diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3e1b655..0adbade 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -2,11 +2,10 @@ name: CI
on:
push:
- branches:
- - main
+ branches: [main, rewrite]
pull_request:
- branches:
- - main
+ branches: [main, rewrite]
+ workflow_dispatch:
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
@@ -15,162 +14,65 @@ concurrency:
jobs:
format:
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
-
+ 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: |
- 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
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 }}-${{ hashFiles('requirements.txt') }}
-
- # single serial download; the concurrent notebook kernels spawned by
- # xdist must find the database already in place (x4i3 downloads at
- # import time outside pytest, so parallel kernels would race it)
- - name: Warm EXFOR database
- run: python -c "import x4i3"
-
- - name: Run notebooks with pytest
- run: python -m pytest -n 4 --nbmake --nbmake-timeout=1200 examples
+ python -m pip install -e '.[validation]'
+ - name: Run the fast tier
+ # the ten slowest tests are listed so the few-minute budget stays visible
+ run: python -m pytest --durations=10
docs:
runs-on: ubuntu-latest
-
steps:
- uses: actions/checkout@v4
-
+ with:
+ fetch-depth: 0
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- cache: 'pip'
-
+ cache: "pip"
- name: Install docs dependencies
run: |
python -m pip install --upgrade pip setuptools wheel
python -m pip install -e '.[docs]'
-
- - name: Locate x4i3 database directory
- run: echo "X4I3_DATA_DIR=$(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 }}-${{ hashFiles('requirements.txt') }}
-
- - name: Warm EXFOR database
- run: python -c "import x4i3"
-
- name: Build HTML docs
run: |
- test -L docs/examples || ln -sf ../examples docs/examples
- sphinx-build docs docs/_build/html -W --keep-going
-
- build:
- runs-on: ubuntu-latest
-
- steps:
- - uses: actions/checkout@v4
-
- - uses: actions/setup-python@v5
- with:
- python-version: "3.12"
- cache: 'pip'
-
- - name: Build distributions
- run: |
- python -m pip install --upgrade pip build
- python -m build
-
- - name: Install wheel
- run: python -m pip install "$(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"
+ if [ -d examples ]; then test -L docs/examples || ln -sf ../examples docs/examples; fi
+ sphinx-build -W --keep-going docs docs/_build/html
diff --git a/.github/workflows/converged.yml b/.github/workflows/converged.yml
new file mode 100644
index 0000000..2d0f813
--- /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=3600 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/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..2e64c02
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,53 @@
+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. 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[1-9]*"]
+
+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
+ - 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
+ 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 cd0cf6d..81b3e4d 100644
--- a/README.md
+++ b/README.md
@@ -1,301 +1,156 @@
# rxmc
-> **The 1.0 rewrite is in progress on branch [`rewrite`](https://github.com/beykyle/rxmc/tree/rewrite)**, guided by
-> [`docs/groundup_design.md`](docs/groundup_design.md) and [`docs/recipes.md`](docs/recipes.md).
-> This 0.x package is preserved at tag `v0.1.0` and on branch `legacy/0.x`.
+`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.
-`rxmc` is an orchestration layer for Bayesian calibration of reaction models to
-large data sets with flexible, composable covariance modeling.
+## Documentation
-It is built around two complementary workflows:
-
-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`.
+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.
+
```python
+import emcee
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.
-
-
-## Installation
-
-### Development / local use
-```bash
-git clone git@github.com:beykyle/rxmc.git
-cd rxmc
-pip install -ve .
-```
+import rxmc as rx
-It is strongly recommended to use an isolated environment.
+# 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])
-### `venv`
+# 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))
-```bash
-python -m venv .rxmc
-source .rxmc/bin/activate
-pip install -r requirements.txt
-pip install -ve .
-```
+# one comparison, one likelihood, one compiled problem
+problem = rx.Problem([rx.Constraint([rx.Comparison(data, line)])])
+print(problem.names) # ['m', 'b']
-### `uv`:
+sampler = emcee.EnsembleSampler(16, problem.ndim, problem.log_posterior)
+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())
-```bash
-uv env create
-uv env use python
-uv install -e .
+# the posterior predictive on the data points, with the error model
+draws = rx.diagnostics.predictive_draws(problem, samples[::20], n_rep=2, return_draws=True)
+print(rx.diagnostics.coverage_curve(draws, data.y, [0.68]))
```
-### Optional extras
-
-Install the example notebook runtime dependencies with:
+The error model is a sum of covariance *terms* on the constraint. A
+normalisation the experiment did not report, inferred alongside the model:
-```bash
-pip install -ve '.[examples]'
-```
-
-Install the full validation toolchain with:
+```python
+from rxmc import terms as T
-```bash
-pip install -ve '.[validation]'
+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']
```
-## 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:
+## A reaction model
-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.
+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.
-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`
+```python
+from types import SimpleNamespace
-Aggregates multiple independent constraints that share the same physical-model
-parameterization.
+import dynesty
+import jitr
+from jitr.optical_potentials.potential_forms import thomas_safe, woods_saxon_safe
-### Model comparison (`rxmc.model_comparison`)
+reaction = jitr.reactions.ElasticReaction(target=(40, 20), projectile=(1, 0))
+R = 1.2 * 40 ** (1 / 3)
-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
+def central(r, V, W, a):
+ return -(V + 1j * W) * woods_saxon_safe(r, R, a)
-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.
+def spin_orbit(r, Vso, Rso, aso):
+ return Vso * thomas_safe(r, Rso, aso) / jitr.utils.constants.WAVENUMBER_PION**2
-## Documentation
-The full API reference and rendered example notebooks are hosted at
-**https://beykyle.github.io/rxmc/**.
+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
+)
-To build the documentation locally:
+# 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())])
-```bash
-pip install -ve '.[docs]'
-cd docs && make html
-# then open docs/_build/html/index.html
+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])
```
-## Testing
+## Where to go next
-Run the full validation matrix with:
+- [`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 documentation website, including API reference, at
+ https://beykyle.github.io/rxmc/.
-```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
-```
+## Installation
-If you want to apply the formatting fixes locally instead of only checking them:
+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 GitHub:
```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
+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
```
-Run only the unit tests with:
-
-```bash
-python -m pytest test
-```
+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`.
-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: 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`.
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/api.rst b/docs/api.rst
index c1d869c..98d5de3 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -1,54 +1,29 @@
-API Reference
+API reference
=============
-Configuration
--------------
+The public surface, in the order the design document introduces it. Every
+name below is importable from ``rxmc`` or the module shown.
-High-level configuration objects for assembling a calibration problem and
-handing it to an external sampler (emcee, dynesty, etc.).
+Building blocks
+---------------
.. 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
+ 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
----------
-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:
@@ -59,139 +34,104 @@ as a latent normalisation) and covariance terms (coordinate transforms).
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
+ rxmc.terms.Term
+ rxmc.terms.TermContext
+ rxmc.terms.KernelTerm
+ rxmc.terms.statistical
+ rxmc.terms.offset
+ rxmc.terms.normalization
+ rxmc.terms.noise
+ rxmc.terms.proportional_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
----------------------
-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
---------------------
+ rxmc.likelihood.Likelihood
+ rxmc.likelihood.Gaussian
+ rxmc.likelihood.StudentT
+ rxmc.likelihood.Chi2
-Posterior-predictive helpers, including Gaussian-process discrepancy
-propagation.
+The structured covariance
+-------------------------
.. autosummary::
:toctree: generated/
:nosignatures:
- rxmc.predictive.predictive_band
- rxmc.predictive.gp_posterior_predictive
- rxmc.predictive.total_predictive_band
-
-Model comparison
-----------------
+ rxmc.covariance.StructuredCovariance
+ rxmc.covariance.chol_logdet
+ rxmc.problem.CompiledConstraint
+ rxmc.problem.ParameterIndex
-Sampler-agnostic posterior-predictive checks, held-out scoring, and
-nested-sampling evidence bookkeeping.
+Diagnostics
+-----------
.. 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
+ 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
-Sampling
---------
+Predictive
+----------
.. 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
--------------------
+ rxmc.predictive.grid_draws
+ rxmc.predictive.gp_predictive_draws
+ rxmc.predictive.gp_posterior_predictive
+ rxmc.predictive.predictive_band
-Low-level sampling functions used internally by the sampler classes.
+Reactions
+---------
.. autosummary::
:toctree: generated/
:nosignatures:
- rxmc.metropolis_hastings.metropolis_hastings
- rxmc.adaptive_metropolis.adaptive_metropolis
-
-Domain-specific models
-----------------------
+ 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
-Reaction-physics observation and model classes for elastic differential
-cross sections and isobaric-analog (p,n) cross sections.
+Units
+-----
.. 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
+ rxmc.units.parse_unit
+ rxmc.units.check_angle_grid
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
index fe892ce..6fbf046 100644
--- a/docs/design.md
+++ b/docs/design.md
@@ -1,245 +1,751 @@
-# 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`:
+# 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
-# 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.
+@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)
+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
+# bases and amplitudes: ones, ym, averaging, x_basis(scale), exp_growth(scale, base=ones),
+# constant_amplitude, exp_growth_amplitude(scale)
+```
+
+`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
+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", prior=gamma(a=2, scale=10), 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(size=n, random_state=rng)` (scipy's
+spelling); `scipy.stats.multivariate_normal` qualifies and is whitened for
+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
+`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, *, 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)
+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))
+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*
+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.
+
+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/`
+
+```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 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
+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.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),
+}
+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
+# 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, return_draws=True)
+```
+
+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 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 |
+| 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 |
+| 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 |
+| 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) | `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 |
+| 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
+
+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.
+
+| 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 | 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 | 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 |
+| `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
+`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/ the notebooks and plotstyle.py; data/ (committed measurements)
+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/examples.rst b/docs/examples.rst
index abeccd8..68ae886 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -1,43 +1,41 @@
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:
+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::
-.. code-block:: bash
+ pip install -e '.[examples]'
+ jupyter lab examples
- pip install -ve '.[examples]'
- jupyter lab
-
-Basic calibration
------------------
+Calibration basics
+------------------
.. toctree::
:maxdepth: 1
- examples/linear_calibration_demo.ipynb
- examples/systematic_err_demo.ipynb
- examples/robust_likelihoods.ipynb
+ examples/linear_calibration.ipynb
+ examples/error_models.ipynb
+ examples/sharing_error_models.ipynb
+ examples/normalization_and_covariance_structure.ipynb
-Realistic nuclear physics calibration
---------------------------------------
+Beyond the Gaussian
+-------------------
.. toctree::
:maxdepth: 1
- examples/30s_optical_potential_calibration.ipynb
- examples/measurement_to_calibration.ipynb
- examples/calibration_config_emcee_dynesty.ipynb
+ examples/gp_discrepancy.ipynb
+ examples/robust_likelihoods.ipynb
+ examples/error_scale_and_usu.ipynb
-Advanced topics
----------------
+Reactions and studies
+---------------------
.. toctree::
:maxdepth: 1
- examples/correlated_observations.ipynb
- examples/gp_discrepancy.ipynb
- examples/normalization_inference.ipynb
- examples/sampling_algos.ipynb
- examples/overconfidence.ipynb
+ examples/local_optical_model_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 fb9ce02..f2a95b7 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
@@ -132,10 +136,14 @@ 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 +170,7 @@ 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
@@ -262,6 +270,9 @@ 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:
@@ -296,11 +307,13 @@ 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)
+ prefix="discrepancy", params=None, on=None) -> KernelTerm
+# 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;
# None derives fresh ones named f"{prefix}_{name}". Pass the same objects to share
# hyperparameters between per-block kernels. Two kernel terms with derived
@@ -567,19 +580,30 @@ gone.
```python
# diagnostics.py
-predictive_draws(problem, samples, constraint=0, *, n_rep=1, rng=None, model_only=False)
+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) # 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)
# 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)
-# term is the kernel Term; its columns and the predictor's come from problem.columns
+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.
```
## 3. Worked example: the α+Ca study shape
@@ -681,12 +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 |
@@ -694,7 +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) |
@@ -705,7 +729,7 @@ 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) |
@@ -756,8 +780,8 @@ 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` |
+| `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` |
| `elastic_diffxs_model.py` | `_xs` body, `extract_dXS_dA`, `extract_dXS_dRuth`, `extract_Ay` | `reactions/elastic.py` |
@@ -774,7 +798,7 @@ 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 |
@@ -798,7 +822,7 @@ 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, 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,
@@ -852,7 +876,7 @@ 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,
@@ -867,21 +891,29 @@ 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; `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 `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) |
+
+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:
@@ -935,7 +967,7 @@ 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
@@ -960,7 +992,8 @@ 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`,
@@ -1006,12 +1039,15 @@ 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/`; `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);
@@ -1049,8 +1085,10 @@ 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 b34b31e..ad23a5b 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,31 +1,28 @@
rxmc
====
-``rxmc`` is an orchestration layer for Bayesian calibration of reaction models
-to large data sets with flexible, composable covariance modeling.
+``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).
-It is built around two complementary workflows:
+- :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.
-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`).
+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 `_
+and on branch `legacy/0.x `_;
+:doc:`installation` says how to pin it.
.. toctree::
:maxdepth: 1
:caption: Contents
installation
- design
- groundup_design
recipes
- bugs_found
- api
examples
+ design
+ api
diff --git a/docs/installation.rst b/docs/installation.rst
index 54b6fa0..4ede0d2 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -1,59 +1,32 @@
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. 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 GitHub:
.. code-block:: bash
git clone 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
----------------
+ python -m venv .venv && source .venv/bin/activate
+ pip install -e '.[examples]'
-Install example notebook runtime dependencies:
-
-.. code-block:: bash
-
- pip install -ve '.[examples]'
-
-Install the full validation toolchain (formatting, linting, testing):
-
-.. code-block:: bash
+Extras:
- pip install -ve '.[validation]'
+=============== =============================================================
+``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
+=============== =============================================================
-Install documentation build dependencies:
+The 0.x package, which 1.0 replaces without backwards compatibility, is
+preserved at tag `v0.1.0 `_ and
+on branch `legacy/0.x `_.
+Pin it with:
.. code-block:: bash
- pip install -ve '.[docs]'
+ pip install git+https://github.com/beykyle/rxmc@v0.1.0
diff --git a/docs/recipes.md b/docs/recipes.md
index 3eaa9f6..d7332de 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.
@@ -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
@@ -168,18 +180,43 @@ 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:
- 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 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.
+ (`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`).
@@ -269,7 +306,12 @@ 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.
@@ -321,6 +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:
@@ -335,6 +381,10 @@ 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.
+- 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
@@ -345,9 +395,10 @@ 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:
@@ -397,16 +448,31 @@ 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
@@ -421,11 +487,12 @@ 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()),
}
+# 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)
@@ -434,6 +501,28 @@ 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 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:
- The same `log_eps` object is reused across models without conflict:
@@ -459,7 +548,10 @@ 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).
@@ -679,7 +771,8 @@ 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.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
# KDUQ additionally scales the whole log-likelihood by k/N ("democratic"), or
@@ -732,11 +825,17 @@ 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 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.
+- 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;
@@ -771,7 +870,8 @@ 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.
@@ -835,7 +935,7 @@ 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])
```
@@ -844,7 +944,9 @@ 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.
@@ -877,6 +979,9 @@ 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.
@@ -1035,32 +1140,79 @@ 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 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.*
-*One experiment reports both a cross section and an analysing power, and
-they share a normalisation or an angle calibration.*
+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`.
-- 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: `_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 `_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 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`)
+ 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).
+- 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. The classic normal hierarchical model (eight schools)
@@ -1123,11 +1275,110 @@ 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.
+
+## 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`,
+ `proportional_error`, `normalization`/`offset`/`systematic` with
+ 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
+ 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
@@ -1137,8 +1388,8 @@ 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/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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uGb6+uzvojhMYs/4ixmy4hMN3NZk6DJkaFAoK/95OwrbweK2xNmasmZ0Vs2SzPsamy3FZCI8V9xuVlMtxICIFJ6PTkcLyj6Vr+our1iN7a02/rjzOwu7rCQZleRm/6TLjNXu9GLLmAlYcvS/K7YetHefyx+WCfj/pmmN0jf+fV57WyUwkKswq+H799dfYtGkTtmzZAktLTY7QHj164NatW3qda86cOQgNDUV8fDyuXr2KcePGIT8/H1OmTIFEIsGsWbOwYsUK/Pvvv7h37x7eeust2NnZYeLEicb+WrWS2wk5GLL6AsJin+luzCI2vQCDV4fi8mONL1l1BDaGryPraVE9jLN3R3AeZ/NfRAoO3knB1vB4Qe3m5gvKQMftJgxiS88vxa/h8dh/O1ktSLD573YyRvyssWaw58zqCsJjN1xS79S5zi9ETlE5glee4YzUp0/uvq62vOcYtS4ckcl5DD/w+2n5aP/VKc729K9L/41vPMnG0oNR6qwXA38MxTs7buB0TDrK5QqUVyqQX1LBqHDF5xqjT1YE1T3+05lY3EnKw1c0txwV5x7o/wwBmjHMKSpHvkhzJKD/PfHNsfu4l5yPX8MeY/T6ixjw43m1hrygtAKTtl7BtfhsUeei93Phf/cYgryyb8CxyFSErArF8XtpoCgKI9aG46V1F5FdVM7Q5mbQsmHQjxeVVTIKEuRzmF11oesnfpBWIKod97nZmkDxn03NK4GVSJc+rr7RNX58Gycxab1iq0z1GYzfT3nBjPxSLeGtoLQCByKStdxGDtxJxuzdd/DV4Wh8/m+kelwBqN0BVNDdEsSy/Eg0hv0UpnXd0zHpWm2jOAIVSyvkmLDlCib/ehV5JRV4lFGAVace8pr/6dr8QtZ9R7fMyTgkowo5hXn7IvHPLe1Ks7qEYfacxJdBRbWBFIJrfvjm2H3svq7ZmBSVVWppyOn3ta7nQpebW3RqvtmDcI2Jhe4mxuPBgwfo06eP1nEnJyfk5ubqda6kpCRMmDABmZmZqF+/Prp3744rV66gcePGAIC5c+eipKQEM2fORE5ODrp164aTJ0/C0bF6JnSzUCTgUyuTATY2Otu+tyEUBRUKvPHrNTz5ZoTu80qlgK1SwPl0zx0kJGXClvZwfLg1HOsndlS+kEgAOzvNm8XFnKuDbXkpKAlQammDCrkCljIp0tJyYFuumbgtSoqBoiKE3n4CWwAlVsrvpqAooKREy+57936y+vOMhaG0FJDTFo3cAtiqTIJFRQAte4h1ZTmkCgX/eNjZaba8ZWVAJXOCTMouxuawx+jdpL7ye0skyomN1fZafBYW/HEdKrGx1NJKM/GWl+NURBKWHLqHH8e3Q3AgM50PbG01Nq3ycqCCeyK3LS9FmYUlFFLlYkuVlQFCLtk2Nsp7CMDaE9HIfZaLLcdy8WmPhoxmVFERZAo55FKZ8ieoqFD2g0ZUSp76tyhWaDayI1adh20lq79VYy0tLoaFvBKVMgssORCFMUGeQFkZZm0LR1ZhBazKivH58Fbq8z5I1AhsispKyEtL1e+5KMrU523lJEVsThkqZJZK0VguB0pLER77DPeS8+Bib4XXuzTS9MfSErBSpp2SUArYVPAHMVXSBRmFQnlfspArKHy6OwLH7mcCFsqxkMsVKM7JQ49lypRA0V8NYS4WFhaAdZX2jqJgW1ElpBQWY9FfV5CaV4otb3ZWfkbguVeNR3ZGDh4+TodCKsWTrCK08HRC9xVnoCgswpToZMQsG6r1GYVEgjJLjQaRKixSvxf5IAX0LY9CIgEFYO4/d1FQVonFf1+F3bi2kBUXwxZAdno20tIL1M/9ruuJeCPYDwDQZ+kR9XxiWVqMvEzNPOBEK5RSWiGHTWW5sL+HvT1kVeNoXVEGKcfc44YKoKgIyTk0ywVrjuA6r7IPCs0cAc0cxYBjjigqq8TI786huFzOGDeGeECbI+SVCsZcWJlfgKgkjXD3NCUHaOKsfq1qW19ioeyPwBxBPy8AlFXdk5fjsvDmpjC83t4Ty0YHqd+f88cNXHiYiSGtPbDmrWD1HHE7LoNxruyMbMBRiriMQvwX/hAyC0vIq+ae+wnZsJVXzX9cc6u1tfKeB5RjUFaGP88ozfRPnoIxZv9ee6L+W6aQw6qyArvP30czBwkmdmus+V7FFer5pKC0AoN/OAfrygpcjUzA7mnB6nbPCspQViHHmgua8/53I4Hx3SoKCtSv7cqtgPJytUBLnyOiYlOAlq7qzy0/Eo2DUc/w7+z+8Hax5Zwj6NeRS2W4n1aA4vJK2FnKEPckXXPdijLm2LHmCBQXIzerWOv33XEqCgqpFK9VzXGtl5yAbXkpejZ1x9Y3Oys/Xlioee6LSwAX2ogXFTHOaVthyZgj2N/lYVwq0mw1bdR9pskRtRrKjAQEBFCnTp2iKIqiHBwcqLi4OIqiKGrHjh1Uy5YtzdkV0eTl5VEAqLy8PPNdVHmLc/8bPpzZ1s6Ot+1l3zZU43mHqT3XE6j8knKKcnfnP2/nzupT9v/+HJXo1IC/batWzD60asXbNtGpAdV43mEqOkU5frGNW/C2zbR1ohrPO0w1nneYGvjjeYrq25e3bZGlNfUwLV/Th+HDhcetisbzDlOHm/cUbltYqDnvlCmCbTt89BfVeN5h6lp8FkXNnCnYtuf0X6nw2GeUQqGgSj/5VLgP9+5p+rBkiWDbUW+uUo9b9pKvhc977pz6tJvGzxZs+9a4JerzKrZtE2w74+X56vPOeHm+YNvPhs9Sn5c6fFiw7dkPF6vb/rBgk2Db5f3ephrPO0yl5BZT1LVrwuOwZIn6fhg0db1g201dx2p+i/h4wbY7OoxQ9/e/k7eF+zBlivq0LWb/I9x23DjRc8SZgM7UmZg0avvFeKrxvMNUkaU1b1vVHKH6V+Hqxts2wrMpNXnrFXVboTnigVsjasz6cHV3H7g14m2bXd+boiiK+iX0EdXk8yNUXpv2vG0LnOpRj58VUruvJVCN5x2mLvu24R8LOzvGdxM7R3Rfcdqoc0Rq7FNNWxFzhKq/dye8J9j28fmrlEKhED1HtP/yBDX1t2vU8n5vC3832hxx+P2Fgm1VcwRFUdRnw2cJn3fPHs047Nkj2PbnyZ+rx+GtccLf7YuQ6VTjeYepJ5mF1GsTVgi2Vc0Rjecdprb+uEu4v0uWUPdT80XPEd8fv09tPP+I+mbjUcG2qjni6uMsqigpRbgPtDmCKiwUbHu4eU8qp6hMPa8JtU3vPZD668pTzf2jQ45IzytRj1umrRP/uWlyRE0gVl4zq6vDtGnT8Mknn+Dq1auQSCRISUnBX3/9hTlz5mDmzJnm7MoLxf/+uYs5e++Ibm8KC4bK1CPWn+9RRqHOVEQ1lN2GE0pkXygK+Pzfe9h++YlJ+qGPT2OSHsEi+SU1Uy6UfrtcfyLOXG+KAhB+84/Ab/4RlJSLHwdKj9/C2Hy087ZJUvaFifSlBJT5m789fl9nGi3VfLPi6H1UyCmk5vHfl2VVBVD0SeOkL8aunGeor7cu8/n7v9/Qq6CIIRkqqhvgbCj1nfTPd65vgYtrIuYTfebT7OJyfHPsPg7dEZc5KDz2GUatNW5hFbFjcC85D5//G4kTUeLShtJz0T8PSCgzz84LFy7E6tWrUVqqnFysra0xZ84cLFu2zJzdEE1+fj6cnZ2Rl5dnvkA3I7g6tFx0XMuM+WRRP/7z0kwUIatCkZicCQnrzmjp5Yj9M3tquTrceZiC1zddhqO1Ba59MUh9fQBqV4dDH/ZCkI8zpm0OwwVa0Etnv3r4451u6vYqVwcAiJrfB/aWyr3ZlceZWHowCk+zNIvivjkhSMktweawx/isTyN0a+SCJQfvISIhFw/Tmf5nMateAaAUYFRmTLrpl4EOV4dOy06pgx9KLK0BiQR7pgWjq7e9uu3RyFR8toe52Si1tMKOd7rjzW3XYCmvgAXN7KrVF5GuDi0XHWe4Opz6oDuautpwtgXAcHVo8r8DsKwyTd77cghkUon6dwCAcpoZM2x2Lwz+VmmynzO4Gd7pHYDQhxmY/sctddu4714CAATOPQgrtqsDADcHSywY3gqf7ItCpUxp8oxfNhiS8nLGdel8PqYtFh1RLu5ShRzWtPOq+gEAL68LR3RmCSpkltj5bjek5RTh6PV4XKTlPLW2kCJiSVVRG0tLHHuQhRl/3RLl6lAhU5qKP+4XgE97a1wmnmYWYehPYerXcqkM5VVm5RWj26ChlQLT/1SO0d2lg2FJdx6kmTH7f38OaancC/Ef73SFpZUl2jX11BykPfeqsXulY0Psu5UMhVSKMgtN9TCVOZJ+j6k+w54jDk/toBWIo4Ld1qailDFHHPm4F648zsKywzHq5x4A1k3sgDk7LmvNJyomdm+ERa92gd98ZUpKHxsg/H/9GG0qqoIcAeYcwefq8L8hzTC1VwD8lp1XH3uydKAoVwf6HKEiZtlQUBSFAxEpOHc/A5+P7wQP5yqTbtUcwXcPl1ha4/2+gfh8eEvGfFJYVokurMpYpZZWoCTKe2Rufz/M7OWHSrkCO68mYGVVoG7TBg6IzShUt33yzQitOYLdF9UcMaS1B87eTYKFXM55PwBAzPej1XPE6qP3sPn0A/V7f7zTFZ39XPH+HzcQ9jBTPUc8+WYEYz6J+nIIo5AHAE5XB74xe7t/M2y4qPRZVbk6sIlZNhQZ+aXosSoclTILbJrcETN/v66eI5o2sMebPfwwrpOv+jr0Z5k9n9AZ2dYL30/sjKjMEoz4OZwxR4zv7IOvXm4DiqLQavEJ9Xlf7uqPf24mcc4n7X2dEZGojAOhzxGgaC5ONGRSCe59OUTL1SEvKw/dV5zh7LNCKkXY4mG4l5KHqdtvaD330al5eGXDZXXbMgsrzBrUFLMGNQOKihi/RfcAV1x5rJyPFBIJ9n82UB2rwnazAJTrR1hsBn69lIAVE7ti/blHePysCPOGNUenxq5a7U2FWHnNrD6+ALB8+XIsXLgQ0dHRUCgUaNWqFRwcHHR/8EVCn2p2PG3piwO97bHIVGwOe4wfx7dDAC1lyfF7afjx5DX8PKEDpBKJetGik1wh5bzemaeFKLGyQQmlvMbluCyt66uiSEstrBnvlVjaAPb2nP2V2NkCVdG+/zt2FYkFFEBrp6AozN4TgYLSSiwoLMPZz/phx50qjRTX969CJRQciM3Fy+0b8rYDoJx0rJnah3JrW5SAKQxTFAVYW+Pow2wsPxKDll6OnN9JpUGokFmqJ2AAwr+5lRUoS0vOYAL2NSotLDnPVSFX4GBEClp6OaGVt3JCqJRZqAXQLFiggb0N930DQGFhoX6PsrcH7O0ht7XTal9cXgm5VIYSDk1RiaUVKm1s1dcEAIXMAjJ7S97rqoReAFCwzvuM0nzXAgtrVMiUv8m3Jx7gjqoEKe28UisZY2xm/KUUSCmJlPf6bKLTmf7iZYUK3s/KASjsNPe2ws4OsJBhw/lHKKtQYHZIM3XbNj4uiOdJHzfuD6V2M3b5MI3gTOuD6vzlNracfVEf4/iMVp85flM+2HOE3NYOxZZFWp9PzC7hnE9UlLHal1pYM/r63+1kfHU4mrNfdEGcTrmNnfZzYCPuewFgbBwAAPb2+O74fWysKmJQ8O89bHurCwAgvYxCTrFccNw2X3isFHxp84lCViH4mYc55YC9PfZeS8DiM0/U93KptQ1KrFiWBysrtc86wP/7Wsik6rnnQYECzavSRjLay2SITS+Ap7MNSiFjvlf17J98Ush4tirlCsZ8Irezg5QrQkzdEQuANqewKZdonnO++QT29lBUytTXvJOUx5gj7ubKMedoHMb1acE9F/OdF1VrnZUVFAqlooU+R1RU3VuUgmKcV2Vp4ppPmgV44nI6R7YZiYSzb1IJtO9fiQTfhSUK3jNyisLU7TcAaD/3CttKrc9uPB8HdwdrTO7emPHeuaRixu9L31vyzTFv7VH6a8/ZewfF5XJEJufVmKVQF2bP4wsAdnZ26Ny5M7p27UqEXjOSnFuCGX/dwu0qEySd6X/eRGxGIT7++zavq0N6PvPBVaUgYzc/EpkCNnzphYTSqtAj+BOztc2fFKXJh/j4mf5FNj7ZFYGErGIciEjG4gP3dJrII5PysPjAPa28hnQW/huJ5NwSnI7J4HzfEPPKptA4dF1xBk+zdH9H1Xe4l5yHkzQz1qE7Kfhs7x289gu3Ni945VnB89LT7xRWfX+u+COh3LxZReWMAhaA8n743sASrI42GgGa/vvzmYi1NFAGwTy3UI5WiqIYDwdFKYO3vjv+AD+diUVGQSk2X4jDmA0XeYsV0NFlMt9zQzvaXF+qY/4rLKtkJL7XnFP4rOyfq1KhwME7KersBbN2RyBbT1OrghJ2mcgpKsdXh6L1Kmm8kVa5K4XmJtRtxRkMXRPG9REG687GYsLmK+qMG5QODwhV0F8sy3rFtmZFJuXh4J0UUa411rQ82r+GP+ZsE5GYi5DVF9D/h/NaKb8KeeY+dpXCu0m5WPTfPSTl8KdFTBRImSimmNGdxFyk0TJcyCQS+LnZabUzxKh9KS4LtxNyBF0d2O8I9dlYZvW/eNL3qRBaw7i+S1mlAl/8d0/nGOly+aDnEs4uKle3r62JH8yi8Z06daqodtu2bTNxT14cGrnaIYE1sfT8RiPcqAQFiqIYSfdjWSlq+Nh3Mwmf7b2DZaPbaN3cFhy1blUPFvsBEprfDt5JVkepcrH4ILNijNBEuurUQ+Rx5JZ9kF6grqjTzd8NI9p6qd+7n5YPeysL+LraQa6gMGodvz/Wl4eicfST3jr96HRNMHnFFfjx1AN08XNF3+b14WRjqc5F/PWRGGx5szMoisKp6HQENtDeNMoVFG48yca4KnP1iVl90NzTUV2Wlk9o1yX00/OLbrnwGLMGNcMTDkFcVxocdrowBUVh/TnDSoGqBFx2TkxeQcsIqw/754sWEPTlCopxTfa9X1ahwIqj4oV++m9UUFoBBQU40zIi6INUwv3s6SMIshmz4RLncV1yB/uZyC1W5uru1cQdf77bzaC+hMdm4rvjD7SOz9l7BxkFZXCyscDhu6nYdjFek/UGELW5NJQfTiqtFy0WHceJWX1Q31HYj1X1LNGLvnChmpccrGUY0MJDK80YHUdaXlo+QU21Yc4sLMdDWvoyAPju+AP0a95A6zN5rI3bKxuV8092UTnWT+rIeZ0Pd/KnMNWVhtLGUoqX119kHKNAoZGbPZ6wLCeGOnNGJuchqKEzxzvcaxk937XWJ/Tsg4JSrj+NXO20chwL8eke/lgeoTleV/907UPoOZ7pMkR1q3+aCrMIvtu3b0fjxo3RoUOHGg34qAuUVsgRk5qPdj4uWhqqknK5qGCD4/fStIReNqpf4X5aAT5i5RRs6GKrs0rOZ1XBcov+u4fZg5ox3nOx016MVQ8d++FTm6Q5mLcvUlDwvZ3A/GxsBn+lrJ/PxHIef+/3G+q/c4rLkVtcji1hj9G3WQO8WqUd7de8vs6k8dGp+Wjy+VHBNoDwBFNaIUe7r5R+jL9f1q5/rgoSuvI4G+//cRPeztomp0oFpRZ6ASAhuxjNPR0Z2nNlP/R7DunCZVG5spTo10e0KwbpW4wgrRqBRKqFm12RqpInwMMYMw9bWBD6uux7PTa9EJN/vWrwtVNyS+Bsawm5gkJQlb/rg695/NQFoCiKdyH7/N9Ig/tnKHyZy1T5qVt4OuJ+Gv+zzYWqMA6bf25ya8VVmkmVP7YuqpvD9MtDUfh5QgfBNtsvPcGSUa1EV5+8+TQHA1p4YNCqUN429DXFgufmpWvXnWyZIkJ0aj5nsCJf0ZgjkamYmZKH1t7aAqQhVjoV9I24CooCrGTa38nQ537xgSjsm6Fd8KqsQoFhP4WhmQdT8cAW/pl90L8XQ9eEoV/z+tj+dlfRn+HK130rIQcdG9UTFF51aXQTs4vh4WStZfXVxQst+E6fPh27du3C48ePMXXqVEyePBmuruZzeK5LvP/HTVx4+AyLR7bC1F7+AIC1Z2LxY1X07r4ZPdCpcT3Bc0z/86bO66gEnxwOLWjDeroFXyHsOXaocrXGV79z5RVX6NR4qCjjmAz1QSaVYOF/93DkbipDC3leZAEDMeY5oSZrz3IL5+zz307MAQAtM6Ty/NwXoM8/OUXlvIIBH+xI5bn/aEfVZxWW6T3R8WkJxaD6ruwFlE9ICqyvh+88D+18XRivhaKoKQoMVwctDZWez8Jbv13D1c8HMczNGXosRDlF5fhkdwS8nMT7uhoDXdkJhCLG0/JKGS4t1YFvs3f8XqpOgZct1FR3Ob8UlyUqY8BXh6NFF+ERU1GX/nzybVJ3XdcUouByr6ErC1RYC8zRI34OZ2jVVVQYuTa3guLekIgpA8wHVwXK/beV5bzFllwHwAiy1Qf62sNXDVMXP52OxY6pXQWVHbqWro/+vo32vi4GCL56NTcbZvHx3bBhA1JTUzFv3jwcOnQIvr6+ePXVV3HixAmiAWahMpdsuxgPQGl6/JGWsmbpwSh88V+kuiKboeMXp3Z10H5P6Jwno9Jwm1V3fPVpZkodLq206pT6lnts99VJ3E8TN8GogpUMZcH+SBy5m6q7oQF0bOQCQHhsrz/J4X0PoAVPCAzh65uvMF5zLVIL9kfiAwEzIxd7RVRlKi6X6y346uu7SUczHuLuqTtJefjo79uIELAy6OLnM7Hwm38EH+68hbDYZwxfT63+6ejXX9e0tfpCqBYd+ve9rMcG5qczsbjw8Bl2G1Bhqzr8qCPl1umYdLV1hU33lWd0Phdi4ROwxWh52dkXVFRn/RIj9/128Yno820KjRMsnQ4wFR1itNYO1tqbDq5KhoakEOTS2lYHChTn3Nj723MGn/OdHdrzpyHEZ1bfhYa+IdEH76pCFdXR+AKG3evGiaswPmbL6mBtbY0JEyZgwoQJePr0KbZv346ZM2eioqIC0dHRJMgNYJTxTMopAUVRGP4zM2giMjkPkcl5+PNKAn56vT2WHozCL290Rld//TXoRWWVnDe8kBbr/T90a5PLObQEfMFtKnzq2fKWIDakpGlt43HVxDePJ//or+HxgloTQKkl+vPKU8GAKr4FiD79HOfJ3SgkhAoF9KlYfiQGs0Ka6mxnLFTftaWX+DSDh+6k4NCdFIxq512tax++m8rwjeciLa8U5x9wBzkCwC+h3MFFQsz48ybmDm2hfq1PPlt9yiabG7EllXUhk0p4n4F3qyHElLMEyujUfLRdekJv1x46+uSIFcvBCO3AYjr7byWr/xZzebbg62xryWnS11fu1VcBIga+7/O85KBll1gWy9/XEtT+33yIsVbq2shzUVtdHWokq4NEIoFEIlH6mhnZ3FGXSS9gmq4P6VhYP9kVgZziCszeHcE4HuAuzqSbX1rBabpguzmMbq+fkGBtqX1bqfzU+GRqK4HUN0dNpIU1J7lVmxq+4Ldlh6PhZm/F+R6dL/67h3XnHul1bYWCEhVdO26T4W4HgFKgzjUgSb6hyBUUSivkBmkVDt0RFhCMwfZLT9S5MI3FsXtpOB2dbtBnLTmCTp8ndr7bDZO68ccEVEfTz0V+aaVBRSFUmKLQyslocQUJAHEavBtPmdr2gS20A9sAYN05YTctQBknUFxeiQq5AkN/uiCuk3pAURTktVyeYLtK0bHjid+RKyiUVcr1nvfpbAmLF7z/2VZcLgwZ2lqq8DWf4FtWVoa///4bISEhaN68OSIjI7Fu3TokJCQQbW8VbE2pGPMyoB3VzhVcxkXwyrOcFd2eFTCF4V5N64s6H6D0Y8yp2mF70Krv/Fzlv8q301ftJne/313rPXObZmuKZzwBItXlz6tPRWkLqhNsooLtamFK5BSFrstPc/obPw9wmZkBiA52YmMsX9naSo8m7tXSwJqbx0Ywf7M5ESV+U+RfpSD59zZ/Gjy2FUjl38pGjA/rS+suotXiE7jNUWDIGBSWyTndMGoTO3mylPRs4obeTd053ysqr8RZnvSY+sBOYUpHzO8nlMGGj+oGgZoKswi+M2fOhJeXF7799luMHDkSSUlJ2Lt3L4YPHw7pc66F0Ad2Jgax5UHppl6KonCrKttBOx+uVCz6Y8kRKcvHncRcddqezrSKLfeSlQ8Nl3mPoig8rUpBYykykO15xNAACF0sPhCFHRxZIuo6v118YnI3mDmDmwm+36sJ92JlDPieOrbZXSweZg5qqwlqq2mViynbrtXo9Z1tLZGUU4zZu8WXs68OqmCw7ZfiTXJ+dnYXQ2hRVdDDVNjzbGY9HG3U1UDZ5BSVI96EafYApX+4Kait+1CzqAA2bdqERo0awd/fH6GhoQgN5U63sn//fnN0p9YilA5FCJXj/Ln7GYw688bQ4AHCEbtCcH0fLvMeffdfX0faMALBnIzp6KPeyHGhK5ioOlQqKDjaWKiLtKhYe9Ywk6chKZXqGrV1oa2NKCiYrbIW3a3CWOuSsflkYFPEZxbpnTrPGMSkFeC93v6cyq5KBYWjkdrufi52lmZ1LTOE2roRNYt67c0330T//v3h4uICZ2dn3n8vOmM66Cify4NKU/z29uuMPJxTevhxtrfn8CUSChDiKkghhvBHmfj2lSDGMS6N74bzmoXc11W78g6BAADfj2tr9mvy5TpVYWzBd0JXX/XfJRVyLBrRyqjnf94x5kL7xYiWRjtXTdC/eX1c+F9/TO3pz/k+BcokfsZc0GNJakKwFCL0f/3wxYiWeL9PQLVSn+ni/Jx+vO/FpObDy9mW8z2FguJ0e9I1N60cGyT4vrGoi5tNsxWwIOjGxlKGb8YGYf5+/ZPIp+dr53R9p5c/LsZlahV6eL1rI/wazjQ3OVjzF8aojvtBx0bMnMNc8+yBqkhkU5uZCHWbcZ180KahM4b9xF0e1tXeqlrp0bjQJUixtbHVhV3S+dUuvrCQSQQrMokltRrFQmoDb/Xww0vtvTFWKPezERfhN4IbcxZpqSuEtPJEIzc7BPDkr1ZQyvLQ5iCrqHrxCxKJJmvD4pGttCpAVofGbvZ4t3eA8jrGvIFo9Ah0g1+VT/Wglh44HZOOJg0cGFX2uILCAWUsA1eQrC5/dp963IK0sbm2cBA686T7E5MtoiYwucb37t27emVuiIqKQmVl3U9fZSivd23EWZFLF91WnNE65mJnib3TgkV9XsgJ3bIaWzo3muuCQkEJprHhq/5DIADKe7SllxNvkJaHkw2uLxzEGxRmCKbS+H48oAnnca6S4WM7+nBaafRFn5ywtZG+zeujAysqfma/QMZrY2p8rS20x3xCV/6sEbWN9lVjxScgFZVVailATEV1NcuX5w/E/WVDcWfxYHVhJ1NgKsv8u701fd46pTPiVgzH8tFt1MdkUn6Ru6iMWwst09FZQy21+iLkClkoIg1mTWDykenQoQOyssQH7QQHByMhofpO6nWZPdODdZazFINEIoEFR5owLgf7PB5fIXcHa7g66E6zxYcFLTCuUkGpXR1+er29Vtv2Aqle6BDN8PPJ3uniNmn/G9Kc83hMaj7qO1ojculg/PJGJ6P0SVeqtCdZwqXB9YVdBrU20dpbfL5kUyCrSoNJ543gxpgzuBn2z1SWljW22XX9xI6M105GzozxTi9/g+7VNg2Ff4vd73dHq6rfi0/w/ebYfZ25qI2FoTlol41ug9Of9oWnsw1sLGVwFpmxyFBM4ZN6cnYfrRy6MqmE8bv41LPlvfYDHtcQXXOThR5B6Xx89XJrBNa3h7tA7I2jjSVvmjZV4abahsldHSiKwqJFi2BnJ853s7z8+Ug2XR186tkJFpHQFwdrCxSWVaJf8/oY2dYbRRy7ML7k9nunBxucG7F7gCtDY3bjSbY6bZklh0CemM3MH9zQhbt08sqxQdUqdVvTvNXDT3QZUlPzxztd8cavNRtdrsLZ1hIjgrxwhCOQg44uja5EIsGQ1p74YkRLUabqMR0a4l9Wmqaghs4Y3MrDZOmxuDakAPDlS230SkllTBaNbIVlAiZkL2cbRKVoUhqN7dCQN72VKeD6LSxlUnw4QFM0pUkD424cRrT1wvCg4fBfcBQA0KahcCxKV39XvYpx+NazhY2lftp8X1dbLB8dpFUCm063ADf137o0g+ZAbBGJ5h6OeJCuEfTe6N7YVF3ixghDdfazvhjwoyaAv5kHt6KGvYlz5cnjHpHInWOXax2no8taJYYWnk4481k/AMDWsMda86nKOu3pZA26M1avJu749a3OnFaT2oDJNb59+vTBgwcPcPv2bVH/goODYWtrHt+U2gxfcvGlo/QPdjnwYU/88kYnbH2zM8Z18uHcKfKZovzc7AzOxbf5zc4Mc8vErVfVwi3XIqaq3a7aPQ4P8uQ8r5OtaXf9pqZHoJvuRmZC30XXlEglwPpJHbFhUkfBdmLzVIsVWuvZaS84hz7qhY8GNjXK4sGFFY950NPZhpH/2py82tlH69jsQc3wehdffD68hVaJ2epUv3u7px+6+NXT3ZAGl0aMfWxMBx+08nJSj++IIC9RbiJCwb0SiQSnZvfB9+PaYmRbL842PZu44btxbTGuk/YYCmFlIdMra86odt4ImztAr+fWGJq/wa34q36x2TMtGM08HBjBgVy+xG72VniH5baga2NhagwZqXp2lvjljU4IqG+Pv9/rzrgnPwsRTodIx9fVDsto7g8q9tzgzrOsq3iKMVwd6NPfu70DMLk709VncGvlGr1weCuGm8jT7KJaK/QCZtD4nj9/3tSXeC7x5PHzfa1LIyw9pJ9jf2B9BwTW12hCuDQACoqCVKIMeJBJJejfvD6aejhCIpGIcpL/6uXWWHwginHMycaS16eXS6BQ9evXKZ3xJLMIBWWV2BKm7YOmS4MhVLaUDzsrGW8exergaGOBjwY0wYqjmuThnf2Ey0sPD/LE0UjxFZj0oWkDB7UfaTd/V3RurJ/wwWZCV1/8fc04BUZUG6zhQV54p5c/r/8hl7WAi1c7+2L39UT0auKOcrkCv/PkMqbnqbaSSRlll4UqClYHK5lUK7hFRU2lAGJvFPbNCEYnWi5udoAN3zO2bHQbLPrvnuC1JJDg05DmmLBFuOBJ6P/6oe/35wFwuzFwzQVHPu6FcrlCvfC2WXJC8BqA7ipmTT0c0ZRHcwcoF/5W3k7Yd5O/GAQXnRrXQ1MdWuoZ/QKx8bwyz6rK+iZ0Wx76sBfjdfeA6m+0+dYjLrr6u+Lk7L6gKEqtIYxK1i5+MKlbI1jTBPiufq7o6l8P+24px5CrmJGpmdrLX8v9w8pCqlVcis6sQc0wpLUnhlQJgY+faZ7paX0D+T7GEBRV6+Eb3RvzPjtezjZYMqo1pv95U+f3AIyz4anvyNyEv9srAMk5JfBzt0dLLycMa6P8zo3c7HD184HoulwZa9TWx6Xa1zYlz021gJUrV0IikWDWrFnqYxRFYenSpfD29oatrS369euHqKgo/pPUIuysLHBp/gCt47ZWMt7ShnQGteTfoXNNmnIFhR9fbQcLqQTfj2uLrVO6YN7QFgC4gzzYsLXCKo0Lnx8Sl6lXtfC6O1ijs58rr4CrS5PX0EV/i8GqV9vB0doCwXosEmK0gefn9MP7fZiTn6u9FeYPa8H7GTd702n86EP6/bh2Bmnzz9HS8tR3NF5RBAnP32z47gu2AGFvbYHjs/rgi5Gt0LcZf/VBusYtbF5/zOynCTwzpBzym8G6zbMhrTywgHUPLDHAmmNM2AI3XegFtEuhy3mExRFB3FpROhVyhSiNL92tRfVbdKVtHO04stFIJBLGnCXmFhdRvVfNh/21AxNVyjV9XWMau9lBKpXg+KzevG3mDW2h9i1WCVf055YunJyc3QdBrMJF+hQuUcWWqOZ+FXzR+bYCmmeJRKIORN3KsYmVSCR4vYsvuvq5YkpwY+yZHgwZTUtpbLcVMXRsVA+3F4XgxheDACh9qT8XmKsB7Y04fajE3g8f0O6pxm7cbqGWMimGtuG2grIZ2trTKNaqxm7MZ97P3R6/vd0VS0a1xqudfeFoo7G+0Uui29RibS/wnAi+169fx+bNm9G2LTPP53fffYdVq1Zh3bp1uH79Ojw9PRESEoKCgtqVR5APbxdbbH+7CxpV5bZVmZvEPUz6aTwpKM2E974cgrEd9TPXAUAlK7p9THvhnMRcmSLYO1Q+zRdXLuDXu2jyn6ryvfq7c6fx4WJoGy9EfjkEf+uhZegjIEyp4EsFJ7xgiO4CAKW2XSz0MeUzt19eoL3hokMfV12aMn2g901oDPiE0dWvtef9THOBgEgrCyl+er09vhkbJCgk0IXn92hR2nTsrGR4uT2/C8D7fQJwbeFA+LraoW+z+mqNyfAgT7xdlW+VK+1YkQmsEWx03XfscefT+MokEp2LbqVCoXPTtWlyR8Zcp/pz97TuOPRhL9xaFCJK+y9Gg86eU4SeqTlDmmNgiwac19Bno+RdFbAFKH0p983oAQ8na0b+1RlVWStOf9oXv73VBS9VuZfQN390q0R1RZ1Rbb1wf9lQrc0bn+Xu2sKBgudrJeBCIpVI4OZgjT3Tg/Hly22qjmneF1rndumYpw1xCVRRz94K7g7WiPpyCA580EvnfcqubNrYzQ4udpbwdbUVHWw5nLZZ/Pl17sB2fQrQBPk488YRiEWf9RMAZLRxuJ+mf3ljc1LnBd/CwkJMmjQJW7ZsQb16Gg0CRVFYs2YNFi5ciLFjx6JNmzbYsWMHiouLsXPnzhrssX70a94AZz/ri93vd8ePr7YDoAy80YWQpf9OUp76b5X2ZGJVmh5DfT5d7a3w29td4OFkjWl9ArBwpHDydy6BkO2TxDVphLTy4EyRMndoC3z5UmvcWhSCbgFuODW7j8GR/W48QQZs3uIpEEJHtSipxlm1mxfy65NAE9QhxtSuT4ql+2kFmD+sBT4Z2JTXfOloY4lrCweif3Pdgj072ndqT3+DNQ10AeXCQ/5y3XyCjFDWAZ96dvjvg56c71EU8HL7hnidZxzXTuiAjwc2xfa3u+B/Q5pj8xudeAUuXXkrm3k4okGVltxCJsXGyZ3weMVwbJhknCwUdHTdO+zNly4BsQHL7MkXjONka4HeTYVLOZdXUjqFND93e3XAY6fG9dDaWznvSSQSBPk4816fjZiNZCtvJ3w0oAmaNnDAkY974c1gP8H2W97szMguorqEmECytRM6oJmHA74b145xvFPjerj6+SCGH3FIlbKjgZMN+rdooBbC6EIhXfASu2m+vnAQ/nynG8N66GJnCYlEAhtLmda9wCeEOtpY4vSnfXivw7e5Vp6T6xhtoyMwj3QPcBM891s8RTv+EZk5BlBajGRSiU7hla2wsZRJce3zQTj3WT9BoZlh4aK94MuQIBRjzk6PSFFUtTW++roLOlhprDP0INjaSJ0XfD/44AOMGDECgwYNYhyPj49HWloaBg8erD5mbW2Nvn374tIl/owAZWVlyM/PZ/yraSxkUnQLcFObFRazdrPrJnbAm8GNMbGbZuHu1YR/4amg+Svter87wub2500RpYsNkzpiWt8ADG3jif7NG+Dq54OwYHhLONkIByBxvc+eXNkT35gODbH5jU5o4ekEL5bQ5mpvhSk9/NSLYVMPR62F/cP+TXSmAQKU6eT4oF+3T7P6+Gd6MGYNaorVrykXsYndGmHfjB7qNqqNxLpJHfDRgCbY+Z5SUzG0jSdvoIxEIsHiUa1w+tM+jN907YQOiF0+DH4sU5gE+rl3TO8biNkCQRcyiQQNHG3w29tdtdJrqa6zaXJHvN7FV0vo/nQw87wLh3NvgA592At3lw7GMppmjT750yO7tfrHM2vp0sy093XBAJamDgCiU4Wf8VHtvPFpSDNIJBJ80L8JBrf25BV8hXwBAe7NnCHuFAAz4MjDyVprUb+2cCCufj6Q16UgfF5/xmtdQht7Y9DN3xW+rtr3nUQiwapX22Pe0BacaQsBpauDLiFNlb5s/aSO2Dejh8GbcjEa39mDmuGzwc1x6tO+agFb8JxSidoSB4BTIFXB1g6PaueNk7P7ohfP5oB+H/MZVKQMwZd+L4q7l+o7WqNXU3fcWzoEj1cMx50lg3F9oWYNpesgXu3sg6m9/LXmU5VGsEkDR+yY2hU9m7ghbC7zntLXR56p4Rf+LotG6q/V7eznqr8rnI5++Ltru2RYWUj10rhWN/NGJ1bcyODWntXOSMNlWRXC0HmsJjCr4Bsfb9xk2bt27cKtW7ewcuVKrffS0pTBQR4eTF9XDw8P9XtcrFy5klFG2dfXl7dtTdHC0wljaeWNR7b1xlcvt8GKMUE4+1lfbJrcCZMF0sB8PLAp/NzssGRUK0ilEvi6Gp65YXiQFxYMa6l3BCfXM+LMytZAn/h6N3XHguEtIJEo8x/S/UzF5hdt6uGAwx/11hKa2dADAdmwu93ZzxWzBjXDmA4+iFgcghVjgtCpcT1cWzgQcSuGq9s1cLTBZ4ObqyddFzsrrGPlCFUR96wQljIpmjRwZEw+o9p5w1ImxcnZfdGzCdMXWUhYp8P20+T8jrQv6cKR8QBQuoZ880pbLa2LvZWMYZB7r08AVo4NQld/V0YFqaYeDnCysWT49Im9BQ29VwHuxVSfwB0VQpWoHj8r4n1PzGLEZUngyvRAzw5CUcp7UTUv9Ah0g4udFTycbDiFp8ZudnBzsMb7fZQVq/o3r69z/Nlp5CQSCf6b2ZNTk17P3goz+gXyliBXCr4Shr/ukY+ZQVlc+cYNoR3L5/XkbKaG0lIm4fWrFIKuaVX9rOzfN/qrITqDWdnQT8FXrIXehi748v2Gm3msX1KpBFKpBM62lozz0J+TEW29EVjfAbcXh2DT5I7oEeiG78e1xX8zNb9732b18de73bV+b7ZPKj0jC5dCkS4A6hIGX+nYEJ0b18OnrE08X9YX1b1OV0yIQdcTa+hsRJ/HDHHHcbW3goVUAltLGWMTNiW4MZp5OFY7uE2o2FRdx6yCb8uWLTFr1ixkZvKbMcWSmJiITz75BH/++SdsbPgXLvYiSVGU4MK5YMEC5OXlqf8lJhonYt3Y8KV9CajvgKFtPAXNQL6udjj/v/5qn0J9oAdlVaeQhEQiwfqJHRmLPNvFgD65f/tKW7WJGFBqUn97qwtGt/fGWp5iH948O/tcHWlghGgiENlNFxIbONqIEnK4JumwWM3zwSW0WFlIsXQUXVMqQUMXW63UQFx8zqOBpUPvN1toEJqfu/m7cj5bE7o2wp5pwfjyJU2fVZogeo5Ksabr6mhHuH4SQ6pKNXbVCPHsVE9CObjFCO0z+wXCzkrG0BQGNXTRateeVg5cpQ1dPiYImyZ3xOY3O6vfS8jWLrKh+s7/G9Icf7zTFesndRTVt8CqzUtXf6Uw5+Zgjfa+Lrz5Vvl+K9UYNaFZFNjPC9/zqy+LRrbCoJYNsGRUK9xdOpiRV9XZ1hIPlg0zaDNFd82SqjW+zDZ2VhaMZ4YdNMYFvYYX3xxCH1e6AM4XOzC4tadeGVwY/vZV/9tYyjC0jRd2vtcd4zv7iiomMb4zU3HUj+Ynz6VRlDJcHYTPbWdlgX9m9MDHA5syjv8ymVvIV927+m50dd0axkjCwtaW3vhiEJaNbsPYbLKHy8vZBtcWDsKVBQPhSlt73q/KJFHddGa5JYavk7Udswq+YWFhiIqKQmBgIJYvX47iYsOrHt28eRMZGRno1KkTLCwsYGFhgdDQUPz888+wsLBQa3rZ2t2MjAwtLTAda2trODk5Mf7VRt4IbowFw1rg8Ee9dDc2IvTHk62h5YMu8KiQSpTJ4Ze+1Br/G9Ic7X1dtEpRSnWYvfq3aIA1r3dAAI+Glr1o+FVFqNIPuztY6wyEaNPQCefn9MO/M3vA18j1z7kEA/rixWdu4lqoxSyqg0Tk46SPNdslojqTfNMGjpBIAHcHK/VvS5c57Wg+YuyMB2togWvVSffFtSE0pFgMXdj4bhwzqJYv2wEgrrJYAyflgkYXXrn89dzsrbBhUkc0drNT5z62tVIKJ/QFc85gbTemSrmmkEzvpvXVY6/KvcoefxU/vd4Br3b2YZRbBYCFI1rivd7++Ps9ZtAR329V31G5UNPflUkkGFHl/sN3fUMIqO+ArVO64O2e/louVsPaeBpsom3SwAFSibKaW4Mqjbyue3MGq8QyF/RT8G0c6H2m320OApXl9DFd04fEmNn16O4qXBXJ6Jcy9DnvxpOZx9A4XP5iwkpMURrY3cEab3RvjKJyTTwLO7jN2dYSrvZWcLazZORJVikVqqvxNaQc+7tVa7g++YtrArMKvl26dMGpU6ewd+9e/Pfff2jSpAk2b94MhQGVwQYOHIjIyEhERESo/3Xu3BmTJk1CREQEAgIC4OnpiVOnTqk/U15ejtDQUPTooZ+pozZiKZNiWt9Asyf8lkiATwY2hUwqwRcjxPlYTeEw3dIntQ/6N8F/H/TUSiLvU88WMqkETjYWoosW8NHQxVYdNECPkL/xxSDeQAgAGNuxIfbP6Ak/d3t0aFTP6LXc6QuYKk2Syl8Y4A9SbORqBxtLKdwdrNWLlJCWH+A3m7ILRtAXPTsrC4bLhtAi0IjHrK3C09kGp2b3xZGPNamb+FIW2dEEt/d6+6uj2QHdmiAhVOZOOmIKHLChb6okEgn6VQUCNvdwFDQRitUuO1QF1qjg+m0tZBIMD/JC6P/6C84D4zv74Pis3ohdPkx9jC8I793eAYhfOZw3/2ibhs74blw7rZy2NpYyLBzRCsGs4izs3+rbV4IQ1NBZnTKOXuXM1kqGdRM64NrCgYL5T43BqlfbITjADR+xtIX64Oduj+sLB+Hi/AHqjUMLT80cpgpM83PTLzqePsdwpWsDmAIxfZNjJ+AHrY9hQyKRoIWnIxytLdCWw9pgDI7d03Y5pKdmq67fawdWuVxDLff9W3AH+b7Vww/DgzzR0sswy6cqfkJIgUR3TVP1f9HIVnCwtsBcmqLDxc4K3s428HSyUVvO9Alua88RUGeIQuDz4S1x5rO++HCAdsq/2oTJC1hwMXjwYAwePBi7d+/G4sWLsWrVKqxYsQJjx44VfQ5HR0e0acPUOtjb28PNzU19fNasWVixYgWaNm2Kpk2bYsWKFbCzs8PEiRON+n1eJNzsrfF+Hx98OKCJ6EICAHDzi0Ho9/15FFRlZBCzm2/gaIMrCwbCSiatdoWxYSLzH7LxqWfHEDre6uGPP68k8FaV0xf63DRnSHO81zuAYULkSxdmZSFFxOLBkEklvGbanyd0wMd/31a/5hvx4UFeWD+xIz7YeUvZTmREt4o/3+mGf24mYmGVtlAoxRlb0H27px/ySyrQnxUARJ+0F7I2WPpUumJDT6zepqET8ksqtSwNYmD65yldgNzsrTFnSDMcEyg+YmiO5rlDm+NqfBbS8zW+xWIFA6UQw9xUCpUhr44PNRv6vePuYIXXujTCa100QXLZtFK2PvWUG6cGRswNzcfYjj4GpW1k48bKauLpbIN7Xw7B7YQcdKny7R3SWpmzWWxSf2sLGb4Y0RLlcgXvWNBTR3Xzd0NnP1c083AQDKh6VsDvl87FgQ97orxSwcjVWl12XRd2HaRXUaxusNSmyZ3QbcUZ9WuuzaOYW93L2Ra3F4Wgw7JTjONLOSyZ+mBnZYGoL4cIamZ7N62PuKqYAdXv904vf7zVw4/xbMmkEpz/X39IJJpnTihlJhv6fNvC0xH30wrQ1kd/pZpUKhGMkakt1Ijgq2LEiBFo2LAh5s6di/Hjx0MuN26uyrlz56KkpAQzZ85ETk4OunXrhpMnT8LR0XDf1BeV1a+1w5W4bHWOUn2EXkC5QOydEYyha8IAiDefsSvH6IuzrSXySirQWkQ2Bzp9mtXHhYfPMJ5VgrRJAwdEfTlEVBERMQS4OzCEGTF+cyp0bQZGBnnh1tMcbL/0BIC2vx2dtj7OkEgAb2dhVw6u361XU3dGhLpMKoFCpLbA2kLG0FyoEFrzuCZWoZKzbDZM6oibT3OwcHhLgxdXdvR5C08ndbrBkFYe+OqwprrixwOb4uczsQDE+zGz8alnhysLBuJpVjH6/XBeqw/6oqvcqbGga7P2TNMOwBRyC6mrOFhboHdTjZZQIpHorcF+t7e2ZYIO3WWjsKxCa3PIRXJuiV59sLaQmb3srJ+7PWYPagZn2+qLJh5ONupqpAAQQivqpHomPx+mO+YBUAZrXl4wAMErz1a7X3R0BXDOGdJcPX/T4Xr22YK9RCKBl7ONOi/4S+28cfBOivr9CV19UVqhwLhOPnhz2zX18dWvtccfV55iUjfxaTLrGmYVfLdt24aoqChER0cjKioKycnJkEgkaNSoEUaOHFnt87PLI0skEixduhRLly6t9rlfdMZ08MGYDtXTkNC1vNVNtSKW397uguvx2RjVVmMqt7eS6SwIsOPtLigul3NOTMaKNgeUZquP/r6FWYO4faJebt8Qe24kiiqWwUYqlWDpS63x6eBmuBKXhb4CeXl9Xe0QNrc/nHT4bYvZ8Pw+tRs+2HkLy17WrjsvFiGXColEgsD69mpNSGM3O4YPsC6GB3kxEsYbgkzCFHzp0INn5g9rwUgfaG1puLaarYk1hW+hsfFytsWmyZ1QqVBw+uLLDTCnEpgY4IpZK2DnAFfxySDD3U/YXFs4CAciUjC+sw9jszBrYFOMbOuls1w0HS9nW+yY2hVTtl1jpJk0JexMKvry65QuWHksBv8b0hyeTjZo4eWI744/AAA42Vpi5Vil4E93wWrp5YQVY4I4z/e8YJaZMysrC4AyY0JERASaN2+OxYsX49KlS8jLy8Pjx49x4MABc3SFUIPQZd3qBCjpQ8dG9TCtbyDDBChmqZVIJEYVcPlo5e2EM5/1w6h23NW+ggPdcOOLEPw6pYuo8/07U9t/3cnGEoNbe+rU3vjUs+PNv7zq1XbwdrbhzcvK7vPNLwapA5UMYWBLpesDXzL3uUNbQCIB5gxuhtD/9ReszGYK6Lcv+1a2kErQI9ANQQ2d8V7vAIZZurouO3Rk1QxeMRfKnNXc97fKN9lQTThB2G2FjsoHn11pzNTQs/XMpAX38WXjMSbuDtZ4p5d2UKNUKkEzD0e93Xr6NquPW4tCtII7Tcn6qrSXP4xvp6OlNq28nfDHO93Q1scFDZxsGOXY6QuhKqf38jHm+141iVk0vk2bNsWyZcuQmpoKaR3QUhBMA9svsqaoa9ZVfYSCDo3q4dCHveDqYFxBQl+fyOr6ibo5WCP6qyG8wvqQ1p64s2SwzkIppqKtjwskEmUAJjtJv0QiwV/vdlP/3T3ADY42Fghwt4enQElkMXi72KKhiy1srWSCgUx1hSUvtcLXh2MYAhFBP3RVC1Sx7a3O+P7EA630X6aGPhdM6xuI9r4uSM8v1QqErCuYe5M2oq0XBrYcatRNM8DM8uFTzw5Pvhlh1PPXZswi+M6ZMwcLFizA5s2b8fPPP6Nv377muCyhlpFJC64wZsCEvuhT87wuEmRAUEJthJ7ejIuaEnoBoLmnI64uGAgHGwtOP2H6Yt+xUT3cXTJY67ghWFlIcXZOX0glkjpVKYmPFp5O+LNqk0AwDLHxBk0aOOKXNzrrbmhk6Leps63S+kTQD2MLvUDdUwAZE7OoXz///HPExsaiU6dOGDhwIF577TUkJSWZ49KEWkQgzZ/K1kjBYYagKufsbUDFLgJBRQMnG53CuQqJhD/7hr5YW8j0Di5VMa0qndtrAoGOhLrBrEFN4e9ujzeD/Wq6K4KYI56jupaUFwlVqWlVEZoXEQkllHvIBNy+fRuzZs3CzZs3MW/ePMydOxfW1tWL3Dcl+fn5cHZ2Rl5eXq0tZlGXSMgqhr21TCsNkDnJLirHH5efYmzHhrwlVQmE5xG5gkJkch5aezsZLDwTCPrQ85uz6owSpjKnH7+XhmWHo7FybJBBgcAvEk8yixCdmo+hrQ0v3lJbESuvmV3wVfHPP/9g3rx5UCgUWLVqFcaMGVMT3dAJEXwJBAKBQDAMv/lH1H+/SH6kBPMjVl6rsS3/uHHjEBMTg2nTpuHtt99GSEhITXWFQCAQCASCCXASKKNMINQEZr8jy8rKEBMTg8jISNy7dw/37t2DlZUVzp41bmJoAoFAIBAINcuLHERFqJ2YRfD98ssv1YJuXFwc5HI5XFxcEBQUhKCgILz00ksICnq+EyYTCAQCgfCi8V6fAKw69bBaeb0JBGNiFh/fdu3aoW3btmpBNygoCD4+1a+Tbg6Ijy+BQCAQCIYhV1C4m5SLVt5OZi+BTHixECuvmUXje+fOHXNcxiSo9gX5+fk13BMCgUAgEOoegS4ylBUXoUx3UwLBYFRymi59LvE610FBQQEAwNeX5L0kEAgEAoFAqM0UFBTA2Zm/kFONpTOrKygUCqSkpMDRUf+63oaQn58PX19fJCYmEtcKGmRc+CFjww0ZF37I2HBDxoUbMi78kLHhpibGhaIoFBQUwNvbG1Ipf9IyovHVgVQqrRF/ZCcnJ/IQcUDGhR8yNtyQceGHjA03ZFy4IePCDxkbbsw9LkKaXhWkdA+BQCAQCAQC4YWACL4EAoFAIBAIhBcCIvjWMqytrbFkyRJYW1vXdFdqFWRc+CFjww0ZF37I2HBDxoUbMi78kLHhpjaPCwluIxAIBAKBQCC8EBCNL4FAIBAIBALhhYAIvgQCgUAgEAiEFwIi+BIIBAKBQCAQXgiI4EsgEAgEAoFAeCEggi+BQCAQCAQC4YWAVG7TgblLFhMIBAKBQCAQ9IOULDYSKSkp8PX1reluEAgEAoFAIBB0kJiYCB8fH973TS74Hjx4UO/PhISEwNbW1gS90R9HR0cAyoEkdbgJBAKBQCAQah/5+fnw9fVVy218mFzwHT16tF7tJRIJYmNjERAQYJoO6YnKvcHJyYkIvgQCocZJzSvBz2diMaWHH1p4kjmJQCAQ6OhySzVLcFtaWhoUCoWof3Z2duboEoFAINRJZu2KwN/XEjH8p7Ca7gqBQCDUOUwu+E6ZMkUvt4XJkycTzSqBQCDw8DC9AACgIMXmCQQCQW9MLvj+9ttviIuLE91+48aNcHd3N2GPCAQCoe6SU1xR010gEAiEOotZXB06duyITp06YePGjcjLyzPHJQmEGqW0Qo4N5x/hXjK53wnGRUqyKhIIBILBmEXwvXjxIjp27Ij58+fDy8sLkydPxrlz58xx6ReSlNwSZBeV13Q3XmhaLDqO744/wMi14TXdFcJzhoM1yUJJIBAIhmIWwTc4OBhbtmxBWloaNm7ciKSkJAwaNAiBgYFYvnw5kpKSzNGNF4K84gr0+OYsOi47VdNdIRAIJoD49hJMRVFZJT7ceQvHIlNruisEgskwa8liW1tbTJkyBefPn8fDhw8xYcIE/PLLL/D398fw4cPN2ZXnlseZhTXdhReeSrlC/bcFsUsTjIyCIpIvQTzPCspw5G4qKmjzEh8bzj/C4bupmPHXLTP0jECoGcwq+NIJDAzE/PnzsXDhQjg5OeHEiRM11ZXnChlN0KLIAlkjlFZqFhg/d/sa7AnheYQIvgR9eGldOD7YeQubLzzW2fZJZrEZekSobRSXV9Z0F8xKjQi+oaGhmDJlCjw9PTF37lyMHTsWFy9erImuPHfQBd9KYhOtEUrK5eq/rWQ1trckPKdUymv+ua6UK1BWKdfd0EzsvJqAvt+fw5PMopruikHklVTgQESySQSQ1LxSAMCp6HSdbeOeGW4xzCgoxcJ/IxGdkm/wOQjm58rjLLRafALLj0TXdFfMhtlW5cTERCxbtgyBgYHo378/4uLisHbtWqSkpGDLli3o3r273ue8cOECRo0aBW9vb0gkEvz333+M9ymKwtKlS+Ht7Q1bW1v069cPUVFRRvpGtRO64Csngm+NUFCqSTelo4AMgaA3Nb2hVSgoNF90HP2+P4/Sitoh/H7+bySeZhXjo79v13RXDOKDv27hk10RmL8v0mTXEDMXudhZGnz+xf9F4a+rCRj+MymsUpdYeTQGALAlLL6Ge2I+zCL4hoSEwN/fHxs2bMC4ceMQExOD8PBwvP3227C3N9wUXFRUhHbt2mHdunWc73/33XdYtWoV1q1bh+vXr8PT0xMhISEoKCgw+Jq1HRltdsspFs7skJhdjM/23MGNJ9mm7tYLRQVNI0es0oTnjYcZBZArKKTmlSKtSptYW6hNWmh9CH+UCQA4eCelRvshgeE79YcZz++6+jzzIuYFN0teHFtbW+zbtw8jR46ETCYz2nmHDRuGYcOGcb5HURTWrFmDhQsXYuzYsQCAHTt2wMPDAzt37sS0adOM1o/aRBHNzJ5dVA4vZ/6qeSuPxeBoZBrCYp/h2sJB5ujeCwFd007kXoKpqFcN7Vx1KCqrvcKlrVX1l7SySjkO3UlFm4ZOaOH5/FQRFSPSVsdCZW1hvLWdYD4Ssl88v26zCL4HDx40x2UYxMfHIy0tDYMHD1Yfs7a2Rt++fXHp0qXnVvClZxSo0OELeOtpLgAgo6DMlF164SDBRwRzIKkhPxr6/V1b7/XySgVyS8rRwNFGr88pFBSaf3EcgNI//+FybsWKsVAoqGr51eqDmPulOrcU8eqqWxSXV2LLhRfHvYGO2SNvwsLCMHnyZAQHByM5ORkA8McffyA83LiJ/tPS0gAAHh4ejOMeHh7q97goKytDfn4+419dorSCLvhyp69ZfiQag1aFIrfE/EUuTkWnY/25R89NxomH6QXYfyuJ8X3owsDz8j1NyZ3EXLzx61XcTco1+BzX4rMxe3cExm64+MJEKBtL0DgZlYbQh89Et6cH19WUuzFFUYLP1hu/XkX3FWf0DrQqpN075SLSf1WX7048QMjqCya/DiBS41uNu8rNwcrgzxqbm0+z8dovl3HzaU5Nd6XW8tmeO1h9+mFNd6NGMKvgu2/fPgwZMgS2tra4ffs2ysqUmsaCggKsWLHCJNdk73IpihLc+a5cuRLOzs7qf76+vibpl6mgB5uUVXBP3FvC4vEoo5AhJJuL936/ge9PPMCVx8+HX/Hg1Rfw6Z47OBGliZiu6zGFe24k4vBdw30NKYrC42eFoChKVIDl3H/uIiw2E3P/uWvQ9R6kFeDVXy7j39vJuJWQi51XEww6j7HYdzMJo9aGIyO/5v1fFQoKmYX8Fp2M/FK8/8dNTNl2DUVl4jYMQhu73dcT8P7vN5CSW2JYh0VQIVdgxM/heO2XK1Cw7i/VzH41PhsKCjh+T1OIgaIo3EvOE/QDNtY+9UBEMk6LyKKwKTSO83ilGYRuLqqj8c0rqT2+ov/bexdX47MxccsVAMDTrCIMWX0B+2+RYlkqjt3jVwA+75hV8P3666+xadMmbNmyBZaWGv+0Hj164NYt4ybM9vT0BAAt7W5GRoaWFpjOggULkJeXp/6XmJho1H6ZmlLapJ6cK953h72AmJqnWXUz7RAfdG1lXc6mkZpXgrn/3MWHO28bHLH/zfH7GPBjKPr9cB5BS0/oFAAepCuDYu6nGRYck84SME2hqbscl4Uhqy/ggg7NaIVcgc/23kFkch4+2HkLMammsxiJMV1/tvcOuiw/rdZ8nYlJR9/vz+FSnDKY6hHNzF4i8vem39/sW33evkicjE7Hr+GmM6FeePgM0an5uPYkG/tvJ+N+Gv8Yy2mS7L5byRi5Nhwvr+NPnWkMC01aXik+2RWBd3+/YdDnnxWUocNXpzDPwI0gH6b2jCksrT2WlsdVae3KqnKqL/z3Hh6kF+DTPXdqsluEWoJZBd8HDx6gT58+WsednJyQm5tr1Gv5+/vD09MTp05pSveWl5cjNDQUPXr04P2ctbU1nJycGP/qEo+faQRKfYINknK4NTR5xRX44/ITZBcZ1y3C1LJhYVklrsVnm02gl9cBv0cx5JdoFi++e0IXv4QqE+U/zSpGcbkcC/41XYomQHtBz8hnajgTs4tx+G5Kte6F//1zBw/SC3RqpenC4/UnORj2UxjOPcgw+LpC8Akylx5lIrlK4/rv7WRQFPD75ScAgFm7IvA0qxgL9kfiTmIuJm65qv6c2PuWKfhyf8aY2R4ePyvE5/9GIjG7GI8yCvHODo1AeS85D+M3Xeb9LN0t4+uqPKX30wpw8VEmp5BrjEeXPlfqEqS5Cjv+eeUpCsoqsfuGcZUuYtwYquM3XhtcHSiKwpRt17SOp+aZzgJBqHuYVfD18vLCo0ePtI6Hh4cjICBA7/MVFhYiIiICERERAJQBbREREUhISIBEIsGsWbOwYsUK/Pvvv7h37x7eeust2NnZYeLEidX9KrWWVac0Pjuf6yFw8GnJuqw4jUUHojDzr5vV7htdg2hq7cOQ1Rfw6i+Xseu6eTT2f9PM63QBq67JwHRBZtlh4yQ0f2bE4MnE7GItTTRbnrW1Ym74Zv51Cx/uvF0t055qE5Cmw32B4niM9hhwD2bkl6qDnkrK5Wphk35vcY3rtfhsTNx6FT2/Ocs4rsr9W1DlzvA0q1j72RB5r4oRfIXiB/Q1iU/YcgU7rybgrd+u4TxrE0FRFAoENI3xtIIW9K5O2nqVc0PC/j76aIDT8kqxNewxYmlpvfTda7XycjJZXECMgGZchbGm5TxaiqyswjKzxTo8KyzT8lenKApxz7QtjOn5pTh7P71WxWFkFZYZHKOQVViG0esvGsXVq1KuQNflpzF1+3XO8XmUUWBSdyZTY1bBd9q0afjkk09w9epVSCQSpKSk4K+//sKcOXMwc+ZMvc9348YNdOjQAR06dAAAfPrpp+jQoQMWL14MAJg7dy5mzZqFmTNnonPnzkhOTsbJkyfh6Oho1O9VWykuF2+qrlRor9gpuSUorzIVGcMnd/JWjXbJlHNNRkGpWuP1z03N4v79ift4ZeMlxqRsLPJpi6+8Fk2k+kLXjOgT8KQLY2gAbyXkoPd35zByrSYQVq6gEM+KimcPf2RyHgCYTPNKh0sQrGevvyZs5NpwhKwKxaOMArRcfBwjqooCXNeRc/vGU+73j9xN1TqmpSkXuUGRU9obu4SsYryy8ZL6OF/p239vJ6Hdlycxa5f4QhPpVRr8uGdFkLI6HZvB/O0jEnMZr0/S3Gwaudox3guPzdK6FvvXS9Xjvl2w/y6+PhKDT3ZFqI/pa/2RSk03f7TzcdHZhn1PGCoUfnviPgBg/blH6PT1afT74bxB59EXLq02u+BLUVklrj/JRq9vz2Lq9hsG5U4urzS+O1VmYRl6fnuWMb/pw+rTDxGRmKuXwouP7048QEZBGc7ez1DPn6oNb2peCQatuoAhqy/UmC96dTGr4Dt37lyMHj0a/fv3R2FhIfr06YN3330X06ZNw4cffqj3+fr166eO7qX/2759OwCl2Wbp0qVITU1FaWkpQkND0aZNGyN/q9oDl3DBNu/y3ahcZVCFgmIM4QYtwjYppxjX4k0T4PYoXbMY3krIRX5VJbX15+Jw82kOXv2F3zRqDOh7CErPTL7GSMBfWiHHr+HxSMjSPz/jurPaFplfQuMw/KcwURsGPneC6NQ8UdcXKqZy4LYyC8wjmrDz8a7bWHqIqZnmG3NzpFviEnTo5myl9qmQUdaai4yCMigoYMelpwA0/s+/XXwi+DlZNUwpI9eG40SUsFY8I78U0/7QWH9U3/eHkw8YEfTujtaMz115nIXolHzM3q30sfwvQlvYKCyrRERirqCwxdbuXorTFl757lO2WwHXfaKl8eXtiTbnHmhvFFXnO3gnBV8fjkaODpexRxmFWlri+Mwio2QqkXH5VbCg++nuvZGIbivO4F6yuGeXzs6rCYhNL8C5+8rN5tOsYq1NiSng+orsQxO3XsX4TZfV6T7DYjNFn7+wrBK9vzuLZl8cM6piAFBu0EsrFHj8rIjHDYcStJbo62PdPcCV973NFx6r//5szx18sus2On19Ck8yi9Sb2oKySrUPdV3D7OnMli9fjszMTFy7dg1XrlzBs2fPsGzZMnN347mEyzTNvjHv8aT34SqDyn72vjwUhYSsYszfdxenotNx5G6qwRqBDefj8Oovl40awKFK3xaXyTRrhT1kTmwP0gtMGoGsj68kXdBdfiQazb84jkuPxE/EXPx48gGWHY5GyOpQvT9rb62d2nvlsfuITs3H1vDHHJ9gwldOV1dOaRXsZOqFZZXqQEgu/0MuTWZpuRz30/JRVFaJFVXlOJWfF742RVG4+TRb0J+9PkugY8P19em3w45LTzDwx1C0XHycNwiSvnlg91lXABq9vSFBlj+djhV8f+9NZlS86hKPM5maV/oGOzWvBK9vvoLhP4ehU+N6jHb0lItj1l/E6PUXcYj1m07s1kj9t5j0S+2+Osl4/bhqoyFqNIysbB2/6TIORCTj479vY2t4PDosUwat8WkMSysUjPvlblIu+v9wHv311Jg+yijQEpYLyyqx/twjzNl7B7kcVT0fZRQwlBP/++cuMgrKMGt3hM7rlVXKcf0JM3VYyOoLjPNFpegvQKv6ved6oqg4EzHC/R2WAC7iI2p+OPEAidlKq9gnelgtxEC3ZtAf3bhnhRi0KhT+C46i/VcnsZfm+52QVYxjkcp1mO/WLa9UIColDxRF4VlBmTp7Szd/N622D9O1A4xjMwpxICIFucUVOHovlbG+1XT5dEMxu+ALAHZ2dujcuTO6du0KBweHmujCcwnXTcv2h+TbFY5efxF/XX0qeP7fLj7B6A0Xset6It77/QY+2HkLZ2K4zcf30/KxLTweFx4+U0eQc2GsAI5vjt1Hq8XH8SijAH9dYX4PrnzGxjbRdPPX7J65TMFchKwKRdulJ9W/kapW+iccC83KozHYcemJqL5cfKTUgtE3PXklFThyN1V9reyicpx7kMEQvMsq5YLaDzHmPT5hi8uiwAVb0Ju89Sr6fn+eoeXVRXxWMYauCUPrJScYmgtdwT1X47PxysbLmP6H4f7sujY9dO0037NIv3/+rdJyq2A/zxRF4afTsfCbfwSH7qQwFs9T0fr7NOvaHFxkbcpU35f9+ybllKgtLQdo2t16dhq3j7+uPkXThcew+YIypZfKbeEA6ztX1/I/4MdQtFl6QtT9y759q+v/eTcpj+H6ACjnvN3Xuf0wHa0tGPeQSgOfnl+GSVuvIFyEdvLGk2wMWnUBM/68xUhRd/NpDr4/8QD/3ExC+69OaX2OvalRIebZE+PHvuWC7o0zV0q9BfsjMXffXbyz47rOz3Oh6xdku88IcZJmEdHnc2Kgn43uerju7CP1b0BRwJFI5cYwObcEfb4/hxl/3YL/gqO8z8nMv25ixM/h+OlMLLosP42Oy05VnUv7Ayp3JRtLbtHwwsNnTMGXYx29m5SLLCNbi42NWSq3TZ06VVS7bdu2mbgnzzfJHM7mzwrLGD6GXL68Khb+ew+TujVWv+Z6jti77vMPMzColXZ6uKFrwhivLy8YwHtdQ0jMLsa/t5PxZnBjuNhZqfNhrjr1UEvQ5RJGjt5LwxvdG2sdZ0NRFLZdfAKferYY0tqTeV7aKkmfBPN1aJMTs4txJylXnXLnvd9vYP6wFur3PZ2U1aZ+DY/Hiag0zBncHL9ULRpTevgxzpWeX4q4jEL0aOKu6Rft+5ZWyHEiKg2rTz3Ek6xivN7FFzP6BWLuP8o8lx8NaILPBjcHoDRHstl+kZaWSsQ8z+efmFNcjhVHY9CvWX1GX9nYsDKRqMyjL60Lx6udxeXU5ks5xl6ncovL8emeOxjXyQfDg7xwtGpBuUZztzgTk46H6eKFbq57jW/hnb0nAjnF5fhneg+Gpoq+eWCb9oMD3XCV5iKkoDRa0I/+vo3FI1up3+Pqt0SiESS5fk6htfxBWoGWa4Fq8WSnossrqcDo9Rdx9rN++ObYfc7zLfz3HgBgxdH7eL9PIG8fjBF8JFdQogRftt5MYLpkoG8A47NC5TzKFrQVFH8/Lz7KwqW4LMSvHKHsK0WhqFwOB5aVZndVX0IfPuMtYkTnQVoBIhJzIBfYnOYVV8BZoER2lght7BMdrlehD59h6vbrmN43AP8bopkPD1X54N5OyIVCQUEi4c8+wXWr6BoD/nNRuPI4G808HODmoLT0+Ne3R0qVS6EpXaeSc0qQmleKnk3ckVHAdGG0kCqFUraii+/XO12lnFpTZc0pq1SAoihO65RqvvFwsuFcD648zkYbb838ylZ0RCbl4aV1F9HWxxkHP+zF/wVrGLMIvtu3b0fjxo3RoUOHWhVBWdfJyC/FnhuJeLWLLxo42sDF1lIroI390Osyf9ILfIjxK/vzSgIC3B0wtZc/CssqsetaAoYFeWm1u5csPp9pSm4JZFIJPJz4y42O3XgJzwrKEJmchy1vdlYfP3f/GTycmOZoBaXtO7vov3uiBN+IxFy1C8mDr4cyUsTRzc63EnIgV1CQSSUMDQnXaA9dcwFFtN8pLDYTYbGagAbVQqa67m904ZPFjD9v4lZCLjZM6ojhVeNONz9N3nqVYW7cdT2REc2/9uwjteDL9WjSNZT3kvMY90deSQUcrC0YQlsZjyn+i/+UQs7mC49x44tBcHfgdhlwtrVETlE55u27i3GdfNTHi8vljHy9g1aFYma/QK5TiGb1qYc4ez8DZ+9n4Mk3IzgXMnrqLD4yCkrxa1g8olPzGX1WsfNqAuYNaYEfTz1gHD9b5f8YkZjLcAHgMx9SFAVvZ1vGMbagTc/OspsljGUXlevUnsYKCPl3OCrrqbrqZGPBCPAEmKkVq4Ox0gO6OVipN5uA0oJVWqHAklGtYGOpfK7ZAryuQLPvT9zHkbupOoU6NnxCk4ICwmladW0NtObvT/fcwZG7qfj3gx5o7e2sPl5I05qKsUSPWheuc1OQlFsMZztn3veNke5y9amHkCsorD8XxxB86bT76iQ6NKqH1t5OOHw3BX+/1x0+9TRBi1xfV5fvK99m73RMBt77/QbsrWSI+mqo1vv0a0Wl5GHevruY3jcQI9t6C14PUMZNSCUSvNdHk82K3o8BPyrd1H6f2lWr/5YyCRKzi/HlwSid1+GjqFyOewKuJ4H1HTgFX4AZMMqeq07HKN+7m2SYW4u5MIvgO336dOzatQuPHz/G1KlTMXnyZLi68jtWE8TRdcUZAMD+W8k4O6cfijmEjoMRKYhMysOluCy093WBpUx4n5qcW6KeSJYdjhFsq+Krw9GY2ssfM/68ibDYTE4/wTMxuqsYAUoNZY+qVEyPlg+DhYzb5KJK5XSZpYEqqZBrLUIKiuI0s5VVytWC7OnodDzMKMC0PoEMQS6zUDOhv7T2Ik7M1uShZlTJq1TgaGQqRrXzxlpagFhBqUb7WyFXwFImZQi9XCgoimEqsrLQjEGrxcfx65QuCA5U+mfdSsgFAITFPlMLvnRN9A0dJTttLTWCvK5AvIuPsrDreiImdG2E+Mwi9P/hPHoEumHne90BKAXhTl+fFjwHoDSd8gm+kCgDpU5GpzMmWIBZaehRRqHeyejZCxxbS2VoDtMlB6LUfeNzFZmw5QqieYpZuLGyPvAFQC38756WKXjTeWblL7p2lW0BOs5K58a1CS6rVKBSruB87rjSp6nuNfGiqf5CrLHcCJs0cNTyQ/37WgKcbC2wYFhL3EvOw9u/Mc3pch0q3/XnuCuvGYqComBBm3+Eck+r3GD23khCZotyeDnboJmHI+M5EePnLUYT/vulp/h2XFtQFIXMwnItX3exPvxCiAl+KyitxIWHz9RWnV7fnsP5Of1AAfB3t+dUrOnSRrM15irCY5XXKCqXo6isUiv+QbmRVFan3BT6GPeS8/HhztuwkEowtI1yLi4pl+Py40y42lujnY8zJBIJsovKsbLqOZ3YrZH6vFyuWGfvZ+AOS4g8di+NMzVjuh4ZSNosOSH4vpCW3NXeSh2LcTcpD94ums14XVFrmsXHd8OGDUhNTcW8efNw6NAh+Pr64tVXX8WJEyeIBtgIPM4sQnp+KXI5opnvpeRh/v5IHLyTgq8OR2PRAeFdIl1bqW/VKdWiX8DhpyUmn+7NpzmMqHIxWpTCskqdE7dCQXFqnBOrHl6KovDu7zfw3fEHCH3I9Fl+QMt9+SC9ABRFqX0X2YFGV+O1I8zT88vw1aForDr5AEFLT4jy0SurVDCC73xpGo3icjkmVJXh5EOfdEj07yCmZK3KD1wVYEE3fR+ISOb8DBsh7dCywzGcmkVjQlEUIhJzGdaRXzhKx4qdm8TkB+YTegFtX8HvTzzgbLfzagLDXxYAfjylO9hLxc9nmBtSvn63/fIkbiXk4FR0Okavv4j4zCL8Gh7P2S8FpRwnscVBTvPEBNBzgl54yO1H/FI73Zo0Ifh+T1XBFa4YCVNla4rkyZSgoCh0aORCuz53dP+tBI0AH5GYiynbrmHw6gsYR0spp2orRH6puCDf3TcSce5BBvwXHEWX5ae14kGMvY7/w+NvzMXAVaEIWRWK0gruAEZdeW3tWHm/VbRuqNFwbwnTVpxIJMD7f9xE95VnGOvE9D81VWjXno3F1O03MHr9RXXatIE/nle/f/NpjtpPlkvxILaMOMB00aouDwSqaNJjR06zFFp1RZ4zi8YXUFZEmzBhAiZMmICnT59i+/btmDlzJioqKhAdHU2C3HRQUFqBGX/ewiudGmJMB21zau9vz6FpAwd1gEhrbydEpeSjd9P66mAnMbz123U8+WaEXg9cdRn+Uxh6N3VX+7Fq+nIN/87siaKySvx7OxmTuzfG2I0XMagl06f4uI4UTMm5JZzCoMqfia4ZKyxjCrM/nGQKFv4LjgIAPh7QBC+1b8h4j0+7so3mqrDunHDUPKD026WfSl9Tr77R/JmFZfjtYryo9DwSSKBQUJz3h9hgj5ziclTIFUjLK4UvK7dqTGo++jSrL67jeqLSSp2ISmMsToAyc4W3s8a1ZsXRGDRpoD0nmcKvb9vFeCx9qbX69RMTlfNmF9/gy2xSXC7H1rDHOBqpfK5G/BzGmxNcrqDw3u83ea0YYhfCHrSCG+xiOiqhuq2Ps0E5V1Xoei7o1g8VQjER1SG/pIKzJLiCAsMawjVvRSTmYuyGS4zXKtgWHl2b4OUirXoAGNpwdjxIDkeWCC4Kyyp5Nax05uy9g37N6zNKwfOh+l3zSys4taZ/sIKd2bA/o3LncrLR9HPN6Vg8yihkrKXNPRxxqsoqRbcM0qGnGFUVwcmhKajerKoyd/rTPiit0L7XHj0TH1+g9dmMAoYbiFjkCkowpzddIcbe8NaVqqVmE3zpSCQSSCSSKk1B3cwDZ26ClipT9IQ/yuQUfMvlCvXNuvmNTjgVnY6olHxsDeP3D+XjYXoBZrEikU1JdGo+p0YsKacEXZZrTOc/VWms2LlM83RMumvPPkJjN+0JQPWQ9vr2nPqYtYU4I8jPZx9pLSpWMqlWIAIbMXkPU/NKGanpxMixf19LxLQ+gWjsZqd3FPzyIzFa2QOEmPbnTfWEDyh982aHNBOVSghQpvQ6FpmG8EeZ2PZWZ6339UkvpA/ZReXYcP4RvjvOrVFNoS1Sm3ki0HWlEzOE7ZeeMARfUyTH1xf6BlCoEM61+CwtrQ8dQ1KqaZ2j6hT0jVWbhk56xQ0AQK5A0OmSA/cg5bjxTLU83XiawxD21dejKMYcwZW3mSuFHx9Cw3/pUWa1supkFJTiv9vJKKtQ4ESUOFe2u4m56uDWrMIyfHv8PkJaeSKEI0C6x8qzvBVFuVAoAKm0evfbschUzPjrFsZ18sHAFg0Y7x1mjTvbH5zO8XtpWHY4mjEnCllFBq26wHn8dpUrGwCM7dgQ+29xz9MNXWwZCpwLD5/hzW3X0MJT/2JduqpT0tl/Oxlv9/RHkI8zsovKkV1kujShxsRsgm9ZWRn279+Pbdu2ITw8HCNHjsS6deswdOhQSKU1klWtzlIpV3BGbKs0CBYyifqBM6QIxSsbLnG6KwjBlRfSXOhy3wCAbI4d+SsbL+Pu0sEGX5fLv6/r8jOCnxFrEqZrX8UWwej3w3mMbOvFmd1DCD6zK19bdvufzsTqJfg+TC9U3787Lj3VCowydpogFapAtupQUFqJAxHJyMgvw9iODdXR3sZETCS+qUnLK4G1hVTnRu2ADg2soVXIvjl2H0ENnVGpUKij+um3l5MNf4YBPk5F8wtnOy5zawVjUvMR5MMf1FUduFx+KArYp8PMvzVcvDJDaL7ZcfmJ6PNw8e6OG3oHMdG7szU8HntuJGHPjSQsHN5Sq60+Qi+gtJQEuNvr9RlAmRXlaXYRVr3aHjP+UlqC/rmZpJe7BZvpf2qnRKzuHtBCD43AnqoNjZBwzge73LkuJm29gq1TumgVhsoqLDPJ/GgMzCL4zpw5E7t27UKjRo3w9ttvY9euXXBz006eTAA6LjuF7KJyhM3tr2UGVtFk4THO46pFSiaVGhyoA3D76OqCKy9kbYLvO03acpXxmr5O6+uvxLd40mEHKoiBS2jv9e1Z/Duzp9ZxtlZCDGKFcSHO3k/Xa2JWIVdQsLGUMQRfY0SImxJVXtYH6QX4YXw7o5wzp6hcnXawNiSFF5vCjS/yW8XAH/UvogJAnZ6QDl0ja8oCNHTm7ruLV7uIS6NnLMSkBhOLkOm5umNoSOQ+fRMfT8v6sfyoeJcLPl7ffAVb39S2IIlh/61kdXab6iKTSjgtHdV1AxCyBLEtUYasA4aSX1rJWQ11xdH7+PFV48yPxsYsgu+mTZvQqFEj+Pv7IzQ0FKGh3JPh/v37zdGdWkt0Sr560f/vdjI+GtgUecUVemtNLKQS8CRDILDg0nbGZxbBy9kGu64JB0WYC67E8kk5JRjxcxhHa/15nFl9n9Kp229g46SOen9OQVFaC4I5Spsag/23kowm+CZkF6sF39rg6mAsVH6NxoA+LiYyCmhB38uVVyoYGVbqAkLCqa6y2aYgv0SzwTXUGiDEu7/rTj/Ih0JBwcXOkjNIXB/43HtuJeRUK3aGL8MRIKwsCGrorJdVjwupRH+NdVq+8Z59Y2MWwffNN9+slgbyRSGrSOOW8OOph5jeL1Cr/KYYZFIJZEYaby9nG6TqkSalNtE9wBVXHusX6frJrtsoq1SghacjfOrZ6v5ADSIUgFATqMyE+iBXULVCw2kIxuw1fQy8XWzr7DNnSpxtNe4Nnk42evv4GsKbwX4AlNXD/r6WgN5N3TGtTyB6NXUXXUmxJlHlz+Yizkh5lvXh5zOxGNFWmeqLK4tGTWNvZVFtwZePsNhMtNaRRkwISwO1WbqE3l/e6IRpOqpVWsp0uz2x4QrWqy2YrYAFQTfsDfAaEXXpuZBKJJyBGlw0aeAgWJLS2AvwS+28qxWZrQ92Vvrf3qqH+35aARq61G7B93lAQVGCFaNqM8ZUWJ2MSlMXsaCX9a2LGENrxkUDRkEb8yhSbKtSXf1dZf1RFpvJxJNvRmBJNQoIsFk7oQM++vu20c6nQsidoZBH++jvbo94I1iBuHhQJexejsvS6SZjbuQKipF3vbbBVR7YGOjK7Q8o18U5g5upsxz9b0hz3rSLKm7qyCFfk5jcbnP37l29MjdERUWhstJ8qbRqE+zoaUOTo1MUJVrj28zDvGnkGvH4LZuC6gZJiUnRI5NKMKGreX0A6xIB9YWDTcrllEE+5VwsGtkKc4cax09PLHlGEvDoqfyEsiTUBb4fZxq/PiuaxstUmT/YlFcVpjE1fm76B2WZCvrYmmLzP+DH82YZU30Z8ON5reqDtYkcE2mixa6T9LLitSEAtzqYXPDt0KEDsrLE55ENDg5GQkLt8K00N1386qGdr4tBn53cvRHjta7iD/UdrdHFrx5vaUhTYU73Aa7F8d1e/qI/f4uWSoYPO0sZ+jVvoLPdi4qljowtd2j+vK9XM4jonV7+jGIf5oCrAMAXI7Qj1Nmsea09PhnY1KBrBrjbc+acrS3ceJoNFzv9sy7ogu5fK5EAhz/qhel9AxGxOATRXw2Bo43xDZi/hsdjJocLjzECQunYWdfc79metebQs7M01zMdVq+qVGVCPH5WpDO3bk1gjJ9UjPbUUEwlbIpNOUh//tL1SHlWGzG5qwNFUVi0aBHs7MQtSOXltTui25S4OVjjwAc9sfTvq9h9nTuVikIqRZmFxhRqW668AVs6ytR/S4qLcPHOU1hLJCiztNZqCwCjAutj8ajW6uMKVlubilJIqp6HMR0aMvK8UhKg1NKGsy0bdtt6kkpGP9iUWGnaWleUQSpgT2a0rSyHlGVZsCkvVV9L1XZQKw/8cf6BVlvGeS2t1dEzVpUVkCn4g0AKKGv1jllX21JLK1AS5eRhKa+AhdywtmxTcpmFJRRSmajz0ttayCthKefXcJRbWEJuQFuZQg6rSmX/bMottH7vCpkFKmUWjLb2VjJ8MzQQBy7G8raVKuSwrhTQepSXY2DLBqLaVspkqJAphTMJpYBNBf+8I9SWKiyEbXkpHKwtMG9Yc5yJzcbk7o3x9ZEYgKJgW8Hth93TyxajW7mrc1ODovDJ1nAENrDnfD5Uz/2eacFo6+OMHl8cAnhyCvPNEZxtBeYIXW35nnsXeTkW9G2Mecce6WwLaM8RfM99kIvyXiqxsoEEErRp6Iw2rlaAogKoAE6+1wmX4rLw++Un6qwUuuYIOvq03XxBY43T9dyLmU9kxcWwLS/Va454qXsAdt9K0dn28+EtsORkHO8c4VhZxvjdLSjNWt3ExQqXBe4J9hzRub4lbkZzt+ebI7jgmiPEtNX13FvbWSO3UiKqrSFzRD07KzzLLxFsK5fKUG5RtTEUmCPYbSsq5cLPpz7PPa1tXkmF3nOEpKiYe55ita2tmFzw7dOnDx48EPYFoRMcHAxb2xfbt3LpxO5YyvPe2YDOmDpe8+7NdZNgV1EGrAYmqQ6uBmIAXPFtg9cnfqNuG75pKtxKtANCYgDc8WyKl6esVh87vXUmfPI1OU9X0No/dGuEwe9uUL8+uONTNMvi1tInOTVArxnb1K+7vz0WMVF3ONtm2Tqh08c71a937F2C7oncwRnFltZo9ek+9euN/67AgMf8Eb1+8w4DUGozrt3fAecjB3jbtpz9j3oRXHFiHcbd48/N2/Gjv9QZNL44uxVv3j7C27bX9F+R5KxM1D7nwh+Ydo0/i0nI1PWIra+sjPTB5T2YdfFv3rYvvbkKd72aAQDevnEQn5//jbft6xNW4EqjtgCACXeOY9mpTbxt3x63BOcCuwAARkefxw9H1/C2nfnyfBxt0QsAMOThZWw48A1v2znDZ+GfoEEAgD7xt/DbP18q3/hWeS/SWRQyHX90HAkA6JoUhV1/f857Xnh9B8tPPwMAtEmPw8HfP+VtuqbnBKzppXximmQm4tS2D3jb/tJ1LFb2n4pGrnZQxMcjfNM7mjdX0/r8DTBu5kzgnR4AANeSfNxaO0nrfKrPYcoUwHM8AMC2ogw/vdcbAPAxR/MjzXvig9EL0NrbCTaWMtz6bgxvf3nnCA7EzhGA7jmC/t3y/Jti3quatvrMEXt2zke7NI4Kh6uB8Ko5Qm1IGDYMqMoS5AXglap/gOFzBACsOvwjRjy4yNs22O4AAKUQJ2aOyLZT5gLmnSOq7iN95ogV3+0FoFwrBeeI1cBePeaIeR/8hGgHpVm7+5n9+HzdV7xt2XPErB/WYBZPW6PMERzoM0covv0WAdlKhY+YOSLjswXYeTVB9BzRu2l9XD13kzlHsPi9wwgsHjwDgI45AsA/bQZizojZAIDhAU7YOa0Hb1vVHKEiZvU43rb0OcLZ1tKgOWIFR1v2HFFbMbnge/78eVNfglCD6DLteDrboH/z+jj3QHc5XHMglWgCVox3TpKxhI4pzX26MCSXsL3I+8HJ1gK5ep/duNT0vTZvaAu808sfv19+AmysuX5wlabVhdgCK2Ioq1AAkpp1Nzl3PwOo31h3Qz2hB0ZbWTxfc5tUIsG7vfxFFQHxc7PHgC6+2HlVvOulKcfr1S6NdDcyAKE0aSpULorutbQghb5IKH2z9L9g5Ofnw9nZGXl5eXBycjLLNePi0zByLbe2gc+csX9mD/wSGof4zCLsnd4DFx89w/SdEQyzw8UPu6pLI74Z3BgLqqrltFx0nNeMaWUhxfhODfHXVU1pS5VpMsDdHn+82w3f/HMLp6PTOPsb8/UwPCmm0O+H8wCAg+90xGsbNXXmo78agv8iUvD5/kgA+rk6LHm9C+ZXfY7LNBnSykNdrUl13n9n9kCgkwW6CaSJ4zJNfjygCX4++0ir7cjugXipQ0O88eu1ark6TOjqi7+vJepsO6NfAI5GpuJpliZHopCrw9GPe2H4z+Gcbavr6hDk44zIqjyh9LYuVhJEzOsLALiblIvXfrnC+ByXGbNNQyfsnd4DLRcd520rVcjRwJI7Uv3krD7w9XQGrKzgN/+IXmbMVeOCsHDndZ1tN03uiBl/3GCYMf/9oAfGrL8ENwcrhM8bAFhYANbW8Jt/hNOM2bSBPQ5+pNTswsICvdZcVOa61WHyVD3395cNhY2lDBX5BWi7lPseNpWrw5WFIXB2U86BcgWF1v/bz+m+sGBYC9jbWOKzIxqtrTFcHWKWDUXLRcdRYmWDEW29sH5iR6CkRKuucGx6AWbtuo3HmcXq5/7r0W0wvo07Kssr8cflJ2jp7YTpfyh9dy/M7Y+P/r6FK+ma8d88vjU++Ys/xZM+7lBcbW8vDkFJuVxdtvjU7D6Y/udNROVWiHJ18HaxweMiBaNte0977JkWjLySCgSvVJ7X1d4SX77UBu/vvQeFVIYBLRogLCqZMZ9YW8iw5rTmt2rSyA2RacqsDuc/6YlJGy7wlqJlzxFOUgo3Fw1CpYJSp9/KKSpHj2/OVtvVQfX7s/l+Ymd8+I/SMqh67u2sZLi5KESrfcw3owArK8gVFJrOPyQ4R7zSzQ+v92qCkWvDRbs6vN7FF7uvPRXl6vBZSDOcu5+OmDjlGrX97S5467frnG0B4MnK4fh6z3XGWtzSyxExqcpMGYa6OoTN7Y/BX3MXxQKUc4SbuzMuLRiI2wk5uBDxBNP6BKIDR9Eq9nzyz/RgdPZz5T23sRErr5mtZDFBPI5uLgwBUEVXf1dci2fmpVW1a9nEC2uaeIGiKEgkEvTp4ICyvUzDsZWzk7p9saUNYK+MJLZwckABK5pVtRC5u9qi0taesz/HZvWGtYUM3g1dUfIol/vL2NnBzw44+1lfFJXJ0aahE+NcEgcHvNSjKa6ml2LXdWbdeF2+Qo3c7LDlzc5Yd+4RI0hKRYWNrVa/ZVIJLOy0j4/r5KMuUdnMwwFfvdwGr2++UjXpWEJhzz0Gc4a2QFxVOjhVWyE+HtAEL3doiIE/hqoFLwDo1b4xtkUwteIdG7ngVkIuKmSW6rYVNnYY0rWJxjeU/Z1pbRcMawGZoyNnvwGgkra46ILdtrGbHea90h6v0DYxKrzdHNX3Vtvm3OOmQi6VocRKBsrOHuAZYxUKqQwlVpYokXNo2hzsASsrVltxGrnezT0ErwsoA6mcbS1BSaSMtmVWynupwsZa/Z3VSCRa5/Xwdme0U2shOdpyoWpv6cT/u7IR027v9GCsOBqD2wIBnSqhV9UPurBKp8LWDq6sbB5cbVe/1g4eTjaYyKqeyPvc0+4Pteabwy2uaYA9jnw+DM2/OAZUpSec3F2pGbUGMH1EOyTlaIRimaMDQ+h9paMPXFydRI+vmOee3Vbq4ABLuUJ9jUpbO9jVcwaVp5nf6c/y8Vm9MXSNpmDNX58MRPeVZxhtPxjZDhIHB7g4AJ1a+SD8USYGdm6MXu0b4+O8SjjZWGJy98Zo9kWG+rwfv9QB6889Uvdj+9td8AEtmK9hAyecWzISzb7QCEabJnfE9D+1A/4qZRawq2cLiYMDYzTq2dtjePcm2HdLE7ci1+P5VLdlzQ+nP+0DK5kMjdzs1IKv6rmXcrQHoJ4jZFIJQoK8cSKKP4OKwspKfZ+xn3s+8ksrRLcd2c4bP556CFS1tXTmf6Z7N3UHJBIM6ByArbc168SOj/pj8OoLnGlHxd6/vq522DS9D6Zsu8bbRlVspEOjeujQqJ7o85+KSTer4CuWulWG5gWBma9Sg5UIk4SqUIhMKtGKcnay1by+Qcuxt2NqV8zoF4jVr2mnIepUdZNzYW2hnLjYypnxnXzgbGuJ/w3RpJYKqO+AIB9nzkImMqkE37zSVuBbceNkY4mQVh74YZzms6Pbewt+xkIqVfebDj1K3sXWCt38XZWTTRV8OS8drC1E50wGgNkhzRBY3wFPvhnBON7AUfs35zJBFZXJ8eGAJjqv883YIEzrGwgvF3GTn74sGtEKpTzBVfOH6Z8pxNpS3FTEl8uSbsb+oH8gZxs+LKQSrBwbxPv+xwOaoE1DZ87fWVV0xkIge4VPPVuM7dAQLTwdMWsQM5ODvoVm9HV1kEqUWpe904MZx/s1r6/+u2kDB3Txc9X73HwR/BRFoV+z+lrZAuh82L8JXm7XED0ChbMAfFyV+WJQSw/GcTE9Ffo69HmI/Rs8zSqCRTXddd7q4cd4vXRUK8ZrmVTCcMuRUxTvl5JJJWjsag9fV6WQf/rTPvB01n6uW3hqNiYrxwbhs5Bm+HhgU9hYyjBrUDNM7eUPKwspfp3SGV7ONvh6dBsAzIp4HRrVw9iOPgCUc5ulTKpVrW4g67eg48GzdrX1ceb9jKE0aeCIRm7KQLxNk5lVI8VUheN6ZulrpgT6u8eUV4o3oLPH1YZjXQKAbW91xo63uwLQvlcdbSyxxcAyzQDQrup36dusPl5qx792isn80KdZfa1jrbzMYyXXFyL41lJOzu6jdYxrMn6vtz/+fq875zn+fKcb4zVd4Cuv1AgtHRvVw7yhLTCmgw/C5/XHtD4B6vf+N5RbiFk0UjOR071luvq74vvx7XB7UQg+6C8soFXXF9S6auKgC4htGmomWK6Fr4WnI+dkRj9mbSmFRCLB58M1aanoi/xC2nGZVKLX5MhXwdDT2QYdG7moX9tayjhzxCooSquCzz80oWZQywZY9nJrvNpZmRrMUibFj0Yqq0vHWSBdVU8RKY3YqKLwvxvH3ACdn9OPkR6sY+N6jHFS4Wqv0fa+31s/wVcqlWBCV37/uSlVQgyXkHonMa/qHPznD583AKtea4/js/qgtTdTANDXZVfoVmvBkXpq65TO6Oznii4srQv9HlowvIXOc3PRvwV/Gj+JRIJ5PHMHAMwZ0lzUhnFG30CserWd1sZETFlnsX7AUilw4IOe6tc3nuaI9hXnUkb8PrUrlr7UGk5VQtTXo9ugV1OmUCCVMH8DhYLi7O1rnX1x5tO+sLWS4b+ZPXFydh80aaD9O7fzdWEIw76udvhoYFNOn8yBLT1wecFAtRa8hLaBlUklWDa6DX57qwuOz+rN+Z2FxkZfz0n2/W+oH/bQNl449ommv2KqhnFdq5mHZmylEgn0KZYmlYjfwH80oAkautgipJVmE2FjKYW7g3Ie6+aveV7LKhTqZ4Wrz/Q1T1/o9+B349pqyQwqxAi+o6oq8tFp0sC8dQLEYlbBNz5et0M5QUkzD0d09WcuVuc5AsQ+GtgUwYFunOdo5+uCxVUCKnvhqOCpluVTzw4fDGiC7gGuWD6mDW8C83yanyV9rtv8RicAELWotfNx0dlGCI+qiZ4+Ebs5aAQg+sPqbGuJIx/3UveLra2a3jcQw4M8IZNK8HpVEAFdexEcoBlj+uQmk0qMEnAklUiwf2ZPRCwOwUcDmuDIx73Qwkt7gVMtLOM7KbUyvZu6oyltsp4zpDneCPZjjD+f9urtnn4G91cmlcDDSXtR9XSyMWjxelZVfpmeH3Vit0bwc7fHu70DsG9GMPo3r48lo1ph3wxmZPPGSR1hQ89rq+fl2cKLv7s9lo5qhX+mB+PQh73gViU8cAm3KpeTxGzD6tILjVVLDm2JUOl3LsGXrwpcTpHGB1GV+zirUL9UklOCG2tpYgHNfGCMYDJbKxnGdvRBfUflbzC4SlAY27Ghzs+KvbxEIkE7Xxf1eLf2dmIIQEIMbeOpdUy1IT/9WV9sebMz56ZKIlFumFt4OsLTyQZ+7vZaQuA/04OxfEwb+Lkr3UbcHKwZ/aJvCHvwrAFioOdWV23u+rdoAB+enNhC96C+uXDpa8fika1w9fOBauFPX/QNbOXaQAfRhMi8kgq95vZjn/RBe5Fr2meDldbQENrzY20pw74ZPTCtbwDWTuwAX1dbuNhZMqwiQlYUQ7Cz1mi4bSxl6NWUuS6qZJChbbSFWjqu9lbqZ5RObY0gM6uPb8uWLTF9+nR88cUXcHfXXyv0osFnSlaxaGQrONkI+5ZN7eWPcZ19dLaj42RjiV3vBwu2KaKZ/ukLtIse5VbLBRJyr36tHe6nFiCgvj2Sc0rw19UEZBUxF2bVd6KbjPo2a4AZ/QLRpL4DozTy7UUhDGHQ390e4Y8yASgFBk9nG6yf2BFF5XI4VE0GrvZWOPpxb1jIlCWgZ/QLRPyzInTz1ywyMol+Gl8+VCY2Fzsr9aTYpL72blk1jywb3QYDWjTQmqi4tFx8C9WIIC/8dvGJ+vWxT3pj2E9hnG1/er09PJxs8PpmZaCao7UFmjRwxLevBKGBkw3ergrKKC43rPLRxG5K4YD+Gy8aobEqdGrsit+qzH0AMK1PAH658BgtPB3RtzlTm1ZWKfzcsGFn+ZgzuDlGcGgv9HVLEIPQwlqPpVX/dyZ/KiNA6RZgIZPi5fbesJRJkZJbovbHA4CZ/QKx4XwcJnVrhL9okeqq5+KxnmVqLWRSvNKxoValOarqLqXvJ3xdbQ3eHNBZ9Vp7xKYXiBIAhAQ0umuT6nfd+W437LyWgLEdG8LGUobA+vaIeyY8Jqtfa69Vgl113QaONghppdw80zfhdAHt6Me9Ia+y4ozr5IsrjzU+vrp8I+maPva9og/0cdJRb0YNX8laX57KnFybMgDwc7NTF1vq17w+3B2sceijXhjwQyhDEy0GXfPw3aWDGa+n9PDD0kPRjGP0SpMN69lqPZ/T+gagnY8LZ1GT5p6O8Klni+VH2UkZ+ckt0cx3NhZSuLnYYsEw5Ybmwv/6o1yuYFhqxWRg0AcrHVbXdRM74HJcFgZwWHcWDGuBlcfuA1BaIrnmMkUtlXzNqvENCwtDVFQUAgMDsXz5chQX165a3bWNxSNb8b7n4WSNd0RWIaMLvQFV2oPeTcVvPLhMmvRqZWM6NMRXL7fGkY97iT4nAASyBLv9M3ugtbcTdr3fHWM6+GDB8JZ4rUsjfDq4OdZPYvpw0U3bDRytMaGrL6YEN4arvRXmDW2BVzr5MBYbtgaa/lKloZFIJGqhV0UrmvZn3tAW2PRGJzTzcMCItl6YEtwYUqlElGZp1/vdtSZeFeM7+Wi5LwDAe30CMLFbI/z2Vhf1MdU8YmMpw7AgLzjaWDImHH1ks2asxYhLw6ji5fYNGdW4VFqo17o0Qn/aveDvrl16lW/Ro6Nyi6mkWSKE0s4tGN4ST74ZgeOz+sDOivmbudtXL+WOpzP3502RSozvnJ0a18O3r7TFsDaesLeS4cSsPgwhFgC+erk143VAfQf8ML4dejetj+4BbmpfTRUfD2yK36d2xUJWZbnqCPTsZc3KQooxHZTXpQtUo9oK+96riF85XPB9B2sLdGhUT1CoVbH5jU6wtZThO474AVd7K3wxoiW+fKm1+j6rZ2+FD/o3gZezUgNKN5dvnNQRbX2ctXyl2cJWQH17tOSw1NCrbtGtE1KpRP3st/fVz2Td1c8VGyZ1xFcvt8bEboanNaN/BbH3wgf9m+CD/oFwd7DCq50199mSUdxrVrcAbo10AG0NUI2ll7MthgVpa9IBYfc4XYKvPWue4LqH6C40bvZWjDnv9Kd9sWBYS4Zw3JW1ObG3tlALiWLW2DMxmjzY1qxqjBKJhDMeRYivR7dRW13FYG+trfs8P6cf+jWvj5Oz+6CBow1ebt8QjhyKs2l9NS5lFMU9/jWdfpEPs2p8u3TpglOnTuHkyZNYuHAh1q9fj6VLl+Ldd9+FVOxW8wWis58rwub2xzfH7qN/iwZYdzZWvTsuNLCm+KrX2uOfm4mYNaiZ6M/0bVYfe6cHo6GLLYasuQArmRQ9m2gmMqlUgjeD/USf79CHvbD7RgI+DWnOON6xUT0c+Zjbr6x7gBviVgxH4OdHATB3khKJBCvHai9uQpo/+qRXJsJfkP3Z9RM1gjifOfnQh72w7Eg0uvjVQ3eeiR8AnGy5tTU2ljKsGMN0UaG0RA3DJxcnG0vsfK8bJm65ymmyVjG9aoJzpX1PLkFd2T9tfhjfDiPXhqOrvytuPc1Ba28n3KlKg6bCpWoM5ALVssQilUqw7OXWuBSXhUtxWZzpz1TQTZtb3uyM+MxCdGrMrWnTJ4hRpVUd3Ip/XAF+NxSVO8eGSR21tD4q3gz2w+IDUQDA65JEx8ZSxhmAolqw3gxujN8vPwWgDI7ccyMRjd3s8e/tZK1gLRV0hc6Q1h5YO6Gj2gJDf0/IDN3O1wV3EnPRlif41VB6NHHHvS+H8ApE7/YO4DyuoqmHA5JzlVrqYUFeGBbEbe5dO6EDfr/8BD+Mb4fGbtobP4Ap+IotEasLqVSC4Tx90us89EA/Pe7x/w1pgTmDm6OwrBLuDtYYHuSld55Xawum25gulBYt7vHT9Xkx56e7ADpYW8DFzgr7ZgTDxlKm9letT/uOXFbLLW92RmpeCWRSiTqtHJ3mNHeVxaNaYURVukmxZchn9AvE3huJyvSJLJxsLdWuWWLgCkb0c7fHdpp1TQwKitIqY9y7qXutDW6rkXRmgwcPxuDBg7F7924sXrwYq1atwooVKzB27FiTXG/Dhg34/vvvkZqaitatW2PNmjXo3ZtbwKpt+LraqbWd3fxd0fu7cwCAonL9zEAq2vu6GOQnpAqOiVg8GFKJsBlRF0E+zgjy4Y+i50MmlajNK1xaHDZCPot2NG3i/bQCvftCx9fVDraWMpRWyhmLvaWFBHum8buMzOwXiH9uJmFaX+EFmA7Xmkn/KXT9Kqp8xJOqXAt6BLrj7tLBcOTY+atQCTINnGywY2pXrWwhumjT0FmdfzavpAKJ2cUYuTac0UYlSLfyVk6ULtUw3QLAG8F+eCPYD52/Pi3Yjj52ykATfkFVHx/CxaNaIaSVB8MthgtrC+0NhDNtI2SI1kdfVAJ9Fz9XteD7etdGeL3KN/Wrl1tzanzY/DC+HcPtiG49GdjSA809nfDZ3ggc+pBpGdryZifsvpaI17ooAzKHtvbE8ag0bGRZeQyhOm5In4U0x/kHz9CpMX9mGwAY1c4bowQi4gEwxm9wa757rGa0Y3QfX33ndYlEAkcbS8wVCGQUgj6XiPqtBJoIZVYRi4Ki8GH/JriTlIthVX6t7I0w3drIle1HJpVo+Ud7Odtg6Uut8SijkKFkaO3tjHNz+sHeWib6Xp03tIVW4Ojcoc1x9XE2hrT2wGMd7jl0jLXPpCggJVfjyvRWDz8sfam1wCdqlhrN4ztixAg0bNgQc+fOxfjx4yEXqEtuKLt378asWbOwYcMG9OzZE7/88guGDRuG6OhoNGpkmkoopoLPf8qcGLP6kSFM6xuIN4P9RFVfE9Lkit1diyViSQhyiytwMjodi/5T5pSsr2PnPXdoC/xvSHO9FhsuU6QuVwd6ZbIPBzTFS+0bql1eAOj0/3aiLU59OTSGYlCZd51tLZHE0UeVwBTSyhMrxgQZMf2RZqcQPq8/lhyIwpn7HGV2RWBvbYExHRoiOacEkcl5DB9EtsbV2kLGcAfiY/mYIEzYfIXh28xOc2QKWnk5ITpVWZpYdU/xPdtihF6udvTzNWnggHa+Lpy+0w0cbfDRQE2at+/Ht8W7vf3RUSCVojkI8nHGzS8G8Vp09IEe3R7EE4XPtQkyBz0D3fHdK23V7ktc2FhKUVqhENwgGwJdQ0yf24a29sT+W8la7bv5uyIsNpMhfKo/b4S1SaGgMGdIc8E29PlabBaLAS0aYEhrTwzhkAW53MP0ZWa/JpjZT/l3Sy8nvNfbH1vCNMkEFo1shWWHo7U+ZyxXBAVFMSxixrJqmAqzPmnbtm3DZ599hmHDhqFRo0ZwdnZGv379kJaWhpEjR5rkmqtWrcI777yDd999Fy1btsSaNWvg6+uLjRtrsN4moVqILTks5FtKN1tWV7sIKAUdDycbtKL594kxOYkVej8LaYaGLracOXx1zfe9m9bHlODG+PKl1rCykKJJAwdes73KZ+1L2m59TAfdEfQqxKwD9DbjO/ngp9fbq1/LpBJM7NaoWil6+K7lU89OK0d2Si5/dSMuVr/WHnumB2ul6Xkj2DAfy2YejrjxxSC8VpV+DtBkuDAly8e0Uf+tUpQZknpIaINC15Drs8A62liis5+rXq4lpsLNwVqrH9Ut28pnOagpxYZUKsGrXXy1sgjR+faVtnCwtjAo37oQ9E0FfZxDWnloZW8BlM/fB/0DOQM96eP6MWuefK83dzzMpyFMl7/mIuIR6PC5fLExtyC4cEQrvN8nAO19XfD3e90Z8UD028+Yj5gvTctdWcsFX7NofMvLy2FlZYUFCxagTZs2CAoKwiuvvIKgoCC0adMG9uyKR0a87s2bNzF//nzG8cGDB+PSJe2KUwBQVlaGsjLNwpOfn2+SvhlKv+b1cf7BM1EBQy86XI77Kpo0cED/5vVx7sEzfKgj37A+dGxUD3+/1x3eRi4c8dHApvhwQBNOQZnpo6c9EVtZSPHly220jnPxx7tdkZBVjKYejnitiy+sLaRG9bsENEGNdlYyfG+CHMN02NMv3awL6J8CSf05lm+umOIyfEgkEsiqmdNaX+gLvKO1cuPXzMMR297qzFuEgAtfVzscn9Wb4f+twpWWlqq6BSFqE9vf7oLQh8/Urhliebm9N0IfPsNogY3krUUh+P7EA4zv7MPbpiZ4uX1DjGrrbZTNSO+m7vB1tcOUYD9ceZylPi5jWK4k6NS4HpxsLJBPi2dxd7DG/4Zwu1XQnyH23M83h308sCk+7N8E99MKcC85j5FXVwwvt28Ii8gU9GoibAmrCW0+PQ89APw8oQO2X4zHuokd1aWyjZWpRkFRGB7khY/+vq18TQRfoFGjRvjoo48QExMDV1fzla/LzMyEXC6HhwfzZvbw8EBaWhrnZ1auXIkvv/zSHN0ziC9fao01p2PVSfUJ/HQPcNNKNURn85ud8TC9wKgO+BKJhDevsjHOzYVUKsFrnX2RV1IBP7fqaY2sLWTqvMA2RnYHUWFrJcO9L4cYLHTqw6i2Xthx+SnaNFT+xu/08kdSTjH+vqYsj22oZoKt6eHK56oPbwY3xs6qFGPsSHFTYGdlgeOzekMCCcOCMqCFfgs/wKwYRsfJxhJ7pwfDgpa54HmgTUNngywSa15rj0qFdgEaOq72VoJVBGsSY2ngm3k4qgsgXXuiSd/GdX59smHRhUu6vykg7MsqlUrQyttJHV+gD7aWUhz+iD9eaPmYNthzPZHhylNTvNTOW6s6WwBHykxDobuaEI0vgHnz5mHt2rVYuXIlpkyZgtmzZ6NJE+Np2XTBFhgoiuIVIhYsWIBPP/1U/To/Px++vvrt7E1JYzd7rH6tfU13o06g0lh29uP2FbSUSbUqadVVvh1nXBOkodDzUgrBThtnKhYMb4mOjeuhT1XlLBtLGVaObasWfJsaWFkoNl0TEGlvJYO3iKwKQtCFx+oUFjH0mqaCXTHuRUYikVS7WmVdZma/QBy+m6quGAdoSuYC3D66+uSBpQeBsoO/TZGDWwyTujXGpGqkmjMVe6YF48bTbMEyxfrAHt+EbP1ygpsbs2zDZ8+ejUePHuG3337D7du30aJFC4wZMwYXL1406XXd3d0hk8m0tLsZGRlaWmAV1tbWcHJyYvwj1E1kUgle6eTDm2KIYHxqW75yG0sZXm7fEPVYwTCHPuyFsR0bYtVrhrla0HMHG5phhc3J2X3w4/h21dYeEwi1kblDW+DC3P6MYC66sMtlATJUccg2tduJjAt5Uejq74qZ/ZpUW4u/YVJHuDtYq4sLvdfbH442FuoiTLUVs9mfpFIpxo8fj0uXLiE8PBwWFhbo168funXrhr1790JhhPydbKysrNCpUyecOnWKcfzUqVPo0UO4ChKBQBDP8KqE8/QgrdpMkI8zVr3aXl2sQF/0SUEnlmYejnilk4/RfaoJhNoKXUvLFQDZ2EDXLZWpfVrfADR0seUsG02oPsODvHB94UB1YOTCEa0QuXSIYN762kCNOF51794de/fuxaNHj9CzZ0+89957JnN9+PTTT7F161Zs27YNMTExmD17NhISEjB9+nSTXI9AeBH5YXw77Hq/O6b3C9Td+DnAVP7PBMKLRIC7PXo1cceIIC/ONH4bJ3fCoJYeOst1s5FXmZ4WDGuJ8Hn99SrqoA9e1XRxeh6oixt1szjaLVy4EHl5eZz/cnNzUVhYiIKC6hUR4OO1115DVlYWvvrqK6SmpqJNmzY4evQoGjeufX43BEJdxc7Kotbv8o2JOQLzCITnHalUgj/f7cb7vr+7PbZO6az3eemBbqYQzNZP7IjbCTmCFS8JtRezCL4rV66EjY0N3nrrLXTt2hXOzs5q/1nV387OpgsymjlzJmbOnGmy8xMIhBcXvhyhBALBvMzoF4g/rzzF+32M74pEZ0RbL85iLIS6gVkE39OnT2PVqlXYtm0bXn/9dcyZMwdt2ojLK0ogEAi1DXrlP12VnggEgnmYN7QF5gxuXuMVRgm1G7P4+A4YMACHDx/GnTt3YG1tjW7dumHo0KE4c+aMOS5PIBAIRqVf8wZ4tbMPFg5vyQjQIRAINQsRegm6MGtwW/PmzfHLL7/gyZMn6N69OyZNmoQOHTrgr7/+glxunJRABAKBYGpsrWT4blw7vGdik6pYfF1JkA2BQCCIoUayOtSvXx9Lly5FTEwMxo4di48//hgBAbVjASEQCIS6wk+vt4efmx2+GNGqprtCIBAIdQKz+PiOHj1ancUhPz9f/X9lZSWoqrQjubm55ugKgUAgPDe83L4hXm7fsKa7QSAQCHUGswi+Li4u8PPzg4uLC5ydnRn/0/+ujagE8/z8/BruCYFAIBAIBAKBC5WcRukoISqhdLV4wUlKSoKvb92oRkUgEAgEAoHwIpOYmAgfHx/e94ngqwOFQoGUlBQ4OjqapUJJfn4+fH19kZiYCCcnJ5Nfr65AxoUfMjbckHHhh4wNN2RcuCHjwg8ZG25qYlwoikJBQQG8vb0hlfKHsJnF1aEuI5VKBXcOpkJV4IPAhIwLP2RsuCHjwg8ZG27IuHBDxoUfMjbcmHtcxBRDq5GsDgQCgUAgEAgEgrkhgi+BQCAQCAQC4YWACL61DGtrayxZsgTW1tY13ZVaBRkXfsjYcEPGhR8yNtyQceGGjAs/ZGy4qc3jQoLbCAQCgUAgEAgvBETjSyAQCAQCgUB4ISCCL4FAIBAIBALhhYAIvgQCgUAgEAiEFwIi+BIIBAKBQCAQXgiI4EsgEAgEAoFAeCEgldt0YO6SxQQCgUAgEAgE/SAli41ESkoKfH19a7obBAKBQCAQCAQdJCYmwsfHh/d9IvjqwNHREYByIEkdbgKBQCAQah8VcgUsZcR780UmPz8fvr6+armNDyL46kDl3uDk5EQEXwKBoDc5ReXYGBoHf3d7TOjaqKa7QyA8d0zaegUXH2Vh9WvtMKYDv6aPAPwSGoc/rz7FnmnB8HK2renumARdbqlke0QgEAgm5EBEMjZfeIwF+yORXVRe090hEJ47Lj7KAgDM3n2nhntS+1l57D4Ss0uw+tTDmu5KjUEEXwKBQDAhhWWV6r9LKuQ12BMCgUBQoqBqugc1BxF8CQRCnYOiKMRnFkFeB2bvCrmmj3J57e8vgUCoHcgVFH48+QD9vj+Hu0m5Rj237AXOUkUEXwKBUOfYdysZ/X84j5VHY2q6KzqpVCjUf8spIvgSCARx/HnlKdaefYQnWcWYuOWqUc8tkO3ruecF/uqEmoCiKFA1vPjfTcpFyKpQnLufUaP9IPCjUFBYcuAe/rmZxPnenL1KX76t4fHm7preVNI1vnVAQ00gEGoHOy49Uf9Nd5kyBjLpi6vxNWpWh4MHD+r9mZCQENjaPp+RhQQmCgWF8b9chlxBYd+MHjX24E374yZS80rx9vbrePLNiBrpA0GYM/czsOPyUwBPMa4TM0o77llhzXSKRlFZJT7ceQtNPRzx+fCWgm0jEnPVfyteQI3vtfhsAEDnxvUgfYEXW8LzQWmFHNYWUrMUtHqcWWS0cxWWVeJ61bMIvNiuDkYVfEePHq1Xe4lEgtjYWAQEBBizG4RaSk5xOW4+zQEAZBWWoYGTTY30I6+kokauy8WjjALM2XsXH/RvgpBWHkY9d35pBWbtikCPQDe827tuPWPZRWW871XUAj/ZsNhnOPdA+e+zwc1gbSHjbVvPzkr9d23Q+BaUVsDRxtIs13qUUYhXf7kMAHB3sMKNL0LMct2aZP+tJNhYyjA8yKumu8Igs7AMu64lYHxnX3iYee7NL63AjSfZ6NWkPqwszGtozi4qx3fH7+PVLr7o2Khetc6VnFuCnt+cxah23lg7oYOResiPq72VOhNMS6/qpVOduv26ehMK6E759Txj9DswLS0NCoVC1D87OztjX55Qi6Ev+jW5/JdXKnQ3MhPfn3iAiMRcvPf7DaOf+99byTh7PwNfH6n9frBsisr4sx/UBq1pGe0e0qc7YgXfsNhneJplPG1PUk4xjt9Lw8bzcQhaehJbwx4b7dxCZBaW0f7WncrtQVoBXt10GaEPn5myWybjUUYBPt1zBzP/uoX0/FKTXuvSo0x8c+w+isvFmcDn74vEDycfotuKMybtFxdfHYrG1O03GKZ7c9Fx2Snsup6IsRsuVftcf155CgA4dCel2ucSg5QmnKpcBB+mF+DvawlQ6LmJpgu9wIvt6mBUwXfKlCl6uS1MnjyZFIV4gaAH9hhDdvnvdjIW7L+LSrl+gmxlLdC6qYjNEDbbP0grwLs7biAqJU/vc+cW1x7NNh8FpRWY9scNHL7LXEi+OhxdQz0Sh0KPe5l+v4kRfEMfPsMbv17D8J/CdLZNzy/FrmsJKCkX2CgoKAxbE4bpf97Et8fvA4DZNkP6PudfH4nGtSfZmPnnTdN0yMTQn7mdVxPw8vqLSM4tMcm13vrtOjaFxmHn1QRR7U/HpJukH7q4nZCj9tXfFBqHxOxi3EvWfz4zhFIjpw+0MLOwaGulEdHupxUAAAavvoAF+yOx75Z2/AMXCgXFuX7QBd8KuQK/XYzHw/SCava4bmBUwfe3337TWSqOzsaNG+Hu7m7MLhBqMUyNb/WET4qiMGt3BP6+loj3/9BvkTT35CVEZgG/SR8AXt98Gadj0jF+02W9z21MzWhEYi7m7L2DjALjarHWn4vDiah0fLjzNm+brw5FI79UI1Cwv9fPZ2LxTMc4GhtaogbBcU7MLmYIHGKyOkzZdg0AUCQgzKr4fH8k5u+PxBYBDW5STgkKjBwYIxZ9n3PV4i7mu9dG6HPcT2dicScxF9P+ML41BwDKqzb8D9MLcCYmXafQ0s7H2ST90MWSg1Hqv+UUhd7fncPIteFIzTPNhoBOWUX1rXsVcgXe2a7cZNhY8rs0GZvsonIkZmvGyNeVqVTcfytZ1Hm2XYzHiJ/DtY7Ttck7Lj3Bl4eiMXj1BQN7W7cgWR0IZoMpLFTvXPQF5qye2Rk8ncX5t6XmlWDB/kg8SDPdLji/VCOQbA17jA923kJZpWbRz6nSIBUbIAjkFGtMy9WtGDZ6/UX8czMJC/ZFimovVtMiRpDedjEem0M1gh373ll16iG+PqK/hnhTaBwGrQo1SCvE0PhCuRHjMm3P33+X8brSCP7J0Sn5iE7JB6AMAgSA3dcTASh9EBcfuId4WlDMBztvVfuaKkrK5biTmGtwZhZdnzPFpjQqJQ8vrwvHyag09TGFgjL6Jg7g1ujfS843+nXohD58hnd23MDwn8KEzd9G8OlUKCjEphfo9fsX0OY4enxFXAa3K09Y7DM8ytA951IUhX9uJvFqj8/dz0C7r06K7icfRyNTceZ+Br45dh8udhrf+Ao9LY36Mn8fc+5gxxHYWYkTwvmsOzKa9EcPwK0ON5/m4NVfLptNo28oJhV8S0tLce3aNfyfvfMOi+J44/h37ziODgoIqCBgV1ARG/beo8YSY2JJYoqpGpNYoiYaY4nJzxhjbIkl0dgSYzT2ir0Lgg0r0kFUejm4298fx97t7u3u7R53gLqf5/GRu5ubnZvdmXnnnbfs3r0bu3btYvyTebE4FpeBzt8fM7ymT5wL9t3ER5uuSJpMxZorbDqfgDfXXWBoDMXaNn20KQqbLySgz5KK2QV/u+cm9sSkYvI266TdpHenOXOQJYdvY8DSkzhzL1OwnBgv490xKWg0az/WcoQa23YpEX9dSuRsoxCUUyTArWHdGS3d5m7hvlu4m5GHZUfvSv4uvQk6ksSSw3fQdv4R7ItNZZSjUqlSJDwpMFt3DVc172eHbqSj/9KT6L/0JKfAPn79Rfxx9iFGrjKeEMRacREaveY8Bv9yGn9xhJnjhHWrCku0uJmawzvWG/gYTwytFfbwm/9u4GpSNj7407gB+OLvGLSZdwSn7wo/71KpjDjN6Tn6045SHSk4L0oVews0pSYb5v8dikOvH09gjYQwgoyTPlrzkrNMx8K9R3kYs+YChos44Tp7/zE+/+sqXv+NO77tm+svim6jEHR/gw1nHxr+pgv0tiCddYplr2SKa+Xdx2y+kIh3/riEi/FPzBcWybAVZ3DhwROMLTu1qqrYTPDdv38/AgIC0K5dOwwaNAhDhgwx/Hv55ZdtdVmZKkipVoc31zEnIWoCzMgtwqrj97E7JhUrjt8TX6dIwffLHbE4FvcIfX48gdwiaTavdGGrItkTk2q+kAjox8znHjwRtC9dcvgOrqfkGIKkl0fo+GSz3myBbaebll2EKX/H4Iu/Y5BSZvdI1/DRHaGEKK9ApNORDJtYS2y+2Ta+Px25AwBYWGZDy4dahEe7pwu/4Et3guQ6xqVMBTJEmH7odCT+PP/QoD0WAzUmNl8QZ1fK5pPN0ej300neUxq6Rs1atvh3y+zoS3WkYQxQ9pGrT1jXya+y/QeEzG6kmj4NXHoKHRYeZQi/vxzTz9HfH4grd5um/2N6enSnzFxDjH/CzVR9WVtH6aHPo7dop3/WfnZMrsvqN3YvsqMykCSJKwlPGf0h5CT6JF+DQzcsM6MzR3lPGG2NzQTfjz76CCNGjEBqaqpJNAet9tm036pqVGRopB1RSZi/96ZkT1IA+C/GVBtHjWn64h15S7wnt1SHttTsIny545ro8qskCOEUZ+5lYtvFRPMFbcij3GK8t+ESzt1/DHoXfbI5CkvLhDM2WQXMSepBZj7aLTjCqbHlUjLcSc/FsqN3zHqX0wXbfA6bUyEHLWs5RiY8LkDwl3vR+Kv9hvc8ne0FvsENvQn0BUooGgUgTvjgMplIySpEy7mHGO8RtNnbEu3PfzEpmLHjGvovPWnyDHy3/xb+PP+Q55viTTbYpSh75y1l44QkSdxIyTFEWqFr7q1hFsKm8az9jNfHbz/Cwn23rLZQWzI/WhOhNSEmSbzmnyRJ3M/MR2GJFtdTslGq1WELbbMj5Zif75HnamopQzss3JcqpeUqz4zcImw89xDZ5XAAXnn8HuKtGGeXzv1HeSb3i90fXqwN8qEb6Ri6/Az60k4ox4nUvFpiSvcsYzPBNyMjA5MnT4aPj3Vjk8roSXxSgLBvDhq8tAFgw7mH6LvkhE0G46dbr2L1ifs4cUd6mKGz9x6bvEftoukTtbljQvrAp0+QdC2REFQIGjHT5YJ9TM0d34J2Ky0HG849hE5H4rVfz2PK9phy51R/p1OQ4OckqddccS0+H/x5GQeup+PV1edM2vzTkTucIbKG/HKa8XrOf9eRnlMsOrJCrx9P4IeDt9HkqwM4cy+TMz5kcakWSU+NR5vUvabblgndfnooHr61XYwm+Jdj0s0auJAS1YGOOWHuWFwGpxD26dZok/fJcpoYRiVkGf6mHwtfS87Gish7mMHaKN5MNWqGxQo+fNpk6gnZfCER/ZeeNJwS+LgZF/MSnXVsKOk9ruFo98rj93g3hVKp7DjNQhpnF7UxbP+d9FzOzScF+5n+LyYF02gaWvplnuRr9EIkj+ZVSlQL+nNlriv5TNbEjPGv/r2Omf9ew3cHhE9oAOHxfddMVB5L+XRrtMl77HvL/vn7rult2FOz9RtnIUUCG6l+Ms86NhN8hw8fjsjISFtV/8Kz9Mgd5BSVYkXkPey6moKZ/8Zi1r/XcCst1yYxYSky8zR4kq/B0Vvpoif56xyLH0kC3x+4hX60kE3sga3VkZixIxY/HIhDblEJuv4Qial/xxg+o6jj6SzpN9Cv8vbvF3H0lvkwP3yaur5LTmLWv9cQ/OVew3spWeVzmnGy588ro9ORGLL8DOp+uRf1Z+xjOBGQJImL8cK2sFz2w/GPmbZ2gvfVzK6BL5/8wKWnMGGj0caSkmk8XYzaVvqRYr8QX95r8N0LMY9jVqGpUGmJtpR+rbmMDQKJUq0OE7dEcWpMzY2Zn3kEsPMPTO3w2P3wQOKGd2e00SucEoJziko400QDejtwilssh8/krEIcuJ5m8vt+pH2HDtXna07pj4v3lzmeEbQHzFoaXzEbInO27XwUarQMYY3v/mbmFUuKH771YgI2nI2X3B4hjTP9een14wl0+f4YVh6/h3WnH5jYi7M3dmfumiovKN754xJm/nsNn0n0TWjg42LyXkmp8brmTkf4HCHFmGFQz5vYUHB8xFsx1jagH5P7r6XhKod2XqqNb4qFUTM6LDyKwGl7yt03VRmrZm6js2zZMowYMQInT55EaGgoVCqmVu6TTz6x1aVfCOgPPaUtobB24HT6zrG4VIuXfj6F5KxCzBnUFKPb1UFRiRbOav5HyY0jS5SOJA32Yob3WJP2jZQc/Fk2+B7nF+Ph4wI8fFyA74Y3Y2oGyqFlOXwzA0dvZeD+AuHUxVIuUV6nAz5NxnsbLmFG/ya4StOSLjl8B7+Na4V/o5IZYYMAMBz6KJ5KPNLNKy7FTzThRcxP4zSHYGlGqEWNLtzQ+9jPnT8eOL/gS0JppoVcX5WSwWjbpURk5BQxtGf/RBkFSJIEdsekYmd0CqfDnTkbUClPMlt7+fqv50zKCGl9nnIc8773x2WcvW8UcrQ60vA8PqRtkNge5d1/iERxqQ5LR4VhUPOahvf5jlCpUEo1PRxx75FeeGg+5yBDa/gkX4PqFpihWILCgkEbk5SFV1adhZeLGocnd4GDSsl7atXq28MAgN/faoMuDbwF69WU6jC1LHpKnxBf1HBlRqHR6kjk08yKHFVKFJYJrkLPF/teZOZpsLDsZKuGqwMGNDNmmmOY8oB7Trr/KA/B3i4Gu2+pcYL7hphmttMwNL7mBF/L9HZ3BMK+ZReU4Ktd1zCsZW1EJ2ahoa+r4Jj8ds9Nq2XFvP8oDxO3RJu8/1rbAE4h1Nwza6kvBKWh/3JHLF5rG2BRHVUdmwm+mzZtwoEDB+Do6IjIyEjG4kIQhCz4lhOhhz6nqBSxSdkItVLcRrqWLKew1DAwDt5Iwz9Rybidlou9EzshyMsZecWliEp4ijZB1Q3hV7iORbmGJLscfRLcfIFpO3v/kXGnLdWhhN1z7K9zaW12RicjI7cYH3arZ7b+DJEbDz57YL756sD1dEb6WwDIK9YLCpM4jsYOXDddiEp0OvwblYx2wZ6iwrr9cCAO62nZlu49Mq/hEHM/riQ8xaIDcbhKMwthhggTqIPnI62OhLkwm1xfFSvyFJVoMaXsxGFgM+50tI/zNZwbDoovd8RiRKvaUCkVyMgtwpN8DRr5GpP4SFmrBi8zmqiQJJCSbfrcSXXAoQu9ADDz31gsGNoMABgxTOnt1OpIQya7/66mMARfPqjpq01gdZy8o9e2so/KK9JsQKkgsPhgHJYevQt7pQI/vxaGPk35Tx0A4GpSNopKdEh6Woi07CIEejmbbfO4tRcQv9DcJttYR2pWEUPw/enwHfx26j7WvdHa8J5apTAIvpbG7v5w0xUUlTTHsPDaJvWQJMm5Obydnotgb1OtrViUHHXSfTcYkVN0JE7fy0TTmu6GzZCdhTa+/QQSwyw6cMtk0zr7pSYWXUcqVHQONpQPAvvemhN8q1CepiqHzUwdZs6ciW+++QbZ2dmIj4/HgwcPDP/u36+YdJnPI3czcvE4r9isVvGlZaYBq7l4Wma2IDbtJT3GrL1SgauJWSgs0eJc2YL5+barGLPmAhbtNx43cYV94Tr2Yw9svh1rqVbHCJdCb5MYuGo9cD0NHRYexT9XkjgF9S/+jsH3B+JwNTELe2JSkfCYPyzVrJ3XTd5LzynC4kO3kZZdhKISLYatOIMprDiNxvbxz1jsyAfn7ksLRZP4pBCTtkZj8C/ino9badw2mgWaUlFH63z38Kud13Hi9iOUaEnOskLrN7+Nr9nmlMsxjr4R2y0QecPcckxpyNrMO4K+S04y7K75mselcUujbbD47CjLexRL33DyzTkptGtTzkK5RSWCkR8okwYhu/5SG9j48qFUEFhaFtZOo9XhY4GEKhQrI40nVtTcJUZY3xmdjO7/i+T0uN90PoFhHsLW6v94+DZyi0qxiHacr6WNIb5Np5hTsc/+Mpoq0G8LSTJjvlJcS84xibebJyFRCtfzxJwPjO//E5WMMWsuMEJLSk25G+ylN4kT2pincIyj2f9JjxF+IyVHsv0v3++hNNtSNzXl3TjS7e2lYoWQ0TbFZoKvRqPByJEjobDwOELGlN9O3kfPxSfQadExxgRRHt76/SLeWn8JX3MIa6fvZuLsvceMAeThaDRbOBZnnLip55zLdiqO42iJy7GCPVD5fmEhyx7t/qN8bL6QICoP/OWHTzg979/bcBnJWYWYvO2qYHzGDece4sNNVxgxicUw/veLWHrkDt5YdwFn7z0WDJVG74aQWuZTelti6sGnXWDD9Zz9dzUFTb46gG4/RJp15JsjYdEQ+zOETB3MY1pG7PpJ72ehRAv7aYkSuGAHd2fY81kxDmyhRosdUeKyOwHcm1E69F9M35zRHcPsy8K1TfsnljNclaGussqEnl1baXy5jo3Z1+JygmND32ycufcYyVmFoto8aWs07j/Kx+xdpnPulztisYqerIWnPrq9Jz0rXwGP4PmHSHthavNJH0sJTwo4tbPLjt01ibfLtY7wsfjQbSSy4lrTN2r0Nuy/pt9o0jM0Cpk6sOsFxAljlpi8sMkuLEH/pSfRc/FxSeYGfFMKpdm+9yifEX2F3Vb2WC9v5k6xawQXVVzutZ3gO27cOGzdutVW1b9wRCU8NWRgKdBoeR1QpECSpMGpha19yC4oweu/nceoX88xBEU1z1ny9wfi8C7Nqc6cMME1nzetyTTN4Jv0uRaX6f/E4utd17Fo/y3B2IXDVpw1Gy926IrTvJ+JDfbNPralsjfdSss1GwOVJEkkZxVi0/kEk8n9PIeG19IJLnDaHvx4yNT5iDp6BrjNVD6m2ZQfMCPkrT8Tj5n/isv2JvZ3UFpCZ5adqZjvl8fGl/7cCWmN2Ekr2Hy75ybO00wK6GNFw7OhtcReb8SqM5LKs0OasaH3E9WcpUfumCSzKCrRmo1FTdUl1I+5RaUYtOwUJm0xr30Vgt11X+4wfR7Lu9+Y+e81dFh4VJTgS12LSzhjM3/fLcRyODqdkph4Q6zWkmob/Wd8ves6XBy4rSLZDohUfGSxzPiXGTnEleYPsoeWDCaZw2FYaI3ptMhUMSHmFkux9+eDLpxL2bspeDW+ZacjOhK3041aZHpT2WulPkmM+GtbA/rJKzvLXFXDZoKvVqvFokWL0KVLF3z88ceYPHky4195WLBgAQiCwKRJkwTLHT9+HOHh4XBwcEBwcDBWrlxZrutWFnP+u46Xl0tbxBrSMiDxQc8D7ufBdCai2ynSj6/4tDiP8zU4eMNoU2rOzpNrgWBPrnxVCGm7l0feEx27kA96v7ChT03HBELACM2f9H7i4uejd9Fh4VF8uSPWJJVkLodGR0q4IDY/mQnjZC72J9tBkYuN58R5B1Mn26fuZHI6yqw+cQ+PcovxJE8voBWzNJRiFhmuIvZmkkpExmVg1fF7Vg1YT/99JGkUbBv5co9bS9Ywdqrcr83YKhZxJMTQ15ONESvPcJ5SLGZtnLQ60mD2JAQ1PIQExUUH4hCTlI1/o1M4w/BZkxup3CY9XJAkyXAwpSMl/bUYGetqYham/RODM/cyTVLYciFF0PqyfyNsfbcd6/umGl8AvM7Lw1rWMnlv/7U0tJt/hBGCkI8Tt/XJhVp9ewjT/4llnCRMoUXwuclxf8TUT+e+CP8ESzJmz9tzg+FQTq9DqyOh05E4cjPdrO8Hn6AaVGaioSCYNtA6ksTkrdEInLbHJDvjsbiMcmt8pUKfP3o0rlGh15aKzZzbYmNjERYWBgC4do25qyvPrurixYtYvXo1mjVrJljuwYMH6N+/P9555x1s3LgRp0+fxgcffABvb28MGzbM4utXJClZhSAIYN3peLNl4xcOQIGmFNP/icXO6BTDYOEiq0CDL3fEonltD8N77o78sXClJosA9ELJlgsJeLUNt1co16Ck4g8/ydfg3qM8ZORyTxRitCo6Hcm7gy4P9GdXKCUmAX6nEGvDjubwrKIjSaw8fs/gac5m/t5b2BOTive71gVgurkS0opqdSTeWHeBoc2mYNvWFZVo8cnmKHRrVAOj2gTgjXX899kafLw5Cssj72Hnhx3gxqNZs8YaZu4Yl21CRDHwZ1N78OJSHaeQpyUF3RJpbSkrLzCW6cIlffOdX1yKa8nZaB1Y3SZj3Byfbo3GvzwpsuPSxdt1lmhJUXNEXnEpb5hANlKEHQKESf9R32bHiH7EkwnQgeMEcMLGywCAV1adxckp3cy2gzKF23whgVNhE51ouuEqLtXiNwlpkynMxba1xNTh15MP8OvJBwanRXodTws0OHUnE5/9dRUuajtcm9MHgP4kTaVU4MzdTLg42KFZbQ/esUBFkNGRYMxFlx8+NSgmBrNisV9PyUH7ul6SfwubohIt5z3mgq51LirRIvFJAfyrO5W7DbbAqoJvTEwMQkJCoFAocOyYNBtIMeTl5eH111/Hr7/+im+//Vaw7MqVKxEQEIAlS5YAABo3boxLly7hhx9+eCYE36ISrT7NrgRnASd7O0Ou+yIBh68fDsZhb2wa9sYaj6lJkoSmVIfoxCy08PcQfawrxLR/YjGilT/nZxvPmcY4pZylJmy4jAsCJgVigudrSRIKELBXKkTZ64lFbCzO0NkHAQAPFvS3ufAbGSc9qUhVhCTBK/RSXE3KNtFkUgg9pqfvZnIKvfrrMr+4+UICDt5Ix8Eb6RjFs3GzNjdTczBy9VlEBHuafGatbGBsIcdFbccQKLky9QlxgsOkSKcjOe1B2VBj4qGI436AeXT6zX83sPVSIn4Y0RzDy6IQ0Pn5yB2cupuJ74c3R4Cnk9VT2vIJvYA+0oEUHmTmm42M8FDAkZYNpWHUaHWIjMtAAx9X3voJwlTQoyKjsAVoLo0rAKwycwoipe0Ad/rhBI5npEhj2ZxuLimKNVyS6H06eNlphAdWA2DcvP185A5+PHwbq8a0MsTcj184gPcUwYmmbaevZUKnC3tiUtGjUfm1rntjUzG0pekY44L+zBy+mYETdzJxfnoPVKugkIRSsKqpQ1hYGDIz9YtLcHAwHj82f+QlhQ8//BADBgxAz549zZY9e/YsevfuzXivT58+uHTpEkpK+CfC4uJi5OTkMP5VNKVaHYb8clq00NvC38PwN7VICglDaRxhj7Q6EsuO3sErq85i2bG7jAmoPIIjn3MNlWWGjmuZtktI6AUg6BhGQQnu1j7ukZKqE+COlSrDjdh7tYwnMxNfHNGYpCyMW8dv/kK/7M7oZLM2utaA66dGJWRxCoLjf7eOxpmtHGV74G+9JC3dNpfJUXGpTpTWjCCAfbGpOGTG7IeC7sxFtXMlR1rxpKcF+N+h2zj/4Ilke1MhnuRrMOvfa5w28ezrS4HuSLuBQxkgFZLUJ5RoNGs/Jmy8gu7/O85bVkEQJqcd1HzNvrNSojXQWX3SKBi/2zkYb7QPFCyfJjIUpKXzOj0EIBflUVKQJIm07CKGCUtaTpGJA/D/Dt2GjjRqxgH9mj9v703Oeh15NK7m4hhzJSuSilB2PzrRiVkIL4tVTaEp1eEmT1SgysaqGl8PDw88ePAANWrUQHx8PHRWCkcDAFu2bMGVK1dw8aK4RSAtLc0kXbKPjw9KS0uRmZkJPz/uOJwLFizAnDlzyt3e8nA/M98kM5IQdPu3FgEehr/5jvtdeRJKUOF8lh65w/DULk8GpSMSgprfTs8TFZqMK2YtG1sJvlKVby3nHsIXfRpatQ3PK+wkF1KZ8ncMXqGdMGhKdfhkc5TZKAskgM+2XcXJO4+QwXOka234BAm2U9jdjFxG9JTyICSQujrYCUYz4YIrxFVscjb+d8h85iwCBKfgygeXAQVXuKi/LhmFXanhroR4f+Nlzux5bKQ69Vx6+BTNyxQXs1iOXpYQm5yNIyzfAz5FAUGYxtKNSniKUW0C8C0rXTnfKYs56KcCPm4OeJIvbXytOn4PjzmS7lg6r5sLwVieqA4L99/CquP3DaZYFHw+I/RT1b8uJ6G6sz1nunIFAYQFeDBSjAOW2f1LRexGYAaHwyiAimmkBVhV4zts2DB06dIFQUFBIAgCrVq1QnBwMOc/KSQmJmLixInYuHEjHBzMB92nYN806khT6GZOnz4d2dnZhn+JidK0INZASmpLAGgTVJ3z70c80QtalR290BGKBytVy8n3Xb4sTOveNAZjv5kq7aiQD+rI0doRkcxFhOBCTApNGeDzv8qvoaDzz5Uks0IvoHfY3H4lqcKEXkDYQZNOz8UnzBcSidASVovl3FqeGtkLNOc3CUgK9skXPYQdf5w+31jTwEiM0AtI14xSHvvmImqIhStiBV/0CAVBmBztb7uUhJd+PsXIRmgNXNR2GNnan5GSWgwL9t3idCq1VXKG8uyVqDB0KyLFb+gofj8Tj/A6pusyoDdRYqcrBqxnAiWE2I3A9ZSqqdnlw6oa39WrV2Po0KG4e/cuPvnkE7zzzjtwdTUfXcAcly9fRkZGBsLDww3vabVanDhxAsuWLUNxcTGUSuZO29fXF2lpzMkyIyMDdnZ28PQ0taOjUKvVUKstD9xsDaTa1HZtaLTloWscHuUWw8fNdKMgdbNcHsH38E2j9uHClz3QedExkwxT3RrWgJuDHXKKSqG10inBy8vPIO7bvlapS+bZRCiDWmXDtym1JULD3hIF2pUE8yZHfPx9OUkwFjKb+XtvoUdjH5OFWFOqAz2ZIX3qLCzRYptE8w06dzPysPHcQxMNnhBcGjshqJ/zCUeqWmvB5zRFENxacXaEAGswa2BjuKjtyiVYUuyMTkbSU8sj2bBJzS7EZ9uuVlh6bD74BFkC3JFnhJK/WIty368qGtDX6lEd+vbVCxuXL1/GxIkTrSL49ujRA7GxzJ3sm2++iUaNGmHq1KkmQi8ARERE4L///mO8d/DgQbRq1QoqFX8Eg6qAVEGzA8t7s46nEx4+LjCYDZCk3tmBEoqlZpSxVrIMpYLg1bZ7u6qRU1RaLrMKNpNsuJjIVD6NfF0FTYKssS7YSqvC5Rhma4T6Q1wsBiblDe8mdYP/NF9jkjDBJOkN7Ucut0DzRmfQslMo0GgZKbutDdVcWz4Pn/GcpBAEAV8OxYgtoDYs1nD0ncgzr4+NqIM/zkq3k94Tk4oz9/R2/cHe/NGQrAGfo6VWR/KOBzdHFbI4fEWkruOWQL9d2y4m4vyDJ/huWCjsuOycuL5fRSVfq5o6xMTEGOx6161bZ1bovX79OkpLzR8Nubq6IiQkhPHP2dkZnp6eCAkJAaA3URg7dqzhOxMmTMDDhw8xefJk3Lx5E2vXrsWaNWvw+eefl+MXVgxiBd9rc/rg1NRuCPBkhgxRl+0OH+XqtQ+9fzyBhjP3Y8ya84hNypZsx2StI2ihSY8y1LdmtiYuB7qqyoQu4rVKMnqa1OTPalei1WGBmQgRYhDrbPOsc1tCGK7KgmtuYL9lyezB53BVYCb0lTWwVXY6MSgIwMOpYrSclGa5HUfUEmuhEimMsaEnA0nlSJRhTZrPOcj5vo4kee2WXR3s0LOxD+dnUmhft3x9P2V7DLZfScJOWlSTjNwiRjIeNlU1dbHVozpIieQQERGBhARxwe3NkZqayqgrKCgIe/fuRWRkJFq0aIG5c+di6dKlVTqUGUmSKC7V4moi91HTytEtGa9d1HaoXc00Th61iJ0uG9CU09DJO5l4admpCg9sTYfPEZWaGC0NnfasM61fI0zsUb+ym/FMIRQ2y1oh3vgiRTyLWKLVrUpwCYnsUHQVYfdoTb7ZfQPT/zGfmMIWUNo4LvtRa2MUfKvjz7fb2uQaCgLwry7dVp3++/liWdsaHcm/CVLbKeHjVj7zy83vtMNnvRuUqw4Kys/l5yN30GbeEYxcfc4q9VYkVjV1IEkSs2bNgpOTuKDFGo3lBv2RkZGM1+vXrzcp06VLF1y5csXia1Q0B66nM0Kc0PluWCj6hvhhxest8f6fV8xmYQL04XEmcNin2fLozhx8Rx9UPvLK1IBUNmI08eF1qokK5/YiIOS1/9RKzkJf7axayUEa+brCw0kl6IzKRyXud60C16Y4I7cYp+9lonltDwR7u1jk9FRaDr8CV7WdpFjrXGy+UPEO1IDRfrMiNkReLnrBjSAIdKhX/sQKXCgIQjDrJh9VYc3RkfymDoDe3KE8RNT1tMgmf93peGw6n4CPuxuVMkoFgY3nHuJ/ZkL7AVXWxNe6gm/nzp0RFyfegz0iIgKOjpZ4Ez+f2NvxPybDyoJI9wv1M2SIEUOHhUfL3S5rwifbvegaX0CcI8H8l0Px8vLT6N6oBnazQl+9aAg54ZhLD/oscvDTzmjg44rJIsL5cVHexbOy4XLmoWeVi184wKLTrPL4FWx5rx0GLDXNbGct6tVwsZktZ0UeQ3MlZrE6Fv6eqrDmPHxcIJjso3ujGnBQKXjTigtBabQt6R7Kh+LtskQbAPDtHu54w1xUfs9yY1XBl62FlZEG20bJyV5psDMTa0xeFejT1AcHrnMfEfNpNdPLIj1cNJO8gk1zfw9Gxpu63s64JyIne0UT7OWM+5n5UBD8oXjEpF9t6OuKK7N6QW2neOEFX3YInYY+rvjwzyvYE/t89otHmeBqqYNQ7ybltxOsTNI5Eu+wEUpbzUd5BB83jpjo1mTB0FCMYDn0WYuKSKdOURGppS2JweuqtqtU0z+xOKiU+GpgU85wdebY/G47ABV7vymqqunRsyNNvQCwBd/PeusTH4xpV0dSPcteC7Nam6SiVBBwUfMvBnzzHxXijMtT/JPu9Ti/888H7Q2OfBTNanuIa2gFc78scPpH3S234+1YdkTooFJWyiRmjs+tZENmKXHpuRUu9I5qw52S21rQQyxRwoOlMoSDSikpNFdVY9o/5hd9S9ZZSzWq1Z3t4e0qzvaSPU+JxUFiQoyejcWnqaUExWdA7hOFJcNCR5K8qcwB6WuvLbHUJIeKD1wBew8TqqjcKwu+VQl2NIe3OgTi8OTOoux56dgqHqG7iKPSs9O7480OgSbvu5TlG7ckm1J3Ho9WBUGYDGZLND4VidCvN6exsFTW3fNJR4bnupj7yKZfiK/ZMk1quiHYy7bhgOh80aehpIXeFgh5ktNTiVsKQ/AtewDoIZGkbnIn9qhfJTMJXp/Th/P9kFr8kTvY7I1NFYyIw3Xc7smTLcscg1vUxLHPusKBJ50snZ9HheHo510lXwOQNuab+3vgiz6NxNdtQXtc1XacwuCaca0k1WMLBzeCANwcpB1ic8XHpdOyjgfvZ5dm9pR0rfJS3rCiQqHFvhnctFx181FVtemy4FuFoAs+Q1vWAkEQqFfDVbKZg628dBv6uOKX11pi6SjmYkvXetVwdUBTjjBTlPAu9TjKQaVATQ/uWJMKjgDsfMPso27cWuOKRujnm9sTWJqCNcjLmdHvV7/uLclOHACWvNrCbJlgLxcc/LSz1OZZTO1qjvh1bCusHhNuvrCNEBJ8BzbzE+yPxa80N1s//Y5TUSyO02K+SrWddFAp8UEV1PryjYv/Puoouo4P/ryCLRf5HcXYEWVebxuA/z7uiCILPPnjHxfA3UncBjIswMPCzHjS2PpuOzT0FR83n+qPjyWcQs0dEoKvOBQxzmppAmeHel7wtLKCJjY5B7+/1UbSd55yxMelY8+RI4BCipnLqDYBosvy0aWBd7m+L7T2nL0nPhqXFGTBV8YsLrTJI4cn0LWoenh2veZ2t+ZQKIABzfwwqHlNxvs1XJmCKdcxfL0aLvo6JAq+zvZ2JvUb2kMQJoIH3zj7qHs97P5Y/CIK6AOi92lqXbtIoV23ub4RCt8lhIIgRGmOujbknlirO9szMgLy4WivrFBb9HsZeSAIwhARpCKZ0b8xJvdqIBhmSKkg4OtufHbZC73UkxmirGuLaSnNLbFPrYpmMnzjgiAIDA2rZZVrsAWV9zrXRU0PRwxspp/PpMQ5teRk6SXWvCmGiog48DGPKRkX/tWdODd7KgvG4Oqx1t2w1vN2gSvP2jf/5VAsGdlCcp18a2a3ht6STAesYetar4YLhofXtvj7QsN+cAvpz6YYqqrga/XMbRQPHjxAUFCQrap/LnG01wsXjpoi2BcXAvkcTlpKJeBAEwQ5ytRU6eCoKYKOIFCsMi7MyoICUHoHL1d7ZOYaj/jYZR1KikCwnlmnkiL99VgjyK6oEI6aIkZ7AtQ6PMrVgCSAIpWDYbF1KC02lqX9Buq9Qnvjb1OXFMNdRzI+Z/P98OZ4Y90F3E3IhEKngz29LTRUSgVCarnr6y3VQCFgL1WoUgMEgW8Gh0BXWISmUfzZgKiy3q5qDG3ihT9O8meLKlLZo6CkFPsmdsKgxUdhp9Vrmra82xbIz4eqqMDQ9iKVPcgySUelLYGdVosABx3jflNli+1U0Cn0z87GMS3wzhpmXEVFQQHUxfq6i+2Mi7+dthQqrTEU03d96uLttCcY1TYA6TlFWHYmCVqFUp9atqQEv7/SBKtP3IeHsz0+6FoXw1cYnW40dirUKLN3VOq0sC/l3rgFezkh7qkGpUo7s2UBoERpZyir0GmhppUtzMoB8vOhLNT/tlKlEiVKFWdZNq+2D8a6S/pA7ASpg0MJ/3E3vV6q7Dvh+g3Rysi7jOdNq1BCU9bHSgJw02pwbEIrqJQKpGQXYu3peBwvizFsV0q7JknCscQ0jbFTiZ1+LCsUjI0PdU1votTkedcpFCi2szcpy/XssMc93zjjKss1Rxh+Ttm4B/SL6oGL9wXL0qcUdUkxFNSCmZ8Px5IiRrvYc4RCYHGll/0oojYir8QbXisL84F8Ep+084OfnRZ929ZFp0XH9PWamSN0OqNm1b60BEodv9aYLBN6Fr/SHAcuP4RSp4W7owqb3mlrEhGCPe6rkRree8IuSxTkA3ZKzvL0OYKaT5BfAOTnQwHmfafK2isV0Gk0jDkC+XlAvr2hvMZOBa1CCU9nNWM+WTS8Gab8TYtPnJ8PqNWAXZnIUVqKcC81728b1bEu1l5IBsCcI+YOaYpZ/zJDDFJzxKtt/EGWajnrDK2mRGgtD3yhLRE9R5QqlQbBlz1HzO0ZBEWBcb7mmiPoHLhwD45lG1b6HME37im6NPEzviBJfNsrCAFqEis4shKaG/f09rLLRvg48t4LS+eIWh6OaOgr3lSpQiFthFqtJidOnEg+evTIVpeoELKzs0kAZHZ2ts2vlZlbRNaZupsk9YpL7n/9+zO/5OTEW/asfwhZZ+puss7U3eSCvTfJTEc33rLRvvXJOlN3kz8cuEXWmbqbTHSrwd+GJk0M9daZupt8VKceb9lEtxpknam7yeiEpyRJkuTtgEa8ZTMd3Rj1nvUP4S2br1KT15ON9+RIcCvBftPpdCRJkmSdqbvJ3Q07CJZt9Onf+vtAkiQ5bpxg2bCP/9S39V4mSX7wgWDZDhPWkAeupZIkSZL7+o4WLNvzrV8M/bCm51jBsi+NXWwomzJjjmDZkaPmG/psZq8JgmXfGP41WWfqbrL9giMkuW6dYNn3B08z1Pv+4GmCZT/rP8nQ3jeGfy1YduWITw1lR46aL1h2Xtc3DWVfGrtYsOy1d4xt6PnWL8JtaDPUULbDhDWCZX8PG2Ao+9e+y4JlHw171VC20ad/C5bd3bADqSnVGp5hobJHglsxxlG+Ss1blj5H1Jm62+wc0WDGXkNZoTkizjPAUG5l5F0yzjOAt2yiWw2yUFNqKB/tW5+3rNQ5gl5W17+/YL9RfStmjhjy3QHD8/5XSA/Bsklx8Yayv4cNECzbYcIaQxukzBE/dhglWJY+R8zr+qZg2ZGj5uufhdwictc7XwqWpeaIuLQc8rP+k4T7d9s247q1bZtg2TMzfxA9R8zsNYGsM3U3eS8jl0zasVewrJQ54scOo8iz9zJtOkeEffynYNnSMWONfZaXJ1h2d8MOjOddqCx7jtCJlCPEzBFUuR8O3JIg/VgHsfKazc4lT548ievXr6Nu3bqYN28eCgr4Y9TJ6PF0UWP56y3NF5RIYz83UQ4tv7/VBpN6NkAjEXZizcscd+p4OomyKbbF6SrdZq92NWEbOlsf74qtnTJn6N5IvFPWWx3En5zYIjd6ZZgSUIh5Fi2BJG1SLQNzpilizEcoOtbzsjglqxTMXaNNUHXJdZIwP/4Jwmjq5SrRXlQs1nyKpRzh0s1/wgI8RH/PzVFcPywa3gwjW1s3uoivmwM8XdTwEGnHXMdTXNIqsahV0p91lVKBmjTTIi6kzmVSTDg+sUHmTSl+HX5mfntFUxUSg/BBkKRtl4CDBw9ixowZSE5OxuzZs/H2229DwZe3tgqSk5MDd3d3ZGdnw82tgtT2XCYOFCJMHQCg8az9hmOHUW38sWBoMzSevN3weYCnIw5M6gIAePAoD2q1CjX99Itanx9P4GHSI86jyZtz+wIEgfRSBX4/E4/X2gagporEmpP3ERbggVaBxoWx5+JIJGUVoUjlgN0fd0RILXeM+ukYoh8+Manz1xP3sfjQbcbRZGMPO+wrs8vt+N0RPM5jHkvtnNobDXzKBKOiIhQXa6C2U6LxrP2m7V6sT1UdOG2PyTHmzbl9AeijauyOScWn/93GyNYB+G54M6C4GCgtxZGb6fhoU5ThO6emdkP843wM+/0qQBDY+m47tK3lAp2mBPce5cHDyR6dy45NKYpU9ljzZht0b+QDaDRACfP3FGq0aDn3EACgbZNauJmRh/c618VbbWqZlDX00az9jGPMfe+3xdCfjpv07/WUbLz+63m0bOiLteMj9NeYsw852QWMchQXHjzGqD+ioVUoUdfbGUc+6aBvM+vaFBo7Fe4tGgQAqDtlF6f5goeTCnMHh2DCtliD+UKfhp5Y9Uoo5z0DgAm9GuHH4/EATI8mJ3QNxsQeDXDyziO8+8dlSaYOa9+JwKjf9ffTElMHqq/uP8pjHFfTjzG/HxaKEU2YdqP0e7z/827osrTMXIQ0Hnl+NzwUU/82hu66ObcvY9wHTttjOG68Obevoe/e7xqMFZH3OY886fcWMN479jHm7rfCeOPG6ggCbZrUMoR/ops6/Pl2G7z+2wVDWbqpw7R+jaDWFGHRvluc9ZIEcOt/w3A3IxeRcY8wLswHKtp6v3DfTfx+xmhuZKmpQ/zsHlh68CYy8zSYM6ip6WbY2RmB0/bo66XNEeM7BeLz3vpoCVS/dWoegNXjWgMAftgVg1tJT3D6LreD0M3/DTVK/sXFKCnWQKVUIDYpC6+sYpol0c0XBjbyxLIRodh6MRGzdxmP95v7u+NqYrahbPzCAYz5hGssUXNE14beOH0jBXZaLb7s3whjIgIBAO3mHzFEC+kRFoBJfRqjXg0X/HY0Dv/bfc1Qz7o3WqFdXS/8cyUJM3ZcM5g6xC8cgHpf7DSYOqwZ1wrf7L6Oh4/1mdRuzu1rYuqA4mLecf/dqHB8sl3/m+mmDtRzTP8eZepwamo31HZTA0X6sfE4rxgdv9PPwf9+1B4NfdxwLb0AA1frn1P2HEGPP9/C3x1fDAwFobbHy8vPgCB1eDvcFzMGMB37SJIESQJ5JAGFWo2Qrw+YnU+ETB3q13BGi4BqeL9LXfh5OOr7S602lEVBAZ7ma9CelZjq+pw+UKjscOxhDr7fH4eM3GLkP8lm9Nmd9FwMWnYagKmpw/UpHdHq28MAAGd7JYK8nXEtWR8nXYypQ+QXXdH1+0hG2fe6BGN6v8a8/WALxMprNrPxpejduzd69+6NrVu34quvvsLixYsxf/58DB061NaXfnZxlhASiqcs6eyE4rIsL/GZeuFm0uAWWFC2+JSonQzfDWLVQYI0LFp81/MBMKWvMXTOO/1CTYqWODihiKUwKFWrGYtRAx8XwNkZhfYOjPcBYOfnvYAyG6tf3umMSVuj8fBxviE2IGPZcnCAukww0Do6QSMQ1og+4Om/SQXg5Y6uiAgNMDotqdWAWg2dkzOjfZ4+1fGgEIYFjSAIQK2GQq1GfVe9I9/xOQPQZt4RxqUMi629vf4fDUdn4NjXA/BPVBJGhPvDy8WeWZ4Ddp8pWP1L/b6m9Z1x5tuX4EgLv3RiRm+MXXMBV5Oy8NvY1oxn6U5+JrSUTaBSAahU+n8C16ZYPq4Ndl1NwR5Wgg1HtT1IZ2eD0AsA1d2dDfefCy3NJnnawKY4fCMDF8qSnGgdnct+mwqF9kbBgMp2VWjPrVGd1q8RIhrXBKAXfElCwXt9ipkDGuPorQycufcYTev7GZ+ZYoL3u3Z2CtPxqdIayivoG1iCVo+zC7NOVh2vtvbHlouJekcXZ2csfrM9jt7KwHv9Q/AE9sgpLMHu2FRoymwKC+0dTOrgazPhwn8v9M00jjr6HNGyiT8K7WO4vgIAGBRRDwuPJzAc8yjGRtQBANSr4Yp6NUw1/PZurrxtoi/IZnFwwCeDhMO/tQ6shovxTxlzhGt1D0P/bfikGzaee4gvBxgX9M8HNQMADPz5pEFYYMAwYFZDVSbIFKuLBfu6UYAn4OyMUkdHRjn9XMSyC6XNJ53D6vAmEAKAEqUKJUoVNA7GNeCPT7rhl2N3MbFHfQR7uxibbm/PuLbC1RVwdka/tvUwec9dRr2lNHv8RvX8kK+6h0L7ssmaPQ7s7AA7O/7fT5vvtAqlcSyX1cP1PTuFQr9BLCujgMpQjnTSzxX1A2j9SK8XwLThrfDvHf18HRzog5b1fRCTlKX/PqGAC+05MPRP2T83AAWaUkNZc/OJsQLm/OFXywvfvMYTGo4gAGdnkLTfRaEoW3O6NXRAt4Y1MHbtBZzIK3tGqDY763jbpXBxwZZPe+CHg3GY3q8xJm2N4i3L9b7CxQUbPumGP88nYEeU3jZbW87wa7akwlSvAwYMwJo1a1C9enWMGDGioi77wkJPbXj2vl4T8V4XYxijIIF4q3wKlNaB1SS1gco6BxjnfnbkAj93bhMFpYJgeNQ29/fAsc+7oncTYzxZPvOF8uae93V3MKmb68jdnPmEh6OpsGoucoOvuwM+6FoP3q5qi8wz2CfVQ2jeuk72dow61XZKbH0vArfm9kPH+l6M7/lXMx5dSj0e7NPUF7+8ZmqyQ5KkiSd0ryZ6kw92rOrX2gagfg0XvEzz6H+llT9WjDbWW73sGLa6sz0jhJ6rgx3+mhDBGcJu3sshmNBFejivtzsFY8Xr4Zg7uClWjzUuTF4uajjxCNhc95p+dMoX1cGO1klT+pqaKE3t2wjfDgnBl/31wle/UD98P6I51HZKzB7UFItHtmCYWdBjOFNseqct+of64g9W+CdLsl+JwdNFjdjZ3LF6zV3z9XZ1DCnbxTKwmR+jH8XyC4epGb15rQKrY8mrYZyRZjiFXgE0HJsAOpQpDPvE2Nzx+6JhzXk/o8/t9P5p4OOKn14NYwi9ADPEYs/GNQzJEBx5nnl6G6UIPuz5lcvchSs+PACMbOWPl8NqmURY4coWJ3S+Tb/PCU/0yiL6s1nNWdjsg+85nijBBEJow2Johwjzk5ea6Z3i6CYoQusJQejX2A3j26JJTTeTfnqvc7Dg9RSEfmz8SIucYUl87IrCZhrftWvX4vr167hx4wauX7+O5ORkEASBgIAADBw40FaXlTHD0lFhWHX8Hqb14w90zjc3eLlI0K4AeJRr1EpQtqfsuqm5aXS7Olh86LbhfV83np0pTbDjW9ea1fbA5YdPJbXVHP7VndC5gTdO0GKo0q/PNadw2WfZOnsOe/Jd8qplWfyqORmFMjsRpkmU1k4IgiAY7Xu1tT+6NdQLvm92CMKbHYIQlfAUCoIw2JBTWhRAH4M2r9j4un8zo8cz/Vmb1LMBWgdWh52CwLJjRq3UkBY18Xpb8+1kQyWJcHdSGY6GKRztlTgxpRsO30g3ySzGZftup1Tg2OddUarV8cY+pfdRPZYgAgDVnO0x2kxGqVrVHA0ZyWYPMg1O376uF9rX9WIkwwDM2/iW5/m1t1Ng0bBmmLKdqRk2J2v7uDlgzuCm2H4lSfS1gr2cEfVVL4TOPiipjVwCbXVnafOeWEJquwt+Tm3g2daI5sYjX3xhNwc7xvwrJjQW/Vn8rcy0g0IoNb2dQoE2QdXxb3SKoM320c+6IOFJAfZfS8OttFzD+/V9TJ/7IS24w9p9N7wZT9t5L8sJASDQ0wnxjwswuOxa9K5PfCLsp8Se710d7PDr2FYIC/DAT0fumJQf0qIm/o1OkdZIiPNXGdayNgKqO6GRn1EhIPQ19mdsG/bp/RtjFUdWVQouob/QgvjYFYXNBN/p06cjJCQEoaGhGDZsGEJDQxESEgJnKcf4MlZnUPOaJnF42fCZfVuaQAHgH3TUIGZrv5KzCjnLp2Yb7Yv4NGYzBzTGy8vPmG3T+jdb88Z95GLV6HCsPnEfvZroQ1mZm4C4ustWGjWK8twjOvRminHw+Gqg+eyCTWu6MeptG1zdpA/DApinCk72dtjxQXsQBGEi+NIFgMc07QIVM5r9FLM1ZzP6N8a8vTfNtttcBjYvFzVqcThX1vHknuvopy2XZvZE0tNCjP7tvOG30bukyIxWkI9vBjXFyhP3zQo37o4q9G3qi/3X0wAAdb2dMbpdAIpKdPj7sqmgSQAI9nbG/UemvgUt/D0QnZhl8j59OgngcIKib1r4kBrDuoGvK1wdVGjg44Lb6ZalJAb0iW/MzZdC1K9hKsBRmEuAQM0V7BiwlsTMBfRzA31uF5N0gktrStGstgev4KtQAHMGh6BeDRcMas4fhznY2wXB3i44UPb8UXAtQfS5bUAzP+yJSTVooLmgl6fqo9cb+XlXdP0hkvGd3Z90wq3UHLQsm4dyi40bw/jHwoKvSqnA3MFNcSstF839PdC8todgQpEJXetizuAQ/HriPmODbg0UCgJtWclt6Hfyk+71sO9aGu6UbY7Z65JUzy/6cxIW4IGohCzGaV1Vw2aCb3q6eZW9TNWE76Hv09R82lo6tTwcDQIsFeOVLY1IldPoKUk9nLgFX7FZkro2lJbu1tFeiYk9jcdWDI0vR3mCIKC2UzDsGm2dO8CShAbm4NMwfTcsFFO367Wc5hJXjGoTgE971jdMtID4TQBdGPZ0tkeHep4gQDCO/OgexNR9YR91s49Cx3cMQss61VCvhgvGrjmPq0nZnNcXo2Hh6nYxPrxeLmp4uagZzw9dQ2ZOy8RH+3peaF/Py3xBMBPeEASBb4fo7fU5BV+CwP9GNOfcWC4Z2QL7rqUhPacIdgoCv516AIBpftOa5vxKQTnLCUHvS09ne7Tw98CRWxm85al7v3pMK4xZex7vdrYsW92gFjXLlfiHih1uCd5lcyb72bJTKNAmsDouxD9B7ybiE+woCALNarvj5J1MUcfl+u8I18eHnUIBd0clPhKdFY5ZF5dSg25yNWtAE9T2cMSIVvwbO0eVEuF1quFpgQZ1y05O6MIwO2KFjtRHFqE7aDev7WH4O6C6+cgV7BMhIZQEAXdHFWPOLm92NiHo92tkmwC81Lwmev14wuQzgP/Ul79u49+b32mH+Mf5aFRVY/jCRoLvw4cPERcXh9DQUPj5+Zl8npKSgpo1bZMpRMY2/PFWG3QUuZBSbH+/PTadf4jeTX3hSZlJsObKpjWlLQwCMeUN1OAxk7A29MmCbw3Y/XFHw+QC2CbcGB1L0q+ag89Wa0S4P26m5iJUxOK+YKhemKJrZumLilgIgsDG8W0NfwsRUtMdXRt643ZaLloHVTcJN6RQEAaNUaCXM7/gK6JdrepUg6NKCQ8nleFUwuJMe7RVhG7qYSukanfYWnmKQC9nvE9Lh5yWU4Tjtx8Zjo0B7hMJZzP2ogCzLze/2w45hSWCgi81NgO9nHFySnez9ZurpyK+7+ZgB1cHlclpVyM/ptYwX1OK1WPDceB6GvqHmq6vfBAEgXc710V+sRYDmon7nlD7hYVi0c0qa5vx78WvNOfURtM3sr7uDpjeXzhiAEEQ+HtCBEjSOKbs7RRYM64VSrQ6E8UJV4g6Z7Ud1r7RCvti0xhKD6m0DaqO8w+Y0Yyo+Wt4eC2sPK5PSOFf3Xaprel9rCCYwi37dj3IFIgsxQF9XDuolFVa6AVs4Ny2efNm1KtXD3379kXdunWxYcMGAHpheOHChWjbti0CAsqft1pGPAc/7SypPNc62LmBt+CxFxe+7g6Y3LshQ+uhpmlP3uwQiPGdgsrdNi4caHEgz3/Zw/C3JXFI+Wjg44qO9bzQNqg6rwBf34e5aFnbxpcuaADitd3meFpgFFDj0nM5yygUBGYPaophEtJoOtCiSnCZB4iBIAhBoZdSoCgUBNa/2QZnpvfAT6+GwVXgaJkKi8flEOXpYj61sLPaDlFf9cKuj4xpsaXITPS+cHNQGWzcuzeybspsLqQ4g8aXLYgzB5gPU/TzqDBcnNETPmY2oiUiTinslAr0aeqD1oHVUM/bxawtsrVMfspbjxQHu1FtAnDs866G14/z9GMwItiTEd+9djVHeDjZY2TrAMFnGgA60ZxWJ/asD3dHFWYPasqpeedC6PcLCcVSHXPppetzRPYwdz2hdrDXrR6NfdA3xFTw54vN3L2RD74f0dysaYoQK0aHmziZUn1Lj2QiEIyIF7Fxr+lKFwVBwIEWR5zdtfUETHS4sLUJn7WxuuA7d+5cfPzxx4iNjUWvXr3w/vvvY8aMGahbty7Wr1+PNm3a4J9//rH2ZWVYRNDsexr4SEsC8Fob221M6Ee3Mwc0kTyZWBJ2mr7w1vW2no25vZ0CG99ui63vRTAEOiGkbh7MMbVvI2x9t53hNZ/5h1TyisqvaeTS5AV5OWNCl7qY0b+xzRIysG0ixTA2og7eaB+INW+0NvlMbKIJB5WSZX8p/l6vHB0OQO8539jPFYcmd8bBTzsL2jBaDQndRQkHb3cKxvEvuqJ2NUd82Z/bUZayy2bD3pyViFztV41phb8mtIdCQQjaTgLWG2eWRIawtB3jOwUxzCqoDRdBEAzNLp/tOJ3gsnlueHht3J/fH5Gfd8XottLn9T5NfOHtqka/EFMzN7asIyV6ARuuCEBf9GkIXzcHKBV6syZfGydosGWSo+rO9hjFWlfdOPxLtGKONKFPA06x5b12AiWN0H8eQejt7T/qVg9f9m9k8tvvZpjaxbcsS74yc0BjrB4TjlVjwjnrfhawuqnDvXv3MHHiRNSpUwe//PILAgICcPbsWcTGxqJx44oNZvwis+7N1jh8M12yHSugt31sEeCBvy8lYeulRLNZ0aRQw83B4CQgtCZ0qOfJ/2E5+Kh7fRSX6tCfY8dfEdhifhArdEvBGovMyNYBWHv6gcn7QhFFrIElKXlcHVSc0Q+kYuniGejlrE9EQGuPOW2eteDTdFExbYO9nRFQ3QmRcY8whOawUsfTGaemSjcj2PFBexyLyzDYh5dY4MDnoFJi2WthOHf/MTaeSzD53FIzEzZSBNflr7fEB39eYbZDwt6Oiiixakw4zt57jH6sOWrl6HAcuZnOG9aLzs4PO+BORh7C/D1AEAQCBcJXCuHupMLZad15ItQw3/NytTz6xa6rxugG1Kbmw2718GG3eijUaKElSTjZ2zbtgLmsb+Ult8joKLf+zdZG8z8aYn00vhvWDK+08kfrwOqibdCZpg76F5+LyOj6vxH68HgbxrfFjdQchAdUg0JB4EaKMXyftcZbRWH1J6mkpASOjnpBqXbt2nB0dMQPP/wgC70VjINKiYHNLLOjVigItA6sjmAvZ6jsCAwPt146zA+61sWFMlsntpBALbQAsJQnDFd58wzW8nDE4ldalK+ScpBbbH2bzWa13fFyWC2rmTkA/HacUrB16DY+bK0ZEsJciLuqCN+Q+nZIKBYfisP7XevB180BB2+kYajEeLpc1HBzwMjWAfjfwdvIyC1GqJmwXnwMbFYTnep7cwu+1opuIqFsXY7Qc5YIBH2a+nI6EvcN8UVfDs0rF64OKkNkgvLC57hq0sXlmJzpzqnskyBzMYOtwcKhoTZPa093jmMrpIK8nPEgMx9tRZriOaiU6CDR58bDyR4KQu946GLGPKKRr6shvBxlyuastmOYyHjTNjovvKkDAGzatAm3bukzhCkUClSrVv4BuGLFCjRr1gxubm5wc3NDREQE9u3bx1s+MjLSYAtI/0e1S8Y8ni5qfDsk1Gw4JylwxTaloDs7ce2GAWBy7wb6slZsk62h22DlsOKmWgOCIPDjyBaG3XvnMs9gqc6I1qay5sLyeOGXF1svnraAT15p6OuKVWNaoYW/B3zdHTA2ItDsgimFZa+1RKf6XuXStPMJuOVZiOcONrZHymkKV1M6i/TS79rQdt78toKt4a26ebrMUxHDtoabA/ZN7IRTU7uZfLZqjD45zsth5d9Y8uGitsOxz7viyGddzD7X3w4JQdOabgZHYi68XdVYNSYcv7/VxuomfLbG6hrfjh074uuvv8Znn32GatWqoaioCD/99BPat2+PkJAQNGjQAHZ20i9bu3ZtLFy4EPXq6bMx/f777xg8eDCioqLQtCn/xBkXF8fI2ezt/exNMM8Tljo0UfRu4oP1b7Y26zVKz1xX2Rz7oitafXsYBAGraWGE+HlUGPbFppoclVY0FakF6N3EBwdvpHNm2JPKmnGtMP73SxZ9V8FxnFjVUVfSRqFNUHVsEFhYxcCnUS2PxndMRCB83R1RWKLljRXOBdfi36qOOA1eZW9SLWFAqB8uRjxB47IkCWH++rnNkpOn6f0aYcG+ylNK1a5mPlSZNWjsx71uNfBxleyLYwli7MMBfRa2PZ90MltOaojTqoLVBd8TJ/Shm+7cuYPLly/jypUruHz5Mv744w9kZWVBpVKhYcOGiInhz+vOxUsvvcR4PW/ePKxYsQLnzp0TFHxr1KgBDw8Pyb9DxjbU8XTG8tdbMjKDUYiREwiCEGW3PDSsFv6JSq4Sx81eLmpcmdULT/KL4S8iFmR5cXdU4VUbOiiKxRrmEmL5fnhztLmcWK5kAxQ9GhsjKQil9uaCEeKu3C2pGD7v0xDRiVlms8FVRfhiJVc3k17WHL0kxMelCPR0RnN/D+QUlhjDQYl8CMrrRFcZEASBOYNDDK9Da7tj23sRqOkh3dTIXOQPW7Hp7ba4+ygP7evaxqdEpmpiM2vx+vXro379+nj11VcN7z148ACXLl1CVFRUuerWarX466+/kJ+fj4iICMGyYWFhKCoqQpMmTTBz5kx062Z6zECnuLgYxcXGTEI5OdLyr8uYhy/2ZId6Xvj1pKkzlCV8PagpfNwdeNNcVjTVne0laY+eB/o09cHPo8LQtKbtYzq6O6nwdifhfPKWICYVMx26plFMZqyqgI+bAw5N7lLZzbAIFY/ka63oJlJQKgj8+0F7XE/JwcCfT0n7ro0inFQ01gwXWRFISfQi8/xQoTNzUFAQgoKCMGLECIu+Hxsbi4iICBQVFcHFxQU7duxAkybcqVL9/PywevVqhIeHo7i4GBs2bECPHj0QGRmJzp3549ouWLAAc+bMsah9MuWjSwNv/Pl2W8kxBLlwd1Rhal/bRg+QEYYgCLxkBQ1sZSLVX0elVODnUWEoLNEynD9kbINCQWDvJ53w9+UkHIvLMGha+QRiW0MQhCGUGKCfh8TwLGp8ZWSeVZ4NlUQZDRs2RHR0NLKysrB9+3aMGzcOx48f5xR+GzZsiIYNjaE6IiIikJiYiB9++EFQ8J0+fTomT55seJ2TkwN/f+tFNZDhhyAIyZ6qMraDIPSCn9Tj/ucJSxx2nnVh/1mjSU03fFWzCd7qGIiO3x0DwExvW9E42dvhyqxektpgq5jWzwodyxJtBHpWjK2tzIvNMyX42tvbG5zbWrVqhYsXL+Knn37CqlWrRH2/Xbt22Lhxo2AZtVoNtVrW1MjI/PdRR/xy7C6mvMCacy8RWdtkqgb0kFiVKfgCkGzWFFaWHOBFxctFjZjZveFog5jkMjJsninBlw1Jkgx7XHNERUXBz69yPd1lZJ4VQmq5Y8XocPMFn0MWDW+G8/ef8Nqjy1Q96AkO7CrJ1EEqp6d1R2ZuMWcM4BeN8qQElpGRwjMj+H755Zfo168f/P39kZubiy1btiAyMhL79+8HoDdRSE5Oxh9//AEAWLJkCQIDA9G0aVNoNBps3LgR27dvx/bt2yvzZ8jIyDwDvNLKH6+0kk2cniW8XdVY/EpzOKqUVktgYWtqeThaNfGMjIyMeZ4ZwTc9PR1jxoxBamoq3N3d0axZM+zfvx+9evUCAKSmpiIhwZjBR6PR4PPPP0dycjIcHR3RtGlT7NmzB/3796+snyAjIyMjY0OskVlORkbm+YYgyfImgX2+yc7OhoeHBxITExmJMGRkZGRkZGRkZKoGVDCCrKwsuLvzp0J/ZjS+lUVurj5ftRzZQUZGRkZGRkamapObmyso+MoaXzPodDqkpKTA1dUVRAWkAaN2LLKGmYncL/zIfcON3C/8yH3Djdwv3Mj9wo/cN9xURr+QJInc3FzUrFkTCgEHV1njawaFQoHatSvebszNzU0eRBzI/cKP3DfcyP3Cj9w33Mj9wo3cL/zIfcNNRfeLkKaX4tmI+SIjIyMjIyMjIyNTTmTBV0ZGRkZGRkZG5oVAFnyrGGq1Gl9//bWcPY6F3C/8yH3Djdwv/Mh9w43cL9zI/cKP3DfcVOV+kZ3bZGRkZGRkZGRkXghkja+MjIyMjIyMjMwLgSz4ysjIyMjIyMjIvBDIgq+MjIyMjIyMjMwLgSz4ysjIyMjIyMjIvBDIgq+MjIyMjIyMjMwLgZy5zQwVnbJYRkZGRkZGRkZGGnLKYiuRkpICf3//ym6GjIyMjIyMjIyMGRITE1G7dm3ez2XB1wyurq4A9B0p5+GWeRYo0JTi9J1MtK/nBWe1PMRlZGRkZJ5/cnJy4O/vb5Db+JBXRTNQ5g1ubm6y4CvzTDB90xXsiUlFv5BcrBgdXtnNkZGRkZGRqTDMmaXKzm0yMs8Ze2JSAQD7rqVVcktsw+JDtzFmzXkUl2ptdo3iUi3yikttVr/M883krdGY/k9MZTdDxgwJjwswbu0FnLmXWdlNkalAZMFXRkbGYrQ6EhWd9XzpkTs4eScT5+4/sdk1On13DCFfH0BuUYnNriHzfJKaXYh/opKx+UIi8uXNU5Vm8rZoHL/9CK/9er6ymyJTgciCr4zNIEkSmXnFlXLtnKIS/HbyPhKfFFTK9dnsjE7G/mupNqm7RKvDzH9jceB6xWp4i0q06PrDMXy46UqFXpfClkJpRq7+uY1NzrbZNdh8ujUaLy8/jaIS22mybc2NlByMWXMeF+Nttymp6pRqjRtBORAQN1kFGuh0Fbth5iI9t6iym1BlOHnnEbp+fwwXHjz/Y7dKCb7Lly9HUFAQHBwcEB4ejpMnT/KWfeONN0AQhMm/pk2bGsqsX7+es0xRkfywVwRf/B2DVt8exh9n4yv82s1mH8S3e26i06JjFX5tNhm5RZi4JRoTNl5Bgcb6GqDNFxKw8VwC3ttw2ep1CxEZl4HEJ4XYGytN4N5w7iE+23a13KYEtlI0f7v7huHvx3ka21yERXGpFjuikhGVkIUrCU9tfj2tjkR6jvXnwVk7r+HknUxM2hJt9bqfFSr4AOSZ4/z9xwj/9rBNNsxXE7MkKQDsBEJeVSSxSdlYefweSrU6UeWzCjT4dvcN3EjJsVobxqy5gPjHBXhl1Vmr1VlVqRp3HcDWrVsxadIkzJgxA1FRUejUqRP69euHhIQEzvI//fQTUlNTDf8SExNRvXp1jBgxglHOzc2NUS41NRUODg4V8ZNeaDSlOvx9OQkAsP5MfKW2ZcHem5V6/YJiowavUGNdbd7yyLv4aud1q9Zpa2b9ew3bryRhT0xKueqxpnyR9LQAf5yNx9N8DX479cDwvs4CKebfqGRsvsA9b/Fx3YoLmBje/v0i2s4/gtN3rWfbeCc9F5cf6oX25KxCq9X7LJOZWzEbp2eJLRcTodWRNvFBGLbiDN7bcBm303NFlVcqKl8lX1SixUvLTmHhvlui++SLv2Pw26kH6L+UXzkow0+VEXwXL16M8ePH4+2330bjxo2xZMkS+Pv7Y8WKFZzl3d3d4evra/h36dIlPH36FG+++SajHEEQjHK+vr4V8XNeeP6NTjb8LfZI62m+Bgv33cLVxKxyXZttc7rqxP1y1Vde6K0ptfLx3qL9cVatTwqW/BT6vckrlr4JsNXx6KQt0fhq53XMY22SCjVa7ItNRXaBOLOKUq0Ok7ZGY/o/sUjLFq9RHUnTslSEFupY3CMAwLrT8RbXUVSixZvrLmD9af1GodePJ6zRtHKRkVOEfbGp0JSK05zZArp5w7ozD/gLPgNodSRm/huLo7fSrVZniUitpiVQ8+v9R3miyle+2Av8TlMMJT0Vt2E8dMN69+NFRHQ4s127dkmuvFevXnB0dDRbTqPR4PLly5g2bRrj/d69e+PMmTOirrVmzRr07NkTderUYbyfl5eHOnXqQKvVokWLFpg7dy7CwsJ46ykuLkZxsdEuNSenYjUx1iImKQtf7ojFl/0ao309L8nfJ0myXJnqHuUa+9DLRS3qO2FzDwEA/r6chEsze4q+FiVMUe3VShSOHj7OR+KTQnSsL72fxEDXGlpb8K1MLNGG0n+/0oLHi35NazrVXSrTVFKnFBQ/Hr6N9JxiDGjmh19ea2m2HvrvS8spgq+7uNOlEm3lPBdcQzy3qARHb2WgU31vVHe2N/m8qESLbZcSEZeWi2Nxj3As7hHe6BBkUm7juYfILizBh93q2aLpnEzedhWn7mbi2yEhGN2ujvkv2BhbCnkVwS/H7mLjOb0pVfzCAVapU+q88SRfg8i4DPQN8YWTPb/IQp8PVp24j74hfmbrblLTDXcyxAnJtiKDtlY6qqqGLvKjTVcQXqca3uQY188DogXfIUOGSKqYIAjcuXMHwcHBZstmZmZCq9XCx8eH8b6Pjw/S0syr/lNTU7Fv3z5s2rSJ8X6jRo2wfv16hIaGIicnBz/99BM6dOiAq1evon79+px1LViwAHPmzDF7zarO67+dR25RKV777bzkCet2ei5G/3Yen/Sob/HiQV9QfUQu/hRSHOISnxTg5eWnobZT4t8PO8DbVS1ZuOzyfSQA4N8PO6CFv4ek73Kh05FQ0I7Q6PO8WBuuZwFz3RydmIUPNl7GjAFNMKCZfhGiO/5YcsyorWADyvQc/bO4JyYVv7xmvjy9eTdScix6nizZUFgK1x1YsO8WNp1PQO8mPlg9tpXJ58uP3cXSo3fN1j3z32sAgIHN/FDH07m8TRXFqTLTjT0xqVVD8C217b2Mz8zHlL9jEBbggen9G1u9/p20kztrIVYxcSwuAwevpyMzrxiHbqTjQWY+PuvdkLf84kO3DX9HJWSJuoZLFUjwQ+8PS+bEhMcFCPB0kvy9+Mx8LI+8Cx0JfNGH2a+7Y1KxOyYVb7QPLJcCrKoiaXuRlpYGnU4n6p+Tk/Qbwe5gsVrH9evXw8PDw0Q4b9euHUaPHo3mzZujU6dO2LZtGxo0aICff/6Zt67p06cjOzvb8C8xMVHy76gK5BZZ7jg0dXsMMnKLDQuXJdCPpG3pvXsl4Sky8zRIzipEbHIWAOkaX4rYpCzG69/PxOPYrQzD6wJNKRbtv4XYpGyk5xRxahy3XkxAk6/3YwPNoa88Gl+djkRqdqFNnOLKC2lG+/rehktIyS7Ch5uuGDRfGprgr5A4yZMkiXyaecR/V8VFyTh9NxPrTlfMkTNJM2wR+/OyC5lmFBUq+HK0cdN5vX3ywRvpSHxSgIlbonCNFt3inESvb+r3XYp/gv+uls+uWyz2drbTnJnbvNJvn601vv9GJ+NC/BOsOnHfJuYdajul4W9r/RZz1eh0JLZcSMCb6y5i84UEw7G+kFlOfnEpfmZtxkiSxEebrmDK31fL22QAes3zpfgnVg/fWNOjfD5HORZGt+mx+Di2XUrC35eT8MnmKM4ye2JtE4moshE9O4wbN06U2QLF6NGjRWc68/LyglKpNNHuZmRkmGiB2ZAkibVr12LMmDGwtzc9lqOjUCjQunVr3Llzh7eMWq02ZGl7UbO1WUNQpVchRtjLsDCsDP2ImPrbGlrBuxl5+HrXdby5/qKhP5YcvoPlkffw0rJTaDv/CJZxaL2+3nUdRSU6zNp5HYUaLQKn7UFvmu1jXtmG5Nz9x2g3/4jZEGeTt0UjYsFRNPnqAM7ee4zHlRQejgu6gMa12aC0pQAMgic9ksCMHddwT6QtHgB8uOkKWpaZwwDA4Zvm7dxIksTrv53HnP9u4AzNkSvhcQGm/H0Viw/dxs1U65kz0btB7DCas4vpnKirQocCH266gp3RKRj48ym0X3AE0RbY3yc+0dstDl95Fh9vjsKtNNubj6kssaMRweoT99DkqwOIKou8wRV6jj4uiqyQZIUkSeyOSeH04C+kXd8WGyZXB6NG9Em+ZY560YlZmLwt2jD2zbXzQvwTTPsn1uT9vOJS3rFaymEqlPS0ELtjUrHtUhJviMA/zxudUM1tHMK/PYThK89iRxS/FvxRbjH+upQoWhjV6UjM33vL8NoSU7hHFq4J9Dn7PM9m9uJzGtpMtOC7bt06s/mP6axYsQJeXuJsJu3t7REeHo5Dhw4x3j906BDat28v+N3jx4/j7t27GD9+vNnrkCSJ6Oho+PmZt/150dDpSDx8nK9/YYWjDa1Eja+liz1dC0FNfloL7SXppwultAZRphfsRf9/tKM1iqIS4/deXn7a5HNKKJ+2PQZpOUWYtDVasE3/Rhs1ZKN+PYfwbw+baAhthU5HYtulRINwmvS0gCE80jUw5jYbV5P0GkP2AjRBQgg2qWHTAOZiTUUcAIA311/AtktJWHrkDvr9ZB3P6DvpuQzb9i93xIoSqi8+ZC4u5gSDjJwifPHXVdHxNoU0VKVaEnN338C4tRc4Ny/3aPaPKdlFmPNf+SOIPHxs+9jaXLagSU8LsOVCgug4yfcf5SE6MYvRf/P33oJGq8OXO65h5fF7CJ19AD8dvsPYuNPHgiXPLJsz9x7jo01R6L/0pEk2OPpcR7/NecWluJaczWh7fnEpp3Cn1ZG8czT96N3Sk7Qhv5zGP1eS8WWZMGuunnP3H/N+9tLPpzjfJzlivKRIjCxizhmV6srJ265iR1QSMjjCAU7bHoMv/o7BkkP8yjU67DjCySKd29wdVbSGifqKRdxMFRcd41nD4vOgoqIiXLhwAbt378auXbsY/yxh8uTJ+O2337B27VrcvHkTn376KRISEjBhwgQAehOEsWPHmnxvzZo1aNu2LUJCQkw+mzNnDg4cOID79+8jOjoa48ePR3R0tKFOGT0lWh3e3XAJXb6PxKbzCSZ2fzlFJVh8MA53M8QPArpAKjSRUbBlbaHJ8fCNdBwuO/765j9jzFVKWBWj8eUSBuhtUNBeUE2RGtj7Vpppf1HCeXzZ4k8XlMUidB/Yv2v8+otIemoUNHKLSnAsLkPUseX2K0mY8ncMevzvOAC93fhrv53HpbLkBDFJxuNvcxsXamFl39Y7GXmMJCPRiVl46edTDK/lW2k5mPmvqQZIDCfuPDL8raRpAe89ypdc1/8O8kfQSHxSgF4/nkC3HyIZ70/7JxZ7Y1MRn8l9PZIkDRpRihscwjI9A9iPh+/gr8tJeG/DJZNyH266gpd+PmUon11Qgg4Lj2L2Lm6B9cKDJ1hz6gGO337E+XyzTc2SnhaWe6HlE8Qvxj/Bu39cwgOevpIC3cyEut7krVcx7Z9YUXHFp/8Tg+7/O44hv5zmPO5VKQks3HcLJVoSPx6+jTfWXjS5nrV4/TdjVrHNF5imd3QNIbVhKtHqEPL1AQz8+RRO39XPvTlFJWi/8CgGLWMKjoUaLbr9EIneS05wCsX0ecJSwZeCciIzt7FbcphfaCzVkZwCKlfTRq4+R/tchPKF1Nfda/Fxs1ETPt16FZ/9pTehOHg9DZ9sjkJGbhGOlJnG/XVZnImkSskUweyU4kSyhr5GJSQJ/YnAZdYG2hqJcC7EP8FBnrjIG87G4+3fL+KfK0kYv/5ilTqNNIdFgu/+/V4EXHwAAQAASURBVPsREBCAdu3aYdCgQRgyZIjh38svv2xRQ0aOHIklS5bgm2++QYsWLXDixAns3bvXEKUhNTXVJKZvdnY2tm/fzqvtzcrKwrvvvovGjRujd+/eSE5OxokTJ9CmTRuL2lhZXIx/grd/v8i7eJaX/j+dxOGb+gH789E7JkLogKUnsfToXYZmLK+4FB9tuoLAaXvwKytc2KQtUVgeec/w2kGlhDmKWQJgo1n7TELSlGh1iEvLxdt/XMLbf1zCo9xixlHfxC364zSuCVpTqsN/V1OwIvIeAqftQdD0vYLpROkTpTWPELkEzusp2Zjz33XRR4lCdu/s337kVgY+/8to4/bW+ot4c91F/CSwuFCwEylQmjq65tRwXTN9dC1Fr33iujff0BJGzN51HbHJ2fh4szG4fd8lJ7HxnLS4uBT0xby8JjxsG0I6fCYAVxOz8MGfV9D1h0isOfUA/0YlgySNaZ5XHjcNtbdw3y3GorU7JgWhsw9gcZngTS3KT1kh1s7czcSemFTEJmcb4oFuvpiAlOwi3ljaubQxwHVv2ElGLBkKbFtnDc+JzMebonDwRjq+3X0DUQlP0e2HSCzYZ1kMbsp+PDYpG0HT9yJw2h5cKNuwidHC0gVMLi1cGMtpkb5ZsXXgFkrAKC7VMu4rddnULKNguO1SIm6n5+L8/SfILizBrbRchmAe/zgfCU8KcDcjj7FBpnBzMGoWuY7hswtKMHvXdfx1iVvQo18rtJY7AP75VOyGYTdH/G9zczQ7EQ1Jklh6hDkH6kgS7RYcwZ2MPLzzxyVeu1eKk3f0p1/vbriMXVdTsPyY+TXvVloOI1znvyyzCbF9QC8Xl5aHjzZFYdgKY0jES/FP0GjWfizYdxOXHz7FlrK44jdScrD9cpKkuXDDuYec78/aeR2Hb2Zg8rarOHIro9LDhkrBIpfGjz76CCNGjMBXX31l1gZXCh988AE++OADzs/Wr19v8p67uzsKCviPzX788Uf8+OOP1mpehXD0VjqcNEVoV9cLxaVa3ErNwZSt0UjLLsb8wgKsfqMtipQqqO0UegEo31QY/vtyIhw1RdARBIpVakQlPEVYQDWTsjodiT/OxiMpKRPqsrIkqff0digpAkECyM9HZtpTUNbd6amPUVSiw+rLadgdo9eE/G9nFN4JNz4HBy7cN5QnCeBxvv7IubqzPVBYiLxCDa4lZyGklgec7ZUgCAI7T9+Bo6YIhfZ6Q/8SLYnFu6Kx7FVj6LlhP5/EnYx8Q910Oyp1qQYKnQ795u1HiwB3OGqYWoEGM/eZlP3v7B282jrAUFZVVKjvIycngwbTvrQEZF4eoNKZ1Kn/gaRRVVxczF2mjCKVvUHwVWlLYKfVCzfDfzis/3pWLuYPDdUXdnQEyuK50ssCgF1hAZCvt2d31BSh2E4FnUI/0Z67lWrShqxHTw33/vL9TEChxLZLifi8ezCg4Re2FbRroqTEUK9dYQFSkzMZ19FqSgDKQ7q01KQvMtOKsOHwDYTUcoOdthSlSn1ZpU4LMj/f0L7ExEdw1GgBDYD8fOSRxr25QqeFupQp7KUkZyKnqASNfN0AlQqg7Py1WqCoCDmZ2YZ2aHJyDddRaUtQotQv6gSpg0MJfz+UKpWGsmN/PYtv+9RFACtKgbKwAI6aImgVSmjsyoQFkoRjiVEL8sM/emF++gb96zMze+O7/bc4y+44dRuj2gQAAJbuioaqRIOlR+9icu+GyMyj9W3Z74lLz8H45WfgCECnUMDdUYXdMSk4HveIUbaoRIttFxMN71FzBADsupqMjjUdeZ9hHUEAMIYmNMwRAGJvJzO+RxJAkUo/lnOKSoCCAsPnKUmPgHruxooJAnByQlrZ0XH07VS8FqNfqP9IeYzpnQOYDXGm9X1hoeG4IT27yHAN+0L9WB78i1HDSY17dXEhYy68mvgUPxy4jS+GtUSz2h6MsoD+3iI/Hzodaaj/ME0jaF9aAqVOi7j7qWjo4wZdXh6zD2lzRFFeAe6mZKFpTTfuDSxt3EOjQVJ6tsn9WHfwGj7v0whX04wCuUpbAl1eHlCqApmXb/jOg/h0DP7uPortVEDZHEEWa0Bo9Rua0txcQ9k9Z+/iw271oHByBJT6sp2D3HGm7F5oc3IBlpvPkKVn8SBb/9wODfWBkjY+b6fnYvzvF+FYNsdUUxvDTSp1WtiXlhjuQ6FGi8G/nEKYfzU4aopQorRjzBH2tHrti5j3D/b2BsGXa44AoJ/D1aRhjjgWl4ElB27CkVaW3m8AsO/KQ5CvttDfJx3P/J+v/06pUmkwCSNIHfyUWpP1VqsjMez7QyghlDg3pz/m/HcdO6OSGeO+hDZHAQDs7AB12ZgjSaBM3rErNI6n1BT9XKyjxf+eu+cmHDVF+OPQDfxxSK9YqONAYuLmaOQWl8LTjhmtRWjdqq5gKYjK2sf+TkpyJlBYiB23HmP9mYdYObol/NzF+4VVKKQFuLq6knfv3rXkq88c2dnZJAAyOzvb5tfKzC0i60zdTZL6R5zzX0GvPmTo1/vJCRsu6b/k5MRb9qx/CFln6m6yztTdZFp2IUl6efGWjfatT9aZuptsM+8QGT73EJnoVoO3bJxnANlm3iFD3XGeAbxlE91qkHWm7iZjk7L07W3VirdspqOboc46U3eTNxqE8ZbNV6nJO+k5hrJHgvnrJQFGvbsbdhAsS+blkdeSs8g6U3eTf4X0EC6bkWG8gR98IFi2w4Q15LKjd0iSJMmVbYYK13vtGkmSJFln6m7yxw6jBMu+NHax4bfN6/qmYNmRo+YbypLLlgmWff/VOYay+St/Eyyb88cmYz9s2yZY9rP+kwz1vjH8a8Gy8XO+M5QdOWq+cJ8tWmRsw4ULgmV/7DDKUG/Pt34RLLuyzVBD2Q4T1giW/T1sgKFs2Md/CpaN7TnEULbRp38Llt3dsIP+npU9E0JljwS3ItecvG+8dyo1b1n6HFFn6m6zc0T43EPkiBVnyDpTd5udI6g6u35/jCSbNOFvc506ht9VZ+puMtq3Pn9ZLy/mpNmlC39ZJyfGbzM3RzScuddQ7ZGQzoJlG336t6FeKXPEqd6vCJd98MD42z7/XLDs5b0nDW2QMkfkzTUzjo4dMzTh9KezBcu+MfxrQ72a34THxpYvfiBJkiRHrDhDvj94mmBZKXMEuWwZmZ5dKGmOOHwjjXxp7GLBsj92GEWWanX6jrh2TbDsyjZDyXFrz4ueI2b9GytqjiDHjTPci0dpjwXL7m7YwVC2/pd7Bcs+bNeVMTaE5ohbjVoyx5zAHEG2amWo8/2Nl8iKRqy8ZpGpw/DhwxEZGWlVAVwGeFpg/qj7RkoOcopKJad7bDv/iGjTPC8X4egYANNjXwy2SNxgyyNFUmTdpNiCZVRmRik6bg52gqYeAPOeFZYIl73wQG9LmFdcih1XkgTLSoGePlgK5u5LVUhV6mhv3gTIUmyXMpiUnO6qmpPKfCELKC7VmjWx+aBrXdH10e3tpY5rc5AkiYlboiyyLefjEYdzlRiWHhXneAWInwcBpiMkF5TDodoGSRqkrAU7o5Mx/ndT+3guSiV4XXNFluDjj7Pc5gN8LD4Yh07fHRNdXmPGh4NtWyyEpUOhPCFVbQ1BWjDCCwoKMGLECHh7eyM0NBQqFXNi++STT6zWwMomJycH7u7uyM7Otnlos9vpuej94wk4aopwZlp3tF941KRMSEA1XCw74tr5YQc8TMzAoOa1GGUaz9oPgHmMCQBx0zsx4jIuP3bXYLdIla3hqkZecSl0+fkgSODm3L6G+ijox5iA/sjz1jd9Ta5PL7v9/QiE16kOFBai8Yy9jPpiZ/dG6OyDAGAwdQCAzrWd8cebrXE9JRvDafZLFDum9MKb6y4iNbuIcTTJBb1equzsQU1x6GY6Tt8xRiq4Obcv4OSEmORsDFp2GvalJegc7IHfxrU26QcA6NM6COGB1TG6XR2UFhah2cy9JmUoilT26NbYF293DMK4VacY5gv065MkiXlH42GnssPK4/dMTB3+fj8CTWu648dDcVh94gHD1IFdFgACvZzwausALNx3y1DWw0kFZ4LEk6f6xapLQ2+seL0lmnx1wPC9OjU9cCtTv7hGTuyAft8f4f1tGjsV7i0ahLm7b2D9ibuMo0k27GNMXzVweloPADDpY3pZvmNMim9fCcOwdsHILy7FwCWRsC/VIOExUwC8OVf/nIbOO4Jcnb7PpJg6UGWpeij2xaZi8rargqYObGYOCcWMvXc4y77XJQiTeuqDyjeetR86hQLFdvaIXzgAgdP2GI4ZT0zpBm9XNaPfqLIUVNmbc/vi/Y2XERlndPhjzxFrhjfGR5uiONtLlW0TVB0XHjxhmDqwoc8RYQEe2PFGGBrTzI3obadMHQKn7QEAqEuKoWAtSzGzexsXa2dnkCSJ0NkHUZqfj6uzeqL/TyeRksUUBK9+3Rt/xDzCt3v0dsLUuA+t7Y5t70UYylF9V2jvYEj2E/HVHmTlGetbNioMbYKro808/RgoVKkN5guUqcNLzfywaERzxCZn4ZWVRseqmEVDkJpdjM7fHzOUDfJywt6JnQ3X/m54qH4eZ5k6NJ72H3cHA6jlVw13y55vlbYE+z9ohyG/nOFUMpibI+jc/H6IwdTh1yO3sHgP0zFyzbhW8HN3gJPaDu1/PA1tWb0OpBZECf/4nNCnMSb2aYIxa87jTFw67EtLMKFLMCb2bIDDN9LxMc2mVsjUAQCivupltKO1t0dKfinaLzzKO0fs+aQjgr1dAJUKgV/po0exy47vGIg1p+INr0uVSlydN1AvsOt0aPz5DtO+KlsfS5VKtKzng/MPnoAgdXiliRe+G96MUTa3qARt5h0RnCOCvZywZ2Jn45fKTB0Cp+0xO5/oFApc/24wtCSJhjP3m5gijG4XYPCVmDkoBDP2G30WhEwddASBuP8NNb5RZurAnqt7N/HBT6+HI3COXm5pG1QdW2njrCIQK69ZZOO7adMmHDhwAI6OjoiMjGTYKhEE8VwJvhUJNdcX2jtg/vEEhrBGkU+oAOgnu8G/6MNl+dX0QuvA6gD0IXu4vgcAxWpHqGnOCkpXF5OyxaU6FGi0QNmi1XHZed76KIpUDgy7O67yht2wo6PJ55tvPOH8zomkfJxNK8JfURmcn+t0xj6jL/TmoMqSTk44/DAPKKtbqSAMv4NaOzR2KsRmlQLOzpxt+PdqKv69mgoPJ3s42SvN9tXRWxk4eisDUKoMwhQDZ2dE3srAb2eMGoESVtkswh5wdsZP51INbecrCwBPYY/Vl9NN2pacX2r4/v4Hufjy4ANGGUroBYBSpZ3Z3wboPbe1CiUKRWoztQol6gd7G/pd6Bo6M/V+9u9NDGsXjDP3HuPB07IFglZfQHUnw3W0dvaARr/4k4RC1G9jlHVm2vjqnDieD4IQrJdxn1hll5xNxbUsLVaODme8v6jMJph6j3RyQpG9SvA6hs+cneFYzQ2F9vxRQcb/fdPkmeKDvvkVIiohC+/8fcOkjb9FpWN6P9NsY3RBnELr6AQVzWFIo9XpHe/s1EjWKHCvACbtLnV0ZAiB1LgvVjuana8KlCoU0qaUdVcfoVVTf86yeiFGBaWbC+DsjFIHDaPcK6vP4YuyjGNU2aKyNlDliu0dUaBSY/eVZPRoVAOeLmrA3l7wvt6lbepKlCoMWheNfDvz6eG55gg6266k4JXW/gCAUqXpsxX1pASvb7kG/2pOBqEXAIoIJSAwPrWE/jMdSRrmiB/PpmDi4DB8uucO72/lmk+K1Y5woIX00pF6AZZvjtA5OZmOWVbZZRfSTJ4hg8OngmeOoN1Dyn+DJBTQODiaXo8oMTtHXM/RocTBkVsja2Y+AYBBy04bHC3ZZR/pjHN4ZimzfrHzHwDeuXrn3Wz4RRrXLb7YwFUBi84cZs6ciW+++QbZ2dmIj4/HgwcPDP/u3392PPuqGvRYhH9d5j4u5gp1RI9+MHbtBd766TFGAaCmh6nhOTtGbJLIuILm+IUW5YENO3oDnVG/nsM/V7gDhutI0uAQYwlcIdQOXk/D0OWncZSVHIHt3c7m481RVjO9YN8nNq//dh7Hbz8SLEMnnyPrW1aBqVZk8wX+yAliI1vYWZKGWEei3pd7eWN08uHpzL3Z0fJo/p3slTh8Ix1XEp7qN3flgG4mUqApxRERyTTYzN93S/Dzwzcz0HreYcZ7y1nj6N0Nl9HkK9OTCFtiSdptrvBQbA2tEOyIE/THkc9sZW9smsUhndjXS3pagJ/NmAhQwgp7HohKyMJrtJBkgN5ahJ7MRUeS+HrndUz5OwZj1ujn8KcSE0bkl/OZppiy3RgrmGvcn7qTCZIEEp5Ii8f8X0wq/ruaggyWmVyHhUclH4tvu5iIb3ffwJ10/SbO3PRk6XE9/TlwNBOdqJhmxsYVoUdsYqUTHHO7l4v5DQ3ALR9QlNB+y4+HTWPQW4OVx/nX+aqERYKvRqPByJEjoVBY31bnRcbSxZigGd3dF7AhYw8ocwG7pTDkl9PIErBR5hrMFJZOSvQ4spZAcBgrvrvhMq4kZGEpLXRVek4xfhIxUSRKXAi40OlIURPkVQkZtLo3qlGOFukRa78WxxG72Bwn72SiVEciNlna/WwX7Gny3uWHT3ifi1tlofCGLj8juY1sqNMWAPhkczT+EcjmxIcYe292yDI20YlZNg+fxabYSnbqUpKrsY/v6QKJgie83+d/XeWMCZv0pACFGi32xqbyhhBk23bGPy4wa29OtUmM9eC9R/loO99oOqTVGdPD3kjNwVc7r4ny+bA1XGGvLA3v+CAzHx9vjsJdlqLDEnv0eXtv4rdTDzBxSzQAEYKv5CvoWXvqAS7FP0GBptSsX8B1Wma947cfGRIfUYiNg8yV9a1eDWeOktLYE2O99MOnaOaBzyIWSa7jxo3D1q1brd2WFx72hBnsXf6HnQ574FkzVWt0YhZnTFI2XFqMaxIFHoovd1iW1MDQFgkLy68nzTtZHS0LXl4egr/cK6oeLi0uHyqlotzJ+ApEXC+vuNSGTlWmcC3Aw1acNdGK2oK7NEceMamTqwxWEJLNbdA71ReXsVPKUSh77qJn5bKTmJ74cb4Gjb/ajw/+vMJIJy50PTFQz6Ml39XqdAzh7Y+zD/Ehj611RcK1CS9vXHO+kxpLoDSc5tpkaZOXHr2L4SvPYvLWq/DgcNIsFBgLrb5lntaI7bdPt141ea+iN7dsJrMyjK4/Y5nTcVXBIsFXq9Vi0aJF6NKlCz7++GNMnjyZ8U/GMpQsDbqQ9pYBz7zfqb4Xfnq1heE1W2virLbIxJuXlcfv4ZVVpk5odO5xmDVc4kiIIIUGPi4Wfc+c+YJUTt21zi7YXNYgAFglYpNB8fflJBOTlTZB1SW1afhK4fsKQFDjbwukRjaxNlwpS18EzGVWe6dTsKh6UrOLMHvXdbPRRQBTDSw98cbuq5ZrsthauZ3Ryei5+DhKLEh7TiW9MKel52LWzusmyW2sqZjgYyEVN5wFlRGTS+C6GF+++dqSvhVCqyM5s+vRuVSW1czSaB37r6dxmv1tOBcvKMjTT1WlbIiWsE4Yy5t8p7z8E5WM4lKt4VRTKInSs4BFgm9sbCzCwsKgUChw7do1REVFGf5FR0dbuYkvDpamhOR7BCf3aoDBLWphZCt/zvrF2hxJQSitb4GmVJQAJRV7O8tMbiortJg5W7GKwBbTlrUXtKrOhI2XK7sJkrH2Zo/NWx2CJJVffyYeTb8+gO7/ixQsd+buY8b8RRcE5u21LLsbFxO3RDO0+VLZEZVk8XNhi5CP5ni1TQDn+2tP6zV6xVZIe8vG2vPupvMP8f0B/nTiADBjxzWMW3sBQdP5o+7QaV/X1IyKiysPswQ1uW//YUxlLU3wZZro2GKtlkrDmfvRadExbDqfIEo5U5URrfKLiYlBSEgIFAoFjh0TH09ORjyWCr5RiVlIelqIgc38GO9Tgoiy7CiQbadZ0bvI7TxOauXFXkRMwohgT5y9/5jxnrerOIcBa1Nog8VEKrbwuOVKx/ys4qhSMu7TuIg6+J0Ve/NKQpZkB6TK5qCNF6z03CKLzGrMnW5N2hqN5KxCfNitHgDL50pbw3VM/SwTlZBl9TrNxZiVilhHXykOwWqRyhQtSSKrkF/Dfy05B49yi+Htqi6XiUhla3zplNfEsCogWlUWFhaGzEz9UW5wcDAeP35s5hsyUrF0Mt90PgE/Hblj4rQT5KW3EVaVGeWzjwupgWipxlQqthq8YoJxcy3G5+7Lz7A1qSrJOawB227UjyMCCgC8sY4/ikpV43a6dMdDqYxs5c/pNGoNNp03Rh2pChowNmIS/zxrXIi3/gbZ2psWWxy7N/ITF7P/0I10s/bDC8pOJIR+d0SwJ+YMasr7eWXIvdboVksiwFQEoiUeDw8PPHigP/6Ij4+HTkJGExlxlNdpgG73VsfTyaDRpGyHTT2j9f93aeBdruvyMb1fI8ZrWyXLslRwP3yz/M5ofLQM8LBJvaPbcR9NVgU+2nSlQq4TwRHNwRq83tbYt/VqMO3G+U4VrpYzsggbJxtmc+Nz5LImdTydbDbO6VSV5acWbUNUhZRyAPQhsA5M6oxmtd0Fy/FFfdHqSIPms5Gvq9XbZy3Ke+zOZXrWwt/DKoIfoLePLdRo8d1+7vCFi19pjt/GtUJILX5hW2rEG2ugtEIHSNGyVySiJYZhw4ahS5cuCAoKAkEQaNWqFYKDgzn/yVgGl43XG+0DLaprxevhhr8p7RV7x2nQ+EpIXyiF19oGYEiLmobXChutiGLsFivaFv+vCe0Nf0/r1whhVhKEHexMJ+lRPHZ6FU384/KHcxPD+xLS0Irll9da4pvBIZjQpS7WvdkaX7/E1L5IjRwgBi5Tm0HNa3KUtJxN77S1an3mUBCEVQ3I6YJlclYhnuZroCnVSUolayt6NKqBQ5M7w9VBbzHo5y4hCUAFkJlXjIa+rvB1E25XSC1uwTglq9AQus63iv02a7J3YieT95QEgT5NfK12jSnbY3DgOreA3qORD5zVdibO7RSVpTW1Rlp3W6355UW0je/q1asxdOhQ3L17F5988gneeecduLpW3V3gswhlCmBvpzAcGwd6OmFkK39svZQoqS5HmuaIeoBNbHzLBF9bLOqAfhF8s0MQ/o1OQS0PR954m3TslQqsHhuON9ZdNFuWYnh4bbO2aL0a++D0XX7Thh9HNkdMUjbWnY4XfV0hlAoCi4Y3w7l7jzG+YxAmdKmLDeceIjWrsFzhtpQc92pij/qCySeeN2xhmtPc3x1KBYFprFMKCjHPrlRUtEVhdLsAPHxcgLERgdhyUdpYF6J9XS+81jaAYSZgSxQKwmqmDm+0D8T6M/GM98Lm6tPNrhnXyirXsJRJPevjtbYBcLK3wy+vtcTYtRcsDpllCe6OKpNkQ3yYe3b5Pu20yOjLY/ccx+wP8nJGWIAHYw2x9s/972oK72fUxokPMU6Po9oEWH0NsIbgaw2tsS2QFM+qb199fvrLly9j4sSJsuBrZSiNrANN8LW3U+K74c3QN8QXb64XLwzWqe5k+JvKprX29AO82sYfDXxcGdcTYyNrCQqCMEy6OpIUtTDsm9QJdb2lhSfzceXXRlyc0RPxj/PhQUtv2dzfwyQBxMthtTEgtCa8XdVYtF/YQ1gsr7TyxytlETUAYEy7OgBMs29J4UmeqTOVI8/x+MwBjfHtHut5vFcVbCGEmjv1sDQDmBB2tGuOblcHjXzdrHqd4DIb/xrldOL8sn8jzN8rnGWOwpoKHn/aHMbG2iYmUpnUs4Hhb+pxFMqaVR7iFw5A4LQ9jPc2jm+Ll5aJy3TIJ8T5uOmfCzHDSWWhcmRij/r46YhwxruqAFtAIwiCkUlVCs1qu0tKrqQwKKaYmt3AaXvwZodAfF6W7hoA+jT1YWiOl7/eEodvpuPdzsHWF3wlzLNvdwziTPBii7naGoiWeGJiYgx2vevWrTMr9F6/fh2lpbYNnfO84efhgFFt/DGAFp2B0m51behtYncoBP2IgW78P5WWjpISRJ3LaVfIJzQQhHFSTc0u4sxI41/deJQZXqeaQejlChbOh52SMDjysfF2VaN1YHVGema+tLr2dgq8KzIGaWXxKM80nTHXzjwswMMqO3ZbYukxvC1+lrnNH1f86fJCP2mhtKQO5Qx117Wh0V4/sGxM8C0+PRv7iKqTr00rXm9p8p6CIKxmViRUzdIKEqbWvtEKX/RpyHjPlRX/vDIW9wa+4tcCPg08FcddjIbe0rmksZ8rvhvGHSvYVswd3FRy6nR2pIny3FG2mZRYuMJBrjsdj6tJWYbX7HvVq4kPFr/SQnQUCinkSgh9yOebUEXlXmlRHaREcoiIiEBCwotz/GoNmtZ0x4KhzTCtX2PDe55lnsIEQVicejbIy6g5oR/nUBrfejVcypUljm/QsRdBrgxtm95uZ/i7Gk3YlXJsqFIqzAbNd1bb4fS07rjwZQ+TneyWd41tsLOR9psO2+lPClyOGOzfM7VvI6x/s43kyd9SpGxS6Lg5SP9eu+DqNvHiVplZOMpjUsrn6EjfMPL9pA71pDny0Z8F6lSDT2ixtxPXj3yCXb9QP4TXqWZS1lonSFVh39a9kY8hhBrFZ70bMF5XxuKutlPiv4864u2OQQynTDqUQ5q59onpZ0vvadLTQtTy4NfcS6WFv4fZMmMiAnFrbl9J9bIj0gRUd7LYdMXcCQvdtKF3E+Pmk++35RQaBVC29p4a7+WxsnGyV3IKrmMj6oiug+/6VWAIcyLa1IEkScyaNQtOTuIeYo3m2YpvWZVwd1Thj7faID2nCJ3qGdN/8k1gobXcBb0+29flTiFKhQRSKAh8NbCJJLtaipkDGuPno3c5P1MqCMaiyXX8o2Is/sayUiJcKBUEXmsbgJ5NasDbRY3vD8RheeQ9E8dAylGG3Y/trBwlYGjLWoKfSzXloMMlhNDfqu5sb3D+yjeTWtYaNK/tDoWC4LWx/vqlJphTlgWKjdTFdOHQUPQN8TWbOYyNq4MdcouEtRdOZjSt5QmfVb+GK66w+mdCl7o4RktNzZfUpG2QJ6dtei0PR7Ppoam4ynyCq9j+F9Jo/v5WG4R8fYBWFgjz90CboOq4/ygPmRymOWK5lWZZ+DVvVzUe5RpPRhr7uTGyoB2e3Bk9F1se4SIsgCnsC6WtFeLV1v5m7bmFTkVCa7sjtLY7ikq0aFbbHQ4qJWbsuGZw9qXmIXpfMCh7pMU4IInV+L7UvCbDnjXpaSGv85wUNoxvg9rVnBDo6YS3f7+EI2bSugspMIaG1cI/UcyY8tVp2dfcHVWoVc0Rg1vUsijutbmNxv9GNEe3RjVMxp+jvRJLR4Xhk83sVNXGuYeu8fV2VVvFeezQ5C7ovfi4yftS7Lp5W1FFJV/Rv6xz586Ii4tjZGkT+hcREQFHR+7YlzLm6dzAGyNa+TMGMH0B6tbQGwNC/bBydEvs+KA9rn7Vm7cuHzcHgzBET1VLpW9UEgTDYejHkc1Ft7N1YHXeSZEt+CY8MfX6d1YbF/zyjuEarg4gCAKf9KiPX8eaHlFSFNAWquoc6Sa9XPh37EtHhZltR5+mwt7AQraLa99ohZruDvi4u1HL1JgWU9KFI800vf/pKTkfPpYmIFrCitHhgoLR2IhA3s/YdoO1qwnPF6+2CYCHkz3v9fry9HuBGcGkR6ManAvIwU87AwB+erWF6BjUn/dugMOTO6MmzQvekyO+q7erGvkaozDO98zRW+VOs1Mf2MzPJMTUb2NbIYKWccq1TKPON67ERnMRKuaitmOcmCgIAgoFgW3vReDSzF6i6ufDUie/74aFYtUYY1QbdkrkejXK55vSkNXv6Tk8gqUZvhzQmDHOuRBj+uKgUmJk6wAMblEL1+b0MbxPCVZ8yWqoustrEkWPP9u5PlPJQpKkxfbBdHzcHBDk5QyCIBAeWM38FwRQqxQIZQnj9Cll98cdobZTon+oL3Z+2EFy+82dSGm0Ot5NJ9eYpKYe9n2in5Cyr/hJj/rmG1oGn+O5FOXTgGbc0WhsFdO7vIjW+EZGRtqwGTJioD/3k3o2QHPa0Yi7k/Ai1rwsliNdMKJMHRQKgrErl5KIgCCAJxzZqxxUCsPnQrjSjrsZg09gzL3eNgB/0rzU2cKrg0qJXk347Rfp2nGutu/6qAPaLzzK+V0xIYu4hGk6DX1dse6N1vB2VWPgz0YHlZvf9IWjvRKnp9UAQRAGTXpcmlFbZW+nQKCnEyN0GP14m95tXDt2Lxc1MjnshKXw69hWeOePSwAAZ3s7wQ2L0Gfs6Axzh4TgYWY++jfzQ5t5RwTq5K505ZhwDFh6EtdTmE5G9koFCnX8wi+fmU8DH1fELxwAADhqRsNEp14NV8bix6V9UhLAgFA/rDpxH4C44/KrX/c2ODlVd7bH/kmdDa89ne3Rs4kPikq0BodGw8aW5yaINevhWsjp4fnowhl7A+HmYIccM9p2ITrU49Z4C+GoskP3RraJ9Ty4RU0TYTTQU/gUtI6nEyb2qI+p22OwcnQ4etBsqxv5CidKoO7h5F4NsPjQbVFtfK9zMI7cysAImmMtF0tebVF2DVHV8kJ/Fkp1JBr5uhq09UFezoKaQ4IQd336mB8eXluUA3Lz2u68TpDsIUFvI6WYIAgCzf090C7YEyfvZJpvZBnmwo8Vl/B/zjVWqZMbJUGgZ5Ma2BObqm8fTahkD9FPe9ZH+7qeeHX1OcG2zB1ctmmxQD6lsluO7xhkshmkqArmSlw8vzFKnkPog5/LJoc6yucKj0LtMC/GPzW8R+0kFQQBNwcVBoT6oZaHI69pBBcejtxC3orR4WV1m37mZK9Em8Dqhl0ptXC0DqRpowWuOXdwCON1eeIQt6Fdk8LP3YERP5SOmHHcimX3yEW3RjVMjgCp6AxsQaNrQ6NtNwkSQ1vWZnzOd9zFZXvbL6R8sSn/nhCBTvW94Olsj9rVHOHiYCeo4RD6jL1B8HN3wBsdglBDIEqHvk7m634hvoZnqTXrfnZp4A0/D+H6BrcQNk0BuDXtXFBCEV1Lb8+hMVIqCIazp1gHqa9faoKIYE+MYdnfUV+nC2XUGOLTeLPbdWBSZ85yns72JjGH6e2lb6bZj2KAGaFQiNkvNcF0mr+DWOr76E2JqHmlVTk1hHS4hDS6lp0LAsDQlrVx85u+DKEXMCoIzF1PigZvev/GODy5i+AzW81JZYjuIwYh4ZS+BpRodYxn87W2dQQ1yux6O9OSKX0zmNtJrIarA68dLRWpAgDWvNGa95r9QvUO5NTJjJBW15xGnL3emjMhEkrZ/CTfVCkRn1lg+N7g5ubnKkA/75oz4RvYzA9jyk7kuPpTyGGuVxMf3JzbF7Gze2PWwCaC7aiKyILvMwR9seEaXIuGN0OwtzO+H25qqiB0lEFV9cvrLXF6Wnfeo/ifXm2BgOpOeKdTEAD9cW2ApxPjCJaiXRA16Eyv6+fugG0TIjC5l95JZNuECGwY34ZhTE8KzLRsQa88cV1vpeWYvEcQBPZN6oQFQ029kevVcMHFGT1N3qdPEpYMdi6B9OSUbnivc7BJOyZ04U/gQO+24eG1TT5/u+zecdHEzw3b32+PD7vx11+rmiMcVEpEftEVBz/tXGbOwlucl1Vjwk02LGLD59Cf5fVvtsaK0eGGZ4mdKahDPU+zZgpinp/3OtdlLKrshdLdUYXWgdXwWpmj0cJhoXijfSD2T+qE6s6mi4pCQaB2NeM44+vDpqxsTm92CMLmd9vByZ4t1PCP76iEpyafUW2gcHOw49XauKjtcPyLrhjVxqhBpEeEoHcve54pT/zXgc1rMqKxiIUSRH4e1RLDw2szwkHxmT+tHhOORr6ujIg6XHBlQRM73rk07GqOhDR0uH6/uYQUYuAT5i7M6CGpnqEtazE2N5pSHUMTaW+nkGTLT2/ViHDj88ZOWLLzow4Y1tJ0fqNndfRyUeP+/P4m9vMkqQ+/tXJ0S+z6uCMA4efUz537GVw0vBnOf9kDsbP7MN6v6eGI+S+H8kZLEjpRdeVw+C0sC3FIEMwxSz+pquZk3Hxw9QuXMox+rVVjwtE2qDo2vd0Wjf3cQBAQPDEYGlaLt710qqjcKwu+zxJ0rQv9QafoUM8LRz/rir4cQhTdzpDSNBlMHUQ+nf1D/XBiSjfMGNAEp6d1x7np+kly2Wumdq+U9pIrO9W9R0zb0xquDuhU35uxMNBFlVNTu5nU8XKYcefrYiYAuBD2PAuPm4OKcYR3ZVYvnJveAx5Oeu3X3Xn98Mtr+nBOg5rXNBuE3BxcoWz8qzthev/G8KEtdCSpX0z4MvrRNwxcJhd0YYtONScVFg1vhvA61TC5F7dwABgXTFcHlUH4oi+i7YJNNehc9GnqayII0AWIL/szI1/QFxG60NmkJlMwHNyCaWtGgDBxTGNv1MQ8/QGeTjg3vQfWv9kaA5v54b3OzM3BhC518deE9oY+8XJRY/agpmjk64aXw2ph3sshWEjbwNgpmBFPuMZgsJczujWsgf+NaI59HNmlGL+B40dQAimf2E9ftChzhA3j2zDKvNS8JloHVoeTvR1DQ0jfQNGfObZAxX5OzQmWdNR2CjirlWa1ogBznqE2VKG13fHDiOao6eGIV1v7o7qzvSHLIduevHdTX+yf1BnfD2/GWf/BTzvj65eaYJwFmTSF5leuTdfkXg3g5aLGrIFNGILv9vcjEBHsifVvcWsypcC3eeY7waPTmqZBf6sDcyPNNY9JSZBE9/lQ2ylQu5oj3Bzs4M+at/zcHfH1oCaoyzJTcmRtCBUKwiA4UmTkFsFOqUDfED+DbX3vpvqNXHOOyAp8z5+nsz1jbqajd7bmNrcT0iBz+SmsPK6P+96s7IRw5eiWaBnggal9jXOkg0qJY593xYFJnfHDCO5n2BTjvapXwxVb34tA+3pe2Di+DfZ+0klS+FSA20+hisq9suD7LPFKK3+sHN0SOz/sAHeJIaToExJlY0RphLgmZkq7Rddk0svV8nA0DOBO9b2x/f0IxmcUYo+I2bzbWR+ebGAzP9Su5oQZ/ZlHnj+ObIGVo1ti+estLQqLRSF0nENPjFHNScVI22mnVKB/qC/++6gjvhvWzGI7uT5lE+4rrUx36VxQl3m3czDaBFbHItZCTW8G133lmnS9XOxxZVYvg+mFUkGYLCgUXJoRugZDjBc/X/gzetvYwmkL2iakrrcLXm3tj3c6mZpFfNCV6Sw0oJkftKzFmN0HIv3WQBAEujasgWWvtUQ11qZCaO/oaK/E623roAstxq6CIARt9Kj3CILAsPDaDNMJLrjWUmrB5vp9jXxdMZ4ltAD6sRy/cAA+69UA3w9vhp9HhRm0TK+2DsDLYbXw06stGFo8evXs3zG4RU0c/LQzwutUw4BmfvhumNhFWX+qpbZT4shnXTFrYBMTIYuOi9oOJ6d0w4Uve3BqYBcOa4aLM3oaNoMLh3K3w8neziCc0zcBDXxc8WaHIMvCekmwcwf0QvmlmT0xviPz94bXqY7N77YzaxcsBroAT0/UwDU/1PJwNMSHblrTDctfNzoOsru6RKszeU8loE1lX697I/182NjPDQoFgQOTOuPElG6GmMN03BxUOPJZV6yjmTRwne58zgo/d/qeqc344Ba18MdbbfDr2HCTz4aGcc/N5vRFWp6JZVAL/rTkQlEaqPTRfUP88M8HHQxxuimCvJzR0NeV+wSCsync1/J0UXPON3SHQK5LzBhgGqazqmp8LZJKHjx4gKAg/klIxjbY2+l3qZZAH0/bryRhQpe6SHqqD4fENdkd/6Ib8opLcfpuJiZuiTapg014HaOmj16fpR7Dn3Svj64Na6BJ2QDs38wPiw7cQpOaxsFnaV/Qw78JZcqq5myP7e9HQG2n5JxMCIJAKOU0aFFLgCUjw3A/M8/wO8VS08MR2yZEmH5Aa4iz2g6dG3jjBOv4n07z2u6Y93Koye87PLkLMnKL0XY+08mM63bS7cbTsot4r3Vqajf8fTmJoa3nq5utPaBvLBQKAgt5BCi6IDGpZ33U9HBEkLczUsradXFGT/RfepLxHWtkSxPzlDvQThdyi0oZiwLf8yX++sayH3Wrh2XH7hqO+P04tFKrxoQzTkrYZjEfc9iUOtor8ePIFibv0zcpXBmwGvi4Yvv77Q3v2SsVvHaO7YKr49z9J4ZygF7wooTAtadNs0MBwIPMfMFoKQBzLhLSZv3yWkv88ppgVZLIEIj6wLXxNhd6TyrjOwZhDSurFl2Ap6c+VhB6reP+62kAgBn9G2NIWC1Ud7YHCX2oOvrGlb25LtXqTDatQhrfV1rVxt7YNGQXlqCxnxsGNa+JviG+cCjrFy6Bl003Wnx79oYUMHUg5DI1UCoIhn0xnVCaeUv9Gi64kyEumU1DH1fsht4RrW9TX6jsFJj3cojFihpLwvtRofz6hfpi26Ukxmf05FHmcFHb4b+POxqcabk2bC+H1canW6+y3q2akq9FGt/GjRtj0qRJyMwU7+koU3VYuE+ffjSrQD/hcYUtcVAp4eWiRrCXcYEQuxCbK/b7W22EC0Av3LTw9zAMsFoejjg9tTu20kInWUpzf+NExmfXSBFep7qoOJR8cVjNfs9eiaY13UX3LZ9mmbL36tGYmeTkK5rjAVeYup0fdeT8fQRBwMfNQdLRNKC3xWNntro7rx9uze2L2tWcMKlnA9Tx5NYm0xfR7o1qGGzJAZhoN8RALcAz+jdBz8Y18NvYVvB2VTPCL1FtloqW9Z0SM57cAHNRzi8uNbskSFky6I/PZ70b4MqsXganvbdomkM7BYETX3RDHU9nKBUENr/TDp/1aoBvh4SwqxRNAx9XzBnUFMtfbykqUgTnhg16D3N6bGJrxCjlw1dEdBYp0E+8ACCEZpvN5ygLcAsQfFmwLGXmgMYmJ2Z0svKNgi9BEIyEHX1DfOHtqoZSQWBQ85rwr+7EGKfU35TpSFhANfRoXANDWtQ0zD1Cgm+XBjVwZVYvxC8cgL2fdIS9nQIuajuLEwm5OZoKylLCcpnDj3YvzVX7TudgTOxRH7s+6oCVY8Lx86gwUUIvZULHprUFTpobx7fB98ObYfYgU0dB9omCENQc+VaHIEQEe6JDPXEO8FVV42vR03Xy5Elcv34ddevWxbx581BQYBqfVaZqwdagxSZlGwKdW8NZgo6QTZufuwO68OyszVHDzaHcKV0BgC6jlDfKAcWqMeGo4+mE5RxpXK0JX6zbLe+0w5xBTTGXJcDQtTPhAXqtPJVC9KXm/EduFPNfZjrWmXNm6FDXy0Ris1MqRN03RgpfgsCMAU2wakw4OjfwxkQJXu0U1HPYpKYbfhvX2mBz1z/UjxH3mssu0RzskER0rZkYxCwIlqbCJQiCYd9Nd8pzcbBjOCNF1PXExz3ql3tcjWsfiP6h4jZJLfw9TDawsbN7Y0xEIEa31Tu4dmvIPUd8JeBBLhV6TPPyEl6nusEuvWtDb+z+2GiT7SggyAZUdzLRPlsr8x0FQRB4p3MwevBk/mRrVc2d2NHfov5e90Zr/DiyObo29IbaToklr4YZNlx8pg4zBzRGn6Y+hmtYIwIAl4MsV/x4qQwsUwBM6Cw+pb2DSolPezVAs9oekq7Fp2wQM1+z8XRRY0QrfxNn2Pvz+5t1rKRD7fO/eqkJNr/bjve7815mrj9CZi6ViUWmDq1bt8ahQ4dw8OBBzJgxA7/88gtmz56Nt99+G4oq+kNfdNjHgFQsQED42C/IglTGQtOXNXfflpJDE1JebcOd7lMqIbXccfwLUyc8a/HHW21w+GY67y69hpsDp+ONl4saK15vieJSnUHgGdayNmq4OaClv3kNgrujCiendENGbhGa1nQ3a7oyvX9jvLz8tPkfxAHXBqxPU1+zCUHYtAzwwJWELE4nTwq6jbwlmfTOsOwEzSXJYJPwpMCs84gUOUCoKF1zWgWGHwCgbbAn9n7SCVO3x2BSz/qGDdW49oGoXc0J7XlChL3VMQjf7ObOBCiVZrXccYEnuYMlvNkhCI393EwEHaH7qFIqcGBSZ3x/IM7gxGRtwZeCT4P+VsdAHIvLMKQ+po9xrrTnBEGgW0NvZBWWGMZOfR9X1OcJj0YX/Ie0qIl/o1Pw69hWgrHWLYVrfrI0sx6dn0eFYd6QUKh5HN3s7RSS4t9LxUGlMDhmWgOxpykDmvlhT0yq6PTFr7etg2Eta2POfzeQml2IpjXLb49uC8rlit67d2/07t0bW7duxVdffYXFixdj/vz5GDp0qLXaJ2Mj1p/R23wNal4THhwRIihc1Ha4PLOnpJBhQhP9Mp5jnIrEkREh4NnYqHVu4M1rg2aOfixNnJ1SgW4NubU/XPhXdxK0n6SnJfZ1d8CsAU0wZXuMoDMSm471vKwW8/GvCe1RWKI161h5cUZP5BeXckYeMQdbwytVe/i0oMSsRldMnNUuDbxx/PYjztB1FIwEJ1VF8oVeE/9fWTgpCgeV0qx5TXVnezzJ1xiEKEv5tFcDZBWWGLR55UWlVKBTfdMxau6pVioIBHkZx5eUKAhS4DO5qF3NCcc+70prj/EzPgFpbZlDmZgx66BS4udRYUjNLsTbHYPx3fBmkrSNUuAaU0Jxc8VCEATcnVQM4ZY+lM5M646PNl3BmHaB5b4WnS/6NGSYnlgDMUmYKBYNa4bX2gRIioXtoFJyhgKtSpQvBlMZAwYMQK1atTBlyhSMGDECWm35d1gytqWo7Kg2wIxDCKA/LpECW5jc/n57rIi8h4+71+MMF1PRvNkhEMdvP8LbEmycZPhpSnM4JAC80tofXRp68waZ54K02D3QFKWCEBVNxNtVbZHQCwBdGnrjRmoO1HYKbHqnLVoGSLO/E0qL/e+HHbDtUiK+6M0fVo5iwdBQHL6Zzhm7k4KR0lpSK6smK15via2XEjG9X2OD4GuJNsxZbYcfRohPz24pYoTDWh7Gedga5lxcTOhSFwlPCvBSc2FBv3Y1J/hXd4SzvR1n2ExAulkC/ZherbDN7wO4Nb6Dmtc0ZMEsL3QNeAHNKdbLRY0t73LbrlvC3MFNse9aGm/YyorCWW0n2p73WcIiwXft2rW4fv06bty4gevXryM5ORkEQSAgIAADBw60uDHLly/H999/j9TUVDRt2hRLlixBp07c8SsjIyPRrZvp0fLNmzfRqJExrMb27dsxa9Ys3Lt3z2CT/PLLL1vcxucNWziQsAXf8DrV8Nu4Vla/jqU0renOmYRCxjLoiw21HvLFt+RDSrbAqsDEHvVR19sFnet7oYaFNvJ8wkMLfw+0ELlBrOnhiLFl2Zf4UCkVUBD6sGY1eYLxP0u0DfZEW1ZWKkudSysCMTMs3Sa7rRVtj+n4ujsYNLVCOKiUOPZZVygIwqYOhraAa0jV93HFpZk90erbw+Wun94ftjRtGBMRaMiqJmN9LBJ8p0+fjpCQEISGhmLYsGEIDQ1FSEgInJ2l24NSbN26FZMmTcLy5cvRoUMHrFq1Cv369cONGzcQEMC/m4+Li4Obm9GOxNvbeNR09uxZjBw5EnPnzsXLL7+MHTt24JVXXsGpU6fQtm1bi9v6PMFlw1VehNI/yjx/WBqyDtBnp7uRmoPeNrD3syUOKqWgeYEYOtX3AkGYhluyNkoFgf8+7ogrCVnoXP/Z2mCY4/PeDbD9SjI+EMg2WNmIUY428HFBQx9XOKuVgrHFKwpLoypUVYROWCzlWesiZ3sl8jVaszHBXwQsEnzT09Ot3Q4sXrwY48ePx9tvvw0AWLJkCQ4cOIAVK1ZgwYIFvN+rUaMGPDw8OD9bsmQJevXqhenTpwPQC+zHjx/HkiVLsHnzZqv/hmeR8ggtbF5vG4A/zyfgMxFHtDLPD5TtoKNKaZKG2Bzm7IefVzrU80QDH1ecmtod1SQmo7GEpjXdGSYpzwsfda+Pj7pLj/hRkYgxu7FTKgzZ+axl6y5jG6b3a4Qz9x6LjmJSVVgxOhzLI+9ixgD+0HYvChYJvlqtFr/99htu3bqF2rVro0WLFmjRogU8Pbk9cc2h0Whw+fJlTJs2jfF+7969cebMGcHvhoWFoaioCE2aNMHMmTMZ5g9nz57Fp59+yijfp08fLFmyhLe+4uJiFBcbA47n5ORI+CVVmx6NauDIrQyb1f/tkBBM6dvIJIC5zPONt6saBz/tDEeV8rnTFFmbs9O743pyjiHeslCMV5lnmx9HNsdvJx/gm8HiYiQ/a2YFzxq1qzki6WkhAj3Lt9F+r0tdvMeT8rkqUx4H6ecNiwTfjz/+GH///Td69eqFX375BQqFAiUlJahVqxZatGiBXbt2SaovMzMTWq0WPj7M404fHx+kpaVxfsfPzw+rV69GeHg4iouLsWHDBvTo0QORkZHo3LkzACAtLU1SnQCwYMECzJkzR1L7nxU+7dXARPDNkRh/VAiCIGSh9wVFTAQCGcDP3RF+z4GdrYx5Xg6rjZd50t3KWJeXmtfE8bgMDBKIdbvp7Xb49eR9vNNJfCxemecTiwTff/75Bxs2bECfPn2wa9cunD17FsePH8ecOXNQp464eG9csI94SJLkPfZp2LAhGjY0HqlHREQgMTERP/zwg0HwlVonoDeHmDx5suF1Tk4O/P39Jf2OqkojX1dEBHvi7H1jDNKuEsJaycjIyMjIVDWWvtoCWh0peOIU4OlkkuBH5sXEIsE3Ly8PTZroM+ioVCoolUp8+OGH0Gg0SEmRHlfRy8sLSqXSRBObkZFhorEVol27dti4caPhta+vr+Q61Wo11GrrG8JXBeyUCmx+t50h3zYA2NvJx2syMjIyMs8uBEHYLP6xzPOHRQZ5wcHBBgG3Vq1aSE5OBgC89NJLDMFTLPb29ggPD8ehQ4cY7x86dAjt27cXXU9UVBT8/IwG5xERESZ1Hjx4UFKdzzuyI4WMjIyMjIzMi4JFGt8RI0Zg//79aNu2Lbp27Yq1a9eiZ8+euHHjBgoLCy1qyOTJkzFmzBi0atUKERERWL16NRISEjBhwgQAehOE5ORk/PHHHwD0ERsCAwPRtGlTaDQabNy4Edu3b8f27dsNdU6cOBGdO3fGd999h8GDB2Pnzp04fPgwTp06ZVEbnxfGRtTBH2cfAuDOdCMjIyMjIyMj8zxikeA7a9Ysw99ffPEF2rRpA29vb+Tk5GD8+PEWNWTkyJF4/PgxvvnmG6SmpiIkJAR79+412AynpqYiISHBUF6j0eDzzz9HcnIyHB0d0bRpU+zZswf9+/c3lGnfvj22bNmCmTNnYtasWahbty62bt36wsfwbV7bAwAl+FZqU2RkZGRkZGRkKgyCtELy9sePH2Pv3r2oXr06BgwYYI12VRlycnLg7u6O7OxsRqKMZ5md0cmYuCUaALD7444IqfX8xfeUkZGRkZGReXEQK69ZpPFl4+npiTFjxlijKpkKgG7eIFs6yMjIyMjIyLwoyNHmX0Do2dpkG18ZGRkZGRmZFwVZ8H0BkQVfGRkZGRkZmRcRWfB9AVESdMG3EhsiIyMjIyMjI1OByILvC0iglzMAQKUkUMPVoZJbIyMjIyMjIyNTMVjFuU3m2aJeDRecnNINapUC7k6qym6OjIyMjIyMjEyFIAu+ZqCiveXk5FRyS6yLux0AEsjJ0VR2U2RkZGRkZGRkygUlp5mL0isLvmbIzc0FAPj7+1dyS2RkZGRkZGRkZITIzc2Fuzt/fgKrJLB4ntHpdEhJSYGrqyuICoiAkJOTA39/fyQmJj43CTOsgdwv/Mh9w43cL/zIfcON3C/cyP3Cj9w33FRGv5AkidzcXNSsWRMKBb8Lm6zxNYNCoUDt2rUr/Lpubm7yIOJA7hd+5L7hRu4XfuS+4UbuF27kfuFH7htuKrpfhDS9FHJUBxkZGRkZGRkZmRcCWfCVkZGRkZGRkZF5IZAF3yqGWq3G119/DbVaXdlNqVLI/cKP3DfcyP3Cj9w33Mj9wo3cL/zIfcNNVe4X2blNRkZGRkZGRkbmhUDW+MrIyMjIyMjIyLwQyIKvjIyMjIyMjIzMC4Es+MrIyMjIyMjIyLwQyIKvjIyMjIyMjIzMC4Es+MrIyMjIyMjIyLwQyJnbzFDRKYtlZGRkZGRkZGSkIacsthIpKSnw9/ev7GbIyMjIyMjIyMiYITExEbVr1+b93OaC765duyR/p1evXnB0dLRBa6Tj6uoKQN+Rch5uGRkZGRkZGZmqR05ODvz9/Q1yGx82F3yHDBkiqTxBELhz5w6Cg4Nt0yCJUOYNbm5usuArIyMjIyNTRVl94h7i0vKwaHgzKBWyaeKLijmz1ApxbktLS4NOpxP1z8nJqSKaJCMjIyMjI/McMX/vLWy/koTzDx5XdlNkqjA2F3zHjRsnyWxh9OjRsmZVRkZGRkZGRjRaHWn4+48zDyuxJTJVHZsLvuvWrTNrb0FnxYoV8PLyElWuWbNmBhOEiIgI7Nu3z/A5SZKYPXs2atasCUdHR3Tt2hXXr1+36DfIyMjIyMjIVF0KNKWGv/dfT6vElshUdSrE1CE6OtrqddauXRsLFy7EpUuXcOnSJXTv3h2DBw82CLeLFi3C4sWLsWzZMly8eBG+vr7o1asXcnNzrd4WGRkZGRkZmcpj/zVZ2JURR4UIvi1btkR4eDhWrFiB7Oxsq9T50ksvoX///mjQoAEaNGiAefPmwcXFBefOnQNJkliyZAlmzJiBoUOHIiQkBL///jsKCgqwadMmq1xfRkZGRkZGpmqwPPKe4e8BzfwqsSUyVZ0KEXxPnz6Nli1bYtq0afDz88Po0aNx7Ngxq9Wv1WqxZcsW5OfnIyIiAg8ePEBaWhp69+5tKKNWq9GlSxecOXPGateVkZGRkZGRqXyKSrSGv1vU9qi8hshUeSpE8I2IiMCvv/6KtLQ0rFixAklJSejZsyfq1q2LefPmISkpyaJ6Y2Nj4eLiArVajQkTJmDHjh1o0qQJ0tL0Rx4+Pj6M8j4+PobP+CguLkZOTg7jn4yMjIyMjEzV5WmBxvC3liQFSsq86FSI4Evh6OiIcePGITIyErdv38aoUaOwatUqBAUFoX///pLra9iwIaKjo3Hu3Dm8//77GDduHG7cuGH4nB3LjSRJs/HdFixYAHd3d8M/OWubjIyMjIxM1UWnI1FUojO8pkd4kJFhU6GCL526deti2rRpmDFjBtzc3HDgwAHJddjb26NevXpo1aoVFixYgObNm+Onn36Cr68vAJhodzMyMky0wGymT5+O7Oxsw7/ExETJ7ZKRkZGRkZGpGDRaHeO1ThZ8ZQSoFMH3+PHjGDduHHx9fTFlyhQMHToUp0+fLne9JEmiuLgYQUFB8PX1xaFDhwyfaTQaHD9+HO3btxesQ61WG0KkydnaZGRkZGRkqjY6lmmDbOogI4TNUxZTJCYmYv369Vi/fj0ePHiA9u3b4+eff8Yrr7wCZ2dnyfV9+eWX6NevH/z9/ZGbm4stW7YgMjIS+/fvB0EQmDRpEubPn4/69eujfv36mD9/PpycnPDaa6/Z4NfJyMjIyMjImONacja+2nkNg1vUwrj2gVapk23aICt8ZYSoEMG3V69eOHbsGLy9vTF27Fi89dZbaNiwYbnqTE9Px5gxY5Camgp3d3c0a9YM+/fvR69evQAAU6ZMQWFhIT744AM8ffoUbdu2xcGDByUl05CRkZGRkZGxHssj7+JKQhauJGRZTfDV6divZclXhp8KEXwdHR2xfft2DBw4EEql0ip1rlmzRvBzgiAwe/ZszJ492yrXk5GRqRrkF5ciX1OKGq4Old0UGRkZkZAkiQKNFo/zNOYLS4Rt2iCbOsgIUSGC765duwx/nzx5EqtWrcK9e/fw999/o1atWtiwYQOCgoLQsWPHimiOjIzMM0zY3EPQlOpwcUZPeLuqK7s5MjIyIpj+Tyz+uZIMO6VwZCVLYJs6pGcXWf0aMs8PFerctn37dvTp0weOjo6IiopCcXExACA3Nxfz58+vyKbIyMg8o2hK9eea0YlZldsQGRkZ0Wy5mAiNVocCjdZ8YQ4KNVqQPJpc9vvXU+T4+zL8VKjg++2332LlypX49ddfoVKpDO+3b98eV65cqcimyMg8MxRauFA87/Atgs8qq47fQ+dFx5CaXVjZTZGRqVJcS85G46/248sdsZyfs00b5JMgGSEqVPCNi4tD586dTd53c3NDVlZWRTZFRuaZ4Ocjd9D4q/04FpdR2U2pciRnFT5XTiwL9t1CwpMC/HjodmU3RUamSkCSJK6nZON/B+MAAJsvcMfVZ5s60F8fvpGOG2Y0wKVaHXQ6ElodiY3nHsqbz+ecChV8/fz8cPfuXZP3T506heDg4IpsiozMM8H/yoSgmTuuVXJLrE95NbZz/ruBRQfiLPpuoUaLg9fTkF9cWq42SCEzr1jU9Vix+G3CxnMP0WvxcaRklX+BT8suwoK9N5H4pECw3LaLiXj3j0vIKSop9zWrAtGJWVgReQ+lFXHDnlOKSoRPs/66nIQBS0/hWNwjwXLsz6m4vtdTsvH2H5fQf+lJPMnndqo7d/8xms05iK4/ROLrXdcw899reH+j6Ql0XnEpFuy7ichKVkKQJClq7iwq0WLzhQQ8fJxv0/ZQpmfPEhUq+L733nuYOHEizp8/D4IgkJKSgj///BOff/45Pvjgg4psiswLwvn7j9Fo1j4ETtuD7ALTBVenEzeJyFiXndHJaDn3EC7GPylXPSuPWyZ4/HryPt7dcBnfcwjOecWlyCqwnuf53YxcHLiehlbfHkazOQdNPr+WnM1YTE/eEV7krcHMf6/hTkYe5u25We663t1wCatO3Merq88JlpuyPQYHb6Rj0/mEcl+zKjDkl9P4bv8t/H05qcKu+TRfg72xqc+ksMEFO+Mam9/PxIuqp7qTPeM1Jfhm0iJInL6byfnd8/efoECjRcKTAmw8p382ufwHvvjrKlYdv4831l2EVkcirwI3zRQkSWLMmgsYsPQUSsz0XaNZ+zH9n1i89PMpm7Vn47mHaDBzHw7fSLfZNWxBhQq+U6ZMwZAhQ9CtWzfk5eWhc+fOePvtt/Hee+/ho48+qsimyLwg/BJ5z5DDfc7u64zPSrQ69PvpJF5adsqqud01pTqzmgwA2B2TgolbokSVJazvCC2ZubtvoPv/IvGUR3MihYlbovG0oATj118sd12n7z3m/UynI3El4SmKS5l9vOyo/uRpPWthJUkSIV8fQItvDpm9Lxm5RaLu3ZBfzuC9DZcBcB/JDvz5FN5YZ+yHjNxis3Vai3P3+ftOLDFJ2QD0pidiSM95dj3uk7MKMfXvGKw8fs/w3r5raRV2/VG/nsMHf17BT0eeLXMYvs1pqdZ03qUrIsTOy34ezNCG1NeUtImTry6xoc/o93n87xfRZt5hZOSW/1nWlOpEb941Wh1O3c3EjdQc3M3I4y1HP1nKKdL/nZlXjNd+PYddV1PK12AaM//Vn0R+sOnZ8tGq8JTF8+bNQ2ZmJi5cuIBz587h0aNHmDt3bkU345mhVKvD4oNx2B1jvYe1IiBJEn+ef4hTd7h32WweZOZjxo5YdPshEn9d4rbjsgS6vPjPlWTGpJqaVYS49FxcS84xOQYjSRLf7r6B9acfSLqeTkei308n0PX7SLNC0UeborAzOgW/nbxvVcHbVqw59QD3H+VjR1Sy1eo0p/ERQ4GA5mV55F0MXX4GH2+KEnXdUtp94DID2H8tDZFxGbgY/wRt5x9B3yUnzNoZC2mGCnmekYqKWPHYCpsYqYg9YCFJEh9vjsKsf6uOmc8rK89i66VELNx3y/BeaC33Crv+rbRcAMCGsw8Fy11NzEL43ENYfJDbFKhQo0Xik4IKM/U5eovbPIAt8O2JSUXreUdwvmxDVipyXswrYv4Oaj7VihCiLdGeR8Y9QoFGiyM3y2f2UKrVIWLBEXT87pgofwX62BFaM7g2FN/vj8OZe4/xyeYojm8AJ24/QuC0PZb5k1T95YtBhQu+AODk5IRWrVqhTZs2cHFxqYwmPDMsOXwHS4/exUebosza0FUlLsY/xYwd1zB6zXlRQt3kbdH483wCHmTm44u/Y6zWDns75iN+h7ZL1miNQge7jdeSc/DbqQeY/d8Ns9fQ6UhsOp+Am6k5eJyvwb1H+UjLKULSU3H364eDt1H3y72Cx/4VpfGNz8zH6hP3BCNJ8AlrlkBp423FmlP6jctBkUdx9OfgIWu8PcnXYMLGy3hj3UVcfvgUJAnEPy4QFN6vJWcLXo9vc/SWSE34rqspaL/gCI7cTMeq4/cs0qaaOzItD2nZRSbzlk6k5PsgMx//XU3BhnMPq4QTo6ZUx6nVXnbsboUrJnKKSqHTkZi7+wZafXsYn227ynh2N51PwON8DZYeNfWpySkqQfuFR9Bp0TG0nX8EjyrghKGEQxADgBLWff1w0xVk5hXj3Q2XodWRou/7L8eYv5NScNCfNS7NbuKTAob2ngudjsTXO7k3X/vLqe1Pzy3G43wN0nKKUCBiXqWHaRNaV7l+61Mz5ltj114AALy5TvopHPmMSb4VksDirbfeElVu7dq1Nm7Js8cy2oA+dCMdb3UMqsTWiCeX5sCS/LQQAZ5OguWjErKs3gaSJHErjenNSz+mP0M7Imcv/lLst/6LSTGE2Zk7uKnhfalr9YiVZxG/cADnZwSEJd+iEi3UdgoQZiTkQo0WO6OT0a1RDfi4mWY+6/a/SJAkkJ5TjFkDm4hvfAXBZY+dLyCkKyTuGOiLycTNUYiZ3cfwOqfQ+EzTaxXSSm0zc3pxiEcg53PEYUNpb8b/fgmAPjLEB13rYkrfRqK+DwDZhSXwcrF++CeSJNFuwREAwLU5xn4Ue7pB71ctSUJhZgzYmpikLN7PPtoUhfA61eDn7shbRqcjQRAwO0bFEpucbdjYbb+ShPZ1PTEsvDYA4IGAQ1PC4wI8LfN3yCsuxeBlp9CtUQ3MGNAYTvbSRIKL8U8w/Z9YzH6pKTrW9/o/e+cd3bTVxuGfPOLsBEImCWEkzDDC3nuPsqGFAi2UlhYoowMoUPb8WsqGtuy2zEJp2XuPsBICYUNCAmQQsnds6/vDkS3Jkiw7zgD0nOMDka+ka4173/tO3nZ8i50HsWn4cf9daEkSmz5ppN+emp2PKj8cFt2PkEim0oAS/OjjBZcQffhOrMljH78Xj608GvbQ6GTRfeRCRnsUxCwI6YsUoXHHXAtiYRe/b1uYTLFofLds2YIzZ84gJSUFycnJvB8JYZRFUPGmOPjjalSRHfvInVjewetYRBxikpgammia9mn5ycf6/7M1b/S5yVTwG12rl0YzuYnVbImB3p/nbzIx+NcrepPUs9cZaDj/JKbu5c5xSWfJ0QeYuu8OBqy/zPk91WUh7XNJat9yOcySfLk9AfOFDLqmJC2Hf/FDF6j5/Re1SOEIqKQj1Hdq4SbGj5jO2rPCGiw21nxOmcc1/D+Olh7q+RtxlhD6nSsNrkCmFsNrz/Bfd62WxAdrLuLD366aHUwbmZiJWxwCFrs/dAvFtUj+95dtoXiVmoO/QqJx5oHpoMo8tRb91l7Su6ON2nIdTxIy8PHGEMH9+NyjvvjjJk49SMCZh6/1wrg1oH5iQppBUGRrQRMzckUV0zj3iP+6tKrqblkHC6CPI6QI2fP0A8NCWei9ZX935ekbTqvXg7g0TNsXXmhrcsm/neZRLBrfMWPGYOfOnXj27BlGjhyJjz/+GGXLli2OU7/VsLMQzPw3Ah1qeMLHlV+rUCT9yM7HwkP3IZcT2HktGjN61DSpeaabtth+hCRJ4nVGLjycjDWOQmTnaZCanQ8vF91+MUlZ+PIvnVP9P181Rz0/V5x79BppOWp8UNcHCw8/MDoG3fWhWWU3HCoQmjdfjsLCvrX139EHJC0JCK056G3pK2eNlsT5R68R4OEo+p7lqjVQKeRG2wkAGy48w42oZDx9nYHHCRkIiUxC1OIe+P3CM2TkqrHrRgyWDKiDzFw1VAoZFHLjde2xCJ1pjr0gMIeSHOToixUKIR89c4U6DY9JFgCUtGcniqZR49K8nLwXjy/+vGlSYBPq3vZr0Zj+z110C/LCuo8bCB6HjytP38BWKUNwhTKi+qDVkohNy8HOa9HoXa88AjyEXdHeZPCbyenvAv0aUe+vOZQGwddUIRkh16bnSVm4+1JnfcpVa2GrNH7H+ei79hJSsvJxcUo74f7QbmQZeyWvIJnP875suxKFehVcUZ42Vmm1JG5GJ6OmtzPsbeT48LcruBWdglvRKRjY0A8ymsry2esMVHbnfl74Yj3oz4U1F2DU4mLqPsPCkv4IxSRlodXSM4LHyMxVw0GlwI5r/FlIFDLuiSE7TwOCgMn7TJip8d19w5BBROh5ZI9Jf4Zwa6x7rrwItZbEjSjmwurn4w/xTedqJvtDQV3vG1FJWHTkAeZ8UAtBxej7bi7FovFdu3YtYmNjMWXKFBw4cAB+fn4YNGgQjh07JqWSEmDxUeNUQ80Xny72SWDjxUjsuhGD7SHR0JLA3IP3TE4CdFcHdtsf/rmDxgtO4ehdndApdrXZaulpNFt8CifvxeNJQgbDHNx37WV8tvUGPtl8HV/v0PlDl3WwMToG3enf1d5QPZDdB/p4Nvs/QzaI7DwNcvI1yFVrDBM7rW08TcNw/lEihm+6huaLTzOOTZIkr2mp2aLTnNuj3mRh/qH7OBoRx/BTPnU/npHUvdK0Q6g165jexExn6t5wxPLUsN9zIwYf0dJRCelJ6VHBN6KS8CQhXaC1eaRm5+PjDSHYdJE7qJDPH49PG1fT29ms8wuZD5W0h4Kuye258qKRS81n227wvqdic+dOL8jdfORunEVa9pSsPHz0+1X0XXvZqC+NKxoUD9SEm5mrRqulZ9Bi8WmsOv0E3/192+Q5Lgtk1KD7w9I1a0fvxmHm/rsmI9npQoHYICc2JEnis6038NnWG2bPNanZ+TgUHqvXuFO/oVFF7kWEnPZ8RCVm8rqxPH3NH43PBfWsPYxjvmds1wv6uCCkPeXztw2JTEKHn88yth0If4WB66+g47JzqDTtMG7RXNIWHbmPbkFe+r/b/3yO95xiDC9iBV8x91GjJfETK1Uh/R06ed+0z/8FEYHZXD8rV61BjR+PosG8Eyb7Ss86kavWmhVsmJrNf4/Z48WhcKZVtPMv5/DRb1f179VjVoaIVRy+4UJQpxuw/gpuPk82aQEoaYotuE2lUuGjjz7CiRMncO/ePdSqVQtfffUV/P39kZFh3kDwvvAonvu6WDuQIikzD6cfxOsHeLVGi5FbrusrSD2ONxZs7sfxV8LZdT2aEaDGHkAoQe3n47rjm0qppNWSiE3NRmJGHkhSJ1R0XHbOKO3TKVrkcHJWHrgW42qtYbKlm80rl3NgtKMP1H9cfY43GbnIU2vR/uez6PDzOTSYdxLtfjoLkiThamcQsENov2XJUabGOSwmBT/+exeVph1G4PQjnL9VrG8nBeXbSUGNs/T8lRQ7r3P7m8al5uC7v8NxRWRqKyqNzsuUbAxYfwUdl503o8c66PfmCM1VZcOFZ7j4JBFzD97DsYg4xKZmIy41h7GQ4oJvEnBzZC5+4lJz0GPlBaN2Gi2JrDy10eSbk6/BuUevkZmrZmi6D9H6HJeWg483iB/oI0xUkeLiYXy62YJbOs1VQ+j6UZPW4TuxDGE1NDoFxyOEg3eEhBV6f5Noz2NGrhp/XH2OfbcM5u/fzz/DtH13eH+jpe41iRl5OHk/Hifvx5sdxFV3znGM3X4Lo7fp3jFKOHCyVXK2JwgC8Wk56LTsHNr+dBajt93gNJNfEVgsCHH4DvNePGEJ0EfuxmHblSiTY0i+ln/BwQ423XwpCgA4F8y/nnsGJYdViQs5j2aUjtjHW4ziR0uSjPgY9n5ifP+jk0wXfiBhLIhTShSh2AOKp68N5+jw81k0mH8CUYniCk6M3xGKtv/j1lqbukaP4jNMjvd8ygc+6NXxTLl4lTQlktWBIAgQBAGSJKEVeAnfd/jMiKtOPxE9iEe8SsXu6zGCk+aYP29i5JYb+mT2px4k4PSDBKw4pTMryzgGLbnAwDGF5Wtaxd2Bsx01+JiKRP9ky3VOTaiprAlc/p2UtiMjV81IPJ9npAVh7vsmMw9xqTmITc3By5RsZOSq8SI5G1oScFQZzFnPBAateQfvYZuJNESA9cy64S9SMGLTNSNtJJ2wmBRO7TDFm4xcXqGJPkBvuRSJiTtDjdwOnr/JxMD1lzH/4D2G8EL/iX/RChrQF0lf/HET/dZeRtNFpxA89wRnH+wKTIl8ghH7sf/9wjMjwTMqUeczXfPHY4hk3b9Vpx9jxKZrmLH/LsM/nA3XQoMPaoG52YxUed1WXMAcVoYRvjKslJBBd+tJZ/krx6YZBFzq2nFdwc8L8g/zwb6+nX85p9e+02/JK44SsHG0937B4fvYcS0aN55zx3pYqvGla13NyUYy76DhWlPPpIudTuDls1ARAP537CFDe3ar4PfwjZa7b8ToA9S4oD/Xe28xC2Vw+af++G+EUWovNnyuDlzwmfIpuATIqMRMo4IGQvMFhViNr5hngcuV636s4X0RIYdzusqxychRo/X/zmB6ga++RksyMj1w/SS6zz49G0VmngY5+VrcixW/MI56k8XIUa7RkkjMyBWdm1iIuQdNZzSiY42c4MVFsfj4AkBubi727duHTZs24eLFi+jZsydWr16Nrl27QiYrEfm79JKpm3zLK7WIz2MKhVqZDE8SMtB1+XncnNkJu889wLPXGRjXPhCOKsPtvPU8Cd//E4En6bqXwtVeic4VnThPd+fhK6gIAn/ffIF5fYJ0Pmj5OSBIXV9sc3NgR+sHSRgGvBn778CNUGNSx6r679lt03Nogm92tv57Va4cMdGvgcxM/bZsG1tGW2i1uH43BlwesnSTsUqdBxltESXLyoJdXrbRcdUaLZCTgwOXnzH6uf/iI9jmZeHcw9eYP7QJ7FW6Sc5GnQ+5VgNZZiagkDH2AYDzDxP0Wh2qLSeZmUjPNCxWlJp8KDTcbfPT0iF3dgQK3guhtgCQq1BCK5Mbtf3wl1MgSWD8q9c4Makt7PJyGG2Rn4/dZx8Y/SZdfxWIjk9D2+UX4GynxNVvWzPaffnrebQMKKffNu/fO9DI5OhQwxO9ankAubrf+tWv5/EsMQt3H8Ui5XUKfhpUF4+SDddBrtXANj9H/8yXI/IZ53mdpAbkCqi1JDT5aqO+OhIKIE8NMiMDcFQANgUaXo0GyMmBTY7hGUBmJpITkmGXlwO1XI58ue4eX3+WiIjHsbAD8Onqs4xnbdOxCCjlcvwT+hKXniSCILWwzecRcjMzAYUCUBVkSCBJ2OUbL1Btc7ORnZyGRf/cBhRKwbYUWpkMWy5HYfYHtfTn6v/TCc73QiuTQa3R6gUJu7wc/H3+ASZ1MvjsJcYlww6AliAMkzMJnmehYDEgkwF2tDNmZSE/LZ2xT0xMDg5cfoyPmvhj+UlDkQVtZpbxsTMzdR+aUHTqfgIS4pPhaiuHk61Sv482IwOQFQh0DszxBALKE3rw4KW7L+Df0Je3Lf24f559CDvacfPT0pGbmgq7vBw083JDt1qeSMvVYHAjP/T++bTuOc7LgVady/id8uwshvRjo87HjjP38Vl9TwDArO26FFLdKjnp4gDs7ACZDNcik3A3MgHdq5fjvicAcmgLUvp7T2ZkMPfJzARsbQG57r1/GP0Gdnk5qFzOHu1qeGDjhSjmgTUafVsVNLznB4A/Lj4FCsYThUYNZGai28KjAICNIxqieYAu00P/6q74IzQemoK2cq0GNmrmgprMMMwD+XIF1HIFZ9vgKfuxYnA9dC5wsyCVBg28TKuBqqAt+924EPYc6F1DN0YQBKMtF2q5QaHB995fDtct2ne/TsOCvrWxP/Qlfj72AHZU28xMhpS9/ORD/H7lBXaNb4Og8i7IyMk3ur75aelAZoGLFns84bgXy/eHomNNT8Rl5OPviEScefgaywbVFbxvWpkMuQqDNYy3bWam8XufaVAO0PdT5mRBlZ+LXKX1M8RYHbIY+PLLL8kyZcqQdevWJZcvX04mJiYW+pgLFy4kGzZsSDo6OpLu7u5k7969yQcPHjDaaLVactasWaS3tzdpa2tLtmnThrx7965Z50lNTSUBkKmpqYXus2h0QyXn51TlhqT/lIOk/5SD5JDfr5CZShVv2yt+Qfq2/lMOkmS5crxtw7wCyWozDpMkSZL/hb0kY5w9eNs+dKtA7roeTf58/CHpP+Ug+dCtAm/bGGcP0n/KQfJ5YqbutzVsyNs20c7Z0FeSJMk2bXjbZipVjN92qjL/cUlA3+73809JcsAAwbZj1p8jb0Qlkf5TDpJ7gjoItm35zU5y0s5Q0n/KQXJrcA/Bti3GbNT3Y33jfoJtyYLn1H/KQfKXFh8Jtu01fJn+uAvafirYdvBHCw3XePVqwbanf96kb5u4ar1g2y97TyX9pxwkp+0LJ8nduwXbRv5vlf64nwyYJdh2Rqcx+raDP1oofM2WLjW8Q9euCbb9pcVH+uMe2XlCsO36xv30bVuM2SjYNmbIp2RyZi7pP+UgGTz+L8G2e4I66I9bfdLfgm0PVmtheC9EjBHf7g4jG80/QfpPOWhyjKg75xj53Z4wsv7c42SinTP/sRs2ZAxT6gr87722Zk3G+xnvV4X/uP7+jLZhXoH8bcuVY46VAmMEaW/POO6zxgJtAcZhLwW3FW6bkUGSJEmmZOWZHCOW/nGeDI1OFjVGRF6/S0a/yRQ1Rnz1wzb9bzM1RsQeP0/efZlCPk1IJ4+NmCz8286c0V+HzUO+EWz7yYBZ+j58032iYFtqjPCfcpD8svdUwbbfdJ8oeozYO3Kq2WPE59uuk72GLxNsu6XTCP0Y3HHkGsG26xv3I/8Le0mO3nrd5BixNbgH+dnW6+SbjFyTYwQ5YoThoczIEGxLjRH68V2gLV2OMDVGkG3aMN85E3IEYw4vZsTKa8Wi8V2/fj0qVKiASpUq4dy5czh3jtsJft++faKPee7cOYwdOxaNGjWCWq3G9OnT0blzZ9y7dw8OBSv3pUuXYtmyZdiyZQuqVq2K+fPno1OnTnj48CGcnLi1n28Tl54UjWnh1P14BJto872ZRSZ234jBt13ER4laGxu5DHkarcmCAoAuyEes2S0zT2MVs1JJkK/RgttbUce2K8+BKh4AdD6tw0Ucc3tINBbUJwWD49aefQoElZ581NaMFT19PwE/8rhlWIOoxExULMftOkRnz80XJttQpGTlM6LFxRKbkgM+/Sm7rHVKVj48zD6DdRHybQV0CfyvPnuDVR8FQy7CPxMQF7S141oMLqSIS6v38cYQvHCJEtU2O08NiEwOcfJBPGacugg7pRwrxO0CQKf8Le2ExqQABVnFxGYSOBYRjzom2tgoZCDNGNvHF+TUFrAp6Hkcn476805AbG4rrZZE1OsMVBbdGwkhCNKcO2shn3zyiah8mps3b7b4HK9fv4aHhwfOnTuH1q1bgyRJ+Pj4YOLEiZgyZQoAnbuFp6cnlixZgi+++ELUcdPS0uDi4oLU1FQ4O5sXIW4pdx69xKD1Vxnb2lZzx9mHr0WZKP78rDE+3nANWoJgmB2iZrZltMvO0+DzP27gRlQytAQBmb097s3tgkrTDutdHf4b1wLT9t1h+EWSBJCjNLgk2Obn4ODYFgjw1C0masw8atRWn1Q/Oxs1pvMnJqdcEtydVAhwlCPsOX8+SrpbBNvVga/tF20qo7GXPcb9cYO3rY2zI34d3hAf/nZV776wYnA9BHo6ovvKi8zjKlX62U/Q1QFAjtIGJCHOfWFo22qo6euKybtvW+zqQNGsSlls+qQxasw8ymj7aSMf7Lz0jPe4eQql3jSp0Kih1PD7DtLb9qrpjpNh/IUbuMyYf33WGEM3XBNsy2WaLOOgRHJmPv4b1wKBvmWNXB2+3X1bH4i2bWQj/PhfBKISsxiuDoLuC4BZbTUyOfJMuC8sHVAHjSuVRaufLxi1bV7FjTNTAv29f7awO2p9y68kEBoj7s/rilcp2ehQEIHPHiOEzKP3F3RnmDyrf7NX5w7FAdcYwW47sKEv5vYOAggCFeee0W9X5edCxpqW/hnbHNW9CsZfka4OuWoNqi0yBDKq1HnwcVJi3ccN4Olki3HbbzGyFNDHk+quSjznyVbi4WyDc7N6AgSBjFw16k8/CLlWg041PaGUy4zyimcrVVDIZVBrSdio8+HjqMCuL5qhnKMKQbOO6X+fUkag4/rroscIvvHk4PiW6LmKOU51CK6AgxG64N+Pg72wNyQK49sH4Kt2AZj6dzj+pWVquf+/PnpXh34rzuL+c34Fi5gxYlSrith4IYrRlsvVgY6Qq4NQ2+87BmDVYe4qa9W9nPDPxLaAjQ0qTj1k0tWhml8ZrBrRFK2WnhE9RjjbKpCenVeoMSLQwxHDmvmjaSU3+Hk6AyoV5hyIwOaLkSbdoUS5L5jR9v68roKuDqO2XGeMV9R44qhSMIrWFBdi5bVi0fhu2bKlyM+RmqrT5FH5gSMjIxEXF4fOnTvr26hUKrRp0waXL1/mFXxzc3ORm2t4uNLSzI/ALgwJaTk4FZ3J9HUF4OnthuxI44GY3Q4AZI6OyLO1Mw6ScmBqiu6+TsKFV9lAwTHsCUNyeWrSSpPbQObkiGwb/hc5R2mLH45HYveYZrx90mtF7ew4v2fzOj0Xr9Oh75sQzrYKiK3U+uu5Z/gVwsfNzlFjWUHGCd0ApYTMyRFvoBTsO9VWDPlypV6Y4mLD5ecAnotqa+q4aTIV+mwJM+r7n7dikS/i+gKAmja5mOLAvdei7hugmwSybeToty3c5D7agrZ0HFQqZOfnQm1nbxB6Ad3E7eCANLmN/ndH5hBIJWyQbcMUlEhCJuqZNLctCIKz7fa7bxBQydMw+dHa5tmafj80JCm+DzC8jwoZATg44MvNobz7Cz7fShVOhMfCyVaBVoHlGIKtKbja5qnsjMYkAJw+glo7B862jAmZxd+s3KW5ChtEZgNLzsegfQ1PXIrL4X3mNDYqZNtwC0Wkvb1+sSsjDO+93MkRMcnZnNeQCsjKUygRlQM0WXEVM3rU0LfV2jmAVBB6QRYQ/97rcvZC3/azvff1xx3QwBd/33yBXNKgeMqEHNk2tiAcHQEHB1yIZfWZ5tuaS8hFP2t8Y8TqkDij66zheJf54GrbwL8MbnIEQsqVCt7+5qjsGGME13jCaE8o9PdN7Hvv7WKHtBx1ocaI8BQ1vjvyFMBTfSXPzZeieMcTPqzSluudK9hGkiTy7eyRbWMc0J2Rq8afV5/j46b+ovtQnLwTUWUkSWLy5Mlo2bIlgoKCAABxcbrISk9PT0ZbT09P/XdcLFq0CC4uLvqPn59f0XWcRVaeGo0XnjJK0n9rZif0qOMNAPAtY7oQglIug0ph+tayc8kSMI4U7r/uCnxEJJy/FpWERYfvI5EvoX0R2hWWDqgrqo/mcI2jctmA9Veseo7i4sbzZITFpBht58vn+TZBZVNiL/Ky8tS4HZPCqFZUxd2BM0NJcXPl2Rv8j5VjlCI5y3R2CLWF961LkBeevc7A7Rem3X24qDrjCMZuv4Xhm67xplo0h0cFaRLFGB3FZjohSVKfVSKJJ9PG64xc3u8o2HlN+WCXEucaN/iYf8iQp70wxRuqeTHd9qjMJG4ONvrrdp6WVo3KhkNVAlUIVOcpDYVDuODLEsFVtIeCJEl88ccNdPlFXPrFB3HpmG9mZgN2VbzCMn5HqOj+FgVC7+a47aGM54rNjP13ccfCsaaoKXLBNzw83KyUZREREVCrxSdxBoBx48YhPDwcO3bsMPqO7WJBkqSg28W0adOQmpqq/8TE8JtsrQ1fYns7pRwN/ctg/9gWjHrmQoipDMS+LQRBcBZW2B8mLm/wr+efYTVP4us9N19YtdABHbmMMKoOZ22KqqyrROGgCoZM3BXG2N59xQX0XnOJsW32f/fwItnyinWFoTyrct9pWs5pOlxV+9jU+PGoyTZc2MhlvO+nuYjxlTcFlTP4XxHji1g/+sVHH6DunOO4/DSRd5/Cvsr06cPMithFQjlH7ih6gjCUCqbnLL9eUKXL3qbAlUBgMVhaBV++8Vgo/dqDuHQci4jHQ4689Hyc4nlP+RCqImkJB26/Mqu/1kbo/h9iufRwwasIK2GKXPANDg7Gmzfig7CaNWuG6Gj+EoFsxo8fj//++w9nzpyBr6/BrdzLS5fqhK3dTUhIMNIC01GpVHB2dmZ8iguuxaK7kwoqhQwEQaCenys8RZT5VWtJUYUQ1CzJNyNXLZirVAx8xTWSMvMwcgu/Xy0AXJvewaJzymXMgb0ouM2qkiRRunjC0tBFvTF+ju9YQVhj06eej6h220Y1FtXO07noUgH9E/oS+0Jfmm4ogm/2mK7qZgpFQbo+9qKFC40I5UnEq1T8eu4ZNFoSy0885s3tnK/RgrSSCcpa62GdIGe+FO3prEKTStwhUlx5fulQQrtQMYc0E4VjSop8nnsrpL0uDl6KrMr4tnDxSaLJCotC2IiwPJcERe7jS5IkZs6cCXt7e1Ht8/LEae5IksT48ePxzz//4OzZs6hUiRklXqlSJXh5eeHEiRMIDg7WH/vcuXNYsmSJeT+imGALops/bYRgP1eGedbZzvQt23eLO0L7WmQSGtMGSa5VMxWZain0RP57xjTDQJp7QHRSFr4TmDDNqV9PR0wVnsKy5gx3qVxz6VjDAyfvm6dFkDCmU01PDG/mj2EbDQFxMUlZ8Ctrz1u+uCgQ+8z6uJh2UQKAnnV8jCp0vavIZMBNgeBVQFdR8VliJqdSgE0PWuDptagkNKrEXVr4+Zssq2mi6C5lHs4qtKvmjjMP+c2/fGTkqPUaWHOIT8uFsx23H7ApwZdaZAlVxaOXYC9N5PIUI+HS+LrYKQXL+45qWQkZOWrsulF81t23hU82X0eP2t5YM7S+RfubKoBSUhS54Nu6dWs8fMjtz8ZFs2bNYCcQsEAxduxYbN++Hf/++y+cnJz0ml0XFxfY2dmBIAhMnDgRCxcuRGBgIAIDA7Fw4ULY29tjyJAhFv+eoiQ5k/ly+rjYwdWeWXKVIAjYKeWCVYj4qoMN+vUKbv/YGS72uoFyyRHx98USqrg7Gm3jSrM0u1dNONsp4WyrxPTuNbDg8H2jNkKIKYcpRDlHG7MqbxUGc6pHSfDz+/CGRtu+2X0bu8c0M1kJ0JqI8aUH9LVIeNk6sjGcbBUI9nMtfKfeElztbNB/nbDfPFUqN+TZG8aiXQzZeXzZHrT486p4q6IQMhmBIU0qYHtINBQywuKxyNFWgaN3TZuOuWhbjT9RXHUvJzyI4zaVUwoDtkvIs9cZqOzuaHGZ6OKA7zcpWC+ajAB+HdYAH/52lbM9hbyENcXFgVxGWOS6cuhOLNbQ/s5Va0S5ZAHA86QsNKnsZvY5i5oiF3zPnj1bJMddt24dAKBt27aM7Zs3b8Ynn3wCAPj++++RnZ2Nr776CsnJyWjSpAmOHz9eanP45rFS1wR6GAuOAHBvbhfka0hUnXHE7HPMORCBZYPrAUCR+w6JnQM+bFxBrznrUcfbfMFXhMb3o8YVsOMa92R3cUp7VJ9p7DfZuqq7oPO+JVgamPSu8m3nqvjp+CPTDUVABRbZCAS4WBsHlbgh1FSfXO2UqPseCb2ALjLflPsJtVD8+cQjjO8QaNbxN5lREtocmlcpx/ibWvwUxiokIwiL3wNnW/5ncM4HtTCYR+ijhHS2MNT+53NQyAh82baKRf0pKj5s5Ied14W1smwXPy0pzoEkLrX4FsslhVjBd+vIxhixyTi1JAAsOHQPf1x9jgPjWlq7e8VK6XTAEAFJkpwfSugFdNrR2bNnIzY2Fjk5OTh37pw+60NphG2a4otAJwgCNgoZutQy+Cp3qeWJAB5BmY61fPzEQBAEOtYwnbaeriUpw9Jwi0FMpL6/G7erTe3yLrxau62fNkJlEcUCzCFPo0XjiuZpruhQ/alfwVW/bcfopoXtlmh+G9bAasc6NrE1xrU3T5gRgzm17guLG09gEZ1WgeVM5jEvDned0oaPq3C8gpjxrCSY3Kkq4+97rwr/vKm1pFEAJAA0rWx6rBB6tioJjF+UwoDLT1mtJbHKSoGQ1uCrtlWwuH8dwd8DcBcpMfXuJaTnoq6va2G6V+I4iViAi1EQAUCbqu5wtTd2n0lIy8HvFyKRk6/F+nP8+d/p9KjtLapdcfPWCr7vIjn5hpe2srtpgWvFh8HY8mkjPJjXFb8Oa4ioRON8eiWJjAC+6Wy6Whv9hbTEVChmn2MR3H6TU7pW5xwYWwS4gSAIlBeRPo7N/D6GxRXbx8mvjD1+HlQXC/vWFn28rrW89P8//W1bRC3ugZ2fN9Nv4xqkiopGFctyuhmYS7/g8kZpmKyBVkviiz9umr2fu5MKI5oVTc5J+4JcoUv6899zMXPSH6MaY3Sr0lPxTiyjWnL3+aJA5UkHGzl+GljXKuf/qhCay1+HNUDkou76v3vX84G7E3OxExIpPoUZHxotyZk6akrX6oL7HRyv07xd+L4d5/dCY6OMR+NbGqDSd1J0LxCg3EUsNNmYmh4qlLWDrZIpCn1tpnXBWnxnYXXTT1tUNNnGScAywCbIh1kBb3tINCM2JSuPGUexoG8QfuxZE5GLuiOioHCFSiETbRErbiTBtxTRL7i8XuDd+qnpKHBbpRxtq3no3QRqeJvOQFGcUZYyghDl3E7X2Foi+CpFmLbdHLgHzEBPplZpUENf/D2mGTYM16WN49MW8LmhAIYUQgDg4aTC2qH1IZcRaFyxLCZ1qgq/svYY0qQCY59bMzuheRVuXygvjhzF9Otqjchysc+FTEaglk/hMp3U9Ha2SKhxKBAgG/hzBy0BwODfLMu1vP7jBhYFV5Y3obUEDAFCbFcG+oJFjMa3oX9ZeDpbnq/6w0bm5ySf1k1Y8DLF1+0D0C3Ii/M7PjeinnW8ET67C+qZcP14k5GLBBH+3IWJAajm6SSq6qgl0IWcw3di8YrD3F7D25lXCPMtY6cv0etX1p7zerF9XunoXR1EDCCFsVJZQu3yLuhf35ClieqrSik8TnH9FFO3TymXMfzH5TICX7SujFUfBYvvsBUY3z5AdMllNslZhvigsg7cVlNbpRyHv26Fw1+3Mqk5vxXNLA7ywz938DLFkClHzVosda7phZEtK4EgCDioFLg9qzPCfuyM0ook+JYiZDICp7/RafT8yorLgkHnEYfP7th2VfB4QTf94Nm2qnuh+ykWGUHw5pjk38f887A1MFxM6849gbNXpAQINKxYFnYFQhZXfw5/3QonJrfhPRfdvCOTEehe2xtPF3bH7jHNOAecP0c1QVkHG/QJLs95vNTsfNyY0ZFRApK+WCjjoMQHdXVptSzR/l77oQOu/9BRVFuCgKDwdW+u6TKVvmXszC4ksfKjYETM7Yrr0zti1+f8rh1UjlJzsbeRm51NiiCASuVMm+MpjRrb1OhAi+I3FfwG6CbkwlRCWty/DuNvLu3SsKb+aEYLRjHX79jTWYU5H9TS/z25czWzE4d93NSfV1i9/DQR20OiodWSaDD/JBovPIWsPDVvGkWgcG4kYoTmKiKsc2y+61INY9sF6N05Nl7k9kdWyAgj1wpAl7t3KmtRwlWkxl7Fv5ijhEQxGt8FfYPg7qTC3N61OJ+JfjxjlxCtBeaisg42GNLEsFCj7oMl/vtp2cJZXhQyAsEVymBWr5oY0MAXi/vVhoNKgZ51vDGuXYDZ5wN0GY3EcmJSa1z7oQMmd6oq2h2BquhGQX9M7ZRyTheD6T1qoKaPM2r6OOPMt20xo0cN3uNzZQSh+7CzU5yxNeYudkr9HFoaKXLBt3nz5vjqq6/w22+/ISQkBNnZ71aeu9IE12r3uy7VoZTL4FUgrOTQ8t1SAw+XRqahgFZNLAQBsx9+giAQMacLr08uFx4mBF8bhQxV3B05Ta7sgYYtgHBNmjULNJ7ePNXi6NoDMZMudY2UPJHF3i62KOeoq39OZ9MnDbHiw3rwdrHDig/r4faPndEq0PyFjYezreAESYfU8gsDC/oGiUrJZEmuTWoPdyeVYHUmsbSu6o7bP3bWBwZVKudgdvL5en6uRgP+F60rG7WrUPAsszWHdM2MmOdEISOMtNLs8/PB5as+onlFfN2eObE7qBRY0FfnquPhpMsRO7FjIOr6mtZEtQhww/FJbfSZGCjEWCTovuM1BSwKQ34PwQ//3GEEOb1Oz8W47fxpGAsj+IpZoHWswZ8XHtAtBthxBJRbgylBh3rX2Pmdb8zoiJ51TOeQFmMNEyP4Bno64doPHTC8WUX8wZGT+huRJvo1Q+pj5UfBuD+3K7aN5Ldq6gRpw7WhboOjCXN9Gw5hOiZZODc9Na5+2qISfhpYFwMb6gRugiDwbZdqWD3EfM1vo4plOV2nuJQ0gZ5O8HC2BUEQggvghX1r44vWlbGUtYAFhAujRi3ugduzOqNLLeY8LzYjDRfslH2WpiItKYpc8O3duzdSUlKwYsUKtGzZEs7OzqhRowY+/PBDLF68GEePHkVsrGVpXCSYCJmsqFKodPMiJX842xprCbly/HIlShfyhZIRhMmXq2cd45Wpg0rB2Sc+lHIZ9o9tgR51vPFgXlej7xtU0AnxM3rUwJoh9bFxhMFHlRLCvu1cFT4utpjQgald4aumB+hW0HQqlXPA+o/rMwRDIY1RjzreqOvnqhcq+CYpvrmxfXVP9K5XvqANARd7JWay+iS2wIJYTYMDh4Ac6OGIaz90wNAmxgM9l+YjN9/8hOiWFii5NbMT53atloSLvRLHJ7XB1WkdYKuUw9XOIIiWYWnOh3FoWuf1DjIyK07qVBWhrHNSV5b9LFSn+TjTn/dZvWpy9pkthCnlBI5NbM3Zls0vBZlcKMq72sFRpcBnLEE9IT0Hld0dsffLZvh7THNdWsiOVfHvuJZYN7S+3t2Ei6DyLnCxU2JgQ52JmvJtFqMRreHtjMNft8L+sS143326RnP6/jv6/5vKNXvyfryRz6hY2G8Ftyld+N0p72pn9PxSsqapzDrUscvyuGoVBkr4FluggOqLs60Su79gvtdilxYudjrrFLXY5zO5K+QyRrYK6txsLTed/WNbcLoKmBLs+wb7Cn4f6GF4Tz9pXlGwLZ0xbasYubSZ8lEWGoc/qOeDad1rYFAjSjA3fEefr+PTcoxuiAtHrueEdOP3RkwwJRdiFliliSLv7ZQpU7B9+3ZERETg6tWr8PT0RHBwMFQqFf766y90794dvr6+gtXUJMRBf/jL2Cuxf2wL/d/0yefbPbeRkJ6j99Ph0sB14NBizO0dhEfzuzG2pWTx57+1Kag4x0fkou5YPUR8Yuy+weWxekgwvulU1SgCup6fK9YMqc+58qSqNBEEgR51vNGhhicW9A3Cig/r6V/Yce0DcWlqeyN/Wr58kYDxIHXm27boGuTNEHCEFEZrhtTHv2Nb6DWYfP54HzaqwLmdCw9nWwxsYBjI2e4TjSoyNfnURCKTESYDJCqXc9D31ZcW9FepnAM8aO4PlCm0RYAbGnH4Bo5iBWiJ0e5zldIGTAculXWwwYFxLVHXzxUtAwxpqKhn3svFVn/PyzryZxT5vitTo1XT2xlB5V3gZKvEqo+C4VvGDue/awdbpRxlHGywY3RTdAvygo+LrT5Aif280P3L6S4qn7YQF8BWxd0R/m7CQqWdUo5T37QxMk8PK9BGsYXMfbd0/ukN/MvqNdUU3Wp7YxLL7P43bWFTu0DoqOLuiFszO+HObJ3bi5ujCue+a4trP3Tg1Y6qlDLU9HEW9Ov9fJuh8iNdAB3zp3AwY/Mqblj9UTBn1gSKkS0q4ejEVmhfnZmFRoybhiml8Jg2xs+ouX75P/C4ahUGSh5k+2uKwZycynRrAXs6ODi+JQ5/3YqxbVDBwqkMbVFJ7ebtYofx7bndD7x4XLC8eYrH7BjdFJGLuuvz2vNRoaw9yrvawdVeyXkv+fB2scPCvrUZGlp2LEUzVo5bFcf8JZcRsFXKYM/6jn4p6c+T2Pv5VwgzveeYNlWwqJ+ur0KBuO8CxSqmf/7551izZg22b9+OrVu34s6dOzh48CC8vb0xevTo4uzKOwn94Q/9sTNjEqnqaVi1/n3zBSbvuo0LjxMBGK/W/Mra4fPWlTGbpXmSEboXl546JeRZksV+UGKDRiq7O+DilHb4ZXA99Kzjg/EdAk2mQqJwVCkw5wPjFHZDm/jrtaXm9oeCroGbQNN8002r5phZy/EIXub6e9MF7zZV3bHiw3qo6+cKlUJmtNCgD+T0RPjVOTIufE+LMBcKWvx7TDP8OaqJUfaHFR/Ww94vmxvlQXVguUeoFDK0CiyHjxr7YWADX5RztEHnmtwC0/ddq2Pzp414+wIAtX1d8O/YFthKM61yWSoGN/RD3+Dy+GWwceCdk60S/eobnpfPaZrSXnV9cHFKe4ag2KyKG9Z93ACXp3XQ378yDoYJtnkVNxC0qYt9PansDUIWE+p5DSrP7xpwc2ZHRiGZT5pXhI+LLT6iLabM0WKxNZf0QEP6wq2sgw3jOfR30y2OHHlcasSYSrm0VoBx7lY2I5pXBEEQvC4U5RxV+LFXTVT3cta7eVCw3Y+4RIpPBBaMl6a2R+daxq5k5pZMtsSFyRTU89ix4N0SCtg1BV+VuE+aV8T8PgYhii34OagUjPuy4sN6WFCQ8Ybelq7Uobsz0BcE1OO2nGbdcFQp0K668bVTKWQIruAqasy3s5EXLNw6GilGBjTwxb6vmjMsRL1ZVrZBtKDSirQxonttL2wYwRwja3NorCPmdEHYj52NLD70vrNl3TGtdeN6XwHfa3uW9WZqt+p6DfzgRhVweWp73n3p7P2yuah2pYlizTVx//591KnD9E/p3r071q5di1WrVhVnV95JqCINXD67bAHs4pNE/f9jkpg+UOe/aweCIPBJi0rIztdiydEHAGBYGdMO9cvgeqjm5YRedX3QZfl5K/0SJqe/aWu07ZvO1TB5VxhGtTL2qaQTPst4wLAWdA0ePSNDMk0Lnp4jvnxuA/8yWPlRMPLUWuy+HqMvyGAu3Wt7Y+f1GDSuWBYEQaB3vfLoXa888jVaKOUynJzcBntuxugHRwp6xbOD41tCIZeh4tRD+m2h0cnoWvBsBXg4IeqN7rlhT+FKuQwtA8uBjaezLWdGhi/aVMaEnWH6v+f3CdL72QE6U6WQy4gfT8o5tpZMLiPwbGF3JGXlcQZd2ihkepeAeQeNi6gs6FMbQ5tUgJuDChUtyO/sRNOu+rC0j+zfN7VbDXxQtzyeJWYwrg2d+wX5iv8Y2QQn78ejTTV37LnxAv87ZqjIyPa5nv1BLczqVZMxaf7QvQa2XI4S9RvYWRToxxGTwYVP0LDjEXzphWeevM4Q1Uc2VElfMcMAe5z0cGIKOo0rGj+/7DZ0+LTM1sjEwqaBfxncfC4+uLO6l07gXNSvNhpXLItutb0wbMM1iwobseMPKGZ/UAskSWJsuyrIzNXwViY8OL4lXqfnoh1N425Lqw5Gf27oYwg9OJkSlPsEl4dCTuCnYw8xr08QY4EJ6LKbzP6glll+qfS4gvHtA3D+cSJ2jm6qd9mgZ1VY3M/YB3fFh/Ww79ZLTOhYFfvDdIGYAR5ORsHV7HGgYw0P3n429C+jT6U3qmUlRoGm2r4uuDuni6BrUt/g8oK5mtljFBfda3sJZtkprRSr4NukSROsX78eS5YsYWyvXbs2QkP5gxMkxDGlazXULu+CThzaMSHNI72M7p4xzRiDzJVnhlybVEQt/VjUap2dk7Wo06Y1reyGy9M6mGxXVEIvALygBU3Qgy7opzTn9ARB6LMzPHudYbHg2yqwHPaPbYFKLDM4pdkP8HDEtG7GEb3da3tjw4VnaFPV3WQAGT2tENfzxgVbw0DRu1551C7vggpl7fE6I9fINGkqsj7Awwm7v2iGQb8aUpk9WdCN8zfIZOIyjdjbyJHESottZyNHA3/rpHX6++YLRlQ1WyCUywjU9nURVeK6jIONfqEwqKEfQ/Dlgn0u+rtqKlUdl18ghbcIKwzfneTzEVzQJwgpWXk4cjfOYmGRGrf4qibSLwf9//Tn9fx37XAtKklQg8bGVyAHOKUVdLJVmLU4FiLYz9UswZfC2VaJEQVaf67YDj6+bFsF684+xfw+QfBysYVSTiCf4xoTBIHvugi7anD55tooZPi6fQASM/MYwZn057ecowoTO+qsN6604kc96/jog//YblK2SnmhgrG+6VxNMD89V0A3pXygwxdA/l2Xavp3uKZAitJ5fYKw8PB9jGxRibPYC99ihGJsuwA42Srg7WLHm0rTFMVZJdOaFKvgu3btWjRt2hTx8fGYPHkygoKCkJeXh59++gkODtatkPU+4mpvY+RMT+Em4L8YVN4Fl5/qBFx2UM8T2uqfEkDECHMbWSac4mRu71r48d8IzO1dy3RjkcgIYHz7QEYwXibNvEcfoOQ0k2+WCMGFi687BMJBpUAHEZXv2BAEYTIHKheOKgWOT2KmaZvZsybmHbwHAGhTzWAyzKeZvE2lMvqybRW8SsnmNONRVC4wx/P545mCPomMbFGp0JkfvutSjVfTWpw0qlgGEzoEiipoAzCjxmf25A6SKwx0wYgSRjZ/2ggvk7NRy4f//lKU44hqFyq5KytYABy5y12Ahovutb1w+I6hPeWuQA/+JQiD1pUu6/G5KVVwszfyeRZiWFN/Rhqyz1tXxm/nn2F0q0oY2y5AL6RxCb3uTiq8Flhg8I2/Y9pWweuMXPwbxp/aDdAFT7bisMgA4nyaKb7pVBW96vjo3aJ61fHRVwYd0qSC4PsulskmCiDpBF/jdG9CmCPcFwXnvmuLx/EZvOncxrYL0Au+Qj2t6umELRz5/sWWmrZVyvF5a+G25V3t8FIgwLs46wJYk2IRfPPy8mBjY4MaNWogJCQEY8eORb169aBUKqHVaqFQKLBx48bi6Mp7C18FFUeVAoMa+uK387oShGxtUJ/g8lh7Vpe/TygZOgCE/NABs/6NwOwPajF8oTrW8GBUfQHASE7ORWFyxg9vVhG96vgwgiMKi7Od0iiwh6kpMvxB73oKzQRmDrZKOcZa6DttTXrW8dYLvjW8DMJ9ff8yOH4vHgBMCpmmqk9ZA7pmv5xT4e9773rlUcvHGeO2h2Ioz2KyMMgIcT7lBEEYPXemeLqwO14mZ5slqIllStfqOBahu+/LCtxC2lUTvzj7pHlFPHudiZP3dcdQKWQ49107wX3YPuCmWNK/DkPwpa7ziOYVcfbha/QLLo+BDf3w0e9XAQC5asPilC7sZuRaroltV92dMf5M7Vod/eqXR1UPJ5NWqBZV3PTmcC74YpfKOaqw4sNgZOaqcfJ+Aq+bhVDwJFf1OD4BTSGXMfxz6S5e5lSmLAxCAYsU7KvNVziluPB3czAZlEphjoy+5dNGuPsy1arzBtcQ5ems0mdSseYcW5wUi+BboUIFjB8/Hl9++SWqV6+OU6dO4fnz57h9+zZkMhkaNGgAb+/SWdP5XWL76CYY8nsIY9u49gGMyG62n16XWl56wZfS+ObwpKPydLbFelo+TorVQ+oj/EUq5hyIQERBXfv/DTD2g7Im1n4hufwX+dxH6JOBOfmISyP0BZOStrr/pHlF3IhKRkcLNNJFDV1ALwwBHk44KjJdmLk0rFgWNhbkMwZ0Fewy8zS8Zka5jDBb6J3XuxZ+PvEISzhyhNKp7O6IyEXdLa5m5uNqhw0jGiIhLQfnHr1G99reJsua8rnI8LdXoFI5B0QmZjKez3bVPBDyQwe4O6ogkxF6n1i6q46lnlHtqrkzcpuy/UplMkLvU2uKeX2C4GKnRK+6zCCpDtU9cOpBAmdqPTprhtbHqlNPGBYaCqrEMR90Qeufr5rjj6vPBVOI0UmzksuGGPZ+2QwZuRrOqpamUCksd3MobswJgmxbzYMRoGwNfuheA1/9dYux7ZvO1fD93+EAgC/NyHJRmigWwXfKlClYtWoVFi1ahBEjRmDSpEkICAiAv7/lVYgkzKd5FV2k/I5rhuTvTrYKuDup0De4PHLyNfArw5wwZRzBK+1reOBQuPjcy7ZKORpXKovto5vicXw6giuUMan1qOPrgvAXqaLPUVRM6lgVv5x8xIhMpuD7CUq5DP+Na4E5B+7hWxOmutKOo0qBlR8FgyRJhs+YrVJuFJFc0hyZ0Ar3XqWhLceEX1qoXd4Fd16mYmKHQFTzcsLGi5FoZqZ/3aL+dfDtntuciewtZVizivi4qb9oLXRh8XC2ZQQwCsElwAkhlxHYMKIhHsalGwX60qsOLh1QB39efY4vaOZeusBqjqVizdD66L7igj7g09JLZKOQwclWiTm9jTPRrBoSjJBnSWgeIPy8qBRyfMtTUMJUSVy6G0BwhTIIriA+cGlh39rosfKCWSm/LMUcX3uFXIaPm1bAn1d1wV9qrWU5wUuCEvbKQPfa3rg1sxOm7g3XW/hql3eBk0qBso42ZuXbL00Ui+A7adIkTJgwAXv37sUvv/yC6tWro1evXvj222/RokUL0weQsBotA9wZgi9Vi56d4J4LSlid3asWMnLUvP7EfLjYKdFQZM33qd1qoKyDirP0YnEyoWMgRrasyIjIpxAKGKzj6/pWpnnh4oO64opglDQ1vJ1RQyAYpDSw76vmSEjP1Ztoz5ow83PxQV0fdAvysnrSeGsItEWBUNYEPqq4OzLSuPG1mdWLGQdA0C6pOQsoexsFQ3NdwYKS84BwZgx7GwUj64G5iCm/XBg5q5qXEx7O7ybqPMXN/D619YJvUfRveDN/bLvy3OrHFROIW9SUdbBBk8puOH4vHt4utqji7ojL09pDKZcVafB4UVJsnskymQwDBw7E5cuXcfHiRSgUCrRt2xZNmjTBnj17oH2LVmFvM3VYpUdNlRSu4qHzRaLnE3V3UmHryMZGJRCtiaNKgcmdqhpliygJuIReAOhcU/f7K77l7gwSxYdSLhPllyjmOBLGfNhInBaZD3ulHGXslbBRyESlc6JDz8BR2YTQzYfYCoqWIEZGef5GuLyvKUqj0EvxvwF14OGkwrJB9ax+7B+618CkjlVNupKIZd3Q+hjYwBdDm1o/vsASRrWshJAfOuDMt231Vom3rUwxnRIZPZs2bYo9e/bgyZMnaNGiBUaPHo2AAPMdss+fP49evXrBx8cHBEFg//79jO9JksTs2bPh4+MDOzs7tG3bFhEREVb6FW8n7FrhFU042dvbKHB7VmeE/di5KLv1VlLBzR7Xp3fEsUlF4wcqISGhY3CBW0SP2t64P9e4LDkAfNqiIhb1K1xQlUIuw6lv2uLi9+14C2bwYUkpbnZGi/RCBNSZorRq9IuLgQ39EPJDB5PuHpZgq5RjQsdAqx27W21v/G9g3VLlj+zpbPtWC7t0isXVYfr06UhNTeX8pKSkICMjA+np5ifNzszMRN26dfHpp5+if//+Rt8vXboUy5Ytw5YtW1C1alXMnz8fnTp1wsOHD+HkVPKaxJKA/uDW8nE2GVgC8FdMkjBeSEhISFifJQPqYHH/2nrhbe+XzfHJ5mv6dGAfNfYzcluwlLIWBsYKpX3iQ6WUA0UcFFbD2xn3Y9M4Cxu9b7zvwr+EjmIRfBctWgRbW1t88sknaNy4MVxcXODs7AxnZ2f9/11czF8pdevWDd26deP8jiRJLF++HNOnT0e/fv0AAFu3boWnpye2b9+OL774olC/6V1AMpdKSEi8LbCrd4XP6gy1lkRyZh48nM33Ay4NFKVrA8UfoxrjWEScUQEFLn4f3hCTd4dh1UfBRd4vCYmSolgkn5MnT6J9+/bYtGkTbt68ifr166NPnz5o3749GjRogMDAQHh4WDcNR2RkJOLi4tC5s8FEr1Kp0KZNG1y+fJl3v9zcXKSlpTE+7yolHDAqISEhYTEEQUApl5UaoXft0PoAgJ8H1hW9T3G4xJZzVGFoE3+TlbwAXRXG8FmdrZ4WS0KiNFEsgm/79u1x8OBB3L59GyqVCk2aNEHXrl1x6tSpIjtnXJwugbmnJ7Ocqqenp/47LhYtWgQXFxf9x8+vcMESpRkPyUwvISEhYRW61/ZG1OIe6N9AuDgPnUBPg8uds60CW0caV+IqbiR3AIl3nWK1dVerVg2//voroqKi0LRpUwwdOhTBwcH466+/oNFYVtrVFOyXmCRJwRd72rRpDB/kmJgY3rZvK78Na4AWAW5WzQMqISEhIWEeM3rUQP0KrvhtWAPcntUZbXiqpElISFiPEnHydHd3x+zZs3H//n3069cPX3/9NSpXrmzVc3h56Rz52drdhIQEIy0wHZVKpfc/pj7vGp1reeGvz5q+teUGJSQkJN4FAj2dsO+rFuhcy0vStEpIFBPFEtzWp08fvQY1LS1N/69ardbXBk9JSbHqOStVqgQvLy+cOHECwcE6R/28vDycO3cOS5YsEX0cqn/vsq+vhISEhISEhMTbDCWnkSZK3hWL4Ovq6oqKFSvC1dUVLi4ujH/p/zeXjIwMPHnyRP93ZGQkwsLCULZsWVSoUAETJ07EwoULERgYiMDAQCxcuBD29vYYMmSI6HNQadbeZV9fCQkJCQkJCYl3gfT0dMFMYQRpSjQuxZw9exbt2hmX/BwxYgS2bNkCkiQxZ84c/Prrr0hOTkaTJk2wZs0aBAUZ10HnQ6vV4tWrV3BycioWU1RaWhr8/PwQExPzTrpZWIp0XfiRrg030nXhR7o23EjXhRvpuvAjXRtuSuK6kCSJ9PR0+Pj4QCbj9+R9qwXfd5G0tDS4uLggNTVVeoloSNeFH+nacCNdF36ka8ONdF24ka4LP9K14aY0XxepgoGEhISEhISEhMR7gST4SkhISEhISEhIvBdIgm8pQ6VSYdasWVCppOISdKTrwo90bbiRrgs/0rXhRrou3EjXhR/p2nBTmq+L5OMrISEhISEhISHxXiBpfCUkJCQkJCQkJN4LJMFXQkJCQkJCQkLivUASfCUkJCQkJCQkJN4LJMFXQkJCQkJCQkLivUASfCUkJCQkJCQkJN4LFCXdgdJOcZcslpCQkJCQkJCQMA+xJYslwdcEr169gp+fX0l3Q0JCQkJCQkJCwgQxMTHw9fXl/V4SfE3g5OQEQHchS1u9aQkJPtJy8uGkUkhWCgkJCQmJ94K0tDT4+fnp5TY+JMHXBJTg4OzsLAm+Em8FFx6/xrCN1zCmTRVM7Va9pLsjISEhISFRbJhS+EjBbRIS7xijtt4AAKw/97SEeyIhISEhIVG6kARfCYl3DBc7ZUl3QUJCQkJColQiCb4SEu8YJEmWdBckJCQkJCRKJZLgKyFRStBqSbxOzy38cSS5953mUXw6xm2/hfuxaSXdFQkJCYm3DknwlZAoArZdicJXf93Eq5Rs0fuM3xmKRgtO4sLj14U6t1bS+L7T/PjvXRwMj8W3e26XdFckJCQk3jpEZ3X477//zD54p06dYGdnZ/Z+EhJvOz/+GwEAqFTOAd91EZdZ4VB4LADg13PP0CrQ3eJzayWV7zvN1WdJAICIV5LGV6L0cz0qCTeikvFpi4qwVcpLujsSBcQkZWHK3nBUKGuP6T1qwMn2/YkNES349unTx6wDEwSBx48fo3Llyub2SaIQaLUkniVmoIq7o5TDtRSQlq02e5/C3jZJ4ftuo5QTyNeUrpucnafBruvRaFvNAxXLOZR0d94rSJLEP6Ev4e1ih2ZV3Eq6O0YM33gN2fka2NvIMaJ5xZLujkQBbX86C42WxOWnb5CTr8HyD4NLukvFhlmuDnFxcdBqtaI+9vb2RdVnCQEWHL6PjsvOY/nJxyXdFQkAJEwLKAduv0KLxaetdk7J1eHdprQJvQCw6VIkZh+4h29K2P0iJ1+Dv2++QPiLlBLtR3ESFpOCybtv46Pfr5ZKa092vgYA8CCu9FsocvI1WHf2KR7GpZd0V4ocDe1Z2R/2Cmk5+SXYm+JFtOA7YsQIs9wWPv74Y4sLPixatAgEQWDixIm8bfbt24dOnTrB3d0dzs7OaNasGY4dO8Zos2XLFhAEYfTJycmxqF9vAxsvRgIAVpySBN+Sgp5VQcw8NH5HKF6a4QtsilI491lMRq4axyLikFMweb6tpGTlYcOFZ0hIK9zYE5tqvefEmhy+o3PTufk8uUT7sT0kGt/uuY0PVl+CWqMt0b4UF7GphmdKXcwvf2auGitOPsaxiDiTbWXFaIE89+g1vt4RitQs84S5tWefYsnRB+iy/HwR9az0Mmj9Ff3/t1yKRN+1l5Ca/W4Kw6IF382bN5ssA0dn3bp1KFeunNkdun79On777TfUqVNHsN358+fRqVMnHD58GDdv3kS7du3Qq1cvhIaGMto5OzsjNjaW8bG1tTW7XxJvLxm5auy8Fm1V4VII+kraEuVrYV1U3iWN77jtt/DFHzcx+7+Iku5Kofhm923MP3QfwzddK9Rx9t58YaUeWZd8mpA55o+buB2TUiL9oAeT5qjfD8GX/r5rilnw3R/2Er+cfIQv/rhp8txyWfEJviM2XcN/t1/hy79umrVfaHTJLtzMIVctThmQq9bgYVy6yTSXD2ha7tkH7iE0OgW/vqNFkCzO6pCTk4Nr167h4MGD+O+//xgfS8nIyMDQoUPx+++/o0yZMoJtly9fju+//x6NGjVCYGAgFi5ciMDAQBw4cIDRjiAIeHl5MT4SOrRaElsvR731ZsEbUUnYdiWK18y35swTTN13B5N2hhXqPKlZ+fhm921cfpIo2E7DGGDMn4iSMg0pzRIzcrHjWrSgGWrVqcfouvy8fnWe+w5N+Gcf6jJc7LweU2J9+DfsJX4//6xQxzj1IAEAc3KxhCvP3pjVXqMlMWP/Hey7pROYc9Ua/HH1OZ6/ySxUP9g8is/Q//9oRFyJuTzQ37bct9xKIBb6cKMxY9H7IjkLk3eFYdq+cIs1eyk0japaazzu0MdkuuBbXAL65afmvS9vi87g6rM3qDbjKFaJsOwO23ANXZafx3+3X4k6Nn0Rm5X3br5DFgm+R48eRYUKFdC0aVN88MEH6NOnj/7Tt29fizszduxY9OjRAx07djR7X61Wi/T0dJQtW5axPSMjA/7+/vD19UXPnj2NNMJscnNzkZaWxvi8rdwzEfV97tFrzPovAh+svoToN1lF3h+SJDFqy3V8tvWGVYssfPT7Vfz4bwROFwgX92PT8N2e2/j5+ENk5Kr12RKuRSWZddyHcekYtjFErwVYeuwB9t56gSEbQgT3o4//L5LN1zLffWm4b3MO3MO0fXew/MRjJKTncAosP594hAdx6dhU4OZCZ+SW63gUXzT+anlqLU7ci8ebDIOgnpWnxi8nHpl89ixl57Voi0yY5vLf7Ve49yoNWi2JCTvDsODwfbP9/jZfisTwTdeQlScc4JiZqxbtm1nJzMCxo3fj8OfVaEzerRNEfz33DDP330Wb/5016zh8UO+xvQ0zWv9JQgYm7gzF8pOPLDpu9JssjNt+C3tumLfgyaMt+mIsePfeRhgaXzP8v3dci8a+0JfYcS1G0FUhOTMPCencLjp09wUuYfZZomG8clLpYukP34lFzR+P8lpxctUa7LwWjRfJRT8nsSkpa9m9V2k4+zBBdPvp/9wBoBv7TUHNe9tDokUdOyGdqXh5F7FI8B03bhwGDhyI2NhYo6A2jcayFcLOnTtx69YtLFq0yKL9f/75Z2RmZmLQoEH6bdWrV8eWLVvw33//YceOHbC1tUWLFi3w+DH/KmnRokVwcXHRf/z8/CzqT2lg7kFh83BGrmFCXnj4flF3B/FpuTj1IAEn78dbpVADBRXsE1kwyHZbcQF7br7AqtNPEDTrGKKTLBtAR2y6hguPE9F37WVk52nwl8iBg651sZEbv2L/hr3EqC3XRQUTHChYpW++HInGC06hzf/OIikzj7Mtl8bl9IMEzNh/V/93alY+jkXEMQQES/nz6nOM3nYDE3eF6betOPkYK049RveVFwp9fC6m7ruD/26/wp6bRacBvvwkEV/vCEX3lRcYPpNvMnJx92WqaCF1zoF7OP/oNf5muSf8dOwhhvx+Fdl5GjyIS0P9eSfw8UbhxRSFOWnudl+PwdjttxjbrrMWf3dfpiIli/t5MsXN58lotOAkNl2M5PQt3R/2yuIg298uPMXB8Fh893e4Wftl0hYZISzteEJ6Di4/1Vlriru64ZuMXKu8c1yY0vgmpOfgwO1XDE0eABy4Hav//6HwWM5rotWS6Lz8PNr/dA6ZucYLOLr3wq/nnuEG6/mi++bLChr/ev4ZctVabLkcxfl71p55iqn77qDlkjP48d+7SBcZdEWSJOdvoIREMTxJyDDdyIocCo/FnRep6L7yAj7ZfF30+S1xhwuJNNybqER+iw/9GrKfGYrMXDUWH3lQYi5NhcUiwTchIQGTJ0+Gp6enVToRExODCRMm4M8//7TI/3bHjh2YPXs2du3aBQ8PD/32pk2b4uOPP0bdunXRqlUr7N69G1WrVsWqVat4jzVt2jSkpqbqPzExJWdiLSxUvk8+bBSG258nEAjyIjkLFaceQo+VF6DVkrj4OBFf/HEDK089xrPX3C8qSZLIZplJ6KtpawVh0DV/Cw7fNylQn34QL+q4Wi2JOFog0srT4idwuubjFofP2ISdYTj1IAHrznL7T/UNLm+0jT6eR/GYqa9HJXMG9FyjDXjDN4Xgiz9u4hcOTZy5EeH7w14CAC48Nrh+sHPLXn32Br+ceCTaH00sfMK/JSRn5qHRgpMYtP4KXiRnMSYI+r0cvyMUPVddxIaLwm4Pe27EoOLUQ/q/E1nP5OozT3D56RscuP0KJyLikavW6k2yr1Ky0WLxacw/eI/z2Obco+/3GguNdDeY3qsvoueqi2i19IzoY9IZtjEEiRl5mHvwntWFOkvv775bL/X/j3qTxVjcd19xAUN+D8HCw/fReOGpQruvCKHWaPX36vmbTDSYfxK9Vl1EZq4aMRwL8d3XY3DuEXfhmgdxaYKLZPpYyrX47bXqIsbvCMVvrN9bztFG//9zj17jDIfGMU+jxev0XGTkqjmL8dA1vitOPcaA9VfQ7qez+m30sYrqZjZtccIl1F6iuZJtu/Icy0RoNUmSxIjN19F/3WWjd+SvkGjGXLQ/9CVaLjnNWfnQ3Ull8lzWYs2ZJxi7/RZ6rb6o3xYpIJBuvhSJYRtDEP4ihbFYFTunUdx5kYq2tHtEJydfg+RMwz05FhHPaQ3edDES6889xZd/mudDXVqwSPAdMGAAzp49a7VO3Lx5EwkJCWjQoAEUCgUUCgXOnTuHlStXQqFQCGqRd+3ahVGjRmH37t0mXSRkMhkaNWokqPFVqVRwdnZmfN5mhCZK+mqOchNgk5aTr68QFfEqDecev8bHG0NwLCIey048wtjt3K4jfdZeRo0fj6LzL+cMfaFJbylZ+dgeEo1hG0MYk5O5XGT5287YL7y6H7nlhlFkvFZLGmUNSGAJK3xC6s5r0fhs63XGwEq/5hqtTgux+3oMuq+4gIhXqfrv+PzqTCV5H7XlOuc1uxaZJLiAAYDbL3Tn3x/6krE9NjUbjRacxOIjDwBAlCDDFazCVkR8uvk6Vpx6zBBI2KRm5WP39Zgid1/gY8f1aLxOz8W1qCS0XHKGkRGFLki8KRDGNlwwdilJz8nHn1ef41VKtpGWks9PLikrz+h67boeg5cp2djAclt5mZKNqjOO4Mu/mBpcNleevsGHv13B3ZepnN/TF0HUs5CeY977p9ZoMWxjSJH6/1maAaC8qyHz0I5r0Wi5xJAmMDFDd/9+O/8Mr9NzscAKVi61Routl6P0mq/bMSnYcyMGbX86qxdoDhVkvHgYn44JO8PQaukZnLhnEFYexqXj+73hGMER+HgrOhldl19AqyX8ixP6OM4h9yI+TTeWUWO8Rkti3PZbuBWdwmhHX8Dqj2ciQw3XbYpMzER2ngZXn73BONr8QI2LdEF9ydEHxgdg8VyEG16uWovzj17jVnQKp4Wv4fwTejexibvC8CI5G5NoliqK4sg8QZIkLjx+jf8de8hxfu598tRazDlwDxceJ+KD1Zf0zzKgm9OoxVRmrpp3HO1V1wcA8PMJ4/NSVJ95lCGIU+1/P/8Mi47c12uDbxRkb3mV+nZmyBJdwILO6tWrMXDgQFy4cAG1a9eGUsms+PH111+bdbwOHTrgzh2mwPLpp5+ievXqmDJlCuRybkFgx44dGDlyJHbs2IEePXqYPA9JkggLC0Pt2rXN6l9J8yQhA/+EvsColpVR1sEGR+/GomI5B1T3Mi2UZ+Vr4Kjivs30SRDQCWIudoZ7GZuajbb/O8vQEn26+TpjH65VMwD9RPAoPgMxSVnwK2vP0Fruu/VCP7lvuPAMEztW1X/3KiUbt6KT0S3I22QkMFuTKGaQPBQei89aGQqr9Ft3GRGvUnF5ageUc7QBQRCiNJQaLYmp+3TP7bYrUfiiTRXk5GsYflFpOWqsPftUP8gtO27QXihlBOI4Bg5TY29yVj6uRyahXXUPI+H5TYY4TRl7gP/9fCTeZOZh/bmncLJVYPnJR9j9RTMEV+APMpVzdJRugkvKzNPn8BTyj11y7AG2h0Tj9osULOhr2bup0ZIWRY3Hp+Vg6VH+iYDLb5FrKbnq9BP8dv4ZWgYYZ7LZe4s7E4NGSxqZLHNoz92+Wy/Qr74vAODH/XdFLUaGbrgKLanTxprL3psv4GirQJdahgDgsJgU7Lv1AhM6BMLNUYW/b74wu1SyVkvqzdxioT+fao0WCg6XIYoFh+7h9wuR2DOmmZHGM6WIF1MfbwzB1WdJsFHI8Gh+Nwxcf0W/+HyRnI38Ao0pxcn7OoF325UodKqps5i+EkhRd6qgPf09Z18PNSMYiX8Rcys6GWvPPkHbqh44GB5r9L2C4x5pWIt4Nnw+sUlZefhmN/M5odww6Mf582o05vdhvvPsYUXMe23K3SMzT4OLTxLh72bwkecy41fzcsIdnkWjtbjwONHs7C6zTGS1uRebBt8ydui24gLSc/JxeWoHJGXlwcvZYEG/EZWEnHyNPmBYLE9fZ+DfMJ3LXf/6vqjq6VToIksljUWC7/bt23Hs2DHY2dnh7NmzjMGbIAizBV8nJycEBQUxtjk4OMDNzU2/fdq0aXj58iW2bdsGQCf0Dh8+HCtWrEDTpk0RF6dzzrezs4OLiwsAYM6cOWjatCkCAwORlpaGlStXIiwsDGvWrLHkZ5cY3+y5jdsxKUjNzseghn4Y8+ctlLFXIvTHzib3jUvNQYCHI+d3KgVzMll2/CHm9Dbchwex6VbJEJCSlQ+/ssxBMpOmLWJrnFotPQONlsT8Pvn4uKk/cvI1eJWSjcruxr/D3JcYAE7ci2cIvmEFQnqjBSfRo4431gypj/M8Zkc6v9BMcCnZ+chTa1Fn9nEjrSt9ZU93n1DIZei6wrJ8kTHJWbgVnYx+ay8ztmeaCKSiYE8mdLmC6u/QDSH4rFVlhEYnY9mgenozYHxaDsJfpOpX/XToR/3+b8PEp9GS+DfsJTRaEl2DvLDnxgu0CHBDgIeTPujir5Bo9Krrg9/OP8OcD2pBpWQ+n3zWiwdxaeiz5hJGt6qMfvV9UaGsPWb+excB7o4Y2bIS7zXQaEkM/vUK7/cAGJoVCva8qtGSejMy2wIB6BYqfOdnt6cLfJN339YLvikc1oHKBYFuJEkiMSMP7k4qvVaO75x8xKXm6DMxRC7qrh/Tv/rzJl6l5oAAMKd3kNlCL6DTVnu52EIpILzSIUmSEYF++0UqGvhzL8A0WhK/F2jgB66/AqXcvBk5MSMX5RwtM2/na7R6dzJqUcJ+93PVWmy+FCV4HCF/48xc5gL8m923cfpBPI5Nag0PJ1vkqbWMgiafbL6OzFw1lvSvgzbV3BnXnCSBpUcfMhZ6Kz8Kxtc7dFpZrsUJfR1Bjd/5Gq3+uHwGxfkH7xmlj9RrfE0E4BFg9oMSyLPzNLCz4VaCMTTTPJ1iH5dLu2tLG3Py1FqGO+AfV5/j3MMErB5Sv1Cll4VKjfM9CjuuCceXfPHHTTyc31Wv7f79wjMsO/EInWsa3FFjU3NQfeZRs/v7KM7gzkhZeUy9ZQnpOXC1s2Fcv9KERb2aMWMG5s6di9TUVERFRSEyMlL/efasaPymYmNjER1tuPm//vor1Go1xo4dC29vb/1nwoQJ+jYpKSn4/PPPUaNGDXTu3BkvX77E+fPn0bhx4yLpY1FBaU9P3IvXR8snZ+UjPSef4X/zmCN6XyjxP3t82HrlOet78/1w8zVaIzMrNRnQz1fTh19bTWkEqECU3qsvof3P54zSiJ15mGCUokVMn0Mik/TpndhQGSDomgE+Vp95YjivlkR0UpZJVwM6O65Fc2qkCOgCh7h8ASlep+cigkMzIfaWsQVfexvjNbC9jQIrTz3GhceJaLTgpD47RJOFpzB62w3O49IPe/K+wX0mNTsfE3aGYfLu26j54zHM+i8CHZcZC/0f/nYVpx8kYPLuMExkpZ9bf97gbvKU5ls+98A95ORrser0E7T76SyWHtVpkOcW+MlqtAZ/8xtRSbj5XCesHAx/hSgTFoKOy85xbGVe5BUWZi7QaEkjqwtbi56alY/Ju8M4C0M42eru2eTdt9FowUm9dtAS6D619JRrlCmTPTaYQ6ulZ9Bi8WnRKbOusFJQ3XyehHyNFkfuxDIyiADGPq1cVe2ev8nUv9dsBq4XXvgIQQmMFGz3IUDnu2sK+jvL9m1mB4DtvfUCyVn52HfrJb788yaqzjjCyJwRnZSFN5l5mHfoHoJmHcNSAVcCHxdbBNCUCQoZgZvPk9Bv7SX9nEPXnt6ISkLv1RcROP0I7hS4yfCNt0fuGmeJOP84EWcfJphOZcaSqnLVWqw9+wQ1fjyKaQUWtuw8DeN5oveTcu0wOizruI85AsnowvHvF5iyzMz9d3HyfgJ2C2QaycxV4/yj14IWQ6rYCxefbbvB699vipx8w7tA+a8fv2f5mEBBn9Oo+y0UXPckIQONF5xCz1VFE+BsDSzS+Obl5WHw4MGQyYpOmmf7EG/ZskXwey5++eUX/PLLL9brVAlDksAO2kDaZOEpZOVpEOjhiDq+rmhX3TjiOydfgwWH7iE+LRcze9ZkOO/zDUDpOflYd/YpHHhcJNgcuROLbrW9AQBdl5/H09eZRt838C/DGCQv0DSqXOZ+Og8LBK6/b71Ac5opmS0YAcx8okJM3n0bPq52aFqZu7Z9GXsbzu18aElSdPQxBZ+P5OOEDPRfd5nzOwqlXIY8jkn+KU+wIRu2codLg5HHGrz33IjB9B41BY/LNyAKBW1w8SI5GxVZiw+6pupYRDxI0thVANBFjVP0X3dZLzSe+bYtBhQIOqEzO+HXc5Yt0lOy8nEoPBZtq7nDQaVgnM8c2Jo+kiSN7svtFym8/tHU3v8UCFyjtnIvRsQwZMNV/f+7rbiA9R83QNcg6+U8T0jPxZOEDF7NLUViRq6RpnDh4QdYeFgnwFV0s8fZ79rpvxOz0BNK3RaZmImYpCxsuhSJUS0rwbeMvekDFsAW7iZy+IzOFSHE0H/D+B238NdnTTnb0Ysr/H3zhT4DQBqHjzbl8rWWJzYB0BX4qFjO8HvdHVXov073fvRecwlRi3sw5ojZBwy/pdfqizg4vqVZeW/vx6bhE5arHMXRu3HYejkKfeuXR1VPR8aC8PSDBL1/8o5r0VjUrzaCZh+DRkvi3twusLdRgKStf8RmSNEdOx7tqxu0ovTh5H/HHmLDhWdoWtkNq4fU128X8omfuf8u9oW+RN/g8ngUn44WAeXwQ/cajDamXCk2XIzEjJ7McbZyOQdGajguptBjC4rIFYHS1tPHqbsvUxFU3kX/98FwnTJK7FxcElgkuY4YMQK7du2ydl8kTJCQnouKboaBihKcHidkYO+tF3rzfNPKZVHdS1dl7+bzZPx+IRL/3X6Fo6xcjVwmtln/3kXdOccZfqmmoAfcsIVewOCfRj9fNk0TLTa12b5bL9Fq6Wm9D21hyykKCYn5XFEiNNiadI0WWC+iyg070wUXfIFJdNRaklPTOI4n2PDo3TiGRpDtQ/k3R3owrgA6rshuiv2hL3mDJD14oqW5rBQUpgQvykVFKEk9XVNK197GJGfhHo9/uinUWhJjt9/Sa9rEmvDZrDz9hPF388WncZ4VYCQkV4S/SOW1XJgL2/JwhuM+TtgpnAPdFKYyNfwT+gIN55/E9H/u8raha+jTcvKtkme01dIz2HwpiiGUPX2dgQWH7lk17SKdC48TseCQTpCkKwQuPeF/lvvS3JqskXYrKTMP9jYKva8xp6uDgGTbc9VFq6WFG/PnTVx59gbf/x0uGAhLQQnkzwrmGzGWPi5ZkB20zG6TnJWPI3fjRCsU9hUsQv8JfYmIV2lGmTQsxcvFdLYr+vxuyp3EUgwWFsOVYrthXDJR4Kk0YJHGV6PRYOnSpTh27Bjq1KljFNy2bNkyq3TufSRPrUVkYiYCPByRk69BKCvyNkNgtUnVbFfKZUguSHey6IjB1MUOjuFS+BbGpMkHZYain4+eE/JaVBKGbQzBH6OaMPYjSeN0NzFJ2Wg4/yQ+E/DdFAtXcBaFqYGDPc7GpWXjssCkRWFq1c51bC40Wi2npoePMay0M3RXh02XIjkXLOzn401mHmbu5xdKuDReFHyX+os/uNPhxKbmmAzoMDcbAV17JZSwXywbL0Zieo+ahcpKQic2NUf/DlOYEiwmswKIhKA0MWLgul9UgIuljN52A1GL+YOQJ+3S/Rax7kIfrLpo0lXFHOjCZIefdYuk6KQs/DqsIQDdYpeyjFhD4Pv9gu754bK2kCRZ6PLWYqFiPbjcaeLThK1xRVGAzVS2EHbWHF0/THfkWWKmUVo4tlWTz2JFnw9eJGdj9n8RGNWyEvzKircQFAZz3Q6zi7hqYRl7g8x3KzoFq049xujWlWGrlON6lPFzVNqwSPC9c+cOgoODAQB37zInQksSK0sY+Oqvm3rfyEAPRyM/pFM8GjXAkI7GRi5DFXdHI18nttO/OeUtC4NWxOB04XEi4tNykEvzUzpyN47TVwzQmYM8nFRGacfMQSha+KQJf0l2TXeCICwoTsyNmEFLKEBCDDKCwOIjD0RpqSluPk9mZP2gw5fPmeIhj2aXLeiZw5ozTwQ10ML7Fr4GfXFUXbXmKdaceQonlQLpIgT1nddjMMoKi8uixJpCLx+U4mHOgQhsvhSF/8a1QB1fV6vee/Y4pNGSyFVrONOLFQXU+bkWNh+sviS4b0kUOhtIC0jtveYSZvSogQ/q+Zjc77fzz4yClptX4XZ1Y0OfuygN56kH8bjwfXtR+1NQ8QXmUkwVnk1CFWSq5G5wQ7sfm4b7sWn4+cQjzOhRg2/XUoVowTc8PBxBQUGQyWQ4c8ayhOcSpqEHBHE534uBILj9ZtmCbnFVL6IWy3TvAXYOSUBntvro96tG2/lwtVeKFny9XWyNhCy5jMBfIdwablPuBuyyxXzBM0WFJdks6DyMTzfb1G9vo0DLgHIIf2F8bUylkYtJ4hZQcwpR2CIkMolRbOJdZPo+8VWnTFHDywlOKoXo0t1CGvySpCjHrfOPXjM0rZTvIpWZYeD6K/BxtTO7dLQQQ1ljydS94fixl7AvvTURsnyZYu3ZJ6YbWRm2Znr+ofs4dZ9fIUTnASutYlxqLoZtDEHzKuXwZdsqSONxn+MquMQe0/gqIFLp/EiS1PtQm4u5xYWKijy1FmqNlpHRiM78Q0VfAdYaiHZOCw4ORmKibgVauXJlvHlj2qwrUTJk5mo4TersYDaT0bVmIJRjVIzGFwBWnDIvOp5ynt/1OXcwCJ1VHwUbbXuTkcfpT7j3pnX8Jkszltx7rZaEG0/qp384ItrFUBIao7cJayaIp7JAiMUSq4KTSsGoCMbFtH3h6L36osXV3pIy87DjmvUran5Q18fIvYAtzOQWuKLx+bKbC5ff856bL6w2NvNZaOjw5VgWs8CwRrpLdkEhS7jyzDJ5ZNOlSFx4nKgvpMGXU1koP/K6s0/RYvFpzsI2gEFoNqdaKT03c1JmHmfqyJLgeVIWGi04yZk95W1CtODr6uqKyEjdjY2KioLWRPCPhPkIpR4zB7762uxVozUXkStO8ZelpSoXmRpHLe2PGPcarsGdL2vFN3tuCwZMva88jE/n9TFnp5V7n+hQ3cN0o1LA1ivPRWt7LeWfsc2x7uMGvN9rtCR2XIvB7Rep2HMzBr+df4pkM8sTX3iciB/+sZ4mnOIRhzsOl2XKmtSfd4JzOzulm6WICQDmKlwBFJ95/cs/hasRFherTz+GZ0HBB3YMCVc+b0AnoC45+gAvU7IZ6S3pUAofcxYJAdOPIDU7H//dfsX7jJQEWXkas3OEl0ZEqwD69++PNm3awNvbGwRBoGHDhrwV1Yoql++7DiWw2uXxa3m0MhlyFQaNCtXWVinT5/Gr5SpHxOMcaAkCuUqDho7IygQyDZpgRXaWfn92W9v8HBA8Ax9JADlKW0bbTccioMrNNuo71Tb6TRa0JAlVfi5kPBLwnYevABvDcYXaAkB2QVsnWwVU6jzIBBZj9CwGVFs57fczjqtU6aN7bNT5kGv5FyTmtM1R2oAkdGtNpSYfCoFS3HxtyzookZTJHHhyFUpoZXJRx6W3VWjUUGr4NRl5CiU0rLbrD4fDzkRbuVYDGzX/4JgvV0AtV5jdVqbVQCXQVi2XI1+uNLstQWphm88vfAm2zcyEjPYcaWRy5CkKtGwkCbt8flccc9ryvfeFbst6781pyzVGyLOy4KIlYZufYzRGECTw4Gms/hzzd+nSr8XGvsGswYbc6qbee/rCXuwYAcDkGPGAFk5Ab3sxLMrouhT1GPHlX7csHiO44B0jMjOhymOO21TbfI3WojFCTFv6e//waZzRmFISY8Sag+Go6eMEu7wc+NpoodTk69vm5OZzvhttZh+EXV6+4BihSc8AVArkZebBLi9H9Ht/4vpTzDv6CIDcZFugeMaIeQfvmTVGlFYI0gyHqaNHj+LJkyf4+uuvMXfuXDg5OXG2oxeReNtJS0uDi4sLUlNT4exsukRwYUjJykO9uScQtaQnb5vTlRti5MDZ+r/vLesPe56X4apfED4cstjQdv0w2Kdym0xuewWi9whDzuOL60bCN43bnPfIrQI6f7ZW//fxDV+h6hvuyjIvnD3Q8stNODi+JfI1WsiaNEHduMecbd/YOaPB19v1f+/cPhVNY7izCGQpVag5eS8A4MSk1ohp3h7tn/HnMb0dnYzea3SBGmv2L0KPh/xBGzUm/a2fMH869AsG3D3F27b++L+QZK/zA5x7fB2Ghx7ibdtyzEa8cNGlDpp2ZhO+uLaPt22nkWvw2N0fADDx4l+YeGkHb9sPhi9DuLeu5PPnIXvxw9nNvG0//GghrlaoAwAYdusg5p1Yz9v20wGzcKZKIwDAgDsn8dPh5bxtv+o9FYertwQAdH9wEWv/Xczb9tvuE/F37Y4AgHZPr2Pz33N4287sNAZ/1Ne9D02jw7Fzxw+8bRe2/RS/NekPAKgT+wj/bZvM23Z5i4+wvOVQAEDg6+c4sWksb9tfG/fDonYjAQC+qfG4uH4Ub9ttwT3wY+cvAQBls1Jxa9VQ3rZ/B3XAtz0mAdBNPPd/GcDb9lC1FhjbZ5r+76IaI26uHAK3bJ4y5EU0Rrx08UD5lHhUnKp7d/7dOklwjDh48rY+44fYMQIANu2ZLThGVJxyUP9/aYyog441POH910ZpjADQ1yYZv8wbxttWGiN0sMcIoSwuRYFYec0sp6+uXbsCAG7evIkJEybwCr4SlmFO1a+3DY2WhJa0MHG0CcTUcpeSjUhIFB+VyjlgRDN/UekRSVJXfU8sXC4JEtbn5P148It67z6U2wMA3H1ZuCw6pZkD41oC71kGWrM0vu8jxanxjUnKQqulZ3hNCTV9nHA3LpPTRDGndy3M+lenBfnzs8b4eMM1I7PD1029MblTNf3fk3aF4WhBurDCujqYarv3y2YgSWDoqrPiTZMizZjnvmuLcZsu4wktS0Hvuj44cT9enxNyzzcd0XPVRd1xeUyekztVxZG7sbiVmFekZswWAW649jDOIjPmqJYVsfFiFKNtcbo6iGn7Nro6LOxSBXMP6iKSD4xrgV60NE5CZsx7c7ug5o/H9H9zmTHvz9MpDJaffIhfz0UKtuXjbXF1OPVNG/i42gEEgR13E/UlZs0ZT8xyX7DQ1cHJVmGUB9octwhruDrM/qAWZrNyVVvDHYoLrjGihrcT9n3VAj8de8AYU6QxQkebIB8cfaTziS+MO9SI5v6Y2q0GrjxNxMgtN3jf+4PjW+rnKAAY3boS1l+MLvIx4s9RTTB6HVdp9oK2HGME1/vD1fat1/jS05mJISIiAtWqVYNCYVGq4PcSKsKXPgDT0do7IFfBfFH1bR0csOebjoh6k4kGNX3ww0ANZv7LHFRzbewAB0ManlyVLe+56BORKcS03XQpCh2qe5jl/yO2rVxGYEqfYHyy+Zo+cta2jDOybVKRDePJgD440BndrTZ8ypfDre2GYAvdoGM6Mtqctm2reuDSkzf6gdIU+XKlvq3W3sHonjnbKvTFLKi2ZeyVJoMQ1LQJwxTmtNXI5Mi24fb/L0xbbRG1JQkZNPYO+HdKZ8Sn5SCgsjvve0ESMsZ3L/JkvG1BELrvCt45rR3z3pVztDEEzVBtRTCuXQBvIA2b3vV8zCo8IbYPAPd7Tzg6AA46j016ZgJzxpOiGCMA5nv/z9et0HX5BVFtTWHpGJFIKgSvN/29N4UlbfNsdfOB2s54TKF4n8cIjcJwPdnvvRDstjkF8y7pkG18DNp7L3dyZHy/8mosQOuDOWMEIP5dzshVm33c5lU9BGsKlHbMSmdmTgqzZs2aITqa26dLghtT6U6UAouOlgHlEFTeBT3r6BJ5f9zUHycnt2G0YafIoRQa83rXwkqOdF/W5FB4rFlVpsxBLiPQMrAcbs/qzNhG926QmfB12PdVcwCAjaIonDGYNKpUFj1qe1u0L9fP+HlQPaNtPep4Y84HtTChQ6BF53nfCPRwRFVPJ7QKdDdrP3apXyHYmUV61TWddJ+LbzpXFd12XLsADG/mb9F5xPBJ84qMv+k5Yc2tNmUKf1q59sIwqKGvyfHAmnStxV16uzhys9av4Mr7HTVCSm5gxthYWIacC+o9MPU6cN0HvowblsDnEqiUmz5Hs8rMQh/1/ctYpU8lhWh1LEmSmDlzJuztxQ0+eXnmpaiRMEQrywjuVDIKgQfUx5UZF0sQBAI8HBnbXiQzcxRSBS3kMhnal1BKpn7B5fX1zS2Fmmzp6cm8nG0Zac5MGSrqV9C9yGIGATEs7lcbU3mKD9jIZVgztD4OTeUPcuHDydZYq1OFVkWH4tKTN5jfp7ZVSvOWNoSuraVwlY2lOD6pNTr/cp7zOyq/p0ohQ6OKZXFRoE690kqTqTnVMeUyAhWKqKyqvY0crQLLYcvlKP02unBvzTzhgE6I/+7v8EIdY1TLSpjZsyYeW9FPuHI5B8FS5FzPxJSu1XnTP7IZ0MAXf1uQW/y7LtUwtl0A5h64h02XjHPMVvPSxehYUbbCig/rYcLOMOsdsISY9UFNnCtkkSAKSvC1ZCGokBOCCrFGFcsgwMOxUHmt6XPK6iHBGLc91KgN1/xp6rkvzYgeiVu3bo2HDx8iNDRU1KdZs2aws+NKfCTBB5XQ3d6Gez2isGDidKCZco5GxKHi1EP6xOSkXvDVtXNzEG/esxaftapc6GOUoxVVWP9xAwxq6IvhzZlaLqb+lx9raXyF7hXXILJ6SDA+bORnUtOg0ZJoXLEsYxvXSp4SOopTs1VcUBM2ANT0to7fvZ2AybOqJ38QL5XIXSEj8OdnTTCuXQBvW2pxRWFuxaxvOlXFr8P4c+R2qeWJjjU8GdsqujkUSRl5DycVFvQNMtJS0Z83awi+nWoafo+YYgxisdY1Gd7MHycmt8HHTSvwtiFJEt2CDFrfjxpXwJdtq4jKk+vprMLQJvzHFoLSKPP91MmddJYDMWOENSvVvQ242llvLnyakInY1GzEpwlXGeWao6gUpRRtqjItUj8PrIdJHcVZgPjCuRpVNIxL9D7QxzL2M0IQb7elQPQsf/bsWZw5c8asj7e3ZebcRYsWgSAITJw4UbDduXPn0KBBA9ja2qJy5cpYv9447crevXtRs2ZNqFQq1KxZE//8849FfSoO9Hl8eSZhJUvA2ftlM/iVtcMIAVPm8g+NXRhepug0vxr9wEiAIAgcmdBKsH9C57EUpZxA55qephvyICOYWqauQV5YOqAuVAo548Wk/19IuOT7bmn/OvjaDLcBIRNVWXvjQbVbkDcW968DZzthI0xOvsZIcOaauL5qW6XgOxGdBYysA6UZuqA/vn0Ajk1sjQaFNL15OAn7itInBzqPE3SaQ+oeTOpUFVO7Vedsy74Xb8ws3DC+QyC68JjNAWD54GDYKJgnkckI0ZpFPka2YCbzr1/BFdemd0TfYF+jttZ2dTj/yKB146saaA5lCxb31pq05/YOglxGMLT5TSuXxd9jmun/lhEE/Ghad+rcYlwdZvSoyakI+bCRn8l9qcPzjQGUlVDMpWAv2vgoikVWSWBFTwdcefYGzRadxrd7hF39fFyF/WybVCoL9hMjlxOwFenTzAf9nnm5GN4x+nNjJPiCeKvvddE7NJrJ9evX8dtvv6FOnTqC7SIjI9G9e3e0atUKoaGh+OGHH/D1119j715D3sYrV65g8ODBGDZsGG7fvo1hw4Zh0KBBCAkJEThyyWGjkKGimz2vaZI+4QdXcEUD/7K48H17zOkdxHvMTjU98VFjbo0BNTBSk5WHsy0qc6zsnVQKPJrfDXN6B+H8d+30kwcAnP6mDSpy+N4p5YQo/1KFXIa1Q+ubbMeHkLaC4Pn/UAHtDJ8fVOdannoNCZt5vWsZ94vnODs/bwoPZ+YAN7yZP+28woNJZq4G7k7M/bkuwcCGuolRrMb34PiWotLCmSKofNFmPgGYv6mCmz2qeTmhVWC5Qh3TmcOFhM7uL5rh/HftjLbPKUjDRXVJLiMwpk0Vzv7Qn4l6fq6MdEnWuG4Ewa01OlxQOdFSqnsbNN7bRzfBH6Oa8LalL8qojCqFge6+pOKxxtjbyLF/bAtUdLOHXEagf31jgZyiX/3yAMQJe+ZArz43qWNV1PZ10f8tkxEY1NCX8T1gcDUToqKbA8PCAejKry/uXwcL+gZh86eN0KOON7xdbDG9ew1GO+r4psYAUZUvRV6w0iQKXZ/e0eJ9ZQRhlqLDGpiy6P75WRMjra1CRoge44Wetm0jG2PpgDqo5mUYh0IiDVUe2ff/LZZ5AZQywTcjIwNDhw7F77//jjJlhFeY69evR4UKFbB8+XLUqFEDn332GUaOHImffvpJ32b58uXo1KkTpk2bhurVq2PatGno0KEDli9fXsS/xDJaBbrj7Hft8Meoxpzf07UKb3hKKHIxrj23+ZXSyNAnqw0jGmJmz5qMdksH1NG7AFRws8fNGR3xbGF3RC7qjsrujoh6k2V07MtTO2BSp6qw4/CdpPsTK2QEFHKZoJuFs23hM4PQB3eha+dO0/zRz8s3OdTxdcGwZhWNtvNpfJuyggQA5oRuakDRaLWY2ZM5wbEHvsX9auuFWD4BnI2MINDbwmArOnKRWV8Kdw7Db3Jz0N0vc90G6NjIZSavE0EQgm4w7P2X9K+DD+r64J+CoEmAeZ961fVh+JNvGtHIrD7b82h5BjY0FvqGNTW21CzpX1v0uTydbdGnng8GNvBF8yrlGMIoW9Cm3xtruDo40s7F59Zyb25X1PNzxcGvW+HSlPZY0DcI33etxmjz12dNcG9uF3i76LSc1nYBepyQof9/dW9nxvPoYqdEgIcTLnzfDtend9SPMWJcGEiWuNItyEsfFDm0iT/aVfPA6o+CcXFKe4xuXRnXpncw7EsaLHpC0L/mihcAxF+v0iQQuZuw4gihkBMIKu/CeH9LGq4YAYVMnBOfs61CMLiudVV3DGrox5i36M2z8zUY06aK/m8CgG+Zt9eVtVQJvmPHjkWPHj3QsaPpldqVK1fQuXNnxrYuXbrgxo0byM/PF2xz+fJl3uPm5uYiLS2N8Slu7G0U2D66CX4eWJexne4CYY4vanlW4Bv1Amg5NAKV3R0xilanvH99X3QNYppXCYKATGYwdbjaG2vLqP4t/7AeY3v/+r4Ms7RQwB5F2I+deb8TmlrpAz71krLNkoAueMTQzh67Pm+KQ1+3xIK+BuGATy6iriU7etpWKTMyEfNBv/5iBjEPJ1uGNoI9KX1I0/Dn5ovTuskIpnbNUtgC/5ZP+QW6FgHMRQDXRMV13Su7O6COrwtaBpTTuyi0DCxncQR0GQfj57cMxzMtdHj2PfBxtcPKj4IRTDMR0/dXygnGRMS2ApjClcPfVUYQaFvNw8jPtwaHwOjmoMLvwxsytvEJLb5l7LD8w2D8jzUecUG/Dqay1JjC28WWITybWpw4qhTwcrGFrVKOr9oGMBauzSq7MVwGrC2g0fvpYqdkaO8o32S/svaMZ9xLxD1nX0JPjn0IgtAvODxo1iCDn7/wOej3jC/bjIezod9CbkFaEvjWjKwjfDSuVNZ0oyKEuiaWjInfdalmupGVUMhkjMXmskGm31E67Kws9CDfprR7EJOUjYkdDXMOQQCL+plePN97VToLf5QawXfnzp24desWFi1aJKp9XFwcPD2ZA7ynpyfUajUSExMF28TF8Ue6L1q0CC4uLvqPn59pX6qioHmVcujfwBcrPqyHMvZKTO1WnSEoiPHxojOjh0FLOGD9ZUzeFaY3RQqt5htXKmNSY0AF5dGhBnv24C6XMZ3sFQUaQiGtg9CEJ+RHSN/NVinH3TldcHd2F9x4nsRoV8uHKRg0qeyGWj4uCPQ0+L2acgP4ZXA9hkDv42qHH3vVxLzetVDOUThQgv77Pi0QlttV406rRf3aHJpAmy9Q8a+syIBFGUHAkSYoPFvYHd93rYaD41tis4DwyoZ9nTyc+Cf3S0+Y6RGX9K+Nr1nWifPftzOaaFUKOf4b1xJ/ftZEf+2CK5RhaNPM4X8DjCcLdpYUQFhzJkaOor9nchmBzDxmAnhH1iQbSPO7ZmtX7DkmZOrwbaoy3Sy4NEUyGTNwDACaVzG2RqwbWh9V3MX7f1szuG3/2BYmjyH0XtL3ZI8hpvRkSjkBbxdakR6l8FQpNA7x/QaFXIYVLMWAqeOKqTdFWQMMWRuEfyv90vA942PaVEG3IC+sHhIsGGCp1ZIY194gIPULLo/fBNrT+ZFmaWQHcRUl7PeuvKsdapfXuaqI1XRT2ntvF1t81bYKqnoKvzMzetTg1a5z0S+4POd2hZyArVKOeb1rYUaPGrzZmfieGqH5oYKboX95Gi0zZgYEvF3sTC4g49P4i2iUJKIF3+bNm+Orr77Cb7/9hpCQEGRnZ5veSSQxMTGYMGEC/vzzT9jaitd8sF9SLtMOVxuhCWzatGlITU3Vf2JiLE8TYg161yuP0B87Y0ybKugb7Iu/xzTDTwPr6gUksQyhmdXi03KxL/QlQqNTADCFKApKWGspIq9pdZYPGj3fJlfUN11O45pQlg8OFjwf3UQsNA+wfaYcVQrY2ciRls3MverAk0WDDt8ASJki/d0csGN0U/12ymd0WLOKmF6w6HDi0R7QJ54vWlfG3i+bY93HwpMF3Q+cPngt7c/0jRcbgEAQusmtrp8rZvasCZmMwFdtAxBU3sWkGwH9/EmsgK2aPuJ9VyuUtcfkztVwf25XjGsXgJk9a8K3jD2uTe+oD4AU8uUt42CDJhZoirgi1pcNqge5jMAXrQ1ZR7hkLGpfcX6ShjYKGYEoViqg34Y1YPiC0g+ZmMGMCGdragCD8P1hQdaA7Z/pfHG5UvRx9Zde1ZGim6l806zD0C1R9AUZ38TNx6CGvvB0ttULFF1qcQfAHvq6Je8x1g6tDxkBzO9jHANh6nbdmd0FK2iBwewFlZOtgrHQFZLPX2fwR/NXdBMWfixx4dk6sjG+61JNn9PdlMaXOV9yt3dQKbDu4wboWcdHMMMDJeQv6V8brQLLYW6fINFaU3rWDraVkoKuwKGgL4w/L3hfuVzs+GDf20tT2+sDKcXGPcztHYSz37bF5antCwLFWxtl36HzWavKOPVNW1GKidCZnbBscD3O76j+DWtWEZ+1qmyVrEQbhjfE6FaV0Jf2zuZrtEzLZMF//x7THNN4gnnp/SttiNbj9+7dG7dv38aKFSvw6NEjAEBAQADq1q2LevXqoV69eqhbt65FmRxu3ryJhIQENGhgmOw1Gg3Onz+P1atXIzc3F3I580H28vIy0twmJCRAoVDAzc1NsA1bC0xHpVJBpSp89HBR0bBiWTQUeKH4sLdRwMNJhYR040E4OsnYR/filPZIy8kX1NhRsFOu0B91tmD6LDETXjRNioLDJ5StgWUTPqsLavx41GS/+DI0pLFKLdb3d+VsR9cK8c0/dX0N+9ooZJjUsSpy1RqGxrB33fJwtbfh/V30yU0mIwQzFFDXkz5h0icWTxfm/RIdlEIQcLFT4t+xLYz7Z+Ig9O8Lk6uWGljtbOT4lmUu/HlQXRyLiDfSUrKZ3ycIB8PNC+biurfVvJxwf25XxkQipP0Rc53pj7rORMm8Vs0DyqF5QDnsvqHL2Up//tQa5os0qKEfyrva4dMt1436p5TLMKWrYTLi1PgWtPV2sUVsag4qlLW3WoEIip51fLD5UhSquDtgft8g0fm6F/QNwtAmOr/k0a0ro2I5B05t9PLB9VDdi3+saBXojkfzu3EGDbFv5exeNTG7IFAR0FmI6NeDPU7dnNGJsaAQytAgpP0zJfhQmkdzaFSxLBrR5wgTwjP9/ZUTumApIQ22kLacEnwHN6qAwY0qCJ6+X/3yuPL0DWJTjbWCXYO8gF3G+4xoXhEpWfnQkCTWnX0KgPl8VyrngM2fNjKZpaVZZTdceaazOP3QvQZGb7vB2Vf2wsPTWcWZlkwuI1CRtiCQywhR1if2pbk8tT2aLz7N2EYf32f1qoWOy87p/2bPcXypUM2hY01PdGSNs/lqLef418C/DOpXcMXFJ4m48Ng4X3VpFXxFz1JTpkzB9u3bERERgatXr8LT0xPBwcFQqVT466+/0L17d/j6+goKlXx06NABd+7cQVhYmP7TsGFDDB06FGFhYUZCL6CrDHfixAnGtuPHj6Nhw4ZQKpWCbZo3Lz0O68UJl9ALAN05tDq2SrkooRcA6voxB2f6C8IePh/GpTO0I1yxUDIZYeQe4EQzw4vxCwb4JxW2aSvAgz9Pq75PHC+9g43cKBBwQsdAfN+VuQKWyQi0q+ZhdD371S8PG4UMQ5qITxNHaZhbBLhhXLsArCqouPddl2roUdsbLQOYGlFrBPFwHePPUU0wr08Qto1sjNe054pPU2PpeSicbJUY0MDXZC5XV45UcZael609obfrUGBSVGu1Rt+JOY/ORCk8/BIE8EFBINMIlobXRiFDO5ZZk68LXIIfNR/9PKguqns5YXH/2ijnqMKhr1saZQcQ7KPAdw38y+Dcd21x6OtWnBPykwXd9BUT6dCFSFulHL3q+nCmMusjQovMFynP1nh/0qKSkRBBFzjobmbfdakGG4WMcQyhDA1Cz4ZfWXssG1SX11+S7aJRwYSGmPv8zL8dVQrG+Rhpq0QIKoTAY8t1HfgE5WWD6mE4LTCYfpn4q4zJ8G2XagwfYHpbLUmiXTUP1PIRXjDQBXv6Yprdffb8xPafB5i58ulM6Gh+VggfVzujd4K+wArwcGTEk3DdL3N8rMVmHMzXkLyLe4IgsG1kY+yhpfCjeOsFXzqff/451qxZg+3bt2Pr1q24c+cODh48CG9vb4wePdrs4zk5OSEoKIjxcXBwgJubG4KCdGaqadOmYfjw4fp9xowZg+fPn2Py5Mm4f/8+Nm3ahI0bN+Lbb7/Vt5kwYQKOHz+OJUuW4MGDB1iyZAlOnjxpMj/w+0Q9P1fO4BdzUClYLz7tWWdrDrLzNQw/NWpF/euwBnC2VWDpAJ2pnsobSWU8oGdDEGv+m9qtOpRywijB93iaH6nYSm3U5NW4UlnYKeW4Pr0jIuZ2Faz4ZYqfB9ZFxJwuDA04F1zCJEEQ+LZLNX2E99h2AVgztL7RQMOedDeOYAY0iYF9zH7B5dEysByGNfVH66ruqENL3WSOwpceJcx1HmvB5RZAR3TEOu23UVomjUZcABH7PKnZ+ZjWvQYCPBwxj8MUD+ju8axeNbGgb5Co1Ep87hZczzjVl+ZVyuHoxNZoXkW3YKrl48K4n+bAdf/83Rz070hd1nEVchlnflgxvsE+Jt4ZU9C7SrlhbBvVGOUcbfTpFR1VCuz9shm2jWyMeb2DcObbtrg0tT3GchQpYWvkzaFffV/essYUO0Y3xRdtKnNm6DBFNVoBliruDrg9qzMjxSX9uXR3UhkFJf7NEmjYd7l3PUM2GK57R7/WQmW66YF7psZ4eiachrQc22IFOXY7KkD7A1b/2M80l5sH33snVPiG4sdeOsUJ3aWK/U6wj0/lXOcbL9tW4/DzJQ2WVEuUEzofX34XUoLgXt5Ys+SyNbFIL37//n2jPLvdu3fH2rVrsWrVKqt0jE1sbCyio6P1f1eqVAmHDx/GpEmTsGbNGvj4+GDlypXo37+/vk3z5s2xc+dOzJgxAzNnzkSVKlWwa9cuNGnCn4fyfaN8EaQkYWh8WSNMnlrLGHSol7eBf1mE/dhZv4Jd0r8Oqng802dcmNc7CGqNFsObV2SscrsF8U8YLQLK4c7sLkbCKT0vZr7AhEXXOlOn/OuzJsjK1cCFI+rfXAiCEBS85/auhbkH7mFWr5r4/I+bAMQP7IZzMP+2ZJFDH2C3jmxsFHjCDtpytVciJYvpR81FQ3/2AG9210TRoYYHcvI12Hmd21/fkhyllNUhr+D5EXNb6IuCkMgkDG9WEScntzFqV93LCQ/i0tGnnk7TOdQMiwAXXDmKha51o4pl0S3IC/5mahf50jBSbPykEcZtv4VrkUnY8il/WzExcanZpp8vIejT9OCCQOGmld1wfXpHxqTewN+gWRTybW1YsQxehmVbLJCbsmI1q+KGZhzuHmLoGuSFzZ80wrGIOHzXpZqRwET/vc0qu6GKuwOevtb5nw9r6m/kWscWemb0qIl/w14B4A6+o7f/oK4PsvM0aBlg/FuCyrtg6YA68HaxNal5blrJDd1reyHA3RFDmvhj3y2dG43Y4ZGtkPl3bAv8F/YKw1mLZLYAzuVDW5hhq3e98mgV6M6ZRQbgHq+/7hCI9By1/rkVonNNTxy/F4/RrSujRx1vHLj9CsObVUT9eTpLODtdnlgyc9VG27iOJDadZnFjkeDbpEkTrF+/HkuWLGFsr127NkJDjes8W8LZs2cZf2/ZssWoTZs2bXDr1i3B4wwYMAADBgywSp/eRdjV4KyBgmF6Mv6e/rLRB0X6S1LGwYbhp+jlYovNtMmynKMNEjPyTKZUMaWRFRIA3BxV+OuzJrBVyvX9VMplcLEvnmQow5tVxKCGfoXSKrO1mXIZgRYBbkYZFYSgT5RcjwvzewKOKoVJwffk5DZIzWYGwlE5Vq2Np7MtxncI5BV8xQYA0ic9qkw2FXTG5acodB61QCaOXV80Q1hMipHbiqXYKuU4MqEVJuwMxaN4Xb5ZQX9lGWEyuJKinp9rwTlkeq0xH+UcVdj5ubE5lI0pCwgAZBayOAb959OfO0urUU3pWh22CjkGNzYWRsRYFLhiHawFQRBoV93DyDWGgv5OK+TMdI9i0vrR01ma0vja28ixgcfqJJcRGNRQXLYimYzA2qEcz6hIzQBb8PV3c8B4DqsKW3DjymAkxJ4xzTBw/RXGth51mK6FXC55n7WshG1XnxvlbAd0KTeF3k/647ZqSDAexqUjyMcFMhmBiSzrJ1+xLIqutbxwNCLOqCgMV9abp7Rc1hSFya9elFgk+K5duxZNmzZFfHw8Jk+ejKCgIOTl5eGnn36Cg8P7VdP7baJPPR/sL1iZU1ij4ABX5gYKtqmjZx1vs7WWXJz5ti1y1VqLfDrpY7OpF7OFlYQPS2ELveZeOvbtlcsI+JWxB2CG4EswBVs29Oh9GUEgyMcFL5KFs74EeDgi/EWK/u8v2lS2mqvDycmt0XHZeQC6DCNVPZ0Qm2roT1kHG0b2CbHnVSnk+H14Q7xOzxXtZ06Hfu3Y2S/ouNgprZ7OqYa3M77vUh2fFQTxWKuAg6u9DUJndirU4ozOJ80rFqqEuVgY/qQW3Es2Pq52WDKAu9qomEvN9QxyZTAoCpjjtYyxwOtU09iiRteW/zGqMUNQ5srd7ESzODixihHRlSBCr2E1TyejnPBcsKta8tG/gS9uRafwFkWhYAeG3n2ZatTGXsX/7DeqWBa2ShkjANyU6xUAzOhZE992qWbRe0WfE1UKOerQArApto9uguuRyehTT9hPfn7fILSr7o7OrOeAS/PN5d9dWn18LRJ8a9SogZCQEIwbNw716tWDUqmEVquFQqHAxo0brd1HCSvxv4F1jQTfuLTCp6VjV5GiP+x1fF3RsYYn3Bxs0KpqObSr5oFlJx4V+pxOtkqY9qDihh48U9gk+8WNuYsGI40vQSDXTK0FXXjmmsTDXxgmA7mMwMJ+tVG+jJ2+itjGEQ0xausNo/3oz8nNqGSz+iQEPVjx4wI3AfptPjW5DYLnGYJezRmbqUCYvTdfmN0v+nmM/OILiV9Z09py+n205nxURmSuaFO0qeqO2R8Yl//morC+g3Thraj9EMUsMtguT7N71TQKaCwq6O4JSpbGl0twof8cJ5YbDVc2iKqejlg6oA40WlIwS4XQdZrUqaqgm9avwxogNDpF9KLpo0YVUNXTyaTrFzuYNpDmt9u/vi9Co5MZae+4oAu964bWN3Lx4sPSxWR5Vzss7FvbaJFBp3mVciYtNIDOSkNl56DDlTWDS9tfShW+lgm+AFC9enWcPHkS0dHRCAsLg0wmQ4MGDSxKZyZRPHClNarLsRo0l89bV8HFJ29wOyYFANM8JJcRRqatiFfGq+bixJzcsqUNrip5QrDnLZmMwP1YQzWdk5Nbm9Sas10ZhM9HoKyDDSPbRYcanoha3AP/hr3EhJ1hGNtOF9RGN+9mcPiMWQMqVR09V7WdjRxVPR31Zn9xRT+ZWDKg0zMMlMSEQL931hJWrQlXOW8220Y2xpS94ZgjUkDmg379rV2+2OhcYtoQBB7M64qD4bFoUqks/EyYoK0JPcxBIZdhUENf3HyuW4iaEnzZFjMuDx6C4HdhoMvJQrfhxL14owqidLrU8kIXjgDBxpXK4lpkktF2mYxgpnwTyaiWlfC/Yw8BAK2rlsPPZlZKM5kX20oMEVEO2xKWD66Hp68zOPOlc7lmFfW7ZSlmC77Pnz/Hw4cPUbt2bXh7e6NChQqoUMFwkV+9egUfH/7ITYnShTW0HVT+14pTDwEATFkOteYpHK0OPXpYqCBCaeKXwXXxb9grfNm2iunGNNg+i3IZAU9nWzyISwcgLpUb/Rkx9bQIZXXoXa882lR15xS0LXEdEOLilHaIfpOlD06i33OVQoZNnzRCyyVndBssOLUlAzo98Mnade7FCO/0PiuL0KfUXJb0r43jEfGiTMCtq7rjyrQOVj1/UVtjxT4qtko5o3x6cUG3gClkBCqVo1esNG7PVcgg0MMRjxMy0LSyecIk3WdWyM+5e23hrBd8bP20MUJjkjHk9xCL9mdD18IWtjLh24hQCsHK7o7oUN0Dpx4k6LeVVsHXrNFvx44dCAgIQNeuXVGlShX88ccfAHTC8OLFi9G4cWOGECxR+uHLc2kJ1MTeLUh4Vftjr5qwUcgwuVPha7oXFjFpokoDfYN9seXTxpwR+kJwuTrM7xOE+hVc9WmbzD2GEKZ8uuhCr4Yx4VpXEPMtY4/mNA2Eo0qBy1Pb48YMXdQ+PZezJYs/S8Zz5iKk+CcEdh7h0sLgRhWw8ZNGsOPJh1oU0DWNRT85l55rzQXd3UsplzELWnC8l/RfQ1277aObYv/YFgjmSE8nfG6D4Cs0dlhSihzQWXdMBXBZitgUdoXJbf628dvwhtj7pSGAtZS6+Jqn8Z03bx7Gjx+PUaNG4YcffsCXX36pz48bEBCATp06YcaMGUXVV4kiwJrO5wfGt8TN58m89cIpgsq7IGJOl0JV+Sos20Y2xtPXGaL9rd5W2LdXKSfgV9Ye+74yrtDGB/0ZMZX+zhwhooyDQYjnq7JnTeiRyK72NvixZ03IZYTokqp0LBWWPm9dGTtCovXuHtZCTHfY0fvvM3Rf1KIXfEu3ZvBFsqFyp1xGMBaCXMG/9AUcNTa4O6ksEk6FYizK2CuRXJAdpjDzVHlXO/So441DZlZ0NIXY+JCg8s54mVL4WJq3AbmMQBDNj9uS4PPiwKwR/+nTp5gwYQL8/f2xZs0aVKhQAVeuXMGdO3dQo0bxRKBKWBeuFCSW4uaoQmcTidgpSlLoBXQm09ZWjpwvjXi52KJCWXtEJ2Vh+2dNLNLw0wUDUwEX2fni00wx0peVgBw2smUli/dlC0tcPm9c/NC9BqZ0rW71aGdzhbfS5OpQEtAFX6FKZO8DbM0lQ+PLWfzE8H+u6P7CnJvOtpFN0Gv1RQCFswgRBIE1Q+rjUPghi/ZvXsUNl58aZ8FxcxQn1JVWc39RoVLIsfPzpsjO11isqS9qzBJ88/PzYWenm6x8fX1hZ2eHn376SRJ63yJGt6qE3y9E6v9uVfXt8HGVsAyVQo7T37QBCcsXG1qSaQoVQihNl+A53jJ/Obbcas4iqihS/Igx59LTDb3vGl96CeWitjZYI31jUZLPCrqQi9D4ftayElKz81HRrXBuBEKCs5hMJcXB78Mb4szDBH1Gl1+HNUBYTAo6cZQv5uI9k3sBiAtULUnMtvFt374dXbt2RfXq1SGTyVCmzLttKn7X+KF7DfiWsces/yIAAJVpgQwS7yaF9eOmFxTgq0tPMbVbdcHv+eBKg1SasbTQgbVZ9VEw1p59ih+6m1Y+aLTiFzDvOi52SqwbWh8yGWG1HMRvK+wgLbqrA5+idQYta0th+LCRHw6Fx3JW4KSPWyU5PjioFOhZxxCwz5dBgg9LssZIFC1mCb4tW7bErFmz8M0336BMmTLIycnBihUr0Lx5cwQFBaFq1apQKCzOkCZRDBAEwRBk3nfNj4RpVAq5LigMpoVoSwM53jKFr1F1sZKSg3vV9UGvuuKy6NBlh6LOXfs2UFyppYozNZklfNS4Av4Ne6UXPu1pPu9FvSjwd3PA+e/bcX5Hz238lq2LGfgXUisuYX3MklLPn9dVQ3r8+DFu3ryJW7du4ebNm9i2bRtSUlKgVCpRrVo1hIeHF0lnJawD3XxVlKUyJd4dqBK9prBUE1pafcH4qOvrAhuFTJ+O6W3w42tQsQy8XWxRqZxDqa2o9C6x/bMm2B/2EpM7l3z2GiGaVnbDycmt4VtGJ6CVd7XDrF41YaeUm51FxprYyGVoFVgOiRl5CPR8ey2T49oHICNXLZiHWKJ4sUg9GxgYiMDAQHz44Yf6bZGRkbhx4wZCQ0Ot1jmJoqGmjzOcbBVwVClKjR+VxPvJ4n61sflSVKlIbWcOBEGgeRU3nH34Wvd3CfdHDM62Slyc0h4yovS4arzLNA8ox0ipV5ph5/P+tIXlgZ/WgiAIbBvZWP//wrL+4/qYuCsMywcLV1qzNvY2CsztHVSs55QQxmp+CZUqVUKlSpUwcOBAax1SoojwcbXD9ekdIZcR772vn4R1MXd6+rBxBXzY+O3M/U23nLwtcqSk6ZV4m7DmAq1rkDcianpJ74CE9QRfibeL9z2gQ6JoeFsEQGvwOiNX///EDMuyWUhISBQfktArAZhZua2oWLduHerUqQNnZ2c4OzujWbNmOHLkCG/7Tz75BARBGH1q1TLUb9+yZQtnm5ycnOL4SRIS7w11fQ0Jy4ujEEVpgV5uVZpOJSQkJN4OSoXG19fXF4sXL0ZAQAAAYOvWrejduzdCQ0MZwizFihUrsHjxYv3farUadevWNXKzcHZ2xsOHDxnbbG2Z0dgSEhKFY9cXzbDjWjRaBbpbtQR2aaealxMexKUDAAY18ivh3khISEhIiKFUCL69evVi/L1gwQKsW7cOV69e5RR8XVxc4OJi0DLt378fycnJ+PTTTxntCIKAl5cUSSkhUZTYKuWlIhimuPmhew1U83JC60B3VHF/e6POJSQkJN4nSoXgS0ej0WDPnj3IzMxEs2bNRO2zceNGdOzYEf7+/oztGRkZ8Pf3h0ajQb169TBv3jwEBwtHdObm5iI31+C7l5qaCgBIS0sz85dISEi8y9gB+Li+BwBpfJCQkJAoaahxmDSV+JksJYSHh5MODg6kXC4nXVxcyEOHDona79WrV6RcLid37drF2H7lyhXyjz/+IMPCwsjz58+T/fv3J+3s7MhHjx4JHm/WrFkkAOkjfaSP9JE+0kf6SB/p85Z9YmJiBOU8giRLR02UvLw8REdHIyUlBXv37sWGDRtw7tw51KwpXBpx0aJF+Pnnn/Hq1SvY2NjwttNqtahfvz5at26NlStX8rZja3y1Wi2SkpLg5uZWLLkv09LS4Ofnh5iYGDg7Oxf5+d4WpOvCj3RtuJGuCz/SteFGui7cSNeFH+nacFMS14UkSaSnp8PHxwcygeJcpcbVwcbGRh/c1rBhQ1y/fh0rVqzAr7/+yrsPSZLYtGkThg0bJij0AoBMJkOjRo3w+PFjwXYqlQoqFbOKlKurq7gfYUWoDBcSTKTrwo90bbiRrgs/0rXhRrou3EjXhR/p2nBT3NeFHv/FR6kNwSZJkqF55eLcuXN48uQJRo0aJep4YWFh8PYunvrsEhISEhISEhISpYtSofH94Ycf0K1bN/j5+SE9PR07d+7E2bNncfToUQDAtGnT8PLlS2zbto2x38aNG9GkSRMEBRmXA5wzZw6aNm2KwMBApKWlYeXKlQgLC8OaNWuK5TdJSEhISEhISEiULkqF4BsfH49hw4YhNjYWLi4uqFOnDo4ePYpOnToBAGJjYxEdHc3YJzU1FXv37sWKFSs4j5mSkoLPP/8ccXFxcHFxQXBwMM6fP4/GjRsX+e8pDCqVCrNmzTJyt3jfka4LP9K14Ua6LvxI14Yb6bpwI10XfqRrw01pvi6lJrhNQkJCQkJCQkJCoigptT6+EhISEhISEhISEtZEEnwlJCQkJCQkJCTeCyTBV0JCQkJCQkJC4r1AEnwlJCQkJCQkJCTeC0qV4Lt27VpUqlQJtra2aNCgAS5cuMDb9pNPPgFBEEafWrVq6dts2bKFs01OTk5x/BwJCQkJCQkJCYlSRKkRfHft2oWJEydi+vTpCA0NRatWrdCtWzejNGYUK1asQGxsrP4TExODsmXLYuDAgYx2zs7OjHaxsbGwtbUtjp8kISEhISEhISFRiig16cyaNGmC+vXrY926dfptNWrUQJ8+fbBo0SKT++/fvx/9+vVDZGQk/P39Aeg0vhMnTkRKSorF/dJqtXj16hWcnJxAEITFx5GQkJCQkJCQkCgaSJJEeno6fHx8IJPx63VLRQGLvLw83Lx5E1OnTmVs79y5My5fvizqGBs3bkTHjh31Qi9FRkYG/P39odFoUK9ePcybNw/BwcG8x8nNzWWUSn758iVq1qxpxq+RkJCQkJCQkJAoCWJiYuDr68v7fakQfBMTE6HRaODp6cnY7unpibi4OJP7x8bG4siRI9i+fTtje/Xq1bFlyxbUrl0baWlpWLFiBVq0aIHbt28jMDCQ81iLFi3CnDlzjLbHxMTA2dnZjF9VetFqSey99QL2NnL0qONT0t2RkJCQkJCQkCgUaWlp8PPzg5OTk2C7UiH4UrBdCUiSFOVesGXLFri6uqJPnz6M7U2bNkXTpk31f7do0QL169fHqlWrsHLlSs5jTZs2DZMnT9b/TV1IZ2fnd0bwvRaZhHnHowAAzWv4wd/NoWQ7JCEhISEhISFhBUzJjaVC8C1XrhzkcrmRdjchIcFIC8yGJEls2rQJw4YNg42NjWBbmUyGRo0a4fHjx7xtVCpVqawtbU1SsvL0/0/Nzi/BnkhISEhISEhIFB+lIquDjY0NGjRogBMnTjC2nzhxAs2bNxfc99y5c3jy5AlGjRpl8jwkSSIsLAze3t6F6u/bjkZriGc89/B1CfZEQkJCQkJCQqL4KBWCLwBMnjwZGzZswKZNm3D//n1MmjQJ0dHRGDNmDACdC8Lw4cON9tu4cSOaNGmCoKAgo+/mzJmDY8eO4dmzZwgLC8OoUaMQFhamP+b7Sj5N8P3t/LMS7ImEhISEhISERPFRKlwdAGDw4MF48+YN5s6di9jYWAQFBeHw4cP6LA2xsbFGOX1TU1Oxd+9erFixgvOYKSkp+PzzzxEXFwcXFxcEBwfj/PnzaNy4cZH/ntKMWqPV/z9fqxVoKSEhISEhISHx7lBq8viWVtLS0uDi4oLU1NR3Jrit9uxjSM9RAwA61vDAhhGNSrhHEhISEhISEhKWI1ZeKzWuDhLFQ2Ripl7oBYC6vq4l1xkJCQkJCQkJiWJEtKvDf//9Z/bBO3XqBDs7O7P3kyga4tNy0O6ns4xtGknhLyEhISEhIfGeIFrwZefINQVBEHj8+DEqV65sbp8kiojY1ByjbVqtJPhKSEhISEhIvB+Y5eoQFxcHrVYr6mNvb19UfZawkGn77hhtkzS+EhISEhISEu8LogXfESNGmOW28PHHH78zwWDvCvdj04y2aaSkDhISEhIlikZLYv7Be/gn9EWRHJ9etEhC4n1HtOC7efNmk/WP6axbtw7lypWzqFMSxYdaknwlJCTeEk7ei8eiw/cZRXjeBc48SMCGi5GYtOu21Y/9y4lHqDf3BPbeLBqhWkLibcPiPL45OTkIDw9HQkICtKxcsB988EGhOyZRPIS/SC3pLkhIvJfkqjWYe+AegiuUwYAGviXdnbeCz7bdAADU9HFG73rlkZSZh8jETNSv4AqCIEq4d5YTn24cf2EtVpx6DACYsf8u+kvPmYSEZYLv0aNHMXz4cCQmJhp9RxAENBpNoTsmUTz4lrFe1g2NlsSrlGz4lZX8uyUkTHH0bhz+ConGXyHRkuBrJvdj09G7HtB66Rlk5Kqx5dNGaFvNo6S7ZRFxqTlQa94tDXZp5MzDBMz45y6WDqiDFgGSNVos+RotlPJ3K/OtRb9m3LhxGDhwIGJjY42C2iSh9+1iX+hL5ORb55598cdNtFp6BkfvxlrleBIS7xpxqTk48yABJEniTYbkd2kp6889BQBk5Opykp99+LokuwMAeBiXjopTD6HO7GOi9/kn9AWaLjqFWf9FFGHPJABg8q4wvEzJxnd7rO9O8q6y/txT1PzxKPbciCnprlgViwTfhIQETJ48GZ6entbuj0QJsO/WS6sc5+T9eADA7xcirXI8CYl3jeaLT+HTLddx+E4c3mLLfKkjNTu/pLuA0QVuGGk5amTniVMmcPn0pmaV/G95F0kuuK6vONJ6FpacfA1+/PcuHsQZB5C/zSw+8gD5GhILDt8v6a5YFYsE3wEDBuDs2bNW7opEUeNgIwcAOKkUqF/BVb89Ps26A4FWSpEmIcEJFZN14XHJayjfds4+TND//59Q6yzeC0N0UpZVjvM8KdMqx2FDQhqXi4omC09h25Xn6Lr8Qkl3pUhIeccWYxb5+K5evRoDBw7EhQsXULt2bSiVSsb3X3/9tVU6J2FdqOCPA+NbwkYhQ/PFpwEAVTwcBfe78yIVdjZyBJhoRyEVxXi/uB+bhs2XIjGxY1X4uEqVGsUgLQ4LzyebrxfJcc8+TMD3f4fjx1410bOOj6h9rjx9w/i7MPnRi+rR0IpM4JOZq8bg367Ay9kWvw9vWKJBg7lqDbRawK5AaVNaKQ0WBwnxWCT4bt++HceOHYOdnR3Onj3LeDEIgpAE31IKlQJILiPg42qHJpXKIiQyCTKBce1NRi56rb4IAIha3AMAQJIkRm+7AXsbBVZ+FGy0jyT3vl90W6HTcjxJyMC+r1qUcG90z2e+hoSNovQGZLzvcm9MUhac7ZRwsVOablzApSfGwdRFASVQT//nrmjBd/ERpim4MOnWQiLfYNq+O5j9QS00rlTW4uOwyROZujLiVRruvtR9kjLz4OaoMmrzxR83kJyVj52jm0ImNIEUApIk0WPlRaRk5ePC9+1KvfBrTfI1WqRl53Nee4nCY9HMMGPGDMydOxepqamIiopCZGSk/vPs2TNr91HCSlCDsUKuG6hkBQsWoUmYq8zxy5RsnLyfgP9uv+L0ZSvKHJtaLYnnbzJBvu+SgxksPfoAcw/c01+ztJx8rD79GE9fZ1j1PA/j0q16PEuZuCsMVWccQWJGbkl3hRcSAF1ceJ+e50fx6Wi19AzqzjmOhSJ9BzVaEkM3hBRxz5iYo8VLYhWIKIzVa+HhB7gXm4bv/i6ZICw1TTXMpbkmSRLHIuJxLTIJzxKtO4bQycrT4ElCBhIzcq3mRmIJSZl5OHo31mpB4GLoteoiGsw/icjEonF7ed+xSPDNy8vD4MGDIZOVXo2KBBOSJJFfMKDJCwReSlEv1uxKDeYymoY/T22sRbCWGZdr8pi+/y7a/O8stl15bpVzFJZ/w17i3qvSG9DwKiUba88+xaZLkXicoJuk1px+gp+OP8K3Vo5uzhQZ0FOU3HuVhn/DXgEAVp9+wvju8J1YHAovuYwjb2iCONuv/n2ykoRGJ+v//9v5Z7gdk2Jyn9nFlPUgK0+t/39VT3GuXQBzTASsUwr++ZuSEfboLhFc7hH0Z7XnqotYdvxhkfQjPcdwL5TyknO3GPvXLYz58xY2Xiy+oO0HBUqEw3dKT4akfmsvodaPR/WZVN5mLJJcR4wYgV27dlm7LxJFSL6G1Gt2VQqdyYg9WJuCGsyTMg3ajVyO9HVcGt9d16PRbNEpnH/0GmvPPsGNqCTBc0W8SkWdOcex4QLTgrDjWjQA4OdCDrZ3X6bi6rM3phsKcOlJIibsDEP3laU3oCGXtjChNBZH7sYBAEKjU0Qf5+bzJDxJMK3RLU6tCBcTdobq/0+fLDNz1fjqr1sYu/0W0nNMa/JuRSejyg+H8ft561mwMnMN1yaNpU0sjkpkGi2JWf/exaZinMC5cFQx3Rt6r7nE25YkSYzYdA1/XDW90DVXa34jKgnDNobg8pNEzDkQgQdxaVDT7kN5M/zV5ayxtLTGOZy4F2+07U1GLoZtDMG/YboAwVy14Tnl0/hS5ORrsZK1wLQWL1Oy9f8vSZ/4KwXzxP+OFY2AL0Quh2LJHMJfpGDc9luIsYLG/FZ0CjLzdNkr3nYs8vHVaDRYunQpjh07hjp16hgFty1btswqnZOwHnSXBMpXypTGd9+tF1hZUPUH0E2cSjkw54BB+5KWrYYHq5I1+2gz99/VT1zDN10DADjbKhA+uwtvfwetv4LMPA3mH7qPz1pVxuUniVh89AHvOcyl5yqd3/Klqe3NmuDo3I8tvZpeCg1NZZNfkCQ/38wy1TFJWei/7gpsFDI8mNtV0KevpOanPLUWu2/E6LXaAHAwPBbTe9TUf0+RlaeBk62wb+kP++5Ao9Wl8RndujJvO5IkERqTgkpuDijjYCN4TPp7lqvWMmIjimNiv/k8GVsLLCUjmleEvIh8M03B9fyRJMkZRJWclY9zj8RlwKg07TC+7hCIiR0CRfmdfr0jFK9Sc3Dhsc53OORZEnaMbqr//owZuYHZXdeQJNaceYI9N2KwZ0xzuDtZx1eT7zqJZdz2W3g4vxtj2+8XInHhcaL+OkzYGab/jkuALwmZ3swhy4hrkcKKluIiT601K/Zg5anHmNypqsXn+2C1blEZnZSF/8a11G/Xaklk5KnhbGIc5GLfrZdYNqiexX0qDVik8b1z5w6Cg4Mhk8lw9+5dhIaG6j9hYWFW7qKENcjK15knFDJC/+IRJnx8J+++jSiauY2anONoZtpMDrMHfRJ//iaTU1uTliNsLmGbzYdsCGGUV043sb9YWhRktnjbycxVIyXLuCACXYNFCRxcfttCUNqCPLXWpAbC3JRJqdn5eleRy08SMfjXKxa5jhwMf4UZ+5maiNjUHFx+koiEtByM3GrIACBmwSLWnHfmYQL6rb2MTzZfM9mW/l7kabQMYak4zIfZNG08W/jMydfgfmxasfgac7lHZfNYCtRmSjwrTz3G8Xtx6Lv2Ev4JfSHYlp3P9V5smtHzS41vuWqNoL+lkauDlsT/jj1E1JssrDljHY3o3psv0GD+SVw3YS0TIletxeUniUimWe3omlW60AtwWyIsSYuWlJnHcPURA/1ZVItNScEiNUs3vgz69YpF+1uT5ScfoeqMI7hFc/UpLiJfM5/dIRuuos7s43j+hv+ZFhoLtlyK5LTuJaTnMCwGpRXRgm94eDi0BQ/fmTNneD+nT78bgsS7BuUvpqKtNqmhmhrbcvI1eJ3OPzhRg2C+2vBCcL0az2gvWYLA8SSsR905x1Fv7gkjMz69FKqpsqj5Gi0+3XwNa848QZ5ai4k7Q7HnRgzjHpuagMyVm9r+7wy6r7yAa5FJmLArDCGRSZix/455BwG/P+SDuHRsuBjJcOsQ41bA1yY9Jx+Xnybqvz8eoTMd36YtyvigHzI3n1kGlO4+RJ3n1P14q7qOKGhaUHaE/4hN19BtxQX8d/uV1c7Hx4Fw43NwCcMAc+Emlun/3EVodAom7bqNndeizRKe2adLzc5HcmYeBq6/gnY/ncV5Hu0zW3sen2YY94SyKZSxF69x+2bPbSRl5mHWv6b9nYXceYZsCEGX5ef1fwu5z16LTGL4PQPmv+MJ6TlosvAkGi88hRfJzPc0PScfj+PTcf7Rawz+9QoS0g2LEfo7EZvCXKRsvhSJJgtP4u5L4ffus23XS4UrWsizN1h+Umc9nXPgXgn3Brj6TLd4Esp/HSGggJh94B5m7r+LrDy1ftEe/SYLjRecQoefz1m3s0WAaME3ODgYiYk6U0jlypXx5k3h/CMlihdq8KJrUqmxWpf+SYvqM4+i6aJTvIMJJfPE0wYnSosVwuMvW0LWVLMwR8v1KD4dpx8Y+8mVNJSA8Cie6YdLF+BMRUYfuRuHMw9f43/HHmLPzRjsD3uF7/4OZxzDEl/UZ68zsPtGDKfZlKqmdPJ+vH7RFSYi2IkNn4AolxFItGDxxSdwfbwhBEN+D8H2a9G4FZ2MndfFl/KkP2fZ+RqGBpjt6jDr3wiM2noDv5nwMX6RnIXVpx8zNHh/Xn2OZotOGfmw05WSbEEzpMAU/FdItLgfYyEZuWq9SZ0Or+BrYrHGxRvatZi67w4OFQQIabUkLj9NNFpk0GGPBe1/PovgeSf01qY9N7m1yGz3A7rgKTS8tKvuwfsdn1DMFh7Z/HLiEWrPPi7oy01XSAi5hXy/NxxzWYIa1+9Ra7SMZ5BO9Jss5GtIaLSk0QJ1+KZr6PTLeQzfdA0hkUlovOAUDhQsvj7/46a+3WfbbiCatu+cA/cQn5ard1nj43qU9bWrR+/GYcnRB4zxrJyjsZsTSZIYueU6Kk49hMG/XdVvfxwvHCthjcp9+RqtqDLDQs+mqawme26+QKdl59H2f2eRk6/RV259kZwtuF9pQLTg6+rqishI3YsUFRWl1/5KvB1Q/p0N/Mvot9FdHcJfpADQCTZ8gWNpBYM5/WWhJoqDrGh5KpWUuf5oe27EoOLUQ4xtdFOcGE7ci0fzRadwkiOQgwuxwtzj+HT0WnURI7fcQMQr0xo+OuYI1yRJIitPjYk7QzFsY4jgRM0mnyUo0AW4M7RKVxSTd4fpAwhzaIuixHTDOSm/bPbxOPtO+w0UXZafx/d/h2PHdX6hiu5+YYkP4ZME7rRKKoXMyKeOfit0kzJHZhJaJ+gmWkqz+2/oSyw58sBoPyHovyvp/+2dd1gUVxeHf7PLUqVKFxDEgkpRQRF77z2xRGOLJqbYS4yJLTFGE2tiiSaxpFpiyZdEY8fewYIdC4oUsVClLbvz/bHsMjM7Mzu7LEW57/P4yM7cnZm9c8u5557yspD1mdsGdxVrYrjOnVwGrjuji9Ch1WzO/usaUjLzsZj7fIxbCNl5S+2txppmqNU0Pt0dh+7fHuc9L2RCY44wVheLhZ/911Mx9Mdz+Oj3WOHn5LS9fCX7uYTqx8+F7SfA7u7CDVpMsE8XEICE+kdyRh6mbruMb4v9Mpg+EWJwHfO4cBd3fPbo436NQbOv+MNvMZ+X+10+B9sJWy7pHQM0ixBzkf6yEB/vuMIyG6FpGseKna/F5oT3f4vB90fv4cPfYzHnr2uCi+7vj93DkVv6Y24uY5wtLFIj+naarj+tPhKPsC8OSPoNL14W4ufTCboFx/dH78H/kz24lpSJ0ZsuYMaOq5KuI4SUeTEpIw/PcgqQllXAWlj/73KSzhG9MiJZ8H3jjTfQtm1bBAQEgKIoREREoFatWrz/CJUP7UTH3O7UaXxBsyae6NtPeQewAd+f1ovGoKY1ggHXjlerwREbVFceuqN37LO/9D1G+exw+VbYWmbsuILkzHyM3xKLY3ee6tkccYVQsdBDLwuKcOruM5x/8AKdVxzX1ZMxdqiJL3IR8eUhlqOgEDEP09Hoi4N44/sz+OtyMk7EP0OTBQcl34s7WDG1MAdvPNH77btik/DlnuJYqoxXJeRsxbz+jeQslhYGAJ5mF+BCwgs0+uIgdsVqtGNaYfzwzTTeZwRgklaWibsDv/PQJ7vi9LSJ2t926MYTtFkSjf5r9aMKMAX8Kdv1w75ZyCmdllQqzDrtEOTOiuxA0/xaa0NOWtoFw+FbaWi+6AgrdvHzl+w6ZbZzoRSk2u56+t4zQU/w74/eQ/C8/Vh/7J7oszH5Ny4Ff5x7hMQX/IvY1t9E89obLtnPL7z9OCJC8r1/PfsQajWNbcUasDMi0VwM2a8mCyzCvRw5gi/jOmJrXlNiXwsJJEv239YtmABhLToXY6P78N398K00KFW0LjIEoNEC/3j8Pit8nbYuUjPzRSP78P1GbZ80ZCO78tAd/H5OPArIgj03sP3iYwxcp7H9PXTjCQJm7cXIjefxzb7bgiYtTPZdT8WvZx9i6/lHeJajr5z4Zp94FIisfCV+Pp2A0ZsuYGHxGLz0gP6cyOVaUiYmbLmEQevPYN7f1/H+bxrN+NfFC51eq07ipBmSvRjjcMs1gZu09TJm7YozWmlVXkiO6vDDDz9gwIABuHv3LiZOnIh3330X9vb2hr9IqBRoNQts7ZdmwFPT0BvN/r6sb4f3NLsAb65jOwmo1LSeUxFQMoFyJ18mKw/FY3KnEo/VgiKV5ME6X6nGqbvP0LxWdT37Ou2knq9UY+TG85jepS7Gd6ijO891pBHbvHjv14s4dVd/ojRGKfnN/tt4/rIQyw/ewcSOdUTLDlx3Gmpaf5uJpjWLE2uFePYi7hRmwTHgyxbR1DFLZgnYCO6MfYyaLna4nJiOH0/ob6V+s+8Wzt5/jsw8JaZuv4IBTXx0547cSsOlR+l468ez+LhrENrUddOds1KULia4mHZiF8eOTTug/1CsTb2WlIUilRoWDJtbZv3zTYJ8GuZdsY9x6VEG5vZugPSXhVhx6A6GRdZEcA1H1n0BTV3U8ywZPz/bHYcrjzPx36TWqO/loDtuIZNBraYlRSl4llOAmIclQoFeiC1GFTGFbu7uRdzjTAz9UZMsQputkYl2gl303y2kZRfgenImJnWsi6jA6sLPJmFh03bJUQDAjS+6wtZSMzVx++qPIyLg62KD9JfGbQc3mLePpb1Ny+J38FxxUHxxevFhOv6LS0H3EC/Wca6QwIqFKyBAxD3OxG0D295a/mXYRQvJqULmZoYQWjQKISYQyShKl+Ai5uELvTFCTdMoKFKh+aLDovcQ6s8FRSoMWHuadeyfK8mQyyj0CPFC/JNsnS3tsMiavNcIruGgp7gY+8tF1metBpZpdiAU13m+CTa7R249wfu/xursv7ecf4RFA0IkfZdr3mHsApyJ2DxmjFmbVjnEJf1loclRk8oSo8KZdevWDQAQExODSZMmEcG3kvPd4XhcfZyJ1UMb6zoYn8ZXXbzFw2QFjzaWDzVN6+LCMknOyIOXow3m/CUt8Hz07TSM3nTBcMFicgqKMOync/isR33RcFOAZhXNFHy52/ViGl8+oRcAcvKLcInHFlWpUkNN07pYyYBxK2ehsWbytss4eOMJ9k1qA7/qtqBpGteSshDgZsd6p0rub+N8/k0kHirTLGXTqQTeMoa0GCfjn0Eu4i3Tv3jS+uLfG6hZ3VZ3/CgndNSN5CzU97KXbCrDNfEQI/rWU3QL9mJpwzONTA/Kp+GZWqwZDvKyx7n7L/D3lWRsOZ/ISPXNLm/HSMGqNaFYHX0Xa4Y20R1/WVCE5osOo1uwJ77oG8w6vvaoeLQA/QUew1ab8TA9vzvJ+I4a1xiC8Jf/3sDsXg0E76EN6n/2/llET2+HAFc71vl911Lg62LL91VBvtl3G1GB1dExyB3NAlxw54lmkeHnYovODTwAGNb6ceGaLAiZ7EjZnl1+8I5BwZfpJCjU/Q/eNGyKRdM01DQw/o+S7X+hXTTmwo2Jo41C1F6TmTba1lLO2ornfybhc+m5hbjyOFOnheTyLKcQzRaKC72A8Jj5y2n98UtrGnF1fhfRhb2Wa0lsoZfPJM7OStM3b6WWlNW2Q0P8ePw+ujb0FC3zzuaLoufNjVC9cOOJa/nj3CN8ulu6k/EcAYfLyhrhwSQ1y6ZNm4jQWwZcS8rEtO1XeE0ATGH5wTs4dPMJ9sal6CItMD3JteNnSkY+1psYqF9oEHzj+zNQq2nJWx0z/jTNHmlnrHjIIi00TWP4hnP46I9YPSer4Hn7DYY+4vLFvzdYWcBUahpZ+UpELTqC8AWHWDaUUr3Kud7TTP53ORm5hSrsKP69x+48Re/VJzFxyyXWpMbePqf1JvgdAs45qZn5ku07xbCQU1AxhNCFe4S1IWKZqXp8dwLDNxgOEQZo6k3MO5nLtouJevXAJ8hyOSVx+zA1Mx83eEKmcSdzPuGLAtt+Nk+pQlp2gV6mwiX7b2NNtLipATOyAPf+Wo0kty/cTMli7aD8ZESyi/ZLj7I+xz3OxPu/xWLID2d5BZm3mvnyXmfz6QSM+zUGf19JZtnAMu19rYyIhcpHabZg83kmc+7PYwm+jOPJGXm63RRmf1s+KIz3XmqG/4UWobWgkMAaGeDC/4ViWAtnCWOVmL/CplMJeFsktfRPJ+5LSgUtJPg+fCEcfosvtKYUxv5yUc8JW9c/TPA3WLj3JtosiTbpWUzF1DCEm08n6B3bFfvYoNBbi7PAFSKvsHL6gpkUzkwK169fR1HRq5/arjxJzczHztjHOueE0sAUtqZuv4J1xfZ4zPi3Wtsubp55Yzh8M00wcoNSQnspSYNs8iO6IDH/AACFtklEQVQA0KzMt55/JDgAPHj2Eifin2HP1RReR5op265g/t/XRQU1MZovOozQ+QfwLKcAOQVFSGU4a+2/Ls3JjrlNLYQ2IYXWu/zIrTTWxDVhyyVE30rDk6x8RC06guUcm7H7T/knjuaLDpvFkcjN3oqlXeAzh5DKybvPJE3EnZfzO0yJMf3PK6xkF8z78Jl5PHqei2EiEzqT5Ix8XlOIZE5IJiHHpiQJXtF8ExagcTJiwtRqM6MdFKnVmLr9Mlp9zbafd+PReidl5GHgutN464ezRmUk08ZLzs4v4l0gh9RwQt9G3oLfn7HjKmtxwLTrZ+6o+Dgbv5VaGs95vmFNLBqGmqZRWKTG1vOP0GLxEXRfeaI4EUVJmea1+M1EVGpaTwjMyi9C929P4Ot9t1ht1ZjQaEBJm6dZx/jfLzPSjyHNp5jj4y2JNs1CfUDMFE5ZRJu8eO/bqAbrs7bOufOJIWfTskYooocpYf+EmMrxaeBLFz000k/StSpremOjwpkZE8IsKioKjx5VXq++yohW00LTwjaWJ+KfIkSChlLIS3pkixK7J+3AqzIhZJCWjaceCK6KpayTEoodWox1sNCi3QrvtvIEPtkVh3+u8uc2Zz6j0OC5+XQCfjzxAPuupRjdYbnxj03JxCXFvjlHu3BhRgTg3Gv05gtYfeQuUrPyJdsQAjDLgsvcSAlnZQ4HCqbg277Y1pSJMRocoV0I7vYvn1Cfr1RDYMfaJEZvLjEfWspIuaqmaeyKTdJL4lBQpNLb/my5+AguJKTjzP3nLOGZD2YbZjp48fUHioJo2nCVmmYt4JnCri8jisIPw6U7umnhi3AiFa4wZMjkRK2mMe/va/hkl0aLlpSRB5WaZkfHEfouTfMuGm6mZOH7o/fwyc6SnTKhIVTo2tpFsBSZiWlX+s5m6SZppiI0josl0NEkhSmpBGO0oNwdI22dcOtG5whcAeQUFKHV1/zj0B6B+jIHjXydEFLsp6BFatbHrg09yuKRSo1kG1+apjFnzhzY2kqz1yosNF2LWFVhrtrin+QgvKYzsvKVeFlQpPMa1m7/Ttl2Bf0b+/BeBxDelu0WXGKbph0kzLlaZJKSaVggeVmg2Z4zNX0q14xAOMxYyW8UCygPAO//pgl3JLT9KAUpVZqWnY+UjHyE+ToBkLbNqI1xy7Sd4kZWkHqtskDIa99UlGo1bCDs0CeWcMUYitQ0MvOUeJqdb1C4Mxd82jVPRyvBRaBUJzcmzJjIzNitQguKZzmFopN7ngH7T6aAy5Q7HvLsJtA0UM/TAU+yhD3ouTbrWqws5Hi7uR+y8opQ38t4s7vSxCtOzsxHYZEaCjmFQpXaoN27iga2nGeHBHtZqGItrl8ImNqoafE4E3vjNP4V0bfSBDWxQvKf1hNfqhZ/7v+uoW8j73LR4gmZh4kpB/68mMiyvZaygyaETuNrQpa6skLM1GrytssmX3fzqQeo7+WASIFdh9xCFf4Z3wq1Pt0LAOjW0FNyrPXSpNcuSyQLvm3atMHt2+IdnElUVBRsbIzbglq7di2WLFmClJQUNGzYECtXrkTr1q15yx49ehTt27fXO37z5k0EBQXpPu/cuRNz5szBvXv3EBgYiIULF6J///5GPVd5wXREeuP70/iqf4jO1ub8px3h7mAt+Vrv/arvXDAiiu3lqm2SqjKKydxBQgYX7cBiav+I52wpC00gzMFfqlDI3fIxBo2mhsaPIltjWiePf8a3QoiPIwqN0LwznXX40jcbk1jBnAilnjWVmIR00SD/gzmpSJcPCjPpvRWpaIR9Li1+prnga4e/nX0kOLnnKVV49CLX6KQOVxIzkJGnRA0nG512fOLWSwa+xY8hzbeqeAEx9ucLLJvTOJ7Mdiqaxmc96sNCRvHGOwWACwxP8XdbB7DOfdlPmhd8WdD6myOwVsixf3Ibg2X5zF66rTzOil3NjTWtpbBILSlTGjMlN5cEgbS0tE6rKa09/XLmoZ6teVkhpIwRCicHAOuP30cPhuDLjUBkDNo6KSOdkEkYY2bER1p2PpotPKynhdVGpdgwMgKt67jpfS8jV8lacLcPcoOtpQX+xxP56VVBsuB79OjRMnwMYNu2bZg8eTLWrl2Lli1bYv369ejevTtu3LgBPz9he5Lbt2/DwaEk/I+bW8mLO3PmDAYPHowFCxagf//+2L17NwYNGoSTJ08iMjKyTH+PKfhygqAzDczjkjJRyFnBauzEpEuM3FBYsjLW+EpBu3VlqqkDF6HMSswtMqkh00oDTdM4fe85vtprOIj8uQfPEeLjiIc8sZP1rlv8PzPm61d7K277ray5mZrFEny5Ws/7nDob0MQHf11OlhSHk8mlRNO1Q6YilP55+0X+NtxlxXGTzDr6rtGPU8yNcmAuTsQ/w+XEDL2MWXE82SCz8pSo52mPDSMjEDBrL+/1qllbIC27AIMjfDGyhX9ZPLJJaB0HDWnAgRJbZyYpHBMTIUX+nSc5vN9ncj05U1A43n89VTC5y5OsfATXcKxUwp0WoYyFhlKDn3tgnoyyJ+48w62UbKw/Lj1WtRCLB4Qg9lE66ns5iKYr7rVKPLWytaV4KEtDaBUtQj4nY36+iLj5XfSOPy2ODb7zgyicvf8Cb4b7muxMV1kwKpxZWbJ8+XKMGTMGY8eOBQCsXLkS+/fvx/fff49FixYJfs/d3R1OTk6851auXInOnTtj1qxZAIBZs2bh2LFjWLlyJbZs2WL231BaGnpr7GhsCvVjTD5PS0dqZj6sigpRYKFx8lh15C4mNud3DrEpzIeaolCgsGIcywNelggKVgV5sCnMhzw3F1bKAlZZa2U+KIG2TVNAvsLapLJWygLIGJ3m7NVHaOpmBVul5lnyLIXLcmGVLSqETERzvZCxfTv91/O8day7rsJKp4K2LFJCrhae3ITK9l6s0R4ylzL5CksUFKk0toqFhbpnsMzPw6Wbifhp/zXYFAvl+QpL0JRGC6RQKWGh0ly3lZcH4u4kISX5GWwKNbaY91JVgEyuV5aPAgsF1MVlLVRFUKiEty0LLRRQmVBWrlbBskjYeUgpt0CR3EJS2XrOmndM0zT+u5KE+dtiMKVLXQxp6ovsgiLWe7S21bTfhf2CMXbjWeTnvERaFv8OQJFcDqVc4wxE0Wps2n8dNgJRNbhlrZXCphAqmRyFFsVORjQNG2WBrs9x25w6n2GmoS0rgFomQ1JGyedIdytdGl2+stoxgu++rLJ6Y4T0snz9fsrGU+jfuAaslfkGxwgfSzXw8qXe4p3Z7y3yKNgU5mNwA2dQucXmEnYMj/K8PECtFnxuY8YIo8oW9/vMPKXJYwSLly9hU5iv1+/tivKxeEcM+PZOtWV7fndSsN9P3XgKNjS732vL/nHkJjr6VYMs9yWr/l61MYJZ9uL9Z6JtmNmXZWoVrASu+/fpeKP6vVjZfnWdMKRhdeQWFuGbnfn8YwSAew/y9d6zSibXxRjPzlOK909j+r1A2Zt3U/THKaWm7YbXdEF4TRfB8QzQHyMqKxRdCUT3wsJC2Nra4s8//2SZIUyaNAmXL1/GsWP6W+ZaUwd/f3/k5+ejQYMGmD17Nsv8wc/PD1OmTMGUKVN0x1asWIGVK1fi4UP+LZuCggIUFJRMQFlZWfD19UVmZiZLs1xW+H+yBwlf9xI8f6RWBN4ZOF/3+cGqQSUTAoezvsEYMnSx7vONdcNhm8mv2briWQd9R67QfT75/TvwyeLffrxT3Q9dxq7VfT7w04eo+5zfZu6xgztafbBR9/l/P09BWCq/E9VzGweET/xD93nrH5+geaJ+cgwAyFVYocHUnQA0QfaPBDZFh/vCsRH9Z/6r+3vNX4vQ87a+FkxL/Sk7dJPg0j0r8OY14biTTSb8jhe2mgXLFwe+x4hLewTLtnp/Ax47emjius6YASxdKli28ztrEO+mMU2ZfPJ3TD4lvFDrM2I5rnppEoG8d24nPj26SbDskLe+wlm/UADA8Nh/seDgOsGyo9+ch+jApgCAN+MOYenelYJlP+z7CfYGtQIA9Lh1Emv/t1iw7PQek7EjpBMAoP29C9i043PBsr+/PQMv3x0HlRo49sN2bN3yqWDZZZ3GYNrBnzQfLlwAmjUTLLuy5VtY2WoYAKDO04c4uPEjwbLrmw3AovbvAAB8Mp/g5LoxgmV/adwTc7t8AABwyc1E7KphgmXPt+mNQVHjAGgmkZsr3hQsu6deS3zUb5buszFjxI3lb8BWQKjmjhEx3w1F9Tx+DWNZjRGoWRP+Q9boPoqNEXB1BZ4ytPnt2gE88wPAHiMAYOOf880+Ruyf3AZxnfubfYwAgFnRGzHu/C7BsmSM0DCn8/v4tYmmP4wuTMC8FeMFy37VbjR+iHwDABCacgd//zJVsGxlGCN2BHfET2Pm4o0mPvC1pNEtqrZg2bIcI5o/YoQ3c3MDnvHbG3PHCL4EOGVJVlYWHB0dDcprZvQfNp1nz55BpVLBw4Nte+Lh4YHUVP3kCADg5eWFH374ATt37sSuXbtQr149dOzYEcePl4Q2Sk1NNeqaALBo0SI4Ojrq/vn68seaLCuq2wmn4i0tldPMvOrx4NlL3pTQBH1uPcnGV3tv6bKFidGqjms5PJH5MEf4OELFUlkD9FdVbqaKm4VUUl8rUW6lZmPh3ptQVSJHu1edSqHxTU5ORo0aNXD69GlERUXpji9cuBC//vorbt0yPOkBQO/evUFRFP7++28AgKWlJX7++We89dZbujK///47xowZg/x8/m2Aitb4Jr7IRZcv/+M91y3YE3tvpOltUYxuWRMfd6vPKlt/zj69bYc57fwwpnVJlrNZO+PwV3FudSnbmFpMNXWo4WSDZ08z9MwXrBUync2hKaYOCYt7ot703WbfxgRMN3XgQ8h8QaxsmK8TbiQ81ZV1slXoUjJr4dvGFKKstjEbBLgiJkljS1hW25hiW5MAcOHz7qhmXxx1RqXC5sM38TXD435ih9ro0tATvVadNMs25g8jwtG6jhsGrz+jMzvg28a8/nlXyGQUhm84h4sM21ehLU8+zLGNyVvWzKYOWqSMEV/2C8Yb4T4ARcH/ixKnOWa/t7eyQHZBEfZObIUAt+KUsTymDlpafX0Yz3M0bcTe2gJp6hJrvrIwdQh0s0NiSoau37vaW+JZdiFvWUB/jJjbuwHeaqbxYQlfcBAvaLnRY4SUsnxjhPbeqw7HY+3Re3pl/5vUGr2XR1dKU4dPewTp/CekjhER/s744s3GaP/dacGyFjJK5/Ni7Bjh6+mE+09f6pXV9n8AmPNXHLZdSpXc75ljxDcDQjBvq7ATY1mOEbeXDSg5UGzqQNM0Gszdr1eWOUZUVo2vZBvfFi1aoFGjRmjUqBHCwsIQGhpqdNQGIVxdXSGXy/U0sWlpaXoaWzGaN2+O3377TffZ09PT6GtaWVnByqribFR8XWzRo3lt3lig2XIrVmMFNIP12gtP8PEbEVi45wZO33uOHe+3YA3iWtS2dqxJQ2ljw1sOYE9ahpBadv3wcL084wCQBwA8im5jbIW49WKusppBR4Ho6e30MlMJlZWCUq7QDapi2FtZsMpaWyqQVyTs5CD1ugBQxJgwSls21L9E8FXJ5MiT6IhhTFm1gbKWNox2KJdD4WjPat9qOztYOFTTa/M0JRPsB1yYZd09qwN2djiXVgDwfZ+ikGdpjUbLTuHq/K5Q2dgiz1LAOa24rFQqQ9nSjhFKaxu2EFuMtt/7V7dFWnYB8mgLyO3tATueUJqcOeipWoGC4jYyoKkfK2yZqWOEnaUcLwWc2O49fQkw+v0ztRx5lsIbqdwxwtrJXlcHk/s2YqV+NaYvm1I239IasLNDoTX/PODtaGPWMYJJqccIOzv+OU7kuiobWwR4O4uWZSphmEgZI2b3rI93Nl/UK0tVs9MtfOa/FYlf4xiOnEb0+493xfGPMwKUVb/XtlfK2O9VIiSbOvTt2xcZGRn49ttv0apVKzg4OKB+/foYMmQIFi9ejH379iElxbQgypaWlggPD8fBgwdZxw8ePIgWLVpIvs6lS5fg5VUSziQqKkrvmgcOHDDqmhUBN+yYlkMiud01obMe4HpyFt77ld+OrXVd9lZwabOlGUMjXydWumRzki2Q7MOcBLjaYaTAeylLynJr7pd3msFaUSmsncwCNySUtYX+BOhmb55F7Zf9glHPU1r82Kz8Ik0yhsroPl+BGKqNIjWtC/Eok9hMmSEhnW0tYW9Vev9tY96aseH8ujPiqg+P8jfqu6VFGxZPKJyZ1DqvCOQymdHzl5RY8QqBH+3hID5ujIiqKRiZiOm8aWq8eoJ5kdy0Z86ciT/++APXr1/H2bNn4eHhgcaNG8PKygq///47evToAR8fH6M0tEymTp2Kn376CRs3bsTNmzcxZcoUPHr0CO+//z4ATUSGESNG6MqvXLkSf/31F+Lj43H9+nXMmjULO3fuxPjxJYbtkyZNwoEDB/D111/j1q1b+Prrr3Ho0CFMnjzZpGcsL8J8nbBvMn/8YiGO3i5x+DgRr294/u2QRgjyZKv+KTNb/QrFopRRwO4PW5SZoM3d+jcHfAPdqJYBPCXLFu5gam9tXFpSMdrUdcOtBd3Rtq5+7EZjaRlY+exre4V5ITLARfdZqVLD1tKCdcxUhklM2aklM0+J84yYtAR2WMFPugfpnVcz0vVKFRgCteYQ0Iw7tlalCwEFCCeAMAfcEJPN/F1YKcjLEm1KeaH1mLlCTJYFchmwemgTI79j+PfIedLzAsIJX2Z1D8L2cVH4tEd9wfnPVGq7VzNcqJIR6lOS4e2nEcZnVCwvTHpT7733HtasWYM//vgDP//8M+Li4vDvv//Cy8sL7777rkkPMnjwYKxcuRJffPEFGjVqhOPHj2Pv3r2oWVOjZUtJSWGlQC4sLMT06dMRGhqK1q1b4+TJk9izZw8GDCixRWnRogW2bt2KTZs2ITQ0FJs3b8a2bdsqZQxfLlwh1RBC8T0PT2uLDSMj9HKRA/wr+m/eDDXqvlp6hXrh1hfdeM/1DvMGRVFGZ52SyrMc07J3jRKICzoowgfLBjbSO14Ri3Vu9jtDDlGmaHAXDSh9IoAAN/0t64rGykKOP95trvvsbKvZvnYxgwMpU4tjozAsXC00MdUpN2lDWbKwfzACXMvvPTK17++3DdQ7n5yZr8tuJ5cohDHHGIqidJkhpTC9S11E8WSvivB3xqc9guBgXXrtMbOt+FfXN93YNq457nzZHW81K3unaq3C4P7Tkji/c3s10P0tRVAMrlG2fi9CwqSvsy3sjXwfCRIcioV+8cAI/vcxoIkPmgW4wFohR2QAf+YzU9g0uikOTW2LWuXYH4WwFTFJ+Wd8K1grZPCvbouzszpiamdN5BDXalbo1KBypisGTBR8b968idBQtoDUo0cPrF27FufOnTP5YT788EMkJCSgoKAAMTExaNOmJCvO5s2bWUk0Pv74Y9y9exd5eXl48eIFTpw4gR49euhd880338StW7dQWFiImzdvsgTj14nMPH6tZ6BbNXSsL9QA9bv5wHAf/O+jltj6XonA8I4ETWdwDUdBwVY7aTE1CL1CvXjLtq9nvPZx2E+mtTmhgf2bN8NgxSNAejhYw7Va2UXd4EMoDakQP41oavQ9vJ2Mt9X/41324pGpadPC3GZmagLEMLeSifmO7Yqfx9PRvHZpv44RDpumhc9mXwqf9WxguJBEGnoLCymrhzbGsMiaiJ7ejnX82yGNzHZ/Ld+8GYqJHWqjW0NPyd+RumhmakvlMgq+Ljx2wQJQFIV1b4frLQRXDG6E99oE4sq8LhgsIABJZUyrAMzqHgQ3eyt82qO+3nmtgmAWz7nFZligMtEmidAK4/0aeaMzQ1iR0hdb1i7bnZ4O9fizNtZ2ryZ5MaQlgSe1OxehhFBC8xJTMJfLKMTO6YzZPfXfnSE2jGRrRy2LzQLNsWNRGiJqOmPvROHd5xAfR9xa0B1HZ7SHp6M12tZ1w5I3Q/HLO4bHxIrEJME3MjIS69bpx/YLCQnBpUumpcIk6HNypn5KZiGW7JeeTloL31xCURTCfJ102jEAiKwlvjW8Z2IrjGtTS/C8dtJi3m9ypzq8ZZn3lUquhOxJfHQLFp54+bb5rBVyHJnezqStyNEt/SWXrcmjCZKK1PSjxiCjgENT28KdoaFrEeiKuh5sYTd6ejv2ZMn420riNuCf46JY95GCtUImqrX2cdYI9q2Lw51N7lTXqOtz+Xt8S9bnCP/Sm06UB2KLj16h/Ilw+oTxH5fCD8PDsetDtj/FsRntMCjCF1O71NMTZvs1Er6XKRpfGQVYCmxd82kL1WoajrYKXZQFAHCwtoBrNU17pCiKd0Gs5dDUtvi8T0PR58tXqjCubSAufNYJXUQEfz77dCnt7NiMdqJjMR9a3wu/6nbwdbHF9nFRODiljaQ6FxsLQ2o4SjKjeqOJj+A5IeWEhVxmdMa5MJ72z9Uo8437dpZywR1YBad9udhZYmxr4+ofAMJrOrM+a3/38xzhSBJclpi4WyvGuLaB8DdC60xRFAZG+KKByCK7MmCS4Lt27VqsW7cOo0aNwtWrV6FWq5Gfn4+lS5fCjsdLl2AaPs62+LEM7WTExjXmOUNbuQ29HXUrZb5BzMmmOFMOy8ifv+lxNQh8W6AA0FNAYyyVuPldRDWdQgOug7UCH7bTPFNjPyfJ95vXuyHqe0kbDNrVdcPMbkF6WgApqMws+B6Y0gZHprVDbfdqODK9HVrVdsWX/YIBAMHFmQa1mt0AVzvWRPceYwKQav8W4e+C8591Mmob89aC7ixhhcuRae1weW5neDhoNL2ONgr4CWgCtUKOFq4QETe/C0J9nCQ/W1kTN78Ldn7QAnN6SdEMSxMetSZAAxrXAEVRugUDk7GtAkTNIgLd7NCloaee8FSzuvB3tKmpHW0U6FSfremTqvFlyiEURfH2Y9dqloid0xnHZ7AVCwUSUplzbXK1ONsqUNu9mqBjspajElNpW1rIsOqtxrrPrtUs4WRr2L7fWiHn1RaLoR0z7Iq3tJsFuKCOh700m1jO+w1wtcM3b4TC29EaSwaGSjIP044nfAjNUXIZBWc74/wdBvDMTdyFCp+p2OlZHeHIU/cL+gXD1pJ/nFrQV3NdOx4zgb/Ht8Q7LQPQkZGK3Ymj8HEo9uXgprYW4vLczugkuLNrOjkFZe84XhGYJPjWr18f586dQ2JiIho1agQbGxvY29tj48aNoumFCcbTWcRORmsjVsOErWpA3HnB3d4KFjIK9lYWLJvIQ1Pb4sGiHggS8GhfNihM71j3EI2Qyrwd34D4cbd66N+4xBa5R4gnr9MLAKwx0rGBi721At6O1uherPXlai7ENKfvtQ3EkjdDJRvvawe/N5rUkJSg5GWhCh+0CxQxURFGLaAGEbJnNkRdD3vdir+alQV+GxuJt5trJvfpXethUIQPfmDUA9Or/qP2JVmGhExxhDCnxYOlhUxvYhEigqN5mdKZrR0Wci5sY4SD4MmZ7fEdQ6gxRI8Qfs3guLa1YG+tQHhNZ4xpFWAwYoXUjYpPugfhtzGR+LK/RiCZ3bMBmvm7YF7vBmhXzw0TO9bBZz3rY93b4YLXKFRphEhjvNh7hnhhYf9g/DYmUq+epV7HQsbeerbgWWA/yymEQi6DH2dnRYpSuZpAlIj0YntZoa1yLcaYSvUO89ZtsX/cNUiSBlZbROg5AeD7YZqxs1axXb52zODWsaHfAugvSKICq2NQU1+cntURQZ4Okt6bDUM4HN68JqZ3KelzQt9XyCmjowRxrxXgaoe+jbx1i/V3Wgbwmmw52rDb4pROdZGwuCeGNxde5Axq6osv+wXjt7H6/kShPk6Y27sBqou0hVpG+EyseqsxnGwtze5cB0BnY/+6YXJNBQUF4fDhw0hISMCff/6J3bt34/79+xg6dKg5n4/Ag6VchnVvN8G+yW1w4bNO+GdCK5Ouk50vHGzcydYSxz9ujyPT27G2qeUyChRFGRUWSvt9OWsbUn9A6xHsJUmz82kPfmHYWCiKwvdvhyNhcU+c/6wTala3xdhWGntmMcVpNSsLDIzwRfVq0upA6308tnUtxMzpbLC8FM2TECoBwXde7wa48Fknk6/Lh7eTDb55MwxRgSVOHUyNKfNd1nUvWShxJwztYmdAk5JFj6uZwo5J4daCEqdMbsg/pobvr4/YJg5M5vVugOld6uLKvC4G71fDycYoW/bpXerxHucuZnqGiO+CSPXSt1bI0aqOq06bVc/THtvfj8LolgHYPLoZpnauC4qiUM/THs0Ett8TX+QZdU9As309LLImQnwc8XZztgZfqnkRU84N9naEBUMFrN1F+Kp/iVlMIEPAGCTBfveNcB8MaFwDU0TMZZqJRA0x1p5+7TCNucib4T6SxkatcFzfS18xMaFDbdz/qgfciyPWaCMVFAkIvlx2f9hCZ3vKvR+gCen1GUfbbCcxnNzuD1tgdEt/fNytHrIY85KQsG8hkyHfyNBx3N9H0zRsLS3w8zvNkLC4J+b2boC0bMOO0hYC5jNMrCzkeLt5TTT2cxYsw30epimSVNMwQLNAAqTtqhmrJBOKZvGqY5Lgq1KpsH79ekyZMgV//vkn7OzsEBUVxYqhSyg71g5rgm7BXrBWyOFmbwUXO0scnNJGb5vNkK0X1+avka8T67O3kw3c7K1YK39TIhs48pg6yGQU/pvUWidoAiUrf+2WXscgtsZzYsc6OPVJB7wrYEM1s1uJQOxazRJf9G2ILQyvfi3v8dSLi50ljs1oj9nFW8alTWj4eZ+GeKuZH2LndNZt4UpF6r2DPO3hxXHUErJF0y5WtINtFwketz8MF9boCfFpj/roEOSuZ6LD1LiOa1sLf7wbicgAFxya2haf9ayPOb0asJx9ZnWvryfszOiqLwAuENkmFWNKZ42N+cBwH5Zw6+Nsi6HFocq0k8Teia2xaVRTvf7BJNCtGsZ3qANHGwWWDQxDoJudoCMURVGoZmUhKiAxqeVWjdeshisUcNt1K47ZkIwSF8pMYX6fhqJmKabGLQ2v6cKyi5cqQDO3hut6VsOEDiW+BAemtMGxGe1YERNWDG6EN5r44MysDixHOO1zc20VazjZYPngRpgk4KMAaHajOnD6vDY8Ip9Dmxg2lnI08XOGTEbpCZ2zeHbD+Opp7bAm6NrQA2Nb1YKMoQV/9CIXlxMzREPGtQisDoWcwr7JrdHYz1mnyQc0Tp3M8F8fta+tJ+hyF2dCEQIa+zljXu+GsLdW4Epihu54ZC0XvQgXTrYKWMgo1PdygH91WzT1FxYumXCHVT7FBbOKhzT1xcL+po0vUuC+qwV9S+4lRdvORSGXoWtDDz17YSZDjQzD6GBT+kgmlRGTftWECROwY8cOdO7cGWvWrIFMJoNSqUSNGjXQqFEjXcpggnnY9WEL/HMlGR+0DUSeUsVrJ1fHwx6Dm/rilzMPdceEzAS0aG0eAY2X7LZx+kIiwK+pFeuY77cNxLpjJSkwtc5FXFOH+l4OmNk9CKfvPYelhUynLTw0tS2uJ2ehdfHEvWl0U+yLS8X7bWuxbKrquFdDfFpJ1IPuwZ74ep8mjeXxj9vD1tJCTwM6MNxH0uRjSq6BTvXdcehmGgBgpATTgsmd6mBHzGM8TmeHLDN062GRfsgpKMJ7bWrBydYSLRcf0Z3zq26Lf8a3grOdAq2+jtb77r5JrbH9YqKe7bStpRy5hSpQVMkEwZ28peBmb4WNo0oiS8zoWg//XEnGO638sfHUAwAajXmLQFe0YMT+HcNYAAEaE5/4hd0RMGsvqwzTiXPd25oFoCn0b+yDyIDq8CzuA8sHheFmShba1HFFYz8neDlYo2mxkNjA28EoZ403wn00qXgBNA1wwfQ/r+iVoSgK295rjjXRd7H0wB0AGifRnt/pZzYE+AUabh/kahO/6h+CNktK2oCKprHl3eYYuO40grwccOjGE0kaLjEaeDvgytwuqPXpXt7zzJ0hY82T2D4B0gSBqFrV8bDYe99KLkfzWi5oXssFlhZyeDpY69VZqI8Tlg1y0rvO3omt8evZBJbgLAZTk+Zmb4XJnergyK003bFzn5Z+t8WGIzSOaxuocT76ZI/uGO9OWogXejB2A5gLvX5rTol+9/exkSgoUvPaNreu44bryVm6z3xaeSuGk96IqJoY36E2mi08rFeOCVNAHdzUD71CvfG/y8k6J+b9k9tAJqMgA4VDU9tCRlF67W9sqwD8dPIB+7rFI+umUU3x/dF7vKE7mbHtF7/B7ywmFh3FGJjzL6CJ33/+044s08KP2gdiTfQ97ldZ32GyfrhG4XD0dhr2xqXgnVYB6LbyhO786Jb+kh3hW9V2RZcG0iOvvEqYJPju2rULv/76K7p27Yq///4bZ86cwbFjx/D555/r4u4SzEcTP2c0Edky0VKfm6DCwKqRaSP1zZuhrEGKCXM80263iV15Rtd6OsHXnaExZml8i/9WyGXYM7EV63ldq1mxnKTa13NHe56wNquGNtZ16n/Gt4K/qx1mdguCtUKmE5D17dZEHpwB08ZXzGlgTq8GWH0kHuveDkdkreo4duepwSw/w5vXxO/nHmJ0ywB0CHJHn9Wn2AUMSL6eDtaY0LFkQj48rS36rTmlc/AKEfHer+Nhzxsia8PIpvjj/CN80DYQrvaWUMhksDBDpr2P2tfW2fquHdYESpVasr0tt/1aK+RYPzwc436NAQBe+01jYAqKTMcXB2sFq35LQ48QT17BF9D8Pq1NoY1Cjobejmjo7cASJrTU97JHzMN01jFDwiC3euKf5EAuo7DrQ43JhpeDNZYdvCP1p4jch8KnPYLw1d5bumNaJ1ftblSRmpbs3KllYoc62FAsvEhVHDO3oS0tZKAoClvfizLqvoDGvOPLftLDh20azQ4jaM5EM3wwo+K0q+emS2AkpUvU9aiGUS38sfl0Auv4ywJ90zeKolhCr72VBbIZ5ZhCKt94wbRptlHI4W5vOJQg17/CzsoCbeq4Yd/1VABsgZHvnnsntkZ9L3s9wVcrmLcPchfchRNrZwemtMGt1GyzJPwBgJFR/jh59xl6Mxy13TnC8KSOdXElMRMn7+onpQL43xkAtKvnjnY8c6aQMx6XuPldyrwNVyQmCb45OTlo0EAzeSoUCsjlcnz00UcoLCxEcnKyWR+QIB1jE0QwJwmhVI0AW2DVDh5iAiRzQmYOEswBlPm3Kds6gGZb/9ynHZGUkacT9j5oxx8FQku/xvqJPPhgPtF3bzUSLDemVQDeaemv+w1SBsUF/YJ1W/R1PfRt8WgeyddCRuls8bghlQLdquHSnM56k0DXhh7Yf104zTWTqMDqLFvdsqCHARtUKXRlhICSaj9YkRjaou/S0BOrhzbWZWkSsu2b2S0INgo5/F3t8Nnua8XXFr83VzB+m2NbbU7rvZEt/OFsa4kwXydcfpTBCl5fh6eNS8HRVoErc7tALqckjxEtA13x+7lHqOdhX+bpuL95MxT/XEnG2mFN9ISEsk4EwtSudg/2LBF8i+tpaKQfLiSk85o0URSF+X0a4lJiBsusQIoD6uCmviyBkimkckN7AWxBTru7sGlUU4zefAEAeJ2km9eqjosP01ntm29MFMKC015sFHI08HZAhyDD5l1i7ayuhz3veG0qjrYKbB8nviiztJDht7GReJyeiy//vYn03EKcY2SAXDm4kdmeB9D4KvQM9XqthV7ARMG3Vq1aSE5Ohq+vL2rUqIGkpCTUr18fvXv3RuvWrbFkyRJzPyehDGDajIkZ7DMHN+3EbCj71Yyu9fDHuUeYyvDQrWZlgTVDm0BF03qesqbi4WCtt2XExcvRWmf7x5eZiY/wms4Ir+mMAFc7g6tkUwV3gD88Ep+JLw1NEPXo208xvLm/3nk+zcc3b4ShW/ATdH7Ntqs+79MQ8WnZaG4gvnRlgCt8cqM5yGWUYAxdJvbWCnzWswGuJ2fqjvEtdCd0qI1VR+5iVAt/nRmHlt6cmLzmjHxnZSHXZbcyt3BgDN1DvBAzuzPsrS1K1S+lMCjCV9Qhbsf7UXhn8wXMNmMSEi1+DHM35k6dVvDt16gGarvZi6a95foSCDnGMnmvTS3Ep+Xwxnc2tAOj3bFoH+SOnR9EYdWRu7xOguM71IabvRXamZDMCCjpc0Ge9rj9JBv/TWotORZtRWTnlIKPsy3WFftcaE1bpnaui+Aa0hIDSWHd2+Gise1fJ0wSfAcOHIh9+/YhMjIS7dq1w8aNG9GpUyfcuHEDeXn8qXMJlQ+mqQPfal2Lt5MNarnZwdZSrosvOKFDHcQ8TMeIKH/e73zUvjY+bBeoN/mUNv6uKTC1blInQwu5DDs/aGG4YBnAJ5B4Olhj46imoGnpmn1HWwX6NxYODv+qIsV+urLAdUATczwBgI71PRD7KEP4eszMZDxteUqnuugV6o067tVAURQW9AvGnL+uGffQrzjmSEltDiL8XXB5bhezpmr/aUQELjx8gR4MAYWp2dbKnhRFiZo8AUAdd3tcfVyykJISusrdwRo/M7Jydarvgc2nE9DY10kwqoB2wc6MmRte0wWbR/Nn97JWyPX6uKFFWq9QL/x7NQVASb/Y9l4UXuQWGp2A4VXBmJBnhrC1lFcZoRcwUfCdM2eO7u8ZM2agWbNmcHNzQ1ZWFsaMGWO2hyMYT6f6Hjh08wkrHq4QTC2vUEIJQCMgH5zSFhRKhK4AVzsc4wSA51JZBpGeoV744fh9vRitlZWCopIwPSsHN8Kc/13Dwv7BoCjK7Cl9CWUL930ZiuP6Xptaos4ntVyroZabHRxtFLwe8jKZJsyYFJQq08PmEaRhTqEXADo18GCZkQD8Gl8pfNI9iJVKmxvKTwr1PO0NhkncOKopsvKKjNbeM+nS0BMHbjwRXNR8NSBEJ/hqcbRVGH3PV2F4XTusCS4mpBuV8tsQr8LvNielNpLz8/PD9evXsXfvXri4uKBnz57meC6CiXw7pBFOxD9F27qGPfLruFeDt6M1rC3l8HYSNxcwNSxRZWBCh9rwdbFFOzM5JZQ1LwtKBN9+jWugT5i32SfQVwWtEw5fKLNXAebi79shjQQdSLUo5DIsHRgm6BBnaaFZhMooaQvLrg09MOeva7zh0Do38MDq6LsmJ8AhVA6YiTikpnYGoBeLvazGR4qiSiX0Aposgu72VoIRFcR8VIzBmIVDRcGN0mGIZgEuOP/ghVGZRl93zOIdUr16dQwfPtwclyKUEjsrC8khnuytFTgxswNLk/s6Ym+tEM2yU9nI4wRmf53fjSHm9mqA4VE1UauMnYXKAzuJHtUDGtdA9WqWCBGw3zNmEepub43rn3fltSUP83XC4Wlt9WyBCa8WgW7VsGFkBFzsLI0eK7Q7hEveDK00O3R8yGSUaHZESwuZzgHYxYjseHpU3iowmTVDm2BHzGO8WRxiMcDVDg+evWSVeT3TVAhT+d2iCWXKq6zJfV1pYGTYp9cZmYziTSP6KiI126FMRvGG7zMVsegXr0vdVnVMSW8OAN+/3QQPn+eKOsG9CsiLEyIVqtQ6PxRT4CYJeR1ws7diRTvaOKopridn4qs9N5Fc7PRd1Ra/RPAlECoRfcK8MbmzeWLIEioHq95qjEcvcvUyJRIIFY1CLnvlhV4tpobNY/Jh+0CcvPvM6AxnrxIBrnYIcLVDHXd7dF15HHIZhR9GGJ+l81WGokubm/U1JysrC46OjsjMzISDA9HEEczP8gO38d2Ru6XKREYgEAiE0pOSmQd3e2uyG/oKIlVeI4KvAYjgSygPsvOVr33QcAKBQCAQygqp8hoxdTCAdl2QlaWfRpRAMCdZhSQGNoFAIBAIpqCV0wzpc4nga4Ds7GwAgK+vcIYeAoFAIBAIBELFk52dDUdHYZ8KYupgALVajeTkZNjb25dLuJesrCz4+voiMTGRmFYwIPUiDKkbfki9CEPqhh9SL/yQehGG1A0/FVEvNE0jOzsb3t7ekInEdiYaXwPIZDL4+JR/2lcHBwfSiXgg9SIMqRt+SL0IQ+qGH1Iv/JB6EYbUDT/lXS9iml4tr1/QOgKBQCAQCAQCgQci+BIIBAKBQCAQqgRE8K1kWFlZYd68ebCykpblqapA6kUYUjf8kHoRhtQNP6Re+CH1IgypG34qc70Q5zYCgUAgEAgEQpWAaHwJBAKBQCAQCFUCIvgSCAQCgUAgEKoERPAlEAgEAoFAIFQJiOBLIBAIBAKBQKgSEMGXQCAQCAQCgVAlMFvmNqVSidTUVOTm5sLNzQ0uLi7munSFUt4piwkEAoFAIBAIxlEuKYtzcnLw+++/Y8uWLTh//jwKCgp053x8fNClSxe89957aNq0aWluU6EkJyfD19e3oh+DQCAQCAQCgWCAxMRE+Pj4CJ43OY7vihUrsHDhQvj7+6NPnz5o1qwZatSoARsbG7x48QLXrl3DiRMnsHv3bjRv3hyrVq1CnTp1TP4hFUVmZiacnJyQmJhI8nATCAQCgUAgVEKysrLg6+uLjIwMODo6CpYzWfAdOHAg5s6di5CQENFyBQUF2LBhAywtLTF27FhTblWhZGVlwdHREZmZmUTwJRAIZuNuWg5+PZOAD9rVhqejdUU/TqWDpmkoVTQsLcrXFUWlpnErNQtBng6Qy4h5G5Ojt9Ow4N8baOznjKUDwyr6caosuYVFsLUs2bDPK1QhI68QXo42ADRtmAIgq2LtV6q8ZvKI8ueff7KE3vT0dOTn5+uVs7KywocffvhKCr2vAvuvp+LD32OQla8063XzClV4kqX/Pl8VXhYU4XlOgeGCHGiaxuf/XMf4P2Kx+dQDPHz+UrR8XqEKBUUq1jGlSi16/dup2biWlIlnIs9H0zTyClWC59VqWldOy8uCIsz93zWcvf+c9Xx8XHqUjvMPXghe31QSX+Si7ZJoLNl/y+zXLkvupmVj+cE7yMgtLLd79l9zCj+feYghP5yR/B2VmhZtN5WVS4/S0Xn5Mfx4/L7k74zefAGRXx3C0+zy/b0L99xEz+9OYtHem2VyfaVKjT8vJiL2UXqZXB8AnucUiI4fprIzNgn3nr7EjpjHkvpKzMMXSHyRa/bn4HI7NRtpZTBfacfrsqhLU1m6/zaC5+3H/y4nAdDMBV1WHkPUoiOIvp0GlZpGz+9OoM+ak7p5gsCm1EvpS5cuISQkBK6urrCzs0Pz5s2xdetWczxblSQjtxAzd1zF/ac5eJ5TgPl/X8fuS48Fy4/7NQZ741IxaN0ZjNl8ASmZeaV+hiKVGp2WH0PkV4ex8eQDFBYJC3KARuDaEfNYJyinZObhZPwzSfe68yQbbZdEo9+aU0jKKHn2QzeeYO7/rqGwSK0T7vIKVZIFk7DPDyD8y0Oi5Yt4BNSE57nYdCoB/15Nwfx/buCjP2IFv5+WnY9mCw+h9dfRyC0sAgDsu5bCGpS4LNl/G11XHkevVScRtegwUjLzwLfpMvuva6g/dx8O3niiO7b6SDx2xT7G7dRshH1xAKM2nUeTBQfx69mHmvPRd/HLmYcY8sNZLDtwG1O3XUb9uftwJTFDd435f19H1xXH0X/taQxafwZZ+Ursin2M8AUHcSs1S/C38nE3LQepmezJpvU30Xj4PBdrou8ZdS0AyFeqsOHkA9x/mmP0d0vDtaRMdFp+HN8djsd3h+8iJTMPN1OMqwsmFxJeYNr2KzjHWIBoufMkG0v338blxAxkF2jaTMJz6YLBqE3n0XThIRxitAsAuPc0B9k8i997T3PQcdlRvTEkK1+Jpftv46M/Yln94Nidp/jgtxhM3X4ZP59O4H2G+CfZOqEtt7AImbmGF92rj9xFfFoOFu69qbdQ5JKVrwRN0zh6+ynSczXPWZ5sPPUAAPDTyQes49G307Bk/y1dX5dKkUqNy4kZuno+cP0JZuy4igFrT/OOQUwSX+Ti7yvJeJwuvY0kZ+QhatER9Fp1wqjnlEIh490VGnj2q48z8Mb3Z9D92xO8Y5y5iH+Sja4rj6PHdyd1xwqKVPjo91jM2nVV8N4FRSqcuvtMtD0evf0UXVceF50HTOVxei4O33xidN2sjr4LNQ18dzgeAJCnVCHxhWbuvPY4E8kZebiVmo1rSVnIMbKtatHstoi/Xy6P03MR8eUhLD94x6R7lielFnxHjRoFGxsbLF++HEuXLkX9+vXxwQcfoF+/fsjLK70QVtVo9MVBbLuYiA7LjmHL+UfYfDoBU7ZdMSgM3ErNxuFbaZi1K67Uz5CVX6QTQr/49wZGbjwvWn5N9F1M//MKpv95BQAwetMFvL3hnCSN4q7YJDx8novLiRnYcbFkch77y0X8cuYh3v7pHMK/PISz95+j/tx9aPTFQVHt9rn7zzFl22UUFa90ryfzCzA/n05Ag7n7WdpRtZrG2J8vsMpdSxIWgO4+yUF2QRHSsguQlK6prxk7rqKgSI1JWy/jZPwz5CvZg+rt1Gzd30oVjclbLyPyq8NIzczH1O2X0XThIdxIzsLv5x4BAN795SLylSr8fSUZSw/cwdTtV7Am+i6y84t0gsGcv66h3uz/cOB6qu7aq47cxa5LGuH7u8PxePQ8F0kZedh8OgG3n5Q8w43kLEzdfgXPXxai28oT+Oh34QGepmmsPHQH0bfT8CQrv1iA10xqS/ffxpbzjwS/K4Xvj97Dgn9voMOyY3hZYNqAbQqjNpW880M3n6DP6lPo/u0JnaYqp6AIe66msJ7pcXouVALalElbLmFn7GNM3nZZ71yXFcexOvou+q05ZdKznoh/BpoG/r2arDt29v5zdFx2DP3XntYrP+PPK7j39CWmbLvCOh46/wBWR9/FnqspWB19V6fRGrnxPP67lopdsUmY9/d13mfovOI4Bqw9jQfPXqLTsmNovuiw6O6KUqXG4Vtpus+D1p/lLZevVGHBvzcQOv8AZuy4qjuepzRd21akUuPwzSeiC+DEF7moN/s/TNxyCT+d0NdI0zSN384+xOhNF7Am+h52xOgrItKy85HwTLM7dOjGE7RcfAS/nEkAACz67xb6rTmFz/+5AQBIyigRYmt/9h9rTGCy7tg9tP4mGhO3XMJbP5bUGU3TeuMKkyuJGShUqXHv6Uuz96P910sWXEqVsMBG07RubM0pKBLsK6Xl36vJ6LziOACwdkJiH2ZgT1wKtpxPxCMBjfPs3dcw7KdzmL37muD1Z+zQ9JsjjPZriNzCIt5FKJdWX0djzM8XETBrr67tGOJaUqbu73tPNd9RcQRn5k4CbZzsquP932IQtegwMvOk7ySP2XwRz3IK8N3h+DJd6JiDUgu+WVlZOHbsGCZNmoQpU6Zg06ZNePjwIZydnTF06FBzPGOVgbvyXHqgZOXUYdkxPYGPTyNz9PZT3msnZ+RhwNpT6LbyOEuLyAdXw3uGR3PF5H+XNZPwifhnutUmAHy227AQzhyYVxy6g5WH2KvF8wkv8OJlIYb8UDLwf7NPs42+4eQDbLtQImwVFqkx+Iez2H2pRNsqZOE07+/rKFSpMWXbZeQWFmH3pcfYEfNYN5gwETJ3UDIG84LiOsvOL/k9b284h9l/sQfVAk7dnnvwAmnZBVhx8A52xSbhaXYBdsayJ9ajt9OwkaF94ptECorUvM8OAIdvpaHNkmi0XHxE79ynnHe0Jy4F6S/5hYQDN55g5aF4jN50Ad8dji/edi/E0dtPsTr6ruRF179XkzF4/Rn0XX0S3x8t0Qwz6/nMPfE2ZwoZuYX482Ki3qTEnDBp0Lqtde2iafLWS/joj1g0nLcfm089wKEbT9Dq62jM3HkVfCRnanc+yt5UiKZpXT+7m5aDerP/Y2mDYx9l8H6HycpD8RgvoNFaf+weWiw6jEc8Wunfzz5EcmY+8pQqhH95CN/su4W8QhUO3niCHTGPsf7YPQxYewo7OYLilcQMpGTm4fQ99q7Qr2ceYkNxO2cKl39fSUbY5wdYk75Ufjr5AGN+vog3vmcvCi4nZiD+STYO33yC1t9Eo6BIjb+vJOPLPfrmDXfTclj9WLvIZdJs4WG0W3oUm049wFf/3URSRh7m/k+zcND+Ju3ODLf7asczLov/Kzmu1egBwNTtVxA0Zx+GbziH384+xMWEF0jNzMe5+8/h/8kefMBYvH5lpLnG3bRsHL2dJklwURaPZV/8cwP9157CrdQs0DSNIpUaPb47ia/2ljw/Uzjbcv4RPt0dp2dKd/9pDrZfTETLxUdwPVnaux7/xyXWZ+1zMxcGfJrL5Iw8/Fncxv7kWcgAmkWTjBHCVIoGlKZpBM/bj5D5B3DqrrRdTwD4+UwCaJrG9ouJogojrtlIQZGKZc6w7OAd1oKbKxQDmoXIqsPxuCRiarP/+hM8yynU21niQ6lS4/dzD1kKlT8vCu9SVwZKHce3TZs2sLGxwRdffIGAgAAMHz4cDg4O2LRpE9577z3s3LkTb7zxhjme9bXnxB3xjpL+shAO1goAwK3ULEGNDJfCIjUmbLmkmwRXHLyDzg089MrlK1XYdCpBtMPmK1VQ07TOsH7ftRSWiUILhnAVn5aDuMeZCPHh967MzFXqJgMtKw/F461mfqK/57ezj5CUnofoYiG/f2MfWFrI8COPtsZQ7GULOYVVR+6yBDAuJ+KfoWZ1O91nmqYx7c8r2BVbImALbZftiHnMcgJhDg5M0hkaKTVnsHr/N7ZQsicuRfBZjeU+j7DceMFB9AzxwpphTVjHmYOuViMNaGwxhRi96TwKitT4bUykztGCOVldeZyJD9oFIiO3EH9dLtFiMrdRaZoGTWscNTLzlAj7/ADeDPeR5Fxz7v5zHLr5BHKZDOkvC7HtYiLuPMlG4os8xCVlYvv7UazyasbcphWQD90s0fbM/+eGztmK+24B6GkW85UqrDoSj4HhvvB3tYMQFxJeIKKmM57mFMDdnt/RLS27RFDQtut911JZC56CIjXG/nIRiwaEoEBAK8i3cDp8K41Xi7ioWPiauPUSdn/YgtWfuJPq2qP3sJanH/EJ31GLNOPEn+9Hoam/Jub7TRFTm8w8JZYduI1No5sJluFDuyhn1tGN5CzJGvf+a09hQBN2WCTumMIUEj//5wacbBWi11TI2fqmOIkC/f2nObCzstAt7E/EP8MJhkkZ333/upSEhf1LfHFuJGfB1d4S6S+V+PVsAiZ0qAMPB017U6tpdFp+XFd2/fBwdG3oKfg8adkF8He105mGdFt5AuveDoeHg5WeqZC2X+UrVboFspyiYGslR6varihS0axxZOKWSzg8rZ3gvaNvpfGOuTQNUBR7PObKq1/+e0PPjIWLUqVG1xXHkcawLz93/wVa1XEtviaNmIfpCK7hwHIyKyhS6xY2w346h90ftkBjP2fRewGa9h3zMB0fF+90JCzuyf9cnL5L09DtbjKPaeEzU/z5dAKWHbyDDace4PLcLqLPJUVTf+z2U3zG0Zqvjr6LPo288SQrnzV3VhZKpfGlaRoTJ07E5MmTcePGDUyYMIF1fvny5cTe14wwG3SPb4Xtt7ir/BaLjyDmYcnqTshTefelJHy97xZOCgi+Z+8/R+jnBxAy/wD+vZoMpUqtJ5RxERvU913nF+D4thu5RDM02yo1DZqmsYTHFpAr9ya+yGVpjhJf5OE/A4Lk7L+uYezPF3SCwfrj91lCLwAkZxjW7ilVakFHHeYEVhkcEpjC9dXHGRj/RyyvNkyMmIfpiL79FKfvPUdyse05nyYpX6nCDc5E+fs5zYJo37UUBMzai1qf7kVhkVqnudsR8xjRt8W3HzNyCzH4h7P48cQDrDt2D9suJgIAfjzxAPuupyIpI09PC17EkHy/FtDE8U0mNE3jlzMJGLiuxFHN08Eai/+7hTXR99Bu6VHRZ90Z8xhf7rmJZgsP4yjndx2++QSD159Bs4WHdcd2X0rCs5wClnaPyaxdcZhfvLXOhU8LBGjsh4W4nJiBPqtPIZmxyN10KkGwvFRO39Vo9pUqtV6f4vLUBKe+Fy9LvpOvVIGmafx3TfrC8dKjDCzg1CN3TOEKB3KRxXbI/P2Qc06nZRdg9l+Gd0uG/niOZa/PJYPH1pr5rh88e4ke353AWz+cRe/VJ/Hb2UeYtLVkEcqt392c95HDMZsYtP4MPvgthnVswb83eJUN2udg7nj9evYh1h+7j+EbzustnvOVatxNy8HxO0/1HMvylSqM3nyBd+5R89yH+X6uJWUKCr3Mna74Jzm4zzE/eMmwl912IRGD1p/R29Hj2j33X3tabz67kZyFriuOs46F1HBkKZA6LT/G62fAZxMuNl9wdw8B6LTpfO0lp6AIMQ9LNM5CYwUTPnMISwsZBqw9jbZLjvL6OlQ0pRJ8//e//8HJyQlDhw6Fq6srNmzYwDpfrVq1Uj1cVaNjfXfR88wmKCYb/XD8Pks7x/UCFxJGxQbVj36PxYZiRzftardIxMZLy6e74xAvoOW8m8Zvt2yMXREAHLiRirDPD/Ce4w7Brb+JRq9VJU4QlnIZpIiZh26mIWjOPiQ8e8nagtTCFdyYrD16FwVFKpZdJndu8HWx0f1d8WKvhhPxT/E4PRd9Vp/Cv1eN1zKP2HBO93dOQRHe/UVjz8ZlV2wSuOPrqbvPsSb6Lmtyqzv7P1abGb3pgs4+8iXDjvBGcpZm69eEqBXpjMngWU6hwS1frblRXFIm5v7vOuIZz6ewoLD1gjS7Z4oq2RJfdeQuAM2E9u4vFzHm54u8v0XKNiQfQlqcs/fF6ysuKZN3cVkatGvwfddSxQtCX3NniPMPXuBJVsnYdyUxA7+ceairX6lwhZnvj95jaRS5GjdmCCnuQjc7v4h3QXLcwG4fAKRm5eO9X2MMlmPCfLQLxW3o3tOXusXb2fsv8M7mC7ienKlnD8zceSooUqH11/qmUv9x3ptCToFPr6Jtc4ac+bQkZeSh0/JjGLHxPGbtYpsUidk3ZxTPHcy5Sc0R/vnYduERGi84iK3Ffgp82mTmNbUOXLtik1j9KSdf36aaqzD4ZNdVvZ0/W0s5LBiZxu6m5fA61PHNuWL2x6fvPcODZy+hVKmRlp2PP849QmERf//ffjERwfP2443vSxbvDyU43nJ3KAGgU30P3ZxoaEFbEZTK1KF3794YOHAgvvrqK6xevVrvPE3TuHfPeO/uqgpFUehU3wOHbvJPaFcfZ+Dq4wz0bVTD4LUePHsJXxdbwfP5ShWsFXLWMUcb4S26PXEpLGGNpqWtBgGNNrOOh73ecSHh3cpCzn9CgElbLwueMxTHsHeYN++qWIg/YxJ5j39/9B5mdgviPffNvtv4Zh9bYOBWXcKzkgHGWG/asmL4BnGnRkO8ZGhqPvw9ltesAgASnr+Ej7ON3nEpQlbXlcdxcXYnRHx5CACwb3Jr9PjuBJxtFegZ6mX0M3O1uYaU7/P+vo5+jWtgb5y+4KZS0egQ5K47JxQlQe97xTe99zRH1B7/hBE2hHzXNwWm/bw50I4pj3nsZrkY4zCTkpmHQevZYeIG/8DvVGcKS/bdxuxeDQDo91fmkCO1L4sJc6Whlqsdvvjnhs4cgY8jt9KgVKnxaY/6rOPM8X79sfusRaEQchnFsovV8jg9F442jnqLBClw24aQsxoARHx5CBc+68Sam5jtXWi3c+ZOjcb9k11xGNLMj1dAPnXvmW5MYSqTMvOUKCxS4/N/rgva9Z+If4rIgOqwtJDh6mN9xRNNa8zumNx/+hLLD9zG1C71dMeUanZ7ikvKxCcivhWn7j5HewO7TVo+3qHvs7Du2D1M6VxHdE7me6PMaqYrjSqnhFJpfOVyORYvXoxu3bph6NCh2L9/PxITE6FUKvHgwQOMHTsWbm5u5nrWKsFX/YMFz03aehmTtl6G/yd7DF5H62S1T2Bbj6nFKFKp0X/tKaw3EGOTOe88f1ko2WPYQk4hp6AIFxJesAahDQJbTraWxgm+YjC3c/gmTmOEXsCwIORlYiIC5vvYcp5fuH6VERJ6Ac174dMaSIWp8Xj7J42wnp6rxG9nSxdlAgDi0/h3K5g8Ts/FumP6C3ylmoafS4l9m7hNfslMYVE8a3AdIbnsMVILX6RSQ6lS42WB6UKWqe1bCO22eHU7S4NlbwlEP+BSWKRmLSTLAuZ2OXcrnmnq8ELAUZRLWnZBmZg43UrNFhV6tSRl5OlFz3ieo3n23MIiySGq7j19yesvoQ1JZ8qiniss91ktbp/98PlL1ljPFIL5hHIuFxJeYOr2K3rH/yj2a+DOI5/uikPzRYfx37VUXBbYNR2+4byoo+GVxxm6fs/kuyN3cYehHd7LMctjmlaZwrIDt/HPlWTRMoZ2YPnm1X+uil+zoil1VIe6devizJkzUKlU6NmzJ/z9/WFtbY3atWtj7969WLZsmTmes8pgIS/1KwFQEgJIyAaXGeYk4XkuLvE4oIjxz5VkdOHYKYkx7teLGLjuDL4/anibUSxMjrG8+8tFHLvzFPlKFZouPGSwfB33aggQcULiOqZwcXeQJhgouIZ+VRg1zXbuMxYrRmYvcyd36LbScCzUrDz+BWCRSq1n0iIEU+OXU1CEG8lZJmnHxOiy8jiiFh2R7EzFh7kjVWjf19E70sJFGdKMjtx4HnVn/8cK/1XWPM5gaySZNq5Msyo++jcu2b27V87xq5ncf/oSAzjh8C4+TEdadj4roowU+JxvtW1ZyNRADOaiWEiwZEKDraBgLij4FL721uyNb0PC5Lu/XGR93nfdsJkOAGw+nSC4a7HlfKLg7g5znhULr2kKq47cxYQtl1BYpBbMkHj4piaq0Pg/Ynntjvl+EjMKSWWMbFbqqA4A4OXlhW3btiElJQXHjh1DUlISPD090b17d7i4uJjjFlUGKzOl57yenIk3w30EzzMHkxUmBpw2xhb3VLETy/8uJ2N8hzqiBu8qtXm3+j/4LQaWFjJeY34u07rURdeGnsjMU6LRFwf1zucZCAguxYbNwdoCdlYW5RLu6lUgO1+pF2fWGLyd9M0kypOkDP5t+vRcpWi0ECbMCf1WajZ6fHeCN/JKadBq3StTjM1NpxIQ5GnPayrCB9dM42ZKFh4+f4luwZot6GN3+MM5mhtnRgQFJUczLzXNsbOtAosGhOjMR2ws5aBpGmfuPUdtjxL/mD5h3vjbgFaurNgZk2SWhATejjbIyleaZD7FtGuVkk1PraZZ7eTbw/FYM6wJHKwVvBpfY0x/pOy2ivHbuUfwdbFhCYZa+OJDM3nw7KXR/i9SyS9SwUYh53XcZYap/PdqCr5+IwSd6nugejUrXE7MEDW1ADRa6iWVLL21WQRfLV5eXhgyZIg5L1nlKM02v2s1S+Qr1cgpKIKVhVw0ji5z+ye7jJMF8M2zYvZ25tZ05RaqkCsx5aS3kw0oioKTLf/W648nxLUfUhz+Otb30IthWpUp7QJAuyVbURjKRCYFPk2YoXjbpjJ+yyXDhcoRrX2lFLh+Bd2Lo9vseD8KEf7lp2RpV6/EETm/SNjGV4zODTxgrZDDUi5DoUoNmgYmbr2Mf64kw5Kxs1TXo2ydxMe1qSVo5iYU2cRYwnydcN6A86QQKrUmtnb0rTRJGmMaYEVbOBH/DLN2xmHNsCa81qZS5wZjCPVx5LXlnfOXcLIMQ/Per2ceip4vDWui70oWqjX9NQ7rh4djnARny8ha1Uv5dObHPOpFgtkwFHdWjAufdcLACI2W93pyJivWKhemxrc8w2cJ2Xgxt1nKKsuPFIS2rQ3haKNAvlIlGKeXSXa+slJu/1QUJzjprQPdKl/cRzEMpfSubLxqz8tEaKy6mZJVrppsppCyl2NrbUwKaqBkl69ITevsLQtVarjbWwEAPB3LbkfjjSY+ek5VZUF6biH2SzQJ4HL7STaiFh3GxzuvsuLqCsHXDLTmF6XxJTCGHiHGO9aKoVLTZZrNcv0xwyFEuXCF3m4CMZ+7NjTvzpU5MFnjGxAQYJKQNnnyZEycONHU21Yp5vVuoEtzKQWKonQ2qFxhAgBmdK2n85RnWhMUmdm0gAszz3zC81xWTGEteye2xge/xSA+LYeVMKC8YdreDorwwXaJGWgy85QImrNPUtmO9T0q9DdWdgZG+PKGjBOmYlcRpUmpWxbIKMNOmJUN12qWeCZBc//P1RQcu/0U15Iy0bZuieP00dtPUc3arBuYohy68QS5hUWwtbSAlcI0/ZFWBpMXjzncBb9WyHOxE0+IURra1XMTDcVoLkobBs+YXUCxBVCmBHM3cxDm42TW691KzdILq1fZEBIHw3ydyvU5pGDySLF582aTvufv72/qLascYk5WQoitRT5qX7tE8GVpfI2+jVFwzQPm/a2/3ePrYoNQHyfEp+WY3UGJj8gAF73YqK3ruOqySAHAnF4NJAu+xiDVBlCIyAAXeDvZmD20VGXBRYKHP5OKFvKuJpruLFYWeDnaCNodV1a0Qq+hxSZzq1ibkATQZJ47LBLP1NzkKVVoMHc/RrXwxy8mbkFrm602CoTQTpeQgmnZwDBM+9N023hAE0GkInfYygKhX3My/hk+Fkgzbm7CfB0llXOxs5QU+aPnd+JOkpUBGUVhxeAwPX8NW0X5LUilYvITtW3b1pzPQTARCxnFWg0bCtdirZAhX6nG5cQMnVOQkMY3eno7yTEAjYHPM5UCxRvOpaxoWdtVT/D9dUwk67OU0Dem4Ckx8gMfNZxssOqtxtgkMSYsH96O1kiuxI51rtWMFXwrduJ+acDhsbyRvcIGbGG+TizBd2rnuvjpxH1k8SQGMDf9GnljWPOayMhV6nnua+HGWd9cin6o0/gWj3tC47DQKNQ9xLPUgq9cRlUqZ0dzIDQefLLLeKG3byNvXdprY5Cq3Ogd6oWfjVw41fWohjtPKi4CiBAvXhaiobe+wG/qjkhZYtYn+vbbbwEAt2/fhrqs1YgEAPodzJAQkK/UvJcPGalOYwVCmWltzMoDGWU42YRU1r3dxGAZKQNTaTWzQpRGUDv1SQe4O1hLdqDhQ6mmYW+lv+bdN7k11r0dLjnxwxtNhKOGlAZjFxzj/6hYZy0pDo3lSW23VzdjJjOJzsfd6uGj9rVxdX7Xcrn3yiGN0dTfBZ0beLCcy7Rsebc5ugfz2zGagjawv3bBLzRlCml8xfpJ6zqukp7B0UZRZv4GQZ76SYvKA6HfIyVJCpd+jWrgpxERRn+PElyusDFF2d68EjqLAcCZ+88RyDP28PWlisasTxQcrEm+MGXKFAQFBaFJkyYYPnw4vv76a+zZU7owIFURoQGvZvWSjGzLBzUCAMzqrskaZqwnuJiXrLECSMvapnXI+b0bwEIuM4vG18HaQhfaqLSUkcLXZMF3w8iSAVheioezs5Tj7KcdceqTDqjHyKgX5OmAbsGemNq5rqTrWJfRSr6Wa/kLboMjfNGs2MxlRFRNSd/ROuGlZpWt9pyb3CG8prNgWRuFHHN7NzT7MyjkFH40QQAwljruJe0xtIZTmS0+DcHXvRr7OZnVEUybzlhmQOMrNF4IDQHfvBmKzaObsc57CyQecXewhoNIxk5DzCnOXgcAJ2e2Z51bO6wJ3mkZYPK1F4okcxLDnDtADb0dEFnL+GghUodnUyLaCEUcqgzw9dfytL2Xillnro4dOwIA9u7dizt37uDo0aP44IMP4OzsjIMH9WOiinH8+HH07t0b3t7eoCgKf/31l+6cUqnEzJkzERISAjs7O3h7e2PEiBFITmZvSRQUFGDChAlwdXWFnZ0d+vTpg8ePzW+zWZ5YyCg0DygRMHuGeuH6510xrm0gABjt+SlmT2uMbNW/cQ38PrY5No1uatT9AWBU8eBojri22niBhjJBnRWJI6ylNMKlGD7OwqmkmXzzZqju72UDw9Cxfol3bGm043IZBTsrC9RwssHbxUJeZEDJ4C71d5uq6axhIO6uX3VbfNnPtEnPVBQWFLa/H4WExT3xRV9p99Y6bZgSlF9Lm7qGM1t+NSAEiwaE6D5/3LWeYFkLOSVqIz2uTS2D9+PTFl6c3RmdG3hg46iyFX6ZobukpkQvC/i6ADfFe2mxKb6eTuMr8HsLlGp82kM/HbqQYsLF1lIwdTAfQ5r6SirHRwMvB93f9lZsAdpKIUdwDQfuVyRjamhPcwq+mt21slt8ZeYZH4qxrBQOZUF9LweDSZ8qArM8Ub9+/XD1qr79jIODA1q0aIH33nsPK1euNOqaL1++RFhYGFavXq13Ljc3F7GxsZgzZw5iY2Oxa9cu3LlzB3369GGVmzx5Mnbv3o2tW7fi5MmTyMnJQa9evaBSVS4vbCH4utuRae10XsBa7Bjb1sYIazRNi25DCHX4uPldWJ/HtgrAisGNAADtGfEtjSW5lA45ng7W6FocUsXQ4CdlgVBWA15dD3tsHt0UddzFNZtuDFMT7iQwMsofHg6mmaIwV+VvR/rhfx+1xM/vNOM9L4apwvfY1oa1QOW9nWeKw7Q5Fka9JIQ9cra1RDDDdi45U7ifhPo4ir4/KfX6/dvhese0Jgj1vRxM3gmR0q6Yu1zMPuznIm2xaC6E+n5pwk1y0S6ctP1oq0Cq8pcFRRjSzE/vuNAzajVsUtun1GyTfDBvQck0zoklzydtK79DkPFzxsBwH9z8ohsG8iRpkpqSm7mDJoYp84CFjNLtIIlhimOhnWXl06AKkfiibFOHm4pZBN8ePXpg4MCBGDhwIG7cKAm/9ejRI9SrJ6ydEKN79+748ssvMWDAAL1zjo6OOHjwIAYNGoR69eqhefPmWLVqFWJiYvDokSZ2bWZmJjZs2IBly5ahU6dOaNy4MX777TfExcXh0CHDqWsrC8xt16/6h8CvuvgEYIwwoqaB+DRhI3kZBXz9RojecXtrBVrVLtEKta1nWGslxLsMIai0iQCCvEq2SdMNhK1xk2C/bC6bYybaCbxdPXccnNqW19YW0MQ1Zt6dKzQ421ni7KyOJj0DcyCnKAphvk4sbZaU392lgYegAOTrIq7RHdXCH9HT2/Gee79450JISBrd0h//jG9l8PmMhRlyTypPJMQUNQQzfrVQylCKAhp4O6BtXTcMDPeBlYWwJmx+74aiAo/QPZhUE2iTgCZixIHJbQxq7fn430ctjSrPdLraPi6qzGzKAaCpP9t8REjYMZffQ/dgT7zbWqN912p8/xTJ3MU3Tgh1U+0ixZ2xMDanwC4EhZIMnYCmDqU4zn39RqjBMlxCfZ1gYynHh+1r652TGqe6ZW1pdtCmVB1FUdg2rrnBcqbY+LpW42+DCYt7Gn8xAVYPbYyfRkSUur2XR4xoUzCL4NukSRPUqVMHu3fvRmhoKFq0aIH27dujWbNmsLcvHwP3zMxMTcYtJycAQExMDJRKJbp0KdFOent7Izg4GKdPnxa4isY8Iisri/WvovCvbofZPUtsqKR0QKEBmy/7j5qm8eKlmKkDhWpW/PZfdlYlky93bPt1TDNIZWLHOrq/e4V6s865VrNEhyB3XuGbD2NshLkavr8EJuXD08wbvWTZIHbqRqGseTRNsyYrvgGEed6Y0HeGNBg2BrZ07y7sjvXDwwXdNxYPEJ/IKIrifd5bC7rhk2JbdaFXOa93Q4T4SAsVZAxCiVXEaCPRgUgM5jbg4Aj+LWcKmoXAz+80w5KBYaITdh0Pe1ZUhzBOXUkRfA1Rx8MengI2o2IYK0AwX4mnozW+fiMEXYxM43xgShvM7lmf91wnhukQN6KL0LOWNqX8N2+GYk6vBvj+7XDdu5AbCMNBUZo+05GjGRUSZrULlwV9g2FrKcdX/UPMkl2Q99kYf3PHFRlFIV9CjGuFgHCULRLJw6q43wS42mF2z/osYVBqX5ZqumLqzh/3/fAtFse00t/9MtRHpVgOGBsSkkn09HboFeqNTg08MDRSf6fBGCqb468Wswi+I0aMgLW1NbZs2YKdO3eib9++iIuLg4+PDw4cOGCOW4iSn5+PTz75BEOHDoWDg8amKDU1FZaWlnB2Zq/kPTw8kJoqnEFm0aJFcHR01P3z9TXd/slU/pvUGn+MjYRfdVtWJxDTxGgR0pQt4LFbVKlpndDaxM9J77yMKvE85uLMMLDnlmhdx01Pg8LH1M51YW9dIli/x7E/3P1hS2wc1RSDm0rrfMY4UnBNIRoJBNnm81I1ldtfdmPFCeZycXYn3d9qmq25EBp8Z3Sthzea+OCIEQK6oVW4s624s4uFXAaKogSFg+pGhiPTwtI6m0lDZWkhQ2cjhSUpLOgXbBbHK+a7+O8a/7jEnUAdDTgjMeuO2zfN5WFtyi+X+k61JjwRHCc+C7kMPxjpYFfHvZrge2JqI7lCEPdZG3pr5hWmtr1ZALsvf96nIUuY5qNniJeesGPolWgf8yeJW/M+zhoBq32QO+Lmd8XQSD/W9n97Azt0vUSiuiwbGIa1wzRRc7jzkYyiWHUto4CGNQwvUoV2mMQ0t8wQnmNb18LF2Z10Y7jSCDXq3YXdDT+fmZSWpz7pgOYMR7lvhzRCN54oIWI2/IA07b2Hg7Xggs8QzKuPLoVzIlBxkT0MYZZRMCEhAV9//TUGDhyIvn37YubMmbhz5w5cXV0xY8YMc9xCEKVSiSFDhkCtVmPt2rUGy3M1aVxmzZqFzMxM3b/ERH67q7KkvpcDWjC0OnN6NUDPEC9dKB2x3SNuJ13QLxi3v+ymy5fNdBoa9tM5nQOJLY/dEEVReqturUd5d4ZtoqHtLCFbVG/OCtiOM5D6Muz66nsZdpJoIXHrCgAmd6pjuFAxQpOZnaVc0mJEiyGhw7WaFZyKhc7Gvk64/7TEDKWOB/8A8lH72lg2KMzgYPgNYzvRkABCURTOf9YR/04QNykQCtnD9Mw3FVPl3rj5XXBrQTfd50ERPjpzIa4gxbqfRFGuf+MaiJvfBcOb1zRLFBJmeuaMXH5HF2PrgmnqwF3gcR1N2go41y0w4Fwo5ZkacPosRQGf9agPB2sLLHkzFO3quaExz4L7+MftcWVuFziXQmsFaMYdiuJ/s63ruIr6ATBf7fzeDbDzgxYAAH/XkjGJ2Z+ndq6Lt5r5GRSSuGMcAFiLmK4woShK54QqlAa2qb8zayzQCqJMHwFmdA7u7k49D3uWsKLdgdHSM9QLPUK8sO7tcD1HZopi77rJKApN/Jx5tZqs7wkcD3SvphsPuaTz9BXtrY1JyW0hYSHIXTjVNGBuuOvDFrq/uenXt7zbHBtGRmDRgBD0CfPWM0v6flgTjGkVIGgW9GG7QNQ24BuixVQ/Ceb8wFxk8ynHmGh3epm2zd+91dikZyhrzCL4RkZGYteuXaxjLi4u+Pbbb7F161Zz3IIXpVKJQYMG4cGDBzh48KBO2wsAnp6eKCwsRHo6Oz1uWloaPDyEV+VWVlZwcHBg/atoxrQKwJphTUzqpD5ONiwtxZsMZ4CYh+k6wVZoe6WAs1X1aQ/NKpI5YfKFw2FON3zaZsCwwMzkxxHhGBrpJziI9g7z5j3OhKnJDPVxwi/FDl0TOujbiTFZNigMX/RtqDeRn/m0I/ZObK373MqA4C1lpb77w5Z4p6XmfTORYlM5v3cDdA/2xKSObKH+6PR2GMTw3JYir7nbWyPYgLZGKOOQmCaUGSqN2Ya4ed5N1fjaWytY2jsLmQyt67jhxMftseU9ts3dgr7Sw35phYGP2tfW7VIY2qKWQm3GIqGJgGBubE0wNWhMRxh3eys9++uZ3fSjBQDA8OY1cWlOZ4yIqmmSTXUjXydWVBIt77aphctzu2BghC82j27Gm9rVykIORwO7DlLQ7nhz+930LnWxaVRT0UTXzIQZarpEI8xUEDDNPfo3rgFLCxmuJxtvGmdjRPSC9cPD8c0boVg6MIz3fGQAv7DDFPIt5DLM690AM7rW0/2GWsWmR1GB1Vl2p++3DcStBd1w44uuuDq/i64eugV7oqm/C6tuKYrd9rR9eE6vBhjXVjiaCF9fn9ChNtrVdcP5TzvxfIPf5ER7HVPMlrh4Olhjc7Fgz1xwAPzj24DGNQBoHOaa+JX0Y27boygKHet74K1mfqAoCjIZxbKjbVLTWed3oYW5mGga4IJAt2rYPLopa+7hkpWnNDkjH/d1aO3LDTmut66jGc9/HBmBbe81x72veugptyoLZhF8v/76a8ybNw8jR47E2bNnoVQqoVQqsWPHDtjZGZ92VwpaoTc+Ph6HDh1C9ersDh8eHg6FQsEKo5aSkoJr166hRYsW3Mu9NnBj/HGjAXAb9ZPiGKRC2sieocIC5YK+DfFOywA0NpCLO5+xAtdqTgDg6O2not9j4uNsi6/6h7DiRjKRErT/p5ER6NrQQzdptKnrhviF3TGti/jWkqONAiN4oig4WCvgV90WC/sHY9VbjUutoQI0NmtzezeAh4O16MTMx6iWAfj+7XCWUwugv6gxV2zUPXEpRn+HeeuVgxthds/62D4uCquHsjUDpbFRY6K1H/R1sWVpOz/v0xBvNy9xHDUkZ68c0gh3vuzO0rYImYz0DPHSbY2LoZ285/duAHd7K3wlELe0NI5JI1v4Y1ybWqjv5YDD09rC3loBB0ZcTW6WPKaGz9nOEl/0Dea1qW7sxy+ka4kMcBE0yWAKR2WZdU97bWb17Xg/Ch+1rw0LuUzUsYgpNNhz4pBuGBmBIU19Mb1LPbSq7YqIms66Cb6sU0U72VpiUFNflokYAATXcMC83g0wXmARzxWCRrcMwEcMx7BVQxvjw3aBmNixjt47sVbIYWtpAQdr8cUIBYrV7inGsOMsEnuWT/Cd1qUeKIoSVMgEeer3r4LieWbxf7dEn9MQrtUscfbTjmjHEPSYvhkKzoI3oqYzlg4Mw5lZHVghJ6WyfnhJFBW+XSSWPX3xq2lXzx0NeMYYbf2/3y6Q9R6HFdvqfvdWY13MfyG4picHprbB0oFhGNc2UFDry9y5cbRRILJW9QqLwS0Fs8TFaNasGY4cOYJp06ahRYsWoCgKcrkcRUVFWLBggUnXzMnJwd27d3WfHzx4gMuXL8PFxQXe3t548803ERsbi3///RcqlUpnt+vi4gJLS0s4OjpizJgxmDZtGqpXrw4XFxdMnz4dISEh6NSJfxX5OrB4QAg6LDum+8zVEnMHmV2xSQA0dpmRAS54nJ6HzDylbpvEzd4KQyP98Me5R3r3Gh7lL+mZbBkaOKaTnTkD/7vaGxaUXKtZYf1wtp2cMTEGuzT0RDSPsD4sUiNA3X9aEs+1VW1XnLz7TPK1zcnAcF98tvua7rP2lc/sFoTvDsdjfh/jExy42Vth6cAwuDGcSN5vG4h1x+6Z/JzOdpYY25pfE1RaW1Rt/fdtVIP3vKONgiVQCmmvtdTzsNcTQJN4MkFZK2SY1qUuqllboNnCw7zX6tzAAwdvPMGH7TSCx6iWARjZwh8URaFnqBf2XGUvKEyZPmJmd8KdJzloXssFPUK8MItxztbSQqfRdHewxg/Dw/HerzEASuxDDfFhu0BYK+ToGOSOvmtO6Z0X0l5zYQpkA5rwvysxbn7RDYVFalhbyjBq4wWcYcTn1gm+jPIRjG1YqTtO3FId63voBJzfxkbqf0GAkQKJUfIKS+945l/dTtQe05Dyr6G3oy7drDFrEWaXkFHA2Fa1dCl+mXON2ALHmHXde21qwdfZhjdZUlxSpvQLicD3qGKOxlpNt5ejadpN1rUZQvVfH7XEplMP8EWfYIR9ofGV4tajdgwe1cIfgEaZcPb+c7zV1BdXHpfUxxd9gzGuTaAuKtQikcUBV171crTR7RR/3icYvVef1PvOn+OiJPzSyoPZAsI1b94cp06dQlJSEm7evInMzEw0atQIgYGBJl3v4sWLaN++ve7z1KlTAQAjR47E/Pnz8ffffwMAGjVqxPpedHQ02rVrBwBYsWIFLCwsMGjQIOTl5aFjx47YvHkz5HLzBiIvb8RsfGpxNJ9cGy6hUEcWMgrbihuvWk2zVn1MIUTI5oqLj7MNzido/m5Xzw0ftAtEQ28HyatAvigUfHzepyGuJWUKesQzKa3D1IAmNTBrV5zg+XFtayG/SIVO9T1Qz9MewfP2686d/9T40GOmKsMsLWRQyCkoVdqJX/O7P2gXiPfa1DJpJe7pYK1nDzqzW71SCb5iMNvfmFYB2BHz2ChnjR9GhCMlM1+yc2Jdjg117zBv/HOlJCEOn9aVrz3Fze+qt5gaEVUTv5x5CAA4OKUN/F3tcDs1m2U6o73+d0Mao2+Yt04Q1ZyT9BNYVK9mhSiBsEdcujQ0Pg2vk62lzmzlzpfdUXf2fwCAn0ZEQKlSo0sDD1aK2PpeDry238wFx1f9pUVvYWJpIdOZCkzqVAdnfigRfLVCtZDGXGr/MsYkS4zPBUy+zKH1NmTXWs3KAjkSkxsZ83uZOzMyimI5tjK1l2oRyduYcVlraleW+BqIGS3F5FBLt4aeWJ12l9eWXQvz1zNj9DfydcK3Q9g7YdxX83HXeujbyFuXgbOOhz2vP4hcRhkMhVryPMLvg2/356cREUbVSWXAZMH30aNH8PPT97avUaMGatTQX7knJSXxHheiXbt2oh1QSue0trbGqlWrsGrVKsn3fRUYEVUTmbmFkrI+1fdidwKhMYZlmyUiGEkVJGb3aoAiNY1BEb6wkMt0toTM8DZi8ler2sK/jaJKBoCRxStdLrs+bIF1R++BRkka59LG5bWykGP5oDBM3X6F97y1Qq77nbmFJZMMRZUuSLwpyGUlgi/3uCnw2YuZsgXfqo7xMZ8b+zlhds/6Rt3P1tJCtK1yL8X9vGxgGEvw5SOUZxLg20GwtbTA3YXdkZGn1IVdErKflssovcx+Uh3vKgpmkwpws9PVu0+xZs5GIcePIyJ4358Ho1+YkhmNee/mtarjj7GRGPrTOQAlC3ahZiNV4DS0xV9azCFWG3L+/XFEBKZsv2xwmxswLrZsoFs1fP1GCFzsrCCTUfB2ssHC/sGoZmXBep9i15RRwKU5ndF4gXHZXcsKvt0mZhNScMZQsf45rm0t2FjKdY7pfPhXt4O1QgZHG4XBUJLcapTJKMF338jXCc38XSQLvFqMGdYX9g9GpzKImlPWmCz4Nm3aFH369MG7776LZs3447ZmZmZi+/bt+PbbbzFu3DhMmDDB5AcllKCQyzBVxC7VtZolnuVoNCl8xvV8mDtLmYudJa9HJ1MLIDZgCMV21HzP8GTRxM8ZP4yIwKStl3THzJFpq2+jGrienMVyduCDFVLKxJlNKJScFDRbZhotkDleramOEkz2TGyl2041BgdrRansXKXAvbqkeLcSH0lGabREQoHnDd7GzD+d73pejtZIycxHiITwU1yYiylvxnYvRVH4fax4EP+JHesgPi2b5XRrDNx2wYzsonUQFRpnxATf8JrOiHmocYw2RSPOxNfFRtDBV/Mc4t8XO715dFOcvvecleiIjxAfRxyaKi3kIV/kCTG44Sa1pl9MxMYPGUXB2c4Sx2e0x2d/xeGDtqbtEvNxaGpbdFp+zHDBYmwUcszrw+9HokXPtl+kf9pbK1i21Hw42ipwamYHKCxkBhUTxkQRkss0adiNRep81THInfddvwqYLPjevHkTX331Fbp16waFQoGIiAh4e3vD2toa6enpuHHjBq5fv46IiAgsWbIE3bsbjpdHMA+/vBOJHTGPMbqlv+Tv5EkING4OWJOkk74WdHCEL/6+ksxyPOJCMVW+BmCu3s0hQMhllKCDHZNySJQkipy1wCg9t59kl/oapgi9QNk6QEmB6wCmRWq9GnIE41KaBY8UnGwtkZLJtq//3/iWyMhVomZ1452RKYrClXldoFbTRkUoADQLZEPCsRBC4eluLeiGrDylbpdFqC9KbValddL5Y2xz0e3zK4kZJl+7XT13lhOWOegQ5I4Vh+6gnYQdRakw+3AtNzuWP4T2/fhVt9VLJFJapIb+AjTZ9FYPbcL7vpltiLuQ8jdSo8pHdQOL4gV9G+ps9ssaQ+NtSA1HxCVlYriBxVZlxmTB18XFBUuXLsWXX36JvXv34sSJE0hISEBeXh5cXV0xbNgwdO3aFcHB4vEgCeangbcD5noLC2d8cmPsw3T+wiiJ/GAOKIqCf3VbJDzPxTAe4XbxGyH4vG9D0W3P0S388dPJB5ISEzAHFDcTNW6mYA4Nemnkvcw8RsrmChTCazjZlNrTvTzEXjGNspADpKF3fHhaW9xMyUKn+sYJJmUt58/pWR/v/HwBH3ct2fZ2t7eGu73p5jiGkmqUBUJjhLVCzkmGwv99sXo2tcs09XfGhQTNWDqtc10E13A0aDNa2ajnaY/YOZ1ZTsmlhSlMHZnWDv6f7NF9NrSbM6qFPzafThDMrinEkKbGJ5+SsshhPq5/dVtM7lRXuLCZkOpIbg4M7e5tGBWBu2k5iDIxTnBloNTObdbW1hgwYAAGDBhgjuchlAN8A36BiHNE14aeglmlTGH/lDZIyyrgnRAoijJo6zezexA6BLlL8hxneR2XY3gVcwi+xqQh5jKuTS2sP36/1M/gaKNgC9EiWMpleL+dZpty5wctcCL+Kaws5Ph6X+nCC/FJvn+8G4mhP55jHStNek2xtyV0ztArDnSrZlLmv7LWcLeo7crrhPeqoI3YMUrAvp+LkKnDlM518daPZ3nbjandd2H/EHRZcRyAxrzA3NrY8sKYLXUpnIg3PcLN/D4NJUWisVHIWTuXYgL1x930TQXF3jmzDTGF472TWvMmf3qVsVKIjwulXSRXBl6vN0aQRJivk9722oNnL/kLQ+Ph7mij4PXoNAUrC3mptCAKuUxylraKUnaaQ8buHuyJT7oHCaZTFkMu0ZbaEMYIALcWdNMtLsJrOiO8pjPupmXj6323TIrL62SrQEaukvf3twh0hbejNZKLt+x3fhCFUJ5kCELoTYoivzM5k3/Hg1uvP7/D7+tgLFyFS1mYzbyqQi8AbBrdFEnpefCXujAUqL+owOq4Or+LLkA/E248dKkwo4OYsn5ZOjAM0//kd559lbnKCK1VXgiNwR93q6cLJchEbJwUMj8yt29MZeBVF2qlUOrRLysrC4WF+jEw1Wo1Vq9eXdrLE8qAbwc30jvGzUrGRC6j0D7I3WTnnKqIOZyxKIrC+20DTUo9yRJ8y2ls5rtPbXd7nPi4PU583F7/pAHOzuqIy3M7CyYGWfRGKKrbWWLN0CYIr+likjCndaoa10bYoUbI0ZL5e3d+ECWY/tdYmBFrImo6oz5PsP6qjEIuky70Qlw4EXKcnNSxDpxtFbyaQalIbY9MR8o3w330dnoMOdK+CtRyK5tEVky4wqnQe28tEDGIm/iHCXP7n/k+Xie5d1QLf2wYGWG44GtAqQTftWvXwsXFBbVr10ZBQQH7wjIZWrVqhW3btpXqAQnmh2/SWMXJnPW68DoNTMbAHPRLUwXGfFdI2Pd1sTXaUxzQ2GqKad7a1nXDxdmd0DPUS/I1talA2xaHVVvyZihuftEN9Tz1Y19q4QsLB3AcXszY0Jga3+3josrVROd1pEeIJ2pWt8WgCOmRI4JrOCJ2TmdezaAhJnWsg64NPRAVKG3Byl0w/TuhFaKnt0Pc/C44ObP9K2cjzEdFLN74us0f70bq7VwuHhCCpv7OeK+NcFplJp6MyCWvqsb37eYa857gGiXvZX6fhiZlnnsVKZWpw7lz53D69Gls3rwZFhb6l2rUqBFmzJiBwYMHl+Y2hHLAFFvEV4HKHgP1k+5BWPzfLczoarpmiY/KnC7SnBgrcO7+oAUKVWqdHTlFUQYjEQjdgtm2zFnb2uxplhYyIvSaAVtLCxyd3s7otmLqYmZKZ+OcnQ7dfML6bGdlgYDihSI3NfGrilIlnmDDHOQr2ffge38tAvVN5IY088OQZuL+AUJmK6+q4PtFn2C83zYQ/7ucjGtJWRX9OOVOqQTf9u3bQ61WY+3atbznL126hDNnzpTmFgTCa824NrXQPdgTfmbW6sjKSBv5qiOTUbCWSfNWf69NLfxw/D6mCnhtl5XG18PBGrs/bAF7a+KCYS4qcx9QyGQoLAfBsCLp1MADB248KVdzubISSplXfVXXpbLiRDlvN6+J7RcT0a2UsapfNUo1sg4fPhzt27dH165d0bNnT4SGhkLGyDW9adMmODgQ+zRC1aRl7eo4dfe5aBmKokyKnWoImZnj+FZFpnWpi071PQTTjVICf5sDY2P/El5d+jbyxp8xj1/rhc6bTXzg4WCNYO/ykwfKSigtqwVvReBoozBpN+RVp1Q2vnK5HNu3b8exY8fQpEkTODo6onv37li2bBkePnwItVqNevXMu4VLIBhDfjkl5qhssGx8q9aYZjasLORoFiDsNEeROiaYgTm9G+DjbvWwd2Lrin6UMkMmo9C2rpsurnq94ugXZakBLiszodetr1c1oRcwQ1QHT09PHDhwAGfOnMGkSZPw8uVLzJ49G7Vr18bx48fh6Vm1VOiEyoWnY8WFZqnIhGMsU4dShTOreoOiVMxVx6YwuVMdAMA3b4bqPObd7EnUlVcRB2sFPmxX+7VwYpPKhlEReLu5H7aPMy1rnxQycvWjTZmDyu43QjCM2fZWIiMjERmpSTdYUFCAs2fP4vDhw7h06ZK5bkEgGE1FxiutWMGXDM5lDXvLs3zvPblTXV3GqEA3O3y2+5qkVNoEQmXAx9kWX/YLKdN7XEksm9jBZGh99SkToyIrKyu0bdsWbdu2RWxsbFncgkCQBF2B0qehDDhlCUvwLcVA3S3YE3+ce2Qw573Da2yfKEzlMHUIr+mCfZPbVNwDEAiVEKGkEyZdi3EpQyl9CZWfMp+ZmzRpUta3IJjAr2NKskxFSEj9+6pSkUPUxI51IJdReLd1QLnf21wJLOb0bIAlb4Zi63viW5JV0SSCqkBTBwKBIE5ZyadWFtKiwhAqL1VRTUMA0LpOSdB0v+qvr21ZfZGMdGVNEz9nXJrbmTclalkjM4/CFzaWcgyM8DVYrgrKveyoDlXw9xMIlZmCIrZjc+s60tLc88HUHleVGOmvM0TwJVSsWrSMaV6rOtYMbYJA97JPmcmHQwUFoC/vxAdV0aaYRM4gECovM7sFAQAeLOqBmynZqOthniRNFWnCRjAPZhF8p06dynucoihYW1ujdu3a6Nu3L1xcXv2c468TkzrWwa9nHxqdaehVw5iUtq8LbKGs7KSywRG+2HYxER+0DSyze1RWiKkDgVB50WZpoygKDUoZP5hp4xsZ4IIuDTwQaMDvgVB5MYvge+nSJcTGxkKlUqFevXqgaRrx8fGQy+UICgrC2rVrMW3aNJw8eRINGhDP48rClM51MaljHZIW9TXEXKYOhpjTuwF6hHohqlb1MrxL5YSqJM5tBAJBH3kZdUoZReGHERFlcm1C+WAWnX3fvn3RqVMnJCcnIyYmBrGxsUhKSkLnzp3x1ltvISkpCW3atMGUKVPMcTuCGSFC7+tJeW3DV7OyQNu6brC0qHrbf2YKnEEgEMzA2mFsR3qZGYek19gasEpilqaxZMkSLFiwgJWe2MHBAfPnz8c333wDW1tbzJ07FzExMea4HYFAMABxwChfiMaXQKhYeoR4IcjTXvfZnGNg9WqWur8rMjY8wTyYxdQhMzMTaWlpemYMT58+RVZWFgDAyckJhYVlk0mFQCCwYWl8iT6yTPB3tYOHgxUsZDLUcHp9I6MQCK8KFvKSsc6cDrcO1gr8M74VFBYUUSq8BphF8O3bty/eeecdLFu2DE2bNgVFUTh//jymT5+Ofv36AQDOnz+PunVfbycqAqGyUJFZxaoK1awscHJmBwBEC0QgVAbU6pK/zS2ghvg4mvV6hIrDLILv+vXrMWXKFAwZMgRFRUWaC1tYYOTIkVixYgUAICgoCD/99JM5bkcgEAxQFcOLVQRE4CUQKg+2liXJJcrKuY3w6mMWwbdatWr48ccfsWLFCty/fx80TSMwMBDVqpWE+2jUqJE5bkUgECRAtuMIBEJVY0rnulh37B7a1nUjjtsEQcyawKJatWoIDQ015yUJBIIJkOQKBAKhqtGytita1jY9QxuhamA2wTcjIwMbNmzAzZs3QVEU6tevjzFjxsDRkdjFEAjljYwkVyAQCAQCQQ+zGKhdvHgRgYGBWLFiBV68eIFnz55hxYoVCAwMRGxsrDluQSAQjIBofAkEAoFA0McsGt8pU6agT58++PHHH2FhoblkUVERxo4di8mTJ+P48ePmuA2BQJAIsfElEAgEAkEfs2l8Z86cqRN6AU1Uh48//hgXL1406ZrHjx9H79694e3tDYqi8Ndff7HO79q1C127doWrqysoisLly5f1rlFQUIAJEybA1dUVdnZ26NOnDx4/fmzS8xAIrxLMaANEBCYQCAQCQYNZBF8HBwc8evRI73hiYiLs7e15vmGYly9fIiwsDKtXrxY837JlSyxevFjwGpMnT8bu3buxdetWnDx5Ejk5OejVqxdUKpVJz0QgvCrUrF6SUIEitg4EAoFAIAAwk6nD4MGDMWbMGCxduhQtWrQARVE4efIkZsyYgbfeesuka3bv3h3du3cXPD98+HAAQEJCAu/5zMxMbNiwAb/++is6deoEAPjtt9/g6+uLQ4cOoWvXriY9F4HwKuDrYotxbWrB1tKCmD0QCAQCgVCMWQTfpUuXgqIojBgxAkVFRaBpGpaWlvjggw9ENbJlSUxMDJRKJbp06aI75u3tjeDgYJw+fVpQ8C0oKEBBQYHuszblMoHwqjGrR/2KfgQCgUAgECoVZhF8LS0t8e2332LRokW4d+8eaJpG7dq1YWtbcfnrU1NTYWlpCWdnZ9ZxDw8PpKamCn5v0aJF+Pzzz/WOEwGYQCAQCAQCoXKildNomhYtZ7LgO3XqVMllly9fbuptzA5N06I2j7NmzWL9tqSkJDRo0AC+vr7l8XgEAoFAIBAIBBPJzs4WzSFhsuB76dIlSeUqyrHG09MThYWFSE9PZ2l909LS0KJFC8HvWVlZwcrKSve5WrVqOie98vgtWVlZ8PX1RWJiIhwcHMr8fq8KpF6EIXXDD6kXYUjd8EPqhR9SL8KQuuGnIuqFpmlkZ2fD29tbtJzJgm90dLSpXy0XwsPDoVAocPDgQQwaNAgAkJKSgmvXruGbb76RfB2ZTAYfH5+yekxBHBwcSCfigdSLMKRu+CH1IgypG35IvfBD6kUYUjf8lHe9SMkWbLaUxeYmJycHd+/e1X1+8OABLl++DBcXF/j5+eHFixd49OgRkpOTAQC3b98GoNH0enp6wtHREWPGjMG0adNQvXp1uLi4YPr06QgJCdFFeSAQCAQCgUAgVB3MEse3LLh48SIaN26Mxo0bA9DYFDdu3Bhz584FAPz9999o3LgxevbsCQAYMmQIGjdujHXr1umusWLFCvTr1w+DBg1Cy5YtYWtri3/++Qdyubz8fxCBQCAQCAQCoUKptBrfdu3aiXrmjRo1CqNGjRK9hrW1NVatWoVVq1aZ+enKDisrK8ybN49lZ0wg9SIGqRt+SL0IQ+qGH1Iv/JB6EYbUDT+VuV4o2lDcBwKBQCAQCAQC4TWg0po6EAgEAoFAIBAI5oQIvgQCgUAgEAiEKgERfAkEAoFAIBAIVQIi+BIIBAKBQCAQqgRE8K1ErF27FgEBAbC2tkZ4eDhOnDhR0Y9kNhYtWoSmTZvC3t4e7u7u6Nevny72spZRo0aBoijWv+bNm7PKFBQUYMKECXB1dYWdnR369OmDx48fs8qkp6dj+PDhcHR0hKOjI4YPH46MjIyy/okmM3/+fL3f7enpqTtP0zTmz58Pb29v2NjYoF27drh+/TrrGq9jvfj7++vVC0VR+OijjwBUrfZy/Phx9O7dG97e3qAoCn/99RfrfHm2kUePHqF3796ws7ODq6srJk6ciMLCwrL42QYRqxelUomZM2ciJCQEdnZ28Pb2xogRI3Sx37W0a9dOrx0NGTKEVeZVqxfAcJspz/5TmerGUL3wjTkURWHJkiW6Mq9jm5EyR7824wxNqBRs3bqVVigU9I8//kjfuHGDnjRpEm1nZ0c/fPiwoh/NLHTt2pXetGkTfe3aNfry5ct0z549aT8/PzonJ0dXZuTIkXS3bt3olJQU3b/nz5+zrvP+++/TNWrUoA8ePEjHxsbS7du3p8PCwuiioiJdmW7dutHBwcH06dOn6dOnT9PBwcF0r169yu23Gsu8efPohg0bsn53Wlqa7vzixYtpe3t7eufOnXRcXBw9ePBg2svLi87KytKVeR3rJS0tjVUnBw8epAHQ0dHRNE1Xrfayd+9e+rPPPqN37txJA6B3797NOl9ebaSoqIgODg6m27dvT8fGxtIHDx6kvb296fHjx5d5HfAhVi8ZGRl0p06d6G3bttG3bt2iz5w5Q0dGRtLh4eGsa7Rt25Z+9913We0oIyODVeZVqxeaNtxmyqv/VLa6MVQvzPpISUmhN27cSFMURd+7d09X5nVsM1Lm6NdlnCGCbyWhWbNm9Pvvv886FhQURH/yyScV9ERlS1paGg2APnbsmO7YyJEj6b59+wp+JyMjg1YoFPTWrVt1x5KSkmiZTEbv27ePpmmavnHjBg2APnv2rK7MmTNnaAD0rVu3zP9DzMC8efPosLAw3nNqtZr29PSkFy9erDuWn59POzo60uvWraNp+vWtFy6TJk2iAwMDabVaTdN01W0v3Mm6PNvI3r17aZlMRiclJenKbNmyhbaysqIzMzPL5PdKhU+I4XL+/HkaAEuh0LZtW3rSpEmC33nV64Wm+eumvPpPZa4bKW2mb9++dIcOHVjHqkKb4c7Rr9M4Q0wdKgGFhYWIiYlBly5dWMe7dOmC06dPV9BTlS2ZmZkAABcXF9bxo0ePwt3dHXXr1sW7776LtLQ03bmYmBgolUpWPXl7eyM4OFhXT2fOnIGjoyMiIyN1ZZo3bw5HR8dKXZfx8fHw9vZGQEAAhgwZgvv37wPQpOpOTU1l/WYrKyu0bdtW93te53rRUlhYiN9++w3vvPMOKIrSHa+q7YVJebaRM2fOIDg4GN7e3royXbt2RUFBAWJiYsr0d5qDzMxMUBQFJycn1vHff/8drq6uaNiwIaZPn47s7Gzdude5Xsqj/7yqdQMAT548wZ49ezBmzBi9c697m+HO0a/TOFNpM7dVJZ49ewaVSgUPDw/WcQ8PD6SmplbQU5UdNE1j6tSpaNWqFYKDg3XHu3fvjoEDB6JmzZp48OAB5syZgw4dOiAmJgZWVlZITU2FpaUlnJ2dWddj1lNqairc3d317unu7l5p6zIyMhK//PIL6tatiydPnuDLL79EixYtcP36dd0z87WNhw8fAsBrWy9M/vrrL2RkZLCyNVbV9sKlPNtIamqq3n2cnZ1haWlZ6esrPz8fn3zyCYYOHQoHBwfd8WHDhiEgIACenp64du0aZs2ahStXruDgwYMAXt96Ka/+8yrWjZaff/4Z9vb2GDBgAOv4695m+Obo12mcIYJvJYKpyQI0jY977HVg/PjxuHr1Kk6ePMk6PnjwYN3fwcHBiIiIQM2aNbFnzx69gYcJt5746qwy12X37t11f4eEhCAqKgqBgYH4+eefdc4mprSNV71emGzYsAHdu3dnaQCqansRorzayKtYX0qlEkOGDIFarcbatWtZ5959913d38HBwahTpw4iIiIQGxuLJk2aAHg966U8+8+rVjdaNm7ciGHDhsHa2pp1/HVvM0JzNPB6jDPE1KES4OrqCrlcrreSSUtL01v1vOpMmDABf//9N6Kjo+Hj4yNa1svLCzVr1kR8fDwAwNPTE4WFhUhPT2eVY9aTp6cnnjx5onetp0+fvjJ1aWdnh5CQEMTHx+uiO4i1jde9Xh4+fIhDhw5h7NixouWqanspzzbi6empd5/09HQolcpKW19KpRKDBg3CgwcPcPDgQZa2l48mTZpAoVCw2tHrWC9cyqr/vKp1c+LECdy+fdvguAO8Xm1GaI5+ncYZIvhWAiwtLREeHq7bJtFy8OBBtGjRooKeyrzQNI3x48dj165dOHLkCAICAgx+5/nz50hMTISXlxcAIDw8HAqFglVPKSkpuHbtmq6eoqKikJmZifPnz+vKnDt3DpmZma9MXRYUFODmzZvw8vLSbacxf3NhYSGOHTum+z2ve71s2rQJ7u7u6Nmzp2i5qtpeyrONREVF4dq1a0hJSdGVOXDgAKysrBAeHl6mv9MUtEJvfHw8Dh06hOrVqxv8zvXr16FUKnXt6HWsFz7Kqv+8qnWzYcMGhIeHIywszGDZ16HNGJqjX6txptTucQSzoA1ntmHDBvrGjRv05MmTaTs7OzohIaGiH80sfPDBB7SjoyN99OhRVgiY3NxcmqZpOjs7m542bRp9+vRp+sGDB3R0dDQdFRVF16hRQy9Uio+PD33o0CE6NjaW7tChA2+olNDQUPrMmTP0mTNn6JCQkEoXnorJtGnT6KNHj9L379+nz549S/fq1Yu2t7fXvfvFixfTjo6O9K5du+i4uDj6rbfe4g0h87rVC03TtEqlov38/OiZM2eyjle19pKdnU1funSJvnTpEg2AXr58OX3p0iVddILyaiPaMEMdO3akY2Nj6UOHDtE+Pj4VFoJJrF6USiXdp08f2sfHh758+TJr3CkoKKBpmqbv3r1Lf/755/SFCxfoBw8e0Hv27KGDgoLoxo0bv9L1QtPidVOe/aey1Y2hvkTTNJ2ZmUnb2trS33//vd73X9c2Y2iOpunXZ5whgm8lYs2aNXTNmjVpS0tLukmTJqxQX686AHj/bdq0iaZpms7NzaW7dOlCu7m50QqFgvbz86NHjhxJP3r0iHWdvLw8evz48bSLiwttY2ND9+rVS6/M8+fP6WHDhtH29va0vb09PWzYMDo9Pb2cfqnxaGMhKhQK2tvbmx4wYAB9/fp13Xm1Wk3PmzeP9vT0pK2srOg2bdrQcXFxrGu8jvVC0zS9f/9+GgB9+/Zt1vGq1l6io6N5+8/IkSNpmi7fNvLw4UO6Z8+etI2NDe3i4kKPHz+ezs/PL8ufL4hYvTx48EBw3NHGgn706BHdpk0b2sXFhba0tKQDAwPpiRMn6sWzfdXqhabF66a8+09lqhtDfYmmaXr9+vW0jY2NXmxemn5924yhOZqmX59xhir+wQQCgUAgEAgEwmsNsfElEAgEAoFAIFQJiOBLIBAIBAKBQKgSEMGXQCAQCAQCgVAlIIIvgUAgEAgEAqFKQARfAoFAIBAIBEKVgAi+BAKBQCAQCIQqARF8CQQCgUAgEAhVAiL4EggEAoFAIBCqBETwJRAIhCrIuHHjMHTo0Ip+DAKBQChXSOY2AoFAqIK8ePECVlZWsLOzq+hHIRAIhHKDCL4EAoFAIBAIhCoBMXUgEAiEKkZCQgIoisLDhw8r+lEIBAKhXCGCL4FAIFQxLl++DCcnJ9SsWbOiH4VAIBDKFSL4EggEQhXjypUrCAsLq+jHIBAIhHKHCL4EAoFQxbh8+TIRfAkEQpWECL4EAoFQxbhy5QoaNWpU0Y9BIBAI5Q4RfAkEAqEKkZWVhYSEBKLxJRAIVRIi+BIIBEIV4sqVK5DL5WjYsGFFPwqBQCCUO0TwJRAIhCrElStXEBQUBCsrq4p+FAKBQCh3SAILAoFAIBAIBEKVgGh8CQQCgUAgEAhVAiL4EggEAoFAIBCqBETwJRAIBAKBQCBUCYjgSyAQCAQCgUCoEhDBl0AgEAgEAoFQJSCCL4FAIBAIBAKhSkAEXwKBQCAQCARClYAIvgQCgUAgEAiEKgERfAkEAoFAIBAIVQIi+BIIBAKBQCAQqgRE8CUQCAQCgUAgVAmI4EsgEAgEAoFAqBL8Hyy3X6sF2Aj0AAAAAElFTkSuQmCC",
- "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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",
- "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/alpha_ca_error_model_comparison.ipynb b/examples/alpha_ca_error_model_comparison.ipynb
new file mode 100644
index 0000000..bb5195e
--- /dev/null
+++ b/examples/alpha_ca_error_model_comparison.ipynb
@@ -0,0 +1,1093 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6999ba52",
+ "metadata": {},
+ "source": [
+ "# A Gaussian-process discrepancy for $\\alpha + {}^{44}$Ca elastic scattering\n",
+ "\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), 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",
+ "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",
+ "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": {},
+ "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",
+ "import plotstyle\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\n",
+ "\n",
+ "plotstyle.use()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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, 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": "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",
+ "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))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "5a03da60",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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\"q from {q_data.min():.2f} to {q_data.max():.2f} fm^-1\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b2e6b303",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ " 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": "f4bbc9b2",
+ "metadata": {},
+ "source": [
+ "## The optical model\n",
+ "\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 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": 5,
+ "id": "91f59c49",
+ "metadata": {},
+ "outputs": [],
+ "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])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d95b3b0e",
+ "metadata": {},
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(np.rad2deg(angles), ratio, \"o\", ms=3, color=\"k\", label=\"data\")\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": "f58bf0a9",
+ "metadata": {},
+ "source": [
+ "## The error model\n",
+ "\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",
+ "### The inferred errors, $s$ and $b$\n",
+ "\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",
+ "### The discrepancy\n",
+ "\n",
+ "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": "6a54d545",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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\", prior=log_uniform(shortest, longest), latex=r\"\\log\\ell\"\n",
+ ")\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",
+ " coords=by_angle,\n",
+ " amplitude=T.constant_amplitude,\n",
+ " amplitude_params=(log_A,),\n",
+ " params=[log_ell],\n",
+ ")\n",
+ "gp_power = T.kernel(\n",
+ " Matern(0.1, nu=2.5),\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": 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": [
+ "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",
+ "colours = {\"constant\": plotstyle.COLOURS[1], \"power of q\": plotstyle.COLOURS[0]}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "c707911e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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:10s} columns: {p.names}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f17a91f",
+ "metadata": {},
+ "source": [
+ "## 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": 13,
+ "id": "f3d12040",
+ "metadata": {},
+ "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": 14,
+ "id": "4cadad4e",
+ "metadata": {},
+ "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": 15,
+ "id": "c213d49e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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: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": "b07bf0f2",
+ "metadata": {},
+ "source": [
+ "## Evidence, and what a Bayes factor is\n",
+ "\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",
+ "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",
+ "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": 16,
+ "id": "efffbe81",
+ "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(logz[\"power of q\"], logz[\"constant\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "578ce107",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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",
+ "power of q vs constant: dlogZ = 5.39 +/- 1.32 -> power of q favoured\n"
+ ]
+ }
+ ],
+ "source": [
+ "for name, (mean, err, _) in logz.items():\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": 18,
+ "id": "360da7f8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "labels = [f\"${q.latex}$\" for q in params]\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=labels,\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",
+ " bbox_to_anchor=(0.95, 0.95),\n",
+ " fontsize=9,\n",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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": [
+ "### The inferred errors and the discrepancy's hyperparameters\n",
+ "\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",
+ "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",
+ "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": 21,
+ "id": "571262cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 22,
+ "id": "94d95f16",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "p = 3.22 (68 % interval 2.39 to 3.84)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f\"p = {power_med:.2f} (68 % interval {power_lo:.2f} to {power_hi:.2f})\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "828bd51e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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.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": 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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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": "ddb882ac",
+ "metadata": {},
+ "source": [
+ "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",
+ "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",
+ "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": 26,
+ "id": "7de4ceb1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 27,
+ "id": "d23a080a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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, c68 in cov68.items():\n",
+ " print(\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": 28,
+ "id": "65192f70",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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.set(\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()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61e636ec",
+ "metadata": {},
+ "source": [
+ "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": "1f01181e",
+ "metadata": {},
+ "source": [
+ "## Where the potential fails, not just how badly\n",
+ "\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",
+ "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",
+ "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": 29,
+ "id": "f59ee077",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 30,
+ "id": "d946253c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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, (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: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": 31,
+ "id": "9c8629e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\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=colours[name],\n",
+ " hatch=hatch,\n",
+ " label=f\"{name} (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 potential\",\n",
+ " title=\"The discrepancy each model allows, by angle\",\n",
+ ")\n",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "- 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."
+ ]
+ }
+ ],
+ "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/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"
- },
- {
- "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\"$d\\sigma/d\\Omega$ [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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",
- "text/plain": [
- ""
- ]
- },
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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": {
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f2gBw//59lS/zV+U2Pbu4uGDJkiXSi1wIIb2Rrl69KiVqffv2xYQJE7BgwQLUrFkTa9asybcvSt26daWkLFfu43Hp0iV0794dERERAHKaeHOPnXv8mzdvIiMjA3FxcVJH1dq1a+f7oVGQunXrwshI9e1RvXp1VKpUKU9/IHt7e6k5HkCxY6tbt67KZTUg5/E/cOCAWrF27twZjRo1Qt++fREQEICOHTuiY8eO8PX1LbSPUHHup26MixYtwueff45GjRqhUaNGsLS0lM49ISFBpW5+z0lERAQsLCywbds2AJAeOyEEjIyMcPXq1QLP5/Lly3BwcMiT5Lq6uqJy5crSeyfX6695c3NzyOXyPM9lSeMpbStXroSPjw+WLVuGjz/+GIMHDy7yPpGRkbC3ty+wvyGQ8yUbGBiI0NBQdOzYES4uLjAxMcHz588B5DyPuR1pi+P1z7Tcsu3bt+Phw4dwcHCQyvMb1VVacZXG66Yg33//fbHjy0/u+ya/UVgZGRlFftb5+flh5syZ+Pbbb3H79m34+vri5MmTiImJgYODg0rfv5iYGHTt2hWBgYFYt26dlCzu27cP3bt3hxACX331VbHr6hImQjrK0NAwT5mRkRGys7MBQJr35/Uv61fLijM30OsdkXP3UVCfBgDSvETp6em4ceNGnttfH6aqVCqlVqoXL14gJSUFtra2ee5X0LkDkM4/99hPnz7Nc+yqVati3LhxKuf0aodCdeTXMdvIyKjA8lfnaCpubAXts7DH/lXm5uYIDw/H1q1b8e+//2LVqlWYNm0aGjdujF27dklJ7ZvcT50Yb926hYkTJ+Kzzz7D3LlzpfL4+Hj83//9X55fg/k9J5mZmTAyMsr39dS9e3eVL8vXKZXKfN8PubG+/n5Q55zeJJ7iyH3N576+cxXWn+TevXt49OgRAEj/FqWwxyjXggULsH//fpw7d04leZk3bx72799f4l/16ryvc+X32iituErjdVMQTfURyh0R9uDBgzz9fB48eFBoopvrm2++Qf/+/RESEoJnz56hd+/eGDJkCGrVqqXSsrhp0yZkZmZi/PjxUmIDAF27dkWDBg2wdu1aKbkpTl1dwkRIR129elXqHAnkJBtxcXFo0aIFAKBatWqQy+X59sKPjo4GkPOLG4DKC1KTqlWrhipVqsDJyUmtIalffPEFjhw5gpUrV2LGjBkYOHAgQkND87Q03LhxA0qlUuWDM/c8c88p943atWtXvP/++xo6I80ordgKex5NTEwwaNAgDBo0CEBOB98OHTrgxx9/LPS5Ken98nPhwgVkZ2ejd+/eKuWRkZFq78PT0xMnT57EDz/8AHNz82IdP7fTelJSksov2sePH+Px48fSa6c43iSe4shNOl9vNSuoxSm3dcTe3h4TJkzA//73PyxYsKDIocmenp44evQonj59WuCPg8jISLi4uORpwSnO85if/M7lypUrsLS0LPJSdXHiKu7nXWm8bgoyY8YMPH36tMh6Xbt2LTQRyu0kfebMGZVLmomJibh27Zra04J4e3urPJ6RkZFISEhQGSSTm7i9/jmdW5bbIlfcurqEfYR01IoVK1R+4SxevBipqanS9WgDAwMMHToUISEhKn2BkpKSsHjxYtSrV08aqp3b30OdN2BxGBgYICgoCDt27MDx48fz3H7t2jUkJSUBAHbt2oW5c+fiiy++wAcffID169cjPDxcpS9QroyMDGkILJDTX2f+/PmoWrUqunXrBgBo1aoVmjVrhh9++CHfvgFlNYttfkortmrVquX7HF6/fj3PB0zz5s1hamqap/+PJu5XkNxfplFRUVJZWloa/u///k/tfYwZMwYA8p10MDk5udBLUcOHD4dSqVRpjQJyLkfIZDKMGDFC7Tg0EU9xeHh4oEqVKiqT4WVnZ2P58uX51v/oo48QFxeHf/75B0FBQRg1ahSmTJlS5GzVo0aNgqGhIaZMmaLSCpOdnS1Ne+Dq6oqHDx/i8ePH0u3nzp2T+ueV1I4dO/Dw4UNp+/r169iyZQuGDBmSb2vR69SNq7ifd6XxuimIpvoINW3aFL6+vvjll19U3quLFy+GTCbDqFGjVOqvWLECf/75p0rZ6z+i09PTMX78eDRr1kxlktmOHTsCANauXatSPzIyElFRUejQoUOJ6uoStgjpqA4dOiAgIAD+/v64cuUKNm7ciClTpqjMVzF79mxcuHAB7dq1w3vvvQdra2v8/fffSElJwZ49e6RfRl27doW5uTk+/PBDdO3aFcbGxvnOI1QSM2fOxIMHD9ChQwcEBgbCy8sLCoUCFy5cwL1793DkyBHExcVh+PDh6NSpkzSE2t/fH9999x2mT5+ONm3a4O2335b22bJlSxw/fhzHjh2Du7s7QkJCcPHiRWzfvl36VS6TybBlyxb07dsXderUwaBBg+Dk5IS7d+/i2LFj8PHxyfPGLyulFVu/fv3w3nvvISgoCLVq1YKlpSU++ugj3LhxA506dYKfnx/q1asHIQS2bt2KKlWqYOLEiQXur6T3K0jjxo0xYMAATJw4EdHR0ZDL5di9ezemTp2KvXv3qrWPRo0a4a+//sIHH3yAEydOSPNRXb9+HaGhofj5558LXK+qbdu2mDVrFr744gtcvnwZTZs2xalTp7Bv3z4sWLAATZs2LfY5vUk8xVGpUiV8/fXXGDt2LJRKJRo0aIAjR45g7NixeTrrLlmyBOvXr8fvv/8u/ZpfvHgxzp49i4EDByIyMhJ2dnb5Hsfb2xurV6/GRx99hKioKHTu3BkvX77E/v37MXLkSNStWxfjx4/HmjVr0LZtWwwZMgTx8fE4deoUJk+ejClTppT4HMeMGYN+/fqhXbt2UCqV+OOPP1C/fn3MmTNHrfurG5elpSU6deqExYsXIyMjA9bW1tI8QvkpjddNQTTVRwgA1qxZg4CAALRu3RoDBgzAtWvXsG7dOixatCjPGnZffvklbGxspB/SQM5nd1paGlq0aIHU1FRs2rQJVatWxc6dO1UuFQYEBGDSpElYuHAhYmJi0Lp1azx8+BCrV6+Gt7e3SgJZnLq6hImQjmndujXGjRuHIUOGoEGDBtI8NceOHcszB4OVlRVCQ0OxZ88eaULFGTNmIDAwUOU6trOzMyIiIrBt2zbEx8cjOztb6mSXe7zXm5NdXFwwbty4Iq81GxoaYsWKFRg/fjz+/fdfxMfHo3r16pgyZQo6dOgAmUyGkJAQjBgxAjNmzFA5ztSpU6FUKhEVFYV+/fqp/CpcuXIltm3bhvDwcAQGBuLvv//OE4uzszPCw8Nx8OBBnDp1Cs+fP4eXlxfGjh2r8uU0ZMiQPBP/5Xr77bfz9E8oqH7uXDKve+edd/L0MXjT2Nq3b5+neXnEiBGwt7fHiRMncOfOHVhZWQHI6asSHR2NXbt2ITo6GoaGhpg+fTr69OmjMuHe69S9X3Fi3LhxI7Zt24aIiAiYm5vjr7/+Qp06dTBu3DiVJvyinpOAgADs2LEDV69ehRACPXr0QHBwcIH3yTV9+nT069cPu3btQkJCAjp27IiFCxeqXN6ws7PDuHHjVEb+5BozZkye+W/eJJ4PPvggz+vWxsYG48aNy9Pp9n//+x8aNWokzau1ZMkS1KtXT+WxS0tLw927d7Fw4UKVy66mpqbYvHkzFi1ahGPHjhXaojBkyBAEBARg27ZtiI2Nhb29vbQMApDz2r169Sr++usv3L9/H02aNMHcuXNx5coVjBs3TurXl99yFfmV5T7enTp1wsiRI7F27Vo8ePAAP/30E9555x2VwQeFPTfqxgUAW7ZswYYNG3D9+nUkJiZK8wgVtMRGabxuSlvdunURHR2NTZs2STOYX7hwId/4Ro0aleeza9OmTThw4ACOHz8OMzMzLF26FJ07d853gMX8+fMxatQoHDhwAPfu3YOzszM2bdqkMslsSerqCpnQ1fFspJd8fHxgb2+vdgsCERHRm2AfISIiItJbTISIiIhIb7GPEOmUwvqOEBERaRr7CBEREZHe4qUxIiIi0ltMhIiIiEhvsY9QEbKzs/HgwQNYWVnp7BwIREREpEoIgeTkZNSoUaPQpVeYCBXhwYMH0gJ3REREVL7cvXsXTk5OBd7ORKgAwcHBCA4ORlZWFoCcB5KjmYiIiMqHpKQkODs7S7PwF4SjxoqQlJQEuVwOhULBRIiIiKicUPf7m52liYiISG8xESIiIiK9xUSIiIiI9BY7SxMR6SClUonMzExth0Gks4yNjWFoaPjG+2EiRESkQ4QQePjwIZ4/f67tUIh0no2NDezt7d9onj8mQkREOiQ3CapWrRrMzc05kStRPoQQSE1NRUJCAgDAwcGhxPtiIkREpCOUSqWUBFWpUkXb4RDpNDMzMwBAQkICqlWrVuLLZOwsTUSkI3L7BJmbm2s5EqLyIfe98ib96ZgIERHpGF4OI1KPJt4rTISIiCqg1JdZcJ26G65TdyP1ZVapH+/ly5dYvnw5goKC8O2335b68UrL5cuX8fHHH2s7jDfy119/ITg4WNthlBtMhIiI6I1NnToVixcvRsOGDeHl5aXtcErs7t27WLVqlbbDeCMnT57E/v37tR2GZO3atfjll1+0HUaB2FmaiIje2OHDhzF69Ohy35pSEQwZMgQpKSnaDkNy/PhxPHnyBGPGjNF2KPliIkRERCWWlpaGsWPH4tatW/j7779x8eJFBAYGonv37sjOzsamTZtw4sQJmJqaomfPnmjfvr1036dPn2LKlCn48ssvsXXrVly7dg3Dhw/HqVOn4OLigv79+wMAdu/eja1bt+L777+Hvb09AGD69Ono0aMH2rRpg9GjR0OpVMLQ0BA1a9bE22+/jTp16hR5nJYtW+Lx48f49ddf8fTpU3h7e8POzq7Ic87MzMSGDRsQHh4OGxsbdO/eHa1atZIej8mTJ6Nnz57o1q0bACA7OxtTp06Fj48PBg8ejFWrViE7Oxuenp7Yu3cvkpOTMWDAAGkfr8b9559/4ubNm3B2dsY777yDmjVrSrfn7qdBgwbYsmULjI2NMWvWLMTGxuLZs2fo1KmTSr3atWvj0KFDUCgUCAwMRJs2bXD06FHs3LkTJiYmGDp0KDw9PUsUg7e3N3bv3o0XL16oPM/btm3D0aNHkZ6ejpEjRwIAJk2ahPr16xf5OJcVXhorQHBwMDw9PeHr61sq+49XpOHkzSeIV6SVyv6JiHI9VKSX2r4NDQ3RsmVLmJqaws3NDS1btoSjoyMAYPDgwZg4cSKqV68OmUyGrl27YtGiRdJ9k5OTsXLlSrRr1w4xMTHw8fFB1apVkZCQgGXLlkn1VqxYgd9//x0HDx4EADx79gxz586FpaUlAKBFixZo2bIlGjdujNu3b6NJkyY4ceJEkcd5/vw5mjVrhr1798LJyQlbt27FqFGjCj3ftLQ0tG/fHr/++itcXV0hk8nQu3dvLFmyBEDOkG53d3cMHjwY9+7dAwDMnj0bq1evRkBAAADg6NGjmDFjBt5//32Ym5sjPT0d7dq1w/bt26XjxMTEwMvLC2fPnkWdOnUQFxcHHx8fnDlzRqpz9OhRfPHFFxgzZgyqVasGHx8fAHkvjR09ehRTp07FpEmTYG1tjeTkZLRv3x7Dhw/H1KlTUaNGDdy7dw/NmzfH/fv3SxTDxx9/DAsLCyiVSnTu3BkhISEAAGdnZ1SvXh1VqlRBy5Yt0bJlS8jl8kIf4zInqFAKhUIAEAqFQmP7/PNkrKg5ZZeoOWWXcJu6S2w4c1tj+yai8istLU1ER0eLtLS0N95XWX/OeHh4iGXLlknbhw8fFgYGBuLy5ctS2YoVK4SlpaV4/PixEEKI2NhYAUB8++23Kvvat2+fMDc3FxkZGSI7O1vY2tqKAQMGiA8//FAIIcTWrVtF5cqVhVKpzDeWKVOmiO7du0vbBR3nyy+/FLVr1xYvX76Uyrp06SJMTU0LPM/vv/9e+Pr6iqysLKls7969wtzcXKSmpgohhMjOzhadO3cW/v7+IiwsTBgbG4udO3dK9UeMGCFMTU3F/fv3pbJp06aJWrVqiezsbCGEEJ06dRITJ05UOfbUqVOFv7+/yn4qV64snj9/rlIvKChI9OnTR6Weg4ODSElJkcoaN24satasKdLT06Wy2rVri59//lnaVjcGBwcH8eLFC6ls2LBhon///tL26NGjRWBgoCgNhb1n1P3+5qWxMhavSMNXOy5L29kCmL7lEtrVqQoHuZkWIyOiikIXPmeOHDmCRo0aqVxqGTJkCD766CNERESga9euUnn37t1V7uvn54fMzEycOXMGlpaWMDQ0RFBQED788ENp3+3atYOBQc5FjadPn2LdunW4desWXrx4gZs3byIuLi5PTK8f5+jRo+jbty+MjY2lsoEDB+Lo0aMFnteuXbuQmZmJoKAgCCEghEB6ejpSU1Nx7do1eHt7QyaT4Y8//kCjRo3QsWNHjBw5Ej179lTZT+vWrVGjRg1pe9CgQZg9ezbu378PW1tbHD58GCYmJhgzZox0nNjYWJw/fz7PftRpYWnVqpXK/FS1atWCsbExTE1NVcpyW7FSU1PVjqFVq1awsLCQtuvWrYsdO3YUGZOuYCJUxmKfpCBbqJYphUDck1QmQkSkEbrwOZOQkJCnv42ZmRksLCzw6NEjlXIbGxuVbQsLCzRr1gyHDx+GlZUV2rdvj1atWuHBgwe4e/cujhw5gvfeew8AcOfOHTRp0gQtWrRAhw4dIJfLIZPJEBUVlSem14+TkJCQZwbvovoIPXnyBA0aNECzZs1Uytu1ayf1XwIAe3t7NGjQAEePHsW7776bZz8FHTchIQEymQxKpRINGzZE7dq1pTrNmzfPs6/Xz6kgubMw5zIwMMi3TKlUAsi5/KhuDK/vx9DQUNpPecBEqIy52VnAQAaVDylDmQyudpxJlog0Qxc+Z5ydnXHgwAGVsqdPnyIlJQUuLi5F3t/f3x9HjhyBlZUVunTpAhMTE7Rs2RJbtmxBVFQU/P39AQB///03XFxcsHv3bpXjqBvj3bt3Vcpu375d6H0cHR1RqVIlqeNvQRYtWoSLFy/i7bffxsiRIxEZGanSIlPQcZ2dnWFtbQ0TExO4ubkVeZzSYmdnp7EYdH2CUHaWLmMOcjPM7u8Fw///wjCUyfBD/4ZsDSIijXGQm+Gb3g2kbQMZyvxzpm/fvrh16xZ27dollc2fPx9OTk5o0aJFkff39/fHyZMnERoaig4dOkhlP/74I+RyORo1agTgv8U3c1sgEhMT8euvv6oVY//+/bFx40aphSolJQUrVqwo9D5Dhw7FP//8g7CwMKlMCIF//vlH2r506RKmTZuG5cuXY/Xq1QCACRMmqOznzJkzOHXqlHT/RYsWoU2bNqhatSpMTU0xYMAAzJs3T6X17MWLF9izZ49a5/amNBmDra0tEhMTNR2ixrBFSAsG+bqgXZ2qiHuSClc7cyZBRKRxgU2d8OX2nH5CBya2h3tVyzI9fv369TFr1iwMHDgQnTp1gkKhwLlz57Bp06Y8l1Ly4+fnB6VSCRMTE2motb+/P7766iv06dNH6h80fPhwLF68GE2bNkW9evUQGhoKR0dHJCUlFXmMDz/8EH///Te8vb3Rrl07nDt3ThrxVpCRI0fi8uXL6NChA9q3bw9ra2tERkaiY8eOCAwMREZGBgYPHoxBgwZhwIABAIB169ahdevWeOutt9C7d28AgKenJwYNGoSmTZvizp07iI2NVWlBW7JkCQIDA+Hp6Yn27dsjPT0dV65cwfTp04s8L03RVAw9e/bETz/9hP79+8PW1lbnhs8zEdISB7kZEyAiKhP28kqlfozZs2fnmVF6ypQpGDBgAE6dOgVTU1P4+/ur9I2xs7PDihUrUK1atTz7s7CwwF9//aXSCbdly5ZYsWIFGjduLJVVr14d0dHROHjwIFJSUvDNN9/A2NgYJ0+eLPI4xsbGOHDgAA4ePIjExER8++23sLS0zHNJ73ULFizA+PHjcfr0aRgZGWH+/PnS3Dr379/Hp59+ikGDBkn1mzVrhpCQEJVJDn18fLBo0SKcPn0aycnJ6NSpk8pjY2Njg4MHD+LcuXOIjo6GnZ0dWrRogcqVK0t1PvjgA2RnZ+eJ7/UJFfOrN3r0aJVO4gDw6aefwtra+o1j6NGjh0qi06pVK1y5cgXh4eFISkrSueHzMiGEKLqa/sp90hQKhcoLhIhI09LT0xEbGws3NzdUqvRmyUvqyyx4ztwHAIj+tivMTfi7V1e89957yMrKwtq1a7UdSrlX2HtG3e9vvjOIiCogcxMjxM15S9thEOk8JkJERERlqKBLWqQdTISIiIjKULt27bQdAr2Cw+eJiIhIbzERqoC4oCsREZF6eGmsglkTFifNHWIgA2b398Ig36JncSUiItJHbBGqQApaaJEtQ0RERPljIlSBFLbQIhEREeXFRKgCyV1o8VVc0JVIT71MAb6W5/y9TCm6/htITU3F5s2bVWYzLs/HKYlHjx5h69at2g7jjZw/f15lDTV9wUSoAuGCrkSkDQkJCRgwYADi4+MrxHFKIjIyEu+++662w3gjv/32G+bOnavtMCSRkZHSwrSliZ2lKxgu6EpERCXRuHFjuLjozuCa5cuX48mTJ9i8eXOpHoeJUAXEBV2JSFtu3bqFmJgYuLq65llhfMuWLcjOzoahoSFq1qyJRo0awcgo79dQSkoKzpw5AyEEWrRoobLw6usePnyI48ePo3Xr1rh+/TqqVasmHff69eu4cOECevToAXPznC4C+/fvh7u7Ozw8PIqMJzU1FSEhIejevTvu3r2La9euwcfHR0oWIiIi8PTpUzRs2FDtxyc+Ph6RkZGwsbGBj4+PFFfusZo3b66SjBw8eBDm5uZo1aoVzp49CyEEGjRogAsXLiA5ORlt2rSR9vGqK1eu4ObNm3B2doaXlxcMDP67AJS7H09PT5w+fRqZmZno2rUrmjZtirS0tDz1GjRogIsXL0KhUKBNmzawsLBASkoKwsLCYGJighYtWsDU1LREMTRs2BAXL17Eixcv4OvrK60JdvnyZdy6dQvJyclSItSuXbt8F+h9Y0IPbNy4UXTo0EH06tVLnD9/vlj3VSgUAoBQKBSlFB0RUY60tDQRHR0t0tLS3nxnGS+E+Mo65+/xtTffXyFiY2MFANGrVy/h5uYmOnbsKMzMzMSkSZNU6g0cOFAEBgaKPn36CDc3N+Hl5SXu3bunUmfLli2icuXKomHDhqJjx46idu3a4syZMyrHuX79uhBCiOvXrws3NzcxZswYoVQqxejRo8XAgQOlfb3//vsCgAgJCRFCCJGRkSHMzMzEwYMH1Yon93h9+vQRtWrVEv369ROhoaEiKytL9OvXT8jlctG5c2fh6OgoevToIUxNTQt9nL744gvpPs2bNxc1atQQJ06ckG4fPHiw8PHxERkZGUIIIfbs2SOMjY3FyZMnhRBCjBgxQjRu3Fi4u7sLf39/Ubt2beHo6CiuXLki7SM5OVn07NlTODg4iO7du4tatWqJFi1aiIcPH0p1cvdTp04dERAQIP73v/8JIYQICgoSffr0UanXqFEjUbNmTdG5c2fh4eEhHBwcxLp164Sbm5vo3LmzcHV1FT4+PiqvWXVjaNKkiahfv74ICAgQDRo0EPb29uLq1atCCCHWr18v3N3dhaOjowgMDBSBgYHi4sWLeR7Twt4z6n5/V/hE6Pz588LBwUHs2bNH/Pbbb8LR0VGkp6erfX8mQkRUVjSaCJ3+9b9E6GsbISL+ePN9FiA3YejYsaP0JX7y5ElhYGAgjh8/nu99cpOJDz74QCq7ceOGMDU1FfPnz5fKHjx4ICULryZCkZGRonr16mL69OlS3fXr14vq1atL225ubqJJkybi888/F0IIcfToUWFiYiJSU1PViif3eIGBgSIrK0sqX716tbCxsRGxsbFCCCESExNF7dq1C02ENmzYIBwdHVUSrZ9++km4ublJ+37+/LlwdXUVEydOFAkJCaJ69eri66+/luqPGDFCABD79++XYu7Zs6fo3LmzVGfMmDGia9eu0vdcVlaW6Nu3rxg2bJjKfgwNDcXZs2dVYswvETIzM5MSrfT0dOHs7CwsLS3FjRs3hBBCvHjxQlStWlWsWrWq2DFUqlRJXLp0SQghhFKpFP7+/uLDDz+U6owePVoEBgYW+JgKoZlEqFxcGnv58iUSExNha2sLExOTAuukp6dLzWq5/vnnH3z00Ufo1q0bAGDDhg0IDQ1F586dSz1uIiKtUNwH9nz+37bIBnaOBzwCALljqR12woQJ0md0q1at0L59e2zYsAF+fn5SnWvXruHWrVt48eIFatSogcOHD0u3rV+/HtWqVcPEiROlMgcHBzg4OKgc59ixY5g4cSJmzpyJCRMmSOX+/v549OgRoqOjYWVlhYcPH2L27Nn46aefAABHjhxBixYtYGb2X9eBwuLJNXbsWBgaGkrbGzZswKBBg+Dq6goAqFy5MsaMGYPp06cX+Nj8/vvv8Pb2Rnh4uHTZTy6XIzY2Frdu3ULt2rUhl8uxdu1a+Pv74+DBg/Dw8MAXX3yhsp+mTZuiU6dOAABDQ0NMnjwZ7du3x9OnTyGXy7FmzRoEBQVhz549EDmNHXB1dcXff/+tsp+2bduiadOmBcb76mNar149AICpqSmaNm0KmUwGDw8PAICFhQW8vb1x9epVAEBWVpbaMXTo0AENGjQAABgYGMDf3x/79+8vMiZN0+lE6Pbt21i2bBlWr16NR48e4fDhw/D391epk5qaitGjR+Pvv/+GTCZD7dq18fvvv8PX1xcA8ODBA5U3oZubGx48eFCWp0FEVLYSb+YkP68SSiDxVqkmQm5ubirb7u7uuHPnDgAgPT0d/fr1Q1hYGJo1awa5XI779+8jISFBqn/79m3Url0bMtlr84C8JigoCJ06dVJJggDA3t4edevWxeHDh2FlZYVWrVqhS5cuGDZsGJKSknDkyBHpO0SdeHLVqFFDZfv27dt5vovc3d0LjTk2NhampqZYu3atSnlgYCCUSqW07efnhx49emDHjh04c+aMSgIG5P8YA8CdO3dQvXp1pKSkIDw8HDdv3lSp16ZNm0LPqSC2trYq26amprC0tMxTlp6eDiBnZJ+6MeS379z9lCWdToT+/vtvyOVyhISEFJi5jh8/HmFhYbh27Rrs7e0xYcIEvPXWW7h+/Trkcjmsra2RlJQk1VcoFJDL5WV1CkREZc/WA5AZqCZDMkPAtvAv6zf1/PnzPNu5X3Zr1qzB5cuXcffuXVhZWQEAgoOD8dVXX0n15XI5nj59WuRxVq9ejTFjxmDmzJn49ttvVW7z9/fHkSNHYGVlhQ4dOqBy5crw9PTEgQMHcOrUKamFRZ14cr2emNna2uZ7roWxsrJCixYtsHTp0kLrHT16FCEhIahduza+//57bN++vdDj5G7b2trC0tISMpkMY8aMwcCBAws9TlHJZkkVJwZdodPzCE2ePBnTpk0rsJd4cnIy/vjjD0ybNg01a9aEqakp5syZg6SkJGzcuBFATvPspk2bkJWVhYSEBBw9ehTNmjUr8JgZGRlISkpS+SMiKlfkjkD3H//blhkCvRaWamsQAJUv7aSkJBw8eFBqkY+Pj4ezs7OUdAA5o8he1blzZ0RFReHixYsq5a8nR02aNMH+/fuxZMmSPIlLbiL0auuPv78/5s6dC6VSiVatWqkdT0H8/Pywc+dOZGf/l2gWNZli165dsWnTJjx79kyl/NU5kZ4/f47hw4dj0qRJCAkJwaFDh/Drr7+q1D958iSePHmiclxnZ2c4OzvD2toaLVu2xIoVK/Icv6zmXtJkDJaWlsjIyNBUaAXS6Rahopw/fx4vX75UaW6ztraGt7c3zpw5g1GjRqFfv37466+/4ODggIyMDEyfPh1OTk4F7nP27Nn45ptvyiJ8IqLS4zMYCPks5/9BpwG72qV+yFWrViE7Oxv16tXDr7/+iho1amDEiBEAgB49euDbb7/FZ599hoYNG2Lr1q2IiIhQGa7etWtXvPPOOwgICMCECRNQrVo1bNu2De+88w6GDh2qcqymTZti//796NSpE2QyGb7++msAOUnPkydPkJqaiubNm0tlixYtQtu2baX+QerEU5BJkyZh9erV6NWrFwIDAxEaGorjx48Xep+pU6diz549aN68OT755BNYW1tL/YXOnTsHAPj4449hZ2eH7777DsbGxli0aBHGjh0Lf39/1KlTB0DO5aNOnTrhk08+QVxcHObPn49Vq1ZJQ9OXLl2KgIAAdOrUCQMHDkR6ejoOHjwIJycnBAcHF3lumqCpGJo2bYqVK1fi119/ha2tbakNn9fpFqGiPH78GABgZ2enUl61alXpOq+RkRG2bt2KS5cu4d69e5g6dWqh+5w2bRoUCoX0d/fu3dIJnoiorFir1x+kpCwsLKSEwNbWFqGhoejWrRuOHTsGY2NjAECzZs1w5MgRvHjxAgcPHkRAQAA2b96M3r17q+xr7dq1WLp0KW7duoWzZ88iKChISoJyj5PbRyU3Gbp8+bK0NIS9vT3GjBmj0nG7ffv2CAwMxMiRI6XjqBPP68fLVa1aNZw5cwb16tXDsWPH4OPjg927d6N///4FPkbW1tYICwvDlClTEBUVhdOnT6NZs2ZS3NHR0cjMzMS6deukx+yDDz7Ap59+qtJS1aNHD8yZMweXLl1CfHw8tm/fjiFDhki3+/j44PLly+jYsSNCQ0Nx69YtjBo1SiUB8fX1lZLEVzVu3BitW7cutF7Lli3zXFVp06YNfHx83jiGevXqISAgQNoeNGgQfvzxR5w6dQobNmzAo0ePCnx834RMCCGKrqZd9+7dg7Ozc57O0lu3bkX//v3x8OFDVK9eXSrv1q0bzMzMNLLuS1JSEuRyORQKRZ4RaUREmpSeno7Y2Fi4ubmhUqVKb7azlynAD/8/AZr+ADApeFJCKh/ee+89ZGVl5elwrc8Ke8+o+/1drluEHB1zrne/niU+evRI7R7xREREpL/KdR8hb29vWFtbY//+/WjUqBGAnOnWL168WOQlMCKiCs3EAvhaoe0oSIN8fX1VhtqTZuh0IpSWlgaFQiH1BUpMTMTDhw9haWkJS0tLmJqaYvLkyfj+++9Rp04duLi44LPPPkO9evUKvVarjuDgYAQHB/NFR0REOiEoKEjbIVRIOt1HaOPGjRg3blye8s8++wyffZYzGkIIgZ9++gl//vknkpOT4efnhx9//FFjl8bYR4iIyopG+wgR6QFN9BHS6URIFzAR+k+8Ig2xT1LgZmfB1e2JSgETIaLi0UQipNOXxkh3rAmLw5fbLwMADGTA7P5eGOTrouWoiCom/j4lUo8m3ivletRYaQoODoanp6e0Zpk+i1ek4asdl6XtbAFM33IJ8Yo0LUZFVPHkzh+Tmpqq5UiIyofc90rue6ck2CJUgKCgIAQFBUlNa/os9kkKsl9LupVCIO5JKi+REWmQoaEhbGxspAlhzc3NS21NKKLyTAiB1NRUJCQkwMbGJs/itMXBRIiK5GZnAQMZVJIhQ5kMrnbm2guKqIKyt7cHgHxXQSciVTY2NtJ7pqSYCFGRHORmmN3fC9O3XIJSCBjKZPihf0O2BhGVAplMBgcHB1SrVg2ZmZnaDodIZxkbG79RS1AuJkKklkG+LmhXpyrinqTC1c6cSRBRKTM0NNTIhzwRFY6JUAE4oWJeDnIzJkBERFShcB6hInAeISIiovJHLxZdJSIiInoTTISIiIhIbzERIiIiIr3FRIiIiIj0FhOhAnCJDSIiooqPo8aKwFFjRERE5Q9HjREREREVgYkQERER6S0mQkRERKS3mAgRERGR3mIiRERERHqLiVABOHyeiIio4uPw+SJw+DwREVH5w+HzREREREVgIkRERER6i4kQERER6S0mQkRERKS3mAgRERGR3mIiRERERHqLiRARERHpLSZCBeCEikRERBUfJ1QsAidUJCIiKn84oSIRERFREZgIERERkd5iIkQ6IV6RhpM3nyBekabtUIiISI8YaTsAojVhcfhy+2UAgIEMmN3fC4N8XbQcFRER6QO2CJFWxSvS8NWOy9J2tgCmb7nEliEiIioTTIRIq2KfpCD7tXGLSiEQ9yRVOwEREZFeYSJEWuVmZwEDmWqZoUwGVztz7QRERER6hYkQaZWD3Ayz+3vBUJaTDRnKZPihf0M4yM20HBkREekDdpYmrRvk64J2daoi7kkqXO3MmQQREVGZYSJUgODgYAQHB0OpVGo7FL3gIDdjAkRERGWOS2wUgUtsEBERlT9cYoOIiIioCEyEiIiISG8xESIiIiK9xUSIiIiI9BYTISIiItJbTISIiIhIbzERIiIiIr3FRIiIiIj0FhMhIiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWEyEiIiLSW0yEiIiISG8xESpAcHAwPD094evrq+1QiIiIqJTIhBBC20HosqSkJMjlcigUClhbW2s7HCIiIlKDut/fbBEiIiIivcVEiIiIiPQWEyEiIiLSW0yEiIiISG8xESIiIiK9xUSIiIiI9BYTISIiItJbTISIiIhIbzERIiIiIr3FRIiIiIj0FhMhIiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWEyEiIiLSW0yEiIiISG8xESIiIiK9xUSIiIiI9JaRtgMobUqlEuHh4QCAKlWqoHbt2lqOiIiIiHRFhU+E0tLSMH78eDx//hwNGzbE5s2btR0SERER6YgKf2nM0tISp06dwvz587UdChEREekYrbcIZWZmYvv27bh69SqGDRuGmjVr5qnz6NEj7NixA8nJyfDz80OLFi2k25KTk3H58uV8992yZctSi5uIiIjKP60mQuvXr8eUKVNQr1497N+/H23atMmTCJ09exadOnVCixYt4OLigq+//hpjx47FrFmzAACxsbEYP358vvs/ceIEDA0NS/s0iIiIqJzSaiLk4uKC06dPQ6lUwtnZOd86o0ePRrdu3bBhwwYAQJ8+fdC7d2+888478PLyQqNGjXDq1KmyDJuIiIgqCK32EfLz84ODg0OBt8fGxuLcuXP48MMPpbK33noLDg4O+Oeff9Q+TkREBGJiYpCYmIhTp05BoVAUWDcjIwNJSUkqf0RERFQx6XRn6atXrwIA6tSpI5XJZDLUqlVLuk0dM2bMwKZNm5Camorx48fjypUrBdadPXs25HK59FdQSxURERGVf1rvLF2YFy9eAADkcrlKuY2NjXSbOvbu3at23WnTpmHixInSdlJSEpMhIiKiCkqnEyELCwsAOcmIjY2NVK5QKFC9evVSOaapqSlMTU1LZd9ERESkW3T60ljdunUBADdu3FApv3nzpnQbERERUUnpdCLk4eGBRo0aYdWqVVLZvn37cO/ePfTr169Ujx0cHAxPT0/4+vqW6nGIiIhIe2RCCKGtg1+8eBE7duxAUlIS5s2bhw8++ABubm5o164d2rVrBwAICwtDly5d0LFjR7i4uGDt2rX44IMP8NNPP5VJjElJSZDL5VAoFLC2ti6TYxIREdGbUff7W6t9hJRKJdLT02FiYoIZM2YAANLT05GVlSXVadWqFa5cuYItW7YgOTkZW7duhb+/v5YiJiIioopEqy1C5QFbhIiIiMofdb+/dbqPEBEREVFpYiJUAHaWJiIiqvh4aawIvDRGRERU/mi0s/TBgwexdetWtQ/eqVMn9O3bV+36RKUlXpGG2CcpcLOzgIPcTNvhEBGRjlErEYqMjMS+ffvQoEGDIuveuHEDlSpVYiJEWrcmLA5fbr8MADCQAbP7e2GQr4uWoyIiIl2i9vD5Pn36YP78+UXWmz9/Ph4+fPhGQRG9qXhFGr7acVnazhbA9C2X0K5OVbYMERGRRK1EqHfv3sjIyFBrh8Wpq8uCg4MRHBwMpVKp7VCoBGKfpCD7td5vSiEQ9ySViRAREUmK3Vn6ypUrSEtLQ5MmTUorJp3CztLlU7wiDX5zDqkkQ4YyGY5P7cBEiIhID5TaPEL//vsv1q5d+0bBEZU2B7kZZvf3gqFMBiAnCfqhf0MmQUREpKLYS2zUrVsXO3bsKI1YiDRqkK8L2tWpirgnqXC1M2cSREREeRQ7EWrRogWSkpIQFBSEoUOHomrVqiq3V65cGVWqVNFYgERvwkFuxgSIiIgKVOxEaOXKlTh79izOnj2LpUuX5rl90qRJao0uIyIiItK2YneWTkpKQmJiYoG3y+VyVK5c+Y0D07ZXR41du3aNnaWJiIjKEXU7S3OJjSJw1BgREVH5U6qrz//xxx+IiIgAACQkJKBNmzaws7PDl19+WbJoiYiIiLSg2InQzZs3MW/ePHh7ewMA5syZAwMDA8ybNw/Lly9HWFiYxoMkIiIiKg3F7iwdGhqK1q1bw8go5667d+/GqlWr0Lp1a1y5cgWhoaFo1aqVxgMlIiIi0rRitwgJIZCcnAwAuH37Nh48eIDmzZsDAIyNjbkkBREREZUbxU6E2rdvjx07duCbb77Bhx9+iN69e0utQ8ePH0fbtm01HiQRERFRaSh2IuTh4YHff/8dR44cgZ2dHX766ScAwPnz51GtWrUKkwgFBwfD09MTvr6+2g6FiIiISonaw+djYmJQqVIl1KxZs7Rj0ikcPk9ERFT+aHz4/OnTp1G7dm14eXlh2rRpOHHiBLKzszUSLBEREZE2qJ0IDR8+HE+ePMHMmTPx4MED9OvXD9WqVcOwYcOwceNGKBSK0oyTiIiISONKPLN0dnY2Tp8+jV27dmHXrl24cuUK/Pz80LNnT/Ts2RN169bVdKxawUtjRERE5U+pziwNAAYGBmjVqhVmzZqFCxcu4ObNmxg4cCAOHTqExo0b46uvvirpromIiIjKRKmsNZaWloZHjx7B1dVV07suc2wRIiIiKn9KtUUoISEB48ePR+3atWFmZgZ3d3eMGjUK9+7dAwCYmZlViCSIiIiIKrZiL7Hx4sULtG7dGmZmZhgzZgycnJzw6NEjrFu3Dr6+vjh//jyqV69eGrESERERaVSxE6Ft27ahUqVKCA8PR6VKlaTyoKAgdOjQAWvXrsWkSZM0GqQ2BAcHIzg4mEuGEBERVWDFvjSWkZGB1q1bqyRBAGBoaAh/f3+kp6drLDhtCgoKQnR0NMLDw7UdChEREZWSYidCvr6+OHLkCJKSklTKMzIyEBISgpYtW2osOCIiIqLSpNalsQsXLiAsLEzatrCwQMOGDfHuu++iRo0aePz4MTZt2gSlUgkLC4tSC5aIiIhIk9RKhM6ePYv58+erlJmYmOCff/7JU/fIkSNsFSIiIqJyoVTmEapIOI8QERFR+VPqM0sTERERlXdqJUKHDx/Gjh071NphceoSUdmKV6Th5M0niFekaTsUIiKdoFYiFBERgdDQULV2WJy6RFR21oTFodXsQxi84jT85hzCxvA72g6JiEjr1J5QcenSpVi9enWR9dLS0vDxxx+/SUxEpGHxijR8teOytJ0tgOlbLqFdnapwkJtpMTIiIu1SKxHq1asXnJyc1N5pvXr1ShwQEWle7JMUZL8yLMIeT+Eme4gHt13g0Kih9gIjItIytRKhunXrom7duqUdi07hEhtUkbjZWcBAltMSNNDwMGYb/QZDmYDYOhvIWgQ0Ga7tEImItILD54vA4fNUUWwMv4PFW44i1GQsDGWvvO1lhsD4KEDuqL3giIg0jMPniUjFIF8X7Bhir5oEAYBQAom3tBMUEZGWMREi0iNVnD0B2Wtve5khYOuunYCIiLSMiRCRPpE7Ar0W5SQ/QM6/vRbyshgR6S21h8+/KiYmBqGhoUhJSUHNmjXRuXNnWFpa4sCBA1AoFAgMDNR0nESkKU2GAx4BOZfDbN01kgTFK9IQ+yQFbnYWHI5PROVKsRKhtLQ0jB49GmvWrAEAGBoaQqlUQi6XY9GiRXj8+DEePnzIRIhI18kdNdYKtCYsDl9uz5mjyEAGzO7vhUG+LhrZNxFRaSvWpbHRo0fj+PHj+Oeff/D8+XNkZGQgJiYGo0aNwsiRI/Hnn3+WVpxEpIMKmqiRS3gQUXmhdiIUExODbdu2Yf/+/ejfvz/kcjkMDQ1Rp04d/PjjjwgJCcGVK1dKM1Yi0jGvT9QIAEohEPckVTsBEREVk9qXxo4fP46AgAB4eHjke3vnzp2xfv16WFpaaiw4ItJtr07UmMtQJoOrnbn2giIiKga1W4RSU1NhZ2dXaJ1evXrB39//TWMionLCQW6G2f29YCiTAchJgn7o35Adpomo3FC7RcjDwwO//vorsrOzYWCQN39KT0+Hr68vunbtivnz52s0SCLSXYN8XdCuTlXEPUmFq505kyAiKlfUbhEKCAjAs2fPMGPGjDzrb8XHx6Nbt26Ijo7WeIBEpPsc5GZo5VGFSRARlTtqtwiZmppi3bp1eOutt7B9+3b4+/vDxsYGMTEx2L17Nxo0aIDJkycjKyurNOMlIiIi0phiDZ9v3749zp07h1atWuHff//FypUrcffuXXz//fcICwsrsg8RERERkS5Ra/X548eP4/r16/Dx8UGDBg1gYmKSb73MzEwIIQq8vTzi6vNERETlj7rf32pdGnvw4AFmzpyJe/fuwdjYGJ6envDx8UHjxo3h4+MDHx8fyOVyGBsba+wEtC04OBjBwcF5+kMRlTYuV0FEVHbUahHK9fTpU0RGRuKXX37BP//8A2trayQlJQEA3Nzc4OPjg5EjR6JHjx6lFnBZY4sQlaUKuVyF4j6QeBOw9eDirkRUZtT9/i5WH6EqVaqgZs2aOH/+PK5cuQKFQoGkpCRs2LABlSpVwq1bt3Dv3r03Dp5IH1XI5SrOrAAWeAJ/9AIWNgTOcRkeItItxUqEAODff/9Fly5dUK9ePQCAlZUVBg0ahPDwcBgZGeGtt97SeJBE+qDCLVehuA/s+fy/bZEN7ByfU05EpCOKnQhVr14dFy5cwOtX1CwsLNCzZ0/8/fffGguOSJ/kLlfxqnK9XEXizZzk51VCCSTe0k48RET5KHYi1LNnTzx+/BgjR45EYmKiym0XLlzAixcvNBYckT6pcMtV2HoAstc+YmSGgK27duIhIspHsTpL57pz5w769++PmJgYtGvXDs7Ozjh//jwiIiJw+vRpNGnSpDRi1Qp2lqayFq9IqzjLVZz7M+dymFDmJEG9FgJNhms7KiLSA+p+f5coEQKA7OxsbNmyBSEhIbh//z7s7e0xatQo+Pn5lThoXcREiOgNKe7nXA6zdeeoMSIqMxpPhA4dOoT4+Hh0794dtra2GgtU1zERIiIiKn80PnxeCIGff/4Z1atXR9u2bTF37lxcvny56DsSERER6ahirT4fERGBO3fuYPjw4Th58iRatGgBd3d3jB07Fvv27UNGRkZpxkpERESkUSXuIwQAGRkZOHToEHbt2oXdu3fj6dOn6NSpE3r27IlevXqhWrVqmoxVK3hpjIiIqPwptc7SaWlpiIiIQMuWLWFkpLpUWVRUFHbt2oVdu3ahTZs2mDt3bsmi1yFMhIiIiMqfUkuE7t27B2dnZ1y/fh3u7u4wMMj/6lp2dnaBt5UnTISIiIjKn1JZa+xVPXv2hJGRESwtLdG3b1/s3btXdccVIAkiIiKiiq3E2YqjoyP++OMPLF68GFZWVujbty+mTp2qydiIiIiISlWxL43dv38f7u7uUCgUqFSpklQeFRUFf39//PHHH+jZs6fGA9UWXhojIiIqf0rt0ljVqlVhZGSUp6O0l5cXPvvsM2zYsKH40RIRERFpQbETIRMTE9StWxfz58/Pc5tcLsfjx481EhgRERFRaTMqukpeP//8M9566y2cPn0aQ4cORa1atXDnzh3MnTsXb7/9tqZjJCIiIioVJeos7e/vj/DwcBgYGGDYsGHw8fFB79694eDggGnTpmk6xjeWmpqKf//9F2FhYXiD+SOJiIioglGrs/TDhw9hYmKS72KraWlpuHnzJszMzODh4VEqQb6JqKgoDBo0CM7OzoiLi4OzszP2798PmUym1v3ZWZqIiKj8Uff7W61LY2vXrsXkyZPh4uKCxo0bw8fHBz4+PmjcuDFq1qyJhg0baixwTcvMzMS+ffvg7OyMjIwMuLi4SJNCEhERkX5Tq0UoIyMDly5dwvnz5xEZGYmTJ08iMjISAFC5cmV4e3tLCVL79u1Rs2ZNtQN48uQJVq9ejatXr2Ly5MmoW7dunjpRUVHYsGEDkpOT4efnh4EDB0otOk+ePMGBAwfy3fegQYNUWn5iY2PRs2dPREVFqT3hI1uEiIiIyh+NtgiZmpqiadOmaNq0KZKSktC2bVvMmTMHzZs3x7179xASEoLFixfDzMwMY8aMwbx589QKMjg4GD/88AO6du2KVatWYejQoXkSoX379qFXr1547733ULNmTUyYMAG7d+/Gn3/+CQB4+vQptm3blu/+X02YYmNjMXz4cGzevJmzXhMRERGAEkyo+Mcff+DgwYNSIpIrPDwcEyZMwJ49e2BlZaXWvq5evQo3Nzc8fvwYzs7OOHz4MPz9/VXq1KlTB506dcLSpUsBAGFhYWjdujVOnDiB1q1bq3WciIgIjB07FmvWrCl2Pya2CBEREZU/pTahYmZmJjIzM/OU+/r6olmzZgW2zuSnXr16MDU1LfD2q1ev4vr163j33XelslatWsHNzQ07d+5U6xhhYWHo0qUL3n77bYSHh2PDhg149uxZgfUzMjKQlJSk8kdEREQVU7EToe7du2P37t35JjwGBga4fv26JuICANy4cQMA4OrqqlLu6uoq3VaU5ORkdO7cGWfOnMG2bduwbdu2QhOh2bNnQy6XS3/sVE1ERFRxFXtCRUdHR6xevRpDhgyBv78/+vbtC2dnZ5w/fx7Lli3DmjVrNBZcWloaAOS51GZlZSXdVpQuXbqgS5cuah9z2rRpmDhxorSdlJTEZIiIiKiCKtHM0v3794enpye+//57TJkyBc+ePYNcLseECRM0OrN07jW9Z8+ewcbGRipPTEwsteTE1NS00Mt1REREVHGUKBECcvr3rF27FkBOy42ZmZnGgsrVoEEDAEB0dDTc3NwAAEqlEjExMejevbvGj0dEuiVekYbYJylws7OAg1zznzFERBoZR14aSRAAODk5oW3btggODkZ2djYAYP369UhMTMSAAQNK5Zi5goOD4enpCV9f31I9DhHlb01YHFrNPoTBK07Db84hbAy/o+2QiKgCKvbweU06efIkfv/9d6SmpmL9+vXo0aMHHBwc0Lt3b/Tu3RtAzsixTp06oWrVqnBycsLBgwfxww8/YPz48WUSI4fPE5W9eEUa/OYcQvYrn06GMhmOT+3AliEiUotGJ1QsLVWrVkXLli0BAB07dpTKnZycpP/Xq1cPMTExOHjwIJKTk7Fw4UKdXNOMiDQn9kmKShIEAEohEPcklYkQEWmUVhOh2rVro3bt2kXWs7CwkFqIiKjic7OzgIEMeVqEXO3MtRcUEVVIXGuCiHSOg9wMs/t7wfD/L5FjKJPhh/4N2RpERBqn1RYhXRYcHIzg4GAolUpth0Kklwb5uqBdnaqIe5IKVztzJkFEVCq02lm6PGBnaSIiovKn1NYaIyIiIqoomAgRERGR3mIiRERERHqLiVABOLM0ERFRxcfO0kVgZ2kqCNfBIiLSXeViZmmi8mpNWBy+3H4ZAGAgA2b398IgXxctR0VERMXFS2NExRSvSMNXOy5L29kCmL7lEuIVaVqMioiISoKJEFExFbYOFlG5pLgPxIbm/EukZ3hpjKiYuA4WVShnVgAhn+X8X2YA9FoENBmu3ZiIyhBbhArAUWNUEK6DRRWG4j6w5/P/tkU2sHM8W4ZIr3DUWBE4aowKEq9I4zpYVL7FhgJ/9MpbPmIX4Na27OMh0iCOGiMqZQ5yMyZAVL7ZeuRcDhPZ/5XJDAFbd+3FRFTGeGmMiEhfyR1z+gTJDHO2ZYZAr4U55UR6gi1CRET6rMlwwCMASLyV0xLEJIj0DBMhIiJ9J3dkAkR6i5fGiIiISG8xESoAh88TERFVfBw+XwQOnyciIip/1P3+ZosQERER6S0mQkRUMXC9LCIqAY4aI6Ly79yfwM5xORMDcr0sIioGtggRUfmmuP9fEgRwvSwiKhYmQkRUviXeVF0iAgCEMmeCQCKiIjARIqLyLXe9rFdxvSwiUhMTISIq37heFhG9AXaWLkBwcDCCg4OhVCq1HQoRFaWirpeluJ9z6c/Wo+KcE5GO4YSKReCEikSkFWdWACGf5fy/Io2EY3JHZYQTKhIRlVeK+8Cez//brigj4c6sABZ4An/0AhY2zJn2gEjLmAgREemaijgSrqImd1TuMREiItI1FXEkXEVM7qhCYCJERKRrKuJIuIqY3FGFwFFjRES6qKKNhMtN7naOz2kJqgjJHVUITISIiHSV3LFiJQoVLbmjCoGJEBERlZ2KltxRucc+QkRERKS3mAgRERGR3mIiVIDg4GB4enrC19dX26EQERFRKeESG0XgEhtERETlD5fYICIiIioCEyEiIiLSW0yEiIiISG8xESIiIiK9xUSIiIiI9BYTISIiItJbTISIiIhIbzERIiIiIr3FRIiIiIj0FhMhIiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWEyEiIiLSW0yEChAcHAxPT0/4+vpqOxQiIiouxX0gNjTnX6JCyIQQQttB6LKkpCTI5XIoFApYW1trOxwiIirKmRVAyGc5/5cZAL0WAU2GazcmKnPqfn+zRYiIiCoOxX1gz+f/bYtsYOd4tgxRgZgIERFRxZF4Myf5eZVQAom3tBMP6TwmQkREVHHYeuRcDnuVzBCwdddOPKTzmAgREVHFIXfM6RMkM8zZlhkCvRbmlBPlw0jbARAREWlUk+GAR0DO5TBbdyZBVCgmQkREVPHIHZkAkVp4aYyIiIj0FhMhIiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWEyEiIg2KV6Th5M0niFekaTsUIt2nA4vjcvg8EZGGbAy/g2lbopAtAAMZMLu/Fwb5umg7LCLdpCOL47JFiIhIA+IVaVISBADZApi+5RJbhojyo0OL4zIRIiLSgNgnKVISlEspBOKepGonICJdpkOL4zIRIiLSADc7CxjIVMsMZTK42plrJyAiXaZDi+MyESIi0gAHuRlm9/eCoSwnGzKUyfBD/4ZwkJtpOTIiHaRDi+PKhBCi6Gr6KykpCXK5HAqFAtbW1toOh4h0XLwiDXFPUuFqZ84kiKgoivultjiuut/fejFqLC4uDqtXr4aBgQGGDRsGNzc3bYdERFoWr0hD7JMUuNlZaDRhcZCbMQEiUpcOLI5b4S+N3bt3DwMGDICRkRGeP3+Oli1bQqFQaDssItKiNWFxaDX7EAavOA2/OYewMfyOtkMiIi2p8C1C1tbWOHr0KMzNczosHjx4EHfu3IGXl5eWIyMibYhXpOGrHZel7dxh7u3qVGVLDpEe0moilJ2djZCQEPzyyy+4evUq1q1bhxYtWuSpt3z5cvz5559ITk6Gn58fvvvuO9jZ2QEAbt68iXnz5uW7/6VLl8La2hqPHz/GxIkTERsbizZt2jAJItJjhQ1zZyJEpH+0mghNmTIF0dHR6Nu3L3bv3o20tLwTj/3000/4+uuvsXz5ctSsWRNTp05Fly5dEB4eDkNDQ1hYWMDHxyff/cv+/+gNU1NT+Pj4wNbWFtu3b8e9e/fg5ORUmqdGRDoqd5j7q8kQh7kT6S+tjhp7+fIlTExMcO/ePTg7O+Pw4cPw9/eXbs/MzET16tUxZcoUTJkyBUBOnx8XFxds2rQJgYGBRR4jOjoarq6u0qWxQYMGoU+fPhg8eLBaMXLUGFHFszH8DqZvuQSlENIwdy6FQVSxlItRYyYmJoXefuHCBTx79gxdu3aVypycnODl5YUjR46olQglJyejVatW8Pb2RkJCAq5evYrFixcXWD8jIwMZGRnSdlJSkhpnQkTlySBfF7SrU5XD3IlItztL3717FwBgb2+vUm5vb4979+6ptY8WLVpg7969OHToEORyOQICAmBmVvCH3uzZs/HNN9+UPGgiKhc4zJ2oEIr7Octg2HpofXh7adPpRCg7O2cdEmNjY5VyExMTKJVKtffj4OCAIUOGqFV32rRpmDhxorSdlJQEZ2dntY9FRERUrunIqvBlRafnEcodGfb06VOV8idPnki3aZqpqSmsra1V/oiIiPSCDq0KX1Z0OhHy8fGBsbExwsLCpLKUlBRcuHABzZo102JkREREFZAOrQpfVnQ6EZLL5XjnnXcwd+5cPH78GNnZ2fjqq69QqVIlvPPOO6V67ODgYHh6esLX17dUj0NERKQzdGhV+LKi1URoy5YtqFWrFtq2bQsAGDJkCGrVqqUyqmvJkiWoU6cOHB0dUblyZWzZsgXbt2+Hra1tqcYWFBSE6OhohIeHl+pxiIiIdIYOrQpfVrQ6j1BycjIePXqUp9zW1jZPovP8+XOkpKSgRo0a0kSJZYHzCBERkd4pxVXhy0q5mEfIysoKVlZWatW1sbGBjY1N6QZEREREOrEqfFnR6T5C2sQ+QkRERBWfVi+NlQe8NEZERFT+qPv9zRYhIiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWE6ECcNQYERFRxcdRY0XgqDEiIqLyh6PGiIiIiIrARIiIqDgU94HY0Jx/iajc0+oSG0RE5cqZFUDIZzn/lxnkLE7ZZLh2YyKiN8IWISIidSjuA3s+/29bZAM7x7NliKicYyJUAI4aIyIViTdzkp9XCWXOCt1EVG4xESpAUFAQoqOjER4eru1QiEgX2HrkXA57lcwQsHXXTjxEpBFMhIiI1CF3zOkTJDPM2ZYZAr0W5pQTUbnFztJEROpqMhzwCMi5HGbrziSIqAJgIkREVBxyRyZARBUIL40RERGR3mIiRERERHqLiVABOHyeiIio4uOiq0XgoqtERETlDxddJSIiIioCEyEiIiLSW0yEiIiISG8xESIiIiK9xUSIiIjoTSjuA7GhOf9SucOZpYmIiErqzAog5LOc/8sMctajazK8dI6luA8k3sxZAJizm2sMW4QKwHmEiIioUIr7wJ7P/9sW2cDO8aXTMnRmBbDAE/ijF7CwIXDuT80fQ08xESpAUFAQoqOjER4eru1QiIhIFyXezEl+XiWUOYvyalJZJlx6iIkQERFRSdh65FwOe5XMELB11+xxyirh0lNMhIiIiEpC7pjTJ0hmmLMtMwR6LdR8/52ySrj0FDtLExERlVST4YBHQE7rjK176XRiljsC3X98pVN2KSVceoqJEBER0ZuQO5Z+UtL8I6Buj9JNuPQUEyEiIqLyoCwSLj3EPkJERESkt5gIERERkd5iIkRERER6i4kQERER6S0mQkRERKS3mAgVgGuNERERVXwyIYTQdhC6LCkpCXK5HAqFAtbW1toOh4iIiNSg7vc3W4SIiIhIbzERIiIiIr3FRIiIiIj0FhMhIiIi0ltMhIiIiEhvMREiIiIivcXV54uQO7tAUlKSliMhIiIideV+bxc1SxAToSIkJycDAJydnbUcCRERERVXcnIy5HJ5gbdzQsUiZGdn48GDB7CysoJMJstze1JSEpydnXH37t0KPeEiz7Ni4XlWPPpyrjzPiqU0z1MIgeTkZNSoUQMGBgX3BGKLUBEMDAzg5ORUZD1ra+sK/WLNxfOsWHieFY++nCvPs2IprfMsrCUoFztLExERkd5iIkRERER6i4nQGzI1NcVXX30FU1NTbYdSqnieFQvPs+LRl3PleVYsunCe7CxNREREeostQkRERKS3mAgRERGR3mIiRERERHqLidAbePr0KcLDw/Hw4UNth6IxmZmZuHTpEm7dugWlUllgvVu3biEiIgIpKSllGJ3mpaWl4fjx44iJicn39ocPHyI8PBxPnz4t48g0KzY2FlFRUQU+p8+ePcPZs2dx//79Mo5Mc54/f47IyEhcu3YNWVlZ+dZJS0tDREQEbty4UcbRldzz589x/PhxPHr0qMA6CQkJCA8Px+PHj9+ojjalp6fj5MmTiI2NLbDO/fv3ceHCBbx48aLAOgqFAmfPnsXdu3dLI8w3lp2djfDwcERFRRVZNyoqCsePH8/3fZuRkYFz584V+NmlC6KionD69OlC6yQlJeHcuXN4/vx5vrdnZ2cjKioKFy9eLPQ76Y0IKpGZM2cKU1NT4enpKUxNTcWHH34olEqltsMqsfT0dDF16lRRpUoV0aBBA1GjRg1Rq1YtERoaqlLv+fPnokOHDsLKykrUqVNHWFlZiXXr1mkp6jf3/vvvCwMDAzFo0CCV8qysLPHhhx+KSpUqSc/xzJkztRRlyUVHR4umTZsKOzs70bRpU1GvXj1x5swZlTpz5swRlSpVEvXr1xeVKlUSgwcPFi9fvtRSxMWXnZ0tgoKChJmZmfDx8REuLi7CyclJ/Pvvvyr1tmzZIuRyuahVq5aQy+WidevW4vHjx1qKumjXr18XH3zwgXBwcBAymUysWLEiT53s7GwxduxYlc+iSZMmFbuONj158kR89tlnokaNGsLMzEyMGzcuT509e/YIb29vUaNGDdGoUSNhbm4upk6dmqfe4sWLhZmZmahfv74wMzMT/fv3F+np6WVwFkV7+fKlmD17tnB3dxdyuVy0b9++0PonTpwQJiYmAoB49uyZym0hISGiSpUqwt3dXVSuXFk0bdpUPHjwoPSCL6bff/9d+Pj4iMqVK4sqVarkWycrK0uMHz9emJmZicaNGwsXFxfx1VdfqdS5ePGi8PDwEA4ODsLR0VHUrFlTnDt3TuPxMhEqgW3btgljY2Nx4sQJIYQQV69eFXK5XCxatEjLkZXc48ePxezZs0VSUpIQQgilUik+/vhjUaVKFZGamirVe++994Snp6d4/vy5EEKIZcuWCWNjY3Hjxg2txP0m/vrrL9GsWTPRsWPHPInQwoULhY2NjYiJiRFCCHHs2DFhZGQktm/fro1QSyQxMVHUqFFDjBgxQkpsbt++LXbs2CHVOXDggDAwMBAHDhwQQggRGxsr7OzsxKxZs7QSc0ls2bJFyGQyERERIYTI+eIfNWqUqF69ulTn7t27wszMTPz8889CCCGSk5OFt7e3GDBggFZiVseuXbvEb7/9JlJSUoSpqWm+idBvv/0mLC0txcWLF4UQQoSHhwtTU1OVHyfq1NGmyMhI8eOPP4rHjx+Lpk2b5psIBQcHiwsXLkjbYWFhwtTUVPzxxx9S2alTp4RMJpNe3/fv3xc1atQQ06ZNK/VzUMfz58/FlClTxM2bN8WHH35YaCL07Nkz4e7uLiZNmpQnEXr8+LGwsrIS3377rRBCiLS0NNGyZUvRvXv3Uj4D9U2dOlVERESIBQsWFJgIjR8/Xjg4OIirV68KIXK+c5YtWybdrlQqRb169cSgQYNEdna2EEKIoUOHCnd3d5GZmanReJkIlUDv3r1Ft27dVMpGjhwpvL29tRNQKTl//rwAIH3BpKamikqVKolffvlFqqNUKkX16tXzZPK67saNG8Le3l7ExMSIrl275kmEGjVqJMaMGaNS1qlTJ9GnT58yjPLNzJo1S1hbW4sXL14UWGfw4MGiTZs2KmXjx48XHh4epR2exixfvlyYm5urtMiuWrVKmJiYSB+YP/74o6hcubLKB+jq1auFkZFRnl/buqigRKh169Zi6NChKmV9+/YVAQEBxaqjKwpKhPLTsmVL8dFHH0nbo0aNEj4+Pip1vvjiC5WEWFcUlQj1799ffPHFF2Lr1q15EqGlS5cKc3NzkZKSIpVt3rxZyGQynWoVEkIUmAg9fPhQGBsbi19//bXA+4aGhgoA4tKlS1JZTEyMACD279+v0TjZR6gEIiMj0bRpU5Wy5s2b49KlS8jMzNRSVJoXHh4OQ0NDuLq6AgCuXLmC9PR0lXM3MDBA06ZNERkZqaUoiy8zMxPvvPMOvvnmG9SpUyff2y9fvpzvc1yezvPgwYPo0KEDKlWqhPPnz+PmzZt5rrEX9Fq+efMmkpOTyzLcEhs0aBDq1q2LDz74AP/++y/Wr1+PWbNmYdasWTAyyllOMTIyEo0aNZK2gZzzzMrKUquvhq4q6Pl79XWqTp3yJjk5GTExMahVq5ZUVtB5Pnr0CPHx8WUdYoktW7YM9+7dw1dffZXv7ZGRkahfvz7Mzc2lsubNm0MIgfPnz5dRlG8mNDQUmZmZ6NWrF+7evYvz58/n+byJjIyEqakpGjRoIJXVqVMH1tbWGn/tctHVEkhMTESVKlVUyqpUqQKlUomkpKQ8t5VHcXFxmDZtGoKCgmBrawsg57wB5HvuhXVw1DVTp06Fo6MjRo0ale/tCoUCSqUy3/PMfQzKgwcPHsDDw0NKAB4/fgxLS0usXbsWzZs3B1Dwazn3NisrqzKPu7jkcjk+/fRTTJ48GZGRkUhMTISbmxv69Okj1SnqPMuj9PR0pKWl5Xtez549gxACGRkZRdaRyWRlGbZGfPzxx6hUqRI+/PBDqayo59jBwaFMYyyJqKgozJw5E2FhYSpJ+6sqwmv5wYMHMDU1xXfffYft27fD1tYWN27cwOeff46vv/4aQP7nCZTO5zAToRIwNjZGenq6SllaWhoAwMTERBshaVR8fDy6dOkCX19fzJs3Tyo3NjYGgHzPvbyc9+nTp7F06VKsX78ex48fB5AzKicrKwvHjx9H8+bNK8R5AjnP1969e3H48GG0bdsWWVlZGDFiBAYMGIC4uDjIZLIK8Vpet24dPv74Y5w8eRKNGzeGEAITJ06Ev78/rl27BgsLiwpxnq8r7HVqZGQkPb9F1SlvJk+ejF27duHAgQMqX5QV4TkeMWIEAgMD8fDhQzx8+BBXrlwBAJw6dQqenp5wcXGpEOdpbGwsJem3b9+GoaEhDh48KH3vvPXWW/meJ1A6n8O8NFYCNWvWzDPM+P79+7CxsSkXv6AL8/DhQ3Ts2BHu7u7YunWryguuZs2aAJDvubu4uJRpnCWVlpaGpk2bYv78+Zg6dSqmTp2KmJgYXLhwAVOnToVCoYBcLoeNjU25Pk8AcHV1RYMGDdC2bVsAgJGREUaOHIk7d+7g9u3bAAp+LZuamqJatWplHnNJ7Nq1C61bt0bjxo0BADKZDJ988gkePHiAiIgIAAWfJ4By9Zy+ytDQEI6OjvmeV+57VZ065cm0adPw66+/Yt++fWjWrJnKbQU9xwYGBnBycirLMEusRo0auHTpkvTZtGbNGgDAN998g3379gGoGK/l3O4WH330EQwNDQEAAQEBqFWrFo4dOwYg5zyfPXuG1NRU6X4ZGRl4+vSpxs+TiVAJdO7cGSEhISr9LbZv347OnTtrMao39+jRI3Ts2BEuLi7Ytm1bnkXwXF1dUatWLezYsUMqi4+Px5kzZ8rNufv7++P48eMqfy1atEBAQACOHz+OqlWrAgA6deqEnTt3SvfLysrC7t27y815AkDXrl2RkJCg0m/t3r17kMlk0i/pzp07Y9++fXj58qVUZ/v27ejYsaP0AaXrqlatigcPHkC8smxi7hwyuc9n586dcfHiRSkBBHLO09HREfXr1y/bgDWoc+fO2Llzp3Tu2dnZ2Llzp8rrVJ065cH06dOxdOlS7Nu3Dy1atMhze+fOnXHgwAGVL87t27fDz88PZmZmZRlqie3atUvls+mHH34AAOzZswcfffQRgJzzvHnzJqKjo6X75V5eatKkiVbiLq62bdvC3NxcJaHLyMjAkydPpPdsx44dYWBggF27dkl1QkJCkJWVhYCAAM0GpNGu13riwYMHolq1auLtt98WO3bsEKNHjxbm5uYiKipK26GVmEKhEA0aNBBubm5i//794tixY9JfYmKiVG/z5s3CyMhIzJo1S2zZskU0b95cNGnSROPDGctSfqPGLl68KMzNzcWYMWPEjh07RGBgoKhWrZqIj4/XUpTFl5qaKjw9PUVgYKAICQkRK1euFPb29iIoKEiq8/TpU+Hk5CR69eolduzYIcaNGydMTU3zzDWkyy5evCgqVaokhg4dKvbs2SPWrFkj3N3dRadOnaRht0qlUvj5+YnGjRuLf/75R8ybN08YGRmpDL/WNQqFQnoPmpiYiClTpohjx45JUzoIIcS1a9eEtbW1eO+998SOHTvE4MGDReXKlUVcXFyx6mhTVlaWdJ5169YVAwYMEMeOHRORkZFSnVmzZgmZTCbmz5+v8tl0+fJlqU5SUpJwd3cXXbp0Edu3bxdTpkwRRkZG4ujRo1o4q/yFh4eLY8eOiZ49ewofHx/pPAqS36gxIYTo0qWL8PT0FJs2bRKLFi0SpqamYunSpaUcvfouX74sjh07Jj799FMhl8ul83x1pNvcuXOFo6OjWL16tdi9e7fo3bu3cHBwEAkJCVKdCRMmCDs7O7Fq1Srx559/iurVq4tPPvlE4/Fy9fkSio2NxY8//oiYmBi4uLhgwoQJ8Pb21nZYJRYbG4thw4ble9uPP/6I1q1bS9v79u3Db7/9hmfPnsHX1xdTpkyBjY1NGUWqeZ999hksLS2lTnq5Lly4gAULFuDOnTuoW7cuPv/8c7i5uWknyBJKTEzEvHnzEB4ejipVqqBHjx4YNmwYDAz+awy+d+8e5syZgytXrqBGjRr49NNP4evrq8Woi+/q1atYsmQJrl+/DktLS7Rp00bqUJsrOTkZ8+bNQ1hYGKytrTFixAj07t1bi1EXLioqCh9//HGe8q5du+LLL7+UtqOjo/HTTz8hNjYWHh4emDx5cp7RkOrU0ZYXL16gW7duecpr166NVatWAQA++eQTXLx4MU8dPz8/zJ07V9p++PAh5syZg6ioKFSvXh1BQUHw8/MrveCLacCAAfmOYMvtr/i6Y8eOYdq0adi7dy8sLS2l8tTUVPz8888IDQ2Fubk5hgwZggEDBpRa3MU1ZcoUnDhxIk/5mjVrVD5DN27ciA0bNiA9PR2NGjXCxIkTUb16den27Oxs/Prrr9ixYweEEHjrrbfw8ccfa7y1mokQERER6S32ESIiIiK9xUSIiIiI9BYTISIiItJbTISIiIhIbzERIiIiIr3FRIiIiIj0FhMhIiIi0ltMhIioXLh//z5WrVqFdevWITk5ucB6Fy9eRN++fdG3b18sXLiw0H3Onj0bK1eu1Gicf/31l3T8AwcOaHTfRKR5TISISOf9/vvvaNy4MQ4cOIC5c+eiWbNmSElJybduQkICtm/fjvfee09acLYgp0+fRlRUlEZjbdy4Md577z2cPHkScXFxGt03EWkeEyEi0mmHDx/GhAkTcPToUaxbtw5hYWF4+vQp1q1bV+j9+vbti6ZNm5ZRlP+pX78++vbtC3Nz8zI/NhEVn5G2AyAiKsz48ePx6aefSqvEW1hYwMvLCxcuXCj2vrZs2YItW7agcuXKBa4zduDAAWzevBkvXryAl5cXgoKCVNZ5unr1KoKDg5GUlIQmTZrAx8cHK1euxJ9//lmyEyQirWKLEBHprFOnTuHixYt4//33VcqVSiWKu0ziL7/8giFDhqBevXrw9vbGhAkTcOTIEZU6P/zwA95//33Url0b3bp1Q1hYGJo2bYrU1FQAQFxcHFq0aIGHDx8iICAAERER6Nu3L0JCQt7oPIlIe9giREQ6a8+ePTA2Nsann36qUn7hwoV8VywvSEZGBmbOnIl58+bhf//7HwCgW7ducHd3l+rcvn0bM2fOxIULF9CgQQMAwODBg+Ht7Y2VK1di7NixmDNnDjw9PbFp0yYAwPDhw9GtWzecPXv2TU+ViLSEiRAR6azz58+jcePG8Pf3l8pevHiB3bt3o3Hjxmrv5/r163j8+LHK5TAnJyc0adJE2j5w4AAMDAzw1VdfAQCEEBBC4NmzZ1KH6rCwMAwaNEhl37169WIiRFSOMREiIp117949dOnSBZ999plUtm3bNpiZmaFNmzZq7ycxMREAIJfLVcptbGyk/z9//hwWFhYYOnSoSp3hw4fD2dkZAPDs2bM8+3h9m4jKFyZCRKSzDAwMYGxsrFK2evVqvPPOO7CyslJ7PzVr1gQA3Lp1S6Ul6ebNm6hXr55U5/nz52jVqhWqV69e4H5u3bqlUhYbG6t2HESke9hZmoh0VqNGjXD69Glpe+fOnQgNDcU333xTrP3UrFkTrVq1wty5c5GdnQ0A+Pvvv3Hjxg2pTo8ePVCjRg2MGzcOGRkZUnlYWJh06WvgwIFYu3YtHjx4AAB48uQJfv/99xKfHxFpH1uEiEhnjR8/Hq1atULPnj1haWmJI0eOYOvWrdKlquJYtmwZunbtivr166Nq1ap4/PgxvLy8pNvNzc2xa9cuDBo0CG5ubmjQoAHu3LmDatWqYfXq1QCAUaNGYdeuXWjYsCEaN26Mq1evolGjRjh37pymTpmIyhgTISLSWV5eXrh48SJCQkJgaWmJpUuXwtbWVq379u3bF/7+/hg/fjwAwNvbGzdv3kRYWBhsbGzg5eWF8+fPq0x8mJvcnD9/HgkJCahVqxZq1aol3W5qaoq9e/ciIiICSUlJ8PLywh9//IF79+5Jdf766y/8/fffSEhI0MyDQESlSiaKOxkHEZEOe/z4MU6cOAEAcHZ21vjs0rt27ULPnj2lY/n5+aF3796YP38+AODKlSuIiYkBkJNY5fZPIiLdxESIiKgYAgMDceXKFTg4OODcuXNo2bIl1q9frzICjYjKDyZCRETFdOPGDcTGxsLV1RW1a9fWdjhE9AaYCBEREZHe4vB5IiIi0ltMhIiIiEhvMREiIiIivcVEiIiIiPQWEyEiIiLSW0yEiIiISG8xESIiIiK9xUSIiIiI9BYTISIiItJb/w9XNj9MRUHAfgAAAABJRU5ErkJggg==",
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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"
- ]
- },
- {
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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/data/alpha_ca_ratio_ruth.csv b/examples/data/alpha_ca_ratio_ruth.csv
new file mode 100644
index 0000000..4c1d737
--- /dev/null
+++ b/examples/data/alpha_ca_ratio_ruth.csv
@@ -0,0 +1,1008 @@
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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 @@
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diff --git a/examples/error_models.ipynb b/examples/error_models.ipynb
new file mode 100644
index 0000000..9c2a573
--- /dev/null
+++ b/examples/error_models.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "099dd40a",
+ "metadata": {},
+ "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 only one thing: the model covariance we use. \n",
+ "\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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6a1a51a0",
+ "metadata": {},
+ "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": "6f7c1693",
+ "metadata": {},
+ "source": [
+ "## Where a $\\chi^2$ comes from\n",
+ "\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",
+ "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",
+ "\n",
+ "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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f938056",
+ "metadata": {},
+ "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 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": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset('biased', n=15)"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "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",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "99cfee79",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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=plotstyle.COLOURS[1], label=\"truth\")\n",
+ "ax.set(xlabel=\"$x$\", ylabel=\"$y$\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4fe9487",
+ "metadata": {},
+ "source": [
+ "## 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 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": {},
+ "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",
+ "comp = rx.Comparison(data, line)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "573b525e",
+ "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",
+ "\n",
+ "\n",
+ "def band(problem, samples, levels=(5, 95)):\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",
+ " x_fine,\n",
+ " samples[::10],\n",
+ " model_only=True,\n",
+ " 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": "aa89658e",
+ "metadata": {},
+ "source": [
+ "## 1. Reported statistics only\n",
+ "\n",
+ "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": 6,
+ "id": "63d1e287",
+ "metadata": {},
+ "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": "a545f1bf",
+ "metadata": {},
+ "source": [
+ "## 2. Inferring the noise level (recipe 2)\n",
+ "\n",
+ "Suppose the experiment had not reported errors at all. We can infer them:\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, $\\sigma_i = \\epsilon$.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "3dee571d",
+ "metadata": {},
+ "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.proportional_error(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",
+ "inferred = np.exp(s_noise[:, 2]).mean()\n",
+ "print(f\"inferred noise fraction: {inferred:.3f} (generated with {noise_fraction})\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "\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": 8,
+ "id": "61371390",
+ "metadata": {},
+ "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": "fb1a2cc5",
+ "metadata": {},
+ "source": [
+ "## 4. The same mode built from the data: Peelle's Pertinent Puzzle\n",
+ "\n",
+ "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": 9,
+ "id": "911272d9",
+ "metadata": {},
+ "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": "ee2ad5ca",
+ "metadata": {},
+ "source": [
+ "### Four posteriors, one truth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5a4feed4",
+ "metadata": {},
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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, 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 % bands of the fitted line\")\n",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "7b2a4566",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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=[(0.3, 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=[\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": "5bdfe338",
+ "metadata": {},
+ "source": [
+ "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."
+ ]
+ }
+ ],
+ "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/error_scale_and_usu.ipynb b/examples/error_scale_and_usu.ipynb
new file mode 100644
index 0000000..dcc6b2c
--- /dev/null
+++ b/examples/error_scale_and_usu.ipynb
@@ -0,0 +1,381 @@
+{
+ "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": {},
+ "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": {},
+ "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": {},
+ "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": {},
+ "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": {},
+ "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"
+ ]
+ }
+ ],
+ "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": {},
+ "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": {},
+ "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": {},
+ "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}, \"\n",
+ " f\"b = {s[:, cols[1]].mean():.3f} +/- {s[:, cols[1]].std():.3f}\"\n",
+ " )\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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "675db827",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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(\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()"
+ ]
+ },
+ {
+ "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/gp_discrepancy.ipynb b/examples/gp_discrepancy.ipynb
index 236a733..fd0dede 100644
--- a/examples/gp_discrepancy.ipynb
+++ b/examples/gp_discrepancy.ipynb
@@ -2,118 +2,120 @@
"cells": [
{
"cell_type": "markdown",
- "id": "c00",
+ "id": "75c9df24",
"metadata": {},
"source": [
- "# Model discrepancy with a Gaussian-process term\n",
+ "# Model discrepancy with a Gaussian process\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",
+ "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",
- "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`."
+ "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** 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",
+ "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"
]
},
{
"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"
- ]
- }
- ],
+ "id": "506a99dd",
+ "metadata": {},
+ "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 matplotlib import pyplot as plt\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 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\n",
+ "import rxmc as rx\n",
+ "from rxmc import terms as T\n",
+ "from rxmc import transforms as tf\n",
"\n",
- "rng = np.random.default_rng(7)"
+ "plotstyle.use()"
]
},
{
"cell_type": "markdown",
- "id": "c02",
+ "id": "c44ee40c",
"metadata": {},
"source": [
- "## A linear model and a non-linear truth\n",
+ "## A line, and a truth that leaves it\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."
+ "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": "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"
- }
- },
+ "id": "b77965a8",
+ "metadata": {},
"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",
+ "M_TRUE, B_TRUE = 0.8, 1.0\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",
+ "def defect(x):\n",
+ " return 0.03 * np.asarray(x, dtype=float) ** 2\n",
"\n",
"\n",
- "model = LinearModel()"
+ "def truth(x):\n",
+ " return M_TRUE * np.asarray(x, dtype=float) + B_TRUE + defect(x)\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)"
]
},
{
"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"
+ "id": "81c09486",
+ "metadata": {},
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cf684d62",
+ "metadata": {},
"outputs": [
{
"data": {
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",
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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -121,251 +123,173 @@
}
],
"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",
+ "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=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": "000c2900",
+ "metadata": {},
+ "source": [
+ "## Four ways to say \"the model is wrong\"\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",
+ "We'll fit the same line four times, and the only thing we'll change between fits is the covariance:\n",
"\n",
- "xg = np.linspace(0.0, 5.5, 120) # fine grid for predictions\n",
+ "1. **Statistics only.** We use the reported errors and nothing else, which amounts to claiming the line is perfect.\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",
- "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\");"
+ "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": "markdown",
- "id": "c05",
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "1a201944",
"metadata": {},
+ "outputs": [],
"source": [
- "## Building the constraint with a GP discrepancy term\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",
- "`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."
+ "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": 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"
- }
- },
+ "execution_count": 6,
+ "id": "54264f30",
+ "metadata": {},
"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"
+ "statistics only ['m', 'b']\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"
]
}
],
"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."
+ "problems = {\n",
+ " \"statistics only\": rx.Problem([rx.Constraint([comp])]),\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",
+ "}\n",
+ "for name, p in problems.items():\n",
+ " print(f\"{name:24s} {p.names}\")"
]
},
{
"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"
- }
- },
+ "execution_count": 7,
+ "id": "699a541c",
+ "metadata": {},
"outputs": [],
"source": [
- "constraint_plain = rxmc.constraint.Constraint([observation], model)\n",
- "evidence_plain = rxmc.evidence.Evidence([constraint_plain])\n",
+ "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(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",
- "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**."
+ "samples = {name: fit(p, seed=i) for i, (name, p) in enumerate(problems.items())}"
]
},
{
"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"
- }
- },
+ "execution_count": 8,
+ "id": "f08f7fc9",
+ "metadata": {},
"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"
+ "statistics only m = 0.953 +/- 0.003 (pull +60.2) b = 0.858 +/- 0.007 (pull -19.0)\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"
]
}
],
"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",
+ "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\"{key}: model acceptance \"\n",
- " f\"{walker.model_sampler.overall_acceptance_fraction():.2f}\"\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": "c11",
+ "id": "bf2aeeb7",
"metadata": {},
"source": [
- "## Posterior of the GP hyperparameters"
+ "### 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 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",
+ "**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": 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"
- }
- },
+ "execution_count": 9,
+ "id": "1b55f904",
+ "metadata": {},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -373,49 +297,139 @@
}
],
"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",
+ "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",
+ "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",
- "fig.suptitle(\"GP hyperparameters (log-theta)\");"
+ "plt.show()"
]
},
{
"cell_type": "markdown",
- "id": "c13",
+ "id": "61c8dadf",
"metadata": {},
"source": [
- "## Propagating the total uncertainty\n",
+ "## Propagating the whole uncertainty\n",
+ "\n",
+ "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",
- "`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."
+ "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": 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"
+ "execution_count": 10,
+ "id": "152c7f05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def mean_band(problem, samples_, levels=(16, 84)):\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",
+ "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",
+ " 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": 11,
+ "id": "d8b317c3",
+ "metadata": {},
+ "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": {},
"outputs": [
{
"data": {
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",
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uLpZmcU2MuGFRGPSgKRnPV3vjC8BKtqDXrsRKNKL4wmiFI3CUEOnGOFbaRPF7URQZLLs1+D6FGvIhqQqOaWHpJqrfgyc3iGU5SEZ8q6/B5tC70gQEAoFAIBDskFRXV3PUUUdx5JFH8r///a/LMbpps6gmyvw1zThASdjnGmDtcSwTvb6cdOW32KkWPPkD0EpGYhleUrUt2BaoIT9S27WKjBbygSRhtCQxWhJgO/gKQvgKIyie3ulz6p2zEggEAoFAsMPwwQcf8NOf/pSqqioAzjnnHJYtW4bHs77Aan1c59uqFlKm7Xq/HMehIWEgSbjFWM1EE0btSuoam5F8IfqUDkaSg6Tq4ziWTRwZw4Y820FpV74iZjnELJuwZRHKD+HJDSCrCgndIqabyBJ0X+51+yCMMIFAIBAIBFuE4zjcfffdXHvttVhWpv7DwIEDee6551wDTDdtVtTFmb2iDgmJ/frnut6vaNpkRUMCVZbwSjZafB1GwxoSpsM6Twm2lofWLOFprkNSZaSIn4qmNEnTQpL8FPgzgfimbrKyuoW4IzO8LJeCgpC7tVnRlKQ2nqYg4CHo3w6LtAHEdqRgh2DJkiVcd91123saAoFAIGilpaWFn/70p1x99dWuAXbsscfyxRdfMHr0aCDj/fq8solFtTH8mkph0JPlvdIUmYAm4zVjWFUL0etWIXsDhPqPxB8oQokZKLqJlhtAkmXsRBqfDAFVwafI4DhYyTSYFrn5IQqKwwSDvqzYsqBHIexV8aq9z+TpfTPahji2hWMam//P2oyxP7Ay3Pfff79ZxkdX4zfFgNnS67YVNTU1zJw5c3tPQyAQCATAt99+y/77789zzz0HZALqb7/9dl577TUKCgrQTZslNTG+qGjCshyGFATZoyTMLoXBLCPMi8lgauifWo5q63j67IJWtCukFPpjMrwwQDDiR/FqqCEvsg0DVBiR58MvOZiJNIpPI1CSy9CyfHYriZDb6h1rozTHx8jiMAUb6D25vdhptyMd2yKxbB5m8zok1YviDW70GttIYesJUvEE8aLSTlkcnZ9hIqkeAsPHIslbVpukurqamTNn8sc//nGLx+fl5XHooYdulesEAoFAsHPx1ltvcfLJJ5NIJAAoKCjg3//+N0cffTQAjQmdRTUxWlJmVuZj+wB8x3Gw4g0YdauwU1G0YD5a/kBsUyNVn7mvN+iH9qUqNBU1BEY0iVkfRcsJ4CsMo/q9SK2Gnax0fi9LkoSmSEAvK5fPTmyEYdutBpgHLbfvRodbyShWohnZ40fO6YsSyNnw7Y00Zt06JI8XbBs2YoQ5jsNTTz3Fhx9+SElJCdOmTSM/P58ZM2ZQVVXFWWedBcDDDz9Mc3Mz1157LZD58k+aNIkJEyZgGEaX4xsbG5k7dy6TJk3arOe0vw4gFovx6KOPsmTJEg4++GB3bFf3LC4u7lLP6upqHnnkEdauXctBBx3EcccdB8DChQv597//zUknncQzzzyDLMtcdtllDBw4sNM9rrzySi666CJGjhzpyq6++mrOO+889thjjw2us0AgEAh+GHvvvTc5OTkkEgkOOOAA/vvf/1JWVoZp2axuTLKyPoFHlbPaDrXHNlIYDRWYTeuQVA1v8TDw5pNuTGIZKVSfF0nt4p3pODiWg6wqePLDbuD9jsxOvR0pqV603H5IirbBf7aexIrVofjDaAUDkVXPBsc7toXVvA5J0VD8kU2ay/3338+MGTPYc889kWWZU045BUVRGDNmDMFgkGOPPZZjjz0WTdOyjvv378/ZZ5/NCy+80O349lt5m/Oc9tfFYjH23Xdf3nvvPfbcc0/efPNN7r///m7v2RX19fXss88+LFu2jJEjR3LnnXe6xmRVVRX33nsvN9xwA0OHDqWqqoojjjgC0zQ73aetD1kbX3/9NX//+98ZOnTopn/4AoFAINgiiouL+e9//8svfvEL3n//fcrKymhJGXxZ2cyyuhi5fo1cv4btODQmDBJ6xgPl2DZGSw2p8q+pq15L0pOD1nd3bHJIVjWTbkoQTdtEzc4FVWNJnfrGGJIsEeiXh68w7GY+NiYMbMfJGp80LBoSOpadLXfIPt7e7LyeMNikLUgr2YLZvA7Z40fNLUWSN2y32kYKs6ESJBktvxTHSGzSXBYsWMDFF1/MpZdeCsC0adOQZZmjjjqK559/3vU6QabzfPvjIUOG8MADD3DKKad0OX5Ln9OeBx54gH79+rlG2SWXXEJdXV239+yKBx98kL333tvtITZp0iSGDx/O7bff7o556aWXCIfDXHjhhfTp04elS5ey2267Zd3nvPPOY8KECfzpT39C0zT+8Y9/cNppp+Hzdf1Xl0AgEAi2nDfeeIO99torq/3Q2LFjGTt2LLbtUN4QZ/byelKmw5gBOcit78mGhMGK+jhBj8qwXAW7sRyrpYao7aXSOwDNCTOoPo1mO2ghP0kpzcqaGFJUZ0TfCEGvCo5DIp5ieVMaye8hmBMg6GstZWE7rGxIENdNhhQEKQxm5I7jsLoxSVPSYFCen5J2HjnDEhXzdxh+mAE2ACRwjE171oUXXsjZZ5/NokWLGD9+PCeccMIGx7/55pu8+eab1NXV0dDQwMqVK7fKc9pYsGABEyZMyJIVFhZu1j0XLlyYFWM2cOBASktL+f7775FlmUGDBhEOh4FMheWioiIaGho63WfvvfemrKyM1157jRNOOIGnnnqK559/fpP0EAgEAsGmYVkW06dPZ8aMGYwdO5b33nsPTVsf9J7QTZbWxllUGyVpOPQJe1wDDECVJTRZQko0oMfXIZkplHAffP6+yOtS2C1J5IIAajBjJHkCXjy+NJZu4iR1LMnBMUw0n0ao0IMty2jtth8lQFMkPIqM2i7Yvy0GzKPIqB0KwW44knvbs1NvR26IH2qASaq2wfEdGTNmDN999x3nnnsu3377LXvssYfraerIU089xUUXXURxcTFHHHEEBx54IPH4prVi2JzntCcQCJBMJn/QPf1+f6d7JJNJ/P5M4Ra5izV2nK5dx+eddx6PP/44r732GqFQiLFjx25UB4FAIBBsGtXV1Rx99NHMmDEDyBRjfeaZZ4DM7+XqaJrPKpqojeuMKAqzd2mEQXmBrHuEZZ1BRjml6VUokoxWsityeCBq3GKIT2J4cRiv3+uO96kyI/pGGF4UQE6lcQwTX2GEcJ9cRvSNMKJPiJB3ve9IkSUG5wcY0SfUKSNyUF6AXYtDrnesDU3pXTFkwgjrgm1tgAG8/vrrmKbJgQceyO23344sy3z99deEQiFisVjW2A8++ICzzz6b66+/nnPPPTcrS7Or8Vv6nPYcccQRPP300zQ2NgKQSqX45JNPNnjPjhxyyCE8//zzbkbN22+/TTweZ6+99tr0hWpl6tSpvPfee9x1112cddZZG81UFQgEAsGmMXfuXDcGGDIhMPfeey9nnnlma9uhGF+taUaTZfqEvMiSREBT3NITjm2hN6whufpLPHoz3ty+qMUjMRMeElXNSIpMIBxA0zpsxjkOimHhkWWCffMIlhagBX1IsoSmyF3W+dIUGb/W2bBS5Mycejs7/XakY2UHflvJqGuAKeE+4Fg4ltXpGsfK7DPaRjorBgwJ91zHe2+I8vJydtttN/baay8qKyvJycnhgAMOQJIkLMvimGOOoaioiIcffpjJkyfzk5/8hG+++YaGhgZSqZR7n5EjR3Yav6XPac/pp5/OrFmz2G233TjggANYvHgxf/3rXzd4z46cc845PPvss+y5556MHDmSuXPncscdd5CTs+FM067Iy8vj+OOP55lnnuHvf//7Zl8vEAgEgmwcx+Guu+7ihhtucIuvlpWV8eyzzzJmzJis0hNFIW/WFmAbViqKvvZ7zEQzaigfNX8gthMg3ZDATKRBknFMGzr4KhzTwkoZKD4NT2EI1df7anptDSSnu/2eHZDKykoGDBhARUUF/fv33+BYx7ZIrZqPnVrv/WmrAybJKrIv3K13pbmlmZxIDo5tYqVaAAnFH0GSOlvdsi+Eb9CoTaoTVl1dzbx588jLy+OQQw5BaXWb1tfX8+GHH9LS0sKUKVPQNI3ly5ezYMECBg8ezODBg3n33Xc59dRTuxzf1NTEvHnz3FITm/qcjtdBJq5r2bJljB49mtLS0o3OvdO6Ow6ff/45VVVV7LvvvgQCAQoKCli3bh3z58/n+OOPd8e+8sorHHjggRQVFVFbW9tpLnfddRfPPfccH3/88UbXdmegvr6egoKC7T2NHR6xjj2HWMueY2uvZVNTEz//+c956aWXXNlxxx3HP//5T3Lz8qloTLK0Lo5Xld3Mx1jawq/JmR6QtonRUEWq8ltiko9InwF4wyWYcRMzqaN4PSRsB1I6mu2g+DQUvwcch5ZoElmSyMkPo4W97u5TwrBwHIdgh+bbKcPCtJ2srUnItEdKmTZhr5L1/jYsm6RhY9k2PjPBAbuWbbV13Fx2WiMMMoYY9uZnStQ31FOQv4k/DLK8xYVadwZ+yC+W/fffnwsvvJCLLrqoh2e1YyJeeD2DWMeeQ6xlz7E111LXdfbaay8WL14MZOJzb7/9dm688UaShs0XlU2sbEgwsjjibglWtaSoaEpSEPQwKGCRXrsIo76caGgAlUpfAqqPQZqELMkoPo3mtMXK5hReWWKwV0a2bCRFImHbrNbBG/Kxa98IvtYtxJRhsaQujm07DC0MugaXYdksqY2TNm2GFATcWDDbcVhaGyeaNhmUH8iKBVteH6c+rpPr0xjgM3uVEdbrYsJs22blypUbjE/qKSRZQVK1zf+nbMZYYYD1OO+99x5HHXUUtbW1nHnmmdt7OgKBQLBD4/F43D9mi4qKeOutt7jpppuojRvMXlbHZxVNFAQ8nWOybAujqYrEkg8xozX4hoxGKRqBmQQzmkL2tHq72u8qSaD4NDfMx1sQwZPjR94B4re2Br0qJuzvf/87N954I5qmYds2U6ZM4U9/+lO3W1uCnZMBAwZw4YUXMm7cOILBjdd6EwgEAsGGueqqq4hGo1xwwQX0KenLktoYKxsShH0aB5TlE/Bkv4cLlRSKvhqlqRw5vwRP8QistIa3tokhQU+m32O78hA5PpVhkg/JsJBNE29xDlrEj6woeIImkiS5XjAAn6YwtDDYaTtSU2R2KQh02o6UpUymZNqyCXWYa1mun6KgF8u2oYsC4NuTXmOE/etf/+KSSy7h2Wef5cQTT8S2bR544AGi0Si5ubnbe3qCXsSwYcMYNmzY9p6GQCAQ7JAsWrSIN954gyuvvNKVSZLE9OnT3cr3jUmDoqA3q98jZBLPjMY16PUV+PU42pB9UbxF6NE0tp5E8WgEHVBtG9r3ijQsvIaZCbzPC6J610fmd4z5aqO77EZfN3KPKuPpJoNSU2Sak5tYuHMb0iuMMNu2uemmm7jwwgs58cQTgcye9OWXX76dZyYQCAQCwY+Hp59+mgsvvJB4PM7AgQM5+eSTgUzS1JrmJEtq40iSREnYmxXc7jgOVqIRo3YVVrIFxRtA7bcnZloh1RBvba7tB9vGiKUwY2nUEMiKgpXWAfDmhbIC7wW9JCZs0aJFVFRUcNJJJxGPx1m+fDm6rm/vaQkEAoFA8KMglUpx6aWXMnXqVLe493333YfjOOimzXfVMb5dFyPoUcnzayRN2y2WbRtpjNoVpCoXEk+mUPoMRS7cnXSzhZVIo/p9pCU506dRltFCPiRFQm9MEG2K43g9+Itz8eT4XQMsbdpdthDSTRu9i96RhmWT7kaeNKxOcst2SHQh7230CiOssrISgDlz5jBw4EAmTJhAJBLh6quvxt5A9mJLSwuVlZXuv6qqqm01ZYFAIBAIdghWrFjBIYccwoMPPujKLr74Yl577TWiaZNPyhupbEpSHPLi1xTWNKdYVB2lqiWFGasnveZbjMZKap0wK5SBVDZq6I1xZEVGCXipT5ssbkhS3tJas9IBZIm4IlGuaKxFgnbthmJpk8U1MZbVxbMMsaRhsbg2xuLaWJZhZVg2y+riLK6JEUuvj+myWntHLq6J0dRhq3FVY4JF1THq4tkOHcPqXYZZr9iObAu8f/vtt1m2bBm5ubnMnz+fQw89lF122YXLLrusy+v+/Oc/c9ttt3WSNzY2uq1wtgYtLS1b7d47G2Itew6xlj2DWMeeQ6xlz7Gla/n6669z+eWXu9cHg0HuuusuTj31VFZVNzC/shlNkRiYF8CIpzAAI57CiiVJRZuJ2i3IqoYcGYATV0nXNJFUJKySCLZlggVGygRDx0Ym2ZxCcmyUgBfFpyLHkthJnVTMQmnd3UylLZxUEluSSUZNrNYTKcPCSWYMuVTUQGqN/dItBzuRxHFsUlEDVc/ILQesRALbsEjFDFJmxqRxHDDjSZy0SVpNk7Iz5SqMtAV6nPr6+i1ay01lc0qJ9Io6YUuXLmX48OE89thjnHfeea580qRJqKrKiy++2OV1LS0tWV/MqqoqxowZs8l1wrYUUfum59jYWr733nskEgkmTpy4DWe1YyK+lz2DWMeeQ6xlz7G5a2kYBjfeeCN33XWXK9t999159tlnGTp8BMvr4yypiaEpMiVhX7uWQzaJ5hqa15UTJIk30gclUooRt0kndGIO+E0Tj6qghXygZLYhm+M6HsskFPah5WYC723HoTlp4lXlrOxKx3FoTpmostSp4GpLKuPRiviyS+rH0iam7ZDjU7Ni1RK6Rdq0yfGryO3kScMiaVjk+DRXt+akgWbEOHDXgZu8jlubXuEJGzp0KAMHDqS2tjZLXldXx2677dbtdZFIhEgkssXPdWw74zbd3OssG6eLvewukfhRBCG+8847pFKpLGNoWxhI77//PnV1dcIIEwgEgs1gxowZWQbY2WefzYMPPoileFiwppm6uEFpjj8r+9HWk+h1qyBaR57Xh5y/K44VIN2YAlnBF/bhkyQc08KMpTBiKdSAF0yTHBU8hTlowfWB97IkkRfo3EtZkqRODbfb6Gh8tdHRWGsj4FE6lc8A8GtKlz0lJXpXn+FeYYRJksQdd9zBFVdcQd++fdl111154YUX+OKLL7jnnnu2yjMd2yZWXocRSyMrMnI3KbJZ15g2lmGSiEXRWqyNNo12LBtJlQkPKtrhDbHZs2fT1NSUZQypqoqmbX6zcoFAIBBsXa666iqeeuopKioquO+++/j5z8+jKppiydomJCT6RtZnPzq2jRmtxaxfjW2mUSJ9kIP9MOIGtp7K9HFsZ6xJqoIa8pFujGPpBr6iCJ6cAErHhtyCjdJrVmzq1KkEAgH+9re/UV9fz7Bhw/j444/Zb7/9ts4DHXAsB0mRsXQTZAkt6NvwNV6Q0jJOs42tm2hh/wYNMcd2sFL6Jnnbli9fzhtvvMGxxx7L66+/TjAYZMqUKW4x0rbzxx9/PLNmzaKsrIyJEydSXl7Oa6+9hmEYHHfccW79rLfeegvbtgmHw3z00UfsuuuuWX0XgY1em5uby9y5c9l9992ZM2cOqVSKW265hdLSUi655BJM08QwjE2+X15eHu+//z5lZWWcdtppyK2G6SeffMIrr7yCJEmUlZVx8sknU1hYuPFFAxoaGpg5cyZ1dXWMHz/e/b501GHixIl89NFHHHDAAaxcuZIFCxZw5ZVXEgqFeP311/nmm2/YZZddOPnkk1EUBdu2ufXWW/nFL37BG2+8QTQa7TY2USAQCHobOTk5PP/889i2zfCRe7CwOsqalhS5Pg2PIrvvLltPkK5ZgRWrx/YE8Rfvipn2kGpMomgqSsCLYTuojrP+fWfZWGkDAh6ChWG0gLdTr0ZFlrK2BzckN20HCdxtwzYs28GBTo3CbcfBsp1ONcy6kzuOg2lv98irLulV7pmTTjqJWbNm8emnn/LUU09tPQOsHZ6wHzXgxdYtzKSOpMgb/KcGvKghH47lYMbTIEvdj289tymsXLmS6dOnc9JJJ1FeXs5jjz3GuHHjMFur+7adP/XUU6moqEDTNL744gv22WcfPvnkE7777jv2339/3nvvPSCzVXjppZdy7bXXUlNTw+WXX861117rPm9j11522WVcf/31xONxNE1DURQURcHn8+H1eoHMVuEbb7yxyfe7+eabaWho4Oabb+aGG25w59J2X4/Hw5tvvsm+++5LY2PjRtds2bJl7L333syaNYuamhpOPPFE/v3vf3epg6Io/POf/+Tkk0/mn//8J07rL5Rzzz2Xq666ivr6en7/+99z7LHH4jgOtm0zY8YMjjrqKD777DPh8RMIBL2WqqoqTjzxRJYuXZol33PPPem7y258VtFEdUynJOSlJqrzfU2MxngKo6mKVMU32IkmqpViFhulrKk2SdY0g+Ugayo1CYNF9QlqEgY4DlZSx0rrtKgqq2SVGsPJMsCakgbf18RY3ZCkfch5LG2yqCbG8rp4ppRFK0nDYklt50zJtozIJR0yJS3bYXldnEUdMiUdx2F1Q5Lvu8iUXNOc4rvqKPWJ3lf6qtd4wrYnWijjAbNaAwLbjrtD9qpoXh9GLIXRkkSLbNgjtqk0Njby4YcfMmLECEzTZI899uC///0vU6dOBTKJCDNnznSTDo4//nguvfRSfve73wGw2267cd111/H5559n5inLzJkzB03TuPjii9lzzz256qqr6NevH7/+9a83eG0oFGL27NmuXnPmzKGpqYlbbrmly7lv7H5FRUW88847ABx33HGcffbZ/PGPfwRg9OjRjB492r3X2WefzRNPPJFVzbkrbrrpJs4++2zuuOMOAH76059y0kknuevVUQeAcePG8cgjjwAwf/58nn/+eVauXElhYSGpVIphw4bx8ssvu9uuN910E1OmTNngPAQCgWB78d5773HGGWdQU1NDeXk5H3/8MX6/n7RpsagmyrLaOH0jfvL8KpbtEDdMWmIxWlpq8dCM7A2hFu5CvMqgsSlJbkEQb14IM6FjxFKkLIjqFmFFx8RC9XnQciM0pUziDQkCenbJh7RpE0ubyBLYDm5GZNq0SegWjpMxpNq8XoZlE9ctVFnCsBzawrgMK1Pny7QdDMt247ss2yFpZGqDpU2bUMYngO1A3DCJpc1O9cTiukU0beJRZOhlf08LI6yVzTXElNbgwZ40xAYOHMiIESOATLzVUUcdxZdffukaFYMHD87K+vzyyy+zjKKJEydy7bXXYrXWQTniiCNcD87QoUMZNGgQ33zzDf369dvotWPHjt0sfTZ2vwMPPNA9N2TIEGpqarKuf++99/jiiy9oaWmhqqqKJUuWbPSZH330EX6/n9/85jeu92rt2rU0NDR0q8Nhhx2WNef999/f3fr0+XxMmDCB+fPnu0ZY+/ECgUDQW7AsixkzZnDbbbe59TSbm5uprKwkv99Avq1qoSqaZpeCoNt4W8aiVGoinF5Dnmaj5g0ArQC9xaDEI5PbN4dcn4qsyKiAmdApwMHrl4l4VLz5ITfwvlhV8CgyoQ7x1AVBD5KUCYxvv72Y69cYUhDAo2S3Fgp7VYbkB5AksgLsAx6FgXl+HCczpg2PKjMwz49u2VnB/YqcKbORNCzyA56sOQ3I9ZMX0JAlwEr+4LXvSYQR1o7tbYh1rBbiONlu3o61zyRJyrqmY2HbDd1vY9dubp21jd1PVbO/au3HXnPNNbzyyitMnDiR3NxcVFXdpJo4juPg9/uz7n377be7dee60qG9rOOc2+a9oTUXCASC7U11dTVnnXWWu7sAMHnyZB559DFa8DG/sgmfqjCyOOyet5JR9LqVeBJN9IlEUHLKsFIyZnMaxacR9nsIt3uG7NWQdBM1qVNSFMaTG8wKvNcUmT5tbqh2qLLUpVyRJYq6kEuSREHQ00kOdDKm2sjpJrMy7FWzDLY22jIom5MG9K5arb0rJqw3oIV8KD4NK2VgxFIbHa/4NLSQD9uwMFqSnV7qm8Pq1av57rvvgEyNl7fffptRo0Z1O36//fZj5syZ7vHLL7/Mvvvu6xoh7777rtv+acmSJaxevZo999xzk67tSCAQIJ1Ob/FcNsTLL7/MY489xl133cWvf/1rNxlhYxxyyCGUlJRwyy23uP+OOeYYcnJyNun6UaNG8emnn1JdXQ1AIpHgnXfeydoaFQgEgt7E7Nmz2WeffVwDTFVV7rrrLp54+r+sTsgsrYuRH/C4hopjmej15aTXfIuTjqMUDITAYFK1aSzDRg16kdTs39OOZWPGUyg+ldCgInyFEZH5uJXY6VfVsR2gg9fG78mk7CbSOLbdKWvSsbPrhMmaghrwYMRSOE22mzXpbGY2RkFBAT/96U854ogjmDdvHnl5efzkJz/pdvztt9/OhAkTKC8vx+v18tJLL/Hyyy+75xVFYezYsRx44IG88MIL/PKXv6Rv376bdG1HRo8ezZ///Gdyc3MZMmQIl1xyyWbNZUMceuihXHzxxRx33HEsWLCAZcuWccghh2z0uj/+8Y8ceeSRfP755wwfPpwFCxZQWFjIM888s0nP3WeffTjjjDM4+OCDmThxIu+99x577bUXEydOdLdRBQKBoDfQlix06623ujsNAwYM4Omn/0PZyH34vKIZWZYoCXndzHMr0YxetxI72YLki6DmlGEkwWiJARKSbePA+spZjtO6E+SgRXx4IgHkDn9Id9yh2RpyYKs+ozex8xphEsgeFVs3uzwtqwp4nIyHK5rMqiNmp81Mem4HVJ8HyzAxWpLI3kxVX9mjsqm14UpLS5k5cyZvvPEGY8aM4dRTT3W32oYOHcrll1+eNX7fffflm2++4Y033sA0TaZPn87gwYPd85MnT+b000/n448/5u9//ztHH330Jl17zDHHdNpOPProo/nvf//LF1984WZHHnHEESSTyc2+X05ODr/5zW/c44cffpjnn3+edevWMWnSJPx+v9sHtP0zOtIW4/b6669TWVnJiSeeyLhx47rV4fzzz2ePPfbIkj366KO8++67fPPNN9xxxx2ccMIJSJKEoij89re/FduRAoFgu6LrOk888QQffvgh//73v93fayeccAIPPPwYDY6PhdUxcnwq66JpamM6ZREVNbYOs3ENjqSwVhtAPOWnr6kT8nnwFkQwE2kqGxLESDKgKEREBks3UP1emlSF+rRN35RFQXC9EVYf16mKpigKeikOr99abE4aVDanyPGr9M9Z/zszrpuUNyYJeBTKcteH6yQNi9WNSTRFYlBeICtIf2VDAoDB+QG31IRlO6xqTGBYDgPz/G6QvuM4lDclSegWZXl+gu3e05XNSZqTJv1zfFnbl+YG+lFvD3pF26KeorKykgEDBmxy26ItrZi/We0jNrFi/jvvvMM111zDV199tfkT6oIbbrgB0zT505/+1CP321qItiY9h1jLnkGsY88h1vKHoes6xxxzDLNnz86Sz5gxg7On/YLvaxIoikRBwEPKtFlUHSMZb2awU0OOlEQO5GL5+7JwTYpo2mB4n7Abl+U4DgvXNFMbTTEkpNG/KIwnJ4AW9LKsIcmapiRDCoKU5a03qsobk6yoj1Oa62dY4fqwkbUtKZbWxikMehhZHHKNrZpYmqW1ccJeld2KQ65R1ZDQWVIbx6PI7FocItBqVLWkTBbXxgAYURQi4ssYVQnDYlF1DN2yGV4UdGPFDMvm++oY0bTJsKKgG4vmOA7ftTbvHlYUpF8ks5vVnDQg1cKhu693Vmxvdl5PGFveTqitDphAIBAIBFsD27Z59NFHOxlgAGktzIflzQzM8RNsDUT3SRbF+lpSyRoiYR9qzi7YdhAnnqY04sWQ/FkthCRJon/ESwSH4uIwgcIwcmvcV9+wl4CmdGo5VBjyoMgSOb4OGZEBDxRBUFOytv/yWjMivaqcVUA1x6cxKM+PqsiuAQYQ9mY8Zm3/byOgKZTl+TEtm5x2bY00RaZ/ro+0aZPn76Bbjo+8gJaZWzvUXvbu3qmNsN5EV9uNP4SutuMEAoFA0Pupra3l7LPPZu3atV2eX7x0KSedEUKRMxneVqKRdOVCchyb/IIipEAJqUYdSUqihQMUdKg471g2VipNxK9S1LcExadlGU8hr9plr8aAphDI6Zxs5VVl19vUHk2R6duFXJElSrqQS5KUtc3ZnsItyKDsKotSFr0jBV0xaNAgLrjggh673/jx43vsXgKBQCDYNsydO5fTTz+9WwMMYNfhw1BkCdtIodcsR69ZgZpTjJYzENP0YrSkM635HLANE9nbaoy0C7z35AbxhP07fF/jHR1hhAkEAoFAsJ2xbZs777yTX//61252dr9+/cgvLOLbrxe44/Y/+FAm/uQMzGgdqcpvsVNRtKJhSL5i9IQFWGihzJaeEUthJvTMi16WsHUT1e/J1PzyiNd/b0B8CgKBQCAQbEdqamr42c9+xptvvunKjjr6GH591wO0OF5mz/wvK5cuou/g4Zx1+k+xapeTql4CgTyqc/fBiKmUGDq+gA9JkXEch6qYTtp0KMLBaoyhRfz4CkLUmhBvSVMakbIq1NfG0jQlDYrDPjcgHjJB9PVxnYKgJ2vrryVlUh1NkevXsoqwJnSLNS0pQh4laysyZWTkXlWmNOJztz8Ny6ayKYUsQ/8cv5spadkOlc1JbBv65/rcmDLHcVjTkiJt2pRGfPjaxZRVtaSI6RalEV+Xuvk1hYJe5vgTRphAIBAIBNuJOXPmcMYZZ7hleRRF4Zqbfs0RZ1xMSlHoF/Aw6fSfsayiCsMyia35Dm+6Hq14BHGngLVVcWzJIT8Uxt9qqOi2Q23SIBbX8Uc0+pbk4cn148gKddVRGhMG4dYq8m3UxXWqWtL4NSXLCGtOmqxuSqLKcgcjzKCiKYVpO1lGWEvKYG1ziryARp+Q1zWqYrrFupY0AY9CUdCLV83I47pFdSyNImeyPNti0ZKGRU1Mx7Id8gIauf5W3SyH2phOQrfI8WmuEWbZDrVxfYO69Y14KehlVYeEESYQCAQCwTbGcRzuuOOOrOKr/UpLufUvfyOyy96EfR438zEg6eTpNTh6DG9QwVM6BiPmQCxJaa4PSVUJtjM6NNumSLLJz/FRUpqDN+B1PU99w15yfVpW30WA4rCXgKdzRmRBUEOWg+T5swPg8wIauxQGiHizx+f6NQbnBwh4sntH5vhUyvL8md6RSnYSQP9cH7IkdeodWZrjw3acrCQBjyLRL+JDt+ysLE1FljaqW+a5vasQtzDCBIJN4PPPPyeVSjF27Ngev3djYyOvvPIKP/vZz3r83j3J5sxza67XlsxHIOhtSJJEeXm5a4AdefQxXPHbvxLOyyc/4EGWJBzbwozWYtWVky8l8fXtD0o+RtxEUlQ8fujjOKh+FUmWWgPvdUBiQL88tLCvU+B9V/0bIZNl2FWmYcSnEfF1zjIMetSs4qht+FrLSXREU2QG5HaWq7KUVeC1DVmSusy43FAG5cZ0a04aYHTffm97sFMbYZbtYG9BrVrDsjGsTSv/IEtS1l8DGyOdTjN37lwqKiro168f++23H4WFhUCm8OGzzz4LgKZpDBw4kHHjxnVqjt2b2BYv423Bq6++Sl1dHWPHjs3S6bPPPiOdTv8g/dasWcN1113X642JzZln+/XaVvP5sXzXBD9O2irfr1ixgl122YVzzjmHu+++m88++5wjJp7MEadfQF7A4xo2VjqGXrMSO9mEpAVRcgZimWEwbFS/D2QJxaNmgu9jKSSvBraNGvDgyRGB9zsKO+2nZNkO8yubiKU33zXZ3NJCTvOmGVYhr8Ko/rmbZIjNnj2bqVOnUlBQwF577UVjYyPfffcdV155JVdeeSUVFRVccsklXHzxxW41fI/Hw4cffkgoFNpsPbYF69atIx6Pb+9p9CjtdXr55ZdpamoSL/5ewI/xuyb4caDrOkcffTRz5sxxZU899RT/ffk1/vrfWSTMjBdHlSUc28RsqsZorADHQY6UgRPGjLXgC8lI7RtpKzJqwEOqIYoqS/iLI6h+X8YrJtgh2GmNMNtxiKUtAh4FVZaojaVJmTbFIS8edb37tiGuEzcsCoOe9f2qdBUch6a0Qb7fk7VfHUubNCR1cr0aAa9KLG1hOw7KRgrEVVVVMXnyZG6++Wauu+46V55MJvnf//6XNfahhx5yzw0ePJiXXnqJs846a6M6p1Ip3n33XWzb5vDDD+ell15i0qRJ5OXlsWbNGj7++GOOPPJI5s6dS35+PmPHjqW5uZn3338fwzA47LDDKCwsJJ1O88QTT3DhhRciSRLfffcdH330kVvn7JtvvqG2tpYjjjiCkpISUqkUAJ988gmO41BcXMy8efMoKytj1113zZpjdXU1s2fPpqSkhN13353XX3+9W8/LwoULmTt3LpIkUVZWxrhx49xej23PKioq4pNPPmHw4MEcdNBB1NXV8dZbb1FQUMAxxxzj3qttfGFhIZ999hkjRoxg1KhRXT63Tafy8nLmz59PMpnkoYceok+fPpxyyinMmTOHnJwc9tlnHyDjwZw1a1bWZ1RTU8Ps2bMpLi4mLy+v0zPaPKJ1dXUcfPDBlJWVbfTz3RK9gS4/4609zzaSySTPP//8Jn1/Nzaf9t81gHnz5rF06VL22msv9t5776w5f/DBB9TX13PkkUe6bXXa1s7j8fDWW29x0EEHMWjQIGpra/nwww+RZZlx48YRiUQAeOGFF9h///1Zs2YNq1at4pBDDmHAgAHuc958801WrlyJpmnstttuHHzwwe65tmvXrVvHkiVLGDNmDMOGDcvSp7a2lo8//hhFUTj66KPRNI0FCxawbt26rM9w6dKlfPvtt5x88smbtIaCbc8999yTZYBB5o/u39/7N477yVkYlk1DQqdA0VsbbjdjeyJUWwU4TTIlARvF50HSVCzboTqhg+NQqMjIjk2gJA8t4qcmaWK1pCgOe7Mq1NfE0qQMm+KwF2+791t9XCeuWxSGPFmV65uTBk0pg/yAh3C791s0bdKQ0Mn1ZRdCTRgWdTGdoFfJqlCfNm2qo2l8muy2FILMblJ1NBOMXxJeH69m2Q7ropntwpLw+qB+x8nILdv5wbrZTu8qYr7TGmFtqK0fcm1cpyVlEvSoFLd+6RzHoTahUxfT8aqKuy+uyhK1SYPyphRqoUxe+4yRtMmqhhRluRDpolpvdzz55JP079+fa6+9Nkvu9/s5/vjju7zG5/MRCAS6bXDdnlQqxaGHHoppmgwfPpzp06ezbNkyRo0aRV5eHt9//z1XXHEFxcXF7LnnnkycOJElS5Zw5JFHMmLECHw+H9OmTWPWrFnst99+/OY3v+Gggw5izz335P777+fBBx9k3LhxDB06lLvvvpsRI0ZwxBFHZG1Lvfjii7zxxhvIsszuu+/OG2+8waWXXsptt90GwLfffsv48ePZf//9sW2b+vp6KioqujXCamtr+eqrr3Ach8cff5zrrruOTz75hEAgwIsvvsjLL7+M1+tl99135+WXX+aKK67glVdeYZ999mHu3LlMmTKFP/7xjwC8+OKLvP7665imyd57782bb77JTTfdxDXXXNPpuW06XXzxxVRXV5NOp/nqq68YOHAgAP/85z8ZOnSoa4RVVFRwww03uIbGd999x7hx49hvv/2wLIvGxsas+1dVVXH00UcTiUQYOHAgv/jFL3jyySc59thjqamp4YUXXuDiiy/OqnDdxubqvaHP+IfMc1Npbm7OWpsNsbH5tP+uXXPNNTz//PMcfvjh3H333UyZMoXrrruOyspKjjnmGPx+P0OHDuV3v/sdr776KmVlZbz44ovMmjUL0zQ56KCDGDlyJO+99x6nn346hxxyCLquc9VVVzFnzhz69+/PHXfcAWR+DouKirj44ot57bXXXK/oypUr+eqrr9B1nRkzZjBhwgQefvhhAO644w4URSEYDFJYWMhFF13EO++8w0EHHQTAW2+9xRlnnMEBBxyAoij89a9/5a233gLgzDPPZO3atXg8md87M2bMoF+/fsII66W89dZb3HrrrV2eq19TDsCq+igBowmvUoemKCg5g2mMq6yoaMbr0ygI+1BaI2cSpsXaphS2YRDqE6GgOAfV5yFhWFS1lm4IeJSs/opVLSlaUiZeVXbjqRzHoSqaoi6mo8hSViX8mlia8qYUQwvJMsIaEjrL6hKU5WY3xW5MGKyoj1MY8pDvX199vylpsLoxQcSnkufXXOMpmjapaEriVWVy/JprJMV1izXNmfdZ2Ku6WZpJ0+4x3Uy7d7XL3umNMMgYVUUhLwGPmpWaK0lSayqt0qlXVtuXILdDwGKuT6Ms19dtK4Xu+Prrrxk1atT6L29TE//5z3+ATPzX+eef74596KGHME2T9957j+bm5m6NtPb8/e9/B+Czzz5DVVVeffVVJk2alDWmtraWd999l5EjRwJw2mmnMWnSJB544AEApk+fztVXX82cOXM47LDDmD17NnvuuSfvv/8+kydPZs6cOQwdOpQ5c+Zw0UUXdTkPy7L4/PPPUVWVN998k2nTprlG2K233so555zjNh2/8sor3TXoinHjxjFu3Dj3ePLkyTz55JNMmzbNXbe2Z/31r3/l+uuvZ+nSpQwYMIAFCxZw2GGHucYIZDxWixcvJhQKMX/+fA499FDOOeccioqKunz+nnvuyXHHHUdTUxP33Xdft/PsyK233spZZ53Fn//8ZwAuu+wyKioq3PM333wzY8eO5cEHHwTg3Xff5ZJLLmHJkiWsWLGCSy65hAsuuKDbWMDN0fvmm2/u9jP+IfPcGmxsPu157rnneOWVV9hzzz0B+PjjjwG48cYb2W+//XjyySeBjCFpGIZ7XSKRYM6cOfTr1w/Lshg6dCgPPvggp556qnv9HXfc4a5XWVkZL7zwAgD/93//xzXXXMO8efMA3O8hQDQaZfjw4SxdutT1eI0ZM4Z7770XgGuvvZbHH3+cgw46CNM0Of/88/nzn//MOeecA2S8dAB77703ZWVlvPrqq5xyyinEYjGef/55Pv300x+0toKexzRNpk+fzu9//3ucbmKPBw8ZQtBOkJ+swGfF0fJykfz9MFMOAUWiX3EYOW0ip3TQMiXwPYZJgUdGy88hv18OqpoxMHyqTFHIi2HZhNrFg22L91uOT6U010/Eq2b9cRjxqZREfARbd5zaCHlU16Pla+e9CngU12PWPlOyJ3XbnBjtbYEwwsh8YF1lYUAmtbWrTIyCoIeCLnpZte9XtanB+5AxTny+9XNo867U1NTw1ltvZRlhX331FaqqcuCBB3LffffRr1+/jd7/008/5cQTT3Rf3CeccAJeb7ZegwcPdg0wyGznPPXUU+7x1KlTufPOO93tzHfffZczzzyTWCzGOeecwwsvvMCxxx7LunXr2G+//bqcx/jx4905jBw5kurq6qw5tvcEnnrqqRs0wgAWLVrEl19+SUtLC4qisHDhQvdc+6SF4cOHM3z4cHe7aPjw4bS0tJBIJAgEAgAce+yxbmzdqFGjKC0t5euvv+bII4/c4Bw2l3nz5vGrX/3KPZ4yZQrPP/+8e/y///2Pk08+mUcffTTTF86yWLZsGc3NzRQXF3PxxRcjb6DVyObovaHP+IfMc0MsXbqUd999F4CWlhbi8bi7xQ5w+umnk5ubu9nr1p6jjz6aW265hYsuuohDDz3U9TC9//77PPHEE+64vn37Zl03YcIE9+di1apVlJeXU1dXxyOPPILjOCSTST777DN3/GmnnZY1n5tuugnLslAUBcdx+Oijj1i2bBnJZJK8vDy+++471wg76qij3GtHjhzJiy++CGQ8aDU1NVnewQMOOMD9/3nnncfjjz/OKaecwrPPPsuuu+7Kbrvt1uU6CLYPFRUVTJ06lQ8++MCV5eYX0NRQ7x6PPmgsx447CGq+Z0hQRQrugmX6cJI2qs+DX5HZJejFNkzMWBojmkSyNLwhLyP65nUKvJclqcvsw631fmtPyKsyrItek35NYVhhsJPco8oMKegsV2WJQfmBTvKe1E2Rele1VmGE9RKGDh3Ke++95x4XFxfz0EMPMW/ePHcboo32L6xNRdd1d/sCMl/ejp6UYDC4wWs8Hg+maWLbNocddhjTp0/n/fff57DDDuOwww5zt2oOPvjgbr00HefQvsl4x+dp2oa3c++8807+8pe/cPjhh5Obm8u6deuydOj4rI7HQNbz259vO9Z1fYNz6IqO24RtLUja6Gpd25NMJlm9ejWJRMKVXXTRRZimyeDBgzf6+W+O3hv6jH/IPDdEY2MjX331lXsPwzDcY6DbbbWNzac9Dz/8MP/73/949913uf7665kyZQq//vWvSafTG7yu/fcnmUyiqipffvll1pjjjjuuyzl4PB4sy8I0TRRF4dhjj6WiooLRo0cTCASIxWI0NTV1eW37n4V0Oo2qqihK50bJkNmOvPHGG6mpqeHxxx/n7LPP7lYfwbbnlVde4dxzz6WhoQHIfM6X3ng7R516JvPeeJE15SvpV1LMsYfuhxOrQfLlInlLMFMOsiahBLONClmWkRQJSVXw94mg+r0i8P5HhDDCegltHog333yzU+D0pvLYY49x9NFHZwUHt7H77rvz/vvvc/311wPw5ZdfbjSTbI899uC9997jwAMPBDLbTbvtthuqqroeswcffJCf/vSn5OfnE4lEePzxxzn88MO3aP5tc2zzos2dO3eD4x977DGeeeYZ93nnnnvuRg2ADTF37lwcx0GSJNauXcuyZcuyPINd4fV6s7azAPLz87Oa737xxRdZ5/fYYw/mzJnj6jl79uys8/vttx9jx47lqquucmUrV66koKBgozFhm8uGPuMfMs8NMWbMGMaMGQNkMhrffffdTfrDYmPzac+qVasYP34848eP5/zzz2f48OHcfPPN7LfffsyaNcsNkk+n08TjcfLz8zvdY9iwYfh8Pi677DJ3W7Pt3m3MmTPH3aqcPXs2w4YNw+v1Ul5ezty5c2lpaUFVVQzD4K233up2W6rjc1VVZc6cOe53e+3ata7HOy8vj+OPP57bb7+defPm8dxzz230noJtw3333ccVV1zhHg8cPIRr//Q39t5nFBGfyilTzsCoL8dqqQZZQgoOxkipSCkb1e+F9h5ux8FKmziWhTcviCppaMGuvT6CHZed2ghLGhYJw6RvxJ+1X92UNEgYFkVBT1YWRkvKJKabKGb2NmNcN2lKmuT6s4vXpQ2LaHrTjIKRI0fywAMPcNJJJ/GTn/yEvffeG13XmTlzJnvttdcm3ePyyy/njTfe6NIIu+iii7j33ns5++yz2X333Xn66afx+XwbfJHfeuutTJo0icbGRrxeL/fee6+7lSNJEoceeigvvPAC999/PwCHH3449957b7dBqBvjxhtv5Cc/+Qm1tbXYts0zzzyzwfnttdde3HrrrZx88sksWLCA119/naOPPnqLng2ZmLCJEydy+OGH8+STTzJ16lQ32L479txzTx5++GH++te/MmDAAE455RQmTZrEcccdR35+Prqud9oyu/nmmzn55JOpq6vDsiyeeeaZrPN33nknRx11FIsXL2b48OFuRtybb765STFhm8OGPuMfMs+twcbm055TTz2V0aNHs9tuu/H2229z0EEHIcsyv/vd7zjiiCNYu3Ytw4YNY+bMmTz22GNdGmFer5e//OUvHHfccZx77rmEQiHeeecdjjjiCG666SYAnn/+eTer9u6773ZjA/v06UMkEmHatGmMHDmSmTNnbnSbtv1z77zzTk455RSmTZuGoii88847fPTRR+6Y8847j2OPPZZJkyZ1G7Mo2PaccMIJ3HLLLTQ3N3PUiady/o13EIxEcBwHM96AUbsKW09iefKoNXNR620iSqapNpKEaTvUJQw0xyZHAsWr4SkMIXs16muSxGJpioIe9/ei42Ra9TgOFIUyBV7bqI/r6Jbtlr5oY2Pvt3y/ltWPsbv3W8qwaEgahDrEYxmWTW1cJ6ApWZXrTduhNpbGo8hZW522k2lDJElsdd0UCTYvWnvr07s2R7cx0bTJ8roE9bG0W4A1ZVisbEiwtDZGdTTlyg3LprwpyffVUapjepZ8bXOKRdVR1jZnj18XS9OYNDY+kVYuvPBCvv32W/baay834Pi2225zf/kWFhZy8cUXd3ntwoULKSsr49BDD+3yfFFRkVt6Qdd1nnnmGRzHIRwOA9C/f/+s+BbIGFUfffQR4XAYWZZ55513OOmkk9zzF1xwAVdeeSVDhw4FMt68adOmuV4OgNGjR7tzOvDAA93YHIBQKJS1lXL00Ufz5ptv4jgOJSUl3Hvvve78uuIf//gHJ554ImvWrGHChAn861//cuO3Oj6rrKzM9VgAqKrKxRdfnLXleeaZZ3LxxRdTX1/P1VdfzaOPPtqlHu3/P3nyZGbMmMGKFStYvHgxAGPHjuWll14iHo/Tr18/XnnllSw9jzzySNcr0rdvX9544w03ABsyHp+vvvqKXXfdlbVr13LUUUfx2muvAWw0Jmxz9d7QZ/xD5tl+jTZEIBDY5O20jc2n/TM//PBD9t9/f8rLy5k4cSKvv/46APvuuy8LFixgxIgRtLS08OCDD7rxVB3XDjLGziuvvILP56OlpYWbb77ZNcAAHnjgAUaOHEkikeDZZ5/lzDPPBDIZkx9++CElJSU0NjZy22238dvf/pYRI0YAGSOxfTmPESNGZCXYTJs2jVdffRVFUfD7/bz88stZ85owYcJmrZ1g21A2cBB/uOdBrvrdX7jpTw8RjuSwvLqZ8pWLSa/5Hsd2kCODqUnlsqIhTa2iIAe82IaFGU/TnNRZURuloikF4UCm7pfPQzRlsi6aoqIpScJYH96QMCwqmpKsakwQTa3/gz9t2lQ0JVnZkMhUiW/Fsh0qmpKsqI9TH88OtVjbkmJJbYy6DvKaqM7S2hg10Wx5XVxnSW2MtS2pLHl9XGdFfZyKpiRWu0zE5qTByoYEFU1J0u0cGdGUyarGxDbRrSGx6e/jbYXkbIp/fAehsrKSAQMGUFFRQf/+/Tc41rIdPlzZQG0sTdirru/QjkM0ZaFbFmGvildd/xdBTDdJGhZOOkGf/PU1ihKGRVzPlLdoX48kaVoENIVDhxRs9YyMTz75hKampg1uZdbW1rp/Nc+dO5fJkydTV1e3wSDvrU19fb27fdXS0oLX63UDo6dPn87ChQu3yXbLDTfc4BbA3VFpv5aCLWdT13H06NH87ne/26ySHD3FRx99xMSJE6mqquqUYNOb+DF/J5PJJLfffjvXXnst+fn5RFMmC9e10JQ0KAh68SgSdXXVlJevJk816JtfhC0XYBs2CUlhbcokpCmUhj3YKQMjliLp2NSqHoIRP0OKw673J23arF5bjRSIMCjP776vDMtmVWMS23YYlB9w62TZjsPqhiRpy6Ysz+++lxzHobI5RTRt0j/Hn+XBqmpJUZ/QKY34s/pH1sbSVMfSFIe8WW2BGhMGa1qSFAQ89G0XHN+SMqlsThL2qvTPWb/bkjAsyhuTeBWZgfn+LN1WNSSQZWmr6xb2qPRRdQ7YdfPqGW5NdtrtSEWWOGRw/ha1Lcr8Yum8fdEVm9u2aEtpnz3VHVOnTmX//fdH0zQeeOABbrzxxu1qgHWkubmZ0047jcmTJ1NRUcG//vUvZs2atb2nJRD0Kh577DEefPBBpk2b1qsNsB8ruq7z+9//nvvuu4+6ujoWLvyO+5/4N0trEyBJ9I34cIwUqeqVBOIN7JrrRw4MxDJVsCXUoI+IJBEOtNbTsmwcx0YLeQnlhynxa51+L3vVTN9FfyQ7eUpTZIYVBt1Y1jZkSWJwQaCTXGrNMuwoB+gb8WUVTm2jKOSlsN02YRt5AY1cv9pJHvGp7OYNdZIHNIURRcFOcq8qM7wL+dbQrSVlgrH5yVZbk53WCIOMIbaxSvZdoSly1n7zjsLTTz/N448/TkNDA0899dQPip/aGgwYMIBHHnmEZ599ln79+jFv3ryNBsb3FAceeGCnLEaBYEN03FLcVixcuJDTTjuNX/7yl9v82Ts76XSavffe2w09AHjttVc5/oMv2GevPfErEmZLDWb9ahzbQg6UYOhBjKiNJ0dDavfekAArZYBt4Qn70SJ+5G4yYgE2lIfTXezsji7fVs/Ynuy025E/hB+zi31bI9ay5xBr2TOIdew5fkxrGY1GmTBhQpeFcX/9x3s45bTTSNcsx040gxpC9pdg6DJO2gJJQvFpKP5MWLhjWFhpHcXnwZMXRPVuvLtKKtqEL5zb02rtVDQnDTxGvFdtR+547hyBQCAQCLYh8+fPZ9SoUd12Jihf/A2pim+wUzEMbz9qjD7EkjKaz4snL4isKaQTOuvqY7S0JLBNE29+GH+fCJKmUhtLZ7bK2mE7DnVxnaYOyV2O41Cf0KlP6J1KnjQlDerieqcwm5aUSW0snRUoD5nMx5rWxLT2JAyL6mg6K4Ae1veCTBrZuwaGZVMTSxPXs3WwWjMie5NuDr3L7ySMMIFAIBAIusBxHP76179y4IEHsmzZsm7HleT6QPGBfzBVMQ/LojrVFqDIIEmoQS/1KZPF61pYm7LxFefgifiRZJmGhM7y+gSrGhNZBkNz0mRFfZxVDYksoyemW6xuSLC6IUFMXy9PGharGhKsqI/TnFxv9GQC3BMsr0/QkFgfD+U4DuWNSZbWxjtlRK5pzmQTVrc2026jOppmSW2MyubsjMi6uM7S2jjljcks46k36rY5nWy2BTt1TJhAIBAIBN3x+eefc+WVV7rHu+2zP7LksPDLz13ZHnvtxcRTz8GSc5FsiWBQI0fSCWitPg7LxkzpBMJe+oT95OUGUD3rtx+9qkLAoxD0KFlJXF5VJuhR0RQJVZZoM0k8ioy/NSPQ0y7GTJUlAh4FzZLdTELIxD4HW/swts/2l6TMeMNyssYDBD0KEZ+KT8uW+zSZiE9179d+rmGvSsCjZMVebapubWwL3XpbaJiICdsCfkxxDtsbsZY9h1jLnkGsY8+xo6+lbTtccfU1PHjPX5h6yVVced1N2MkoL/3jHspXLqff4F2ZeOLZ2AkJLRLIFF0l421RJbB1C2wLrTXw3iJjdHQMEjcsG0WWsgqSQqbAqUTG2GgfE9a29dYx896yM5ttage57ThYttMpocxxHMzNkLu6daPDlurWUYetpVtvjAkTnrCdgLq6Or7//vtNKp4pEAgEOyttGdKKohBPmyyvj3Pc+Vexz2FHM2b//dEbKzHWfM8JRx2OnHMetuUHBySfjZ0ysFUZWVNRbQdL11F9HrTcsBt43138T3fZ9h0Njja6K3vUnVyWJGSl8zlJktA2Q76huW6ufHvp1tsQMWG9hPr6+g32wvsh9/nqq6+y+pkJBAKBIJvKykqOOOII/vCHP7CmKcmn5U3UxHT654cZvddIUhULSJd/jRQsRg6PxDZ9KB4NNejDE/YhKRJGNIXRLvDeVxTZpMxHwc6LMMJ6Cd988w3Tpk3rNfcRCASCnYWZM2ey99578/777zN9+nT+9crbpEyLAp+EWbeSZPlXmPEWrIK9abBLMBwVNeBDao1DMhxotkE3LNSgF39JLp5wZvuxIaF3yjK0HYeGhJ7VpgcyW2hNSaNTliFALG11yjKETHZgcxft8TI9H41OWYYJw6Ih0TnLMG3aNCR0zA5ZhoaVkXcMaDdtp8d0a04a20a3Xhh9JYywXoBt23z88cfE43FeffVVXn31VUzTpK6ujrlz52IYBvPnz2fhwoWsXbuWTz75JOv6t956i3g83u192jAMg88//5z58+d3+vIKBALBzkYqleIXv/gFkydPpqGhAYA+/fpjyiqrq+tYs/Rr0jWrkLQ8CI9kdbPMioRNg+2sj/C2bCpqoyxtTtGSF8LfJwdFy0T6VEfTLK6JUd6YyHpuWzbhqoZElsHQmDRYWhdneX12NmFLymRtS4oV9fFO/RVX1GfGtzdiDMtmeX2CpXXxrP7FtuOwqiHRZdZgeWOCxTUxamLZGZFrmlMsqomxpkNGZE/qtqI183Fr61YX613V8kHEhPUKLMti5syZNDY28tBDDwEwfvx4vvrqK8477zxKSkoIBoNMmTKF3NxcHn30Ud555x33+qlTp/LBBx+wyy67dHkfgMbGRg499FB8Ph+LFy/myCOP5F//+te2V1YgEAh6AYsXL2bKlCksWLDAlR09+Sfc8rs7WVNTQ3PDaqSghpQ7nHQUJMPE7/eQ1G0USQLHwUqbYFsEIn4iSPiDvqzAdFWW8KqdO6wosoSnVd4+akmVJTyKhEeRO91HkSUURc48u+0+koSmyFiOkxVjJUmZezgd5WRitDyq3CnGSlMymYeK1Fnu60KHHtdNkra6bh2TA3oDO7URdvfdd3P33XdvdNyxxx7rGjVt7LPPPjQ1NW302v/85z8ceOCBGxyjaRq///3vmTZtGq+++mrWucrKSp5++mkOOeQQ935bcp81a9bw4osvMmrUKKqrqxk4cCB/+MMftmoWqUAgEPQ2HMfhiSee4LLLLiORyHhxVE3jhhl3cdLkiZh1qymVkhTl5+LzFYEj481TsOI6fXAoyvXilSTMeArVnwm8H+hRKTZtfB3KIfQJeQj7VLwdDJWCgIeApmQMlXaGQcSnMaIohCJnl24IeJRM78hwMMvoaeu7aDtklWJQZYldCgJYtoNPyy7dMDg/gGHZbimINgbk+ukT9uLvoEPfiJe8gLZVdRtRFEKW2Oq66YYFZuftze3JTm2ENTU1sXr16o2Oq62t7SSrqKhw3dcbIpVKbXTMhhg4cKBrgP0Qhg4dyqhRowAoLi7eJqU8BAKBoDeRTCY5//zzefrpp7PkpmEw65knOGbUYBRPCCUwCJ/tQdZU5NatRSkk47QksZvi2GEf3oIwWtCL1NpsO6B17vsoSVKXcqCTodCGrxu5R5G6zDTcUFZiV7dSZQlV7nxCkSUCXcjlbnToSd061vJqo6d1M8zeVagVdnIjLDc3l4EDB250XFFRUSfZgAEDCIfDG73W5/Nt0dzayMnJ2eiYTYnv6jgPRVFEw2qBQLBT4fV6WVO1rstzX3zxBa+9/TETTzwXCQU14Mmq7OnYNpIq442E8RaE3LgvgeCHsFN/i6688sqsasibw1dffdWjc9E0bZOMopycHOrq6tzjdevWZXnkNvU+AoFAsDOhmzarGhKcP/1uvvxqHNGmxk5jVlTUIns8yJ71r0ZDN4nF00QCGv7iXNSAB0mSMG2HhG51qgZv2Q5x3SLgUbK23RzHIZq28GvZcVSO4xDTLTyK3MkjFEubKLLUybOUaG3pE+hQuT5pWFi2Q8ib/WpPmza6ZRPqUNHesGyShk3Ymy3/MesmekcKumTIkCFUVlbyj3/8o1NWY3vGjBnD8uXLmT59Ok899RRTpkxB07TNvo9AIBD82Fm3bh3z5s2jIaHzRWUTS2pj2IFcTvr5pV2OV3P70tS2ZeU4mEmdFXUxVhkOsZA/s/3Y+kKvbEqyqCbKug79FddF0yyqiVLZlMyS18R0FtfGWNmQyO6vmDRYWhtjRX08qwl1SyqTTbisLo7ebhstoVssa5Un2vVX1E2bZXVxltbFaUmtj3uybIcV9XGW1sZoaJdN6DgOKxsSLK6NUdMha/DHrJvZy3pHCiOsl9C3b1+eeOIJ3n77bR566CHS6TRFRUUcdthhWeMKCgp45513KC8vZ+7cufz5z3/mtNNOIxQKbdZ9xo8fT35+/jbTTyAQCLYls2bNYq+99mLipBN5e/4SDMuhj8/Brl/JAQfsz55775M1fte9xjDm8ImY8TS2YWLGUyianIn9CvuRuog1Egh+KKJ35Bawo/dD602Itew5xFr2DGIde47tsZbpdJobb7yRv/zlL67shFNP57bbf4PZUEEi7WCqBWiSjzfenEnVukpKBwxkwvGnYqYdtEQCLeDF1ycHLejFdOgVW3bte0eK7cgt0605aaAZMQ7cdeOx4NuKXhMTdv3117Nw4cIs2WGHHcZ11123nWYkEAgEgh0BXdd54okn+Pzzz5k1axYVFRXuuQnHTeSXF/8MvWYFjhzE6++DR9ZQvR5OnnK2O87WDWzVRC3Jw5MbcAPvNbrO0lNliYiv8ytU6UYuSd3Lw96uX8UdDY42OhoobXSXlehVOxtB0JZluHPpJtG7aoX1GiPsww8/pKysjKlTp7qy0tLS7TgjgUAgEPR2dF3n6KOPZs6cOVlyj9fHNTfcwKQjDsZxJCy5ENv0ogUyPR9dLBszpSNrCr6iHFS/J8ujIhBsTXqNEQYwfPhwJk6cuL2nIRAIBIIdhIceeqiTAQZwzs/OZOK4MZi2D1MtwkhbeBUZ27SRPYDjkE7oWLZFKDeIFvEjKxlvi2k7XRb9tGyHtGXjV7MLkdqOQ6q1WGv7quyO45A0bbxK5yruScNCU+SsbT2AlGGhyJ1rgumWg2LZneSGZXcqaNom71jQtLfqljbtTsVat4ZuvTH6qlcZYa+//jrffPMNffv25YQTTuC4447b3lMSCAQCQS/m//70py7lyUQcR+tP2vZSnrCwFY0yVcKXNjFMC0uC8pSN5fcy1O/F22qAtWXWxdImZXl+CgIe954VTUkaEjqlOX6Kw15XXtWSpjqaojjsozRnfU3GmpjOmuYk+QEPg/IDrrw+oVPemCTkVRlaEHCNnpaUwcqGBF5VYWhh0DViErpFRVMSUipDC4OuUZJuzRq0HIehBUF3K8+0HZbXJ0ibFoPzA0R8Wq/WbVl9HEWStrpuYa9G//Wq9Qp6TXZkJBJh/PjxnHbaaeTm5nLaaadx7bXXbvCalpYWKisr3X9VVVXbaLYCgUAg2J7ops2SmhgHHzO5y/OlpbuDGkLxezFlGdN2kDwajmPh2A5aXhAp4sdSMufacMh4WnTTziqrQKs8bdpYTmd5yrSzGlNDxmBIdyG3bAe9Ve50GK9bDnoHr41pO66Xp/2zLScjMy0nWwcncw+9o7y36mZtG93sXugJ6zXZkS0tLUQiEff4mWee4YwzzuD7779nxIgRXV7zm9/8httuu62T/Ouvv6Zfv37bbK6CLUesZc8h1rJnEOvYc2yttWxJmaysj5M2bfyyybknHZvVgm6fffbnrnseQfNm3B4Jw8I2LPwSKH4PWtCLrCokTQvLgqBHaV8cn5SbcafSfkdNtxxSpkXQo6K0k5u2Q8KwCGjZWYOWA3HdxKcqeJT2W3wQ0008rc2x23AciOsWigJ+NXtLram5Bc0fJNghcD2uWzgOhLwdsgl3IN1iaQtJYqvr5gA+M8XIgcVsTTYnI7jXGGEdSSQSBINB/v3vf3PGGWd0OaalpYWWlhb3uKqqijFjxogSFTsQYi17DrGWPYNYx56jp9fyv88+x30P/Y0b//o4Ib+PoBPHqF2JHo/y6psfUFlRR/+BgznxtKmontbtqLbAe4+CJye4wwbety9RIdgympMGHiPOAbuWbe+puPSqmLD2NDZmWkq0rwbfkUgkIv5iFQgEgh858XicS6/4JU/+4zEAdnnw/7jkvDNJx+qxHS+SbxCTTt4VLAfHdpAdCRwHK2UADp4cP1p4feC9QNBb6BUxYStXrmTWrFnusWEY3HjjjeTk5HDEEUdsx5kJBAKBYHvy2efz2WvfUa4BBlC9fCFGSwMWeRhyP9CCaCE/WsSPpMro0STJxhioCr4+OXhzQ64BZtnZsURt2K0xSB1xNlMOmRikrjaZNiTvKl7JtJ1O8U1tOnQnF7ptXIfeRK/whOXk5PDII49w6aWXMnjwYBYtWkQkEmHmzJmitY5AIBDshNi2zR1/vIvbp9+CoWf6//kDAa6+/AKOPfZk0PqQQqMybaPaJoO9GiogKRKmIlOlqmiSzBBFcV90hmWzsiGBYTkMzg+4GXd2a2ZdXLcYlBfIKjxa3pSkOWnSP9dHfruMu6pomtpYmr4RH31C61PuamJpqlpSFIW89IuszyZsSOhUNqXI8asMzFufTdiSMlnVmCDoURicH3DLQCR0i5UNCTRFYnC77MO0abOiPg7AkIL12YQ7sm5tpSm2hW6Gvb4nZW+gVxhh+fn5vPDCC6xZs4YlS5bQt29fhg0bhiJcxwKBQLDT0Fb5/ssFC3j7nXdZtniRe27g0OFc8MtfMXbkPuDJQ/Z5sCxIxJNojo2e1AEHNejDk69hNqUwWwO1217mbe149NZsubaXeVs7noRukTYt2l6NjpMZH02bpM1sr0pct2hJmeT5s+Upw6YlZRLwdG6vE02baIqE4zhuXFratNxWPZbtICuSOz7eGuBu2o5b5123bJKG5f7/x6BbW1mvbaFbb4uC7xVGWBulpaWiSr5AIBDshHRX+R7g7NNP5sQpF+HzlSBZGorPg6ypRFSHgWEPkm7g88hu4L0PGChnXuChdhl3fk1hUH4A03bI8a9//WmKzKC8AGnTyvKaSJKUqTkV9JDnz45P7p/jI+JVye0gLw578apypzY+hUEPiiy1ZvStTwxoe55XVbKKleb4VYYUZOpp+TWFVApXn4GtnrEfi25tbE3d2orOhrtoZbQ96VVGmEAgEAh2Th79+z+6NMBOP3UyF027GdQIikfDShlY8TQEwTFt8hTQ+oQ7Bd63L9bZno6GRRsZw6LzKzHoUQl6Osv9mtJlP0OvKmcVO21DU+Ssrb02FFmiqAu5LEkUBjvrIEmS0K0dG9MtbdrUxNLYtkN+0EOB7Ot8k+2IMMIEAoFAsN1wHIfamM5HC77v8rziK0L25aP4NJAkZFUh3RzHbIjhKwjjzQ9m94IU7PQ4jkM0bRLTTbyKQllupgtAxKfS0NCwvaeXhTDCBAKBQLBdSOgm/5n5Bv322J9+/Qd2OaZsyDAUf6t3xHaw0jqyT8PfNxdP0Ickty8I6nRbA6y7czuOvJOoF8xp68o39xrHcWhOGiRNm1y/yp4lEQpDHrxq740v712bowKBQCD40eM4Dt+uqOCo4yZy/mmTePX5Zxkzei/23XvPrHG77nMAex8xGcdxsNMGZjKNrmmsUTXW6uC0ewmnTZsltXGW1sWzShMYls3SujhLauNZQdq247CyPsHi2jgJY33GnOM4VDQl+b4mRkvKzJpPVUuKhdVRGhNGlrw2lmZhdZTaWDpL3pgwWFgdpaollSVvSZl8XxOjoimZVdYhYVgsro2zsj6RVdYhbdpUNCV/tLr1xOfWnDT4uqoF3bYZ1T+H/QfkUZrr79UGGAhPmEAgEAi2IQnd5F/Pv8INv5hGY10NAP+482Z2/9sT3PWnR3nt1Vmsq1tLbv+BDDr4BGKGgx5PoXlVfEUR0jY01sUJOAaG5cOrZgyxlGHRlDJQZQnd8rqB4Lpl05IyMG2HlGG1K3vg0JwySBgWRUEPAa2t7AE0JQ2aUwb5fi0rCL05ZVIX18n1aeQF1m+BxtIWdTGdoKZSFFqva1zPjFdlmb7t6oondJOGhI5lO/SL+Nw2QUndoiGhE9CUTrrFdBNHNn6Uuv2Qz63N+xX2qRw1vIiikBdF7tqr1hsRRphAIBAItjqO47Cypolrr7+RF574mysvKCjgsitvYlCfYfhygpx69rmYsTQG0GhaBDQZf14QLeRDVhRybYfB+Q4eRXZfzABhn8qgvACShGt0QOb/A3L9OE5mTBteVWZArh/dsslpF/StyBIDcv0UGB4KOgSP94v4iPhU8jsEifcJe/BqMjldZA3KskSoQ/B7QdCD3Tq39gZDjl9jcH6gS91Kwj7UoP9Hqdvmfm6lOT6akgZJ00RVNPboG86sTy/LfNwUhBEmEAgEgq1KQjd586PP+dW081m5+DtXftjYsVx37Qxyi/qh+n20dV2WfRZyS5J++UF8BeGswHtVlugb6ZzhJktSl5l7kiR1mbkHdDJE2sj1a11m40V8aqfyDNB9lqFPU+jXRZahpshZxU7b2JBu+QENXwc9fiy6bernljAytb8c26F/ro/SHB99Qj486o5nfLUhjDCBQCAQ9ChtRVe//fZb+u8yglXVjTx61+/Q05n4IZ/PyxWX/JJJJ5+HFvAhaa2vItvGSulIskxoYCFawJsVeC/Y+dBNm6hukjYtgh6VIfkBCoMeIj51h2zE3hFhhAkEAoGgx9B1nWOOOYbZs2d3eX74sGH8+qY7Gbzb3qg+D7S+SO20gW1aqCEf3ogfWROvp50VvbUCv27beGSZwqBGSSRMnl/bIbccN4T4lgsEAoGgx/jHPx7v1gA7Y8qZnP/za0g4KssbkhTmSeR7VayUjuxRMHMCrDMcIimL4nZGWMKwWNOcIuhR6Bv2uh4Q3bSpbE6iKZk4obbehKbtUNmUBKB/rh+1dZvTdhzWNKcwLJv+OX53G8txHKqiaeK6Rf8cX1ah0upompa0Sd+wl5B3/Zzq4zoNCZ0+IW9W3FVz0qAmliY/kB13FUubVEXTRLxq1vbbpurWxo9RN1mS0E2bxqRBVTRFrldjSKGfPiEfOX6112c4/hCEESYQCASCHiGaMpn3dddFV4884jguv/p3KF6VutoY5bUxLN0kpziEJzeAFvLRFDNY0xwjbdn0CXncl3Y0ZbKuJUXEp1IU9KC1ptzFdJPqaBqvKlPQLgswoVvUtJZUyA943FinlGlTG0uTNm1y/Rr5asaQMG2H2lialpRJxKu6horjONTG09TFMll97Q2VhoROeVMKjypnGSpNKYPypsy2a3tDpTllsqYpSTrk2SLd2vw/PybdNEVCVSRwHDyKQr+wl31KI+T4tB06zmtzEEaYQCAQCH4Qlu1Q0Zjg9bmf8PqLz3U5Zswh4zJV78m8wK2Uh/y8AIGSXDfwPi8AQwqCBL3ZPQhz/RoD8wL4NDmrB2HYqzIg148iS/jbvbSDHoXSHL/7/zb8qkzfiA/Ldgi3Mzo0JSPP89tZQeuSJNE37CPizS7bANAn5MWjylk9CyFjGA0thFxf9vi8gPaDdGur0vVj0C3TM1Im4FEo8GuU5vrJD2g/ao9Xd0hO+2pqOziVlZUMGDCAiooK+vfvv9WeU19fT0FBwVa7/86EWMueQ6xlzyDWcfNoThosrm7hkQfv44m/3IFh6Ph8PlKp9UU8R+1/EPc8+G9UJbP1iCzhyQmgBUXg/aaSijbhC+du72lsMW1xXmkrE2DfL+KjKOQh7N22Afa97edbeMIEAoFAsNkYlk15Y5JPv1vOXTdewRcfve+eG1Q2hPFHnEBLvJGyIcM5YdJpyDaYeho16MObIwLvdwYMq9XwMm08ikyfsIfi1jgz7UcWYL+liJ8CgUAgEGwWDQmdpbUxXn35Je6Zfg0tTY0AyLLM2WddxDk/uxxV1bBlHV8oBzOlg0fBVxRB9Xt+FKUFBN2TMiwakwaKLFEU8lAS9pH7Iw+w31KEESYQCASCTUI3bVY1JPiuooZ7f3sT7738jHuupKQft/7mzwzeewx1hkWOZSHHUyh48BaESGoadWmLPNnKKgra1v6mY2xSyrCoien4NZmidkU7DctmXTSd8ay0CwI3bYd10RSyJFES9rqZkrbjsC6axnacTFX11mxCx3Goienolk1J2JvlmamNpUkameQAX7tswoaETjRtUhj0ZBUwbUkZNCYN8vyeraZbTVxHdVK9WjfbcVjbnCRl2uzbL4eSDtmYgs4II0wgEAgEG8RxHOriOktqY3z15Vf84aoLqCxf5Z4/5pgTuebG3xHOy2dxfYKq5iSDAyrFYT+BfgWoXo219QmWN8TZJZ+sl3ljwmB5XYK+EW+WodKUNFjZkCAvoJEf8LgtcJpTJuWNSQIehVy/5vYgjKVNKptSKLJExKu62X4JPVMmwbIdQh7VDU7XLYe1LSkSuoVfUyhszfaz7ExJh8aEgUeRKOlQ0qGqJY0iSVmGSn3c2Oq61UbT2Eaq1+oWTZtE0ybFYS8j+oSzsi0F3SNWSSAQCATdkjQsltTEKG9KkeuVKfUb1NeuAyAQDHPltbdz4kk/yRRdtR1yJRvJr1FUEsHnMVG9GcMgx68yMNdPjj/7tRPxaQzI9XVqpRPxafTL8RHyZPcgDHkUSiJevKqMR1kvD3oUikNeZJks74tfU+gT8mDb2dmEHiWzVZY2bULt5IosURT04NcUIh2yAAuDHlRZ6iTfFrrlBzzIAU+v0y3oUVgXTeHXFPYtzcny4Ak2jsiO3AJ6W3bFjoxYy55DrGXPINYxg+M4VEfTLKmNZWpPqSZ2zTLMRJKXZr7DO7Nn8+vf3k3poEEA2LqBbVioQR+eHD+Kpu7wGX29id60lo7jkDAsYrqF5EBZnp+B+f4dIuart/18C0+YQCAQCLKIpU1W1MdZ05zig5n/IVW/hprKVfQr7s+xx5/BiWdczMRTLkBGwkoZOJaFrInA+x8ztuOQ0C0SuokNBDXF7eOY00VDcMGmIYwwgUAgEABgWjZrmlMsq4/TVF/PPbf8gvfffTtrzDsfzOOeh/6N5vWQbohDPIm3KIInJ4Cs9H5PiGDTMG2HtGmRMmwM20YCQl6VoYUh8gIaYa+KLAtj+4cijDCBQCDYyXEch4aEwbK6GI0Jg8/mvstfbvwFjQ31ncbO//xjZr7wDCdOPBVvXgBPXoi4DQ1xg4KAhLdd5fqkYVHfnCIvoLkthSDTU7AxYZDjU7MCuNOmTX1CJ6gpWd4Vw7Kpi+t4O1Rxt1pb8qiK7Aaft+lTE9MBOsUo1cV1TMumKOTNisdqSOikTZvCoCcrm7A5aRA3LAoCnizdYmmT5pT5o9FNliCWtkibFmnLxrIdCgMaJREvuX6NoEfZ5oVVdwaEESYQCAQ7MUnDYnltnLXRFBhpHv39LTz/7yc3eM3yVSvw5ofRgl6QJCqrY9TFdSiCfpH1zabrEgZrUjGGFAQpy/Ovl8d0VtTHKc31M6x94+iEztLaOIXBTDmEthd+Y9JgRX2CsFcl7FVdQ6I5ZbCqMYlHybTAaTOGommL8tYm15kg9NZsQsOivDGJbtlZbXkMy6ayKUU0baLIEn1ay0Y4jkNlc6pL3aqiadY0JbeZbm2mU0/qVt6UpLIpRf9cL8XhTAX7wmCAgKbg1xS8qiyMrq2MMMIEAoFgJ8S2HdY0J/msogm/ptCwYiG3XH4+q1atcsfssssIli9f3OnaXUaOwBNeb3i0Zc4FO9SECmoKhaqHoLeD3KtQGPIQ6VDGINhaTiHHn+1xyWQHagQ8ilsLCzJGSK5fQ1MkvO08PH5NJqfVOPFr6+VeRSbHr2JYTlaWoSpL5PhVNEXKyjKUJKlb3SJelXRo2+lm9KBuCcMimjJQZYn9BkTYtU+I/ICHgEeYBNsakR25BfS27IodGbGWPYdYy55hZ1jHaMpkaV2M6miasCbznwf/j4f++mcsywLAHwhy5a9uZcJhx/Gra87nqwWfudeOPmgsDz39EpqWHYztOE4nr0kq2oQ3lNOlN6Wr8T0tB7bbs3ta3j47ckt0S7VmMxqWTcCj0C/i6+SV2xnobT/fwuwVCASCnQTLzlQ0X1oXB6A4IPP5Oy9w/5//zx2z59778Zs77qG0tAwjkebP//co73z4JlW1aykbPIRJp03tZIBB1wZBb5T3xjltLd069m4sCXspDnvJ8amoondjr0AYYQKBQLAT0JIyWFobpzauk+tT8ejN6BWr2a3/YE468Se88tpLnD/tSn523uUojoSZSKH5PARKSpmy+8Xbe/qCTcRxHKLpTCkJuV3vxjy/hkcVhldvQxhhAoFA8CPGbA3MXlTVyNsvPcPKxd8xoKSAUyccjmV4USOl/PL6GRx56jkMGbY76Ca2JLuB9y1pCz2WzmqvAxBNmyQNi/yAJyuWKaFbxHSTvA61o1KGRXPKJMenZvUsNCybxqRByKMSaBePZdoODQkdv5bJymvDapV7FDkry7Atw1OSyMoyhEx2oONAfkDL8hQ1Jw10y94i3dpnGW4L3ZpTJglF71Y3vyYT0y1sxyHHpzKyRMR57QiIT0cgEAh+pDQlDZbUxKhuivGbaWcwf96H7rnZb77Dn+58FE3x4ng0tKLBLK9pYdd+uRT2CaNoKrpps7oxSdKwkCWJgnY9CFc3JmhJmexSAMXh9U2oK5qS1MbTDMoPUNjO8bIumqaiKcmAXD+D8gOuvDqWZlVDgqKglxF9Qq68Pq6zvD5OxKeyW5+wayQ1tWYT+jWFXTXF9e5E0yYrGhKosoRPVVyjJ6FbrG5MYtoOmhJ02/L8UN3656xPTNgWuq1tSYGuZekWTZl8uy6K7TjsVhRkUEGQolBmu3FnivPakRFGmEAgEPzIMCyblfUJvqhsok/Iy4cv/TPLAAP4+pv5vPfBmxw34SSsRBqfKhPMjRAuyhhgkOk16NdkJImsGlmyBEFNxXay5ZDJ9kuZKj5VAXu93KfKRHwqvg7jfWrGG9Q+KxEy9w15VYKaSvuaoF41U7LBp8pZ3itNkQl6FBRJQlPayyUCmoLlOFneqx+sW5YOW183vyYjaQqaImFYNk2tXrx9S8MMKwyTF8j2zgl2DIQRJhAIBD8iGhM6S2ozdbuG5PlZ/On/ePCuP3Q5trJyNbKmECkMkxfxIynZZRIUWWKXwiCWnW3ASJLEwHx/JzlAaY6P4rAXTZFJRROuvDjspSCYvb0HmcbROT61k7ytQKgiS1lenZBXZdc+IRRZQm4n92sKw4tCSK3zbkNTZIYWBnGgR3Vrz7bQbUBuAMnnpzGhgyTRN+KjNMcnvF47OMIIEwgEgh8BhpXZXltZH8ejKBSpBo/ccTP/+PvjWLbd5TUDBg4iUJqP6u2+958sSchK55d8d3KpgydqY3KgWw/O5so7GjttKN3IdwTdHMchplu0JHR8isXA/AB9I76savyCHRfxKQoEAsEOTkNCZ3FNjJaUSYFfZfU3n3DrdVfz3feL3DHBYJh4POoe7zfmYE75+XkbNMAE24+0adOcMrDsTKD94IIAg/rlZQX+C3Z8hBEmEAgEOyjtvV+aIqPZKea/+za/uPQy0uk0AKqqcdZ5v+C0qefx/hsvUVW9hoHDhzP5jLNIWpBKGW6wehuxtInZ+vJvv9WV0C3Spk2OX83aLksaFknDIsenZXmdDMshkdCz2vG0zTuaNgl51KyyCZbt0Jwy8Le2zWnDdhyak6YbM9VGW9agKkudPEMtqUyN+a2lm27axHSzR3XzaTLRlEnCMPEoCv0iXvpGfOT4NBobG4QB9iNEGGECgUCwA9KQ0FlSE6M5ZVLgV6hbV87SihpUfz+GD9+db76Zzy5DR/DL6/+AE+5PRbPJpLN+TijHjyTLxNImy+szRVuHFQZdIyZt2qyoT2DaNkMKguS2lkowbYeVDQniusmQgqDbVNpxHFY3JmlKGgzK81PSrr9ibTxNdZPDgFw//XPXZxNWt2YTFoe9DCkIrh8fS7OqMUmuX2NEUdA1khoSBivq4wQ9KiP6hNxtx+aUyYr6OKosM6JPyA2kz+iW2Kq6VTYnqY6me0S3xTUZD+Wg/ABFQQ/DinLIC2h4VWF0/dgRRphAIBDsQBiWzfK6OF+uaaI0J0Afj4FetQSrLo5seAnkFnDrHfcw84V/c94Fv0A3oSLl4M8L4ItkDDDIxEm1GTPtPTyyRGt8k5wVYyWRkXuUDvLWeCiPIneqwq7KMt4OmX5tz/Oqcqf4J1WR8SgympIdsK7K0np51v0lVDkjb/+IbaGbpvww3dpivRqTmdpfe/QN0z/Hv9O1EdrZEb0jt4De1ntqR0asZc8h1rJn6M3rWB/PZD7WRNOUhFRiFd/zx9/ezlHjjuLAQ47DDETQPCoqYKXSIMt48wJYmgdFkToZBrqZCdjvWEndsOwuSzQYlo1pZzeIhsxWW9qyCXSQJ1qawB/Gr8pZhoXjOCRNG58qZ239Qaa5tFfpbNwkDQtV7qxD2rRbjattq5vtOKRMe7N1k4G4bmHYNmGvyoBcH4VB70a3Gnvz93JHoreto/CECQQCQS9HN21WNSRY1ZDApymU+U3mzvwPv/vtHdTW1zH/q2946tDjyfFp2LqBaVioQR/eXD+yqtJd6H13bWw2lLnXla2gyBIBufMJWaJL40KSpE5GTRvdyTsaR210NKba2Pq6da1Dd7rFdZNo2kSWJErCHvpG/OT5NeRuMjcFOwfCCBMIBIJeiuM41LV6v2JpiwKfTGLNd9zxp7t47qWX3XGGrrNqyRJ232NfZFXGVxRB9XvEttZ2xrQdWlIGacsmpCmMKApRFBKthATrEd8EgUAg6IWkDIuVDQnKG5P4NRmfGWX+m2/xu9/dQUVlpTtu/4MO55orb6O4oA9awIsnN4CsKsTSmazBjp6ouG526a1JGBaO4xDsYCCkDAvTdjplH+qmTcq0CXuVLGPPsGyShk3Im31/03ZI6BYBT3ZBWNtxiKUt/Fp2HFVbI2qfKnfyavUG3dpvNbbXTZEyz4ulLcAhP+Bh97yM16tjXJlAIIwwgUAg6EU4jkNNLOP9SuoWhX6ZVUu+5dG/PcqbL/ybjmG8TdE48Ug+wUGFePyZPodtPQi9qszwoqBr3MTSJsvq4siyxPDCoGvEpAyLZXVxbNthaLtsQsOyWV6fIG3aDCkIuNmEtpPJJoymzUyPyOD6ptLlTUnq4zoDcv3ktXPEVbWkWNuSol/Ex4AusgkLgh52aZdNWJ8wWNWQIOxVGVYUdI2e3qJb33aZklUtKcqbEoQ9KkUhLyGPwrDCIIUhjyiqKtgg4tshEAgEvYSEbrJwXZQltXEG5vkplGKsmjeP626+jRXLl3R5zdJvP2feR2+w754XbePZCizbIa6b1McztdD2LInQP9dPrl/rtkq/QNAeYYQJBALBdsa2HdZFUyypidGYMhmaq0H198SjURQjQEtTEwCSJOM4nVsQpevXZm2P5fo1hhcFO2UThlq9SpKUvZXn05RMf8UOW3aaIrNLQaDTlp0sSQzOD2RinTo0py7L9VMU9BLyKuixlCtvKzoa6DC+OOwl6FHxa9lbdQUBDa8awqvIvUq3woAHWYbqaAqQyPOrTBheRH5AE8VUBZuNMMIEAoFgOxJLmyyvi1MVTZHj1ShTm0iuXIqlq0iePhSNKOCmm+7kgQfvZOyhE3j8H/d1usegwUM6ybrbBusYF9VGd1mJ3RkWni5itaAty7CzXJUlIr7Oz5alruWSJBHuRoftoVv7NkKZ7cYQBUEPoQ5xYwLB5tArjbA1a9awdOlSRowYQd++fbf3dAQCgaDHsWyHtc1JltbFcYA+PtDXfsuz/32WPXY/kMG7j0EN+kGSOGjcEew9chRyQOOb777ki08+dO+z/8GHMum0qdtPkR8xtuNktRHqn+ujT8jbqYWRQLCl9DojLB6Pc9RRR/H999/zyCOPcMEFF2zvKQkEAkGP0pw0WFoboy5hEPHI2NEaKhZ9wR//fD8fffoJe+/zCfc/9ixJ08YrgZXSCZbk4s0P87dnXub5//yLdRWrKRs0mEmnTUXTMkHlva+gaSZTcEcr1poyLFrSJo7jkOPTGFqUQ75oIyTYCvQ6I+zSSy9l0qRJLFnSdRCqQCAQ7KgYls3qhgQrG5KoskSx12b58kW8PvNVnnv8YaLRZgAWfPUZb8+ZQ8muo8lXYXC/XDw5ASRZpiFuMXz8KezvV7OyCZuSmWzCgEdhl4Kga8TE0iYr6hNoisTQwvXZhMnWrEGAoYVB17AyLJtldXEMy2FIQcDd+rNa+ysmdItB+euzCQFWNSZoTpqU5fmzsgnr4jp1LVH6RnxZ2YTrommqWlIUhbxZmZJ1cZ3yxiQ520G3FfVx6uI6RSEPBQEvZbl+isNe0UZIsFXpVUbYU089xTfffMMjjzzCXXfdtb2nIxAIBD1GfVznk9UNNCVNhhUEkKJVrFn6Hddfd0tW5mNRUTE333YXpbvtT2VzCikvgid3fcNn07LRLRvDcnAcZ73cdtAtG82SaV/EwrQdTNsGZOx2JyzbwWwVWO1O2A4YVuYas53cISPXrQ5yx1kvt7KTBkzbJm3aWfdve17atDE6jt8OuhmWTUPCIJo2GVIQYK9+ORQEPN1W3BcIepJeY4QtW7aMq6++mtmzZ+PxeDZ+gUAgEOwAtBVdXV6XwHIchudKWOu+Z/ab73LL736LaZru2Ly8Ap74z+vk5xWRTunkDS0iJ+zP8sQUhbx4VBm/lh0Qnh/QUKQQXjW7CXWOT2VIQSabsP0WXMirsktBwP1/G15VZkhr1mBOu4B5VW7NGjRtcvzr5ZIkMTDPT5+QhxxfdoOkoqCXXE+wU4B9cdhLwKMQ6hBIvy118ygS1dEUkiTRN+JhvwE55PpEGyHBtqVXGGG6rjNlyhSmT5/ObrvttsnXtbS00NLS4h5XVVVtjekJBALBZuM4DtXRNEvr4qQMm5KQitNUSdOSCu6972Fmvv5Kp2saG+uZ/eYsTpw8BX9OgNy8YKcxiiyRH+j8h6osSeQFOneJlCQpa+uwPRFf1/Lusg8DHqVTiQnI9HXsqrejpkiEu5irpshd6rC1dXMcBwdIGiaOo7BLYZDikJegKKgq2E70im/eH//4R+LxOCNHjmT27NlA5odl8eLFfPrpp4wZM6bL6/785z9z2223dZI3Njbi9/u7uKJnaG/4CX4YYi17DrGWPUNPrGPSsFjbnKIurhP0KITtNIl1VZiJNKtX1THr7Te6vbZ89TL0VAwlRyEVNbsdtyOgJ2LbewpAZhsyrlvoVqa10MCQlzy/gkqKVCxFqndMc4OIn++eYVusY0FBwSaP7RVGWCAQoKSkhN/85jeuzLZtZs6cSXV1NU8++WSX11199dVZ2ZNVVVWMGTOGvLy8zVqELWFr339nQqxlzyHWsmfY0nV0y05E4ziynz7/z959R0lylXcf/1au6jg9eXY2KwcQEpKQMDIYAQKLjGSDjMnRyGAwBpzAGBtbJgtMxjbBZCTiK4INCAMCRBTKm8OEndypctV9/+iZnu6d2ZnVsrsT9n7O4aCtre6p7t7t+u29z31uV4ZwfB9JbZqkAkZxI+dfdh4vecXr+Mj7304YBgueY3DT6WR6u3FKhbbjUZKiq8qCIvEoSdFUZcEqwyMdj1OBAgtWHyZpY5RIP+x4KgRJKhasPjzScSEa9Vhzx+18R9vxI72GE/Ha4lQw7YZEIqW7w2RLZ4bOjLlm20vIv9/Hx2p6H1dFCHvNa17Da17zmrZjuq7zV3/1V0u2qCgUChQKhSP+viRJ0sky40Xce6jKjB83NmsOK+y4Zze//fXdnL/ldPq7B1ENB1SFRz7tuWy46FF89t1/x29/+dPmc5x/4cM47YqrmVZ0WsfyJ+ohQ2WProzJxpbVhDNexP4Zj7yps7VzvnasFsTsmXKxdZXtLasJvdn6NE1R2N6VaQalKEnZPdmoWdvWmWlOLc6tGvTjlG2d86sJhRDsm/KohjGbZ7fpmTNU9pl0QwaLDrmW13CoGnCoFtCXs+hvWSl5Il7b5g6bWpSQptCfN5tbCclVjtJqsypCmCRJ0loVxim7Jmr8aO80XRmTbR0mwdj9TI9P8smPfoZvfvkzXPXkP+bv3vBPJH5EXA8IEoHTtYG/f+fH+dX3vsrQgX0MbtrCOZc8linDIWxfTIgfJ1SDGPuwPlVBnFILYlSlMeWmKfPH3TBBiEaQmgsqUZJSDxv9tqJEMFfGFSUCN0qIU0GUpG0hzItSvCghiFNyjf3BG9N7UUwtiAni9hWO9bBxrX6ckGsZJPPjlIofLyjeP56vrezH+FFM1tTY0plhU4d9xLo3SVoNVm0Ie+QjHym75UuStGoJIRivhewYrzFSDdhYtOlSXbz993PvHffzL+96Ozt2NlpP3PLlT/PUp17L+WdfQOJH9GkKHSWHDltn89Mb3e7TKCaIEvqKGToyVtvP6stZmJq6YDVhV9ZEURqF8a1TbB2OwfauDKbWvv1O3tLZ3plBUWgrsM+YGltKDkLQtpLR1FW2lBzCJG0b7dJUhS2lDF6ULCik39ThUMoYlByDxJ2fbu2fXRFZOHyl5HF4bWHcCIoDBZPtpRJbOjPkF9kKSZJWm1X7p/R///d/V/oSJEmSFuWGMbsn6wyVA7KmzhmdFv7IDurTZT79mS/xX5/+L6IoAsAwTF7yitdy/oMvQtMawcf2IzIibau/SoOYXFcOM79wUZGhqfTmrAXHdVVZ9LimKvQsclxRFLqyi7cAWmxVIkDxCKsP85a+6N6OrSsok5bjtqEtulfjsb62KEmZdEPCJMVUVTaVHAYLjgxf0poi/7RKkiQdpSQVjFQ8doy7xELQkzUR7iT1gwfYeecubnjvu7n73jub55959vm86Z/ezWlnnN08pjmNsDM3NalnLdIoRrU0jOzC0CHNi5KU6uwUqKmpdGcN+vI2HY4hm6tKa5IMYZIkSUeh4kfsnKgzVgvpcAxsYvyhuwlmqnzmczfz0U9+jCRptJRQVZXnv+RVPP9Fr0Q35keS5rrAtwaxqOYjSMn0dqCoC1caLlZMfjyPAyf8Z/yux5NUMOUGpEKhN2/Sn7fpcHS5l6O05skQJkmStIQ4Sdk/5bFrykVTFfpzJmH5EHfuPoBfTxnbfT8f+q8PtT1m82nn8EfPe0VbABuuBpSDmA15i6KlN4PY0FiViq6zrRu6W55jsh4yUvXpyVr05edHyMpexMGyT9HR2Vicn7qshzH7pz0ypsbmjvnVhF6UsG/aw9AUtpYybYXse6ZcALZ1zq+UTFLB3mmXKBFsKTnNIn0hBPtnPNwwYXPJIdtSw3Ww7FH2YjYW7bbpyyk3YqpeZSBvt02DHu1rE0JQ9mNmvJD+vM3ZfTlZaC+tK3L8VpIk6Qim3ZDv7Zzgq/eMoioKnUaCf+AuZvbuY6oM9Uwfh2oLO33u3XEXX/3iZ5u/FkJQDmImvRg3mq+U0mwDDyjrGm7cviSyHiZM1EIqQXvD1nqUMFEPKXtxcyRr7vwpN6LsxW17O3pRwozXOB607NXoRY3VhGU/xovmjwdJStmLmfEivJZrjVNB2YuZciPq4fxxIRrHJ+oh9ai1Cmz2Wmth2/lH+9pqQcRINcDSFR6+tZNLNnfIACatO3IkTJIk6TBhnLJ3ymXflMuMH3NOT5a8f4j60CFGDozTu+ksTu8sEKPw89GhRZ9jamgfiReiOSaKorAhb9Fh63S2jBSlUcxgT57ufIbOXHthfHeu0VS0eFiheVfGhB7IHra/Yml21aClq20NVIu2wdaSg66pZFoK4/NWY8Rs7r/nZAyNzSWHOEnb2kkYmsrGDpsgTim1vAZFUdhYtClljMa1tb6GjEEmn12w5dBSry0oNTb2jlPBg/rz9BfsNdtcVZKWI0OYJEnSLCEE027EPeVp6mFCd9akx07xh3dRn67xja9/hw984oP8+Wv+nqdd+2wANm7avOhzbd68lcRvrJDUHJOipVM8bDVhGiUUOxdfEZkxNDLFhTVPlq6yoaXZ6RxDUxlY5LimKm3NUecoitI2Fdiq+xhWUC62itIxNEr5hT97sdfmRwkzfkTB0tnS6TBYtGXNl7TuyRAmSZIE1IOYPVN19o/XKHR0Nmq/pg7gjY8xcmCcd3zgffz0Zz8C4MZ3/CMPu/z32bBxM1c/+Vq+/f++zC9/flvzuS665HKedM2zIEzbglibNEVRFbRTfIptLnwZqsrp3Vk2FOxFW1lI0nokQ5gkSae0uf0ed040tu3pzJhYik997y7Ciss3//cH/PuH30utVm0+5qqrnkohXwQafcBu/OCn+cbXvtDsfH/1k65tFOUbgrgeLBrEkjBBsww049T8GnbDhEogw5d0ajs1//ZLkiTR2J/w7pEK5SCmlDFxNJjae5BdY1Wmxqb47Cc/zI9//P3m+d19G3jp697KlRdfjp42aroUXWPMT3nwo5/GVTkLu6Vf1Wg9oh6m9KgKdksQm3AjJmfqbN7Y2bZH5JQbMlkP6cqabVN/FT/mUNWnwzHamrC6YcJQxSdnam1TkX7UOG7pKoMFu1k7FiUpB2d8VBU2Fp1mrVWSCg6WPdIUNnbYzZoyIQRDFZ8gThk8LCSNVHxqYcJgwW7rvj9eC5jxIvryNq1jf1NuyKFqgKEpZC2djKHJ8CWd8mQIkyTplDNXeH/rrkn8OOFhm0sYcZ2Z+3/LF276f9z601+y8+5fEgZ+8zFXP/VZ/MFzXoPpZIksAyOKiWsBiWMw7kW4UULB0pshLEkFE17EjB+TK9lkEM0RsYlawIgb0wl0tlxX2YvZN+Ohq+phISziwIxPnIq2EFbxI4bLPqWMQW/OaoaqWpgwWgnImBo9WQtLbxyvhwmHagGaqtCVMZsbcntRwlgtJEkFpYxBhzO7HVDS2JrJDROKttEMS0kqGK+HTLsR+ZYO+dDYkHukEuAYGt1aI8hVg8bm2nGactFgkS2lDB2Oga7JBfrSqU2GMEmSThlCCCbqITsm6kzXQ3pyJhkd0tH7mBwa5i///s3ccfcdbY/p7unj7978Di59+CM5VA8RArKWgW7pRDUf1Q3ps1Ri26DQEkY0VaE/a1K0NIq2ga4pzanJzjQh25ej67BtebqyBqqapXRY/VgpY3Bad4aC1V4/1uEYbOvMkDHb91cs2jqbS05jf0Vt/njO0tnYYaMqyoK9IweLNqkQzWAGYGoKGwo2YZK2rWTUVIWBvEWHbbTtKQnQN7tHZIejU626TImAkqPz2DO76cpazb5jkiTJECZJ0inCixJ2T7ocnPHImhqbShmi+hT+vvvxqiHf+r/bFwQwgD99/p9x2e89CoCBttCkYORsoppPZyLQcxrqYSM73RkDmA8petYirgcUooj8QBH9sE2rC7axaC+srKm3NUedY8+2kzicoals6lh4XFeVtgavc1RFWXTF5VIrKBfbmxIarTJURaESJOR1hQs2FunMmKiyzYQkLSBDmCRJ61qaCkYqPjsm6sRJSk/OQhMx7oG78A+N4/sqhc1nMlb55qKPn5wYO/KTq2oziMW1AD0H6lKF9oqCamroqo22SKhay4QQVPwYN04o2Qbn9OVQfJXuI4Q1SZJkx3xJktaxih/x6+Eyd4xWsHWN3ryNqE1Svf/X1A+O872f3sl1L38+d953Nxs2Lt7va3Bw8eNNs0FM0RTiWkAaxUueLuIEs5hZsE/kWhWngik3ZLQWYOoKF24ocvGmDnpy1qJ7Q0qSNG99/VNMkiSJxirAfVMud49VyZkGA3kbkojpXXdxcLhCvVzjM5/5GN///rcBeMsbX8NbP3QT5114E3f96qfN53nIQy7lQZc+juGyR19+vnO7EIJD9YhECPqyJnrLiNjoRJ3Y0ukrOlgt05OTXoQbJnSQ0tVS81X2Imb8iM6MSb6lHqsaxEy5IR12eyNUd3YroKyltXWoD+KUQ9UA21DpbRl9ipLGcU1V6M/PB6MkFYxWAwD681bbaxutBiSpoC9vtXXfH6sF+FFKX94iEYKqHwEKpYzOeaU8nRlTdreXpAdAhjBJktaVyXrIHcMVbts3xYaizeaiQzJzCG/0ICOHfL72g5/w9U+/l3p1pvmYM8+9gOF6yJ/980fZeetNTI6PMbhpC1c85insOOTCeI28qZGfDU9+nDJaDwmSlIyhUrINUFVwTA5NelTKPpam0jdbf9UIbSHjFZ8zShl6WqYsx2oB+2d8Tu+mLYRNuSE7J1w2d7Rvij3tRuyerNOdM+l0jGaomvEi9k27FGydkmM0w1M1iDkw42HpKkXHaG5dVA8Thsoe0Pi5hdnCey9OGZltS5ExteYqzShJGZrxGK0G1EKHwaLDad1ZenNWWzG/JElHT/7NkSRpXfCjRhuEAzMeSSI4qzdPXkvw996FX64zOVnl/f/5Yf5vdvQLoLunl9f/3Q087IorGaoGGKrKeU/7I2wnDzSm2vpjhdgN0P0ITB00FUtX6XYMojQl27LaT9c1+ruyZCoeThiTRjGqoaMoCt2OgR5GlDozbdN0cyGn47CC/A7bYHOHvWCroKKtM9jhULD0tucp2Dr9BZusqaG3jEblTL05otXawyxjas0Rs9aVkrau0pOziJKU3GzdWpSkTHshGVPjiu2dnN6do5Qx2kbJJEl64I4phKVpSq1Wo1AoHO/rkSRJekCEEIzVQu4fr+GFCV1ZE11ViKaG8YaHcd2UH/zyV7z7xhsoz0w3H3fVVU/lVa/6Ozo39IGisG12NWHoh81zdFVhS2cGUbCIaz5RzcfI2aiaysbCwoJzRVEYKNgM5MwFxfq9jk6X6uActgqxK2vStchejUfajzFn6ZyxyMiTY2ic0Z1dcNzUVbZ3LTyuqwpbOzMLjquK0lxZ6UYJU1UfTVUYLDpcsqlEwdZlrZckHSfHFMLCMOTss8/m7W9/O9ddd93xviZJkqSjUg9idk3WGa4EFKzGSFASutT37iSccRGqyZe//13e++63Nh/T3dPH6//uX/m9yx9F7IbNYMUSwULRNfSc3RbEWGoUaJFVkyIRa2KbornmqrUgImvpnNmdozdvkllnqzklaTU4prFky7J4zWtew0te8hIe/ehHc8899xzv65IkSTqiJBUcnHH52f4ZxmohfTmLrKHiH9pL+e478cZdRL6Hb//sZ4weGsFxGiM+T3jiM/j0Tf/LFY96LKploGdMRJwS1XwQYsmfORfEEKJxfpIufZGHr5oMQ/Tswl5cq4UQghkvYqTqo6lwwWCRy7aU2NqVkQFMkk6QYwphiqLw2te+lnvuuYdiscgFF1zAG97wBur1+vG+PkmSpDYVP+L2/VPcvr+MM1vXJMI6E/f9hl07hpmugsj28peveTn/+o+v5wuf+U88z2XraWfxt//wdgqFDqBR7zUWCSqq0hbEUgFj9ZBxN0S0BDMhBBNhwpSqkqZpWxCb9CJGayFx2h7kymHKBCpRKiAFbbb4veLHDFd8/ChpO78exgyVfephe5sLP0oYrvhU/PbjUZIyXPGZ8aK24/Fsb7TJeth2PBWCQ9WAsVrQfG2pEEzWA+4fr6ECFw12cOnmEgMFW9Z8SdIJ9jv9Ddu0aRM333wzN910E5/73Oc455xzuOmmm47XtUmSJDXFScreSZdv3zvO/+yYQAEcDbyRXVTvu4uRcZ8v/99Peemrn89NX/ssv2ppNQGwd9d9fO0rX2j+uhzE7Cv7DAcpiaU3g5gbJeyvBBysBLjx/GiXG6ccrAQcqMd4ptEcEQvCmKFqwL6yTzmYD0lJKjhYDdhTCZhJBUZHBlVrFMAPV3zuH68xcVhIGquG7BivMVZtPz5Rb9S8DVf8tuOT9ZDdk/XGYoSWAFj2ouYihaDlNVT9mL3TjePVIGayHjJWC8hbOo8+o4eHb+ukt6VdhSRJJ9ZxGWN+4hOfyJVXXskb3/hGnvGMZ/D4xz+e9773vZx++unH4+klSTrFzXgRO2dDS9bU2N6VxUk9qjvuI6qGTFQ9bnz/u7n9x98H4Ktf+eyizzM8tK/53xldpeTomJqK5ZigqcRuiJZEFE0DVVXb+nxZmkrB0kmFwLEbe0fGNR/cgILeWHmYaVl9qCpQtHQ0VSGjpo06sllFWydOzQVbEeUsje6cSc5q318xa+p0Z822/RsBMqbe7C/Wmpuc2dYSlqZitOwdaRsaBVOnFiXUgpgNRZtNHQ4dLa0uJEk6eX6nEDYxMcFtt93Gbbfdxo9//GNuv/12AH74wx/yoAc9iLe+9a28+tWvPi4XKknSqSdKUvZPe+yedDG0xspDREJHZR/BZJkwVvjOz27nvTfeQL1WbT6uq7uHoYP7Fjzfxk1bmv/tGBpnlJz58GEZ6IBecdliKpj59mJ9XVU4reQghGg+Zq5Yf1Br7Aup6PPhSVEUNhYs0rAxVdi6TdFAwW5rnDqnJ2fRnTUXHC9lDDqchasSC7bOOVZuwfGMoXFWT7bteJIKqkFMh6NzTn9Ohi9JWgWOKYRFUcR5553Hjh07ADjttNN4xCMewbOf/Wwe8YhHcNZZZ/HFL36R66+/nlqtxt///d8f14uWJGl9E0Iw5UbcP16j4sd0ZUxMXSWqTuPu340/5TPpebz7w+/hp7f9oPm4vv4N/PUb/42HXvpwXvWyP+GXP7+t+XsXXnQZVz/p2rafc3gAUS0DzTIgEUdcNdn6mNZVk3E9WHTVpIhTjKKzYJuiI4Wf4308SQXTXkScpPQXLDaXihRlmwlJWhWOeSTsCU94Am9961t5xCMeQX9//4Lfv/baa9myZQtPf/rTZQiTJOmoeVHCnkmXg2UPR9cYKNikcUR9z90EUzWSROFbP72N93/wHXie23zc0655Nte/+m/J5hqNVm/84Kf5xte+wNCBfQz0DXLVlU+GKIWFrbfaqKaGrpjHp32FEAhAdxb2ATvRUtEIX1GS0p+32FzKyPAlSavMMYUwwzB4z3ves+x5F1xwAeVy+Vh+hCRJp5g0FYxWfX4zXEEIQX/BQVcV3PEhPvexj7Jn/0G2n3YGu/ft4Stf+nTzcb39G3nDm97Gwx9+RfNYnAqmI8FjnvTHzY72sRsQ+RETXkQ2b5NvmR5MhWDaj9EVBYfGiJgORPWAsYkaetaidNjUXdmPiYWgZOuoLUFsaqpOapuUsiZKHKMZOqqhUw9j6mHStqUQNBqiVv2YDsfAaqkpC+KUGS+iYOs4LV35G93rI7Km1lZTlsxupG1qKqkAP07oyZls7cwsuHZJklaHE9r8xbIsDh06dCJ/hCRJ60DFj9g5UefnB2aoBwnn9udRk4ip++7kL/7iVfzmzjua55597oNQFAUhBI978nU84hkvp2egu+35JtyIPWWPkm1wVmej7kvPWEx4EbsnXbJuxHmDHc3tfSpBwt4ZH0NT2J4VmDSCWBAm7JuqotRCzt5QbO6R6Mcp+yo+USLQSg4dtt6oB8tY7D84gzfjc0Z/gQ5NYJUa3ef3T3tMuRHbuzKN2rZZQ2Wf0YrPllKGzbPnAhyqBuybdukv2G2d8CfqIbsnXTozBmf3zteDTdYDfjtaJWPqXDRY4PyBPJ0ZGb4kaTU74R34MpmF22JIkiRBY/TmwLTHrsk6iqLQl7eo6DFKdYLynmG+9vVvtAUwgHvv/i2PuepJXPPM5zF41oUcHK9ixQlpEKFajblGS1fImzoZQ20LIdm8TdaNcIQg9QKYbZ5qagoZQ0VXFTRlvtWDlTHJ5SwSPwIvAFMDRUFXFRxdxVAFZsvqQ83QKBQdKHsoXgBZA81uBKGMqRElom20CyBrahRsHdtoP24bKgVbJ2u2r5S0dJW8pZMxtWYYrfgx1TDhQQMFLthQoCtjoso2E5K06sk2yJIkrYiyF7Fjtu1EZ8bENjQ6tJjq2B6iqTp+pPC9n/x40cduGNzMQy56GAAdVgfCDYjdEJ3GCFbJNsibOtphOSRv6pw32NEIYGFCrAToGYuMoXFGZwYFSMJa83xLUzmzN08aRAg/atZ86arC9g6nUe/VEnZUpbEfY5K3SKbraOb8NkWbOxzigljQAHUgb9GTNRcc7801pkD1w8LUXEsKTWm07nCjhE7H4Pe2dtKVNWWPL0laQ2QIkyTppIqTlAMzHrsmXTRVaU7N+WNDuMNDiAgOVmv86w3/yH333bnocwy2tJowdA3yTmOfxpYgdnh4maOrCmRtYiUgDWJiQM9YzfOTw87XVAXNMUlVpa1Y/0hhR1UUVFMHx8AozE8vKorS1rNruePAoh3rhRDUwwQvSujKGJzTl5MjX5K0RskQJknSSdPadLUra2HpKrFXx917H1E1IFFVPvONr/Px/3w/SRwv+hwXXXL5glYTKMr8htktQWwpesYihrYgtpS5Yv2jWTUpkhTV0NHsZZZiPgBJKpjxI8I4leFLktYJGcIkSTrhwjhl37TLPWNV8qbOQMFGiBR3eC9T+/cTpBYTM1PccMOb2bXzvubjtp9xLq99zZvZu/s+RsaH2bhlK4/7w2dQTSCnpm0jRbGAmq5hJCm0BLFUCMpBjK2pbasMhRDUVQ1FTbAPC2LlIEZVaFtBCVBXFBJNJTO7xdFcEKtHCXEiKFiNOq00jNEzJn4KfhDS4RioLYEtiFPqYUzBbh+xi5KUahCTt/Tma4uSlIl6iBclbCk5bC41VjvK8CVJa58MYZIknTBCCMZrIfePVfnNSBVNVTi71yBxa9R23kNQ89gdGnz55s/wvZs/Tpo29jk0DJOnPvd6Ln/KcxkoZnnQeReAEOg5mwP1iJGax0DOZHNxfpXhmBtyoBLQZetsMZTmiNhkIthb9smbOmd2Os0wNBPE7J7xsTSV0xwVZoOYmyTs83wU4IyS0wxuXpSwZ8YnFbAto+PESWMTb8dkz4xPkKRs77Ap2QYiTVAzJrunXKp+zLauDL25+ZG2/dMuE/WQLZ0ZNhy2UnK44rOhYNOXtyh7EZqmMFiw2NyZIW/JPl+StJ7IECZJ0glRD2J2T9UZLgfYukZPzsILIoLhvcyMDZOEBkbPRt79yuex6975FZDnP/gi/vYf3o7WvZkpP0bTVQzbbEw11nx0VCxdRT+sjkpXFCxNxdRUjJzVnJpU1NljqkLrIzRFwVQVTE3ByNjgh6RBjEhjNMVAU2gbbVJVBUNVSAQYtoGeasRuSFIPMFUQQkFTFEQUoxo6uqljaDGmri6oHzO0xmvQlIXHFQRlPyJv6Zzek6U/b5Ex5Ve1JK1H8m+2JEnHVZykDJV9dk3WSUVjlZ+mKthRncrQDlS3jt7RjV3aQFKPuOapz+KGf70Dy7Z52fWv54+uewGappGkgp6Mga2rbTVfXWlCR9HEPqzmqydjkDc1TK39/EKckskamE77nowFS+eMzgy62mg5wWyNmFkXbLNVzIzVNt1paSqnlxzS2f8GrfEF6oZs0hWUgo1taCRugFF0UDWNbZ0ZoiRtmwYF2NTh0Ju3cFraVdTDGBCc0Z3jtO4s/QULS29/nCRJ64sMYZIkHTfTbsiO8TpTbkgpY+IYGmkcU9u7g9rQCN/6v+9zqFxj82lncfWTrkXPWfzhVU9jeGgfT7n2OjZu2d58Lk1VcNSWEKKpzWCl+lGjOephG2a3hZ2WIKaHMaquwmHBzT6sZ5eesYhCDS1OUYIIDivWP3y14lyxPm6I4ocIzWrbpqgR8BYGKU1VyMwerwUx1SAiY+qc35+nL29j6gtXRUqStP7IECZJ0u8sjFP2Trnsm3YxNJWBgo2iKIQzU9T37cavVHntW97EnXfNTzt+6//dzI0f+DRG3ubFL3w1iqZAmoK6RABpCWJxzUfP2Y0wdiTHsGpSsw3UVD+mVZPhjIuZt1GNpb9ahRBUg4RaGJM3Nc7vL9CbtxZtSSFJ0vol/8ZLknTMhBCMVQN+vHeSuw/V6MyYdGZM0iimct9vOXTHPVTcmA989tNtAQzgl7ffxje++oVG/VTOQiQCt+JR9UOEEG3nxqmgGsYkqWgGMRSFoOpRcUPitP18IRrnR0naDGKKrhLVAyrVRhH94ephgh83jusZC9VqBLFKxcWNDu8e1ti6qB42jquWgZ4xScKI2DKohcmC1xAlKWUvouyFjFQDVBUu2FDgYVtKDHY4MoBJ0ilIjoRJknRM3DBm92Sd3ZMuI5WArKkTxinJ9ATegb241YjfjNf57Kf+nd/+7PuLPsfOvXsAGj21soJdwxWqicvWvjy9uflVg8PVgHE3YiBvsiFnNYPYyKEKIxMe/V1ZtnTOb5E27kYcqAYUTI3TS429I42czdhEjX1TVXI5izN7882C+UoQs6fsoysKW2f3jtQzFpU4Yde4i2IGnNlfIDM73RkmKbumPWIh2Fa0KVg6qqGhOiYHvJjArbGlM0NXpjEtmaQpdwxXqEcJDx7Ic+FgkW7Z3V6STnkyhEmS9ICkqWCk4nP/eJ1YCLoyFhU/IQkDqjt2Y7geimnyvTt+wvvecwOeWzvicw0Mbm7+t2roaBkTUfaJ6wFkzGWnJvWsBW5E7AaIgrXs1KSetVBqIYkfkQYR2mzt1pHojoViBogwIfYCMI68F24SJhhZG1XRYHbkLEpSyn5MGCds785wbl9eNliVJKlJhjBJko5axY+4f6zGhBvR4RhkDA2RCgaTCt6hIQxNMB6E/Ntb/5Ff3D6/72N3Vy+FYge7d9/fPHbBQy/n2qf/cfPXiqKwrStLX9bEDub3aURV2ZC3KDk6mcNCVl/eJm9q6H7UViPWkzFwDBVba9/Au+QYnL2hCF6A8CNSVUG1DAqWzukdDpqqoMb15vkZQ+PM/gKxF2Angtht7DVpaiqnlRySVDQ32BZxgl3KsN0yqfgRUZwwnQr68yaDxQIdjiF7fEmS1EaGMEmSlhUnKQdnGm0nFEVhIG+hKApR3cXdex/UPHKOjWfkef6zHonrzgeZp137p7z8Za9FFxrf+s5XGBkfZnDTlsbqSKO9SN7QVIpZi9TUiGtBy4bZ6oLu9dBYZZh3TDD1BcX6i52vKAo5SwdTW1CsPxemwsN2S8oYGhgZYrd9r8nWlZUiSVE0FRcF1w2wdI1tXTn6chZZS37NSpK0OPntIEnSklrbTszt95gmCe7IQer7hlA0Bbt/A3quEwuFJz/9WXz2Ux9l0+Zt/PWb/o2LLr4cgMQLufoJ16AaWmMacYlRoUaxPm1BbKVXTS6112SlFuDrGt2GxoN683RnTdlmQpKkZckQJknSooI4Yd+Ux87JGpamNdtORNUq9T07cGsecargFPtQtDzM9qN//sv+klyhxLOf+xJs22k+n7AM4jhFjxLietAMYnEqiFOxoGeX0DQi20D3wrYglgpBEKdYutq2H6NQFWLbQPFCOCyI+XE635R1jqKQ2CbCDdr2mgQIkhSVhX3BhGUQJSnGbBDzdZ1aGGOlCRdu72WgO4cuVzlKknSUVk0Iq1QqfOADH+AHP/gBiqJwySWXcP3119PV1bXSlyZJp5Tmfo/jNQ5VAyp+RNYyKJgq8egQ/qFRqlHCv3/mM/zoO1/lwQ+6iH/+x/diAbGpMxyoXPb0F5Fq80XvcSrYPeMRxCkbLY3sbBDTMib7ygG1KGFT3qLTmR+NGqoGTPkxA7ZGKZnfMHvUjRirR/RmjcZKyVnjbsRwLaTTVBlQaY6ITUcpB6oBOUNje4fdrMuqBDH7yj6WprBJV5pBzI9T9lc9NAW2l5zZ7viNYLZ72iMR0KWmRNMexZzJed1ZuqwsHd05FBnAJEl6AFbNN8bjH/94XNflla98JS94wQu45ZZbuOKKKwiCYKUvTZJOGW4Yc9dohV8NlQGFrqxJIsAtVyjfeQf+6AjV0OMFL38e377pk9SrZW778fd4xZ9fR1BzidyQaHZkK2npkyWEIEwEYSrA0tFsgzRKiOoBUZISJmnb+QBhKgiSlFRTm33EoppPHCcEcUqcHNZLTDTOD1GafcTimk8cJYRJSpQKWh+RiMb1hCmNVZO6SuyGxGFMnAqiVJC29B9LkpSZIKbsRzgZmwf153hIwaIUhjjFjAxgkiQ9YKtmJOx//ud/yGTml3+fccYZPPjBD+a3v/0tF1988QpemSStf0kqGKl43HOoBorS3O8xDkIGaodgehojZ/DTe3fyln/8G2rVStvj77nnDr71na9w9ROuYbOlodhGW2G8oals67CJE0HB0pqjUYkfMagrxFmbot1ek7WpYNHl6ORNHVVVmjVi3QrkS3ajwL5Fb8bE0dVGIX1LjVgxiTktb+FYetv0ZcfsikhdUzB0DWbPt/yUrY6GZhs4hoYQgkqQ4McJ53dn2F7K0O0Y6KpCWPWIaz5Gdumu+pIkSYtZNSGsNYAB/OAHP6BQKLBt27YVuiJJOjXMeBE7x2uM1yNKmbm2Eyn+xDS13Tsx3YiaGvGWd72X//3ON474PCPjw6iGRiZK0NKFHemzhgYtOWuuR5flRzizNVitLE1tTgXCfLE+tQAjjjHs9q8vXVUotQa5liCWj2P0w85XFIVi67G5Yv3IJZOkqGnKtB8Rxikl2+DMTocux2hrsKqZOlopu2xRvyRJ0mJWTQgD+PSnP82NN97I2NgYQgi+973vLVkTVqlUqFTm/0U+MjJyMi5TktaFucL7/dMemqawodBoOxH7Ae7+3UTTMyimxv/+6mf8+wffSa1WWfL5BjdtQc9axPWAxI8Alm2GOvf7iR+1FesfyclYNanZBhU3xZty6StlOK8/T8luH0Wbk0Yxdk9B9v+SJOmYKOLwDc5W0MjICHv37mVoaIi3v/3tpGnKrbfeiuM4i57/D//wD7z5zW9ecPyOO+5gw4YNJ+w6K5UKhULhhD3/qUS+l8fP0b6XQgim3YgDZR83TCg5OoamNuq2Zsr4YyMkSYKZy3H7HXfyhte+ovnYzs5urn/1X/PVmz/Lr395e/P4Qy66lLe/56OYpgECEj8kjVPQNVTL4PByqVSAaGwD2fh1EJOEcaOuytIXrDAUolHDNbe6MY0TEi9CUZVGjZmmLchucSrQFKVxPBXEbggIsAxUXePwpvVJCl6cEPgu+UyGHlUhrzZaUWiL9BwTaUoSxjh9RdSlgt0pTP79Pn7ke3l8nIz38YEsKFxVIazVzMwMvb29fOhDH+L5z3/+oucsNhJ26aWXcuDAATZu3HjCrm1yclKu2jxO5Ht5/BzNe1nxI3ZPuoxWfZJUMO3GFB2dDaaCu3cnca1KXTc5ZHSTzWTYXLB41Uufxc9/9iMef/W1XPWsV9LV183mosW3vvFFhg7so29wM2df8UR03WBrh92YQhQCv+qzZ8olMXS29+aa+y6mQrCv7FOPUrYUrWbtWOKF7J2oU00FW3pzlFpG0UZqARNuRH/OpGd2P8Y0ihmdqHPIj+nvyjJQmN9rctqPGKqGFCyNzXPHk5SpqTr7qwGFosPWzkxzdGvSDdkx5bGtw2ZbLmVrVxeqAlHFI40SjJyNdljNWuyF6JZOZrDzd/vg1jH59/v4ke/l8bHa3sdVNR3ZqqOjg0wmw8TExBHPKRQK8l8GknQUoiRl35TL3mkPVVHoz9scqgaUvZB4YpysP4GiCsY8n9y2M3HLAUQpAoXX//2/MjY6zGnnX8J9I2Wq1YA0b/GUp18HQC1M2DntEcUpUSKwNBpTio6Jr3gEbohXD8h0NOo+UwH1KMWNEoJYkJ/NWqptEGgqVTegXg0a9V2zIcmNUqphQkc8X2umGjqxpVMp+2QqHgO5+b0mg1hQDWN0VUEI0Zgu1FRS28Sb8aHskeQtPEWhGiSoCjx+e4n+nEXdrTbrvoyCQ1TxiGo+QFsQE1GC0ZM/oZ+bJEnr26oIYffeey+/+tWveOYzn4miKKRpyrve9S6q1SpXXXXVSl+eJK1ZQggm6yF3jlap+BEDBQdLb0w9doiIzpEd/PCbX+cbEyPsPzTK7bf/mH9++4d48MMfi6U3mptu2ryNTZu3kQrB6f0FhBui+xGprqIaOllDZXOhsTowa8xPI9qGxvbePH49IJumJF6I5pjoqsKWokUQC0othfGKorClJ0enrVMQoq1GbDBvkTe19kJ6oK/Y6OPlhHFbjVh3pjEFmjG09r0jsyan9+Xxaj6jEzUKHQ7ndGXozZqYi7SYUBRl0SCWJimKri5b8yZJkrSUVRHCBgYGePvb387111/Ppk2bGB4eJp/P87nPfY4HP/jBK315krQmeVHC7kmXgzMeGUNja2cWgCSK8UdHqe7bxQ1v+Tt+c+cdbY97x7/8HZ/78iPIZdpHmVVFoStrgWPMFroH6LnGiFRrk9VWRcegaOsLivXzpt4cAWuVNTSynVkSL2wr1rd1FVtf+ABLU+krOqRRvGCvyblpyzlCCGphQqIqdHVmGVAEXZZGJmOgLtHja7EgJpIUI2/LWjBJkn4nqyKEFYtFPvrRj3LjjTeyc+dOurq62LBhg1xxJEnHIEkFoxWfHRN14iSlJ2c1puXSlLBap77rPtLQ5av/e8uCAAZw4YWX4mSyR/4BauuKw/kgdkSKsqKrJlMhKAcxYZzSYeuc2ZmjyzFQhSAsu0RlF6OYWTJQLQhiKmQGOpZ8DZIkSctZFSFsTiaTkSNfkvQ7KHsROyfqjNdDipZO5+xoUByEeEPDBIf2IzSF7/3y13zk4/+x6HP09W5Ao2VPxrmaqlaq2gxWiwWxBY+ZDWJRzV80iC32MzTHRAjR2DD7sCC22PmqoaNlBUm9sdekmrEoR406te6MzsauLJ1Oa6sJBbOYecBBLJisoarKgkJ9SZKkB2pVhTBJko5NGKcMlX0OTcxuVK3AnmmX3oxJPvCo77ofEdfYOVPjPR98P/f86idHfK7BgU3N0SQvFRyoBphao+5rLsAEScr+coAqBBs0YDaIpZrGvrJPKgSbi3az2WoqBPsrAWGcMqApWLNBTLUNhqohtShhMG+2ddkfrYVM+TF9ukK+ZdPvCS9mzA3pzZh0Z+aD0IwfM1wLG20l6hGpGzHQnWNTj0PJ1hcdWVc0dUEQW4qiKGi2jtmRldsUSZL0O5PfIpK0hgkhGKsGfH/XBEMzHiXHoCtrUg9TRqfqHNq5h+p9d5AEPkZpC9/47vfbAlhnV0/b8511wcP4w2c8E0Rjn0Y3iJn2YipBTNSyj6Ifp5SDhEqUkjomiqYQ1wI8P6QSxJSDBL9lJWOUCipBzLSfEJo6qqGR+BGRG1IOYqa8CDdq77JfCWMmvQhf05p7Tcb1gFoYM+FG1KKk7fyZIGLvjE85FvR1ZXlIp8NZlkrJ0pYsbZgLYigKUdklTRZ2+299v5nbm1KSJOl3JEfCJGmNqgcxOyZq3HuoTk/OpCNrYuoqaZyQ92oMTB+gIDysUh6tsJHET3nx817Bbd+7BdM0+Zs3/hsPuehSvvG1L7B//16KvRu5+snXYmcchJkQ13wyUcyWvIlhaG1bCOVNjc0FC0WBjKmjGBpRzcf0IzY4OoqukTfnp/YsTWUwbxElgqJtoCsQ1wPSMGbAUOh0bDoPW/k4kDPJmxol20DTGz878SM6AavkUJg934sSKkGMrao8+fQuNhQsipZOEsZEFY+w7GEWHZQlOuu3jojFNZ80lyw6NZmGMZqlo8ltiiRJOg5kCJOkNWZus+0d4y6JEJzVm0NTFbyKR1T3qO/fh1KdoajUuPfgOL939hMBUNSIDnK88+0fY/OZp2M7jam3uX5frRRdQ8/ZUPPpEimG2V5IryoKvdmWY3P7LtZ8SkmKbhoLtvnpOmwF5VxNWS5KKGoK2mHTe40VlPNfUXM1ZLYf4ZDipymj1RjH0Dij06EnYzYbwkJjX0dmi+kfSBDDqx2xRqy5TdHh7fYlSZKOgQxhkrSGVPxG4f1YLaTDMZqhw6+7fPE/P8rQrp0MDvRS7Ozixg++n5npKT7x+bPYtv0MVMtAB7ZuPg0lVhp7AS01TTcbxOKaP7/icKk6qJOwahLLoOLHeJWAYjblvL4C3Ufo8QXHFsSMnAWJsiCIiTRFURT0jLX0NUqSJB0lGcIkaQ2Ik5SDMz47J+uoisJAvrHZtkhTapPTvOLZz+A3d/5q0ce+951v4Z3v+wRAM4jFbjgfrNZAEIuSlBk/RiDoLtr0dznkkhST+f0kj+QBBzFVxcwtXDWZBDG6Yy66j6QkSdKxkN8mkrTKTbkhO8frTHkhXRmLGS9i56RLr62iTU9z0yc/fMQA9sg/uIoXv+Yf2DntUbL1Rn+s2SBWLnuMlX1KpQy92fnRHS9KGK6FZAyV/qzZDGJu2WXvSBknZ7OhaDenG+NUMFwNANiQt9Bng1hQ9dh3qIqwDTaWMs3RKiEEo/UQN0oZzDcasc4FsdFpF7fss6EzS9bUmvVeYSrYkrfYWLQpmI1C+9gNGptyV330vL1k8f2xTk22BjERy22KJEk6vmQIk6RVKogT7h+rcf9Ene6MxUC+sSJvrOozMlYhCibo0wLu3Xnvoo9/1KOfwL++6yPsr/jsnm50ep+ry1ItA1cLGap6BImgxzGaoaQaJhyqh+RNjW7HwNAUFF0jsEzGJz3MIKHL0XFmi9O9OGHcbYxilZzZOi61sU/j5KRHUI8oWjpds9cfp4IJN6IaJuRNrdEJX1HQMiZTUx7jdQ9VgWzWwtLUReu9gOa04MkIYuFUDS1jym2KJEk6rmQIk6RVRgjBWC3kjuEKwxWfM7uzZC0dIQSJH1EoTyGqh8gZMR/97Ge55dvfXPR5Lr/iDwDotA0oQdFq/+veWbA5XQisuNH6YW5qsmjrbCo0RqiMlqnHQsZkc08OvBDNC0HXQFPJ6BoDs3sQZVoK2W1TZ2NPjqDm44QxaRSjGjqGptKfM+mI07a9IBVVpbPDIUoFdirY7mhs7M5j6UcOSicriLmjM1hZuU2RJEnHlwxhkrSK1IOYXZN1RioBOUvnwsEiAGmcEExVcIf2kkt8OjotjNI27tu5a7Z3VbsLH/IwrnrMkwHImRo5c2F4cAyNzd050iBqqxGzNJVNhYV9sHRVYWMpg8hbbTVimqayIbewWF1RFPrzNmTNBTVih+/r6McpZT/CMTQeeUY3XWmKkaZoYQT60oXwJyOIGTkLa5lGrpIkSQ+UDGGStArEScpIxWfnRKPtRG/OQlMVRCqIXZ/6gWGC8VF0R8Xs7EEv9ICi8vq/+1de9KdP4rrnvoxsxmZ8fILBTVu46jFPRk0adVPLreZbyWJ9L0ooBzGGpnJayWEg1xiBE0IQV/1GsIJlX8OJDGJpFKPZhtymSJKk406GMElaQUIIJuohuybqzHgRpYyJM1v7lIQx4VQFd3gPaeJxyw++Q8+GbTzqqrNAaQSGzVu38+Vv/YxMJkvoVzHt+cLx2A0a+y6yfIg52UEssBLqqYKlKZzZ6dCXbYSv5vMrCnrehlUQxNIgxuyU2xRJknT8yRAmSSuk4kfsmawzWg3JGBoDBZuxWsh41acTQTJxiLgyzt6hvdzwvn/nvrvvoKu7lwsvegTFnk5Uy2Daj6jGGl1RQus4TSWImYkFeUUh2xLE6lHCpBdRMHU6Wuqx/DhlPEiwFIWOOG0GqygVHKpHmJpCT8Zohpo4FYx5MaDSlSbN81NV4VA9RAjozZrN9hFCCMa9mOkwxQwTClHCmf15BjoyR+zxtVJBrJUQAnEUP1eSJOlYyBAmSSdZECfsmXS5bd8UHY7J1lIGTVUI4pQDkzUmhyfY9T+fYXpsP/sOHOT2X/6cNG3skzg5McZPf/5/PPqRT0AHxuoRo/UQTVHoaUlhU37MnhmPbR0OeVNpjojNJLBnxqc/a7aFsHIQs68c0GHrlHIGwo+Iaj4VTeNA1SdjaBRtHUtrBJp6mHCwGqCpCoWihRU0zg9Ng+FaSJIKsrOPEUIw6UXsnHIZzNtcuKFAMU7QhUBL0iVH0FYiiAl1fu9IuU2RJEknkgxhknSSpKlgtOqzc6LO7kmPvGmwucNBUxXSJCGZrmENH+Bjb7ye++757YLHb9x2Jq/7u3/h0ode2pjWc0NKmoKWtyhYGrTsO120NDYVbIqWhm4bxDSm1TKKwmDeouOwlZJ5U2MgZ5I1NAzHJFUVYjfESlL6MwaWrmG2NEXNGCq9WRNVAccyUA2NuOajBSE9lk6qKji6QjmI8aKEoqXzlLN66HYMdLXRZDYse0QVDwrOkg1QT3YQi7wAkU9RVFVuUyRJ0gklQ5gknQRlL2LXZJ2xWkDRNrhoY2PVoxCCyA3wx6dw9+/j1q9/btEA9qhHP4F/+rf3oxuNEZm5+qqOOKU7Y6CaOqE/f37JNii1FJLrGYsYyAYxeUtDz7SP7GQMjdNK81NxczViuCEbdQUjZ7bViBmaytZiywpKdX6vyT4lxdMNpvyYDlvnzFKOLsdAawkyiqpiFp1VGcTEWI2w7GHM7kogpyIlSTpRZAiTpBMoiBP2TXnsm/bQVYWBliCQRjHBTB1/bJjUL3Pfgbv5r89+ctHn2bx1ezOAAW0bZsdueFR/keeC2Ikq1kdTqRs6tbJHhy24YEMH3TlzwUbezZewSoOYnrEQSYo/XsPpy8ttiiRJOmHkch9JOgHSVDBa8fnZ/hn2Trt0Zgy6smZzv8ew6lE/eAh3//0Qz2BkMpx3+eOwM4v3ohrctGXhwdkgpugqsRuShsmy16VnLFRLbwQxN1j2fNUy0DMmYrZYn0V6kgkhqAQxh+ohtqVz0bYuHtLlUIwilHTh+W0vYTaIKZpKVPFIwnjp82eDmGbqxG54VK9Bz1joGZMkjImr/qJ91VpphoZRcBBpjFnILvv8kiRJx0qGMEk6zqp+zC+HZvj1cBlttmGpriocqgaMTNXxDk1T37Mbf2IvuqVSzWxgKjOIlS3w1296G909fW3Pd+HFl3P1k65t/rocxIzUAoIkbQti9XrA0GQdL2oPY429IAPqsyFtLoh5bsjB8SrloD34xKlgtBYyPbuh9lwQi6OEg2NVJmdHoaBRoD9aC4jTlAf35rioL09fwcbqyIIQhGUXkaQsZTUGMUVRMItZ9IzcpkiSpBNHjrNL0nESJykHpj1u3TNJxY+5eFMHudmprIoXsevgFP74DKcZZX7z6x/xwf/4GP/8rv/EL5mEno+pKTz6MX/I7z/ysXz15s/xm3vvp9A9yLOeeV1zKlKIxmbZk14jqAzkrGYQm56eZv9MjdOFYHN3rnldE17EnhmfwbzFaWaj7kvPWJTrITsnXLqDhMJgsTlNN+1H7Cl75M3GPpC6qjT2mvRj9k+4WG6E0psjFI1asgf15OhpaUcBoOoaRjFDVHYJyy5mMbNkn63VNjWZBhFWSfYGkyTpxJIhTJKOgyk3ZMd4jdFqgK6qbOpwcGb3GYz9ECbL6IeGcQ/u5q1f/Bg/uf02AN59w9/zund+kliozfN1w+Bp1/4JF8/4VCoe2QREnKDoGoqiNPeAbNvQWlHI5226BVhxQhpEqLNtFbKGRnfGIH/Y1kX5gkN3kJBDkHhhM6RkDI2SbZAxVLSWjJLNWmSzAa4bknohZ28o0p+z2vaXbLVWg1ijN5ho1MBJkiSdQDKESdLvwI8S9k677J/ysA2NraVGTZeiKKRxgj/jEk5M4k/s4/bv3MwnPvMZ/GB+GWOx2EGfmZLJZNvCgKIobC45iLxJXA+Iaz56zkbRNTbkLQZy5oLwUHIMeovZxvmzxfqqZdDpGJRsfcH5RUunMFgk8cK2Yv2soXFWp9N2fpCkVIKIjZ1ZNm8o0CVSHETb6Ndi1mIQS6MEvcNshlhJkqQTRYYwSToGaSoYqwXcP14jiAVdWbM5IiSEIKoH1A6N89XP/Qd33HE7v77rbiYmJ5qP7+vbwF/8xd/zB4+/GpbaPFrXWrb7mQ9iR5pGU1q3B2oJYkc8f7YFw+GrJufOj5KUaT9GVRS2Fh025BrbKiWzzVyjiodRcJZccbjWgpiIY8xCZsnXJEmSdDzIECZJD1DVj9k1WWe06lOwDDry83+NkigmLHvUR4d55auex1333t32WFVVedZzXsLzX/DnmKk+3/phiSCGpi4axI5okfYVy43qHB7EhGUw7cdoCmzKmwzmbbIt05lzm1mvtyAmUgGqhubIgnxJkk48GcIk6ShFScr+aZf7xurYhkp/3kZVFJJUMFkLwA8xpytE0yP8v//50oIABvD8l7yKF7/8L5n2I/wwJh8nbUGsHMREiaBk623NTWuJwFM1cnEMLUHMjRLqUbKgA76fCCqqSkZJoSWIRUnKTBCTNbS2mrI4FUyjNEbxyh6aGbG1J89A3iJnLh741mMQQ1XQMyaqsUTIlSRJOk7k0h9JWoYQgolawI/2THHTHaOM1QNKznwT0omyyz27x9i58yDu1H5EVGd0qr7oc0VhiBsl7K8E7HNjPEtHJKKx72IUc6ASsKfsMdPSNiJJBfsrPrsrAVVNA0UhrvmIOGGoGrBjymPcjdp+zqF6yM5pn0nU+T5iQcS4G7FjymOo2t7W4VAt4NeHqkymCls6s1xUtNhqqUcMYHM022hs3B0lRBVv2dYPc0FsNbaviIOIxA1kWwpJkk4aORImSUuo+jF7p+sMlwOiJGWgaJM3dVQF0iQhqnqkYzOkh/YyemgPZ194PkZ+G4Obzlj0+QY3bcFQFTK6SiLAtkx0MyWuBaRugK026sDslhEiVWmsWEwFWKaOYajNqUlHUcibGpbe/u8pW1fJmxq2oWFkjObUpDF7/twoWJCklL0YATxhexebC41px6jmk8z2CVtuleB6GRFLwhhVU0hlh3xJkk4S+W0jSYsI4oSDMz57p1xQoDdnoakKA0mKqkDiRwTTNaKZSX5+69e58UPvJ0kFv//VW9FzOa5+yrV853++xq9//bPmc150SaPpqq6pbO9wEDC7ulBFz0FcC9ikg5a10VtqvhRFYXPBIhU0VyPO1Xz1ipTeooVpGYT+/KrL3oxBp93o8dVaI1aMUwo5g9TQOVQL0DWVbR02/TmzbXpyLnidSkFM1VWs7jxxuvzomSRJ0vEgQ5gktUhSwaGqz65JFy9M6MyYmC2jTJpICaZdonKVgzt/w3s/9G5+/NOfNn//ox98N6967RuxCzne875P8o0vf4Hh0QNs2n4aVz/52mbTVe2w1g6qoTeDmHADOKxYX1UU2h7SUqyPFyIOCzCKomC0NvmaDWLVGZeZaZdMzua07iz92cZqx8WcSkFMzO4+YGZt6lUZwiRJOjlkCJOkWdNuyK6JOuNuSNEy6C/Mhw6RCmLXxxurUBse4otf/Tif+uJnCIL5G/YVj3ocf3TdC5q/tvJZnnLNdSR+hGpo6PrSf91ag9ixrJoUxpHrq7wooRzEmLbJmRmDbl0lb+toyxSgnypBLAkijKyNKqciJUk6ieQ3jnTKc8OYvVONVY85U2NDS8+oJBVMV30010er1/jZj77Je/79PewfOtB8fP/AIK95/T/y+39wFUIIyn6MripkzflWB4kfMTXtomdMCnZ7u4h6mBCngoKltQWx8oyLsE0KjtFcBADgxylenFAwdbSWIObXfOrCppCZ30LIjRKmvRhDUzirM0Nf1sTUFKKK1xhFYz44HcmpEMREkmLkZYd8SZJOLhnCpFNWnKSMVHx2TrgcnHGpBgkbii1NO9OUoZEy9+0f5/avf4KffPcr7Nu/v/l4VdO56unP4zWv/ivy2SwAlSBhT9lHVxXO6HSwNBXNMalHCXvGaiiVgLMGCmRnW0oEScqesk+cCrYVbYq2jmrokBHsG67gTXuc3l+gK9sIDUI0VkrO+DFbijZ9WbM5IjZZnmG0XGZzT45SzqIaxGQMjfO6HfpyFmZLsDEKjgxis0EsDWNUU0OXvcEkSTrJZAiTTjlCCCbrIbsm60y7EaWMSW/eJhF+s+t9HESE03X8Q+N88O9fyM577mh7jgddeClPe+Ffs23TdqyWv0a6qjT/1xohzIyFaQckYYzwQjAbrSZU5ovtW7cA0gwdO2sSVQOEG4JjgKo2ar1UBVNT27cM0lRMxyTxUian6+QtjXO7MvRkzbbwNUdRFBnEmFsVGWF15uVm3ZIknXQyhEmnFDeM2TVeZ6jikzV1BgqNkS9bVyk6BqYi8CeqhOUaqT/JT7/z+QUBDOCJT7qGR192EXghhDGJqqA5JllT44xOBxXaNra2dZWzBgokXogWJcT1AD3b2Pz6tA6bFLBaztdUhe3dOcK8hT67RdBcjdjmgk2YpM2CeiEE1TBB1RQevKmDjaqg29bJZAzUpUKMDGKkcYKqq5gFORUpSdLJJ//pJ50SklRwcMblp/tmGK2H9OVtik77nopGFOOPVRi+517+5c2vZtd9dzAyMb3o8w0d3IdtaNh5G3V2L8XEa4yuWJraFsDmmJqKk7PRbIN0NoghBIamtgWwObqqkHFM9JzVbOhKmqKpCo6hNevPxuohpqpwemeGyzZ1sGVDB4amEpVd0jhZ8n2ZC2Kq0d4bbCnG7GuY2z9yOau5oWtUdjEKmcYUsCRJ0kkmv3mkdW/Gi9g5XmPCjehwjLZ+WABpFBOUPYKZKl+/+b/40H98gEqlwtDYGI9/0jMWfc7BTVsa/6Eo6FmLuB40A8xy+w62FuvPjYix1OjQYasmtaxFOUoJ45SSbXBmp0OXY1CtV2anKBXMYoaw7DZCRjGDusRek6fqiJiaMdHjGKuYWfLaJUmSThQZwqR1K4xT9k657J50iZKUjR02WkvLhyhOqZTraPWAHb+9nXe+883cfe89zd/fvXcn5515ARc+9DJ+9YufNI9feHGj6WqTohAYOiQpHBbE6lGCCgt6cYW6RqQl2C1TkygKfpySpKJts2yAWFWpGRpB1SdxI/q6c2zuytLp6G0rJ5uXpKkyiC0TxIQf4vQWZVsKSZJWjPz2kdadNBWM10N2jNeohwmVIKLix5i6Sret8tUvfJoDe3Zj5XsZPOcyfvD5G/nfb32NNJ2f9rr8cU/neS9/Hdu6O3nHDR/l29//Orv278PsHOAxVz8DtaXnV9mP2VP2sVSFbZbaDGK+prFrxkNVFE4vOdizTV/9OGXXjE8qBFttrRnEcEz2zHgEyfxKSYAwSfnNoRoKcH7JYaOmULJULFtbMsTIIHbkINZozqo2pj0lSZJWiAxh0rpS9WPuG68yUY/IW43Cezdq1EVFYcjLnv9Mfv6THzXPVzWNNJmvmzrjrHN54avfTH7r+RQcHSPXaGPwh495GmVN5YAbt20p1EahMaLlhY0aMW3peiwAzTbRkoTEj0jTAFrKpaIkZcaPSYXgwt4c20oORUtr1laFZa9RC7VEQ1cZxBYPYokXYnZklhwpkyRJOtHkN5C0LoRxyoEZj3vHqti6Sn/eak7Tbe5w6Mla3PKZ/2oLYEAzgGVzeV7yZ6/lGX/8XFRNox4lOLoG6vy+i4U44fS8iW23N08t2jpnqA6aqqBrKszWiNlRwlZbw3DM5igYNFZKzq2IzBoaMBuK/IjNpkJoGARxSpymDOZNBnIWBWv+r6pm6jAblGQQO8JrWCKINUbBGr3SJEmSVpIMYdKaJoRgvBayY6JGLUjozdlYevvNWRMCy/PZd9/9iz7Htu1n8r6PfJau7t7msXzrCIk635XeDmN0U4PDAkBbDVdLsb4dJWhxAofVhB1eI6Y5Jn6cMl31McyULX15NuSsBbVhzfMXCWJLkUFsPoglfoiRd9CspV+PJEnSiSZbVEhrVsWPuGO4wi+HygihMFBoD2BCCKJ6gHtohn333M1AZ27R57n2mufQVepe+ofNBjFFU4hrAWm0dBuEuSB2ePuKxdSjhNFaSKhrnNmX46Elm626QsZY+q+nZuoYBQeRpIRlD5Eu08phNoihKKds+4rYC0GIxvsgSZK0wmQIk9acME7ZNVHnZ/tnGK0GFG2dgt0+qBv6EdOjZe76yc/4sxddw0tf8kc84mGX8ZALL24776KLL+cJVz2VsOrhBRHpIiHAixKSVCwIYq4XEqcLzw+SlChJFwQxr+YTzoYGIQS1MGG0GhAnKed0OTxsQ4HT+ovkCzZJGBNX/WVDSWsQi+qBDGKLvYaWIBZMVtBnr0+SJGmlyelIac1IU8FYLWDnRB03TCjaBgfKHoeqAVs7M3Q4BiJNCWs+v7pjN5/9xPu49ZbPNVc9fvZr/483vPNTfP2rX8AbH+LM07Zz9ZOuRVNUhsaqjE569Hdl2dgxP7U36UUcqAQULY1tHU4ziE1M1tk3XCGftzitO4c2u4VQPZzfO/K0DhtDU9GzFrWyx86xGqqhMdCZJRGCvKlzfk+W7ozR1tx1bk/D2A2h6qO3bCi+mLmpSTFWkzViR3oNqoqRtxBRjFXKLnktkiRJJ8uqCmETExPccccdFItFzj//fCzLWulLklaJih/xm+EyY9WQgYJNf8EmTgVRIgiTlDgVs/s91vjGzZ/nve/7V8ozU83Hb91+Blc8+vEIVeOSxz2DjXmLwXzLny/HJKyFeDUf8laz5itOG88fpSpCiEYwUFWUjElc8fHrIUkxbtYXxalojo41x2cUBWyDaiIgDNlStDmjv0C3YzTD2+GOJYjpGas5NSmD2EJpkGD1FtBsuVG3JEmrw6oIYdPT01x//fV897vf5dxzz+XgwYP4vs+nPvUprrjiipW+PGkFxUlj1eM9Y1XCWLC9K9NsuKqrCts6M3hhRCYMuPOnv+adb38Tv/nNL5qPt22HF7zglVz3/JeimyZhktJh6+2F90B/0SFjaFhBOL9Po6bS7RiYmoKjt/fkKmVMzhooovghuCGpqqAaOgVLY1vRRlcVLK0R3CpBgh+nPGRzka2GRlEBSxFHDGBzHnAQMzQMS66aXCyICSEQaYrVIUfBJElaPVZFCKtWq1x99dV84hOfQNMae+K97GUv45prrmFkZAR1iRuJtH5NuyE7xutMeSE92YWrHoUQGHFM/dA473jv27jpS58iSedrnB77+Kdw/fVvoJTrRAQJGAJTUzGPsE9jZ85C2DpxzW8GMU1TKS0SNFRFoSNjgq0T1XziWoCea2wxVLT15r6OXpzQYeuc2ZmjO2OgAPHs5tEwH7SO5FinJmUQa3/NaZSgWrpcESlJ0qqyKkLY5s2bue6665q/VhSFP/7jP+bDH/4wBw8eZPPmzSt4ddLJFsYp+2c89k666JrCwCLBI6i73PTJj3Nw7x56u3v4wfdvaQawbdvP4LV//U889NLfAyANImK3ZYRrqS7zuoaes9uC2OHtKNq0tK+IawFaVlBLFbw4oWTrnNWVo8tp7yum522QQWzJ8493EBNhhNWVR1lm9FGSJOlkWhUhbDG33norhUKBwcHBI55TqVSoVCrNX4+MjJyMS5NOECEEk/WQ+yfqVP2YkmOga2rbzVekgtp0mVc851p+8+ufNY9v27IFx8nwwpf/Jc+87gXoxnwoUEy9sYIuiNuCmBCN+i1dVdp+hqJrCMcEN1gQxOJUoCq079eoquCYzMy4+ON1ejqznNW3MHw1n19RZBDj5AWxudWWelbWmEqStLqsyhD2k5/8hBtuuIEbbrgBTTvyjeCd73wnb37zmxccn56exnFOXDfs1uAn/W7m3ssgThmpeIzVQixNI2+o7B+ZJkUwkG9MRaZxwvjBEV732j/n3jt/2fY8e/bt4zkvfy0XX/V0Kl6dXDL/52bcjagEMV2GSjYWRJGL7phM+THTfkTJNuh0WvaCDBIm3JC8rtKpCKJpFz1jUotTxtwIR1fpz5ooCgjRWEE5Vo/YlDc401HJKy6qn1JJjvxnF0AgSOKQZKqGVjfQj6JtQiwikkoNzdXQMmZbKKm6tQXnJ2pjX0rFq2JkrSWDGIDQUqJaAF4NPWejLjUKCAhFEEcB6UQN3TGPahugOA1JZmporo7uLF8knygxcTVEdavoWWvJIAaQailxzQevhpGzSOMUVVcJq2WU2vIjYfLv9/Ej38vjR76Xx8fJeB+7urqO+txVF8LuuOMOnvjEJ/KiF72IV73qVUue+5rXvIYXvehFzV+PjIxw6aWXUiqVHtCbcCxO9POfKoQQ+JrDTw7OYGgGG3uK6KqCGybUaypxKhiwHFI/5Ob//iQf/MDbqZRnFn2uiZkK5cSiU7MxW1bAua7LdBJRzNk4pkrshiixSqDoTCcqGdXCtOdHZaIoYDpR0C0Dp2A1buiRQqyolBOVVNfQTAc3TnHjhELO5qJNGbocA0WIRuPUJMWwFt88uu315wVx1ScJY3TNXHZEjDzEbkDshmjo6Ln2EbGOfHHBQ5JcTFTxUFIVM7/0iBiAyKWEZRcSgZFbekRs7jVEFY80SjCMo+jBlafZc0xTjGVHxIBmzzFVaBj5pUfEANJcQlR2IVFQdAWnv+MBFeXLv9/Hj3wvjx/5Xh4fq+l9XFUh7Le//S1XXnkl1157Le9973uXPb9QKFAoFE7ClUknQtWP2TnhcsAPyJp6W3+ujKmxpeQQ+SH3/t8PeccNb+S+++5c8vnO2LSZ7SV7QSH9YN6iw9bpsHRUTUWnMa3XpUCm5JC32v8a9GQMDE0ha2htNWL5NGF7wSJVFSa8iKKlc0Fvo+C+Oe2oKMtuHt1KTk2e2KnJcLpOmgiM5cKtJEnSClg1Ieyuu+7iyiuv5JprruH973//sv/SldauOEk5OOOze6pOHESc0VNcUDuVxgnegYO8921v4ZZbbmr7vT984tM5cGA/v/3Nz5vHLrzoMp7yuKdi0qjxapUxNDItezWqltH4g++GWGmCobUHBkNT6cnMj6TNBTF3xsXzfTo6MmwvZejNmou2mVhq8+jFHK8gtpRTNYiptoFu6qhHMU0qSZJ0sq2Kb6aDBw9y5ZVX0tfXx+Me9zi+8pWvNH/vkY98JKVSaQWvTjqeptyQHeM1pt2IzowJaXvxuhACv1zjvz/yAf7jw++h3lLndPbZ5/Lav/lnzr/gEuIo4htf+wJDB/YxuGkLVz/pWohS0iAmZvkQMxfEjmbVpBcllIMYyzE5O5PSl9HIZowlV9qtRBATLL/F0akWxBTA6lp8z1BJkqSVtipC2MzMDJdddhkAH//4x9t+79xzz5UhbB3wo4S90y77pzwsXaM/b6GqKn7L1oJJGBOUXd72lr/mpi99qnm8WCjysle8lidf+5zmQg3dMHjK0xttTZqd7A2IoRnENMdc9AY9d/7hQUw/rHDdjRIqQYylqZzZ6dCftTAQD2irnJMZxJI4ROSFnJqcJZIURVNkh3xJklatVRHCzj//fL785S+v9GVIJ8Dcfo87Jur4UUpX1iROBfdPuBiawoABIm1sPl2frLK37HPpVU/la1/5HEma8NSnXsvzX/iXTKc2O8brbO3OYc82bRVCcKAa4EYpmwoWWUNDz1jEwMEplxoem3pyFFtqvsbqIRNeRF/WbLSQmA1iY1MuY1Me/d1ZsqZONYjJGBpndzr0Zq22RrEPdM/CkxXEkqkasawRa0qCCD1roxpLLyyQJElaKasihEnrU9WP+c1wmTtHq5zTm6Nvdq/GahAy40WN7vXlKb70jncQBQHdA4NsftAlWI7NK65/NQ+99ArOOO9CvDBm/1CZoOLT4xjYxUYBf5w2tgSqhjFdjk529marOSY1PCaqAR2WRrEnP39NYcKEG5E3Nbqcxo1ctQxCQ2No0sVPUs7eUOTcrgw9WXPR7vrHsnn0yQhiWt0gCWNZrD9LJOlRrbyUJElaKTKEScddGDf2e9w1WWfSDTmzJ0uppdC9aBtsKpj88Gs389I3vpowCpu/d975D+bN7/gIlz3vehSlEQocU2drXx6v6pNJEtIgQrUMDE1lMG8SxEbbikhFUdjUk2sEMKXR0mEuxPRnTRxDpdQyOuZGCamuc8GGImdYKn05g0x+6X5UqzKI2Qa6ZspVk4pCGicounpUfcgkSZJWigxh0nEjhGCsFrJzvEYtTOjMmPQftmpPCMHuu+7ibW/+a3562w8WPMddd97BL358Kxuffl3b8a6MCY7R2B7IDdFpjGAttq8jQNHSKfbkid2grVg/a2pkzUZw8OOUGT/C0TUu7MvSlzVRo+S4bx49Z6VWTZ6KQSwJIqxlPg9JkqSVJkOYdFxU/Ijdky6j1YCcqTNQWHjjLE9N8YG3/Quf/+//IEniIz7X0IF9i/+Goszv09gSxJYyVyPWGsT8OKXsN6ZD5wrumzVfszdtGcTWbhALyy6qpshtiiRJWvVkCJN+J3NTj3umXFSFxqrHwwuk44Sb//vjvO9t/8T09GTzeLFYpFwuL3jOwU1bjvwDf4cg5rkhVTfCylqcVnIYyFnNIv9Wx3vz6AUvQQaxxs88QUEsmK6j5szlO/dLkiStMDlWLx0TIQRj1YD/2z3Jzok6RdugO2vhRyk7JuqMVBo32oN79vLsP/wD3vI3r24GMNu2ecnzXsgXb/ofLrjwkrbnvfDCh/GHT3g6SSrYX/HZW/aJkrTt5w7XQg7GglBphIw0aNykR2shu6Y93Chpe87hasCvZgLKAjZbGhcVTLZ1OIsGsDma3dhOJ40SoorX3AT6SOaCGEIQlt3GhuFLmAtiiqYSVbxGQf1S588GMc3Uid2Q2A2WPB8aQUzPmCRhTFz1l30NmqljFBxEkja2XkqXeQ2zQQxFISq7pHGy9PmzQUw1tOa2Rcsxco1tkOa2LVqOZhtopoZZyi67PZMkSdJKkyNh0gNW9WPuH6vy/d2TlByDizfN93Gr+BHDZZ+SrVFMYoy6YHpyfvTr4oc/itf++V+w9awLQDN4yzs/zKe//DWmRg9w/vbtPOmxT4YgwSNi3I1IUkHJ0inOjiyFqWDci3CjhELJwUqSxl6QqWDCi5jxY3Jmo0N+mKTMeDG1KOERGwtsKtjoQdS8oZ+orXLkiNjKjYgJIVAtAzPvHPEcSZKk1UKGMOmoRcns1OOkixcnnNaVpTS7+iwKQ776hU+zZ/duCt0beOyVTyaJdZx8hpc//6V84rP/xXUvfAUP/b3H0FsqNbvT522bP7r2T1AUGoXxqWgEJD9kwNZBU5uF9ACmqjCQNQkTQcHSMdRGsb7wI3oNhaJlkdFVxushiqKwtcNmIGfOb1s0+/8nes9CGcRWJoilYYxmaaimnIqUJGn1kyFMWpYQgsl6yP0Tdap+TGfGpEefv/FHYcjLrnsaP7/th81jt91yM+9+zwcRlXEe8bCLuPzSK7A7+zAK2bbtgTQVNrSGCG2+5qtHpOiO2bY9kKIo9Gbb2w7MnZ8LE4RQCOKUjXmTwbzdFuBazwcZxNZjEEujBLs7v+SWUpIkSauFLJqQluRFCXcfqvGLg2XSVDBQsNu6xwN87N/f1RbAAH75i5/w1c/+B4qm4QycTnbTVoRQGzfcZWqN0NTmXo5xzUcsU2sUCyirGtOJoEdJuajT4cyu7KIBbM6x1BrJGrHVXSMmUoGCQM/I3mCSJK0NMoRJi0pTwdCMx8/2TzNS9ujJWRQOG6molGf4tze+gQ++418WfY7R6TJm73YUO4tq6Og5C5GI4xbE4lQw4UZMeSG9WZPLT+vinN4cVhidsKLvVR3EjuI1r+cgloYxmmOiLjGKKEmStJrIbytpgYofcddohZFqwIa806z7qgYxU25I3lD57pc/w/tueAvTU5NHfJ6B7edyoBZj6yk9GXM2iIFf8Rk+VMHK2ZRa/gQmqeDQ7PRaX8ZEmw1iYdVjaKwKjkn/7JZFM35EKqDHMdhctOmwG08kLH3F9yxsdTKnJpNKrW13gCNZr1OTaRxjdXUs+VokSZJWExnCpKYgTjgw4/PzA9PcP1bnIYMFMi1TelNuyLe/eytf//e3sPe+u5rHTcsmkyswMznWPHbRJZdz+WOfyt5KQN7U6LB0DE1FNXQ8U+fghIvlxWS7dazZhWxunDBSbYSwvKmRNxuF+YljMjrp4VYC3DillDEZyJkM5i0KVvsf4dWwZ+HhTlYQ01ztlK0RE2mKoqnosjeYJElriAxhEkkqGK347Jqs48cpJcfkvH61OQI2p2Cq/OQLH2sLYFc95nE862lPIchv5le3/5Ta9Dibtp/G1U++lljR8NKAjKGitxRK5zMmA10ZFD9CDaPG1KSq4ugaPZnGTdRpuWGrmoqZNcmEMWdaCpt7sxSXqPs5VYOYljHR0E/JIBaWXZy+opyKlCRpTZHfWKcwIQTTXsTuiToTbkTR1ulYZMNjIQSxF2JWXP7qz9/As39yK6effiZ//oLncfZp2zB7tqPnuzj/zPNI/AjV0NB1HV1ROK20sF+Tqals786RRjHezHSzZ5euqmwuzoefOG1cnwJcsqHAQMbE9ALwAlJTWzUd2ldTENNzp+aqycQNsDqyS54nSZK02sgQdoqqBzF7pz2Gyx6GpjKQt9puvkIIbvnyF1EEPOoRjyOqeGgZk83bNvGB97yPbf3d6E4Bq2crGI2wos0GuMSPiOtBY+++pUKJoaM5BiIS881TVbUZvgAGZ1tN5GanRYWprXgbhFarMoidYu0r0iRFz9lojpyKlCRpbZEh7BQTJSlDMz67p+okArqyVttUIcCvb/8Jf/PKlzK0fy9OJss5n/w2xY4O0ulpCKc5fdMgRtcmtGwJaH/sAw5iuoZuWcS1AK/iUdN1hKKwIWeysWA16sJarJZ+VG2vWQaxFQ1iaRCh56wln0+SJGk1ki0qThFzez3+aM8UP9w7iaWr9OXmA1jFj/ntzr284foX89ynXsXQ/r0AeG6dP3v1s5mZ2Ec4NUaKiT1wNpHVwUgtpHpYG4UoSRlLBDVFIY0S4noAQhCngtFayKTX3oIgFTDqJ+wKEibdkC6RcnFflnO6swsC2JyV7ke1mFXfvmId9xETSYqZk9sUSZK09sgQdgooexG/Ga7wi4Mz3D9eo+LH1IP5G6DvebzvnTfwgqsu55abP7/g8UP79/CV730Lq3cTam6QJFaY9CJ2TnuM1MK2c6f8mD0zPiORAFNvBrGyH7Gv7DNUDQhmA0ecCg7VQ0ZqIWf0ZrlsezdnFSzs2dVuS5FBTAYxaHTIV3Vt2dFNSZKk1UhOR65jfpSwb8pl/4yPpir05y1S0ej3lTF1hBB8+2s38+5/fhPDB/cv+VzVmofZ2U1cD0j8CFtR6HIMCoeNVmUMlU7HIGdoGBmTVFVI/AhTSSnZGqauoQATbkgqYDBn8vu93c1WE8lsn6+VnuKaI6cmV/fUZBpGy75nkiRJq5X85lqHkpZu9/tmPDozBt1ZE01V2dThcE5vjoKt87UvfIbXvfz5zQCm6waXXvaIRZ/zjNNOB0VBz1qohkZeCM7M6PTn2ldT5k2dszodNhYahf6aY6LZBpYQbNIVMrpC2Y/pz5pcMpBnW4fT1utrpUdWFiNHxFbniJgQApGKxopQSZKkNUiGsHVECMGUG/KrgzPcMVLF1DT68zbGYaMEiqIgUsGjH/OHbNiwCYDf+71H818f/jz/9Lo38JDzH9R2/kWXXM7VT7p27sHNIJYGMYnXPh059/xt12UZTAuYdEN6FcFD+3Oc051d0Gh1jgxiMogd6TW0fm5x1Uc1dbQj/DmSJEla7eS31zox13JiqOxhaRobCu0tJ3zP46c/vJVHPvbxJGHcuNG7Ia/7m7eiKoILz9hKMDMDQuc97/sk3/zetxk6sI/BTVu4+knXohst03azQWxuahLmV0W2au3ztak7S6+m4MQJWhgjLH3VTnEdyWqfmkzU5UPMepqaDGfq5E/vW/L5JUmSVjMZwta4OEkZKje63c94EXnLoOg0Ak4Uhnzl85/me9/5Jr+5/SdUy9N88ovfZvvG0wDwDZ1t55xPzhsjCWtYPb2gFnEjuOzKp9GRs7BaQkCQpJT9mIKlY+tqM4gFbkjFi8jnbbKG1gxfAsFA1mRT0W6udIzdoO2GvhQZxB5YEIvrAUkuPiVqxPS8TRpEGNmlr12SJGk1kyFsjRJCMFkP2TVZZ9qNcAyNWpAw5UaYmkpOF/zpkx7DPXf+pu1x73rrm/j3j3wW0pA9e/cyVXPZXnLYOLAVxc5CmjI6XGZ00mVbb47NnfNdyMfqIQcqAX1Zs9EJf3ZEbNSN2DPtUnQjBkoZADbkG3s7Ht5m4vAbumD5KS4ZxI4uiCle9ZQp1hdRgtmRRbXkqkhJktYuOY6/BtWDmF8NlfnJvhnCWDBQsMlZOhlTI2vqlMdHeP7Tn7AggAFc/LCHk/gzhOO7cIRPsaOLXN9sAANQVQqFRrG8HsSk0Xytka2r5E2NjNHyx0ZRsLMmQgUtTujR4eKBPGd3HbnPV2utUeKGq67WaK3WiBlZ65SpEUujGKO4dJiVJEla7eRI2BoyN/W4Y6JGksKGot1stmpoChsc+K8P3MgnP/hefN9b9Dmqh4aIJvahOXm2b91AamQWdMzvz1t0Ozp4IXEtQM81thjqyZh0WHrz/CQVzAQxKfDI07sZUBWyCPQkYbk/WnMjK8lUjXgVjKysmxGx/PpvXyFS0bj2ReoQJUmS1hIZwtaAw6ceSxkTx2i/Mc1MTXHNYx/O+OjIks810NMDZi9GVx+Kbiw6FKooCoahg6Y2VqG1BDFDU0lnN/6OEkFf1mBz0aZoNfqOxQ/whq7VjcaojQxii5J9xBZ+bmkYoTkm6hKvS5IkaS2Q05GrXD2IuWu0yi8Olglmpx4PD2AAHZ2dnH/BRQCYpsl1f/ISLrjwkrZzLrzgITz1T1+KnikR1yNYZooLVcXI2SiaQlwLSMKIGT9mvB7SYWk8dCDH+T1ZirMtAo5piss2Vs0U19xrWA9Tk+u5fUUaxZh5ORUpSdLaJ/8puUq1rnpMBJiaiqoozRvPwX176B/chBs3RspyGrz8Za9FT1Ve/uevo9i/gdrkIf7vG59l5NAoG7efw5OueQ6RouMFMdmoERiMnA2aSpCkuFFC3tTbpicjAVVNI/ADovE6vZ1Zzu3P02kv3mJiLY6sLPYa5IjY6vzcFEVBUVU0RxbkS5K09skQtso0Gq5G3D9Wo+w3ph5dL+ATn/w440P7OWPrJsaH9vO5j3+Uv3zzDZxz5TMI6z4bRUpPpoc3vvEdpGrC3bt34Qc+v//4p9DbvwUMm1QI9k15VMOYLVmTUpo0g9iBSsCkF7G5YDGQs5rXsmPKY8wNuaAnw3mGSslQsTRlyZvtWrmhyyB22Plr4HNTTB0zb6Ma8qtLkqS1T36TrSJuGHPfWI07Rqps6nAYKNjEUcRfPu8afvXTHy04/8PveCtvOfv3MAwHqzePKRKCQ8NE/gy6opPp7Mfu7YHZRqsKYKgKpqaiGxq6YRDXfKKajwlYmoo2ewOthQm1MKY/Z/CoLR10OwYqYl3d0GUQW+T81f65VVyyG0pLXo8kSdJaIUPYKpCkgpGKx45xl3oYc3Zvrln39ZXPf3rRAGYYJtdc8xzOLOWwinmMqEY4sQ+SCN3McnqxH6WQw26pH1MUhS1FmzgV2HrjpqfnbOKaT68CPR0WiaIwWg3IWRrn9+ToyRgt05PK+rqhyyC2+Pmr9HPTsxYgln3/JUmS1gpZmL/Cpmf3erxztIZjamwuZZoB7J47f8NHbnzboo978lP+mBe+7C8oFDKo0/vxD96NomrYA9uxB7ejqTqaH8JhBdO6qjQDGICia+g5mzBOmZhySeKUc7szXNyfZyBnLmhfsd6Lvo/0GmSx/sp/biJOsbryS4ZmSZKktUSGsBXiRwn3jlX5+YEZamHCQN4i0zJqdcMbX8+zHv9IRocOLvr4M897EGlQxT/4W6KZUYyOfqze01EzHWhWY8WhiNNGAFjiZujHKWN+TGSbnFU0uSCjsSFrLtj0u9V6uKGvyyC23Gte45+bSFPMgrPsNUiSJK0VMoSdZEkqGJrx+On+afZPeZQcA0NTCZP2m1VPX3/zBmbb7TeeBz/kUh5x8UPx9/0KNA178FyM7i34ik49bIQJtSWI1coutSBuuyEGScpQxaPsRZzR6XD5pg62DZYwVeWUGVlZb0EsqgXr9nNLoxjV1NDkNkWSJK0jsibsJJp2Q+4YqeCGCSXHpOSYTLohe8arqEnIBVv70Wan/5707Bfx1Ztv4uFXPpVnP+UZ/ODWWxgaPkCpUGLL2Reyc3iMs7dto9i9ATSDMEnZNe0RC8G2ok3B0lEtAyUV7D1UxZ/22d6XJ2/pTHsxqgqnd2QYLFhtfcdOdq3RcmSN2NHXiOHV1m2NWBrGmB1Lvy5JkqS1Roawk8CPEnZP1vnR3ilUReGhg0XU2RvM7f/3XW785zdyxnkP4Z3//iEA0iQBL+Gvbvgkhq6T7czxlKdeQzg1RHXmELvrBmp2A4rTBdrSAUC1DDTbIK74HJqqk5YybC46DObNtunP5vknueg7FhHkl37/ZBA7uiCm52xIxLoLYkIIRCrQs0u/Z5IkSWuNDGEn0Nyqx50TLmGccEZXDltNufkzn+C3v/w5d/zydnbdfy8Aw7vvZ8f113Pa1rMIy3XsOOGs3gKarqD6MwTlYRLfpdCzifO29BKHCXYiiN0APWNhaiqnlRySVJA15wOEAIpZi6yusslQ2ZgzKJWWvhmezCCWVGrN17AUGcSOIohpKkZu/a2aTOMU1dLlVKQkSeuODGEnyJQbsnuizoQb0WEbdGZMojDkBdc8kTt+cfuC8x9y8WVE0z41ZQwtY2PkbPTII5w6SOxVwTBxBs9DsXMYADbEbkAaxMQ0bnatqx7nNtdO0pSNeYtNgwWyQqy6Ngiaq62qNghrPoitw/YVqgZ2TxFFPfJjJEmS1iIZwo4zN4zZN+1xYNrD0jUG8haKouDWa7z+z164aAB7xjOfx6tf9XdAYxl+GoaEkzMIdxyBQOsYwCj0gNr+cekZixjagpgQjfAVxoLejM7mYo4Ou/1xq+mGrmVMNHQZxJZwKgexsOwiIrHs80uSJK1Fssr1OImTlAPTLj/dN8PQjEd3zqLD0fHilO9+8xs86REX8YP/+eaij3V0C80w0DM2sZYQTB8kOHSQVDGxek/D6BggECrRIivfhGUQ6SqJHzE1U+dQPSSna1zYn+NBvQsDmOxHdeqtmlzLn5tmG+hZC9WS/16UJGn9kSHsdySEYLIe8t2d49y6awLH1OjN2+iqwlgt5N5DVeqKxcTYoSM+x6btp6FogqnxEe7es5t9foRS7EPNDCAwcaOEHVMeO6c9gpabaZCk7Jz2uKsaMRSmaFHK+TmDh/Tn6HKMI440nKo3dBnE1t7npgBWd37JUTNJkqS1Soaw34Ebxtx9qMa37h3jjuEqecsgY2j4ngdAnAqCOOXsiy7jD666mque/AzOv+Chbc9x0cWX84dXPYHg0C7C6RFi1UQpbcTq24BqGMRuSORHxKkgSgVpOn/Tqvgx037ExpzFw7Z3cfHGIiUFkvryN8NT8YYug9ja+txEmqKoipyKlCRp3ZJj/McgSQX7p112TbjEQnBad5YNRZv6xAh/8w+vZsc9d/HZb/2AvryFY6hkTZ0b3vcxUjeiNjrFLd/8MvvHR9iyeQtPvPL3Sab2o6gqXQObcfQCuq5h6BrkbKKajxMnbHU0NNvAMTTqUULVj8maGk8+vZue7Pz2Qq03f1lrtMhrljVia+ZzS4IYzTGX/NmSJElr2ar5dguCgC996Ut89KMfZXh4mJ/85Cd0dHSs9GUt4EUJ94/VqKkJpYyJY2hUyjN8/H3v5NMf+yBh0BgJ+Mrn/5unP+s5lGydqB4Qll1IBdmuDp76tGeRBDVEOAm1SVQnj9m5CQybYusPUxSM2SCWiVPCMGY0TMgYGud2Z+hdZHsheUM/dYPYUtbi5ybiBLNnmSZykiRJa9iqmY78oz/6I772ta/xyEc+kvvuu484XnoaZKV4UUI1jBko2GhpzKc+8n6e+PCH8F/vf08zgHV292DbDkkY409UCaaqqJqKlrFQlBTCCeLJAyRuiFbajNl7GhhHuOkqColtMh4lePWQM3MGlw7kGSzYR9zfUU5xyanJxaylz00kKYqmoNnmss8hSZK0Vq2akbDPf/7zWJbFN7+5+ArC1USkgm9+5Uu891//kaED+5rHbSfDc156Pde96M9wFAPv0AygoGdsUCCozSBmhiH2MTs7wOgEYRJFKZqhoB42euNGCWU/xtRVzh7soCtNMFKBGiWwzPYtckTs1BsRi6MAkRfr4nNLggg9Y6MusquDJEnSerFqQphlrY3i2/vuvYe/eM6fsuPu3zaPqarK0571p7z01W8gNQvct3eKTlNlY1cORdcgChkbO8iByRk6HJNtg9tRM0VIBFNTdfYfnKFQdNjamUFVFKIkZbga4MYpF/bm2Di7v6MQYt3VGi1FBrEH9rmlE7V1E6AFKUa+uORjJEmS1rpVE8KORaVSoVKpNH89MjJywn9mZ2cXB/ftbv76kkc+lje86S1sP+1MworL7oPTTJU97A4HVEjr00Qzw3i1AM/IkS30oTgFQAFNIbVNvBkfyh5B1qSWCBTgtJLN5oLTtgXReiz6TtTlp/VkEDu6z013zObU5FoOYmHFQ3car0eSJGk9W9Mh7J3vfCdvfvObFxyfnp7GcZYeZTlWpmlwzbOfz+0//AHPf9XrePjDfw81jinvH0YkCb15Dce0MMM61X0jKLiouk137wCOYmPpEAW15vPlNNjWqeLWA6YmJ+gtZejP22SMhCioMbNIWY1QBHEUkE7U0I9y9VichiQzNTRXP6qbW6LExNUQ1a2iZ61l+zSlWkpc88GrYeSsJUMMgFBTIi+g5rkAaMtMOwkESRySTNXQ6gb6MiEGIBYRSaWG5mpoGXPZ15CoCXE9QPGqGNmjeA1aSlQLwKuh52zUZaaIT/Tn5sY+6JyUz02M1dAz1gn53ILYxdKyJDPTy557IrT+w0763cj38viR7+XxcTLex66urqM+VxHLVfOeZN/85jd5whOewPj4ON3d3Uueu9hI2KWXXsqBAwfYuHHjCbm+KTfkx3fuZuOGfkScEpbrxG6Iauqohg4iJa5PE00OEbsRWrYDZ2AjirFwFGBuf8c4SelzDPpJKRjqsiMrQHNqMo2SZkH3cuYKxOcKupczVyCuGtqyIysAaZwQlV1QlGVHVqDRB2p8dIyCmcVYZmQFGq85rvokYYyeMY+qf1TsBsRuiGbqy46IASRhTFTxZke7lh4RA2YL+10QYsU/t5lqmY588aR8bmHZQyTpcf/chBDENY/sxq4V6w82OTn5gL5EpSOT7+XxI9/L42O1vY9reiSsUChQKBRO+s/VdJ246hNWPGB2XztFgdkNt1Ovimo4OBs3k8YGsZdgaCnM3tBTIZjxY6Kksb/jpmKWDkuHVKy6KS44sVOTRtZCSVVZrL+E1fi5naipSRElqLPbFUmSJK13q6ZFxVqRRgnBVBV3ZBpFV9EcC0iJyofwR3eQ+nX0jj6s/tPR8x1oWRORCKKaT5okTPsR4/WIDkvjotn9HUt2Y4sh2QZBtq84klPlc0vDGCO//AikJEnSerBqvun+7d/+jbPPPpsXv/jFAFx++eWcffbZfPvb317hK2uXRgl1LwZVJQ1iUr/G8N77uGvfEFOJgdV3OnrHIGgGM37MfZWQMWDajRger5HXVS7sz/Hg3hydi+zvKG/oMogdyXr/3IQQiLmRZUmSpFPAqpmOfP7zn8+Tn/zkBccHBwdX4GqWFiug2RrhxChpfYyqMJgxO+kodqPYueZ5tTBmf8WnL2NwbinDBgSdloplaUtOD8kpLjk1eSTr+XNLowTN0tEsORUpSdKpYdWEsJ6eHnp6elb6Mo6KQ0I8uYO0XkMxOugv9FJw8hTsxtsphKAaJggBDx8scFZnli5Hb45IyBv6EV6DDGKNn3mKfm5pEGH3FFDUpRcSSJIkrRerZjpyrUj8KmL6ACKJsAdPwxrcjmM49CiCrK5SCxMO1UN0VeGhA3kuHyzSnWlMO8opLjk1eaTXcKp/blHNR1FAz8jeYJIknTpkCHugFAUtU8TecA56oQ/dadwMa17E0HiVVAjO785ycX+evqy5YCsieUOXQexIr+FU/tzCiodi6qhH0TtNkiRpvZAh7AHSrBx6vhvFzADgxynjiSA1NM7IGlyY0+nPmWhLTKnIG7oMYkd6Db/L5xZ74bLnr9bPTVVoTHsu09NMkiRpPZEh7BiN1gJuH6ow7UWc2elw2bYutvTmUeN0XdzQZRBb3Kr+3MJ4TX5upAI9b2PIVZGSJJ1iZAg7BrGAahBz2WCBKzYV2VJ0sHR1fd3QT1YQW+41yyB29J+bqa/JAJ2EMUbGklORkiSdcmQIe4AsXWFj1uRx27s4tyeLc9jeeevmhn6SglhUC+SI2BFewwP93HTHXJMjmSJKMAqZZa9XkiRpvZEh7AHKmjob8iY588htB9bDDV1OTcrPbTHH+3NLk3R25wnZG0ySpFOPDGHHII2WvnmCvKEf7Q1dz9kyiC3zGlbj53a8glgaROg5a9mNzyVJktYjGcKOQRLG8oZ+pNfwQG/omipHxNbi53acgphIUozs0s1mJUmS1isZwo6Bqqvyhr7Ua5BTk/JzW+w1HPa5RfWgMRrqyAatkiSdmmQIOwaqpcsb+jJkEJOf26KvoeVzCyar6DlryS2RJEmS1jP57XcMFOQNfbXd0GUQO7LV+LkZBRvN0pbd91KSJGk9kyHsGMkb+uq5ocsgtvY+NxKB0ZFddrNySZKk9UyGsN+BvKGvjhu6DGJr73NLwwgz76Co8itIkqRTl/wG/B3JG/rK39BBBrGo5i+/PRCr43MTQiAEGFm5TZEkSac2GcKOg3V5Q5dBbFGr+XOLvXBNfG5pGKPZBqolpyIlSTq1yRB2nKy3G7oMYke2aj83TV0Tn1saxRgFB0VRlv25kiRJ65kMYcfRurqhn6QgFtcDGcQWu6Zj+Nz0rLXqA7RIReNaZW8wSZIkGcKOt/VyQz9pQSxJ5YjYka5pNX9uxxjEEi9sTEWa+rI/S5Ikab2TIewYpMsFAHlDP+obuu6YcmpyqWtapZ/bsQaxYKaGWcjIqUhJkiRgXf1zNI4bN9mRkZET9zO8gKEDB/DDEM1YetNhIQRxPSAZTdAd46imYGIvJPYiNFNDz1rL3qySKCGq+iijCmbeXnbJv0hTwooPo6JZR7Tca4hqPuloipG10Kzl/8hEbkDix2i2jpFZegVcuV4lpzuNLWwMFSNnL/ua50bPGFUwC0f5mqs+YlRg5O11+7mV61Xq1VrzNZzIzw0gCeIH9LklYUxU8/EmNdTK6v3qmZ6exvO8lb6MdUG+l8ePfC+Pj5P1Pvb396Pry3/Prd5vwmMwPj4OwKWXXrrCVyJJkiRJ0qnqwIEDbNy4cdnzFLHcXMIa4vs+v/3tb+np6TmqBHosRkZGuPTSS/nZz37GwMDACfkZpwr5Xh4/8r08PuT7ePzI9/L4ke/l8XEy38dTciTMtm0uueSSk/KzBgYGjirlSsuT7+XxI9/L40O+j8ePfC+PH/leHh+r6X2UhfmSJEmSJEkrQIYwSZIkSZKkFSBD2ANUKBR405veRKFQWOlLWfPke3n8yPfy+JDv4/Ej38vjR76Xx8dqfB/XVWG+JEmSJEnSWiFHwiRJkiRJklaADGGSJEmSJEkrQIYwSZIkSZKkFSBDmCRJkiRJ0gpYV81aT7Rbb72V//7v/6ZWq3HFFVfw4he/+IR15l/Poiji5ptv5ktf+hJnn302b37zm1f6ktakNE256aab+O53v4vneTz0oQ/lhS98IY7jrPSlrTlRFPHpT3+aH/zgByiKwiWXXMJznvMc+V7+DsIw5GUvexlTU1N89rOfxbbtlb6kNeVrX/saH/vYx9qOaZrGl770pRW6orWtVqvx0Y9+lNtvv52BgQGuv/56tm7dutKXJUPY0frc5z7Hs5/9bF7/+tdz4YUX8ra3vY3vfOc73HTTTSt9aWvKzMwMD3rQg7jssssYHh5mcnJypS9pzbryyivp6enhUY96FLZtc+ONN/KRj3yEH//4x2Sz2ZW+vDXlD/7gDzjvvPP4/d//fer1Ou9+97v5z//8T374wx/Kf2gdo9e//vV8//vfZ8+ePcRxvNKXs+bs2rWLX/3qV7znPe9pHlNVOXl1LEZGRnjkIx9Jb28vL3rRi/B9n6c97WnceuutK9+uQkjLSpJEDA4Oije84Q3NYz//+c8FIG699dYVvLK1x/d9MTo6KoQQ4rnPfa648sorV/iK1q7h4eG2X4+Pjwtd18UnPvGJFbqitWt8fLzt17/4xS8EIH7xi1+s0BWtbV//+tfF2WefLT7+8Y8LQFSr1ZW+pDXnXe96lzjvvPNW+jLWhWc84xniggsuEEEQNI9Vq1URhuEKXlWDjNVH4Y477mBoaIhnPOMZzWMPfehD2bZtG7fccssKXtnaY1kWfX19K30Z68LhG9CWSiUcx6FcLq/QFa1d3d3dbb++7777sG2bDRs2rNAVrV3Dw8O8+MUv5lOf+hSZTGalL2dNO3ToEM95znN44QtfyAc+8AHCMFzpS1pzpqenufnmm/nzP/9zTNNsHs/lchiGsYJX1iDH2Y/Cnj17ANi0aVPb8U2bNjV/T5JW2n/8x3/gui6PecxjVvpS1qSvfOUr/Od//idjY2McOnSIW265hf7+/pW+rDUlTVP+5E/+hFe+8pU89KEPld+PvwNFUbjiiit41KMeRa1W453vfCcf/OAHue2222S4fQDuvPNO0jTlrLPO4m//9m/ZvXs327dv56UvfSmbN29e6cuTqyOPRhAEAAv+4OdyOXzfX4lLkqQ2t912G6961at4y1vewtlnn73Sl7MmnXfeeTz3uc/luuuuwzAMbrjhBqIoWunLWlP+6Z/+CSEEr3vd61b6Uta85z3vedx000284AUv4JWvfCU/+tGP2L9/PzfeeONKX9qaUq/XAXjuc5+Lpmk88YlPZMeOHZx77rncc889K3x1ciTsqBSLRaAxrJnP55vHJycnOeecc1bqsiQJgNtvv50nPOEJvOIVr+Cv//qvV/py1qzTTz+d008/HYBrrrmGjRs38oUvfIHrrrtuha9s7Xj3u9/NWWedxdOf/nSgMTUJ8KxnPYs/+ZM/4ZnPfOZKXt6aMnffmdPb28v/b+9+Qpr+4ziOvzTSSxNhhwQHobhgDKpDwkTQJrSBZX4pIrJLMXHnCEQ8hYoQUSp1qPASdQysU3TRQ8FqlRkspmgJ/qOBwgbByox1CMZPfhd/P3783ny35wN2+Z6eB8GXn+/3O1taWvTu3TujIneqra2V9OdncGhoSJLU09Ojo0ePamxsTA8ePDCsY4TtyZEjRyRJHz9+LB5fbm9vK51O68KFC5ZpKHPv379XJBJRLBbTzZs3rXNKRl1dnTwej1ZXV61TXOXRo0e7Tg8TiYTevn2rS5cu6dixY3ZhJSKbzdq/zecywWBQ+/fvL/6BJf251dvY2KhMJmNY9ge3I/egvr5eHR0dGh8fL75qff/+ff348UPnz583rkO5+vDhg06ePKlYLKZbt25Z57jWly9fNDMzs+va48ePlc1m1d7eblTlTqdOnZLjOMVPc3OzJOn06dPcJv+HHj58uOtB/KmpKb1+/Vrd3d2GVe7j8Xh07tw5PXnypPj7e319XS9fvlRra6txHSdhezY5OaloNCq/36+DBw8qlUppcnJSPp/POs11ent7tbm5qdnZWX3//l2O4/AlhP/C2bNnlc/ntbS0JMdxitcdx9Hly5fNutympqZGt2/fVjweV2Njo9bW1vT161fdvXtXoVDIOg9lam1trXiLPJvNamFhQcPDw7p48aJ1muvcuXNHnZ2dOnz4sJqampRMJhWNRnX16lXrNFUUCoWCdYRb7Ozs6M2bN/r27ZuOHz8ur9drneRKL168UD6f33WtsrJSZ86cMSpyp+fPnxdfGvkrv9+vYDBoUORuKysrmp+fl9frVSAQ4A20/8DGxoaSyaS6urq0b98+6xzXyeVympubU3V1tQKBwN+eE8PeFQoFzc7OamtrS36/Xw0NDdZJkhhhAAAAJngmDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDAAAwAAjDEBZy+Vyevr0qdbX13ddT6VSevbsmba3t43KAJQ6RhiAsubxeHT9+nX19fUVryUSCYVCIS0vL6uqqsqwDkAp4x94Ayh7MzMz6ujo0PT0tGpraxUOh9Xf36/BwUHrNAAljBEGAJIcx9HS0pIymYzi8bhGRkaskwCUOG5HAoCknp4effr0Se3t7QwwAP8LTsIAlL25uTmFw2H5fD5tbm5qcXFRBw4csM4CUOI4CQNQ1tLptCKRiGKxmF69eqWdnR2Njo5aZwEoA5yEAShbnz9/Vltbm7q6unTv3j1J0sTEhAYGBjQ/P69Dhw4ZFwIoZYwwAGXp169funbtmqqqqnTjxg1VVFRIkn7+/KkrV67oxIkT6u3tNa4EUMoYYQAAAAZ4JgwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMAAIwwAAMDAb+vQ85/r4oUwAAAAAElFTkSuQmCC",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -423,79 +437,64 @@
}
],
"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",
+ "fig, ax = plt.subplots()\n",
+ "for name, colour, hatch in (\n",
+ " (\"statistics only\", plotstyle.COLOURS[1], plotstyle.HATCHES[0]),\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",
+ "plotstyle.band(\n",
+ " ax,\n",
+ " x_fine,\n",
+ " *band_gp,\n",
+ " color=plotstyle.COLOURS[0],\n",
+ " hatch=plotstyle.HATCHES[2],\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",
+ "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": "58b57dfa",
+ "metadata": {},
+ "source": [
+ "### 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",
- "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",
+ "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",
- "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\");"
+ "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": "markdown",
- "id": "c15",
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "30f35b57",
"metadata": {},
+ "outputs": [],
"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)`."
+ "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": 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"
- }
- },
+ "execution_count": 14,
+ "id": "21bc61b5",
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
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TnwC44IILCtoOHDiQQw45pNPv3amnnup9Z0LWBwpg0qRJBbZNnToVoGCcdvVZp512WsE9x44dS2lpaa/mszuam5sBKCsr63Qu9w4oLi7mhRde4IQTTuCUU07h3//+N5qmdYpSvO6663jwwQe54ooruOeee/jtb3/LgAEDOOuss7z57o65c+dy+umne9uiyWSSX/3qV9x11100NTVx/fXXc+mll/LHP/6Ryy+/nHA4zKGHHtop+jzXj1y/9kakY34fpn///mzbtq3HNnV1dV7bfHb0CaioqADYqY9JeXk5Z5xxBn/84x+5+eabiUQi/L//9/8oKSn5THIq9cauDRs2oOu652sjsiu2CCFwXdfrc1fMmjULXdf597//zcSJE3n33Xf53//9X6qrq7n++uvZtGkTL7/8MsBORdiWLVu6dNYfMmRIp2O7avOwYcN6fHZvnjlkyBCvLwDPPfccX/va1zjzzDO55JJLqK6uRtd1fv3rX3cpdnozF7syBp/W/hw7js2GDRuArI/kM888gxACyI5xvr9Y7ou8u2ftjJzf14gRI3batitGjRrFXXfdxc9+9jOeeuopRo8ezRFHHMG5557LtGnTerx27ty5vPbaa/zsZz/joIMOori4GEVRmDFjRq/8ZQYPHtzpWFlZWbfH8/3keju+o0eP7pWdn2YceqKmpqbTHysbNmxA0zROOOEEz+ac/evWraOoqKjTPXYci56O7/i99GmeBdnfsd74+XVHJBIB6PJnoqSkBMj6WeX/EVFeXs7hhx/OP/7xDzKZDMFgkDfffJNbbrmFG264gWuuucZre/TRRzNy5EguvPBCXnnllW7tUFWV3//+99xyyy3E43GGDx/OJ598wjXXXMPf//53NE3j/vvv57HHHmPOnDkcccQRPPXUUzz99NOcd9553n1y/cj1a29EirA+zIwZM/jd737HsmXLuk2dkAs73tGRVde7ntrcl0ZPXHTRRTz88MM89thjHH744bz++ut873vf6xQO/d/QG7sikQj77LMPd955Z5dtc2KhK4qKipg+fTr//ve/mT59Oo7jcOSRRxKNRikuLubf//43L7/8MqNGjerk2LojkUikS0f1tra2Ltvuis27Opbd2ZH/5XXLLbdwwAEHdAoG6M7Jtrdz0dsx6Ine2J9jx7HJtfnWt77lOTLvyOjRo70AhP/W3mg0CvCpcp9dfPHFXHDBBbz//vu8/fbbPPXUU9x33308/vjj/M///E+X16xatYq//vWv3HHHHfzwhz8ssDk/oq0nupvL3s4x7Hx8d8XO3oxDOBzusn/diZSufmcikQh+v5+7774bRVE6nd9xRfPTjtNn8azefAd3R3V1NZqmdTlG48aNA+gkBiG7Oua6rifC5s2bB8CRRx5Z0K6kpIRJkybx1ltv9cqeyspKKisrgewK4f/8z/8wa9Ys1q9fj2manvO+3+9nxIgRnZJONzQ0ANnVxL0VKcL6MP/7v//L73//e37xi1/w1FNPdTq/bt06nnjiCY477jhGjx79mT130qRJTJ06lXvvvddbju/NVmTuS8i27U/1/MMPP5xXX32VsWPH/ld/IR199NHccMMNPP3000yZMoXS0lIAjjjiCF566SVee+21bl+I+UybNo33338fwzAKtjC6+oL6tDbvjLfeeovZs2d7n3PJUo844gjvWFtbW6dw8Hg8zptvvvlfP3dXxqAnemN/TzYEg0E2btzYYxqRSZMmoes6b731Fqeddpp33LZt76XTE1OnTiUYDPLSSy/1uN3q9/t7/Bn3+XwcdthhHHbYYVx22WXU1NTw5JNPdvszlxOIO/5R0NO2+2dJb8d3V+3c2TgMGTKkYLsPsnOVcyXoDYcffjhPPvkkrut+qlW2PfWsXf3ODAaDHHDAAV7kaz5Tp06lqqqqy3MffvghAwcO9L4Lc//funVrp7Zbt271zveWhx9+mBUrVvD0008DHSuJuah9yP787LiNumjRIgAOOeSQXXrelwnpE9aH2W+//bjjjjt45plnuPjiiwt+oOfPn8+xxx5LdXV1j/lc/lsuvPBClixZwn333cf+++/fqxxaubxLO36x7irXXXcdpmnyzW9+0/tLCbKh9nfddddOv6SPPvpoHMfh0UcfLcg/c8wxx/DXv/6Vtra2nW5FAvz4xz+moaGBH//4x7iuC2TTH7z33nufuc0746233vKEhOM4XHrppbS1tRWE+B966KG8/vrrrFq1CsgmdTz//PM/VT6sXRmDT2t/d5SWlnLttddy//33c//993t2QHYV6Sc/+QkAVVVVnHvuuTz44INeGgYhBD/96U97XD3Nf85VV13FU089xd133+2tWKRSKa6//nocxwGyP+ebN28u+H2EbOqC++67r2CraO3atcRisR63OPfbbz9KSkp46KGHPL+epUuX8sgjj+yWbZrejm9v7eztOOT8zZ555hkg+3Nx5ZVXUlVV1Wvbzz33XPbff3/OPfdclixZ4h13HIe//vWvPProo7s4Grv3WcXFxVRUVOzSd+acOXOYP39+p1VEXde54YYbePnllwvS9Nx6660sX76cq666yjt2yimnUFJSwvXXX+/57QkhuOOOO1i2bBnf+ta3em3Ptm3b+NGPfsS9997rbYmWlZUxefJk/vjHPwLw7rvvsm7duk7fu2+99RZjx47dZfeGLxW7MQhA8l/y4osviilTpohQKCTGjx8vhg4dKqLRqDj//PNFQ0NDQdtchNmyZcsKjucidu6++27vWFcRgTnS6bSoqKgQgLjzzjt7bes555wjdF0X48ePF5MmTfIitnbFLiGEWLp0qTjiiCOE3+8XI0aMEEOHDhVlZWXikksu6TadQw7btkVJSYkAxFtvveUdX716tQCEpmmira2t4JruxuLxxx8X5eXloqSkRAwfPlzMmTNH/Oc//+kyMrA3Nnc3Dt2Ra//WW2+Jo48+Wuyzzz6iuLhYVFZWiqeffrqgbWNjo5g1a5bw+XxizJgxorq6Wvzud78TP/zhDwsiA3d1LnZlDD6N/Tsbm9/97neipqZGFBUVif3220+UlZWJ8ePHi4cffthrk06nxdlnny1UVRXDhg0TAwcOFLfddlunMRCi6xQVQghx++23i6qqKlFUVCRGjx4tysrKxHXXXeed/+STT7y5nThxopg2bZoQIhsld+GFF4qSkhIxYsQIse+++4pwOCy+853viHg83uM4/fOf/xT9+vUT5eXlYsSIEeLAAw8UH3/8ca/SssyePVtMmjSp0/E5c+aIAw44oNPx7qI7ezO+vbGzt+Ng27Y499xzhaqqYsSIEaKmpkY8/PDD3UZHdtVHIYRobm4W5557rgiFQmLgwIFi9OjRIhKJiNNOO82L9O3uZ7uurk4A4vbbby843l3U4qd5lhBCjBgxQpx22mmdxj33nTFp0iRx4403dtnPHOvWrROKooinnnqqy/O33nqrKCkpETU1NaJ///6itLRU3HrrrZ3aLVq0yPu+GD16tKiqqhKlpaXiF7/4hbBtu0cb8jnttNM6RVoLIcTixYvFkCFDxLBhw0Q4HPYi+HO0traKQCDQaez3NqQI+wLR3Nwsli5dKlavXt0plDy/zfz58zuFxJumKebPn18g2tra2sT8+fNFIpHo8l6HHXaYCAQCXq6v3tLQ0CAWL15ckCdsV+zKp62tTSxdulRs2rSpxzD5HVm5cqWYP39+py+ThQsXiiVLlnT5nO7GwjAMsXz5clFbWyuEyIb5z58/X7S0tOyyzd2NQ3fs2H7Tpk1ixYoV3c6/ENkXy4oVKwquWbhw4U5t6GkudnUMcuworHqyv7djs3HjRrF06dJOQjqfpqYmsXTpUi8n045jIET2ZbZ8+fIur3ccR6xZs0Z8/PHHXb6QcucXLFjQKR+S4zhi7dq1Ys2aNbuUmsKyLLF69WqxYcMG79jixYvFxo0be7zu448/FitXrux0fPXq1QWpZnKsWbOm234LsfPx7a2dvR2Hbdu2iWXLlnl5tjZu3CgWLVrUqz7mk8lkxMqVK7v8fuzuZ9uyLDF//nyxbdu2Lo/X19d/Zs8SQohly5Z1maohkUiIpUuX7jRPWI7TTjtNHHnkkd2eNwxDrFy5UqxcudJL19MdiURCrFixQtTW1nabKqc7bNsWCxYsKMj3l4/jOGL16tVdnr/zzjtFeXm5aG1t3aVnftlQhPgUXoKSLy319fUMGjSIuXPnFixtSyS7wmOPPcZZZ53FsmXLGD9+/J42RyL5UrB27Vr2228//v3vf3P44YfvaXN2mXQ6zYgRI7jyyiv5wQ9+sKfN2aNIx3xJl/z2t7/Fdd1e+exIJBKJZPcxcuRIlixZ4kX0ftFwHIfnn3+eAw88cE+bsseRIkxSwG233caDDz7I6tWrufbaa2VRa4lEIumDdFcY+4tANBqV75btyO1ISQG1tbU0NjZSU1OzS1FKEklXtLS0sH79esaPH/+psqVLJBLJl5E+J8JeeeUV5s+fz4ABA5g7d+5enUlXIpFIJBLJl5c+kycsmUwyZ84cvvWtb9Hc3MyCBQs47LDDelWyQyKRSCQSieSLRp/xCbviiitYtWoVH374oZdYccOGDV2Wh5BIJBKJRCL5otMnVsKSySS///3vueSSSwoyWw8fPnyXauzZts3mzZs/ddkciUQikUgkks+bPiHCli1bRiaT4ZBDDuGJJ57gpptu4oknnthp8dpYLMbmzZu9/xYvXkxNTQ319fUF7T5NQV7J54ucm76LnJu+jZyfvoucm75LX5ubPrEdmSsMe/HFF1NTU8Po0aO55ZZbuPrqq5k3bx79+vXr8rrbbruN66+/vtPx1tbWghW0WCz2qSrXSz4/5Nz0XeTc9G3k/PRd5Nz0XXbH3PSmVm2OPhEd+fbbbzNjxgy+973vce+99wJgGAZjxozhq1/9KrfddluX18VisYIiunV1dUydOpXa2loGDx7sHW9ubt6lQZHsPuTc9F3k3PRt5Pz0XeTc9F362tz0iZWwMWPGoCgKBx98sHcsEAgwceJE1qxZ0+11xcXFFBcX7w4TJRKJRCKRSD5T+oRPWGVlJUceeSRvvfWWdyydTrNgwQL222+/PWiZRCKRSCQSyedDn1gJA7jnnnuYNWsWxx9/PGPGjOGll16irKyMK6+8ck+bJpFIJBKJRPKZ02dE2OjRo/noo4/429/+RnNzM7fccgvHH388mqbtadMkEolEIpFIPnP6jAgDKC0t5ZxzztnTZkgkEolEIpF87vQJnzCJRCKRSCSSvQ0pwiQSiUQikUj2AFKESSQSiUQikewBpAjbS7j44ovZvHnzTtutXr2an/zkJ33GHonk8+T//u//ePfdd/e0GRKJZC9lrxZhjutiuc7n+p/junu6mwD8+c9/9spD9URDQwPPP//8brVndwk/ye5jT4mbm2++eZee++qrr7J+/frP0SKJRCLpnj4VHbk7cVyXeY0bqU/HCWg6Ed2/02sytk3KMdEVlSJfAEVRemxvuw5+TeewfsPR1L1a7/ZIWVkZM2bM2NNmSD5DJk2axIABA3b7c1955RUGDx7MIYccstufLZFI+jYrV65k0aJFHHTQQYwbN25PmwPsxSLMRVCfjuNXNQaEinbaPm5maDfThHQfA8PFqDsRYIZjUZ+KEdB8uAh2lu1sxYoV/OlPf2LOnDn87W9/Q9M0Lr30UjKZDPfddx/pdJoLLrigoILAtm3beOCBB9i6dSsHH3ww3/zmNz1huGHDBu6//35c1+Ub3/hGp+clEgkeeOABVq9ezbhx47jgggvw+3cuRPP56U9/yg9+8INevWx7sqe1tZW33nqLE088ESEEjz/+OO+88w79+/fne9/7HtXV1Z7NDz74IKtXr+aQQw7hm9/8ZsHYnXTSSTz77LOMHz+es88+m3Xr1vHoo4/S1NTEUUcdxSmnnALAU089heu6CCF45513GDNmDN///vfR9eyvw8svv8wjjzyCoigMGTKEc889l1GjRhVcq+s6//nPfxgyZAg/+MEPCgrGr1u3jscee4zm5mbOOOMMDjnkELZt28ZPf/pT7r//fu85DQ0NXHHFFdx///34fL5ux669vZ1rrrmGu+66q1fz0lW/Y7EYP/zhD7n22msZPnw4AL/61a8YMWIEp59+OjfddBMzZ85k4cKFrFmzhiOPPJJZs2YV3Pftt9/m+eefx3Vdvv71rzNlyhQAbrrpJg4//HAWL17MkiVLuPXWW1m4cCHBYJDhw4dz0003ccghh7BkyRI++ugjDj/8cP7nf/6Hxx9/nDfeeIPx48dz8cUXo+b9odLTs2bNmsWiRYtYuXIl06ZN45xzzkFRFP785z+zYsUK7r33Xl566SWOOeYYzj77bO69917eeecddF1n9OjRfP/736e0tLRXYymRSL74NDU1ccYZZ/Dqq696x2bPns2TTz5JZWXlHrRsL9+ODGg6A8PF+FStx//StkWTkaLIH2BotIyApvfY3hEu9ekEPlWj2BfolS11dXXcdddd/PznP2fUqFEsW7aMOXPm8PWvf51BgwahqipHHnkkqVQKyBYhPfDAA1m7di3jxo3jlltu4cILLwSgsbGRadOm0dzczKBBgzjrrLMKtiJTqRRTpkzhgw8+4MADD+Tll1/mq1/96i6P39///nfa29t32m5n9uRvgd5zzz3cdNNNTJgwAVVVOfXUU4GsAJs4cSKvvfYaEyZM4F//+hf33HOPN3Z33303P/7xjxkxYgRjx45l0aJFTJ8+nXQ6zbhx47jqqqu4/fbbAVi8eDEXX3wxf/nLXxg3bhx/+MMfOPfccz17hg4dyrHHHssxxxyDYRhMmzaNrVu3etf+4Ac/4IUXXmD//ffn2Wef5aKLLvKuff/995k0aRItLS3su+++/PSnP2X58uVUV1fzwQcf8OKLL3ptH3vsMRoaGnoUYJAt4fWXv/xlp+MMdNvv4uJi9tlnH8466ywcx+Gvf/0r/+///T+OOeYYICs8TzvtNDZt2sTIkSO56KKLePDBB737/uEPf+DrX/86lZWVVFdXc+KJJ/Lmm296155++unU1tZy+OGHEwgECrb5Xn75Zc4880yam5vZd999ueCCCzjqqKN45ZVXOOigg7j33nv59a9/3etnnXHGGWzdupX99tuPq6++mgceeACAUaNGUVFRwQEHHMCxxx7L2LFjAbzPhx9+OEuXLmXGjBm4fcRNQCKRfP6cccYZvPbaawXHXnvtNc4888w9ZFEe4ktEbW2tAERtbW3B8aampk5tTccWr21dIxY0bhJLmrd0+99bdevEM+s/FP/YtEIsbtrcY9slzVvE+w2fiGc3LBHPfbJUzNu2Qby2dY0wHXuntr/88suiqKhIxONxIYQQmzdvFoB44403vDajRo0Sb775phBCiBtvvFHMmTPHO/fJJ58Iv98vNm3aJG644QZx4okneucWL14sALFs2TIhhBC33nqrOProo73zlmWJYcOGiUWLFom33npLjB49ujfDLfbbbz/x0Ucf7bRdT/Y0NTUVPPM73/mOuP322722DQ0NQgghbrnlFjFz5syC+zY2NgohsmMXDAZFc3Ozd+64444Tt9xyi/d51apVory8XAghxBVXXCH2339/71x9fb0IBoNi/fr1Xdr/ve99T/zqV7/yrj3qqKO8cx988IEYOHCg9/noo48WN9xwg/fZsizR1tYmhBDiN7/5jTj11FO9cxMmTBBPPvlkl8/Mp66uTgwaNGin7YToud+2bYvp06eLSy65RFRVVYlXXnnFa3f44YeL888/3/v84osvin79+gkhhHAcR1RUVBT8LP7hD38Qxx9/vHftZZddVmDHnDlzxKOPPuqd/9nPfuad+973vidmzJjhfX7yySe9ue3Ns/LvdddddxWM6ezZs73ndsfEiRPF66+/3snOLxpdfa9J+gZybvoOy5cvF0C3/61YsWKP2rfXbkf2hpiZoT4dJ6T7GBQu2ekWZMax2JxsR1UUaiKl2YOO3evnDRs2jGg0CuBtwY0fP947369fP1paWoDsFly+H9XQoUOpqanho48+YuXKlRx66KHeuQMPPNC7L2RXcxoaGjj33HMRQiCEwDRNPvroI4YMGdKtfe3t7QWrPps3b+YnP/kJxcXFAJxzzjkcffTRna7bmT35nH/++Zx11lmsWrWKI444ghNOOAGAJUuWcNRRRxW0zV9GHj58OOXl5QV9FEKwcuVKr4+tra00NDQAFPgMVVdXM3LkSD766COGDx+OYRg8/vjjLFy4kFgsxkcffVSwcjJhwgTv3wMGDPDmJGfn9ddf733WdZ2SkhIAzjrrLH7+85/T0tLChg0b2LhxIyeffHKX4/Duu+/y//7f/wMgk8nQ2trqbb9C1vG9q23gnvrdr18/Hn74YcaOHcsFF1zA7NmzC67Nn6MZM2bQ0NBAS0sL8Xic5uZmHnjgAX7/+98jhKCpqYlVq1Z57SdNmtRlP7oas+rq6gJ/yurqam8Ma2trd/qsnsa/K5qamvjDH/7A6tWrSaVSNDc3s2HDhk7brRKJ5MvHunXrejyf203aU0gR1g2fVoD5VA3LdXbpmWoXzvs7HhNCABAKhUin0wXn0uk0oVCIYDBIJpPxjruui2ma3udAIMCIESMKRM0xxxzDtGnTqKur69a+QCDAscce631+4403mDFjhicGhg4d2uV1O7Mnn6lTp7Jy5Urmz5/PP//5T6644goWLFhAOBzu1N98dvRnCwQCTJkyhX333begj5FIBKDAHsiOXTAYBOB73/se69atY+7cuZSWlmJZFslk0mvb3ZwAPdrZr18/jj76aP70pz+xatUq5s6d6z1zRwYMGOCNdXt7O6+99lrB2Of6sSM76/ef/vQnBg0axH/+8x/v5yVH/pjk+hAMBjFNE1VVmT17tufPBhRcuzN/wh3HrLsxDAQCO31WT+PfFTNnzmTixInMnDmTSCTCqlWrCuZTIpF8eRkxYkSP50eOHLmbLOkaKcK64LMQYJ83hx56KL/5zW+46qqrCIfDvPzyyyQSCfbff38OPfRQfvvb33LllVcSCAR45plnCkTPUUcdxc0338xpp53mvdzefvttiouLexRhwWCwYDXm5ptv5sQTT2TMmDE7tbUne/J54YUXmD17NtOnT2fatGk8/vjjLF26lCOPPJKrrrqKyy+/nLKyMjKZDEuWLGHatGld3ueoo46iqamJG264Aci+qP/2t795YuTFF1+kra2N0tJS5s2bR319PQceeKA3Fg888ACzZs3CdV0eeeQR+vfv32Mfcxx55JHcd999zJo1C1VV2bhxI0IIhg0bBsC3vvUtrr32Wmpra3nuuee6vc/w4cM9B/r6+np+9atfFYx9d/TU73nz5vHb3/6WRYsW8YMf/IArrriiwNn/6aef5vzzz0dVVR5//HHGjx9POBwmHA4zduxYVFX1bEin07zxxhu9GpNdoX///p/qWdFolEQi4X1ubm7m448/9oR8a2urTIcikewlZGwLdVAl+x86jaXvfgB5f7ApisLs2bP3eJTkXi/C7B0cdONmhvp0gpDuo18wiiNcnB7+0DYci83JGKqiMCic3ZbLrYDteO/PknPOOYdnnnmGCRMmMG7cON566y3uvPNOSkpKOPvss/njH//I/vvvz8iRI2loaPC2DCHrpPif//yHkSNHMm3aNDZv3kw4HP7c8oPtzJ58Nm3axNixY9l///3ZvHkzJSUlTJs2jXA4zIsvvsjYsWOZNm0aH3/8MXfeeWe3z8wJxAkTJniBDieeeKIXITl48GCmTp3KmDFjeOONN/jlL3/pbWeedtppnHHGGRx22GGsXLmSYDDYaxF20003cfzxxzN+/Hj22WcfamtreeGFF7zzxx9/PBdccAHFxcWfS1qO7vp99NFHc9ZZZ3HbbbcxbNgwHnzwQfbff39OOukkb0XUMAwmTpzIgAEDeO+99/jjH//o3feRRx7htNNO44EHHqCsrIxly5Zx3XXXfeb2f9pnzZ49mxtvvJE333yTY489lrPPPpsZM2YwadIkxo0bx5IlS7ztYYlE8uVECMG2dJzVsSbazBT/e8cvufOHV7H83fe9NrNnz+aJJ57Yg1ZmUcTO1vK/QGzevJmamhpqa2sZPHiwd7y5uZmKioqCto7rsqh5Mwm7Y0Xmv8kDFrNMFKDYF+gyF1hU93NQxeCd5gmrr69n0aJFHH/88QBeqoavf/3rXvTcv//9b8aNG+f1TQjBggULqKurY+LEidTU1Hj3syyLd955B9d1Ofjgg3nhhRc46qijCl5Aa9euZcWKFQwePJiDDjoIRVFobGxk3rx5nHjiiT3aC/CPf/yDmTNndiuo8unOHtu2cV234Jnbtm1j3rx5lJWVceihh6JpHSuLK1asYO3atUyePJlBgwZ1OXY5XNdl0aJFbN68mf3335999tkHgCuvvBLbtrn00ktZtGgRo0aN6rSa9/7771NfX88BBxxAJpOhpaWFQw45hA8//BDXdTnooIOAbKTp3/72t4IoG8uymD9/Pm1tbcycObOT/9txxx3H5MmTufHGG3c6bpDdJnzxxRd7HcHaVb/Xr1/P4sWLOe2007x2H374Ic3NzcyePZtZs2Zx8cUXs99++7FmzRqmTJmC3+8v+L1JpVIsWLCAeDzO1KlTqaqqArIRi6NHjy7wJ3zttde81bwdzy9duhTDMLy0E9u2bWP+/Pl85Stf2eVnbdq0iY8//rjAF3H+/PmsW7eOESNGMGXKFGzb5vXXX8e2baZMmcKqVavo168f++67b4GdXzS6+l6T9A3k3Ow5kpbBulgzW9MxdFRazBSxFKSTPtatXkWgaQvnzDlqj6+A5dhrRRhkhZjL59t9FUUmau2BPfFllRNhv/nNb3brcyGbrqOmpobly5fvcV+EfHIi7Gtf+5p3TL5I+jZyfvoucm52P47rsjXVztp4M47rEtR01ra10RgTxOMajiMI+gVjinXmHrT/njbXY6/ejtRUdadJVCWSz4q77rqL+++/n69+9at9SoBJJBLJF5l2M83aWBMNmQTl/jBp12Hh1hbaki4DI0VoQYdYxgF2LVhud7BXizDJ3skZZ5yxR5J1Tpw4kV/96lfMmTNntz97Z1x99dWMHj16T5shkUgkvcZyHWoTbayPN6MpCv2DRWxpT7O4vgXhqowsKyWgqRQFdCosl3Yzs/Ob7makCJPsdeSiIHc3fbk+Zlf53SQSiaSvsHLlStauXcvIkSMZN24cLUaKNe2NtJppKgJhLFth2dY4q1vbKQ7oDC0rQdvu162rCnpAI2717Oe9J5AiTCKRSCQSSZ+kq7qP02fO4LLf3kJ1VT+qAkXUxwzWNSVpNpNURnwMCBd7AqyvIz3GJRKJRCKR9Em6qvv4/ltv85uLL0fYPpZvTbCyLk6KNBVhvyfAHFeQsfp+jVi5EiaRSCQSiaTPsWLFioIVsBxCCD548x1efudDRPkgYnaGgSV+qouKvBWwre0mCcOhf7GfsnCH1LGcviXM5EqYRCKRSCSSPseaNWt6PN9et4mka4BQKfdHPQEmhMB2BZYjcNyONFS2cLGdvhUhKUXYXsItt9ziFa/uiU2bNvWYiX532yORSCSSvY9WI4VZWdRjm+DAKgaV+Nm/f2nBapeiKAws8TO0POAdt4VLYyaJpvUtX7G9WoQ5rsBy3M/1v3wVvie5/fbbey3C7r333t1qz+4SfhKJRCLp2xiOzer2BhY01dJvxFCmzDi0c/UaRWHU1CnUjBjFkOISykK+TjWeA7pKSUhHUxVs4VKfjiMQu6W2866w1/qEOa5g0eY2EsbnuzQZDWgcNLgUTe1b6rsvoSgKgUBgT5shkUgkkj2EEIKGTILV7Y2kbYvyQIh4RnD29b8hfsWlrJo/z2s7aspkzv3lr6gOFe00CjInwFwhqAxEdlqOcHez14owVwjihk1AUwn6tE7nXFega4ULhUJk95e7O66pSsEE266gPWPjCoFGzxO/bt06XnrpJebMmcOLL76IpmmcffbZ2LbNE088QTqd5swzz2TAgAHeNalUir/85S9s3bqV6dOnM3PmTO9cS0sLzzzzDK7rctJJJ3V6nuM4PP/886xevZpx48b1qlbkjtx9992ceeaZVFZW7rRtT/YIITAMw/v89ttv884779C/f3/mzp1LOBz2bP773//O6tWrOfjgg728W7mxO/7443nxxRcZMmQIX/nKV2hpaeH555+nqamJI444gkmTJgHZGpyu61JUVMS7777LmDFjCvr//vvv8/e//x1FURgyZAhf/epXvT7mri0rK+PNN99kyJAhzJ07FzWvNFVLSwt///vfaW5u5uSTT2bEiBE0NDRw3333cc0113g/I7FYjN/85jdcc801Xn1QiUQi2dtIWAbrYk3UpeMU+QIU62E2NhvUx0zCJaXc+Mij1K5dw6YN69H7VzJon5FUBTsLMFcIXJHNCwYdAsxxXSoDUeKGtSe61yN79XZka9pibXMKw3bxaSo+TUVXFba2G6xpTpE0He+4T1NpSJisbkrSnrELjrekLFY3JWlJWQXH29MWzUlz54YAGzZs4Nprr2Xu3Lls2bKF3//+95x44onMmTOHNWvW8NZbbzFjxgwsK/tDlEwmmTp1Kg899BANDQ2cccYZ/OIXvwAgHo8zffp0/vKXv7BmzRrmzJlDPB73nmXbNrNnz+Y3v/kNbW1tXHfddZx//vm7PH6/+93vaGpq2mm7ndmTvwX6+OOPM3fuXJqbm3nzzTc57rjjADBNk5kzZ3LDDTfQ3NzMz372Mx577DFv7H7+859z2mmnUVtbi8/nY+3atRxwwAG8+OKLNDQ0cNJJJ/GnP/0JyBaXvvDCC7n88stpaGjg4osv5vLLL/fs0TSNYDCI3+/nX//6FxMnTqS1tdW79qKLLuLqq6+mpaWFq6++miuvvNK7dtWqVYwfP94Tx3PnzmXt2rVUVVXx8MMP85///Mdr++STT/Lmm29KASaRSPZKbNdhQ6yJ9xs20pRJURWIkkzBsi1JtsUskoZDY9LCsF0qhw1j8CFTGTR8JFbGz6Zmg5TZsZMlhKCu3WRDU4Z4xi5YAVPtIJtbLGJpew/2tmv22pUwANsRpEwHw3aJbt8NcwUkLZuEYWPYhaGsSdMhbthk7MItzIztEsvYlAQLX6YZx8HchXDYdDrNv//9b6qqqjj//PMZOXIk//jHPzjhhBMQQjBs2DAWLFjAwQcfzO9+9zuKiop49dVXURSFc845h6lTp/Ld736Xxx57jIEDB/Kvf/0LgP/85z8cccQR3nMeeughMpkM7777Lqqqcu211zJixAhWrVq1K8PXax544IEe7cnn9ddf56c//Sk/+MEPAFi9erV3j1Qqxfz589H17I/t2rVrvetisRjPP/+8V7j99NNP56yzzuKXv/yl9/mUU07hf/7nfwBQVZU33ngDn8/HBRdcwIQJE7j00ksZOHAgkydPZvLkyd69zzrrLB555BEuueQSAKqqqnjllVcAOO644zjrrLP49a9/DcBPf/pTvv71r3P77bcDWQGaSqVQFIVzzz2XP/zhD17fH374Yb7zne98mqGVSCSSLyTNmSTLW+tJ2SYVwTCmqfBRfYpY2qEkpOEKaElaOK4gbpgkSeNTNMoDETalTAzLxbQFYX/2fq6AtOWSMh1Stkq7m8QVgupQlLqMTcp08Pv6ho92Pnu1CCsO6pQGdUpDHeJJUxWGloVJWw7ludndTk1piLKwj7JQodjqXxQg7NcoDhQOZ3UkgKb0frFxxIgRVFVVATB06FAApk+fDmT9pmpqajxn9sWLF3Pcccd5W1sTJkygf//+LF++nMWLF3PMMcd49501axahUMj7/O677+Lz+bjhhhsQQiCEIBgMsnTpUgYOHNitfYlEgptvvtn73NDQwJ133klFRQUAJ554ItOmTet03c7syedrX/saP/jBD0ilUhxxxBFMmTIFgA8++ICTTz7ZE2BAQRHs4cOHewIs18dQKMR1112HEALXddm6dSstLS0AHHnkkd4K1MiRIxk2bBjLli3z+v/aa6+xcOFCYrEYdXV1nhiEjjkB2GeffQoCHj744AN+9KMfeZ+LioooKspG+Jx77rlMmDCBe+65hy1btrBkyRLmzp3b5ThIJBLJl5GMbbEm1sRH7duoDkap9EfZ0mbySVOGooBGVVT33msDSvykbYuESOFXdc8HbECxH9t1KQp2uBJpqsKAEj9J0yFNCkVAdShKQNWpLlYpDmqk7d7tTO1O9moRFtQ1ioN6J6f5ooBOUaDz0IT9GmF/58iKoE/r5FcGoGsq4S6Od4emdW674zEhskpeURTv3zlc1/V+eHc8l/85J7ryBc15553HvvvuSyKR6NY+RVEIBoMFnwOBgHesK/u7en5Xn3Mce+yxvPfee7z88sv89re/ZcWKFbz55ptd9jefHUWdEIJQKFTQxxtuuMGzsSt7cmP34x//mL///e985StfobS0FF3XicViXtv8e+54r57sHDp0KFOnTuXPf/4zq1at4uSTT6a4uLjbPkkkEsmXgZUrV7J6zWpKBw2gvSpCwjLYt7iSRAaWt2RdgqqL/Ph2SB/h9wna3Ax+RS9wws+Kr87vm4BPodVOFwgwgJBPJeRTqU/0PZ+wvVqEfZGZNGkSjz/+ONdccw2qqrJ48WIaGhoYP348kyZN4tlnn+Wqq65CURReffVVMpmO6vGHHnoojz32GFdffbUnPNauXUtlZSXLly/v9pmRSISf/exn3ucnn3yS733ve4wZM2antvZkTz4LFixg8uTJnHHGGZxxxhmMGjWK999/n4MPPpjf/va3XH311fj9foQQrF69mtGjR3d5n0MPPZT+/fsX2Dt//nxKSkoAePXVVzFNE7/fz+rVq9m4cSMTJkwA4G9/+xsPP/wwhx12GACnnXZaj/3L5+CDD+bJJ5/0ggba2tpIp9NeQMV5553Hvffey/r163nggQd6fV+JRCL5otFV3ccJh0zllt/9jk8si6aERdivUhnp7Bebti0ajAQ+RdvlKMh8AdbX+WJY+TliuwLI+m3lr4bk898etz/HHGHnn38+Dz30EDNnzmTixIk8/fTTXHvttVRVVfGd73yH++67jyOPPJJx48bx5ptvEolEvGu//e1v89xzzzFp0iRmzZrF5s2b+fjjjzvV5/qs2Jk9+bzwwgt8//vf59BDD6W2tpZMJsPEiROZOXMmTz75JAcddBCzZ89m/vz5/PCHP+xWhP36179m9uzZLFiwgH333ZclS5ZQWVnJU089BWRX7Q477DAvYOCHP/yhJ5RmzJjBBRdcwHHHHceSJUtYu3Ythx56aK/6+qtf/YojjjiCtWvXMmrUKN5++23+8pe/eOdPPfVULrroIgKBQMEWrUQikXzZmPv103nj9f8UHFv+3nwu+/aFXHrP76mI6J3yewkhSNsWjWaykwDr7p1ruQ7bMolOAqy79n2JvVaEqYpCNKCRMBwEgljGxnYFRQEdf14KirhhY9gO0YBOUO9Y/kyaDinLJurXCeVtOaYth4RpE/bpRPwa0YDW6YesK0aOHMnFF1/sfdY0jRtvvLFgm+273/0u48aNAyAcDnupFOrq6nj++ec9f6xoNMr777/Pc889h+u6XHvttTzxxBNUV1d7937xxRd55ZVXWLFiBYceeignnHACwWCQoUOHeg7oO+MHP/iB58PWEzuzJ/+Z1157LSeddBJvvPEGBx10EA8//LC3Zffqq6/y4osvsnbtWs466yzPeX7HsQM8H68XXniBzZs3c9JJJzFr1izv/Mknn8wZZ5zBe++9x0MPPVQgiO6//36effZZ6uvrOfHEEwmFQtTV1QEwZ84cXLcj2KKkpITrrrvO+zxy5EjvuW1tbVxxxRXU1NR454PBIIceeiijRo3qtK0pkUgkXwZs1+E/C9/nP6+93umcEIJVH7zH6o9WM2jESAaW+D2XIMsRrG9O0mKmGFQSoDpa5BXj3tpuYruCgSV+Arrq3Wtzu8HmRJyKqMbQ4mJPgG2LmyQyDv2K/AW+Y47Tt5zzFdGTo80XjM2bN1NTU0NtbW2Bk3Zzc7PnPJ6P4wrcz7n7qqLIRK090N3cfJ5ceeWV2LbNb37zm936XMhGSw4YMIB33nmHAw44YLc/f1fYE3Mj6T1yfvoue+vcCCFozCRYG2vm3/98geu/fVG3bU+//g7GzziC4RVBgr6sqGpKZliyrQ1d0Zg4oIziYFZQZSyXDc0ZLEcwtDxASWj7cdthwdYWUobD/gNK6R8Nenasb8rQlrYZUhakqii73bk1kaLMsThr+qTPcxh2ib36T3FNVXaaRFUi+ax48sknefDBBznkkEP6vACTSCSSXaHdTLMh3sK2dJyI7qdm+PAe248ZvQ/9i/2eAMtFQQ4oDlAVjFAU6Fi9CvpU+hf7cVxBdPtxW7g0mQnKIyrDS6NUhjuqriiKQr8iP8VBnZJQtr2LoDmTpDxQmPVgT7NXizDJ3smOW4q7C5/Px8knn8w3v/nN3f5siUQi+TxI2xYbE63UJtvQFIXqUBG1sQRN4Qr2mTSF9YsWQP6Ok6IwYdrBTNh/bME9GowEflWnpqJrJ/z8At35Tvgjykq6dMIvCmreNqSLoCGdwHBtfGrX6ZH2FFKESfY6uksU+3mzK1GWEolE0pexXIe6VIx18WZs16E8EMZ1FZbVt7G6KU6R389P77yLu378I5bNe9e7bsK0g7nk1tu9z593FKQnwBybcl+4oMRcX0CKMIlEIpFIJL3CFS5NmSRrY83ErAxl/hABf4imhMWKhnaakin6RUP0j0RQULj2oUeoXbuG+k2b6D9kCDUjR3n32p0CrCIQIYEsWySRSCQSieQLSJuRZn2imYZ0gqjuZ2C4mHjGYUNTmo1tCWwMysNBTEOnSdhURbMO8TUjR9Fv2Aga4hbb4ib9oj4yjk2DkUBxVWzbT4Nl0a/I5wWyOa6gIW7hCkG/Ij+KKrxi3KoTpKHdoV+R6kVKAjQmLNKmQ1WRn4BP8QSY5gRojru4OBDqfQL13YEUYRKJRCKRSLolZZtsSrSxebvfV/9QEY4rWN+YYtW2NP6Ai6qblPuCYPvYlDAoDgrKwx0VaVKmS1PCIuRTCfoEbU4Kn6IR1sNsihloSrZmZK4qjWG72dqRAsIBlaRI4QpBuT/ClhabtGURDWieCHNcQVvKJpaxCfpV2m3DWwFrbHdpTFiEg64UYRKJRCKRSPo+tuuwJdXO+niL5/elKSqtSZv1TWm2tJuEgi5CtYjqASqDYUxbMLAkG9GYn54pGtAYUOLHEQ6tdtKrBYmA6mIfKooXKQnZ6/sV+bGEQ8xJoijZUkR+RaOqSMFyOiIlIZvtoDKqEwmoZEQG4ThUBCJEfX6ciE3Qp2K4snbkF5o77riDtrY2SktLe53Q9NPwxz/+kRNPPJGysrLP/VkSiUQikeRoNVKsiTXSYqQp84cIBcIkDYfa1jTNyWy5oYEVKu2GQVj3UxkMo6AQ0LOFtHfEpymUhBUajB1qQSpQXdS5vaoolEU16tOpTrUgK7oocwRQEtYx0okCAQZQEtIpCUF9QvqEfaG544472Lhx4y5llf80/OQnP+Gggw7aqQjbunUrb731Fl//+tc/d5skEolE8uXFcGw2JlrYmGjFp2oMCBVhu1DbmmFrm4miQEVEJ2YbtBvpAgHWE7vbCT8nwPo6fStWs49jmmbB//sKq1ev5uc///meNkMikUgkX1CEEDSk48xvqmVDvIXyQJgyf4hFH37Eo396nvcXrCDiVykNSQH2WSJXwnpBrhJ8rn5gXV0dRx11FE8++SSVlZWf2XMymQyvvvoqruty+OGHF5xra2vjySefBKCiooIZM2bQv39/HMfh+eefp62tjfvuuw+A8847j1Qq1WV7iUQikUjySVoGa2PN1KVjRH1+BoSL2VrXyOXf/j7L33/Pazd26nROueYmQqURKiNBT4BlLJfWlE3Yr3olhQBM22VrLE3MSVMZ8XsCzHYFzQkLVVWojOhekW3HFTQkTBozCYpDKgMiRQRUHSEETUkb1xVURH3oeb5mTUmTumSSUEDQPxz1BFhbyiZtuZSF9QJfM8ftW5Ua5UpYLzjjjDN47bXXCo699tprnHnmmZ/ZMwzDYNasWVx11VU8+uijHH744aRSqYLzH374IR9++CGPPfYY48ePZ9GiRQghWLt2bcF5x3G6bS+RSCQSCYDjumyKt/BG/XoaMwmqQ1FCaoDa1gwXnvVdln8wr6D9R/Pf5w9X/wTDVAtWwGIZm81tBo0Ji/xy1I1Jg1XNceIpQYU/6q2AJQ2H+phJU8LCsDvaJ0ybVc3tNCVsivWwtwJm2IKmhEV9zCRpOF5703FZ0xxjS5tBQAQ9AZYVbRab2wximUI/MMt26EvIlbCdsGLFCl599dVOx4UQvPLKK6xcuZJx48Z96uc8/PDDWJbFggUL8Pl8PPfcc5x66qne+erqam+lC+Duu+/ml7/8JX/+85+57LLLWL16dcH5UCjUbXuJRCKR7N20GWmWtGxlWzrO0GgZQU2nLe2wqSXDhx+uYMPiDzpfJAR1Sxfia65HGdDhqxwNaFQX+YgENG9VK21bpEhTGfExOFqEX+tYvQr7VSoiPnRNwa9nj9vCpc1OUhxUqQiGKcmr8ejXFcrCOrYjCPuza0cugmYjQTAAZcEI/bYX74Zs7ciysI5fUwoiKFvNNKV635I9fcuaPsi6det6PL927drPRITNmzePU045BZ8vG/VxyimnEAgECto0NTXx5ptv0tTURH19PStWrOjxnrvaXiKRSCRfbkzH5pN4C/ObagnpfkaXVJGxBeub0tTHLIK6ih5v6PEeqYatQEftx7BfY0h5h9jJ+YBFfDr7DOjsA+bTVAaXdbzfcj5gigLjq0s7+YCpikL/4g5RlvMBM12HUeXFXfqAVUR8BVGUzUaKpG2i+/uWv5gUYTth5MiRn+p8bzFNE3/eD4eiKOh5iv2dd97hK1/5CocddhjV1dXEYjHa2tq6vd+utpdIJBLJlxchBE1GkiUtW1kfb2ZYtJyqQJRtMZNNLQauwEuu2n/IkB7v1dP5vuiE32ykSFgGEd2PpvWtZK3SJ2wnjBs3jtmzZ3tLrDkUReGoo476TFbBAMaPH88bb7zhfV6wYAHJZNL7/Pjjj3PxxRfz97//nQcffJCTTjrJ23sPBAJYllVwv57aSyQSiWTvIWWbrGir59Wta9icaGNUcRUhQqysS7K2MUPIr1Ie6chuXzNyFOOnHww7CihFYcL0QwrqP+bTlwVY1BegzB/aafvdjVwJ6wVPPvkkZ555Jq+88op3bPbs2TzxxBOf2TMuuOAC7r77br75zW+y33778cQTTxAMduxx77///vzqV7+iuLiYlpYWnn76ae/cqFGjqKur48Ybb6Sqqorzzjuvx/YSiUQi+fLjuC51qRhr4k1sTbajqyr9g6W0tCvUxxKEfCpV0Wx0YtJwSJkuxaFsKaDzbr6Fey//EWvmz/fuN2HawVxy6+1AtqxQLO0Q9qtEAponwBRXRRVBUoZDUVDPs0XQkrLxqQqlYd0TYLbr4hdBkmmBPyy8BQ8hBK0pGwGUhXVQ8ASY7gZIGwohTRRk5Y9nbDKWoDSs49MUT4Cpwodr+rAcA7S+tfYkRVgvqKys5OWXX2bgwIHU1dUxYMAAXn755c/0GRUVFcyfP58//OEPWJbFM888w0MPPUR5eTmQFWlFRUUsXLiQ4cOH87e//Y1HH33Us++ll17iH//4B1u2bMFxnB7bSyQSieTLTZuR5uUF77J81Soqhw6i3/BhKLaflfUmnzQbjO4XojhPJDUkLJoTFjVlAQIhFzcc5NLfPcDGjz5h9eoNjN53HyZN7Nj5aUvZ1LYaVER99NdcbwVMI8jWdpNoIFsHMieS4hmHrW0GQZ+K3wctVhJXCIq0MFtbbTQlW1cytL12ZMZyqY9ZOELg0xRSIo3h2BTrIRraXTKW4Qk6yIq8+phFwnBQFFB8lrcCFkuobExkCAZcBgf6lghTRB/Zo/rGN77BwoULC46dcMIJ3Hrrrb2+x+bNm6mpqaG2tpbBgwd7x5ubm6moqPjUNg4bNszLmP/JJ5986vtJPru5kXz2yLnp28j56bvsybnJ2BaLN67lonPOY/E7HSkmRk+Zxnm/uBUtXIItBP2L/ETyIgcbExbtaZtA0MVVTC8Ra1vKpjlpUxHRKQt3OLrHMjYNcYuAT+D6DG8LMmO61MdNIv5sxGRuZStjudTFTFRVoPgNIFuKSHFVtrabqAoMLA14OcBsV7C1zcAWAl/AwiVbiiis+6hrNzEdwYBiv5cDTAjBtrhF0nTwBxxcxSLqC1ARCHt9Q7EZEdGYe9D+n/s89JY+sxK2ceNGjjrqKC6++GLvWHFx8R60qDOXXHKJVztSIpFIJJK+Qm7rcW28iUvPOY8l775fcP7jBR/w2M9/wrUPPYIQopOfc1XUh+63iZlmQSb8srCP0pDeqX1xUEfXXBrNZIEPWCSgsY8/2Kl90KcyuMzHtkwCN78WpApDywOd2uuqwuDyANtScUy3sBbkoNJApz4o2yMomzJJknaHAMv1rTKisy2Z/nSD/DnQZ0QYZLfVxowZs6fN6JbdUS9SIpFIJJLeIoSg1UyzPtZMk5GkZf0mPnxnXlcNWTbvXWrXrunSsb7NyhAzM12WItpRIEHWCX9HAdZTe1u42wVYZyf8rtrnp6Hoygm/q2tyaSjyBVhP7fsCfWpz9LHHHuOAAw7g2GOP5e6778Zx+lZmW4lEIpFI+gop22Rl2zYWNtWStE0GhIrYuG5Tj9fUb+p8vs3KfOFrQeZHQe4owPoyfWYlbOjQoZxzzjlMnDiR5cuXc9VVV/HWW2/1GNUXi8WIxWLe51xtR4lEIpFIvqzYrsPWVIx18Wac7StFjgsbmtNsUop6vDY/x1fadNiWTuMqJkX+QEEtyKTpUBTQ8OsdazWm7dKYNEiRJuLTC2pBxtJ2gWM9bI+ITFu0WUkCPsUTYK4QtKcddJWCCEohBG0Zm6ZMEp/uUhnsqAUZz9jYLpSENNQ80Zc0HLalkiiaTXEg6AmwtOmQtlyKQ3pBrck+4gbv0WdE2B//+EcvidrkyZMZNGgQxxxzDIsXL2bixIldXnPbbbdx/fXXdzre2tpKKNSRDyRfqEn6FnJu+i5ybvo2cn76Lp/X3AghiFkGW5JtxG2DIj1ASNVobGunrt3g48Y4xWVVjJ40mY8XLYR8waEojJs0hSGDBkE6hRCCrS1JmlMGA4oDVPkCKOn0dvsN2tIORHxURjuc8VtiaTa3pygN6fSvCKBlsu2TKZvmeDYiMlTi97b+EmmLTa0JfCrUVBURNEzAJGPatMUsdFUhWOLHvz1tRNqy2dycwHZdhpdFKNJtsG1Mx6Wt3cR2BYFiHxF/R0Tk5qY4cdNiUEmQSh9e39raTRKGg1bk9yIo/UYGRzg0Nzd/LvOTY1eCMvqMCNsxi+2MGTOAbO3G7kTYZZddxne+8x3vc11dHVOnTqWsrKzTIMgoor6LnJu+i5ybvo2cn77LZz03Ccvgk0QLWzJxwtEgg3ylxA2HDS0GrSmXlApm2EdIi/KDW+/mvit/xLJ573rXjzhoKt//9R2IUHalqM3K0KopEIpQXFIEIR85yabYPgQWStiHCGVFWNq2aFBT2IEgJSUlqGE/HRLPRjg+CGiw/bgtXBoMg5TuY1A0gj8SRmwXZ4ru4poGrqqghAMIVcFF0OTEies6Jb4Q4eIIYvsqnOIKXEPHdUW2/faIyOZMkjZdQ9MCFJcUI/KiPbF8CM2BsA8RykqdtoxFSFX71O9NnxFhO1JbWwtASUlJt22Ki4v7XASlRCKRSCSfFZbrsCXZxrp4C0II+oWi2A580mxQHzPRVAG6ieI4jK4qosQXRFMVrn3oEWrXrqF+0yaqa4YwcMRIb1su5wM2pDREWSCMTy10D6+M6l7CU+jwAauM+BldFi0oxg1QEtKJBDRyh3M+YAGfwkGDyghpesEWYsinMqwim4xc2y7AGtIJXBzG9ysh4vMXJGHVVcWrNZk73mykSNkm+1SEKfOHC7YcAfoX+6gSPu94wjJpsVLUhCOfaj4+a/qECFu1ahUvv/wy559/PsFgkObmZi688EIGDRrE7Nmz97R5EolEIpHsVoQQNGYSrI01E7cylAVC+BSdprhJbauBaQtKwhptZpq0bVESCFHqCxbco2bkqE6RkL1xwlcUBd/2RaXeOuF7+b166YSfE1O9dcLPF2W9ccJXFAV9+yUJy6TZSBJQdXStT8gejz4RHVlTU8OmTZsYMGAAQ4YMYeDAgWiaxmuvvUY4/MWJcpBIJBKJ5NMStzIsbdnK4uYtuLgMCBdjmgqr6lOsaczg01QqojptZpqUbXYpwLpib4yC9ASYplMR7FurYNBHVsIikQj/93//xy233MKWLVuorq7G79/5ZEgkEolE8mXBch02xVv5JNnCxtXriG2uo/+QYQT7D2VjcwZVgf7FflCgKZPdjvMpfjTHB77Ce6VNB9sVRAMaiqJ4AkxDxy/8CFFYn9u0XdKWSzSQLTWUXwsypIVwXch33XZcQcJwCPlU/LpaUAsyooQQjlqwzCNEtr2uKoT8WoEACxBEcQv9wiEb+egKKApmz+UEmI5vex8KE7ZmLBfDdikKZiMocwJMRSNIkKRrgta38oX1CRGWQ1VVampq9rQZEolEIpHsNnJbjx+1NVDf2MCtF1/O/Lfe8c6PmjSNU666mZLyckrDLgknQ8o2CaoB2uLQLgyGlAW8MkSWI9jcZmLaLoPLAgjdpt1IE9R8JJIarYbJoFIoj3Qot7qYSVvKZmBJgGgIbwXMcQJsajeoKnIZWBLw2jcnLeraTUrDOoPK/N4KWJAQ9W0OsYDB8Iqg5wsWyzhsbjXw6ypDKwK0mEkMxyakBGmJC9oVg6HlQa8MUcZy2dxm4AoYUhYgg0HCMgiqfmIJlTbbYHBZgJLtTveuEGxpN0gZLoNK/fj9wlsBy6R1NqYMfD6Hgb4d1Ooepk9sR0okEolEsjeSsAwWNNXy3raNaKrCbf/7Exa8/W5BmzWLPuAvv7wSTYXWvC3IMn8QXVXQVQU1z2dKVUDXFHRNIeEY3hZkVTCMT1XwaUqBjxWAT1Pw6yqmaxdsQQa2H9/R8V1Ts8dVlYItyJCWdej3qYWbnZqatUfToDHTsQVZ5A9kz6kK+Y9QFby+tVlpbwuyMhj2+pbfBwW8vmUcyxNg/UJR/Fp2tU7tg1nz+9RKmEQikUgkewOW67Ap0cKi5q0EVZ0hRaWsWv4xH7z5dufGQrBu4fvE6tfgGz6kwAdsSHkQIURBUlVNVagpC9BipEk7hbUgB5UGsvm29MI1mOoiP2G/SZuTxK90JGKtjPooDur49UIBUx7WCfgUWswEIq8WZCCUrROpq0rBVmE0oDG0IkCzkcQWhaWIcrUjfXlbhX5dZUh5kGYjheEW1oKsKQvg7NAHRcn2rc0wSDhpT4CpKFQX+SkPC1ozsnakRCKRSCR7LUIImowkS5q38kmihZFFlRT7QzTGTN5Z+HGP19bXfsI+Y0YXOOFnhUvnFZ6EY5B2jE5O+JraeRUMwHRt2t0UflUvcMJXFYWgr3N7B0GbnQSFTk74Owo8yDrht1mpTgIMKBCQ+cTsNIbbuRZkboVsR9KO1UmA5fdZMeRKmEQikUgkeyUJy2B9rJmlrXU4wmFcSTW2rbGyLkk84zBkn2E9Xj9sn5EyCrIb8qMg8wVYX0eKMIlEIpFIPkdyCVfXxJrZmmwn5NOp1Muob3eoj6URLpSGNKpG78v46Qez/P15nUoOjZ06jXFj9gMgbblANulpPobt0mJkMEXhCphpu1iuIOxTC7YILUfQnjFIiMIVMMcVZCyXoE8tWDVzXEHCtGmzkyh5K2BCCFKmi19XC7YUhRAkLYdWM4VL4QpYynTQVKXTqlnacmkxUtgUbkEatovjCsL+wihK03ZpMwxSbpqg7vMEmOUITNsl7C/sc1+rHSkd8yUSiUQi+RwQQtCUSbKgqZYVrdtoM7MRipoVZl2DSVPCRlcUWlI2W9stHFdw6a13MGHawQX3GTNlKj++7U4gK1JqWw1qWw1PjAFYjsvKhjgfN8TB0T0B5riCLW0mm1oMYhmnwLb1zUmWbGsjlVEKVsC2xU02NGdoTloFdjQkTBbVtdIUtwtWwFpSNp80Z9jaZhSInLaMzYd1bdS2ZijzhT0BljAcNrUYbGoxsJyOPqQtl+Xb2lnXlETH5wkwy3G99gmjow+OK1jTlGTFthiWrXoCTAjB1jaDT5oztKTsgj5YrktfQq6ESSQSiUTyGZO0DDYkWtiaiuFTNGzhEs84xNp9+FWHkpBOQFdpTxeKhOKycq556A988OFSVn60lkHD9uGIqQfg03peM2mzDBJWhqDmozwY6tUWZIuZQlc0qoKRXm1BNmYSCCGoCIZ7tQXZlEliOQ79AmEivdiCbDFSpG2TkO6nvBdbkEnLpN1K49M0KoORnW5Bmq6DI6QIk0gkEonkS4ntOtSnY2wzWnCFoMwfYn17O5tbM1iGn4qQj7Jwx6u3OKihawF8253HBYKmTIr+I4cwZNQoygPBAgEW8qnUbK+jmNuObLMypOwM+1SEKfWHiPg77q+pCoNK/d52JGQFWKOZZFBJgFJfhKJA4RZfdZGfkqDu5ezK+YAVh1QGF5VSEigUVOVhnaCeTQOhKB21IH26y37VxZQGAgXbmtGAxpDy7LFc35q3b0GOqIxQHggXbLX6NJUh5YGC7ciEZdJqpagpC1LmDxPxdfRBURQGlga87UgAw7VpMpL0D/WtKjxShEkkEolE8ikRQtBspFgba6Q10UZFRTnCVVhU18zWNoPKYJTBZYFOkYmKohDZLixyAmxnpYjyBUpvnPD9ukpONhU44Ue7dsLXVMVL/JrvhD8gUtTlCpiidLTPd8KvDEa7dcLP9+3qjRN+vu9Yb5zwfZqCb3uKf8O12ZZOoKCgq50z8+9JpAiTSCQSieRTkNt63JJsJ6z7aN+0hflvL6AtUkJ44BCGFxdREgz0eI/eCrB8ZBTkzqMgcwJMVRSqgpECJ/2+gBRhEolEIpH8F+SiHtfFWxBC4EuZXPrt77Dw7Y6SQ2OmTOOy2+6EHUSYYbvoO2xBpmyTkBYgqnUWbKbtFiQ0zQkwv+KjWA92EmC2KwoSmuYEmCpUSv2dfcAcV2A5goCeTbKaXwuyzBfBpxSuIAkhMGzhZd/PF2BRLURI61weyLBdLzs+dAiwgOqnWA91Hl9HFCSizQkwXdEo9YU7CTDHFQWJaHMCDAGlvghxx+j0jD2NjI6USCQSiWQXyNV6nN9Yy6r2Rop8fkp9YS4553ssfKew5NCqBR/wm8suKTjWnrbZ0JRhS5uBK1xPgOn4aY1BbauB43ZEGaYtl40tBptaMtmUDNsFmE/xkUypbGoxCyIlHVdQ25qNDkwYjifAdFQcI8CmZqNTQEAuIrIlZResgOlOkK2tNk2JwkjJlpTNhuYM2+JmgQDzuQEa21227BApmTAcPmnOeH3LL8YdT6he33KYtsumlgwbW7JRoPkCzMj42NhcGCkphGBLm8GGpgztabtgBUyxg9S2mLSnHfoaUoRJJBKJRNJLEpbB8tZ6FjVvwRUu1cEi2hIuT/5rAcvmvVeY3wtACFbPn0ft2jXeodyqk+UKGvO2IKNaANsR2I4gT4Phbl/hsV1Bq9mxBVnmD+G42VUvN+8CV+DdJ2l1bEH2CxXhiOyz80Ue4OXVMp3CLUgVFdN2sXdo77gd7fO3IIOaz+ub2KF9zqamTMcWZKkv5PWtoM8C73jcNLwtyKpgFMfJ9i+/DwKwto9r2ukQYP1DRbhuVtS5fSxHGMjtSIlEIpFIdorlOmxOtrEh3oIQ0C8YIZFxWdWUoilhEavf3OP19Zs2UTNyFAClYR1NhbiTIePYng+Y0AVDygPoamEdxUhAY3BpINveLawFObg0gCvwHOMh65Q+uNRP3LRIkSqoBTmoJIBhuxQFC7cXBxT7KQqqxN0USl4tyKqoRsSvdUqSWhHx4dcV4k4aw+lIxOrqwkvCml8wuziYjYiM2RkyO9SCHFwaQFXwojEh++/BpQHilklaZAp8wAaX+rFdQTSvz+r2vsVNk6RIoysq/UNF6IrKgGI/pSGdhNX3tiOlCJNIJBKJpBtyW49rY83ErQzlgTCuq7Kx2aA+ZuJTFaqKdCqH9e/xPv2HDPH+rShgKiYOdoETvqIoFAW7fi07moVlm52c8COBbqL9VJe0ki4QYJAVN0Ff500wVYMU6YJM+JAVdCWhLiIiVciQ6ZQJX1W6aa8omIrZKRM+0EkQ5hCqg0GmkxN+yN91e0VzSZNBVzsEGGyPDtVV0o7Z9VjtQeR2pEQikUgkXRC3MixrqfO2HisDRTQnHJZtTrAtZlEW0okGNRoyCSqGD2XctOlZhZWPojBh+iHeKpiMgvz8oyDzBVhf54thpUQikUgkuwnTsVkfa+aDxk00ZpL0C0awrWyh7Q1NGUJ+lfKwhu0KtqXjWMKhXyDKj267s1PJoQnTDuaSW28HsuV3GtPJTgIs6w/V2V/JcQUtRrqTAHPczj5dufYJ0+wkwFwhOvl0QXaVL2M7nQSY2O431vXYuGxLxTsJsFwk447YrqAxk+wkwHrqQ8wwOgmw7tq7QpC0rU4CrKc+9CXkdqREIpFIJHRsPa5ubyBhW1QEwti2wkd1abbFLMojOlXRbOqF+pjJutZ2isIKo8pKCOk+QmXlXHz3g6xbsoRYazP7jt7HWwFrT1t83BRH1R1GVhZ5AixpOGxtNwn6VAaV+j0/qrTl8nFTnLRjMLw8XFCMe0tbdlttUKnfS99gu4J1TUkaMykGlfipLu4QYFvaTDKWy8ASf8H25eZ2g42xOOURlRFlJd4KWFPCpiVlURX1UR7pSDXRlDRZ0xwjGIBR5cWeAGtP22yLWxQFNAaUdKyKJQ2HjxpjOIrNPhVhT4ClLZetbQa6qjCoLOClrDBtlzVNSdqtNDVlQU+A2a5gS6uB7QoGlga8ZLWuEHzSkmJLIkn/Eh/DS4q9FbD6mEXccKgu8hVsj9qOLFskkUgkEkmfot1Ms6qtgaZMkpJAkEp/lG3tJsu2JqlvtxjVL+g5gjtCsDkRJ246DCoqIqR3CBXDFpQMHEb1mPHUlGeFlkBQn0rRmjHpXxSiRO/IA2Y6rpdewhWQS6jflEnTnEoT8fsp9XfUgrRcQWZ7KgfLFV4m/IRpUp9Kwg55wFwBGSv7DNNxiaBtv9ZhcyJOynAYXlq4BZm2HJKmS7GdF3GJoC6ZpD1jUxYs3II0HUHScNDVrJDNJUTdlkrSmjEoCwYo83dsQVrb++zTsitcORHWZhg0pFL4NI0yf0ceMGd7ny1HYDmuJ8LSjs2WRBLTyeYBywkwIUS2D4aDmVciyhEC2+1baSqkCJNIJBLJXkvGttgQb2ZJSx1lgRADwsW0Jh3WtCbJWC79ojrlYZ3i7aspjshuQZZEFAZESqiKFCZWrYjoRBw/geKsSMn5gPl8DqP7RakOhQqythcHdWrKwK+pHWLEymBjMLQ8TL9QmKDesXoV9qkM3L7alF8Lst1JMbDET7k/Skmec7+uKgws8WM6LsXbj9vCZVsmQUVUY3hpEZXhwj5UF2dXzIp2KEUUCgj261dMv2ihH1tZWEdTsuWUcn1rNlIoms2IijDV4YjXN8jWjhxUGkBXO8oRJSyTlJtmcFmAymCkoBZkQM/22XbxhLDh2jQb2RWwUl+E8lCHEFYUZXtEpFswb02ZFJWhnfuv7U6kCJNIJBLJXoftOtSn4ixsqaUpnWK/smpsS2X1tjStKZuIX/W2HnPkBJglHAZHClfAcvh1lUBIR+hqgRN+eSjcpRO+piqUhTvuk3PCj/oCVBZ1dsJXdog+zHfCH1zatRN+JKB5K2D5TvhDi4u7dMIP6KonjvKd8PuHu64FqatKwbZlzgm/OBDs0glfVZSCIuY5J/yg7mNIUddO+PlRo/lO+PlbkPmE/JoXRZmbNxsbXZEFvCUSiUQi2SPkCm2vbm9kZds2oj4fo4uqqWu1+XDJR7RsqWWfEcOpGjWq4Lp8AdYvEO1SgBU8R0ZB9okoyPx5q/BFUNWe77+7kSJMIpFIJHsFcSvDhlgLG5OttBgpivUgITfCBx/V8cBPL2PV/Hle2/HTDubS2+6guKy84EVe5Y90KcDyfaHyBVixP9ilAMtvDx0CLKT5uhRgO7bPF2D9gtEuBVj+NfkCrF8w0qUAy2+fL8DK/eEuBdiONuUEWET3dynAdmyfE2B+VetWgOVfky/AqoPRLgVYfvsd5y0m7E7t9zQyRYVEIpFIvtQYjs3a9ibeb9jEllQ7KdvEtXTqGzXe35DgwZ9exscL3i+4ZvkH87j1sksKXuSqFWBrm0MsU/gyb01ZrGvK0JqyEAjazQwp20QVfprboXGHuotJw2F9c4b6mIkQwhNgGjrJlMbWNrMgZYVpd9SOtF1RUIzbTPvZ3GoW1F10t9dR/KTFIGO5ngBzXBcsP1tabZJGoYN6YyLbh1jGLhBgmhNgW7tLa6qwD7GMzbqmjNe3nABThY/2hOr1LUfGcvmkxdheL1N4AkxFI532sbnFKEijYbvCqx1p2m5HMW7AMfxsbrXI5NXLFEJQHzNZ35whaThdzlsi07ec8kGKMIlEIpF8SXFcly3JNt5v3MT6eDNBTacxmaG2yaGt3Udj3Kbpk3V8NH9e5xxXQrDy/fdYsmKJtwVpOyptKZu0WZjmIGW6tKZskqZDUyZFxrYoCYTwCx9tKbug0DRkUzTE0g7xjFNQCzKiBomlHRKGg52X48qwBXHDIWG4xIyOPGBlvggp0yVuOBh5kYy2I0gYDrG0TcKy81bAohimQiztFBT8hmyB7bbtfcjfglSERmvKJrVDn9Om6/UtfwsypAS8vuWn9cpYbtYew6E9Lw9YiR4mabgkDBcrT0hadvZY3HCIWx15wCr9UTKmIJa2C0SYKyCecbLjZzoFW8e5ecvYfSs9BcjtSIlEIpF8yRBC0GKk2BBvoclIUuILIhQ/C7Y005Z0GRiJUhb1UeTXaVpa3+O96jdtZOyY/QjpPvpFVaJ+jeJQYdmc8rCOT1MwMEjZNhX+AFFfEENxURWFsL9wvaMkpCMEGJjErY5SRK4LA0vBpype/i+ASCAbHWg4NjEniV/NliJSgf7FPsT2Njn8ukp1kZ+M45B0UwWliPoXK2QsvVNpoX5RH2G/ikEGkVcLUgs7+DWVaKCwD6VhHVVRyGCQsDpKEVm6QAgI+hS0PP+roqDGwNIApmsRs9OeDxgCBpb4UehcO7J/sY+MaxN3UgWliKqLsuk58ssdaapC/2IfSVPDII0Qrue7l5u3jCNrR0okEolE8rmRsAw+SbSwNRkjoOlU+iNsakuzdFsrCgqjyksIaNmXd2lYZ9999+nxfvuO2NfzAYsEtC5rNQb9Kgk3g2tna0EW2S6CbJRhVVHnDSefpuALOqSMwlqQmkqniEzIRhOGA5AwMp1qQeZHJeYTDakk0smCYtyQjTIs6iJGIBTIFu/OF2BQGGWYT0BXUf0ZXKuwFqRPU6gq6myTpiqEAoKUYRQ64SvZYuA7oigKkZBCIm0UFOOG7Lx1RTigZfuQJ8CgY97qd9gW7gvI7UiJRCKRfOGxXIcN8Wbeb9xEfSpOmT+Mbeos2hxjcV0LkYDG6IpST4DlqBk5ivHTD+6y5uN+06YzcvTYHp8royD7XhRkb6JX+wpShEkkEonkC0uu1ND8xlo+bm+kSPcTIMjahgzL62M0GUkqoj5qol3nkwK49NY7OtV83G/qNC677c6eny0FmBRgnxK5HSmRSCSSLyRJy2BDooUtqRgRzUepHmFzq0Fdu0lj0iDjZqgq9nkvclcIGuIWtiOoLvbh07Iv96LSMr57xwN8uGIpZvNmxo0a7a2ANSctkoZDZdRHOG9brjVlsbE9QcDvMqA44gmweMYhFTMICH/BtlnKdNjQlsTBZEBxyBNgGculIW4S8mlURnUvvYLluGxsTdNup6gu8nsCzHEF2+LZ2pHVRX7P78oVgrqYQX0qQVlEY1C0yCvG3ZSwSVsO1cV+LwkrZGtBbooliAQFg6JFngBrS9m0Z2zKw74Cv6t4xmFDWxxVtxlQ1FELMmU6NCUsIgGtYGsxY7l80pokLQz6F/k9AWY5LttiFrqm0K/I59XLdFxBbXua5kyKyiKd/uGe500Iwba4xeZEtoJBfgLd7ubN6WNFveVKmEQikUi+UNiuw6ZEKx801Wa3Hn1h2pOwbEuSpoSNqro0pFLEDUGp3lFT0LQFrSmb5qRVEO1nOoJ1re1QOZhJM+Z4AkyIbPttcasgwlEg2NieYHN7BhV/wQpYwnBoTmZFTD5bkyk2tiYxTJWKQEctyKTp0JTMFszOD95rSZtsaIuTykCZryMPWMZyaUnatCQLowNTlsPa1hgtSYciLeStgNkutKQsmpKFUZqOcFnXGqc+ZhAgWLAC1p6xqY+ZnaI6a+MJNrWlcW29YAUsYThsi1u0puyCKNPGZIZP2pJkDKgIdKyApUyX5mS2vZkX1dlumKxvjRPLuLs0b01JizAhT4B1P29gOX0rTYVcCZNIJBLJF4YWI8XaWCMtRppSX5C0rfBhbYLNa9eQ3LaF6mE1qP2rqYzqVAWjRP0dr7mArlAZ9eG6wnOwd4Sg2UxQFFYYVFRUUAtSURQqIz4ifs2ru5jbggz4XUZVRRlcVLgdVxzS8Bf58e9QishVTIaWhxgUjaDmba8VBTQGFPsJ6Ao+LStS0rZFUqSoLvZTHYp6NSIhGzXYb7vjey6a0BYurVaC0rBGuT9MWaijDz5NoSrqo9gWBbUgGzNJIkFBZaiY/tFQQR/Kwz78mloQBdpspFA1m30qwtQURQv7HNQZXAphf0ftyIRlYigZBpUGGBCJ4Nfyoz01+hf7UVWFgN6RiDVmpz7XeWs1UpT5+9ZWpRRhEolEIunzZGyLDYkWapOtBFQfxWqY2haTjZsbeOhnP+KjDzqy3Y+aMoUf334X5cWFhakVRSmIPsz3JRpVVtKlL1FpWKd0+7/zfcDytyDzifg1olEfYvs2Xs4HrCwYpLK0sw+YX1cZUNKxCpXzAQtqOkOrOvuAaapCv6KO9jkfMAGMqSzp0gcsP4Iy3wdsWElxlz5gRUGtYBsy5wNWFQl16QMW9BX2IecDFvX72Keksw+YripUF3e0z/mA6ZrKuKqSTj5gn9m8ORY+ded+e7sTuR0pkUgkkj6LK1y2Jtv5oKmW2mQbxXqIRFJh+dYU7WmbR675MavmF2a7X7NgAb/98Y96vK+sBbl3OOEXlJDyhVDVviV7+pY1EolEIpFsp91M82HLVpa21qGh4HOCrKrLsKnVoCioEt+8geXvv9dltvtl896ldu2aLu8rBdheKMD8QUJa53xnexopwiQSiUTSpzAdmzXtDXzQWEu7kSaqhtnS4rBqWwrHFTiuIJZxqN+0scf71G/ahCsEjXGLbXEze+32F7np2uh2kPaUwNohYq41ZVHXbmLYbsGLXBV+0imVtFno3J0wHOraTa8eY9w2aDfSqELHMnTiO9QsNGyXunbTq8eYE2CKqyKsAG07OLjnIiIb4xauEJ4As10XzQnQlnRx8moECSFoTlrUx0wsR3SqBRlPZW3Ipz1tU9duen3LCTDF1TEzeqdak2kz2+dcHc2cAFOEhmv6aU8VBiZYTra2Y3PSQgjhCTAhQLUDtCacgnqZn8W8NaaTNBtJ4pmsrSo6Jb7CLeo9jfQJk0gkEkmfIJfza0nLVmzhUqKFaIg71Lcn0VSFiohOezr7Qg35VIoGDuzxfv2HDPFSQDgim+U9JVJYwqFMj1CXcEhbJgFdpWx7OgnHzaZ0iGVsdA3QLW8FrLUd6hIGqhIoyCLfnrbZ3GowuCyApVoYpkE4UoRj6mxqNbOO8cGO120847C13aA4qOPXBU1WEp+iEVBDbGozsn0LaPj1XDShw7aYhaZAwA8xJ4UrBCV6mK0tNo5wCftUirY/w3KyAiZtufh1BVPJYDg2Zb4w21IusYyBT1MIRDvWYVqSNo0JC1UJkBKGtwKWSWtsas0wuCxQUC0glnGobTWoivpQNddbAVOdALXb+1Yc1L0UGjmhGvKp+H2CViuFqihE1DC17Saa4hAJqF46iU8zby42KSWNIxwGhIpJC51EWqEyHKbE17dSVEgRJpFIJJI9TtIyWNXewMftjQwKF6NZQRZtya5iDKsIepGDYb9KZdQHqosbHcCoKVNYs2AB5G9JKgoTph1MzchROK6gPOLDcl1idhKhZkvaBDWdTMQiYouC2o6aqlAa1vHrChlheKWISn1BRNhCVSG6Q+miaECjf4kfV7NpN0xKdR/FwTApxaW6WFAcLGwfCWhURX0oqusJsOpQEbYjqIz68OdFSgIEfRoVER0XQZuVhO21IHU0MhEFVwiCvo5n+DSFsohO2HZJOGmE0lGKyAhbBHSlU/ml0rCGqoKpmGTyShG1uzb9S/xd9rm62IemuTQbHbUgDUtQFXUJ+bWC2pH589ZiJtG214IUrkJFJFtjMz+HWUBXd2neHFwU3Ub32zgKjCupoiIYIaD62EiGmGrRryhAcaBwhW5Po4hOm+lfXDZv3kxNTQ21tbUMHjzYO97c3ExFRcUetEzSHXJu+i5ybvo2X5b5sV2Hzck2FjRuJu1Y1IQqqI85tCYtIn61INFmjnxfonDG4rc//hHL5r3rnZ8w/RAuufV2isvKgd3vA1YlgFCkx/bSB+zT+4DZwiVpm5iug0/VqPCHKQ9GCKs+MpYgbTvZaNKIn/7FQUpDPmJtrX3q90auhEkkEolktyOEoNlIsap9GytbGyjWQxRRxsf1BpoCFRHdy6SeT6cXeVjl2oceoXbtGuo3baL/kCHUjBzltd8jTvjpND2tbkgB9t8LMAdB2jLJuA6aolDqD1EZjBDV/Vi2QtKwMXEoC+mMqIxQFvYR8vU9h/wcUoRJJBKJZLeSKze0NtZEczpFwA2RjAdoc0xKgnrBVlw+Pb3Ia0aOKhBfIKMgvywCLKjrJG2LjJMNZCj2BRkcLaXYFwJXIW7atBoO0aDOqMooFZHs9qmyk7HtC0gRJpFIJJLdguU6bEm2sT7eQszM0JwyiSV9BISPooBCSFdpS9kEfWpBslDbFWxLZGi3UhSHdO9F7rjZ8jSaqngO2pBdZWtKWjRmkoSDgupgR03B1pSN4wrKwnqBz1J72qI+lcLncygPhQtqQWYsl5KQhj/PZyllOmxNpnAVM5uINa8WZCZl4decgq1U03bZlsiQcNOUBH2eALO398GvKZSEOvrguIKmpEmzmSQaVD0BJoSgZXvkYXlYLxAaLSmLbakEQb+gKhT1BFh72sZ0sn3W8/oczzjUJZOouk1ZMFhQCzJhOBQHdS8rP2Sd5esTaQwylAQ7akGatkt72vnM563EFyLtWCRtC8tSKPNHGV6arYkZNx1akjYKgiGlYfoXBygJ+lDVvi+88pEiTCKRSCSfK7mtxzWxRtrNDI4Na7al2dbuMqayhJJgVkw0xi02tWYoDelEA0FPYDSlDFZsaycc0NinNOqtpCQMh63tJj5NIeRTPcEQNxxWNrZjC4cDQqWeAMtYrpe2wZcneizH5eOmOK0Zk9H9op4AE0LQEDdpS9sMIUhVUYcg2dCWZGNrkqHloYJM+K0pm/aYRVixGFLeIUi2JTIsb2inLORjVGlHLcjY9sjKSEAjEtA8kdSesVje2I6mwqBoqbcCljJd6tuzBbyDuuo52Kcsh48a20lZNvtVl3gCzHKyRa6ThoOmdGTPF0KwpiXGtniGEZWRghWwpoTFtrjF4FIKMuFviaX5uClG/+IAo8o6VsDa085nNm9LG1pJ2yZj+hXhC2hUBMMElQCbmg0a2x2CikVZmAI/L5/2xc22JUWYRCKRSD43cluPW5Lt+FWdZAoWbmmlNSEYX11CaahjizDoU7Mv8mDHVlKupmBRSGNwtIiAWlhHsSiYFS65LUxHZCMI/T5Bf3+UkmBhHcVoQMN2hffiFwhazRSq7tC/KER1qKOOoqIoRLev7OSvCLVZGRxMqiIBBkUjBVuQYb+KG9QI5kUTpm2LhJumLORjaEkRel4fQj6VkpBGyKeR24W1hUubnSTsV6gOhYn6OsbIr6tepGJuZc5F0GYm0XXBoGCEirzakbqarU+pq9ln5Wgx06DaVBcFGRApDCKIBDTKnMLow4RlkhZpKiJ+hhQXbkF+2nlzhCBmZdiSjhH2q4wsqeKAfhWU+gMYNsQNm7Dfx8iqKKMqo33ez2tXkNGRkj2KnJu+i5ybvk1fn5/8rUfHddFEgDWNCWpjCcrDPgZFi9C2r4zkO9UPHjGy4EWe8yWqDkbxqZ1fvEIIr32+L1GVP0JI93XpF5S7ZkcfsBI90GN7KPQBqwiECopxe6SSEM4Km3wfsH7BaIEA6+r++T5g/YIRglpnP7bca1tRlE4+YJGd9BkKfcDK/aGdts/3AasKRrx56659b+bNFS5pxyFtWygKhFQfg6MlRHU/tqOQNB2EyBYHH1gSpCz02fh59bXfG7kSJpFIJJLPDCEETUaStbEm2s0MQcVPa0Lhk5Z2Um6G6iI/1eEiVBRirS3c/qNLWD7vPe/68dMP5tJb7yBQUtwrZ+6uBNjOnPC7EmA9OeF3JcB6csLP3r/3TvhdCbCenPBz7XfFCb8rAdaTE35XAqwnJ/yuBFhX85ZxbJJ2djs1ovvYp6icEn8QITQShk2b6RLxa9kt0rCf4qD+hXCw/2+RIkwikUgknwkJy+CTRAtbUjH8ZMvXrGk1SVomrmZQlufMDXD7jy5hxfvzCu6x/P153PajSzjv7rt2S01BGQXZPZ9VFKTlOiTsbPmhoKYzOFJCqT+ELnQSpkNbyiXsVxhaFqIy4qc46CsImvgyI0WYRCKRSD4Vua3HdbmtR8fPxlaTtGWh6w6OZqAJHd0NYNmCgK5Qu2ZNwQqYhxCsmPcedevWM3yf8RiWQM/TF9m6kTa6mvUryq8pGBQhTEshtMObrT1tIwSUhDRQ8ASYjh9haTiaKHjpJ00Hw3IpCWUjKHMCTBU6quPDdiDfJcmwXRKGQzSgEdBVDMemwTVQXBWfEsS0BSFfx/0dV9Cetgn4VCJ+raMWpOMSVEKYpkIgTxcKIWhPOygKlIT0AgHmE0FsS0XoomDFKJ5xsF3XKx2UE2Ca8KHYPhxfYZ/TlkvadCjaniIkvxZk/rx5c+4I4hmbkF8j5FMLakH6RQjDcjFUh5RjoisqZb4wmvAT0X340ImnXYK6y6DSIFWRACVBHf0L7GD/3yJFmEQikUj+K3bcevTjZ1uLYOnmVvoV+6gq1rySNrbhY1ObSXWRYFBpgPraTT3eO7a5gbpyk0hAY3hF0BMM8YzD5laTgE/Fp/tpMZNYwiGqhNkWc9BUE7+uek7oaStbLNtxBZoWICMMUrZJWA/SGoMWy6SmLFuqCLICKVeMWwCa3/FWwBJJlU0pk4GlUF3UoQxz0YTVRT7KoyoxM4UvGEYRATa3mZSFXYZVdKiqtryIyCHlfhqMBK4QhNUQ29ocYj5B0Bfwov6SpsuWNgNNVfDpCjE7heHYRNQQTTGXVtdA14Kew77luNTFTAzLpaZMwdFMEpZBWPPTnlBpNgwEUBHpWDHcFjNpTdkMLBVEgngrYDvOW46WlMXWNpOysM6AMt1bAVMdP2taEvh1GNsvyshoJVE9wLaYxfqWJINKVPbrH6K66Isf2fhZIEWYRCKRSHaZmJlhQ7yZ+kwCXahYGT+b2kyaEybFIQ2/TxRsZbU4NhG/69UH7D9kSI/3rxk2DG37Kkv+zpRfVwj6VHQNmjIJHCVbU1BFo91voClKQS4sXc22z0bgpbHJ1oIs0gKkdAMF1SuUDaAq2ShCISDtmjiG6W1BuqaF6Rf4dxAOAV0l4lcRikODkSKqaFSGioilHcJ+pyAqEcCvKYT9Gj4dtmXiCLK1IB1bod0n8OtKQbUAn6YQ9KsoCrQYSRyytSADqo+Ez8ARoiDBbbYOo4KCSsLJ4LiW54RvGmZ2tWqHhLghn4rhV7GFTbNhdDtvHX1QCftVNE2wLZ3AchyivgAGNv3CIQZGowwNRzFsQdIV1JQFOXh4OaUhnYD+5Yhs/CyQIkwikUgkvSZjW9Qm29iYaM3WzLZ9bGqzMGybkqBOWThEu2HSbqcKfIkqIjolIZ3cu7xm5CjGTz+Y5e/P67L49phxY7BdgapQsM0W9msMqQjQmInj4Bb4gA0rz6425W+z+TSFmjI/jZkUhmsX+IDVlAVwBQWiTVEUBpT4aTEzJC2zwAesX5GP8oivU0b/iohOwCdothL4FZ2ygB9VySYijW5PD5FPUVDH51NoyBNgAVUHPwyvDKIqFIiwgK4ypDxAYzqBJZwCH7CaskCnPmuqQk1ZgKZMirRjFfiADSjxd+ozQL8iHwG/2Om85SgL6+i6S22yBcVWGRQupn+wiKAaJGMKdFUhEtAZVRykLOwj7Jdyoyv65Ki0tbVRX1/PgAEDKCkp2dPmSCQSyV6P47rUp2Osi7eQtkw04aex3aEtY1DkVynavrWVsDoLMMiKmx1TO1166x3c9qNLWJHnGzZh2sFccuvtQGehAFkn/GYj0UmAAV06cwsELWYaw7U6OeGrikJX/t8x2yBpZTo54XfVB8hG/LXYSfyqnnXCz3TUjuyqBJMtXJqMBEAnJ/yu+uySXVXcUYB112eAVjNN2jE7OeF31+ekbfV63gzXpiWTImVbDC+qoDJQhCZ0hKsS0DX2qQ5RHvFRFPhyRzZ+FvQ5EWaaJkcddRQLFy7kgQce4Dvf+c6eNkkikUj2WoQQtBgp1sWbaDHS+ISPRFKnMW7g1xWqIvoupzPIESgp5ry776JhwwbsbU0MGjqsU/3HfGQU5J6LgnQRpCyLtGOjqzAgWEJIDaGhEVQ1BhQH6RfduyIbPwv6nAi74oorOOigg/jwww/3tCkSiUSyV5OwDNbGmmhIJ1DRsNI6a1oM/JpSUHvRsF2a0waGSBPy+bwXueW4pEy3oBwPZGsKtqYN4k4av65y4H4HoI9XvajBoE8t8EFyhaA9Y9NmJlF14QkwIQQJw0FTlYI6jZDNst6SSYNWuAWZMh0cV3RK/Jm2XJoyaWyy4iUnwAzbJWO5RANagbiwHJeWtElSpAhqekEtSMOw8fvdAqdzxxW0Zyza7CS6pngCzN3eB5+mFviOCSGIGQ4tZhJVdQsEWMJwADxH/Bwp06Exk0IoFkX+jlqQacvFcrJ9yN/m7M28CVwStoHjQkDVCbkRSvUgxX4/VV+S0kF7kj4lwv75z3/ywgsvsGjRIh566KE9bY5EIpHslZiOzcZ4Kx+1byOg+rb7fZmsa8ygAEPzohVr16xh/rKPsUsrmLD/GIbmlbTZFrNoTlr0L/ZTXdyxglMXy7CqKUZ5ROOA/iVePqnWlM3WdpOioMaw8o7s9a0pm+UNbWia4MD+HbUgE4bD5jYTTVUYVh7wyvgkTZsV22JkHIvR/Yo8AWbaLpvbspGSg0v9FAU7IiI/borTnEoztDxMZVFWgAkh2NpuEs84DCzxUxntWHnb2JpmQ1uc6mI/Q6s6VsCaExaJdpOAsBhc1hFN2JQ0Wd7YTtivMHFARy3I9rTDljaDkE9lWN64xgyHZdvacIXDhP4dtSBTpsPmVgOAIeUBT3yatsuKhhgJ02SfyrAnwBxXsLXNIG25DCoNeAWzhRCsa06yJZ5kcGmgYN7qYyZb4xmKwwrVRT7CaoiQL4hrq1QWBxhTHaUs5CP4JSkdtCfpMyJs69atnH/++fztb38jskMdK4lEIpF8/rjCpSGdYFHzZtrMNP18ZTS2O7SmDUK6QnlYJ2O76CpdZrtfNmU6l99xJ8Vl5QDomoJfV1HzVpAM16bFTOHXFfqHowWJWLXttQR1VSnIhN9iJkBxqQpGOvmAaaqCrnY47wsEbWYaS1iUBIKU5yXcUhRlu4O5UrCq1W5lSDsGEb+ffqFCH7BcfcOCnFrb/aeCukZ1KFqwBamqCrqmouf5gtnCpdlMoqlQHQoXlCLSVbw+e/OAoMVM4gqH8mCYqK9DzOX6nPt3jlYzjelaRP1+qoKFW5C6N64dx5K2RdzOEPbpVIezAswWLnErQ8Kx8CkKZWoRA3xRKiNBBpUEKQ/7ifg/fekgSQd9QoS5rss3vvENLrroIqZMmdLr62KxGLFYzPtcV1f3eZgnkUgkX3pajRQftzWwon0bYSVIwClmbUuh31fIr+G4goCucv2FnbPdr1rwPnf86FKufegRIBtxl01JUFjSpjyiMaK0hPAOKyllYZ2QTy0oxr0tHcfvhwP6lxL1+wuER9jfsWLm0zpKEdnYjKkupjQv1xbkIiWDCCG8VbM2K0PcyjC8PEypP0Rwh/QJA0v8WE5Hwe+cD1h1kZ+y8ijhHdJPVEZ0bOFH275qlvMBiwZVBkVLC4pxQzZScpiueuIq5wOmqu72FbBAgXN/QFcZVhHY3p/ss5uNFBnXZGRlhDJ/yOsbZIXaoLKAN2/Q4QNWUxqgxBcB1aXRSKIrKhElxJjSUiKVfoaWRRhYHKAk6CsQ0pLPjj4hwu68807q6ur46le/yqpVq7zj9fX1bNiwgeHDh3d53W233cb111/f6XhrayuhUMj7nC/UJH0LOTd9Fzk3fZvPan4Mx6Y+HWdDooX2TBq/G8KwHDLEKfdraEKBTLatb/t/tavWdpvtftm8d9m8Yhk1+4xAA0IANpiuTbuRJqQoVATCaI4JTudb5No7QtBmpNCEQ6U/TEA4YKQ7tc+tEQkEMTODa1tU+AMUCQFmplN7b1PUgrhtYJgGpbqPEk1BsQywCtvr2//Dzo5VzMzmASsLhNEco1MfFMDvmJBRcYRLq5HCLwRlgRB+1wbD7mRTbq3ORdBmpFEcm37+EGHhgtm5z/mu+e1mBts2KdP9lGgKWJlOfcjNGxakbIukmSasqgRVgW23E1A1+ilBdEWnOKDTL6pTFlYJ6CZu2qS1swlfWHbH99quFAjvEyKsvb0dgFNPPdU75jgO99xzD++++y4vvPBCl9dddtllBdGTdXV1TJ06lbKysk6D0JeqpksKkXPTd5Fz07f5NPNjuw5bUu2sj8epM2O0Oyppu5iQGqSkSPNWU0QX126o29rjveu2NTB4vwne5+wKmIkaDNI/VISqqF3eN4cXBenX6Rcoxa/7emzvRUFqKiXhUqK+YI/tYXsUpAPhSBHFwTCg9HhN2rZocA18wTCVoSJUpfv2CmAFg9koSL+f6lAUn6r3eH8vClLXqIgUE/L5d9qHZiNFQlOIBospDoR32j5hGWw2E6Cr9AtGCelh/ASI6H6qogEGl4QoD/u+9OWD+tL3Wp8QYddddx3XXXddwTFd17nxxht7TFFRXFxMcXHx52ydRCKRfLlozmRLDbUYKVrTFptabAxDZUA4QHm0cLvMsF0cV3gO4AnLxF9d1eP9S/oPxhUCVVEKagoWaWEUoZCfAcEVgrTpEvCp6KpSUAsyqoTRlc7O3ykzGxEZ0NWCNBQBNUCQzikcMpaLEILQ9j7k0lDo6ITVQKc0FJbjYtqCsF9FURRvC1IVKlEt3CmBg+MKMpZL0JfdVnRytSBdl2ItjEZhH4QQpEwXv/7/2fvzIEmv9L7v/b77knvWXtVVvTf2WTgGMJBoySHaYUqiQxJJhcgrKcK6omSFRJHAgHNJyeTItGlSNDmD4SJey1KMRNpXtByU5AiFJcUVNfaVzEEDg+FwBo21sXVXL7Xn+u7ve879I6veyuyqbjQwALq6cT4RiOjOejMrs7Kr6odznvM8GpahT7Sh8DUPSzv4qzlKCzRNK7dF99pQWFj4mnPg+vH3LZeC9WjIIItpmj4NvUbddKk7DksNl/m6o3p63SFHIoQpiqIoH74oz3hnuMPqsIsUOqudlNVuhGdYWNJiM8hxbaMMXLmQXOkkpMXoNKFmiFEt0ZmznPnM47zx+88f6HZ/9jOPkzaPsR3k1H2tnCmo5Q6X+ykzVcn82EnJ7SBnvZ/S8EwWm3bZB8wRHutBwTBOWWk7ZWuFQZxzpZtiGxorUw7dNCLMU2zNoTeAgZaw0nbK+qckF6x2EoSULLccUi2jl0SjkT+BTn+YcqxJeVJSSMnVbkqQFiw2HFxbln3AssTmcj9hrm4zMxZWN4cZm8OMmarFdM2kk4QI28YSLtf6OYG339keoBsVXOslVGyDY22brTggKXIc6bI1FPTDhONTblmsH6YFq50EXdNYaTsMi5hhlmBrFsPAYBAkLLecA+/bIM1oVMG3daadKsvODN1AMFt1eXi+znTVViOE7rAjG8Luv/9+ms3mnX4aiqIod729bvcX+1uEWU4am7y80ScuUhZqHg3L4/JOgpQ339AKsowkj3EMk2m3wl/4b36J3/o7n+et33+uvOaRx5/gz/7ML1IwqgFbj0bBYd6rsZXnHFoEtkvc0Ig1TjUguen1Esl2HJad8G1p0b/F9QC9LCGVCb5p07Q9giDh8A3XkTjP6Mu4bMR6LUlv+fh7K2D2biPWfiiBm99nbwUsE6NO+LLQ6dzieoCdJCSVo1FEVcNlGEy+ZolkmCV00ggDk3mnQdPx8E2LuZrN4imPpiq0PzI0eavvurvMlStXWF5eZnV1lWPHjpW3b29vH6k9YGWfem+OLvXeHG23+/50k2jUcDUeIjOTtW7BlcEQxxG03P2Gnjdu2e1JckE/SQllNNFRPSsEaSHZufQWa6uXmV9ZYfnMWXIh6Scp/SLE1HXmvRqmph/Ystuz16y0lwcITUw0Yg0zgW1oBxqBBmlON90fxr3XB+zGLbs9cSboZjGJSCY64d+41bonK0aveSDC/VFEe41YM4Fn6xNNTwshGaY53TxA02ABHatSP7DVukdKSZAVdNIQweQoovGt1nFRWtBJIzImZ0HuvW+2rTPIEjIhqBoWtvQxMJmruiw3PGaqNhXnyK67fGSO2s819Y4oiqLcg5Ii59Jwh8vDLkEiCQODbpyTyISqL6la7sRImxuDy55M5gcCGIzaI1gGVM6eZfns/qihgoKhiCYCGIxaJVScg1tfEhiKcCKAwahHV8U+7HpJKJIDAQw4ECD3xKQHAhhwIOjsyWXBUEYTAQxG/bbMw16DJhmIEE0bzYK0kxTJaE7joa9Zg2ERHQhgwIFAuCeUyYEABmCaMEhTwkRSMRxapk/VtJmuOBxrjgrtVTf7o0uFMEVRlHvIXsPVi/0tOlFCEhvsBGLUANVMKfKDv8hv5r3Ogtwrwt/bghxvxHqYe3cW5LtvQX6nsyATkTPMUjQknuZTMVxajsuxpsdcTRXa3y1UCFMURblH9NOYtwbbrA77JLFGZ6ghpcC3dPp5TFykE7/I3379ddYvX2LpxOTg7KwY1RX188kVsFzIiaafewohGWajLvLjAUxISZpLHFObCAR7W42dbEjB5ApYnIkDHeoB4rygm0ZlDdj4KKK9Zq3jskLQzRLCPJ4IYFkhJ5q17smFZJim9IpwIoAVQk40a90jpCTMCjrZEAllAJNSkuUCY/d06LgwK+imAZmcXAFLcrHb+X/y+jQXdNKIWOy/b1Ge0UkTNKnTNH3qlst81Wel5dP2LVVof5dRIUxRFOUul4mCy8MOb/a36UeC/lAjySUN1yAXkgsbfVKRcWa6wpTj0+/s8KXP/fhEx/uHP/sET33xy3j1Fq9tDtmJQ5bbLrPVUQArhORqJyHOBYuN/bmLAJc6IZcHAbM1k3PtZrkCtjHI6IQ501Vr4jThxjDj9e0evgsPTu/PguyEOWv9lKpjTJwm7Mc5L2/2kVrOfTO1iWHcV7oppg7LLbcMYkkueHljwDCLOTXlTwSw1U5MLuBY0y63/goheXMrYC0MWGzYHGvur4Bd66UMk4L5ul3OXQS43k94o9On6RvcP90oZ0FuBTlhL8WR9sQp0J0w45XNHqYpeWh2fxbkIB7Ny3RNnWMtpwyfYVrw0u77dnrKx9FNNqIhaWYwGFocb1T5zFyLhYarOtrfxVQIUxRFuUtJKdlKAi72trg2DNjcgWu9jFNTbhl6rvcHDNOUqm3TskeTRJ55+klefv65ice68Nx5vvz0U/zN3/j77MQhumbQtisTW5D57qpQLvbvFxcZ61FIIWDKrkxsQeaFJM0FQuyf/xptQQ6J84IFszaxBVmUjz9ardK00SiijSgkSFOmfI/G2BZksbsyx+6w7b0GZDtJTC+JcQ2Lpu2VW5BSjp77/v1GojxjMw5B6rTt/VmQo+tHz2n8+lwK1sIhaS5p234ZwACEkOSFwCj2rxdI1sMhYZaz5FaojL3mXIxWHk198ozcZhwySBMMHTIhCRJB224yV/c5c7bCTM3FUwO073oqhCmKotyFojzjpe4ab/a69IeSYaSR5oKzM165wrOdhEgt49S0z4zrY5s6qxcv3nLk0Ouvv8TyiTO07Qr1saJyQ9dYbDpkhaC6e3sicjbigLm6SdOs0HAna7rm6hY11yiL0/dqwKoezPlN2t5kPVTLN7GM0enGvQC2FYcYZs652RrTrjexTVl1DI41R/Mkx2dBpjLhZNun7XpU7P1fc7apc6xpUwhZvoYoz9jOApYaNk27QmNshU/TNBYbNrG//5r3asBaFYOVepWWN9kodapqURU2dm30tdirAXNtyUNzDaY8Z2JrtuEZGPpoxuXea1uPBvSyAa2KRduqsuI3ONWssdz0afn2ga1a5e6lQpiiKMpdJC1yroY9XtlZY1uahJGBJnXavjHxy3mvmLtmT56CXFu9fMvH71+7zqOf+K5Di/A9S8fbrY0aL8JfqdUPLcK3DJ2GN7p9vAh/wasdWoRv6BoNb/RrabwIv+X6hxbha5o2sS06UYRfPbwIf/z04UQRfv3wInzH1MsauPEi/KVqbWIFbI+pa1iOibyhE/6MVz20CF/XNOquiUQS5BnXgh5JBvPONCdmmtw/VWe25lJV7SXuSepdVRRFuQsIKdiMA17vbbLaC0m7BZHpUHeNA4Xyh52m2zO/snLLz3PfmfvUKchDHH4K8uZu9xRkLgWDLCHMCrIMlr0ZTs83OD1Vo6XaS9zzVAhTFEU54rpJxOu9dS71A3pDyDKDmq4zU7XIhSznNMJ+APOM/RqwcYunzvDw409w4fnzB0YOPfz4Z1kZOyUJo1OAQlKe3NsLYADTTvVAANuru9orkh+fBTllVXGNg792RjVRTGxBhnlKxXKpmwfnIuZComuUr3kvgDm6RXusBmzPXj3X3krhXgAz0JlyqgcC2I2veS+AFUIw7RwMYHuvee81jAewhulP1IDBKGT204woT8hyDd9wOVlp8cB0g/maR9UxVHuJjwkVwhRFUY6oKM94c7DFy9tbdPqS7lBjumoxXTHQY4N+nLPWz/BtncWGzU4aMcwSDGnRG+hEUcKx5v6JuyQXXOkm/OBP/yL89z/FhfNfKz/Xmc88xp/523+XJBflylohJFe6CWkuWWraGOaoqF4DROqwGqTM162JLcGNQUY3ypmt2dQ9o1wBMwuXa0HOVGX0GvZ0wpyNQUrTM5mtW2UAM6RNpweJnbLYsMtQEqYFV7sptqlxrOkwKJJyGHcQGsRRylLTKUNgVkiudhMKKTnWdBAUowAm9dEsyCBhaeykpJSS672UMBXM1y08Ry9XwPTc5UqQMVvTJk5Kbgc520HGVMWiXTXpJhGJaWAKh/VeQeJlzNXtctVrc5iy1ZOcatZ4fHGK01M11VT1Y0qFMEVRlCMmFwXXgh7Pblxha5ARhzaXtzMWG3ZZM7X61pu8fukaojHPiXPnRuFltw+YUdh0sgTJqFeXwX4giVOBUWnwN3797/PmGy/Rv3adEyfOUjSXy9OJe+VHQkqSfDSqZ5hlxFmMrmnMOlUuhylhWpAWk79GokwQpoI4L4iisNyC3MkEQZpRdcTE9Wkxut42C7birNyCLFKD7TRB00BI2GsDluaj8UcSnZ00LvuAuZpDt59g6KN+ZnshLN8dl1QIySBJCYiwNIO2U+FymJJkgjSX+Pbea957DQVhrtMTQbkFeT3Od1/z5GtIckGQCipOwUYUoxU5U5U6QQxBmiA1AVZKmkHFcDlVrfGnTrVYbvmqqerHnAphiqIoR4SUku0k5Fvb1/j99Q2M1Mc3fKZ9nZpt4tkG/c4Ozzz95MQJx7P/0WP8hZ//OeZm5phyfAohOdayMXV9YnWlYussNR3CIiMQEcfPnmN2twi/F+VIObpmj2XoLNRtgjwjFBGmsT+KaKFhk2SiDIV75uo2FUcnkhFS7jdina4KPEs/MMan7VuYOoQiIcz3RxEVhkQDnBtmTdZcg2Mtm1CkhHla1oAhYanpoGmUhwdg9OeFhk2UZwzl5CzIhbpNLgQ1d/IU6ELDJkgLIkI0ud+Ida6uU3eNiZU/gOmqhWNphCImKQpmbQ/LNInNGNctsHWbpl5lZbbK2ak6bd8+0CxW+XhSIUxRFOUIGGQxb/W2Ob9+jdWdhKZRZabilfMQ98LL//D0kxNNVgEufuPr/PbP/Az/3T/6X4BRkGj5B4vfNU3DtASZiA+MIroxTO2xbUmniA/MgqzYxqGzHW1TI8viiQAGk6cMx5kGCCNDyMlZkIau0a4cfoJSswryJJ0swteg6d/kNViSroixtclZkKPwdfA1OJZGJ48mAhhMng4dZ5kaeZYgRE7FcEiSmK1hgK+7fGqmzbl2nYW6R91Vq17KJBXCFEVR7qCkyLkSdHlpe4uXNjoMIsmi32Cu6h74hX2rHl+vPvccq29cnBg/dCM1C/LDOQV5LezTTSJ8wyHNDJqaz30zc2rVS3lXKoQpiqLcAYUQrEcDXuls8vZOyNVeTCE1zjTr1J3RicC9rvF73q3H19qlS4eGMClHPahuDGBy93TkYaszcZGxEQcHAtiNz2lPLgQb8fBAALvZ9UIKtpPoQAC72fVSSnp5ciCA3er6KM/YTIMDAexm98lEwXo8PBDADrteMpo1uRp2kYXBnD3FYrXGuXYdX0SsLMyoVS/lXakQpiiK8hHrJCGvdbd4fafHZkfwxnbIVA3OtPdnCnbDnK0go+WbTO1uy71bj6+8Mc/13ujE4l4ASHLBm9sBgzxmuemUASwrJNe6CQCLY6cJCyG51AlZj0Lm6mbZiFVKyVo/I8oKFup2uU0KozmKl3oDGhWNE/V6GcC2g93ZkRVrYquwE2a8sTPAsQUrrWoZwAZxwcYgpeoazNX2+2pFacHrO0MymbLS9MoAluSCa70UUx91tt+rHcsKyVvbATtpyFLDYa66P4z7Wi8lF5LFhl1uj0opudJLuDIcMFU1OF6vlwFsfZAyjAtmazY11yhPOK71Ejb6OQ/PzHD/XItz07VygPb2dqYCmHJbVAhTFEX5iARZwtuDHV7Z2WGzVyBzg+vDgKTIqZn1iYaeUSbohDm2oZUhbPnMWU595lHe+v0XDvT4uv/Rx3HnTjBICmakhbmbAbajhKuDAN8yaY7NgkxzwTApyj9bxihUDbOMy4OAQkDT3J8FmQsYJAVBUtD0RBnCciG41BvQiTKWao2JLcggKeiEORXboLl7m0RyLQjYDBKO25WJLcg4E3SjHIDZ6v7q03oUsTaIaLk2LWd/CzLOBIO4wDJGoXIvhPXihCvDIaZm0LQq5QpYVkiGSTE6Jervt+JICsGl/oAwKTjZrE2sgA3jgk6UY1qSGEGag6u5rFSqfN+pJifaFZqepUKX8r6oEKYoivIhS4uca2GPC9ubXOkk5KmFb9sEZkirqjFfrTNfnWys2vJNDJ1yZiGMGrH+P37u5/jtn/kZ3nzh+fL2Rx5/gh/9pS9ROA62oZVNRodZSiIjjjUd5vwq/tjAZ9/WmW/Y5Z9hVAPWy0NmayZT9uQsSMvQmKtZpL5JfbeIv5CSjXhIo6KxVBvVsY2brlq4lk7dnRxFZFkF52arLFYmu/k3PIMV3HJ2JIxqwISWcqLtMe/7WPp+fVXVMcoVMHe3YH7vFORC3WHGrVAb+/q5ls58fXJ2ZC4FW+mQdkXnZLPKtL/fHLZA4rgFLgIpdOpag2PtCmemakxVbFw1QFv5DqkQpiiK8iERUrARDXmls8lbOwFRrOMaDs2qzlYSkIqCpWrt0JE2rqXjjt2+1wn/2Pw8f/e3/j9ceelFrq9vML+ycmgd2F4RvmdZHK8fLMLXtP0VNpgswj/Xbh5ahD9+gnK8CH98C3Kcbxv7TVDHivBnK5VDi/BtU2emtv9594rwa7bDdP1gEb6hTzZ+3SvCt3WT5anDi/DHm6yOF+GfbjVw9P0ZjsFeN3vd4/hUhXPtGgsNj6ZroasB2soHRIUwRVGUD0EnCbnY2+L17R69IRiaxZRnYBjabc0UHHfYLMjlU6c59tAjh16vTkG+91OQhqbTSSPSQiBynbZdZ7FV5ex0lamKg6dWvZQPgQphiqIoH6AwT3m7v82rOx3W+znkJnXHwLON2x7qPO5Ww7gPowLY7QewQgjqtssgS8hycDSXBbfCmfkqSw2flqdWvZQPlwphiqIoH4BMFFzd7fd1tRuTpSZhqqNTUHPNiQBmFA7bA4FWKSY6yPeinG6U0/RMGp5ZBjBdWgwDHb3IJpqwRplgc5Di2QYzVasMYJo0SGOLzTxjtrpfNJ4Vko1BOho9VLPIKUbDuCXIzGE9zZit2eVJSSklG8OMNJfM1ixMQysDmJ45bCYFMzVjooFpJ8zoxwXTFQvf0csApkub3gA0L5/Y1gySgq0go+4atHyrDGC6NAlDg608Z2ZsyzHJBRuDDNvUmK1axEXORjJEEzp5brORZczWrLJIvxCSjUGGkJLZmo2mS64GPYZZQpFadPoZp5t1TtdrnJ2qMVuz8W31q1H5aKh/aYqiKN8BIQWbccArnQ3e3h4SxgaW7tDydPpRQj/O8RyDXp6UK2AXvnWRt954mwcfOMWnPvlg+VjDpGBtt+VCrqflCliemFzqJwjBRAgLkoLNYUbdlTiWoJOFOIaJLV0udRM8S6flmdjm3mnCgu0gx9DAdSSDIkLXNOqmz2o/pZBQdw0sY/SrISsknSAnygS+rZNoEZksmLYqXAsK+nGGb0+GsG44ek6OqRHKvFwBCwKNa70EU9cmQtgwKVjvZwgBmlWUK2DkFpeHCXVX0vbNMlSFqWBrmOFZOq4l6RYhlmbgmz6X+wmGVtDw9mvRklywE2QUEgyjYCsfYGsmU3qL3LB4aL7BJ5aaTPkWphqgrXzEVAhTFEV5n7pJxBuDUd3X9Z0cR7dp+2bZIX26alJxdGIZI4sCO0z54o/9eNn1/l8AD3/2CZ764pept9o0PBNd2wtgWbkFGWkFyy2HijMZEmquwWLDQWh5GcBmvSpFAQuN0RDrvVUtGBXKz9UtclnQy8NyFJGOxmxt1P3dH+v/ZRkaMzWLOBcEIkTq47MgM6qOMTF3EaBdMXEsjUxLScZmQZpeMQp8N1xfdw2WWw65ntEbG0WU5pLFxuiAwvjsyKpjsNCwKWRBJw/KWZBImKtb6OyflASwLA3Pk4RZRlhonHBnmfEqnGzVWGy4NNQoIeUOUiFMURTlPQrzlHcGO7y6s8NGv6DIDGZ8byLAADR8kyQaIouCKafCF3/sxw/Mfbzw3Hm+/PRTfOErv0nVMUhISMcCGIBnGxPNUfc4pk7Nh+0kmagB0w2Yrx+sNzN0jWZFZz0KMbXJWZAztcNnTTZ9k/VocGAW5GGzKQHqnkGqJWT55CzIqmNMtNvY49kGiTYZwDQ0HHM0SPtGlqHR8DU2khtmQWpMNHiNi5wgTxFC0nIdjleazFUrnGxVmK2pQnvlaFAhTFEU5TZlouBK0OHC9hab/ZQoNnBNi6mqcWA15cYi/M47l2469/HF819j9Y2L+MtLqgj/OyjCF0jCLCUSOZrU8fFpuC5L1QorLZ+22nJUjhgVwhRFUd5FIQSb8ZAXNq+wNkhJIhNTs5mqGGVj1HGHnYJ85V3mPl588yKn56dvO4CFecZ2kagABuUooUIILGyaeoO253G85bNQc6mrLUfliFIhTFEU5SaklGwnIRc61/nWxhZ66mLi0HB0Bolge5gxVbUmgthWkHI9CPAcybxfLdtQVGaXbvm5zOnZiQAWpAX9qDhQdxVngqv9CD0N8Jq1MoCluWA7yHFMjfZYE9ZcSK73Y3bSkHbFKANYISTbQQbAVGX/NOHeicj1aEjVgwWvVgawnSAjySVTlf26NxidiLwWBFhWMdGIdRAXDJOCumdQGdtODdKCK4MQQcp0xS0DWLw7qsm39Yni/TQXXOtH9IuI6YpdBrAgz1jthugaLFSqtE2f+YrHSstnpuqojvbKkafWZRVFUQ7RSyO+tX2Vf/bmS/zuxevEA4+a5e22S9DYGmas9VOC3fmLAGkhuLjd52o3wZFuGcCklNhzx1n51GNw44qMprHwic8wvXJ2YgWsHxVc7sTshNnE5WvDiNe2+kSpxoy7PwtykBRc76dsDjOyYn+uZCdKeHWrTzcsaFnVcgUszkatHjYGGXEmyuvDTPD6do/1fkpF88sAlhWSzWHG9X7KYOw1Cyl4Y2fApZ0IXdoTK2A7YcblTkw/2r8e4Mog5OLmkCTVJ1bA+nHOlW7C5jBDjs3G3AwSXt0eMAglU3aVpMjZSkI2g4TBwKBOg4fbs/yRk7N89sQUyy1fBTDlrqBWwhRFUcaEecqlYYfXO9u8vNlloyupmVVWZj0sQ2f1jYtcv3QJa3qRmZXT5dxFgWQ7GeI60HIrzI7NUdQ0jZZv8pd/7pf5p//dT/Lyc/u1YSe/6z/i+3/651muVSeeR9UxmK/bNNyxdg5ZSkLMfN1hyTEwtMk5itMVE88y2FukSkTOoIhGK2A3zI50LZ12xSz/DKMtyE42xHdhwazR9vYL3U0d2r6FZxVlgb1Esp1EOLbguF05OAty97mPF+R3sxhByrGGy/FGdWILsuoYzNUsKs5+jV2UZ4RETFcsWq5NLwuxdZu6VmOx5vDHjnmcVEO0lbuUJsf/d+M2CSEYDofU6/UP4zm9b1euXGF5eZnV1VWOHTtW3r69vc3U1NQdfGbKzaj35uj6uL03aZFzJejxZn+b64OYt7cjCiE53WxQsSz6nR2eefrJieL6vfYS1VbrPXXCX33jIhffvEhlYY4z9z3wvorwjShCeje/371SA7YW9UmEoO14uJqDr/tMOS7HWxUW6i419+itJXzcvnfuJkftvXlf25FpmnL//ffzT/7JP/mgn4+iKMpHKt/tdH9+4xLf2Fjjnc2MNzYjbEPjvqkmFWsURp55+slD20s88/RT73kUkb+8xOk//MT7DmAfhyL8fprwxmAbIWHZbTFjTnGyOs1nl2b47lPTnJutHskApijvxfsKYY7j8LnPfY6/+lf/Kn/sj/0xXnnllQ/6eSmKonyoRp3uh7ywtcrz69e4vJ2yvgMbUUjD01mp13H00S/51YsXuXD+WQ5sHEjJhfNf49LF19UsyFt4LwEsLnKuhn2244BT/gznKkucbc7yh5an+cMnpjgx5aseX8o9432FME3T+Imf+AleeeUVGo0Gn/zkJ/mpn/opgiD4oJ+foijKB66ThPzBzjWeW1/lne2IrY7OIBKkWoRjgSVdgkiWoWvtXdpLdC+vEyUahZgMaYM4Z3OwXyg/PgtSpBZJLiauj9KCjUFKlI4K2cdnQWq5Q5RMXp8Vks1BxiDOgf0AJiWYhcswmrxeyNGJyJ1gVPi+F8BSkWMVLmEyumbitYU5m8OMQsiJAGZImyw2Jg4BwO4opUFWvrbxWZAiM4nTyec0mgWZshFEbMYBQZowbdY5W1niTH2aP3x8hsdXWiw1vYkTmYpyL/iO/kUvLy/zL/7Fv+Cf//N/zj/9p/+UBx54gH/+z//5B/XcFEVRPlDDLOGlzhrPbV7i7c6Q167kfPNSjKYLEj0GDWqGz/ZAsNbfPzU4v7Jyy8f1Z5a51k0YxPunAAshWetnXOkm9KJ8YgUsT00u7cR0w3zicXbCnEs7CTthPrECZkmH1Z2EjeHkScleNDpNuNbPiPKsXAGr6D7rvYz1QUo6FvSCRHCtl7LWzwjSolwBqxsVuoHkWi8lGAt6aS5YH6Rc645mYO4FsJrlEkV6+drGbQyz8rWNr4BphcXlnZSdsddcSMnlfsCL6z02Bjlto8GKN8sjMzP8kVMz/KGTbWZrzsTYIkW5l3wgG+rf933fx/d8z/fwhS98gR/4gR/ge7/3e/m1X/s1zpw580E8vKIoynckLXJWgy7vDHcYRAVBqLMV5ASpZLZu0i9CAOa8KprQqTkSXQNrd+Vl+cxZHnr8CV56/jyMrxRpGg8//gSn7jtHWsiJmYW6NprtqOsQyQQxNopIZKOVJc+e/P9g39Zp+SZSKyZGEQ21gqZvHhj741k6dc/AMiUb8RBjdxZknkPdE9iGhjk2O9IxNWq7j9HJAoQ2GkVk6SZDJ0HI0TV7TEOj6hjYpkYgYgoxGkXUMB1SN0PXmRjeDaMTjoWQpFpGPDaKqCtyWr7Et3XiYjTYWwINy+FkzeN0u8aDc3WWGi5199Zboopyr3hfpyP3bG1t8eyzz/Lss8/yta99ja9//euEYUi1WiXPc37+53+ep5566oN8vrekTkfefdR7c3TdC+9NIQQb8ZA3+lvsxDFpZLITCEx9NDg6l4L1eIiQkjmvWtaASSkPtDsYnY58igvnv1be9shn/xBPfvEZ6q32ofcB2IoDgjw9UAN2s+sHacJOGh6oAbvxei0KkZ5PXGRsxMGBGrCbPX6++zU5rAbssPtIJJtRQFRkB2rAbvY5OmlEP40nasAkkmGaEosCU9eo6j6u5jLtuyw3PGZrNr59bxTa3wvfO/eqo/bevK9/8VmW8dBDD3Hx4kUATp8+zXd/93fzF/7CX+C7v/u7ue+++/id3/kdfvRHf5ThcMjP/MzPfKBPWlEU5d3sJCFv9be5Hg5JE43OQKeQgoY3GjV0swAGHBosqq0W/9Xf+3UuXXyddH2TEydPs3zm7C3vs52Ehwawm10/zNJDA9jNrk9EfmgAu9n1hZQ3DWCH3WevBuywAHazz9HN4okAVkjJIIsppMTVLKbMKhXDZq7qsrw7z9FS8xyVj6n3/b8df/yP/3F+/ud/nu/+7u9mfn7+wMf/7J/9sxw/fpzv//7vVyFMUZSPTJAlvDPssBp0CGON/lAjyiR118DZ3V7MpWAtGhwawA4zPgvyoQceofqJO38KMhU561F6ZE9BVk2bnSRC18DTXDzDo27bLNV9FhsuDTXPUVHeXwizLItf+ZVfedfrPvnJT9Lr9d7Pp1AURXlPst1+X2/2t+lGBYMhdKOClm8yUx39qCuEZCfK6GYBjqWVAUxISS8qMHUmek9JKenGOVtxgGUKpt39WZCDOCcX0PAM9LEwESQF62GAZuTUHbcMYFFaEGWCumdOzJqMM8FmEJNoMVXbmpgFOUhG3emdsVOBuZBshQlZGKJXvYlZkP04xzZ0KmO1Y0JKOmHOTjrEtikDmJSS/u5BgrprTASifpyzEYUYZk7L9csAFiQFaSGou+ZEsXyUFqxHETnJ6HYJmRB4wkMWJsvtGidaPjPVe2fLUVE+CB/qd4PjOKyvr3+Yn0JRlI+5Ub+vgDd6m2yGMXGic2Wn4EonYaFu4Y/17nrppVf4+oVXmFo6xn/2h76rXAEbJgVXuwmWoXHC1MvQE2QFL230yIqCh+bqZQBL8tEpw6yQGLpDfTe45ULyymafTpxwesovA5iUkuv9lF5UcKzF7vzJkXc6Ae90A5aaDqca+ytg20HO9X7KdMVkpb2/CrU+jHlpvceSXXBuen8WZD/OWe2keJbOyWm3DHq9OOfCRhc0wSfnm+UKWJiNXgOAaTjlgO04L3h5s0+QppybrZUBLBej05NRJlhuQcu3ytf28lafy70hx9su97db+JqHbzrUHZNzMzXavoWpthwV5YAP/X9JfP/dl+EVRVHej24S8UpvnY0gJEo0BoGOBOqOsVvoPVrh6Xd2+NLnfnyi4/23Hn+Cp770ZeqtNpah41k6pq6VKzwCSScN0XXBrOPTdJzyvoau4Zo6pi4n6pk6aUih5bRchzm/Ut6uaRqeZZALsMdOKw6zlEgmNFyThUplYgvSMTUqtj7RmDQROb0sxHcMZlwHS9//mL37GlxLZ2+RqpCSbhpgGJIZt0LV3g+k1u5r2PszjLYgu2mE1HKmfI9p1yuv17X9GZP27msejRUaUJBzpt7mVKXOklthqekxX1Nbjorybr6j05FHjTodefdR783RdZTfmyjPeHOwxYvba5BbRJFJmo/qvvYaeuZCYmijAPTf/KW/yEvPP3egvcQjjz/BF77ymwBlo1VD1yZqwFqWT8WyD/SqGr8e9mvAfNOmZfsTW47AbnNUytv3asBs3WDKqZbBZlxWSEx99BrGO+FPO1XcJIaxoLf3mnUNdE2bqAGbsip4pnXL1zBeA1azXBqWe+B6ISW5lKRFRlQUaFLSMKv4ukvbczjR9pmuOh/7jvZH+Xvn4+6ovTdqc15RlLtGLgquh32e31zl2iCkIuokmUbV1qhXJ3+c7YWdty++dmDmIwBS8uL5r7H6xkWWz5ydWAG7nVmQ4wHldorwNU1jrwXX7RbhW7urZoeNItI0jRv/D3rvNd9uEb4xtgL2bkX4uRQMsmTU30y3mTar1CyHuarLsaZH2z8YVBVFuTUVwhRFOfKklGwlAa911/nm5jpxbOLLGpqlM10xbrrllUvBa2+8dsvHXrt8uWw1cbsBbNy9PgsyLnKCPAUp8XQf33SZckfBa67mUHPUlqOivF8qhCmKcqT105i3Btu80d3mzZ0hUWRS0T2adQtnrFt7ISTDpMCzdGxTL9tQtJaWbvn4rYVR6cJ4AHNw0cTBLbUgKRBy1Akf9gOYiYUt7QPNS+NMkORi1Dlf08oApmPg4iIFE8PjskISpgW+bWAZ2sQsSF/zEAUTP7WFlAziAscc1YKNz4L08UEcDGyDuEDXoOIYEwHM0myMwgJrFMyCPCPKc9JMYkuXhUqFhZrPSstnyrfVHEdF+QCoEKYoypEU5xmXgw5v9Tus9SPe6cTo0qJiePTDHMfMWGzsF8tvBxnXeylN32SpZZd9wI6feoiTn36Mt//g6wdqwk59+jHk1AppIdhJA5Iix9NcdgaSnpZwvO2WxehxJrjSHY32WWk5xCQMswRXt+kPdbp5wrGWQ8Mb/VgVUnK1lxAmgqWmjW3LcgUsjkwuhQmLDZip7a9UbQ5TNgcZMzWLqZpRroAZhctqN6XpC46PnZTshjlXuym+o7PSdtjcbcTq4bPZL+iZkhNTbrmtGeyeAtU0WGk7DIuYME9xdYfuADoyplUDywATk4qsUNFNHp5rcHLKp+Fa6GrLUVE+MCqEKYpypOzVfb3R32IzSLneydkMYxqeyUqtwfYwIzb1A4Xvhq5hmzq6zkQj1jiBP/eF/4F/9t//FBdf2K8Ne/Cxz/Kn/tYvYBiwGQ/JZcGUU8HApKcnuwXu+4+va6OaKyGhm0XkjGZBNi2PMEoQUpuoidIYnTq0DI24yBiMzYJcS1NsU6LfsJhk7r4GgWA9isotyF5YYJt6GabGX7NlaBiaxkY0IGc0C7IodLqGwDS0ydewe/pT0ySdNCKTo074pjS4WgzJC8mUqNC2ayxWfVaaqreXonyY1HeWoihHwl7d15v9La4OAoaBzno/JyZipmox79cxNI3pqkXdNbHNyUDS9k0cS2MnHW3f7TVidTx4+PQCn/rHv8W1t95g7fJl5ldWWD5zljgv2E6CMoDt1YAdbztomjYRemxTZ6Xtsp2EJCKbqAFbbjkUQk40VdU0jaWmQzdJGBbRRA3YXM2m7cuJYdkA01ULxx4N1zY1vawBa/sald0tynENz8QyNbaTYRnAPNMCE05MjU43jgdDz9JZaY862ScixTEtMlGQCMGJep2243O6XVOF9oryEVEhTFGUO26v7mt12KcfSIaBTipyNCehqdvMeTWM3VorXdNwrUPmIiLp5gFoHBhFtBeOls+cnSjC72bhgQAG3LTeqZ+PwsuNRfimrh1YmQOIiuxAAAMOhKM9mSzo5SGmrk8U4Ws3e81S0s0ChCYOFOE7h7wGiaSThWynIVXTxhAWVc1jrlLl9FSV2aozMTFAUZQPl/puUxTljhmv++oGOYNAp5AajiUZ5jGOZk4EsJv5TmZBflxOQcZFzmrQRQjJgt2iaVdZrlU4OVVRhfaKcoeoEKYoykfuxrqvIDCIMx3X1CmKjO0sxNb3A1ghJG+++jqd66ssHj9ermYVQjJMc7p5gDa2AialJEzFgToqKSVBVtBJQwSTK2BhWmDo2oEVpCgT7CRhWQO2F8CSXFAIiW9PnqJMc0E3SQhFhGvuz4LMCkmaC3xbnzhBWQhJL0np5yGmoU/MgowzgWvpE6tmUkoGSUEcBWSuNTELMswE1m5dGeyfctwJE7IiZ95t0XIqHG9UWd7dclSF9opy5xypEHbhwgX+w3/4D2iaxqOPPspnPvOZO/2UFEX5AN1Y99Uf6sSpjmdpTPk6F7dCrgyHLNQdlqdGAazf2eEXf/zHeP2F58rHefizT/DUF79MYFR4dbtH3dV5eK5ZroDthDlrvZSqY7CyW98F0I1zXtrooeuCh2cbZQAbJqNZk4aucWLKKUcRRZngwnqPKE85PV0pA1hWCC7vjBqXHms5VHcHZhdCcnErYCMMOdZyWKmNApiUkmvdhGFSMN+wmarsr1qt9iLe6gyYrpo8ONMoV8C2g4yNQUa7Yk6cAt0KMl7e7DGtZ5ys78+C7McF13oprqmz1LIIi5RMSrJEYziwOdFs8+hSi8W6R12NE1KUI+HIhLA//+f/PK+99hpPPPEEQRDwkz/5k/zAD/wAX/nKV+70U1MU5QOwV/d1edCnN5S8s5FTcQyOtRx0TSPMUnbSEFMzmHEr5RbkM08/yevfeH7isS48d55nnn6SP/N3n0FKyZTr39YW5FYckBUFs85oFNG72UlCojzFM23at7EFGWQpvSzCMgym3cptbUFuxyGapjHjVm9rC3IzHh0kqFvOgS3ItCiIZUo1yamYPg3Do9G0OX2qykLDUaccFeWIOTLfkT/6oz/KE088Uf79h37oh/jP//P/nJ/4iZ/gwQcfvIPPTFGU78RhdV9RBotNh7o76nYf5RmbacBSw6FpVajtriytXrzIhfPPHnxQKblw/lm+9/o7fOrcwzScyUDV9k1cc9S0VdP2Z0FapuChuTpNx5nY4ttbMRu1fNhdidrdgjw9XaHt+HhjjWEtY9SXa3w7cpildLKQ5ZZLy/apjM1P1DSNxaZTbkfCfg3YdM3kTKtC9YaANFWxqNhG2adsrwbMdyWf9Jq0ZFG+tiBLSciZqxvUzRp122Ou6rHS9JipOqreS1GOqCMTwsYDGIDjjLYQbPvd/29VUZSjZ6/u62J/i62xuq+aY9Dy90NBlGdsJEMszWCuOlmEv7Z6+Zafo9japv1J58DtmqZR2Q1y40X40271pkX447Vdt1OEP147djtF+JahYRmjzzFRhO8fXoRv6PuvYbwIf86t4ZkWRTCgk0QIJL7hMGtX8T2H+Zpb1nupFhOKcrQdmRAGcP78eX73d3+XjY0Nfvd3f5df+ZVf4cyZMze9vt/v0+/3y79fv379o3iaiqLcwl7d1xv9La72A8LIIIhHdV8zNwzZnghgh5yCnF9ZueXnWj5+8pYfv9dOQc7YVQSwmQR4RU7dq2NJh7rlsNBwWWq4NFS9l6LcNY5UCCuKgiiK6Pf77OzssLGxccvrv/SlL/GzP/uzB27vdDp4nlf+fTyoKUeLem+Orvfz3gR5wuVBl80oJI51onTUaX7aMZA5ZJnE3t3uS4qcfhriSZ2GbWLG0cRjFUIyP7fAw48+xoUXDo4ceuTRx1hZWoIoBEbhLytk2YNLIOkmEVqR0zIcfC1Dy/OJz5EWAkPb79nVS2PyPKWmWzSQaCKcuD4vJBJZblmGeUaQRlQ0g4ZtYkQHX0Mh919zKnJ6SYQroWZ5mFE0EZiElOSF3O10r436gCUhsshwMEiyPr5psqB7aLmgUmjMVmCqInGtlCJM2Zl8ysodoH6uHV0fxXszNTV129dqUo7/ZDs6/uAP/oBPf/rT/Ot//a/53u/93kOvOWwl7LHHHmN1dZVjx46Vt29vb7+nL4ry0VHvzdH1Xt6bKM94e7DFt7fXiSODtY7Es03mahamPmoxcbmTkOaCpaaDYQg2kiEmOlnsEGeChYZdzl0EuNZL6EUFfjHkH//MT0zUhp36rsf5r37hi5xantt/vrunCRuewXzDLlfALOHQD8G3dZZb+yclh7tzFG1TZ6Xl0M2ichh3GBro2qgT/l49VZoLVjuj2ZHHWg4FOdtJgKkZJLFFlkuWmvsnJaWUrHYSwnT02lyHcgVMpg7DWDBbsyZOSm4MUraDnKmKSbticinoEGYpjvTJU4s5z2elWaXh2bS1mJNLczjmwUHjyp2lfq4dXUftvTlSK2HjPvWpTzE1NcUf/MEf3DSE1et16vX6R/zMFEXZk4uCa0GP5zavsNaPMLMq72ynOKbOQt0ou8gLOVpFygtJkGXEeYSlGcy4VS5HCVkhKcTk/w/u9dVq1hv85V//Na69+Sb5xhbu1DGM6RXc2uTJwEKMrk8LbWILMk01touUTEgklJuFhRg9H12TbMUh8W4nfBeHvhjNjhx/SkJCLiRCwiBNiGSMY5i07QqXgoT8htcggUyMVueiIqcXxeUW5NU4Jc0FQtz49ZREmWA7jujIlLbt8XBjkbVeQapJHpqrcW6mxlTFptvZUQFMUe5yRyKEra+vUxQFi4uL5W0vvPAC29vbPPTQQ3fwmSmKchgpJZvxkJd21vnm5gZ5bFHVa1QdgwfnTYSkLCpffeMia5cv01o4xszycUJC7LFO+EsNhyQX1NzJQLFQt6m5OgMRokn45EOfwHnEJCskYVocaJI6VbGwTY1BEZEU+41YhSnLJqz62NZf3R2diOznMfENsyCPNR10jfJkIoz+fKzpMMjSMoDt1YAda9rkQparYDAar7TUcBikKYGMJmZBLtRtmp45cX1cZOhWTrsumfMrzPmLiFzH1HQeX7Y51vRVvZei3GOORAiLooj/4r/4L3jkkUc4deoUV65c4Z/9s3/GX/pLf4nv+77vu9NPT1GUMb004u3BDt/e3OSdnQBduCxWRm0lxgNCv7PDM08/ObGNeObRR/lLv/ALLC+slEX4rqVPhJ09ugEh0UQnfBidMhzfttyj6RATH+iEr2s3uV7TSLX0QCd84EAg3CP1goT4QBG+Zx9+vWYIIuIDsyDt3fYZBZJeFpMWBZ5pc6rWxNUcskLHkjor0x7zNYeKcyR+VCuK8gE7Et/ZJ06c4IUXXuD/+D/+D1599VWeeOIJPv/5z6tVMEU5QqI849Kww8XuDm/tDNnoZ9Rsj5PtKsYhJ/yeefpJXnru/MRtb7zwAr/901/g73zlN2/5ue71WZBxkRPmKRJoOh7T1QpaYZIWEscxOTcz6u+lthsV5d52JEIYjPqCff/3f/+dfhqKotwg2+339Wp3i/V+zLVuxjDLWah5zPkVNLSyFmrvlOGtm6x+jcsXL7J4+kxZM7b/YUlSCLbS4UQAk1KSCybmQO5JC8F2MiQVkytgWSExdQ5s3+VC0klDwjydCGA3voY9hRg1Q+1k4UQAu9n1Qo5qwLaTYCKASSkZphmxzLANg6VKg7rpkqQaaSKZqdqsND2mKqq/l6J8XByZEKYoytEipWQjGvBad4vLvSG9gcZrmxFSzzg3UykDWJoLrnZTAJaaNrapv2uT1ZdeeZOkcYzFhl3WjgFc6SVc6g9oV3ROtxrlCtjWMGcnzJipWrTHThNuBSkXt/u4Dpxt18sA1oty1gcZNcdgobG/KhYkBa9s9im0nFNTfhnAokxwrZtg6hpLLacMh2kuuLgV0MsilltuGcByIbnaSciFZLHplN30hZS8sxNydRgw37A42ahjaBrDPOXSTkieGzwy22SxViVOJEmmMVO1WGn5ND1L1XspyseMCmGKohzQTSJe729ypSPoDiV5btJLY1KRUjFtmraHtrsdlwlJnIvyzzbv3mS1Nn+MKBOkhaCCsXvfgivDAWFScLI5uQUZZQVBKqjn+6cPBZLrQUAvzmm5k1uQaSEJkgJTH4XJvXCzHgZ04oSW69Cy97cgs0IQZQLLGK1w7YWwbpKwEYZYhkHL9sstyGL3NWeFJCtEGcKiIufqMCAtoG76hHlGUhRUTZMZp8GggDw1yXONk1M+czWHqqr3UpSPLfXdryhKKczTUd1Xp8P2xoDIqOKZBpqdYcqM422fWc/HHatV8i2dxd3VJn83jEyfOMGZRx/ljRdeONhk9fEn+PQnHyAtBHV39CMol4L1eMhU1eBks8a0PzmKaK4+WjGr3TCKyHMkD83Wma26E9e3fBNDA8/SywC2nYRoRs7pKZ85vzKxFVp1DJaaDqa+P45omKWEIuJYy2HarUzMgnTM0WvOBeUJx0SMtiBn6gYWFugZvulzrNKC3KCpS9qzFqemK0z5tprnqCiKCmGKooxWoa6FPV7tbLHWSwgiHV+YTNUsBkVCP4mpWg7TNb9cAduj3XD6cG8U0V/6hV/gt3/6C1w4/7XyY488/gRPfvEZKo5RroCNF+Efr9cPLcJ3TL0MR+NF+PP+4bMgTV2b2LbcK8KvO+6hRfi6ptHy9z/vXhG+a1qs1A4vwq+5+9cnIudq0CPenU854/pUDR9dmljCYKE1mumoWkwoijJOhTBF+RgTUrAZB7zW3eRSNyAIdMCk5RnYRkGnSOglEb5pM+0eDGA3Gp8FeWxhhb/zld8s+4TNr6ywfObsxPX3winIYZbwTtClYtg8UJ/D0Vxs3aLhWCw3XaYrDq6lTjkqinKQCmGK8jHVTSLeHGzxRqdPdygpMoOaa5b1TYM84bWLbzG4dp37ztyHdkOAGq+1gskANutWyz5gy2fOluFr/D7jAWzWrRwawMavHw9gbds/NIDd+Jz2AljFtA8NYDdevxfAbN24aQDbu09c5OwkIakQ3F+Zw9VdarbDfM1hoe7SdC10dcpRUZRbUCFMUT5m9uq+XtvZZmsgSBMdKTUGSY6Q4JoWVzbX+B+fforXv/GN8n4PP/4ET33py9RbbdJccK2Xomuw2HTIRM5GMkSXOmlscyVKWWzs1z0JKbneS0kLyULdxjRhLRpQCIGWO1yNcuZr+sRJyc1hRi/Kma1ZVF2jDGBG4bDeE+SVjJa/v+XYj/Pd2ZEmM1WrDGC6tOgNdfI0Za62fwIxzgTX+ym2obHQsAnzbNRWAoMosrgSJyw2909K5kJyrZsQ5jn1CjiGzrTVoKq7OKbJfTNVZmsOvq1+rCqKcnvUTwtF+ZjIRMHVoMurnS2udhOSxMA2TKYqBp0wZxALpCywnYLf+PzTvPn7vz9x/wvPn+fLTz/FF77ymyS5ZJAUGJpGP0kZiBBLM6ibPpcHKYWUJLlkL4/khWSYFMSZoObpxFm0uwJW5cpOzjApaLhiIoQNk4JumFNxdEIZlVuQvUDSCVM8S6c1trgVpYJumGPoGrqVlVuQMrPYihKEgJmqxV6rsTgT9KMc19LpJQn9PMIxTHzN41I/wdA0slxg2gYSSSeOuTIIkUJn1m2yXKmy2PBZqru0fNXbS1GU906FMEW5x+3Vfb3a3WS1G7DZBSkNFup22fy04ZlICQkpF199hTe+/vWDDyQlL57/GqtvXGTp9BkWGzZJkdMvAmx9NAtSB+brFhKoOPun/2xTZ65mExcFgQgnRhHN1zXizDwwWmi2auHbOgkxcmwWpOEX2IZO1Zk8Xdj0TXRNIyZhmO2PIspMiZTgWtpEUKq5BotNh1RkZQCb9aogYbFhowGWpdHdHStkYnJfa4rlWoX75+rMVlV7CUVRvjPqJ4ii3MM6Schr3Q3e6Q3pBxp5ZjJdMcq6rz2WoWG5BWGSMlxbv+Vjrl2+zPKZs/gODJN4Yhg3MHEqcVzV0xlGAZqcnAVZc01q7sHrPWc0vHs8gMFoTuNhsxodU0e3Y0Q2OQvSMjRmagefk6FreI4kTJLJInwNGr7BIE3YiVNMaTNj1zlWq3CiXWG6YmMZqr2EoijfORXCFOUeFGQJF3ubvLSzTRTpZImBaxm0qvqhLRK6WVyegjx3+twtH3t+ZWWiCH88gN3M3XIKMhE5wyylEBJbuiy4FU41qxxr+rQ8VWivKMoHS4UwRbmHpEXO5WGHZ9dW2R4KbOFh6TrtinnTmqXxADbt+mhnzvLwZ5/gwnPnD220On3ixD0XwJI8Z1ik5LlGVfeY8yqcna4zX3Mn+oEpiqJ8kNRPF0W5BxRCsB4NeGHzKt9e38IqqjTtKnXPIM4EV7oJDdekOdaQNEwL3u4GFKQs1L2yD1icCf7sT/8ixX/zE7zywvPl9Q8//ln+1N/+73l5q8tczS4DWCEk64PR7Mi52n6BupCS6/2EtXBIq2KwVK2Vw7i3hjlRVjBXt8smrDCaBXm5P6TiSpaqtTKAdcOcXpzT9i1q7v5W5CAueLs7QDdzFmr7syDDtGBrmFFxDKbGtkfjTPBOJyCSCXNVi6ppsxkFpJmBTB3Otet8crHJTFX19lIU5cOnQpii3MWklOwkIa91N/n6tTVevZ5Qtyt8Yr5ahputYcFafxSSxkPYtSDkUidgpuIw5ezPggzSgtiq8Vd+4dfIg222rowardrzy3x7rYsbQ6u93wcszgQ7QQ5AwzXLE45hVvBGp0+aS1bq+ytguYCdMCNIR6ch955nIQVvdgZsBykPuvWJFbBenLPWT7ENfSKErQ6GXO5GrDS9iRWwYVKwPshoFZK2v9+lfjOIeaszxLE0plyboRBMO02W21XOTtVo+xamqvdSFOUjokKYotylBlnMm70dXt7e5o3NkH5UMFOpcrpVnVhdqnsGK7jljEMYbUEKLeV422OpWkHX9q+vOaOTk9VCUFs6y8lzZ4nyjOvRgLm6zZxXLWdEAriWzuxu4bu7e3suBZ1sSNM3aNs+LW9/FqRlaMxULeq5nJgFuRkHVFzJtFdnvupNvNa2b2EbOnVv/zVsJyG6kXNqyme5Vp24vu6aHGuCb+/XwPXSiK2sR93XmHFrHK+2ONdusNzy1TghRVHuCBXCFOUuE+cZl4cdXtza4no3YydK0XW4f65B09o/ZnizcUF7NWAt12W6eXAUkW3qLDRstChHst8J3zVMjs8crAEzdI3Z2v6q1V4NmATun24cWgM2foJyvAbsRKN+aA1YzTUmVsD2asBmKt6hNWCuNXoNAHGRsx4NGMQ5i+40x2fqPDDTYL7mUlEtJhRFuYPUTyBFuUvkouB60OcPtja40onJM51YFviOpOlWygDW7+zwzNNPcuH8s+V9H/7sEzz1xS8jqv77ngV5NxXhSyRBnhFkGVFa0LIafGqhzv3TDWaqTtnJX1EU5U5SIUxRjjgpJZvxkG9vbXBxa0CSGlQtG2El6EVBw/EnVsCeefpJXnru/MRjXHjuPL/8uR/nL//ar952AEuKnA2R3FUBrEAyyBLiLEcWBlN2nc8s1Dg9VaOtutorinLEqBCmKEdYN4l4ubPJS+sd1rqCluswXzXppBFxnqFLmyjUcbwCzzZYvXhxYgWsJCWvPHeejTcvs3TifgYU1MdaLyT5qLjetTRavkWUZ/TTEM3ykMKhK/OJAvdCSLaCDB2NqaqJQLIWDciFwBIu3UAwXZFl6JFSshPmZIVkqmJhGEzMghyEYFXERC1bL8oJU0HTGzVn3QtgmjBJY5OAojwEkIqCrTBia5hTtxxONtucadVZafk0PUvVeymKciSpEKYoR1CYp1zsbvOt9W02+hlbfRBCMu1DJ40I85SG49HpwfVhgq45eLbB2urlWz5ud/U6snFqVBg/FsIGccG1XkLdNbFNyVYWUNUMTN3ncjfBs3RqjoFtjsJMmBas9zMMDRwb+kWIkJKG6XNtJ6eQAt/Syx5bWSHZHGREmcA2NVItJilyWpbPeijoxwmWoeFU90PYTpCzOczQNYdQJuUKWBwZXO7EHGs56IYgKFLyQiOJTRacGv/JyWmW2xU1UkhRlCNP/ZRSlCMkLXIuD7p8Y22dq70UR7NZqFaomAVhWhDLBJHnNByPpuUi/Qxdpzz5OL+ycsvHP37qJE7dou5O9sCqOAYzVQtNF2xlAZZm0HJsMttgumphm1o5ZxLAtQymKqMVsG4WwO4sSBODuKIhpJzos2UZGq2KiZ8LhkWE1PZHESV+hmNqE8O7AZq+ga5DqqXEY6OIuiKjXtGIZcwgcaibVeZrVc6cqDJfd1V/L0VR7hoqhCnKESCkYD0c8ML6Bm9uDdGkyYxXKWc8Tlc1tuKUcCyAAbR8i5a/f9Jw+V263Z978L5DP79n6czUDTaSaL8GLI7QTZ3llnPgesvQmGtYo1OQN8yC3DuVOE7TRvMbR1uQk7MgZ6oWcHC2Y8u3EEZWDuNu2C6dNCSTktmqQ8P0Wa5XOTVVVfMcFUW5K6kQpih3kJSSThrxrY11XtrskSY6bc+nMtbfSiLZisNyC3K8CP8wT33xy/zy536cV8aK8x95/Ame/OIzN73PUT4Faemjla1OEmFLl3mnwvFGleMtj5YqtlcU5S6mQpii3CHDLOGVzibfur5DNxQ0bIfZhok+FoDeawADEFWfv/xrv0rv8irJ2gbzK8cn+oTd6CgGsK04YDMeYusmNdPDw6Xl+Jxs11hquKq5qqIo9wQVwhTlI5YUOW90t/nmxibXuxlFblKzberuZADrRRlrYYhlFbS9/TYUg7ggzgQNz5jodxWmBdeCEKGltFyXlQcfQXtwNAtyY5BSdQx8e79eKs0F68OYoYhouFYZwHIh6YQ5fp5TGWtcPzoRmbKdBlRdvQxgeycfgYkTlDAaT7QeDnFtyYxXLQNYL8pJC0nLNzHHVrJ6cc6b3R1MW7DgNmiZNaY8j+NNn8WGp4rtFUW5p6ifaIryEclFwZVBj+euX+dKN8XWLKYcj6txynqS4Vo6DW/vNKHgta0BnTjlvtlqGcCklGwMUrpRzgouM7X9EPZ2N+BSJ+B425vohN8Jc650E+ZqFivt/RC2Poy5sNGj5Vmcbe7PguxHOVc6CdNGgVOXZUjqxRkXNnsYOixVm+UKWJgK1nqj2ZSuqU/Mjnxls0eY5Tw01ygDWFZI1gcZQVJgaKPu+YnIGaYJr64H5JnFwzNt7m/WOdGqMFdTw7QVRbk3qRCmKB+yvWar//fVK7y0NsDTXWYrPr5tUAhJ1THIhSznLkokL7/6Im+++iYzSyeZO/6J8rE0TaO6e7LRHZvf2M1iClJmKg5L1cpEI1bf1mn55sTpwyjPGIqIlmdxvFHD1Pcfy7N0Gp6BL0chCUZbkN08wLc15jyfqrVfSG+benk6c29lTiDppgGmKVlyK0yNzY409dF8SkMHoQs24wBD16noFR5q1zhWq/DIQp3ZmqOK7RVFuaepEKYoH6JeGvGN9TV+78o19NxhsVKjPlbPZOgayy0HKSWaptHrbPNLT/0Yrz3/fPkYv7c7cqjeagMwV7OZrcryMfZmQS7UPaYcb2IYN0DDM6m7Rnn9Xg1Ywx2tgI0HMADPNjg55aLHEqlpZQ2YaWh8eqGJa0yeZLQMjZX2KGRpmlbWgGWy4KHZBhVzslmq0MD3BUZe4BgGFa2BbznMV1xWWj5TFVVsryjKx4MKYYryIQjzlNc7O/xfq6tc6oScrLaZa7s3DReapiGRowD29a9PfOzCc+f58tNP8YWv/ObE9bAfwN5tFNGNAezdivD3rr/dIvy9629VhJ+JgkGWIIGq6dKw69RMh4W6y7GmR0t1tlcU5WNGhTBF+QBlouDSoMNz19f59loHE4NPTM1Qc259IlAi+fZL355YAdv/oOTF819j9Y2LE6ccbzeA7XmvpyCLD+gUZJRnBEWKoek0rSq2dKhYFseaHksNl7p7sEeYoijKx4EKYYryAZBSshYOeP76Gq9vDugkGQ3Hom74CHGwrqkX5UgJDc8ADbbikKuX3rnl53jj9bdYPHUGQ9fKAKZLE72wyAsYr11PcsEwKag6Bo6plwFMEzqW5pLmEs/aD2GFkPSiHMfSqdgGuRR0kpDctHA1jzTVcMa6Y0gp6UUFmjba7hwPYJZ0STONvoxJRIFn2szZTeJYp2rZ3DdXZb7mUFEnHRVF+ZhTPwUV5TvUiUO+ubHB+ctbIA1MSzJVNajgsd4rMPQU29TL7vdRJrjeSymExDAcYpkQ5inHTpy+5ecpGvN0oxzDLsoVsGGgczlMWWyOasX2bA0z1gcZczWLdlUvV8A06XClm9LyBSem9lNVd/dEZMUxWGnbbCRDbCnxdY/1bkHfkrjWfqF8kAqudhMMXcMyNfp5SFLkOJrNaicmE4L7Z2sseg0sTCq2xYk5j/mGi2Oqk46KoiigQpiivG9RlvKtrU2ev7LF21sJddumWcsRumTWqaJj0LMTDE2b6IVl6hqupVNIST+LyBmNIjp2/0Oc+czjvPH7zx8YOXTuM49z/Ow5IpFSJGm5BSnSjNSW2DecInRMnYqtI7WCjSQstyD7UYFvF2Ug3GMbGr5tYJmwHg+QQMvxyCyLniWxTW2ih5llaLi2jqbBThIQFAmeYWHoGjO+j15YzNt1FirOqNjetzDVSUdFUZQJKoQpynuUFTmv7XR49uoam8OUad/jM8dctpIhBaMA5pmjOqcT7dFq03hBvmVoLLdsNuOQREzOgvzJX/kVfuUnPseF818rr3/k8Sf4sV/+EnkFgiydqAGbrVm0K9bEcG2AqYqJY0m2syG2ZpY1YC3fpOoYmDfkoZprYlkaG7sBbM6rYicplm1wctpF15gIYc7uTMlLgx26WcFSpc6sU8eQNk7FYL7mcKzp0VTF9oqiKDelQpii3CYpJe/0+nztynXe6QY0HYcz7ToSWI8GFIiJAAYcehpSItlJIxKRHRhF1GxP8Xe+8pusvnGRtcuXmV9ZYfnMWbpZTHBIEb6maRzWxzQucnbyAFs3DxTh3xjYYHQKcisZAuPDuEcNWM0bXkMuBb00pptGzPpVZp0GhrTwDJOVlsdczVGd7RVFUW6D+kmpKLfh+nDIc1fXeWW7h2cYnGnVMPTRluJ6NCCTRRnAbgxQ4253FuTymbPlfT/sU5C324YiLjKGWYauQdPyWHSmsDSTumux0nSZqTqq3ktRFOU9UCFMUW6hGyc8d3Wd37++jaHDSqOKo+sIKelEGd00QDdHW5DZYMD/8LknufDcs+X9Hx5rtDpIcnbiCIzJLcgwLcrO+eNbd1Em2IojchKqllMGsCQXxJmg6hgTK21ZIdiJUgIZ4hrmxCzIICnwbX2iA30hJL04o5sHmIZWBjAhJcOkwM0FJpIwS4mKHFc3qWk+lubQMh2mKjbLTY+2r5qrKoqivB+qUlZRDhGmGc+urvH/fuEVfufbV8kzjZPNUQCD0TzGb691udpLmbIqeKbFM08/yUvPn594nL1Gq0Ga89J6n7d3QmzNKQNYmguudFOudFOGSVHerxCS17YGvL4xoMjNMoBJKbnWS7m0k9DZHZq951In4ttrXYYREytg28OMd7Zj1vvZxPVbQco317qs93Nm3Uq5AtaLCt7ZibncDdmIQmzD5Lg/hSfqyMLl/pk6j620+PRSg5mqowKYoijK+6RWwhRlTJoXvLbd5WtXNtgKQ1zdZKVZmVilKqRkJx2CJphxRwFs9eJFLpx/9uAD7jZaffXVl8ga8zQcl/ZYwy1N03aL5LWJMNPLYqIioWLbzHqTNWCmrmEZk9dHeUYvD3FNgzmvOrEFqesatqljjtWC5VKwnQYYOsx5fjmKqJCSYR4TFynTpsmKO9pyrFoWDyy7zNUdfFv92FAURfkgqJ+misJo5entbp/fu7zG1cGQlm9zbrqOhkacibKYfa8GzLbhk/NNqvZoK25t9fItH3/9yjs8eu4cTWtyKPXopKSLlLIcft3NYgZZzMm2T9P2cG+os1ps2GTF/sDvvRqwuZpNq13Fv6H9xHTFpOYY2ObkKKKqq7NUbVK1LHIp6KcxElisVVlyLfw8ZbZSYaXlMVWx1TBtRVGUD5gKYcrHmpSS1X7A+SvrvNnt4VsGp6fqEytJe2HnsCL8PfMrK7f8PCdOnWHG9Q792Cjg3f4sSEPfXwW7nSJ8TdNwrcNnQWpo9LJo1PnequBIl6ppM9+yqciY4/NNdLXdqCiK8qFQIUz52NoYRpy/us5Lm9vYls6JZgVLP/x0360CGIxOMz782Se48Nz5A41WH3jscR68/6F3fT4f5SnIlu0ySFN0DWpGBVdzaTnOqMVEdTRSaHs7VwFMURTlQ6RCmPKx040Tfv/aJv/hynVyITndqlK1RyN/Vt+4yPVLl2guLHPq/nOYulYGsFTkVDUfUzsY1MK04G/84pf4jZ98mhfHGq3e9+hj/M1ffObA9XEmkFLi2aPH2gtgJia+7hwIYFkhSHOJb+tomlYGMF3qVA3/QFwrhCTOBK6lY+haGcCCLMWQFpEuaBgVaqbHXNXd7Wpvl1uiiqIoyodPhTDlYyNMM17c2OH/unyVjWjItFVDFDrDGESwwzNPPzlRXH/2M4/z+V/5FSLXIpMFjvBYDwqGccpK2yk7yA/inCvdFNuo8Lf+4T/ilVcvcOWdt5lfPkVl9jRdNOq5wNkNOEkuWO0kCClZbjmkWkYviXB0i2Gg0x+mHGuOutgDCCm52k0J0oLFhoNry3IFLEtsLvcT5uo2M9X91bnNYcbmMGOmajFVNXhn2CEpCszMJYhtjk3X+cRMk8WGR8uz1IqXoijKHaBCmHLPy/KCl7c6fPXta1wJezQ8k0cX5tka5MTZqG3DM08/yUvPTbaXuPj7z/PLn/sx/urf+3VmnSpxqgHJTT+PRLIdhzRWllk5ew5bWqx2bn49QC9LSGWCb9o0bY8gSAB50+vjPKMv43IL8lqS3uLRJb0sZqcfUdUrrPh1apbLfdM1lpqe6mqvKIpyh6mfwso9SwjJm50+//fqGq9vd9HtlGMNjwW/jqnpzFQtao7B5qU3b9pe4vXnnye7soZ33wO4hsQ2XWxjcph1zTVZaUM3jQ7MglxpOWiaVq6Cwf7cxW4Wk4hkogbsWMuhEBLf3t/y1DWNpaZNP0kZiHBiFuRCw6btm3j2WBNWJLZT4OY5pm6w7B9juV7hRKvCTNVRW46KoihHhAphyj1ndOJxNGbo4k4P29SYqmmYhs+8V8PURiHE0DUqjsHLV1Zv+Xidq9fgvgfQNI2KfbAeTCIJRUJOfmAUkXfI9QAx6YEABkyEtXG5LBjK6MAsSFPXMB1j9xrBIItJcolWmJytz3K6XWOlWVFbjoqiKEeQCmHKPWUjiHj+6joXNjuYOizWPXbSEF0zJgLYuHdrL3Grj9/uLMhxH/QpyFQU9NOYOJN4usvxSo37puos1L2yrkxRFEU5etRPaOWe0I8TvnF9k2+sbVFIwVLNJxViN4BpZQATUpLmEsfUyg74y2fO8vDjT3Dh+YPtJR55/Almjp+mEPLAeJ44L3a3ILOJAJbmAk3Tygave7JC0M0SwjyeCGBZISeate7JhWSYpvSKcCKAFUKSFRJ0QT9JiHNJw/I53arxwExDDdJWFEW5S6gQptzVkiznW+ub/IfVDWKRs1TzqFo2b20HXB4EzNZMzrWb5QrYxiCjE+ZMV62J04R/4b/9Jf7eTz7J6h+8UN72yONP8F/+3C/z9nZM1TFYbjnlx/pxzsubfaSWc99MbWIY95VuiqnDcsstg1iSC17eGDDMYk5N+RMBbLUTkws41rTLWrBCSN7cClgLAxYbNsea+ytgb+6ErA8jWq7F6WaTT881ODNdU4O0FUVR7jIqhCl3pUIUvLbT49++eY2X1wecbfvcN9UAIC4y1qOQQsCUXZnYgswLSZoLhNhf8SqkJHAs/vh/+wx+dwO7v8n8ygrLZ86yNczo9lJyMVqt0jQNiWQjCgnSlCnfozG2BVkISSEk7A7b3uuEv5PE9JIY17Bo2l65BSmlJBfj9xuJ8ozNOASp07ar6BoMs4SdKKETCk7VWvzRk7OcbFepu2a5qqcoiqLcPVQIU+4qUkou9fp87eo6b3cGuIbBo8ca1N3RqlYicjbigLm6SdOs0HAnO9vP1S1qrkFlt5h9rxFr1YM5v0n75OzEjMSWb2IZGq6llwFsKw4xzJxzszWmXW9i9anqGBxrjlakxmdBpjLhZNun7XpUxgZg26bOsaZNISTV3ecU5RnbWcBSw6Zu+Ugj451+jI3FmfoMf+p0k8WGj2epLUdFUZS72ZEJYa+99hq/8Ru/wTe/+U0ajQZ/4k/8CX7kR34Ey7Le/c7Kx8J6EHD+6hovb3VxDJPT7clC+0TkrEdDdE1jpVY/tAjfMnQa3sFZkAte7cAoIhidoGx4o2+T8SL8lusfWoSvadpEMfxEEX718CL88XYUe0X4GjqeadJLYmqWy6enpnlotsVMxcFUg7QVRVHuCUcihL3yyiv84A/+IH/tr/01fvAHf5DV1VU+//nP8+yzz/Jbv/Vbd/rpKXdYL074xvo6v39tm0LCSr2Gc0MQSUTOt176NjtXrnD/mfsxzzZu+ZjvNgvyRh/VKcirYY8wy/G1Cg3X5dHlNmfbdVq+rbYcFUVR7jFHIoSdPn2aF198EV3f/8VqGAY//MM/zK/+6q/SbDbv3JNT7pgkz/n25hbPXtkgSDIWaz4V20JISS4k5u424Nb2Jl/63I9z8etfL+/78Gef4Kkvfpl6q13WXe0VyY/PgpyyqrjGwW+DrJCYOhNbkGGeUrFc6qZz4PpcSHSNsonrXgBzdIv2WA3Ynr36L0PXdjvtR1zq97E1m3ONeT45N8WpVo2K6mqvKIpyzzoSP+Ht3eHJ4xzHGf3yzPM78IyUOykvCl7vdPl3b13lja2QM9MVzu4W3RdCcqWbkOaSpaaNYcpRAHvhhYnHuPDceb789FN84Su/ycYgoxvlzNZs6p5RroCZhcu1IGeqojE9dlKyE+ZsDFKansls3SoDmCFtOj1I7JTFxv7KVJgWXO2m2KbGsabDoEjKYdxBaBBHKUtNpwyBWSG52k3IpKTuCwZphiwMHp1e5hNzLZabFdViQlEU5WPgSISwG2VZxi/8wi/wR//oH2V6evqm1/X7ffr9fvn369evfxRPT/mQSCm51O/zf15e5fJ2RDeAluNSGwvpQkqSXJJkgmGW8c6rL02sgI09GC+e/xqrb1ykaC0TpoI4L4iisNyC3MkEQZpRdcTEXdNCEKYC2yzYirNyC7JIDbbTBE0DIWGvDViaS+JMINHZSeOyD5irOXT7CYaukQtZhrA4z1kPAoaJ5LRW4VOzizw822Su5k4cClAURVHubUcuhEkp+St/5a/wzjvvcP78+Vte+6UvfYmf/dmfPXB7p9PB87zy7+NBTTla9t6bThzzrY1NXtvp0TAdHqi6RI5ASkG1iNGiUYCxgWWnIDJy0jhh8M47t3z89Tcu8ok/vECLgiQLkblg2vZxsgzTFNQrAtfM0KKivM+0JvEqBbGMEUHOlO1QywUFBbYvsEyJmUTl9XUpOekXxCKmCDKapkXDAETCca9A08DPY9IkZ5hlpLnkjGtzYr7K6XaNhmehFxH9bnTIK7hz1PfN0aben6NLvTdH10fx3kxNTd32tUcqhEkp+et//a/zr/7Vv+KrX/0qJ0+evOX1n/vc5/iRH/mR8u/Xr1/nscceo9VqHfgivJcvivLRCdKEbw0GfPXyVdIcPjE7j7u7FVcZu26sjz26kzOMhuiax+kHHrnl48+dOYtRrRBEAzJpMetUsU0LCVi7/x14fCRJHBLlgoZTpWq5SEAHapXDrofcjgmSAt+sUXd99vqDVXxJkKVcSzPywmSm0uSTrVFz1ZZnHflie/V9c7Sp9+foUu/N0XWU3psjFcJ+9Ed/lN/5nd/hq1/9Kg8//PC7Xl+v16nX6x/BM1M+aEme8eLWNs+/eYlXooTlWpUTMw30dzlBON6GYt6rYZ5t8PBnn+DCc4ePHFo8feaOnIIspGSQxYRZgSZM5twmDyw1Od6qlD3NFEVRlI+3IxPC/ubf/Jv8b//b/8ZXv/pVHnnk1qsbyt2rEILXdzo8e3WNt7t9qkXKuXadOb+GXnaZ59AVorjI2IiDiVmQAE/+8jP8yk98jhfPf6289pHHn+BHf+mLhwawvc73NxJSsJ1EBwLYza6XUtLLk4kAlgtBL41JcomDw4lqmwem6yzWPXXSUVEURZlwJH4r/N7v/R6//uu/ztzcHH/xL/7FiY/9z//z/6xC2T1ASsnlwYDnrl7n1c0B24Mcy5Yc821auwEsKyTXugkAi2OnCQshudQJWY9C5upm2YhVSslaPyMSPp//H7/C1uW3WLt8mfmVFczZFV7vDWhoGSfq9TKAbQe7syMrFk1//59/J8x4Y2eAYwtWWtUygA3igo1BStU1mKvtHxCI0oLXd4ZkMmWl6VEzbbbjkH4siCOTB6cbPLY8zWzVwVWd7RVFUZRDHIkQ9slPfpJvfvObh37s9OnTH/GzUT5oW2HI89fXeHGji6kZ1GyLqyJGL0yqllNuQaa5YJgU5Z8tYxRehlnG5UFAIaBp7s+CzAUMkoIgKWh6guUzZ1k+c5ZcCF64ukMnyliqNSa2IIOkoBPmVGyD5u5tEsm1IGAzSDhuVya2IONM0I1GbVJmq/srYutRxNogwrd18kKylac0zAqfmqry8FyDmaqjTjoqiqIot3QkQli1WuVTn/rUnX4aygdsmKZ8Y22dZ69soSFZrFUoZMFmNORY02HOr+JqWVnk7ts68w27/DOMasB6echszWTKnpwFaRkaczWL1Dep744WKqRkIx7SqGgs1RrMVSdruqarFq6lU3cnRxFZVsG52SqLFX/i+oZnsIJbzo4E6KQRQRHQ8KFh+7StOmdaDU5PVWn79sQsSUVRFEW5mSMRwpR7S1rkvLS1zdeurNOLchaqPjXHZJilbCcBnmVxvF4drYBFGQCrb1wstxKXz5wFJovwz7Wbh86C3JvrCJOjiMa3IMf5tlHOahwvwp+tVA4twrdNnZmaXl5/NRywFYU0jCr3z7W4b6rOiVaVhmse+ZOOiqIoytGiQpjygRFS8Eany9eurnGlFzHlupxte2iaVgYwxzCZ9arlFmS/0+FLf+OvceH8s+XjPPzZJ/gbv/RFQtc6UIR/Mx/mLMgCyTBLWA9CLGnz6dYKD003Odb0qapie0VRFOV9Ur9BlO+YlJKrwyHnr6zx+k6fimlztlUvt+VuFsAAnvlb/y9eemGy4/2F587zpaef5K//xm/c0QBWSEkvjRgmBaa0+ERriYdmGizUPVVsryiKonzHVAhTviNbYcg31jf41vUdDE1noVKhE+TshDkzVasMYJo0SGOLzTxjtjpqUrp68SIvfv35gw8qJReff57rr11BO3mG2ZpdnpSUUrIxHHWdn61ZmIZWBjA9c9hMCmZqBp61H9w6YUY/LpiuWPiOXgYwXdr0BqB5+cS2Zi/OeKczRNNgsVrlkfYsD82qYntFURTlg6VCmPK+DNKEb66t8/97ZwtL11hqVPBMg61hxuYwo+5KHEvQyUIcw8SWLpe6CZ6l0/JMbFNjbfXyLT/H5TffwZk9Qd01sIzRP9WskHSCnCgT+LZOokVksmDaqnAtKOjHGb49GcK6YcHmMMMxNUKZlytgQaBxrZdg6hoNzyQucvppwuWdFLOw+Y9PzfJdC23avo2uiu0VRVGUD5gKYcp7kuRZWXR/qZNwrO6zVN/fzqu5BosNB6HlZQCb9aoUBSw0RkOs91a15ldWbvm5zp49xVzdKgvpYXQicqZmEeeCQIRIXZRbkNPVjKpjUHMntwrbFRPH0si0lCTPyy1I0yvQNQ3TFFwLhqQZzHpVfuC+Y5ydrqtie0VRFOVDpUKYcltyUXCx0+HZK+tcH8RMeS6PLfkHQopj6tR82E6SiRow3YD5uj1x7fKZszzy6GO8+MLXDx059IlPPnDgeWiaRtM3WY8GSCkmasBa/uG1YHXPINUSsrEAJpBIPacwUrLcYMlv8vCxJsdbVVVsryiKonwk1G8b5ZaEFFzq9/n3l69yrR9TNW3OtuvoN1khulUR/mH+xs//PF/8r/82F5/frw175PEnePKLzxx6/XdahF8zHTpJyDAVmNLiZG2Kh2ZaHGv6qtheURRF+UipEKYcSkrJWhjw71dX+fZ6j2nH50SzhqUfLEzf6/FVX1zAPbZw2wEsETmF5/LXf+M3yK+us7m6OtEn7EbfSQDzLYesKLgSDfE1lweaDR6eaTJbc7FNVWyvKIqifPRUCFMO2IlCnr9+nf/rnTVMQ+eBdhvXNAjSgq0oLeuu+p0dnnn6yYkeX+cefYzPf/lX0T2NNBdsBzmOqdGu7AemXEiu92N20pAFk1EbirMNlk+fZTvI2BikTFWsssXF3onI9WhI1YMFr1YGsJ0gI8klUxVzIkx1woxrQYA0UnzDICwETdvn7FSNT8y1maqozvaKoijKnaVCmFIapgnf3tzi//vmVb51dcADs1U+NTtVfrwfFVzuxMzXbWquwTNPP8lLz52feIzXX/g6v/r05/jCV36TQVJwvZ9SsXVqrlkW5HeihFe3+timxrm2V/YBizPBxmDUQb9iG1Sc0fZgmAle3+4R5wVzfrMMYFkh2RxmBKnANjWmdkNYIQQXNjtc60ejcULTLc5NNTkzVaPlWeqko6IoinIkqBCmkBY5r2xvc/7aBm93BmS55OG5Oqda9Ynrqo7BfN2m4ZqsXrw4sQJWkpIXz3+N1TcuMnviNNMVE88y2FukSkTOoIhoVwzm/SquWZR3dS2ddsUs/wyjLchONsR3YcGs0fb2i/tNHdq+hWcVVB2DAskgjVkbBviWyX96/DiPH5viZLtKXZ10VBRFUY4YFcI+xnJR8Fa3y/lr61zpRYBgqmJSt12mHP/A9TV3v/3DxXfp8bV2+TLLZ86y0t5vX7E3C9I2dT4538DUdLQoLAd4G7rGYsMpr9+rASsQPDjdPFADpmmjdhW5NBhkMcGgAGHyYGuJh6ebLDU8Kuqko6IoinJEqd9QH0NSSi4P+jx3bZ23dob4hk3TM4mKlKp1eAC7UX1x4ZYfv7EH2Pgw7g9qFFEqCvppTJQKfMPjvkadB2cazNVcddJRURRFOfJUCPuYWQ8Cvn59nQsbXWzNYKVeYZAnDLOUquXcVgAbZinusQXOPfoYr9+kx9f4CccPOoDFRUYvSUgLjYbhc990nQdmGkxVbDVWSFEURblrqBD2MdGNI76xtsHzV7YYxJJz06OmpNtJyDBL0KWFSC0SQ+CMnTKM0oJBUlBzDDzbmJgF+f/8uS/xW1/4PC8/t18b9vDjT/AXf/aXGMQ5NdcsA5iUYAqXYSRo+vuPL6RkJ8jQgJZvIoD1aEAqcmzhESbgGBJNgyDPGCQpnUAw7VZ4Yn6KM1M1NVZIURRFuSupEHaPC9OUP9jY4uvXNhkmOUmqowmNOJMkjAJY1XLoD3UuDWOWWw5zY53td8Kca72UxYZNQyvKRqwys9kQGv/lL/9PmJ1V1i5fZn5lBW/+BKudhLyfYZqSzSRA1zRczePqToprFfi2XraTiDPBtSDF0DRsU6NfhGSyoG5UWBsU5DIhkRkSgRQGM06DJ041OTutiu0VRVGUu5sKYfeoNC94eXub//vyOt0kZb7isVj1WR9kBGlBJBNEltG/fIWNtQ2c6UWaCyfw7MntPN/WafkmUismRhENtYKmb1J1DGbOnC23H4OkoO4ZWKZkIx5i6DrzXo08h7onsA0N09gPTpahUdttRdHJAoQ2GkWkawaFFhFlApF7nGhO8dB0i6WGi2+rf7aKoijK3U/9NrvHFIXg9W6XZ1fXuDoImfFc7p9qlB+fr9tsxQEbW5v89k//NK+OjQt6+PEneOpLXwa3Xd7W8i1MU7CThhOd8OuuSc0xDqxEVRyDJdNkIw4wNL2sATMtONF2DlxvGTrHWjYb8ZBMClqWzzBPCVPBQrXCiUaDh2aazKnO9oqiKMo9RoWwe4SUkku9Ac9eXeetbp+GZXHfVB39hiL47SQkyFN++6d/mte+/vWJj114/jxffvopvvCV3yxvG2bpgQC257CtwETkbMTBoUX4h11fyNGKWZAnOLpNJ8poWj4PzjR4YKapOtsriqIo9ywVwu4B1wYBz1/b4NWtLrapcbp1+AnEvSL8/uXViRWw0lij1eUzZ9/zMO73dwqyz4aEml5httLgvvkmp3c726t6L0VRFOVepkLYXWw7iHlhbYNvrW+jadByHHRNPxCWgqRgPQzQjJy647KxtnnLx119+x2YXiLRYqq2VQawNBcMklF3+vETlLmQbIUJ/TzEt40ygBVC0o9zbEMvRxABFFJwtR9yLRiyqOk8Mr3Ew7NNVppVaq76J6koiqJ8PKjfeHehQZzx/NUNnru2jm5IFms+BgbvbMdkhcTQHeq7YSYXklc2+3TihNNTPlOOT3hDI9Ub5Y1ZXtros9R0ONXYXwHbDnKu91OmK+ZEJ/z1YcxL6z1qnsHJxUa5AtaPc1Y7KZ6lc3LaRddhkCasdiI2+wX/0eICn2nbnF1exFPNVRVFUZSPGRXC7iJRlnFhs8u/fO0y68OITy80ma6MwlAuJK6pY+pyomFpJw0ptJyW6zDnVwBYPnOWhz/7BBeeO3+g0eqDjz1O7fgSVgILlcrEqppjalRsfSIwJSKnl4X4jsGxag1L3/+Ybeh4lo5tanSTkEEicHWLRxcW+NSn28xWXfrdjgpgiqIoyseSCmF3gTQveG27y79fvc5bvQ5LFZ+HZmcx9P2wZeoax1qjuYt7hezbSUiYp5ya8mnZPuZYgftTX/wyX376KV48/7Xytgcfe5w/99/9t7RqNlPTVewbus+3KxY115wYxr0eDal7JqeaVRx98nrH1qj6BWEqoPB4bK7Fg7MtplRzVUVRFEVRIewoK4TkrU6fZ6+s88rOFppR8PBMm5rlsPrGxbJB6l6PrvFThHtF+DcbRVRvtfnCV36zfJz64gLusYV3LcK3dnt83aoIPxUF21FEmEpmvQqPz7c5N92goZqrKoqiKEpJhbAjSAjJaj/g+avrXOz2iEVEyzeZ9ZqI4ZCfffqvcOH82Kigzz7BU1/8MvXWqL/XuwWwcctnztI6fvwDOQUZFxmbYUyWayxWavwnx9qcatWoOOqfmaIoiqLcSP12PEKklKwPI56/uskrOx0MXeA5AkfaOLhowuCZp5/kpefOT9zvwnP7/b32ApiJhS1tpJQTq09xJkhyQc010DWtbEOhY+DiIgUwtquYFZIwLfBtA8vQJmZB+ppHUUgimbIVJmhC50SjyafmplhuVnBMVeulKIqiKDejQtgRsRMkPH9tk3/35hpRkfPpxRqRTEkKiae57Awkr7/z0sQKWGm3v9eLL79IfeUYrm7TH+p084RjLYeGN3qbhZRc7SWEiWCpaWPbslwBiyOTS2HCYgNmalb50JvDlM1BxkzNYqpmlCtgWu7w6tYQgeB4q8JDrWkemWuzUPMwDdXZXlEURVHejQphd9ggzvjW+g7fXNtkK4pxTI2ZaoVhkZDLgimngoFJT0/orV255WOtvvMWj58+TdPyCKMEIbWJOjENsHQNy9CIi4zB2CzItTTFNiU31NZj6hq2qSMQrEcREomlmVwNIiqmxR86PsNnFqaZrjiq2F5RFEVR3gMVwu6QKCt4aWOHF65v0UlDZnyHh+sNklywnQRlAKtaNgDH2w7mg2dv+ZjLJ06VNWDLLYdCyImmqpqmsdR06CYJwyKaqAGbq9m0fYljTgap6aqFY8Nm0ieMcxw8qq7LnzyzwAMzTVqerYrtFUVRFOV9UCHsI5bmgovbPZ67usl6NKTpmZxu1dHREEi6WXgggAHYps7Jczfv73X/Y4/xyIOPlDeZujbRkmJPVGQHAhiMTlYeNqMxyFPeGuygCYtTtVk+Odfm3FSDmmsduFZRFEVRlNunQthHpBCSdzoDnru6yeqwR8XWOdWqYeyuIgkkG9GQpMgPBLBxh/X3uv+xx/j8l371XZ/De5kFGeUZa0HAMCl4oDHPp+anOdWu4dvqn4yiKIqifBDUb9QPmRCStWHEc1c3+PbGDlVHQ9+6zurqFbKVFY6dPkOQFXTSEMHkCliYFhi6NrGlWG+1+fzf/0e8/MqLrF95h+UTp8oVsCQXFELi25OnEtNc0E0SQhHhmvuzILNCkuYC39bRNA2JZJimbIYJcaLx4FSbT52ZYbnpq5OOiqIoivIBUyHsQyKlZDtI+cb1LV7a2qYTJwy2+vwvv/hf88Y3niuvu//Rz/JHnv47VFp1Hp5tlAFsmBRc6SQYusaJKaccRRRlggvrPaL6PJ/8I6dZqlYByArB5Z2EQkiOtRyquwOzCyG5uBWwEYYcazms1EYBTErJtW7CMCmYbVhYVkEnyrCwOF5p88T90yzU/YkRSIqiKIqifHBUCPsQ9KOMr1/b4uXtbaIiZbbiUrEsfvHpH+XtP/j6xLWvvvAcw1/4GX7kmf+Jyk22IMftJCFRnuKZNu13acQKEGQpvSzCMgym3clZkDmCXhaT9RMWqlU+NT3FI7MtZqruofVhiqIoiqJ8cFQI+wBFWcFL6x1+78oawzxhruow79TQ0Nh553Xe/ubzB+8kJVe+9QJe7zrG/Lny5qpjsNJ2MHStXI3aTkJyMk5PV2g7Pp61v0plGTorbWdiO3KYpXSykOWWS8v2qewOyk5FQTeJERScbNb55FybB2eatNVMR0VRFEX5yKgQ9gHYO/H4tStrrEcB077NQqM+seq0vrp6y8fYvrrKqfvOTdw2Xtt1O6OIxmvHDivCj4ucbpwQZ5Jpt8Jnjre4T810VBRFUZQ7QoWw78DeicdnVzd4tbdF27U4226UJx7Hza+s3PKxbvXx9zILEiYD2IxbJcpH4SvPNeb9Kg8vtjnVrlFz1duvKIqiKHeK+i38PuydeHz+6ibf3FojKRJO1tq0PAd9LIBJKUlyiWVoLJ+5eY+vRx5/guUzo0asSS4wxnp87QUwR7epm96B55IVEikl9u4q2P4sSB0Dg2vDIQiTpUqTT8y2WGlVVJsJRVEURTkC1G/j9yjOcv7dW9d5bXuHjaxPwzFpWS02+oKiyJit7RfX74Q5G4OMhmcw37D58z/3c3zlb/0UF7++X5z/yONP8OQXnwFGJyKvdhNsU2el5dDNonIY92CoEwQxyy2nDFxpLljtJAgJx1oOBTkb8YCkEHR7OrrUePzYFN+1MMViw8O1VJsJRVEURTkqVAh7j64NA/795WvUa4LZisecV2V7UJDmGbmQE9cWYtSHKy00NqIhVr3G3/6H/5h3Xn2TV199i8WTx/nsZx4qV88KIckLia5JtuKQWKRULQcXh75I0DUY/xRCQi4kQkInCdlKB+jSZMpocHaxyh8+NsVc3StDm6IoiqIoR4cKYe/R66++yuvn/0/O3neKEw99Akc3makaVGzjQJPUqYqFbWoMioik2G/E+uDD97N85iyOqU9sX9bd0YnIfh4Ti2yiBuxY00HXwB07EelaOjN1g7VwyHYiWXSmOdNq8fBsk+mKjal6fCmKoijKkaVC2G3a2trih37oh/h3/+7flbc9/NkneOqLX6beatPwDn4pNR1i4gOd8HVNO/x6TSPVUnKyA0X4NXcy4MVFRi9NGMaCJbfFfdNN7ptuMKXaTCiKoijKXUEtldymH/qhH+KrX/3qxG0XnjvPl59+6tDrb3cW5Lh3OwUpkQzzlLVwyGaQ0dDq/LFjx/kzD57gDx+fYabqqACmKIqiKHcJtRJ2G1566aWJFbCSlLx4/musvnGxPN0IkBaC7WRIKiZXwLJCYuoc6MmVC0knDQnzdCKAFbsFYJo+6nw/SHMQOrNukwcWmpxoVairHl+KoiiKcldSIew2vPnmm7f8+Nrly2UI2wpSLm73cR04266XAawX5awPMmqOwUJjf1UsSApe2exTaDmnpvwygEWZYLUbk4qMuqdh4bBUafHgdIPlVoWqo946RVEURbmbqd/kt+HMmTO3/Pheo1WB5HoQ0ItzWu7kFmRaSIKkwNRH/cP2Vq/Ww4BOnNByHVr2KIDlUrAZhlzrx1RMh0emp/nUfIuFuqt6fCmKoijKPUL9Rr8NDz74IN/zPd/DV7/6VeQNjVYfeuyzLJ85W9aAeY7kodk6s1V34jFavomhgWfpZQDbTkI0I+f0lM+cX0Eg2E4S4kxQMRz+05NTfGq+zULDxTFVjy9FURRFuZeoEHab/tf/9X/lh3/4h/nd3/3d8ra9RqvjRfjzfvXQInxT12hXrPLve0X4dcelYloMsog01KgbPqebNR6YaTBbc8rh3YqiKIqi3FtUCLtN09PT/Nt/+2/5N88+xz/598/y6Qfvm1gBe6+nIAdpjKkbo6L8MKdhVXloqsF9M3WmfEv1+FIURVGUe9yRC2GdToft7W1OnTqFrh+9IHL2/vt5UNgst2sTAaxt+4cGsPH6L4DNOGAzHmKi4+DRNKucma9zql1VPb4URVEU5WPkyISw8+fP86u/+qv8y3/5LxkOh2xubjI9PX2nn9ahorSYCGBG4bDeE+SVjJa/v+XYj/dmR5q0qyaXBzuEeY5TVKjoPo8uTHNutkrLs1SbCUVRFEX5mDkyIex//9//d/7kn/yT/Lk/9+f403/6T9/pp3NLaSEmtiB7gaQTpniWTmusx2qUCrbDjCBPGAiJr1U41WhwplHj+FSFhurxpSiKoigfW0cmhP3dv/t3Afg3/+bf3OFn8u5yLSMptLIGzPALbEOn6uxvnyYiRxgpVU/QtH1O1hrcP91goe5Sc4/Ml11RFEVRlDtEpYH3Q5MTRfiebeDtDu+O8oxhkVIUGlW9wum5CvfPNJivOarHl6IoiqIopbs6FfT7ffr9fvn369evfySft2rZE0X4EkmQZYR5CtKkpldZrNc4O11lruaoHl+KoiiKohxwV4ewL33pS/zsz/7sgds7nQ6e55V/Hw9q36lgENAsBFoUUiCJ8pSsKNCkyZThMuP7LNZdWr6OKSKGvYjhB/bZ7z0f5HujfLDUe3O0qffn6FLvzdH1Ubw3U1NTt33tXR3CPve5z/EjP/Ij5d+vX7/OY489RqvVOvBFeC9flFvp6iahtcU7aUEnyJn2PRYrdVbqVU5OVZiu2BiqzcR78kG9N8oHT703R5t6f44u9d4cXUfpvbmrQ1i9Xqder3/kn9fUDAYhVLU6j89Pc3amSlv1+FIURVEU5T04MiFse3ubTqdT1nW9/fbbdLtdFhYWqFQqd/jZ7WvYDo9MzTFf9Tje8miqHl+KoiiKorwPRyaE/YN/8A/4h//wHwJw+vRpfviHfxiAX//1X+d7v/d77+RTm9DyHP7jE9NUnCPzpVMURVEU5S50ZJLET/3UT/FTP/VTd/ppvCtD11QAUxRFURTlO3b0hjMqiqIoiqJ8DKgQpiiKoiiKcgeoEKYoiqIoinIHqBCmKIqiKIpyB6gQpiiKoiiKcgeoEKYoiqIoinIHqBCmKIqiKIpyB6gQpiiKoiiKcgeoEKYoiqIoinIHqBCmKIqiKIpyB6gQpiiKoiiKcgeoEKYoiqIoinIH3FOTqPM8B+D69esTt3c6HaIouhNPSXkX///27iUk6rYP4/hlic9gox0oaqiQnAypyLIDQQczKiwzhwo6DJV2JqqNpVZIJyuyoOOiRRtpI4SYaFQgFaEEFWZoKaVlZEZllhpkOjrvovLR3p7Du3i9/818P+Bibl1c8GOca+7/f+5hNtbFbKyN+VgXs7Gu3prNsGHDFBj4zxXLp0rY+/fvJUnTpk0znAQAAPirV69eacSIEf/4dwFer9fbC3l6RWtrq8rLyzVkyJCuBvrmzRtNmzZN9+7dk8PhMJwQ3TEb62I21sZ8rIvZWFdvzsYvd8JsNpumTp36y985HI5/1UrR+5iNdTEba2M+1sVsrMtKs+HGfAAAAAMoYQAAAAb4fAkLDQ3V/v37FRoaajoKfsJsrIvZWBvzsS5mY11WnI1P3ZgPAADwu/D5nTAAAAArooQBAAAYQAkDAAAwgBIGAABggE8d1trd69evdf78eT1//lzh4eHavn27hg8fbjoWJLW3tysvL0+5ubmKjIzUwYMHTUfCd42NjcrOztbDhw/Vv39/LVq0SAsXLjQdC98VFxcrLy9Pb9++ldPp1Pr16xUWFmY6Frppa2vT1q1b1djYqJycHNlsNtOR/N7evXv15MmTHmuzZs1SSkqKoUR/8smdsPr6ek2ZMkUVFRWKi4tTRUWFpkyZovr6etPR/N6nT58UHh6uy5cvq66uTiUlJaYj4buamhpFR0errq5O8+fPl8Ph0MqVK5WWlmY6GiQdOXJEmZmZCgsLU1xcnB4/fqzx48ervLzcdDR0k5aWptu3bys/P18ej8d0HEi6c+eObDabkpKSun5iYmJMx5Lko0dU7NixQ0VFRSovL1dgYKA8Ho/Gjh2ruLg4nT171nQ8v/b161d9+vRJQ4cOVVJSkurq6lRUVGQ6FiR9/vxZkmS327vWLl68qC1btqi5uVn9+vUzFQ2SGhoaNHjw4B5rTqdTK1as0NGjRw2lQndXr17Vrl27tGfPHq1bt04tLS09nk8wY+bMmZo3b54OHDhgOsp/8cnLkdeuXdPy5cu7vjwzMDBQLpdLeXl5hpPhjz/+0NChQ03HwC/86sVi+PDh6uzsVEtLCyXMsJ8L2Lt379TY2Cin02koEbqrr6/Xpk2bVFBQoBcvXpiOg59cv35dlZWVcjgcio+P1/z5801HkuSjlyNra2s1cuTIHmsjR47kiQH8D7xer86cOaOoqCgNGzbMdBzoW/FyuVxasGCBxo8fr7S0NCUnJ5uO5fc6Ozvldru1c+dOTZ482XQc/CQkJEQzZsxQQkKCbDabEhMTtWfPHtOxJPngTpjH41FHR4eCg4N7rNvtdnV0dMjj8XTtkAH4axkZGSouLlZxcbHpKPjObrcrKSlJzc3NCgkJ0blz55SQkKBx48aZjubXMjMz5fV6lZqaajoKfiEnJ0f9+/fvehwVFSW3263k5GSNGTPGYDIf3AkLDAxUcHCwPn782GP9w4cPstvtFDDgXzh27JhOnTqlgoICTZw40XQcfBccHCyXy6W1a9cqNzdXERERysjIMB3L750+fVpfv37V0qVL5XK5lJWVJUlatWqVcnJyDKdD9wImSUuWLJHX61VpaamhRH/yyUYSFRWlR48e9VgrKyvThAkTDCUCfh/Hjx/X4cOHVVhYqNjYWNNx8DciIiJUVlZmOobfu3Tpktrb27se3717V/fv35fb7eZNjAU1NTVJkiU2Zcwn+D9Ys2aN0tPTVV1drdGjR+vp06fKz8/XiRMnTEcDLO3kyZM6dOiQCgsLNXfuXNNx0E12drbcbnfXC0dtba0KCwvldrsNJ0N8fHyPxz+Opli8eDGfjjSstrZWz54967oR3+PxaN++fQoJCbHE/zifLGGbN29WSUmJoqOjNWnSJD18+FCJiYnatGmT6WiQtHHjRjU0NKi0tFStra1yuVzq27evcnNzTUfzaw8ePNDu3bsVHh6us2fP9jjOJSsry/i9E/7u5cuXcjqdcjqdamtrU2lpqZYtW8Zhx8Df+HHv5LZt2zRq1ChVVlbKZrMpPz9fgwYNMh3PN88J+6GqqqrrxPzIyEjTcfDdjRs39OXLlx5rffr00ZIlSwwlgvTtHKq/ugk/JiZGAwcO7OVE+Flzc7PKysoUEBCgMWPGcNyLRdXX1+vevXtKSEhQ3759TceBvr2Jefr0qRwOhyIjIy1xKVLy8RIGAABgVT736UgAAIDfASUMAADAAEoYAACAAZQwAAAAAyhhAAAABlDCAAAADKCEAQAAGEAJAwAAMIASBgAAYAAlDAAAwABKGAC/1tTUpCtXruj169c91isqKpSfn6+2tjZDyQD4OkoYAL8WEhKiAwcOaPPmzV1rd+/e1fTp0/XixQsFBQUZTAfAl/EF3gD83q1btzR37lzdvHlTAwYMUGxsrFJTU7V3717T0QD4MEoYAEhyuVyqrq7W27dvtWXLFmVmZpqOBMDHcTkSACStXr1ajx8/VkxMDAUMQK9gJwyA3ysrK1NsbKxGjBihhoYGPXv2THa73XQsAD6OnTAAfq2yslILFizQhg0bVFxcLI/Ho6NHj5qOBcAPsBMGwG/V1NRo9uzZSkhI0IULFyRJZ86cUXp6uqqqqhQWFmY4IQBfRgkD4Jc6OjqUkpKioKAgHT9+XAEBAZKk9vZ2JScna86cOdq4caPhlAB8GSUMAADAAO4JAwAAMIASBgAAYAAlDAAAwABKGAAAgAGUMAAAAAMoYQAAAAZQwgAAAAyghAEAABhACQMAADCAEgYAAGAAJQwAAMAAShgAAIABlDAAAAADKGEAAAAG/AcyriKb2hxCEQAAAABJRU5ErkJggg==",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -503,84 +502,115 @@
}
],
"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",
+ "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",
- "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",
+ "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",
- "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\");"
+ "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": "markdown",
- "id": "c17",
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "6b2e05c6",
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "fraction of the measured points inside the 68 % predictive: 0.97\n"
+ ]
+ }
+ ],
"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."
+ "print(f\"fraction of the measured points inside the 68 % predictive: {inside_d:.2f}\")"
]
},
{
"cell_type": "markdown",
- "id": "6e491de1",
+ "id": "7f6aebb1",
"metadata": {},
"source": [
- "# GP discrepancy on a differential cross section\n",
+ "## 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",
- "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"
+ "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": 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"
+ "execution_count": 16,
+ "id": "940dde08",
+ "metadata": {},
+ "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",
+ "resid = data.y - line.bind(x)(*theta_gp[p_grow.columns(line.params)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "7a96ab0c",
+ "metadata": {},
+ "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": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -588,26 +618,53 @@
}
],
"source": [
- "import jitr\n",
- "from jitr.optical_potentials.potential_forms import (\n",
- " thomas_safe,\n",
- " woods_saxon_prime_safe,\n",
- " woods_saxon_safe,\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=\"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 departures the data allow\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a4be8ce",
+ "metadata": {},
+ "source": [
+ "## The same thing on a cross section\n",
+ "\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",
- "from rxmc.params import Parameter\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",
- "Ca40 = (40, 20)\n",
+ "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": 19,
+ "id": "9cf85214",
+ "metadata": {},
+ "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",
- "rxn = jitr.reactions.ElasticReaction(target=Ca40, projectile=(1, 0))\n",
+ "R40 = 40 ** (1 / 3)\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",
+ " 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",
@@ -615,206 +672,260 @@
" 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",
+ "def spin_orbit(r, Vso, Rso, aso):\n",
+ " return Vso * mso**2 * thomas_safe(r, Rso, aso)\n",
"\n",
"\n",
- "omp_full = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\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]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "0f3057a0",
+ "metadata": {},
+ "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",
+ "]\n",
+ "omp_full = rx.reactions.ElasticXS(\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",
+ " full_central,\n",
+ " spin_orbit,\n",
+ " lambda ws, *x: (tuple(x), so_args),\n",
+ " full_params,\n",
+ " lmax=10,\n",
")\n",
- "omp_vol = rxmc.elastic_diffxs_model.ElasticDifferentialXSModel(\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",
- " 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",
+ " volume_central,\n",
+ " spin_orbit,\n",
+ " lambda ws, *x: (tuple(x), so_args),\n",
+ " volume_params,\n",
+ " lmax=10,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "b6343957",
+ "metadata": {},
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\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",
- "\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",
+ "ax.plot(\n",
+ " np.rad2deg(angles),\n",
+ " omp_vol.bind(angles, meta)(*volume_truth),\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",
- "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",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c3199959",
+ "metadata": {},
+ "source": [
+ "### The same three rungs\n",
"\n",
- "plt.errorbar(\n",
- " np.rad2deg(obs_xs.x), obs_xs.y, stat_xs, ls=\"none\", marker=\".\", label=\"data\"\n",
+ "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": {},
+ "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",
- "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",
+ "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",
- "plt.xlabel(r\"$\\theta$ [deg]\")\n",
- "plt.ylabel(r\"$d\\sigma/d\\Omega$ [b/sr]\")\n",
- "plt.yscale(\"log\")\n",
- "plt.legend();"
+ "xs_problems = {\n",
+ " \"bare\": rx.Problem([rx.Constraint([comp_xs])]),\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",
+ "}"
]
},
{
- "cell_type": "markdown",
- "id": "f1652855",
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "8c902ca0",
"metadata": {},
+ "outputs": [],
"source": [
- "## The discrepancy term acts on the angle grid\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",
+ " return sampler.results\n",
+ "\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"
+ "xs_results, xs_samples = {}, {}\n",
+ "for i, (name, p) in enumerate(xs_problems.items()):\n",
+ " 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": 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"
- }
- },
+ "execution_count": 25,
+ "id": "26258f3e",
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "GP hyperparameters: ['discrepancy_k1__k1__constant_value', 'discrepancy_k1__k2__length_scale', 'discrepancy_k2__noise_level']\n"
+ "bare log Z = -581.75 +/- 0.64, 85485 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"
]
}
],
"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])"
+ "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": 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"
- }
- },
+ "execution_count": 26,
+ "id": "2f8e7c6e",
+ "metadata": {},
"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"
+ "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",
+ "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"
]
}
],
"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",
+ "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": "56cddf05",
+ "metadata": {},
+ "source": [
+ "### What the three rungs did to the potential\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",
+ "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 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",
- "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",
+ "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",
- "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)"
+ "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": 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"
- }
- },
+ "execution_count": 27,
+ "id": "9f34319b",
+ "metadata": {},
"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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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -822,67 +933,103 @@
}
],
"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",
+ "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",
- "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",
+ "fig.legend(\n",
+ " handles=[\n",
+ " plt.Line2D([], [], color=c, label=n) for n, c in zip(xs_problems, xs_colours)\n",
+ " ],\n",
+ " loc=\"upper right\",\n",
+ " fontsize=9,\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\");"
+ "plt.show()"
]
},
{
"cell_type": "markdown",
- "id": "65f86f5a",
+ "id": "c580f46a",
"metadata": {},
"source": [
- "## The learned discrepancy vs the missing physics\n",
+ "### The learned discrepancy against 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"
+ "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": 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"
+ "execution_count": 28,
+ "id": "1799733e",
+ "metadata": {},
+ "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 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",
+ ")\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": 29,
+ "id": "b1b56ab1",
+ "metadata": {},
+ "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": {},
"outputs": [
{
"data": {
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ejCtXrhTZ99mzZyEIAlq1aoWIiAikp6eja9eusLW11aun0+lw9uxZPHjwAE2bNkWLFi2K7Cs+Ph6RkZFwcXFB27ZtoVQqERkZiZiYGKSnp4sxPf/883BzcwMAXL16FdHR0fDw8ICPjw/k8n9HTxTE1rJlS5w6dQp5eXkICgpCQEAAsrOzSxVfSft62utx8eJFpKWloWvXrrCzs0NmZiZOnDgBS0tLdOzYEVZWVkW2f9r5XLt2DZcvXwYAODo6onXr1qhbt26xx2/dujUuXryIjIwMtG/fvlLXRTRFTG6qmPj4eNSvX1/qMIioCvvggw/g5eWFr776CgqFQu85Pz+/p3ZvW1hY4KWXXsIbb7yBnJycIt1T33//vXgRb9CgAe7evYusrCwcOHBATBCSkpLQv39/3L9/Hy1btsTp06cxcOBA/Pbbb2I8CxYswBdffIHAwEBkZGQgOzsbf/75Jy5duoTo6Gjk5uZi06ZNAIDmzZvD1tYWo0aNwrlz5+Dr64ubN2/CxcUFO3bsEL8AFsSWmZkJDw8PeHt7IygoqEi3lCHxlbSvJ33//fe4cOEC0tLS0KxZM8TExCArKwuLFi3CRx99BC8vL9y8eRNOTk44ceKE+HpmZGQ883wiIyPF1yAlJQWnTp3CvHnzMGfOHL3jX7p0CdnZ2XB3d0dCQgKSk5Nx+PBhNG/e3IDflmpKqIbS0tIEAEJaWprUoYhycnKErl27CnK5XLh9+7bU4RBVC9nZ2cKVK1eE7OxsQRAEQafTCZm5eZL86HQ6g2JWq9WCpaWl8OGHHxpU/8MPPxSaNGmiVzZr1izBycmp2Prjx48XAAj79+8XBEEQNBqNEBwcLPTp00esM3HiRMHPz09IT08XBEEQoqKihBo1agg///yzGKOVlZWwZ88ecZurV68KFy9eFARBEKZNmyYMHTpU77jTp08XgoKChJycHPG4gwYNEl5++WW92BQKhXD27Fm9bWfMmCG89NJLBsf3tH0V93rY2NgIV69eFQQh/2+1h4eHYG9vL0RFRQmCIAgZGRmCq6ursGbNmlKdz5POnDkjWFlZCTExMXrHt7a2Fi5fviwIgiBotVqhe/fuwqRJk54atyl78nNZmKHXb7bcVBFWVlZQKpUAgKNHj2L06NESR0RU/WTnadHy472SHPvKvCDYWj77T3JqairUanWRgcMHDhwQZzt3cnJC7969xecyMzOxZcsWaLVahIeH44cffsAnn3xS4jECAgLE7RUKBd555x1069YNycnJcHFxwaZNm/Djjz/C3t4eANCkSROMHj0aGzduxOTJkyGXy6FUKnH9+nX07dsXMpms2G6rAhqNBr/99htmzJiB3bt3QxAECIIAT09PbN68Wa/uc889h4CAgKe+Rs+KrzT7AoDu3buL8VtZWSEgIAAymQxNmjQBANjZ2aFt27a4du1aqc8nIyMDERERSExMhFarhYODA86ePYtGjRqJdXr06CHOiyaXy9G9e3fs37//mXFXZ0xuqpBly5bB2dkZ7u7uUodCRFWUnZ0dACA5OVmvfO/evYiNjUVERATs7OwQHh4uPpeeno5NmzZBoVCgbt262LlzJ/r27VviMQpfWAGgcePGAIA7d+5Aq9UiKytLLCvg5eUlXnAVCgV+/fVXzJ49G/Pnz0e3bt0wcuRIDBo0qNjjJSYmIjMzE2fOnEF0dLTec127dtV7/Ky/j4mJic+Mz9B9FXB2dtZ7bGVlJSZOhctycnLEGAw5n+3bt2PixInw8PBAgwYNxH0kJiY+8/gFx6LiMbmpQlq3bi11CETVmo1SgSvzio67qKxjG8LOzg7e3t44c+aMXvnChQsBALNmzcLhw4f1nis8oNgQT653V/DY2dkZNWvWhEKhKFInJSUFLi4u4uMhQ4ZgyJAhuHr1Knbv3o3x48fjs88+w6xZs4ocz97eHjKZDNOnT8fw4cOfGtuzlqsxND5D9lVWhp7PjBkz8PHHH+u9Jq6urlxM2Qg4iV8VlZeXJ3UIRNWOTCaDraWFJD+ludDOmjUL27Ztw8GDByvkdTh+/DgePnwoPt62bRs8PDzg4eEhzs21bds28XmdTocdO3agS5cuAIDs7GykpaUBALy9vfHWW29h0KBBOH78OID8i39ubq64vYODAzp16oSff/65SCz3798vVeyGxFfRDDkfrVaLxMREvWV4/vnnH73XncqOLTdVTFZWFl5//XXs3LlTHIFPRFTY1KlTxfEsY8eORfv27eHo6IiYmBhs3boV7du3L9f+rays0Lt3b7z22mu4desWFi1ahDVr1oi3MS9atAi9evWCUqlEhw4dsHnzZqSmpuKDDz4AkN8N1rlzZwwZMgQ+Pj6Ij4/H1q1bxYt9QEAAVq1ahZUrV8LZ2RnPP/88fvjhB/Tq1Qu9e/fG8OHDkZOTg7///hv169fH8uXLSxX/s+KrDM86H4VCgQEDBmDWrFmYM2cOkpKSsGTJkiK33FPZMLmpYmxsbHD69Gk8fPgQ27Ztw4QJE6QOiYiqoMWLF2P8+PHYunUrTp48CSsrKzRs2BCbNm3Sm5yvdevW6N+/f6n2PWDAAIwdOxahoaFIT0/Hjh079PbRpUsXnDlzBmvWrMGBAwfQqVMn/PLLL3B1dQUAuLm54fTp01i9ejUOHDiAmjVrIjQ0FM899xwAYMSIEcjIyMCJEyegUqnQvHlz+Pr6IjIyEqtXr8aRI0dQq1YtTJ06FQMHDhSP2759e2i12iLxPjmJ37Pie9q+nlRcvU6dOhW5hb5r166oU6eO+NiQ89mwYQO+//57HDx4EC4uLggJCcG6devg5eX11OO3aNECvXr1embs1ZlMqIadeyqVCo6OjkhLS6uSEyEdPHgQ1tbW6NixY5E5LIjIeHJychAbG4tGjRpJuhxBVfLKK69Ao9Fg3bp1UodC1dTTPpeGXr/ZclMF9ezZU+oQiIiITBaTGyIiEhnaXUNUlTG5qaLu3r2LNWvWQK1WY968eVKHQ0TVxIwZM6QOgajceCt4FRUfH4///ve/WLp0qd4tk0RERPR0bLmpotq3b48xY8age/fu0Ol0UodDRERkMpjcVFFyuZx3KxAREZUBu6WIiIjIrFSZ5KasXS/mPk1PRkYGtmzZIq42S0RERE8neXIzf/581K5dG0qlEj4+PgatlRIZGYkBAwbAxsYGbm5ueO+998x20O2rr76KYcOGYfXq1VKHQkRmLisrCxqNptx1KurYRIaSNLlZsWIFvvzyS6xfvx5paWkYMmQIgoODERsbW+I2UVFR6NKlCxo1aoS7d+/izp07cHV1xYULFyox8srz4osvonHjxqhdu7bUoRCRmWvWrNkzx/r5+/vjf//7nyTHJjKUpMnNkiVLMGnSJPTu3Rv29vb49NNPUatWLaxYsaLEbT744AM0atQI33//PZydnWFjY4M5c+agU6dOlRh55Rk6dCiioqIwZ84cqUMhIjNnZ2cHpVIpdRhE5SbZ3VLJycm4efMmunXrJpbJZDJ069YNJ06cKHYbrVaL0NBQfPTRR5DJZNDpdOIqtebK3M+PiEqvoBveysoKgiAgJycHNjY2enU0Gg0sLEr+E6/VaousXXfhwgVYWloWqfu0fWVkZMDGxkZvX1lZWbC0tBS3yc7OhlarhUwmg52dnWEn+ZQ4iZ5FsivngwcPAEBvlVYgfzXZgueelJSUhMzMTAiCgHbt2sHS0hL16tXDBx98ALVaXeKxcnNzoVKp9H5MjSAIiIqKkjoMIiqBSqVCbGxspfx9mTZtGoYOHYoxY8bA3t5ebLlOT0/H5MmT4eDgADs7O7Rq1Qo7d+7U23bhwoWoW7cubGxs0LRpU/z666/ic092Dd26dQvPP/88rKys4O7ujvfff19vXIxGo0GNGjXwzz//6B2jTZs2el1XQUFBqFOnDtzc3ODo6IgRI0YgMTHxqef4tDiJnkXyZoEn75LS6XSQyWTF1i24M+rrr7/GkiVLkJOTgy1btuCnn3566hIF8+fPh6Ojo/jj4eFhvBOoBLm5uWjRogWaNm2KhIQEqcMhoiecP38eS5cuxdq1a7F06VKcP3++wo+5a9cuBAQEIDk5GREREQDyu7EfPXqE69evIysrC1988QVGjBghPn/s2DF8/PHH2L59O9RqNfbv34+wsLASjzFixAjUqFEDiYmJuHz5Mq5cuYLo6OhSx3rkyBFkZGQgMzMT165dQ3p6Ol577bUS65c2TqInSZbc1K1bFwCKZO+JiYmoU6dOsdvUqlULSqUSY8aMwfPPPw8LCwsEBgZiwoQJ2Lp1a4nHev/995GWlib+xMXFGe9EKoGVlRVq1KgBpVKJ8PBwqcMhokJUKhVCQkLEL1+CICAkJKTCW3DatGmDt956C9bW1gCAM2fO4O+//8b333+PmjVrQq1Wo2/fvujVqxc2bNgAALh37x4cHR3h5+cHAPD09CzxTsyTJ0/i9OnT+PHHH+Hi4gJnZ2f8+OOP5eoqFwQBDg4OePfdd7Fz584SpwApTZxExZEsualZsyZatmyJQ4cOiWU6nQ6HDh1Cly5dxDKNRiN2OSmVSnTq1KnI7YJ5eXlPHQRnZWUFBwcHvR9Ts379eqSkpKBfv35Sh0JEhSQnJxeZb0sQBKSkpFTocVu0aKH3+MKFCxAEAV5eXqhVqxZcXV3h5uaGQ4cOiS2+QUFBcHNzg7e3N95++23s3r0beXl5xe7/2rVrcHFxQYMGDcQyd3f3Er98Ps327dvh6+sLa2tr1KpVCwMHDoRarS6xa6o0cRIVR9Juqblz52L16tX4888/ce/ePbz11lvIyMjAq6++KtaZPn06/P39xccffPAB1q9fj507dyIlJQW7d+/GmjVr8PLLL0txCpWmefPmsLe3lzoMInqCi4tLka50mUwGZ2fnCj1ucV/orK2toVKpkJGRofdTMF7FwcEB58+fx8qVK2FlZYVZs2ahbdu2SE1NLbIvhUIBrVZbpLxwWUlDCAq3yMTGxmL48OF44403kJqaiuzsbHGMTknz2pQmTqLiSJrcjBs3DosXL8b7778Pb29vnD17Fvv370f9+vXFOkqlElZWVuLjfv36YdWqVfjoo4/QuHFjzJ07F59//jlvlSYiSTg4OCA4OFi80MtkMgQHB1d6C3H79u2RnZ39zIlQLSws0KtXL3zxxReIiIhAXFwc9uzZU6Req1atxPE7BWJiYvRaWxQKBRwcHJCcnCyWZWRkID4+XnwcEREBW1tbTJo0Cba2tgBQ4h2xZYmTqDiSL5z52muvPXVg2Y8//likbPjw4Rg+fHhFhlUlhYaG4qeffkKfPn3w+uuvSx0OET3m7+8PLy8vpKSkwNnZWZKubz8/P/znP//BxIkT8cMPP6B9+/aIi4vDunXr0LFjR4waNQorV65ETEwMRo4cCQ8PDxw4cADZ2dlo3rx5sefUo0cPTJo0CStXroRcLse0adOKdMF1794d33zzDfz8/KDVavHee+/pdSG1bNkSGRkZWLVqFV544QWcOHECn3zyyVPPpTRxEhVH8rulyHAxMTH466+/sH37dqlDIaInODg4wNPTs1ISG2tra3EgcWEbNmzA1KlTMXfuXLRq1QozZsxAkyZN8J///AdAfmu5i4sLJkyYAG9vb3zzzTfYtGmT2PX/5CR+GzduRJ06ddCtWzeMGDECgwcPhp+fn16dH374AS4uLujcuTOGDx+Ovn37wsfHR6zTrFkzrFmzBosXL4a3tze++uor/N///R/s7Oz0BicXPvaz4iR6Fplg7itPFkOlUsHR0RFpaWkmNbj41q1b2LhxI/r37w9fX1+pwyEyeTk5OYiNjUWjRo2KTRaIqPI97XNp6PVb8m4pMpynpyfef/99qcMgIiKq0tgtRURERGaFyY2J0Wg0OHLkCJYsWSJ1KERERFUSu6VMTHp6Onr06AGdTodhw4aZ3FISREREFY3JjYmpWbMmgoODYW9vj5ycHKnDISIiqnKY3JigHTt2SB0CkVmphjeNElVZxvg8cswNEVVbCoUCAMT164hIellZWQCKX2LEUGy5MWEFC/bVqlVL6lCITJKFhQVsbW2RlJQEpVJZrhWviah8BEFAVlYWEhMT4eTkJH75KAsmNyZq7ty5WLhwIebNm4ePPvpI6nCITJJMJkPdunURGxuL27dvSx0OEQFwcnIq0+rzhTG5MVENGzaEIAiIjo6WOhQik2ZpaYmmTZuya4qoClAqleVqsSnA5RdMaPmFwh49eoScnJxyZ7dERESmgssvmDknJyepQyAiIqqSOHqOiIiIzAqTGxN248YNTJw4EePGjZM6FCIioiqDyY0JEwQBa9aswebNmzlbMRER0WMcc2PCmjVrhk8//RQdO3Y0yuhyIiIic8C7pUz0bikiIqLqxtDrN7uliIiIyKwwuTFxgiDg4sWLWLZsGbKzs6UOh4iISHIcc2MG+vfvj3v37sHHxwc9evSQOhwiIiJJMbkxcTKZDMHBwbh16xYsLPh2EhER8WpoBn766SepQyAiIqoyOOaGiIiIzAqTGzOSm5uLtLQ0qcMgIiKSFJMbM7FgwQLUrFkTCxculDoUIiIiSTG5MRNubm7Izs5GeHi41KEQERFJigOKzcSgQYMQGBiIFi1aSB0KERGRpJjcmAlnZ2c4OztLHQYREZHk2C1FREREZoXJjRmJjY3FnDlz8Prrr0sdChERkWSY3JiRrKwsLFmyBKtXr4ZarZY6HCIiIklwzI0Z8fb2xsyZM9G+fXvodDqpwyEiIpIEkxszIpfL8e2330odBhERkaTYLUVERERmhcmNGYqLi8PGjRvZNUVERNUSu6XMjEajgbe3NzIzM9G6dWv4+PhIHRIREVGlYnJjZiwsLPD8888jKSkJKpVK6nCIiIgqHZMbMxQSEgK5nD2ORERUPfEKaIaY2BARUXXGq6AZEwQBGo1G6jCIiIgqFZMbM/Xf//4XderUwbp166QOhYiIqFIxuTFTeXl5SExMxNGjR6UOhYiIqFJxQLGZmjRpEoKDg9G+fXupQyEiIqpUTG7MVNOmTdG0aVOpwyAiIqp07JYiIiIis8KWGzN28+ZN/PHHH3B2dsb06dOlDoeIiKhSsOXGjF24cAEffvghfvrpJ6lDISIiqjRsuTFjzz33HP7zn/+gW7duUodCRERUaZjcmLG6devijz/+kDoMIiKiSsVuKSIiIjIrTG6qAZVKhdOnT0sdBhERUaVgt5SZi42NhZeXFywtLZGWlgZLS0upQyIiIqpQbLkxc56ennB2dkbdunURFxcndThEREQVji03Zk4mk+HmzZtwcnKSOhQiIqJKwZabaoCJDRERVSdMboiIiMisMLmpJmbNmoWWLVviypUrUodCRERUoZjcVBMRERG4evUqjh49KnUoREREFUomCIIgdRCVTaVSwdHREWlpaXBwcJA6nEqxd+9eqNVqdO3aFTVr1pQ6HCIiolIz9PrNu6WqiaCgIKlDICIiqhTsliIiIiKzwuSmGomKisLKlStx8uRJqUMhIiKqMExuqpFly5Zh2rRp2LBhg9ShEBERVRiOualGevXqhcjISLRu3VrqUIiIiCoM75aqJndLERERmTpDr98m3y2l0Wig0+mkDoOIiIiqCMmTm4ULF6JevXpQKpXw8/NDWFiYwdvOmTMHSqUSb731VgVGaH40Gg0SEhKkDoOIiKhCGDTmJjw8vFQz2/r5+aFLly7PrLdy5Up89tln2LJlCwIDA/H1119jwIABiIyMhKen51O33b17N/bs2YNmzZoZHBcBoaGhGDFiBPz9/UuVSBIREZkKg5KbAwcOYP78+XB1dX1m3ZSUFIwbN86g5Gbx4sWYNGkS+vXrBwD4/PPPsXbtWqxYsQILFiwocbv79+9jypQp2LlzJyZMmGDIKdBjTZo0QUZGBm7cuAGtVguFQiF1SEREREZl8N1SEyZMwKJFi55Zb9GiRQZ1eaSkpODGjRuYP3++WCaTydC9e3ccP368xO10Oh3Gjh2LN998E35+foYFT6JmzZrh4sWLaNWqFeRyyXsliYiIjM6g5KZr167IzMw0aIeG1i1IgJ5sDXJzc8Pp06dL3O7LL7+ETqfDnDlzDIoHAHJzc5Gbmys+VqlUBm9rbmQyGXx8fKQOg4iIqMIYlNx06tRJ/H9SUhLy8vLg7u7+zLplodPpIJPJin3u9OnTWLp0Kc6ePQudTifeJSUIAjQaDSwsij+d+fPn47PPPitXXERERGQaSt0vsW7dOixZsqTcB65bty4AIDExUa88KSkJderUKXabs2fP4tGjR/Dy8oK1tTWsra0RERGB7777DtbW1tBqtcVu9/777yMtLU38iYuLK3f8piw9PR3vv/8++vXrx9voiYjI7JQ6uWnQoAFiY2PLfeCaNWvC29sbhw4dEssEQcChQ4fQuXNnsUyn04lJy2uvvQaNRqP307ZtW8ycORMajabEwbFWVlZwcHDQ+6nObGxs8N1332Hv3r24fPmy1OEQEREZVamTm+DgYNy6dQsrVqwo99iVd955B6tXr8aOHTuQmJiIt99+GyqVCq+++qpYZ+rUqWjbtm25jkP6LCws8Omnn2L16tWoV6+e1OEQEREZVanXlvrxxx9x8eJFvPrqq3j11VdhZWWl9/zs2bP17oB6mgkTJiA9PR2zZ8/GgwcP4OPjg3379sHDw0Oso1AoShxLA+RfqHk7c+m9/fbbUodARERUIUq9tlRMTAyuXLlS4vNNmjSBt7d3uQOrSFxbioiIyPQYev0udctN48aN0bhx43IFR1VDQkICjh49iueeew61a9eWOhwiIiKjKNMsbv/884/YepOVlYXp06eje/fu2Lhxo1GDo4o1aNAgDBs2DPv27ZM6FCIiIqMpdXLz4MEDTJ06FfXr1wcAfPPNNzh48CACAgIwefJkXLt2zehBUsXo3r072rZt+9QxTURERKam1Fe1gwcPokOHDmJf159//olvv/0W/fv3R25uLkJDQ9GiRQujB0rGN3/+/Keu4UVERGSKSt1yk56eLn7TT05OxtWrV9G9e3cAQK1atZCenm7UAKnilDQTNBERkSkrdXLTrl07bNu2DX/++Sfmzp2Lbt26wcbGBgBw/vx5BAQEGD1IqliCIOitvUVERGTKSp3c+Pv7Y+bMmZg+fTpOnjwprhR++fJlxMXFYcCAAUYPkirO//73P9SvXx8ffPCB1KEQEREZhcHJTcGCmQDw6aefIikpCZcvX0br1q0BAN7e3jhz5gzk8jLdgEUSsbGxwb1793D8+HGpQyEiIjIKgzOR9evXo1atWhg2bBh+/fVXJCUl6T3/rJmEqWrq168fDh48iIMHD0odChERkVGUaobiCxcuICQkBCEhITh//jzatWuH4OBgBAcHm9T6T5yhmIiIyPQYev0u9fILBRITE7Fr1y6EhIRg//79cHJywsCBAxEcHIyePXuKg4yrIiY3REREpsfQ63eZB8i4ublhwoQJ+PPPP/Hw4UOsWrUKlpaWePPNN+Hi4oLly5eXdddUyRITE7Fo0SK8++67UodCRERUbmVuuXmaq1evIj09HR06dDD2ro2CLTf6YmJi0KRJEyiVSjx69Ai2trZSh0RERFREhS2cCQAajQa//PILdu3ahfj4eNStWxe9e/fGtGnTYGVlVeVXBSd9jRo1wvjx4+Hj4wONRiN1OEREROVS6pYbjUaDnj174uLFixg8eDDq16+PBw8eYPv27XB3d8fx48er/Dd/ttwQERGZngprudm5cydiY2Nx/fp11K5dWyxftGgROnfujLVr12L69Olli5qIiIionMq0Knj//v31EhsAcHBwwODBg/HgwQOjBUeVKysrC3///TeXYiAiIpNW6uTGx8cH58+fh1arLfLcmTNnxBmLyfS0aNECvXv3xqlTp6QOhYiIqMwM6pa6e/cuYmNjxceCIKBXr16YNGkS3N3dkZSUhHXr1uHmzZvw9fWtqFipgnXq1AmCICA5OVnqUIiIiMrMoORm48aNeOedd4qUh4WFFSn78ccfxcU0ybT88ssvsLGxgUwmkzoUIiKiMjPobimtVisumvksFhYWVX6NKd4tRUREZHqMereUQqGAQqEwWnBU9QmCwBYcIiIySQYNKL506RJOnz5t0A5LU5eqnlWrVqFNmzZYuHCh1KEQERGViUHJzd69e7F582aDdliaulT1ZGRk4NKlSzh06JDUoRAREZWJwYNjfv/9d5w9e/aZ9eLi4jB48OByBUXSGTRoEOrVq4fnn39e6lCIiIjKxKDkJjAwEI8ePTJ4p127di1rPCSxhg0bomHDhlKHQUREVGYVsip4Vce7pYiIiEyPodfvUs9QTOYvJSUFa9aswddffy11KERERKXGlhu23BRx8eJFtG3bFnZ2dkhJSYGlpaXUIREREVXcquBk/lq3bo1+/fohICAAOTk5TG6IiMikMLmhIuRyOXbv3i11GERERGXCMTdERERkVkrdcnPt2jXs2bMH4eHhSE1NhaOjo9iN0aZNm4qIkSSi1WoREREBb29v2NjYSB0OERGRQQxuuTl27Bh69OgBb29vfP/993j48CHs7e3x6NEjrFmzBr6+vujUqRP27NlTkfFSJQoICEBAQACOHj0qdShEREQGM6jl5rfffsOHH36I119/HevWrUO9evWK1ElMTMSmTZswc+ZMTJ06FW+//bbRg6XK5evri5iYGMTHx0sdChERkcEMuhU8Ojoa9erVg7W19TN3qNFoEB0djebNmxslwIrAW8ENk5KSAgcHB1hYcNw5ERFJz6i3gjdp0sSggwqCALlcXqUTGzKcs7Oz1CEQERGVWqnvlrp9+zZmzpyJtm3bwsvLC7169cKPP/4IjUaDxYsX4913362IOImIiIgMUqrk5tSpU/D19cXOnTvRpk0b9O/fHwqFAm+88Qaef/75Ui2uSaYhNDQU3bt3Z9JKREQmw+DkRq1WY+TIkXj55Zdx/fp1/Pbbb/juu++wb98+3Lx5E1qtFgsWLKjIWEkCmZmZCAsL46R+RERkMgweKXro0CHY2Njgm2++gUKh0HuuUaNGOHjwIAICAoweIEmrZ8+eWLFiBXr27Cl1KERERAYxOLm5efMmOnfuXCSxKWBnZ4dz585Bp9MZLTiSnouLC6ZNmyZ1GERERAYzuFvK2toaqampT63z999/4/Tp0+UOioiIiKisDE5uAgMD8ffff+POnTvFPn/06FEMGzaMYzPMUE5ODv766y988sknUodCRET0TAYnN61atUKfPn0QFBSE/fv3Q61WAwDu37+PefPmoXfv3gbPh0OmJS8vD0OHDsW8efMQFRUldThERERPVaqpZ9esWYNx48ahb9++UCgUsLW1RXp6Ouzs7PDtt98iPT0dCQkJFRUrSaRGjRoYPXo07OzsIJdzIXkiIqraSpXc2NvbY+vWrbhw4QLCwsKQnp6Ohg0bYuDAgXBxccGePXvg4eFRUbGShH799VepQyAiIjKIQWtLLV++HGvXroWvry/8/Pzg6+uLNm3awNbWtjJiNDquLUVERGR6jLq2VO/evfHo0SOEh4dj0aJFiImJgUwmQ7NmzeDr66uX9Li5uRntJKjqiY2NhVKpRP369aUOhYiIqFgGDaBo3rw5PvzwQ/zxxx+4efMmRo0aheeffx4vvvgi5HI5li5diqCgINSuXRv//e9/Kzpmksjbb7+Nxo0b4/vvv5c6FCIiohKVaswNAOzatQsJCQk4dOiQWJaTk4MlS5YgNDQUw4cPN2qAVHX4+flBoVAgOTlZ6lCIiIhKVOrk5tatW/Dy8tIrs7a2xgcffICIiAgkJSUZLTiqWgYPHozg4GA4OjpKHQoREVGJSn1fb2BgILZt24Zbt24Vea5FixYICwszRlxUBdna2jKxISKiKq/UyU1AQABGjx6Nzp0747vvvkNcXBx0Oh3Cw8Oxdu1auLq6VkScVMUYcJMdERGRJMo0I9vSpUvxySef4IsvvkCDBg1gYWEBPz8/uLi44JVXXjFyiFSVJCYmYujQoWjWrBkXSSUioirJoHlugPxv6jKZTK9Mq9Xi3LlzuHv3LurUqYOOHTuaxAy2nOem7PLy8uDi4oL09HScOXMG7dq1kzokIiKqJow6zw2Q31qzefNmBAcHIzg4GG3btoVCoUCHDh2MEjCZBqVSiZ9//hmNGjWCn5+f1OEQEREVYXDLTUpKCnbs2IGQkBDs378fTk5OGDhwIIKDg9GzZ0/Y2NhUdKxGw5YbIiIi02Po9dvg5KYwtVqNsLAwhISEYNeuXbh37x569eolturUq1evXMFXNCY3REREpsfQ63eZBshYWlqiT58++PbbbxEVFYVz587h+eefx4YNG+Dp6Qk/Pz9s27atzMFT1RceHo5PP/2Ut/4TEVGVU+rkJikpCWPHjtWbpdbb2xvvvPMOwsLCkJSUhHfffbfI4GMyL7/88gs+++wzrhZORERVTqlnKM7NzcX69evx0ksvITk5Gfb29ujZsyfc3d0BAE5OThg1apTRA6WqZejQoYiPj8fAgQOlDoWIiEhPqcfcxMfHw8PDAwBgb2+PvLw85OXl4cUXX8TPP/+MWrVqVUigxsQxN0RERKanQsfcAMDatWuRlpaGrKwsHDlyBOnp6ejUqRPS0tLKuksiIiKicit1cmNpaQkHBwe8/PLLkMvlkMvl6NKlC/bt2wcPDw/88MMPFREnVVGPHj3CX3/9JXUYREREolInN25ubtBqtYiLi9PfkVyOMWPG4NixY0YLjqq2rKwsuLu746WXXkJUVJTU4RAREQEoY7fUiBEjMHz48CIJTmRkJCwsSj1GmUyUra0tOnfujJYtWyIhIUHqcIiIiACU4W4pAFi0aBHGjBmDJk2aoFu3bvDy8sKdO3cQGhrKW4OrmR07dsDOzk7qMIiIiERlarmpWbMmdu3ahV27dsHLywuXLl2CWq3G6tWrMW7cuFLta+HChahXrx6USiX8/PyeOSnc33//jf79+8PJyQkuLi546aWXcP369bKcBhkBExsiIqpqDLoVfM+ePThz5gx8fX3h5+eH+vXrG+XgK1euxFtvvYUtW7YgMDAQX3/9NZYuXYrIyEh4enoWqa/VahEUFIR33nkHnTp1QnZ2NmbMmIEzZ84gMjISNWrUMOi4vBXc+ARBQEZGhsHvARERUWkZdW2p0NBQ/N///R8uXbqEzMxMuLi4iImOr68vfH190aJFCygUilIF2bx5c/Tr1w/ffvstgPwLZIMGDTBmzBgsWLDAoH3ExcWhQYMG2LdvH/r06WPQNkxujCskJASvv/46OnTogM2bN0sdDhERmSmjznMzYMAAnDhxAiqVClevXkWfPn1w/PhxhIaGYsaMGWjdujVq1KiBDh06YMOGDQYFmJKSghs3bqBbt25imUwmQ/fu3XH8+HGD9gEADx48AJA/MzJJw9XVFbdv38aRI0eg1WqlDoeIiKq5Uo25kcvlyMnJwYULF3D79m1ERkbi0aNHOHr0KNq3bw8ABt8tVXB3jaurq165m5ubmLA8S15eHmbPno127dohICCgxHq5ublQqVR6P2Q87du3R0hICGJiYkrdekdERGRspR5QfPToUfTq1UsvKenSpQv+/vtvKBQK+Pv7lysgnU5n0KKbOp0OEydORHR0NH7//XfI5SWfyvz58+Ho6Cj+FCwfQcYhl8sxcOBA2NraSh0KERFR6ZObhg0b4tixY1Cr1XrlFhYW6N+/P7Zu3WrQfurWrQsASExM1CtPSkpCnTp1nrqtIAiYPHkyDhw4gIMHD6Jx48ZPrf/+++8jLS1N/Hlyfh4iIiIyH6VObvr37w9LS0sMHjxYb1ZarVaLsLAwaDQag/ZTs2ZNeHt749ChQ2KZIAg4dOgQOnfuLJbpdDq9cRwFiU1oaCgOHjyIFi1aPPNYVlZWcHBw0Psh41u3bh369u2LM2fOSB0KERFVY6VObiwsLLBnzx7I5XK0aNECbdu2RXBwMJo0aYKTJ09i6NChBu/rnXfewerVq7Fjxw4kJibi7bffhkqlwquvvirWmTp1Ktq2bQsgP7GZPn06QkJCsH//fjRt2hQajQYajQalXNycKsCuXbuwf/9+bNq0SepQiIioGivTDMXOzs7YuXMnTp48idDQUNy9exdt27bFK6+8gqZNmxq8nwkTJiA9PR2zZ8/GgwcP4OPjIy7AWUChUIiDlFNSUrBq1SoAgJ+fn96+Vq5ciYkTJ5bldMhIpk2bhrZt22L48OFSh0JERNWYQfPcmBvOc0NERGR6jDrPzdGjR3Hz5k2DDhwXF4f9+/cbFiURERGRkRmU3OTk5KBLly4YMWIEdu3ahbS0NL3n09PTsWfPHrz88svw9fVFampqhQRLVZ9Op8M///yD9957DzqdTupwiIioGjJozE3v3r0RGRmJRYsWYdy4cUhNTUXt2rXh6OiI9PR0JCQkwM7ODuPHj0dERITR1p4i05OXl4cXXngBaWlpCA4ORteuXaUOiYiIqhmDBxS7urriq6++wueff45Tp07h0qVLSE1NhYODA1q1aoXAwEBYW1tXZKxkAqysrDB+/HikpaVxSQwiIpIEBxRzQDEREZFJMPT6bXDLzf79+yGTydC7d28AwIULF3D06FE4ODigV69e7IoiIiKiKsHgSfwWLVokDhTesmULAgIC8M4772Dy5Mlo0KABXnrpJTx8+LDCAiXTc//+fRw+fFjqMIiIqJoxOLlJSEhAgwYNAAAfffQRZs6ciUePHkGtVuPYsWPIyspCp06ditxJRdXTyZMnUa9ePYwYMcLgJTmIiIiMweDkxt7eHllZWYiKikJUVBTmzZsHa2tryGQyBAYGYu/evfDw8MAPP/xQkfGSiQgICICzszOaNGmCBw8eSB0OERFVIwaPuenUqRO+//57ODg4wM7ODgqFQu95uVyOMWPGYPv27caOkUyQUqlEdHQ0HB0dpQ6FiIiqGYNbbt577z3cvXsXGzZswOjRozFo0CDcv39fr05kZKS4DhQRExsiIpJCqea5OXnyJLRaLRQKBRYsWIDGjRuja9eu8PLywp07dxAaGoq1a9dWZLxkgtRqNRITE3lHHRERVQqDW24KFHRHvffeezh69CiaNWuGS5cuQa1WY/Xq1Xj55ZeNHiSZrr1798Ld3R3jx4+XOhQiIqomytWHFBAQgICAAGPFQmaoRYsWSElJwfXr15GRkQF7e3upQyIiIjNX6pYbotJo2LAhjh8/jlu3bjGxISKiSsHRv1ThOnXqJHUIRERUjbDlhipVbm6u1CEQEZGZY3JDleLQoUNo3749pk+fLnUoRERk5tgtRZXC0tISZ8+eRXR0NNRqNSwtLaUOiYiIzBRbbqhSdO7cGT///DOuX7/OxIaIiCoUW26oUshkMkyePFnqMIiIqBpgyw1JQhAEqUMgIiIzxeSGKtWlS5cwatQovPvuu1KHQkREZorJDVWqe/fuYdOmTVi5ciWysrKkDoeIiMwQkxuqVH369MHcuXNx+PBh2NraSh0OERGZIZlQDQc/qFQqODo6Ii0tDQ4ODlKHQ0RERAYw9PrNlhsiIiIyK0xuSBKJiYn45JNP8Nprr0kdChERmRl2S7FbShKXL1+Gj48PFAoFYmNj4eHhIXVIRERUxRl6/eYkfiSJ1q1bY/bs2ejUqRPq1KkjdThERGRGmNyQZJYsWSJ1CEREZIY45oaIiIjMCpMbkpRGo8HGjRsxZswYLslARERGweSGJJWWloYpU6Zgw4YN2Lt3r9ThEBGRGeCYG5KUi4sL5s6dC5lMhg4dOkgdDhERmQHeCs5bwYmIiEwCZygmIiKiaonJDVUZFy5cwMiRI3H58mWpQyEiIhPGMTdUZXzxxRf4888/YWlpibVr10odDhERmSgmN1RlvPfee7C0tMScOXOkDoWIiEwYBxRzQDEREZFJ4IBiIiIiqpbYLUVVTlpaGpYsWQKtVovPP/9c6nCIiMjEMLmhKufMmTOYN28eLC0tMX36dNSvX1/qkIiIyIQwuaEqp1evXpgyZQqCgoJQr149qcMhIiITw+SGqhyZTIaVK1dKHQYREZkoDiimKq8a3tBHRETlwOSGqrRt27ahTZs2uHTpktShEBGRiWByQ1Xapk2bcPnyZSxYsEDqUIiIyERwzA1VafPmzUOzZs04azERERmMMxSb+AzFKpUKycnJcHFxMflzISIiehpDr99suTFh58+fR0hICARBgEwmQ3BwMPz9/aUOq0Klp6ejRo0aUodBRERVGMfcmCiVSiUmNkD+HUUhISFQqVQSR1YxEhMTMXLkSPj7+0OtVksdDhERVWFMbkxUcnJykVukBUFASkqKRBFVLFtbW4SFhSE6OhqHDh2SOhwiIqrC2C1lolxcXCCTyfQSHJlMBmdnZwmjqjj29vZYs2YN6tSpA19fX6nDISKiKowtNybKwcEBwcHBkMlkACCOuTHnQcX9+vVjYkNERM/ElhsT5u/vDy8vL6SkpMDZ2dmsE5snPXz4EJmZmWjYsKHUoRARURXDlhsT5+DgAE9Pz2qV2ISEhMDLywtTp07l0gxERFQEkxsyOd7e3sjKykJSUpLZ3h1GRERlx24pMjlNmjRBWFgYOnToAIVCIXU4RERUxTC5IZMUGBgodQhERFRFsVuKTJpOp8Pq1atx/fp1qUMhIqIqgskNmbQPPvgAkyZNwuuvv87BxUREBIDJDZm4KVOmoFatWujXrx90Op3U4RARURXAMTdk0po0aYI7d+7AxsZG6lCIiKiKYHJjZEnpucjT6mAhl0Ehl8FCLodcDljI5Y8fyyCXy6QO06wwsSEiosKY3BhRckYu9kcm4FF2HizkgIWFHEq5HHK5DHLkJzUKOaCQy2CpkMNCIYelQgZLCzmUCgUsLeT5yY9MBgvF438fJ0kFiZGiuB9Z/r8FSzGUhkqlQnJyMlxcXEx+IsDr16/j1Vdfxddff4127dpJHQ4REUmEyY0Rvfj9Mdx9lK1XJpdBTELkMv1kRC7+i/wESO95QCGX621vIZdBoZBB+bgVSKn491+lIj9hsrSQw0qpgJWFHNaP/2/9+LGlhfzxv/mPH8RexaUThwBBgEwmQ7feQWjb1g8Kxb8tTs9KnKpCclQQw1dffYVDhw5h1qxZOHr0qCSxEBGR9CRPbr755hssXboUDx48gI+PD7755ht07drV6NtUBq2u6N06OgHQaQXkaavWnTy2UGOY9UUU9JAJgoCD+/bgtb/uIVdupddSlJ9U5SdQFvLHiZSFHHXyEtAw8ypkAAQAqS4+UDs1FJOs/BYpuV5iZaVUwOrx89bK/ETLWimHtYUCVkqFuG3BMSwt5IXK8lu2Cida58+fR0hICARBgKenJ6ZNm4b33nuvVK9FeRM0qbcnIiJ9kiY3q1atwocffojNmzcjMDAQX3/9Nfr164crV66gQYMGRtumshyd2wN/no+HOk8HO2sFtDoBGp0ArVb49/+FfjQFP1odtEIJdbQ6sVwnCNBohX/rivvNr6MVCpcVfg5iHc3j7VygFhObAnIZUEOeg0ydJTQ6AblPOVdbqNHB+ioK8gwZAKeHl/BHvAxZsKyolxgyQGypqqHIQz+cR8FpCIKAOnXr4u0dUZBZ3YelRX6CZPW4y09MsMT/KyA8jEH6jdPIT89k8PDtijqNvfUStIIEy+qJx5YWcly9HIE9oaEQHrd+BQcHw9/f3+DzKZyclWV7gMkVEdGTZIKEk4O0aNECffv2xbJlywDkX5w8PDzw8ssvY/78+Ubb5kkqlQqOjo5IS0sz6h9znU5AyMX7SMrIEctk4r+FMomChECvqHIHGWtyMhH/z5/Iv6j/G4VTu5cgWNqICZemUCKmK5Q8IT0R9nHHi+z3rmt7ZFq6QFcomSqcyGmf+FFqc2Cty4JKsEamYKn//OMkriR15Cr0t7pRpHx3bjMk6J79vj7ZegXkt7T9kdPGoASt2O0BHFK0g9bCRkyClBb5XYaFW60sLeSwFnJhH7XviXdehjqdB8PGvoZ+glXo/1YWclg+Tthu34jEycP7xOQoqP8AtAsIgMLAQevGSK4A6RMsJmjlJ/V7IPX2ZBoMvX5L1nKTkpKC69ev48svvxTLZDIZunfvjuPHi140y7pNZZLLZXiuaS2otfnzrYiXl2KSGFnRXOffx8WMbylP6lM4PSicy16x6YdD+/eIF7ZuvfuhpU8LCIW3EP79RxD/LyAjvS42rj6htz+ZTIa3ggNga1dDrCcIBdsKhfaRXx519RJOHzmaXyiTod1zveHZolX+NkL+9rrHSVFunha5Wh3yNDrk5OmQp9MiMz0Dt8Ju4skEbUA7L5w4dgzHD+5Fl/5D0dTHH3mPW6wKWso0OgGW2Q8hT9V/reQywMtRwENY6yVmei1kOgE6AXCQ5xRt/QKgzlIhwYApd/KTs6Lv1ppDl8uUnAmCgNBduzDhzzjkyCz/HbRuoT8mK7/lSw47mRotkv/Ra/n6a2cIfr+uhqWNHSwtFOJ2Vsp//698vI/8/cuQeucGYs4dQUHrV5vOPdDE2wcWcjksLfK7Mi0UMvG4For8rs6CLs4LFy6UK8Eyl9YvKWMo72to6tsXqAq/B2QckiU3CQkJAABXV1e9cjc3N5w5c8Zo2wBAbm4ucnP/7WSpyJWka9pVXJeMsT0X2AFtW7VASkoKnJ2dS/dhdLRBcHBwkT8oLRrUNmhzlUqFjUcOFMqYBJw7egDPtWtdqjjOOxSNwd/fH0tu/YM94Xvg5F0TS7+YBEEAdEJ+UqJ7fMy0tDT89MP5Ignasleeg619Df1tdEKhx/kJz6O0R9i96ea/55C/A8zs7wed0gbZeRrk5OmQm6dFjib/3zytgDytDnlaAbpcRwi3b+glrgIAjzquqAUr5OkEaB8nYhoxOfu3RcxFl1Fs16KDPAdZOkvkaHSARoeS+hfryFXwfiK5kkFA2KVYg5IroLjWKwHhxw7iw78flqn1SxAE7Ni5EzN33UOewlqcTqGkOwatdLnFJGg7sTI8EzJL28cD9aE3iF8sezxgX55yC7I758SYFJ7toHRrApkM+fVlAB7/K4MMMln+l42C7yC5CdHIuHlK3N6+WUfY1m2a/3o+8f482U5e8EUi+34UMm78uw+7ph1hXaeJ+OWgYNvCXxjw+HdTgABNYgzUMf/+DVR4toO8VmPoCuoX+nIh7uNxuU6dBctre4pNcnUWNsV+OSockywvGw7R+4ps/9uVHAjKotM0FLyG+f8H5Joc2N3cW2T7P27m5b+HBa+9+H7IxDK5TAaos5ATvlMvxp07Q3AyyQKWtvb/3rjx+GaNguk4Cv+rkMvw8NY1RJ39N0n36dwDjZq3Fn//lAoZLB6P+xOT9ceJu4VChssREdi1S9okm/4l+YDiJxX8Yhhzm/nz5+Ozzz4rb2hmycHBocwfIn9/f3h5eZUpOUpOTi6yXIIgCEhJSSnVfkqKYebMmWjatCmCg4Mhk+X/MZU/0f5lXcu52ATNs65rcYcqqnYNWBSzvb9/C4PjP3/eVm/7fv0H4N22vtDq8i9GOt2/CZlOEPQSrnSVCpt/uV4kOftqdGfILW2Rq9FBXdDapdEiN0+H7DwtcjVa5Gp0yM5Mh+7Sk8mVDH5N6yNPbl0oofq3tUv7RCtWjTw15E8kT3IZUMc6Dw90NtAJgFbIb4HTPo6/sJJavzTZ6Xige/bfgeITNCA8Kr5UrV+F/3zkxZ7FhqvqMndNqq6fwqqIbIPHnhW3j/Qbp7D6omH7KG77vNiz+MPAcyiuBbE0SW5J25+4crtc2x+KiCnz9oCAtWGR5UrSI44dxEflTNLfCLkHrYUNlIVaLpUKGZQFXdYFd7paKFAjIw72iRHiDRoWnu1gU9fr8ThBxeMbMgq6qQs9Vvx7h+y/4woVsFIWdGHLyzRNiKmTLLmpU6cOACApKUmvPCkpCbVrF//tvyzbAMD777+Pt956S3ysUqng4eFRprhJX1mTIxcXF8hksiIXZmdnZ6PEYGFhgRdeeOGZ25YnQZN8e6fiW8/8Wzc0eBfnPRV627/w+NumUKiVS3jiX53wb+KlUqnwy8pLRd7HFRO7w9beXr+LUcgf2J6nyU+ONFoBKlUawrY+0bUok2H2QH8orG2R9zhBU2vyk6u8x61XeVodtFogLycDwuWiCdpzrT2hVdjotdY9eU6CIECZnQz5A/3XRC4DunhYIcvKOT8ZK+haxb8tIHj8r606pdiuzXZ1lMiwdCr2NX9yfJ1dXgrkj4ruo4O7ElmWNQHZv1v829WdXyYDYKNOhjy56PZdPayQa+OSf8RCrU2ygige78xCUwNCfNHXMKCZB3QWNnrHLW6coFzjBOFW0e07NG8A3ZMtN4W6tsX95BXXgimDr1d9aC2sCzXuPv69e9ySisf/V2jtICQUbQFtWNcNdeTW4u+sTlf8lwSdIMAxLxfyvKKvobuNBg8EW73ttY/HHxYeDlhSkq7NSUeCAUn6k0m2DKVLsp9Fb+yfhSL/ZovH4/asLOSwVMphVShJsrKQi1OIWCsVsFHmJ0vWSgWsxTILWFvKYaNUwNYyf8qRgn9tlApYPJ6qRCqSJTfOzs5o3rw5Dh06hCFDhgDI/+U9fPgwXn75ZaNtAwBWVlawsiqS2pOEHBwcir0wV0RTrFqtxsKFCzFp0iQxQX4ylvIcV8rtKyq5kslkUMgAxTNGeznZ1ir2fWzm4WZgBDXhkFdc61dzg8/hfKPiEzRDpKWl4dtvTxdJzhaM7iy+FkIxF+SCMpVKhRXLi3ZtLnq5i8HvRUn7+HqM/j5KugEhPV2F5d+dK/EcCr61Fx3b9+//L1ywL/NrCADnz9uUc3vbErcXu8KeGApYuIss/EIN7A7dpdcCOretn/ieFTv2r1AXXXq6Cr+svFzkNfxxYjfY2dcolBD/m6jrdDox2VapVNj/R9Eu6tkD/SFYWEOtFZCrye+eVufpkKvNb0lVa3XQagUI6YmQx+m/JnIZ0NHdEmkWTmIrap4uv37e4y8HBa2oBS2shccIFqbW6qDWApnQAngii6sgSoUMO1/vihZ1pelek/Ruqf/973948803xdu6v/rqKyxfvhyRkZFo2DD/2+fkyZNx8uRJXL582eBtnqWi7pai0lOpVGW+MBvq5Zdfxrp16zBo0CBs3bq1WjbRVrTyvo9Sbl8VBrNWhRhM+T00xvZSvgcqlQpLly4tklxNe+112NrXEFurdILwuJtXvxWpcGtSftKjQ65Gh6xcDbLytMhWa5Gt1iD7cdd0dp4Wao1O7LpW5+mg1uaPCRRbSguND9QUJHKFHj/ZZZ2n1eGJnAr7Zj+PZrVrGPwaGvpaGXL9ljS5AYAlS5boTci3dOlSvQn5nkxuDNnmWZjcVC8XL15E//79sXTpUgwbNkzqcKgKkvrCWlViqO5MOckuLUF4YpoP3b93p+p0RVuCClqJdDogT/tvd/G/yZEOao0WOXk65ORpoZDLMNi/PpyNfJONySQ3UmByU/3k5uaya5KIqjRTT1ALJ0UCBNhaGn/kS5Wf54aoMhVObHJzc2FpacnuKSKqUso7fk9qcrkMlhIOIi5MLnUARJXp9OnTaNOmDTZs2CB1KEREVEGY3FC1snfvXty4cQNffvkldDoDphEmIiKTw24pqlbef/995OXlYdasWZDLmdsTEZkjDig24f5NIqKqIi4uDlevXoWrqyv8/PzE8hs3bsDOzg61a9eGhQW/T1P5GHr95ldXqtYOHz6MqKgoqcMgMhlZWVkYNWoUOnbsiIcPH4rl27dvR1BQEBYsWKBXv0+fPqhfvz7Onj0rlkVFReG3337DtWvXKi1uql6Y3FC19dNPP6FHjx545ZVXoNVqpQ6HChEEAQ8fPtQbF/Xw4UNcu3YNjx49ki6waiYmJgbz5s3Dd999J5bZ2tri2LFjOH36NGJiYsTy2rVro23btmjQoIHePhQKBRQKBdzc/p21OjQ0FOPGjcPcuXP16j65tA5RWTG5oWorKCgIDg4OaNmyJdRqtdThVGuFe8d1Oh3s7e3h6uqKhIQEsfyPP/6At7c3Jk6cqLftgQMHcPv27SILsVLpCYKgl+ifOnUKn3zyCX7++We9esuWLcPWrVvh5eUllg0fPhzh4eFYuHChXt2YmBio1Wo0atRILKtVqxaee+45dOnSRSzLzc1Fw4YN4e3tjbi4J9YiIColJjdUbXl6euLatWtYuXIlbGxsnr0BGd1ff/2Fbt264dVXXxXL5HI5nJycAACJiYliuUajgZOTE1xdXfXKBg8eDE9PT71ZzKn0fvvtN/j4+OhNkzBw4EAMGjQIb7zxhl7yOGjQIAwePNjghW7lcv2VqUePHo0jR47g3XffFcsuXboEtVqN9PR01KtXTyw/ffo07t+/X55To2qIA4o5oJgK0el0vIuqEu3evRsDBgyAu7s74uPjxQtgQkICnJ2dYWlZdOr2gunpAeDevXsYPHgwYmJikJCQAIVCAQBYt24d7t+/j7Fjx6Ju3bqVd0Im7L///S8+//xzDB48GFu3bpUkhrS0NERHR+stO+Dr64vIyEhs3boVL7zwgiRxUdXBAcVEpZCRkYHJkydj5syZUoditlJTUzF37lysX79eLOvXrx9WrVqFf/75R++bfZ06dYpNbADo1XN3d8epU6dw9+5dMbEBgIULF+Ldd9/F77//XgFnYvp27dqFbt26ITIyUiybMmUKvvvuO6xevVqyuBwdHfUSm4yMDNjb20OpVOp1Yd2+fRsqlUqKEMlUCNVQWlqaAEBIS0uTOhQhLS1NiImJqRKxVGd///23AECQy+XC9evXpQ7HLH366acCAKFp06aCRqOpsONotVrhp59+Evr27SuoVCqx/Ny5c8Lu3bsFnU5XYcc2FUOGDBEACBMnTpQ6FIPcu3dP7/F//vMfwcHBQfj9998lioikYuj1m91SEnZLVfYqsPR0X375JTp37ozu3btLHYpZys7OxvDhwzF16lQEBwdX+tpeL774Inbu3ImPP/4Yn332WaUeW0pZWVlYvXo1xo0bJ/69O336NLZu3YqZM2fC3d1d4ghLR6PRICAgABcvXsTFixfh4+MDQL+7kswXF86s4lQqlZjYAPkfzJCQEHh5eXEckEQ++OADqUMwG4IgYM2aNTh+/Dj+97//AQBsbGywc+dOSeLR6XTiZ2vs2LFieU5ODqysrMz6oti/f38cOXIEmZmZ4q3XHTp0QIcOHSSOrGwsLCxw4cIFnDp1SkxsAODzzz9HeHg4PvvsM7Ru3VrCCKkq4JgbiSQnJxe5dVUQBKSkpEgUERX28OFD7Nq1S+owTNaVK1cwZcoUrFq1Cvv375c6HMjlcixZsgT37t1D06ZNxfJPPvkE/v7+OHz4sHTBGVlaWpre35ZJkybB09MT9evXlzAq45LL5QgMDBQf5+bm4ttvv8XWrVv1xhFR9cWWG4m4uLhAJpPp/RGSyWQG31pJFScuLg6dOnXCw4cPcerUKfj6+kodkslp1aoVvvrqK+Tl5aFnz55ShyOys7MT/69Wq7F27VokJCQgPT1dwqiM56uvvsKXX36J9evXIzg4GED+bdejRo2CUqmUOLqKY2VlhbCwMKxevRr/+c9/xPJjx45BoVCgU6dOEkZHUmDLjUQcHBz0xh0UjLlhl5T06tevD39/fzRq1EjvDhx6ukOHDiE5OVl8/Pbbb+P999+vsq+hpaUlLl++jG+//VZMBID8leO3b99ukqvGp6amQqVSYdOmTWKZhYWFWSc2BVq1aoXFixeLv286nQ4zZsxAYGAgfvnlF2mDo0rHAcUSJxMqlQopKSlwdnaWPBb6V2pqKiwtLfW+6VPJVq1ahalTp6JXr17YvXt3lU1onkWj0aBVq1a4ceMGVqxYgWnTpkkdUokiIiKwePFivP/++/D29gaQP+nhP//8g8GDB1f7+ZoyMjIwc+ZMbN++HVFRUWKruEaj4QKeJozz3JgIBwcHeHp6MrGpYmrWrFmkC4NK5u/vDzs7O9SrVw8ajUbqcMpMrVZj6NCh8PT0xOjRo8XymJgYpKamShhZUZ999hl+++03LFmyRCxzc3PD0KFDq31iAwD29vZYvXo1bt26pdfdP2HCBAwZMgQ3btyQMDqqaPwEED3Dn3/+CS8vL64e/hR+fn6IiIjA6tWrYWVlJXU4ZWZra4svv/wSUVFRqFGjhlg+a9YsuLu7Y+PGjZLEFRsbi48++khvFe533nkHw4cPr9KtS1VB4S+O9+/fx6ZNm7Bt2zZkZmZKGBVVNCY3RE+h0+mwbNkyxMXFYfHixVKHU2VkZmZi/PjxiI6OFssaNWpkNrdUF+5WU6vVuHv3LnJycvTmoYqJiUFkZGSlLNg5fPhwfPHFF/jtt9/EssDAQPz+++9o165dhR/fXNStW1fszvPz8xPLQ0JCcP36dQkjI2NjckP0FHK5HBs2bMBHH32EZcuWSR1OlfHaa69h7dq1GDJkiEkOvC0NS0tLnD17FpcvX0bz5s3F8mXLlqF169Z45513jHYsrVaL//73v+jSpYteV+iUKVPQp08fzt9iBC1btsRbb70lPlapVBg/fjxatmyJo0ePShgZGROTG6JnqFevHv7v//6vWtxxYqgFCxagU6dOWLFiRbUY3yGTydCqVSu9soIJAJ977jmxLDIyEvXq1cPIkSP16p44cQJhYWF681hduXIFH330EX788UexTKFQYPXq1Th+/Lje3DtTpkzBvn370KdPHyOfGalUKnTp0gVNmzbVu2U8Ly9PwqiovHi3FAfyUikIgoDvv/8egYGB1a474MkV0zndfX73nFKpFBf5XL9+PcaOHYsuXbrotQJ06NABZ86cwV9//SWubP3XX3/hpZdegp+fH86fPy/WXbx4MVxdXfHCCy+gZs2alXtC1ZhKpRKvB4IgIDAwEC1atMD8+fO5snwVwuUXiCrAsmXLMGvWLDRs2BARERFwdHSUOqRKcefOHbzwwgtYtmwZunXrBgDVPrEBUGSqgEGDBuHYsWPQarV65Z6enlCpVHB1dRXLmjdvjtdeew0tWrTQqztnzpyKC5hKVPhCeerUKZw6dQqXL1/GwoULJYyKyootN2y5oVJIS0tDYGAgpk6dijfffLPaXOCnTZuGlStXwsfHB+Hh4dWiK4qqtzNnzuDq1asYN26cWPbDDz+ga9euaNOmjYSRVW+GXr+Z3DC5oVJSq9ViN0R1kZOTgzfeeAMff/wxPDw8pA6HqNJFRUWhefPm0Ol0iIqKQpMmTaQOqVriJH5EFaRwYqNWq81q0cXCVCqV+H9ra2v8/PPPTGyo2lIoFBg2bBiCg4P1Epvbt29XynQAVDpMbojKKCsrC/369UPv3r2rxMrXxhQZGYnmzZtj5cqVUodCVCU0atRInACwQGZmJgICAtC+fXvcvXtXwujoSUxuiMrIxsYG9evXh42NjUkvOVCcLVu2ICEhAT/99BNviSUqpPC6VOfOnUN2djZSU1NRp04dCaOiJ3HMDcfcUDnk5uYiNja2yB0vpk4QBCxduhTjx4/XW5eHiPQ9fPgQMTEx6NChA4D8z06/fv0QGBiI2bNnV5s7KisLBxQ/BZMbqigJCQmws7PTW5fIVERGRsLb25t3QhGVw+HDh9GjRw/Y2toiLi6OXw6MjAOKiSrZnTt38NxzzyE4OBhZWVlSh1Mqu3btQrt27fDmm29ycCRROXTt2hWbNm3C559/rpfYfP3119i3bx8/X5WEk/gRGcnDhw+RmJgIjUaDhw8fokGDBlKHZLC0tDTk5ubizp070Gg0XGqCqIwsLCwwYsQIvbLbt2/jgw8+gFarRWRkJFq2bClRdNUHkxsiI/H398e+fftQr1491K9fX+pwSmX06NFwc3NDt27dmNgQGZmlpSVmzJiBO3fu6CU2e/bsQfPmzdGoUSMJozNPHHPDMTdUgW7cuIFGjRpVuYRBq9ViyZIlmDZtGj8DRJWk8HpsOTk58PDwQHJyMo4ePYrOnTtLHJ1p4JgbIokdP34c7du3xwsvvICMjAypw9Ezffp0vPvuuxg1ahTHABBVksLLtSQmJsLPzw8NGjQQ77QCgMuXLyM1NVWK8MwKkxuiCpKamoq8vDzk5eVVueUapk2bBmdnZ4wfP77arI9FVJU0aNAA+/btw6VLl8S5cwRBwLhx4+Du7o7du3dLHKFp45gbogoycOBAHD16FI0bN64SyU1ycjJcXFwAAO3atcOtW7dM8pZ1InNS+DOYmpoKrVYLQRD0WnPi4+Ph6OjIz2spsOWGqAL5+/vDyclJfLxixQqEhIRUagy5ubmYM2cOmjZtiri4OLGcfyiJqhZnZ2eEh4fj8uXL4hcRAJgzZw7q1q2L9evXSxidaWFyQ1RJwsLC8Nprr+GFF17AhQsXKu24crkcR48eRWpqKrZv315pxyWi0pPJZPDy8hIfazQaXLt2DZmZmWjVqpVY/vDhQyQlJUkRoklgckNUSQIDAzFz5ky8/PLL8PPzq9BjZWVlIScnBwCgVCqxbt06/PXXX3jjjTcq9LhEZFwWFhYIDw/HmTNn4OvrK5YvXboU7u7u+Pzzz6ULrgpjckNUSSwtLbF06VL88ssvYllmZibeeecd3Lp1y2jHWbduHRo3bozly5eLZU2bNsULL7xgtGMQUeWRyWRo166dXtmVK1eg0WjQtGlTsSwjIwMnTpzgHZBgckNU6Qqv3fThhx9i0aJFGDVqlNH2n5eXhwcPHmDz5s38I0dkprZu3YpLly7hpZde0ivr3LkzBgwYIGFkVQOTGyIJjRgxAn369NHrLtJoNPj2229x5MiRZ27/559/ok+fPli1apVY9vLLL+PXX3/FP//8w9u8icxY69atYW1tLT5OTEyEnZ0dunTpIpbpdDp8+eWXCA8Pr1ZfdjhDMWdnpSqg8Mylf/zxB4YPHw4/Pz+cP39erDN06FDcvn0bmzZtEgcczp8/Hx988AEGDBiAXbt2SRI7EVUdmZmZ0Gg0cHR0BACcPHkSgYGBcHBwQFJSkjgtReG/OabE0Os357khqgIK/5GRy+UYMmQIWrRooVfnwoULiI2NRVJSkpjcDBs2DJaWlhxPQ0QAADs7O73HlpaWGDJkCOzt7fXm2xo4cCAsLCywYMECs1zIky03bLkhE7F7924IgoDAwEDUrFlT6nCIyIQUbqlJTU2Fq6srtFotYmNj4enpCSB/yZjTp0+jZ8+eaNOmjYTRloxrSxGZmf79+2PAgAFMbIio1Aq3Djs5OeHs2bP47rvvxMQGyL/Tcvbs2fj111/Fsry8PCxcuBB//fUXtFptZYZcLuyWIiIiqkZkMhl8fX315s0B8mdUf/HFF9GjRw+xLCYmBu+++y7s7OyQnp4uls+fPx+nT5/G1KlT0b9/fwD5LUJbtmyBg4MDRowYUSnnUhImN0RERITJkydj8uTJemUymQwjR46ETCbTa/05evQoQkNDERwcLJbFx8dj6tSpcHNzY3JDREREVVOzZs2wcePGIuVvv/02goOD0bVrV7HMzs4OL730UpVYt44DijmgmIiIyCRwQDERERFVS0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyKxZSByCFgoXQVSqVxJEQERGRoQqu2wXX8ZJUy+QmPT0dAODh4SFxJERERFRa6enpcHR0LPF5mfCs9McM6XQ63Lt3DzVq1IBMJjPaflUqFTw8PBAXFwcHBwej7dcUVNdzr67nDfDcq+O5V9fzBnjuVeXcBUFAeno63N3dIZeXPLKmWrbcyOVy1K9fv8L27+DgIPkvgFSq67lX1/MGeO7V8dyr63kDPPeqcO5Pa7EpwAHFREREZFaY3BAREZFZYXJjRFZWVvjkk09gZWUldSiVrrqee3U9b4DnXh3PvbqeN8BzN7Vzr5YDiomIiMh8seWGiIiIzAqTGyIiIjIrTG6IiIjIrFTLeW4qgiAIuHbtGnJzc9GqVSsolUqpQ6oQarUa169fh729PRo2bFhkEqXIyEikpqbqlTk7O6Nly5aVGaZR5eXl4dSpU0XKvb294eLioleWm5uLyMhI2NnZoXnz5pUVYoUJDw9HRkZGkXJHR0f4+PgAAO7evYvY2Fi95+VyOTp37lwpMRpTVlYWLly4AA8PDzRo0KDYOqmpqYiOjkbdunVRr169Mtepam7duoX4+Hi0a9cO1tbWRZ7Py8vD9evXYWdnV+xn/8qVK0hJSdErq1mzJlq1alWhcZeXIAg4d+4crKysxN/pAhqNBidPniyyTYsWLVCrVi29MrVajcuXL5vUZ//Ro0eIjIxEkyZNUKdOHb3nIiIixNn8Cyv82b937x5iYmL0npfJZOjSpUvFBW0ogcotOjpaaN26tVCrVi2hYcOGQp06dYSwsDCpwzKqrKws4e233xacnZ0FHx8foW7dukLz5s2FEydO6NULCgoS6tWrJ3Tp0kX8effddyWK2jju378vABD8/Pz0zuvQoUN69UJDQwUXFxehcePGQs2aNYWAgADh3r170gRtJK+88oreOXfp0kWQy+XCsGHDxDoLFy4U7Ozs9Or07NlTwqhLLyEhQXjzzTeFunXrCtbW1sKHH35YbL0FCxYI1tbWgre3t2BtbS2MHj1aUKvVpa5Tlfz9999CUFCQ4OLiIgAQbt68qfd8dna28O677wrOzs5C69atBXd3d6FZs2bCsWPH9OoNHDhQcHd31/s9mDNnTmWeSqloNBrhq6++Epo0aSI4OjoKXbp0KVInKSlJACD4+vrqndeBAwf06u3du1eoVauW0KhRI8HZ2Vnw8/MT4uPjK+tUSi06OlqYNGmSULduXUEulws//vhjkTqTJk0q9rM/ePBgsc4333wj2Nra6tXp1q1bJZ5JyZjcGEFgYKDQt29fIS8vTxAEQXjzzTcFNzc3IT09XeLIjOfu3bvCwoULhYyMDEEQ8v8wTJw4UXBzc9P7wx0UFFSl/6CVRUFyc+nSpRLrJCUlCTVq1BDmzZsnCEL+BaFTp05C//79KyvMSnH69GkBgLBr1y6xbOHChULbtm2lC8oITpw4IXzzzTdCSkqK0Lx582KTmwMHDghyuVy8sMXGxgq1atUSvvjii1LVqWq+/fZbITQ0VDh27FixyU1CQoLw1VdfiX/PNBqNMHXqVKFWrVpCTk6OWG/gwIHCm2++WZmhl0t6errwzjvvCFFRUcK0adOemtxcuHChxP0kJycLjo6OwscffywIgiDk5OQIXbp0Efr06VNRoZdbaGiosHLlSiEjI0Ows7MrNrl50vnz5wUAwo4dO8Syb775RmjVqlVFhlpmTG7K6cqVKwIA4fDhw2JZYmKioFAohI0bN0oYWcU7efKkAECIjIwUy4KCgoRp06YJZ86cEW7fvi3odDoJIzSOguRm165dwrlz54RHjx4VqfPDDz8Itra2QmZmpli2ZcsWQSaTmXzrTWHTpk0TPDw8BK1WK5YtXLhQaN26tRARESFcvXq1SrdSGKKk5Gb06NFC165d9cpmzZolNGnSpFR1qqoTJ04Um9wU5+zZswIAISIiQiwbOHCgMGnSJJP87D8rufnrr79K/OyvXLlSsLa21vsyu337dgGAcOfOnQqN2xgMTW5mzJghuLu7CxqNRiz75ptvhBYtWggRERHClStXqtRnnwOKy+nChQsAgICAALHM1dUVDRs2FJ8zV2fOnIFSqSwyNuGXX37B5MmT0bZtW7Rq1arY8SqmaOLEiRg3bhxcXV0xfvx4vbEoFy5cgLe3N2xtbcWyDh06QBAEhIeHSxCt8WVlZWHjxo2YOHFisWOtRo4ciT59+sDNzQ2rV6+WKMqKc+HCBb3POZD/HkdHR4tjEwypYw7OnDkDhUIBT09PvfJ169Zh8uTJ8PX1hbe3N06cOCFNgEY2efJkjB8/Hq6urhg7dixUKpX43IULF9C8eXPY29uLZR06dAAAs/ns5+TkYMOGDZgwYQIUCoXec9evX8fIkSMRFBQEV1dX/PzzzxJFqY/JTTmlpKTA0tJS7xcbAFxcXIoMrjMnN2/exH//+1/Mnj1b79zHjx+PpKQkhIeH4/79+/D398egQYOKDDI2JVZWVti8eTMSEhJw+fJlXLp0CQcOHMCcOXPEOikpKUUGFxc8Npffgy1btiAjIwMTJ07UK/f19cXNmzdx5coVxMXF4csvv8TkyZNx5MgRiSKtGIa8x9Xh9yA6Ohoffvgh3nzzTb1FFMeOHYvExETxs9+xY0cMGjQIycnJEkZbPpaWlti4cSMePHiAS5cu4fLlywgLC8Ps2bPFOtXhPd+6dSsePXqESZMm6ZW3adMGN27cwJUrV3Dnzh0sXLgQ06ZNw8GDByWK9F9MbspJqVQiLy8PWq1Wrzw7OxuWlpYSRVWx4uPjERQUhG7duuGLL77Qe27UqFGoUaMGAMDa2hpLly5FQkICDh8+LEGkxlGzZk0MGzZMfNy8eXPMnj0bv//+u1imVCqRk5Ojt112djYAmM3vwapVqxAUFFSkpa53795o0qSJ+PjVV19FmzZt9F4fc2DIe2zuvwf37t1DUFAQOnfujAULFug9N3LkSDHZsbKywrfffovExMQqcaErKwcHB4wcOVJ83KxZM8yZMwe///47hMeT+5v7ew7kf/Z79+6NRo0a6ZX37NkTXl5e4uMpU6bA39+/Snz2mdyUU8OGDSEIAu7fvy+WFTwu6VZSUxYfH4/u3bujdevW2Lx5Mywsnj6bgLOzM5RKJe7evVtJEVaO2rVrIy0tTeyaatiwYZFzLHhsDr8HN2/exJEjRzBlyhSD6teuXdvs3vOS3mMrKyu4ubkZXMdU3bt3Dz169EDz5s3x559/PnO6C0dHR1hbW5vd70Ht2rWRmZmJtLQ0AOb/2Y+NjcWhQ4dM7rPP5KacunTpAhsbG/z1119i2dGjR5GcnIw+ffpIGJnx3b17Fz169IC3tze2bNlS5FtJbm4uNBqNXtmhQ4eQl5eH1q1bV2aoRpWZmVmkbN++fWjYsKHYJdenTx9ER0fjypUrYp0dO3bA2dkZ/v7+lRZrRVm1ahXq1KmDF154ochzT74+qampOHv2rEm/58Xp06cP9u7dC7VaLZbt2LEDPXv2FMchGFLHFN2/fx89evRAkyZNsHXr1iKffbVaXeSzf+TIEeTk5Jj070FJn/369evDyckJQP57fvv2bVy8eFGss2PHDjg5OaF9+/aVFWqFWbVqFWrVqoWXXnqpyHNPvj5paWk4ffp0lXjPOYlfOdWoUQMfffQR3nvvPSgUCjg6OuKDDz7A8OHDzeKiViA1NRU9e/aEXC7H7Nmzcfr0afE5Hx8fODo64v79+xgyZAgmT54MLy8vXL16FZ9//jlefPFFdO/eXbrgy2nZsmW4cuUKBg4ciBo1amDHjh3YtGkT1q9fL9bp1asX+vbti2HDhuGzzz7DvXv38MUXX+Cbb74x+QkdNRoN1q5di1deeaXYlrqgoCAEBQUhICAAqampWLRoERwcHDBz5kwJoi0btVot/k5nZ2cjLi4OR48e1Zuw7I033sD//vc//Oc//8GUKVPw999/IywsDP/884+4H0PqVDVxcXG4ffu2mJifO3cOCQkJaNq0qdhC2atXL2i1WsyZMwdnzpwRt23dujWcnJyQmJiIF154AZMnT0bTpk1x/fp1fP755xgwYAB69eol1ak907lz55CdnY2EhASoVCocPXoUANC1a1cAwPLly3Hx4kUMHDgQDg4O2LlzJ9atW4e1a9eK++jWrRv69++PESNGYN68eXjw4AHmzZuHr7/+usp2S6WnpyMiIgIAoNPpEB0djaNHj8LV1VVvAkKtVotff/0V48ePL/ZcCt7fdu3a4dGjR1i8eDFsbW0xa9asyjqVEnFVcCP57bff8McffyA3Nxe9e/fGm2++WWV/scvi+vXrRQaTFVi6dCnatWsHIL/74ocffsCVK1dQu3Zt9OvXD6NGjYJMJqvMcI3uzz//xLZt25CcnIymTZti+vTpRWZdzsrKwpIlS3DkyBHY2tpizJgxemN1TNX58+cxc+ZM/Prrr3pjawqkpaVh+fLlOH78OGxsbNCuXTu8/vrrsLOzkyDasklKSsLgwYOLlLdp0wY//PCD+Dg+Ph4LFizA1atX4e7ujpkzZxb5dm5Inapk7dq1WLlyZZHy9957D8HBwYiKisIrr7xS7LaLFy9Gx44dAeQPNF6+fDkiIyNRu3Zt9O3bF2PGjKnSn/2RI0ciPj6+SHlYWJjY0rZt2zZs3boVDx8+hJeXF6ZNm1akZSI7OxtLlixBWFgYbG1tMWrUKIwYMaJSzqEsrly5gqlTpxYp7927Nz799FPxcUREBGbMmIHVq1ejWbNmReqrVCrxs29lZYWAgAC8/vrr4rhLKTG5ISIiIrPCMTdERERkVpjcEBERkVlhckNERERmhckNERERmRUmN0RERGRWmNwQERGRWWFyQ0RERGaFyQ0RSebu3btYs2YN1q9fj/T09BLrXbx4EYMGDcKgQYOwdOnSp+5z/vz5WLVqlVHj3LBhg3j8AwcOGHXfRGR8TG6ISBKrV6+Gn58fDhw4gK+++grt2rUrdi0fAEhMTMSOHTvwyiuv4Lnnnnvqfk+dOoVLly4ZNVY/Pz+88sorOH78OG7dumXUfROR8TG5IaJKd+jQIcyePRthYWFYv349Tpw4geTkZL31uoozaNAgBAQEVFKU//L29sagQYNga2tb6ccmotLjwplEVOlmzZqFmTNnwtvbGwBgZ2cHHx8fcTG/0ti6dSu2bt2KmjVr4sUXXyy2zoEDB7BlyxZkZGTAx8cHM2bMEFd0B4Br165h+fLlUKlU8Pf3h6+vL1atWqW3QCIRmQ623BBRpTp58iQuXryICRMm6JVrtVqUdqm7FStWYMyYMWjRogXatm2L2bNn4/Dhw3p1vvzyS0yYMAFNmzZFv379cOLECQQEBCArKwsAcOvWLXTs2BEJCQno1asXzp07h0GDBiE0NLRc50lE0mHLDRFVqt27d0OpVGLmzJl65REREejXr5/B+8nNzcXHH3+MhQsX4vXXXwcA9OvXD40bNxbr3L59Gx9//DEiIiLQqlUrAMDo0aPRtm1brFq1Cm+88QYWLFiAli1b4o8//gAAjBs3Dv369cPZs2fLe6pEJBEmN0RUqcLDw+Hn54fu3buLZRkZGdi1axf8/PwM3s/NmzeRlJSk1xVVv359+Pv7i48PHDgAuVyOTz75BAAgCAIEQUBqaqo46PjEiRMYMWKE3r5feOEFJjdEJozJDRFVqvj4ePTt2xdvv/22WLZ9+3bY2Niga9euBu8nJSUFAODo6KhX7uTkJP7/0aNHsLOzw9ixY/XqjBs3Dh4eHgCA1NTUIvt48jERmRYmN0RUqeRyOZRKpV7ZL7/8gpEjR6JGjRoG76dhw4YAgJiYGL0Wn+joaLRo0UKs8+jRIwQGBqJ27dol7icmJkavLDY21uA4iKjq4YBiIqpUbdq0walTp8THO3fuxJEjR/DZZ5+Vaj8NGzZEYGAgvvrqK+h0OgDA5s2bERUVJdYZMGAA3N3d8eabbyI3N1csP3HihNjtNHz4cKxbtw737t0DADx8+BCrV68u8/kRkfTYckNElWrWrFkIDAxEcHAw7O3tcfjwYWzbtk3sJiqNH3/8EUFBQfD29oarqyuSkpLg4+MjPm9ra4uQkBCMGDECjRo1QqtWrXDnzh24ubnhl19+AQBMnToVISEhaN26Nfz8/HDt2jW0adMG58+fN9YpE1ElY3JDRJXKx8cHFy9eRGhoKOzt7fHDDz/A2dnZoG0HDRqE7t27Y9asWQCAtm3bIjo6GidOnICTkxN8fHwQHh6uN9leQcISHh6OxMREeHl5wcvLS3zeysoKe/bswblz56BSqeDj44Nff/0V8fHxYp0NGzZg8+bNSExMNM6LQEQVSiaUdmIJIqJKlpSUhGPHjgEAPDw8jD5LcUhICIKDg8VjdenSBS+++CIWLVoEALh69SquX78OID9ZKhjvQ0RVE5MbIqr2hg4diqtXr6Ju3bo4f/48OnXqhI0bN+rdeUVEpoPJDRERgKioKMTGxsLT0xNNmzaVOhwiKgcmN0RERGRWeCs4ERERmRUmN0RERGRWmNwQERGRWWFyQ0RERGaFyQ0RERGZFSY3REREZFaY3BAREZFZYXJDREREZoXJDREREZmV/wf6zBjLBFQgtAAAAABJRU5ErkJggg==",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -890,67 +1037,60 @@
}
],
"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",
+ "fig, ax = plt.subplots()\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",
- "\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",
+ "plotstyle.band(\n",
+ " ax,\n",
+ " np.rad2deg(fine),\n",
+ " disc_lo_xs,\n",
+ " disc_hi_xs,\n",
+ " color=plotstyle.COLOURS[0],\n",
+ " label=\"GP discrepancy (68 %)\",\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();"
+ "ax.plot(\n",
+ " np.rad2deg(fine),\n",
+ " true_defect_xs,\n",
+ " \"--\",\n",
+ " color=plotstyle.COLOURS[1],\n",
+ " label=\"defect: log(full truth / fitted volume)\",\n",
+ ")\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": "35d64f79",
+ "id": "67bd0f36",
"metadata": {},
"source": [
- "## Takeaways, continued\n",
+ "## Takeaways\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"
+ "- 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",
+ "- 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."
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
diff --git a/examples/hierarchical_calibration.ipynb b/examples/hierarchical_calibration.ipynb
new file mode 100644
index 0000000..b977413
--- /dev/null
+++ b/examples/hierarchical_calibration.ipynb
@@ -0,0 +1,905 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "9ca1c7a6",
+ "metadata": {},
+ "source": [
+ "# Hierarchical calibration: a hierarchy on the physics parameters\n",
+ "\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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b449277b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import corner\n",
+ "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\n",
+ "\n",
+ "plotstyle.use()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3416ec59",
+ "metadata": {},
+ "source": [
+ "## 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",
+ "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": "b9a36078",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "columns: ['mu', 'tau', 'eta_0', 'eta_1', 'eta_2', 'eta_3', 'eta_4', 'eta_5', 'eta_6', 'eta_7']\n"
+ ]
+ }
+ ],
+ "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",
+ ")\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",
+ "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",
+ "print(\"columns:\", p_schools.names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b4c01254",
+ "metadata": {},
+ "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",
+ " 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]: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:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f019a0e5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": 5,
+ "id": "ec0f35b1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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": {},
+ "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 + 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",
+ "\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)\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1d0d39ab",
+ "metadata": {},
+ "source": [
+ "### The data, energy by energy\n",
+ "\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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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.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": "bfafc8d9",
+ "metadata": {},
+ "source": [
+ "## Three fits of the same data\n",
+ "\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**,\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",
+ " $\\delta_j = \\tau \\odot \\eta_j$ 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": 8,
+ "id": "943e52d1",
+ "metadata": {},
+ "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": {},
+ "outputs": [],
+ "source": [
+ "def global_params(prefix, n_per):\n",
+ " return [\n",
+ " [\n",
+ " rx.Parameter(\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",
+ " 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_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, 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_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_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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "c94dbbec",
+ "metadata": {},
+ "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",
+ " \"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": 11,
+ "id": "ce3ad648",
+ "metadata": {},
+ "outputs": [
+ {
+ "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"
+ ]
+ }
+ ],
+ "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": "cc95901b",
+ "metadata": {},
+ "source": [
+ "## The coefficient mappings against the truth\n",
+ "\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": 12,
+ "id": "cdceaa49",
+ "metadata": {},
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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",
+ " 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",
+ " plotstyle.band(\n",
+ " ax, E_grid, lo[:, k], hi[:, k], color=colour, hatch=hatch, label=name\n",
+ " )\n",
+ "for k, ax in enumerate(axes):\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(fontsize=7)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c473b0aa",
+ "metadata": {},
+ "source": [
+ "### What each case predicts, dataset by dataset\n",
+ "\n",
+ "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": 14,
+ "id": "e2b80ed8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def case_curves(name, E, rows):\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",
+ " 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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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1W4/U1FR++OEHHnroIb755hseffRRTjvtNE4++WSOPvpojj/+eA4//HCGDh3K888/z/r16+ncuTNvvfUWI0eOjB/H5XIBEIlEWnbjpD2GDBSlXe6II47g6aefpqysjNmzZzNkyJD4viFDhnDttdeyaNEiZs+eja7rHHrooY2O0dTA84YfluFwGADLsjjllFMoLCxk/vz5eDyeeNk2bdrEf4m0VkP6iPnz5zeZNPbEE08kLS2txedvqO/2flFAy667KS05x666X1sep4Gqqrjd7ka/JDIzMxulQdpV9Wo4t9frbbSvYduW93RH772qqsyZM4dnn32W999/n2uvvZby8nIGDhzICy+8sN2AqqmJGE6nk9TU1Eb3qiHga9DSa9wyoGquXIOWfv5b649+Thrq9frrrzc6jtPpjP/BGYlE0HU9Hqhsqan7tLWG+9uSsk3p27cvCxYs4JlnnmH27NnxoOySSy7hwQcfbDYNWGpqKrquN3nP8/PzAeJ/cG+pR48ezJw5k6KiIrp3786sWbNYsWIFTz75ZDxIBDj88MM55phjePzxx3nggQeanRQFsT9I7rvvvvjrN998ky+++IJFixZh2zbffPMNr7zySrxuBx10EJ9//nlCoFheXg5AVlZWs+eR9kwyUJR2uSOOOIKnnnqKjz76iK+//prJkyfH9/Xt25eUlBRmz57NZ599Rv/+/fH5fDt0ntWrV7Nu3Tr+/e9/J/wSKisrY9OmTQllW5OjsWPHjiiKwjHHHMMdd9zRbLkVK1a06PydOnVCURR+//33FtehtVpyjtbcr9aorKyMt0A1KC0tJRAI0L59+4SyTT2HXfUcG869fPnyRvsatnXo0KHFx9sWt9vNhRdeyIUXXogQgtmzZzNy5EiuuuqqJnOH7iwtvcaGgHflypWNyq1YsSLhdUs//631Rz8nDS2099133zZnX7dv3x7TNFm7dm3CcUOhEMXFxY1mCW+tc+fOACxevLjJ1u9t1XHLY9x1111ArAX01ltv5f7772fw4MGcdNJJzR6vW7duTT6j/fbbD4/HQ0VFRaN9DQFZw31tCDSb+gPE7/cTjUapq6trcn9TqqqquOSSS5g8eTJZWVmEQiGi0WjC96rH46G2tjbhfQ3X0aNHjxadR9pzyDyK0i53xBFHoCgK99xzD6FQiMMPPzy+T9M0Bg0axJNPPklxcXGT3c4tlZ2djaIoFBUVJWy/6667GrUmZGRkUFtb26LWqTZt2jBixAimTp1KaWlpo/3FxcUIIVp8/uzsbEaMGMG0adMadaMGAoGd0jXTknO05n61htPp5PHHH0/Y9sgjj6AoCqecckqL6r4rnmNeXh4HH3ww06dPp6amJr49EAgwdepUDjjggEYByo6orKxMSGKsKApDhw6la9eubNy48Q8ff1taeo15eXkcdNBBPPvss4RCoXi54uJiPvjgg4RjtvTz31p/9HNy+umn43a7ufvuu5vc3/C5HzVqFKqqMmXKlIT9TzzxxDZb0RqMGTMGt9vNnXfe2eg6t7wfGRkZCfd863o08Pl8jB8/HmC7n4fDDjuMH3/8Edu2E7YnJSUxbtw4Zs+ezYYNG+LbA4EA77zzDv379yc3NxcgPrzg3XffTThGbW0ts2fPplu3bi0OEgGuvvpq9t9/f8aNGwfEgsJ27drFlz20bZsFCxY0au2cO3cuiqLscBe+tPvIFkVpl8vOzqZnz5789ttv5OfnN/rrv6H7GZrPn9gSPp+Pc889l8mTJ+N0OunUqROzZs3C7/c3CgCOPPJI7rnnHq6++moGDRqEpmnbzKM4depUhg8fTu/evbnwwgvp0qULZWVlfP/993zzzTcsX768Ved/6qmnOPLII9l///257LLL6NixI0uWLOHVV1/l66+//kOBWkvPkZmZ2eL6tkZWVhYbN27ksssuY8CAAcydO5cpU6Zw3XXXsf/++2/3/bvyOT799NMcfvjhHHTQQVx88cUoisKUKVMwTZOpU6fu8DVvadWqVYwaNYqRI0fSq1cvkpKS+OSTT5g/fz4vvPDCTjnHtrT0Gh9//HEOO+wwBg8ezHnnnUc4HOa1115jwoQJCa3+0LLPf2tXUvqjn5O2bdvy8ssvc8YZZ7Bq1SpOOukk0tPTWblyJe+88w5HHHEEd999N927d+fmm2/mxhtvpLKyksGDB/Pzzz9TV1fXos9527ZtmT59OmeeeSZHHHEEo0ePxu12M2fOHIQQPP/880Dss/jWW2/Rv39/srOz43kUH374YWbPns0JJ5xAp06dqK6u5vHHH6d9+/bbDYjHjBnDo48+yhdffJEwZAfgnnvuYe7cuQwePJgrr7wSh8PBo48+iqIoCc+5e/fuXHfdddxzzz0YhsGIESOora3l8ccfJxAIxOvfEp988glvvPEGCxYsSNj+r3/9ixtuuAGfzxfPVXn22WcnlHn33XcZOnRoQi5Hae8gA0XpT3HRRRcxa9asJgfZH3vssXz99deoqsrBBx/caH///v2bTOTavn17TjzxxIRxeI8//jiHHHIIc+bMYe3atRx77LGMHTuWSy+9ND6uB2I/1N944w3ee+89XnzxRWzbZujQoc0GGNnZ2Xz//fe8+eabfPbZZyxatIi8vDyOO+44pk2bFk9429Lz5+bmMm/ePF5++WW+/PJLFi9eTM+ePfnyyy/JzMxs9XU3pSXnaGl9+/Tp02isVFPbGur84IMP8txzzzFr1iycTifvvPMOxx13XJNlm7IznmNT9evevTsLFy5k6tSpfPPNNwghOPPMMzn77LPj92RbdWvJve/bty+//PILL774InPnziUSidCxY0cWLVrUaJLK1po7b9++fZsMxPbff//4pIfWXmPv3r359ddfeeyxx5g9ezYdO3bk1Vdf5bvvvmPVqlU4HI542ZZ+/pu659vyRz8nI0eOZNmyZbzwwgt89913CCHo3LkzTzzxREKwOWnSJAYMGMBrr73GRx99xGGHHcY555zD5ZdfnvB5gqaX8Bs9ejT9+/fn+eef58svvyQ1NZXhw4cnTCR59NFHefTRR/nss88Ih8P079+fgQMHcvfdd/Pzzz/zv//9j3fffRefz8fFF1/M6aefvt3v4UGDBtGnTx+ee+65RoFieno6P/zwA1OnTmXu3LkYhsHpp5/O2Wef3Wgc4F133cVpp53G66+/zqxZs/B4PIwfP56zzz67RRN6Grz//vs89thjtG3bNmH7xRdfTHZ2Nu+99x4ZGRn88MMPCeNWV65cyddff82bb77Z4nNJew5F7OjIcEmSJEnaAWeccQZz5sxh3bp1u7sqe7yZM2dy8skns2zZsvjqKHubCy+8kJ9++onvv/++1S3P0u4nxyhKkiRJ0h7quOOO46KLLuKLL77Y3VXZIcFgkIqKCh5++GEZJO6lZIuiJEmS9KeSLYqStPeQYxQlSZKkP9W2xh1KkrRnkS2KkiRJkiRJUpPkGEVJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSTJQBEzTZN26dZimuburIkmSJEmStMeQgSKwYcMG2rZtm7Bm5s7Q1ILt0p5NPrO9i3xeexf5vPYu8nlJIAPFXUpOKN/7yGe2d5HPa+8in9feRT4vCWSgKEmSJEmSJDVDBoqSJEmSJElSk2SgKEmSJEmSJDVJBoqSJEmSJElSk2SgKEmSJEmSJDVJBoqSJEmSJElSk2SgKEmSJEmSJDVJBoqSJEmSJElSk2SgKEmSJEmSJDVJ390V2BtZtsBuQcZ6w7IxLLvVx1cVBU1VWlS2uLiYmpoaAFJTU8nNzW31+fY0gUCAyspK2rVrt7ur0iJ7W30lSZIkqaVkoNhKli34ZnUFhmWjsO1grryyCmWDgYJCiltHU7Zd3haCQMQkLcnBoA4ZLQoWH3jgAd577z1KS0s5/vjjefbZZ1tzOXuk9957j8cff5w5c+bs7qq0yLfffssLL7zA9OnTd3dVJEmSJGmnkoFiK9lCYFg2XqeOvo1ALmJYVEdtUl2CglQ3urbtXn7TFqyvDhE2bDwODVsItO0EohALFB944AH+8Y9/UFZW1urraVBRUYHT6SQ5ObnRvpKSEjIyMnA6nfFtK1asID8/H4fDwZo1aygsLGT58uXss88+hEIhysvLKSgoiF2babJx40by8vJQtgqWLcuK7wMwDIPi4mKCwSCLFy9GVVW6du3aqE41NTXU1NRQUFBAaWkpLpeLlJQUAMrKylBVlfT09Hj5YDDI2rVrURSF7Oxs0tLSmjxWWVkZycnJuFyuZs+VkpKSsP+ggw6iW7duTZb1eDxN3tOKigpcLhder5fFixfTpUsXNE3bxhOSJEnac0QiEV588cVG2wcMGMC+++67U8/1/PPPYxhG/HVhYSFDhw5NKPPNN9+wcuVKcnNzGTJkiPx5uhPJMYo7QEFBVxUcmtrkP8sWbKiN4lAVOqR78Tj1Zss6NBVFUdgYiCAEtPW7cW4nqNyZFixYQN++fenYsSOFhYXce++98X3ffvst7du3Z//99yctLY1JkybF9w0dOpQrr7ySvLw8Ro0aRWlpKT179uSSSy6hU6dOXH/99QDccccdpKenc+CBB1JQUMAXX3wRP8Ytt9xCamoq/fv3p3fv3qxatYpNmzbxwAMPsGDBAkaOHMno0aObrPc777zDCSecwIABA+jbty9t2rThueeeY/z48fTp04f8/HxuuummhOscOXIkJ554Iu3bt2fYsGHxLvt33nmHUaNGcfDBB3PYYYeRlpbGk08+mXCuhv19+vRptP+jjz5i/PjxCWWHDBlC3759yczM5L///W+8rGEYjBs3joKCAjp16sQll1xC9+7dqays3KHnJ0mStDtEIhHeeustnE4naWlp8X9b/hG9s7z77rs4HI74Obxeb8L+p556imnTpmHbNu+88w533333Tq/D35kMFHeysGGxrjqMqkB+ihNHC1oS11WFMCybvFQ3Sc4/r5HXNE1Gjx7NcccdR2VlJRs2bMDj8QAQjUYZO3YsV199NRs2bGDhwoU8+eSTzJw5M/7+devWsXbtWubPnw/EWgfT0tIoLi5m+vTpvPnmm0ydOpVFixaxbt06nnnmGc444wwikQivvPIKTz/9NL/88gvFxcW89NJLLFu2jPz8fB544AEGDBjA4sWL48duyoIFC/jvf/9LUVERjz76KGeffTZ9+/Zl/fr1zJ8/n3vvvZeKigqA+PEWL15MaWkpPp+Phx56KH6sH3/8kX//+98sXLiQ2bNnc8UVV7Bq1apG+4uLi5vcv6X58+dz5513UlRUxMcff8x1110X/2t46tSp/PLLL6xfv54NGzbIv3olSdqrDR8+nFGjRsX/denSZZecZ9iwYfFzDBw4ML69vLyc9957j0mTJjF+/Hhuv/12fv75ZxYvXrxL6vF3JAPFnWjLILGt39PqINH7JwaJEAu0iouLuemmm1AUBYfDwWWXXQbAb7/9RnV1NZdeeikA7du3Z/z48bz77rvx91966aWN/nr8xz/+Ef/6lVdeYdSoUYTDYZYtW0bHjh3RNI2FCxfy+uuvc/HFF9O5c2cA9t13X4YNG9aq+h9wwAHxHxiHH344ABdddBEAXbt2JTs7m9WrVye8p6ysjFWrVjF06FC++eab+PaePXty/PHHAzBw4EAGDx7MrFmzWrx/63odfPDBAAwePBhFUSgpKQHggw8+4Lzzzot3fV977bWtumZJkqS/ow8++IBXXnmFn3/+OWH777//TnZ2dnwyoc/no0ePHvz222+7o5p/SXKM4k6ytwWJAKWlpWRmZjbZqlVeXk5GRgaquvk62rRpw8qVK+OvMzMzE96jaRp+vz/+euPGjXz99dcJrZAul4tAIEBpaSk5OTl/qP4+ny/+tcPhwOFwJASuDoeDaDQKwKJFizjllFPYsGEDmZmZhMPhhPpnZWUlHDs7O5vy8vIW72+uXlvXo7y8POFYW99DSZKkvcmMGTNwu93x18OGDYuPT9/as88+i203nQmkd+/e9O/fv8l9Z555JqZpUlVVxX/+8x8GDBgQb8SorKxMGHMOkJaWRlVV1Q5cjdQU2aK4E+zJQeL69espLS1tcl/nzp0pKipK2G9ZFhBrQVy3bl3CN9uvv/5Khw4dWnzuLl26cO6558a7fBcvXszChQs59NBD6dKlC/PmzUso33BuTdOa/WGyo+644w5GjRpFRUUFS5cu5c4770w4x5IlS+Lnh1hra/v27Vu8v6Xat2/PokWL4q+3/FqSJGlvk5KSkjBG0eFwNFvW7/cnlN3yX8Owp6Ycf/zxjBo1iokTJ3LPPffwySefsHbtWgAURUFsla5u69fSHyNbFP+g3R0klpeXU1paSkVFBdXV1SxevJj09HSys7MBuOSSS+jcuTP3339/o/d26NCBE044gVNOOYWbb76ZaDTKk08+yRtvvEHXrl055JBDOOuss/j3v//NTz/9xOuvv96o2X9brr76agYPHkxOTg4HHnggK1eu5IEHHuDTTz/liiuu4KCDDqJjx44MGjSIWbNmUVBQwIQJE2jXrh1Llizh+++/x+/3NznrubW8Xi8LFy5k3rx5FBUVcdNNNyW0/G3atInLLruMUaNG8fnnn1NSUsKJJ57YaP/EiRN5++23G+1vqbPPPptRo0bRq1cvCgoKuO666wAazQaXJEnaGwwfPrzFPSNVVVXNNgKEQqEWHSM3N5f09HQ2btxIu3btSE9Pb9S7U15evkN/yEtNky2KO8i0BbVhg9WVQWxhk+uLdXk2JNk2LBvTFgmvQ4bF6oo6QoZFdrITp6Ym7G94T2u89tprjBw5km+++Ybff/+dkSNH8swzz8T3FxQUxIPGprz44osMGTKESZMm8fDDD3PDDTfE97355pu0a9eOyy67jA8//JCPP/6YffbZB4BOnTol/AWo63o8RUyDHj168Nlnn/HFF19w/vnn89prr/HII4+QlJREr169mD17Nl999RWXXHIJkUiEcePGAXDggQcyceJELrroomZnPaemptK2bdv4a4fDEa9bgy3reNttt5GcnMy5557Lyy+/zPXXX5/QOjp06FAKCwu54YYb+O2335g9e3bCzLqG/VdeeWWj/SkpKfHxMVvXC2LjJRtSCx1++OE8/vjjTJkyhdtuu40rr7wSYJfMFJQkSdpRwrYQtrX9gq2wIy2KWy4qAbB69WoqKirIz88HYuPHKysrWbp0KRAbh/7777+z33777dS6/50pQrbRsm7dOtq2bUtRUVGzYysaWLZg3roqqkIGNRFzm8m0q2uqSU1JBTYn07aEINmpbzMFTrJLo2+Bv8Wrs0h/zAsvvMALL7zAhx9+GB+b2dz+ncGyrPi40K+//pqTTjqJjRs37pRj/9009bykPZd8XnsmYVuIaAg7GsII1RIoLSJYVUowKYcefQ9p8j01NTWcccYZDB8+PGGMYs+ePTnwwAN3Wt1WrVrFAw88QIcOHVBVle+++47jjjuOM844I17mlVde4b333uOAAw7g119/pXfv3lx++eU7rQ5/d7LruZU0VaFvgb9FS/iVlwsyMtK3W25rrVnCT9r7nHTSSVx11VVEIhGuueYaJkyYsLurJEnS34SwbYQRCwrtcC3RQAV1gSrC4SDVIYOasEFp0OLdGS+huHz02v+HeM/HllwuFxMnTmy0fVtjDXdEhw4duPvuu5k3bx6RSISTTjqJwsLChDJjxoyhd+/erFy5kkMOOYR+/frt1Dr83clAcQdoqtKiVVMaEmpLe7amuotbs7+1LrzwQu68805CoRCnn346//rXv3basSVJkhps2VJohWuxqjcSqa0gbArCpkVNxCJgaRiqE7QkUBWqFBM1yeSN9+dQE6glfeZHXHLJJY0mqbhcLkaNGvWnXEdycjKHHnroNsv06NGDHj16/Cn1+buRXc+0ruu5NWQ3y95HPrO9i3xeexf5vHYNIQTCjCKMMMIIY0VqsYPV2OEA0UiYsGESMm0qA0EiNRWYrmSslDycugOXruLUVKKmzcbaCKZpcNv1/+aneb/Ejz9kyBBmzZq1zRnN0l+XbFGUJEmSpL1EvJXQCMdaCmsrMKs3IqwIiu7CtCzClkJIaJSGFCI4MYUTRQF3cipudwp6sBSMckjKwzJNFi9ZwbfzfmHp4sUsWbyEFauLEs45Z84cnnvuOc4999zddNXS7iQDRUmSJEnaAwnTwDZCiGgo1kpYV4kdrkWYBoJYZ6CBTqg2QF0wQEBNpULzU1EXRVVsclPcJDs1dFUFywQrSnllJRluFQIbeWzqCzz/1icEg7HUNE6Xi6SUlCbrsmLFij/tuqU9iwwUJUmSJGk3s40IIhqKdR2HAxhVJVi1ZSiqjupwg6IidBeG6iKke6mLWlSFDIKGhSAd1bQRoXIiopb05DSyPRCoLOOHxctYuGQ5CxYvZ8HvyyirqOStTz/FyOtGoF0J7Q81yO/enT79+tGjd3d+nvk+D18/qVH9OnXqtBvuirQnkIGiJEmSJP1JYrOOw/UthWHscA1WXVUsSLRNUBRQNFAd2ChEQkGiipdakqgJGITMCIgQqmXiUixSVQtNmFSYUeYtXM7K1WuYOG4sSnIbnpr6JtOfnQZAZm4eBX36MqBndxaqCplujf3HHEOf04bRPS2bQp8fgN4Tz2D2O2+zcO6P8ToPGTJEZmf4G5OBovSXFA6H0TTtDw2+blifuSFZtiRJUkttPcHEjtRhBqswq0oQoQCqJxlFd6GoOoruQk1KxUQjZFiETYvSQJTK2iScNeWo0Y0onjScnmTSNAVVVUB3Mm/RUt79aA6/LlzMyhXLseuXGW17zJlk+FPIO2YE5+3Xh069u5OWnUau10ea243P4UBRFEJmOiHLINW5OQ+iw+nk6TdeYdi+/QhUVpOeni4nsvzN7TGBYnV1Nf/5z3+YMWMG69evp3379lx44YVcdNFFzb5n+vTpnH322U0ea8tVNXY2YVvQgrWIhWUgTKP1J1BVFFVr1Vu2TOK8KwWDQdxuN6q656T92bJOa9as4dhjj2XFihXccsstXHHFFaiqukM/5G699VZqa2uZPHnyzq+0JEl/GbGxhPUBYTSEHazCDgewjTDYFrFsaiqK7kLz+DGNCJYRwXanE8ZBbW2ImroawuEwNcEggbCJz+UgOyUJNT2XVUUlLFq8koVLlrNo4ULueeRJ/LmF/PLF77z30SfkdenGoNNOp2OvHnTtsy8FHfPxOaHzoL6kOA9Eb+b3iUd34NFjPxuFEERti4hlEhYWHq+XQGU1Pp9PBol/c3tMoDh16lSSk5N5++23adOmDR9++CHjx4/H4/Fw1llnNfke27bJyclh9erVCdt1fdddlrAtgsu+QZjGdtfnjVSVU+NUAAXNk4KibDuQE8LGDgfQvGkkdR3UomDxlVde4Y477mDJkiXk5+dzyy23MH78+NZcUqsMHjyYRx55hIMOOmiXnaO1tqzTlClTOOyww1iwYAEQy1nYtWtXrr766t1cS0mS9nbCjMbGEhrh2KzjUAA7EsAOVmPUbAJho/uyUZ2eWFCYlIaixX4fRaIRwuEIwXAdtbWCYEUxL777EjVRSE7xc/q4MdQ601i5yaRDh+4U5Gbzwcz3uPlfVxEOBQFwOF2026cbX69dQ06SRoejD+HJE78g2eMi1e0i1enEqW3/958QgohtxoJCy6QhR55T1Uh2uMhNSuHSyy+jalMZbfN3Xso4ae+0xwSKV111VcLrU089lcmTJ/P11183Gyg22JWBYSO2jVmzCQBHejuUbbSsqVGBnuTGrFiHVVuFI70tynbqquhurNrSWIvldgLFSCTC22+/zcsvv0yPHj14//33OeWUU9hvv/3o3bt3iy5ny9a4rVslm2ql/PLLLxOWa2quXFPbtzyXbdvNtkpua19D2s8tg/SGOoXDYVatWsWAAQOora0F4P77729yHeUdPb8kSX99sYAwjDAiWJEgIhzADtcgjAi2ZYACilBQtFi3sebNQHX7MCrWY0dqweEhHKolEo5QGzGpjZiELDA1JziT0L2FqP7OPPu/SQRqqnG6XHz+w0KWL15AOFjHfc++huXPQmTnMnDE8eT36EaHfbvRoXtnUjwefC4HKQ4nSQ4HNgIhiM1s3oItBJawURWFiBVrKYzaJqYtUBRI0hykON209brxOlx4NAduTY+3Pk7653Uy76UEwB7529AwDD788EMWLFjAiSeeuM2yGzduJDMzk6ysLI466ii+++67XV4/1ZmEMA2swCZQNBTN0cw/Hc2djCOrPYqqYlaXgGAb5R2oTjeq29eierhcLl5++WX23XdfVFXluOOOo2vXrvzyyy8tvpYePXrwz3/+kw4dOuByuTj55JOZN28evXv3xu12c/DBB1NWVhYv37dvX7755hsArr/+elJSUvD7/Zx22mlUV1dvc3uPHj248cYbad++PV6vl1NPPTUe0EFs8fcTTjiB1NRUMjIyuPHGG+OBYVFRESeccAIpKSnk5uby/PPPN6rTxIkTefPNN7n++uvJyckhJyeHtLQ0HnjggRad47fffuPQQw8lKSmJQw45hDVr1rT4PkqStPcRlokdrsWsKSVatpZw0QKCy74huOQrgsvnUrd4DnULPiK8fgFCgOpOwZGag14fGKJq2NEgkUAp1dUVVBgKRTUG368t5/MShXnRbEqSuxLK3Z9NWhu+W7yRaS++RUl1hCsuvpRATexnYzQSYenvv7H/0GGcdv311OW4qVbK6dSvA/+4/2YmnDeWffp2wZfsoIM/hVxvMl6nE1PYLK8pZ2lNKSHTwLRt6owopeFavi9dy7zy9RQHA5jCJs3lYZ/UbA7IasvgNh04pE0H9svIp70vgyx3MskOV7Nd1NLf2x7TogixsYUZGRnxFp17772X4447rtnyPp+PyZMnc9JJJxGNRrn33ns59NBD+eGHH7bZolZTU0NNTU38dUlJSavqqTrcKKkerNoyzKr16P78bbcsOtzo6QWYFeswKorqWxabH/OhKDsWv5eUlLBkyRL222+/Vr1v/vz58XU0+/fvz2mnncbMmTPp0KEDo0aN4uGHH+aWW25JeM+vv/7KtGnTWLRoEdnZ2bzzzjt8++235OXlNbl9+PDhALz//vt8++23OJ1OTjzxRG699VbuvfdehBCMHj2aAQMG8Oqrr1JVVcWIESPo3r07Y8aM4eSTT2a//fZj/fr1RCIR/vOf/zS6jpdffhkhBIMGDeLSSy8FSGiN3tY5Tj31VEaPHs3IkSO57bbb+Oqrrxg+fPg2x8hKkrR3EEIgjEhsjWMjjBWowApVgxWJ5SRsaCHUHfGJJaqiIZL8qLobo2YjRulKtJQshICorRJGJ6QmUW37qBNOoroDO9XBJs3GVhQ6ZqWwbukCbr7mCpYs/I1gXeyPYk3XKa4o46e5XyXUMRoK0v3AnvQ84Uj8Thfd0vy46n9P1ETDREImlrAxbAuHqmHYFpWRIMXBamwh8GgOfA4Xbs1BjttHt9Q2sTGI9S2F2xsqJUnN2aMCxdTUVMLhMLW1tXz44YecffbZ+Hw+zjvvvCbLn3zyyQmvH3nkEb7++msmT57M1KlTmz3Pgw8+2CjwAaisrNzugubCMojWVKO4UxCKB7tiE0qgBjU1t1GAV7dFaxmA0HxY1cUotTWo/nwUrXGwKCwTEa4hVFHe5P7mRCIRTj31VMaOHUt+fj7l5eUtep9t21x00UXYto3D4WDAgAG0b9+ezMxMAoEARxxxBJ9//nn8eJZlUV1dHe9+LikpwePxcPjhhwOwatWqJreXl5dj2zYXX3xxfBbxVVddxdVXX82//vUvli5dyvz583nppZcoLy9HCMG4ceN49dVXadu2LQsXLuR///sfhmGgqirXXHNNozqVl5cTiUSoq6uL74tEIgSDQcrLy7d5jry8PDZs2MA555xDTU0NvXv35phjjiEcDrf4Xkp/vi3/4JP2fLv6eQkhwDJis43rZxxbkTqIhrCtKAgbAdjBaoRloidnoHpSwTLrf/aGgVD90RTQdCw1lWh6FqbmodZUqbVUIkJDKDqWYVC8agWrli5i6aIFLFzwG6uWLuaKG+8k99gTqQqGqK6p5uCjjqZ99+506N6Fdl078caTTzVZf2PdRvZ1pqKrKqIuTJhwLLG2ZZFlqBjCprKikkpAVzXcqsbg5DySHU5cqo5L09EVFUUoEDYRmAQJEdzB+ym/v/Yuu2qYwB4VKEJsvKHf72fMmDF8+eWXPPLII80GiltTFIXevXuzbNmybZa7+uqrE5YiKikpYcCAAaSlpW33RgvTIFiWiupJQdEysVJSMas3oNp1TbYs+lP9Ca/t1FTMinVgBnCkNG5ZFJaB7VRISs/YZqvjlgzD4JRTTiEzM5Onn366VWM2VVWlsLAwft0pKSnk5eXFXze08Da81jSN1NRU+vfvzxNPPMGNN95IWVkZhx12GJMmTWp2e2pqKqqq0r179/ix9t13XzZt2kRGRgaBQIBQKETPnj0T6jdw4ECCwSC5ubnk5+c3eQ0NdcrIyMDlcuH1euPncLlcJCUlbfcc4XCY3Nxc0tPT4+/t0qULhmHIMTp7OPl89i4743kJ20aYkfrUMxGsaB1W9SasugqEoqCqesJMYyXZA3gQthnLRuHWsOoqwCxHswRaUiqq24/p8BJRnISETmUESkIgFBVFUQjX1VG0YjGrFi/ggIEH0b1nb+b98B0XnHY8AE6Xm/wu3Tj0xJNJ79SOgMcgu08h9733Gi5dwaPruDUdt+ZgWa+evNXEdXXYpwv+9DQilkl1/SQTBQWH6iLPn0Ka00Ny/XhCj+7A8Sd0FcvvL2mPCxS3ZFlWfPxYSy1cuHC7GeRTUlJIaWaZotbSPLHjmNUbdkk39PaYpsnYsWOJRqO8/fbbjYLEcDiMqqq7JBfgCSecwAknnIBhGFxwwQXccMMNPPzww81uB1i5ciWHHHIIAMuXL6egIDajrn379qSmprJp06ZG17B06VLWr19PIBDA52vZ+M2mbOscCxYsiHdrN1i+fDmFhYU7fD5Jkv4YYVuxLmMzEptlHKnDDtVgR2oRRgQafj+oGqBihQNgmigpWSiqHktlZoQQZhhFd6K6vKjuLFRXMrbqIIyDOqETMBUqQwZ1tbEchuurQqiqSlK4nGf/cydLF/7K6hXL4r+Pxl9+HZ68DrjbtuPyu+6nQ69u5HVuh8Oh4dQUnLpa3+XraXIW8gljRvPe6//jp6+/jW/rfdAABo4cQcg08TlcFHj9eHVnvPtYjh+Udpc9JlA877zzOP300+nfvz9CCN59912ee+45br/99niZqVOncv7558fzJJ5//vmceuqp8Vane+65h/nz5zc5fm1X2l3Bom3bTJgwgQ0bNvDWW28RjUaJRqO4XK543qsxY8bQuXNn7r///h27uGa8//77fPnll0yYMIGUlBSqq6vxeDzNbm9wxx130Lt3b5xOJ5MmTWLcuHEAdO3alQMOOIDzzz+f66+/HqfTyXvvvUcgEODaa6/l4IMPZuLEidx1111Eo1EmT57MU0813X3TnG2d45///CedOnXilltu4aabbuLLL79k5syZXHLJJTv1vkmSlCiemNqMbA4KwwGsUG1s1ZKaTQjbRPOmozjcKJoTFBXF4YH6FkJsC7DQvBkI20Bze1F92WgeH6rDja06CeMgaAlqIyaVdQY1EZOoGWXDuuWsXLyANUsXsWzhbyxe8CvHjr+A8eddTHWFh+++mkOHbj05aciRdOzRnU69u5NdmIulVpOcDMedeTwuTcOt6bjU2FhAIUTCmEDTtutT0RiY9d3ft73wFGP6DqKmsoq09DQ++OhDUtxJeDQHmsy6IO1B9phA8aKLLuLmm2/myy+/xLIsunbtypQpUxImI9i2ndDKeNFFF3HTTTdxyimnoKoqffr04bPPPmPw4MG7vL7CMhNeq04PWnImZvUGhLUG3Z8XG/NiNZ1wW1E1tNQczIp1REtXxVPnbH3cbSkrK+Ptt98GYq1lDW699dZ43kCPx9NkepgGXq83IYWN2+1OaH10OBwJgV5D+WHDhrFw4UJGjRpFTU0NQ4YM4bbbbiMlJaXJ7Q3OP/98zjvvPIqLixk5ciTXX399fN+MGTO44YYbOOaYYzBNk2OPPTb+h8Jrr73GP//5T4466ij8fj/33ntvk9ewdf23ft3cORRF4c0332TixIn079+fgQMHctFFF23z3kmS1HLCMmNjBmsrsM0IVrgWEamL/TMi2MKK9RYLAZoDRY1llFCTMzHL12JUb8ThzwHbjk06cXpQXemo7mRUpzvWxay7Yi2FlqDOsAiETEqrI4SiIerClaxZvoRVixfRuWtX+h5wIMHS9Uw4akC8jtkFhbTftw8F7dsSUUJ4spw8/u1n1JhB/C4XfpcbZ/1YwOpoiPJIkFyHD78z9jNSCEFJsIaiuip8DhdpLg8KCqqioqPid3nIdvvw6LEJJjdPupHq6mr8fj95vrTd82AkaTsU0dq+3d1ICIFlWTs9b+K6deto27YtRUVF8a7QZutgW4RXz8MO1za53zbC2NEgiqoTiNqNxig2Pp6JFa5hy6TcqjsZd/u+rV6dZU/Xvn17Xn/9dfr377+7q9IsmTds7yKf154lNnawvnXQjGBHw9jhWuxIABENUVkbxu9WsSJ1WIEyVLcXPTUv1gNjW7E/lBUQgKpoKA4XisuL4k5Gc3lRdVdsm+5C0XQsWxAyLEKGRV3EojIUpSZsUhWMUBG2qK2u4p1Hb2f1koWsXr4U04z9IX7iGedy8Q13YFom7748jU7du9GueydMr4KmKiQ7HThUFYeqsipQQcCMsk9qFnlJm4csLakuZU2ggjxvClluHwKBAoQsA6eq087rJ82VFB9P6FS1vW7msfz+kmAPalFsCUVR/tzk2k3VQdVwt+/boiX8ohXlJKXvwDfZDizhJ0mS9GdpNHYwGsQKlGEFyrAtE62+hQ1FQVF1UFQglqAaRaC5k1EUFRENYkfr0HxZ6B4fiis5ln6sIRjUnfHgyrBs6gyLUNSmNhChOlRLdSjKimVLWblkASsXL2L1kkWsWLKQwceOZsyl/yIvP4ufv/2Sth27cOy4s+nYvTtd9+1Jfud2WM5qhGJxwvknoakqtUYEXUC+NwWPvrkXQldVAkYUr+6gKhoiUt+r5dEc9EzLJTfJR5rTQ5LDiVvT5XhC6S9nrwoU9xSKqm131RSoT6z9Byaq/NVs3c0tSdKea+uxg7YRRkRqsUIBRDQYG1YjBChKbMxgffJpK1QDLi+qNx2FWCYHRXOgOFyobg/OzKzYEncNwaDD1egP46hpx1oKQxGqglE21UWpKK9g2e8LWLlkEQXtO3LYkUejmBHOOy421EhRFPIKO9J+337kdumC021gOgTT536JKUwUVaBrCrqqoCsqLk3Hpbk3zxz2+OJL29VEw0TsWN5CUHBqGkJAitNNmtdTv5KJjkd3oO5g3ltJ2lvIQFH60yxcuHB3V0GSpK0Iy9wcDJoR7GgQOxRAROqwjBBWTVms1c+bhp6UFgsMoX5WcawrV9gWAA5/Hs7M9ihJqWhuX2ymsd7QOuggVF6Oc4uuTCEEYdMmFI4SNmyqwwblNXVUVNdiOpMoqQjw1A0XUbxiMaUbNi+MMGzkqfQ99AgMFK685yFyC9tRuE9H3D4ntmKjawpOLYymqLg0DafqwaXpqFt0/Rq2RdSyqDEimLZFfT4dXA3rHTtT8OpOXPUpbVyaJoNC6W9JBoqSJEl/cZu7imMthFYkGMs5aETqVycxia0v2tA6GPvVoKoOFF8WRmUEK1iFouqxANARW2pU9aRsnkjicCd0FW/NsGyCUQu7NkJdxKI6bFAVjPDVnE9YueR3Vi1ZxJpli1m7chlHjjyV8dfdTZs0H2aojn3268/Qzl1p160b7bp2IattG4J6JYoqGHraUTg0JdZC2JB0eotZww2thAEjHOs2rt+mqypuzUEbdzKpTk99jsNYUChnHUvSZjJQlCRJ+guItwya0c2tg+HaWM7BaGiLrmIQpoVZsxEQ6P48VN0Zm0giBGAjbCMW9DmS0D3JuPK6xbuKVYd7u0Nq4l3HhkVVyGD5+k0sXrSQ1Yt+Zs3q1RR23odTJ5yHQ9e448rzCQXrSMvIpF2Xbhx+yhkU9htAwK5B1Q2umv44yU4HTk3Fpaug2FSE60jWnbRPTkOtD+oM22JNbSWWsMlwebGEjRDw5tPPUVNdTXZ6OpdfcSXeLcYSNpXjUJKkRPK7RJIkaS8RCwaj8YDQjoZiwWA4gB2pxawpBWGjJWegaHpsGdD4RBJHfTBoo2gaemqb2PhB3YHqy0LzpKBuOW5Qd20zHyyAZQvChkXYtAmbFpsqA/y6YCFhS9B+n33ZFAhx9cjBlBUXxd/jcnsYOnI0m0IBIrbF5VOewpnlx98mnSy3B5dDxamr1Fph1geryNS9dE7LjI8lLAvXETAridgGqc7YUqKmsKk1olRHw6S5kkhzJpHhTkK3YezDT1BRUUF6ejr3XXdjPMesJEktIwNFSZKkPUhCMGjEuontUPXmJeu2aBmMBYBOUDUUhwctOQOzYh1m9QZ0fw5CCFRVj6eZ0TwpqM6kbU4kabJOQhC1bEKGTdiwqI2YVIdNAmGDp/5zN6uX/c7qZYspWbsaIQSDjxzOlQ8+Q9Ay2f+woaT4U8ns2IGcghx8HQtJ8biIOmpwaRoHDNqXiG2Q6faS7UmOjyMMWwZ+pwu35sC0berMKBHLxLBt0txJJOsuPLqDVKeHVKe7fiWUWH5CRVGIRqMcffTRVFRUAFBRUcGwYcOYNWuWDBYlqRVkoChJkvQn23rMoG2EG3cTx0pi1VXx6PP/oyZikZ6VwyUTTt28IokQCDMSS6mlO9GSM3BktIvlZI13FbtiCaxbmMPPtOzYBBPDIhg1+eW3hfz4ywIW/76A4pXLWLdyKfv02o8bH3iMJKfOnJlvIBC07dyVA44cTnaHDvg6dOT3mnUku1XG3XAlLodKssOBOxIlLSMNvX795IR7IgSGbRGxLaKWiSlsnKqOAKK2RYrDjd/rJkl3xgPCbXUdP/fcc8yZMydh25w5c3juuec499xzW/G0JOnvTQaK0l/Kpk2bGD9+PD/++CPXXXcd//jHP3b4WHfccQe1tbXcddddO7GG0Lt3b6ZPn85+++23U4+7M61atYohQ4awZs2a3V2VvZYwDYQVjQeE8WAwWocZKEeEqsHhQnMn09A1jKqDqscmk9QHg5rXz+NvfkpRSSntcrO59JxxiSuSaM4WdxVvybYFESsWEAaCERYuXsL83xawdPFiUjMyGXHaeCwhOHPkMQSqYq1yWTl5tO3chZzOnVkeKCNoRLjy1VdxunS8ThWPrpHk1PE5nCQ7HXh0PSEgDFcHcKhabEk70yBimxh2bIKJAuiqRpLmICMphRSnG7cWW+d4R2Ycr1y5ssntK1asaNVxJOnvTgaKO8CybWKrdW6bYVsY9WkjWkNFafGsu+XLlzNp0iS++uorsrOzueqqqzjjjDNafc6WGjBgAP/9738ZOHDgLjtHa21Zp0cffZTMzEyWLFlCcnIyF198MV27duXKK69s9XFDoRChUGin17empia+QkRT9oR7bFkW1dXVu+38e4PNeQaj8YAwlmOwLtYqaEa3WJIzNqNY0Zwomo7mScGMBrHqKsGy0JJSEWYUVLM+52DD8nReTKFSF4kdpzZi4mjXt1Vdpw0BYdiwqKkL8evCxdSEwrTtui8Ry+Ky0cNZvug3rC0+kwcPHcbQ0WNYURFg5LU3k57tJ7NjO5J8Xty6gtel43EYtHG4+eC514jU1pLq93PGheclnlsIIpZB1LIIWyZGJIgWAl2JzTjOdHlJcbrx6A0Bob45r+Ef1LFjxya3d+rUaaccX5L+LmSg2EqWbTOvfB1l4SAR26jPr7X5NjYs/O5SHUTqgqSaNQAYlkXIMnCoGp4tZgyatk3QjKKrKkn1qwEk6076ZhS0KFi84447OOWUU7j//vuZN28eY8eOpbCwcJetd/3pp5+SlJS0S469o7as04oVKzjwwAPjy07df//9e12S7z3xHv9dJS5JFwsK7Uh9F3EkFgzaW+QStGo2ggBHekFsLKDuRFhGfVeyiAWUVhRFc+DIaItT74Ka5EdzeTavRKI74+MGo9Eoxx59NBUVlcC2x9lZtiBiWkRMm7qIQdgU1IRNHr7vThYv/I3Vy5eycd1qhG3T/5DDePD519Gw6dKrD5367E9m+/akF7YlvX0hdpLOj1UryU/2ctzoI0jWdbxOJ0ErjCVs2nh88Z97bzz9LMVF68jKz2PYhLFoioIhYitXhS0DXVHJcnnJTUrBVtxkZ2bh1nb9knYTJkzgpZdeSuh+HjJkCBMmTNhl55SkvyKZLKqVbAS1ZpTKaB3Lq8sImQYpDlf8X8g0WF5dRo0RIkXfvD1im6ysKacsXIdPd8a3m7bFmtoKNoVqSdJ0kjQHtWa0RS2WANOmTePkk08mPz+f448/nkGDBvHdd9+1+Hp69+7N/fffz6BBg8jPz+eKK65g9erVjBgxgry8PE499VRqazevaz106FB++OEHAB599FG6d+9Ox44dueaaa4hGo9vc3rt3bx599FEOOeQQOnTowNVXX53QslZTU8PFF19M586d6d27N08++WR8X1VVFRdffDH77LMPffv25ZNPPmlUp3HjxjFjxgz+9a9/4ff78fv95OXlMWXKlBado6ioiDFjxlBYWMhpp50WHwTfElOmTGHSpEktLv/9998zePBg2rVrx7nnnkskEml0Pdurb+/evZkyZQqHHnpofI3yY445Br/fT5s2bTjyyCNZsGDBNsv//vvvnHDCCRQWFjJixIhGXc1PPfUUvXv3pnPnzjz88MPx7WPHjk24rxD7Jfzmm2+2+B7sCWKtggZ2uBarrhKzagPRsjWE1y0kuOJ76n6fQ+Dnd6j5ZSahVT8QWb8Qo7IYOxKKTSBxelCdSaiaA1XT0VPaoDicsaXsjCBoDjRfNs6crrjb9sHToT9JXQ4hqesgkjodiKewD66sQvSUbLSk1NiKJVu0qDU3zu6pZ6ZSEYxSUhPmpXc+5Pq7/sNpZ1/IoUOPYp/OHTl19CmsqQwRNCzmfPgOK5YsIqttIceOP5ezbr6DIRddxJcbVvB+8W90u2gsw6+5gOPGn8ywIw/mwC755Kc5yEtz0DU7lW7paRSk+Eh26pRF6lgRKGd9sJrySJCiqnICgQAAobo6FMsiNymFfdNy6J/ZliPzujK8oBsHZBfSKSWTdJeXVGcs+fWuXvfY6XQya9Ys0tPTAUhPT5cTWSRpB8gWxR2U5fLi0nQy3UkJXSWZ7iRs0kl3JqGFzfi+DFcSUV8aKQ5XwgDsdFcS+V4/3voB2qawofW91QAsXryYX375hZtvvrnF76mpqeHVV19l2rRpRKNRhg0bxgcffMCUKVPo3Lkz48aN45FHHuG6666LlzdNkyVLlnDjjTfy3nvvkZeXx5tvvsn7779P9+7dm9w+cuRIampqmDx5Mi+//DIul4tx48Zx9913c8MNNwCx4CM1NZVZs2ZRXl7O2LFjKSgoYMSIEYwePRqv18tbb71FNBrloYce4sgjj0yo05NPPkk4HGbgwIGcd16sC+ziiy8mHA7Hr3db5xg1ahS9e/fm6aef5osvvmDixIlceumlLbqPkUikVd3UU6dO5ZlnnsHlcjFq1CimT58er/OWXdPbqm9NTQ1Tpkzh2WefjXenvfbaa5imSTgc5sUXX2TUqFEsXrwYTdMala+oqGDQoEFcffXVPPzww/z+++9MmzYt/vmprq7m22+/5d1332XZsmUcf/zxjBgxgk6dOnHyySdz1113cdFFFwGxVXfmzZvH8OHDW3wP/iyxVsHNLYLCjGIFq7HDNbGuYcuo7yIWm1cdqR8vqDpckJyJXV6EWbUBPTUHRVGwLQOlfgKJ4khC82WgOL1bjRl0/qE12y1bsGTZ8ib3PfzEM3QcMhJVUbj64vPZuL4I3eGgoH1Huvbejy59D2B1XRnVRpQxU57C1E1SXTq5ycmkuF34nA7cqkaV6UVVFQqSUuNJqm0hMBSTkGmgABWRYGwcoQC3plPgTSPbnUwSGtdPOItAVWyIQm1VNbdPuHiPCsYcDgc+n4+Kigp8Pt8eUy9J2pvIQHEHpbu9tFFTGm2PpWrwAFAV3jzGK9nhoovD1ai8R3fQJSVz84aWNSQmmDRpEpMnT6a2tpbbbrut1WPbbr75Zvbdd18g1ppVWFjI0KFDARg3bhyfffZZo/dYlkVycjLdunXD7/fHxwAuWrSoye0NbrjhBvr37w/Eus2vvvpqbrjhBpYvX87s2bOpqKjA4/HQsWNHrr32Wl544QU6d+7MV199xcaNG0lJid3zqVOnNqqT1+vF4XDg8Xjw+/1ArFWhwbbOUVhYyJIlS3j77bfJz8/nzDPP5PXXX2/VfWyNW265hT59+gCxYPDnn39uVGZb9R0xYgQAN954I3379o2/Jzk5Of71Nddcw1NPPcWiRYvo1atXo/KPPfYYPXv25P/+7/8AKCwsTAj0VFXlkUceISkpicLCQg444ADmz59Pp06dOOGEE7jooov47bff6NWrV7xle3d1mSckm45PHKmLdREb9bOIbUHDN5gdDWPVVaA6PWipeShOB49Me5nq6hpSfV4uOfMkUAzQdFSnG3e73ijOJDR38ubu4Yau4j+4iodh2URMm9LyShYu/p2FixajONwcOHQEli+7yfco2ESIUBeJMuH2u9F9Xnw5bdAdKm6HTpJTxecU5Do99CtIwedwoGuN65mOOzbT2DIJGBZR20QIcCgaHpcDh6rHej/qxxE2rFziUDWeeuop5n71dcLx9sRZxVdeeSVVVVXxnwmSJLWODBT/Av7v//6PK664ggULFnDWWWfRrl07xo8f3+L3t2nTJv61x+MhOzs74XUwGGz0nh49evDvf/+bYcOGkZqayrHHHstFF13U7HaXKxYkd+7cOX6MLl26sG7dOiA2QzEajdK2bdtYd6AQRKNR9ttvP1avXk1+fn48SNxR2zrHunXrKCgowO12J9RvW5NOxowZw4cffgjEWhSFEDz99NMA9OrViy+//LLZ9255z5OSkpq8x9uqb4PCwsKE98yYMYMHH3yQVatWEYlEqK2tpbi4OB4obll+9erV9OjRo9k6+ny+hMBvy3o6nU7GjRvHs88+yz333MOLL77Iiy++2Oyx/ihhWwktgrFk00Ei5eUEK5YgzAhmXQUiFACHGz05Y/MsYkVD0TWwrdh4QWGjON1oSnrsWNFa1JRsHnvxLdauK6awXVv+cdPdmwPCnbB6hxAiNnYwHGXZ8pUEQkEyCztTE7K47rzTWbLgFyrLSuPle/Y9gP2POIoho0byytMPs2Ht5iEBHfr2ZcLDD1MUKsXr1Dhw8P54HTpe3YHX6UBTG3fpxvIgmrHUM7aFYVmgbJ5p7FJ10l1J+BzOFqef2VtmFe/IRDZJkjaTgeJfgNvtxu12M2TIEE4//XTef//9VgWKO+rCCy/kwgsvpLS0lPPPP58NGzZw9913N7sdYP369fH3r1+/Ph4w5eXl4ff7WbZsWcLYJV3XWb16NRs2bCASicQDzh2xrXMsX76cjRs3Ylmb+/23rF9TnnnmGQwjlu9uypQpFBcXc9tttwHslAk026pvgy23r1u3jnPOOYcZM2aw//7743a7GThwYLyOW5fPy8vj119/3eH6nX322QwbNoxBgwbhcDgYMmTIDh8rPmnEisaCufpl6Ky6Ssy6ithqIooSaxVsuARFQ0RA6E5QHei+NpiWFXuPoqJ708AyYrOIdRdqUiqqy4vi9NQHgM54MGgYBrXB2BCFQG0dttO7Q92UUdMmYlpU1tSiuzzURU3uvvUmFv++kKKVKyguWo1lmuzT/2BufOJlsnwOklJ99Bp4MBnt2uEvKMDfti1pBfnMqyzCoapc/9qrXD/saGqrq/Gl+Zn+1gy8HneTAaEtBOH6WcZR24otYwcoKDhUFY/mIN2TRLLuxF3fQuhStx0QNkfOKpakvwcZKO4Ac6uUN3VGlCojhN/hwevY3NUZsUyKgzUkO5ykODa3VBm2RWm4jiTdgb++mzp2XJsNwUCLB3nX1dVx0003ce2115Kdnc3ixYv53//+xznnnBMvM27cODp27BgPYHaW2bNn8/vvv3PmmWeSlpZGeno6paWlzW5vcN9993H44YfjdDq5/fbbOemkk4BYC2Xnzp257777mDRpEqqqMmvWLNavX8/5559P9+7dueqqq7jvvvuIRCJMnjyZW2+9tVV13tY5zjvvPDIyMnj44Ye5+eabmTt3Lm+99RYXXHBBs8fzer3xrz0eDy6Xa6d2b22rvhdeeGGj8pWVlTgcDnr16kV2djYvvvgiS5cubfb4p5xyCpMmTeLFF19kzJgxLF++nFmzZnHZZZe1qH69e/cmPz+fyy67jPHjx8fX3G3K5kCwPregGUVEw9iROuxoXf2KIyZii+8tRVFA0bADZdjREFpKGzSXZ3MyattEWDaYgO5AdXrqu4i9saXoGgLB7aw+0toVPBq6iiOmTdSyeenFl1iyZDErV6xg7arlrFu1gj4DB3HLo9OxsJj90fsYRoTcwg50OegQXG3yaNO5A0vDRawKqwy//jqcmoLP5cSpg4VNTpKXDI8Hh6qgKArn/uNySisqSE9LI8XriXUXmxZ1ZpSSYACP7sDncKGg4FI1kvT61DMuNy5VjwWEmo6+k1LPgJxVLEl/FzJQ3AEhy8C07fjr4mANa2sraZecRntfWnz7xnAtpZEobTw+9knN2rw9VMuKmjJSnG66+7PR6hPJVoTrWBEoo43H16J6eL1eOnfuTP/+/dm0aROpqamcddZZXHXVVfEydXV1uyQX4IABA3j77bcpLCwkHA4zYMAAnn/+edLT05vc3mDo0KHsv//+lJWVMXz48PjECVVVeeONN7jsssvIzs5GURSOOOIIHnroIVRV5bXXXuOCCy4gKyuLlJQU7rnnnlbXeVvn0DSNV199lTFjxnDffffRu3dvRo0atbNu1w7ZVn2b0qtXL8aOHUunTp1ISkpi6NCh8bGnTSkoKOCtt97i8ssv5+yzz6ZLly5Mnz69VXU8++yzufTSSzlj3OnY8dyBDS2CYexIMBYImpFYAmrbAtvCrN4AloWe0RbVmQSoscBOteKBoBAChInqy0Axo6gOF6o3Hc3tQ3G6UXQnrpognqzsWEC4g7Nom5tZ/OTTUzl9wkR++PEnFi9dytKly1ixYjlrV62kfdfunP7POwhEDO6+7SbKStaRnpVNXoeOHDziONr26sVv1UVUGLUc+/iDpLqS8GpuPLpGVpKbMrOGWjNMN3827VPScNS3Dq4KVLCmthq/0NFUTyz3oG3Rb8wJrK6tJNeTwsZQAF3VcKoafoeHwqw0fM5YQOjS9B1KTr0jGmYV5+TkxNdS3pMmskiStHMoQogdmD7x17Ju3Tratm1LUVFRPG1Icyzb5rvStUTszWPXgmaUOiOKt358T4NNFRUoHice3UHyFhNZYgPHIzhVLdYKUP8LzrAsAkaELI+XQ7I7tDjpNsRaRbacuNGgrq4OTdMSxt5tqaamBq/XG+8qDQaDaJoW7+KNRqMYhhFvPQsEAiQlJcXL27aNbdsJ3aHNbW/fvj2vv/46/fv3xzTNRu9pIITAtu0mu28ty0JVE5f/2rJOwWAQXdfj92Lr69neOcrLy0lNTUXXdcLhMEIIPB4P29MwRrG5+7ylre95JBLBsqz4eMCt73Fz9d36OA1M00RVVVRVpba2Frfbja7rzZZveM+Wz8O2bQKBAKmpqbHzWya11ZU4VNBV4mMEn352OlNffJ3PX/4vVqAcgUDz+kGJpT+JjRPUGi4CRP04QdPArC0FRUNPyYqlhdHdqC5vrHu4YdZwQ6ug5mhy0kh5eXk8Z2ZrmVasRfAf11zDlEf+22j/aeddxtnX3MCpB/eguqIcgJS0dHLaFVLYbwADzpiI26lQXVyEmeIBj4NMh5dUtxufy0mKQ6fGCmNi0t7nJ8PlQa0PCEuCNZRHguR5Ukh2OInWjx3cGApQGQmR5YmlkXEqGkkOJ1491ivREAi61Fhi6l2dYqYlJk+eHJ8ssr3xgH/keUl/Pvm8JJAtiq2mqSoHZrVrUZ7Dcj1lh77JWrMyS4OmgkRI7B5tytYTRLaetep0OhOO7fMltnY2BCRba257g+aCRIh1OTY3xq+p7VvWaev6NzcLd1vnaKhbS4K+Bq0ZO7n1Pd/6vVvfY2i6vs1N7tny3m45C7q58kIINGzscO3m1kDLwBUKENy0EDsSRNE0FMvEBEwRSyMTNS2efn4GZ592YmwlkeR0zIr1mOZGtJRs0HSEGYmlkYmvNtK6QPCPEEJgWLEk1CUbN7F0+XKWLV3O8hUrSM/N54gTTiNkmDz15BNNvj85P4vldSWcetPNOJKTyWhXgMfrI2oJkhw6mV43aW4H3rZZuHUVh6LgdGjoCWMHU7BtG1PYBK0ohmETtU1URSHDlYQhLGrNKB7NQbbbQ0dfRrybODZ28M9pHfwj5GQRSfprk4HiDtBUlZaM9HGo2k5bjkqSdlQsdUx0izGCBlY0CNFwfXdxGEwD0dBKriixLDKajh2qwQpVo7q86CltYjOHbZPPvvmJUy+bxH49unD68UNAxJah01PaoLg8aK4t0sg0BIM7ORCEhtnEFsGIweo1a1m2fCWLly3Dn5lNv8FHEjFtThrYnerK8oT3HTz8WHocPYzV1QGOuPAyfn77dUrXrI7v79SvH72GD8fr1BgxYhjJTgcuXcWhqjh0Nd5VvGU9YmlmjHiamfqbCQgcqoZb08lwJZHscMZXdGoICFv7h6EkSdKfRXY907qu59aQzfaJttX1uafY256ZsO3YmL76ALAhGIxNEgkiIsH6mcRmvLwVKMWOhtB9WegpWaBogAAhELYZO179EmwIYsGksNG8fnRvOorbi604iJg2Pn96fSDo+EPJpbfFsgVRy6a8spplK1ayYuUKPL409tl/AJXlZZxz0jGUFK3BNDfP7h5wxFH865EniNgGLz50P6rbgz8vj9S8fDIL8tA9SUQt8Ok6WUkuknSFcw4ZTKCqipS0NGYtmIfb5Ujo2hVCYAqbqGVhCIuoZWFjg4hNONFVFbem49NdjYJBp6rt1Ikke6u97fvr704+Lwlki6L0J/qjeRD/boQQjQJAYRrYRggRCcb+NyPYRgS7tgxhRFG9flRHEmg6iqqjaDqK7kHRQdgmimVASjZKbRlmsDI2ptDji6eQ0dx+FLcX1eGp3+ZstP5wg+2P3Gz5dZq2oDYUYdWaNaxYsQp3cipt9+lBbTDM+accS3HRmoRWwUHHnMA1D+yDaQUp6LUvuf364c/LJ699W9IL8knOzuan8rV4dRcjLrwEr1Mn1eXE7VSpMyMkOxwJs4ptITjzyoupqwmQnpaGpQmqomEilkm1EUJFxedw4agfH+h3ekjWY2OSGwLBnT2rWJIkaU8gA0VJ2k2EuVUQaBnxmcJWJIBVW44wI6iOpFjS54bGf1WrH9Ono6hONJcTVXMRrViLVb0JUtqguNyxINNUYsGi5owtL+dMx+Hyouq9QXfEjrOLWwQhllImHDUpKi5mxcpVeH2p5HXoSnlVNZefNYbidWvZVLIeuz6P5cDjTuKqu+5DqDYuv499Cg8iqU026Xn5ZBe0I72ggN+rNxKuraTjRRPITUqhT3obfC4nSS6dikgd60IVZHuc7OvPQKtflaQ0XEtpsIpqS8ftUBDE0lVVG2F6nnoMSbqTAq8fpX4MYZLukMGgJEl/azJQlKRdILaSiBFPII1pYJtR7FAVdqgWIexYINcwlk2IWGJkpX7ih6ajulMwK9ZjGUZsjWFiXcPYFnZ9vsH4RBGnG3fb3qjOJFRX0uYJIvX/0PRdOkPWsgVhw2Td+hJWrFqFLy2drPz2FG0s4/pLzmbDuiKKi1bHA8Fhp53BWdffhIFJTbiWvG5d6XrYoYi0dLw5WXTeZx9W1paiKipn3H8/dWaEsIjQLjmVdsl+XA4Nh6pQWanT3mmR40km35sar4+qGdTaTnQVqowQZjTWlR6yDJLrl6RzqTo+hwuvw4lHd8YDQdlNLEmStJkMFCWpFWLdwWZ8LN+WQaAwgrHxfA35BG2zPhegqF9MRME2wli15ajuZHR/fmxmuG3GxhDaJgi7PudgBBQFPSUTYZkomobqzUBze1F1V31roHNz9/AunAzRMHs4FImydt06Vq1eQ3p2G7IL2rO+eAP/uvQ8StYVsbF4HUY0AsARp0/kiPOvwO0w2Vi6gfT2BZSWlhCpC+JNS2Pfk09kZV0ZDlXjoieeAQG6ouJ3OUn2aPhcTnRVxaErONTYP6GI+OQwu37yiHA7SfY4sRFsDAVomDyiKSqdUzJJdrjx1qencqoaTnXzmEE5gUSSJGn7ZKAoSWwRAJoRrFBN/QSRWDDYEPzZRjgWwDUEisTCErOuAruuCs2bhp6ai6Jq9d25rvoE07FAUAgb1eECbxp2uBarZiNqchqqw4PmSUVxeVB19+bWwPpgcFe2BjYEgYZlU1kTYM2ataxZu5acgna0aduBpStXc8MV57NxfRGVpRvjLYLHn3sRJ1x8KaFwHcUb15NeWEDHgf3x5+SipWfha9eBiFqHW3dy1fMvoto2/3f0kUQARQiO2K8XSW43Do1YQKip6OrmJQYbZhEbtk1EWNQaFrZouOMCRVFwKBoqCm08Prz1k0e2bBXcU/IMSpIk7c1koCj9pcUDwIZu3oav64NCOxpCGCGEEUFYUcK1EYIuBbu2HNsIxlYC8fhjYwQVFVCgoUtX2AjLRE9KxyQ2O9is2YiWlJo4LtCRVP+/e3N3cP34QFR9l7UGWnYsADQsQTgapWjdetYWFbFy9Ro6dO9JVrsOLF6yhDuvvJDSkvXUVlfF33vCJZdzxFlnsa6qlMq6GvJ6dqd37hGk5uSQmpNDTqfOsUkeusa/XpqBQ9NxqyqaqpLk1Eh2aTg1DYcGwjS5auyZ1FVXA1BbVcXNZ0/kv68+D5pGVEQJGvYW6xID9WsTO1WdVKcHr+7Aozlxalq8ZdCpalRVVpKRLmdlSpIk7SoyUJT2OkKIWD6/+u7aWGudVR8EmrHZwFvmCLRMbCOCVVcOtonqzUDVnbFJIaoGav23gepAdSioThUlpQ125TrsugoURUNxemIrjah6rIVPd9WvJuJCdSXhbhgHuOW4wF3UotUwS7ghCIwYJsUbNrB27TpWrVnDmrVr2fegwWS378CiX3/hnsvPo7J0E2KLZSdPvOpqDh83jhqlFkeKl55dDyUpO4v0nBwycwvIbt+e8roo3mQ/l019lhSXixSnC7eu4naouB0aQTOKU1dJd7rRdRVdibUI1hhhDMskSdcwhc1bL73CT998m3ANP371LW++9ConjT8dn+4iSXOQ7HDHA0FH/RJ1cqygJEnS7iUDRWmPsGXwJyyjPgCsb/kzIggzHFs2zqgfv2dbWEYIO1ABqoLmTY8FfQJQ1c2pYVQHqA40hxtFd2GUr8GqKgF/HooO2DYoBoqiga6DqqJ6UlCdHpw5XWIBpb45+IsHin9CV7Bhx8YFri8uYdXaNaxeU8Ta9UWsK1pHv2NG0qZjR1b+NJf7Lj0XyzQTjnPCtf9gnxOOxvSEade7F53TM0nJaoM/LxOR7qNdYScc6GRntuWfT0wjYAYpi9aQ5nbTPbUNbmdsskjADFEUrCRJN+iRlo5D1TBtm8pokJK6cuyIwCYNhxkL6AxhsSlUh0vTaOOGFKebqnUbmrxWtayGg7Lb75L7KEmSJO0cMlCUdolGXb7xFsDYyh6xoC9cv2JIBGFGwTaxwrU8+uyrVNeF8adncun4kwEl3vIXH//n0FDdPmxHEkZ5EVZtBbovK3byhlnBZqR+veFYN7DmTUH3D0BxJqE6XAmtf4qmg6oTrqzEswsSzDYEgKZt1/8fCwZLNm3kh3k/s3bdejaUrGdDcQmbNhZz4gWX0aZzJ777+AOe/tc1CcdSVA29Q1sO7ZJNIMtJ75NOIDsnj9zctqRl55Cek4vlcxGwDDKy8jn3zgfwODRS3Tq1dojyaC1Zbi8dUvw4FAVdU6mO6qyqtfDoDvxeBQsLy7aJiiiGMDGEyqZwLbqioSkKCgqdfJmkOF2kOT14Ha7YqiVbtAY2jBH8pWdvnmninnTp3Hmn32dJkiRp55IrsyBXZmkJYduxVr4tu3zt+u5e24rP1LWNCMIII6woWBa2ZWAHqxDREIrbi+ZJ3dzqp6jx4C/WDawTNW06DhhKRXWA9FQfK75+D4euxCeX1A9gq08lU98VrCgomgPVnRJrDdy6FbAhEGyB1j6zrQNAw7IJBEMYCgRNmwULF/LN55+xccNGSjeWUL5xAxWbNnDWXffhzstn7vvv8+E9t8WPp7tcpGZlcfqNN9Cl3wAqikv4bc4cUrKzcaVlkpyRS3pWBuleF5qqoSkCVEGq04nboeHUY2sNqyrYtkWSw4GqCkwhsISNYcXWFtZVFbU+4BNC1LeQCpyqhqc+b6Bb0/FoDhyqFptwosT+d9a/bukaxNFolKOPPpo5c+bEtw0ZMoRZs2bhcDhafK+b8lf6Hvs7kM9r7yKflwSyRfFvJ2F8n7ASgr0tW/uEFUUY9f/XJ4TGthG2iW2b2LWV2FYEPSkN1emNBWtbtvrpbhSnhqrqkNIGs7oEO1wLiobmTU1oXbSNMAiLqGFw4vn/pqI6AEBFdYATzr6MmS8+iSslMzYpZOsgsIkVQ3YG07Ix7VjLX9SyCJsmlYFaitavZ13xejr03I8o8M1ns/n8vbfZWFJCVVkpteVlBGuq+eeM18kqbMsPn33Ia3feiaIoJKen48/KIrNtAYGogWLCvgMOotN/HyM9uw2pWW3wpqaga7Fgz6Ep5HXtQr/uXXHpseBM12JpZFAFsdm/sWXlTNvGEhavP/UMtdU1JKemMOqcCRimWR/kqbh1nVSHmwI9FY9Wn35G1dAVNR4M6oq607vVnU4ns2bNIicnh4qKCtLT03dKkChJkiTtejJQ3Is1mtRRH3jFAjAL24jGJ3M0dO/Gx//ZNtgWDelGbDOKVbMJgcDhz4+lcVE1UOrH+zk88UBQVxSErw1G5TowDVR3MorDU3/c+pyA0WCs+9e2YqdQVIRtYdZsQBhhVE8yiu5GcyfHZgM7Pbzwwqt89eOvCdf45ffzmTHnV84999w/dK/s+qDPtGNr9UYsm7BpETYMijduZH1JCeuLSyhZs5r15RUMHjseVRPMfvVFvnp9BoGyMsJ1dfHjHXXeuYw4/wIWLvuNeV/OwZueSXpOGzr17kVqZhZ+nx+f5uGgYcey/yGHk5qRgcPlRFdUBAKnppLs1PF0zEY/oDta7BahKCI2uVrYWMRaAW1hIzAxUDAEKEJBFQqaouBQNdyaA49DR7UFr/z3caoqKklLT+f2f/ybJJd7lwWAreFwOJg0aRJVVVX4/X4ZJEqSJO0lZKC4m8USMtv1rXXW5i7d+mCv4f9YPr8wdjSIImKB3uagzwJbxLtlY6t8KNjh2DJwqsuLnlaAWj9WT3Ek1U/4SGyJs1OyMSvWxQI5XxaK7tiiq9nCrh9H2FAvRXdihQNENq1AS05Hc3q2SAnjQXG6Y3kB62cDb15/uOmVQtZsKKcpK1asSLhfsfF9FlHLJmrHgr74a8siZJhELEHQNJn340+sXrGM6vJSqspLqSgtJVRXx5l33wPA63fdyY/vvN3onH2PH0FGVgapqT6y27Wja9/+pKSl8+nLLxANhfj61RkMO+tcDhg5lkNOPhOfSyfJqeHSVCxMPA6NZIcTsryxwA9B2I4ihMDt0LFsAYoFwiJsW9gIvIoTTVHRFBW37sKlari0WFewrqjo9a1/W7YENiSNbujeraqoBKCyooJTjjsh1nK3h8wcvvLKK3d3FSRJkqRWkoHiLiKEwA7VYFab8Zm8wgzHunPthjx+Jla4BmFEYsGVpscCvtgREg+oKKDqWLXl2OFaNK8/lty5maAPgNQ2WCnZmNUbEJEAij8/nrNP2HZ9C6NZ3wpZ3xKoO7ECm7AjAbTkzHjXrqI5UF0eFN2D4nTVJ4ZumAW8eRxgS8YC2sLGsq3YzF5hY1g2UcsmNTe3yfJ1yak88d0PFJVsoKa8AhWbdn16YxgWn776PEULFxGpqiFUVUVtRQWarnPmK9PZFAzy8xMPs+yzz+pvoUKS348n3Y8tDLI9aRxy9AgKO3aBFC+1Hhcd0jMp7LgPaenpqEKl9zHHUHDkIJI1necv+wfRUAiAYE0Nj19xIZc9NZmu6W2ILSWsELQiVIQChG0dl5KCS9fRUFAVcODC53Dh0124dUe8pa/hf0crx/5t6bnnnksYAwgwZ84cnnvuuT/cGitJkiT9fclAcRcRZpTIhmWEK0V9omY2T9xQYhM5FFVD8/ixjA1YdVXoaXnoPv82j6snZ2DWbMQKVmOHa9BT2iSed+vJJrYJqo5RtR6zrhLdlwmqxmPPv0l1bRB/qp/Lzjtzq6TQ+ha5AGPpYISiYiOwhcCybcJ2LMAzhU3UsDAjIUwhiFo2pmUTtU1CpkEgahCyTIQFEdsiYllUhMOUh4OgQHDlGmpKy6mrrqSqtJTkrCxqS0vj1+NJSeGxW2/CuuHf8W3Zbdty/wcfonoEJQsWsv7X30jNyCSrTS4du/UkKSOTaETDq7s57oIL8J5/Cb70DHxpaZRbQQJWEI/LSbJLZdDQw9CHHUZpJMDGcIBcoZGdkY2iClQVAlENV9TJj2+8z8Lvv0+41yt+/IWyz77j2HMm4lT1hHF+sf933bi/ra1cubLJ7Vu2xkqSJElSa8lAcRd56KH/snbh9+Tm5XP5BWdvs6zq7IBZtR6rZhOKoqK6fcSmKgiEiLUtCiFi3c+2idBdRG0bY+NKlJqNaN6M2PJmAoSqghZrARSaE9vpw07KQLTphq3oWIpKyLC468lXqK6qIjUtjUMuvBqhaJhRi0hdmKBhErFiS6ZF61v+TGKBYdiyUOv7uE3bpjZiEqytI1JXDXVBQjU1eHwptOvRHQS8M/lBasvLCVdXU1dVRU1lFW179mLC3XeRmeTh+hvPYuPaNZtvhqKgahq2ZZGcmsqg0adTXlaGNy2NjOx00jOy8WdlUReOJY++6N7/YAgbr6rjcKioikBVADVWZ2+ndqgqqEpspY+2OLCFD6em49BiqV50RSU3JQuH2gZRGyYtPR2npqHVB3yaorK85q0mn525qZLOKVl//APzB3Xs2LHJ7Z06dfqTayJJkiT9lchAcRd56OFHYmvmtsng4JOHI3QHthAIiLXKCeonKYBt21iWiVFdidhUDO5k0F3YNtg0vMdGKBoCFVN3guLGcBWC5kBEdGxFxULFVjRMQ8W0IWTaKBg4NRMbQCiYRpRpV1xBdVUVANWVlYwfdTIT/vNfKusiRAIBctJ8+NP8WJEIP8/6iLqaGkI11dRVVxOsrqLXwYM4YvRplFTVcc9Rh2FGIwnXfsixx3H4wQ8CsPLbuZiGgS8tjeT0LDLad6bjvr1Ic3kxTDj9/25C0VRS/Gkk+f0k+XzcOPI4KkqKcSZ5OfHiC3A7FJy6gqrFunA1pf5/VUGrH6vn1DU0FBza5uXdGpaA2zro0xU1FiBuNc4PoFwpJyOlcTqI7l26Nvmc95RAbMKECbz00kuNUtBMmDBh91VKkiRJ2uvJPIrs/DyK0WiUnNwcKisqSUpO4q4nbkb1ZSLQUIRFKBLFFuDSVZyaWj/dVUWoGqCCw42ie0GPdf1WGwq1UfC4nWR4PSj1OfBURUFRQEEhNkROwTYNQoZJdVRgh4LUrPqdcCBAXU01wUCARd9/z6/ffN2ozpqux1f3OO3yKzjl4kuoq6nhrAH942W8KSkkp/o55MRRDBgzgagl+Pyp/+L1+UhK8eP1p+Lzp5GWm0tWQdtYK6caaxWNWCaKIvA4NJy6gqaCU1NR1dh1aGr9LF5NZeYzzxOqDZDqT+WMC8+LJXDW9PrJHXrCxA5NVXZqF29zecN2ZS7AncUwjIQUNBs2bNhj6raryDxvexf5vPYu8nlJIANFYOcGik0FFD16deP/7r4eLTmNiqhKBAdJSQ5SPUlomgM0Byg6QlWpDdbhcjpxOpxUbtrEgl8XUllVjREMoEbDhGtrGXrKaLILCvj9xx948cEH40FgXSBAJBhk/B330/eoIylf/Bu3jT8zsYKKAk088tyu3eg1eDCp/nQ69dmPwp77Yts2xatX4UxOIjnVj8PpQCCImjaqKtBUC7dTx6XrOFQFTa3vlFZsnKqOS4+16rk1DbceW7fXVd/C1zCmb8uWvYYWP01R0HZTOpdt/WDcGwKx9u3bs2bNGgoLC1m9evXurs4uJ3+R7V3k89q7yOclgex63umamn266LfFPDT5eVLz21EXqGXUVZexyWEz++13+X7GmwTragkGAgQDASzT5JrXXuSAffvxxaxPeOH2mxudY5++/UnJyaXOtKgzIriyMmnTqTOe5FSEOwlPbi7F4QrqMpI5++FHSEtLx+3z4fJ6+eLDd/nk/v80OubA0SfQ58SjyfYk43d6UBXQVZWUlHaYwsbvdJHmSsKtx1r1VEXBqWok6c54wNeoe3cPyN+3MzkcDnw+HxUVFfh8vj0uSIRYCpqGXIWSJEmS9EfJQHEna2726fcffwyA0+1h+HkTCXh1DEXFk+onLT8fj9eH4nETdSnUajq/b6rE37svE/7zCJ7UJAIOgdOXRPuMPDwuD5tCtWT06MrpU/6DrirkeJLxaE6cmoquqwSMMFqGj/R9uuJzOHFpsQDviK6XUPXNXH785tt43Q4+dDCP/Os6XI5YHr+Gbu2Gr3dnC9+eZk8PxGSuQkmSJGlnkoHiTtbc7NOjL7+CwaeOJsnlQlEEbmFRcMKxHD1qZP34vFgLnqXYuDU9NhavXTqugT1w1ufY8zhiq3C4dR2HWt96V9+Cp9Iwzq/ha7V+0kfjnHzfzPk8oQt1zief7pGtY3siGYhJkiRJfycyUNzJmpp9OmDQITx847/xut2bAztFQUWJt95t+bph/65qwZPLqUmSJEmS1BIyUNzJnE4ns2bNSmix+2r2Z3tcMCZbxiRJkiRJ2h4ZKO4CDS12xcXF5OXl7XFBoiRJkiRJUkvIQHEXufLKK2VqAUmSJEmS9mqNZzpIkiRJkiRJEjJQlCRJkiRJkpohA0VJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSTJQlCRJkiRJkpokA0VJkiRJkiSpSfrursCWotEoc+fOZf369bRv356BAweiKMp23/fdd9+xdOlS2rVrx+DBg1FVGf9KkiRJkiT9UXtMoPjJJ59w6aWXkp+fT5s2bfjyyy/JyMhg1qxZZGdnN/key7I4/fTTmT17Noceeijff/89HTt25P3338fr9f7JVyBJkiRJkvTXssc0vbndbr788ks+/fRTXnrpJRYvXkxFRQUPPPBAs+957rnneO+995g7dy5vvPEG8+bNY9myZdx7771/Ys0lSZIkSZL+mvaYQHHQoEFkZWXFX3u9XtLS0ohGo82+5+WXX+aYY46hU6dOAGRlZTFmzBhefvnlXV5fSZIkSZKkv7o9pusZYmMUp0+fTjgc5pNPPsHr9XLttdc2W37BggWcf/75Cdt69uzJ5MmTiUQiuFyuJt9XU1NDTU1N/HVJScnOuQBJkiRJkqS/kD0qULQsi7lz51JbW8u8efM4+OCDmw32IBbwpaWlJWxLT09HCEEgEGj2vQ8++CC33HJLo+2VlZV4PJ4/dhFb1U/au8hntneRz2vvIp/X3kU+r71LRkbGLjnuHhUoejwenn76aQCCwSADBw7kyiuvZPr06c2WDwQCCdsaXm8r4Lv66qs599xz469LSkoYMGAAaWlpO/1G76oHJ+068pntXeTz2rvI57V3kc9L2qMCxS0lJSUxYsQI3njjjWbLdO7cmdWrVydsW7VqFTk5Oduc9ZySkkJKSsrOqqokSZIkSdJf0h4zmWXrgE8Iwdy5c+nYsWN825IlS3j66acxTROA448/npkzZ8ZbEQ3D4LXXXuP444//0+otSZIkSZL0V7XHtChec8016LrOAQccgG3bzJw5k0WLFjFr1qx4mS+//JLzzjuPMWPGkJyczOWXX85LL73E0KFDGT16NLNmzaKiooIbb7xxN16JJEmSJEnSX8Me06L4xhtvMGHCBMrKyigrK2P8+PGsXr2avn37xst069aNc845B4fDAYDP52Pu3LmMGzeOVatWMXToUH755RcKCgp212VIkiRJkiT9ZShCCLG7K7G7rVu3jrZt21JUVLRTg8zy8nI5EHgvI5/Z3kU+r72LfF57F/m8JNiDWhQlSZIkSZKkPYsMFCVJkiRJkqQmyUBRkiRJkiRJapIMFCVJkiRJkqQmyUBRkiRJkiRJapIMFCVJkiRJ+lsxTTO+eIcQgnA4vEPH2d57I5EIlmXt0LFby7IsotHoTj+uDBQlSZIkSWq1UCjU6F9D8LUrbC/gas25/+///i++Itz69es555xzdqhOmzZtYvz48c3u//e//80vv/yyQ8felqYC1FAoxBVXXLHDQW9z9piVWSRJkiRJ2jvU1NRwxhln4HK5UBQlvv3MM8/cqcvoCiH4+OOPeeONNygtLSU3N5dzzz2X/fffP15m5cqV/Oc//6GoqIjMzEwuvfRS9ttvv2aP+eOPP+J0OuncufNOq+efraSkhCuvvJIZM2bEtyUnJ9O/f3/ee+89Tj755J12LhkoSpIkSZK0Q6ZMmUJmZuYuO35tbS2rV6/m1ltvJSsri48//pi7776badOmkZSUhGVZ3H333QwZMoQHH3yQzz77jHvuuYennnqK5OTkJo/54YcfMmTIkGbPads2qtp0h6tlWWiatt16b+sYLTmubdsYhoHL5Ypvi0Qi6LqOpmnxLuZQKASAy+VCVVUOO+ww7r333p0aKMquZ0mSJEmS9kg+n4/zzz+fNm3aoKoqRx55JJFIhNLSUgAWL15MdXU1o0ePxuFwMGzYMPx+P999912Tx7Msi19//ZWePXs22vfCCy9wxhlncNppp/HCCy8k7Fu4cCFXXHEFo0eP5rzzzuOHH35o8vizZ8/mzDPP5PTTT+ehhx7CMIxtXt8333zD+eefz+jRo7niiitYsmRJ/LquvPLKhLLXXnstv/32GwC33nor4XCYCRMmMGHCBNavXw9Ax44dqa6ujr/eGWSgKEmSJEnSDolEIgljFLc1jjAcDjc5rjEUCm03oIpEItTU1PD222+Tn59Pfn4+AEVFReTl5eFwOOJlCwsLKSoqavI4GzduxDAMcnJyErYHAgEApk2bxgMPPMDbb7/NunXrgNhShrfffjunnXYar732GldddRWTJ0+mvLw84Rjr169nypQp/OMf/+CFF16gTZs2rFmzptlrKi4u5sEHH+SCCy5gxowZHHXUUdxxxx0Eg8Ft3guA22+/HbfbzYwZM5gxYwZt27YFQFVV8vLytnne1pJdz5IkSZIk7ZArr7wyYYzi9ddf3+z4wIkTJzYbSI4cOZLTTz+92fNcf/31rFy5EqfTybXXXouux8KXSCSCx+NJKJuUlNTshI5QKITb7W60Xdd1xo4di6ZptGvXjo4dO7Ju3ToKCgr46quv6NGjB/3798c0TTp16kSvXr2YN28evXv3jh9j7ty59OvXjz59+gAwevRo3n333Wav6dtvv2W//fajX79+ABx33HHMnDmTBQsWNNtt3hIej4e6urodfv/WZKAoSZIkSdIOac0YxWnTpiGEaHJfQ+DXnAceeADbtvn555+59957ue+++2jXrl2TQVEwGMTv9zd5HK/XSygUQgiREOB6PJ6EMYJOpzM+DrCsrIyff/6ZM844I+FYW0+GqaysTLgXmqaRnp7e7DVVVlaSlZWVsC0rK4uKioo/FCjW1dX9ofdvTQaKkiRJkvQ3J4QgYpmELIOgaVAZCUJdmIyMjJ12jj/SogixbtV+/fqRk5PDsmXLaNeuHYWFhaxfv55IJBKf+LF8+XIOOuigJo+RnZ2Nx+OhuLg43n29PdnZ2QwYMIDrrruu0b6NGzfGv87IyGDhwoXx14ZhUFZW1uxxMzIymD9/fvy1EIKSkhIyMzMTAtUGVVVV8a9VVW0y6LYsi+LiYjp27Niia2sJGShKkiRJ0t+IEIJwfVAYMg2qoyEqoyFCloFtCxQFTGGTairbPVbDGMUGuq4njBfc0ssvv9zqun7//fesXr2aQYMG4Xa7mTt3LiUlJXTr1g2ALl26kJuby7Rp0zj55JP57LPPiEQiDBgwoMnjNQSbv/32W4sDxUMPPZQZM2bw/vvvM3DgQAKBALNmzeKQQw5JCKQHDRrESy+9xBdffEHPnj158803t9kF3FD+008/Zb/99uODDz5ACEHv3r0xTZNAIMDXX39Njx49eO+996isrIy/NzU1FcMwWLVqFTk5OfFZz0uXLiU7O5s2bdq06NpaQgaKkiRJkrQbTJ48maqqKvx+f6MZrjuLZduELYOwZRI0o9REw1QZ4XhQiAIOVcOj6WS4ktCU2BzXmmgYtrPKh9vt5qqrrkrYNmLECM4666ydVv++fftSVFTEbbfdRigUom3btvzf//1fPMhTVZXrr7+exx57jCuvvJLc3FxuvPHGRuMWt3TMMccwffp0hg8fDoCiKI3KO53OeHd4amoqd9xxB9OnT2fGjBmkpKQwbNgwunXrRnl5eXzMY1ZWFtdeey3Tp0/n+eefZ9CgQXTv3r3ZdDpZWVlMmjSJ559/nueee4727dtz880343Q6cTqdXHrppTz//PPYts0RRxxB165d48fyeDycfvrp3HbbbdTV1XH//ffTtm1b5syZwzHHHPPHbvpWFNHcgIG/kXXr1tG2bVuKioooKCjYacctLy/fqc320q4nn9neRT6vvYt8Xonat2/PmjVrKCwsjK8SsqMauo7DlknYMqg1IlRFQtRZBlHbindTKgiEopDm8AAqli0wbZuQYVFnGuhoGLagLBgkXQhO3kbi6r3Z7bffzimnnBJvmfwrqKmpYdKkSdx///3NturuiB1qUbRtmw8++IA33niDr776inXr1mGaJm3atOHAAw/k2GOPZcyYMduM6CVJkiTp7yoajcZTsgQCAQzDaNEv960DwppomNJIHRHLxEZg2TYR02JFoAJTCNp70klzeTFNm9qoyapABVVGhEynD5/uwbJjAWRJuJoaI0gbdwqpmpdVVUF6+nZesLGnueGGG3Z3FXa6lJQUHnrooZ1+3FYHijNmzOD666+nuLiYAw88kJEjR5KdnY3D4aCsrIxffvmFa665hmuuuYZ//etfXHXVVTidzp1ecUmSJEnaG0WjUY4++mgqKioAqKioYNiwYcyaNSshWLSFTcg0CFkmtUaY4mANdYZBxLQxbJs6I8qq2gqErZDvTsetODFsCFuC0qCFYVkYgSCRaASHrpCV7CRs2SgA4TB1gQpyC9uhaRqL5i9i3pw5GJU11JRVUVW2iUsn3QgH9t89N0naY7QqUHz++ee55ZZb+Oc//8nYsWNJSUlpspxhGMycOZMHHniAtWvX8uijj+6UykqSJEnS3u65555jzpw5CdvmzJnDE888zUlnjqM6HKEsHKQiEqLWiBI0LGoiUYwo2ELDoWj4XA5AJ9VOQ1EUFNtJFIgGA9SWliI2baC0ZANthh5Lik9j4ayZvPjmG1RuKqWqrJRI/QSUx2bPIT0nl/LfV/L19JdISvXjS8/Al5EOdvPJs6W/j1YFikcddRRjx47dbr4jh8PBqFGjGDVqVDyzuSRJkiT9XQkhiFo2QcNk/u+Lmyzzv7nfUde3H7YtcKoaDlXHoeg4dB2r1qS0pBi7pgIRqKJy00ZqKisZd80/AHjxgft5//npRLdKNH1j/0NIzc0iUBciEjXI7dyFfQYehD8zC19aGlFhE6guZ9DwIzj4qENJ0kySiBK1wqR5ms8BKP19tCpQ3HrJm5bYmZNDJEmSJGlPY9sC0xYYlo1hC8KGQa1hEohGqA5HqYiEqYpEqDUjhE2D5WrTv3odtmDVrE+o3LSR8g0biIbDXH7f/ZTXRXn5jhv55eMPE8qrus4RZ52Hw+0mvUNnDj5hFP6sbJIzM/GlZ5KVm0PXXD9JDosxp4/kzNOPRzWjqOEqMIKomoYmSlGFiuKJJYhWVB10L88+N5NQbZTlPy7ZZTOypb3DDqfHmTp1Kt999x1PPPFEq/ZJkiRJ0p9lZ6Sgse1Ya6Bhxf6PWjbBqElVOML6QIiaSASwCAmTkGkQtMIIYmlXdEVF1xSKFyygZNVKNq3fSEVxCUkpKQRrauLnSPb7+ej56fHXbm8y6Xm5LCutQgiNgcedwL4DBpCZk0N6m2yyc9uQl9MGt0NHw6LbaccjRh+FsAySFBvFCIERAnMNRtjAFP/P3nvHyVFdeftP5aqO0xOVswAhRI4mGmPAGWOccMA24LRrL9512vXavH6d9+dlWdv72l7jgNdpHdd4HdaGNc4kAyJIoJxH0sTOle69vz+qpzWtkQBJI4OgHj76MKru6bp3SlP97XPO9xyJqxug6UgRIIMKys4g8v24lg0iRskYpMBvVPnGV75PrVLjJ92/4K/+6q+m1UWbcmRx0EJRSrnfDuthGO63b1BKSkpKSspfgjAM+chHPsLo6Cjd3d37FTyiFQ2cLAb9SFAPBfUwphJGNOIQP4rxVfL1aFhjS6WMbVgsKOZZf8cfGdq4icHt29i9Yzv1XcPki0Xe/+X/QCjJf33+86y9804AvHyeUv8AQaOBiGO8Qp5Xf+pj9Bsus+bMZsasmZg5l231MRxTY2m+hLfkUnQZ48c+68q7Ia5SbPg4IgQR4TfGWF8dJ9ZNlnQNkLNdMCyklWGzqNEQkvmmSVHT0E0HZXtsrA5TrY8zL99Ln+OBYRFrBn/1Nx+lVqkB+zfapDxzOGChODQ0xPr169mwYQNDQ0PccccdHY83Gg2++93v8tznPnfaFpmSkpKSknIg7MtZ/JznPpfv/tdPUbrRIQSrfsxIIyKSMZ6tIRCEStCMfcqiQTVusPbXv6W8eRsjg4OM7tjJru3bcHq6+at//xJZw+PWr/0Ha+/9M6Ztkxvop3tgBvOOOgbH1HBMi9f/w3sYEw0WzJnPCbPmYekG373pywyNjRC7Bhc85wyOznWjS4GKQ8aaY5iNnRA2UOUtSF1HtFzQftAETSPOFHFsFywPLW8hgpDIr+I3xsjoPRCHxEAY1ImAWMugZ4volotpWJjNKrqIMHM9uPluNN3gB1//Jg/f80DHz/L222/n5ptv5pprrvnLX8iUJ50DFoo//vGPufbaa9t/v+WWW6Y855hjjpnWzuwpKSkpKSmPhWilh8M4iQx+9Ss3TXEW/+43v+GfPvfvPO/lryFSAqEEDRGyrVLjjp//iPHtmxgbHGRkxyCjg4P0z5/PW/7t82S0DH/45nfZsuph8qVuSjNmsnj5ccw7eikn9veSsS0+/NkbyRWyFLu6aKqIou2QmRSBW3j2GZRDH1cDV8VI0eS1r3spImwy3ihjjW0nHt2KlAI0yCiYFwv0sI4RVJDZHnTbI68bLNQ0UIoMEhU2QNNwDJMlPXMQuk6XV0J3PTTDwjVMjpaKEJ0u10PX9ozlW5Qt0RQRRdtFa01k2bZpyz5/vuvXr5/+i5ZyRHDAQvEVr3gFF1xwAd/73ve47777+PjHP97xeKFQoL+/f9oWmJKSkpKSsq86wclRQT+SlP2Ish9j6Bp/fujRfb7O927+PD/54X8wtH07/YuX8ML/+wkcw+T33/o6OzdsoKuvn95Zs1hx+hkcteI4TpjRg6npHP0fX6Grq4t8PoupJ/WHkxk4ZlH766I0UXGIaDSRIoTIRwYNvLCBigPq1RGUitHcLgzboaiboOmgG+i6gZIxSimKpoEyulBREyUjMHLoXpEex0MzbDBMNMNK/ugmnr7v2cy5/fxMPdPCMzvTyXMWzNvncxcvXryfV0l5unPAQrFQKFAoFHjHO95BEATpOKaUlJSUlGlhIioYxElksBnF1AJBIxI0o6TJtBDJfGIAXdOwdB3L0Lj15z9h7bp1jAxuYWTHVtavfmif5xjasZ3Zixez/JRTOe6Uk3jR0fMxDTjr+9+hu6dENuNh7ENw9WQ7O3gopUDGyDhCxQEyCpDNCir2IQpRIgZaE3I1HU1P6vY1zcDIdhOVd0B9FKX3gKGSxw0b3cqhOR6a6aCZ9h4haJhTxOnh4MWvejk/+/6PuOcPf2ofu+CCC7jqqqsO+7lTnpoctJkll8uRy+UIw5Bf/epXrFu3joULF3L++edTrVbTtjgpKSkpKVOYiAz6LTHox4JaEFMLBc1QEArBWFNQ9UPyrkVfzsHUNFb/+Q62b9nElk0b2LJ5I9s3b2Th8hW8+n3/yIjf4MYPv5fayDC259E7ZzYLVixn18aN7NqytX3uk848g3//0Xfa08KkUgglsXSD4vzZe9Y46biSCiVCVBwi44AobGKEPjKsQxwipUChiEIfvT6GZloYuT40wwApiZTAVIAGmmGC4WJkPbSeeRiWjWHaaIaNZlpoukEkBYamd6SIAWIp0VAYrRTxBEJJlAJT7zw+eQ9P5LhSilhJLNvmCz/4NhcuO5HK2Djd3d2pkeUZzkELRYBbb72Vq6++mu3bt5PL5bjiiis488wzueCCC3jwwQfTWc8pKSkpz1DCOIkMBrEgEJJKM2Z0rIYYh1BIZGvGsKZrGBqM7tzOjs0bWL1mLZs3bGBkcAtHLV/BZW/5awIR8rfXXEmjmrSTyff20DNnNvmBAl424uiSxz99+ytEOYcZA/0sLvRg6QZRFPGcZSdRHhsj11Xks9//RlskCiXZUB3BFzELc91kDRMVBYg4ZFNlmKpfZRaCXFBHiRjdzrAj8hmNYmbZDj1W8joaGrujgF1xTF+2iwHTQndcjGwXI1JjMAro8fLMzfe2UsQa42GTLbVx8ugscPPtSGEtCthYG8U1TBble9qisBlHbKyNYmg6i/LdbZEXScGG6ihCSRbmuttp5L33lrMcIBGDm2tjVKOAebkuuuw979HbG2VGggazM0V63SxXXffX1EfGWLFwSSoSn+EctFAcHR3lZS97Gddddx3vfe97+fa3v80dd9xBf38/5513Ht/5znd44xvfOJ1rTUlJSUl5ChEJyb/8y78wMjZONl/gdde+neqk6OBQLcCPJd0Zi4ypM7R5C7uHdrN10wa2bFzPvMVHc8kVr6EZxbzmuWcSBgEAumHQM2sm3txuNkZbmJsr8sF//yz9A32UZvWzW4VkTYtjiv3YRvI2VlyxnEcrQzRFRCQFlm5gWRZeLkN5bAwn6yF1DSUiZBwShE2q47uo+3XGx7ZjoFBCIFCMN2vUREy345GNA0SzgnID6rpNTcaEuofm5DHcDJrlIUKfIGig5XrIFvvRWqlrUS/TqI2SNSz0lrAECERMLQ7QNQ2Jwmjl0gMR04gjlFIIKTGMRChGUlCPQ0xNb+9t4ngjDomVJJICj5ZQlHtmRAcibgtFiaIeh9TigEDEHdeyHkdUowC/dfwVb34jeiPkWYuOOSz/dlKOHA5aKP7v//4vJ598Mh/+8IenPHbcccdx7733pkIxJSUl5QgnajmJg1btYDNKUsX1UFCt+3z0ox+jWh4jXyxx2guvpFkZZ8vGdTz8yBoKvQM8+6KLMZG88OSF7fnCAKZlcdHlL+O8V72Amhbw5o9dT09vD/MWLWD+gvk4lkU9DslZDnnLYdnFFwFJtKwQNLB1oy0SAfKWw6JcN5qm4RkWMgpRIuQ1b7yS3cPDeBkba/ARmiJENMrIsMmAbhLbGQrKQmk6StPQNY35jocPlJwMtpNHsz1022MxMENBd6aAPckEMkdEFMKAgu20RSLAgJfD1g1yk0QiQI+TRUPDM62OVHKX47GI7sfcW8bc81oZ02Z+roRSinxLDALYhsn8XIlQCrqcPVFDQ9OZnyvRjCO6nUzHmuZmi5Rsj5KTZgJTOjlooRgEAcVicZ+PhWH4uPOgU1JSUlKeGsQtERiICROJaEcGG0HMUC0kkorerI1t6kSNOpvWruYzH/kHquUxAKrlMV54wjyklO3XPefS5/O8l1xAqEIuf/PV5DIZ5ixbzLxFCxmYMwvHsrENg5m6y/I3vH6KWaNgu1PWamg6fW7i41VSoeKgVUMYkQtqqKCBHweoOAApueL5Z6BkMhxCBXUAdCeHQlFQoDsehldAdzJoVgbNtHFNG91MTCSTybf+7I1rWLje1PSspRv0e1M9x6au7/P45L1NRtM0etzsPs7MFME3QdHet+DLt4T33mRMu0OEpqRMcNBq7vjjj+ctb3kLq1at4thjj+147Oc//zlvectbDnlxKSkpKSnTg5SqHRUM4qS1TDWIqQUxzUgSS4VCgQJd19CVolavMyZM4jDgh5/9CNs2rmPbxvWMDu3azzkkF7zw+Tz78hcza9E8Zs6dg2b7FAyT6z7wbmStSaZYOOC1KylR8R5DiQobyKCetI2Jo+Q5IiIu7wIh0HPd6I4HWpLU1S0HTAfdzqC72Zab2E6cxa26wZSUlH1z0EJxxYoVXHjhhZx22mm84Q1voFqtsnXrVq688kp27tzJy172sulcZ0pKSkrKEyBuOYqDtqM4EYTjzYiReogCSp6JriVtZUxdR5OC3//8v9iwdg1bNqxj64a1bNu0gQte/HL+7qP/TF8mywd+dys9vX2ccc6zmL14IaseWMkff3nblPPPXjCP573wedi6gaZpKKXakUIfv/28ycfbx6RCRj6ICBkHyKCOrI0gghqgoxsGSkpQe6KWE61nNMPC6l2AZhjoTgHNy6OZLVexaSeO48c495F2HKb2cjwc50hJOaT88Le//W3e+9738vWvf51arYZhGFx66aXcdtttOM7U0HZKSkpKyvQQThKDfiSotkbRNcIkOgjJm71l6PixYP3WQUa2bqC8YyMb1z7K1g1rWXHaWVz5tuswNY1Pvf86gsBn1py5LFqyhPMuOJczzz+HGb0xtbjO1++8jSG/TiUKmJ8t0fu9/9qnUFyyZDFOS5TtalYZ8uvM9PIdqdPhZo3B2hi9pkWfaSGjJtKvMN6osL1Zo6gbzDQtUBIpQmrjQ2zTDfK5buZ6OXTLRbczhJbLlijANh0WFPswTRdN14ikYGN1FOKIhV6+bf4QSrKpOkakBPOzpbZLWCnFlvo4jThkXrZEdlJN4bZ6mXLYZE622JHO3d/eRvw6g80qfW6WAW9PorocNtlWL1O0PeZk95Rt1aOQLfUxMqbNvGxXW7Q144jN9TEszWBBvtSuZWzvDVg4yQE93XvbVB3jaHNfifaUZxqHJBSz2Sz/9m//xmc+8xlGRkbI5/NpS5yUlJSUaUKpVs/BaI+RZLQRMlQLCYXCNrSkPx9gGjqmpjE+NMimdWtYv+YR+mfP5fhznstotc7fPO+Udv2gbdssWLSY/rzFMTNdpIr53u3/Q26gl9g2khYqcUhPpsh42MQxDLocj91+DVDYhsEVr3k1t/7oJx2NmY8/63Re9KqXt/9eiQKGGmWySlIUAXG1TNAYZGRskG3l3QjTJJ/rBpU00a7FISNhiLIcZjkZNNvFtDOo3oU0/Tqmm8MszcK2HDRNox40qJaHsHWDSDexWinkpogoR3776wkxFQhBOfIJRUy/m2uLqVhJymGTahTQ42TbYkopRTlsMhw0KDmZDjFViQKG/ToFy6GHPUKxHocM+3Ucw+wQivU4YjhoADA7U2gLwnocMho0iKQgzhSwNKO97vGgiW2YBEKQMfW/6N5GgjrK3N9Ml5RnEtPiODEMg/7+fsIwZNu2bWmz7ZSUlCOOG2+8kfHxcbq6urjuuuv+oueeLAj9WNIMBZUgourH+ELyva9+gXqlgubmuODlb6TkmajKMA0R0ztrHk3f529edxmb16+hUau2X/fFl1/BW1/7CtCLfPyfP03/zAFmLV1EbkYfoaYYDRr8aWgDXbZHfv4sbN2gYJgYmk6sJH1utsOVOyfbRSBiSo6HpRt84QffbvcpzJeK/L9v3oQW1onqPjKoU2qMo/t18rUhAiDyJViSbiXQvCx5mUwvMXIldCfHDMvFkZKsk8HLFNpmkj4p0Friy5lkcCnaLgvy3ZiaTmaSCzlvOszLdrW/niBjWszLdhErSXHS61i60bG3CTRNY062SMnJ0LOXaWSmlydjWpTszuO9bg5D1ylanUacHicDhV6yptWR6i05HovyPTiG2dEE+8nem1ASQ3Q28U55ZqKpQyhE+PSnP83xxx/PxRdfzM6dOznjjDPYsmULl156Kf/93/+NYRiP/yJPAbZt28bcuXPZunXrtIrckZGRdMThEUZ6zY4sput6hWHIzJkzGR0dpbu7m507dx62JsNhO10saYQxY35MI4gJRCtlrJI3ccdMagjjMOIFZyyjOj6GadnMP/pYBjeuo1Gv8fzLX8m/fOEmcrbJ1a95OX29vRx97DEsWXY085cuxuvrphaH+CJGKAkKLF3HNkxcw8TU9AMeC6eUQsURKvaRcciLzr6Ewe07mTGznx/85/+HaFbQNAPdS9KrmmG2ZxiHwsItFtCdLJrlopk2+l71gylPDSqhn/ZRTAEOIaK4bds2vvjFL7J69WoAPvOZz7Bw4UK+/OUv87a3vY1bbrmFl770pdO20JSUlJTDQRiGXHLJJYyOJnVfo6OjXHzxxYc8tkxIhR8J/JYwLDcjyn5MI4wZqofUg5jujE13xsE2NYKxcdY+soq1j6xi49pH2LT2EY45/mTWr3qA6njSgiaOQnZv2cDLrriCZcct56TTT8H1IsbCKtd/5bM0RUwkY0CjikYQ+jiGQcn2pox4eyIoESPjMJllHDZRfh0Z1FAiBClRIuLll19ErdYgl8+i21mUZqIbJka2hJEtodkumukkgrDm4xTTureUlCOJgxaKd911FyeddFK7X+Ktt97Khz/8YS666CKuvvpq7rrrrlQopqSkPOW5+eabuf322zuO3X777dx8881cc801j/v9k9PGzUhQDwXjzYhaEBOKiYSNwjR0LF1jcLTMukcfYWTzWnr7ejnrwktphpLLzj6eMEhqzzLZLMcsO5ZobCf33/XHjvNVKxX6j1vKKa96EWjwaHk3lm4kaWPLwdL33VfvMfcw0Y8wDpGRjwrrCL8GkY8UMaI6hPSr6JlS0m/QMNE0E93OcOXVr8fwcmhONhGDppO4jPcZqfT3cSwlJeWpzEELRaUUvp/80lcqFR544AHOPvtsACzLIoqi6VlhSkpKymFkw4YN+zy+fv36KcekVPixoBklUcKhWsDuWpiYMVquEqOVNjZ1CBo1vGwBPxZ88K/ewLpVD7F7+5Z265FnX3Qxb3/tKzBNjU//yw309PeyeNnRlGYO0JAx//zhj+5zbSNbtzMjk6cRh2xvVMiZdkfjZV9EbK9XcAyzwzgRScHW6iiaiJhlO2hBE+lXicMG2xsVhJLMNHQs1RJ5us6gEIROnpm5XpxsF2a2G81y2RUFNIDZuRL2pEbNQ36N8WaFAS9PYVKd3mjQYKQxTo9rdKy1Evnsalbpsr2OZtOT9zYzs6f34mPtbVu9jN6qvZuorRRKsq1eRirFnGyxXQeolGJ7o0IgYmZnC7iTmmsPNirU4pDZmUJHE+ohv8Z42Nz33oI6PU72abe3lJSDFoqnnHIKr3/96/nyl7/MHXfcwWmnnUahkPyDv++++3jBC14wbYtMSUlJOVwsWrRon8cXLFyYmEliQT0QjPshFV8QCEEUK3ZWfTRNY36Xh2eb3PWH37Jm9UOsf3QVGx9dzaa1j3DWBc/lhi99nVlFBy1ssGLFsSx71cs5avkxzDxqIQPz57CmMUgoBce8+EJ2Nav8sraTOUM+iws9zJk/f59rm79oIZC4U3c0KpRsj34v1xYQtShksFkhg063klhKoII6o9VRtlaHMJQiY5o4lWFA4Ge72S0UAo1upwsvU0D3CkS6TaVZoaGgvzQDrzVNRCjJyNguxsImeSfTITiG/QaDzQqeYXUIjnLos6tRw8ztJaZCn631MrFUHWLqsfa2s1khY9r0udl2K556HLKrWcXQdXqcTHu+cTOO2O3XEFJScjy6Wu7eUAqG/BqNOKRou20xJZRkyK8ne7OcJ7y3zbVxTM142u0tJeWgheKCBQv40Ic+xLve9S6KxSI/+MEPANi6dSt33XUXX/nKV6ZtkSkpKSmHi6uuuopvfOOb/Pa3v2kfO+Wsc1h87ou4Y/MoSawwiRIiBTvWr+WOP9/L5jWrmT1zgDlvejuBkHzs3W9naNcghUKRZcct58zXv45Tzjqd0BlnWyT4P9/8IkJK0KAc+NRFRF1JippOwUqMJZ5hUWzN2y1YLle85lXc+qNbOlrQnHbOs9otaLpsl4W5Ep5uoUUBcRwiwyZ2dYSBRhlLxaiySagESghcpZiFhm5aZB0PY0Y32C6uV2SBlGCY9OR7sVolRZZSzHG8fc4MnpnJ02W7dO01Zm/Ay+3TDdzjZNCyRbrdzuMlO8PifM8UcTKxt4xpdzivi7bLvFwpmYc8ySWcMx3mZLvQ9zEPeXamiFSK3CSXsK0bzMoUCKXocAkf7N50TZsyJ/npsLeUlENyPcPUru6+7z/mHOinIqnrOWWC9JodWRzo9VJKtfsRNiPJeCNkRzVgvNbkTReeSLU8Rr6rxC13rML3fSpjI/TMmodQive94QpW3Xd3u45Q0zSe94IX8c3vfheJ4E933YHbXcTpK9EUMQqFBriGiaWb2LpxUIaSKIraLWiKpRK3PnwPBhIV+6igifArqLCROJGVRPo1RG0EdAOzOCNxFusGWC6Gl0dzcuim06oltA7Y9Xwo+OUqbmpmOSJIXc8pExxyT4K9bzKu6+K6adg6JSXlyWVyPWEjjCn7MeVmRDOWSKVoRoLhWohnGcwqZjDMJIIT+j6vvugMdm7bwkmnn8X3fvorsrbBaSeu4KyTj+e4449j6fJjmXfUYhqG4v7xrYRCYC2Zha4lYrDPyqJPgwBTIkIXAZmMR3lsDM+1ENsfJJYCJQXIOHkeSRsaDQMz14PZPQfDySVzjS03EYZpC5qUJ4BUCqEkkZKk89VSYBqE4m233cadd97J0NBQx2zIs846i1e+8pWH+vIpKSkpj4uUqhUlFDSixHVc8WOasZgY+oFl6Cgh2L5hLQ8+cD8P3L8Sz3P56/d9EEvXsFutcDzX4dSTTuSkN72BM844nYGiSSMOec8n/w+jQYNaHBJLyZaohiNNsqZNt3PoIkzGESrykXGACmqIZrXtOn7FFRdRGR0l6+pE44MY+d5kvrHtoTt5dC/fEoR20p/wICKXKU8PlFJIFFKpluhLhJ9UEtE+Jlv+K4WGRvIbolCArmkYmo6BRsZMP1ykHKJQfPvb387XvvY1ent78X2fTCbD5s2b6erqYmBgYLrWmJKSktJmsijcVQnYWB9jsBoQxpKcY6BrGrZhEIY+o7t30TNrLlIqrn/H1dzx61+2U8eGYXDu+Rdw7qIeHFPnt7f/mlJvD04+m4xV8xuMhU1+se0RAiHo87JkTZuS7WFoGjubVWpRQHZSzRjA7mYNX0QMePm2GQGSGcD1OKTXyeJC0rA6Chir7GasOkq3YZDVtCRSqAQ1IRiTkqJl86rXXJakiu0MgZ1ht1LknSw92RJaa2xdIGJ2NKu4UtLv7TFORFIkRghNZ4aXb2eBhJLsbCZTXGZ4+XatnFKKnc0qQkkGJs1JfkJ7c3MdU0TKYZPx0KfbyZC39sSnqlHAaNCgy3Y7Rsc14ohhv0bWcjqmhQQiZlezimtYz7i96Wj0ullipYiVIBSCwWYFpaDHzWCgg5bsrRoGaJpGwXIwDQMdDVPXkUphGyZ508E2DCzNwNQNDE3D0PX28wxNx9R0DF3H0DTGRsdISTloofjoo4/yjW98g4ceeoj//d//5Y477uCmm27i9ttv5/LLL+fyyy+fznWmpKQ8A5mcPq6HMWONpGl1EEskiupYhYoWk3MMRjes4n/vu4e1Dz/A+lUPsHn9GubMX8htd9xHzjE5/YRjOXbhbE45+WROOukkli9fDqZBPfbZ5QcM5g3uL28lW3cSM4FhEEnBWNBA0zQyZldbEDTiiMFmpTWDd09rmkgmb+KVyMcxTPrdHCqOkFGTraM7GKqNEhkGM3UNpERKwbbRHWwL6izOdbOw2J/UDVp5quhsiULm5XsYKM1q1xOONMpsLg/Ta1j0TMpuj4dNNtfHKFhue8QeJMJla72MYxgUba8tdupxyPZ6GYC85bQNF00RP6G9TcwxVkox2Kwy7NcxdJ2Muac+fbdfY0ttnCWF3g4xNRo0WFcZZl6uq0NMjYUNNlRH6XWzdNteW/g9HfcGyRzmwUaFRyu7yZoOQkl0TUOhqIaJ0zljWhi6jmtYeKZJ3nCYlSmQsxws3cDUdExdR9c0dDR0rfW1pmFoyd9TUg6FgxaKDz30EM95znNYtGgRt99+e7tv4gUXXMDVV1/Nd7/7XT74wQ9O20JTUlKe3iilkjnHkaAeJDWF481oSvq4Ua2xZtVK7rv3Xsoju3nduz5IX87hXz/zKf74m9soFIuceOJJvOQFl3LaqaeyuDcLwKc+8TGacUQ9DimHTVZWdlGLQ4SSxEIwEjbIWw5zssV2fWEsJU2R1AFmjD2RQ9cw6XNzRFKQa0UUlVLocUw3ClvE2MNb8OMAJSOQkq6giS5C8qaNMm003cAwbfr7F+JoBgPFfpxMAc3y0AyTvshH+TW67Qy6tefcRctldrZIwXI6asQLlssMr0DWtDEniYOcaTPg5bB0A3dSpCxj2PS3BNHj7Q3A1HT63BwZ0+5w8Wqa1m7lsvd84247AzmmOGy7bJd5ua7k8Uk8XfY2O1sgZ9jUo7Bd7xeKGM+0MDWdXX4NDTB1g6LtckbfPPKGS852WuanJOJn6TqWbrQF4V/SeJSSMsFBC8WxsbG227Cnp4ddu3a1H5szZw5r1qw59NWlpKQ8bQliQSNMooVDdZ+dlZBQSBxTb4vCKAixHJtIKr7+2f+P2//7h2zbtKcR9oyZs/iPz/8rWcfii5+7kUwmw8KFSY9BX8T4ImJrbYyxsMl40CSQAoVCR8MzrY7RdnPomrJGU9dZkCtNOa5rGnPcLDL0kY1xQr+KaFZRUZMeKemKAkR1N4EUmIV+NDvDQCbPwN7OY8tljmmxr14LRdvriEhNkLMcllpTbQaeabG00DvluG2YLMpPdYY/1t7mZqf+LDRNY9ak5tCTGfDy7SjcZHrcLD1udsrxp8Peup0MBdslFIKxoEnYGp0Iim4ni6UZKCBr2rimxcJcCcewWsIvFYApRw7TUqm6YsUK/vCHP3DvvfeyYMECvv3tb6fj+1JSUtpEIokUNkJB2Y8Za4Q0IkEkJMP1iGYkyGoxwxtXs+qB+1nz0P2seWgl1fExfv3AWrozDv0Zg2VHLeH1V76SU089lRNPOgk346GZinLUpHfRPOpxyH0j26lFAaESSKnQNLB1E9cwKdruQb0pJzOPA1ToIxplZJCMt1NSIEUIMjHyaYaZtKVxs5j55eheFsPNp87jIwylFLGSCCURMjF/xEoSSdF6PBGXpq7jGiY9rTpF17RwWv/WLN3ASE1FKU8DDvquNWPGDJYuXQokkw1e/epXc8oppwAwb948rr766ulZYUpKyhGFaJlNGqGgGsSMNSOqfkwkJZBEe+IwYN3qh1j50MOcfunl9GYd/u0f382tt3wfgO7uHk4+5RROPfUU5hV1NBv+7vr3U40CalGILyI2y5hwbCdE42hoKCSGpmPrBo6R1HIZB1GfpaRsuY99ZNBANiuooI4UMTJsIio7AR2zawaa6WDYWTQng+4VkjnIpotuOWiTjBIpTw2kUsRStEVfLBMBOJWWuQMNyzDIGElUMGOYuIaFY5jtf2dmep1TnuYccsPtydx5550MDw9z7rnntsf5HQmkDbdTJkiv2YEx0cC6EQlqfsTOWkgtiBEyabahaxpmy5V79+9/w29+8RMeffA+Nq1dTdyqa161cRtzZgzw5zv/yPadO1h24vEUZ/QzHNQZCepoJM5MjaSWLJSCehzSbWdwg5hMMbnX+CJiNGiSszrrzCIpGPLrZEyrPeIMkvrD3c0qlhSUdB0ZNpCtFPKQXwMp6QE0XUczDJRmMKokynTpL/RieXk0y0O3HEZDn1AK+txcR1Pt8bBJI47oc7MdDttK5FOLQrodr2MObz0KGY+adFke2Ul1iQeztyG/hq0bHalfqRRDfg2NpPZuIrqqlGLIr6NIRs1N7gE54tenbW+jY6N0l7oP2952NirEStFlu8RKEsskKlgJk3GLRdttp3w9w8LVTTKmhWNamHrL8dsyh+wxiTxzo4Lp/TAFDiGiuGPHDsrlMsuWLWsfO+OMM9qPbd++veOxlJSUI59YJKJwIoU80giphzG7qgH1QDC76FAdGuSRlfeyauW9rHl4JZ/68nfoymXZsupebrvlu5x00sk8/21v57TTTuXYk06g2JtlfW0XculM+pfMYEgpRmojbK2PMxY2OabQx2xvj9t0Y3WUbfUyhqaTYY8QGfbrrKuOMMPLUyjuERwjQYMN1RGKtktW09HjABn6DFWGWTM+iKsESzQwq0Nopk3NK7FFRJi6Sa7QSyFXQnfyNHSd3fUKQtPoKvbhtcRLIGK2Nso04rBDvAgl2Vofpxz66JrGjEl1bjsaFXY2qyzJ9zBnUs3cbr/G5toY83MlFlrdB7S3vLUngloOm2ysjZIxbXKW0273Uo18NtXGMDWdrGm3BVsjjthaHydWspWiPzx721kbJ/bsQ9rbsF9jdXkXlm6yIC5hG0ktoC8iyqFPxrAxdI2SmSVj2bitukC7JRAtXU+jgCkpB8BBC8Wf/exn7ZY4B/JYSkrKkYFSe1LItUAw2gipBDFhnMwrNjSN8bExqjiEEh781fd5/2c+xfjoMJD0KTx2+XEsdEOOmj+HJR94Dx+6/n34SjAWNhkLmgyLiId3rqMehyzKd9Pn5tqCQCqFa1jk9nKb5iybXjdLzrIh2JMQyZoOvU627U5VUiIjH8evkW9WcKu7iUa2gBLIOMSKYwpxiGvauF4OI9sNbo6i10Vf5GNYLsViXzta5klBUYj2uiawdIOi5eIaJt4kJ62ORpftJYJ2Uv89SNy9sZRkzU7jRsfeJrH33ibImFa7j18Sc03wWq1fHN3siPa5RhKdm+g1OYFtGBRs97DvrWh7T2hvE7WAtm6glGJ3M4mCoiW1gccWB+h1cvS4mSQN3EoFTwjC1BySkjJ9HJbK6lqtRi6Xe/wnpqSkTCs33ngj4+PjdHV1cd111x3Q90ZC0ggF9VAw1ggYb8Y0JlrTaBoiCtmw+mEevv/PPPrgvTyy8l62b97Af952BxeetILC4FKGnnsRp512KieedgpLjjsWZRmUQ5/f79rIt794E5VyhXyxwKvefDWeadJlu8zIFKbMjAeYmSl0NFGeoM/N0eskaVM/SJoqK6Uo6gY524WwgV8eRPk1RGMMvTbGAt3AyJRQpoWmmxhenoKT5zg3j+5k0C0XzbTb5zpmH+uxdIOlhd4pa9U1jYX57inHtZbD9mD3NpmS49G1DyNOwXJZVnSmHM+YFkcX+qYcdwyTowq9T9recnmJ5+baZpFICjQN+t0sUsGuZg2FwtINsqbNqb1z25HPCUOSY3SK35SUlMPLAdco3nvvvdxyyy3ce++9bNu2jRe/+MUdjzcaDb7zne/wwQ9+kGuvvXZaF3u4SGsUUyY4kq9ZGIbMnDmT0dFRuru72blzJ5Zl7fO5k6OF1SBmuBayrexTjwTdnoVj6uzatoVVK+/lxGedR6FY4mff+Rr/+uH3AVAqdXPq6adz+umn8aqrXkfPzAHKYZNy6NMQEZFIIkK6puEYBrZu8tJTz2Fw6zZmzZ3Dz++/85D2qqRARj7+6DiWFSEaFVTUBBEh4zixpeoammmDFGiWh1Hox/By6Kabjrn7CxFJ0f4TSkFYqWPkk96JE9G/nOWQM20ypt02iExEB9PI4JPLkXw/TJk+DjiiuH79er7xjW9QqVQIgoBvfOMbHY8XCgUuv/xyXv/610/bIlNSUh6bMAy55JJLGB0dBWB0dJSLL76YX/7yl1iW1Y4WNiLBWCNK2tO0ooWRkOyuhgwPbuHh3/ycRx/4M4+s/DPjI0kK+avf+h7PecmLmXXlZSxb0Mvyk0+iNGcG5SigKSJ2KMmO0UEsXccxTAqWg+V0RnyiMKRRqwFQr9WJomi/InZvlFKoKEBGPipsIJpl4soQslEm0jIoWwdNQzNMNMPEyObQ3QK6l0e3XHTbRTOe2LlSDhypFKGMCaUgEqLDRWxoyb+JvJXUGQpcerp7cNLIYErKEcNBu56///3vs3LlSj7ykY9M95r+4qQRxZQJjtRr9qUvfYk3v/nNU45/4l/+jYteduWe9jQKTF1j++YNPHzf3ay85y7OetErWX7CSex44E7e+brLmTNvHqecdhqnnHEqx518EnOWLSUwNJpxSCX0QYNuJ4urJ2/2hqYxGjYBOsauQeKMbfo+H3jttfz5D39qHz/pWWfyiW/dRH+u2NHCph6F1MIGRTQMESKbVaRfoRHUqYYBeQ2cpFANGfs0hEtQzNNd6MPzCkkK2XKIlWQsaHYYNiAxYYwGjZag3VMTJ5ViNGhg6nqHw1Yp9Zh7i6Wk28l0uIQrkU8gYrqdzJS91eOwYwQdJEaSauTTZXsdM4YDETMeNilYLt6kOsBkrOBffm+RFIwEdWpRSHZSml5DQwcypk3J8chbbksIGu0ayYnnHqm/X89U0uuVAodQo3jFFVdwxRVXTOdaUlJSDgIhFavXrN3nYytXP8pZgcDQNTavW8fnP3k9q1f+mWp5DADX83j+xedz5qIctZmn84sH70F1ZdlaG0cgcbMlmrrC1gwMTWcsbGIbJnMyVlu8VKOAzbUkkmkX+tpzb5txxKbaGLd++wcdIhHgvj/ewTe//g1e/6Y30GtYyMhH+jXW7FrPcG2MRZkcA6aVNLLWYEcUsUvGzMt2saBrBpqbR7c9RkfH2aoHhHaGpZMctsPNOhuqI3Q7GY4p9reFymjQYH1lhIxps6yrvy3YyqHPhuoItmFydMFs760Wh4+5t1DEGJpOyUkEWCQFm6pjNOIQCkldHiSibEt9jNGgwaJ8DzMnTQHZ3iizs1lhfrbEvEnTRHY1q2yujzHDK3RMJRn2D9/elFKUQ5+Hy7toRiHz8yXyptsyL+kICTO9AjO8HJ5pt3sKOobxjG4jk5LydOaQzSxSSrZt28bQ0BCTg5N9fX3Mnz//Cb9OEATcfPPN/OpXv6JSqXDCCSfwrne9i5kzZ+73e37605/uM6L561//Gs+bOh4qJeXpQBhL6mFMLYgZbUaMNyLCTP8+n3vbT37A3MVLeckVr2ZOX5HBLeu48JKLOOn0U1l84nLseTNRhs6q8k5AkentIpQC1zDJWQ4zM4VJ0TJF1rRbc2j3iAJbN/Ba83TtSZEyU9fJGBbl7Tv3ubaR9WthxyP4SqBEjJIxjl8nGzUwAx3dG0D3ChhekS6ZzGMu5ruxJ7VicYw6BVMja3Y6aZN0p0NmUuRr4njGtMmadke0zzGMg9qbpek4k9zDRqvtzMS5JtA0jYxpE0nRcRySEW8Fy+1wG0PiUC5Y7mHZm1IKlEIpCKVgLGhQj0O0VqPpZcV+euwM3W62bSBJU8UpKc9MDkko/uxnP+Paa69lx44dUx67+uqrD6g9zmWXXcb8+fN5xSteQT6f5zOf+Qwnn3wy9957737F4tDQEBs2bOCWW27pOO44U2eFpqQciSil8GNJPYipBDEj9ZBqEBNLlUyVjSPGqnWOu+glLPrht9jw0H3t79U0jblzZzNvRo58wYdshi///n9aTlVaaWOdrGGh72XsmGh9MjmlmjFtji72o7WiSxM4hslRxSTiNfm4pRssyhY4buECOn9DE5b3d5EJ6mBaaKaFbuVZ3DMP6eSw3WySRm4JkzlKMUPJKUKl5Hj05jOYe0WzJlrG7H28YLks6+qflr0tLvSgFB3CcsIlLPax1nnZLuJMYcrxmV5+StNqgH4vR8nxDnlvlm4wK1MkVoLRoNF2sVu6zrFdAxRtl4KdCFVHN3ANKx09l5KS0uagheLY2Bivfe1r+Yd/+AcajQYPPfQQ73znO7n55pv5yU9+wgc+8IEDer3vfe97HS11nv3sZzNjxgy+/e1v87d/+7f7/T7btjnzzDMPdhspKU8plFLtFjVlP2K4HlIPk959GhqN8igP3Xc3D9xzJw/fexdrHlrJZa+/lvdd/2F+8LOfcvqSBURhSDaf4zt//h2256FpMBo1cQ2LPjfbISL2x/4iR+Z+BISh6SilkFGADJuooE5cH4ewziXPWsrPTjia+1c+2n7+yaccx2WvfjlOsQ/N9qa0p9kbTdOwtH2vaX9rPdDjj7W3/R7fx3J1TUPfx1r3t4fp3Jup6fgipikiIilQredmDIuCnWtFLpPooJuOn0tJSXkCHLRQvOuuuzj22GN597vfzU033cSmTZs455xzOOecc3jJS17CrbfeekDtcfbuu2jbNo7jEIbhY37f2NgYl156KZqmceKJJ/J3f/d39Pb2Pub3pKQ8VZBSUQ8F9TCJFm4Zb+LHgqxtogM7t24kiiLmLDqKRr3G5c9ajhQCXdc5avlyrnjdlZxzyVlUjVFCIeju72XXth3ki0VmlXr3K36mAyUVKmoio2Yy+q5Rbs1IDiFOfm8108F2XD73pf+PFzz3tZTLFYqlLr74kx9jO+7jnCHl8RBK4sd7hKGuaTi6SY+TodvJtg0vaco4JSXlYDloobh9+3aWLl0KJCKvUqm0H7vwwgt54IEHDmlhN910E8PDw7zwhS/c73M0TeMVr3gFl19+OWEY8pnPfIavfvWrrFy5koGBgf1+X6VS6Vjv4ODgIa01JeWJIqSiHsbUw2TSyWgjohkJRuvJ1JORDavY+vCfeeDuO3j4vrsYHxnm3Iufx403f41S0eFdH72eGQvmsuTEFWTyOSIhkChc3aTbyfD6t72ZarlCJp+jEvnkLadDJMRSUol8sqbdUSsnlWI8TKKOkydtKKUoRz6WZpAxDGTYTP40yozVRlHNMl59FHQdI9uNbjk0dBOVy9Od70Nzsui2h2bavOHv/oZyeZzeUk+HSGzEEb6I2hNDJghETD0OKVhuh+CNpKAaBW1jyXTsLbvXtJBy2ETTtA73MCSOZqVUe8TdBPUoJFKCotXZFPtQ9rb3dSuHTSzdQKEIxESUGSzdZFamQMnJkDGt1gi7NHWckpIyPRy0UJRSYrSKuOfPn8/dd9/d7o22evVqSqXS47zC/vntb3/LO9/5Tj7xiU9w3HHH7fd5r3rVq7jqqqvaf3/BC17AMcccwyc+8QluvPHG/X7fDTfcwIc//OEpx8fGxqbVBDNZjKYcGTzeNfvCF75AuVymWCzy1re+9XFfT0iFHwkasaDSjKlOjMADorDJIyvvY6RS55jTz6PbNviHv30TI7t30dvfz4mnn8qxJ5/A8tNPZuXgI9TigLNfdGmrnk1HC2Fro0o58DEyAbqT5YpXvwqAwWaV9YPb6XWzzJxk/hgK6uxqVCk6LnMzXe3jY0GTwWaFjGkzL9eFjoYUMZVmlW21EcwoYK6uo8sYRERDxOyIY3TDZHZ2Np6bR8uWCDWDnWETpeuYuGRjA+KQWPmc8ZJLCaVgdqaIX04mqkgUW2rjNOKQmV6h7R4G2NoYpxz4zMjk6XWy7eODzSrDfp1eN0uPNA5qbwCVKGB7o4ytG8zPldo1f3URsr1eTmo8M124LdHpi5itjXGkUszJFsm2TC6xkmyujbX3VmgJ2EPam5Ohx80SqaQ34UjQpBmH9LgZ+t083ZbTch0nzak1oUEjJPmv/rj/Lp8s0nvikUV6vY4sDlcro2kZ4XfGGWfgui6nnXYas2bN4he/+AV/+MMfDuq1/vjHP/LCF76Qd7/73bznPe95zOfubVpxXZdzzjmHlStXPub3/e3f/i3XXHNN+++Dg4OcfvrplEqlaf9Bpz2ojjz2d83CMOSGG25oTz5573vfO6VpdCQk9VBQCyJGGhFjjYhQaICJZdjcc9et3PPH3/DA3XewbvWDSCGYt3gp333NS2gIn7/+14/h9JU4atEiet18Uk+mG6yvjaJHAV25EgUn0z6fZcTotoaVyeJOEoS2JTEsie1lcTOTjjdBN2IsJ4ubm3Tc10HVgRgjLiMqQ4j6CCIWxHGErmuYmTyW7aBZRTQ7gykEpu3RVZqF24rIBSLGqAwjlMIr5HFbjt1YSgwtQBcxbi6Ha7fmMSuFoYVokYGdy+Ee4N7sSMct5h97b4GBZkQYloOX3zNaLgwtdD3EMEzcfL4d2ZNxCITomoZXyLejk5qI0bUANc17s90cgYgJZExFaDQxCTyL2LXJWw5dtssJlkvGtPAM64ivK0zviUcW6fVKOeiG27VajSAI2v+Itm3bxqc+9SmGh4e58soredGLXnTAr/mnP/2JSy65hHe84x187GMfO5hlcdFFF+G6Lv/93//9hL8nbbidMsH+rtnE5JPbb7+9feyCCy7gpz//BaHSqfoROyo+g5UADcjYJmO7d/Dg3X9iePduXvrGt4JSvO+NV/DgPXdwzIoVLDv9ZI47/RSWn3YSXd3dLXNB4ljdu1VKLJO5uJMbL0NSoxYIgWeYHSlPqRS+iHENsyPlqZSiKWJsTUeLA1TYRAZJfWHTr2FIgSEiFKCiJppuEWVLuPl+7GwB3fLQTBdN1whEjK5pU+rfIpmkRfduAxNJgVByWvcWVmptoTixN8cwphhQmnE0pfUNgC8iDE2fsofDtbdkTzGNOKQWRbiGmTjQdZOi41G0HLKWg2dYT0uzSXpPPLJIr1cKHIJQnG7uuusunvvc5/KOd7yDj370o/t8zi233MLHP/7xdp/EG2+8kde97nXtf8jf+ta3eO1rX8vXv/51Xvva1z7hc6dCMWWC/V2z/U0++buP3sBzX/YadlZ8hIINd93GXbf9ggfu+RO7tm8FoFgqcct99xLqgsFtmyn2lshkskmEyLT266qdTpQU7frCuDqECmogBEoEKCFA09FMG9200ZwshlfsqC98qs7c9cvVtlB8qhGKmEAKAhEjlESROKVdw6RouRRtl4xp45lWS9A//esK03vikUV6vVJgmlLP08Eb3vAGms0mt956K7feemv7+Ete8hL+/u//HoDdu3dz5513IoQAkqbeJ598Mp7n0Wg0qFar3HjjjQckElNSHotYSGqhYOWqR/f5+G9+cQt/vuduXv2ej9OTM7n/T7fzp//9BStOP40XXfVqlp9xMktXHEtkhWQMi+OOOuovJwyDBjJsIBsVZLOMjENk1ERUhgAwu2ZgODk0L4/hFdGdTCIM07nIB4RQklAIAhkTiBhF0jXH1JNG171utt2WxmtNMnmqCu+UlJSUvTmgiOIPf/hDPvShDz2h577sZS/bp2Fkf9x///34vj/leH9/P4sWLQKSBtvr16/n9NNPbzcIVkqxbt06DMNg/vz5bYPNgZBGFFMmGBoexskVqQWCkXrASCMiFJKf/ed/8K/Xv3uf31Po7uZzP/8Jpf5ugkYdXIOM5VByMh2p30arZUxmr0kbzThCKEluLxdvIGJCKcjtFdGLpKApIvKm0z6uREzo16g3KzhhA92vIeMAFUcIGdPUNDKWl9QYOgWMbBea5VLXk7VOTrEqpajFIbZuTEmx1qIAQ9OnpIkP594gSeE2RDhl6kijXCHO2GQMuyOtrJSiGgd4hjWte4ukwBdx27UMtCKCFjnTpqsjSmilbWn2Ir0nHlmk1ysFDjCiOH/+fC677LL233/1q19x//3388IXvpCFCxcyNDTET37yEzKZDCeddNIBLeTEE0983Of09fXR19fXcUzTtHabnpSUA2WiwXUtFOyq+mwZHEfzYnSl2LpuFQ/c+Ufuu+uPvO36T3DsT85k1T13tL+3b/5crr3xY1xw6mlkLQfHMGm6NuurI/hKdDS3bsQh6yrDACwp9LYFVShi1lWHiaVkcb6HQssIIZRkQ3WUpgiZn+ump2WEUEqxsTrKuF9jru3Sq2mIxhiyWWHD8FZ2Bw3mZLuZnc2jGQ56NsuwbjKoYCDXzcKuGWgtQbWrWWVLdYSi5bK00LtnZnDYZHNtFM+wOarY2xZmldBnfXUEU9c5utCH3RJa0723cuQzL9vFwCQDy7ZGmd3NKrOzRWZniu3jI0GDwXCcfi/Pgklzknf7NbbUxw9pb0JJxoIGq8d3I4EFuS5ylkvGsOjJFCjaHq5h4rbqCdMoYcoziXq9zvXXXz/l+Ite9CLOP//8aT3X9u3b+fGPf8zg4CDz5s3j8ssv7xCwH/jABwiCoP334447jje84Q3TuoZnMgckFE855RROOeUUAHbt2sVXv/pV7rnnHlasWNF+ztjYGGeffTaFQmF/L5OS8qTiR4JaEFP2Y3bXAsp+xGDFRymN0Yfu5off+hor776DRjVpDdE10M/qnQ9x/Tf+H9ec+Wyq42XypSIf/9l3yLsZer1cW3A0iQ7LmpWIkEETEdQJhrcQNisEukGgJQkBzbQxMkVMJ4dVmo3TMwvdzqCZLpZfwaiXk7Ry2l9vv8QySR/vbtYwdKPVvNrgWQMLGPDyZAybjGm1BXJKyjMZIQRr1qzhH//xHzve7/v79z13/mCp1WrccMMNXHLJJZx99tnceuutfOADH+Bzn/scppn8Lq5du5Z3vetd7bZ8+fxTs275SOWg73i//e1vOeOMMzpEIkCpVOINb3gDP/3pT7nwwgsPeYEpKYdKGEtqYUylGTNUD6gEMVEs2LRmFSvv/AN3/P63XPSmt3PMimMZrA7xyKoHOPnC8zjhrNM59eyzmDF/NiUng2davPU976JarpArFFjWMxNb73TYFuwkgmVoeoegyJg2Swq97a8nsA2TJfneKelZTQjmGyZ+WMfdvoq6XwPDhDhgjhL0mhYF20N3MhheF7qXZ7HhMEvXyFlOx5pmeHnylkPG6EwL97s5vFbLlcnRsG7bwy70Hba9GZrOonx3O/Xc3nNrTvJE6nkyczJFuh2P7F6p7R4nQzHTdcB7szSdSAoqcUAg4+Qc2S66bI9+L0d+UjuaNFKYkrJ/Fi1adFinoXmex6c+9am2KDz22GN5+ctfzo4dO5g3b17HOh5r0EbKwXPQQjGKInbv3r3Px3bv3k0cxwe9qJSUQ2Gil2HVjxiqhZT9mEhKNE1jx/q1fOlfPsrKO/9IrVIGkoihCrbT1XUUF196Pq+88iX7NRy89q2PP5Zy73q8Cfau35vAMy1kHBE3yqigjqiPo4IaKg6x44A4bCLjACPThVnow852k3dy+3Qk7+sMhqZPmTAC7HPyyMTxvaeeHMre9oXTmje8N5Zu7LOuz9R1CvrUtersfw8Tx5VShC33cSBjRGuiiaOblByPbjtD1rLJmnYaLUxJeYqxt+9g/fr1OI4zpXbys5/9LIZhcNRRR/HSl76UTCZDyvRw0HfF888/nze96U188IMf5L3vfS/5fJ44jvnOd77D5z73OX74wx9O5zpTUvbLxFi8WiDYNt6kHibTT3Zs3sADd/2ee+/4Hadf8gKOOe88tsRjPHjf3Rx37rM44awzOOeCczjmqKVtgeCXq7j7ETfTSTuV7FeRjTIqqCHjEBWHgIJWqxo9U8TJltCcHLqdQbf2LchSEib6FAZCEMoJB7KGpevkLIeZdoGc5aSj7lJSpomPfvSj7WgfwDXXXMMxxxyzz+e+733va3ct2ZsLL7yQ5z//+fs9zz//8z+zZcsWhoeHef/73082u2ei0cc+9jGEEJTLZf7rv/6L++67j0996lMHZW5NmcpBC8XZs2dz880387a3vY2Pfexj9PT0MD4+jq7r/P3f//1jXvCUlENhsgFlpB6yq+qzvewTSyjQ5PMf/wD33vF7RnftBCBXKjHnlOWcmT+HRScfw0tW34upG+3GzXs3NT4cjZtNpZJWNUEDUR8lbFYJqyOYzVF0O4OR70MzbaSXB6+Il+nqEIaxlARSsPeAyYNtuP1Ubkr9RPbWjCMMTaMhImp+HaEkUimUgpxl0+14FG2vnX72DCsVhSkph4Err7yyo0Zx7ty5+33um970JvbXaOXx3NWXX3451WqVe+65h8997nPccMMNFIuJsW2yoXXFihW8/vWvZ926dRx99NEHspWU/XBIeZZXvvKVXHzxxfzyl79k8+bNDAwMcP7557NgwYJpWl5KSsKEAWWsGTFUD6kHgtGR3fz5T7/jjt/dzozFS3nOa17Nw40x7v7Tb1mw/FhefO0bOPu8c+lePJuKCCh4Jj2TZnkPNivsalYZ8PIdTtrdfo3tjTLdTrbDSTsSNNhSGyNnOSzJ97TFSyX02VgbxWnV5Jm6jhIxtUaZdWODENRYqGlYMkJFIYEUbBQxyrBY2LeUfNcARqaINF22+FUCEbPQcim0RKJSio21UWpRwLxcqe0SBthaLzMa1JmdKXa4hA/n3mDC6TyCoWksKfS2RWEgYta1RvgtKfS0U9KxlKyvjiR7y3W3HdBPZG+zvMRhHMoYX8QM+XWaccSAl6NL0+nLFtM+hSkpTxIHUqP4la985aAjigsXLgTg+OOPZ+XKldxzzz085znPmfK8TCZDNpulVqs9oTWlPD6HXJBTKpV45StfOR1rSUlpEwmZOJMnGVBiIYlkzFc+/WH+/IffsWXNIwCYjsM5V7yMEbULK6fxrbt+zZx8V/u11laGCEKB2OuTbCQlvkjqFycTtyJZkeyssxVSEkpBJEW7qXL7+XGECpqEIiKqjxBXhqhJQSMM0JUizuaxLQ89n8d08mhxiGY4WN0zcGyvtR5BWBeEUhCrPWtSE49JgdhrrZFM0qyHc2+hFGhoKFTH68RKItE6zi2UImpF9zr3oAjF4+9tIhIaiJidjSq+iMhbAa5hUbBd5mZdTuie1e5TWBkbp6eY9nlLSTkSOJiI4qOPPoqmaRx11FFA0vd4586dzJw5E4BHHnkEpRTLli0D4Ne//jWNRoPFixcfhh08M0krt1OeEkzUGVaDmKFawFgjptyos2rl3Tx45++JUVx49dVUQp87//Q7TNfisre+hbOffT5nnXU6+axHI47wRUSX3ZmgnZct0eNkp5geZmcKFCxninFjwM3jGdYUh22vm8XUdVzDQlMKEdSRQYNMfZx5tRF0ESKVQAmBDOvkTIfF+T6sQi/FfDe6lUGzHDxNY2nko5SiOGmtlm6wON9DpATFSWvVNY0Fue6/2N4mp6pLtsfSQi+W1mkyKVgui/PdaJpGZlLKOGNaLMp3o5TqWNO+9paYTGK6bA8DjVhJhvw6nmlRcjwunXN00rzaMPFM6xkx4i4l5clGKUUkFEEsCeJ9R/8ms3eN4rnnnstLXvKSfT73YFLBvb293HjjjQwODuI4DsPDw1x22WUce+yxQNJf+YYbbmDXrl3ouk6z2eRd73oXXV1dB3yulH3zlJn1/GSSTmb5y6OUohkJqoFguBawbqTKSNPHtuGz73sHm1Y/zMjgICJK+hIuOe00PvjVrzCQzZA1dXKuha5Pf3pxf7ODlVSoqDUruT6G8svIKERFASiBZphopoNmexiZErqXawvDNA2aEEuJLyJ8ERMriQbYLedxyfbaE00OpCVN+jt2ZJFer6cOsZCJGGz9vxEKhusBY40IQ9eQStGlBZxx9Lx9fr8QgrVr10453t3dPe29FAGGh4dpNBoMDAzgOFO7LwwPDxMEAQMDAx3CNeXQSX+aKX8xIiGpBjHlZsSOSoPdzSar16/nT7/7NeM7NnHF376TRj3kz7f9ChHHmLbNu2+4gfMuOp9ZMwewDoMw3B9KKVQUIKMGsllB1MeRfo24shMVBej5fgw3i5EpYGS60DPFpMG15abCkD3u46aIiVvpbEPTyZgWs7NFumwvdR6npBxmlFKEE4IwljRbtd7VIKYZSSKhgCSCuLPqY2g680seedekGQlEMHWs7gSGYezX3Xw4eLw6yMPZy/GZTioUUw4bUirqoaDiR2yt1tlZbVAOA1b++Q/c88tfsPqOPzG8ZQsAmUKRl7/5rXzj3dchWj044zDkNz/8Pq+68nJ0TRFLNcWRK5VCKDnFeataNXJP9DhAEDSJm1VCUUbURlFhAyUiwijA1A10y8HqnofmZDEL/QjLwbKzU1owxFKiaUxxFYtWbd6+jivFYd1bJAWmpk8RsY913ND0jjT0Y+2tGYf4IiaUEqVUOyU94Obocjw8wyJj2ti6kQrplJRpRkhFEAuCWOLHkqofM9YMCWNJKBVCtiY4AZahYxs6OdvEMjRiqdg63qTLs8mrJtseWcPmDetYs2YNl73sClg2/8ndXMqTzkELxf/5n/9h9erVXHfdddO4nJQjEaUUUcsIUQlCxvyQ4brPtkqNwVqZNQ+uZNOf/8zzr34jumXxwB9/xx9+9ENmHXccL33xSzn/ORdw4inH8/PvfpdVd9/d8dp3//6P/Ne3/pMTLruESAoW5rvbTlrZmg1cj0MW5EsdNXFb6uOUwyZzsl10T3LSDjarDPk1ZnoF+hwPGTSQQZ3B3RvYXhmiz8gxwwY0Hc2wKGs62+0M3bleFpRmJFFDw6QS+WyqjpGNIhbmuztmOm+sjmLpBgvz3W3RFoiYDdVRABblu9su4UgKNlZHp31v/V6ufXx3s8Zgs0Kfm2NWZk8bi9Ggwbb6OEXbY/4kB3R7b6a9z73pmsasTIFYKeKWEcUXMX1ulqWFHnKWS9a00/nHKSnTiFKqHRlMBKGg1uof2wgFkZA0I8nuWoCpa8zvzuBZBllDx5yUjWk06mxdt4HNG9bhZnPMOeFZRELyjhecya4d2zrOuXj50bz0wrP+0ltNeYpx0EJxZGSEe+65ZzrXkvIURypJI45oxGHrT0QtCtlaq7KlMs64H+PpHuO7drPmD79nzZ13sO6eewhqdQBOPufZLD1+BZe96Y1cdO2V9HTlWFEaINNqA7N98+Z9nnfLxk0sFRFhq5FypvWvVihJvbWWQMTQ8lQopWjEIdUoSI6z53itUWGsOkK2spu8EsioiYpDavUK5aBG3nExemdj5rrRnRxlGRPWy4ROBt3Nt4VPIGIacdheh65NCEJBPQ6xDZNY7onuhVLQFGH76wmhGEtJYxr2BlCPQyqRT2kvw4svIiqRP2V6SiBiqlGApRvtKODeewtljFTJsaFmjZGgQcFykUoxw8vvSSGb9j4jmSkpKQeGlIpASPwoiRBW/ZhaGFMPBYGQCNGKDmpgtqKDWdskEoKKH9Ofc5iZt9m9fQvVWLBgyVJiKfjr172c9Y8+wu7B7e1zLTr1DF7yyX+iy7NYfunFnOy6zFwwD2d2L8bMLk4dWPJk/RhSnkIctFA85ZRTuP766wmCYJ+FpSlHPhOCoR6FjIYNRvwGo2EdJXWk0GmEkuFayK7hcTbefxennXEG82cV+e3tv+VHn/oU+Z4elp9zHiec/Swuuvgi5s2dRdYy0LQio0EDU9fbIhH29Mnam4WLF7EgVyKWkqLd6aRdkC8RiLgjsqZpWtsNXDRM4vp4UmfYGKO3WcUKGhQAYdnoloueyzO3fzFdmoknbby+vvZr9UmBaSTj3TpmBjsZKCSj6CYLpKLtsijfg6nrHQ2kc6bN/Fx3++sJPNM66L2VnE5BOCdTpGA5U5zRA14exzCnOKN73SyGprf3FrXG3E04lj3TohIFeIZFt5NpRzzTGcgpKYeOlEn9oN+KENbDpH67GgjGmyHjzRhDh4Gcg2MaWGYiCI1J0cEoirAsk4of8s8f+z9s27iO3Vs3smPzJuIo5IQLLuRN//TPNETImAyZcewxHP+8S5i1aAFzFy5kzpL5ZAo5ZmRyXPzx63ENA13XaMYR2xtliiKtTks5BNfzjh07eP3rX08QBFx77bXMnDmz441j9uzZ7b5GT3VS13OSAm22ooXlyGcsaFCPI4RKPsEKCVGkUQskNT9k08OruO/3v2ft3X9iy8MPIuOY133wwzzrsssRjQbh6G5OOPE4cq6Jaz4xs0IUhrz1iiu55w9/ah877Zxn8fnvfwvLemJj9ZQU7XSyqI8iaiNEld0gBUauB9320C13vwaU/bmen25ErXRxIGJiJQANU0vEbZftUbTd9kSTp3ID6yPpdyzlmXm9JuoHJwRhLYhbUULBd778eaqVMtlCgSve8FYcQ6fWahNWcC1m5pOuCUopbv/Vz1m35hE2rF/LpnVr2LpxA8ee+Sxe/qH/y3Cjwedf83J0Dfrnz2fGwgXMWbSQY45fwQmnnYJnGmRMC8c0MHWe0O9zJfTRGyHPWvSXM6ykPDU5aKF40003ce211+738auvvpqbbrrpoBf2l+SZJBST3nUCX0Q044hqFDAeNqnFYasJs4aBhiZ1hNSpBILxZsTQzl1oKAZmzmL7Iw/xj698OQADCxez4uxzOOXcc3jWec+iu5gjaxkdn3oPhCiKeM6ykyiPjVEslbht9X2PKRIn2taIsIGsjyObZWTYRMY+GqCZDqChewXM4gC6m0O3PLT9OG2fbkJx4noHIiaQcdIcW4FlGGRNmy7bI285iSg0rSlj957qPBV/x1L2z9P5egmp8KNEEPqxoBYIhmoBo/UQBXh2knkwNA3H1LEMnZeecwKD27Yya848vvur33PfQw/z0KpVDG5ez9CW9cxaehTPu/btNETA+y86n0a5jJvLMdASg8efdToveNUr8UwDC8h7LqamTUvrsJs+93nqI2OsWLgk9SI8wznod4XXvOY1vPCFL9zv45lMZr+PpRxeJswlYetPIGLqcUAtCqnHIX4ckTQsURiajmOYeLqFKU1qgWR3M6RSb7Dmvnt55I4/sOqPf2Drmkd5/hvexIvf8S70mYt4zQc/wrkXnstRixeQdYyOqOH+PntMroPb33HLssjmspTHxsjmsvvshyWjIGl2XRtDNEZACGQcgIhRuoHhZDAzRcxcD7qTQ3c80PcdFXsiazocxw/HOaRSbUEYimTeMhrYukHOcphpF8hNEoVpTWFKyoEzWRAGcdLyq+JH1ENBJBQKhVKK0Ubyezgj7zJjUmRw547trFqzmkdWP8TY6AgA5WqZZ590FJGftKPRNI3uWTPJz+knl43pNz0+/e2bmTtnNrNmzMA0Oj/oHo771fe+9FV2btvO/PnzU6H4DOeghaLneXie9/hPTDlsTNSUTaQQay0x2IhDdjdrNOKQLsfD0ZO6FkszGApq+CJmfq6EhUUjlGyvNNkwPs5QrczsQg8FzeQ9F51Ho1pF03VmLlvGRddcy7MufT5zii7LZ+Q5fdGr2OVXCLUmPeYeJ20l8tlWL5O3HOZkiu2bTiOO2FIfw9FN5udKbSdtIGI21cZa00dKWLrBa956DePj40SuxZrKMPO9PFYcIoMaYXWEzbVh/ChgZrOMpwRGoR8j081O06JuOswrzqDg7TGeDDYqjAQNZmeKHXV9Q36NXc0aA16OPnePS3gsaLK9UabHyTAzM717m7hum2pjSKVYkCu1o3hSKTbXxghkzLxsqT3xRCnFtkaZahQwpzXXeKJP4ebaGENBndmZAjkzmXdcsFyKGYdcK33sGiZmKgpTnqHceOONjI+P09XVdUCCZ3LK2I8kY82Qmh/jx5KwJQg1NExdw2oZSmwj+d3fNFJh2+a1ZCygdDRrx2u8/8qXsW3dGoJGY8q56uUysxYu4Mq/+SuKC+eweOlilvXP6riX+McsINA0lLbng/h03EsmmLhPzvDyZNCpt0yI1Wq1VQv5xMp/Up5+HHKeqVKp8KMf/Yh169axbNkyLr74Ynbv3t0er5MyPYQiThzHIqQc+lRafzbXx2jEEbMzRbodr+1gLUc+oYyZYxbpcbNA4sgdGQ/Y3agzUlY0KoKH7rqLbffewUN//C2l+XN5+403ohk2z3ntG5i5cAFzTz0OWbA4qtjNslJvWxxtq9cZDuqYus5M9oipRhwxGjQQSjIrU8BoTQ1uxiGjQYOMaRNNcv36ImI8bGJqOqEQiVB889VUm2VWD29jaOcaippGXkmUiAiEoBJH+LrBwMxjyHbNRHezKNOlXtlNOfRpahrFSZ+Wy5HPcFCny3Y7hGItChn262RNm75JPo96HBy2vSXXUlAJfWIl8UWuo1VOOfJpxCF9TrZ9cxdKsqtZZdivI6Siz8uiaxqeYXFUsZ9z3Fxr9rGJa5jpqLuUlBZhGPKRj3yE0dFRuru7+au/+qspgmciQpg4jSW1MKbix9SDGD8WDNcjGqFgZsGhy7PJ2CZdLUEYSUGkBI3I53Of+hTr1zzC5nVrGdm2FRnHLD3nbC748PvocT365s5m0bKlLFq6lK1bNvOzr3+zYx07Nm5CRQG9R80jMrVpvZd4htma8x6zo1FmLGiiAU0nmXylSNpr1eMAXSg++Ya3Ux0fB2B0dJSLL76YX/7yl6lYfIZySELxBz/4AW9+85uRUpLNZrn00kt53vOex4te9CLuv/9+8vmnT63XXxKpJM04ot5qgzIS1Nnt1/HjkLzlYusGjmHSZbtACTToc3MdzZEXqsQxa2MzXA8Zb0aMNyMqvo4SLl/76Id58LZfEYcBhmmx8IQTOPr0s3B1l56Mzbv+/u/IOQaxiilHPsW9Jo70ull0TSdndbZc6XEyyHwPGdPqaMpctD0W5rrba58gb7nMz3ZBHGH7FYLRClF5B6o+Th866AZZx0UzHXSvQD7XzULNRFg2/ZmujibVc7Nd9DgRPU5n2cOsTIGC5dK9l0u4301urMV9uIGna28LciU0ps5DnpvtQqHITzq3Y5jMzRRpxhGGpjPk15JGuVqytxNKs+j3ci3ncdqnMCXlsQjDkEsuuYTR0aR/6ejoKM957nP5zx/9N1I3qPgxtSDuSBlraBi6hm3oWKbOeDPCMmBxn8P6Vffzm0cfYe2jq9m0dg3bN6xj3ooVXPGh/0OkYn7+g/9EB+YtXcLZF57P4qOWsvT45cxZsoSi4/Dqr/97e23/8uGP7XPNI1sHee4B3Escw2DAyxPLZCbzWNAkVknZiWeYOLqBL2N2+8kHX1PTObY4gGtYSRlKqwTF0g1O79UxdZ2vf/mrrPzTXR3ruv3227n55pu55pprpuvypBxBHLRQ3LVrF1dddRWf+MQneNvb3sbXvvY17rjjDkqlEhdddBHf+ta3eMtb3jKda33aEklBPQqTFgZ+g7HIpxlHKBQ6Gq5pMtPL77OmbMC09notRSOMafo6ow2NtZVhHr3nLlbf8QdGtm3h7794EzvKPvnuPk5/wYs4/uxzOeFZz2LuQImCa5K1zY7mrA422b0EE4BrWMzKTP10aelGR1PnCUxd70jjKhEn7mS/SrE+igrqhCJGiRilJJqm0Wd5WN1zMHIlDDuDZntomsbAfn6OXbY3pTUMQMFyp7SGAchah2dvE+iaxoA39cOSpmn0e7l9G00Ax7SwDYMZToGinTSv9gwrHXWXkvIEiFqRwZu+dBO33357x2O/+81v+ORnv8gLX/k6TF3HbrWcsXSIkYyVx3lkzSoeXb2alQ8+iNvbx3mvfiXrm4qPvO6VNCsVNF2nf+4cFhx9FGeceTrnzp+BY+g8794/4rhT7zP7Yt7CBfs8Pn/RAvq9HEImPWuFksRStqc6KRS7/TqK5F4xMRbT0l0cw8DVLVzTxDUSAWhqevJ/XW///fE+XG7dTz/b9evXP6G9pTz9OGihePvtt3P22Wfzjne8Y8pjy5Yt48EHHzykhT1dUUrht/oTViOf4aBBNQqIpAAFtmHgGhZ9bnbK+LT9vl4kqUWC8UbIuB8TxpIHfvtr/vij7/PI3XcR+j6GabH05FN4aNsQuWyWa973XgbyNjnHJGMZ0+KSe7x1qjBxJ4vaCKpZQcURSglQEjQDzbDQJ0wobq49BeXpglKKpohpiohIxmhoWC2jyQw7T850cFtGk3TUXUrKYzMhCP1Y4EeiZSpJZhjHUnLng4/s8/vGdm1BhBUeWbuKZhwzsGwZlcDnI1e8lKFJIkk3DE6/5BKO7S+Stx0+/u+fp7+/jyVHLcLdV32++fgiUShJJAUXvuzF3PLdH3D/n+5sP3b8Wadz+ksuZSRotAWeYxgULBenVVYyEf0zNb0dITR1fVprkBctWrTP44sXL562c6QcWRz0u7AQYr/1CvV6HfcJfrJ6OqOUohlHNFutaMphk7GwSVNESJm4yzzDJG/a2AcgiIRU1CNBpRGxverj+wHr77+XR/70R171zr+hL5ehsm0z29eu5Yznv4hjzz6H5aefSV93gf68S9ZOXMqHW4goESFaUUNRG0E2q8TVXSAkRr4P3bTRLQ8904WRLaE7WTTLedoIpFhKmiLCFxGxVEldYWv+ccnJkG01r06NJikp+2fvtjMjtZDRZohSEMsktjZ5hnHG1lEo5i3c94zi7//HV/mP//cZAJaecQbXfeELlLIO5178XHLZLEuWHc3SY45iydJF2JOGSVz43Av2v8ZJkb9YSiIl2+ngZH0aqtVlwtJ0PNfjKz/6LhceeyLjo2OUuru59Ze/wnOcwyL+DoSrrrqKb33rWx3R2AsuuICrrrrqSVlPypPPIU1medOb3sSdd97JGWec0T4upeTHP/4x733ve6dlgUcqkRQ8Ut5NMxhFKoUvIkaCBr1Ohrm5rnaNWyOO2FwfJ2vazJzk1A1FzLZGGUs3mJ0pEsSSRigZaQQ8MjbM1u272XHHPey4905W3fFH/EYD3TBYcN5ZHHfC6Zz1itdw0evfRHfGItB8lCZZkPfa00K+8YUvMTg8hJnLcO1fvY2cteeGOOLXGQ0b9Ls5ipNSueWwyW6/RredaRtkAGpRwGCzSt506DeMpKdhbYx6fYTtzRqepjHDtNF1HTM/QKgZ7HQLOJkic7sGMFpriqVkW30cSKaMTNQfSqXY3igTScGcTLEtqpVSrQLskDmZYscklF3NKpUoYKaXP6C9FUJw2ZMunthbwXI60siN1uSCiesGEMg4MRjVxvFMi343R95ymJctUWilkDOmlZpNUp5WHKyreG8mZhknLmNBPUx6uNaCmFAo6mHM7mpA1jGZ1+ViWRpKk612UDH3PHAvj6xexeY1j7Jt3Tq2r1uHUygQVCrtc9iuy7IzTmXu0Us5acXxrDjheJbM60cpRe+H/m6/95Lx0KfPyeIYJpEShEIwFjZoxBElxyNrOhiahq0ndYEFwyFvOTjmXilgTcc2jPY94PoPfqj9s+vLTS1feTKwbZtf/vKXzJgxo20CSo0sz2wOWigeffTRXHHFFZxzzjlcfvnlRFHE4OAgz3/+84njmMsuu2wal3nkceO/3MjKjWsZmDHAVW9/C9sbZWpRkLhS2RMxq0Y+O5sVCpZLn5vFas0MLocBGytl4gh2mYIwgo0PPYjb18ugJdjy0L38z6c/RqGnh1MuupilZ55F9vijyZay9OZN5hbyZCwDiWTVeJlK6NMduZOE4k0Mbt1G96wZvOLNb+wQU6Nhgy21cWzd7BBT46HPlto45GgLRSUiRitDbBreSimsk3My6EiUgkoUsisKKVgOM/L9WIUeDDuDrxSjlWEcDfqBCetJQ4TsblYB6HY8CnoSlfZFzJBfIxCCLtuje2JOspIM+TUqkU+hVZgNyRvOkF9n2K+TMa0D2ts8ze2ogSxHPtvrZQI3S7+bawv58bDBptoItm4iVTLT2dZNuhyPJYVeCrZHxrBwzfTmmvL05sYbb2Tz5s0H1G8vjCXNVpSwEcaU/YhakDiPpQRIIvCGrkBThCpkKGgSGz47dmzht/+7ms1r16JlM8x/8XNRCv7zbW+hvGtXq4ZwLvOXH8Xik0/kf7/8H1THximWSvz3g3exrjFKIARHF/va4zEDGbO9Ps5Y2ASl6HK8dnP6bY1xIilbk01MuiyPnOngmTOwdQPbMLC0JCV8oHXET9X+hJZl8cEPfpAdO3Ywa9asVCQ+wzmkArCvfvWrLF68mK985Sts27aNQqHAS1/6Ur75zW/us1HyM4UwDPnkxz/O6OgoxVKJK699EyU7w6J8N9m9Uqtdtsf8bAnXsIgFVFru5KFGwPDuJhvvvostd97Dg3/4LZXRUV5y3Xs45rIXM/PcCzj+5mNYfNwKujybnqxNXTQwDZiRKbSNLwYGM70CpUlGjygMadRqydfNJnmt8ybQ7+awdbNjxjAk840X53soAFFlCFEfQzXLZCOf2fVxvGYVmcmh5frQ3Sw9PUUUOhkvTy7b1X6dghTMzRYxNB1vUso9a9rMzhbbX0/gGSYzvQJCSfKTRJ+lT90bJGaRmV6eguVQsjv38Fh7W1LoJRuIjuMT180xTKpxQDOOkAqU0jihNLvdmzFjJvOgU8NJyjOJMAypVpMPd/vqtyelSmoIW8Jw11iDB8cUw/UAlEbBMxNjhiZBV2i6xCekFvrs2L6VcqWCM3suu2o+P3jPdexetQoRRe3XP/m887jyrW/EMw2O++wN9Pb0sPjoJViuw87Wh84lXd1UymUy+TzKSNpK6Wg04pBQCpLBAwazs10sLvRSsr0OR/AZ+rzWB0HjGVUmct111z2tJ+mkPHEOeoTf3gghMIwj85doOkf4TbRkmFzfsb95xbFUNCJBPYgZqYfUQkEQBHiuS2XXDt77wuch4hg3k2X5WWcx+5Rnsezsc1kybyY9GYdSxiLnGNjGExcnBzNPee9aQxU0AYlKPvqj6TqaYaF5eYxcb4dD+UhjYoSfbNWX1uMQ0YoY5i2HHidLwXbJmMkc5CNxj08n0jeyJ4993evOPe98vvnDnxAqnXIzSu5pQiCkIhIx4+OjjOsWBVenkNWTSVEiIogFv/vhD9n08Cp2rlvPrg3r8et1Fp1wIh/42jfoyTr8xz99El1Jlhx7DMccewxLjjmaXCEp+1BKEStJKJK+hpFMzgmgaYk72NINPNNKSkBaM8xtw8TSdSzN6EgJpySkv18pMA0NtyGpS9y1axe9vb3Y9tR2I88kbr755iktGe7+/R/5yXe+x0tf++pWqkWyu95kuBahRMzGlffz8O9/ywO/u50Tzj6Hqz/4IQrz5vKiq69lwYknM++Ek7FMi9lFl55s4lQ2D9KlfMt3vtchEiev7/LXXQkkN10ZNpFBHVHZTVQeBCkwnNZNWQM0Hd3NYGS70TNFDCeDZhzZ6YlQxFSigPFmUnyeNW3m50qUnExSb/Q0cmCnpBwsE+aSf//Sl6e2n/ntb/inf/sCF17xKkASExMREsiYHaNlBlc/wvZtWxnbtIGd69eTL3Vz1Yc/jms63PnDHzG4cSMLjj6K57z4hSxddjTHnXgCp84rAfCBT/6fpMG1TJzDdSmoN6solWQRTF3HaZWU5Fq9RidSw46eOIbTD3aPj1IKFYeoOEBF/pO9nJSnAIf0zrdjxw7e97738b3vfY8gCDAMg/POO48bbriBE088cZqWeGSxYcOGfR5f9ch6lu6sMtqMGKkFWIbOzz/9f7n/9l9Tr1TQDYNjTj6F2UcvY6QeoICXvP0ddGcsShlrSn/Dg2Xbpi37PL51wwbi+jiyWUE0xlDBnjFTKgpRsQ+Gg1Xox8h1t9rXZNEOc1udw0nSriaiFoUIpbANg6LlMKc0k7zlkDXt9I0l5RmLUopQSJrRHnPJWDOi5kfsqgX88q6V+/y+tZtXMbDlQbauWUsQhBx//oX4keJfr7qKoU3J/VHXdWYvXMiypUs4dU4J29T52n/9J/muIjGSSEpCKYilSFLICgw9cQy7pkXJ9siYFu5EZFA3WtHBIzOr9WSg4ggVB8g4QIV+EhiojyZZIxSaYRHqRZgx+8leasqTzEELxSAIePazn41pmnz6059mwYIFDA8P881vfpNzzz2XlStX7rcf09OZ/e050C1+8KWbGB0e4cXvfCemFRMETU449zxWnHM+S888CzeXx8cn40kWFIvk7GRGcywlO5sVdE1jhpdv91eUSrGzWUUqxQwv33YJK6XY7dcIpWDGXo26i7P33a46n5Ws33gv3ZZD1jABDZSkqmlUumbTW+wnn+tGb9UI1qOQ4cY4BatzLJ4vInY3a3im1TE/OWrd8G3d6DCFJHurTsvehvwazTii38vhTopujrZ6Vfa2XIuNOKTRalvjGCYLs930uFnylkN1vEzPPppnp6Q8nRFSJeaSSNCMBGU/puzH1KMIP4oJVYxQgoiIHbUGjTjAmd21z9f63299h//5ytcAmLVgIa+64nJcy2T0HW8m9Bscf+rJLFt2LLptEUrB7qDMeLVJ3nYIwya2bpBpicG85eCaFo5utqODaXr4iaOkbEUGg0QUhk2kX0P6leSYiJioPdN0EwwLKSJUFKDbHjjySV1/ylODgxaKt956K81mk4ceeohCYc8b61VXXcXzn/98br75Zj784Q9PyyKPJPbVg8pyHW75f58FYObiJRh/81bWVce48APvZ2G2l6Jr05u1CFTIlmYVaWq4VjJKCqAWB2yrj2PoOgXLabt4G3HI9kYZISU5y24bOkIp2NGo0IhDPMOit+VQjuOQ5c89k8UnL2f9vQ+313fyScey4qIzWR800XWdnJfDyPVguAV2Rj6b62VM06E0yUgyFjZYXx1JzCROp3t4Y22Mku3R7WTabYDKoc+W2hgZM1nnRBp3uvYmlGSwUWUsbGIbJjO8PQ7oLbVxNtdHWZTrYSCTTwxEuaRlTd5y0jeelGcUQSzwo8RcUvFjdtV8gkjQFIJQxUQyTlLGKiZQIcNhhWrURA7V2LJqPTvWrGN80wa2rV2N5XlEzWb7tb1MhhMvOp/eoxdy6vEncuqJJ5LxIBQBy1/0bOrjZXLdvYSmhqslJrJ52SIZy8HRzXZ0MDWFHRhKxKjIT6KDUdAWgzJsIMMAURtCBU2MfB9Gpohm2uhuAc20UEqBiFEyKYUy3CKRv514bAz6up/sraU8BThooVir1TjrrLM6RCIktSIXX3wxW7duPeTFHYlM9KAamDGDsdaM0TnHLOOYZ13A0Wefx9KjlyCRLMh1MTOfZVGhC89KomK+MIgotG+WE2RNm4FWtG1yf6+JXn1SqQ6XsK0b9Lk5fBGRUZKoOoysjSGb4/T4dT746XfzjsveQbVSo5DPcsM/XUet0E3OztHX1Y+bKbVTyl2ByXxNo2h3NlAv2C5zs8UpI/MKlsOsTIGcaXfMQ85ZNjO86dtbIOKOWcyGptPnZvFMC88wGQ+bBCJGKRjwcpzQPZOZmQJ5y+04T0rK05UJx3GzNb2k3IwYbUZUg4BqFLG9UsePI7pyOpopqEYNIiWxI8Xguk1sX7uWsN7gxCsuxyXLf1z/fjauvB+AvpkzWXT0EuYtP5rbvvV9qmPj5Etd/Oe9v6OmBL6I6HVyFOzkw1/BcjiuNINmvkZ/T0+aJj4IlFLtyKCKA0TQRNRGUVETRICK4yQRpEDTDTTTRjNdTCeP4RUJRzYj62PJY5NHlyrQDDMRj7aLluvG7l+IZjmIRvzkbTjlKcNBC8Xly5fzoQ99iDAMpxhY7rjjDi6++OJDXtyRimVZvOf97+eX99yD29XLWVdeTSljMafoPaZT2TUsFhemOsws3WBhfuonO0PTmZ8rdRxTUiCDOv3NKqI+goqaRCg03QJNZ4btMcOyufrqV1ILBMXePoorLuqIFk6m28lMaSUD+5+fnDFtlhZ6D8veIPkgMqfVQmeCSAqacYSu6eRNh1gpipbLolwPedshZzpphCLlac3EOLtmJGiEgpGmz0gjpBoFhDImEBG+ilBaTCQlI40QISQZGSIpISON2/7fV1n569vYtWVzu6NBqa+Xv/nbv8Y2dPo/9D4kktlHLSZf6mqZSGB2/wBBtU5vdzfH9c5up4onRs5NrvMdaUZk93OvSUlQIm6ni2U8ER2sIoN6YjKRAk3TkHGAqAyhmQ5Wz3z0TDaJDIoo+RM1ExEJaJqBVegHw8DwCuheF7rltMSkjWbYYJhTarJ1MfJk/AhSnmIcdHuccrnMy172MuI45l3vehcLFixgaGiIb3zjG9x666389Kc/xWmNP+rq6mLGjBnTuvDpZDrb40xQDQO+fe+9lErdDORcso6JdZiMHzL0kWEdUR9H1seQkU9cHUYzTIxCH5pSoBSa5WDkepKReU72iJ2jPCEMmyJGKoWl6xQsl143Q6HleDyQkYiTSdtBHFk8E69X0IoSNiPBeDNk83idkYYPuiRUEQ0RIohBV1i6hqYBUmPX5q1sePBh1j38CLvXr2HX+jWYls0Xbr+dnG3ytU9+kk1rHmXpccey+Nijmb/sKGYtmo/Ziug7ukG2FR3MWQ6ukaSKHd18wh/EnonXa190Oov31A6KxhhxZReIGN0rgKYnEUDDBk1D07Sk7lBEoAQyDhH1MXTLwcj1ojsZdDubjEO13SRKaCaCkINo55VerxQ4hIji9773PW677TYAfvOb30x5fLLr+eqrr+amm2462FMdkVi6zvySR39vtiMFOx0oKZPaE79GXB1BhTUQAs20wHLQdQMTEH4FpMTsnouRKaA7ObQjMLIWipiGiJJUMrR7Gs7JFslbLrm0dU3K0xClVHuc3Wf+9V8ZHB4GN8NFV76eahgwEjTZVW812S+42CQtYkSzyfZH17Pl0UcpD+3mxW9/B6au89+f/Rz33PYrdF1n3pLFnH7e2SxZvoxSToEWc83170bXNBzdJGvaFG2XjGm3BaFrmGk97wHS4SyOguS+3ayggkZyTIrWnC4t+eCuaWiGTVwfR0U+RmEApEBJP3nctNG9PLqTRbczeyKCE3/SdH7KYeCg310vu+yyJ9wCp6+v72BP87QhEDEjQYOsaXWMjoukYNiv4xid00KEkgz5dUxNp9fNIuMIGdSJK8PsHN0KStHneOiWi2bnIA4Y8qsIv0FfroQzsCS5oVgZxqKkXq8XxeTqvHLYpB5H9DiZDqFViwLKkU/JzpCZVM/XiCPGwgbFljibrr1NMOFoVkpRsFzqIiSWSduavGWzIFsi35qZfLARw5SUpyJywnUcS8p+wFDDZ6juMx74jPlV/ulTH6NRLpMpFln64oupqYimr+GGEXNmDIBS/PRLX+JPP/kvRrZuZSJRlM3nefv730FVC3nZ37yJq/72zcw/ailexsPWDBzDoNvJkLdcXMNqi8K0LdQTZ//O4ioq9lFxjGiMIRrjGF4BI9+ffKOmo2kKlAQUSsRopomZ68bqno3u5JI2ZOZEitjZZ3o4JeVwc9Dvtr29vfT2Tq1FS9k3I0GDtZVhep0MBctt/7KPBU02VEfIW8kQ+YkC7/GgyYbRQSwZYWjghg2kiKgETTbXRsGw8CyHfNQEERJYHrsy3USmQ6FnNrmWAIukYFt9nGoUYGg6/V7SskYpxbZ6meGgAYVeZk1qCTPYrLK9XmZRvpt5k+oEh/0aG6qjzM4WWTpJKB7o3sqhz6bqKLZhkjFtMqaFUEmbnFVju8lZNkcVepmb7WpPQkkL31OeLky0oqmHEaONgB21BlsrDcb8OpopiWSMpilq0mewNswv3/cRGuUyAI1ymY+85HIys2dR2byZsFbjG/feR8Y18RxFae4AJ11yPqeedBJLj1vO3IXz0XWNbiPH6eddRM7e4y520lYzB8TezmLRKCOqQ0gRJlHBiSouRVK8OYEGRqYL0JJ0c+RjZLtaQjCPbntoE/WChp1khlJSnkKkYZm/EFnTotfJUNxrtF3WtOl2MmRMG0MpRLOC9GvolV1kKqNYmsLyutCcLLpSZDSDYhyghMDz8til2ehOFsey6a6OESlBZpJL2NR0iraHpRsd7mFN09rRv+xeN6aC5RC42SlF51nLodfNUtj7+BPYmznpDckzLIq2i1SKauRTiXx0TaPL8njp/OV0O0lPw9SAkvKX5MYbb2R8fJyuri6uu+66aXlNIRX1MGI8CBlr+uyq+Qw1GpQjn6aIGPcDhv0Gwgg5qqtEn5tHRQY7NmxizYP3c8fPf8qme+/veM3yrp2IKGDZ6Sez4sQT8OyArGfx1+/+GzzzPW33/0SEMJ1IcmCoOERGyVQSGfnIZhUZVFFho8NZjJTElZ2tFHF/UheoaYBC01suYjeP7ubQbC+JDFpOKgZTjjimbdbzkczhMLP4ccRv1j9Mf29vu0ZRKTXlhi2jEBFUkfVxotGtyDjAcAtJs1PTSepTwgZKxuiGhZ7tRs92oTt5DGvquMR9neOpcjwUMfU4qTUERcZy6HWydDseOct5SsxOTou3jyym63qFYcjMmTMZHR2lu7ubnTt37nf2+f6IhaQaRIz5ASN+k53VJjsbNSpRSKySVk2WruFaBhnTpBHD6HiTjOeStw1+/+Mf8bsffp/tax8lCgIg+UC3r1v0Ndf9Ndd/9COtySQGrmEdEVH3p8rvV9tMEvl70sXNCqJRIa7uQgYNzFw3aEYSLeyIEGqtoKGOZjpoponulTC8PJrltFLFztNCDD5VrlfKk0saUfwLMnHTT2pYqoj6aFLYrCSaZiSd8YmQkQ9oaEEDzXIwC33omRKGm3tcp/L+hNaTcVwoST0OacYxUklsw6Rke/R7OXKmQ86y09RXypNOGIZccskljLb6no6OjnLxxRfzy1/+cr9iMRaScT9kzPcZ8X02j1XZUq3hywjTBFC4hkHWshjIuliGgV9vsm71I6x/6CHWPfwQ2x59hN0b1/Ovt/+GQqYLCx/LhItfcTlHrVjOsccfx4YHHuYT7/3AlPOfsfwE5mS7Dt8P5WlCR+/ByG8LQulXkZHfNpMoGcOErUS3QISI2ghGrhfNyaBZGQwvSRVrE5HBidrBNFqb8jQnFYp/AZQUSL+O9CvEtRFU0ESh0FupCXQzGb6uJJquoxsWZtcAZrYH3c0eMU42pRS+iGnEIaGU6JpGwXKYUyhSaI3jOhKiHinPLG6++eaOSUoAt99+OzfffDPXXHMNYSwYCwLG/YCRRpPttTq7m/VkRriUVIIYKTUGci6Li8lYyVq1xtqHVnPnww9y9stegW3b/OSLX+SnX/oCAF4ux6Llx3DGea9hRl5nTneWd7/rnfzf9763lTZOXMbxs5/Hn372q471XXDBBVx11VV/wZ/QU58OQ0nkI8MGollB+bWkF2HYIC7vBhljFmckH7h1HTQDNA3d8ialiifXDbZEYXrfSnkGkwrFw4RSiqhZYdvW3Xj1EdxmGc32MPP9yEyRsTjCFRHZRiURjbaHVppF3bBx3QJZN9PxWqNBA03TpjS/Hg0aKKXodjIdn2zLYZNQio4xegDVKKAZR3Q7mfb8ZEhG5tWikJLjdYg5X0SUQ5+i7XbMT46kYNRvYBk6oBEKgaYlrWv63Bx9Xo78UySdnJLyWGzYsGGfx2+/7z6yq1Yz1GzSiGOUUjTiENc0GPBydLt5xpsCQ0ZIXbD2z3fznR//hG2PrmbX5k3tlPHC05Zx6qmn8MJXPJ9TT1nGySefzLFLj6LgeLiGhWfuP208MelpxowZ7bT4Y0U6n+60I4SRj4p8Yr+CrI2iRIiKQ5ASpQSISRFCw0A3HcyuGSAFRqYLs9iP7uTbYnCi+XRKSspUpkUobtmyhd///vcEQcCZZ57JsmXLpuNlj2iUCKmP7mCbqjOQKbI414MI6sjmOMMKNvgNim6O5QMLsbwiupNjNGywuTqCJ8fJWHtawFSjgA21UUxNx205hSERd5trY8RKYukGhdaYvVDEbK6N0RQxOho9k+Yhb66NUYl8FtPDgJdvr3drvcyQX2NBrrtj8snORpWtjXHmZrqYnysRyJhGHLG1Pk4lCpibLbIo10O3myFr2ukUlJQjgkT0RYz5Ps5+2ndt80y212r0eB4zMlkCJVg1uImH73+Uu9ZtYcuqR9n26Cre+OkbYFaebYPreeSuP7J0xXKe/cJLOP6kEzj+pBM54ahj8Ewbd95RuM858F6ElmWRz+cZHR0ln88/I0RiRw1h5CODBqJZbkUI/UQQighRGyFujKPbGczS7HZzat0tJAZAL49hZ9PoYErKIXDAQvGTn/wkp556KhdddBEAX//613nLW96C7/sA6LrO9ddfz4c+9KHpXemRhlJoKApegYymJY1UAUyHTL6fbgUFr4hT6G5H3BzDJNNKO00WWxOOZUPTOyIPlm6QMW1ESyhOYOg6nplE8ib3R9TRyJo2UqkpDaqzpoXfmrYwGV3T0NCoxSG7mjUcw6TXyXJ0sZ+saSfrSoVhylMYpRSNKGLUT/oSDtYa7KhX2d1ostOvEK44mjknnMi21hxjgEWnnMz5z/v/2bvvMCurc+/j36fuvqcXelVRVEQFRCygCIol1tjFgsYW5Zi80ZzEJJ7ElBOjppqoybHEkhgTU1USI0YFBQsWVHqfPnt2L099/9jDKAGNwFAG7k+uXDDP7Jm99jwy85u11n2vk2h7933MffajFInw0p/+wCP/8/WeNijx6mpGHnQA+1UFOGif/agdfQjRL3+tp+K4N7dZzJ49u6cie09TrjIu9uwhdDMdOJm28v5BzQDXxnfd8oMVpVwkomioZhitoYqAEUaNVKIFwt1hMCh7B4XoRVtV9bxq1SpOPvlk3n33XVRVpa2tjeHDh3PjjTfyuc99jmAwyN/+9jeuvfZannvuOSZOnLgjx95rdkTVc76Q4bkFzxAzIRiMYsTrUMMV5W76ioLtuWiKivpv38w+7rrjeSgKm53y4voevs8my8gAnu9vFiA/6brv+zh++XzXvGNR7D4FxVQ1qswwtcEIUWPPnzGUKr++5d/vl+/7FBybzmKBrkKJlnyWpmyOjkKBkuOjKaBrOhHNIGLqqCh05GzyxSLfOnUqhUwGzTCoGTCA9jVr8H2f2ff8mCOOn0zH6tW88penGXf4YUwcP4Fhg4cQNkw5seRT2DhD2NneSlU01FPQ5xXS5YDoWuVA6Nj4voeX68IH9FgtWrgCxQyjBmLdhwgEUYygLBfvBPL9UMBWzii+8sorjB07FrU7KCxYsICxY8dy++239zzm0ksvZfHixcyZM6fPBMUdQdEDaBUNROsbMALRzX67/bjZho+7/u9BcCNNUTduxdmEqiioyuaf69+ve937rnKO3VOZXGEEGRqtJmYEiRimFKCI3ZLv+5Qchw3ZNIlCkZZclrXpDC25Igo+qqKgd8/G14eiBHUdVVEoFkssf+99lr79Nh+8/TbjzziHkQftj9a9pOs5DgMG9ufE02Yw/vDDOW7KFPrXNxAcPorrTjhlF7/q3dtmbWdK5SPr3EIaN9NGLp3HjHYv/3o+qGp3b0EDJRBFiUXQwrHy/kEzuEe1mhGir9qqoFhVVcWKFSt63s5kMjQ0NGz2uPr6etatW7f9o+vDFEUpH9fU04R199HTz9BzUFCIGiYjYjVUBkJE5Xg8sRvaWFGfLBXpLBRpK+RoyubIJlMkVR3bcUkXXUK6ydDKKNGAgaooWJZNyXawXHhv0Zs8+u3baFq2DNdxAAhFIkw/+TiOHVbP9bOvp6OljRHDhvPFm25Cl1+QPpHv2JssGXuFVPcMYQnPtcG18B27/GBVA81ANcpH0WnxevRIVbnC2AiWw6IRlP2DQuyGtioRHHvssaxatYqf/vSnXHfddRxzzDHcdNNNLF68mNGjRwPQ1dXFgw8+yBe/+MUdMuC+yvU9UlaRUHeV40ae75OyigQ0bZMTVXzfJ2UX0RV1k3OVAdJWeT/oxuKVjbJ2Ccf3qPjIMXobryetYs/pKIamUW2Gqe+uTI7Ifh6xG9kYCjNWic5CkdZCjqZMhuZcFttVwAdd1QhpBlHVwDV0cq5BY1jB6Wzh1fn/Ytk7b7Fy8Ts0LVnC2bfczJQzP0O/fnGi4SBnXXoREyccwRHjxzN6v1GEu08BGnfrbbv6pe+Weo6u6w6Fbj6NV0iV/17K4XQ14TtF9HjDh0Ujmo5ixlEDEdRgvHxmsRHEyhaJ1TdIIBSiD9mqoBgKhXjooYc4/fTTuf/++znxxBMZO3Yshx56KEcddRTBYJD58+ez//77c+GFF+6oMfdJ7cUcqzMJKgMh9ovX9QSzRCnPykwnEd1kv4r6niXmlF1kZaYTXVHZr6K+p/gka5dYkekEYJ94bU+ILLkOKzOdOL7H8FgNIc3obnZt01HMURMMMyxW1XM8nswait2B7/uUuntvJopF1qSTrEx1kShZqJ6O6/noikbSyVHwLAZHKhkQqcT3fVpbWnhh3ku0VVcwaMS+mE0t/OjSSwBQdY3B++7DSeecydmTjuDYffYltP9oPr9wuuwn/Bgftp4p4NnF8lnGhQxYuXLBiWOBZ4PnlZeM9QCqbmI2jEBRDbRYbfkM4+49hFuqMFZKnRIShehjtjotTJ8+nTfeeIPbb7+de++9t+c0g3/9618ccMAB3Hzzzdxwww17RQuHraErKqamYyibnruqq93XVW2TU6J0RUXvrnL+aGGLpqg9YfKjhS1ezw9cm2SpgGf61AYi1FREZNZQ7DZszyXvWCSLJdZl07QW8qQKRVJFm6zlsDqdxMXloJp6GiNxTE1FURQ25HxWt6b522P/R8v7H7DmvcWk2toAOP6KSzju+GMZeMBIar7/XY4YP4HDDz2UqkhM/pv/GJssG5dy5dNKCmk8p4hvW7i5BE66FcUIYVQPRDWCaMEIihlGC8XL7WfM4IehUMKfEHus7T7rOZVK4TgOFRUV6HrfnKXaWWc95x2bgKZtVrlccGx0Vd2saKTkOqiKstl1y3VwPBcHn6Lj4OGjKSpRw6BfqIKYESBmBGSP1TaQKr/e43guBdcmY1l05As05bK0F/IkCiVytoPn+UT1ICHDwHchYzkEdIWQ5tG0dAVL3nqL5e+8xfDDD+OQk07EKWS5dcpUavs1cuChY5kwbhwHHTCa4487jobqGgmFW+B7bnnPoF3At4q4uXLfQcVzu9vSlPBdu9zOSzPKp5PoARQziBKIooYqygFRD5RD4XYWlci/r75F7peAXmi4XVFR8Z8fJAAIf8w32dDHXP9or0PLdSi4NkXXxe8OhnEjwIB4BXEjSEQ3CUploNhFPN+j4NjkupeQm3JZ2vM5EgWLrOXidy8hhzSDSjNKQ1BB11Q8z6MtkabdVinmUzx+8400LV2KY1kAhGMxxhywL58dNZKKQJCLN6xncL/+PaFQfpCV/fuJJa6Vw8sn8YrZchWyU8JzSrjpDny7hB6vQ4vXoUeqUYJRtHBl+XQoM9QdCKXtjBCirNenAFOpFM3NzVRWVtLY2Njbn36v4Ps+Bdeh6NpYrgvdR+NVGEEGRULltjUSDMUu0rOv0LVJFQs05XK0FvIkCkUSeZtUwcbUNGqCAcJ6kHhYxdRU/vbQg3S2tpJKpYnU1LDqnbdZ895iDpg8mcu+cSv1/RuYW1PN0ZdcxJFHHMGRRxzBgaP2R//IL0w1/SO78JXvHnqKS6xCuUF1thMn3YLv+SgK5cDoud3NqcszhGogihZvJDjw4J7CEmXj/2UmVgjxCXo9KD7xxBNceeWVXHHFFdx///29/en7FN/3SdtFonpgk+Vj3/fJOCWCqo6p6bjdszEF1yZjW5jdx/HVBiJUB8OENVP6GYpdxvZcCo5N1i7RXizQlEmTKJZoy+XJ2y4B1cRUdIqWj+1p1IV0aqImmuOzbPG7pEsl6vYZyW9++hMK6XTP5x0wdAjHnzCVU089lQtHH0hINzhn7gu78JXuXv69uMQrZHDzKfye4pJiuf2M6+IWkii6iR6rR69oQA1VoIbiqBtnCM2Q7CMUQmyTXg+KRx99NPfddx+jRo3q7U/d5xRch7XJduqCUUbEP1weay/mWJJqw1A1Bker0FWVqG4yMFxBVSBMWDcJ64ZUZ4qdrjybbfcURa1OJVmRSdCZL2IqQfBUdEXDw6PTKhDUNfpFI2SLHgUs3p07hyXvLKRp8ft0LF+Ba1nsd9hhBHR9k5A4adIknn/+eSl667ZpT8I8bj6J09WEV8yUl4F9vzxLqCo9Tai1cCVqIIoarkALRD+cIZTCNSFEL+r1oLjffvux33779fan7dM2HiuWdywszyXv2AyJVjMsVk1dMELUCBDSDPnmLnY61/PIuxZZq8S6XIa16ST5kktnwaLkeFi+S8LKEjWC9ItGCRs6mqrQkezizUWLWP3Ou2iezvjzLiIe1PnXQ/fTunoN+x04mhMvnclREyfSsmEDt3711k2e9+WXX+bBBx9k1qxZu+iV7xrlWcKPNKku/vssYQkcG3zwu5eR0TT0eCNauKr7OLsgqhHqrjaWXyaFEDtW3yxT7iNCmk5tMEBIN7A8l4ZQjOpAmKi0qxE7yN13300ymaSyspLZs2dv9v6NPQtTpXLBSUs+R7JYIlWwydoWVknB93T6xUM0xkwMXSXvVOC6Ppbn8ugdd7L4xRdpWbEc3yufDX7A2LFc8d3bqQkFOefppxk8YCChUKjnOZXnKdcAAHkxSURBVL/85S9vcawfPeVpT/Tvewm9fBI3n8BJtuI7Vnk5GAVUpfuYOrNnlrD8Z6R8prEZkuISIcQus01BccWKFTz88MO89tprtLa24rou1dXVHHTQQZx33nlMmDCht8fZ5xiqRr9wnLraWqJ6ORhq8tu/2MHuvvtu1qxZw5AhQ/j8DTeQdy3yjk1HIceGXJZEoUBXwSJneeApmJpOUDOoDgTQPAc74BF0i6x6/RX+8uYbrHz7LaxCket+8QsqggH8RCf9a6o5/eTPc8yRkzj6yCMZMGBAz/PXjtxnszENHz58i2MdMWLEDvs67Gzexopjq4BbzJSPs9tYceza+I4FvgeqjmKGQFXRwpVolf3KfQkDYZklFELslra6j+KvfvUrrr76akKhEGPGjKG2thZVVUkmk7z33ns0Nzdz3XXX8ZOf/GRHjbnX7Yg+iiCtO/qivnrPfN8nXcgzbNBguhIJ4lVV/HL+C2Rdl1TRwXJ9dDQCqk5INzB1BVNT8X2fDevXkQnGSZYsnr/ru7z19N/Y+G1h4LChjJtwBPf/8pdUhoLbtG/WsiymT5/O3Llze65NnjyZOXPmbPcexZ19v3zP22Tp2M2n8ApJ3FwXTqYD8MrNqFHKPQf1AKpuoJhRtHC8XGBilNvQ7I17Cfvqv6+9ldwvAVs5o7hhwwauv/56fvjDHzJr1qwtfpOfM2cOF1xwASeeeCKnnHJKrw1UiN3Bf1ra3Vmc7r2uOceis5BjdVeCL114KV3dJyWlu7q45YLLueln91IRioDhEjZMcpkMixe8yntvvMGKt95i3bvv4tg2P3jhRY4cNJCqaSdwzAH7M/moozh60iRqa2u3e6ymaTJnzhwaGxtJJBJUV1f3Skjc0XzPxd+4bGzl8fKp8ukldgE8t/tIOw8UupeNK0BR0eP16PF61GC5wEQ1QtvdqFoIIXaVrQqKL774IlOnTuWaa6752MdMmzaNm266ieeee26bgmJnZyfpdJrBgwejaZ+unUMul2Pt2rX0799fGoCLHeqjS7s7MyiWXIecY5GzS6xKd7Ey3UXJ9XEshZILr/7hKd5f8NomH7PijdeY++gDUFtNzaTDiRkhXrjzxyx46ikABg0bzkknn8xRRx7JFQceQDgcZuqNN+6Q8RuGQSwWI5FIEIvFdruQWK46LpSXjktZvFwXXjGLm+nAyXehaiZqqAJUFVU3QdXQI9XlGcJwZXnp2AzJcXZCiD3OVgVFTdMolUr/8XGlUulTh7yN/vznP/P1r3+dtWvXEo1GSafT3H777Z8YSgH+93//l2984xvU1dXR0tLCNddcw1133bXXLemIHc+yLDKZDACZTAbbtndI4NlYiVzeW5hhQy5HxrLJlGxSBYt1+RQFx2ZAsIr+4QqqTJViW+sWP9cTP/oRAL+aO5dxo0Zz/E1fID9zJkdNmkRdXV2vj/2TzJ49u2c2dlfy7FJ5ptAu4BXSPVXHnmODa5d7EwKKpqEEo+iaiRIIoVc0lsPhxkAo+wmFEHuBrQqKkydP5rLLLuPrX/86//Vf/7XZN3zLsnj88ce58847+dOf/rRVA3nzzTf51a9+xSGHHAKUG3efe+65jBo1iilTpmzxY5555hm+8pWv8PTTTzN16lTeeustjjrqKPbff38+97nPbdXzC/FJNu6zS3Qv7SYSCaZNm9YrS6i255KzLXKORVshR0s+S6pUoj1bIGXZhJUQAU3HUHUqzQBVgQg2Drm2BPNeepn3Xn8No3bLoe+sz36WW/7f/+OQQw5B13UOrDtqu8a6PXb2Uv0mrWhKedxCqnysXSmPk27FK6TLs4FmGEXTQDPLf4+Fy61oQvHykXZSdSyE2IttdTHLU089xSWXXEI+n2fEiBHU1dWhKArJZJLly5dj2zZf//rXufXWW//zJ/sPhg4dysyZM7ntttu2+P4zzzyTTCbD3//+955rl112GW+//Tavv/76p34eKWYRG33cPbvvvvu46qqrtnh9a3oB+r5PsbtFTcYu0prP05rPkbEsMiWHfMknU3BR0aiPBomYGqauogA5y+btN17jn489xpq33yLZ0gKApuvc/7vfc+8d32f+Sy/2PFdvFYzszjber41FJp5dwCtt3E+YKu8ndF081wLPB3wUzcBXFXBstFAlWlU/9I2hUPYT7lDyPbFvkfslYBva45x++umsXLmSJ554goULF9La2orneQwePJgLL7yQs88+m5EjR273wNra2mhpaWHIkCEf+5jXX3+diy66aJNrEydO5Ne//jWO46Dr0iZS9I6VK1du8fp/6gXo+V5P0UmylKc5n6WzWCJXdMhYDo6joPoqhqrh+QZWyaEyYBBWLN5b+BKLX3+dlYsWMeOGGxg+6gD0QpHVCxdw2IQJHD3pKI475mjGjRtHKBTiwhkn9rmCkW3he25P1bGTaqOYW49bSJebV6fbca08Rryh3IrGMFAUDS0YQw3F0MJV3UUm3TOFmnyPEEKIT7JN3yVra2u55ppr/uP+wW3l+z5XXXUVAwYM4Nxzz/3Yx23pt52amhocxyGVSn3sb0LpdJr0R44Ta25u7p2Biz3Wp+0F6Hhuueikuxq5KZclWSqRs13yRQ/XU9DQ0FSFoBYgEtbRNZXOXJ71mTyr3n2LuT+6k9YVK3oaWg/bZx+OjFVw+piDMcaO4XtXX7XFPbiGYXDrrbf27APcE0LiJpXHpRxuvguvmMG3Svh4WDkLy/RBKbej0WJ1qJ6DHq1Fr+yHGoyUl5PlrGMhhNgmu+Wv09deey0vvfQSc+fOJRKJfOzjDMPYrLhm49uf9EPyzjvv3OJydldX1yYnSmyvj4ZR0Td83D075ZRTmDRpEi+//HLPtaOOOooTT57BmpYm8rZFWzFHxiqRt10yJYd0ySbjFDAUnfpAjCpDx9AUbKfEa28vYumiRTS99wGr33mXk678HMeffAqjRgzlg4oKPnPNNRx1xETGjx/X8wtPLp36j+O/+OKLe/7e2dm5nV+Nncv3PbBL5TOPrQJuKYNfyuOWcri5LvB9tEh1uYBE01FQyBFAC8TRgrHuVjQBMALYG/s92oBdgtx/LsITO558T+xb5H71LTtqm8BuFxQ///nP88QTT/Dcc89x4IEHfuJjBw0atNlsYFNTE/F4nHg8/rEfd9NNN22yr6y5uZnx48dTVVXV619o2d/R93zcPXv++ed7lnarqqv4/uMP8lYpTXM+y/td7fieTp1aRclWCBkhKmJhkpZHsVhEsXPoRoz1S5bw8yuvpJTLAVBRVc34iRM5/8iJnDpxPACXnHTiTnutu8pmM4W5RHmm0C7h45fPOwZQFYgG8QPVoCjdPQobUAMRVDNEIlug9mMKecTuSb4n9i1yv8RuFRRvuOEGHn30UZ577jnGjBmz2fsTiQRr167l4IMPRlVVjjvuOP72t7/heR5qd5uKv/zlLxx//PGf+Dz/KUgKsVGxe39hxi7SkstyytVXkEymMCMxFrQkUXwVTTHpH6jDcwBPwyl1seL1RXzw+uusfGsRzUuWcNKsK7n+/93McUf3p/XMs5hy1FEce+wx7Lvvvnt8K6dyoUl3kUkph5tP4hVS3T0Kkyiqhh6tARTQDRRUlHAcNRjvOfNYDYS32KNQkZlCIYTYoXaboHjzzTfz85//nHvvvReARYsWAeXfZgYNGgTA73//e6688koymQzRaJQvfOELPPzww1x22WVceuml/OUvf+GVV15h3rx5u+pliD7M931KrkN7MUvaKtKaz9JeKJC1bHIlh6KtMO6MC9FVDVPTCOgKmgJr16yiKZXFr2lALeX44Wkn4/s+qqYx+uAxnHbNtZx79tkcO2IoAI899OCufaE7UE/1sZUvt6TJJfCK6Z49heWZQh9Qyj0KAXQTvaKxvL+wJxSGpEehEELsBnaboPj+++9zwAEHcPfdd29y/cwzz+RrX/saUA6NY8aM6WnmPWjQIF5++WVuv/12br75ZgYPHszcuXM59NBDd/bwRR+0sVVN1imRLBVoyWfo6Oii0GXQmbOxbTA1A0PVMDSD2qCKoni8v/hd3l24kOVvvMGat94m3dHOhBNn8MP7fklDNMyAH/yAQ8eMYcKECZ+4x7av833/w8bVxRxePombT+KkW3GLGVQjhBqIdBeamOWZwlAMNRQvVx9/wkyhEEKI3cNW91Hc6Omnn+aDDz7g0ksvpaqqqrfHtVNJH8W9w0eDYdoqsiGbob1YIG/ZZIsutqPgZ4t0eAY1UZP6SADbLvLuojdJZrIMGzcO1YfbZ5xENtlFVU0NR0yaxHHHHMMJU6ducbvEnqKnebVVwC3lyn0K88lyn0Lfw7NLGycKQTPwS1kUI4hR0R8tVtOzp7C3q4/l31jfIverb5H7JWA7ZhQ1TeMb3/gGX/nKVzj33HO55pprGD9+fG+OTYjt4vs+Bbe8x7CrmKcpn2FdJs3aTArF14moEXQ0dEWj5Dmsy6Wodnxal7zF/DdeY+Wbb7Bu8Xs4VomRB4xm9nkXUBcOcshjjzFs2NA9en9h+Zi7PJ5VwM0lcbMJnHQzfikPgTCqaoCqomgGiqajRarQQhX/du6xLB8LIURft81Bcdq0aTQ1NfHII49wzz33MGHCBA499FCuueYazj///D16yU3snjY2t87aJdoKGd7raqdgu/ieRr5U7mHYVszQZRcYEqmmPhgi0dXJ6wteZcnaNfQ/4XiqQ1Gev/8XLF+0iH0POIBLLr2U4489lmOPPYYBddUAnHji9F38SnuX7zp4Vr7ciqaQws114Zfy+J5dnin0PFAUVDNULhwLRNEr+6FFqst9Co2QnHsshBB7qG1eev53r7zyCvfccw+//e1vCQQCXHLJJVx77bWMGjWqNz79DiVLz32T63kfNrcu5mjOZ+kqlEiXbNanc9ieT30gSkg3CeoahqbQZeV5+fl/sPylV1iz6C1aV5ZPVolXVrJw2UoCVol8qouGhgaqq6t38SvsfZtUIBezOJl27K4mfLuAagTB98szhbpZni00AmihyvJM4W4YCuXfWN8i96tvkfsloBeLWY444giKxSLNzc38/e9/5//+7//46U9/yqWXXsrPfvYzAoFAbz2V2Et5fjkYZm2LjkKWpnyWZLFEtuRSsH1cB9IFD9eF/pWVxAI669euYcGrr7Ju6VJmfP4GQrpB04sLWPS3vzL28HFcdt55HD9lMhMnTiQSidDZ2cmQ/fff1S+1V2zcV+hZ+Q+LTQpJfKuI55TAsbof6JUDom5iVPZHi5SPuZPlYyGEENsdFJPJJA899BD33HMPS5cuZcaMGTz99NNMmzaNZ555hmuvvZZ77rmH2bNn98Jwxd7g7rvvJplMUlFRwVXXX0vWsUiU8mzIpukslMiWbAo2+K6KrqjoqkFUU0mULFBs1r3zCn98+mlWv7mIZGsLAOFIhB/9z7fYd9BAzv/5T6mo+PUe98tLz77CUh4n24mT3IBXyqPoJr5jge+jaAaqGew++zjec/bxjig0EUII0fdtc1BcvXo13/rWt3jssccIhUJcfvnlXHPNNQwbNqznMTNmzOCLX/wi77zzTq8MVuzZNhaf3HnXXaxbu5aGgQMYdOo0MiWbjOXhuwqmoqOrJhWGhhGA1cuX8fr8+by7YCHTbvp/DKmro7h6HSvmz2fCxCM5fvIUjj9uMmPHjkXXy/+5h+vrd/Er3X6b7SvMJ/GLOTynhG8XwfPw7Dy+56LFajFrh6BGqrqPuguVg6G223THEkIIsZva5p8U//jHP1i0aBE/+clPOP/88wkGg1t83JQpU/rEPkWxaxQcm6xT6qlKbkllSKTKZxqn02kWbWhjQLSGSjNIIKhScm3mvvwCLz32OE1vv0uuqwuAyupqToxHmXzIQXDQ/xC443s9p/X0dT39Cq08biGDm2zCLWbK77OL4Lrd+woDqEYQJVqNFq5EC1WgBMLlfYX6x599LoQQQnycbQ6KF1544SbnJX+c0aNHM3r06G19GrGHKbkOWbtEyirSnM+wNpPig1QHqYJFjR/lVzfOJtcdFAvpDL+8+moOO/FEVi96m6mf+xwDRowgXLTpWPw+xx5zLFOPO45pxx/H/vvv/5Fg2LdnynzHLu8rtPJ4uS7cXFf573YR3y7hZjvwUTAq+qHH6lDDld1H3YVRzDCqsWctqQshhNh1tvknaigUAsC2bd58801Wr15NY2MjY8eOJRaL9doARd9mey5Zu0TGLrE600VTNoPjQq7kUXIU8MG0wzQYUV576i+sXvTGJh/f8sFS/vrBUmrqGxijG5xz8BhChx7Kj2+4YY/oYfjRKmQ3n8bNdeJbOTyrWG5w7bkf7isMxVGrK9CGj+/uVRgu7yvcA74OQgghdk/bNfXyr3/9i1mzZrFs2bKeazU1Ndxxxx1ceuml2zs20Qd5vkfWtsg6JVryWVpyWdIli3TJoWSB2t3g2tRUoiZsWL6UN19+mbdeeQVV23IhxVVXXcXPf/7zPSIQfbTgxM119Zxu4pZy2B1rAQ+jelB56ThajRapRgvFyzOFgbAUmwghhNiptjkodnZ2ctpppzFjxgx++9vfMnToUDo6Ovj1r3/NFVdcwT777MOkSZN6c6xiN/TRJtedxTzrsxm6ikW6CjaWA5qvois6hh6gOqhiaCob1m/gp7d/k+VvvE4hnQagur6Bo4+bwtItPMe4ceP6ZEj0PRevlMe38riFNG4usVnBCbqOagTRI1WYNUNQozXowWg5GJpb3vcrhBBC7CzbHBSfe+45hg4dyiOPPNLzQ7yyspJvfOMbNDU18fvf/16C4h7I933yjkXWsego5FifKwfDVNEmU/DIFj00NBrjQaIBlY7Vq1gwfz5vvzqfwWPGMPGcczHDQVqXL2Py1KlMmTyZU044gVH77Ydt20xvaWHu3Lk9zzd58mRmzpy5617wp/TvPQvdXAKvkMIrZrE614JtoVf1Qw1Ey02sN84YhipQAhGZLRRCCLFb2uagGAgEPvas2/3224/Ozs7tGpjYPWxsWZO1S3QW8ixJdZIsFrEchXzJB0/BxydRyuO5Go2RKDHD5ydf+gIfvLagpyq5oqaGIw4/nAsO2I/6UJgvrFu32XOZpsmcOXNobGwkkUhQXV3NnDlzMIzdr2J3k4KTfLKn4MR37PJsoe+BZqAaQQL9RqEGo2jxBpktFEII0adsc1A89NBDue6662hubqZfv349123b5sknn+Tmm2/ulQGKnWtjMMw5Fp2FLM25PO3FPKmizapUkpZimn5mFcPitdSGVFLNG3h+7j957eWXidbUcOEXvkTRV0h1tDH2iAkcd9xxnHzCCRx+0MGfql2NYRjceuutJJNJKisrd4uQ+O/H3rm5zu6G1q34bgktXI2iaSh6AEU3N9lbqAYi0shaCCFEn7VVQXHJkiW8+OKLPW8PHz6cQw45hEsuuYShQ4fS3t7Ob37zGzKZzB55Tu6eyPd9iq5Dxi7SVcrTlM+yLpNiXTqDikGIEKaiYeoBBkerqAmGiOpBnrjje7z2z3+QaGoCIBiLsc/JJzJ9+GAGxGLMfm0hxjaGo119io/v2HilXLlvYS6Jl+9uT+OUwC4fe6cYJlqkEhQFvaIfekUD2sbZQmlPI4QQYg+xVUFx/vz5XH/99Ztd//GPf7zZtUcffZSjjz5620cmdhjLdcg6FimrwNpMmo5CnkzJIZG3yRY8TE2nPlRL0NAopZMsXvAqb8+fTyqZ4vxvfosux6Kzs43BI0dw/hWXc9IJUzl63Dhigb7XqqWnmXUph1fMUmxeS3plB3guim6C54Kql/cVBiKoFY09J5yU29OE5SxkIYQQe6ytCoqXXnqptL3pgzYGw6xdYn02TWs+T6pYblnjuyqaopEr+tieSl0oSG3U5E8PPMDc3z/J+mXlOmQ9EGDU4YdxYF2c4VXVfO73TxI3g2h9LCR9dG+hm+3qmS30HavczLoEnlpAVTTUYBS9oj9atArV7O5bKCecCCGE2Iv07SMsxBY5nkvWtkjbRTZsDIYli0zRwXEVdDT+9fhjWLkskUiYuhH7smjePJqWvMe1P/oZa9NFmtuaCUTDnHv9tZww9XimH3ssdbEKAn3ofOCPViK7+XT56LtSFlA+bE+jKuWj78wwSqyegB8kVt/44d7CPjZDKoQQQvSmvvNTX3wsz/fIORZZ26I5n2FDJkNTLkNbvkhYCWEqBqamE9UCOJqF7Ts883/3kUkkPvwkisKA/fYh6KQ4eMQIzv3RXVQGQkR0s8+EJd91yrODpTxuvmuTSmTPLuKmWgEfvXIAWqQCLVzVvYwcLQdD3aTQ2Yker9nVL0UIIYTYLUhQ7IM+2rKmOZ/hvUQ7BcchW/BI5j0MVSXpZnAVl9pohMK69bw2fx5vzZvHUbOvwwobFPN5AHTT5Et3fZ9Tp09nnwEDiRkBzD4ya+hZRXwrj1PI4GU78YppPMcCz8F3bKBcdKJoOkaonmD/UWjhKpRAuBwMpRJZCCGE+ER9IxHs5TZWJmedEsligbXZNB2FAol8iZWZJHgQ8mJoik7/eJiKkMGCNz/g7w88xL2vvUkmUe5pWdnQwKTONl749i+xi0UAHMti3hN/4BtXXrNbtKL5OB+2qMnh5lPlhtbFLL5dwupcA66LXtUPLRhDMUIo0Tr0WA1qMCbLyEIIIcQ2kqC4G/poMOzqPhavo1AgVbTJllwUX8VUdAJGiDGVEdY1t/H+gn+x5s0FHH36WQzcfzTkfVa8upBR48dxyKQjmDj5KCYcdDDPPvY7li58Y5Pnmzt3Lg8++CCzZs3aRa94cz1FJ6VcuUVNLoFnFfDxwHXA90HVQNMJNO6HGgijVfZHD8V6lpGFEEIIsX22OSiuXLmSzs5Oxo0bt1XvE1tWdOyeljXlYJgnXbTJlDw8V+npZVgXUgloKsmODv708wd4/eWX2LDkA/B9jECA4YccyOhxBzBp8qF85r3XaYzEqQ6EiZtBDFXjkfVNW3z+FStW7ORX/KFNjr8rZHBzCdxsJ3ayBVwbLVZXbmitqCiqhhKKoYWr0aLVqIFIORj2keVyIYQQoi/Z5p+u//znP3nllVe2GAY/6X2irOQ6ZO0SGbvYXXhSIFW06MxZ4KkEdANTNagyNQzFZ8377/P2/Hn0H7kP+x15NF15i7888H8MHLUfp1x5OYcdfSTjj5jA4OpqaoMRokZgi4Uow4cP3+J4RowYsTNeNgC+5+KVcuWik0ISN5vAs/Lgefi+D/gAaOE4XjGHohvo1YPQPxoMZX+hEEIIscPtkGmYVCpFPB7fEZ+6z9rYyzBtFWgv5GjN50lbNsmCRUe+SKGoEFACDKgIEw8auLbF3373OB+88iofLFhALpUCYMq55zPoiLFUDojwyKJ5DK2rozEco9IMEzVM9P8QoGbOnMmjjz7K3Llze65NnjyZmTNn7rDX7tkl/FIOt5TDTbViJ5vw7SJquAKF7iDr+/iqgqKoqIEIWrQGLVzZvb9QmloLIYQQu8JWB8X58+fzyCOP8N5779Ha2rrZSS35fJ4///nP/OAHP+i1QfZFd999N2vWr6OirpbPzLqYlekuPuhqx3KgXqtCQUNFJed6rM+lIVvAWLGGjK4xduo0WopFfv+jH6MoCqMnTmTs0Ucy9piJDBo8gOpAiKGxauJGgPBWtq8xTZM5c+bQ2NhIIpGgurqaOXPm9FohyyZFJ8UMbqYDv5jD9xzoHqdvF3ELSXzfR4/VogbjUngihBBC7Ia2Oii2t7fz2muv0d7eTiaT4bXXXtvk/fF4nBtvvJELLrig1wbZ11iWxTe/+U0SiQSRigrqj5tKxvNJWRA3glSEQwR0lXcXLOCFfzzH+wvm0bZ0Gfg+Q0cfxOjJU6mJGNzw+H0MHzyI/apqaQzHqQlEiPdC+xrDMLj11ltJJpNUVlZuV0j8aNGJnWrFyyXA98rFJopaLjihvJis+D6KbmI27lueMfxI/0IhhBBC7H4Uv7wpbKv95S9/YfHixdx88829Paadbv369QwaNIh169YxcODA7fpclmUxffr0TZZ2R40bz1fu+yXNa1bStnY946dOJVdy+dpF57F+8TsMGrU/YyZN4rBjJ7L/4YcQj4SoCATpFyovKceMwG5zVJ5nFfGsHF4xh5vtwCuk8Bwb3/fLx+GVcujhKpRgFABVN8tNraM1aIGNwXD3bcPT2dlJTY003O4r5H71LXK/+ha5XwK2IyjuSXozKN53331cddVVm10PRaMUslkC4TD/+48XMQyDfMtq6vrVUlNXialpVJkhGkJRKgMhonpgly+/+r6Pb5WXkZ1CqrupdRY8F1BQjACglBtcuzae76PgocXqMSr79cmKZPnG2LfI/epb5H71LXK/BPRCMctzzz3H/fffz/LlyznhhBOYNWsW//znP3ernnw708qVK7d4PVZdw3mz/4vxkyfRrzGIqSmEBuxPbSBCbTBC3AwS3sVLsOVq5Hy58CTfhZtN4BZzOOkWsEvotUNQzTC4Nr5n4zslFCOAFq/ftFWNVCQLIYQQe4TtCoo33HADv/jFLzj11FOpqamhra2NIUOGcPfdd3Pcccd9bCuWPdnHveZzr72CUy46h6gepCEUpSpQXlI2dmGo8l0Hr5QrF55kE7j5BL5dAnxQDVQ9gBaMAI04qRbcTAdqVX+0eANatAo1EEUNhCUYCiGEEHuobQ6K7777Lg899BBvvPEGo0eP5v777+eVV15B0zROPPFEHn/8cf77v/+7N8faJ2yp/cwhR07gxqs+R10kTtQwUZVds9/Qd6zuauQsbrodO7EOr5RFDVWhBcMoegDF1PHsArg2nuegmmHMuiGEhoyRVjVCCCHEXmabg+Kbb77J1KlTGT169GbvGzp0KO+99952Dayv2lL7mVef/xemufOXlXsKTwqZ7sKTDL5rg6Kg6AHUSBVeMYubbkFRG1FcGzUQwageiBap+jAYSqsaIYQQYq+0zUExGo2yYcOGLb5v/fr11NbWbvOg+rqN7Weampro37//TgmJmxSe5LtwuprwPQfFc0FRUYwQSiCK4jnlM5OtAqgqRu0QtEg1erwWNRCVHoZCCCGE6LHNQfHoo4/mggsu4Pbbb9+kRU5bWxsPP/wwv/vd73plgH3V7Nmzd2jFmO95+N39C918EjfbWT4WDx+vmMcrpNBCFajRWny3hG/l8e0CqhnGqB5QPvUkGJUZQyGEEEJ8rG0OirW1tfzgBz/g+uuv56677qK6uppCocAf//hHzjrrLCZOnNib49zrbTwf2Svl8HJd3ecjFygXnuioZggtXF2uREZFUcC3CvhOsTxrGK6QpWQhhBBCbJXtqnq+9tprGTNmDA888ABr1qyhqqqKM844g3PPPbe3xrfX2lJFspNux8t2ooYrMWoGo0Vr8O0inpXHLWXK5ySbke5gWCnH4QkhhBBiu2x3H8VJkyYxadKk3hjLXm2TiuRsJ14+iedYQPl0E8UIYdYNx9Z0nFQbvr8GPVqNGoh+GAyDURQjKMFQCCGEEL1iu4JiLpfDtm0qKysBeOaZZ3jttdc4++yzGTVqVG+Mb4/l2aVyY+tCGrtrA06qBUXRUIOR7jY1EfRwFb5dKBefFFKgKOixOsyGfdFjNVJ8IoQQQogdapuDou/7zJgxg29/+9tMmjSJ559/nhkzZjBgwADuvPNOPvjgA+rr63tzrH2aZxXxSlm8QgYn04FfzOB7NqCCHkBRNXzXAUUF38PLd4GioBohjMpGtEi1FJ8IIYQQYqfa5s7Jr732GrZt9yw7/+pXv+LLX/4y69atY8qUKTz++OO9Nsi+yPd93HyKYstScsvmkV/2MoXVr2O1r8B3SqjhCvSKfmjRahRVRTHD+I6FW8ighisIDDyQ0IgjCO1zJIH++6NXNJT3HEpIFEIIIcROss0zisuWLWPEiBE9b7/44os89dRTAEyZMoVly5Zt9+D6Mt+xsFqXU8iuQ9FNjIYR6IFoua2NXcDNdeH7HoqqoYUrysUpw2OowRiKtt1bR4UQQgghtts2J5Kamhref/99ABYvXkwymeSggw4CoLW1da9uuA2A7wM+gYEHYXeswmpaghavQ9UDqMHuyuRI93nJZnBXj1YIIYQQYjPbHBSPOeYY1q5dyzHHHMP69es599xz0TQNgOeee4677767t8bYZykouIUutHg9RqgCs6p/eZ+hLCELIYQQog/Y5qAYCoWYO3cu99xzD8ceeyxf+tKXAFi6dCnjx49n/PjxvTbIvkgxAuh1QwnX1pfDoSwnCyGEEKKP2ar0MnfuXFasWMEVV1zB+vXr8X2fH//4x5s8Zt9995XZREBRFPRoDVqkclcPRQghhBBim2xV1fOqVauYN28eUO6ZeNddd+2QQQkhhBBCiF1vq2YU+/fvz8KFC1m9ejWFQgHLskgmk1t8bCAQIBQK9cYYhRBCCCHELrBVQXHKlCnous6wYcN6rj388MNbfOwVV1zB/fffv32jE0IIIYQQu8xWBUXTNFmwYAGLFi3ikUce4f3332f27NlbfOygQYN6Y3xCCCGEEGIX2aqg2NTURCqV4vDDD8e2bVatWsWJJ564o8YmhBBCCCF2oa0qZvnb3/7GD37wA6Bc2PL666/vkEEJIYQQQohdb6uCYkVFBe3t7QDk83lSqdQOGZQQQgghhNj1tmrpeeLEiVxyySVMnjyZVCpFV1cXp59++hYfe8IJJ3Ddddf1xhiFEEIIIcQusFUzigMHDuS5557jwAMPxDAMNE0jGAxu8f+GYeyoMQshhBBCiJ1gq8+VO/LIIznyyCN58MEHWbhwIT/5yU92xLiEEEIIIcQuts0HEM+cOZOZM2f25liEEEIIIcRuZJuD4kavvvoqv/3tb1m9ejWNjY1MnTqVM844ozfGJoQQQgghdqGt2qP4726//XYmTpzI7373Ozo7O5k7dy5nnnkmZ555Jp7n9dYYhRBCCCHELrDNQXHt2rXcdtttPPDAA6xZs4a5c+eyePFiXn31VebOnctTTz3Vi8MUQgghhBA72zYvPc+bN48pU6ZwySWXbHJ9/PjxXH/99bz44ouceeaZ2z1AIYQQQuxeMpkMV1999WbXL7zwQmbMmNFrz+N5Hv/85z959tlnSSaT7LvvvlxyySU0NDT0PKatrY1f/OIXrFy5kn79+nHllVcybNiwXhvD3m6bg6LneZimucX3maaJ67pb/TlLpRJPPvkkL774IqeffjrTp0//xMfPnz+fBx98cLPrP/zhDwkEAlv9/EIIIcTeyvc8fNfGdyxwbbxSDqjZ8mN9n0wmw49+9COqq6t7rvf2z97XX3+dxYsXc8UVV1BRUcGjjz7KN7/5zZ6OK77vc/vttzNy5Ei+9a1vMXfuXG677TZ+8YtfSA7oJdscFMeNG8cVV1zB3LlzmTx5cs/11atX84tf/ILvfve7W/X5XnzxRc4//3yOOuoonnnmGYYMGfIfg+KSJUv4zW9+w3e+851NrmuatlXPLYQQQuzpfNfpCYIb//RKObxiDt+18J0ivmOD74HnYGtV0H/wJ37OaDRKPB7fYWM+/PDDGTduXM/bl156KZdddhnJZJLKykqWL1/O+vXr+e53v0soFOLCCy/kueeeY+HChRx11FE7bFx7k20Oivvssw9XXXUVU6ZM4cgjj2TYsGG0t7czd+5cxo8fz2c/+9mt+nxDhgzhzTffpK6ujoEDB37qj4tEIluc/hZCCCH2Fr7vw7+FQN+18Up5PCuPb+V7ruGDr/jlPJhtx7NLGPE6tHgDqhlF0XTcQhqcXV+UqijKJm+vXLlyk3C6atUqBg4cSCgU6nnMPvvsw6pVqyQo9pLtao/zwx/+kKlTp/LYY4+xcuVKGhsbueuuu/jc5z631bN6gwd/8m8tHyebzXLLLbegKAqHHHIIZ599tswoCiGE2OP4rlMOe46F75Tw7BK+lcMt5vDtAjg2nmvhZjrwHQstWoMaiqOoOoqmowaioOoo6od1rH5FA3ZyA24+DWoCLVzZEyxRo/9xTDfccMMmYe5LX/oSY8aM2eJjL7nkko/dlnbqqady3nnnfeJzpdNp7r33Xi6++GLU7tdQKBSIRCKbPC4SiZDL5f7j2MWns919FE899VROPfXU3hjLNhk5ciSRSATLsvjCF77AD37wA+bOnUs4HP7Yj0mn06TT6Z63m5ubd8ZQhRBCiI/le173rF/3rKBdwrMLeIUMTqYD38qjaAaKqkF3OFNUHTQDRTNRzDCqqqPH6rESa/GtAgRjKKpanl20CuC5+PhsjHY+oKgGaiCM71p4dgE1GEMLxdHcLdchfNS3v/3tTfYoftLP3k86ye0/7SfMZrN87Wtf48gjj+Skk07a5OOKxeImjy0UCjt0OXxvs91BcVeaPn06l1xySc9vFp///OfZf//9+f73v8/Xv/71j/24O++8k9tuu22z611dXZtMX2+vj4ZR0TfIPetb5H71LXv7/fJ9Hzy3vETs2viug+dY+HYR3yp0B0QH398466agKCqKquP7Pl6uCKqLHq8tJzzPxfdcfDdH+YIP3RHQJ4iPgpLNowY1VDOMYhgougmajqJoKJoGH/kTVdtkdjCfTtPZ2bnF15LJZACwbRvbtnuup1Kpj339N95448f2WD7hhBM47bTTtvi+QqHA97//fYYNG8Zpp522yZji8TgbNmygtbUVXS9HmlWrVjFq1KiPHfueqqZmy4VH22urguLDDz/MggULuOWWWxgwYMAnPjadTnPPPfeQSCT43ve+t12D/Dj9+vXb5O36+nqmTp3KvHnzPvHjbrrpJmbNmtXzdnNzM+PHj6eqqqrXv9A76saJHUfuWd8i96tv2dPvV8+soFPqWSb2Slm8Uha/VChf8xwA3FION92GGoxhVA9CMaLl2ULf/7DwxHPAdUABXw3jFTJgtaFFalDNCGogjGqGUPQAim6gaEZ5eVkzUDR9s/C3tT7ufhmGAbBVPzd/9rOffez7AoHAFmcVi8Ui3/ve99hnn3247rrrNnst48ePJxqN8sILL3DWWWfx4osv0tXVxZQpU2RWsZdsVVA84YQTeOaZZxg6dCjTpk1j2rRpHHrooTQ0NKBpGh0dHbz11ls8//zz/PGPf+Swww7jpz/96Y4a+xYVi8X/2JonHo/Lf0BCCCG2yYd7Bcth0LOLeMUcXimLm+vEy6dB1dFCsfIHaEb3krGOEtBRfPA9B10zwPfxsp3YnevQYjUomlZ+nG6ihmKoZgg1ECl/vG72/IlmbFcA/E/uvvtumpqa6N+/P7Nnz/7Yx/37HsWTTjqJiy66aIuP3Zafuy+//DIffPABGzZsYP78+T3Xv/3tbzNkyBB0XeeWW27hjjvu4PHHHycWi/HFL35Rfsb3IsX3fX9rP+itt97ixz/+MU899dRmU7uRSIRp06ZxzTXXcMIJJ2zToAYOHMj111/PLbfcssn1l19+mYcffrinT+LTTz/N9OnTe5ae33zzTSZOnMi3vvUtvvjFL37q51u/fj2DBg1i3bp1W1Vx/Z90dnbu8b8972nknvUtcr/6lr50v3zPxbdL5TBol3CLObxSprxE7BTxXQd8v3ulV+mZyQNwM204uSRqIIwWqQHFR/EVUEDR9O79hKHyTKAZQdVN6J4N7AmE6q4tyhw6dChr1qxhyJAhrF69erP3b+yj+O9M0yQYDPbaOGzbplAobHY9EolsVriaz+cJhUI7NEDvjbZpj+KYMWO4//77ue+++1iyZAkbNmzAcRwaGxs54IADeqakt0Zra2vPvsJkMslTTz3F6tWrGT16NJ///OcBeP/99/nFL37BHXfcQSAQYP78+fzXf/0XhxxyCPl8nr///e9ccMEF3HjjjdvysoQQQuxFfN//cGbQLnXPDGbwitlyOxm3vPfO9zzcdCue66BX9EMLxVBUo/z+jcvCjgWKjaKb6FUDMGqHoQaj5dnAj8wE9iwJ78LX7Pgerufh+l75776P23PNJ1vMk0yX9xpmMhls297s57qiKDtl1s4wjE+dKT6pkEZsu22aUdwRkskkjz/++GbXBw8e3HMc0AcffMDcuXOZNWtWz6bV9evXM3/+fDRN4+CDD2bkyJFb/dwyoyg2knvWt8j96lt21f3qmR20i3hOCc/K4+VTOKlWvFKuPLOnGZRnBsv7+no+1nXALxeNuPkkiqKiRWvQwhXdQTBaDoB6oPvPnTcb6HcHPMcrBz7nI+HP8Twsz6HkOlieW/6/61JyHCzPo+R4ZAs5ulrb6WhtZ8B+++JpJotfWcADX/kq6Y6OnueZPHkyc+bM2aZJINH37TZBcVeSoCg2knvWt8j96lt29P3yHbu7v2CxPENYTOMWMh/ODvp+ua2MoqJoBr5jY3dtAM9Fi9ehGgF8QFW0cuALhFED3bOCRmDTQLgDlzfd7uBney625/b83XIdCt3Br+SWQ+DGoGh7Ho7n43k+uXyB9pY2ulrbaG9pJdHawTFnnYNqBHjpj0/x9wcfIN3RQSH74dLxVfc/xsj9R/HH7/0PL//pD5uN6b777tukCFTsPfp0exwhhBB7H9+xy2HQKeFZ5T6DXjGNbxdwi1mcbAeKD0bVwHLhh6KiaGZ3WPTKM4S+h6KbmP32QwuEUEKVaGaoOxD2fhj0fb8n9H04A+hiex5F16LglANg0XXKodBz6Srm8YCwbuJ64Hs+mXSGFatW0dnSSqEtRWdLC4nWNi7+6jfosgv86Wc/Y97DD272/EMnHsl+I/altrKSAUOHcuC4cURqqiBWQbyqnlEjhzCoJkJdQ90Wx79ixYpe+1qIvkWCohBCiN1STyC0i7ilPF6uC98ulPsOek65cbTfvVysKICCFoiC5+HkEjjZTvRYbblyOBhFDca6+wl2h0Ej0CvLxJ5fntGzPAfbK8/+FR2bkudScG1KrkNnMU97MYuqQF0wiufD7+97kLZEJwVTZcrF5zE8XEvb+mbWrVjF2rVrWbF2Fdm2DpRMjs//8B4UVeHhb9/JvCef2OT5oxUVnH3jDTRjUzl6X6bPmsWQgYOpbmggVldDJqKjxMNYWEw66SQmnXQSvu/zZkczzfkMw6M1NFZUU3RcqvtvufXdiBEjtvvrJPomCYpCCCF2qfIewmJ5dvCjM4RW4cOeg7ku3EIGNRhBjzeUG0R7DlDuOajoRrmSOBjDaBiJagRRjACqEdyuVjKu52F1LwFv/L/lueRti6RVwPY9vO4CEc/3SNsl2otZIlqAmkAE31fAV0lZNi3pIsk168msaaV57Vr+eM9PcCwLRdOYeMYFdHnw+I9+xrw/PdXz/OHKSur79SOmeQTDYaaddQZDDjmAivp69h08gvp+/dFNk+Z0ESXXxSGTJjFk+mkYioaLj+t6OKU0JdcmX/Tx7BIKCtmSg2OrVJth4maQvOOionDkaafzr7/8ieWvv94zhsmTJzNz5sztucWiD9vmPYrvvfce9fX11NbW9vaYdjrZoyg2knvWt8j96ls6OjqojkfKodAu4uZTuOk2PCuPomg9R8spmgFK93nEvleePfT98l5DH7RoDUZFPWow1hMGFT2w1dXEG2cCPxoAbc8l79gUXJucbXXPErr0/KRUfBQUfBR8xwdVxXfLY7Vcn2XvfcDKpctoW7+BtvXr6WjaQKFYYuZd91AfCfDAV77Egmef3mwsB4wfz62//D9WLVnC2qZ2YvUNDB8ykEg0guv5uH55/6Hj+bi+i+/7KArlKmWr3KonZGqYmoqhKWgaaKqCripoqoKi+OiqiqoqaAooqKhqeUZWVRUUBbTuMO06NuceehSZrhTV1dW0tLRIIctebJtnFBcuXMhNN93Ed77zHWbNmtXTy1AIIYQoLxuXl4m9Yg63kKKUSJA3Peg+xs1XVNxsB14xhx6pRA3G8AFcB0XXUcwwWiiOGox/GAiN4KcOhG53COyp+vUcCo5NwbHJORZF1+mZCSxTAB9NUbtDUzkE6r6O4/sUSi7Ll69i9YoVdG5YT9u6dbRvWI/veVx7149RVZVf33kni+Y+D4AZDFHVrx91AwexX20URVE486qrqR84gL/88v5NxvreggU88egj7Dv9ZCprGogHVbKaR6GUxVTL4c8MqER0FV3R0TUVU1XQVBVTUwmoGgFNx9Q1dMrXdVVFV1Q0RUNVFFRFQYHyn4pC+X90/737OuXHffUrX6WttZWB/QdISNzLbXNQvPjiiykUCtx888388pe/5Gc/+xmHHXZYb45NCCHEbs73/e7WM4VyH8JCGjvVgpvtRNEMVD0AioKi6uVzjhWd8tEkPvgeRrwBPw5quPLD/YRbEQjt7gpgq6cquDwTuDEI2r6L55UbY5d3MSoYqoahqgRUnQAKrge262O7Hp2JJKtWrGL96jU0r1lD27p1oCic/qWvkim5PPb1W1m28FUANF2ntl9/+o8YjqH7WL7FCbOu5JiZMzFra7HMCAFDpyJosDKdRFMVtIGNFBx7i68lkklx3v7DCZsGAU0noJXDnqaoqIqCpiio3X/f+LaCskMqsL/0hS/KjL0AtiMoqqrK1VdfzZlnnskXvvAFJkyYwOc+9zluv/12Kisre3GIQgghdge+53XvJczjlfJ4hTReIYVnF8Fz8aE7EHp4xSzYFsRrUc1wucpYM9BjNaihinLvQiPYvWz88TNWnu9hueUZwZLnYLkuWbtI1ilR7L7ueOVZwZRdREWhLhjFUFWCqs4f7n2Its5OgtEY5195FZbrsa4jyYplK1i9cjnt69diqhonX3kNCvD9K65gxaI3e54/FIvRb5996SjlUFSPSZdfzNGXXETtwIFU1jeg6+Vj+DKWQ9QwGX/oOKKGQcQ0iJoGhqYS1FVMrbwsHNJ1QkccwXMPbl6ZfMyYQxhT37gD7pwQ267X+ig+//zzXHvttSQSCf73f/+XSy65pM8coyN7FMVGcs/6FrlfO045FHYXl5TyuNlO7OQG/EIWJRxHVY2PVBvT06fQB1TdADOCFoyhR6rKs4NmiEQqs8V97b7vd7eGsSm6DkXHJmUXac5nyDsWUcPsboGo0GXlWZtNEtUNDqhsIKiZeB50lQp8kOzA9aAxWIVdsFm9dDnfu+ISSvk8gViMr//tWe6fPZuVry/Y5Pkbhg3jv598Asf1eXvuXNLZDDUDBjJ86EhCsRo0RaMyGKAmbBIzTWKmQcTUCeoaIV0nqGmYuoahfbrZPcuymD59OnPnzu25tjs2tZZ/XwJ6ueH2mjVruOCCC5g3bx5HH300P/vZzzjwwAN769PvMBIUxUZyz/oWuV+9o2em0C7gFXN4+SRuIVleUvY9FEVFUXU8x8LpasLHQ6/sh6oZ/OyRP5Iq2FTV1DP7huvLlcdGaIuzhB0dHcSrKim6DqXu/YKJYo6mfJqS5xIxTKC8RFxwbFoKGSrNECPjNfioWLZHV6nIikwnrgMRwjRvaKOYTdMwYhgbil3M+f5drHv9DRJNTZs9/5CxYxkx8RjUUpGhw0fQf8hwBg0dQXVVFUFdJ6obxIM68YBJ2NAxdRVDVTF1BaO7EKS32LZNY2MjiURity0YkX9fArZj6dlxHN5++23mzZvH/PnzmTdvHqtXr0bTNMaOHYumaRx22GHccccdPWc1CyGE2LU2aUVTyuNmOsozhXYRJRBDVbUP9waqOopXrqj1PQctGEUfOhY1XIUWjOCg8b17z+sJOzfe8jV0w8DzPUqO/ZFAaJG2S3R0deCXEniUZx4VFExVI2YGaNBMVEXFcj1s10NTAzTqJkXL553mLJbjYbkOc5/4DSveXkTzqtUk1q3ByudpGDaUr/3hSQbplVSGoyj77U//ocN5d95Lm7z2NW++ySXnz+Tyy68kaKiYerkQZGOl8M5cBTMMg1gsRiKRIBaL7XYhUYiNtjkoPvDAA1x55ZWEw2HGjx/PRRddxNFHH83EiROJxWIA/O1vf+PCCy9k+PDhnHzyyb02aCGEEP+Z77n4G3sTlnK4+WR3f8JSuRXNxiITH9x8CsUqoMTqQVVQjRBaqAItUlXeT9i9p3BjmLIsixOnTSORSACQSCQ4+vgp/PA3D1PCw+pu4VIOhBDQdHRFIxIIga9iOR4l18OyPFIFm1QpjeP5rF7yAeuWLaFl1UpaVq2kfc0aQtEo/++hB9AMhXdf+CdrP1hC3ZBhTJh+MkNH7MOQfUYxJNBAWDeY+L8/Ixow+Ml3b9ssKALYiRaG1oR34l34eLNnzyaZTMq+frFb2+ageOyxx/LKK69w6KGHfuxvQjNmzOBLX/oSzz77rARFIYTYgcrVx92FJsUcbroVO9UCjoUajJUrj7XupV1NLzer9n1830WPVmPUDkGP1mwxFLqeVz5hxCpQdG0ydomHfvl/vPDCC5uM4dUXX+b3jz7OmRdfQEwL4PkKll0OhIWiSzbrsLKQIZnNsXb5MtavWM7qJUtRFIVz/uvzhAydJ777LVa89RYAtf0HMHD4SEaMPphBZgMBVeP7v/gtdZVRasImkYDeMyNo6gqmpvaMecwB+27x67Q7nTAye/bsXT0EIf6jbQ6K++yzz6d63Lhx45g/f/62Po0QQogt8B27HAqtfHlPYTaBZxe6exQqKLqJ71i4+RSqa6NFqsG1UI0QargGNVyBFohsEgpdz6PkOeWCkkKarG2Rtos9jaeLroPlOVQaIdasXr3Fca1ZsZb1XSWSJYvOXI7VK5ZipbOMOOxQorbNvV/9KotffBG/u5eioigMO+AgRoT7oSsqn//y7USDQUaNGkVDdQWRgE6ge4k4oKsY2qfr2Ttz5kweffTRzQpG5IQRIbbODj/Cb+rUqUydOnVHP40QQuyxfM/D7w6FTj6Fm2wuh8LuhsnlI+pUFNXA963yTKFjocdqMWsGo8bq0YKR7nOOg6AoFF2HnGtTdGyyhQxp2yJrl8g5FuvzSfKOzZBoFXXBCGHdIIrJe6k20qUSStCgrv/gLY51yeo1fOvGG2hfuZLW1atxbJvafv259x/zUfI5xo8/hlEjD2LAsH0ZNfoADj/oAOqrKwjqGkFDZdq+J24yM7itTNNkzpw5mxSM7G5VxUL0BXLWsxBC7GY8u4RfyuGWcni5rnIFslUCfHwf3Fwnnl1Ej9SgmEF810bRDdRgDC0yCDUY61lCdoCiWz6NJG8VSOWSZOwSGatEUz5FybMZEqmmwgwS0nXCukGyVEDzNIIEKZQUmgsFEsUiy9NJkpkUr697m0RnF/G6OtLt7T3jPnD8ERgFm6XvLGLYPvszfuIURozan9GjD2T/mmqI6hz99a8S0Mu9BfVPOTu4rQzD4NZbb+3ZByghUYitJ0FRCCF2oZ6Ck1IOJ9uFm0/gW4XuU0zK+wp/+tDvSGayVESCXHvRGWjRWnRFQa+oR4vVoZphMIKUFIWs61B0bdLFHKl0J3nXpuBYABgbj3lTdRrDUWrMEJbng69Qsn1aU0VacwWSRQvX91i65D2aly2jmEpx3FnncFj1IL53yzdY1F0kopsmiqrhey6xyip+/shTRAyVuspoeYZQLy8XbwyEnZ0laiLmTv36yj5AIbaPBEUhhNiJtjxbWMR3HZxUC4qqoVX1765GdvCcEj996AnWNrUyZNAAvnDrt3D1AEVFI+855ByLVCFLOtVByStXGisK6IqKqenEjQA1gTC261NyXEq2T7tl0VkokiraFFwbz/XIWh4lB5b+9SlWznuRtUs+IJtKARAIhbj8wutQFZVzL7uOs86/jIMPPoiD9t+PR351L/lMmprqKiaNbNjFX10hRG+ToCiEEDvIR/cWuvkUbi5RPtruI7OFKCqKHgAU9MpGvFIOfB+9uj96uJK8r5Apls8GTmXzLEwnKSo+jud2n1YCQU0noOnEzSAKUOoOhbmiy4ZijkShRNa2sV0XK1+gbeUKOpYvp23ZctYt+YCujk6++fsXiMcDLG9PUExnOfK4E9nvgAM5eMwYDh97CAMaqwjoKpNHfhZT/3DJ+Ks3f3HXfHGFEDuFBEUhhOglPZXIpVy5CjmfxMkncdJtKKqKUTMURQ/gO1bPbKFqBMoVyJFqbCOArZnkFJWMU6Ijm+a6cy6kq7tXYbKri+vOuYh7fvcIoVAY1/XoLBUp2QpZx6GzkKUlW6CrYOEpDlrJomvlSrIbmjn9gouJmQG+feP1PP+XpwAIR6IM2W80E4+fwOiaCANrKpj6s58SND5cNu4rR7EKIXYMCYpCCLGNfMfCK2Zxi1ncbAdeIY3XvR9Q1U1QdbRQBfgeTrYTN9OOXtUfvao/TiBKSTcoaSZpx6KrVKAl20ZrIU1ENxkYqeTZx3/HW/M3PZf4zXmv8OgDj3PYaaexNpNmXa6LqB6gxoxSshWW/+tFVr08l7XvL2bDmtU9H3fGaRfhxw3OuOByTjrldMYfdiij9h1JJGAQMjS0XjyeTgix55CgKIQQn5JnFfGsHF4hg5Nqxk5swPfc8uklRhBFM1AVFd8p4TsWiuaDGYF+++OaIXKaQdrzWJ9P05JoJqjpVAfC6KpGUNXRFAVd1agyw0S0IOtXrdviON54512oqadt2TKa33+f1UuW8PWH/4xrmLy3dj0fvLaAfUaP4YRTz2LsoYdxxLjDGT50IGFT4/iRp/TqmcVCiD2bBEUhhPgYG4++8wppnEz7pvsLjRCqEcBOt4FrQ6wO1QjgBuOUKvtT0k3SvkKna1FyXbxiDgXQVY2wrjM0VkWFEerZU1i0XQwnACWH97pyzC+lyEbiWxzXS489xkuPPQZARXUNI0aPIWRbHDyykRO+/U1id32PkKkTlKVjIcR2kqAohBB85Ai8Ug4nl8BJtYJj4Xtu+UxkI4RqBPEdC98t70UkGMOLN1KMVJFTdFJAxnOwHRfcPIZSbkdTHQigq+UCENvzKdgujuOxPJWlo1Duaeh4Ho7n0by2iba33yG7ajkr336rXK3i+z3jHDFqNBOPPY5x4ydw5BHjGTF0CGFT65Um1UII8e8kKAoh9ko9wbCYxS2mcTMdeMUMnuviZTvwXQc9Wo0SiIJn49sFbM2kGKqkFIySVhSSPhQ9D991UXAJaQYx3cQM6D3PUXQ8MkWXdKlAe75IsmhRdB1UoJDsouP9JbS8/wGfnXUNWS3Cewuf5ck7byMQDLHP6IM559Ir+etvHiGfz1FVXc2iN14jGgru2i+eEGKvIUFRCLHX8KzCZsGwPGOolc871oOoWCjRWrxChqKiYAXjFAJRuoAsKjYeuB6qAqaqUxeMoHbP5NmeT7Zk0WIVSeUdmnMFHM/GVxQUXyFqGhSWr+DZhx7ig7fepL25CSifd3zQ+OM48uhjufqSC7j8jBkccvCBxMMBArrG3Qfv33O6iIREIcTOJEFRCLHH8uwSXimLl09jp1txujbgFtLo0Rq0SDWKHgTHgu6l5JIeKC8j6yES+CR9WJ9PUip2sF9FLVVmAFPTsT2Xpcl28rZDY6gKx/FpzxfpLBRYn0+RLjgEE1mU9Wtpfm8xS95axHW3fpt9DzqM13JLWfz6axw49nAuvOwqJh4xgQnjx9FQXdndjqZ+s9chp4sIIXYVCYpCiD2G79h4pSxuIYObacPLp/E9u7updRAtUoNXzOKk2/EVFSsYoxCpIq0HWG2VSLgONWqAgKIS1g0inoep6pgqmIpB0YHOfJGmbJZ3OjsouA71AYuADfFAgMHRajoWreCh/76BdKITAMMMsO/ogwn6HvvWRjnks6dxw0VnEjZ1qT4WQuz2JCgKIfos33Nx8ymcQhq3a0P5VBPPBRQUIwC6CQ7lGUO7QEEPUBx0MGk9QBdQVDQ8H3RF4c8PPYyVzVFXXcOFn5tF0fGwXA/NDtJZKLIwmcByXVRFwU4kcd5/n9Z33mHuG2+w7L13uP4bPyA27XRGDBnJuIlHM37CERw1aSLjDz+Mykio57xjIYToSyQoCiH6DN/zyu1qSlncTCel9lbyhovvWLiZDhRVQ483gOLhWUWKRoBCuJKMESQBFBQNfNDV8oxhTDNQFYVSscTjP/w56WSSaGUFjcefSHvBwcenwtAobmhiQE0dI4aNZPm773H1Z6YDoBsG+x5wMOdfdhXTjzqMI0Y3EjYHcPWJf9i1XyghhOglEhSFELutDyuTM7i5rnIBipUH30fRDEBB1Qx8gHgdBd8nHY6TDcToRKGoqPgomwVDp7tFTXvBojmZ4n8un0U6mQQgm0zx/YsvYcJx09nw3ju89+br5LMZzvvcjQy/6SscsP9BfOGrt3H0UZM4csJ4quNROdVECLHHkqAohNit+I5dDoaFNHbXBpzEejzXQo/3QzVMFM3Ac2w8x6LkKSQC1WTNMJ0+ZFFQVAVD1TYJhgXHoeT4pPJF2vJF2vNFSo4D+Lz62ydY8cbrm4yhZdlS/rhsKQOGDOP4k07lyCMnccLU4xg9rJqArnL0N7+2a744Qgixk0lQFELsUj3LycUMbqYDN9eJb1ugqih6ACUQwe9K4mbbsWJ1FIIx0pFqWj2fUtFFDUbQFIW8Y5MoZakLRqk1YxQsh6ZsnmVdSVZkOzEVgxojSkTTWfOvf7HyzUW8u/BVVi9bssVxXXXNdfzkxz/CkL2FQoi9mARFIcRO51nl5WQn04GXS3y4nKyboGig6fieg22XyIeqyFcNoks1yCgqTcUs67raGRatYlggTDgUw/Z8OgsJ1qayrE0UUNwMyYJFUFfxuzqw3l1MKBhh3Onn4toK3/jf75JLpxk99nCGDhnK3H88u9kYxx16iIREIcReT4KiEGKH8z2Xu+74X7o62ogHFK4572ScbAdePo0WrUaP1uA5Fq5jUdBMcqFKOjWDds9H1U10VSWkGVTqBrFgiPpQDB2DrlSWpfkE7fkiecfG9QwUz2TJ3//B2tcXsOT1hXS0NgMwasxhnHnOxdRWBvjbs8+x38hhVEbD2LbN9OnTmTt3bs94J0+ezMyZM3fRV0sIIXYfEhSFEDtE+RSUDG62k3xnE7d/+zskUhmqK+Jc+dlT0KM1OL5PznMpKSrpeCMJFOzuIpWgrtOgGZiajttdfJLIObTm8rTlixQdh7Bl0dnWTss772Cls0y96HpyJY8nnv4r65Z/wNjxEzn66BuZMmUyEw4bSyRoAjCi9sCecZqmyZw5c2hsbCSRSFBdXc2cOXMwDGOXfN2EEGJ3IkFRCNErfM/FK2Rw8imcztW4xSyKpmO7cPpVt5BIZQBIpNLMmPUFfv7wz8n0qyOvGPiqhqGqhHWToKbj+VCwXZIFj9ZMko5CgbzjgKIQ0Q3WvTiPN/7xd955dT5dnR0A1PUbwKzrvsTg6jDHP/k7BjXWEzQ/3bc4wzCIxWIkEglisZiERCGE6CZBUQixzXpmDTMd5aVkqwi+j5vtxLMLaJEqfv3nf/HiwkWbfNz8BYt44I9/5+xLLqRWN1BRKNoeuZLL6myaNaksnYUSIUPD6+yg5a23aVm6jGu/ejsKOs+++hqL5r3MoeMncNzUEzhu8mQOG3PQh8GwOrLVr2X27Nk95ykLIYQok6AohPjUfM/raV3jpltx80l8z0VRDRRVRVE1fN/FidWSUzSSoWpebPnTFj/XsuUrSBYc2hyb5myBdNEiYWfJFV3y769g1fP/4L2Fr9C2YT0AsYpKPn/Tf7PfsIH8/Ed3UVf5S3KZFDU1Nb3y2uQ8ZSGE2JwERSHEJ/IdC7eQxsl2YretxMkl0EJx1EC0XKGMh+faFBSTfLiKTtUkpajkfI8VmQRa/7otft5kNMKL6zdgFhza33mHVa+/yYlX/BdmpILXN8zj1b8/zWETJjHrqms5YerxTDhsLKHAxiXhMAC5nfMlEEKIvZYERSHEJnzfxy/lcIsZnHQbXjaB51ooigqKClYR17FxVYNcuIJsrI71jkvK94kZQYK6QaVuUquoaOjUnn0+b//1Bd5fuLDnOQaPHk1VssST19zI8sXv4Ps+hhngpFM/y/Sp+3DKF2/g7m98iWgouAu/EkIIISQoCiHwXae8pJxP4aRa8IoZ8L3yjKGiAAq+71MygmSHHkaXZtKFgqOoqIpCJGxSqxvYrk/eclmTyrM2nSfnWPiewwlXX8OKt9/BKhWJVVRx6bX/j299/nL2O3AMV91wEydMPZ4pxxxNdTy6q78UQgghPkKCohB7Kc8u4RXSuJl27GQTdlcTeC56RX8UrXxGso9CzoyQC8XpQCXjg68omJpGTDfRFY2c7ZLO2yzJ5mjL5WjLlShkMiRfm8+q117n3Vfmk0klAThyyjSOmTKFKz57BleeOYMBDbUoipyTLIQQuysJikLsJXzfx7fy5f2GyRa8XBeeZ5eXlAEtVIFbSGP5LoVYA2kjRLsPJb88qRjSDWo0A9uFvOWxJl2gNVsgY5fIZzOse/MtYtX9GHXAoWCpXHf7t6isruWoKVOZevxUTpx2AqNGDkNVJRgKIURfIUFRiD3YxiplJ5fETbfgFdLlJWXK4VDxwVegFIiRqxpEi+eTU1Vs30dHQVdVkoUcvqcQVTSW5lMkCkWydol1771L2xuLWL5gIUvfWoTnuhx/xvlcePxJNO43mDEL3uDwQw4iYMi3GSGE6KvkO7gQe5C7776brkSCikiQ6y79LG6qFTvVipttRzHD6LF6FEXB13QKoQqygSjtKDRZRZZ0bqDCCHFoTX/wykvKS5JJFnU24fs+0bSNmi8w5uBD0e0Kvnvjf1EqFBi+3wHMvOpaTjpxOlMnH0tV9z7DIdVjd/FXQwghxPaSoCjEHsCzSxRTHXzztm+QSKaorohx2QmHYBg6ajACioJnmGTijaQCIZodD0cFxfcJ6Tq1wSgl28NxVd5Yl6Y9X8B1i2x45y1WLVjI0vmvsH7FckYfNp4TH/sr1VUGDzz2Ow47+CBGDBkoy8lCCLGHkqAoRB/llfK4hRROVzPFVDunXfklEskUAIlUhs9c/d88+cBPsCpq6VJ1OnwoeQ6aB2EzgOprFGyf1mSJ1lyB9nyBDS2tNDbUM7KyktvOv5KVi99B1TQOOnQ85553ISefdBKThlVj6ipjPjNjF38FhBBC7GgSFIXoIzb2N3TySdxUC062CzfTilvI8sg/3uCl197e5PEvLnyL7/7tBaafdza67xPSTHTFJFdyWNlVpKNYIJXNsGbRGyyd9wrvz5uPnc/z638tIh4Mct2NX6QiZHLStBPoV1ct1clCCLEXkqAoxG6s58i8XBIn3YKXT+P7HgqgKApapBYlVMXK5Ctb/Pi2NRswCZLK26zMZUmWCtieS9DQ+NcDD/LX++/DLpXQDZODD5/AiSeexLj+cWorokweecnOfbFCCCF2OxIUhdjN+J6LV0jjZDpwks14dhHweyqUURQUM0whUkXWCNMGKMNGbvFzeZV1PLdiJcveeJ2mha+z/JUFfOOB39AQb6Rr5Bj0z17IyTNO4qQTplJfXSGzhkIIITYhQVGI3YDvOuVwmO3ETbXiljI4Xc34joVW0YgWiEAoSjFSTVIP8EEhS9G2qVQsAqrOyZ/9LH994g8se+2Nns85YNQo3n3xBZ688w5K+TyarnPwYRMYoiscPqSeGZ+/HEObtQtftRBCiN2dBEUhdhHfsXGLadxMB066Fd8q4vs+4KMoKnr1IHwjQL6iHxnNpM3zyLsWHZkERdel3qygUFBZlyuxPpVh//MvZuXb7+JaFtGKSi6/6av8+MuzmXby6ZxyysnMmD6dAfWy11AIIcSnJ0FRiK1w9913k0wmqaysZPbs2Vv98b5jlc9TTjbjZDrAs7vDISgoKJqOF6okF6kiqeq0uw4F10bxSpiqjuYGCLoa2XyRd1raeW/+S7zzrxdZNn8+ua5Ez/PE43GuO/dMvjzzs4RMo7devhBCiL2MBEUhtsLdd9/NmjVrGDJkyKcOij3hMN2Gm2nDLeZwEutBUdFrBqAZYbxwNdlwnISi0+5YlGwbVXHQ0dFck0TBJpHPs3bdGiyrwMBhQ1GKRR6+5RYCoTDjjjqWU04+lWRHC4YK1VVVNMRDO/aLIYQQYo8nQVGIT8myLDKZDACZTAbbtjGMLc/W/Xs49BwLPBcAVTcwG/fBj1STjVSTQCmHQ6uErthoGCiuQXvOJpHPsPSdRSx+6QXef/ElmpYtY+KJp/CVu35Obc0w/vD0Pzhq4gRq4hFZUhZCCNHrJCgK8SlYlsX06dNJJMrLu4lEgmnTpjFnzpyesLgxHNqJDbj5Lnz3w3CoqDpKMAbRWrLBKB2ewppiBqWYx1Q1VF/Htw0Wd6VIFovlZWYVfnDe+bSuWgnAfgeP5fNf+ipnnXkGE0Y0EjQ0GHr8rvmCCCGE2CtIUBTiU3jwwQeZO3fuJtfmzp3LA7/6JZedf9aHy8q5JHayCdUIolf2Rw3GUGP15IJREopKa6lAVzbFsnQHvq8yKFBDKu+xYt1a3n/lBd576XmsTIab732I2mCM0z97MYMb6jj9tFPYd+ggdE3dNV8AIYQQeyUJikJ8CitXrtzi9SWv/YvCkcPA8wAfNRDGbNwXPd6PfKyKLlSW55L4uTSaoqL4OtgmxbxBxrJ44dknePPPT9H07jv4nke8qoajT5jOSUOHURMNcc63vy7nKAshhNhlJCgK8SkMGzJki9eH9q8rn5ISCKPFGyiGK0gpOs2lHOl8Bs9TUDGwLGjNWnzw7iIWvTiXyTMvBscktW4DbirN+bOu5ZwzzmDKMZOIh0zZbyiEEGK3IEFRiI/hey5uPombbueciSN4+NADeOmN93ref8z4Q7j08ll4FXVkNJPmYo6uXAbH81A8FdvRaU8VWLTgVd6c+xzvvjCXZEsLAOMnTWPGUZO47s47qY2HCRnarnqZQgghxMeSoCjER/i+Xz5XOdeJ09WEX8rjey66An+6738Zedz5JFIZqqur+PUzz7Lcs+nIp7FdD99VcFyV1s4CWcvGC2i89sxfefxrX0fTdQ4cP5Grb/wiF5x1JvsOG0xAl3AohBBi97bbBEXLsnjyySe55557ePHFF/nOd77DLbfc8h8/7q9//Su33HILS5cuZfDgwXzlK1/h0ksv3fEDFnsM3/fxihmcTCdW81ryaglcp/xOTUMNhNHj9ZixWv7rv79Mc2cnhAO8lNgAnorpBmjuTPH6C3N545//4P15LzPjxhuYetYFzDjhdA6sbuC8z5zO4H51GFKMIoQQog/ZbYLiH//4R/70pz/xP//zP1x00UWf6mMWLVrEGWecwXe+8x0uv/xy/vKXv3D55ZdTV1fHySefvINHLPoy3/fxSzmcbAI72YTTuQY324mt10J1HDUQQo83oEZryRshmqwirYUsh513OkXXpyNXZN66NlIdaRbe/SOWLXgV17YJxyuYNG0GZ06awsn7j6IiqKMfcfCufrlCCCHENtltguI555zDOeecs1Ufc/fdd3PIIYfwhS98AYCLL76Y3//+99xxxx0SFMUWeaU8bj6J09XU3evQAc9FCcTQ9SBGoJ7Q4JGUzBDtjsPqbIIlzaspOi51ZiVN69qYP+fvdLkO9cdNoqGmimxnJ1PPOpfTTz+T06efQG0sLG1shBBC7BF2m6C4LebNm8fpp5++ybXjjz+eL33pS3ieh6rKD2sBnl3Cyyexk8242Q58xwbPAU1H0U20aA1GZT+cQJR8IsFi26Y1nSBTsrFdlfbVaV7/+9958/l/sHLRm/i+z9BDD+eymbMYVVPJf7/2JpUhE03a2AghhNjD9Omg2NLSQl1d3SbX6uvrKRQKpFIpqqqqtvhx6XSadDrd83Zzc/MOHafY+XzXKc8cptqwWpbgZDvRQhWowQiKZqBV1KNX9odgnDTQXsyxobOZZGeSghGgsz2DGYnjYfOLW77I6rfeonbQIE689ErOPessTjrmaGoiQQmHQggh9mh9Oihuie/7AJ/Yh+7OO+/ktttu2+x6V1cXoVCo18by0TAqdrzyvsMsbj6Fm+nEswvl2UNFAa0CLVCLXtEfNRCloKqk0kXa2jpIFkuUHI+mNc0snPMsC154gebVq/jmH/9EMBjkkutnM6S2lqMPHUtFKFAOh1aepJXf1S95ryf/xvoWuV99i9yvvqWmpmaHfN4+HRQbGxtpb2/f5Fp7ezvBYJB4PP6xH3fTTTcxa9asnrebm5sZP348VVVVvf6F3lE3TpRtrFh2swmcZBNuIY1vF0FRUcMhtEgDWmU/9EglJc0kUcqxPpeiKZujM1+kK2+x/OWFPPvLe1mz+F0AGoaPYNrMyzio/yDGDBpE3ZFHyp7D3Zj8G+tb5H71LXK/RJ8OikceeSTPP//8Jtf++c9/csQRR3zi/sR4PP6JQVLs/rxSDifXhdWyFDfdBrqJoqgoZgi9sh96RT/0aDWuESRpF1mRbGdVOonjqixZspY35vyDfY47Crc2QnspRalY4NSrr+P0GSdzwjFH0xgNSysbIYQQe70+FRTvv/9+rrzySjKZDNFolBtvvJEJEybwgx/8gMsvv5y//vWv/PnPf+YPf/jDrh6q2AF8x8LNdWF3NeFmO/GdUvcMYgEtXEGgcV/0aC1KMEbGKdFRzLGivYn3E+28u3wVa19YwOJ/PM+G98szh0QNjjrzTGaceSF3XHUDA2IRsukkNRXRXftChRBCiN3EbhMUP/jgA/bff/+et7/85S/z5S9/menTp/PMM89s8WPGjh3LH/7wB2655Ra+/OUvM3jwYO69915OOeWUnTVssYP1HKOXasVJt+JZRXzHRtF1VCOEUTsUvaIBLVxJ0fdpLuZY2bKGpmyWzpyF52p0revi/rMvBqBmyFCmXXkVp5x+BieMn8DgeJSwafQ8X3ZXvVAhhBBiN6T4G6s/9mLr169n0KBBrFu3joEDB/ba5+3s7JT9Hdug56SUVCt2+yp818V3LVBVVCOIFq1Gr+yPFq7C1026rALrsylWpLpYua6Fhc8+x6K//52BI/flzP/3/2gv5Jn3+OMcO2Uqpx41kaEVcSqCgS0+t9yzvkXuV98i96tvkfslYDeaURTCswo42QROVxNevgsn3YKbS6JXNGLUDkGv7I8eqUIJRMg6JdryWZZ2JWjK5vjnH/7KG397mqULX8VzXar69WPEhAkENY0ZQ4dy3fe+S1049InV8EIIIYTYlARFsUv5roOb68JJtuBk2vDtAr7joBgBjNphBEf2w4jVoYbiWJ5LazHHsua1LG1p4c2Fixg65lAMXePtZ5+lacUyJp5zDkeedDIzjj2WEVWVDIhGpPG6EEIIsY0kKIqdzvd9vEIaO92Gm2rBK2bx7RKKbqCaEfS6BrSKBrRQBZ6ikrKLLGtdx2st61nw/DzeefY53vvXCzi2zdVP/QY3FOIzX/0q40eMYHRtLYNiMUxd29UvUwghhOjzJCiKnaa8tNyJ07UBu2MNTqoFLVKJHu+HXl2HXj0ALVyFagbJ2SVasimWdHWyPJHk97/7Ha/89BeU0mkC4TAHHDuZg6edwIH9BjFp4FCGVMSJB7a871AIIYQQ20aCotihfMcuH6W3cWnZKuB7LmowRiBciVE3DL2iATUYw/U92op5lrQ3M/fVhcz941856IQZVA0YwchB+9J2yCGMmTaNI48/jkMHDmJ4ZSWNkYjsOxRCCCF2EAmKotf5vo+XT+FkO3C6NuCV8viujaJqKGYII96AXtmIFq4EVSNjF1nd0coL77zD00/+gZd+81synZ0ABGr6ceiggRw0/hDOmj6F/aprGBiLYmryn64QQgixo8lPW9FrvFIeJ5fAalmG3bUe1QiiGiHQTfRoNXrVAPRoDaoZwnIdNuSyvJ/oYHlXiuamVr5z6in4vo9mlPsaxuvquejKixhTW8eQeKUsLQshhBA7mQRFsV181ykvLXc142RawbXxbAtcFwI6RuM+GPEG1HAFAIlSgfdWreDxPzzF3D/+iXAszhX/8y2G9R/M2bf8N/0PHM19115DPpVCcx3OHr4PQQmIQgghxC4hQVFstXJD7Cxupntp2crhey6goGg6RnUd4RET0KLVKLpJ3rZYk+jgkWf+yl8ef4IPnn+BUi5HtLKScaecxuJ0CxFD4/QLz+buz11HPpUCoCuR4KQTT2TOnDkYhvHJgxJCCCFEr5OgKD61jWctl1qX4xXSoCjg+6AqqIEIevVA9FgdajCG5/s05bP8652FtBsh2ktZfvt/D7LqxXmMmXwcY6efyD4Tx1ERNRkcjzO2th9P/fpRFr48b5PnnDt3Lg8++CCzZs3aRa9aCCGE2HtJUBSfyPc8vEIaJ92Kk2zCLWZxEhtA0zCqB5VPTelua6PoBhmrxOvvv899Dz/E83/4I81LPuDbf3mG4QMHcdbnb8K85as01lfSPxphVFU1/aNxonoARVFYs3r1FsewYsWKnfuihRBCCAFIUBQf46M9D918CgUF8FF0A7P/KIyqQRiVDajBKI7rsiab5emXX+an3/0u78+bh+e6NAwdzhmfn41nmmTcAgfuN4ShlRWMrKimOhDGUDdtij18+PAtjmXEiBE74RULIYQQ4t9JUBQ9fM/98Di9dCt4Dqg6G7sUarFajOqBaJFqFE2nM5/nj3/+Cy34KHX1tHWlWP3OO4w7/RzGzTiFEQftTzSk0BAJsW9lNf0icWJG4GP7Hs6cOZNHH32UuXPn9lybPHkyM2fO3PEvXgghhBCbkaAo8IpZnEwHVttK7M41KJqBXtEffFBUDb1uaM/eQ9v1eHnxe9z7q18x5/dP0rFuHUeddTYzv/I1/MH78v9+/zTVFSZ1UYNB8RjD49XUBMKfqu+haZrMmTOHxsZGEokE1dXVUsgihBBC7EISFPdSvmPj5hLYXU242Q4AFCOEFqnGzXeBphIYeDB6tAZFN2nPFVi6dgNXnP9ZlrzyCvg+ww86mOkXX8rIY47hnVQT/WJhxtbUMjJeSb9IBZVmaKtPTTEMg1tvvZVkMkllZaWERCGEEGIXkqC4F/F9v7swpY0f/vCHdCWTVFVWct2l5+FZebCLmHXD0av7o4WrKLkuTzzzd/4xbx6jPvMZ2ksZ9NpKjrnkIo479Rwahg3FMD0ctcRBwQCH1Q2kIRQnpG9fuJs9e3bvvGAhhBBCbBcJinsBzy7h5hKUmpfgZjvRQnF++vCTrN3QwuD+9Vx/2fkEGvdFj9WiBCK88cEy7vnl9/jjbx6nY/06AuEwP/zMZ4jFarnwK18nZphUhnXiQY1BsSoGRiqpNsNoqrqrX6oQQgghepEExT1Uua1NCifVipNsLvdALGXxillKhQLZbA6AbNFGH3IYjqKzPJXmW7d+m8d/8F1832f4wQdz6uWzOPi4aWCEiJhQG4tQHQoyOFpBQyj2icUpQgghhOjbJCjuYTyriJPt6G5rk0RRNNRgFBQFTQHXiHDOdbeRSGUASCS6GDBkKBd8727UAf2oOXAUMy67nAmnnk7t4KEENJVw0CdoQl04zJBIFXWhKIFPUZwihBBCiL5NftrvAXzPw8snsZPNuKlWfN9BMcJooUq8YhqvkOlpbfPoY7/nX/Ne2eTjO1pbeeE3v+bqb34bqmo46ODDiAVVTNMjEtCoC4YZFKmkJhCR5WUhhBBiLyJBsQ/zrAJOpgO7cy12xxq8Ug6zfgSqGcWzsuAZGLVD0Ssa8c0InTmLOa8s3OLnilZX0WQlGV4RpypkEg3o9AvF6B+uoMIMyvKyEEIIsReSoNjH+J6Lm0+Wm2KnWsBzUQMRzMb9sFqXYHWsIlA/ArP/AeixOt5dtpp7/vd/aEp0cfx115Oti2/x8w4cOYBQyKYxHmBURT31oRhh3dzJr04IIYQQuxMJin2EV8qXj9RLrMMrZkHV0UKVALiFJL6VJ9BvP4zqweSVIA8/8Qfuu/9e3pj/MgD7T5rEZwIKJ551Hkv+MZfVb77Z87kPnjiea2Zdzn7VjdQEI5sdrSeEEEKIvZMExd3YxtlDu2MtVttyFCOEHqlGizfg20WcXCeKqmJU9ker6EdWjbAuV+Lz117FnN89TkV9PSfOuoLjzjyb6v6DKTkelaEodz76MDOPnESmK0VFVRXPPPssDdE4qiL7D4UQQgjxIQmKu6F/nz30fR988O0Srl1AKaZRjCCBhpEkvQAP/faP/OpXv+Rz374Dpb6aMWedyr6Tj+SQI4/F9VV8FGIBnXhIBdXF0BSu/eJNUCjRUF1Lv1jlrn7JQgghhNgNSVDcTWx572EULd4AvoeraDjJJghXoPcbzYvvruDn37qNv/7hd5QKeWoHDWJ1ywoOGlzL/qMOoeh6KKpK/6hJ0ARXcQhoKoMiNTSGYhz/31/b1S9ZCCGEELs5CYq7mFfKY2c6cBLr8Eu5nr2Him6UT1TJtoMPWrwWZcBBJAjz4pvvceHUKWiGyZjjpzD57LM44PAjaClkWZNKMSJexT7VERTdxXZtFE3ngFgD9eGY9D8UQgghxKcmqWEX6Jk97GrGSqzFSWxADcYwG/ZB0XT8UhY314GimagVA3jh3dXc+3934AYCnHbDF3nbzzH5y1/k+ONPYGDdQAq2R9FxUQ2HeNgnEnVw1RI1RoTBVQ3S/1AIIYQQ20SC4k7klfI4mQ6crnV4hSzoJka8AT1chd25jlLTYvRYLVq4kk6zHw/87q88+NBDrFr6AUYgwITTTmN1IUFA0zjhtDOpMCKoqsrI2iAhQ2FtwcFQdUbGahkUq6LKDEn/QyGEEEJsMwmKO5jvebj5LpyuZpx0C77noQWiaBWNKIqCZ5fwSlmUQBijZjDFisG0OmGuvvxiXnr2L/QfOZLP3vz/OPykGTh6HNuB/jGDmohJQyyIqftknRIWKofWDKJ/OE7cDO7qly2EEEKIPYAExR3E932cdDvZDa/j5rvQY3Vo4SoUzQDKs4tuMY2iGXQpFTz07D95+JHHuflH92PWVDLl8ouYeOFZ7D/2MGxboSltEdd0DqgPUxc18RWHrFMAX2efiloapEG2EEIIIXqZBMUdxLdL2J1rQS2BouK7Fr6i4RXSeKUsqhlhwfoCP//1r/nzU09SzOcZMGIkSza8z9CaAxk5+kBcRyVbcNAVn4mDK6gKm1i+TcYpEDVMRlc2Uh+KYkqBihBCCCF2AEkYO5SPXj0QJduJ1bEGN59Eqx5KrmZ/XnhnNefNOB3dMBk3bRoTzz6d4YccQtGxSRdsfF2nMqgxtDpOyFRYne0kkfUZFq1mTHU/aoMRdDlBRQghhBA7kATFHczNdgAKSwtRfv7gs9ihKs697guoAwZywdduZczkY6mtq0PHoDmbYUMpRWMkzL4NtVQFDVJ2iaZCgbpQlIOr+1FthqWCWQghhBA7hQTFHURRVRwMHpv7Jvf++klee3U+qqYx+TNn0uq2s6GQ5LDPzKBKq6BYckD32bemgiGYREwDFIfOkk1dMMrBVf2oDoSlglkIIYQQO5UExR3EUw2u+Ood/PPZp6morWfGlddwzDmnU9FQi6nqDArWYTkeuqawT1WUqpAJikdXScEHGsIxBoYrqTCDEhCFEEIIsUtIUNxBbNfjmDPOY8AxxzPiqCNoqAgR0YIUHZ+i41MZ0mmsiRAP6ri+S8LKoqDQPxxnYKRSWtwIIYQQYpeToLiDWK5DaJ9h7HPgfgyMxbAcn4LnUxcJUB8ziZoalufSUcqiKipDolX0D1cQNQK7euhCCCGEEIAExR3Gwwc8Ks0gng8NcR10h5qggqH5tBWzaIrK0Gg1AyIV0gNRCCGEELsdCYo7iK4q1EYC9K+LUhcJ0l7KsDjZQVNeZ/+qBobHaugXjktAFEIIIcRuS4LiDnLPj3/CW6uW0divkXOuvIysbTE0UsmY6v4MjFQS1I1dPUQhhBBCiE8kQXEHsCyL73772yQSCWJVFZx48bkcXjuAxlBcAqIQQggh+gzp3NzLLMti+vTpJBIJADJdKb418xoGBCUkCiGEEKJvkaDYyx588EHmzp27ybUX5r7Agw8+uGsGJIQQQgixjSQo9rKVK1du8fqKFSt28kiEEEIIIbaPBMVeNnz48C1eHzFixE4eiRBCCCHE9pGg2MtmzpzJ5MmTN7k2efJkZs6cuWsGJIQQQgixjSQo9jLT/P/t3XtMU2cfB/AvQmHcioi6ilYF8b5RiMglQyfYbDNOilEnRFQydNGJSuYS50x2NcuMtzmdc1OnbsuGc8tE4yCEhS1ehySAIEEn1nmNSAMUrVxanvePvZzXvhwuxRaofD8JiTw9zzm/np9tv5ye07ojNzcXgwYNAgAMGjQIubm5UCh4IQsRERE5FwZFB1AoFPD19QUA+Pr6MiQSERGRU+LnKDpIRkYG7ty5g8DAwN4uhYiIiKhbGBQdJCMjAwaDAQEBAb1dChEREVG38K1nIiIiIpLFoEhEREREshgUiYiIiEgWgyIRERERyWJQJCIiIiJZDIpEREREJItBkYiIiIhkMSgSERERkSwGRSIiIiKSxaBIRERERLL63Ff4nTlzBleuXMHIkSMxY8YMuLq6trvs5cuXkZ+f32Z82bJlcHPrc3eNiIiIyKn0mTRlsVjw2muv4fTp05gxYwbOnz8PtVqNnJwc+Pj4yM45d+4c3n77baSkpLRZF4MiERER0ZPpM2nq4MGDyM3NxcWLFxEUFASDwYDQ0FBs3rwZH3/8cbvzBg4ciL179/ZgpURERET9Q585RzEzMxOzZs1CUFAQACAgIADJycnIzMzscF5TUxMyMzNx5MgRVFRU9ESpRERERP1CnwmKly5dwqRJk6zGJk2ahMrKSjQ2NrY7z2w24+jRo/j+++8RFhaGtLQ0CCE63JbRaMStW7ekn7t379rlPhARERE9TfrMW89GoxEDBw60GvP394cQAvX19fDw8GgzZ+rUqbh27Zo0r6ioCDExMYiIiMDKlSvb3db27dvx4YcfthmvqamBp6fnE92PxxmNRruti3oGe+Zc2C/nwn45F/bLuQQEBDhkvX0mKHp6eqK+vt5qrPU/qZeXl+ycyZMnW/0eHh6Ol19+GTk5OR0GxbfeegvLli2Tfr979y4iIyPh7+9v9x3tqMaR47BnzoX9ci7sl3Nhv6jPBMWxY8dCr9dbjen1egwbNqzdoCjnmWee6fStZKVSCaVS2a06iYiIiPqLPhMUExISsGPHDhiNRiiVSjQ1NeGnn35CQkKCtExFRQX++OMP6XMSKysrMWbMGOn2qqoq5OXlYfny5TZt22w2A4Ddz1WsqanBo0eP7LpOciz2zLmwX86F/XIu7JfzUalUdv94QBfR2ZUfPeTBgweIiYmBh4cH5s2bh9zcXPz9998oKChAYGAgAGD//v1Yvnw56uvr4ePjg6SkJDQ0NCA6OhomkwkHDx6ESqVCbm4u/P39u7ztCxcuIDIy0lF3jYiIiMjhysvLMXHiRLuus88ERQB4+PAhDh06hMuXL2PkyJFITU3F4MGDpdvPnDmD7777Djt37pQubsnNzcWff/4JV1dXaDQazJ07FwMG2HYxd0NDA0pLSzFkyBC7JfHW8x4LCgowbNgwu6yTHIs9cy7sl3Nhv5wL++VcWvul1+sxevRou667TwXFp8mtW7egVqtx8+ZNjBgxorfLoS5gz5wL++Vc2C/nwn45F0f2q898jiIRERER9S0MikREREQki0HRQZRKJd5//31+DI8TYc+cC/vlXNgv58J+ORdH9ovnKBIRERGRLB5RJCIiIiJZDIpEREREJItBkYiIiIhkMSgSERERkaw+813PTwuLxYKTJ08iMzMTzz77LHbs2NHpnJaWFnz77bfIy8uDh4cH5s+fj1mzZvVAtQQA2dnZ+Pnnn9HY2AitVoslS5Z0+O0+6enpuHXrltXY7Nmzbf6OcepYcXExDhw4gPv37yM8PBzp6enw9va2+xyyj9u3b2P37t24du0agoODkZ6ejuHDh7e7/IkTJ3DgwAGrMVdXV/zyyy+OLpUAmEwm/PDDD8jOzkZ8fDxWrVrV6ZyHDx9i9+7dKCoqwpAhQ5CWloawsDDHF0s2Z4vm5mYsWLCgzXh6ejq0Wq1N2+YRRTtqaWlBSEgI9u3bh+rqauTn53dp3ooVK/Duu+8iOjoaQUFBmDt3Lvbs2ePgagkAvvjiC8ydOxfBwcGIjo7Ghg0bsGLFig7n5OXlYejQoUhNTZV+oqKieqji/uHUqVOIiopCS0sLtFotMjMzER8fD7PZbNc5ZB937txBREQEysrK8Morr6CsrAwRERG4c+dOu3MqKytRVFRk9ThaunRpD1bdf1VUVCAkJARnz55FeXk5ioqKOp3T3NyM+Ph4HDlyBFqtFhaLBVFRUTh9+nQPVNy/dSdbWCwWZGVlISoqyuoxNnbsWNsLEGQ3LS0t4saNG0IIIdatWyc0Gk2nc0pLSwUAkZeXJ419+umnQqlUCpPJ5KhSSQhhMpmEUqkUW7dulcZycnIEAFFWVtbuvPHjx4tdu3b1RIn9VnR0tEhKSpJ+v3v3rlAoFOLw4cN2nUP2kZ6eLiZMmCCam5uFEEI0NzeLsWPHitWrV7c7Z8eOHWLy5Mk9VSI9pra2VtTW1gohhHjxxRdFWlpap3MOHTok3N3dxb1796Sx+fPni5iYGIfVSf/qTrZ49OiRACBOnTr1xNvnEUU7cnFxgVqttmlOTk4O/P39ERcXJ40tWLAARqMRZ8+etXeJ9JgzZ87AaDRi3rx50phWq8XAgQORk5PT4dyjR49i4cKFWLduHf+itrOamhqcP3/eqi8qlQqxsbHIzs622xyyn+zsbOh0Ori5/Xs2k5ubGxITEzvd9/fu3cOSJUuQlpaGL7/8Ek1NTT1Rbr/n5+cHPz8/m+ZkZ2dj2rRpGDp0qDS2YMECnDt3DrW1tXaukB7XnWzRauvWrUhOTsbGjRtx5cqVbq2DQbGX6fV6DB8+3OqcuNb/EHq9vrfK6hda9+/jX6Du6uqKwMDADvf9oEGD8MILL2DOnDmwWCyYOXMmtm7d6vB6+4vr168DQJsnRrVa3W5fujOH7Of69es273sXFxdMmzYNM2bMgEajwfbt2zF16lSYTCZHl0vdoNfrZXsM/O/xR31LUFAQoqOjMWvWLOj1eoSGhuK3336zeT28mKUDDx8+xKJFizpcZurUqdi4cWO3t9HY2AgvLy+rMYVCAXd3dzQ0NHR7vf3VG2+8gaqqqnZvHzx4MPbv3w/g333v7u4uHQVp5ePj0+G+z87Olv4aT0lJwZgxY7Bu3TosWbLE6q9t6p7GxkYAaPO46Kgv3ZlD9mE2m2GxWGT3vcVigdlsbvMYA4DU1FSsXbtW+j0pKQnjx4/H559/jnfeecfhdZNt5F6rfHx8AICPsT7I3d0dJSUl8PX1BQDpyP2bb75pc7BnUOyAu7s7UlNTO1xGpVI90Tb8/PxQU1NjNfbgwQM0NTXB39//idbdH82fP7/DIxKenp7Sv/38/NDU1ASTyWT1BGgwGDrc9///lo1Op8OaNWtw8eJFm68mo7Za9+//Py466kt35pB9uLm5wcvLS3bf+/j4yIZEoO3jaOjQoYiJiUFhYaHDaqXuk3utMhgMAMDHWB80YMAAKSS20ul0+Oabb1BdXY3Bgwd3eV0Mih1QKBRITEx06DY0Gg127dqFuro66YmzuLgYABAaGurQbT+NXnrppS4vq9FoAAAlJSWIiYkB8G/QuHHjhk37vvX8nPZeEMk2ISEh8Pb2RklJCaZPny6Nl5SUtNvf7swh+9FoNCgpKbEaKy4utvk5rLa2Fkql0p6lkZ1oNBr8/vvvVmPFxcXw9vZGcHBwL1VFtmh9rXJ1dbVpHs9R7GENDQ1ITExEXl4eAGDOnDnw8vLCzp07AQBCCGzbtg0ajQbPP/98b5b61AsNDYVGo8G2bdsghAAAfPbZZ/D29sacOXOk5VauXIl9+/YBAC5dumR1kVFDQwPee+89qFQqfkSOnSgUCixcuBB79+5FfX09gH8/c6+iogIpKSnSctu2bcP69ettmkOOsXjxYhw7dgxXr14FAFy5cgVZWVlYvHixtExWVpbVxUaHDx+2unjl119/xfnz56HT6XqucGpXWVkZEhMTpfNMU1JSUF5eLp3jZjQa8dVXXyEpKQkKhaI3SyW0zRb5+fmorKyUbr9//z62bNmC6dOn234E+ImvmyYrGRkZQqfTiZCQEKFUKoVOpxM6nU48ePBACCFEfX29ACD27dsnzTl+/LhQKpViypQpYty4cUKtVovS0tLeugv9SmlpqVCr1WLcuHFiypQpQqlUiuPHj1stM2bMGLFq1SohhBA3b94UM2fOFOPHjxdarVaoVCrx3HPPiYKCgt4o/6llMBhEZGSkUKlUIjY2Vnh6eorNmzdbLbNw4UIRFRVl0xxyDLPZLBYtWiR8fX3F9OnTha+vr1i0aJEwm83SMlu2bBGurq7S75s2bRJqtVrExcWJ8PBw4eXlJTZt2tQb5fc7FotFem0KCAgQo0aNEjqdTqxYsUJaJj8/XwAQRUVF0tjmzZuFp6eniI2NFSqVSkRGRgqDwdAL96D/sTVbXLhwQYSFhYmwsDARHx8vlEql0Gq10sfs2MJFiP8eSiG7yM/PR11dXZvx2bNnQ6FQwGKx4MSJEwgPD8eoUaOk2+vq6lBQUAAPDw9ER0fD3d29J8vu1xobG/HXX3+hsbERkZGRbc6dav2A7cffRqusrMS1a9cwYsQIjBs3zuZD+dQ5IQQKCwthMBgQGhqKwMBAq9sLCwthMpms3mrubA45VkVFhfTNLBMmTLC67erVqygvL0dCQoI0VldXh+LiYnh4eGDixIk2f2QLdY8QAllZWW3GfXx8pPOsq6urcfr0acTFxVn15fbt2ygtLUVAQAAiIiLg4uLSY3X3Z93JFi0tLbh06RLu3buH4ODgbp8iwKBIRERERLJ4jiIRERERyWJQJCIiIiJZDIpEREREJItBkYiIiIhkMSgSERERkSwGRSIiIiKSxaBIRERERLIYFImIiIhIFoMiEREREcliUCQiIiIiWQyKREQOUFVVhWPHjqG6utpqvLCwECdPnkRLS0svVUZE1HUMikREDqBUKrF27VqsX79eGjt58iRiY2NRV1eHAQP49EtEfZ+LEEL0dhFERE+jH3/8EYsXL0ZxcTGqqqrw6quvYteuXUhLS+vt0oiIuoRBkYjIgWJiYmA2m1FRUYFPPvkEq1ev7u2SiIi6jO99EBE5UHJyMgoLC7F06VKGRCJyOjyiSETkIPn5+Zg9ezaCg4NhsVhQWloKNze33i6LiKjLeESRiMgBzp07h4SEBHz00UfIycnBP//8gz179vR2WURENuERRSIiOysqKkJcXBwyMjLwwQcfAAA2bNiAr7/+GlevXoW/v3/vFkhE1EUMikREdvTo0SOsWbMGo0ePxsaNG6Vxo9GI119/HcnJyZg3b14vVkhE1HUMikREREQki+coEhEREZEsBkUiIiIiksWgSERERESyGBSJiIiISBaDIhERERHJYlAkIiIiIlkMikREREQki0GRiIiIiGQxKBIRERGRLAZFIiIiIpLFoEhEREREshgUiYiIiEgWgyIRERERyWJQJCIiIiJZ/wEeaGBjdBXEnAAAAABJRU5ErkJggg==",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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=\"What each calibration's model predicts (68 %)\",\n",
+ " xlim=(-1.1, 1.5),\n",
+ ")\n",
+ "ax.legend(fontsize=8, loc=\"upper left\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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, 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": 16,
+ "id": "8022b6e5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "case held-out log predictive 68 % predictive width\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",
+ "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(\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",
+ " rx.diagnostics.coverage_curve(draws_out, ch.y[ch.active], levels),\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",
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ " title=title,\n",
+ " xlim=(0, 1),\n",
+ " ylim=(0, 1),\n",
+ " )\n",
+ "axes[0].legend(fontsize=8)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd066d32",
+ "metadata": {},
+ "source": [
+ "## The learned spread\n",
+ "\n",
+ "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": 18,
+ "id": "b93c1029",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ " **plotstyle.corner_kwargs(title_fmt=\".3f\"),\n",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ " energy.\n",
+ "- 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 (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/linear_calibration.ipynb b/examples/linear_calibration.ipynb
new file mode 100644
index 0000000..10b0f88
--- /dev/null
+++ b/examples/linear_calibration.ipynb
@@ -0,0 +1,624 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "96a94733",
+ "metadata": {},
+ "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 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",
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4487f98f",
+ "metadata": {},
+ "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": "26b7c934",
+ "metadata": {},
+ "source": [
+ "## Parameters and a model\n",
+ "\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 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "bd5cc6df",
+ "metadata": {},
+ "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",
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fd08ef0d",
+ "metadata": {},
+ "source": [
+ "## Data\n",
+ "\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`. 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": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset('toy', n=20)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rng = np.random.default_rng(49)\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",
+ "data = rx.Dataset(\n",
+ " x, y_true + rng.normal(0.0, sigma, x.size), sigma * np.ones(x.size), label=\"toy\"\n",
+ ")\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "00228baf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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=plotstyle.COLOURS[1], label=\"truth\")\n",
+ "ax.set(xlabel=\"$x$\", ylabel=\"$y$\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eb2f8834",
+ "metadata": {},
+ "source": [
+ "## A comparison, a constraint and a problem\n",
+ "\n",
+ "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, 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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3e2eb131",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Problem(ndim=3, constraints=1)\n",
+ "columns: ['m', 'b', 'log_sigma']\n"
+ ]
+ }
+ ],
+ "source": [
+ "comp = rx.Comparison(data, line)\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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b84afe70",
+ "metadata": {},
+ "source": [
+ "### 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, 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.\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": {},
+ "outputs": [],
+ "source": [
+ "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_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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "fee892f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "plotstyle.band(\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(\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()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "28b9123a",
+ "metadata": {},
+ "source": [
+ "### Calibrating to data \n",
+ "\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",
+ "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 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)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b416f0f2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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, np.log(0.2)]) # the prior mean, in problem.names order\n",
+ "on_data = line.bind(x)\n",
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "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",
+ "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": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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",
+ "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",
+ " f\"acceptance {sampler.acceptance_fraction.mean():.2f}\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1b1fd4cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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(\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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ " **plotstyle.corner_kwargs(),\n",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48d80632",
+ "metadata": {},
+ "source": [
+ "## The posterior predictive on any grid\n",
+ "\n",
+ "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 $\\theta = {m,b}$ 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.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": {},
+ "outputs": [],
+ "source": [
+ "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",
+ "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,
+ "id": "046ac161",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.axvspan(x.min(), x.max(), color=\"0.92\", zorder=0, label=\"data range\")\n",
+ "plotstyle.band(\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",
+ "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",
+ " \"--\",\n",
+ " color=plotstyle.COLOURS[1],\n",
+ " label=\"truth\",\n",
+ ")\n",
+ "ax.errorbar(data.x, data.y, data.y_err, fmt=\"o\", color=\"k\", label=\"data\")\n",
+ "ax.set(\n",
+ " xlabel=\"$x$\",\n",
+ " ylabel=\"$y$\",\n",
+ ")\n",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef728bd6",
+ "metadata": {},
+ "source": [
+ "## Is our error model calibrated?\n",
+ "\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",
+ "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": {},
+ "outputs": [],
+ "source": [
+ "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_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,
+ "id": "c59df024",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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_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",
+ ")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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/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/local_optical_model_calibration.ipynb b/examples/local_optical_model_calibration.ipynb
new file mode 100644
index 0000000..f429d60
--- /dev/null
+++ b/examples/local_optical_model_calibration.ipynb
@@ -0,0 +1,956 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c1391575",
+ "metadata": {},
+ "source": [
+ "# Calibrating a local optical potential to a real measurement\n",
+ "\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 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",
+ "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": "b2831405",
+ "metadata": {},
+ "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",
+ "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",
+ ")\n",
+ "from scipy import stats\n",
+ "\n",
+ "import rxmc as rx\n",
+ "from rxmc import transforms as tf\n",
+ "\n",
+ "plotstyle.use()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b4359118",
+ "metadata": {},
+ "source": [
+ "## The measurement\n",
+ "\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",
+ "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",
+ "`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. 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": "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",
+ "output_type": "stream",
+ "text": [
+ "59 angles, 6.3 to 163.3 deg\n",
+ "relative statistical error: 2.3%\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_deg, y_meas, y_stat = (\n",
+ " measurement[\"x\"],\n",
+ " measurement[\"y\"],\n",
+ " measurement[\"statistical_err\"],\n",
+ ")\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": "d62f3f00",
+ "metadata": {},
+ "source": [
+ "## `from_measurement`: the unit contract\n",
+ "\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",
+ "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": 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",
+ "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": [
+ "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:\", {key: v for key, v in data.meta.items() if key != \"reaction\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c897b34a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.errorbar(\n",
+ " np.rad2deg(data.x),\n",
+ " data.y,\n",
+ " data.y_err,\n",
+ " fmt=\"o\",\n",
+ " ms=3,\n",
+ " color=\"k\",\n",
+ " label=f\"EXFOR {measurement['subentry']}\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xlabel=r\"$\\theta_{cm}$ [deg]\",\n",
+ " ylabel=r\"$\\sigma / \\sigma_{Rutherford}$\",\n",
+ " yscale=\"log\",\n",
+ " title=r\"$p + {}^{40}$Ca elastic at 35 MeV\",\n",
+ ")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf89a76a",
+ "metadata": {},
+ "source": [
+ "## The potential\n",
+ "\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 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": 7,
+ "id": "22704ba5",
+ "metadata": {},
+ "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, no Ay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "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",
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8fec4194",
+ "metadata": {},
+ "source": [
+ "## 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",
+ "$$\\Sigma^{\\log}_{ij} = \\sigma_{\\mathrm{stat},i}^2\\,\\delta_{ij} + \\log\\!\\left(1 + \\frac{s^2}{1+b^2}\\right)\\delta_{ij} + \\log\\!\\left(1 + b^2\\right),$$\n",
+ "\n",
+ "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",
+ "### 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": 9,
+ "id": "21d928bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": "code",
+ "execution_count": 10,
+ "id": "86942c24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 11,
+ "id": "f2d363c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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": [
+ "\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",
+ " 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",
+ " 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": 13,
+ "id": "103e4e7f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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, res in results.items():\n",
+ " print(\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": "706a6b87",
+ "metadata": {},
+ "source": [
+ "## (A) The evidence\n",
+ "\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 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": "638d57f9",
+ "metadata": {},
+ "source": [
+ "### The two posteriors\n",
+ "\n",
+ "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": 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": [
+ {
+ "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": [
+ "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",
+ "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": 18,
+ "id": "ccdbbd11",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": "0f16759a",
+ "metadata": {},
+ "source": [
+ "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",
+ "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": "markdown",
+ "id": "5df6b5e2",
+ "metadata": {},
+ "source": [
+ "## (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": "code",
+ "execution_count": 19,
+ "id": "2012d2ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 20,
+ "id": "26893f77",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": "c2130314",
+ "metadata": {},
+ "source": [
+ "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",
+ "- **(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",
+ "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": 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": [
+ "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": [
+ "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": 23,
+ "id": "85fd7f5a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": 24,
+ "id": "57c5d001",
+ "metadata": {},
+ "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"
+ ]
+ }
+ ],
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2e2cc8f",
+ "metadata": {},
+ "source": [
+ "## Takeaways\n",
+ "\n",
+ "- `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."
+ ]
+ }
+ ],
+ "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": {
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",
- "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": {
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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_and_covariance_structure.ipynb b/examples/normalization_and_covariance_structure.ipynb
new file mode 100644
index 0000000..a8300e7
--- /dev/null
+++ b/examples/normalization_and_covariance_structure.ipynb
@@ -0,0 +1,997 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f78307d3",
+ "metadata": {},
+ "source": [
+ "# Normalisation, systematic errors, and the structure of the covariance matrix\n",
+ "\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",
+ "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"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "867ac683",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import corner\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 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\n",
+ "\n",
+ "plotstyle.use()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "240f15bb-1321-402c-915b-ef269093abfd",
+ "metadata": {},
+ "source": [
+ "### Generate synthetic data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f6b7fce0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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 P\": {\n",
+ " \"domain\": (0.05, 0.4),\n",
+ " \"n\": 25,\n",
+ " \"noise\": 0.05,\n",
+ " \"sys\": 0.12,\n",
+ " \"rho\": 1.10,\n",
+ " },\n",
+ " \"exp Q\": {\n",
+ " \"domain\": (0.55, 1.05),\n",
+ " \"n\": 25,\n",
+ " \"noise\": 0.05,\n",
+ " \"sys\": 0.25,\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",
+ " 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": "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": {},
+ "outputs": [],
+ "source": [
+ "rng = np.random.default_rng(42)\n",
+ "a_true = np.array([1, -0.3, 0.4, -0.2])\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_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\"], np.abs(s[\"noise\"])), norm_err=s[\"sys\"], label=label\n",
+ " )\n",
+ "x_fine = np.linspace(0.0, 1.5, 140)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5cdc4ada",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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",
+ "ax.set(xlabel=\"$x$\", ylabel=\"$y$\", title=\"Four experiments, four normalisations\")\n",
+ "ax.legend(fontsize=8, ncol=2)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5a2ac08",
+ "metadata": {},
+ "source": [
+ "## 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": {},
+ "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, 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(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": "277a09fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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",
+ " rows = samples[np.random.default_rng(0).choice(len(samples), n, replace=False)]\n",
+ " return rx.predictive.grid_draws(\n",
+ " problem, cubic.bind(grid), grid, rows, model_only=True, return_draws=True\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "90bd9bc0-e6f4-4999-a7b2-1e3504ceff74",
+ "metadata": {},
+ "source": [
+ "### The three good approaches (1,2,3):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ad183ccd",
+ "metadata": {},
+ "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(comps)\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(comps)\n",
+ "}\n",
+ "comps_scaled = [\n",
+ " (\n",
+ " rx.Comparison(d, cubic | tf.scale(rhos[label]))\n",
+ " if label in comps\n",
+ " else rx.Comparison(d, cubic)\n",
+ " )\n",
+ " for label, d in datasets.items()\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "6cd9b231",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "correct = {\n",
+ " \"quoted systematics\": rx.Problem(\n",
+ " [\n",
+ " rx.Constraint(\n",
+ " list(comps.values()),\n",
+ " terms=[t for label in comps for t in comps[label].reported_terms()],\n",
+ " )\n",
+ " ]\n",
+ " ),\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 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.names}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7c9ff53",
+ "metadata": {},
+ "source": [
+ "## Three things not to do:\n",
+ "\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.** 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 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": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "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 comps\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.proportional_error(log_eps)],\n",
+ " statistical=False,\n",
+ " )\n",
+ " ]\n",
+ " ),\n",
+ "}\n",
+ "for name, p in wrong.items():\n",
+ " print(f\"{name:24s} {p.names}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "problems = {**correct, **wrong}\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, 1, 4, 3, 6)],\n",
+ " plotstyle.HATCHES + plotstyle.HATCHES,\n",
+ " ),\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b96c4d72",
+ "metadata": {},
+ "source": [
+ "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": "code",
+ "execution_count": 12,
+ "id": "f2ad67f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 13,
+ "id": "84a548b9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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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": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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 = bands[name]\n",
+ " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\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": 14,
+ "id": "816316c6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "for name in wrong:\n",
+ " colour, hatch = style[name]\n",
+ " lo, hi = bands[name]\n",
+ " plotstyle.band(ax, x_fine, lo, hi, color=colour, hatch=hatch, label=name)\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=r\"$y - y_\\mathrm{truth}$\",\n",
+ " title=\"90 % bands: the bad three\",\n",
+ ")\n",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "dbd240fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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",
+ " p = problems[name]\n",
+ " fig = corner.corner(\n",
+ " samples[name][:, p.columns(cubic.params)],\n",
+ " fig=fig,\n",
+ " labels=[f\"${c.latex}$\" for c in cubic.params],\n",
+ " truths=a_true,\n",
+ " range=span,\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=style[n][0], label=n) for n in correct],\n",
+ " loc=\"upper right\",\n",
+ " fontsize=9,\n",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58d79de7",
+ "metadata": {},
+ "source": [
+ "### 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 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": 16,
+ "id": "a640c45a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dataset quoted true rho inferred rho inferred eta\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"
+ ]
+ }
+ ],
+ "source": [
+ "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 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",
+ " 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": 17,
+ "id": "a9e36535",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\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(comps)),\n",
+ " [settings[label][\"sys\"] for label in comps],\n",
+ " fmt=\"none\",\n",
+ " ecolor=\"0.6\",\n",
+ " capsize=8,\n",
+ " label=\"what was quoted\",\n",
+ ")\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 comps],\n",
+ " \"x\",\n",
+ " ms=11,\n",
+ " color=\"k\",\n",
+ " label=r\"true $\\rho_i$\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xticks=positions,\n",
+ " xticklabels=list(comps),\n",
+ " ylabel=r\"$\\rho$\",\n",
+ " title=\"The normalisations, recovered\",\n",
+ ")\n",
+ "ax.legend(fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7331f252",
+ "metadata": {},
+ "source": [
+ "## A gallery of covariance structures\n",
+ "\n",
+ "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": 18,
+ "id": "1c04818c",
+ "metadata": {},
+ "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",
+ " ax.grid(False)\n",
+ " return im\n",
+ "\n",
+ "\n",
+ "first = comps[\"exp P\"]\n",
+ "theta_map = np.median(samples[\"quoted systematics\"], axis=0)[: len(cubic.params)]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d2ae5f53",
+ "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 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": 19,
+ "id": "7348683c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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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": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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, 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": "6fba0a03",
+ "metadata": {},
+ "source": [
+ "### 2. Correlated statistical errors: bring the matrix, do not diagonalise it\n",
+ "\n",
+ "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": 20,
+ "id": "1bedc753",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "diagonal only: m = 0.779 +/- 0.029 full matrix: m = 0.796 +/- 0.050\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_ = 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 = 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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = corner.corner(\n",
+ " s_diag,\n",
+ " labels=[\"$m$\", \"$b$\"],\n",
+ " truths=[0.8, 1.0],\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",
+ "fig.legend(\n",
+ " handles=[\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",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bd65faf",
+ "metadata": {},
+ "source": [
+ "### 3. Unknown or misreported magnitudes\n",
+ "\n",
+ "A `proportional_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": 22,
+ "id": "c32c89dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gamma = rx.Parameter(\n",
+ " \"log_gamma\", prior=stats.norm(np.log(0.1), 0.5), latex=r\"\\log\\gamma\"\n",
+ ")\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.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",
+ "ax.set(xlabel=\"$x$\", ylabel=\"total standard deviation\")\n",
+ "ax.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "574374c1",
+ "metadata": {},
+ "source": [
+ "### 4. A systematic shared across datasets\n",
+ "\n",
+ "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": 23,
+ "id": "2ddf9ef6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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",
+ "for ax, c, title in zip(\n",
+ " axes,\n",
+ " (c_shared, c_each),\n",
+ " (\"shared: off-diagonal blocks\", \"per dataset: block diagonal\"),\n",
+ "):\n",
+ " show(\n",
+ " ax,\n",
+ " rx.Problem([c]).constraints[0].matrix(theta_map),\n",
+ " title,\n",
+ " dividers=(datasets[\"exp Q\"].n,),\n",
+ " )\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "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/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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G4na7D+v5Hemzyc3NVVJTU5Xly5cr4XBY+dvf/qYASv/+/ZWvv/5aCYfDyp///GfFbrcrdXV17b4PRVGU6dOnK1OnTlX279+v1NTUKLNmzVKAhOf+ox/9SJkxY4ZSXFysRCIRZdGiRYrZbE74O9zyO3Iw7Smvre/09u3bFUAZPny4snnz5gOW3+bv9GZEANmgJwUj3SGAbAwKF+1cpBRWFya8mgePzY8VAeTxrc1fNiGvovzGcexfIW+7692RALLxHzpFUZQVK1YokiQpfr8/4Zg9e/YklJWVlaX885//VCKRiBIOh5VQKKS88MILyrBhw+LHXHPNNUp+fr4iy3J83xtvvKGYTCYlGAwqiqIo6enpyp///OeEss877zxl1qxZCeU0D2obtRVAtqdeaWlpyvPPP59w3tixY9sVQHq9XgVQvvnmmzbff+SRR5TRo0fHt//zn/8oNptN8Xq9hzz3YNr7vNt6Trm5uQlB+6E0BjjvvvuuoiiKMnr0aOWJJ55QkpKSFFmW4wFSdXW1oijtDyAfeuihhH3jxo1THn/88XbVqb0B5KGukZubq7z66qvK7NmzlYKCAqW4uLhD9SwsLFQAZfXq1fH3A4GAkpqaqvz1r39VFKXjz+9In03L82OxmGIwGJTf/e538X3BYFABlBUrVrT7Phr/TW7+73FVVZViMBjiz72oqCj++6H5d/PKK69U7rjjjvh57Q0g21teW9/pxgBy4cKFB73GoQJI0YUtHJHGJQubv8R618LxyOFwkJaWFt9OTk5GURRcLld8n8VioXfv3vHt0tJSysvLuemmm7DZbNjtdhwOB3fffTdlZWUJ5Y8ePRqNpulX8rhx4wgGg+zevZuamhqqqqriXa2NTj75ZIqKihL2DRky5JD30p56VVdXU11dzdixYxPObd7leDBWq5XbbruNc889l5tuuol///vf1NXVxd+/8cYb2bBhQ7wrcN68eVx++eVYrdZDnnsk99XoQM+pPc+vkSRJTJ48mWXLllFfX09hYSE33XQTFouFtWvXsmzZMkaMGEFqamq7ywTIz89P2E5OTqa+vr5DZXTGNR566CE++eQTvvrqK/r169ehMoqKitDpdIwePTr+vslkYuTIkfHv7OE8vyN9Ns3PlyQJu92ecG9GoxGj0dih+ygqKsJoNDJs2LD4MWlpafTp0ye+vWbNGgAGDhyY8N3873//y/79+9td/8MprzO+620Ra2ELgnD06C3wQGnXXLedJElqta+tAeVtHdfqsnp9m/s/+uij+Ni+A1EOMOFGo9HEA8u2Jr9otdp21aGj9Wq85oHq1R4vvvgit912G4sXL+aVV17hjjvuYOHChUydOpVevXoxY8YM5s2bx3333cfnn3/OihUr2nXukdxXowM9p448P4Bp06bxyiuvsHz5ckaNGkVycjJTp05l6dKlfP3110ybNq1D5UH7vmsdOfdwv8/nn38+b731Fm+88Qb33HNPh8o42Pen+Xe2o8/vSJ7Ngc4/0vvQaDQoao9uQlktn7tWq6W+vh6TyXTY9T+c8jrru96SaIEUBOHokSQwWI/9qwP/yDidzlaTObZu3dopt5+dnU1OTk6rSQ1tWb16dcJEj++++w6LxULfvn1xOp1kZ2ezcuXKhHO++eabhFaPA9Hr9Qllt6deKSkpZGVl8d133yXsb7l9KKNGjeIXv/gFy5YtY+rUqbz88svx926++WbeeOMNXnrpJQYMGMCpp57a7nPb0pHn3VmmTZtGYWEhb7/9djzYmTZtGl988QUrVqw4aADZ8nPpDE6nk5qamvh2KBRi9+7dh1XWpEmT+Pjjj/nNb37T4XyGw4YNIxqN8sMPP8T3+Xw+NmzYkPCdPZLndyy05z6GDRtGOBxm3bp18WPKy8vZs2dPfHvcuHHIssxnn33WKfXq7PIOhwggBUE4oZ1yyin4fD6eeuopamtr+eKLL3jsscc6pWxJknj00Ud5/vnnefbZZykrK6O4uJiXXnqJOXPmJBy7d+9e7rnnHsrKyvj666956KGH+OlPfxpvJZg7dy5/+tOf+Pjjj6msrOQvf/kLX3zxBb/4xS8OWY9+/fqxceNGysrK4uk72lOve++9l8cff5wlS5ZQXl7OQw89xKZNm9p17zt37uTSSy9lxYoV1NXVsX79egoLCxk0aFD8mAsvvBC9Xs+f/vQnbrzxxg6de6TPu7OMHDmS1NRU3nrrLU4//XRADYA+++wzamtrmTJlygHP7devH99++y1ut7vNND6HY+rUqfz1r39l165d7N27l1tuuQWPx3PY5U2ePJmPPvqIX/3qVwmzow9l4MCBXHrppdx2221s2LCBkpISbrnlFqxWK9dee238uCN5fsdCe+5j0KBBnH/++dx2221s3ryZXbt2ceONNyakgRo0aBDXXXcdt99+O4sWLaKmpoYNGzbw4IMPMm/evA7Xq7PLOxwigBQE4YSWl5fHv/71L1555RUGDx7MH/7wBx544AGMRmO8O0qr1bbqJtJoNIc8BmD27Nm88cYb/Oc//2HIkCHMmDGD9evXc//99yccN2vWLCKRCKeeeiqXXHIJl1xyCY888kj8/TvvvJNf/vKXzJkzh/79+/Of//yHDz74gFGjRsWPMRgMbXZLzZ49mwEDBjBy5Mh4Gp/21Ouee+7hpptu4tprr2XChAns27ePW2+9FYPBcMjnWlBQwHXXXceDDz5I//79ueyyy5g1axZz586NH6PT6bj++usB4n+299wDac99Heg5mUymVkMCDkWSJM444wxMJhOTJ08G1H/c8/LyGDduXML4vZaJxB999FG2bdtGXl5ePI1PW3U4UH3b8tRTT5Gbm8uECRM499xzGTlyJOPHj0+4bnuu0fyYKVOm8NFHH/Hoo4/G0xC1p4z58+czYcIEZsyYwciRI6mvr2fJkiWYzebDen5H+mzaOr89+9pzH6+99hp9+vThtNNO47zzzuPUU0/l5JNPTnju8+bN44477uD+++8nPz+f66+/HofDwVVXXRU/puV35GDaU15b99fyd9fhkpSWHfsnKLfbTVJSEi6XC4fD0dXVOajCwkKGDx8OwKZNm9rVhdXZNtds5ooPr+CtC95iaOrQTju2I3rSZ3YiCAaD7Nq1i379+nXaGJ8TxbXXXotOp+O1117r6qocc9dccw1er5eFCxd2dVUEQWjmUL/TxSQaQRAE4bAcrNv1UC0c4XCY1atX8+6777J48eJjcs2OisViba6YA2rLmdFo7LRr9cT6dCfH27PpCfcjAkhBEIQuZjAYOtx12tV8Pt9B09N88cUXrSbFNHf66aezZcsW7r///nZPlDjSa3bU4sWLueSSS9p8T6fT4fV6O+1aPbE+3cnx9mx6wv2IAFIQBKGLHatB753JarUe0cSPr7/++phfs6POPffcY3q9Q+lu9elOjrdn0xPuR0yiEQRBEARBEDpEBJCCIAiCIAhCh4gu7B5OU70FSiNNOyypkJzXdRUSBEEQBOG4JwLIHs604AZwNht8r7fAHd+LIFIQBEEQhKNGBJA9XOiMR+GkM9SN6m2w4GbY+y34axIPFC2TgiAIgiB0EhFA9nBKch/IGa1uWFLVFsgFN7c+ULRMCoIgCILQSUQAeTxJzlODxJatj40tk/6aLg0gi13FCdtOo5NsW3YX1UYQureXX36ZrVu38uc///molnu0rtPc66+/zvfff89zzz131K4hCMKxJQLI401yXrdrZXQanZh1ZuauSFzH1qwzs3DmQhFECkIbiouLWb9+/VEv92hdp7k9e/awdu3ao3oNQRCOLRFACkddti2bhTMXUheqi+8rdhUzd8Vc6kJ1IoAUhDbccsstXHXVVcfNdQRBOL6IAFI4JrJt2SJQFLql5cuX88QTT/DAAw/w+uuvs2fPHsaMGcODDz7I2rVreemll6itreXMM89kzpw5aDRq+twdO3Zw0003AeoazP379+fOO+9kyJAhrcp+7LHHeOqpp9i3bx/z58+nd+/efPrpp7z66qsATJkyBYfDwerVq+PdvEuWLEnoWn7mmWeoqqpiwoQJvPfee7hcLs455xxuvfXW+PrP7777Ls8//zwADoeDkSNHcu+99+J0Og94/82v43K5mDlzZqtjUlJSWLBgQfy+n332WbZt20Zubi7XX389U6dObVXmSy+9hKIoTJkyBVmW2/151NXV8cwzz7B69WqcTiezZs3iwgsv5IcffuD+++9n4cKF2Gy2+PFvvfUWCxYs4K233jrgue1xqPtqfP5jx47ljTfeQFEU3nvvPX784x8zY8YM9u3bx9KlS5k4cSK/+tWv2n2/gtBTiQCyh9tT60Pe74pvO60GcpPNXVgjQWiiKArRcOyYX1dn0MSDqkOpqqri008/Zc+ePfzyl79kypQp3HvvvSxZsoRQKMT999+PLMvcfffdKIrCPffcA0BWVhYPP/wwAMFgkCVLljBu3Dg2btxIQUFBQtk7duzgl7/8JX379iUtLY3Fixdz4YUXcv/993PSSSfxr3/9i48//pgxY8bE69Wya3n79u289tprTJo0iVtvvZXq6mruu+8+otEod955JwATJ04kLS0NgPr6el599VVOO+00NmzYgE7X9q/75texWCzxewKIxWLcdttt8SXV1q5dy/Tp07n11lv56U9/yo4dO7jkkkv429/+xuWXXw7A0qVLOe+887jvvvuYMGEC//znP/n0008T7u1gLrvsMgDuuusuQqEQ8+bNIxaLcd5557Fp0ybefvttbrzxxvjxTz75JFOmTDnouW0Fxc215762b9/O/PnzOfnkk7nzzjvJysoCYNWqVbzzzjtce+213HnnnfTp06dd9ykIPZ0IIHu4P3y8ld3ffRXfTtdo+e25Q0ky6+P7jPVuBshp2LuigsIJLRqO8fKc5cf8urc8MxW9UXvoAxvIssx//vMfhg8fDqhj9ubOncuOHTviweCWLVt4//334wGkzWZj2rRp8TLOOecciouL+dvf/sYTTzyRUPZrr73GxIkT4/sefvhhZs+ezWOPPQbARRddxLBhww5Zz6SkJBYuXIjRaARg27ZtLFiwIB5A9urVi169esWPv+CCC8jNzWXx4sWcf/75hyxfr9cn3NP9999PbW0tixcvBuDee+/llltu4fe//338GK1Wy8MPPxwPtH71q19x44038rvf/Q6ACy+8MKFV9mAUReHLL7/kiy++YPLkyQBcccUV+Hw+9Ho9N9xwA/PmzYsHkJs2beKHH37gtddeO+i5h9Ke+wIwmUwsWrQIq9WacP6kSZP429/+1q57FITjhQgge7hrRxYw7JSxANTUBtixYBc7/72j1XGrpOeYURqgb86xrqEgdH8WiyUePALk5uaSkpISDx4b95WXlyect2TJEt58801KSkoIhULs3LkTRVESjtHr9UyYMCG+HYvFWLt2Lb/4xS/i+yRJYvr06axZs+ag9Rw9enQ8eATo3bs3H330UXw7Eonw+uuv8/nnn1NeXo4sy/j9fnbsaP074VD+85//8NRTT/HZZ5/Rr18/IpEIK1asoKamhlWrVqEoCoqiUFtby7Zt25BlGY1Gww8//MC9994bL0ej0TBjxoxD3lvjc5g0aRL33HMP9957L9OmTSMrKysesN100008+eSTbNu2jYEDB/L3v/+dk08+OR58H+zcA2nPfWm16n9GxowZ02Z5kyZNavdzFYTjhQgge4L6koTUPJrqnfGfq1YlsaawKL5t0msZOisfvaWpBbJiYyH7vzPhKSmGnGbd2yK5uHCU6Qwabnlm6qEPPArX7Qi9Xp+wLUlSm/tisabu+LfeeovZs2fz4IMPMnPmTOx2O88//zxerzfhPIvFEh83CRAKhQiFQtjtiX0CLbfbW8/mdfrpT3/KkiVLuPfee+nbty8mk4mbbrqJQCBwyLKbW7NmDbNnz+bpp5+Ot0gGAgGi0ShXXnklp5xySqtzJEkiGAwe9r01+vDDD3n55ZeZN28eN910E2PHjmX+/PkUFBQwcOBAJk2axLx58/jtb3/Lv//973gr7qHOPZD23Fej5mMvmzvQfkE4nokAsrurL4G/ToCIP77LVNc0IL33RC1nX3BS03s2PfYUU0IRq/yV7P+uhqwfnoT1zXIxiuTiwlEmSVKHupJ7ksYAcu7cpvRUzbuuD8RsNpORkcH27duZPn16fP/htBK2Vae///3vXHLJJQBEo1Gqq6s7VEZlZSUXX3wx1113HT/5yU/i+x0OB2lpaYRCoYRu7ubMZjOZmZls27Yt4d62bt3a7utbrVbuvvtu7r77brxeLxdccAEPPvggb775JgA333wz999/P2PHjsXn83HllVe2+9y2tOe+BEForWP/TReOPX+NGjxe+grcshxuWU7w0n/E39amp5Le2x5/tQweAaKWDABKTn8mXgaXvqKW2zLpuCAI7WK329myZUu8BXDx4sV8+umn7Tr32muv5bnnnqO2thaAdevWsXDhwk6p0+bNm+PbDz/8MC6X6yBnJAqHw/zoRz+iT58+8dnczd122208/fTTrF69Or5v//79vPzyy/Ht66+/nueee46aGvV3y6pVq/jggw/adX23280zzzxDOBwG1JY9h8OR0Ap42WWXEQgEmDNnDj/60Y9wOBztPvdA2nNfgiAkEi2QPUXawPiShbE6/cGPPYBQ8gDIEal0BKEzzJ07l7POOov8/HwcDgc1NTXtHgv3q1/9im+//Zb8/Hzy8/OpqqpiypQp8eDncD355JP83//9H++88w4ul4v09HTy8/Pbff6qVav46quvGDlyJGeddVZ8f2Man9/85je43W4mTZpEQUEBsiwTDAYTWl4ffPDB+L0VFBRQVVXF9OnTcbvdh7y+2Wxm165dZGdnk5+fT2VlJVarNSEANZvNXH311bzwwgvMnj27Q+ceSHvuSxCERCKAFAThhDZ16lQ+/PDDhH1nnXVWPO9ho4suuighFc3gwYPZvn07mzdvRpIkhg0bRklJScKs37bKBkhOTubrr79m06ZNSJLEoEGDuOWWWxLGKrZM8D1nzhxCoVBCOZdccknCBJ1Zs2Yxffp0tm7ditPpZODAgaxZs4b09PQDltt8e/jw4SxdurRVfQ0GAwA6nY5nnnmGRx99lC1btuB0Ounfv3/CGM+kpCRWrFjBpk2bABg0aBClpaXx1taD0ev1PP300/zud7+jqKgIh8PBgAEDWrUi5ubmUlBQEE/f05Fz29Ke+2rr+QPMnz+f7GzxH3PhxCMCyBNISa0fe0POSFO1l/5dXB9B6A7S09MTAiyAzMxMMjMzE/bl5OSQk5OYxsBkMjF27Nj4dsvJGm2VDVBeXk5hYSFnnnkmoOYhfPvttxNSwbRsORwwYECrcnJzc8nNzU3Y53Q6E1IGjRs3LuH9luU2305KSmrXOMCkpCROPvnkgx7TfFZ7nz59OpQf0Wq1Mn78+DbfCwaDvPzyy8yZM6fN4PBg5x7Kwe6rrecPcNJJJ7W5XxCOdyKA7CF2VHkJKmrwt6PS06FzHWb1Y37ys61UfrEFgGHSLj4yQqU3REbnVlUQhEOw2+386U9/4pZbbsFqtbJ161buuusurr322q6u2lF35plnHnBlmscff7zNmdCN7r33XhYsWEBSUhK33nrrMbmmIAhtEwFkN9cY4M15cx2FDQFksGpP/H2r8dAfYYZdnVjzzJVjsGdbAKjaZoDl4A5ERAApCMeY1Wpl8eLFlJSUUFlZSf/+/UlKSurqah0Tv/71r1vlymw0cODAg557/fXXc8UVVzB69Oh4t/rRvqYgCG0TAWQ35w5EMMtp3DF+EM4+Datk7HJw1Tz1/VRb+3+J9s+wkZ6r5mPbUS2WOxSErpaXl0de3omVRqvlutkdMWrUqGN+TUEQ2iYCyG4u4FF4o/o5ov/Tsgs1YbjbXxd/32Buf4692rKmwf3uqhgeOa3zKioIgiAIwglDBJDdXCSoEFVMjDhTy5CT1cH623dsg9fV982OQ6f0Mdn06AwalszfnLB/lfQcp3ra7tYRBEEQBEE4EBFA9hA2p0R6b7X7udLTOln4wdhTTFz98ESC3kh8X9F3a9j4uYlIsH0BZJm3jLpQU8tnsav4IEcLgiAIgnA8EwHkCcKeYkpYpaZk26FzozUq85Yxc+FMAtHE9XTNOjNOo7PT6igIgiAIQs8gAsgTnLdOoWpvU1qgttbSrgvVEYgGeHzy4+QnNeWMcxqdZNtEAl1BEARBONGIAPIEpTdJ6KQgGz83sfHzVfH9OoOGqx+e2Oaa2vlJ+QxNHXosqykIQjezdOlSampquOyyy7q6KoIgdCERQHYz23fXU13d1FVcURIGNAc+4TCZ7RJXp93F9qnzyRuoLs9WW+ZjyfzNBL2RNgNIQRB6pk8++YRIJMJFF110xGV99NFHbNmyRQSQgnCCEwFkN7J9dz0f/WE1epqPT9Sgk4KkJHf+WEO7thpHuiY+OUcQhOPTe++9h9fr7ZQAUhAEAUQA2a1UVwfQI+E8I5t++ckAGOu3M2D5Xdhz3jsq1yypDRBsWB/bU+k/KtcQhO5s3bp1fPfddwlL4+3du5c33niD+++/H0mS2Lp1K5988gm33XYbn332GXv37mXUqFFMnjw5fk7jMbfccgsLFy5k3759zJ49m5SUFCKRCJ988gnFxcXk5eUxY8YMbDZb/NwPP/wQSZIYNmwYX375ZTzY69WrV0Jd3W43ixYtorKykoEDB3Luueei0WhalTN8+HA+/fRTtFotAwYMYMOGDYRCIf7whz8AcPXVV9O7d+9D1gugoqKChQsXotfrmTRpUoef75dffsmGDRtIT0/nrLPOIiUlhfXr17Ns2TLmzJmTcOx3333HmjVruP322w94bnu093k3f0433XQTL7zwApMnTyYYDPLNN98wePBgZsyY0eF7FoQTQef3jQpHrF9+MqeMz+aU8dmMHerArq3u9Gs4zGr+yCc/28oFz33FBc99xZw31wJQ6Ql2+vUEobtauXIlTzzxRMK+4uJi5s6dG18/eePGjcydO5dTTjmFN998kw0bNnDOOefw+9//Pn5O4zETJ05k4cKF1NTUIMsyZWVljBkzhscff5zi4mJeeOEFhg8fTnFxUyqsN998k7vvvpupU6fy1VdfsWDBAoYMGcJ3330XP2b79u0MHjyYZ599lm3btnHHHXcwZcoUQqFQQjn33HMP06dPZ+3atbjdbgKBAKFQiEgkQn19PfX19USj0XbVa/PmzQwdOpTXX3+d7777jrPOOotPPvmk3c929uzZXH311RQVFfHhhx8yefJkNm/eTEpKCvfccw/ff/99wvG//OUvWb9+/UHPPZT2Pu+Wzwng97//Pddffz033XQTu3btIhAIHOgygnDCEy2QJ6gMmxGAv51jI5SsrsG7bUeAXf8DdyDalVUTjiOKorRK/3QsmHVmJKn9qaraIxgM8qtf/YpLL70UgJNPPpl77rmHBx54IOGYe++9lxtuuCG+79JLL+Wkk05i/vz58X2zZ8/mvvvu4733mnoWiouL2bhxI0OGDAHghhtuYM6cOaxcuRKAu+++m5EjR/Lxxx+j0Wiorq5myJAhPP/889x7773xcvbv38/WrVvJzc2N73v33Xfxer3xFsj21uu+++5j8uTJ/Pe//0WSJPbu3cvgwYPp16/fIZ9XIBDgtdde49tvv2XChAkAVFdX4/V6ycvL4+yzz2bevHnx94qLi1m+fDl//OMfD3ruodxxxx3tet5tPScAvV7P119/jV5/6EUaBOFEJgLIE5UlFfQW8pY2dSElRfLZxZ/R+SsBkZ5HOHKBaICT3zj5mF/3u6u/w6K3dGqZer2emTNnxrfHjh2L2+2mpqaG1NRUACRJ4sorr4wfEwgEWLRoETfccANPPvkkiqKoQXUgwLfffptQ/qRJk+LBI8Btt93GqaeeSnV1NU6nk8WLF/P222/Hu6zT0tK48sor+eijjxICyKlTp7YKilpqT72i0SiLFy/m/fffjwfjvXv35txzz01o9TwQo9GI0+nkvffeY+DAgSQnJ5OWlkZamrqE6s0338yNN97IU089hdlsZv78+QwdOpQJEyYQi8UOeu6R3NehntOsWbNE8CgI7SC6sLshY/12KF2nvqq3HZ2LJOfBHd/DLcvjr/LxPwdAG3YdnWsKQg9msVjQapvWnm8MMiKRphWeHA4HRqMxvl1ZWUk0GiUSiVBdXU1NTQ21tbX07t2b2bNnJ5SfnZ34n7acnBwASktLKS8vJxqNxvc1ys3NZd++fQn70tPTD3kv7alXRUUFsiwfsF6HotFo+OCDD/j+++/Jzs7mpJNO4i9/+Uv8eV144YWYTCbee+89YrEY//jHP7jxxhvbde6R3NehnlN7np8gCKIFsltRW/5QWwW/arZUoN6ithh2tuQ89dUgYncDovta6DxmnZnvrv7u0Aceheu2l06ni491bNSertL2SElJQaPRMGPGDK6++uqDHltZWZmwXVFRAUBWVhZOpxOtVhvf16i8vLxVgNdZ9crIyECj0RywXu1x6qmnsnTpUnw+H//73//4yU9+gtfr5de//jV6vZ7rr7+eefPmkZ6eTnl5Odddd127zj2S+xIEoXOIFshupLHlr3z8zxNaBrnj+4RATxB6CkmSsOgtx/zVkfGPffr0Yf/+/dTU1MT3LVq0qFPu3263c+aZZ/L0008ndPvGYjHWrVuXcOyKFSvYtWtXfHv+/PmMHDmSjIwM9Ho9p59+Oq+99lr8fa/XyzvvvMPZZ599yHokJSXh8/k6VC+9Xs/UqVP55z//GX+/oqKi3ZNoXC4XO3fuBMBqtXLxxRczefJktm/fHj/mpptuYvny5Tz66KNceOGF8da/9pzblo48b0EQjoxogeyGIvY8yBndZddvubxhnUek9xGOX6effjqDBw9m+vTpXHjhhaxbt+6QgUpHvPzyy0yfPp3Ro0dz0UUXEQgEWLZsGVdeeSWjR4+OH5eTk8OZZ57JrFmzKC4u5oMPPkgI1p566immTJnCjBkzGDduHAsXLiQ9PZ277777kHWYOnUqL774Ivfffz9Op5Orr766XfV64oknmDp1KhdccAHDhw9nwYIFZGRktOu+GxOXDx48mOHDh7N3714WLVrExx9/HD9m4MCBTJo0iS+//JIPP/ywQ+ceSHuftyAIR0YEkF3EUxsk6E0cz+OtU7qoNqoDLW9YZS2BkeB3haGTe9KLXcWt9ok1toVjSafT8c033/DGG29QXV3NbbfdxuDBg3n55ZfjE1YGDx7cKlBLT0/n/vvvj+cXbOsYgL59+7Jp0yYWLlxIUVERvXv35ic/+QmDBw9OOG7atGncd999LFmyhKSkJH77298mHDN8+HC2bNnCO++8Q0VFBQ888ACXXXZZwpjLCy+8MCEvZKOLLrqIRYsWsXLlyngan/z8/EPWa9y4cWzcuJG33noLg8HAm2++SXl5OVVVVYd8rmlpaaxbt45FixZRWFjI+PHjefTRR8nLS+xNOfPMM9mxYwfnnHNOh89tS3ue94Ge0x133MGIESMOeQ1BEEBSFKVro5Zuwu12k5SUhMvlwuFwHNVreWqD/Ps3K5EjsVbv6aQgp15jZ8SkyW2cqSosLGT48OEAbNq0iWHDhnVKvXas/4rMd29gxei/4ewzPL5/a9km/lx7D38bP5/Tho3vlGuVecuYuXBmmylezDozC2cuPGQQeSw/M+HQgsEgu3btol+/fphMYinMjrj22mvR6XQJXdQnAkVRGDlyJJdccgmPPvpoV1dHEIRmDvU7XbRAdoG9ZR7kSIwPLWFqNE1BZIFUysumxwlkvNEl9XKY9di11fz1h60UrgrH96fpSmEA1PkOnbqjvbJt2SycuZC6UF3C/mJXMXNXzKUuVCdaIQWhG4tEIvz5z38+4PvXXnttq5V0mvvHP/7BJ598Qnl5OXfdddcxuaYgCJ1HBJBdoDFR94/PGcDQYU15zUzVG7H/txq7zXigU4+qxuTiz1w5mmBaUzfO5+u+49Vq8IbkA516WLJt2SJIFAQO3KXanSmKQn19/QHfbzmzvSWPx8OwYcN45JFH2p0650ivKQhC5xEBZBfKS7EwPDepaYdkO/DBx1D/dBvkNNVrU7EJOn81RUEQGlxxxRVdXYUOMxgMCSvbdNSdd955zK8pCELnEQGk0FqL5OUGz94uqoggCIIgCN2RCCC7kLriTLOkvEdr1Zn2aljekAU3J+zO1PSDPqAL1nZRxQRBEARB6E5EAHkMtEzZE9pXCrSx4gwcvVVn2qNxeUN/TcLumm8+B99rBGu9CfkhTTY99hQx21ZIFIu1zi4gCIIg9CyH+l0uAsij7EApe3RSkNrT7iF9dIucY5bUrl11psXyhgA4C8EHO76XcS1tyg+pM2i4+uGJIogUAHV8mkajobS0lPT0dAwGQ4dWhBEEQRC6nqIohMNhqqqq0Gg0GAyGNo8TAeRR1lbKnni6nv5vdOmKM20p85a1Sq1TpVG72XNPjjE8fwgA/uoAW97fTdAbEQGkAIBGo6Ffv36UlZVRWlra1dURBEEQjoDFYqF3794HzBAhAsijrDFlz12naRnYX51lbayvxL6069L1HMhBk3vHYvywaT3vr1XX03VETUwin0pPkHTsx7qqQjdlMBjo3bs30WhUpFQRBEHoobRaLTqd7qC9SCKA7GTbd9dTXd0UgO0v2gXASWt+QfrGZuMdu3Ks4wHUheoIRAM8Pvlx8pPym97wVJD0n2vI1cyHhpi3SpPP2/yZojXb40Fyo7Q0MwP6Jh9RXVoucSiWN+w5JElCr9ej1+u7uiqCIAjCUSICyMPUMlAEqKkNsHNBMToSm3t1UpDg6b+AoUOadnb1WEdad1c3Bm35SfkMTR3adGDqULj9u4TJNcHNRejeC1LxNVR8XZRQbpQYk28cRl5WU17L9k64cRqdmHVm5q6Ym7C/vcsbCoIgCIJw9IkAskHjkuBvvf8aZov5oMd6vDK7f4ihofW4AI0uzET7vzFK/vg+nS5CWeaTlIWblgckXAb1ZYdV113lu+I/byrfhM/q63AZ9cF65q6YS0BODILNWjO6kA632514giYJbE3JxZMGGjgv+VIikcTBtYGYnf/V380Xf/shYb9WByPP1KFvx3DJB0w/w6s03VNZtIL59W/wrw//TrYus+lafrXuYjl3QRAEQTi2JEX86wvAvn37yMvr2hZB4fCUlJSI9W8FQRAE4RgSAWSDWCxGaWkpdrsdj8dDXl4eJSUlOByOrq7aMeN2u3vUfSuKgsfjIScnp8etIywIgiAIPZnowm6g0WjirViNs44cDkePCKQ6W0+676SkpEMfJAiCIAhCpxIBZIOWLZBA63GAx7nG++0p991WC2Tzz1Ekse4ZREuyIAhCzyO6sBuIMZA9V/MxkOJz7LnEWFZBEISeQ7RANrDb1WTYPWH8X1FRERMnTgRg5cqVDBkyhEhIJhKS0Ru16I3aVudEQjIBbxgFsNgM6A2tj2nk94QJ+sKYrAYsjqZZ1rs2ViGHZLRGLf1GpHf6fXVU45jNxs8Oetbn2NO09b3rDG19joIgCEL3JgLIBj1p3KPNZkv42eFwtAogXdUBvHVBbE4TSWlm/O4wSlAHEpiNRiz2tte2BIj4vGjN6rrGDkfTtXr10+Cq8pOUbknY39Wad1X3pM+xp2nre9eZxJADQRCEnkMEkN2E3x0m4A1jtqmtfn53GE9tEG99ADmikJprw5FqJhKSiYZj8fMaf46EZJSYEg8ivXVB5LBM8Z56wh4/GWY9qWY9CrTdQhmWKd1Rx86NlQSDMjn5yQwcnZkQmKbm2EjN6T6BoyAIgiAIXUMEkMdIyxbClkNPA94wsWiMgDeMyaajeHs15fu8KIEIUYuOFcVVDB6azug8J5Fw0xrDO9dVUFJmZFuNn4wUI9Mn9iZQGmbJ9yXsrg9gzbYwwmWhrDLAmBGZ7NHFKNzkZni2g9MGpFO110NduQ+zw0B9hQ93dYhoRKayxEV2bwfRSAyjWYfFYURnSJzgEPBEmoLe5i2aoiFJEARBEI5rIoA8Rlq2EDYPvgCCvggoCqZUE6UVPkpKvCghGY1Oy95SD7W+EKGwjNMn88X/dsbL9QYj+LbX4zFLePaGSNLtJ7bbT1WZG/RQE4tRVxpCQmJXYTVFlhg+b5TN7jB5JgMbvihBE4X0HAvpeXbsqXo8tWCy6inZUkNKjh1XTQCd1oc1w4Qx1YzFoMWs1yYEvS27xFu2qDZX7Q1S64uQYtWTZmvH0jSCIAiCIHQrPTKArKys5IknnmDHjh28/PLLpKd3/YSOQ9EbtfHgEdQWR1dVgJ1bqokaNKSnWUhxmFCMWvy1AWImWLeuAtkFVrOEMUmPJihTuLGKyrKmZf427nNR0DcDu15DQJYp3elCuyeAMawQM0KuSUM0EESOQDDFSFJtjGh9kNRUC6v+t5d9e+uIKAp9DdB/bBYFIyTqyv3I0RjWZBM1tT5qqwLYHUY8wShpeg3eSAx9KEZduQ85qpCV3zoXY0Jw2SKArPVFCEdj1PoiIoA8gJbrlIO6TrhYC1wQBEHoDnpcAFlTU8OECRO4+uqrefXVV0lNTe3qKrWL3qhlV52P4l1eelmN5CVbCPkj+GMKgQofVWU+XFqZXd4AuqiEWa8l5gaLDLoAOK1QFAqyvTyC7G3qwg5WBaiRPRCDOhMYoxqMXhmdBNaAQsjlI6KRUCSF4tVVuCXQxqBmbwA5GiMSk0AP1SVuSopq2L+tnnBMxpJsoNwV4IetVdTJ0Muk5+TB6RhtOgjGqCr14nKHyOplIxqJtbpfs82Q0MLaXIpVH2+BFFor85Yxc+FMAtEW65TrzCycuVAEkYIgCEKX63EB5JNPPsmpp57K73//+yMqJxQKEQqF4ttHI3l2867aFIuBPXV+Av4wJWEZazBGWYWH+qiMJqogRRUMWonUqBZtUMbqjpCSY0UnSWRmWaiu9RFzhTCYtPidTYFXlsNCnzQjQa+Moz6CJhgFIB7SKRCTG8ZbRhUaE6VEUffZ9RIaRcIS07BjSyUaswFPiR93lR9MEqawjl42DTXBMLX1AepW+kGOUV8XQGvWEehtJegLs2bJbvRGHb2HpuLWxCh1BclJMpFqN0DjcM+GsZFpNpNoeTyIulAdgWiAxyc/Tn5SPgDFrmLmrphLXahOBJCCIAhCl+txAeTXX3/NrFmz4tuffPIJv/nNb9i7dy/nnHMOTz/9NMnJyYcs5/HHH+eRRx7p9Po1nyxT64vgrvZTszNMTroFswxub5hks4maGg++gExdlQ93fRB9FDJ62dB5orAvgCcgE5MgqIWte11UE8NFjJgEMVdTi19llYLe58eqSGgAGYVtepnVxij1GgVHTMIRk7ArEkkxDUkxCWdMIiWmQaNALBIjFgG3X8ZbHcKYZibgjWHWSOBXsAF+n0JaCCq214FVhzaooDfpiEVj6DVayne58NeH0Bm1pGRbKdXFCIZlSqp8pOr06sShg+SdLKn1xQPOvBRrp38mPVV+Uj5DU4d2dTUEQRAEoZUeF0DabDZWrVoFwHvvvcftt9/O3LlzkSSJ3/3udxQXF7N8+fJD5pSbO3cu99xzT3y7MZlxe1R7g+ys8gISBenWhNa0SEimxhPEVRXFYNVRXxMg4o0QDclEzToq68K43VEGJpmxWnR4XEEiPtDFIOZTYK8fOaQGiBoFLFGwRCWy0FJcvoUadxmBcNMYyPKd3+IyWAkhU2e1kHzSZHIcdi7R6TCY9URtWsJGCUUrIccU1pa4eH9fPShgVyQyJQ2nOuxklUeIRWIEyvwoEgQMGtBr0GkksrQQUIAghGJRjHYtFque5CQ9VknCJyvoTDoUvYTOpiPHqKGkykdSTCLc0CJa7gseMEgsdQUJhmVKXUERQAqCIAhCD9DjAsgLLriAn/3sZ9x333089NBDfPDBB/HVMcaNG8eUKVNYt24dY8aMOWg5RqMRo9F4WHWo9UWo9oQBcFoMpNlMbCt3s7vWT57NiBxRkDWAJJGRYaGeAJJJS0lhNb4NdQRk+LbASNLAVMIGMMZAUhQqdrvQhRSCksICaxiTRce4NDsDHRZ2rV7GXxbOaVWXt79+NmH7Hu2z5GcNi2/rAatWwplpwZltZXpOJrXJTrZVeNhR5aM+EGFbyMsSg0yuVstJER1WWYJQDG0MZAk8OolIGNCBRiehsRsxZJtx2E346oMEFJl99X4CNh3R4jr620z0MeuwppqQozH0Ri2lNZ4DBok5SaZ4cCkIgiAIQvfX4wLI2bNn88ILL3DZZZdRWVkZDx4BJk6ciEajIRZrPamjM6VY9aTZDYAUnwiyu9aPa48LlztKrTbG914/o7LsXDA8B8mkxWzWU1tYS9BsQAHS7A5c3jDp+cmENX7q9nixRyX8ksLn6Qr3nj+cGYMzCAdlNi3fx1db97WrbuXufdhzhuDWKDgkDc6ohE5WqCn1UVPa1HJpBNQwU62/gp5NxiivW4LkyFpOCunIjqjHaj1RNGYtOqsOi0aCQAypKkhYr8XhNFO1LYC7LoTsibBPqcGY5iAmx0jOsaGxaLH6glARIGaQyOnvbFXnvBSraHkUBEEQhB6kxwWQRqORDz74gKlTp1JfX8/bb7/N5ZdfDsD8+fMZPHjwIVsfD6aoqChhybaWAhGZYFjG1JALsWIPVACRai+715di0mgpqnRRk6xlzTaZ6Obt7AuG8ekUDGVBjPUKBmLUYsXni7E9Eqavz0SyoqUcmbohOh6a0gdjrJoPX9vO7vX1xKIKAX/ogHVqrsJXhdVfSUiJsU2KskcKE0YhFS2pig4HWjLsRjLNRtLtRjQShHwydfuD9PFDHy3UF+hY7qlEqQxzquzAiRZcoEgxwoYQhobRAdYKI1q9Dk+NH58vQlirEKi3Ekg2obVq8RfHMMoKBp2WnHQrWqMGtzWVwrLD/ngSeL3ezilIEARBEIQO6XEBJEB+fj7ffvstV199NVdffTXvv/8+4XCYlStX8tlnn6HRaA5dyAE0b9HsKn89gnM//34+n38/v9Pq8nWnlSQIgiAIwvGi2wWQsViMDRs2YDAYGDJkyAEnw/Tq1Ysvv/ySxYsXs2LFCtLS0nj55ZdJSUk5xjUWhOOTpzZI0BuJb7sqgl1YG0EQBKE76VYBZHFxMRdffDHbt28nGAwyePBgnn/+ec4888wDnjNjxgxmzJjRaXVYuXJlQhd2iyWrcVX5qCh2q0sLJulJybTgMBtIMusAiUA4whvf7kEJRLDXR+md7SDkiWCIKShRmT01MXqFjEhIhCWFvqc4GTrGiRxR2PBpOTV71X+kS6QwyzVupkoO8qIG1hV/2WrCTFtGjLkMZfRZVBEFCZI1WvrrTfSTTKSFtehDUptLVa/R+Phe7+PSXhlk7YaYDANPSyFrkJU/LNtB3i6JPMVALDmCyaZFQUYx6Ciq8ZKp15Gaa2X6Gf3R6iQkCSrcAcpqA6SYDOSmWtHqpfh/Bspdfio9YTLsBrKSLIf5Sald2N2hxfh45KkN8sbDK4mGm8YTu/11BzlDEARBOJF0mwBSlmUuuugirr/+en7+859TXFzM3Xffzdlnn80zzzzDnXfeGT82FArxk5/8hF/+8pcMGDCgU+sxZMgQzEZrPJejrkX+wl0bKlGq6vBH/BitemojEpkWG3a7mf75TvZV+RhRomfnHi9pJsgx2fARIByRce0PMUyrAQuEsozceNMIHMkmAp4wH/51A5FqMzaLia9MUWqSddxRn4MSlMEIGlvkADVONDZ5ACcZ+hOQFJabImw0ymwCNjW8r1dgkM7A5SlOkrRa3DVB3FUBpuGkzBbiv4EYM3vbGFghU10oMXnGIF68eTAvPvkDjgBs0EeZYDahAaIGDYrFSzDJRCzNQdFODbk5DgrSrOyuribJ4MSQYiY1y0FKqhmLQf26Nc4RP9L8j0cj+fuJaH99gDpfOGGfp8xPNBxj8MV9saSZAdi2bgu83hU1FARBELqbbhNA/vDDD5SUlHDfffchSRL9+/fngw8+4P777+euu+7Cbrdzww03AOpyhkuXLuWHH35g3bp1h8z52FGRkIwSU4iE5FYBZFk0yo5AEJ1FgyHZiGt7LZ9vqiUt3UKdRmF/YTW+Yi/JdUGSAQ1GTOiodIdwyhr8GoXB5/fhjEl5aLRqvWtLfXiqg0RReMceZurobH6W7GDNot0AZPZx4NvjalfdGxtMzYrETJ2V87PNVMlR/HYd+30h1lR72BQNM9gkc/upvdm/tZ6Ny9QZ3uekJvGqq47NHj8DMRINx/DUhsjoY6fAaSEQ8FOuxAgAWsAF6BTw1ASpqQ9SnWpBkRWSggp2rRaXP0KSVY+k1+APy/EAspHI/9j19tcHmP7n5QQicsL+jKjEDZiYu2QLlbqGVYsq93dFFQVBEIRuqNsEkBqNBq/XS1lZGTk5OQBIksSf/vQnvF4vt99+O1OmTKFfv37k5OSwbNky6uvrOz14BHXd6sYWyJa2BUNUZRrIsJu49KQ8/lRUgy4GZRVejNtq2FdYizEQwwlEAQ0QIYo2rP4jXJWu554Z+QR9TS2Kab3tKIAOiVF9ndxxcl8URaF2pIfdG2qo2O2mT9qIdtU9zaEuc2d1GknNsaLVa+irMZJTkAzAy1v28+HeGvS+KN/+dyeuSnW95ZQcKwsUH3kRDRcF1PyY/cdlkN7bhqIoBKrUrnWNXsIMNLZX2fRGauQQuihUuUPYgZItNUiShmGnZJHdL6UheGz9LEX+x65X5wsTiMg8fcVo+mc0Dd3wlPlZ82oRz1w5Bnu2Oszgf598zz2dNz9LEARB6MG6TQA5duxYevXqxX333ce///3vhPeeeeYZPv/8c5599lmeeuopAHr37k3v3r07vR413hCZJgsWhwFoPQYyzarHFQiTatBCMEbBwCS2F9WRYzYTrvJjsmqxm/WYIybMKGT0S8XqDuPZXw4o+CwaYnKMcDCKVqfOFg9EZSp0MbKiGk7RmfC71JQ9AydkEpMV9hbWMtgygdPOe4itFesYaZD54ttPALj8tJ+iN5jRoSHVkU2v3GEkp5kxmHTxCRBmhwGfqyHk88mc59PTqz6EC9QAc0QqX2vDeIqCXOU3olGg12An48/rhxwFnysIkRgyCr1TTCQnW4gAaWY99d4qciw6ao1aktPMpFrM1NZG0OgkTFYDZr2a7qgtIv9j99E/w8bw3KT4dpWsYU3D/vRcdQX1TSLQFwRBEBp0mwBSq9XyzDPPcMkllzB8+HDmzp0bf0+v1/PjH/+YTz/99KjXQ44pCd2tLds3T+mfxqDsJCwxBSWmMKZ/JsP6puHa5WZ9WR1hBYJKjNSGFIV1pT48rjC6oBqJerQKSBJGix5tQxf2+5vL2aWVyYpqMLuj2FNNaLVqcDlxZj6SJLFnUw1X5Z3OuoLJnDyUeAA5pGAS6eZUwih8Y4rysSnII3lppBr08TrrjVrS+9jZta6KgbtCaGPqvfUbncbIM3qxyxfk239v4DKfEYMikd0/ibNvGo5Or9Zhy9oKAGo1CunBGJWVfkKAzaQj2yfjx4zOCnkBiSq3nwAKdgP46kO4a4IkNYyhEwRBEATh+NBtAkiAiy++mN/+9rc88MADeDweHnvssXhOx5qaGgYOHHjU66DVSG12tzZKs5pIs5qIhGUiIZngfi8Ve91YrDqksEKFEqWfRn2sIcBOhLAcQtIAMRi2M8xPnv6GObOGMyBT7TLcW+dnh17mlJCe8p1uPn2lkAnn9SW9t10dDzounTpFxl1Yz+iwjqpvmmbDrrR4SDI6KDRE8Takv9wTDJNm0KMoCmW+EOXbvBiW70MbU8cu7tfK2Eek0HtiJm98soP6HW4uiagtrsmZZmY0BI81+718/s426nd5AKjUxxgp6TFgwEsYIxAALIA2Ct5QmHqTlr5n9CZc7sdk0eGtEwGkIAiCIBxvulUACfDQQw9ht9u57777+Oyzz7jxxhvZt28fb7zxBt98881Rv77ZoG012aOlrT+UUV7sIis/CfceN3J9GE9dBJ9VwmDQURuFTCCG2oJp0kroM/XUVkWwRSVGFYf547OrGHNWHjdPzuf2yfnUByJ8+F05pwf0UBPki9e3kN0/iYETMtHpNUw5ozf/s2opLapD52/qVz/VZ6dap8EaMRDSKoQVWFtaz5oKN063zKCQBnNDO2qlJsZWk8wwpxX9Ng/L1xViBaxoiElQMC6dEZNyKNtZz/ZVlRSvqwJARqHIKJOXpMMgSUhIpGIkgEQMMxo7SDLYYqDEFEJyjMx0C1q9FptTdHsKgiAIwvGm2wWQAHPmzGH69Ok8/fTT/OMf/2Do0KF8/fXX9OnT56hfOxCWD/jetgo3u2v9BNZXYdVp2L+lBgWIRWVsThNWAwRdMklmLXoDxKKgN2kxRnRIFh3mFBs1+9zgkpkY0FHy0T6u2lDOAzOH8eh5Q/k8K4lnv9pFv4ooI8Nayna4KNvhQrFo6TcyjcnDMtCOymLN+sJ4OhU9GgZFtNA8y098yWu1JdWlV9A79KR6ZSb7NeBvStlSZ4KcAcmMKHBSvcfLp3/bRDjY9Ay26KPUZeuZrDFi1WiIoUVCRwywJmvw2SAjx47BHUGKKehtBqyuKLU1QdIGpuBINaE0DCQ9GhOeBEEQBEE49rplAAkwbNgwXnnllWN+XXPL7utmMc/uWj/+UJRwio7ksAZbihFHipn9JW4MWRZC++sYlGrDG4jij/lx18gEQzIaBUwA2hBhRYNkCqOX9eRFIG1PjD+8soZxE7L5ydkDOH18L15asZN/Ld/NUJ+GEWEtBr/M7pUVbPu+gmiyjmB9feuKa0DSatT6SmpLoN6sIxZTSPJGoSYKQASF3boY2lwzZ52Ug7kqwq51VXy/sanMkA42aqMUGWWG6XWciQ5vpY8gIKNBwQBImEIgSRp8OQG8Gh+ZERMGk5ay0jIMQS17ChWcWTbMdn3r+grdhj0m4SnzUyU3LQFaW+Y7yBmCIAjCia7bBpBd5WDd131TLOyu9TNofDaWsEJ1qY+YXsLU30EgppBm0+OXJHLtRjIGpeOtCVC3sRrFH0Nr1FBjhJgb9HpI6qWnfm8Qc1hiht8Ay2p4dUU16QVJnDMhm2vvOI0tngBfF1WybU0VvWplkmMaDLVRon6ldeVioMRiCbsiEbVZUkahWBej1CHRd3AK5zrs1BfWsfPd3fFjdUYtNU4tn3m97NHKWExaLowZKNBYiEkSMdSGTQk9OiSiNjCYDbhCQfxuD0mRGLJVRqOEyOxjxrUrgCPVTMAbFgFkNxZ0hbnRbWTNq0WsafGezqDBZBOfnSAIgtCaCCA7YGCWg4FZDvzuMFs3VRJTQAZSMizUeMOcMiyTPklWImE1EfnunbXYI9XUlmgwmqHOLRHVKSiRGFqDQlKOQtQVw+XVoo2AQ5YIbXOzbJu6woo9xcS4fg7OnNAHY6aJDcV1VNUGKCmtjdfp35Zagg4TdkUiKSbhjElkRzVkyRo8GoW9Zhg+KIUZKXaUyiAlP9SyI1wPgKSVMPWxUuLQ8ObeKgLBGOggS6NlYsxAnkZPSAu2VAN7o27qTNWkeNIoMPXCmqKnPstMZtiKZI+RFPMS8ITonZlM32HpfKFfxjvVnzK2djQXZV/QBZ+W0B4RfxQDEoMv7svIoekJ75lseuwpYgyrIAiC0JoIIA+D3qglJcOCuz5EaoYFu9NMitWI7AlTV+HDZFVbbWxGPbqUZPLS6qlyWyiRo7gCYfQxCa0RFFmDOUOLIwO8UQ07JS379nvJDmvIlCU8tUE8tcH4dSWdhN2io4+rqZv9mkAKDsxoLVp0Vj0xg0TYqMEvQZYMQ2rCBH5wUUzTSjY+g8QGQ5TVmjCBWj80xKO9THrO7Z1CpNxHml0mbPQSNNkwZSpUSpsIBfXoTBp0un70HupgUJ8oe10eIEp9sJJefdPp1SuVFFMKO/3bkQ0hijyFXMQFKChNy+Q0I8ZFQpm3jLpQ08z6YlfxMa+DJc1Mem/7Mb+uIAiC0DOJAPIw6I1aehc4E/Zt3FfPxrWV5CWZGN7HSUZvBxtWl1Fb6iU1zULvnCSW76xAE4jiD2rZsV0mEpPQEkUXC6EHeqdIpFu0fGYKsqWhJTFX1pATVV/GKETcEWKBZl3VDUGZ7JeR/erkFwmwos6riQBRFEp1MfbqYuzRxSjVxkACjaKQpkhkKBqGKCEKtBuoKskn6Nbj19TgDerYjYP1JTXIioRi8BLR9cE8xkMw2UWZt5Ld/t1Ueivpm9wXv06iOlxJ4d6NmLVmyvxl9LH2IRBVV7sJRAOYdWbMOpHWp1GZt4yZC2fGn1Ejs86M0+hsdXxbwaXT6CTbln3U6igIgiAILYkA8jD4w1H8YZn9dX4qPCH6pljYXOYhoI9R4goy0WlCZ9DgDkep9MXY7XVjMlVycup+1lWko7c5kLQQMsbQKkE0fglNVIMZhRRzBTeGZNwxqNTpqFEM1FuT+NofJBqRQAFXpKkL+wvDToYmS+yM9iaoOLDHJGwxCXtMwq1R2KuLUa6LYZdknJoA2VgYJcEgc4yc+q146EWAVCyaGlJ0tUiShVpbBladjpjNhzuaQVKSnZjWTq5pMAOyUkhK8rKmYiNfl3yNO6x2t2+u3cz4jPFE5Aj1oXrqQnWkm9LZ6d7JO0XvYDVYSTYlMzh1MLm23K766LqdulAdgWiAxyc/Tn5Sfnx/y6DQaXRi1pmZu2JuqzLMOjMLZy4UQaQgCIJwzIgA8jD4wzJyTGF7pRedRmJ3rZ+h2XY2A0Oz7ThS1XFjBUNS2VTlRWMxo6n4lDOsVaQNGMWO6GQCMQ8BOYTFFED2Owi5YlgyZTT1AcI6K5YoDAy58StZOBwavArYrZVYNPVsDAZZ3lCXl6y/Jd+kzp71kkaFeRTbwoPZqgzCK2Vymk5DjtOBq2In/Rxr0IS1RGISKXY/ETzsj9kx2bQYw9Ukmdx4tFaUnDRG5fpxGQazb5+L8tguDJp69rKa7aUh5H0y1cFqZI1MFHV2dzAc5LN9SyjxlpBsSiPL1JevShcTlkOsrVjLwJSByFGZ4ZnD8YV9lPpKGZ85nmuGX9MFn2D3k5+Uz9DUoQd8P9uWzcKZCxO6ukFtkZy7Yi51oToRQAqCIAjHjAggD4PFoMUflhmQYYu3QA7ItHNSLzNS2A8RP+gtDBuSjs+kYUuZh1673OCrZmTGHgaeeTu7tm1DV74bub6UQPZAnLnZxKJBdmzsBR7QmmSsNhmLv5rMTImAppyU4Foygt/RW7ODuxrqIqGgJOWBax82qrEFPqeAzzkXUJCIKlbk+mTCeiO6iAQGE77kYSjOXEImFwaNCwkjKXj4TmNnn76SrYpMfp8hjMzMYzvVaFwyZYHdeCJ1DcnRNTiMDvom96UmWEMwGiSqRNFr9CSZk6jyVFLjq8JoMGBEXeHGHXWzy7WL6lA1YTlMmjmNTdWb1LGR7SS1WljyxJJtyxZBoiAIgtAtiADyMFgMOiwGHWk2Y8J+KexXs4eH/WCwAnByv1RO7pcKmlFQZYb0gVhqVpHnW4u7dgdGWypW0z7YsoxA7R4slcNxMxy9JJNlWI0ke9DttjMisBCzorY+VTcLpPyXvQHjJ0PIjbJ/DZR8D2XrYf8apGgAfcyLPuhV81A25A83urdRF5lOXr+h5BUvAb0JnPn4ZC2lUTdhwx6+K6thW+0O5GA5UqgYWXETamhtBFAUhVJfKfu9+wnKTRN9drp2IiGhlbToNDpsehsWvQVZkVFQKAuWARCIBOjr6EthVSFBOUiWNYtcu+jaFgRBEISe4KgEkOvWrcNut1NQUHA0iu++DJaG4NHS+r1B50LWSEjKha2f4tDImC0SEe8+9GENVK4DjYF0XTkeOY+YJKGXAmQqG8nwb0dCAVMSTLqHoG4YPHs2AIqlIfWK0QH506DPqerPMRklUAe+KvCUQ9AF3gr4YR7a+j2kVn5BsG4VWmMGEdmGXpfEKVIdAUuIYo2OPfW72CHvwhxwU6PzUEcIADkmE5SDhKIhaoI1AOg1enJsOdQGavFEPCgoRJUoUTmqBpdBqApUEVNi6DV6tJKWmBQjqkR5vfB1RqSPAIgHkN+UfMPyfctJNiRzdv7ZFDhPsO+RIAiCIHRzRyWAfPXVVxk8eDB33nnn0Si++zJY4y2PrTh7qy+ArOFQsRF9ai/0kTXgd4PsQau1kGvcScTcF5t/Pf20a5CiDRNmhl0KZz0CtkyUwsJ4scYvfwff1YA1HRw5YM+ClAI1UM0aCRlD1G1dQ2vp6GtRFt6OtP0zTLEooZiBEu051G1JJcce5bxMNxtTMvnAW4gvDCXRcgwN5/ojfoJykJiizgJPNiQzLmscu1272efdR1huWiKxkcPgwB12x2cZh+QQWkmLoldYVbqK/JR89rr3IiGxtWYrKaYUlpUso8pfhdPkZK97rwggBUEQBKGbOaIA8oUXXuCDDz5otb+oqIjBgwcfSdE9k9LGeD5FAX8N+GrBmqK2TlpSIXMEeMqgcjNofGBOhuyR2GqKGVE3HymsBo5K+mA49S6k3HGwaQHsWo5548p48brdy5Cc2tbXBRS9BQaeA0MugN6nNL1x3pMon85F2voRhlAp9fqV7E6eiE+TjsWiZWv9HpL1ZkJyGI/GyvD04RTWFlIdqAZgVPooTs4+mSV7lvD53s8P+kgaZ2lnWbLItGSytW4rQTmIrMikOlIpri/GH/FTH6wnGA3iiXjQaXRoJS0FSQX0dvRuyh95Yg+BFARBEIRu44gCyM2bNzNo0CAuueSShP3PPPPMEVXquBDxN3Vn+2pBDkL1Ttj8IZR+D1mjoPcEKJgGRR+AyYameitUrFPHUepMMG0u0vgboXQtLPqpOr4RkEJy02WGXAqjT4FgPXgqoLYYQh5wlSD5qqBwgfrqfSqMvR4KTge9GeXcP6FY05DW/IOTvV8DHooyB7E3GGZLoAqPEkZCg5sA66vXU+orBeD6oddTE6jh1Q2vEiOGVtIyMXsiYzPGYtAaKKwuxGlyUh2opiZYQ12wjl3uXZT7yyn3lzMibQTusJs97j2srVyLUWvEFXVhdptxmpxY9VZkRWZyzmRSLCkEo8FWj1YQBEEQhK51xF3YAwYMYNq0aQn7li5disPhONKie6b962DzRxCuh/yp0Hui2vJYvx+2L4X174ASgoAXKreBEgU0gAI1W9XgMbk3/OjvkDVCDUQ/vheqt6vd0GOuJRjpBc/eC0BkyCXQt3/T9V37wZENSgz2r4bC/8Keb2BvwyttIFz4LDj7wnlPoBjtSN8+z8neDWi0VZQYRiJrJGJyDC0gyzKlYTV4vH3U7URjUT7a9REAg5yDmNF3BhpJw/a67ezz7mNH/Q58EV+8Or1svbik4BKKaovYVreNjdUbcRgcnN3nbD7b8xkhOYRG0iBpJcKxMFas6BU9G6o30DepLwDD0oYd7U9NEARBEIQOOKIA8tlnn21zKbpHHnnkSIrt2Tb9F7Z+DFoDhAOAAqkDITkX/FWgBSIxCLvAXwFowJKmBodhD+jN8H8fgzVN7f7++D41eDQ74dKXwdmX2Jbt8csZ1vwdftgBSb3U8Y7WDHUijTUNep2kvur2wK4vYf1/oHobvHkVzHwRBpwJZ/5aDSKXPc54Vzkuk5NYziB2+yuowIMSVUCCbGs2PxrwIx5d+SgANwy9gSxrFq6Qi79v+juRWCThMSQZk/CFfezz7iOmxLi44GLuHnc3j3//OLvd6pjJa4dcy7+K/kVIDmHQGHCFXQTCAcwGM7aojQxLBgXJYvxjd+SqCFJl9wBizWxBEIQT0WEHkLFYDI1G05l1OT5EAiAZQJHUIFKRoH4fWFMhfQDUDQY5DDHAr6a0wZELlRvVnwedB5IG/LWw5p+w6T11e/yN6j5/LZRui19Ot3sZOLXqeMp9q9SdKwCDHdIHqsGkPQcGzlBbQ5c9DjU74J3rYeYLUHAGjL8JpX4v0rp/c2bNTpwmJ9+m5lAUdqMx+PB5feg0OupD9ex27QYg15bLd2Xf8U3ZN0RiESw6C06jE5vBhqIoaDVa/AY/JZ4SSn2lvL39bWxGG+f1O4+/b/o7m2s2k2xIjk+ySTImkWRMQq/RU1hdyMCkgcwaPIsdtTv4eOfHjEwfyTkF5yQ86tpALcKxozE2/X1f/tpu1lrU9dV1Bg1XPzxRBJGCIAgnkA5HgC6Xi3POOQej0UhKSgpTpkzhpz/9KfPmzWP16tWEw61n4p4wJAkGnQMDzoCh50P2SIiEwJamdi17KsFgA71VbV0M+kFvU2dOBxuCodHXgFavdj9/+YS6b9ilkDkMNFrY8Tnm756LXzKGRh0vqTWCRqf+jKS2Zu5fDSv/BpvfBzmitmJOfwRyxiDJYaT/3opUuABJq0M661EoOBMpGmJsZRHhqh2Uhjz4/fWAulyezWCjzKcGvYNTBvNV6VcEogEkJAxaAwE5wNa6rWyr30ZRbRF7PHuIoc7Yrg5U8++if2PRWbgg/wIAvin7hglZEwAo95XjDrlxh93EiJFuSWfZ7mU8s+YZVles5vuy76kJ1FATqMEf8QNQF0xclaVHq9oGpeuaXvUlXVyh1gz2pslaU/+vL5c/cBLTfzyUaDhG0Bs5yJmCIAjC8abDAeSLL75IVVUVy5YtY8SIETidTr744gtmz57N+PHj25yV3dm+/PJLZs2axXnnncfTTz+N3+8/6tdst4FnwrmPwfBLodcYyB6hptTZ8hFsXgg1xeCvBrMDMoZCSn/Yu1JtlcwYpo57BFj6O1BkGDpTze8IsHMpbHwbqSElDkCQhsBRq1eDx4whcPKtMPhCsGVCLAIl38HiB9SckHozTJurlqnI8Okv1ck3Gh1c9DxK1kikQC1n1uwGgvGJ5cFoEFfQFe+qDskhQrKaG9KsM6ORDv1V2u/dz9qqtQxOGRwPHHe5dmHUGgnHwgnd4Hs9e/l498cA1AZrGZw6mLpgHbIix1MCOU3Ow/uMuhNPhfrngpvg5alNr79O6JZBZKOkTBPpve2kZB8gbZUgCIJwXOtwAFlYWMhtt93GaaedRnp6Otdddx0bN27k/vvvZ86cOZx11llHo55xCxYs4PLLL2fkyJEMGjSIX//614waNYqNGzce1et2mDUVtCZ1Ao3eDDuWQcgNvko1LY2jF6QPAqMNGmY4M7ChizYaUsc9Akz+udqyGZNh26cARPpMO/i1tQZI6QcjLle7xPVmqNsFn/wCKovUYPGUn6oBbCyqBpigzhg/6SYAkqNh5JiCUaNDr9Gz17OX/d79DHIOAuCfm/+JXqMHIBANEI1F26xKczpJR7+kfgCkm9UE6A6jI75EYfOlCuu8ddR6azFqjUzvM50z+56J0+REK2kx68wApJhTDnnNbqW+JLGVsXQdlG9Q3zvjV3DLcvV16Svq5Cl/TdfVVRAEQRAOosNjIAOBAE6n2vLjcDhwu91IksTvf/97hg8fzty5c0lKSur0ioK6fN5dd93FP//5T84+W12J5Z577uGSSy5hypQpLF26lNGjR7errFAoRCgUim+73e7OqGDTz9Y0NSDb9S2Ur1dbAo02tRva5ABLEiT1hZKV0JiqJnOIGjTU7FRbBw1WNdir2qoGfv4a0JmJNARfAMiy2j0dr0MMipcl1qvgTDVI9FXB/34Fo65S81DmjFHzUO75Wp0xDijRMBKg1RkYZMykSqfBro2wpX4b725/l5+M+gl3L7+bFftXYNaaiUkxZEXGHXaj0xz86zSl1xQkJCr9lWyvVwPkgckDWVu5FlDXejZrzez37ker1ZJuSSfLksUA5wDqgnX0d/anJlBDqbcUp8mJHv1hfEhdpL5EbVWMtGgtN+ghN1tN+p4zukuqJgiCIAgddUSzYPr168f69evVgjQa+vTpw+rVqzulYm2pr6+ntLSUvn37xvfl5eWxdOlSBg0axIUXXkhtbfsmVjz++OMkJSXFX3l5eZ1f4bAfKjbB7q8h6Fa7mM0pEI1B2QYofB8qNqrBotEBuePVP70N3ZopBeryhbGYur41QO44dRxlI2cepA1oepWtV1u1mr9c+9Sg0dlXDTB3fgFySJ1UA+o5DavoSA0r6cSiIbYFyij17MWsqIHhyrKV5DnyuGrwVQCY9Wb+MOkPjEwbCRBvhbTpbYxJH8Mp2afEu7ZHpI3gsoGXMSZjDGMyxuAKqRMwejvU1Xk0koYyXxl7PXvVrmwZQtEQsViMQDTA5urN+CN+6oJ1hGPhnjf+0V+jBo+XvtLU0njLcrj0VfV9e2bX1k8QBEEQOqDDAeRll10WD+CuueYa5s+fz0svvcRrr73GsmXLyM7O7uw6xjmdTnJzc3nttdcS9tvtdhYuXEgkEuGxxx5rV1lz587F5XLFXyUlnTzerG6vGrxZUiEcVFsf0wfBhJtAr4doRE3r42novu5zqto6CVC3W/0zJV/901eldn1r9IffSqXVw4AZaoump0wNKnPHqe9VbYGwr+k4wKTECGEEjQWL1o5FayGmxLjvy/s4Lfs0sqxZ1AZreWL1E5yedzqPnvIoZ+adiU7S4Y14WVu1lm/LviWmxOhl68XodLXeckxm4Y6FVPjVIHnF/hXqZaXE1XRqY7WU+krZWreVCn8FsiJT6i3FpDNh0Bh67vjHtIHqZ9j4Sh944GOru+/Emu2VHjbtd7Gj0tvVVREEQRC6QIe7sK+88sr4z/n5+Tz11FP84he/wOv1ctdddzFmzJhOrWBL9913Hz//+c85++yzExKYZ2Zm8sADD/Dkk0/yl7/85ZDlGI1GjEbj0auoa7/apZ02AIZeANs/U1PweKshOR/qtiV2Pztyms4N1Kt/WtLUPxu7uM3J6nhGDrO7Xd8szYrOpE6yMTshUAf1e9UJOFkjUDR6MiIhJgR9fG8ygaQh3ZqOO+xmZ/1OfvLFTxiTMQZP2EOlv5Jn1j7DwOSBjEwfycTsiYTlMDtcO3CFXOTacpmQNYHaYC3/2/M/VpWvYp93HwBGrZGVZSvRa/TYDfZW1Y0SxR12U5BUgCRJyIpMMBokx5aDRW/BHemEYQfdkSUV9BZYcHPifr0F7vgeko9Ca3k7JJmbhgzc9q81mNJryIhK3ICJSk+QdFp/hoIgCMLx6YhXopk9ezazZ89GlmW02rbXZO5Md911F59++ikzZ87k448/5rTTTou/N3r06O6TRigpt2lVGMf5aotfoFZdjtCWCtljgTUQqoOwGyStGmBKmqbJE5Kk/ty4LYfVHI6uZt23IT8EY4euj3u/2jXeuEyipiHXpCNXDSArCtWAUtLA4HNh8wfc5ndzusXJNnMmIcIk65PQKBrqwnWsrVyLhESONYdyXznb6rexrV7NT2nRWciyZtHb3pvqQDUf7PyAcKzpc9FpdERjUUJyCL1GT5YlC5NODW4bWyItWIgRI82cRiQWYXTmaAD0Wj2BaACL3nJEH89RV1+SOAmmetuBj20pOU8NFFuev+BmdV8XBZAZ9qb/gPzt2rH0HziEzYXVVC3Ygztw6ElUgiAIwvHjiAPIRscieAR1rOW7777LzJkzOfPMM/njH//I7bffDqgr41x11VXHpB5tar4qT0of9dU4sab/mbBpgdqKGHCrwZurGiINXYBag9oaqNGqgSKALQMsKeoL1FyPqf3BXdF0nT4ToHda0/a+1ep5zRntkDdRTSUEak7JtIFqt7qznzpO01cJRhuSEoOxN8DmD8h076dUr2eQHOYbvOzT6Inpwaa1EpADRJQopb5SDBoDA5wDCEQDlHhK8Ef9FLuKE6pg0BjItGYSiAaoDlQDYNfbkRWZ2lAtNM1nwhQzMTxzONnWbFItqYzOHE2OLYfahlyZjbOwu60DTZjRW9TWxfZIzuuyQLE9BmTYGZabhKfMT1VXV0YQBEE45jotgDyWrFYrn3zyCY888ghz587loYceQqPRcNZZZ/H44493dfXaljNSncBSug5qd4OjtzqxJtAwBrLZ+tE05nkMtJgoosiHd21FUVs+93ytbqc1G3eX1Ev907WvaV/fSZAxFG3lZnK9Nay0ZdDP5ydmNiBpzQQ0Rio0WhxGB2W+csKxMIU1hfHTU4wppFnSkGMy0ViUfd59hGNhSjzqGD67wY5W0pJqSqXcX55QVafWyYS8CUzMmYjVYMWit5BjyyEQDWDQGtBK2u7f+th8wkzzZ21J7fKgcH99gDpfU2twSW03yqEqCIIg9BjdLoAsLCzknXfewWAwcPHFFzN06NA2j9Pr9Tz22GP8/Oc/Z82aNWRmZjJs2LBjXNt2iPjV2dgAGQMh5FVnWtfuVMc4OvOhfA1seBvGXKeuStP7FNj9lZr3ceJPwNzQauWvUYPOjgj7oGxdU3eoIxeyRzW935i8W2to2idpYMbv4fWLSQ35sCsSk+QoJ/tjfKexUWHUUUMUi85CqjmVsBwmJIfiicBrQ7Vqq2IzJq0JSZLQa/SYtCa0mtYt1jnmHHrZenF237MZkzWGzTWb2Vi5ka01WxmVMUpN89PdWx+ba5ww003srw8w/c/LCUSa/iPSOIbRYe52vwoEQRCEbqxb/avxzjvvcNtttzF9+nQ2b97MQw89xF133cWTTz6JXp+Y88/r9WKz2UhOTuaMM87oohq30DwPZKP966B2j9qd3ecUNYm4az9IeqgpAmcfcO2BQA18+zyc/hAMvgC+/SvUFquBpBwGa4bazbzvOwg2S/ni2ge1zYLKWARCHrW1012qzrhGUWdf550M2aPV/eYUCNY3tTyaHOp2LKYGuNmjwGDDEPYyKKWASMBNcjiKIaplryWNcTEva3RW/NEABo0Bu96GAvSx92GneyeesIewHMaqt+IwOMiz5VHsTuzWbj7zephzGL3tvUk2JYOkpghyh9xUBatAUXNEDk8f3hmf0gmrzhcmEJF5+orR9M9QU0F5yvysebUoYXyjIAiCIBxKtwkgXS4XN910E59//jnjx48H4NVXX+XOO+9k+/btvP/++xgMaitZSUkJEydO5LHHHuPHP/5xV1b7wIo+hn1r1FnW1hR1zGP6QHD2Vl8hL2xZqE5qabT5AzjtZ+o4x+GXwdp/QtEHMOBsQIb1b6pL3/WeAbyjnmPPgeRm4+qc/dQE5hvfVdP/gJoOaNyPE8ffafXqduOMb2c/dVuOqu9p9Wpqoe2fkbznO1zmNPy4GRGT6FtfQW1qPt+69lMWrQTAjAGbzsYm/zr8sTBatESIUB+qV9e49rmJxWK4W8wglxu65UekjmBs9ljqgnUkGZMIRAKggDfkJcWUEs8XKRy5/hk2hueqyf6rZA1rurg+giAIQs9zRInEO9O6deuQJCkePALcdNNNfPzxxyxbtoxbb701vj8nJ4dp06bxxhtvoLTV6tcd7FsDoXq1NVBvAWcv8DWMjfNVg84I+ZPV1kVDEmgM6hjH//1Gbckc939qOdsWqyvT5IxWgzpPGebvn219PTms5nPc/D6sfFENHs1OmHgHDLv0wJM33PvVP23prd9rSDRuCtRgcPYBRy+0Gi3JRivZ3ip+Yh/AMFNvrJhJ0droi5ZexlTyk/IZljqMfvZ+jE8bz0X9LuLaodcyNGMoybpkkrXJFNgKyLPkYcRIvi0fvVbPsLRh2PV21lauZW35WpBgQu4ERmaMpMBZcIQfiCAIgiAInaXbtEDm5ubicrn4+uuvE1LznHHGGbz22mtcccUVXH311Zx11llotVr++c9/Eg6HkZrPfO5Oeo1Vg8he48CRBZXbYM9KtWUyfYA6o9qcCsN+BGtfB50NZB/sWqa2PI69AYZcpLZArnoFJt2tjpFc9wZSuCl5s2nvMlCy1DyOja2ZOiP0PxsGnaOm7Kna0nYdq7aBq0RNUJ4+pPX7e74BQEKDtraYlKQ8LJYsQAK9gVyNkdscg9g58ALMe1bRT5b5NupClzWBnJQB5NhyeKPoDXZ7dlPiLUGjaEg2JZNiSmF6n+lkWDPY49qDJ+xhdMZoUswp1ARrCEaDlPpKGZM5hr3uvaL1URAEQRC6mW4TQPbv358zzjiDW2+9lZUrV2KzNS3Xd/nll/OPf/yDefPmcdZZZwFq2iCzuRtPqBhynvpSYrDne7Cnw/al4C2H4s+h3xTIHKpObjFawZQK5jyoWA+fP6qm3Tn3CXUM475V6vjIiXfAGb8itGYF8DYAmrAHGmfSGu3qxI0hF6lJw8Ne9RXyNHVnN9Jb4gGiugqOHkI+NQg1OdSgc8cSQEKXPpTUQI3aSqnRQMFZUF4IYQ+DjHYyIlqcYZmUcJCk3PF8r0AsFqPUW8oQ5xAW71lMri2XPe49GLVGFBRGZowk2ZTM4NTBpJhSMOvVz3JI6hB21u+kILmAAmeBaHnsZPaYpKbekdXOh9oy3yHOEARBEITWuk0ACfDSSy8xfvx4Zs6cyaJFi7BYmtK1zJgxg4ULF3Zh7dqhzdZQDSTlgKtUbZVc+ns1oIvKkNwPgl6w54KkUwNKd4macPyje+DHn8CV/4H550LNdlj/Blz+OrJmKI0BZHj0j8EeUIPA3HHqBBlHi+UkbVmJK92AOtbx66fVn0dc3pRrMiarrZbfN6zRrDOryy2G/eCpgt4TQCNB/mQCe76FiIucvVux2LNB0tF/8EUEQ9V4dFoC4QA52TnoNDp2u3eTmZWJRW9hZPrI+IQYf8RPoCFtkUVvYXj6cDFZ5igJusLc6Day5tWihHGPOoMGk01/wPMEQRAEoaVuFUD279+fDz/8kPPPP5+pU6fyn//8h/79+xOLxVi8eHH3mW3dUY0TZwC+fUHN8xhxq0m/Q7Xqe9mjIBqC3pNhzzI19c6Cm+GCp+HiF+GdG9Ru6n9cgFHf1N0c7TsNBg/oWH181bB6vjr+0WCD/tMT31//JmxUA1TSBqspfgwhINY0ySa5F4FqO7IvTMBix2JMgozBYE0l22zFJIdwOp2kmlNJNiZzcuxkDBoD/Z39Ey4ViAaQFblnrC7Tw0X8UQxIDL64LyOHNo15Ndn02FPELGxBEASh/bpVAAkwadIkvvrqK6655hqGDh3KtGnTKC0tpVevXtx3331dXb0jN+g8Nam3rwp2fQEGuxo8usohWNswJlGnvrZ8CKVr4ezfqkmp37sRPOVoy7+NF2f+9KdQMl5tZTQ7AQlS89U0PVqdmpbHVaLmg1RisPdb2PRe0/raJ9/WtEa2HIYlj8C6f6nbRoc6czzoB38loKhL6tVsgT6TMdt7EVBimPtOVSf5+KohFiVVYyLV0ZQw22lyUhesw2lytnocZp2ZQDTQs/I7NtdyyULo2LKFXcCSZia9t1i3WhAEQTh83S6ABBgxYgTr1q3jk08+YcOGDQwdOpQLL7wQjabbTBpvv5azxE+9FcLXwce/hKqtgKLOwk6zwc6l6lKH4YA6qcZbqrYSvjsbTroJrn0fqrcR+fYj4BUApKALipd1vF7pg2H6I9BrvDru0VsBC+9QA1YAayakjwRvFdCQGzJ9iDoxSAMUfoBl6r1Yss4Da8NSigaL2tVtSGxJTDWnkmpuexa4RW/puS2PB1qyEDq2bKEgCIIg9DDdMoAEdc3r888/n/PPP7+rq3JkWo6L1FvU1+DzwJIBmQMhb4y6PrakUWdqBwMQC0PqUKjdCnJQnYldugbOepTo0EtoDCCD5z0HVpeaZNxbCe4ytUXMV6mOZ9Ro1XIbWzWtaXDqXWpuSV1Dy+Peb9Ug1VepBooykD5KzV9Z8jUYrBCNgMGglhENgj0VvNWQObj1vXXXmfGd7UBLFsLRW7awZeume3fnX0MQBEEQDqHbBpDHjcalDA0NwVWjEZfAiEvV9+tKwJGppvcZci58/CAkpYMsg84CwSq1C3n/anjtfMx1TUvRGb59CgaPgNFXQf+zIBJQg7zm5HBiq6CnHIoWqeXt/Q4qNqGuVqMHS446Yzy1rxpQSjqIhtWANhxuSFRugppdagtc4zKNPbUVsTMciyULLanqM15wc+J+gx5ys9UE86ltL/spCIIgCJ1NBJBHW9ivdhHvWQmeSsgYBLlj1Pe2fwGl69TJJ9nD1WNtadBvEuxfC1lD1dQ8tbth3/dQ8i3IQRSaAkhNXTFs2aOOl0wfAhNuhmGXJK5tHfKq+SV3fA67vlQn47Skt4E5SR036amCnDFqfSxpoERAa1ZbFvueAhWbwWiDrZ9AoBJ6nQw5I9VE6dbUtpOSC0cmOQ/u+L71eMu9y2HrqxB0dU29BEEQhBOSCCCPtsZxgXUlaite5damALJ0HYTcULlFTY8Ti6rHjvqR2hJpsMDG98CWAsm9oW43xIIEvCFAXVc6ZEgHI6CEoapITf/z5RPqmElJAzs/V1sZY5GmOkka0FvBlAJ6o9qyqGkMSiPgK1WDw96ngcWpBoaSFgacBYMaur7r94HRBGWbwb0Panao9dfbYOh5Iog8GpLzWneLd5MubH91gKq9noR9Yna3IAjC8UsEkEdb47jAnJFq8JgxqOm9nNFqEJkzOnECir4x6NynztJGo6baURSoKARjU1Jw2dYLslLUZOSxMJR8pyYf/+K3ifXQGtVWQ6Md9Ha1tdLZV+22Ll2jzgqPhECvB0kPcgiSMiD7JDWRebAOkNS8kINmqDkri7+EncvAnAa1e8DcMLPXVyMCyDaUecuoC9Ul7Ct2FXdK2cW+/VCzOb7tNDrJtmUf5IzOobfoCKOw5f3dbHl/d8J7OoOGqx+eKIJIQRCE45AIII+2xgklvcaqr0YKMOAM9QVqChxfQ/ekwar+LAchpQ8k5amBZVIObPsUar5oKseeDRGXuixiUl+IKlCxEcKuhgk0OrUVMeiCmFadrBMIQExRczoOOlttOdz5hdoyGfI3tFA6IG0IpOTB+v9A3Q412Gy8H2dvGHctZA1TA2OTAzXANKnd2EKCMm8ZMxfOjCdNb86sM+M0tk5x1B5OvQ1zLMbcTX+DTX9LKHPhzIVHPYg0JRmY5wjx8uVj6J/RtHpUbZmPJfM3E/RGRAApCIJwHBIBZFdqDBqtqU0BY2PrXeM+a2pD4u9P1NVszEmQMQz4XC2jYDJoS0BngPr94C0DrQJmB6AFZLX1UW9X95nSwV8OSOqYzFhUXTvb4lSXMlQs6hhMowPyxqmtoTojmJLVGd6+6qa0PdA6MBbaVBeqIxAN8Pjkx8lPyk9470haC7PNaSzcV0bdrHlqzk7UVs25K+ZSF6o7Jq2QHo2CPdtCeq7ILSkIgnCiEAFkV2oeNDYPGMMN6xM789TWyL0/qEsZ+uvUQDGlWQBiSoHMdFAkdfJL2N3sAg3jGiUD6LXqkokFZ0Dxcgi5wJYNe1ep629Ho2pXut6sBojJvdRWUoMFep8Ce76EzOENdWwWQAodkp+Uz9BOni2dLctkO/qKWdiCIAjCMSMCyK7UPGi0pqkvSVKTdzdOqDFY1ZQ6yX1AY4K0QVCnbSojdxRYPVC5GUJ1YMqASD0gqTOx5TAkZTfkgdSDKUlN91O9RU0WXrIS0IDGoI6dlDRqV/jAGWrwqDOrXdVDz2uqa/Pk6CdKzkfhoHZUehO2PZVtJFcXBEEQjhsigOwqkqR2Vbc12aTlii7WNOg3Re2CtmXCF+82Heuvg7IvQZLVFW2U/eokGFMKeErVvJDlW8CSrE58qdisBomOXLU7PH0I1O8BOQJVm9UWyP3fw+Q7EutqTVO7r/eugtR+auoh4YTntBow67X87K11CfszohI3YKLSEyQd0bUtCIJwvBEBZHdksKqvRr4atSWxdq8aQCblNr239RN1VrbBACf9WA06d36pdj9XrIey9WrrYu5IMKWpLZtKTC1fa4KxV6nB4LKnIFAHwXrIGqUmOG+ZHLxmF0S86p8nYgDZct3r7rTmdfO6HMPUPrnJZpbcO5U6Xzhh/+bCaqoW7MEdiB6zugiCIAjHjgggewJrKvhrIaVh7GFqszGQNbsgPwuc/dWVbPRmcFdA1Kum9uk1Drx1kNILrOnqRBt7mroWtjm5adWa3FEgASl91WTh4TYCyNR+6vVS+x2Dm+5mDrTudVeved3WCjXHeHWa3GQzucnmhH2eMj9VBzheEARB6PlEANkTWNNad2s3siRB+lBIK1BbFyMBNfVP7R7oN0zNO6korccqNl9iEdQ8lVlD1WULNTp1f8uxjhmDT8yWRzjwutdHa83r9mprhZqG1WlKysqI6ZtWqCmpFeMSBUEQhM4hAsieQJISu7WbB4ODzoNTLlJnbjcGhLZ0yByceH5LLbvJGwPU9W+oyyb2mQST7zx4GSeiY7HudUe1WKGmtlztzn5y8Va2B5Pi+xvHJTrM4q+9IAiCcGTEvyRdyVt15OtHpxV0Tl0aA8p936spfvZ8lRhACj2GL6SOO7zulL6MHT4pvt9T5mfNq0Vk2EVib0EQBOHIaLq6Aie05nkgW/JWQcUW9c/2CPubUv8ciT6TwJik/in0aDZZT6asib9SZNGKLAiCIHSOHtsCuX79enbs2MGll16K1FO7V5vngWyp5co0h9LWGMnm3drNu6sPZvKdouWx5Wxr6F4zrg8h5FfHrm74XKbsg1UJ7+n0Eiab/pjVxV8doGqvJ75tsunF0oaCIAjHgR4XQCqKwq233sqXX37J7bffTm1tLampPXTt5QPlgYSDB5dtaTmmERJbJdsbQPbUYLyzHGi2NXT9jOt2CsfUzzo35TWGJtUlvJehiWLXfAIc3Yk/eouOMApb3t/Nlvd3x/frDBqufniiCCIFQRB6uB4XQL7++ut8++23rF27FrPZfOgTeqqDBZftdaCZ20KTtnI7tjXbGjo047rMW0ZdqCl4K3YVd0Zt2+SpDRL0Rpp2hLPRyQae7VXd6lhzTGHhjk/JzjmpaedRmEluSjIwzxHiV9MHkZeifv/81QG2vL+boDciAkhBEIQerscFkG+++SY33HBDPHj0eDzMnz+fvXv3MmPGDM4666x2lRMKhQiFQvFtl0tNd+J2uw90Srfh9XoTfj54nY0QlCHYcEzYB+EAGMztb5XsphrvW2mWbqjx57fefw2z5eD/wdBEfKQW/hMpFknYr2gKqCiOEi0ta3FGGbDpkPXyyW7+vu8PhJVQwn6DZGTTmjpK9Z3XHR4JRCh6fzexSCxh/3mGOQy6IovB/ZLj+3aXreXh9c+w4pOH6Btpds86E5zxEJiSOZjde/bFfy7c8hG+wPoDHlvnD5Nt2MqLS7+M77PIBkbqcnhr8TaSnU3LcQb8ASDxcxQEQRC6N0npYb+1p06dyuTJk3nsscfYs2cPZ5xxBsnJyUiSxOrVq/nd737HAw88cMhyHn74YR555JFjUGPhaCspKaFXr14A7Nu3j7y8LszLKBy25p+jIAiC0L31uADy3nvv5a233mLr1q1cfvnlnHLKKTz00EMAPProozz66KPs3r37kP8QtWyBjMVi8fGUHo+HvLw8SkpKcDgcR/V+uhO3292j7ltRFDweDzk5OWg0akKBWCzG1q1bGTp0aI+5j5Z62ufQ3OHUva3PURAEQejeelwAuXv3boYOHcoVV1zBRx99RFlZGVqt2h0WCoWwWCwsW7aMyZMnH/Y13G43SUlJuFyuHvcP+JE4Xu67p99HT65/T667IAiC0H49bgxk3759efnll7n++usB2Lt3L/36qWszb9myBZPJxMiRIztcbiwWo7S0FLvdjsejph3pCeMhO1Pj/faU+z5QC+T+/fuBnnMfLfW0z6G5w6n7gT7Hxr+PPTZN1wlGtCQLwomlx7VANnr99de55ZZb6NevH08++SThcJh7772XX/ziF9x6660dLk+Mneu5xBjI44P4HI8PYiyrIJwYemwACbB161Yee+wxVqxYQVpaGvfffz+zZs06rLJcLhfJycnHbtxZG0/9QB+EoigJ6RmLioo45ZRTAPj08+Wk9y7AadGTajOhKBANy0RCcvx4vVGL3qBtWSxIsKPSQzgaw6DT0D/DfgQ3dOw1jrerr68nKUld8/mYf449SMu/6jurvE2ffXriZ6/Q9D3SG7XoDNqG791EQP3epWT1xabTkOG0oje28f1qJ/E5Hj1FRUVMnKh+ZitXrmTIkCFH7VptfY6CIBy/ulUXdiwW449//CP//ve/MRgMXHrppdx9991YrW2nmxk0aBCvv/56p1y7sZvM4XAck3+wIqGmf5z1Bi01ZV5cVX6S0i2kZNvYV+ej1BUkJ8lEbrIlIYCMagzxn1ft83HxIBsRrQaHw54QQH6xsZTNFV6G93Jw3km94+eUNCu7d1Yqtb4IKVY9DlvPzM3XvIvzWH+OPUnLALK31siidfvZXePntAKJcSkOdhTX4ZUUSsNhyir9DMmwMmVEDg6HGZvNFj83qjXhwsAeTwRbmgGzRhv/HqUd5vdIfI6dr/lnZrPZjsmzFEMOBOHE0K0CyNtvv53Nmzfzm9/8hsLCQp588klef/11Fi1axMCBiUmdP//8c6ZOnYpO161uod0iIRklpqhBpEGLq8pPNCirf4Zl1m+owG+EkjQz0wZpSbUaAfhhSxWffFsSL2e/K4hBpyHF2rQ8XcATJhyIsrnEjZcYGzdUk14dJaOPnT5D0yl1BXH5w9S5Q5zUK5l+yZZ4C5LfHSbgDWO2GbA4DAg92/+z99/xltX1vT/+XG33fnqfXhlght5EUQQERVQskJhEY4zXxKs3xZCem+SS6PXnTfI1MUZz0VzsoIiCCoj0AQZmmF7PmdPr7m319fvjc+oUHGBgzoH1nMd+7LPXXnutz1qftWe99rvOn8/JwSIT/WXKYZlcQmF1c4yJsonremx/YZKgUmJCt5CCMjvKVRJI7LddrtwEdcumPztXfzQZVhmrWwQViZGiTtSFatnCjL98Aenj4+Pjs3RYNOqrt7eXb3zjG4yOjpJKpbj55pv5zd/8Td75zndyxRVX8MQTT7Bq1SoA+vr6uPbaa7npppv4zne+s3h+8Z7QLX1ix7QWVGYtkK7nkWyKUJyqYVkOe58cwjU8dNuiORVi4HCOkYKNElJ4un+KXEWf3c553SlWNQkL7dM/PszkQIlkc5jl57fRE1IYzVo06S59+6YY2J8FSaI5E6BuWERlhb6JCuZYifaGCM2REOV8HVmSqFdMIvFTEJCL5NT7LMRzxXU3dCBHfrxKuiVKcaqGadgMjlWR16U4PFHh3NY4hw7nSYzVOJwrgQNyRuPcpiBZx2NVR5zRYo2BEYOdw3OddZyCSaNjUgpJpFsTKBWLXMFEyukUkxGSDWH/2vDx8fF5HbNoBOTAwACSJBGJzLXdW7ZsGY888ghXXHEF7373u9m2bRuhUIjly5dzxx13MDw8vHjE4ymQrRqzbr5MJICizVkN061RUs0RDj43RiCoUBsrU43JDA6VCIWDqDUX0wO17hIoz8U3BvI22x7sJxTVOLoniyxLFPYX2VWqY02ZJOIqlu1ilCxCqkTvvimaz21mVXMc1ZO4Z2sfwwWDnsYwH7xoBaomLJHh2ELxWCvPs0yeirD0OeOUcjqjvXlc10MLKERTQfRhi1BKY/9UlZURjRYlxHJZY6zuoRviN1C4aJFoiVIfqzDiFMjn6kSjQfYfmROQg4dzrFzRTCsymViQSCSEOWmgKjKVvC4EpI+Pj4/P65ZFIyDPOeccXNfla1/7Gp/4xCdml2cyGX70ox9x9tln89WvfpXf+73fA+DWW299zcd4cKzE0VyNZZkIa1p/dSzR8KEcU0MVYq0hkt1JBscreA44psNIvs7BkRItkkJHKMDQgSzlrEUkJRNpjLDbrlIZBs3QUduT5AcqpDNBuhuj5MPB2X30vTBJvS9E3XQJByAQ1Oi3bCqHa8QlFadoEkwoqLZNLBZGN2zqho09YpJWVEYGyuTzBuGjFQ6qYbrXN86Kx1rZnI3RrFdMXNs9dcukz2vKfFf1yHiJwcNF7HydkckaEzWbnoDHuliSQFMIS4cuBQ73FjhatnCLHhguYTyCKEwaYB0so1csrDGTWqtJPaMTKdqz+3v2hTGsUBsr1jTwzJEJarbH6mSIhKYRS/subB8fH5/XO4tGQKbTaX7v936PP/qjP+Liiy9m8+bNs++tXLmSj370o/zkJz+ZFZBngqO5GjXD5miudkoCcmqogqnbDO7PczBX58h4mbTt0eKpTIZczESQvpEiNWRGDhXx8LCGPaQ2HTUHnuThVSxGBorUyxYFS6chGsArm7P7cAsmNdfFBSYUULsDHDxco9kBWQKCYNZcihWYsuuEoxLFXRNIQzVcyyVYM+gwIGJ47Hiwn4MvTBI8p4GE4RHzoKEjRmNngsJkDVVVaOo6PlO7OFWnkteJpUMkG33L05lgvsAfG6pSK1tUJ3WmyhalsMNzE2VCnoxp2jiOS99kEW/EIVCHUMyl6jhYgAKEAQkFGQkNBb1kotkOofJcv223BJM7ChhTNdRNDRh5E601xobLek6c8b+IGK2MkjfyC5alg2naYm1naEQ+Pj4+S49FIyAB/u7v/o4nnniCt7/97fz0pz/lvPPOm31v2bJl9PX1ncHRwbJMZNYCORNjNh8PFmRLN3bG6O/NYwZljo5WMIsmR0fKZIMqTW0xUukwWljFMVwSrREUTaZumuiGx7LWKB02SEmZxniISWogQX6ohuPOFelNN0ZpSIeQZAlDhrgaJpZ20JMSSsClZkEkoVEKlXE8FaNk0a7XaWyPUZiq0RVW0WVI6iJkzVVl+p8epSkdobUxxNCeSYLZMm3xMA0Bsd8dvVP0leqsaoyxqStNJa/jmI5wXfoC8jXFnbkOgzKlmomdN7EKdRRcmruiuGWV6mQNpW6zM19CMhxiFnSXVIiqEIWOdTGmSiaHagY1z6NNVelIRMj2lTHKDuG6RzQRYHVPana/mZhGKh5ArkFjzqZ/os5I2eI+2yGZDtPREqUjFT1x2MMZjDoZrYxy4z03UrfrC5aH1TD33HiPLyJ9fHx8TpFFJSBDoRD33XffbOLM3/7t3/Kxj32MoaEhvvSlL/FP//RPZ3R8a1oTs5bHEwnIY2lfnaaa1AgXdPK7JxmpmtiuS81wMas26UMVbMfF1G3UqIYaUxka1nHqBlE0QMbwXBzHhryNa7sYtoNcnLsD18dlSuXq7Gu9t4IMRIBJ2WVvwGFMcZlUXOrTujMuyVzmGGy0FGRbQnY9KoqMJEkEAW/KIRD3qOkutRDYjsNk1SAWDzDUl2P37kn6cXhSy3J1sU6LoqLZLh0tJy63BDCYmysd1JU5+Xo+Lw8vqBBrjrD3oUmoOjQ2R1h+XgsH7j9I1bCom5ByFeyCTgwQtsQQjufx5ESFR0dLNOsSy20Zy5bpp7Zg+8X+CjXJmn2tOx627RG2wRurEczWyQVharTMspYElWVx5NUegelfVIsl7CFv5KnbdW6/4nZWJFcA0Fvs5bbHbiNv5H0B6ePj43OKLCoBCcKV/Ytf/IK///u/53/+z//JH/7hHxKPx/n85z/PDTfccKaHdxx3/r8XGD1Som1lgpYtzWwfKtKZCrGyJUF7MsThsQLP7c3RPWWSqbl4HtSTMpQNRswaGJBIh6iP1QhlgshVl4m6zVjdJOxA1JYY86oL9ilbcy5CG48p2UUBNE9C8yAASEg0uTJX6nPWSkOFouShOR7RvIE8bQpyAV2FYEaFYIQYUK+aSEGJsKciRV0GTZ3+bWXC4wZqVMUwDaLLYmwfLHL92e2oqvyi1seRoo5uOowUdV9AnkZ026FmOoCHIktEQioTw3XKhRwjfUUm+4s4jsuUZNAeUAgCIaDmeWx3y1QNj5WHFN5va0jzTINVyaNfdehXXRKuxEWGil2dez/oCUOiDpQrFqjglcFWIVfQaSWGGlExazYRIDtaWVQJWCuSK9jQsOFMD8PHx8dnybIoBKTneXzqU5/i+uuv59prryUQCPA3f/M3/Nmf/Rn9/f10dnYSDi9O1+jokRKObjN6oMBoQqWCy7aDVRpdhYnBErtGSig5nVrZJlBzSQUDNNoq4KHrDlUT8mMFbDdAtWQQrti0uDKwsJdsRfLIKi5F2WNEL88u377a4axlGVIBjbisoLkeigtW1cYqW7i6jZuzcMoWQRuakZjvQ7SmywxptoSZdZAcg6LnoRRN9KKJpIA6ojIquQTzDiVAq0p0npMh3BylJR48rg7liWhPhmYtkD6nSGUSqlmINkCs6YSrHN45we5dk+QVj/UrMjRHg0hSCfCYHC4Tt8HWDbwgmI5DyPPYatnEdJktpoo271poWp5g1eYmWlcmmdJNjo5V6R8ts2O0xNfHS5xXN2bXDTtRzJqOGgqCJVEpQhmDkOFRC0NR9fjB7hE296RJEfATsHx8fHxeZ5xxAel5Hp/85CfZtWsXt99++4L3AoEAq1evPkMjE5ys06NtuViGQ+uyOGOHizS1RUl4MoPjOquSAVTXozpRZ00sxJTlkQoHoGwKc58mk+pKEMjq6CNlKpMyGcMmhgTIGJKH2Roi0REllAkSiCh0JUOEVZmIDcO7NX78dTGO8w4ppIYLANSnH/Mx8ejVHPrCLhXZQ/NgRVuc37y4m3JA4o7nB9nel+eaWoDVtkI9axALy8hJlbAWIiqBnAqQHyriKQoRoBaCSkCiVtHZe3CKi9tSvHlLJ5Y2v/XdQgHcmYrQmY5On9O55ZLku7dPSjULji6eTyIgK2N18gUd0/N4qj5GSyxEMizTnomQ101CqkIqpHAxNq0bG/nR9jHOK6kEp4WjktBYvj6NElbZ+/wE4z/qRbbnJkgGtgCXphPsaJ8rJO7hoVoyHjbR5UkawjJhSSYAaASp7ipgBmHHWB02tdAY0uhsis1l9r+C1oc+Pj4+PmeeMyog54vH+++/f0HbrcXOTCeZ9753IyCyYI/unKBnWYj+bI2DwyVCtkeLKxOVA5RcE1tTIWvhuTaD2Rzl8TpBl9mb+VQY2s9p5L3XrCAVF6V6PNfj6O4pju7Pc2jXFF7NoVSbyyCVkTDxqEsepgS2BJ4iIUuQtCTCrsQ6S2WdJbTrmOqSG6jx5ZH9XHpuK//yrk08M1rkz+/bR2/e5Kq6BnWXwLhDJVLBiIaJWhY5wyVpStQkD9OSODJQYNKyiZZcmtA4P6+jBZTZ7jrHCsgXw3dvn4Row5wF8iR0LE8wVqgzik1WN5ksGeQMFyWi0me4hGoGMWAMlyefGubyynR2dUuI6z64Dtt2ufe/9qKUbOZLOhePugR1ySPpyZA3aa/NrfFEus55tkuLJTO2s4CheYTTCrIq4WHi1jTGh6qEwzJ7kFm+IcPyjuRc9yVfQPr4+Pgsac6YgFys4jFbMVBDNpHAyU+NbbrYpkPdsqmbDhoS6XSY1hUpciMVJgJQyxroeYOy7FCvW4SQiShhcEAvGEi6SxARa1bvDPHWa5dz7oo0qqbiOh79u7Mc3jlJ/94cnj5XONzBY3xeMkNfyKRNg4ADCVcSYnR69brk8YLmYEoeyx2FRkem3ZZpRwYL7Kem+PbWKZq74/zLRav59/4x7jiU44ZagLa6DXVwjRqhYJi46WEjEUaiqQx1w8VLyNR1k0TFJpcV3U4kWSI/VqF/j06mLUpzz68ud7Ro3dvHupDNKpg1CEQg8DKE7kv9fKzppJbHGbo3NNK9oZHxXJ1th6d47vFhGssO416FkCe+4A4eDzkWH6oEkZFo3pjmHR/eQHaqxr3/shPFFZbq54I2+wIOVclDl0SggwpoLmw2VTboc5bJa6Uk0Te38ctnR9hSkglaEk7OxW6S0SQXF5OkYaB7IaqTFY4MyPTma6zUAqxvSpBqifgZ+z4+Pj5LmDMiIF+peNy5cyf/9m//RrVa5e1vfzsf/OAHT1tPbMf1qJnOiwrI/U+PMN5XItoWRm2JkC0ZNBfrrFvXSK9nMXaoRvJojbAK9YhLFAgi4zlg6nUk3cPF44kGjz/+yGbO7k4BYFQtxvuK/OK7B6lNzrUrNPDo0xye63+EgYk9hOpz9fiKe7diTouRhkQbK1o34gKuDGFX4hxLxcRjR9DmpyGTlCfzlrYUSs2lOl6jwZWZ6C8z0V/mXSuTXHHZcv71hUE25BwuNlSouJT7a0RVGU+DsAt40KRDLQYdsRCdLTEOj5TZ71hk6h7VfUWiKRETGW+PTp9PhbAmzunOoTyHJiqsbo5xTlearkx0cVoej3UhmzUwSuJ1ukuIwJciCs0auPb0+qf3eDXgnM4UFWeMsmGhGg6V1hBuHQ7YNXosYXlsXpHght/ayGTZ4I4v7aDBhWHF4aGwxZt1jS11DckFxfVQpi3jh8Z28+TEC9xXODq7v8e3PUR491PITUXutJO8r/FtxGwJyh5SQkJF/OcSkCWilsSBg1OUkZiIBtlwVXxBySfLcI47Hh8fHx+fxc1rLiBfqXh84IEHuOWWW7jllluoVCp89KMf5Ytf/CLf//73Wb58+SsenyJLRH5FIeSx/hK1iok54kImwHhJxwrKNI5X6D9cpNNVCCWCSB50rY5gFi1AxrRt+ne5RJDYHXH57Ic3szIVoVYy0asWT37/MMMHC4CwHu7RHMpJBTcgMbj1Ybbd9w/HjeW7T/zzgtef+Y2/5h1vfh8A5ZxBdqgCNZsLDY0LLI3nVZs7szm++JZ1/FffBN/vzXGhG2BLXWHsSBG5r8Q/XtLJQysNvrd9jOtrAaImqHgEYh5aIkQEkc0rdyn0jlepGRZTdQsUh+qYQVNApVqwyLQJ8TgjymcE5KGJClXD5tBEhXO60q94zl415ruQPU+IxGoWVE2IQC2yUBRq0204PQ/6t8L4HmjZCMsuAasm1pGAUGKh8NQiC/freQsLis7nZMsNF2tKx9VtJMclkQmRSIaZ6JbZM1jlqpK4ppdtbGBPX55//uYuLqrJWHg8GrJ4dy1Iwp2/bfF379ge/ume/37c7o697szrPH6n++04VY9EZ4R4S5hCQaJFATmkkDY0LNkjoEkoAWVBtxpfQPr4+PgsPV5zATk6OsrExMTLdlt//OMf5z/+4z9497vfDcBtt93GTTfdxKWXXsqjjz56ykk3hmFgGHNZpaVSCYBwQHlR6+POoQKHsInh0t0RxfI8wh4M9ObIH8zjmDZ6Ucc2AAvCWYNaycbxHHaP6SxzFEqyxy2/tpHVzeL4Dz47zrb7jmJNu6p3ajbBVIBlloKatUm5Es/WJbadwnH9bOfzTKy8EtvzWBkMcPHyOJbuUCmYlLM655kqZ5sKDz06yJ/dcha/U9zJg/kaB9MaH5RjuBM6B58YY306SM+5nXxp9wDvqAboMcGecJHLVYxkmIgsIY+4tEeCSMkAjVGVvOsSaAhQK3t0rW9ADShM7M+jJTXae5Kz2md1c2zWAnk6mJm7Y/9+xcx3IXueEHpaGMb2gBqCxpVCZ3lAbJ6r3q7D3h9DbRyyfRDJQK4X6kXxfigF4SQ0LIf8ACAJkRptfNlD1asWVcuh7nl4mkJh0qI6ZTFh1qnVHRpcDUmRaFyT4hPf3s41eTEZTwct3lkPEnMlVE0m3RZFViTKOYNwTOWe7Y+d0v4rhSPsWGVzrqky1VvFdF0kxxOtOycMOtMKaVWjsz1OrDVCMj7XjtOPh/Tx8fFZerzmArK9vZ3vf//7L+uz+Xyevr4+Nm3aNLtsw4YNPPHEE7z5zW/m+uuv5/nnnz8lYXr77bfzN3/zN8ctf2HnHrItmdnXx2Zh37t9mImyTs2wWTWZJXLUJh0MMrJvgoCmUq8a1PHQSnUiyBhqnFrFZNC06SzFKSGRflOCUH2UvfvHKE0YPHvXCAAT2Dymlbk2nSHaP7ffIh4FI3dK56i3nid7dC8AvwTIZOiJBmlanURptBk6VMWtuMRqcNe3n+B3z03yucfGOFg2+J8efGZVJ4Ehl5FhB2l0jN+5KsEXHzvCJivMxW4MqQZuxcELOHhZmTFHR1OaaImF6WoIM2YYmCGJ/eUCww8fQfYgHFHQjQzStIJUgfUhoFRiz56RUzquE1GpiKzgrq6u497bt2/f6Y2rnbkOsn0wdQAqEyCp0H4OJNqh8ox4r2E5xFphpAgjB8HZDaMVsW44JgRjshNcD3ouAtcAZCiPAxIkO6BhxcJ91/JQL0A4BdHMMcMS46oUDPoGSozqk9gVE7Oo4Hkme22LVkOj5MZoWhbmPx58nOSRArob4Yhk0qGquBWZsuIRjsJ4YQqAcl4nqGuUa4VTOj0b1CQ/qRwmLjfSVFMo7Hcp1ku4qIQDELNCeGGZwnCU556r0NI058KfmUef089EeS4U5tBEGS8lfsSkowE6Un4Mqo+Pz8tH8k5Wp2YR4nkera2tfOpTn+LP/uzPFrzX39/P5s2b+cQnPsHf//3f/8ptncgCeSIh4rP4GRwcJJEQFkB/HpcuxWJxwTwmk8kFy04He7N7+cCPP8B3bvjObCHxEy17PTBcqHP5Z7/B0a/8LgAtH/kSoaYeAMKawoN/cOVpFZGv1pz5+PgsTs54HciXgiRJfOpTn+Lv/u7vuPbaaxf0yu7p6eGzn/0s//7v/35KAjIYDBIMBn/lej6Ln0Qi4d+wfN7wHDpaYGpqrhLsYK6GZs3ZB778a1tYtWY9hycqfPo7O8hXTd8K6ePj87JZUgIS4I//+I+5//77ue6663jwwQc5++yzZ9+75JJL+MIXvvCKtr9169YXdX3WLQfddJgo6+waKmA7Hu2xEC3RAF9/qo9KzaYjoLCxNUbtQIlYIoKtu3imi1QNMiSZPBAt839vPY/wdKzl4a05jjyb46Ck81S0yj9euJq9v8ihaBK7l9n8/OgUpSe/TX3/r45Hu+nCj3PB2qtnX5uWTrxVo2vDXLKK53ocebIMHvxfdZLO5gh/9qbV/J+netk5XOLjTjOSBxe/v4NYY5CpisGnvrmDj9hNSBI48QquBGpYwy7aDDo2nuMSUhVWdCXp6UhQytaxTIdVm5vJtMdRNXl+A5zTQqVS4eKLLz7he8fN44sZ2o9NTDnRur2PQ+EoGEVoWg/hDISSUByBib0iNnLZZcKF/cx/Qu/DIHkQ64RwVMRJZlZCwyqwbaiMwtgLEIhB29nQdZFwU7sGZI9C/ii0bIDG1cLtHU5AZhmo0zd8uw4Dz2HbHlY5jxZQqYd66Ks3sbs3i1Ow0IoONiaP2x7vcNLIEZm3/Ho3D/5yGOmARZ9kYEdgdTVIIAah1Nx5GDkkao3e//z/Y3vvL198IoCbr/gkj67eQJOncovdAIpH0RHbsNFERXIJ1JYgG1c30NESIxhVSTVFxTxecuJ59Dk1Dh0t8JN/eG5BZyGAX7cb+cvpv5s9jRZHpuzIxN3T/GX08fF5w7HkBKSmafzoRz/i2muv5YorruBf//VfueWWWwC44447uPHGG1/R9tevX084GJ0tdnyyAP+pik6sNU9Jt9jYFCdbMoj0OigVh4ZokLXLG9k7OkZHWwzDdCBvUvNMNgCPR5PcP6rxZ9euQ5IkGqMlJve8wLl45IMWL0gpGiJCxLwrkEDpbuL7208tni8RzZCILMxsbm6Js7KnGQDX9Rg6VCAeUZE8aItG6MXlCBlWrw4QLk+R1DVSLREuvHwzAI7rcvb9RRJVDVeFSGsTOhCNRqlKNWKeh4mBJaske9JcdEk3U0MVGjtjdKyeF7N3mu9ZL5Yws379+oVWyfk1HY9NVnkxAVmdmv7cJsgnxTGkeiDfD8UDkAwIQVkrwOj3YaACwRA0qeCaEHcgLENhUCThOAYEY6CYkFoH5TFIWyAdBbUIzRthw1mQ7RVitHndwnHNH2tbTIjNiRolOchI3uSsTWfRJGcpuQ5D+4vYjkGTbZIoBvCAjqblXHNpE1sHD7NK8tgZsEl4Gpqm0NKcQFGnC8DnsgDEgvFTmotIMEaoqYeoLZGohJAUiVgwgcglC2DKJoYLejBIWM2wvKuVSDJIS0/i9CY+vUGZmqqLerRXtbF8RQoAq2bx1B1zqXeP3HGU7RERA/kRguhFEzrOxGh9fHxeDyw5AQmQyWR4+OGH+YM/+AN+4zd+g89+9rNIksSqVav40Y9+9Iq3P9Nl5mQdM6YqOrmqxZaeNA1R4QYfytc4b3kDU/0VOmMhhnZMELUAF0KyjRs0cSIqRs3mw5Ug9z01zL9GA3zyTStp6oqz7OxGju6c4up6gIPbpqinVDqLHuN9Jc4HCvEufngKY7fdhSVRbDzGHIv8WJHSWA1psE7I8pCAMcVlXHFZno5w38EJjg6X+U1dHM/GK9pnt/Hsnkkur4pLRY1qKARQFNEFR/JAQSJICEmGptUp3MYguubhJsW2sqMVipM1kk0RGtrPUMH4ahbKo0KYdV9w4oxnswr5QbDqkOoU61SzkO8TyxtWQft0AtfeH4NVBtuBVCvkR2DoWTDKkF4hrJOOCYURyB4GbJAC4NQBFbIHoTIMahKGn4VoO8TSMLITZA1cS1gbz30/LLtU7HPygBCMiRZIdYkSQPUcjO3BkBNYHe/G8jzauxMUe3NU4lAvwzJXYUB16LYVfvz1fbz3dzfxRFghWndYayrUJQ8Mh7HeIi3LEqi/oozViajJIlnjUlmU5wlEFeq2hYKMI0vokSBUTFor4BZMPDzimcVVOL632HvcsnQwTVus7QyM5uWxfEWKS86fG29Ey8H/FX9f+ZvLWL1qDTv3TrL/h0exavYZGqWPj8/rgUUnIPv7+/nBD35AIBDghhtuoLu7+4TrRaNRvvzlL/Onf/qnbN26lZaWFt70pjfNZvq+ErSgMiceT+DNHOgtMHm0xLCmsOWCduINITrSETa3pTjoSIRdDzwJI2gRT3tgWBCOEEl6TA24UHd5TzXI1p/2c4ei8OGLe3jzrWt5MqRw8Nlx1lgK5aLDYKSPTZJCobKMFemzTmnsR2UDM2iCBEOKSwCJjWMOqwbrhKdNgBXJY0fMIbo8zlVKnKfHivTlanywHkBDonVFguXnNOLYLq7jsvNHfQSQyAU8VrdrSFIAFYd4TEKfkJCRcICQCoHDJZ7tq0BDkMlkkMAyl+JwFVyP4mTtzAnIaIMQj8HwtEWxUdRmHH4BhneI2o4tG4XruDoGUghCcWEtNCrCZTy+c64AuOuI7OimNdMWS0u4rLUA6BPiM2YFvLmi79RzMDH9t6SAEgDFFYK13A9OFdLLIZ4W7u3ms2DvT+DoU2K8xSHQ82Kds98rrs3sEQjFCKsJvO5ziGkq0aRLWxCGRyokFQVPkXkOk6aCRzhr8OTP+rnpd87i7n/bScaUmZBdLDwSJgwfLBCKabND7mhYeUqn92gyTNiFFdMJ1a1rEgwcspDRUJGIByU8L0QsIEHNJpoMUa+YjPUVkENnVsikg2nCapjbHrvtuPfCaph7brxnSYnI+YQTc3OZbAnR1B0nMuFnvfv4+LxyFpWAvP/++7n11lu54IILOHDgAJ/+9Ke57bbb+Ku/+itkeWFvZcuy0DSN7u7uk4rMl8sC1/UJBKRctbHKNlrQpZLXsUISuarF08N5vAkDreaQwcXSHSYHs5jOJGErDkTRQuC5MpYBFxsaffcc5R5N4f1XLOOym1ez4aI0D37teahFiFdXEtH2sCXxT3QHr4Ab/5lsaZS6WZ0t5PzfLl5BczhH0Umhp8/GW7GZvOsRcxVuNDRi3pygrgZgMqMynFHYNVmiMjBXGujNapguGxRV5i2/to7otPXw6Xt7CZQd6pJHPuyQG68iYaCjUVEd9LJDDdASoJqQG6zhhBVqkzW61zVQLptEkwGMuk2y6ZiC2a8mZhVIzPsbaN0oxFq0QbiCzRr0PwVDT4GWBNsSbudaHoa2ibjGaDNsvBHMshCE43tg5AUY2SYKiBvlaWviKCe8WGYLRR6D5whRWp5LeqAyClP7ILUctKiwTJp1kGUhZDvPh/HdIiYy3gLrroPui2DgaWKd5xHp205toJ9SqpPhyArsnEFFN5FwWY7HcwmJy4sKg9sm2XBeCx/4zBa++4XnabZlhhSHMg4djoJeEa0ybQXWXfFubsm0opWLC667N7/tXfRI68BWaF7VxHcza7jcUJFckAMShVwFs67gYlIDorJKIiARVoKkWyLEMyHG+grYukO9Uj/+/LyGtMXauOfGe8gb+QXLe4u93PbYbeSN/JIVkD4+Pj6vFotGQFYqFX7t136NH/zgB1x55ZU4jsMXv/hFbrvtNvbv3883v/lNFEWIutHRUd7ylrfwD//wD7MFxV9LunpSqBYomkwsHWK8amHaLm2ZMNlhncaYRquao1AfwSVNIZoEL024WECSQgSjLpmOKGP9Ot2Owth3+vjc1nE+ePNaup//73wo9ijbpJvZWX0nI9ZGRqyNDKoW1Y0aV6QvJntwcPZG3rn6cyQiaWYdsrWFY7Ukmz2ay37NxUUiXjKhBJ1IhDWN1Y0x1oSCWAdKuHhsenMHicYw9bLJEz88wpFnhcns4bDJlbKGDdgolIMQ9FQIOIQyGtXmIJ26RyykYTgeoZCCkzeQbIfmlQ2oAfm0WIdPGXOeKJnpFiMhxGNgWsgGIkKchRrBNiHdAXIARndCICyEmlmHoedBkYXV0ChAoV/UgXSqUJ+EmogXZNnlIh4y1QWNayCzQtSIlGThkq6X5v6uTkFpRGyrOgmlYeh7DKoTkDsESlC4smUZ1Ok4xGBGjE8vwLNfhVoOui+mtulj1L041Wd/AJaJM9FPYuM6okFAt6lToCSrXNTRw26zxFl1hfu+toe3fXAN7/7k2fzwX16g01GYkl0eCZnEXIk1lkrckWjMOTS2XYDZ5nG3cRSeEEN5/5aP4RYjhOMav+hRWN9b4mJdWLuCCRkkCRkwUXAliaIHZ29opKMnTduqNFpQIRhWsU13UfTEbou1vXYisTA4d83MJ9Igrh0fHx+fJcCiEZDPP/88ruty5ZVXAqAoCn/4h3/ImjVreN/73sdnPvMZ/vmfhWjKZDKsXLmSz3/+89x4442vqTCZiedrW5Ui0ybcsVZV5/n+PJqiclFnDW98irj9DFK4HS8WpGQsIx0oIaLEHMAg3hjEw6Nv0CNqSQT66/z4fz/POZFuzo2nueDX38raxkvZ9rN+Dm2boMvW6MpCMZclq89ZDl3Jmn62CUhlknKWiFxGk3Q0Scf2NDL2Ss41WzlhFktRx5geWfvqFCvPa+aJn/ax58EhPFO4X58OWrQGJTRJwgYKCZlgXKEhHma8oDAWgHPObeAD5y9Dtx127Z9icG+WREOIkKahBuTj9/tqE5gnSgLTLQdtc2Ev6kAU1l4HWhyGt8Hue2DFm6H9XJjYL7ahBYWbONMtlsmauPmbJdEn26xC01q49h+hezqT2NZBPaZElBIQ41Cn4/6iTdC8HmxjTtDaOuy6C575iihW7kzXKTWrEIxDZQRKQ0LEAjz6Beg4j3rPr+P2XE68tZP8SC9T0TSVbJVkMgD1ESJuEQLjWME2Us0aI0M27bbMw//vALHmMGdd2sauraM02jJX6gFcPEZllz7VI+pJBD3o01zOc2I8Pn04hVGDdCLGjgaJ5IEKl1gBcZghCTki4aJhY6EioYagIa4hu8KlmmwIU8zWCUY0Ypkwkmad5slfxBQG4UsXivCJY9Ei8MlnfBHp4+OzJFg0ArK5uZlCocBzzz23oL7ju971Lr785S/z0Y9+lJtvvpkrrriCYDDI3Xffja7rr61VCyhO1rB1h+JkjXAsQL1iEokFQJKIajIN5fsJ2f3U61lk2cMOt5JKKURqebxIFJF6UqWtVaE5VKSltYVdYypTAwaNtsKu2vXsql1L59Zm1p5bYcvV3fRsTNL/0MMcHWoEL0SKuSxr2dOmn1VsL03WTXMC2wYAUTlLUh3DTXRjBVtxZQnDdtAdj6mUwvNGmV1f2EaDLc7puOLydMzmhjUNhPpF3FRBC5DSIWQ6lKUa/SEYrOo894sjjOZq3HBuF8Q1WtalCejegp7HrykzsYozfweiC/tPz9C0RlgaD90vWg0OboVVV4OiQLBBWB21oBB6jikshPmDQjxKMpxzC1z0cSEQ8/1im7UpCB/T49tzYXAblAZh6rDYZmaV6ErTumkuu7r7ImjfDEYJ9v0Y9v5QbC+3H4w8BKOiW83MZhWVsDuBjkFrk0Fu7CDjlRXsr2SJ1SzSmkZYs2huSdPaHaSqOuwNqjw1VGNzTYGJOvsm6jS2RrAiMtUpHblk0+EqdMwL3+x0FErG3H8XLWuj7FIUWgZ0Uq4KEsSaAkSbA+I7GQiSHa4Ti0OTpmCUbQYOZNGCKj3rm8RPGe+0J+YvfmpZIR7f8x/CSj3D1EG4+2PifV9A+vj4LAEWjYBct24dl156KR//+Md54oknFhT5/shHPsKdd97JV77yFa644grgNSwEfswdLtkUmc0orldMzJqFXrXoigUZBBqVCrlagXE7wvONa2hrS1KjzuDkFPF6ibhbYVVgH+aRZiqlFDWrQhtxLs38jCbrEE9Wb2HSXM/QjixDO4QUDCp1GpU860IvoIQijMhzSQchpQCkUTDQJANN0lE1DzUaQ2tuxGuIEXD7adIfp6fwIK31QwA8YZ/Dp42PM0mKmAtv7tPYYqmARF3y2BG0qGse17gy6pECpYqNGwijJhysnEsFcKuQODdBabJCZ8Wl+Og42wsuZ7+phynVorFRQwsox5/EM8WMkJyPJEG6W1gMJURZnRe+KzKkFQ1CzcJyWS9A/ohwNbsWpJfBO/9JuB2VgBCnhf7p50FQVCFWa1PCcjm5T8RLnohgQgjZ5o2w5u0QaxEJPS0b4dLfh21fEy7r6jhUEbGZoZRwc2cihKMS4drz0Hs3ifIkq8nzE2kNiajCgKTRXW+jYSjJ8NgkFdOl0dMIBSR+phq0GgpbDJXCmLCIpVsjJJelAKiXDKaGqziWS6Y9Sjhqw3+JIT8/UWKtoSEj42oSWtjDcEyMURMXsGWLiukSssB0JSzdxbXB1l2QIBwPoAZErHHdeANZIGdoXCMs3T4+Pj5LlEUjIAG+8pWvcNFFF/H+97+f733vewQCgdn33vWud3HPPfecwdEJGtpjs5nEtZKJXrUIhlR6kkHWd6VAvYmS+gS9wytJWsvpqo3Qpu2h15gkooLnBJBkDdNqICiXiAWLBJxxNnE/kqxzxTUh/vwZBXXIoNNxaXRkDCfMsLOJYTZBHUq1uWB/3UkRiauoikHUnqLR6SMqFwlZJaRhi+1DK/ilew6TvBmXK7lc2sdVHMRw0/yxk+OI20zMVoTEk8BNymAXOV8KoxLCw8KRIbIiTE9jAyPDVaqKjuGAHYPGhigf6Uxy4Kf9RGQFadykKREkYXnU6hb7j+bpXpGkIbq4SrYsoHktvOmP4Il/hvF9wjooyRBphGASxveKWMTCUbH+uhvgnf9HiNHsEeF6/tYHRFzkiyFrohRQ42rxmeyR6eLkJZG0M7QNnv86xFth+ZXQc5lwi1/6+3D2B+Dx/wN77hKJQB7QuB7ijUL0Th2E6iQNap1CspMrG1o4MlLBRSGUD2Kg0iBFMGUd07FRkblGljgS9vhO0uGsAmwyFfJjNfJjc+5VS4JsGKbGy6jluXqNHXkFOSKhRySWrY6THZpfyzGIokFYAwmFaCKIFnaJZ0IsP0dE685PVKsb+Pj4+PgsMRaVgNy4cSN33XUXN910E9dccw133nkn7e2iHuFjjz3G5ZdffoZHuJBIIrCw5A/A6qtYpYXITw5iynmUkkFjbJi0spMSTViyQtFpR5EcFMmkKdBH2BlDqup4iQ5az7uRr5wHP9qdxXvgL7nReZBnzQv5kvFRVsXSZJCpji8se2KXbWyC6Kwgy4rjxnnhglfnsp1zZ1/NlInWQpBucsmkK/SPDVOSAlT0bjRFIRZwUQMBGtMRJo+WCSkQaglAd4z25ghr21K05F0qw1VaVyRpiAYZLhvsH6gQSwTJVa0TCsjBXJWRok57MkRXJnrc+68pVl2U8DHyYAJKBMINIiPbNsCazuRedwO85ysLC3r3PiLEoxIUSTNqQFgIAzERu9i0Bpo3gGNB/JhEjcq42NbkATj6OAw+LQThzu+IBxKc9V5461/C2/8WLv09+Nlt0P8kTO4R1k85BHoW3ZORYu2sDuY5f/CP0FHZ034J2+rL0GoxCAcI2SHq9QoeohbochTOb4xzt1fkCUOn1ZFpnn602RIJT6a1DiBTmmdJnpItlEaJjlQQSZlb7hKkqkIyDPG4jCYrrNzUxMbLOwnHNXx8fHx8Xh8sKgEJcM011/CLX/yCW265hTVr1vCOd7yDoaEhZFnmG9/4xpke3nEsLPkzXa5lbA/NSZuJgkPr6hYyUxYEHYLeOFNuJwXSrEhbNJiD2F4QuTbtik904ul5JODG1kkkHkKSbH6aehOP5cM8Zuq0keUsfjG7/5uSnyQeX0HZTdMnb2AishlVTRB0JcJWFaoTeEYNz5NxUJEjSaLdK4gE66QOfZW4NMZk+moKmTfRHDhEMBlFrw4xIXfgxXPE7WZUSUHOl9nuDuLWQInLtCTCvPntqxl3bEzbpXlzExe8uWf2XNRkiXRrhJrlkomeWDiMFHV002GkqJ85Ael5opzO0DYIpqEwIAqBO7XpCjyuqAfJdIH2xjVCUM5QGIBd3xN/r78BNr1fiECjNLf9Wk6Iw0BUuLnnU8sJt7djCdf1WTeLEj/VSZjcL4qf7/4+DDwFl/132PBuuOkr8OPPwOEHkEafw4s2QyCCF2zAi7YQrIyBrBIuDnF+4yTn37iWp7JRhg55kFNJEsc0iwSQsYBYROED6Rglx6XiujhAojVGNlunUnPRDI9oRCWjzIuBbHbpagjjIcJFJUmIxxqgNgWoxlTW9qRJpoKsOKvZF48+Pj4+rzMWnYAEuPjii9m3bx/f//732blzJ9dffz0f+tCHFri0FyUzVqnWjfSwm57WZXg9l8AzfWBWqUwYlKw0EWmUSP4oYa9I3lIIm9NiI92DNJM9/MKdSK6F13E+f9UwwIdqkwT7fsEyp4++kMNXp3cZkGu0hw4iuRbreBxs8KLd0HGecJVKMl4tjzS5D+noowB48tl4b/sC0oarkH52Gx36frYPTxCLj2GWUnRV8+CWyXkrsZQisUAaU5OZLDqUAjHaZIVgcxhN0kmaJSZrKunGFJHE3PzMiMZMVKMxdmL3dXsyNGuBPGPM1IPMLIfYC1BrB7MoXMTlCRHvGG4QpXNAWBDVeeO16jC2S/y9/l0QTomMbkmBiX0ibjEQF4XAYy2w+u0gT//gMMqw90ciqaKeBzyRiZvqgU3vg3XXiy422/+fiL184K9Eh5tVb4N3/Qv85H/AgfuQquNgiYLnIUvHlgPITlUUOw9GQA4TCnjITUECuSoBr06xaFHHxiVI/54pLNsjBGgEUTGwciZpy8VDtLE2qjCRn5w97JHBEtSqSCgoUh7T9qgGoGNNnEpMZXVngkvPaccDIvHAa57s5uPj4+Pz6rIoBSSIJJlbb72VW2+99UwP5aWz/DLxAHBdkRih5/DGnyXs5MCTiKBjOxYRJDTPFOvOZF8aFdj1ffH3xZ+AQw+xJvsQknMUDwk3lEZkU0At0gGpCJ6tC5FjVpGKA1AcwIs0wtp3QGYlbL4Vb2Iv3PeHSGM74cE/h1+7G+/oY0gH7mNz8B4O1t5Cze1C8mooRiNRRScaqqJ2eATtAEY9RSQapqujkZXdKer5CvGITCwmIWkLLUyNsdBJheMMXZnomXddg8jK7rlYiL7KV8CuidjH4oBIntFLwkIIovzOfPoeATxoO0fELoKY86Gn58rwGCXxyPUKsdl6tsi2nTrIcUXGrZpIuHn4f8HZ7xc/BC7/DGz/L+Fi/8HH4V1fgrXXwg1fBFmFfT8Co0zEcxCW0oDoo61ooquOVaVn+UaSrTZYLzA2FWeXrhM0UlQsd9oxrVEJqGgKuPUwCjYSEJzeonLMSEuRIOe0RyjnDcp6EBMHKQpvubCTWCpEKKqhqDKSLJ20n7yPj4+Pz9LlFQlIx3H48z//c7Zt28ZHPvIRPvShDwHwv/7X/2L58uWzr9/wpLpAjdMQ1okYVRxqBHDw8NDdFEEKYj1zusWYayM506KydRMcemjaQgWseDP1UgT4lngtSeKhhUEL48VahAt0ZDtSbQpv+zeQui4RyRjNG+C6z+H9+DNIg0/j/exP4Yb/g1fLIQ1uZS0/Ykw/nyG7h4iSIxjKkFSP0lV7Eim5jJXnvIdIVxdauIl6xSQcVNAwsQgubZGgRcSj+wLY+mUhFisTQjxiAmGRRAPC4jifyrh4bj173kJPlPyZXe4Jl3Q1K+Z44MmF+461TGdzB6E2CZVJ0bLwhW+L7XRfAhd9AvbeIz77i/8Jy68QLvHrPi+eX/iWsGh6HgSAan6uSHmtSMMHrqU28jRlJUdXeohqbC37ppIEzSDBooVpe2gSTIUl6nEPqSGCMaSTNhVqkkPCUfGYszCHNIgnAkSDCiHFY7JYoz0RQdcdVMkjFteQPF88Hlc0fOrgi69/7Pt+cXEfH59FyisSkN/97ne59957+eIXv8jXv/51xsfH+fSnP83IyAiJROJ0jXHpML/OoDZda9DzRA/kWBPhlg7C2hGoe9RTy5CsGoHQKqSxqrjRj+0W21A0vFgzUmUCRnZAfUoUwAYhQkqF2V0q9SwUJkXChqxCogPUGHRfije5H6k8AoNP4X3rA7D6Gkj3wNp34O39IdLzX8eLNcG7/xVv67/Cs1+j1d5GKniEydRVNC5vgHwdiQjW1AsomQ4cpUpi9SWEWxsYKtQZKbq0JzW6AvLC4z+2XM6ZxvNOvNx1xfxUcxDNCLE2vgtKo6BGABWUKChxsV5uOut6Bn26NI9ZFfGQIKyIWkQk3uh5IQLirSIDuzQqhKmsCmumo8/Vl5yh/VyxrdwR2H2XyNZuWClaGeb7RFzkw38P539UrP/2vxPn+9mvCoEaTkHbJrF/NSAEcX4YDv6YlrHt5EOrOfusq9jYojOYaKL3BRMnb2OoLslMkOaQwoRlUeuSKBkeATXAIA4xa14fcxmskEzbujTnN8Xo3zOFrMjkp2qkViawJEjGF3nIyavNyYqGaxFxTcwn0iCW3/2x49f1i4v7+PgsQl6RgNyxYwe//uu/ztVXX83VV1/Nb//2b/Of//mfp2tsS4+ZlnkTB8DSIdmBlOoS4qR5jUjIqJ0N2+9EKg7gORZBWSbiFQCQskcg1iy21bIJKg8hlUdF/JwaEu7pWCOSM+caDjoVJEcV4gaQ8iFoWC2sks3rhUiZ3I+kF2DXd6BpHfRcCiuvgiMPIT36BUh2w+X/A7ouhp/+CaHaFF2l++BQM7oSAUVBNsuolUFq2QIjoyaxteeQL2Wx5QQjtNCVFt1e6oZOrWoQTgeJBBZthITAqok5Kw7B1CHIHhU1GdWIcGFXxiAUE77bGQFaGlkYA6lMu+6jjZCcvsk3TM/16AughKDjfLH82a+ygOo4SKoQeQBMW5PVELSeJWIlpw7C2E6INYHTKEr6/OxPhbBs2SjmUlbhqr8Qc/3L28XxDG4VHXYq41AtwqNfIFUYwLVLpK2jqGOPIRX6WNG+Eefs96NP6oxUalghhcZEiK6YxtaDWWIJie5kiHDdw7Hn6jWm2qLQEWXVxkY0w6N1eYpKXifaHKI6WSckSRB7Deq0LmZOVjT8RFbFVJcQisdaK/3i4j4+PouUV3SHP++887j77rtnX3/5y1/mpptu4siRI6xbt+4VD27JMdMyT88LwVEcFv/xRzNCRDatgScfhNIE4VAQbFvUAfQ8QBKCZfKgWK9lIxx5CI78AhJtIj4PhKWSuZvJr0xOiDaCNt3ZZGKvyOzFgw03CbH6wrfg/j+GX79HiJFbvw8P/Q30PgylfkKRZoi0gFGDqX2UpPNxrD6GLSjFQ0juGB2tTbPHX6sa2GqYmuksfgE5I/hLY3DoYfAsyPYCLiQ6YeN7hMvYqs0lvhzrYpyxDLvuwuXxNiEgCwMipjUY44R4NljzyjLNWK5nfgDUc6Jv9vge0Vu7+xJY+VZxbTzwV3D9F8QPBoCLfhfCGfjpZ6HQJ4SwGgBUGHyKeDAGkgMrLoT6CATLJGqHka9opFoy0XtzmAWD5ZExtsSKvOPq5VipNQzuz1ItmBw6PDw7zFWrGzl/YxOdbQmG+wsYeLRvzIDp4ZgOdmVhqakzwWhllLyRX7Cst9j72g/kVIuGp7p8oejj47NkeEVNim+++WZisRh9fX0AqKrKt7/9bVatWkVzc/NpGeCSQosIwZZZJW7eyQ5hPajmhAixdDByENCYrhEDLiBZwooEcNdHRPzdqreKYtZ9j8DAVmGBAhjbRWjoqdldeidzzc5HVmHZ5aJnMwhBI0nw5tugfYtwye79gXgv0gAf+hasf6d4bdfEOGJxsC1iUhZFkzBlkwa9n67cVjr7fyxEjhYhnG5FCcaIBJZA7FsgIs6Nq0PTSjDrQmg7jnD/NiwX1sdADCLNQsQPb4PeX85tI9UjnvfcNZepDRBrBS0qEmn23DUt3F8ikiTiVkFcE54nlr3lNui+FPBEeZ/5nP1+uOZ28bczz9WuamJuJQmGn4fJw1ArQrSJCAbBoEooFmDFsiQRfRCsCmT70IIKDR1xoqkADR1zIviiTS2s7hbtGitVC1lTqFQtYukQSkA5cy0spxmtjHLjPTfygR9/YMHjtsduI6yGSQfTv3ojPj4+Pj4n5RWZiCRJ4qtfXeiWi0aj/OhHP3pFg1rypLvFA2Bium9y/5OABIlpwSFPdzuZ3AuGLQpOW9Pu1O/9BtzyHXjnP8O9/11k36Z6hPt56iByfWp2V+ZLMfTMaM2ZTGJZhXNvhZHnYcc3YcuHRckZSYYVV8G+e0VCjueCaUAoSDJYIansQW1dSSVXJGVPQt+o2F7TWiLJDiKZnldw8l4DHvv/oP9x6LkcLv6IsLCmDJAjgCRiThPdcOQRITKNshBfwSToOfj5n8NvPyTaH258D/Q/IRJWHv8iXPWXYh+KKsrw9P5SxDceffzEY5EUUQxckph1Yc8nlBLz4dpzrRBlFXouEdbR/NHjt7n8TeLZcyGYgXgHOPVpS7cMdkVYX9dcC6kOIphEMgksyWOkqJMMrAF7TAhoINkYJhIPMFoZmN2Fbc5ZXGPRALapE4sGSDaGSTaGX+KEnH7yRp66Xef2K25nRXJhcf10ME1brO0kn/Tx8fHxORVeloD88Y9/zAMPPEAymeSss87i3HPPZdWqVcjyKzJovj6ZcV8HEqBPQftmuO5voTAMhx+GZCcMPisSK7ovgcMPiPqBd30M3vef8K5/hnt+TwiUZCesuQ57z3ZAWLSC2glc2Lkjx4yhSQT0FwfFa1kRBazzfaJWZHqZECKP/m+48OMiCWMmCUZWoWEZjJZFDUJXh8gyWiaepaVzC1TjEAiI7ae7hNt+sQvI/sfBKIrnLR8UQinSAOFByHSIguK5I5AfEJZDLQThpMioHnhCvPfMV0SJJdeES/4b/OzPRAHxx78Aali4pkFYfrOHhAvac48fiyQD3lyMpQfgipI/MygBYSUuDs4Jxpk4zOwRkcAzn2BcfMYxhfW0PiE667SsEiWdsr3i2TZEElA1C9FGutIREctKZnpsc9eWFlTwtDmrsmU6s383t8ZIp8OLMuN6RXIFGxo2nOlh+Pj4+LzueMkC8ic/+Qk333wz7373u/n+979PNpvFMAyi0SibNm3in/7pn7jwwgt/9YZej5woHjHaKKxYVhWm9kO4Ubh7G1eJG384LRIdCv0w1SeKVptlIUbu/RTc+CW4/n/D/X8irJPxNqxL/wAQ2ZreTHs5RYO2c0XChRyYG4vnCbHhWHOxfKveJtztMyLxwt8RiRn77oXNvybi5mZc5pYOxX7hendMEc818ASoUeGF33wz1IvCGqeGhdt+MTJ/bnouExbBnsvm4lajGcATZXpSnRBvmk5AGRfHmpuem0SniC985t9h9dXQcpaoASlrcN8fipjVlo1w1vugaf3cfksj8Mv/NR3DilhfVsU+1aCwRKoBEcNYy4rkm0BECMwZAVkviJI/86nnprvlzKt64LniR0NpGMoDYrtSTWxj3bVwySfEekPbRAvFbf8FG94xV7v0JITmhSVo8/+e343Jx8fHx+cNwUsWkPfeey+f/exn+eu//mve97738f73v5/GxkY+8YlP0NbWhqL4N5LjmDggLHOBJCTb51zJWgRa1sLRR0U2rqSBlhZCzNbh8EPwb5fDm/4Ibvj/wU/+EIaeJbTr2dlN65t+HapPQb4XhqaXS5YQKHhCOM6Pv2vfIgTLDLYhhOMMM0khM3Un5wsv2xRCyJ0Wo8UxsAxYf514vzolrFmVyTkBuhh506fEA4TAnklciTbCxH4Y2CasrkpQWH2rUyJTvTgEoTTI07X9/vMakb2++ddgzTVizn7+F8LaOJP0svYGUQw80S6SX0rDIm7RtcQDFpYFKo+J55n6kpIiwghgzupYy8GDfyP+DsbnEnxAiMef/anYD9Jc/UpPmYvDPfggTB0RorlegXBMlJD6FQIyPM8CqQZ8b4OPj4/PG5mXLCCz2SxvectbANA0Ddu2ueqqq3jooYe44YYbOOecc077IJc8tYJwdSphYUEKzAiWafe2HISmVWKZ6wjxFWuHkaehNiWyatdcA+/9Gmz7KtJzD81uWin1w4W/JbqmDD0LozuEiJjpggJChLSdLSxj6eVzyysTIp5v6qCwcr397+YsXEPbxPN8sYksxEs4KcZZHYV9PxYu3mSHsN45uhCRi1lAvhjZPrCrQkhmeoTVNd0N+38s5tHRId4ljt3W4ZF/ENbit/8dbLhRWCMf+ZyIT8z1wlP/LARewxoRF9m4Bs77LfFZ1xZhBLFWcT6tmji/2YNC+DumEI+KJuYt0QHP3QH7fzJd7zENV//tnCXZc+Gnt4nMeiSR+T2TnIUjMv4lCca2C3E8vlMk+nim6Gzjs2Qo53T0ylxZpdpU/UXW9vHx8Tn9vGQB6XnerJWxra2NoaEhADo7O2lububpp5/msste3JLxhqNxpbBA4sHkISG2Ul3C4hVthDVvg6EYlCaFWxgPWlqEK7t0VGRNH/yZiJV80x+hRy6Bf74NgED/I2DtEQIj1jztvkYIEkkSlsjWs0R8JYBeFM+1HDzxf4SlMZiAt/6l6Hpj62Bbc9bMWCteZgXUy2JcgRhEMuAJgSKN7oJ4oxC8TdO17poWqRv7VGhYDoPPgVMUSUvBtLDmeRIoioglTHULi2TukOgc0/sw/L+b4C1/Ic7zObfA2R8U8axHHxNu68l9Yvtju/AUTYjGeIso/6OGhEgMp4Sl0qoihZLg2EKweoht7L93Lo4y2QlX/oko8eTYYvnP/xx2fhuQoOvCaSvmjNVQFuETw89BtH26fNG4KOdj63OWT59FTzmn882/3rogkQnAxEOLnPp/6YO5GvHh4uzrdDRAR+rMJ0D5+PgsDV6ygIzF5kp5XHnllfzlX/4lv/d7v4csy/T19VGtVl/k029AJElYsjI90PekKItzbKLJ+utg5Ztgx/dgZDskWoW1KdcLk7tFgkc9Jx4/u42AtGz2o14oIUTgjMt5BjUkBFDHFiFWui8S4s82YNf3hNBwbVFr8L3/OVd/zjHh4P2zAlK3DIK1El+T4bmoxHm6y0ejzdPlb4BMTJS/MUvgGmAsMUvIsXGrLeuFpbF4FAYeF1ZjeTpTWgkIK6HrTLenDEN5ELIHhDX33t8X573nctj4brj2H4V1sNAPfY/iHXkQBp9B0osiIWY6qckbfQFaNkDXRWJOGqfrOhoVqNkwsWtufO2b4YKPiTJPsiKEo6zAfX8Cu74rYiZDKREvWS2K2EgtAqGMaHcZSEDrepGp/eS/w65vix8QA8/C2e999c+3zytGr1jYpsvbfmsDmTZhfT48UeF3vrudq5O/uvtPIiz+2/+/Pz1E9ucHZpd7AZkf/vGVvoj08fE5JV6ygLzjjjtwHBGT9c53vpPPfe5ztLW1oWkakUiESy+99LQP8nVDskOIxxMlmpg1WHcNbLhevK5mId8vBF0tC/Ee4TY1i8hjc1nWxpV/Bc0ByB4WGbmT+4UwsXXhzh7dIVaUZBGTp5fm2uatfQfc8EUhcjxXWDkf+8JszKStBKgrEsH6BI+GoSTLPBCWWK24dOGyvON8CMeFYEm0ioLcgfDSdmGDiFMt9AsxXDgoanpaurA65o9Oi2dHCL1QXBQCr42BVRbn/ciD4qGGRNzjmreLc73uHSCreBP7ROmk4edh6Fmk6iSM7RKPEyIJwbfpfbD2uoVvje8VyTvZw2K9UErMZ6gRWs+F4qio6ZjpEZboQBzSK0QYRdd5omRRcVgc89B2kUC0lOfuDURO8bAUYYUcV1zK8inUhAW62+IomswNtYVi08RjdKTiC0gfH59T4mWV8ZlxYcuyzC9+8QvuuusuSqUS73nPexZYKH2OYcYSeSJmsoFn+mhHG0XyRWEQgimRxS1JUA9iSwYgLL3Bn/0hXP5eWHs9XPDbwmqlBoWIHN0phMrg08KdmT0s9hVrhvM/Bhf/roiVPHC/cGeP75keS4y6rDAZThGXJI4oKjgWjqxhAY9MvsAqV+aIWWRbQyuJzFqs6kE0s8JFoU42JNqJvMqn8lWjMiliOdvPgck9EG+GypQodVQeFqJMDUC0QcSwZveL+NZQcroPeQTcGkwdFmLywE/EA0lkbK98Myy/UiTebPkwZI/gmVXofUSU+9HCgIQUbxUW42BMfC7RLsY1g+fCtv8rSi+5Fq4awgo2EFRlIAiZ5SJWtfcxsOsiU37jTdC5WVxbIKyeoZSIYTUrYBSgGvYF5CJnoiySrv77t7czoc6JxrCmkI7+agtkPBPi1r+5eEEM5c69k+z/4VGs2pnvIOTj47M0eMW95oLBILfccsvpGMsbm0BUuERhriZg9wWiFV55RLQ4DLZAohWz3gT8HAAJD/b8QDwkWbS167pQiM+2c0QdwsqYSKQZ2y1ETbJD/P2tD8Hws8KtPT0GL70cXJsDwQh7NIUAEvFIC+sKFoOhIDXT42AshGu4VMMRLDXM7qndxAIxcrVxykmLUH0lG701xx3ir2y7uBioZkXcoRKC1dcJUd20Xpy/xuVCKMoKoAgLY6QJojkh7prWCytf41p44ZsiOcnIC1Fv15HGd8H4LnjyX/CCCWg/V5zvji2i848msqy9Wh452ijmyqoJK3RhAM+oIhklYQ196l+QpouTm7E2KqluZLtC0PVEUlYwIYrXe+50oXIZkEQm+cwPlVijSBCyapAfFMcQbThz597nlCjVhcj7w7evZcPGxtnlLyWGMZ4JEc/MdQuKTFRO7yB9fHxe9yzyZsVvMI4VWIEINK2G3FGINIo2e5FGaO9iRkDqRERDSs8SYmHqgHhs/69jthUT1iazsrDlHlCTFXZEkwyG47zfscnVJ3gq1MEOx6ToGbRUHaYUmYptU5Ah6jjsD0NJkcnWx5AlmaMTvdiOQVEvUHcdUpnldMQ6qNt16nadsBomoi0Bu2S0YbqwdgO0rIPl0yEZnjddjmkAKllR5ieUFnGLtinqenZdBPUsTO0VYjAQ44htcbhrJe2WTaqSpVafoMesETJK0PcoUt+j8PzX8WRVWDBdG8msgFFGOqbw+LHy21PDHI01EiBIuDBKwClNlyVKwOBTYp4tGyJhURdyfDfU2yDaDIlm4c7O9IgfLy1vwN71S5yuTISzOpJnehg+Pj5vUHwBuZjRIrDqKmhYBWN7QAuKuou7t8+u4mop0FwhPt2aqAuol4Rr2nWYLTppVsQDELFySZACIKt8LxJkJBig3TQAhUK8nWSih4I9zghQDoYomXPurjJgE8Ay8kzpU9Q8HUmSkCUZz5DoLfdz35H7uLTjUgbLg+i2Tle8i+Wp5YtfSMaaTu7CTXcJYWnWhFDEg46zoaFH1PFMd8PTD0/HSWqgRRmMQcm1yTYvo96ynB2lXgqmTZteZbVZ5mJXobM6Rb8Cz3h5kp5Lo+zQEFBpcBwyjosKOJKEKylYeLhI5LQgDyebWRFMsCI3SoeaFN1vPE/0z/amu9u0nA1nvw8KQ8IdbukQSYnjWQIGYR8fHx+fxYkvIJcCqa65GouVnLB8zdB9IYTHhcvVrIl1ZgpLxztBjYGZE2VaZixaSgBiPaI/sq1zsWTSb0GPlIbGZaQiaVY4ZeJGHW9ejWrbtTEcA8dzKHoeo8bEgmHKyDiyTTV7mJHSCI8MPkJbtI1kKEnNqpHX8/QV+9jSsoXz285/lU/aq4A2E5+KsA67NtSBoCRiF6ONoqROaVLUb2w7C6N8gMMYVEpDFIMqg4ZDUpbYk4qxX4rxeLSdw6WjlOyTuxBDUoC0kmJFtIuJch8rbQ9bUWhyXcJykICqsD8WIxDtIK4kaGpcS8qFUDRDpHGdEIqBOIRiopB8rHHOje3j4+Pj4/My8AXkUmHGtdq+CczmueXr3gEbNwCSaEv3zH+KbGAALSaSKWpJcD0hblwLkPEiSQ4pKQZqWbqRWeZ5DARDqJ3nsnzFW2h+/PNoTpiwB2tb13GkcIShyhCWay0YVkSNkAllGKmM4OKiuwayZCFLMoZpUA1U6U510xRu4rHBxzhaPsrzY8+TrWfZ2LSRznjna3YKTyszSU/pbvHarInn1W/DG99DHY+6IlFbcx0rx3fycGmAeKydVLiCDcRch4n6BIP5/diejYzMZR2X4eExVZ+iaBTJ6llMx0T3TEbtCWp1g2QkTkVJk5BlDEmi6sX5nmPSJHukMVjv1TAViXjzBurxBiKhZlH+Kd0hYmxnxjt/zFrkxG04fRYHUwcXvAwWSmdoID4+Pj5z+AJysVCZnIu9O9aFKkkLXavSPMufFhTJHC3rRCmWo4/CwaOALASjGhAu1XpRuC9xwXHBNDgYMJkE9khgSaDJDewefYbGRJz7rCH6nRoWsDu7m9HqKACXtl/KTatuoiXSgiRJhJQQX931VXoSPXh47MvuI2/kKZklKlaFUXOUgeoADysPM1WaAiAaiPLc+HMAS1dAzlgjQbQ6dG1qtSz1rnMJr7yK+tQenECcKbPEPtmjIoOhF5nwyhi2gemYFE1RxPmC1gs4p/Ecnpt4jrAapi3axobMBmKaqGgwXhvn8eHHKZpFimaRIUZIahF6Au2MByR2amWCwJtLZSJahmY1jKyFCatxiGbIOjr5yjDpzGoarJrIKj9wv3Brd2yGFW86+bXnc+aINIhr7O6PLVjcZa3gKb6AWpsA2s7M2Hx8fN7w+AJysTCT/ftSayjKgbnM2UAELvmk6CjjOuB4kO6EzotEF5u+h0XGtaKB47LCKjOhQEDRGEdlKOCieVUKh+9lhBpOUHQeGq+Jvsy3rLuF3z3nd5El0Qd56+hW/vez/5ux2lwXEwmJrngXrucyXBmmZJawXIu2aBs1aigoBL0guq1TsSrUrNrijok8ERP7RcvDhuXQvE6c9+oU9cooTi3ASMdGCvE0WFXqpcN0RRsZqudwFQVsKJklDMdgRXIFHznrIwSVIJ997LO4xyTNzKcn0cM5sXM4VDjEZG2SvFWh6hylVWrFABxgd1SlDJyt57m+YbnoVqNFyFtFzLYN5OUADWZNdDia2g+2LYqgt2xc+i0oX0V6i70LXqeDadpir4FwS3XBJ58RdWDnMfb0HvgZKGbxJB/08fHxefXxBeRiYX7270uhccXcTT8QFYJGi0BxRFgna0VRBqbrPFHOpzou6kqWx1lf1WmxTR5RYmxPhWiKL2fQKOK6Js705j08XM8lIAf4nbN/Z1Y83t93P59/9vOYrklrpJXLOy5n+8R2jhSPMFgenB2ejEzdrjNcGcbzPBzJIRVM4TgOuq2zfXw7m1s2Ly0Rme0TxbmzfdMCMgpmjbCkUrfq6IEIOxSLo1N7sc06ddNlopYlpsQBEUsKcNtFt6FJGr//8O/jei5v6nwT57ecT07P4XgOFbPCVH2Kp8eepr/UT3+pH0VSWJteS1+xD9M1Ga0Jy7AtSUzi4QUC9Jf2EZ7cwXrP5Kyms0iH0uT1PGklKEr2SAp0XCgK1Xdc+PKvvdc56WCasBrmtsduW7A8rIa558Z7XjsROdMlahorXgL8eo0+Pj5nFl9ALhZeLPv3VDCrc4kRgYgoMyOrImtb9iDUCu/+J1HvrzwiBEN1koyk0KCPcparM2VX2RBIsKfSzzKlhSwFKo4JQHeiW1gTHYv/2PUf/PToTwHoinXRFe9ioDzA+ob1XN5xOSWzxMH8QfZm9+LiIiNjOAaKpNASbsHAwMFBlmSKRnG2zM8Mi75eZMPyOQskiGznQIRIvUikMAANK3h+8AnGKgOE5QCXdV7BjmIvbdE2TMdkuDwMQCaY4ZMPfZKyWaYr3sV1y69DkzVqZo3+cj/95X7GqmN0x7tJBpKMVkcZqY5wIC/azwWVIIZjIEsy6UAaTdYoYhKw4cHenxGwDTaGW8gEImRSKzky/CwvVAbojnWzavOHFh6Tb3k8jrZYG/fceA95Y65NaG+xl9seu428kX9tBKSPj4/PIsUXkK8XzJrICp4RkTPPyfa5VnUz9f5a1iFVJkVdSduiQ7Jw6pMYtsFP93+PEDLj9jgqCoYrioyvSq3Cw+NPH/9TDuQPICGxOr2agl7gydEnFwylJdJCZ7yTyzsu55nRZzBcAwlJZG+bRUJKiIHCAJZncSR/hLv238XN627mgvYLFm+Zn/mitmW9eMBC4W4ZEIiSqU2hmFVc28L2YLg8immbjFRG0G0dD494IM6/vvCvjNXGSAQS/ObG30RG5oeHf8jWka3Y3pyFKafnkJBYm1nLp7d8mgcGHuCB/gcwHIOIGqFm16jbdUzZRJM1TEx6K0e5r/d+cnqOC5q3sLH7cgbMKWqOxYA5xarFLtIXCW2xNl8o+vj4+JyAJSsgPc9b/Jaq15L5ojEQFQ+ATPTE7RNnSrjEI6wKRFkFfG//95C1MHbNxMGjhj67el+xj73ZvbPWr7++9K/5xeAvOJgXGaIdsQ7yep6aXWO8Nj4bN7kqtYqCXmBKn0JCompV0W1diEm3yGRlksnqJD889EOWp5fTGG5cnALyZMwX7jOWyXCSS5o2k8ruJhiIUZQlwloYDY2qJ1pQdsY6eWjgIQB+Y+NvkAwmeW78OR4fFt1lgkqQtmgbjeFGBsuDjNfG2Z/bz8/7f85vb/ptVqZW8h87/4OaXWNVahWHC4dRJRUtqAFg4XCoMsxg330cro7yd92X051ew4AyQHei+8TH4vPyKQweF6t4bPa0j4+Pz+uJJSkgJycneec738mdd97JypUrz/RwFgfzRePLXL892s6GlnM5K7WO+wd+hqHnUCSPsBrmQP4A28a2kQllyOk56nadRCCBIik4nsP6zHoRa2fkiWtxjpaO8sLkCxwuHKYz1snq5GoOFQ8B4HgOBaOA4zqYnomFxZHCER4deJRbN956Os/Kq8884V4L9lBPNBP2PN4UjpNuWc+e/GEss0wymERCwptuU6lM1+oMq2F6EkLgPzv2LADd8W7Oazlv9gdSd6Kb4cowT48+zYMDD9Iea+f6FdczXB7m3t57UWUVGRnbs3FcZ3bbum1hux6Hi33k9BwrUytZlV71Wp+h1z+FQfjShSK+9Fi0iMim9vHx8XmdseQE5OTkJG9961u5/vrrffF4mtncupl1jesIq2Ga08u4++DdjFfHaYo18fzk89x16C6u6rqKXwz+grsO3kV7rJ1kMElOz3G4cJjuRDeu5zJZmwRgWWIZR0tHGaoMEdfiXNx6MVvHts7ur2yVCcgB4locWZIZLg8TVIJ4M91zjkFajK1T5pXzqU8nv9QlhXCyA6c6QDKYpG7XGauMsaF5A5ZkkTNys0JSkzWOFI5QNsscLhwGIKpF2Tm187hdbWjYwN7sXr6x9xuUzTLdiW40WWN/bj8bMhvYm9uLIit0x7tRJZU95h40NDKBDHfuuZPLOi7jvLbzXrtz80ahlhXi8T3/AY3H9ICPNByXBOPj4+PzemBJCcj54vH2228HhCu7UqkQj8df0rYMw8AwjNnXpZJfnDeiRajbdbaPbycaiPL+de/nv/b+F1k9S0yLUbEqPDf+HJqscahwiE2Nmzin6RweHnyYul1nU8MmvvTCl0647bJVZk9uD1d3X82DAw/i4SEhYbomBbNAXItzMHeQz2/9PC2xFnoSPSRCCVqjrXTEO1503PPn7jWfx3lhFGE1vCAhaOroU/RN7cYKJah6VUZro5RNUeR9JhM7qkVpjjSzL7sPgOWJ5ezL7Tvhrs5uPJtzm85lx+QOftz7Yz6y8SOc13IeW0e3UnfqSEjk9ByKpJAKprCxqVNHVVRqbo0nhp9gWWoZDWHfIvaq0LgG2s8906Pw8fHxeU2Qz/QAThXDMHjrW9/KsmXLuP322/E8j7//+7+noaGBRCLBxo0beeSRR055e7fffjvJZHL20dXlWwkA8nqe0eooU/oUiWCCdel1BAmSDk5n+ZpFNFnE2d3bey+KpCAhMV4b52u7v/ai2y6bZVRZ5SNnfYSAHMDDI6pFcTwHwzGoGBV+OfhLHh54mKdHn0Z3dMaqYy+6TYCurq5FMY8RLUJDuIGwGiKshvDKwzRJCqXSCOr0b7WZMkgztQUnahMczB+cjRkdKA+86D664uL4LNeit9TLha0XAiJG9ZZ1twDMilQAF5ejpaPkq3mao830FfuoncjV6uPj4+Pj8xJYMgIyGAzyW7/1W9x3331885vf5JOf/CR33303d9xxBz/5yU/IZDJcc801PPPMM6e0vdtuu41isTj7GBwc/NUfegOQDqVF8kaokZ5kD5FAhK5EF3E1TlO4CVVSqdk1AnIAy7V4cOBB1qXXkQqm0B39Rbe9sWEjF7VdxNrM2tnM1jd1vAmYjot0C9S8GgdyB+jL9/HDgz+kL9/3K8c8ODi4KOYxW89yOH+YnC7Kvmxqv4R0IEk03sLZjWcTIEBYDaPJGpZr0RBqwPEcvnPgOyxPLGdZYhmO55x0+/tz+7m3915AWDtXp1ZTMoTFtTnSzMqUCOmY7+pXUIhoETriHaxOryYeiFO366/WKfDx8fHxeYOwpFzYn/nMZwD48Ic/TFdXF7t37yYaFYkgb3vb27jwwgv5i7/4C372s5/9ym0Fg0GCweCrOt6lSCaU4dKOS2dfJ7QEtmuTCqYwPIOOWAfD1WFM15wVQvvy+1ieWM76zHqeGn3qhNtdkVzBNT3XUDJKlAxRTxJYUCKlPdaOIinsy+7jUPEQ7XY7e7W95Oo5MqEMJwuBTCQSJBKJ03cSXiZ5PY/pmuT1PJlQmvMu+BitleuR+3/BYwOP0RptpWbXUCSF0eooWT1LW7SN0eoo9x+9n3eueCfntZzHXYfuOuH2TdckqATZ0LCBDZkNWK7FkeIRQMRHTulzrSI7Yh0MV0S9yUKtgGVbtERbCKvhBTU3fXx8fHx8Xg5LSkDCnIjMZrOz4hEgEAhw66238uUvf/lMDe11wbGlkba0bqGv2Md4bRzFVRisDiJ5Yh3LtYhrcap2lb5SH32lPjY1bGJtZi1PjT7FSGWEKzuv5KZVN5HVs3TE5mIZQ2oIgJ64yEA2XZOCUUCTNTw8FE+haBZZnlw+Lcgyr9EZePnMdnwJpZGkOeP+Re0XsTK1kmdGn+HZ0Wcp1efiNAt6gSs6ruCx4ce4t/de3tbzNv7kgj/hcOEwP+77MbotrLotkRYuar2I61ZcR1CZ++HzzJiwuJ/TdA75acun4RgMVgaxXCHSC26Bg7mDjFfHWd+wfmmVSfLx8fHxWZQsOQEJQkSeKFliYGCA888//wyM6PVLa6yVLa1bKBpFOuId/PDAD9k2tQ0NDUmSKFtlQkqInkQPB/MH2ZXdxe7sbi5vv5z/ds5/oznSDEBWFzXyPM/jwYEH6SsK1/RwdXh2X7Zrz8ZXupLLmvSaF+0PvdhoCDccl6CSCWUIq2EawyIkIBlIcrh4mB1jOxiuD1N36uzP7edt3W/jwYEHeaD/AR4aeIiL2y7mf2z5H+zL7aMj1sFZjWcxUZuYFY8Vs8I393+TFyZfAISl+M59dwKgSuJrnZbTGK6BoihkjSwvTLyAh8f5ra+/78hoZfS4jjGvB4YLdfJVc8GysaIBKGdmQD4+Pj7TLEkBCRznsnzooYe48847efLJJ0/yCZ+XQyaUYUPDBiZqEyQCCd6z9j3U3TpHckdojjczVB1Ct3UO5A/QGGokpIYYqgzx2MhjPDX6FIlggogaQZEVMsEMNbs2W4x8TXoNX935VQAiamRWPAKUrBKTtUl2ZncSUAIAS7KGYUSLzFr8svUsg6VBnhp8ChubgCISiSbrkzw7/ix/cdFf8NDgQzw58iRPjjzJ1tGtXNZ+GfFAnMeGHiNv5IlNxTAdk4eHHqZoFJGRWZFawd8+/beAKAsUna7vWXJLXNR0Ec2xZlxcJFmaLbFUs2qzGeNL3SI5WhnlxntuPC62M6yGSQfTZ2hUr5zhQp23feER6tbCuNhLnFEuZwUJYxRGdsy94ZcM8vHxeQ1ZlALypXSZefDBB7nzzjt55JFHuOeee1i3bt2rPLo3FhEtwobGDWxgAzk9h6ZovKXrLUxWJ8loGeSYTN7Ik9NzszF4YSVMQA1QNIrk9Bw5cgD00w+ITOSLWi9i6+hWPDxSwRRdsS4kSSIgBxitjKKiUnNqZGtZ+sp9SCPSkhSQ8ynoBXrLvbiSi4ODJmlsbNjIzqmdFI0in9v2OS5qu4iruq6iv9TPkeIRHht+jMeGHzvh9mJaDN3WOVw4jIREIpigJdKCKquosoqDQ3+ln8u6L8O0TTLBDKvTq/HwqNt1UbPSrhPWwouzxuYpkjfy1O06t19xOyuSK2aXp4PpJd2GMF81UQ2Xz71tHV2ZOZFvDI2w56dFkk/+Azw7z9KqReCTz7wiEVmbqjM5UF6wLBTTiGdCL3ubPj4+r08WlYAsFov8wR/8Ad/61rdQVZXbbruNP/mTP3nRz3R3d/OhD32If//3fycQCLxGI31jMSPmw2qYTDjDBW0XzLqiq2YV3dCRkJAlGQmJulOn7tTRJI2mSBPJYJKYFiMaiNISbiGoBrlz3514eASVIKqkMlYT5XpmOtsoKJT1MrZtM1WZIqNlGCoPkQlllpzF7HD+MAOlARKBBHEtjiIrVM0qNjaHC4cJKAFM18RwDB4denT2c03hJoJKEEmaO7czZYDGq+NUrAogzllQCeJ6LqPV0dnPW65FSA3RX+4XWfK2zvf2f49zm8/lwvYLKRkl0qGla6E7lhXJFWxo2HCmh3Ha0IsmHykFmby7n8lj3lM1idCvfRWS067sqYNw98dEUfOXISC1iIqJx/4fHmX/D48ueE/RZG79m4t9Eenj47OARSMgdV3nqquuYt26dXz/+9/noYce4rbbbmP9+vXceOONC9b1PI9/+Id/4GMf+xhr1qxhzZo1J9mqz+lkxh2bCWUIqSG6k93cc+geBkuDFM0ihm0QC8Wo2lWqVhXLsxipjjBSHTnh9jLBjBBGJ7A2p4NpHBxKRonR2iiXtF9C1aouSZfrQGmAmlNjLDfG+S3nc07TORzIHeCZYZEAI0kSMS2G4zlYroWMjOEYTNaPlQ0LUSSF5kgzZbN8wnPYFGzi8o7LsTxRMmh/bj91p86OiR1c2H4hieCZz1z3OTlWzSaAxLp3L+PsDU0L3jvdVsG29hjfzFhI5sKY4wZX5oZagIHRMht9Aenj4zOPRSMg/+3f/o22tjbuvFMkAlx33XU899xz3HHHHccJyP7+fj7/+c/zgx/8gKeffvqU3d0+p4eIFqE91k46lCYdTPPdA99l79ReTNskraSRkQkqQRzPwfVcHM8hqkYpmkV0W0eRFWJajK5410nFpWVbFJwCKiqpQIpEKEFUiy7JEjTdiW4GSgOUKNEUayKshNncshlZlkXZovF95MihSsL13BZtI6/nMRwD0zFnWztG1Sg1WxQBV2WViBohFojNWiI1NEJSSJxzHFYmVrK5dTMRLcLy5HJiWowdEzs4t/nc47rm+CxeIo1hmrpfWqetl0pHKswP//jK4xJ29u6ZYvLufkp1+1Xdv4+Pz9Jj0QjIb3zjG3zzm99csOyaa67hO9/5znHrLlu2jAcffJCpqSlfPJ4hIlpEuLRbhTXyH5/5RwJKgGQ4SavSylR9irJRpoYQPF3xLqJ6dME2gkoQRVqYTarJGmEpTM2tkdASKLLCBa0XsCq9asmKnVXpVaxKr5p1ZXcnummPtfNh9cP05nvpK/Yh12VchPVHlVSCSnBBuR6A9kj7cYI7IAWQpv99YN0H0C2d58afw7AMimaR8eo416+8nrAW5tqV13LtymtnPxvWlub5XBRMHgRjWmxNHTyzYzlNdKTCdKQWXhPl0dpx7nMfHx8fWEQC8i//8i9Zv379gmXNzc3Y9ol/+W7ZsuW1GJbPizAj3jc2beSdK97JtvFtNIWbaAg3sH1iO4lggu1j26lRI6/nZy1pMwxXhrFdG5k58SRLMhElgmd7qIrKVZ1X8aGNH5pN+hipCAGVDqXR0FhKzAhJEBnZzdFmQmqIs7NnUx+tE1WjIEO2lsU2bVzZpWAXZj8/WZucre0IoKIyyiiu65JQE3TEOhgqDRGUg4yZY8QDcZ4YfoL1Detpi7UtSff/oqMsWk5y92+DOTcXaBGRBe3j4+PzBmHRCMibbrrpuGWapuF5c6Lj6aef5q677uJzn/vcazk0n1Pg5vU3c/P6mxkqDzFWHeOKrit4euRpooEouyZ3MWVMza57QcMF5KwcRaMIQCaQoWAUqBgVQoEQjuvQme7k/JbzyRt5fnL4J1zSeQkAuq0jyzJ5PU+z0nxGjvV0MONCbo+186eX/CkgSut8d/93mapPUTJKqJLKk6NPUtALmCx0LcaIsaVzC3W9TtWq0hJtIaAECKgBGsINaLKG7uq0xlo5UjhCc7SZul33BeQrRRfXLFf9BXRfObfcL6Hj4+PzBmPRCMgTIUkSrissU08//TQ33ngjX//618/wqHxejI5Yx2zHGd3WiQVibG7azM+P/pyhyhArEiu4oP0CdFdnvDIukkZkmaAcpDHUyPbx7SiyQmuslW0T26gYFZ4bew4Xl7ctexvhWHi22wvWrxjMImZ+fcgZ6nad9Q3r6S300h3vZqg8xKbGTRzMHWS0JrKrY8S4ef3NZPUso9VRzm89n45kB3WrTq6eY2VqJZ3xTrriQsxM1CZojjSjSMqSDQFYjPRqKgTnqj6kVZWlWzDIx8fH56Wz6AWk53kLxOM111xzpoflc4psbNzIxsaNPDf+HMvSy8jVcsQCMYJKkFggxuHcYfbl91ExKzDd+e+SzksomSVqdo3mUDPDlWFaIi3sm9rHO1e9k4gWme32UrKO70a0lAmr4Vk3d92ucyh7iOpIlUQwQckskQllWJ5azpbWLfzo8I+oOTW2TWwjrIWJBWOzn51t+yjBRjae2YN6nZHWYoRdl9t2fxl2z7VNDath7rnxnjNfd/KYeEypkj1DA/Hx8Xm9s2gEpK7rhEILy0TIssz4+LgvHpcQJ0pqao22MlYdY3PrZjrjndSsGiMVUd5HkiTqdp3lieVEA1HWZtYSUkNMVCfI6lnWZtaSNbKc23wuIGIHX6+xfMdaJTOhDFP6FJlQhoHiAA4OF7VeRCIo4h0HygNEQ1Esz+LqZVfPZlW/nhPLjm1ZCK9t28K2cCP3DI2Sv/k/oWnN7P5ve+w28kb+zAnISIOIw7z7YwsWh8qnpzbusQXG/eLiPj4+i0JAVioVrrvuOj796U/z3ve+d3Z5JpOhVqvxrW99yxePS5jOeCed8c7Z1zNlgDY0bGCgPMDmps00RZtojbbOrdd0/HaGykNUrSpRLfq6EpAnaysY0SKsb1hPOpTmyq4rOavprNn122JtbGnZwtHSUdZm1p7QJf5642QtC+FVbFtYGBTFuWeYOkib49CWWAanuWj5oaMFpqbmjq2vt3DqH051iS40x4xV+tpHXtGYTlZg3C8u7uPjc8YF5Ix43LRpE+95z3sWvHfNNdewZ88eVq5ceYZG5/NqEdEiXLXsqjM9jEXB/LaCx4rAFakVsxnUM8yIxcu6LuMyLnuth3vGOFnLQniV2hYWBuFLF4JVW7j8JBnXJ7KEnuq4Dh0t8JN/eA7tmJaSFh6NjacYu5rqetFEHnlqP4xMBw6fYtLPiQqMzxQX/8VjAzR3zNWnrFbKJ9qEj4/P65QzKiDni8cvfelLJ3S9vVbicSbbu1Ra/HF1lUplwd9LYcynA9VSCdpBVE+lVCrRPyF6a8/P1F9K8ziDZVnotk5IDZ0wrrNcLzOgD5AOpcmEM2dghIJX67qb2c6J5vE7P7yDcEQIqFF7HKfuUHh+lCltYf3QKfo5xI7TMp4ZtMoQLfkmxjf/PlZszoLuBBI4O+qAiDfMWXkUXeWPf/7Hx20jIAX5aOefEFVevOvP2EiFspolvqWB1vbY7PLWZJA8B3nm6Ms4gPIEI2oEqAJw4O7fppyc/j9WDcFVfw6h1K/czN9da1I158qpFQo2B37pMvTAwnhLwxTW0/nz6OPj8/pF8s7Qt/1UxONrydDQEF1dfhmOpcjg4CCdneIG78/j0sWfx9cH8+fRx8fn9csZEZCLTTwCuK7LyMgI8XiccrlMV1cXg4ODJBJvnH7BpVJpSR2353mUy2Xa29uRZZHG7bouBw4cYMOGDUvmOI5lqc3DfF7O2E82jzPfx1fr/4elfJ5fDq/28Z5oHn18fF6/vOYu7MUoHkFkfM/8ap4ZUyKReEPcWI5lKR13Mplc8FqWZTo6RB3KpXQcJ2Ipj/+ljv1E8/haWbGW8nl+Obyax3vsPPr4+Lx+ec0FpOM43HjjjfzBH/zBohGPcLwFEpZWHN3pYOZ4l8pxn8xyNTw8DCyd4ziWpTYP83k5Yz+TFsiXOtalzKt9vGdqHn1OH74V2eelcMZiIBcbfszV0sWPnXt94M/j6wN/Hpc+fhyrz6lwxsv4LBbicVGOYinEQ+3bt4+LL74YgK0P3c/6s8+BQFS8OXEAcgOQ6Z4tdAzA6/DX/0xM18zcwWmax5P9pjrROfQ8mDwIuX7I9Myd8/nrmlUw6xAIz83TEmTBdbd1K+vXrz8t233V5vHV4kTzOXPNHHstnOr37sV+x7+C7+6rNWcnYjHO42t5/K8HTjSHPj4nwxeQ0yyluMdYbK7MRyzTQCKkQkARN7OxEtT6IZWCRALyA1AchmSHuKmdYYbKQ4xVxxYWDX+FzHeNnZZ5nLmZb/829G+Fnoth8wdPfCM3q9C6DLo3iPqAcwOZt9LcOF6N43+tWHDdxWKn/XtyOufx1T3PJxjPzDWTOB9Wnj+3/GTiL9e/8Ht58CEY2QHt58Kyi8GsQSAirqlXICBf7Tk7Eaf9+/gKOBPH/3rADzfwORV8AbkUONY6Mf+1qYNrixuOFoHR3WCWYGQnrHqLuEnZdfFcywoLZfNa6NgsCiRXp8ADYo3TAsgDXsJ/Hsf8RzO/q0pYE/X7cvUceT1POpRmrDqG7uiMVccAZm/yHfGOuU2+lP2/Uk5k+fE8cc4OPwxWUTyf9a6FAnF4B0wegGgLtK4Ds0bW0WePsyE0V6+xZtep23Xyep6nR54mFhA3tY7Y9DGf4HBf03OwxDlRFM7862y+gBwqD7FrchcAm5o2vfh190qie0743Zq33eIwjO2Eww/B+uth5AUwStMi8uyF3+njD/jk+/Vv/D4+Pq8RfpTs64F6ce7vTBeYhnBhS5KwcKhh8TxxAMyyeAZxgzLKYplZO/G2T8TEfth3v3g+dijzuqrMkNfzmK7JaGWUkBJC8qTZ/tjzxeSiQZLE+eg8F6SAeJ5/fib2w/PfgtFdUDgCsgqBCHk9j+Wa5PWFvZpnzsnAxG6i9RKVihDNC9ax6uTqOerW8W36fF46rdFWQkqI1mgrNatGtp6lZtUYq46JR23s1K67k13r1SnxPapOzS2TpLmHWQOjAmYFKlNivfkdbZIdUJkEVYOJg9B+DgQTwgIZiMxeU74g9PHxWaz4FsjFSmUSqlmINkC08eTrOTp49tzNqXkDNKwAZbpHbbpb3IiqWYg1QwVhgQSxPBiHwhBUxiGzCsZ2w9Bz0HkerH/HifeZ7QOrIp5bREzRjMswFUyRDqUXtN5Lh9Lk9TzbJ7YzWBlkfWb9rOVnxgJZt+on7Ad92ph/PmPHNNq2agtdhiD+Pvtm8QDoewzG9kLHFkCCYBRqeYg0iu3OHOf4XtJGDTxNnHtEn+a6XYd6kYn6BBtTa+gv9HPfkfvY2LCRS7suXSC8Zyy3Pi+fjlgHHbEOJEkiW8/ieA45PUdICZEJZegr9PHLgV9iWAaXdl163OdnXeDD++lUA+Jab143t0I1C/k+GN4Oyy6dnetZAhEIxoQF8sgvhQBtPwfO/YB4P90tLI8TB8X1mOwQlseZazMQFeERlUmxrVONnX2x69zHx8fnNOILyMVKNSvEYTW7UEAefRJe+Pnc63oJrDQEZoKePSiMQMNyYfXoexRG94iA/vZzYfW8/tNaBFLdUBgGS4exPTD4lEgQGHru5AKyYbm4oTYsn100Y00sGAVWphe2n8yEM2TCGX7e93Pqdp2+Yh8AHfEOwmqYvJ7ngb4HGCgP0B3v5urlVzNSGWGgNEB3optV6VWv4EROM/98ztxYzaoQjmZNuKNzfdB1gXDvB6Ji+czNeOA5qI2Lc3P+r4ltxVpAi4JjQDVLQ/NaGsb2wcReYZ289BOgRYioYSJqGMJJ2t12jECYw9k97J3ay2NDj7Fncg8t8RY6Yh1saNzwyo/1DUpOz/HC+AuzLurzW89nXaMQfYokWh82R5tpi7UxXh2nbJfZk91zQgE5ax0Ph+kkvOBaB8Q1MbwdtIBwRx8nIKNzP0ayfWCVYfwYK2bHZiiNwd67INkDy66Y/mxk7vqrjIvveNPqhYJwePvCcJQZTnSd+yxKRiuj5I38cctflb7uPj6vAr6AfLV5sXil6tQxVsZ58YfRhrn3PG/OlTW+F6zq3Db23AOHhiG9Ci77hHBX9z4KT/1/0LoFImkw6kJExlqgnhd9cLUIRDNiv8kOqBcg3gTNZ8HU4WlL2/yxT+8/PyAsb60b8ZKd4LqAcBmOV8cJK2H+c/t/0lvu5ayGs7iw/UJ0W+eF8RfoK/QR1aKsbFlJb74XgEP5Q8iSzL7cPpCE5adu1xkoDVBzagyUBk6PgIw2wOQhoCasNNFGcYN2bahnRbKMqombcvu50/OTnRWHxFuhMipuyk1rxGN0lxDdfY9BdYKaolEvDRAOJKkrKvm995A262RSy3jOLnCg2I8bCHJWKEWT51EwCmiyxgODD3B+6/k4rkM6lGbv1F56C71satrEW3resuAw/OD24/Gmr9O8nme4OsyR4hHy9Tw7JnZw9bKruaj9IopGkZJRIhVKEQ/EaQo1YVUt2qPt/OTwT5isTpKJZDiv9TzCahjDNjAdk2VdF+HFOo7bpxRtFJbHmUSYme95fgBKI+I7tuNbMPAUqBFAArkXnvx3uPi3mY0eGtsJrieuI0kV3oTm9eL7GYgI8SjLC39Iep6wXJoV8TxfQM7/AemzaBmtjHLjPTcuCPWZIayGuefGe3wR6bPoWZICMp/P87WvfY3+/n4+/OEPc8EFF5zpIb08TmZlBPF6/g1jhpaN0Ds89zp7GBIlYUGcPCgSZ8Z2iO1O7IQLfkuIpIYVUBoEJQCFAUivgES72Ee6WwT6mzUIxCDVA8l2QDrevVscBrs2fePsZG92H73FXgJyACSwHZtHhh+hbJQ5lDtE3akzXB6mv9SPqqisSq8iqAbZn9tPxahQNstUrAqGZXC4eJhMKMP+qf10J7pnLZCnhWjj8ec7EJmLbWxeIyyxM+59WCjiW9ZBbDoxZmAbRJLCCoQL4y+AEqReGsSJNVK3SuRbzqJUPErBqhHODfKoUmLKsyjiMl4b53D+MEW9AJ5EOBimd+ogUaNOoTTCYxPPEQ8Ki/KxAtLneCQkcnqOglEgqSVZllhGX6GPRCDBE8NP4Houz449S82ucWnnpTSFmpgyROzikeIRdk7spGpWWZVeRUe8g5ASQpZlEmpiNvygt9DLkcIRVqZWsrFx43RMY138QLPqUBgU11P/ViiNgmfC8LNiPS0MoSRoceh/Ai75nbkfhG3ngquD5UA4Ka7BzvPEe1pEWB6rWbGN6pTYhxoW1+vEQfF8zNkgNf3d9Vm05I08dbvO7VfczorkitnlvcVebnvsNvJG3heQPoueJScg9+zZww033EB3dzeFQoGvfOUr7Nq1izVrjv2PdAkwX6Aca408+qSIR2w9C3oumftMxznQ0Dv3OpiAgCssY7l+eOL/Ay0BigLL3wLtW0CNgueArYFeAM+F4ReEtSTZIQRkZUqIwsKgsMRZulg+Y6WbcfVaOtgmNKyG0Z30HvwBAzLsqI8SlINEtAg1u0bdqWM4Bruzu6noFaJalIHSAFvtrfQV+oiFYpT1MkWzSMWqkAgkZmMf92T3sLl18+mxPM5HC0MtB8n09OuIWAbCStS4RlggR3dD9wVzotzWoToJ+X5hvUy0QyQjxLUWhbbzYHwX4WCKuhIlvPJqHk+k2TqxlbNsiVB6BZO5I+iqRrE6SbU8xrAxhYtLSAmRltPYdpWjxV76JrZheTIDpQFWJkQowHBleMmW/nktGK4M89DRh7Bci5WplaxKrWL35G7qdp3mSDNDlf8/e+cdL1ld3v/3adPr7X1770tZYOnNpUkRUbBj1EQTTTQmkkRFTGJsvxhbgqixomBBiiAsbVlY2M7Wu3vL3t7v9HJmTv398b1lFxbE6AKL97Ovee2dMzPnnDnfM+d8vs/zfD5PP8lSkmQpyeaezQS0ALql4+JStIqkSilUV6VgFagL1vFUz1PsGdvD/Nh8WiIt6JZOZ7qTglWgM90pCORYGxTHRDS+YrbYEU8YerdCKQP+OHjiwAiE6qFiLmT7oXIeHHwQCiNiWaQWOAP6noOBfbD4UvE7S/WJa0Fg4nowsBuG900L4mLNYrJjFMV5mOqFRDf4IxOfqXztBmQGrxhzo3NZWjlTtjKDkxMnFYE0DIMbbriBT3/609x8881YlsXy5cvZtWvXH0wgy+Uy5XJ56vlr0s7s6Cjj6OFjo2PD+0WkMNEN0abpGqvN34Cn7ppeR6ASQn5wJdh/F/grRdRixXVQuwJG9oHkgJ4TKevR/cJoXJLETa57i0iflbIQroK2B8XNqPkMmH26ENiUUlAxT0RaQhVCoBNvgZ5thC2HA7lDDNkl8naeqBqlaBWRFAmv7KVYLpIpZ+jP9TNujpMupXEcB0/JQ9gbZiQ/Qm2oFsM2aA43gwRzonOmUjtHC2uOtgg6Wmhz9Ni97Dh6AiK1t/0HwjZl1no45yPT49D9rBBGSBIkq8UxKY1Dzw7AgaHnQfGKm38+BbID8Tmw/HoIVhCQZQLeKImlV/B8653Y/giHJAVZUwiF6vGme6mydBJWkRFk0hiYmBzJtlNHiB5KlABHcrFtm3s77yWgBljTsOa4ljRvNBxvHItmEVM3X1ZcNVwYZrw0znBhmKJRZKAwIKLhrkVHuoPGYCOarJEr5yjZJbyyF9u2MW0THZ2gEiTiiXBa3WlU+CoYLAyiqRrDxWF6k21glqnShPXSvNhEfa+EyAxoXqGY1kJw4DcwuB1sW0wyGlfBnLNA88HgPkHqtBDsu0v8Ln1xaDoDEofF7zfeJFZ8dKTcmIj2p3rEtorj0P2UsOmadTaccpN4T6JbCNtwYc76EzZGM5jBDGYwiZOKQD744IMsXryYm2++GQBVVdE0jc2bN/OLX/yCSy+9lA9+8IOvqE7sC1/4Ap/73OdO9C5P46WK3idxdDQSF+pWCKIXbxSF9qEqESXsfhqsaeJLYRgiIVHDKPtEREz2wmCruGkVkiI66QIdGyE/CshiO6WsSGmbBcgnBGHNjk0QqC4wihRLKfTBHfgPPUJg1ulC5R2sBLOIG60jWhjA0v1YThq/qtIYbmC4PDK1e6lyClVRWVi1EF9OKMNzdo6gE0R2ZBRFoVAu4NN8LIov4uyWs4WHpOrnYOIgfdk+miPNnFJ7yjFK5aPJxCtulTaZsu7fKW62Pc/A2R8BaaJEIFAhbuqSJAhAxxPQ87Q4Xv6oEDbIMvgiwn9TckUEM9svPDZNHbwxCgO7mG27HBzroAWZXKjAAEXigQp0xQQJKlEJYIlUfa4XCYhOPCJahCPZIygo7B3fS97Ms3dgM4stlTnzrqRi7buO+VpvlLrI441jySohu/LUmCf0xJTXZoW/gqSexLZtksUkyWKSTDlDopigaBSpi9aRKWcYzA1SEaigJlRDU7iJ3lzv1PpjxAjgJeKJ83D3w5hGgfn+OvYYWeZH5uMYJeyezdQmh1gz9yL0pnp2De9kIN9No+xndqQRv20TiFbA0F4oFUBWxITLGxHnXKYfMt3itWJSqLNVP9gmJA+DxwtKAHyVotRET0H7I1DIgS8AVfMhNyrOsVizEHw5BozsBvndExOj2YJEVs5+tYZrBjOYwZ85TioCWSwWp8gjwK233srg4CCBQIBYLMaHP/xhBgYGuO22237vum655RY+/vGPTz2fbOH0J8fkzf1oD8amtS9+X6j62DrI2WeKVFVmUBCTA78Fb2Ba9TsJowimItJk+QGI1AmSk+oUNzRfBLJVkO6G3IBIb2ua6H5RGgZsGBkS0RRZEvWN5Qw0ng6eALongJ0eQDfLBHp3kqhfTsrMEC+lIFzFE+Vh2ovdGDioQNpMYjgGrutiOiayJCNLMj7DR8kuTe12iRIFt4DjOGSdLNWhanaN7eKSuZdQ4asgWUryRM8TpMop0qU0p9SeMlWPdrRFEBzbJu0lx1GSBAH0BGHOuYKIzzprenxSvZDshnCNSPFnh2GsdVrA4A2KMgPJD5YD9kTxu+wXBtCJbignQS8QGT/AXEWmzjEYVmSecbIMAJlgnJRdPEaalDfy6KZOwSpgOzYhTwiv4sVxHWxsVEll+9BzYKTpklR+feAXDBTaCHliRH1RllYuZX3zGyPidLxx9Kk+FEmZGvNJT9FD44fwal7KVplKfyWJUoLh4jBZK4sXL2XKopTC0hkzxziSP4IjOeStPHkzj4w4L72KF0sqUpJMhgpDjGdHWFw5j1nhFhqjDVQoAUqjR4hbBvqh32BLEu1mAjcU47BTolnxoJfGCRy4B9I9Ip0se8QEpTAGboXwg8yNiN8WNsgtoOcBR5x/8RZoOlXUJVslUUaS7IFEO0QahdWW5IoylPEOCDZA6TBULZu+ZtQsFo83yGTi1UAuWaKUN49Z5gtphCt8r9EezWAGJxdOKgJ50003TXWd2LdvHz/72c/YuXMns2fPBmDOnDl88Ytf5HOf+9zvjcp4vV68Xu+J3uVp1CyajkD+Pjz+RUFwZp8NF/4jHHpEpE2HRqZrnJisg5SEarhyrqjHSnXBwE4RrShPGIx7giLa6FpQTkOwTtTySRKMHBb/K14ROfGExfoaVoMWwF+/Ej03ir/rGaicQyrdixFvIOXaSKZJd2EQCWdKQG45JplyhpyRw3btqa/Ul+ubIpMhLUR4wnZoWB9GRkY3dZZXLKcn0wMIoiBLMrjgUTwABLTAcdOYf3CbtHM/Kh4wLVAa3A3jbUL1GmuEZCeoXpA0iM8Tdi3ekFCd62lQZBFhilQKI/f8IJSSIEG0oLKsXGQkEGfU70eWgzh2BrcsMpVHY6gwxGBhcOpY5c08mXIG27XRZI2iVcS1DFI4OJTZpDjUltK0ptpZ17iOg4mDbxgCebxxDGgBIv7pZXFfnKH8EOOlcUJ2CEmS0GQNv+zHsAwAyogJlmEbDBeGsVxr6vM5I3fM+hVJIaAFqJbFtSBHgd2JVnYnWmnNdXNDywbWtZyN3r0ZfDEUPcGC3DADnY/S6HpRFm7A74mKiV6kUSiwY7PEpEP1wch+SByZ/i0auigJcfKABOWCOMeSPUIU4w+DXoBM30SKPCDqoEs58VsFsd6174DYbPH86Prpl7PuKSSFldCMRyS5ZIk7b30Oy3COWa56ZG669YwZEjmDGbwCnFQEEqbTdStWrGDv3r3HkMD169dj2/ZLffS1RdPa40cej4fup0U7ws7HYfk1gjQmj4CeBKcsahknIBsZ6HwUDqRF+72ju11M4gWdUTA6INUBsgaSIgQk9atE9C2XFPVWtUvALArBQTnLYGEIn2RC1SxcxyEeamBr71MMlzOUXBfXtclbBpnMkSkyJEsymqxRnoiYOq6D4zqky2kAfH4fLi42Ii3dmevk3vZ7CXvCnN9yPmtq19CR7ACgM935pxfVgCD144dELWi6T9SrBauEiMETFupZxxE1b7Jn4vjaYBuQL0Pb76AwekxUWNJTxIF4cYQWT5CAO5f/pQAxE0pl8HlxXZeskSVnCkKztGIpQU+Q7cPbyZt5AEzZZNv4NqJKBFVWyEgaBTePVRyjKdyE67h/dgX4uqUzWhwloAWQJImFFQupD9VzasOpZKwM48VxUmYKx3XoyfZguRY1gRoun3M5ZbtM1shSNIvkjBytiVYKVkE4ARh5ZEnGL3uxZBtJkmhPtfP13CBXtFzI3OVX0mI7NCgh1uZHOCVXADcHmSE4/TJRGlLKCdJXGBGTinQPFNJQHhcpZxCTjGzX9BcqZyDfD6E6cU5VLxbnXbhepL6RoO1RqFsqIoy5YbGNVA/EZ4nzsXe7mDjG58DSl/BuBTHxscN/th6R+w4myepDABTHdSzD4Yvg+OwAAQAASURBVOL3LaWiXpi0J4cKPPq/BynlzRkCOYMZvAKcdATyaLwwgnjXXXdx/fXXn3w1YZOG1p6ASKWG6iBZgKolkB8WdYnFpIg0lDNo+enWhb5CD6SU6XXJqrAGaV4nbhKODY4pIoSuLW5AfVtFtM0xAVMU7A9shdnng8cP+kSbtsE9EGlkR8cjdBlDRJMpllkX0D28lx35B3k+1U7KKFCwCzju9Ex+ZdVK1tSsIeKJcHrd6cyOzCZZSlK0i/yu+3f88MAPyZQz+JTpi7SBgWmaDOYHqQnUkDWyXNAiLGyKdpH2VPtUh5vJKGRST/7hx/eFHT2S3aLmzCyKSGziiCADtinen+gSpQPe2IQIwi9u7OWMIAWTWXlvRKi4c0O4uWEk1wbXJljO8pa+5zlN9fBLyc9Gjx/XdafU5wAfWf0R3rf8fQB0Z7rZ2LORx3ofoyMtyHOGLB7Xg0/ykXHy1DoOF829iKgW5azGF5tgv5GQ1JPHRCWHC8O4rosiKZzRcAZ+zU/reCu5co6lFUvpkDpIpBLkzTyO69AYauSOS++gJlADgOmYdKY6uX3v7ei2zoqqFcjI7Bnfg+3a5O0iki3hV/1YskVvsZ9nRnZxSAtydsUKWiljajILA1GWK0ExCdtzF1g2+EIwNAY4YOcglxMRfxBp6FjLtGranJjsORZgwUTknfyQmNT54uI3q6eE8KZ7E7ScIUpUinkI1ojJTaoPRlsnRDejL9+5RvNBuTjtQvBnhq77R0kEplPWJi69io2piGtXTvkjep/PYAZ/hjipCeQkHMfhS1/6Eg8//DBbt259rXfnleFob8ejrXISPVA9T9wcWk4XEY7+nSIFnWiFYgItOx1ldbUgzD0Dmk4Tj8qFQgxyNMySuHkcjewgjB6A/u1w+EEhoGl/EDPajOmpIJDqnej/K2HWLMAcNxiSJPTuJ3hOHyBh6ngCQdJuBsd1CGkh1jesx3Eddo/tZu/BvVObqvJXcWrtqVzccjE3LbyJlJ7iviP3kSgliHlj+FSxb1685Et5UnqKwdwg393zXeZH53PB7AuIeCMvEtC8sOf0S+Lo4/vCG2vFbFEfiiTGpJQB1SPMnWVZpBetMrQsFQS87UHQR8VnJRmaz4SlbxbHHhcClbjlHK4kCT/A7qeRdnyPFiPPx3v3cl24mn+sr2dggjy+f/n7ubjlYvqyfYBIqV7UfBE3Lb6J7mw339//fTb1b8JyLKr8VeimTqqcpGOslYvmbJgy0T4epDeAF2CqlGI2s6eeT/YQrwvW4Vf9DOQG+M7u7zBWGkNyJWzJpmSVcFyHmkANXz3vqwTUAHkjT3uqnW/v+Ta7R3dPrW/f+D5UWeWC5gtoDDbycM/DjOljlOwSdYE6PJKHQX2YlA592R4kycfs2CyyC85iWc06ocxP9YE/Io53fI6IaLtMk8dl18G5fz9ReoIgjr6YON9yg8KeZ3C3+L0N7xHnoD4+4cm6QtQuyyqMHBCf8caES0OwAoK1UD0fcmNiAjN5nk+Wehx9nVF9woVAfkNc9v9geNcGWXu2aL2a0U0+/dBBxn75/NTrNZbEe/AxmitRTfgl1jKDGcxgEif9leSHP/whX/va15g7dy5btmyhpqbmtd6lV47CuKhL0nwTUYMAVM6CtoeFMjM/IMyHM93Q94xIk4brsSuWAvcAoN94Lyw7Ko1p6qKW8WjI8ouXBSth4ZvE45xPwNP/CVtvR8v0oQX0iVTcOIy3swCFROVCOvO9PJPei20bDLoldF0ISWZHZuNTfTzc8/D06rUgC+MLOZg4yLg+zu+6f8fvun9HxBPhxsU3cm7TuTzV/xRFq4jlWPhkHxYWeSmP67iMlEao8FbQnmnncy2fQ7f0Fwlo4r5XGEmZVF97jmMDU7NIKM7T/aLmcfSgIH62LYQyTgFcFXJD0LdFlANoAXHMlr9VjMn4YfjB5UIAEWtBal4Hi68UpLJxLax9N+72O2DnD+mzchw0/cgufHbWVSxtuRiAOw/dyZN9T7IovoillUu5uOViZkdmc+uZt/K9/d/jJ60/oTfXiyzJFF2ZXSO7uGjOBlKl1DGq5DcaXjjGFb6KKZU+iIhkT76Hol3EdV3GS+OYjklNoIb/Ov+/aAo3YdgGX9v1Ne7rvA8QxPqshrM4q+EsHup6iP2J/TzR9wRhT5h3L303HakOHu55mCPZI6iSSlgLY8oqXkxUbEYLg/hLWe4cPciSQok1pSKoKlguDGwWHWJApKMvvnWiq9MLIEniEW0Sj7nniyil68K+X8DGT4v1DO8Ab+WEW0JJTHYMHRpXi996MC4mjIFqyPROp7bhxc0JPD5BHo/3O/gzQENjkDNPnTbnXrGimlTBmHp+8MA4Y7/uIatbx/v4DGYwgxfgpCeQ11xzDVdddRUVFSfZzXPgeejcJLpG1K0SSkzXFXVOq94qog2RRpGW6tsiiMrCDXDNf1Pu6GGSQOK6MHZI3EysklB8SpKop6pcIKISvw+aHy74J0F6fv1BYUtjO9D1LEW7QHW4lnMidWxxy6g4ZAwTXRbkcUXVCvaNi97DsiSzsmoll86+lDPqz8CjeCjbZZ4ffZ7NA5vZNbKLrJHl9r2389E1HyWhJziQOIClWCgeQXBVVJJGkmqtmqJdZGmFIMfH8wF8WcJkFICJ1Oek+vqlEK4TN2Froo1c6og4rq4sIsGKV9S0TUY8b7wLmk8Xf6d7IdEpjv3EcyndC/t+gasFhBDqlPfCuZ/EWP0OHt30D1Ae4K25HNdt+hbmjrt4cMHZ/CKzC4AxfYynB5/mO/u+Q3O4mVNqTuHaBdfSGGrkKzu+gu3aFCnRnu7iU099ivmR+WyYv4F5sXlvSAL5wu80aeU0mB+kPdlOV7qLCk8FRb2Ig4PpmEhI3H7x7VT4KijbZf5+09+ze3Q3EhKn153O9QuvpyHUAMCq6lXsHd/Lna130p/v51vPf4sFsQX85cq/5MetP6ZgFshZOXySh7haSUEqkTFz9OR78BBmWPJQq2dp8FXA0C5B+lQfXPRZWHKF+Pt4SHTCkSdE6cPy66aXSxKsvEGUoNz7ERHhdBxYeLkgf4kO4RKQ7BGq7ZHDgjCOd4hrhVUWbg89z4quVHim160F/ixrH18KjTE/jbHpCWluqMjYa7g/M5jByYaTnkBGo9HXehf+bxhrE1Y73U/D3DERdciPiq4zy94sWqR1PwOP3ybI0Oxz4C3fFZGIo+B74INA7/G3ARCfDfMuhiVXQuMpIu36UqhfCXPOgT0/w3As8oU+SuUSmqOTlYFggDG3TMkngQHLK5dPkcd3LX0X186/FsuxCGgBWhOtdKQ7OLX2VNbVr2NF1QoqfBV8b//3+Nmhn/HN57/Jh1Z8iAOJA9iuje3aKJIgkRoaFYEKFFlBU7WpFnJ/EIwX95h9ScSaYPnVQgUbqxcR4EICFL/wfzRyIjo5iReS8qajWmle+BncwV3QtxWpMCb8/NofwW05A+mMv2ar6kIZzpl7BW75EZJ6gi8nt4OicIUcI9yynl2ZdjrTnfTl+ujL9bGxdyP/tv7fuOX0W/i3rf+G7doUKFIuGuT1DJJjEA/X0RRq4pI5l9AYfnHv5jcSejO9DOQH6Eh1kDNyBL1B0Jmqw415Y8yKzCJbzvIf2/6D3aO7CWpBbjvrtqmSiaf6n2Jjz0bOaTyHi2ddzL+t/zf2J/Zz+97baU+305Xt4oYFN9Cf7+epgafIYzHqugS0EBIw7jiEZJt5qpfeUJyGoUMTSmsJrrsDFl0mCN0kHBuG9kDHY8KPNdU9/dquH8Elt01PSkCQwuu/D9+5QKy3dwvUrxH1kQqiJnqsVXScOvywqMcNxKDhVBGlVD2ijtmz4oSPxwx+P4byQ6TK0yU3RzJHXubdM5jByYGTnkC+7uG6QhiTGZhuGwhQvQgO3i+iW/3bBOHx+mHoIBQz0LkRBrYJj8GqhXDlf02QIh0p2T21ejnVBdVB0VVG8YgIhuYHZCGUSXXDju+KR6ASFrwJzvqYeF/oxcPvhmpFkw1NJRttpAMHqWYJg06WkpsnbxvodonGUCP7E/sBuHre1ZzTeA5jxTEe6nqIbcPbGCwMAnA7tzM7MpvTak/j3OZzWd+wnq5MF88NPcf3D3yfxfHFHEodQkGhMdiIR/awr7yPtJ4mqAWxXIvOVCdzo3P/MHGUx//732MURJp+5JAQ04weEKnscK2wYinlIVQjxq52MbQ9ID5nFqdbIOop8XfFXKGUtw3cVTfCaX+Bm+xCan8EOjYi9T7HyOAOBpobUCWFxed/hsz5n+Uj911PxkgxzzBYkuuiYqyX5jlno6/8S8b1BM8OPUtProdPbPoE71zyTj552if50vYvYbs2UW+U+kADaT2B6g3SmelkRWEFjaEJAnnyl0DiTvx74bKAFsB1XQZyA8R9cRqDjSSNJGP6GHFfHMux+Prur7OxZyOKpPCxNR+jLljHw90Ps2VgCx0ZIVA6kjnCfZ33sWH2Bs5vPp9Pr/s0Pz30U54fe547D9/J4vhiPrzqw3x333dJOzouHlZXrKE7cQBFiqF6/MRLSShOkMVzPgEtZwrFc3ZowsrnIDzyT8IXchKyCrXLRURxZD/89Ho49f2w7i+nJyuKB9Z/TEwiy2kh3oo1C4FNZlBYTvU/L7raOA5UzofViyYswyb6ZKdP9AjN4PdhKD/E1fdePdVdaxJ+1U/c++cpaJrBGwMzBPLVQGZAmAhnBqYJZMNKYRY+dliYDOf6wKkRxGX/3SK6YBVFevUt3xMRyd7n4MCv8W25d2rV1txL4C3/Av7YxLb6p2ufjIJQXHc+LsQyxQTsuVO854qvHj+tG58DgGMZ3C2Xed4DNWaGrD3KocIgul0ipIUYzAuCuGH2Bq6ceyWHU4e5+/DdU10+ZEmmylfFqD5Kd7ab7mw3Tw08xd+u/VvesfgdpMtpDiUPibZzCP/DRCmBJmtYjoVhG5zdfDYNoQYq/ZVT5PHoTiQa2ksf85dLWU/CKIqU/8hBcayH94ljNHYQHAW8PqhbKcYq1Tf9OVmdvsmHakW0J9okCOTADqSGNaBoUL1QPFbegLv3bp4dfgaAVcUCVXe/l582LeGwkUKVVEb8Pr7imYguZ/fCHiFCCmpBagI1jBZH+dHBH/G5sz7H3679W76262skS0kc2+ayhgvwBmJU+iqnRCZvVPhVPxW+CgJqgJ+P/5zuXDeRUgTLsciYwp2g0l/JMwPP8OuOXwPw/hXvZ1XNKgzb4DcdvyFriDaJ9cF6xopjpMopfnb4Z2SMDFfOvZIPrvwg24e3c9fhuziUOsSRzBHeu+y9/Ojgj8iUM2wZ2Uo9GiPSCKfqPhYNHhSq+6VXwxkfnjbz1gJQzsGjnxXk0ROGOWeL0pI554pztJiErf8D3Zth+x3Q8Shc8C8w9zyxjtU3Cauo/m1CROe6sOY8MZkMVQqXBlkFIw2yK4jj8qumu12lD7yawzOD4yBVTqFbOl845wvMjc6dWh73xqkP1b/MJ2cwg9c3XiafOYM/GaKNwmsx+oLU4pIrYfWNIkqh+oXQJdUriIg1oRi+8efCE27r7fDzG2HfL5COmskap35wmjy+EJ4gzLsQzr8F/vIZuPyrIuLZ/RT84r3C0ueFmCCfZddhHNANsIABM0/RKuKRPYQ8IVxczm48m6vnXc0d++7gKzu+Qm+uF1VSWVG1grcvejtXz7+aGxbewMqqlXgUDyPFEZ4eeBpFVvjAig8Q9oRFClILUrJLWI4oXpeQQBZdWuqD9SytWopu6ST0BEP5IQzHeOUK7JeDJyDMwYNV4uZevUREdXDByQCOOEaF1LTNCkxHHycxvB+6nhJ/O8fxIQ3VwFl/jbn6JgBGVBW7ZwsH2n4DwAWuj/xEl54FcpBm08LjiKhbwSwwWhylJdyCi8u/b/13zqw/k8+f9XkUSSFtZugtj9E23kZHqoN0Kf0HHQKzbFPMGpjl16l/6gR0UyepJ0mWhHWTT/UxWBykZJWwEfs+mcKOeCLc1Sb6xb9p9pu4qOUiAH508EdkjSyqpLK6ejXLKpdxZsOZ1AUE6X6w60F+1f4rAE6vP53PnPkZTq87HcMxeKLvCb563lfFMTcySI6JF4fGkcNIpTRu7QrY8MVjO8GMHYKf3ySU1tEmeP/DcNmXRJnK5AQnUCHqj8//JwhWCz/HX78fHvoHMcGRZDHZU7yivjFxGPp3QNMqTF8dRe98TE81eKPCpd614dDDsPsu0T51Bq8bzI3OZWnl0qnHDHmcwcmOGQL5aiDeIqKNZhH23SNMqy1dLK+aL7qcyKoQvmS6YbJW5ro7RO/pxz8Pm/5DLFt6DaVLvvSH74PigcVXwFv/V6S7Rw/A9zcIsc7RmCj6d12XtJHFcnRcWycx4bl4duPZDBeGCXvCXL/gen7V/iu2DW9DQuKC5gu4Yu4VnFF/BkFN3CCj3ijr6texrm4dAHvG9gAikjTpA6mbghDLE/WZYcL4VB9ZM0tbsk28Z0I84VN9eGTPK1dgvxw8QeHNt3iDqD+bd77w0FQDoIbFDdtxYGSPiEyCKCfwHmXxkRuGe/9K/N1yJmz4j5fc3IbmC6nyVtCvqfx49ZXMkgUR9eWGkSbsVm6qW89V86/hTt9iHh0c5wNpEVXry/VSF6ijZJf452f+mXObzuV9y4R35MH0QfYm9vL04NM80PHAS27/eGTRLNu4jvv6J5CWzsbujfzXjv9iU+8m+nJ9LI4tnuo6YzLt7ychsXNkJyAIJMCzg8+ysWcjIIRfVX4xUfIqXpZXLWdNtYjYPdb7GL9s+yUgaik/e+Zn8cge2tPteFXvVAQpj004b3CePtHZ5oJ/OrZOtn0j/OavoDguzpnr//fY8+aFaDkTbn4ETrlZkMYDv4afvkWkuGuWwLt+LQimXYKujZQOP4M+NoobqMRUoxCZaMEZrBTEMd0trjVHNv9xB34GrwmOZI5wMHFw6jGUH3qtd2kGM3gRZlLYJxwT9VupPmi9F4wSjHsE8ahdLtS7lQvBLAvFb6uIgHDm30DT6fCL98Hh34pla98NS67CaT8qGjZyEMJHtWdLdYu6vrFD4rkvJiJssRaRUpMkOP1DsPMHIjLygyvgiv+cSpm5tokEeHGoDNfhk2VUf4RyxiCgBqaKv6+Zdw33dd7H432PA3Bu07k0hhrpTHfSnmp/0VFoibQgIdGf7+f5seeJeWMYtrDQcBCRo7pAHZqikS/laQo1UbJKeBXvlH2Pbuk0hBqm1NhZM/tHjYz4wsK7EX9SKN2b14rasUw3yAHAFcRgIu1J/UowC+Lvch7u+aA43rEWkb7M9ose5LiihGD88AQRtQgrKh9xJD4H/DTfwd+e9xnY/q8cDIaps02GVJV5O37EwlAddzQtJLPiAm7q2ce1uTz3hEMkiiME1SAd6Q7+9bl/5T3L3sPDPQ/Tl+vDI3uIKTH2jOzh37f8OzFvjHWN6zil7pSpr3o0WdS8E4Ilr3LM89cr/KowDFdllb1je5EkiVPqTmHr2FYagg3otj5l0J4sJTEdk7hXtD1sTbTyw4M/BIQNUKKUIFFKHLP+RRWLOLP+TJ4depbH+x7HdEzW1a2jPljPGfVn8NTAU9zbcS+zo7NpT7dT1gJclxxHsg3c5nW48XniPADY83N4+v8h4Yrf9qqbhJfrJAqjIlJtlUU0W/WKSLhZgIWXikzFlm+IXtg/uQ4u/VfhNfqOX8K9H4aRA/j6N+J6GmC4FrWyAdSQqKt2HEE0s8PgC0L2qEi96077Qp5szRZeRfQli4QHMscsiwc9xyi2TxTiXtEs4ZbNtxyz3K/6uffqe2eiljN4XWGGQJ5wTFyoM4Oio0SpAJYj2hJ2Pw0V80Qq1ReGzocF6Wg5E879BNz1LlEbJatwxkdg9vpjbwIgVJiKKgyE+3fAvl9OR8teCNUHkQZYdSOc9RFo/S30PSciaFf8P1h8BdKE+ER2LMxkNwPeEAO6qP87s+FMHut9jIAa4ILmC/jAxg8AsCC2gApfBQWzwLNDzx53037Nz9zYXDrTnQwXhllcsfiY7jWyJDNUHEKWZBRJoWAVWBBbgIPDntE91ARqqA/VTxWiH68n9v8JkzfSYAVM3vBdE5BESzrbgoZ10DORom44ZVoJ/7tPiXZ1vpiwbZkoJZD0hBij3T+ZJpsIQ5UrJIl/ndXEmF2k9nf/BJUB+lSV5kAz5HvZ5/Px7kQv/57o5dvxKB+NhPlw2mJ9UeeZgB/PhF3Qxt6NLKpYxIdXfZh/fuafRVrfTKHKKu3pdiLeCDtHdzI7OpuLWi7irKazjksWNa/yuiaP0sQ/gOVVy2lPtxPWwiiywmB+ENVRGcgPYLrmVGefSXI4NzYXWZL57ZHfYtgGTaEm+vP9U2nwo+FTfZxaeyo5I8f+xH6eHXp2yt/0olkX8dTAUzwz+Aw3Lb6JjT0bKeppzstMlICc/Qkkj0+UoGz5JtLW/wbArVuJtPhKQRhb7xP1kFZJEMdJyBrMOmuin/1Eq87aZSKSveUbIlPw4N8LQdx5/yBKWn53Cxx6AH+pF5wE1DeK8ot554sGARXNwlmglIP0yAkZlzciIn5xO/zKI4cZffzQMa/5NYVHP3HeCSeR9aF67r363hcptm/ZfAupcmqGQM7gdYUZAvlqIRCDaAM0rBVRh8HnRUqr60nIDgglZn4EAlVwzbdFV4ruzYL0nf1xcYM5HkwdDj0oiu/1yYuOJMQ4mn+iq0VKpMytkqivHG8TbdGu/x488i9w4B546ivCeiRYgyvJ+Bybqw2XzRV1tBVbgWlT5/Obz2ewMChSyorvFfdjlicqJnRLJ11OT93wgSkLHwDTNXFch4OJg3RkOhjXxzmr8Sx8qo+IN3JMN5o/GdJ9QoDgiwjSqAWEcMlFRCMnSfuuHwq/zEAceoQohtPeL+ocj0b3ZkEePSEhTFJUHFPH64+zhFH2Y7JNNonbNinAKYgb/derqpFdh3dm8/xtKkODZfPvlXFuyOZIlhVavR4WoNGOyY9bf8w3L/gmNy6+kZ+2/hTDMchZaaqpBgdKdokjmSNkD2XJpkqsqFtGU83JKbLRLZ3zZp3HhbMvZDg/zJ0H72SwMEhVoIqoL8qIPk2UihP94OPeOI7rMDqhkF4QX0B/vv9lt7O8ajmHUoewHGuKiM6JCGFZqpSaaomIayFZJdyKucIEPD8ssgrb7xAvn/1xQBKksvvpib7Wx4FjQtcmkTGINAmVNYjJyAX/DAd+Bft/Bdu+A0uuEunsK74KDWtEaYtRgOEDwsO0lAd/GKKzKCrV6E4M3Xfio2ZvFNSERUnNf719DeH66etLx2iev73reVIF41WJQtaH6meI4gxOCszUQL5aCFaJtniHfyf8HeNzBVksjgtSNyZIGld8VSh7+7eL57PWi4jES8C/5avCQ1JPCVPi2uVw6s0i5bXgElhxvfj7wn+BCnEjnIpQKh64+HOC5GT7hco7WCWUn8Di/Ci7bRGtUSSFgCouqqqsMj8moiVlu3xMJPGlkDfytKfbkSWZdXXr2DWyCxeXSp9o7+Y5yt+yaE6IdbQQiqTQne2mNlhL3BdHkZRjutH80Rg9BK2/g44nhN9jKQvhGqhdBKEGUDXRP7h+rYgWjR0SYiY9LQRKIEyhj4ZRmC4hWHGDuPEvvAy36RTcOefwnmrh9/fTymo+pkvIrkuXW6bZtCg7Jl+siPOJ6koc4IZcnq+NjHFvOMQSvyB/R1yDagcy5Qy/6/7dVJ2f5VhYrkulv5Jzms5hRdUKwlqYoBxmpDDEcHqU46FoWIznyxSN128HDr/qnxr7ObE52K5NyBNCkiQUSUFBmaqhnTw/+nP9KLLCmQ1nArB16Pe3OU2UEliOhSqrU3ZIe8eFIn5BfAG2K2pFPZNJAG94Oor9/E+RHAu36TQxqZAkMcEbPSheX3qtKHNYchWc83E495Ni0qZ6xeTx4U8JsjghJkOWYcVbxYQFxCTPdcV61/8trH2vWJ7tFf2wR/ZAfhyyA+itW3CG2ikbx4kRdG+BZ2+Hrmde2cH/M8P8mhDLG6NTj/k1oT9sBek+ESCYfIy1nYC9nMEMXnvMRCD/lDg6tTwJxxHRh1SXMPzN9UO/CePtIvWshYWPGy7u8rdAyzrh/dj7rPBjrFsBoweRJm8qehrPzsenVi/ZJfDGRdu0WAtubhAO/VaQRE9ARCJjzZA4Ap6wWGeyS3jUTfrSzb8Y6eBvcJ//KU7VIqRzPgH7f011Oc87Egk+HTq2r3LeyONX/XgVL2W7TFem6/f6mU36Qq6uXo0kSWwb2ib2f+LmG/PGCHvCqLJKophgTnQOjcFGnux/EkdyGMgOsLx6+f9pWF4WiW4w8yB7hRLbF4H8mGhl6AkLYi6rotQACQa3imjxxk+L7iAH7hEK7IUbpolE31YhaPDFBDHPiuiTqwVgZD8Xui6LJB+HnRIPzz+Lq0tZ7kkfpF9VmG2YdHs0HgkGyMgy3xwd53y9xI+GRvjOsuUsLGu06SM0l0qM+X38uu2XvG/Z+/ApPkp2iWp/NXkjz2i2j2VanHNnryJna2Ao1MVqcI9zjhYNG9txKRo2fm06EvwH+W6eYAS0AAEtwCNHHmHr4FZKZol0OY0iKUS9UWzXxpVc0uX0VG/1nlwPKT3F4vhi9o/vPyYteDyMFkc5nDwMQJWvipJVomAWpojnyqqVJCfEZEF5YsLjuriOBclOpD1C+c2iy0VLwWSXiPY7liCajiWuBaUsdD4hzhEXYeujJ0X6ed8vRIeaRVeAL4IbrIFVb4e2h5C6NuHu/D7ugsuQ1Il2mofuRyomcGVwo81Ivhik+vD7K9Gz3XgLRxlWT7o3DO8X15jh/aIs5mi8fob85ES6D751uhBMTsKjQWO9uC6Up1snEqicjjjPYAYnIWYI5ImEWRRWHL3PCUJn5EDxYabGsPR2VCOFZo8J8UasBS789HTrsyGhVqb5dBFd0AJw6AEY2ImanFacluOLoHkisti/TbTSm3yxlMEd3itUnfOXiBqs4b0ile2NTKtCV74NDv4GjjyJZJtIlbPhqq/Brz/IumQPhBqRJImoR3T9cXGJeqPMisyiLdVG2BPm9HoRVdNt/UUK6XF9nN2ju1EllQ+t/BBZI8uoPooma4zrQniQn7AJUiQF1VWJe+Ncu/haOrOdmK7J82PP86Z5b/rTjg9A5WxIdMGii6B2CXRtETfWvu0iLRmIQM0qqJ4P/pBoc1gch4P3wjl/L8ZLT4JrQdViQRJSXWLdkcZjbH2U3ufAG0QBPqI6fDQAu8ee50eX/QjjwA/5bddv6dNUmk2TPk1jbzjOd2dfxPuev59F5QK3HniKH5/1Htp6fssen5dmw6TPA3fv+gara1bz3NBzUxG0UX0Yn+0wO9zC+2ZdylyrQKkQJhGII5VtevqzdHQmCRsSc5fEiTZFkIDennHSIzpVTSEaF063ETTLNnre5LXGrpFdFKwCRbvIksolJEtJBrIDFMwCWUsInRRJwaf40C2d2dHZNIebqQ/Vc9tztwGwOL6YmDd2zHqDniBzY3PZMrgFgHObz2VBfAFV/ioOpwSpXN+4frqDyOSETpKQZBU6n0AyC7jRZph3gfjNOYboTw2ivnGyB3XrfS/+YrUrhOfokSeFGKftIRG9tstCHHP6h+C5b8PTX4emM0VqPFgFF34GHvgY5AdJZjK4xNDHogyWq4n6E0h219Qm9MQ4puGg1S0X53jdCZiQvQGQHCoc8zw3WiTsvEJmXUyI6/51d0x3rBrdDXv+Hzz4STCO+g1pAfjIthkSOYOTFjMp7BOF1gfhkX+Fto1gGoAr0qA1izDVGC4ulivDpG/f+f8kUskARgEpKwy2qZtoRVZMiLS262BHWqY2Y/sqRORr/PAUeXSRcCUZl4mAQnmi1nCSnB5dxA9Quwy3Yh6SbUDvhAhm6ZtxL/hnyhNRKE3WpiJSk2m8xqBI8XVnu1/yMCRLSfaOiRTgJbMuIeaLcfve2wFoCIp+xLIkI0syXryECBHzxejKdNGV7mJRbBEls0Stv/b3HfH/G2qXwNLLxf8gFLD5MfHLcG2REhxrFZGipVeJMZJVET3q3w7NZ4jPtQuLGMp5sCduErbxwq1N4TxLYoUlUbJLfGbLZ/ir1X/Fhc0XYksSI4pKk2mi2yV+mtrD51Zfxm8q69ilONR1PElQC2JLEisnjNSf6frdlE1SzsxhuzYmNp3FYVq7R7lz62P0pgfIdbdRLlmkUyX6Uzr5/iLjSZ3EkRx+V8KnKIz25DBKFuP9+WP21yzbx41evtpYWrkUn+LjtJrTaAw1sii2iDmxOQS9QbySsNHpTHeyokr8bn5w4AdYjsXC+MIpP8i2VBuZ8rTKtmyXaU20csfeOyhaRcJaeKrusWgWp2ooF8UXYU6OrTxx6dQn1pOdsFmJtUy3C3VsUYMMEP09JEGSBFFcfeNESnt0ehIJsPbduLXLkcwCHD7KqmnFW3ErFyCZOlpmELf/WYZzFRTTBr2ZWYzoDVNvdW0xjsw+C8740Iujj3/m8IU0VI/Mo/97kLv/ffvUY9d3W7k566WUeenf84tQtVDUrTesFn3NAa77Lnxwk3hcd4cgmi8leJzBDE4CzEQgTxT6d4JdhOFWWHmdEGIEa6HnabTaeVjpYVQ3D0YAjMx01AqmSKWreKZJZW7iBhVppFz9ZqB1+v3pHkiLSIcrKeIG5joTZBKRlgWYNN9+ofG4JEHTqSINN7Jvevnyaxl99iuA6O4xKXqJeMT65kTn8ET/EwwVhsgZOcKeY33uxvVx9oztwXEd6oJ1vGvpu7iz9U460h0E1SAjRSF8mEw5+vEjKzKmbTKsD/Orw79iRfUKLpt3GQHlTyyaeSlUzIIll4tIZN0qUSPpDYrUo6KKYyWpgCUiuQs3CCFUx6Nw+gfEsY3NEmOSaBek/YUCG0RJwL+XFN5REeBQ8hBfeegD/MfICH83dyXPJvaSchUaTItBCjw0uo2HIh7h9UeBSctDufkMGHmOvJnnutoz+KH3h0KcpOeJqTGMgommBxkveXk8kCKoL8Kb6iVWHeTQSJYCBgtUPx6fStu2IQrZMqW8gTegUarw8NiDrYQMm6WhIHNaoq+LlPb65vWc0XTGlOjKdm12De0ifSRNxBMhOZrEcAx8qg+/6udw6jB3Hb6Ldyx5B+9Y/A62D28na2Q5lDrEvOg8CmaB4eLwVLvEBbEFXNhyIYos1j/ZtUaTNYJacOocTyuCJErpblw9BYs24O7/JVLPM7iT0T1Jhqlf4Ssk394IzD5b9MzufU6IZUBEHJdcBSP7kfq2Tb9fkoQQB+jVVzOWBZ9/AFWqwuOm6ZQWTL81GJ9S3PccHGO0J0fNrDCzllb/ocPwhkS4wsdNt55B6QWR9r0Hxzj0m27M4h9ZI1y9EF6h4HAGMzgZMEMgTxSaToH+nbgt64RFjFkUno3jR1A9fpTZK5Fy1bipEFLfFtzup3FXvh0kBdIDInLoi+KWciIaMbx/okd1QNQwTkBOdeKWEuK1QBUOEpJjiUiFbYPswfVGkdK9oi4LQFZxiymx3knEZ4ttDu7GMYVVDP4KhjUR1alQAowVRc2kR/aQKWco2SWq/FWM6+PsGt3FgvgCMuUMmXKGvJlnuCBuzHFvnLPqz+K5oee4t1O0YVxUsYhdo7vQZI2YFkOSJAwMvHhRZRXJlQhMpPwUFMLeMEWz+KdXXx8PDavEI90HrixaG0oqdD8nlLKJTmHobOrC6D1UK0QQhx4Uwhp/pXitMCrqJY3ZEKjEdR1R0jCBFuBWbTH/aLbyZGmI290sXx4Z4epQJYlSAq/rcm6xyIA/SsofJlZI4rUMdM2HFJ+FExQCpLwk499zF+9d+l6+tvtrFNwC59acQ7aYocmci8/vpZUwYTNAtj1JaDCD169QqWmE6/2oXpnMaIGxoSKKT0FybMYzOmNDGTIZB1+tQdyr4Yu/9peLST/Qyf/bE+2EvCFOqzmNQ6lD1AXq6Mv3sXlgM9fOv5Z7Ou7hoe6HaIm0cGrtqUIYkxfEcLIfNkDUE+WsxrOoCdRQsksM5AeIe+NT0ceQFhKdmCbEXpLiYcwbprqcwxnYCdFG5AUXi0j0s9+Ay748UT+rTJc1/L5zt2vC9Nt1QQsKFf/B+4TND4i2pgDD+3CLCVGC4jpIGZGtcBtPIV324Gg2lYERSsEY6cFpMqQGgiiahOO6jB5JYqSSjGaGmDWL6fanf+ZFkOEKH+EK3zHLAqP547853ffiCOL4jGBmBn8+eO3vCG8oHHXxXXIFidh5ZMeLRIZTVIS8MNouohJmiWQhj5Max7BaaGKLSIfKKpInIOqeAPwxJG8QV/WK+kkQ9TKl6NRmvKVxJL+E6wmC6kNSPEiqD8YnVMDRZiTNJ1Lhg7vEsuolwlIoMF3jRotQqjLeLr6FItKjo+EqwKJe8ZExRDou7osT9oRZXLGYrJHlgSMPsG98H7tHd0+ltydxSs0pfGDlB9g/vp//3iP88S6bfRmP9j4KCGuftJGeer8fP6fUn8La2rWEPCGWVS0j7otju/aJse85HiYjbQM7RBRZ8wAOZPugmAV3IpVllYR4adXb4Zn/EvVrp74f1r5TtKbc+t+i5jTVBZKMVDX/RanM8488yW1unltqKvnfWISm8Vb+e8GH+cT4M/Tl+uh2Nb4+0IP//H9iDyaXbfwCriSx6ZS/o19V+e2R31KQJbx77+La9U/zq45f0ZfrY/f4bkxMUr4kDb4o3oyP4XIa7EqMbJElQx4UWSWVThBbUc3oSJF0WgevTN714kY8ePI2Pp+MYzioUZWx4jQZyaVKJ34cjgO/6p9SWeuWjm6LDkUFq8DiysXsHNmJLMnYrk1bqo0bFt7A3W1386ODP2JudC7vXfZeVFnlJ60/YffobppCTVwx9wpUSaUxfGyrUU3WMCeiexFvhLAnTG1AlFLoZpFWj5/qcg5peB+sfRdc+GncrqeRRltFVNIXmzCht8TfAUH4qZwvzL6PxuDuaX/RoyClu3ETHdB4iphQhuuRckMwsFNEv7NDSI6Jg8xQKoZp5DBMm32lpUQdHQrTpv69ff2E/RXIpobPa1HUdRQbEkeGqFxR9aJtz+BlcDyxzCS0wPRYz2AGb2D80QRy586d7Nmzh+uvv549e/Zwzz33cP3113PWWWf9Kfbv5EWqh8y+HmxPBdlIlIrmCph/PvqBezED1WTzNq5az2B+DvWSF8Uu42b6RB3UZI9q/1EET0+L/wOVcNS9W8LBlTwwKVwpZaDUjeQ6EynwiZ67uSFhFwQQfkE9oesg7fmZ+Ns2xDqCVTDailzKgDfI9kwnCzyi6P7HB39MY6gRVVZZUbmCB7sepDxBer2Kl1mRWTSHm5kbncuC+AK+u++77BjZAQgl6+aBzZiOSUgLHWMBFCBAY6SRFVUrOK/lPJrCTYCoQ5uMOr2qcBE3CMUnIo6heiinp2/0z/23MB5feo34e/Qg3P1O8Tw+B9b9pYhIHX4Qkp2ivZzLMUXzkl3mSr1Ad0rl9niUz1dV8Paeh7nlwn/js898ll7GuKapgZUHv0vTnItZ2LiCeQP7qN71U74aFZESGQkpP4K/9bcsqVhCX66PtJkWnFfPEshGMH2HqAprJHWXWXnhdenaLnpGo2t/ErNokDWgYNkkzSItSZ2ILVH0yOwuFuhuHUQzpiPWevYPqAc7QdAtnUe7H6U/109TqIk5sTksji1m59hOLMniQOIAjuOwsmole8f38o+b/5GlFUu5buF1vHvpu7li7hVU+CqQJZmOVMeL1j9cGObOQ3cC0BhqxHIs7joslNYSEkd8fs7NgTSyXySoQ7WwaINQ5nc/LVaieIGCELD9IaRCkqdKURjYKQgkiJT24SHo2SII5IRRvaR68JHDRcZxw3hNGWPMg12OTK2y48kjOP0BlIZaetMF5lXI1Ie8ZAoBZujOH4jjiWUmMIRNys5BQtg3TQmvZjCDNxj+KAK5bds2Lr/8cq655hp+9atf0dvby7vf/W6uuuoqWltbqal5ce3Xnw0yA0QjBtnMKJFoVCgwF22geGQrdG6mZMdwLJmgXkZxJyKOkxHBvufE/zVH1ctMEi1ZBaZJV9GuoL24Ag0vXnMEPy5eKUxCCfG4ci7+vEKTLHFq36/wuA5uzRKRhp2EbSJt/DRS28NCfHPePwjyWEgg/eJ9vCWX5J5ggG7yyOlOWsIt9OZ6ue2521hWuYzrF1zPX636K3JGjsZgIy4u8+PzcVyHpwee5tYtt1K0ikhIzI7MZt/4PlxcfIqP5kgzPZkeVFSCSpB3LX8XTeEmfJqPkjXNkictXF51NJ0KSKLNXKgGOjeBlYfcuCAE5Sz85i9hwxfgqv8SHUJGWwVRX/NuaDpN+Pw1r4Md30NKtEPvFtzCfNFzW1ZwVR+uJPPhdAZHgjtiUX7upGjdczufPO2T3HXo5+we2cVej8regSd5xCtzXk0V++1+RrIqPsXHF2rOQOr5EU9t+xqPBMW5MSswi2S+j6hlYjopDKOMY4/hVQpoxlImVELIWGhlmUy+jIJMEfDqUNC8WGWLgZRJygc9h8usiE+TRn/kxdGyVxslq8RgfhAkSJaTXFRxEYqikCgl6Mp2EfaEaU21EvfGubD5Qjb1b+Jg8iCHtx7mguYLuGTWJVPekS/E4eRhfn7455TtMo2hRj6y6iN8/rnP88zgM3gUD1VyJf3eidTm0T6gtcsFgRxvE5GocJ1Q6WcGID77lX85xwLXwUUStkCTqFkiJiSDu8XzinnCvifTR9g8zJC7kCQaCi5eSaNoTF8rsuMexsiTGCgx7nNgXohZsXrkqjBF0yZwlIVTYjBPZqxItDpAZcMf6IP4GmIgrZMqTJ+nHaO5l3n3nwCTYpkJDOWHuPreq6c6Zk3Cr/p/r9XZDGZwsuGPIpD33XcfH/7wh7ntttt4+9vfzg033MAnP/lJtm/fzhNPPMHb3va2P9V+nhQ4RqUabaSCAeItQQiruGMdkO5HG95L2taoq6kkZfqoLY7BCLiyhuMoSOkh5MO/A8BpWQ+FFA4+1Ak7GKdcxC5M3xTuy36ViFUJx7ZuBWBQcXjea/Hm9E85SxthzI1y8+hfctFBmxsWeqgzDdj1A0EeZRX3ws9iz9+ApGdQfnkzUqYP1xfnbYtv5htddzGmjzErPIvrF17PPe33cCBxgK5MF1fOu5LF8cUM5AdoS7Xx267f0pnupGiJ9E5YC1O0inRlRe1mhbeCpnATMTVGRslQEaygLlDHrOgsHNchrIVJl9I83vM4LZEW5sfnn6ARewm4rvDM8wRg7jlimTZRF6VqIqpUSkFpon/2g5+E8z4p2sw9fIu4uW+/Q/h7Lr0GVA+c/kHcLV9HyvQhJTpwi+OiLaKsYfuiqHqKj6YyLDId/rmujj1jexguDPP3p/49H4ufwt4tX+K+cJA2j4fHgoJMzzFN3jfnetauej/d++/ncz4LkFkaWcSFjeex2dhCixanulRPViswq2kVhYCMEo7SVXDxyxIRRSZQH6J2oIAsSTRIEiYQjHnpbksSRCUD+IJepPR073FVE20RFU0QMFl+9Wvn4r44q6pW0Z3rZl50HiO5EfyynyUVSwh5QhiuwcHkQVLlFI/3Pc7amrWMFcfoy/fxaO+jbBvexrLKZbREWnBdd8p+auvwVjZ2b8TFZVX1Km4981a+s/c7PNb7GKqscl7DOeTKaYpKHjgAySO45Zyod4w1ixKQsUPC2WDyvCmOCyGW6hUq/fzwMd/FVTyi/hkmzr+yWE/lAvE8LdqJ4p0oXxnag5PPgKLhzr+MxHOPM5yEPYUaKkyJUcVlJAJa7XS5S7gpRLS+ipBfwm4folpSQSkz1usy3JZmwbIqamdHaRvOsm/nMJUelRY4aQjkQFrn4q9uQjeny2hKYz1Tfwe9J75iK1VOoVs6XzjnC8yNzp1aHvfGZ7rLzOANhz/qF1VZWcnwsLgQtrS0UFcnirw1Tfvj9+xkR6xFPI5sgq6nKR16jlImQ9aw0c0wBU+IoNVH0J6IXoTrkLx+pLZNwk8u0ojUvE7Uznn8lG0vObOBH++5jOygAvx0YkMy1apIv6WcSopuFM2VkYAGW6ah6EEtXcdTvmrukxYyItXwjU6Xb3TBtUe6+Y/BH4lY1IYvwoobkPQ0ys9vRBrag+uN8MWmtSjFJPVaHT1OLz25HrJGlr9Y8Rds7NnIkcwR7j58N4qkvKj+UZM1JCRypogCKJKCV/FiuiZd2S4CBFgQX8D7V78fn+LDp/qI++JU+it5vOdxinaR3mzvq08gJUmIXRxLRHwn2xzWLIQlV4i/D28ULQ+9FVBOwqYviQ407/4NPPZ52P69af/Py74ElfORZEWYS+/4LpKehlIGFRlCtbhmEckqc6kcprGg8QmvxWBxhM9s+Qx/vfqvub5yDe/peor9iy7h3jlrcNse5mMDewkmf8j2+lP4VH0NOVtnpWFRX3Y5kjpC1srQLpcoSQaNY6txAjG29+sczORYnBeEzwF2HM7iTOiQFZ/MygofzT4Vx3bRgDgqNZbLSGF6fDsPjbFgnnGM4KCYNdDzBv6Qh8AJilAerQRPlVKsqV/D1YuuBmBMH0M3dSoDlWS7s7SOt+KVvFiSJdTao6IGeF50Hjkzx2hxlGeHnp3q3x7UgjSEGmhPibrBDbM38A+n/QPf2P0NHup+CFmSqfREGNdHGSqM0Gu4mEholo5slUVpQtNpYj9zQ7DqJiF0yY8LP0hJgbkXCc9Vf5RjULEAGtcK65+OR5GGduN6I7jLr0UKH0U8QrW4nhCSkefwtoM88ZwHe+RNKM5lAEzmFgKWRFMSRovTUcWn0gWuqtDx2h4UXWPooEEqlSKgeAh7VbzdCrWzo3Qni9h+mYRusaL6NYj8/x+RKhioZYcvXbyY5gqx3z1dEW78vni9MvTqRc3nRue+4havM5jByYo/ikD+xV/8BRs2bGBgYIAvfelLAAwODtLe3s5ll132J9nB1yPMso1ZttG8ypQtxgthmY54z0gvSrKd0ugAjuSl7FRSVKqRnQbyej0oAWbRKeqnAEYOMGwsoLP4TkZ+OMjwSBkjuxLN+SEAXsDrTnfUKDXt56zqp0guex8Vq99EpV9DcQro+x6l/dFdHBhdQ96pZl/xSuYAcxCkISE7VO7pYr/nInRPmQHjbK5xXeTNXxHkMVDJrXXLGfaGaJTAg4cWbzMD5QFS5RS3772dllALFzRfwFP9T2G7NhW+CuqCdTiOQ0+2B90WaRyP7KE+WE+ilDiGAES8EdbUruHUulNfdPxaIi30ZntpOcrz8lWFJyBIpCcAo4dFtLFvB6y4VhCF094jyGDFLNj7S0i2wbPfgvZH4fxPCSLx8C2i49CdN8AZHxHG4nUrhCn0lq8LkQ6SKF2INOA6NniCLHQVfmxX8ik5zXZL58s7vswdspcbY1He3ruVj1xzByx9H+M/uYbPkODRLbcAsMBR+M/hAczxHN8PNWIBpmTR68tghcZJZCQ2Ji3ek/NS5byEBWwZjIzB3pEUcdXF71FwJIVCHgrZaRuTA0/3oaQPcNrFs6hujqLnDUp5A49PRc8bJ4xAHo3ebO/UJGNVzSqx0A/bBrcxUBhA0zTichyf4SPn5jBsAxeXzkwnHtnDDQtvQFM0DowfoDXZSsEs0J5qR0LiAys+gE/1cf3915MsJZEQRvoaAZrwEkIm6XEY0bw0mSUhgos1gz8uJn/ZAeGpuuBSkeLM9Aqj//E2UZfsC0/7RYJQ7XdtgqE9SJPCjHkXvVhYU0zgui6Hihfw+D02EqL0wCMVaPAcoLBoLavXr6bzQIIj20fxHUUgV+cjPDpcZn2tgoRMLlNiUAGPVqYi5scqeVmWNWgOeaEBZlcEiMT9FLMGmlchY5okCyYVQY2q0LFK5dcDShmDm7Nexn7dw0SPLbLF6Wulx3/8a/UMZjCD/xv+KAIZDod55plj+6nKssz9999PJBJ5iU+d/DDLNq7jTpHIF6FnC2bXIdyqRRw06/H2pLHcZqKUqK70Q6pM0oxQMBwqJVHrV/Y1cXhLmoPPXUYy+WZIAogbiTbh9+6VctRq7cTl6U4WF61dyiHvTXjSA9j3fw8rtZWKzA78UpZTJJ01jRG66j/JoO9iEkMlEgN5ykWLakeG8jyeKc8DIH1nP5997AC3uUI08M3YWvr9ETTZImBpIIMXL3N8c0haSUpSmd58L735XuZF51Hpr+T50ec5WDo4tW+KpBD3xqf888JKmNHiKDqCWM6Pz+espuOLrebH57/6kcejoQWmbVdqFgnyONlLPFgpVLSuK3w6F1wG+9KgjwtD91++T/Qwvv4HsPmrghg88zURkT71/aK29cy/gQO/RsoIKxBXTwohlKyCRyWOzLecOL90i/xUyjNAmf+OR/m+pHD5rv9EkzV+HSxhEUAGrpt/HR+dew2xO29Eyg3x7sO/Y0/VQkwVpBKMK0UeHddZU1aocmR8QY3l5zXi2C5l3WSoM0NysCC+EhK1RQCJkmSjekuoQXCZVmEXC5AbLzHYnsa2XDRNxnElkGxCgVcnA3H0JKPCV4Ff9XMwcZCiWcSn+AgoAcp2GZ/iwyk7eCUvtcFaevI96JbO3W13U+2vZnXNatbVr6MmUEPRLFIwCzzY9SC9OeGtqkoqUW90SsQlo1ClVWGZOfaEKmhKTbQgXH6d2LHz/gHu/xhS7xYhpqpfDZYBPZuFx2spLVLa0WZRDpEbAj055eHgekIw62whqDsKiUKZ0gP/wXDqMrbm34EEjIdN3hv8Ms3uTtz5l+Bc/3Es02XWgjgXXj2fJ367DX4sPu9D4Zy8RodZpijbxBwFrQjZCFgRmYgGRsmiRtWYPy+C5lUoZo2pa11SNzEsh2TBfF0SSLNo4UFi8TWzWTnhbdne0Tb1/f2RmczYDGbwp8T/iUBalkVXVxfRaPRFQpnJNPYbGZpXOS55dF2HUsFGb+tCdUysoW56PevQYldQ7e9FapxHwiowbhWontOAP1PC7KvhsfG/oePZc7GcMQRdNFjge4ZNcgMjNctYM7+Si9bWo/Y8grrvCYzOvVPb3HKfSzgwUR/FionHX4j9waKkyXgLXuprS1TW+mlZVkFVc4jCwWcpbL2XMWcRXdapxAyZ2FCcu9WvUBneBtHTWBLJIEs2sypmM+yIHsBIUO82YikWWSvL4fQhOjOddGZEKj6gBvAqXsKeMH7FP93rWonh8XpAgnQ5jUfxcMXcKyhZJX564KcsqljEKfWnTGzideZF17hGkMjCuIhKSkDzqZAbFapqxQOBapGuLI4Jcc2+X4h+x+f9I8y/CDZ9WZi9P/550ed47nlw9sdxn/wClDJIdlmILfQkqhbACVSi+Ct4myvz9rEBHvN7+H5lDa2KzT0d90zt2tlFnb+tPYe5Kz8MgHXdHag/v4lZuRE+r/r5xfyrUB0vG/cvoZw3OccQJGjthhaU2SG2PtaLvi+FMpGdnjzyZQUc28XvSlACW5ZpmDMtAnB8Kr5KL6EKL67johctghEPruNiGTbTbZBOHCYnGY4rEvC6pRP2hCmYBdbUrqE2UEuqmGL72Hay5Sy2ZFPprcQn+xjUB0mVU4zpY2zs2Ti1To/swXCMqb/DnjART+SYyLnr8ZM0UlT4K2ir83B5agip91nc8XYxSVi4AXfVTUh77oQDvxKCqrrlghAO7YHuzaIbVGJa+S0BbrQJt24lVMwV9ZSWjqNFccplvv68TfXBHzArfyEHdNHSM7oiygfVf0Ib2o4dX8Deys/R9a19DHVk8PgUaudGKanTXadqV4QpdML8skxB8nEk5rK0zkOFKoMjU12Sad09TLjShyW5VNcGyZom4+kyVTEvFUFtKgL5ekagyk91izB8H829/ojuMTieb+RMj+wZnCT4gwnkwMAA5513Hp2dgjBUV1ezevVqVq9ezapVq9iwYQOVlW9sU4iXTl1L6Ie24Ix2saljNqlSM8GWLEa0DtnQKHeWyRfK6AUZXfEw1AnZ/LVTnzaCCk85Ohf6f8XFnrs5p/405Pe8n0P9eb74ZAePHQ7TbP01S9NvA94DgItCjdaGhEParUR3wuB4hbULKn4TSJqMJNOMkBb7H9Y4t+4pTgv9Auf0D1M+dz077n6cw9vLJK0WkqkWRp92SFSGCM8+RNY8RFe6G5H8lpFNCSdqE/FE8ck+JFfCVV08soeQFiJtpCnr0zcuL14yaobmSDOzI7PpyHRQ66+lbbyfntwYAU2IjyYJ5GuO43VcmYxIlrPCCzJYMRFVehaGDoDmh3IG4gtEO8SO34ko00OfhMVXws0PwUOfgp6nofVeGHoe5l2EdPHnoHaZUN4fuAf6tiGbReRMETc7CFoAybF4k1zNJUs+xI5oFT/qfoC0keeviHB21z24tRruhG8ntctw3/JduPtdLEl1c3HHg3zLuZmujJ8NZQ3NAbXCy62Pt7EuLRF1ZRRgVHbY7DepsWXWlVS8tgRIjPkgVHLxFx3GWqfVrUmPixnVGBsuIPcXUTSJqqYQoagP6yjl76uBSXLnV/3sHdlLR7oDGZmIFqEqWMVqVqO5Gp2ZTtpSbZRcEfX3yl5UScV2bSRJomyXMRwDryLM88OeMDkjh2Pa1PpjaCiM6ymGjBKybTCLMou0akxZxeOYSAfugYs+I3ZqwxeEmGqsFXqfgWu/IwQ0vAsOPwxDu6H9EdFFpuUs3JYzYOGGF3FuqVzitq0Oyb0PsqS4mgPl0wGXdW+eyyrrdtixmz3mdewdeS/ZQ0NTnzNKNn0Hk8ekcENBlfUfXMbDdx4imLdZkZLoKuZpqvfhK0FRK3O4P49RtnEUh5qaCBUtIaqWVZG1HBZWBl+XkceTEoFKcT359Qde/NpMj+wZnCT4gwnk//zP/zBv3jy2bt3Ke97zHlpaWhgeHubLX/4y8Xic73znO1x//fUnYl9ftxjPl6Zm5oFMK3ooSKpYhQVYWYOoL0B6VCfjQEgZpWhVkumWyeUDSLgs8D0F4S4+YNyALEtcfc7NuNvvwTu0nf5vXcLfjb+fmF7LBw0vUUcm6wantr3kwjDnrW7Eji8ExUMxUyYQ9WCbDr0jeba2jbO/K00qXSJkSyw0FUI5k8dyl5EKFtmePot5e1q5LPV3nF6dZrP/Vg72LqDOlKkbrqSvtByiBwAHLFBUGU0NEZS8mKaFXJLx+6f9GY+O1Ex2lZkVnYUruQS1IMurl1PlryLqj9I1NkyqlCQtw6WTaufXE4zCdB2kFhD/FxLgj4j/O56AZDdYWahZBivfLmoli0lYcRO0/VaotQ89IFLbV30L+rfCpi+KVoc7vy8e9auEWvvSf4VyHnfLf8HoIaTcIBg5XC0Aa96JpHo5vXIZp1cuE/v3yKfF/9ILJjNNp+GufifSju9xVqqTv3dDhB2JFWXxc3/AzHNxTkVFoqyBPivA8pVVvEnT+M2RMb53eISzSxrLDYXqkoQlSRRwUKzpmr0K3aYwWGTYZ+BDwhv0EIx6qZkVxf8qihWOhl/z05HuwKN6sByLhkgDCgohLURvpnfKYFxFxcJCkiRUSUVFpdJTScbMILsyiqRQlspTFj+yrFATbCEgy2ian4hhssK2OSs6l8WpIUY8PupKJjz3bRFZvPBfxITi0n+DX74XRg7AvR+By78M/rhQ5S+4VDxcV0xY7PJxv9NY0WFsz+/4G2cPm8ofQ5FtzrlxCbOXV9H1pVY2p79DyY0ANmVc9nts9ngtfK7EXFOm5qj2e53PpdAT3bzlQyt5elMfozvGmFNWGOsvM6tFpqCbJIZKooxBgn6Ex2TDqprXfdTxpEOsWZDE43Wy+fUHxPIZAjmD1zn+YALZ0dHBTTfdRGVlJX6/nwsuuIC3vvWtfP/73+eZZ57huuuuOxH7+bpDZlwnnyoRivtI2haG5TAwME6VaaKOHyDoqyJVnIXjhcRoCccM4Dpgmg3oho9SQUZWJAKnFji3+5t4JYu/DcZYe8O/cObcSg4pXyb21NfoGjiFt+qVWK64gHt8Ck2zp+tLzaCP+w+G0ULj+GIeMBwiRS9eVWZufZgbm6PoOYOyV+LJznFyBx9hZXc7e4tvZlfhLfRssVhx4BPI2iBK5XzOfe8NnG75+NH39yJ1FmhOR0ltX05w8X4KagGPFcayTfJJBW9YAXuiL/RxUKZMVIsyOzIb0zEZK44xUhzhjIYzqA3VUtAfR1FUmsKVr5/o49GYVGIbxekIZLxZPE/0QDAuOs3oBQg1CnuWQgIiDVDKQuPpgjhmuoWY5q6bhPHwB58URtOt98ORJ0Rac2iPENOcd4sQ4My/GDc3DGOHRerT/wIPue5nkDofw5Vk3JUvsMs6/BDSzh8A8HR8EelkkNVlQTIDjQHmjOZQkaiZE+HyDy1ntCdHYrDA1ns6maNI/NPiBu438/xoIMcluocGW0ZFZohpFXbY9WEmTIqKhRv34MrHngOm8RL1wScYq2tWs2NkB03BJiq8FcR8MRpCDWzq30TAE6BslrFcCwWFsl2mNOHKLyHhk3wUpSIW4nUFsf9lp4xtlbii6WzGzAJ5I0eNLwZKGCyHtmANjidMQ3YQdnxPRJnf9lOI1MMVX4UH/k5EnH98Dax9N4SP8mF9uf7iVpng9m9wu+cBnsqKspSl5zTRsqySvq4MT6Q+jINGXjZ41guHfQ6zqoKcVSH8ZMeKBk93DEytzsAhP1Dggf/Zx1V/uYLB1dVs+t5Bqi2ZkcEyfr9C1i0TAAqul6hp42oSwayFpqhwcrj5nDyINc+QxBmc1PiDCaRpmgSDIgIWj8dJpUSK5Oabb+ab3/wm3d3dzJ079+VWcVLhGG/Ho5BPlbANm3yqREW9n4G+LOZYjjJVGOEV1Dc7OFmTeHMVpTEDN6jj4sF1qsh2OoDLkbjMLw7LtCnv5t+07/Mx9yckcm/mMz+Iou9tYr71/6bqASvUXhY3HCFy/k082zk4tR+t949QGRA3bwuXpOwyrjiMKS7ZgMRbljdwWqWXmDbM5bkD1GY+jxzR8VdH2Np/AXMtlcH0e+iKlLit+Bf8w7DN7HqFmz+4mi//8HliB/PEkxFO2fsOhtduwa4qgwmKplHhryZnjoFXwUeQ2kA1ll2iVLYwKOFiUzSLZPQMZadM0BukaBTZObyTQ8lDNIebOaVxKfNi8074OP6fcLQSexKTRLJqnvCGLOaglACzDKOHID8g1LXZEUE+qxZBqA4Gt4uU9t3vhCv+E+ZfLB49W0RN3OEHRdThoU9C46m48y4QUaxJk+JSBkdzkPU0GEWkTf8BgLvsOtzapWJbAJ2PId//USTXZk/dcp6adxm+LQorDEGGhlWHRaaCC6y9pIVSwaKnNcGezYOoLjiWS2p/ivWqxJVL6nnULtLTmefMskaQaULow4uqQOXiOHJAprM9hZUpYLdJxBtChMMeauvDJ36MXoAN8zawYd4GAO5vu5/fdv6WNTVrOKXmFBLFBLFojKAaZKw0RkpP0VvsxcFBkRVqQ7X0Fnqn1hXTYtiGDQoczBzmG8URFkdncemcN1GFRrQwDsFqmirmMBasQRvvoHpojyD9P7oaNnxRCKau+W947HOi5vG5/xbCq4UbhMG8LI6p6ziiA9QkUj1IT32JcFr4GO43TgegenaM4c4MD/24FQ8ac7xbKa5pYNXs8whnLIyyzZ7OFApwVshHsbqGbROr3BIpsN6JU1mwuOc/d3P6lXPwnVON+dQY4ZJEMONixDwgSdheFaPKhyTDM787Qk1TiPOuPrbjivQaeH/OYAYzeP3gj1JhL1y4kGeffZYPfvCDAFRUVNDa2vqGIpAvhVDcx6H2BGM4LAwrVKkaBX+AYr4O2SkzNFZHSfKRHysjeSS0chqcDCPDldimSl52+Y2ZJ+hVWH3532L3ZODgb9j9s400FC9FnrhZ18yLsNj/DMvGP49smIw89Cu6Bt45tR8aMgXZxeuC6krUOBI1jgwmUALtmb3Y/o00BR9AlUQNW7HmHKKnfpDLiwM89Yse0nYjD6ZuY1bG4T9+uIfPfvg0GmJ+Pv6e1fz9d3cwv6NEIB+kYet6Dqx5mKI/AWWFjJEinbNRSlBJJXP71jO3vol59dt4qO8+cmSRNAkLi1XVq8haWRbEFnB/x/0U7SJSXuKWs255DUbvFcITnG4F+UJUzBLEMj8MKQVa5sLOH4oayZH9wiOynBcRJl8E1AjIOhh5uPfDcOGn4bT3w8JLYfHlcPGt8NhtsOuHSAM7wLWFACfSMLVJuZBA0pOw4/tIxQRutBnO/XskX0y84ciTSPd9FMmxeL5iHv9ZNRc300aTvoKIKyN5JKwhEXELzA9T1RQiWSizacsAVbbEqOyw1WdxVkml0pJJ70uxUpMwZ0W5bzjDmcXp2kYLBzdn07N3nLrFUfLjJlv2j5JsKnFq3IslQy2vzPLqj8ULRVeTrS+3DW1Dd3R2juzk1rNv5S2L38JAfoDB/CCmbeJVvPzNxr9BRydlpbik5RKOHDxCgQI+fFTHqol5Y7Sn28nbBcp2GUX18M9zLkZv/S3+bT9GUlXmBioh2ARlA2RN2PVkB+C3H4cbfwbzLoS558OB38DmCTHV/l8K65/l14E/juTYkOoWk4axVtjyTSSrhO2r5H2ZD3KqVYUC1M4O84vb9+IxXDxykouiXydf/VkKNWHu3nQId7hEwBXHI0mRbHE6NX5OKURHk8x4wmaRqbDtvi7mrqnmF40yawYcCv06QR8kgi6GZaLJEgWnTDlnY5VsjhxJoktQVxukMuA9IWM5gxnM4OTBH0wgP/axj00pr9/97nczb948YrEYXq+XTZs28e1vf/tPvpOvN7TtGGa4M8OIx8EzJ0J3ssi6migHhzL0yg3U+Oox3DRWxoaAi14wMcoKHlvCLHsAh8d9JitaYnz52hU0xwPoC7/Clu1x2otvQgZCs3yc/+ZFRKv9DHU0sHFwHtrW39CZvxrVmhYzaDWwYpHwkLQMByV1mHjuGVKlKnrKp1Cw63gu/y4Olc7nguYHCDY0kFz8MaI9d1F96CvUVmhs4R9pTy2l2pKpTsLPf3mIj//FGjRF5vPvWcNf3LGVU/tsKssB5rWto33hs5j+PDgKQU1DzTeQc012W9uxhizOaj4H/H56vG3IQYsbFl3PmU1nIiGhWzr9uX72je3j1NoX+z++7nF0XWQhAZVzRZSpZhGMHYGR58FXCVYR+neIfrk4EKoGWYb0EbHs0c/C1v+BMz8sDKdVL7zp36D5dNzffgJpcDfuz98BCy4G1QejrUiJTqSjzdovvlUQjglIj38eyTZoV1t4vP46FNrwGBAti/IHpdZHRZ+Ojcuai0Tq7O7Hu6gyJExcqpfF+euqIJvH0hzqzbEiJxExZTwdRa6uC/MzfTodqiBRxsVbhkRnDq9jg1dlUDfQkyXK4yUSAS/+kOflLa9OAHRLx3ZtllQtoS3ZxtratVOvTdr9TD4unX0pu0Z2sbhiMac3nU5AC7BjdAcj+RFUVcW2bE6rO42n+55GVVS8niCtiVZqB3ZRKUmiR33DadBymuiTnh2GikWQaoPCKPzkLXDDj4VJ+PLrYMmV8Oy3Rao73QNP/6cYu+N8D3fWetLrP8vI/6RQkPBHPKSHi1jDJSxcLqv8Nl65iF4c5PDuUfxDZUAio7jkNJBNF/uosgPFkDg1KfNci8qTAzrnllSO7B7jLRc08D+pbjYUPbglCHuhV3NxTJs5Hg+mbYBp09ORIVgXIBkyZwjkDGYwgz+cQJ5zzrTYoaamhnvvvZd/+Zd/IZvN8q1vfYuFCxe+zKffGBjuzGDoFt68g3+xSnPIi+ZReLp3nFSyTKUm01wUF+6yY0BJx0YlV67AMhzSsoPS7Ocn7z0NVRaF+ndvHyeXvwwVOD/ybRZW6RRiwjzcMmye2RqkNncTABXq4al9qQ5mkSQJyTVZUvouc6yfIPldytEG+qq97ByfR19vGKxm7un5K5bEfCx96haqsw8jSS6e5jM57U1XsZIYv/jlIWjPE27LkxnTiVb7ifg0rlzXxPcT7bwr7yOWbuDsgbfzfMODyLEygbKPgmRgSxamZhCuk+ka7qdab0EyPJyxZDVnNq2Y2l+/6ufDaz/8Ko3UCcDRdZHBSkEizSJ0PwvNa6B5rahl1PzwwN9DflBEIjW/iEb668FbEOnq3JAQwjz3P7D+o7DiBlh6Nag+3K3/g9S/HdoePmbzrr9CWMIsvQaaT5/ukQ5CjDO8l7zs48zsIgKlGAXF4YjHBN1HoWDiRZC/h9vH+KvGCNm8SQhw/AqnNsYAuLapGqM6zlhQ5skt/SxIOijDJd7qr+LpiU1JSHiQsHBR8zaq7FAq2vhUg537Rmmq9lMZ8DBrWRWSLL2q9ZB+1Y9u6Vy78Nop70aAZClJqpQi7otPLb/1nFsB2Dmyk5JdwnRNYp4Ylt/CdV1WVa3CdEzes/w9lO0CEXyUE52MSA5NoRrwzIXahaBnwLWgohnsGkH6k23C8/Fnb4O3fA/mnCssn1a9HVa+FZ6/U6S7zSJuKQuOIQzFQfiIrr6JkOFwniF6boeXRDn4jFBaLwpsZK6yE0cNccS5mLanxfJdPou3n9qMV1WwXZen9yanvn8Bm4jh8NZAjJ8v1thyKMvZJY3soQyLT63l0LNjLDZVtDI0q9BvWBQrQ8SRkQMK7d0pYuUSUrJEb22JUNx/QrsOzWAGM3h9449uDnrhhReyZcuWP8W+vGK4rsu2bdsoFAqsW7duqibzRKN9JEd3soinwoMnBXV1QWoCfqyyg1GyCBmgqzL4NGL1KuOjJlUNfvRCGQ2VkU4LcGjTbDYsbsYxHQwciobNrkd6WYKMEjRZHNqCMlAg/L+r6Ym+ky3tF1JrqNi4hBuC+JzpC/ba1Gd5yv0brpV/RgPCWqlH28A+z0eQMxa16hCFxg5yI/WoZiOtu0u08iE80rsIhy28uWZCj+WI1zsUKjTGVZtZlsITP2nl0puXISsSUVVlXBGt7iQHQhU+VqnrkXxZwp4+Bg2HtKbj80dZvrKFYrdMadxHPNyCX4+TLJgEPAp+7Q3QCeLoukhPEIJVgjxaOpiGIBOWJcjl4svhwP2gBUFPCesOWYHZV0JmAHqeFCnt7AA89I+w+T9FXVyoBta8E3f1jULp7QlCzWLcUD1S49pp4YVVBtuYiHKCu+QqpN0/ZoXZzeHKELFxPwSSBCuADEhpCzmo4hQsNu8e4sY1jZQn+wbbLqnh4tTXlGTwjNqcURPhB8YYl+c1SolpI/E+r8kypiNnfjRWKRqWCWoAyrkybTuGeWL/EHt9DmfMqeA960Vpi/RywpE/AQJagIAWeFH9cqqUwnAMUqUUFb6KY16rC9YxXBieaj9XHahmZfVKmqPN+FQfftfFn0uQtHVGhvdQ23gqeLpAcoUfaNOp4K8Bsxd8NVAhQawJujcJJf7d7xbEcdZ6iM8WAptT3ze1fTc/LiYeR8NxOLyzQNSS0CWXLq9LaNs4ILEucC9JtYWDdV9j3ybhu7nXY7HLZ3NJV25qFRFr+ljvVYrUA92tKT76zoV8qGcv60ouyaECV5xWzb95R1hsCmehproQEVlCDiqYjsPwaA4l4sU3JqP4fIx0ZQmEvaLrUHjienT8cvE3DLzpdhgcEU+O5984gxn8meHEd5f/E2N8fJyrr76a1tZWdF3H6/Xymc98hr/7u787QTem6XV2J4sUyxblWi9nntVIOVHCKFrYloMd1Fi2oJK2/iw5yeJJ3cCnmTgjQwScHsaVSoJ50R2hTbP5zMo6NI8gVD97tINFBbGd1ecvYMR3B9W7PsnhxHI291+Ag4pPTqDE9lIfu4iO4capfQpJST4i/ysASTfEbcYHqChWcrHzUxZ6n6VK6+F8wK2Ag/oltOoXMW7NxXADJLJANgNk8Ic11HkBHgqY/EVeITFQ4NBzw6y6qJnamA9LgoIKIQtSwzmKQYvYWA2z+5qoCeYoz5Pw1FURyTTS0uylx1vAE/dSUMB2XIqGTUA76U63F+OFdZGSJHwfMwOiXtEyQdVEJOm09wqfyPFxkFQRiXRdIbYJVILsBZ8fjKQggflhaL33qDNOguqF0HgKBCqQ5l0A3hdIYU1ddK8BaDkTN9qEmulH6XgYM3k2Ja+GJPdjsBiPI1HTEGS4PUNEd3lyIM1EuRyyIh3T1/rw1mEUTUTHW1SNu0JlNhxVA/mElWFt4yzKwyIlrjggjRt4qxVG0iWUoBc3ZzCSK2HWq+zMj/KO02ajel6iheIJwAuvB3FffCoC+cLX/KqfmDdGPBpnffN6sfBoAloYB1+YgOWjadZ5kBkCuQesvJhE1C4GMw9DCngjcKhNlCvICih+sHXY/WPxAKheArPXC0LZvA6pZvH0OE7Ath2ef3o3ALu8Za7Yu5G0u4YGbT87pHraKm9D3WvhOA5S3MNGR6fakkgM5KfWkdanJwUx1SUrOUQsmUJPnnee3kLrEwOsNFR69yVYtaqa/JY0IVsiPaLjBhTMgslQ2sCQQS2YzIoFkWQJ07Q4uGWQunlRKuvf2PJstTgKQPMTH4Onj0wtd1Q/cuCN7Xk8gxm8HE66O/r73/9+1qxZw+bNmykWi3zxi1/kk5/8JNu3b+fHP/4xqvrKvlK5XKZcni4wz2azv/czsysCdCeLRH0qtuPiemSKowalvMne3iRDJZPlc+Js6hojbVk0yCAHY4wW/KgpF8twyEoO9XMiNESnU2utzw2zAhmqPNQ0hSlxOm1nPsKmO8Ust9mzk0tjX8Mn57FSd4BxyfRn7SbmMMST9io+Y76H95j9lArL2M0yDhcv4fqqT1HW6iiEmnnM9nNAHaFh1lpOMb3IikwhXSY1XETPmWgDOjnZpa1eZemAxd4n+ll+XiMxn6ihSyoOIUvGsk0U2UJSdMBDoBjGH4hTEw3hBFQUj0Lz3AiGT8WvySiyRMBz4qKPR4/dKxnHPzkqZomHUYD8uJhzTCq3SxPRoGiTSD0P7RRpzXgDLH8r5AZgcI8wIbdLIvwjqYJQupZIcY4dFunOUC2c/XERyZo0Dj8ariv6bWf6qbb2UqM0Y8klFNdLv2rTYqlkiiKKuNBQ2NQ2SkQSJEmyXFzXPe4k7Ay87FQN7lKn06EXOBEeDJtc4/jJjeoilW1LUHQJhyTUPGSHSnjCEJAVVlWFMMv2yxLIEz2Olf5KKv3Hv+EfHZ087nsmxzM8ocIPVoE/LAQx8Tni9VA9RFPCl9NIApII5UZbxLgWRkWk2rWFUGasFbZ/V6w3PkdEn1vOEJ6iuUGGDoyQSy4A4P+FPs4zmY8CkA2afNz8O/6qzyFoOPgjHjILQjhtGTzuS0+ilygarX6HdUWZ1meHeNfH1nBbdwYOl8h0ZXn7hUv49s4Ep5RUcgWbyoD4zRq5MqYqzreuXAHX8lHlumgemeyY/n8YiZMLEUn4YX7ZfCsHnGkLNd2O8WOqaHypD/6JcSRz5EXL4t449aH6V2kPZjCDY3FSEchCocD999/PkSNHkGWZUCjE5z//eU455RTe9ra3EQqFuOOOO17Rur7whS/wuc997g/a/oLaCAtqI+imRdGwCVf6SbdnKOZM+jpSWDVeOttTNAU0jLJMyOvic2Vsx0BOeLBw2eqzuGRJ7dQ6syWTnGkDMnUV06TSG/AQjHkppMv8lmUc5N38lbKRqN3OQnO6ld3f6B/im2UPfrfEp8pH6NIvmnqt6FTwK+OHRGN+mhbFSQ8kebhzGKV7nLqmeta2RKluCROK5zjy/Dgl3QIVPHOCMJjBthzKusVoXhDt0ISxsoqG6yg4tkhdeaoVVp1WTzDoRc6VKRdMosEAFTUiUpccKjA8liFaHTgh0Yrm5teJl5pRFCbRsjrdR3vZm6HzUYjOhvoVglxYJsQbRcpySBYWQP648IssF0DzijR4OQXF1ESaOg/5EfjdP8LW/4ZT3ic+I8mihaLqg32/QOrbiitrZP2L0XQDyW7GkdIM+gxa8iqFwSKaR6bGgLb2HMvPbkBvH8ZvQnqkSLzuxeUgGhJn4eXX0rQgo9n1sHkkj7JuDmzUmaSFruEyad5jeyBe4ePGSxcSjfl+bx3kazmOR0cnj4uje6ODIIzx2VC7VCx3XahZALFGcR6kukT/dH8EGk4RRHNkL2SGwRcUdk9WGduyUZwyUqpLfGbvz6c2Uev4CctfI+fUsCd/I0G1F8zFZMxTcdU8zxtl1qNgmw6VE5O8rPzSeWRJkoj6VYyii6fkkBvT+dSbl3LH13ZQb8oc3jlK7YIY7MtjGy7gBUw0HBJymUrZT0/JwM3oNDTF0bIWNbNefaumVxsVAQ9Q4srT1nPtHFHj35cs8vlHD5MqGDTG/C+/gj8Sca+o2b1l84sdK/yqn3uvvneGRM7gNcFJRSBVVUVVVfbu3cvs2bOnll9zzTX87//+L+94xzu4/PLLufbaa196JRO45ZZb+PjHPz71PJvN0tzcTH+ywGxfgIBn8tC8+ILs1xR8qrgZFm2LpF4mVuHDjfuIeGVkCzxVaeTEILOq5tLZ6UM3LAY1h+c9Np+ZXYmeN1BVha7RPJmJi345a1IqTNeZzT+lmj2P9bPc8DLsbOAfdkF+xEPM2jf1nov33UparaHKt5rHIucytw5i3mGyVj2O7VIqmERsH7lEifVeP3sjQXZkC/zP4Cif8aiEFAU9J7ZZMhxQYUFFCM2bxyjZZMd1OkcmivgtCXCJxaLgj6IGPfSpBU49u4V82iBWdCikDDSPQmasSKTSj1m2RUrNccmMFU8Igezr6yMSEZGByXF8TXB0feRk+nP12wTRGD8EqR5oWgtIoHhFRCraBHXLRJSqe7MgGaFGyPaLSKYDIItol6lD4pCwe3n0s8CL1buupNAbuJxS7VqS+Ro8BKmmhlkhm96STYulEKrwkRkusjIvMa8yxE+9FueVNAY603gCKpIk4Tguti68JTfvv58jY+1kzOm2eM8feQpve5aPPzHOO1ouZl61EErZFtTNiuDx+Ggr6GgNQXrMMpWOimuaxFUPygv9AycO1Ws5ji8XnZxCqleUKkQbId5yLKEEUP3i4QnA6e+H9R8Rzy0dHr5N+DwqHgg1T3xnk3FrMY4axKflCOT24JomajmJIUUoaxFOXXyYJ1ur6Sidw2ZfiXMUCaVgszKisU0xOFMNYOgWsRExycsoLgYu2w48wECik5I1nc7+9ePfw/WFORRpYmXlYg5vq2DuqmrsOh/0GYzuT3LOO+bTta+DgC3hq/RQVRMjlTKYi4rkAZ8KNR6VxpYwLc0xANyJAXTfoEWQvoCEKpXY95gPaJ1afjNeShmDEx2CrA/Vc+/V95Iqp45ZfiRzhFs230KqnJohkDN4TXBSEUiv18uVV17Jpz71KS6++GICgekL+E033cQDDzzAV77ylVdEIL1eL17vi60oCoYt6vU8v//QSEDOI5PHpW5BBaOYPLp/jCZNRcuM0uB0MXSoDj3tICkSD/oM4kGNZfURDN1C1WSGC8YUgTTLNnXzolPrH/K5HNQslpoq+uFdfOc3//GiffjO9iyQBTqAX/L+iz7FmvmXMHlXtk0HF6idHcEoWXxIrSV3qJ/2ssGPkkn+cWkzjjOxfUfUuK1ojtETTmKUdBzLZSBfRnNBs8T78ukydlYCn4WRKzO6Y5SSaXPEcQj6NKoiPurmRjHLNq7jEox6KOsW0erA8f1K/khEIpEp4vGaYrI+8oXm89EGYRiu+URUqnYxFHvBH4PCiFDfKios3gCzzoL8EAwoQqXt2uAaoFRCblyYUFtl8IQnWuCZYBbENl0bSQsTsjpIDteSScZxyQEKsyjy81CMprRMZriIqUDElhluT5Gp0Sj2uQTKDl6/SuPCOO3bhVjg6QP38/Onv/air3r3M1+f+vs/dzzAx6/+OrPqlqJYkOgvoMYMMi6YZZNDbUmavQXM5hAVq+pe8vC9bsZxEi9M52cGhD1TZkCULAAke44llTAdrZxsUyjJUByG3JiINsdaRJ1kOYvPGaHgLqBUsQwjPpdcMYh37BCFYp6k7icdOZVQpUxu3GVdyUuhxUewV+cSfOzFZE/QZk1GRh8rEbChqMDGQw/w0HHGbHvbE1N/Pwrg+RoLT3sbZ66u43B/DwFLos6R2RSAliIcOjjOSrkGj6ng4CJnIGJCqE9nR2cHm6MKA81+1jRHuWTZG5fAhKMKN1X9DaVr7hJdoYC9B8c49JtuzKNaRZ5I1IfqZ0jiDF53OKkIJMBXvvIVVq9ezdve9jZ+/etfo2nTtWDve9/7eOtb3/pHrT/oUV62Xm9vf5rWQ+OEyzC7OUyiO0NxtMRQ2eTRfB4zb2JpGmeG6iiOSBhjgpSlqjRS5SLrasLIR0VgkkWDjCIIRyFjHOOXt6krwZN+k/mWQjIx9or2v2gYL1rmui7jfTkOPTeMZTi8GQUbH+mcxfOZIQITqWnHBVmCZq9Ga06sxyhZtI3lidsT+ywBsoSFAzjggpK3yOgldMMmEPEya06c8bEivpiXUNBDzawoqkc+4erb1y3iLbDkckh0iWgkEtQ1iv+TPdC/S/hIpgZhdDeYJtSvhDXvFK+lu8EfFQTS8oAmi/R11WLwV4g2iKWMiHyVswScLPX+YRoDbeSKlbiUyNLIrAoPe4omawwVRRLR5Pz2JKctC7JtNM35JY3WZ4eO4b8Dic5X9BUT2SFq65cSdMEcMwnHNCKlMqX9oNSZlCsdiopEaZ41dX6/mt6QfxJMiqWiR4WcjiaVk96gwUoRic4MislDZgD8lcLKKVIHOEJEVUoR1TJEvW0UC0VcxQt6AtsXQKFEyRTXtlCsRDKj4TFlejI6izUZshbnhDw8pZRZrQbIJUtcoXq4J2iwL9n+ir5OYnwEQ7dYWBlkYwAWF2Dv1mE2vG0h+//3MFVliZF8ARdEiYILRRuUMR1P0MNgR45Or0VWN97QBBIgrIwTrlehQaTsA6P53/OJGczgjY+TjkDOnTuXX/3qV1x11VVceeWV/OxnP6NiovdrX18fy5cv/6PWnxvppaswLRZ4oRXIYwdHSHRkKBdNFveFyQ3pOJaDkXApeCwKWRNTknjegHgBAn4oF0HqgtlKhnZjhH37g1hlC0WVqbFK6Nl+eoox4ihsemgnTUtEmncOeSgO8puSTE35lV2wbMciW5xOdZimQeFIgvb9giTKHgnDcFCR0IBUPkMKkX56VsmyIRDkl1/dhKk7aD6ZzlQ3T+/awwrLT9aJYMsW+qhOCbCiXsiVwfKSLpTRFQj5InT05DAdm8GEh6pKP9Ut4qL7pyaQ+fxLH5PW1lZCoddIHfoS7S+hZfrPwsT/vQnIFaF/K6iqUPcqMiQ1qF0Ao0UouBDxQ+A0GN0sut1IGRjdL6JZo8OiBtKnQEmmaNm42U585YNYhp+kG2OvtZaW4gJ+VSwQN+JUoArhCxLKtgRD/iwH9ADNrofnHkliWiaGWSRfzh33m7wQulEgV0hgIiMXJfLdw4yPZ3Dw4Dga4yMye9sldh06yNL5lcTrQmRHC+SzBr7oS6c+X9NxPC6iUMzD0AHxNFOA3AhYJUg/DMEKiM8VhN4ugXIEhvdAYgDKtWBpkM6A3k3JNnB8NUiKheb2kcz60csyQV+JZNFPd6GBsN2BbUiU3SB6MUZl0SXb4sHuNVlShC4lxT2S8v/Z++84Se76zh9/VuzqnCbnndkctCut4kooEiQZEIdJNhjbgP07c8DZPnP8wD7ng+997Ts/OIOPA6fDZ4INCCGQQQgFhKRV2qTNOzs593QOVV3x+0ft9OzsaldpV9oR/dzHPGanuru6qj4V3p93eL15oxMj5cFbSxZftY0XtSclO8vYzDA5w4LFRUqeSkBwkF2BUi2PiIC1kCG/WEJDwkLG1aArFsCrehRck/KMRktriEOHpPNej02aNHn9seoMSIA3velN3H///bz3ve9l06ZNfOQjH0EQBL7yla/wve997xWt+9prr71AW3ludv7V2cv+Yuk///TK1v0vj/3PFeHFl8qDZy44VZM0C9z/stf66vNqjOPF5aEXfgsAe1/k+37Q+N/Bc7zjsRe5pufjXx77n/AKzrtzsfrH8dXhrOv2RXD3o1/m7ke/vHLhy7z/fO7lfaxJkyarmFVpQALceOONHDlyhL/5m7/hZz/7Ge3t7Tz44INs2bLltd60Jk2aNGnS5OVzplB5KA2JS0RtokmTU6xaAxIgkUjwmc985oKuc/fu3WhqCMt0UFQJWVmpWzc+kWdxTscOSjhhia64Rns8iGE6BBSJJ38yxuhcGdvzMG2HgCgimw6iB0FboWYqpNxTeV8ibLm5lYHLfWHju//hBErWZQGbw+0mO9ck2NgWpU2S+fI//BV33/udF9z+91z/CXYM3tj4Ww+4nEha2BERw3ZJagrvu6yb52byfOeZWfpMle1iCMkWECWBdbuSDO70q1H/dc8Uf//4GDd4Ea5wwoiSgCVUEDUBMaISjimYtodne8hxmbAnoEVVWtvDdK5JLh/DiyQeXalUzumh2r179yUW+nweqjm/l3Iw4Yc+XwjP82V9xh6D4jjMH/GlgKJdfrXvyKN+wU3ndhacIZzcNKatownzTEvryAc2MT2hYJtQwqTqxtjmBHFFDx2XsCth4uL0K+RUm+KIgXni6Rfl0V46774qLfJrYiuu5VHzqli41JMCQ9ta2NKdIpoOEoys1LBc9eN4LjwPHvuCL0KOANF2v/NQyzq/9WFl3k9HiLaii53MVmRynoZgypDJYNsOdkcbEydjBBHQ8chZGr1uiIrosO72VoQRi5mjfprBUcEg2+YiPv0A9z7y1RfcvH/3tncyeN0HiByxCSCy6eY0vTuS/OVXDnJZVaOs2LR0wOKEjgvYgJKU6B2MU3c9FgWPWHuQKwdSBGSJ2cUC7/mF217oa5ucj1DaL8D6zm+sXK6E4D881TQim1xSrGoD8mKwadOmlZWgZ6RnrVnva0DuHs5ge6AEZHZu8itLPdejPdDJtx8YISPYBGWRHlfBzNeRXZcQAgctg5+YDldURfpticndNoUFg3f+1mW8/1fa+Ld/OETM8uioeIwcdXjoqIknmWSL59CnOwNZlens7kAAUl1hBra1ANAx6Fd35+ar3P3AKNZkgA+Kg3Cq+UisS+MN711PvC3ISMXgn5+e4EfHPT4YWkef7Ru8ckBEDbWRaA1gh2WmbRPPcGmPB9m8rRW3ZhOJBWjrjJDuijCXrbJYqJNOBOhIX/h2k+cTmz5rHF8PeJ4vMN4qQqYTKn1++8Suy/wqh+07/CpvEXqKBSrxXqTyAYh10te6hSlpF7ZdQjQqSI7BcS9AqSLR40jEZahI0FIHMhCUPDZdk+Sb4z9+UZsWVMOUozG2d6WJTLoIAYGQmgZBIJoSKJdV7t9dpyMK77qrj+71ywbz63YcPQ/U9/hFTnrOz1MtTkGoDgkN9Dg4QejaSbao0m/ZiKJAtW0DP3uuzkKxSufmNaw1TmJYIUx0XEdDKEl0uQKzjzjIN3Vw0+Yh9v5onKs9KFRd/rb24sS9K0aStokY4ZCAlFLZddtl/GQmxxahnVhIJNUmk24PotRq1HGYTUhYmsDgZT3E20OIIQXDdNBUiasH0q+6iH85Z2BUlmXPaouvA1HzRK9vKNayy8sWj/sGZS3bNCCbXFI0DciXSM10cFyP1liQTEknrinolk1QkZgdzTM6XsIOCYiySm9rmN5EkMxEGX2xjm3aXEWYIddlX5fL2LjO9TWJ8kiZP/v9R8lsCnPdze0Iz+Qgb3KZedrwSG389EVsX1WeZccbl28yjuNSLdSZHisydTRPvWCeEnoW8UTo25RiYFsLXnuAbx6f57775hnP6wxYIr9a0wh7AkpAwg2IhDUJZAj1Rni6WsUKCFjAljVREkEFOaLR2htFCUhkszWOTRRRTullXgwD8ucOz/M9jf3X+T/gawyaNZh5DhbHfCFzNUxMhViqh0PP1ihm4wScCpF1cVI9NtOZKrGaxBZT5PGoCGWHHlsiJggEtsTIHSkQcwTYVyDuvDjjbcIs8GzK4b+E02QoEO/UKOsGAgqSBUbRoc2RMKsuw3sWiKSCxFsurgDzJcG6W/yf6X2+cV+aBVWF/DR0bPLbHA7eSHAxgz41TjAVJdgZZ/jAAo4YRa3oXPnGdiZHypycDBG1ZNZdluLkE4vELJHqIxn+R9si7/mFHvIPzpGo2bwhMsTYi9i0LruNsCdQ0QTe98sbMU2b7983wi5XxBGhbzBKMKywOGXgIBFxLcSqwN7HZ9hyfQeXb+9kpmjQFdde+MsuMOWcwT//0W4cy12x3MRDCb2Cx1phcqXxBq9+3+tEb9NQbLIqaBqQL8QZhcMhVaJmOmzriVMzIziuRy6rE/QE5kaKzC/qRCxwBJG1nspcyWS3aZAvFNlShmkgisI16TC5kMWzks3OikSfKWIcrvDZsRwycEtHmL6wRktARnIgVG15UZvb1X0FT47mMBSB8mwNZc4gYiy7UR08JlSPndd1ctdb13E8V+WPv3+YPT8sABB3BP5dXWWteSrMLgnU0gpu1qRedwhGYV5wKEse41WTbf1xutan0VwBVRDQKyagUjNsogGJmuvRkjhbb7PJS6CSWZaHCZ9xHpg1X1g8ewLSAxDtADmA0L4J2jexcOyH1CpThJQeNrRFmJ6ukE2n2FMu0q57tAhwPCVCzqHHkjAOFenpjZKpW7BgsD65/kVt4r6IyP++dQOZfxkFQFA86jlfCkqSJTQFDN1v/R1NalTyxuvfgDxddaDncv9n9DGYO+hXamsJ/zVHJ9TSQmjd5Q0h+vUpkZP1Kr14XHtZPx3dBs/96xSBhRLlkyLxGFQKHmFH4JZ5+OH9Y+hDIe7yErTOdb2ozQslW5G2xvm1X9xAIh7gX5+dZF3WBUSUsEB2sowSkDCMOjVBQXcE4raAKzgsjFfpuTnMkZkiDx9d4PLeONf0vnqpBhOzZRzL5fshk6y4bER6qsh7ul7mdhQm4YtX+17+M1FCfnj5NMpqjnH9BPHssurF87UbbNLk9UrTgHyJhFSZkCqzWDGYLhgEFZGw6eEKApG4SkvdoRISiJZMjswXmS+ZSIKDKkJeE+g2AFVGQyAtBLlBq1MUXJSqwHpL4q5AhHusCvcbVTCqje/t2bKDD3/qs9jzoxwfy/DEw98F4J27/gPRgO8lSsc6GQxswX7GN0KXgt4OHmOyy1REIDwQ4f//1k20h1T+12MjfOnRESzHQwTeGY2xZsYGx0MQBRxVhG4FRRNRTil0JHoTtK5NsThTYOvaFtrjQTriGlbVQvQECIiULBtBhK72KLGo+qJE2Zuch2rWl4SpZs82INWQL1CtRcA0YP0bV7wnuukyrNgQWipAsiNCd2cEd9ThuCqQiQkEizq3x+J806lSKHtssiRykxUkID0YQ09v57f5PPnSHLpZbeRDvuf6T6CpIfYaU0zF4vz7X/sIwcOlU05SAUnz814dHKq2RNiFsAiEJGoKRJKvvtfqkmDN9f7PEqdPDpaE6IE3XdPH1Zka8QQ4lkdcixJIBAhO+0apKAqEkxpOxQLDYacpkzlm8JfhEr/1K2/ksxu/yPCxk5QKVb718P8G/DGTQjJi0OTK6zfxWx9bzrM7Nl/m3vtGuNEVQRGQIiI1IIqMjYKlgCODI0JQhGjVYWG8yN7JImXDYu9k8VU1IEunuiT9+u3r2Lxl+XxPhtWX31qwlvWNx3d+BVrOmDidUcSSNRf45o7PYY+YcIbNGJSDJAMvLuWoSZPVTPPJ/jLJVS0kQeCRo/NMz+tsawlxx5V9rLtKZma+wrfuH4HZGh2ejCTAxkScoYDG8HQJy4T6qfVYQgA1UMdxHcSKwDWyxqd/52p2j2Y5Nlfm2EKFPRN5pgo6U1xG9/qrufbqasOAjNx1C/l6koQjYiMwa0HYhoDpIUZk4mvjDGxK8ktr08SDfvHC7tEs/7+v7WEs58+0b+9Pc0tJYnHUT8b3ZHDDCtGgiOVBQlSpBixMy0GQYFt3nFhQ5kcn5tlzbIHcdIk3XdFDKhUkWzURXA8tEKAl0vQ8XhDCad/IqGXhyL9Beg20bfRfU0J+K8QlgeszDMw1G1IEExpxzb/UFRvaYxpXtMQ4ZltUKzqSIPCeWIh/lWo8UjfYZsrsMmSyI2XkK5P8yq+/n0qxzv6nnmsYkF07b+AJJUxd9PiQFmFgd5Fxw++VrSVkIIBAEAk/PdN1QQdE1yWvQCiqUiuZq0tI/GIQafV/ziDd5ecRu66LUbHRsxUWczpBqqQI4eASlgS8hEqp5uCV67S6Iu8vB/jOz8YJrN3O1mvewNrCdMOA/O0//xV27LwMq+4QOC3M+9RYjv/0tX28p+AvEzVAFPBHRkQGVBNinkCqT2NACmDaLsf2znP5ujh7J4tc3hs/cxdeFXpTIbZ2X+DvblkPXTvO+5aKU8SWTD7c/SnefPkVK15LBpLNrjFNfi5oGpAvk1RYIVe1GF6oYnsez5UN3nHqphz0RLYOJRiZrBGKSHQ4EqFwENdxEWw/Kh6KKwSCIKkShaqCHPEo18rkZqpIVZvbN3Vw+6ninFLF5F/2T/GVx8aYLhp8bXiqsR1r0kF+/Y6rCKsSrudRKZmIiojrgeW41B2Xmm4zsljBsB3u3jfDdw/MggebVZX3pFPUj5RZNF1kVSS1JkKxaiPKAprs19jEkgrlLJQkEcm1mRktUq1beIaDZMLUZIWFSAH6XYSAiIdvIJzVGvfntBHNK2bJyDh8H1gVv6PNkgEJfqebZN/zfrQlohFXlEbXl5aeCI7tcvWODq5NqPzvr+vUAzJhFX41pJKzHZ6tGezPOeysSVjP5njfiRlMAYzMeGO9d5cXuSEW41pbRSg5WICiicS7gwQTCkpAYn5KQAAUwMCfNFVjIrd0RhptLq26cxEP3OsAD5SAxMyJPOsklQUtDcEwAcHDEcAUQAlKqGETwVQha3KnrjJ+pMbukyUOFUYbq/pP3z/ItjHoi2ms7YgymA4xmq3x+z84xB0lhaAnIAYEUt1BXEHABsKaQn5eIIyH4HhUqjaBvihOwaBed7hiIMVAa5RUWAHn7C5Yr3c6An1sTm9+rTejSZPXhKYB+TJJhzXSYY0b1qV5ZrzIzv44oiQiCBCMqKzpiCFtM5k/nieUDKJoEsWMhax6lCs25bkKFdEjElQQchYVQNIk7JrD5JE8l92yXHSSiAX49zcN8Z5t3TxycI67f1Lha6dee/zReQ4cfIxZyWVO8vBewEgLu3CNJXOdqKEUHaqZov+CAl5EJFMzyVdsZA9CEtgiZCSPE66DLYFWrNEyW8I0HFI1Fyug0BcKIMkixUyN9v44QVEgpDRPrQtOeo1vPKbXnN2n+TwoAQklIOF50LUuRde6FN/fP8XRiSy0BBivmQQMj3QJwGAnkOgKkxutE3UE/p0RYDIuEggm+cdT6/xNt424oQAesib6XqsAVKsG1aqBIIrouo2jQAKoAYV2gcEdHWxd24JVdxpGrV1/vq1uAiCIArIqIAgCm7vi9MaDlLMGtXKd2qnjpgNB6sRaAQMEHfptiX5b4ga3jT86ta6RfI2Zo/Nnfcd6U2SDJSGIAoLqUpivnmpUqlGVHEzDwcDBFIFogGhHCFcRaR+IkatamLZLrmrR8TpPaX3NObOYpzT2mmxGkyZLNJ/yr5C3bu/lrdv93Bjd8iV+QppEZ1+MeEcYsTfC9GSRlKaQVERCcYXOmkMxX4UyWFh+aA8IRETsmsPTPxhlfrSI53pUCyaVQh29ZOK6vktva2152G5xY8R0FYA6HpOyy4TsMCm7yJJACxItnkjSEYk7kDRO9bXFQVZFHMFF0EBQAriCH3QsWTay7Hddy1tg2xZ2XGTec7m6K8rc0TxyxSTdHuHtbxkEz2NxqkKiPYggCpRzOnOjBeKtIdKdq1TD71LkVGHMhWBmuIA1XaEFkcGBGIGaS/V4Ed2U8bCRJZFYWkJfcOiri/QtQKmmNj4vuCAqAmpcItkTpDDnp0N4+I5mFxcLBxsJPS7S1xFm3bok7S3hhuG4FL7WmwbkC6IEJPSyRf+6JKkbQuz5ySQLE2UMC0KAQAhBEFDDLrrmoegergGKuzzR+E+RbsLxNAXPZd6xmdDreHWX20wV8Ii0KuimPxgCCv70wJ+rBDyJxW646ooO4t0Jgr02FcdF8zxUWfQ9kO7PnwdyvmRwcLq4YtkrysN8Ps6lDakq0N3pt9JsekGbvAY0DcgLxIn5Egemi6RDCt2pMD3JIC2RAIueg92qkc+bbGuJUI9YBGMBDjw2hRG1EVwXUQDLg1gsQC1rYxkOJ/dknv+LRPBOqzq0ZdsPG5suAU9grS2x1j5/XpmoCqgtIRbrVRRR8B8SIYEWw89cCwGhANQcEOLgibCpPcbNIQW55FIyXARFwi6aRJIahfkaoYgKiASjCs8dmGexYNBSrjcNyEuUHkFG1FQSEYUbbxzgxLPzTGYNarOgY+MAsiaiRD3sqosgCyjSck6CG6uhdbQhCsKKHudLBqQIeJg4SpBgXKVvIEmtYOLoLiO6R+eaBMGo2syBfJH4RrfA9IkCnWsTtF/eythomdCp1yv4KQISEJQUhIiEFzYIBJdTBOy8jVevEQfiwHqkU5/wvcjhjgCViToiCgJSIwMlFAY6Qtx+fSeD/QlSYZWjYwVc3UF3YV13AkWR0Os/PwZk5NR5+09PjPNXj1orXgsqEg/8p5sunBH5fNqQABOPwLG/ZSR72BepP0UzB7PJq0XTgLxAjOVquK7LWE5nTWuUmumH6JKiiOHCmt44UUmhNRwl1RmhVjLITtewdJOiXKdc85AjAWJrAN1BNxxs10OWBRKtEWoVg0BQoKZ7zM0v50B6CYFIRwTH83Bdl4gkUpjTsQ0bQRAQFREtpqJbLpoiIqkigioRSmkUZnQWJKAlwJXtCdSChVmsE9JBESASUqiGBLZvSLE1FWVkXwZFFRCSASRBpHNdglhaw6iY2KaNFvZPp5oq4Aj+7yaXJju2tdEzVaGlJ0I0HCDnmix4LromILhBUDUc0yHQYhFscRAB0VvuKKRoIpom4wJaJIikeYjUGwakJ4JKECEoMNidoL0twtTxHK5pURRrpDsiyKrvhWzmQZ4b23Sx6g6prjALE2XCcYX8XI1aWiU0GKI2W6MlrpGPCdhVCJguKoAEdSdIILV8i5cSFqG4jBYMUC3WcU0X1/XQkgqhNhUppmLEgqh6o78AwR6NSk8Cbc6g/GSGuiGQur6HTk1hfKpG5mSJuC3Q1h8/O+f5AnGmYDi89qLhybBfIPixawbY0Lm1sXwyV+PPHjhGvmpeWC/k82hDJvVFgkdcPn3wS3DwS43lQTnIPXfd0zQim1x0mgbky+TMFLSBVIgxYENHgIgmo3iwOF+lLxZifUeM7o7oivf3bkwTTgc59uw81ZpFseBSsmziMZXOLXHcmRr6RIW6DtRLxJIqAiIjTpXFzPLNtFDWmY7UKEvQJQtUayJlV6cSDtASE5BkkZOSjVNyaalCuOhr8eUXa1RVj3hEYaAsodfK0BYklNBQBZFyrs7AmiQ70hobr+1iZN8Cdt0hP2Ow/poOrnjzwKnjIJDqjBBNBVECEgICa9cmmWkNEtYdRg4svGAoezJXbQgS96aaguMXi9PP2e71KSKpID96aoLDT09QLenIpoUtOsRkgZlilVQswNiEQVi3UbGZL5cbny8s1Ai4/t+lrI2seyyp5y19jah4DHYn2LS1BVmViaWDmLpDJKmhBuWG97FpQJ6bpWKjzsEkkYRGbrZKsj2EqAk4t/bSYkGw5rLOtsnXTKSSTVSVmZ8oUTpSJFs9bcwKOgHZwBEc6p6NqUAOSNRsSmM6piwTSYfIV3X0EogytCbjmLEA+oEiDjB8YBEprhIOyURdcMMqxYxO9/oUtnHh97+cM/jaH+/GNt2zXnvFouGvgEDQ/96xh2eoVld60T9EAKNoQvfF3YbOYAv3TM2Sf/ffQ6svOzRSHOHTj36afD3fNCCbXHSaBuQFYn1HjPUdy107aiWTkCb7gtoRdcV7c7MVFicrmDJoURktGkA1dSJBkVhYQVAVAhGFZFeYoCqRbAkAEpYIuxJJnpqfbKzL9aDWq9KViBAYrVCyHQqKhKG6pMNBunriXDEQ49GfTuApFlYFtIBAdE0EpT1IaraOWLSxdJuADV2DcbKjJdSQAoJH+0CMYEShtS/K9Ik8rQMxXMfDPlW1DWAJHjXRIyR4KEBvMkxvMszIgQVsw6GYqZ3XgJwpGhimw0zRaBqQryKVvMHxmQqWY1NUBTraNbS6g225JCUJRxHp7QlRmqqhyjLqaU4gFwUbBRkHAb+QY8lwDALEoLsrybW3DpBsD2OZDtFCHbVTIhhVsUwH+YxcyCZns+ShVQIS7QNx2gfieJ6HljUIKwEWZ8vUHI9YVGHtthSL0xUqOZ16zUIMAadpYpuigEmAICIGNgLgS2NrWHjIiohXN1F1cE65ktWKy7p4mPHeMGrOJpRSseouVWz6NqfJzVZJdYZRVAn9IhiQRsXCNl3e+OubSXUu3xuGFyr85r/s5U1x9TyffgHO7DrzEjrOhE5975s+tIX10Q2N5QcOZzj63TGsmv3yt+sl0Ok4dMYGmjmQTV4TmgbkRUIJSMRjGi2tErK68gFZzNSwTId6ySaS0HB1h4SmEIwr2B7UxkoIHrSlg0Q6wzgJBV23SUYDPLZ3hry0PBsPAdcPtbFtUysnO3IcPJhBxSYsQ1KSaNNkwnWPtb0RZg7n8TQBKa3Re0UHt27tZHY4z/ihRcp5g0QqRDSm0Xpd1DeAYypdaxPgQVt/jK1v6GZmuECyI4RVdxoG5FJ7x5rprKi+jreGKGZqxFtDK/b/TI9jV1x7zVqi/TwTSWqs74pwuGTw9o0pbt3cSc10eOqRCfJFg9a4RkcowKSSw3E9itmJxmdtARxE6oKIioiOg4wv2QMgRhRcEfLztcZkQ1bEU4VgAq7tolf8c6xpQJ6bcxnYlbxBMaMzMZyDsEwLIfpCaTrWxBmrmEiSiKrIaMllA0tNSWhREUIStqERxoJTao9WQMCLQlwJIDp1KnmHsAbhsMoaLcD1v7wNgIW5CtmFGum2EFJUQQqKeLJIrWximRfPk5zqDNPatxzFmZdcyuIriJmfq+vM83ScOR/JjhCt6eXtCi1UXv42NWmyymgakBeJ06VTljgxX2IsV6MF8EwHz3ZRJYFwJICcktDSGoX5Kk7dxaxaVOZ1Jk/mCA/EMPvChBWNkKZgnxa2UUMCA/0xOtvDdLaHWdsV48iRDLnRMoGgjFexIeSwqSNJiyMyN6djC9AZ9h8snWuTdK5NMnkkR7lgIMsSyfYwWlgheIbntGtdkrb+eMMjssRSe8fQGYZyujPyvJ7HMz2OSz9NXl3iLUHec+cGvNNO0qAicc3lnRQXawQ02fccZoJ4gBJePu9cAYQ09GxrI5FQkQ5mMRYMv5rDgeqihbGoUxurEekM0nddB17RpH8wSTSloVfMs86vJi+eSFJjcbKMmgrgyhJGQuXYRJ6wLJJoC9K1Nk6qM4iZLzU+o+YdtLCL3BEl7OmQ84udNBFCrRqR9hCJiEr2UI7OjRqJRIDutXFs00EvWygBibaOCG0d/jV9YqGCabssViyiafmipiIML1SYP23iPPxKDbVzdZ05o+NMkyZNzk3TgHwVGcvVqNVtFgMyW4bizJwoICkS7QNBHMvDkz0iqQABRcJLOowfzIDuUZut0LetBbXicFl7lPppnRc2X99LX0+CXM0ipEiE4xrxaACr3QHDRQr4enxKVCHRHyecCJFIabQGV3r7WnujRJIBCIh+OLpFI6ic7fmQVbHheVxiqb0jcM5E+sn8stex6XG8tEl1hkm0h3Btj1rZxHVcCvM1BjakGu9R4xLtA0kuX5+msyfOflfkpJxHyNaoLbiEPDDLIKQ9yosGriQiJjXfGDllPIZiTQPy5RJLawxc1ooyrWKpIoYmUpszqOg2Qz0xrn37OmzL5chXl73GoicgGgLb16WZi1eYO1FEsD2iURU1oNDVG2dxvkq4J0wlXyeQiFCSIaoK1OoWIVhx7S81U4glZARRuCie5IWyHxf/j9/Yy4K88uYSVCSS4Vd4Dr2IrjNNmjR5fpoG5EXm9MKFgVSIsVyNgVSIZMh/mDq2R7o7SiVv4JgOyXSYrrUJFsZLTB3JI0k2IUkhWHIIhBXS6TCXDS6HWBIpjZp1KoRsOaTTQdZtaSOVKGOZLo4qIAUlqoZDS0sQz3JRTA9ZlRBFgcWKQa5qkQortHRFWKzUl8PRS0bhSymmPsd7T/c6DrWGSYZUgmozdHkpIDyPKLkkCUgSxANBQlGVeKvOeGG5E80VV3Vz1a5eTN2mtKhTNB2koITeGyVgVjDrNnJCIhQLoHVpZG2bDkUGYWX4uslLZ2m84i1B4i1+pe9ixeDkvOEbcaeklay6QzK03E7UMR1kTWLj1nb6xFYO9eewZ3VwXLSWIF5IJtERJpvTqQQgnFTIyNCW1DAsl3hMWnGutEQ0WiLLk0A9U73g+7rU8/r33rxhRc9ruAh6i+dhtjJLvp5v/D1SHDnPu19lTs/dbIqLN3kVaRqQryJnFtr0b1nZA7eSN4gk/RtyW3+MXe9cS2aijCiLxJNBBMGv3talhcZnurtiZ4WQFVWivT+OIAqgidRMh/QpgRWh7qBJftcJYEUniZaIds5w9CvldK/jmd/Z5NJGCUikuyL0rl/2QLaviaGKIkgitunQJsvUIgFaIwrh9ihetk7X2jibdnY2Qp2qLJIMBprh64tAS0QjtLltxbFVAhKD/YnGe3q3pNh6dTfBqMLUQoVwMoCTCDDUGkY3XcAjHQkwMllCylYp1W029YYIaDKhmITyAveEixnCvig9r18ks5VZ7rrnLnR7pXRQUA6SDCRfk20Cnl9gvCku3uRVpGlAXiLEW4LE0hq26aKXLSzBI9wfI9wfpZI1cKs26Ta/i0cxszLx+/QQ8vG5EiPzFXrCATb0JpAViaAiNzyhluKsyGFcCkOlwspZ67qQ9CTD9CT9PMds1VjxnU1WB5q8bEAszFfJ1xbpTgbZMJAkFQmQylQoiRBMBkiFVNIR3/t1+jkWDCtNz+NFIhRV/R70p1BUiXBi2QM5dHk7ay/3BadVw8aZM2jvDNES1iAMtVOdtMJhmR7CtCQCzNdMHj62wLq2CNt7z28sXYgQ9pmajxdS71FcPAozp9b9Eiqu8/U8uq3zuTd8jsH4YGP5+QS7J3M1oqd1qLko3tLnExg/h7j4C21vkyYvh6YB+RrhPU+uoOct676VdRMlrFDULeJJDSktEAur5GaruM65qw/HcjUM12XGtNhymsdg6ftkdakq3MPzPNLhANXxMqPPZqj2R+nb3PK8Ic0LyVIf8Sarl9nFKq2KxZwu0TZfYWG8TFt/lLb+GJlynUrdIag6BBWJdDhAOhx44ZU2uai09kWJpf3rTjGhMxpAOq15zJKaQjQSQMzWmXxynuN1Hbs9xImFykU3IM+l+Xih9B617/wqJE/bxpdYcT0YH2TzC3j1Yqf0If/hhyfI3n+ssdxTRb77ny9gd5olzhAYP5e4OEBQCnDPO+5tGpFNLhhNA/ISQhCEhu5bNKJiCZzy0gmEVD//KBhREaVlA6+cXxZfm8xVKdVM6o7L5s5Yw+v4fMbq6SyMlzENm4XxMn2bW87/5pfJRbZJm7wKnD6GnS1h1LDKms4IC8dLjfNny7okkYBMJVejlDfJqFCShKZI/KvB81xjp08GK4U6M8MFIkmNSFJbTpkR/HvHyGKVZFBhbXuU0VP3hGgdzIDMuraL35J0SfNx4zsGCJ3K7Vzq7PKK9B5PUb/1T+GqW5cXXISK677OKJIi8tbayu018ZidqVz0nM3O9AbumS+Qd1c2mB9RFD7d1kJ+8WjTgGxywWgakJcYLySsHIqpJNqWH8R6admFMFM0aI1qaKq0ItfyhWjrjzY8SE2avBg2b2hhyxY/nDde9xrnT1CR6UnKzGTrOJ7DyekKakeoKRJ/CaCXTJykQyVv0L0u2SjAAf/egQe67RJU5MY9YfuG5Fm52heLpYrrTz9wdEXFdTDwEqqtzxAHFxdPNv7vJfpfsOL6zGIZeGkFM9GUxvv/5NoVYfglcfHiRJnMaTnfWkQhmrrAkZhEL53/fjed5+ibzdwBUE/zJDdli5q8ApoG5CmWtPBKpdILvPNCfd/Zy54dy3J0vszG9ihXrjl3aKVSWdZAcyWzsc1RwaRarxMNBFbsx5nfdXKhxHhepz8ZZKgtSqJHJdHjf1+pVLroIewLxdI+nq5j+GqP488Tp593lUqlcYwTPQESPQFOLpTY8/QJ+pNB5marnBwvkkirdNc565w8neY4XjzOvFfUzCrhkEapVGJ4fvk+EFUknh2bp1SzqJZK7BxIk+jx0w5e7Bicbxy/+d1/JBg6v/dtfsFiURb5wFaXnp5lgzGsykwP3830C22AUYAH/5zTeyrOFJe35UBlgfLYU+f8eMEo8OlHP43unJ13GZSCyHX5xR0LGQKJ5T+lFJTMCnv/5RB7T3ubqIhsescASvBi5IKvbN4wU2jF0R2e+OFXmDL/trHcExWyWz6Iq/iTO73m77v3QmGrJk0AwWueKQBMTU3R29ucia1GJicn6enpAZrjuJppjuPrg+Y4rn5OH8MmTc5F04A8heu6zMzMEI1GKZfL9Pb2Mjk5SSz24kPBq51SqbSq9tvzPMrlMl1dXYiiL3Dsui7Hjh1j8+bNq2Y/zmS1jcPpvJxtP9c4Ll2PF8sjvpqP88vhYu/vazWOr4TX4znwSvbp+cawSZNz0Qxhn0IUxcaMa+lGF4vFXjc3lZfCatrveHylNpwoinR3dwOraz+ej9W8/S91259vHF8tD8hqPs4vh4u5v6/lOL4SXo/nwMvdpzPHsEmTc9E0IE9xpgcSfv7yr5b2d7Xs97k8HtPTfrbUatmPM1lt43A6L2fbX0sP5Evd1tXMxd7f1eqBPP3364FXsk/n8kBe6uPYZCWvlie5GcI+RTNXZ/XSzLl6fdAcx9cHzXFc/ZyZA9kcx9XJxc5lbXogTxGN+hI2qyEX5siRI1x77bUA7N69m02bNr0m23H63EO3dQzbQJN9WYrZyix1p05HuIPnMs9hOAaapHFj740AjBRGmCpP0RPtQZM1JkuTxAIxBuIDBOXlas0HRh/gwOIBLmu5jJ2dO3E8B0mQSAVTjVyfpbGD1TWOAB5nz988z0M4Jep3JHuE+eo84/lxMvUMXaEuLu+8HMM2yOpZ0qE0G1MbCSpBdGt5DDRZ49HJR9EdnaAUbBz3V+I9uFjn3YsZx5yeY7oyzY9Hf0xOz7GrexcVq8JsdZbOcCfv3vTuFevULZ2x4hileoneWC9dka7lF89xCISX1PR9dfBq3isuxevxpe7/C12Pp9/nNFlDQOCnkz9tXGdv6H0DAkLjfUu/k1oSTdYwbIPvHfsez2Wf44q2K/jlrb9M3sg37mu6rTNfm6c91E53tPvCH5AX4PnGEF69cbxUnm2rnXON44WmaUCeYjXlPUYikRX/v5jbW7Nq6LZOUA4SUlZKQ3iex6HFQ5wsnKRFa2Fdeh2luh82Ga4NkzEytNZbSUfTeKbHppZNxGIx7j1xL3cP301LoIUdwg4iagRTMBFFESWkENOW9+e4fhw7YHNcP84dqTued1tON4pW0zjCygdWTs/x1OxTzFfmaQ+30xvrJU8eNawyPD9MTIsxUh9hh7aD8ew4WkAj7+VRQgoZI8Oh3CGSgSQpMcUz48/w7OyzpEIpbuq9CVu1SWmpV2RAXuzz7nzjKAdlRowRZpwZHNnhaPUo61Pr0dAIRUKNbXl88nEOZQ+xJb2FJzJPsC+zj4HoAL922a8xX52n7tRZn1pPd7Sbk/mTTJQm6Iv1MZQcel0akK/mvWKJS+l6fKn7f6YB+b3j3+Oh8Ydoj7TztrVvQ5M1FE3BFm0c2UG3dcKRMIZhENbCTJlTdEY6aY/5bQSfnXuWWXOWPHnWp9YTCoZ4rvoceSHPgwsP8gbzDQymBhv3tcPZw8ghmapUfU3vX2feJ16tcXwtztfXMxc73aBpQDY5L7qt43j+jfJMAxLgZOEkC7UFpsvTjBXH2L+wn7ZIG1PFKeb0OURE3rf5fUyXpnli+gl2tu/k4OJBSlaJsdIYeTPPNZ3XkNASdIQ70G2dk4WTJLUk++f3+55Mu86OgR0ApIMvvvXYamO2MsvhxcPojs5MdYaqUyUshRktjFIySpiWyXR5mt994HfpDHeyqWUTNbuG7diYrknJLDFaGGVtai0/m/oZkihRMkt0RDvIG3lSWqrxXeebGFyKhJQQdbsOHniuhyRIPD33NAvVBU7kT5DX82xq3cSTM09iOiZPzjzJycJJLNvCsAwmShPkjBwuLuTAsA0eHH+QQr3ARGmCpJZcNceiyauDbuk8PvM4C8YCi/oiAG3hNtbE1tAX76NqVVmsLdIR7QABpspTTFYmqY5XeWbhGfJGnmQgyZaWLWxv3w6AJEhc3X41Pxr/EV2RLr43/D1USWVdYh07O3fy3PxzjJXGuLbr2tdy15s0eVE0Dcgm5yUoBxuGxpnpsjkjR1AOcnTxKLl6jrpdJ6Wl/Fm0IFM1q9StOt8f/j5RNYqHx575PWxIbWCyMklMjhGQAxSMAle0X0FnpJOHxx/mSO4IG5IbmK5M0xppRUbmpv6bzmnErmZ0S28cX8M2MBwDy7JIBpPMl+fRJI356jzpYBrHdSh7ZdSAStEuUrbKKILCgxMPEhSDjJZH2dqylT67j00tmxgvjrMpuYm5yhx9sT48VobizjcxuNSoWTUmy5Nc3nY5yUCSifIEWDBTnSHmxHhg4gEiWgRFVMgbeRZqCyiygiVYSILETHmG+do8ADOlGYr1IvPVeSRJwnXcxrE4PX0CLv4MvsmlRU7P+Yaf5ndr2dqyFd3UiQViyIJM3a5juRZ1u06mliEgBXh47GH2ZvYSESMkQ0kOZA5QqBdwcVFEhYAcYDA+SN2uc3jxMP3Jfn4z8Zvsnd/LU7NP0RJqwXZtWkOtzBvzKLJCRs+Q1bME5WDjnGyei00uNZoGZJPzElJCZz1Ul8LWhm0gCiIZI0OhXqBqVjEdk7pbx3XcRkeH+eo8tmOTr+fpi/YxWhxlobKA67lUChVG8iM4rsObBt/ERGkCwzWYrkyzJr6GPfN72NiyEUmQztqO1wMzlRlyRo6QHKLu+AZ4IBTgcPYwk+VJomqUqeIUFSrE1TiO5wDg4jJeHEcURaJSlLH6GJIgcbxwnC3pLSQCCa7deC3tkXZM1293efoD6PSJwWpAt3XWJNYwXhxnXWoddafOycJJFFHBtm0QYLgwjG3ZHM4dJlPPEFX8SUvOyPH07NMUrSKGbZAzcqiSyobEBhRJIR1Is3duLy4uMTXGxvTGFd7aJj8/5I08U+UpDi0eYkvLFu4cupNb+m/hyZknOZQ5hCZr5PQcT848iYBAPBBnz/weam6NnJ3DFm2KRhEDvxtOxaxQMv1oS6aW4cejP2ZBX/ANVAHyep5FfZG+aB8AYSlMzauRUBOMFEZIakmGEkOv5SFp0uScNA3IJi+Ibi97yXJGjh+O/hAA0zEZKY4wXhxHQKBOHcmRqFpVFPz2XFE1Ss2sYXgGZavMfH2+sV5VUjE8A9VTeXjqYZ7LPUdaTdMX7+Oy1svoi/exLr0OVVRJBVOvuxy1mlVjvjrPbHWWfC1PZ6yTkBTCci0WjUXmq/MMl4b9sCtQt+vYro2HhyiI2J5Nm9ZGxshQN+uUKNHutfPd4e+Sr+cJK2F+adMvEVEj9EZ70a1lb2NICa0Kz+MSQTnIFe1XcH339QTlICWzxOHsYUpmCVmWSakpKkaFx+cfP+uzVa/K04tPA6BJGmWzjIzMAfsAMjKu56KKKhYWlbrf+m9X9y5g9YX6m7wyklqSQ4uHUGSFQr3AUHKIJ6aeYN/cPo4XjlOxKqQ1PxpQc2q0BdtYNBaxHdv3UiJTdatYnoXjORi2wT3D9/DEzBOktBTHc8exBZs5fQ5N0qi6Vfoj/eTNPLtndjNWHCOjZ6jUK8gZmcnSJLf13sb7trzvtT40TZqcRdOAbHJOlh6euq2jSiq6rTNXnSOv5xktjpJUkwznhjHxPVye52E6JoZtUPWqOJ5DwSogCRJhIYzruYjCSk0qBwfd0hERMW2TWWZpj/oJ6JqssXd6L9Plaa7quIrre69/1Y/BxWQpjLxvfh8Vq8JkZZK0liZTyzBdmabklLBcq/F+G7vxf8dzqNk1pvVpLMdqeBcX64sU60UcHGpWjYyRIaElUCSFHwz/gOHCMDvadnDH0B2v+v6+EpYM3ppVI2f46RLPLT7nT1qQmDfmmTfmV3zG9Vwsx8JyLT/9QvCPueu6mJjoro4kSDw79ywncyeZrc0ieRIPjz/M5tbNXNd9HVtathALxFZNqL/JKyMVTLGzYyePTDzCWH6M6dI0I8URCkaBqdIUDk4jtzuuxMnomYaxOF+f51jpGK7nnrXescoYY5Wxxt8CAo7n4HgO2WqWXCVHd7ybxdoiruCSM3LE1BgRNcKT80/y5qE3E5SDzFRmGoVfa5NrX8Uj0+RCMFuZJV/Pn7U8GUjSGel8DbboldE0IJus4ExpHsdzyFQzTJQneHjsYeZqc1iORTqcJqpGWZdYx7QxTaleomJXqNrVFetbukmadd/IVESFlJaiI9RBIpDgaO4oLWoLVbuK7uoECPDY5GM8l3mOy9sux3ItwmqYw9nDq96APDOHdCl8HFJCzFRmwIXx0jhHC0ep2TUAru64mjd0vwFZlLFdm5ASQhREfjDyA56ZfwbTNZEFmWTALwJRUX1vmmcxX5nn2MIxkkoSWZA5nD1M3auzb2Eftw/dvmJbVot3V7d19s3t47Hpx+gId5A3/bSIpTC967lUrApls0zBLKwwwPFY+XD3/PPTFEwS4QQ5J+d/h6cznBtGkzVUUWVb2zZSWmrF+DXz0V6neNAd6UZEBAH++dA/o8oqkigxlBxiquIbkQB9iT4my5PU63XqTr2xCk3SuLztcrakt2C5FlWrSsEoUHfrFOtFjuaPYjpm45zNOTkCUoBFc5GeWA8nSydpU9uQBAlFVNiQ2EBWzxJSQkyWJjEcg8nSpB/a/jmSpFrtzFZmueueu9Bt/azXgnKQe+66Z9UZkU0Dssk5WZrxHs0dZd/8Pvbm9gIgImKVLEqBEseLxxs3QoCwEmZn+06ubL+Swfggo8VRjuaOcjh3mPHSOJZrMV+bp1gvsjaxFt3RmdQnG583BRPZk6EOs9VZ+qJ9ROQIW9Jbztq+nJ67+AfhAjNdmWauOkdHuIPuSDeDiUEeGn+IrJHlSP4IFdsPobYEW/jE5Z/gpp6bGsZKySwRUXyZi5t7bubhqYf5wt4vkDWyZPQMmqTRprYhyiKWa6FJGjO1GRKhBI7nsCW9hROFE+xo2/Fa7f5LJqfnVkh56LbOM/PPYDgGYSXM7f23sy+zj7pXx7ANanaNqrU8iRmMD3Jb321sbdmK6ZgUzSICAoZtcCh7iH8b/TcK9QL7F/cjIBCUg7iCS8bKcCR7hIgcIW/kydQybGvdxi39t7wWh6HJq8z61Hr2LuwlX8/j1l3qbp1r2q5B8AROlE/geR5Hc0cpW37XssH4IDd238jWlq1sb93O4exhvj/6fRzX13f08IiqURKBBFd1XIUmaczV5nhu8TmO549Td+rMVGcom2Usz6KgF4hqUTRBQxIlHhh7gN5oLxE1guM6xIKxhlpFKtjM110N5Ot5dFvnc2/4HIPxwcbykeIIn3700+Tr+aYB2eT1xXx1Ht3WyRiZxjIXlwVrAatuYbomESXCu9a/ixu6bkASJI7mj7Ivs48HJx5kW8s27hy8k9/a/luElBAPTz7MXz7zl9TsGqPFUURBXOHNkZEJyAHWRNcwGBvk1oFb2dKyBd3WG1WJS6HEvHF2KOBSZ646h+EYHM8dx7ANBAQK9QJj5TFcXCRB4p3r3smHt364EbItmkXqdp1FYxEREcMx6I50c0vvLVzVfhVfO/o1vn706xiOQc7OERbCqIKK7ugUzAL3HruXW/tv5c61dxJUVkfRzBJ5I88AA42/DdugM9LJQm2BdCiN5VjIyNSpUzbLDcH69296P9d3X89gfJD/tf9/8T/3/E/qbr1RRWs6JpIo8QuDv8BEaYIDiwcAP683pIRQJRXTNhktjjKcH2bRWOS+0fs4mTvJRy7/yGt0NJpcLLJ6tlF9ndJSDCQGqDt1TMvEVVy/aK0yzobkBk6WTzYmLIqo8B92/Ad+eeMvIwoihXqBhycf5nNPfa5R8HYuJEFifXI9bx98Owv6ArtndzcM0nlhnlKtREbPMFoaJaJG8ASPNw28iWs7r2WkMMK+zD7WxNc0JzWrjMH4IJvTm1/rzbggNA3IJmex5CUrGSX2ZvayUFlohO88z/PzIk9VWO/q2sVdQ3fx1NxT/PmTf85YaWzFuvZl9vFPR/6JtJbmhu4buL77ev7mtr/hYw9+jIpVQRZlglKwYUTa2KwJr6Ez1smR/BFqTq3R3UaRlIbRmNSSDamN1cDSA+q5+ed4cu5JRE+kK9rFodwhJiuTuLgMxYf47A2fpS3Uhu3afOXAV/j6sa9ju/ZZ6xMQ+OSVn+SONXfwyas+SX+sn8899TlEQcT1XFzBpU4ds26yL7MP3dXZ0bGDoeTqqug8c4w1WSMRSHBz3808MPoA05Vp6vjhwyUR6M9c8xnuHLyTrJ7lvzz2X3h0+tFzrv/7I9+nN9rLh7Z8iH8b+zdmq7NUrApBL0iVKiPFEQaiA0xXpgkrYR6bfYz3b3t/Mx/ydUbeyGO6ZkMvNW/kGS4MY2EhIQEwU5uhalYbhTIA79vwPj6w6QON9dx94m6+uP+LALyh+w1sb92O67noto4iKliuxUhxhEPZQyzqixzJHeFI7ggA/bF+WoOt7FvYh+3ZVJ0qOjq2Z1NxKigo/Hjsx3zj4DfIOTlScopbBm7hwMIBLmu9jFsGXl+G5ImFMl6i2Pg7GVbpTqyuCfDrnaYB2WQFHh4TxQmKVpGZ0gwVs4IgCCS1JBYW46XxhvF4S+8tuJ7Lpx791Ip1dIW7WBNfQ1JLcrJwkpOFk2SNLPecvId7Tt5Db7SXD2z6AF957ivYro2qqrSGWgFQRZWqXeXp2aepWlUmS5P0R/vZ1bOLcr1MsV5s5A62Bdte3YPzCsgbeQ5mDvL4zOOYjkmmnOHZxWfxBN8g35jayJ/f8OfE1BhfP/p1vnPiOywavnixLMqoooosygSkAAAZPcNfPPMXTJQn+O0rfpu3rnkr3zz2TUaKI7SH2umL9pGtZ1foPk6VphoG5On6k5eyV1KTtRWe56AcpD/ez3B2GAQoGSXWpNZgYlKsF32vkWNSrpf5nYd+h8M5X5P03evfTWuolZpVI6pGUUSF6co03xv5HpPlSf7+0N9z55o7uUq4iu+NfA/d1kkEEhTNIocKh0gH0iiywpaWLRzNHaU/1v+6FrX/eSOpJRseSM/z/PudY5EMJBEUAdPx03QcHMJKmJzlp89c3Xk1NbuG53n83cG/46uHvwrALT238O71724UDc7V5hr3rY2pjdwxcAcZI0PJKHGyeJInZp9gvDTOeGmcqzquoifSw08mfkLJLFFxKsSDcQzHwKz7HtGEkkASJR6dfJRt7duYHZ1lsjxJRI1wTdc1pLTUqlcP+Pf/dw9aa7bxd1CReOA/3XS2EVmYhFp25bJQGhLN3t0Xm6YB2WQFeSNPySwxW5nlePY4o6VRAlKAol1kQV+galWRRZmP7/g4dw/f3fA4Xtd5XSPPrCXUwvH8cXJGjtvX3E5ACnAsd4xivchTc08xWZ7k7uG7eff6d/ONY98ga2RxcQkrYSRBoqpXUQTFF8qWFOpOnYgaQRIlFEmhbJZ9z5R1/n25lNBkjZHCCLFAjMnyJFkni+3ZmI7J5W2X8ye7/gTXc/nLp/+S+8fvByCmxrhz4E62t21HFEROFk/SG+3F8zzuG72P3bO7+caxb7AhtYG3Dr6VT1z+CX774d8mb+SZkWZ8cWK5lUQowY62HfTEeladkLhhG4ie2NjOoBwkrIQJqkF6o70czh1mujqNg9N4yC9Nag7nDqOICh/a+iHWJdcBcKJwopEztjGwkf54P/eP3c8z889w3+h9pLU0dw3dxb0j9zJbnUUSJDRJI+tlSYtpxFP/8ka+aUC+jkgH06SD6UakJW/kCcpBruu+jj0LeyhaxUbxzJKUVlSNsqN1B7Io85fP/CXfPvFtAN4++HbuXHPnitScrJElraV5dPpRHNchqSWRRInLWi7jstbLuH3gdn4w+gMenX6Up+eeZo+wh9sHbke3dR6cfJCpypSfu6sYKJLSWK9R91M2LM9iQV8ga2QZiA8QlIOr4vo+H5+5YxO3XncFAMMLFX77m/vIV82VBmRhEr54NVi1lR9WQvAfnmoakReZpgHZZAVjRV/wdrI0SbaeRRRE6l4d3dapWlUkQeLzt3ye+8fuZ6w0RlAO8sHNH2QgNsCBzAEenX6UmepMo9r1ucxzvHvDuxmID7AlvYXb+m/jfzzzPxqFNB/e+mH+7uDfUaqXCCthAGp2jYgSIR6IMxgfZGvb1obwdVgJ0xvtJaSEKFml1/JQvWiyepZvHfkWz8w/g+M4hOQQhmPg4nJ91/V85prPMFIc4c93/3mjW8o1HdegSirfPP5Nfjr9U96z4T2N9QmCwJ1r7gRg9+xuvnLgK7x18K3s6trF9tbt7M/sXy4kEeANPW9ga3orT888TcEocGXnlatGSFyTtRUi8kE5SFekC8M2mC5P0xfpa8j3LD2wn5l/hqfmnkISJD6y7SPPK8S8dK7uaN3BW4feyuWtl3Pf2H1MV6a5d+RePrDpA3zr+Leo2TVM1yQgBSibZQ7lDnFl55VE1MhZ62zy+mE4P4wkSVzZeSV1t87+hf0smn5EYMnIjKtxwDc2l4zHd617F2/qf9PzrvOHYz9k9+zuFcu+dfxbiIJIZ7iTtw2+jZt7buaBiQd4bOYxfjD6A3qjvfzhtX/I3x/8e6YqU9iuTUJJEJbCvswXFicLJ9mc3EzJKFG1qmSqGTalN62K6/t8tHky7Y7vwS07IlH3eSrLa1nfeHznV6Blvb9s8Th85zf815oG5EVFfOG3NPl5oWbVKNVLFOtFiobf6m2uMofruQ3tql9c94u0Bdv4/sj3AfjI1o/w9NzTfOZnn+Hrx77OVGUK13NpC7WRDCQpmSX+z8H/w5Gsn+fTHmrnneveCcCh7CHuWnsX4MupNPIs8UhoCQJSAAnfA7SkRbkaQzL7F/b73tryKeO86Oc8yqLMp676FCWzxB/87A+Yr83TGe7k3evfzWx1lkenH8XDY6Y6w1/v/Wv2zO/BcX0viCAIXNN5DQBFs9hY1hXuApbzAfNWnrnKHI/PPM5MbYbnMs8BvnRQOpi+5I/lmds5XZnmcPYwCS3RSKsQT93GlnJlJ8t+Vb/nefRGz36APDv/LN86/i3mqnP8cOyHfGn/l1Akha+8+Svc1ncbrufy9aNf513r3wVAQAr4GqeYTOQnmKpMYdgGWT1L7UzPR5PXBS2hFg5lDvH9k98nqfqVzvIpf4skSsTVOFOVKf71+L+SCCToCHUAnNMrPVwYbhiPO1p3sC6xjmQgiSz6QvbTlWm+dOBLfPvEt/n45R/n87d8nrZQG5PlSb6474v84bV/yLWd12I4BjP6DJ3hTtIB/7tG86Mcyh4iHUwT02I8NfsUM5WZxras1vN09N4F/uWzT/Mvn32aPX97hA+VAhhF8/nf3LIeunb4P0uGZJOLzqr2QFqWhaIoL/zGJmexZGCcjm7rzFXmmChPMFoYxfVcJFEiqSU5VjhGQArwjrXv4G/2/w2O59AT6eGhyYc4mD0IQEpLkQwkUUQFTdawXRvHcyiZJX449kMytQw3996MLPqn3WR5smEQAXSEOgjKQWzL1zuMBWKkwikWa4sMJgZXzYz6TC3N2coslmXRGmvFxfVb79lwRdsVHFw8yJef+zJFs0hnuJNdnbu4+8Td2J6NJEh0R7t9g75e5Jn5ZxgtjnJDzw2ktBQV05f8MR0Tw/Fbpy3pHiYCCSpWhaSSxHRMusJd2Nj0x/pf/QNyAVmqYp+rzhFRIygotGqtmJ5JWAqTN/KMl8ZJaSlyRo6DiwfpjnQ3Pv/I5CMczR0FfA9SxaowV53jb/b/Dbtnd3Nj941MliY5XjjON45+g+5wN9PVaVJairSWRkbmePY4qqDieH4+XFeki6AcbGpDvo7YnN7Mt49+G0XyC19u6bmFcr1Mzszh4FC3/cKtrzz3Fd7Q8wau6byGe07ew7PzzzYmcUvkjBw/Hv8xAOuT6xseccP2z2PLtVioLTBfm+dg9iDvv+/93NhzIx/Y+AH++eg/M1+b5xMPfYI/2fUnjJfGma3Osie3hzXRNcQDcWRkWoItpINpFmuLJIN+N52hxNCqSVV5PrpuTHHnm68E4MDhDEe/O4ZVO7ugsMlrx6o0IP/t3/6N3/u936O9vZ1vfvObtLa2vtabtOrJ6TmemnmKR6cfJayEKTtlX/LEs3hu0fdavX3o7dTsGg9PPgxAZ7iTZ+afAWBX5y76Yn1849g3zvkdzy48S87I8f5N76c12EpGz3A8f5ywHKZqV5mrzqHJGvPGPLZt8+7N76ZiVpipzDBTmVlVnRcen3qcBycfxPM8NqQ2sLNjJzWnhmEbHK8cB/wqzR+N/4ix0pjfrlFL8e3hbzfW4XgOE6WJxt+SIJE1stx78l5u7b2Vy1ovA3yjURZkJFFqTAyK9SK2axNTY8iiTCQQYWfHTgYTg6xmOsIdDR3NjnAHPxz9IZqscbJwkrLhS6DU7BpbW7by1NxTgF+0APCTiZ80jEdY9twusWdhD0dzR3lj3xt9fdLyZMPzbrkWFauCKIgsVBc44B3AdE22tm4lZ+RWGKlNVi9Lk4CckaM93M5UZYpN6U1sbt3MaGmUB8cfpOyW8TyPgBRAt3W+uO+L3DV0F/ecvIex4hh9sb5lVQnX5h8O/QO2a9MSbOGy1ssahTU/GP3BWd8fVaKUrTIPTT7Es/PP8tbBt/Kz6Z8xUZ7gDx77Az511af47vB3OZg9yMHcQVRJJSAFOJE9QbFeJKbGkCSJhepCQyfy9BSQS5FyzsCo+BPf4rzRWB6Iy7T2RQEILVRek21rcn5WXQh73759/Mqv/Apf/vKXefDBB5vG4wUib+RZMBZo1VoREAgIAb/fMjZVu4oiKrx3w3v5ycRP8PC4vut6MnoGD4+eSA99sb7zrn9NbA0Ao6VRDi4ebMyGR4ojjW4KSz2fAXJ2DtETERCQJInJ0uRZ67yUOZQ9xGxllpmKX8zy3279b/zq1l8FYXk/d7bv5GfTPwP8jjOHsofO20FiY2ojvdFeXM/lgYkHmK3OAjSEigtGoWHsLz2kIlqEueoc/fF+NFlbdV6IM+mOdrOzYyfd0W66o928ZeAtfr6sGscSfEMaljvO3D92P3kjj+d5fHf4u+dc75rYGpKBJDW7xgMTD/D2wbfTGe6kZtca/bOX1uPgUDbLFI0iC7UFCkbhVdjzJq8mc9U5ruy6kvdtel+jA9a7NryLt699O0kpSUSMkNJSSILEw5MPM1ed87vJGIs8Pf90Yz1jpbFGoWFYCb+gNuRQfIj3bngvESVCySzxk4mf8CubfqXR1eYvn/lL/mjXH3HHmjsankXDNqhQYaQyQtEqIngCiPDEzBMcXTzqF9VVZhjOD5PVs+f9/lebcs7ga3+8uxGqfuQfxxqviYFVZ56sYLYyy+Hs4cbPSHHktd6kC86qG6G/+qu/4iMf+QjXX7/c1m5ubo5nnnmGer1+nk+upF6vUyqVVvz8PJLTc5zMnwSgTWtjU8sm3rvxvaRDaWRp2UEdVsJ+ztmpEGlPtKfxkH4x1ahLN05VVHFwGC+NIwkSAkKjOEcV1cb7o0IU0zURBAHHceiNnTsZ+lIcxy3pLXRGOklraWpWjcPZwxzNHmWqMoXpmnSGO7Ecv4duRIkQVf2Z9tb01nOu0/M8coYvH7IxtbHhCX7LwFsA+P3Hfp+F2gKyKBNWwqioxJQYG1Ib6In2XPK6mS9mHJfO16UuRLcP3c6NvTc2JjBLnpY983tIBpIsGov81Z6/ompXubn35nN+d0SN8Esbfwmg0UbyXWvfhSRIGI4vGG04BoV6gZJeolKvULNruK5LsV4kZ+RWba7ZheZSvB5fKh3hDjRJ89tlntKIBHjf5vdxa/+txNQYaTXdKPz7671/zXvW+4Vu/3TonyjV/f0ejA9yY8+NgN+m9EejP2KuOnfO7xUEgU2pTXxw8wcBP39RFmXev/H9XN52OYZj8Pk9n+cPr/1DfmPbbwBgOAaGbfj3h1qO/Qv7eWLyCWzHZqw0humaTJQmyOgZ9i/sv6SMSKNiYZsuG98xwBUf2cSaty1Ls6lR6TXcslfGUtvC937/vY2fTz/6aYJykGTg0r4PvxRWXQh7eHiYyy7zQ3e2bfO7v/u7fPGLX8R1Xbq6uvjOd77DNddc84Lr+dznPsef/MmfXOzNvXQ5laaXN/Iczh5mrjLHlpYt7OzcSaVeoS/WR9WsYrgGdaeO53mU6iUsxzcgDdto9H8tGIVG4cK5mKnOAHBl+5U8OPEgADd03cAPx34I+K37ImqEoBAkmAiyJb2Fil2hI9jBjX030hXpOue6e3svjUq707tZXNdzHdf1XMfDEw+T0TM8OPYgJwonKNn+g+WjOz7KodwhAOKBOBPlU6Hq86TRjZXGqNk10lqatmAbR3NHCUgBPrbjY/zrsX/l6bmnCcpBeiN+lXpUjvKOde+gL9bHYGKwUfhxqRYivZhxXCH4fEqOpzvSTaaW8bUyVRnLtTAcA1EQiatxpivTfH7P5/nEjk9w3+h951z3VGWq0XP8SO4I/bF+bum9hQcmHmBdYh1Hc36PclM0ydVz5PU8+xf20xHqYKw4Rlu4jU3pTZfksX01uVSux1dCT7SHnmgPcNp1HUqSDCQJqSGSoSTpQBrLsxotNJOBJN2RbqYr0/z9wb/nI9s+giAIvHfDeynXyxzN++fPT6d+Sl/03BGb0aKffy4gYHv+udgeaufXt/w6BzIH+Nn0z/jth36b37nidwhIAb6w7wsYjkEsECMSjKCKKs/lnqMj3MHOzp0sVBYQ8VMv2kJtjWYMS/eq11KOaqHsh6w//cBRFmQPIzPeeC0eXL31DedqW5gMJFddu8Lzseo8kP39/dxzzz0A/OEf/iH79u3j6NGjnDhxgqGhId7xjndQq72wF+DTn/40xWKx8TM5ubpCpBeKpJZkrjKHIAjMVGYa4dCucBeu4Da09QRBIB6INzw8sigTkv0HZTQQpTfWS2+sl3WJdVzdcfWKn/ZQO7Zr0xXuIqyEqVpVuiPd7OraxVhpjLASpmbVmCxPMl4a57qu69jZuZM1iTWkw+kXLFCYnJy8JMZxtjJLRs8wW5lFwD+eWT3LXHmOk4WT7F/cj+3a3NhzI2/qexPD+WHA70Cx5M1VJZWbe27m3evf3fh517p30R/rb8gbffLKT7J3we9L/ssbf5nOSGdDRgR8kfFcNcfaxFqenXuW+co8giCsSKi/FHkx45jUkqiiSlJLIpz6t2Qc1+06WT2L5Pqei6yR5aqOq4iqUUaKIzw49SAf3vJhUppveKa0FHcM3MHbh95ORImgiAqu649DWA2TCqZ494Z3A3Akd4T3b3o/4GtJztRmOFo4ynxtnpOlkziec17P0s8Tl8r1eKFIaSmGEkOktJTfVCGQpGyUGc4PM1eba6SLfHv42/z+Nb+PJEjsy+xjpjrDpvQmNqU38bErPsaX3vglbh+4HYCJ8gRBOchtfbfx61t+nQ9t/RAf2voh1iTW0B3tpjfW2/BuarJGXIszEB/gd3b+DrIo8+Tck/z+Y7/P24bexn+84j8CsFBb8DuImSU8PJ6Zf4aAHGCyPInlWbQEW2gNtTZE05cmYq8lJd0vivm9N2/g+x+/gS994IrGa21R7bXarAvGUtvCpZ/Xk/EIq9AD+Wu/9mu85S1v4f/+3//LP/7jP7Jv3z7a2ny399e//nV6enrYs2cPN9xww3nXEwgECAQCr8YmX9Kk9Aq7iHDSMxhKb+J49jgPjj/IvD5PVIk23lezaiuqoPcu7G0YddPladYn16NK6op1LxWBZHS/j3Z3pJsfT/jViB/c/EH+8dA/AmA5VkOCxcXl0KIvSXE0f5TOYCc72nacdx9isRixWOwVH4tXiiZrfs7cqX2Zq85hOZZfBFSdQXd0AlKA/3zlf0YQhIbmo+d5DW/us/PPEpACdIY7aQ+1E1Nj7MvsI6NnEBB434b38df7/pqyVaY32sttvbfxmUc/w0R5gogSQRZkVBQ2t2zGcAxqeo2n556mPdLOfGWeRWORocTQJSmC/WLGcUnw+Uxu6Pavd9u1yRt5xqpjOJ7DAxMPcHPPzTwy9QjfO/k91ifXc8fAHXz7xLfJGTn+bezfnvd7lqps18TXNLQ1s0aWD27+IF89/FWqTpWZ2gyVeoVru64lHfRTFZ6YfoJN6U1sbT13KsLrnUvlejwvuXEoTkO8G1IvrEyg28udmzrCHRiOgSr49ztVVKk7daYr03z5wJf5xXW/yL8c/xf+n6f+Hx6afIg71txBe6idSCDCr2/9da7tvJYv7f8Sc7U5fjLxE1qDrWxMbcR0THJGjqO5o34HGssvHGlougI39txIa7CVP3r8jzhZPMk3j32Tj+74KBOlCe4evpuiWSQRSABQsAo8NPYQHeEOZiozXNVx1YpCxCUP5KVAbyrE1u44QiH6wm9ucsmw6gzIN7/5zXzgAx/gN3/zN32vWDzeeC0SiSCKIl1d5w53NjmD4jQ4OtOFYUatEjkjR8EsMFeZIxqI+gU1UoC6U+eB8Qd4U/+buHv4bkaKI6S0FKqkkq/n+d7w9wjKQSzXYlFfbHSMWfJ2qaLaSC5/97p3c8/wPcxWfU/dkuEpIpLSUoSVMIZjYDs2Fn6IaDXQFekiqSUbRnZHuIODCwcpm2VKph+6Nh2zsb9v7n8zj808xpNzT3L7wO24nstkeZK6U1+RfA9+oUxPpIe/P/T3uJ5LIpDgw1s/zH996r9yJHcEWZDxPA9BFBjQWuiJ9lCq+56I7kg3pmsyVhojHoyzUFt41Y/NxeaD2z7IB7d90A8f7v97alM1HNFhQV/g4amHWZ9Yz3BxmOP545TqJd7Y/0YOLR7CcAws10K3dTzPw/ZsOsOdK/Qjt7VsY39mP47n8AuDv9BoVwdQdss8t/gcPdEev9WkEuBk4eTPtQG5KihOg13zf79IA3LJe58zcnSEO6hYFWRHxhVcgnIQD499mX2MFce4ou0K9izs4YmZJ3hi5gliaoybem7i5t6b2ZTexP970//L5578HMfzx8noGTLTmef93p5ID2via1Yscz23Idu1q2sXsFw0d3oeeUD079sFs8BAbIC6u1wjcK6JWJMmL4VVZ0AC/O3f/i3lcpl77rmH//gf/yNf+MIXcByHj370o7zrXe9icHB1S5WcxZKu4IkHYWYfVOPnffuLWuVSEmS8m2fHf8y+6gySXaAz1ElEjjCUHEIWZE4WT9IZ6eRg9iDfPvFt3tT/Jv74uj/mU49+ype6CLVTqBeoO3XKli+jsnRzA//G5noupmuSCCT4re2/xWMzj3E4d5igHESTtIYuZG+4l7ZgGzvad6BKqi+Wq6UvmVnyCxFSQoSUEK7nNgznrW1bma5Mc3TxKCE5RM2u8dTcU9zcezO39t3KPx35J4YLwzw+8zi/vOGXEQSBh6ceRrd1FvVFSmaJuBrH8RzGy35+0HVd1/HWNW/lC/u+wGx1logSQRREVEmlRYrRFulGN3U2tWwirIQJSAHytTwbUhsomaUXrJhfbXiex+7p3RzJHQEPbM8Pi/VF+wjIAeaqcxwvHGcoMcRkaZK52pzvGRq4g5ZgC+CrA3SGl8NLjudQd+oUjWIjzKdJGtnTeu4ueegrdoUHJx5kKD7E2tRaLqsaeA//d+jagbD+tlfrMDR5KcS7lz2QL4B34kGCU09zOJIk09JHRIlwTec1hJUwD088zIK+QEJI4HkeGSFDwSywd2Evbxt8G7Io8/Dkw+Tree4duZd7R+5lbWItN/XcxLrEOja3bObQ4iFyRo6gHEQSJLoifqpPVI3SHmqn7tRxPZeqVcV2bb6w7wsA3NJ7C9tatlGzavxo7EcAdIW76Ax3kjWy9If66Y50M5gYZKYyw+7p3QTEALf037Ji/163+qWLx89e1uyRfUFZlQZkIBDgO9/5Dn/xF3/BZz/7Wf71X/8Vx3G47bbb+OpXv/rCK1itzOyDehEWZl/c+80qmDVQQ6CGV7zUkItJ9hHq2EwK3+C5vONywmqYA/MHeHr+aSp2BdMxCckhJsoTjBRHuK7rOj53w+f4vZ/+HvO1ee5ccyc399zMfG2esdIYhm2QM3KMFEcaYdqrOq7iv17/X/n2iW/z06mfIgkSqUCKRcNvD6ahkRNybEpuoi/WR2ekk8vbLyepJRvFEqsFAT/fMGtkeWrqKeZrfg6ihJ+bt3dhL3esuQOAz1zzGT75yCfJGlmeWXiGW3tv5YbuGwgpIRzXYao8xfdOfg/bs4mqUf7gmj+gLdTG7zz8OxTqBSRBQrd1BEGgZtdoibZQFwUkWQLBr1pOhpKUzTI39NzwuvQ6CILAoewhynaZmdIMqqRSMkuU82Vcz0UURMJKmJOFkwzEBrBcq9GycHvrdnoiPfREetiS3sL65PpGIc1EeYK56lwjt7Et1NZITwAaIUYREdERCbWH2Nq6lS1HH4F6yb9e19923uuwyWtEqv9FeR4BhJl9BK0apcwMTks30UCUj677KB4eYTXMeHmcmlnDMMv8dDaPIvri4/eO3IsqqlzffT290V5Gi6M8PvM4w4VhhgvDBKQAN/bcyG9e9ptc1nIZgiAwWZ5sKDKcTt2u0xZs4/8c+j9MV6ZJBBL89hW/jSqpPDD+QEOjtGyWsT0b0zEZKY+Q1tLUnTqL+iJBJciTs09ybfe1jSjJdGW6oau6VDR0KTOZqxGdXtZv1RYrnKUOHEr7vbC/8xtnr6DZI/uCsioNSABRFPnUpz7FJz7xCfbv309LSwtr164eoemXRdcO/6HU9iI9SGYNXPvUw+vsB9ehxUOcLJzEsq1GcctEcYLOaCdBNUiu7kulSKLU8BL+2e4/45NXfpLru6/nI1s/wpef+zL3jd53zupWVVL58NYPMxgb5A8e+4OGuHMikCCshFk0FlE41bnGs5mrzRFUghi20Qi1rjYDcolqvcrehb1k6hmqdpWo5osE/2jsR7x7/btZl1xHVI3y8cs/zh8/8cc8NfdU4/icydUdV/ORrR9h99xuPvOzz+B4DrIg06q1kqn74a+gEGRtYi07O3b6eVlunc5wJ2WrTDwQJ2/kX5cGJPidQw5nDxNRIhzPH8fCQsUP50mChOAKJLUkY6Ux0lqazanNHM4d9o85y8c8IAXQZI1ivXjWd8QD8UYLz9P1OmVkOiOdOI5D3aovX6ddOwAYXjjERGWCvkgfa3uuvmjHoMlFYmk8o0mGs8MNAX+A67qvY011DZlqhqnccZ4RAiD7qSp1p47pmjw0+RAAvdFePrbjY1iexX0j9zFWGuPH4z/mx+M/pj3Uzs72nfRF+7iy48pG1MX1XI7njvPI1CM8Pfd0Q9j+o9s/SkyN8dDkQ3z2qc8CEJRWFhs6OBzOHaY73o1lWYwUR1ibWMvhxcPs7NgJrOzsdCkbkLGg//z5y/uPsfDgcjOALcIoPwjAQqVOQwAo0esbibUz5IqaPbIvOKvWgFwiGAxy7bXXvtab8eqw7lb/59ChF/d+NbTs+XgeThZOUrNrTJQn8ASPueociqSQDqXZkNrQ8JiBX/2q2zrztXl+76e/R3+snzf2vZEPbf0Q3zj6DWp2DVmQCSkh4oE4UTVKa7CVW3tv5ScTP+F/7f9fACii0gjPLKGhEVWjmLbJVHmKh8YeYtFYpD3UDsBQcuhlHrDXlu5oNx2RDmZrs0hIRKUoOTFHxarwqz/8Vd674b3c3Hsz21u38xvbfoNHph5BQPC7XMgBZFFGERWu77qejlAHf7r7T5mqTAF+ODWmxpBEf4wkJNKBNJ2RTn5xwy+yZ34PuqOjSRrb27dfUgnzF4NdPbvY1bOLrx/+Ok/PPU1UiCIiIkkSmqxh1A0KVoGAFCBr+LIsv7juFxmIDTBTneFE/oSfH2mWGgVNkiA1Whj2RHvYPbu7Ify+lGsWIEA6lEYURcYKY+wL7GPXFb8F65bDhBPmIjXHYsJcPNtb0uTSZ+m+O/EQa53aCqmt7kg33ZFuckaO1kCUvkg3x0rjVNw6e+b2MF2bxnRNHM9hsjzJ5/d+npAc4pqOa7iu8zoyeobHZx5nvja/PAk/4Oc+DiWGOJQ9xKK+2Pi+uBrnrrV38YbuN/A/nv0f3DtyLwCyIDeqtk9HERUUFGzBJhlMokpqo6hRt/37Q92u0xHuuHjH7wKwVJH9+fddTrRz+XmWOa7CI1DSrWUDEnwDsWkkXnRWvQH5c4/nLedILrE0C1XD/s+Z71k4CtlRhiSRk3KI7W3bma/OI3piI3zXGmwlpsRYqC8QUkJIosTaxFoyegbDNhgvjfN3B/+OqBrl9oHb2ZTehIhIsV4kokawXZtD2UP82e4/w/ZsBASiapSWYEujiCQgBtAEjdZQKzf13sTT808TkkPszexlMDnIfG3+0s/XO/PYA57nNkJE7930XlqDrTwy+QhRJUqv1OtXZNs6Xz38Vb51/FvctfYufmHNLzQEwRf1RdrC/u2w7tT55tFv8vk9n8fFD8cmA36hjoLiex00gbSWpjvW3RB6bw+3N0JTP08J81d3Xc3x3HFGS6Nc034Nl3dczlOzT3Hf6H0ErAAb0hs4UThBvp7n2ye+jSzKrImtoTfWywc2foBoIEpQDhJRIpiOSVSNYrkWXz38VfZl9iEJEpvTm5kp+7qmGgFiSgzd0jFdk8PZw3z+mf/JZa2XcXP/zQieR19yPRPShH8uL50vr9e8s9XO81zPS/TF+pgoTSzfkywdpvdBeYFU+0ZIbyKkpSjiMl2Z5or2K/AWPCr1Cp3hTiaqE+TqOWp2jYemfK+kiMim9Ca6Il1YjsXJ4kmmylNMVaYak8WgHOSKtit46+Bb2dm+k5HiCL/5wG82tHfjapxkINnwPsaVOHWpTmu4les6ryMZTJLTc0yUJlgTX0NMiTFdmQagLdzmNzxYJfeHlCOQcpbVB00nQNlpeQ236MJxZqea1aAZ2TQgVzvT+6ArCpGW8+dZWbXl1+ePIBg5tsphtmoxyA5DzxWw7Vd5cOIhak6N/Qv7cQUXx3E4nj/e0CkEv2hBFVUcz2/p9p3h78DwuTdRERXSWpqSWWrkk8nI9Ef6GxWG+XqeOwbv4MjiEdYn1xNSQ2xJb1lV/a9rp0l9LP2ktBSarJE1suyd20vOyiEiokkapmNSs2t8/ejXuX/s/obmYMEoUDJLFOoFRgojzNX8YxaU/Cr3klmialbpCHWwTU2zS26DeA/DmsqWVj+X6nQh5BW8DvPxTg/brU2u5Y/f8MeAXyj2vRPfI6SGaA22Yns2hmWgoaGKKqZrYrs2JwonOFE40RC4PxchOYQsymT1LBWrggLEBYH2UDuz1VnqZgmrNENZ1DgAjWKFtYkh1iZWpxf954Kla2IJNeTnyo09DnOHEDq2wJrrWZtc69+PKhlYOIYgeJAbA8eA7BjE2mgJtTBTmfHzxpUQO9t3MpY9QrmWRfUEAl4AAwMPD0EQcD2XQ9lDHMr6USVJkBiKD9EaakUWZboiXXRFuqjbdY7nj7N7djc/GP0BtmsjCRKy4AvnL+h+uo+MTDLhi50HpAAlq0Q6mGZr61buGLqDpJbkx2M/5uGph9ma3soNvTdc0n2yl9AiCrIq8sA/HD7rtaeFv2ZX+dyG/6XOkkPg049+esXyoBzknrvuuaSNyKYBudqpLcDYE9C+HiQVAtHnNwxOz4eMtvsGpRaD+UP+/+cOwsCuxixb0iSu6rgKx3XAhbyVp+L4RQOCIKBICh2a3+bLcIyGgSmLsv8ZwZ9dB+UgqqSepREpIVExKyRDSRD8z7UGW9HjOltbt3Jl55UX/dBdaPTZAzi5MWYiKZ40MkyUJ7im8xpiWoxqvUrOyuHgIAqiX1QjSISVMFkjS9bI8qX9X3re9YqIRNQIESXSCD91hbpYk1jD+9w4ilkiUily6/aPg/gCl/QL5MW+3hhKDHEid4L2UDs39d6ELMr8bOpnlAolZFHGw8P1XAJSAN3WsV3bf7gjIApiQ61gqcpdPu34RoB+NC5rvww1pxISNDRcPLPKZdF+yE/4XqpwGsKvDy/J65Kla0IvQjDu/62EYO6QXww1dwjWLLfOpZr1jUbXhdQAlBdAVglOH0APp7is9TIOZA6wNrWWkBSi06wTiEBXuJNn3So/m/oZs9VZWrQW8lYewzVwPAfXc3E8h+HiMMPF88zI8XN1W4OtjdamS6SDaRD8TjonCyeZqExQN+tsb9vOOze8k65IF1PlKWzXZroyTSqYWpHPe6kSTWn88h9fi1GxViw/8uQenvuJhmWsXgOyM9LJPXfd08hvBd8b+elHP02+nm8akE0uInPHwDkK+VEYvAHEc/QPzRz3jcSOrdC9A1rXL7+WHYH0WqguMhTuZCjcyXTuJK2BKLqtM1uZxapYKCiUnTLuqX+CIKDJ2orK1LSWJmu8uF6rouQbRo7j8MaBN3I8dxzTMzmWO3bpGpDn8eAFi3Poto6RG+NIfQLDMTiQOcC7Nr6LxfoiEhIODgoKJn6f76gaRRREv0WeYyIKIiElhG7pDUNTEZWGzpsIuPgVwFtTW+kKdpObfBI9PUTNtQhpLyDg/AJ5sa83trRuYf/CfiRJompV2ZzeTH+sn+nCNBWhgoeHIzgkAomzJjkpLXXWA3qJPsI4VKlKArPlWWQkApFWWkybjGiB5/gyMYGQb3AsHfeJp/wUkq4dfoX2mbwOPcSXPEtjM38I5g770ZhNd0LHFt947Niy/F6zCoLnG4+h9PJ1NPwgBJPQuo5re3dybWItuigQDKa5JTZELnOUfCBISziF6ZrsWdiDKqpU7SqS5OCioKGSDLWT03NU3WojHUUU/IhF3a0j4N8PVElt5D+DPyH38MCDkBCiI9TBXGWOmcoMFhaTlUkmShPc0n8L13Vex6HsIdpD7Tw79+yqqcCOihmi8spny5w8BVy6BtaLpTPSeUkbiueiaUBeKlQy/oPmdG9FddFfhgcIvqfQqkOpsvy5wgTEEv4sWAmBHIJ6xfd8KMHlHMjClG9cTjwFRtmfObdt8G+US9+15JkCgpJMn5RmMD7IYmURAQEJkRAqqqwBIqqoYov2it3QZG2Fl2aJgBigRW4hEUr4BR9aGtM1SQaSjcphgOO546xPrT/r85cM5/Hghdo2EMqOQSTFmqzIcOEk7aF2UlqKtwy8hfuG72OxniEqB7HtOnl0P3cJgYgSaaynM9zOeOnsFnAhRaNkltAIMBQf4paBmyHShdG+jmxulBMH/4V1cpieNTf7Y/t8noWlvNifIzakNkDO/121qlzVfRWZWoaKVfFDz3YdTQhgCwYCEhYO4Ht5zjQqASJyhJJZwyaAJNg8M/sMtyU3EbYdFgMquu1xeOJhbmm5DgQX2rdCfgpqizD+BKhBGH/c1yBcut6XUkysmh9JWPKCQTNf8qVwZg7j6X+f/Cm0iNC2ceV7lFMh69wYiKI/ofY86L8OurbD4kkYfhhSa3yDUUtAcQ5mDviFGvkxUELoxSmccBp9+BFSbRsJRtqgnIHSLMnWjQSjrei2zkJ1lqCgMF9bRJZkVDdIHAlVDRMPtqHbOjIyKiouLja+uP18bZ4AAer4RV6apCEKIjIyQ4khHNehLdSGXtfRLZ2h+BCarFGoF+gKdTX6b+/q3cWu3l08O/fsqqjABqAwCV+82r8+TqPDGgT+O476yrWRm7x0mgbkpcJSWGTiKUCA9Br/d3kWFg6DGoPyDCT6YHpq+XNWBeanoDcGwqkZ6eQzICmQ6IHFYf8GyKlCGqMAIw/D4TJs+8XlatFTs3Dh1O+gaaIHQlzddTU2NoFMgExxnLjYRkgOsuBZTJQmWLQWV+xGvu73WAUIEyakhnBxaQ218qbNb6Iv2sdseZbR4iiJQILuWDed4U4EQeDKzisvXc/jEufz4LVthLaNpDyPa4MR+pJ9BGcOoz/4Od7XtoXuNXfy4PyzZItjKJ6CYlcolmdYsMtLsu6IQMUsYlhVTAAXYqJICAXbqhB2RURZJelJBI7/BF2JoCS6mM8dJVKdZ66Woyc7CdvfBd2X+yvNTyyLJr9QruzriKXQ3JrEGhJaotEDeKI0wbaWbfxk8ieNCU/IFdkUWUcikORgbZbDucOUzXIjNUMENEBAxJbKiK6Jg4eitBCXg0hGgfX1Gh1GhYNBla2xfoTF4xBKQmkeSlMwfxAK02BW/OVjP/OvyZ4rIZQCWfXniqL8c+MhvmhYNags+vfPJewaZEdXGpBLk/Rw2l8++jMIRPyIDUD2KOSmwMjDoXsh2Qt9V0PmMOh5MIrQdxXkpwjaJvrUboLlHIKkwtBNIGlQLyPoRfRgBMdz2Bwb5IhrgetRdGqkpSBXJtcSVKI8WjhGza5hWAY90R4M22C6Nk2hXsDzPHpCPeg1HU3VKBgFZE9mXXIdv7T5lzixeIITxRO+fBAmETXCne13MlL09SBjWmxFuLoj3NEotLvkqWX9MX3nV6Bl2cEwebgE37KxQ23n+XCTi8UFNSB/+tOfkkwm2bZt24Vc7euLc4Wowmn/RmaUQRL9Gx0eHLsf1AgoWT8sNv4k6KclPddLEAbGH4XyvB8yEyXo2gm2BbmT/t9yEKSAf0PNHPdn0yd+DPGuZS+IEvK/06wRirYTEmWCgTBXtF3BUHyIfk8mVa8xI0s8XDzuy0ucSklJqknWx9czVhpjvj5PRIwQUkJc0XkFlmWxvW07lXoFK2TRHe1mXXodruvSGelcFUncDc7lwVs4CvNH/fzS7u1ocgAtO07wxP0ElSQYVejfQUIQ6E5t4ujsU4SACBKbCDCGxSISBh4qASyqbEbmSk9lUUvRJ4S51pa5u3qQcqhGS3mWYGQNwUoGRInNsQEKjkhHuQSyDMfvh1oBWoaW27bNHYJgAoIxP1f258SQzBv+pCZv5BlKDDGUGOKWvlvY0rqFv3vu78CFzckBPjrwdoLxbj755H8jpASpuToKMgoyV4f7SNomh+sFstQQEOkTk2yI9LHR0FkLtJcypG2Hmws6lGr+9WZVoTgLE4/7G1MvQbTb90yNPgrRLv+6TvRBtNMPj+pZiPcueyCbvHTMGphl/367hBzyJ+anT6iK01BdgFqbbxgiwNTT8Mh/9++N+RE/oqPXQFXALEC6H5Sof492s9B5GXRdRmjiSUKO7U8Wwm2w92v+OEbSEG4lmJtAn97D+22FUM/bmFIU5mQRTVQ5uXiYMi43dd/E+uR6YkgkBJEfL+6jpCcIiAEGE4MUzDypUMrvxa2qpANp1qXW0RnuZE18Dbd5t7Fvbh+jxVH6on1kahl0S+eYfoxYILYiXH3OQrtLmZb1DX1VgPrMLHDkNducn3dekQH5xBNPcOg0TcKvfe1rdHZ2csstt7Br1y42b978ijfwdce5QqCRVv+3FgOjBB1rYORR37izSpAYgokn/Qd+YTm8KSBQq+iYXhitdggtJPsztckn/AeYqEDrZtj5K/Ds/4XccRBkf71mFZ79qm9QrHvzsgE5fwSqGei9ilD35Wxp2QwIjTB3l2sRLJ8kqUap2kkEQWJDagMdoQ5kWUbIC3h4bG/ZTnuwHTfoh2FyRo7nFp+jP9rPOm0dyVBy1chHnJOFozC93z+uWhxKk2CU6UwPkFicYMyu85PyYQbrGebcAiXPoORaCIgIgkYZk6RhMuS63Clr7FcFhrERCNAqJ/lo363giXDga2AU2SlUOOy10icYBC2bYGmRUDBNOt4HGy6D9h2+oWjpUM+vfFAi+CE6vQRq9BUX0xQXdSp5g0hSI95y6U4CljyPZ+pg7urZxUx1hqPZo6SjPeipXoKSxm2hXhY5SEUJUrAs4moAV5LpjXTz3OJe4ijISFwd7SNanKZbryI4IxSi3aSNLNSzUBwDLwDo4LkgCn7eXKwXPPtU5e6IP5nL90HfdX5oFOfUuBjLnrFI6/KxVmvEtXJj+c8jL+q8U0P+OX76uT34Bt/LOPYEVGYgP+5fs0v+fzUEgugbkILg3wPzJ8F1wBP8vHGjAjOHwTX8iYFXhyc+D6E2cE1wHGjf5keN6iVwLd/jLGqEDn6HkChC6zooTdHddzXdksZJfYFeMYRbW2CzWUaXAjiBCBOSwGykj774IKIn0p3o5qnpp2hTgpwsnMRzHaYK46wL96BIfjMGLJ3r05t428Bt6ILAEzNPoNs6KS1FKpjiucxzq6rzTJNLm1dkQH7zm99kz549DUNxYWEBy7J45plnWLt2bdOAhLNzck4PgZ75WjUHyW4Qh07dZOawpg5jiyHk2WEUKeB7lcRlHSytOk5IcQmROVVd8TybUBpHmH7yVIsnDaL9eK0b4fAP/Nm358L8CbjifaBXEMpT/g10Zg+1wjS6GmDMrjBdGmPQgS2pjWxXW5mMDyAKEA2miSpRAnKANfE1rE2sxXZtymaZgdgALcEWSlaJvJ7H9VwqVoWhS1XW5PRc1NMf0EtjlZ+A4gyNvNTcCOSGfaP+VG4ocoTg3AGCs4fZp2ogdzAnqnSJAapunZhp0WWL1KwacVScap5ewyDumLxVUqiFW5lVZNy4xnPTjxM1dDpkDaurh6sXR7hajKFpA2jlOX87MkcguQbmyn6ox65BMOV7Pesn/PDowLX+fi3t2+nn4bn2+Qy8085Xz/Oo5A0c06EwX0NRJZSAhKKeVsR1iaTuna6D6Z0mRwV+J5GuSBfxQNz3hNer3K62QGIz04LLcVWiljlOW26cK4ICj7o2hhojIrlE9DKpep1Crcpcx1q685NQyYOeAafuGx7uaTnCogwVEbqvA7PoG5Se7U/cilPQusGfpI09AfoiDOyC9XdAuKVxrCu5ReK93qnxasUyHay6c/axX2IVFad659FgFBAa+1qYryHgn39nGpBLVfMoQVgcxjv6o+UX930DjI2+Vx4JNM0fk1Dar77OT0K9DOkh3+iTg1CahVQ3VLJ++gceFMdh4QjU5qGywNkHWTiVVxmEesH3QrsmhFr9yVt5FsLt+IkRAsFyloPVGWrzhwihMVDNofdeweaWLWzuewO6KHDsma9y/+Q3EJDpTG+gJ305zyw+RzTYRlQQWawtEpbrBIszoEUhUCMYbmFjaiPbWrZRNssYjkGmllnOe4yc6gPezLFt8jJ5RQbkn/7pn/K7v/u7XHvttfzar/0aH/vYx9i4cSMf+9jHLtT2vf5YSti2av7DYklzDCCc8vMVK6N+Qnd1Aau4ANFO7OICiliB4jiBmeXKUMGzoGMHJPpBjeBJqn8zlDXIHIOJJxCqGf+BtNTRQM/7D/fKjH9jDCYhd8LP8Yn3+CFzARh/Gl1wcSNtHEskEUrzjAgCWwOtbO3YypYrPg5qiOl6nv0L+8kaWT/UHe/nK/u+go0vJv7h7R9GEqSGtMRQYmiFdt8lxVIuajX7/MZUceZULtVJ/0EjqWDq/jG0LIgmERaPweJRqCzQo2jMRqKszc8SyU2wvTpH1Kr7YctzENdzfl3h7LJ335ICoHdAIEQsvsb3HhdGQFD83NfpPb4xUpqDeCc4LrSugcln/QKA7e+BDW9ZKSezdN7lJ8+/z+cgktSo5A1kRcRzPd+QeT4j5hLiTMmSnkj3ygep51GLdxLLBTCAW9ObiU2PkDYFWmon6IgIVIwaUVnimsI0YLJPltmQOcFQ5jg45tlfGu3wvWHZE/6E7fg9fmQg3O4X2IjyKe/1FDg2zB8G0YHD9/nXZ/91RJJXU8mUiaRDIDm+sQ9YdWfVHPtXytK+amEZ23KJJLXneddp4zuzz88RX2L+MKQs/x7XfQXMHgBJ9sekOA5zR3xDTy/6qT/VvD9OeP4kbP6Q/z6r5k+6l+i83B/jwrjv1bSqyz8A1XlYPBVmFRU/fagwgTC9B1qGCHZswSiMY9lVRs08mwNpQoIKa3aBVSNUXmDXxFP81MnRIoqQGuSTA3dwcPDN7J99ilS0l/ZwO2ppgVmzgGIWCUfb6JaDdEW60G2d1lArITnI1Gm9r5s0eaW8IgMyFovxt3/7t/zDP/wDH//4x9F1/UJt1+uP04W8ldDKUPaSQWnWoDIPC8f86uqZvSiOSN2solSn8GrjCJ6FZCwbH8Ydfw23vnf5e8yq72VcwvPwMscQZvbB+M/g6A+gNIlQmsQTVRA1sCt+nmR13v8tCFCagcIEQUFElxQ2pPsZNUdJeDJZWyXmqcjVLIeyR3m6OEy8MM0VtQqDYguhnh5u6r2JA5kDfktEQSIoB9naupWtrVtfvWP+cljKRQ2fEVpfGp9QAmr4HsnjP/ElkZYeRq4Lhu5XuA//GGpzbLAMNjyfsRhMQrLfD2mmBiAx4C+rZfHKs6AX/AdbYRLmDqA4dZTiOEHwvR/yqWrQSDtorTC9F/QFsAywt0LfNbA47huWUsA3Ije85aXt83lwLI9QVG0YLZbpIJz6vWoMmRMPLvesXnerv0wNo3dvp6deor06S5dRh8QQT1af4AnRo6+k4wUFBusyIcGDSonbjCxJ26+MJdwCV/0GtG/xxyY1eMozjT9u/x977x0nyV3e+b8rdnV1np6cZ3PS7iqvMhIIIQkQ2RgMNvY5Yp99GAeO8+8AG+NwhzlsfA5ng8nGBIkgJIKytCutpF1t3p0NM7OTp6dzqFy/P74z0zOrFQoIscL7eb16e7ZDVXWFb32+z/N5Ps/eL8KBr4r0ZmVc+LgaLdC1XUSNG0VcH1wngmZoMLYH98C9mNteTyrdCif3w+prxLGszaFJEVxZLL9ecZ45EvkygucES1FVoBlhjSi4tk9LV1z8xh8xB/WcADe9HV+eWfaqL/a1XRTXSapbTNhHHhET9txBQeoDF6rTTa/HxhR4Z9zburbD2tdAx0ZBLFUDXvEBQSQ9G4ojYpI5fxxyw6IPc3FULDt3TDwUHfQk5oW/wFD+FFP1IkOeBAProX2TGG/GnwACSPSwrVTiqajBtlCkqrcM7mBV98U0FrfNDzDcGv78MXjiCzBwDSczHUuT9i2tm1dOmM7jPH5MvChFNO95z3u44oor+JVf+RWuueaaF2ORL2/kR5vas8xC2yunLm4ac8NQnYJGRVRdqoYgEK4lBq3JvYK8jT8J1Sm0wEJbPttVo3jrrgG+Kv4vK/DoP4jUZX1epFSsoogyJnuEtqr3Utj+83DxL4pB7YcfIhz+HlLgiBl3tUyoFkFRxKy7Mg6uB4GFaWQwpQjZcoHB8jxVstS9I0THn0Id3MAJaky7RY5M7aI/2kd05jBsvIUbBm/ghsEbXtLd/qIg3nb2KFzhtIgYx1oFiZw5DHpEVLxXphZuIrqIIg9/B0pjK7+fGYK+y2DwanFMFvu0Ogu2LYG3UGnYEJWdhOJvz4FUL+HkHhi+G2n0YfG+V4dqXUSRlYiIiCiaOJ8KJ4SI30yCXYPGaVAj4ibo1Bd0d6fFOVqfh75LYOubnnGX1B2PuuNjec3zcDGdGAYhkiyh6cqPjITVyw6NqkM0rmMmn26N81PB5F6wS+J5bfNcjapRaBki2ihjlqfBd5lq6cG15onlZrnQtRmJGJQ8lQ2NHJLvEMoa0rW/D5f9WlN3t3hs54/Dga8JQrn15+Ca3xdOCE9+RujtatNw4nui0CbWiUuGUFJwlRTkhgk9F3ffnbidXUK/fOIhzKFrIPDQZNDiSeoVh3rJxrY8Mh0xzMQ5so+fI5afH8DSubT87+UTlmfEwkTPtTXCwaupjpea7xXGYfhuOL0bNt8Gq66F8AhUpiF3CPDFZLo8sTKSHCCixF3bYN0tIgU+8STs+5IghYu45yPQuh7W3wLrXyOO9WKmxXcBCab3wc5Pie3wHWjkMB75BJdHkkLyoGZEAKH3IiGN0SLCum3Hf+Hm0hg3L2qWG3k4fj+NTA9+qgulNEXWqhFN9rPn6HfYZ+XYeMLixJorqHk1ThRPsKV1wc9yoZUt2aGnWxqdx3k8D7xoVdgbNmzg4YcffrEW9/LGYtVraULYPsBC8cu4sOKZOUoYS4uKasmDeK8oZKnMItXmsCcPEimdWFpcKGswdC1suAV6L8V76NssEkjjO78FmeagumJSnj8JIw+KZagR6NwmLEMu+WW44rcJp/eJSNmph5C8OkH+BE7vDeiREqgWKFlhQ5EZQi4OE5UNPCeP6mhosg/FCVavvZonx35AW2qQCdfj4mUVci8LnKm7mj0i2pJlBwX5X4wae7aw8tjzRSENMLOiatZMiaKnsCGixvOLnRIkWPUKcRPpuUQM+uVJQfLGdolIVOm0IPS1WXETW9DLnRlYEcf/GtjwOrj5r8U5NLlHeAmOPbKgw0JEN0AQSglRha9HQc6Kdez9D2GKPL4HnAIM3wstA4IEr7pmmQ9p24rWlXXHp1a2mRhr3oz3HZxleFojYcHq/jRmOoIR1Um0GMyMlrDqHul2k1RWaNQaVYfAC2hUnXOHQHZvb0Ygl8HUTMyubaJC+olPg6yw3q5xLAhZ5znkOtciByGZ3MkF8qjCLX8N614NniUeIHwHD34VTtyzMuWpRqB9szD1H7oWxnaKY+mUwSljRDI4yQ0omTaIRXGnTzDDZk7Pb0XTZmjpHCA97VMYr6AnDCKpAoapYdsesiT2dWG6Sm6iSr5e4uWA5ecHwNTxIqWc8PzrWp2icyjDyX0z5E7XSGQjDGxuw4g//fYVOnWOTeaZHhmmW3MJZ/Y037RKUPfEZH3msCCKVgXcCsS7oDEtMkC+I1LN294uzo1ISkhOTt4Lj/2juJYWIWvQeYEgiLMHIXdUPB7+GyFLUI0laYRo8iBD6xq49v1CejJ/HE7dL65/EJ8pjsDD/xfaN4o0dv8OQVqPVcTYpMeEJELViNbyNNK9RGt5CH3MGuSSHbhehRPxJKvTq5cikEuYPyVS+2daGp3HeTxPPG8CGQQBH//4x/n+979PKpViy5YtbN++ne3bt9Pf3/+T2MaXHxarXlM9rKADUigGo3QPpXIeaf4UugRGEEBJEpqZiUeJNBbc9gevhs1vEinS6hw89k9w1wcwcs10SqhGCQcvX0hlpgkDDyndL7zMimMitTq1F8kuw/hj4rHr7wnTA7DhVrjqv8HVv0/47d9Dnj+OMfodscxYl6hIlFVxA3QqmMkuzAtuE4OulYfBa9gycCWYmaa28VxPUT8b5k+BVxMDday1KTNIdcPh78Low4hIRQ36LofiKZh8tJniimZg0xtg+zuF1gqEMP/Yd+HwN2H8cSHQPxskWdxwFm2XVB18D6k6LY7BiXug9zK46N0iyrHxdSJCaRXh1APw8CdERHH2gNBsxdohNSgikpGU0OBFkyLycDoHbRvEja/v4pXaz1gzAuu7ISPjJU6MFJmbqSy9PjVWoiNoxUYicrRAqjWKJEtEoirVkkUqY6Jq8hKBjMb1FRGmnxqWa2/XvfLs3WAWEWuF/qvh+D1cnL6Riyf2gnOMY9U8NPJ0VudAUpDe9E/iJr9ooG9X4LF/hsf/tRnJ6touSMT8MRFZnnxSPADW3Ej42v8DIw/AwduR7QLG3E7c4iGU9s24msFUbS2FMInvmQRz01Tnn8AKe8gfrNLW75LtjtE+kKI4U6VRc5kdK6OoMoWZ+tl+2TmHxfPDc3ymT5aoFW0K0zW0iMrMSIVUW4z5yRrVoo3r+pjpCoSgqhJGXEQmowkdWTc5MnGa1Mgh8I/hzzQdKzwMCGRBIKf3Cu1y4IhxtzIlzn+Anovh1X8mrv0j34ZDdzQJHgjJ0cCVkOoXY7S2UMhjV8X4mj8pxgmrBJyFwJfHRQRaVqH/Srj8t8T48eg/iMyRXRZyFKcqskKHvg2qJiYm7RtE7U3rarDrmKkUpmIIqcTCPWd1/HXNtHVqiC3RjoUK84VzPzvUjECeq1r083hZ4HkTyM9//vP85V/+Jb/927/Nl770JXbv3s3Y2BhBEJDJZPjKV77Cq171qp/Etr580DIgHiBuGiM7cU/uxo31igvWlak6OobZhVIdBbuOF2lFGfs+klMU5OG1fwOb3yCWsfPv4J6PIi1U+4WxDuAkAPVb/g42rm+uuzAqdDggblobX09olYEQJh6HkYfh9KNIxVHY9feCTL7qw/Cu2wnv/wukPZ9H8hrQmCNMXYwfqLh1B8130YpjgpRsfp2wrimPwamQLT3bz31t43NFdkiQR0I48QMoz4s0kqTC2MOAJCJ72R5hJnzqh2Lw12Nww5/Atp8TEUnfg71fEDefmQMrI1BGWnjHpfoEYWxbL+QGsVZBImcPiegmiPMndwyO3gmnH21OAla/El7/SXE+ta4Vj77L4AtvEQRm7pC44fguZNeJyv30gEilt68TRLc6LaIc7RuWDJVdNYNbcZgo1vjhQ2NUpuoEaY3B/gSV+ebEZWSuysAGlbSk4JZcJsfK6IqMmTaozNYIfVBNhcO7JmnpitExkHp5pFUXfVoX4VrCT/DkfSLCG29jnQcsRvZu/ktB5GuiRzmeLY5B/pT4f+s6MZloW4j0hAHMHRGp1oknxaTg+PdFFPLq98FVv0e46+9h37+juSXCyV34SjeOX6JUyBLX5/DcEtP+FNUAFFlmuFDm4VMREsMma8wobWkT3VBQVIVENrL0UxpVl/nJ6rklI1iAFlFoVGFurEIYimpqI65RmbeRpJAjO6eIJlVsAgoNG2+8iOwEOBWProEUiajN/U+NMDE5i+RMk9RPI6s2c4XmLa7RfQ1020Ao/B2tmpgIWjlB9CNJERnc9nY4+A347h81N1A1RLR4zavExFGNCH3iInkEMWkfuBJ2/Ia49gojQkNJKAIA0ZRYz/jjcOJe0X525AHxUCNw+W+IjmL7viy+b80KohuEgrT6DQgkaF8tpFBWVUwYy1OQ7BbOHbFWtgBblIQglPZRSHattOvq2Cge53EePyaeN4G8//77+aM/+iPe//73s3//ft7+9rezY8cO3v3ud9Pf338+CnkmRnbCrn/CJU2YkbG8CAZFND8gSA9ALAUzTxKO7UZ2i+LG/vP/LggGwNG74N4/RyIkXHMjXP7r1HPAx14t3j9L28CnQZIEcWhZBRe8DewK4Yl74NR9SKcegEf/rxg0X/Uhgv4rkO94L3gN7hu+kFVts7R1aLi5g2ixFEw9Rd3VaTzxTaJyBbN/vfBH+1kxo27fIAjdkbuhPCtSi4luUdQiK4IAGClB6uozwq6lYwu8+Z8hMyiivo/8HRy6XRC5RbSsEqnsNa8UAvnFPrb5UxBvF1qqkQeh7YyBXZLE9rStFzeX6b3wxGfgxA9hzxfEcVtE5wXwmr+AO94ryKHrikhkKIvCqgvfIcijZjI6HmV2ooP2osVAOxBr48RYyNihIuk2i6nA5/RwAdcJiectamUfvdYkwUrBo8MC1ZTJ2QH1fAO34RAxNFraYsQzEcpzDVw7wGl4dAy8TFqNLRa3NUoi6jPxmJAsVGdF9LZjM8g65I+Kz2+4deX3D3xdHFMzC9veAQNXrYzySLKYLLRvEMRz69vgwf8tutbc8xHCJz4NN/wPuOw3CO//GNKxu0h441wUfJ6K9FtUnA4K9TSOL1OzAlwvAHL4hVZKszYzXR4t8Qi25dMo1ilYTTJs189BGcECXNunUXFQdZnSfIPQh0SLSTSuk5+uQWAxN+5QqjsoLRFGqw7udANXAzusM6DOMnHQJ0IUX8pQDNtpOJsYqRwH7gTgyEQH6YhNv3kQM5LEUg2U3Bya7wjvxp//kpCYSDJM7xcb1rFFZBTWvHIlWVyOypR4RFILWmNrYWK4LD0cnWpqq3suhst/XchlpveJyeHsYXj4/wi9+s99Hh76OEw8ISaCenzBAqgIWlwQypkDCxHtw2BfKCaIWrTpsrAoo/IbQm/9XDsbLTdeP4/zeBY8bwJZKpUYGBDRNcMwaDQa9Pb28o1vfIMdO3bQ19f3om/kuYizW5aFT9OvBbnjkO5FrdbwsquJzhyA2hR+dgv5MIEhTaPWpxkIjojPX/cBSA2ICMfEE0jf+HWkMCAcvI5w27vAbsDMsgKNkYcIS/cIsuJUwa6LlKZbg2gLYesGwu5Lmzq5xS1tXY+87jWEn74ZqTJNePRuGLiSsOdywraNSHOHySaqFCKDGNk4yd6LqLsWmtvAevBfCGslrGgKUzNWeFqKf8+eFnlZZEsKY6L1nBYVZs5aVEQNWtcIjVPrWrEvy6OCPKb7hf5t+iB89wMimrAYbYy3w+obhM2H2SLSYHZFRBJBfO7EPULPWF1WLRpJCoug9ICo1F68cekJcTOTNUH6H/zfwhJk9bI0bOtCNDrwxA63SkK7mR4gyB0RxTOqTm7vLF7dY6Y2SOu2zUzkKux7cpZKsUF1EgYuaKFrKAlzPgk3QLFlNKWptc20meTm6oRRBTmiUPYkdE1oL80Wg0xPnGreopq38DQ4NluhJaaRjUWeZqVzTmHRH3OpMr1DRIp9TxyXwWvF36d+KK6zE/fCljeJYxuG8Og/iuVsf6eIGk3tWbl8zxJWSwe+Jo6LFoM1ryTs2AIn70MqnYZv/Dph72Vw5e8QdF2M9OinSFqzvDb11xxI/Q7O0A2cPurSggRiakmSCLGUzvhEldMnZvBqEEtGqRXtpVVHTB1ZlVF1hXpZVGyrurxi814qe60zPR+1iIIlB1QjEr4UYERVPNsj1WGimTLTw0VK8zZ4UKs16OozqRkqHlAuVZnJREmnQlJ9MRq5EFvfhKpmidSatzjfiDHubiLfGKSnP0Isv5OsLa7F8HWfENdocVykja0SEhCuvh6yq5HKk0//EfPH4diwcLY40wtS1gSZbP7i5k0j2Q3dF4kgQc/F4u+T98Kez4uiqq//Klz7hzB4Hez62wUjclXomZ2i0E3LSrMnt3xIbLsSFZFyLSm65ajmQr/11sWdLp7PdARZDue8k8p5PHe8oCKaxUGmp6eHU6dEqiaVSjEwMMDOnTu54YaXYfXti4mRR2D6gBDJt68HQtRNG/BqIaUwjlavQX03mq8yGrZzaW0fUmgTDl4Dm98sbvy5Y0h3/AaSbxN0XQgXvafJwFxraVXm3n9BzjzDoF8aQ5reS3joayI61bVNRL9UQ5AfMwubXg97PifSaOtuQgpD2PBamDtMl/MQucZWzOkSlEs0aCHnmahWGTWsY8ZV6onNNJ7YhTdxGK9lI/GtV5LMnrtdSZ4VpckFLZQkihrqJci2iAhuPS9SjcXjIlUZaxPkcfRhuPdjTW/H7BpYe5OIVkkLN+jAF+L7zKAgEad3C1ul2rJe4lp0oYNMWZDKyT1iO4auFp2CQl8M+FveDKd3ifcf/j/CbHoxopldI74TeKKiVFKAUFTXz5zCkls4ckxifCqJpMCqtmkIQsZPVwjdgNyYhUTAdBjgVx00RBEqwLIibJIe1Bohnuch6R6q18BFwvMiWIHPzESF/lUZOgZSTDVsHC8gX3PJxpbfVM9BLG9VqZsiRShJsP5m0fO4Ng+P/K3oTALixr/t58R3Rh4WBU6RJFzwVqF5jSSEru3wt8V7Z4vwjO8WGsrXf5Lw8LfgwNeQxh8j/PbvwTV/CL96L+Ed70Uae4QL5v6CMWmaEec24QGKjE0ABASuTcyuU5iAWEQm53qcnmnqbcdOFmhJ1pFcKAcBVRX6uxN0xowVVjkvJcrz1lJXmSAZoeF6zKnQQYDeazIZlUnJOo2KR2CFLMZT3UpA0AhpAHYjoDADPhBphBQsj6jnYgU2tt00cq/UkxTsOFXbxJqNsHHimKDf624WEUgQJtyKJq5XEBmhSFwQ/TOROya0zYTCginwFrIO4UIHGvfp3wFBPOePi3Nn+zug51Ix0ezYLHSQc0fhhx+Coevgjf8M9/+FkD1YRfHwQzEWxDvF+JI/Lia3WkxEOdMDIiKZ6T+73/CiXVyjvJT2XkJp/AUeyZcRcsdW/t/MNl0xzuN54XkTyPXr12Oa4kS89dZbefe738073vEODMNg3759565B9EuJ6QOCBEwfgMv/i9Cz5Ud4bE+exgmHntCmt7tCaMusdY+RDCcItRjBq/5U7L8wQLr915HsMmH3RXgX/xbVkspDx0OM2b2kTt++tCqJAD/aQWhkKYe9FKxWikEfRTuL5pdI+UdJMUp6bJrExD5kw4Srf795k1x1gyCQk082Z6gbXgsP/jVtykk0VUdzNNzKKBVvHY4coES7iEpF6N5Eoy4RTB0hnw+JOcep9l20RCDnaxb5mrsQeTqb6e85gMXZuFOHekGQ80ZOPM8dE/q3ib3ixtDIQ21KkEc9JlJeY7vgnj8Ty1r1CrjsV0Ua28iICGNuWKS3qtMicuBUBRFdjFhIsiCi8Q4RsQh88T0t2qzQPvWgeL9za/M71/wBfP1XRCrrwFdFtTeIZSS7RMV34Iu+6hjQvo6cleb0qYCJUhbL15DCKCdKSfZ9Zh92yQIXTMDBJU4EF2j4PmUvxFAlgqBpbVIJfKK+TeBDhx5ZaIAUUgNmR0oUp+qc3pejb30GT5UoFWy6V6WgPf4TP6QvGjRzoUvJHAS2iE6VJ0WKWl4Y58Z2NiPOB78hnjfcutKL9fC3BGFYhBIRPckjCbE8p4p0/AfC//PK/wq9lxA++DdIlUn43geAAH7+3wl/+CGkx/+F/tnPYKlF9mm/SuCHBEqIJNnoGPhIKF6DmlIlFzhYVjPCuG/XBEePyfQPpEjEdZRkBN8NyK7WVloAvYTp7aVuRrM1ilNlTh0ukOg0SF/YxcnTRY7uzeP5Pp0NDzFtDmng0wBqgIIHyGKiE4Y06j5BOUvFCZHDAL/elE64dgTXihFGQlSvQmfwlHjjqv/29A1zFyJxz5S2Lp6GY3eJY9+xWWQGJFlUVJtZMUYsWQFJwmM32StI5vwJoX+szYkJYPtmuP4D4hq//n+Ia/qhj4tlTe+Hty9oqR/7J7FdxZMLfpUhaBoUpsRyXVs0EwgDsc5EK8xNQbhenGvpfpFhyR8XE+NU90KUfRmBrMye5cf+jMDMimv667+68nXNhPc+dp5EvgA8bwL50Y9+dOnva6+9lquvvpq1a9cCcNlll3H11Ve/eFv3MsLwTJmRfJ1kySM6vo5WZYSewQjze79LuZRn7OQBCmMJEoHPlLuaoA7x4DSdlkhdc/lvLFXt1vMlDp3eQd57G/ngWop7XcJgkZhfTbm+GXgLAP9W+Bc0N0sykJDPmhq8fOkviYCkMk3L3VV6N0XoUBpk2y5AUSJI5QnCmf1iJt6+mbB1PVLuKIVyC47Uh+6BSgk0g/n0BtJ6FSNMEE0aNLo20BIcxmtZQzxjMF6oMVmysB2PlrixEHk6RwnkouatNClIwv7/ELqkSBoiUTGjzw0L0lccFSRSUuCt/yYI3Td+XSxn8xsXrDlkQSADTwz40/vOvt5YGwxeI/wjjWTzdVmBaFYI9gGO3yMiFce+t1IfmegUqesj3xZdM54BbmDgtl2M1fk6Th2oYNVnyJcj2GESCGAa5Egd35KxCJnBYRKPQr5CrBxwoaWSCCVcAk4vy27tsh02eR7bVBVZLAkJUHGoV3XsukexbJNIGYRBSLIlgj1n0ai4NKoOqq4stT88p1Gfh8n9YsIQa4dTj4h2hYomXiuNw1NfFgVMixGe0Ydhx281l7FYwbvhtSILML67WegWiM4z4cwBpJmDhN/5fTEW3PJXhLv/H9LJ++DuDxDW83Djnwl5yXffT9raz/h0Fc3T8QmpKiGHVJuKFFCVVUwvyWrFB5oR7nrRI6Y5nB4rk0KmEJfJ9CeIJ3WMqk9UV5g6WUCSJFJtJtnuF4/sH58pc1Ey+bTX4xmDI7unyE1UqJVcYrqEN2nhdNUIjpexT1WJmToTIUSwcAiJAC42Yehge6DYIYETggOFaWvZ0iUCuxnx9qoJCoFPqEHKnSMIJOTOLU+zcQKahD/ecfYftP8/RBQwMwgbb2tmGiRJeH8qZ5BwtyaimSCu/a5topBn5rCIHo7tEgU4sixs1gavgW//N+GccNcfw9s+L8aY239DTGBqM2I8kJWFbEMA2T6xvbEseD4UJkQWxW6IczNEaCPDUGShjPTTmwcsFn/+LCLdJ4hifb75Wu6YIJT1+RdMIKeqUxTsworXTpZO/jhb+rLBj+0D+fnPf573ve99lMtlrrzySjRNezG262WHkXyduu2RP1VkdXqAnLGaHuMA5bk8nlUhnB+jVY4gua3kw17mnQ5yTg997l1iAYOCeM+M1rj7s5PUagvFEbMeC3FG2tQxJCmgpDR1KxFUkoEYvBxC8kpIXg4oyCEakA5k0r5EOpDQkSn53ZRywvWFB06hahLt5t8wFNzF2ie/Q/TmC8QgePlvwnd+j85gL/fV3obZiGDIFUzFRXaKNMw0RiKCmYnDqu3UL7iahJMn6o5yfFbCUjKUpuvIgUVnbwzaEy/dwXg+0E1RUZ47LoTsJ34g9EZ2acHfzRNemLUF3SMIQ/ZV18HJ+0U0QY2Km/7ydPXufxHkUdaEmD7eIW44besXCnEykGgXfax/FIauEdFhqyTS1quub763GCWJtjRf82xRlQkgK7iBSVgvMrfzO4RhH7WwAxRJVHOGMhgWfiDzJBKPRENqls8WR+GKikQqFNdyQIiGRGfYvLZv9DL4BTgUdbkwLl5fnL5IeMho+D6MHpqjpTdBpjtGe39iye+vWnNJt5lLZtHnLBolcbP16uIYFIYBD6QISAtk5QcfFtHoa98vjlVxDI58q7mMxQhlZvAMXRwLBtVboX0j4enHkPIn4MH/Rbj5TbDjvYSJbqSnvoj04F8TNgo0rvmfPPjtHCdzF6MtDN8KEilfIuWD8HgBrx4yrPvU3abuQPYdZM8jKHmULQVvxmO4bpOIR7h0VQuBLOMUHRRZojRXf1EJ5GihwepcYyldrenKkq2TZXs4XkA1X8e1AohJcERl/mQRuebjKw08N0AOIR6A6kk4kw5+I4BQpK4X4RAyoQacVgMcKSS0min805LDWkJ0V+L4xCBT8j+wLnKCC+sehrnsVpg/iVQ6Lfw9uy9cGT0GkRI+ea/4e/Urm/KR5wMtKvSP2XWw/yuw79+FRdeizrh1LbzpH+Gzb4Cpp+CRT8JrPga/+B343BuF1+TYQyCb4jvJLnjtXwsT8tN7IJkQDSGQRdaja2vTWq40Adm1zSYXy9G27vn/lueIiWKDQm1lq8/T+ZfYZird96JGGqeqU9x2x23NbkDLEFWjZCKZF21d5yJeFCPxiy666MVYzMsagy0mo/k6nUNpdCugtSfO109pWPMeqUQrUu8tZGZPEpNKOHWNlk2D6BOPEZksE2omVutWDt0/zWPfnSPwQ9LqBFuid/Gnwes4Jif5UurP2eztx93xfva1vI4Pfkasd8fbeuiN6wRRqMgKo2Me+Rmf2qxLKEmEMQXfkCGl48surznw3ym6A9xt30LMS4ILk6UuJnkPO+/z6J04wcZrPPrXvQ7N/DPMeo4NnSfZU7uAEI2EXCOtuxgtGazWNAU3ysHjOXrrR+jLPwbtA3TH1jKmtmIoKhlDRa6Lm9jZCo9eMsXD2aueBDm0yqI70Kn7BFnQ4yLdZJeFfY8sgxEXKSk9BjveKyKXj/ytWMbAlSISVUKQhd3/IlJUkiyIZrJbvD5zSMgFFnVwqrHQzsxcMP9e2BmascDwF5DqFeTl5P2w6U0iHQVNQ2M93jSvnj8BhOLmZ2TR1CyHypuYdNbS0qnRFvMpzsfxyxGieDhahG+rOo/nGqyvKFznGKQ8sR2KqbBhRye9a9JIssTunfvhcwu7U4FMIJOpwdyYTUs2gplRUSSZAA1FkxgrO8iA1/Ax4hpTx4tkuj1hel33UDWZlq44i10AzzmEobB1OvgdyB0Q0WLFgJbV0JgDpQ8mH4d6TlhiXf0+UTzz8CeE4fyqV4A0tdCFBGEcbxUXotmnV66rc5vwgxxPIU0+iXTw64RzR/Eu/R3kWCfKIx8nt/MevnHX5bieyCqsjjyCctVqaq2X08jb1HMWQdXDmrNQSy4bHZVSo5nCzfYl6etuJRpTmRjOE6Bg1jy6tIDabAP8EENTUA2VVJu5VOjyYsiSBjLRpXR1tWBhxLSlivDOoSTVqoORVJGUAMeGkZEKXqCgmgFDqzM08jaV0QaBK7bJW1DmhqrEqOwzIvucVn1mlJD1HXEiikxClmGu6cOY6NCZMzXGSxaX2za1IMuesSy7//hRjHVx3vKWVSS1OuqB28UXei8D3yes5IBlPpAHvo4U+oRmqyiuWV5gY5XEBM6piutcUhai1XGRHl6s8gahj88MiTGlNieimoNXCX9JEJPPa/8Avv8nsPufRZBh8Bp422fhK+8S0bPAA6NdpLN3f3bB/3dYEMmWNYKItq0X61jsjHY24vgTxkSxwRv+6n4kJ1jxejaQeS06yeiL1tPkJUXBLtDwGnzsmo+xKrVqxXuZSIaueNdPacteGrw8j9o5gDPH1HWdSdZ2LJuxH76L4PEHcCOrOarK3NRaphHCfDWBF8xQnyvSkrsHgNutC/nq/3yCK20RyRnWfP44/WE6lTlCrudPd3SycedBZr0h/veeSxifPrS0mqe+cQ9FM04taKHqZ/EREY5nigPfq7yPbmU/a8zD/J13IY4UskZSeIM1Rs5dzdiww9jwMGZSY8f6P2RD7Q9pnf46VeuP8RQTpx6lRzsKyXXk2y9nvu5A6JA7cQACi9baIbqu20FXS5b5SITSXJ1U28pKv8UUd3fKoK/lp2z/I0kibXP0LpFKViOw8fVQmhICebchiiLyC8LrHb8l0o/5U0KjJEmw6TZxgwCRzlwij68Q5BHg2N1Nr8BFLHYtsctigO+5WEQz8idX6pLUyEIUzBKdZy79LwtWM0XxfqKjqdWa2iueZY26XQC7ykT5etzQoVhPMpiYIVEfxnfWMx4qfKkO1bDBrQ2dTY6IfgS6zEyrQs32qd03zsF7JghVCTnenGVf/0sDHDqskH8qj+lK1KcdrHkHo11FkT1CJaQRBAR1idCoc/ToPHIQYochaza3LTTKCYT/37lKICVJ2O1EF8h9bU4cIz0lqlynD4hWkbN7hUZt68+LSPTjnxZm0VZxIT26MHmJJEQUcs/nn74uRRPRrs4LRNeosZ1IswdR9n+O8NUf4992Z6hNbyREAdli0+Betpe/yOF9b6LSt4nA8gkbLp4XQFRwBXr3AABzzElEQVQh1hGlPG8h1ZsD1cQUVKN1tqomfiAcI6KEVE+VCJIhVcvDa/hs3dGJ5wQ0Ki7RxIuTUVrTnmRizmE8V6W3J05cN6jWXOKmxvptnfRtbOXxu09ybNc0dURhjKW4mK5Gdc6hMW4RuCF6VKV7TYrWgQT/emKa700WCCXoy0R5y9Z+jk5WGD40z3V1jWQoU643b3HpaUiaHp2SSioyQUu4n1PuJtQgiXe0yhc++hR0atykzbE61FBSfeKYGWmhNQQR4R/bKf5eZri/hMm9z7AHlrksKBFxvZoZ0dZ08Bqhp5zZL+yCjGWWV5tuE5mM/f8Bd74ffvNhcb2//Yvw5XeKSGR1BqIyjD4osh9eDeplkTLvfZtwcjh+Hzzyj4KgXv3elZu2wmbqJzOjn5qs8o68hn4WmZWiyfR3naMZqueIValVbMpu+mlvxkuO8wTyRcLpvCBFmahGUlPRTuwlSQIjqCApCZz5KU7NX4CHgkGBROhw0IkwAPTZySXy+JDhsjPi8ZthHMfu4Vf7+zh2r8P/K38ONzTpn4d0fZk5sLueSbcZJtelGl36Ybq0wxwIB3nYvYJ0IJH2JRKhhOfrjPkXgwPvAHwpxFDGuTT+H6SUST5Z+wNMuxfKLvfsXsuw/v9xafY7+DhIcpSIPIdTGUXZM4YWjdGuJYjmT1PQMnhewESsh/YFz7Fsd3wpDbY8ADhZsrAcn8mS9dMhkA/+naiAHrhaDKbtG2Db2wSJ9F0hUD9x70J/aQXiWahNiO8uRpMW2wZKsogULMIui+dIskkeQXSM+VHwXZ5mBbKIer4ZYQwD8bjvL0RES9GFVx0If8i7/7v420gyUtvCvLMexa/h63EScZidVAltjf1ByDcVn7gj8Z5GhJQnERDypO6hILF5UqFrwSYGQPJCSpNNpvfwF05z/ZsuJH9xO//65YNcUlcwXImgESLFfBwkXCw8NYqma9RkMF3wVAkjpmEh9G8vC3RfCrlToKjCz7N7uzgmyR7htJA7AoEFX/0l4eF3yS+LVnbH7hITgXi7iFLt/aKQRTwbWteJSUR1BuwKJ2cc5sY3E0ViKLKLq1Y9SOGqv+er/3IBThiD3PyKr8tAbdpCJsRZNqftCDSMSY87KgWyUshqCWQUTgw7yF1FVBd0U2VuokZ5sk697DC4JcvA5rMQpReAsiJhNTwOPzqNsi5Lz9oWJLlJKDZdP0DB9eBIkXLdQ1cixF3ACvHdgHSnyRvedyGRqMq/PniKu6cKmLrCh1+7iddv7eIfHjhJfV+Bt9j6klWUvyy7fFBuMKSk6PRlStYAJRb1fgF1ScIMJZj2+AHv4H7pDax96EEutf4Mc/0OSC1oIcNAXHOeJfSJWvSZdZLPiDOu8/TCdiyOHWfi2j8Qtk+1OWEhluoV59XbvwCfukxsU1CHaiAyJhqAIzqGFcfFJOf0biHLGXkYsquEiX3PRbDx5ue57S8Mbt1DR2LDGwbZumnl+WTENRItL5Ox4DxW4DyBfJGwSIpOlW3K83XmT7TR4jSIxQZoS0KtmKZBFIjgonNPLsb3rZ8nLfk8Vf5lADbf0MPP3zTAkQcm+eF3/lpEGwqgAC4qmtTANMrU403SNd/poJsniERDlLiOo8uMqj08bvczqgwiSRIx+xiX2vdzUfV+fDvOfnsHd9qvp8vXiQYSrtfHd4t/jJae5p7WdiYqFm9z5hhsdHLauZDJuQuR5DpyWKPsJhmbXE8sWcfbO4wfGoQqGLF25jsuYVWf/qymtYYqczpfZ+1PqyJ39CExmI4+1JyNmxkxYw8skZLUogspZg20hEj/5E/Czk+JCGXbemH6O75bEIXt7xDLueCtIv1sFUXKunOB3G24BZ760tO3RYuKNJaZfbopfLjQwaK0kO7MDMDF7xEp0uPfF+mxV35IENXd/w++9z8A8CIpyu2XM3VsLUEgo+lVLkp+hpPOuynVFG53MtwThFzQkLnRiqCEUCFkVg24yNFYrNtV0jo9F7Uyl1L4j4dHkcaaxuiuFbDr9pNc8cbVXPbKfnZ9b5RrLQ274hOPyZhAFJnQAzUiY3ZFSRsabQs2Pu39yXO/gGYR617VlDAoUdFdpHRaVLym+kRUcmafqNj/yrvhbZ8TUaMTP4R9XxG2SyMPis4kU089+/rKk0jVGUIkgsFX8K27q0RDCddwuCn1v8hXL+TOz44ShjHa1WGSkRPcpb0CXzeJRlVUSSacqNPjyujLumY6WkgSicsrKsOaz78bDpfIsBqL2pTEnARqQyWdjlCftYgoCg3Ho60v9aJUZnenDCbmLUxk8lM1eteLAo7xqTJjZYvZU0VypyrICgy1RCjmbDGFWSCZ0bhGJKoylq/zN/cOA/BHr17Pbdu6ufvJSSa+NcaVnpjMrbusg0tvHWT41DH+5DNi/b/2nnVI6T72HcszPpxnaPYIupOiFrRihuBIAfmoTKzukwhNDjVu4vhDNS5+6qtsvlBG33qbyBTc9DHChz4u9KqTTwoC2Lr+R2shZVWMJ0jNFPYiFj1gn4mILk4aQURDF6EazdclHfDF5NcpArIgnLlhMQZ1bBbj0eBVgjzaRfH8EhHIRZitUdr6X97RxvNo4jyBfBacaXibn6pRmqsTmDJBMrJkUdOdMpgpWwyc+hbTh/cwU1/Lo/p2rhhI0a7kcJQhEqGIFO61JL41bzPgRnnK+i0CZNZEH2bzprfwwFeGmXhiDlBIKtMUlDpHU2t47doGN534BWQp4Ha7WelenQmRo6uoSCE1GepSSF0OSWk1Xm3ezav0r9LmN43HvXjA5Mabuf+EwlrrCf5O/gxHrRvYU3sjbrGTXw50Lr65lbW7fg+7pvLF4v/A97oAE12JEe9QCfV+qgSEbkBjPk8krtDR20F3eystrQlKdojr2Ji6QlRdNqgupEeiusqGziS6esZA+lJh4OpmBHLx+ObHBPGdPAFSICIMmX5h51PPQcd2oW+qz4tU0i98DS79VUEgj39PeLYtWrdkVwvftqm9giD+qC49sVZx47GKzdcCHypzUJ9rFsqYWei+GJ74tEiXIomijd6LRQXnDz4EgJfowk10IZlZsskCBWeACBaj6mtwe7bzzxWH/Q2HW+oam11x+U8oPslQZrUnjlW2N04iG0HTFPJHS6gNn1/JZvh+fzPS9YRc43oy7PzGCbZf38Od7Rr+WIjiSiQyJukOg2rBwkEDO6C2bx5XVSEbxVQVogv9i4FnDLyeM9BNEW20CkJX5i5Utc6fEJFlIy28/CYfF1Gk//hFeMdXhAVLbVbY+Gx7u5AcnH5USBzOQNjIC9uoSBzp9C7xYmaQWWkDymlR4z54eZLSwS6+OfHbhEHAnOLw1vZP0R6Mcl16Jw91/S3fH68zlqujtClYEY3CiWZVsu5KuAaoPqx1Fbo8gztNh2NplXd2ZTCjCo8cyDF9pIisgxyLUNF1fvjUJOvXtrCu8+lV1M8VIdCbiRFs72B2tIITV3loPE+3rhGEIHkBpakGuiRBSfhappFpGJBpN6hM1HBtH8/1+eDtB2i4AZf0prltQwcPPXiaw984yUCgEMiw/fpeetZlyE/XyU00Jz1f/vQJ+qJlAk2mzVAxsltJhidYU/oGY9bFFPx+OuuQiyjkuyJkxwvEvRg7y7/I0YdHufzg39Jx4WaiG14Dr/wz+OH/h5QfhuIoYXVWnCOSgrToB7scktLcE6EvdkgQQH5EkDwQxXi1uZVNBaDZBEJWEf60C4UnSzpaCWIZkKLQOgToMPqAkNU4ZREx7b8C1r4K2tbA2G5BHjs2NT0if1Y6iZ3HS4rzBPIscG1/yVj3zE4Ns5NVZifKzE7VSffFaaxKkt1g0JuJ0R0D6+E9FPwaa6QnGQ87mJosccCy6LVUDLdALtD491Bis61ws6UThNCTHOWV0U9y578nmZgTKS7X3Mu7kh9muv0GCtv/joYq8w8Tv86vW/9AzzKz14Ewgo6EHkokV5QjpsjXruHb8mauT32CY3qMmY5b2LDtdVyTSHJh+jADe/+JhDfHqDTOFyWHW+salB0e+vccRmo9g90z3NvWgTficG1DgwbMn/bQWwJUQ0aSNfJ1hVBK44xaVE8dYireoKZmqMgJLGDz+hbWrc6iavKSvKYlpi35Q75kWK7tufZ3gN8Rfy9NEALRIaQ+K0iAawmdoVVY6M4gC+2bZ4uow6HbRcHEPR8RBTQz+4UHJIgIZeCJ6s2pp4RmyXcFCZTkZt/dVI9YT3bNghGxL4TxpXFhPg1i8N/6NtjwOqGD2vsF8fqrPwIXvku0Q7v9NyHw8BQd17MIiiMElkvWS5CVDnOqsZlpe5C/39dgpO7zjmqETl8mIOSw5rPRFVY8NQ12tYToSZeW0SobSqAs7J5K3mK12xwuHpKrpHSPixyVw/dN8N7X93Pn9Em2OSoTx8uEKvh+iEwDu2RgKD6W7zM5b2PXvBctLfoTxeI5o8egd1mhYHVOFNdM7RdRoPKMMAmXFPGwy8J65fV/C9/+PeEDeupBuOmjcN0fCt/O4/csdCQSO1guja8wcQ61KFz8i9z9UAtqKJHTQ37+mtV888EPYQUp5hSXPX0G1cv/kewjb8co7iM38Vl6KrfSjwyFkMNanaNqU7daxSfpi6p61VSJ133eVotwn+/yv6wZfru/jRbbx7N9/JpCVYG2SJy663J0pvJjEcgnRua5fmuS/k1t9G9q49v7JiiWGsz6dQbTJq4EqW6Duck61JsDmQWiAw3g2T7fOTTDzlN5DFXmI7duYuxwgYNfO0UMiZohcf3NQ7R2icyG4wd87MGmxVVfoJMMZXAAx6NS9qjQyQRvRJfqbI7exVHrFbTaBi0jFkfaYqzuiKEeKpD3Bvhu7oNsefi7bBv9CMnX/wXhTR+DqSdh598h2WXIHxfFa60bRY/5zJC47t26sAAzW8BeKK5xquK8ODrZdE3ovEBkE8xlrgrQjFqbLSstv9wFIilJ4Hmg2mIyE20FrhYTlY4twod47qDIoHRdCNvfKiKPxTFBViOJ8wTyPF4QzhPIs8C1fcIgxLX9pxFILaXhHPep2R5zwwVsL2BtT5poQge3Ti6xlnB2L7lgIz2yCoGDHrh4bgyIst93ubQKOxY0j2subuf6qyWmP7uO0wVBHod7VN558QA8Bp2z92Af+HPeM/0GpuvXcNzvprPaCrwTgA3GXWzpqmE6Yzh+hEaQph6kKfvtHLevpB608I3Ch/n3uM30WIg2foS/v6DA9Sf+GNUr48RXURn6ExInKxzLP8ZbGy4j1hXssd7G4M9fz79G2/mtL+/h08N5/ottgh3gOyGqIcbh0LegMsFcXaMUxombLjV3hvmoQzJlMJk2GOpNo2rN/dgaN2iNnwOaF6fWNBEvTYhZfC0vUpEh0DIoKrQVc2FQTwlCCSJNfeG7xOO+j4nIoGeJFLYkweY3wCOfEjeK+erT122Vmqnpqb2CWEqyIJ4gIlzrXiO8A906fO+DTTuRq35PrPfoXfCd94FbJ5RVlHgfJUuiHhiYEY0qLYzWL2LGaeMEGU4oPtc7Op2+jKvAtyMOr7MjImXdEeH/WUUcG7rHHC6v6ihIzMkBo5rPxa62JP8EuKYnzQ8dl0FDp6UcYIw3mOxU2TYGUl1o1gBchCSrsRB0kRxQCiIqVq84L5809nLEWsWjPCN0cGYC7Bj4krBVCarimH77d4Vp/z1/Krz+PvcG6NshUoo7fkuYv+eOQmmCcOagSCvWC0iEovONHsNzBIlNujDz1U9RC14BwLdNi/duHCBItpDb9Ie07v0TKrVrUJBwNQnVDdnoqmTqSRZKPphfLyPloasm4dY9utammRku8QpLw5Yc7i9U2Ya4KUiAX4d0MoKf1GlP/HgdhI7MVFhmQEV7IkLD9ZE1SGei1ByPTVcMcDhUGN45gVMSpgaeBumIAhIUZxuEo0In2GLqdCYi7J2cRQaKcsArf+UCUstsIEdKDcxi8/9W3KdrdYJTNZuJipAcxUKJHk8mG5gctm7gxuTfMGxdy0n7CjbNhRyulXntL2xkevcs1cMlDtRvJjitct3n3oC09kYhXbnt7wlPPQDDdyEVx4Q117PZc50JWV3oIrUMgQe7/1VUYIPIQCzHkp+hJIip0SX6Z3duF5HHVK+IkKsRmDkGXmXBwDwnTMUX587negbgOeB0vk5iollxn4np9KRfxh3RXiY4TyDPAi2iLEUgz0Qka9DoilLMV4lKMo3QpzBTQ4soOOgczF7PsHcV8liDZMlFbmhI9RoqOmEg0VGW6XHFci96TT+X3DyIa6/mh5XfBaBqTvPB97wOWZKoKR8htvP/Y2Dy8/yuN8HfH9nEfBkmnOas+qF9n+HgcUHO1rbFuWr79Wgd2wmTV7BZSnH8iVmqeZt32yYPZmCw/k1eceQzqJKP3XIhs1f+AzdFWrnB/meGqn+Jo8X5tHU5ObuXipshkVDoz5g8xjzyggWDGZVQCbGwwAuBGqFrUsIhYuhIKZ0oGlpKp7vDRIsojBfr507l9SKcukg1H39QFMUoKhCKu2fgCrG5GhEFEPGsIJyRpCCR1sJgte5mkXba/f+ExvGpL0GiWxRaXPJLIv0kyUJLWS+IZcqaMCQvnRaVvFZZtJYMAxFB6Noq0uOSLHrbPvlZ8b5mCi+4ja+DB/4X7Pw7AEJZh3gftK6hXE4jF0aoBWkqcjeTXj8hJvsliQ4PLrbEufc902VbQ0X1IdUW5butAc4E3NbVwtphC4mQnB7y3bjH8JPf5tH8PBfo7Uu7bvL+LzBfDfmMmuAN3VcTObINdVBjUrHp9mXsomCbGhKmAQUXXN9DkVVaOowVk7RzGouTjLOl+VZdI9KA2TWCzM8utJvzVXHsKtPCgmXHbwmZwdhO4eV5epfQRg5dJ3w+B68RN/5kF/gOoVMXnWqAN9xQ4x/+I6TFjvDI0W2klClKfhetfoQnZspc09uCXj3JqH0xTpAiVCTWbmrh7gfuYH6mhOc0W/l94fOfYlVXK61eK9tbNiFJm1m1rZVTT+W4saFx17xNEUgj4RCihxCO1xnaEqMr+uNpIDd0NHVvDdejO23SvXCDPzVXpdRwMTWZ9u44w4NJ5kbLSDZUHI9cCrSYjFIN6Jty6UhEmCxb/MuuUd55cReH75sgGUjsPDrHrQMisv2lr36Wh/Y8iZJr6qzvf/xf2XNEyIj62tbwxte/g/tyJb4xW+SGksIqT+eu8u9zW/tf0Fk/wiOVX2RjXeaRLx5k6A1rueGybu75t8McatyIIZe44vgXCE/vEpO5tTfC2lcT7v2C6CA0e0TorEFct4ouMhB6HCIL7TJ9T/gxRuJC/xhJIIwtXXG+PPHpZvRx/c1wy/9euVMXCaQkiwmurIn0eGlMrFNWFiY5xyHdLZwBOjc3CWO8FRzzWTXrPy3IuSMwuTBrPbP94AIW7X8+fdcw8987uvR6qMvc/ofXvexJ5JmG5OeaNdB5AnkWLO8Je6YGsuEGKBkdfWMaz/JoT8aIGCqu7TPTkBh3DKYCnwsMDwohnasyJIsxfLdBbtijx1XwCFl/Ux9bru3Fqnnsuv0kVa+VhDLDlZ23U51/NQDfD27lKW+SP1P+kdKReR6642M8dMa2fvxRb9n/8vxXdxNXrO4Bb5hW4zSXRo6xN/JKCnYHr5qo8OaW76JJPrf7V3Is/j+5cV6j7+Qf0TrzVZDgzmA7LX0J5k/XOLFnlvU7OslXHVZ5ClIImiHR0h0BfLRUC6OFgJAYVUI8uYWhHZ3kcjZ61aE3E6MzLQanFZXXGXEjfqaJ70vmDambUJwSthduDfquhMoMzB4SfWKn9oluM32XQnVehGQqU1AaFXo23xXp6O2/IEjj4TvEdyqTIjUFIgqQ7BGWH5IkbhLaQkQzu2bh7zYRqfRskcauz8G9HxXaqEU9VcdmuPy3BDH9yi/BiPCJDFs3ghoHsxXUKB1mg3ljA1Z8LbnpLuQwjhyGHCsFvK0eQQohsSqBOjXPKk9BViRaVyfYfWyMlC+x/rhN6IUoqsyAqbLuybt57O5PUQCWx1QeefBOAKrAx/kK77vtkyipzQxrPt2+TGCJ4hkPCUVXCGKQkwIiUYVXXN2zYpLmLYsanXNY7FTk1J9OIBcjkZk+6L0EHvwEHP8B6DpobYAs/EUf+jhc9G6hWT3ybdj/VZHmPvIt8TDSEO8gbN8kqryNFOROIM0dITt/kj9OT/Pl3P+h7HeSabcpTcFaV+bB0wXe3ddOx9RODjdeD4Ciy3zt21/i03f+5dN+SvXIfexbaHx1D/C+2z6JJG2ma02KqeMlbqqoHEwF9CYjOEjopk7V8qlWbOxkhCBYecXK8nO/UC8ebHY8qTs+uiqjyBLZmM5kySKJxPhICW3WQtEkTEUiNCAe1Wg3deyBGPahCuMH8vzXqzv54IFR/nnnCDevb0duj8CszYknZqkmE3ztu1/kzz/x35+2DffvvnvF/z3H51WXvYntsSyPqDXG5116PZX/mP3vDLZ+jVuUj/H90n+jwzOpfv0QX7qwjVfcPMCR747xZO0t+JrM1XwOdn2K8OA3oGsbIbLQw/ZfLTISakQQvOo0UsugqIyWZaF3rM+LinsQk8fp/aK//V0faFZk6zG45v2iM5WiseJiWdRKajHhFqHHhD5XjYoJZ7QVogmYPgxtq6Bnu+iYpJswsUd4RbavXynPOIdgfP0XIbMsiLPYWnQZ+rsSKJrMa+srJzgOIVOT1ZctgcxEMkTVKB948AMrXo+qUe647Y5zhkSeJ5DPgjNNdFtiGq0JHV2V6c1EyegaEjBTt/jCIyfYe7xEa0zFylWgppOfrmLVXWrjDr4jUZNCftji80vX9iErElPHixx7TAwENyT/ji77BPNM83glyQcfOIYfXkuLN8TB/Bg8jT4+HaWix3wlDcB8o4PWxD7elPp9bs//KfPeEHfkP0y6fye/V3wlbcdO8Ob8R2itPkyIxP/y38Gn3Fv4h80p5k/XGD9SYPM1PVQcj7ULUVNFh+JshQY60UqFekXGQWNcgYhh46UMWm3QHZ/qdJ1Kd4NoXKc7ZSxFIBex6AdJGIIk0Z0SWtKXFOlekdZJ9YgZ+cAVIi3ZyIHfAKsqUo5aFAonBMEEQRKdmqjGllXxvOM3xaC+/6swfLcosDlD28bROwXZM1tYijZIiriB1HIifb5i+wbgkveIVNmTn4dPv0b05VYi4mCoMRHNlFXIHyMaBnRqLTzAK5gvqyhemYOewVY7QlsgEzFVJjtVrj8pJBTbX9XPzokC+CFvrxmEQUDEVLHrHrWiw+jYkee0G+fLU2QqG3lc87nO0qjOWziaiy1BqQbVboV4NsbWDS1sXt2CpiuU5xtMn6ojG96zr+CnBd1sRiAXceYMpzAm9GVmesGbc6Fa22wVNz23LqLIw98XZuPvfUwUce39Eow8BFYRySoi5UQEJVSjSAudLSTEGNQWe4Ry+U3MFk3AZ7WrcLdjMe4V2VyZZNQW6c2g4XP81CGeC/bPH2FV52ZmRipEu6I0phpsKMmUcZA0GduWicghT+yc4Hh7gW11hy0bWjHU5y87kKTmbjN1hbrjY+oK0sJ1P1myKBVsPNvDrIf09yapzDYozlvkT1TwXAfNlFBqIeWdc9wylOLOXImP3nOM372ujyf+4zi9xZAJ32Nk/OiP3pgFDI8cZsdaQcguQaURlanYHnFH5kTuLXw5cZIPdf0rT8y8hbLfSc+TBXb1H2XgkgspPT7PU8U34W7ayiuqHxJRx8okEhBKstAz9l0O86MiAlieENftip0iC02jkRbvLW+xF2sT8pXLfk10TvHsZqtMEGNmYUT8HXhQLYpJrhaBag5aVwvttaqJ6N3Eo7DmRuEGAYI8OhXxfI4SSPuGj8ClNzRfMLNP6yKTaDF454d3YFWb+pp9h+Y4cvsIbv0cHleeBV3xLu647Y4VLRJPlk7ygQc/QMEunCeQPw6+853v8IlPfIJarcarX/1qfvd3f5dM5sVpGXT48GHi8R9tLxMHFNdn8pTNJBKFksVsyWbvvkksy2E+UJkJJLyyTUkv4tR8wqKBpEr8G3MoVYUjRw4hKzJP3TVNuV5nn1xnY3UGt14l+NyrONn2X6nNpgE4SZrGYveSZ0HDqVGuN0+6hxoXM5bWyXSPMn48QdlTgFfym/bt3OzfgXy6wUlUTq/7Yz57OIvVGGPcilKuF7GOlzl0WCY/foJUWaYc6lRCG9lyaOChRVJ4hTrTMjRwsXSF+56ssSURY2a8TCwZwT9ZZP/JgNAKybZFKbfFOLgQnNs3UcR2fcYLDXozUU5qChf0pJ9zBLJaPYu2cAHP5ThSy4sIn7IJLBsOL5jyzgZQ1MHNg+ZB9ZSIFEzmoOwSDXSkwgThRy/BueL38PuvWbnc6GVw4dVI6+dQJp9ALo4iFUeQi6eQ6vNQmGGFsfAyhFqMoH0Lfv8V+F0XE8a7UMYfQf/L65DKgogGqNiKTCjFCJ0q9VKBQKoiBTYqIZXQYzx2D7P5XtxQ51FfZ5vbSjnUWH9RC9/bfZAL61EacQkpk2B413HWzNgQJKhpEGnRmDwtTM+rZ970ngENp4Y/N0lacin7aQDKQR6LAB/wCyprt7Ui5RsMH6yh6jKnj87j2wGW//Q2YIt4Tsfxp43ZQ1AriM40La+B049AJAWFeQhUFN9ADxpIhXH44vsIM3+LfdlvE2z5Y9jgIM8dRBl7GGXuEFJuGIkqoWoSdG3H7bgQf+g6bn+0SnY6R7yuoOgSvhNyTcPii3c+wGpFp1QXEasJt/ycj9nh2lHWNGboC3VSUZ0Rr0GLozBtWSR1QazC+SiNmsXkcdh38iiX7Ojm4kFR5HHm5PrHuR7jwIybY65Ux9QV2rIxxoYnmM5ViVcjuAFEwgDN0ZB9jf4DsEopce/cKHOjx7nWjqL5Ev/+jV3MFwrPuJ4V29soMzG3MkWYyJo0PAWvGjBYS/HW6E18Yu0J7CNjzDlrSRy1WVd9L7vU26iVV7Hr8SyzF/8jF2wcRp16AnXyCaTKOOSfhENPPm2doRKFMEAKbEQTxvmFh7j2/d4r8Na9hqBju4hUTlZh8rDQMi66PNgVIg//Fcrog+K/EQPfqwsDUMkXE9LGPEQX0ubHTgr2XrgPet4illFUYb4I2QwcPNjcJz/iGL7UCNMDZ+9TfgYSLcYKH0lz9tz5DT8OuuJd5wxRfEaELzP827/9W9jb2xt+8pOfDD/0oQ+FbW1tYU9PT7hr164fa7mlUilEZFXPP15mj1KpdP44/gw8zh/Hn43H+eP48n8sP4bLj+OZrz9XPLJ7Mvy7X/9h+MjuyR/5uQMHDixtw6H7vvoTXVcYhmE4sScM/2dSPL8AHMwdDLd8Zkt4MHfwBX3/J7WuH/d4PVe8rCKQYRjyB3/wB3zlK1/huuuuA+C9730vb37zm7nhhhv4wQ9+wBVXXPGclmXbNrbd7KxRLj9DF4DzOOex/NidP44vX5w/jj8bOH8cz+M8/nPgZUUgi8Uis7OztLc3q0FbW1u5++67ueWWW7jtttvYv38/HR3P3lrqYx/7GB/+8Ief9vq9Dz5MWya19P8zamj43qFp5qs2hiqzpj3O1x8aQQ+AMCDuikYm6kJTgIgLXWkTqRjgVxXaVsWob9L587uP0BnX+TWzk9KETde6GOmLEvzG155CluCvXrWRoeAUifHvEp++D8lv8KHHb+Czj3zrWX/Xa7ddwJtv/jVqUgcZf5h+9146/T3U/BbuLH8MgM0t3+fjzhqO2Ca/e9kgl3al8IKQP/jhEebqDn967QZyPywA8MrfGOKRU/P8xT3DvFNppdVSQPcIYg1mNZXiTJHBqEo0aeJk4gRphZgV4pUculpitHTHWbVJCJ/PTHn9uKhWq+zYsYO+vr6nvbdr166np8zcOjgW6EZTTxSGMH9SePpVZ0WayEhCehAKo0LbNnsUQgfCQGgS2zcJPZtdRAYijWkkhAYnlHXC9s0EmUGC9ABhvAu8BpJdRmoUkNwaUn0eeX4YShNInL0KOUQiQMZHwicmfAFDT2gmw1AYiNfmqSPjuw2UeBcnw6uY1DcTlhtM1eqYVp25sMY3rTjv9cQ18Uhfg/hEyNbAZPDCFIMXpbj3X04hofC4XOO43GBdqBANJZKaykMPfZanjtzzrMdiw7Y38u6t70TRJNxoDUkLyU97RDUwUhE0XaWj02T9Fd0kzmhhWMyXufraK5/7cTwXEYZw+E6YOSikEVtug55tMHNEeADKuvD4O3gHjO1GCmvoOAtH+LljzL2MR6rvBTySLQ3K+QSGVOKEsRclcSWtsyoHTj7IVx7+5LMua82FN/OXv/cH/MXuE9xUSmKiIHVohDMu85JHst0lrulMj5QBBQzIrE6yemsbHS0msZi+olFAtVplxxXP43p82i4Mn/ba8OMz5MbLSIZEfs6iOibkDnKbgt7QkGxRPLFTrnJMsXhX0Mq+4Qee0++/dMMNvPHq96x4TdZkkq3i/Jyf9VHqMnk8XvHWHhKGxj37Z7D3WiQRUgJ94CjO2CS+/QoALjY/x9rID1b+LiTCjgtwtr8bv/eyxR+LVBpHndmHMrUHefYgUmXimfeN2DpCZAJkHCUuOmfJOgSO0EwqUaHbjrcLfXRqQKSuh64SFf5nQ3lK6LoTHZDqXhpTz+M8ngteVgQyk8kwODjIP/3TP/E3f/M3S68bhsHXvvY1tm7dykc+8hE+9alPPeuyPvCBD/C+971v6f/lcpm+vj4u2rqFZLJp1nrmmOanujkwWaY3bXDpUBbbamF4tMigojGnBYwWHTRJIqlIQgye1ajLVaxAwp6GVZ2trOpezaTrMVc1aDVNauPQ0ZrghtWbeaRc5S8O2fzP667jykveiOTWqD3yZTIHvv6c9tHlLUf5pcgH8dEJAonj4VU85X6YaW8jyQXO9H/0NzBi+BjAtdu2ossyH3nwOFWziytUBf8JmaSZQTcUNqzfyD8d2U9XyyCrKgaYIGsSRqeKoUGHZNCTjNGQQ6RVPbg+dA1GSSoKm1a3EM8YJBf0KdLzqNp8LliMbpw+fXrpmC0ex42r+0m2Lgyay/0eVV0UnMQXjKzDENgMvG6BYC4UTGgm7PkynBoRA7Fvi440RgC1nWDkwHDEwGtrwnfNKUPog71PtLKb/hEbLwMZQNIWPCAVYcNhpCDRC/lRCBqgxYXI3kiK3+HWhK6pcx20rqVUKlBTO7EiA1SnMuhFCSnawKhY+E6DOS8kakOiFBUFGckskapN0tYIyybr1mxgT6xCGMJ6Jcl41GU2lIiFEvVAIpoZfE7H4rKWDeKcScpE0m2gSKRcF7lTZeCiDvSKR3trnGxHgp61K/XK0+O5Zz6OGzeuuB7PWYQhpB2Y7ROVrT0XIm77l8Dh7wrT8FgR9AkYaAE7ALWVmuuhhD6ypiDZNeTARg58QiTRz1lRCUOwAhMtbDDIE+SUGabdDRh2kVSiTskfZB393Od7uN060fHnVoiWiGbZtHYDN7oJ5CeKJAOZobVtnKrMkQRGUhLr0jH8ahEFnRCQG6DLnWzoEROS/FSNlq4YHQOpH309PofjeCaBLM9bHJioYnoR9EBn+5t7ODCSZ/5EhVgYoPsBXtnHrYTcRIYBzWO3EpB+jqbYHW1bGI614+syKVkmhURaUcmkTbQQisUSScAkpKV9iNWdCdJdQ7xzZje/XDLQkbj88nfwwfgIa07abHJVJjO/R8ilrNYfoEM7iuE30EILnIPw2B8Rzl0nKvdHH0GqTDU3RkVUHCsaIIkxymhhsRc98a6FzlSl5heSbcJezEiL4hK7KgpxZE1U/yunYMNrIB2DwU0wslPYhnVugcErxWJGytDWCaoJQ5vPR4zP43nhZUUgQRC/3/zN3+RVr3oVt95669LrmUyGD37wg/zZn/3ZcyKQkUiESOTp5rjzVRvV8DD1s++arb0ZtvZmCMMQCdjcl6bVlsDzqeXrZFIKubJLe1HBtEHLgBIH0wuplyRG9+b4BUXncELjW4bFdaHGKk/h5N4cVwHrTJN7XIs/+N4R3rCxg1+5sI9yy21MRJ+CJUvgZ8Ypfok78hvQJItx5wLcULDGADip+uyJeIzIAaviBm/b2s23js3y7eFZzEbAu60I7Z6MT0BrX5wr37SGf3t8jOMH5/mFuthXkgLR1iguHgkZqpqCnHSQnBTh0QrtvXE29qbItpu0d7w0kaNkMvn0m5OzrDBj0YpFQgzMyytqZ4/A3CGIdULbupXLaF8PXkP4tSkqTO5daGc4hxDAL/ahDSCShmiHKLzR44JsBp6w5ZEQO06Pi+VJshjkFU1U6taKgjgme4VPmxqFY3eCXRPblOoVkVMjDoSiWrt9Iwxdg5bLk6rMMj4yT82WsYIkgRWS0qJUHIhLAa5kUdIFv3lVJsk/zk+xzdUoTNU5+OAkelTBrvv0+Arvrq6ssN1tdLDrORwDCZBjEnpq0TBex8NFL3tsXtfK/hPzPDRbZF1SooeVBHLRMuusx/HlhJ4LF4jjGbBKYuLi2ZBZDZUx4fOomcjlCQJJIYy2YHkeql1GDiEW2oBFkB7Cqs8zZm3hRPVaouQY2CxROJjDcltxfQNPq6K6cV7Z0Dnh+jxUH31Om9uXDSnaLvP7C1wQKISqxBWvX8WJfTlkL6TkBCiAjkpAgzrgelGMqseRR6cIvIBEi7BJ6RhI4Toimv58j+Mzdf6qFizUiILjeGhRhWrOwqp7mH6AEdWx6x5yIkRFxq95bHBVer2QO6zZ57TerBrjYksV7W4AQfhd5ucFSUsClhRi9UdZ3ZkgDEP+/P7jtHgyOhKKJrPu4nZuw6Z2TDQFSHaYTE1vwvck8kovaqRMjDpxP6Tbfwzp1P1L6w9ljUDWULQoKIaYTJrxBaeHKKSHoDwmIoqRBHgL45iPqPhP9QoLMqsIxUC0wjRbRCYl1io+nx4UDhMgyGN1Co6dFp/L9Iv3ShPNz5zHeTwPvOwI5K/92q/x/e9/n7e+9a184xvf4Kabblp6b8OGDQRB8GMt3w/CBYuJxV3z9LTKciiqjJNSiZZgTW+CVMWhJlWJ1CCigmFqBOj4KR+tKmFN+9i1kA1FWKtFuUezedhweaUUpbsa0lYP+TkizCgBR/bO894jOS7tSDHYv+E5bX86uYVxZ/PS/wtywD7d56DuIQFb1QiXGRHyNYe77h1FCeHKQGaroyMBqi5z0WsGWXtpB4+N5bnvrlO8uaEjI6FGZZK9CWRVRidCOe0TyDHc3nakwzUyURXDCtl4Qfszbt9LBn2Z/9eiFYuRfLqX3/wpYfDtNERkL5paiFRGBYHUzGbv43ibaP9ltoJ9rzAb17Ni9r503mWF51o1t9DOwxUebUgiolA6Kf5W42AkBGmUgNWvFOSxOCZSoBtuFVGFdD9UZoW1UKpX9L/NrgItAW1rmR45yfRRi1rgoGoBEWUSL0yS6EmhFXR814Epi1Oyx4WoDHoKYUThbs/hdXWdAw9MoukKelTFtb1FRyUkJGRVwjSf2yTATEUxO3QUSSIEFEVBB/CgXvcYK9pU4jLHpstsHC4QzxikWsUxell2o3k+6NkuJgjZIdh4C8wfE3OP4gjR6cNQnYbB63Bnj+LVS3iBiZNaizH2fXQ9AVYFRdFIGrNYQQJl6FJWt6gcfKKEXzJQXcjJAZlA9DO3Oi5hP1981s3atuYCvvHwGBcsmMtfcHU30YSOElUIKx62LNExlCY3UcZBQwPktEp5rk7VDlAUhWhCx0zquI7/gk3hn6nzVzxjsPaidmplGzMVIZKMYNkutYiGYvvMOhI6MrQo+DEfK9cg7sEV7duew1QblGiEhgqGJ+K9AD4hPuBJMK8EJDtNrujNYNVc7jg6w67TBd62kDbv39SCbqhk5zzCUKKmhPRtzXKyVieklUm1E89s0KUVKRrrOD7xGvq5F0Ox0CMKLQNdKL4jxoDpfWKSmlklCF0kIbwgOzaLiUfbJtGSUNaEF60eBd8XE8nSFCCBngA5Aj2XiXOq/yoYWEhHh6GIPA7PiMlyPdf0MM30Nz9zFhnBeZzHM+FlRyABPv/5z/P2t7+dW2+9lT/5kz9ZSkX/1V/9Fe95z3ue5ds/GoosYerPfkNbvMk6DY9oIyTaEiEmy7SnTRrHHIpujVoZRk+DoTWwZJsO9wS2EkczO/G9GDghN7o6JSXkB4YFHRq/2dnG/MECHS50+DJYUC/VmTF28Kv/5S9pNE4wV5jj7ju/AcDqS2/EkxNIoUSkZYg9q9dwOHSIhJCTQ2b0kMtbEryrLhGbdpBDAJcBJGCl+eqqC9u49NZBUu0me07m+c4XDnNDQ3zGVT1sRaI0XcDEoBwBTZpl0JpBnrTR011orkQso3Hk0amltNZPDcuJor7Q+aE6B4XTEMs2U9jZIZEqrpfh1H2iM8TADnGAZ4/C8fsE0YukhO+aXxfao76LoZKD7q2in/XUU6LDRMtqGLxC6OGqsyKtlBwSWqPp/YKoahHo3yRS3qVRkfY+qcG+r4l1mS3iOQjEunwLnE7xG7KrhKVH2xqQJObzMmGyB9/2SJpj+AWoleu4gU1ouWihSF2PKD4XolKarPHuy/v4h4dGWKuJqE2gSVz31jXEkiLKPFmx+ObhaY5OVwi1i/ll5a/xK3kaTm1JW3bz5b9Gu5klICCeNVjffSljExUCKUpk4RxLSCHxbJRy3WVVd5zjFYuBiIbv+FQL1hKB/JnAWfW9C691bBSPRbgNmH4KcqOiC1HXdpAlkroODZv5Cnil/dQtMGYPY8kmQX2SRL1CQg4p7syTsweJS63UDY3AUWgNZGxCLAU2d23jfbd9krnyJOPVGe7d/WkANl5+E9sGuwn8gI7+zSR7r0M5WgEkshdl2Xp9H4omEzFUrIpHreoyP1khcMOlG4VaCyjbNrg+sqGS7jQITOHp+UKx3FR+vmYzV7BIqDKJeISeyzqxKjaNiku14tC+Oo0RwrGD8+RKBfx6QByZGqBLIcmEzCo2877bPslkaZzj5TEef/LLALztqv9KdGFcSCQ7yA5sodam07m5lUs2t9GVjlKcrRNGZGaqNtmoTjyiMpWvc/tojk/uHqHbkxnwFCQZtt7Qi6JKNA4XAXhcd1l1okSu4pBnCJUKLb6NnwyQS0eZr/Ux0Xg7SSZpM8a5SH4IrWeT8GlMtoPrgW/hSjHc5Ga0TCdaUBYdq6oz4Fbh4l+Giadg+kkw2oQGsm0t5I6Lie36m2Ddq0SkcuQReOAT0L1NTDxHHhEkcuhKMUHWzeY5eh7n8QLwsiSQkUiEr33ta3z84x/nox/9KH/6p3+Koii8613vOmthzPNBNh55xvT1Io49Ps30iRKdq1MYIURjGum4Rro7TrVo0ZOI0rXBoF62GXFtZms2A7E6ZdJISkhDkmntM6jM1PHLkPIl3lyL8Ljn8SfOBB94wzq2uCoTRwuMDxcxPRhqAMolhPGLOaLMcjeCQFYveANG2wAhoGoK8aRBT8YkoausbXhop+p0nHBQFgaKGSUAXaYtGaGnxcQ0VBRdoX1DmmFcPn7HIeonq/TVYV0oEwJmpw5Vn6IEUUICHRQZWskR2hJJrULLts2s3tzG7OE8ruWRn6r9dAnk2VCbF2SsNt8kkO0bxGPPl8FxRbRPXSA2s8eEVnH+lIgOVsZFQLowIgpqtDi0roXOTbD952Byn4geeg50bhMFFcleoa1M9Ynoge8IgpjqhoIHQSiI4vijC0bDEgxcDZkBEQGNZQXh6EhDrEVEDbSFThK7/412tZ9ZrYeOziRhvYNGLU/Dlcn4LjYGEUmmXZI5rQR4hJRzFtunk/zm1YN85sER+jyFWMXlrk8fojgQZTzw8GYaDLoK1wcyoEHXRUj9MpPRPCwQyGvWvZpoIkO2x8BIqIwez+MZUVqiElFZJmpqRDpM6lHQ2iJc29bGKyMK9YpDtWARP6OQ5mca+dFmmrBlQExMPFeYS2d6RQQ80yeOdW2GqNqgUbOIRnxIrSGz5gYyY8JwvFH3OIaFOz9Jw20hFnUIdINiLSTiy0R8yMsBA2u3saqymY31whKBvOKa95CkjdZaQLomw9EqIFFJKrzjtrUEQcj3v3oMa07kdK0zsi8mYNcDGjIYEZjAo1GskjI0MrEIrvPCds/yzl/5Yp1Gw8OVJEBCi2lUvAAzrtEIfZLxCFIQ0tGfYHa+TugHtHTHGJ2sEo5YqJkYoe6wStnMqs7NbK0XlgikuvFSNsZ6MK1Q9H+3gXGH0vgk3/zeBHNGSJDWaRtIsHYwxaHZKt8/MsP46QptnsSNvspGX9wbBra0Es8YPPL1E9RyFpYUcsQMcQiQWdh2YrRLJ4lqKYy0TqFYx/U0it4AWaOI23EJGlVR4KKYoNagnsd1dULHxu2/Dq1yCCafEl2pJBke/WdYfQNc8FYxdiRawXWFJKJRgPkTUNsuxon5Y0L6MvmUOOfsokhjX/M7L+xA/SzjbK0Sz2Jcfh4r8bIkkACyLPP+97+f3/md3+HgwYO0t7fT29v7kqx7+kQJq+oyfjjPlmt7SZo2qTaTaKuBqisMlB1q8xbR1Rm+9fgpEl6URi5CEG2nYQTE6yBJOnrcpWrW0cs6fhUusVV6PZm/uesYr760hz981wZMVebkvhz3754kd7JMqyvREzYjh78kteLqcaarNmEjRC3bWKdFpHGdq6AuDGZTUTC3Zbjlil6296ZwLR8rDPnm/knue3QCfc84612FnlB8HqCmhMQ6dFJtCeo10HCp6RrdpkQsGyE0Oonbc6hmJ2EInheiJjU0F1q6zpF+14uozkE9L/5uO4veJ94Ok5Ni0Jg7Jm7m1TkYfUzM4FtWiYhi8bRIL/mueE72CHIIgnC29kO8BzQdDBPMDLRugCN3ia4QtZyIFlRmYN1NYsC3SqIXcuiLzjKpLrjgTeImAM3n5Zg9Co08fTGLvisuwNIz7D4gM5twoVLDCcUN2SXgjRGDr9sWd5our61rnHhili0XtvJ/33kR33lsnMpTJTo9mbYRC0GrtaXVJLpMpE6DO8fyDJ1udnZwIz79A1FkVSIAfCQ0EzqSBh3tMVrWpBixXDRNxpUlzIQ4Z1PZ6M9W5PG5oDQh9GilCUEg29eL4zd4ldCv9ayB4riYqBhZzNg0pl+A9vWEbRsh0QmtG6E6gza0Cd9bhbb3OLHSDFXakJUamUQUJWFQHLdoCWS8isvJuERsmcx7zYy6UEgn4wONlEJqMMEv3rKG+fkG3/rsQfSiOMaHDJ8dHTECIEARHtX4RPCxAxnSOlOKyxPHZmlPRNhgZPClH1+K0BITEWoRgdRxJWjLGIBEqwyFhkt3ykBZm+GqoRSaKjM3V2PfSBGpRTSM0kwVdAXN8gitZlp90NbZGbcZSwWsTZsMyBrJgkey5BMLJWINCRoeTBU49WgeWwq5KpCBlVr5jqEkF948wNe/NUxjtzABvyvqcFU8iiSBR7jwjRJ+GGE210WlPoQi59F0iZSZR27dgNY1C7Ijsgybb4MDt8Pw99E0A7d9HVr5JEztEWORvxrqM2I8carQdu3CeBZAuhMI4dA3xURk5shCVfWgyHx0bxOT1pFHRHaklhOT6FhWjC3D9wh9d/d26Ljkxz6GAJW8taI7TD33wiPUP1GYWTEh//qvPv09zRRdo86TyGfEy5ZALiISiXDRRS9tK6bO1SnGD+dp608wP1WlOF0nDEMynSbRUKK9K4mbjZFuN+k+Nk5XzieOhuxARBKtcmOxEEmx8NHwE3FiVRs3H9LZ8PjFSoTv75rilXsmuLwvw2VdaV5x/QDmjTK+H7Dn4YPwObEtKUchOeuz6hkOpZtWWXdZB7/0qkEkSSIMQx47Ps937xmlPFJhwJHZEUosngqeAn5MJt6iE40pJCUJzVBQFAkJHb1NJpY0aV+VJOZnKZoWRkKnJWOgqhID67Ok4k8vTvqpozYPEVOI1Rejj8CSxjXVIyoaRx+F4z8UWiS7KCJEgQcDl4vPdayH3Ij4WqZLpJRBEID6PMwNw5pXALIogkkNijaHsVYRndz1z+ImoMVEX9pIXHxn05tEhWXow+DVKwt9zob29QtFGQPis1ZAteaQ2JRhJqoSqbk4FZCxSQC/opk82avxrRMlXlfXGdmTg+EiN9/QS9sFXex+co7KoSKSBG39CZKdJqdiIXcdn6f1QIWrXYUyTYIQbw+JxFRCQNMVJEnHAfRVCaYtj8cPTWNmDC7Y2EZ3yuD+YzPsnyhzQU+S69Y/u83WzxTOLFTouVAcv8KYKKpK9cET/ypu6JG4KJzKHReR7GQfJNogfgN0b0HVTTKTszjFAu1TNbr8A5zKb8CTAnrWJEikNU4cr2I0YFUV8vVmFNGNQGRVnMGhFBdc1oWmKwRhyDfvOUXh4VmMUMIh5HAGdgxlaNVVIjEVWY+gEMDCdFSLKETXJLHmK2SlkCcmylx/QdcK/eILRWvcoDW+Mjq9XJaXMnVKDYdi3QVCVqXiHJ/20NMGjfEqkYxEaEQJW32CkoRbri99N47CjQ0dGjBf8RhTHfaoPpPxgF5VZTDUaLeg1YZoKBENxURajiq09cRp64kRa4+ys1HnNz69m1fNSihIPBrz2NCb4Ma2FJGkQnGiQkiITpacdCGOkkWXTdAU+tqmkPwI7d0BSjQOugYdFwjddWUaOjahJbrRhjaCVQCzTbRY3fIGkeEY3SnIYNs6kdpe7DqkRcW441gghZA7KuQ6vRcLmc2a6+HSXxSfnT3azMToJow8LM7Dyb0vCoGs5C2++KFdeM7KegSHUJD7cwnpPkESl7eRBBGR/PqvitfPE8hnxDl2NM9FPF0jsu6SLlZtbce1fZ64+xS+GzBzqszQ1jaicR2r5mJVHQ4N59khR5moWcihCzrEojqu7zA1OU8gj2NVsswR0ENIpiuON+6hOxK31nWoA4UG9r4G9yEsH0JgTi4sbcupVp8uQ8EPIQhDdDfEcENUHwIJPNtnzyNT7HlsGhQJt+6RaYT0IsECIfAJCTQJRVPR4gFuNWQ672I1XE5YPmsCC8f2iRsgx1K0DSTIGhqFqRqSD37Vpf/SbpJZ45l22U8fsWxz1r0C0sr3Q0RxjFWEnotg/AnxLMsi7VwKYdOtIq3sW4IYZIfEMurzkO4QkcreC4VmMZYWA3N5BrKDMHQFjO2GzKDQQGYGYf2rxfcXLYSeSa+5HL0XQe9FSAt310auRnssQu7hOVrmbVwloOwFUEGcG57PRapCJmlwt2RzbV0jVvU4/M0RHtJDlI0pbv1vWzk8Vea+B0+jPpan31O4KhTnSShBaDYjCnPjZVSrgIxBIMGMElIwAzprLoXxKo4aEgYy16wTZPGOvZNULJf9E+X/fASyZUA8FiFJ4lgvVuqrJnRdAuFeEQXyLCF16NiM1LlFnGNms3J9aGCQIbVAee9DzNc6OFiwcfIaI49PQRiSlqFiSFiOvED5BEYDm1yhysFyne8dnEFVZcKKS28pxEAipwZYsZBLZQV/tMwMoEQU3IZHGQ8JFQ0ItZBuTeWNF3Xz5cdOM5qr8q29E7zpgrNEyl8EnNlDu1B3cb0ATRVx0Z6WGAcUiUZEoVRw6R80cCIq+ZrPeG5ZFEyqk5DTSKFENpDJOjIXOiohIRUppCT7zCgheyMBthxiSTCrBFgykKuiF2XUg9DdgFfXdRQkCpGQHakI0VpIvlZENSTqJRsdmTwRdFqRFCioDXRTZWDTFjYlZ4nqlpDFLBawHLlTyGckYO2NzXFq0y3NDMTsUaG9ViNipyzKWdw6RH0ghMG14tza91XhG1s4JTIa0Bxblo+FTl0U4sweFufei+DVa1VdPCdgwxsGMReyDafzdf70B0e5MaU/y7d/Ckj3nSeJLxDnCeTzwPBMmZF8ncEWk7UdCVRdpnttmplTZTqGhG1FNKGhRZI8+p0T5Mcr6DGZtrROEAZEe+OEqoRqmVSkCqWZKDUpQjIEGwlFixBpU3DLdbxqCCEEhASAB4SSmBkby1p9evMuT7U0GHIVhjx5SevY/MBiZ6jlkKgrIYESYmoBnhJFlyRCGaJxDdMICRMqR3WfWhBQrfmk5mWIawx1J7ji0l60iMJxbZZCzqKzL94kj+cq4m1nJ2KLiLUuDNShSCVmh0Thw6amVdQSEXBqInoUBiKqJKtCnziwA/ILeslYO8iKqL4d3S0G84kSbHkdbH2zMCm3KyINvljws7zw52x6zR8BVVfoThicrPgEqkJc0bjq+l4O7ZqgVLKoLWTyBlWVnrTC4aiDXAtYbclkHQmeKvPdfXvQQ4mtNCPSASFSRMJMq9RrzRS2BAQYyHEwMzrRtIEe0ehpj5FSJCZn66xfnV76/AU9yaUI5HkgbuZmq4j8xLLQsQHWvVK859Sg/7LmDb9lQEwoAk/c8DUTytNoqSzYOutu2MqB7x5HXOcekuShqgZxxadsNI/ZGjdCsgAsJKaXYy4OW9ZnKIyUVry+KGiJAhYeDTRcDeplG7Pk0Vr3qaR0Rubr/KRh6iqmrpKv2cxXHSDED0J6MiZhTMPQFSzPZ1VfhqIq4c826O5tRvKLsk0iHmKGCpG4il12ke0QPIlkKJH0oc+HbQufDyThmOMT4i086zQjk5qp0JOWkGRp2QgrIyNjExBd2HcYEMkamF0mT02GTLGKjUMGa3RFkD/NhMoctA6CkYXBK3GdADeaQtOUpqBkUfrQvp6FDRDnh1MTf7cMif+Hoaj2n9grnCfUaPPc0WMrx0KnJiKXm24R770IPpCzFaGh/cAPjjCrNvdMNKKQiZ2FQBZPL0UA5dyJH3v9P0lMVaco2M0AzsnSyR/x6Z99nCeQzwMj+Tp122MkX2dtRwKAVdvaWbVN2NYsGuGquoxdcZEkGasBLf0GWkRh47U9KBGVWExn3hpgeNc0wyeLhLkZ4mGUCA6SoZCXVKLJAE+CEJ9sd4rh6SonXZfAddGma0vbtDY0SDaaF6VvSDS0EF8HR5LwgxAjquHWXIIQkKAjpjGUNZgYtZASEhFLSPrUEBwfGq5LLBrlpkt6MHQFz/Kh4uEXbFraYjSqDkY8xpot7UvVkz8zWCyq+VEzcacuROtyazM6MPLIAnncIlLSj39WRC63vgna1wl9ZLq7OYgvRg8WU9WLZudnixI8B2i6QmAqhFGZ+kyDbG+c6nwdqUVMXnJFiyFEzDkiSVxqRJAMsL2AeiDDnEMyFClIV4FEhwGuixyRCCUJBQibpx0+OnEdEp0JWloNuqIK2e4EOAGX9C8YyGebWsdr13Vw7br/ZJHHHwU9trIy+8z3lk8mxp8U6ctMX9OWJTsEdUiu6WGNlmR6Y5ZarkLBcvCDCmbDQwqimGaTQDaiLnpSRo0oWA2PIAyFEiOtsT6poatCG+ki/BlCQEZZapCsoFJMQkKG8lN5imrIQGuUUUPjqtXP7Tx9MdCbMWmJRRa2SrhmXLillV11j4FEBMkDTQXXDvDrTQ2k06ojEQEJ4nGTetRFs0H1Q/ADfM9Hj8jUiw6BHSKHooZOW5qUi2dJAT2l0r4mwexkeSnKu0iVQsCSZaItKrITEkohSV3Dd0JCxSEc3oc7eQguuJx6y6U0wpBocgumOizI4cQe3NFjhAG4MmgD65oeo2fzGT3zfIHmOAYrGyQ8l+/+mCg3xDn3/levZ9PmZlQ6E9PpSZ+hfy6ehk9dJrYRMArN4xUa6R9rO+q5BnNjlRWvGXGNRMsLC3ZMVae47Y7baHgr9ZxRNUomknmGb/1s4zyBfB4YbDGXIpDPhEUS2b8ly9RwESmuIKkK857HAw+N07kqxda1WQxFZWBNhoSskOcU1aKFosVBUpClCNgWugpRLU4qEaWnDKspoRLhuLtsth/3UQyZMK6STETpGTCYPV3EJcRHYRLoGkjAZA0VCNGQAdlQiSgObd1xKq5HfcIiAExDpmJGsA2FkuXS3RrD1lxqskxXdxxJgoYUEvV8DE15UXRPLwssF2It+krqJku3jcKYIJ2FMaEpauRgfDfs+FUh1G5fv3IQP3PgXjQ7P1uU4DlAiyiU5jxiHTHirTHiKqQ6YuybKhMmVUoNSWgVZRF5shGlAWZEZs3qFkpzdapFB1WVMRIq6TaT8aPzWICEjoaPojaLaxRJQpIkqvk6ZnuUmAuaIhNPiu++2F2H/tMiDEXUKXBEinOxBWf7BrT0OkzbJ+UWuODSJGNzBg/ui7C5MYrsQdxxqSrNG3I8E9DbZdA2kGJ+ovq0VWmGSsVQ8CXQQxUVGdlQcYoNDDQSKVi7pY38oSKy5uM0PDZvbuOtF3XQmzFfsi4mpq4S1Vbeum7c3M22bIL52TrZdpO6FDIT01ZkRgb6UrTOAQ4YMnS3GHStSbNr9zTRmoekS3SvaqFUtvCDEKUeEoY2rT0J8jM1EEkh9KiMLUnUEwooEjLSQpZIQdEkHFUGVOzAR4kIr0o3CNCckNbWkPbGcZKSBXPHacSvIJACGskNmOsvFhu659/Rgiru5FG07vVi8nk24ricGP4oEqgtdNZ6kVvJPhv6Wky29DyLE0d9XvyON/0ztK7DGj4Bn3wLAGG88wWtVzNVHEKO3D7CkdtHVrynaDLv/PCOF0QiC3aBhtfgY9d8jFWpVUuvZyIZuuLP0CryZxznCeSzYPk1t64zybrOJK7t06i4K+wnzkTnUArPDlCSKpEOk9yBOSQV5gsN9IhKa0Kny4ywf96iHI+ilSxyhRmcWDe50Kfd8tBkyAyoFCZrnJ6so9tVZEqM5ZsD9US5TM1sp5wN2FCawd8HjUodiFPEwDFhWPYpztTodCTiRIghMS9LqK5HcazKplf0Mhkt45YckCGtQBiVyUoq5eEiUkpn7boWWuMGuap9FrP1lzFeyKCqLRDIRY1irHVleqk6B+OPQedWmDvaTIn/qMjmClL6fDZ/IZ0WUejsMCn1JXBzFu0dMZLZKK1xnenJKq1+wOk5B2oNcRMEZFRCPE7PN/BtmXZUbMCnyMiwRN7y0VHBdInXoVxpTlzkwMOuiK4YOcvhokt7yXQ2Tce1iPJS369+dtG+HobvFdrI2SNIC+fR0viTt+jx50mlW/mmpjCaT7FaLpHtaGF64ujSYiqjFuO1eU6NFfE8MfgbSELHioNvQHHBDLwBaMhEZQPV8tEUaO3I4FYcFN8n9KF3dZprLuvFTIoMyIvd6/5H4Wyr6uhK0NGVWPq/vKOXx8O5pf+3uhJGW5TGTINioYyhJvAqHk9VajQqDfobOro3RyhsBSg1QKbBdLGBZPnYgImBAkQNyJsSDcsnnvNxgZrsIhkSiqSCDZoFaBKOqWA5Hn1r0qT7U6zr20a0OorUdwnRTIpG1SEa18WPCkNoX4c2ewytvRNKJyCyMLacqYc+c9L5o3bOuY7WddC9naCgPftnnwVd3XG+2OIinVHEkw1kXlvXGZuqsPkFRiEBVqVWsSm76cfdzBeMs6XNf1ok9meAAbz0WN454ZkI5MypMo2yg2J7bNzeCabKiYkyq3uSdLSYhGGIH5Fo7TSp1DcQxMrorowZLTJQH+MUGQpaF52rWyjPlFEaNrNeiojZQhA0NRjK+iReRqNN0xgyc0TlkPlZk1zDIO1Y+BENS48yotmkfRnFD4S9jASqJqMoErblseniDpR6gG17xBI6nhuw74FxGiWb1p44l1zUDTSF7M/FbP1nCmf2yS6OiypttyEIZLpPvB7Lwo5fAefnxWeP3y8MgIe/LzRJ7etFAcyZeBFSSa1xg9ZrhCg/CERkNPvULJGWGImYTrE7wq6nZjAtj/6GRlyRqfoBwk44oIFQxjUQptSqpGCiIoXQsiaGOt0kiHpEhRiUDIW2/hjtA6mfLSnDuYJaTpxXqW7R0Wj+VDM1uQi3QdWK4Ntl3jioUE4GZB2Trp4hTiabqTUlAtk2E3VVkkfGqnTP2IRoIvOATp5ARJqBMpBLq1zUAMUBNJjEI1IOaR9MEVNkkuvSPHpkljVrMkR1hbHZlenCnxZO52tMlizaszqZNU3NbU9viqtfuRkJuPvbw7h1j9x8ncsv72LngRmiKMTNKLV5h3rJQwdyqBgIDXoU8AgIkSkE0KpqbHnDBqx5i9mKhd1wmDtawC9BLAJGUkfTFDJr08R7YkTSEdqjGlHzFrTcfigcw9SimGeOB+3rxXgy8gC09EG9uLJqeinqaDYr+eF5ZSx+ltGTjnL7H15HobbSmPTQwRxzXx9dSrG/3JCJZIiqUT7w4Aee9l5UjXLHbXe85CTyPIF8AVjeOeGZ0DEkIpWptijRuM4qIOtAKtWMMKmazKoNbfSvzi4tr77zqzxxJCTq5hmPdTHlOkQVhUyHTr/l0m7EeWi6OShe2ZUiISdoWB5qtJ3CbA09Y2AGNWItDmXboaWzk6lynkpoI8c1Otdl0cKQSNEnmY4Q7TDxDZU1a1JLRs9u2cYuObiWT7VgL61vUcj+nw7LZ/uaKWwznKp4hpVFL4u6RacOyU4oTwuzX6ciIglnI5A/IWQGEwTjEulsmlRKRx2vMVVokOiOks6aNA7PE/EUUYkP2FGQ/AikJKI1jzgyZovB4LosRyuTzeUOxUklEmQ6DbJm5GdPC3uuYPG8MhZSgYsV/8sRyxJPu1RLAZe1QzKeZzZv4riTDHU2daeaLmPIUS66pJ8nJ48wH9WQGiFtUZDiKtEISBMyOkLt1x5RaW2JEA0k5vUQJ6Zhqz7ZDpOOrjhjjkugyUyWLDKmjuP9eG1kXyxMliwsx2cWaCyLxnUMppZS2oPrWzh9qkjfUJo37+jh169fR73iMDtV5r4HxlA8Dyki09eSYqJgY9o+siJq4WQCXF+mXHVItEZpb4mijZV56okKZgOCKBgRlb7BNLFshNVrs8iqTLYrRr3iEto13JE9aLp19vFgcayJdUKjBJ09wn5ssWp6cRyKtwGSMBh/jsV2/1nQk44+TW9Zmaoz9wyffzmgK971/7d35wFRlXvcwL+zAsMMM8wMIossAuJ2RUv04oaKWyVerYtmmvuS3dTbzddK89Vcy3rTNPMqV0vNDUvR1LypLZqmFSoZXHNBEFlkZ2YYGGb5vX8gI+OggAJnBp/PP8U5M/U985yZ+c05z4KDfztoM4gHqLoi+fbpt1FsKGYFpDN42K3rajUH1wA85N4sganCjNJ8PZQ+tusL67WVMFaYIHIVQicOhEJxE3fEUiiVUqjkrgj09sCd9EoU3ihHmaUCMrd7t2l0JQaoVQJY+Ba4e/tCIK+EycyDn08xpK4aiFXe4LmpYSQj+OXlEMjcERKqQrnFgjatpJBJxSgqM0LpXnXrQCQWoFWAB6TaSngFy6DJr0Cb9k9mB2Eb999irl45pPrv+we9VH/QK9pU9V/yaG07grKZBIaroPSTIU9vQMovufCWu+MvAZ5oE6ZEUbEeLmRB3jUNTAagUgZIXIUQuvDh5yKEtJMUMhMPCqULKvIN4NXo7uobqEB4twBoBEArNzErHptK9XkVEFn7hPIAIPWCvIMXPCrLqq5YoiNk/AKUywLgqb/XaAIhD77tFJDLXeHTSoIcow6uvm4I8nBDscYA32AZ8o1FqNRXwsUICMQCSN3F+MugAFxPL0F6URmU/lK0i/SDUuICcYke2aUV8JW7wk0sgE7rGP2hfeWu1lxGt3tfca7u9/69+1/90P2v9xYUEIr58FC5QuopRoVCjCt/5MGlwIhAfw8UFJXDGGKBVluJ7LRSGMv5cOMBAX7ucLMAmkozyitNICLwXPmA2QKVnxTqABlcRALwhfyqW9S4e/GhrAKiVgGA9mbtnwfVnzV+Xe/1eQXuTv9UZvs51MDBdkwD1VyhRpPOWYxqPlIfh+pvyQrIRvSwPkByLwlK8/WQe0mq1tGu8VgeAFDVP6XtIgCvcPiJ+KgU8WDhAQY+D3q9BAKtARVFPLRtq7A+V6h0hVgqhE97BfzClDCSBRUmC5SqELiJqr7Uy7VGlBnMuF1mQHiQAmFyd5CFwOPzIJGKrRP36jWV1lvzcrUbBoztyK4sVbv/FvP9f98/6KVmwcnjWedtbC78u4NYRMSDp5sYN3K1sLjwUV5hhHsrCVy83eAqEyKyjQcygjS4cr0Qfi5CqKUuEJVZ4NlaAlWwHAKpCCIA+ddLcCvn3mj/gBBPtPVXWPu/MU2kAYOpeNXnpGcgJKhaelCRkmLdHzU8CFExYQCA7h29YWzfCgIzIVjqCoMA0BnMEOuMKMgqR4WQ4KWUQNpagrQcHTwkYvzFRQSRZ9UPTrXUFW2U7mijvPceELeSwRHUzKWpsRQP7+7Ar4cR8AX4i78nQuTuKMrRwaA3QmMywaQzISTCC2FBCtxMKQRfIYLaR4Gbd3SQiIXI1VdC5CVBhUyEvr38EdRKZl2ys+bKSyIXAUQqD0DWDRD3tu+2wuM9vDtLXZ87zez+FWeAh6w6U2O6HqvalhB0BLWtUCMWAX4+VauIcdgH0pGwArKZqHylUPlKa93nJhNDKK66qikSC2CqNKMwWwehRACJtztK9QZ4yOQoDpegZ7ASpdnp1ueGdFbjqfbBIAvBbLJA4iJCaVYxUi7mo1WgDAEd1XCTidDrbh9GoKoPZ22F4f235utzpZV5gCaYHqM+CrN11h8qKl+ptU29ZS4wBsmhkIgQ3laJ28XlMPN5EAj56Bfph/AQBbQmC7w8XSEXiazngUgsgLHSjEI+D/wapwLv7iAOxnmoA9XWNgsK8EBWgR5+agn8Pd1hvDt4xhioAA9Aua4SZaUGpP4vH9qbpTB4u6NbXz9oTBbr3YqWTOQigIfKDVmF5YDODJ82UoR2aQWJWIhOffyRmlaESrMFJiPg5i5C2yAFTDxAZgZkRsBUaYZf2APu3HD02dDYtEUV2Ln4HMxG+64LdqvO3Dddjw2RpKpgayb3T+9T69Q+ijbImXwYxZpM66a03CQgI7Fq6VkHVHNwjU5rP8tCU2AFZDOpWbTVVriJXATWGWFK8/VVy4cYCa2VEghd+HAVCxHhLoLK3RWl97qiwdPdxVokVBWifBRll6GywoS8DC0COtrf9npQYcgKRudXmq+3dpWoLiBFLgKEuCgQUKMoVLqLrF0XRC4C+PvYTvAtqjFISiQWoDS/3FpkAFUTzrNzxXkFt/ZAcOt7bS5yEYCI4HZ32uqCLC1MFWYY8gwQi6rWlW6tcsejTazifERiAUQqN6QWlMNcYYI+Ww9J96qvS5GLAH5tZCgqM6KNkA8Pkcj6PktLzrN5/7U0v1zIhbu0akLYvCwtzEYLLkluQyewHbDCE1kwTigFsrOqNhRctZmux4ZE1SwrwTxoep8y9xL4DlNA6HqvHNKZS/HvzKUwWCps/htuFgs8dflVq4vVpZmOq7bBNeZy80Oe0XhYAXlX9fyNTTWXWfXtYR6f9+DbfncLSL6rCeW6cshd3aDRaCAC4O0KwFwJTU42dLf+sD5Fp9Oh3FD1hjYZAKoguKkAXWY55CqZ9Xiac4qN5lJ9bFRjjsambkcAgK4A0BdVrUkrbZrl2x7V/efO/UwGoNxQNY1PazcAlkpoNJV2j7t/8SI3FVBpujeYSqfTNdprzFk7PgF0untXInR3MqFxKX/geVvz9a8+j9T+Qhj0Jii9az+fanLEdrQ5/vqcs/ed92KZBRpNOTxkApvnWt8/MMMEs/V9Vdf7r0k04udRbW1Y8++ju47ARXzvljxfWIlxkrWQ8Evs/lvpXwDpNTcI3QGeBKi87/OmMgcoqVqq92buTevmP3L/QJl7GRpLvtmAbZ6p4JnuHZtIoEdFmz0w3bD/DBSaxRir/j9oLa/qIiCsKEboL/8P5SnrUZ/1ZywCMe50nw+zuOm7dixw/Sd0NVZ6uGnOwP/FCrt2bGw8aur/g5O4ffs22rRh62E6o8zMTPj7+wNg7ejMWDu2DKwdnV/NNgRYOzqr+9uxsbEC8i6LxYLs7GzIZDJotVq0adMGmZmZ8PB4ctbu1Wg0TnXcRAStVgtfX1/w+VUjQC0WC/7880907NjRaY7jfs7WDjU9SvYHtWP1+7Gprp478+v8KJr6eLlqx8fREs+Bxzmm2toQaL52bIntUVNzHd+D2rGxsVvYd/H5fGulXv0G8fDwaJEncV2c6bjlctulsvh8Pvz8qqbncKbjqI0z529o9trasSl/OdfkzK/zo2jK4+WyHR9HSzwHHvWY7m9DoPnbsSW2R03NcXy1tWNjc4yJuxiGYRiGYRinwQpIhmEYhmEYpkFYAVkLFxcXLF68GC4uLnU/uAVpKcft7MfhzPmdKbszZW0MT9rx1kdLfE2c+ZicOXt9tLTjY4NoGIZhGIZhmAZhVyAZhmEYhmGYBmEFJMMwDMMwDNMgrIBkGIZhGIZhGoQVkAzDMAzDMEyDsAKyAUpLS1FRUVH3A1uIwsJCTJ48GXq9nusojaKoqAj79u3D5cuXuY5SL1qt1mlf+9zcXOzevRtpafVZNdZxlJWVYerUqSgsLOQ6SpMxmUwt+vjqYjabUVBQwHWMRpeVlYVr165xHeORtOTvVp1Oh8TERJw/f57rKI2OFZD1kJ2djUGDBkGhUMDDwwNxcXHIyMjgOlaTKiwsRExMDNRqNSQSCddxHtu5c+fQuXNn/PTTTw7/5VlcXIznn38ecrkcHh4eePbZZ5Gamsp1rHo7evQounbtit9//x35+flcx6m3srIyPPfcczCZTPD09OQ6TpNYt24d1Go11Go1QkNDsW3bNq4jNatNmzbBy8sLXl5eCA4Oxn/+8x+uIz02rVaLsWPHok2bNmjXrh0mTpzIdaR6u3XrFqKjo6FQKCCXyzF27Fjcvn2b61iN5sqVK+jUqRMOHz7s8N87j4SYhzKZTNSlSxd66623KDMzkw4cOEDt2rUjhUJBP/zwA9fxmkRBQQFFRETQvHnzuI7SKIxGIwUHB9OePXu4jlIvAwcOpOnTp1NGRgb997//pW7dupGbmxsdOHCA62h10mg0pFKpnO69odPpKDo6miZMmEBms5nrOE1ix44dFBYWRr/++iulpqbSrFmziMfj0YQJE8hoNHIdr8klJCRQcHAwnTt3jq5cuUJz5swhHo9HY8aMIYPBwHW8R2KxWCg6OpomTZpE6enp9MUXXxCPx6MrV65wHa1OBoOB2rdvT4sXL6bMzEz68ssvqW3btqRSqejMmTNcx2sUkZGRtHbtWq5jNBlWQNbh+PHj5O/vb7NNo9HQkCFDSCKR0Pnz5zlK1jQKCwspIiKCZs6cad22adMmioyMpLCwMJo9ezaVlJRwmLDhzp07RzKZzGbbyZMnac6cOfTuu+/SnTt3OEpm7/Lly+Tm5kaVlZXWbRUVFfTiiy+SUCikb775hsN0dUtMTKTQ0FCbbYcOHaLXXnuN3nvvPYc8d8rKyig6Opqef/55a/G4e/du6tWrF4WEhNCUKVMoJyeH45SP7+mnn6YdO3bYbEtISCCxWEzjx4/nKFXz6d27N8XHx9tsS0xMJFdXV4qLiyOLxcJRske3Z88eGjhwoE12f39/OnPmDJ08eZL0ej2H6R7u66+/ppCQEJttJSUl1L9/f5LJZHThwgWOkjWO9PR0AkA6nc667dy5c/T666/TwoULKSMjg8N0jYMVkHU4ePAgyeVyqqiosNleXl5Offv2pYCAANJqtRyla3w6nY769etHbdu2pVu3btHMmTMpPDycVq9eTW+//TZJpVKKiIhwqmP+7rvvSCQSWd/ICxYsIJVKRbGxsaRWq8nPz49u3brFccoq58+fJ4FAQAUFBTbbTSYTjRw5kpRKpUMXM7t27SK1Wm0txKZOnUo+Pj4UGxtLHh4eFB4eTkVFRRyntFVZWUkjR44kb29vSklJocWLF5O/vz+tXLmSlixZQmq1moKDgykvL4/rqI+lQ4cOtV4N+fLLL4nH49HmzZs5SNV8unXrRu+9957d9q+//pr4fD6tW7eOg1SPZ8KECTZX+/fu3UsikYi8vLxILBZTSEiIwxYqe/fuJbVabXf1u6ysjHr27EkhISEOXQDXJTU1lQBYX/+PP/6Y5HI5DR8+nHx9fcnT05P++OMPjlM+HlZA1qGwsJAkEgmtWrXKbl9OTg7J5XL6+OOPOUjWdKqLSLVaTR06dLD5BZWcnEyurq60fPlyDhM2TFFREbm4uNDq1avp7NmzFBwcbL3qeOfOHQoMDKRp06ZxnLKKwWAgLy8vmjt3rt0+jUZD/v7+9Pbbbzd/sHq6fv068Xg82r59O+3fv5+6detGpaWlRER048YNUiqV9M4773Cc0l51EalSqcjPz4/y8/Ot+9LT08nT05Nee+01DhM+vrlz51JAQIC1Pe7f5+vr65RX4errzTffJB8fHyosLKx1n5eXF5lMJg6SPbq0tDRrAfbDDz+QWq2mhIQEIiLKyMig4OBgGj16NJcRHyg3N5dcXFxozZo1dvsyMzNJKpXSpk2bmj9YI6msrCSlUkn/+te/6Nq1a+Tt7U03btwgIqLS0lLq0qULDR8+nOOUj4cVkPWwbNmyB94+fPXVVykuLo6DVE2ruojcsmWL3b4xY8ZQbGwsB6ke3ezZs0kikdD48eNp5cqVNvsWLlxIMTExHCWzFx8fTwBo+/btdvuWLl1Kffv25SBV/cXFxZFKpaIXXniBtm7darNv6tSpNG7cOI6SPVx1EblixQq7fXPnzqXIyEgOUjWenJwcUqlU9Mwzz9h0kSAiunXrFgGwfsG1RPn5+eTt7U0xMTF2d5Ty8vIIAKWkpHCU7vFlZWXRTz/9ZLNtzZo11KFDB44S1W3hwoUkFovp5MmTdvumTJlCL7/8MgepGs+KFStIIBDQlClTaM6cOTb71q9fT506deIoWeNgo7Br0Gg0eP/99/Hyyy9j5cqVuHPnDgDg7bffxvDhwzFq1Cjs37/f5jkCgQA+Pj5cxG0UZWVlWLp0KWJjYzFnzhzraF93d3ccPXoUw4YNs3uOwWBAWFhYc0d9LO+99x46d+6ML774AikpKTb7kpOT0bt3b46S2Zs2bRpmzJiByZMnIz4+3mafM5xvGzduhFqtxldffWXzWhMRLl++7FCvdU0ikQgJCQl46aWX7PY54zl/v9atW2Pfvn347rvvMHLkSGi1Wus+gUAAkUgElUrFYcKmVX1Onj17FrGxsSgtLbXu4/P5EAgE8PLy4jDh4/H19bV7b6Wnp+Ppp5/mKFHdlixZgkGDBiE2NhaHDx+22ecMn3V1mT9/PgYPHoytW7fazaThaN87j4TrCtZR5OXlUfv27WnEiBG0ePFiCgoKIg8PD9q9ezcRVd1aHD9+PPF4PJo0aRKdOHGC1q9fb3NZ2tno9XqKjIyk2NhYevfddykiIoKEQiG9//77D3zODz/8QAqFgtLT05sxaeMoKSmhQYMGEQD617/+RSdPnqSZM2dS586dHa5Pp9lspn/+858EgJ5//nk6duwYbd26lVq1akVJSUlcx6tTTk4ORUZGkkAgoCVLltCJEydozJgx1KdPH7urX44uOTmZ5HI5Xbx4kesojeLEiRPk6elJbdu2pa1bt9Lx48epT58+NH/+fK6jNYtTp06RWq2mwMBAio+PpxMnTtCAAQPsrhA5u1OnTpGnpyf9+eefXEd5qIqKCho9ejTxeDyaNm0anTx5ktasWUOtW7d2mL7pj6O8vJzi4uIIAE2ZMoVOnjxJb775JgUGBlJubi7X8R4LKyDvGjt2rM3I4zt37pBarSYej0cbN260bk9ISKCoqChSKpUUExNDv//+OxdxG8XatWtp4MCB1r/NZjO9++67BIDeeOMNm8deuHCB3nnnHfLz86MTJ040d9RGY7FYaOvWrdSnTx8KDw+nV155xeEGddR07NgxGjBgACmVSurVq5dTTW9RWVlJH3/8MfXs2ZM6dOhA8+bNo7KyMq5j1VtKSgqtWrWKWrduTfv27eM6TqO6ffs2zZgxg/z8/CgwMJDee++9Ft3/8X45OTk0a9Ys8vf3p4CAAFq2bFmLmb4pOTmZFi1aRP7+/k71Wb1z507q2bMnKZVKGjJkCKWmpnIdqVElJCTQwIEDKSwsjCZMmEBZWVlcR3psrIC8y9XV1a4fRmxsLEVFRZFAIKBz585xlKzpTJkyxaZorrZhwwYCYDPlxWeffUaffvqp3ehgR3Dx4kUaNmwYBQYGUmxsLP34449cR3ogjUZDr776KoWEhFBkZCR98sknTtVx//Tp09S/f38KCgqiuLg4p7gaSlQ1F+iHH35IvXr1osGDB9OpU6fqfM5XX31Fa9ascZoP+h07dlC3bt0oLCyMZs2a1SKu3jANt3nzZof9rGZaFlZA3qVWq21uYdy5c4d8fHwoMzOTunTpQkOHDuUwXdP48MMPydvbmzQajd2+OXPmkFwur3XEpiNJTU0ltVpNH3zwAe3Zs4eGDBlCPB6PFixYYPfYvLw8u07mzclkMlGvXr3opZdeosTERHr99ddJLBZT7969bUb9VktMTOQg5YOdOXOG1Go1bdiwgXbs2EFRUVEkFArpo48+sntsenq6w9zyNRqNNHToUOrZsyctW7aMoqKiyM3Nja5evVrr4w8ePOh0V6M2btxIISEhtHPnTtqwYQO1bduWFAoFHT161O6xZ86ccai5TxmGcU6sgLxrzZo1BIBGjx5NK1eupKCgIOtozF27dpGLi4vTfanUpbCwkDw9PenFF1+0u32l1WrJw8PD4W/dvfzyy/TWW2/ZbFuzZg3xeDy7Pk3Tpk0jiURCp0+fbs6IVgcPHqR27drZvNa//fYbtW7dmjp16mRzK/3YsWMEwKGm7Bk8eLDNPIIWi4UWLFhAAOxGLg8fPpyUSiVdvny5uWPaWb9+PfXt29d6pVev15O/v3+tUyVduHCBeDweTZ48uZlTPjqLxUIqlcrmx5FOp6ORI0eSUCikI0eOWLcbDAYKCAiwO98YhmEaihWQNWzdupV69uxJ/fr1s1n27vfffyehUNjiCkiiqqtcfD6fZs2aZVdERkZG1jqVjCOJjo6mpUuX2m2vngrn888/t27TarU0adIkzibiXr9+PXXp0sVu+59//kleXl52c4KtXLmSDh8+3Fzx6hQeHl7rZNPLli0jADbTXOXl5dHEiRMdYuWZzp07203P8uqrrz7wrsLmzZtp27ZtzRGtUWi1WgJgt3KHyWSiF154gWQyGaWlpVm3X7x4kWbPnt0iP88Yhmk+rICshzVr1tCgQYO4jtFkPvvsMxIIBPTMM89YR1efPn2aVCqVw9/qqh7NVtvgjFmzZpG3t7fDjPr99ddfCUCt60SfOHGCANQ6H5qjmDhxInXt2rXWdZNHjRpFHTt25CBV3RYtWmS3bdWqVTRgwAAO0jSNTp060aRJk+y2l5WVUXh4uNPPp8cwjONhBeRDpKenW/sJOvuSQ3X5/vvvqV27diQQCCgoKIhUKhUdP36c61h1ys7OJrlcTmPGjLG7gpqfn+9wA6CeffZZ8vf3r3VgxsCBA2nevHkcpKqf1NRUcnFxodmzZ9e6D4DTTO/00UcfUXR0tPXva9euOXx3jYdJSEggALVeOd2+fTup1WoOUjEM05I9MROJ6/V6bN68GYsWLcKRI0dARA99vMViwebNm1FcXIzffvsNnTp1aqakjUev19f7sf3790dqaip++uknfPrpp0hLS8OgQYOaMF3j8PHxwbZt2/Dll19iypQpMJlM1n1KpRJSqRRCoZDDhLa2bNkCoVCIgQMH4tatWzb7AgMDHSrr/Tp06IBPPvkE69evx/z5823eQwEBAQDg0Plr4vF4sFgsAIDr168jJiYGGo2G41SPLi4uDq+88gqmTp2KnTt32uxz9POKYRgnxXUF2xzy8vKoQ4cO1KdPHxo8eDAJBALq3r07Xb9+3e6xJpPJYW55Po6SkhKKiIio9fZdbcrLy5s4UdPavXs3icVi6tWrF/3888+k0WhowYIF1LNnT4eb3+7GjRsUGhpKarWaPv/8c9JoNPTtt9+Sl5eXw0/6S1TVl5PP59OwYcPo0qVLVFxcTDNmzKARI0ZwHa3e1q5dS3369KFr165RQECA3ZKLzshkMtH06dMJAM2aNYsyMjLo9u3bD+wnzDAM8zieiAJy4sSJNGvWLOvfly9fpg4dOpCXlxclJyfbPXbEiBFOX0SuXLmSIiIiCECdReQbb7xB0dHRTjXJc20uXLhA/fr1IwAEgGJiYhx2pv+SkhJ65ZVXyNXVlQCQv7+/U3QZqPbjjz/S008/bX2tR40a5fBTPtW0bt06CgkJaTHFY03bt2+nwMBAAkAikYjmzZvHBswwDNPonogCMigoyG5OvaKiIurevTv5+vraDBRJSEgghUJhV1g6E7PZTG3btqXr16/TunXr6iwiv/32W5LL5fWaXNmRXLp0iWJjY2vt+8jVSOuG0uv1dPPmTaeaTLym3NxcysvL4zrGAxmNRho3bhydPXvWZvu///1v4vF4La54rCkjI6PWOV4ZhmEaA4+ojs6ALUDv3r3RuXNnbNq0yWZ7fn4+nnrqKfTv3x87duywbi8pKYFCoWjmlI1Hq9Vi+fLleP/99wEA69evx5w5c7Bo0SIsXbq01uc42zEnJydj2LBh+PTTTzFq1Ciu4zAOyGQyYezYsaisrMS+ffsgFout+/R6Pb7//ns899xzHCZkGIZxXk9EAblt2zZMnToVx48fx4ABA2z27d27Fy+//DKKiooglUo5Stj0aisir169ipCQEAgEAo7TNQwrHpm6PKx4ZBiGYR7fE1FAEhGGDx+Os2fP4vjx4+jevbt1n0ajgVwuR0ZGhnUkaUtVs4gcMWIEhg8fjsOHD9u8Ho6OFY9MXVjxyDAM0/SeiAISAHQ6HYYOHYrLly8jPj4eY8aMAQDs27cPCxYswNWrV8Hj8ThO+XiSkpKst+LFYjHCwsIwbtw4SCQS62Oqi0gPDw/s2rXLqW7hPSnF4/fff4+DBw8CAFxdXdGxY0e8+OKLrBCqB0cqHg8cOIAff/wRAODu7o5u3brh+eefB5//xMye1iJ8/vnnuHTpEgDAw8MDUVFReOaZZ7gNxTTYunXrkJaWBgBQKBQYMGAAoqOjOU7l3J6YTzKpVIoTJ04gLi4OY8eORdeuXfHcc89h9uzZ2LVrl9MXj0DVldagoCAEBQVBLpdjw4YN6N+/v818fVFRUVAqlax4dGB8Pt/ajm5ubli+fDleeOEFrmM5PEcqHoGqH3HV7cjj8TB37lz84x//4DQT03Bubm7WdqyoqMC4ceOwYsUKrmMxDSSVShEUFITAwECUlJTg2WefxZYtW7iO5dSemCuQNf3xxx84duwYRCIRXnzxRXh7e3MdqUlkZWXB39/fenv++vXr6NOnD7Zs2eJUxSMALF++HJ06dWrxxWNtkpKS0KNHD5SVlcHV1ZXrOA7r6tWrWLFiBeLj4zkvHmtz8OBBTJ8+HXl5eVxHYR7Dhg0bsGXLFly4cIHrKMxjWLRoEZKSknD06FGuozitJ3J5gs6dO6Nz585cx2h0BoMBiYmJuHr1KoqKiqDVagHAWnQEBQVh/vz5yM7Oxu3bt+Hv789l3AZ55513uI7QbHQ6HQ4cOIAbN26gtLQUubm5EAgEEIlEAIDVq1fDYrEgICAAL730EsdpHUe7du2wbds2rmNYFRUV4cCBA0hPT4dWq8WNGzdsfgBkZWXhyJEj8PDwwN/+9je4ublxmJZ5kOzsbBw6dAi3bt2CXq9HcnKyTTvm5ubi0KFDkMvlGDlyJFxcXDhMyzxIWloajh49itu3b6OiogJnz561+w40GAxYv349hgwZgi5dunCU1Hk8MbewW7qkpCQEBQVh8+bNMBqNCAgIQFlZGdRqNVq1agUAmDp1KhISEnDu3Dn06NEDmZmZHKdm7nfy5EkEBgZi7969ICIEBARAo9EgPDzcOlq+tLQUqamp2Lx5M8dpmQfZvXs3QkJCcOzYMQiFQgQFBaGoqMi6JOqZM2cwZMgQJCUlYePGjYiKirIurcg4jrVr16JTp044c+aM9VZ2fn6+tR1TUlIwdOhQXLx4EfHx8ejXrx/HiZnaLFy4ED169MDFixchk8kQFBSE3NxcuyWKFyxYgF27duGXX37hKKmT4WLySabx9e3bl6ZPn26z7aWXXqJBgwYREVFmZia1atWKKioqiIhowYIFtHDhwmbPyTxcWFiY3aTvAwcOpPHjx9ts+/nnnyk6OroZkzH1VVlZSXK5nDZt2mTdZrFYKDw8nN566y0iIrpy5Yp1km+z2UxeXl6UnZ3NSV6mdnfu3CGBQEBHjhyxbquoqCBPT0/65JNPiIgoPT2dtFotEVW1sUKhoOLiYi7iMg+QkpJCACgpKcm6raCggEQiEX355ZfWbYcOHaIPPviAZs6cSfHx8VxEdTrsCmQLkZOTg8DAQOvf33zzDfbs2YOIiAgAVf3DunTpYr29EhUVhf/973+cZGUe7P523L59O7777jtrOzKOT6/Xo7S01KYdV61ahT///NPajuHh4ZDJZACq7h6EhYXBx8eHk7xM7fLz82E2m63tSER44403UFxcbG3HwMBAFBYWYtWqVRg/fjzGjh3rVAsyPAlycnIAwNqORqMRr776KoxGo7Uds7KysH//frzxxhuc5XRGT2QfyJZo3LhxWLlyJa5fv46CggJYLBZIpVJ07doVQNWHX82R5jwez2Z0NuMYxo0bh3nz5uHnn39GRkYGFAoF+Hy+tR0ZxyeXy/Hcc89h8uTJGD58OFJTU+Hr6wsAdu14/vx5vPnmm9i/fz8HSZmHCQ8Px9NPP40RI0YgJiYGFy9eREBAAHg8ns0POqPRiOLiYusPh8rKSoccxPWk+utf/4qQkBBER0cjKioKv/76KwIDAyGTyRASEgKgqntXREQE3n//fVy6dMm6St1TTz3FcXrHxgrIFmLJkiXo168frl69io4dOyIqKgobNmxATEwMACA0NBSXL1+GyWSCUCi0XvVgHMvGjRsxYsQI3Lp1CzNmzED79u3Ru3dv9OjRg+toTAMkJibi0KFDKCgowJw5c+Dp6YnevXujXbt21sccOnQIa9euxf79+6FUKjlMy9RGKBTi9OnTSExMhF6vx5tvvgmTyYShQ4darx7fvHkToaGhWL16NQCgV69e+O2339CrVy8uozM1uLu7IykpCYcOHYLZbMbixYuRn5+P0aNHWy+qREdHo7S0FCUlJTAYDCgvL4fBYOA4ueN7IqfxeVKNGjUKOp0OERER2L59O86ePYvQ0FCuYzENFB8fj0uXLuGbb77BjBkzEBsba9cZnHFs33zzDf7+97/j9ddfty6hOmXKFOuAN8Y5bNu2Dbt27UJUVBTS0tJw6tQpJCcnQy6Xcx2NeUSvvPIKunfvjmnTpnEdxeGxK5BPkL1792Lnzp3Iz8/H6dOnWfHopLRaLWQyGUaPHo2SkhIYjUauIzEN5OHhgdmzZ8NkMqGkpAQAYDabuQ3FNNjEiRMRGhqK7777Dn379sXatWtZ8ejkhg0bBj8/P65jOAV2BZJhGIZhGIZpEDYKm2EYhmEYhmkQVkAyDMMwDMMwDcIKSIZhGIZhGKZBWAHJMAzDMAzDNAgrIBmGYRiGYZgGYQUkwzAMwzAM0yCsgGQYhmEYhmEahBWQDMMwDMMwTIOwApJhGIZhGIZpEFZAMgzDMAzDMA3y/wER0TLv6ZutPQAAAABJRU5ErkJggg==",
- "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": [
- {
- "data": {
- "image/png": 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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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",
- "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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",
- "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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5U/+86uGLc7dly5YoVaoUfvzxR3IeREREwGq1enynUqVKGDx4MNasWYPo6GjynBaE4oAsOkowAQEBmD17NjZv3oxx48a575IeP34cI0aMQFZWFkJCQvD8889j5cqVePvtt5GZmYmUlBSMHz8ee/bsweuvv16gOkybNg2BgYHo06cP/vrrL7hcLtjtdvzzzz8YN24cfv75Z/a7Xbp0wYYNG9wX0pMnT+L+++/Hbbfd5lGubt26AJCnryt54YUX4HQ68cADDyAuLg5OpxNr1qzBG2+8gT59+qBly5b5bG3hH6cwfBZkjCZNmoS0tDQMGzbMvbvx559/jtOnT+vK+mI8vfXRu3dv/PDDD+4f6GfOnMFPP/2E2rVru+8yettuo/OrS5cuiIqKwpQpU9x7sHz88cd53i31BiN9XZAxzYur9UXnzp3hcrmwatUqj8VF586dsW7dOmRkZOgWHQWZ096Mcw6jR4/G4cOH8eWXX6Jdu3aoVauWYV95tb+w+pyisGLjlClTEBISgn79+mHv3r1QSuHy5cuYP38+Vq9ezX6voH13tbZ6ez55U/+rxZeCnrvBwcF46623sHTpUkyaNMl93U1OTsb69evd+ZNr1qzBc889h8OHD7vzOTZs2ID4+HjccssthvpIEIoM1yuDXSg6fPvtt+rmm29WmqapoKAgVbduXZ0G/pw5c1S1atWUxWJRJpNJNWzYUKc81LVrV9W0aVOd/xz1qsmTJ5PHP3PmjHrooYdUmTJllJ+fnwoKClJNmjRR7777rkpLS1NK0UoqKSkpasiQISowMFAFBASounXrqvXr16vRo0er4OBgj2NMnjxZhYSEqJCQEA/ddU4Va+fOnapdu3bKbDYrf39/FRERoSZMmKAyMjLcZXLUSXKr0Zw8eVIBUAsWLCDba/Q4RtSrCqvu3owRx5YtW1TDhg0VABUaGqoee+wx9csvv+gUlXwxnt76WLNmjercubPbR1hYmLr33ns99oYw0m6uPhxbt25VdevWVSaTSZUuXVo9//zzaseOHawCjrfj5G1fe9s27vh5kVdfOJ1O974zV+7/kNOegIAAj3magzdzmsLbcVYqO05FRUUpAOqzzz7Lt6+82l+QPv/8888VAJ3yEqW2VJix8cSJE2rgwIEqKCjIPRZjx45Vly5d4oahwH13tXno7fnkbf3zGsOCnrs5rF+/Xt1xxx3KYrGooKAgVaZMGdWzZ0+1ZcsWpVS2xPLMmTNV/fr1VWBgoAoMDFTVqlVTr776qrLb7WxfC0JRRlPqKi+eCyUGm80Gp9PpfrxMkZaWBrPZTL7PnpycDJfLRb5jffHiRQQFBSEoKCjPOqSkpCA4OFj33rLNZkNycjJKly4Ni8UzFcnlciEzM9PtOy0tDZmZmQgPD/cop5RCUlISHA4HwsLC4Ofnl6dfIPsRe0ZGhjtZ80qysrKQkpKCMmXKeLz77XK5cOnSJYSEhHj93n9ex1FKISEhAcHBwXmOzbWqOzdGVyMtLQ0BAQEwm81wOBy4fPmyexyupCDjmR8f6enpXu32frV251UfjtTUVAQFBcFkMpF9kt9x8ravr9Y27vhXI6++SEpKgt1u1+12nZCQAJPJhDJlyrB+85rTV6uPN+PcvHlzHD9+HOfOnWNjlTe+vJkLRvucs+fEMGqMCjM2ulwupKWlufMcvCG/feftPLza+WSk/lcbw/yeu7lxOp1IT0/Psx9tNhs0TdO9diUIxQ1ZdAiCIAglnmPHjqF27dp47LHHPORzBUEQBN8gOR2CIAhCicblcuG1116D1WrF008/fb2rIwiCcEMiiw5BEAShxPLcc88hNDQUq1atwvvvv69LIBcEQRB8g7xeJQiCIJRY0tLSkJWVhTJlyshOz4IgCIWILDoEQRAEQRAEQShU5PUqQRAEQRAEQRAKFVl0CIIgCIIgCIJQqMiiQxAEQRAEQRCEQkUWHYIgCIIgCIIgFCqy6BAEQRAEQRAEoVCRRYcgCIIgCIIgCIWKLDoEQRAEQRAEQShUZNEhCIIgCIIgCEKhIosOQRAEQRAEQRAKFVl0CIIgCIIgCIJQqJTYRcf69evxyiuvYMKECR6fxMTE6121Ys1nn32GmTNnXtV2Petzrdm4cSPefPPN61qHhQsX4p133rmudcgvP/74I15//fXrXQ1B8Bl79uzBhAkTEBsbe72rUuI4fPgwJkyYgFOnTvnMZ1JSEiZOnOhTn9eb3377DRMmTEBSUtL1roohFi9ejI8++uh6V0NgKJGLjmeffRb33nsvsrKyUKFCBVSoUAGnTp3C7Nmz83WC5ZycsmABVq9erTvhKZsv+eijjzB79myv63MtSUtLw7Bhw2CxWK5bHQBg2bJl+Pzzz69rHfJLnTp18Morr2DVqlXXuyqC4DX//PMPJkyYgNOnT+v+dujQIcyePRsXLly4DjUzTl5tKYrkVd+TJ09i9uzZOHPmjM+ON23aNKxfvx5VqlRx206fPo0JEyZg69atuvKHDx/GxIkTMXPmTDidTp/Vw5fs2bMHs2fPRkpKCjIzM3U3aLnPjh07rmu9o6Oj8eijj+LQoUPXtR4CTYlbdLhcLrz77rsYM2YMXn31VfeJcuedd+bbZ87JWdzuCFwrhg4dimeffbbQ/H/zzTf4+OOPr8uxr8a7776LzMxMPPbYY9etDsWdihUrYujQoXjuueeglLre1REErzh69Chmz559QzzNKG5tyau+9erVw5tvvonq1av75FhnzpzB3Llz8fzzz8Nk+u8nVWxsLGbPno3ff//do/zvv/+ONm3aYMWKFejbty/MZrNP6lGYaJrmvkGb89m4cSNmz56NyMhID3twcPB1rWu7du3QsmVLTJ48+brWQ6C5vrdfrwPnzp1DZmYmoqOjr3dVSgy9evUqkcd2Op1YsGABBg0ahICAgOtWjxuBoUOH4v3338eWLVsKdINAEISSTbVq1TBhwgSf+fvggw8QEBCAfv36XbXspk2b0L9/f1SvXh3r168vNr9D/P39dX3222+/Yd++fXj66aev+5P83AwdOhSPPfYYzpw5g0qVKl3v6ghXULRmSiHz9ddfY926dQCy744fP34cwcHBmDp1Kln+8OHD+OCDDwBkr/QDAwPRoEED9OrVy72aX7t2Lb7++msAwOuvv46wsDAAwCOPPIK6deuSfn/++Wd8++23mDJlChITE7F06VKkpqaiffv26NChA/udn3/+Genp6ahduzb69evnPlZunwkJCfj6668RFxeHN998EwsXLkRGRgaefPJJbNmyBT///DMiIyMxZMgQhISEAMh+b37btm0IDQ3FAw88gMjISI/jr1q1Ctu3bwcAmEwmlC5dGrfddhs6dOgATdPy7PfPPvsMcXFxeOaZZwBkPxn64osvyLJhYWF46aWXvD7m7NmzcfDgQSQlJbmDotlsxowZM8hj55CVlYVVq1bhn3/+gdlsxq233oq77rrLoy1X9tvPP/+MLVu2ICgoCAMGDEDNmjXzbDMAbN26FTExMejfv7/ub1f63rZtG3766SdUqlQJw4cP97qvC1q/zMxMvPnmm3A6nXjmmWcQFBR01e/s2LEDW7duRXp6OmrVqoV+/fqhVKlSHn9fuXIlXnzxRSQnJ2Pp0qVITk5Gu3bt0KlTJ9LnoUOHsH79ely4cAFRUVHo16+f7kLRqlUrVKhQAZ999pksOkogZ86cwZo1axAbG4uoqCi0b98eDRo0cP89J6Y8/fTTUEphyZIlSE9PR58+fdCkSRMA2Xeely5disTERHTt2hW33347eaw//vgDW7ZsQXJyMqpVq4Z+/fohIiLCULmff/4Zn3zyCQBgzpw5WLFiBQBg8ODBuPnmmz38nD9/HosXL0ZCQgJuu+029OjRo8B9cuTIESxcuBAPPPAAmjVr5vG9jIwMvPTSS2jXrh169ux5VV/etuXkyZP4/vvvERsbi3LlyqFPnz6oUaOG++++GKPY2Fj873//A5B9TQ4ICEDt2rXRu3dvlC5d2qv65lzXx4wZg2rVqnn4//fff7F27VqcOXMGVapUQa9eva66MPj888/RrVs3BAYG5lluyZIlGDJkCFq2bIk1a9a465tDfHw8Vq9ejRMnTiA0NBRdunRB8+bN3X+Pi4vDrFmzcN9996F27dpYtmwZjh49ihEjRuDMmTP4/vvv8eqrr+LcuXNYtmwZ0tPT0alTJ7Rt25asz/79+7Fx40ZcvHgRFStWRP/+/REVFZVnG7zh9OnT+O6773DmzBmEh4ejZ8+eHr+FDh48iEWLFmHMmDEIDg7GsmXLcPr0aTzzzDP46aefsGfPHrzyyivYvXs31q5di6CgIDz44IPu3yR//vkn1q5dC7PZjEGDBnnMsRz69euHkSNH4quvvtJd+4XrS4l6vapUqVIoV66c+98VKlRA+fLl2fL+/v7uR4aRkZHIyMjA5MmTUb9+fcTFxQEAQkND3T+6ypUr5y7v7+/P+t29ezdmz56NDRs24MEHH0RSUhKOHDmCjh07Yvr06R5l7XY7+vfvjzvvvBPHjx+Hy+XCjBkzULt2bezatUvnc/Xq1bj//vuRkJDgfpc0533+yZMn44MPPoDT6cRbb72F2267DSkpKXj66aexYMECOBwOvP/++7jpppuQkJDgUY/Q0FB328qWLYuTJ0+ib9++GDBgwFX7PXdeRWBgoO5RbUhICN59910sXLjQ0DHDw8Ph7+8Pq9XqLnvlmFI5HcePH0fDhg0xceJEZGRk4MKFC7j33ntxxx13eLwil9Nvr732Gt566y0opbBixQo0atRI98ic4scff4Smabj11lt1f8vxPWnSJPzvf/9DRkaG+11Yb/u6IPU7f/482rdvj/fffx99+vS56oLD4XBg4MCB6NChA44fPw6lFGbNmoVatWrht99+c5f766+/MHv2bGzcuBH3338/Ll++jOPHj+POO+/Eq6++qvP7zDPPoFGjRtixYwf8/PywadMm1K5dG998842u7G233YYffvghz3oKNx7ffvstatSogQ0bNsDPzw+HDx/Gfffd5yEQkZMjsX79etx7771ITEzEX3/9hebNm2PdunX47bffMGDAAFy8eBEHDhxAmzZtdDlOSik88sgjaNmyJfbt2wdN0/Dee++hZs2a2Lhxo6FywcHBKFOmDIDsGJVzPuf+Ybp//37cfffdOH/+PGJjY9G7d2+MGzeuwH1SpUoVfPLJJ6QAw1dffYVZs2YhNDTUK1/etGX69OmoU6eO28evv/6KevXqYdGiRT4do9xx3uFwYObMmahduzaOHDniVX25nI7XXnsNtWrVwjfffAOz2Yw9e/agY8eO2Lx5MzsO//77L06ePInbbrstz/GaO3cu7rvvPnTr1g0bN27ULThWrFiB6tWr46OPPoLJZMKJEyfQunVrjx/MFy9exOzZs7FlyxZ069YN+/btw6lTp/Dvv/9i586dmD17NtauXYuHHnoIqampOHDgANq1a4e3337b41hKKYwZMwbNmjXD77//Dj8/P6xZswa1a9d235TNL++++y5q1aqF1atXw8/PD3/++ScaNWqEOXPmuMscP37c/fund+/e+Pfff7Fnzx4kJiZi06ZNmDNnDj788EO8+OKLsNvtWLx4MZo0aYKYmBjMmzcPzz//PGw2G1atWoWmTZvi4MGDunpERESgVq1acr0oiqgSxtGjRxUAtWDBAg/7ggULFAB18uTJPL+fmpqqatasqYYPH274uzm89dZbCoAaMGCAstlsbvvDDz+sQkJC1OXLl922adOmKQBq3bp1HnVo1qyZqly5ssrMzPTw2atXL5WVlaWUUm4/nTp1UqVKlVL/+9//3D5Onz6tzGaz6tmzp5o+fbrbfubMGWWxWNTLL7981Xb8/vvvCoD65ptv3LYBAwaounXrepSjbFficDhUjx49lKZpavny5YaP2aNHD9WwYUOyPHXsli1bqqioKHXhwgW3bd++fcpqtaphw4a5bTn9NnPmTLctMzNTVa1aVXXr1i3PeiqlVO/evVV0dDT5txzfr7zyitt25bjnhmq3kfp16tRJtWjRwt3WqlWrqubNm6uzZ89etR1KKfX6668rAGrNmjVuW1pammrRooWKjo5WGRkZSiml5syZowCovn37uuehUkqNGjVKBQcHq0uXLrltH374oQKgvvzyS49jTZw4UQUFBam4uDgP+3PPPacAqPj4eK/qLNwYtGjRQvXu3dvD5nQ61d9//+3+/+LFixUA1bFjR/dcVEqpbt26qXr16qn+/furtLQ0t71Xr16qatWqyul0um0LFy5UANTHH3/sttlsNtWxY0dVqlQplZCQYKjcypUrFQD166+/6tqUU98OHTp41OuFF15QFotFnTp1qsB9MnHiRGW1WtW5c+d0361Vq5ZyuVxe+8qrLatWrVIA1DvvvONhnzFjhrJarerIkSMebS7IGFHYbDZ18803q+7du3tV33Xr1ikA6ueff3bbli9frgCoGTNmeJRNS0tTx48fZ4+9evVqBUCtXbtW97dff/1VAVB169ZVANSwYcOUw+HQlTtw4IDy8/NTI0eOdI+JUkp9//33HjF33759CoCqWrWqiomJcZe7fPmyeuONNxQAdd999ym73e7+2wMPPKBKly6tUlNT3ba3335bAVDffvutRz0ee+wxVapUKZWYmKiU+u93zZXHupIBAwYoAO7jbdq0SQFQr732mke5efPmKZPJpPbs2aOUUmrNmjUKgGrevLn7WA6HQ6WkpKhHH31U+fn5qfHjx7u/n5aWpsLDw1WXLl3U448/7rZnZGSoyMhI9cADD5D169mzpypfvjz5N+H6UaKedOSHpKQkfPXVV5gyZQomTpyIKVOmwGKxeHWn+2o8/PDDsFqt7v/369cPqamp+Oeff9y2Tz75xP3qTw7BwcGYOHEiYmJisGnTJg+fw4YNg5+fHwB4vPbicDgwevRo9/8rV66Mxo0bY/PmzXjyySfd9ooVK6Jp06Yed69zOHbsGObNm4dJkyZhwoQJWLJkCaxWa4H74oknnsD333+PGTNm4O677y7UYx48eBA7d+7EY4895n7qBQCNGjXC3Xffja+++gqZmZluu91uxxNPPOH+v7+/P7p37072T24uXLiAsmXLsn/PzMz0uKt55Xh5226j9Vu/fj1uv/12NGvWDNu2bfN4dWDr1q06JRKXywUgex42b97c/ToGAAQFBeGZZ55BbGws1q9f73GcESNGuOchkD2309LSsG/fPrdtzpw5aNSoEe677z6P744fPx7p6enu1xZzCA8PB4Bio/gj+AabzYazZ88iOTnZbTOZTGjcuLGu7JAhQzzyp3r06IFDhw6he/fuHk/zunfvjn///Rfnzp1z2z755BNUq1YNQ4cOddusVismT56MpKQk99M3b8t5w5AhQzzq1a9fPzgcDuzevTvP73nTJ4899hicTqf7FWEA2LVrF3bv3o3hw4e7X9U00r8Uc+bMQXR0NMaOHethf/LJJ6GUwuLFi3Vtzu8YAdmKgMuXL8fUqVMxceJEPP/883C5XAW6Ds2dOxeVKlXC+PHjPexBQUHk6zs55MSivOJ8Tv2rVq1KJo2///77sNvteO211zxen+3evTvq1auHzz77zKN8jx49PF4/vfK68cgjj3jkV/Tr1w+XL1/G4cOH3bY5c+agVatW6N27t4ff8ePHIykpCatXr2bbkhdz585F6dKldcItjz76KIKCgvDll1962AcPHux+4mM2m92vettsNo+5FBQUhA4dOmDjxo14+umn3faAgAB06NCBvdaFh4fj4sWLIj5SxChROR1G+fXXX90neJcuXVCuXDmYzWYEBQX55IdP7pyPnNeCchQ3HA4HTpw4gXbt2um+26hRIwBwP1LOoX79+uSxqlat6vEjEMh+BFmlShVdknO5cuV0gX7mzJmYNGkSunbtiubNm6N8+fLQNA0mkwmXLl26WlNZZs+ejfnz52PUqFGYOHFioR8zp78aNmyo+1ujRo2wePFinD59GnXq1AGQnXSY+1W58uXLIzExEZmZmXkmiJvN5jzlECtVquQOtFdipN1G6nf48GH07NkTvXv3xooVKzyUVoBsVZXc0sM5r/sdO3YM999/v66u3Dy82txWSmH//v2oU6eOhyqVUgpKKZjNZhw/ftzDR05fFge1F8F3vPzyy3jwwQdRsWJFdOzYEe3atUOPHj3InLmc8zaHnByL3PacGw7nzp1DxYoVAWTP4VtuuUWXo5Z7jntbzhuudp5weNMn1atXx1133YWFCxdi0qRJMJvNmD9/Psxms8eCyUj/Uvz9998ICgrC888/D+C/c1gphYCAAN15XJAxOnDgADp37oygoCD06NEDFSpUgMViQUhISIGuQwcOHECLFi0Mx5ac8nnF+eeeew67d+/Gyy+/DJvNhtdee83j7zn9N2vWLACe/ZeZmanrP+4aD+Q9n5o3b4709HR3LmvuuJvThtzH85acduSoRl3ZDqvV6nU7/P39PaSHgew5YrVadYpj1G+VHJxOJ0wm01VzToVriyw68uDJJ59ExYoV8ccff3j8YM9Jei0oud/vzQlgDocDQHainKZp7rvNV3JlmSvJ/a4od6yc43H2HP9AdtCfNGkSnnzySY8fpRkZGQVK0vrmm2/wzDPPoFu3bpg7d67H3wrrmDn95W2fcv1zZXmOyMhIHDt2jP07NVZG222kflWrVkX16tWxdu1afPfdd7o7Xe3bt9dtYpjjy+g8vNrcziEkJIRM0p0+fTpuuukmD1vOj4rcIgfCjU3//v1x++2347vvvsP27dvxv//9z32HO3eeEDfvvJmP3s5xo+dCXnh7nuTG2z4ZPXo0evTogTVr1qBt27ZYunQpunXr5vGE00j/cgQHB5Pn8ZQpU1CvXj2v2uxNXzz33HMwmUz4888/3TkpQHaSekHQNC1f+2XkxKK8FjxWq9WdRP7666/DZrPp4qy/vz/Zf6NHj9bZuWs84P18Cg0NJY/35ptvomXLlqz/qxEYGEj6nTRpkk7chGtHQECA7hwym83w8/PT3SjL/VvlSi5duuTxNoNQNJBFRx4cOXIEAwcO9FhwZGRkYO/evR53l3NObF8/xjObzahTpw727t2r+1uOjbpj72uOHz8Op9OpU8HYuXNnvtu8c+dOPPDAA2jSpAmWLVumu8Nk5Jhms9nreuQosuzdu1f3KtfevXsRFBSkUzTJLzfddBNWrlyJS5cu5fn4/UoKo69zCAgIwMqVK/HQQw9hwIAB+Oijj/Dggw+6/37zzTfrlHVyqFevnk/noaZpaNKkCZxOp9fylfv370f16tXdSaJCyaF8+fIYMWIERowYAZfLhcGDB+O1117DxIkTPV4vKQgNGjTA/v374XQ6PeJR7jnubbnCui7k4E2f3HXXXahRowbmz5+P48ePIzMzEyNGjDDsK6+2NGvWDEeOHMH48eML/a7ykSNH0KxZM48Fh8vl8hBVAYz3fbNmzbB3715kZWXlKQKTmxx1qQMHDni8epobi8WCL774An5+fpg1axaysrLw7rvvuo+9detWPPzww3kuKHxBUFAQateuDZPJ5FPZYCC7Hdu3b8dTTz1VJJ5G79+/Hy1atLje1RByITkdeVC3bl3s3LnTYyU9depUjzwMAG6ZuRxFK1/y6KOPYs+ePVi6dKnblpCQ4Faw6tixo8+PmZtatWrBZDJ57DSakpKCmTNnGgrQOZw8eRK9e/dG2bJl8d1335GvGBk5ZlRUFC5cuEDefaT8duzYEe+99x5OnTrltv/yyy/49ttvMWLECN345pecsfEm/+PK+vmyr3NjsVjw+eefY9SoURg6dKiHqkhePProo9i/f7/He7mJiYmYPn06atasic6dOxuuy7PPPos///wT8+bN0/1t165diImJcf/f5XJh586drOyucOOyatUqj7vQJpMJkZGRsFqtPv2R++ijj+Ls2bMeT13T0tIwbdo0REZGuqWvvS1XmNcFb/vEZDJh1KhR2Lx5M2bNmoXIyEidJK83vvJqyzPPPIPTp0/jlVde0f3I37dvH44ePVrwBv8/devWxZ49e5Cenu62vfvuu0hNTfUoZ7Tvx48fjwsXLuDFF1/0aEN8fDz279/Pfq9ChQqoV6+eVzHeZDLh448/xsiRIzFnzhyMGjXKrSQVGBiI0aNHw2azeXwnJiZGt6AqKM8++6yHrPCV7NixI9/zdcKECbh06RImTZqkuxYfOnQIBw4cyJff/BAbG4vTp0/L9aIIIk868uDNN99Ez5490aJFC7Ru3Rp79uzBLbfcgo4dO+Knn35yl+vQoQOqVKmCYcOGoUuXLvDz88tznw4jPPnkkzh48CDuv/9+fPHFF4iMjMSGDRsQEBCA1atXX5NNeSpUqICXX34ZL7/8Mv755x9ERUVhx44dmDFjBn755RfD/ubPn48LFy6gW7dueOuttzz+lrNPh5FjPvDAA/jwww9x1113oVGjRrBare59Oig+//xz9O7dGy1atED37t2RmZmJ7777Dj169NBJFheE1q1bo06dOlixYgW6d+/u1Xd83dcUmqZhzpw5KFOmDJ544gkkJia690bhGD16NA4cOIChQ4di8eLFKF++PDZu3Air1Ypvv/02Xwu1QYMGIT4+Hs8++yw+/vhjNG/eHJmZmdi/fz/8/f09Fto//fQTEhIS8NBDDxk+jlC8Wb58OcaPH4/mzZujQoUKOHLkCLZv34633nrLY6+ignLvvfdi3759GD9+PL777jtUq1YNP/zwAzIyMrBq1Sr3zRFvyzVr1gxNmzbFuHHjsGXLFgQEBJD7dOQHI30yfPhwvPTSS4iLi8PEiRN156o3vvJqy5133olPP/0UTzzxBFasWIFbb70VTqcTBw4cgM1mw1dffVXg9ubw6quvomPHjmjWrBk6deqEw4cPIzIyEvfeey/eeecddzmjfd+lSxe8//77eOqpp7Bhwwa0bNkSFy9exP79+/Hxxx/nWaeHHnoI06ZNQ0pKiscTGIoceWV/f3/MmTMHNpsNH374Ib7//ns8+OCDqFOnDtq2bYuAgAAcPXoUZ86cwfz58413VB6MGDECCQkJePzxx/Hee++hadOmSEtLw99//41SpUq59zUxSuvWrbFkyRKMGjUKa9aswW233QZN03Dw4EGkpKToEuILkxUrVsDPz08nUiJcfzRVwlL7L1++jA8//BCdO3dG06ZN3fa9e/di06ZNGDlypEfQPn/+PDZv3oyUlBTceuutaN68OdauXYuYmBg8+uij7nKJiYnYsGEDzp07B6fTibvvvpt9TefPP//EDz/8gMcff9xDrSM+Ph6ffvopevXqpVuwHD58GD///DMyMjJQq1Yt3HnnnR4XD84nkH1RycrKwgMPPOBh//rrr5GWloYhQ4Z42FeuXImkpCTdD7y///4bO3fuhNVqRdeuXREVFYW5c+eiXr167g3b1qxZg4sXL2LYsGHu7+W2bd26lVUaCQ4OxmOPPWbomED2Y/dt27YhKSkJmqa5VS6o+gDZd863bdvmsTnglRsx5dVvv/32G7Zv344nn3zyqj+2586di0mTJuHs2bMeFyTOt5F2G6kfV3bFihU4deoUBg4ciKpVq+bZFiC7n3M2qaxZsybuvPNOj9cP9+zZg82bN2PUqFEeT7ASEhLw8ccfo0ePHroEwsuXL+PHH3/EmTNnULZsWTRu3Ni9WVgOQ4YMwf79+/Hnn39etY7CjcfZs2fx22+/IS4uDhUqVECHDh08Xlk8fPgw1qxZg6FDh3q8x33kyBGsXr0aDz74oMf+PceOHcOqVatw//336zZEO3XqFH766Sf3pn+dO3cmc6e8KZeamooNGzYgJiYGDocD3bp1Q8OGDdn6pqenY/78+ejYsaMuHhntkyvp0qULNm3ahIMHD+pyLLz1xbXlyr//+OOPOHXqFEqXLo0GDRp4vN7iqzFKTEzExo0bkZCQgKZNm+L222/H1q1bsXv3bg91I66+p06dwooVK3Dvvfe6E9RzuHTpErZs2YK4uDhUrVoVHTp0uOpC4uLFi6hWrRrefvttPPzww277uXPn8OWXX6J9+/bkYufTTz9FfHy8u152ux3btm3DoUOHEBAQgDp16qB169buV5XyiqG7du3Ctm3bMHbsWI8n4jl16Nu3L2rVqqVr648//oizZ8+iXLlyaNq0qceGmzm/iXLH8hzWrFmDw4cP616rS09Px08//eTe5LB+/foee1WdOHEC33zzDdn/mzdvxqFDhzBmzBgP+5YtW/DPP/94KDUC2Xth7d27V7e3TYsWLdCwYcNrutARvKPELToE4Vpis9nQsGFDPPjgg1d9miDQHD9+HPXq1cOGDRuuyeuExYWzZ89i2bJlCAwMxKhRo7z6zj///IPNmzfDYrGge/fuOjUYb8sIxYfMzExERESgadOmHq9tCr5j2rRp+PTTT3H48OFr8vaBwPP999/j7rvvxqFDh7y6kVZccDqdWLNmDfbv34/BgwfrFpEUaWlp+PbbbxEbG4v69euje/fuuldSvSnjSySnQxAKET8/P3z55ZceajGCMS5cuID3339fFhxXcPfdd6NVq1b49NNPdTsOc7z//vu4+eabsXv3bvzwww9o0KABvvvuO8NlhOLFihUrkJaWprtLLPiOCRMm4PHHH2flW4Vrh8vlwtKlS2+oBceqVatQs2ZNLFiwAC+++CIOHTp01e9cuHABN910E2bNmoUTJ05g1KhR6Nmzp0f+ljdlfE7h7z8oCIIg+JJVq1Ypu92uJk+erOrWrXvV8ufPn1eBgYFqwYIFbtv48eNVVFSUstlsXpcRig9r1qxx7zLdoUMHj92uBUEoPvz2228qJiZGJSYmeuxSnxcjR45UDRs2VBkZGUoppU6ePKn8/f3Vxx9/bKiMr5EnHYIgCMWMPn36GHqN4/vvv4fT6fTI6XnkkUdw7tw59ys33pQRig+hoaGoVasWFi1ahPXr18smaYJQTGnZsqXHLvTe8PXXX+P+++93bxCck3d2pVCAN2V8jbx8KAiC4CMyMzN1spfeopTS/TD09/f3iVTyoUOHEB0d7ZEQmqPXf+jQIbRv396rMkLxoV27dmjXrt31roYg3NDkN+YXZryPj49HQkIC6tSp42GvU6cOvv32W6/LFAaFtuhQSiE1NfWqyg+CIAg3ApmZmQgPDEE68vc+bEhIiG6/gSlTpuDll18ucN1SU1N1m+iZTCaPY3pTJi8yMjJgsVh8ts+NIAhCUaYgMb+w4z0AXTwvXbq0R7y/WpnCoFAWHW+99RZeffVVpKSkIDw8HDNmzNDJsgqCINxI2Gw2pMOJIagIP4MaHTa48FnqWcTExHhIdvvirheQLUWdnJzsYXO5XEhNTUVwcLDXZSiOHDmC4cOHuzcx6927Nz744APZOV4QhBua/Mb8axHvAejieVJSkke8v1qZwsDni44VK1bgueeew8qVK9G1a1d8+eWXGD58OKpXr4477rjDKx8ulwuxsbEIDQ2V91AFQbgmKKWQkpKC6OhomEz5T3fzgwl+msHv/79weVhYmE83vMuhbt26iI2NRXp6unsfn+PHj8Plcrn3BPKmTG7sdjt69uyJpk2bIiEhAampqbjrrrswfPhwrFy50qu6SbwXBOF6cN1ifiHH+8jISJQpUwbHjh3zsB89etQdy70pUxj4fNExZ84c9OvXz70D85AhQ7Bw4ULMnTvX60VHbGwsKleu7OuqCYIgXJWYmBjDSXtXYtY0mA3+eDZDc1+IfIFSCq+99hruuusu3HzzzejRowdGjx6NxYsXY8SIEQCAjz76CJGRkWjTpg0AeFUmN+vXr8fRo0exZcsWhIaGIjQ0FFOnTkW/fv1w+vRpVKlS5ap1lXgvCML15FrHfF/HewDYtGkTDh8+7N5YsV+/fvjyyy8xbtw4+Pn54cyZM9i0aRPeffdd93e8KeNrfLrocLlc2LVrF958800Pe7t27fDll1967ScnD+T+XI+sZu96nyyvbJm0I5OZNgfoHx05Uy+TZTWDq19TkH7VqhzGkowMtcdFv0uo+QV47YOrn+YfRNpVWlKh1c+VmU6XNzAO1BgAgMuWoffLtFFj9sx0ZaZ5XQ/lsJN2UxCT58T0lSLsJj/9Dsl5QbUdADSLn87GzT/NQr+rT5Znzj1u3JWBfjUytwG+PbnjQHJqGqq27VPgPDSTBpgN3rA3AYYuQp988gnOnDmD7du3IyEhAa+++ioA4KmnnkJwcDCcTidefPFFlC5dGjfffDMqVKiAmTNnYuzYsdi5cyfS09OxfPlyLF682L2jvDdlcvPbb7+hatWqHouGnOTlXbt2ebXo4OI9AMw8uYn8jjkpVm9kzll7VEPSbrl0mrSbMpNJO4UtuhHtI+OyzqbM9KsT5qSzpF1TLtpOxGtHGfoHkyuAjoXmy/QxnWX0iz/ruX/IsjDT8YCqHwDYI2rqXaTG0z7szHXA6dDZbFENiJL0GACAK4jetZ3CGneAroeLHhuXv37nbgBwlqpAFKbnq+agY7UrsDRpB3GH3ZR2kfbBtN16Xr/vg7MUvbcUN3c47JG19ce7cJQsy1137eH0BqXKqj+nrBeO0D4q6OdJSkoKatWufc1jvtF4f+jQIaxYsQKZmdnXsqVLl2LPnj1o1aoVOnXqBCBbfXDVqlXuRcerr76K1q1bo23btrj99tuxcuVKtG7dGg899JDbrzdlfI1PFx2pqanIzMxEuXLlPOzlypVDfDwdXAAgKysLWVlZ7v+npKQA+P9HVldchMJC6PfMlI3+scEuOgKJRQeYH96MDw4T8S6cshtLrDTUHp8sOpiLRwCz6ND0gd9X9XMxM9LIOFBjAACuLH1w5trILjoMnDHcYs4UxLwvaWTR4W9w0UG0HeAWHcyCgV10EOWNLjoM9KvxRYf3cQBAgV/xyfeTDgNkZWUhMzMTbdq0QZs2bdwXI/X/89ZkMmHy5Mm45ZZb3N954okncPvtt2PTpk2wWCx48cUXdY/RvSlzJfHx8bp4X7p0aVgsFjbmexvvASCM+TFgdhE/7pgf6Xbm9QWLnf6BaLJ6nxRqY3ybLHofysIsOqi2IK9FR5bO5mD6yRXILDqc9I0jJ9EeayoTr8z0QpSqHwDYw/R1NGvMzRA7E6+c+hs5RsYAAFzB3r/OYk1jxoaJ1eyig6ojs3DR7HQwdDE308hFh4keA67t1nR9vZ3cuec09r4/df5ZM5jYyy06iLkDAMqqvxZYM+gx4OIAcO1jvtF473Q63TF+8uTJALKT2O32/86HLl26eNz8iYqKwt69e7F8+XLExsZi5syZ6NevH8xms6Eyvsani46cgcu9m6HD4cjzfbk33ngDU6dO9WVVBEEQrgvmfDzpMBriH3300Tz/bjKZ3E8/rqRFixZo0aJFnt/1pkwOmqbp4r1SCi6Xi435Eu8FQbiRMBrzjcb7hg0bkvH8Srp37+5Oa8ghLCzM/aoshzdlfIlPNwfMeaf3/PnzHvbz588jKiqK/d7zzz+PpKQk9ycmJsaX1RIEQbhm5Nz1MvopjkRHR+vifXx8PFwuFxvzJd4LgnAjUVLivS/w+Y7kd9xxBzZv3uxh27hxY55J5P7+/u4s/sLK5hcEQbgW5Nz1MvopjrRt2xaxsbE4ePCg27Zx40aYzWa0atWK/I7Ee0EQbiRKSrz3BT5Xr3ruuefQsWNH/O9//0OvXr3wySef4MiRI1i8eLFhX7N3ve+RxzG6Ab3Xx9z9H5N2Lg9AEXbuPXGjOR2kbyv97ivrg8uDoPIxXMy7r8wxFbHCZue/iZkezLv9dF8xZZn6ce/Ken88egwAOg9CMW1U3PvUTNvpAzJregt9TE0xeRCEjWsjB5W7wdVFcxkbM2q+sucN03ZuThmqB3P3iGtP7j5UBZBMvJJrkdNRVGjfvj1at26N4cOHY8GCBUhKSsJzzz2HkSNH6nI9rsbMk5t0ORxPRLQmy75zebfOZsqk8xRMGbTdUZZOcifLM3KYprQE0m65fEZn4+YnleAKAJZEvQ8AcJTWJ41bLx4nyzozS5N2k40WbnARSfSO8BpkWXPKBfqYIfS4WxJO6Gxc2+Gi8wZNhCCGOfkcXb9UOpnazuTWaESfuILovWaUhclJZMpTidpcG7m8BqeDFsSg6mKhhBYAOG10gr4roJTOZkmkhRYoQQAAMGWlkHZr/DGdjZtTXF4WNXcAuu3cGJgI0QJTGl1noxR2TseNRKE86Vi5ciWWL1+O9u3bY8eOHdiwYQMaNGCCiyAIwg2EhuzAauRTXC9BmqZh9erVaNCgAfr06YNHHnkEDz30EN5+++3rXTVBEIRrgtGYX1zjvS8olB3Je/bsiZ49exaGa0EQBKEIER4ejkWLFl3vagiCIAhFnEJZdAiCIJRUStLrVYIgCCUdeb3Ke2TRIQiC4EOuhWSuIAiCUDQobMncG4kivehQtkyPjb24hPExjYaR9rl7FpJ2E5GI6jK4qzkH6TuL2QDJ4A7KoJKBuU2KaA+GdqA2MRvEcDttKxNxVAOJ4XnVhYTbgI6xu4jN+kx0TiELO08IjAgCAPwGpWSiNpMYrjFjRrUdYJLUufHlxpIor0D7YOMyc0yyHkbFHbj5netc5XYZNkr2Bcjokw7BnBSr2yiPShgHgCdL6/cRmXvqW7KsM4yW7uV2myaTWbmNB6Ob0HYrIVrBnLNcPajkXoBOGreX0+/6DADKj9nglUmAV8QO5n5n95Jlnczu1lxit718PZ2NTLAGvQkgQCfjc2PA9Z9iNvCj7BYiCRrgxQnMKedJO9V2bk5pdiZhnKk3eTwmWZ7zYblIJPmXpzcFtZ4/TNpdxJzP9qNvu1/sPrp+nNgC1X8AKXZDtQUAnOUq6mwul8EfAAxGY35JjvdFetEhCIJQ3JAnHYIgCCUHedLhPbLoEARB8CGS0yEIglBykJwO75FFhyAIgg8x5eNJh8+1ywVBEIRrgtGYX5LjvSw6BEEQfIg86RAEQSg5yJMO7ynJCy5BEARBEARBEK4BRftJh8nsoVDEqf5wKlVjmo0k7fOOLdXZON9G1aso9QWjvvny3q8ROR+UupFi/HJKEmyfGKif4bYb8cEoOMFFtJ1po6YYLSkD9WPvZRicU5Qfro1se4zMB64enOoWpVrG4aPz7Jr7NoAkkucTpXSKPqZMWiWIUqoaU60PWfbt9IOk3RkcTtpJBSKNiW2MApEpI1Ff1EorSbkCy3jtAwCU2ep1WaeZvsSbU+PpuhB9ohglJO6YHKasNJ2NGwOTjVF8zErWG10O2kc60yecChQxliabvs554QyNJO1aVqrexqkbEmUBwGkNoA9KzE0To07mZBTUTFkpOpti+o+dD3ZmzGzpXvtgxzJTXz8AUIRiFjtmBlTpjCKJ5N5TtBcdgiAIxQxZdAiCIJQcZNHhPbLoEARB8CGS0yEIglBykJwO75FFhyAIgg8xIx9POrh32gRBEIQijdGYX5LjvSw6BEEQfIgpH086TAbLC4IgCEUDozG/JMd7WXQIgiD4kHzldJTca5AgCEKxxnBORwmO90V60WEKCIYpMNj9f8Wo0JistCIDpVIFAKNrDdLZ5h9dko8a6lFmoi7+jPIJo4hiMqBMxKkscX2lTPohZ1WJqLYAMAUyxzRSP0KBBeDbbgRWGYNoJ9dGTtVCC/I+WhhtOwtRF2oc84SZg+R84NrIzVfi/OOVz+h6Gxl3o/2nWehj5h57ZbEb8suRr5yOEnznKwd7VEPYw8I8bKYMWr3KGRals3EqVeOC6pN2rrwrtHxe1fTA7+xe0m4Pr66zaYy6j7NMZdpeuiJ9UEqtiOkn68UTdP0iapB2qj22ik3penDKP0ycMCef09mocQQAI5pC1nP7SbujTBXSzo0ZFbNs0Y3pg3JqZgzmxBj98RglKVcQrWZmPX+IrgqhgmWPqEn7YOYD1U5OMcrOjBmH+dK/eh/l6xnyYWLU1szJcTobN2Z+sfv0thRj6mQchnM6SnC8l306BEEQBEEQBEEoVIr0kw5BEITihrxeJQiCUHKQ16u8RxYdgiAIPkRerxIEQSg5yOtV3iOLDkEQBB9i0jTD6iQlWc1EEAShOGM05pfkeF+kFx3O1Mtwwub+v+YXQJZz2TJJO5cgTSWNP157MFl2zp8LrlZND0xBYTqbctiIkjxG2uMiksgAvq80InlNMce7Mon/SpwpiV7Xj5WjttDJwK7MdLq8yfv0I2oMAHocTP6BdFkn3a8q0/vEM5eDTkw2BYXSvpmxpKDGMS+4MabmCdtGbswI39y5x81Lp4F+Zec2J4jAtT0g1/y20Ym+RtHMGjSTsYuKVsgXIaUUnMyctlyRaO9wOHR/N5vNhV4/ALBcOg2LPcTD5ihLJwNb4w7obM7gcLKs0QTzOWfW62xUsi6QR5K1S9+PKoCOS1xiM5cITSXVOsrVoqsRWIq0aw76nKDaY7lwhPbNJNxrGZdJu7NsVZ3NSFI3ACh/fR/aoxqRZakxAPIYMwIugVmz0/1nTr9U4GNy9WbbacQHk8BtSkvQuzCY0O5kylPjzvngBFIcjPCBK6SczsaNGTUGtuRksqxRjMb8axFPnU4nFCFmYzKZYPr/31Mulwsul6dsg6ZpMPtA0IdDEskFQRB8iMms5etjhNjYWPTt2xeBgYEIDQ3F0KFDkZzHBXTx4sUICAjw+Pj7+8NqteLAgf9+wFutVvj7+3uUW7DA2I0XQRCEkkRhx3sAmDNnDqpUqQKLxYLGjRtj48aNeZavWrWqLuZbrVY8/PDD7jLPPPMM/Pz8PMrUrVvXcN2MIIsOQRAEX2I2QTP4gdn7UOxyudC7d2+kpKTg+PHj+PPPP/HHH39g2LBh7Hfuu+8+OBwOj0///v3RsGFDNGjQwKPshg0bPMo9/vjj+e4KQRCEG55CjPcA8NVXX2HixImYM2cOLl26hMGDB6N37944eJB+igsAZ86c8YjjO3bsAADcc889HuW6dOniUe7YsWPG228AWXQIgiD4EM2kZT9uN/Ix8Gh+69at2L17N+bOnYvo6GjUrl0b06dPxzfffIOTJ0965ePixYtYvXo1Hnnkkfw2UxAEQUA+Yr7B129nz56N+++/H3369EFYWBgmT56MatWqYd68eV77WLRoEapUqYIuXboYbZ5PkUWHIAhCMeKXX35BuXLlUL/+fzkJHTt2BAD8+uuvXvn4/PPPoWkaHnzwQd3f7r77bvj5+aFOnTqYPn067HbfbJooCIIgGCMjIwN79uxBu3btPOwdOnTAL7/84pWPtLQ0LFmyBCNGjHDnc+Swbds2BAQEICIiAr169cKhQ3S+ja+QRYcgCIIPKUhOR3JysscnKytL5//8+fMoV84zgTI4OBhBQUE4f/68V3VctGgRBgwYgLJly3rYhw8fjl27duHy5cuYNWsW3nzzTTz33HP57AlBEIQbn8KM9/Hx8XC5XLqYHxkZ6XW8X758OdLS0jB8+HAPe7Vq1bBkyRIkJCRg165dsFqtaNu2LS5cuJDPnrg6RVq9SjOZPBRpOHUaGLUTcCpVY5s/Rtrn7llI2jUq699lUAnAQHu4h3RsX5F+6bUnpyBieByM+GAUkoxAjgFAjgPbRsaHMtJG7uzilJ2Y4pSqFdtGzocB9S+2fkbG3eAcMTJffRUHcvehoXMmD7LjlrH7Odr/q4xUrlzZwz5lyhS8/PLLXvlQSnmlivLbb7/hn3/+wdy5c3V/W7RokfvfvXv3xpQpUzBhwgRMnz4dVmvBz828MGUmw2T1nOumjCS6sHLpTOYU+gLMqSxRKlUAMLbSXTobpXiYF1S9lcWfLOvyo1UCzcnnaOeEMpEpnVYU5BSIzElxpN0RXk1n45SuuPppDv0PJwBw2fRKQ64AY+papkxiPhBzAcijTwjFIw62jYQqEGCwPU5aYUqz0yp6LkadDZo+1lBqVADfdrKdhF8AgJNW4+TOP0UpqDE+TMzc0bJSad9WvZIhN2ZGxt0oRmO+L+K9y+XyWgXrww8/RLdu3VCpUiUP+5gxY9z/rlGjBr744gtER0fj888/x/jx473ybZQivegQBEEobuRHncT0/0vOmJgYhIX9Jwvq76//oRoVFaW7E5WamoqMjAxUqFDhqsdatGgRateurXtcT9GoUSPY7XacPn0aNWvWvGp5QRCEkobRmG8k3kdGRsJkMulifnx8vFfx/vDhw9ixYwdWrVp11bJBQUGoUaNGoSaTy+tVgiAIPsRwEvn/fwAgLCzM40NdhG6//XZcvHgR+/fvd9u2bNkCAGjVqpXb5nA4dDrtaWlpWLp0KR5++GGv7pLt3bsXFosFkZGR+eoLQRCEG53CjPcBAQFo0aIFfvzxRw/7li1b0Lp1a/f/XS4XuRfTokWLEBUVhR49ely1HampqTh+/DgqVqxotAu8RhYdgiAIPiT7omJUNtf7u2R33HEHbrvtNjz++OM4ceIE9u/fj2eeeQaDBg1C1arZG3E5HA5YrVadusmyZcuQmZmJoUOH6vx+9NFHmDFjBo4ePYrExEQsW7YM06ZNwyOPPILQUHpDS0EQhJKO8Zhv7En4xIkTsXjxYixduhTx8fF48cUXcfbsWYwdO9Zd5umnn9Y9jXY4HPjss88wbNgwj01gc+jRowd+/vlnXL58GQcPHsTgwYNhNpvzlF8vKPJ6lSAIgg8pyOtV3qBpGlatWoUnnngCzZs3h8ViwYABAzB79myPMmazWadU8umnn6Jv374oX16f5zBo0CDMmjULvXr1QlxcHKpXr45p06bhscfovDZBEAQh/69XecvAgQORmJiIF154AbGxsahfvz6+//571K5d213GbDbrFhZr167FpUuXMGLECNLvxIkT8corr+CPP/5AWFgYWrdujd9//71Qn3TIokMQBMGHaJpxHXbNZax8+fLlsXTpUvbvZrMZDoc+UfWnn35ivxMcHIwpU6ZgypQphuoiCIJQkjEa843GewAYOXIkRo4cyf599uzZHjeegGwxEJuNTtoHgPbt26N9+/aG61IQZNFxFTiVqjHN6MGnVE4o9SHAd2o5RRqm7QCthFOYfUWqQBXY6/9D+GbbwvaJIAgecAo6lGIRV5ZzzZyHVAx/vPZgsuzb6cyOwFRdmPpphBpVnuUp5SSTsUu5IRU+TqmJqx/niFL3YZSnKIUuwxicD4Z8KCaGM+1RhB+N823UTmFwPhiar8x84OYU2XbGBwvXHgP1FooGsugQBEHwISazCSazsQufScmFUhAEoThiNOaX5Hgviw5BEAQfcqU6idffUT575iYIgiBcQ4zG/JIc72XRIQiC4ENk0SEIglBykEWH98iiQxAEwYfI61WCIAglB3m9yntk0SEIguBL8vGkAyX4zpcgCEKxxmjML8HxvkgvOkxBYTAFB7v/z6kjmKx+pF0xO+4qs768KSiMKAloZvqYlMIJQKuczDu+nCzLqSwYaY9RJQnqmBqxaQwAKEsAXb9g5phG6mem1atMTL25cSB9W/S7egK0AhY1F7L/QKuQaEH0nKLaqYjdQQEAfnS/cmhEXZRBdRJW/cvAfDAyX7lzj1MhMRkZXwNtAfKY37nGXlnsXtchL0yaBpNByVyTF7uD3+jYohvBFuYZh01pCWRZe3QTvZE5Z/3O7qWPV7Gp13XjVKrGBdUn7e9e/EVnM2UkkmXt5evRB+UUnIhziOsna9wB0u4oV4suH/u3zsb2k4H6AYA5MUZfj4gatA8DUHUGAGeZqnT5c/tJO6VmZotuTB/UoEKSJeGUzqYsjIKjXwhp59pJXXscZavRPpj5YK/QQGczpdPzlZ0PzPlHtd3IuQcA5uRzpF3LTNHZqLYAdBzwS0kzVA8OozG/JMf7Ir3oEARBKG7k7Dpr6Duukvu4XRAEoThjNOaX5HhfclsuCIIgCIIgCMI1QZ50CIIg+BCTWYPJYE6HKR871AqCIAjXH6MxvyTHe1l0CIIg+JB8SeaW4IuQIAhCccawZG4JjvdFetGhHDYo+3/JVhqTYO3KyiDtbPKsv/6tMuWw0WVdtA9FJJ0BdNL46JoDybJcMrqR9riYepj8A0m70ohkLwedEKhpdJ8YqR+dRg5wp5yLGQdFVJFPjma8E+3UuIRAJikOtiy6OOmCHhsusZlNuqcS0ukcRB47M7+pujBtNHL+sWPD5O1zc4rC0NwG2Pbo4gCXGGsQyenIH6aMyzBZPOe65fIZsqzdqp8DbKJ2eHX6gMx4mzKS9EYmTlAJ4wDwRERrnW3esaVkWWdIOdJuSaTb7gyJ0Jdl+skZWJr2HX+MtDvC9YndWlYqWdaccoG0uwJLkXZTpr5fna4osqzGjI1m08cJV3A4WdZy6RRpt5erzfjWJxWbL58lyyorLQbiCipD+7an621ZTBtTL5J2Z2gkXRdC8IVrOzcfqCR/c8ZlsqydEWqhxhcANKf+2sPNKSqZHwBMqfGknWo7N2aO0pX0NrM+ET0/SE6H9xTpRYcgCEJxw2RGPl6vKqTKCIIgCIWK0ZhfkuO9LDoEQRB8iGbSoBmUzDVaXhAEQSgaGI35JTney6JDEATBh5hM+diR3FlyH7cLgiAUZ4zG/JIc7w23/MiRI3j66afRvn17bNq0iSxz4MABjBw5El27dsWYMWPw77//FriigiAIxYGcpEKjn6JIZmYmPvvsM3Tv3h0PP/wwWcZut+Ptt99Gz549MWDAACxZQueqCYIg3IjcKPH+WmBo0fHBBx+gV69eiI6OxtatW3H+/HldmUOHDuG2226D0+nEqFGjcO7cOdx66604d47eUVIQBOFGIiep0OinqOF0OlGzZk1s2rQJZrMZf/zxB1luyJAheOeddzB48GB06NABDz/8MGbOnHmNaysIgnB9uBHi/bXC0OtVAwYMwMMPPwxN0zBx4kSyzCuvvIKGDRti0aJFAIBevXqhdu3amD17NmbNmlXwGhOwSjmsulHBB5xXTtL75lSqHq89mLTP3f8x7Zs4puH1spG2M2UN9TejRqE4hSm2Kswx6cK02ey9uhYLVw+mnUbg+sRQvQ20PfuYRHkjfQ1mbHzhg8Ho3DFaFyEbs9mMffv2oWzZspgwYQJiYvQKN3/99ReWLFmC7du34/bbbweQvViZPHkyRo8ejeDgYK+Pp8z+ULmUcbixVha9DJqyBpFlNTutjKYCwhjfhDoPc15xilmUUtXoWoPIsm+n/UPaXf5031HKSVw/mbi2c33l0Cu9uQJCaR9+jIoco25EKhOZ6J8gbHwjfJvSEmgfzHlPtREANJteYUr5h9C+zYx8INMezWmny1O+uXoTyl3ZX9BnJXM+uPngJBTAFKNGRZ17AD+nQIwP54NTjeSUHeHI1Jk4NTNqfNk+FQoNQ7++y5YtC+0qF/yNGzeid+/e7v9bLBb07NkTGzduzF8NBUEQihGayZSvT1GkbNmyef5948aNCA8PR+vW/0nE9u3bF2lpadixY0dhV08QBOG6c6PE+2uBTxPJ09PTcfHiRVSsWNHDXrFixTzzOrKyspCV9d/dh+TkZF9WSxAE4ZphMucjkbyYPm7/999/ER0d7XEzqmLFitA0jY35Eu8FQbiRMBrzi2u89wU+bbnNlr0JTGCg52PXoKAg998o3njjDZQqVcr9qVy5si+rJQiCcO3Iz/u9xfQiZLPZdPHeYrHAYrGwMV/ivSAINxQlJN77Ap+2PDQ0FBaLBQkJnu/wJSQkoEwZeqdOAHj++eeRlJTk/lDvDguCIBQHNFM+EsmL6eP2MmXK6OJ9SkoK7HY7G/Ml3guCcCNhOOYX03jvC3z6epXZbEbjxo3x559/eth///133HTTTez3/P394e9PJ58JgiAUJ/Lzzm5xvQjddNNNePvtt3Hp0iV3/sfvv//u/huFxHtBEG4kjMb84hrvfYHPNwccNmwYXnrpJUyYMAF16tTBrl27sHnzZnz11VeGfSlbJpTtPxUGxSkh2fQKBgCviGMilHxcjA+jyjcmq16VwZVFKyRwKlVjGg2jy+9ZqLNxfUIqhQDQKNUXpu1UPwGAMzPNa9+cqhMnR8DVhdKu4MaXs7sc+tc9TMxvH+Vk5hrTdrIs13amfuz8pnxw6h8MVNs5P1wblYVWbKHGjB0bP73yDgC4DPQrO7e5fuXiQ26Dw3uFmbzIvptlUL3LXHD1s+tBr169ULp0aUyfPh0zZ86E0+nEjBkz0LJlS9SvX9+QL3PSWZhdnmpB9goNyLLWuAM6myuQfrLiLEO/vuV3di9pd/npVaM0l4Msay9fjz5mSDmdjVOpGhfckLS/e/4n0m69cERfD6afWDWlTDqPhlL0sp7bT5Z1hpYn7ebkONJui26ss3FjwOGy6hWzHJF16MJO5pVuMxM7g/Tzx5x0lixqyqCVnUwJJ0m7rWJTvZFRagIz19h6E1DzLy8f5mT9lgbsuXf+EGl3BZQi7fboJnofzJziVOKoucOV58aMigNOs2/yyYzG/OIa732BoUXH7t27MX78ePf/X3vtNXz44Yfo0aOHW0L38ccfx549e9C0aVPUqVMHhw8fxrhx43D33Xf7tuaCIAhFkPzosBdV3faHH34Yx44dw/Hjx5GYmIj27dsDAL777juEhIQgNDQUy5Ytw6BBg/DNN98gLS0NpUuXxnfffXd9Ky4IgnCNMBrzi2q8vxYYWnTUqFEDL7/8ss4eFRXl/rfZbMaiRYswdepUxMTEoEaNGihfnr4jIgiCIOSPn3/+GRs3boTFYkGfPn3QrFmzPMuPGzdOZ7v77rvRpk0b1m+HDh3wwAMP6L4XEPDfE6sOHTogJiYGf//9N/z9/dGoUSOYSvDrA4IgCL4mISEBX375JWJjY1G/fn3ce++98PPjn36tXr0aP/zwg4etbNmyeOmllwrkt6AYujKUKVMG7du3133q1q2rK1upUiW0atVKFhyCIJQoTCZTvj5GmD59Orp164a0tDTExsbi1ltvveorrO+88w5sNhuqVavm/oSGem78ltvvsGHDEBsbq4v5Fovn/Sp/f3/ccsstaNKkiSw4BEEoURR2vD99+jSaNGmClStXws/PD9OnT0eHDh3yVIXdtm0bNmzY4BHvK1WqVGC/BcXnOR2CIAglmcJ+vers2bN46aWX8NFHH7mfQkRFRWHs2LHo37+/x1OI3PTv3x933nmnz/0KgiCUVAr79aoXXngBUVFR2LRpEywWC0aPHo2aNWti0aJFeOyxx9jvVa1alXzCXVC/BUFuSQmCIPgQw3K5Bi9Ya9euhdls9siTGzJkCC5duoSff/45z+8uXboUzz//PBYuXIj4+Hif+RUEQSipFGa8V0ph9erVGDx4sPsJc/ny5dGlSxesWrUqz++ePn0akydPxvTp07F161af+S0IRftJh8nsoR7FqdOAU/JhHmEpTa+dxPo2qF7lC9+UShUAjGk2Ul/270VkWVY9iFJY4FSJiLYAeSgnGXlkaFB5ysg4sCoSLr2dayMszKnB9BVceiUSTqHLcNsJtSaujeyYcYpPhB9OpcrQ+cfNBa7tBtS4uLJsnzBt142x2TfhUNPyIZn7/yosuXfnpuRljxw5gujoaI8nD9WrV4fZbMbRo0fRuXNn8hihoaHQNA1BQUH44osv8Nxzz+Hbb7/FHXfcUSC/vkJTLmi5FH0siWfIspRSjikjkSzrLF2RtDvKVCHtlJIPp6rDKQ1R9Xb561WxAF6l6ony7Un73H/XeHU8AHCGRjL1O03aKcUiZQ2ifSTFknZlpuOHKS1BZ+PGQLOn0z4yU/RGRqXKcoluo6NcLdJOjaU59SJZVDFxjGsP1XZu7pBtBOAoS/um5ibXdmepaNJuTrmgd2vPIsty84E7/5R/iN7G+OD6xJxyni5OKM1xY0aqrXFKYQYxGvONxPsLFy4gKSkJNWvW9LDXrFkTX3/9dZ7HiYiIgJ+fH06fPo1XXnkF99xzDz7++OMC+y0IRXvRIQiCUMwoyOtVuXfnnjJlik68Iz09XZeLoWkagoODkZ5O/1gDgH379qFq1aoAgBdffBGDBw/G8OHDcfTo0QL5FQRBKMnk9/Uqb+M9AF1sDgsLyzMujx071h3vAWDo0KFo3bo1+vbtiz59+uTbb0GRRYcgCIIPKciiIyYmBmFh/+2VQG2iFxoaisuXL3vYnE4nUlJSdBeQK7nyAgRkvzq1dOlSxMXFoUKFCvn2KwiCUJLJ76LD23gPQBebExMTDcX7li1bom7duvj555/Rp0+ffPstKLLoEARB8CEmswkmg4uOnPJhYWEeFyGKBg0aIDY2FsnJye6yhw8fhlIKDRowm8QR5CiU2O12n/oVBEEoSRiN+UbifUREBMqVK4dDhzw3ZTx06JDhuGyz2dzx3pd+jSCJ5IIgCMWIHj16wM/PDx999JHbNn/+fLdMOQC4XC6MGzcO27dvBwDs2bMHCQn/vVdus9kwd+5c1KtXz/2I3xu/giAIwrXlnnvuwRdffIG0tDQA2fl3W7ZswT333OMus3LlSkybNs39/82bN3v4WL16NY4fP44uXboY8utrivaTDpfTI4lWubhkPmNbymtK6V1wibaGPPvGN5f4SiWNj2kygi67/2PauVPvmzueiWhLXuVJuLKMnfVNJVMzyXyKaCPnmxsDahyzfegTxrn6cW3hkro5SD9MG7lkam4Okn6YNtI9AmPnn8H5YKQsXz+6PbnHmBtzo2gmzXgiucn7SBMeHo758+fj0UcfxbZt25Ceno7t27dj5cqVbhUSl8uFd955B7Vq1UKbNm2QnJyMNm3aoE6dOoiIiMC2bdugaRqWLFliyG9hojls0ByeyauO0pXIstaLx3U2LoGZSwI3pcaTdiq5lJ0bJrpfnCER+vpZaclh64UjpJ1KGAeAMVV76WzvJP5OllV+dMKuKzictFN9ZcqiE5td/vQrGJotlS4fqE/+tyScon04Mmk7NQ5mWliCS6JnIcZSWfSvu2Q7p5PXuYRnO5W8rphriYWRpmbaSeEKKUfa2flgDdTZqDkM0OcewJ9/rgD9POGEDDjsTBK9IvqEHTPqXGXOX6MYjflG4j0ATJ06Fdu2bcPNN9+MW2+9FevXr0fv3r1x7733usts3boVq1atcm/+9+6772Ly5Mlo3Lgxzp07hy1btmDSpEno0aOHIb++pmgvOgRBEIoZhb1PB5Cdj9GmTRv88MMPsFgs+PjjjxEVFeX+u9lsxltvveVWpmrbti3++OMP/Pjjj4iLi8N9992Htm3bwmq1GvIrCIIgeFLY+3SEh4fj999/x/r1692btrZv396jTL9+/dCkSRP3/1evXo2///4bf/zxB8LCwjB//nxdnoc3fn2NLDoEQRB8yLVYdABAjRo1UKNGDdqfpuk2hQoODkbPnj0L5FcQBEHwpLAXHUB2knmfPn3Yv7dr1w7t2rXzsDVp0sRjIZIfv75GFh2CIAg+pCD7dAiCIAjFi/zu01ESkUWHIAiCD9HMZpi4DSrz+I4gCIJQ/DAa80tyvJdFhyAIgg+5Vq9XCYIgCNefa/F61Y1CkV50aH4B0Pz+U3LQrLR6A6MBwasbEfYrj+ONDw7Kt8lfrwyRF5y6EVUXTqVqTKNhpH3esaV6v0w9WDUYC22n+4opy4ylEWUndmwY39SdCMWoVyhGWURj2k62k2uLH62uwSnkUONDzTOAV3DSLIzyCaFKpLmMjRnVU+zYMG03ol7F1UNp9Ezm2pO7D5XBV6I4ZNGRPxxlKsGRa1MqTinHXq62zmbKSCTLmjKS6ONRikIATOmEHyZOmNISSLvl8hmdjZuf9gq0Jr4lUe8DoJWqnixzC1l2zpn1pN1kSyPtlCKVPaImWdaccoG0O0tVJO2UShfXdkpBDABMWfp6m5PO0vVLvUjaqbkDABrRJy6/YLKsstLqX66gMqSdVChj2shdBzg1KUWoXVHzDwCcgaVpH4R6FXvuMfOBUzmzxh/z2gen6GVhFN6otnNjRp2rpnS6zkaRRYf3lNyWC4IgCIIgCIJwTSjSTzoEQRCKG5opH4nkPnrKIgiCIFxbjMb8khzvZdEhCILgQ+T1KkEQhJKDvF7lPbLoEARB8CGaSTO+6DC4Q60gCIJQNDAa80tyvJdFhyAIgg+R16sEQRBKDvJ6lfcU7UWHyZz9+X9YdRpGmYfTQqYUi1h1H6MQm74ojdHXYjaIMdQeJ636Q6lUAcDoWoO8LuuL+immfpz6kk/Ggak3OQ4GN+lhlZOIdrIKTmz9mINSih5GNxciVKoAkKo8mpVWTzFy/rHnnsE5RfowqCjHq13lqouPNmzSTGbDqndGy9+IuALC4AoM87A5M0uTZZVfkM7mNNNz3HrxBH28wFK0nVEgIn3HHSDtlEqQyZ5BO2GUsZyhkaSdajunUjW20l2k/d2Lv5B2c1KszuaIrEPXj4tjVloJ0pxyXm/kzjkzo5QXqO8rv8TTdP2CytKuk+NIu2ZL1dns5evR9ePqzYwlGcOZNjoD9ApiAK/GRcVDTumKVfQqX1dn45QklX8IaXcyMZxSjeJ8cOpVlEoVAGiOTJ3NEV6NLEspiJlT9GOeH4zG/JIc74v2okMQBKG4ketmidffEQRBEIofRmN+CY73sugQBEHwJSZT9sfodwRBEITih9GYX4LjfcltuSAIgiAIgiAI1wR50iEIguBDNLOZzWnJ6zuCIAhC8cNozC/J8b5ILzqUwwblsLr/z+bZ2vTJRACgmEdYVBIP58PoYzCNSth1OAz5YOtisepMikn24vqKShqnkssBYN7x5XT9HDbauYvoV6Z+Juakc3FtJ51wScnMtKbGgUsIZBLaXFlMMihZmPbBtZ1LuicxeuZyc5Dww7WRS/am5it37pn8A0k7O+6EH25uc4GcbU9ALk/MXDWM5HTkC/PlszA7kzxsJlsaWVYRyanm1HiyrD2iBmmnklABwJykTzTmxAsc5WqRdkv8Mb0Pqz4BHAC0zGTaB5Mg7QoO19m4fuISxp+IaE3a307dp/dNJYCDSQwHoPzDaDvRfs2WTpbVnHbannFZZ7OHVyfLcv3HJYdrxHWNEyFQXBJ4WAXS7grQixZwbTSlJ5J2rp0g4rKFSJoGAEeZKrQLpp0U7Hxg6k0JM3A+NBdznWKu066QcjqblTj3AMARro8DDmsKfTyjSE6H1xTpRYcgCEKxw2TKx6JD3nQVBEEolhiN+SU43suiQxAEwYfIPh2CIAglB9mnw3tk0SEIguBLtHy8XqWV3MftgiAIxRqjMb8Ex3tZdAiCIPgSyekQBEEoOUhOh9fIokMQBMGHyOtVgiAIJQd5vcp7ivSiQ/MPghZwheKFia4uqwak0To3lPKEKTDYkA8OZQnQ2TSNUXtiFBmMtMekFF3WrFe64o7JqVSNrjmQtM8/usTr+rGKY9xYMuPg7fEAAIyyCNV2ToVEY9SrNP3wsmhGx4azU3Vh+k8xc4qdg4Qfto0Gzj/23DM47qQfw21n6pLrXFUWYypzgm9xlqkMZ5in8pGLUXZSAXqFJErVCQD8zu4l7baKTUm7I7xaHrX0xBr7N+NDr5SjObLIslRbAMBeoQF9UGKeu/xDyaLmpFjSTqlUAcC4kMb6sukHybKUchBXPwAwEepiyo9W9KIjJ4BAvQqU9fwhsig1BgDgd+4f+pjE3Wd7VCOuJsbITNKZXFzbGQUsy6VTtG9C8ckRQauqmS+fIe3UXONU1bj56gotTx8z6azXZTk4RS+qjtx5Q52rlhRa9U0oPIr0okMQBKHYIa9XCYIglBzk9SqvkUWHIAiCLxHJXEEQhJKDSOZ6jSw6BEEQfIjsSC4IglBykB3JvUcWHYIgCL7EZDJ+Jyufd75SU1NhNpsRGEjv9E6RkpKC0FA6ByAuTr8bd1hYGIKC6PfPBUEQSjxGY34+473T6URKSgpKlSrF5irmJisrC5qmwc9Pn7+akpKCtDTPvBaz2Yxy5Zh8LR9Qcp/xCIIgFAY57/ca/Rjg2LFjaNOmDcqWLYuwsDD06NED8fH6RN0c4uPj8cwzz6B8+fKoWLEiwsLC8OSTTyIzM9OjXFRUFBo0aIBmzZq5P0uXLs1XNwiCIJQICjneA8CUKVNQpkwZREVFoWLFili2bFme5VeuXIlWrVohPDwcpUqVQsuWLfHrr796lJk6dSqqVq3qEe+7d+9uuG5GKNJPOlRaEpR2hTKDhVb3UQ477YAZWFOgXhvDmcKoIxicHKZgvW9XVoYh385MWlFBs+hXqsrlpCvC9BXpw0ErG3EqVY/XHkza5/69SG9k6qf50RJJXNtJH0z/aQGMEhLRTg91tCvtjPKUK4OpH9FOF9N2E1M/DmqMqXEEeOUzdg5S88GWSZQEe3eGPP+4sWHmpYs7JuWDmTvcMbn25I4DJrv3dcgLzWQ2HDeMlHc4HOjduzfq1q2LxMREZGZmolu3brj//vuxceNG8js//vgjypUrh3379iEyMhIHDhxAly5d4HQ6MXfuXI+yy5Ytw5133mmo/r7Aeu4fWFM9zw1WgYhQpFIWf7Isp1JluXCEtGsO/Tzg4gHnW8tK1dlcAfTTJeu5/aRdWenYZMpK0dnsETXJso7IOrSPlPOknVKqGhdUnyw7N2YdadeyaNUje/l6OhunPAUnfU2ixoEdA0Z9iStPYbR+nHKivRyhJsUpJDJxiFUzIzBl6NWyAH4+UOeCs1Q0WZZTg+NUGe3EMTkfHPbydUm7y4CamT26id6WTM8RoxiN+UavDwsXLsT//vc/rF+/Hq1atcIHH3yA++67D7Vr18ZNN91Efmf58uV4++230aJFCzidTkyYMAHdunXDoUOHUKHCfwppHTp0wPr16w3VpyDIkw5BEARfopn+e9zu7YeRGaXYsmULDh48iFmzZiE4OBjh4eF45ZVXsGnTJhw5Qv+QvueeezBx4kRERkYCABo0aIBhw4bh+++/15V1Op1ISqJ/tAiCIAi5MBrzDcR7AJgzZw4eeOAB3H777TCZTHj00UdRv359LFiwgP3OV199hZYtW8JiscDf3x+vvvoqkpKSsGPHDl3Z5ORk2O3MzXsfI4sOQRAEH5Jz18vox1t27dqFChUqoGbN/+5ut23b1v03bzl58iTKl9fr5ffs2RPR0dEoW7YsJkyYgPT0dK99CoIglDQKM96npaXhn3/+QZs2bTzsbdu2NRzvAehi/ubNmxEVFYWQkBC0atXKkM/8IIsOQRCEIkJycrLHJytLv6FcfHw8IiIiPGyBgYEIDg7GhQsXvDrODz/8gMWLF+PJJ5/0sD/zzDOIi4tDWloavvvuO3z11VcYN25cvtsjCIIg0HgT7xMSEqCU0sX8cuXKeR3vbTYbHn/8cdxyyy1o3bq1296wYUNs27YNKSkpuHDhAurXr4/OnTsjJiamYA3LA1l0CIIg+JIczXZDn+xQXLlyZZQqVcr9eeONN3TuNU2Dw6HfhdjpdMLshRTjX3/9hQEDBuDJJ5/Evffe6/G3GTNmIDw8e2fv1q1bY8qUKfjoo4/Ii6EgCIKAfMR8Y/EegC7mOxwOr+K90+nEgw8+iJiYGCxfvhymK3Izhw0bhtatW8NkMqFUqVJ47733YLFY8NVXXxWkN/KkSCeSC4IgFDsKIJkbExODsLAwt9nfX58cXbFiRd0drqSkJGRmZiI6mk7+zGHv3r3o3LkzHnjgAfzvf/+7arVq1aoFp9OJmJgY1KpFJMMKgiCUdPIpmetNvC9fvjwsFgvOn/cUfzh//vxV473T6cSQIUOwY8cO/PTTT6hatWqe5f38/FClShX3q1iFQdFedOSSFuPeg1MmWgWCVdsh9I3Zd+wMqgz4wjenTGRkUrPKTtTK2MX0K6MDTapUARjTZITXZdm2+2Ac2I13qHYyCV0KxuYUWQ/uD0aVjSgb00ZuzIzMB8W00dD5x/WTL84zI3MbfHty9xXXd0YpyOaAYWFhHhchirZt2+LZZ5/F7t270aJFCwDAhg0bYDKZcPvtt7vLxcXFeeyx8ffff6NTp04YPHgw5syZo/OrlNJpv+/cuRP+/v5Xvbj5BLNVp/5jTqFfH3AGldXZTBm0AiGnEuQK1eezAIA5+Zy+LJf46dI/cQLoeis/ei8VJ1MPS1IsfUh/vQoW209Mvc2MepUrRK/Pz6lUjancjbTPO/E1aTel68fHGRZFltXsdB6RlqlX7qLUxgC+Txz+IaSdHEtOpcpE/3TixpJSk9I4hS6ijQDgZH4XKKIunDoZpzBlshPqhkz/uQL0ilEAr1pGtZ3zwZ1PprQE0q78iLHkxozyzRzPKPndHNCbeO/n54eWLVti06ZNGD58OIDsWL1x40bcc8897nIpKSnIzMx077HhcrkwdOhQbN26FT/99BN50yh3zE9MTMTRo0cxeDCtUOoL5PUqQRAEX1LI+3TcdtttuPPOO/HII49g165d+OmnnzB+/HgMHz7cvThwOByIiorCRx99BAA4cOAAOnXqhE6dOuGFF15AXFwc4uLiPO6eLVy4EBMnTsSvv/6Ko0ePYv78+Xjttdcwbtw42RxQEASBo5D36Zg8eTJWrFiBefPm4eDBgxgzZgwuX76MsWPHusu8+OKLuOWWW9z/Hz58ONauXYslS5YgJCTEHfOvFAZp06YNVq5ciWPHjuHnn39Gnz59EBYW5l7cFAZF+0mHIAhCcSM/mz8ZLL9ixQpMmjQJ9913HywWC4YMGYKXXnrJ/XdN01C+fHkEB2fve7Fx40aYzWZs3boVzZo1c5ezWCw4c+YMgOyLVM7CIy4uDtWrV8eHH36oy/sQBEEQrsBozDcY77t164YlS5Zg1qxZmDlzJurXr48ff/wRVapUcZcJCwtzP+XIysrC+vXr4efnh7vvvtvD1yuvvIJHHnkEALBgwQJMnz4dzz77LMLCwtC6dWssX768UHckN7zoiImJwY4dO2C323HzzTejfn39xkEOhwMbN27Ev//+i9q1a6NTp05eb9kuCIJQnNFMJmgGczqMli9VqhTmzZvH/t1sNiMuLs79/3Hjxl1VhcpqtWL06NEYPXq022a32/HDDz/g+PHjqFSpEjp16gQ/P/1rHseOHcNPP/0Ef39/dO3a1b0fiCAIwo2O0ZhvNN4DwIABAzBgwAD279OmTcO0adMAZOeGXBn/OZo0aVKoSeMUhlo+atQotG/fHqtWrcL69etx88036y5kqampaNOmDcaOHYvffvsNDz30ELp3737NNh4RBEG4rmj5eNSuGXwycg3YunUrGjRogNdeew27du3CM888g7p16+L48eMe5T744AM0adIE69evx6effopatWph27Zt16nWgiAI1xijMb8IxvtrhaEnHd26dcO8efPcMl3bt2/HHXfcgX79+qFdu3YAgOnTp+Ps2bP4+++/UaZMGcTExKBhw4ZYuHChxx00r3A5sz9u6CQozzJXR1NKZ9NbrgJzTEO+uXobaQ9blu4r5dSXV1xbDB6TShqnkssBYN6Bzzjvho5ZYJgkU2ocs4szCea+gGkjNT7c2HD1dnH9R8wHcG3kHgn7Ymx8MOf588y7Meb6rqQSHByMLVu2uB/hO51OtG3bFuPGjcOaNWsAZCerP/HEE3jrrbcwatQoAMDDDz+MYcOG4dixY4aecGsOGzSHpzSvk0hsBuhkb94xI4yQcZmph14emBeFoC+hrkB9oqyy6JVpAMCcTN+R5JJ+NVuqzuYsVZH2YQ2g7f5M8irRV1yCMJcwProGfTf2ncu7dTZTajxZlkxsBj02DjOdYE2NQbYT5l4r44d04aRvolJJ0wDgLFVBZ1NOej5ozJxSXP2I9nBtZ+cDEdtdQWXIsubLMXQ9GJR/sM6mJZ0ly3Ix2B5KPzml2sP+nKf6lelrofAw9KSjT58+HrrAbdq0gZ+fHw4fPuy2LVu2DIMGDUKZMtkTtnLlyujZsyeWLVvmoyoLgiAUYTQt+4eAoU/Re/305ptv9nhn2Gw2o127dh7xfvXq1dA0DUOHDnXbHn/8cZw4cQJ//PHHNa2vIAjCdcFwzC968f5aUaBl3tq1a2Gz2XDzzTcDyN718NixY6hXr55HuXr16mHTpk2sn6ysLI/Np5KT6TsrgiAIRZ6cC4vR7xRxnE4nvvvuO3e8B7JVsapUqYLAwP/kYHPy/A4cOOChppKDxHtBEG4ojMb8YhDvC4t8tzwmJgYPP/wwhg4diubNmwMA0tLSoJRC6dKlPcqWKVMGKSm09jQAvPHGGx67MlauXDm/1RIEQbiuKM2Ur09RZ/z48Th9+jReeeUVty0lJUUX7wMDA+Hn58fGfIn3giDcSNyI8b6wyFfL4+LicOedd6Jp06Z4//333fYcLffcF5vk5OQ8dd6ff/55JCUluT8xMcbeGRQEQSgyGH61Kh9PRq4x06ZNw4cffog1a9agZs2abntQUJAu3mdlZcFms7ExX+K9IAg3FDdYvC9MDL9eFRcXhw4dOqB69epYuXKlx7bt/v7+qFKlik7d5Pjx46hduzbr09/fn9z+XRAEodihacbf2S3C7/i++uqrmDFjBtauXYs77rjD42916tTBp59+CrvdDqs1O/E5J/5zMV/ivSAINxRGY34RjveFjaFFx/nz59GxY0dUrVoVq1atQkCAXjmgT58+WL58OV588UX4+/vj8uXLWLNmDZ5++mnDldP8AqD5/XcMzeq9ugQAVm2HVAWxMEoh7CYujDoU4ZubXoqZeLxSClEXTnmK6StKpcJkZvqJUXa4ckyuVj9OpWp0gyGkfe7+j+ljGthMh2ojQPcJ10bFqVpxbSfGQbmYuxkW7rSj7SZKEY3rD+YOion5jUf5YZWxmHlCH5Apy7Sd7VcKP2M/WDUwii25zlVl9pGSicmU/TH6nSLIG2+8genTp+P77793KxReSc+ePfHUU0/h22+/dW9C9dlnn6F8+fK47bbbDB3LHlET9rBQD5sl4QRdtnw9nc2UlUaW5ZSunGWrknaXjVDMYsbHnEg/pTFl6lWMNCZW26Ib0z7SEuj6EcpE1gtH6PqlnCftyko/haLUpKi+BgBTeiJpp1SqAODJ0i10trfTD5JlnVz8ddp0Nkv8Mbqsy0Ha2RiZqX8d0FmafvWPUxZT/iGk3e/sXrouBnxzylhUeZONOReYOeUMi9KXZVSq2PlgoxXHrOcP62y2Cvr93fLCL46eJ9RvKG7MzJf+1dtS9Epw+cJozC+i8f5aYOgq27VrV8TExGDIkCF477333PbbbrvNfYF54YUX8N1336FDhw7o3LkzVq1ahYoVK3ps1y4IgnCjkp93doviO75ffPEFJk2ahAEDBmDPnj3Ys2cPAMDPzw+PP/44AKB69ep44YUXMHz4cPz2229ISkrCp59+iq+++sr95EMQBOFGxmjML4rx/lphaNHRuXNntG/fXrfT4ZVqVZGRkfjrr7/wxRdf4PTp0xg7dizuv/9+D3UTQRAEoWhTtmxZPPnkkwCAU6dOue25X416+eWX0a5dO2zevBmVK1fG7t270bgxffdeEARBKLkYWnS8+eabXpUrVaqU8Y0ABUEQbgRuEMnc7t27o3v37l6V7dChAzp06FDINRIEQSiCiGSu18h2jIIgCL7kBll0CIIgCF4giw6vkUWHIAiCL5FFhyAIQslBFh1eU6QXHa7MdLiuqCGn/qFsmaSdUzyi1Jpcmel0JRhVK8XUxUQc0+XQK27khZH2cPXg+kqz6BWcXMzxTIHBpN2ZSStjGFGY4lSqxjQaRtrn/LnAa9/UGAD0OLCqTk5mrjFtJ8s67KSd6yduLPXaVfQ45gU3xpRqFNdGxZ0LhG+ujZxKlctAv7Jzm+tXru25DfYsqphhlKblI5G85Eoo5mBOjYdZ81TAsVdoQJa1nj+kszmDw8mylDIPwCsKuQL06lBg1JQcETXoY7qIYzJKeVw9HGWqkHZLwimdjesn7seNZqOvd8pPr2pF9TXA9yulgAXQSlXjgmgVo3nHljL106tDOSLrkGXBqVcx4+Ai5g/XFlMWrXpkvnictNsqNqXrQlbEWL0pnAZ9kKplUY3IspxSGqWqBtBt5+YUpybJKbxR85sbM0qtzmlJpv0axGjML8nxvkgvOgRBEIod8qRDEASh5CBPOrxGFh2CIAi+5AbbHFAQBEHIA9kc0Gtk0SEIguBL5EmHIAhCyUGedHhNyW25IAiCIAiCIAjXBHnSIQiC4ENulB3JBUEQhKsjO5J7T5FedGgmkyFFpAJhMjYJWHUeQhlLMUISrNoOd1CqPKPkc10wUD+u7ZxK1djmj+lsc/cs9L5u1wMfzSlO1Yr0Qcw/AFAG61JUKMzzP3df+exYmsnw2Jfkx+05aPZ0aPZc/cCo8GhOvTKcyZZBlARo3SleQUZzEGpnnBoQg0aUZ+M658NOK0wZqp+ZVrmj+g9g6uik1Re5+pns9Dg4CQUwTqVqdK1BpH3eia9JOwU1BgCvkERhymJU9Zg+YVWJKPUzRhHNF/XWGMVM5Uf7INXMGDUqdj4w5x/ZTs4HN4+ZviLVq5gxc4WUo334AqMxvwTH+yK96BAEQSh2SE6HIAhCyUFyOrxGFh2CIAi+RBYdgiAIJQdZdHiNLDoEQRB8iSw6BEEQSg6y6PAaWXQIgiD4ENmRXBAEoeQgO5J7T7FadLCJnkbt1xijCapGyrMJ7UaOySRAsYmW16FfqaTxMc1GkmXnH11C2qmEfraNbEK2gbZzCeCcDx8kjPsEo3OKshs8J30x54tMHJAnHflCczp0Cc5ccjh13mpZyYaOp/zDSLspM8mQHwoyqdbiT5Z1WQOZeqTQvpU+3ZtNng1kEoczLpN2KnmYOh4AaFz9HFm0nUgeVn4hZFkuYXx0jQE629tp/9DHY/pEWQJIO+nDlkrbmVjt8g+lHREJ0pqTEUlgEvEVIwpAxQ7NxrTdSrfdRJRXXGI4A9dXRkQYuH7V7IR4Auj2sPUoTORJh9cUq0WHIAiCkM2qVauwYcMGWCwW9O/fHx06dPDJd/LjVxAEQSg8Tp8+jQ8//BCxsbGoX78+Hn30UYSE0It2I9/Jj9+CUHKXW4IgCIWBpuXvY4Bnn30Ww4cPR+XKlREWFoa77roLCxbQctNGvpMfv4IgCCWaQo73R44cQbNmzfDPP/+gSZMmWLJkCVq3bo30dFq22tvv5MdvQZEnHYIgCL6kkF+vOnnyJGbNmoUVK1agX79+AIDSpUvj2WefxZAhQxAcHJyv7+THryAIQomnkF+veuGFF9CgQQOsWLECmqZhyJAhqFq1Kt5//3089dRT+f5OfvwWFHnSIQiC4ENydqc1+vGWdevWISAgAD169HDbBg8ejJSUFGzbti3f38mPX0EQhJJOYcZ7pRTWrl2LgQMHQvv/JySlS5fGXXfdhe+++y7f38mPX18gTzoEQRB8SQGedCQneyZD+/v7w9/fMwH5xIkTiIqKgp/ff4mllSpVgtlsxokTJ0j33nwnP34FQRBKPPl80uFNvD9//jzS0tJQtWpVD3vVqlWxa9cu0r0338mPX19QpBcdpqAwmK54pM8pB7EKTpwCEaEiYgqilUyMqgRRvtn395hJaqQ9yskoHlkNKF1YmGnAqGVoAfRrFkb6ihtLkwGlIU6l6vHag0n7vGNL9UamjbReC2CihWZoH05GJYUbGwZNufS+TcyYcXOKm4OEH5NB5S5qvhpV/zL0hquBuQ3k0Z5c56qy2MlyRsmWTzT2zm5O+cqVK3vYp0yZgpdfftnDlpGRoUv00zQNwcHByMigFWe8+U5+/PoSW1QD2MI847A5+RxZ1h7dRG9kVHKs5/bTPqIa0RUhzjcOa+zfpN0VHK6zmdISyLKOyDq0c0LtCQAZs8xJZ8mifomnSbs9vDppt54/pLPZKjYly2oOWlHIwcRUS/wxfVmu7QyUUtW44IZk2XfP/0TX48IR0m4iVKNsUbRv9gcmY6f6lYvhrgBaAcsvdh9pp2KNM7S8IR+2CvV1NnNqPFmWPPcA/vwj+pv1wWC5SN/00Oz63AN7+Xp0PYhz1ZpCq3wZxWjMNxrvAehic2hoaJ7x/mrfyY9fX1CkFx2CIAjFDaWyP0a/AwAxMTEIu+KHd+67XgBQqlQpJCYmetgcDgdSUlJQqpRe8tTb7+THryAIQknHaMw3Gu8B6GLzpUuX8oz3V/tOfvz6AsnpEARB8CEupfL1AYCwsDCPD3URaty4MWJjY3Hp0iW3bf/+/VBKoXHjxmSdvPlOfvwKgiCUdAoz3pctWxbR0dHYv9/zie2+ffvYuOzNd/Lj1xfIokMQBKEY0b17d4SEhGDevHlu29tvv41atWrh1ltvBQC4XC488MAD2LRpk9ff8aaMIAiCcG2577778Nlnn7mfSvz111/YunUr7rvvPneZL774wkNxypvveFPG18jrVYIgCD5Egc8Jyus73lKqVCl88skn7kVFeno6/v33X6xZswYmU/Z9JJfLhS+//BK33XYbOnfu7NV3vCkjCIIgeGI05hu9Prz00kv47bff0KhRIzRr1gzbt2/HiBEj0L9/f3eZP/74A6tWrcJbb73l9Xe8KeNrZNEhCILgQ1wq+2P0O0bo06cPTp48ie3bt8NisaBdu3YIDf0vAdVsNuPzzz/3eEJxte94W0YQBEH4D6Mx32i8Dw0NxdatW/Hrr78iNjYWM2bMQKNGnkIYDz74INq3b2/oO96U8TWaUkZTHguf5ORklCpVCgm/rUFYyH9KSSZ/WjrIlUVn2nMqUCD8qIxUQz6Ui1aN0gKJ7ePttAoJp/BjpD1cPbi+IhUWHLTqBKcSZLSvyLKMb67tRtACgkj76FqDdLZ5J76mnXAKNgbqZ3RsWD+UQhmnOMapqjBzkPRjy6JdGxgzVoHNB+NuaG4DbHtyx4HklFSUbdYRSUlJHsl93pITt/49e87w95OTk1G1YlS+j12cyem3+BMHEJZrgWO5fIb8jj2ips5mSk8kSgKuQDopUvnRcYL0w51XjAKR5dIp/fGYc8LOKDhZLtHKU87QSJ3NmnCSLhtUlrSbMpNIuyO8ht7IqBKZUy6Qdq6/KTUkru0ac0wti1AbYso+Ub49aX8n+S+vfWtZ9LVOWWllQkq1DACshHIXV2+NuW44wiqQdkrNzMKolnHzgTomN0c45TNTBl1eI1TBnExbNObnqDnxX9KurPpzWJmtdNkAfVxNTklBuer1rnnML8nxXp50CIIg+JBr8aRDEARBKBoU9pOOGwlZdAiCIPiYEnxNEQRBKHFIzPcOWXQIgiD4EHnSIQiCUHKQJx3eI4sOQRAEH6KUgtFUuSKYWicIgiB4gdGYX5LjvSw6BEEQfIjr/z9GvyMIgiAUP4zG/JIc74v0okPzD/JQIlKMUohJv4ljdnlGzUYRag9GFXEYnRzSt8Yon3BrXSPtYevB9BWpwsLVj2gLwKtDkX4YFSijY0n64NSKmHpTSlWjawwgy8499S1p1/y8Dxcm5m4Gp67BOyJUb5j+U8xYcvOE9EN3HztmWgDrXe+Dm1PcWBqoB6swxLUnV124ugnXBldQWbiCPZVc7BY6ICh/vUqgk7ABgN/ZvaTdVrEpXY+QcnlV0wPruf2k3V6uts6mORgVNWbeOcrV8roe1PEAwJwcR5cvX4+0+537R2fj+snB9Dd7HlJ2Lo5xdoteNcpy4QhZllOpejLsJtJOXR/sFRqQZY3iDCqjNzLjrvzo3yKWhFO0c0IFy16+LlnUfDmWtDsi9KplrHIXM+5OQh0KAMzJ5/Q+mLL8/f+qpFWz6evoLFWRLEudq+YUuo1C4VGkFx2CIAjFDaWyP0a/IwiCIBQ/jMb8khzvZdEhCILgQySRXBAEoeQgieTeI4sOQRAEHyKJ5IIgCCUHSST3Hll0CIIg+BBJJBcEQSg5SCK59xTpRYemFLQrVoSKSUrO6/skhB/ldNI+zEQSb15QdTRYb64usOiHi2ujob5iymqsnTkmcSoZrR/bdqoezNiw9xCIY3IJ42Oq9SHt848u8aZq2Yfj2sIlkhucJxTcmLEQ5Y2OGXueXWMfHN7GAcN9x6CQj5wOnxz5xkOzpZF2MpmViykGRAqMormY6wZRb82WTjuhkowBMkEYAJl8zfUTlWgLAJrDRtoVJVrBwdWPSZA2ZaboXQSHe388BpM9g7S7sug+oRLGAVpU5O00fWJ9njBJ9FTbWUERLkZyQgRUWRvdJ9x8oMbS0LkH8PUm2o6wKNoHg5aZTNpNRB2dYUw9iHNVc12fmF+S432RXnQIgiAUN1xKwWVw1WG0vCAIglA0MBrzS3K8l0WHIAiCD1Ewfier5F6CBEEQijdGY35JjveMoLYgCIIgCIIgCIJvkCcdgiAIPkQkcwVBEEoOIpnrPbLoEARB8CX52BywRD9vFwRBKM4YjfklON4X6UWHKzMNritqqFlotQeXLZN2wChxaEF6NROVySg1GFHzYHzDxqhOML65uoBov2LUF7i+0qx6ZRFXFqN0EUBXw5XB1M+kf1uPrZ8f7ZxtO1WW6T9TIFPerlds0fzo+nEqVY/XHkza5/69SG/klG3o6vFQqhvEOAJ5KPUwc9DQfGD62+Ww08ekfDDjzp7DBnxwcL5156rT+3bkeTwouAxeVYyWvxGxxh2ANc1TGcfFKDtZ4o/pbJSSDQDYohuTdlNqPGk3J5/TGxlVIs63+fJZnY1T/TEn6csCgDn1ImlXFn+dzeUXTJa1l69H2q0XT9Dloxrpy54/RJaFk1bA4nCWrqyzcWNgYpSnKPUlW1RDsiw1BgBgr9CAtFNKVeOCad/zji0l7c7Q8ow9UmfTmHijZdEKU1w7qblpuXSaLMrNB79z+rY7QyLIspYLR0g7pyJmD6/utQ+NUURzhpQj7Q4iPvjF7iPLUueqLZlWxTKK0ZhfkuN9kV50CIIgFDdUPp50lGAxE0EQhGKN0ZhfkuO9LDoEQRB8iOR0CIIglBwkp8N78qVe5XA4cPny5auWiY+Ph8tHm68IgiAUB3Luehn9FGUuX74M51U27rx8+TLS0rx/PVIQBOFG4EaL94WJoUXHgQMHMGDAAERERKBq1aqIjIzEzJkzdeXeeOMNlC1bFtWrV0e5cuXwwQcf+KzCgiAIRZmc93uNfooaNpsNs2fPRpUqVVCzZk0EBQWhW7duOHnypEe5AwcO4Oabb0ZUVBTKlCmDnj17IiEh4TrVWhAE4dpyI8T7a4WhRcfGjRsxcuRIXLhwAUlJSfj0008xadIkrFixwl1m8eLFmDZtGlatWoXU1FQsWLAAo0aNwo8//ujzyguCIAiFw+nTp5GSkoJdu3YhISEBsbGxyMrKwr333usuk5WVhV69eqFu3bpITEzEuXPncO7cOQwdOvQ61lwQBEEoihjK6Rg3bpzH/7t164bo6GgcOHDAbZs3bx769euHjh07AgDuuecezJs3D/Pnz0eHDh0KXuOiAqdMRDw3Y9e0jA/+mMSraqwPWr1KXeUVCe/qUUR8XAdIlSoAY5qM0Jfds9CYcx/0CTX/AH4OkvOBeyWSU3Kj6m1Q9c1Q27myRpXmcvUV13dGuVESyWvVqoWXX37Z/f/w8HD07dsXL7zwgtu2bt06nDx5Etu2bUNAQAACAgLw8ssvo3fv3jh16hSqVavm9fE0lwtarrFVFlqpzJSR5H1DGOUpzc6omlGDoZg5x/hWVn29lZmOyVxbWOVEQjVKWcON1c9Mq995ezwAgIn++cCpMlHtNzFKTdwxc8+PbKP3Y2AUTqVqdK1BpH3OmfV0XfxD9TbuoFy9mXZSdmXVK5zl5YNSPWTPvfRE2jeHRT/XNG5OKfraQym2Zdv1vlkFR6rtXJ8aRBLJvcdwIrnD4UBcXBzS0tKwYsUKZGRkYNCg7BPQ5XLhjz/+8LgTBgB33HEHPv/8c9/UWBAEoQjjUgoug1cVo+WvJRcvXkR6ejqOHj2KBQsWYPTo0e6/7dq1C1WrVkXFihXdtjvuuAMA8PvvvxtadAiCIBRHjMb8ohzvCxvDi47jx4+jU6dOSEpKgsPhwNy5c1G3bl0AQEpKCrKyshAR4anvHBERgfh4Wo8byH5En5X13z4CyT7SThYEQbjWOF3ZH6PfuRb8+++/sFgsHouEvHA4HHjkkUfwyy+/ID4+Hp06dcKECRPcfz98+DCCg4Oxf/9+ty06OhoWi4WN+RLvBUG4kTAa869VvE9OTsaFCxdQqVIlBAR49+Tv/PnzAIDy5fV7zsTFxeHiRc/9g/z8/FCnTh2v62T42VLdunVx5swZpKSk4Ouvv8aYMWPwySefZDv7/83hHA7PDV7sdjvMZv7VhzfeeAOlSpVyfypX1m8iJAiCUBzIuetl9FOY7N27F/Xq1UOzZs1Qt25dtGzZEqdP0xuIAUBMTAyGDx+OMmXKYO/evUhKSkK/fv2glEKnTp3cSlbffPMNjh49isGDB7s/69atg8vlYmO+xHtBEG4kilq8d7lcGD16NMqVK4e2bduiXLlyeO+99/L8zocffojatWujadOmaNSoEWrWrIn16z1fF5w1axZatWrlEe/Hjh1rqG4FeqGte/fu6NWrFxYvXgwACA0NRVhYGOLi4jzKnT9/Ps87a88//zySkpLcn5iYmIJUSxAE4brhUgpOg5/CvAjZbDb07dsXLVu2REJCAi5evIjg4GDda7BX8tdff+GOO+7AxYsXceLECRw7dgx79+5FYGAg9u7d65HHl/OkI+fTqVMnuFwuNuZLvBcE4UbCaMwv7EXH22+/jcWLF2PPnj2IjY3FJ598gscffxy//PIL+52///4b69evR1xcHC5cuID7778fAwYM0MXn22+/3SPeb9iwwVDdDL1e5XK53E8zcrh48SJKlSrl/n/btm2xadMmjB8/3m1bv3492rZty/r19/eHv78+UUg57FCOKxKOTEwSFJfUzRyPShR0OejkN43pIe6YVGIuV5bDSHtY35wPKjmRSRzmEmtdBvubQrmYsWTGgRx7dgzox4hUX5m4xGsu4Z45JpU0PqbZSLLsvAOfeV0/FoOCAOycIucD06/cmBHl2bnA+i54IrnhY+bqQ6PnKXs4Zfyd3cLcLGrDhg04deoUtm3bBpPJhICAALz00kvo0KED9u/fj0aNGum+07t3b494X6lSJdx///3uu2VBQUHusomJiVi3bh06deoEPz8/rF+/HhaLBa1atSLrw8V7l38IXP4hnragMqQPc8p5nc0ZGsn0AI05/RJpdwWU0huZBFcOst5M4rUp4SRpd5SpQtqptnP9xB3TGVaBLk8ekE46d4bqX78A8kiMzzW2AGC+eJwuyyQDu4iEbC4Z2BXMJNdzEH64NnIJ42Mr3UXa304/6HU1lF8Q/QcDSc/OkHL0H9j5EKWzsedeKv3apJPpbzIhnamH4hLdibkDwNCYFSZGY35hbw74/vvvY8iQIahfvz4AYMCAAWjevDkWLlyI1q1bk99599133f/WNA0TJ07EK6+8gp07d3o8jXa5XDh58iRCQ0N1qRTeYGjR0bFjR4waNQpNmzZFRkYGvvzyS2zbtg3r1q1zl5k0aRLatm2L119/Hb169cInn3yCkydP4uuvvzZcOUEQhOJGUcvp2L17N6Kjoz0uHDkLgt27d5OLDiD7bll8fDx69uyJcuXKYcOGDUhKSkL37t1Rs2ZNdzmTyYS+ffvC5XKhffv22Lt3L0aPHo3wcIM/+gRBEIohRSmnIyUlBUeOHPFQHgSA1q1bG9q64p9//gEA3euvmzZtQtu2bZGYmIioqCjMnz8fnTt39tqvoUXHBx98gOnTp+PVV1+F1WpFw4YNsXPnTrRo0cJdplWrVvj+++/xxhtvYNGiRahduza2bNniTjYXBEEQaHInVVNPBZRS7gsCR0hIiFs5KiEhQbcA8Pf3R0hIiC4p8ErGjh2LhQsXYtKkSTh69CjOnTuHRx99FO+88467zMyZMzFkyBBMmzYNa9euxdatW1GvXj3MmjXLm+YKgiCUWLyJ9wBw7ty5q264WqtWLQQEBODSpewnuLljfnh4eJ7x/krS0tIwatQotG/fHi1btnTbb731Vhw+fBh16tSB3W7Hc889hz59+mDv3r2oXbu2V74NLTpq166NRYvofQqupEuXLujSpYsR14IgCDcEBZHMzX1XacqUKbo7Vna7HYMHD87TX6tWrfDBBx8AACwWC2w2vS6+zWaD1UrvHQEAVqsVo0ePRuPGjdGtWze88sorHnt0AMDEiRMBZO/PBACffPIJRowYAbvdDovFsDiiIAhCsSO/krnexHsA+Pjjj/HVV1/l6fPrr79G3bp13XE3d8zPysrKM95fWW7AgAHIzMzEkiVLPP52zz33uP9ttVoxc+ZMfPHFF1i2bBkmT558Vd9APiRzBUEQBJ6cZEGj3wGyVaPCwsLcduqul5+fn4dE7dWoXLky4uLioJSC9v/vyickJMBms11VOWr79u3o0aMHJk6cqFtwUFSqVAkulwtnz55FrVq1vK6jIAhCccVozDcS74HstIVJkyZ55TsyMhJWqxXnzp3zsJ87d+6q8d5ms6F///44ceIEfvrpJ1I290rMZjOioqIMiYH4ZjtGQRAEAQDgQk5ioYHP/383LCzM48NdhIzQoUMHJCUleSiXfP/997BYLGjTpo3btn//fvejeQDYsWMHunXrhqeffpq8+0Y9Pdm6dSuCgoJQqVKlAtdbEAShOGA45v//9woj3lutVtxxxx1Yu3at2+ZwOLBhwwZ06NDBbYuLi8ORI0fc/89ZcBw9ehQ//vgjoqOjdb5zx/wcH0bSJ4r0kw5TUChMQcH/GZjH9aQCDwAwdmXWP2IyBRGqGHn40DilGz+9UoNm8DUDI+3h68FMXkLtwcTo6VP9BACmgGDSzvUVidGxNHI8K622QrWTayMYuxGFLk6lanSDIXT5Y0vpYxJ3UBTXdkb9gx1jak7Rno2NGVc/xoehux8G5na2malL7nPV6qDLGcTpUnAalCcxWt4IN910E/r164fhw4fjrbfeQnp6OsaPH48nnngC5cplq9w4HA40btwYc+bMwZgxY/DHH3+gW7du6N69OwYOHOh+sqJpGho2bAgg+5H/b7/9hv79+yMiIgKbNm3CzJkzMXXqVK83oroSZ6kKcF5x1w8ArOcPkWXt5evpbFpWKlnWnEjfhbNVbEraNUemzsap6lgSTtE+7Ol6m5NW5uPqYUqj3+G2l9M/QbJeOEKUBKu6RSp0AUCmXnmKOh7Aq1Q5S9HKWH5n9+psXNtZtTCX/hzl5giHk1FlMmWm6MsyimiKUtECr1I1Lqi+zjb/6BKiJKAsxn54kupQTv0NAQDQGLuztP4OOHvuRdIbwWk2/ZwHmHGPakiW5cbdeo5+sktdG+3h1cmylosn9LYUOmYYxWjML8x4D2S/ptWpUydMnToVHTt2xPz58+FwODz21Jg+fTpWrVqFU6dOQSmFgQMH4pdffsGSJUuQmJiIxMREAEBUVJQ7P+TWW291v3J77tw5TJ06FdHR0Rg+fLjXdSvSiw5BEITihspHTocqZN32L7/8EtOnT8e0adNgsVgwadIkPPHEE+6/5ywmci4uf/75J6pUqYJ//vnHI3/EYrFgz549AICRI0eidOnSWLRoEeLi4lC9enV8//33uPPOOwu1LYIgCEUJozG/sON927ZtsWHDBrz11ltYvXo16tevjx07dni8LhUVFeV+QmG323H8+HFER0fj6aef9vD13HPP4YEHHgAAfPvtt5g9ezY+/vhjhIWFoX///njqqacQGsrctCeQRYcgCIIPcarsj9HvFCaBgYGYOnUqpk6dSv7dbDZ75ImMHDkSI0fS+8zkoGkaBg0ahEGDBvm0roIgCMUJozG/sOM9kL3FRceOHdm/P/vss3j22WcBeJ8nWLVqVY/9PPKDLDoEQRB8SEHUqwRBEITiRX7Vq0oikkguCIIgCIIgCEKhIk86BEEQfEhRSyQXBEEQCo+ilkhelCnaiw6XM/vz/2iKUeDxwaEUowJlRK2Ig1JYAACl0d7ZuhhQduKPSdicjAIWp+xkBE5di5l6htrO+vYBnHoKB1EXri2cStXoWvS78ZzKybWGnVPXuB4sRscsd3mj32eQ16vyiUsBrlxjQKgVASDHilPyUxZazY7FqT+mxqhXKQujcpdlQAnNgFITW54ra6bbzilpufyCvDseeCUk5Sy47Cd/TH07lYlR1SNUyACwfUIpGXL9ZPRspWL447XpDT7nHV9OH9NKjA1oBUITIWed/QfmJx81lkbmH/I4/6jfOZwP1u79byh2zIjxZdUrDSKvV3lP0V50CIIgFDOKYiK5IAiCUDgUxUTyooosOgRBEHyIPOkQBEEoOciTDu+RRYcgCIIPcbkUXAbf2TVaXhAEQSgaGI35JTney6JDEATBh7jy8XpVCb4GCYIgFGuMxvySHO9l0SEIguBD5PUqQRCEkoO8XuU9RXrRoVxODwUgTknKsPKUAZUazjcHpb7AqUNpZoNqXAYUknzRdq6skWNyZU0GlZCMjAOngEGOgwFFMAA+UczilDg4lSpK5YRTwOJgFcooZRGujdx8Jcpz849ToHMZ6Fd27jBqcIWqckbgVApOgxcVo+VvRDRHBjS75yWJO1c0u16ZSMtKJcu6gsrQB2TUeTR7BmFk1Kv8QmgfqRf1ZblYw9TDlJlCl7cE6I/H9JMzIJT2nZ5I2lVYBb1voq8BQGPqpzEKSaRSEDcGBsbGxbTRkpxG18MvkLSTimjMnIJVPwbZvhmFKYte0YtTqRpdcyBpf/f8T3RdqPmQfoks6mTOBVOWvq+MnHsA31eKqh/nw4gCFujzj61HQJjeZvNN3DUa80tyvJfNAQVBEARBEARBKFSK9JMOQRCE4oYkkguCIJQcJJHce2TRIQiC4EOcyMc+HYVSE0EQBKGwMRrzS3K8l0WHIAiCD5FEckEQhJKDJJJ7T5FedJj8AmHy/y/pi0vE0yx+tJ1LfCUS3Yz64KB8g8ifA/ikaUN1YRKE2aRFKiGSmwVMQqCR+rHJ/wbH0tvjZftmGkSZubLcMa3e18/w2DBQSeOjaw2iy55cSTuxMAIC1LlgZRK1DYwZPzYFP4cNzW3k1Z5cbTc4FzgkkTx/uAJLwxXkmezpdNAJp8pfn0DqZJJ7recPkXZ7VCO6HsHheiMzt6yxf5N2Z2ik3oWNSFAHADM99x1lq3hd3hkSQRclEtoBwB5enbRbLp3Sl63QgCzrZM5ZxbTHlJFEGJmkczYZXe/bL3YfWdZBJMUDgCXhFGnXHFk6my2qIVmWmw+snUBZ6aRzLmH8ifLtSTt1fWDbnhxH2m0V6uts5lT6hwuVkA0ATiaJ3nrhiNc+uChIzh0Amk2fNO4oX48s63fuH53NkkKLDRhFEsm9p0gvOgRBEIobLpeCU3I6BEEQSgRGY35Jjvey6BAEQfAhznwsOoyWFwRBEIoGRmN+SY73sugQBEHwIbLoEARBKDnIosN7ZJ8OQRAEQRAEQRAKFXnSIQiC4EOcLuN3spxMnr8gCIJQtDEa80tyvL8hFh2sUo7GaScVEQwoXQB0e9i256tCuY9H189koL81TqXBYNsNqYgZUBbh2qgpOipwc4ptp5f1MAqnUjW6ej+6/PHlXvs2et6QClOcD66/DarEGeFaxwF5vSqfaCbd/FAWWpGK/T5ldhlUxTdwfnLnPVlvJqb4oh5cP7FKb5xKoMvh/TE5tTem3srMyDgagYzhzPnNqGgZaaMvVKoAenz4saHHklKpAmglw3cTfqPrwR2TGEtD5x7Aj7tRP5QPqz/9B6fN63oUJvJ6lffcEIsOQRCEooIsOgRBEEoOsujwHll0CIIg+BCRzBUEQSg5iGSu98iiQxAEwYc4VT6edJTgzaIEQRCKM0ZjfkmO97LoEARB8CHyepUgCELJQV6v8h5ZdAiCIPiQorjocLlc+Oijj7BhwwZYLBb0798fAwcOzPM7d911l842evRo9OrVq0B+BUEQbiSK4qJj//79mD9/PmJjY1G/fn08/fTTKFeuHFv+ww8/xIoVKzxs5cuXx6efflogv7kp0osOly0Drqz/lAg0RnHD5SAUDABonFKDP6GAYcskiyqTQZUl6ph2pn6MYg/bHkKFxcUos3B9BQsx5A5azUPT6Hq4sjLo8kTbufqZGDEKlw/GQePUTIh2GtY1smWRZiqEKK7tBpWalJPwY6GVcDiVqtE16R+CpCIK00bNypx/xHzgzj3NSgdbbk5RmPwDSbvSGHUgpj26OGBE1aaY8dhjj2H16tWYOnUq0tLSMGzYMBw/fhzPPfcc+50NGzbgjTfeQLNmzdy2Bg0aFNgvhyntIkwmz7GyJMWSZe0WfQAxpSXQZSNq0gdkxpv0wyg1OcpWI+2WS6d0Nk45yBlCX7Atl06TdhdR3nL5DOM7gvZ94Qhpd0TU0tlMGUlkWVPKebp+gaXo8rY0ff2YMdC4ayDlI7Q8WdaSSPefvXxdxrc+BnFjwKkpcWNJqSyZbEwb0y+RdkdYBdJOKVU9EX4bWXbOuc2k3ZJwSl+/rBSyrJ1RITOlJ5J26jzTMpPJopzSHHduK2uQzsaNmaNMFb3NQrexuLN37160bt0a9913H+655x4sWrQIt912G/7880+UKkWfn4cOHcLFixfx6quvum2BgZ7X2vz4zU2RXnQIgiAUNxwuBbPBO1mOQrzzdfjwYXzwwQdYt24dunbtCgDw8/PDxIkT8fjjjyMsLIz97s0334w777zT534FQRBuFIzG/MKM9wDw4osvonXr1vjggw8AAH379kXlypUxf/58PP/88+z3IiIiyCfcBfV7JbIjuSAIgg/JedRu9FNYbNiwAcHBwR6LhwEDBiAjIwNbt27N87uzZs1Cv3798PTTT2Pv3r0+8ysIgnCjUJTivdPpxObNm9G3b1+3LSgoCN26dcP69evz/O7BgwcxYMAAPPTQQ3j//ffhuOLtkIL4vRJZdAiCIPgQVz4uQDkSisnJyR6frCzm1TADnDp1ClFRUTBf8VpfhQoVYLVacerUKfZ7NWvWRJcuXXDfffchNTUVt9xyC5YtW1Zgv4IgCDcSRmN+Ycb78+fPIyMjA5UrV/awV6pUKc+4bLFY0LVrV9xzzz1o3rw5Xn/9dXTo0AHO/3+9O79+dcfxuqQgCIJwVZxKGZZEzCmfO6BPmTIFL7/8sofNbrd7JHNT3HTTTXjjjTcAADabTfduLgAEBATAxrxXDgB79uxBSEgIAGDgwIEIDAzEmDFjcM899xTIryAIwo2E0ZhvJN4DwPz587F69eo8fc6fPx81atRwx97csTkoKCjPuPzCCy+44z0A9OjRAw0aNMAXX3yBoUOH5ttvbmTRIQiC4EMKol4VExPjkQvh769PWDWbzRg3blye/iIi/ksgLl26NC5d8kxOtdvtSElJQZkyZVgfV16AgOyL0LvvvouYmBhUrlw5334FQRBuJPKrXuVNvAeADh06oEaNGnn6zFGQKl26NADoYnNCQoKheF+zZk3UrVsXf/75J4YOHZpvv7kp0osOzeLnqcJEKS+BVyDi1KEUoUSi+QUYrR5zUOKNNabeiioLXnmKbA+lbJTHMUkVFm4WMIotvqgfp+Tik3Fg6k22kyuraCUkTsGJUpjiFJy4trNQalxcvRlIlSoAo2sN0tnmH11CllWMKhg1H/hzjxl3Tm3NgA8Odsxyn3/M+WiUgiw6wsLCrpqAbTKZ8kz2y02zZs3wxhtv4MKFC4iMjAQA/PXXXwCApk2beu0n52Lj5+fnU785uILKwhXs2XanLZ0sq/xDdDYnM4esF0+Qdnv5enQ9OAUiynfcAdLuDCyts5nsjEKbma63s1Q0aVd+esUe6ngAYE69SNopJR8AMBMqWI7IOnQ9GBUjZaVjuNmAKpjyY+yEb7/YfWRZZ1BZuh6XaUU0zZaqs3FzhI0V3DWTUK/iyjqD6B9wluQ40k7FQ06lamwULQrxTvJfemMSc7wAOj45mXG3EkppnA/FXHdNqfGk3ciYUfUwp+i/nx/yu+jwJt4DQP369VG/fn2vfJcuXRpVq1bFX3/9hUGD/ru+//XXX4bj8qVLl9zx3ld+JadDEATBhxS1RPJu3bohIiICM2fOBAAopTBjxgw0atQIzZs3B5C938Zdd92FNWvWAAB+/PFH7Nv334+5Cxcu4I033sDtt9+O8uXLe+1XEAThRqcoxXsAGDp0KD777DOcO3cOAPDzzz9jx44dGDp0qLvM+++/7/H/efPmufM3gGwRkdjYWPTr18+Q36tRpJ90CIIgCAUjODgYS5YswcCBA7F27VpkZmbC5XLhu+++c+9p43K5sGHDBvTs2RNA9utZjzzyCBITExEREYG///4bt99+u1sq0Vu/giAIwrVl0qRJ2LNnD+rVq4d69erh77//xvPPP+/xhPzgwYMeKoP//vsvKleujBo1auDcuXNISfm/9s47PKoy++PfqSmkAAkQEkINBIRIC+gmKLAUV0Bc3RVBSixUYUWXVWGBH+4qD4htFZQiCrKrstYAFlwNwgIiVZrSSwhptJBCypT7/v7IZmS45w1zJ5PMTOZ8nid/zLln3nvee997bt6593zfYqxevRopKSma2r0ZPOlgGIbxIHahwK5IFiqs5ju1Sf/+/ZGVlYW9e/fCaDSiZ8+eMJl+fT3GYDDg66+/diz+l5SUhB9//BEnT55EXl4e2rRpg7i4OM3tMgzD1He05vzazvdBQUFYt24djh07hpycHCQmJiI21vl1zcmTJzs9xVi0aBHmzJmDw4cPIyIiAu3bt1fVmLjS7s3gSQfDMIwHUdx4fK7U8uN2oFJ1pE+fPuQ2nU5H/lqVkJCAhAT1KtWutsswDFPf0Zrz6yLfA0BiYiISExPJbVVPK64nIiLC6cmGO+3eDJ+edAhLOYTl1yIpnSIpXLNZabukXV2o+tG/KL9GO2stWqUKuC0S7WVZobEkFmEk+i+ZXcuOlc6kPipKBV3gqJPUdAtLOW3XEyVCsvjopuXngUJy/PSyImYrUcwnqV/WSeTvZMeK7KdCF9FrfvGEaIc6j4C82Fs2Bqmi8cfbjyR93/xlDb1P4vqTXnsSoQDZmCLbcNmzEsVGy/npgm9oSXK+tGJXBPRuFpIHMqb8ozCVOheIK8GRpK+RKA7XVxSTvpbYJNKupwqbARiKctVGSeGwNeYWuo2CLJXN3iDK9f0BMBRfIO2KSS1TLAgbAFib0f8YUMcPoPtjJApwAXlhvEzowR7RXN2GtECYFhDQW9T3B0sMXWBrlBSM26IlKkCKTWUy5/5MusryLNVHALA3jCeMdF7SV9D3QFk/qYJ04+WzpCtZMA5gekR3lW1x9n9IX63jwRrVxuU2dJInALLiemFW/8puzjtC+lJ5wBpaRPpqRWvOD+R879OTDoZhGH/DpgA6jTcVW+0+bWcYhmFqCa05P5DzPU86GIZhPAg/6WAYhgkc+EmH6/Ckg2EYxoPwpINhGCZw4EmH67i9TsfGjRuRnJyMZ599VrVt7969eOihh5CamoqHH34Yx44dq1GQDMMw/oKvrdPhCSwWC+69914kJyc7NNqrKC8vxwsvvIB+/frhrrvuwsqVKyEkNVEMwzD1jfqW72sTtyYdubm5mDhxIoqLi3Hq1CmnbYcOHcIdd9yB6OhoPPfcc7Db7UhJSUFWlrqwjmEYhvF9nnnmGWRmZmLv3r2oqHAWJRg1ahT++c9/4sknn8SoUaMwY8YM/P3vf/dSpAzDMIyvovn1KkVRMGbMGPzlL3/Bf/6jVjd4/vnnkZycjDfeeAMAMGDAACQmJuLll1/G66+/rmlfOqMJuusUm3QmWmpIyFSCZMpTlBIJpQxVXRsyiLZlccsgVaoksUhVgmTHilDd0Bkl8RGqGJV2eq5KHivJ8dPJFKYkfXd5f5Crp1DHREj6KCQqGtLxQJ0bRTKnpxTOIFfMAnGsZH2UoWU8yFSqpt4yjrQvObxKvT9ZfJK+w+b6mJKOHYmijE6mSnXjteqhBe18VTLXXb788kt88803eOWVVzB06FCnbbt27UJ6ejp27tyJ3r17AwBKS0sxY8YMPPXUU4iIiHB5P/bIWNjDw51sxoJzpC+lyiRKC0hffTmtaqVIFHHI+4NEvUov2aeh7Ko6vvJC0lemgKWz0opz9rBolc106RThWc3Yl6ArV6v52CMlOvwydS3JcTVcVf/waG3ehW47hFYtExa1QpJBooCllxxvXUUJbSeUsahjDQDCSKvwyfpuyj9KOKvVsgD5fcBQIlHvJGKRKbmhMI80U0pVf4obTPq+XrCbjkOyT+PV8yqbrVFLOj7Jfdd0+Qxpp5TcZOeMUqvTl0qOk0Z8VTLXF9H8pGPBggUwmUz405/+RG7PyMhwujHp9XoMGTIEGRkZ7kfJMAzjJ9Sn16uys7MxYcIE/Otf/0JoaKhqe0ZGBpo0aeKYcADAPffcg/Lycvzwww91GSrDMIxXqC/5vi7Q9KTjhx9+wJIlS7Bv3z7oiF8Fr127hitXrqhWKIyNjcW5c/QvVgBQUVHh9Mi+qMgz2skMwzB1jRACQuNNxRdrIKqeaj/xxBPo2bMnNm/erPI5d+4cmjd3XpsgNjYWOp1OmvM53zMMU5/QmvN9Md/XFS4/6SgoKMBDDz2EpUuXqm4yVVitlYuE3bh0ekhIiGMbxYIFCxAZGen4i48nFtJhGIbxAxRFuPXna7zwwgsQQuCZZ56R+litVlW+NxgMMBqN0pzP+Z5hmPpEfcj3dYXLk44tW7YgNzcXL7zwApKTk5GcnIxt27Zh06ZNDkWT8PBwmEwmXL7s/O7cpUuXEBVFr8YKALNmzUJhYaHjj4vOGYbxV4QQbv35GmvWrEFWVhZ69+6N5ORkTJo0CQAwfPhwvPTSSwCAqKgoVb4vKiqC1WqV5nzO9wzD1CfqQ76vK1x+vapfv37Yvn27k+2pp56C2WzGiy++iKioKBgMBnTt2hW7d+/GlClTHH47d+5Ejx49pG0HBQWpfi1jGIbxR4TixutVPvjLV3p6OsrLyx2f9+7di8mTJ2P+/Pno2rUrAKBHjx545ZVXcPHiRTRp0gRAZb6v2kbB+Z5hmPqE1pzvi/m+rnB50tGwYUMkJyc72SIjIxEcHOxkHz9+PJ5++mk89dRTSEpKwpYtW7Bp0yZ8+umnmoMTlnIIy69KNTKVKtjox/hCTysh6AklH8VSTnhCqr4kg2y7Qq24AVSjviSLhVKZkhwTuue0UpVsf3qZSpCW461RPUXadwqZSpXErtgsat9gieKR5JcIRdJ3qp9aVdVkaYhqR6Y4JlN2ko5BajxI+kipVAHAtC6PqH33r6D3R1ohv4Yhfy1They4Stq+8VrVKbKrRhvuPD73xcftXbo4KwuVlFSq/iQlJaFly0r1mWHDhqFJkyb4+9//jsWLF8NisWD+/Pm444470KFDB037M1zNhsHewMlmjW5H+pry1Ws/CSM9kbFG0K8Dk4pCAGAn8oQkH1jiutL7JGIRkmtWFocwqQv3AVqpSnacRFAYadcX59P+wWq1MXP2AdJXCaYVpiiVKgCwNuuospkuHCd9qXMgwxp7K2lXgsJJu+yYUHajJD6paplEScvalLgWZAqJVvoeSJ0bGVaDROlK0gbVT5lK1fRGvUj7m2c+p2Mh+m7O/Zn0lUGp1QGAMKivKePFk6SvvWmcyqYonvnxQ2vO98V8X1d4fEXyiRMn4pdffkFycjJatGiB7OxszJ07F8OHD/f0rhiGYRgv0qBBA3z22Wd48MEH8cknn6CsrAzt2rXD55/T/4AwDMMwgUuNJh3/+Mc/oL9hzQadTofXX38d8+bNQ3Z2Nlq2bInISPoXEYZhmPqGUKQ/Ylb7HV+nZ8+e2L17t0qd8De/+Q3OnDmD48ePIygoCG3btvVShAzDMHWP1pzvD/m+tqjRpCMhIUG6rXHjxmjcuHFNmmcYhvE73CkU9IfCwvDwcNUrtlUYDAZ06tSpjiNiGIbxPlpzvj/k+9rC469XMQzDBDL1paaDYRiGuTlc0+E6POlgGIbxIPVFvYphGIa5Oaxe5Tq+PenQG5wUaeSqPxoUbgAIYjV1Wdta1as80bbc3+VlVaRtUOpGQtIu1ZfKOGoen+a+a2lDouAERdu51AQRi1SpSeOYotqR9VF2zjSNB1kckjYopapp3SaSvm8efV/SugSNx8oncGPSgQC+CVWHvqKYtCumELWvlVZok2EPbUTaDYSyk5CNQ8nL2fryQrWrRI1KpgKlL6MVkgShTCQ7TnaJYpZBor6khDdzaX8AoKvQtpK83qI+P0oI3Xcd4VtpL1EbFRu9vzL1OQAAu0wFijiXmsdUA3ptGp2lVG2TqRtWEH0EYDfT4wc69X1Xpq5lNwWTdqqfQjKmZCpVU9vcR9opFSyZ0pzsXOoqrtH+Jg3njLpWPVVcoTXnB3C+9+1JB8MwjJ+hCCGVWK3uOwzDMIz/oTXnB3K+50kHwzCMBxHCjderAvgmxDAM489ozfmBnO950sEwDONBuKaDYRgmcOCaDtfR8BI+wzAMwzAMwzCMdnz6SYfOHAyd+brCJyMdruaCXb26Haf9uNKGdJ9EjHQtnxRNsUiK0WCmC7UEVXQWpC7KBABB9QWAzkgXFmo6VrJzKeu7hv3Jij51JvWJEAb65AhJgZmm+GTnRtJ3GTpBFf9LjjVxfgFAZ6J/WaHakfZRw/UnKxif2nE0aX/zlzX0Pql+yo6frO+0t6pAVhg8kw4VBdBplsz1yK79GmvT9rBGOBf5mi6epH2bdVTZqGJdADBcySTt9satSLsgipupvAkAxstnSbvOblEbr10mfa2xt9JxBIWRdiU4XGWTHSe9ZJ+KrIi+MFsdX9MOdNuSQm0R1IC0m/KPqWyWuK6kr7TAlyg0Nl04TjchuX8ZinJJu65cXThtjWpDxyEp0BdGOneasw+ofSWiH7I2pP2k/CUF2bI2qH4ar56nfSXjgSoYB4DpjXqpbP8oOUT6yjDl/ULaqToK2TkzXjqtthXTRfta0ZrzAznf+/Skg2EYxt/w1cUBKyoqsH//fhiNRnTr1g0GmcobgLy8PBw+fJjcdttttyE8vPIf3++++061vWPHjmjRooVngmYYhvFxfHVxwNOnTyMnJwcdOnRA06ZNq/XdunUrKioqVPaYmBh06dIFAHDixAlkZjr/iBMSEoLU1FSXY+JJB8MwjAcRinYlRk8pN8r473//iwceeACRkZEoLy+HyWTChg0bcMstt5D+P//8MxYuXOhkO3XqFDIzM5GZmemYdAwaNAjdu3dH48aNHX5/+tOfeNLBMEzAoDXn13a+t1gsGDVqFP7zn/+gffv2OHLkCGbOnIl58+ZJv7N48WJcuXLF8dlms2HLli148skn8dprrwEAli9fjtWrV6Nbt24Ov5iYGJ50MAzDeAtFEW68XlV7v3yVlpZixIgReOihh/Daa69BURTcf//9GDVqFA4cUL/2AQADBgzAgAEDnGx9+vRBQkIC4uPjneyLFi3CwIEDay1+hmEYX0Zrzq/tFckXLFiAH374AUePHkVcXBw2b96M3/72t/jNb36DwYMHk9/56KOPnD6np6djy5YtePjhh53sycnJ2Lhxo9uxcSE5wzCMB6lSMtH6V1t8/fXXuHjxImbOnAkA0Ov1mDVrFg4ePIh9+/a51MaxY8ewfft2TJgwQbUtMzMT27dvR05OjkfjZhiG8Qd8Kd8DwOrVqzFu3DjExcUBAPr164fU1FSsXr3a5Tbeeecd9OrVC127OtddlZeXY8eOHTh8+DD5OtbN4EkHwzCMB6nJpKOoqMjpz52kfiP79+9HbGwsmjX7dbXpHj16OLa5wjvvvIPo6Gj8/ve/V22bO3cu/vznP6N9+/a4++67kZeXV+OYGYZh/AVfyveFhYU4e/asI8dX0aNHD5fzfU5ODr7++mvyR6YdO3bgiSeewNChQ9GiRQusXbtWU3w+/XqVKL8GcX2EMtUkm5W2SxR+9EQBpb38Gumr06heRbWtVJRpaluRxUIpZsgUkiR2qg3FUk766kNoFRKZvxZ1LZlCkqzvZBsylSqJv2JTK8roJAoistVFpX0n+ikkfdc601eIdsixAEAnKQ6WjkGiHSHro00Suez6I5CpVE29ZRxpX7J/hcqmVWlO1p8br1WdnVZ80UpNViS/8dWlefPm4bnnnnP2VRRs2rSp2vaioqLQvXt3AEBBQYFTzQUAmEwmhIeHo6Cg4Kax2Ww2rFmzBmlpaTCbncfLmjVrMHbsWABAbm4uBg0ahEcffRRfffXVTdu9EdOFEzCVOeccW1Rb0teco1a/EUZasY9SugIAU/5ROhBCeUp2PmXqS7oKtSqOkFyzply6YF+YQkm7seCcymaNbke3IVHA0hfnk3YlvJnKRikvAYASrFb5AgAdoYAFAJaYTiqblnMgQ6b+pSsvIu0iOIK0I6K5ymSUqD2R6mQArV4JwNK8MxGIRCHRSucradxUGxr7TvXT1qgl6WvO/ZluW3L9UUpVT4Ylkb5vnaD/gbU2S6T3SahPGiVKbjZCdcsWRB8nrbi7Irkr+R6ofNKclZVVbZu33347wsLCHDn9xpwfFRXlUr4HgPfeew8hISEYOXKkk33AgAF49tln0aRJEwDAwoULMW7cOHTq1En1RESGT086GIZhAomsrCxEXCcbGxSkvpHb7XZVkfeN9OjRwzHpMJvNKCtTTzrLy8tVkwiKL774Avn5+Rg/frxqW9WEAwCaN2+O2bNnY/To0SgpKUFYGP0PL8MwDONavgeAb7/9Funp6dW2tXz5coSFhTly+o05v7S01KV8DwDvvvsuRo4c6RAMqeLuu+92+jxz5kwsWbIE6enpPOlgGIbxBjVZkTwiIsLpJkRhMplIqVoZrVu3Rl5eHux2u0MmNy8vD1arFa1bt77p99955x3ccccd6NiRfmJwPdHR0RBCIC8vDwkJCS7HyDAM46+4uyK5K/keAKZNm4Zp06a51HazZs0QHByM8+ed11k5f/68S/l+y5YtOHnyJN5/n15r60aioqKQm0uvfUPBNR0MwzAeRAg3ajpqUbd90KBBKC4uRkZGhsP2+eefIzg4GHfeeacj5u+++051o6ru3d7CQvXicF999RUiIyPRqhW98B7DMEx9Q3POr8V8bzAYMGDAAKxbt85hKysrw8aNG52Uq44fP47t27ervr9y5Urceuut6N27t2rbjTn/zJkzOHr0qMtPOQB+0sEwDONRhCI0SyLWpppJp06d8Nhjj+GRRx7B888/j9LSUsyaNQuzZ89GZGTle/l2ux2DBg3C4sWLnX5RW716NcLDw/HHP/5R1W56ejrWrl2L+++/H9HR0fj222+xcuVKvPnmmzCZJPV3DMMw9QytOb+21auef/55pKamYsqUKfjtb3+Lt99+G+Hh4Zg6darD56233kJ6ejrOnj3rsBUWFuLTTz/FokWLyHZTUlIwcuRIJCUlITc3F4sWLUJSUhLS0tJcjo0nHQzDMB7EF1ckX758OVasWIH169fDaDRixYoVGDVqlGO7Xq/HgAEDVIWN586dw6xZsxASEqJqMy0tDa1bt8aHH36IvLw8tGnTBrt379b0qxfDMIy/42srknfv3h07duzAkiVLsHr1atx6661Ys2YNGjZs6PBJTExEnz59nL63Z88e9OnTB2PGjCHb3bZtG5YuXYo1a9YgIiICs2bNwqOPPgqj0fWpBE86GEBPv2UnJMpO3kCrilh9R6ZSVav7lKlDgVCvkp0viZ1SqQKAad0mqmwyBSxfoSY1HbWFwWDAlClTMGXKFHK7Xq8n60SWLVtWbbt9+/ZF3759PRKjjlKAkSj8kLlJ0aY+JiRKQ3pbzWUrdZRynWwZYp3kLWct/ZG1LVNI0nisSCRtaFHxkZ0DWXzkcZUgi0PLlSY9TtJxKTmXhL9Oem7oPmqLW9KGbJ+UXeuy2RrGlEyl6vH2I0n761f30g3piePqibGtEXdrOmqTrl274u2335Zup+4H1KKw19OoUSP89a9/rVFcPOlgGIbxIIoiAB9akZxhGIapPbTm/EDO9zzpYBiG8SBCsUvXaKnuOwzDMIz/oTXnB3K+Z/UqhmEYhmEYhmFqFX7SwTAM40H4SQfDMEzgwE86XMenJx06czB05uBfP5vo1RSFxiJjYVDLOV6/Hye71rYJf32QWvkFkBdqy4rAdEai/zJfDcdKWi4uK/KTHCuySFh2cZnplTc1FQrKzo2k73qi+FpWyCgtuJP1nYpbY99l6ImCSK1jXjoGNYwHjxSvS1QuZMeVKhqfess42vfkv+m2Qbd9Y9+FRFBBK0JR3Jh0aCzarIdYo9rAGuG8Aq7x8mnat5l6oUJ9eTHpqy+5SNpt0W1Ju66ihGiEHreGInpRLGqfssJmS2wS3XZxPmm3Nm6pshkvHCd9hVGSryQFz/rSAvX+miXSvtcu0/GFNyXt5rwjKpus79ICeGu5yma8RI8RnYU4jwAAeg0ZXXmRymYPa0KHZ6RzuAgKI+2m3MPq/ckK3SX/F+jL1OviAIAwqWORnRvZtWAPbaSymS6fIX1l40FXcY20m/J+cbkNWcH49IY9Sfubpz5W2ezhzUhfQ2G22lZM5wytaM35gZzvfXrSwTAM428Iux3CrnHSodGfYRiG8Q205vxAzvc86WAYhvEgQrjxepUI3JsQwzCMP6M15wdyvudJB8MwjAfhmg6GYZjAgWs6XIcnHQzDMB6EJx0MwzCBA086XIcnHQzDMB6EJx0MwzCBA086XMe3Jx16g5MikkzVodrvu4hUCUmjShClCiJ02pQKZLFQ6kGydS21HCuZKpGQKJzIjomW+KSxaDneMl9J3OR5kPVRK1pUwWT7lCi2kOdSY9xax6DLcQDarhGNY4pCplI1NeFB2v/o+67F4qmxwLiFMAVBmJzVlqTqS4SalDDRCm2GojzSrsiUiUzEPiVjQydRzCLjtqmVl6prWzE3oNs2qNX5ZMdJJ9mnrO+UgpMSEknHYaaVmsjjB415TJbDibZ11lKJbyjdtETVSm9Rqy/ZCFUnABCUkiQgHyeUAqEkn8qOq1SNy25Rt6Gx78Icq7IpkuuJGn8AAJNEcYzqu6wNPd0GpVIFAFPbPaCyvV6wm26aUGbTVdBjh6k9fHvSwTAM42ewZC7DMEzgwJK5rsOTDoZhGA+iKHb5Gi3VfYdhGIbxO7Tm/EDO9zzpYBiG8SBc08EwDBM4cE2H6/Ckg2EYxoPwpINhGCZw4EmH6/Ckg2EYxpPY7RB6jTeVAF6hlmEYxq/RmvMDON/79KRDWMohLL+q2ugUk9SPRKayZCSUTzS2IYNqG5YKTW3LYiFnx5KCJNmx0pnUqhFKRRntK1PXkMWnJ5Q7ZPFBonAiOw8UkuOnl6lxWdUqH5CJkBCKGwCgaIlP8muGJoUuSTs6Ex2fVGFKMgbJ8WAjjhMAnaQ/wmal90m1IbFrOe+ysSNTqZracTTtf6MKlod+fRJCe01HIK9QW4XpwnGYypyVexSJepDx0mmVjVIfAgBLbBJp15dcJO2Goly1UaJKZI25hW7jarbKpjSIon0L1b4AYCi5RNqFMUjdtkTpyhbVmrSbLp4k7VR/TPlHSV9KNQkAZNnN3jBeZZOdA30FfS4p9SVrs450HMQ5AAB7ZBxtj1Dfq8w5h0hfWZ61hzcj7daoNiqbzk7nTV0FrTBlk/STGpvGK+foOCRtmPOOqGz2sGjS1ygZO3or/X8E1XdZGzrFRtplx5VSqpreqBfp+49rP6tstiC1Wps7aM35gZzvfXrSwTAM428IRdE+6QhgNROGYRh/RmvOD+R8z8L0DMMwDMMwDMPUKvykg2EYxoMINyRzA7mwkGEYxp/RmvMDOd/zpINhGMaDVD5q1/b4PJAftzMMw/gzWnN+IOd7nnQwDMN4EH7SwTAMEzjwkw7X8elJhz64AfQhv6pyCJlaEaHAUx3CoPbXBdPqHzqJEpKWthGkrXRGpvADQhlLprIkO1aCULrQBUtUqoy0SpA+xHXlJGl8Bom6FmnVdh4odRcApNoVeb4AQEhUt0LpCKl+Cpksnpk+rrJ9kq56jZeuZAxqGQ8yBR8t15/svMsUx8g2ZOpfkvhUKlX/Y2rCg06fLfDMr0886XAPa8wtsEZEONlk6kb2JoQCkeT6kSkQWeK6knYlrEk1Ud7QdvYB0m5r2EJl01lKSV97I7WqEyBX7AFx7euvXSZdTReO0/FFtaX9cw6qbNbYW+k4JEpDVHwAYLiSqbLZG7eim9ZwDqiYAVotCwBMuYdJO6XOJ1M+k+UaGZTamiwXiuAI0m7OVasvybA1aknaZeOB6qdsTNmb0upfsuuP6rutaQe6DQkyhTd9aYHKRqlUAcCTDTqrbN7K+YGc73160sEwDONvKIpdKi8sI5BvQgzDMP6M1pwfyPmeJx0MwzAeRNgVQKdx0mEP3Hd8GYZh/BmtOT+Q8z1L5jIMw3gQIewQisa/Wl4sqqSkBMuXL0ffvn3xxz/+0aXvVFRUYOHChejfvz8GDRqEpUuXQrmhANIVH4ZhmPqM5pxfB4sD7ty5Ew8//DCSk5Oxbds2l76zfft2jBgxAn369MGECRNw9uxZt3yqwyefdIj/vR9fVOK8Kim54jUAnValGCOxEqhFsiq3xtWjybZl777KkK3wbNBS0yGZT1LvospWmjbSceutkhXJNdV0SIaeVbJytobzQJ4DgDwPspoOnay+QraKLFXTIXuEapKMBw01HbL3pqXIxqCG8QDJKrxarj/ZedfZXb9GNI1tQNqfG9/nrfosJGPWn+nUqRPuvvtuxMTE4MABug7hRtLS0rBr1y68/PLLuHbtGqZPn45z585hwYIFmnxuRtXxLi4uVm3TX1PbAEBRiLotWU1HsWSl8qKar0Ysa9tmUMetk9xj7AZJHBpqJvSl9HEyFEtWtzbR/kaiP1bZcdJa00HEYjfW/ByYJOfATpwDWRwAncekY0RrTQexT2lNh4XOP9S5kWEzauu7NVTdT9mYIq89QF7TQexT60rgBiI3AICuQl0nJWubqt+orzl//vz5WL9+PUaPHo333nsPV69evel3duzYgf79+2PGjBkYP348li1bhpSUFBw8eBDR0dEu+9wMnfDBo33+/HnEx9NFYAzDMLVJVlYWWrRQFwLfjKKiIkRGRsLc/RHoZAIFEoTdAstPq1BYWIiICLqQtCYUFxcjPDwcc+bMwSeffIKjR49W63/w4EF07doVW7ZswZ133gkAePfddzF58mTk5uYiKirKJR9X4HzPMIw3qeucX1f5/urVq2jUqBE2bNiAYcOGVfudwYMHIzg4GOvXrwcAWK1WxMfHY9KkSfjb3/7mss/N8MknHbGxscjKykJ4eDiKi4sRHx+PrKysWjk5vkBRURH3sZ4QCP2sr30UQqC4uBixsbE1a0exa6/pqOXCwvDwcE3+GRkZiIiIQJ8+fRy2e+65B4899hi2bt2K3//+9y75uALn+/pJIPST++jfeCvn+1q+t9ls2LJlC9544w2HzWQy4a677kJGRgb+9re/ueTjCj456dDr9Y5Zp+5/r3RERETUuwF/I9zH+kMg9LM+9jEyMrLGbQhrufabyv9e2yu64XWOoKAgBAVJXmeoRc6dO4eYmBjor3uVrUmTJjCZTDh37pzLPq7A+b5+Ewj95D76L17J+T6W7/Pz82GxWFSTr9jYWHz//fcu+7iCT046GIZh/A2z2YyYmBjk/fKRW98PCwtTvWY0b948PPfcc042i8WClJSUatu67bbb8Oabb7oVB1D52Jy6+QUHB8NqtbrswzAMU1+pSc53Nd8DwEsvvYR//5teb6qKDz74AB06aFv/pIqqfH1jPg8JCXHK9zfzcQWedDAMw3iA4OBgnDlzBhaLxa3vCyEcv/RXQf1TbzKZsGzZsmrbqukvklFRUbh82XlxMIvFguLiYkethis+DMMw9ZWa5HxX8z0APPjgg+jfv3+17dWkLq5x48YAoMrnly5dcuRyV3xcwecnHUFBQZg3b55XHjnVFdzH+kMg9DMQ+uguwcHBCA6WrDjvIXQ6HZKTk2t1Hz169EBubi6ys7MRF1e5AvHOnTsd21z10UogjK1A6CMQGP3kPjJ1kfNbtmyJli3pVeY9QUREBBISErB7926MGjXKYd+5c6cjl7vi4xKCYRiGCQhmz54tEhMTVXabzSZ69uwp/v3vfwshhCgrKxPx8fFi/PjxQlEUYbFYxODBg0WvXr0c33HFh2EYhvEOBQUFAoDYsGGDatsrr7wi7rnnHsfnhQsXiqioKHHy5EkhhBAbNmwQOp1ObNmyRZPPzfD5Jx0MwzBMzXjkkUdw6NAh5OTkoKCgwPGk5JtvvkFUVBSEENi7dy8uXLgAoPLXu88++wwPPPAAYmJiYLFY0LJlS3z++eeONl3xYRiGYeqWTZs24ZlnnoHdXlnc/tRTT+G5557D2LFjMX36dACVQiAHDx50fOcvf/kLjh8/jltuuQUtWrRAbm4uXn31VYccuqs+N8Mn1+lgGIZhPMfRo0dRUqJepKtbt24wGit/e9qzZw9atWqFJk2aOLYrioLjx4/DaDQiISGBbNsVH4ZhGKZuKCgowKlTp1T2mJgYh1JgVlYWrl69iqSkJCefixcvIi8vD23atEFYWBjZvis+MnjSwTAMwzAMwzBMreLTr1cdPnwYJ06cQKtWrdwuTPQ1Dhw4gCNHjqB///5o1qyZaruiKNi1axfy8vLQuXNntG/f3gtRuo/VasX+/ftx4cIFJCYmSn/5PH36NA4ePIimTZvitttug8FgqONIa0ZOTg7279+PBg0aoEePHuRiPEVFRdi+fTsAIDU11W81zrOzs7F161Z07NgR3bp1c9qmKAp+/PFHXLhwAUlJSWjXrp13gmT8nvpyvVxPcXExvv32W0RFRaFv376kT25uLnbv3o3w8HCkpqbCbNa2mr23OX/+PA4ePIiGDRuiR48eZFFteXk5tm/fjmvXruH2229H06ZNvRCp+1gsFvz000+4ePEi2rdvj8TERNLvp59+wtmzZ5GQkKD6Bdmf+PLLL1FcXIyRI0eqtmVnZ2PPnj2IjIxEamoqTCaTFyJk/BY3alNqHbvdLsaNGyciIyPF4MGDRXR0tBg6dKgoLy/3dmhuk5GRIW6//XbRvn17AUB8++23Kp/CwkKRkpIiYmNjxaBBg0SDBg3EjBkzvBCte6xdu1a0a9dO9OrVSwwZMkRERESIESNGCIvF4uT3f//3fyI0NFQMHDhQtGjRQvTs2VNcunTJS1Fro7y8XIwZM0a0bt1aDBs2TPTs2VNERkY6CnCr+P7770WjRo1EcnKy6NWrl2jUqJH4/vvvvRN0DbBYLKJ3797CbDarxuKVK1dEr169RFxcnBg4cKAIDQ0Vs2fP9lKkjD9TX66XKq5duyYmT54sYmJiRExMjLjrrrtIvxUrVojQ0FDRr18/0b59e9G2bVtx6tSpOo7WPfLz88Xw4cNFy5YtxZAhQ0Tnzp1FXFyc+O9//+vk98svv4j4+HjRqVMncccdd4jQ0FDxr3/9y0tRa2f9+vWiffv2IjU1VQwdOlRERESIIUOGiNLSUodPRUWFGD58uIiKihKDBw8WkZGRYvTo0cJut3sxcvd4//33hdlsFtS/h4sXLxYhISGif//+ol27dqJDhw4iMzPTC1Ey/opPTjpWrVolGjRoII4ePSqEECIrK0tERUWJhQsXejky91m3bp3Yvn27yM3NlU46pk+fLtq1aycKCgqEEEL88MMPQqfTiY0bN9ZxtO7x8ccfi/Pnzzs+nzlzRkRGRopXXnnFYdu6dasAIDZt2iSEEKKoqEh07NhRjB8/vs7jdYfi4mLx6aefCkVRHLa//vWvIjw8XNhsNiFE5cQkNjZWTJ8+3eEzdepUERcXJyoqKuo65Brx9NNPi7S0NNG5c2fVpGPSpEmiY8eOoqioSAhR+Y8jALF582ZvhMr4KfXpeqniypUr4q233hJFRUVi9OjR5KTj1KlTwmQyiVWrVgkhhLBaraJfv35i4MCBdRyte5w+fVqsW7fO8VlRFDF+/HjRvHlzJ7/bbrtN3HPPPY5/wF977TUREhIicnJy6jRed/nyyy/FhQsXHJ9zcnJEWFiYeOONNxy2l156SURHR4usrCwhhBDHjh0ToaGhYuXKlXUeb004efKkiI2NFXPnzlVNOo4cOSIMBoP48MMPhRCVE62UlBQxbNgwb4TK+Ck+OekYMGCAeOCBB5xsjz/+uOjSpYuXIvIc1U06oqOjxfz5851sKSkpYsyYMXUVnsfp37+/GDt2rOPzpEmTRPfu3Z18Xn31VREWFiasVmtdh+cR3nvvPWEymURZWZkQQoivv/5aABBnz551+Jw+fVoA8JsJpBBCbNy4UbRr104UFRWpJh12u11EREQ4TSiFEKJHjx5+M4FkfIP6cr3IkE06FixYIKKjox0/VgghxGeffSZ0Op3f/EN+I19++aUAIPLz84UQQpw4cUIAEBkZGQ6f0tJS0aBBA7FkyRJvhVkjFEURcXFxTvfqrl27iilTpjj5jRgxQvTr16+uw3ObiooKkZycLFavXi1WrVqlmnTMmzdPxMbGOv3g9sEHHwi9Xi8uX75c1+EyforeG6903YxDhw6hS5cuTrakpCQcOXIENpvNS1HVLrm5ubh06RLZ70OHDnkpqppRUFCAffv2OfVJdm5LSkpw9uzZOo7QfXbu3IkPP/wQCxYswLx58/D666873mU+dOgQwsPD0apVK4d/mzZt0KBBA785l3l5eXj00UexZs0asl4lMzMTRUVF9Wq8Mt6hPlwv7nDo0CF06tTJqZ4tKSkJQgj8/PPPXozMfb777js0bdrUUbNRdf6uzxMhISFISEjwq3NbXFyMtWvX4p133sG9996LVq1aYdKkSQAq69p+/vlnv8+FM2fORJs2bZCWlkZuP3ToEDp37uy0inZSUhIURcEvv/xSV2Eyfo5PFpIXFhY6llyvIioqCna7HSUlJWjYsKF3AqtFCgsLAYDs99WrV70QUc1QFAUPP/wwGjVqhMmTJzvssnMLwK/6uW/fPmzatAknTpxAWFgYOnTo4NhG9RHwn3MphMDYsWMxfvx4pKSkkD71bbwy3sPfrxd3qS+5sIqMjAwsXrwYK1ascNjqS54oLi5Geno6CgsLsXfvXowePdohFVpaWgqbzebXffzqq6/wySef4MCBA1KfwsJCREdHO9n8ebwy3sEnJx1BQUEqTfmqz7W93Ly3CAoKAgCy3/7WZyEEJkyYgJ07d2Lz5s1OKjT15dxOmTIFU6ZMAQAsWrQIw4cPx6lTpxATE0P2EfCfc/n+++9j7969SEtLw9q1awFU3nCOHTuGtWvXYuTIkfVqvDLexd+vF3cJCgpy/FNehT/mQgD48ccfcd999+GZZ57BI4884rBfnyeu/7GwpKQE8fHxdR2m28TGxjpyYV5eHnr27InQ0FDMnz+/XuTCRx99FPfffz+++eYbAJVP8gFg7dq16N69OxITE+vNvZvxLj75elW7du1w7tw5J1tmZiaaN29ebwd3ixYtYDabyX63bdvWS1FpRwiBSZMm4YsvvsCmTZvQsWNHp+2yc6vX69G6des6jNRzjB07FqWlpdi3bx+Ayj4WFBSguLjY4VNUVISrV6/6xbmMiorC4MGD8cUXXyA9PR3p6ekoLi7GiRMnkJ6eDgBo1aoVDAaD349Xxvv4+/XiLrJcCMCv+r1z507cddddePzxxzF//nynbVUS2jf289y5c37Vx+uJiYnBwIEDsXXrVgCAyWRCfHy8X+fCAQMG4MqVK458X3UvS09Px4kTJwDUn/HKeBnvlpTQzJ49W7Ro0cJRmGuz2USXLl3qRYFqdYXkQ4cOdVIuuXz5sl8V3CmKIiZOnCiaNm0qDh8+TPqsWrVKBAUFOQoNhRBi2LBhflNwl5+fr5JB3LBhgwAgDh486PAxm81i9erVDp+VK1cKs9ns1G9/glKvGjBggJNySX5+vggKCvI7xRbGu9TH6+V6ZIXkVWpvP/30k8M2bdo00aZNG6diXV9m165dIjIyUjz77LPkdqvVKpo1aybmzJnjsG3evFkAEHv27KmrMGvEjUX9NptNdO3aVTz00EMO26RJk0SXLl0cYihlZWUiPj5ezJw5s05j9RRUIXmV4MORI0cctvHjx4uOHTvWdXiMH+OTr1f9+c9/xgcffIC7774bI0aMwFdffYX8/HzMnTvX26G5TWZmJnbs2OF4nL5582ZcunQJnTt3diwitGDBAqSkpGD06NFITU3Fu+++i4SEBDz22GPeDN1lZs+ejRUrVmDOnDk4dOiQo4guJiYG/fr1AwCMHj0ay5Ytw6BBgzBx4kTs2bMHGRkZ2Lx5s/cC18CuXbvw/PPPY/jw4YiLi8ORI0ewfPlyTJgwwXEemzZtijlz5mDatGnIzs4GUHlu586d63eLYlXHiy++iDvvvBNpaWno3bs33n77bdx6660YO3ast0Nj/Ij6er2sW7cOZWVlyMzMdBQiGwwGPPDAAwCAfv364Q9/+APuu+8+PPnkk8jMzMTSpUvx6aefOhXr+iqnTp3C4MGD0bZtW3Tr1s3x+hEA/O53v0PDhg1hNBrx6quvIi0tDVarFc2aNcPLL7+MtLQ09OzZ04vRu84f/vAHdOvWDd27d0d5eTk++ugj5ObmOvV3zpw5SE5Oxr333othw4bh448/hl6vx4wZM7wYuWf53e9+h6FDh2LYsGF44okncPLkSaxevRobNmzwdmiMH6ETQghvB0Fx+fJlLF26FCdOnEDLli0xefJkxMXFeTsst9m2bRuWLFmist9///0YMWKE4/PJkyfx9ttvIy8vD126dMGUKVMcBWu+zvz580m1jqSkJMyePdvxuaysDMuWLcP+/fvRpEkTPPbYY+jUqVNdhlojjh07hg8//BCZmZmIjY3FkCFDkJqaqvJbv349vvjiCwDAsGHDMHz48LoO1WM8/fTT6NatG0aPHu1kP3bsGFauXIkLFy6ga9eumDx5MkJDQ70UJePP1KfrBQAmT56sKrA1m81Ys2aN47PdbseqVauwfft2hIeHY8yYMejdu3cdR+oeBw4cwIIFC8htL774opMa2bZt27B27VqUlpaib9++GDt2LPR6n3y7W4XVasUHH3yAHTt2wGg0onPnzhg7dqzqvpyTk4Nly5YhMzMT7dq1w+OPP64qvPYXtmzZgqVLlzpNrIDKY/Huu+9ix44diIyMxLhx4/xm8sj4Bj476WAYhmEYhmEYpn7gHz81MAzDMAzDMAzjt/Ckg2EYhmEYhmGYWoUnHQzDMAzDMAzD1Co86WAYhmEYhmEYplbhSQfDMAzDMAzDMLUKTzoYhmEYhmEYhqlVeNLBMAzDMAzDMEytwpMOhmEYhmEYhmFqFZ50MAzDMAzDMAxTq/Ckg2EYhmEYhmGYWoUnHQzDMAzDMAzD1Co86WAYhmEYhmEYplb5f0j06HtlyXkfAAAAAElFTkSuQmCC",
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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": "stream",
- "text": [
- "Batch: 9/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.342\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 10/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.384\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 11/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.338\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 12/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.370\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 13/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.368\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 14/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.352\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 15/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.362\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 16/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.300\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 17/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.396\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 18/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.342\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 19/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.340\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 20/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.366\n",
- "CPU times: user 3.66 s, sys: 5.09 ms, total: 3.66 s\n",
- "Wall time: 3.43 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "walker_scaled.walk(n_steps=10000, burnin=1000, batch_size=500)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "eeee43dd-2c5d-43df-9ed2-7610abe4899f",
- "metadata": {
- "execution": {
- "iopub.execute_input": "2026-08-11T03:10:35.106658Z",
- "iopub.status.busy": "2026-08-11T03:10:35.106520Z",
- "iopub.status.idle": "2026-08-11T03:10:38.552877Z",
- "shell.execute_reply": "2026-08-11T03:10:38.552229Z"
- }
- },
- "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: 0.382\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 2/20 completed, 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",
- " Model parameter acceptance fraction: 0.364\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 9/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.340\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 10/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.352\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 11/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.376\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 12/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.320\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 13/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.344\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 14/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.398\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 15/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.298\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 16/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.306\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 17/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.384\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 18/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.340\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 19/20 completed, 500 steps. \n",
- " Model parameter acceptance fraction: 0.378\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 20/20 completed, 500 steps. \n",
- " Model parameter 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"
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- "outputs": [
- {
- "data": {
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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/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/examples/robust_likelihoods.ipynb b/examples/robust_likelihoods.ipynb
index 78a8863..d2c6be0 100644
--- a/examples/robust_likelihoods.ipynb
+++ b/examples/robust_likelihoods.ipynb
@@ -2,274 +2,370 @@
"cells": [
{
"cell_type": "markdown",
- "id": "d8f105f7",
+ "id": "5bcc02f5",
"metadata": {},
"source": [
- "# Robust likelihoods: Student-t vs the multivariate normal\n",
+ "# Robust likelihoods\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"
+ "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",
+ "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\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, 12, 39"
]
},
{
"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"
- ]
- }
- ],
+ "id": "f6300218",
+ "metadata": {},
+ "outputs": [],
"source": [
"import corner\n",
+ "import emcee\n",
+ "import matplotlib.pyplot as plt\n",
"import numpy as np\n",
- "from matplotlib import pyplot as plt\n",
+ "import plotstyle\n",
"from scipy import stats\n",
"\n",
- "import rxmc\n",
+ "import rxmc as rx\n",
"\n",
- "rng = np.random.default_rng(21)"
+ "plotstyle.use()"
]
},
{
"cell_type": "markdown",
- "id": "8e3985d0",
+ "id": "b128b9e4",
"metadata": {},
"source": [
- "## A clean linear signal with a few gross outliers"
+ "## A clean signal with a few gross outliers\n",
+ "\n",
+ "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": "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"
- }
- },
+ "id": "5a862393",
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
- ""
+ "Dataset('with outliers', n=25)"
]
},
+ "execution_count": 2,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"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",
+ "rng = np.random.default_rng(21)\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",
+ "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 # 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();"
+ "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",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0fac5bbe",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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/hqWlpcZv8WWfwaJFi9CoUSOtx3Fycqr0fbRNUs/OztY4ftnaetraZmVlVWkpk4o+/+p89mvWrMHy5cvx559/4vjx49i8eTPWrl2LEydO1PodytXJV9s5trW1hY2NjXijwsPK7vCsajtt7OzstJ6TnJwcqNXqOlsTsSqfTXX7VdExa/p1Y2Zmhh49eiA6OhpnzpyBXC5HWFgYAgIC0LBhQ0RHR+PWrVtwdXVF+/bty+3/8Dl91PfAg20qy/vB3Mu+byr7HiTjw8KNjIKNjQ06d+6M6OhoCIKAvn37AgA6duwIFxcXrFq1ChkZGRq/GTdt2hTe3t64ceNGrf7QtrCwgEqlqnD7xIkT8fbbb2PDhg34448/4OLigqFDh4rbAwICtF6BqS6pVIqXXnoJK1aswK1bt7B582YMGzYMLi4uYpvu3bsDANLS0jBixIgavc8///yD4uJiWFlZibHjx49DIpEgODgYwL1hQwA4evQo2rVrJ7YrLi7GqVOnxKsqteFRn7+NjQ3CwsIQFhaGV155Bb6+vvjhhx+q/TXwqPd5XN27d8fXX3+NgICASq94VrWdNk8//TS2bNmC1NRUcSgZuHeeANTovJQNAz7uZ/M4/aotffr0wU8//YQvv/wSTZo0Eb8v+/Tpg0OHDuHWrVvo1auXxjBlRVq3bg1bW1scPXoUERERGtuOHj0KOzu7ai+FU3bMEydOlJvucPLkyWodiwwH57iR0ejTpw/OnTuH8+fPi/NapFIpevXqhW+//RZSqRQ9e/YU20ulUqxevRq//vorli5dipKSEnFbQkICXn/99RrdSt+4cWOUlJRUuMyEi4sLRo0ahfXr1+PChQsYN24cLC0tq/0+VfHSSy9BrVZj9OjRyMjI0LiyB9ybnzZmzBgsWrQIBw4c0Nh28uRJrFy58pHv4ePjg3nz5olz4f7++2+sXbsWo0aNEq/ihYSEoFu3bnjvvfdw7tw5APd+sM+aNQsZGRl48803a6O7AO59/teuXdNY7gQAtm7dit27d2sUFGULJQcGBoqxLVu2ICgo6JHDtxW9T21ZsGABHBwcMGrUKCQkJIjxoqIibNy4EYcPH65WO23mzp0LiUSCqVOnivOm4uLiMH/+fLRo0QLDhw+vdt6NGjWCRCLBf//9V+19H/Q4/aotZb/offPNNxpz5cLCwnD06FHcunVL6zCpNlZWVpg7dy727t2LLVu2iPGoqCjs378fr7/+erX/H7C2tsbMmTOxY8cOjTl/H330kcb8OjIuLNzIaPTp0weCIMDa2hqdO3fWiJeWlqJDhw7iHJEyY8aMwQ8//IAdO3bA2dkZrVq1goeHB7p16wZPT88aFVQTJkzAk08+ibZt26J9+/biOm4PmjFjBhQKBQCUK6ZqU0BAAHr37o1Tp04hICAAvXr1Ktfm22+/xezZszFu3Di4ubmhdevWcHBwwIIFC7QOAT2sXbt2aNeuHRo2bIjAwEDxqRZfffWVRrsff/wRoaGhCA4ORpMmTeDi4oLffvsNu3btQo8ePWqtz4sWLcLdu3fh6+ursb5au3btsHv3bjg5OaFly5Zo1KgRwsPDsXjxYvHGEOBe0X7mzJlHXjGq6H1qi6+vL06ePAkrKys0adIEjRo1QmBgIDw9PXHmzBm0bNmyWu20adWqFQ4ePIhr167Bzc0NzZo1Q/PmzREYGIhDhw7V6Ovfy8sLERERWLRoEVq3bl3hOm611f+61LRpU/j7+6O0tFSjQOvVq5c4XPnwupCVWbJkCd566y1MmzYN3t7e8PLywsyZM/HOO+/grbfeqlGOy5cvx8svv4xhw4bB398ffn5+yM/PF2/qqGzIlQyTRKjqBBEiPSEIAs6cOQMXFxeNR2Gp1WqcPXsWNjY2Gnfo5efn4+rVq3B1da1wHhdw70aE9PR0eHt7lxuaiY+PR3Z2Ntq2basRl8vliIuLwxNPPKExVAjcu/lBLpdDrVajbdu2GhONCwsL4eLigjZt2uD06dNV7nt8fDyysrI0hhsB4OLFi7C1tdX4PB7s1927dx/Zf7Vajbi4OCgUCjRq1KhKc+s8PT3Rr18/fPvttygoKMCtW7fg4eGh8aSLh+Xm5iI+Ph62trbi1ZkHXb58GdbW1hp9+ffff+Ho6FhuCDkuLg4FBQXlnhyhVCpx8+ZNFBYWwtraWuPrQalUIjY2FlZWVvDx8Sk3AXzIkCGIj4/HuXPnHnnVoqL3qU6+FbV9UF5eHuLj42FnZyfeOPA47bS5c+cOsrOz4ePjU+4XHODeIq/u7u5aJ8JrI5fLkZCQAKVSiYYNG8Ld3R25ubm4fv06mjdvDnt7e7Ft2fe0t7c3vL29q9Wvio6Zl5eHa9euVRhv1qxZuZuNtLlx4wZycnLQpk0bjUL233//FX8hfFBF+TxIoVDg5s2bkEgkCAwM1LjLFKj46zo9PR3x8fF48skny+2TnZ2NhIQENGzYEI6Ojpg6dSq2bt1a45ubSH+xcCPSgV27dmHkyJFYv349XnnlFV2nU2MPFm7GoLS0FK6urti2bRsGDBig63SIakStVqN169bw8vISlzMh48GhUqJ6Vlpais8++wzu7u4V3s1KuqFWq3HkyBEWbWQwkpKS8PXXX4tD+yqVCm+++SauXr2K2bNn6zg7qgsc/CaqR2PHjsWxY8eQk5OD7du3w8bGRtcp0QNkMlm5oS8ifebs7IxTp07htddeg5eXF+7cuQMbGxt88cUXGDJkiK7TozrAoVKievTff/9BpVIhMDCwSmuX6buqzM8iorqnUCgQFxcHMzMzBAYGVmt+IxkWFm5EREREBoIlOREREZGBYOFGREREZCBMvnBTqVRITEys00fXEBEREdUGky/cUlJS4Ofnh5SUlDp9H7lcXqfH11fst2lhv00L+21a2G/9YPKFW30x1XtA2G/Twn6bFvbbtLDf+oGFGxEREZGBYOFGREREZCBYuBEREREZCBZuRERERAaChRsRERGRgWDhRkRERGQgWLgRERERGQhzXSdgCgR1KRQ3jiP7Sg7UxfmQWtlB5toQNs27QSI103V6REREZCBYuNUhVW4aso9tQtYfG6DMiEfWQ9tlbgFw7jEVTt0mwdyhgU5yJCIiIsPBwq2OyA+vQ+r2uRBUClg36QzrsDfQ4InukFrZQ12ch6LbZ5B1ZD3Sdr2J9B/fgcfoNXDpPUPXaRMREZEeY+FWB9J2v4WM/Stg27o3PEathpV/O2RmZsLS1VVsY+nTCk5dxqMo/hzSds5HyuaZUGUnw/355TrM/NHu3r2L+Ph4dO7cWetrIiIiqju8OaGWyQ+vQ8b+FXDsPA4N5x6AlX+7Sttb+7dHw7kH4Nh5HDL2r4D88Lr6SbQKEhMT8ddff2nE9u/fjwkTJlT4moiIiOqOXhZu7777Ljp16oR9+/ZV2m7q1Kno1KmTxp/FixfXU5blqXLTkLp9Lmxb94b3pE2QmMuqtJ/EXAbvSZtg27o3UrfPhSo3vY4zrZqffvoJL730UqVtfH190aVLl3rKiIiIyLTp3VDpwYMH8f333+PKlStIS0urtO3ly5fxxBNPYPLkyWLMzc2trlOsUPaxTRBUCniMWl3loq2MxFwG95GrcOudjsg+vgluA9947HwSExMRFxcHHx8fBAYGamy7efMm8vLy0L59ezGWnJyM2NhYhISEIDU1FdeuXUNhYSEOHz4MAGjevHm592jfvj1cXFzKxbOysvDff//BzMwMTk5OMDO7f/fsnTt3kJycjODgYPz3339ITU1FaGgopFIpCgsLceXKFVhaWqJFixYwN9e7L1EiIiKd0aufisnJyXj55Zfx888/IygoqEr7+Pr6olOnTnWc2aMJ6lJk/bEB1k2eeeTwaEWsAzrAOrATso5sgGv/eTVeKqS0tBSTJk3Crl270Lp1a1y7dg1dunTB999/Dzs7OwDAV199hX/++UcsygDgt99+w1tvvYXExERcuXIFhw4dQmZmJlauXAkAmD59ern32r9/Pz788EPcvHlTjC1evBhr165Fy5YtkZKSAgcHB+zevRvNmjUDAHz//fdYt24d/P39kZGRAU9PT3Tt2hXR0dEYO3YsfHx8YG5ujsLCQmzZsqXKXwtERES1KTIyEklJSfD29kZERISu0wGgR0OlarUa48aNQ0REBNq1a1fl/Xbv3o3u3bvjhRdewObNmyEIQt0lWYnCa8egzIiHc89pj3Uc517Tocy4jcJrx2t8jI0bN2Lfvn04f/48/ve//+HatWu4evUqVqxYUeVjhIaGYubMmfDz88Phw4dx+PBhPPfcc4/c75tvvsGWLVvw33//4e+//8Y///yD7t2748UXX9Rod/v2bbzwwgu4dOkSDh8+DJlMhjfffBNz587FhQsXcPbsWfz222+Qy+XV7T4REdFjUygUWLZsGVavXo1ly5ZBqVTqOiUAelS4vffeewCA1157rcr7eHh4IDw8HIsXL0anTp0QERGBl19+udJ9cnNzkZiYKP5JTk5+rLzLKDPvAACsAzo+1nGs/DtoHK8moqKi8OKLL6Jp06YAAE9PT7z66qv49ttvHyu3qtiwYQO6d++OGzdu4I8//sDx48fx5JNP4q+//kJubq7YztXVFVOnTtXYVxAEZGdni8W3v78/wsLC6jxnIiKiBykUCvTt21e8eCCXyxEWFqYXxZteDJWeOHECa9euxblz5yCRSKq837Zt22BpaQkA6N27N5o0aYJBgwZh9uzZaNOmjdZ91qxZg6VLl5aLZ2VlwdraumYdAFCYmQoAyClSwSwzs9z2B4uWypQWqwAAeZkpKNVynKqIjY3F8OHDkfnA/t7e3khJSUFiYiKsra1RVFQEpVKp0SY/Px9qtVqMFRQUoLS0tNI2D7++fv06srOzcevWLQCASqWCubk5unXrhtu3b8PHxwcFBQXw8PAodzVt6dKlePXVV7F161aEhISgf//+GDRoULW+JvRFVc+3sWG/TQv7bVpMod+CIKD4zC5Erf8YMTE3NLbFxMRg3bp1GD9+fK2/r+sDy4U9il4Ublu2bAEADB8+XIyVlpbi/fffx+HDh7Fjxw6t+5UVbWV69eoFALhw4UKFhdvcuXM1bmYomyTv7OxcrQ/uYWauHsgD4GhtrrFe24OqcvziohRkALB39YRTDfNxdnaGWq0u937W1tbw8fGBRCKBra0tzM3NNdpYWFhAKpWKMVtbW5iZmWm0sbOz02ij7fWYMWPwzjvvAAAyMzPL5WFrawuZTFYuPmTIEAwePBgXL17EoUOHsHDhQpw7dw5r166t0eega4/z9WTI2G/Twn6bFmPud8nd/5D83XQUXj2K27Ha26Smpur8M9CLwm3+/PkIDw/XiIWEhGDUqFEYNWpUlY+TmnrvqpetrW2FbRwcHODg4FCzRCshc20IACi6fQaWPq1qfJzi+LMax6uJp59+Gr/88gvmz58vxvbv34+nnnpKvHrl6emJ+Ph4jf1Onjyp8drGxgYKhaJa792zZ09s374dCxcuhEx2/87a7OxsODk5VbpvWZsnn3wSTz75JEpLS+tleJeIiEyXuqQA6XuXIfPXj4DSe6Nevnba2z68QoMu6EXh1qhRIzRq1KhcvHHjxmjbtq34evLkyfDy8sKyZctw48YN/P333xg3bhwkEgny8/MRERGBBg0aiFfe6pNN826Qufkj68h6OHWp+WXUrN8/h8wtADbNu9b4GEuWLEG7du0QHh6OoUOH4ujRo9i1axf++OMPsc3gwYMxf/58zJ49G7169cLRo0fx008/iXedAkDbtm0RHx+Pr776CgEBAVqXA3nY8uXL0blzZ4SGhmLq1KlQKpW4dOkSzp49i6NHj1a6b9euXTFw4EAEBwcjPz8fGzduxKBBg2r8ORAREVVEEATknd2L1K2zNeaVW3i1wKy5nyAm4n3ExMSI8dDQ0HIXmXRBb25OqIpLly7hxo17Y84eHh44duwYXF1d8eSTT8LLywspKSmIjo6Gvb19vecmkZrBuccrKLr5F4riz9XoGEW3z6Io9m8493ylxkuBAPcK3n/++Qf29vbYuHEj8vPzceLECY3HUgUEBCAmJgY5OTnYvHkzfHx8sGXLFnTter9g7NixIzZt2oTo6GisWrUKp0+fLrfg7sOv/fz8cP78eQwYMAB79uzBL7/8gsaNG+PXX38V2/j7+2tdwuXEiRNwcXHB1q1b8euvv+LNN9/EBx98UOPPgYiISBtFWhwSPh6ExLXDxKJNYmEN9xHvI3D5v3BuG4bo6GiMGjUKTz/9NEaNGoXo6GiNkSRdkQi6Wj/jEU6dOoXGjRujQYMGYuzy5cuwtLREkyZNxFhBQQHi4uLg5eVVo8V3ExMT4efnh4SEBPj6+j5WzqrcdNyY4wub5t3QcO4BjUV4tc31epCgUuLOR/1ReP04mn6cCHOHBhW2NSSP6rexYr9NC/ttWthvwyaoFLjxWgBU2fdXlbDvMAQeYyJh0SCgXHt967feXnF7+umnNYo2AGjdurVG0Qbcm8/Wpk0bnT4xoYy5QwN4jF6DgsuHkfT1RAiqqt02LKiUSPp6Igr++x0eo9cYTdFGRESkbyTmFnAbtAgAIHMLgF/EPvjN/klr0aaP9GKOmzFx6T0DquxkZOxfAVVOCjxGra70SQpF8eeQtnM+Ci4fhtugRXDpPaP+kiUiIjJyyqwkSKRmMHf0EGPOPV+BoC6Fc/fJkFra6DC76mPhVgfcn18OcycvpG6fi7i328O6yTOQBY9HcetuMLO2R2lRHorjzyLr989RFPs3JOYW8Bz/GYs2IiKiWiKUqiD/fR3S9yyGXbtn4Tttm7hNIjWDa9gsHWZXcyzc6ohL7xlwCB6J7OObkHVkA4q2TcfDSxfK3ALgPnIlnLpO5PAoERHRY4qMjER2djb8ZdnoVhyDkjv/AgBy/96Ogu6TYduqp44zfHws3OqQuUMDuA18A6795yHl9M+wVuZAXZwHqZU9ZK4NYdO862PdPUpERET3KBQKrH5vKRrLstHHH8gPBGRSABIpXHrPhNVjPpJSX7BwqwcSqRksmoTU+EkIREREVLGS4mL0eqYdktKzkQTgzyTglzhg89Sn4DfxC1j7t9d1irVGb+8qJSIiInqU4sRLWDOqNU6cv6YR/18qcNRnslEVbQALNyIiIjJg6qJcxMXFad0Wd+tWPWdT9zhUSjVSVFSEkpKSRz5/lIiItBPUpSi8dgzKzDtQF+dDamX3//Ofu3H+czXYNO2MZh26AJdOlNumD88WrW0s3IxQcXExiouLa62o0na8devWYcuWLTh//nytvAcRkalQ5aYh+9gmZP2xAcqM+HLbZW4BcO4xFU7dJnHFgYeUpFyH/Nc18Bz3qfh0IkEQMHPtXvxy+zkcPXZMbKsvzxatbRwqNUJfffWV1meB6svxiIhMlfzwOtyY44e0XW/C3MkH3lO+Q+B7l9F0zR0EvncZ3lO+g7mTF9J2vYkbc3whP7xO1ynrBbWiCGl7FiNuURtk/fEFMqM/EbdJJBJYO7ri0OHDGDVqFLp06aJXzxatbbziZmQUCgUKCgpQWlqKjIwMAICdnR0EQRCHNktLS5GTkwMXFxcUFhZCpVLBwcFBPEZJSQkKCwvh7Oxc4fEeVlxcDCsrq/rpJBGRAUrb/RYy9q+AbeveFT5Vx9KnFZy6jBefqpOyeSZU2clwf355/SesJ/LO/4KULa9CmX5/vlru39vg2m8uJNL7159kMhl27NihixTrFa+4GZlffvkFy5Ytw61bt9CiRQu0aNECUVFRWLduHTp37oxZs2bByckJzZo1Q0lJCd59910899xzGsfYvn072rRpU+nxgHtF4ty5c9GgQQM4ODggKCgIN2/erPc+ExHpO/nhdcjYvwKOnceh4dwDlT4KEQCs/duj4dwDcOw8Dhn7V5jklTdl5h0kfDIMCR8/KxZtEpklGjz3LgLeOqlRtJkS0+y1ERs2bBhWrlyJJk2aICMjAxkZGZg6dSoA4MqVKwCA9PR0ZGRkwNLS8rGPJ5PJkJiYiKysLDg5OWHu3Ll11zkiIgOkyk1D6va5sG3dG96TNolzsx5FYi6D96RNsG3dG6nb50KVm17HmeoHQaVAxi+rcHNBS+Sd/UmM2z3ZH4ErLqPBkMWQWpjuCA+HSmso87dIyKMjH9nOrk0/eL24QSMWu7gd1IXZj9zXZ9oO2DSpvblldnZ2WL16dZUKtqpwcXHBe++9BzMzM1haWmLixImYM2dOrRybiMhYZB/bBEGlgMeo1VqLtrLHNDk5OSEiIkJjm8RcBveRq3DrnY7IPr4JbgPfqKesdUMQBNxe2RNFN+7fIWru4gvPsWth33EoJBKJDrPTDyzcaqi0MFvr3UAPU+WV/w1JlZmA0gL5I/cVlMU1yq0i/v7+tVa0AYCvry/MzO7fsu7g4ICcnJxaOz4RkaET1KXI+mMDrJs8o3V4VKFQYNmyZZDL5XBxccGMGTPKTai3DugA68BOyDqyAa795xn1UiESiQSOz4y5V7iZmcO17xw0GPI2pFbl51abKhZuNWRm4wSZm/8j25nbl7+V29zVD1Jr+0fuK5HV7qVgc/Pyp1vbby+lpaVVOh5/8yEiqlzhtWNQZsSjwXPLym1TKBTo27cv5PJ7v8jL5XKEhYVpvRvSudd0JH05AYXXjsO2ZWh9pF4vBHUp1CUFMLO+f4Occ4+pKEm8BOde02Hl+4QOs9NPLNxqyLVvBFz7RtRo38Bl52s1l4dZWFhUufhycXFBerrmVcH//vuvxscjIqL7lJl3AADWWh5wHhUVhZiYGI1YTEwMoqKiMHnyZI24lX8HjeMZg6K400iOmgYLj6bwnb5djEukZvAK/1yHmek33pxghJo0aYKEhAT8888/yMjIQHFxxUOuvXr1wsWLF7Fp0ybcuXMHmzdvxhdffFHj4xER0X3q4nwAgNSq/ChLRY9pio2NLRcz+/9RGnVxXi1mpxulBVlIjpqOW+8+jeLbZ5B7agfyL/+u67QMBgs3I9SjRw9Mnz4dY8aMQcuWLREVFQUbGxs4OzuXa9uhQwd8/fXXWLt2LXr16oVDhw5h+fLlcHFxqfbxLCws4ObmVuf9IyIyFGVzs7QVXI0bN9a6j7bHNJUW5f3/8R49zUZfCYKA7D+/w803miP10Hrsuibg47PAltvOUFtwDltVSQRBEHSdhC4lJibCz88PCQkJ8PX1rbP3yczMhKura50dX1+x36aF/TYt7PejFVz5A/Ere8J7yndw6jJeY1vZHLcHh0tDQ0O1znHLPrEZSV9OgP+CP3Q2x+1xzndx4mWkfDcdhdeOQVEKTDkMnE69v72ifusDffs65xU3IiKiOmLTvBtkbv7IOrK+3DYLCwtER0eLIxwuLi4VFi9Zv38OmVsAbJp3rfOca5O6OB+pO+cj7u12KLx27zmie2M1izbg/tw+ejTenEBERFRHJFIzOPd4BWm73kRR/DlY+7fX2C6TybB48WJxHTdtRVvR7bMoiv0b7iNXGtxSIMWJl5B5YLX42sK7JfIs2gN/byvXVtvcPiqPV9yIiIjqkFO3SZCYWyBt53wIKmW57REREViyZEm5xXcBQFApkbZzPiTmFnDqOrEesq1dNk06wan7ZEgsbOA+chUCl51H8+BQrW21ze2j8li4ERER1SFzhwbwGL0GBZcPI+nriVqLN20ElRJJX09EwX+/w2P0Gpg7lF8XVJ+oFcXIjF5brn/uI1ci8P3/4DbwXgEaHh6O0NBQjTahoaEIDw+vx2wNF4dKiYiI6phL7xlQZScjY/8KqHJS4DFqdaUPmi+KP4e0nfNRcPkw3AYtgkvvGfWXbA3kX4xGyuYZUKTehKAqgduA18Vt5naugN39yf1lc/s8PT3FJ0bo640J+oiFGxERUT1wf345zJ28kLp9LuLebg/rJs/Auec0WPl3gJm1PUqL8lAcfxZZv3+Ooti/ITG3gOf4z/S6aFPK7yJ12xzknt4lxjJ+fh8uvaZDamlb4X5VmdtH2ull4TZ06FD8/PPP+PLLLzFxYsVj+iqVCgsXLsSWLVuQn5+Prl274tNPP61wbRwiIiJdcuk9Aw7BI5F9fBOyjmxA0pcTyrWRuQXAfeRKOHWdqDfDo5GRkUhKSoK3tzciIiIglKogP/Qp0n98W1xkGABsW/WC54R1lRZtZbTN6aNH07vC7ZNPPhFX5ler1ZW2feONN7B9+3b88MMP8PHxwaxZsxAWFobLly/X6sPUiYiIaou5QwO4DXwDrv3nofDacSgz70BdnAeplT1krg1h07yrXt09qlAosGzZMnFY86U+7ZGxfRZKEi6IbcydvOAx5mM4BI/kc6zrmF4VbufOncPq1atx+vRp+Pn5Vdq2oKAA69evx5o1a9CpUycAwBdffAEvLy/s2bMHY8aMqY+UqyUyMlK8LMzfNIiITJtEaqb3D4wvWyRYLpcDAORyOcJ6heLLPoBMCkAihUufWWjw3FKNB8VT3dGbu0rz8/Pxwgsv4LPPPoOXl9cj2589exZFRUXo0aOHGHN3d0ebNm1w8uTJuky1xiIjI7F06VJERkbW+3vv2bMHHTp0qPf3JSIiwxUVFaXxZAcA+F/qvUV0rZs8g8ZLz8Bz7Mcs2uqR3hRu06dPR/fu3TF06NAqtU9OTgZwr1h7UIMGDZCSklLhfrm5uUhMTBT/lB3H2BUVFSEtLa1a+3zzzTfo2tWwVukmIqLaU9GiuFmN+iNg0Z+V3hlLdUMvhkq3b9+OU6dO4ezZs499LKlUisoev7pmzRosXbq0XDwrKwvW1taP/f4Vyc3NFefsqdVqZGZm1tl7aZOXl1ft901LS0NKSspj5Zqbm1vjfQ0Z+21a2G/TYgr9VhfnoeDgSjjcPq11u/9TYZBnZdVzVrpRH+e7Os9C1YvC7ejRo7hx4wYcHR3FWGlpKaZOnYrIyEhcunSp3D4eHh4AgPT0dDg7O4vxtLS0Sldfnjt3LiZPniy+Tk5ORnBwMJydnev0IbIKhQIFBQUA7s3Pc3BwqNPbn3fv3o2VK1ciPz8fQUFBaNu2LaRSqdjHX3/9FS+++CIAwMbGBi1btsTixYvF+YJ79+7FihUrUFhYiNatWwMAli1bBj8/v0r300afHs5bn9hv08J+mxZj7bcgCMg9tROp2+dClZ2MAebAwaAn8ec/929ECA0NxYwZM0xqCQ99Ot96MVT6+eefQ6FQoLi4WPxjZmaG9evX499//9W6T4cOHWBpaYmjR4+KsYyMDFy8eBGdO3eu8L0cHBzg6+sr/qnKfLrHpVAoMGLECM3JnWFhUCqrtnp2dZ08eRJjxozB+PHjsW/fPnTs2BELFy7UaNOjRw+cP38e58+fR3R0NLp27YpevXohISEBANC3b1+8+eabaNSokdhu7Nixj9yPiIgMU0nSVcR/0Bt314+GKvveNCIrWzv8sHo2XFxcAICL5eoBvbjiJpVKIZWWryGlUinMzO7fEh0SEgJ/f39s3boV9vb2mDJlCpYtW4bg4GD4+PhgxowZ8PX1xfDhw+sz/UeKiorCiRMnNGIxMTGIiorSuPpXW1auXInhw4dj9uzZAIBmzZrhr7/+0rhpw9LSEp6enuLrBQsW4ODBg9i1axfmzp0LKysr2Nvbw8zMTKMdgEr3IyIiw6IuKUTG/hXIOLAaKL1/QcEheCQ8Rq+BzMUHixfniuu4sWjTLb0o3KpKpVKhtLRUfP3hhx8CuHfZtqCgACEhIfjtt9/qdK5aTcTFxWmNVzTp83FduHAB8+bN04iFhIRoFG4lJSVYvXo19u3bh+TkZCiVSmRnZ6Nt27aVHrum+xERkf7JO7cfKVtehTIjXoxZeDSF54R1sHuijxiLiIhAZmamXg0Zmiq9LdxKSkrKXYU7ceKExsJ+FhYWWLt2LdauXVvf6VVLRU9yqGwu3uNQKpXlfiOysLDQeP3mm2/i119/xZo1a9C0aVPY2tpiwoQJUCgUlR67pvsREZF+KUm6ioTIweJricwKboMWwXXA65DKuIi9vtLbwu3BIdLKYoYgPDy83HBpaGgowsPD6+T9mjVrVm5u4Pnz5zVeHz58GNOmTUO/fv0A3LvT9caNGxpFprm5ebmnV1RlPyIi0q2qLPhu6d0CTt1fRvbRjbBrOxCe49bCwp3/l+s7vbg5wdhZWFhg9+7d9Ta5c9q0afjuu+/EGzdiYmKwefNmjTZ+fn44dOgQiouLoVAo8Oabb+L27dsabXx8fJCcnIycnJxq7UdERLpT9oiqpUuXYtmyZeKNcIXX/4Sg0hwdcR/5Pvxm/wS/OftZtBkIFm71RCaTwd7eHgBgb29fp5M7R44cidmzZ6Nfv36ws7PDjBkzMHHiRI02H330EW7fvg0nJyc4OTnh4sWL6N69u0abvn374plnnoGnpyc8PT2xcePGKu1HRES6oe0RVX16huL2ujG4vaIrMn/7WKO9uZ0r7DsM4fNFDYhEqGy1WhOQmJgIPz8/JCQkwNfXt87eJzMzEx07dkR8fDz8/f3r5SqVQqGASqWCjY0NiouLkZeXhwYNGmi0ycvLg0wmg5WVFbKzsyGVSuHgoPnokpKSEmRnZ8Pe3h42NjZV3g+AyU5mZb9NC/ttWvS53xs3bsSUKVPKxZc+AzzfFJBY2KDJBzcgc/au9rH1ud91Sd/6rbdz3OjxWVhYiDclWFlZwcrKqlybsquAAODk5KT1OJaWluKCx9XZj4iI6ldFqxgk5AFmDu7weOFDmDvV/fqlVHdYuNWjiIgIcbIoERFRbQvw9tAab/5UKJqs/AFmts5at5PhYOFWjyq6s4eIiOhx5fy9A53/W46nPIDTqffj3Tp1xOwvomHGhXONAgs3IiIiI1BamA2zwkxs7AN8fx348QaQLnHB4WN/8WkHRoSFGxERkRFwDn0Z2ce/gaNXC/g82QQjC0rh5OTEos3IsHAjIiIyIIIgIO+fH1B4/Tg8x0aKcYnUDAFvxkBqYY3pukuP6hgLNyIiIgOhSL2J5M2vouDirwAAuycHwK5NmLhdaqFfz+qm2scFeImIiPScWlGM9B+XInbRE2LRBgD5D/ybTAOvuBEREemx/Iu/Ifm7GVCmxYoxWYPG8Bz/KezbDtBhZqQLLNyIiIj0kFKeiJRtc5B3ercYk5hbwHXgArg9u4DDoiaKhRsREZGeUeVlIHZha6iLcsWYbeve8JywDpaezXSYGeka57gRERHpGXN7Nzg8/cK9fzt5wWf6TjR8PZpFG/GKGxERka6pctNhZuMIibmFGPMY8T7M7Fzh9uwCmFk76DA70ie84kZERKQjglqNrD++wM0FzZH56xqNbWZ2LvAY8R6LNtLAwo2IiEgHim6fxa1lzyD521egLshC+t53oUi/reu0SM9xqJSIiKgelRZkI+2Hxcj6/XNAUItxx2fGQmptr8PMyBCwcCMiIqoHgiAg969tSNnxGkpzUsW4ZcO28ApfD5smz+gwOzIULNyIiIjqWEnSVSR/Nx2FV/4QY1IrezQYvgwuvWZAYsYfx1Q1/EohIiKqY4XXjmoUbQ5Pj4LH6DWQOXvrMCsyRCzciIiI6phT95eRdWwT1AVZ8JywDnZP9NF1SmSgWLgRERHVIkX6LeSe3gO3AfPuByUS+M3cDTMHd0hllrpLjgweCzciIqJaoFaWIPPXj5CxbzkERRGs/NrArk1fcbvM1U+H2ZGx0LvC7datW5BIJPD19YW5eeXpXbt2DXl5eRoxFxcXNG7cuC5TJCIi0pB/+XekbJ4BRfI1MZZx4AOxcJNIJLpKjYyM3hRuX331FZYtWwYLCwsUFhZCrVZjzZo1GD16dIX7TJo0CXFxcfD2vj+5s2/fvlixYkV9pExERCZOmZ2M1O2vIffv7feDZjK49p+HBoMX6S4xMlp6U7ilpaXhf//7Hzw8PAAAH374IcaPH4/Q0FB4eXlVuN+UKVOwZMmSesqSiIgIEEpVkP/+OdJ/WAx1Ua4Yt2kRCq/wz2Hp3VKH2ZEx05tHXi1cuFAs2gCgX79+KC0txd27dyvdr7CwEBcvXkRGRkZdp0hERARBVYJb73ZC6tbZYtFm5ugBn6lb4L/gCIs2qlN6U7gBQGpqKv755x8cOHAAM2fOxLPPPouOHTtWuk9kZCRGjBgBf39/PP3007h06VI9ZUtERKZIYm4J60ZB//9CCufeM9Hk/atw7DyWc9mozunNUCkAHDp0CB9//DGSkpJgZWWFb775ptJvgokTJ+LAgQNwcHBAbm4uxo4diyFDhuDixYuwsbHRuk9ubi5yc+9f1k5OTq71fhARkfEQ1GpAUGs83cD9+feglCegwbB3Yd2o8gsMRLVJIgiCoOskHiYIAtatW4c5c+bg/PnzaN26dZX2i4uLQ2BgIA4dOoTevXtrbbNkyRIsXbq0XPzChQsaNznUttzcXDg4ONTZ8fUV+21a2G/TYgr9ViZdRt7u12HZKgyybtMxbdo0JCYmws/PD+vXr4dMJtN1ivXGFM63NvXRb1dX1yq31cvCrYyzszMWLVqEefPmPboxgOLiYlhbW+Obb77Biy++qLWNtituwcHBSEhIgK+vb22krVVmZma1ToyxYL9NC/ttWoy536VFeUj/aQnk0Z8A6lIozaww61pbHDt5SmwTGhqK6OhokynejPl8V0bf+q0XQ6UlJSWwsLDQGBbNzMxEXl4eXFxcxNjVq1dhYWGBxo0ba93n999/BwC0bFnxxFAHBweT/I2BiIgeTRAE5J3ejZStEVBlJ4nxn1NcNIo2AIiJiUFUVBQmT55c32mSCdOLmxOuXLmC7t2747vvvsOff/6JHTt2oG/fvmjcuDFGjBghtnvxxRexcOFCAMCNGzcQEhKCqKgoHDt2DOvWrcOLL76I5557Dk8//bSuukJERAaqJOUG7nzYD4nrRopFm8TSFh4vfIi8NmO17hMbG1ufKRLpxxW3du3a4ZNPPsH69evx1VdfwdXVFaNHj8bUqVNhZ2cntmvZsqU4D+2JJ57AunXrsG7dOmzatAleXl74+OOPMXas9m8uIiIyPZGRkcjOzoaTkxMiIiK0tlEripDx80pk/rISgkohxu2feh6eYz6GzMUXgYkbte4bGBhYF2kTVUiv57jVh7JJppzjVjfYb9PCfpsWfe+3QqGAl5cX5HI5XFxckJKSonU+Wvaf3yFpY7j4WuYeCK/xn8HuyX4ax+rbty9iYmLEGOe4mQZ967deDJUSERHVprJCSy6XAwDkcjnCwsKgVCrLtXXsPA7WgU9DIrOE29B3ELjikkbRBgAWFhaIjo4W5127uLiYVNFG+kMvhkqJiIhqU1RUlMbVMeDezQTfbtqEUR3d4RA0TIxLpFJ4T/4GkJrD0rNphceUyWRYvHgxkpKS4O3tzaKNdIKFGxERGZ24uDit8TNRb6HLyQz4zT0A+7b9xXhVH1MVERGhd0NnZFo4VEpEREancePGWuPeuPdc69Ttc+49EYHIwLBwIyIioxMeHo7u3btrxII9gCGBgHXTLvCdsQsSKX8EkuHhUCkRERmd0rsX8UXXfOxUAAl5gJ898FxbV/iOXg3HkHAWbWSwWLgREZHREAQBqVtnQ374M0AQ8Pz/32vgFDoF7iPeg7kd56aRYWPhRkREeqEqi+VWRhAESCQSCCol8P9LlFr5t4fnhM9h06RTLWdLpBss3IiISOcUCgWWLVsmLpY7Y8aMai+3UfbsavcR76Hgv8Nw6TMLzj2nQWLGH3VkPDjIT0REOlWdxXIfpC4pQOr3C5DxywdiTBAESG2cEPj+Fbj0eZVFGxkdFm5ERKRTFS2WGxUVpbW9IAjIPfMTbi5oicxfViH9x3egSL+Fsic4SiQSFmxktPiVTUREOlXRYrmxsbHlYoq0OKRsmYX8f3+5H5RIUHz7LGRuAeJwKZGxYuFGREQ6VdFiuYGBgeK/1coSZB5YjYz9KyAoi8W4fYch8BgTCYsGAXWdJpFeYOFGREQ6FR4ejm3btmkMl4aGhiI8PBwAkH/pEFK+mwFF6g1xu8zNH57jPoV9+0H1nS6RTrFwIyIinbKwsEB0dDTGjx+PxMRE+Pr6YvPmzZDJZMi/fBh3Vofdb2wmg9uA+XAbtBBSSxvdJU2kIyzciIhI52QyGXbs2FEubtuyJ6wDO6Eo9m/YtOoJr/HrYOndQgcZEukHFm5ERKQ3SpKuahRmEqkUXi9uQMnd/+DQ6QXefEAmj8uBEBGRzqnyM5G06WXEvtkSef8e0Nhm1bAtHJ8ZzaKNCCzciIhIhwS1GllHv0bsG82RffQrAEDK5lehVhTpODMi/cShUiIi0oniO/8iOWoaim7+Jcakts5we3YBJOaWOsyMSH+xcCMiqgFBXYrCa8egzLyDwsxUmLl6QObaEDbNu0EiNdN1enqttCgX6T+8A/mhtYCgFuNO3SbCfcRKmDs00GF2RPqNhRsRUTWoctOQfWwTsv7YAGVGvBjP+/+/ZW4BcO4xFU7dJrEA0SLn1PdI3RYBVXayGLP0bQOv8PWwadZFh5kRGQYWbkREVSQ/vA6p2+dCUClg3aQzGjy3DNYBHZFTpIKjtTmKbp9B1pH1SNv1JtJ/fAceo9fApfcMXaetVwqvHRWLNqmVHRoMe5cPgyeqBn6nEBFVQdrut5CxfwVsW/eGx6jVsPJvJ24zy8yEpasrLH1awanLeBTFn0PazvlI2TwTquxkuD+/XHeJ6xn34cuRe3o3bFuEwmP0GshcfHSdEpFBYeFGRPQI8sPrkLF/BRw7j4P3pE2QmMsqbW/t3x4N5x5A0tcTkbF/BcydvIzuyltkZCSSkpLg7e2NiIgIrW3yzv8CpTwBLj1fEWNmts4IXHER5g7u9ZQpkXFh4UZEVAlVbhpSt8+FbeveVSraykjMZfCetAmqnBSkbp8Lh+CRRjPnTaFQYNmyZZDL5XBxccGMGTMgk93/XBQZ8UjdOht5Z/dCIrOC3RNhsHC//yB5Fm1ENadX67glJiZi165d2L17N+Li4qq0T25uLn744Qd89913uHnzZh1nSESmJvvYJggqBTxGrdZatEVGRmLVqlWIjIwst01iLoP7yFUQVApkH99UD9nWPYVCgb59+0IulwMA5HI5wsLCoFQqIagUyPh55b1FdM/uBQAIymLk/LVVlykTGRW9ueL26quv4tdff0VQUBAKCgowYcIEzJw5Ex988EGF+1y6dAm9e/dGw4YN4eXlhWnTpuG9997D7Nmz6zFzIjJWgroUWX9sgHWTZzTmtJV51JUnALAO6ADrwE7IOrIBrv3nGfxSIVFRUYiJidGIxcTE4MuVb6Jv4QEokq6IcXMXX3iOXQv7jkPrN0kiI6Y3V9z69euH69evY/v27di3bx+2bduG1atX4/r16xXuM3nyZHTq1AmnTp3C3r17sWHDBsybNw+xsbH1mDkRGavCa8egzIiHc89p5bZVduXpYc69pkOZcRuF147Xec51raLRkPPff3S/aDMzh+uA+Wjy/hU4BA3jo6qIapHeFG4DBw7U+Ob28vICAJSWlmptf/v2bZw6dQrTp08X9xs9ejQcHR2xe/fuuk+YiIyeMvMOAMA6oGO5bRVdeYqKiirX1sq/g8bxDFnjxo21xv3s7/1t07wbGr97Hh6jVkFqZVePmRGZBr0ZKgWAf//9F3/99RfS0tKwfft2vPPOO2jZsqXWtpcvXwYAtGrVSoyZm5ujWbNm4jZtcnNzkZubK75OTk6usC0RmTZ1cT4AQGplX25bRVeetF3xN7O2///j5ZXbZmjCw8OxdetWHD16VIwFewDPtXWD97g1cOw8jlfYiOqQXhVu6enpOHfuHO7evYu8vDw4OztX2Las+HJyctKIOzs7axRmD1uzZg2WLl1aLp6VlQVra+uaJV4FleVkzNhv02Js/S5S3ftbnpIAc9hobHN3135npIeHBzIzMzViqpQEAEChSgLhoW2GRF2SD6mlHbZv347p06cjISEBTb0csLR/QzgPWoxSGydx6NiYGdvXeVWx33XH1dW1ym31qnDr3bs3evfuDQA4fvw4unXrhrZt2yI0NLRc27IiKy8vD3Z29y/H5+bmws/Pr8L3mDt3LiZPniy+Tk5ORnBwMJydnav1wdVEXR9fX7HfpsWY+l3g3wq5ACyzY+HUurPGthkzZmDfvn0aw6WhoaFab1DIvnrv6pyzfyvYGuDnIwgCck5sRsaOefCe/A1c2w3Ejz/+iMzMTKM639XBfpsWfeq33sxxe1jXrl3h6uqKU6dOad3etGlTAMCtW7c04rdv30aTJk0qPK6DgwN8fX3FP2Vz6YiIHmbTvBtkbv7IOrK+3DYLCwtER0fDxcUFAODi4oLo6OhyRRsAZP3+OWRuAbBp3rXOc65txYmXEP9edyRtDEdpXjpStrwKtaJI12kRmSy9uOKWmZkJc3NzODo6irErV64gMzMTgYGBYuzHH3+Evb09evfujdatWyMwMBDbtm1D5873fhOOiYlBYmIiBg0aVO99ICLjI5GawbnHK0jb9SaK4s/B2r+9xnaZTIbFixeLTxDQVrQV3T6Loti/4T5ypU6XAomMjER2djacnJwqfNLBg9TF+Ujf+y4yf/sYKFWJcZumXSAoiwGLuptaQkQV04vCLSsrC0OGDEGvXr3QuHFjJCYmIioqCoMGDcKwYcPEdqtWrUJAQIA4nPrZZ59h8ODBKCkpgY+PD9avX4/Jkyfjqaee0lVXiMjIOHWbhPQf30HazvloOPdAuUV4IyIiKhwyFFRKpO2cD4m5BZy6TqyvlMupynpzZQRBQN6ZH5GydTZU8kQxbuHdEl4TPodty9B6ypqItNGLodImTZrg5MmTeOKJJ3Dnzh04Oztj9+7d2LdvH8zM7v+G+txzz6FPnz7i6379+uHs2bPw9PRETk4O1q9fj40bN+qiC0RkpMwdGsBj9BoUXD6MpK8nQlCVX6dNG0GlRNLXE1Hw3+/wGL1GZ4+7qs56c4rUWCSsGYjET4eLRZvEwgbuI1chcNl5Fm1EekAvrrgBgKOjI6ZMmVJpm/nz55eLPfHEE3jiiSfqKi0iIrj0ngFVdjIy9q+AKicFHqNWa32SQpmi+HNI2zkfBZcPw23QIp0+YL6y9eYevFELAOSH1iL/wkHxtX3HYfAcGwmZa8P6SJWIqkBvCjciIn3m/vxymDt5IXX7XMS93R7WTZ6Bc89psPLvgNJiFYqLUlAcfxZZv3+Ooti/ITG3gOf4z3RatAHVW2+uwbClyDm1A1JLW3iO+xT27QbWdXpEVE0s3IiIqsil9ww4BI9E9vFNyDqyAUlfThC3Zfz/3zK3ALiPXAmnrhN1Njz6oIqedBDg6YLcf36AQ9BzYszM1gkN5/0KS8/mkFraaN2PiHSLhRsRUTWYOzSA28A34Np/HgqvHYcy8w7yMlNg7+oJmWtD2DTvqlcPkg8PD8e2bds0hktDnmyCzpeW4u7FUli9dxkW7veLu4fvnCUi/cLCjYioBiRSM3GyfmlmJpz0aIHOB5WtN+fp6QlZkRwjWphhcuubMFMAAoD0n5bCZ0r556sSkX5i4UZEZOQkxTnYPqEpfOWnAJT+f1AKlz6z0OC58o8AJCL9xcKNiMhICWo1so99jbTvF8C34P4zRK2bPAOvCZ9XemcsEeknFm5EREZIlZeBhI+fRVHs/ccGmtm6wH3UB3Dq+hIkUr1YxpOIqomFGxGRETKzdQGk9/+Ld+o+Ge4j3oe5vZsOsyKix8XCjYjICAiCAIlEIr6WSKXwmvA5kr6eCM/xn8KmyTM6zI6IaguvlRMRGbiSpKuI/6A38s7/rBG3avgkGi05zaKNyIjwihsRkYFSlxQifd9yZB78EChVQpkWB9uWPTUWz33wKhwRGT4WbkREBijv7D6kbJ0FZUa8GJOYyaCUJ8DSq7kOMyOiusTCjYjIgCjSbyNlyyzkn98vxiQyK7gNWgTXAa9DKrPUYXZEVNdYuBERGQC1sgSZv36EjH3LISiKxLhd2wHwHPepxmOriMh4sXAjItJzgiAg8+eVSP9piRgzd/GD57i1sO8whPPYiEwI7yolIjIALmGzYeboAZiZw3XgG2iy8gocOg5l0UZkYnjFjYhIzwjqUhTfPgvrxk8BuHdnqJmtE3ymbIbM2QeWPq10nCER6QoLNyIiPVIU+z8kR01DSdJlBK64DAuPQHGb3RN9dJgZEekDDpUSEemB0nw5kr99BbeWdUJx/FkIyhKkbJ2t67SISM/wihsRkQ4JgoCcP6OQuvN1lOZliHGrRkFoMGyJ7hIjIr3Ewo2ISEeKEy4i+bvpKLr+pxiT2jjBfcT7cA59GRKpmQ6zIyJ9xMKNiKieCSoF0nYvQuZvHwPqUjHuGBIOj1Ef4LNN25B9bBmcnJwQERGhu0SJSO+wcCMiqkeCIABmMhTG/i0WbZY+reE54XPYtugGhUKBZcuWQS6Xw8XFBTNmzIBMJtNx1kSkL3hzAhFRPZJIJJBIJPCa8DmkNo5wH/UBGr97Tiza+vbtC7lcDgCQy+UICwuDUqnUcdZEpC9YuBER1SG1ohjpPy5F3vlfNOJWfm3QdE0C3Aa8Don5vStqUVFRiImJ0WgXExODqKio+kqXiPQch0qJiOpI/oVfkbx5JpRpsZC5+cO2ZQ9ILW3E7WbW9hrt4+LitB4nNja2TvMkIsNRo8JNrVYjPz8fDg4OtZZIcXExdu3ahXPnzsHR0RH9+/dHcHBwpfusWrUKN27c0IgFBwdjypQptZYXEVF1KeWJSNk2B3mnd4sxVXYyCm+ehF3r3hXu17ix9gfFBwYGao0Tkemp0VCpQqFAixYtsG3btlpJIj4+Hq1atcLhw4fh5+eHnJwchIaGYtWqVZXut3//fqSlpaFTp07in6ZNm9ZKTkRE1SWolMg8+BFuLmihUbTZtu6DxisuVVq0AUB4eDhCQ0M1YqGhoQgPD6+LdInIANXoipulpSXmzp2LKVOm4KuvvsK6devQsmXLGifh6OiIv/76Cx4eHmKsUaNGmDt3LmbNmgVra+sK9+3QoQMmT55c4/cmIqoNhdf/vPeoqsRLYszcyRueYyNh/9TzVXoYvIWFBaKjozF+/HgkJibC19cXmzdv5l2lRCSq0RU3iUSCefPm4cqVK3B0dETbtm2xYMECFBQU1CgJJycnjaINAJo3bw6VSiXeXVWRY8eOYcaMGVi+fDnOnDlTo/cnInocRWd24faKrveLNqkZXPrOQeDKK3AIHlGloq2MTCbDjh078Oeff2LHjh0s2ohIw2PdVern54cff/wRP/zwA3bu3ImWLVvihx9+qJXEvvzySzRr1gw+Pj4VtrGwsICfnx+aNm2K27dvo3Pnzvjggw8qPW5ubi4SExPFP8nJybWSLxGZLstWfWHmeO+XT+smndF46Rl4jlkDM+vamwdMRATU0l2lzz77LHr16oW3334bw4cPR79+/fDpp5+iSZMmNTremjVrsH//fhw5cqTSdlu3boWXl5f4ulOnTpg2bRpGjBiBRo0aVXjspUuXlotnZWVVOiT7uHJzc+vs2PqM/TYtptJvdX4GpHZu4ut8JWA3bBWEohxYPTUaBVIpCjIzdZhh/TCV8/0w9tu01Ee/XV1dq9xWIgiCUNM3ysjIwF9//YW//voLJ0+exOnTp1FYWAg7OzuoVCq89957mDNnTrWO+eWXX+LVV1/Fzp07MXTo0Grtm5eXBwcHB+zcuRMjR47U2iY3N1fjJCQnJyM4OBgJCQnw9fWt1vtVR2ZmZrVOjLFgv02Lsfe7tCAbaXveQvbxbxC4/F9YeNz75dTY+10R9tu0sN/6oUZX3JRKJVq3bi0uxREYGIiQkBCMGzcOISEhaN68OXbv3o2ZM2ciPz8fixcvrtJxN27ciFdffRXbt2+vdtEGAEVFRQD+/5EyFXBwcKjVZUyIyPgJgoCck1uRunMeSnNSAQDJm2ei4WsHqzV/jYjocdV4qLR///547733EBISAk9Pz3LbR4wYAX9/fzz33HNVKtw2bdqEmTNnYvv27Xjuuee0tnn//ffh6uqKKVOmIDExEampqejYsSOAe/+xvvfee7C2ti53Oz0RUU2V3P0Pyd/NQOHVGDEmtbKH3ZMDAEEAWLgRUT2qUeEmk8nwySefPLJd27ZtkZOT88h2//77L15++WU0bdoUBw4cwIEDB8RtCxcuFBel3Lt3LwICAjBlyhTIZDJERERApVKhUaNGuHjxItLT07F9+/Zyd6gSEVWXuqQA6XuXIfPXj4BSlRh36DQaHqM/gszJq5K9iYjqRp0+8srS0hKpqamPbOfu7o4vvvhC6zZ7+/uPhFm4cKH42sPDA8ePH8eZM2dw/fp1TJkyBUFBQbCzs6ud5InIZOWd3YeUrbOgzIgXYxZezeE5fh3sWvfSYWZEZOrq/FmlNjY2j2zj5eVVpUV0Bw8eXC7WsWNHcbiUiOhxCYKAzIMfikWbRGYFtyGL4drvNUhlljrOjohM3WOt40ZEZEgUCgVGjRqFkJAQvPDCC1AqleXaSCQSeE5YB0jNYNfuWQS+/x8aDFrIoo2I9EKdX3EjItIHCoUCffv2RUxMjBhLTU3FDx8vgExmpvEcUSu/NghcfgGWPq10kCkRUcV4xY2ITEJUVJRG0QYAMTEx+HR6PyRvmgx1ieYj+1i0EZE+YuFGRCYhLi5OazwhD1BmxCPn5JZ6zoiIqPo4VEpEJqFsWaGH+bvbw2fqejg8M6aeMyIiqj4WbkRk9FT5megj/I2nPIDTD6xQ1LmlD+btOQcrpwa6S46IqBpYuBGRUSu8+TcSIgehNC8DG/sAe2OBs6nAtWIHxPx7CzKZTNcpEhFVGQs3IjJqlt4tIZHe+6/O0s4JrXr2gWDWAqHOLizaiMjgsHAjIqMiqEshkZqJr81sHOEx5mPkX/wNHqNWoYWDO57XYX5ERI+Dd5USkVEQBAE5p77HzfnNUJJyQ2ObY6cX4PPyNzB3cNdRdkREtYOFGxEZvJKU67izui/ufj4KyvQ4pGyeCUEQdJ0WEVGt41ApERkstaIIGT+/j8xfVkFQKcS41NoBgqIIEstHPyuZiMiQsHAjIr0VGRmJ7OxsODk5ISIiQmNb3r8HkbJ5JpTp9xfWlbkHwmv8Z7B7sl89Z0pEVD9YuBGRXlIoFFi2bBnkcjlcXFwwY8YMyGQyKDMTkLItAnn//CC2lcgs4TbwTbgOfANSCysdZk1EVLc4x42I9E7ZA+HlcjkAQC6XIywsDEqlEsnfTtUo2mzb9EXgiktoMOwdFm1EZPRYuBGR3qnogfBRUVFwH/UBYGYOc2cf+M7chYavHYSFRxPdJEpEVM84VEpEeqeiB8LHxsbCyncy/Gb9CJvm3WFmbV/PmRER6RYLNyLSO40CArTGAwMDAQD27Z6tx2yIiPQHh0qJSK8U3foH3RK/xFMemvHQ0FCEh4frJikiIj3BK25EpBdKC7KRtmcRso6sBwQBG/sAP90EouOB2ypnREdH89miRGTyWLgRkU4JgoCck1uQumMeSnPTxLh9o/Zo2KI7+igd4eTkxKKNiAgs3IhIh5SZCbj75XgUXj0qxqTWDnAfvgLOvaah8QMPiyciIhZuRKRDUhtHKFKui68dnxkLjxc+hLmTpw6zIiLSX7w5gYh0xszaAR5jPoaFVwv4LzgCn1e2sGgjIqoEr7gRUa2IjIxEUlISvL29yz1XFAAUaXFI3f4a3EetgqVnMzHuEDwSDh2HQWJuUY/ZEhEZJhZuRPTYKnquKAColSXIPPABMva/B0FZDHVJPhq+Hg2JRAIA9/5m0UZEVCV6M1R67do1zJ49G926dcOgQYOwfv16KJXKR+733XffISwsDJ07d8Ybb7yB3NzcesiWiMpU9lzR/EuHELeoDdJ/eBuCsvhe+9SbUOWk6DJlIiKDpReF25UrV/Dcc8+hSZMmWLFiBUaPHo3ly5dj0qRJle63du1aTJs2DSNHjsTixYtx+PBhDBgwAIIg1FPmRFTRc0UjX+yMO6vDoEi9cS9oJoPboEUIfO8yZE5e9Z8oEZER0Iuh0sDAQFy8eBFS6f060szMDKNHj8batWvh5ORUbh+lUoklS5Zg8eLFmDx5MgCgadOmaNq0KQ4ePIgBAwbUV/pEJq2i54pev/APBna492+bVj3hNX4dLL1b1GNmRETGRy+uuFlYWGgUbQBgaWkJQRCgUqm07nPu3DlkZWVpFGhNmjRB8+bN8fvvv9dpvkR0X+PGjbXG/ewBc0dP+LyyDf7zD7NoIyKqBXpxxe1hSqUS77//Prp37w43NzetbRISEgAAXl6aQy7e3t7iNm1yc3M15sElJyfXQsZEpis8PBzbtm3TGC4N9gRemjID3iNXwMzGUXfJEREZGb0r3ARBwMsvv4zbt2/j77//rrBd2Y0LlpaWGnFLS8tKb2pYs2YNli5dWi6elZUFa2vrGmb9aKZ60wT7bbwEtRrq7ESYuTTE9u3b0apVK2RlZaFbgA227PoJNo06ILtIBRRl6jrVOmcK51sb9tu0sN91x9XVtcpt9apwEwQB06dPx4EDB3DkyBE0atSowrZlnczMzISDg4MYz8zMxJNPPlnhfnPnzhXnxAH3rrgFBwfD2dm5Wh9cTdT18fUV+218iuPPIzlqGpRZd9Hk/f8gtXLF22+/La7j5hfUR9cp1jtjPt+VYb9NC/ute3pVuM2cORO7d+/GkSNH8MQTT1Tatn379pBKpfjf//4nFngFBQW4dOkSXnrppQr3c3Bw0Cj0iKjqSotykf7D25Af+hQQ1ACA9L3L4DFqFSIiIpCZmalX/8ERERkbvbg5AQBeffVVfP/99zhy5AjatGmjtc2ECROwYMECAICbmxuGDRuGVatWIS8vDwCwYsUKWFhYYOTIkfWWN5EpEAQBOX/vQOyCFpBHfyIWbZa+bWDffrCOsyMiMh16ccXtxIkT+Oyzz+Dh4YHx48drbNu8ebNYyF2/fh0KhULc9sUXX2DEiBHw8PCAg4MDpFIp9uzZw9/4iWpRSfI1pHw3AwX/3b9bW2plhwbD3oVLn1chMdOL/0aIiEyCXvyP27ZtW5w7d07rtsDAQPHfmzdvhoXF/UfjuLq64siRI7h79y7y8/MRGBgIc3O96BKRwVMripCx/z1kHvgAgur+L0wOwSPhMXoNZC4+OsyOiMg06UWVY2dnh3bt2j2yXdOmTbXGfXz4A4SqT1CXovDaMSgz70BdnA+plR1krg1h07wbJFIzXaenc0KpEtnHNolFm4VHE3iOXwe7NmE6zoyIyHTpReFGVJ9UuWnIPrYJWX9sgDIjvtx2mVsAnHtMhVO3STB3aKCDDPWDmbUDPMZ8jKSNE+A2aBFc+78OqYWVrtMiIjJpLNzIpMgPr0Pq9rkQVApYN+mMBs8tg3VAR0it7KEuzkPR7TPIOrIeabveRPqP78Bj9Bq49J6h67TrnKBSIPO3j2HfYSgsvZqLcYfgEbBp2hkyF18dZkdERGVYuJHJSNv9FjL2r4Bt697wGLUaVv7tyrWx9GkFpy7jURR/Dmk75yNl80yospPh/vzy+k+4nhRciUHyd9OhSLqCgsuH0fD1aEgkEgCARCJh0UZEpEdYuJFJkB9eh4z9K+DYeRy8J22CxFxWaXtr//ZoOPcAkr6eiIz9K2Du5GVUV94UCgVeGjMCyri/8IRVOoYEAjIpUHA1BiUJF2DVsK2uUyQiIi1YuJHRU+WmIXX7XNi27l2loq2MxFwG70mboMpJQer2uXAIHmkUc95KiovQq1NbnPj3BgBgF4Bf4oAt07vAb+IGWPlWvvg1ERHpjt4swEtUV8rujPQYtVpr0RYZGYklS5YgMjKy3DaJuQzuI1dBUCmQfXxTPWRbt4riTuOjES3Eoq3M/1KBGK9wFm1ERHqOV9zIqAnqUmT9sQHWTZ7ROqdNoVBg2bJlkMvlcHFxwYwZMyCTaRZ31gEdYB3YCVlHNsC1/zyDXSpEfugzpGydhVu3Ba3b4+Li6jkjIiKqLl5xI6NWeO0YlBnxcO45rdw2hUKBvn37Qi6XAwDkcjnCwsKgVCrLtXXuNR3KjNsovHa8znOuK7ategJSM/jaad/+4GLXRESkn1i4kVFTZt4BAFgHdCy3LSoqCjExMRqxmJgYREVFlWtr5d9B43iGQK0o1nht6dMKDYYtxdR3PkH37t01toWGhiI8PLw+0yMiohpg4UZGTV2cDwCQWtmX21bR0GBsbGy5mJm1/f8fL68Ws6sb6uJ8pO6cj9iFrcT+l2kwaCE8+83CoUOH4OLiAgBwcXFBdHR0uSFiIiLSP5zjRkZNanVvXFBbwdW4cWOt+2gbMiwtyvv/45UvAPWFIAjIO/MjUrbOhkqeCABI3/suPEZ9UK6tTCbD4sWLkZ2dDScnJxZtREQGgoUbGTWZa0MAQNHtM7D0aaWxLTw8HNu2bdMYLq1oyLA4/qzG8fSNIjUWKVteRf6Fg2JMYmEDcwePCveJiIioh8yIiKg2sXAjo2bTvBtkbv7IOrIeTl3Ga2yzsLBAdHQ0xo8fj8TERPj6+mLz5s1arz5l/f45ZG4BsGnetb5SrxK1ohiZBz5Axs/vQVCWiHH7jsPgOTZSbwtNIiKqGRZuZNQkUjM493gFabveRFH8OVj7t9fYLpPJsGPHjkqPUXT7LIpi/4b7yJV6tRRI/sVopGyeAUXqTTEma9AInuM+hX27gTrMjIiI6gpvTiCj59RtEiTmFkjbOR+CqvxSH5URVEqk7ZwPibkFnLpOrKMMq0+tKEbS1xPFok1ibgG3wW8hcMUlFm1EREaMhRsZPXOHBvAYvQYFlw8j6euJVS7eBJUSSV9PRMF/v8Nj9Bq9etyV1MIKnmMjAQC2rXuj8YqLcB++DFJLG90mRkREdYpDpWQSXHrPgCo7GRn7V0CVkwKPUau1PkmhTFH8OaTtnI+Cy4fhNmiRzh8wX3j9BMwcGsDSs5kYsw8aDv83fodNyx6QSCQ6zI6IiOoLCzcyGe7PL4e5kxdSt89F3NvtYd3kGTj3nAYr/w4ws7ZHaVEeiuPPIuv3z1EU+zck5hbwHP+ZTos2VV4G0r5/A9nHNsG2VS80nH9ILNIkEsm9pyEQEZHJYOFGJsWl9ww4BI9E9vFNyDqyAUlfTijXRuYWAPeRK+HUdaLOhkcFtRrZx75G2vcLUFpw75FcBf/9jq3vTsW4d77USU5ERKR7LNzI5Jg7NIDbwDfg2n8eCq8dhzLzDtTFeZBa2UPm2hA2zbvq9O7RovhzSImahqLYUwAARSmw87oEP9wQkCndg1EL13HBXCIiE8XCjUyWRGoG25ahuk5DVFqYg/Qf3ob88GeAoAZwr2ib/o8n/rqW8v+t5AgLC+MjqoiITBTvKiXSA/mXf0fsghaQH1orFm2WDdviRMs3Hija7omJiUFUVJQu0iQiIh1j4UakB2TO3lDlZwK49zxUj7GRaLzkHyTma79bNDY2tj7TIyIiPcGhUiI9YOndEq7950GZfgseoz+CzNkbANC4cWOt7QMDA+szPSIi0hO84kZUz/LO7cftFd1QWpSnEXcfvhy+07eLRRsAhIeHIzQ0VKNdaGgowsPD6yNVIiLSM3pXuAmCAJVKVaW2paWlUKlUGn/UanUdZ0hUM4r027gTOQQJkYNReP04Mva+q7FdIi3/7WhhYYHo6Gi4uLgAAFxcXHhjAhGRCdObodLbt2/jyy+/xNdff420tDSkp6fDzc2t0n26d++OkydPQvrAD7xRo0Zh69atdZ0uUZUJKgUyD36E9H3LICiKxLgi9QYEtVprwfYgmUyGxYsXIzs7G05OTizaiIhMmN4UbkuWLEHTpk2xatUqvPTSS1Xe7+2338aSJUvqLjGix1Dw3xEkfzcDiuSrYszcxQ+e49bCvsOQKj+qKiIioo4yJCIiQ6I3hdu3334LAPj11191mwhRLSjNTcXdXbOR89cDV3/NzOHa7zU0GLIYUktb3SVHREQGS+/muFXXmjVrYG5uDl9fX0ydOhWZmZm6TolMnCAIyNowXKNos2nRHYHL/oXHyJUs2oiIqMYMunDr2rUrfvvtN+Tn5+Onn37CqVOnMGjQoEpvUMjNzUViYqL4Jzk5uR4zJlPwySef4HBRMwCAmYM7vKdshv+CP2Dp00rHmRERkaHTm6HSmnj//ffFfwcFBWHTpk3o2LEj/vrrL3Tp0kXrPmvWrMHSpUvLxbOysmBtbV1nuebm5tbZsfWZKfRbXZAFQVEAM2dfKBQKvPvuu8jKykJoY2tsPXwMpQ5ukMvluk6zXpjC+daG/TYt7LdpqY9+u7q6VrmtQRduD2vdujUA4ObNmxUWbnPnzsXkyZPF18nJyQgODoazs3O1PriaqOvj6ytj7bcgCMj5MwoZO1+Hpd+T8Io4gH79+iErKwsAEBNXhLETXzG55TuM9Xw/CvttWthv06JP/Taowq20tBQSiURj+Y8HXbhwAQDg4+NT4TEcHBzg4OBQJ/mR6ShOuIjk76aj6PqfAIDC/47gi3dmIiYmRqNd2XNFH/xlgYiIqKb0Zo6bWq3WWEC3bHFdQRDENl26dMGYMWMAAOfOncPUqVPx77//Ijc3FydOnMBLL72E9u3bo0ePHjrpAxk/dXE+Une8jri324tFGwA4hoTjrkL7UDufK0pERLVFbwq3N998E1ZWVhg8eDDMzMzg4+MDKysr/Pjjj2Ibc3NzmJmZAQDatm2Lzp07Y/LkyfDz88OkSZPQr18/HDlyRGxDVFsEQUDu6T24uaAFMg9+CKhLAQCWPq3h/+ZR+Lz8LZq0bKN1Xz5XlIiIaotEePCSlglKTEyEn58fEhIS4OvrW2fvk5mZqVdj5PXFGPqtSL+N5KhpKLh4f41BiYUNGgxbAtewCEjM781fUygU6Nu3r8ZwaWhoqEnNcTOG810T7LdpYb9Ni77126DmuBHpgqAsRsF/v4uv7YOeg+eYSMhc/TTalT1X1NPTE3K5nM8VJSKiWsfCjegRLL1bwG3A68j5ewc8x38K+7YDKmxb9lzRpKQkeHt7s2gjIqJaxcKN6AFKeSLS9y6DxwsfwszaXoy7DX4LboPfgtTi0Wv9RURE6N2ldSIiMg4s3IgACCol5IfWIu3HdyCUFEBqZQ/P0R+K26tSsBEREdU1vbmrlEhXCq//ibh3OiB1xzwIJQUAgLx/dkOtKNJxZkRERJp4xY1Mlio3Hak75yPnz2/vB6VmcAmbjQZDl/AqGxER6R0WbmRyBLUa2Uc3InXXm1AXZIlx6yad4RW+HlYNn9RhdkRERBVj4UYmJ3HdSOT9s0d8bWbnCvdRH8Ap5EVIKnicGhERkT7gTykyOY6dRov/dgqdgsBV1+DcbSKLNiIi0nu84kZGTRAEqItyYWbjKMbsg56DS985cAgeCZsmnXSYHRERUfWwcCOjVZJ0BclR0wGJBP5v/A6JRAIAkEgk8ByzRsfZERERVR8LNzI66pICpO9djsxfPwJKlQCA3L+3w/GZMTrOjIiI6PGwcCOjknd2L1K2zIIy844Ys/BqDnNnHx1mRUREVDtYuJFRUKTfQsqW2cg/v1+MSSys0WDwYrj0mwupzFKH2REREdUOFm5k0NTKEmT++hEy9i2H8MCTDuzaDYLnuLWwaBCgu+SIiIhqGQs3MiiRkZHIzs6Gk5MTIiIioMyMR/qPS8S5bDI3f3iOXQv7DoN1mygREVEdYOFGBkOhUGDZsmWQy+VwcXHBjBkzYOnZDG4D5iPjwAdw7T8PDQYvgtTSVtepEhER1QkWbmQQFAoFwsL6QC6XAwDkcjnCwsIQHR0Nt0EL4dh5HCy9W+g4SyIiorrFpeLJIGxc/Q6OHj2mEYuJiUFUVBSkljYs2oiIyCTwihvpNVV+JtK+fxPnd2zUuj02NraeMyIiItIdFm6klwS1Gtl/fou0nfNRmp8JXzvt7QIDA+s3MSIiIh1i4UZ1JjIyEklJSfD29kZERESV9yu+cwHJUdNQdPOkGBv2pBMOFbnjxL/XxVhoaCjCw8NrM2UiIiK9xsKN6oS2O0BlMtkj98v4eSXS9rwFqEvFmFPXl+A+chX+sHaCp6eneMzo6OgqHZOIiMhYsHCjWqdQKNC3b1+td4A+qtCy8GwuFm2Wvk/AK3w9bJqFiNsXL14sruPGoo2IiEwNCzeqdVFRUYiJidGIld0BOnnyZI24IAiQSCTia/uOQ+EQPBLWjYPh0mcWJOaaxVl1hlyJiIiMDQs3qnVxcXFa4w/eAapWFCHj5/dRFPc/NHztoFi8SSQS+M7YWS95EhERGRq9K9xiY2ORnJyMTp06wdy8auldv34d+fn5aN26NSwt+TBxXWvcuLHWeNkdoHn/HkDK5plQpt8CAOSc3AqnLuPqLT8iIiJDpTcL8B44cAA9e/ZEcHAwunbtiuzs7Efuk5KSgqeffhpPP/00RowYAW9vb/zyyy91nyxVKjw8HKGhoRqx0NBQjHm2BxLWPoeENQPFok0is0RpfoYOsiQiIjI8enPF7dKlS1i0aBEUCgUGDBhQpX1efvllSCQSJCUlwdraGsuXL8cLL7yA2NhYuLu713HGVBELCwtER0eLd4C6ubpgW0QY4hc/CUFRKLazbdMXXuM/g4VHEx1mS0REZDj05orb/Pnz0atXL42J6pVJTU3FL7/8gtdffx3W1tYAgNdeew2CIOD777+vy1SpCmQyGRYvXowPZ7+AX4bLkPXDQrFoM3f2ge/M3Wj42kEWbURERNWgN1fcquvff/+FIAjo2LGjGLO2tkbr1q1x7tw5HWZGZWa+NBrX5y4AVCX3AlIzuPadA7chb8PM2l63yRERERkggy3cytYIc3V11Yi7urqK27TJzc1Fbm6u+Do5ObluEiSYO3rAtsdMFBz6CNbNQuA14XNY+bXRdVpEREQGy2ALt7LFV4uLi2Fvf//qTVFRkcbrh61ZswZLly4tF8/KyhKHXOvCg8WisVImnINZgyaQWt3//EuDJ8HBoxms2j+HAokEBZmZOsyw/pjC+daG/TYt7LdpYb/rzsMXoSpjsIWbv78/AODu3bto0KCBGL979y6CgoIq3G/u3Lkai8AmJycjODgYzs7O1frgaqKuj68rpQVZSNvzFrKOrIdLn9loMPZjje2uzaboKDPdMtbz/Sjst2lhv00L+617BlW4XbhwAZaWlmjevDnatWsHNzc37Nu3D+3atQMAXLlyBTdu3ECfPn0qPIaDgwMcHBzqKWPjJggCck5uQeqOeSjNTQMAyA+thVP3ybDyba3j7IiIiIyP3hRut2/fRmJiIi5fvgwAOHXqFBwdHdGyZUux0p0yZQoCAgKwY8cOmJubY8WKFZg1axYcHR3h4+ODJUuWoGfPnggLC9NlV0xCceJlpHw3HYXXjokxqbUD3J9/D5beLXSYGRERkfHSm8Ltl19+wfbt2wEAXbp0wfvvvw8AWL58ubiYa9u2beHl5SXuM2XKFLi5uWHz5s3Iz8/HyJEj8dprr9V77qZEXVKA9J/eReZva4BSlRh37DwOHqNWw9zJU4fZERERGTeJIAiCrpPQpcTERPj5+SEhIQG+vr519j6ZmZl6NUZeE/kXf0PSppehkieIMQuvFvAK/xy2LXto3ccY+l0T7LdpYb9NC/ttWvSt33pzxY30n1pRJBZtEgtrNBjyNlz7zYXE3ELHmREREZkGFm5UZfYdhsCu7UBIzMzhMfYTWLj56zolIiIik8LCjbTKv3QIuX9vh9ekr8XHkEkkEvjO3AWpRd2td0dEREQVY+FGGpRZSUjdNge5/7v3vFeblj3g1GW8uJ1FGxERke7ozUPmSbeEUhUyf4tE7IIWYtEGAPkXDuowKyIiInoQr7gRCm+cRHLUNJQkXBBj5o6e8BjzMRyeHqXDzIiIiOhBLNxMmCovA2nfL0D2sa/vByVSuPR5FQ2GLYWZjaPukiMiIqJyWLiZKLWiCHFvtYUqO0mMWQd2gmf457D2b6/DzIiIiKginONmoqQW1nDqPvnev22d4fXSlwh46wSLNiIiIj3GK24GKjIyEtnZ2XByckJERMQj25cW5QKQwMzaXoy5PbsAgrIIrv1fh7lDg2ofk4iIiOoXCzcDpFAosGzZMsjlcri4uGDGjBmQyWRa2wqCgNxTO5G6fS4cgkfCc2ykuE1qYQ2PUR9U+5hERESkGxwqNTAKhQJ9+/aFXC4HAMjlcoSFhUGpVJZrW5J8DXc+6IO760dDlZ0M+aFPURx//rGOSURERLrDws3AREVFISYmRiMWExODqKgo8bW6pBBpu99C7KI2KPjvdzHu8NTzMHNwr9ExiYiISPc4VGpg4uLitMZjY2MBAHnnf0bK5lehzLgtbrPwaArPCetg90SfGh2TiIiI9AMLNwPTuHFjrXF/dyckfDIMeWd/EmMSmSXcBi2Ca//XIbWwqvYxAwMDHytXIiIiql0cKjUw4eHhCA0N1YiFhoZiZOcmGkWb3ZP9EbjiMhoMWVxp0VbZMcPDw2spayIiIqoNvOJmYCwsLBAdHQ1PT0/xDtDo6GiYm5sj/+SzKL5zHp5j18K+41BIJJLHOibvKiUiItIvLNwMkKRQjk0vP41zlk/BydlZLLC8J34FqaUtpFZ21T6mTCbD4sWLxXXcWLQRERHpHxZuBkRQlyLryAak7VmEpoU56P7yC3AKmSBuN3f0eKzjc9FdIiIi/cY5bgaiKPZ/uLUkGCmbZ0JdmAMAyDy4GoJarePMiIiIqL7wipueKy3IQtquhciK+QIQhHtBiQTOPafBffhySKSsvYmIiEwFCzc9JQgCck58h9Qdr6M0L12MWzUKglf4elg3CtJhdkRERKQLLNz0kCAIuLO6LwouHxJjUhtHuD//Ppx7TIFEaqbD7IiIiEhXOM6mhyQSCaybdhZfO3YejyYrr8Gl1zQWbURERCaMV9z0gCAIQKkKEvP7S3C4DVyA4ttn4dr/Ndi26K7D7IiIiEhfsHDTMUVqLFK2vAoLz2bwHBspxqUWVmg4Z5/uEiMiIiK9o1eFW1ZWFg4ePIj8/Hx06dIFrVu3rrT9rl27cPfuXY1Y8+bN0b9//7pMs9oiIyORlJQEb29vca00taIYmQc+QMbP70FQlgAXf4NjSDis/dvrNlkiIiLSW3pTuJ0/fx69e/dGy5Yt4e3tjTlz5uCdd97B/PnzK9znk08+gUqlQqdOncSYi4tLfaRbZQqFAsuWLRMfJTVjxgyUXD2ClM0zoUi9KbaTuTaEujhfh5kSERGRvtObwm3y5MkIDQ3F7t27AQA7duzAuHHjMHToUDRr1qzC/fr164clS5bUU5bVo1Ao0LdvX8jlcgCAXC5H99Y+WP90OmT/f1uIxNwCrgPmw+3ZNyG1tNFhtkRERKTv9OKu0ri4OJw5cwavvPKKGBsxYgScnZ2xZ8+eSve9evUq1q9fj59++gnp6emVtq1vUVFRiImJ0Yj9dSMde2Pv/du2dW80Xn4B7sOXsWgjIiKiR9KLK27//fcfAKBFixZizMzMDE2bNhW3VeTGjRuws7PDpUuXcPXqVXz99dcYPnx4he1zc3ORm5srvk5OTn7M7CsWFxenNX5XYQuf6V/DIXgkJBJJnb0/ERERGRe9KNzy8vIAAE5OThpxZ2dncZs2kZGRCAq6/wSBhQsX4qWXXkLXrl3h7u6udZ81a9Zg6dKl5eJZWVmwtrauQfYVqyiHFqPegqpJb3EI1Zg9WCSbEvbbtLDfpoX9Ni310W9XV9cqt5UIQtkDMHVn7969GDp0KJKSkuDl5SXGu3btCj8/P2zbtq1Kx8nMzISbmxv27t2LwYMHa22j7YpbcHAwEhIS4Ovr+3gdeUjZHLcHh0tDQ0MRHR0NmUxW8Y5GJDMzs1pfkMaC/TYt7LdpYb9Ni771Wy+uuJXdfBAbG6tRuN26dQs9e/as8nHMze91Jycnp8I2Dg4OcHBwqGGm1WNhYYHo6Gh4enqKd5WaUtFGREREtUsvCreWLVuiWbNm2LJlC0JCQgAAhw8fxt27dzFkyBCx3c6dO+Hg4ID+/fsjMzMTarUaDRo0ELd/9913kEql6Ny5c7n30BWZTIbFixeL67ixaCMiIqKa0ovCDQA+//xzDBw4EAUFBfDx8cFXX32FGTNmoEOHDmKbjz/+GAEBAejfvz/y8vIwYMAAdOnSBY0aNcLFixfxww8/YOXKlQgMDNRhT8qLiIjQu0utREREZHj0YjkQAOjVqxcuXLiAFi1aQCKR4LvvvsNnn32m0eaFF17AgAEDAAABAQE4ffo0QkJCUFBQgC5duuDSpUt4/fXXdZE+ERERUZ3TmytuwL25bosWLapwe9njosrY2toiPDy8jrMiIiIi0g96c8WNiIiIiCrHwo2IiIjIQLBwIyIiIjIQLNyIiIiIDAQLNyIiIiIDwcKNiIiIyECwcCMiIiIyECzciIiIiAwECzciIiIiA8HCjYiIiMhA6NUjr3RBpVIBAJKTk+v0fbKyslBUVFSn76GP2G/Twn6bFvbbtLDfdcvT0xPm5o8uy0y+cEtPTwcABAcH6zgTIiIiMlUJCQnw9fV9ZDuJIAhCPeSjt4qLi3Hx4kU0aNCgSpVuTSQnJyM4OBj/+9//4OXlVSfvoY/Yb/bbFLDf7LcpYL/rvt+84lZFVlZWeOqpp+rlvby8vKpUTRsb9tu0sN+mhf02Ley37vHmBCIiIiIDwcKNiIiIyECwcKsHDg4OeOedd+Dg4KDrVOoV+81+mwL2m/02Bey3/vTb5G9OICIiIjIUvOJGREREZCBYuBEREREZCBZuRERERAaChRsRERGRgTD5BXhrQ15eHj799FNcuHAB7u7uePnll9GmTZta30ffVLcPFy5cwNtvv10uvm7dOvj4+NRlqrUqPz8fW7duxW+//Yb+/fvj5ZdffuQ+xnC+q9tvYznfR44cwc8//4z09HS0bNkSU6ZMgZubW6X7GMP5rm6/jeV8X7lyBVu2bMGtW7fg4+ODsWPHol27dpXuYwznu7r9NpbzXSY9PR3Tp0+Ho6Mjvvrqq0rb6vp8s3B7TCUlJejWrRtkMhmmTp2KU6dOITg4GEePHq3w+ac12Uff1KQPaWlp2Lt3L3788UeNuKOjY32kXCv+/fdf9O/fHwMHDsSFCxfg7e39yH2M4XzXpN/GcL6nT5+O69evY8CAAWjfvj22bt2KyMhInD59Gv7+/lr3MYbzXZN+G8P5/uGHH7Bq1SoMHz4cgwYNwsmTJxEUFIQ9e/ZgyJAhWvcxhvNdk34bw/kuIwgCwsPDcfXqVSgUikrb6sX5FuixbNiwQbCxsRHkcrkYe/bZZ4UePXrU6j76piZ9OHTokGDoX3JZWVlCTk6OIAiC8PTTTwszZsx45D7GcL5r0m9jON9JSUkar5VKpRAQECDMnz+/wn2M4XzXpN/GcL4zMjIEtVqtERs0aJAwaNCgCvcxhvNdk34bw/ku8+GHHwphYWHCu+++K/j4+FTaVh/ON+e4PaaDBw+iR48ecHZ2FmMjRozA0aNHUVhYWGv76JvH6cPLL7+MCRMmYNWqVcjKyqrrVGuVk5NTtRdiNIbzXZN+lzHk8/3wQ6XNzc3h7u6OnJycCvcxhvNdk36XMeTz7erqColEIr4uKSnB7du3ERgYWOE+xnC+a9LvMoZ8vgHgn3/+wUcffYRvv/1W4zOoiD6cbxZuj+nWrVvw8/PTiPn5+UGtVuPOnTu1to++qWkfunTpgvbt2yMkJAR79+5FixYtDKbPNWUM57umjO18nzx5EqdPn0a/fv0qbGOM57sq/QaM53yPHj0aAwYMQOPGjRESEoIVK1ZU2NaYznd1+g0Y/vnOy8vD6NGj8fnnn5f7ZaUi+nC+OcftMZWUlMDGxkYjZmdnBwAoLi6utX30TU36EBwcjOPHj4u/1bz00kto3749Fi5ciC1bttRtwjpkDOe7JoztfMfHx2PEiBEYPXo0hg4dWmE7YzvfVe23MZ3vsWPHIi8vDwEBAdi6dSsGDx5cYdFqTOe7Ov02hvP9yiuvoEePHpV+XT9MH843C7fH5OjoWO7ycGZmJgBoXEp93H30TU368PBQm0wmw4ABA7Bv3766SVJPGMP5rgljOt+JiYno1asXgoKC8O2331ba1pjOd3X6bUzn+9lnnwVw7wqURCLBrFmzcP36da1tjel8V6ffhn6+09LSsG3bNvTu3Vss3K5fv47MzEwMHToU8+bNQ0hISLn99OF8s3B7TG3btsWZM2c0YufPn4eTk1O5y6mPs4++qa0+ZGdnw9zcuL8MjeF81xZDPN93795Fjx490LJlS+zatQsymazS9sZyvqvbb20M8Xw/rFmzZti0aVOF243lfD/sUf3WxpDOt6OjY7k7Ynft2oW0tDS8+OKLaNSokdb99OJ819ttEEbq2LFjAgDh999/FwRBEORyuRAYGKhx193Zs2eFIUOGCImJiVXeR9/VpN87d+4UsrOzxe3nzp0TbG1thYULF9Zv8rWkorsrjfF8P6iq/TaG852UlCQ0bdpUGDRokFBSUqK1jTGe75r02xjO986dO4Xc3FzxdXZ2ttChQwehb9++YswYz3dN+m0M5/thy5YtK3dXqT6ebxZuteDdd98VrK2thZCQEMHd3V3o0qWLxhf0wYMHBQDClStXqryPIahuv7ds2SI0atRI6NKli9C5c2fByspKmDp1qlBcXKyrLlRbUVGRMGTIEGHIkCGCs7Oz0KhRI2HIkCHCrFmzxDbGeL5r0m9jON/PPvusIJFIhP79+4v9HzJkiLB06VKxjTGe75r02xjO93fffSc0btxY6NKli9CjRw/BwcFB6N27t5CQkCC2McbzXZN+G8P5fpi2wk0fz7dEEAShfq7tGbeEhARcunQJ7u7u6NChg8ZtxWlpaTh58iR69+4tTmJ81D6Gorr9Li4uxvnz56FQKNCiRQu4u7vrKvUaKS0txf79+8vFHRwc0LNnTwDGeb5r2m9DP99//vknMjIyysU9PDzwzDPPADDO813Tfhv6+Qbu96GoqAiNGzcut+CwMZ5voGb9Nobz/aDr16/j1q1b6Nu3rxjTx/PNwo2IiIjIQHAdNyIiIiIDwcKNiIiIyECwcCMiIiIyECzciIiIiAwECzciIiIiA8HCjYiIiMhAsHAjIiIiMhAs3IiIiIgMBAs3IiIiIgPBwo2IiIjIQLBwIyKqppycHPz000+4e/euRvzSpUvYu3cvFAqFjjIjImPHwo2IqJrs7e2xZMkSTJkyRYz99ddf6NSpE27dugULCwsdZkdExowPmSciqoE//vgDPXv2xJEjR+Dk5IQePXpg/vz5WLhwoa5TIyIjxsKNiKiGhg4dips3byI1NRVTp07F8uXLdZ0SERk5DpUSEdXQmDFjcPnyZXTv3p1FGxHVC15xIyKqgfPnz6NHjx7w9fVFRkYGbty4ATs7O12nRURGjlfciIiq6cqVKwgLC8OkSZPw559/QqVS4b333tN1WkRkAnjFjYioGmJjY9GtWzcMGjQIGzZsAAB88sknWLBgAa5evQp/f38dZ0hExoyFGxFRFZWWluK1116DhYUFVq1aBYlEAgBQKpV46aWXEBoaismTJ+s4SyIyZizciIiIiAwE57gRERERGQgWbkREREQGgoUbERERkYFg4UZERERkIFi4ERERERkIFm5EREREBoKFGxEREZGBYOFGREREZCBYuBEREREZCBZuRERERAaChRsRERGRgWDhRkRERGQgWLgRERERGQgWbkREREQG4v8AHqbDapwKb0UAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": "34ea6351",
+ "id": "c21e4f8d",
"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"
+ "`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": "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"
- }
- },
+ "execution_count": 4,
+ "id": "ff2eba9a",
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Gaussian constraint params: []\n",
- "Student-t constraint params: ['degrees_of_freedom']\n"
+ "Gaussian : ['m', 'b']\n",
+ "Student-t: ['m', 'b', 'nu']\n"
]
}
],
"source": [
- "nu_param = rxmc.params.Parameter(\n",
- " \"degrees_of_freedom\", float, latex_name=r\"\\nu\", bounds=(1.0, 100.0)\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\") # uniform on its bounds\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",
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3aaaedd7",
+ "metadata": {},
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "69bbceda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s_gauss, s_t = fit(p_gauss, 1), fit(p_t, 2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "76f55c31",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = corner.corner(\n",
+ " s_gauss,\n",
+ " labels=[\"$m$\", \"$b$\"],\n",
+ " truths=[m_true, b_true],\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)],\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=plotstyle.COLOURS[1], label=\"Gaussian\"),\n",
+ " plt.Line2D([], [], color=plotstyle.COLOURS[0], label=\"Student-t\"),\n",
+ " ],\n",
+ " loc=\"upper right\",\n",
+ ")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d32705b6",
+ "metadata": {},
+ "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}, \"\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": 9,
+ "id": "fd2ad161",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "nu_col = p_t.columns(nu)\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",
+ " title=rf\"median $\\nu$ = {np.median(s_t[:, nu_col]):.1f}\",\n",
")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2484ac30",
+ "metadata": {},
+ "source": [
+ "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",
- "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])"
+ "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": "89342ff5",
+ "id": "6314776f",
"metadata": {},
"source": [
- "## Sampling $\\nu$ alongside the model\n",
+ "### $\\chi^2$ is the same for both\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"
+ "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": 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"
- }
- },
+ "execution_count": 10,
+ "id": "e7ea32cc",
+ "metadata": {},
"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"
+ "chi2 at the truth: Gaussian 322.62, Student-t 322.62\n"
]
}
],
"source": [
- "model_prior = stats.multivariate_normal(mean=[1.0, 0.5], cov=np.diag([0.3, 0.3]) ** 2)\n",
+ "theta = np.array([m_true, b_true])\n",
+ "print(\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": 11,
+ "id": "3ab4d1e4",
+ "metadata": {},
+ "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 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",
+ "def residual_band(problem, samples, levels=(5, 95)):\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",
- "\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",
- " )"
+ " return lo - y_true_fine, hi - y_true_fine"
]
},
{
"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"
- }
- },
+ "execution_count": 12,
+ "id": "22d9487d",
+ "metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -277,53 +373,120 @@
}
],
"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\");"
+ "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 = 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.errorbar(data.x, data.y - y_true_data, data.y_err, fmt=\"o\", ms=3, color=\"k\")\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": "883c01ed",
+ "id": "c86dc41f",
"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",
+ "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 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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5fc464d7",
+ "metadata": {},
+ "source": [
+ "## Rejecting the outliers instead (recipe 39)\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"
+ "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",
+ "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": 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"
+ "execution_count": 13,
+ "id": "26724634",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "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": 14,
+ "id": "60313e72",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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"
+ ]
}
- },
+ ],
+ "source": [
+ "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": "2353e732",
+ "metadata": {},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -331,84 +494,172 @@
}
],
"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}\");"
+ "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",
+ " 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=\"kept\",\n",
+ ")\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\")\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": "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": "fd330f6e",
+ "id": "afe6bf44",
"metadata": {},
"source": [
- "## `chi2`: the fit statistic without the normalization\n",
+ "## 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",
- "`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"
+ "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": 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"
- }
- },
+ "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": [
- "Gaussian chi2/n = 10.98\n",
- "Student-t chi2/n = 10.98\n"
+ "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": [
- "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}\")"
+ "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": "1ab2a3ba",
+ "id": "551b1d2e",
"metadata": {},
"source": [
- "## Predictive comparison"
+ "### 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": 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"
- }
- },
+ "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": {
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jwayKOjiIYjJjnTWLrfWb2LbhNXY2bOMjl3wKhzuX7mCctKZx7IxCTqopwmUbH8egTNrFtAabDUNOzsH99QGCUF9fH5/61Ke4/vrrueeeezjllFOora1lyZIlfOUrX6G+vn702mQySWVlJZWVlVRXV3PGGWfwwAMPjHnea6+9RmVlJaqqjr7m9/uprKyksbERGNnKduedd3L88cdz5JFHcvXVV9Pd3f2+7/X19XHqqaeO/rwv7WlsbKSyspLnnnuOSy65hLlz53Leeefx5ptv7vf/VkIIMdlEEmme2NLDf7b2YlAUqryu0ZCSCQ4S27CR5M6dGJzO0QWzWiKO6vdjzMvHfvQCrNNncPzZF3Hx1Tdy6x8ex2B30tQ/hMtm4sJ5U1k6t2TchBSYhCMq491f//pX4vE43/72t/f6vsn01h+ZxWJh9erVAKTTadatW8cNN9yAyWTi8ssvB0ZOFG5vb+fth2Srqkp7ezupXet0fv/73/PjH/+Ye++9l4qKCtatW8fXv/51/vKXv7zne++c+tmX9qRSKdrb21m+fDl33nknFRUV3HbbbZx//vk0NDTIQYZCCPEu2gaiPLejn47BGBX5Dhy7p3pUdWTBbOtO9FQKk8czsmBW01BDIUCnW1F5+rE/sPz7vwRAURQu/uyN9EUSdIfizK/I5+RaDwU5lndvQJZIUBlntm3bRmlpKfn5+e97raIoVFZWjv5cU1PDzp07uffee0eDwb7YvHkzJ598MmeffTYAs2fP5oorrnjf9z5Me37xi1+MPvNHP/oR5eXlNDU1MXv27H1utxBCTAYZVWNde5CXmgZIZNQxJx5rsehIhdmeXhSHA1ORBwA9lUINDmLMzYVp0/j18ksJ+vv415/v4qOf+zLJjEp7IEaew8x5R03hqLK80WeONxJUxplMJoPtHVNGjz/+ODfeeOPozw888AAnnHACAP/+97/57W9/S1tbG9FolOHhYQoL92+F9vnnn88FF1zAZz/7Wc4991yWLFlC3q7V4e/13t7sa3uOOOKI0d+XlJQAI1NSQggh3hKOpVnV4GNzV4h8u4UpeXZg14LZvn6STU2okQimggIUs3lk2/FQBD2ZwlxRgbW6GoPdwdXf+AEvPvkYSy+/moGhJIOxJDNL3CyZ6aXYfXDXa35Yk3aNynhVWVlJR0cHmUxm9LWPfOQjrF69moceeoj29nbi8TgAzz//PJdffjnnnHMODz74IKtWreJLX/oSyWRyvz7zzDPPZMOGDZSVlfGzn/2MkpISVqxY8b7vvdP+tMdo3PM0zrdPTwkhxGTX4h/mb+s62dARpCzPQZFr96nGKZL1DcQ3b0ZLJDB5vSMhJZNB9fvBYGBzuJdOLYVhV1XZ+Seczudv/SW9CQMpTeOsOSVcvKBs3IcUkKAy7lx00UVkMhnuvffe0ddycnKorKzcY3fN008/zZIlS7j++us56qijmD59Oj09PWOucblGtpYNDw+PvtbR0bHH586aNYtbbrmFF154gUcffZRbbrmFvr6+931vf9sjhBDivaVVjVeaB3h0fRf+4SQ1Xhd2y8g/7tRQkNjGjaRamjHkODDl56MoCunhCD2hHfQb+lnX28Jvf7aCu1bcSCw6BEAolqLFP0xlYQ6XLyxncVXR6Pk/493EaOUkctRRR3HTTTfx1a9+ld/97nfEYrHR9965M2bq1Kls2bKF/v5+AJ599lnuu+++MdfU1dXhcrlGg084HN5joe6PfvQjVq9ePbozqK+vD4vFgtPpfM/33mlf2iOEEOLdhWIp/r25h2e29WM3G6kszMFoGNlenOpoJ7ZhA2oggLHIg8FmR9dUMgN+4sm3aoOV5Rs55bQzOOmcj2Iy22gLRIkk0pxa5+XSheWUF2T37J79NWnXqGiJxLj9jDvuuIPZs2dz5513cu2111JcXEw8HmfKlCnccccdLF68GIBrrrmGZ555hoqKCpxOJ3l5eVx66aWsWbNm9FlOp5N77rmHa6+9lh/84AcYjUaWL1/Os88+O3rNaaedxte+9jXWrVuHxWIhJyeHBx98EKfT+Z7vDQwMjGn3vrRHCCHE3jX7hnm+vp/uYJxpBTmjoyhaPEaysYl0dzeK3Y7J4x15PRFHC4fJuHOIRPt4+1LYJSefhOe4C2kdjFNe4ODUOi/V3j3/gTkRKPoEXhgQiUTIzc0lHA7jdrvHvJdIJGhtbWX69OljFqdOlMq0uw0NDTE8PIzX693ruo7d18TjcbxeL8PDw4RCoT2miTRNIxAI4PF4UFWVzs5Opk6dOmY7cCwWI5FIUFBQsMdn7O29TCZDV1cXZWVlY7ZNv1d70uk03d3dlJeXj+lPW1sbJSUleywk3h/v9mcuhBDjWSqjsbY1wMstATKqTkWBY3QHTrq/n1RTI5lweNeCWQu6rqOGgqDrKKWl3P/Az/nIqSfu8VxD7SkcPbduXBVv2+29vr/fadKNqBhycsi//LIJc9aPy+UaXWeyL9c4nc69TssYDAY8npFta0ajccw24t0cDgcOx96HBPf2nslk2utz3qs9ZrN5r/fs7TUhhDjcDUZTrKzv583uCIU5Fgrzdy+YTZNsbSXV1gaAyVs8UmH2bduOLTW1mIq95JdOQ9O0d5xyrHDW0dUsrC7BME63He+rSRdUgNHKsUIIIUS2NPQNsbLBR18ozrTCHGzmXQtmw2GSjY1kfD4MbjcGu31kFCUSRk+mSBbm46iuwVw0MgV0ybVfw9e0kXj37srlCqedtZRjag+P400mZVARQgghsiWRVnmlJcDrrQHQoabYhUFR0DWNdHcXyeZm9EQCY1ERitE4etqx4rDTYVT57f99mTkLj+fz37sTAN1kJlY4E2VXUPn85z+P1+vJZhcPKAkqQgghxCHSG46zcoePhv4hStw28hwjJeu1RJxkUzPpri4UqxVj0chhgrtPOzaVlGCtqcHS2kgo4KNl+2aiQ2Hi2AhEk8yams+S078+Ieqi7C8JKkIIIcRBpmk6m7pCrGnwE46nmVHkHK1jkvH7STY2ooaCGPMLUCyWkW3HgZHTjo3VVdirqlGMJmqPXMiXf/R7aucdS29Mx2HVOHtuCUdPK5gwdVH21+HZKyGEEGKcGEqkefLNXp7Y3IOq61R7R0KKnkmTbG4ivmkj6vAwRo8XxWIZPe3YkJvHKz3NfOvrVxMJh0afN2PBSXQPaxOyeNsHcfj2TAghhMiy9kCUv63r5I3WQUpy7ZTm2kemdCIR4ps2kWhoBKsNU2EhKAqZ4CBaNIZlRhWWI+ay8unH8Pd28fw/HkDV9NHibadM0OJtH4RM/QghhBAH2JgTj9MqVV4nJoMBXdfIdHeTaGpGj8cxFRaiGw10D2wFoNgxA3tdHabiYhQUrl/xC7avf5XF532CJt8QFRO8eNsHIUFFCCGEOIBCsRQr631s7Q6T97YTj8cumLVg3FXbSo28VYD0yfXPUx4b5ISzLwKgrKoOpaCCQDTN4hmFnDgOi7cdbDL1M8lFo1Guu+46OTxQCCEOgGbfEA+v62RzZ4iyPAeeXSceZwb8xNdvIN3RjtHtxujOBVVFHfAT16Kj98+vm8b6//6N6FCYWCpDU/8QLpuJixZM5ey5JZMupMAkDSrRZIbBaOqQ/IomM/vdPlVV+fvf/863vvUtvv3tb/P444+PHgoI4Pf7ue666wgEAh/6f4t4PM7dd9/N4ODgh37W2+1rGw9kX4QQIltSGY01jX4eXd9FYChF9a4Tj0cXzG5824JZqxU1FiMTCKAU5DGkvvX3r8Fg4OyzzyQUz9AdijO/Ip+PH1PBnCm5KMrErjD7QU26s36iyQwPru0gFE0fkjbm5Zj5xDEV5Fj3bZYtHo9zyimnEA6HueKKK7DZbLzxxhts27aN9evXY7fbaW5upqamhtbW1g9den5gYACPx8PWrVuZO3fuh3rW2+1rGw9kX+SsHyFENgwMJ1lZ72NbTxiP00ZBzkhtFDUSIdnUSLqvH4PLhdHhQNc01OAgitFI1O1k4/bXqS7J3eOZOXNP47RFczmqLG/03J/DiZz18x6SGY1QNI3NbBgtV3ywJNIqoWiaZEYjx7pv9zz88MNs3ryZ3t7eMQcAtrS0YDQaicfjfPe73wXgW9/6Fi6Xizlz5nDWWWdxxx13cNddd42e9xCJRPja177Gd7/7XaZMmQJAKpXi3nvvpbGxkZqaGs4+++y9tuO5557jueeew2AwcMIJJ3DuueeOvtfT08Ott97KLbfcwr///W8aGhqYPn06V199NTab7V3b+IUvfGHMZ+zrdUIIMR7puk593xCr6n30RxJUFuZgNRtHFsz29IwsmI3FMBUWophMaMkkWiiEsSAfZdo0/vfai1GTMW666cuMHSxRuPi4OmZMzc9W18aVSTn1A2AzG8mxmg7qrw8ShAYGBrDZbOS84yyiqqoqLBYLRqORWbNmATB79mzmzZtHVVUVPT093H333WiaNnpPLBbbY1rnvPPO48c//jGFhYVs3LiRU045ZY82fP7zn+eaa67B4XDgcrn40pe+xDXXXDP6/uDgIHfffTcnnXQSW7Zsobi4mF//+tecf/75AO/axnfa1+uEEGK8SaRVVtb7+MfGboYSGWqKXVjNRrRkguT27cS3bgVVHVkwazSihkJoQ0NYKqdhX7AAx5SpnH/V5ykorcBUPOOtBysK55x7LjOmerPXuXFm0k39DEZT/OHFVvIc5n2ejvmgoskMoViaq0+aPjoU+H4aGhqYN28eRx11FFdddRWLFy/mqKOOGnMq5t6mS1avXs2SJUtIp9OYTCP96uvro7S0dHRa54knnuCSSy6hpaWFsrKRw6q+8Y1vcPvtt49e88wzz/Cxj32MxsZGioqKAOju7mbGjBm8+uqrLFiwgDfffJMjjjiC3/72t1x77bUAbNq0ifnz54+2SaZ+hBCHq/5Igud29NPQN7YMfiYwMFJhdjCIIS8Pg9WKnk6jBoMYnDl06ynyqmspnjoNGBmRGYhEGYxrTHMpzC0yMrNyyvtOhRwOZOpnAqurq2Pjxo3ccccd/OQnP6G1tZWCggJuvPFGvv3tb3+oxVSrVq3ihBNOGA0pAB//+Me5/fbbR3/+97//jcPh4JZbbkHXdXbnWKvVysaNG1mwYMHotWeeeebo73ePjHR2dn7owCGEEOORpum82RNmdYOPwehbZfB1NUOqo4NUy070TAajpwjFYEQdGhqplVJawvqORu750Tepmj2P7/zmb2Aw0jEYxWRQOLXOy+KqwoO+HGGikqAyDs2cOZN77rkHgP7+fu6++26++93vMnXqVD7zmc984Of6fD4KCwvHvLZ71GS3gYEBCgsL91hY+6Mf/YhjjjlmzGt2u33090bjruPJ37Y76Z3uuOMOGhsbAXA4HNxxxx373wkhhMiCWCrDmkY/69qCWM1GarxOFEVBiw6TaGwk09uHkuPAlJeHrqpkBvwoFgvW2bOxlJczs9iD1eagsHgKg5FhAikD0wocnDLJird9EBJUxrni4mK++93v8ve//50XX3yRz3zmM3sdVbFYRoYeU6nU6NRPKBQac01ZWRmrVq0a81pHR8eYn6dMmcL69eu57rrrPlS799bG6upqHI6Rcs9Wq/VdrxNCiPGkOxTn+e39NPuHmZpnx2Uzo6OT7u0h2dSENhzFmJ+PYjajxeNoQxGMRUUEHVbKdo0we6aU83/3P0nSWsiQpnN8VT4nVE++4m0fxKRdTDtevfLKK9TX1495raenh46ODmpqagDIzx9ZCR4MBkevqaqqQlEUXnvttdHX/vSnP415zoUXXsjatWtZu3YtMDI/+otf/GLMNR//+MdpaWnhd7/73ZjXX3zxxf2qdbK3Np5//vlcd911XHfddfy///f/3vU6IcThLZVKsWLFClasWEEqlcp2c96Vqumsbw/ytzc6aQtEqfI4R0JKKklyxw7im7egp9KjC2Yzg4No8ThKeTn3/ft+vr38MjqaR/4+j6UyBA15uO1mLp5fxllzJmfxtg9i0o6oJNLvPkWRzc9QVZVLL70Um81GXV0diUSCZ599loULF/KlL30JgIKCAk444YTRxbZHHHEEX/jCF1i+fDkXX3wxy5Yto729fXRkZbdjjz2W5cuXc/rpp7N06VLa29tHR2J2W7hwIXfddRdf/OIXuf/++5k2bRrbt2/H5XLx+OOP73M/3q2NH/Q6IcThKRKJ7DEFPR5EEmlW1/vY2BnCZTNT7XUBkAkGSTY2kBkYwJibh8FmQ0+lUENBjO5cLDU1GL0eYvdFUdUMzW9uwFQ0jWgqw4KKfE6p9ZC/j5srxIhJt+tnvBd8g5GRjo0bN7Jjxw4sFgszZ87kiCOOGHNNMpnk2Wefpbe3l6lTp3LOOecA8PLLL7Nz507q6uqYO3cuf/7zn7n88stHRy4AXn311dE6KvPnz+dPf/rTHtf4/X7WrFlDPB5n7ty5zJs3b/S9YDDIww8/zKc+9anRdSq6rnP33Xdz/vnnj9Zsebc2vtO+Xvd+ZNePEBPDG2+8wZNPPgmMTP8uW7ZszEL9bNvpH2ZlvY+OwRjl+Q5yrCZ0TSXV0UlqZwt6KoWxoAAUA9pQBD2ZQikpwVZTjdk5EmjCAT9dHW2YptRRkGPh5BoPRx6mxds+iP3Z9TPpggqMhJVkRuNQsJoMB30btBghQUWI8S8SifCzn/2Mt3/1KIrCjTfemPVtuWlV443WQV5uDpBSVSoKcjAaFLRYdGTBbE8vit2O0eVCz2RQBwdRHHZCOVZ+95sfctTiJVzyuZsA8A0lCMXSzJ7i5tQ6D16X/J30drI9+X2MFGTLdiuEEGLyCQQCvPPfx7quMzg4mNWgEhgtgx+hIMfC1Hz7yILZ/j6SjU2okQimgnwUswU1OowejWIqKcE8YzqJ1/7JJy+7iJ//6i5Ov/T/MZAaKSh61pxiFlYWYDbKctAPY1IGFSGEENlRWFiIoih7jKi8/ciQQ2lMGfyhJNMKHNjMRvR0iuTOnaTa2sGgYPJ6QddGth2bzFhnzsQybdqYfnzpJ3+mP2GgpjiHU+u8lOU7stKnw43EPCGEEIeM2+1m6dKloz/vXqOSjdGURFrl+R39/GPDrjL4Xic2sxE1FCS2cSOp5mYMDgem/AL0ZALV78eYl89OQ5o7f34L6XSGSHfzWw9se41Z5gAfPbpcQsoBJCMqQgghDqlFixZRV1fH4OAgBQUFWQkp3aE4K3f00+QbpjTXTq7dPLJgtrOLZHMLeiqJscgDBgOZ4CBoOpYZVShlU7jnY6cTCvh5+qF7mFniGvPcxnUvkDpxPjbz4V8G/1CRoCKEEOKQc7vdWQkoGVVjU2eIF5sGGEqkqfI4MRsNaPEYycYm0t3dKHY7piIPeipFZsCP0Z2LtbYGU3ExCgqf+9aPWbfmGeqO+wi0vTbm+eNhvc3hRoKKEEKISSEUS7G6wc/mrhDut9VGSff3k2pqRA1HMBbkg8mMGomgJ5OYp5bx0o51THPbqCsuAaBqwYnYZxxNiUOnv12BcbLe5nAlQUUIIcRhr6l/iJX1PnpCcSoKcrBbjOjpNMnWVlJtbQAYvV5QVVS/H8VuxzZ3Ds+/9Ax/+fn38ZSW8X/3/5dAykhG0zh2RiEn1RRRn7d0j5owMppyYElQEUIIcdhKZTRebRng1Z0BdB1qil0YFAU1HCbZ2EjG14/B5cbgcKDGYmhDQ5iLvVhrazG6cznl/MtZ+c+/ctJ5H6dzSKU0z8optV5mlbpQFGVcrLc53I2LoJJOp7nqqqvo7e3l73//Ox6PJ9tNGiOVSnHbbbcBcPPNN+9Rdv5QfP6TTz7J6aefjsvlev8bhBBCMDBaGyWMx2mjIMeCrmmkujpJNjejJxIYC4tGFswGAihGA4bplWzpauY49yIAbA4nX/jVP0jrBo4qy+XkWg95jrHfAdlabzNZjIug8s1vfpNNmzZRX19PMpnMdnPGnUgkwkUXXcTWrVuZO3fuPt0TjUZ59tlnOfPMM0dPLBZCiMlA13V29A6xusFHfyRBZWEOVrMRLREn2dRMuqsLxWpFKSykZ3A7AJ6cSszTq7n1a5+ha2cjNqeLuoWn0BGM4XFaObnWw9wpuRikBP4hl/U6Kk8//TT//Oc/R0csxIHR29vLRRddhM/ny3ZThBDikNldG+XxjbtqoxS7sJqNZPx+4us3kO5ox5ibi8HlIhrqHb3PH20jQ5y5i04kt9BDOKnTFYpxVFkuHz+mgiPL8iSkZElWR1T6+vr4zGc+w2OPPcbQ0FA2mzLu7Ny5k+bmZmpqavaY7tE0jX/9618AmEwmKisrmT17NgbDSO5Mp9M899xzADzzzDN4vV6Ki4s59thj3/M+IYSYyHrDcZ7f4aOpf4iS3bVRMmmSbW2kWlvRNR2jxwuaRsrfx5AhOOZ+35uvcN5V1zPn3E9RVlrCybUejpyai0lK4GdV1oKKpml88pOf5POf/zzHHnvs6Bfre0kmk2OmhiKRyMFs4l4diiPJf/CDH3DrrbeyaNEienp6OPLII8e8r6oq9913HzASSjZv3szUqVP5z3/+Q1FREalUikcffRSARx55BIfDwYIFC1i4cOF73ieEEBORpuls6Q6zusFHOJZmepETi8mAGomQbGwg3e/D4HJhcjh2ndMTgwI3hAbe8SQdXzDMMbOqObXWg9ctBwmOB1kLKj/84Q+Jx+PcfPPN+3zPbbfdxooVKw5iq/Zu8+bNo7+/6667DuqR5Nu2beM73/kOTz75JGeddRaZTIYLLrhgzDVms5nHH3989Od0Os0555zD//3f/3HnnXeSk5PDb37zG2pqarjnnnuorKwcvfa97hNCiIkmmszwQqOf9W1B7BYj1V4noJPu6iTR3Iwei2MqLASDQiYwgGI0YZ1Zh6G4iIEXd77jaQpLjqri+FlT5SDBcSRrQeV3v/sdDoeD008/HYBgcGQI7tJLL+WKK67ghhtu2OOem2++mS9/+cujP0ciEcrLyw9qOyORCE899dToz7qu88QTT1BdXX1QVnk/9NBDzJs3j7POOgsYmaL52te+NrpP/+2amppoa2sjGo1SWVnJq6++uk+f8UHvE0KI8aRzMMZzO/rZ6Y9Slm/HZTO/Y8GsBaPHg55MooZDmAqLoKIM65QyADyzjsO/Y3dlWYWTTj+LU+ZWZK9DYq+yFlT++te/kkgkRn9ev349X/nKV/jyl7/M/Pnz93qP1WrFarUeqiYCh/5I8vb2dmbMmDHmtaqqqjE/x2Ixzj//fNatW8f8+fNxu910dHQQCoXe89kf9D4hhBhPVE1nU2eQ1Q1+osmRwwRNRgOZAT/JhkbUUBBjXj5YLKihEGga5sppPP3Kczz/f3/lf+97AmdeEQPW0tFnfu6665hS7M1ep8S7ylpQOe6448b8nMlkAFi8eDFlZWXZaNJeHeojyQsKCti+ffuY194ZJO655x7a29vp7u4mJycHgNtvv53f/OY37/nsD3qfEEKMF5FEmtX1PjZ2vlUGX8+kSTa3jF0wq2bI+H0YXW6sNTXoBXms/eHXCPr7ee5fjzB36ZVUedwsueGrVBRKCYfxbFzUURnPdh9JfqhKJB9//PH87ne/IxAIUFhYCMA///nPMdf09PRQVVU1Gjb2ds3u996++Hhf7hNCiPGqbSDK8/X9tAdilOc7yLGa9r5gdmgIPR7HPHUqtpoaDI6Rv/OWr/g569ev46hTz2fR9AJOrCnCYZGvwfFO0d85r5ElwWCQzZs3s3jx4n2e3olEIuTm5hIOh/cIDolEgtbWVqZPn47N9uFWbr+9Mu31119/UHfIaJrGMcccg6Zp3HDDDbS1tXH33Xfj8/lGC76tXr2aM844g29+85vU1NTw8MMPs2bNGgoKCmjbdWaFpmlUVFRw5plnsmzZMkpLS0kmk+9730R2IP/MhRDjR0bVWNce5KWmARJplWmFORgUnUxPD4mmJvRYHOOuUW41OIhitaGWFvPAX3/LkYuXcNLSi4mlMnQOxih221gy08vMkpES+CI73uv7+53GzbLm/Px8Tj311EO+BmV/HewyyQaDgeeff57zzjuPZ599FoAXX3yRCy64YPSzTz31VJ588km6u7t56qmnOPPMM3nwwQc588wzxzznySefxOFw8MADD/Dss8/u031CCDGehGNp/r25l6e39WE2GpjhcaKkksS2bqVt67P0JdpQigrRUinUQABTkQfH0Ufz6qZXeeWZf3H/HbfQ2uunOxRnfkU+HzumglmlbgkpE8i4GVH5ILIxopKNs37EvpERFSEOL82+YVbW99MVjDNt14nHmYEBko0NZIKD+IwjdVAK8WA2WrBMn46lshLFZEZTVe669X+YedrFHLngGE6u9XBUWR5GqS47LuzPiIpMzgkhhBhXUhmNta0BXm4JkFF1arwuDLpKsmXXgllVJeEyQ2zk+gB+LM4yXnryYT5+wzdRgIFYmjOXf59ZpS6W1HmleNsEJkFlH1gsFr73ve9luxlCCHHYG4ymeH5HP9t6IhTmWCjMt6INDxFvbCTT14eS4wSng0ioYcx9iWAHLz3xMPnFU6ld8lFyrCbOmlPM0dMKsJjGzSoH8QFIUBFCCDEuNPQNsbK+n75wgmmFOVhNCumeHpJNTWjRKMaCQtB14oP9e6ywNBgMHHHMiXjmnMAMj5NT6zyU5cu248OBBBUhhBBZlcyovNYS4JWdARQdaopdKOkUyR0tpDo6UEwmjB4PWjSKHothLS5F9wcZu9pEYdn1t3L87AqOmV6AzWzMUm/EgSbjYUIIIbLGN5TgHxu6WVnvw20zU1GYgxYKEtuwkWTrTgxOJwa3CzUwAJqKddYs1rY38Z8nnhh9ho5C4ezFfPykmZxc65GQcpg57EdUJvCmJrGf5M9aiIlD13W290ZYVe/DP5yksigHiwGSbW2kdragp1KYPB70VArV78dY5MFaW4Mpv4AjbWYeuuuHOKbWccZHP83C2TM4de407BYJKIejwzaomM1mYOR8G7vdnuXWiEMhFhvZArD7z14IMT4l0iovNfl5vXUQs9FAjdcF8djIgtmeXhS7HWNhEWooCJqOefoM+klTmT9S1M2SV8w1v/wXc6uncWqdhyqPM8s9EgfTYRtUjEYjeXl5+Hw+ABwOhxT4OUzpuk4sFsPn85GXl4fRKP+qEmK86gsneG5HP439Q5Tm2nHbTWT6+kk2NaJGhjAV5IMOGV8/RncuVJTxy5+vYNsbL3PL7x9HKaxEUWDpopmcUF1EjvWw/RoTuxzWf8IlJSUAo2FFHN7y8vJG/8yFEOOLpum82RNmZb2PcCzNjCInZj1Dsr6BVHs7GAyYvF60oSH0ZBJLWTnWmmoUuwOjYeQfHxs2b+XsC+o4pc5Djdcp//icJA7byrRvp6oq6XT6ELZMHGpms1lGUoQYp2KpDGsa/axrC2IzGynNtaGGQiQbG1EH/Bhyc1FM5pFzemw2KCvDNq0Cs8WGpus0tHUTCvhZevIxnFhThNsm07sTnVSmfQej0ShfYkIIkQXdoTjPb++nyT9MWZ4dp8VAuqOdZMvIglljkQctmUQNBDAXe+kzavzmO5/nmCVLOff/3UjHYIwpJV4+dvIcZssZPZPSpAgqQgghDi1N09nUFeKFBj9DiTTVHifGZJz4lrctmC0oRA0GUYwGbHV1WCqn4Vv9ND1tzaz699+ZdfYnOaZmCqfUesjPkTPWJisJKkIIIQ6ooUSaFxr9bGgP4rSaqfI6yfT1E2tqQo1EMBXko2v6yLbjgnws1TWYPR4AFpx6Dudc8y0Wf2QZSxdVM69cDhKc7CSoCCGEOGA6AjGe29FPeyDK1HwHOQZt7IJZjwctEkHPZLBMr2RzVzNPf/sOvv7z+xnKGAhEk1x59TUsqfNSkisHCQoJKkIIIQ6AjKqxoSPEmiY/8ZRKldcJ4TCxpkZU/64Fs0YT6sAABqcT2+zZZHJd/PlrnyY04OPBe3/LWZ+4jjNnF7OwsgCrSdYVihESVIQQQnwo4XiaVfU+NneGyLWbmVFoJ93VSbK5+a0Fs7EYWmQI05RSbDU1GHKcmIFPfPWHbFr7Mh//zOf5yBFllBfIQYJiLAkqQgghPrCd/mGer/fRORijosCBLZMkvrWeTE8Pis2OMb9gZNuxxYJlVh3/XfUf6vQUNUcuomMwxrQjF3PZBedy7IxCOaNH7JUEFSGEEPstrWq80TrIS80DpFWNGq8Lze8bmeoJhzEVFKCnM6gDfkweD9baWv7zr7/y93t+Sr53Ctf84h/UlXk4tc7L9KKcbHdHjGMSVIQQQuyXYDTFygYfWzvDFDgtTHWaSTY3kmprBwVMHg9qMASKgqWmFuv0ShSzhdMu+iRrnv4XJ174KU6fW84JNR4cFvkaEu9N/gsRQgixzxr7h3i+3kdfKM60whzMsWHimxrI+PwY3G4Ug2Fk23FeHpnSEtbu2MTJtbUMJdJ0R3W+94d/cfqsYqq9rmx3RUwQElSEEEK8r0Ra5fWdAV7ZGQAdqj05qN3dxJqb0ZMJjIWFaNEoWjqFuWIa+tRSvnf1Bfh7u4gb7NQsPIXFMwo5saYIl5TAF/tBgooQQoj35BtK8PwOH/W9EbwuG7kGleT2baS7ulCstpEFs4ODKA4H9plHYJoyBUUxMO+ks1j3wtN4PUVcOH8qc6ZICXyx/ybFoYRCCCH2n67rbOuJsKrBx8BwkumFTgzBgZHDBEMhjPkFaOkUejSGqaSYoN1C3tQKcty59EcSDEai1HlsnHN0FQVSAl+8jRxKKIQQ4kOJp1RebPKztnUQq8lIdYGNdGszibY20HSMhUWooSCKyYx1Zh0bW7Zxz/99jaOOP42lX/ohuXYLFxw9jXnleZiMhmx3R0xgElSEEEKM0ROK89z2fpr9w0zJtZOTjpPc/CaZfh8GlwsMCmoggLGwEGttDaaCQjyJYdLpND6/nzKXgaXzy5maZ892V8RhQIKKEEII4K0Tj9c0+Akn0swocqD09RJrakaPxzEWFqIODYGmYZkxg4y3EFNBIaqmY/RWc82P7+eSs07lxBqPFG8TB4wEFSGEEEQSaVbX+9jUGSLHamKGy0Sqfgfpzi4Uq2Vku3EggNHtxlA5jUce+xMv//cffPsPTzBkcFKWb+fST5xLTbFsOxYHlgQVIYSY5Fr8w6zcVQa/PN+BdShE4s0G1FAQY14+WjKJGg5hLpuKraYG1WRi+/pXiQQDvPjsk1x73ec5saaIXLtsOxYHnuz6EUKISSqZUXl95yCv7gygajplbgtqezuptlZ0VcPodo8smLXasFRVYS6bCrpOy3N/AaDf6GX5//sEs0vdGAyy7VjsO9n1I4QQ4j35hhKs3OFjR18Ej9NGrpYguXUL6d4+DE4nBouCOhjA5C0m4Sngtz//Hsecdg61x58z+owvX3UB1VNzs9gLMRnInjEhhJhENE1na1eYh9/oZEdfhGkFdlwhP7ENG8j09WMqyEdPJNCSCSw1tdjnzeO1V1ay6ZVV/OUXPyDR2zD6rAfv+z0bNmzIYm/EZCAjKkIIMUlEkxnWNPpZ3x7EajZSlWsm09hAvLMTxWzBmJuLOjiIMS9/ZNuxxwvAwnOuoL6xkUs//kmSbW8FE13XeeKJJ6iurpbpd3HQyIiKEEJMAu2BKA+9vpO1j92Duv7veJJhkhs3kmxrw+AaCRnqUARzxTT63Tbu/+PPSaYzNPuGyCgKv/zVr7n4+Nm8c1mjrusMDg5mo0tikpARFSGEOIylVY11bYO83BwgnkiOvj68eSMmXcFUUIAaDI2e05PKdXHbRScSG4pgLqrgY5/6HEtmeikvcBCJWFAUZUxYURSFgoKCbHRNTBIyoiKEEIepwHCSf23q5uk3+7EYDeRHWt96T+sjbkqjBoOYSopxHL0A89QyrA4XZ1x1E3OOP4NrP30lly0qp7zAAYDb7Wbp0qWjz1AUhWXLlsm0jzioJKgIIcRhRtd1tvdEePiNTrZ0RygvsJET6CKwc9OY6yLpAYwzprN1sBd/MEAknqbZN8x5l3+Sx//xGBcsnrVHhdmjjjpq9PfLly9nwYIFh6JLYhKTOipCCHEYiadUXmrys7ZtEJPBQKldId3SQrSrmSDBPa7vHlL53U+/T1ntEXzuR/dzTJWXE2s8OK2yMkAcPFJHRQghJqGuYIyVO3w0+4cpzbWTEw2R3NCIGgpidudB9J1BRWHG0Sdjd+Ux95gTuWhBGXPLClAUKd4mxg8JKkIIMcFlVI2NnSHWNPqJJjPMyLeid7YRb21F13SMeflkQiHc9iIiqYFddylYZiwk7ZrC7//zMuceXUOuQ0rgi/FHgooQQkxgoViKVfV+tnSFcNvNTLdpJLduIdPvQ3G5UFQVNRLGXDYVQ7GHyKZnAOh3VjF7ag0n13qYOyVXSuCLcUuCihBCTFANfUOsavDRE4pTkWfH5O8l1tyMnkhgzM9HDYdRrFZsc+ZgLitDR+GRZ16iceOr3PS/P+djx1RQ5LRmuxtCvCcJKkIIMc6lUiluu+02AG6++WY0xcgrLQFebw2ADtVuI+nG7SS6e1CsVgw5TtTgICaPh8zUUgzeEtKaQnsgyuU3fZ8jS6ycs/goTEbZ+CnGPwkqQggxgbT0+Fjfp9LYP0Sx24pzaNeC2XAEQ14e2vAwuprBUlNL21CAu67/GEcvOY/jLr+e2hIXp82cQWmuPdvdEGKfSZwWQohxbvPmzaO//9uf/kDLjq1Mz7Ng72wlvnkTajyOITcXLRzC6HTimDcfW20toVCQQF83G154ihMqXVyyoExCiphwZERFCCHGsUgkwlNPPTX2xY4NJCMRCIQwuHPRUym04SHMFRWYZ8zA5MhhOJkhf86JfPobt/Olz32SeTNKs9MBIT4kGVERQohxbEtz5x4HAYJOKjSAMS8fbWgIjEZsc4/g1ZatfO/zl7Gzd4D+SIKFlfn87Ls3SUgRE5qMqAghxDgUT6m8ujPAa52Jvb5vsjlRw2FMJcXYamtJKPDo735GaKCfTc88yve++TXmTHHLtmMx4UlQEUKIcaZzMMaq+pEKs8VuN9rUWQx27xh9300uxgxYZs3EUlEBBiPhSILzv/R/MNDKT7//bYpctiz2QIgDR4KKEEKME2lVY13bIK+0BEYqzOZa0Np2kuoJUWQoQbMYUKIJrIXFKJUV/P2h3zP3+NOxTzuCghwLX7rqYo4qy8MooyjiMCJBRQghxgHfUILVDX6290TId1ioNGdIbtmGOjCAITcX4gmMSRXLjBqsVVU89uff8NRDf+Dl5/7Dr/+xhjOPLMfrllEUcfiRoCKEEFmkaTpbusOsafQxGE1RnmfD2NdDvKUFPZXCkJePFg5jcDmx1tRgKikhndGpO+PjVL6ymi98+atcvrgKsxRvE4cpCSpCCJEl4Via1Y0+tnSFsJtNVLmMJOu3k+jpQbHZUaw2tHAY05RSkoUFvPrSsyw4+3IGoknmVHp5+eWXmJLnyHY3hDioJKgIIUQWNPUPsbLBR3cwTnm+A2s4QOLNJjLhEMbcPLThIRSLBduc2aTzc/nmx84gMjiA7sjnc5+8jIWVBVhNxmx3Q4iDToKKEEIcQom0yus7A7y6M4CmQXWBjUz7TmJtbaCDweVGi4QxFhVhranFlJ9PLJZizsnn0fXmq1x55rGcUO3JdjeEOGQkqAghxCHSH0nw3I5+mvqH8DhtuNUEyS07yPj6UXKc6MkkeiKBpaqK3kycfEUnFIiiKPDdW1Zw3IxC8lwy1SMmFwkqQghxkOm6zraeCCvrfQSjKablOzD09xJrbkaPx3eNokQw5rqx1NTy8hurufeH36Jm0an8z+2/4dQ6L9VeV7a7IURWSFARQoiDKJFWebl5gNd2BrAYjcxwG0k17CDZ1YVitaDYrCPn9JSXYa2pQbfaMRdNR9d1CuxGLjyymKJcZ7a7IUTWSFARQoiDZGA4yXPb+6nvi+B12XBGQyQ2NKKGQiOjKNFhFJsd29y5RG1WMoqZHt8Q8+Yv4B/PrGHZqcdiMMi2YzG5SVARQogDTNd1GvqHWLnDR39wGMPWf+IHNMWLCROK04k2PITJ64XyMu695yesX/Ms1//qcU6ZV8dJtUW4bTOy3Q0hxoVxEVR0XUdRpOSzEGLi2z3V83rrIAagaKiVwV3vBXQfbnJxpBSstbVYKisZTmZo3rGN+HCEvGA9S+eeIgcJCvE2WRtTVFWVP/7xj8yfPx+bzUZRURGf+tSn8Pl82WqSEGKcSKVSrFixghUrVpBKpbLdnH3WG47zyPpO1jT6ybUZKQp1M9i2Zcw1EcIYZ9ZgqaqmP6bSF83wzR//ln899Szf/NJ1ElKEeIesBZX6+no2btzIX/7yF2KxGK+++irbtm3jyiuvzFaThBDjUCQSyXYT3peq6WzoCPLw2k5afMNUOg3YmhuINWzf6/X3/eY2/vbAn1CAZUeWct0FJ7LsrNMPbaOFmCAUXdf1bDditzvuuIPvf//7BIPBfbo+EomQm5tLOBzG7XYf5NYJIQ6VN954gyeffBIARVFYtmwZCxYsyHKr9i4cT/NCg49NnSFyrCYK42HSzU1kwmHIceCLt8PbBkl0XefOO+/EaLHzxtYdVJXkZ6/xQmTJ/nx/Z305eTqdJhaLsWnTJv785z9zxRVXZLtJQogsikQiPPXUU6M/67rOE088MS5HVlr8w/xtXSfr2oOU5JjI7+8ksWULaiyOwZGDEkuQ65rytjsUjJULOeviT/DqKy9LSBFiH2R9Me0FF1zAs88+SyaT4ayzzuL2229/12uTySTJZHL05/H4F5cQ4sMJBAK8c6BX13UGBwfHzchpKqONlsFPqxozbBqZ7W+S8vlQnDno8TjoRqyzZtEd6MY0PHJf4bHn8ZF51Xz7U+fKBgIh9lHWR1SefPJJUqkU27dvJxQKce655+7xl9Rut912G7m5uaO/ysvLD3FrhRAHW2Fh4R5f4oqiUFBQkKUWjeUbSvDYhi6eq+/HZjJQFg+S3LiRjN+PkpODPjyMMb8A+/wFpArz+fFXr+GWW27Bl7byyVPmUlfikpAixH4YV2tU1qxZwymnnEJ9fT11dXV7vL+3EZXy8nJZoyLEYWY8rlHRNJ03e8KsbvATiCapyDFC607SXV0oFgu6qgJgmVaBdcYMYpqBzmCMnS/9i2jndu67+y5yc+XvKSFg/9aoZH3q5+3i8TgARuPejy63Wq1YrdZD2SQhRBYsWrSIuro6BgcHKSgoyPo/RIaTGdY0+tnQHsRqNjLdkCC9uQk1HEJx5KBHhzHm5mGurubF11dSkFJxl0zj6Gn5fPkjXyHPYclq+4WYyLIWVH72s5+h6zrnnnsuRUVFbNq0iRtvvJFTTz2V6urqbDVLCDFOuN3urAeUVCrFbbfdBoA680ymegqx9HaRbGtFVzUUixU9HsdcUYG1uoZH/3IPj//hDspr5/KPp55nfqVH6qII8SFlbY3K1VdfTTAY5KKLLqKmpoabbrqJj33sY/zrX//KVpOEEGJUKqPxj+deHv3ZWP8MiY1rSDQ2gMEIqgpGI/a5c7HNmYsvrTDjhHPJK/Tw2U9dyfzKIgkpQhwA42qNyv6SOipCiINhYDjJMxtbaVz5MGOihg4ey1SURBpzsRelspKG5nqcM+bhspk5ubaImgILbpecdizEe5mwa1SEECKbNE1ne2+ENY1++rt79hxyViCjJnHVzSJdkMetN1xBb3szK37/Dz520emU5tqz0WwhDmsSVIQQAhhKpHmxaYANHUEsRgPlTivde7nOOXceppIyugNR8ksriIb8HF1qkZAixEEiQUUIMent9A+zqsFHeyDGFJcVq6+HVEsLLlwMMTR6nbNsNgl3IW2+IcrzHfzxD/dQnmejuLg4i60X4vAmQUUIMWklMyqvtQR4rXWQjKoxI0ch07iNZG8fisWCXbMzZBgJKsOOEn785WuZ/5GL+fp3b+XkWg9umznLPRDi8CdBRQgxKfmHkjy/o58dvUMUuSy4h8IktzehDQ+PFHBLpTCXlFBVezIpq4On//MvopEgAw1vcHpNAXYJKUIcEhJUhBCTiq6PLJhdXe/HP5xgmtsC7TuJd7SDYoBdW4qtM+swTJ2CP6kQHozx0Usu5szZHq687BIpPCnEISRBRQgxaSTSKi81+Xm9dRCz0UClOUN66w7UAT+KzY6WSGAqyMdcU8NTTz7Cqice4cu//BvnHjmNeeV5mBd+IttdEGLSkaAihJgUekJxVtb309g/TInTgt3fS7J1J3oqBWYLejqNdXollqpquoIR/vvwH4kE+qF5DYvO/1K2my/EpCVBRQhxWFM1nc1dIdY0+Akn0kx3KKhN20cXzKLpGOwWrDW16F4vrYNx7DYn//vz32KO+rn2c5/NdheEmNQkqAghDlvheJoXGnxs6gyRYzUxLRMhtallzIJZU0kJetkUfnvXj6hefDYfOeMMTq3zUlG45wnuQohDT4KKEOKwtNM/zPP1PjoHY5Q5TZg6Wkl0dICivLVgdtZMDGXl/PFXP+K1/z7Kzs2v8cubPkGey5Hl1gshdpOgIoQ4rKRVjXVtg7zYNEBa1ZhuSpF5czvJwAAGqw0tmcBUWIS1tpaYw0VPIM5lV38Btb+F/1vxXQkpQowzElSEEIeNUCzFynofW7rC5NuMeIL9pHa2omfSKCYzuprBOmMGQZuZlY8+yOILP83xVYWcWONh+Ueey3bzhRB7IUFFCHFYaOofYmW9j+5QnDKLhqGlkWRf/8haFFXD6M7BWlND0GjiW5cvIRkb4iPHzuWscz+Joijv/wFCiKyQoCKEmNAS6d1l8AOomkZlJky6voVMLAZm80iF2alTsNXWMqAaCcXSnHPZJ+mp38iFHzlZQooQ45wEFSHEhNUXTvB8fT9N/UMUmsHR106qqwuMRlBAMRqw1s6mwdeN1hPA6/FwzhGlfP3MOzCbjJhM8legEOOd/L9UCDHhjNZGafQTjqUp0+PoO5pJh4IoVht6MoHR48FWW8sTTz7G3375fY4+6Uwee+xRKgpzst18IcR+kKAihJhQwrE0qxt9bO4MkWOEslAPmY52dFUDkxld07DU1GCurKR7KIOn+khMRhNzq6ZS4pKDBIWYaCSoCCEmBF3XaewfZnXDyILZKcY0pp07Sfv6UWx2SKUw5uZhrq6mL5EgPpikJNfGBR/9CF86exs1NTXZ7oIQ4gOQoCKEGPfiKZWXm/280RZE1zWmJYNkWlrIJBNgsY4smK2oQC0p4ac/+AbNW97gF397jo8uqiU/xwK4s90FIcQHJEFFCDGudQVjrNzho8U/jMesY+tuJd3dA2Yz6KCYTNhmzkQvKaXDHyE86COTipMTbuYXP3kSgJtvvhmLxZLlngghPgjD/t7w5JNP8uc//5lYLHYw2iOEEABkVI032gZ5+I1OWgNRyrUolu1bSHd1v3VOj9eD5cgjiRYW0xqIUVWcxwMPPMAba9fy0UsuGX1WJBLJYk+EEB/GfgeVVCrFF7/4RUpLS7n22mt5/fXXD0a7hBCTWCiW4oktvTy5pRdDJk15oIvM1i1osRiK0QDo2Orq8LvsfPP6j/HMYw9wUk0Rly4s56Sjj2DevHls3rx59Hl33XUXGzZsyF6HhBAf2H4HlQsvvJDe3l5+/etf09TUxOLFi5kzZw4//elP8fl8B6ONQohJpKFviIfe6GRjR5ASkjibtpPc2YJiMkEmgzE3F/u8+agVlax+YTV9bY2s++cfOakqH7vFCIyMoDz11FOjz9R1nSeeeEJGVoSYgPY7qADY7XauvPJKVq5cSUtLCx/96Ef55S9/SVlZGRdffDFPP/30gW6nEOIwl0irrKz38diGLgaH4lTE/LB1E2ooiGK2oKfTWKZXYps/nwGLk+5QnGuXf4Gv3fwt1r/xOlardfRZgUAAXdfHPF/XdQYHBw91t4QQH9IHCipvpyjKaAlqm81GNBrlggsu4IQTTpC/FIQQ+6QnFOeR9V2sbvDh0lJ4O5tJ1zeMvKnpGKxW7EceyWZfFz/85hfRVZVlR5Zywfwybv/B/1JcXDzmeYWFhXuUxlcUhYKCgkPVJSHEAfKBgko8HueBBx7g9NNPZ8aMGTz99NN85zvfobe3l6effprOzk4MBgP33nvvgW6vEOIwomo669tHFsy2+IYoT0Uwb99Mpq/vrQWzJSU4jj6aPgz89pYb2f7KM2gNz3P0tAKMhr2f0+N2u1m6dOnoz4qisGzZMtxu2aYsxESz39uTH3/8cT796U9jMpm48sor+cUvfsGcOXPGXOPxeDj//PPp7+8/YA0VQhxewvE0qxt8bOoMkaNoTPV1kOnedU7PrgBinTUTY1k57eEUJpuLm757Gwl/G1+87nPv+/xFixZRV1fH4OAgBQUFElKEmKAU/Z0Tue/jqaeeIhQKcfHFF4+ZE34nVVXRdf2gHvoViUTIzc0lHA7LX0JCTCDNvmFWNfjoHIwxRY9j3Nk8shbFbkeLxzEVFmGumsG///MI3jnHs+CouSyp81LtdWa76UKIA2B/vr/3O0W8fTj1vRiNxv19tBDiMJfKaLy2M8CrOwNkUhmmDfWRaWtD1TSwWNDTGazTZ2CpmsEf7/opKx++hxl1c/jfN9ZS6HJku/lCiCyQyrRCiEOiP5JgVb2PHX0RCkiT07mTtM+HYrNBOoPBbsdaU4Pq8dIUiHHqRVey4+WnuPkrN1HgtGe7+UKILJGgIoQ4qFRNZ3NXiDWNfkLDSaYmw+itLWTicRSrDT2RxFRagl42lVc2b6TkiOOZVeLmtJnVfP2iZil9L8QkJ0FFCHHQhGIpVjf42dwVIgeVMn8nme4uFLN5ZMGsAtZZs4g6c/ju5y4m7O/lNw/9h4sXzMZi+tDVE4QQhwEJKkKIA07XdXb0DvFCo4++cIJSLYaxtZlMKIRid6An4hgLi7DW1hK3O+kPxqiafQS9TQpHlrklpAghRklQEUIcUJFEmpeaBtjQHsSERnmkF7W9HVXTYXeF2RlVhG1mBtIG1EySxVVFXPPAfbjtFnJzc7PdBSHEOCJBRQhxQOi6TmP/MGsa/XQFYxQbM1jaW8j0+1DsNvR0AqPLjbW2htc2vc69P7yZE5Zdzs/u+Clzp+RieJfibUKIyU2CihDiQ4smM7zSPMC69iBaOoVh0+P4Aa/uxWC1oieTmKdOxVZTQ0g3M5iEVDxKvLuBWcU5ElKEEO9KgooQ4kNpG4iyqsFH60CUEouOubeVnl3vqagYDAZsc+aQKSykIw5GQ4brP/UxPnFcJcvOOUdqLgkh3pMEFSHEB5LMqKzdOcirOwOkMhrT9CjqtmYiQ32jp4gFlAAFpVN4/J8PsnbVf7nl3n+xdP70kQqztedltwNCiAlBgooQYr/1hROsrO+nvm+IIqtC0UAXqY4OVDSGDcNjrh1sf5MtLz3LYE8bpo61VJ91RJZaLYSYiCSoCCH2WUbV2NwV4sXGAcLxNBXGFHpDM6nAAIrdgRoPwx4zOTrfvOV/8eaYuPTSj2aj2UKICUyCihBinwSjKV5oHCne5jIrlA/3k25tQ8+kRyrMJhNYS8vAFxpzn6IofPLic+TgUCHEByJVlYQQ70nXdbb1hPnr2g42dgSZYlZxtzaQamgEgwF0wGDANmcujqPmU98dHL1XURSWLVsmIUUI8YHJiIoQ4l0N7Sretr49iMkAlZkIqYZm9GgMxW5Hj0UxeTxY6+pI2V3s9Ec57pLP8uxvvseNN3yeY445RkKKEOJDkaAihNirpv4hVjf66RyMUeowYO1sI9nZiWIyg8k0UmG2pgaflmL1A79n8SXXMr88nyUzPXzprEez3XwhxGFCgooQYox4SuWVlgHWtg2iaTDdkCCztYlUKISSk4MWHcaYm4u1ppawEb57ySmkk3GWHLuA8869CpNRZpSFEAeOBBUhxKjOwRgr6320+Icpdphw+LpJtbaiazqKzYYej2EpK8daW8MQZgLhOGd/7DNEe5r59CVLJaQIIQ44CSpCCFIZjfXtg7zcEiCWzFBp0dAat5H0+VDsDkgkQFGwzZnL1vYG7H2FONz5nFRTxFd/cwc5NjOKImXwhRAHngQVISY531CC1Q1+tvdEyLMZqUgESbY0oycSKI4c9Ojw6ILZfz72Fx69+8cccfzpPPT3R5lV6paAIoQ4qCSoCDFJqZrOlq4QLzb5CURTlNsV9JZ62vs3A1CoeDGlUliqa7DOmE4goVM081gsViunLDqSWm+OhBQhxEEnQUWISSgwnGRN4wBbukPkWI1M16OkNjcz9PZzevBRMGUOgyYdLZTCZjbyqfNO4eZLmplWXpbdDgghJg0JKkJMIpqm82ZPmDWNfvxDScqcZoydbSQ6OlCVPc/pGeh4k7t+ew9fvetvfPS4+VQW5WSp5UKIyUqCihCTRCg2UgJ/S2cYm9nIdEua9LbtpAYGUJwu1FgQ3jGTYzAoFOS5mW4ISEgRQmSFBBUhDnOaprOjL8ILDX76IgnK3FbM/T0kd7agp9MoThf68BCWwiIIBd9xt8JDDz3E/PnzstF0IYSQs36EOJyF42meerOPf2zsZiiRodqpYGjYRnLHDjAYwWhETyawVFfTZzWz6sWXR+9VFIXzzlsmIUUIkVUSVIQ4DOm6TkPfEA+90cHrrQE8ORZKEiESGzaQ6e3D4HSixaIYbHbsR83DXFvL+o0beGXN6tFnLF++nAULFmSvE0IIgUz9CHHYiSYzvNw8wLr2IApQlWtBbW0m3tGBYjKh2Gxow8NYysqxVFeRttjZ2T/MWZf9P+q8Dr70pS+Ql5eX7W4IIQQgQUWIw0qLf5jVDT7aAzFKc+3kxMIkNzeRCQxicLnQo1EUqxXrnDm8/uZaXrr3Di7++s84oryAJTO9FJ3+nWx3QQghxpCgIsRhIJ5SeW1ngLWtATKaTlWhHa2jg3hrK3omg8HpRB+KjFaYDadT3PeT75GMRzm/4QUuOO8GLCaZCRZCjD8SVISY4DoHY6zadZCg123DnUmQ3LqFTF8fiiMHdG10wax1xgzimgFfPMYV/3Mr+VqY73/1eoxymKAQYpySoCLEBJXKaKxrG+SVlgCxVIZpeWY6Vz/AAFCoFWJx5aNGIhjdbozTp/P4vx9gVvIcHCWVHD0tn/858wvk2s3Z7oYQQrynrAeVlpYWdu7cSXl5OTNnzsx2c4SYEHrDcVY3+Gnoi1CQY6XEamJg4yuj7wcMAdxDadxl1Vhrqrn/tz/hmb/dy7oXn+cfz6xh4XQPRoOc0yOEGP+yFlTWr1/PDTfcgN/vZ/r06WzevJm6ujr+8Y9/UFRUlK1mCTGupVWNTZ0hXmoaIBJPU1HowBgIMNywnXC0Y0xl2YghQkHNdIYNFuYs/SRbX3+BFd/7HsdWebPXASGE2E+Krut6Nj74ueeew+VyceyxxwIQiURYvHgxCxYs4P7779+nZ0QiEXJzcwmHw7jd7oPZXCGyzjeU4IUGP9t6IuTazXhsCqnWVlLtHaT0JEF9YI97rLOWkMkpYuG0fE6oKsBlt2ah5UIIMdb+fH9nbUTljDPOGPOz2+3m3HPP5YknnshSi4QYnzRNZ3NXiDVNfoLRFBUFOZiGI8Q3NaLuOqfHGFf3cp/GQF83n7r4KOZOdaMoMtUjhJh4xs1Sf13XWbNmDbNmzXrXa5LJJJFIZMwvIQ5n4XiaJ7b08O/NPWRUneoiB4beLuIbN6AGAhjcuWhDEcw5TgrKZo/ep+k6L726lnMXVnNEWa6EFCHEhJX1xbS7/fCHP2TTpk3cfffd73rNbbfdxooVKw5hq4TInqb+IVY1+OgKxinPd2DXUiTf3Ea6uxvFbkex2dEiEcxlZQTMCoXTZkDXdgCOXHIBN375K+S7nVnuhRBCfDhZW6Pydr///e9Zvnw5Dz30EBdffPG7XpdMJkkmk6M/RyIRysvLZY2KOKwk0iqvtAR4vTWArkF5gQPN7yPZ1IgaDmPMzUMbHkKxWLBWV/Pyxpe576ff4+izLuPT/3MLp8/0UlPsynY3hBDiXU2INSq73XvvvVx//fU88MAD7xlSAKxWK1arLAYUh6/ecJznd/ho7B+ixG0j16yQbG4k1dYOgNGdixYOYSzyYK2txZSfDw3byaRSqOE+Pjq/lCK3I8u9EEKIAyerIyr33Xcf1157Lffffz+XXXbZft8vu37E4ULVdDZ1hljT6CeSSDOtIAdjdIhkYyMZnw+Dy4WWTIKmYamoQC32Ys3Lpz0Qw24xkjPYyGc/eg5mkzHbXRFCiPc1IUZU/vnPf3L11VdzxRVXYLFYePzxxwEwm82ce+652WqWEIdcOJZmVYOPzV0hXDYzVYUOMt1dxFqa0RMJDLm5aJEIBqcTKsr4y0P3sHXtS3zmJ39jZkUxp88spqKwLtvdEEKIgyJrQSUSiXDeeecRiUS47777Rl/PycmRoCImBV3Xaegf4oUGPz3hkQWzNjVNcvs20l3dKFbr6IJZ05RSbDW1xLUMm155gaCvB0P3Zi69+Gqc1qzP4AohxEEzLhbTflAy9SMmqlgqw8tNA7zRHsQATM13oA/4SDY1oYbDGNxutOFhFIsFy/QZWKZVkNYU2gJRwu1vMttj4+rLzscgZfCFEBPQhJj6EWKyag9EWVnvo3UgSmmuHbcZks1NpNrbQNMxOF1o4TBGj4fhPDc///E3WHLp5yiadQwzS92cduoFFLtt2e6GEEIcEhJUhDhEkhmVdW2DvNoySCKtUu1xogxHiL/ZRMbnQ3G50JMJtEQcS3U11hkzePy3P2HHhtfw9fXwt2df4/gaLzazLJgVQkweElSEOAT6IwlW1vuo74tQlGOl1G0h3dVJsrkFPTmyYFYNhzG63VhrazAVFxNNqhx1/mcJDvj4/orvcdrs0mx3QwghDjlZoyLEQbR72/GLTX5CsTTTCh2YUwkSTU1kuntQ7HZQFLRYFJ/BB8Cb7X6OufwLJNIq8yvyOLnWg9tmznJPhBDiwNmf7+9xc9aPEIebYDTFvzf38MSWHlRNp9qbg2HAR2z9etLd3Rhyc9GTCdBU0iW5o/fNLi+k85V/c8G8KZwzt1RCihBiUpOpHzFhpVIpbrvtNgCuv/56ioqKstyiEbqus703wgsNfvojCcoLHNhRSdY3kOpoB4MRo9uNGgpiKizCWFlO36ZnRu83GAwUKTEq3QbZ1SOEmPRkREVMWJs3bx79/V133cWGDRuy2JoRQ4k0T73Zxz82djOUyFBT7MIyHCG2YSPJnS0YcnJQFAUtGsUyfTobBjqIxPc8BVzXdQYHB7PQAyGEGF9kREVMSJFIhKeeemr0Z13XeeKJJ6iurs7KeiVd12n2DbO60U/XYIwpeXacZoVUWxuplhb0TBpTXv5IjRSnE1vtHB77x/3860+/5sgTP8LFZ5ww5nmKolBQUHDI+yGEEOONjKiICSkQCPDOdeDZGoUYTmZ4dns/j6zvwhdJUu11kZNJEt+8heSOHWAyoVhtqOEwpimlOI4+GnNJKUecfA4Wm4O6ufM4/tTTR5+nKArLli2TBeJCCIGMqIgJqrCwEEVRxoSVQz0KsXsU5YVGPx2DsZHibTYjmd5ekk1NaNEoxtxc1KEIisWCsbaGvlSMSoeDzsEYjuLp/HXlOs5ZWItBV3ll9fMALF++fNystxFCiGyT7cliwnrjjTd48skngbdGIRYsWHBIPns4meGV5gHWtwfRdSgvcKCkk6RaWkh1dKKYTChWK1okjLHIQ8JTyB0rvkR/Vxufu+MR6mqqOLXOy6xSF4oiC2aFEJOLlNAXk8JRRx01GlQO5SjEyCiKj/bAyChKrt1MZmCARFMj6mBw5LTjeAw9FsNSNVJh1mYwoJgsYDCSq4W4bFE5RU7rIWmvEEJMZDKiIsQ+iqUyvNoSYG3bILoOZfl2jJo6smC2rQ1d1TA4naihEEa3m3Spl9yqGnRdoWMwxnCgl0XTPSxbPAeLSZaHCSEmLxlREeIAax2Isrph5CDBEreNPIcFNRIm3tg4ck6P04WiqmjhMOayqTSG+rnni1dwzieXU3PaRynLc3D5omOo9jqz3RUhhJhQJKgI8R4SaZW1rYO8tjNAKqNR7XFiVHRSHR0kW5rREwmMefmo4RCK1Ypt7hzMZWX4Ht1EeNDP6v/8nUuu/AxnzCkl1y4VZoUQYn9JUBHiXfSE4qxq8NHYN4THZaMs34IWixJvaiLT04Nis2NwuVCDgxiLPFhqajAXFJBWNaaffDGXpDRuuv5zHFczBaNUmBVCiA9EgooQ75BRNTZ2hnipaYBIPM30Iidmk0K6r5dkUzPqUGSkeFs0ip5KYaqczgsbX2Lj/T/juh/ei384TbXXyWdv/TpT8+zZ7o4QQkxoElSEeJvBaIrVDT62doVx283UFLvQkkmSjS2kOjvAYMSUn48aDGFwOrHW1jBkVHj0j78kHh1izVP/5JrPXMUJ1R7sFmO2uyOEEBOeBBUhGCnetqN3iNUNPvojCSoKcrBbjGQCAySbmsgEAhhz89AzGdRgCNOUUmw1tRhycrDGE3z9q/8DwEWf/gxHVBRKbRQhhDhAJKiISS+azPBy8wBvtA1iMhioKXahaCrJ5uZd244zmAqLUENBFLMZplfy8L8f4HTnJ7F4KxmKxkefNSUHCSlCCHEASTEHMal1BGL8bV0nLzUPUJhjpbzAgT40RHzTJhKNDWA2Y8hxkgkMYMzLx75gAY8+8SDPPfYXfvbtL6CrKnNtwdHnjZdTnIUQ4nAhQUVMSmlV49WWAf62rpOuYJxqjxOX1Uiqs4PY+vVk+n2YCgrRUym0aBTrjBk4FszHlF/AGZ+4ntIZs/h///M9zptTyLa1a0afu/sU50gkksXeCSHE4UOmfsSkMzCcZFW9j23dEQpyLEzJs6PFYySamkh396BYrRjz88gMBjC63AznOdnUup0TZ86iOxgjY3Vx3z+f56RaL33dHe96irNUSxZCiA9PgoqYNDRNZ3tvhNUNPvxDSaYV5mA1G0j395FsbEKNhDHlF6Alk6jBEOapU4nlufjOZy8kHh0m6fCwYOGxnHtkKbNL3SiKMi5OcRZCiMOZBBUxKQwnM6xp9LOhI4jFuGvBbDpFcsdOUh0dYFDeWjBrsYxWmHUYjMw5dgk9Ha0cVT2Njx0zDY/rrcME3W43S5cu3eMUZxlNEUKIA0MOJRSHvZ3+YVY1jJx2PDXPjstmJjMYGBlFCQxgyM0FHbRIGKPHQ7eWonLeIowWK52DMTKpGCfUlHDizBKspr3XRolEIgwODlJQUCD/LQohxPuQQwmFAJIZldd3jpzTk1Y1qr1OjLpGcmcLqZ2t6Jk0xiIPaiQMuo6luppV617ggV//kNMuuorjP/k/TM2zc9rMMmqKXe/5WW63WwKKEEIcBBJUxGGpNxxnZb2Pxr5hPC4rBTkWtOGhkdOO+/pRchwYcxxkBgYwut1Ya2swFRczZbAPXdPwBwIcOdXNGbNLyHNYst0dIYSYtCSoiMNKRtXY1BnixdFzenIwGyHd3U2iuQk9GsNYUIAWj49UmJ06hZSnEGdxCRlNw1W9kC/98lE+ds4pLJyWj8koO/iFECKbJKiIw0ZgOMkLjf7Rc3qqvU70ZJJEfTPprk4UswVjYSFqcBDFYkGrLOfPD/6Ws05cyMBW0I64gAqvm09+/CymFeZkuztCCCGQoCIOA5qms60nwguNI9uOR8/pGfCTbGhEDYUw5OWBrqEO+DEWebDW1pKymGhr3AYnLgRgbrGFs48ux2UzZ7dDQgghRklQERNaOJZmTZOPTZ1hbCbjyLZjNUOyuZVUWyu6qmH0FKGGI6BpmCqnY6+pQbFY0FWNy677KqR8AOx4/hGq7MtYsGBBlnslhBBiN5mAFxOSruvU90V46I0O3mgLUuy2MTXf/tY5PQ0NYLFidLtRBwIYrFaChW5+8OOvs3ndS0TiaVq6feTvCim7nynl74UQYnyRoCImnOFkhme29fHY+m4GoylqvC5yzAbSnZ0j5/T4/JgKC0HTUINBTKUlOI4+mpdeeZ7W+q386c7/xReJc0TRnjVRdpe/F0IIMT7I1I+YUFr8w6zeVbxtSq4dt92MFo+TaG4m3dWFYh27YNYycyaWigoMJhMXfObL+IIRLvz0DVxwdDnlTtjxopS/F0KI8UxGVMSEkEirrKr38ci6TvrCCaq9Ttx2MxlfP/H160l3tGPMzUWxWEYWzObl06TG+Nvjf8ZgMhEYTtITzfDVW3/MNUsXMXdqLrm5uSxdunT0M6T8vRBCjD8yoiLGvY5AjNUNPpr9wxS7bOTnWNDTaRLNTaTa2gEwejy7FsyqWGZUMeS08bOPXYGqZiionse8k87kjFnFHDO9EIvprXy+aNEi6urqpPy9EEKMUxJUxLiVzKi80TrIazsHiacyVHmcmI0G1HCIZEMDGf8ABpcLxWweWTDrdI5UmC0pwYbCuZ+6gb5+HyctOYOz55UzvWjvtVGk/L0QQoxfElTEuNQdirO6wUdD3xAep5UpeS50TSXV0UGyuRk9mcRYVISWiKMFgxi8Xl5u2MCxc2aSq0NPOM7RF36Wo6cVcHJtkdRGEUKICUqCihhXUhmNDR1BXmkZYCiRYUaRE4vJgBaLkmxqJt3djWK3YywsQB0MophMWGfW8dA//sQzf/8TG9e+xEU3/4r8HAvnz5vKkVNzMRiUbHdLCCHEByRBRYwb/ZEEq+p91PcNkecwU+N1oaOT7u8j2diEGgljKihA13RU/wDGgnystbWYCos4/aIreenpf1I+7yRqi12cMbuYYrct210SQgjxIUlQEVmXUTU2d4V4sXGAUDzNtAIHVrMRPZUi2dJCqqMDDAomjxdtaAg9nUIvLaZTTzGzsAhN11Fzp/LVPz7LkjkVHDujEJt5zxopQgghJh4JKiKrBoaTvNDg483uCC67mRqvE0VRyAQHSTY2og4MYMjNRTGZUQcGUBwO4p4SfvS/NxEa8PO9e/9D3FZAaZ6d044uo7bYle0uCSGEOIAkqIisUDWdrd1hXmzyM/C2gwR1NUOyo5PUzhb0dBqjx4OWSKIGAphLirHW1JKTk0NufhHxWIzmjm7OOX0GS+q85OdYst0tIYQQB5gEFXHIBaMpXmj0s6UrhMNioqbYhUFR0IaHSDQ2kenrQ3E4MLrcqMEgikEh4S3CeeSRGM0WVE3ngptuJyfHwUfmV7GosgCTUWoXCiHE4UiCijhkNE1ne2+ENY1++iIJKvIdOKwmdF0j3d1DorkJPRrDWFAAqorq92HMy2ezv4M/3v4/XPK5mzjlok/RPhilesY0zphV/K61UYQQQhweJKiIQyIcT/Nik59NHSEsJgO1u0dREnGSzS2kuzpRzJaRqZ6hCHoyhbliGtaaaqJPtBAbjvDq808x/eRLmFeRz+kzi8l1SG0UIYQ43ElQEQeVrus09A/xQqOf7mCcsnwHTuvIf3YZv29k23EoiDEvH4xG1AE/is2OZdZMrBXTUAwGllx0JcO6mQWnnsMpM0s5ZrpM9QghxGQhQUUcNMPJDC81+dnQHkJRoMbrwmhQ0NMpkq1tpNrbQNMxerzoiQRaKAiFhfx33Sp2PPRrvvXrh0hldNoHo5x5/qUsmeml2uvMdreEEEIcQhJUxEHR7BvihQY/bYEYU/PsuO0j0zSZYJBkUyPq7nN67HbUYBAAS00twzk2nvn2X4kNR1j1zJNMW7iEBRX5LKnzylSPEEJMQhJUxAEVS2V4tSXA2rZBdB1qip2YDAZ0VSXVuWvbcSqFsagINJWMrx9jbi7WmlrMxcXYgKtvvp2+0DCzj/8IJ9YUsXBavkz1CCHEJCVBRRwwrQNRVjf4aB2IUuK2kecYqWuiDQ+TaGok09uHYrdjKvKQjoTxpTrACC+uWskZxZ+joriY4WSG/DknclShg9NnFlMpu3qEEGJSk6AiPrREWuX1nQFebx0kldGo9jgxGQ3oukamp5dkcxNaNIYxPx8MBjIDfuLG5Oj9Jxw9h+cf+CVnfunHJDIai6YXcEqtR048FkIIIUFFfDidgzFW1fto8Q/jcdkoy981ivL2bccmM0aPBz2ZQBsMQ0EeQ+G+0WcYDAZOOG4RxkySZUdO46iyPDnxWAghBCBBRXxAyYzKurZBXm0ZJJbKMMPjxLxrHcnbtx0b8vJRLBbUUAg0jYDdQndfC8X2sc9TgFMq7cyvyD/kfRFCCDF+SVAR+607FGd1g4+GviGKnFam5I0cBLi3bccjFWb9GJxOIrk53PrFT2CzWbnpppt4+5iJoijUVJRmpT9CCCHGLwkqYp+lMhqvt/Sz8qF7AKhc8glsVivw9m3HfgwuNwaHA3V4GD0Ww1Ragq22DmdODseevoxBfx/x/GocwWZgJKQsW7YMt9udtb4JIYQYnySoiH3SF06wusFHfXdwdCTEkI6jm0zv2HbsAQUyA34Us5kWNcas2loMOSO7dy684XuEkxqzSpw0PD0SVJYvX05RUVGWeiaEEGI8k6Ai3lNG1djUGeLFpgHC8TQFyR6Cu95rf+kf5LnLsIbSo9uOtUQCNRzCVFjEk2+s4vEHfstHGjZz5U0r6AzGMBtNfGROIcdML8Ry3Pey2jchhBDjnwQV8a58QwleaPCzvTeCy2ZmuluhbePaMdeEwl0U51ZhsNhRQyH0TAbr9BlYqqqoU1IoD96Nqis0+4aYVpjDkpleZnikDL4QQoh9I0FF7CGjamzpDvNik5/BaIppBTnYzEZigd49L1Ygo6XAPwx2OzFvIa5ZM1EUA0cccxJfuec/uEoqmF+RL7VRhBBC7LesB5XXXnuNhx9+GK/Xy80335zt5kx6A8NJ1jT62dodxmk1Uet1oSgjq1IMidRe71GG4sRznNz7t3vo7Wrjtr/8F7PdRVsgypTKKk6p9UhtFCGEEB9I1oJKJpNh0aJFmM1mjEYj6XRagkoWaZrO1l2jKP6hJBUFOdgtRmDstmMXboaIjN7nNuTjqJuDtbAA/y+7GQoNsnnjBgprFzKz1MVpM70Uu23Z6pYQQogJLmtBRVEU/vjHPzJv3jxuvPFGXnrppWw1ZdILxVK80OhnS1cIm9lETbELw65RlHduO3ba8xgKbAOgwD6N3COOwlRQCMD13/81g0komVbFcdMLOHZGITazMWv9EkIIMfFl7Uhao9HIvHnzsvXxAtB1ne09Ef66toMN7UFK3Ham5tkxKAq6qpJsayO+cQNqIICxyINit6OHIxQrU1DVfH7wq1vZsm0jANFkhnT+NGbPnsUlC8o4pc4rIUUIIcSHlvU1KvsjmUySTL51mF0kEnmPq8V7GU5meKnJz7r2ICaDYcwoyh6nHXu86Ok0aiCAwZmDrXo26x/9I32drTzyuzsoOeJ44mmVRZX5nFzrJdcuC2aFEEIcGBMqqNx2222sWLEi282Y8Fr8w6xu8NE2EGNqvh33rp04b5123IwWjWLMz0cxm1Gjw+jR3RVmazHkOLns2q+g6wpHnHMVJqOBZbOKZcGsEEKIAy5rUz8fxM0330w4HB791dnZme0mTSjxlMrKeh+PrOukL5ygptg5GlK0RILk9u3Et25BT6cxejxgNJAJDEA6w6ZgDw/+92EUx0iF2eG0wjGX38CR1VP52KKR7ccSUoQQQhxoE2pExWq1Yt11tozYP+2BKKsb/LT4hyl228h3WEbfywz4STY0ooZCGPLyMFitaMkkaiiEqbCAkMvO7753DZqqcuTi0/AecSJGBZbUeWXBrBBCiINqQgUVsf8SaZW1rYO8vnOQREalyuPEbBwZSNMzaVJtbaRaW9E1fWQURVHeVmG2EktVNTlWK5de+xWGozGcNYvwuKwsqfNS7ZUKs0IIIQ6urAaV73//+3R3d/PKK6/Q19fHddddB8DPf/5zGTk5ALqCMVY3+GnqH6LIaWVqvn30PTUcJtnYSMbnQ3G5MDkc6JkM6uAgus3Kc40bWLLgKGxWK7qus+iC/0cspTK/Io+Taz2jU0ZCCCHEwZTVoFJXV4fH49ljm7LRKFMJH0Yqo7G+fZBXWgJEkxmmFzmxmHaNomgq6a5uks3N6MkExsJCFJMJNRpFjw5jKinlgX//hdVPPUpTyw5u+sl9dAzGyHWYOe+oKRw5NVfWogghhDhkshpULrvssmx+/GGpL5xgdYOPHb0RCnKsVHtdo+9psSjJpmbSPd0oVhvGIg/oOpnAAIrRhLVuJpbKaZyT/3k2vfEii868mNZAlNpiF6fP8lKaa3+PTxZCCCEOPFmjcphIqxqbOkO81DRAOJ6msigHq2lXCXx0Mv39JBubUCNhTAUFKGYLWjKJFgqRsJrpU1IcWVUFQGllNV/5w7MoRjPHTM/nxBqPLJgVQgiRFRJUDgO+oQQvNPjZ3hvBbTNT43WOHiSop1IkW1pIdXSAQcHkLQZG1qjo6RThHCu3//SbxKPD/F/tbPJLy2kLRCl2OTi1zsus0rcOJRRCCCEONQkqE5iq6WzuCvFik5/BaIppBTljRj4ywUGSjY2oAwMYcnMx2OyjC2YVhwP7zCPJKfZS8vB0QgEfA6EIYXOUWaVuTp9VjMclC5qFEEJklwSVCSownOSFRj9bu8M4rSZqvW+NfOhqhlRHJ6mdLaPF2xSDETUWQxseImTUmTJvHua8PACu/99fEkgaMNvsLJ5eyOLqwtFpIyGEECKbJKhMMJqm82ZPmDWNfvxDSSoKcrBb3goV2vAwicZGMn19KA4Hptw8dE0jEwigGA2s7WnhgT//iss//zWWfvyzJNIqvoyd4jwrp83yMrPEncXeCSGEEGNJUJlAQrEUaxr9bO4KYzMZxxwk+NY5PU1o0djoOT16MokaCmLMy8daV4thyEc6lWT7+ldZtOxKAtEUc6fmctpML4VOmeoRQggxvkhQmQB0XWd7b4Q1jX56wwnK8x3kWN/6o9MSCVItzaQ6O1FM5pEKs4AaCaMnUzBlCvZZszDY7Jx24Sdw5RVSNPcEEhmNM2YVc+yMwtE6K0IIIcR4IkFlnIsk0rzUNMCG9iBmo4Hat42iwN7P6dm9YDZtMvKvtc/R2t7Ed+95FAMQT6u4Zx1PsdvOabO8VHmkDL4QQojxS4LKOKXrOo39w6xp9NMVjFHiMuN78SFagKozrkSBPc7pUQwGtF0LZk1eL8n8XF79ybNEI2G2vr6G0tnHMvT63zECF934FYpyc7LcSyGEEOK9SVAZh6LJDC83D7CuPYgCVHtdKFoG3673kwP96F29pPt9GHaf07N7waxBwVJTg3X6dBwmM9d++6coRhOumoVouv7Wh6TjgAQVIYQQ45ssTBhnWvzDPPRGBy81D5DvsDCtMAejQSHS3Tx6TdemZxnyd2AqLMTocKCnUqh+H0Nqmt/880/0aSkU08ihgbXHnIqj6mgqChwcYQ+OPuOuu+5iw4YNh7x/QgghxP6QoDJOxFMqK+t9/H1dJ33hBNVeJ7n2kbCRTkTx73htzPURJYKKhhqJoIbDmCum8a/Xn2Xzupf4/W3fQNM0ukNx/ENJjq8q5OzaXF5fs3L0fl3XeeKJJ4hEIoe0n0IIIcT+kKmfcaA9EGV1g58W/zDFbhv5Dsvoezo68a62vd6XCvqw2nKxzZ2DeWoZV5Z/j+HhCJct/ybN/igFOWbOmjOFuVNyaW9vQ3/71A8jYWVwcBC3W2qnCCGEGJ8kqGRRIq2ytnWQ13YGSGY0qjxOzMa3Brn0VJJky07U9vY9b9ahPxajw9fD0iVLAHDlFfC579+NbyhBXYmL02Z6KXbbACgsLERRlDFhRVEUCgoKDm4nhRBCiA9BgkqWdAVjrG7w09Q/RJHTSlm+Y8z7mcFd5/QEBjDn5pKrGwlHe0bfN1oLuP1/v4ymqVQeeTR1846hczAGCpxa6+W4qsIx5/643W6WLl3Kk08+CYyElGXLlsloihBCiHFNgsohlsporG8f5JWWANFkhulFzjHF1nQ1Q6q9ndTOVvTMW+f02BMQ3nXN1Nmn4qiYzomvvEgiHsdTUUNT/xAleXZOq/NSV+La62cvWrSIuro6BgcHKSgokJAihBBi3FP0dy5cmEAikQi5ubmEw+EJ8aXbF06wqsFHfW+EghzrHqcTq5EIyeamkXN6cnIw5jjRdR1tKIKeTLHV18H88z+KI78QgEwmTTCmEoynOWJqLktmeinIsezto4UQQohxY3++v2VE5RBIqxqbOkO81DRAOJ6msihnzOnEuqaR6ekm0dyMHotjLChEMZlGKswGB1Fsdv65YTX//fdDnNbXztVf/wGqptMRTGIzGzlrTjELKwvGrG8RQgghDgcSVA4y31CCFxr8bO+N4LaZqfE6Ud5WAl+Lx0k2N5Pu6kKxWkamehRlpMLs0BCmYi/W2lrmO808++Qj5BV4GE6k6QrFmVbo4PSZxVQWSeE2IYQQhyeZ+jlIVE1nc1eIF5v8DEZTTCvIGbO4FSDd30+qqQk1EsaYl49isaBrGmowiKqrRHLsVCw+cbR420BfN6qjkEgizbzyPJbM9OK2mbPRPSGEEOIDk6mfLAsMJ3mh0c/W7jBOq4lar2vMKIqeTpHc2Upq17ZjY9HIOT16KoUaHCSiaNzz8N0EgwF++JenyXHnktE0wgY3Dl3n3CNKmV+Rj9GgvFsThBBCiMOCBJUDSNN03uwJs6bRj38oSUVBDnbL2FGUTDBIsqkRdWAAg8uNwW4HQB0aQk8kMJeXU1A2leF7f0oiHqVrZyNTZ82nKxhjelEOZ8wqprzAsbePF0IIIQ47ElQOkFAsxZpGP5u7wthMRmqKXRjePoqiqqQ7O0nubEFPpTAWFqEYjeiqijo4SFoB15w5mMvKUAwGbrzttzhcbjKOQvrCcY6ZXsCpdV6cVvkjE0IIMXnINpEPSdd1tvWE+evaDta1Bylx25iabx8TUrToMPGtW0hs3w6KAVORB8VoRIvHUQf8NAV6+N6vvsuW1h0ohpE/kikz6ggZ3CjAuUdO4Zy5pRJShBBCTDryzfchRBJpXmwcYGNHELPRQO07R1HQyfT0kmxuRhsexpifj2I2o+s6aigIuo6luoaG/24kOODjPw/cw9Enn8lwMkN3KM6MohzOmF28R9VaIYQQYrKQoPIB6LpOY/8waxr9dAVjTMmz43rH7hstmSTV0kKqsxPdoOAz9EO4H4+zEiUcw+h2Y62txVTs5dLpX8PuyuXsj11NbzhBIq1yfFUhJ9V4yJFRFCGEEJOYfAvup2gyw8vNA6xrD6IA1V7XHrtvMoEBko2NZAYHMebmEdOjEB15zz/URp8/xNbXdvL5E3+BgoLZYuXcq26gLRAlP8fImbtOPDbIrh4hhBCTnASV/dDsG+aFRh/tgRiluXZy7WNHUXQ1Q6qtnVRrK3omg8njQdM1wsG3DhNEAa/XzdYHV7P51VXMO/40QrEU/ZEEtSUuzphVPHrisRBCCDHZSVDZB/GUyqs7A7zRGiCj6VR7nZgMY9chjz2nx4kpLw+A1HBoj+cZDAYuv+ZG5h57Cu2BKChwSp2X499x4rEQQggx2UlQeR/tgSirG/y0+IcpdtvId4w99E/XNTLdezmnR9dRQyHUZAzdqI8p+AYKR595KS3+6PueeCyEEEJMZhJU3kUqo/HazgCv7xwkkVGp8jj3OPRPS8RJNu15To+eTqMODmJwuXj42b+RiQ2ybNmyXXcp2KsX0RtXOKo8lyV1XvLlxGMhhBBirySovIsW/zCrG3zkOyxMzbfv8X7G7yPZ2IQaCo6c02O1AqAOD6PHYpimlGKrreOSaVO582uffevG2WdhyM3j7OoiFk7LxyQnHgshhBDvSoLKu1A1HU2HQqd1zOt6Ok2ytZVUWxvoOkaPd+ScHk1FDQYJR4foU1QWHnkWitHIlJwqfvjAs8QyOl3BODM8OZw+s5iKQqmNIoQQQrwfCSr7QQ2HSDY0kPEPYHC5MDhGwoaWTKKGggxkktz+m++TSaf4wdHHUlIxHV3X6R9Ok0irLK4q5ORaj1SYFUIIIfaRfGPuA11TSXd1kWxuQU8mMRbtOqdH19HCYfRMBuv06UybPoPKZx8hOhQBRta5tAWiFOSYpTaKEEII8QFIUHkfWjRKoqmRTE8vit2OyeMBQM9k+P/t3X9M1fUex/HXOUfAA8gPTZKTxx/4C4MCU9LRLIVU8OZiu6l/WGvZ5HZbLlvkbatZtt3twsVdp7uu1ZjWbl6d2x1Kay0V2sQ1y7Ga0xS8pXEzQwXhcEDgHL73D8WbGmY38PPB83z8d76HP17bh8N58fl+v+9vuLlZZwPNGv/QPEXf45PL5dbqP2+RNzZOgW7p2/PtSk9NUH56ilKYjQIAwK9GUemP06tQ8wV1nPjuynN6kuSKunx3TjgYlBNsV/XRz/Wvyn9oeWitfrfiD5Kk2BFJamzukNst5aWnaHYas1EAAPh/UVT60fP9GfWcOi0nqud/tx339irc0iKXx6OYqdOU0HVR4XBIpxu+luM4utTTq++ag/IlX56NMuVuZqMAAPBbUFT64XR3ywmH5RmddPn1lQtme7zDlXh/tobddZfmT5qklLHjlTHrITUFutTW2aMZ45I1b9poJcUyGwUAgN+KovILHMdRb1ubOgKt2lmzW00t5/RmRaUkyeVyKf2BXP37XLviYoZp8f2pmuFPYjYKAAADhG/Um3AcR+Fz5ySPW660iTr69Zc6dfJrHf/yc0lS26UenTzXrvGj4rRsll85E0ZSUgAAGEDsqNyEy+3WsJQUxUydqhEJCfrjmxsVNyJBkzIf0H9aOtQd7lXupFGaO2W04piNAgDAgOPf/35839GuHf/coLPDPfIkJEiSsnPna9z0bJ1sCig22qOi7Hu0KGMMJQUAgEFCUenHu+/+XY0nj2jb39bLcRxJUnOwW6cuBJU+JkHLcvzKvCfxuqciAwCAgcRWQD/+9OZf9G1Tm4pL1qnXkb67ENQwj0v56XdrdtpIxQxjNgoAAIONotKPxORk/b7kr/ImedXQFJA/OVbz01M0OSXedDQAACIGReUmQmFH37d2aub4ZM2blqJEb5TpSAAARBSKSj88bpd8ycM1e+JIZfuT5eFhggAA3HYUlX5MTolXauJwJswCAGAQd/30I8rjpqQAAGAYRQUAAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtYaZDvBbOI4jSWprazOcBAAA3Kq+7+2+7/GbGdJFJRAISJL8fr/hJAAA4NcKBAJKTEy86c+4nFupM5bq7e3VmTNnNGLECLlcLtNxbqqtrU1+v1+NjY1KSEgwHQc/wdrYiXWxF2tjp6G0Lo7jKBAIyOfzye2++VUoQ3pHxe12a+zYsaZj/CoJCQnW/wJFKtbGTqyLvVgbOw2VdfmlnZQ+XEwLAACsRVEBAADWoqjcJjExMXrjjTcUExNjOgquw9rYiXWxF2tjpzt1XYb0xbQAAODOxo4KAACwFkUFAABYi6ICAACsRVG5DXbs2KElS5YoLy9P69evVzAYNB0JkhobG7Vu3Trl5uZq8+bNpuPgivr6epWUlGjBggVavny5Pvjgg1sas43B9/HHH+vpp59WXl6ennnmGdXW1pqOhOusWbNGc+bM0cGDB01HGTAUlUG2efNmrVy5UoWFhVq9erV27dqloqIi07EiXm1trebOnSu3262mpiadPn3adCRIqqurU1FRkXw+n9auXau8vDy9+OKLeumll0xHi3ibNm1SRUWFFi5cqNdff10+n0/z5s3Thx9+aDoarti6dav279+vQ4cOqaWlxXScAcNdP4Oou7tbY8aM0dq1a/Xqq69Kko4eParMzEzt27dP+fn5hhNGrmAwqOHDh8vj8Sg7O1uPPvqoysvLTceKeJ2dnYqJiblmpPY777yjF154QYFA4I677XIo6ejoUGxs7DXHHnnkEU2cOFHbtm0zEwpXnThxQvPnz9eePXuUk5OjqqoqPfbYY6ZjDQh2VAZRXV2dWlpatGTJkqvHMjIylJaWpn379hlMhri4OHk8HtMxcB2v13vDcz/i4+MVDocVCoUMpYKkG0rK2bNnVV9fr6ysLEOJ0Kerq0vLly9XWVmZJkyYYDrOgBvSz/qxXd/pBJ/Pd81xn8/HqQbgFnR3d6u8vFwLFixQXFyc6TgRr7u7Ww8//LC6urrU0NCgV155RWvWrDEdK+K9/PLLmj59up588kmdP3/edJwBR1EZRD09PZJ0w3a11+u9+h6An+c4jlauXKkffvhBu3fvNh0HkqKiorRx40YFg0Ht3btXZWVlmj17tgoKCkxHi1iVlZWqqqrSV199ZTrKoKGoDKKRI0dKkpqbm6/ZNr1w4YLS0tJMxQKs5ziOiouLtXfvXtXU1Mjv95uOBEkul0tz5syRJOXn5+vHH3/Ua6+9RlEx6L333lM4HL66Bn2nSEtKSvTRRx9py5YtJuMNCIrKIJoxY4ZcLpe++OILjR07VpLU3t6uY8eOqbi42HA6wE6O4+i5557Tnj17VF1drXvvvdd0JPQjNTVV1dXVpmNEtNLSUjU3N1993draqoKCAq1atUqFhYUGkw0cisogSk1NVWFhocrKylRQUCCv16vS0lJFR0friSeeMB0PsNLzzz+vyspKVVdXKyMjw3QcXLFp0yatWLFCo0aNknR53s37779/x9xZMlRNnTr1mtd916hMmzbtjin5FJVBVlFRoaKiIqWmpioxMVGdnZ3atWvX1Q87zGhtbdWiRYskSQ0NDWpqalJtba2mT5+urVu3Gk4XuT755BO9/fbb8vv9evbZZ695b/v27ZwyNSglJUWzZs1SdHS0pMsDE5966ilt2LDBcDLc6ZijcpucPHlSHR0dSk9Pv/pBhzmhUEiHDx++4Xh8fLwyMzMNJIIkXbx4UcePH//Z97KysuT1em9zIvyU4zj65ptvFAqFNG7cONbDQn1/29LT05WUlGQ6zoCgqAAAAGsx8A0AAFiLogIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAsAaPT092rlzp+rr6685XlNTo08//dRMKABGUVQAWCMqKkoHDx7U4sWLFQgEJEkHDhzQwoULFQqFDKcDYALP+gFglUuXLiknJ0czZ87Uxo0blZWVpaVLl6q8vNx0NAAGUFQAWOfIkSN68MEHNWXKFHk8Hh06dIinjgMRilM/AKxz3333admyZTpy5IjKysooKUAEY0cFgHUOHz6s3NxcTZ48WaNHj1ZNTY3cbv6vAiIRn3wAVgkGg1qxYoVWrVql/fv369ixYyotLTUdC4Ah7KgAsEpxcbEOHDiguro6eb1e7d69W0uXLtVnn32mmTNnmo4H4DZjRwWANU6dOqVAIKDt27fL6/VKkh5//HG99dZbqqqqMpwOgAnsqAAAAGuxowIAAKxFUQEAANaiqAAAAGtRVAAAgLUoKgAAwFoUFQAAYC2KCgAAsBZFBQAAWIuiAgAArEVRAQAA1qKoAAAAa1FUAACAtf4LIdGtW6YA8eYAAAAASUVORK5CYII=",
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -416,50 +667,160 @@
}
],
"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",
+ "# 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",
- "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();"
+ "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": [
+ ""
+ ]
+ },
+ "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": "9e4a0f63",
+ "id": "306562f7",
"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"
+ "- 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",
+ "- 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`."
]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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",
- "Batch: 21/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.450\n",
- " Likelihood parameter acceptance fractions: [0.52]\n",
- "Batch: 22/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.380\n",
- " Likelihood parameter acceptance fractions: [0.44]\n",
- "Batch: 23/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.240\n",
- " Likelihood parameter acceptance fractions: [0.48]\n",
- "Batch: 24/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.330\n",
- " Likelihood parameter acceptance fractions: [0.5]\n",
- "Batch: 25/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.470\n",
- " Likelihood parameter acceptance fractions: [0.34]\n",
- "Batch: 26/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.380\n",
- " Likelihood parameter acceptance fractions: [0.51]\n",
- "Batch: 27/50 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 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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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_chains(walker_metropolis, my_model, true_params)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "5de6d5d2-c745-4a72-b24c-d75c108be3a9",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "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/sharing_error_models.ipynb b/examples/sharing_error_models.ipynb
new file mode 100644
index 0000000..e196d02
--- /dev/null
+++ b/examples/sharing_error_models.ipynb
@@ -0,0 +1,559 @@
+{
+ "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": {},
+ "outputs": [],
+ "source": [
+ "import corner\n",
+ "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\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": {},
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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",
+ "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": {},
+ "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": {},
+ "outputs": [],
+ "source": [
+ "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",
+ " \"\"\"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",
+ "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": {},
+ "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=[\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",
+ "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.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)"
+ ]
+ },
+ {
+ "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": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m = 0.502 +/- 0.138 b = 2.115 +/- 0.068 log_eps = -2.334 +/- 0.114\n",
+ "inferred noise fraction: 0.098\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": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "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"
+ ]
+ }
+ ],
+ "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": [
+ "Here's what each of those error models did.\n",
+ "\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** 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 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."
+ ]
+ },
+ {
+ "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": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "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": "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], \"-\"),\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": "d7b36626",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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dO3bghRdegCiKmD9/fpevoSWD4CuvvAJRFBEYGNjmOmXtueuuuzBjxgxkZmbi6quvxogRI6BUKrFv3z789NNPUKvV+PXXX7tcz87Uu733OlNPZ3++nubptiYi8jovJhkhInI5i8UiRkVF2a3hFBcXJxYUFNiV/+WXX+zW9gIgXnzxxQ6zMrZn8eLFYkpKitjU1GSzfdWqVaJKpRIfffRR8T//+Y8YHBwsvvHGGx0eryVb3RNPPCEGBgba1C88PFwsLi6Wyn7xxRd21xAUFCQ+//zzIgDxv//9r91xW6//1WLKlCnigAEDpNcWi0U899xz7Y49f/58m3WsjEajGBcX53D9rJEjR3a4TpsgCFIGxpO/LrnkEpufRWezL5rNZjE1NbXddcqcaQtRtK47p1AoHNbzyiuv7LAuncm+2F6923uvM/V09ufbns5mX/RUWxMR9SR8UkZEvYpcLse+ffuwbNkybNy4EQqFArm5ubjiiiscJu2YOnUqSktL8c4772D37t0IDQ3FzJkzcf7553fqvI2NjSgpKcE777wjzfNqcfbZZ2PNmjX46KOPcODAAXz88cdOrbGUmJiImTNnYtasWZgzZw7eeustlJeXY+jQobjppptsEjFceOGFSEpKwieffILKykqkpqZi/vz5EEURa9askbIhtj5uv3797M55yimnYMCAAdJruVyOb775Bh999BFWrVoFmUyGqVOn4qqrrsItt9wizetpWZft+++/x7p161BWVoZ+/frhtNNOw7x58zqc/yOTyfDRRx/hkksuwQ8//ICjR48iJiYGs2fPxqxZs2zKKpVKzJw502ECFEcUCgX+/PNPvPHGG9ixYwcMBoO0pldn2gIAFi1ahNmzZ+OTTz7B9u3boVQqkZqaihkzZtgMu2xLe3WfMWMGMjIynKp3e+91pp7O/nzbExsbi5kzZ9oMM3S0zdNtTUTUk8hEURS9XQkiIiIiIqK+quMUSEREREREROQ2DMqIiIiIiIi8iEEZERERERGRFzEoIyIiIiIi8iIGZURERERERF7EoIyIiIiIiMiLGJQRERERERF5EYMyIiIiIiIiL2JQRkRERERE5EUMyoiIiIiIiLyIQRkREREREZEXMSgjIiIiIiLyIgZlREREREREXsSgjIiIiIiIyIsYlBEREREREXkRgzIiIiIiIiIvYlBGRERERETkRQzKiIiIiIiIvIhBGRERERERkRcxKCMiIiIiIvIiBmVERERERERexKCMiIiIiIjIixiUEfVgDXozjjYYfP6YntYbroGIfItg1MNcVwnRYvZ2VZxi0TXAXH+043JNdTA3HHfNOZtqYa6rdMmx3Ek0m2Cuq4Rg8nw/0dN+j8hzGJQRuVGD3oyqbgYH7R1j8S87MeCJ33zimL6kN1wDEfkWTcHX2HV7PAwVO7xdFacc++Yh7L5rQIfljrw7HweePK1b56r59T/YeVMUdt89CHsfHuuVYKczdPvysOv2eDRt/dXj527Y9F2P+j0iz2FQRuRG9/+0HalPrXbbMULVKsSF+PvEMYmIqOdRBEVAEdKvy/sbjuxA5Se3o9/5T2DY6zVIe6USchX7kLbI/QKgCIuFTKHydlXIxyi9XQEi6rqHzx6Kh88e6vPHJCIi39T/2v92a3/dvnxAFBCcfraLatS7hWSci7SMc71dDfJBfFJG1E2CIEKjN9ltr9OZoDVaIAKo1OilL0EQAQBao1nadrTBAK3Rfnx5R8dob+5UV+vV0Xwsk0VweNyOzuuMRoO5zX01epPNkEut0eywzRyp15navCaTRUClRg+9ydL5ChNRr2PR1kMUxQ7LiRYzLNr6Nt8z11Vav+qPtlnu5PlFomCBWVNld37BqIOlqa79+ghCh2UccTSnzNJUC3Nj9Ynz6xsdDkk0a47BVH3Quo9eA3NdpcNrba/+ducyNEllzXWVEPSNNmUFo85mf9FihqWxBqLQ/me4RVvfYRlH9TbXVUr7iWYTLDpNx/uZDLA01Tn8PepoTllbvyutOXvN1LPwSRlRF20qq8OiH7Zhw4FaBKjkUCnkuGRcAh49ZygiA/1w67el+Ka0EjqTBWOXrpX2K1o0CfGhanywsQyLf9kFABBEEdVNRgyJDsJDU4fissxEAOjwGIt/2Yn/rD8A/TMzXVYvR8cEgD/2HMfDq3Ziw4FaqFVyhPqrcMekgbhzUiqUCnmH523Pp5vK8Ngvu7C3Wgu5TIb+Yf6478zBWDg+RSqz6Ptt+G5LBbbecwau+qwI6/bXQGuyYOqQfvj4snHoF9z2cJlPi8px0/JS5N1+GnKSI2zeW/rnPtz343Zsu+d0DI8NabeeRNQ7mRurcfTj26Ep/BYypQoQRQQOOx2x85bAP2GEbWGLGZUf34669R9A0GngFz8MCfPfR8CgHKmIoXwrDj5/zoldGmugCIpA+KTrEHP+45DJFQCs89SOvH0FBj66EXVrl6H+n08haOsx9D/HoAyJhmbjNzj2/WMwHC6FTOUPRWA4ombei6izb5eOLQoCjn37CGp+fQWi2QhFSDTiLn3R6Ws/8u58GMq3YPDTJ+Y4lf1nHizaOiTc+CnK37oChrItEC1GhGZdgP7XLYPcPxAAcOj5c2A8usf67+fOAeRyhOVchLjLX7ZenxP1bzlX/+vfx5F3roWhrBRBI6Yg8RbrHL7oOY/AL2Ywqr6+HxZNFWIvexmRZ94AQ+VuVH1xNxpLV0KmUEEULAjNugBxl78CRVC4dPzGkpWo/Pg2GKv2Qh4QgvAJVyEkc65TbVO37gNUfnAjBj29A9Url0KT9zkEfQP8E0chYcGHUCePsSmv3bUeR7+4G7q9eZCp/CH3C0T45PmIOe8xyJTWfrBh03cof+MSpD5ZCnViOgBr8Fn58W1o2LxCKhc04izEzFsC/7gh0vGdvWbqmRiUEXWBRRAx8518jE+JwNHFZyM8QIU6nQmfFJZh/f4azB4Zh48uzUDYN6V4v+AwKh+1H9Zx46kpuPHUFOm1zmTB0j/34srPipASGYgJAyM7PIY76uXIT9uPYvayfFyWkYivrsxCbIg/KjV6LP1zH8rr9UgMD+jwvG15P/8wrvmiGPdPGYwHzxoChVyGV/86gBu+LoVGb8bdZww+cX0isOj7rXh65nCMTQhDUVk9pry5AXf/bxvev2Rcm+e4IjMR9/24Ha+vP2ATlAmCiLc2HMSpKREMyIj6sIr3b4D+YBEGPVkKv5hUCEY9mratRsPmH+2CsmM/PI6wUy5F7KVLYWmqxaEXZqDszUsx+OkdkCmsf1apk8cg7ZUTWQhFQUBj8f9Q/tblUASEInrWfbbH/PYRhE24AnGXvwLtng2QyZWo/eMdVLx3PaLnPIKBD/4NmUqNhk3fo/ytyyCaDIieeQ8AoGr5g6j++Xn0v/a/CBt/GQRDEyo/ulV6gtVVgqERx75+EIk3fga/mFQ0bvkVh5bOhF98GmLmPgoASH1sE6pXvYSjn96J1Mc2QRWZKO3vbP0BQNA3oOqrB5B446dQ9RuIxtJV0nvanWthqT+KQU9tAwQzDOVbYazahwOPnQL/AWMxaMl2+PUbCEPlbpT95wIcemE6Uh5cD5lcDu3ePBx6aTbCci/GwEcLIPcPQv36j3D8f091qi2OLX8IYaddifir3oBZcxSHnjsH5W9djtQnSiCTyQAAuv2FOPjMmQgePR1DXz0KRVAkGop+QPlbl8N0bD8Sb/6izeOXv3MNzDWHMfjpHVBFJUMwaNG45Rc0lvwsBWXOXjP1XPzpEXXB4TodKhsM+L8RcQgPsE7WDQ9Q4ebTBrYbgLSlTmeCRm/GdTnJ6B+qxudF5T5Rrxa3fbsFI+NC8P7FYxHbnAQkLlSNZ2ePwIDIwC6fVxRF/PvnHchOCsdTM4Yj0E8Jf6UC/zpjEGYOj8Hjv+62GaJYpzPhquxEjE0IAwCMSwzDVdmJ+GRTOcwWoc3zBPsrcUVmIr4oPoIarVHavnJnFfbXaHFdTnKX24aIej79/gIEDj0NfjGpAAC5nxohY2ciesbddmX94tIQmn0+ZHIFlCHR6Pd//4apai+0u9c7PLZgaIKl4RgCUnMRPHoG6jd8Yn/M2CEIy70IMoUSQWkTIVP54+iX9yAk41zEzH0Ucv9AyORyhGbNRcSZN+H4/560DpHT1qNm1YsIn3AFwk+7CjKFEorAMMRf8xYMR7Z3q02MlbvQ77zHpDYJTp+KkDEzUb/ugw73FYw6p+rf+lwx5z0Gv9jBkMkVCBkzQ3rPdGwf4q78DxQBIVAERSBw6Gk49s3DEAUzEm/+En79BgIA/OOGIP7qt6Db+w8aS34CABz/4QkogqMQf81/oQgMg0yhRPika6CMSOhUW/gPGIuQsbMgk8uhCo9H9Mx7YSjbAv2BTVKZY98thswvEP0XfAhlSLT1ejPnIGr6v6DJ/xL6g8VtHl+/vwCBw06HKsraF8n9A637nn3bieM7ec3Uc/FJGVEXJIapMSAiAPf/tB3Hm4yYNqwfRsaFSHfMnHGs0YB7VmzHt6UV0JsFhKqVkMtkqG4yYl+N1mv1Otn+ai32Vmvx4FlDIJc7Pk5Xz7u3WosjGj2uP8U+KJo5IhY/bq9CcbkGpw6MBADIZcAZg6Ntyg2LCYZZEFFer8eAyMA2z3XTqSl4bf0BvJd/GHedPggA8Pr6Awj2V+DCsf3brScR9W4BQyZA889nUARHIzRzDtQDs9vMIBg0YorNa7/4YQAA0/EDACYDsM4NO/79E6hduwzmuiNQBIYDCiUEXYPjY56UJEO3Lx9CUy0C0yZb53tJc5NEqBNGQtBpYCjfCktTDUST3m5/uV8AAtMmoXHzik61Q2vKiAT49x9md60Nm1dAFCzSEExHnK1/QEoGAEARGgP1gLEOjxU0YorduRq3rII6JRMQRZg1x6Rjq6IHAAoVtLv/RsjYWdDu+gtBI6dC7qe22T941DmoX/e+s03R7s88YGAmAEC7ay0Ch5wGRUCoTdmQMTNx/LvFaNq5ts1rDBgyAXVrl0GuDkFIxv8hICVTGsbY2WumnotBGVEXKBVy/HHTqXj81114/s+9uHvFNvQL9sPFYxPw2LQ06WlRe87/YCP2V2vxy8JTkJ0ULgUwQ5f8DovQ8SRzd9XrZC1PlmLbSZPf1fPW66xJPfoF2c85a9lW1yrxR0SACiqF7QP+ID/rx1iDof2kHyPiQnD6oCi8ueEgFk1OxaFaHX7eUYVrspMR7M+PQqK+rP81b+N4zCBo8r9EzcoXIPMLRHD62dY5PScFJsqwWJvXcv8gALAJuI59txjHVyxBwvUfWp+qNf+BfeTdBaj/+0O786vC421eW5oTbxz74XEc/+kZu/KKsFiIRi0EbZ31dXC0XRlliP22zlCGxtptk/sHARYzRKMOMnVwm/s6W3/pXCddv009wuzfszRWQ7f3H+z9d7r9sYMjAdE6ckLQaaB00DaKTrbNyW0h/cz1J37mgk7jcGmBlm0tPytHEhZ8iOMrlqBh49eo/vFpyPyDEDJ6BmLmLYFfrPUmorPXTD0X/xIh6qKUyEAsu2gsAGDP8SYsL6nAgz/vQI3WiI8vy2h336oGA/7aV4OnZgyzmeNksgg4VKdDalTbT3zcWS9H+odZ7zAeqtW1W64r520J9Mo1erv3yuut22LbSeDRWTdPSMG8Dwvx665jWLOnGoIIXJeb5LLjE1HPJPcPQsx5jyHmvMdg1hxDY+kqVH15Lw4+dzaGLD3Y6dEGmoKvETT8TISNv8Rmu/HYXsc7KGz/HFOGWYd9x12yFOETr27zPE07/wIAmOuO2L1nqrXf5inO1r+FTN7On6MK+/eUobFQp2QieVH7TwIVIf1gctA2Zje0jSI0BuZa+6kHLdtODuZt9g0MQ+yFTyP2wqdhrj+KxpKfcfSLe3DoxZlSAhZnr5l6Ls4pI+oC4aQnWYOjg3DvmYMxPiUCm8pPpLMNVSthMAt2aXHVKut/PfNJx/loYxkMZtu7XW0dwx31ciQ+VI3c5HB8sqncYQp6URSdPu/JEsMDMCI2GF9trrB7OvhpUTniQ/0xpn9oG3t33pz0OPQPVeOltfvxbv4hDI8NxviUSJcdn4h6JlE48bmrDO2H8AmXI3zydTDXHIal4Vg7ezomV6khCrafl4bK3dDuXNvGHrYCUnOgjExE3foPHX5Ot9Q3YGA25EER0OR/ZfO+ueE4mrb/3ul6u4qz9e+qkKzzod35J4zHHSczaTl+cPo5aNq2GpbGGpv3Nflfduv8jgSnnwPtrrV2wXD9hk8AmRxBI6e2uW/rNlKGxSJ84tUIO/VyGCt2QjBYnyg6e83UczEoI+qCvEO1mPTaenxSWIbSCg32V2vxXv4hFByqw5z0E4ktMhLCYBZEfFBQhvJ6nbQeWKhahenDYvDquv3439ZK7D7WiLc2HMAnm8qRHmebBbCtY7ijXm15a95oaE0WnPHGBqzaUYW9x5uwckcVznnrH+yv0Tp9XkdemZOOQ7U6XPDBRhQcqkNxeT2u/LQIGw/X4ZU56VAqXPcxpVTIseCUZPy8owpVjUYm+CAiAMC+f49C9S8vQ7s3D8bjB9FY+gvq//4EAYPHQxka0+njheZeDO32Naj+5RUYj+5FQ/EKlL9xCUIy5ji1v0ypQsL1H0C3Nw+HX56Dpu1/wHjsALS7/8bxn57H/sdyAVgTksRe+Awai/+Hys/ugr5sK7S71qH8jUsRPOqcDs7iPs7Wv6tizn8cquiBOPTsWajf8CkMlbugP1SC+g2f4cCSM6SkK/3mPgqZ0g+HXpwF7a510JdtReUndzocZthd/c57DHJ1KA4tnYGmbb/DcGQ7jn33GGrXvIXoWfdJCVNOJpgM2PvASNT89hp0+wpgPH4QDZt/gqbgK+t8uOblB5y9Zuq5OHyRqAvGp0RiyYzhePufg3hmzR7oTAJSIgLw5gWjcWXWiZTA54+Ox/1TBuP5P/figZ+3QxBPrAf2yWXj8NDKnbj3x+2wCCLOGtIP31ydhfM/2Gizrld7xwhVqxDXaq6XK+p18jEBYEz/MBQvmoRn1+zFoh+2Qm8WMDwmGHefMQipUUFIjQpy6ryOTBnaD+tvnYBn1+zBxR8XQhSB0fEh+OOmUzExNUoqFxagdDivLUAlR2yIv81cM0fX0GLB+AF44rfdkMmsqfKJiJIWrUDNb69B889nMNcfhTI8HuGTrkXk1FulMnK/ACjCYiFTnDRHVq6AIixWmmcEAFHT74JMrkD9+g9R88tLUA8Yh4QFH6I+73Po9xd0fEwAQSPOROrjxahZ9SIqP74Ngq4eyqhkBA45DUm3fSuVizj9eigCw1G96iU0FHwNv/7DEXPh02jY9B30Bwo7vHZFUIRdkKIIinQ4bFCuDoYiLBaQnfi8lfsHWbedlIzD2fq3dS7IZM3taj93TREUgYEP/4Oa315FzerXYa45DEVQJPwT09Fv7mIEpU0EAPjFpGLgg3+j6puHrMsRBEUh4swboU4ajcbSnyFTqe2ObXO9foHWn89J1yZTKK3b/QKkbX7RA5C6uBDH//cUKj66BaKhCap+qUhY8CHCTr2s1TFtf+ZylT+Sbv8Otb+9hrp1H8DScAzK8P6IPOtWRJ51c6evmXoumejM+CUiol6ivF6H5Md/w5z0OCy/Otvb1SEiIiLi8EUi6lu+2lwBQQSuP2WAt6tCREREBIDDF4moj6huMuJAjRYvrt2HnORwnJPm+jkFRERERF3BoIyI+oSLPirErmONGJcQhlfmpHdrQW0iIiIiV+KcMiIiIiIiIi/inDIiIiIiIiIvYlBGRERERETkRQzKiIiIiIiIvIhBmRPMZjPKyspgNpu9XRUiIuqh2JcQEVFbenRQZrFYYDabbb4EQXD5eSorK5GUlITKyspuH6umpsYFNepd2Cb22Cb22CaOsV3s+WqbuKov8dXr8za2iz22iT22iT22iWOebhefCco2bNiA2bNnIzw8HOHh4Zg6dSoKCgra3Wfy5Mnw8/ODWq2Wvq644gqbMkajEbfddhsiIyPh7++PKVOmYNeuXe68lHYx2aU9tok9tok9toljbBd7vb1Nevv1dRXbxR7bxB7bxB7bxDFPt4vPBGWvvvoqbrvtNpSXl2PXrl1ISUnB1KlTUVdX1+5+Dz/8sM2Tsk8++cTm/X/961/49ttv8fvvv6OsrAzR0dE4++yzodPp3Hg1REREREREzvGZoOzTTz/F1KlTERQUhJiYGNx5552or6/Hzp07u3zMhoYGvP3223j44YcxduxY9OvXD6+99hrKysqwfPlyF9aeiIiIiIioa3wmKAOsjwnNZjOOHDmCpUuXYvjw4RgzZky7+yxduhRKpRKJiYlYuHAhqqurpfc2bdoEg8GAyZMnS9uio6MxatQo/P333267DiIiIiIiImf5VFD24osvQq1WIyEhAatXr8aXX34JtVrdZvmJEydi1apVaGxsxHfffYe8vDzMnj1bSvZx9OhRAEC/fv1s9ouJiZHec0Sj0aCsrEz6qqiocMHVERFRX8K+hIiInKX0dgVaW7RoERYtWoTKyko88sgjOP3001FaWor4+HiH5ZcsWSL9OysrC++++y4yMzOxYcMGTJgwoc3zCIIAmUzW5vtLly7F4sWL7bbX1tYiICCgE1dkT6PRdGv/3ohtYo9tYo9t4hjbxZ4r2iQqKqrbx3BXX8KfuWNsF3tsE3tsE3tsE8c83Zf4VFDWIi4uDq+//jo+/fRTfPXVV7jtttuc2m/kyJEAgD179mDChAlSMFdVVYWIiAip3LFjx5CWltbmcRYtWoT58+dLrysqKpCTk4OIiAiXdNSuOEZvwzaxxzaxxzZxjO1izxfaxJ19iS9cny9iu9hjm9hjm9hjmzjmyXbxyaAMOLEGmVwut9kmk8lstrVWUlICAEhISAAAZGRkQK1WY82aNVIQVlVVhS1btuCee+5p89yhoaEIDQ111aUQEVEfxL6EiIic5RNzygoKCnDzzTejpKQEjY2N2LFjB6644gqo1Wqcd955UrkJEybg0ksvBQAUFRVh4cKF2Lx5MzQaDdavX49rrrkG48aNwxlnnAEACAoKwk033YTHHnsMeXl5KCsrw8KFC5GSkoLzzz/fK9dKRERERETUmk88KcvMzMTWrVsxf/587NixA1FRUTjttNPwzz//oH///lI5pVIJhUIBABgzZgxOPfVUzJ8/H7t27UJ8fDxmzZqFBx98UCoDAE8//TQUCgXmzJmDxsZGKTmIv7+/x6+TiIiIiIjoZDKRy3h3qKysDElJSTh8+DASExO7dazq6uo+O2537dq1eOSRR7BmzRqb7e5qk5EjR2L58uUYNmyYy4/tbn3596QtbBPH2C72fLVNXNWX+Or1eRvbxR7bxB7bxB7bxDFPt4tPDF8k91qxYgVOPfVUJCUl4aKLLrJbDmDLli2YMmUKEhMTMWPGDOzfv7/NY9XX1+O8887D4MGDsXTpUpv3rr32Wvz5559t7ms0GlFbW9u9i+mE6upqmM1mj52PiIiIiKgrfGL4Ym+zakdVm+9pGjQIPWbp9jnOGRbjVLmioiLMmzcP77//PrKzs/HEE09g3rx5WLt2LQBAp9Nh+vTpuOKKK/Dmm2/ihRdewLnnnovi4mKHCVVefPFFxMTE4H//+x9mzpyJ2bNnY8iQIVizZg3q6upsFuomIiIiIqKO8UlZL7d8+XKcc845uOiii5CamoqXX34Z69atw5YtWwAAK1euhMViwZNPPokhQ4Zg6dKl2Lt3L/755x+Hx9u6dSvmzZuH4cOHY/Lkydi+fTtMJhPuuecevPTSSx3WRxRFPPnkkxg5ciRycnLw66+/Su/t2bMH0dHRiI6ORlpaGu6++24YDAYA1qGPp59+Op599lmkp6dj9OjR+Oabb2yO/eWXX2LcuHEYM2YMXnvttS62GBERERGRZzEo6+UMBoPNIqX+/v6QyWQoKioCYF1GICMjQ1pMOzAwEOnp6di8ebPD4yUlJSEvLw8GgwGbNm1CUlISnn/+eVxwwQVITk7usD4lJSU4dOgQfvjhB9x4440499xzUV5eDgAYOHAgduzYgR07duDrr79GXl6eNETSaDTizz//RE1NDVasWIF//etfuPrqq9HQ0AAAKC4uxnXXXYeHHnoIX331FVavXm03TJOIiIiIyBcxKOvlzjjjDPz4448oLS2FKIp47rnnIAiCFMxoNBqEh4fb7BMeHo76+nqHx7v99tvxwQcfICIiAjk5OYiKisI333yD66+/HhdeeCEGDRqEJUuWtFmf4OBgvPTSSxg0aBCuueYanHXWWVi+fDkAQKFQSE/KRo0ahSeffBLff/+9tG9cXByWLFmClJQUXHnllQgPD8fu3bsBAB9++CEuu+wynHfeeRg6dCjefPPN7jQbEREREZHHcE5ZLzdjxgzcddddmDBhAsxmM84//3wMGjRIWmogMDAQjY2NNvs0NDQgKCjI4fEGDBiAnTt3wmAwwN/fH3PnzsWzzz6LZcuWQa1W46effsLs2bMxe/ZspKen2+2fmJho8+QuLS0NR44cAQDo9Xrcf//9WLVqFY4fPw6TyYTg4GCpbGxsrPREr6XuWq0WAHD48GGccsop0nsxMTF2wSYRERERkS/ik7I+4JFHHkF9fT2OHz+O5557DmVlZRg7diwAYPjw4dL8MgAwm83YuXMnhg8f3u4x/f398eOPPyIoKAhnnHEGtm3bhvPPPx9paWmYMmUKtm7d6nC/o0ePQhAE6XV5eTn69esHAHj++edRUlKCzz77DCUlJfj0009hMpmcusaYmBgpuAOAxsZGaDQap/YlIiIiIvImBmW9nFarxZIlS2A0GqHT6XD99dfjoosuQkpKCgBg+vTpOHbsGD744ANYLBa8+OKLCAgIwKRJk9o9rl6vx0MPPYTnn38eAJCcnIy8vDyYTCYUFha2Ob+strYWL7zwAgRBQF5eHr7//ntMnz4dAFBVVYWkpCSMGjUKgYGBePvtt52+zrlz5+Kjjz7C9u3bYTab8fDDD9sEf0REREREvopBWS8XEBAAvV6PuLg4JCQkIDw83CYzYUREBL788ks88MADCAgIwBtvvIGvv/4afn5+7R73qaeewjXXXIO4uDgAwE033YQVK1YgNDQUaWlpGD9+vMP9hg0bhs2bNyMiIgJnn302nn32WYwYMQIAcNtttyEvLw/BwcEYNmyYU4lDWpx11lm4+eabkZ2djX79+sFoNEpP4IiIiIiIfJlMFEXR25XwdWVlZUhKSsLhw4eRmJjYrWN5a9V0o9EImUwGlUrl8H1RFNHY2IiQkBCnjldXV4fQ0FC7tcxa5po5YjKZ0NTUhPDwcJhMJigUCsjlcrs20Wq1CAwMhMViQX19PSIjI232bV2H4OBgKJUnpkZaLNY14BQKBWpqahAWFgaFQuHUNfkSb/2e+DK2iWNsF3u+2iau6kt89fq8je1ij21ij21ij23imKfbhYk++oiOnnzJZDKnAzIAbSbRaCsgAwCVSiXt11ZwCFgTeADWwCoyMtJu3/bq0DoAa9mXiIiIiMiXcfgiERERERGRFzEoIyIiIiIi8iIGZURERERERF7EoIyIiIiIiMiLGJQRERERERF5EYMyIiIiIiIiL2JQRkRERERE5EUMyqjL1q9fjxkzZni7GkREREREPZpPLR69atUqrF27FjKZDNnZ2Zg9ezbk8rbjRoPBgG+++QZFRUUICwvD9OnTkZGRYVPm+eefx549e2y2ZWdn47rrrnPLNfiaf/75B//+97+xevVqlx9Hp9PhyJEj3a0iEREREVGf5jNPyqZOnYrXX38doaGhkMvluPnmmzFz5kwIguCw/KFDhzBixAisWLEC/fr1w9GjRzFhwgS88MILNuW+++47lJWVYezYsdLXgAEDPHFJPkGv16O8vNxnjkNERERERLZ85knZm2++iUGDBkmvp02bhgkTJqC0tBRjxoyxKx8SEoJ169YhPj5e2jZo0CDcfffduPnmm6FWq6XtWVlZuOGGG9x7AT7IYDDg4osvRnV1NVJSUgAAJSUlKC0txZNPPokZM2bg008/xdSpU5GVlYUvvvgCH3/8sbR/RkYGvvrqKyQmJjo8TovXX38dH374IeRyOR566CFMnz7dk5dJRERERNSj+UxQ1jogA4CamhoolUpERkY6LB8REWG3bfjw4TCZTKiurkZCQoK0fd26dbj99tsRHx+PadOmYezYsS6tu6/y8/PDSy+9ZDPsMDg4GDqdDqtWrUJiYiL++9//Ij4+Hr/88gsqKytt9j906BBMJlObxwGA0tJSbN++He+88w7Wrl2LSy65BOXl5QgKCvLsxRIRERER9VA+E5QBwG+//Yavv/4aVVVV2LZtGz777DMkJSU5vf+yZcswZMgQm4BMpVIhNjYWSUlJ2LJlCx5++GE8/fTTWLRoUZvH0Wg00Gg00uuKiopOXcdLL72El156yeF7giBI8+SmTZuGN9980+b9sWPHoq6ursNzfP755zjllFPaLSOTyRAXFweVSiU94WoRGhqK1157DSqVqsNztXec6OhovPzyy5DL5UhPT8dTTz2F3bt395nAl4ioLd3tS4iIqO/wqaCsX79+GDNmDMrLy5Gfn48ff/wR5513XrvJPlq8/PLL+Pbbb/Hbb7/ZbP/4449tgrTx48fjlltuwXnnnWcXYLRYunQpFi9ebLe9trYWAQEBHdblyJEjOHjwYIflysvLUV1dbbPt0KFDqK2t7XDfo0eP2u3rSH19PSwWi01ZjUaD/v372/yx0NDQID1lbCGKIurq6lBdXd3mcWJiYmzqGxAQgPLy8k4F063rQVZsE3tsE8fYLvZc0SZRUVHdPkZ3+5K28GfuGNvFHtvEHtvEHtvEMU/3JT4VlI0ZM0aaP3bttddi8ODBmDVrFs4///x291u2bBnuuecefPbZZ5g0aZLNe60DMgC45JJLcMMNN6CgoKDNoGzRokWYP3++9LqiogI5OTmIiIhwqnH79+/fZjKR1k/KEhIS7I6XnJyM0NDQDs8RGxvrVF3Cw8OhUChsyoaGhkKlUtlsa/l3y3dBENDU1ITw8HBERUW1eZyTtykUCoSFhXX6DxpX/AHU27BN7LFNHGO72POFNuluX9IeX7g+X8R2scc2scc2scc2ccyT7eJTQVlrqampiIiIsEtnf7L33nsPN910Ez7++OMOgzfAmvwCACwWS5tlQkNDnQqM2nLHHXfgjjvucPhedXV1uz/g4uLiLp/XkcjISBw/fhwmk6ndoYpDhgxBcXExysvLkZCQgBdffFFqq84ch4iIrLrblxARUd/hEynxDx8+jNLSUpttq1atQk1NDbKzs6Vtzz77LJYtWya9/uCDD3DDDTfg448/xrx58+yOW15ebhfkPPPMM1Cr1Tj99NNdeg2+auTIkRg9ejRiYmKQkpLS5qPYUaNG4dJLL8WgQYMQGxuLvXv3Ijw8vNPHISIiIiKizvGJJ2UKhQILFy6EUqlEamoqysrKsGHDBjz00EM488wzpXLffPMNUlJScN1116GkpATXXnsthg4ditWrV9ssanzvvfdi4MCBUCgUuOmmm6BQKDBw4ECUlpaivLwcH3/8MeLi4rxxqR4nl8vx+++/o6amBhqNBsHBwTjttNPw008/2ZV944038Nxzz0GpVEKtVuP++++X2snZ46xevRrR0dEeuTYiIiIiot7AJ4Ky/v37Y/369SgoKMCOHTsQFRWFTz/9FDExMTbl7r33XoSEhACwZv177bXXHB6vJR17XFwc/v77b+Tl5WHXrl248sorkZubKx2jL4mMjJSWF1Cr1ejfv7/Dci2p7gE4TNbR0XFOnsNHRERERETt84mgDLCmXc/JyUFOTk6bZebOnSv9u3///k4vCJ2bm4vc3Nxu15GIiIiIiMjVfGJOGRERERERUV/FoIyIiIiIiMiLGJQRERERERF5EYMyIiIiIiIiL2JQRkRERERE5EUMyoiIiIiIiLyIQRkREREREZEXMSgjIiIiIiLyIgZlREREREREXsSgjIiIiIiIyIsYlBEREREREXkRgzIiIiIiIiIvYlBGRERERETkRQzKiIiIiIiIvIhBGRERERERkRcxKCMiIiIiIvIiBmVERERERERexKCMiIiIiIjIi3wuKDt27BiOHz/eqX2qq6tx6NAhiKLYrTJERERERESe5jNB2WeffYbhw4dj9OjRGDJkCAYOHIjvv/++3X3q6uowY8YM9O/fH2PHjsXAgQPx119/dboMERERERGRt/hMULZlyxb89NNPqKioQHV1Na655hrMmzcPR48ebXOfG264AWVlZaisrMTx48dxwQUX4Nxzz0VtbW2nyhAREREREXmLzwRlTz75JAYOHAgAkMvluOCCC2AymXDo0CGH5aurq/H111/jvvvuQ0REBORyOR555BHodDp8+eWXTpchIiIiIiLyJqW3K9BaTU0NqqqqUFVVhSVLluDMM89ERkaGw7JFRUWwWCw45ZRTpG0hISFIT09HYWGh02WIiIiIiIi8yaeCsu+//x5PP/00KisrERkZic8++wwKhcJh2erqagBAdHS0zfbo6GgpUYgzZRzRaDTQaDTS64qKis5fDBER9WnsS4iIyFk+FZRdc801uOaaa2CxWPDcc8/h9NNPx+bNm5GWlmZXtiVYMxqNNtsNBgNCQkKcLuPI0qVLsXjxYrvttbW1CAgI6NxFnaR1B01WbBN7bBN7bBPH2C72XNEmUVFR3T6Gu/oS/swdY7vYY5vYY5vYY5s45um+xKeCshYKhQL33Xcfnn76aaxYscJhUJaUlATAeuex9ZOwyspKjB071ukyjixatAjz58+XXldUVCAnJwcREREu6ahdcYzehm1ij21ij23iGNvFni+0iTv7El+4Pl/EdrHHNrHHNrHHNnHMk+3iE4k+TCaT3bb6+no0NTUhNDRU2nbw4EEcOXIEADBu3DiEhYVh5cqV0vv79+/H9u3bcfrppztdxpHQ0FAkJiZKX/Hx8d28QiIi6mvYlxARkbN84klZaWkpHnjgASxYsACpqakoKyvD008/jYSEBMybN08qd9FFFyElJQWff/45/Pz88OCDD2Lx4sXo378/EhIScN999yEnJwezZs0CAKfKEBEREREReZNPBGUZGRl46KGH8Nprr2HHjh2IiorC1KlTceuttyI8PFwql5KSgoSEBOn1v/71L4SGhuLNN99EY2MjJk6ciEcffRRyubxTZYiIiIiIiLzFJ4IyAJgwYQImTJjQbpnPP//cbtuCBQuwYMGCdvdzpgwREREREZE38HERERERERGRFzEoIyIiIiIi8iIGZURERERERF7EoIyIiIiIiMiLGJQRERERERF5EYMyIiIiIiIiL2JQRkRERERE5EUMyoiIiIiIiLyIQRkREREREZEXMSgjIiIiIiLyIgZlREREREREXsSgjIiIiIiIyIsYlBEREREREXkRgzIiIiIiIiIvYlBGRERERETkRUpvV4CIqKdau3YtdDodAgICMGnSJG9Xh4iIeiD2JQQwKCMi6rK//voLtbW1iIiIYEdKRERdwr6EAAZlREQ+hXdMiYiou9iX9DwMyoiIPMDZDrIzd0zXrl2LmpoaREZGstMlIurlOhNoOduXsB/xHT4TlFVUVOCdd95BUVERwsLCMGPGDFxwwQWQyWQOyx89ehSXXXaZw/duvvlmzJ07FwBw5513orS01Ob9M844A//+979dewFERO1wx/AUDnkhIuo72I/0bj4RlO3evRvTpk3D5ZdfjiuuuAKHDx/GDTfcgLVr1+LVV191uE9YWBjuu+8+m22//PILnnvuObz44ovStoKCAqSmpuLKK6+UtsXFxbnnQoioT1i+fDkqKiqg0WgAABqNBsuXL8f555/v5ZoREVFPwb6EWvOJoCwhIQHbtm2Dv7+/tC0sLAzXXnstnnrqKYSEhNjto1arcdZZZ9lsW7p0KXJzczFq1Cib7ampqXZliYja0tEQkYqKChw4cEB6bbFYUFFR4cEa0sl0JgvqdCZo9GZEK7xdGyLq65wZasi+hFrziaAsMDDQbltUVBQEQYBWq3UYlJ2srKwMq1atwttvv2333g8//ICCggLEx8dj5syZ0tBGIiJHvDGcg3dMnWe2CKjXm1GvN6FOZ0K93gyjWQAAyGUyREdzCU4i8i5vDQtkX9Jz+URQdjJBELB06VJkZ2cjNjbWqX3ee+89BAUF4eKLL7bZHhERgSlTpmDcuHHYsmULrr76aqxZswavvPJKm8fSaDTSLzMA3rUgIrfrzB3TvtTpiqKIRoMF9Xpr8FWnM6HJaIYoertmHWNfQkSe5mxf0pf6kZ7CJ4Oyu+66C5s2bcLff//tVHlRFPHee+/hkksuQVBQkM17n3/+ubRtzpw5GD58OC644ALceOONGD58uMPjLV26FIsXL7bbXltbi4CAgE5eja3WHTRZsU3ssU3sebJNBEGQvldXV9u9bzabHW5zVHblypU4duyYTcf3ySefYNq0aV0+5uHDh1FWVia9tlgsOHz4sMOyPY3JIqDRYEGj0YxGgwVNRjMEwfkITJTJoNF0/0lZVFRUt4/hrr6Enw+OsV3ssU3seapNOupHAOc/953tRzpzzN7cj3SVKFggGJogGpoAmQxamf1Ivs7qTF/ic0HZv//9byxbtgyrVq3CiBEjnNrn999/x/79+3H99dfbvXdykDZ9+nQAQFFRUZtB2aJFizB//nzpdUVFBXJychAREeGSjtoVx+ht2Cb22Cb2PNUmcrlc+u7onElJSVAqlTh8+DAsFgsUCgWSkpIclq2trbXr+Gpra+3KKpX2H8dKpdLhMTtT1pdZBBGaVk/A6vUm6E0iADkAP+s3tfWbs+QyGUJDHf/cPM2dfYkvXJ8vYrvYY5vY80SbdNSPAM73Jc72I4Dz/UNv6Ue6SrSYIeg0sOjqm79rIBq10vuK0Bgog+M82h4+FZQ99NBDePXVV7Fy5UqMHz/e6f2WLVuGcePGISsrq8OyLXcA1Gp1m2VCQ0MRGhrq9PmJyDneXMzSmXM7O5yj5fWTTz6J2tpahIaGcsiHExoN1nlg9TprENbYQ4YhdhX7EiLX8/aiyB2dvzPDAtmXeIYoihD0jRC0dbBo6yDo6iEYmrxdLTs+E5Q9+uijePnll7Fy5UqceuqpDsvcdtttiI2NtVljrLa2Ft9++y2WLl1qV37v3r0oKSmREnvo9XrcfffdiIiIwJlnnumeCyGiNnlzPRRnzu3NTFjx8fEAYHPHtGVbT2Q0C9LTr3q9GfU6E8ydGIZIROSIt9fV6uj83s6o2Nv6kq4QzSZYWgIwbR0sunpAsHi7Wh3yiaAsLy8PixcvxoABA/Dwww/bvPfqq69Kwwzz8/ORkpJi8/4nn3wCuVyOSy+91O64UVFR+PLLL3HzzTcjJSUFu3fvRnx8PH766SeEh4e763KIiDqtM3dMfa3TFQQRGsOJIYj1OjN0Jt/vAImIehtn+xJf60e6QzSbYGmqsX41VvvkUzBn+ERQNnToUPz6668O30tISJD+/eqrr9pNjp48eTL+/PNPhIWF2e0bHh6Ozz77DMePH5cCsuTkZGmcLxGRu7mj4/P2kBd985pgdToT6vRmaPSmXj0MkYjIm3pjP9IdosXcHIBZAzFB3+DtKrmETwRlERERTi3unJ2dbbft5IWiHYmOjkZ0dHSX6kZE1B09ueMDrGPxNc2JOOqa1wXTmwRvV4uIqM/o6f1Id1mDsFrpaZigawDQ++4E+kRQRkS9mzfXQ+nMuTt7N3LixInShO/ewmC2oK45EUedzgSN3gyBj8GIyMu8va6Ws+fvylOt3tiXdIcoWKxzwhqrYWmsgaDToDcGYSdjUEZEbufNic+dOXdn70Z6Y5K5KwmCiAZDz30KJpfJEOSnQIhaiRD/E18N9bXerhoRuZi3E2g4e/6uPNXq6X1Jd7UEYUJTrXVIoq4eEHtOX+QqDMqIiHyIO++Y6kwW1Dc/AavXm3vUUzA/pRwh/kqEqpUI9rMGX8H+CshkMm9XjYjI5/jy0zfBZLAGYNra5hT1vXM4YmcxKCMi8iGduWM6ceJE1NTUIDIy0u49iyDarAlWrzfBYO4Zdx4D/RTSU6/Q5qdgapXC29UiIuoxnO1L2utHXEFaI0xX3zwvrBaiSeeWc/V0DMqIiDzAHXctJ02ahOrqakRFRaHJYEZdD1uYWSGXIVgadngiEFMqmCGXiOhk7u5HXEEwGaS1wQRtfY9ZI8wXMCgjIrdz53ooa9eulTopR3cGfWUtFlfOGTCaBWlR5qPHGqGvFnx+YWa1Sm4z7yvEX4lAPw4/JCLneLMfcff5neVrc89EwQJBp4FFWy8FYqJJ7+1q9VgMyoj6EGc6HndwZzrfv/76C7W1tYiIiHB4TV05ty+NxW+djKO+OTV964WZBZ0J8kDfCcj8lXIE+ysR7Kewfm/+N59+EfUe3uhLvNmPdOX8vtSPuIqgb7QGYLp661wwfSM4F8x1GJQR9SHOdDyd5a1Az528eR1ao1kKvur1ZjT4aDIOlUIuJdsI9mv+7q+EisEXUa/HvqRjPf0aRLMRFm1z8KWrh0VbDwhmb1erV2NQRkTd4o7Oua9oPQyxZU0wk8W3knG0zPsKbk6+EdwciPkrmXiDiFyHfYn3iKIIS1OreWDaOibj8AIGZUREHmARRGiaA7CWrIithyH6ggBVc7IN9YnkG4F+PaubEATfCmqJiHyNYNDaPAHT19ZBp+b83ta80Zf0rN6WiKjZ8uXLUVFRAY1GAwDQaDRYvny5y+YYdEfLPLB6vfXpV73ejCYfyobYm7Ie1tbW4vvvv0d+fj7y8/OhVCrx448/ertaRNQD+HI/4iqixWwNwFoNRRQtppNLAejbQdnuA2X45a8CFG7ZicItO3HBuTNwz6PPeLQODMqI+oDe2PFUVFTgwIED0muLxYKKioo2y7tr0rUoimgyWmwCMF+ZByaTtXr61Tz0sCc+/QKs7VxWVgZRFJGcnCxtr66uxjXXXCO9ViqV0Ok47IbIHXpbX9LZfgTw7QQe1jXBGmwCMMHQ5O1q+RSLxYKd+w4jOSEWwYEnfoa/b9iE+557S3q9sXiLx+vWpZ7ZYDBgwYIFeO655xATE+PqOhGRi3Wl4+lIVzpnb3ZmrpqjoDdZbIYgavQmn0hH76eUI9jPGnQFtwrCFPKeefezrq4OBQUF0hOw/Px8VFZW4pZbbsGrr74qlRs0aBAiIiJQW1sLpVKJ0aNHo6qqComJiV6sPVHv5At9ibeDIl+a72azJlhTHSx6DdcEa0UURZRVHkNh6U5sbH4CVrR1N5p0enz1n8WYNilXKpuZnib9OyQoEEGBgR6vb5eCMqVSid27dyMtLQ1PPPEEbrzxRsjlPW/YCxF1XVc6Z1/qzJxhtghSANaSDdFo9u6cJblMJmU6DPFXQtSJ6B8b1eMTb+zZswcrV65EXl4e8vPzsWvXLofl8vPzbV7LZDIsW7YMcXFxGDt2LAICAlBdXe2JKhORC3S2L+lp/YirtDwFszTVWgMxbR3XBHNg3cZS/L2pFBtLd6Jwyy5UVdc6LFdYussmKBuVNhBvPn4XMtOHYkhKIvwi4qH1VKWbdSkoUygUWL9+Pd555x3cd999ePfdd/HGG28gJyfH1fUj6rN6W3pgXyeKIhoNFpsAzNvzwFon3mjJfnjygsvV1boeFZAJgoCdO3ciMTERISEh0vbffvsNt956a5v79evXD7m5uTjttNPs3ps7d65b6krUG7Av6ZlEwWIdhthUC4u2linpT6I3GLFr/2GMHjbIZvt/PvoGP67Z0OZ+gwYkICt9KMaOGGyz3d/PD5edO9UtdXVWlycWyGQyXH/99ZgzZw7uvvtujB8/HvPnz8eSJUsQGRnpyjoS9UlMD9y++Ph4AMDhw4dhsVigUCikbc4wmgXU6Uyoax6GWK83weKlYYi9behha+Xl5TZDEDdu3AiNRoNvv/0Wc+bMkcrl5p64YxkYGIisrCzk5ORIX8nJyTbBKBE5h31J27rbj7iSaDZZg68m65eg04ALM1sJgoBd+w9bhyCW7kLhlp0o3bUPgiDiyN/fIChQLZXNTE+TgrJ+keHIGpWGrFFpyExPw7iRQxEZFtLWabyu27O9+/Xrh/fffx/XXnstbrzxRqSlpeHpp5/Gtddeyw6UyEd0pePx9burLfMNnnzySdTW1iI0NLTNOQiiKDYn4TChTmddE8wb6ehbZz0M9jsRgPkpe8/w7w0bNuDPP/+UhiEeOXLEYbn8/HyboCw9PR3//e9/kZubi+HDh0Op7HnJSIh6u872Jb2pH3E1wWSApakGQksQZmj0yHl7ArPZgh//2IDCLbtQWLoTRdt2o6HJ8WDC4u17MCEzXXo9Z+ppGDygP7JGDUNiXL8eFYt0u9czm80oKSlBSUkJRowYgW3btmH+/PlYtmwZ3n33XQwbNswV9SSibuhKx9PR3VVfusN4spZkHHU6k7Qos6ezITpa8ytApehRHUR7DAYDdu7cidGjR9tsf/HFF/HVV1853CcjIwNxcXGIj49HZmamzXsqlQrz5893W32JqPs625c485TOl/sSVxKMOlgaa6xPwxpruDhzszpNI47X1mPwgARpm1wuw40PLW0zEMvKykRSfBzi+oUjPCTI5r0hKYkYktIzEz11KSizWCx46KGHsGHDBhQUFKCpqQmhoaEYP348Hn/8cWRmZuL9999HVlYWfvjhB5x55plOHff48eMoKSlBWFgY0tPT4e/v3275v/76y25Cd2JiIrKysmy2mc1m5Ofno7GxEVlZWRxeSS7n63cD3cGbdxgdMQsiNh+pR53OBL3Jc8k4lK2ffvXwNb/aIggCdu/ebTMMsbi4GCaTCbW1tQgLC5PK5uTkSEFZdHS0NPwwOztbGroYERHRY1NoE7kT+xLv9yWuIhi0sDTVNA9HrGFSDgAGoxGlO/db1wJrzoi4+0AZThk3Ar9+sFQqJ5fLkTFyKP7MLwYADEruj8z0NGSMHIqM9KHYsKcaGq0BYUFqjBw60EtX43pdCspMJhM+/PBDnHbaaXj66adx2mmnYfTo0TYZGKdPn45XX30VixYtQnFxcbvHq62txS233ILff/8dI0aMQFlZGfR6PT7++GNMnDixzf3uv/9+HD9+3OZp3MSJE22CsgMHDuCcc86B0WhETEwMtm7dirfeeguXXXZZVy6dyCFXjtlfvnw5Dh8+3GvWgXE1ncliTcShM6FOb5aGIZosAio1BredVyYDgvxsg69gfyUCVD0nyYazLBYLfvzxR+Tn5yMvLw8FBQWor693WLawsNDmxtucOXOQlJSEnJwcpKSk2DwZLCoqcnvdiXoy9iU9l2BosgZgzU/DGIQBx2vrbRZkLtmxDyazfbKSzdv3wmQyQ6U6EZb8a/5FuOOaCzBu5FBEhYfalP9n719ur7s3dCkoU6vVKCsr67DcNddcg/vuu6/Dcg0NDZg5cyY+/PBDKBQKiKKIG264ARdccAEqKiraTbd/8cUX49FHH23z/euuuw6JiYlYtWoVlEolXn31VcyfPx+TJ0/mOjbkkyoqKmz+f7liHZieyiKI0npgR481obRWhMEDKemVcpk07DBU3TIHTAl5L0i8cTKNRoOjR49iyJAh0ja5XI6rr74atbWOUwnL5XKkp6cjJyfHbuTB4MGDMXjwYIf7ecLatWtRU1ODyMjIPvOkgcgR9iXuI4oiBJ1GyowoaOsgmo3erpZXVVRVQy6XITb6RJ+w/3AFFj74fJv7BKr9MXbEEGSmp0FnMNgEZaefMs6t9e3I38U70CAe9Ghf4taZ1EFBQVi2bFmH5ZKTk3HppZdKr2UyGS666CK8/fbbKCsrQ3Jycpv7VlZW4ueff0Z8fDxGjBgBPz8/6b3y8nL8/vvv+OGHH6RJ4wsXLsSDDz6Ir776CnfeeWc3ro6o53Fm0c2uLArtCq1T0tfrzajXmdDYKiW9oDVC3irDUovk9CyYDXoo/e3fc0aASoFgfwVC1SopCOuNT78AwGg0orS01GYY4vbt25GdnY28vDypnEwmQ05ODlatWgUASElJscmEmJGRgaCgoLZO41XMNEfkfh31Jd7qR7qqo+uxT09f16cXadY0NqFo625pQebC0l04UnUc999wGR646Qqp3OhhqVAplTCZzVAo5BgxOAWZ6WnITB+KrFFpGJY6AEofXdJlQ/EO1DU0ebQvcWtQJpPJcPHFF3dp3z///BOhoaFISEhot9x3332HPXv2YOfOnVAqlfjwww+lIY8lJSUAgDFjxkjl/fz8MHz4cOk9RzQajfRBAoB3lqjXcOaDpSuLQndF62Qc9XprMo6WlPQHt2yUAq0B6VntHqej91v0hblfJ6upqcHPP/8sBWBFRUUwGOyHeBYXF8NgMNjM473nnntw6623Ijs7GzExMZ6sdq/BvoR6q476Ek/1Ix1xdo7eye8JhiZYtPXSIs2CvhF9OT397gNl+OOfouYgbBd27T8M0UHyrMItu2xe+/v54fXH7kRy/1iMGTbYJnU92fPJnMP//PMPnnnmGTzzzDNQKNqOoO+9915Mnz4dSqUSJpMJCxYswLx587Bjxw6Eh4dLcyAiIiJs9ouKikJdXV2bx126dCkWL15st722trbdJwzOaN1Bk1VPbpOVK1fi2LFjNncDP/nkE0ybNq3LxzQ7GG9tNpvtktq4S2fPn5GRIf1B31YZs0VEk9GMRqNF+m5uZxjiodJ86JsaoQ4KRlLqUADWSdPO8lcqEOjX/KWyfvdXygEIAIyAAAg6oL4XJL9q+d2rqqqCKIqIjY2V3ispKcHll1/e5r5qtRqjR4/GuHHjUFZWhvDwcOm91jezXPW7JwiC9N1dv8+uOkdUVFS36+KuvqQnf2a6U09ul97Wl3Tl3M70JZ31559/or6+HmFhYRg5ciQA+98T0WKGYNRC1DfAYmiCqG+A2MeegjUaTnxuHjh8BP3jYqD2PzHy7POf/8Kzb3zQ5v4RYaEYl56G3MwxqNPb9u3TzjoDAGAC7N7rqpYlRQXRdce0P4f1JJ7sS3wuKCspKcGsWbMwf/583H777e2WnT17tvRvlUqFJ598Eu+//z42bNiA6dOnS0MZtVotQkJOLBbX2NjYbrrVRYsW2aRmrqioQE5ODiIiIlzSUbviGL1NT22T2tpauzH7tbW13bqepKQkANbfu5b0wElJSR5rI0frQymVyjbPP336dJvXZosAjcGMep0ZGoN1PpjOJACQN3+pAD9A7ufoaM1kcum7PPDEBN/W/wasyTeC/axp50P9T3zvzU+/AOtnWGFhIfLz87Fu3ToUFxfj0KFDuP/++/HUU09J5U477TSo1Wro9XrI5XKMHDnSZhjiyJEjoVKpPFbvlvnBcrncbb/PnjiHs9zZl3j72nxVT22X3taXdLYfAez7Elc4+fNAtJhh0WkQImisc8K09fap6f0Aa1/V+x09XoONpTuxvngHtu7YjU1bdqGuoREr33veZu2v08al4dnmfweo/TF2+GBkjkpDVrp1UeYBCbEeXe6lZXq3XAaEq93zs5I3X48n+xKfCspKS0sxZcoUzJs3D6+++mqn929Jy1xVVQUASE1NBWBd+6L13ePDhw/j1FNPbfM4oaGhCA0NbfN9Inc6//zzUV1djTfffNPn0wMLgohGY8sQRDM0ejOaWs0DcyWVUo6IID+EqK1JN1qGIfaWdb/as3fvXvz222/SMMRt27ZJT4Ray8/Pt3mtUqnw3//+F8nJycjIyEBwcLCnqkxgX0Le1ZP6EncQjDrpiZdoNqJpx1qIJh2MegFGN/0h7+s2FG3FP0XbpGyIZZXHHJYr3LLTJijLTE/DKw/fjqxRaRg+yHfngfV0PhOUbd26FVOmTMEFF1yA119/3eEfWmvXrkVgYCCysrLQ0NAAPz8/mzkQP/zwAwDrI3AAGD16NPr374/ly5dLafILCwuxf/9+t9yRIeoN2lrIUxRFNBkt0OjNUjKOBjcsyiyXyaQ7VCqFHFlJ4QjxV6KhXoGoqHCXnsvXiKKIffv2oX///jbD27799lvcfffdbe4XHh6OnJwch2tCtjd8kYjIHTy5ILRoNsKib4Cgb7R+GazfIZiljIiiYOlTizUbTSYcKKvE0IFJNtuffO0jae0vRwb0j0XmqDS7xZdDg4NwzQX8u9ndfCIoKysrw5QpUxAbG4uzzz4b33//vfTe5MmTpTlh99xzD1JSUvD555+joqIC559/Pi6++GIMHDgQpaWleOWVV3DLLbdg1KhRAKyPHF944QVcccUVUCqVSEhIwLPPPos5c+YwKxdRG1rupD7xxBOoq6tDQFAwkrLPxOrdx6VEHK7ip5RLCTdC/JVYu+p/OF51FAZtIwBA29iAP1b+r9fe3a2qqkJ+fj4KCgqkp2A1NTX47bffMGXKFKlcdna29G9/f3+MGzcO2dnZyM3NxZAhQ5Cdnd0nnhgSUc/grgWhRcHSPOywOQGHth6i2T550YoN23G0rhGNOut7jToDVmzYjlnjh3e7Dr5GFEXsPXTEZkHmkh17IZfLUb5+uU2a+cxRJxZkjggLQebIocgclYbhaUMxadww9OvlNz59nU8EZXV1dTjllFMAAB98YDuRcMSIEVJQNnnyZCkL2NChQ/Hzzz9j2bJl+PHHHxEfH48ff/wRp59+us3+F198MeLj4/Hxxx9jz549+Ne//oXrr7/e/RdFPsvZbEzO8OTdQHfSGs3S8MN6vQkNejP0zYk4zIKIWq2pW8eXyYBA1Ym08yH+CoSolfA/aQjE8aqjPpGxy13y8vLw119/SQHYwYMHHZYrKCiwCcoyMzPxxhtvICcnB+np6TZLf1RXVzMgI/IC9iXuJxi01uBLV9/8vQHOZEE8WteIw1V10muLIOJoXaP7KupBRpMJv60vlIYgbtqyC7Uax9e2fe9BjB42SHo9b/rpSB8yEJmj0jAwMV7qO+r0gtvmZvkkuRIypZ/1S6Fq9W+/Vv8u8Hi1fCIoS09Px3fffddhuWeeecbmdWJiIh555JEO95s8eTImT57c1epRL+PKdYzcdTcQcG5Nsa5oSUXfkoZeozfDZHFd9qKW1PMtiy5b534poeiFCy+3xWw2Y9++fRg6dKjN9kceeURa+8uRpKQk5OTkIC0tzWZ7cHAwbrjhBrfU1VPMZrOUjt9gMEh/eBL1VH29L3EHwaCFpakGlsYaWJpqHD4F60satTrU1jcgKf7EsiSiKOLyRU/A5CDDJWBdjmr4oAHIHDUUfirbP/PTh6YifWiqW+vsbmaLAKPJeu1GkxkWQYBS5W8TVKF1oNU64GrZLnciAPXCzU6fCMqIfJEr74J2hSvOaTQL0vwvTfN3Yzup6Duj5elX68QbIb144eW2iKKIAwcO2CzIXFhYCIvFAo1GYzPvtfWCzGFhYTaZELOzs3vtnXGz2Yy3334bWq11WQOtVou3334bCxYsYGBGvV5v6EvcRTDqpADM0lQD0aT3dpW8xmQyY/veg9a1wEqtT8G27z2EKeMz8M0bT0jl/P38MHpYqrQmWFJ8DDLThyIzPQ1Zo9IwZvhghAQFeusyXEAGmVJ10lMrazBlkSnwwRc/QGe0BmU6oxmf/n2g1/QlDMqoV3BHp+fKu6Du1HLt/v5qjMoe3yoRhwl6k2sCMKVcZpN2PsRfiSC/vvX0q4XJZLLJhJifn4/jx487LFtSUmIzH2zevHkYMmQIcnNzMXjwYCldc2+3ceNG7Nu3z2bb3r17sXHjRuTm5nqpVkT22Je4L3gURRGCvqF5QeZ6WJpq+1TyjZMdr61vtSDzTmzevhc6vf2TwY1bdkIURZth6vcuvAyCICBrVBpioyM9We3Ok8ltgyvFyU+wbIcPQqFsc0j+P//8g/0HD9ts6019CYMy6hWc6fSWL1+OiooKm8U5ly9f7tEkEq7q9ARBRIPBGnyt/uNPNGnqoQ4OhSGu+5OYA1QKhPgrkZF7KmA2Iiw4EFOG9uv2cZ3ha/MqtFotjh8/juTkZJvt5513HvT6tu/oDh8+HDk5OVCr1TbbR40aJSUi6ktqamocbnf1IrYcIknd1df6ktZcHTwKRj0mZI2FtrEefjILmratBjy0KHNsuHX5jyPH62ERRCjkMmmbNxyrqbOudxV+YomMLTv34Zp7n25zH38/FUYPG4Ss9DTo9AYEBpzoT6ZP9nIAIpNDpvSHTOUPefN3u2BL6Wcto3BdqNHb+xIGZdRnVFRUuC2JhLNj9rva6bUk4qjXmVB3Uir67mREDFApEKpWIkytav5+YuHlcYlndfm4XeXOeRUdMZvN2LZtm80TsC1btuD000/Hb7/9JpVTqVTIyMjA33//DQBISEiwGYKYlZUlrZlIVpGRju/kunJBTg6RJE/pyX2JO4hmY3Mq+qYTX/oGiGYDsvorATT/P/dQQAZAyrL44td/ob5Jj+AAf49lXmzS6lG8fY+UiKOwdCcOHjmKJxbNx+1XXyCVGzfyxJxjmUyGtNQk6xDE9DRkpA9F+tCB8FOpPFLnExVxEGyp1LbblP6QKT1cr2a9vS9hUEZ0kq7cBXVlp6gzWVDTZES10OjSRBxqlRyh/ieCr1C1Cn7KvjF8zpGamhqsXr0aeXl50jywlg/h1goKCiAIgs1QwwceeAAmkwnZ2dlISEjwZLV7pKysLGzatMlmCOOgQYOk9SNdgUMkydd4uy9xNcGotw4/NJwIwERDE0RL97Lz9nR7Dx3B2vzNUhC2bc9BCIJ9n124ZafN67CQILz04K0YkpKIsSMGIzQ4yH2VlCsgU7YKtE76rm7QISgmzqVPtdyht/clvt365HXenqDsDe68C3oyrdEadGkMZpsATNA2QR7Y/h2Z7et+QWPtMRi0TQAAg7YJ29f9guGnnW0TgLU8CevrAZgoijZ300pLS3HhhRe2uY9KpcLYsWORk5MDrVaL4OATQ19mzpzp1vr2NkqlEgsXLsTixYuh1WoRGBjo8ruOnhrWQl3DvsR3l/hwFDx+/cVnOHfqJGsQ1vzV14MvURRxsPwoEuP6QdlqOZcPv12Jpcu+bHO/sJAgZIwcipwx9k/qrruwm31J6ydbrZ5oyVRq2wCsg2BLZqj2+YAM6P19ie//BMir3DFEojOdc0dlfWFsvzO+/vprlLeqZ71Gg5eXfYxhE87u8jEba4+h7mi59FoULBCbanHG4Og+HYDpdDoUFRXZLMi8Z88eLFmyBPfdd59ULjMzE3K5XLqjmZaWZpMNccyYMTaZE6l7FAoF/P39odVq4e/v7/JhIJ4Y1kJdx77ENVxdT1EUUVFehgOHTiRPsFgsKD+4F4ZyH08g4WbHa+uxacsuFG6xLsi8acsuHK+tx/ovX7NZ+ysz/cQSJn4qFUanpSJzVJqUEXHwgIQuJXWSKVTWoYPNwZZcpT4RcLUEYF4aRuhNvbkvYVBGHteZzrmjsp25E+mpJBKCIKLReOLJl8Zgxo79h20CKMFiQUPNsU4fW6WQS8MPt/kpUWf3vqxXBGSdWVdn3759+OOPP6QArLS0FGYH67fk5+fbvA4ODsbrr78uDX0IDw93VfXJCzwxrIV8S2/vSxzp7tM3waiDoK2HpWUxZn0DLPoGN9TUN5wyIhl6oxlqv47/3M3fvB35Jdub09Hvwv4yx+26cctOm6Bs/LiRWPrAzcgclYaRQ1Lg7+fnRM1k1uDKL6DV9wDI/NSQq6zbnFpLi1zOm30JgzLqM9yRRMJsEdBgOBF8NejNaDSaIXY994ZEpZAjpHkR5pZALLBVx6JS9N509I7+aBJFEYcOHUJ8fDxUrSY/v//++3j88cfbPFZwcDCys7Nxyimn2L23cOFC11SYvM4Tw1qIAO8mJHKWaDFD0DfCrDkGg+GYNBdMNBu9XTWPGj9igN02i8WCg+VVGJAQZ7P9nmfekNb+ciSuXySyRg1DQmy0zfZ+keG4/uLZDvaQQeYXALl/EOR+gdbv/kGQ+QVan3h5YXFi6pg3+xIGZUQnaesuqN5kQYPBLAVhDQYztEbXZJPyV8qbgy8VQvyVsGhl6H/SB39fU1tbazMEMS8vD1VVVcjPz7dZ+ysnJ0f6t1KpxJgxY2yGIaalpfEP8z7C3cNaiDrDY0/UBAtMtUesCTj0jdb5X82LMJv0AkzqvvvERRRFHK6osg5BbH4CVrRtN/z9VDi49kubwCgzPU0KykKCApExcigyRw2VMiK21SfLlH6QtQq6WgIwmV8gn3b1UN7qSxiUkUPuGF/f1jFPP/10j5zfWeeddx60RgteePZpaOrr4B8YjMgxk/HnXtdO8lSrFMhMDHOYBbHa2PEHua+t6dVdBQUF2LBhgxSE7d6922G5k4Oy8ePH4+WXX0ZOTg7Gjh1rtzYYEXlPX+5LXPlETRRFa6ZDs32yDYu+AYay0i7X09fW9OoOg9GIvwpKmoMw63ywYzV1duW0Oj32l1UgNam/tO3iWVOQmZ6GzFFpGHLSPDCZQmV96uUXBLl/IGStg68ekCCDegb+JpFD7sga1ZljOlu2u4GJRRDR2OrJl8ZgRqPBDIsgwtichl4QRRjNnU9JL5MBgSoFQtUqJPTvj2B/JSrKy6R6DkxKQHRw1xNJ9IQhNI4IgoCysjK7BZnvuOMOae0vR2JiYpCbm4ukpCSb7VFRUbjtttvcUte+pi9myCP36it9iauIgiClmm+del4wNAGigH6BMggx4S4NoLy5pld36PQGaBqbEBt9IjGD3mDC3BsfbHe/1OQE5IxOg8VmqRkZcjLH4ZRTJ0CmCoDcP9A6t8s/EHK/QAZencS+pGv4W0Y9WmcCE6PZOv/L3LzYst4sYPXuYy6Z/yWTAUF+zfO/pHlgKijk1qERoy+7yOl69iaiKKK8vNxmQeaNGzdCJpOhtrbW5k5kTk6OFJQFBwcjKysL2dnZ0jDEwMBAREf37SGd7uZLC9ISeZKnb3KJoigNNZSGHRqaIBp1ANrulHpqANVdFosFu/YfxsYtu5oTcezElt378X9TJuCD5x6QyoWFBGFISiJ2HygDAMRGR1iHH44ahqxRQ5ExOh2ygAhERkZYA6/W87w4x8tl2Jd0DYMy6pV0JsuJp196ExoMZuhN1rti8cMz0M+gh9Jf3aWATCYDgv1OJOBomQfWEoD1ZUajEWvXrrUJwtq6g71r1y4MGzZMen3JJZdg1KhRyM3NxbBhw+zGcHO9KSLyJc5nibV2NKJghr5sS/O6X42A2PkRGH3F8dp6rN9Yio3NCzIXbd2NRq3Orlxhqe2CzJDJ8e/broOfOhDZWZlISk6xBl+thhpWV1dDzaUyyAcxKKMerWX4YcvTL4NZwOpdx6TXjgxIdy6taXJ6FsxGPQIDAtA/TI1QfyXCAhiAtTAYDKirq0NsbKy0zWg04pxzzpHW/nJk0KBByMnJgXhSRNzyRIyIqCdo/QRAFEWIRh0Ek6556KEWgkkHQd+InMEx0BsjofZTwlxb3s4R+6ZaTQMUcjlCg4OkbX9v2oLL73qizX2USiVGj0hDVsZYKOJHwC8gxDrnS+WPq9KneqLaRC7HoIwccsf4+s4c01HZfrFxqGowSBkQGwxm6EwWiCJgajX/q72ArCMBKgXCApQIU6uQPeMshPoroVT4dvakzqzp1VWCIGDXrl02T8CKi4sxa9YsfPPNN1K54OBgjBw5EqWl1knn/fr1s8mEmJ2dzcV8ifoQX+xLunN+waiHYGyZ89UcfBm17Q47dJSW3Rd1Zk2vrtIbjCjZsReFzU/ACrfswp6D5Xj54dtw7QUzALkCcv9gnHLaGQBOLHUyZMgQm76ESZ2oN2JQRg65Y3x9W8c8eViaRRAxZcb/oUFvxrJXn0ejph5+AUEIHzUJReX1Do+dnJ4Fc/OQRGf5KeUIU1sDsLDmYYg9ceFld4zXrqmpsRmGWFBQIGUvay0vL89u24MPPghRFJGbm4sBAwb0mXH6nNhMZM+bfUlXzj9x4kTotE1Q+6lgrj9qk2hDMDQBgv3C9L2FO4LHfYeP4O9NW1DYnAmxdNc+mM32S8kU7zuGwCETIPMPgkwmQyqA5557DqNGjUJ2djYiIyPtD95LsS/puxiUkVfpTBbUak2oE5vsnn4BkJ56dfTsq6MhiUq5DKFqFcIClNIwxACV59cw8sRTrc5qCbZCQ0OlbRs2bMDcuXPb3EehUCA9PR05OTkwGAzw9z+RRfLCCy90X2V9GCc2d8wXf/+pjxEFmOsqIVqMEM1GCCaD9d8mA0SzERlRRiBCDsAC/aFib9e2TZ54qtVZR44eR3xMlM2NuNc++hZvf/6/NvcJCgpCVlYWxmTlQq62zSL5r3/9y2119WXsSzrWW/sS3/nfTG3qDXdNzBZBCrpahhrqTBas3VsNQdsIeaDrnlDJZTIpCUfLU7Agf9/4Vff2z89oNKKkpMRmGOKOHTvwyiuv4JZbbpHKtV4HDABSU1NthiBmZGQgMDDQ09WnHs7bv/99WW/oR5wlmAythhdaU8u3LKYsmAzQH97s5Rp2n7eHRNY3NKFo667mRBzWp2AVVdUoWfUJBg8eArk6GHL/YIw/c4YUlCkUCowePdqmLxkxYgQXeadO662fYb7xlyqsC8f+5z//QVFREcLCwjBjxgzccccd7UbBzuwzb948FBQU2Ow3e/ZsvPrqq267Fmd0poN09q6JL3S6LYk3Go1mNBos0r9bMh8C6Nacr5O1ZEIMC2gZgqhEiL+yzwyZ68iBAwewbt06KQArKiqC0Wi0K5efn2/zOiYmBs8//zxGjBiB7OxspqKnHmPixImoqanpM8OdnP3c78zdd1/oSzoiChYpe6FoMUN/qASGulo0VpodDjE8ObEQdY51Mead1nT023Zj175DDsttqZZh1NknbupNmTYTL774ojQPjDfzqKfwRl/iE0HZli1bcMstt+CWW27Bv/71Lxw+fBi33HILNm7ciOXLl3drn4qKCsyZMwd33HGHtC0oKMjBET3LHY+n3XHMth4Ri6JoDbqM1qdfjQZrEKYz2Y8VP1ln5n+dXDZApUB4gKr5KZjtWmB9XWVlJWJiYmzW/nr55Zfx0ksvtblPQEAAMjIyMGrUKLv37rrrLndUk8itJk2ahOrq6j6TUKan9yXtEQULBIMWokln/W7UWp98GbUQTXrkDImF3hhlzWpYXwHBIABqx6MufHG4ny8SBAHHauoQFxsLWXMaeblfIBY8cjN27d7b5n7R0dHIzc21+wM2KSnJ5u8vop7CG32JT3w6DR8+3CZhwKhRo/D8889LE3cdNUhn9gkPD0dKSopbr8Edli9fjoqKCmnOj0ajwfLlyz266PCkSZNgMFvQoDdjf7X2RABmNNut8XVwy0YpgGpvjpezKelVCjkyc09FWIAK4Wrr0zCVj2dC9JSGhgYUFhbaDEM8fPgwtm/fbrP2V+sU83K5HCNHjrTJYDVy5EioVCpvXAIReYAv9CNA28ONRLMJgrE5g2FzJsN1BcXQ67TwV8raHabXmSF83h7u56uO1jagcPsBFG7dg8KSbdhYvBnh4RE4cOCATblTxk+QgrLAwEBkZmba9CV9KakTkbv4RFDmaDyxxWJ94tL6rn9X93n//ffx2WefIT4+HjNnzsQdd9zhtT9EV65cidraWqc6yIqKCpsPRovF4nAhXld1uqIoosl4YtHlli+j2bkFLg9t2Qh9owbq4FCnA68WMhkQ4q9EeIAKYWoVwgOUCOQdTUlRURHy8vKkAGzbtm0Oh+Pk5+fbBGWTJk3C888/j5ycHGRkZPjEU2LyTWazGQaDAYB1DbqW9OHkm5z93He2H+nMMTtDFEVrEo3mp13W9PEn0siLFpPdPhs270R9kx5hQWoGU64ik8MoUyG/dA8Kt+7Gxs1bUbCpGOXl9uum1ddrcPToUZs1KK+++mpMnDgROTk5GDFiBJRK9s/kGPuSrvPJ/1V6vR5PPPEEpk2bhoiIiG7tM3jwYCxcuBDjxo3Dli1bcPfdd2P9+vX47rvv2jyWRqOxSf/dVgfWFceOHUNZWZn0ur0O0lmd6XRbtE680RKENRosEDww7l4ukyHYX2HNhqhWQtCJSIztBzmHIUIURVRVVdl1eFdddZW09pcjkZGRDtMGJyQkcBgidchsNuPtt9+GVqsFAGi1Wrz99ttYsGABO9NucGdf0pXPfVcfUxQsEM3NmQtNeggmPcTmL8Gkh2g2QDQZ0HH+XHIlmSoAZoUaOoscfqFhCIzrD5lfIOqPHMH0S85od9+UlBTk5uZKnwUtzjjjDJxxRvv7ErEv6R6fC8osFgsuv/xy1NbW4ueff+72Pu+++6705Cw9PR2xsbE488wzUVhYiMzMTIfHW7p0KRYvXmy3vba2ttvpN1ue5rVmNpsdrq9iNttPVnZUtr1yoijCaBGgNQrQmizQGq1fBgfrhHRb86RriAIErfUPEZlMhkA/BYL9FQhUKRDkp0SASgaZTARgBCxGaPRa1NbWuL4+PUBVVRWKioqwadMm6btarcbff/9tU2706NFSUKZWqzF69GiMGzcOGRkZyMjIQEpKijR0xNHvUm/gaJ00XyIIgvTdkz8DV7RLcXEx9u3bZ7Nt7969+OOPPzB27NhuH9/TXNEmrphH4K6+RKPRuKR/cLT9ZCaDFkf3lEA0myBabL+kz3wXa8kFJYhAnd75czQa3FMfXyWTKyDzCwSUahw4cgzFW3eiqHQLioqKsWXLFlxwwQV4/PHHoWjUA9BDrVYjLi4OlZWVAKw38zIyMqS+ZNy4cTZJndiXeIc3+hJXtQn7Enud6Ut8KiizWCy48sorkZeXhz/++AP9+/fv9j4nD2U89dRTAQDbtm1rMyhbtGgR5s+fL72uqKhATk4OIiIiut1RO7pToFQqHR7X0fAAR2UdlTOKMuxulKPRYIY1A728+UsF+AFyvy5egAPb1/2CxtpjMOh01nPrdajaUYg5c89DiL/SqSdg3pqU78ksY0ajERs2bLBZkPngwYMOyzY1NWHgwIHS6+uvv14aOpKent5n54H5cvKGls8auVzu8Xp293wtQ00cbfflNm+PL9TbnX1Jd/oHpUKOMH9Y53CZdNKTLrlJa1dWbjEiSNfqaZkM1r8clIC1T3GdFRu242hdI7R66++jVm/AuqKdmDV+uNPHCG8j0Ye7bdh2UEok4vIhl3IF5P7B1jTz6hDUNhmxobAEGzcVSX1JXV2d3W4lJSUIDQ21+Z148sknERgYiJycHAwcOLDPzgPzhc+HtnirL3HFudiXdI/PBGWCIOCqq67Cn3/+iT/++AODBg1yyz779+8HgHZTXIaGhtospOst8fHxAIDDhw9LY3JbtmmNZjQYLGgwmNFktH/qZTQLqNfZj9V3JbVKjlB/FUoba1B39MS4dMFiQUPNMYQF+H7g4K5FGk0mExoaGmx+z+rq6nD66ae3u19ycjKys7Ptho5MnjwZkydPdln9qGdy102Etj4Pe2on6iu82ZeIogjRqEVsVBhEUxzKKqtgsQhQyGWI9heg37/RwT7efdJ0tK4Rh6vqpNcWQcTRukbvVagT/tl2qPvz4GRya7ZDKQALRpMJUAUE26SSX/XTh7jqqqvaPExLUqfx48fbzT2+9tpru1Y36jXceTOafUn3+ERQJggCrrnmGvzxxx/4448/MHjwYIfl5s6di8TERLz66qtO7bNt2zb8+OOPuOGGGxASEoKKigrccMMNGDBgAM4880x3X5ZD/fr1g1KpdBhonez888+HIIh48qknUV9Xh4CgYCRmnYnVu47ZrPXlHxqFcIuA+mOVEAULZHIFgiP6ubTeLQFY61T0fkrr3Zz1yr6dEVEURezfv98mE+KmTZswb948fPDBB1K5mJgYDBgwQHo6Fh4eLi2gmZubi+zsbMTFxQHovcNGeitPTWx2102ErKwsbNq0yWbYyaBBg5CV1bmEPeQZoiggLiYaosWMsiMVzb9vcsQEq6Dd84/NXK7p6TFAegxe/Pov1DfpERzg3+aTp9jwYADAkeP1sAgiFHKZtI1cTK6AIiAM8sAwKNShkKuDYZb7YcuWLchf/bdNUqdPPvkEl1xyibRr66y6ADBgwACpH2lJ6hQcbP25sS/pWTzRl7irHwHYl3SXTwRl69atw4cffojw8HCcddZZNu99++23GDduHADr0A9/f3+n9xk4cCA0Gg0GDhwIuVwOjUaDadOm4ffff+/23LCumjZtGqKiovDkk0+itrYWoaGhUmYrU3PyjZbEGxq9GU1GMwzN2Q/Ngog6B0+/hp92NgDgr8/fhL5RA//AIGlbW9pLX6+Uy6xp6JsXZA5rFYCR9YnX33//bROEOer4Tl6QGQAeeeQR+Pn5IScnB4MHD+6zQ0d6k94wsVmpVGLhwoVYvHgxtFotAgMDe1T9+xpz9WFMGx4BDI/Ai18fR32TBcFqP8zITIGgq+/ycVuCNWcCOMDNQ/Z6GZlfIBSB4VAEhkMeGA65OhgHDx7E37+f6EuKioqg1+vt9s3Pz7cJyoYOHYpHH30UmZmZyM7OtsmSSD0X+xLyiaAsJydHGlZ4stZPkb799ltpfLwz+wQEBODxxx/H448/jmPHjiEqKqrNFPue1jKiwGQRsamsDg0GM/Qmzw0daZ2+fkRGLsKbg7DwABWC/BQMFpo1NTVZk5W0Gjry888/49JLL21zH5lMhuHDhyM3NxeCINj8zl1zzTVurS953saNGx1ObN64cSNyc3O9VKvOUygU8Pf3h1arhb+/PztR6pBLhuz1RtJTsPDmQCwM1XUamyQagPUm3YcfftjmYfz9/ZGRkWEzvxiwDk985JFH3FJ18h72JeQTQZlarXZqcefWAZqz+7To18+1w/m6otFgxvajjTBUC9A3Zz80CwKONRo9VgeFXIbwABWUzYGCWqXAaandG+vb3ty3nsRsNmPbtm02T8C2bNmCd999F1deeaVU7uShI4mJiTaLaGZmZvrEnETyjJoax5lDOWyIqHN66vBJuX8QZHLrH55ylT/E5FwUFBUhP/9PqS85ePAgKisrbZ5q5eTkSEGZTCbDiBEjbPqS9PR0+Pm5MCsX+TT2JeQTQVlfYTALaNCbIA/s3JotyelZ0lDDzgpQKRARqEK42voULNjf+hRslcL6JMwVz8Nahl86GpLpyw4dOoSmpiYAQGVlJcLCwuwSbADWoSOtg7LU1FQ88sgjGDt2LHJycpzKEupOnswgSfY4sZl6glNGJEtDDX1VZ4dPeoVcCUVgmHUYYkAY5AFhKNm6DY066zygIxWVCA8Pl9Kat1ZQUIBZs2ZJr6dOnYpnnnkG2dnZyMrKQkhIiMcuwxH2Jd7FvoR899O5l+tMoHXynK+2pIzKgsJiQmhwIMYlhCE8wH4u2PLly1FRUSGtvaDRaLB8+fIeEUR1R01Njd0H3mOPPQaZTIawsDDo9Xq7gMzPzw9jx461Gzoik8nw6KOPurvKTnPnpF3qGCc2kzc5G2x1ZnhhR8dsSV3fEog06gxYsWG77wVQLiD3D5KGIcoDwlCnNdr9kTxr1ixMnz4dYWFhMBqNdgFZeHg4srOzoVbb9vdDhw7FPffc4/ZrcBb7Eu9iX0IMyrzE2UCrPUF+CmtCjpanYGnTO5wLVlFRgQMHDkivLRYLKioq2t6hB9LpdCgqKrIZhrh3716UlZUhISFBKpeTk4OCggLp9bBhw2yGjowePVpKLEM9n7vuAnNiM3mTO+ZydXTMnpy6vl2tn4IFhqOm0YiNRcXIz/9N6kvi4+NRWlpqs1t2drb0b5lMhlNOOQXZ2dlSdt0hQ4b4zHx26h53Pk1kX0IMynoIlUKOMLVSyogYHqCCUuFbH/ITJ06UPqw8qbS01CYAKy0thcViv3ZbQUGBTVA2ZcoU7NmzB4IgIDk5Gdu3b/dktcnD3HkXmBObiVzHM0MtZda1wAKsQZhZoUZh6Xbkr1or9SWOkonV1taisbFRSjkPAPPnz8dff/0Fs9mM5ORkvP32226sN3mTu58msi/p2xiU+SCZDAhVq2yCsEAfngfQwt3DHURRRF1dHSIiImy2z549W1r7y5GQkBCHQ0cGDBgAtVoNrVYLk8nktrWliIjIee54+idTBUAREAp5YBhEVRD0ohLBESeGtO/dsQMTJ05s9xgJCQnIyclBXV2dTVB2zjnnIC8vD2azGUajkX0JEXWJ7/+l3wcEqBQn1gQLUCHUXwm5nCnpa2pqUFBQYPMULDIy0u6JVm5urhSUqVQqjBkzxmYYYlpamt3Qkd6wHgj1LZ5aoJqop5Mp/aVFmWUBoSirqkXBpmLk5/+A/Px8FBYWYsGCBXjxxRelfYYOHYrQ0FBpvnVoaKg0BLFlGGLrkRYt2JdQT8J+xLcxKPMwhVyGyCC/Vk/BPLsws6+mrzcajSgsLLQJwPbs2WNXrqqqCvX19QgLC5O2XX311TjttNOQk5ODMWPG2D0Rc6S3rAdCfUN7f/gReZovpa6XKf0gV4dCERgGP4MMBnVQcwB2YhjisWPH7PbLz8+3eS2Xy/H4448jPDwcOTk5GDp0qFPzwNiXUE/BfsT3MSjzoIgAFTISwxAdHe61OvhC+nqLxQKtVmuT/vfw4cM49dRT290vNjYWubm5qKurswnKpk+f3uk6cD2QzmGqZO9q7w+/wYMHe6lW1Fd5LXV9cyIOeUAoFAFhMMAPSnWglJBJUV2Nzz//HLfccku7h0lLS0N6errd9ttuu63TVWJf4jz2I97FfsT3MSjzILlc1mF2xN5GFEWUlZXZPAHbuHEjrrrqKvznP/+RyqWmpiIyMlLq4IKDg5GVlWUzDDExMdFl7cf1QDqHqZK9q70//FzZmXorWQ+RIzJVABSB4VAERQDqUOzcfxj5a070JSUlJfjuu+9s1v7KycmxOUZ8fDxyc3OlfiQzMxPh4eEuqyP7EuexH/EuT/UjAPuSrmJQRi5VX19vE4Dl5+ejsrLSrtzJQ0da1v4KDg5GTk4Ohg0bBoVCId1Z279/P5KSklxWz96yHgjHh/cNnvrDj38okfdYsyEqgiIgDwzHkepGFBRtRn7+CulmXlNTk91e+fn5NkHZ6NGjcd9990lBWEJCgtSPqFQqlwZkAPsS6jk8eQOBfUnXMCjro1xxF0Ov10Mmk9ms5fXFF19g4cKF7e43ePBgjBw5EqIo2jz5uvXWW+3KuuvOWm9YD4QTzPuO9v7wq6ur817FqE/rTup6mUIlLcrcaJYjIjYRMsWJ49xxzVx89913be6vVCoxZswYxMbG2mz39/fHkiVLbLa58wkN+xLqKdiP+D4GZX1UZzsmQRCwc+dOmydgmzdvxhdffIG5c+dK5U4eOhITEyNlrsrNzUVWVpbPDOvo6euBcIK583r6XeDe8Icf9T7Op65vfgoWGA6jXI3S3QexsfjE+pL79u2zSzOfk5NjE5QNHTrUJhvi2LFjnUrq5AnsS/oG9iPkbgzKyKEjR44gLy/PZh5YS6rg1vLz822CsvT0dNx9991S55mcnNzn5tF5CieYO8dTd4HdPYa+p//hR32HTOkHeUAY5AFh2HnoKApLd6Bg40bpZp7ZbLbbZ9OmTTY3C6dPnw5BEJCTk4OsrCy79SnJddiXdMyTTxPd2ZewH/FtDMoIjY2NNncoAeDOO+/El19+2eY+CoUCo0aNshs6olQq8eyzz7qlnmSLE8yd46m7wBxDT32TDPKAEMgDwqATVAiLSYDcPxCAdYTFxKEZDm/otQgODkZmZiZEUbTZPnbsWIwdO9adFadm7Es65smniexL+i4GZX2MwWBASUmJzTDEXbt2obq62mYCdE5Ojk1QNmjQIJtFNMeNG4eNGzdCp9Nh7dq1/BDxgt4ywdzdeBeYyIXkCigCw9FgUqBo535sLNmGggLrU7CBAwdi/fr1J4rK5cjOzsbq1asBWG/ajR492iar7rBhw7B+/Xr2JV7EvqRj7EfIExiU9WKiKGLXrl02AVhxcTGMRqNd2Y0bN+Kss86SXp999tlYvHixNAzR0R0zprf1Lo4Pdw7vAhN1nUzpD7MiEKX7j6Bw625sLCqRbuadrLa2FiaTCSqVStq2YMECzJ49W7qZ52hIFvsS72Jf0jH2I+QJDMp6kaamJgQFBdlsmzBhQrt3cgICApCZmWm3fdSoURg1apTL60iu5Ynx4Z6Y3OzORUV5F5ioE1QBMCoCERLdH4rAcMj9A1FQUICJ0+a2u1u/fv2Qm5uL2tpaxMTESNsvvPBCd9eYXMDdfYmnkmS4qy9hP0KewKCsh9JoNCgsLLR5ChYfH2+z/pdMJkN2djZWrlwJwDqUJD093WYYYnp6OpRK/hq4kzsDDnfz1ORmpqwm8gYZjmoM2LTzEAq37sLG4lIUbCzE7bffjscee0wqNXr0aPj5+UmjLAIDA5GVlWXTlwwYMIBJndyI/YhzuIwO9WQ+9dd4QUEBioqKEBYWhsmTJyMuLq7DfY4fP46ffvoJjY2NmDBhAsaMGdOlMr7MZDLZzQPbvn273cToo0ePwmAw2Kwbdt1112Hq1KnIycnBuHHj7J6k+bKenn62RU8emtNbUiUz4xQR0GAQULzrMDZt34uNm7ehYFMRysvL7cq1vrkHWNf+euSRRxAbG4ucnBwMHz68x9zMYz/ifexHiJzjE5+qR48exbnnngtRFDF27FgcPnwY1113Hd599912hz4UFhZi6tSpGDNmDPr374977rkHDzzwAB544IFOlfEloihCr9fbjLvfunVrh4/IIyMjkZOTg+rqavTv31/afsEFF7itru7ExSx9Ayc3E/VMJgugDAqHf2gU5AFhUASG4T/PL8X999/f7n6pqalISUmx2+6rfWZ72I/4BvYjRM7xiaBMFEW88sorNgsPP/LII7j22msxd+5cm0nDrS1YsABTp07FF198AQD46quvcPHFF+P8889HWlqa02W8qbKyEgUFBdITsIKCAixcuBBLliyRyowcORIBAQHQ6XQAALVajczMTJuhI6mpqb1q6Iin7qy5e22pno6Tm4l8nyAI2HukBkW7DqFwyy5s3LwFxZtL8Ouvv2LSkMFSudZ9LABER0fbZELMzs5GdHS0p6vvNp58QsO+pG3sR4ic4xNBWVxcnN1QxcmTJ+Oxxx5DRUUFkpOT7fbZu3cvNm3aZLMm1vnnn4+oqCgsX74cDzzwgFNlPKmxsRHr1q3Dzp07pSDs8OHDduVOHjqiUqnw0EMPISoqCjk5ORg5cmSbgWpv4ak7az1tGIincXKzb+EffgQAR49XY23pZhRu34fCku3YWLQZ9fX1duXy8vJsPuOysrKwaNEiKQhLSUnpVTfzTubJJzTsS9rGfsS3sB/xXT4RlDnyzTffIC4uDklJSQ7f3759OwDYPO2Sy+UYMmSI9J4zZRzRaDQ2i11WVFR0/UJaeeutt/Cvf/2r3TLJyckYMGCA3faOhpz0Nryz5hs4udm38A+/nsVdfcnt/34Sf/75Z5vvy+VyjBw5EiEhITbbQ0ND8cILL7ikDj0B+xHfwH7Et7Af8V0+GZR9++23eOONN/D555+3eRevoaEBAGwWPG553fKeM2UcWbp0KRYvXmy3vba2tlt3Fk4eLhkeHo6MjAyMGzdO+t6SStjXx1pbLBbo9XoAgF6vR1VVVZc/YFv/0dIiNTUVycnJOHTokLQtOTkZAwcO9Pm2OZkgCNJ3Z+vuqE1cefzOankyq1KpUFdX5/LjO3MNnWmTrp6jJ+puu/RGrmgTV/zh7q6+ZOTIkTZBWXJystSPZGRkYPTo0VJSJ1//XXdnX9LX+xHAt/oSd/cjgPv7EvYjfYun+xKfC8p++eUXXHrppXjuuecwb968NssFBgYCsDZYcHCwtL2+vl560uRMGUcWLVqE+fPnS68rKiqQk5ODiIiIbnXUZ5xxBhYsWIDJkycjJycHgwYNcuvQEXel0G2ZPN0yx02n02H58uXduvPlqF1vvvlmmztrN998c4+8syaXy6Xvnfn9cbZsV4/fGe4+h7PH7865PdFO3tLbrscVfKFN3NWXzJ49W5oPlp2dbbMumKu5MxW7J/qSvt6PAL7Tl/hSX9XV87Mf6Xs82S4+FZT99ttvmDNnDh577DEsWrSo3bItT5327t0rZRsURRH79+/HWWed5XQZR0JDQxEaGtrt6zlZUFAQnnrqKY/9gN2VQtdTk6d7Q/pZT6Rj7unjw3tLymqik7mrL8nOzsa0adNcflxH3JmK3RN9CfsR57EvIfIuubcr0GL16tX4v//7PyxevBh33323wzKffvopVqxYAQAYNmwY0tLS8OGHH0rv//LLLzhy5AjmzJnjdBnqPKa3dU5b6ZgtFotLzzNp0iScc845PXKcuKfaiIh8D/uSjnnyM5J9CZF3+cSTst27d+P//u//pOF8zz//vPTeZZddhvj4eADAK6+8gpSUFMyaNQsA8MYbb2DGjBlobGxEQkIC3nvvPdx+++0YO3astL8zZahzOHnaOb1lwUx3Yspqor6LfUnH2I84h8voUG/gE0GZXC7HjTfeCMC6bldrJpNJ+vdll11m8yF+xhlnYMuWLfj666/R2NiIzz77DGeffbbN/s6Uoc5helvn8C5wx5iymqjvYl/SMfYjzuEyOtQb+ERQNmjQIJunY2259dZbHe577733dnj8jsqQ85je1jm8C9wxthFR38W+pGP8jHQO24l6A5+ZU0Y9S8vkaQA9dvK0u2VlZSE1NdVmG+8C22IbEfVt7Evax89I57CdqDdgUEbkJi13gVuWZujJd4EnTpyIqVOnYuLEiS49bm9qIyIiV+tNn5Hu6keA3tVO1Hf5xPBFcj2mhvUNvSEdM+DecfS9pY2Iehv2I76ht3xGuns+Vm9pJ+q7GJT1Qm2lhu2Jd42Y6YiIyPPYjxAReRaDsl6oN6XQZaYjIiLPYz9CRORZnFPWCzGFLhERdQf7ESIiz2JQ1gsxNSwREXUH+xEiIs9iUNYLMTUsERF1B/sRIiLPYlDWC3kqNaw709sSEZH3eDLFOPsSIiIm+ui1PJEalpOniYh6L0+lGGdfQkTEoIzI7ZiOuWNsIyKitvEz0jlsJ+rJGJQRuRnvAneMbURE1DZ+RjqH7UQ9GeeUEREREREReRGflHnJ2rVrpUfsvLNDRERdwb6EiKh3YFDmJX/99Rdqa2sRERHBjpSIiLqEfQkRUe/A4YtERERERERexKCMiIiIiIjIizh8sRdjalgiIuoO9iNERJ7hc0GZIAgwGo1Qq9XtlhNFEQaDweF7KpVKWuTSaDRCEASb9xUKBVQqlWsq7MM4v4CIiLqD/QgRkWf4zPDFvXv34p577kFsbCwCAgJw/PjxdssfOnQI4eHhNl9hYWEICAjAu+++K5U788wzERwcbFPuuuuuc/flEBEREREROcVngrKnnnoKUVFRWLp0qVPlBwwYAL1eb/P19NNPw8/PD3PmzLEp++CDD9qU+/DDD91wBURERERERJ3nM8MXly1bBgBYuXJll4/x7rvv4txzz0W/fv3s3hMEAXK5b8SgZrNZGnppMBhgsVik4ZZERETOYF9CRNR7+EaU4gJ5eXnYsmULrr/+erv3nn/+eahUKsTFxeG6667DsWPHvFBDK4vFgrfffhtarRYAoNVq8fbbb8NisXitTkRE1LOYzWb2JUREvUivCcqWLVuGgQMH4qyzzrLZfvrpp2PNmjXQ6/VYuXIlioqKMHv27HY7Lo1Gg7KyMumroqLCZfUsLS3Fvn37bLbt3bsXGzdudNk5iIjI+9zZl2zcuJF9CRFRL+Izwxe7o6mpCZ9//jnuvfdeyGQym/eeeOIJ6d9jx47FsmXLkJGRgQ0bNuC0005zeLylS5di8eLFdttra2u7nRb46NGjDreXlZVh8ODB3Tp2T6XRaLxdBZ/DNrHHNnGM7WLPFW0SFRXV7WO4qy/RaDQoLy93+B77EmqNbWKPbWKPbeKYp/uSXhGUffXVV9DpdLjmmms6LDtixAgA1juKbQVlixYtwvz586XXFRUVyMnJQURERLc76tjYWIfbExMTXfJHQE/Vl6+9LWwTe2wTx9gu9nyhTdzZlyQkJDjczr6k7157W9gm9tgm9tgmjnmyXXpUUGY0GiGTyezWGFu2bBlmzpyJ/v37d3iM4uJiANaOqy2hoaEIDQ3tVl3bMmrUKOzcudNm2MmgQYOQlZXllvMREZF3uLMvycrKwqZNm9iXEBH1Ej4zp8xsNkOv18NkMgGwZpLS6/U2Cz9PmjQJV1xxhc1+O3fuxLp16xwm+Ni0aROuu+46FBYWoqamBn/++SeuvvpqZGZm4vTTT3fr9bRFoVBg4cKFCAwMBAAEBgZiwYIFzJhFREROUyqV7EuIiHoRnwnKHnroIYSHh2PevHnw9/fHoEGDEB4eju+//14q4+/vDz8/P5v9Pv74YwwcOBDTpk2zO+a4ceNw5pln4pZbbsGQIUNw880349xzz8Xvv//u1Y5LoVDA398fgPWa2IkSEVFnsS8hIuo9fGb44pIlS7BkyZJ2y/z555922x5//HE8/vjjDsvLZDJcdtlluOyyy1xSRyIiIiIiIlfzmSdlREREREREfRGDMiIiIiIiIi9iUEZERERERORFDMqIiIiIiIi8yGcSffQ1EydOhE6nQ0BAgLerQkREPRT7EiKi3oFBmZdMmjTJ21UgIqIejn0JEVHvwOGLREREREREXsSgjIiIiIiIyIsYlBEREREREXkRgzIiIiIiIiIvYlBGRERERETkRQzKiIiIiIiIvIhBGRERERERkRcxKCMiIiIiIvIiLh7tBLPZDACoqKjo9rFqa2uh0+m6fZzehG1ij21ij23iGNvFnqvaJC4uDkql67pJV/Ul/Jk7xnaxxzaxxzaxxzZxzNN9CYMyJxw7dgwAkJOT4+WaEBGRpxw+fBiJiYkuOx77EiKivsfZvkQmiqLogfr0aHq9HqWlpejXr1+37ppWVFQgJycH+fn5iI+Pd2ENey62iT22iT22iWNsF3uubBNXPylzRV/Cn7ljbBd7bBN7bBN7bBPHvNGX8EmZE9RqNbKzs112vPj4eJfefe0N2Cb22Cb22CaOsV3s+WKbuLIv8cXr8wVsF3tsE3tsE3tsE8c82S5M9EFERERERORFDMqIiIiIiIi8iEGZB4WGhuKRRx5BaGiot6viM9gm9tgm9tgmjrFd7PX2Nunt19dVbBd7bBN7bBN7bBPHvNEuTPRBRERERETkRXxSRkRERERE5EUMyoiIiIiIiLyIQRkREREREZEXMSgjIiIiIiLyIgZlREREREREXsSgjIiIiIiIyIsYlBEREREREXkRgzIiIiIiIiIvYlBGRERERETkRQzKiIiIiIiIvIhBGRERERERkRcxKCMiIiIiIvIiBmVERERERERexKCMiIiIiIjIixiUEfmwJpMBBovZ29XwiL50rUREvshiMEEwW7xdjR5HMFtgMXqu//L0+cgzGJQR+bCELx/H7XnfebsaHtGda200GWASfOcPCV+rDxFRRwSTBcWLl6Pyj23erkqPU/ZTMUqe+s6lx2wv8HLH+cj7GJQRUY92XN+EkI//jZe3/uXtqgDwvfoQEVHPc+SXEhQ/ttzb1SAPYlBGRERERETkRQzKqFdrMhnQ2PzVmflKoijaDUGzCILDsmYnh6kZnTi/IAoQRMfnccQkWCCKolNlHV2Ts0Ps2jqPo2MaLOY226o1Z661o5+fIApoMhsAAEbB4rBsZ38HnKk74LhNnKkPEfmetuZRiYIIi8EEsdXngmhx7jPC2XJtnqeNzyJnj+vM3DBREJzuQzpz7hYWg0n6cnaumqNzOGofwWSBKIh25Zypo6vKtLmvINrVrc1yDs4jmCwQLdb9W7dhWz8rZ89Hvk3p7QoQudOg5U+j0WT9I1lvMSPcT425A0bh2ayZiPAPbHO/cm09kr58Aq/mzkGQyg+PFP2Ccm09sqOT8OHESzA0rB+W7crDY8W/4nBTPYaF9cN7Ey9Cbr8BNsepMWhx38YfsfxgKeqMOgQr/fF/ySPwbNYsxAeGSuUqtBrc8s+3WHF4G2QyGab2H4JlEy50WDejxYynSlbj/T0bUdZUD4VMhjPiB+OF7NkYGRHn1DUFKFV4pOgXHNFqMDAkEo+NOweXDcro9HlaHzNE5Y/Fxb/iYFMtvjj9clyQMsZhPTpzrR39/DbXVGDiT68BABYX/4KnSlYDAK4enIX/jD/PqWO0eLZ0Dd7a+Q8ON9UhUKnCuMgEPDrubEyOG+R0mzhTHyLyDcZ6LcpXbUbd9iMQjGYoAlSISE9CwjljoAzwAwBoj9Rix+u/IPWSCTA16VG5ZitMjXr4R4UgaVYGwobG2xzTYjDhyOotqC05BFOjHjKFHGFD4pE4cxz8I4LarEvr8xjrm1C5dgfMWgNCUvoh5cLx8AsN+P/27js8qir9A/h3+qQnJISEFHoS0hO6AgoCdmGxrw0VK5Zd3VXXjrrrrrqui/4srLr2srq66iqsXbCAQjoBAoQWCCGkTZLpM/f3xyQ3GWaSzCRT7iTfz/PwwNw59865J2HOvHPOeQ+ObNyBhu93wNpuRHhKHMafPxthiTFe3xMAmJraceDjrdDtaYBMIUfs1BSknVV8fLWGdE82sxUVj34kPrZbrFBGajEqPx0pi/MhV/d8BLWZLKj/ehuaKw7AojNAEaZCTOZYpCzJhzom3Ll9dHo0bNwBi86AzGtPQeT40WjbeRiHv6qC/nALAEAbH4Ux87OQMG2i+BrGRh3q1pejvfYoBKsNqigt4vLSkXRyttg2npTpj6XDiP0f/gLdriOAICAmayzSz54GVXSYU7mB6rv/w5/RUnkQsAtObZh9y2nQjIrsaTejGfs/3ILWHYcAu4DojGSMXz4TygjNgHUl6ZEJ3nxFQhTCbHY7NjcewOUb30ZObBI+WnRln2XrOluR9q9HcHpKFgpGjcVd+Qtgttuw9Mt/os1ixH0Fi1DSdAh3558Cq2DHr75+BXWdrdh17l1QyhUAHCNGs/67BseMnXjrpF/jhMTxKG+ux8XfvQG7IGDrOb9BjDoMRqsF0z55CiabFe+cfCkKR43FD0f3YU319/jy8C5cPLEQz59wHgDHSMyZX7yErU11ePHEC3BqSibazAbcuvkj/O/QTpQtvQ3pkXH93tNpKZnIjEnE/YWLIYcMf678Gn+p/AbvnHQpLpxY6NXr9G6nKdEJuL9wMXQWI44Y2jEncbxLHby5V09/fseMnRj99gN4fPpZ+F3eyYP6HfjHzk248acP8N6Cy3FW2lRY7HZsOXYQ/9z9C16ee6FXbeJNfYgoOKwGM7Y/8z/IFHJMvHAOwpJi0bH/GPb+6yeoIrXIvH4R5EoFOuuasePZzxEzNQURaaOQODsDgt2Ove9tQse+Y8i742zxw7rdasPOtV/B2mHEuOUzETUhEZZ2A/Z/+AsMR9uQffNpUIa7/7Asvk7mWEROTMToWZNh05uw65XvoIrUIjY7FXaLDaNnT4bNaMGuV76DXCnH1FWnen1PVoMZ1X9fB2WEBhMumANtQhTadtWjuWw/WioOIHlhDsYuyhvyPR1PsNnRXnsUe9/bhNipKRj3qxmO17BYsXPtV7C0G5G+bDqiJ42B3WpH247DsOpNGHNiptg+0RnJiEiLx5gTM2Bu1QMAjE0dqH3reyTMmISUxfmQKeQ4uqkGhz+vRMrphUialwUAqHriv1CPisD4c2dBHRMOc5seLZUHoIoOx6j8dI/LuHPg461o2lqLmKyxGD1rMiLHjYb+SCv2vv0jZF0/J7nK8dmgZVudR/Wt+6wUDT/WYNojF7p/vZK9iMtNRXzxBESOG43OQ83Y/eoGxGSNxYTzZ3v0MyFp4fRFGjHMdhvyRyXj9tyT8PHBbWgx6Qc8p8nUiUenn4EYdRhGayNxb8EiVLc24OntP+CxGWchVhOGBG0E7itYhH0dLfimfo947pt7SlDefBjPzP4V5idNglKuwLSEVLwy9yLsaW/Cczt+cpSrLUF1awPWnnAepiekQSlX4KSkSThvfD50FqNTfT7cX4X1h3bimdm/wjnpOdAolEgMi8LLcy+EWqHEY5XfDHhPh/Q6PDVrKUZpwhGrCcOfp5+JmQlpuLtk3aBf50BnC56atRTx2ghMiIp3G5B5e6/HG8zPz9Nr/Hh0H1IjYrBsXC6UcgXClCrMS5ooBmSDaRMikq7GzbtgbunE+PNmITxlFGQKOaImJiLtrGLoD7eguXy/8wmCgOSTc6DQqqAM1yD1tELYTRa0Vh0Uixz7pRb6umaMP382oicnQaaQQx0bgQkXzIFNb0bjpt0DV0wuQ9K8LCjUSqhjIzBmbhbaa4+ife9RJJ00FQqNCuqYcCTNy4L+UAsMR1q9vqfGTbtgaTdgwgVzEDYmxjFSlpWCiNR4l+r45J7gmOou2OyISI/H6JmT0FS2T5xu1/jzHugPtWD8ebMQm5UCuUoJZZga8UXjMebETOfrWG0Ye0ouFFo1wpJiEZYUi0Pry6FNiEb6OdOhjNBAoVUh+eQcxGSNRf1XVbCZLLC0G2Fq7kBcThrUMY4ZEuqYcIyZmyUGW56U6Y/dYkPs1BRETRwDmUKOiJRRGPerGTAe1aGpbJ9YzpP6esJutiImq+f1ItMTkDB9IprL98NuYfbfUMTpizSsfXqwGo9WfI0tTXWwCwLUcgVsXeuY9rQ3YXo/UxgBYPHYDKfHmTGjAQDzxkxwOp4R7Ti+t6NZPPbtkT1QyOQ4K22qU9kTxozHaG0EvqnfjbvyF+LbI3ugVSixcOwUp3LHnwcAn9XtgEImx+KxGTBaLRAACHB0bNPiU7CxYW+/9wMAZ6RmuRw7Ky0b95f+Dwc7WpEWGev165yROhUymWzA1/bmXoGh//w8vcb0hDS8snsLrtjwNlZMmYHZo8chTKlyuo4v2p6IpKG99iiUERpEpic4HY+dmgLIZGivPeo09S16ivPUcO3oKEAmg6mlUzzWVnMYijA1IlLjuz4UC4AAyFQKaBOj0bG/ccB6RU8e4/o6ACLHj3Y6rklwHDe1dCIsKdare2rfexTqmHCEjXGe+hiTNRZ1n5U6HRvqPTWX7UPDDzVi8ChTyB3r2Kx2WNoNUMeEQ7frCBRhakRP7nv6fe869mbWGWBqaseY+VmQyZ37oNjsVLTtOAx9XTMiJyRCHRuO+m+2QbALiMlMdpoGCADKCM2AZQYSnelcv6hJYyBXKdBRexSjZ0zyuL5Rk5x/D/p8veN/LxOjAbsAc5se2q7fEQodDMpo2Np4pBZnf/lPXJ81G++efBlSIhwd0Du1pbj4uzdh9SChw5gw5ze1CKXa7fFIlWP6hs7cM9rTYtYjTh0mTmfsLVEbiRazAQDQajYgQeM6Lz9KpYVW4fxf9KixHXZBQNq/HnFb39SIGLfHexutdX2tRK2j42k265GGWK9fZ2yv9XH98eZeffHz8/QaN2TNgU2w48Wan/Ha+uehUSgxb8wE3FuwSFxT5ou2JyJpsBksUEZqXY7LFHIowlSwGcxOx1XHlZXJ5ZCrFLAZe0Y1rB0m2IwWlP/xQ7evGT524I9cx7+OXKV0e1zRtR6r9+t7ek82gwXKCNdyqkjXaYhDuaeWqoPY+69NSFqQjclXzBfvoeGHnaj7tFRM2mHtNEEVFdbndZzqeFy57ntSubkfZdf9WA1myOQyTLl6AQ5/UYnDX1Tg4CdboYpxTElMXuAYAfWkTL/kMrfrzpSRWljFtvesvp6QqxRQaJzrpFA7Hvf+vaDQwaCMhq1395ZBo1BgzaxlToFRbXtzP2c562vsZ+AxISBBE4Fmsx5mmxXq4wKOekM7Zo9OF8sdNXbALtghl/XMKG41GWA8LmtfojYKaoUCzb9+yOWanjpiaHc5Vm/QAegJ2Lx9HaXMs5nQ3tyrL35+nl5DLpPjlux5uCV7HpqMnfj2yB78qeIrLPnfWmz71e8xOTrBJ21PRNKgjNCgs67J5bjdaoPNYHaTKGHgd31lpAaqSA3y/7BsCDXzpHfp4/U9vCdlhAaGhlaXchad6xTyodxTc/l+qKLDkLI43+m4qbnD6bEqSouOfQOPIgKOYNipfl3r2SztBpeylnbH/XTftzY+ChMvOgGCXYChoQ2t1XWo/2YbzG16TLzoBI/L9MkuwKo3Oa2xEwQBlnajONLpTX0H5MHsFAotXFNGw5ZSroDsuA7OLtjx2p4tAXn9JSmZsAsC/r2/0un4l4dr0GzSi1MjF43NgNluw2d1O5zKvb+/wuWay9JzYLJZ8cFx1/TGxweqnVLRC4KAD/ZXITt2DMaGx/jsddzx5l49/flFdo1emuyuaecH8zsQr43AuePz8fj0s2C221DSVAfA8zbprz5EJA3Rk5Ng05vRvqfB6XhL5UFAgEdT6Y4Xm50KS7sR7bUNAxf2A0/vKXrSGFjaDOisc/5yqmXbQRxvKPckk8tdAge72YqWigNOx2KmpsBmtKC1us7r11BFaRGWFIuW6jqXLQRaKg50Tb0cdVy9ZAhPjsXYU3IRNTERnQddA1lPyrjTus35Htp2HoZgtYnTUr2pr1ytBDxM8U/DA4MyGrZ+NS4XRpsVt27+CHWdrahoPoxzv34NMxLSAvL654/Px9wxE3Dzpg/x730VqNfr8NnB7Vix8V3kxyXj2kxHdqQLJhRgZkIarv3hfXx6sBr1eh3e21uOrw7vQozaeYrD2ek5uGzSNFz347+xpnojdrU14oheh+8b9uI3mz/C6tLPB6xXwahkrNj4Lna0HsWutkas/OE9bGs9gsenn+XT13HHm3v19OenVaqQET0anx+qQV1nq9O+YJ5e47INb+GRsi+x5dhBHDN2YntrA57Z/gOiVBqcmDjBqzbprz5EJA0JMydBmxiDve9vQltNPSw6A1oqD+Dgf0sQOWE04vK87ycSiicgOiMZtW//iGNb9sDU3AGzzoD2PQ3Y/8HPOPpTjR/upNfre3hPCTMnQRMfhb3v/oj2PQ0w6ww4tqXWZQRrqPcUm5MKS5seh7+shEXnCAJ3v7bBZb1UQvEERE0ag33vb3a8RksnjI06HNm4A/XfbBvwvlPPKIS5VY/ad36CoaENpqZ2HPxvCdr3NCD1tALIVUroDzWj5uVv0LKtDqbmDlg6jGjZVofOg03itgaelOmPXK1E+96jYlr/tp2HceA/WxCeOgqj8nu2y/GkvgAQluzIpNxceQA2o7nffcpoeOAcHBq2TkqahHdPvhR/qfwGRR/9DeMi4/D73JMRoVLjw/1VUPQz9C+XyRChVLtMUxOPH7dOTAZ0le85rpQr8L8l1+BP5V/hvpL1qDe0Y7Q2AhdOKMD9hYsR3jWiopIr8L8l1+IPWz/DdT/+G3ZBwGmpmXjhhPOQ/eHj0Cqc54y/Ou8ivLZ7C17ZvQV/LP8KSrkcU6ITcN64fFw5ZcaA7TJ/zESkRsTi19+9if2dLciIHo1PF12N045LAOLJ6/TVTn3x5l69+fn9c+6F+P2W/6LgoydhslnFfcE8vcZfZ5yN53b8hBt/+gB725sRpwnDnNHj8NOZN4vr0Lxp+77qQ0TSoFArkXndKaj/ugoHPymBVW+CKlKLxNlTkHTyVHGanEwug1ythEzh2l/I1UrIlD3fbcsUcky+fB4aN+/BsS17cWh9hSMhRkIURhWOw6iCcS7XEM/t43V6jh/3Hbqb457ek0KjQuY1C1H3v3LU/usnyOQyxGWnIe3MIrRUHvTZPY3KT4fN6MjQ2LhpNzQJkUhemAuLTo+2HYfF5FAyhRyTr5iPoz/W4Oim3ahbVw5VVBhispKRfHL2gD+H6MlJyLz2FBz5tho1L30D2AVox8Rg0uXzEJuVAgAITxmFpPlT0fjzHtStK4XdbIM6NhxjF+UhcfYUj8v0+fNTyiFXK5G+dDoOflqKuk9LAJkMMVkpSDk13+nn5El9ASA2OwWJczNx+ItKHPhoCyD07FPW/Xou9VB0tZOcUxtDEfcpIxohuvcUe3rWMtyUPTfY1SEiIiKiLpy+SEREREREFEQMyoiIiIiIiIKIQRnRCOHt+i8iIiIiCgyuKSMiIiIiIgoijpQREREREREFEYMyIiIiIiKiIGJQRkREREREFEQMyjxgtVpRV1cHq9Ua7KoQEVGIYl9CRER9YVDmgSNHjiAtLQ1HjhwZ8rWam5t9UKPhhW3iim3iim3iHtvFlVTbxFd9iVTvL9jYLq7YJq7YJq7YJu4Ful0kE5Rt27YNl112GcaNG4fx48fj/PPPx7Zt2/o9Z8mSJYiMjHT6s2LFCqcyNpsN999/PyZOnIjExESce+65OHDggB/vpH9MdumKbeKKbeKKbeIe28XVcG+T4X5/g8V2ccU2ccU2ccU2cS/Q7SKZoOyuu+7COeecgx9//BGff/45lEolFixYgPb29j7P0ev1uOWWW3DkyBHxzwsvvOBU5t5778ULL7yAl19+GT/88AOMRiOWLFkCs9ns71siIiIiIiIakGR2kf3kk0+cHt9333145513UF1djVmzZvV5nlqtRmRkpNvn9Ho91qxZg8ceewwnn3wyAOCll15CSkoKPvzwQ1x44YU+qz8REREREdFgSGakrLfOzk784x//wPjx45Gbm9tv2WeffRbx8fHIzc3FnXfeiY6ODvG5kpIS6PV6nHLKKeKxpKQk5Obm4vvvv/db/YmIiIiIiDwlmZEyAFizZg3uvvtudHZ2Yvz48fj0008RERHRZ/mioiLcc889KCoqQlVVFVatWoWtW7fiiy++gEwmw+HDhwEAiYmJTuclJiaivr6+z+vqdDrodDrxcX9liYiI3GFfQkREnpJUUHbDDTfgyiuvxKFDh7B69WosXrwY5eXlSEhIcFv+6aefFv+dlJSEV155BSeccAI2b96M2bNni8/J5c4DgnK5vN/Fe08++SRWr17tcrylpQVhYWHe3paT3h00ObBNXLFNXLFN3GO7uPJFm8THxw/5Gv7qS/gzd4/t4opt4opt4opt4l6g+xJJBWUqlQoqlQpZWVl47bXXEBsbi3feeQc33XSTR+cXFxcDAGpqajB79mxxhKyxsRGxsbFiucbGRkycOLHP69x2221YuXKl+Li+vh4zZ85EXFycTzpqX1xjuGGbuGKbuGKbuMd2cSWFNvFnXyKF+5Mitosrtokrtokrtol7gWwXSQVlvclkMgiCAJvN5vE5O3bsANAzXbG4uBhqtRobNmzAlClTADj2HKisrMStt97a53Wio6MRHR09hNoTEdFIx76EiIg8JYlEH6WlpbjnnnvE/cOOHDmC66+/HnK5HMuWLRPLLVy4UNyHrKKiAnfeeSf2798Pu92Obdu24eqrr0ZWVhYWLlwIwNEhXnXVVXjkkUewY8cO6HQ63HrrrRgzZgzOO++8QN8mERERERGRC0kEZTk5ORg9ejQWLVoEtVqNqVOnoqGhARs3bsS4cePEcnq9HkajEQAwdepUjB07FosWLYJWq8Upp5yCwsJCfPvtt1Cr1eI5f/vb37B48WIUFxdj1KhR2LNnD9avX99vAhEiIiIiIqJAkQkS28bbZrNBoVC4fc5gMEAul0Oj0Xh8TrfuqZBKpfczNuvq6pCWloaDBw8iNTXV6/N7a2pq4rzd47BNXLFNXLFN3GO7uJJqm/iqL5Hq/QUb28UV28QV28QV28TBbrHBqjfBqjdDJpdBr7SO7DVl/QVXfWWrGiggAxxr1AYTkBERERER0fAi2OywGsyw6k2w6c2wma3ic6pIbcCjJEYpREREREQ0rAmCAJvRAlvXaJjNaIaU5gsyKCMiIiIiomHHZraKQZhVb4Jgl1AUdhwGZUREREREFPLsVhtsBrMjCOs0wW71fGutYGNQRkREREREIUewC7AZzWKCDpvREuwqDRqDMiIiIiIiCgk2kwXWzl7rwiQ8JdEbDMqIiIiIiEiS7BZr15owM2x6E+w2e7Cr5BcMyoiIiIiISBLsVhtserOYrt5uCZ11YUPBoIyIiIiIiIKie7+w7kDMZgrddWFDwaCMiIiIiIgCwikIM5phN1kktV9YsDAoIyIiIiIiv2AQ5hkGZURERERE5BPHT0e0mxmEeYJBGRERERERDQqDMN9gUEZERERERB5hEOYfDMqIiIiIiMgtBmGBwaCMiIiIiIgAMAgLFgZlREREREQjlGAXYOkwMggLMgZlREREREQjxPEjYfrWZqjCRuaGzVLCoIyIiIiIaJgSbHZY9WbYDI4gzGY6LgDjqJgkSCoo27RpEzZu3AiZTIYZM2bgpJNO6re8zWbD559/jtLSUsTExGDJkiWYMmWKU5nnn38e+/btczpWUFCAiy++2NfVJyIiIiIKquNHwlyCMJIkebAr0G3ZsmW488470dzcjAMHDuDcc8/FhRdeCKGPSa2HDh1Cbm4unnnmGXR2dmLz5s3Iz8/H2rVrncq98cYb2LJlC2JjY8U/ERERgbglIiIiIiK/Emx2WNqNMB7VoWP/Mej2NEB/uAWm1k4GZCFEMiNlq1evRkFBgfh4+fLlWLBgAe677z7k5ua6lNdoNPj444+dRsZyc3Nxyy234IorroBGoxGPz507F3fddZd/b4CIiIiIyM8GnI5IIUkyQVnvgAwArFYr5HJ5n6NaCQkJSEhIcDpWXFwMk8mExsZGpKamise3bNmCBx54AMnJyVi0aBEmT57s+xsgIiIiIvKx7iDMajDBZrAwCBumJBOUAcDGjRvx6aef4ujRo9i0aRP+8Y9/YMKECR6f/8Ybb2DcuHFOAZlcLocgCLDZbFi/fj1+85vf4Omnn8Y111zT53V0Oh10Op34uL6+fnA3REREIxb7EiIaDAZhI5OkgjKNRoOYmBh0dHSgubkZlZWVHp/7z3/+E2+88Qb++9//Oh1/+eWXnUbGnnzySdx88804/fTTnYK33p588kmsXr3a5XhLSwvCwsI8rpM7vTtocmCbuGKbuGKbuMd2ceWLNomPjx/yNfzVl/Bn7h7bxRXbxJUU20SwC7AZHcGX3WSF3WIN6Ot3GPUBfb1QoBRMMJmGnpbSm75EJvSVSSPIqqurkZubi48++ghnn312v2Xfe+89XHLJJVi7di1WrFjRb9nW1lbExcXh3//+N5YvX+62jLtvN2fOnImDBw/2Gch5qqmpySed/XDCNnHFNnHFNnGP7eJKKm3ir75EKvcnNWwXV2wTV1JoE7vVBpvBIpmRsDZ9O2LCo4JaB6lRRWph0NgD+rsiqZGy3rKzsxEXF4dt27b1G5S9//77uPTSS/H8888PGJABgN1uBwAYjcY+y0RHRyM6OtrrOhMREXVjX0JEgPSCMJImSQRlhw8fhslkclo/9sMPP6C5udkpAcizzz6LuLg4cY+xDz74AJdccgmee+45XHXVVS7XPXLkCDo6OpymLz799NNQqVSYP3++H++IiIiIiEYi5yDMDJspsNMRKTRJIiiz2WxYvnw50tLSMHHiRNTV1WHdunXi2q9ur732GsaPH4+LL74Y1dXVuOiii5CRkYGamhqnlPerVq1CWloabDYbzj//fKSmpmLChAmorKzE1q1bsXbt2iFPQyQiIiIiYhBGviCJoCwtLQ1btmzBV199hR07dmDGjBl47LHHMHHiRKdyq1atQkxMDAAgMjISDz30kNvrKRQKAEBKSgq2bNmCL7/8EjU1NZg/fz7mz5+PxMRE/94QEREREQ1LDMLIHyQRlAGOQGrJkiVYsmRJn2Uuu+wy8d/p6ekebQitUChw6qmn4tRTT/VJPYmIiIho5GAQRoEgmaCMiIiIiCjYGIRRMDAoIyIiIqIRi0EYSQGDMiIiIiIaMRxBmBlWg5lBGEkGgzIiIiIiGrZ6B2GGxlYom8zBrhKRCwZlRERERDRs9Dcd0W6xAaogVo6oDwzKiIiIiChkCTY7rHoz14RRSGNQRkREREQhQ7DZHevB9F3rwkyWYFeJaMgYlBERERGRZIlBWNe6MLvJAkEIdq2IfItBGRERERFJhmC3w2a0wKo3wapnEEYjA4MyIiIiIgoawS7AZjSLUxJtRgsERmE0wjAoIyIiIqKAEewCbCYLbHpT17REBmFEDMqIiIiIyG/ENWFGRwDGkTAiVwzKiIiIiMhn7GYrrEaLIz29kSnqiTzBoIyIiIiIBkUQBNhNFnEaos1ght1mD3a1iEIOgzIiIiIi8kjPVMSukTCTBYKdUxGJhopBGRERERG5JQiCY38wvRlWvQk2IzdqJvIHBmVEREREJLKbrY49wjod2RE5EkbkfwzKiIiIiEYwwWYXR8KsehPsFluwq0Q04kguKOvs7IRcLkdYWJjH5xgMBhgMBowaNWpIZYiIiIiGO0EQYDNaHNMR9Y4MicxQTxRc8mBXoNvHH3+MGTNmIDU1FaNGjUJeXh4+//zzfs/p6OjABRdcgJiYGKSmpiInJwe//PKL12WIiIiIhjO72Qpzayf0h1vQvqcBnQebYGrqcExPZEBGFHSSCcq+/vprvPzyy2huboZOp8OZZ56JpUuX4tixY32es2rVKlRUVGDfvn1obW3FSSedhLPOOgs6nc6rMkRERETDid1ig7XTBMORVrTXHkX7vkYYjupg6TByjRiRBEkmKHvqqaeQl5cHmUwGlUqFyy+/HEajEbW1tW7Lt7S04K233sLdd9+NsWPHQq1W49FHH0Vrayvee+89j8sQERERhTq71QZLuwGGhja07z2K9r1HYWrugFlngN3KNWJEUieZoAxwTDWsq6tDSUkJ7r33XsyZMwfFxcVuy5aWlsJqteLEE08Uj8XExCAvLw8///yzx2WIiCi02QU7Wkx61LY3oeRYXbCrQxQQgl2AVW+CsVGHjv3H0F57FPr6Vpjb9EzUQRSCJJXo480338RDDz2ExsZGpKSk4P3334dS6b6KjY2NAICEhASn4wkJCeJznpRxR6fTOU1vrK+v9/5miIjIL2x2O9osRjSb9Ggx6dFmMcLWtShGDhnGaT1PFOVP7EvI12xmK2xMVU80LEkqKLvuuutw3XXXwWw246GHHsL8+fNRWVmJiRMnupSVyx2DfFar1em4xWKBQqHwuIw7Tz75JFavXu1yvKWlxauskO5wLZsrtokrtokrtol7I6FdbIIdHRYz2i1GtFtM6LCYIcD9h1EBMujMqiG/Znx8/JCv4a++ZCT8zAdjOLaLYBdgM1lgM1ocGRKtdq/O7zDq/VSz0MU2ccU2caUUTDCZhv6lhzd9iaSCsm5qtRoPP/wwnn76aXz00Uf47W9/61ImNTUVAHDkyBGnG25oaEBeXp7HZdy57bbbsHLlSvFxfX09Zs6cibi4OJ901L64xnDDNnHFNnHFNnFvuLWLxW7rGgUzoMWsR7vF5AjBFAAUCsi0YZD1ca4cMkRroyXRJv7sS6Rwf1I0HNqlO1W9tdMEm9HSlRlRBahVgNr768WER/m6iiGPbeKKbeJMFamFQWMP6HuKJIIyu90ujmp16+zshMFgQHh4uHissbERCoUCo0aNQlFREaKiovDFF18gJycHAHDw4EFUV1fjoYceAgCPyrgTHR2N6OhoX98mERG5YbRa0GJ2BGAtJgM6rOZgV8kn2JeQJ+xWG2zdGzd3mmC3eTcaRkTDgySCsq1bt+LPf/4zrrvuOkycOBF1dXV45JFHMHr0aJx33nliubPPPhvjx4/HO++8A61Wi9///vdYvXo1JkyYgJSUFPzud79Dfn4+zjnnHADwqAwREQVWh8UkjoK1mg0w2KwDn0Q0TIgbN3eaHJs3Gy3BrhIRSYAkgrIZM2bgmmuuwTPPPIMdO3YgPj4ec+fOxRtvvOE0bJiYmOj0+N5770VERAQefPBBdHR0YN68eXj00UedkoN4UoaIiPxDEAS0mY1oNRsco2EmPSwCRwJoZLFbrLDqzWIgxgQdRHQ8mSBwH/eB1NXVIS0tDQcPHhTXqQ1WU1PTsJjz7ktsE1dsE1dsE/ek1i42u90pAOudGTEQ5JChWBsvqTbp5qu+RGo/c6mQUrsIdjtshl6jYebgjAa36du5Vug4bBNXbBNXI3ZNGRERhSazzdoVgB2XlINohLGZLOJomM1gBr/zJiJvMCgjIiKPGawWMSFHi9mAzmGSlIPIW4LN7gjCuhN0WLlhMxENHoMyIiJySxAEdFhNYgDWYtLDZOcHTxqZBEGA3dQ9JdHs2DeMg2FE5CMMyoiICABgF+xoMxvF6YitZgOsTMpBI5jdauuZkqhnunoi8h8GZUREI5TVbkOL2YDWrpGwNrMRdq4IoxFMEATYDN1TEs2wmZiunogCg0EZEdEIYbJZ0WLSiyNhHVYm5SCym63iujCrwcx09UQUFAzKiIiGqU6LySkzIjdpJnKkq3dK0GHhOkkiCj4GZUREw4AgCGi3mJwyI5qZlIMIQFe6+u49wwwWpqsnIslhUEZEFIJsdjvaLEYc6mzDXkGPVrMhoJs0E0mZYLOLQZhVb4LdygQdRCRtDMqIiEKAxW4T14O1diXlEADY9XrIFeHBrh5RUAmCAJvJCmNTu2NKosnCdPVEFFIYlBERSZDRanGsB+uajtjBTZqJnNgttp4EHXoTjB06aMI5IkZEoYlBGRGRBHRYTGJCjhazAUYm5SByItgF2IzmnrVhJv4fIaLhg0EZEVGACYIAncUoJuRoMelh4SbNRC5sZmuvBB1MV09EwxeDMiIiP+tOytF7TRiTchC5Emy90tXrma6eiEYOBmVERD7WOylHi8kAncXITZqJ+mAzWsS1YTajmQk6iGhEYlBGRDRIGzZsgMFggEqjxtSZ05iUg8gDdqvNMRrWaYJNb4Ldxqm7NLJtrtwKo8kErUaDWXnTgl0dChIGZUREXuq0mNBiNuCr775FZ5sO2uhIWKaMDXa1iCRJEATYDGZxWqLNaAl2lYgkZXNlCdo6dIiJjGZQNoIxKCMi6ocgCGi3mMRRsBazAWa7Y52LzQ/JOfaXVMJqMkOpUWNccZ7Pr08UCHZLd4IORyDGBB1EgcXRt9DDoIyIqBd/JeXwNNg6UFoFo64D2ujIAYOy/SWVsOg6oYqOYABHQSXYeyXo6GSCDiJ/8CbQ8nT0bXPlVrR26BDLUbqgk0xQ1traitdffx2lpaWIiYnBGWecgcWLF/dZvrGxETfccIPb56666iqcccYZAID77rsP27dvd3p+7ty5+M1vfuOzuhNR6LLYbWjtSsjRYtZDZzbB7oe0HN4EW8G8JpEnBEGA3WTpCsTMjnT1zNBB5Ff+mObIqZPSIYmgrLa2FgsXLsTSpUsxb948HDx4EOeeey5uvvlm/PGPf3R7TkREBC666CKnYxs2bMDTTz+NP/zhD+Kxb775BmPHjsUFF1wgHktLS/PPjRCR5BmtFnEErMVkQLvV5PU1tn/1PTqONcPUqQcAmDr12P7V95h6ylxfV5dIEhxBmFXcL8zKPcOIhuyzjV/iaHMjOvSdAIAOfSc+2/glzpi3KMg1o2CQRFA2evRoVFRUIDo6WjyWnJyM66+/HnfffTciIiJczgkPD8d5553ndOyNN95AUVERpk1zjvSzs7NdyhLRyNCdlKN7JMxgsw54zkBTDTuONaP1cIP4WLDZ0XGs2af1JgomMQgzmGDTMwgj8pYnUw2PNjfiYMNh8bHNbsPR5sZAVZEkRhJBWVRUlMux1NRU2O126HQ6t0HZ8RoaGvDpp59izZo1Ls99/vnn2LVrF5KTk3HGGWdg4cKFPqk3EUlLf0k5vBGMaYEcfaNgEuwCbCaLmCXRZmQQRjQUwZoWyNG30CWJoMydZ555Bnl5eUhOTvao/Kuvvgq1Wo1f//rXTscjIiKQlZWFoqIiVFVV4cwzz8Ttt9+ORx55pM9r6XQ66HQ68XF9ff3gboKI/Mou2NFmNoojYa1mA6x+yIgYCN6MvjGACw1S7ksEu71r02bHejCb0cI1YUTDgKejbwzepEeSQdkDDzyAb7/9Fhs3bvT4nJdffhkXXHABYmJinI6/8847iIuLEx8XFRXh8ssvxxVXXIEpU6a4vdaTTz6J1atXuxxvaWlBWFiYx3Vyp3cHTQ5sE1dsE1c6nQ42wY52i6nrjxGdFgsEPyTlQPcIgV2AvV3v8rTgZrNbwWZ3W3bH9z+jo6UVpo6uAKpDj+p13yJr7sxBX7O94RjaGhqdyrU3HHNbdqQRIIPOrBrydeLj44d8DX/1JYN5f+geCbObLLCZrLBbrPDHf51g6jDy9/94bBNXgWoTe9cXhHbBjjZ9u9syVjczOax2m0v5bzZvxLHWZnToOwAAHfoO/Oebz7Bg1rxBX/PwsSOob+z5ItBmt+HwsSN91nWkUQommExDf5P0pi+RXFD2+OOP4/HHH8fHH3+M4uJij87ZuHEjdu7ciZdfftnlud4BGQAsW7YMdrsdW7du7TMou+2227By5UrxcX19PWbOnIm4uDifdNS+uMZwwzZxxTYBTDarmJr+qE0Pk8ns+BypAKBQQqZVQuaPF5bLxL/lUeEuT0eNSYBMIUdbQyMEmx0yhRxRYxLclu3U6ZwDKLsdnTqdS1mZQu5yrkwhd3tNb8qONHLIEK2NlsT/H3/2JQOdb7faxIQcNoMZNpMVjlBVBShVEuz9fSMm3HU5xEjHNnEViDaRy+Ti33293tiEJCjlChxubIDNboNCrsDYhCSX8q26tuMCKDtadW1ur6uUK9weO76sp+VGKlWkFgaNPaB9iaTelv/617/i/vvvx8cff4xFizwfPn3ppZeQnZ2NE044YcCybW1tAACFwvWXsVt0dLRT0hEi8o0NGzbAYDAgLCwM8+fPd3lebzWLCTlaTAbobRbxObvVDPkQ3rI82SfM02mB3Y83vvQ2jLoOaCLCOXWQXASyL7FbrLAauteEcZ8wGr6CvSnyQK/vzbTA7mNr3voH2jp0iAyP4PTBEUwyQdnf/vY33Hffffj444/73J/s7rvvRmJiotMeY+3t7Xjvvffcps7fv38/amtrsWDBAgCAzWbD/fffj8jISCb7IAqCjRs3oqWlBXFxcZg3bx46rCYxIUeLSQ/TIJJyeMqT5B3BzKoYmTAKAJxG37qPER3PZrb2JOUwmGG3MgijkSHY+2oN9PrBzqiYOGo0ADiNvnUfI2mTRFC2detW3HbbbZg8eTJeeOEFvPDCC+Jzjz76qDjN8Ouvv8b48eOdzn377bdhs9lw2WWXuVw3IiICjz/+OG688UZMmDAB1dXVkMvl+M9//iOJqS1EI4ldsMPelUjAbLPi6/rdIZuUwx+8GX1jADeymds60dFkDnY1iEiCPB19Y/AmPZIIytLT0/Hee++5fa538PToo48iPNx5zURBQQHWrVvnNshKSEjAZ599htraWuzcuRPJycnIzs6GWq327Q0QkQur3YZWs1FcE9ZmNsJkd+wRZoMwYgIyfwRQnD45wo2M/zpE1MUfARSnTkqPJIKy0aNHe7S5c/c0xN5mzZo14HkTJ07ExIkTB1U3IvKM2WZ12qS53WIabsndBoUBFBERDQUDqJFBEkEZEYUeg9XitElzp1Wa06m82dPL21Gt9KJcMXkIERENX54m8BjMqNasvGIxeQiNXAzKiGhAgiAMKSlHMDc79iZ5h7ejWn0lDCEiIt8K9mbHnibwGMyoVjASlpD0MCgjIhd2wQ6d2eQ0EjaUNWDBzGoYajj6RkTkKthZDUMNR99CD4MyInJsRGnuGQVrsxhhE7giLBi8GX1LL8qFRdcJVXSEH2tEREShxtPRt1l5xWjt0CE2kvvzBhuDMqIRiEk5As8fI2DjivNgb9dDHhU+cGEiIgpp/hj9mpU3DW36dsSER/nsmjQ4DMqIRgCTzYpmkx7NXenppZqUYzD2l1SKwY67USap7OnF9WdERNK0uXKrGOz0NcIkhX29uPZseGNQRjQMWe02NJv0aOr60x2EDRTA+Is/A6MDpVUw6jqgjY50e09MSU9E5FueBDG+5s+gaHNlCdo6dIiJjO7zfpiWnvyNQRnRMGAX7GgxGdDUNRqmsxjdTkccKIAZDE8CvVALjJhsg4iob54EMd5fs/9AL9SCIibaIG8xKCMKQYIgQGcxOkbCjJ1oNRthD9KqMH8EesE2XO6DiChU+CPQC6bhcA8UWAzKiEJEp8UkjoQ1m/SwDCFFPRERERFJB4MyIoky26xda8I60WTSw2izBrtKkhLMDalJOvQdHSjZ8CNqyiqxs6wCMpkMX37032BXi4hCQLA3pCbpOHCoDhs2fY+t5aXYWlmKpWecjVvvuj2gdWBQRiQRNrsdzWY9mox6HGk5CpPxmM+uPRwDGG5IPfJ0tOlgMZsRNzpBPNbUcBSPXHuL+FipUsFkMgWjekTD3nALYrgh9cgjCALq6g8hPm4UwsN6tpP59Mv1+MOf7hcfJyYm4tYA121QQZnJZMJtt92GP/7xj4iNjfVxlYhGBkEQ0Go2iKNhbeae5Bx2qwVyqHz2Wv4IYAYT6AUzgQaTd4QWs9GE2uod2FlWgZ1do2CHavdh2dWX4/rVd4vlUiaMR0R0FDp17QCAxJSxqK+vx9ixY4NVdaJhyx9BjLeBXrATaAT79ck7rW2tKKksw9aKMmytKEVJZRmOHmvE+/94E4vmLxDLFecViP9WKpWw2WwBr+uggjKFQoEff/wRmZmZeOyxx3DFFVf4ul5Ew1K7xehIVW/Uo9msh00I3S2bBxPoBTOBBpN3SFvj4XqU/7gZO0orsLOsAnurd8JqsbiU21lW4fRYLpfjhofuQWx8PDIKcxEbNwrjtfGBqjYRDZG3gV6wE2gE+/Wpf2XbKrBp688oqSjD1soy7NlX67bc1opSp6AsPzsXf77nIUzLL0Le1BxExcfCgMCu3R9UUKZUKrFlyxasWbMGN910E1566SX83//9H/Ly+KGHqDej1eK0Lsxs9/ybl2DtKUbkb8fqGxAZGw1tWJh47KfPv8az9z7c5zmasDBk5Ocgd/YMl+cWnbfMH9UkGhaCsacYkb/ZbDYcOHQQE9LHOx1/9Okn8L9vvuzzvMSE0SjOK8SUiZOcjodpw3D95Sv9UVWPDXpNmUKhwG9/+1tccMEFuPXWW1FcXIxbbrkFDz74IKKionxZR6KQYRE3be5Ek1EPvc31m35PDcdU877kzw2pyXc6de2oqajCzrIK1JQ6piE2NRzFg/98FrMXLxTLZRXli/+WKxSYkJWBjMI8ZBbmI7MwD+lTJkGh5DJoIm8Nt1TzvuTPDanJdwRBwOGGemytKENJRSm2VpShrKoceqMBdSU1CNP2fME3La9IDMoiwsNRmFuAaXmFmJZfhOL8QqQmp0AmkwXrVvo15B4uJSUF77//PtatW4ebbroJ77zzDp588klceOGFvqgfkaT13rS5ydSJdospSLuF9W84BjChtiH1SFFbvRPbftmKnaXl2FlWiYO73U8d2VlW6RSUTZiaiWvuvxNZRQWYlDvVaRSNiKRhuAUxobYh9UhhtVrx/c8/9qwDqyjDkcYGt2Urqqswq7hnBsVZi09HUuIYTC8oRuakKVAoFIGq9pD55GvHmpoaNDQ04MQTT8Trr7+Oiy66CP/4xz/w4osvYvz48b54CSJJEAQB7RaTOB2xxWQI2qbN3hhMADPQ9MnhGOiR5+x2O47WHUZSeqrT8Tf/9gx+WPeF23OKi4sRExuLmNHxSJs00ek5lVqNc6+90m/1JaKh8zaI8WTq5HAL9Mg7JrMJx5qbkJLUk5xJJpPhklVXoVOvd3tOcXExkhLHYMzoRGiOS7iSnZGF7Iwsv9bZXwYVlNntdvz5z3/GTz/9hJ9++glNTU3QaDSYPn067rzzTkybNg2vvPIKCgsL8emnn+LEE0/06LoGgwHbt29HTEwMJkyYALlc3m/50tJStLW1OR1LTExEdna2S9nq6mp0dHQgLy8PYfwGlrygt5rRZHSMhPW1afNwXP810PRJjlSNLE0NR8W9wHaWVaKmvBL69g78e/svCI+MFMtlFuaLQZlGq8Xk/BxkFuYhoyAPONICi94AbXQk5i0/O1i3QiRZw239lydTJzlaNXLY7Xbs3rsHWyt7piFW7tiGGYXT8NkbH4jlFAoFCnML8MPPPwEAEkbFY1pBEYpzC1GcV4htB3ehXd+BmMhoFObk9/VyIWdQQZnZbMYTTzyBE044Ab/73e8wd+5czJgxwylaPf/88/GXv/wFt9xyC7Zu3drv9XQ6Hf7whz/g7bffxoQJE3DkyBHExMTg9ddfx7Rpfb8p3Xzzzairq3MajVu4cCHuv79nn4H6+nqcddZZ2L9/P+Lj43H06FG8+uqrOOeccwZz6zQCDGbTZl+u/9r+1fdobzg2rPYUo9Bis9lQtXkLasoqsaNrGuKx+iNuy+6urEb+nJni49lLFiIqLhaZhXkYlzHZaR3YxpfexuBXWRINf75c//XZxi9x+NiRYbOnGIWetnYdNm7+wZEJsaIUpZXl0HW0u5QrqyqH1WqFsld/ccvVN+CaX69AcX4R0sY6rwPb/taegNQ/0AYVlGm1WjQ1NQ24UO7GG2/Eww/3nU2rW1NTE3JyclBfXw+NRgOr1YoVK1Zg2bJlOHDgQL+vs2LFCjz44IN9Pr9y5UpoNBocOnQIGo0Gjz76KH79619jz549GDNmzIB1o+Gv96bNzSY92q3B3Xi241gz2hp60gFzU2TyJ4vZjLamFiQk97wfymQyPLTyJnHvL3fGpKUgszAfmuNmHqRPmYT0KZP6OMv/9pVUotOmwKhRozB//vyg1YMo2I42N6K+1zocboxM/tTWroPdZkNcbJx4bOfuGly66uo+z1EoFJg6JRPT8ovQadAjJipafO7Uk4P75cGPJT+jw2oIaF8y6DVlnmQuiYiIwN///vcBy02YMAE33nhjT6WUSlx55ZV48803cfDgQaSnp/d5bmtrK3766SckJydj3LhxTvVqaGjAunXr8P7774ujeL/5zW/wpz/9Cf/6179w8803D1g3Gn4EQUCb2SiOhLWaDSGwKsw3PNlAeTCbQgcTN4X2nCAIOLx3v7gZ886yCuzZth2Tc7Pxt4/eEcvJ5XJkFOShdOOPAICo2BgxC2JGUT4yC/IQmyDNvcD2l1Zip64DcXFxDMqI/GSgDZS93RA62LghtHfMZjO27dyOrRWl4lTEmtrd+MPNv8Mdq34rlsubmgOlUgmr1THjaFxqOqblF6I4vwjT8gqRn52HiPDwYN1GvzaV/IxWXVtA+xK/5heWy+W4+uq+I+T+/PzzzwgPD8fYsWP7Lffiiy/i+++/R21tLVJSUvDKK6+IUx7LysogCILTFMiwsDBMnToVpaWlg6oXhSaj1YJjpk4cMzoCMaubdWEjgSdTKwezKbSvebNGb7is4fMHfUcHKn76WQzCasqq0HHcOlwA2F1VDavFAqVKJR771corsOTC5cgsyEPy+HTJphAmosAbaGqltxtC+4una/SGw/o9fzp4uA4//rIZWysdmRArqqtgtphdym2tdP5sHaYNw1/ueRhpKakozitEwihpfpknFZLc9KWiogJ//OMfcc899zjNLz3eqlWrsGzZMoSFhcFgMODyyy/Hr371K1RXVyMyMhItLS0AgFGjnDPCJSQkiM+5o9PpoNPpxMf19fVDvCMKNEEQ0GI24JixE43GDnRYXd88fCHURpX8wR8jVdyjzXuGzk5YzRZExcWKx+r27MWDV97Y5zkymQzjMiYjozAfho5Op3NnnnKSH2s7MrAvIU+F2siSP/hjtIp7tHnvyNEGjIqNg1rd06e//eF7+NOax/s8J0yrRWFOPqblFbk8d/Wvr/BLPYcjyQVlu3btwmmnnYbly5fjrrvu6rfsxRdfLP47LCwMf/3rXzFu3Dj8+OOPWLJkifgLZTAYnDa01uv1/W5w/eSTT2L16tUux1taWoacubF3B00OvmoTi92GVrMBrWYDdGYTbAEYDWtvOOay/qu94Rjs7e7TuHoiIjoagsUKXVMLBLsdMrkcEdHRQ7qmNwSba7sJNnufr5/Wa/2Qz+poF8S/u69p1wd3rZ+UWC0W7N+1Bzsrq7CztAI11TtwcE8tLrj2Klx+a08QNi41HUqVClaLI71G4thkZOTnIjMvBxm5OZiUk4XwiAixvN9/x9z8XP31Gna7HU1NTYO+THz80L/R9Vdf0q7vgMUuue476DqMgXmP9IfDx464rP86fOwI2vR9r+scSGx0DExWC5pammCz26GQyxEbHTOka3rKare5Pdbfa2dNyhD/7as62rs+B9gFu3jNUP498bWOzg5Ubt+Gn8u2YvuOHSjfVoHDR+rx73++jemFxWK5rMxM8d9yuRyZk6agIDcfhbn5KMjJR8akyeIgSiB+v9z9XH3+Gna7+Heg+hJJvavv3r0bCxYswIIFC/DPf/5zwJT4xxs9umuvi8OOIfNx48YBAA4dOoTExESx3KFDhzBz5kzXC3S57bbbsHLlSvFxfX09Zs6cibi4OJ901L64xnAz2DbRmY04auxAo7EDOkvXh3a1DFBr4d1vz+DIFK6vIlPIIY8a/Bzp7NNPhr1djx/+9ZEj1XxkOLJPP3kItfSOP+7Ja3KZ+Hfv1w1oHSTkWH0DKjf/Iqak311ZDbPJNUit2b7dqY00AG546B4kJI9BRkEe4kYnBLDWbvTxc/XHa8jl8qC/1/qrLzG3dCLcrhq44AgUE973F65SppS7bnCrlCuGdD/LFpyBNn07Xv3PO12p5iOxbMEZQ6mmx/xxP4Mhl8nFv3u/dqj+ngxV5Y5t+KV0q2ND5soy7NhdA0FwXVW/s6YGp5zQM1ti/swT8fCd92NafhEKJLAOrK+fq09foysGCWRfIpmgrLa2FgsWLMD8+fPx2muvud2Bu6SkBFqtFtnZ2TAYDFCr1U7l/ve//wEA8vIc050KCwsxevRofPzxxygqcgypbtu2Dbt378bixYv7rEt0dDSio6P7fJ6Cx2a3o8nUicauaYkmN9/G0dBwU+jgaj3WhPCoKKh7TQf9+oOP8fKjf+3zHJVGjUk5U5FZ6Lpfy5mXXeSXetLA2JfQSMUNoYNLEATUHz2CsWOSnY7f9cf7xb2/3ImLjcO0vEKkJDmfFxsdg5uvut4vdaUekgjKjhw5ggULFiA2NhZXX301Nm7cKD5XXFwsdmo33ngjxo8fj3feeQf79u3DZZddhiuvvBITJkxAZWUlHn30UVx22WViYg+FQoFHH30UN954IyIjI5GSkoKHHnoIixcv7jcoI2nRmY1oNunRZHKkrLePmFyJwRHMTaFH2ho9o16P3ZXV4obMO8sq0HDwEP7yr1dRcMIssVxmYc+6OplMhrQpk5BRkIusogJMyZiCicX5UKmZfZKIpCHYG0KPtDV6R481OjIhVpShpLIUJZXlMJtNOLBlp1Nuhml5hWJQptVokZ+dg+K8ImRnTcW8GXMwPm0ckzoFkWSCsgkTJgCAy75mzz77LLKzswE4ArSkpCQAwNSpU/Hmm2/i+eefx2effYbk5GS88sorWLZsmdP5V199NUaPHo3XX38dHR0duPTSS/Hb3/4WJF2dFhOaTQY0mTrRbNLD4uO1Yd5k9hsIR5V8SwqZH/1p344aVG8pFQOwAzW7xXnrvdWUVToFZVMKcnH13b9DRmEeJuflICIqUnzO3q6HnAEZUcB5mtnPExxZ8i2pZH/0B6vVik0lv6CkKwjbWlmKusOH3JbduWcXcjKnio9/dfo5mJA+HsX5hciekgVVV8bdNn37iJ3SKSWSCMoKCwvx7bffDlju2WefdXqcmZmJv/3tbwOed8455+Ccc84ZbPXIz0w2Kw7r2xybN5v1MNqsfn09X2b28+eoEvffCl2CIKD5aCPixyQ6HX9h9Z/Fvb/ciYiOQmZhHhLGJjkdD4+MxPk3ruzjrNBgt9lgNTsSjljNFthtdsjdrF8kChW+zOznz5El7sEVuiwWC1p1bRgd37Me2GK1YNmVF4p7f7mTOjYF0/KKXNaLFeUVoCivwG/1DQSbzSam4zdbzOKXGMOBJIIyGlkMVguau6Yitpj16GzVQW6RXgIHX46oDQZTwYeOtuYWMQnHzrJK1JRVQN/ZiQ93bHXa+yuzsGdDZpVahUk52cgozHNszFyUh7Hjx3md4CgU2G02lHywDlajIzmJ1WhCyQefoXj5GQzMaNjz5YjaYDAVfGgQBAH7Du7vmYZYUYry6iqcNGcu3n3hNbFcmDYMOZlTUb6tEgAQEx3j2JA5r6jr70KMGZ3Y18uENJvNhjc/ex8GkxEAYDAZ8ean/8YlZ57rk8BMJpdBoVVBoVb1JKYKIAZl5He9gzB/jYT5I4AKlb2ygh08jjQ2qxU7Siu6ArAK1JRVon7/Qbdl9+6owZS8HPHxiacvxqgxicgqyseEqZkjZh3Y4epdaKlz3qOrpa4eh6trkJqXFaRaEbnyRwAVKntlBTt4HGna2nXYXPKLIwgrL8XWyjK0tLruobu1ohSCIDit9frNylUwWyyYXlCEieMmjJh1YOU127C/vs7p2P76g6io2YaiLNdEV32RyQCZUgGFRgWFRgm5RgWFRgW5StGrDIMyGgb0VnOvkTCD36cjAp4FUFJIIhFKwWMwpk9KbY2ezWZDe2sbYuN76mC323HXhVfA0jUVz534MYnILCpweVOfkp+LKfm5fquvVBna3O9F2Nfxweo9RdJkMsFms7nN5EvUF08CKCkkkQiV4DFYUyeltkavU6+HzW5DdGTPuq2t5SW44NrL+jxHJpMhc9IUFOcXwmA0IDysZ0bRr84YmUtyWtrb3B5v1rk/DnSNfmlUkGuUPUGYWglZP7NSrFYrTF3bzgSyL2FQRkPWaTGhxWwQAzGppqn3ZxIJTwOYUBl9A4IzfTKYmR8FQUBD3SGnaYi7K6uRPaMIf3rzJbGcSq3GpJxs7CgtBwCER0UioyAPmYV5XX/nIyF5TEDqHCrCYtynhe/r+GAcP0VSr9dj7dq1uPbaaxmYkU/5M4mEp0FMqIy+Batuwcz+aLVasX33TpRUlIlTEbfv2oGH77wfq1ZcK5YrynVe25WSlIzi/CJMyytEcX4RCnPznYI4AuKiYtweHxXtOC5XHT/6pYRc5V2oY7VasXbtWuj1ji/wA9mXMCgjr3VnR2w269Ei4SBssAYzoib1AItc6Ts6HJkQxamIlWhrcg3Sa8oqXaaOLL/uSpiNRmQW5iNl4vhhuQ7Ml8ZmT8GRHbudpjDGpSZjbHaGz17D3RTJPXv2YMuWLZg1a1YfZxH5z2BG1KQcYJF7hxvqsWnrz+I6sLJtFTAYjS7lSipKnR7Hxcbhod/fi0njJ6I4rxDJY5JcziFnBRk5qNq93WkK44Rx4zFn4XyowzSQ+WCN8pYtW1BbW+t0LFB9CYMy6teGDRvQ1tkBQanAuGl5wzIIO16opGWXwnTMUGEyGGG1Wp1SydeUV+HeS6/p97zUSROQWZgPQ2cnwiN7zp1/1ml+q+twJFcoULz8DHy39g1YjSYotRqfJ/mwtne6Pd7U1OSz16DBG4nrlUIlLbsUpmOGiqbmJsTGxDqNmDz/6otY89JzfZ6jVqmRNzUHWZMzXZ67ZeWNfqnncCJXKhwjXl0JOK6/8QY8/OgfodfrER4ejutvvMGnI1jNze4/7wWiL2FQRi4MVguaTJ1oMunxxbdfw9A13U6TPd4n1/dmXdVAZUdyYBIqwWOg2Ww2HNxdi5peGzLv3V6DFXf+FuffcLVYbkp+LmQymZgyOC5xNLKK8h3TEAvzkZGfi0gfTq8b6eQKOZRqlSMoU6uGFJBp5ArEqMMQo9YiRqVFjFqLLWkt2LW5xKVsfHz8UKpNPuKP6XbeBHoDlR3JgUmoBI+BpjfoUVFdha2VZeKeYPsO7scPH3/ltPfXtPwip/MyJk52TEPML8S0vCLkZE2FRs3tCAYik8kc677UPVMPFRqV29EvjUYDvV4PjUbj8ymFo0a5X8ceiL6EQRnBbLOiqWs9WJOpE4ZeiTmEfs4bLG/WVQ1U1pvAJJhJJEZy8DgY3iQZaWo4iu29NmTeVVEFQ1c797azrMLpcURUJK6+53dISk9z7A2WnDRiMliFEqVMjhi1FtFdwVeMSgutUuVSbvr06SgpKXGadjJp0iRMnz49kNWlAPIm0BuorDeBSTCTSIzk4HEwvEk0sn3XTmwpL3GsBassxbad22Gzuc4M2lpR6hSUzZ42Ew/c/gcU5znWgcVE8cu8gciV8p7AS+1IwiFXKyXRBwezL2FQNgKZbVa0mA1oMTmSc7RbTcGuUkAEM4kER7W801ew3t7ahvDICCiUPW9dn7zyJt55+oU+r6VQKjExOxPjpkxyee686692cwYFixwyRKk0juCrKwCLUHn2DbNSqcR1112H1atXi9NamOSD/CGYSSQ4quUddwG4IAhoam5C/CjnkY/r77xF3PvLnaiISBTlFSIuNs7p+JjRifjttTf7psLDjEwmg1yt7Eq84Rj5kquVkCul+74czL6EQdkI0J0dsbUrENPb+k7lTdJLyz5SmY0m7Nm2XUzCsbOkHIf3H8DT6/7ttPdXZqHz3iQpE8Yho9AxDTGzMB+TcqZCreXUEamRAYhQqruCL8dUxCiVZkjflCoUCr9OayHyhtTSso9ULa0tKKksd2RCrHTsCQYAu36scHq/mZZXJAZlKpUKuZnZjs2Y84swLb8IUyZMYlKnfsgVPaNf8t6p5yUw+uWtYPUlDMqGGUEQ0GY2osWsR4vJEYhZBLvX1/HHdLu+rpk5szggr++pYI6oeWO4BY8Hd9die0mZmJK+tnonbFbXPe5qyiqdgrKp04tw+e9vdawFy89FVFxsAGtNngpTKKGQOT7QaORKnJI8BQp+wBn2/DHdrq9rnjjNNTNaMKf7BXNEzRvDKXi0WCworSoXMyFurSxD7f69bsseOFSHcalp4uNzz1qGrCkZKM4rRG5WNrQabaCqHVJkMjhGu9Q9677kGmmPfoUKBmUhzma3o81idGzUbNKjzWKETRj6SjB/TLfz5pqelh1ugYk3QiV4dKe9pdUlePrrb/8g7v3ljjY8DFPycxEV67xPSWz8KPz61hv8Uc0Rx1ebm6tk8p5EHF3TENUKJTbKFegEIJfJGJCNEP6YbufNNT0tO5wCE2+FSvB4PJvNhvbODsRG9/QJHZ0dWHJR/xsrj45PwPSCYhhNzmnrT5wxGyfOmO2XuoYqmULutO6re/qhTN7/6NeGDRtgMBgQFhaG+fPnB6i2oY9BWYix2m1oNXcFYWY9dGYT7H5JxxEaQiUwGcnBY0ebDjUVVagpq0BNWRV2llXAZDTivarNTtMaMgrzxKBMoVRiwtQMcTPmzMI8pCQlQxXLjTT9aTCbmytkMjEJR7RKi1h1GMLcJOIgkrJQCUxGavAoCAIOHTncMwJWUYayqnKcumARXnqyJx19XGwcJo6bII6ORYZHoDC3ANMLilCUV4hp+YWIjI5CbASTcTiRoSvrYU/gpdCoIFcNbvRr48aNaGlpQVxcHIMyLzAokziL3YYWkx7NJgNazHq0W0wjOATzDW8y+/lKqASPQ2WzWrG7srpnHVhZBer2uJ86cnjvfqRMHC8+nn/26Ugel4bMonxMyp4KTZjz1BF7u2tGRQosGYBIZa9EHGotIpVDWwdGFKq8yeznK6ESPA6VrqMdW8tLsLWiDFsrSlFSWYaGxqMu5UoqXWdX3HbdzZDJZOI6sOPXA7Xp2/1W71Agk8vEKYeKrrVflg4VIhMSgl21EY9BmcR0Z0Z0TEc0jJjMiIE0lGlZ1MNut8PQ0YmI6J7RK6PBiN+cc6G495c7MfGjkFmYB7PJ+Xc7d+Y05M4cGZvKhopwharXFMQwRKk0nHZI1GWkbILtb0aTEXa7HeFh4eKxb77/Dlfcem2/500aPxHT8gphtVqh7JWR99JzL/JbXUONXKUQAy8x+YbK9aO/rJNfrEkBg7IgM1otPUGY2YBOqznYVQLgn+l23lxTCtP9gjGiNhiBqmfTkQbUlDumH+4orUBNeSWK55+Ie1/4u1gmIioSaVMm4UDNbgCAJiwMGfk5jmyIRfnIKMjFmNQUjqxIkEaucErEsTB5MlRyLtymofHHdDtvrimF6X7BGFEbjEDU0263Y9fePY7Rr65piFU7q/HXBx7F5ef/WixXnF/odN7o+ARM68qCOC2/EEW5BS6p6UcycfRLTD/vCMBk/BItpDAoCzCTzYrD+jY0d23W3HujZinxx3S7vq7pblqat6/vj8AkVEbU/FFPQ2enY/phqWMaYk1ZBY4daXApt7O0wuXYeddfBbvNjoyCPIzLmOS0p9hw5qskGYGglMkRrdL0JOPo2pD5h16JOBiQkS/4Y7pdX9d0Ny3N29f3R2ASKiNq/qhnfcMR/FJegtLKMnEdmK7D9edUUlnmFJSlJqfg9zf+BjkZUzGtoAipySPny7zNlVvF30F3P5Pu0S95VwKO7tTzFPr4UwygJmMnypsPQ24JH7gweUXqH4K7SXH0zWI2w2azQRsWJh4r+2EzVl91Y7/nJaWnIrMwH2aTGepe97PkguV+q6uUDSZJRiAMZUNmX5s3b56YkYtIikIngJLe6Ftbuw7RkVFOwdPjzz6Fl995rc9zFAoFcjKnIj0lzem4TCbDPbfe4be6StnmyhK0degQExmNE2bMdp5+qFZCpuDo13DtSxiUhYBQ+gae+hfsn5/dbsfhffuxs9SRhKOmrBJ7tlXjugfvxlmXXyyWyyjIdTovZlScuCFzRkEeMgrzEBs/MrJHhpLeGzJ3Z0KMVKkhl0mjE2cWruAZ6Nt3Ci3B/hmazCZs27HdsRlzV0bEmtrdKP9qs9PeX8X5hU5B2YT08Y4NmfOKUJxXgPzsXKe1ZCOVXKkQA6/uoEuuUiAynck33BmufYlkgrJt27bh2WefRWlpKWJiYnDGGWfguuuug1rd94iCJ+dcfvnlKCkpcTrv9NNPx+OPP+63e/GEN4GWp9/AM3ij47U0HsOO0oquAKwCNeVV6GjTuZTbWVbhFJTFj0nEJb+5EekZk5FZmI8xaSNn6kgo0SqU0GjCMCY6ATHqMESrNFBy2uGI4mmw1fvb94E+0DOAo+PV7NmFrZWO4KukshyV27fBbHFdA19SUeoUlM2deQL+cPPvMC3fEYSNihvZX+bJZDKXdV+KXoEYgAH3AKPhSxJB2fbt23HxxRfjhhtuwKWXXoqDBw/i9ttvx6ZNm/Dmm28O6Zza2lrMnz8fN97YMxUrNjbW37c0IH9MdfLHNf0x3c6ba0pxup9U6Ts6EBYR4RQ8vb3meXz8zzf6PEculyM9YzLGpKa4PHfZ727xSz1pcPrakLmpqQnxUfHBrp5kzJs3D83NzRg1amR8+PMm2ArmNf2zVsvza0pxup9U6TraER3pvCfkRTesEPf+cic8LAwFOfnQap23Mhmflo47b7rNL/WUOrlC7gi8tD2bL8vVSn7BGSKC0ZdIIiibNGkSysvLxV/UOXPmAAAuvPBCPPPMM4iLc82w4805iYmJyM3NdbmG1G3/6nt0HGuGqdORCMPUqcf2r74P6P5W3gR3no7UeXNNjvi5Z7VYsHdHDWq69gLbWVaJAzW78eJ365z2/soszHc6LzF1rLgZc2ZhPibnZSMsIiLAtaeBdG/I3L0pc4xai3Alv5jwxPz58x2BajwDVQD4bOOXONrciA59JwCgQ9+JzzZ+GfC9rbwJ7jwdqfPmmhzxc0/X0Y6yqgpHJsSukTCVSo2yL39yKjc9v0gMyuRyObIzssRMiMV5RcianOGUln4kkckAeXfCjV6bL8uVnLUQyoLRl0jif5C7KYrd/7ntdvuQz3nrrbfwySefIDk5GWeeeSauvfZal80EA2XH9z+jU6fzKNDqONaM1sM9Ge8Emx0dx5oDVldvSTXRwXBweO9+pw2Z91Rtd9nnCwB2llU6BWV5s6fjkt/ciIyifGQW5CE2gR9UpUYqGzJbrVaYun6nTCYTbDZb0N4naWCeBltHmxtxsOGw+Nhmt+Foc2NA6+otf4zUEWCxWFC1oxpbewVgNbW73e4r2dTchPhRPf3FBeeci/ycPEzLK0R+dh4iwkfmOjCZQu6YcqhWQa7t2feLUw57sC8ZPEkEZcczm8149NFHsWDBAo8j1L7OSUtLwyWXXIKioiJUVVXh3nvvxcaNG/HWW2/1eS2dTgedrmfdTX19/eBv5jgdLa1oa+jpEH0RaElhRI18x9DZCQ2c3+AfvHqVuPeXO2qNBpNyp0KtdZ6ak5gyltMQJUaKGzJbrVasXbsWer3jPUSv12Pt2rVB/QJrOPBnX+KPYEsqo2o0dHa7HXqDAb27kiONDVhw3un9njcqNg7T8ovQ1q5zCsoWzV+ARfMX+Ku6kiOTAXKV88iXQqOCXMX3w/6wLxkayQVlgiDg6quvRl1dHTZt2jTkc15//XVxBG327NlIS0vDaaedhjvuuAOFhYVur/fkk09i9erVLsdbWlqGlH5TZzYCbkb+BJvd7V5dgs2zsu0Nx1wCvfaGY26v6Vd2Qfzbm9e2611HfEYKQ6ceu7dtx87KKtRUVaOmogqCIOCf//3QqVxGzlQxKJPJZEifPAmZeTnIyM9BZl4uxk2ZBKVKBQCB/7kHiOR/T9z8/qvkCkQo1YhQqhGp0iBCpYZSJgcEACYBNpMerRjaz6v3h/7BKisrQ21trdOxPXv24Ntvv+3zfVLKfNEmvpiy4q++pF3fAavd5nLcare57NXlaTkAOHzsCOobe2Zn2Ow2HD52xG1Zf7ILdvFvb167wzg83/s8cfRYI8qrKlC2rQLlVZUo31aJs5acjnt+d5dYJiomGqPjE9DYdAwAoNFokDc1BwU5+SjMdfxJS0kVR+kD/XMPFJffE7kMCpUSMpUCCrUScpUCMpWiqx3sgGACTCYgQF1Q92wvu92OpqamgLymL94zAfYl7njTl0gqKBMEAddeey0+//xzfPPNNxg3btyQzzl+jvNJJ50EAKiqqurzF+S2227DypUrxcf19fWYOXMm4uLihtZRGzsBN9+IyxRyyKNcpwK424vCXVlPy/mLOFJnMAAATAYDdv5c4tVIXaDqerxAZqy0Wa3Yu6PGMQ2x1LEh84Fde9xO0W3t0CFhTM/v8oLzlmJcdhYyC/MwOS8b4ZGRfq2rVAXr98QT3dNXlAoFisdNFjdkDoShBhAmN1Nhu4+H6tosKdTbX32JuaXTbZZNpVyBmPAol2OelPO2rD90j9Tpu/oSvcGAH7Zu9mqkLlB1PV4gM1Z2dHaitKrcaR1YXf1hl3KV1VWI1IY7tcnvbrgVGrUGxfmFmDo5EypVYN6jpEKuUkAZpkZ8QryY/VCuktRHYci7PifK5fKAvo/54rXYlwyNZH4TBUHAddddh48//hhff/01srOz/XJOXV0dACA6OrrPMtHR0f0+HyiRCY6ML20NjRBsdsgUcvGYlITa2rfe/LUOThAEmAwGaHvNu29tasZNp/W/sXJkTDQyCnLR2dGB3ruTTDtpLqadxOmoUuFuQ+YtChUMcIyOjQnzzwfDDRs2iBtm+nKflr6yS4VqJyoVUuhLEkeNBgAcbmyAzW6DQq4Qj0lNKK5/6+avdXAWiwV2wQ6Numdq+n/Wf4Kb7u4/o2FaSioyJ01xWS923WVX+6xuUiaTy3pNO+xJPy+Ty2FuaoImPjjBuxT4qx8B2JcMlWSCshtvvBEfffQRvv76a+Tk5Lgtc8kll2Ds2LHiHmMDnbNz5058/fXXWLlyJVQqFZqbm3HTTTdh7NixOOWUU/x6P32JjIuFTCH3KNDqHmna+NLbMOo6oIkI5zoxiWptahYzIXb/PXvJQtz21z+JZeLHJCIhOQnH6o8AAFQaNSblTEVGQR6yigqQWZiHsRPGQSaTDdspiKGoe0Pm3pkQo1Qapw2ZA7WweePGjWhpaUFcXJxPO9Pp06ejpKTEadrJpEmTMH36dJ+9BvmWp8FW9yjTmrf+gbYOHSLDI7hGTKIEQcDeA/sciTgqylBSWYqK6m147s9P4VdnnCOWm5Zf5HRebEwsivMKurIhFqE4rxCJCY7fheE6BbE3uUrhCMC6EnAoulLPh6JA9CX+6kcA9iVDJYnf2u+//x7PP/88EhMTceGFFzo999ZbbyE/35HWe8+ePbDZbB6fk5qaipqaGowePRqjRo3C4cOHMXfuXHz55ZeICFIa8Ky5MyGPCvdpoDWYETVuND14RoMBuyu2YWd5pRiAHTlQ51JuZ1mly7ELblwJuUKBzMI8jM/KgKqfzdEpOLQKJWLEAGzgDZmHw8JmpVKJ6667DqtXr4Zer0d4eHhI1X8k8kew5e2oGjeZHprGpmNdAVgpSirKUFJVjpbWFpdyWytLnYKyjImTserK61CYk4dp+UWYkD5+ROx9JZPLxIQb4uiXWul2CUcoYl9CkgjKioqKUFnp+gEWcETY3d566y0xFb4n50REROBvf/sbHn/8cRw8eBBJSUlDWlwtVYMZUWP6es/YrFbYbHaoe21e/cNnX+DxW+/o97yE5CSkTZ4Iu90uzg8HgHOuvNRvdSXv9bUhsze2bNnidmHzli1bMGvWLF9W168UCgU0Gg30ej00Gg070RHI20CPqes9pzfoER7mvCb2nkcfxL8++aDPc2QyGbImZyAhznnql0KhwB/vesAv9ZQKuVLhNO1Q3pX5cDgHn+xLSBJBWUREhEebO0+cONHrcwBH5D5hwoRB14/6Fypr3wYiCAIaDh7q2g/MsSfY7spq3PqXh7Bw+dliucwi5yA2IjoKGQV54obMGQW5iE8aE+jq0wAUsq51YKown27I3Nzsfv1koLJmEQ0XobT+rT8WiwXbd+9ESUWZOBVx554a1Hxf5pRmvji/0CkoS00ei+L8IkzLK0RxfhEKc/IRNcyTOslksq5phz0BmEKjGjajX95gX0KSCMqof+lFueJUQykK1bVvupYWmAxGAEBrYxMuKjgBbc2uU0d2llU4BWVjx4/Dr665ApNypiKzKB8pE8Y7jYYFGqeiugrkhsxc2EyhYFZesTjVUKpCcf2bIAjYX3cAeoNjyllD41GkT8+EwWh0KVtSWY7FJy0UH598wnz87vpbUdS1HiwpMbhf5vl7OqpcKe8JvNSONWBytXJYj355g30JMSgLEm8CLU8/aHtyzZG60bTJYIQmTOt07B8PP4Zx0fGIiYmByWh0G5ClTZ6ImHjnN0q5XI7rHviDX+vrDU5FDe6GzFzYTMHkabDlzYfsga45kjeZNhgNCNM6L4M49eKlOHvpOY6+xGxyCcjUKjXys11n9mRNzsC9v73Tr/X1hq+mo8pkgLw74UavzZflSk5j6w/7EmJQFiT++PDsyTVDOX29p2w2Gw7U7MbOXtkQ9+6owRu/fItRiT1TYTIL82GsPSQ+HjVmNLIKC5BRlOeYjliQh4jokZs2V6rUcoVTIo4YtRaqfhJxdPNXGmAubKZg8seIxkDXDOXU9d7QG/Qo31Yp7gW2taIM0VFR2PifL8QyMpkMxXmFTudlTprSMw0xrxC5WdnievjhRq6QO637Ugzz0S9/ppNnX0IMyshngjXNsqHukCMAK61ATVkFdlVWw6h3TSlfU16J2Yt7po4UnDALVcccnevo5CS8tXVjwOpMnlHK5IhWaRCjDoNcFo7UxKRBb8jszzTAXNhM5DvBmGpptVqxY3cNSiodwdfWilJs37VTzPjcTaFQuCTtuGjZ+ajcvwMCgNSxKdj82XcBq3egOEa/lOIIWHcK+pE2+uXPfgRgXzLSMSgjnwnE1Dmz0QS11rmjvv1Xl4h7f7mjVKkwITvT5XjKhHHYHRYGq9EEu90Ou80O+QhcXCwVcsgQqVIjVh0m7gkWoVSL37g2WZsGHZARUejwdyZHQRBgMpug1fRMad+1dw/mLu1/+mV0ZBSK8grQ1NLsFJSdteg07HpjHwwmI6xWq5ikJGTJAGW4Wlz31T39UCYfnqNfRFLBoIwky2QwYk/1dtSUVorZEFUaNV746hOncllF+fi+V1CWMnE8MgvzxWyIE7OzXAI5u82Gkg/WwWp0bNJoNZpQ8sFnKF5+BgOzAIlQqntNQ3TdkJmcBWqDaqLhprmlGSWV5Y79wCodo2AXLTsPj9zZk1Y+Y+JkRIZHiOvk1Co18qbmdG3KXIzivAJMnjDJJamTzWbDm5+9D4PJsY7MYDLizU//jUvOPFfygZlMBshVPeu+ujMeypUKRKQyucRwxH5E2hiUjTBSTV9vs9lwcHctarqCr51lFdi7vQY2q9WpnEwmg76jA+G90gQv+NXZmJSbjczCPEzJz0VUbMyAr3e4ehda6uqdjrXU1eNwdQ1S87J8c1Mk0sgVPfuBdQVi/W3ITM7621SUKNCknLreYDSgoroKWyvKxKmIew/scym3taLM6bFCocBvr7sZ0VFRmJZXhJysqdCoB54+WV6zDfvr65yO7a8/iIqabSjKyh/KrfiUTC4Tpxwqeq/96hVkciRseGM/In0MykYYKaSvFwQBFrPFaUPm+v0HcP0pZ/dzFhAWEY4p+bnQNbc6BWUnnr4YJ56+2Ks6GNp0Xh0f6bxJu6+QyRCt0iK2VxDGaYdD09+mopMnTw5SrWikkkrqepvNBkEQoFT2fJR59V9v4q4/3t/veUmjxyAlKdnl+O3X3+J1HVra29web9a5Px4IcpVCDLyM4UDUmNGQq4L/cc+fSTJoYOxHpC/4/0tp2Oto06GmvFIcAdtZVon5Z52GGx66Rywzdvw4RMZEo6MrKFIolZiYnSluxpxRmI+0yRN9NsweFhPt1fGRrq+0+4HcD2wk629TUV92pvPmzRM/NBFJiSAIqKs/1LMhc2UZyqrK8cpTa532/pqWX+R0XlREJIryCjEt37Eh87T8Qowd4xqQDVZclPuZGaOiB56xMVTi6Jda2ZP9UK102nhZ2WSWREAG+D9JBvUvUP0IwL5ksKTxP5WGDbPRhNrqHWLwtbOsAodq97mU21lW4fRYLpfj/BtWQqPVIqMoD5Oyp0ITphVHaGSdRp/Oex6bPQVHdux2msIYl5qMsdkZPnuNQLDbbLCaLQAAq9ni92QlMsgwRhvZk45epQ3YfmAjWaA2FeUHJZKKltYWlFaVi5kQSyrLcPSYa9r9rZWlTkFZ3tQcrPz1FV0BWBGmTJiEX7aVitkcfRmQAUBBRg6qdm93msI4LjkN+Rk5Pn0duVLhCLy0Kii6MiDKVAqffQHGtUbDXyA3p2ZfMjgMykYoX6Svt9vtEOx2KHpNHfn8Xx/gmbtX93teTPwoJCQnQRAEpw7lwptc5zX7a2NkuUKB4uVn4Lu1b8BqNEGp1YRckg9/JyvpnY5+s1wBIwCtQonC+JQhX5u809+moq2trcGrGI1ovkpdbzabXfbxuuGu32D9N1/0cYbDpPETEdErCyIAaDVaPPHAo07HfLUpsjsKhQKXnHke/vb68zCYjAjTaIeU5EMmkznWfal79v3qnYTDH/pba8TAbPhgPyJ9DMpGqMEEOMfqG7pGwBwbMtdUVOH3f38Mc5b0fEuZWeh8XU1YGDLyc5BRmIfMogJkFuYhMWWsJKa3yRVyKNUqR1CmVoVUQAb4NllJ72mI3WvBeqejV4R4VsRQ/xaYm4qSFA0mwLHZbNi1d49j9KtrFGznnhrs2VTllGZ+Wn6RU1CWmDAaxXmFmNY1BbEotwBxsXE+uY+hUsgVUKvUMJiMUKvUHgdkcmXPxssKtQpyrQpyH45+eaq/tUazZs0KaF2kjP0I+RuDMnKrU9eOmooqRwDWlZK+qeGoS7mdZRVOQdn4rAyccemFyCjIQ2ZhHtKnTHIaSSPfGUqyEq1C6Qi+ujIhRg/jaYiB+hbY33PouakohRpBEHDoyGGUVJajpMKRCbGsqhztnR0uZSuqqzB72kzx8SlzT0ZHZ4e4DiwlSRpf5g2GTCbrte6rZ98vqWy83N9aI3II5GiiP/sS9iPSxk/LBKvFAqXKOTve3353D77/7PN+z0selwZtuPObhkqtxi1/7n/6IvmGp8lKlDK5Uyr6GHUYNIqR818/UN8Ccw49jXQWiwWqXn2J3W7H7DNOFvf+ckepVCI7IwvGrn2+uhXnF6I4v9BfVfUfGaAM1/Qk3uhOPS/hgDKQa41CVSBHE9mXjFwj55MZAXB0kof27sPO0kpxT7C923fi7bIfEBHVk2Y+szDfKSiLGRWHjF4bMmcU5qF1Xx2sJjP2l1T6dL0XeaavZCVZBbmIC4sQR8IiVENb7xHq+C0wke+ZzCZU7ah2ZEKsKENJRSlGJ4zGZ298IJZRKBQozC3A9z//KB6bOG4CpuX1ZELMm5qDil3VMJpM2Fy51edrvvxFJoNjtKs76Yay98bLwd/70xv9rTUiB/YjFAgMyoa5poajqOmVir6mvBKdunaXcrsrqlBw4mzxceG8OVh+7QpkFOQhq6gAY9JSXL7pq/hwvV+ScJBn5AoFTjz/bHz+/GswG4wICwvDHTfdAhX3BHPCb4GJhsZut/daB+YIwqp2VsNisTiVO3TkMKxWq9O+Yb9efgHmzz4RxXmFKM4rwKg41/+P/kzE4Qsyhbxn3Vev6Ye9N1uW8kjYQLjWaGDsRygQGJQNIzar1Wn9liAIuG7h2eho63sTS7lcjnGZU2A2mZ2OT8nLwZQ836b0paFRyeSONPTde4KptFArlPhBGwazwQitVuuXgCwQi5v9uakovwUm8s7xgdXWilIsvvDsfs8JDwtDYW4BmltbkJgwWjz+619d4Ld6+ppMBshVzuu+FBpH8o3hzt9rjQKVJMNffQn7EQoEBmUhymI2Y9+OGqcNmSOjo/Dkf94Wy8hkMmQU5KBkQ8/UkaT0VDEJR0ZBHqbk50AbHu7uJchHuvdaU2rUHo8oyiDrtQbMkRExXDn47QsGK1CLm/25qSi/BSbqW1u7DmW99wOrKMMVF16Cu266XSyTNzUHSqUSVqsVgOMDfHZGFqblF3WNgBUia3KGUyAndeLGy10BmLj2S6IJj/z5xZW/BTJJhr/6EvYjFAiSegfdvn07SktLERMTgxNOOAFxcQOnu9XpdPjyyy/R0dGBE044we2u5J6UkTK73Y7D+/ajpqxKTEm/Z9t2WI4b3VJp1LCYzVD12u9l4fKlmDqtSFwHFhsfOnPdA70xsr94stdauELllI7eoulEQkJCgGvqarikSmbGKSLHOrBtO7Zja2XPOrCa2t0u5UoqypweazVa3HzV9UiIT8C0vELkZ+c6pa+XMpvNBrPF0VeabRZoxkRDHa6BXCWpjz8D8ucXV/7GfoTIM5J4V2psbMRFF12EI0eOoLCwEAcPHkRlZSVee+01nH1231MmqqqqsGjRIqSnpyM5ORk33HAD/vSnP+HWW2/1qozUHD+sv6eqGjefcV6/56g1GkzOy0ZbUwsSkseIxxedt9Rv9fQnf2+MHExqucIpE2KMWgvVcfvaNMn0QaqdMy5uJgpNdrsdgGOKerenX3oejzz1l37Pi48bhdiYWJfjD9x+t0/r5xdyGZRhajH9vF0hw8uvvwJDV2ZHg8GAf771Gq699togV3RkYT9C5BlJBGUWiwX33HMPFi7s2e/qzjvvxGWXXYZjx471OSVi5cqVmD17Nj788EPIZDK8/vrruOqqq3DWWWdh0qRJHpcJJn1HB3ZVbENNeZW4KfPCc8/Bijt+I5YZn5XhGAXrGhmTy+VIz5iMzO5siEX5GJcx2SWtfSjz5cbI/UkvyhWnFvqDQiaDHI4F4Gq5AvPHTERYCCXi4OJmotBw5GiDY/phpWMaYmlVBd59/lWnvb+mHZdiPkyrRWFOvpgJsTivCONS00IiaYVcpeiZftiVAdHarkZEr/emTZs2BWyExt/7FIYy9iNEnpFEUDZ27FiMHTvW6dipp56Kxx57DIcPH0Z6errLOfv27cPmzZvxv//9T+xALr74Yvz2t7/F+++/jzvvvNOjMoFksViwe9t27Nq1S1wHdqBmNwRBcCpXU1bh9FilVmP5NSsQFRuDzMJ8TM7LRlhERCCrHnBD2RjZG77MGqmQyRCl0iC614bMEUo1NiuU0ANQyOQhFZABXNwsNfzgRwDQ3t6OLZXbHZkQKx3TEA8dqXcpt6Wi1CkoK8otwOXn/9qxFiy/EFMnZ0p+HZhMLhMTboh7f6mVkHkwYyKQIzShNqUwkNiPSAv7EemS7Lvxf//7XyQkJCA1NdXt89u2bQMAZGdni8eUSiUyMjLE5zwp445Op4NO1/Phv77etbMbjOfWPIOH7r2v3zKRMdGIjIlxOX7lXbf5pA6hwtONkYNFIZMhWuUIvKLVGjEAC4VvmL3Bxc3Swg9+ocVffclVN16DHzb91G+ZtJRUyGXOgUtsTCzWPPKET+rgFzJAFaHp2XS5K/PhYN9XOUIjDexHpIX9iHRJMihbv3491qxZg5dfftlpPnxv3R1dbGys0/G4uDjxOU/KuPPkk09i9erVLsdbWlqG9M1CVlam02OVWo3J2VnIyMtBRn4uMvNykJzumDpib5fGmqK+2G02WLumU1pNZljbOvr8WQ14Lb3J5VhSWgrqkxLReuSoeCw2ORFJ6akBbxuFTI5wpQoRKjUilGpEKNTQKJSODwo2AAYrTIYOuN6FQ/faDrvd7vE3tP39fvri+t5SdU2NValUaG1t9fn1PbkHb9pksK8RiobaLsORL9rEFx/c/dWXZE/NdgrKYmNiUZCTh8LcfBTk5qMgJw8Joxz1b9O77ksZdDIZ5CoF5CoFBLkMJmtXIg6rBR0qKxQyATCbAfMA1znO8T/3iRMnIj09HQcOHBCPpaenY8KECSH3HjDY9y8p9SX+7kcA//cl7EdGlkD3JZILyjZu3Ihzzz0X999/Py6//PI+y3V3aO3t7YiMjBSP63Q6pKWleVzGndtuuw0rV64UH9fX12PmzJmIi4sbUkc9Z/48LPrV2Zg6cxoyCvMwPnOKU6ZEXxtMKnZP2G02lH2wzikoK/v82yEl4ZBHOWfykgOYdsHZ+G7tG7AaTVBqNZh2/tl+T/KhkskRpdIgRh2GaJUG0WrtkFPRdwercrncq98fT8sO9vre8PdreHr9obx2INopWIbb/fiCFNrEX33JaacsgdIux7T8QkzLL8L4tHF+G6XfXLkVRpMJWo1mUBs7y5VyyNUqKLQ9my/L1Y4vtbpTpRuNPYk4/v3vfw9pFOX4dl21apXTCM2qVatCcoRmKO9fUulLpNRXDfb12Y+MPIFsF0kFZd9//z3OOOMM/P73v8f999/fb9kpU6YAAPbu3Yvk5GTx+L59+7BgwQKPy7gTHR2N6GjfT5OLiorCbX9a7RKA+IsnqdgHI1BJOOQKOZRqlSMoU6t8HpCpZHJEd639iu4KxEJtzRcQ+vPDA7WpKFGg+asvmTNrNk6ZMc/n13Vnc2UJ2jp0iImM7jcok8lkYtbD3psvy5V9/18ORKp0pjH3HPsSouCSTFD2448/4vTTT8dtt92GBx980G2ZDz/8EFFRUVi0aBFycnIwadIkvPXWWzjhhBMAAN9++y3q6urENPqelCHvBSoJhy+p5Qox+OoOxAIRgAWikwjl+eGB3FSUiHxDHP3qTrzRa/TLG0yV7plABRvsS4iCSxJBWW1tLU4//XSMHz8eycnJeP7558Xnli9fjsTERADAX/7yF4wfPx6LFi0CADzzzDM455xzYDKZkJKSgueeew4rV67EjBkzxPM9KUPekXoSju59wBzBlyMJhzYII2DsJAYWyE1FQ/1bYKJAk8lk6NrRAzKFDBGpowYc/fIGE3EMjP2IZwLVl7AfIX+SRFBmtVpx8cUXAwDKysqcnjv11FPFfy9fvtzpzfq0005DSUkJ3n33XbS1teG5557D8uXLnc73pAx5Z2z2FBzZsdtpCmNcajLGZmcEvC6a7hEwdU8aeo1CEr/WAQ04QhVTVhNJg1wpF0e9eq/96g7AZHI5lOEan74mU6UPjP2IZwLVl7AfIX+SxKfXjIwMp9Gxvtxxxx0ux3Jzc5Gbm9vveZ6UIc/JFQoULz/DKQnHUJJ8eEqrULpMQZRKAOYOp+YMjN+UEwWWTCbr2nDZeeqhr0a/vMFU6QNjP+IZ9iU0HEj3Ey1Jmr+TcIR1BWAqmQJGOAKyk5Im+fQ1/I2dxMD4TTmR/8iVCqcNl+Xaoe375Q9MxNE/9iOeYV9Cw4F/hzaIPKBRKDBGG4mM6ARMj0/FwuTJmJ80CYXxKVB2pZ+VQTofIjw1ffp0TJw40ekYOwln3d+Uh4c7MpLym3Ii78nkMii0KqhjwqEdHY2I1FGInjQGURMTEZ4yCtqEKKiiw6AYRDIOCi72I55hX0LDAUfKhim7zQar2QIAsJotsNvsfp9eOBAZgHClWky+0Z2Io62ldVh+6zecpub4c3Ezvykn8o5crYQmIlKcguiv0S+mGA8+9iOeY19CoY5B2TBkt9lQ8sE6WI2OztRqNKHkg88Csu6rmwxApFKDaHVXAKbSIkqlgULu3euHeqaj4dJJcHEzkXQoIzTQxkf59TWGU9Y/9iPSwH6EqH8MyoahQG3u3JsjBX1PABapUkMuG3oAyDdxIqLAG05Z/9iPEFEoYFA2DPlzc2elTI5olQZRKi02yx1JOMIUKsxOHDfkaxMRkTQw6x8RUWAxKBuGfLW5s7p7D7CuFPRRKg3ClWrxeYUPRsKIiEh6mPWPiCiwGJQNQ4PZ3DnU9gAjIiL/YYpxIqLA4qfuYai/zZ17Z0CM6pUFUSX3fuFwqC+eJiIi9wKZ9ZMmOcUAABulSURBVI99CRERg7Jhq/fmzhqNBjmjkgadAbEvXDxNRDR8BSrrH/sSIiIGZcOGQiZzSUG/RaGCEY61YemRccGu4ojFb4EHxjYiIuob3yM9w3aiUMagLAT1zoDYHYRFKNV+2TyUho7fAg+MbURE1De+R3qG7UShjEGZxA2UAZGIiIiIiEIbg7Ig2V9SCavJDKVGjXHFeQBcMyBGKTXQKlVBrikREUnVhg0bxOlaHCUgIgpdDMqC5GDpNhh07YiKicG5i08fdAZEIiIauTZu3IiWlhbExcUxKCMiCmEMygIoUqXB1NgxSEtMwhaFEgYASrkc8dqIYFeNiIiIiIiCxDe50ckjGoXSpynpiYiIiIgo9HGkbBhjalgiIhoK9iNERIHBoGwY4/oCIiIaCvYjRESBIZl5dA0NDfjTn/6EiRMnQqvVoqmpqd/y+/fvh1ardfvnxRdfFMudcsopLs9ffvnl/r4dIiIiIiIij0gmKLv11lvR3t6OO+64AyaTCYIg9Fs+PT0dra2tTn8effRR2Gw2nHnmmWI5k8mE3/3ud07lXnrpJX/fDhERERERkUckM33xnXfeAQCsX7/eo/IymQxardbp2Ouvv44zzzwTycnJTseVSqVL2WCyWq0wmUwAHEGjzWaDQsF0+ERE5Dn2JUREw4dkRsqGqqysDKWlpbjmmmtcnnvmmWcQExODrKws3H777dDpdEGooYPNZsPatWuh1+sBAHq9HmvXroXNZgtanYiIKLRYrVb2JUREw8iwCcpefPFFpKam4rTTTnM6Pn36dLz99tvYs2cPnn/+eXz22WdYunRpv9MjdTod6urqxD/19fU+q2dlZSVqa2udju3Zswdbtmzx2WsQEVHw+bMv2bJlC/sSIqJhRDLTF4fCaDTirbfews033+wydeOpp54S/33yySfjlVdewezZs7Fp0ybMmTPH7fWefPJJrF692uV4S0vLkNMCNzQ0uD1eV1eHyZMnD+naoSqYI5dSxTZxxTZxj+3iyhdtEh8fP+Rr+Ksv0el0OHTokNvn2JdQb2wTV2wTV2wT9wLdlwyLoOyDDz5AW1sbrrrqqgHLFhYWAgBqamr6DMpuu+02rFy5UnxcX1+PmTNnIi4ubsgd9ZgxY9weT01N9cmHgFA1ku+9L2wTV2wT99gurqTQJv7sS1JSUtweZ18ycu+9L2wTV2wTV2wT9wLZLsMiKHvppZewZMkSjBs3bsCy1dXVAICkpKQ+y0RHRyM6Otpn9estLy8PO3fudJp2MmnSJEyfPt0vr0dERMHhz75k+vTpKCkpYV9CRDRMhNSaspNOOsllj7G9e/fim2++cZvgo7y8HLfffjv27NkDq9WK8vJyXH311cjOzsbChQsDVW0nCoUC1113HcLDwwEA4eHhuPbaa5kxi4iIPKZUKtmXEBENI5IJyv7whz9Aq9XinHPOAeCYgqHVavHhhx+KZUwmE8xms9N5L7/8MhITE3H22We7XDMnJwcTJ07EWWedhfDwcJx55pmYOXMmvv32W6hUKv/eUD8UCgU0Gg0AQKPRsBMlIiKvsS8hIho+JDN98eGHH8YDDzzgclytVov/3rBhA2QymdPz999/P+6//363QZZSqcSqVauwatUqCILgci4REREREVGwSSYoUyqVUCr7r07vAK2bpyNeDMiIiIiIiEiKJDN9kYiIiIiIaCRiUEZERERERBREDMqIiIiIiIiCSDJrykaaefPmwWAwICwsLNhVISKiEMW+hIhoeGBQFiTz588PdhWIiCjEsS8hIhoeOH2RiIiIiIgoiBiUERERERERBRGDMiIiIiIioiBiUEZERERERBREDMqIiIiIiIiCiEEZERERERFREDEoIyIiIiIiCiIGZUREREREREHEzaM9YLVaAQD19fVDvlZLSwsMBsOQrzOcsE1csU1csU3cY7u48lWbJCUlQan0XTfpq76EP3P32C6u2Cau2Cau2CbuBbovYVDmgcbGRgDAzJkzg1wTIiIKlIMHDyI1NdVn12NfQkQ08njal8gEQRACUJ+QZjQaUVlZidGjRw/pW9P6+nrMnDkTP//8M5KTk31Yw9DFNnHFNnHFNnGP7eLKl23i65EyX/Ql/Jm7x3ZxxTZxxTZxxTZxLxh9CUfKPKDVajFjxgyfXS85Odmn374OB2wTV2wTV2wT99gurqTYJr7sS6R4f1LAdnHFNnHFNnHFNnEvkO3CRB9ERERERERBxKCMiIiIiIgoiBiUBVB0dDQeeOABREdHB7sqksE2ccU2ccU2cY/t4mq4t8lwv7/BYru4Ypu4Ypu4Ypu4F4x2YaIPIiIiIiKiIOJIGRERERERURAxKCMiIiIiIgoiBmVERERERERBxKCMiIiIiIgoiLh5tA999913ePPNN9HR0YF58+bhmmuuGXAH78GcE0r27NmD5557DgcPHkRWVhZuvvlmJCQk9FneYrHg3XffxYYNG2C32zFr1iysWLECKpUqgLX2r/b2djz99NOoqKhAYmIirrnmGuTl5Xl0bmNjI2688UbExMTgxRdf9HNNA0cQBLz++uv4/PPPoVarce655+LMM88c8Lz9+/fjxRdfxJ49e5Cfn4+bb74ZERERAahxYHj7/mCxWPDWW2/h+++/h16vR2ZmJq655hokJycHsNb+VVdXh3/84x8oLy/HnXfeiTlz5gx4jrfvQ8Fkt9vxyiuv4KuvvoJWq8UFF1yAU0891efnhJqvvvoK77zzDjo7O3HyySfj6quvhkKh6LP8sWPH8Oqrr6K8vByxsbE4++yzsXjx4gDW2P9qamrw/PPP4/Dhw5g6dSpuueUWxMXFeXTu119/jTVr1mDx4sVYtWqVn2saOG1tbXj66adRVVWFpKQkXHfddZg6deqA523cuBHvvvsudDodTjvtNPz6178OQG0DYzDvD42NjXjxxRdRXV0NtVqNWbNm4fLLL4dWqw1Qrf3v559/xksvvYSGhga88847Ht2bt+9D3uJImY+8++67WLRoERITEzFv3jw8/vjjuOCCC3x+TiipqanBtGnTcOjQIZx22mnYuHEjZs2ahdbW1j7PmTFjBj7//HNMnz4dM2bMwBNPPIGFCxfCYrEEruJ+ZDKZMH/+fPznP//B4sWLYTQaMXPmTPz8888DnisIAq644gps3boV69evD0BtA2fVqlW44447MGvWLEyePBnnnXce1qxZ0+853333HXJycnDgwAGcc845sFqtuPDCCwNUY/8bzPvDJZdcgnvvvRcFBQU444wz8P3336OoqAj19fUBqrV//eMf/8DcuXNhsVjw0Ucf4dChQwOeM5j3oWBauXIl7rvvPsyZMwfjx4/HOeecg7Vr1/r8nFDy6quv4vTTT0dKSgrmzp2LRx55BJdddlmf5bdv344ZM2bgyJEjWLx4MUaPHo3ly5fjgQceCGCt/Wvbtm2YPn06GhsbsWTJEnz11VeYPXs22tvbBzz36NGjuPLKK/HLL7+gtLQ0ALUNDL1ejxNPPBGfffYZlixZgra2NkyfPn3Ae3zkkUdwxhlnICEhAWeeeSa+/PJL/PnPfw5Qrf3P2/eHlpYWzJgxA+vXr8eSJUswffp0/PnPf8Y555wTwFr714UXXohVq1bBbDbjo48+gtVqHfAcb9+HBkWgIbPZbEJKSopw1113ice2bNkiABC+++47n50Tai666CJhzpw5gt1uFwRBEPR6vZCYmCisXr26z3MOHz7s9HjXrl0CAOGzzz7za10D5fnnnxfCw8OF5uZm8dhZZ50lLFiwYMBzn3jiCWHJkiXCQw89JKSkpPizmgG1fft2QSaTCevXrxePPfHEE0JkZKTQ0dHh9hyj0SikpKQIN9xwg9Px3u0aygbz/tDZ2SnI5XLhjTfeEI/p9XpBqVQK//znP/1d5YCoq6sTrFarYDAYBADCe++9N+A5g3kfCpaysjIBgPDtt9+Kxx555BEhLi5OMBqNPjsnlFgsFmH06NHCgw8+KB77/vvvBQDC5s2b3Z7T1tbm8t7x9NNPCyqVali0iSAIwrJly4STTz5ZfNze3i7ExsYKf/nLX/o9z263C0uWLBH++te/CieeeKJw9dVX+7uqAfPUU08J0dHRQltbm3hs0aJFwumnn97nOT///LMgk8mEjz76yOn4cOlLBvP+8OGHHwoAhGPHjonHPvnkEwGAUF9f7/c6B8L+/fsFQei51/b29n7LD+Z9aDA4UuYDFRUVOHToEM4991zx2LRp0zBhwgSsW7fOZ+eEmvXr12P58uWQyWQAgLCwMJx55pn93t/x06ySk5Mhk8nQ1tbm17oGyrp167BgwQKnKSbnn38+vvvuO+j1+j7P27JlC/7617/ilVdeEdtzuFi/fj2ioqKwaNEi8dj555+Pjo4OfP/9927P+d///odDhw7h9ttvdzru6dQdqRvM+0NYWBjS0tJQU1MjHqutrYXVakVWVpbf6xwIKSkpXk8VGcz7ULCsW7cOCQkJmD9/vnjs/PPPR0tLCzZt2uSzc0LJli1b0NjY6PR/4cQTT8TYsWP7/BlGR0e7TGNOSUmBxWLp9302VNjtdnz++edObRIZGYnTTz99wN/rxx57DDabDb/97W/9Xc2AW7duHRYvXuy02e/555+PL7/8ss/ZNv/85z8xceJEl1Gg4dKXDOb9YfLkyZDL5U59yc6dOzFmzJhh0y7p6elelR/M+9BgMCjzgb179wIA0tLSnI6npaWJz/ninFDS0tKC1tbWId/fmjVroNFocNJJJ/m6ikGxd+9et21it9tx4MABt+e0t7fj4osvxrPPPjus1gZ127t3L8aOHev0YTs1NRVyubzP35Xy8nLEx8fDbDbjxhtvxOWXX44nn3wSnZ2dgaq2Xw3m/UEmk+F///sfvvjiC8ycOROLFy/GaaedhnfffRezZ8/2e52lyFfvQ4Gyd+9epKamOn3x0v3hob++xNtzQklf/xdSU1M9vj+73Y41a9Zg1qxZw+JDZUNDA/R6vde/15s3b8bf/vY3vPrqq8Puyz2g7/7VYrH0OdW5vLwcs2fPxn//+19cdtlluP766/Gvf/0rENUNiMG8P+Tm5uLDDz/ExRdfjEWLFmHOnDl466238MUXX0Cj0QSk3lLji/chTzAo8wGTyQQACA8PdzoeGRkJo9Hos3NCiS/ub/369bj//vuxZs2aYROMmEwmt20CoM92uf7667FgwQIsW7bM39ULCndtIpfLodVq+2yTzs5OmM1mLF++HNnZ2ViwYAHeeOMNzJo1CwaDIRDV9qvB/v956aWXUFdXh0suuQSXXXYZsrOz8fjjj6O5udmv9ZWqUHufdfd/QavVQqFQ9NuXeHtOKPHFz/COO+7A1q1b8dJLL/m8fsEwmDZpa2vDxRdfjP/7v/9DSkqK3+sYDIPpXzs7O/H111/jiSeewOLFizF58mTceOONwyb5yWDeH9rb2/H4448jPT0dl112GS677DK0trbiueeeC0SVJSlQfcnwSfMXRDExMQAc38pGRUWJx5uamvrM+jOYc0JJ9/SBlpYWp+NNTU0efVP59ddfY/ny5XjkkUdwzTXX+KWOwRATE+O2TQD30yWOHj2Kt956C4sWLRKDspqaGjQ1NWHZsmX43e9+h7lz5/q93v7krk1MJhP0en2fvyuxsbFiFsvuaY9nnnkmkpOT8eGHH4Z85qzBvD9UVVXh8ccfx9dff40FCxYAAC666CJMmjQJTz31FB566CH/V1xihvo+FGju/i+0trbCZrP1Wd/BnBNKev9fSExMFI83NTUhIyNjwPMfeOABvPDCC1i/fj1ycnL8Vs9A6t0mvfX3e/3RRx/h6NGjeP311/H6668DcCREqaurw7Jly/Dmm2+GfOZab/tXwNGX7Nu3D59++ql4/0lJSbjsssvw8MMPY9SoUf6ttJ8N5v3hhRdewLZt23Dw4EGxTebMmYPi4mJceumlOOGEE/xeb6kZ6vuQpzhS5gP5+fkAHMPg3cxmM7Zv3y4+54tzQkl4eDimTJnidH8AUFZWNuD9ffPNNzj77LNx33334c477/RnNQOuoKDAbZvExsa6DIsDjjeCDz/8EKtWrcKKFSuwYsUKFBUVISIiAitWrMCECRMCVXW/KSgowMGDB506ju5sWX39rhQWFgJwzH3vlpiYiKioKDQ0NPivsgEymPeH7uk5U6ZMEY+p1WqMGzcOdXV1fqytdA3lfSgYCgoKUFtb65RBr6ysDEDf/xcGc04oKSgoAOD8f0Gv12PXrl0D3t/q1avx17/+FZ999hlOPPFEv9YzkOLi4pCWlubV7/XJJ5+MN954Q+xHVqxYgTFjxmDKlClYsWIF1Gp1IKruV331r4mJiRgzZozbcwoLC5GUlOQUkHb3K8OhLxnM+8OhQ4cwduxYpzbp7ldGal8ylPchr/gsZcgIt3DhQmHhwoWCxWIRBEEQ1qxZI2g0GuHgwYNimTvuuEN44oknvDonlD300ENCUlKScOjQIUEQBGHr1q2CSqVyypj22muvCStWrBAff/vtt0J4eLjwpz/9KeD1DYQNGzYIAISvvvpKEARHhqdJkyYJq1atEsuUlJQIS5cuFerq6txe4+GHHx5W2RdbW1uFuLg44b777hMEwZEd7NxzzxVyc3PFMmazWVi6dKmYodFkMgnp6elOvyefffaZAED46aefAnsDfuLte0p9fb2gVquFP//5z+Lz1dXVQlhYmPDss88GtvJ+1l/2xePfUzx5H5KKY8eOCVFRUcIf//hHQRAcWTjPOussYdq0aWKZjo4OYenSpeJ7iCfnhLoTTjhBOO200wSr1SoIgiA89thjQnh4uHDkyBGxzG9+8xvh73//u/j44YcfFiIjI4UNGzYEvL6BcPfddwupqalCQ0ODIAiC8NNPPwlyuVz45JNPxDIvvviicM011/R5jeGWffHzzz8XZDKZsHHjRkEQBKGxsVFIT08Xbr/9drHMpk2bhKVLl4rtVlJSIqhUKuGXX34Ry9x6661CYmKiYDabA3sDfjCY95S33npLUCqVwpYtW8Qyzz33nKBQKIRdu3YF9gb8rL/si8e/p3jyPjRUDMp8pLa2VpgyZYowfvx4YdasWUJERITw+uuvO5WZNWuWcOGFF3p1TigzGo3CWWedJcTFxQnz588XwsPDhZtuusmpzJ133imMGTNGfBwZGSlER0cLS5cudfrz73//O9DV95uHHnpICAsLE+bOnSskJiYKJ554otDa2io+v27dOgGAsH37drfnD7egTBAE4dNPPxViYmKE4uJiITMzU0hJSRHKysrE57s/hD/33HPisU2bNglJSUnCtGnTxN+vgdJBh5LBvKe88sorQnR0tFBUVCQsXLhQCA8PFy6++GKxEwl1v/zyi7B06VLh7LPPFgAIM2bMEJYuXSqsXbtWLHP8e4on70NS8p///EeIiooSpk+fLkyePFlIT08Xtm3bJj7f0tIiAHDa5mCgc0JdTU2NMHHiRGHixInCzJkzhcjISOHdd991KlNQUCBcccUVgiD0pKqeMmWKS19SW1sbhDvwPb1eLyxZskSIj48Xf697Bx+C4Aguxo0b1+c1hltQJgiCcM899whhYWHCvHnzhISEBOHkk092+sDd/SF879694rE1a9YIUVFRwoIFC4Tc3Fxh7Nixwtdffx2E2vuHt+8pdrtduOaaawStViucfPLJwowZM4SIiAjhmWeeCdId+N7atWuFpUuXCjNnzhQACGeddZawdOlSp+C893uKIHj2PjRUMkEQBN+Nu41sVqsVmzdvRkdHB6ZPn474+Hin5zds2IDw8HBMnz7d43OGg8rKStTV1SEjIwOTJk1yeq66uhoHDhzAaaedBgD4z3/+4/YaOTk5TtOyQt3BgwdRVVWFxMREFBcXO2VGOnr0KH788UcsWrRIXKTcW01NDfbu3YtTTz01kFX2O51Oh82bN0OtVmP27NlOWZ7sdjs+/vhjFBYWYvz48eJxo9GIzZs3QxAE5ObmIiEhIQg195/BvKfodDpUVVVBr9cjIyPD69S/UtbQ0ICffvrJ5fjEiRPFKSTHv6d06+99SGpaW1vxyy+/QKPRYPbs2U5TyywWCz799FMUFxc7/Wz7O2c4sFgs2Lx5M/R6PWbMmOGyHuabb75BTEwMiouLxfdQdxYsWCCuDxkOysvLcfjwYUydOtXpvRFw/M7X19djyZIlbs/duHEjoqKixOngw8X+/ftRXV2NpKQkFBUVOT1XX1+PzZs3Y8mSJU5JG44ePYrS0lLExcUhLy8PYWFhga62Xw3mPeXQoUPYuXMn1Go1cnJyhsUa1W4VFRWora11OT5nzhxxqmvv95RuA70PDRWDMiIiIiIioiBiog8iIiIiIqIgYlBGREREREQURAzKiIiIiIiIgohBGRERERERURAxKCMiIiIiIgoiBmVERERERERBxKCMiIiIiIgoiBiUERERERERBRGDMiIiIiIioiBiUEZERERERBREDMqIyEllZSU++eQTl+Pr1q3D5s2bg1AjIiIKNV999ZVLn9HS0oL//Oc/2LdvX3AqRSRhDMqIyInJZMLSpUvx3nvvicduu+02XHnllYiNjQ1exYiIKGRs3rwZp5xyCo4cOQIA6OjowOmnn441a9ZgzJgxQa4dkfTIBEEQgl0JIpKWK664Aj/++COqq6vx8MMP4//+7//w7bffIi8vL9hVIyKiEGA0GpGVlYVTTz0VTz31FE4//XSYzWZ8/vnniIyMDHb1iCSHQRkRuTh06BAyMjIwc+ZMlJaW4quvvsK0adOCXS0iIgoh77zzDi699FLMmTMHer0eX3/9NWJiYoJdLSJJYlBGRG6dccYZWLduHT755BOcddZZwa4OERGFmPb2dqSmpgIAamtrER8fH+QaEUkX15QRkYsHH3wQP/30E1JTU7F+/fpgV4eIiEKMwWDA0qVLkZqaivb2dvz000/BrhKRpHGkjIicPP7443jooYfwxRdfYM+ePVixYgXKy8uRnZ0d7KoREVEIMJvNWLZsGerq6vDtt9/i1ltvxS+//ILKykqoVKpgV49IkhiUEZHoueeew+23345PP/0UCxYsgCAImD59OhITE7Fu3bpgV4+IiCTOZrPhggsuQFVVFTZs2IAxY8bgwIEDyMjIwF/+8hfceuutwa4ikSRx+iIRAQB27tyJL774Av/+97+xYMECAIBMJsPf//53aDQa1NbWBrmGREQkde+99x7kcjm+/PJLMfV9eno6HnvsMZSXl8Nutwe5hkTSxJEyIiIiIiKiIOJIGRERERERURAxKCMiIiIiIgoiBmVERERERERBxKCMiIiIiIgoiBiUERERERERBRGDMiIiIiIioiBiUEZERERERBREDMqIiIiIiIiCiEEZERERERFREDEoIyIiIiIiCiIGZUREREREREHEoIyIiIiIiCiIGJQREREREREFEYMyIiIiIiKiIPp/pFoqOshYuoMAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "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": "e82ade5a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = None\n",
+ "for label, (p, s, colour, style) 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,\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, ls=ls, lw=1.8, label=lab)\n",
+ " for lab, (_, _, c, ls) 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 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."
+ ]
+ }
+ ],
+ "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/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": {
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",
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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",
- "Batch: 51/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.340\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Model parameter acceptance fraction: 0.300\n",
- " Likelihood parameter acceptance fractions: [0.89]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 53/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.220\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
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- " Model parameter acceptance fraction: 0.250\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Model parameter acceptance fraction: 0.330\n",
- " Likelihood parameter acceptance fractions: [0.74]\n",
- "Batch: 56/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.660\n",
- " Likelihood parameter acceptance fractions: [0.82]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 57/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.390\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
- "Batch: 58/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.87]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 60/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.250\n",
- " Likelihood parameter acceptance fractions: [0.84]\n",
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- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Model parameter acceptance fraction: 0.270\n",
- " 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",
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- " Model parameter acceptance fraction: 0.470\n",
- " Likelihood parameter acceptance fractions: [0.82]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.320\n",
- " Likelihood parameter acceptance fractions: [0.81]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.270\n",
- " Likelihood parameter acceptance fractions: [0.78]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Model parameter acceptance fraction: 0.340\n",
- " Likelihood parameter acceptance fractions: [0.92]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 75/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.220\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
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- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.86]\n",
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- " Model parameter acceptance fraction: 0.390\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
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- " Model parameter acceptance fraction: 0.240\n",
- " Likelihood parameter acceptance fractions: [0.8]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 79/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.290\n",
- " Likelihood parameter acceptance fractions: [0.82]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.310\n",
- " Likelihood parameter acceptance fractions: [0.86]\n",
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- " Model parameter acceptance fraction: 0.340\n",
- " Likelihood parameter acceptance fractions: [0.83]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 85/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.360\n",
- " Likelihood parameter acceptance fractions: [0.81]\n",
- "Batch: 86/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.84]\n",
- "Batch: 87/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.350\n",
- " Likelihood parameter acceptance fractions: [0.81]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 88/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.82]\n",
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- " Model parameter acceptance fraction: 0.530\n",
- " Likelihood parameter acceptance fractions: [0.84]\n",
- "Batch: 90/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.520\n",
- " Likelihood parameter acceptance fractions: [0.79]\n"
- ]
- },
- {
- "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",
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- " Model parameter acceptance fraction: 0.360\n",
- " Likelihood parameter acceptance fractions: [0.81]\n",
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- " 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",
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- " 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",
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- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.87]\n",
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- " Model parameter acceptance fraction: 0.320\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.260\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.530\n",
- " Likelihood parameter acceptance fractions: [0.77]\n",
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- " Likelihood parameter acceptance fractions: [0.84]\n",
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- " Model parameter acceptance fraction: 0.490\n",
- " Likelihood parameter acceptance fractions: [0.81]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Model parameter acceptance fraction: 0.290\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
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- " 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",
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- " Model parameter acceptance fraction: 0.490\n",
- " Likelihood parameter acceptance fractions: [0.84]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 25/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.250\n",
- " Likelihood parameter acceptance fractions: [0.77]\n",
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- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.85]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 30/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Model parameter 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",
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- " 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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- " Model parameter acceptance fraction: 0.470\n",
- " Likelihood parameter acceptance fractions: [0.88]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 42/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.350\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Likelihood parameter acceptance fractions: [0.81]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 46/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.320\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Model parameter acceptance fraction: 0.480\n",
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- " Model parameter acceptance fraction: 0.390\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: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.87]\n",
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- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.91]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 53/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.340\n",
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- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.85]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 57/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.450\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.82]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.79]\n",
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- " Model parameter acceptance fraction: 0.370\n",
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- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.8]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 63/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.310\n",
- " Likelihood parameter acceptance fractions: [0.82]\n",
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- " Model parameter acceptance fraction: 0.380\n",
- " Likelihood parameter acceptance fractions: [0.89]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 67/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.500\n",
- " Likelihood parameter acceptance fractions: [0.84]\n",
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- " Model parameter acceptance fraction: 0.240\n",
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- " Likelihood parameter acceptance fractions: [0.85]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Likelihood parameter acceptance fractions: [0.88]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Model parameter acceptance fraction: 0.340\n",
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- " Likelihood parameter acceptance fractions: [0.87]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 78/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.570\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Likelihood parameter acceptance fractions: [0.79]\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: [0.89]\n",
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- " Model parameter acceptance fraction: 0.320\n",
- " Likelihood parameter acceptance fractions: [0.82]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 86/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.300\n",
- " Likelihood parameter acceptance fractions: [0.86]\n",
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- " Model parameter acceptance fraction: 0.370\n",
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- " Model parameter acceptance fraction: 0.320\n",
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- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.77]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 90/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.230\n",
- " Likelihood parameter acceptance fractions: [0.77]\n",
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- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.82]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 93/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.330\n",
- " Likelihood parameter acceptance fractions: [0.85]\n",
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- " Likelihood parameter acceptance fractions: [0.8]\n",
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- " Model parameter acceptance fraction: 0.390\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: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
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- " Model parameter acceptance fraction: 0.450\n",
- " Likelihood parameter acceptance fractions: [0.75]\n",
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- " 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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- "output_type": "display_data"
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",
- "text/plain": [
- ""
- ]
- },
- "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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AgU8IIYQEAAp8QgghJABQ4BNCCCEBIKijD4CQ7kqtVnvdRqlUQqVStcPREEICHQU+Ia1MqVSCYRhkZWV53ZZhGKjVagp9Qkibo8DvJjQaDfR6fZPb+NLiJC2nUqmgVqt9ej+ysrKg1+sp8AkhbY4CvxvQaDTIyMgAy7Jet2UYBkqlsh2OKrCpVCoKcUJIp0KB3w3o9XqwLIv169cjIyOjyW3pmjEhhAQmCvxuJCMjA5mZmR19GIQQQjohmpZHCCGEBAAKfEIIISQAUOATQgghAYACnxBCCAkAFPiEEEJIAKDAJ4QQQgIABT4hhBASACjwCSGEkABAgU8IIYQEAKq0R0gHo2V0CSHtgQKfkA5Cy+gSQtoTBT4hHYSW0SWEtCcKfEI6EC2jSwhpLzRojxBCCAkA1MLv5DQajU9dvoQQQkhTKPA7MY1Gg4yMDLAs63VbhmGgVCrb4agIIYR0RRT4nZherwfLsli/fj0yMjKa3JambRFCCGkKBX4XkJGRgczMzI4+DEIIIV0YDdojhBBCAgAFPiGEEBIAKPAJIYSQAECBTwghhAQAGrRHSBdBi+wQQlqCAp+QTo4W2SGEtAYKfEI6OVpkhxDSGijwCekCaJEdQkhL0aA9QgghJABQ4BNCCCEBgAKfEEIICQB0Db+D0LK3hBBC2hMFfgegZW9JW6L5+oQQTyjwOwAte0vaAs3XJ4Q0hQK/A9Gyt6Q1+Ttff9++fXTCSUgAocAnpBvxZb4+9QQQEpgo8AkJMFS5j5DARIFPSADyp3IfDQIkpHugwCeEeERd/4R0LxT4beDo0aMIDQ1t9O80v550BdT1T0j3QoHfBi655BKv29D8etIVtGbXf21tbWscEiGkmSjwWxHHcQCAVatWYejQoU1uGxMTg8jISFRXV7fDkRFfma122M11BZGqq6thldI/EW9kMhkUCoVPXf/Av/9OCCHtS8TRv75Wo9VqkZyc3NGHQUinVlxcjKSkpI4+DEICDgV+K3I6nSgtLUVYWBhEIlGr7be6uhrJyckoLi5GeHh4q+23rdFxt6/Oftwcx6GmpgYJCQkQi2ndLkLaG/VXtiKxWNymLZfw8PBO+UXuDR13++rMxx0REdHRh0BIwKLTbEIIISQAUOATQgghAYACvwuQyWRYtGgRZDJZRx+KX+i421dXPW5CSPugQXuEEEJIAKAWPiGEEBIAKPAJIYSQAEDT8lpRW83DJ6Q78HUePv07IqRxLalnQYHfikpLS6nSHiFeeKu0R/+OCPGuORUrKfBbUVhYGAB02kpnxDuz1Y5bVu4CAHz5yCTI69XS9zbGVa/XQ6/XQ6lUIjY21uvjtcWY2dZsFftzfN4el68EyP87aQz9O2ob3j7bpGvw9d+RJ/SOtyL+C68zVzojTZNa7QiSMwDq3kd/Az88PBx9+vQB4FvwGo1GGI1GhISEgGEYr9uzLOt1+84a+L5uR/+O2oa3zzbpWprz75wG7RHSgYxGI+x2O4xGY5tsTwghvIAO/KqqKuzYsaOjD4MEsJCQEAQFBSEkJKRNtieEEF7A9ulUVVVh8uTJyM3NRXFxMUJDQzv6kEgAYhjGp6785m5PCCG8gGzh82Hft29f1NbW4rPPPuvoQyKEEELaVMAFPh/2mZmZWL9+PaZPn44333yzWaOlLRYLqqur3X4IIYSQziigAt817N9++22IRCI88sgjyMvLww8//OD3/pYuXYqIiAjhh+YOk0Cg0+lw8uRJ6HS6jj4UQogfAirwrVYrrrnmGiHsAWDkyJEYM2YMVq1a5ff+Fi5ciKqqKuGnuLi4tQ+ZtBDHcT7/dMT+OorrcRqNRpSVlcFoNDZ4Dk6ns8FtOp0O58+fx8GDB6HT6brE8yWEBFjgx8bG4umnn24wf/GRRx7BTz/9BLVa7df+ZDKZMFeY5gwHBpFI5PNPS7AsC51OB5Zl/X5sfzU11c/T/mNjY1FbWwuGYaDT6Vrl+RJC2l5ABX5jrrvuOvTq1QtvvvlmRx8KIQD+DeGioqI27z73d6pfbGwsRo0ahR49evhUTZAQ0jlQ4AOQSCSYP38+1q5di4qKio4+HEKEEDaZTLBarW0a+AzDIDY21q/pfrGxsRgwYAAFPiFdCAX+/7v77rshkUjw4YcfdvShECKEsEqlglQq7fBgLSoqwr59+1BUVNShx0EIab5uXXiH4zifry2Gh4djzpw5Qh10QjqD2NjYDg97oG4hG7PZjOLiYqSkpHT04RBCmqHbtfCNRiMeeOABhIeHIywsDM8884zP912+fDluuOGGNjw60t15GmzXHSQnJ0Mul9PUU0K6sG4V+A6HA1OnTkVJSQk2bNiAxx57DC+//HKjlfRef/11lJSUCL+Lxd3q5SAdwN/FbbrKCUJKSgrGjx9PrXtCurBu1aW/du1aAMCWLVsgFosxbdo0HD9+HJ9++inuuOMOt23Lysrw2muv4aOPPsKxY8cQHBzcEYdMupmQkBBh+Vpf8CcIZWVlCAkJ8XmZXNK9aDQa6PV6r9splUqoVKpm7c9qdwr/f/ToUUiDxD7vj3QP3S7wX331VbeW+uTJk/HKK6802DYuLg6//vorcnNzKexJq/F3cRv+BMFisQg9AxT4gUWj0SAjI8OnXh6GYaBWq5sM6cb2JwmWYdJzWwAA48aNg8Nm8Wl/pPvoVoH/0EMPYdSoUW63xcXFwW63e9w+IyMDGRkZ7XFoxEf+VGxrbrEXlmWFVrhruNavGOepgpw/A0F9oVAooFAo3HoGOlvVuvrHYzKZhGNVKBTC7VR8p3n0ej1YlsX69eub/D5Sq9XIysqCXq9vMqAb25/V7sSiH88DAPbv34/C/Fyf9ke6j24V+Ndee22D24KDg92+sE6cOIE1a9bgrbfeascjIx3FUwi5Xmfnu947KmT54+usy956e/064zF3VRkZGcjMzGyz/ZmtduDHHwEAQ4cOhTSIxiwFmm7/jotEIjidddeuTpw4gcmTJ2PChAkde1CkQ/lbWa4z6QyD/Lry60dIIOtWLXxPRCIROI4Twv7NN9/E9OnTO/qwSAfiB8d1RZ2hdd1ZeyMIIU3r9i18sViMmpoaCnvSIkVFRdi/fz/OnDnTocdBrWtCSHN1+xZ+VFQULBYLhT1pEj+QTyKVe/y7VquFyWSCVqttdC56Y4MBfaHT6aDT6cAwTJPT89qidd2S4ybtz9uqnv6u+kkCR7cP/FGjRkGtViM1NbWjD4V0YnxXudnmuWBOUlIStFotkpKSvO6jOd3tOp0OFosF5eXl6Nu3b7t22XeGywTEO6VSCYZhkJWV5XVbhmGgVCrb4ahIV9LtAx8AhT3xip8WJ2ukhZ+SkoKUlJQGo/ldW8f+Ft1xFRsbC51Oh6ioqDbvsq/fom/JcZP2o1KpoFarW7VADwksARH4pGvpiC5mvqvcbPVcs6Exrq1jf5eYdeW6SA7//Pnjam31W/Q0CK/rUKlUFOSk2br9oD3S9fhbj74jtcUgurZ+/jTwj5DARC184lFrV7zzdX8sy0Kj0aC8vBxpaWlNLg3ryz59rYzH78tbpT2n0+lWulmhUIDjOBiNRnAcJ7SUOY7zaTEmp9PZ4PgYhgHLsmAYxu24fNmfL8+XYRgoFAqfXxeqoNd8vtTIp0F2pL1Q4JNOxWg0ora2FhKJBCaTqU32z4cp38LlbwsJCYE4SCpsazKboZCFud1fLBY3CECWZWG324V9+EMkEjXYX1euE0D+5W+NfBpkR9oaBT7pVEJCQhAXFweWZdvkC1Cn06G2thahoaFCqPKBbTQaERbxb+AbjUZER4Q12Ef9kwa+Rc7vn66JE8D3GvkADbIj7YMCn3QqCoWi3Rc04gO7fqu6sVZ2/RY9/6PT6YTbu3Lguw6adF0chzRPa9fIJ6S5KPBJl9OSUfyxsbEN7ufahe46Sp9pJOxcr7H7cntn4str5zpokAKfkO6DAp90OS0pFNMa18cb24fr7fxgO76Cnuu0u6YcPnwYOTk56N+/P4YNG9ai46yPZVkUFRVBJpMBaHzKH83LJ6R7osAnnVJTLVHXQPKntd/YgD2dTgcAiIuLcxu05wudTgeNRoOKigoAdUWeXEvv8hX0+ND3dmzHjx+HzWZDTk5Oqwe+0WiETCaDxWJpEOb1X0fX2Qauz8WfkxdCSOdC8/CJX06ePIkdO3bg5MmTrb5vlmWFgU5NzUVnGEYocuPPnHXXa++ut9XW1qK2trZZ8971ej2qq6uRm5uLqqoqlJSUuP09NjYWMpnMa0Dyx5acnIyQkBD079/f72PxJiQkBOHh4UhJSWnQVe/L6+h68kII6XqohU/8UlhYCKPRiMLCQgwYMKBV9+0aOr52K/vT/dzYaHqJROL3+vJ8i1yhUCA8PBzp6ekAgMTERLftfG0N88c2evToRp+Lry3sxno9Gmu5A769jnz5X2rdE9I1UeATv/Tt2xeFhYXo27dvq+/bNXT4cGJZFjqdrlVWj/M0mj42NhZKpRJ2u38ldfkWeUhISKOr5/myD1/GIeh0Ouj1euj1eoSGhvp0ecDfMQ6+vI7UlU9I10aB38m1dsW7lj5uRkaGMG2O36a1HpevAOe678ZGjPtTec7T4/DX8o1Go9CNHRYWBqfTKWzndDrdfgcAh8MBsVgMhUIhLJnLcRxiYmLctnM6nZBIJAAAg8EAvV4PpVLpth3/3Pjehcam9BkMBthsNohEIuF4+N4FT8/X9cSpsfexfsVAnl6vF04oqBAMId0LBT7xqKPKqXqqOseHV3OOia9kV39VO77rWqfTQSaTQSKReFw8R6PRoLS0FAkJCVCpVML++LK3YrEYBoOhQTi6Pm5+fr4Q/q7b1Z//39jzjImJgV6vFy4bVFdXo6ioCCkpKQ1ODvhj89Za91QxkH89rFarWy8CldYlpHugwCftorlz51ural39bm7XFekqKytRVVUFg8GA2B4JbvcrLS2F2WxGaWkplEql0AJnGAYxMTEwGAxCq51lWRQXF8NkMiEpKQmxsbFgWRYRERGoqqpqcFLA92jwLe3Gniff2jaZTCgrK0NlZSUiIyN97rL357Wn6/SEdF8U+KRdtGTufGuoPyiNP56goLp/Alarte4aeXik2/0SEhKEFj7LsnA4HG4D/JKTk4Xnw88ycDqdKC8vF2YSxMXFNWiNsyzbYIpgfa5BDUCYQx8TE+NXPQF/Xnu6Tk9I90WBT9pFRxdz8dRTUFlZCaVSCaVSiaqqqgbX64G6LnjX+/Ih7Rr+/N/4BVBMJhOio6MbfVx+P94W3OGD+syZM6ioqBBOTjx15Telo197QkjnQIFP2kX94CsqKoJWq0VSUlKzR7nzmnO5wGg0wuFwwGg0Ii4uDjKZDHa7HYby8gb75oNdqVRCLpcLXfD1B9gxDCNcZ/d08uDKlzK8fFCzLAuJRAKr1ep32POP1ZnL/RJC2gcFfhdXfzBaez9mc4OEH+Gu1Wq9Br63x/Ony5rfl+vSu3z4m81mOLh/B6jl5ecjMuzfx3Rd19zbcqYmkwlms7lBz4BrbwA/8I9lWZSVlQGoq/ZXfxv+sfnehtbAjweo/5j8sbb0/SWEdD4U+F1c/WI17f2YzQ2EpKQkoYXf0sfzp8ua35dCoXA7SeK74i32f1vmBQUFSIiPxahRo6DX6+FwOJCXl4eamhokJCRg6NChjT6OyWSCw+GATqeDyWQSpsGpVKoGz8FoNKK2tla4H388/LV0ftBea46Wd33M+sHe0eMtugKNRuN2AuiJWq1up6MhxDcU+F1cR1yfbY3HTElJ8bkr39vjeeqybqyVyu8rIiLC7XaVSlVX1tdsbfQxWJaF1WqF1WrF+fPnodfrG31s14F9MpkM586dQ48ePdyKCPH3CwkJQWhoKIC6OfcSiQR6vd7vwXP+tMxdH5N/XV1nLgQFBdE1/0ZoNBpkZGT4VJ3RW08QIe2JAr+L64jrs635mL6ElC+PV38/nlqp3h6LYRhYbA7h99TUVCTEx7oFeGxsLCQSCSIjI+FwOFBcXAygbq68UqmEXq9HQUEBLBYLzp8/j/DwcPTu3RtpaWkA3IvtuHbd8yc/ISEh0Gg0wvH68zr72zLnezj4bV1nLtBI/cbx6z2sX79eKELVGKVSCZVK1U5HRkjTAjrwLRaLsFRooPC1ch/HcT51Ifu6P6fT2WB/JpPJbblWhULhcwW9+vs7c+aMcJ07PT1daJErFAphAB3fjW0wGJCcnCxUqjOZTB4XjUnt2xdyaRDKy8vhcDhQVVWFiIgIyOVyYRS+xWKBRCJBeXk5oqOjUV5ejuDgYOTl5eHMmTOIiopym7evUChgMpmE43I4HMLoe9fufKlU6lbvny8O1BS+bLDVavXaA+OpgqGnCn2+vh9A4BXoycjIQGZmZkcfBiE+C9jV8v766y+kpqZi7969HX0oAYGvPCcSiWAymaDX61FWVua2XCv/d3/3x+/T4XDAZDIJ1eaUSiUUCoVbdTyz2QypVAqWZd2q4TkcDhgMBjgc/7bwKyoqUF5eDpZlYbFYEB0dDYvFAqn03yV0WZZFTU0NoqOjIRaLER0djdDQUMhkMshkMphMJkgkEpSVlaG4uBgikQhKpRIhISEQi8WQSCRux8HX9OdPAvjeAL4yXlM//MmBVCoVSuTypYP5/+cxDCN027veH6ibQaHX6/16PwghnV/AtvDffPNNmM1mTJs2Dd999x0uvvjijj6kZnOd4ta7d++OPhyv+NYlAISHh/s9Gtx10Rn+fsnJyUILvzEMw0ClUjXoKtfr9SgsLATDMBAH/zstz+FwwFBdV9WOL73LsixMJhPEYjHKy8sREhICqVQqPG79/6anpwsV+WQyWZPd9AzDCAPB+Oem1+thtVo9FujxdInCdayBpzr9/D4aK/jjugQudesT0r0EbOCnp6ejV69e+Pvvv5sd+haLBRaLRfi9urq6tQ+zgfrV14xGI/Lz8wEAxcXFnS7wPU05a2zgnK8auw7uy7S1+kV0WJbF2bNnhd+dMAvbSiQSKBQKnD59GgAQFRWFyMhImEwmyGQyoSs8JibGrXKeVqtFbW0tgoODERMTA5VK5VaW1/W1cZ2ux/+4PjeDwSBckggJCXF7/z1ds3d9fV0fz9fxAFRal5DuK2ADv3///ti8eTO2bt2K66+/HtOmTcOOHTvw5ZdfYtasWRgzZozXfSxduhRLlixph6P9l+uXPFDXcouOjhbqt3c2/HVzkUjk1ir3NpCuqQF2fKAxDIOcnBycPn0akZGR6N27t18D3fjueABCK10cLAOOngFQF+S11ZVC4R2z2QyFQiHUznctq8t3oVdVVQGoW5yG75YHGp9J4OnExbWan9PpbFAzwHUaJv854Ef/u16Pd23B+zrinkrrEtJ9BXTgZ2dnQyaTCaE/adIkXHTRRbjwwgt92sfChQvx6KOPCr9XV1cjOTm5rQ4ZgOea8BkZGWAYplNeb9XpdMjJyUHPnj099j40NrLc2yj7mJgYiEQinD59WrjGnpaW1mTYe2pR6/V6pKSkIDQ0FEql8v9Xy6sLfIPBAIetblCe0+lEfHw8FAqFx8sGDMOgqqoKEokEwcHBwhx9b5cY+C531yl+rs+BD19+P67vP7+tTqcTXiuj0dhgX65h79ryd72diu0Q0v0FbOCnpaXh1KlTsNlsCA4OhlKpRHh4OE6cOIE///zTp+59fmBWe6ofCK355ez6pe9prfX62/jy2OXl5ZDJZKioqPD495CQEJSVlQkD9zyNGOcf98yZM8LrLZfLAQC9e/fG6dOn0bt3b6/znfnLC1VVVUhLSxOmTLEsK+zPFT8IsHfv3rBYLGAYpsF2ricRqampwup5CoUCZrO5wT5d8fsrLy93u95ev8XPMIzwunjqKXB9rc6cOQOLxYLy8nL06dOnwfK7Go1GeA35XpfGLg+QwOBLgSCaXtg9BGzgS6VSJCYmIicnB6+99hrOnj2LoqIi3HbbbZg2bRp++OEHjBs3rqMPs115mqrF44PeaDRCJpP5HAxJSUmoqKhAfHy8x/vwLc36j1s/2PjHtVgs6NGjh3BMfMhnZ2djx44duP/++5sshMO3wl1L1TIM4zY6nzd69Gg8cN89uO2224Rt629XfxEdrVaL7OxsREdHIz4+3mOIuxZsUSgUbt34er0eNTU1CAsLc7ue39gJWP3Xiq8FEBUVhaCgoAaXSfjXkH8/6l8e8KXrX6fTCdf5qfu/6+I/01lZWV63ZRgGarWaQr+LC9jAB+q69WfMmAGVSoWvv/4aCoUCW7duxQMPPICEhATvO+hmPLWq67cAAf+qsKWkpCAuLq7JMPE1bCwWi/Al5XQ6hWvgGzZswBtvvIGamhps3LgRW7duFVru/HOQyWQICgoSlrv1FKAsy6L0nE74Xa/X47nnnkN2djY++OCDRo+rsrJSGLhXWloKu90OiUSCxMREIczrj5jn17Q3mUweBxu6nhSEh4c3+bq44kvy8l33rvjHiI+PFy4B1b884Asayd89qFQqqNVqn0oEZ2VlQa/XU+B3cQEd+Pfffz/eeustbN68WQgAmUzW5Jd7d+b6pc9xnBDyRUVFQrGY5q7Wxs+H9+VxefVPOGpqaoQuarlcDrlcjpdffhmvvPIKgLou6iNHjmDkyJFYu3Yt0tPThfn2rhX4goODcerUKZSXlyM5OVnoJSgsLMTsufdAefVzAICZt9+OtZ98hI0bNyI7Oxvvv/8+Bg4cKJSkZVlWmG7HP4+EhARUVlYiPj7e7fnp9XpUVlYKv/OPWf/Egw9/lmUhlUpRWVkJjUbjVlu/KfxJDn9/1y5914F8HMf5FfKuaCR/96FSqSjEA0i3LLxjt9vx22+/Yffu3UKr1JMpU6Zg586dTXaXdjSO41r9x9fHDQkJQVBQkFA8Bvi3Gh7/43Q6fXpMh8MBp9Pp9YefP26321FbWwubzYba2lpUVFSgqKgIVVVVqK2thUajwW233SaE/ezZs3Hw4EEMGjQIOp0OV199Nd5//304HA4oFApERkYKBXP4ffOBbbfbkZOTgxtvvBHHjh4VXoNly5Zh+/btiIuLwz///IPJkydj/fr1bscnk8lgtVrhdDqh1+sRHR2NYcOGISIiQjj24OBgyOVy4TkCdZUCZTIZgoOD3V4nfhaAUqlEUFAQzGYzbDYbysvLG33NXAvr1C/e4/p+1dbWQqfToba21qf3jR8AyLKs2+1KpRIZGRlQKpV+f64IIR2n2wX+sWPHkJGRgcsvvxwTJ07ERRdd5LFsalfhrbqa609rPy7DMIiNjYVKpYJUKkVsbGyDx3StAKfX65GTk+NWpc11O5PJBIPBIFTD8/Tjuj+FQiGE1rlz51BcXIzTp0/j5MmTmDx5Mnbu3AmpVIpVq1Zh+fLlSE1Nxffff4/rr78edrsdy5Ytw0svvYTQ0FBUVFSgoKAAABAZGYng4GAhzI4dO4apU6ciPz8f8f8/PgCom1o3YcIE7N+/H2PHjkVtbS0eeOABPPbYY7Db7UKLOTExESKRCFqtFv/8849wyaCqqgolJSWoqKgQxiqIxWIYDAbU1tYKBXw8/fD7lsvlsNlsYFkWBw4cEKr1uf64XjLgXzO+R4B/X/iCQXa7XXhcb58nfr9Go7HdP3+EkNbXrQJfr9dj8uTJeOKJJ2AymXDo0CHk5OTgxRdfbLBtTU0NJk6ciO+++64DjrT9sCwLnU7n08pejYmNjcWAAQO8duHq9XpUVVUhLy/P40kWHyB6vV4Ioabw9ef5bmeFQoGCggJcf/31yM/PR48ePfDNN99g5syZbvf54IMPsHjxYojFYmzatAlXX3011Go1rFYrysvLheekVCqhVqsxZcoUlJSUIC0tDTt27GhwHD169MCOHTuEKZhvv/02Lr30Uhw/fhyFhYUwGAzC8+fnzfOj+W02GwoLCwHUDWCMjo5GRESEUD2vKUajEZGRkVCpVDCbzTCZTCgpKfH4OrnW43edcld/rn/9gXxA3TV5tVoNnU7ncb+0ah4h3UO3Cvw333wTN910E+bOnQuJRIIRI0bg7rvvxk8//dRg26CgIEgkEjz88MOw2WwdcLTto36hnqY09sXvK6VSCYfDgYiICI9h7hpMdrsdGo1G6BHwpn///jh27BiWL1+OyspKDB8+HLt27cLIkSMbbCsSifDggw9i48aNiIiIwMGDB3HzzTdDrVZDoVBAKpVCLBYjOzsbc+fORXl5OYYPH46ffvqp0ToKQUFBWLx4Mb788ktERUXhzz//xDXXXIM9e/agtLQUAJCYmIigoCBhYZ3o6GgYjUY4nU7hpIBhGKGCX2P498FoNAqBm5CQAIVCgcTERI+vKz8mQKPRYM+ePThz5ozba86fBMTGxjaYl5+Xl4eqqqoG70NISIhP1QsJIV1Dtwr8Xbt2uRXCAYBhw4Z5DDCFQoFvvvkGu3btQnBwcHsdYrvjr8P70krT6/WwWCw+BbAnsbGxGDp0qDAKvD4+mPjr0yaTCVVVVcjPz2+ytW8ymbBgwQJ88MEHcDqdmDVrFrZv346ePXs2eTyTJk3Czz//jIEDB+L8+fOYNWsWdu/ejdDQUBw4cADz58+HyWTClClTsH37dqGCXlOmTJmCgwcPYvjw4aioqMCzzz6LNWvWwGg0wmazua3Cx8+z51vnQN3nTqFQwOFwQKvVepyrz78PJpMJsbGxYBgGycnJGD16tNsAK5Zlhd4S/j3j/5udnQ21Wg2WZRuEvCuWZREREQGHwwGlUtnkSV9r9BYRQjpOtwr8zZs3o1evXm63RURENBhQ5Prl29aV8Toafx3el1aaUqmETCbzWsCmKZ5akZ6OSalUIjk5GU6nE+Hh4UKImM1mGAwG4ef8+fO44YYb8NVXXyEoKAirVq3C+++/73PBoz59+mDfvn249tprYbVa8cADD+Diiy/GAw88AIfDgaysLGzdutWvbuuUlBTs3r0b9913HwBg3bp1ePfddxEfHy8ssgMA+fn5yM/PR3V1tVuLnmEYWK1WYRR9/ZBt7H3gA951bj9/iYQfqNe/f39ERkZCLpf7dPLGMAzi4+MxdOhQxMbGCov1eLqfP71FhJDOp1tNy/M0d14sFgsjowHggw8+wIYNG7Br1672PLROg1/73VOlPNdCKhzHtVq5Vddyrq77cZ2Cxt9eXl6OqqoqHD9+HIcPH8bOnTtRWFgIuVyOb775Bpdeeqnfjx8WFoaNGzfilVdeweLFi5Gfnw+pVIqFCxfimWeegUgkclsEyRcymQxvvPEG0tPT8dBDD+Gdd97BTTfdhPT0dCgUCmGuvVwuR0VFBU6dOgWg7jq+QqFAUlKSENx5eXnCLAj+PfA0XoIP+NzcXJjNZkRFRQk9JsC/0x/519S1MFFj6tfc5wv3eLqfP8V5CCGdT7cKfE9EIpHQwv/ggw/w0ksv4ddff+3go2qZoqIiFBcXIzk5GSkpKX7dV61Wo6SkBImJiRg2bFiT27ZWuVVPi8S4lqRVKpUwmUzYunUrNmzYgL1797p1KctkMqxbt65ZYc8Ti8VYuHAhxo0bh++++w5z5sxBnz59mr0/3j333IOtW7fit99+w8KFC7Flyxbhb3369IFcLkdZWRmqq6tx8OBBt8V3gLoTsIiICFRVVXkNZz7IXcsU9+/f320bvrXPMEyD3q76PNXVb6p6XnPn7RNCOoeACXzXsG+NL/qOVFxcDLPZjOLiYr8Dv6KiAlarFRUVFV5b8M1p0el0Oo8LwVRVVYHjOLfW49mzZ7Fjxw7s2bMHf/zxh9u14ZCQEEyePBlXXXUVpk6d2qLLDK7Gjx+P8ePHt8q+gLrP11tvvYVhw4Zh165deO+993DvvfeitrYWMTExSEpKglarxcGDBxEZGYny8nKhBK9UKoVEIkF8fDz69OnjNUz51zM1NVU4aXPlWkLYtTofP58eqGvBu47gP3/+PKqqqtCvXz+3oOffR18L/hBCOr9uH/gSiQQ6na7bhD1Qtywr38L3l2tY8NXr9Ho9evXq5bHOfWMhxJ8s8K1DvrV4/Phx1NTUIDg4GJmZmUIARURE4PTp08ICLytXrsSGDRvcxlckJSXhyiuvxNVXX41LLrnE44I2nVF6ejoWLFiAF154AcuWLcMll1yC0NBQWCwWJCUlCd34/JQ6g8EAqVQKq9WKpKQkhIWFue3PU9iaTCahNd5YdTS+ul79QZquywC7tuZdV/jT6/Vuwe56Ld9b4LueOFJ3PyGdV7cP/LS0NAwcOBDbtm3rkmHvqYJZr169hO5a/u8cx0Es9j4GU6VSCfdlWRZarRYOhwM6nc6t/jx/LdiVwWBwa6Hz3f38Yjp2ux0OhwPBwcEIDQ2FRCIRQruyshLh4eE4dOgQXnjhBWHa2IUXXogpU6Zg2rRpGDx4MIxGo1BspqnpkufOnfPphMBkMvk07bKyshJhYWGw2P5dHKemuhrWYInbdna73eOsjocffhgbNmxAfn4+li9fjscffxy1tbXCMcrlciQkJMDhcAjhq1AoUFtbK/w/z2AwwGazwWAwICoqCgUFBSgsLERUVBTS0tIafd4KhUIogew6bkWhUAgnFfzfnE4nGIZBv379hBX+XD9rDMMI9f69VdHz9dIPVeMjpGN1+8Dv27cvjh492mUrgbVFBT1+nyzLory8XAgw1+pqfEU811a+a6vPdVlZviqf68lAUlKS27Vqh8OB9957D8uXL4fdbkdiYiI+/PDDBisS8tX2vJHL5T5dT7bZbD69hsHBwXX1/oP+DXy5QgG5h8Dnawm4CgoKwltvvYXJkydj27ZtuOKKK5CZmYmKigqh0E5ISIhQlEcqlaKqqgoOhwOVlZVISkoSnk90dDQMBgOio6MhEolw+vRpmEwm1NTUYMiQIW6vj+tYCP7SjOvz5Svs1Z9Pz1fa42dw8Ptxbf2npKQgKCjI6+tHg/kI6Rq61bS8xnTVsPeGn9LFdwHz/+/rPGm9Xo+QkBBIJBK3kd6uxXFc96VUKiGVSoXw4O/jWlSmR48eDcK+tLQUV111FZYuXQq73Y5rr70Wf/zxR7dbfviSSy7BzJkzwXEcVq5ciYiICMTExMBqtQqvY0xMDGJiYhAUFISYmBjYbDZhTr7rNv369RNew5SUFERERGDIkCENlrvVaDTCokL16fV6HDhwwGNxI5PJ1KD+vus+GqvK5wn/WaABfYR0bgER+N0V35Wq0+nc/uvrPGmlUomIiAj069evQevM0xe+UqlE//793QbQ8fXZ+VZmVVUVtFqtcBKwY8cOZGZm4vfffwfDMHjrrbewdu1aREVFtcIr0PksXboUMTExyM3NxdatW4XSwFKpVKj/wJcMVigUkMvlwpx8/u/19evXDxMmTGgwIp9lWdTW1iInJ8fje873yPCr9NW/r+v7xq/Kx8+O4Osp8K1/X08kqTgPIZ1Xt+/S7874rlR+UBX/38a6VusPBvM0BYsPAr5VajKZ3JaQ1Wg0wqA/lUolXDfm54ADdQMlz549i8WLF+O9994DAAwePBiffPIJ+vXr1yavRWehVCqxbNkyzJkzB88//zyuvPJKxMXFoaqqCjExMSguLkZpaalwwiOVShEdHS2cWBkMBo/jJ1yxLIvc3FycO3cOFosFCQkJHk8W+OmOCoWiwSwH1/oH/OfF0+A9lmVx5swZtyWAm9JaUzkJIa2PWvhdGF9Fj18Ihv//xr5odTqd1+prrl36er0e1dXVbtuXlJS4LeLCzyvnwz4mJgZSqRQffPCBEPaPPPIIfv75524f9rysrCxcfPHFMJlMGDp0KG6//Xb89ddfcDgcKC0txfnz5/H7778LgyD5Fj8AYTU7fuleTy1llmVRWloKi8UCmUyG8PBwj9MWlUolLrzwQvTv39/jZ8J1Wd2mqvvJZDJYLBafrtHXL+VMLX5COg9q4QcQvnRqY3PaXXsAYmJi3AZ88eRyOc6cOYP09HSP+4iJiUF1dTU+/PBDAMD69etx0003tWk51nPnzmH16tXYs2cPxGIxZDKZ8CMWixEaGup2W2xsLO6+++42m18uEonw/vvv45577sGePXvw008/4aeffoJKpcLKlStRXl6O8PBw1NTUQKVSoaCgAFqtVlh0p6qqCmfPnoXT6URiYiJMJhOKioqQkpKC9PR06PV64XkOGDCgQYEdvra+axW++vjLAcC/3feeXg/+RMHXaov1p3K6tvh9LYdMCGkbFPhdFD9Ar6nKaPXxPQGeBjHyq6bxJV7T09OFa8Z8DXe+237AgAGQSqUA6q7hu3YnMwyDZ599FlarFZdddhluvPHGlj7VRhkMBrz99tv47LPPPC5C05R169Zh+fLluPLKK9vk2JKTk/Hzzz8jNzcXH374IdatWweNRoMHHngAn3/+OZxOJyIjI4W17m02m9BbUltbi4qKCgQHB8NkMiE/Px82mw15eXnC+gO9evWCxWIRVupzDX1+mWK+yE9jQW2xWFpcPc/b59B1BD9f758Q0jEo8Lsovnue/7JtKX7VNNcSr/z1erlcjp49ewq12V17CfjpYufPn0d8fDwOHz6MTZs2QSQSYdmyZW0yQ6KyshIrVqzAZ599JnQVjxgxAvfddx8iIiJgsViEn8rKSgQHB8NsNsNqtcJiseDrr7/GyZMnMXv2bEyfPh0vvfSScKLT2tLT07F8+XI8/fTTGDduHPLy8vDEE09g/fr1CAsLQ3V1tRC4/KWR0NBQ9O7dG0Dd9EYAQgvf9Zr7+fPnAdTNgnANfKVSifLycmGZYn7/Go0GWq1WmAKYkJDgdSVFfkxHY9fkdTodrFZro59D1xOK6upqv18/QkjrocDvomJjY1sU9vUH8PGrpvXu3RshISFwOp0oKChAVVUV5HI5kpOThS9v125ihUIhDDRbuXIl1q5dCwC4/fbbMWTIkFZ5rryamhqsWbMGq1evRlVVFYC6wYBPPvkkJkyY4PHkoqqqSugq591zzz14/fXX8dZbb+Grr77C77//jqVLl+Kqq65q1eN1FRUVhe3bt2Ps2LE4fPgwnnjiCSxatAghISHCfHf+Oj6/HC4vJiYGgwYNEsZWuIb7kSNHUFVVhTNnzgi3e1qUCICwHG92djYSEhIgl8s9Vuyrv9iR6/z8+lr6OSSEtB8K/E6usepk/PVZk8mEkydPgmVZ9OrVy63lzXelulZxczgcEIvFbtXc+JYlv53T6YTVaoXT6cT58+eRmpqKiIgImM1mlJWVCdPJ+OPLy8vDggULkJeXBwAYNWoUlixZAqvVKjyuwWDwqRV97ty5BuFiNpuxceNGfPTRR8LCMb169cK8efNw8cUXQyQSCZX76ispKUFERESD26+88kqkpKRg+fLlKC0txR133IHbbrsNDzz4sLBNWVkZZEHu41qtVqtPvRZms9mtnj1Qt5rjp59+iuuvvx47d+5E7969MX/+fKEioWt1vPr4981VcnIyzp07B7PZjHPnzgnd/fx0yvot8qSkJJSUlEAkEiE4OFgomFT/M8a36nU6nfBe8GMwXD9LAJocJ0AI6Vwo8Ds5b+HCj7R2Op1uLa3GpkfxFdZiYmKg1+sRExPjVqed31YsFkMikSA0NBTnzp0TgtbhcAjT8EpKSvDEE0/gq6++AlDX2nvppZeQlZXVIJyCg4M9lqStTywWC+MD7HY7vvrqK7z11lvC/PCUlBTMnz8fAwYMQM+ePb3uz2azNXqiMXjwYLz//vt4//33sWPHDnz++ec48Odh9M16DQDAKBjIgt2fh0gk8lhprz6JROKxYuCll16K5cuX49FHH8Xq1auRmZmJmTNnet2fWCz2uL+ePXuioKAAUqkUZrO5rlpgI58ZviSzTqcTTvQ8bctfd7dYLLDb7aioqEBUVJTXqXbePqvdtQAWIV0FBX4Xx4+wZlnWrVvVW7lT15YZv6Sq6yp3wcHB6NOnj3ANv6SkBGlpaWBZFsHBwVixYgVefPFF1NbWQiwWY968efjvf//bKgV1OI7D3r178eqrr6KgoABAXev4/vvvx7XXXougoKBGW/T+UigUeOihhzB8+HC88847KDp9Gn3//282uw2y4NYfWX733XcjJycH77//Ph544AEMGjQIQ4cObda+VCoVGIYRiujwLXBPKyHWX+TGteveFf8ZqF/nISQkpFmDRQkhnQMFfhfHMAwGDBgAjuPcWlCeunRdV7hz/Rt/ndZqtQrBwZeF7du3r1C8RaFQ4ODBg3jooYegVqsBACNHjsTq1atb7Xp9Xl4e3n77bfzxxx8AgMjISNx333245ZZbhJZ/Wxg2bBh++uknPLf4efDj/W+5+WZ8sOZd9OjRo9Ufb9myZcjPz8fu3btxxRVX4JNPPsGll17q14h51+Vw61dF9NTDYzQahboKCoUCDoej0ZUSAQizMlw/V0VFRa06WJQQ0n6o8E4A4UOADwl+uh3wb4vfNThYlkV8fDwSEhIgk8mQlZWFK664Amq1GrGxsfjwww/x888/t0rYnz17Fg8//DBmzpyJP/74A8HBwZg9ezZ+/vln3HHHHW0a9ryIiAgsf3W58PvJkycxa9asNhldHhQUhE8//RSpqakoLy/HY489hvLycuE9aaxQDf93vV4v1NEH/h2ox6tfAIe/jS/jazabcfbsWTgcDr9qJMTGxgq1DAghXQsFfhfTksplfAi4BrrrCYDrIihmsxkmkwkSiQQ9e/bELbfcgk2bNkEsFuO+++5DdnY2br/9dp9WtmuKyWTCypUrMXbsWGzcuBEcx+HKK6/E999/jyeffLLBwLf2pFQqkZOTg2XLlrXJ/qOiorBp0yZhCdwtW7aAZVmh5e0a/K5BX1NTg7y8PDgcDlgsFrAs22CBHL4KY/2eHIVCgbNnz4JlWfTs2RMSiQQhISE+f65iY2MxYMAACnxCuiAK/C7GtavWX3wJVz4EGIYRaubXD5by8nJIpVIoFAp88skn+PPPPxEREYE//vgDb7zxRouv1XMchx07duDiiy/Gq6++CpPJhOHDh+Pjjz/GihUr3KaldZTlr9UN3tu8ebNQ4Ka19e3bFw888AAA4Pjx42BZFqdPn0Z5eTlqa2tRXFwsVDzkC9dYLBZERERAIpFApVLBYDDg/PnzKC4u9vp4JpMJoaGhAIDw8HChO78lnytCSNdA1/C7mJauPe5av50PfqlUKgS+RqNBQUEBysrK0Lt3b8THx+O///0vAODll1/GhRde2OLnkJeXh6effhr/+9//ANQNyHv22Wdx7bXXQqvVtnj/reWikRdh1KhROHDgANasWYMlS5a0yeMMHjwYAJCdnY3y8nLYbDYAdVMUjUYjTCaTMEefH2jpOqtCoVDAaDQ2mDLnaeCea+Gk+oM8y8rKhJr5tPANqY8ft9MUpVLpsbYD6Rwo8LuYxkqhus67b+rLml8ytaKiAhERETh37hwSEhKgUqmEhVLy8/MRFRUFrVaLpUuXwmg0Yty4cZg9e3aLjp1lWaxcuRLvvfce7HY75HI57r//ftx3332dNmAefPBBHDhwAF9++SXuv/9+hIWFtfpjDBw4EEDdmAGj0Qir1Yq4uDhUVFQIl1169uzZ4HKMRqMRvmA9rUfvaeAeP7q+/tx7flQ+rXRH6uM/W1lZWV63ZRgGarWaQr+TosDvJup3yTYW/nwLLzIyEiUlJUJPget2o0aNQnFxMf7880/s2rULUqkU77zzTrOv13Mchx9++AFLliwRVtm74oor8MILL3T6L4bRo0dj2LBhOHz4MNasWYPHH3+81R+jb9++kMvlMJlMQs8Kf0nD4XAIg+34yy8Mwwgnbnq93m01PNcg97c3qKW9R6R7UqlUUKvVTa6yCdT1AGRlZUGv13f6f9eBigK/DXAc12iFPFe+FCLxZT+A+5e1a/jX7+aNjIwUSs0yDCMUYHE6nZDL5eA4DmlpaRCJRLjvvvsAAI899pjQA1BfSUlJkwV1NBoNXnjhBfz2228AgB49euDhhx/GuHHjYLPZUFhY6LZ9bm6uUMWvKTqdDmfPnvW6XUlJiTCSvSk2mw0ikQg2x7+vt1p9EsESEf7zn//g8OHDWL9+PS6//HKMHz/e6/5qamp8qizIv+4ZGRk4cuQISkpKMGzYMMhkMiQmJiImJgZyuRxmsxkOh0O4Ti8SiSCTyRATE+P2GXE6ncLnyrV6Yv3PkafPlaftff388cdEuieVSkUh3g1Q4Hdyvn6JKhSKBvOwQ0JCGtxfIpEIt3kqi8oPDlu2bBkqKirQv39/PP74442GF8dxHlv+FosF77//Pt59911YrVYEBQXhjjvuwJ133tngJMSV2Wz2qdvcbDYLg8+aolAofBoAqNVqIZfLIXZwwP/PxJfJ5ZBKRBgzZgzS09ORm5uL7du3Y9q0aV73V39efGP4krkXXHCBEPgxMTEQi8XC+2cwGGA2myGXy8GyLCQSCaRSqbCaoSu+kqI3vn6uXLfzNCaAENJ10Cj9dlZUVIR9+/ahqKjI5/v4OxXP05QsnslkEqZ7eZrzzTAM/vjjD+zYsQMikQirV6/2ew78b7/9hqlTp2LVqlWwWq0YO3Ys3nvvPdx3331Nhn1nJRKJhPK327dvh8FgaPXHGDRoEIC6gXuu75vJZEJtba3QtZ+cnAypVOp3/frmfO7qo5H8hHRtFPjtrLi4GGaz2acpVLzW/KJ1nXtffx4+UBduCxYsAFBXAnbUqFE+79tkMmHBggWYPXs2NBoN4uPjsWrVKnz66aedYppdS4waNQqpqakwm814++23W33/rgP3WJaFwWAQSuWGhoa6XVfnB+n5ozmfu/o8FfMhhHQdFPjtLDk5WVhu1lfN+aLV6XRQq9UoKipy6x1gGMatu7mystLtfi+88AIKCwvRs2dPLFq0yOfH02q1uPHGG7FlyxaIxWLcdddd+PHHH3HllVd2i2u7rq38NWvWCIsJtZYLLrgAQN3rWFFRAbvdDpPJBKDuPePHXbieoOXk5OD7779HTk6O1/0353NXX1M9R4SQzo8Cv52lpKRg/PjxSElJ8fk+zfmi1ev1sFgs0Gq1Qu8Ay7LIy8vDiRMnhNXnIiMjhft8/PHHePXVVwEAr7/+usdlZT3hOA7z58+HWq1GdHQ01q5di6efftqna+xdyZgxY9C7d2/U1NRgzZo1rbrvnj17IjIyEg6HA6WlpQgKCoJCoYDJZEJNTY1Q+MdisUCv1+PMmTPIyckRCvV405zPHSGkewnYwP/++++bXH+8s/Dn+r3rtkqlEjKZDElJSULvgNFoxNmzZ1FVVYVTp04JS6AyDIO3334bc+bMAcdxuPfee3HVVVf5fIwHDx7EiRMnIJfLsW3bNr8uA3QlYrFYmIv83nvvteq1bJFIJIxvCA4OFioiKhQKof49UNfb43Q6YTQaER8fD4Zh0Lt371Y5hpaUbSaEdH7NHqW/c+dOrFy5Uqi+NGDAADz66KM+jWDuaBs2bMAtt9yC2267DWvXrm1xPfi21Ni69t62bWz50p49e6KiogJlZWXIz89HWloaNm3ahKeeegoA8NBDD+H111/360v/448/BgBcf/31SExM9OPZdT2XXHIJ1q1bh6KiIqxduxb33ntvq+3b4XAAqOu2NxgMwsyLpKQkmEwmYVVDi8UChUKBIUOGCJ+JnJwcnD59Gr1790ZGRkazHt+fz1p3pdFofJpvTkhX1KzAX7VqFRYsWICsrCzceOONAIA///wT119/PZYvX4758+e36kG2tmXLlmHx4sV46aWXAKDZoW+xWGCxWITf22JVNW/FUDytcc4vhuLaAuWnUg0ZMgQikQhr165FbW0tVqxYge+++w4A8Mwzz2DJkiV+XXMvKirCr7/+CgC48847W/BMuwaJRIKHH34YDz/8MFavXo3Zs2e32kp+fK18u90uXMNnGAYmkwmlpaWQyWRQKBQNqu4BwOnTp8GyLIqKipod+IFeeEej0SAjI8Onk11+oSlCupJmBf4rr7yCtWvXCmEPAPPmzcMVV1yBRx55pFMH/oEDB6BQKLBo0SJceOGFmD59OoDmhf7SpUvbrL46r7FSurz6rXp+W51OB7vdjsrKSkRGRjZotfXp0wcrVqzAiRMnANTVyedH5/tj3bp1AICJEye2WtdyZ3frrbdi6dKl0Gq12Lx5M2677bZW2S8f+DabDUVFRUhISEBMTAwMBgNqa2tRU1OD5ORkj3P8e/fujdOnT7foGr23z1p3x09RXb9+vdeTJqoZT7qiZgU+y7KYMmVKg9unTJmCuXPntvig2tLgwYOxatUqAMA111yDr776qtmhv3DhQjz66KPC79XV1T6Pgm6qgplrq91b1Tm+m1culwuBAdQVnGFZVlh0RaFQCCcGxcXFuPfee3Hy5EnIZDKsXr0aN998s1uPQHl5udfHLi8vx7Zt2wAAkyZNwsmTJxvd9sSJEw2eM8uyqKmpEcKstrYWOp0OFosFtbW1MBqNqK2thcVigUwmg0wmg1wuh0wmg0QiQXh4uHAbf3twcDCCg4MRFBSE4OBgWCwWnDp1Svid/6n/PtvtdkilUtidAFB3KURbXIygeh+HoKAg9OzZEzNnzsRrr72G1157DZdcckmD/VmtVvTo0aPJ1w+o68aXyWTCMfC3hYWFwWw2w263Q6FQQCKRQKFQICYmRrjWz18CAIC0tDSkpaX5XOURaP3KeN4e15+qfR0pIyMDmZmZHX0YhLS6ZgX+iBEj8O233+KWW25xu33nzp0YOXJkqxxYW2EYxu0YPYW+0+nEyy+/jMcee6zJ7k0+hOoTiUQt+jL1VBpXo9FAq9UiKSnJrWXBt8ocDofbY7qWSeWZTCb8/PPPuPfee2EwGBAfH48vv/wSI0aM8PjcvAX+F198AavVirS0NIwcObLJ58xXkOOPY+fOnT5NJ3M9dn6aWkuFhYXhzjvvRJ8+fYTbLBYL+vTpA6uDA0pqAQApvXtDKnF/TkajEVKpFDNnzsSaNWtQWFiIvXv34oorrnDbTiwW+1xpLyio7p8hH/gxMTEICgpCdHS00Jrv1auXT1MzOY5r9SBvzRLQhJCO43Pgf/rpp8L/jxgxArNmzcL333+PESNGgOM4/PXXX9i4cWObLC7S1uqHvsVigdVqbbJGfFvydC1Vq9XCbDZDq9UKgc+vmGYymZCUlCQEKs9kMrldj3zvvffw4osvwm63Y+jQodiwYUOzB9lZLBZ88MEHAIDp06f7HDKlpaXYsmWLMP9foVAgJCQEoaGhCA0NhUgkQt++fREeHi78KBQKmM1mIfRNJhPOnDmDsLAw4Tnyf7fZbLBarbDZbLDZbGBZFhzHCb8DdXXu165diyeffLLZXdhhYWHIysrCu+++i3fffReXX355i4OWD/zY2Fi33gF+el5Tx8oXUmIYxufpkFQql5DA4nPgP/vss26/x8bG4tdffxUGbPG3ffbZZ8JguK7kmmuuwZdffonp06fjmmuuwebNm1ttMJa/XK+l8i2npKQkoYXP40vj1tbWwmq1CtO4XP/Oj/hetWqV22j6d999t0Vf8lu2bMH58+cRExODiy++2Ov2HMfhzz//xM8//wyHw4HIyEhMnz4dPXv2dNvu/PnzwvrwTQkPD0e/fv28bqdWq5GWlgagbmEZk8mElStXQq/XY9OmTbjjjjuaHdSzZs3Cxx9/jGPHjuHHH3/0eJnLV06nU5gmWlZW5hb4DMOA4zgh1PnbXN8/lmXhcDjAsqzPgU+j8gkJLD4Hvlarbcvj6HB2ux0bNmzo8LBvjKfVqviRwlarFWFhYUIY8MFQWlqKmpoavPTSS9i/fz+AupH4CxYsaFFrlOM4vPXWWwCAq6++2mtPCMuy+Omnn4SV8dLT03HNNdf4tCpea+IXpLn99tvxxhtv4OjRoxgwYECzL0PFxsbi9ttvxwcffIDHH38cycnJQolcf7lej+eLIrniV8tTq9VgWRYJCQkYOnQogLrBZlqttsFCQY3N1HD9PZBH5RMSaDrvBPQWOHbsGJ544gk89thjPs+Z3bZtG6xWa6cM+/r4lj0A9O/fH6NGjRJG6POtej7sH3/8cezfvx8Mw2DTpk144oknWtz1/PHHH+PkyZMICQnxqVX77rvvorCwEGKxGFdccQVmzJjR7mHvSqVSCce9fft2t7D11+OPP46xY8eCZdkWzdgwm83C/7v24vDkcjkkEolwucm1JLLBYBBWM3Tt4SkrK8P58+eh0Wg8rsVApXIJCSzdLvDfe+89XHLJJcjNzcXWrVsxYsQI5OXledzWdSGRGTNmYOvWrZ067Pmgz83NRU5ODjQaDYB/a63zhVqCgoIQFxeHJUuW4J9//kFUVBT27NmD6667rsXHsH//fmH63oIFC7wuZavVavHjjz8CAG655RZcdNFFnaK2/qRJk4QTJP51bI7g4GAh6LOzs5t98pCfnw+gbroXwzA4ePAg8vLyhEV0eCkpKUJ3/48//ojc3FwoFAq3AZ718Z8JaskTEti6VeAfPXoUzz77LA4dOoRvvvkG//zzD+Li4rB48eIG25aVlWHo0KFu0+oaW/O9s+Bb7xUVFXA4HB5HrfNTt15//XUcOHBAKHd74YUXtvjxz5w5gzvuuAN2ux3Tp0/3qd4CP+uhV69ebqPiO5pEIhHGAPgzW8CTXr16QS6Xw2w2N/vkge+JysjIQGlpKcxmM4qKioQCPHyXfkxMDEaNGoWysjIUFBTg2LFjQnnd+i31uLg4xMfHo1evXtSSJ4R0r8B/88038fLLLwtf5AzD4O6778aRI0cabBsXF4fHH38chYWFbvPXOzO+pZaamor4+PgGc/5NJhMMBgPefvttrFmzBiKRCOvWrcPYsWNb/Ni1tbW49dZbYTAYMGTIELz55pteW+pFRUXYvXs3AHTK6Zr9+/cH0PLAl0gkSE1NBQDk5uY2ax98DYMBAwYgISEBcrkc8fHxqKqqAvBvlz4f2hEREQgKCkJERAQYhnH7G6+xLnuqmU9IYGp2Lf3OyGw2N6gN0KdPn0YXOVm4cCGcTmenrqXvylslNJZl8fvvvwtTI1944QVce+21LX7ciooK3H777UKPyeeff+5Ta/Gzzz4Dx3EYP368x7r+HS09PR1AXY2DCtaOszUO2J3/zic/X+tAkLjupIYJFiFC3vjnJD09HdnZ2cjNzW3WaH0+8NPS0sAwDAYNGiRU2GNZFlFRUQgNDRUu62RkZCAlJUVYZKf+rA6grtegsLAQffv2dasc11Gj8+kEg5CO1a0C//PPP2/Q6mQYxm1VPLvdjsOHD+Oiiy4CgE4f9k6ns9GWNMuyMJlMUCgUcDgcOHv2LObNmwe73Y7rr78e8+fPb9DtbzAYfBqnUFBQAJlMhqKiIjz55JPCKPAXXngB1dXVQkBlZ2cLhWNcabVa7Nu3DyKRCKNGjcLJkyebrMTHCw0NRXZ2ttftevTogfPnz3vdzul0ora2ttG/R0REwAIp/hYNxN9/uwfS2qP/vnZicLgmsRJSzuJx2lt8fDyAustKWq0WIpHIp1rrNpsNUqkU//zzD4C6ojuFhYWIioqCXC4X3vuamhrY7XawLAuZTOa2qI5erxcKLTmdTlgsFhiNRuTk5MDhcOD06dNCbwbg/+j81iqqQ4FPSMfqVoHvKRhFIpEQ+Ha7XegB2Lx5c7seW3M1VbXPZDKhrKwMVVVVCA8Px+233w6dTodBgwbh3Xff9TgmQSqV+jRC3ul04sCBA3juuedgNBrRo0cPLF++XJjT7ioqKqrBbWvXrgUAXHTRRcjIyMDWrVsbHVTmas+ePV63AYDU1FSf5utXV1c3WVyoT58+KDhXDYib/qfghAhRcT0hs1V7fL5DhgwBUDfOgf+7L89XKpXCarWiqKgIAJCYmIjq6mqYTCZccMEFQm0Fg8EAo9EoVPBjGAZisRgmk0kYzxESEgKRSISqqirY7XbExsbi3LlzCA8Ph8lkgtFohF6vh1KphFKpbPfBkzSGgJCO1a0C3xOxWAyO44Sw56fetSWz1Q6ptelxAb62mhpr4WuKi5GjzkHRmSKEhYbiiy++wMmcPCjjeuCz9V9AIpXDbGs4YtxicwCSpkeScxyHDZu31BXq4TgMHTYCzy9ZgsjIKFjt7sft4ET/X3/+X6dOn0b2yRwEyRS48ur/wO4EREFSiCSe5+s7nU6YLRawLAtJcMNSxZ6IgqTgvIQ0AEAcBGcTQ1WSU/rgtP4fnx7T5gDETsBS/wkD6NUnFZJgGbRnz6O61gSZXAaz1fuIfYfTAfVJNcRBUsTExCA9YyAKT51CWFgYLDYHoqOjAQBVtSz0egNiYmIQGh4JoO5zJg6WwWJjESyXobyqBiaWBUQiACIMGDQEvfqkwmG3w1BZDYPBAKvFAou9DKHhke0e+OKgzjsDhpBAIOK6eRHs7777DrNmzcIll1zS5vPsq6urERERgYlPb0KQnFozhLiym1n8+vKNQo9UY/h/R962a21///03hg0bhsOHD3fLxXPMVjv+80rdFNmvF0yGXNq67T3+9aPVBttWS/59dPsWvkQigU6n6zJFdQghpCvia0hkZWV53ZZhGKjVagr9dtbtA3/06NF46qmnsGTJknYL+y8fmeT1zMuXjhX2/6+7MgwDppHrweMvvhjHjh7FlKlT8emnn3rtpq2sqICskWv4zzzzDD5fvx7h4RF48sknMHbsOK/HuH//PrfBaW+88QZyc3MxfvzFuPXWf2dMvLp8ORT//7gVlZU4W1oKSVAQevToAYVcgeDgIIhEIp8G9rkKCQ3FpEmTGv17TXU1BgwY0OQ+DmSfQuTFd3t9rKzBcsjs1ejbt6/Hvy986in8+uuvuHvOHMyZMwd9+3jezpXD6cC8ufOwffs2XHzxxdi0eTPkMhn+/OsvaDQaqFQqjBg+HA6HE0FBdWMy8vLzYbNaESyVop/LmIpffvkFZosFwUHBmDq16ZkCbbGqnjfV1dXo8XK7PiRpRyqVCmq1WqgC2hi1Wo2srCzo9XoK/HbW7QM/PDwcS5cubdfHlEuDvHaX+RL4NVVmBIk4OG0WyCM8V7S7fOIE/P3nQfyxfy8qDboGi9HUJwuWQB7sucDQ6YI8OGwWPL/4OQwYMADSIO+BIBFxwprxZ86cwcns4xCLxbhy6hVua8lzdis4x/8/rsMGh80CuTQI4SH/fyLjtIMD4LBZvD6mK1ViGkTOJsZLOO0Qo+E1d57VasWpgjxkel//B8ESINgJyIIajgkoKirCr7/8BIfDgYuGZ0IWJIZc6r2Qk8MB3HlHFnZ8vRW7d/2Mq6ZOxpIlS5CcnIwB6WlQKBSQS4OEZXT1ej3Ymrq5+b2SE90+Z2l9eyM/Px/R0ZFw2q0A0OhqeB0R+NZW7kImnY+nNT9I59G556QFuJCQEGE9dB7LssjJycGRI0eg1+uxePFiDBo0COXl5Zg7d67bFER/8fXZfZlO5sn3338PoK7ITv2lel3xPS1Wq7VZjwPUzV4YOXKkTyvmNeXvv/+GxeLfSYYnb775JhwOBy6++GKMGDHCr/tOnjwZ3377LcLCwnDw4EHcf//9OHPmjDDH3pVWq0VNTQ3Kysqg1WqRm5uL3Nxc6PV6pKenY9SoUejbty+MRqPbfPv69Ho9Tp486XGhnuagYj6EdH4U+B3Ely9IhmEafOnzhVeqq6uh1+shlUrx8ssvQ6FQYPfu3cIqds3BV3WLjIz0+756vR5//fUXAHgtPMOvrmez2Zo9xzsxMdHjIjP+YFkWhw8fho2tBprqJQAgEQGKRno8jh49ip9//hlisRgPP/xws45lwoQJ2LVrF+Lj41FYWIgtW7Z4/GwoFArYbDawLIuamhoUFRXBarXCYDAAcK+bz58weppvr9frYbFYWi3wmzq5IIR0DhT4HaS5X5D8krjh4eFQKBRC0ZU5c+YAABYtWoRjx44165j4Fn5zAv+nn36C0+nEgAEDvHbpBQUFCd3JzW3lt8Zqe3/++SdsNhvCZSIMMB3GnRfKkTX43/1mDZbjzgvrfuYNV3istMdxHFasWAEAuPbaaz3WKfDVhRdeiFWrVgEAvv76a5w6dUpYQIeXnJyMvn37okePHggODkZKSgqkUqnQo6JQKIRyuk2thqdUKiGTyVqlAiK/DK/FYqEFegjpxOiiWhvgOM5ry1WhUIBlWaE6mqvc3FwUFRUhJSUFffv2dSugI5fLhW7s8vJy2O12hISE4Nprr8Xhw4fxv//9D3feeSd+++03j1/0VVVVbkux8hwOB6qrqwHUdZeXlJT4NMixqKgIubm5QsGcPn364M8//2ywncVicav6J5FIYLfbUVRUBJFI1KyWfkFBgddtwsPDcfbs2Qa3G41G4cQoIyMDpsoy6IvUsHMiACkAAIMmB0Gif4+rGHV1HRISEoTb9u7diyNHjkAmk2HWrFnCCZzT6fT4OtdX/3LC5MmTkZCQgNLSUuzcuRNXXXUV9Hq9UC+fXxwpMjISEonE7dIJ/7nz5bWMiYkRLt14297btX6j0QiZTNbg8hMhpHOhwO8gTdXFLyoqAsuyKCoqQlpaWqPlf/nlXfnW3quvvorrrrsOeXl5+O9//4t33nmnwX2io6M9VuArLy8X/j8pKQlVVVU+tdYcDgfy8/Nht9sRFxeHjIwMjwERGRkpFJEB6rrzdTodbDab18cQi8UYPnw4BgwYIOx73759PlWyi42NxeTJkxvc/tFHH8HpdCIjIwO33367cM3c5gRQWbdNdHQ0guu99MHBwcIofY7jMHfuXADAPffcg9GjRwvbWa1Wn06YOI5zez8kEgnmzJmDJUuW4KuvvsK1116LyMhIBAcHCyVzRSKR0FVfXFwMrVaLpKQkpKSkCK1tTwP16vN10J637VxL9Ta1bWdYFpmQQEaB3wmlpKQILXygrsuUZdkGJwn87/yX/AUXXIB169ZhypQp+OCDDzB58mT85z//8ekx+ev3ISEhwjV2X9jtdvz9998AgBEjRvj8pd63b19ERtZVexOLxcKPw+EQejX4H5lM5tcxeaPVavH7778DAKZPn97sIDp06BCOHz8OuVyO++67r9WO76677sLSpUtx7NgxVFZWCivx8ZeBgoKChK54rVYLk8kErVaLlJSUDlkYhx8vQAjp3OgafhsrKirCvn37hFrpvkhPT8fkyZOF1dxYlhUWTqmPPxmQy+VgGAYTJ07EvffeCwCYN2+ex+5sTyoqKgB4rovflIKCAhiNRoSFhbkt0OKNVCpFz5490aNHD8TFxUGpVCI6Ohrh4eGIjY1FdHQ0IiIiEBoa2qphDwBbt24Fx3EYNmwYevfu3ez9fPjhhwCAG264wa33oqXi4uJwww03AACWLVsmXMP3NAgvKSkJCoVCGMDY1EA9Qkhgo8BvY8XFxTCbzSguLm72PviR155G6+v1etjtduF6McuymDlzJlJTU2EwGLBs2TKfHoNv4UdERPh8XE6nE8ePHwcADB8+3OOlgs4mPz8fR48ehVgsxvXXX9/s/ZSWlmLHjh0AgLvv9l60x1/33HMPgLpiOoWFhQA8r2+fkpKCcePGCb1BTQ3UI4QENgr8NpacnAy5XI7k5ORm74MfmV8/8IuKinD48GGcPXtWGLVuMplgs9kwZswYAHWr1nkblGW1WvH5558DgF8t1d9++w2VlZWQyWQ+rVzXGfz4Y10t8XHjxnktUtSUDRs2wOFwYPTo0bjgggta6/AEI0eORO/evWGxWHDixIlW3z8hJPDQNfw2lpKSIrS+XLlel8/Ly0NeXh769euHfv36gWVZGAwGVFRUIDEx0eM0N4ZhUFxcDIvFInTHGwwGVFZW4p133sHGjRsBAFdddVWT16iNRiNmz56N3bt3Izg4WLgc4I3dbseaNWsAAMOGDYNM5tsqdzydTofz588jNTW1VabY+YLjOGFk/7hx3ssGN2XXrl0A6sYAtIU//vgDp0+fhlgsbvGxNldRURGKi4uRnJzcoksfhJDOgVr4HYRlWTgcDrAsi7y8PBiNRuTl5Qm3FxUVwWw2N7gGr9frkZOTA5ZlkZKSgtDQUCgUCmi1Wnz88ccYM2YMNmzYAI7jcPPNN3scqc+z2WyYNWsWdu/eDYVCgfXr1zdZl97VN998A61WC4VC4XdlOZPJhNzcXJSXlyM3N7fZxXf8xRcskkgk6NWrV7P3U1VVhcOHDwMALr300tY6PAHHcXj22WcBADfddJMwlqO9tcblKEJI50GB30H4edUMw6Bfv34ICQlBv379hNtTUlIgl8sbdDvr9XpYrVa3UqoVFRXIysrC008/jfLycgwYMAC//PIL1q1bh7AwzzX4gboiPXv37gXDMPjqq68wYcIEn46dZVl88sknAIDMzEy/WvccxyE/P1+oPVBVVeXzwMKWOnXqFIC6et8tGQi4d+9eOBwOpKWltehSTWO+++47/P7775DL5XjmmWca3U6n00GtVrdatbz6WuNyFCGk86Au/Q7iOsVu6NChiI6ORmlpqbCCFF8UpX5RHqVSCb1eD6VSCYvFgqeeegqfffYZnE4nQkND8d///hcPPPCA10Bbt24dPvroIwDA22+/jeHDh/t87F9++SUqKiqQlJTkdd3r+s6dO4fKykqIxWL07NkTJSUlOH36NKKjo9u8a58P/D59+rRoP7t37wbQNq17h8OB//73vwDqBu41tjIf8G95XL1e3yoV8+rjL0fR/HlCugcK/A7icDjcvkjPnz8Pm82G8+fPu9WIt1gsCAr6920KDg5GREQENm3ahCVLlghLUV533XV44YUX0LNnT5hMJreqdq5OnDiBf/75BwsWLAAAzJo1C8nJyQ0Ghp04ccJj4Zjq6mphgN/ll1+Oc+fOQavVen2+JpMJBQUFQms+PDwcYrEYUqkUVqsVx48fR2xsLJRKJfLz873uz263uxULakxMTIwwJZJfejc8PLzBNMmqqqq6GQ+cCEDd0sYGg8Gt0h5QV+mQv34/evRo1NbWenxcm82G0NBQr8dns9mE1/ns2bN4++23cfLkSURFRWHOnDlwOBwwm80wm82IjIx0KzbkevLnelmEApoQ4gkFfhsQiURev3QlEonbwL34+HiUlpYiPj7erbKeRCJx29dff/2FhQsXCteQMzIysHLlSmRmZvrUTX327FksWbIEDocDl156Ke644w6Px2q1Wj2O2N+6dSusViv69OmDcePG4YsvvvBpZH9JSQnsdrtQWc7hcKCqqgrBwcGwWq0wm83Q6XQICQkRCs00RSKR+LRSXlBQEAYNGgSbzYaysjIAdS3z+pdKampqMGTIEFjtTvzw43kAwOhRoyCttxRufn4+SktLIZPJcMUVVzQ6/c3hcPhUCbCmpgZr167Fxo0bsXfvXiG4b7zxRqHsst1uh0gkalBMJzY21mPL3pfAp5MCQgIPBX4Hch2457qONH8iANQFB8MwcDgcePHFF7F69Wo4HA6Ehobi2Wefxf3334/g4GChDn5TamtrsWTJElRVVSEtLQ0LFizw64v/7Nmz2Lt3LwBgxowZft3X4XDAbq9bkU4ulwv35SvpWSwWmM1mYZvWdubMGdhsNoSFhaFHjx7N3s+BAwcAAGPGjGn2XPfq6mp8/fXX2Lx5M3755Re35zxkyBBMmjQJl156qVCXX6lUCtUUdTpdo0FPCCFNocDvYJWVlW4LoOj1ehQUFCAiIgJisRhhYWH45ptv8OyzzwqjpW+44Qa8+uqrSExM9PlxnE4n7r33Xpw+fRpRUVF46aWX/L5mvnXrVjidTgwZMsSvkeNms1momS+TyRoU6JFKpbDZbHA6nTh37hw4jmv1Fih/mSA1NbVF+z548CAAYOLEiX7dz263Y/v27di4cSN++OEHt0VzBg0ahCuuuAI333wzkpOTYTAY3C7JMAwjrIzIL2lLgU8I8RcFfgdzXYp2z549+PXXX5GYmIj09HTIZDI89thj+OGHHwDUjS5/8803va4378krr7yCnTt3Ijg4GC+++CLi4uL8un9BQQEOHz4MkUjk99zzI0eOAIBwzb4+kUgEhUIBo9GI2tpaaLXaVh8Zzge+L5cBGmM2m4V1A/wN/LvvvhtffPGF8HuvXr0wY8YMXHrppRg2bJgwSBP4t0XPX+7hxcbGUtgTQpqNAr8D8Qvf8F/qx48fh91uR0lJCSZMmIBXX30VP/zwAyQSCR566CE888wzzaqRnpeXh1dffRUAMH/+fAwcONDvfWzduhUAMHbsWL96FrRarTCoT6FQNNq6lkgkwgC+3NzcVg38wsJC/PXXXwDQovXqd+zYAYvFgoSEBL9mJxgMBiHs7777bkydOhWjR49GRUUF7HY7DAaDEPiuQe96EgBAeO+pTj4hpDloHn4Hci2ZyxfSiYmJwcSJE5GcnCyUxwWAa6+9ttlf9Js2bQJQN6r+iiuu8Pv+BQUFyMnJgUQi8Xn1PaBuhgHfIg4KCvJaa58frNiai+WUlZXh+eefh8lkQkZGRrPL4J4+fRorV64EANx///1+XRYIDQ0VejYeffRRXHbZZRCJRDCZTDh37pzbtq7jOupzXQnPdXudTudxe0IIcUUt/E6CZVlcdNFFGDNmjNCymz17Nn755Rds3rwZd911Fw4ePOh36HMch6+++gpAXdW25vjuu+8A1E1Dcx1v4M2xY8dgsVgQFhbm07r3DocDgH/1/Jui0+nw3XffwWq1on///li4cGGzFvixWq145plnYLFYMHLkSGFhG1/JZDJkZmbiwIED+P3333HllVcK0+2ioqJw9uxZKBQKt5M/TwMCXded53XEcriEtAa1Wu11G6VS6bG0OGkeCvwOpNFoUFpaioSEBOG6bf0v7TfeeAO///47CgoKsGDBArz11lt+PcahQ4dw5swZhIaGYurUqX4vxFJcXIyjR49CJBJh6tSpPt/v7NmzOHPmDABgxIgR+OOPP7zepzUDv37YP/300z5Nk/PknXfeQW5uLiIiIvDf//7Xbdqkr8aMGSME/owZM8CyLBISEqDRaMCyLIqLi2EymZCcnNygK5/nWqyJ5+kkgJDOjD+xzcrK8rotwzBQq9UU+q2EAr8DlZaWwmw2o7S0tNEv+aioKHzwwQeYNm0aPvzwQ0ydOhVXXnmlz4+xefNmAMCVV17ZrBYg37ofNmyYz6vL2Ww2oU5Av379fOoV4DhOqCrY0sB3DfsePXq0KOwPHjyIdevWAagrRdzcAXPjxo3DihUrsH//figUCrfj4Wv8G41GHD9+HKmpqY1+HurzdBJASGemUqmgVquFomGNUavVyMrKEqqPkpYL6MA/dOgQjhw5gssuu6zJEqb+4jjO64IwTqcTiYmJOHv2LHr27Cm08gwGA9LT05GYmAiTyYSamhqMHDkS8+fPx+rVq3HPPffg0KFDDUbZ6/X6Bt3VNptNGGx3+eWXo6ysTOg+9ub06dP4+++/cejQIQBAYmIi9u/f32C7yspKoaANr6SkBCaTCVKpFMHBwcjJyYHT6YTZbG7y9QDqrvXX1NSgpqamyeOzWq0wGAwNbq+oqMD+/fths9mE8RC+VORzOp2oqamB1fHv+6YtKcFzzz0HALjmmmuQmZkJo9Ho0+UJm83mth2/wFBeXh5KSkqE9y8sLEwI7MLCQtTW1uK3335DbGwsevTogaSkJCgUCp8+UzwqqkM6O9e6I6T9BGTg22w2zJ07F1u3boVUKsUjjzyCAwcOtOua7hKJpEGxHX4Al1arhUqlEgLSbrfj5Zdfxu7du5GdnY358+dj69atbl/sDMM06Gr+/fffUVFRAaVSiYkTJyIoKAi9evVymwrYmK+//honTpwAx3FISkpqtGV77tw5hIeHC78bjUYhYHv06AGn0wmn0wmbzdZkVzgf+OHh4T5dOqisrMSwYcPcbissLMTzzz8Pm80mdOPX1NRgwIABXvdnt9uRnp4Oi80B/O8oAOCDDz6AwWBAWloaVq5cKRRAcn2+jbHZbG4lkePi4jBw4ED8888/+OKLL3DzzTcjKSkJYWFhwoA+kUiEgwcPwmg0wmg0QiQSISYmBqGhoW1Sm4AQElgCcpT+ww8/jLNnz6KkpARarRaZmZn45ptv/N6PxWJBdXW1209zMQyDCy64AJGRkUhKSkJOTg727dsHjUYDhUIBuVyOzz77DFKpFDt37sT333/vdZ/bt28HAFx99dVu4eMLlmWFuetDhw716T52ux2lpaUA6i5F+HNdmW+9NvdadHZ2Np5//nmwLNvia/a8Xb/8AqlUinfeeadVus3Hjh0LADh8+LDHFQJjY2Nx0UUXITU1FUlJSYiLi3N7PXQ6HU6ePNlmq+MRQrq3gGvhazQarFmzBiUlJcLiJoMHD4bNZsOKFSswaNAgXH755T7ta+nSpViyZEmrHZtri/+7776Dw+FAdXW1MHI7MTERN954I9avX48//vgD06ZNa3RfRqMRP/30E4C6hXX8xXfD9+jRw6dStBzHobS0FHa7HVKp1O/CPnzg+7LgjKvi4mJ8/vnnwpiB1gp73sKFC5s9la++cePG4f3338fx48dhNpthMBgajFeIjY11G4jneqKh0+mo0l4zaTQan64ZE9KdBVzgnzlzBg6HAwaDAfHx8Thy5AjWrVuHIUOGQCwW47HHHsNjjz2G1157zeu+Fi5ciEcffVT4vbq6utUKxvTp0wenTp2CSqWCwWBAQUEBwsPDhYIv3kbb//jjjzCZTEhJScGQIUP8euzKykoUFBQAgM/31ev1Qjd0YmKiXyPZXa9N+9rCZ1kW77zzDvbs2QOO4yAWizFp0iTMnDmzRWHvWtd+9JgxmDt3brP3VR/fwj916hQsFgtKS0sRHR2NoqIiaLVaJCUlISUlpdGpdlRpr3k0Gg0yMjJ8qlXgqeARId1FwAX+qFGjMHToUFx88cUYOXIk9uzZg6VLl+Khhx4CALz22mt44okncPfdd6N///5N7ksmk0Emk7XJcfbv3x/9+/eH3W5HQUEBJBIJiouLhRZwbm5uk/f/7bffAABTp071+9rvL7/8AofDgejoaLelehvjdDqF1lNUVJRfNfpdR+cD8PnSw549e3D8+HEAdbXoZ8+e7VcFwMaOZfHixUBqXengV5a90qwpeI05e/assFLg6dOn0atXLwB1YzbMZjO0Wi1SUlIanWpHi+Y0j16vB8uyWL9+vdcKiTTvm3RnARf4wcHB2Ldvn3ANPC8vTwh7AHjwwQexYMECFBUVeQ38tqTRaIRWHz+tje+ZAOC1FcIHdUlJid+Pzc+HDw8P9+lkQSwWIzw8HNXV1SgvL4dCofBpYBvgPqPBn3AdMGAA8vLyhKCsrq5uceC/+uqr+PLLLzHpubrAT0xMaNH+eCUlJdiyZQsWL14Mh8OBIUOGYMqUKcL7mpSUJLzXAE21aysZGRnIzMzs6MMgpMME5KC90NBQzJgxAwzDoKKiwq2r78iRI2AYBhdddFEHHuG/rb6zZ88iJiYGSUlJkMvlKCwsBOB9EZiLL74YALBv3z4hwH0VFhYGoG7qm68SEhKEkC8pKUFeXh5Onz6NkpISlJWVCS1514DnR/ADdWHvT+CnpKRg2bJlSE5ORkVFBRYvXoxvvvnG56lr9W3btg1vvPFGs+7ryZkzZ/DGG2/gkksuQZ8+ffDEE0/AaDRi/Pjx+OCDD2A2m4UV8VJSUjBu3DikpKS47YPK5hJCWlPAtfBdDR8+HFarFdOnT8crr7wCjUaD++67D2+//TaioqI69Nj4Vh9f7Iaf185PefO2PG1mZibCwsJQUVGB7Oxsv67j88HtuoSrNyKRCAkJCZBIJKioqIDD4RDKx/IaC2ORSNSsrvPExES8/PLLeP/997Fv3z6sW7cOsbGxGD16tF/7+fnnn/HOO+8AAB559FEc9/tI6hQUFGDbtm3Ytm2bsIYAb9SoURg7diwuvPBClJSUoFevXigvL2+yyJDrtfzWGoRICAlcAR348fHx2LRpE26//XYMHjwYsbGxWL16dbNrzvuKZVm3BVA8deGqVCoolUro9Xqh7Or58+eFIPE2VS44OBhjx47FDz/8gD179vgV+M1p4QN1wd2jRw/ExcXBarXCZrMJ/62oqGj0Pi25Ti6XyzF//nyEhobi+++/x65du/wK/IMHD+KVV14BULd2wf333Yd5nx3z+f46nQ7vv/8+tm/f7jaQUiwWY+jQobj66qtx4403IjU1Fbt27QLLsrBYLJBKpV4rClLZXEJIa+p2gW+1WvH555/jzjvv9Gn7qVOnQqvV4syZM0hJSfG4Xntr41tuVVVViIiIaHSxFP7EgOM41NbWora2FufPn4dMJsNFF13kVsmturq6wYC3UaNG4YcffsDu3bsxe/ZsAHWXCrzN4+Z7Efjr403hpw42Jjg4GMHBwaiqqoJIJGq0lc/fznGcUN2vKeHh4Thw4IDwO98Tcvz4cezatUsISYVC0ejMiZycHPz3v/+Fw+HA+PHj8eijj6LK5blUVlVBFuR+MsJxnFtQ33///UINB4lEgosuuggDBw7E8OHDccEFFwjFgTiOg0qlgkajgUqlQmpqKkwmE8rLyxEeHu7Wgs/NzcWpU6fQp08frz059flzSYMK+RASWLpd4D/44INYs2YNTpw4gRUrVjS57YYNGzBlyhRERkZ6vSbemviWGz9oKyQkxOOXb0hICGprawHUfTlv2LABQN30Lr4VzouLi2sQ+P/5z3+wePFiHDt2DDKZDBEREYiOjvbaPcwfi91ux5gxY5psgYtEIp9G8hsMBp8qGf76668+rWin1+vdgpxhGCQmJqKkpASFhYUYNWoUACAiIqJBRT6gLlRffvllWK1WTJw4EStWrEB0dHRdpb3/FxkRAVmw+7FwHCecFFqtVuzatQsA8PLLL2P27NngOA67d+8WLmm4nkAOGDDArepfVVUVOI5rMP2uoKAARqMRBQUFbgNHKaAJIS3RrQbtGQwGfPPNN3jzzTfxxhtvuM2Rr6+srAzz5s3DFVdc4VNt9NbEz/Xlp1k1NiKbDzG+njpfXOayyy7z6XGSk5ORlpYGh8OBvXv3+nx8/MkEx3F+d+t3pEGDBgHwXqNAq9Xi1ltvFcrzrlmzBsHBwX4/3oEDB2A0GhEbG4vLLrsMv/76K7RaLS699FJceOGFXlvnISEhsFgsMBqNbgPz+vbti5CQkFZd34EQQrpV4H/xxRe49dZbMX/+fHz44YdNhn5cXBy+//573HXXXc36sm8vJpMJBoMB1dXVfgc+AEycOBFAXcvZV1KpVGhl8yPJu4KBAwdCLBbj3LlzjV62MBgMuOWWW3Du3Dmkp6fjs88+a/YUuF9++QVAXQW9kydPwmg0Ij8/H0qlUuh9aGqEPcMwCAkJgUwmcxvTkZGRgauuusrrnHFCCPFHtwr82267DY888ggA4K677vIa+mPGjME999zTnofoRqfTQa1WN3lN3WQyweFwQK1Ww2QyIS4uTmjJ+sI18P25vssXFGpqhbvOhmEYoVXcWCv/ySefxKlTp5CYmIjPP/+8RbMx+MAfP348gLoKevzrxrKsMMK+KSEhIQgKCqKBeYSQNtetAj86Otqt+Iqn0NdqtZg/f36z52u3Jr1ej8rKSuTm5jbaEjSZTDhz5ozQuh8/frxfo9rHjBkDmUyGkpISHDvm++hzvlqet/rjnQ0/pZAvDezqs88+ww8//ACRSIRPP/1UGOjXHJ9//jn+/PNPAMCECRMQHh6OCy+8UAh8hmF8CnKFQtHkZR1CCGkt3SrwPXEN/Xnz5uHSSy9F3759O8UAKKVSCYfDgcjIyEZbgmazGQzDCNfVKysr/XoMhUKBK6+8EgDw4Ycf+ny/Pn36AKgr3NMZTo684TgOv/32m3BiVH8a4ieffIKnn34aAHDffff5tGRuYz799FPMmjULHMfh7rvvRmRkpHAtni+eQ0FOCOlsut0ofU/uuusu6HQ6PPXUU1i5ciUefvjhjj4kAO4rowF1Xfz1V0iLiopCeXk5RowYAcD7gDRP7rnnHmzduhVbt27FlVde6VMRl2HDhuH48eMoLS1Fbm5uh5YZbkxtbS20Wi20Wi2Ki4uFKYSTJk1yq5T4wQcf1NXIB3Dvvfdi4cKFzX7MdevWYcGCBQCAO+64A6+99hqys7MRGxsLuVwuDNQzmUwwmUzC+6nT6YSFb6gePiGkIwRE4Gu1Wnz44YedKux5fNEdnU4Hu92OM2fOAIAwij86OhoxMTFITEyESCRCWVkZzp0759OStbzMzEwMHz4cf/31F77//nthTn5TFAoFRo0ahb179+LXX39Fenp6h/aKOJ1OVFZWwmAwwGAwQK/XY+fOnQ22u/zyy4UpeUDdIjv8dg888ACeeuqpZj+Pjz76CM8++ywAYM6cOVi0aBHMZjMSEhJQWlqKhIR/a++zLAuHwyFMudPpdLBarULo8zUW6p/gtZa23j8hpOsJiMD/6KOPcP/993do2Hv7AuZb+izLQiKRQK/Xu7UEGYZBamoq8vPzceLECb8CHwDmzp2Lv/76C9999x1mzpzpU4GhsWPH4sCBA+3Wync6nbBYLMJSu/zrUV1djYqKCrdV9XhxcXFISkpCYmIiVCqVW1Gc33//XZgn/9BDD+GJJ55odti/++67eP755wEAt99+O1577TWYTCZhiqVKpQLLstDr9cJJHN/CBxoubdvYEritpa33TwjpegIi8BctWtTRh9DgC7j+dXGFQgGj0SiMik9MTATHcbDZbEJIXXDBBcjPz8fRo0cxYcKEBvtvKsQvu+wy9OjRA+fOncO3336LSy65pMnjraiogNlsxsCBA3HkyBH88MMPHgsE8SV/vbHb7Th37pzwe2VlJQoLC1FTUyP81NbWegx1XnBwMKKjoxEVFYWwsDCMHj26wfLE/Ot34MAB7Nu3D0Bd2D/44INNrg1gsVjcFvMB+MV9RFi1ahVeffVVAMCsWbPwxBNPQKFQCJdG+PvwrXqWZREZGSmEPcdxUCqVwgqHHMe1edlcKstLugu1Wu11G1rW2DcBEfidQf0vYJFIBJFI5Nby12g0Qp114N8Fc/iQHTJkCLZt24aTJ082CPeoqCiva8nPmzcPS5Yswa+//or58+c32do9f/48IiIiMHjwYDzyyCNCy7X+8qJhYWHCAL+mlJWVYciQIaisrMTGjRvx448/egx3iUSCnj17CgWHEhMTkZycjAsuuAApKSnCMdfU1Lh1obtauXKlEPaPPvoolixZ4vX4lEolQkJCYLbahdtiY2PxytKXhLC/4447MHfuXCiVSkgkErAsK5RF1uv1KCgoQFRUFNLT0xEUFNTk6+u6fkJrXirh9xUSEkJhT7o0pVIJhmGQlZXldVuGYaBWqyn0vaDAbyeNrXFev+XPl9Llb4uMjBS25UvTHj/evPXcZs2ahWXLluHkyZP466+/hIGATQkLC8Pll1+OnTt3Ytu2bbjwwgubFVA2mw1fffUVtmzZIhTzyczMRHp6OuLi4oQfhmE8lsL1BcdxWLFihVBS+amnnsK8efOatS8AWLR4MVYsr1tYZ968eZg5cyYGDhyI0NBQAP/OtWdZFiUlJQD+nVXRFWY2ENKZqVQqqNVqr1OD1Wo1srKyoNfrKfC9oMDvYK4t/5CQEKHbl/+bKz7wc3JyYLVa/V7oJyYmBldddRW2bNmCtWvX+hT4ADBt2jT8/PPPKCoqwksvvYTLL78cw4YN89qjANR1d//555/YunWrsMhO3759ceedd+KCCy5osH1zC/1wHIfly5dj1apVAIBnn30W9957b4tKA6964w0AwIIFC3D11VcjNTXVbYYDwzBCC5+v4+9aB4IQ0jIqlYpCvBVR4Hew+i3/+r0ArnX+k5OTERERgaqqKqjVar+WvOXdeuut2LJlC37++WefAyosLAw333wz1q5di9zcXOTm5iIiIgKXXHIJ5HI5IiMjERkZ6VYQyG63Q61WY8eOHSguLgZQ10WelZXld/GgxlRWVmLnzp04fvw4jh8/LkxZXLRoEebOnev3/mpra7H4+ReByAl1N4hEWLFiBW699VbhfXK9DOH63tEXEyGks6PAbwMsywoV31qTSCTCgAED8Mcff+D222/H6tWrcfHFF/u1j7S0NIwZMwa///47Hn74Ybz//vs+lZe97LLLkJmZid27d2PPnj2orKwUloXdtGkTgoODERsbi7i4OIjFYpw8eVJorcvlcowbNw5z5sxpMMiuOcxmM9atW4fPPvsMVVVVwu1isRiLFy/2adqhp31Onz4dvx84hEnPTQAArF+/Hml9UgC4n4jp9Xro9Xq3gXiujhw5gvz8fKSmpnq8PEFT5gghHYECvw00tWBKU/fxJQQWLVqEW2+9FWq1GpdddhluvfVWLFu2TLiu7Isnn3wSd9xxB44dO4abb74ZH3/8sU8t/ejoaNxwww34z3/+g7///ht//PEHTp06hcrKSthsNpSWlqK0tFTYPjw8HMOHD8eUKVNgMplaHPYOhwPbt2/H6tWrhRH//fr1wxVXXIGBAwciMzPTp6V6Pe337rvvxr59+xAR/W+AS4OlwomLa7Dn5+ejrKwMFRUVHgM/NzcXJpMJarXaY+CXlZWhtrYWoaGhQmU+QghpaxT4baA5rTZf5k1rtVqEhoZi8+bNeO211/DDDz/giy++wM6dO/HMM89g7ty5Pq0lP2DAAHz55ZeYPXs2Tp8+jZtuugkrV670+Zp+UFAQRo4ciZEjR+L48ePo1asXysvLUVZWhrKyMphMJvTv3x8pKSlC131LVt3j15hfuXIl8vPzAQDx8fF46qmncMMNN/j0nJva96OPPoodO3ZAKpVi0aJF+OH/Ow0kEnGzTlLS09OFFj4hhHQWFPhtoDmB78u86dLSUqGb/LXXXsPNN9+M119/HcePH8eCBQvwxRdf4PXXX8fIkSO9Pl5qaio2btyI2bNnIz8/H1lZWbjrrrvw8MMP+x1yEolEKBk7cOBAv+7bFIfDgf/9739Ys2aNUCM/IiIC8+bNwzXXXIPevXu3+DGWLVuGjz/+GCKRCM899xwmTJiAH76uq3Z44YUXora6EjExMW73SUtLQ1RUlMfWPX+/oUOHNjpOIS4ujqbNEULaXbdfPKerYBim0cVWDh06hM8++wxlZWWQy+Xo06cP0tLScMstt+DgwYN4/fXXERERgWPHjuGyyy7Dgw8+CIPB4PUxe/TogY0bN+KGG24Ax3H46KOPcN111yE7O7stnqLPNBoNVq5ciYkTJ2Lu3Lk4fPgw5HI55s6di59//hl33XVXq4wF+Oijj/Dyyy8DAJ544gksXLgQMS6V+mJiYpCenu4W7HxPhUqlajTwvWEYBkajEX///TeKioqa/wQIIcQP1MLvIL7O0+Y4DgUFBbBYLKiursa0adMA1E13+/vvv5GXl4fMzEx88cUXWLVqFX766Sd8+umn+Oabb7B48WLMnDnTbd58/WpzQUFBWLRoES6++GI8//zzKCgowI033oiJEyfiP//5j9fucovFgpqaGq/Pw2KxNNmtb7FYcOjQIezatQu5ubnC7ZGRkZg2bRpuv/12xMXFAYBQ8MaXKXcWiwUOh6PB7d98842wZPLMmTNxww03wOl0Qu4y7a5+5T2gLvD5anpNLULkdDqbrFeg1WphMpmg1Wp9vo7fGVZ4JIR0XRT4HYSvtOdNcHAw+vbtiwMHDiAyMhJVVVVCy/LYsWOoqamBTqfD0KFDcc899+DGG2/EG2+8gezsbDz44IM4fPgw1qxZg+DgYABodBGc3r1745prrsEjjzyC7du346effsL58+exatUqpKWlNXp8w4YNcysO1BiTydSgMh7HcThy5AjWr1+Pbdu2CScOIpEIEydOxMyZMzFlyhSPrXmLxSIsGdwUp9MJuVzudtvevXsxZ84cOJ1OXH311bjpppsQFhYGiUSCUJdudolE0uCEJyQkBCzLIiQkpEGXvevAS4VC0eT7m5SUhOLi4mYNMiSEkOagwO8CxowZg+joaFitVmE6GFBXWz87OxupqalITEwEy7IYO3YssrKysHz5cixevBifffYZzp07h02bNnkdya9UKrF27Vp89dVXeOSRR3Ds2DFMnjwZCxYswJw5c1pl7jwAnDt3Dlu2bMGXX36JnJwc4fZevXph+vTpmDVrVpsF4dGjR3HdddfBYrHgsssuw2OPPYbExESfTh6AujUPGrv27jrw0tsSxCkpKUhJSaGKfISQdkOB30UolUq3sAfqTgTGjBkDoK4lywcyy7K4/vrrER8fj4ceegg//vgjJk2ahB07dngdKCYSiTBjxgz0798fzzzzDHbv3o3nn38eP/30E1auXNns4jIsy2Lz5s3YvHkz9uzZI3SVy+VyXH311bj11lsxduxYmEymNqlhAABbtmzB7NmzYTQaMXr0aLzyyisIDg6G1WqFyWRCTk4OQiO81yRw5dqq93fBGpqPTwhpTxT4nczJkydRWFiIvn37YsCAAcLtjRV5qY9lWWg0GjgcDowdOxbffvstZsyYgb/++gvjxo3DV1995dN0sfj4eKxbtw6ff/45lixZggMHDuDSSy/F4MGDMWjQIFxwwQVudeU9cTgcOHjwILZv344ff/zRrT7ByJEjMWPGDFx//fWIiIjwejwtYbPZsGjRIixfvhwAMGHCBGzatAm1tbXCOvYmkwlWqxUlWq1f+64/p54P7qZa7nzQG41GyGQyWsKWENIuKPDbgT8tucLCQhiNRhQWFroFvj+PJZPJYLFYoFKp0L9/f+zfvx/Tpk1DYWEhJk6ciC+++ALjxo3zui+RSCSUwn300Udx4MABHDp0CIcOHRK2CQoKQmpqKjIyMpCRkYH+/ftDLpfj+++/x7fffouysjJh25SUFMyYMQMzZszwaYW91nD8+HHcf//9OHr0KIC61fNeeuklBAUFQafTISQkBCaTCTExMTAYDODE7v8kXFfEa61Q5rv+gbrXLyQkBDqdDjqdTpjeSAghrY0Cvx34UlSH17dvX6GF7ws+kPhry/z+4+Pjhf/v168f9u/fj6uvvhp///03rr76aqxcuRKzZs3y6TF69eqFr776Cnl5eThx4gROnDiB7OxsnDx5EtXV1cjJyUFOTg62bdvW4L4RERGYMmUKpk2bhmnTprXbSHOz2YxXXnkFK1asgN1uR3R0NObOnYuHH34YVVVVYBhGCHkAKC8vh1KphDhYBqBuHn5uXh5kQXXFd1wvp/Bd93q9HgqFAvHx8UI3vi8nd/z9IyIihMF9RUVFsFgsQugTQkhro8BvB/5c2x0wYIBfLXt+iVaTySSEjKeg6dGjB7Zu3YoHHngAO3fuxPz585GTk4MXX3zRp1XvRCIR0tPTkZ6ejunTpwOo67Y+fPgwSktLoVaroVarkZOTg4qKClxyySW4+uqrMX78eEilUphMpnYL+4MHD+Lee+8VpvdNnToVt912G/r16wetVgupVIrQ0FDhdaqoqEBYWBj0ej1S+vx7ucNmtcJpq+ua50M/MjJSCHt+iqPrtDpfTu5c3yO+6z82NpbCnhDSpijw20FrdQfzrfn6+/U2J5xnNBpx//33Izk5Ge+++y7efvtt5OXl4ZNPPmnWdXSRSISEhAQMGDAAl112md/3b21GoxGLFy/Gu+++C47jEB0djWXLluHOO++EyWQCy7IwGAzCvHyDwSDM5Q8ODm5QUc/hcCCldy9hvfuoqCihCx5Ag0GUgH8nd66oK5+QllGr1V63USqVAb2qJQV+F8K35isrKxEZGQmWZaFUKhss29qY0tJS2Gw2XH/99Rg9ejTmzZuHn3/+GRMnTsTLL7+Myy+/vNWm3rU1juNgMBhw6tQp4Wf9+vVC5brbbrsNy5YtE2oEKBQKKBQKtzXs+ROAnj17Ijo6GhKJBGarXXiMxKQkKJVK5OTkCAEeExMDsVgsVEasjz8Jo+l2hLQP/jswKyvL67YMw0CtVgds6FPgd3Icxwld4XxI8a1K114DjuMaDRm+dcsHUUREBEaNGgWr1YoFCxYgLy8P06dPR//+/TF//nzcdNNNcDqdHivU1edwOHwKN4fD0az9mc1mHD16FKdOncLp06eF/54+fdptaVxeXFwcFixYgLlz5wKom66Yn5+P7OxshIeHY+jQoUJLXqFQCP/vdDobvIb86+U6JdJbBT1CSPtSqVRQq9XQ6/VNbqdWq5GVlQW9Xk+BT1qPL1X0fA0N1xZ3UwuuSCSSRvfJ9wz07NlT2AfDMJg4cSLeeecd7NixA19//TVycnJw//334/nnn8d9992HefPmNejmri8+Pt6nMQB2u92n5xwZGQmO4/DLL79g48aN2L59e5Ole+Pi4pCRkYE+ffogISEB48ePR3p6uts18sLCQpSUlODcuXMICQmBwWCAUql0a6GLxWKIRCL315thIBaL3brbXU/AeEVFRdBqtUhKSnK7nu/L86WTh5bRaDQ+fdGT7k2lUgVsiPuDAr8LaGmBlpCQEBw5cgTnzp3DwIEDhfDiq73NmDEDhw4dwsaNG/Hll1/i3LlzWLRoEV555RXMmjULDz30kE/T6Ph57TU1NUhJSfF6suDK4XBg7969QsiXl5cLf4uPj8egQYPQq1cvJCcnIz09XailL5PJcOmllwpd6Z5enz59+qCqqkoYp8BXLGzONXOTydTgvWhOXXzSchqNBhkZGW7jWhrDMEyzFzsipLugwO8C/JnW5wnDMKioqIDD4cCpU6fQv3//Btv07t0bd999Nx577DH8+OOPWLZsGfLz8/HOO+/gvffew3XXXYdZs2bBbDajpKQEpaWlKC0tFVrOJSUlqK6udttndHQ00tLSkJaWhtTUVKSmpgq/h4aGwul04uDBg9i0aRO2bNmCc+fOCfeNi4vDjBkzcPPNN2P06NENxhZoNBrs378fDMMgOzsbGRkZwt/qz5vnawQAgE6ng16vh8lkwv/+9z8kJSWhV69ewt+0Z8//ux+TCXKpe8ldT+9FUlKS0MIn7Uev14NlWaxfv97t/fck0AdrEQIEeODzg986u+aO/HbFz+/31FIvKipCSUkJEhMTkZCQgDvvvBPTpk3DunXrsGHDBvz999/YsmULtmzZ4vVxwsLCEBoairNnz6K8vBwHDx7EwYMHG2zXs2dPiMVilJSUuN13woQJuOmmm3DllVfCarWC+f9u9fqSk5Nx+eWXIy8vD1KpFBaLBfHx8cLlCz706+O75//3v//BbDZDq9W6Bb7NZQU+o9GI6Aj3wPf0XvA9JaRjZGRkIDMzs6MPg5BOL2ADv6CgABMnTsTXX3+NCy+8sKMPp0mNdVW7Vmfz1F2p0+mg0WjAMAx69eqFjIwMjwPsSkpKYDKZUFJSIgRXTEwMHnnkEVx55ZXYv3+/cI0/NjZWODFISEhAeHg4IiIiEBUVhczMTMTHxwOoa2UXFBQgPz8fR48exYkTJ4QTi/Lycpw9e1Z4blOnTsWkSZMQFRWFiIgIZGRkwGq1wm63Q6PRAECDa+78ba6j7vnXqLGw5/GL25jNZrdWeWxsLCwuLXxPJ1j8SH9CCOlqAjLw+bBfuHBhpw/7puh0OqE6m6fA1+v1qK6uhtFoFMLRk8TERKGFD9QFZm1tLRQKBSoqKiCRSHDrrbdizJgxSE5OBsuyMJlMUCgU4DgOJSUlkMlkbkvJMgyDwYMHY/DgwbjhhhtQVlaG4uJiaLVaREVFgeM4nDp1CgMHDkR4eDhMJhN0Oh3kcrnwXPjHkUgkHq+56/V6YfQ8/9xcQx+Ax/oELMuiZ8+eSE5OdttnbGwswiKigG/qTjIYH2obEEK6lkCerx9wge8a9vfeey+AukFXp0+fxoABA/waaGaxWIRqawAaXMNua96qsymVSqG1y7dWPQ06q98lzV+nNplMQqvbdU15k8mEmpoaGAwGJCYmQqVSeWxVu9ahVygUQhe9zWZDYmIiUlJSYDKZEB0dDQAN5snHxsaCZdlGr4/r9foGSwbzj8t363sKfNfHIIQEBpqvH2CB73A4cPXVV6NPnz645557YDabcc8992Dt2rXgOA4ymQzPP/88nnzySZ/2t3TpUixZsqSNj7px9aeLNfV3ni8DAENCQoQWflJSkhCarvPXDQYDpFIpzGYzYmNjwTAMfv31Vxw7dgxDhgzBxIkT3YI3MjISffv2RUlJCcxmM6RSqbB/Hl/bnr8Pf5LSWBVAT0sGA94DnWVZt/u5FuJxHbRHCOk+aL5+gAW+RCLB+++/j2nTpuGBBx5AWVkZrFYr8vPzERoaiuXLl2PBggWIjY3FnXfe6XV/CxcuxKOPPir8Xl1djeTk5LZ8Ci3mywBAhmGE+vf8SnKu4ckwDJKSkmAymdxa/seOHUN1dTWOHTuGiRMnNghefj/8gjWeAlmhUAi9JjqdrskpV40tGeypVr2r+j0D/AlG/UF7hJDuJdDn6wdU4APA+PHj8d1332HatGlITEzE8ePHIZVKAQCvvfYaNBoNXn31VZ8CXyaTQSaTtfUhtypfB51xHAeTySR07SsUCpSXl8NgMCAmJgbR0dFQKBRChToAyMzMRHZ2Ni644AK3qnUcx8HpdEIikQjjA3Q6HUwmk1sPAn98/EI2drvdbX69a3h7KoDjK9eegfqXECzUwieEdFMBF/jAv6H/66+/CmHPmzJlCvbs2dMxB+aBr6HW2tvZbDahdR0ZGYmgoCBhhTi9Xo+4uDgA7sE7YcIETJgwQdiHwWBATU0NwsLCoFKpIBKJEBYWhry8PJhMJpw/fx5KpRJhYWENHt91CVlPJygikcingkSeqh7GxcUJxw8AoaGhwn/DI6OFQXue7kuV8QgJDN1xcF9ABj5QF/pjx45tcPuff/7ZKVZ+62hGoxEymQxBQUFCmLbGEq4Mw6B///44c+aM22BCT9t564loaUEiQgiprzsP7gvYwAfQoKDLhx9+iG3btuHQoUMddESdh6dr/f4u4apUKlFQUIC//voLgwcPFoqjtNZSsK1RkIgQQlx158F9AR34vO3bt2PZsmWQy+XYt29fl3nz2pIvLWxvQkJCcP78edhsNuTm5nqthubvmgGtcYyEEFJfdx3c1zUWP/fDqVOncMMNN6CystLn+1x22WXYsmUL9uzZg7S0tLY7uG6IH91uNBrdbtfpdFCr1YiPj0dISAjS09O97su1i54QQkjr6naBf9ddd+HHH3/EFVdc0WToW61WzJw5E4cOHUJoaKhQZY74x3WuvSt+6ltCQsL/tXenYU2daRiA37AWiMgSlU3rihV1XIaKWwV0FKsoKo5SN6x1o+I62tZlFKeD01pal0t7ueAoaq3iSF1ALSq1ZbSMoCIuFbdWE5SIAmICignP/HDImCYR1JCTmPf+Zc45hAdMeM7y5Ts0atSoWs1o6OLiQnZ2dnyKnjHG6sBrdUr/0qVLVFhYSFlZWdSnTx/q168fpaen671BTnl5OV28eJGioqIoPz+f7O3tTR/4NWBokhtDk+LU9Fx8ip5Vy83N1XyCQh++zz0zB5Y0mv+1KvzMzEyKjY2ldu3aUUZGBvXu3dtg6bu5udHRo0epoKCAy/4VGBppb6yBecx6BQcH17gN3+eeCeVFR/OnpKQY5W+iQqF46a99rQp/ypQpVPm/mdLatm1rsPSfPHlC9vb25OHhoZnHnTFmXjZs2EB//OMfn7uNuRw5MetT29H8RUVFNGzYMOrfv7+Jkhn2WhU+EWlNpKOv9E+cOEF/+9vfKCsry+iTqFTPBGfqm+jUBX1T0hrarqbfo1KppIqKCsFO2b/I//OjShWpHj0dj1BWVkaVDq/dW0Qw1e+Lml5b1ev9/PyoZcuWtX5e9nz82jY+Nzc3vZeMn9WyZUs6deqUZkrxV5Wbm0szZ86s9d/oZ4nwMl9lYS5evEi9e/cmiURC9+/fp9TUVAoMDDT695HJZGY/lz5jQpNKpXrvfliN30eM1aym95E+VlH4RESrV6+mJUuW0JEjR+qk7ImIqqqq6Pbt21SvXj2jnj2ovimPVColV1dXoz1vXePcpmXuuQHQw4cPycfHR2fSq2e9zPvI3H92fSwts6XlJXo9M9f2faSPVZzTSUtLo2XLltVp2RM9nbnvRfe4XoSrq6vFvGifxblNy5xzG7rV8bNe5X1kzj+7IZaW2dLyEr1+mWvzPtLntfscvj4qlarOTuMzxhhjlsAqjvAjIiKEjsAYY4wJyiqO8C2do6MjLVmyhBwdHYWO8kI4t2lZam5jsMSf3dIyW1peIs78e1YzaI8xxhizZnyEzxhjjFkBLnzGGGPMCnDhM8YYY1aAC58xxhgzMw8ePKCKigqjPicXPjOptLQ0OnjwoNAxrIZMJqPPPvtM6BgmoVarKS4ujkpKSoSO8kIuX77Mt/qtY3v27HmpueeF8uDBAwoLC6OFCxca9Xm58C3Qnj176K233iInJycKCQmho0ePCh2pVtLS0mj8+PHk7u4udJQXkp+fT3369CEnJydq3bo1rVy5ktRqtdCxaiSTySg0NFToGCahVqspOjqaMjMzLeYjWEVFRRQWFkZt2rShgIAAmjFjhtCRapSenk75+flCx3ghy5Yto+HDh9PUqVMtovSry14ul9OmTZvo4cOHxntyMIuSmpoKHx8ffPfdd0hPT8egQYNARJg/f77Q0Z4rNTUVEokEJ0+eFDrKC5HL5fD29sby5ctx8uRJfPzxx3BwcEBwcDCKi4uFjmeQVCpFy5Yt8Y9//EPoKHVOpVJh9OjR6N27N5RKpdBxaqW8vBwdO3bEnDlzcO/ePWzevBlEZNavqfz8fDg6OsLLywuXL18WOk6txcbGIiwsDLa2tpg8eTKqqqqEjmRQaWkpgoKCEBMTA5lMBnt7e6xevdpoz8+Fb2G6d++OxMRErWUrVqyASCTC3LlzBUr1fKmpqXB3d9eUfXFxMebPn4/g4GBERUXh1KlTAic0bNmyZRgyZIjWsqysLEgkEgQGBkKhUAiUzLDqso+PjwcAqNVqJCYmIiwsDP3790dSUpLACY2nuux79eqlKfuTJ0/ivffeQ3BwMBYuXIgHDx4InFJXQkICgoODNY+rqqrg5eWFc+fO4ccff8SjR4+EC2fAjBkzMH/+fHTq1MmiSn/NmjWYPXs2vv32W63Sv3XrFlQqldDxNJ4t++qdkjFjxqBVq1ZG20nhU/oWpqioiFQqldayWbNm0Zo1ayghIYH27NkjUDLD7ty5Q2VlZXTx4kWSyWQUGBhIOTk51KVLF7pw4QL16NGDDhw4IHRMvfT9voOCgujYsWN09epVio2NFSiZYaWlpVRaWkq5ubn0+PFjGjlyJH3xxRfUrl07srW1pejoaJo+fbrQMY2isrKSbt++Tbdu3aKioiLavHkzDRgwgOrXr6+5/NKjRw8qLS0VOqqWnJwc8vT01DzevHkzlZSUUHh4OPXp04c6duxIMplMwITa1Go1paSk0Lx58+jo0aPk7e1NISEhFnF6/6233qILFy5QVFQUbd++nTZt2kSjR4+m7t27U2ZmptDxNHbv3k2dO3emtWvXau4SOXv2bLp69arxxj0ZZbeBmczEiRPRpk0bvUcAY8eORcuWLQVIVbP169fD1tYWLVq0wNKlSzXLHz9+jAEDBqBBgwaoqKgQMKF+e/fuhZ2dHc6dO6ezbteuXRCJRLh06ZIAyZ4vLy8PEokErVq1Qq9evbR+tytXrgQR4fjx4wImNB6lUonQ0FA0adIEEolE68jzwoULEIvFmDVrloAJdSUlJUEkEuH999/H+++/D7FYjGPHjgEArly5Al9fX0RGRgqcUtvFixc1/75//77FHOnLZDL4+PhoHi9fvhxEhNDQULM+vV/tnXfeQd++fY3yXFz4Fub69etwcXHBuHHjdF6sV65cARHht99+Eyjd/z18+FBn2fr169GwYUOd02jnz58HESE7O9tU8WpNrVYjKCgIrVq1QlFRkc56f39/rFmzRoBk2qRSKQYNGoTKykrNsurSP3HihM72TZs2xZIlS0yYsG5Vl/68efN01s2cORMdOnQwfagaJCcn46OPPkJUVJTOGJwvv/wSjRs3FihZ7egr/fj4ePz73/8WOJkuV1dXFBcXIz8/H35+fpgyZYpFXNMHgJSUFBARLly48MrPxaf0zVhOTg5FR0dTZGQk/ec//yEioubNm9PmzZtp+/btNHHiRHry5Ilmew8PD7KxsSGxWCxUZCJ6Ohq/efPm9PPPP2stnzx5Mu3bt49sbW21ljs6OpJIJCJvb29TxtSRn59PgwYNos6dO9PVq1eJ6Om92Xfu3EkKhYJCQ0OpoKBA62s8PDwEv8929Wj8AwcO0O7duzXL27dvTxkZGdSxY0edr3FwcCBfX18Tpqxbzs7OlJqaSmPHjtVZ5+joaJY/65///Gf6/PPPSalUkpOTk9Y6uVxOnTp1EihZ7Xh4eGid3p8+fTrt3LmT/P39hY6mw9/fn1JSUqhPnz4UHx9P69at05ze37Fjh9DxnisiIoKaN29Oq1evfvUnM8IOCKsDSUlJkEgkmDp1KoKCglCvXj2UlJRo1u/YsQOOjo7o0qUL0tPT8csvv2DIkCGYMGGCcKH/Z+DAgfDy8oKrq2utRuWPHz8eI0aMMEEyw+7duwcfHx+sXbtW7x7/5cuX0bRpUzRo0AAbNmzAtWvXsHz5cjRv3lzQgXvPjsbv168funTpUuPX/Otf/4K3t7dZDmYzttu3b6Nhw4ZIT08XOopBH330Edzc3JCZmYmqqirs3r0bEonELC8V6XP//n14eXmhffv2uHv3rtBx9Prggw9gZ2enM2A1KyvL7I/wgaeX4ZycnHD//v1Xeh4ufDN07do1NGrUSHOaTKlUwtXVVecPQF5eHgYMGAA7Ozu88cYbiI2NNYvRvXPmzEFCQgL+9Kc/GSx9lUqF7OxshIeHo0ePHigtLRUg6f/Fx8cjIiJCa1llZSVu3rwJtVoN4Oko2unTp6N+/foQiUQICQnBr7/+avqw//P7j94dOnQIRISff/5Z7/bXr1/H4sWL4efnh6ysLFNGNbni4mJ8++23aNy4MRISEoSO81xlZWXo2rUriAhOTk7w9fW1qI+vxsXFmXXZA0BJSQn27dsndIyXVlZWBk9PT4Pv7driwjdDn3zyCb766ivNY7lcDolEgiFDhuDtt9/GqlWrtLavrKzEkydPTB3ToA0bNiAmJgbl5eWa0j927BgGDx6sGfx248YNjBkzBlu3btUUqpBGjRqFmTNnah4nJibC3d0dRIQmTZpoXZesqqoSfMfq8ePHaN26tdbn7KuqqtCmTRtERUXpbF9ZWYnY2Fh89tlnuHfvnimjvrLqncMXsXHjRsyaNQtnz56tm1A1kEqlkMlktd5epVIhPT0dBw4cQHl5eR0mM+zq1auYNm0awsPD8fXXX9fqyFculyM0NFSwsj906BCioqIwYsQI/PTTT4JkeBGVlZVYtWoVBg8ejJiYGBQUFNT6a2/evPnK358L3wx99dVXKCwsBPB0go6QkBAEBQVh48aNmDlzJkQikVkMFDPkp59+wjvvvAMAmtInIoSHh5tFuevzl7/8BQEBAaiqqsKuXbvw5ptv4uDBgzh9+jRCQ0Ph5uamd9CekJ4dNV1t/fr1sLOze6GyMXeTJk2Co6Mj0tLSatzWHAZ+KpVKtGjRAi1btqzx/0GtVuPMmTMmSmZYRkYG3NzcMHbsWAwfPhwikQh///vf9W57584ds3h9LV68GN7e3oiJiUGXLl1gZ2eHnJwcvduePXtW8M/cK5VK9OzZE4GBgZg2bRq8vLzg7+9vcAevLl7LXPhmLiUlBVFRUVqjr6dMmYJOnToJmOr57t69Cw8PDwBPC79v375o2LBhra/pCyEnJwdEhDVr1qB79+5aRwulpaVwcXHBjh07BExYO+Xl5fD09DT7mRdrSyqVws/PDyNGjKix9C9evAhbW1vMnj3bhAl1bdiwAQMHDqxV6SckJNR6Z6aulJeXw8fHRyvDvHnz4OLiovV3p1pISEitdmbqUmZmJnx8fDQHRiqVCu3atcPQoUN1tpXL5XBxcUFUVJSgpf/xxx9j6NChmoOey5cvw97eHps2bdLZdufOnbCxscE///lPo2bgwrdAK1asQLdu3YSO8Vyenp64fv06+vbti3HjxkGhUGhO75vDUZg+MTExcHBwgKurq86pNi8vLxw6dEigZC9mwYIF8PT0FOzUsDH99a9/RVxcHFQqVa1Kf9myZYiLizNhQl2dOnVCTk4OpFJpjaVfXl6OiIgIQedE2LZtG0aNGqW17NKlSyAi5Ofn62x/6dIl9O3bV9Br9iNHjtTZAV++fDlatWqld/stW7ZgypQpgg3Qq6yshJeXl85Zwl69eundQVWpVIiOjsbu3buNmoML38I8evQIHTp0wIYNG4SO8lzdu3dH06ZNMW7cOM0ebXl5OWJjY832GvKTJ08QGRkJIkJ0dLRmXMQXX3yBtm3b6j3aMUcFBQWwt7fHxo0bhY7yyk6cOIE7d+4AQK1LX2h79uzR/Ls2pS+05ORkHDlyRGuZUqkEEemdcMoczJgxQ2eirl27dpnt3AWlpaV654gYO3YsYmJiTJaDC9+CyOVy9O/fH8OGDTP7j5Ls3bsXH3zwgdleszdErVYjPj4ezs7OaNKkCQICAhAQEIAbN24IHe2FjBkzBkFBQULHMDp9pS+Xy7Ft2zaBkxmmr/QzMjLM4tq9IWq1GkSkNegxOTnZrAYH/96+ffvg6+ureaxQKMx+ZP6kSZMwZcoUzeO8vDycP3++zr4fT7wjkKtXr9K6desoOTmZFApFjdvPnTuXevfuTX369KHdu3dr5lo2tcLCQkpMTKStW7dSUVGRwe0iIiIoMTGRbGzM4yVWVlZGSUlJtGnTJrp165bB7WxsbGjBggUkk8lo7dq1tGrVKjp37hw1a9bMhGn/7/Hjx5ScnEzr1q17oXumz507lxISEuowmTBsbW1px44dFBERQcOGDaOkpCQKDQ2l3377TehoBvn5+dHx48cJAIWEhNA333xDUVFRpFQqhY5mUPX7tqqqioiIli5dSp9++imVlZUJGeu5bGxsNHmVSiUNHDjQeHPQ15FnM58/f57CwsI0k37ViTrblWAGbdq0CfXr10fPnj1Rr149SCQSJCcn6922+gjZHOaZP3LkCNzd3dG9e3c0aNAAzs7O+PLLL/Vua05H9ufPn4ePjw/efvttNG7cGHZ2dpg7d67eU/TmlPv27dsICAhA+/bt4e/vDyLCmDFj9E6YY065TUGlUmkuv3z66adCx6kVqVSKN998E2KxGJmZmULHqRERIScnxyI+Zw88vSunl5cXFAoFgoODMWnSJLM/Ezp16lRMnDgReXl58Pb2Nvo1+9/jwjexK1euoH79+prBMCUlJYiOjgYRYfny5Vrb5uXlISAgQO/Hr0zt4cOH8PT0xNGjRwE8HUuwaNEiiEQiTJw4UeuNdefOHXTo0EHnuqBQ2rZti3Xr1gF4Woxr166Fo6MjwsLCtHaklEolevXqZfSRsS9r8ODBWrc83rNnD9zd3dGxY0etwT9VVVUYPXr0azMyvzbkcjkCAgIspuwB4NixY2jYsKFFlD0AiEQiDBs2zCLKHgDS0tLg4eFhMWUPPB0o3LNnT5OUPcCFb3Jff/01evbsqbN88eLFICJs3bpVs6ygoACtWrXC+PHjTRlRr4yMDEgkEp3l27Ztg42NDRYsWKBZplAo0KtXL3Tt2lXwN92tW7dARDoz+f3www9wdnbGyJEjNctUKhVGjRqFJk2aaO6tLiQHBwedzxVfunQJXl5e6Nq1q9YZiiVLlkAsFgs6858pjRw50qLKXqlUonnz5hZT9gBgb29vMWUPAN9//z2IyGLKHgCmT58OOzs7k5Q9wIVvcrt27UK9evX0TiU7depUiMViyOVyzbLCwkLBZ3UDgHPnzoGI9J5tWLlyJUQikdbH7RQKxSvP+2wMDx48gK2tLfbv36+zbv/+/RCJRNi1a5dmmUqlMpvR1N7e3lozLlY7c+YMnJycEB8fr7XcGDNxWQpL/MihpWVOSkqymLIHnp51XLNmjcWUPfB0dsO9e/ea7PuJAKDuRgiw31MoFNSsWTMKDw+nzZs3a62rqKigFi1a0Jw5c2ju3LkCJdQPAHXu3JnEYjH98MMPZGdnp7W+W7du1LZtW0pMTBQooWHDhw+n3NxcOn36NNWvX19r3dixY0kqldLx48eFCfccn3zyCa1fv57Onj1LTZs21Vq3dOlS2rhxI8lkMmHCMcYsjnkMobYiYrGY1q5dS1u2bKGlS5dqrXNycqIBAwbQzZs3BUpnmEgkoo0bN1J2djaNHz+e1Gq11vrIyEizzE1EtGLFCiotLaWIiAidkdHmnHvRokXUsGFDevfdd6mwsFBrXWRkJBUUFJBKpRIoHWPM0nDhC2DEiBH0+eefU1xcHH344Yf06NEjIiJSqVSUm5tLXbt2FTihfoGBgfTNN99QcnIyDRkyhIqLizXrsrOzzTZ348aNKTU1lc6ePUuhoaFaBW/OucViMR06dIgUCgX16NGDzp49q1mXnZ1NgYGBOmdaGGPMED6lL6CkpCSKjY0ld3d3evfddyknJ4eaNWtGycnJZvP5dX2OHTtGY8aMocrKSoqIiKBbt25RWVkZZWRkkFgsFjqeQXl5eTRixAiSSqU0ZMgQqqiooNzcXMrMzCRfX1+h4xkklUopKiqKTp06ReHh4SQWi+nIkSN0+PBh6tixo9DxGGMWggtfYHK5nLZu3Uo3b96kbt260XvvvWfWZV9NoVDQtm3b6MKFC9S2bVuaMGECvfHGG0LHqlFlZSUlJydTVlYWNWnShCZNmkTu7u5Cx6oRADpw4ABlZGSQm5sbTZw4kfz8/ISOxRizIFz4jDHGmBUw/0NJxhhjjL0yLnzGGGPMCnDhM8YYY1aAC58xxhizAlz4jDHGmBXgwmeMMcasABc+Y4wxZgW48BljjDErwIXPGGOMWQEufMYYY8wKcOEzxhhjVoALnzHGGLMCXPiMMcaYFeDCZ4wxxqwAFz5jjDFmBbjwGWOMMSvAhc8YY4xZAS58xhhjzApw4TPGGGNWgAufMcYYswJc+IwxxpgV4MJnjDGmY//+/fTrr78KHYMZERc+Y4wxHR9++CH9+OOPQsdgRsSFzxhjjFkBLnz2Wti/fz8NHTqUDh06ROPGjaPQ0FBasGABVVRU0MGDBykyMpL69etHK1asIABCx2XMYhQWFtLhw4cpMzOTqqqqhI7DXoGd0AEYM4a7d+9SWloaFRQU0MKFC+nx48cUGxtL6enp5ODgQPPnz6eHDx/StGnTyMXFhSZPnix0ZMbM3vbt22nRokX0hz/8gU6fPk2tW7emw4cPk7Ozs9DR2EvgwmevDZVKRd999x35+voSEVFubi4lJCRQQUEBNWjQgIiIsrKy6MCBA1z4jNXCL7/8QmfOnKFGjRpRUVERde7cmVatWkXz588XOhp7CXxKn702vL29NWVf/bhx48aasq9eVlhYKEQ8xizOuHHjqFGjRkRE1KBBA5owYQIlJycLnIq9LC589tqwt7fXeiwSifQu42v4jNVO06ZNtR43a9aMbt68KUwY9sq48BljjOlVUlKi81gikQiUhr0qLnzGGGN67du3T+uMWEpKCvXo0UPAROxV8KA9xhhjel27do2GDh1KAwYMoO+//57y8vJoy5YtQsdiL0kEvqDJXgNFRUUkk8moU6dOmmVyuZwKCwupQ4cOmmW3b9+m4uJiateunRAxGbMY06ZNo+HDh9ONGzcoOzubnJ2daerUqeTv7y90NPaSuPAZY4wxK8DX8BljjDErwIXPGGOMWQEufMYYY8wKcOEzxhhjVoALnzHGGLMCXPiMMcaYFeDCZ4wxxqwAFz5jjDFmBbjwGWOMMSvAhc8YY4xZAS58xhhjzApw4TPGGGNW4L9o2OwyJkLtRgAAAABJRU5ErkJggg==",
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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": {
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",
- "text/plain": [
- ""
- ]
- },
- "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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",
- "text/plain": [
- ""
- ]
- },
- "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",
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- "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: 1/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.820\n",
- "Batch: 2/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.760\n",
- "Batch: 3/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.740\n",
- "Batch: 4/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.830\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 5/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.810\n",
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- " Model parameter acceptance fraction: 0.780\n",
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- " Model parameter acceptance fraction: 0.820\n",
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- " Model parameter acceptance fraction: 0.830\n",
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- " Model parameter acceptance fraction: 0.800\n"
- ]
- },
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- "Batch: 12/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.810\n",
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- " Model parameter acceptance fraction: 0.730\n",
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- " Model parameter acceptance fraction: 0.860\n",
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- " Model parameter acceptance fraction: 0.770\n"
- ]
- },
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- "output_type": "stream",
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- "Batch: 28/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.760\n",
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- " Model parameter acceptance fraction: 0.810\n",
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- ]
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- " Model parameter acceptance fraction: 0.860\n",
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- " Model parameter acceptance fraction: 0.790\n",
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- " Model parameter acceptance fraction: 0.780\n"
- ]
- },
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- ]
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- ]
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- ]
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- " Model parameter acceptance fraction: 0.830\n",
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- " Model parameter acceptance fraction: 0.730\n"
- ]
- },
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- " Model parameter acceptance fraction: 0.730\n",
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- ]
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- " Model parameter acceptance fraction: 0.820\n",
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- ]
- },
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- " Model parameter acceptance fraction: 0.850\n",
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- ]
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- "Burn-in batch 1/10 completed, 100 steps.\n",
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- "Burn-in batch 5/10 completed, 100 steps.\n"
- ]
- },
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- "text": [
- "Burn-in batch 6/10 completed, 100 steps."
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\n",
- "Burn-in batch 7/10 completed, 100 steps.\n",
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- "Burn-in batch 9/10 completed, 100 steps.\n",
- "Burn-in batch 10/10 completed, 100 steps.\n",
- "Batch: 1/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
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- ]
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- "Batch: 7/100 completed, 100 steps. \n",
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- ]
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- "Batch: 19/100 completed, 100 steps. \n",
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- ]
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- "Batch: 27/100 completed, 100 steps. \n",
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- "Batch: 28/100 completed, 100 steps. \n",
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- ]
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- ]
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- ]
- },
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- "Batch: 45/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.490\n",
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- " Model parameter acceptance fraction: 0.540\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Batch: 48/100 completed, 100 steps. \n",
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- ]
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- "Batch: 55/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.380\n"
- ]
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- "Batch: 56/100 completed, 100 steps. \n",
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- "output_type": "stream",
- "text": [
- "Batch: 63/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- "Batch: 64/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 65/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.420\n",
- "Batch: 66/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- "Batch: 67/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.430\n",
- "Batch: 68/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- "Batch: 69/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.520\n",
- "Batch: 70/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 71/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.420\n",
- "Batch: 72/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.520\n",
- "Batch: 73/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.500\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 74/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.370\n",
- "Batch: 75/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- "Batch: 76/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.430\n",
- "Batch: 77/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n",
- "Batch: 78/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.490\n"
- ]
- },
- {
- "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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lM6xPWV3Or+szpE1+984Qn2d97serdHlui3LdkpOi5zVplz2iav369RrbIyMjBQCxZs0aje15jdh68uSJeOedd4S9vb1wdnYWw4YNE6mpqTqNAhPi5YipmjVrCmtra2FtbS3Gjx+fZ/lXY16/fr2oXLmysLa2FnXq1BF79+7VOG5e58vMzBRff/21qFevnrCzsxNOTk6ifv36YtGiRblGXeRU0L43b94Uzs7OokePHrmGA/fq1Us4OjqK69evCyH+GzFx79498eGHHwpXV1chl8tF586dxc2bN3Od+/Hjx2L06NGiYsWKwtraWnh6eor27duLXbt2qYfbZx8z5wgOIfIf8ZOzfPZomYcPH2psz2v0SFFiCw0NFQDEjh07NLbPmjVLlC1bVlhZWQlra2tx+PBhIYQQSUlJYuTIkaJMmTJCLpeLNm3aiH/++UcMGDBAlC9fPt/rzcv9+/fF4MGDhZeXl7CxsRHVq1cX//vf/0RaWpq6zMWLF8Wbb74pHB0dhbW1tahevbr45ptv1COa9L3Go0ePip49ewpvb29hZ2cnqlSpIiZOnKgxVDi/+5Df77oo98hQv6e8KBQK4eDgIABojLy7evWqACA8PT1zfXby+l3qe2/yugZt9Pl96/IZ1vfzrsv5dXmG9L13Quj2edZGn2vUFpeuz62unx1zIxOC85abk6ysLFhbW+dqK9d3uzZKpRIymUyjyrSg/bOysmBlZaXeJ2f5O3fuwN/fH+vXr8eIESMKfX3GIv5/aQ9t58x+L/v6li1bhkmTJiE6OjrXiIuinl/bDKva3survL7bixpbfr+n/O7pq1QqFYQQ6r8AixJrYRX2GvU5dvYxCnN9utwjY/+eAKhrHXL+tZ6VlQWZTJZre0HXqs+9MeT/Cfp8ho3xedeFvvfOmOcqbNmcz21xxU7QZiavh0/f7dpoe1gL2j/n+0X98jL1VOv5Te9uiqnf9T1/XuX13V7U2PI7pq7n1NbZ2px+/0WNJeexC3N9utwjY/+eAO3/N+R3/IKOrc+9sbTlF/S9d8Y8V2HLStnH0ZBKxlUQERER6YEJEBEREVkc9gGiIjN1vx5jkaKPChEZjrH6vFDJxASIiIiILA6bwIiIiMjisO5PC5VKhcePH8PZ2bnYN+sQERFZCiEEkpOTUa5cuQJHqzEB0uLx48eoWLGi1GEQERFRITx8+BAVKlTItwwTIC2yV9h++PCherViIiIiMm9JSUmoWLGi+ns8P0yAtMhu9nJxcWECREREVMzo0n1F0k7QJ06cwDvvvINq1aqhcePGmDNnTr4LcGZlZWHz5s3o2LEj/Pz80K5du1yLWb548QLe3t65fnbt2mXsyyEiIqJiQrIaoKtXr2Lp0qUYNWoUateujTt37mDMmDG4efNmnsnKypUrcfnyZUydOhW+vr44ffo0hg8fjoSEBHz00UcAXnZgfvLkCQ4cOICGDRuq93V1dTXJdREREZH5k2weICFEriqqJUuWYMGCBXj+/LnWfVQqVa5e3R9++CEuXryIixcvAgBSUlLg7OyMc+fOoXnz5oWKLSkpCa6urkhMTGQTGBERUTGhz/e3ZDVAOZOfp0+f4rfffkOHDh3y3EfbkDaFQgE7O7tc2wcPHozMzEz4+flh1KhReO+994oedA5KpRKZmZkGPy6VXLa2tsV+BWUiopJA8k7Qffr0QXBwMJ4+fYqOHTtiy5YtOu97+fJlbN++HUuWLNHY3qNHD0ycOBE+Pj44ePAgBg0ahOjoaIwfP17rcRQKBRQKhfp1UlJSvucVQiAmJgYJCQk6x0qUzc3NDd7e3pxjiohIQpIvhfH8+XOkpKTg2rVrmDBhAho2bIidO3cWuN/Dhw/RunVrNGnSBHv27Mn3y2T69OlYu3Ytnj59qvX9uXPnYt68ebm251WFFh0djYSEBHh5ecHBwYFfZKQTIQRevHiB2NhYuLm5wcfHR+qQiIhKFH2awCRPgF51+PBhdOnSBXfv3oWvr2+e5R49eoR27dqhTp06+Omnn2Bra5vvcX/99Vf07NkTT548gZeXV673tdUAVaxYUesNVCqVuH37Nry8vFC6dGk9r5DoZXNvbGwsqlevzuYwIiIDKhZ9gLSxt7cHAKSnp+dZJioqCgEBAahVqxb27NlTYPIDAHfu3IGdnV2eN0Mul0Mul+sUY3afHwcHB53KE+WU/exkZmYyASIikohk8wBt3rwZ+/btQ1paGgDg7t27mD59Ol577TXUrFkTwMtM7tU5fKKjo9XJz88//6y18/OWLVvw008/IS0tDUIIBAcH44svvsCgQYPUCZYhsNmLCovPDhGR9CRLgNq3b499+/bBx8cHLi4uaNCgAapXr45Dhw6pvyCy5/TJTpJWrFiB8PBwnD17FpUqVVJPclijRg31cQMDA7F//374+PjAyckJQUFBGD16NFauXCnJdVqSjIwM3Lx5E0qlUupQiIiI8iV5HyAhhHruHm3vPXnyBK6urihVqhRSUlKQkpKSq5yVlVWuvj0qlQovXryAk5OT3jHl14aYnp6OyMhI+Pr6GrRGydgyMzMREREBPz8/nZoNC3O8a9eu4bXXXkN0dDS8vb2LfI6Sqrg+Q0RE5q5Y9QGSyWR5Llomk8k0vkidnJx0TmisrKwKlfyUVPfv30etWrUQGRmJKlWqmN3xiIiITEnStcDINFQqFe7evQsAiIiIwM2bN/Ho0SONJqv09HREREQgJSUFL168wM2bNzWOoVQqcfPmTWRkZOR5vJznfPToESeKJCIis8QEyAKkpqZi9OjRAICRI0eiZ8+emDdvHm7fvo1atWphxowZ8PHxwZtvvonz58/jwoULqFWrFrKystTHiIuLQ61atXD79u08j5ft22+/RdmyZdGsWTO4ubnpNbklERGRKUjeBFaS5LeSvbW1tUZ/j/zKWllZoVSpUvmWdXR01DkuZ2dnHD58GP7+/jh27Ji6yeratWsAgJCQEDx48EDdFBkcHFyk4125cgX37t2Do6MjVq5ciTFjxuCdd97Js6mTiIhKPoVCAVtbW63LWknBPKIoIbL7KGn76d27t0ZZLy+vPMu++eabGmWrVKmSq4whzZ0716DJybx589QJ2sCBA5Gamorbt28b7PhERFS8hIeHo0WLFli8eLHUoagxASJUrVrVoMcrX768+t/ZyVpycrJBz0FERMXDzp070ahRI4SGhmLZsmVaR3NLgU1gBpTfLzXnjL+xsbF5ls1ZPXjv3r0ixVWQnLFpm6jv1f5AREREBUlLS8PEiROxbt06AECbNm2wY8cOsxmhzQTIgPTpl2OssnnJ7n+ky6gsT09PAC9n3q5YsSIAICwsrNDHIyIiy3Lz5k306dMHV69ehUwmw/Tp0zF37lzY2JhP2sEmMAvh7e0NV1dX7NixAzdu3Mg1bP1V1atXR7Vq1TBp0iRcvHgRP//8MyZOnFjo4xERkeVITk5Gq1atcPXqVXh5eeHw4cP4/PPPzSr5AZgAWQwbGxts374dp06dwnvvvYd58+ZBLpejRo0auR5KGxsb/P7775DJZBg1ahR+/vlnbNy4ETVq1FAvGqvr8WQyGWrUqMHFY4mILISzszPmzp2LwMBAhIWFoWPHjlKHpJXkS2GYo5K4FAaZDz5DRFTSXL9+HVlZWXj99dcBvFzKSqVS5epjamz6LIXBGiAiIiIqFCEENm3ahCZNmqB3795ISkoC8LL239TJj77Mq0GOiIiIioWUlBSMGTMGP/74IwDAz88PGRkZEkelO9YAERERkV6uXLmCxo0b48cff4SVlRX+97//4eDBgyhTpozUoemMNUBERESkEyEE1q1bhwkTJkChUKB8+fLYsWMH2rRpI3VoemMNEBEREelECIFffvkFCoUCb731FsLCwopl8gOwBoiIiIh0ZGVlhS1btmDnzp346KOPzGZh08IovpETERGRUQkhsHLlSowZM0a9zdPTE+PGjSvWyQ/AGiAiIiLSIiEhAcOHD8fevXsBAO+++y4CAwMljspwmABJKFOhxLoJJwEAI5e3ha3cvOdMICIiyxASEoKgoCBERkbC1tYWixcvRkBAgNRhGVTxrr+iEuWnn35CixYtpA6DiMhiCSGwdOlStGrVSj1j/ZkzZzBhwgTIZDKpwzMoJkASUqn+W4XkcXiCxmtLlJKSgocPH0odhtquXbvQunXrAsvdvn0bkydPhr+/v0Y7ORFRcTNixAh8/PHHyMzMRO/evXHp0iU0adJE6rCMggmQRCJCY7Fj3t/q17+vvIwt088iIjRWwqik9e677+Lvv/8uuKCJJCcnF7jK/Z07d/D222/D29sbFSpUQGys5f7+iKj4e++992Bvb49Vq1Zhz549cHNzkzoko2ECJIGI0FgcWnsNqQmaU4anJihwaO01oyVBCQkJ+OSTT1C/fn00bNgQ48aNw7Nnz9TvDxkyBH369EH2+rgqlQo9e/bEyJEjAQBbtmxB+/btsXXrVnTo0AG1atXCmDFj1Gu/ZLt58yYGDBiAWrVqoXXr1li8eDGysrLU72cfZ8eOHWjVqhWqVq2KpKQkHDp0CO+9916ucuvXr0fHjh1Ru3ZtjBw5EklJSVi3bh1atGiBevXqYebMmRrH1yeG3bt3o1OnTnjttdfwwQcfqO/HkSNH8Nlnn+HRo0eoUqUKqlSpgjVr1uS6p76+vrh58yamTJkCV1fXwv5qiIgkoVKpcOvWLfXrLl26IDIyEmPGjClxTV45sRO0ialUAqd3hedb5q/d4fB93RNWVoZ7+BQKBQIDA1GjRg2sW7cOtra2+OKLL9C+fXuEhITAxsYGCxYswOuvv44VK1Zg/Pjx+Oqrr/DXX3/h6tWrAF6ushscHIzExEQsW7YMKpUKY8eOxZAhQ7Bv3z4AQHh4OFq2bInJkyfjs88+Q3x8PCZNmoQHDx5gxYoVGsdRqVRYvHgxypUrBycnp1xNYNnl5HI5Fi5ciJSUFAwYMADHjx9H3bp1sXLlSjx79gz9+vVD+fLlMXr0aL1jsLGxwfz58wEAI0eOxPjx47F161a0bt0aU6ZMwYoVKxAcHAwAcHd3z3VfzX2xPyKivMTHx2Pw4ME4e/YsQkND4evrCwDw9vaWODLTYAJkYtHhCUhNUORbJuW5AtHhCShfI/cXbmFt27YNiYmJ2Lp1q/pL+8cff4SnpydOnDiBjh07olKlSli7di0GDx4MZ2dnzJ49G7t374aPj4/6OCqVCtu2bUONGjUAAD/88AMaNmyI69evo06dOliwYAG6d++OGTNmqPdZu3YtWrVqhcWLF8Pe3h7Ay5WCd+zYUeAHzdbWFjt27FDXrgwaNAirVq3C5cuX4ejoCAAICgrC0aNH1QmQrjHY2tpi9+7d6mN/+umn+PjjjwEADg4OKF26NGxsbFClSpXC3XQiIjN1+vRp9OvXD1FRUbC3t8fly5fVCZClYAJkYqlJ+Sc/+pbT1ZkzZxATE4MaNWpACKFu5kpLS0N4eDg6duwIAOjTpw8OHDiAYcOGYeTIkejZs6fGcby8vNTJDwA0aNAAzs7OuHz5MurUqYMzZ84gMTER1apVU58nKysLSqUSd+/eRe3atQEA5cuX1+mvjIoVK2o0LXl6esLX11ed/GRvu3Llisa16hJDzmN7e3sjLi5O11tKRFTsqFQqfPHFF5g9ezaUSiVq1KiB3bt3o169elKHZnJMgEzM0UVu0HK6Sk9PR+PGjfHDDz/kes/Dw0PjdXx8PADAzs4uV1lt2+RyOdLT09XnGT58uLo25lXlypVT/7tUqVI6xa2tiUnbtuyETp8Y2HxFRJbkyZMnGDRoEI4ePQrgZY366tWr4eTkJHFk0mACZGI+/m5wdJPn2wzm5C6Hj7+bQc9bu3ZtHD16FGXLls03+Vi5ciXOnDmD7du3Y/DgwXjrrbfw5ptvqt+Pjo7G06dPUbp0aQDA48ePER8fD39/f/V5rl+/LmmzkaFisLa21kisiIiKs+XLl+Po0aMoVaoUVq1ahaFDh5b4js754SgwE7OykqFNkH++ZVr38TdoB2gAGDZsGJRKJUaOHInk5GQAQFxcHGbMmIEHDx4AAG7cuIFPP/0Uq1evRr9+/TBjxgy8//77Gs1CSqUSkydPRmZmJhQKBSZNmoS6deuiVatWAIDJkyfjjz/+wLfffgulUgng5YisSZMmGfR68mOoGMqVK4fY2FgkJiYaI0wiIpOaPXs2+vfvj4sXL+L999+36OQHYAIkCb8GXugyqi4c3TSbk5zc5egyqi78GngZ/Jw+Pj44fvw4IiMj4eHhAU9PT7z22mtwcnKCj48PMjIyMGDAAPTq1Qv9+/cHAMyaNQtVq1bFiBEj1MepUqUKbGxs4OXlBTc3N1y5cgXbt29XL4rXpUsX7Ny5E8uXL4eTkxPc3Nzw7rvv6jShoKEYKobAwEC0bNkS5cqVy3MYPADUqVMHVapUwZEjR3Dw4EFUqVIFDRs2NMSlEBEVWnR0ND799FP1H4L29vbYtm2buh+kpZMJ1vHnkpSUBFdXVyQmJsLFxUXjvfT0dPX04NmjiQpLkZaF7yedAgB0G/s6Ktb2MHjNjzbJyclQKBQoU6bMf7EoFIiOjoaPjw/k8v/6H6WkpCA+Ph4VK1bEmjVrsHLlSty8eRNZWVl4+vQpypYtm+d54uPjIZfL4ezsnOv8SUlJKF++vMb2lJQUJCQkoEKFCnmWS0pKQkpKikZfnoSEBKSnp2vtVK1PDOnp6YiJicnVdJacnIxnz57Bzc1N61w/Dx48gEql0thmbW2NihUrarstBn2GiIi0OXr0KAYOHIjY2FjMmzcPs2fPljokk8jv+zsn9gGS0KvJTjl/N5MkPwDg7OycKyGQy+Va+8w4OTlp7SBnY2OTb/IDQCPBKuj82s6lrZyLi0uuhzq/mUr1icHe3l7rPcgr3myVKlXK8z0iIlPKysrC3LlzsXDhQgghUK9ePfTp00fqsMwSEyAJ2cqt8dF3gVKHQUREJcCjR4/Qv39/nD59GgAwatQoLF26VOdRt5aGCRDpbMiQIXjnnXekDoOIiHI4ceIE3nvvPTx9+hTOzs5Yv349goKCpA7LrEmeAKWkpODmzZtwc3ODn5+fTr3SFQoFrly5Ant7e9StW1frPrqUIf0U1BRERETS8PLywosXL9CgQQPs3r0b1apVkzoksydZApSUlIQpU6Zg7969qFq1Ku7fvw83Nzds37493xE0x44dQ9++feHq6ork5GSUKVMGv//+u8YU3rqUISIiKs5evHgBBwcHAC9Ho/75559o2LAhB1foSLJh8E+fPkXbtm0RExOD8+fP49GjR6hduzaGDh2a5z6JiYno06cPRo4ciTt37iAqKgo+Pj4YNGiQXmWIiIiKs99++w2+vr44e/aselvLli2Z/OhBsgTI19cX/fv3Vy9HYGNjgxYtWiAmJibPfX799VckJydjypQp6n2mTp2KM2fOIDw8XOcyRERExVFGRgYmTZqEHj16IDY2FkuWLJE6pGJL8j5Aly9fxtOnT3H79m0sW7YM//vf//IsGxoaCj8/P41hz40aNVK/5+/vr1OZnBQKBRSK/5amSEpKKuJVERERGVZkZCSCgoIQEhICAJg0aRK++OILiaMqviRPgLZs2YK///4bt2/fRr169RAYmPew8OfPn+dauNPd3R1WVlZ4/vy5zmVyWrRoEebNm1fEKyEiIjKOn3/+GcOHD0diYiLc3d2xefNmdO/eXeqwijXJl8L45ptvcObMGURFRaF8+fLo0KEDMjIytJa1s7NDWlqaxraMjAyoVCr1KuW6lMlp2rRpSExMVP88fPjQAFdWsFSlEt4nwuB9Igyp/z9VuSU7d+4cpk2bJnUYRERmJTg4GO+++y4SExPRokULhIaGMvkxAMkToGx2dnYYO3Ys7t27h1u3bmktU7lyZTx+/Fhj26NHj9Tv6VomJ7lcrp5hWNtMw2Qat27dwo8//ih1GGpnzpzBjBkzCiwXERGBpUuX4pNPPsGaNWvYhEpEBtW2bVv06tULn376KU6ePJnndxnpR7IEKHtF8lfdunULMplMvXxBVlYWgoOD1R2jO3bsiCdPnuD8+fPqfX755Rc4OTmhefPmOpcxF8pXlmH7OyFF47UlatGihVm1Z//777/Ytm1bvmWWL1+Obt264eHDh/Dx8cGuXbtQvXp1REZGmihKIiqJfvnlF/UfUzKZDLt378aXX34JW1tbiSMrOSTrA7RhwwacO3cOXbt2RZkyZRAWFobFixdj9OjR8PHxAfByksSAgABs2rQJQ4cORdOmTfHuu++if//+mD9/Pp49e4bZs2dj/vz56rkQdCljDg7EJWDG7Sj16wFXIuEjt8Xn/uXR1dPNaOf9888/8eeff8LKygqtWrVC165d1e99/vnn8Pb21lj9PbtGY+rUqQgODkZwcDAGDx6MvXv3IioqCgEBAbmqYjMyMrBr1y5cunQJpUuXRrdu3VC/fn31+9nHGTp0KH788UdER0fjm2++wbNnz3D9+vVc5fr374/9+/cjKioKrVu3Rq9evXD79m3s2LEDqamp6NatG954441CxTB8+HDs2bMH0dHRaN68OXr37g3gZef8rVu34unTp/jwww8BAO+88w46d+6scZ4uXbpg7Nix6tGMEydORP369bFw4UKsX7++EL8hIrJkaWlpmDhxItatW4e+ffti+/btkMlk6v9jyHAkqwGaOHEihgwZgpMnT2LVqlW4f/8+du7ciVWrVqnL2NjYoG3bthqrfG/btg1jxozB9u3bceLECWzcuBGTJ0/WOLYuZaR0IC4BI67dQ0xGpsb2GEUmRly7hwNxCUY57+jRozFy5Eg4ODjA2dkZEyZMwMiRI9Xvt2zZEqNHj8apUy9XqD969CjGjx+PVq1aAQCuXbuGJUuWoG3btkhISECpUqXUiWa2lJQUtGrVCt9//z3KlSuH5ORkBAYGYsuWLeoy2cfp0KEDMjMzUa9ePVhbW+dqAssu17VrV7x48QK2trbo168fBg0ahG7dusHGxgZKpRIdO3bEsWPH9I5h6dKl6NSpE5KSkuDo6Ijhw4dj4cKFAABXV1dUrFgRdnZ2qF+/PurXr6918dcaNWpo/MdkbW0Nf3//fKdzICLS5tatW2jevDnWrVsHmUwGPz8/CAtvGTAqQbkkJiYKACIxMTHXe2lpaeLGjRsiLS2tUMfOUqlE/TPXRNnjoVp/vI+HigZnrokslaqol6Hh8OHDwt3dXcTFxam3PXr0SNjZ2Yl//vlHvW3KlCmiUqVKIjw8XPj4+IhZs2ap31uxYoUAIP744w/1tj179gh7e3sRHR0thBBi2rRp4o033hCqV+Lfs2ePcHd3F0qlUuM4ISEhGjFu2rRJlC9fXuN8VlZW4t9//1VvGzFihLCxsRERERHqbX379hUDBgxQv9Y1BplMJm7cuKEus3TpUlG5cmX16/Xr12u81sWDBw+Eo6Oj+Prrr/MsU9RniIhKnh9//FE4OjoKAMLT01McOXJE6pCKpfy+v3OSfBi8pfk7IQXRisw83xcAHisy8XdCClq5G27drf3798PBwQFz586FEEL9V4VcLkdoaKh6+ZHPP/8cx44dQ/369VG3bl3Mnj1b4zguLi5488031a979eqFAQMG4O+//0bPnj3V5xk3bpz6PMnJyXj+/DkiIyPh5+cH4OW6NY0bNy4w7goVKqBmzZrq1/7+/qhatSqqVq2qse3kyZO5rrWgGCpWrIhatWqp96tVq1aRRgCmpqaiV69eqF27NsaNG1fo4xCR5Xjx4gXGjRuHjRs3AgACAgKwbds2dVcQMh4mQCYWm5Fl0HK6io+PR+nSpVG3bl2N7V999RWaNm2qfm1nZ4fAwEBcunQJffv2hY2N5iOSc44lKysreHh4IDY2Vn2e1q1b5zpPq1atNCandHd31ynuUqVK5Tqftm3KV6YR0DWGnMextraGSqXSKa6cXrx4gW7dukGhUOD48eN5TrlARPSqlJQUHDx4EDKZDHPmzMHMmTPZ38dEmACZmJedbrdc13K6KleuHP755x91h968nDlzBsuWLcOQIUMwe/ZsdO/eXaO2JSYmBpmZmeqRCOnp6YiLi0OFChXU53FzcyvwPMZkqBhkMplO5dLS0vD2228jLi4OJ06cUI9iJCIqiJeXF3bu3AmlUomAgACpw7EoZjMPkKVo7uYEH7kt8vpqlQEoJ7dFczcng563X79+iIiIyDUy6fTp03j69CmAl0uADBw4EBMmTMDmzZvRtm1bDBw4UKN2JT09HRs2bFC/XrlyJdzc3NCmTRsAwIABA7Bt2zZcuXJFXUalUuHXX3816PXkx1AxuLu7IzExMd9OiOnp6Xj77bcRGxuLEydOwNPTs9BxE1HJl5KSgsGDB2tMsfHGG28w+ZEAa4BMzFomw+f+5THi2j3I8LLPT7bspGiBf3lY61j7oKvGjRtj9erVGD9+PH788UdUrlwZN27cgLOzM3755RcAwJgxY+Dq6qpej23jxo2oV68ePv/8c8yZMwcA4OnpieXLl+P333+HSqXCn3/+iY0bN8LZ+WV/pQkTJuD69eto1qwZOnToAAcHB1y6dAldunRBjx49DHpNeTFUDNlD6zt16gQ/Pz+tw+CnTJmCY8eOoVevXpg1a5Z6e8WKFXWaRJGILMeVK1cQFBSEmzdvYv/+/ejWrRtcXV2lDstiMQGSQFdPN3xftwpm3I7SGArvI7fFAiPOAzRq1Cj06tULp06dQlpaGiZPnqyeGycpKQlt2rTBvHnzIJfLAbxMdg4cOIDQ0FB1LZCHhwcuXryIs2fP4vHjx/j2229RrVo19Tmsra2xYcMGTJ06FRcuXIC9vT2++uorjZlLAwICtNaU5JwIUVu5jh07okqVKhrbunXrhgYNGhQ5hlq1amHNmjXq12XKlMGtW7dw8uRJPHv2TOsw+B49eqBOnTq5trMZjIiyCSGwfv16TJgwAenp6Shfvjx27NjB5EdiMpFf/b6FSkpKgqurKxITE3Mti5Geno7IyEj4+vrC3t6+aOfJykL109cAANvq+aKdh4vBa34MaeXKlVi5ciVu3rwpdSjFmiGfISIyb0lJSfjwww+xY8cOAMCbb76JLVu28I8kI8nv+zsn1gBJyMXGBjEB9aUOg4iIjCA1NRWNGzdGeHg4rK2tsXDhQnzyySewsmL3W3PABIh0llfTFRER5ebo6IiePXti586d2LlzJ1q2bCl1SPQKNoFpYaomMLJMfIaISq6EhASkpqaifPnyAIDMzEwkJyfnmkONjEOfJjDWwxERERlASEgIGjZsiN69eyMjIwMAYGtry+THTDEBIiIiKgIhBJYvX45WrVohMjIST548QVRUlNRhUQGYABVSYZdMIOKzQ1RyPHv2DO+88w4mTpyIzMxM9OrVC6GhofD19ZU6NCoAO0Hryc7ODlZWVnj8+DE8PT1hZ2en85IJZNmEEMjIyEBcXBysrKy4XhhRMff3338jKCgIDx48gJ2dHZYsWYIxY8bwO6GYYAKkJysrK/j6+iI6OhqPHz+WOhwqhhwcHFCpUiUOhSUqxoQQmDBhAh48eAA/Pz/s3r0bDRs2lDos0gMToEKws7NDpUqVkJWVpbFOFlFBrK2tYWNjw78QiYo5mUyGrVu3YtGiRVi2bFmBI47I/HAYvBb6DKMjIjKmVKUSfqeuAgAi3ngNjtbWEkdkuU6fPo3Q0FCMHz9e6lAoD5wJmoiIyEBUKhW++OILzJ49GyqVCvXr11cvlkzFFxMgIiKiPMTGxmLgwIE4evQoAGDgwIHs61NCMAEiIiLSIjg4GP3790d0dDRKlSqFlStX4v3332cfvhKCw1CIiIhy+PLLL9G+fXtER0ejdu3aCAkJwbBhw5j8lCBMgIiIiHIoXbo0VCoVhg4digsXLqBOnTpSh0QGxiYwIiIiAKmpqXB0dAQADB8+HNWrV2dn5xKMNUBERGTRsrKyMGvWLNStWxfPnj0D8HKeHyY/JRsTICIislhRUVEIDAzE559/jnv37mHPnj1Sh0QmwgSIiMiMKV+Zq/bvhBSN11Q0Bw8eRP369XH69Gk4OTlhx44dGDVqlNRhkYkwASIiMlMH4hLwxvlb6tcDrkSi8bkbOBCXIF1QJUBmZiamTp2Kt956C/Hx8WjQoAEuXbqEvn37Sh0amRATICIiM3QgLgEjrt1DTEamxvYYRSZGXLvHJKgI5s6di6+++goA8NFHH+Hs2bPw9/eXOCoyNSZARERmRikEZoZHQVtjV/a2WeFRbA4rpMmTJ6NevXrYs2cPVq5cCXt7e6lDIgkwASIiMiOpSiXKB19GtCIzzzICwGNFJv5OSDFdYMVYRkYGtm7diuy1vz08PBAaGop3331X4shISpwHiIiomIrNyJI6BLMXGRmJvn374sKFC0hPT8eIESMAAFZW/Pvf0vEJICIqprzs+Ddsfvbu3YsGDRrgwoULcHd3h5eXl9QhkRlhAkREZKbyWnVKBqCc3BbN3ZxMGU6xoVAoMG7cOPTu3RuJiYlo3rw5QkND0b17d6lDIzPCBIiIyIzlTIKyXy/wLw9rLsyZS0REBFq1aoWVK1cCAD799FOcOnUKlStXljgyMjdMgIiIzNSq2pVQ1s5WY5uP3Bbf162Crp5u0gRl5h48eIDQ0FCULl0aBw4cwJdffglbW9uCdySLI2kDcmxsLPbt24e7d++iYsWKCAoKgqenZ57l//rrL+zcuVPre7NmzULZsmWRkZGBjz/+ONf7gwYNQrNmzQwWOxGRsXUu44oOpV1Q/fQ1AMC2er5o5+HCmp8chBCQ/f89CQgIwKZNmxAYGIgKFSpIHBmZM8lqgPbu3atul/Xw8MChQ4fg5+eHixcv5rmPu7s7atasqfETEhKCHTt2wM3NDcDL4Y6rVq2Cvb29RjlXV1cTXRkRkeG8muw0d3Ni8pPD7du38cYbbyA8PFy9bfDgwUx+qEAyIaSZSSs8PBwVKlRAqVKl1Nu6dOkCpVKJo0eP6nSMzMxMVKhQAf3798fSpUsBACkpKXB2dsa5c+fQvHnzQsWWlJQEV1dXJCYmwsXFpVDHICIqjFSlEn6nrgIAIt54DQA0XjtaW0sWm7nZtm0bRo0ahdTUVHTq1AmHDx+WOiSSmD7f35LVAPn7+2skPwBQp04dxMTE6HyM/fv3IzY2Vj2vw6s2bdqEKVOm4LvvvkN8fHyR4yUiMgUuflqwFy9eYMSIERg4cCBSU1PRrl07bN68WeqwqJgxm07QKSkp2LNnD9q2bavzPhs2bEDLli1Rp04dje0eHh6wtbWFm5sbtm3bhho1auDChQt5HkehUCApKUnjh4jI2FKVSnifCIP3iTCkKpVaFz999TUBN27cQNOmTbFhwwbIZDLMmTMHf/75J3x8fKQOjYoZs5hFS6lUYuDAgbCyssK8efN02icqKgqHDx/G999/r7Hd3t4e169fh7e3NwBgxowZ6NGjBz744ANcvnxZ67EWLVqk83mJiIzhcHwiPrrxINf6X08y8l4Sw9KEhISgXbt2ePHiBby9vbFt2zYEBgZKHRYVU5LXAKlUKgwdOhQhISE4evQoSpcurdN+mzdvhqOjI/r06aOx3cbGRp38ZBswYACuXLmC5ORkrceaNm0aEhMT1T8PHz4s3MUQERXS/DvR+S5+CsDim8Pq16+PevXqoUOHDggLC2PyQ0UiaQ2QSqXCsGHDcOzYMQQHB8Pf31+n/YQQ2LhxIwYMGAAHB4cCyysUCgBAVpb2dXPkcjnkcrnugRMRGViMDjU9IYmpaF/aska03rx5E35+frC1tYWtrS0OHDgANzc3ruVFRSbZEySEwPDhw3HkyBGcOHEC1atXz1UmLS0NY8eOxfnz5zW2nzhxAnfv3tXa+TkkJATPnj1Tv37x4gVWrVqFJk2awN3d3fAXQkQWL2dfHmOxpMVPhRBYv349GjRogBkzZqi3e3h4MPkhg5CsBmjZsmXYvHkzOnfujBUrVqi3Ozg44KuvvgLwsuZm1apVaNy4scYkhhs2bECjRo3QsGHDXMdNTExEq1at4O/vDzc3NwQHB8PNzQ27du0y/kURERlRJXs7qUMwiaSkJIwaNUo98e3169eRlZUFGxuz6LZKJYRkT1Pz5s01Ep9srzZFOTg4YMWKFbnm8wkMDMS4ceO0HrdDhw4ICQnBqVOnEBcXhw8++AAtW7aENefOICITyjmfT0Hz93jb2eJJRqbWfkAyvFwCwxIWPw0NDUWfPn1w584dWFtb43//+x+mTJnCWh8yOMkmQjRnnAiRiPShLdnRJQF6tczq2pXw0Y0HADQ7PmfP+1zS1/8SQmDNmjWYNGkSMjIyULFiRezcuRMtW7aUOjQqRorFRIhERKZmqr46hdG5jCu+r1vFYhc/ffz4MaZOnYqMjAy8/fbbCA0NZfJDRsUGVSIiieSc9blLGVe0cXeyyMVPy5cvj3Xr1iE6OhqTJk1SL25KZCxMgIiIJHAgLgEzbkepXw+4EgkfuS1m+f03o3FJXvxUCIFvv/0Wr7/+Otq1awcA6Nevn7RBkUVhAkREZGIH4hIw4tq9XB2eYxSZ6n5AJdnz588xbNgw/PLLL/D29saNGzc4TQmZHBMgIiITUgqBmeFRec76XDLre/7z999/o2/fvrh//z7s7OwwY8YMuLm5SR0WWSB2giYiKiJ9VnD/OyEF0Yq8Z30uqcNyVSoVvv76a7Rp0wb379+Hn58fzp49i7Fjx7K/D0mCCRARURFoW8G98bkbOBiXoN72alJkSbM5Z0tPT0f37t0xZcoUZGVlISgoCJcuXUKjRo2kDo0sGJvAiIgKKa++PNGKTIz9979FlbM7OH/uXx5edpb3365cLoezszPkcjmWL1+OkSNHstaHJMcaICKyGPo0VelyrLz68mgTo8jEiGv38CwzCz5y2zz7+pSUtEClUiE1NRUAIJPJsHbtWly4cAGjRo1i8kNmgQkQEVmEvJqqDrzSVKWPgvry5JSdKM258xjzq5UDkDvZKSlpQWxsLN566y0MGDAA2YsNuLi4oF69ehJHRvQfJkBEVOJlN1XFZGgmLNm1MoVJggrTl0cAeKzIhIetTb6zPscE1EdMQP0C1w8zRydPnkT9+vVx+PBhHDlyBDdu3JA6JCKtmAARUYlW0LBzAJgVHqV3c1hR+vL0DotAOw9nnGpWQ71tWz1fhLSoXWyXvFAqlZg/fz4CAwMRHR2NWrVq4cKFC6hTp47UoRFpxQSIiEo0XYadP1Zk4u+EFL2O29zNKd++PLp4dZbn4jzrc0xMDDp37ow5c+ZApVLh/fffR0hICOrWrSt1aER5YgJERCWark1V+jZpWctk+Ny/PADd++4Uz/Qmf0II9OjRA8eOHYODgwO2bNmCjRs3wtHRUerQiPLFBIiISjRdm6oK06TV1dNNa18ebUpi8gO8HOG1bNkyNGzYEP/88w8GDRokdUhEOmECREQlWkFNVTIA5eS2aO7mVKjjd/V0y9WXZ32dyvDW0sF5Ve1KhTqHuYmKisKBAwfUr1u0aIGQkBDUrFlTwqiI9MMEiIhKtPyaqrJfL/AvX6T+Nzn78rzt5a61g3PnMq6FPoe5OHToEOrXr4/33nsP165dU2+3suLXCRUvfGKJqMTLq6kqe9i5MUZelZQOztkyMzPx2Wef4c0330R8fDxq1KgBuVwudVhEhWZ5c7ITkUXq6umGNu5OqH76Za3Ftnq+aOfhUuwTE1N4+PAh+vbti7NnzwIAxowZg2+++Qb29vYSR0ZUeEyAiKjYS1Uq4XfqKgAg4o3X4GhtrXWb1LUyOZfiaOfhgpiA+iaNQV/79+/H0KFD8ezZM7i4uOD777/He++9J3VYREXGJjAiIhMw9FIcpnLx4kU8e/YMjRo1wqVLl5j8UInBGiAiIiM7HJ+Ij248yDUbdfZSHMbqh1RYQgj1gqWzZ89GmTJlMHLkSPb5oRKFNUBEREY2/060wZfiMJZ9+/ahQ4cOSE9PBwBYW1tj3LhxTH6oxGECRERkZDkXYX1VYZfiMDSFQoHx48ejV69eOH78OFauXClpPETGxiYwIqIicrS2LnJn5sKsLm8oERERCAoKwj///AMA+OSTTzBhwgTJ4iEyBdYAEREZQXZS9HN9P53KF2V1+aLYvXs3GjRogH/++QceHh7Yv38/Fi9eDFvbgpf3ICrOmAARkcVKVSrhfSIM3ifCkKpU6lxGl/2yGXspjqJYunQpgoKCkJycjFatWiEsLAzdunUzeRxEUmACRETFXs75dZRCaN2WXSsTE1AfjtbWJonNFEtxFNY777wDDw8PTJs2DcHBwahYsaLJYyCSCvsAEVGxdiAuATNuR6lfD7gSCTcbzeRmwJVI+Mht8bl/eUmGm2cvxTHjdpRGh2gfuS0WmDimy5cv4/XXXwcAVKlSBbdv30bp0qVNdn4ic8EaICIqtg7EJWDEtXu5RlklZCmRkKXZNJU9545UEw9qWzU+pEVtkyU/L168wAcffID69evj4MGD6u1MfshSMQEiomJJKQRmhkdpnV9HG3OYc0eqpTj+/fdfNGvWDN9//z1kMhmuX79ukvMSmTMmQERULP2dkIJoRd7z62hjLnPumNIPP/yAxo0b49q1ayhbtiyOHj2KTz75ROqwiCTHPkBEVCwVZd4cKefcMZXU1FSMGTMGW7ZsAQC0b98eW7duRWl3T6z68DgAYOTytrCVm6YzOJG5YQ0QERVLRZk3R6o5d0zp8OHD2LJlC6ysrLBgwQIcPnwY3t7eUodFZDaYABFRsVTQ/Dra5JxzR9tQ+Zx0HWJvbnr16oXPPvsMx48fx8yZM2FtomH/RMWFZAlQVlYWNm/ejI4dO8LPzw/t2rXDtm3b8t3nxYsX8Pb2zvWza9cujXJPnz7FyJEj4efnhzp16mD+/PnIyir5Vd5EliS/+XW0yTnnzoG4BLxx/pb6/QFXItH43A2NUWLaytT56xqanvs33/2kkJycjHHjxiEuLk69bdGiRWjbtq2EURGZL8nqgVeuXInLly9j6tSp8PX1xenTpzF8+HAkJCTgo48+0rqPSqXCkydPcODAATRs2FC93dXVVf1vIQTefvttAMCuXbvw/PlzDBo0CM+fP8fSpUuNe1FEZFJ5za/jbmMNAWgMhX91zp3s4fM5622yh8p/X7cKAGgtk3N4fc79pJhnKCwsDH369EF4eDju3buH/fv3mzwGouJGsgRo/PjxsLL6rwLKz88Pf//9NzZt2pRnApTNw8Mjz7bsY8eO4dy5c7h58yZq1Hg558bChQvx4YcfYvbs2XB3dzfcRRCR5Lp6uqGNuxOqn74G4OX8Ou08XJCqVObaZi2T5Tt8XuBlTdHM248AmUyvIfYyvBxi36WMq8mGtwsh8N1332HSpElQKBSoUKECPvvsM4McO1OhxLoJJwGwszSVTJI1gb2a/GRTKBSws7MrcN/BgwfD19cXHTp0wJ49ezTeO3XqFCpWrKhOfgCgc+fOyMzMxN9//130wIkoF33WxjLG+bTNr5PXnDsFDZ8XAKIzsowyxN6QS3EkJiYiKCgIY8aMgUKhQLdu3RAWFoZWrVoV6bhElsJshkJcvnwZ27dvx5IlS/It16NHD0ycOBE+Pj44ePAgBg0ahOjoaIwfPx4AEBUVhbJly2rsk/368ePHWo+pUCigUCjUr5OSkopyKURkQKlKJfxOXQUARLzxWpETB2MPgTfFEPtbt27hrbfewt27d2FjY4Mvv/wSkyZNgkyC9cSIiiuzSIAePnyI7t274+2338aYMWPyLOfk5IRffvlF/bpGjRqIjY3FvHnz1AmQEAI2NpqXZWVlBSsrKyjz+Mt00aJFmDdvXtEvhIjMnrGHwJtiiL2Pjw9kMhkqV66MXbt2oVmzZkY/J1FJI/kw+EePHiEgIAD169fHjh079P4LplmzZnj27BliY2MBAJ6enoiPj9co8/TpU6hUKnh5eWk9xrRp05CYmKj+efjwYeEuhoj0YuqmM6Dg4fMyAD52NkUeYm9oycnJEP8/3N7FxQW///47QkNDmfwQFZKkCVBUVBQCAgJQq1Yt7NmzB7a2tnof486dO7Czs4OLiwsAoGnTpoiIiEB0dLS6zKlTpyCTydC4cWOtx5DL5XBxcdH4IaKSKb/h89mvP69eoUhD7A3t/PnzeO2117By5Ur1tpo1a3JQB1ERSJYARUdHq5Ofn3/+WWvn56SkJI15frZs2YKffvoJaWlpEEIgODgYX3zxBQYNGgR7e3sAQLdu3VC5cmV88sknSEtLw5MnTzB//nz07NkTFSpUMOk1EpF5yh4+X9ZO848uH7ktVtWuhOHX7mH4tXtYVbtSrjLuNtZws7HOtZ8xhsALIfDNN9+gdevWuH//PtasWYPMTP06ZxORdpIlQCtWrEB4eDjOnj2LSpUqqSc1fHX0Vva8P2lpaQCAwMBA7N+/Hz4+PnByckJQUBBGjx6t8VeRXC7HgQMHcPv2bbi5uaFChQqoWrUqNmzYYPJrJCLz1dXTDaea/ff/zbZ6vghpURudy/w3r1jnMq65ylxrXRcXWtTKtZ+hk5+nT5+ie/fu+OSTT5CVlYX33nsP586dK1RNORHlJlkn6OnTp6s7Lr/q1eHxrq6uiI6OVk90WKFCBfzwww9QqVR48eIFnJy0t7XXrl0bISEhSE5Oho2NDUqVKmWciyAiveQc0VVYOZeiaOfhgpiA+hplsoec5yevofIFldFlv6I4c+YM+vbti0ePHkEul2PZsmUYNWoUR3kRGZBkCZCTk1OeCUw2mUymdcJDKyurAvcFAGdn50LHR0S605aQGCopyHnsF0oVZoX/N6XFgCuR8JHb4vP/n+W5uIuJiUGHDh2Qnp4Of39/7N69G/Xr15c6LKISR/JRYERUvOmyppYhj/3B9fsay14A/y1FIfV6XIbg7e2NuXPnon///vjnn3+Y/BAZCRMgIiq07DW1CpuQ5Leqel7H1iZ7r1nhUWa5MntBTp48ievXr6tff/rpp9i6datRarFVqv/uz+PwBI3XRJaECRARFUpBa2oB+Sckh+MT86w5yu/YedFlKQpzo1QqsWDBAgQGBqJPnz5ITU0F8LL53xj9fSJCY7Fj3n9LAv2+8jK2TD+LiNBYg5+LyNwxASKiQtFlTa38EpKPbjzIs+Zo2b0YvdfiylbUpSjyq5UypJiYGHTu3BmzZ8+GSqVCkyZNjHKebBGhsTi09hpSEzI0tqcmKHBo7TUmQWRxzGIpDCIqfnRNNPIql99q7OsfxWt5Vzf6LEWRc6TYgbgEzLgdpX494EokvO0MP+z82LFjGDBgAJ48eQIHBwesXr0aQ4YMMfh5sqlUAqd3hedb5q/d4fB93RNWVhxpRpaBNUBEVCi6Jhqjb9xXL3OhS22KAJCQpf+yGEVdiiKvPkdPMjIhA7ChbhX1QqyFXdVdqVRi9uzZ6NixI548eYK6devi4sWLRk1+ACA6PAGpCYp8y6Q8VyA6PEH9mn2FqKRjAkREhaLLmlqvyjmiqyBuNtY6r8VV1KUoitqfSVdCCJw8eRJCCIwYMQLnz59HrVq1Ct6xiFKT8k9+cpZjXyGyBEyAiKhQdFlTK9vh+ESdR3Rl+6BCGa3H0qaoS1EUtT9TQbIXMbWxscGOHTuwY8cOrF+/Hg4ODoU6nr4cXeQ6l2NfIbIUTICISG/Zq7jntV5W9ppa2ebfidZ5RFd2U9bEKt5a1+sqJ7fFyloV1a8NsRRFUfsz5SUrKwvTpk3DxIkT1dvKlSuHvn376nWcovLxd4OjW/5JkJO7HGX9XHXqK8TmMCoJmAARUZFoWy8r55pautb85GzKymu9rjdfSXYMsRSFrv2Z9Olg/fDhQ7Rr1w5ffPEFvv32W4SFhRUyuqKzspKhTZB/vmVa9/HHk4hEvfsKERVXTICIqMgMtTaWtqYsY6+7lX3cgvoz6dPB+vfff0f9+vVx5swZuLi4YM+ePZLP6OzXwAtdRtWFo5udxnYndzm6jKoLvwZeevcVIirOOAyeiMzCjKreGFOprFESnIJk92cace0eZNAcoq9PB+uMjAxMmzYNS5YsAQA0atQIu3btgp+fn1Hi1pdfAy9UqOmB7yedAgB0G/s6Ktb2UA9916evEFFxxxogIjI6b7u8a1eyDSlfRpLkJ1tXTzetfY507WAthECPHj3Uyc/48eNx5swZs0l+sr06z085fzeN17r2FfLxdzNWeEQmwwSIiIzi1SHjfX3cAeQ/WkzK5CdbXn2OdOlgLZPJ8MEHH8DNzQ379u3D8uXLIZcXr5oSXfsKcbJEKgmYABGRweWc82fZ/Vi42ljD1UZz0kBvueFnWS4qffocKRQKXLt2Tf26V69euHv3Lnr27GnMEI1Kl75CRCUB+wARkYZUpRJ+p64CACLeeE2vmY6Bl3P+fHTjQa5h74lZSo1t2+r5oomrI6qfvobiKCIiAkFBQbh//z7CwsJQvvzLOZHc3d0ljqzoCuorRFQSsAaIyIJlz+fjfSJMvVyFLvJbMDSvOX+y1/nKVpQRXYVdisJQfvrpJzRs2BD//PMPVCoV7t69a/IYjC2/vkJEJQFrgIhIL9oWDPWR22JD3Spws7FG77CIPPctzPR5ORcslVJ6ejo+/vhjrFmzBgDQqlUr7NixAxUrVixgTyIyN6wBIiKd5bVgaIwiEyOu3cPh+ESJIjO+8PBwtGjRQp38TJs2DcHBwUx+iIop1gARUb5e7RPkbWebb/PWz0+e63Vsc6rdKcjSpUsRFhaGMmXKYOvWrejcubPUIRFRETABIiKd5bekhQDwNFOJ0rbWeJap1Joo5ZxkUJuidsI2lsWLFyMjIwPz5s1Td3gmouKLTWBEZFC9y3oAKHiFeHOVXSt1wluO6R9/DJVK9XK7oyO+//57Jj9EJQRrgIhIQ84RXk1cHfXav3MZFzRzc8SM21EaNUY+clss8C9fpFXbTWXLli0YPXo0Xrx4AV9fX43V3M1RpkKJdRNOAgBGLm8LW7l51JoRmTMmQEQWLGey80Kpwqzwx+ptA65EwvuVpSG87WzxJCMzz+Ytn/9fMNRaJkMbdyf1HD/b6vminYeLWcz2nJ/U1FSMHTsWmzdvBgC0b98effv2lTYoIjIKNoERWaicszUPuBKJD67fz9XP58krr2dX8wGQd/PWqwuGmmIVd0O6du0amjRpgs2bN8PKygoLFizA4cOH4e3tLXVoRGQErAEisiCvdjDWpUMycpTpUNoF39etUqybt7TZs2cPhgwZgrS0NJQrVw7bt29H27ZtpQ6LiIxI7wTojz/+QHx8PN599104ODgYIyYiMoHCTEr4Q1Q8xlQqWyybt/JTtWpVKJVKdO7cGT/++CM8PT2lDslobOXW+Oi7QKnDIJKc3k1gGRkZGD9+PHx8fDBq1CicP3/eGHERkRn6390YND53A38+TVJvM3TzVn7LbBhSYuJ/kzY2atQIZ8+exR9//FGikx8i+o/eCVDPnj0RHR2NVatWqWdGrVOnDr755hvExsYaI0YiMiMxikx8dOOBUY6trV9S43M3cCAuwWDnEELgu+++Q+XKlXHp0iX19kaNGsHKit0iiSxFoT7tpUqVwsCBA3H8+HFERETg3XffxYoVK1ChQgX06tULhw8fNnScRGQmjFMfU/AyG4ZIghITE9G3b1+MHj0aiYmJ2LhxY5GPSUTFU5E7QctkMsj+v/rb3t4eqamp6NGjBxo1aoT9+/fDw8OjyEESkeHp2glaG13202eZC6UQmBkele8yG7PCo9CljGuhm9v++ecf9OnTB3fv3oWNjQ2+/PJLTJo0qVDHKoriMmcP+wpRSVeoGqC0tDRs27YN7du3R9WqVXH48GHMmjUL0dHROHz4MB4+fAgrKyv+dUVkZl7tT5OdWJiDvxNSEK3If5mNx4pM/J2QovexhRBYsWIFWrZsibt376Jy5cr466+/8PHHH6v/eCMiy6N3AvTLL7/Ax8cHEyZMwGuvvYarV6/i3LlzGD58OBwdX84Y6+npie7du+PJkycGD5iICidn/xogdwJUTm6LlbVMv7p5bEaWQcu96ueff8b48eORkZGBnj17IjQ0FM2aNdP7OERUsujdBCaXy7FmzRr06tULcrk8z3Iff/wxhJFGbxCRfrL71+T8RKpe+Xf2cPZ0lQr49yGAvJvJsmd9DmlR2yAjwLzsdPuvSNdyr+rVqxe6d++O9u3bY9y4caz1ISIAhUiA3nzzTZ3KWeu4gvPTp09x9+5dVKxYUecZV/PbR6lU4vTp07n2qVmzJmd0JYuUX/+aVzVxddSazORMgrTN+lxUzd2c4CO3RYyi4GU2CiKEwMaNG9GvXz84ODjAysoKv/zyCxMfItIg2ZjP69evo0uXLqhZsybGjBmD6tWr4+2339aYm6Mw+6SlpSEgIAAff/wx5s6dq/4JCwszwVURSSdVqYT3iTB4nwhDqlKp3l5Q/5psIYmpubatql0JZV9ZCwx4mYh8X7eKQWd9tpbJ8Ln/y1XWdVlmIy9Pnz5F9+7dMWLECIwfP/6/YzD5IaIcJEuAIiMjMWnSJMTFxSEkJAQRERG4efMmJk+ebJB9Vq9ejeDgYPVPly5djHk5RGarKP1rOpdxxalmNdSvt9XzRUiL2kZZ8qKrpxu+r1ul0AnXmTNn0KBBA/z++++Qy+Vo1KiRxTTDq1T/Xefj8ASN10SknWRrgXXr1k3jtaenJ3r27IkDBw4YZJ/79+9DJpPBz88PZcqUMUzQRMVQUfvXmHJR066ebnovs6FSqfDVV19h5syZUCqV8Pf3x+7du1G/fn2jxWlOIkJjcXrXbfXr31dehqObHG2C/OHXwEvCyIjMm1lNe3rhwgX4+/sbZJ+JEyfiww8/RIUKFdC7d288e/Ysz2MoFAokJSVp/BCVFNn9awpKW5q4OpoknoLok3DFx8eja9eumDZtGpRKJfr3749//vnHopKfQ2uvITUhQ2N7aoICh9ZeQ0QoZ+cnyovZJECrVq3C2bNnMX369CLtY2Njg+3btyM6OhqhoaG4efMmwsLC8NFHH+V5nEWLFsHV1VX9U7Gi6YcBExmLLv1rsssB/01gGBNQH446DmaQSnp6Oi5evAh7e3usX78eW7duhbOzs9RhmYRKJXB6V3i+Zf7aHc7mMKI8mEUCtHv3bkyaNAnff/+9zvNz5LWPvb09+vXrp35dpUoVTJ06FXv37kVWlva+ENOmTUNiYqL65+HDh0W7ICIzk1//mg11qxSLZCfbq/16KlSogN27d+PChQsYMWKERXV2jg5PQGqCIt8yKc8ViA5PME1ARMWM5AnQTz/9hEGDBuG7777DkCFDjLKPp6cnMjIyEB8fr/V9uVwOFxcXjR+i4ibnKupJWVkao8K6erqZrEOzsTx58gSdO3fGvn371NsCAgLw2muvSRiVNFKT8k9+9C1HZGkkTYD27t2LAQMGYPXq1Rg2bFiu97OyshAcHIyYmBid90lOTs617fDhw/Dy8oKXFzsEUsmkbRX1nLM+A6bt0Gxox48fx+uvv46jR49i3LhxUCgs+4vd0SXviWgLU47I0kg2CuzIkSPo27cv+vfvDz8/PwQHB78MyMYGrVu3BgCkpKQgICAAmzZtwtChQ3XaZ/PmzTh58iS6d+8ONzc3/PHHH9iwYQM2btwIKyvJK7yIDC6vWZ6fZBQ8948u9FnUVBf6LgaqVCoxf/58LFiwAEII1KlTB7t37853JnpzlnPIesXaHrCy0j8R9fF3g6ObPN9mMCd3OXz83QoTJlGJJ1kCdO/ePbRs2RL37t3D3Llz1dudnJzw+++/A3iZ2LRt21Y9g7Mu+4wbNw7VqlXDnj17EBcXh6pVqyI0NBR169Y12bURmUpBq6i/Wq64eDXhevz4MboNGKD+Y2fEiBFYvnw5HBwcpAuwCAw5ZN3KSoY2Qf44tPZanmVa9/EvVHJFZAlkwlJmCtNDUlISXF1dkZiYyP5AZNbOPE9G77CIAsttq+eL9qVdkapUwu/UVQBAxBuvSdLxWdcaoLi4ONSpUwdxcXFwcnLC2rVr0b9/f1OGalDZQ9bz0mVUXY0kSNf7lJ1UvToU3sldjtZ9OA8QWR59vr8lqwEioqIz5irqUvP09ESfPn3w119/YdeuXahRo0bBO5kpXYes+77uqXeNjV8DL1So6YHvJ50CAHQb+3qhm9WILAkTIKJiTNdZnuMzMotFM9ijR49gbW0NHx8fAMA333wDIQTs7e0ljqxo9BmyXr6Gu97HfzXZKefvxuSHSAfsFUxUjOk6y/P/7sag8bkbCH6WbLaTHB44cACvv/46+vfvD+X/L+Yql8uLffIDcMg6kTliAkRUjOU3y3NOMYpMjLh2DwfiEowelz4yMzMxZcoUdOvWDc+ePUNycjKePn0qdVgGxSHrROaHCRBRMZfXLM85ZTeAzQqPMpvmsPv376NNmzb4+uuvAQDjx4/HmTNnStycXdlD1vPDIetEpsUEiKgEyDnLc14EgMeKTPydkGL8oApw5d4ZNGnWCOfPn4ebmxv27t2L5cuXF9v5ffKTPWQ9PxyyTmRaTICISgh9ZnWWelSYUpmF3y5sQEJCApo2bYrQ0FC88847hT5epkKJVR8ex6oPjyNToTRgpIbj18ALXUbVhaObncZ2J3d5riHwRGR8HAVGZIF0HT1mLNbWNhjWYRYUlW5i4cL/wc7OruCdSgAOWScyH6wBIiqB8vo6lQEoJ7dFczcnU4YDAPj555+xcuVK9etyHr5Y9L8vLCb5ycYh60TmgTVARCWUDJrLYWR/zS7wL2/SRVDT09MxefJkrF69GtbW1mjapLnJzm0q+q5vRkTSYw0QUQm0qnalXKPCfOS2+L5uFXT1dDNZHOHh4WjRogVWr14NAJgyZQpq16qjfv9xeILG4qBERKbCGiCiEqhzGVd0KO2C6qdfrj21rZ4v2nm4mLTmZ8eOHRg5ciRSUlJQpkwZ/Pjjj/Av2xA/LfxHXaYoi4ESERUFa4CISojsVdSzZ3l+Ndlp7uZk0uRn3Lhx6N+/P1JSUvDGG28gLCwM/mUb4tDaaxqLdgJAaoICh9ZeQ0RorMniKw5erRljTRmR4TEBIiKDq1atGmQyGWbOnIljx47Bx6ecTouB8kv+pYjQWOyY97f69e8rL2PL9LNMEokMiE1gRGQQz58/h7v7y4U8x48fjzfeeAMNGjQAAETdem7UxUBLkojQWBxaey3X9uyaMm1zBtnKrfHRd4GmCpGoRGANEBEVSWpqKt5//300bdoUSUlJAACZTKZOfgAuBqorlUqwpozIRJgAEVGhXbt2DU2aNMHmzZtx9+5dHDt2TGs5Lgaqm+jwBJ1ryoioaNgERlRCZXeKNgYhBDZt2oSxY8ciLS0NPj4+2L59O9q1a6e1fPZioPl9uZtqMVBznrOHNWVEpsMaICLSS0pKCgYNGoThw4cjLS0NnTp1QlhYWJ7JD2D8xUBLyogp1pQRmQ4TICLSyyeffIJt27bB2toaCxcuxMGDB+HlVfAcPsZaDLQkjZjKrinLj6lqyohKOiZARKSX+fPno0mTJggODsa0adNgZWWl82rsfg280G/Of0thdBv7Ogb9r2WRkp+SNLeQsWvKiOg/TICIKF9JSUnYsGGD+rWXlxfOnz+P1q1bF+p4hloM1JxGTOnTBJc9ZP2j7wK19j8yVk0ZEWliJ2giytOlS5fQp08fREREoFSpUujfvz+Al8PcpabPiCljzi0UERqL07tuq18bYnkPvwZeqFDTA99POgXgZU1ZxdoerPkhMiDWABFRLkIIrFy5Ei1atEBERAQqV66MqlWrSh2WBnMYMWXMJjhD1ZQRkXZMgIhIQ0JCAt59912MGzcOGRkZ6NGjB0JDQ9G8efOCdzYhqUdMmVMTHBHpjwkQEamFhISgQYMG2Lt3L2xtbbF8+XLs27dPvcSFOZF6xBQnLSQq3pgAEZFaXFwc7t27h6pVq+Ls2bMYP368WfT30UbqEVPm0ARHRIXHBIjIwgnxXxPNW2+9hW3btuHSpUto3LixzsPbpSLliCmpm+CIqGiYABFZsHPnzqFBgwa4f/++elv//v3h6uoqYVT6MfTcQrqSugmOiIqGCRCRBVKpVFi8eDHeeOMNXL58GdOmTZM6pCLRZ8SUoZbNkLoJjoiKhvMAEVmYuLg4DBkyBAcPHgQA9OvXD2vXrpU4KtMw9Jw92U1wp3fd1hgK7+QuR+s+hZ8HiIiMjwkQkQU5deoU+vXrh8ePH8Pe3h4rVqzA8OHDzbajsyFlz9mTU/acPdr6DOmycjwnLSQqntgERmQhDh06hICAADx+/Bg1a9bEhQsXMGLECIMkP8Zajd1QnbCNPWcPJy0kKn5YA0RkIdq1a4d69eqhXr16WLVqFZycnAxyXH2blbLXwjIlc1k2g4jMB2uAiEqwkJAQKJUva07s7e1x8uRJ/PDDDwZNforDauycs4eIcpK0Big2Nhb79u3D3bt3UbFiRQQFBcHT07PA/Y4cOYJjx47B3t4evXv3Rr169QpVhqikUiqVWLBgAebPn485c+Zgzpw5AAAXFxeDnUPXZiXf1z0lbxIqjnP2SFFTRmRJJKsB2rt3L5o3b47Q0FB4eHjg0KFD8PPzw8WLF/Pdb9q0aQgKCoJMJkNMTAwaN26MvXv36l2GqKSKjo5Ghw4dMG/ePAgh8PjxY43JDg12nmK0FATn7CGinCSrAXrttddw/fp1lCpVCgAwdepUdOnSBdOmTcPRo0e17nP79m189dVX2LdvH7p37w4AcHV1xdixY9G9e3fY2NjoVIaopDpy5AgGDhyIuLg4ODo64rvvvsPAgQONcq7i1KyUPWePtlFg2ThnD5FlkawGyN/fX538ZKtTpw5iYmLy3Of333+Hi4sLunbtqt42aNAgREdHIyQkROcyRCVNVlYWZsyYgS5duiAuLg6vv/46Ll26ZLTkByh+zUpSLptBRObHbDpBp6SkYM+ePWjbtm2eZcLDw1GpUiVYW/83F0fVqlXV7+laJieFQoGkpCSNH6LiJCIiAkuWLIEQAh9++CHOnTuH6tWrF/m4+Q1vL47NSlItm0FE5scs2oOUSiUGDhwIKysrzJs3L89yaWlpcHZ21tjm6OgIa2trvHjxQucyOS1atCjf8xKZuxo1amDt2rWwt7dHnz59DHLMgoa3F9dmJc7ZQ0SAGdQAqVQqDB06FCEhITh69ChKly6dZ1knJyckJCRobEtKSoJSqVSPbtGlTE7Tpk1DYmKi+ufhw4dFuiYiY8vMzMTUqVNx7tw59bbBgwcbNPnRZXi7uTQrZY+Y+ui7QK2zNRMR5SRpAqRSqTBs2DAcO3YMJ06cgL9//gsL1qlTB/fu3UN6erp627///gsAqF27ts5lcpLL5XBxcdH4ITJXDx48QNu2bfHVV1+hX79+SEtLM+jx9Z01mc1KRFQcSZYACSEwfPhwHDlyBCdOnNDaXyEtLQ1jx47F+fPnAQA9evRAVlYWfvjhB3WZVatWoUaNGnj99dd1LkNUXP3666+oX78+zp07B1dXVyxZsiTXYIKCFLS8RGGGtxurWclYS2wUl/MTkfFI1gdo2bJl2Lx5Mzp37owVK1aotzs4OOCrr74C8LJz8qpVq9C4cWM0a9YM5cqVw4oVKzBhwgQcOnQIz549w5UrV/DHH3+o1zPSpQxRcZORkYGpU6di2bJlAIAmTZpg165d8PX1Nfi5zGV4u6FXbjfm+TlpIVHxI1kC1Lx5c43EJ5tc/t+oEgcHB6xYsQLNm/9Xvf7BBx8gMDAQp06dglwuR+fOnXP1G9KlDFFx8fz5c3Tu3Fk9jcPHH3+MRYsWwc7OroA9C8cchrcXZuX2knR+IjI+yRKgFi1aoEWLFvmWsbOzw9ixY3Nt9/Pzg5+fX7776lKGqDhwc3ODj48P3N3d8cMPP+Dtt9826vmyh7fn1wxmzOHtUi+xIfX5icg0JB8FRkS5paenIyUlBQAgk8mwadMmhIWFGT35Af6bNTk/xhzeLvUSG1Kfn4hMgwkQkZkJDw9Hy5YtMWrUKPUaXh4eHqhUqZLJYpByeLvUfZCkPj8RmYZZTIRIRC/t2rULH3zwAZKTk/Hw4UNERUWhQoUKksTi18ALFWp64PtJpwC8HN5esbaH0Zt9pO6DJPX5icg0WANEZAbS0tIwatQo9O3bF8nJyWjTpg3CwsIkS36ySTFrstRLbEh9fiIyDSZARBK7desWmjdvjnXr1kEmk2HmzJk4fvw4ypcvL3VokjB2H6SCZo2Wug8UEZkGEyAiCSmVSnTr1g1XrlxB2bJlceTIESxYsAA2NpbdOi31EhtSn5+IjM+y/5clkpi1tTXWrl2LL774Alu2bIG3t7fRz5lzdmNT9OspDKn6IJnL+YnIuJgAEZnY9evXERkZiW7dugEAAgMDERAQYJKZyo01u7KxZkKWeuV2qc9PRMbDJjAiExFCYOPGjWjSpAn69euH8PD/JtszVfKjywrvRESWgAkQkQmkpKRg8ODBGD58ONLS0tCyZUu4urqa7Pz6rvBORFTSMQEiMrIrV66gUaNG2Lp1K6ytrbFw4UIcPHgQXl6m60jL2Y2JiDSxDxCREa1fvx7jx49Heno6ypcvj507d6J169Ymj4OzGxMRaWINEJER3bp1C+np6XjrrbcQFhYmSfIDcHZjIqKcWANEZGAqlQpWVi//tli0aBHq1auHgQMHqrdJQeoV3omIzA1rgIgMRAiBlStXon379sjMzAQA2NraYvDgwZImP0DhZzcuaNZkIqLiigkQkQEkJCTgvffew7hx4xAcHIytW7dKHVIunN2YiOg/bAIjKqKQkBAEBQUhMjIStra2WLx4MYYOHSp1WFpxdmMiopeYABEVkhACy5cvx6efforMzEz4+vpi165daNKkidSh5as4zW5srBmmi8v5ich42ARGVEifffYZJk2ahMzMTPTu3RuXLl0y++SHiIheYgJEVEjDhw9H6dKlsWrVKuzZswdubm5Sh0RERDpiExiRjlQqFf7++2+0bNkSAFC9enXcu3cPTk5OEkdGRET6Yg0QkQ7i4+PRrVs3tGnTBsHBwertTH6IiIon1gARFeD06dPo168foqKiYG9vj6ioKKlDIiKiImINEFEeVCoVFi5ciICAAERFRaFGjRo4f/48BgwYIHVoRERURKwBItLiyZMnGDRoEI4ePQoAGDRoEFavXs0mLyKiEoIJEJEWBw8exNGjR1GqVCmsXr3abCc2fFWmQol1E04CAEYub8ulK4iI8sEEiEiLIUOGIDw8HP3790edOnWkDoeIiAyMCRARgOjoaEydOhXLly+Hu7s7ZDIZ/ve//0kdllFwdmMiIiZARDh69CgGDhyI2NhYqFQqs1zIlIiIDIujwMhiZWVlYebMmejcuTNiY2NRr149zJo1S+qwiIjIBFgDRBbp0aNH6N+/P06fPg0A+PDDD7FkyRKUKlVK4siIiMgUmACRxTl//jy6du2Kp0+fwtnZGevXr0dQUJDUYRERkQkxASKL4+/vDwcHB1SuXBm7du1CtWrVpA6JiIhMjAkQWYS4uDiUKVMGMpkMHh4e+PPPP1G5cmXI5XKpQyMiIgmwEzSVeL/99htq1KiBTZs2qbdVr169xCU/KpVQ//txeILGayIi0iRpAhQZGYnPPvsMvr6+6NKlS4Hlt2zZAm9vb60/Dx48AAC8ePFC6/u7du0y9uWQmcnIyMCkSZPQo0cPPH/+HFu2bIEQJTMpiAiNxY55f6tf/77yMrZMP4uI0FgJoyIiMl8yIdE3gkKhQO3atTF8+HDcuHEDN2/exMWLF/PdJy0tDYmJiRrbhg4diqioKFy9ehUAkJKSAmdnZxw4cAANGzZUl3N1ddV5hE9SUhJcXV2RmJgIFxcXPa+MzEFkZCSCgoIQEhICAJg0aRK++OIL2NnZSRyZ4UWExuLQ2mt5vt9lVF34NfAyYURERNLQ5/tbsj5Acrkc4eHhsLKywsSJE3Xap1SpUhpJTFxcHI4fP46vv/46V1kPDw94e3sbKlwqRvbu3Ythw4YhMTER7u7u2Lx5M7p37y51WEahUgmc3hWeb5m/dofD93VPWFnJTBQVEZH5k7QJzMqqaKffsmULrK2tMXDgwFzvDR48GL6+vujQoQP27NlTpPNQ8REeHo733nsPiYmJaNGiBUJDQ0ts8gMA0eEJSE1Q5Fsm5bkC0eEJpgmIiKiYKNajwDZu3IjevXvDw8NDY3uPHj0wceJE+Pj44ODBgxg0aBCio6Mxfvx4rcdRKBRQKP77EklKSjJq3GQ8/v7+mDVrFtLS0vD555/D1tZW6pCMKjUp/+RH33JERJai2CZA586dw40bN7B69WqN7U5OTvjll1/Ur2vUqIHY2FjMmzcvzwRo0aJFmDdvnjHDJSPavXs3GjRoAH9/fwDAnDlzIJNZRnOPo4tuI9l0LUdEZCmK7TD4DRs2wN/fH23bti2wbLNmzfDs2TPExmofETNt2jQkJiaqfx4+fGjocMkI0tLS8OGHHyIoKAhBQUFIT08HAItJfgDAx98Njm75JzdO7nL4+LuZJiAiomKiWCZAqamp2LVrFz744AOdyt+5cwd2dnZ59giXy+VwcXHR+CHzduvWLTRv3hxr166FTCbDW2+9BRubYluhWWhWVjK0CfLPt0zrPv7sAE1ElINZJ0BJSUla5/DZtWsXFAoFhgwZkmufLVu24KeffkJaWhqEEAgODsYXX3yBQYMGwd7e3lShkxFt27YNjRo1wpUrV+Dl5YXDhw/j888/t8gECAD8Gnihy6i6cHTTHOLv5C7nEHgiojxI+o3Rrl073Lx5E8nJycjIyFAPW7979y4cHBygUqnw5MkTpKWlaey3YcMG9OjRA15euf9jDwwMxIwZMzBixAhkZmbCyckJo0ePxvTp001yTWQ8aWlpGDt2LDZu3Ajg5fOzfft2+Pj4SByZ9PwaeKFCTQ98P+kUAKDb2NdRsbYHa36IiPIgaQL0888/IzMzM9f27Ll+XF1dER0dDVdXV4339+7dC2dnZ63HrFChAn744QeoVCq8ePECTk5Ohg+cJGFtbY1r165BJpNhzpw5mDlzJqytraUOy2y8muyU83dj8kNElA9JE6DSpUvn+75MJtM6mWHZsmULPLaVlRWTnxJACAEhBKysrGBnZ4ddu3YhMjISAQEBUodGRETFmFn3ASLLlpKSgiFDhmg0X1apUoXJDxERFZll9hols3flyhUEBQXh5s2bsLGxwahRo+Dr6yt1WEREVEKwBojMihAC69atQ7NmzXDz5k2UL18ex48fZ/JDREQGxRogMhtJSUn48MMPsWPHDgDAm2++iS1btqBMmTISR0ZERCUNEyAyCyqVCu3atUNoaChsbGywcOFCTJ48ucgL5hIREWnDbxcyC1ZWVvj4449RqVIlnDp1ClOmTGHyQ0RERsNvGJJMQkICLl++rH49cOBA3LhxAy1atJAwKiIisgRsAiNJhISEICgoCAqFAmFhYfD09AQAODo6ShxZ8WUrt8ZH3wVKHQYRUbHAGiAyKSEEli1bhlatWiEyMhJ2dnZ48uSJ1GEREZGFYQJEJvPs2TO88847mDRpEjIzM9GrVy+Ehoaibt26UodGREQWhgkQmcS5c+fQoEED/Prrr7Czs8PKlSvx008/wc3NTerQiIjIArEPEJnEqlWr8ODBA/j5+WH37t1o2LCh1CEREZEFYwJEJrF69Wp4eXlh7ty5cHFxkTocIiKycGwCI6P466+/MHbsWAghAAAuLi5YsmQJkx8iIjILTIDIoFQqFRYuXIh27dph1apV+OGHH6QOiYiIKBc2gZHBxMbGYtCgQThy5AiAlxMbvvvuuxJHRURElBsTIDKI4OBg9O/fH9HR0ShVqhRWrVqFoUOHQiaTSR0aERFRLmwCoyJbsWIF2rdvj+joaNSuXRshISF4//33mfwQEZHZYgJERdawYUPIZDK8//77uHDhAurUqSN1SERERPliExgVSmxsLLy8vAAArVq1wpUrV1C7dm2JoyIiItINa4BIL1lZWZg5cyaqVq2K69evq7cz+SEiouKENUCks6ioKPTv3x+nTp0CAPz6669s7iIiomKJCRDp5NChQxg0aBDi4+Ph7OyMdevWoW/fvlKHRUREVChsAqN8ZWZm4rPPPsObb76J+Ph4NGjQAJcuXWLyQ0RExRoTIMrXxo0b8eWXXwIAPvroI5w9exbVqlWTOCoiIqKiYRMY5Wv48OH4448/MGjQIM7qTEREJQZrgEhDRkYGlixZAoVCAQCwsbHBr7/+yuSHiIhKFNYAkVpkZCT69u2LCxcu4N69e/j222+lDomIiMgoWANEAIC9e/eiQYMGuHDhAtzc3NC+fXupQyIiIjIaJkAWTqFQYNy4cejduzcSExPRvHlzhIWFoUePHlKHRkREZDRMgCzY3bt30bJlS6xcuRIAMGXKFJw6dQqVK1eWODIiIiLjYh8gCyaTyRAREYHSpUvjhx9+QNeuXaUOiYiIyCSYAFkYpVIJa2trAICvry/27t2L6tWro0KFChJHRkREZDpsArMgt27dQuPGjXH48GH1tsDAQCY/RERkcSSvAVKpVAgJCYGNjQ0aNWqUb1mlUonTp0/n2l6zZk14e3trbFMoFLhy5Qrs7e1Rt25dyGQyg8Zd3Gzbtg2jRo1CamoqJk+ejI4dO8LKivkvERFZJskSIJVKhcWLF2PdunV4/vw5qlatiosXL+a7T1paGgICAtCgQQO4uLiot3/22Wfo0qWL+vWxY8fQt29fuLq6Ijk5GWXKlMHvv/8OX19fo12PuXrx4gXGjx+PDRs2AADatWuHbdu2MfkhIiKLJtm3oFKpxLNnz3DkyBEMHjxYr31Xr16N4OBg9c+ryU9iYiL69OmDkSNH4s6dO4iKioKPjw8GDRpk6Eswezdu3EDTpk2xYcMGyGQyzJkzB3/++SfKlSsndWhERESSkqwGyNbWVr3Ipr7u378PmUwGPz8/lClTRuO9X3/9FcnJyZgyZQqAl0s5TJ06FZ06dUJ4eDj8/f2LHHtxEBkZiSZNmuDFixcoW7Ystm/fjsDAQKnDIiIiMguS9wEqjIkTJ8Lb2xv//vsvunbtivXr18PDwwMAEBoaCj8/P7i5uanLZ/ctCg0N1ZoAKRQK9dpXAJCUlGTcCzABX19fBAUF4eHDh9i6dSvKli0rdUhERERmo1h1BLGxscH27dsRHR2N0NBQ3Lx5E2FhYfjoo4/UZZ4/f65OhrK5u7vDysoKz58/13rcRYsWwdXVVf1TsWJFo16HsVy9ehVxcXHq16tXr8bhw4eZ/BAREeVQrBIge3t79OvXT/26SpUqmDp1Kvbu3YusrCwAgJ2dHdLS0jT2y8jIgEqlgp2dndbjTps2DYmJieqfhw8fGu8ijEAIgfXr16Np06YYMmQIVCoVgJf3i52diYiIciv2346enp7IyMhAfHw8AKBy5cp4/PixRplHjx6p39NGLpfDxcVF46e4SE5OxoABAzBy5Eikp6dDCIEXL15IHRYREZFZM+sEKCsrC8HBwYiJiQHw8ss+p8OHD8PLywteXl4AgI4dO+LJkyc4f/68uswvv/wCJycnNG/e3DSBm0hoaCgaNmyIHTt2wNraGl988QUOHDgAJycnqUMjIiIya5J2gg4JCUFqaioePXqE5ORkBAcHAwDatGkDa2trpKSkICAgAJs2bcLQoUOxefNmnDx5Et27d4ebmxv++OMPbNiwARs3blQ39TRt2hTvvvsu+vfvj/nz5+PZs2eYPXs25s+fDwcHBwmv1nCEEFizZg0+/vhjKBQKVKxYETt37kTLli2lDo2IiKhYkAkhhFQnf//99xEZGZlr+8GDB1GqVCmkpKSgW7duGhMdHjx4EHv27EFcXByqVq2KDz74AHXr1tXYPyMjAytWrMDx48chl8sRFBSEoKAgneNKSkqCq6srEhMTzbI5LDU1Fa+//joiIiLw9ttvY9OmTShdurTUYREREUlKn+9vSRMgc2XuCRAA/PPPPzh16hQmTpxo8ct8EBERAfp9fxfLeYAsjRAC3377LeRyOT788EMAL+c2KmjtNCIiItKOCZCZe/78OYYNG4ZffvkFdnZ2aN++vcXMZk1ERGQsTIDM2Pnz5xEUFIT79+/Dzs4OX3/9NapVqyZ1WERERMWeWQ+Dt1QqlQrffPMNWrdujfv378PPzw9nz57FuHHj2N+HiIjIAFgDZGaEEOjduzd++eUXAECfPn2wfv16s+2MTUREVByxBsjMyGQyNG3aFHK5HN999x127tzJ5IeIiMjAOAxeC1MPg1epVIiPj1fPZq1SqXDnzh1Ur17d6OcmIiIqKfT5/mYNkMRiY2Px5ptvIjAwUL2Gl5WVFZMfIiIiI2ICJKGTJ0+ifv36OHLkCO7evYuLFy9KHRIREZFFYAIkAaVSifnz5yMwMBDR0dGoVasWLly4gDfeeEPq0IiIiCwCR4GZWExMDAYMGIDjx48DeLke2ooVK+Do6ChxZERERJaDCZCJjR07FsePH4eDgwO+++47DBo0SOqQiIiILA4TIBNbtmwZnj17hlWrVqFWrVpSh0NERGSRmACZWIUKFdTNX0RERCQNdoImIiIii8MEiIiIiCwOEyAiIiKyOEyAiIiIyOIwASIiIiKLwwSIiIiILA4TICIiIrI4TICIiIjI4jABIiIiIovDBIiIiIgsDhMgIiIisjhMgIiIiMjiMAEiIiIii8MEiIiIiCyOjdQBmCMhBAAgKSlJ4kiIiIhIV9nf29nf4/lhAqRFcnIyAKBixYoSR0JERET6Sk5Ohqura75lZEKXNMnCqFQqPH78GM7OzpDJZAY9dlJSEipWrIiHDx/CxcXFoMcuaXivdMd7pTveK93xXumO90o/xrpfQggkJyejXLlysLLKv5cPa4C0sLKyQoUKFYx6DhcXF35IdMR7pTveK93xXumO90p3vFf6Mcb9KqjmJxs7QRMREZHFYQJEREREFocJkInJ5XLMmTMHcrlc6lDMHu+V7nivdMd7pTveK93xXunHHO4XO0ETERGRxWENEBEREVkcJkBERERkcZgAERERkcVhAmQCKpUKf/zxB3r16oXmzZsjMTFRp/12796N7t27IzAwEHPmzEFqaqqRIzUPx48fR58+fdCuXTtMnDgRsbGx+ZZfsGABmjdvrvHz/vvvmyha07hy5QqGDBmCtm3bYvjw4bh9+7ZR9ikJHj16hDFjxqBdu3bo168fzp49m2/5U6dO5Xp+mjdvjri4OBNFLJ34+HgsXrwYbdq0wfTp03XaJyEhAZ999hkCAgLwzjvvYP/+/UaO0jykpqZi/fr1aN++PQYMGFBgeaVSqfW52rdvnwmildaNGzcwceJEdOjQAX379sWePXsKXJpCCIENGzbgrbfeQocOHfDVV18hIyPDqHFyIkQTePfdd5GWloa6deti3759yMzMLHCfNWvW4OOPP8bixYtRvnx5zJkzB6dOncLx48cNPju1OTl8+DC6deuGmTNnYsiQIViyZAlat26NsLAwODg4aN0nIiICbm5umDt3rnqbk5OTiSI2vn///RetWrXCgAED8Nlnn2Hbtm1o0aIFQkNDUalSJYPtUxI8f/4cLVq0QIMGDfDpp58iODgY7dq1Q3BwMFq2bKl1n2fPnuHKlSs4fvy4xnZdJ1Mrru7du4dWrVqhb9++UCqVOiXIWVlZ6NChA2xtbTFt2jTcunULvXr1wo8//oi+ffuaIGrp+Pv7480334SbmxsuX75cYHkhBM6fP49Vq1ahYcOG6u1Vq1Y1ZpiSO336ND766CMMHz4c3bp1w61btzBixAhcvnwZn3/+eZ77zZ07F8uXL8eSJUvg7OyMTz/9FJcuXcLOnTuNF6wgo0tISBBCCHHw4EEBQMTFxeVbPjMzU5QuXVosWLBAve3ff/8VAMShQ4eMGqvUGjZsKIYMGaJ+nZiYKBwcHMTKlSvz3GfIkCFiwIABJohOGv379xfNmjVTv1YqlaJ69epi7NixBt2nJJg/f77w9PQUCoVCva1r166iQ4cOee6zb98+4ejoaIrwzEp6erpIT08XQgjRo0cP0bt37wL32b59u7C2thbR0dHqbRMmTBBVqlQxWpzmIvv/8alTp4o6deoUWD4zM1MAECdOnDByZOYlNTVVqFQqjW3ffPONcHJyEkqlUus+z58/F3K5XKxfv1697cSJEwKACAsLM1qsbAIzAX3/kgwLC8PTp0/x9ttvq7fVrFkT1atXx59//mno8MzG8+fPcenSJY3rdnFxQbt27Qq87jNnziAgIAC9e/fGt99+q1MtW3Fx7NgxjXtiZWWFbt265XtPCrNPSXDs2DF07twZdnZ26m09evTAyZMn830mFAoFunbtis6dO+OTTz5BdHS0KcKVlFwu13sOlmPHjqFJkybw9vZWb+vRowfu3buHiIgIQ4doVgpbIzhr1iwEBARg2LBhBTbHlgQODg65WimcnJyQmZkJlUqldZ+//voLCoVC4/+sN954A25ubkb9P4sJkBm6f/8+AKBcuXIa28uVK6d+ryR68OABAP2v28nJCYMGDcL06dPx1ltvYcmSJejUqVOeH7biRKFQ4MmTJ3rdk8LsU1Lcv39f63VnZmbmmdTIZDK8++67+OCDDzB69Ghcu3YNtWvXRmRkpClCLlbyur/Z75Gm+vXrY9iwYZg2bRo8PDzQtm1bbNmyReqwTOrFixdYunQpunXrBhsb7b1u7t+/D2tra3h5eam3WVlZwdvb26jPFfsAFcL777+Pf//9N8/35XI5Tp48WejjZ/+lmvOvs1KlShW7mo1Zs2bh6NGj+ZbZv38/PD09C33d33zzjcY+LVu2RO3atfHbb7+hZ8+ehQ/eDBTmnpSk50dfmZmZWq87+z1tunTpgh49eqhfd+vWDfXr18e8efOwefNmo8VaHBXm/loqGxsbnD9/Xl0b2alTJwDAxx9/jMGDB0sZmskolUoMHDgQL168wKpVq/Isl5mZCTs7u1w1R8b+P4sJUCFMnjwZKSkpeb5vZVW0ijUPDw8ALztnvrpK7tOnT/Haa68V6dimNmTIEHTt2jXfMtlVy69e96uePn2K0qVL57l/zv+Qa9WqhfLly+Py5cvFPgFydHSEXC7X654UZp+SwsPDQ+t1Z7+nTc7nx8bGBu3atcOZM2eME2Qxlt/9LenPVmG82hQLAB06dMA333yDqKgolC9fXqKoTEOpVGLw4MEICQlBcHAwypYtm2dZDw8PpKWlIT09Hfb29urtxv4/iwlQIdStW9eox69fvz6srKwQEhKCKlWqAHhZjXjt2rVi95dDtWrVUK1aNZ3KVqlSBR4eHggJCUGHDh3U2y9cuIBu3brpfM6MjAw8e/YMjo6OesdrbmQyGerXr4+QkBCN7efPn0eDBg0Mtk9J0bBhQ63XXaVKFbi7u+t8nJiYmBLx/Bhaw4YN8e2330KlUqn/0Dt//jzkcjlq1aolcXTmLyYmBjKZLM8RrSWFSqXCkCFDcPLkSQQHB8PPzy/f8tmj5EJCQtCmTRsAQHR0NB49emTc/7OM1r2acslvFNjw4cPFzJkz1a+7d+8umjRpIlJTU4UQQsydO1c4OzuL2NhYk8UrhY8//lhUrlxZxMTECCGE2Lp1q7CyshKXL19Wl5kzZ44YOnSoEEKI5ORk8c0334iMjAwhhBAZGRli7NixQi6Xizt37pj+Aoxg3bp1wsnJSVy5ckUIIcTZs2eFnZ2d2LNnj7rMpk2bREBAgF77lESnTp0SMplM/PHHH0IIISIjI4Wnp6fGiMrjx4+LZs2aqT9LK1eu1BjV9Ntvvwlra2uxYsUK0wYvobxGgd2+fVs0a9ZM/PPPP0KIl/dTLper783z589FzZo1NUZulnR5jQJTKBSiWbNmYu/evUIIIQ4cOCDOnj2rfv/u3buiWrVqonPnziaLVQpKpVIMGjRIlC9fXoSHh+dZrmfPnmLp0qXq182aNROdO3cWmZmZQgghPvzwQ+Hj46P+DjQGJkAm8O2334pmzZqJmjVrCgCiUaNGolmzZuL48ePqMs2aNRNBQUHq10+ePBEtWrQQLi4uonLlyqJMmTLq/9RLshcvXogePXqIUqVKiWrVqglHR0eNoZFCCDFgwADRqFEjIYQQWVlZ4rPPPhMeHh6ibt26wt3dXVSvXl0cPXpUivCNQqVSiXHjxgk7OztRvXp1IZfLxfTp0zXKLFiwQGMoty77lFRLliwRpUqVUl/3gAED1AmyEELs2bNHABAPHz4UQgixd+9e4efnJ/z9/UWVKlWEs7OzWLhwYa6hvCVRQECAaNasmXB3dxceHh7qL6FsoaGhuYZy79mzR7i6uoqqVasKR0dHERgYqB4iXpINHTpUNGvWTJQrV06UKlVKNGvWTDRr1kx97WlpaQKAWLNmjRBCiJs3b4rAwEDh7e0tateuLezs7ES/fv0KnAaluPv5558FAFGlShX1Pcr+efz4sbpc5cqVxYQJE9SvIyIiRN26dYWHh4coV66cqFChgkYCaQxcDd4EHjx4gMePH+faXr16dXW/hOvXr0Mul+dqLoqIiEBKSgpq1aqVqz25JHv06BHi4uJQvXr1XE0RERER6okls2VkZOD27dsoXbo0fHx8TB2uScTFxeHRo0eoXLlyrv4sUVFRePz4MZo0aaLzPiVZUlISIiIi4O3tnet5ePbsGW7fvo2GDRuqP1NCCERGRkKlUqFKlSp5jlYpaUJCQqBUKjW22draolGjRgBeNr1fuXIFtWvX1uiPmJaWhlu3bsHNzU3dTF/SXbt2TWvfz8aNG8PGxkY98WHVqlU1RjPFx8cjJiYGvr6+FtGs+vTpU4SHh2t9r0GDBuo+d6GhofDw8EDlypU1yty6dQsZGRmoVauW0T+HTICIiIjI4nAeICIiIrI4TICIiIjI4jABIiIiIovDBIiIiIgsDhMgIiIisjhMgIiIiMjiMAEiIiIii8MEiIiIiCwOEyAiIiKyOEyAiKjEy8zMxK5du3D79m2N7SdOnEBwcLA0QRGRpJgAEVGJZ2trizNnzuCtt95CcnIyAOD06dPo1KkTsrKyJI6OiKTAtcCIyCKkp6ejSZMmaNSoEZYtW4bXX38d7733Hr7++mupQyMiCTABIiKLcfXqVTRt2hT+/v6wtrbG+fPn1SvCE5FlYRMYEVmM1157DX369MHVq1fx1VdfMfkhsmCsASIii3Hx4kW0bNkS1apVg6enJ06cOAErK/4dSGSJ+MknIouQmpqKAQMG4IMPPsCxY8dw48YNfPnll1KHRUQSYQ0QEVmEkSNH4vTp07h06RJKlSqFX3/9Fe+99x7OnTuHRo0aSR0eEZkYa4CIqMS7d+8ekpOTsX37dpQqVQoA0KNHD8yfPx/79++XODoikgJrgIiIiMjisAaIiIiILA4TICIiIrI4TICIiIjI4jABIiIiIovDBIiIiIgsDhMgIiIisjhMgIiIiMjiMAEiIiIii8MEiIiIiCwOEyAiIiKyOEyAiIiIyOIwASIiIiKL83/bXOc1Wpm6IgAAAABJRU5ErkJggg==",
- "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",
- "text": [
- "Batch: 19/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
- " Likelihood parameter acceptance fractions: [0.7]\n",
- "Batch: 20/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.520\n",
- " Likelihood parameter acceptance fractions: [0.77]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 21/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.520\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 22/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.71]\n",
- "Batch: 23/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 24/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.280\n",
- " Likelihood parameter acceptance fractions: [0.83]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 25/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
- " Likelihood parameter acceptance fractions: [0.78]\n",
- "Batch: 26/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.420\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 27/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.570\n",
- " Likelihood parameter acceptance fractions: [0.73]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 28/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.530\n",
- " Likelihood parameter acceptance fractions: [0.69]\n",
- "Batch: 29/100 completed, 100 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": "stream",
- "text": [
- "Batch: 34/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.560\n",
- " Likelihood parameter acceptance fractions: [0.71]\n",
- "Batch: 35/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.320\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 36/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.65]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 37/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.69]\n",
- "Batch: 38/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.620\n",
- " Likelihood parameter acceptance fractions: [0.8]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "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 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: 45/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.450\n",
- " Likelihood parameter acceptance fractions: [0.8]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 46/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.7]\n",
- "Batch: 47/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 48/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
- " Likelihood parameter acceptance fractions: [0.78]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 49/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.73]\n",
- "Batch: 50/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.360\n",
- " Likelihood parameter acceptance fractions: [0.77]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 51/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.480\n",
- " Likelihood parameter acceptance fractions: [0.72]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 52/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.500\n",
- " Likelihood parameter acceptance fractions: [0.77]\n",
- "Batch: 53/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.450\n",
- " Likelihood parameter acceptance fractions: [0.78]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 54/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.550\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 55/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.7]\n",
- "Batch: 56/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.380\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 57/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.390\n",
- " Likelihood parameter acceptance fractions: [0.73]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 58/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.77]\n",
- "Batch: 59/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.510\n",
- " Likelihood parameter acceptance fractions: [0.65]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 60/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 61/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.340\n",
- " Likelihood parameter acceptance fractions: [0.71]\n",
- "Batch: 62/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 63/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.290\n",
- " Likelihood parameter acceptance fractions: [0.68]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 64/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.71]\n",
- "Batch: 65/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.67]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 66/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.400\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 67/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.72]\n",
- "Batch: 68/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.560\n",
- " Likelihood parameter acceptance fractions: [0.69]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 69/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.440\n",
- " Likelihood parameter acceptance fractions: [0.72]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 70/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.540\n",
- " Likelihood parameter acceptance fractions: [0.8]\n",
- "Batch: 71/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.420\n",
- " Likelihood parameter acceptance fractions: [0.68]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 72/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.360\n",
- " Likelihood parameter acceptance fractions: [0.77]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 73/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.510\n",
- " Likelihood parameter acceptance fractions: [0.74]\n",
- "Batch: 74/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.510\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 75/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 76/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.390\n",
- " Likelihood parameter acceptance fractions: [0.72]\n",
- "Batch: 77/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.410\n",
- " Likelihood parameter acceptance fractions: [0.67]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 78/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.420\n",
- " Likelihood parameter acceptance fractions: [0.67]\n"
- ]
- },
- {
- "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",
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- " 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": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 84/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.460\n",
- " Likelihood parameter acceptance fractions: [0.79]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 85/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.580\n",
- " Likelihood parameter acceptance fractions: [0.66]\n",
- "Batch: 86/100 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: 87/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.370\n",
- " Likelihood parameter acceptance fractions: [0.68]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 88/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.510\n",
- " Likelihood parameter acceptance fractions: [0.73]\n",
- "Batch: 89/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.430\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 90/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.540\n",
- " Likelihood parameter acceptance fractions: [0.72]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 91/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.500\n",
- " Likelihood parameter acceptance fractions: [0.72]\n",
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- " Model parameter acceptance fraction: 0.510\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 93/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.390\n",
- " Likelihood parameter acceptance fractions: [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": [
- "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: 1.000\n",
- " Likelihood parameter acceptance fractions: [0.83]\n",
- "Batch: 2/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.940\n",
- " Likelihood parameter acceptance fractions: [0.76]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 3/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 1.000\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 4/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.69]\n",
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- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.79]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.8]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- " Model parameter acceptance fraction: 0.970\n",
- " Likelihood parameter acceptance fractions: [0.75]\n",
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- " Model parameter acceptance fraction: 0.980\n",
- " Likelihood parameter acceptance fractions: [0.73]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 9/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.970\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.73]\n"
- ]
- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.960\n",
- " Likelihood parameter acceptance fractions: [0.72]\n"
- ]
- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- " Likelihood parameter acceptance fractions: [0.72]\n"
- ]
- },
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.930\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
- ]
- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.970\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
- {
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- "output_type": "stream",
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- ]
- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.940\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
- ]
- },
- {
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- "output_type": "stream",
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- ]
- },
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- "output_type": "stream",
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- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
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- ]
- },
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- "output_type": "stream",
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- " Likelihood parameter acceptance fractions: [0.76]\n"
- ]
- },
- {
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.970\n",
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- ]
- },
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 1.000\n",
- " Likelihood parameter acceptance fractions: [0.76]\n"
- ]
- },
- {
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.68]\n"
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- },
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- "output_type": "stream",
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- },
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 42/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.910\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
- ]
- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- " Likelihood parameter acceptance fractions: [0.74]\n"
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- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- },
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- "output_type": "stream",
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- " Likelihood parameter acceptance fractions: [0.75]\n"
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- },
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- },
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- " Model parameter acceptance fraction: 0.990\n",
- " Likelihood parameter acceptance fractions: [0.74]\n"
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- },
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- },
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- " Model parameter acceptance fraction: 0.950\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
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- },
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- },
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- "output_type": "stream",
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- " Model parameter acceptance fraction: 0.960\n",
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- },
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- },
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- " Model parameter acceptance fraction: 0.980\n",
- " Likelihood parameter acceptance fractions: [0.75]\n"
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- },
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- },
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- " Model parameter acceptance fraction: 0.970\n",
- " Likelihood parameter acceptance fractions: [0.71]\n"
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- },
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- " Model parameter acceptance fraction: 0.990\n",
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- },
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- },
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- "Batch: 92/100 completed, 100 steps. \n",
- " Model parameter acceptance fraction: 0.940\n",
- " Likelihood parameter acceptance fractions: [0.77]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Batch: 93/100 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: 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": {
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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/pyproject.toml b/pyproject.toml
index 254beb5..7fce863 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",
]
@@ -71,16 +75,16 @@ write_to = "src/rxmc/__version__.py"
[tool.black]
line-length = 88
-target-version = ["py310"]
+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
-target-version = "py310"
+target-version = "py312"
[tool.ruff.lint]
select = ["F", "I"]
@@ -88,9 +92,16 @@ 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 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 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 with -m slow on pushes and PRs to main",
+]
diff --git a/requirements.txt b/requirements.txt
index 9987df3..685b026 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,4 @@
-scikit-learn>=1.0.0
numpy>=2.2.6
-pandas>=2.2
scipy>=1.15
-pint>=0.2
jitr>=3.0
exfor-tools>=0.4
diff --git a/src/rxmc/__init__.py b/src/rxmc/__init__.py
index 5685810..d88c5b8 100644
--- a/src/rxmc/__init__.py
+++ b/src/rxmc/__init__.py
@@ -1,45 +1,65 @@
-from . import adaptive_metropolis as adaptive_metropolis
-from . import config as config
+"""rxmc: Bayesian calibration of reaction models with composable error models.
+
+See ``docs/design.md`` for the design and ``docs/recipes.md`` for the
+supported use cases.
+"""
+
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 data as data
+from . import diagnostics as diagnostics
+from . import likelihood as likelihood
+from . import model as model
from . import predictive as predictive
-from . import priors as priors
+from . import problem as problem
+from . import reactions as reactions
+from . import terms as terms
from . import transforms as transforms
-from . import walker as walker
-from .__version__ import __version__ as __version__
+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 .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
+from .problem import Problem as Problem
+from .terms import KernelTerm as KernelTerm
+from .terms import Term as Term
+
+try:
+ from .__version__ import __version__ as __version__
+except ImportError: # pragma: no cover - source checkout without a build
+ __version__ = "0+unknown"
__all__ = [
"__version__",
- "adaptive_metropolis",
- "config",
+ "Chi2",
+ "Comparison",
+ "Constraint",
+ "Dataset",
+ "Gaussian",
+ "KernelTerm",
+ "Model",
+ "Parameter",
+ "Problem",
+ "StudentT",
+ "Term",
+ "from_measurement",
+ "polynomial",
"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",
+ "data",
+ "diagnostics",
+ "likelihood",
+ "model",
"predictive",
- "priors",
- "walker",
+ "problem",
+ "reactions",
+ "terms",
+ "transforms",
+ "units",
]
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
index 73ef398..ef5e802 100644
--- a/src/rxmc/constraint.py
+++ b/src/rxmc/constraint.py
@@ -1,483 +1,333 @@
"""
-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.
+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 .covariance import ConstraintCovariance, StackContext, stacked_supports
-from .likelihood_model import GaussianLikelihood
-from .observation import Observation
-from .physical_model import PhysicalModel
+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"]
-class Constraint:
- """Pair observations with a physical model and a stacked covariance.
+
+@dataclass(eq=False, frozen=True)
+class Comparison:
+ """One dataset compared with one model in one space.
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.
+ 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.
"""
- 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():
+ 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__}")
+ 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"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"
+ 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"
)
- 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,
- )
+ 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"):
+ # 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))
- def complement(self):
- """The held-out counterpart: every currently inactive point becomes active
- and every active point inactive.
+ @property
+ def n(self) -> int:
+ return self.data.n
- 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 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 _validate_constant_covariance(self):
- """Fail fast on a singular constant covariance (also warms the cache).
+ def log_jacobian(self, mask=None) -> float:
+ r"""``sum(log |space'(data.y)|)`` over the active points.
- 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.
+ 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.
"""
- 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)
+ 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. 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 = []
+ 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 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))
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
+ 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"
+ )
- # ------------------------------------------------------------------
- # Stacking
- # ------------------------------------------------------------------
+ def basis(c, eta=eta, t=t, inv=inv):
+ ym_raw = inv(c.ym)
+ return eta * ym_raw * np.abs(t.derivative(ym_raw))
- def _stack(self, model_params):
- ym = [self.physical_model(o, *model_params) for o in self.observations]
- return self._stack_from_predictions(ym)
+ terms.append(Term(basis, kind="mode", on=self))
+ return terms
- 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 __repr__(self):
+ label = self.data.label or "dataset"
+ return f"Comparison({label!r}, {self.model!r}, space={self.space.name})"
- 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 _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 _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 _as_mask(mask, n) -> np.ndarray:
+ # 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
- 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)
+@dataclass(eq=False, frozen=True)
+class Constraint:
+ """The maximal block of mutually correlated data: one likelihood.
- def predict(self, *model_params, raw=False):
- """Predictions for each observation (all points, comparison space).
+ 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.
+ """
- 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
+ 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 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))
+ 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 y(self) -> np.ndarray:
- """Stacked observed data on the active points (comparison space)."""
- return self._y_stacked[self.active]
+ def n_total(self) -> int:
+ return int(sum(c.n for c in self.comparisons))
@property
- def x(self) -> np.ndarray:
- """Stacked independent variable on the active points."""
- return self._x_stacked[self.active]
+ def n_active(self) -> int:
+ return int(self.active.size)
@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
- )
- )
+ """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)
- 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]
- )
+ # -- masked views ---------------------------------------------------------
- def empirical_coverage(
- self, ylow: list[np.ndarray], yhigh: list[np.ndarray], xlim=None
- ):
- """Fraction of active data points within a predictive interval.
+ def masked(self, masks) -> "Constraint":
+ """The same constraint with new active-point masks."""
+ return replace(self, masks=masks)
- ``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
+ 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/src/rxmc/covariance.py b/src/rxmc/covariance.py
index 86e349e..36739cb 100644
--- a/src/rxmc/covariance.py
+++ b/src/rxmc/covariance.py
@@ -1,904 +1,367 @@
"""
-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
-coordinate :class:`~rxmc.transforms.Transform`) and its parameter values, plus a
-``kind`` that says how the returned array enters the covariance:
-
-``"diag"``
- ``fn`` returns a standard-deviation vector ``v``; ``Sigma_ii += v_i**2``.
-``"mode"``
- ``fn`` returns a vector ``v``; ``Sigma += outer(v, v)`` (one correlated mode).
-``"matrix"``
- ``fn`` returns a full block ``M``; ``Sigma_block += M``.
-
-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=...)``.
-"""
+The structured covariance of a constraint.
-from dataclasses import dataclass
+The three term kinds describe a decomposition of the stacked covariance over
+a constraint's comparisons (one block per comparison):
-import numpy as np
-import scipy as sc
-
-from .params import Parameter
-from .transforms import as_transform
-
-__all__ = [
- "StackContext",
- "TermContext",
- "Term",
- "ConstraintCovariance",
- "stacked_supports",
- "chol_logdet",
- "as_2d",
- "ones",
- "ym",
- "averaging",
- "x_basis",
- "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",
-]
-
-KINDS = ("diag", "mode", "matrix")
-
-
-@dataclass(frozen=True)
-class StackContext:
- """Bundle of stacked arrays passed to every :meth:`Term.add_to`.
+.. math::
- 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.
- """
+ \\Sigma = \\mathrm{diag}(D) + \\mathrm{blockdiag}(M_b) + U U^T
- x: np.ndarray
- y: np.ndarray
- ym: np.ndarray | None
- supports: tuple
+``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``,
- @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))
+.. 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
-@dataclass(frozen=True)
-class TermContext:
- """A term's local view of the stack on its own support.
+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.
- ``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.
- """
+``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
- x: np.ndarray
- y: np.ndarray
- ym: np.ndarray | None
- support: np.ndarray
+from .terms import Term
- def __len__(self):
- return len(self.support)
+__all__ = ["StructuredCovariance", "chol_logdet"]
-def stacked_supports(observations) -> tuple:
- """One contiguous index array per observation, in stacking order.
+class _SingularCovariance(ValueError):
+ """A parametric covariance that is singular at the requested ``theta``.
- 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).
+ The compiled constraint reads it as zero density (``log_likelihood`` is
+ ``-inf``) so a sampler stepping onto such a point moves on.
"""
- 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)
+ L = sla.cholesky(np.asarray(Sigma, dtype=float), 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
+class _Entry:
+ """One term placed on rows of the stack, with its gather into theta."""
+ __slots__ = (
+ "term",
+ "rows",
+ "gather",
+ "pos",
+ "keep",
+ "block",
+ "segments",
+ "labels",
+ )
-# ----------------------------------------------------------------------------
-# The term
-# ----------------------------------------------------------------------------
-
-
-class Term:
- """One additive contribution to the stacked 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
- 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).
- 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`.
- 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`.
- 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.
- """
-
- 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:
- 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))
-
- # -- structure ----------------------------------------------------------
-
- @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)
- 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 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:
- """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:
- raise ValueError(
- f"{self.kind} term fn returned shape {v.shape}, "
- f"expected {self._expected_shape}"
- )
- 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 __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})"
-
+ 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)
+ # 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
+ # 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)
-# ----------------------------------------------------------------------------
-# Constraint-local covariance: gather-by-identity over the stacked space
-# ----------------------------------------------------------------------------
+def _meta_rows(meta, rows):
+ if meta is None:
+ return None
+ return {k: np.asarray(v)[rows] for k, v in meta.items()}
-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).
+class StructuredCovariance:
+ """``diag(D) + blockdiag(M_b) + U Uᵀ`` over the active rows of a constraint.
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.
+ 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, 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]
+ 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))]
)
- 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.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, self.labels)
+ 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.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
+ 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
)
-
- 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])
+ self._cache = None
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
+ self._factor(self._D0, self._U0, self._M0, self._X0)
+ 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 -------------------------------------------------------------
+
+ 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),
+ segments=e.segments,
+ labels=e.labels,
+ )
+ 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
- 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.
+ # -- factorisation ---------------------------------------------------------
- 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)
+ 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:
+ error = ValueError if self.is_constant else _SingularCovariance
+ raise error(self._singular_message(D)) from err
if self.is_constant:
- self._block_chol_cache = factors
+ self._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
-
-
-# ----------------------------------------------------------------------------
-# Standard bases and amplitudes (numpy-style callables over a TermContext)
-# ----------------------------------------------------------------------------
-
-
-def ones(c: TermContext) -> np.ndarray:
- """Constant unit basis."""
- return np.ones(len(c))
-
-
-def ym(c: TermContext) -> np.ndarray:
- """The model prediction — the prediction-scaled basis."""
- return c.ym
-
-
-def averaging(c: TermContext) -> np.ndarray:
- """``0.5 * (y + ym)`` — the averaging model-error basis."""
- return 0.5 * (c.y + c.ym)
-
-
-_AVERAGING = averaging # factories take an ``averaging`` flag that shadows the name
-
-
-def x_basis(scale: float = 1.0):
- """Basis ``x / scale`` (e.g. ``x_basis(np.pi)`` for ``theta/180`` on radians)."""
-
- def basis(c: TermContext) -> np.ndarray:
- return np.asarray(c.x, dtype=float) / scale
-
- return basis
-
-
-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,))``.
- """
-
- def basis(c: TermContext, slope: float) -> np.ndarray:
- return base(c) * np.exp(slope * np.asarray(c.x, dtype=float) / scale)
-
- return basis
-
-
-def constant_amplitude(c: TermContext, log_amplitude: float) -> np.ndarray:
- """Kernel amplitude ``exp(log_amplitude)``, constant over the support."""
- return np.full(len(c), np.exp(log_amplitude))
-
-
-def exp_growth_amplitude(scale: float = 1.0):
- """Kernel amplitude ``exp(log_amplitude) * exp(slope * x / scale)`` (two params)."""
-
- def amplitude(c: TermContext, log_amplitude: float, slope: float) -> np.ndarray:
- return np.exp(log_amplitude) * np.exp(
- slope * np.asarray(c.x, dtype=float) / scale
- )
-
- return amplitude
-
-
-# ----------------------------------------------------------------------------
-# Term factory helpers (the assembly-time builders)
-# ----------------------------------------------------------------------------
-
-
-def _masked(magnitude, mask=None):
- """A scalar/array magnitude with an optional mask.
-
- Scalar-like values include 0-d ndarrays (e.g. ``np.array(0.05)`` as stored by
- ``exfor_tools`` distributions), not just Python scalars. An array magnitude
- must have exactly the support's length; this is checked by :func:`_full`
- when the term is evaluated.
- """
- v = float(magnitude) if np.ndim(magnitude) == 0 else np.asarray(magnitude, float)
- if mask is not None:
- v = v * np.asarray(mask, dtype=float)
- return v
-
-
-def _full(v, n):
- """``v`` as a length-``n`` vector.
-
- Scalars are broadcast; arrays must already have shape ``(n,)`` — a length-1
- array is *not* treated as a scalar, so a magnitude or mask of the wrong
- length fails loudly instead of being spread over the support.
- """
- v = np.asarray(v, dtype=float)
- if v.ndim == 0:
- return np.full(n, float(v))
- if v.shape != (n,):
- raise ValueError(
- f"magnitude/basis has shape {v.shape} but the term's support has "
- f"length {n}"
- )
- return v
-
-
-def _coefficient(parameter, log):
- """Parameter -> multiplicative coefficient ``exp(theta)`` (``log``) or ``theta``."""
- if parameter is None:
- return (), (lambda values: 1.0)
- if log:
- return (parameter,), (lambda values: np.exp(values[0]))
- return (parameter,), (lambda values: values[0])
-
-
-def _scaled_term(
- kind, parameter, log, basis, basis_params=(), *, support=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)
- nc = len(cparams)
-
- 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)
-
-
-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 offset_term(
- magnitude=None, parameter=None, mask=None, log=True, support=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")
- 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)
-
-
-def normalization_term(
- magnitude=None, parameter=None, mask=None, log=True, support=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``.
- """
- if magnitude is None and parameter is None:
- raise ValueError("normalization_term 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)
-
-
-def noise_term(
- parameter, log=True, basis=None, basis_params=(), support=None, coords=None
-) -> Term:
- """Unknown statistical noise ``diag((epsilon * basis)**2)``.
-
- ``basis`` defaults to ones (constant noise); pass any
- ``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``.
- """
- return _scaled_term(
- "diag",
- parameter,
- log,
- ones if basis is None else basis,
- basis_params,
- support=support,
- coords=coords,
- )
-
-
-def noise_fraction_term(parameter, log=True, support=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).
- """
- return _scaled_term("diag", parameter, log, ym, support=support)
-
-
-def model_error_term(parameter, averaging=True, log=True, support=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)
-
-
-def systematic_term(
- parameter, basis, log=True, basis_params=(), support=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.
- """
- return _scaled_term(
- "mode", parameter, log, basis, basis_params, support=support, coords=coords
- )
-
-
-def _kernel_params(kernel, prefix) -> list:
- """One :class:`~rxmc.params.Parameter` per free kernel hyperparameter element."""
- params = []
- for hp in kernel.hyperparameters:
- if hp.fixed:
- continue
- if hp.n_elements == 1:
- params.append(Parameter(f"{prefix}_{hp.name}", float, latex_name=hp.name))
- else:
- params.extend(
- Parameter(
- f"{prefix}_{hp.name}_{i}", float, latex_name=f"{hp.name}[{i}]"
- )
- for i in range(hp.n_elements)
+ 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 params
-
+ 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 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, blocks)) from err
+
+ 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 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:
+ return msg + (
+ " although its diagonal is nonzero: a matrix term dominates it "
+ "(e.g. a kernel amplitude large against the diagonal)"
+ )
+ 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."
+ )
-def kernel_term(
- kernel,
- coords=None,
- amplitude=None,
- amplitude_params=(),
- jitter=1e-10,
- prefix="discrepancy",
- support=None,
-) -> Term:
- """A Gaussian-process kernel ``a a^T * K(x, x; theta)`` over the support.
+ # -- public --------------------------------------------------------------
- One :class:`~rxmc.params.Parameter` is auto-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.
+ def distance(self, ym, theta=()):
+ r"""``(d2, logdet)`` of the residual ``y - ym`` on the active rows.
- Parameters
- ----------
- kernel : sklearn-style kernel
- Duck-typed on ``hyperparameters``, ``theta``, ``clone_with_theta`` and
- ``__call__``.
- coords : Transform or callable, optional
- Coordinate transform of ``x`` the kernel is evaluated in (e.g. angle to
- momentum transfer). Default: ``x`` itself.
- amplitude : callable or array, optional
- ``amplitude(c, *amplitude_values) -> vector a``; the block becomes
- ``outer(a, a) * K``. Like the kernel, it sees the *transformed*
- coordinate: ``c.x`` is ``coords(x)``. See :func:`constant_amplitude`,
- :func:`exp_growth_amplitude`.
- amplitude_params : sequence of Parameter, optional
- Parameters consumed by ``amplitude``.
- 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
- See :class:`Term`.
- """
- kparams = _kernel_params(kernel, prefix)
- nk = 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]
- else:
- K = kernel.clone_with_theta(np.asarray(values[:nk], dtype=float))(
- as_2d(c.x)
- )
- if amplitude is not None:
- a = amplitude(c, *values[nk:]) if callable(amplitude) else amplitude
- a = np.broadcast_to(np.asarray(a, dtype=float), (len(c),))
- K = np.outer(a, a) * K
- else:
- K = np.array(K, dtype=float)
- K[np.diag_indices_from(K)] += jitter
- return K
-
- return Term(
- fn,
- tuple(kparams) + amplitude_params,
- kind="matrix",
- support=support,
- coords=coords,
- constant=nk == 0 and not amplitude_params and not callable(amplitude),
- )
+ ``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=(), 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)
+ 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/data.py b/src/rxmc/data.py
new file mode 100644
index 0000000..db6a3f4
--- /dev/null
+++ b/src/rxmc/data.py
@@ -0,0 +1,248 @@
+"""
+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 types import SimpleNamespace
+from typing import Any, Mapping
+
+import numpy as np
+
+__all__ = ["Dataset", "from_measurement"]
+
+
+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:
+ 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
+
+
+@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})"
+
+
+# ----------------------------------------------------------------------------
+# EXFOR measurements
+# ----------------------------------------------------------------------------
+
+_QUANTITY_KIND = {
+ "dXS/dA": "differential",
+ "dXS/dRuth": "dimensionless",
+ "Ay": "dimensionless",
+}
+
+
+_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:
+ """A :class:`Dataset` from an ``exfor_tools`` measurement, in internal 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``: 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
+ 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 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
+ 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
+
+ measurement = _as_record(measurement)
+ 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}"
+ )
+ 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)
+
+ 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/src/rxmc/diagnostics.py b/src/rxmc/diagnostics.py
new file mode 100644
index 0000000..85be867
--- /dev/null
+++ b/src/rxmc/diagnostics.py
@@ -0,0 +1,453 @@
+"""Sampler-agnostic posterior checks on ``(problem, samples)``.
+
+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` — 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)``.
+* :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 problem (``Problem([fit.complement()])``).
+* :func:`logz_summary`, :func:`compare_logz` — nested-sampling evidence
+ bookkeeping with replicate-based errors and a conservative tie verdict.
+
+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
+
+import numpy as np
+import scipy.linalg as sla
+from scipy.special import logsumexp
+
+from .likelihood import Gaussian
+from .problem import CompiledConstraint, Problem
+
+__all__ = [
+ "predictive_draws",
+ "coverage_curve",
+ "coverage_error",
+ "sharpness",
+ "heldout_log_predictive",
+ "log_posterior_predictive",
+ "logz_summary",
+ "compare_logz",
+]
+
+_DEFAULT_LEVELS = np.linspace(0.02, 0.98, 49)
+
+
+# ----------------------------------------------------------------------------
+# Helpers
+# ----------------------------------------------------------------------------
+
+
+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) -> np.ndarray:
+ """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:
+ return np.linalg.cholesky(Sigma)
+ except np.linalg.LinAlgError:
+ pass
+ scale = max(float(np.mean(np.diag(Sigma))), np.finfo(float).tiny)
+ try:
+ return np.linalg.cholesky(Sigma + jitter * scale * np.eye(len(Sigma)))
+ except np.linalg.LinAlgError:
+ w, V = np.linalg.eigh(Sigma)
+ # 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]:
+ """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."""
+
+ 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 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)
+ 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(
+ problem: Problem,
+ 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 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,
+ 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.
+
+ ``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
+ 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``.
+ 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
+ model_only : bool, optional
+ 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.
+ 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: 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)
+ samples = _rows(samples, problem.ndim)
+ 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 if return_draws else np.percentile(out, levels, axis=0)
+
+ out = np.empty((n * n_rep, N))
+ for i in range(n):
+ theta = samples[i]
+ if cond:
+ mu, Sigma = cond(theta)
+ else:
+ 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 if return_draws else np.percentile(out, levels, axis=0)
+
+
+def coverage_curve(draws, y, levels=None) -> np.ndarray:
+ """Empirical coverage of central predictive intervals at each nominal level.
+
+ Parameters
+ ----------
+ draws : array_like, shape (n_draws, n_pts)
+ y : array_like, shape (n_pts,)
+ 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.
+
+ Returns
+ -------
+ np.ndarray
+ Fraction of points inside the central ``level`` interval, per level.
+ """
+ draws = np.asarray(draws, dtype=float)
+ y = np.asarray(y, dtype=float)
+ 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)
+ out[i] = np.mean((y >= lo) & (y <= hi))
+ return out
+
+
+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, dtype=float)
+ return float(np.max(np.abs(coverage_curve(draws, y, levels) - levels)))
+
+
+def sharpness(draws, percentiles=(16, 84), transform=None) -> np.ndarray:
+ """Per-point width of a central predictive interval.
+
+ Parameters
+ ----------
+ 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 %. :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
+ physical units for a log comparison space).
+ """
+ draws = np.asarray(draws, dtype=float)
+ if transform is not None:
+ draws = transform(draws)
+ lo, hi = np.percentile(draws, percentiles, axis=0)
+ return hi - lo
+
+
+# ----------------------------------------------------------------------------
+# 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_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,)
+ """
+ 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:
+ """``log E_post[p(y_held | theta)]``: the joint log posterior predictive
+ density of the held-out block.
+
+ A log-mean-exp over posterior samples of :func:`heldout_log_predictive`
+ values; pass ``logw`` (unnormalised log importance weights, e.g.
+ nested-sampling ``logwt``) for weighted samples. This is the joint
+ predictive of the whole held-out block, not the pointwise-summed ``elpd``
+ of Vehtari et al.; divide by the number of held-out points for a
+ per-point score.
+ """
+ logp = np.asarray(logp_samples, dtype=float)
+ if logw is None:
+ return float(logsumexp(logp) - np.log(len(logp)))
+ logw = np.asarray(logw, dtype=float)
+ return float(logsumexp(logp + logw) - logsumexp(logw))
+
+
+# ----------------------------------------------------------------------------
+# 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). 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.
+ """
+ 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)
+
+
+def compare_logz(a, b, sigma: float = 2.0) -> dict:
+ """``Delta log Z = a - b`` with a conservative tie verdict.
+
+ Parameters
+ ----------
+ a, b : (mean, err) or (mean, err, n)
+ As returned by :func:`logz_summary`; a trailing replicate count is
+ accepted and ignored.
+ sigma : float, optional
+ A difference no larger than ``sigma * hypot(err_a, err_b)`` is a
+ ``"tie"``.
+
+ Returns
+ -------
+ dict
+ ``{"dlogZ": ..., "err": ..., "verdict": "a" | "b" | "tie"}``.
+ """
+ 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:
+ verdict = "tie"
+ else:
+ verdict = "a" if d > 0 else "b"
+ return {"dlogZ": d, "err": err, "verdict": verdict}
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/elastic_diffxs_observation.py b/src/rxmc/elastic_diffxs_observation.py
deleted file mode 100644
index d4cc6e5..0000000
--- a/src/rxmc/elastic_diffxs_observation.py
+++ /dev/null
@@ -1,328 +0,0 @@
-"""
-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`.
-"""
-
-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,
-)
-
-
-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}",
- )
-
- # 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
-
- # 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
- ),
- )
-
- @property
- def k(self) -> float:
- """Entrance-channel wavenumber in fm^-1."""
- return float(self.kinematics.k)
-
- @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}"
- )
-
- conversion_factor = 1.0 / measurement_unit.to(rutherford_unit).magnitude
- return self.constraint_workspace.rutherford * conversion_factor, y_unit
-
- 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}"
- )
-
- return 1.0 / measurement_unit.to(y_unit).magnitude, y_unit
-
- 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 set_up_solver(
- reaction: jitr.reactions.Reaction,
- Elab: float,
- angle_rad_constraint: np.ndarray,
- angle_rad_vis: np.ndarray,
- lmax: int,
- wavelengths_beyond_range: float = 2.0,
- zeros_per_node: int = 5,
-):
- """
- Set up ``jitr`` workspaces for a reaction at a given 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.
- lmax : int
- Maximum angular momentum.
- 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``.
-
- 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.
- """
- 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
- )
- visualization_ws = jitr.xs.elastic.DifferentialWorkspace(
- integral_workspace=integral_ws, angles=angle_rad_vis
- )
-
- return constraint_ws, visualization_ws, kinematics
-
-
-def momentum_transfer(observation: ElasticDifferentialXSObservation) -> np.ndarray:
- r"""Momentum transfer :math:`q = 2k\sin(\theta/2)` (fm^-1) on the data angles.
-
- 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))``.
- """
- return 2.0 * observation.k * np.sin(np.asarray(observation.x, dtype=float) / 2.0)
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/ias_pn_observation.py b/src/rxmc/ias_pn_observation.py
deleted file mode 100644
index c448fe2..0000000
--- a/src/rxmc/ias_pn_observation.py
+++ /dev/null
@@ -1,242 +0,0 @@
-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,
-)
-
-
-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`.
-
- It is designed to handle (p,n) IAS reaction measurements in differential cross
- section form.
-
- 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.
- """
-
- 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
- ),
- )
-
- @classmethod
- def from_measurement(
- cls,
- measurement: Distribution,
- reaction: jitr.reactions.Reaction,
- ExIAS: float,
- **kwargs,
- ):
- """Construct from an ``exfor_tools`` ``Distribution``.
-
- ``**kwargs`` (solver settings, ``transform``, ``mask``, ...) are
- forwarded to the constructor.
- """
- return cls(
- reaction=reaction, ExIAS=ExIAS, **measurement_kwargs(measurement), **kwargs
- )
-
-
-def set_up_solver(
- reaction: jitr.reactions.Reaction,
- Elab: float,
- ExIAS: float,
- angle_rad_constraint: np.array,
- angle_rad_vis: np.array,
- lmax: int,
- wavelengths_beyond_range: float = 2.0,
- zeros_per_node: int = 5,
-):
- """
- Set up the solver for the reaction.
-
- Parameters
- ----------
- reaction :
- Reaction information.
- 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.
-
- Returns
- -------
- tuple
- constraint and visualization workspaces.
- """
- 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(
- reaction,
- kinematics_entrance,
- kinematics_exit,
- core_solver,
- angle_rad_constraint,
- lmax,
- channel_radius_fm,
- tmatrix_abs_tol=1e-8,
- )
-
- 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,
- )
diff --git a/src/rxmc/likelihood.py b/src/rxmc/likelihood.py
new file mode 100644
index 0000000..1808cd3
--- /dev/null
+++ b/src/rxmc/likelihood.py
@@ -0,0 +1,128 @@
+"""
+Likelihood functionals over the stacked residual of a constraint.
+
+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:`Gaussian` — the multivariate normal (parameter-free).
+* :class:`StudentT` — a heavy-tailed variant carrying a degrees-of-freedom
+ parameter ``nu``; one radial tail factor for the whole residual.
+* :class:`Chi2` — drops the log-determinant normalisation (pure chi-squared).
+"""
+
+from __future__ import annotations
+
+from math import inf
+
+import numpy as np
+from scipy import stats
+from scipy.special import betaln, gammaln
+
+from .params import Parameter
+
+__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 in
+ ``params``. The chi-squared statistic is likelihood-independent (always
+ the Mahalanobis distance).
+ """
+
+ params: tuple[Parameter, ...] = ()
+
+ def log_likelihood(self, d2, logdet, n, *values) -> float:
+ raise NotImplementedError
+
+ 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)."""
+
+ def log_likelihood(self, d2, logdet, n, *values) -> float:
+ return log_likelihood(d2, logdet, n)
+
+
+class StudentT(Likelihood):
+ r"""Multivariate Student-t likelihood with a degrees-of-freedom parameter.
+
+ .. math::
+
+ \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",
+ 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",
+ 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:
+ # lnG((n+nu)/2) - lnG(nu/2), without the cancellation at large nu
+ return (
+ 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)
+ )
+
+
+class Chi2(Likelihood):
+ """Generalised chi-squared functional: drops the log-det normalisation."""
+
+ def log_likelihood(self, d2, logdet, n, *values) -> float:
+ return -0.5 * d2
+
+
+def log_likelihood(d2: float, logdet: float, n: int) -> float:
+ r"""Multivariate-normal log likelihood from pre-computed statistics.
+
+ Parameters
+ ----------
+ 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.
+ """
+ return -0.5 * (d2 + logdet + n * np.log(2 * np.pi))
diff --git a/src/rxmc/likelihood_model.py b/src/rxmc/likelihood_model.py
deleted file mode 100644
index a819cf5..0000000
--- a/src/rxmc/likelihood_model.py
+++ /dev/null
@@ -1,155 +0,0 @@
-"""
-Likelihoods over the stacked residual of a :class:`~rxmc.constraint.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:
-
-* :class:`GaussianLikelihood` — the multivariate normal (parameter-free).
-* :class:`StudentT` — a heavy-tailed variant carrying a degrees-of-freedom
- parameter ``nu``.
-* :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.
-"""
-
-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",
-]
-
-
-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).
- """
-
- params: tuple = ()
- n_params: int = 0
-
- def log_likelihood(self, d2, logdet, n, *like_params):
- raise NotImplementedError
-
- def chi2(self, d2, logdet, n, *like_params):
- return d2
-
-
-class GaussianLikelihood(Likelihood):
- """Multivariate-normal likelihood over the stacked residual.
-
- Parameter-free — all uncertainty lives on the covariance terms.
- """
-
- def log_likelihood(self, d2, logdet, n, *like_params):
- return log_likelihood(d2, logdet, n)
-
-
-class StudentT(Likelihood):
- r"""Multivariate Student-t likelihood with a degrees-of-freedom parameter.
-
- .. math::
-
- \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)
- """
-
- 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 log_likelihood(self, d2, logdet, n, nu):
- return (
- gammaln((n + nu) / 2.0)
- - gammaln(nu / 2.0)
- - 0.5 * n * np.log(np.pi * nu)
- - 0.5 * logdet
- - 0.5 * (nu + n) * np.log1p(d2 / nu)
- )
-
-
-class Chi2(Likelihood):
- """Generalised chi-squared functional — drops the log-det normalisation."""
-
- def log_likelihood(self, d2, logdet, n, *like_params):
- 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):
- 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
- $\log \det \Sigma$.
- n : int
- Number of data points.
-
- Returns
- -------
- float
- Log likelihood value.
- """
- return -0.5 * (mahalanobis_sqr + log_det + n * np.log(2 * np.pi))
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/model.py b/src/rxmc/model.py
new file mode 100644
index 0000000..2003160
--- /dev/null
+++ b/src/rxmc/model.py
@@ -0,0 +1,157 @@
+"""
+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.
+ meta : mapping, optional
+ The dataset metadata the model was bound with; a term evaluated at the
+ predictor's grid (:func:`~rxmc.predictive.grid_draws`) reads
+ it through ``c.meta(key)``.
+ """
+
+ 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):
+ 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), meta)
+
+ 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:]), meta)
+
+
+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:])), meta)
+
+
+def polynomial(order: int) -> Model:
+ """A polynomial of the given order with parameters ``a0`` to ``a``.
+
+ ``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/model_comparison.py b/src/rxmc/model_comparison.py
deleted file mode 100644
index 162c679..0000000
--- a/src/rxmc/model_comparison.py
+++ /dev/null
@@ -1,333 +0,0 @@
-"""
-Sampler-agnostic model-comparison and predictive-checking utilities.
-
-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:
-
-* :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.
-* :func:`heldout_log_predictive`, :func:`log_posterior_predictive` —
- out-of-sample scoring on a held-out constraint (e.g.
- ``constraint.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.
-"""
-
-from __future__ import annotations
-
-import numpy as np
-from scipy.special import logsumexp
-
-__all__ = [
- "log_jacobian",
- "predictive_draws",
- "coverage_curve",
- "coverage_error",
- "sharpness",
- "heldout_log_predictive",
- "log_posterior_predictive",
- "logz_summary",
- "compare_logz",
- "split_samples",
-]
-
-
-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)
-
-
-_DEFAULT_LEVELS = np.linspace(0.02, 0.98, 49)
-
-
-def _psd_factor(Sigma, jitter=1e-10):
- """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.
- """
- Sigma = np.asarray(Sigma, dtype=float)
- try:
- return np.linalg.cholesky(Sigma)
- except np.linalg.LinAlgError:
- pass
- scale = max(float(np.mean(np.diag(Sigma))), np.finfo(float).tiny)
- try:
- 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))
-
-
-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
-
-
-def predictive_draws(
- constraint,
- model_samples,
- cov_samples=None,
- *,
- n_rep: int = 1,
- rng=None,
- model_only: bool = False,
-) -> np.ndarray:
- """Posterior-predictive draws on the 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).
-
- 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).
- 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.
- model_only : bool, optional
- Return the predictions ``ym_i`` themselves instead of draws around
- them (no covariance is assembled).
-
- Returns
- -------
- np.ndarray
- Draws in the observations' comparison space: shape
- ``(n * n_rep, n_data_pts)``, or ``(n, n_data_pts)`` 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))
-
- 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]
- 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])
- )
- L = _psd_factor(Sigma)
- z = rng.standard_normal((n_rep, N))
- out[i * n_rep : (i + 1) * n_rep] = ym + z @ L.T
- return out
-
-
-def coverage_curve(draws, y, levels=None) -> np.ndarray:
- """Empirical coverage of central predictive intervals at each nominal level.
-
- Parameters
- ----------
- draws : array_like, shape (n_draws, n_pts)
- y : array_like, shape (n_pts,)
- 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``).
-
- Returns
- -------
- np.ndarray
- Fraction of points inside the central ``level`` interval, per level.
- """
- draws = np.asarray(draws, dtype=float)
- y = np.asarray(y, dtype=float)
- levels = _DEFAULT_LEVELS if levels is None else np.asarray(levels)
- out = np.empty(len(levels))
- for i, lv in enumerate(levels):
- lo, hi = np.percentile(draws, [50 * (1 - lv), 50 * (1 + lv)], axis=0)
- out[i] = np.mean((y >= lo) & (y <= hi))
- return out
-
-
-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)
- return float(np.max(np.abs(coverage_curve(draws, y, levels) - levels)))
-
-
-def sharpness(draws, percentiles=(16, 84), transform=None) -> np.ndarray:
- """Per-point width of a central predictive interval.
-
- Parameters
- ----------
- 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.
- 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``).
- """
- draws = np.asarray(draws, dtype=float)
- if transform is not None:
- draws = transform(draws)
- lo, hi = np.percentile(draws, percentiles, axis=0)
- return hi - lo
-
-
-def heldout_log_predictive(heldout_constraint, model_samples, cov_samples=None):
- """``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).
-
- 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)
- ]
- )
-
-
-def log_posterior_predictive(logp_samples, logw=None) -> float:
- """``log E_post[p(y_held | theta)]``: the joint log posterior predictive
- density of the held-out block.
-
- A log-mean-exp over posterior samples of :func:`heldout_log_predictive`
- values; pass ``logw`` (unnormalised log importance weights, e.g.
- nested-sampling ``logwt``) for weighted samples. This is the joint
- predictive of the whole held-out block, not the pointwise-summed ``elpd``
- of Vehtari et al.; divide by the number of held-out points for a
- per-point score.
- """
- logp = np.asarray(logp_samples, dtype=float)
- if logw is None:
- return float(logsumexp(logp) - np.log(len(logp)))
- logw = np.asarray(logw, dtype=float)
- return float(logsumexp(logp + logw) - logsumexp(logw))
-
-
-def logz_summary(logz, logzerr):
- """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).
-
- 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.
- """
- logz = np.atleast_1d(np.asarray(logz, dtype=float))
- logzerr = np.atleast_1d(np.asarray(logzerr, dtype=float))
- 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)
-
-
-def compare_logz(a, b, sigma: float = 2.0) -> dict:
- """``Delta log Z = a - b`` with a conservative tie verdict.
-
- Parameters
- ----------
- 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).
- sigma : float, optional
- A difference smaller than ``sigma * hypot(err_a, err_b)`` is a ``"tie"``.
-
- Returns
- -------
- dict
- ``{"dlogZ": ..., "err": ..., "verdict": "a" | "b" | "tie"}``.
- """
- ma, ea = a[0], a[1]
- mb, eb = b[0], b[1]
- d = float(ma - mb)
- err = float(np.hypot(ea, eb))
- if abs(d) < sigma * err:
- verdict = "tie"
- 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/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/observation_from_measurement.py b/src/rxmc/observation_from_measurement.py
deleted file mode 100644
index 3dfc871..0000000
--- a/src/rxmc/observation_from_measurement.py
+++ /dev/null
@@ -1,82 +0,0 @@
-"""
-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`).
-"""
-
-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.
-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
-
-#: Unit ``jitr`` reports cross sections (and the Rutherford cross section) in.
-RUTHERFORD_UNIT = ureg.millibarn / ureg.steradian
-
-#: Millibarn per barn; divides ``jitr`` output to land in :data:`XS_UNIT`.
-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)")
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
index a5fc547..6fd32d7 100644
--- a/src/rxmc/params.py
+++ b/src/rxmc/params.py
@@ -1,66 +1,71 @@
-"""
-Parameter definitions.
+"""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.
-The :class:`Parameter` class describes a single scalar model parameter —
-its name, data type, physical unit, LaTeX label, and optional bounds.
+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.
"""
-import numpy as np
+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
- Human-readable name of the parameter.
- dtype : type, optional
- Data type of the parameter value. Defaults to ``float``.
+ 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 (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.
+ Physical unit string for display.
+ latex : str, optional
+ LaTeX label for plots; ``name`` when omitted.
"""
- 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)
+ name: str
+ bounds: tuple[float, float] = (-inf, inf)
+ prior: object | None = field(default=None, repr=False)
+ unit: str = ""
+ latex: str | None = None
- def __eq__(self, other):
- if not isinstance(other, Parameter):
- return False
- return self._key() == other._key()
+ 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))
- def __hash__(self):
- return hash(self._key())
+ @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):
- 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})"
- )
+ prior = ", prior=set" if self.prior is not None else ""
+ return f"Parameter({self.name!r}, bounds={self.bounds!r}{prior})"
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/predictive.py b/src/rxmc/predictive.py
index a7fc92d..17c4f88 100644
--- a/src/rxmc/predictive.py
+++ b/src/rxmc/predictive.py
@@ -1,31 +1,99 @@
+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.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
+ 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
+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 .covariance import as_2d
+from .diagnostics import _psd_factor, _rows
+from .problem import Problem, _per_point
+from .terms import KernelTerm, TermContext, as_2d
__all__ = [
+ "grid_draws",
+ "gp_predictive_draws",
"gp_posterior_predictive",
- "total_predictive_band",
"predictive_band",
]
+# ----------------------------------------------------------------------------
+# GP conditioning
+# ----------------------------------------------------------------------------
+
+
def _train_noise_matrix(train_noise_var, n) -> np.ndarray:
if train_noise_var is None:
return np.zeros((n, n))
@@ -37,45 +105,45 @@ 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 +151,491 @@ 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
+ Xt, Xp = as_2d(X_train), as_2d(X_pred)
+ r = np.asarray(residuals, dtype=float)
+ 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
-def _gp_condition(k, X_train, residuals, Xp, *, train_noise_var, jitter):
- """Shared GP conditioning: returns (mean, v).
+def predictive_band(draws, levels=(16, 50, 84)) -> np.ndarray:
+ """Percentile band over ``draws`` of shape ``(n_draws, n_points)``.
- ``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)``).
+ Returns ``(len(levels), n_points)``.
"""
- Xtr = as_2d(X_train)
- r = np.asarray(residuals, dtype=float)
- n = Xtr.shape[0]
+ return np.percentile(np.asarray(draws, dtype=float), levels, axis=0)
- 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
+# ----------------------------------------------------------------------------
+# Draws on a new grid
+# ----------------------------------------------------------------------------
-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 _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
+ 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(_same_space(comp.space, s) for s in spaces):
+ spaces.append(comp.space)
+ if len(spaces) != 1:
+ raise ValueError(
+ "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]
- 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.
- """
- 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
+
+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
)
- prior_var = np.asarray(k.diag(Xp), dtype=float)
- var = prior_var - np.einsum("ij,ij->j", v, v)
- return mean, var
-def predictive_band(draws, levels=(16, 50, 84)) -> np.ndarray:
- """Percentile band over a matrix of predictive draws.
+def _grid_terms(c, terms, rows) -> list:
+ """The terms a grid draw carries, or a ``ValueError`` saying why it cannot.
- 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.
+ ``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
- Returns
- -------
- np.ndarray, shape (len(levels), n_points)
+
+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.
"""
- return np.percentile(np.asarray(draws, dtype=float), levels, axis=0)
+ 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 total_predictive_band(
- mean_fn,
- kernel,
- x_train,
- y_train,
+def _draw_on_grid(
+ problem,
+ c,
+ space,
+ chosen,
+ predictor,
x_pred,
- draws,
- n_model_params,
+ samples,
*,
- theta_cols=None,
+ model_only,
+ joint,
+ physical,
+ n_rep,
+ rng,
+ levels,
+ return_draws,
noise_std=0.0,
- train_noise_var=None,
- levels=(16, 84),
- n_draws=400,
- rng=None,
+ condition=None,
):
- r"""Data-space predictive band that propagates *total* uncertainty.
+ """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 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 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_*^{(s)} = \mathrm{mean\_fn}(x_*) + \bar f_*^{(s)}
- + \mathcal N\!\big(0,\ \mathrm{var}_*^{(s)} + \sigma^2\big),
+ y_* = y_m(x_*; \theta) + s\,L(\theta) z, \qquad
+ L L^T = C(\theta), \quad z \sim \mathcal N(0, I)
- 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).
+ 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
----------
- 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.
- 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``).
+ 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
- Percentile levels for the returned band.
- n_draws : int, optional
- Number of posterior rows to subsample (if the chain is longer).
- rng : np.random.Generator, optional
+ Percentiles of the band.
+ return_draws : bool, optional
+ Return the draws instead of the band.
Returns
-------
- np.ndarray, shape (len(levels), len(x_pred))
-
- Raises
- ------
- ValueError
- If the kernel-theta selection does not have ``len(kernel.theta)`` columns.
+ 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() 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:
+ 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"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."
+ 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"
)
- theta_cols = np.arange(n_model_params, n_model_params + n_theta)
+ rows = np.arange(c.offsets[-1].stop)
else:
- theta_cols = np.asarray(theta_cols, dtype=int)
- if theta_cols.shape[0] != n_theta:
+ i = next((k for k, u in enumerate(c.comparisons) if u is comparison), None)
+ if i is None:
raise ValueError(
- f"theta_cols selects {theta_cols.shape[0]} columns but the kernel "
- f"has {n_theta} hyperparameters."
+ 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,
+ physical: bool = False,
+ n_rep: int = 1,
+ rng=None,
+ levels=(16, 50, 84),
+ return_draws: bool = False,
+) -> np.ndarray:
+ r"""Grid draws for a Gaussian-process discrepancy, optionally conditioned.
- 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
+ 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_* = 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
+
+ with ``\bar f_* = K_{*t}(K_{tt} + N)^{-1} r``: GP regression on top of
+ the model, which interpolates the residuals.
+
+ Parameters
+ ----------
+ 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)``); 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
+ 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; ``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.
+ return_draws : bool, optional
+ Return the ``(n * n_rep, len(x_pred))`` draws instead of the band.
+
+ Returns
+ -------
+ np.ndarray
+ ``(len(levels), len(x_pred))``, or the draws when ``return_draws``.
+ """
+ rng = np.random.default_rng(rng)
+ samples = _rows(samples, problem.ndim)
+ if train_noise_var is not None and not conditioned:
+ raise ValueError(
+ "train_noise_var is the noise the GP regression conditions on and "
+ "applies only with conditioned=True"
)
- 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))
+ 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)
+ 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)
+ 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()}
+ meta_p = (
+ None
+ if predictor.meta is None
+ else {k: _per_point(v, x_pred.shape[0]) for k, v in predictor.meta.items()}
+ )
- return predictive_band(samples, levels)
+ def condition(theta, mu):
+ values = tuple(theta[cols_term])
+ ym = c.ym(theta)
+ # 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,
+ ) # fmt: skip
+ 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(values[:nk], 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
+ 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])
+ 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):
+ 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/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/problem.py b/src/rxmc/problem.py
new file mode 100644
index 0000000..208a523
--- /dev/null
+++ b/src/rxmc/problem.py
@@ -0,0 +1,702 @@
+"""
+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(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, and a one-parameter block holding a scipy univariate distribution is
+ 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.
+"""
+
+from __future__ import annotations
+
+import warnings
+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, _SingularCovariance
+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"
+
+
+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.
+
+ ``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 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(
+ 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:
+ # 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"
+ )
+ 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)
+ 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:
+ """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)")
+ 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)
+ 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:
+ 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
+ # 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()) - self.log_mass
+
+ 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: set[Parameter] = set()
+ univariate: dict[Parameter, Any] = {}
+ 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.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, univariate.get(p))
+ for i, p in enumerate(index.params)
+ if p not in covered or p in univariate
+ ]
+
+ 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 _per_point(v, n) -> np.ndarray:
+ """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
+ 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:
+ keys = set()
+ for c in constraint.comparisons:
+ keys |= set(c.data.meta)
+ if not keys:
+ return None
+ meta = {}
+ for key in keys:
+ parts = [_per_point(c.data.meta.get(key), c.n) for c in constraint.comparisons]
+ 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)
+ ]
+ # 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
+ ]
+ 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."""
+ 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]
+
+ def _stats(self, theta):
+ ym = self.ym(theta)
+ if not np.all(np.isfinite(ym[self.active])):
+ return None
+ 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)
+ 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 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})"
+
+
+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
+# ----------------------------------------------------------------------------
+
+
+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)
+ _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
+ 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 ----------------------------------------------------------
+
+ @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, 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)
+ 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/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/reactions/__init__.py b/src/rxmc/reactions/__init__.py
new file mode 100644
index 0000000..04e0d8a
--- /dev/null
+++ 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
new file mode 100644
index 0000000..0352cc4
--- /dev/null
+++ b/src/rxmc/reactions/elastic.py
@@ -0,0 +1,259 @@
+"""
+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``.
+"""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import jitr
+import numpy as np
+
+from ..model import Model, Predictor
+from ..units import DEFAULT_LMAX, MB_PER_B, check_angle_grid
+
+__all__ = [
+ "ElasticXS",
+ "set_up_solver",
+ "rutherford",
+ "momentum_transfer",
+ "extract_dXS_dA",
+ "extract_dXS_dRuth",
+ "extract_Ay",
+]
+
+QUANTITIES = ("dXS/dA", "dXS/dRuth", "Ay")
+
+
+def rutherford(kinematics, angles_rad) -> np.ndarray:
+ r"""The Rutherford cross section in mb/sr on ``angles_rad``.
+
+ :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)
+
+
+def momentum_transfer(angles_rad, k) -> np.ndarray:
+ r"""Momentum transfer :math:`q = 2k\sin(\theta/2)` (fm⁻¹) on the angles.
+
+ 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)
+
+
+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,
+ Elab: float,
+ angles_rad: np.ndarray,
+ lmax: int = DEFAULT_LMAX,
+ wavelengths_beyond_range: float = 2.0,
+ zeros_per_node: int = 5,
+):
+ """Set up a ``jitr`` differential workspace for a reaction at one energy.
+
+ Parameters
+ ----------
+ reaction : jitr.reactions.Reaction
+ Elab : float
+ Laboratory energy in MeV.
+ angles_rad : np.ndarray
+ Angles in radians the observables are wanted on.
+ lmax : int
+ Maximum partial wave.
+ wavelengths_beyond_range : float
+ Number of wavelengths beyond the interaction range used to set the
+ channel radius.
+ zeros_per_node : int
+ Basis-function zeros per node in the R-matrix solver.
+
+ Returns
+ -------
+ (jitr.xs.elastic.DifferentialWorkspace, jitr.reactions.Kinematics)
+ """
+ integral_ws, kinematics = _basis(
+ reaction, Elab, lmax, wavelengths_beyond_range, zeros_per_node
+ )
+ 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
+
+
+def extract_Ay(xs, ws) -> np.ndarray:
+ """The analysing power (dimensionless)."""
+ return xs.Ay
+
+
+_EXTRACT = {"dXS/dA": extract_dXS_dA, "dXS/dRuth": extract_dXS_dRuth, "Ay": extract_Ay}
+
+
+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`.
+ """
+
+ 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)
+ 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
+ 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, meta)
diff --git a/src/rxmc/reactions/ias.py b/src/rxmc/reactions/ias.py
new file mode 100644
index 0000000..db6f2f3
--- /dev/null
+++ b/src/rxmc/reactions/ias.py
@@ -0,0 +1,157 @@
+"""
+(p,n) isobaric-analog-state differential cross sections from ``jitr``.
+
+: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.
+"""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import jitr
+import numpy as np
+
+from ..model import Model, Predictor
+from ..units import DEFAULT_LMAX, MB_PER_B, check_angle_grid
+
+__all__ = ["IsobaricAnalogPN", "set_up_solver"]
+
+
+def set_up_solver(
+ reaction,
+ Elab: float,
+ ExIAS: float,
+ angles_rad: np.ndarray,
+ lmax: int = DEFAULT_LMAX,
+ wavelengths_beyond_range: float = 2.0,
+ zeros_per_node: int = 5,
+):
+ """Set up the ``jitr`` (p,n) workspace for a reaction at one energy.
+
+ Parameters
+ ----------
+ reaction : jitr.reactions.Reaction
+ Elab : float
+ Laboratory energy of the incoming proton (MeV).
+ ExIAS : float
+ 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
+ -------
+ (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)
+ ws = jitr.xs.quasielastic_pn.Workspace(
+ reaction,
+ kinematics_entrance,
+ kinematics_exit,
+ 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
+
+
+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, meta)
diff --git a/src/rxmc/terms.py b/src/rxmc/terms.py
new file mode 100644
index 0000000..bb31130
--- /dev/null
+++ b/src/rxmc/terms.py
@@ -0,0 +1,661 @@
+"""
+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:
+
+``"diag"``
+ ``fn`` returns a standard-deviation vector ``v``; ``Sigma_ii += v_i**2``.
+``"mode"``
+ ``fn`` returns a vector ``v``; ``Sigma += outer(v, v)`` (one correlated mode).
+``"matrix"``
+ ``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.
+
+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``
+ 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:`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=...)``.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Mapping
+
+import numpy as np
+
+from .params import Parameter
+from .transforms import as_transform, identity
+
+__all__ = [
+ "KINDS",
+ "TermContext",
+ "Term",
+ "KernelTerm",
+ "as_2d",
+ "ones",
+ "ym",
+ "averaging",
+ "x_basis",
+ "exp_growth",
+ "constant_amplitude",
+ "exp_growth_amplitude",
+ "statistical",
+ "offset",
+ "normalization",
+ "noise",
+ "proportional_error",
+ "systematic",
+ "kernel",
+]
+
+KINDS = ("diag", "mode", "matrix")
+
+
+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(eq=False, frozen=True)
+class TermContext:
+ """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")``).
+
+ 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:
+ 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 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
+ 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.
+ kind : {"diag", "mode", "matrix"}
+ How the returned array enters the covariance (see module docstring).
+ 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`` before ``fn`` sees it (e.g. angle
+ to momentum transfer). Its parameters, if any, are appended to
+ :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.
+ ``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``.
+ """
+
+ fn: Any
+ params: tuple[Parameter, ...] = ()
+ kind: str = "matrix"
+ 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:
+ 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"
+
+ def expected_shape(self, n: int) -> tuple:
+ return (n, n) if self.kind == "matrix" else (n,)
+
+ # -- evaluation -----------------------------------------------------------
+
+ 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) # 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(
+ x=x,
+ 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, 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):
+ 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, 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(
+ f"{self.kind} term fn returned shape {v.shape}, "
+ f"expected {self.expected_shape(n)}"
+ )
+ return 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)
+ 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.gp_predictive_draws` reads these to condition the
+ discrepancy at new points; the covariance machinery treats a
+ ``KernelTerm`` exactly as a ``matrix`` :class:`Term`.
+
+ Parameters
+ ----------
+ 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 of every kernel block, relative to the block's mean
+ variance.
+ """
+
+ kernel: Any = None
+ n_kernel: int = 0
+ amplitude: Any = None
+ jitter: float = 0.0
+
+
+# ----------------------------------------------------------------------------
+# Standard bases and amplitudes (numpy-style callables over a TermContext)
+# ----------------------------------------------------------------------------
+
+
+def ones(c: TermContext) -> np.ndarray:
+ """Constant unit basis."""
+ return np.ones(len(c))
+
+
+def ym(c: TermContext) -> np.ndarray:
+ """The model prediction — the prediction-scaled basis."""
+ return c.ym
+
+
+def averaging(c: TermContext) -> np.ndarray:
+ """``0.5 * (y + ym)`` — the averaging model-error basis."""
+ return 0.5 * (c.y + c.ym)
+
+
+_AVERAGING = averaging # factories take an ``averaging`` flag that shadows the name
+
+
+def x_basis(scale: float = 1.0):
+ """Basis ``x / scale`` (e.g. ``x_basis(np.pi)`` for ``theta/180`` on radians)."""
+
+ def basis(c: TermContext) -> np.ndarray:
+ return np.asarray(c.x, dtype=float) / scale
+
+ return basis
+
+
+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(..., basis=exp_growth(np.pi), basis_params=(slope,))``.
+ """
+
+ def basis(c: TermContext, slope: float) -> np.ndarray:
+ return base(c) * np.exp(slope * np.asarray(c.x, dtype=float) / scale)
+
+ return basis
+
+
+def constant_amplitude(c: TermContext, log_amplitude: float) -> np.ndarray:
+ """Kernel amplitude ``exp(log_amplitude)``, constant over the support."""
+ return np.full(len(c), np.exp(log_amplitude))
+
+
+def exp_growth_amplitude(scale: float = 1.0):
+ """Kernel amplitude ``exp(log_amplitude) * exp(slope * x / scale)`` (two params)."""
+
+ def amplitude(c: TermContext, log_amplitude: float, slope: float) -> np.ndarray:
+ return np.exp(log_amplitude) * np.exp(
+ slope * np.asarray(c.x, dtype=float) / scale
+ )
+
+ return amplitude
+
+
+# ----------------------------------------------------------------------------
+# Term factory helpers
+# ----------------------------------------------------------------------------
+
+
+def _masked(magnitude, mask=None):
+ """A scalar/array magnitude with an optional mask.
+
+ Scalar-like values include 0-d ndarrays (e.g. ``np.array(0.05)`` as stored by
+ ``exfor_tools`` distributions), not just Python scalars. An array magnitude
+ must have exactly the support's length; this is checked by :func:`_full`
+ when the term is evaluated.
+ """
+ v = float(magnitude) if np.ndim(magnitude) == 0 else np.asarray(magnitude, float)
+ if mask is not None:
+ v = v * np.asarray(mask, dtype=float)
+ return v
+
+
+def _full(v, n):
+ """``v`` as a length-``n`` vector.
+
+ Scalars are broadcast; arrays must already have shape ``(n,)`` — a length-1
+ array is *not* treated as a scalar, so a magnitude or mask of the wrong
+ length fails loudly instead of being spread over the support.
+ """
+ v = np.asarray(v, dtype=float)
+ if v.ndim == 0:
+ return np.full(n, float(v))
+ if v.shape != (n,):
+ raise ValueError(
+ f"magnitude/basis has shape {v.shape} but the term's support has "
+ f"length {n}"
+ )
+ return v
+
+
+def _coefficient(parameter, log):
+ """Parameter -> multiplicative coefficient ``exp(theta)`` (``log``) or ``theta``."""
+ if parameter is None:
+ return (), (lambda values: 1.0)
+ if log:
+ return (parameter,), (lambda values: np.exp(values[0]))
+ return (parameter,), (lambda values: values[0])
+
+
+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)
+ nc = len(cparams)
+
+ 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, on=on, coords=coords)
+
+
+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(parameter=None, magnitude=None, mask=None, log=True, on=None) -> Term:
+ """A correlated absolute-offset systematic ``outer(omega, omega)``.
+
+ 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")
+ 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", on=on, constant=True)
+ return _scaled_term("mode", parameter, log, basis, on=on)
+
+
+def normalization(parameter=None, magnitude=None, mask=None, log=True, on=None) -> Term:
+ """A correlated normalisation systematic ``outer(eta * ym, eta * 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).
+ """
+ if magnitude is None and parameter is None:
+ 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, on=on)
+
+
+def noise(
+ parameter, log=True, basis=None, basis_params=(), on=None, coords=None
+) -> Term:
+ """Unknown statistical noise ``diag((epsilon * basis)**2)``.
+
+ ``basis`` defaults to ones (constant noise); pass any
+ ``basis(c, *basis_values)`` — e.g. :func:`exp_growth` with
+ ``basis_params=(slope,)`` for noise growing along ``x``.
+
+ 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",
+ parameter,
+ log,
+ ones if basis is None else basis,
+ basis_params,
+ on=on,
+ coords=coords,
+ )
+
+
+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).
+ """
+ basis = _AVERAGING if averaging else ym
+ return _scaled_term("diag", parameter, log, basis, on=on)
+
+
+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` 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, on=on, coords=coords
+ )
+
+
+def _kernel_params(kernel, prefix) -> list:
+ """One :class:`~rxmc.params.Parameter` per free kernel hyperparameter element."""
+ params = []
+ 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}", bounds=tuple(bounds[0]), latex=hp.name)
+ )
+ else:
+ params.extend(
+ Parameter(
+ f"{prefix}_{hp.name}_{i}",
+ bounds=tuple(bounds[i]),
+ latex=f"{hp.name}[{i}]",
+ )
+ for i in range(hp.n_elements)
+ )
+ return params
+
+
+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",
+ 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 derived per *free* kernel
+ hyperparameter **element** (sampled in sklearn's log-theta space): an
+ anisotropic hyperparameter (``n_elements > 1``) contributes that many
+ 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
+ ----------
+ kernel : sklearn-style kernel
+ Duck-typed on ``hyperparameters``, ``theta``, ``clone_with_theta`` and
+ ``__call__``.
+ coords : Transform or callable, optional
+ Coordinate transform of ``x`` the kernel is evaluated in (e.g. angle to
+ momentum transfer). Default: ``x`` itself.
+ amplitude : callable or array, optional
+ ``amplitude(c, *amplitude_values) -> vector a``; the block becomes
+ ``outer(a, a) * K``. Like the kernel, it sees the *transformed*
+ coordinate: ``c.x`` is ``coords(x)``. See :func:`constant_amplitude`,
+ :func:`exp_growth_amplitude`.
+ amplitude_params : sequence of Parameter, optional
+ Parameters consumed by ``amplitude``.
+ jitter : float, optional
+ ``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
+ 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`.
+ """
+ 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)
+ fixed_K = nk == 0 and not as_transform(coords).params
+
+ def fn(c, *values):
+ if fixed_K:
+ 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)
+ )
+ if amplitude is not None:
+ a = amplitude(c, *values[nk:]) if callable(amplitude) else amplitude
+ a = np.broadcast_to(np.asarray(a, dtype=float), (len(c),))
+ K = np.outer(a, a) * K
+ else:
+ K = np.array(K, dtype=float)
+ K[np.diag_indices_from(K)] += jitter * max(
+ float(np.mean(np.diag(K))), np.finfo(float).tiny
+ )
+ return K
+
+ return KernelTerm(
+ fn,
+ tuple(kparams) + amplitude_params,
+ kind="matrix",
+ on=on,
+ coords=coords,
+ constant=fixed_K and not amplitude_params and not callable(amplitude),
+ kernel=kernel,
+ n_kernel=nk,
+ amplitude=amplitude,
+ jitter=jitter,
+ )
diff --git a/src/rxmc/transforms.py b/src/rxmc/transforms.py
index 9b8483b..c3f6f3e 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 +112,25 @@ 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)
+ # 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":
"""``(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
@@ -223,11 +195,11 @@ 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 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 +215,7 @@ 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 +230,3 @@ 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/src/rxmc/units.py b/src/rxmc/units.py
new file mode 100644
index 0000000..a12af7e
--- /dev/null
+++ b/src/rxmc/units.py
@@ -0,0 +1,99 @@
+"""
+The unit contract, without a unit library.
+
+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
+
+__all__ = [
+ "DEFAULT_LMAX",
+ "XS_UNIT",
+ "RUTHERFORD_UNIT",
+ "MB_PER_B",
+ "parse_unit",
+ "check_angle_grid",
+]
+
+#: Default maximum partial wave for the reaction solvers.
+DEFAULT_LMAX = 20
+
+#: Internal cross-section unit: every ``y`` in b/sr.
+XS_UNIT = "b/sr"
+
+#: Unit ``jitr`` reports cross sections (and the Rutherford cross section) in.
+RUTHERFORD_UNIT = "mb/sr"
+
+#: Millibarn per barn; divides ``jitr`` output to land in :data:`XS_UNIT`.
+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:
+ """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/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
index 90eb5dc..7ebfad6 100644
--- a/test/helpers.py
+++ b/test/helpers.py
@@ -1,22 +1,248 @@
-"""Shared helpers for the test suite."""
+"""Shared helpers for the test suite: dense references built by hand.
+
+``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). 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
import numpy as np
+from sklearn.gaussian_process.kernels import RBF, Matern
-from rxmc.covariance import StackContext
-from rxmc.likelihood_model import log_likelihood, mahalanobis_distance_sqr_cholesky
+from rxmc import Parameter
+from rxmc.terms import (
+ Term,
+ constant_amplitude,
+ exp_growth,
+ exp_growth_amplitude,
+ kernel,
+ noise,
+ normalization,
+ offset,
+ proportional_error,
+ systematic,
+ x_basis,
+)
+# ----------------------------------------------------------------------------
+# Dense references
+# ----------------------------------------------------------------------------
-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 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_distance_sqr_cholesky(y, ym, cov)
- return log_likelihood(d2, logdet, len(y))
+ 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=(), 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). ``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.
+ """
+ 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)
+ 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, 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[r, r] += out**2
+ elif term.kind == "mode":
+ Sigma[np.ix_(r, r)] += np.outer(out, out)
+ else:
+ 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; 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",
+ "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)",
+ "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 = proportional_error(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/recipes/common.py b/test/recipes/common.py
new file mode 100644
index 0000000..3ae251f
--- /dev/null
+++ b/test/recipes/common.py
@@ -0,0 +1,78 @@
+"""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
+
+
+# ----------------------------------------------------------------------------
+# 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/conftest.py b/test/recipes/conftest.py
new file mode 100644
index 0000000..fc73c0e
--- /dev/null
+++ b/test/recipes/conftest.py
@@ -0,0 +1,9 @@
+"""Make the recipe helpers and the shared test helpers importable."""
+
+import pathlib
+import sys
+
+_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/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/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..33e5eb6
--- /dev/null
+++ b/test/recipes/test_recipe_02_unknown_noise.py
@@ -0,0 +1,60 @@
+"""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 averaging in (False, True):
+ term = T.proportional_error(log_eps, averaging=averaging)
+ 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_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_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_07_gp_discrepancy.py b/test/recipes/test_recipe_07_gp_discrepancy.py
new file mode 100644
index 0000000..75dbf9a
--- /dev/null
+++ b/test/recipes/test_recipe_07_gp_discrepancy.py
@@ -0,0 +1,157 @@
+"""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
+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 gp_predictive_draws, grid_draws
+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)
+ 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)
+ 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)
+ 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)
+ )
+ # 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]
+ )
+
+
+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_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]))
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_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_17_posterior_predictive.py b/test/recipes/test_recipe_17_posterior_predictive.py
new file mode 100644
index 0000000..6d9792a
--- /dev/null
+++ b/test/recipes/test_recipe_17_posterior_predictive.py
@@ -0,0 +1,90 @@
+"""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, 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, return_draws=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, 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
+ )
+ 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, return_draws=True
+ )
+ 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, return_draws=True
+ )
+ 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..484df5c
--- /dev/null
+++ b/test/recipes/test_recipe_18_evidence_comparison.py
@@ -0,0 +1,137 @@
+"""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,),
+ )
+ return {
+ "L0": Constraint([comp_log], terms=[T.noise(log_eps)], statistical=False),
+ "E0": Constraint(
+ [comp_lin], terms=[T.proportional_error(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(), # the default nu, as the recipe writes it
+ ),
+ }
+
+
+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))
+ # 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():
+ 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_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/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..345c11e
--- /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.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
+
+
+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..c3ec6c1
--- /dev/null
+++ b/test/recipes/test_recipe_27_peelles_pertinent_puzzle.py
@@ -0,0 +1,91 @@
+"""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_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), 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 = {}
+ 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
+ # 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
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_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_30_leave_one_experiment_out.py b/test/recipes/test_recipe_30_leave_one_experiment_out.py
new file mode 100644
index 0000000..d618413
--- /dev/null
+++ b/test/recipes/test_recipe_30_leave_one_experiment_out.py
@@ -0,0 +1,65 @@
+"""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, 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)
+ 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, 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
+ 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, 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
new file mode 100644
index 0000000..656fbdd
--- /dev/null
+++ b/test/recipes/test_recipe_31_sbc.py
@@ -0,0 +1,80 @@
+"""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, return_draws=True
+ )[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/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_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_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
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/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_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
index 9b9e632..feec160 100644
--- a/test/test_constraint.py
+++ b/test/test_constraint.py
@@ -1,720 +1,231 @@
-"""Tests for the stacked Constraint: multi-observation stacking and case A/B."""
-
-import unittest
+"""Comparison and Constraint: comparison space, reported terms, masks, support."""
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)
+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_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_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)
+ 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_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)
+ 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(
- 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):
+ 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="finite"):
+ Constraint([self.c1], weight=np.nan)
+ 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_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))
+ 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.obs],
- self.pm,
- likelihood=StudentT(nu_parameter=Parameter("nu")),
- extra_terms=[
- normalization_term(parameter=nu_clone, support=np.arange(2))
- ],
+ [self.c1, self.c2], terms=[Term(np.ones(3), kind="diag", on=self.c2)]
)
-
-
-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()
+ 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]))
diff --git a/test/test_covariance.py b/test/test_covariance.py
index 86d4840..886cbf7 100644
--- a/test/test_covariance.py
+++ b/test/test_covariance.py
@@ -1,812 +1,326 @@
-"""Unit tests for the stacked-covariance core (:mod:`rxmc.covariance`)."""
-
-from types import SimpleNamespace
+"""The structured (Woodbury) covariance against the dense reference."""
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 sklearn.gaussian_process.kernels import RBF, Matern
+
+from helpers import (
+ STUDY_LEGEND,
+ assemble_dense,
+ index_params,
+ mahalanobis,
+ study_form,
)
-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)])
+from rxmc import Parameter
+from rxmc.covariance import StructuredCovariance, chol_logdet
+from rxmc.terms import Term, kernel, noise, normalization, offset, statistical
-def assemble(terms, ctx, theta=()):
- cov = ConstraintCovariance(terms, len(ctx.x), blocks=ctx.supports)
- return cov.matrix(ctx, *theta)
+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
-# ----------------------------------------------------------------------------
-# Term
-# ----------------------------------------------------------------------------
-
+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
-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 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
- 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)
+# ----------------------------------------------------------------------------
+# Single block: every study form
+# ----------------------------------------------------------------------------
- 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)
+@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 and structural properties
+# Gather by identity
# ----------------------------------------------------------------------------
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 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_value_fed_to_both(self):
+ def test_shared_parameter_is_one_slot(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))
+ 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_first_seen_order_deterministic(self):
+ def test_distinct_parameters_are_two_slots_in_first_seen_order(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)
+ 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_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)
+ 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, [])
# ----------------------------------------------------------------------------
-# Factories
+# Several blocks: modes across blocks stay structured, matrices crossing go dense
# ----------------------------------------------------------------------------
-class TestFactories:
+class TestMultiBlock:
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),)
+ 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"),
)
- assert np.allclose(S, 0.05**2 * np.outer(self.ym, self.ym))
+ self.stat = 0.1 * np.ones(9)
- 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
+ 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)
)
- assert cov.is_constant
- L, logdet = cov.cholesky(
- StackContext.constant(self.ctx.x, self.ctx.y, [np.arange(3)])
+ 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)
)
- 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),),
+ local, _ = build(
+ terms, self.x, self.y, self.offsets, rows=[np.arange(9), self.b[0]]
)
- 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,),
- )
+ assert not local.dense
- 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
+ def test_masks_restrict_to_active_rows(self):
terms = [
- statistical_term(self.stat),
- offset_term(magnitude=offset),
- normalization_term(magnitude=norm),
+ statistical(self.stat),
+ noise(self.eps),
+ normalization(parameter=self.eta),
]
- 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)
+ 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 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 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),
)
- 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
+ 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
)
- 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,
+ 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),
)
- 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)
+# Constant parts, caching and the singular check
# ----------------------------------------------------------------------------
-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``."""
-
+class TestConstantAndSingular:
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,)
+ 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"],
)
- ]
- 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))
+ # 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_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_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"):
+ build([offset(parameter=Parameter("w"))], self.x, self.y, self.offsets)
- 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_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_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_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))
- 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(
+
+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.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"
+ self.x,
+ self.y,
+ self.offsets,
+ active=active,
+ rows=[np.arange(9), rows],
+ labels=["L0", "L1", "L2"],
)
- 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))
+ 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_data.py b/test/test_data.py
new file mode 100644
index 0000000..51678a4
--- /dev/null
+++ b/test/test_data.py
@@ -0,0 +1,58 @@
+"""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])
+ 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():
+ 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)
diff --git a/test/test_diagnostics.py b/test/test_diagnostics.py
new file mode 100644
index 0000000..a4033cf
--- /dev/null
+++ b/test/test_diagnostics.py
@@ -0,0 +1,306 @@
+"""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 (
+ _psd_factor,
+ 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, 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, 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, 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]], 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, 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
+ 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))])])
+ 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)
+
+
+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, return_draws=True
+ ),
+ predictive_draws(held, samples, model_only=True, return_draws=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, 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
+ 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, return_draws=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"):
+ 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_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)
+ 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
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.py b/test/test_likelihood.py
new file mode 100644
index 0000000..4a54d50
--- /dev/null
+++ b/test/test_likelihood.py
@@ -0,0 +1,79 @@
+"""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)
+ # 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)
+ 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)
+ 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))))
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_measurement.py b/test/test_measurement.py
new file mode 100644
index 0000000..656dcf5
--- /dev/null
+++ b/test/test_measurement.py
@@ -0,0 +1,187 @@
+"""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_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)
+ 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)
+ 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
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)
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_notebooks_index.py b/test/test_notebooks_index.py
new file mode 100644
index 0000000..af18dda
--- /dev/null
+++ b/test/test_notebooks_index.py
@@ -0,0 +1,106 @@
+"""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
+of ``docs/recipes.md``. Nothing here executes a notebook.
+"""
+
+import ast
+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, 2, 17, 40},
+ "error_models": {2, 4, 19},
+ "sharing_error_models": {5},
+ "normalization_and_covariance_structure": {3, 4, 6, 27},
+ "gp_discrepancy": {7},
+ "robust_likelihoods": {9, 12, 39},
+ "error_scale_and_usu": {34},
+ "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},
+}
+
+RECIPE_LINE = re.compile(r"^Recipes?:\s*(.+)$", re.M)
+
+
+def _headings() -> set:
+ return {int(n) for n in re.findall(r"^## (\d+)\. ", RECIPES.read_text(), re.M)}
+
+
+def _present() -> list:
+ return sorted(p.stem for p in EXAMPLES.glob("*.ipynb")) if EXAMPLES.is_dir() else []
+
+
+def _cited(name) -> set:
+ nb = json.loads((EXAMPLES / f"{name}.ipynb").read_text())
+ first = nb["cells"][0]
+ assert first["cell_type"] == "markdown", f"{name}: the first cell must be markdown"
+ m = RECIPE_LINE.search("".join(first["source"]))
+ assert m, f"{name}: the first cell needs a line 'Recipes: N, M, ...'"
+ return {int(n) for n in re.findall(r"\d+", m.group(1))}
+
+
+@pytest.mark.parametrize("name", sorted(NOTEBOOKS))
+def test_each_notebook_exists_and_cites_its_recipes(name):
+ assert name in _present(), f"examples/{name}.ipynb is in NOTEBOOKS but missing"
+ cited = _cited(name)
+ assert cited, f"{name} cites no recipe"
+ missing = cited - _headings()
+ assert not missing, f"{name} cites recipe(s) {sorted(missing)} with no heading"
+ assert cited == NOTEBOOKS[name], (
+ f"{name} cites {sorted(cited)}; the design's section 9 says "
+ f"{sorted(NOTEBOOKS[name])} (update both or neither)"
+ )
+
+
+def _python(source) -> str:
+ """The cell's source with IPython magics and shell escapes dropped.
+
+ ``%%time`` and friends are not Python, and ``ast.parse`` chokes on them.
+ """
+ lines = [ln for ln in source if not ln.lstrip().startswith(("%", "!"))]
+ return "".join(lines)
+
+
+@pytest.mark.parametrize("name", sorted(NOTEBOOKS))
+def test_no_latex_escape_lands_in_a_plain_string(name):
+ r"""``f"$\rho$"`` is a carriage return, and matplotlib then fails to parse it.
+
+ 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(_python(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}"
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
index abc8bcd..7350521 100644
--- a/test/test_params.py
+++ b/test/test_params.py
@@ -1,52 +1,49 @@
-import unittest
+"""Identity semantics and validation of :class:`rxmc.Parameter`."""
import numpy as np
+import pytest
+from scipy import stats
-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()
+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()))
diff --git a/test/test_predictive.py b/test/test_predictive.py
index 6f8a6e1..36c04da 100644
--- a/test/test_predictive.py
+++ b/test/test_predictive.py
@@ -1,200 +1,594 @@
-"""Tests for the predictive-uncertainty helpers (:mod:`rxmc.predictive`)."""
-
-import unittest
+"""GP conditioning, and posterior-predictive draws on a new grid."""
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,
+ StudentT,
+ Term,
+)
+from rxmc.diagnostics import predictive_draws
from rxmc.predictive import (
- _gp_posterior_mean_var,
gp_posterior_predictive,
+ gp_predictive_draws,
+ grid_draws,
predictive_band,
- total_predictive_band,
)
+from rxmc.terms import (
+ constant_amplitude,
+ exp_growth_amplitude,
+ kernel,
+ noise,
+ normalization,
+ proportional_error,
+ systematic,
+ x_basis,
+)
+from rxmc.transforms import log
def make_kernel():
return ConstantKernel(1.0) * RBF(length_scale=0.7) + WhiteKernel(1e-6)
-class TestGPPosteriorPredictive(unittest.TestCase):
- def setUp(self):
+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 # log-space
+ self.theta = self.kernel.theta
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,
+ 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=noise_var,
+ 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(self):
- # a noiseless kernel (no WhiteKernel) interpolates the residuals exactly
- kernel = ConstantKernel(1.0) * RBF(length_scale=0.7)
+ def test_noiseless_interpolation_and_2d_inputs(self):
+ k = 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),
+ 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])
+
+
+# ----------------------------------------------------------------------------
+# gp_predictive_draws 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 TestGPPredictiveDraws:
+ 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 = 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)
+ 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 = 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_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()
+ 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, {})
+ 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):
+ 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 = gp_predictive_draws(
+ p1, gp_amp, m1.bind(X_PRED, {}), X_PRED, chain1, terms=[gp_amp], 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)
+
+ 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 = gp_predictive_draws(
+ p1, gp_meta, m1.bind(X_PRED, d.meta), X_PRED, chain1, terms=[gp_meta], 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)
+
+ 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 = 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()
+ 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"):
+ 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 = 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"):
+ gp_predictive_draws(p, Term(np.ones(10), kind="diag"), pred, X_PRED, chain)
+ with pytest.raises(ValueError, match="not part of any constraint"):
+ 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_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)))
+ 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_proportional_errors_scale_with_the_prediction(self):
+ ym = 0.5 * X_GRID + 1.2
+ 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)]))
+ # 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)
+
+ 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"\(n, 3\)"):
+ grid_draws(p, pred, X_GRID, np.zeros((4, 2)))
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_problem.py b/test/test_problem.py
new file mode 100644
index 0000000..df419bb
--- /dev/null
+++ b/test/test_problem.py
@@ -0,0 +1,608 @@
+"""The compile step: index, priors, compiled constraints, the flat interface."""
+
+import warnings
+from dataclasses import replace
+
+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,
+ constant_amplitude,
+ 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
+ # 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):
+ *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_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_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
+
+
+# ----------------------------------------------------------------------------
+# 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_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_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(
+ [
+ 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_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(
+ "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))
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_reactions.py b/test/test_reactions.py
new file mode 100644
index 0000000..bb47a5d
--- /dev/null
+++ b/test/test_reactions.py
@@ -0,0 +1,197 @@
+"""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_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)
+ 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
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
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}"
diff --git a/test/test_regression.py b/test/test_regression.py
index 2cd8de0..634bae2 100644
--- a/test/test_regression.py
+++ b/test/test_regression.py
@@ -1,141 +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.
"""
-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:
+import numpy as np
+import pytest
+from scipy import stats
-1. the before/after log-posterior gap (the default changed), and
-2. that re-adding the explicit terms exactly recovers the old number.
+from helpers import manual_mvn_loglike
+from rxmc import Comparison, Constraint, Dataset, Model, Parameter, Problem
+from rxmc import terms as T
-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.
-"""
+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
-import unittest
-import numpy as np
+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])
-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 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):
- # pinned old log-likelihood (auto-included systematics)
- old = manual_mvn(self.y, self.ym, self.old_cov)
- self.assertAlmostEqual(old, 1.195784087817536, places=9)
+ 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):
- 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),
- ],
+ p = Problem([Constraint([self.comp])])
+ assert p.log_likelihood(THETA) == pytest.approx(
+ manual_mvn_loglike(Y, self.ym, np.diag(STAT**2))
)
- 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])
+ 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],
+ )
+ ]
)
- obs2 = Observation(
- np.array([3.0, 4.0]), np.array([3.4, 4.6]), y_stat_err=np.array([0.1, 0.1])
+ 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 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_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__
diff --git a/test/test_terms.py b/test/test_terms.py
new file mode 100644
index 0000000..1d8894f
--- /dev/null
+++ b/test/test_terms.py
@@ -0,0 +1,512 @@
+"""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 STUDY_LEGEND, assemble_dense, study_form
+from rxmc import Parameter
+from rxmc.terms import (
+ KernelTerm,
+ Term,
+ TermContext,
+ averaging,
+ constant_amplitude,
+ exp_growth,
+ exp_growth_amplitude,
+ kernel,
+ noise,
+ normalization,
+ offset,
+ ones,
+ proportional_error,
+ 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))
+
+ 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
+# ----------------------------------------------------------------------------
+
+
+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_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_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(
+ [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):
+ 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
+# ----------------------------------------------------------------------------
+
+
+@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)
+
+
+# ----------------------------------------------------------------------------
+# 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_))
+
+
+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))
+
+
+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
+ # 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 == ()
diff --git a/test/test_transforms.py b/test/test_transforms.py
index f1e4edc..13059f1 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -1,41 +1,32 @@
"""Tests for the low-level ``rxmc.transforms`` type."""
-import unittest
-
import numpy as np
+import pytest
-from rxmc.observation import Observation
-from rxmc.params import Parameter
-from rxmc.transforms import (
- Transform,
- as_transform,
- exp,
- identity,
- log,
- per_observation_scaling,
- scale,
-)
+from rxmc import Parameter
+from rxmc.transforms import Transform, as_transform, exp, identity, log, scale
-class TestTransform(unittest.TestCase):
+class TestTransform:
def test_callable_is_wrapped_parameter_free(self):
t = as_transform(np.sqrt)
- self.assertIsInstance(t, Transform)
- self.assertEqual(t.params, ())
+ assert isinstance(t, Transform)
+ assert 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)
+ 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)
- self.assertEqual(out[0], 0.0)
- self.assertEqual(out[1], -np.inf)
- self.assertEqual(out[2], -np.inf)
- self.assertAlmostEqual(out[3], 1.0)
+ 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])
- self.assertIs(log.inverse, exp)
- self.assertIs(exp.inverse, log)
+ 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):
@@ -50,23 +41,31 @@ def test_compose_order_and_params(self):
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,))
+ 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])
- self.assertIsNone(t.inverse) # parametric -> no inverse
+ assert t.inverse is None # parametric -> no inverse
u = log | exp
np.testing.assert_allclose(u(np.array([2.0])), [2.0])
- self.assertIsNotNone(u.inverse)
+ assert u.inverse is not None
def test_wrong_value_count_raises(self):
- with self.assertRaises(ValueError):
+ 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(unittest.TestCase):
+
+class TestScale:
def test_log_scale(self):
t = scale()
- self.assertEqual(t.params[0].name, "log_rho")
+ 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]
@@ -74,55 +73,7 @@ def test_log_scale(self):
def test_linear_scale_names(self):
t = scale(log=False)
- self.assertEqual(t.params[0].name, "rho")
+ 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)
- 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()
+ assert t2.params[0].name == "eta"
diff --git a/test/test_units.py b/test/test_units.py
new file mode 100644
index 0000000..be31804
--- /dev/null
+++ b/test/test_units.py
@@ -0,0 +1,61 @@
+"""The unit contract: a fixed vocabulary of labels, consistent constants."""
+
+import numpy as np
+import pytest
+
+from rxmc.units import (
+ DEFAULT_LMAX,
+ MB_PER_B,
+ RUTHERFORD_UNIT,
+ XS_UNIT,
+ check_angle_grid,
+ parse_unit,
+)
+
+
+def test_unit_constants_agree():
+ assert MB_PER_B == 1000.0
+ 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
+
+
+@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():
+ 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")
+ with pytest.raises(ValueError, match="finite"):
+ check_angle_grid(np.array([0.3, np.nan, 1.0]), "angles")