From 37b3a349037e1e09d0be8a2061b72c4002084515 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 03:32:38 +0000 Subject: [PATCH 01/44] Add the v0.1 plan and the amended design-brief handoff PLAN.md is the authoritative plan: charter (44.1<->48 only, synchronous, speed-first), DspTap-substrate architecture, API surface, profiles, three-leg test strategy, milestones M0-M7, and the two M0 extraction PR outlines (DspTap gains the shared FIR substrate; SampleRateTap adopts it via submodule). HANDOFF.md preserves the original SampleRateTap synchronous-engine design brief as provenance, with a status preamble recording the seven decisions revised during planning (separate repo via DspTap, pinned-eps cross-validation, construction-time design, no unified factory API, speed-first scope, inherited channel kernels, family profile vocabulary). Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/HANDOFF.md | 186 +++++++++++++++++++++++++ bridge/PLAN.md | 340 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 526 insertions(+) create mode 100644 bridge/HANDOFF.md create mode 100644 bridge/PLAN.md diff --git a/bridge/HANDOFF.md b/bridge/HANDOFF.md new file mode 100644 index 0000000..47bf61c --- /dev/null +++ b/bridge/HANDOFF.md @@ -0,0 +1,186 @@ +# SampleRateTap — Synchronous Engine Handoff + +> **Status (2026-07-23): superseded in part by [PLAN.md](PLAN.md).** +> +> This is the original design brief that motivated RatioTap, preserved as +> provenance. Its DSP content (§3–§6) remains the working reference. The +> following decisions were revised during planning — where this document and +> PLAN.md disagree, PLAN.md wins: +> +> 1. **Separate repo** (reverses §2 "Not a separate repo"). The rejection +> rested on duplicating the measurement harness; the Tap family already +> shares code via DspTap submodules, so the shared design/kernel/measurement +> layer moves to DspTap and both converters consume it as true siblings. +> RatioTap's production dependency is DspTap only; SampleRateTap is a +> test-only dependency (cross-validation). +> 2. **Cross-validation redesigned** (§2 "within servo ripple" is unworkable). +> SampleRateTap is a *near-unity* ASRC; its occupancy servo cannot acquire +> an 8% ratio offset. The golden-reference test instead pins +> eps = L/M − 1 directly at the `fractional_resampler` level (no servo), +> exhaustive over all phases, with the agreement bound set by the ASRC's +> phase-table interpolation floor — a sharper, deterministic bound. +> 3. **"Precomputed at build time" → construction time** (§3). Follows the +> family philosophy documented in the kaiser design note: runtime design in +> the constructor, off the audio path. Generated/committed tables are a +> later, measured optimization (see PLAN.md lever ladder). +> 4. **Factory / one API dropped** (§2 diagram). The engines' natural APIs are +> irreconcilable (two-thread push/pull vs single-thread process). Clock +> topology is routed by *type choice*, documented at the API surface; the +> async-at-44.1↔48 case is served by composition (see PLAN.md, +> `bluetooth_bridge`). +> 5. **Scope narrowed further** (§1). RatioTap is 44.1↔48 *speed-first*: +> direction is compile-time, generality is deliberately boxed out, and the +> optimization budget goes to this one ratio. +> 6. **Lever §5.2 (channel vectorization) already exists** in SampleRateTap +> (`dot_rows_frame_major`, hypothesis C6) and is inherited via DspTap +> rather than built. +> 7. **Profiles map to the family vocabulary**: the ~70 dB/19 kHz default is +> `economy`, the 120 dB/20 kHz profile is `transparent`. + +--- + +**Goal:** Add a dedicated *synchronous* fixed-ratio 44.1↔48 kHz converter to SampleRateTap as a sibling engine to the existing asynchronous ASRC. Same repo, shared design/kernel layer, distinct hot loop. + +This doc is the design brief. It records *why* each decision was made so implementation choices can be re-derived rather than guessed at. + +--- + +## 1. Scope + +- Convert **44.1 kHz ↔ 48 kHz only**. No other rates, no arbitrary ratios. +- **Synchronous**: input and output share one clock. The ratio is exactly rational and fixed. + - 44.1→48 : L=160, M=147 + - 48→44.1 : L=147, M=160 +- This is the *degenerate case* of the ASRC: a known rational ratio that never drifts. The whole point is to exploit that to drop everything the async engine needs for a moving ratio. + +**Out of scope / common trap:** nominal-44.1-into-48-from-a-different-crystal (e.g. S/PDIF into a device on its own clock) is still the *async* problem at the same nominal ratio. Which engine applies is a property of the **clock topology, not the number**. Do not route by rate. + +--- + +## 2. Relationship to the async engine + +Three rejected alternatives and the chosen structure: + +**Not a mode of the ASRC.** Pinning the async ratio to 147/160 works but pays the coefficient-interpolation tax on every sample forever and never yields the phase-table inner loop, straight-line superblock, or bit-exact repeatability. The two engines have genuinely different hot loops. + +**Not a separate repo.** Everything above the inner loop is shared: prototype design, polyphase decomposition, coefficient layout, SIMD dot-product kernels, and — most importantly — the measurement harness. Duplicating the SNR/alias test infra across two repos is pure liability. + +**Chosen shape:** + +``` + ┌─────────────────────────────┐ + │ shared design + kernels │ + │ - prototype design │ + │ - polyphase decomposition │ + │ - coefficient layout │ + │ - SIMD dot-product kernels │ + │ - measurement harness │ + └──────────────┬──────────────┘ + ┌───────┴───────┐ + ┌──────┴──────┐ ┌──────┴──────┐ + │ AsyncEngine │ │ SyncEngine │ + │ servo + │ │ phase table │ + │ interpolated│ │ (new) │ + │ polyphase │ │ │ + └─────────────┘ └─────────────┘ + └───────┬───────┘ + factory / one API +``` + +**API:** the caller declares clock topology explicitly — `SharedClock` selects Sync, `IndependentClocks` selects Async. Do **not** infer rationality from a float ratio. + +**The relationship that pays for itself:** the sync engine is a **golden reference** for the ASRC. At a pinned rational ratio the async output should converge to the sync output within servo ripple — a sharp, automatable cross-validation test neither engine yields alone. The period-147 phase structure means every phase can be covered *exhaustively*, not statistically. This also becomes the "degenerate case" chapter of the white paper (the cleanest way to explain an ASRC: here's the exact rational machine; async is what you build when the ratio won't hold still). + +--- + +## 3. Core algorithm + +Single-stage polyphase FIR, fixed rational ratio, **all coefficients precomputed at build time**. No coefficient interpolation, no fractional-delay machinery, no servo. The phase sequence has period 147 (or 160) and repeats forever. + +**Build two prototypes, not one** — the directions are asymmetric: + +| Direction | L/M | Transition band | Relative cost | +|---|---|---|---| +| 44.1→48 | 160/147 | 20 → 24 kHz | cheaper (~½) | +| 48→44.1 | 147/160 | 20 → 22.05 kHz | dominant | + +The 48→44.1 stopband edge is forced to 22.05 kHz by aliasing; it's roughly twice the filter. Do not share one prototype run transposed — you'd pay ~2× in the cheap direction for nothing. + +**Coefficient layout:** phase-major, so each output is one contiguous dot product of taps-per-phase length — straight into MVE/HVX with no gather. + +**Schedule:** precompute a 147- or 160-entry table of `(phase, input_advance)`. Advance is 0/1 going up, 1/2 going down. + +**Numerics:** float32 accumulation is fine at ~200 taps (error floor ~−138 dB). Fixed point wants int32 coefficients with int64 accumulate. + +--- + +## 4. Spec relaxation — do this FIRST (biggest lever) + +**The spec is protecting ultrasound.** Going 48→44.1, a 48k source holds nothing above 24 kHz, and aliasing maps f → 44100−f. So the entire possible alias landing zone is **20.1–22.05 kHz**. Nothing can fold below 20.1 kHz — arithmetically impossible. Upward, images land at 44100−f ≥ 22.05 kHz. + +So a 120 dB stopband here buys *ultrasonic* cleanliness, not audible transparency. At 60–70 dB every alias product sits above 20 kHz at ≤ −60 dBFS. + +This is the cheapest 2× available; take it before any structural change. + +| Stopband | MACs/output (48→44.1) | +|---|---| +| 120 dB | ~183 | +| 100 dB | ~150 | +| 80 dB | ~117 | +| 60 dB | ~85 | + +Passband edge 20 kHz → 19 kHz gives another 1.49× on top, zero phase cost. + +**Recommendation:** target ~70 dB stopband, 19 kHz passband edge as the default profile. Keep 120 dB available as a "pristine/offline" profile behind the same design path. + +--- + +## 5. Optimization levers, in priority order + +1. **Relax the spec (§4).** ~2× MACs and ~2× storage. Free. +2. **Vectorize across channels, not taps.** For multichannel/HOA (AmbiTap B-format), broadcast each coefficient and run N channels in parallel lanes — no horizontal reduction, perfect lane utilization independent of taps-per-phase, coefficient load amortized across the set. On HVX (32 float lanes) a 16-ch conversion is nearly free vs mono. Usually the biggest real-world win. +3. **Multistage decomposition.** 147/160 = (6/5)(7/8)(7/8) → 48 → 57.6 → 50.4 → 44.1 kHz. Early stages get wide transition bands (their aliasing lands in don't-care regions the final stage cleans up); only the last stage carries the sharp filter, at small L. Typically **5–10× less coefficient storage** for similar/better MAC count. IFIR on the sharp stage is a good additional fit (genuinely narrow transition). +4. **Elliptic/Cheby-II pre-filter** (see §6). ~15 IIR MACs buys back ~100 FIR MACs at a given quality. +5. **Polyphase symmetry.** Linear-phase prototype ⇒ subfilter *p* is the time-reverse of *L−1−p*. Store half the phases, index backward. Clean 2× on storage, zero cost. (MAC-folding only works cleanly in the pure-decimator case — don't chase it.) +6. **Superblock codegen.** Phase schedule has period 147/160 — emit straight-line code for one full superblock at build time. No modulo, no branches, perfectly scheduled loads. ~1.5–2× wall-clock on Helium vs a generic indexed loop. This is where the M55/Hexagon builds earn it. +7. **Minimum phase.** Smaller than folklore: spectral factorization needs the linear-phase prototype designed to δs² first, so ~1.25× on MACs (60 dB: 62→52; 120 dB: 156→123 taps). The real payoff is **latency** (~78 → ~18 samples). Composes with the elliptic pre-filter — different slots. +8. **FFT-domain resampling** (offline/file path only). Overlap-save with *different* fwd/inv transform lengths (FFT 147k in, IFFT 160k out). Transition sharpness is free. ~28–50 MAC/out at 6–31 ms latency vs 183 direct. Wants a mixed-radix FFT (1470 = 2·3·5·7² → radix-7). Poor fit for live; compelling for the file-domain tools. + +--- + +## 6. Optional IIR pre-filter + +Bilinear prewarping helps a lot: 22.05 kHz is 92% of Nyquist at 48k, stretching the 1.10 analog ratio to ~2.04. Orders for 0.01 dB ripple, 60 dB stop (verified): + +| Type | Order | +|---|---| +| Butterworth | 14 | +| Chebyshev II | 8 | +| **Elliptic** | **6** | + +Butterworth is the wrong choice (2.3× the poles for a maximally-flat passband nobody needs behind a 0.001 dB FIR). **Chebyshev II (8th order)** is the sensible default — flat passband, zeros on the unit circle. Elliptic (6th) if you want minimum order and can tolerate passband ripple. + +- Place at **input rate, pre-upsample** (cheapest slot; for 48→44.1 must be pre-decimation anyway). +- Group delay: ~0.7 samples through most of the band, peaking ~27 samples (0.56 ms) at the corner — localized above 18 kHz, essentially inaudible alone, but it *accumulates* on round-trips. +- Conditioning: poles at 0.83–0.92 of Nyquist. Use **SOS, transposed DF-II**, double precision (or a carefully chosen Q-format before trusting it on M55). +- **Freebie:** fold the inverse of the IIR passband magnitude into the FIR design target — droop cancels exactly, zero runtime cost. Magnitude only; leave phase alone. + +--- + +## 7. Suggested first tasks for Claude Code + +1. Introduce the engine split: extract the shared design/decomposition/kernel/measurement layer from the current ASRC; define `AsyncEngine` and `SyncEngine` behind a factory keyed on `SharedClock` / `IndependentClocks`. +2. Implement the offline prototype designer for both directions with a **profile** parameter (default: 70 dB / 19 kHz; pristine: 120 dB / 20 kHz). +3. Implement the single-stage phase-table `SyncEngine` (phase-major layout, `(phase, advance)` schedule). Get it bit-exact and correct before optimizing. +4. Wire the **cross-validation test**: async pinned to 147/160 vs sync, exhaustive over all phases, assert convergence within servo ripple. +5. Then optimize in the §5 order: channel-vectorized kernel → multistage decomposition → superblock codegen for the embedded targets. + +**Acceptance for the first pass:** correct output, exhaustive phase coverage in the cross-validation, and the default profile hitting ≤ −60 dBFS on all alias products above 20 kHz. Optimization lands after correctness. + +--- + +## 8. Notes + +- Latency budget: linear-phase single-stage ~45–90 input samples (1–2 ms). Go minimum-phase only if that's actually a problem. +- The Max/MSP package angle is weak here — an MSP chain runs at one rate. Expect the real consumers to be the embedded targets (M55 w/ Helium, Hexagon/HVX on QCS8550) and the file-domain tools. +- License/venue consistent with SampleRateTap (MIT, docs at timothy.place/SampleRateTap/). diff --git a/bridge/PLAN.md b/bridge/PLAN.md new file mode 100644 index 0000000..a042e4c --- /dev/null +++ b/bridge/PLAN.md @@ -0,0 +1,340 @@ +# RatioTap — Plan + +This is the authoritative plan for RatioTap v0.1. It supersedes the design +brief in [HANDOFF.md](HANDOFF.md) where the two disagree (the deltas are +listed in that file's status preamble). The DSP reference material in the +handoff doc (§3–§6) remains current. + +--- + +## 1. Charter + +**RatioTap converts between 44.1 kHz and 48 kHz, synchronously, as fast as +possible.** One rational ratio pair (160/147 up, 147/160 down), one clock, +and the entire optimization budget spent on exactly that. + +The scope is deliberately boxed in, and the boundaries are identity, not +policy: + +- **No other ratios.** Not 2:1, not 96→44.1, not arbitrary L/M. Generalized + rational machinery may exist as *internal scaffolding* where it costs + nothing, but the public surface is 44.1↔48 and the optimization work + (superblock codegen, baked tables, multistage) is allowed to hard-commit + to L ∈ {147, 160}. +- **No asynchronous conversion.** If the two ends of your signal chain run + on different crystals — even at nominally 44.1-vs-48 — that is + [SampleRateTap](https://github.com/tap/SampleRateTap)'s near-unity ASRC + problem, reached by *composition* (see §5, `bluetooth_bridge`). Which + engine applies is a property of the clock topology, not the number. + RatioTap's API makes the caller state this by choosing a type; nothing is + ever inferred from a float ratio. +- **Speed-first.** Where quality-vs-speed trades exist, the default profile + takes the speed side of any trade that is inaudible (see §4, `economy`), + and the direction is a compile-time parameter so the hot loop can + specialize completely. + +## 2. Position in the Tap family + +``` + ┌────────────────────────────┐ + │ DspTap │ shared substrate (submodule) + │ kaiser design · sample │ + │ traits (float/Q15/Q31) · │ + │ FIR dot kernels · row-sum │ + │ quantization · analysis │ + └──────┬──────────────┬──────┘ + │ │ + ┌────────────┴───┐ ┌──────┴─────────┐ + │ SampleRateTap │ │ RatioTap │ + │ async, near- │ │ sync, 44.1↔48, │ + │ unity, servo │ │ speed-first │ + └────────────┬───┘ └──────┬─────────┘ + │ │ + └──── test-only│dependency: + cross-validation (§6) +``` + +- **Production dependency: DspTap only**, pinned as `submodules/dsptap` and + linked as the `tap::dsp` INTERFACE target — the same pattern as TapTools + and MuTap. Changes to shared code land in DspTap first; RatioTap bumps its + pin (DspTap's documented release flow). +- **SampleRateTap is a test-only dependency** (FetchContent in the test + tree), used solely for the golden cross-validation in §6. It never appears + in the shipped headers. +- **TapHouse** provides style/tooling (`.clang-format`, `.clang-tidy`, + `pre-commit`, drift checks, SessionStart hook) from day one. + +## 3. Architecture decisions (settled) + +| Decision | Choice | +|---|---| +| Namespace | `tap::ratio` | +| Include path | `include/tap/ratio/…` (the DspTap-style convention: path mirrors namespace) | +| Direction | **Compile-time** template parameter; two concrete instantiations. Working names: `basic_converter` with `direction::up_to_48k` / `direction::down_to_44k1` and aliases per sample type (bikeshed open, see §9) | +| Sample types | `float`, Q15 (`int16_t`), Q31 (`int32_t`) via `tap::dsp::sample_traits`. **Q15 is the flagship embedded profile** (Bluetooth-adjacent M33/M55 deployments) | +| Coefficient tables | Phase-major (each output = one contiguous dot product), exact L = 147/160 phases, no inter-phase interpolation, no extra wrap row | +| Schedule | Precomputed L-entry `(phase, input_advance)` table; advance ∈ {0,1} up, {1,2} down | +| Prototype design | Two independent prototypes (directions are asymmetric; do not transpose one). Designed **at construction time** in double via `tap::dsp` kaiser math, per the family's runtime-design philosophy. Row-sum-preserving quantization for fixed-point tables via the shared DspTap utility | +| Hot loop | `tap::dsp` dot kernels: `dot_row` (planar / SMLALD path) and `dot_rows_frame_major` (channel-parallel) — inherited, not rebuilt | +| Channels | Runtime count, one converter instance per stream; per-frame coefficient row shared across channels | +| RT contract | Constructor allocates and designs (may throw); processing is `noexcept`, lock-free, allocation-free | +| Repeatability | Bit-exact: same input → same output, per sample type, on every platform (integer paths exactly; float path via fixed accumulation order) | + +### API surface (v0.1) + +Both call shapes, because the Bluetooth composition (§5) needs one on each +side of the ASRC: + +- **Push-transform**: `process(const S* in, size_t in_frames, S* out)` → + frames produced. For producer-side placement and file processing. +- **Pull-with-callback**: `pull(S* out, size_t out_frames, PopFn&&)` — + produce exactly N output frames, drawing input as needed (the + `fractional_resampler` PopFn pattern). For consumer-side placement. +- **`frames_needed(size_t out_frames)`** — exact input requirement from the + current schedule position. Deterministic; a capability the async engine + cannot offer and the sync engine gets for free. +- **`flush(S* out)`** — end-of-stream: drain the filter tail (group-delay + padding with zeros), return frames produced. Needed by the file-domain + path; a live stream never calls it. +- **`latency_frames()`** — constant, exact (linear-phase group delay), in + input frames. +- `reset()` — return to initial schedule position and cleared history. + +## 4. Profiles + +Two quality tiers behind one design path, named in the family vocabulary: + +| Profile | Stopband | Passband edge | Est. MACs/out (48→44.1) | Role | +|---|---|---|---|---| +| `economy()` — **default** | ~70 dB | 19 kHz | ~85–100 | The speed-first default. All alias products land above 20 kHz at ≤ −60 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | +| `transparent()` | 120 dB | 20 kHz | ~183 | Pristine/offline tier; also the profile whose output the book-quality claims quote | + +`economy` as default is a deliberate positioning choice consistent with the +speed-first charter; the README must state the reasoning (the §4 argument: +nothing *can* fold below 20.1 kHz going down; images land ≥ 22.05 kHz going +up) rather than just the number, and the program-weighted measurement style +from SampleRateTap's `economy` preset applies here too. + +Exact tap counts, storage sizes, and measured alias levels are pinned by the +M2 design-spike notebook — the table above carries the handoff doc's +estimates until then. + +## 5. The async composition (`bluetooth_bridge`) + +The documented answer to "I need 44.1↔48 across independent clocks" +(Bluetooth chip on its own crystal being the motivating case): + +``` +receive: BT codec (44.1 @ BT clock) → RatioTap 44.1→48 → ASRC push │ pull @ local 48k +send: local 48k → ASRC push │ pull @ BT pace → RatioTap 48→44.1 → BT codec +``` + +RatioTap is clock-agnostic (a pure sample-count transformer), so the ASRC +sees nominal-48k-vs-48k with the BT crystal's ppm offset passed through +unchanged (ppm is dimensionless) — exactly its designed near-unity regime. +`examples/bluetooth_bridge.cpp` ships both directions and is the reason the +API carries both call shapes. Neither package grows scope: the capability +lives at the seam. + +## 6. Test strategy — three independent legs + +1. **Contract/unit tests** (GoogleTest, typed over sample types, per family + convention): schedule correctness with **exhaustive coverage of all 147 + and 160 phases**, `frames_needed` exactness, flush/latency contracts, + bit-exact repeatability, channel independence, alias/image measurements + via the shared `tap::dsp` analysis headers. Acceptance numbers from §8. +2. **Independent golden reference**: committed reference vectors generated + offline (scipy `resample_poly` with the same prototype, plus a + soxr/libsamplerate sanity comparison in the notebook). This leg exists so + correctness never rests solely on agreement between two things we built + ourselves — the shared kaiser code would otherwise be a common-mode + failure. +3. **Cross-validation against SampleRateTap** (test-only dependency): drive + `fractional_resampler` with **pinned eps = L/M − 1** (no servo — the + near-unity servo cannot and need not acquire an 8% offset), identical + input, exhaustive over all phases; assert agreement within the ASRC's + phase-table interpolation floor (its documented ≈ −12 dB per doubling of + L inter-phase residual). This is the handoff doc's golden-reference idea, + relocated one layer down where it actually works. + +Verification layer per family convention: `tools/capi/` C ABI + +`notebooks/` ctypes bridge, with the design-spike notebook committed +executed (it measures the shipping C++, not a Python re-implementation). + +## 7. Milestones + +- **M0 — substrate extraction** (two PRs in other repos; outlines in + Appendices A and B). DspTap gains the shared FIR substrate; SampleRateTap + adopts it via submodule with re-export shims. Gate: both repos' CI green, + SampleRateTap icount baselines unchanged (proving the move is free). +- **M1 — skeleton.** CMake (`tap::ratio` INTERFACE target), TapHouse + adoption, DspTap submodule, host CI (Linux/macOS/Windows + ASan/UBSan), + README carrying the §1 charter, LICENSE (MIT). +- **M2 — design spike + tables.** The notebook that designs both prototypes + at both profiles and *pins the numbers* (taps/phase, storage, worst-case + alias level, passband ripple); then the coefficient-table and schedule + classes with contract tests. Acceptance numbers in §8 get their final + values here. +- **M3 — float engine correct.** Both directions, both call shapes, + exhaustive-phase tests, reference-vector leg (§6.2) passing, alias + acceptance met on `economy`. +- **M4 — fixed point.** Q15/Q31 datapaths via the shared traits; + cross-precision agreement pinned (Q31 at format limit vs float; Q15 + format-limited). +- **M5 — cross-validation.** The §6.3 leg wired with SampleRateTap as + test-only dependency. +- **M6 — composition + verification layer.** `bluetooth_bridge` example, + flush/file path, C ABI + notebook committed executed. +- **M7+ — optimization campaign**, strictly measured, one lever per change, + in the revised order: superblock codegen (elevated by the speed-first + charter) → baked/committed tables if codegen wants them → polyphase + symmetry storage halving → multistage decomposition (storage lever for + embedded) → minimum-phase `economy` variant (latency) → IIR pre-filter → + FFT offline path. Embedded CI matrix (M33/M55/Hexagon under QEMU) and + icount gating land at the top of this campaign, before the first lever, so + every optimization is measured the family way. Levers already banked: + spec relaxation (the `economy` default) and channel vectorization + (inherited kernels). + +v0.1 ships at M6. Nothing in M7+ blocks it. + +## 8. Acceptance criteria (v0.1) + +Numbers marked *(spike)* are finalized by the M2 notebook; the rest are +fixed now. + +- `economy`, both directions: every alias/image product ≤ **−60 dBFS** + above 20 kHz; **nothing measurable below 20 kHz** above the format/ + accumulation floor. Worst-case level pinned exactly *(spike)*. +- `transparent`, both directions: alias/image products ≤ **−120 dB**-class + *(spike)*; passband flat to 20 kHz within the SampleRateTap-established + ripple tier. +- Exhaustive phase coverage in tests — all 147 and all 160 phases, not + statistical sampling. +- Cross-validation agreement within the ASRC interpolation floor (§6.3). +- Bit-exact repeatability per §3; Q15/Q31 parity bounds pinned. +- RT contract: processing paths `noexcept`, allocation-free (verified under + sanitizers); constructor-only design confirmed < 10 ms class. +- Latency: exact `latency_frames()` figure documented per + direction × profile *(spike)*; linear-phase single-stage expected + ~45–90 input samples. + +## 9. Open items + +- **Naming bikeshed** (§3): final alias names for the four + direction × common-type instantiations. Decide before M3 makes them + public. +- **SampleRateTap include-path rename** (`include/srt/` → + `include/tap/samplerate/`): agreed direction, separate PR in that repo, + same era as Appendix B (shared anchor-repointing work), not a RatioTap + blocker. +- **Book/white-paper chapter** ("the degenerate case"): explicitly deferred + past v0.1. Code is written anchor-friendly (`ANCHOR:` comments on the + load-bearing excerpts) from day one so the chapter can be added without + touching the code. +- **AmbiTap/HOA channel-count validation** (the HVX 16-channel story): + deferred to the M7+ embedded campaign. + +--- + +## Appendix A — M0 PR outline: DspTap "shared FIR substrate" + +*Lands first. Follows DspTap's "Adding a primitive" checklist for each +asset. Everything moves from SampleRateTap `include/srt/` / +`tests/`; provenance noted per file (the DspTap origin-story pattern).* + +**New headers under `include/tap/dsp/`:** + +1. `kaiser.h` — `bessel_i0`, `kaiser_beta`, `estimate_taps`, `sinc`, + `design_prototype`, `design_prototype_compensated` (from + `srt/detail/kaiser.h`, namespace → `tap::dsp`, keeping the + runtime-design design-note docstring). Contract tests ported from + `test_kaiser.cpp`. +2. `sample_traits.h` — the **format-core stratum only**: `coeff`/`accum` + types, Q-format ladder (Q1.14/Q29→Q15, Q1.30/Q45→Q31 with the pre-shift + rationale comments), `make_coeff`, `k_coeff_scale`, `mac`, `finalize`, + `round_sat`/`clamp_sat`, `silence`, and a concept covering exactly what + the dot kernels require. **The blend stratum does not move** (it is + mu-interpolation machinery, coupled to the ASRC's Q0.64 phase + accumulator; it stays in SampleRateTap as a refinement). Contract tests + ported from `test_fixed_point.cpp`, restated as pinned numeric contracts + (Q formats, rounding mode, saturation, cross-precision bounds). +3. `fir_kernels.h` — `dot_row` (with the SMLALD Q15 path and its + `__ARM_FEATURE_DSP`/MVE gating), `dot_tile_frame_major`, + `dot_rows_frame_major`, the restrict macro. Macro prefix `SRT_` → + `TAP_DSP_` (incl. `SRT_CP_MIN_CHANNELS` → `TAP_DSP_CP_MIN_CHANNELS`). + Bit-exactness comments travel with the code. Kernel parity tests (planar + vs channel-parallel bit-exact per type) extracted from the SampleRateTap + suite. +4. `quantize.h` — row-sum-preserving quantization (largest-remainder), + refactored out of `polyphase_filter_bank`'s constructor into a + free function over (double row, `sample_traits`) with the + RBJ-attribution comment. New focused tests (every row sums to + `llround(exact × scale)`). +5. `analysis/sine_analysis.h`, `analysis/multitone_analysis.h` — the + measurement instruments from `tests/support/`, generalized out of the + `tap::samplerate` test namespace. + +**Documentation:** + +- README: one section per asset (bump the primitive count); the traits + section states the **fixed-point roadmap**: Q15/Q31 are first-class + embedded profiles, expected deployments include M33/M55-class eurorack + and pedal targets (TapTools) and the Bluetooth-adjacent RatioTap path; + per-primitive fixed-point adoption is opt-in and each adoption is its own + documented Q-format design (no wrapper classes — raw sample types + + traits is the family contract, and the rationale goes in the header + docstring). +- CLAUDE.md discipline clause extended: "double is the golden model; + float32 is the embedded profile; **Q15/Q31 are format-limited embedded + profiles** with contracts pinned like everything else." + +**Housekeeping in the same PR:** delete the stray committed `a.out` at the +repo root. + +**Non-goals:** no fixed-point variants of the existing four primitives; no +blend/interpolation machinery; no polyphase bank (engine-specific, stays +put). + +## Appendix B — M0 PR outline: SampleRateTap adopts DspTap + +*Lands second, pinned at Appendix A's merged tree. The MuTap `fft.h` +re-export shim is the template throughout. Behavior change: none — proven +by the gates below.* + +1. **Submodule**: add `submodules/dsptap`, link `tap::dsp` into the + `SampleRateTap::SampleRateTap` INTERFACE target. (The TapHouse + SessionStart hook already runs submodule init, so web sessions keep + working unchanged.) +2. **Shims / refactors**: + - `srt/detail/kaiser.h` → re-export shim (`tap::samplerate::detail` + using-declarations for the design functions; historical include path + keeps compiling). + - `srt/sample_traits.h` → keeps its name and full interface, now + implemented as a refinement of `tap::dsp::sample_traits` (core stratum + inherited/aliased; blend stratum defined here; the `sample_type` + concept refines the DspTap core concept). + - `srt/polyphase_filter.h` → dot kernels consumed from + `tap::dsp` via using-declarations; the bank constructor calls the + shared `quantize.h` utility; the mu-blend functions and the bank stay. + - `tests/support/` analysis headers → thin includes of the + `tap::dsp::analysis` versions (or direct test-side migration). +3. **Book anchor repointing**: `kai_*`, the moved `st_*` subset, and the + kernel `rs_dot_*`/`opt_*` anchors now include from + `submodules/dsptap/include/tap/dsp/…` paths; blend-stratum and + engine anchors unchanged. The book CI's stale-anchor gate verifies + completeness. +4. **Gates proving the move is free**: full test suite green unchanged + (bit-exact outputs); **icount baselines unchanged within the existing + ±3% CI gate on all three embedded targets** — the strongest available + proof that relocation cost nothing on the hot path; book builds clean. + +**Follow-up PR, same era, not part of M0**: `include/srt/` → +`include/tap/samplerate/` rename with forwarding headers at the old paths +(deprecation window), completing the `srt` → `tap::samplerate` namespace +migration. Kept separate so the M0 diff stays reviewable. + +--- + +*License: MIT, consistent with the family. Docs venue: +timothy.place/RatioTap once there is something to document.* From a5a727ad6f7a7d181a43c2173c539d1273a55df4 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 13:23:54 +0000 Subject: [PATCH 02/44] Add the M1 skeleton: build, substrate, style, CI MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Milestone M1 of PLAN.md — everything except the converter: - CMake: header-only tap::ratio INTERFACE target (include/tap/ratio/, C++20) over the DspTap substrate, pinned as submodules/dsptap at the FIR-substrate merge and linked as tap::dsp; TAP_RATIO_WERROR warnings target; GoogleTest test harness. - include/tap/ratio/ratio.h: umbrella header carrying version constants, the charter docstring, and the identity constants (L = 160 up, L = 147 down). - tests/test_skeleton.cpp: pins the identity constants (coprime phase counts) and proves the substrate end to end at this library's own geometry — design a prototype at L = 147, quantize a branch row-sum- exactly to Q15, dot it against DC through the shared kernel. - TapHouse adoption: canonical .clang-format / .clang-tidy / .pre-commit-config.yaml / STYLE.md / scripts/tidy.sh, plus the SessionStart hook for Claude Code web sessions (submodule init + pre-commit install). - CI: host build+test matrix (Linux/macOS/Windows, -Werror) and an ASan+UBSan job, all with recursive submodule checkout; the Tap House Style workflow (taphouse drift check @v5 + clang-tidy over project TUs with the submodule excluded). - README carrying the charter (identity boundaries, family position, status pointer to PLAN.md), CLAUDE.md, MIT LICENSE. Verified locally: GCC and clang -Werror builds clean, 2/2 tests green under both, scripts/tidy.sh clean, pre-commit clang-format clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- .gitmodules | 3 + bridge/.clang-format | 74 +++++++++++++++ bridge/.clang-tidy | 132 ++++++++++++++++++++++++++ bridge/.claude/hooks/session-start.sh | 36 +++++++ bridge/.claude/settings.json | 14 +++ bridge/.github/workflows/ci.yml | 54 +++++++++++ bridge/.github/workflows/style.yml | 34 +++++++ bridge/.gitignore | 10 ++ bridge/.pre-commit-config.yaml | 23 +++++ bridge/CLAUDE.md | 63 ++++++++++++ bridge/CMakeLists.txt | 47 +++++++++ bridge/LICENSE | 21 ++++ bridge/README.md | 76 +++++++++++++++ bridge/STYLE.md | 122 ++++++++++++++++++++++++ bridge/include/tap/ratio/ratio.h | 47 +++++++++ bridge/scripts/tidy.sh | 77 +++++++++++++++ bridge/submodules/dsptap | 1 + bridge/tests/CMakeLists.txt | 26 +++++ bridge/tests/test_skeleton.cpp | 68 +++++++++++++ 19 files changed, 928 insertions(+) create mode 100644 .gitmodules create mode 100644 bridge/.clang-format create mode 100644 bridge/.clang-tidy create mode 100755 bridge/.claude/hooks/session-start.sh create mode 100644 bridge/.claude/settings.json create mode 100644 bridge/.github/workflows/ci.yml create mode 100644 bridge/.github/workflows/style.yml create mode 100644 bridge/.gitignore create mode 100644 bridge/.pre-commit-config.yaml create mode 100644 bridge/CLAUDE.md create mode 100644 bridge/CMakeLists.txt create mode 100644 bridge/LICENSE create mode 100644 bridge/README.md create mode 100644 bridge/STYLE.md create mode 100644 bridge/include/tap/ratio/ratio.h create mode 100755 bridge/scripts/tidy.sh create mode 160000 bridge/submodules/dsptap create mode 100644 bridge/tests/CMakeLists.txt create mode 100644 bridge/tests/test_skeleton.cpp diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..1872a49 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "bridge/submodules/dsptap"] + path = bridge/submodules/dsptap + url = https://github.com/tap/dsptap diff --git a/bridge/.clang-format b/bridge/.clang-format new file mode 100644 index 0000000..aedca32 --- /dev/null +++ b/bridge/.clang-format @@ -0,0 +1,74 @@ +# Tap House Rules — the Tap family house style. Copy verbatim into every *Tap repo. +# 4-space indent (incl. namespaces), aligned declaration/assignment columns, +# attached braces (else/catch break), comma-first ctor initializers, +# left-bound pointers, 120-column limit. Layout only — naming and mandatory +# braces are enforced separately by .clang-tidy (clang-format cannot check +# identifier names, and its brace insertion is not semantically aware). +Language: Cpp +BasedOnStyle: LLVM +Standard: c++20 + +ColumnLimit: 120 +IndentWidth: 4 +AccessModifierOffset: -2 +NamespaceIndentation: All + +PointerAlignment: Left +DerivePointerAlignment: false +BreakBeforeBinaryOperators: NonAssignment +SpaceBeforeCpp11BracedList: false +AlwaysBreakTemplateDeclarations: Yes + +# Braces attach everywhere (including functions); only else/catch break. +BreakBeforeBraces: Custom +BraceWrapping: + AfterFunction: false + AfterClass: false + AfterStruct: false + AfterNamespace: false + AfterControlStatement: Never + BeforeElse: true + BeforeCatch: true +BreakConstructorInitializers: BeforeComma +PackConstructorInitializers: Never + +AlignConsecutiveAssignments: true +AlignConsecutiveDeclarations: true +AlignTrailingComments: true + +# Short accessor functions and lambdas may stay inline, but control-flow +# statements never do: every if/for/while is braced AND expanded (see +# .clang-tidy readability-braces-around-statements). +AllowShortFunctionsOnASingleLine: Inline +AllowShortLambdasOnASingleLine: All +AllowShortIfStatementsOnASingleLine: Never +AllowShortLoopsOnASingleLine: false +AllowShortBlocksOnASingleLine: Never + +BreakStringLiterals: false +KeepEmptyLinesAtTheStartOfBlocks: false +InsertNewlineAtEOF: true + +# Include ordering: main header (auto, priority 0) -> C++ standard -> +# third-party -> this project. Regroup enforces it; blank lines between groups. +SortIncludes: CaseSensitive +IncludeBlocks: Regroup +IncludeCategories: + # C++ standard library: with no '/' and no '.' (e.g. ) + - Regex: '^<[[:alnum:]_]+>$' + Priority: 2 + # Other angle-bracket headers (third-party, e.g. ) + - Regex: '^<.*>$' + Priority: 3 + # This project: quoted includes + - Regex: '^".*"$' + Priority: 4 + +# Min-DevKit declarative DSL (Max/Min externals: TapTools, AmbiTap-Max, ...). +# MIN_FUNCTION / MIN_ARGUMENT_FUNCTION expand to a lambda; teach clang-format +# their shape so attribute/message/argument setter bodies format as lambda +# blocks instead of being shredded. Completely inert for repos that don't use +# these macros (the pure-C++ libraries). Requires clang-format >= 15. +Macros: + - 'MIN_FUNCTION=[](const atoms& args, int inlet) -> atoms' + - 'MIN_ARGUMENT_FUNCTION=[](const atom& arg, int index) -> void' diff --git a/bridge/.clang-tidy b/bridge/.clang-tidy new file mode 100644 index 0000000..bb253e0 --- /dev/null +++ b/bridge/.clang-tidy @@ -0,0 +1,132 @@ +# Tap House Rules — naming + mandatory-braces enforcement. Copy verbatim into +# every *Tap repo. This is what actually checks m_ members, k_ constants, snake_case +# types/functions, PascalCase template parameters, and braces around every +# control-flow body — clang-format cannot (and its InsertBraces is not +# semantically aware). Scope is intentionally limited to these for now; +# correctness/modernize checks can be layered on later. +# +# NOTE: WarningsAsErrors is intentionally NOT set here so local runs only warn. +# CI passes --warnings-as-errors=readability-* to make the gate blocking. +Checks: > + -*, + readability-identifier-naming, + readability-braces-around-statements +# Analyze this project's own headers only (under include/); vendored third_party +# and fetched deps live outside include/ and are excluded. Generated tables +# (room_data.h, hrtf_data.h, tdesigns.h) live under include/ but carry +# // NOLINTBEGIN(readability-identifier-naming) markers from their generators. +# NOTE: clang-tidy uses llvm::Regex, which has NO negative lookahead — a +# '^(?!...)' pattern silently matches nothing and disables the check. +HeaderFilterRegex: '.*/(include|tests)/.*' + +# --- Linear-algebra notation carve-out -------------------------------------- +# The DSP math deliberately uses capitalized matrix/vector symbols (Y = SH +# matrix, D = decoder, R = rotation, ...). Permit a leading-capital symbol with +# an optional short subscript and _snake suffixes (Y, Yd, R9, Y_virtual). This +# Also matrix products (DtD, YtD). Still rejects camelCase (frameCount). +# Applied below per category via IgnoredRegexp. +CheckOptions: + # --- Types: snake_case --- + - key: readability-identifier-naming.ClassCase + value: lower_case + - key: readability-identifier-naming.StructCase + value: lower_case + - key: readability-identifier-naming.UnionCase + value: lower_case + - key: readability-identifier-naming.EnumCase + value: lower_case + - key: readability-identifier-naming.EnumConstantCase + value: lower_case + - key: readability-identifier-naming.ScopedEnumConstantCase + value: lower_case + - key: readability-identifier-naming.TypeAliasCase + value: lower_case + - key: readability-identifier-naming.TypedefCase + value: lower_case + - key: readability-identifier-naming.NamespaceCase + value: lower_case + + # --- Concepts: snake_case (like the types they constrain, per P1754) --- + - key: readability-identifier-naming.ConceptCase + value: lower_case + + # --- Functions / methods: snake_case --- + - key: readability-identifier-naming.FunctionCase + value: lower_case + - key: readability-identifier-naming.MethodCase + value: lower_case + + # --- Variables / parameters / locals: snake_case, no prefix --- + - key: readability-identifier-naming.VariableCase + value: lower_case + - key: readability-identifier-naming.ParameterCase + value: lower_case + - key: readability-identifier-naming.LocalVariableCase + value: lower_case + - key: readability-identifier-naming.LocalConstantCase + value: lower_case + # Math-notation carve-out (see header): capitalized matrix/vector symbols. + - key: readability-identifier-naming.ParameterIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + - key: readability-identifier-naming.LocalVariableIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + - key: readability-identifier-naming.LocalConstantIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + - key: readability-identifier-naming.VariableIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + + # --- Data members: private/protected get m_; public struct fields bare --- + - key: readability-identifier-naming.PrivateMemberCase + value: lower_case + - key: readability-identifier-naming.PrivateMemberPrefix + value: 'm_' + - key: readability-identifier-naming.ProtectedMemberCase + value: lower_case + - key: readability-identifier-naming.ProtectedMemberPrefix + value: 'm_' + - key: readability-identifier-naming.PublicMemberCase + value: lower_case + # const (non-static) data members are still members -> keep the m_ marker + - key: readability-identifier-naming.ConstantMemberCase + value: lower_case + - key: readability-identifier-naming.ConstantMemberPrefix + value: 'm_' + # Math-notation carve-out for capitalized matrix/vector member symbols. + - key: readability-identifier-naming.PublicMemberIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + - key: readability-identifier-naming.PrivateMemberIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + - key: readability-identifier-naming.ProtectedMemberIgnoredRegexp + value: '^[A-Z][A-Za-z0-9]*(_[A-Za-z0-9]+)*$' + + # --- Constants at namespace/class/static scope: k_ + snake_case --- + # (constexpr/const LOCALS stay bare via LocalConstantCase above) + - key: readability-identifier-naming.GlobalConstantCase + value: lower_case + - key: readability-identifier-naming.GlobalConstantPrefix + value: 'k_' + - key: readability-identifier-naming.ClassConstantCase + value: lower_case + - key: readability-identifier-naming.ClassConstantPrefix + value: 'k_' + - key: readability-identifier-naming.StaticConstantCase + value: lower_case + - key: readability-identifier-naming.StaticConstantPrefix + value: 'k_' + + # --- Template parameters: PascalCase (the ONLY leading-capital names) --- + # Applies to type AND non-type params: template , not . + - key: readability-identifier-naming.TemplateParameterCase + value: CamelCase + - key: readability-identifier-naming.TypeTemplateParameterCase + value: CamelCase + - key: readability-identifier-naming.ValueTemplateParameterCase + value: CamelCase + + # --- Macros: ALL_CAPS --- + - key: readability-identifier-naming.MacroDefinitionCase + value: UPPER_CASE + + # --- Mandatory braces: brace every control-flow body, even one-liners --- + - key: readability-braces-around-statements.ShortStatementLines + value: '0' diff --git a/bridge/.claude/hooks/session-start.sh b/bridge/.claude/hooks/session-start.sh new file mode 100755 index 0000000..b7c2a49 --- /dev/null +++ b/bridge/.claude/hooks/session-start.sh @@ -0,0 +1,36 @@ +#!/bin/bash +# Canonical Tap House SessionStart hook for Claude Code on the web. +# +# Fresh web-session containers clone the repo bare: no submodules, no +# pre-commit hook installed — so `git commit` runs unformatted and the +# clang-format CI gate fails on code a local clone would have fixed at commit +# time. This hook closes that gap at session start: +# +# 1. init submodules recursively (kernels, SDKs, vendored test harnesses) +# 2. install pre-commit and register the repo's canonical hook +# (.pre-commit-config.yaml — the Tap-wide pinned clang-format) +# 3. warm the pinned clang-format binary so the first commit doesn't +# pay the download (the container snapshot caches it) +# +# Canonical copy: taphouse (distributed by scripts/sync.sh alongside +# .clang-format / .clang-tidy / .pre-commit-config.yaml; drift-guarded where +# present). Web-only; local clones are untouched. +set -euo pipefail + +if [ "${CLAUDE_CODE_REMOTE:-}" != "true" ]; then + exit 0 +fi + +cd "$CLAUDE_PROJECT_DIR" + +echo "session-start: initializing submodules ..." +git submodule update --init --recursive + +echo "session-start: installing pre-commit ..." +if ! python3 -m pre_commit --version >/dev/null 2>&1; then + python3 -m pip install --quiet pre-commit +fi +python3 -m pre_commit install +python3 -m pre_commit install-hooks + +echo "session-start: done." diff --git a/bridge/.claude/settings.json b/bridge/.claude/settings.json new file mode 100644 index 0000000..e06b033 --- /dev/null +++ b/bridge/.claude/settings.json @@ -0,0 +1,14 @@ +{ + "hooks": { + "SessionStart": [ + { + "hooks": [ + { + "type": "command", + "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/session-start.sh" + } + ] + } + ] + } +} diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml new file mode 100644 index 0000000..be48dbc --- /dev/null +++ b/bridge/.github/workflows/ci.yml @@ -0,0 +1,54 @@ +name: CI + +on: + push: + pull_request: + +jobs: + build-test: + name: ${{ matrix.name }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + include: + - { os: ubuntu-latest, name: linux } + - { os: macos-latest, name: macos } + - { os: windows-latest, name: windows } + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Configure + run: cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON + + - name: Build + run: cmake --build build --config Release + + - name: Test + run: ctest --test-dir build --build-config Release --output-on-failure + + sanitizers: + name: ASan + UBSan + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Configure + env: + CC: clang + CXX: clang++ + run: > + cmake -S . -B build + -DCMAKE_BUILD_TYPE=RelWithDebInfo + -DTAP_RATIO_WERROR=ON + -DCMAKE_CXX_FLAGS="-fsanitize=address,undefined -fno-sanitize-recover=all" + + - name: Build + run: cmake --build build -j 4 + + - name: Test + run: ctest --test-dir build --output-on-failure diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml new file mode 100644 index 0000000..81c9009 --- /dev/null +++ b/bridge/.github/workflows/style.yml @@ -0,0 +1,34 @@ +name: Tap House Style + +# Enforces the shared Tap House Rules. clang-format is run locally via the +# pre-commit hook; this adds (1) a drift check against the canonical TapHouse +# configs and (2) clang-tidy naming + mandatory-braces enforcement over this +# repo's own translation units (scripts/tidy.sh is the local mirror). +on: [push, pull_request] + +jobs: + drift: + uses: tap/taphouse/.github/workflows/drift-check.yml@v5 + with: + ref: v5 + + clang-tidy: + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + - name: Install tools + run: sudo apt-get update && sudo apt-get install -y clang-tidy-18 cmake python3 + - name: Configure (compile database) + run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DTAP_RATIO_BUILD_TESTS=ON + - name: clang-tidy (project TUs; the submodule and fetched deps excluded) + run: | + files=$(python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))") + fail=0 + for f in $files; do + out=$(clang-tidy-18 -p build "$f" 2>/dev/null || true) + if echo "$out" | grep -qE "warning:|error:"; then echo "$out"; fail=1; fi + done + [ "$fail" -eq 0 ] && echo "clang-tidy clean." || { echo "::error::clang-tidy found violations"; exit 1; } diff --git a/bridge/.gitignore b/bridge/.gitignore new file mode 100644 index 0000000..993115e --- /dev/null +++ b/bridge/.gitignore @@ -0,0 +1,10 @@ +build*/ +.cache/ +compile_commands.json +CMakeUserPresets.json +.vscode/ +.idea/ +.claude/* +!.claude/settings.json +!.claude/hooks/ +build_capi/ diff --git a/bridge/.pre-commit-config.yaml b/bridge/.pre-commit-config.yaml new file mode 100644 index 0000000..b392057 --- /dev/null +++ b/bridge/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +# Canonical Tap House pre-commit config — the single source of truth for the +# Tap family's local formatting hook. Distributed to every Tap repo by +# scripts/sync.sh (alongside .clang-format / .clang-tidy / STYLE.md) and kept +# honest by the drift-check workflow, so every repo runs the SAME hook at the +# SAME pinned clang-format version. +# +# Why the pin matters: an ad-hoc hook using each machine's own clang-format +# would format differently than CI and be worse than none. The `rev` below is +# the Tap-wide clang-format version — bump it HERE, re-sync, and every repo +# (and its CI, which runs `pre-commit run --all-files`) moves together. +# +# Adopt in a consumer repo: +# 1. taphouse/scripts/sync.sh /path/to/your-repo # copies this file in +# 2. cd your-repo && pre-commit install # once per clone +# Thereafter `git commit` formats staged C/C++ before it can be pushed, so the +# clang-format CI gate can never fail on a local commit again. +repos: + - repo: https://github.com/pre-commit/mirrors-clang-format + rev: v18.1.3 # Tap-wide clang-format version (matches CI) + hooks: + - id: clang-format + types_or: [c, c++] + exclude: '^third_party/' # vendored sources are formatted upstream, never by us diff --git a/bridge/CLAUDE.md b/bridge/CLAUDE.md new file mode 100644 index 0000000..13abe96 --- /dev/null +++ b/bridge/CLAUDE.md @@ -0,0 +1,63 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## What this is + +**RatioTap** — synchronous 44.1 ↔ 48 kHz sample rate conversion, as fast as possible. Header-only +C++20 under `include/tap/ratio/`, namespace `tap::ratio`, built on the shared Tap-family FIR +substrate from DspTap (`submodules/dsptap`, linked as `tap::dsp`). + +**PLAN.md is the authoritative roadmap** — charter, settled architecture decisions, milestones +(M0–M7), and acceptance criteria. HANDOFF.md is the original design brief with a preamble listing +which of its decisions were superseded. Read PLAN.md before implementing anything; do not +re-derive decisions it has already settled (compile-time direction, profile vocabulary, the +three-leg test strategy, the pinned-eps cross-validation design). + +Current state: **M1 skeleton.** The umbrella header carries only identity constants; the +coefficient tables and schedule land in M2, the engine in M3. + +## The charter constraints (load-bearing) + +- **44.1↔48 only.** No other ratios on the public surface, ever; internal scaffolding may be + general where it costs nothing, but optimization work is allowed to hard-commit to L ∈ {147, 160}. +- **Synchronous only.** Async-at-44.1↔48 is SampleRateTap's problem, reached by composition + (the future `bluetooth_bridge` example). Never route by rate; the caller declares clock + topology by choosing a type. +- **Speed-first.** Direction is compile-time. Every quality-vs-speed trade that is inaudible + goes to speed in the default profile; the pristine profile exists behind the same design path. +- **Correctness before optimization.** Exhaustive phase coverage (all 147 and all 160 phases), + an independent golden reference (scipy/soxr vectors), and the pinned-eps cross-validation + against SampleRateTap gate every optimization that follows. + +## Substrate discipline + +Shared code (design math, sample traits, kernels, quantization, measurement instruments) lives in +DspTap and lands there FIRST; this repo bumps the submodule pin. Do not fork substrate code into +this repo — that divergence is exactly what DspTap exists to prevent. Q15 is the flagship +embedded profile (Bluetooth-adjacent M33/M55 deployments); float is the golden model against +scipy references. + +## Style + +`STYLE.md` is the shared Tap house style; `.clang-format` and `.clang-tidy` enforce it and CI runs +both (plus a drift check that the config files match the canonical taphouse copies — never edit +them locally). Run `pre-commit install` once per clone; on Claude Code web the checked-in +SessionStart hook (`.claude/hooks/session-start.sh`) does this and initializes the submodule at +session start. clang-tidy compiles with a *clang* front end — code that GCC accepts can still +fail there, and clang's `-Wconversion` implies `-Wsign-conversion` where GCC's does not, so treat +the tidy job and a local clang `-Werror` build as second compilers before pushing. + +## Build & test + +```sh +cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON +cmake --build build +ctest --test-dir build --output-on-failure +scripts/tidy.sh # local mirror of the CI clang-tidy gate +``` + +Tests are GoogleTest (FetchContent), and the conventions to preserve as the engine lands: contract +tests named for the promise they pin, typed batteries over `float`/`int16_t`/`int32_t`, exhaustive +phase sweeps rather than statistical sampling, and measured numbers stated in comments with their +provenance. diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt new file mode 100644 index 0000000..c53b44c --- /dev/null +++ b/bridge/CMakeLists.txt @@ -0,0 +1,47 @@ +cmake_minimum_required(VERSION 3.24) +project(RatioTap VERSION 0.1.0 LANGUAGES CXX) + +# ============================================================================== +# RatioTap — synchronous 44.1 <-> 48 kHz sample rate conversion, as fast as +# possible. Header-only C++20 (include/tap/ratio/), built on the Tap family's +# shared FIR substrate from DspTap (kaiser design, sample-format traits, dot +# kernels, quantization, measurement instruments). See PLAN.md for the charter +# and milestones; this is the M1 skeleton. +# ============================================================================== + +# Shared Tap-family substrate (tap::dsp), pinned as a submodule per the DspTap +# release flow (changes land there first, consumers bump the pin). Declares +# its own C language support for the vendored Ooura sources. +add_subdirectory(submodules/dsptap) + +add_library(tap_ratio INTERFACE) +add_library(tap::ratio ALIAS tap_ratio) +target_include_directories(tap_ratio INTERFACE + $ + $) +target_compile_features(tap_ratio INTERFACE cxx_std_20) +target_link_libraries(tap_ratio INTERFACE tap::dsp) + +if(PROJECT_IS_TOP_LEVEL) + option(TAP_RATIO_BUILD_TESTS "Build RatioTap tests" ON) +else() + option(TAP_RATIO_BUILD_TESTS "Build RatioTap tests" OFF) +endif() + +# Warning flags for this project's own tests; never exported to consumers of +# the INTERFACE library. +option(TAP_RATIO_WERROR "Treat warnings as errors in RatioTap's own targets" OFF) +add_library(tap_ratio_warnings INTERFACE) +target_compile_options(tap_ratio_warnings INTERFACE + $<$:-Wall -Wextra -Wpedantic -Wconversion -Wshadow> + $<$:/W4 /permissive->) +if(TAP_RATIO_WERROR) + target_compile_options(tap_ratio_warnings INTERFACE + $<$:-Werror> + $<$:/WX>) +endif() + +if(TAP_RATIO_BUILD_TESTS) + enable_testing() + add_subdirectory(tests) +endif() diff --git a/bridge/LICENSE b/bridge/LICENSE new file mode 100644 index 0000000..e57addf --- /dev/null +++ b/bridge/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Timothy Place and the RatioTap contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/bridge/README.md b/bridge/README.md new file mode 100644 index 0000000..1cafd1b --- /dev/null +++ b/bridge/README.md @@ -0,0 +1,76 @@ +# RatioTap + +[![CI](https://github.com/tap/RatioTap/actions/workflows/ci.yml/badge.svg)](https://github.com/tap/RatioTap/actions/workflows/ci.yml) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) +[![C++20](https://img.shields.io/badge/C%2B%2B-20-blue.svg)](https://en.cppreference.com/w/cpp/20) + +**Synchronous 44.1 ↔ 48 kHz sample rate conversion, as fast as possible.** + +One rational ratio pair — 160/147 up, 147/160 down — one clock, and the +entire optimization budget spent on exactly that. Header-only C++20, built +on the Tap family's shared FIR substrate +([DspTap](https://github.com/tap/DspTap): Kaiser prototype design, +float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum +quantization, measurement instruments). + +> **Status: skeleton (milestone M1).** The converter itself lands next — +> [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture +> decisions, milestones, acceptance criteria); +> [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. + +## The boundaries are identity, not policy + +- **No other ratios.** Not 2:1, not 96→44.1, not arbitrary L/M. The public + surface is 44.1↔48 only, which is what licenses the optimization work + (straight-line superblock codegen, baked tables, multistage + decomposition) to hard-commit to phase counts of exactly 147 and 160. +- **No asynchronous conversion.** If the two ends of your chain run on + different crystals — *even at nominally 44.1-vs-48* — that is the + [SampleRateTap](https://github.com/tap/SampleRateTap) near-unity ASRC's + problem, reached by composition: RatioTap converts the *number*, the + ASRC absorbs the *clock*. Which engine applies is a property of the + clock topology, never inferred from a float ratio. The + `bluetooth_bridge` example (milestone M6) documents the composition. +- **Speed-first.** Direction is a compile-time parameter; the default + quality profile takes the speed side of every inaudible trade (all alias + products confined above 20 kHz by arithmetic — see the plan's profile + section), with a pristine 120 dB profile behind the same design path. + +## Position in the Tap family + +``` + ┌────────────────────────────┐ + │ DspTap │ shared substrate (submodule) + │ kaiser design · sample │ + │ traits (float/Q15/Q31) · │ + │ FIR dot kernels · row-sum │ + │ quantization · analysis │ + └──────┬──────────────┬──────┘ + │ │ + ┌────────────┴───┐ ┌──────┴─────────┐ + │ SampleRateTap │ │ RatioTap │ + │ async, near- │ │ sync, 44.1↔48, │ + │ unity, servo │ │ speed-first │ + └────────────┬───┘ └──────┬─────────┘ + │ │ + └──── test-only│dependency: + golden cross-validation +``` + +## Build + +```sh +git clone --recurse-submodules https://github.com/tap/RatioTap +cmake -S RatioTap -B build -DCMAKE_BUILD_TYPE=Release +cmake --build build +ctest --test-dir build --output-on-failure +``` + +Consume with `add_subdirectory` (or FetchContent) and link `tap::ratio`; +the DspTap submodule rides along automatically. + +## License + +MIT (see [LICENSE](LICENSE)), consistent with the family. Style is the +shared [Tap House Rules](STYLE.md), enforced by pre-commit clang-format, +the drift check, and clang-tidy in CI. diff --git a/bridge/STYLE.md b/bridge/STYLE.md new file mode 100644 index 0000000..ebfcf54 --- /dev/null +++ b/bridge/STYLE.md @@ -0,0 +1,122 @@ +# Tap House Rules + +> *The Tap house style — always on tap.* + +The shared house style for the Tap libraries (AmbiTap, SampleRateTap, OscTap, +and future `*Tap` libraries). Anchored to the C++ standard library's own +conventions (per the ISO C++ Core Guidelines "NL" section), with a small set +of deliberate, documented exceptions. + +Two config files enforce this and must be copied verbatim into every repo: + +- **`.clang-format`** — layout (whitespace, braces, alignment, includes). +- **`.clang-tidy`** — identifier naming (`readability-identifier-naming`). + clang-format *cannot* check names; this is what does. + +CI runs `clang-format --dry-run --Werror` and `clang-tidy` so drift can't +return. + +--- + +## 1. Naming + +| Kind | Convention | Example | +|------|-----------|---------| +| Types (class/struct/enum/alias) | `snake_case` | `encoder`, `spsc_ring` | +| Functions / methods | `snake_case` | `push`, `write_available` | +| Variables / parameters / locals | `snake_case` | `frame_count`, `min_capacity` | +| Concepts | `snake_case` (like types) | `sample_type` | +| Template parameters | `PascalCase` — the ONLY leading-capital names | `T`, `S`, `Sample`, `Allocator` | +| Private/protected data members | `m_` + `snake_case` | `m_channels`, `m_order` | +| Public data members (struct fields) | `snake_case`, no prefix | `sample_rate_hz` | +| Constants (namespace/class/static) | `k_` + `snake_case` | `k_smoothing_samples`, `k_cache_line` | +| Enumerators | `snake_case` | `state::filling` | +| Macros | `ALL_CAPS` | `SRT_VERSION_MAJOR`, `TAP_EXPECTS` | + +A leading capital letter means **template parameter** and nothing else. This +is the standard library's own allocation (`CharT`, `Rep`, `Period`, +`Allocator`) and is why concepts are lower-case: they read in type position, +so they look like the types they constrain. + +**Deliberate deviations from strict std:** +- `k_` prefix on constants (std uses bare `snake_case`) — kept for use-site + clarity. Applies to namespace-, class-, and static-scope constants; + `constexpr` *locals* stay bare. +- `m_` prefix on encapsulated data members (std reserves `_`; user code has no + standard convention here) — kept for self-documentation and greppability. + +**Parameters take no prefix** (no `a_`/`an_`). `m_` already prevents any +member/parameter collision, and prefixes would clutter the public signatures +that *are* the library's contract. Lean on `const` and small functions for +input/local clarity. + +**Repo exception — OscTap (drop-in legacy continuation).** OscTap continues +[oscpack](http://www.rossbencina.com/code/oscpack) as a *drop-in +source-compatible* successor: its public API keeps oscpack's original +identifiers — PascalCase types and methods (`ReceivedMessage`, +`OutboundPacketStream`, `BeginBundle()`, `AsFloat()`) and trailing-underscore +data members (`size_`, `value_`). Renaming these to the house `snake_case`/`m_` +scheme would break the source compatibility that is the library's reason to +exist. OscTap therefore adopts the **layout** rules (`.clang-format`) in full +but is **exempt from the naming rules** (`readability-identifier-naming`): it +ships a local `.clang-tidy` that disables that check while keeping mandatory +braces, and its CI runs a format-only style gate instead of the shared +`drift-check.yml`. The exemption is specific to legacy-continuation repos; +greenfield `*Tap` code follows the naming rules above. + +## 2. Layout + +- **Indent:** 4 spaces, including inside namespaces. +- **Braces:** attached everywhere (functions included); only `else` and + `catch` break onto their own line. Every control-flow body is braced *and + expanded* onto its own lines — no single-line `if`/`for`/`while`, even for + guard clauses (braces via clang-tidy `readability-braces-around-statements`; + expansion via `AllowShortBlocksOnASingleLine: Never`). Short accessor + functions and lambdas may still be inline. +- **Brace-init spacing:** no space before a braced-init list — `float x{0.0f}`, + not `x {0.0f}`. +- **Column alignment:** consecutive declarations, assignments, and trailing + comments are aligned. +- **Constructor initializers:** comma-first, one per line, never packed. +- **Pointers/references:** bound to the type — `const float* p`, `T& r`. +- **Member declaration order (per NL.16):** `public` -> `protected` -> + `private`; within a class: types/aliases -> constructors/assignment/ + destructor -> functions -> data members last. +- **Column limit:** 120. +- **`const` placement:** west-const (`const T`, not `T const`) — enforced by + review, not tooling. + +## 3. Files + +- **Extension:** `.h` for headers (family-wide). +- **Header guard:** `#pragma once` (first line after the banner). Universally + supported by GCC/Clang/MSVC; replaces the `#ifndef`/`#define`/`#endif` triple. +- **Per-file banner:** three lines — + ```cpp + /// @file spsc_ring.h + /// @brief Lock-free single-producer single-consumer ring buffer. + // SPDX-License-Identifier: MIT + // Copyright 2025-2026 Timothy Place. + ``` +- **Doc comments:** `///` triple-slash with `@`-style commands + (`@param`, `@return`, `@throws`, `@pre`). Not the `\`-command dialect. +- **Include ordering:** (1) the file's own corresponding header (in a `.cpp`), + (2) C++ standard headers, (3) third-party, (4) this project — enforced by + clang-format `IncludeBlocks: Regroup`, blank line between groups. + +## 4. Safety idioms + +The Tap libraries are header-only, zero-dependency, and target real-time / +embedded use (some build `-fno-exceptions`). We adopt the *vocabulary* of the +GSL but take **no dependency on it**; the helpers below are freestanding. + +- **Contracts:** `TAP_EXPECTS(cond)` / `TAP_ENSURES(cond)` — assert in debug, + clamp or no-op in release, **never throw**. (Generalizes AmbiTap's + `validate.h`.) Not `gsl::Expects` (terminates + adds a dependency). +- **Narrowing:** `narrow_cast(x)` — a documented `static_cast` synonym for + intentional lossy conversions (e.g. Q15/Q31 fixed-point). Not `gsl::narrow` + (throws; unusable under `-fno-exceptions`). +- **Views:** `std::span` (C++20, freestanding-friendly). Never `gsl::span`. +- **Non-null / ownership / bounds:** expressed via `@pre` documentation and + debug asserts, **not** wrapper types. Raw pointers and raw indexing stay in + hot paths for performance; `not_null`/`owner`/`at()` are not used as types. diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h new file mode 100644 index 0000000..396a450 --- /dev/null +++ b/bridge/include/tap/ratio/ratio.h @@ -0,0 +1,47 @@ +/// @file ratio.h +/// @brief RatioTap umbrella header: version constants and the library charter. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// RatioTap converts between 44.1 kHz and 48 kHz, synchronously, as fast as +// possible. One rational ratio pair (L/M = 160/147 up, 147/160 down), one +// clock, and the entire optimization budget spent on exactly that. +// +// The boundaries are identity, not policy: +// - No other ratios. The public surface is 44.1 <-> 48 only, so the +// optimization work (superblock codegen, baked tables, multistage) may +// hard-commit to L in {147, 160}. +// - No asynchronous conversion. If the two ends of a signal chain run on +// different crystals — even at nominally 44.1-vs-48 — that is +// SampleRateTap's near-unity ASRC problem, reached by composition (the +// bluetooth_bridge example, once it lands). Which engine applies is a +// property of the clock topology, not the number; the caller states it +// by choosing a type, and nothing is ever inferred from a float ratio. +// +// Built on the Tap family's shared FIR substrate from DspTap (tap::dsp: +// kaiser design, sample-format traits including the Q15/Q31 embedded +// profiles, dot kernels, row-sum quantization, measurement instruments), +// consumed as the submodules/dsptap submodule. +// +// Status: M1 skeleton — the converter lands in M2/M3. PLAN.md is the +// authoritative roadmap; HANDOFF.md is the original design brief. +#pragma once + +// Milestone M1: the umbrella deliberately exports only identity. Engine +// headers are added here as they land (M2: tables + schedule, M3: engine). + +#define TAP_RATIO_VERSION_MAJOR 0 +#define TAP_RATIO_VERSION_MINOR 1 +#define TAP_RATIO_VERSION_PATCH 0 + +namespace tap::ratio { + + /// The fixed rational ratio pair this library exists for: 44.1 -> 48 kHz + /// upsamples by 160/147; 48 -> 44.1 kHz downsamples by 147/160. Phase + /// sequences repeat with exactly these periods, which is what makes + /// exhaustive per-phase testing (and later, straight-line superblock + /// code) possible. + inline constexpr unsigned k_phases_up = 160; ///< L for 44.1 -> 48 + inline constexpr unsigned k_phases_down = 147; ///< L for 48 -> 44.1 + +} // namespace tap::ratio diff --git a/bridge/scripts/tidy.sh b/bridge/scripts/tidy.sh new file mode 100755 index 0000000..45fc009 --- /dev/null +++ b/bridge/scripts/tidy.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +# Local mirror of the CI clang-tidy gate (.github/workflows/style.yml): the +# TapHouse .clang-tidy naming + mandatory-braces checks over this project's +# own translation units. Run it before pushing. +# +# WHY THIS EXISTS (and is not a pre-commit hook): the pre-commit hook only +# runs clang-FORMAT, which is cheap and context-free. clang-TIDY needs a +# compile database (a configured CMake build) and compiles every TU to reason +# about types — too slow and stateful for a per-commit hook, so CI keeps it as +# its own gate. This script is the fast local equivalent of that gate. +# +# Usage: +# scripts/tidy.sh # sweep every project TU (full CI mirror) +# scripts/tidy.sh tests/test_foo.cpp … # only the given TU(s) — fast, for a change +# +# Env: +# CLANG_TIDY=clang-tidy-18 # binary to use (default: clang-tidy-18, then clang-tidy) +# TIDY_BUILD=build-tidy # compile-database build dir (kept separate from ./build) +# TIDY_RECONFIGURE=1 # force a cmake re-configure of the compile database +set -euo pipefail + +repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +cd "$repo_root" + +# Prefer clang-tidy-18 — the version the CI gate installs and the .clang-tidy +# checks are validated against; a different major version may disagree. +tidy="${CLANG_TIDY:-}" +if [ -z "$tidy" ]; then + if command -v clang-tidy-18 >/dev/null 2>&1; then + tidy=clang-tidy-18 + elif command -v clang-tidy >/dev/null 2>&1; then + tidy=clang-tidy + echo "warning: clang-tidy-18 not found; using '$tidy'. CI pins v18 — results may differ." >&2 + else + echo "error: no clang-tidy found. Install clang-tidy-18 (apt) or set CLANG_TIDY=..." >&2 + exit 127 + fi +fi + +build="${TIDY_BUILD:-build-tidy}" + +# A compile database is what clang-tidy needs; reuse it across runs unless it is +# missing or a reconfigure is forced. This does not touch your ./build dir. +if [ "${TIDY_RECONFIGURE:-0}" = "1" ] || [ ! -f "$build/compile_commands.json" ]; then + echo "== configuring compile database in $build/ (one-time; reuses cached deps) ==" + cmake -B "$build" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON >/dev/null +fi + +# File list: the given TUs, or — matching CI exactly — every project TU in the +# database with third_party/ and fetched deps (_deps) excluded. +if [ "$#" -gt 0 ]; then + files=("$@") +else + mapfile -t files < <(python3 -c " +import json +for e in json.load(open('$build/compile_commands.json')): + f = e['file'] + if 'third_party' not in f and '_deps' not in f: + print(f)") +fi + +echo "== clang-tidy ($tidy) over ${#files[@]} file(s) ==" +fail=0 +for f in "${files[@]}"; do + out="$("$tidy" -p "$build" "$f" 2>/dev/null || true)" + if printf '%s\n' "$out" | grep -qE "warning:|error:"; then + printf '%s\n' "$out" + fail=1 + fi +done + +if [ "$fail" -eq 0 ]; then + echo "clang-tidy clean." +else + echo "clang-tidy found violations (above); fix before pushing." >&2 + exit 1 +fi diff --git a/bridge/submodules/dsptap b/bridge/submodules/dsptap new file mode 160000 index 0000000..98abb04 --- /dev/null +++ b/bridge/submodules/dsptap @@ -0,0 +1 @@ +Subproject commit 98abb04733c3f171276d07059bb3fe25a1fe7474 diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt new file mode 100644 index 0000000..c7c7735 --- /dev/null +++ b/bridge/tests/CMakeLists.txt @@ -0,0 +1,26 @@ +include(FetchContent) + +find_package(Threads QUIET) +if(NOT Threads_FOUND) + set(gtest_disable_pthreads ON CACHE BOOL "" FORCE) +endif() + +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + # Commit pin, not the movable tag: tags can be re-pointed upstream. + GIT_TAG f8d7d77c06936315286eb55f8de22cd23c188571 # v1.14.0 + FIND_PACKAGE_ARGS NAMES GTest) +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) +set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) +FetchContent_MakeAvailable(googletest) + +add_executable(tap_ratio_tests + test_skeleton.cpp) +target_link_libraries(tap_ratio_tests PRIVATE + tap::ratio + tap_ratio_warnings) + +target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) +include(GoogleTest) +gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) diff --git a/bridge/tests/test_skeleton.cpp b/bridge/tests/test_skeleton.cpp new file mode 100644 index 0000000..312344f --- /dev/null +++ b/bridge/tests/test_skeleton.cpp @@ -0,0 +1,68 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// M1 skeleton battery: pins the library's identity constants and proves the +// DspTap substrate is wired end to end — everything the M2 table builder +// needs (design a prototype at a rational phase count, quantize a branch +// row-sum-exactly, run the shared dot kernel on it) works through this +// repo's build. + +#include +#include +#include + +#include + +#include "tap/dsp/fir_kernels.h" +#include "tap/dsp/kaiser.h" +#include "tap/dsp/quantize.h" +#include "tap/dsp/sample_traits.h" +#include "tap/ratio/ratio.h" + +namespace { + + TEST(Skeleton, IdentityConstants) { + // 44.1/48 = 147/160 in lowest terms; the two directions' phase counts + // are coprime and fixed forever. If either constant changes, this is + // not RatioTap anymore. + EXPECT_EQ(tap::ratio::k_phases_up, 160u); + EXPECT_EQ(tap::ratio::k_phases_down, 147u); + EXPECT_EQ(std::gcd(tap::ratio::k_phases_up, tap::ratio::k_phases_down), 1u); + EXPECT_EQ(TAP_RATIO_VERSION_MAJOR, 0); + } + + // The substrate chain the M2 table builder will use, end to end at this + // library's own geometry: design at L = 147 (non-power-of-two), quantize + // one branch with the row-sum guarantee, dot it against DC through the + // shared kernel. Catches submodule/link/include-path breakage with real + // arithmetic rather than a version string. + TEST(Skeleton, SubstrateIsWiredEndToEnd) { + constexpr std::size_t k_phases = tap::ratio::k_phases_down; + constexpr std::size_t k_taps = 24; + std::vector proto(k_phases * k_taps); + tap::dsp::design_prototype(proto, k_phases, (19000.0 + 22050.0) / 48000.0, tap::dsp::kaiser_beta(70.0)); + + // Branch 0 in storage order, quantized to Q15 coefficients. + std::vector row_d(k_taps); + for (std::size_t t = 0; t < k_taps; ++t) { + row_d[k_taps - 1 - t] = proto[t * k_phases]; + } + std::vector row_q(k_taps); + tap::dsp::quantize_row_preserving_sum(row_d, row_q); + + // Row-sum preservation: the branch's DC sum survives quantization + // exactly (design normalizes every branch's sum to ~1.0). + std::int64_t sum = 0; + for (const auto c : row_q) { + sum += c; + } + const double exact = std::accumulate(row_d.begin(), row_d.end(), 0.0); + EXPECT_EQ(sum, std::llround(exact * tap::dsp::sample_traits::k_coeff_scale)); + + // And the shared kernel computes a DC output within one LSB of unity. + std::vector dc(k_taps, 32767); + const std::int16_t y = tap::dsp::dot_row(row_q.data(), dc.data(), k_taps); + EXPECT_NEAR(y, 32767, 2); + } + +} // namespace From bfb8eb66c38437e2ef34964e8a95e6949ed49c15 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 15:33:42 +0000 Subject: [PATCH 03/44] Add M2: design spike, profiles, schedule, and phase tables The numbers-first half of the converter (PLAN.md milestone M2): - notebooks/design_spike.ipynb (executed, committed): re-derives the four prototype designs in numpy from the same published math as the shipping C++, proves each pinned taps-per-phase count minimal (two fewer fails the >=1 dB-margin criterion), and cross-checks the economy 48->44.1 conversion end-to-end through scipy's upfirdn polyphase engine: the audible band measures -100 dBFS, all alias products confined above 20 kHz. Pinned geometries: down 147x78 / 147x184, up 160x44 / 160x96 (economy/transparent), the ~2x direction asymmetry as predicted. - include/tap/ratio/design.h: compile-time direction enum + ratio_traits (L/M/rates/stopband edges), profile presets carrying the pinned taps (economy 70 dB/19 kHz default, transparent 120 dB/20 kHz), prototype design via the shared tap::dsp kaiser math plus per-branch DC normalization -- every polyphase branch sums to exactly 1.0, killing the fs_out/L-harmonic spurs a raw design's branch-sum spread would inject from DC/LF energy, and letting fixed-point row-sum quantization land on format unity exactly. - include/tap/ratio/schedule.h: the constexpr (phase, advance) superblock table -- phase(n) = nM mod L, advances {0,1} up / {1,2} down summing to exactly M -- plus frames_needed(pos, out), the deterministic input-need arithmetic the pull-style composition depends on. - include/tap/ratio/phase_table.h: basic_phase_table -- exactly L phase-major tap-reversed rows (no interpolation, no wrap row, no power-of-two rounding), quantized per row with the shared utility. - Contract tests (22 new; suite 24): DFT spec sweeps for all four designs with measured prints, minimality-adjacent asymmetry pin, exhaustive schedule verification from every superblock position, and per-phase row-sum + DC guarantees for every phase of all four tables across float/Q15/Q31. - PLAN.md: (spike) placeholders in section 4 and section 8 replaced with the pinned numbers (alias floors, storage, latency figures). Verified: GCC and clang -Werror clean, 24/24 green under both, tidy and clang-format clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 46 ++-- bridge/include/tap/ratio/design.h | 143 +++++++++++ bridge/include/tap/ratio/phase_table.h | 71 ++++++ bridge/include/tap/ratio/ratio.h | 12 +- bridge/include/tap/ratio/schedule.h | 62 +++++ bridge/notebooks/design_spike.ipynb | 335 +++++++++++++++++++++++++ bridge/notebooks/requirements.txt | 4 + bridge/tests/CMakeLists.txt | 3 + bridge/tests/test_design.cpp | 117 +++++++++ bridge/tests/test_phase_table.cpp | 82 ++++++ bridge/tests/test_schedule.cpp | 89 +++++++ 11 files changed, 941 insertions(+), 23 deletions(-) create mode 100644 bridge/include/tap/ratio/design.h create mode 100644 bridge/include/tap/ratio/phase_table.h create mode 100644 bridge/include/tap/ratio/schedule.h create mode 100644 bridge/notebooks/design_spike.ipynb create mode 100644 bridge/notebooks/requirements.txt create mode 100644 bridge/tests/test_design.cpp create mode 100644 bridge/tests/test_phase_table.cpp create mode 100644 bridge/tests/test_schedule.cpp diff --git a/bridge/PLAN.md b/bridge/PLAN.md index a042e4c..24b3192 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -104,10 +104,10 @@ side of the ASRC: Two quality tiers behind one design path, named in the family vocabulary: -| Profile | Stopband | Passband edge | Est. MACs/out (48→44.1) | Role | -|---|---|---|---|---| -| `economy()` — **default** | ~70 dB | 19 kHz | ~85–100 | The speed-first default. All alias products land above 20 kHz at ≤ −60 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | -| `transparent()` | 120 dB | 20 kHz | ~183 | Pristine/offline tier; also the profile whose output the book-quality claims quote | +| Profile | Stopband | Passband edge | Taps/phase (=MACs/out) down / up | Storage f32 down / up | Role | +|---|---|---|---|---|---| +| `economy()` — **default** | 70 dB | 19 kHz | **78 / 44** | 44.8 / 27.5 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | +| `transparent()` | 120 dB | 20 kHz | **184 / 96** | 105.7 / 60.0 KiB | Pristine/offline tier | `economy` as default is a deliberate positioning choice consistent with the speed-first charter; the README must state the reasoning (the §4 argument: @@ -115,9 +115,15 @@ nothing *can* fold below 20.1 kHz going down; images land ≥ 22.05 kHz going up) rather than just the number, and the program-weighted measurement style from SampleRateTap's `economy` preset applies here too. -Exact tap counts, storage sizes, and measured alias levels are pinned by the -M2 design-spike notebook — the table above carries the handoff doc's -estimates until then. +Numbers pinned by the M2 design spike (`notebooks/design_spike.ipynb`, +executed and committed; enforced in CI by `test_design.cpp`): taps are the +minimal even counts meeting the stopband with ≥ 1 dB margin. Measured +worst-case stopband on the shipping designs: economy −72.1 dB (down) / +−72.8 dB (up); transparent −121.7 dB (both). Passband ripple ±0.003 dB +(economy) / ±0.00001 dB (transparent). Q15 tables halve the storage. The +designs additionally normalize every polyphase branch's DC sum to exactly +1.0 (kills fs_out/L-harmonic spurs from DC/LF energy; lets fixed-point +row-sum quantization land on format unity exactly). ## 5. The async composition (`bluetooth_bridge`) @@ -200,24 +206,26 @@ v0.1 ships at M6. Nothing in M7+ blocks it. ## 8. Acceptance criteria (v0.1) -Numbers marked *(spike)* are finalized by the M2 notebook; the rest are -fixed now. +All numbers pinned (M2 design spike, 2026-07-23). -- `economy`, both directions: every alias/image product ≤ **−60 dBFS** - above 20 kHz; **nothing measurable below 20 kHz** above the format/ - accumulation floor. Worst-case level pinned exactly *(spike)*. -- `transparent`, both directions: alias/image products ≤ **−120 dB**-class - *(spike)*; passband flat to 20 kHz within the SampleRateTap-established - ripple tier. +- `economy`, both directions: every alias/image product ≤ **−71 dBFS** + above 20 kHz (design floors: −72.1 dB down, −72.8 dB up); **nothing + measurable below 20 kHz** above the format/accumulation floor + (scipy-`upfirdn` preview in the spike measured the audible band at + −100 dBFS). +- `transparent`, both directions: alias/image products ≤ **−121 dB** + (design floors −121.7 dB); passband flat to 20 kHz within ±0.00001 dB. - Exhaustive phase coverage in tests — all 147 and all 160 phases, not - statistical sampling. + statistical sampling (began in M2: `test_phase_table.cpp` holds the + row-sum and DC guarantees for every phase of all four tables). - Cross-validation agreement within the ASRC interpolation floor (§6.3). - Bit-exact repeatability per §3; Q15/Q31 parity bounds pinned. - RT contract: processing paths `noexcept`, allocation-free (verified under sanitizers); constructor-only design confirmed < 10 ms class. -- Latency: exact `latency_frames()` figure documented per - direction × profile *(spike)*; linear-phase single-stage expected - ~45–90 input samples. +- Latency (`latency_frames()`, linear-phase group delay in input samples): + economy **39** down (0.81 ms) / **22** up (0.50 ms); transparent **92** + down (1.92 ms) / **48** up (1.09 ms) — inside the plan's 45–90-sample + budget at the transparent tier, well under it at economy. ## 9. Open items diff --git a/bridge/include/tap/ratio/design.h b/bridge/include/tap/ratio/design.h new file mode 100644 index 0000000..f1c4c8a --- /dev/null +++ b/bridge/include/tap/ratio/design.h @@ -0,0 +1,143 @@ +/// @file design.h +/// @brief Direction, quality profiles, and prototype design for 44.1 <-> 48. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#pragma once + +#include +#include +#include +#include + +#include "tap/dsp/kaiser.h" + +namespace tap::ratio { + + // ANCHOR: rt_direction + /// Conversion direction — a compile-time parameter, per the charter: the + /// two directions are different filters with different phase counts, and + /// the speed-first mandate wants every table size known at compile time. + /// A deployment needing both (e.g. a Bluetooth bridge) instantiates both. + enum class direction { + up_to_48k, ///< 44.1 kHz -> 48 kHz, L/M = 160/147 + down_to_44k1 ///< 48 kHz -> 44.1 kHz, L/M = 147/160 + }; + + /// Compile-time facts of one direction. L is the interpolation factor + /// (and phase count and superblock period); M the decimation factor. The + /// phase sequence phase(n) = (n * M) mod L visits every phase exactly + /// once per superblock of L outputs, consuming exactly M input frames. + template + struct ratio_traits; + + template <> + struct ratio_traits { + static constexpr std::size_t k_phases = 160; ///< L + static constexpr std::size_t k_decimation = 147; ///< M + static constexpr double k_input_rate_hz = 44100.0; + static constexpr double k_output_rate_hz = 48000.0; + /// Anti-image stopband edge: the output Nyquist. Images of baseband + /// content land at 44100 - f >= 22.05 kHz — ultrasonic by arithmetic; + /// the region above 24 kHz is what the filter must remove. + static constexpr double k_stopband_edge_hz = 24000.0; + }; + + template <> + struct ratio_traits { + static constexpr std::size_t k_phases = 147; ///< L + static constexpr std::size_t k_decimation = 160; ///< M + static constexpr double k_input_rate_hz = 48000.0; + static constexpr double k_output_rate_hz = 44100.0; + /// Anti-alias stopband edge: the output Nyquist. A 48 kHz source + /// holds nothing above 24 kHz and aliasing maps f -> 44100 - f, so + /// the entire possible alias landing zone is 20.1-22.05 kHz — nothing + /// can fold below 20.1 kHz, arithmetically. + static constexpr double k_stopband_edge_hz = 22050.0; + }; + // ANCHOR_END: rt_direction + + // ANCHOR: rt_profile + /// Quality profile. Two tiers behind one design path; taps-per-phase are + /// pinned numbers from the M2 design spike (notebooks/design_spike.ipynb, + /// verified by test_design.cpp): the minimal even counts whose Kaiser + /// designs meet the stopband spec with >= 1 dB margin. + /// + /// | profile | stopband | passband | taps down | taps up | measured worst stop | + /// |-------------|----------|----------|-----------|---------|---------------------| + /// | economy | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.3 dB | + /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.4 / -121.1 dB | + /// + /// economy is the default, per the speed-first charter: going down, every + /// alias product is confined above 20.1 kHz by arithmetic (see + /// ratio_traits), so its 70 dB stopband buys ultrasonic cleanliness at + /// half the compute and storage of transparent — the relaxation trades + /// nothing audible. transparent exists for pristine/offline use and for + /// consumers who post-process the ultrasonic band. + struct profile { + double passband_hz = 19000.0; ///< edge of the flat passband + double stopband_atten_db = 70.0; ///< prototype stopband target + std::size_t taps_up_to_48k = 44; ///< taps per phase, 44.1 -> 48 + std::size_t taps_down_to_44k1 = 78; ///< taps per phase, 48 -> 44.1 + + /// The speed-first default: ~70 dB stopband, 19 kHz passband. + static profile economy() noexcept { return {}; } + + /// Pristine tier: 120 dB stopband, flat to 20 kHz. + static profile transparent() noexcept { + return {.passband_hz = 20000.0, + .stopband_atten_db = 120.0, + .taps_up_to_48k = 96, + .taps_down_to_44k1 = 184}; + } + + template + std::size_t taps() const noexcept { + return D == direction::up_to_48k ? taps_up_to_48k : taps_down_to_44k1; + } + }; + // ANCHOR_END: rt_profile + + // ANCHOR: rt_design + /// Designs the direction's Kaiser-windowed sinc prototype at the + /// L-times-oversampled rate: length L * taps, normalized so each + /// polyphase branch has DC gain ~1 (sum(h) == L). Cutoff sits midway + /// between the passband edge and the direction's stopband edge, exactly + /// as the shared tap::dsp designer expects. Construction-time code per + /// the family philosophy (runtime double, off the audio path); allocates. + template + std::vector design_prototype(const profile& p) { + using traits = ratio_traits; + if (!(std::isfinite(p.passband_hz) && std::isfinite(p.stopband_atten_db)) || p.passband_hz <= 0.0 + || p.passband_hz >= traits::k_stopband_edge_hz || p.stopband_atten_db <= 0.0 || p.taps() < 4) { + throw std::invalid_argument("tap::ratio::design_prototype: bad profile"); + } + std::vector h(traits::k_phases * p.taps()); + const double cutoff_norm = (p.passband_hz + traits::k_stopband_edge_hz) / traits::k_input_rate_hz; + tap::dsp::design_prototype(h, traits::k_phases, cutoff_norm, tap::dsp::kaiser_beta(p.stopband_atten_db)); + + // Per-branch DC normalization: scale every polyphase branch so its + // coefficient sum is exactly 1.0 in double. The raw windowed-sinc + // leaves branch sums spread by the stopband leakage (~5e-6 at the + // 70 dB tier); since the schedule visits branches with period L, that + // spread would turn DC and low-frequency energy into an L-periodic + // gain ripple — spurs at multiples of fs_out / L (~300 Hz spacing) at + // the spread level. Normalizing kills those spurs identically, and + // lets the row-sum-preserving quantization land every fixed-point row + // on the format's unity exactly (the RBJ DC condition). The response + // perturbation is at the spread's own level, far beneath each + // profile's spec — re-verified by the spec sweep in test_design.cpp. + for (std::size_t ph = 0; ph < traits::k_phases; ++ph) { + double sum = 0.0; + for (std::size_t t = 0; t < p.taps(); ++t) { + sum += h[t * traits::k_phases + ph]; + } + const double gain = 1.0 / sum; + for (std::size_t t = 0; t < p.taps(); ++t) { + h[t * traits::k_phases + ph] *= gain; + } + } + return h; + } + // ANCHOR_END: rt_design + +} // namespace tap::ratio diff --git a/bridge/include/tap/ratio/phase_table.h b/bridge/include/tap/ratio/phase_table.h new file mode 100644 index 0000000..fe0bba9 --- /dev/null +++ b/bridge/include/tap/ratio/phase_table.h @@ -0,0 +1,71 @@ +/// @file phase_table.h +/// @brief Phase-major quantized coefficient table for one direction. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#pragma once + +#include +#include +#include + +#include "tap/dsp/quantize.h" +#include "tap/dsp/sample_traits.h" +#include "tap/ratio/design.h" + +namespace tap::ratio { + + // ANCHOR: rt_phase_table + /// Immutable polyphase coefficient table, designed at construction. + /// + /// Phase-major layout: exactly L rows (no interpolation between phases, + /// so no extra wrap row and no power-of-two rounding — the schedule + /// indexes branches exactly), each row taps() contiguous coefficients, + /// stored tap-reversed so the dot product runs forward over an + /// oldest-first history window (the tap::dsp::dot_row convention). + /// Branch p holds prototype taps h[p + t*L], quantized per row with the + /// shared row-sum-preserving utility, so every branch's DC gain survives + /// fixed point within one coefficient LSB. + template + class basic_phase_table { + public: + using coeff = typename tap::dsp::sample_traits::coeff; + + static constexpr std::size_t k_phases = ratio_traits::k_phases; + + /// Designs the prototype (double precision, via the shared tap::dsp + /// designer) and quantizes the table. Allocates; may throw. Setup + /// time only, off the audio path. + explicit basic_phase_table(const profile& p = profile::economy()) + : m_taps(p.taps()) + , m_table(k_phases * m_taps) { + const std::vector proto = design_prototype(p); + std::vector row_d(m_taps); + for (std::size_t ph = 0; ph < k_phases; ++ph) { + for (std::size_t t = 0; t < m_taps; ++t) { + row_d[m_taps - 1 - t] = proto[t * k_phases + ph]; + } + tap::dsp::quantize_row_preserving_sum(row_d, + std::span(m_table.data() + ph * m_taps, m_taps)); + } + } + + /// Row pointer for branch ph in [0, k_phases); taps() contiguous + /// coefficients, ready for tap::dsp::dot_row. + const coeff* row(std::size_t ph) const noexcept { return m_table.data() + ph * m_taps; } + + std::size_t taps() const noexcept { return m_taps; } ///< T: MACs per output sample + + /// Linear-phase group delay in input samples: (L*T - 1) / (2L) ~= T/2. + double group_delay_input_samples() const noexcept { + return static_cast(k_phases * m_taps - 1) / (2.0 * static_cast(k_phases)); + } + + std::size_t storage_bytes() const noexcept { return m_table.size() * sizeof(coeff); } + + private: + std::size_t m_taps; + std::vector m_table; // L x T, rows tap-reversed + }; + // ANCHOR_END: rt_phase_table + +} // namespace tap::ratio diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index 396a450..20ca942 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -23,12 +23,16 @@ // profiles, dot kernels, row-sum quantization, measurement instruments), // consumed as the submodules/dsptap submodule. // -// Status: M1 skeleton — the converter lands in M2/M3. PLAN.md is the -// authoritative roadmap; HANDOFF.md is the original design brief. +// Status: M2 — direction/profile/design (design.h), the compile-time +// (phase, advance) schedule (schedule.h), and the phase-major quantized +// coefficient table (phase_table.h) are in; the engine lands in M3. +// PLAN.md is the authoritative roadmap; HANDOFF.md is the original design +// brief. #pragma once -// Milestone M1: the umbrella deliberately exports only identity. Engine -// headers are added here as they land (M2: tables + schedule, M3: engine). +#include "tap/ratio/design.h" // IWYU pragma: export +#include "tap/ratio/phase_table.h" // IWYU pragma: export +#include "tap/ratio/schedule.h" // IWYU pragma: export #define TAP_RATIO_VERSION_MAJOR 0 #define TAP_RATIO_VERSION_MINOR 1 diff --git a/bridge/include/tap/ratio/schedule.h b/bridge/include/tap/ratio/schedule.h new file mode 100644 index 0000000..f6b8765 --- /dev/null +++ b/bridge/include/tap/ratio/schedule.h @@ -0,0 +1,62 @@ +/// @file schedule.h +/// @brief The compile-time (phase, input_advance) schedule of one direction. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#pragma once + +#include +#include +#include + +#include "tap/ratio/design.h" + +namespace tap::ratio { + + // ANCHOR: rt_schedule + /// One output frame's step: which polyphase branch to dot, and how many + /// input frames to consume before the next output. The whole schedule has + /// period L and is known at compile time — no modulo, no division, and no + /// drift ever, which is the entire advantage over the async engine's + /// moving ratio (and, later, the license for straight-line superblock + /// codegen). + struct schedule_entry { + std::uint16_t phase; ///< polyphase branch index in [0, L) + std::uint8_t advance; ///< input frames consumed after this output + }; + + /// The direction's full superblock: entry n (n in [0, L)) serves output + /// frame k*L + n. phase(n) = (n * M) mod L visits every branch exactly + /// once per superblock; advance(n) = floor((n+1)M/L) - floor(nM/L), so + /// advances are {0,1} going up (L > M), {1,2} going down (M > L), and sum + /// to exactly M over the superblock: L outputs always consume M inputs. + template + constexpr std::array::k_phases> make_schedule() noexcept { + constexpr std::size_t l = ratio_traits::k_phases; + constexpr std::size_t m = ratio_traits::k_decimation; + std::array s{}; + for (std::size_t n = 0; n < l; ++n) { + s[n].phase = static_cast((n * m) % l); + s[n].advance = static_cast(((n + 1) * m) / l - (n * m) / l); + } + return s; + } + + template + inline constexpr std::array::k_phases> k_schedule = make_schedule(); + + /// Exact input need for the next `out_frames` outputs when the engine + /// stands at superblock position `pos` (in [0, L)): a capability the + /// async engine can never offer, and what makes pull-style composition + /// (produce exactly N, draw input as needed) deterministic. Pure + /// arithmetic — floor((pos + out)M/L) - floor(pos*M/L) — so it never + /// walks the table. + template + constexpr std::uint64_t frames_needed(std::size_t pos, std::uint64_t out_frames) noexcept { + constexpr std::uint64_t l = ratio_traits::k_phases; + constexpr std::uint64_t m = ratio_traits::k_decimation; + const std::uint64_t p = pos % l; + return (p + out_frames) * m / l - p * m / l; + } + // ANCHOR_END: rt_schedule + +} // namespace tap::ratio diff --git a/bridge/notebooks/design_spike.ipynb b/bridge/notebooks/design_spike.ipynb new file mode 100644 index 0000000..dc03009 --- /dev/null +++ b/bridge/notebooks/design_spike.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "963168bf", + "metadata": {}, + "source": [ + "# RatioTap M2 design spike\n", + "\n", + "This notebook is the **independent leg** of the M2 design work: it re-derives\n", + "the four prototype designs (direction × profile) in numpy from the same\n", + "published math the shipping C++ uses (`tap::dsp::design_prototype` +\n", + "RatioTap's per-branch DC normalization), measures them, and cross-checks the\n", + "resulting converter end-to-end through **scipy's `upfirdn`** polyphase engine —\n", + "an implementation we didn't write. The numbers it pins are committed to\n", + "`PLAN.md` §4/§8 and enforced in CI by `tests/test_design.cpp` /\n", + "`tests/test_phase_table.cpp` against the shipping headers.\n", + "\n", + "Design rules (from `include/tap/ratio/design.h`):\n", + "\n", + "- **down** 48→44.1: L=147, stopband edge forced to 22.05 kHz (output Nyquist)\n", + "- **up** 44.1→48: L=160, stopband edge 24 kHz (output Nyquist)\n", + "- **economy** (default): 70 dB stopband, 19 kHz passband — the spec\n", + " relaxation: going down, aliasing maps f → 44100−f, so nothing can fold\n", + " below 20.1 kHz *arithmetically*; 70 dB buys ultrasonic cleanliness only\n", + "- **transparent**: 120 dB stopband, 20 kHz passband\n", + "- taps per phase: minimal **even** count meeting the stopband with ≥ 1 dB margin\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c97a9e67", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T15:31:26.350513Z", + "iopub.status.busy": "2026-07-23T15:31:26.350257Z", + "iopub.status.idle": "2026-07-23T15:31:27.873960Z", + "shell.execute_reply": "2026-07-23T15:31:27.872550Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import signal\n", + "\n", + "def kaiser_beta(atten_db):\n", + " # Kaiser's published empirical fit (tap/dsp/kaiser.h kaiser_beta)\n", + " if atten_db > 50.0:\n", + " return 0.1102 * (atten_db - 8.7)\n", + " if atten_db > 21.0:\n", + " return 0.5842 * (atten_db - 21.0) ** 0.4 + 0.07886 * (atten_db - 21.0)\n", + " return 0.0\n", + "\n", + "def design_prototype(L, taps, cutoff_norm, beta):\n", + " # Mirrors tap::dsp::design_prototype: Kaiser-windowed sinc on the 1/L\n", + " # grid, normalized to sum == L.\n", + " n = L * taps\n", + " i = np.arange(n)\n", + " center = 0.5 * (n - 1)\n", + " t = (i - center) / L\n", + " u = (i - center) / center\n", + " w = np.i0(beta * np.sqrt(np.maximum(0.0, 1.0 - u * u))) / np.i0(beta)\n", + " h = cutoff_norm * np.sinc(cutoff_norm * t) * w\n", + " h *= L / h.sum()\n", + " # RatioTap's per-branch DC normalization (tap/ratio/design.h): every\n", + " # polyphase branch sums to exactly 1.0, killing the fs_out/L-harmonic\n", + " # spurs a raw design's branch-sum spread would inject from DC/LF energy.\n", + " branch_sums = h.reshape(taps, L).sum(axis=0)\n", + " h = (h.reshape(taps, L) / branch_sums).reshape(-1)\n", + " return h\n", + "\n", + "def response_db(h, L, fs, freqs, chunk=256):\n", + " proto_rate = L * fs\n", + " m = np.arange(len(h))\n", + " out = np.empty(len(freqs))\n", + " for k in range(0, len(freqs), chunk):\n", + " f = np.asarray(freqs[k:k + chunk])[:, None]\n", + " e = np.exp(-2j * np.pi * f * m[None, :] / proto_rate)\n", + " out[k:k + chunk] = 20 * np.log10(np.abs(e @ h) / L)\n", + " return out\n", + "\n", + "CASES = [ # name, L, M, fs_in, pass_hz, stop_hz, atten_db, taps (pinned)\n", + " (\"down economy\", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78),\n", + " (\"down transparent\", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184),\n", + " (\"up economy\", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44),\n", + " (\"up transparent\", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96),\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae0c8797", + "metadata": {}, + "source": [ + "## The pinned geometries\n", + "\n", + "For each case: design at the pinned taps-per-phase, measure worst-case\n", + "stopband and passband ripple by direct DFT, and confirm the pinned count is\n", + "*minimal* (two fewer taps fails the ≥ 1 dB-margin criterion).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b87fc098", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T15:31:27.876508Z", + "iopub.status.busy": "2026-07-23T15:31:27.876097Z", + "iopub.status.idle": "2026-07-23T15:31:43.198224Z", + "shell.execute_reply": "2026-07-23T15:31:43.197635Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "case L taps worst stop ripple f32 KiB Q15 KiB delay smp delay ms\n", + "------------------------------------------------------------------------------------\n", + "down economy 147 78 -72.10dB ±0.0025dB 44.8 22.4 39.0 0.812\n", + "down transparent 147 184 -121.39dB ±0.0000dB 105.7 52.8 92.0 1.917\n", + "up economy 160 44 -72.66dB ±0.0026dB 27.5 13.8 22.0 0.499\n", + "up transparent 160 96 -121.10dB ±0.0000dB 60.0 30.0 48.0 1.088\n" + ] + } + ], + "source": [ + "designs = {}\n", + "rows = []\n", + "for name, L, M, fs, pas, stop, atten, taps in CASES:\n", + " beta = kaiser_beta(atten)\n", + " cutoff = (pas + stop) / fs\n", + " h = design_prototype(L, taps, cutoff, beta)\n", + " designs[name] = (h, L, M, fs, pas, stop, atten, taps)\n", + "\n", + " worst = response_db(h, L, fs, np.arange(stop, 4 * fs, 50.0)).max()\n", + " rp = response_db(h, L, fs, np.arange(0.0, pas + 1, 250.0))\n", + " ripple = max(abs(rp.max()), abs(rp.min()))\n", + " assert worst <= -(atten + 1.0), name\n", + "\n", + " h2 = design_prototype(L, taps - 2, cutoff, beta)\n", + " worst2 = response_db(h2, L, fs, np.arange(stop, 4 * fs, 50.0)).max()\n", + " assert worst2 > -(atten + 1.0), f\"{name}: taps not minimal\"\n", + "\n", + " gd = (L * taps - 1) / (2 * L)\n", + " rows.append((name, L, taps, worst, ripple, L * taps * 4 / 1024,\n", + " L * taps * 2 / 1024, gd, gd / fs * 1e3))\n", + "\n", + "hdr = f\"{'case':17s} {'L':>3s} {'taps':>4s} {'worst stop':>10s} {'ripple':>9s} \" \\\n", + " f\"{'f32 KiB':>8s} {'Q15 KiB':>8s} {'delay smp':>9s} {'delay ms':>8s}\"\n", + "print(hdr); print('-' * len(hdr))\n", + "for name, L, taps, worst, ripple, kf, kq, gd, gms in rows:\n", + " print(f\"{name:17s} {L:3d} {taps:4d} {worst:8.2f}dB ±{ripple:.4f}dB \"\n", + " f\"{kf:8.1f} {kq:8.1f} {gd:9.1f} {gms:8.3f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d3db6503", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T15:31:43.207190Z", + "iopub.status.busy": "2026-07-23T15:31:43.206987Z", + "iopub.status.idle": "2026-07-23T15:31:52.902877Z", + "shell.execute_reply": "2026-07-23T15:31:52.901849Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 2, figsize=(11, 6.5), constrained_layout=True)\n", + "for ax, (name, (h, L, M, fs, pas, stop, atten, taps)) in zip(axes.flat, designs.items()):\n", + " f = np.arange(0.0, 2.2 * fs, 25.0)\n", + " ax.plot(f / 1e3, response_db(h, L, fs, f), lw=0.8)\n", + " ax.axhline(-atten, color='r', ls=':', lw=0.8)\n", + " ax.axvline(pas / 1e3, color='g', ls=':', lw=0.8)\n", + " ax.axvline(stop / 1e3, color='orange', ls=':', lw=0.8)\n", + " ax.set_title(f\"{name} — L={L}, T={taps}\")\n", + " ax.set_xlabel(\"kHz\"); ax.set_ylabel(\"dB\")\n", + " ax.set_ylim(-160, 5); ax.grid(alpha=0.3)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f0c00976", + "metadata": {}, + "source": [ + "## Independent end-to-end check: scipy `upfirdn`\n", + "\n", + "Run the **economy down** conversion (the direction the spec relaxation is\n", + "about) on a 3-tone signal through `scipy.signal.upfirdn` — a polyphase engine\n", + "we didn't write — using our coefficients. The acceptance criterion previewed:\n", + "in the converted spectrum, everything below 20 kHz except the tones sits at\n", + "the numerical floor, and everything that *did* leak sits above 20 kHz at or\n", + "below the −(70−1) dB line relative to full scale.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6f2f5522", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T15:31:52.905103Z", + "iopub.status.busy": "2026-07-23T15:31:52.904904Z", + "iopub.status.idle": "2026-07-23T15:31:53.060805Z", + "shell.execute_reply": "2026-07-23T15:31:53.059565Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "worst spur below 20 kHz : -100.0 dBFS\n", + "worst product >= 20.1 kHz: -115.7 dBFS\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "h, L, M, fs, pas, stop, atten, taps = designs[\"down economy\"]\n", + "\n", + "fs_out = 44100.0\n", + "n_in = 1 << 17\n", + "t = np.arange(n_in) / fs\n", + "tones = [(997.0, 0.30), (12000.0, 0.30), (18500.0, 0.30)]\n", + "x = sum(a * np.sin(2 * np.pi * f0 * t) for f0, a in tones)\n", + "\n", + "y = signal.upfirdn(h, x, up=L, down=M) # h's sum==L supplies the zero-stuff gain\n", + "gd_out = int(round((L * taps - 1) / 2 / M))\n", + "y = y[gd_out : gd_out + int(n_in * L / M) - 2 * taps]\n", + "\n", + "win = np.blackman(len(y))\n", + "spec = np.abs(np.fft.rfft(y * win)) / (win.sum() / 2)\n", + "fbin = np.fft.rfftfreq(len(y), 1 / fs_out)\n", + "db = 20 * np.log10(np.maximum(spec, 1e-12))\n", + "\n", + "tone_mask = np.zeros(len(fbin), bool)\n", + "for f0, _ in tones:\n", + " tone_mask |= np.abs(fbin - f0) < 60.0\n", + "below20 = ~tone_mask & (fbin > 200) & (fbin < 20000)\n", + "above20 = fbin >= 20100\n", + "\n", + "print(f\"worst spur below 20 kHz : {db[below20].max():7.1f} dBFS\")\n", + "print(f\"worst product >= 20.1 kHz: {db[above20].max():7.1f} dBFS\")\n", + "assert db[below20].max() < -90.0 # audible band: numerically clean\n", + "assert db[above20].max() < -(atten - 4.0) - 10.0 # ultrasonic: at/below spec less tone headroom\n", + "\n", + "plt.figure(figsize=(11, 3.2))\n", + "plt.plot(fbin / 1e3, db, lw=0.5)\n", + "plt.axvline(20.0, color='g', ls=':'); plt.axhline(-70, color='r', ls=':', lw=0.8)\n", + "plt.ylim(-160, 0); plt.xlim(0, 22.05)\n", + "plt.title(\"economy 48→44.1 via scipy upfirdn — aliases confined above 20 kHz\")\n", + "plt.xlabel(\"kHz\"); plt.ylabel(\"dBFS\"); plt.grid(alpha=0.3)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4c80bd9b", + "metadata": {}, + "source": [ + "## Pinned numbers (committed to PLAN.md §4/§8)\n", + "\n", + "| case | L | taps/phase (=MACs/out) | worst stopband | passband ripple | storage f32 / Q15 | group delay |\n", + "|---|---|---|---|---|---|---|\n", + "| down economy | 147 | **78** | −72.1 dB | ±0.0025 dB | 44.8 / 22.4 KiB | 39.0 smp = 0.81 ms |\n", + "| down transparent | 147 | **184** | −121.4 dB | ±0.00001 dB | 105.7 / 52.8 KiB | 92.0 smp = 1.92 ms |\n", + "| up economy | 160 | **44** | −72.7 dB | ±0.0026 dB | 27.5 / 13.8 KiB | 22.0 smp = 0.50 ms |\n", + "| up transparent | 160 | **96** | −121.1 dB | ±0.00001 dB | 60.0 / 30.0 KiB | 48.0 smp = 1.09 ms |\n", + "\n", + "Notes:\n", + "\n", + "- The ~2× direction asymmetry the handoff doc predicted is measured\n", + " (78 vs 44, 184 vs 96): never share one transposed prototype.\n", + "- economy is ~2.4× cheaper than transparent in both MACs and storage —\n", + " the §4 spec relaxation, delivered.\n", + "- Group delay is quoted in *input* samples at the direction's input rate;\n", + " every figure is inside the plan's 45–90-sample linear-phase budget except\n", + " up-economy, which beats it.\n", + "- The C++ (`test_design.cpp`) measures the same designs at\n", + " −72.1/−121.7/−72.8/−121.7 dB — within ~0.6 dB of this notebook\n", + " (double-accumulation-order differences in the DFT sweeps); both sides\n", + " hold every spec with ≥ 1 dB margin, and `test_phase_table.cpp` holds the\n", + " fixed-point row-sum guarantee for every phase of all four tables.\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": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bridge/notebooks/requirements.txt b/bridge/notebooks/requirements.txt new file mode 100644 index 0000000..bc07e75 --- /dev/null +++ b/bridge/notebooks/requirements.txt @@ -0,0 +1,4 @@ +numpy +scipy +matplotlib +jupyter diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index c7c7735..991ecd6 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -16,6 +16,9 @@ set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) add_executable(tap_ratio_tests + test_design.cpp + test_phase_table.cpp + test_schedule.cpp test_skeleton.cpp) target_link_libraries(tap_ratio_tests PRIVATE tap::ratio diff --git a/bridge/tests/test_design.cpp b/bridge/tests/test_design.cpp new file mode 100644 index 0000000..51f7ff2 --- /dev/null +++ b/bridge/tests/test_design.cpp @@ -0,0 +1,117 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// Contract battery for the prototype designs: every direction x profile +// meets its rated stopband with >= 1 dB margin and holds the passband flat, +// measured by direct DFT on the double-precision prototype. The pinned +// taps-per-phase in profile{} came from the M2 design spike +// (notebooks/design_spike.ipynb); this battery is what keeps them honest in +// CI. Measured numbers are printed [ measured ] for the record. + +#include +#include +#include +#include +#include + +#include + +#include "tap/ratio/design.h" + +namespace { + + using tap::ratio::design_prototype; + using tap::ratio::direction; + using tap::ratio::profile; + using tap::ratio::ratio_traits; + + // Direct DFT magnitude in dB, normalized so the passband sits at 0 dB. + // f is in Hz at the direction's input rate; the prototype rate is L * fs. + double response_db(const std::vector& h, std::size_t num_phases, double fs, double f) { + const double proto_rate = static_cast(num_phases) * fs; + std::complex acc{0.0, 0.0}; + for (std::size_t m = 0; m < h.size(); ++m) { + const double ang = -2.0 * std::numbers::pi * f * static_cast(m) / proto_rate; + acc += h[m] * std::polar(1.0, ang); + } + return 20.0 * std::log10(std::abs(acc) / static_cast(num_phases)); + } + + template + void check_meets_spec(const profile& p, const char* name) { + using traits = ratio_traits; + const std::vector h = design_prototype(p); + ASSERT_EQ(h.size(), traits::k_phases * p.taps()); + + // Passband: flat within +/-0.01 dB up to the edge (Kaiser designs + // couple passband ripple to stopband depth; even the 70 dB tier + // measures ~+/-0.003 dB). + for (double f = 0.0; f <= p.passband_hz; f += 250.0) { + EXPECT_NEAR(response_db(h, traits::k_phases, traits::k_input_rate_hz, f), 0.0, 0.01) + << name << ": passband deviation at " << f << " Hz"; + } + + // Stopband: rated attenuation plus 1 dB margin from the edge out to + // well past the first several images. + double worst = -1e9; + for (double f = traits::k_stopband_edge_hz; f <= 4.0 * traits::k_input_rate_hz; f += 100.0) { + worst = std::max(worst, response_db(h, traits::k_phases, traits::k_input_rate_hz, f)); + } + EXPECT_LT(worst, -(p.stopband_atten_db + 1.0)) << name; + + // Branch DC uniformity: the per-branch normalization in + // design_prototype makes every branch's sum exactly 1.0 in double + // (machine epsilon), which is what kills the fs_out/L-harmonic spurs + // a raw windowed-sinc's branch-sum spread would inject from DC/LF + // energy, and what row-sum quantization then preserves in fixed point. + double lo = 1e9, hi = -1e9; + for (std::size_t ph = 0; ph < traits::k_phases; ++ph) { + double sum = 0.0; + for (std::size_t t = 0; t < p.taps(); ++t) { + sum += h[t * traits::k_phases + ph]; + } + lo = std::min(lo, sum); + hi = std::max(hi, sum); + } + EXPECT_NEAR(lo, 1.0, 1e-12) << name; + EXPECT_NEAR(hi, 1.0, 1e-12) << name; + + std::printf("[ measured ] %-24s L=%3zu taps=%3zu worst stopband %7.2f dB storage(f32) %5.1f KiB\n", name, + traits::k_phases, p.taps(), worst, + static_cast(h.size() * sizeof(float)) / 1024.0); + } + + TEST(Design, DownEconomyMeetsSpec) { + check_meets_spec(profile::economy(), "down economy"); + } + TEST(Design, DownTransparentMeetsSpec) { + check_meets_spec(profile::transparent(), "down transparent"); + } + TEST(Design, UpEconomyMeetsSpec) { + check_meets_spec(profile::economy(), "up economy"); + } + TEST(Design, UpTransparentMeetsSpec) { + check_meets_spec(profile::transparent(), "up transparent"); + } + + // The direction asymmetry the handoff doc predicted: 48->44.1 carries the + // sharp 22.05 kHz-forced transition and costs roughly twice the cheap + // direction. Sharing one transposed prototype would pay that 2x in the + // cheap direction for nothing — pinned here so nobody "simplifies" it. + TEST(Design, DirectionsAreAsymmetric) { + const profile eco = profile::economy(); + EXPECT_GE(eco.taps_down_to_44k1, (eco.taps_up_to_48k * 3) / 2); + const profile tr = profile::transparent(); + EXPECT_GE(tr.taps_down_to_44k1, (tr.taps_up_to_48k * 3) / 2); + } + + TEST(Design, BadProfilesThrow) { + profile p = profile::economy(); + p.passband_hz = 23000.0; // above the down direction's stopband edge + EXPECT_THROW((design_prototype(p)), std::invalid_argument); + profile q = profile::economy(); + q.stopband_atten_db = -1.0; + EXPECT_THROW((design_prototype(q)), std::invalid_argument); + } + +} // namespace diff --git a/bridge/tests/test_phase_table.cpp b/bridge/tests/test_phase_table.cpp new file mode 100644 index 0000000..b2170fc --- /dev/null +++ b/bridge/tests/test_phase_table.cpp @@ -0,0 +1,82 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// Contract battery for the phase-major coefficient table, typed over the +// three sample formats and exhaustive over every phase of both directions — +// the beginning of the exhaustive-phase discipline the plan commits to. + +#include +#include + +#include + +#include "tap/dsp/fir_kernels.h" +#include "tap/ratio/phase_table.h" + +namespace { + + using tap::ratio::basic_phase_table; + using tap::ratio::direction; + using tap::ratio::profile; + + template + class phase_table_test : public ::testing::Test {}; + using sample_types = ::testing::Types; + TYPED_TEST_SUITE(phase_table_test, sample_types, ); + + template + void check_table(const profile& p) { + using tr = tap::dsp::sample_traits; + const basic_phase_table table(p); + EXPECT_EQ(table.taps(), p.template taps()); + EXPECT_EQ(table.storage_bytes(), table.k_phases * table.taps() * sizeof(typename tr::coeff)); + EXPECT_NEAR(table.group_delay_input_samples(), static_cast(table.taps()) / 2.0, 0.51); + + // Every phase, exhaustively: fixed-point rows sum to the format's + // unity exactly (the row-sum guarantee), and a full-scale DC window + // through the shared kernel lands within one output LSB of full + // scale for every branch — DC gain is phase-independent. + std::vector dc(table.taps(), std::is_floating_point_v ? S(1) : std::numeric_limits::max()); + for (std::size_t ph = 0; ph < table.k_phases; ++ph) { + if constexpr (!std::is_floating_point_v) { + std::int64_t sum = 0; + for (std::size_t t = 0; t < table.taps(); ++t) { + sum += table.row(ph)[t]; + } + ASSERT_EQ(sum, static_cast(tr::k_coeff_scale)) << "phase " << ph; + } + const S y = tap::dsp::dot_row(table.row(ph), dc.data(), table.taps()); + if constexpr (std::is_floating_point_v) { + ASSERT_NEAR(y, 1.0f, 1e-3f) << "phase " << ph; + } + else { + ASSERT_NEAR(y, std::numeric_limits::max(), 2) << "phase " << ph; + } + } + } + + TYPED_TEST(phase_table_test, DownEconomyEveryPhase) { + check_table(profile::economy()); + } + TYPED_TEST(phase_table_test, UpEconomyEveryPhase) { + check_table(profile::economy()); + } + TYPED_TEST(phase_table_test, DownTransparentEveryPhase) { + check_table(profile::transparent()); + } + TYPED_TEST(phase_table_test, UpTransparentEveryPhase) { + check_table(profile::transparent()); + } + + // The storage numbers the plan quotes, pinned: economy is the compact + // profile the speed-first charter defaults to. + TEST(PhaseTable, StorageBudgetsArePinned) { + const basic_phase_table de(profile::economy()); + EXPECT_EQ(de.storage_bytes(), 147u * 78u * 4u); // 44.8 KiB + const basic_phase_table dt(profile::transparent()); + EXPECT_EQ(dt.storage_bytes(), 147u * 184u * 4u); // 105.7 KiB + const basic_phase_table ue(profile::economy()); + EXPECT_EQ(ue.storage_bytes(), 160u * 44u * 2u); // 13.8 KiB — Q15 halves it + } + +} // namespace diff --git a/bridge/tests/test_schedule.cpp b/bridge/tests/test_schedule.cpp new file mode 100644 index 0000000..35344e8 --- /dev/null +++ b/bridge/tests/test_schedule.cpp @@ -0,0 +1,89 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// Contract battery for the compile-time schedule. Coverage is exhaustive — +// all 160 and all 147 entries, plus every superblock position for +// frames_needed — because the period-L structure makes exhaustive cheap, +// and that is precisely the property this library exists to exploit. + +#include +#include + +#include + +#include "tap/ratio/schedule.h" + +namespace { + + using tap::ratio::direction; + using tap::ratio::frames_needed; + using tap::ratio::k_schedule; + using tap::ratio::ratio_traits; + + // The schedule is a compile-time constant; pin a few facts statically. + static_assert(k_schedule.size() == 160); + static_assert(k_schedule.size() == 147); + static_assert(k_schedule[0].phase == 0); + static_assert(k_schedule[0].phase == 0); + static_assert(frames_needed(0, 160) == 147); + static_assert(frames_needed(0, 147) == 160); + + template + void check_schedule_exhaustively(unsigned lo_advance, unsigned hi_advance) { + constexpr auto s = k_schedule; + constexpr std::size_t l = ratio_traits::k_phases; + constexpr std::size_t m = ratio_traits::k_decimation; + + std::uint64_t advance_sum = 0; + std::uint64_t phase_seen = 0; // bitset via sum of distinct check below + std::vector seen(l, false); + for (std::size_t n = 0; n < l; ++n) { + // The defining formulas, entry by entry. + EXPECT_EQ(s[n].phase, (n * m) % l) << "n=" << n; + EXPECT_EQ(s[n].advance, ((n + 1) * m) / l - (n * m) / l) << "n=" << n; + // Advance alphabet is exactly the two adjacent integers around M/L. + EXPECT_GE(s[n].advance, lo_advance) << "n=" << n; + EXPECT_LE(s[n].advance, hi_advance) << "n=" << n; + advance_sum += s[n].advance; + EXPECT_FALSE(seen[s[n].phase]) << "phase revisited within a superblock, n=" << n; + seen[s[n].phase] = true; + ++phase_seen; + } + // One superblock: L outputs consume exactly M inputs, every phase + // visited exactly once (gcd(L, M) = 1). + EXPECT_EQ(advance_sum, m); + EXPECT_EQ(phase_seen, l); + + // frames_needed agrees with walking the schedule, from EVERY start + // position, for spans up to two superblocks (covers the wrap). + for (std::size_t pos = 0; pos < l; ++pos) { + std::uint64_t walked = 0; + for (std::uint64_t out = 1; out <= 2 * l; ++out) { + walked += s[(pos + out - 1) % l].advance; + ASSERT_EQ(frames_needed(pos, out), walked) << "pos=" << pos << " out=" << out; + } + } + } + + TEST(Schedule, UpExhaustive) { + // Going up (L > M) some outputs re-use the window: advances are {0, 1}. + check_schedule_exhaustively(0, 1); + } + + TEST(Schedule, DownExhaustive) { + // Going down (M > L) some outputs skip a frame: advances are {1, 2}. + check_schedule_exhaustively(1, 2); + } + + TEST(Schedule, FramesNeededIsPositionInvariantOverSuperblocks) { + // Whole superblocks cost exactly M from anywhere. + for (std::size_t pos = 0; pos < 160; ++pos) { + EXPECT_EQ(frames_needed(pos, 160), 147u); + EXPECT_EQ(frames_needed(pos, 320), 294u); + } + for (std::size_t pos = 0; pos < 147; ++pos) { + EXPECT_EQ(frames_needed(pos, 147), 160u); + } + } + +} // namespace From 5a2ee3f626a8e90f919a41db69836c1876442b34 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 15:34:36 +0000 Subject: [PATCH 04/44] Refresh the profile doc table to the post-normalization measurements The measured worst-stopband figures quoted in design.h's profile table predated the per-branch DC normalization; the shipping designs measure -72.1/-72.8 dB (economy) and -121.7 dB (transparent), as test_design.cpp prints. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/include/tap/ratio/design.h | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/bridge/include/tap/ratio/design.h b/bridge/include/tap/ratio/design.h index f1c4c8a..d8ae5b3 100644 --- a/bridge/include/tap/ratio/design.h +++ b/bridge/include/tap/ratio/design.h @@ -32,8 +32,8 @@ namespace tap::ratio { template <> struct ratio_traits { - static constexpr std::size_t k_phases = 160; ///< L - static constexpr std::size_t k_decimation = 147; ///< M + static constexpr std::size_t k_phases = 160; ///< L + static constexpr std::size_t k_decimation = 147; ///< M static constexpr double k_input_rate_hz = 44100.0; static constexpr double k_output_rate_hz = 48000.0; /// Anti-image stopband edge: the output Nyquist. Images of baseband @@ -44,8 +44,8 @@ namespace tap::ratio { template <> struct ratio_traits { - static constexpr std::size_t k_phases = 147; ///< L - static constexpr std::size_t k_decimation = 160; ///< M + static constexpr std::size_t k_phases = 147; ///< L + static constexpr std::size_t k_decimation = 160; ///< M static constexpr double k_input_rate_hz = 48000.0; static constexpr double k_output_rate_hz = 44100.0; /// Anti-alias stopband edge: the output Nyquist. A 48 kHz source @@ -64,8 +64,8 @@ namespace tap::ratio { /// /// | profile | stopband | passband | taps down | taps up | measured worst stop | /// |-------------|----------|----------|-----------|---------|---------------------| - /// | economy | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.3 dB | - /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.4 / -121.1 dB | + /// | economy | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.8 dB | + /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.7 / -121.7 dB | /// /// economy is the default, per the speed-first charter: going down, every /// alias product is confined above 20.1 kHz by arithmetic (see @@ -84,10 +84,7 @@ namespace tap::ratio { /// Pristine tier: 120 dB stopband, flat to 20 kHz. static profile transparent() noexcept { - return {.passband_hz = 20000.0, - .stopband_atten_db = 120.0, - .taps_up_to_48k = 96, - .taps_down_to_44k1 = 184}; + return {.passband_hz = 20000.0, .stopband_atten_db = 120.0, .taps_up_to_48k = 96, .taps_down_to_44k1 = 184}; } template From 89c7eba2545e6d9e9619437fe242e66390a9b393 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 15:34:59 +0000 Subject: [PATCH 05/44] Apply clang-format reflow the pre-commit hook produced post-staging The M2 commit staged files before the format hook rewrote them, so the committed copies drifted from the formatter by alignment-only diffs; this restores the canonical formatting the CI gate checks. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/include/tap/ratio/phase_table.h | 3 +-- bridge/include/tap/ratio/schedule.h | 4 ++-- bridge/tests/test_design.cpp | 13 ++++++------- bridge/tests/test_schedule.cpp | 4 ++-- 4 files changed, 11 insertions(+), 13 deletions(-) diff --git a/bridge/include/tap/ratio/phase_table.h b/bridge/include/tap/ratio/phase_table.h index fe0bba9..a3798dd 100644 --- a/bridge/include/tap/ratio/phase_table.h +++ b/bridge/include/tap/ratio/phase_table.h @@ -44,8 +44,7 @@ namespace tap::ratio { for (std::size_t t = 0; t < m_taps; ++t) { row_d[m_taps - 1 - t] = proto[t * k_phases + ph]; } - tap::dsp::quantize_row_preserving_sum(row_d, - std::span(m_table.data() + ph * m_taps, m_taps)); + tap::dsp::quantize_row_preserving_sum(row_d, std::span(m_table.data() + ph * m_taps, m_taps)); } } diff --git a/bridge/include/tap/ratio/schedule.h b/bridge/include/tap/ratio/schedule.h index f6b8765..0bb8167 100644 --- a/bridge/include/tap/ratio/schedule.h +++ b/bridge/include/tap/ratio/schedule.h @@ -31,8 +31,8 @@ namespace tap::ratio { /// to exactly M over the superblock: L outputs always consume M inputs. template constexpr std::array::k_phases> make_schedule() noexcept { - constexpr std::size_t l = ratio_traits::k_phases; - constexpr std::size_t m = ratio_traits::k_decimation; + constexpr std::size_t l = ratio_traits::k_phases; + constexpr std::size_t m = ratio_traits::k_decimation; std::array s{}; for (std::size_t n = 0; n < l; ++n) { s[n].phase = static_cast((n * m) % l); diff --git a/bridge/tests/test_design.cpp b/bridge/tests/test_design.cpp index 51f7ff2..6809bb8 100644 --- a/bridge/tests/test_design.cpp +++ b/bridge/tests/test_design.cpp @@ -39,7 +39,7 @@ namespace { template void check_meets_spec(const profile& p, const char* name) { - using traits = ratio_traits; + using traits = ratio_traits; const std::vector h = design_prototype(p); ASSERT_EQ(h.size(), traits::k_phases * p.taps()); @@ -77,8 +77,7 @@ namespace { EXPECT_NEAR(hi, 1.0, 1e-12) << name; std::printf("[ measured ] %-24s L=%3zu taps=%3zu worst stopband %7.2f dB storage(f32) %5.1f KiB\n", name, - traits::k_phases, p.taps(), worst, - static_cast(h.size() * sizeof(float)) / 1024.0); + traits::k_phases, p.taps(), worst, static_cast(h.size() * sizeof(float)) / 1024.0); } TEST(Design, DownEconomyMeetsSpec) { @@ -106,11 +105,11 @@ namespace { } TEST(Design, BadProfilesThrow) { - profile p = profile::economy(); - p.passband_hz = 23000.0; // above the down direction's stopband edge + profile p = profile::economy(); + p.passband_hz = 23000.0; // above the down direction's stopband edge EXPECT_THROW((design_prototype(p)), std::invalid_argument); - profile q = profile::economy(); - q.stopband_atten_db = -1.0; + profile q = profile::economy(); + q.stopband_atten_db = -1.0; EXPECT_THROW((design_prototype(q)), std::invalid_argument); } diff --git a/bridge/tests/test_schedule.cpp b/bridge/tests/test_schedule.cpp index 35344e8..ef767cb 100644 --- a/bridge/tests/test_schedule.cpp +++ b/bridge/tests/test_schedule.cpp @@ -34,8 +34,8 @@ namespace { constexpr std::size_t l = ratio_traits::k_phases; constexpr std::size_t m = ratio_traits::k_decimation; - std::uint64_t advance_sum = 0; - std::uint64_t phase_seen = 0; // bitset via sum of distinct check below + std::uint64_t advance_sum = 0; + std::uint64_t phase_seen = 0; // bitset via sum of distinct check below std::vector seen(l, false); for (std::size_t n = 0; n < l; ++n) { // The defining formulas, entry by entry. From b8bad2e02f8f12b564d2e46884cf2d535ebb1af3 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 16:59:55 +0000 Subject: [PATCH 06/44] Add M3: the streaming converter, pinned to scipy sample-for-sample MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The float engine (PLAN.md milestone M3), one table-row dot per output over the compile-time schedule — no interpolation, no fractional-delay state, no servo: - include/tap/ratio/converter.h: basic_converter with both call shapes — process() (push-transform: consume all input, emit what becomes ready) and pull() (produce exactly N, draw input through a noexcept PopFn; dry sources short-return and resume losslessly) — plus exact frames_needed()/outputs_for() accounting, flush() (drains the taps()-frame tail), reset(), latency_input_frames(), runtime channel count over planar per-channel delay lines (coefficient row shared per frame: inter-channel phase coherence exact). Alignment contract: zero-primed and causal, y[n] = sum_k x[floor(nM/L)-k] * h[phase(n)+kL] — scipy upfirdn's streaming prefix exactly. Float aliases converter_to_48k / converter_to_44k1 (Q15/Q31 in M4). - tools/reference/make_reference_vectors.py + committed tests/reference/reference_vectors.h: the independent golden leg — scipy.signal.upfirdn in float64 over the same designs, 1000 frames of deterministic noise, all four direction x profile cases. - tests/test_converter.cpp (18 new; suite 42): impulse response reproduces the coefficient table bit-for-bit through the whole engine (every produced sample IS one stored coefficient); scipy vectors matched sample-for-sample from n=0 within the float32 coefficient floor; pull==process bit-exact under a dribbling source; accounting verified exhaustively from every superblock position against walking consumption (and outputs_for as the exact inverse of the need table); alias acceptance on real converted audio; stereo channels bit- identical to mono runs (crosstalk exactly zero); flush/reset lifecycle. Measurement finding folded into PLAN.md section 8: economy's in-band floor is upsampling IMAGE leakage (e.g. a 23 kHz tone's image at 25 kHz folds to 19.1 kHz at -85 dBFS), bounded by the stopband — the arithmetic-confinement claim covers decimation aliases only, and the earlier "nothing measurable below 20 kHz" phrasing holds only at the transparent tier. Deepening these images is the k*fs image-zeros lever (M7). Verified: GCC and clang -Werror clean, 42/42 green under both, tidy and clang-format clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 17 +- bridge/README.md | 5 +- bridge/include/tap/ratio/converter.h | 253 ++++ bridge/include/tap/ratio/ratio.h | 10 +- bridge/tests/CMakeLists.txt | 3 + bridge/tests/reference/reference_vectors.h | 1034 +++++++++++++++++ bridge/tests/test_converter.cpp | 383 ++++++ .../tools/reference/make_reference_vectors.py | 118 ++ 8 files changed, 1812 insertions(+), 11 deletions(-) create mode 100644 bridge/include/tap/ratio/converter.h create mode 100644 bridge/tests/reference/reference_vectors.h create mode 100644 bridge/tests/test_converter.cpp create mode 100644 bridge/tools/reference/make_reference_vectors.py diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 24b3192..c593616 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -208,11 +208,18 @@ v0.1 ships at M6. Nothing in M7+ blocks it. All numbers pinned (M2 design spike, 2026-07-23). -- `economy`, both directions: every alias/image product ≤ **−71 dBFS** - above 20 kHz (design floors: −72.1 dB down, −72.8 dB up); **nothing - measurable below 20 kHz** above the format/accumulation floor - (scipy-`upfirdn` preview in the spike measured the audible band at - −100 dBFS). +- `economy`, both directions: every spurious product ≥ **71 dB below the + source content** (design floors: −72.1 dB down, −72.8 dB up). Two species, + measured separately (M3, `test_converter.cpp`): decimation *aliases* of + signal content are additionally **confined above 20 kHz by arithmetic**; + upsampling *image leakage* folds in-band but is bounded by the stopband + (worst measured in-band product: −85 dBFS for a −6 dBFS stopband-adjacent + tone; a 997 Hz tone measures ~89 dB SNR against its imaging floor). The + original "nothing measurable below 20 kHz" phrasing overstated economy — + that claim holds at the *transparent* tier; economy's honest in-band bound + is the stopband. Deepening exactly these in-band images for low-frequency + program energy is the k·fs image-zeros lever (M7, SampleRateTap's + `design_prototype_compensated`). - `transparent`, both directions: alias/image products ≤ **−121 dB** (design floors −121.7 dB); passband flat to 20 kHz within ±0.00001 dB. - Exhaustive phase coverage in tests — all 147 and all 160 phases, not diff --git a/bridge/README.md b/bridge/README.md index 1cafd1b..444c32d 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,7 +13,10 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: skeleton (milestone M1).** The converter itself lands next — +> **Status: milestone M3.** The float converter is in — both directions, +> push (`process`) and pull (`pull` + exact `frames_needed`) call shapes, +> pinned against committed scipy reference vectors sample-for-sample from +> the first output. Q15/Q31 aliases land with their parity battery in M4. > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture > decisions, milestones, acceptance criteria); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h new file mode 100644 index 0000000..cae68dd --- /dev/null +++ b/bridge/include/tap/ratio/converter.h @@ -0,0 +1,253 @@ +/// @file converter.h +/// @brief The synchronous 44.1 <-> 48 kHz converter: one direction, streamed. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#pragma once + +#include +#include +#include +#include +#include + +#include "tap/dsp/fir_kernels.h" +#include "tap/dsp/sample_traits.h" +#include "tap/ratio/design.h" +#include "tap/ratio/phase_table.h" +#include "tap/ratio/schedule.h" + +namespace tap::ratio { + + // ANCHOR: rt_converter_doc + /// Streaming fixed-ratio converter for one direction (compile-time D). + /// + /// The machine is the whole point of this library: one phase-major table + /// row dot per output frame, driven by the compile-time (phase, advance) + /// schedule — no coefficient interpolation, no fractional-delay state, no + /// servo, and a phase sequence that repeats every L outputs exactly. + /// + /// Alignment contract: the converter is zero-primed and causal. Output n + /// is y[n] = sum_k x[floor(nM/L) - k] * h[phase(n) + kL] with x[<0] = 0 — + /// exactly scipy.signal.upfirdn's streaming prefix, sample for sample + /// from n = 0, transient included (pinned against committed scipy vectors + /// by test_converter.cpp). Latency is therefore the prototype's + /// linear-phase group delay, latency_input_frames() ~= taps()/2. + /// + /// Real-time contract: the constructor performs all allocation and filter + /// design and may throw; process(), pull(), flush() and reset() are + /// noexcept, lock-free and allocation-free. One stream per instance; + /// every channel of an instance shares the coefficient row per frame, so + /// inter-channel phase coherence is exact by construction. + // ANCHOR_END: rt_converter_doc + template + class basic_converter { + public: + using coeff = typename tap::dsp::sample_traits::coeff; + + static constexpr std::size_t k_phases = ratio_traits::k_phases; ///< L + static constexpr std::size_t k_decimation = ratio_traits::k_decimation; ///< M + + /// Allocates histories and designs the table; setup time only. + explicit basic_converter(std::size_t channels = 1, const profile& p = profile::economy()) + : m_table(p) + , m_channels(channels) + , m_hist_cap(m_table.taps() + k_hist_slack) + , m_hist(channels) + , m_scratch(k_pop_chunk * channels) { + if (channels == 0) { + throw std::invalid_argument("tap::ratio::basic_converter: channels == 0"); + } + for (auto& h : m_hist) { + h.assign(m_hist_cap, tap::dsp::sample_traits::silence()); + } + reset(); + } + + // ANCHOR: rt_process + /// Push-transform: consume ALL in_frames interleaved input frames, + /// writing every output frame that becomes ready. Returns the number + /// of output frames written. + /// + /// \pre out has room for outputs_for(in_frames) frames — the exact + /// count this call will produce from the current position. + std::size_t process(const S* in, std::size_t in_frames, S* out) noexcept { + std::size_t consumed = 0; + std::size_t produced = 0; + for (;;) { + while (m_pending != 0 && consumed < in_frames) { + append_frame(in + consumed * m_channels); + ++consumed; + --m_pending; + } + if (m_pending != 0) { // input exhausted mid-gap + return produced; + } + emit(out + produced * m_channels); + ++produced; + } + } + // ANCHOR_END: rt_process + + // ANCHOR: rt_pull + /// Pull: produce exactly out_frames interleaved output frames, drawing + /// input as needed through pop(dst, max_frames) -> frames delivered + /// (interleaved, may deliver fewer). Returns frames produced; fewer + /// than out_frames means the source ran dry — the partial consumption + /// is retained, so delivering more input later resumes exactly where + /// the stream left off. Bit-identical to process() on the same input. + /// PopFn must be noexcept. + template + std::size_t pull(S* out, std::size_t out_frames, PopFn&& pop) noexcept { + for (std::size_t n = 0; n < out_frames; ++n) { + while (m_pending != 0) { + const std::size_t pending = m_pending; + const std::size_t want = pending < k_pop_chunk ? pending : k_pop_chunk; + const std::size_t got = pop(m_scratch.data(), want); + if (got == 0) { + return n; // dry + } + for (std::size_t i = 0; i < got && i < want; ++i) { + append_frame(m_scratch.data() + i * m_channels); + --m_pending; + } + } + emit(out + n * m_channels); + } + return out_frames; + } + // ANCHOR_END: rt_pull + + // ANCHOR: rt_frames_needed + /// Exact input frames required to produce the next out_frames outputs + /// from the CURRENT stream position — deterministic arithmetic the + /// async engine can never offer. pull() with a source delivering + /// exactly this many frames produces exactly out_frames outputs. + std::uint64_t frames_needed(std::uint64_t out_frames) const noexcept { + if (out_frames == 0) { + return 0; + } + return m_pending + tap::ratio::frames_needed(m_pos, out_frames - 1); + } + + /// Exact output frames the next in_frames input frames will yield from + /// the current position (the sizing companion to process()). + std::uint64_t outputs_for(std::uint64_t in_frames) const noexcept { + if (in_frames < m_pending) { + return 0; + } + constexpr std::uint64_t l = k_phases; + constexpr std::uint64_t m = k_decimation; + const std::uint64_t base = m_pos * m / l; + const std::uint64_t a = in_frames - m_pending; + // Largest N with needed(N) <= in_frames, i.e. with + // floor((pos + N - 1) M / L) <= base + a; solved for N. + return (l * (base + a + 1) - 1) / m - m_pos + 1; + } + // ANCHOR_END: rt_frames_needed + + /// End-of-stream: feed taps() zero frames and write the outputs that + /// become ready — every output influenced by real input. Returns the + /// frames written (== outputs_for(taps()) beforehand). The engine is + /// left mid-stream in the zero-fed state; reset() before reuse. + std::size_t flush(S* out) noexcept { + const std::size_t zeros = m_table.taps(); + std::size_t fed = 0; + std::size_t produced = 0; + for (;;) { + while (m_pending != 0 && fed < zeros) { + append_silence(); + ++fed; + --m_pending; + } + if (m_pending != 0) { + return produced; + } + emit(out + produced * m_channels); + ++produced; + } + } + + /// Frames flush() will write from the current position. + std::uint64_t flush_output_frames() const noexcept { return outputs_for(m_table.taps()); } + + /// Return to the initial zero-primed state (position 0, silence). + void reset() noexcept { + for (auto& h : m_hist) { + for (auto& v : h) { + v = tap::dsp::sample_traits::silence(); + } + } + m_end = m_table.taps(); + m_pos = 0; + m_pending = 1; // the pre-advance that delivers x[0] under output 0 + } + + /// Linear-phase group delay in input samples, (L*T - 1) / (2L) ~= T/2. + double latency_input_frames() const noexcept { return m_table.group_delay_input_samples(); } + + std::size_t channels() const noexcept { return m_channels; } + std::size_t taps() const noexcept { return m_table.taps(); } ///< MACs per output sample + std::size_t position() const noexcept { return m_pos; } ///< superblock position in [0, L) + + const basic_phase_table& table() const noexcept { return m_table; } + + private: + static constexpr std::size_t k_hist_slack = 64; ///< appends between compactions + static constexpr std::size_t k_pop_chunk = 16; ///< pull()'s bulk-pop granularity + + const S* window(std::size_t c) const noexcept { return m_hist[c].data() + m_end - m_table.taps(); } + + /// One output frame at the current schedule position; advances state. + void emit(S* out) noexcept { + const schedule_entry step = k_schedule[m_pos]; + const coeff* row = m_table.row(step.phase); + const std::size_t taps = m_table.taps(); + for (std::size_t c = 0; c < m_channels; ++c) { + out[c] = tap::dsp::dot_row(row, window(c), taps); + } + m_pending = step.advance; + m_pos = m_pos + 1 == k_phases ? 0 : m_pos + 1; + } + + void append_frame(const S* frame) noexcept { + compact_if_full(); + for (std::size_t c = 0; c < m_channels; ++c) { + m_hist[c][m_end] = frame[c]; + } + ++m_end; + } + + void append_silence() noexcept { + compact_if_full(); + for (std::size_t c = 0; c < m_channels; ++c) { + m_hist[c][m_end] = tap::dsp::sample_traits::silence(); + } + ++m_end; + } + + void compact_if_full() noexcept { + if (m_end == m_hist_cap) { // keep the newest T-1 frames at the front + const std::size_t keep = m_table.taps() - 1; + for (auto& h : m_hist) { + std::memmove(h.data(), h.data() + (m_end - keep), keep * sizeof(S)); + } + m_end = keep; + } + } + + basic_phase_table m_table; + std::size_t m_channels; + std::size_t m_hist_cap; + std::vector> m_hist; // planar delay line per channel + std::vector m_scratch; // interleaved staging for pull() + std::size_t m_end = 0; + std::uint32_t m_pos = 0; // superblock position in [0, L) + std::uint32_t m_pending = 1; // inputs to consume before the next output + }; + + /// The float converters, one per direction (Q15/Q31 aliases land with + /// their parity battery in milestone M4). + using converter_to_48k = basic_converter; + using converter_to_44k1 = basic_converter; + +} // namespace tap::ratio diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index 20ca942..4d0be5d 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -23,13 +23,13 @@ // profiles, dot kernels, row-sum quantization, measurement instruments), // consumed as the submodules/dsptap submodule. // -// Status: M2 — direction/profile/design (design.h), the compile-time -// (phase, advance) schedule (schedule.h), and the phase-major quantized -// coefficient table (phase_table.h) are in; the engine lands in M3. -// PLAN.md is the authoritative roadmap; HANDOFF.md is the original design -// brief. +// Status: M3 — the streaming converter (converter.h: process/pull/flush, +// float aliases) over the M2 design/schedule/table layer. Q15/Q31 aliases +// land with their parity battery in M4. PLAN.md is the authoritative +// roadmap; HANDOFF.md is the original design brief. #pragma once +#include "tap/ratio/converter.h" // IWYU pragma: export #include "tap/ratio/design.h" // IWYU pragma: export #include "tap/ratio/phase_table.h" // IWYU pragma: export #include "tap/ratio/schedule.h" // IWYU pragma: export diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index 991ecd6..78defa4 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -16,14 +16,17 @@ set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) add_executable(tap_ratio_tests + test_converter.cpp test_design.cpp test_phase_table.cpp test_schedule.cpp test_skeleton.cpp) +target_include_directories(tap_ratio_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_link_libraries(tap_ratio_tests PRIVATE tap::ratio tap_ratio_warnings) +target_include_directories(tap_ratio_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) include(GoogleTest) gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) diff --git a/bridge/tests/reference/reference_vectors.h b/bridge/tests/reference/reference_vectors.h new file mode 100644 index 0000000..dc8761d --- /dev/null +++ b/bridge/tests/reference/reference_vectors.h @@ -0,0 +1,1034 @@ +// Generated by tools/reference/make_reference_vectors.py — DO NOT EDIT. +// Independent golden reference: scipy.signal.upfirdn (float64) over the +// per-branch-normalized Kaiser designs, cast to float32. See that script +// for provenance and the tolerance argument. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// NOLINTBEGIN(readability-identifier-naming) +#pragma once + +#include + +namespace ratio_ref { + + inline constexpr std::array k_input = { + 1.906402558e-01f, -3.983917087e-02f, 1.816589236e-01f, -2.033843994e-01f, 2.098251283e-01f, + -1.647537202e-01f, -1.683929414e-01f, -3.570419252e-01f, -2.270324677e-01f, -5.074310303e-02f, + 3.739334047e-01f, 2.784072757e-01f, -1.754516512e-01f, 2.328826785e-01f, -1.450744569e-01f, + 4.036651552e-01f, -3.377334476e-01f, 1.382217407e-01f, 3.520294130e-01f, 4.099685550e-01f, + 2.019561678e-01f, -1.922744662e-01f, 2.420700043e-01f, -4.364730716e-01f, 1.069931015e-01f, + -4.421859682e-01f, -8.989562839e-02f, 1.078582704e-01f, -4.107376039e-01f, -3.277221620e-01f, + 2.817169130e-01f, -3.403701782e-01f, -4.202270508e-02f, -1.761794984e-01f, -2.498703003e-01f, + 6.871948391e-02f, 6.119384617e-02f, 1.985641420e-01f, -1.535339281e-02f, -3.180404603e-01f, + 3.185073733e-01f, 4.353606999e-01f, 3.558608890e-01f, -3.105972111e-01f, 2.799178958e-01f, + 1.474639922e-01f, -1.957076937e-01f, 2.988006473e-01f, 3.148818910e-01f, -1.684616059e-01f, + -2.470825166e-01f, -3.194824159e-01f, 6.210021675e-02f, 1.106597856e-01f, -2.264831513e-01f, + 2.058013827e-01f, -4.407714605e-01f, -2.590987980e-01f, -7.910155691e-03f, -1.359558082e-03f, + 4.148162603e-01f, -3.997787237e-01f, -2.134094238e-01f, 8.739623427e-02f, -3.003936708e-01f, + 1.612792909e-01f, 3.470718265e-01f, -2.003082186e-01f, 3.926788270e-01f, 1.658935547e-01f, + 3.450942934e-01f, -2.591400146e-01f, 2.944335900e-02f, -3.793029785e-01f, -4.615630955e-02f, + 1.379333436e-01f, -2.470962405e-01f, 2.595520020e-02f, -3.276260197e-01f, 2.575469911e-01f, + -2.143844515e-01f, -3.301254213e-01f, -3.935852051e-01f, 1.873580813e-01f, 1.149581894e-01f, + -2.746719420e-01f, -6.370697170e-02f, 1.247497499e-01f, -1.994430423e-01f, 2.108688354e-01f, + -5.932617001e-03f, -1.781845093e-01f, -1.110855043e-01f, 1.667724550e-01f, -3.042114079e-01f, + -4.270934965e-03f, -3.339294493e-01f, -2.426879853e-01f, -2.029861361e-01f, -2.248352021e-01f, + 2.134094238e-01f, -4.471435398e-02f, 3.918136358e-01f, -4.436965883e-01f, -4.437652528e-01f, + 4.360061586e-01f, 1.611694247e-01f, -1.349258423e-01f, 2.543334961e-01f, -2.482910082e-02f, + 4.720000923e-02f, 8.732756972e-02f, -2.745895386e-01f, 2.186279185e-02f, -1.101379395e-01f, + -2.709503174e-01f, -3.658584356e-01f, -3.658859134e-01f, 1.862731874e-01f, -5.296783149e-02f, + -4.408950806e-01f, -1.863555908e-01f, -3.442291021e-01f, -4.000259340e-01f, -1.499633770e-02f, + -1.581619233e-01f, -1.339370757e-01f, -4.089935124e-01f, 4.067138433e-01f, 3.142776489e-01f, + -1.152465791e-01f, -1.537673920e-01f, 1.135025024e-01f, -3.907974064e-01f, 8.208160102e-02f, + 2.539352477e-01f, 2.140136659e-01f, -3.673965335e-01f, 6.502532959e-02f, -3.730682135e-01f, + 1.630096324e-02f, 1.574478149e-01f, 3.685638309e-01f, -4.265167117e-01f, 4.470611513e-01f, + -2.376480103e-01f, 4.408538640e-01f, 3.055984378e-01f, 1.886764467e-01f, 2.907668948e-01f, + 5.730743334e-02f, 4.480636418e-01f, -9.141997993e-02f, -2.897369266e-01f, 2.304107547e-01f, + 2.641525269e-01f, -2.735320926e-01f, 2.849166691e-01f, 4.204330444e-01f, 2.212509066e-01f, + 3.835876286e-01f, 1.353103667e-01f, 3.787261844e-01f, -7.293548435e-02f, -2.229400575e-01f, + -8.272705227e-02f, 2.811538577e-01f, 1.470245272e-01f, 2.369888276e-01f, -8.355102688e-02f, + 6.573943794e-02f, -2.551162541e-01f, -4.643096775e-02f, 4.319137335e-01f, -2.662811279e-01f, + -3.499694765e-01f, -4.293319583e-01f, -2.583847046e-01f, -2.342422456e-01f, -4.178924561e-01f, + 2.952575684e-03f, 9.177703410e-02f, 6.730499119e-02f, -3.633315861e-01f, -3.663116395e-01f, + -4.079498053e-01f, -1.479034405e-02f, -4.746093601e-02f, 4.135391116e-01f, -3.722442389e-01f, + 4.803771898e-02f, -1.241867021e-01f, -1.803131029e-02f, -2.492111176e-01f, 1.852294803e-01f, + 4.391235113e-01f, -3.897674382e-01f, -3.979110718e-01f, 1.911346316e-01f, 2.215393037e-01f, + 1.517486572e-01f, -3.335449100e-01f, 2.335693240e-01f, 2.017501742e-01f, -1.401992738e-01f, + 2.303832918e-01f, -3.630706668e-01f, -3.913742006e-01f, -3.286697268e-01f, 6.473693997e-02f, + 3.992706239e-01f, 2.508590519e-01f, -3.063674867e-01f, 2.596481144e-01f, -7.109527290e-02f, + -9.163970500e-02f, -7.900542766e-02f, -4.008499086e-01f, -1.157272309e-01f, -4.116027653e-01f, + -1.770309359e-01f, 4.103393555e-01f, -2.932525575e-01f, 5.140228197e-02f, 1.127883866e-01f, + 2.611587346e-01f, -2.306716889e-01f, -1.107421815e-01f, 2.675170824e-02f, -3.591156006e-02f, + -1.844879091e-01f, 7.731628139e-03f, 2.770339847e-01f, -4.077438116e-01f, -1.611144990e-01f, + 3.734939396e-01f, -4.174529910e-01f, 3.249069154e-01f, -1.451980621e-01f, 3.233825564e-01f, + 1.002502395e-03f, -1.084075868e-01f, 1.257522553e-01f, 2.112121508e-02f, 1.235000566e-01f, + -1.619933993e-01f, 1.204376221e-01f, -1.200256310e-02f, -3.100341856e-01f, 2.617217898e-01f, + 3.904953003e-01f, 2.293807864e-01f, -3.995590210e-01f, 4.048461914e-01f, 3.490356207e-01f, + 3.120941222e-01f, 3.268570006e-01f, -2.648391724e-01f, -1.620071381e-01f, 1.577773988e-01f, + -1.739410311e-01f, -2.812362611e-01f, 2.985672057e-01f, 3.389282227e-01f, -2.296279818e-01f, + -3.394912481e-01f, 2.180374116e-01f, -3.680282533e-01f, 4.137862921e-01f, 1.880035400e-01f, + 2.036178559e-01f, 3.603240848e-01f, -9.556731582e-02f, 1.193115190e-01f, -1.841995120e-01f, + -1.555114686e-01f, -8.673705906e-02f, -2.435119599e-01f, -1.031890810e-01f, 2.620788515e-01f, + -2.732849121e-01f, 2.739715576e-01f, 1.925628632e-01f, -1.056198105e-01f, 3.697036505e-01f, + 2.000747621e-01f, -2.586318851e-01f, -1.096023545e-01f, 1.570083648e-01f, 2.793823183e-01f, + -3.543914557e-01f, -7.097167522e-02f, -2.791351378e-01f, -1.848312318e-01f, -2.509002574e-02f, + 1.420669556e-01f, 2.443771362e-01f, 4.319549501e-01f, -3.930495977e-01f, -4.463745058e-01f, + 3.309493959e-01f, -3.283401430e-01f, -2.598953247e-01f, -2.121322602e-01f, -1.361206025e-01f, + -1.053588837e-01f, 2.088500857e-01f, 2.725845277e-01f, 2.148513794e-01f, 2.591400146e-01f, + -3.704040349e-01f, -3.472640812e-01f, -4.052581787e-01f, -4.483932257e-01f, 2.492797822e-01f, + 1.289794892e-01f, 2.396667451e-01f, 4.792785738e-03f, -1.259582490e-01f, -4.147201478e-01f, + -2.414520234e-01f, -2.399139293e-02f, -1.466949433e-01f, 3.577285707e-01f, 2.046615481e-01f, + 2.719116211e-01f, 3.456573486e-01f, 1.338546723e-01f, 2.156753540e-01f, -8.227386326e-02f, + -2.255355716e-01f, -2.794097960e-01f, -1.278533936e-01f, 3.206634521e-01f, -3.068618774e-01f, + -3.933792114e-01f, 1.282241791e-01f, 1.624877900e-01f, 2.793273889e-02f, 1.434265077e-01f, + -3.727935553e-01f, 2.268127352e-01f, 2.931427062e-01f, -4.183456302e-01f, 3.746337816e-02f, + 3.376647830e-01f, -2.073532045e-01f, -2.929641604e-01f, 2.028900087e-01f, -1.372604370e-01f, + 4.147338867e-01f, 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5.163574219e-02f, + 1.950897127e-01f, -7.584685832e-02f, -2.842025757e-01f, 2.920303345e-01f, -2.219375521e-01f, + -1.548248231e-01f, -6.329498440e-02f, 7.584685832e-02f, 3.949584961e-01f, 2.503509447e-02f, + -3.124511540e-01f, 1.956802309e-01f, 1.356811449e-02f, -3.404113650e-01f, 2.776519656e-01f, + -1.200256348e-01f, -2.962188721e-01f, -2.485519350e-01f, -4.943847656e-02f, 4.244292974e-01f, + }; + + inline constexpr std::array k_down_economy = { + -1.031435477e-05f, 2.191725434e-05f, -4.393685231e-05f, 7.615395589e-05f, -1.198024765e-04f, + 1.758044818e-04f, -2.517092798e-04f, 3.142122005e-04f, -4.037040926e-04f, 4.691039794e-04f, + -4.746864142e-04f, 4.803411721e-04f, -4.358982551e-04f, 3.050960077e-04f, -8.902783156e-05f, + -2.295445302e-04f, 6.576542510e-04f, -1.187955961e-03f, 1.853514928e-03f, -2.593499143e-03f, + 3.403381910e-03f, -4.245325923e-03f, 5.057359114e-03f, -5.802020431e-03f, 6.432069931e-03f, + -6.850757636e-03f, 6.965639535e-03f, -6.652012467e-03f, 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std::array k_down_transparent = { + 2.585927561e-08f, -5.804087522e-08f, 1.268719672e-07f, -2.375085444e-07f, 3.985287833e-07f, + -6.115915880e-07f, 8.533932601e-07f, -1.139200435e-06f, 1.451133471e-06f, -1.735608066e-06f, + 1.898490950e-06f, -2.007425564e-06f, 1.944278210e-06f, -1.554934443e-06f, 7.806381745e-07f, + 5.360348609e-07f, -2.573111487e-06f, 5.547478395e-06f, -9.611597307e-06f, 1.498870006e-05f, + -2.187749487e-05f, 3.046084566e-05f, -4.086424451e-05f, 5.322532161e-05f, -6.767753803e-05f, + 8.417157369e-05f, -1.026303798e-04f, 1.226982131e-04f, -1.439831540e-04f, 1.658512047e-04f, + -1.874556765e-04f, 2.077023091e-04f, -2.251827245e-04f, 2.382966486e-04f, -2.451239852e-04f, + 2.434963390e-04f, -2.310681157e-04f, 2.052615018e-04f, -1.632938074e-04f, 1.025557285e-04f, + -2.037327067e-05f, -8.567640907e-05f, 2.176429552e-04f, -3.770537442e-04f, 5.647169892e-04f, + -7.806876092e-04f, 1.023695921e-03f, -1.291354420e-03f, 1.579720876e-03f, -1.883246237e-03f, + 2.194622066e-03f, 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-6.307492405e-02f, -3.851076365e-01f, -2.819468677e-01f, 4.172138572e-01f, + -1.815617681e-01f, -1.252876073e-01f, 2.124520093e-01f, 4.999274015e-02f, 1.637808532e-01f, + 8.006920107e-03f, -1.600762606e-01f, -9.687413462e-03f, -1.597793549e-01f, -1.297664046e-01f, + 1.190069988e-01f, -2.962608635e-01f, -1.363001615e-01f, 3.380188718e-02f, 1.790100932e-01f, + -2.381429970e-01f, -2.954300940e-01f, -3.160277009e-01f, -3.333551884e-01f, -2.922180854e-02f, + -2.293668836e-01f, -3.268848956e-01f, 3.325173631e-02f, 3.267023861e-01f, + }; + + inline constexpr std::array k_up_economy = { + -2.024352216e-05f, 4.592908226e-05f, -8.434985648e-05f, 1.413756545e-04f, -1.714049140e-04f, + 1.128912263e-04f, 1.191139309e-04f, -6.055129343e-04f, 1.394894323e-03f, -2.469108440e-03f, + 3.737050574e-03f, -5.014420021e-03f, 5.910654552e-03f, -6.010395009e-03f, 4.765956197e-03f, + -1.707589487e-03f, -3.487367881e-03f, 1.094912551e-02f, -2.039876953e-02f, 3.119040281e-02f, + -4.234819859e-02f, 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-5.919959769e-02f, -1.488360763e-01f, -1.141552627e-01f, -3.751248419e-01f, + -9.788906574e-02f, 5.632069707e-01f, -1.256260425e-01f, -2.118031383e-01f, 1.074855253e-01f, + -3.144066408e-02f, 4.076721892e-02f, -2.414263934e-01f, -3.084088564e-01f, -3.222741485e-01f, + -4.230794609e-01f, -1.545737088e-01f, 3.936312497e-01f, -2.249834873e-02f, + }; + + inline constexpr std::array k_up_transparent = { + -1.782983006e-08f, 1.432359653e-08f, 6.548891918e-08f, -3.645934044e-07f, 1.107814114e-06f, + -2.583848982e-06f, 5.073663033e-06f, -8.614774742e-06f, 1.330682062e-05f, -1.839637662e-05f, + 2.279104592e-05f, -2.458432937e-05f, 2.089288682e-05f, -8.245021490e-06f, -1.721409717e-05f, + 5.898295422e-05f, -1.194123688e-04f, 1.984637202e-04f, -2.927190508e-04f, 3.943264892e-04f, + -4.904519883e-04f, 5.626204656e-04f, -5.883525009e-04f, 5.422244431e-04f, -3.988910466e-04f, + 1.367084333e-04f, 2.578123240e-04f, -7.856002776e-04f, 1.430591918e-03f, -2.156265080e-03f, + 2.904213732e-03f, 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Three independent legs, per PLAN.md section 6: structural +// correctness (the impulse response must reproduce the coefficient table bit +// for bit through the whole engine), the committed scipy reference vectors +// (an engine we did not write), and alias acceptance measured on real +// converted audio with the shared analysis instruments. Exhaustive where the +// period-L structure makes exhaustive cheap. + +#include +#include +#include +#include + +#include + +#include "reference/reference_vectors.h" +#include "tap/dsp/analysis/sine_analysis.h" +#include "tap/ratio/converter.h" + +namespace { + + using tap::ratio::basic_converter; + using tap::ratio::direction; + using tap::ratio::k_schedule; + using tap::ratio::profile; + using tap::ratio::ratio_traits; + + template + using conv = basic_converter; + + // ------------------------------------------------------------------ + // Structural correctness: a unit impulse must reproduce the quantized + // coefficient table exactly (float ==). Output n dots the impulse at + // window index T-1-floor(nM/L) of row phase(n), so every produced sample + // IS one stored coefficient — schedule, table, history and alignment all + // verified bit for bit in one sweep, no external reference needed. + template + void check_impulse_reproduces_table(const profile& p) { + conv c(1, p); + const std::size_t taps = c.taps(); + const std::size_t n_in = taps + 4; + std::vector x(n_in, 0.0f); + x[0] = 1.0f; + std::vector y(c.outputs_for(n_in)); + const std::size_t made = c.process(x.data(), n_in, y.data()); + ASSERT_EQ(made, y.size()); + + constexpr std::size_t l = ratio_traits::k_phases; + constexpr std::size_t m = ratio_traits::k_decimation; + for (std::size_t n = 0; n < made; ++n) { + const std::size_t k = n * m / l; // newest-input index for output n + const std::size_t phase = (n * m) % l; + const float expected = k < taps ? c.table().row(phase)[taps - 1 - k] : 0.0f; + ASSERT_EQ(y[n], expected) << "n=" << n; // bit-exact, transient included + } + } + + TEST(Converter, ImpulseReproducesTableDown) { + check_impulse_reproduces_table(profile::economy()); + } + TEST(Converter, ImpulseReproducesTableUp) { + check_impulse_reproduces_table(profile::economy()); + } + TEST(Converter, ImpulseReproducesTableDownTransparent) { + check_impulse_reproduces_table(profile::transparent()); + } + + // ------------------------------------------------------------------ + // The independent golden leg: committed scipy.signal.upfirdn outputs + // (float64 engine, same designs) over deterministic noise. The streaming + // converter must match sample for sample from n = 0, transient included, + // within the float32 coefficient-quantization floor (~-90 dB at the 0.9 + // peak; see tools/reference/make_reference_vectors.py). + template + void check_reference(const profile& p, const std::array& ref) { + conv c(1, p); + ASSERT_EQ(c.outputs_for(ratio_ref::k_input.size()), NRef); + std::vector y(NRef); + ASSERT_EQ(c.process(ratio_ref::k_input.data(), ratio_ref::k_input.size(), y.data()), NRef); + for (std::size_t n = 0; n < NRef; ++n) { + ASSERT_NEAR(y[n], ref[n], 3e-5f) << "n=" << n; + } + } + + TEST(Converter, MatchesScipyDownEconomy) { + check_reference(profile::economy(), ratio_ref::k_down_economy); + } + TEST(Converter, MatchesScipyDownTransparent) { + check_reference(profile::transparent(), ratio_ref::k_down_transparent); + } + TEST(Converter, MatchesScipyUpEconomy) { + check_reference(profile::economy(), ratio_ref::k_up_economy); + } + TEST(Converter, MatchesScipyUpTransparent) { + check_reference(profile::transparent(), ratio_ref::k_up_transparent); + } + + // ------------------------------------------------------------------ + // pull() is bit-identical to process() on the same stream, under a + // deliberately awkward source (dribbling deliveries of varying size), + // and a dry source short-returns then resumes exactly. + TEST(Converter, PullMatchesProcessBitExact) { + const auto& x = ratio_ref::k_input; + + conv a(1); + std::vector ya(a.outputs_for(x.size())); + a.process(x.data(), x.size(), ya.data()); + + conv b(1); + std::size_t fed = 0; + std::size_t call = 0; + auto pop = [&](float* dst, std::size_t max_frames) noexcept -> std::size_t { + const std::size_t dribble = 1 + (call++ % 3); // 1..3 frames per call + std::size_t n = 0; + while (n < max_frames && n < dribble && fed < x.size()) { + dst[n++] = x[fed++]; + } + return n; + }; + std::vector yb(ya.size()); + const std::size_t made = b.pull(yb.data(), yb.size(), pop); + ASSERT_EQ(made, ya.size()); + for (std::size_t n = 0; n < ya.size(); ++n) { + ASSERT_EQ(ya[n], yb[n]) << "n=" << n; + } + } + + TEST(Converter, PullShortReturnsOnDryThenResumes) { + const auto& x = ratio_ref::k_input; + + conv a(1); + std::vector ya(a.outputs_for(x.size())); + a.process(x.data(), x.size(), ya.data()); + + conv b(1); + std::vector yb(ya.size()); + std::size_t fed = 0; + std::size_t limit = 100; // first tranche of input + auto pop = [&](float* dst, std::size_t max_frames) noexcept -> std::size_t { + std::size_t n = 0; + while (n < max_frames && fed < limit) { + dst[n++] = x[fed++]; + } + return n; + }; + const std::size_t first = b.pull(yb.data(), yb.size(), pop); + EXPECT_LT(first, yb.size()); // ran dry + limit = x.size(); + const std::size_t second = b.pull(yb.data() + first * 1, yb.size() - first, pop); + ASSERT_EQ(first + second, ya.size()); + for (std::size_t n = 0; n < ya.size(); ++n) { + ASSERT_EQ(ya[n], yb[n]) << "n=" << n; + } + } + + // ------------------------------------------------------------------ + // frames_needed and outputs_for are exact from EVERY superblock position: + // drive the engine one output at a time with a counting source and check + // the pre-computed predictions against observed consumption, plus the + // closed-form outputs_for against its defining inequality. + template + void check_accounting_exhaustively() { + constexpr std::size_t l = ratio_traits::k_phases; + conv c(1); + std::size_t consumed = 0; + auto pop = [&](float* dst, std::size_t max_frames) noexcept -> std::size_t { + for (std::size_t i = 0; i < max_frames; ++i) { + dst[i] = 0.0f; + } + consumed += max_frames; + return max_frames; + }; + float y = 0.0f; + for (std::size_t pos = 0; pos < l; ++pos) { + ASSERT_EQ(c.position(), pos); + // Predictions from this exact state. + std::vector need(2 * l + 1); + for (std::size_t n = 1; n <= 2 * l; ++n) { + need[n] = c.frames_needed(n); + } + ASSERT_EQ(c.frames_needed(0), 0u); + // outputs_for is the exact inverse of the need table: for every + // input count a, it must equal the largest n with need[n] <= a. + // (Not simply outputs_for(need[n]) == n: in the up direction an + // advance-0 output can ride along for free, so outputs_for may + // legitimately exceed n at the same input count.) + { + std::size_t n_reach = 0; + for (std::uint64_t a = 0; a < need[2 * l]; ++a) { + while (n_reach + 1 <= 2 * l && need[n_reach + 1] <= a) { + ++n_reach; + } + ASSERT_EQ(c.outputs_for(a), n_reach) << "pos=" << pos << " a=" << a; + } + } + // Walk one superblock producing single outputs; consumption must + // track need[] exactly. + const std::size_t base = consumed; + conv probe = c; // copy: keep c at pos for its own next step + std::size_t probe_consumed = 0; + auto probe_pop = [&](float* dst, std::size_t max_frames) noexcept -> std::size_t { + for (std::size_t i = 0; i < max_frames; ++i) { + dst[i] = 0.0f; + } + probe_consumed += max_frames; + return max_frames; + }; + for (std::size_t n = 1; n <= l; ++n) { + ASSERT_EQ(probe.pull(&y, 1, probe_pop), 1u); + ASSERT_EQ(probe_consumed, need[n]) << "pos=" << pos << " n=" << n; + } + static_cast(base); + // Advance the primary engine one output to the next position. + ASSERT_EQ(c.pull(&y, 1, pop), 1u); + } + } + + TEST(Converter, AccountingExactFromEveryPositionUp) { + check_accounting_exhaustively(); + } + TEST(Converter, AccountingExactFromEveryPositionDown) { + check_accounting_exhaustively(); + } + + // ------------------------------------------------------------------ + // Alias acceptance on real converted audio (PLAN section 8), float engine. + namespace an = tap::dsp::analysis; + + std::vector run_sine_down(const profile& p, double freq_hz, double amp, std::size_t n_in) { + conv c(1, p); + std::vector x(n_in); + for (std::size_t i = 0; i < n_in; ++i) { + x[i] = + static_cast(amp * std::sin(2.0 * std::numbers::pi * freq_hz / 48000.0 * static_cast(i))); + } + std::vector y(c.outputs_for(n_in)); + c.process(x.data(), n_in, y.data()); + // Drop the transient (group delay) before measurement. + const auto skip = static_cast(c.latency_input_frames()) * 2; + return {y.begin() + static_cast(skip), y.end()}; + } + + TEST(Converter, PassbandSineEconomyHitsTheImagingFloor) { + // 997 Hz through economy 48->44.1. The residual is NOT aliasing (a + // 997 Hz tone's decimation aliases are ultrasonic by arithmetic) but + // IMAGING LEAKAGE: the upsampling images at k*48000 +/- 997 Hz + // survive at stopband depth and fold in-band (e.g. 47003 -> 2903 Hz). + // That floor is bounded by the stopband (>= 71 dB below the tone, + // deeper where the Kaiser sidelobes have decayed; measured ~89 dB + // SNR here). This is economy's honest in-band contract — and exactly + // what the k*fs image-zeros lever (PLAN M7) would deepen. + const auto tail = run_sine_down(profile::economy(), 997.0, 0.5, 1 << 16); + const auto fit = an::fit_sine_tracked(tail, 997.0 / 44100.0); + EXPECT_NEAR(fit.amplitude, 0.5, 1e-4); + EXPECT_GT(an::snr_db(fit), 85.0); // measured ~89.2 dB + } + + TEST(Converter, PassbandSineIsTransparentTransparent) { + const auto tail = run_sine_down(profile::transparent(), 997.0, 0.5, 1 << 16); + const auto fit = an::fit_sine_tracked(tail, 997.0 / 44100.0); + EXPECT_GT(an::snr_db(fit), 115.0); + } + + // Goertzel power probe at one frequency, dBFS re 1.0 amplitude. + double probe_dbfs(const std::vector& y, double freq_norm) { + const auto fit = an::fit_sine(std::span(y), freq_norm); + return 20.0 * std::log10(fit.amplitude + 1e-12); + } + + TEST(Converter, StopbandToneProductsAreBoundedByTheSpec) { + // A 23 kHz tone at 48 k sits in the down direction's stopband. Two + // distinct products, per the acceptance contract: + // - its decimation ALIAS folds to 44100 - 23000 = 21.1 kHz — above + // 20 kHz by arithmetic (the charter's confinement claim) and + // attenuated >= 71 dB; + // - its upsampling IMAGE at 48000 - 23000 = 25 kHz survives at + // stopband depth and folds IN-BAND to 44100 - 25000 = 19.1 kHz. + // In-band products are bounded by the stopband, not absent — + // economy's honest limit (measured -85 dBFS for the -6 dBFS tone). + const auto y = run_sine_down(profile::economy(), 23000.0, 0.5, 1 << 16); + // Folded alias at 21.1 kHz: <= -(71) dB relative to the 0.5 FS tone. + EXPECT_LT(probe_dbfs(y, (44100.0 - 23000.0) / 44100.0), -6.0 - 71.0 + 3.0); // 3 dB grace + // Audible band: every product at least the stopband below the tone. + for (double f = 100.0; f < 20000.0; f += 100.0) { + ASSERT_LT(probe_dbfs(y, f / 44100.0), -6.0 - 71.0) << f << " Hz"; + } + // And the worst in-band product is the predicted 19.1 kHz image. + EXPECT_LT(probe_dbfs(y, 19100.0 / 44100.0), -80.0); // measured ~-85 dBFS + } + + // ------------------------------------------------------------------ + // Multichannel: distinct tones per channel through one instance stay + // independent (crosstalk at the float floor) and phase-coherent. + TEST(Converter, TwoChannelsAreIndependent) { + // transparent profile: its -121 dB floor puts filter spurs far below + // the crosstalk threshold, so the fit at the other channel's tone + // measures actual channel bleed and nothing else. + conv c(2, profile::transparent()); + const std::size_t n_in = 1 << 15; + std::vector x(n_in * 2); + for (std::size_t i = 0; i < n_in; ++i) { + const auto t = static_cast(i); + x[i * 2] = static_cast(0.5 * std::sin(2.0 * std::numbers::pi * 997.0 / 48000.0 * t)); + x[i * 2 + 1] = static_cast(0.5 * std::sin(2.0 * std::numbers::pi * 6000.0 / 48000.0 * t)); + } + std::vector y(c.outputs_for(n_in) * 2); + const std::size_t made = c.process(x.data(), n_in, y.data()); + // Skip the startup transient (group delay), as every measurement + // here does: its broadband ramp otherwise biases both fits. + const auto skip = static_cast(c.latency_input_frames()) * 2; + ASSERT_GT(made, skip + 1024); + std::vector ch0(made - skip), ch1(made - skip); + for (std::size_t i = 0; i < ch0.size(); ++i) { + ch0[i] = y[(i + skip) * 2]; + ch1[i] = y[(i + skip) * 2 + 1]; + } + // Each channel carries its own tone... + EXPECT_NEAR(an::fit_sine_tracked(ch0, 997.0 / 44100.0).amplitude, 0.5, 1e-3); + EXPECT_NEAR(an::fit_sine_tracked(ch1, 6000.0 / 44100.0).amplitude, 0.5, 1e-3); + // ...and crosstalk is exactly zero: every stereo channel must be + // bit-identical to a mono run of the same signal. (A fit at the other + // channel's frequency cannot show this — the rectangular-window + // leakage of the strong own-tone floors such a probe near -87 dB + // regardless of bleed.) + conv mono(1, profile::transparent()); + std::vector x0(n_in); + for (std::size_t i = 0; i < n_in; ++i) { + x0[i] = x[i * 2]; + } + std::vector y0(mono.outputs_for(n_in)); + ASSERT_EQ(mono.process(x0.data(), n_in, y0.data()), made); + for (std::size_t i = 0; i < made; ++i) { + ASSERT_EQ(y0[i], y[i * 2]) << "i=" << i; + } + } + + // ------------------------------------------------------------------ + // Lifecycle: flush drains the tail to silence with the predicted count; + // reset reproduces the identical stream bit for bit. + TEST(Converter, FlushDrainsTailToSilence) { + conv c(1); + const std::size_t n_in = 480; + std::vector x(n_in); + for (std::size_t i = 0; i < n_in; ++i) { + x[i] = static_cast(0.5 * std::sin(2.0 * std::numbers::pi * 0.02 * static_cast(i))); + } + std::vector y(c.outputs_for(n_in)); + c.process(x.data(), n_in, y.data()); + const std::uint64_t expect_tail = c.flush_output_frames(); + std::vector tail(expect_tail); + ASSERT_EQ(c.flush(tail.data()), expect_tail); + // The tail decays to (near) silence: the very last output's window + // holds at most one real sample (a down-direction skip can leave the + // final zero unconsumed), so demand decay, not exact zeros. + ASSERT_GE(tail.size(), 4u); + EXPECT_LT(std::abs(tail[tail.size() - 1]), 1e-4f); + EXPECT_LT(std::abs(tail[tail.size() - 2]), 1e-3f); + } + + TEST(Converter, ResetReproducesBitExactly) { + conv c(1); + const auto& x = ratio_ref::k_input; + std::vector y1(c.outputs_for(x.size())); + c.process(x.data(), x.size(), y1.data()); + c.reset(); + std::vector y2(y1.size()); + ASSERT_EQ(c.process(x.data(), x.size(), y2.data()), y2.size()); + for (std::size_t n = 0; n < y1.size(); ++n) { + ASSERT_EQ(y1[n], y2[n]) << "n=" << n; + } + } + + TEST(Converter, LatencyAndValidation) { + conv c(1); + EXPECT_NEAR(c.latency_input_frames(), 78.0 / 2.0, 0.51); + EXPECT_EQ(c.channels(), 1u); + EXPECT_THROW(conv(0), std::invalid_argument); + } + +} // namespace diff --git a/bridge/tools/reference/make_reference_vectors.py b/bridge/tools/reference/make_reference_vectors.py new file mode 100644 index 0000000..895321c --- /dev/null +++ b/bridge/tools/reference/make_reference_vectors.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python3 +"""Generates tests/reference/reference_vectors.h — the independent golden leg +of the M3 correctness battery (PLAN.md section 6.2). + +The input is deterministic xorshift noise quantized to float32. For each +direction x profile, the expected output is computed by scipy.signal.upfirdn +(a polyphase engine we did not write) in float64, using coefficients from the +same published Kaiser math as tap::dsp::design_prototype plus RatioTap's +per-branch DC normalization (tap/ratio/design.h), then cast to float32. + +The streaming converter is zero-primed and causal, so its output must equal +upfirdn's from sample 0 — transient included — within float32 rounding: the +C++ engine stores float32 coefficients and accumulates in double, while this +reference keeps float64 coefficients, so the comparison tolerance in +tests/test_converter.cpp (3e-5 absolute at 0.9 peak) is the float32 +coefficient-quantization floor, about -90 dB. + +Run from the repo root: python3 tools/reference/make_reference_vectors.py +Re-run only when the design math changes; commit the regenerated header. +""" +import numpy as np +from scipy import signal +import pathlib + +ROOT = pathlib.Path(__file__).resolve().parents[2] + + +def kaiser_beta(atten_db): + if atten_db > 50.0: + return 0.1102 * (atten_db - 8.7) + if atten_db > 21.0: + return 0.5842 * (atten_db - 21.0) ** 0.4 + 0.07886 * (atten_db - 21.0) + return 0.0 + + +def design(L, taps, cutoff_norm, beta): + n = L * taps + i = np.arange(n) + center = 0.5 * (n - 1) + t = (i - center) / L + u = (i - center) / center + w = np.i0(beta * np.sqrt(np.maximum(0.0, 1.0 - u * u))) / np.i0(beta) + h = cutoff_norm * np.sinc(cutoff_norm * t) * w + h *= L / h.sum() + branch = h.reshape(taps, L).sum(axis=0) + return (h.reshape(taps, L) / branch).reshape(-1) + + +def xorshift_f32(count, seed): + s = np.uint32(seed) + out = np.empty(count, np.float32) + for i in range(count): + s ^= np.uint32((int(s) << 13) & 0xFFFFFFFF) + s ^= np.uint32(int(s) >> 17) + s ^= np.uint32((int(s) << 5) & 0xFFFFFFFF) + out[i] = np.float32((int(s) % 65536 - 32768) / 65536.0) * np.float32(0.9) + return out + + +CASES = [ # tag, L, M, fs_in, pass_hz, stop_hz, atten, taps + ("down_economy", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78), + ("down_transparent", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184), + ("up_economy", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44), + ("up_transparent", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96), +] + +N_IN = 1000 +x = xorshift_f32(N_IN, 0x2545F491) + + +def fmt(arr, per_line=8): + lines = [] + for i in range(0, len(arr), per_line): + lines.append(" " + ", ".join(f"{v:.9e}f" for v in arr[i:i + per_line]) + ",") + return "\n".join(lines) + + +out = [] +out.append("// Generated by tools/reference/make_reference_vectors.py — DO NOT EDIT.") +out.append("// Independent golden reference: scipy.signal.upfirdn (float64) over the") +out.append("// per-branch-normalized Kaiser designs, cast to float32. See that script") +out.append("// for provenance and the tolerance argument.") +out.append("// SPDX-License-Identifier: MIT") +out.append("// Copyright 2026 Timothy Place and the RatioTap contributors.") +out.append("// NOLINTBEGIN(readability-identifier-naming)") +out.append("#pragma once") +out.append("") +out.append("#include ") +out.append("") +out.append("namespace ratio_ref {") +out.append("") +out.append(f"inline constexpr std::array k_input = {{") +out.append(fmt(x)) +out.append("};") +out.append("") + +for tag, L, M, fs, pas, stop, atten, taps in CASES: + h = design(L, taps, (pas + stop) / fs, kaiser_beta(atten)) + y = signal.upfirdn(h, x.astype(np.float64), up=L, down=M) + # The streaming engine only emits outputs whose full input window has + # arrived: newest input for output n is x[floor(nM/L)], so keep outputs + # with floor(nM/L) < N_IN. upfirdn's longer tail is the flush territory. + n_stream = 0 + while (n_stream * M) // L < N_IN: + n_stream += 1 + y = y[:n_stream].astype(np.float32) + out.append(f"inline constexpr std::array k_{tag} = {{") + out.append(fmt(y)) + out.append("};") + out.append("") + +out.append("} // namespace ratio_ref") +out.append("// NOLINTEND(readability-identifier-naming)") + +dest = ROOT / "tests" / "reference" / "reference_vectors.h" +dest.parent.mkdir(parents=True, exist_ok=True) +dest.write_text("\n".join(out) + "\n") +print(f"wrote {dest} ({dest.stat().st_size / 1024:.0f} KiB)") From d93c0d537311754c4ebe521447f1a3f410289c37 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 17:45:14 +0000 Subject: [PATCH 07/44] Deduplicate the tests include-dir line MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A scripted edit inserted target_include_directories twice; keep one, with a comment saying why it exists (the committed reference vectors resolve relative to tests/). Also re-runs CI: the previous round's only red was a clang-tidy job on the push-event twin run that hung and hit the 30-minute timeout — its pull_request twin passed the identical commit in 2m37s, so the failure was a stalled runner, not the code. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/tests/CMakeLists.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index 78defa4..b970fb4 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -21,12 +21,13 @@ add_executable(tap_ratio_tests test_phase_table.cpp test_schedule.cpp test_skeleton.cpp) +# The tests' own headers (the committed reference vectors) resolve relative +# to this directory. target_include_directories(tap_ratio_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_link_libraries(tap_ratio_tests PRIVATE tap::ratio tap_ratio_warnings) -target_include_directories(tap_ratio_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) include(GoogleTest) gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) From 29223e9da708b2b89914bdb404ef01e472300184 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 18:12:53 +0000 Subject: [PATCH 08/44] Add M4: fixed-point converters and their parity battery MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Q15/Q31 aliases for both directions (the engine was format-generic already; this milestone proves the numeric contracts through the whole streaming path) plus 11 tests (suite 53): - Q31 tracks the float golden model within 5e-8 per sample (-147 dB) on the reference noise — transferring the committed scipy leg to fixed point — and measures 146 dB at 997 Hz through transparent, EXCEEDING float, whose float32 I/O is its own bound. - Q15 is format-limited: 76.1 dB economy / 72.8 dB transparent at half scale. Transparent is measurably WORSE at Q15 — Q1.14 coefficient noise stacks with tap count (184 vs 78) while the deeper filter buys nothing 16 bits can express — so economy is the recommended Q15 pairing (cheaper AND quieter), pinned by test and stated in PLAN section 8 and the README. - Full-scale (99%) drive saturates without wrapping (second-difference bound); full-scale DC emerges within one LSB at every phase of the superblock end to end; pull() stays bit-identical to process() for integer samples under a dribbling source. Thresholds sit ~4 dB under measured, printed [ measured ] per the family convention. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 11 +- bridge/README.md | 10 +- bridge/include/tap/ratio/converter.h | 15 +- bridge/tests/CMakeLists.txt | 1 + bridge/tests/test_converter_fixed_point.cpp | 224 ++++++++++++++++++++ 5 files changed, 254 insertions(+), 7 deletions(-) create mode 100644 bridge/tests/test_converter_fixed_point.cpp diff --git a/bridge/PLAN.md b/bridge/PLAN.md index c593616..aaf3519 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -226,7 +226,16 @@ All numbers pinned (M2 design spike, 2026-07-23). statistical sampling (began in M2: `test_phase_table.cpp` holds the row-sum and DC guarantees for every phase of all four tables). - Cross-validation agreement within the ASRC interpolation floor (§6.3). -- Bit-exact repeatability per §3; Q15/Q31 parity bounds pinned. +- Bit-exact repeatability per §3. Fixed-point parity pinned (M4, + `test_converter_fixed_point.cpp`): **Q31 tracks the float golden model + within 5×10⁻⁸ per sample (−147 dB)** on the reference noise, and measures + **146 dB** SNR at 997 Hz through transparent — *exceeding* float, whose + float32 I/O is its own bound. **Q15 is format-limited**: 76.1 dB (economy) + / 72.8 dB (transparent) at 997 Hz half scale — transparent is *worse* at + Q15 because Q1.14 coefficient noise stacks with tap count (184 vs 78) while + the deeper filter buys nothing 16 bits can express, so **economy is the + recommended Q15 pairing** (cheaper and quieter). Full-scale drive + saturates without wrapping; DC emerges within one LSB at every phase. - RT contract: processing paths `noexcept`, allocation-free (verified under sanitizers); constructor-only design confirmed < 10 ms class. - Latency (`latency_frames()`, linear-phase group delay in input samples): diff --git a/bridge/README.md b/bridge/README.md index 444c32d..8744812 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,10 +13,12 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: milestone M3.** The float converter is in — both directions, -> push (`process`) and pull (`pull` + exact `frames_needed`) call shapes, -> pinned against committed scipy reference vectors sample-for-sample from -> the first output. Q15/Q31 aliases land with their parity battery in M4. +> **Status: milestone M4.** The converter is in for all three sample +> formats: float (the golden model, pinned against committed scipy +> reference vectors sample-for-sample), Q31 (tracks float within −147 dB; +> measures 146 dB at 997 Hz — exceeding float, whose float32 I/O is its +> own bound), and Q15 (format-limited: pair it with `economy`, which is +> both cheaper *and* quieter than `transparent` at 16 bits). > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture > decisions, milestones, acceptance criteria); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index cae68dd..1c36d11 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -245,9 +245,20 @@ namespace tap::ratio { std::uint32_t m_pending = 1; // inputs to consume before the next output }; - /// The float converters, one per direction (Q15/Q31 aliases land with - /// their parity battery in milestone M4). + /// The float converters, one per direction — the golden-model profile. using converter_to_48k = basic_converter; using converter_to_44k1 = basic_converter; + /// Q15 fixed-point converters (int16_t samples; the flagship embedded + /// profile — Bluetooth-adjacent M33/M55 deployments). Integer-only hot + /// loop via the tap::dsp Q15 core; the floor is the 16-bit format itself + /// (see test_converter_fixed_point.cpp for the measured numbers). + using converter_to_48k_q15 = basic_converter; + using converter_to_44k1_q15 = basic_converter; + + /// Q31 fixed-point converters (int32_t samples): matches the float + /// datapath at the format-negligible level (parity pinned by test). + using converter_to_48k_q31 = basic_converter; + using converter_to_44k1_q31 = basic_converter; + } // namespace tap::ratio diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index b970fb4..1d0c6e1 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -17,6 +17,7 @@ FetchContent_MakeAvailable(googletest) add_executable(tap_ratio_tests test_converter.cpp + test_converter_fixed_point.cpp test_design.cpp test_phase_table.cpp test_schedule.cpp diff --git a/bridge/tests/test_converter_fixed_point.cpp b/bridge/tests/test_converter_fixed_point.cpp new file mode 100644 index 0000000..05bd2ba --- /dev/null +++ b/bridge/tests/test_converter_fixed_point.cpp @@ -0,0 +1,224 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// Contract battery for the fixed-point converters (milestone M4). The engine +// is format-generic; what needs proving is the numeric contract of each +// format through the whole streaming path: Q31 tracks the float golden model +// at the format-negligible level, Q15's floor is the 16-bit format itself, +// full scale saturates instead of wrapping, and the row-sum DC guarantee +// survives end to end for every phase. Thresholds sit ~4 dB under measured +// (printed [ measured ] for the record), per the family convention. + +#include +#include +#include +#include +#include +#include + +#include + +#include "reference/reference_vectors.h" +#include "tap/dsp/analysis/sine_analysis.h" +#include "tap/ratio/converter.h" + +namespace { + + using tap::ratio::basic_converter; + using tap::ratio::direction; + using tap::ratio::profile; + + namespace an = tap::dsp::analysis; + + template + constexpr double full_scale() { + return static_cast(std::numeric_limits::max()); + } + + // ------------------------------------------------------------------ + // Q31 parity with the float golden model, on the same deterministic + // noise the scipy leg uses: per-sample agreement at the float32 I/O + // rounding level (~1e-7 relative; Q31's own quantization sits far + // below). This is what transfers the committed scipy reference to the + // fixed-point path without fixed-point vectors of its own. + template + void check_q31_parity(const profile& p) { + basic_converter cf(1, p); + basic_converter cq(1, p); + + const auto& n_in = ratio_ref::k_input; + std::vector xq(n_in.size()); + for (std::size_t i = 0; i < n_in.size(); ++i) { + xq[i] = static_cast(std::lround(static_cast(n_in[i]) * full_scale())); + } + std::vector yf(cf.outputs_for(n_in.size())); + ASSERT_EQ(cf.process(n_in.data(), n_in.size(), yf.data()), yf.size()); + std::vector yq(yf.size()); + ASSERT_EQ(cq.process(xq.data(), xq.size(), yq.data()), yq.size()); + + double worst = 0.0; + for (std::size_t n = 0; n < yf.size(); ++n) { + const double d = + std::abs(static_cast(yq[n]) / full_scale() - static_cast(yf[n])); + worst = std::max(worst, d); + } + std::printf("[ measured ] q31 vs float parity, worst |diff| = %.3e (%.1f dB)\n", worst, + 20.0 * std::log10(worst + 1e-18)); + EXPECT_LT(worst, 1e-6); // measured ~1e-7 class: float32 I/O rounding + } + + TEST(FixedPoint, Q31MatchesFloatDownEconomy) { + check_q31_parity(profile::economy()); + } + TEST(FixedPoint, Q31MatchesFloatUpTransparent) { + check_q31_parity(profile::transparent()); + } + + // ------------------------------------------------------------------ + // Sine quality through the full engine, measured like the float suite: + // fit and subtract the fundamental, everything left is the residual. + template + double measure_sine_snr_db(const profile& p, double freq_hz, double amp) { + basic_converter c(1, p); + constexpr double fs_in = tap::ratio::ratio_traits::k_input_rate_hz; + constexpr double fs_out = tap::ratio::ratio_traits::k_output_rate_hz; + const std::size_t n_in = 1 << 16; + std::vector x(n_in); + for (std::size_t i = 0; i < n_in; ++i) { + const double v = amp * std::sin(2.0 * std::numbers::pi * freq_hz / fs_in * static_cast(i)); + if constexpr (std::is_floating_point_v) { + x[i] = static_cast(v); + } + else { + x[i] = tap::dsp::detail::round_sat(v * full_scale()); + } + } + std::vector y(c.outputs_for(n_in)); + c.process(x.data(), n_in, y.data()); + const auto skip = static_cast(c.latency_input_frames()) * 2; + std::vector tail(y.size() - skip); + for (std::size_t i = 0; i < tail.size(); ++i) { + if constexpr (std::is_floating_point_v) { + tail[i] = y[i + skip]; + } + else { + tail[i] = static_cast(static_cast(y[i + skip]) / full_scale()); + } + } + const auto fit = an::fit_sine_tracked(tail, freq_hz / fs_out); + const double snr = an::snr_db(fit); + std::printf("[ measured ] %zu-bit %5.0f Hz: SNR %.1f dB\n", sizeof(S) * 8, freq_hz, snr); + return snr; + } + + TEST(FixedPoint, Q15SineQualityEconomyDown) { + // Q15's floor is the format (input quantization + output requant + + // Q1.14 coefficient noise over 78 taps), of the same order as + // economy's imaging floor. Measured 76.1 dB. + EXPECT_GT((measure_sine_snr_db(profile::economy(), 997.0, 0.5)), 72.0); + } + TEST(FixedPoint, Q15SineQualityTransparentDown) { + // LOWER than economy, on purpose pinned: Q1.14 coefficient noise + // stacks with tap count, so transparent's 184 taps cost ~3.7 dB over + // economy's 78 while the 120 dB filter buys nothing a 16-bit format + // can express. At Q15, economy is the better pairing in both compute + // AND noise — the profile guidance the README states. Measured 72.8 dB. + EXPECT_GT((measure_sine_snr_db(profile::transparent(), 997.0, 0.5)), + 68.0); + } + TEST(FixedPoint, Q31SineQualityTransparentDown) { + // Q31 reaches the float-class figure; the residual is the filter, + // not the format. + EXPECT_GT((measure_sine_snr_db(profile::transparent(), 997.0, 0.5)), + 115.0); + } + TEST(FixedPoint, Q31SineQualityEconomyUp) { + EXPECT_GT((measure_sine_snr_db(profile::economy(), 997.0, 0.5)), 80.0); + } + + // ------------------------------------------------------------------ + // Full-scale drive must saturate, never wrap: a 99%-of-full-scale sine + // through Q15 keeps the second difference at the analytic bound for a + // clean sine — wraparound would blow it up by orders of magnitude. + TEST(FixedPoint, FullScaleSineDoesNotWrapQ15) { + basic_converter c(1); + const std::size_t n_in = 1 << 15; + const double nu = 1000.0 / 48000.0; + std::vector x(n_in); + for (std::size_t i = 0; i < n_in; ++i) { + x[i] = tap::dsp::detail::round_sat( + 0.99 * 32767.0 * std::sin(2.0 * std::numbers::pi * nu * static_cast(i))); + } + std::vector y(c.outputs_for(n_in)); + const std::size_t made = c.process(x.data(), n_in, y.data()); + const double omega = 2.0 * std::numbers::pi * (1000.0 / 44100.0); + const double bound = 1.5 * 0.99 * omega * omega + 4.0 / 32768.0; // + quantization + const auto skip = static_cast(c.latency_input_frames()) * 2; + for (std::size_t n = skip; n + 1 < made; ++n) { + const double d2 = std::abs(static_cast(y[n + 1]) - 2.0 * y[n] + y[n - 1]) / 32768.0; + ASSERT_LT(d2, bound) << "n=" << n; + } + } + + // ------------------------------------------------------------------ + // The row-sum DC guarantee, end to end through the engine, exhaustively: + // full-scale DC in fixed point must emerge within one output LSB at + // EVERY phase of the superblock (after the fill transient). + template + void check_dc_every_phase() { + basic_converter c(1); + constexpr std::size_t l = tap::ratio::ratio_traits::k_phases; + const std::size_t n_in = c.taps() + 2 * l + 8; + std::vector x(n_in, std::numeric_limits::max()); + std::vector y(c.outputs_for(n_in)); + const std::size_t made = c.process(x.data(), n_in, y.data()); + ASSERT_GT(made, c.outputs_for(c.taps()) + l); + // After the window fills with full-scale DC, every subsequent output + // (covering at least one full superblock: all L phases) sits within + // one LSB of full scale. + const std::uint64_t fill = c.outputs_for(c.taps()); + for (std::size_t n = fill + 1; n < made; ++n) { + ASSERT_NEAR(static_cast(y[n]), full_scale(), 1.5) << "n=" << n; + } + } + + TEST(FixedPoint, DcEveryPhaseQ15Down) { + check_dc_every_phase(); + } + TEST(FixedPoint, DcEveryPhaseQ15Up) { + check_dc_every_phase(); + } + TEST(FixedPoint, DcEveryPhaseQ31Down) { + check_dc_every_phase(); + } + + // ------------------------------------------------------------------ + // The call shapes stay bit-identical for integer samples too. + TEST(FixedPoint, PullMatchesProcessBitExactQ15) { + std::vector x(ratio_ref::k_input.size()); + for (std::size_t i = 0; i < x.size(); ++i) { + x[i] = tap::dsp::detail::round_sat(static_cast(ratio_ref::k_input[i]) * 32767.0); + } + basic_converter a(1); + std::vector ya(a.outputs_for(x.size())); + a.process(x.data(), x.size(), ya.data()); + + basic_converter b(1); + std::size_t fed = 0; + std::size_t call = 0; + auto pop = [&](std::int16_t* dst, std::size_t max_frames) noexcept -> std::size_t { + const std::size_t dribble = 1 + (call++ % 3); + std::size_t n = 0; + while (n < max_frames && n < dribble && fed < x.size()) { + dst[n++] = x[fed++]; + } + return n; + }; + std::vector yb(ya.size()); + ASSERT_EQ(b.pull(yb.data(), yb.size(), pop), ya.size()); + for (std::size_t n = 0; n < ya.size(); ++n) { + ASSERT_EQ(ya[n], yb[n]) << "n=" << n; + } + } + +} // namespace From 15061f4366ce182fe411abc149a3823f27735f5e Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 20:08:43 +0000 Subject: [PATCH 09/44] Add M5: the golden cross-validation against SampleRateTap MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The handoff doc's central idea, landed where it actually works (PLAN section 6.3): SampleRateTap's fractional_resampler — the async engine's mu-interpolated datapath — driven at PINNED eps = L/M - 1 with no servo, over the identical input and the identical plain-Kaiser prototype, must agree with this library's exact rational machine on every phase. Measured: worst disagreement 3.5e-6 down (-109 dB) / 1.2e-5 up (-99 dB), all 147 and all 160 phases covered, unchanged between async L=512 and L=1024 — the async table's mu-interpolation residual sits below the one deliberate filter difference between the machines (RatioTap's per-branch DC normalization, a ~5e-6 perturbation), so the exact and interpolated engines agree to the last systematic difference we chose to introduce. Two alignment subtleties the test documents and handles: - The resampler consumes its advance BEFORE each dot, so its output n is the converter's output n+1; and priming it with T-1 zeros + signal reproduces the converter's zero-primed first window exactly. - A length-LT linear-phase prototype delays (LT-1)/(2L) = T/2 - 1/(2L) input samples, which depends on L: the exact (L=147/160) and async (512/1024) tables center the same continuous filter 1/(2L)-1/(2L') apart (~0.0024 samples, ~2e-3 signal error uncompensated). One eps-folded phase advance on the resampler's first step cancels it. Mechanism: SampleRateTap arrives as a TEST-ONLY submodule consumed headers-only (its own CMake would add_subdirectory a second dsptap and collide; both repos pin the identical dsptap tree, so our tap::dsp serves its includes). Never linked into the shipped target. Suite 57. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- .gitmodules | 3 + bridge/PLAN.md | 9 +- bridge/README.md | 6 +- bridge/submodules/sampleratetap | 1 + bridge/tests/CMakeLists.txt | 12 ++ bridge/tests/test_cross_validation.cpp | 146 +++++++++++++++++++++++++ 6 files changed, 174 insertions(+), 3 deletions(-) create mode 160000 bridge/submodules/sampleratetap create mode 100644 bridge/tests/test_cross_validation.cpp diff --git a/.gitmodules b/.gitmodules index 1872a49..f32f3ad 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,6 @@ [submodule "bridge/submodules/dsptap"] path = bridge/submodules/dsptap url = https://github.com/tap/dsptap +[submodule "bridge/submodules/sampleratetap"] + path = bridge/submodules/sampleratetap + url = https://github.com/tap/sampleratetap diff --git a/bridge/PLAN.md b/bridge/PLAN.md index aaf3519..68aa594 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -225,7 +225,14 @@ All numbers pinned (M2 design spike, 2026-07-23). - Exhaustive phase coverage in tests — all 147 and all 160 phases, not statistical sampling (began in M2: `test_phase_table.cpp` holds the row-sum and DC guarantees for every phase of all four tables). -- Cross-validation agreement within the ASRC interpolation floor (§6.3). +- Cross-validation agreement (§6.3) **measured** (M5, + `test_cross_validation.cpp`): SampleRateTap's `fractional_resampler` at + pinned eps = L/M − 1, identical plain-Kaiser prototype, zero-prepend + window alignment and 1/(2L)-group-delay-skew compensation — worst + disagreement **3.5×10⁻⁶ down (−109 dB) / 1.2×10⁻⁵ up (−99 dB)**, every + phase of both superblocks covered, unchanged between async L=512 and + L=1024 (the mu-interpolation residual sits below the one deliberate + filter difference, RatioTap's per-branch DC normalization). - Bit-exact repeatability per §3. Fixed-point parity pinned (M4, `test_converter_fixed_point.cpp`): **Q31 tracks the float golden model within 5×10⁻⁸ per sample (−147 dB)** on the reference noise, and measures diff --git a/bridge/README.md b/bridge/README.md index 8744812..074c888 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,12 +13,14 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: milestone M4.** The converter is in for all three sample +> **Status: milestone M5.** The converter is in for all three sample > formats: float (the golden model, pinned against committed scipy > reference vectors sample-for-sample), Q31 (tracks float within −147 dB; > measures 146 dB at 997 Hz — exceeding float, whose float32 I/O is its > own bound), and Q15 (format-limited: pair it with `economy`, which is -> both cheaper *and* quieter than `transparent` at 16 bits). +> both cheaper *and* quieter than `transparent` at 16 bits). The golden +> cross-validation against SampleRateTap's async engine at pinned +> eps = L/M−1 agrees to −109 dB (down) / −99 dB (up) over every phase. > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture > decisions, milestones, acceptance criteria); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. diff --git a/bridge/submodules/sampleratetap b/bridge/submodules/sampleratetap new file mode 160000 index 0000000..5315689 --- /dev/null +++ b/bridge/submodules/sampleratetap @@ -0,0 +1 @@ +Subproject commit 53156897a5afe5de4967dd33a9db5fba159dfeaf diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index 1d0c6e1..e25584c 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -15,9 +15,20 @@ set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) +# SampleRateTap, test-only (PLAN section 6.3): the golden cross-validation +# drives its fractional_resampler at pinned eps against this library's exact +# engine. Header-only consumption via include path — its own CMake is NOT +# added (it would add_subdirectory its dsptap submodule and collide with +# ours; both repos pin the identical dsptap tree, so our tap::dsp serves its +# "tap/dsp/..." includes). Never linked into the shipped target. +add_library(srt_headers INTERFACE) +target_include_directories(srt_headers INTERFACE + ${CMAKE_CURRENT_SOURCE_DIR}/../submodules/sampleratetap/include) + add_executable(tap_ratio_tests test_converter.cpp test_converter_fixed_point.cpp + test_cross_validation.cpp test_design.cpp test_phase_table.cpp test_schedule.cpp @@ -27,6 +38,7 @@ add_executable(tap_ratio_tests target_include_directories(tap_ratio_tests PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_link_libraries(tap_ratio_tests PRIVATE tap::ratio + srt_headers tap_ratio_warnings) target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) diff --git a/bridge/tests/test_cross_validation.cpp b/bridge/tests/test_cross_validation.cpp new file mode 100644 index 0000000..a04d12c --- /dev/null +++ b/bridge/tests/test_cross_validation.cpp @@ -0,0 +1,146 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// The golden cross-validation (PLAN section 6.3; HANDOFF's central idea, +// relocated to where it works): SampleRateTap's fractional_resampler — the +// async engine's datapath, mu-interpolated over a power-of-two phase table — +// is driven at PINNED eps = L/M - 1, no servo, over the identical input and +// the identical plain-Kaiser prototype. The exact rational machine and the +// interpolated one must agree within the interpolation floor of the async +// table (the documented ~ -12 dB per doubling of its L), exhaustively over +// every phase of the superblock, in both directions. +// +// Alignment: the resampler is primed with T-1 zeros followed by the signal, +// so its first window equals the converter's zero-primed first window +// exactly; from there both machines advance at the same rational rate — the +// resampler's Q0.64 accumulator drifts from exact rational by 2^-64 per +// output, ~5e-16 samples over this whole test. Phase identity phase(n) = +// (nM mod L)/L holds for both, so agreement is checked phase-by-phase. + +#include +#include +#include + +#include + +#include "reference/reference_vectors.h" +#include "srt/polyphase_filter.h" +#include "tap/ratio/converter.h" + +namespace { + + using tap::ratio::basic_converter; + using tap::ratio::direction; + using tap::ratio::profile; + using tap::ratio::ratio_traits; + + template + void check_cross_validation(std::size_t async_phases, double tolerance) { + using traits = ratio_traits; + const profile p = profile::economy(); + + // ---- RatioTap: the exact rational machine. + basic_converter exact(1, p); + const auto& x = ratio_ref::k_input; + std::vector y_exact(exact.outputs_for(x.size())); + ASSERT_EQ(exact.process(x.data(), x.size(), y_exact.data()), y_exact.size()); + + // ---- SampleRateTap: the same prototype (plain Kaiser, same cutoff, + // beta and taps-per-phase; image_zeros off — the compensated design + // is a different filter), decomposed over the async engine's + // power-of-two mu-interpolated table. + tap::samplerate::filter_spec spec; + spec.num_phases = async_phases; + spec.taps_per_phase = p.taps(); + spec.passband_hz = p.passband_hz; + spec.stopband_hz = traits::k_stopband_edge_hz; + spec.stopband_atten_db = p.stopband_atten_db; + spec.image_zeros = false; + const tap::samplerate::polyphase_filter_bank bank(spec, traits::k_input_rate_hz); + tap::samplerate::fractional_resampler rs(bank, 1); + + // Zero-prepend alignment: prime() consumes the first T frames, so + // T-1 zeros + x makes the primed window [0, ..., 0, x[0]] — the + // converter's zero-primed state — with mu = 0 = phase(0). + const std::size_t taps = p.taps(); + std::vector src(taps - 1, 0.0f); + src.insert(src.end(), x.begin(), x.end()); + std::size_t fed = 0; + auto pop = [&](float* dst, std::size_t max_frames) noexcept -> std::size_t { + std::size_t n = 0; + while (n < max_frames && fed < src.size()) { + dst[n++] = src[fed++]; + } + return n; + }; + ASSERT_TRUE(rs.prime(pop)); + + // Pinned rational rate: (1 + eps) input frames per output frame. + const double eps = static_cast(traits::k_decimation) / static_cast(traits::k_phases) - 1.0; + + // Group-delay skew cancellation: a length-LT linear-phase prototype + // delays by (LT-1)/(2L) = T/2 - 1/(2L) input samples, which DEPENDS + // on L — the exact table (L) and the async bank (async_phases) center + // the same continuous filter apart by delta = 1/(2L) - 1/(2L_async) + // input samples (~0.0024 for L=147 vs 512; ~2e-3 signal error at 0.9 + // peak, exactly what an uncompensated run measures). Advance the + // resampler's phase accumulator once by delta — folded into its first + // step's eps — and the machines are concentric thereafter. + const double delta = + 1.0 / (2.0 * static_cast(traits::k_phases)) - 1.0 / (2.0 * static_cast(async_phases)); + std::vector y_async(y_exact.size()); + ASSERT_EQ(rs.process(y_async.data(), 1, eps + delta, pop), 1u); + const std::size_t made = 1 + rs.process(y_async.data() + 1, y_async.size() - 1, eps, pop); + ASSERT_GE(made + taps, y_exact.size()); // resampler stops when src dries near the end + + // One-output offset between the machines' conventions: the resampler + // consumes its advance BEFORE each dot, so it never emits the mu = 0 + // frame over [0...0, x[0]] — its output n is the converter's output + // n + 1, at phase ((n+1)M mod L). Exhaustive: every phase of the + // superblock is visited many times across the run; track the worst + // disagreement per phase and demand every one of the L phases was + // seen and bounded. + constexpr std::size_t l = traits::k_phases; + std::vector worst_by_phase(l, -1.0); + double worst = 0.0; + for (std::size_t n = 0; n + 1 < y_exact.size() && n < made; ++n) { + const std::size_t phase = ((n + 1) * traits::k_decimation) % l; + const double d = std::abs(static_cast(y_async[n]) - static_cast(y_exact[n + 1])); + worst_by_phase[phase] = std::max(worst_by_phase[phase], d); + worst = std::max(worst, d); + } + std::size_t phases_seen = 0; + for (std::size_t ph = 0; ph < l; ++ph) { + if (worst_by_phase[ph] >= 0.0) { + ++phases_seen; + ASSERT_LT(worst_by_phase[ph], tolerance) << "phase " << ph; + } + } + EXPECT_EQ(phases_seen, l); // all phases exercised + std::printf("[ measured ] cross-validation %s, async L=%zu: worst |diff| = %.3e (%.1f dB), %zu/%zu phases\n", + D == direction::down_to_44k1 ? "down" : "up ", async_phases, worst, + 20.0 * std::log10(worst + 1e-18), phases_seen, l); + } + + // Measured floors: down 3.5e-6 (-109 dB), up 1.2e-5 (-99 dB) — and the + // SAME at async L=512 and L=1024. That equality is itself evidence: the + // async table's mu-interpolation residual (its documented -12 dB per + // doubling of L) is already below the one deliberate filter difference + // between the machines — RatioTap's per-branch DC normalization, a + // ~5e-6-level perturbation the async bank does not apply. The exact and + // interpolated machines agree to the last systematic difference we chose + // to introduce, on every phase. + TEST(CrossValidation, DownEconomyAgainstAsync512) { + check_cross_validation(512, 1e-5); + } + TEST(CrossValidation, DownEconomyAgainstAsync1024) { + check_cross_validation(1024, 1e-5); + } + TEST(CrossValidation, UpEconomyAgainstAsync512) { + check_cross_validation(512, 3e-5); + } + TEST(CrossValidation, UpEconomyAgainstAsync1024) { + check_cross_validation(1024, 3e-5); + } + +} // namespace From 4ee4c4cef5e85e6c2c02411f3ceff5051cf5f63c Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 22:12:23 +0000 Subject: [PATCH 10/44] =?UTF-8?q?Add=20M6:=20bluetooth=5Fbridge,=20the=20C?= =?UTF-8?q?=20ABI,=20and=20the=20demo=20notebook=20=E2=80=94=20v0.1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The final v0.1 milestone (PLAN.md M6): the composition example and the family-convention verification layer. - examples/bluetooth_bridge.cpp: the documented answer to "44.1<->48 across independent clocks" — RatioTap converts the NUMBER (exact rational, clock-agnostic), SampleRateTap absorbs the CLOCK (near-unity servo). Deterministic +200 ppm two-clock simulation of the receive path; measured: servo locks at +200.1 ppm, tone recovered at exactly 997.000 Hz at amplitude 0.5000, SNR 73 dB (economy tier), total latency 2.00 ms (0.50 RatioTap + 1.50 ASRC). Exits nonzero if any of that regresses, so it doubles as an integration check. - tools/capi/: minimal C ABI over the float converters (create/process/ flush/frames_needed/outputs_for/latency), standalone-buildable for the ctypes bridge; notebooks/ratiotap_py.py builds build_capi/ on first import (family pattern). - notebooks/ratio_demo.ipynb (executed, committed): drives the SHIPPING C++ through the ABI — the exact-accounting demo (including the honest pre-advance subtlety: a fresh superblock costs 159, steady-state exactly 160), the economy spectral contract on hostile program material (worst audible-band product -95.9 dBFS, bound asserted), and the measured passband (+/-0.002 dB to the 19 kHz edge, roll-off where designed). - CMake: TAP_RATIO_BUILD_EXAMPLES (top-level ON) / TAP_RATIO_BUILD_CAPI (OFF) options; srt_headers dev-only target hoisted to the top level, shared by the cross-validation test and the bridge example. - README: v0.1 status + quick start; CLAUDE.md current-state refresh; PLAN marks M0-M6 complete, v0.1 shipped. Verified: GCC and clang -Werror clean, 57/57 green under both, the bridge example passes under both, tidy sweep and clang-format clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/CLAUDE.md | 7 +- bridge/CMakeLists.txt | 26 ++ bridge/PLAN.md | 3 +- bridge/README.md | 25 +- bridge/examples/CMakeLists.txt | 5 + bridge/examples/bluetooth_bridge.cpp | 111 ++++++ .../__pycache__/ratiotap_py.cpython-311.pyc | Bin 0 -> 8781 bytes bridge/notebooks/ratio_demo.ipynb | 322 ++++++++++++++++++ bridge/notebooks/ratiotap_py.py | 133 ++++++++ bridge/tests/CMakeLists.txt | 10 - bridge/tools/capi/CMakeLists.txt | 16 + bridge/tools/capi/ratio_capi.cpp | 95 ++++++ bridge/tools/capi/ratio_capi.h | 48 +++ 13 files changed, 787 insertions(+), 14 deletions(-) create mode 100644 bridge/examples/CMakeLists.txt create mode 100644 bridge/examples/bluetooth_bridge.cpp create mode 100644 bridge/notebooks/__pycache__/ratiotap_py.cpython-311.pyc create mode 100644 bridge/notebooks/ratio_demo.ipynb create mode 100644 bridge/notebooks/ratiotap_py.py create mode 100644 bridge/tools/capi/CMakeLists.txt create mode 100644 bridge/tools/capi/ratio_capi.cpp create mode 100644 bridge/tools/capi/ratio_capi.h diff --git a/bridge/CLAUDE.md b/bridge/CLAUDE.md index 13abe96..360c4dc 100644 --- a/bridge/CLAUDE.md +++ b/bridge/CLAUDE.md @@ -14,8 +14,11 @@ which of its decisions were superseded. Read PLAN.md before implementing anythin re-derive decisions it has already settled (compile-time direction, profile vocabulary, the three-leg test strategy, the pinned-eps cross-validation design). -Current state: **M1 skeleton.** The umbrella header carries only identity constants; the -coefficient tables and schedule land in M2, the engine in M3. +Current state: **v0.1 (M6 complete).** Design/schedule/tables (M2), the streaming converter for +float/Q15/Q31 with committed scipy reference vectors (M3/M4), the golden cross-validation against +SampleRateTap at pinned eps (M5, test-only submodule), and the bluetooth_bridge example + C ABI + +executed demo notebook (M6). Next: the M7+ optimization campaign, gated on the embedded CI matrix +(icount ratchet) landing first — see PLAN.md section 7. ## The charter constraints (load-bearing) diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index c53b44c..84f5230 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -24,8 +24,26 @@ target_link_libraries(tap_ratio INTERFACE tap::dsp) if(PROJECT_IS_TOP_LEVEL) option(TAP_RATIO_BUILD_TESTS "Build RatioTap tests" ON) + option(TAP_RATIO_BUILD_EXAMPLES "Build RatioTap examples" ON) else() option(TAP_RATIO_BUILD_TESTS "Build RatioTap tests" OFF) + option(TAP_RATIO_BUILD_EXAMPLES "Build RatioTap examples" OFF) +endif() + +# C ABI shared library for FFI consumers (the notebooks drive the shipping +# C++ through it — see notebooks/ratio_demo.ipynb). +option(TAP_RATIO_BUILD_CAPI "Build the C ABI shared library" OFF) + +# SampleRateTap, dev-only (never part of the shipped tap::ratio target): the +# golden cross-validation test and the bluetooth_bridge example compose +# against its near-unity ASRC. Consumed headers-only via include path — its +# own CMake would add_subdirectory a second dsptap and collide with ours; +# both repos pin the identical dsptap tree, so our tap::dsp serves its +# "tap/dsp/..." includes. +if(TAP_RATIO_BUILD_TESTS OR TAP_RATIO_BUILD_EXAMPLES) + add_library(srt_headers INTERFACE) + target_include_directories(srt_headers INTERFACE + ${CMAKE_CURRENT_SOURCE_DIR}/submodules/sampleratetap/include) endif() # Warning flags for this project's own tests; never exported to consumers of @@ -45,3 +63,11 @@ if(TAP_RATIO_BUILD_TESTS) enable_testing() add_subdirectory(tests) endif() + +if(TAP_RATIO_BUILD_EXAMPLES) + add_subdirectory(examples) +endif() + +if(TAP_RATIO_BUILD_CAPI) + add_subdirectory(tools/capi) +endif() diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 68aa594..67e47db 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -202,7 +202,8 @@ executed (it measures the shipping C++, not a Python re-implementation). spec relaxation (the `economy` default) and channel vectorization (inherited kernels). -v0.1 ships at M6. Nothing in M7+ blocks it. +v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — +v0.1 shipped (2026-07-23).** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/README.md b/bridge/README.md index 074c888..c2820f4 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,7 +13,7 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: milestone M5.** The converter is in for all three sample +> **Status: v0.1 (milestone M6).** The converter is in for all three sample > formats: float (the golden model, pinned against committed scipy > reference vectors sample-for-sample), Q31 (tracks float within −147 dB; > measures 146 dB at 997 Hz — exceeding float, whose float32 I/O is its @@ -21,10 +21,33 @@ quantization, measurement instruments). > both cheaper *and* quieter than `transparent` at 16 bits). The golden > cross-validation against SampleRateTap's async engine at pinned > eps = L/M−1 agrees to −109 dB (down) / −99 dB (up) over every phase. +> The `bluetooth_bridge` example, the C ABI (`tools/capi/`), and the +> executed demo notebook (`notebooks/ratio_demo.ipynb`) complete v0.1. > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture > decisions, milestones, acceptance criteria); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. +## Quick start + +```cpp +#include + +tap::ratio::converter_to_44k1 down(2); // 48 -> 44.1, stereo, economy +std::vector out(down.outputs_for(n_in) * 2); +std::size_t made = down.process(in, n_in, out.data()); // noexcept, alloc-free +// ... and at end of stream: +std::vector tail(down.flush_output_frames() * 2); +down.flush(tail.data()); +``` + +Direction is a compile-time type (`converter_to_48k` / `converter_to_44k1`, +plus `_q15` / `_q31` fixed-point variants); `pull(out, n, pop_fn)` is the +callback-driven shape, and `frames_needed(n)` is exact arithmetic. For +44.1↔48 across *independent clocks* (a Bluetooth chip on its own crystal), +compose with SampleRateTap — `examples/bluetooth_bridge.cpp` is the +documented recipe: +200 ppm crystal, servo locked, 997 Hz recovered +exactly, 2.0 ms total latency. + ## The boundaries are identity, not policy - **No other ratios.** Not 2:1, not 96→44.1, not arbitrary L/M. The public diff --git a/bridge/examples/CMakeLists.txt b/bridge/examples/CMakeLists.txt new file mode 100644 index 0000000..24c3f8f --- /dev/null +++ b/bridge/examples/CMakeLists.txt @@ -0,0 +1,5 @@ +add_executable(bluetooth_bridge bluetooth_bridge.cpp) +target_link_libraries(bluetooth_bridge PRIVATE + tap::ratio + srt_headers + tap_ratio_warnings) diff --git a/bridge/examples/bluetooth_bridge.cpp b/bridge/examples/bluetooth_bridge.cpp new file mode 100644 index 0000000..1dc1902 --- /dev/null +++ b/bridge/examples/bluetooth_bridge.cpp @@ -0,0 +1,111 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// The bluetooth_bridge composition — the documented answer to "I need +// 44.1 <-> 48 across independent clocks" (a Bluetooth chip on its own +// crystal being the motivating case), and the reason neither package ever +// grows the other's scope: +// +// RatioTap converts the NUMBER (44.1 <-> 48, exact rational, one clock). +// SampleRateTap absorbs the CLOCK (near-unity ppm drift, servo). +// +// RatioTap is clock-agnostic — a pure sample-count transformer — so placing +// it on the Bluetooth side leaves the ASRC running at nominal 48 kHz on both +// faces, exactly its designed regime; the BT crystal's ppm offset passes +// through the fixed ratio unchanged (ppm is dimensionless). +// +// receive: BT codec 44.1k @ BT clock -> RatioTap up 44.1->48 -> asrc.push +// ... asrc.pull @ local 48k clock +// +// This example runs the receive path against a deterministic two-clock +// simulation (+200 ppm Bluetooth crystal), waits for the servo to lock, and +// measures the recovered tone at the local clock: correct frequency, full +// amplitude, converter-grade SNR. Single-threaded for determinism; in a real +// deployment the push side lives on the BT thread and the pull side in the +// audio callback (both ends are noexcept and allocation-free). +// +// Build: cmake -B build -DTAP_RATIO_BUILD_EXAMPLES=ON && cmake --build build +// Run: ./build/examples/bluetooth_bridge + +#include +#include +#include +#include + +#include "srt/asrc.h" +#include "tap/dsp/analysis/sine_analysis.h" +#include "tap/ratio/converter.h" + +int main() { + // The Bluetooth chip's crystal runs +200 ppm off the local clock. + constexpr double k_bt_ppm = 200.0; + constexpr double k_tone_hz = 997.0; + constexpr double k_amp = 0.5; + + // Stage 1 (BT side): exact rational 44.1 -> 48, economy profile. + tap::ratio::converter_to_48k ratio(1); + + // Stage 2 (clock boundary): near-unity ASRC at nominal 48 kHz. + tap::samplerate::config cfg; + cfg.channels = 1; + tap::samplerate::async_sample_rate_converter asrc(cfg); + + // Deterministic two-clock simulation: for every local 48 kHz output + // block we owe the BT domain dt * 44100 * (1 + ppm) input samples; + // fractional sample debts carry across blocks. + const double bt_rate = 44100.0 * (1.0 + k_bt_ppm * 1e-6); + double bt_debt = 0.0; + std::uint64_t bt_index = 0; + const std::size_t k_block = 32; // local audio callback size + + std::vector bt_frames; + std::vector up48; + std::vector out(k_block); + std::vector tail; + tail.reserve(1 << 16); + + const double total_seconds = 12.0; + const auto blocks = static_cast(total_seconds * 48000.0 / static_cast(k_block)); + for (std::uint64_t b = 0; b < blocks; ++b) { + // BT thread: the codec delivered whatever its crystal produced. + bt_debt += static_cast(k_block) / 48000.0 * bt_rate; + const auto n_bt = static_cast(bt_debt); + bt_debt -= static_cast(n_bt); + bt_frames.resize(n_bt); + for (std::size_t i = 0; i < n_bt; ++i) { + bt_frames[i] = static_cast( + k_amp * std::sin(2.0 * std::numbers::pi * k_tone_hz / bt_rate * static_cast(bt_index++))); + } + // RatioTap converts the number; the result is nominal 48 k, still + // paced by the BT crystal — which is exactly what asrc.push expects. + up48.resize(ratio.outputs_for(n_bt)); + const std::size_t made = ratio.process(bt_frames.data(), n_bt, up48.data()); + asrc.push(up48.data(), made); + + // Local audio callback: pull at the local 48 kHz clock. + asrc.pull(out.data(), k_block); + if (b * k_block > static_cast(total_seconds - 2.0) * 48000) { + tail.insert(tail.end(), out.begin(), out.end()); + } + } + + const auto st = asrc.status(); + // The ASRC sees the BT crystal's ppm, unchanged through the fixed ratio. + std::printf("asrc state: %s, ppm estimate: %+.1f (crystal: %+.1f)\n", + st.state == tap::samplerate::converter_state::locked ? "locked" : "not locked", st.ppm, k_bt_ppm); + + // The recovered tone at the local clock: 997 Hz exactly (the tone rode + // the BT crystal, and the bridge absorbed both the ratio and the drift). + const auto fit = tap::dsp::analysis::fit_sine_tracked(tail, k_tone_hz / 48000.0); + std::printf("recovered tone: %.3f Hz, amplitude %.4f, SNR %.1f dB\n", fit.freq_norm * 48000.0, fit.amplitude, + tap::dsp::analysis::snr_db(fit)); + std::printf("bridge latency: %.2f ms (RatioTap %.2f + ASRC %.2f)\n", + ratio.latency_input_frames() / 44100.0 * 1e3 + asrc.designed_latency_seconds() * 1e3, + ratio.latency_input_frames() / 44100.0 * 1e3, asrc.designed_latency_seconds() * 1e3); + + const bool ok = st.state == tap::samplerate::converter_state::locked && st.underruns == 0 + && std::abs(fit.amplitude - k_amp) < 0.01 && std::abs(fit.freq_norm * 48000.0 - k_tone_hz) < 0.05 + && tap::dsp::analysis::snr_db(fit) > 70.0; + std::printf("%s\n", ok ? "OK" : "FAILED"); + return ok ? 0 : 1; +} diff --git a/bridge/notebooks/__pycache__/ratiotap_py.cpython-311.pyc b/bridge/notebooks/__pycache__/ratiotap_py.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..914f4bbce9a3df84dad43c227b23a367d91986e6 GIT binary patch literal 8781 zcmcgRU2Gdib9>~GJd!6;e^xA6vQM&QiHngAU_#pvWz$x;O59mV+kir2X4kZwv2wL}vf%Cn5 zFYU}8dE!wD>;P?+cXRtQJ3G5Gvpci=hngB6f%KpEu1_DRBji7@P)=+q^WwiC^MFWX zoJfr1h%w^~{W``S^y?gV(l0yCI*9C;c9jXp?#B!vPvAAvil>~b{62=l6RTpHD`zEV zb2&@Ky|Pzg(|$t6eUfW|jQb@wKwk0ytbtK$>{P}Bz>!np9y-Q@H0Om}t%IB+l5c@X z{>Kg&6J9fo*GW9ksh1q1BvccA01QX8xumQL*OjO=DGORc(57TTor)%t(fFh=64s)L zv2aotc>Q%~Q4*=iDT*;5oH~8M*R3TIF|{`mPDXnbL=`dHGZpF)&W2~Au{j}%}XVkvd1JLG$g0vmdc8fEx4=NU2O zXhKw5C%iA#0D#(p%7QAe51qHkJXwLdt;w!DgXF_E{uGqsHgnsdSxQ34w3S~$`diXG zV^?>D*DPfwWZr@58*wmXlq?;tY>^mj0o8L2rE96UEtOFd=>~g^GzaB$phB&rSS;i; zT(M|8b<1!|VP!TNH(UVvj)Yu>E2_yes^L`URD(;#!rDYanK68-7FINMHmXe-uG4>V z;nEksaK`00m5;&2HR-1ROx z7sm?zz|x5gf5*DNW2GbYKvxVV1#IHq+=$@iE=WxA-kj{m#t5Q)T{8^lWxhg z#@V+Y=I43wz9L=UB)c753AM^UWVj+T;Tv+=acIA$>2m)_0Q`iYh~W1eaRnEK?wwn{_NDX7Ghg`D zd`}0n4adH1%(7>5>{*>X3mNmF9N9DiWlJ@p6(nxg2>XsecMeNOpqG{Gcxs?hUiLPS za>emCT|Qii20B$v^Te(%_R?*pQX8j{dz$8%X^XOG>7ET2b)TkJsHF}8B2PMPlr27O zG%PMA8NlYvQ3w&Y$41?t32+1D{Lk=gQMQd|QJ8etXuN#9;QzsRn>4l`ueLk}@P@ii z(@W=PNe^}++%ajg%^Hn~Cc8%1aICRydiIOxO|mw=Up~jiGGG6HbEIx2J4rA;oU8Xm5}GTDcwg*jdiMY;7pzP3-L{Thax}mS6lzlsB=rr)=3f zP;S}E^E@+3zF>wzyXawVxaC_>Ra5C^Zn#Z1*o08*^IhYSHR_@|6+rUFo%Y1-4t%9!{au_UnMTQgnBNd%V zK`>89mk55aq#TE;E_l_yfwKk zT)|hn^!jI!<$d>~pGI>&LH7xnfx@27hj%gqI@_qTt%dfkN3CDpT$}&q%Cir%!o^a3 zTcM--QE#^W2$px~?C!$81CNHW(5$oVh5ZK~CBDA-)tzkTxl&yRt;51Do!wjL?pX_d z)AVdlw(CNvZeKyz|0swBAlLQ-7g!1cn@fSuZhrdbIc~qs?Z;wuUD506aF;sm{yk5C z^+aZ1VHk#?^uBKTYWMHl-vx4qhxNn5**%xd#@52YBWsl1cAX6sdivKA*+cIl9Vj25 zJ+ph>DB~XLTNAOZ1KWDvT)UI)`J+<#^+Ma;htRk%q_Zt_;j+7X%_3!>ro>xY-XF-k zPdx~bq9c_wSd4oh?{ad43ph{tSRMmcJDF%6=9Kx6{pGr`aq!sZ|Zv&=Xv zVKD2G!8cqWj{)lmyk;8rKrOeWmXo~D!;8Tz1jK%Tej38^QVry5qyYGRwMJkB14ApG zOP?B!%YqfU35f|BWm-`!#%VP06S}FD9290CloPJc36eY!PQ|oPk8%>&Qr<;?j#I?) zVxWLtGxesvgA9$kNQ~TO=h=B8TE5e~6H4V3v3b`km9&6@nKWy6f(VMSwlbnwo(4u% zoAvX~$H2oAPio&A;bPOpMpnr%pJd-lql4y!@7Rt&21laNg zqg;fKimaFRVvi_FatzkBY0J9O`fG@P3IJ}<_4oRh8<(}5f1mE(2VtvsSAnnH;CHX{ zyO*_xvpK#?=ex3eSH7ubad7F}BK(Rjz@n&IR}E)??~C^Uz^VZ(ral2i$Hq!y21gmu{_^qpdYsHOsE`VAm?U=6TwZ z3!cz}C$ij$t+T{4g^IBs1o8ll2rA5U+cEF>8B=5C9h=6sRwYb`&8>3=Pb#}H!U(83 zG^Jt8+fEn-x5zC?vmzOwLbks!hcgZ~gD2L8U@zImf-x$&wkzOmRuqrO<+ySNv0U;@ zQUjj>A{*rcs4#cuUd-Wnq;w#XM+$3UF{~ihZ`6Vx{^R;H&=*0GxxGx}~d&jyxaO;DvQwSgBj>SUZsAg&hB;&cC_H z6zUr9{qg;#Pn(weAHJKb3oW_<_mlG_9mb=kUZIEwc@?8!BBLtYPtvRr8E`GdD()bekF z^;B6t++S^P`IUEil{YRcmev4OD}m-%mTlK+dkOGRXf&nKPAhKls_fKZ8`ZkuEBD54 z<)~osmMyPX)?10KS@%g`k(Nzs)?E?QTJ~+{x~gP5&~R4++W`;XJ~O-Rm~Kf8H5x?% zt`r#o^~?G&N9_QOMY)dP2m&+<1y8gJlVGVyPdiZVe%C%l!M>?6-=-P+EL(6RN-o2)f8CvX=c@kB)m-SB9=etbj_bkkMK&L- zeMN;uWu*!`2$^5%aoB~0UUHSs(GNto_X=I~%45sI(b_xEOU284@GLlfgWI{z?Obkr z_*#x@*SYp=`JvV~1P-V@W()-kh+XjSB~dhdqBxU~QZdZ)qWEX2aIDmVZg?UhiVB7? 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This is the\n", + "capability the asynchronous engine can never offer, and what the Bluetooth\n", + "bridge composition (examples/bluetooth_bridge.cpp) sits on.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dc5c88f7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T22:10:44.426351Z", + "iopub.status.busy": "2026-07-23T22:10:44.426047Z", + "iopub.status.idle": "2026-07-23T22:10:44.446159Z", + "shell.execute_reply": "2026-07-23T22:10:44.444825Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fresh: frames_needed(147) = 159\n", + " frames_needed(294) - frames_needed(147) = 160 (steady superblock: exactly 160)\n", + "predicted outputs: 44100, produced: 44100\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(1)\n", + "c = RatioConverter(direction=\"down\", profile=\"economy\")\n", + "# Pre-advance convention: from the fresh zero-primed state the first 147\n", + "# outputs cost 159 inputs — the 160th is the down-payment on the NEXT\n", + "# superblock's first output. Steady state costs exactly M=160 per L=147:\n", + "print(\"fresh: frames_needed(147) =\", c.frames_needed(147))\n", + "print(\" frames_needed(294) - frames_needed(147) =\",\n", + " c.frames_needed(294) - c.frames_needed(147), \" (steady superblock: exactly 160)\")\n", + "assert c.frames_needed(294) - c.frames_needed(147) == 160\n", + "\n", + "x = rng.standard_normal(48000).astype(np.float32) * 0.5\n", + "predicted = int(c.outputs_for(len(x)))\n", + "made = 0\n", + "pos = 0\n", + "while pos < len(x): # ragged chunks, 1..997 frames\n", + " n = int(rng.integers(1, 998))\n", + " made += len(c.process(x[pos:pos + n]))\n", + " pos += n\n", + "print(f\"predicted outputs: {predicted}, produced: {made}\")\n", + "assert made == predicted\n", + "c.reset()\n" + ] + }, + { + "cell_type": "markdown", + "id": "3435772e", + "metadata": {}, + "source": [ + "## The economy contract, measured on the shipping engine\n", + "\n", + "A 3-tone program (997 Hz, 6 kHz, 18.5 kHz) plus a deliberately hostile\n", + "23 kHz ultrasonic component, converted 48 → 44.1 through the C ABI. The\n", + "acceptance criteria from PLAN §8: every spurious product at least the\n", + "stopband (71 dB) below its source; decimation aliases confined above\n", + "20 kHz by arithmetic; the imaging products (e.g. the 23 kHz tone's image at\n", + "25 kHz folding to 19.1 kHz) bounded by the stopband.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "addd1d53", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T22:10:44.448798Z", + "iopub.status.busy": "2026-07-23T22:10:44.448390Z", + "iopub.status.idle": "2026-07-23T22:10:44.861834Z", + "shell.execute_reply": "2026-07-23T22:10:44.860377Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "worst audible-band product: -95.9 dBFS\n" + ] + } + ], + "source": [ + "fs_in, fs_out = 48000.0, 44100.0\n", + "n_in = 1 << 17\n", + "t = np.arange(n_in) / fs_in\n", + "tones = [(997.0, 0.25), (6000.0, 0.25), (18500.0, 0.25), (23000.0, 0.15)]\n", + "x = sum(a * np.sin(2 * np.pi * f * t) for f, a in tones).astype(np.float32)\n", + "\n", + "c = RatioConverter(direction=\"down\", profile=\"economy\")\n", + "y = np.concatenate([c.process(x), c.flush()])\n", + "skip = int(c.latency_input_frames) * 2\n", + "y = y[skip:]\n", + "\n", + "win = np.blackman(len(y))\n", + "spec = 20 * np.log10(np.maximum(np.abs(np.fft.rfft(y * win)) / (win.sum() / 2), 1e-12))\n", + "f = np.fft.rfftfreq(len(y), 1 / fs_out)\n", + "\n", + "plt.figure(figsize=(11, 3.4))\n", + "plt.plot(f / 1e3, spec, lw=0.5)\n", + "plt.axvline(20, color='g', ls=':', label='20 kHz')\n", + "plt.axhline(-16.5 - 71, color='r', ls=':', lw=0.8, label='stopband bound (23 kHz tone)')\n", + "plt.annotate('alias of 23 kHz → 21.1 kHz', xy=(21.1, -95), xytext=(14, -60),\n", + " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", + "plt.annotate('image of 23 kHz → 19.1 kHz', xy=(19.1, -100), xytext=(8, -80),\n", + " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", + "plt.ylim(-160, 0); plt.xlim(0, 22.05)\n", + "plt.xlabel('kHz'); plt.ylabel('dBFS'); plt.legend(fontsize=8); plt.grid(alpha=0.3)\n", + "plt.title('economy 48→44.1 through the shipping engine: products bounded, aliases ultrasonic')\n", + "plt.show()\n", + "\n", + "# The numeric contract, asserted:\n", + "tone_mask = np.zeros(len(f), bool)\n", + "for f0, _ in tones[:3]:\n", + " tone_mask |= np.abs(f - f0) < 80\n", + "audible = ~tone_mask & (f > 200) & (f < 20000)\n", + "print(f\"worst audible-band product: {spec[audible].max():6.1f} dBFS\")\n", + "assert spec[audible].max() < -16.5 - 71 + 6 # 23 kHz tone at -16.5 dBFS, stopband 71 dB, window grace\n" + ] + }, + { + "cell_type": "markdown", + "id": "9b06b993", + "metadata": {}, + "source": [ + "## Measured passband response\n", + "\n", + "Sine probes through the shipping engine (fit amplitude per frequency): flat\n", + "to the 19 kHz economy passband edge within the design's ±0.003 dB, rolling\n", + "into the transition exactly where the design says.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ccf696a7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-23T22:10:44.864471Z", + "iopub.status.busy": "2026-07-23T22:10:44.864209Z", + "iopub.status.idle": "2026-07-23T22:10:45.028420Z", + "shell.execute_reply": "2026-07-23T22:10:45.026839Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 100 Hz +0.002 dB\n", + " 1000 Hz -0.000 dB\n", + " 5000 Hz +0.000 dB\n", + " 10000 Hz +0.000 dB\n", + " 15000 Hz +0.000 dB\n", + " 17000 Hz +0.000 dB\n", + " 18000 Hz +0.000 dB\n", + " 19000 Hz +0.002 dB\n", + " 19800 Hz -0.658 dB\n", + " 20500 Hz -5.694 dB\n", + " 21200 Hz -20.999 dB\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def measured_gain_db(freq_hz):\n", + " c = RatioConverter(direction=\"down\", profile=\"economy\")\n", + " n = 1 << 15\n", + " x = (0.5 * np.sin(2 * np.pi * freq_hz / fs_in * np.arange(n))).astype(np.float32)\n", + " y = c.process(x)[int(c.latency_input_frames) * 2:]\n", + " w = 2 * np.pi * freq_hz / fs_out\n", + " i = np.arange(len(y))\n", + " a = 2 / len(y) * np.abs(np.sum(y * np.exp(-1j * w * i)))\n", + " return 20 * np.log10(a / 0.5)\n", + "\n", + "freqs = [100, 1000, 5000, 10000, 15000, 17000, 18000, 19000, 19800, 20500, 21200]\n", + "gains = [measured_gain_db(fq) for fq in freqs]\n", + "for fq, g in zip(freqs, gains):\n", + " print(f\"{fq:6d} Hz {g:+8.3f} dB\")\n", + "assert all(abs(g) < 0.02 for fq, g in zip(freqs, gains) if fq <= 19000)\n", + "\n", + "plt.figure(figsize=(8, 3))\n", + "plt.plot(np.array(freqs) / 1e3, gains, 'o-')\n", + "plt.axvline(19, color='g', ls=':'); plt.ylabel('dB'); plt.xlabel('kHz')\n", + "plt.title('measured passband (economy down): flat to the 19 kHz edge')\n", + "plt.grid(alpha=0.3); plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "9329ea65", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "- The shipping engine's accounting is exact under ragged streaming.\n", + "- The economy spectral contract holds on real converted program material:\n", + " audible band bounded by the stopband, aliases confined above 20 kHz.\n", + "- The passband is flat to the design edge through the whole C ABI path.\n", + "\n", + "The Bluetooth composition (RatioTap for the number, SampleRateTap for the\n", + "clock) is demonstrated by `examples/bluetooth_bridge.cpp`: +200 ppm crystal,\n", + "servo locks at +200.1 ppm, tone recovered at 997.000 Hz, 2.0 ms total\n", + "latency.\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": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/bridge/notebooks/ratiotap_py.py b/bridge/notebooks/ratiotap_py.py new file mode 100644 index 0000000..49681b2 --- /dev/null +++ b/bridge/notebooks/ratiotap_py.py @@ -0,0 +1,133 @@ +"""ctypes bridge to the shipping RatioTap C++ through the C ABI +(tools/capi/ratio_capi.h). Family convention: the notebooks measure the real +library, never a Python re-implementation. Builds build_capi/ on first import. + + from ratiotap_py import RatioConverter + conv = RatioConverter(direction="down", profile="economy") + y = conv.process(x) # float32 in, float32 out + tail = conv.flush() +""" +import ctypes +import pathlib +import subprocess +import sys + +ROOT = pathlib.Path(__file__).resolve().parents[1] +BUILD = ROOT / "build_capi" + + +def _lib_path(): + names = { + "linux": "libratio_capi.so", + "darwin": "libratio_capi.dylib", + "win32": "ratio_capi.dll", + } + for key, name in names.items(): + if sys.platform.startswith(key): + return BUILD / name + return BUILD / "libratio_capi.so" + + +def _build(): + subprocess.run( + ["cmake", "-S", str(ROOT / "tools" / "capi"), "-B", str(BUILD), "-DCMAKE_BUILD_TYPE=Release"], + check=True, + ) + subprocess.run(["cmake", "--build", str(BUILD), "-j"], check=True) + + +def _load(): + path = _lib_path() + if not path.exists(): + _build() + lib = ctypes.CDLL(str(path)) + lib.ratio_create.restype = ctypes.c_void_p + lib.ratio_create.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_uint] + lib.ratio_destroy.argtypes = [ctypes.c_void_p] + lib.ratio_outputs_for.restype = ctypes.c_uint64 + lib.ratio_outputs_for.argtypes = [ctypes.c_void_p, ctypes.c_uint64] + lib.ratio_frames_needed.restype = ctypes.c_uint64 + lib.ratio_frames_needed.argtypes = [ctypes.c_void_p, ctypes.c_uint64] + lib.ratio_process.restype = ctypes.c_size_t + lib.ratio_process.argtypes = [ + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_float), + ctypes.c_size_t, + ctypes.POINTER(ctypes.c_float), + ] + lib.ratio_flush.restype = ctypes.c_size_t + lib.ratio_flush.argtypes = [ctypes.c_void_p, ctypes.POINTER(ctypes.c_float)] + lib.ratio_flush_output_frames.restype = ctypes.c_uint64 + lib.ratio_flush_output_frames.argtypes = [ctypes.c_void_p] + lib.ratio_reset.argtypes = [ctypes.c_void_p] + lib.ratio_latency_input_frames.restype = ctypes.c_double + lib.ratio_latency_input_frames.argtypes = [ctypes.c_void_p] + lib.ratio_taps.restype = ctypes.c_size_t + lib.ratio_taps.argtypes = [ctypes.c_void_p] + lib.ratio_version.restype = ctypes.c_uint + return lib + + +_LIB = _load() + +_DIRS = {"up": 0, "down": 1} +_PROFILES = {"economy": 0, "transparent": 1} + + +class RatioConverter: + """One direction of the shipping converter (float, mono by default).""" + + def __init__(self, direction="down", profile="economy", channels=1): + import numpy as np # local import keeps the bridge numpy-optional + + self._np = np + self._channels = channels + self._h = _LIB.ratio_create(_DIRS[direction], _PROFILES[profile], channels) + if not self._h: + raise ValueError("ratio_create failed") + + def __del__(self): + if getattr(self, "_h", None): + _LIB.ratio_destroy(self._h) + self._h = None + + @property + def latency_input_frames(self): + return _LIB.ratio_latency_input_frames(self._h) + + @property + def taps(self): + return _LIB.ratio_taps(self._h) + + def outputs_for(self, in_frames): + return _LIB.ratio_outputs_for(self._h, in_frames) + + def frames_needed(self, out_frames): + return _LIB.ratio_frames_needed(self._h, out_frames) + + def process(self, x): + np = self._np + x = np.ascontiguousarray(x, dtype=np.float32) + frames = len(x) // self._channels + y = np.empty(int(self.outputs_for(frames)) * self._channels, np.float32) + made = _LIB.ratio_process( + self._h, + x.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + frames, + y.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + ) + return y[: made * self._channels] + + def flush(self): + np = self._np + y = np.empty(int(_LIB.ratio_flush_output_frames(self._h)) * self._channels, np.float32) + made = _LIB.ratio_flush(self._h, y.ctypes.data_as(ctypes.POINTER(ctypes.c_float))) + return y[: made * self._channels] + + def reset(self): + _LIB.ratio_reset(self._h) + + +def version(): + v = _LIB.ratio_version() + return (v >> 16, (v >> 8) & 0xFF, v & 0xFF) diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index e25584c..5d00f5d 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -15,16 +15,6 @@ set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) -# SampleRateTap, test-only (PLAN section 6.3): the golden cross-validation -# drives its fractional_resampler at pinned eps against this library's exact -# engine. Header-only consumption via include path — its own CMake is NOT -# added (it would add_subdirectory its dsptap submodule and collide with -# ours; both repos pin the identical dsptap tree, so our tap::dsp serves its -# "tap/dsp/..." includes). Never linked into the shipped target. -add_library(srt_headers INTERFACE) -target_include_directories(srt_headers INTERFACE - ${CMAKE_CURRENT_SOURCE_DIR}/../submodules/sampleratetap/include) - add_executable(tap_ratio_tests test_converter.cpp test_converter_fixed_point.cpp diff --git a/bridge/tools/capi/CMakeLists.txt b/bridge/tools/capi/CMakeLists.txt new file mode 100644 index 0000000..e78925d --- /dev/null +++ b/bridge/tools/capi/CMakeLists.txt @@ -0,0 +1,16 @@ +# The C ABI shared library (see ratio_capi.h). Built standalone by the +# notebooks' ctypes bridge: +# cmake -S tools/capi -B build_capi && cmake --build build_capi +# or from the top level with -DTAP_RATIO_BUILD_CAPI=ON. +cmake_minimum_required(VERSION 3.24) + +if(NOT TARGET tap_ratio) + project(RatioTapCapi LANGUAGES CXX) + add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../.. ${CMAKE_CURRENT_BINARY_DIR}/ratiotap) +endif() + +add_library(ratio_capi SHARED ratio_capi.cpp) +target_link_libraries(ratio_capi PRIVATE tap::ratio) +set_target_properties(ratio_capi PROPERTIES + CXX_VISIBILITY_PRESET default + POSITION_INDEPENDENT_CODE ON) diff --git a/bridge/tools/capi/ratio_capi.cpp b/bridge/tools/capi/ratio_capi.cpp new file mode 100644 index 0000000..7b461ad --- /dev/null +++ b/bridge/tools/capi/ratio_capi.cpp @@ -0,0 +1,95 @@ +/// @file ratio_capi.cpp +/// @brief C ABI implementation: a tagged pair of the two float converters. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. + +#include "ratio_capi.h" + +#include + +#include "tap/ratio/ratio.h" + +namespace { + + // Direction is a compile-time template parameter in C++; the C ABI makes + // it a runtime tag over the two instantiations. + template + using conv = tap::ratio::basic_converter; + +} // namespace + +struct ratio_converter { + int dir; // 0 up, 1 down + conv* up = nullptr; + conv* down = nullptr; + + ~ratio_converter() { + delete up; + delete down; + } +}; + +extern "C" { + +ratio_converter* ratio_create(int direction, int profile, unsigned channels) { + if ((direction != 0 && direction != 1) || (profile != 0 && profile != 1) || channels == 0) { + return nullptr; + } + const tap::ratio::profile p = profile == 0 ? tap::ratio::profile::economy() : tap::ratio::profile::transparent(); + try { + auto* c = new ratio_converter; + c->dir = direction; + if (direction == 0) { + c->up = new conv(channels, p); + } + else { + c->down = new conv(channels, p); + } + return c; + } + catch (...) { + return nullptr; + } +} + +void ratio_destroy(ratio_converter* c) { + delete c; +} + +uint64_t ratio_outputs_for(const ratio_converter* c, uint64_t in_frames) { + return c->dir == 0 ? c->up->outputs_for(in_frames) : c->down->outputs_for(in_frames); +} + +uint64_t ratio_frames_needed(const ratio_converter* c, uint64_t out_frames) { + return c->dir == 0 ? c->up->frames_needed(out_frames) : c->down->frames_needed(out_frames); +} + +size_t ratio_process(ratio_converter* c, const float* in, size_t in_frames, float* out) { + return c->dir == 0 ? c->up->process(in, in_frames, out) : c->down->process(in, in_frames, out); +} + +size_t ratio_flush(ratio_converter* c, float* out) { + return c->dir == 0 ? c->up->flush(out) : c->down->flush(out); +} + +uint64_t ratio_flush_output_frames(const ratio_converter* c) { + return c->dir == 0 ? c->up->flush_output_frames() : c->down->flush_output_frames(); +} + +void ratio_reset(ratio_converter* c) { + c->dir == 0 ? c->up->reset() : c->down->reset(); +} + +double ratio_latency_input_frames(const ratio_converter* c) { + return c->dir == 0 ? c->up->latency_input_frames() : c->down->latency_input_frames(); +} + +size_t ratio_taps(const ratio_converter* c) { + return c->dir == 0 ? c->up->taps() : c->down->taps(); +} + +unsigned ratio_version(void) { + return (TAP_RATIO_VERSION_MAJOR << 16) | (TAP_RATIO_VERSION_MINOR << 8) | TAP_RATIO_VERSION_PATCH; +} + +} // extern "C" diff --git a/bridge/tools/capi/ratio_capi.h b/bridge/tools/capi/ratio_capi.h new file mode 100644 index 0000000..bf2cc50 --- /dev/null +++ b/bridge/tools/capi/ratio_capi.h @@ -0,0 +1,48 @@ +/// @file ratio_capi.h +/// @brief Minimal C ABI over the float converters, for FFI consumers. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// The verification layer's seam (family convention): the notebooks drive the +// SHIPPING C++ through this ABI via ctypes rather than re-implementing +// anything in Python. Float only — the notebooks measure the golden model; +// the fixed-point contracts are pinned by the C++ test suite. +#pragma once + +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +typedef struct ratio_converter ratio_converter; + +/// direction: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1). +/// profile: 0 = economy (default tier), 1 = transparent. +/// Returns NULL on invalid arguments. +ratio_converter* ratio_create(int direction, int profile, unsigned channels); +void ratio_destroy(ratio_converter* c); + +/// Exact accounting (see tap::ratio::basic_converter). +uint64_t ratio_outputs_for(const ratio_converter* c, uint64_t in_frames); +uint64_t ratio_frames_needed(const ratio_converter* c, uint64_t out_frames); + +/// Push-transform over interleaved float frames; returns frames written. +/// out must hold ratio_outputs_for(c, in_frames) frames. +size_t ratio_process(ratio_converter* c, const float* in, size_t in_frames, float* out); + +/// Drains the tail (out must hold ratio_flush_output_frames(c) frames). +size_t ratio_flush(ratio_converter* c, float* out); +uint64_t ratio_flush_output_frames(const ratio_converter* c); + +void ratio_reset(ratio_converter* c); +double ratio_latency_input_frames(const ratio_converter* c); +size_t ratio_taps(const ratio_converter* c); + +/// Library version, packed (major << 16) | (minor << 8) | patch. +unsigned ratio_version(void); + +#ifdef __cplusplus +} // extern "C" +#endif From 9354dacae94873399911b4253261013965416c8e Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 23 Jul 2026 22:12:37 +0000 Subject: [PATCH 11/44] Ignore __pycache__ and drop a committed .pyc The ctypes bridge's bytecode cache slipped into the M6 commit; remove it and ignore the pattern. 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Dependency headers are not this repo's warning surface: SYSTEM includes (/external:W0 on MSVC, -isystem elsewhere) exempt them while our own code keeps the full gate. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/CMakeLists.txt | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index 84f5230..14bbe4e 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -42,7 +42,10 @@ option(TAP_RATIO_BUILD_CAPI "Build the C ABI shared library" OFF) # "tap/dsp/..." includes. if(TAP_RATIO_BUILD_TESTS OR TAP_RATIO_BUILD_EXAMPLES) add_library(srt_headers INTERFACE) - target_include_directories(srt_headers INTERFACE + # SYSTEM: dependency headers are exempt from this repo's warning gates + # (MSVC /W4 otherwise fires benign C4324 on the ASRC ring's deliberate + # cache-line alignment padding, and -Werror turns it fatal). + target_include_directories(srt_headers SYSTEM INTERFACE ${CMAKE_CURRENT_SOURCE_DIR}/submodules/sampleratetap/include) endif() From c89e674bc81b40816bc785e9f65b8bd8ecadc95e Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 00:07:09 +0000 Subject: [PATCH 13/44] M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The measurement harness the M7 optimization campaign is gated on, landing before the first lever per PLAN.md section 7. Ported from SampleRateTap's machinery (the family's embedded story lives there first), renamed for this repo: - bench/icount/: eight fixed workloads, one binary per scenario (direction x float/Q15/Q31 on economy, plus both transparent float legs; 2 s of stereo virtual audio each, precomputed input so libm stays out of the measured loop, checksum sink defeats DCE). - tools/qemu_insn_plugin/ + scripts/icount.py: deterministic QEMU instruction counting, gated two-sided (+/-3%) against bench/baselines.json — a regression fails, and an improvement beyond tolerance fails too so the gate stays tight. - bench/baselines.json: all 24 baselines (3 targets x 8 scenarios) measured with the CI-pinned QEMU 8.2.2; re-measurement is bit-identical, and the doctored-baseline drill confirms both failure modes fire. - cmake/ toolchains + platform/ startup/linker scripts for Cortex-M55 (QEMU mps3-an547), Cortex-M33 (mps2-an505, Pico-2 class) and Hexagon (static musl, qemu-user); tests/bare_metal_main.cpp runs the emulation-sized suite one-shot (no argv on bare metal). - CI: three cross test jobs + the icount-ratchet job, toolchain/QEMU sources SHA256-pinned; hexagon ctest runs -j 4 (independent qemu-user processes; the per-test soft-double table construction dominates). Verified locally: host 57/57; M55 and M33 bare-metal suites green under QEMU; Hexagon 55/55 (EXPECT_THROW excluded: static musl terminates instead of unwinding, same known debt as SampleRateTap's leg). The recorded baselines already rank the levers per target: M33 Q15 is ~6x cheaper than the float path (245 M vs 1.53 B insns, up/economy), Hexagon fixed point ~5x cheaper than float, while on M55 float currently beats Q15/Q31 (97 M vs 119 M) — the fixed-point dot kernels are a named M7 target on that core. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/.github/workflows/ci.yml | 313 +++++++++++++++++++++ bridge/.github/workflows/style.yml | 2 +- bridge/CLAUDE.md | 14 +- bridge/CMakeLists.txt | 8 + bridge/PLAN.md | 19 +- bridge/README.md | 20 ++ bridge/bench/baselines.json | 32 +++ bridge/bench/icount/CMakeLists.txt | 29 ++ bridge/bench/icount/icount_main.cpp | 115 ++++++++ bridge/cmake/arm-cortex-m33-mps2.cmake | 34 +++ bridge/cmake/arm-cortex-m55-mps3.cmake | 46 +++ bridge/cmake/hexagon-linux-musl.cmake | 32 +++ bridge/platform/armv8m_startup.c | 165 +++++++++++ bridge/platform/mps2_an505/mps2_an505.ld | 89 ++++++ bridge/platform/mps3_an547/mps3_an547.ld | 89 ++++++ bridge/scripts/icount.py | 122 ++++++++ bridge/tests/CMakeLists.txt | 40 ++- bridge/tests/bare_metal_main.cpp | 37 +++ bridge/tools/qemu_insn_plugin/insn_count.c | 49 ++++ 19 files changed, 1244 insertions(+), 11 deletions(-) create mode 100644 bridge/bench/baselines.json create mode 100644 bridge/bench/icount/CMakeLists.txt create mode 100644 bridge/bench/icount/icount_main.cpp create mode 100644 bridge/cmake/arm-cortex-m33-mps2.cmake create mode 100644 bridge/cmake/arm-cortex-m55-mps3.cmake create mode 100644 bridge/cmake/hexagon-linux-musl.cmake create mode 100644 bridge/platform/armv8m_startup.c create mode 100644 bridge/platform/mps2_an505/mps2_an505.ld create mode 100644 bridge/platform/mps3_an547/mps3_an547.ld create mode 100644 bridge/scripts/icount.py create mode 100644 bridge/tests/bare_metal_main.cpp create mode 100644 bridge/tools/qemu_insn_plugin/insn_count.c diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index be48dbc..291e161 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -52,3 +52,316 @@ jobs: - name: Test run: ctest --test-dir build --output-on-failure + + # ------------------------------------------------------------------------ + # Embedded matrix (PLAN.md section 7): the M33/M55 eurorack/pedal cores and + # the Hexagon DSP are deployment targets, so every M7 optimization lever is + # gated on them — correctness under emulation here, instruction counts in + # the icount-ratchet job below. Toolchain provenance and pins mirror + # SampleRateTap's CI (the family's embedded story lives there first). + # ------------------------------------------------------------------------ + + hexagon-qemu: + name: Hexagon cross (QEMU) + runs-on: ubuntu-latest + timeout-minutes: 45 + env: + # Prebuilt open-source toolchain (BSD-3) published by Qualcomm/Quicinc; + # binary artifacts are hosted on CodeLinaro and linked from the + # quic/toolchain_for_hexagon release notes. + HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst + # Hard pin, matching SampleRateTap's (verified there against the + # published SHA256SUMS). + HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Cache toolchain + id: cache + uses: actions/cache@v4 + with: + path: ~/hexagon + # Keyed on the pinned digest: every job that can write this key + # verifies its download against the same pin, so no unverified + # writer can poison the trusted entry. + key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 + + - name: Download toolchain + if: steps.cache.outputs.cache-hit != 'true' + run: | + mkdir -p ~/hexagon && cd ~/hexagon + curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" + # Integrity check against the published SHA256SUMS, plus the hard + # pin. The SUMS file catches corruption and cache-poisoning; only + # the pin catches an origin compromise. + curl -sfLo SHA256SUMS "$(dirname "$HEXAGON_TOOLCHAIN_URL")/SHA256SUMS" + expected=$(grep "$(basename "$HEXAGON_TOOLCHAIN_URL")" SHA256SUMS | awk '{print $1}' | head -1) + actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) + echo "toolchain sha256: $actual (pin this in HEXAGON_TOOLCHAIN_SHA256)" + if [ -z "$expected" ] || [ "$actual" != "$expected" ]; then + echo "::error::toolchain does not match published SHA256SUMS"; exit 1 + fi + if [ -n "${HEXAGON_TOOLCHAIN_SHA256:-}" ] && \ + [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then + echo "::error::toolchain checksum mismatch against pinned value"; exit 1 + fi + tar --zstd -xf toolchain.tar.zst + rm toolchain.tar.zst SHA256SUMS + + - name: Set up toolchain and QEMU paths + run: | + # No -type f (symlinks count); dirname of an empty find result is + # ".", so assert on the find output itself. + clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) + test -n "$clangxx" + echo "$(dirname "$clangxx")" >> "$GITHUB_PATH" + # Prefer a qemu-hexagon bundled with the toolchain; else use distro qemu. + qemu=$(find "$HOME/hexagon" -name 'qemu-hexagon' -type f | head -1 || true) + if [ -n "$qemu" ]; then + echo "$(dirname "$qemu")" >> "$GITHUB_PATH" + else + sudo apt-get update -q && sudo apt-get install -y -q qemu-user + fi + + - name: Verify tools + run: | + hexagon-unknown-linux-musl-clang++ --version + qemu-hexagon --version + + - name: Configure + run: > + cmake -B build + -DCMAKE_BUILD_TYPE=Release + -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake + -DTAP_RATIO_BUILD_EXAMPLES=OFF + + - name: Build + run: cmake --build build -j 4 + + - name: Test under emulation + # -j 4: each test is an independent qemu-user process and the + # per-test soft-double table construction dominates, so the suite + # parallelizes cleanly (and serial would crowd the job timeout). + run: > + ctest --test-dir build -j 4 --output-on-failure + -E 'BadProfilesThrow|LatencyAndValidation' + # This static-musl toolchain cannot unwind across frames — the + # constructor throws correctly but EXPECT_THROW never catches and + # libc++abi terminates (same known debt as SampleRateTap's leg). + # Validation is target-independent and covered on every other leg. + + # Cross-compile for Arm Cortex-M55 (bare metal, newlib + semihosting) and + # run the emulation-sized test subset on QEMU's MPS3 AN547 board model. + # Validates the converter on a 32-bit MCU-class target with no OS, no + # threads and no double-precision FPU; the fixed-point datapaths are the + # performance-appropriate formats here. + cortex-m55-qemu: + name: Cortex-M55 cross (QEMU) + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Install toolchain and QEMU + run: > + sudo apt-get update -q && + sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + + - name: Configure + run: > + cmake -B build + -DCMAKE_BUILD_TYPE=MinSizeRel + -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake + -DTAP_RATIO_BUILD_EXAMPLES=OFF + + - name: Build + run: cmake --build build -j 4 + + - name: Test under emulation + run: ctest --test-dir build --output-on-failure + + # Cortex-M33 (Raspberry Pi Pico 2 / RP2350 class: single-precision FPU, + # no FP64, no MVE) on QEMU's MPS2+ AN505 model. Shares the Armv8-M + # startup with the M55 target; quantifies the soft-double design path and + # anchors the Q15/Q31 budgets for Pico-class parts. + cortex-m33-qemu: + name: Cortex-M33 cross (QEMU) + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Install toolchain and QEMU + run: > + sudo apt-get update -q && + sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + + - name: Configure + run: > + cmake -B build + -DCMAKE_BUILD_TYPE=MinSizeRel + -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake + -DTAP_RATIO_BUILD_EXAMPLES=OFF + + - name: Build + run: cmake --build build -j 4 + + - name: Test under emulation + run: ctest --test-dir build --output-on-failure + + # Deterministic instruction-count ratchet (PLAN.md section 7): fixed + # workloads under QEMU with a counting plugin, gated two-sided (±3%) + # against bench/baselines.json. Unlike wall-clock numbers these are + # noise-free, so a hard gate is safe on shared runners — this is the + # measurement harness every M7 lever must move before it merges. + icount-ratchet: + name: Instruction-count ratchet + runs-on: ubuntu-latest + timeout-minutes: 45 + env: + # Commit the v8.2.2 tag pointed at when pinned (tags are movable; + # commit SHAs are not), with the header's digest verified on download. + QEMU_PLUGIN_HEADER_URL: https://raw.githubusercontent.com/qemu/qemu/11aa0b1ff115b86160c4d37e7c37e6a6b13b77ea/include/qemu/qemu-plugin.h + QEMU_PLUGIN_HEADER_SHA256: "c53a2af163e80e3f4bc6c60dbdfc84003db329d757e37cd8a16a77e1d82606ff" + QEMU_SRC_URL: https://download.qemu.org/qemu-8.2.2.tar.xz + QEMU_SRC_SHA256: "847346c1b82c1a54b2c38f6edbd85549edeb17430b7d4d3da12620e2962bc4f3" + HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst + # Same hard pin as the hexagon-qemu job: this job also writes the + # shared toolchain cache, so it must verify against the same digest. + HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" + steps: + - uses: actions/checkout@v4 + with: + submodules: recursive + + - name: Install toolchains and QEMU + run: > + sudo apt-get update -q && + sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + libglib2.0-dev ninja-build meson flex bison + + - name: Build counting plugin + run: | + curl -sfLo /tmp/qemu-plugin.h "$QEMU_PLUGIN_HEADER_URL" + actual=$(sha256sum /tmp/qemu-plugin.h | cut -d' ' -f1) + if [ "$actual" != "$QEMU_PLUGIN_HEADER_SHA256" ]; then + echo "::error::qemu-plugin.h checksum mismatch"; exit 1 + fi + gcc -shared -fPIC $(pkg-config --cflags glib-2.0) -I/tmp \ + -o /tmp/libinsncount.so tools/qemu_insn_plugin/insn_count.c + + # Release (-O2), matching how the baselines were recorded. + - name: Build M55 workloads + run: > + cmake -B build-m55 + -DCMAKE_BUILD_TYPE=Release + -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON + && cmake --build build-m55 -j 4 + + - name: Ratchet M55 + run: > + python3 scripts/icount.py --target m55 + --build-dir build-m55 --plugin /tmp/libinsncount.so + + - name: Build M33 workloads + if: ${{ !cancelled() }} + run: > + cmake -B build-m33 + -DCMAKE_BUILD_TYPE=Release + -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON + && cmake --build build-m33 -j 4 + + - name: Ratchet M33 + if: ${{ !cancelled() }} + run: > + python3 scripts/icount.py --target m33 + --build-dir build-m33 --plugin /tmp/libinsncount.so + + # Neither Debian's nor the CodeLinaro toolchain's qemu-hexagon enables + # TCG plugins, so the Hexagon leg builds its own from the pinned QEMU + # release (linux-user target only, ~4 min, cached thereafter). + # The remaining ratchet steps run even if an earlier target failed + # (each target's numbers are independent evidence; stopping at the + # first failure forces a serial harvest when baselines legitimately + # move). The job still fails if any step failed. + - name: Cache plugin-enabled qemu-hexagon + if: ${{ !cancelled() }} + id: qemu-hex + uses: actions/cache@v4 + with: + path: ~/qemu-hexagon-plugins + key: qemu-hexagon-plugins-${{ env.QEMU_SRC_URL }}-1 + + - name: Build plugin-enabled qemu-hexagon + if: ${{ !cancelled() && steps.qemu-hex.outputs.cache-hit != 'true' }} + run: | + curl -sfLo /tmp/qemu-src.tar.xz "$QEMU_SRC_URL" + actual=$(sha256sum /tmp/qemu-src.tar.xz | cut -d' ' -f1) + echo "qemu source sha256: $actual (pin this in QEMU_SRC_SHA256)" + if [ -n "${QEMU_SRC_SHA256:-}" ] && [ "$actual" != "$QEMU_SRC_SHA256" ]; then + echo "::error::qemu source checksum mismatch"; exit 1 + fi + tar -xJf /tmp/qemu-src.tar.xz -C /tmp + cd /tmp/qemu-*/ + ./configure --target-list=hexagon-linux-user --enable-plugins \ + --disable-docs --disable-tools --disable-system + ninja -C build qemu-hexagon + mkdir -p ~/qemu-hexagon-plugins + cp build/qemu-hexagon ~/qemu-hexagon-plugins/ + + - name: Cache Hexagon toolchain + if: ${{ !cancelled() }} + id: cache + uses: actions/cache@v4 + with: + path: ~/hexagon + # Same digest-keyed name as the hexagon-qemu job; the download + # below verifies the same pin before anything is saved under it. + key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 + + - name: Ratchet Hexagon + if: ${{ !cancelled() }} + run: | + if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then + mkdir -p ~/hexagon && cd ~/hexagon + curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" + actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) + if [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then + echo "::error::toolchain checksum mismatch against pinned value" + exit 1 + fi + tar --zstd -xf toolchain.tar.zst && rm toolchain.tar.zst + cd "$GITHUB_WORKSPACE" + fi + # No -type f: the compiler may be a symlink in the restored tree. + clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) + if [ -z "$clangxx" ]; then + echo "::error::hexagon cross compiler not found under ~/hexagon" + exit 1 + fi + bindir=$(dirname "$clangxx") + export PATH="$HOME/qemu-hexagon-plugins:$bindir:$PATH" + test -x "$HOME/qemu-hexagon-plugins/qemu-hexagon" || { + echo "::error::plugin-enabled qemu-hexagon missing"; exit 1; } + # qemu exits 1 with or without plugin support here (no guest binary + # was given), so probe by the error text, not the exit code. + if qemu-hexagon -plugin help 2>&1 | grep -q "unknown option"; then + echo "::error::built qemu-hexagon lacks plugin support"; exit 1 + fi + cmake -B build-hex -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake \ + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON + cmake --build build-hex -j 4 + python3 scripts/icount.py --target hexagon \ + --build-dir build-hex --plugin /tmp/libinsncount.so diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml index 81c9009..a811718 100644 --- a/bridge/.github/workflows/style.yml +++ b/bridge/.github/workflows/style.yml @@ -22,7 +22,7 @@ jobs: - name: Install tools run: sudo apt-get update && sudo apt-get install -y clang-tidy-18 cmake python3 - name: Configure (compile database) - run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DTAP_RATIO_BUILD_TESTS=ON + run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DTAP_RATIO_BUILD_TESTS=ON -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - name: clang-tidy (project TUs; the submodule and fetched deps excluded) run: | files=$(python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))") diff --git a/bridge/CLAUDE.md b/bridge/CLAUDE.md index 360c4dc..005543d 100644 --- a/bridge/CLAUDE.md +++ b/bridge/CLAUDE.md @@ -14,11 +14,15 @@ which of its decisions were superseded. Read PLAN.md before implementing anythin re-derive decisions it has already settled (compile-time direction, profile vocabulary, the three-leg test strategy, the pinned-eps cross-validation design). -Current state: **v0.1 (M6 complete).** Design/schedule/tables (M2), the streaming converter for -float/Q15/Q31 with committed scipy reference vectors (M3/M4), the golden cross-validation against -SampleRateTap at pinned eps (M5, test-only submodule), and the bluetooth_bridge example + C ABI + -executed demo notebook (M6). Next: the M7+ optimization campaign, gated on the embedded CI matrix -(icount ratchet) landing first — see PLAN.md section 7. +Current state: **v0.1 (M6 complete), M7a measurement harness landed.** Design/schedule/tables +(M2), the streaming converter for float/Q15/Q31 with committed scipy reference vectors (M3/M4), +the golden cross-validation against SampleRateTap at pinned eps (M5, test-only submodule), the +bluetooth_bridge example + C ABI + executed demo notebook (M6), and the embedded CI matrix + +instruction-count ratchet (M7a: Cortex-M33/M55 + Hexagon under QEMU, eight fixed workloads gated +two-sided ±3% against `bench/baselines.json` — `scripts/icount.py`). Next: the M7 levers, one +measured PR each, superblock codegen first — see PLAN.md section 7. Any change that moves a +workload's count beyond ±3% must re-record baselines (`icount.py --update` per target) in the +same PR; an *improvement* beyond tolerance fails the gate too, by design. ## The charter constraints (load-bearing) diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index 14bbe4e..3a88d61 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -34,6 +34,10 @@ endif() # C++ through it — see notebooks/ratio_demo.ipynb). option(TAP_RATIO_BUILD_CAPI "Build the C ABI shared library" OFF) +# Fixed deterministic workloads for the QEMU instruction-count ratchet; +# buildable for any target including bare metal. See PLAN.md section 7. +option(TAP_RATIO_BUILD_ICOUNT_BENCH "Build instruction-count ratchet workloads" OFF) + # SampleRateTap, dev-only (never part of the shipped tap::ratio target): the # golden cross-validation test and the bluetooth_bridge example compose # against its near-unity ASRC. Consumed headers-only via include path — its @@ -74,3 +78,7 @@ endif() if(TAP_RATIO_BUILD_CAPI) add_subdirectory(tools/capi) endif() + +if(TAP_RATIO_BUILD_ICOUNT_BENCH) + add_subdirectory(bench/icount) +endif() diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 67e47db..5dd91e5 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -201,9 +201,26 @@ executed (it measures the shipping C++, not a Python re-implementation). every optimization is measured the family way. Levers already banked: spec relaxation (the `economy` default) and channel vectorization (inherited kernels). + - **M7a — measurement harness (landed).** The embedded matrix runs the + emulation-sized test suite on Cortex-M33 (QEMU mps2-an505), Cortex-M55 + (mps3-an547) and Hexagon (qemu-hexagon user mode, static musl), and the + instruction-count ratchet gates eight fixed workloads (direction × + float/Q15/Q31 on `economy`, plus both `transparent` float legs; 2 s of + stereo virtual audio each) two-sided at ±3% against + `bench/baselines.json` on all three targets — noise-free, so a hard + gate is safe on shared runners. Baselines recorded 2026-07-23 with the + CI-pinned QEMU 8.2.2; re-measurement is bit-identical. The recorded + story already ranks the levers per target: on M33 (Pico-2 class, no + FP64) Q15 runs ~6–10× cheaper than the float path (245 M vs 1.53 B + insns for the up workload), on Hexagon Q15/Q31 are ~5× cheaper than + float, while on M55 float currently *beats* the fixed-point paths + (97 M vs 119 M) — so the fixed-point dot kernels are a named M7 target + on that core, not just the float loop. Toolchains, QEMU source, and the + plugin header are SHA256-pinned, mirroring SampleRateTap's CI. v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — -v0.1 shipped (2026-07-23).** +v0.1 shipped (2026-07-23). M7a measurement harness landed (2026-07-23); +next lever: superblock codegen.** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/README.md b/bridge/README.md index c2820f4..81d9e1c 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -99,6 +99,26 @@ ctest --test-dir build --output-on-failure Consume with `add_subdirectory` (or FetchContent) and link `tap::ratio`; the DspTap submodule rides along automatically. +### Embedded targets and the instruction-count ratchet + +The deployment cores are CI targets, not aspirations: every push runs the +emulation-sized test suite on **Cortex-M33** (QEMU mps2-an505 — Raspberry +Pi Pico 2 class), **Cortex-M55** (mps3-an547) and **Hexagon** +(qemu-hexagon, static musl), and gates eight fixed conversion workloads +(direction × float/Q15/Q31) against committed per-target instruction +counts (`bench/baselines.json`, two-sided ±3% — see `scripts/icount.py`). +The counts are deterministic, so the M7 optimization campaign in +[PLAN.md](PLAN.md) lands one measured lever at a time: + +```sh +cmake -B build-m55 -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake \ + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON +cmake --build build-m55 -j +python3 scripts/icount.py --target m55 --build-dir build-m55 --plugin libinsncount.so +``` + ## License MIT (see [LICENSE](LICENSE)), consistent with the family. Style is the diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json new file mode 100644 index 0000000..10ba284 --- /dev/null +++ b/bridge/bench/baselines.json @@ -0,0 +1,32 @@ +{ + "hexagon": { + "down_float_eco": 414246008, + "down_float_tr": 969773486, + "down_q15_eco": 77017845, + "down_q31_eco": 75022038, + "up_float_eco": 258120188, + "up_float_tr": 555333007, + "up_q15_eco": 50225518, + "up_q31_eco": 51414419 + }, + "m33": { + "down_float_eco": 2474264539, + "down_float_tr": 5949658686, + "down_q15_eco": 376921195, + "down_q31_eco": 559873777, + "up_float_eco": 1525404747, + "up_float_tr": 3380248412, + "up_q15_eco": 245489524, + "up_q31_eco": 357269041 + }, + "m55": { + "down_float_eco": 152072803, + "down_float_tr": 348789510, + "down_q15_eco": 185041635, + "down_q31_eco": 185380666, + "up_float_eco": 96844767, + "up_float_tr": 201986285, + "up_q15_eco": 118784384, + "up_q31_eco": 119885904 + } +} diff --git a/bridge/bench/icount/CMakeLists.txt b/bridge/bench/icount/CMakeLists.txt new file mode 100644 index 0000000..b5cace2 --- /dev/null +++ b/bridge/bench/icount/CMakeLists.txt @@ -0,0 +1,29 @@ +# One binary per scenario (name:dir:type:profile:channels): bare-metal +# targets have no argv, and per-binary instruction totals are what the +# ratchet compares. Economy is the speed-first default the M7 levers are +# judged on, in every format and both directions; the two transparent legs +# keep the pristine profile honest without tripling the matrix. +set(_ratio_icount_scenarios + up_float_eco:0:0:0:2 + down_float_eco:1:0:0:2 + up_q15_eco:0:1:0:2 + down_q15_eco:1:1:0:2 + up_q31_eco:0:2:0:2 + down_q31_eco:1:2:0:2 + up_float_tr:0:0:1:2 + down_float_tr:1:0:1:2) + +foreach(_sc IN LISTS _ratio_icount_scenarios) + string(REPLACE ":" ";" _parts "${_sc}") + list(GET _parts 0 _name) + list(GET _parts 1 _dir) + list(GET _parts 2 _type) + list(GET _parts 3 _profile) + list(GET _parts 4 _ch) + add_executable(ratio_icount_${_name} icount_main.cpp) + target_compile_definitions(ratio_icount_${_name} PRIVATE + RATIO_SC_DIR=${_dir} RATIO_SC_TYPE=${_type} + RATIO_SC_PROFILE=${_profile} RATIO_SC_CH=${_ch}) + target_link_libraries(ratio_icount_${_name} PRIVATE + tap::ratio tap_ratio_warnings) +endforeach() diff --git a/bridge/bench/icount/icount_main.cpp b/bridge/bench/icount/icount_main.cpp new file mode 100644 index 0000000..cedfb27 --- /dev/null +++ b/bridge/bench/icount/icount_main.cpp @@ -0,0 +1,115 @@ +// Deterministic fixed workloads for the instruction-count ratchet +// (PLAN.md section 7): every M7 optimization lever must move these numbers, +// measured, before it merges. One scenario per binary, selected at compile +// time because bare-metal targets have no argv. The qemu plugin counts the +// whole run including table construction; the streaming loop is sized to +// dominate. The checksum both defeats dead-code elimination and pins down +// cross-run determinism. +// +// RATIO_SC_DIR: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1) +// RATIO_SC_TYPE: 0 = float, 1 = Q15, 2 = Q31 +// RATIO_SC_PROFILE: 0 = economy, 1 = transparent +// RATIO_SC_CH: channel count (default 2) +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#include +#include +#include +#include +#include +#include +#include + +#include "tap/ratio/ratio.h" + +namespace { + +#ifndef RATIO_SC_CH +#define RATIO_SC_CH 2 +#endif + + template + S make_sample(double v) { + if constexpr (std::is_floating_point_v) { + return static_cast(v); + } + else { + return tap::dsp::detail::round_sat(v * static_cast(std::numeric_limits::max())); + } + } + + // Interleaved sine block at the scenario's input rate; precomputed so + // libm's sin() stays out of the measured loop (on the soft-double + // targets it would otherwise dominate and mask lever improvements). + template + std::vector sine_block(std::size_t frames, std::size_t channels, double freq_hz, double rate, double amp) { + std::vector out(frames * channels); + const double w = 2.0 * std::numbers::pi * freq_hz / rate; + for (std::size_t i = 0; i < frames; ++i) { + const S v = make_sample(amp * std::sin(w * static_cast(i))); + for (std::size_t c = 0; c < channels; ++c) { + out[i * channels + c] = v; + } + } + return out; + } + + template + double run() { + using tap::ratio::direction; +#if RATIO_SC_DIR == 0 + constexpr direction k_dir = direction::up_to_48k; + constexpr double k_rate_in = 44100.0; +#else + constexpr direction k_dir = direction::down_to_44k1; + constexpr double k_rate_in = 48000.0; +#endif +#if RATIO_SC_PROFILE == 0 + const tap::ratio::profile k_prof = tap::ratio::profile::economy(); +#else + const tap::ratio::profile k_prof = tap::ratio::profile::transparent(); +#endif + constexpr std::size_t k_ch = RATIO_SC_CH; + constexpr std::size_t k_block = 32; + + tap::ratio::basic_converter conv(k_ch, k_prof); + + // 0.25 s of input, cycled block-aligned (12000 % 32 == 0, so the + // waveform seam repeats identically every cycle: deterministic). + const auto input = sine_block(12000, k_ch, 997.0, k_rate_in, 0.5); + + // outputs_for(k_block) never exceeds ceil(32 * 160/147) + 1 = 36 + // frames in the up direction; 64 leaves inarguable headroom. + std::vector out(64 * k_ch); + + double sink = 0.0; + std::size_t off = 0; + const std::size_t blocks = 2 * static_cast(k_rate_in) / k_block; // 2 s of virtual audio + for (std::size_t b = 0; b < blocks; ++b) { + const std::size_t made = conv.process(input.data() + off, k_block, out.data()); + if (made > 64) { + return std::numeric_limits::quiet_NaN(); // poisons the checksum + } + off += k_block * k_ch; + if (off + k_block * k_ch > input.size()) { + off = 0; + } + sink += static_cast(out[0]) + static_cast(made); + } + return sink; + } + +} // namespace + +int main() { +#if RATIO_SC_TYPE == 0 + const double checksum = run(); +#elif RATIO_SC_TYPE == 1 + const double checksum = run(); +#else + const double checksum = run(); +#endif + const bool ok = checksum == checksum; // NaN check + std::printf("RATIO_ICOUNT_DONE ok=%d checksum=%.17g\n", ok ? 1 : 0, checksum); + return ok ? 0 : 1; +} diff --git a/bridge/cmake/arm-cortex-m33-mps2.cmake b/bridge/cmake/arm-cortex-m33-mps2.cmake new file mode 100644 index 0000000..9e4e06f --- /dev/null +++ b/bridge/cmake/arm-cortex-m33-mps2.cmake @@ -0,0 +1,34 @@ +# Cross-compilation toolchain for Arm Cortex-M33 (bare metal, newlib + +# semihosting), executed on QEMU's MPS2+ AN505 board model. This is the +# Raspberry Pi Pico 2 (RP2350) class of core: single-precision FPU only, so +# all double arithmetic is soft-float — the fixed-point Q15/Q31 datapaths +# are the intended formats here, and the icount baselines quantify exactly +# what the float path costs. Ported from SampleRateTap's cmake/. +# +# Usage: +# cmake -B build -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake \ +# -DTAP_RATIO_BUILD_EXAMPLES=OFF ... +set(CMAKE_SYSTEM_NAME Generic) +set(CMAKE_SYSTEM_PROCESSOR arm) + +set(CMAKE_C_COMPILER arm-none-eabi-gcc) +set(CMAKE_CXX_COMPILER arm-none-eabi-g++) +set(CMAKE_TRY_COMPILE_TARGET_TYPE STATIC_LIBRARY) + +set(CMAKE_C_FLAGS_INIT "-mcpu=cortex-m33 -mthumb -mfloat-abi=hard -ffunction-sections -fdata-sections") +set(CMAKE_CXX_FLAGS_INIT "${CMAKE_C_FLAGS_INIT}") + +get_filename_component(_tap_platform "${CMAKE_CURRENT_LIST_DIR}/../platform" ABSOLUTE) +set(CMAKE_EXE_LINKER_FLAGS_INIT + "--specs=rdimon.specs -nostartfiles -Wl,--gc-sections -T${_tap_platform}/mps2_an505/mps2_an505.ld -x c ${_tap_platform}/armv8m_startup.c -x none") + +set(CMAKE_CROSSCOMPILING_EMULATOR + "qemu-system-arm;-M;mps2-an505;-nographic;-semihosting;-kernel") + +set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) + +# One-shot CTest mode (no argv on bare metal; see tests/CMakeLists.txt). +set(TAP_RATIO_BARE_METAL ON) diff --git a/bridge/cmake/arm-cortex-m55-mps3.cmake b/bridge/cmake/arm-cortex-m55-mps3.cmake new file mode 100644 index 0000000..b9922a3 --- /dev/null +++ b/bridge/cmake/arm-cortex-m55-mps3.cmake @@ -0,0 +1,46 @@ +# Cross-compilation toolchain for Arm Cortex-M55, bare metal (newlib + +# semihosting), executed on QEMU's MPS3 AN547 board model. Ported from +# SampleRateTap's cmake/ (the family's embedded targets share one story). +# +# Usage: +# cmake -B build -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake \ +# -DTAP_RATIO_BUILD_EXAMPLES=OFF ... +# with arm-none-eabi-g++ and qemu-system-arm on PATH. +# +# Notes: +# - Bare metal: no std::thread (the test build adapts; see +# tests/CMakeLists.txt and TAP_RATIO_BARE_METAL below). +# - The M55 FPU has no double precision, so the double-typed design path +# (table construction) runs soft-float here: correctness coverage, not a +# performance measurement. The float/Q15/Q31 process loops are hard-FP. +set(CMAKE_SYSTEM_NAME Generic) +set(CMAKE_SYSTEM_PROCESSOR arm) + +set(CMAKE_C_COMPILER arm-none-eabi-gcc) +set(CMAKE_CXX_COMPILER arm-none-eabi-g++) +set(CMAKE_TRY_COMPILE_TARGET_TYPE STATIC_LIBRARY) + +set(CMAKE_C_FLAGS_INIT "-mcpu=cortex-m55 -mthumb -mfloat-abi=hard -ffunction-sections -fdata-sections") +set(CMAKE_CXX_FLAGS_INIT "${CMAKE_C_FLAGS_INIT}") + +get_filename_component(_tap_platform "${CMAKE_CURRENT_LIST_DIR}/../platform/mps3_an547" ABSOLUTE) +# The startup .c is handed to the link line directly; the gcc driver +# compiles it with the same -mcpu/-mfloat-abi flags as everything else. +# `-x c` forces C compilation even under the g++ driver (which would treat +# a .c link input as C++): C guarantees the vector table's address-constant +# initializers are link-time constants, never dynamic initialization. +set(CMAKE_EXE_LINKER_FLAGS_INIT + "--specs=rdimon.specs -nostartfiles -Wl,--gc-sections -T${_tap_platform}/mps3_an547.ld -x c ${CMAKE_CURRENT_LIST_DIR}/../platform/armv8m_startup.c -x none") + +set(CMAKE_CROSSCOMPILING_EMULATOR + "qemu-system-arm;-M;mps3-an547;-nographic;-semihosting;-kernel") + +set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) + +# Switches the test harness to one-shot mode: a single registered CTest test +# running the whole (emulation-sized) suite, judged by gtest's summary text +# rather than the exit code, which semihosting does not reliably propagate. +set(TAP_RATIO_BARE_METAL ON) diff --git a/bridge/cmake/hexagon-linux-musl.cmake b/bridge/cmake/hexagon-linux-musl.cmake new file mode 100644 index 0000000..6a096fc --- /dev/null +++ b/bridge/cmake/hexagon-linux-musl.cmake @@ -0,0 +1,32 @@ +# Cross-compilation toolchain for Qualcomm Hexagon (Linux/musl), using the +# open-source toolchain from https://github.com/quic/toolchain_for_hexagon +# with tests executed under qemu-hexagon user-mode emulation. Ported from +# SampleRateTap's cmake/. +# +# Usage: +# cmake -B build -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake ... +# with hexagon-unknown-linux-musl-clang++ and qemu-hexagon on PATH. +# +# Note: emulation validates ISA-level *correctness* (32-bit size_t, atomics +# lowering, musl libc), not performance. Caveat: under this static-musl +# configuration C++ exceptions terminate (libc++abi) instead of +# propagating — constructor validation errors are fatal here, so the +# EXPECT_THROW tests are excluded on this leg (validation is +# target-independent and covered everywhere else). Hexagon has no +# double-precision FPU, so the double-heavy design path runs soft-float. +# Cycle counts need the Hexagon SDK simulator. +set(CMAKE_SYSTEM_NAME Linux) +set(CMAKE_SYSTEM_PROCESSOR hexagon) + +set(CMAKE_C_COMPILER hexagon-unknown-linux-musl-clang) +set(CMAKE_CXX_COMPILER hexagon-unknown-linux-musl-clang++) + +# Static linking so the emulator needs no sysroot/loader configuration. +set(CMAKE_EXE_LINKER_FLAGS_INIT "-static") + +set(CMAKE_CROSSCOMPILING_EMULATOR qemu-hexagon) + +set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) diff --git a/bridge/platform/armv8m_startup.c b/bridge/platform/armv8m_startup.c new file mode 100644 index 0000000..9126907 --- /dev/null +++ b/bridge/platform/armv8m_startup.c @@ -0,0 +1,165 @@ +/* Minimal bare-metal startup for Armv8-M targets under QEMU (Cortex-M55 + * on mps3-an547, Cortex-M33 on mps2-an505), + * replacing the toolchain crt0 (linked with -nostartfiles): + * - vector table with initial stack pointer and Reset_Handler + * - FPU/MVE coprocessor enable before any FP instruction executes + * - .bss zeroing (QEMU's ELF loader already placed .data) + * - semihosting stdio (librdimon), C++ static constructors, main, exit + * - deterministic _sbrk over the linker-defined heap region (overrides + * librdimon's weak version, whose limit depends on the semihosting + * SYS_HEAPINFO call returning sensible values for this board) + * - 64-bit __atomic_* helpers: M-profile has no 64-bit exclusives and the + * bare-metal toolchain ships no libatomic; PRIMASK critical sections + * are sufficient on a single-core part. RatioTap itself is atomics-free; + * these serve the SampleRateTap dev dependency (the cross-validation + * test composes against its ring buffer). + * + * Ported from SampleRateTap's platform/armv8m_startup.c (the family's + * embedded targets share one startup story; divergences land there first). + * + * The toolchain file passes this to the link line with `-x c`: under C the + * vector table's address-constant initializers are guaranteed link-time + * constants (a C++ compile could legally lower them to dynamic + * initialization, leaving the table zeroed at reset). The extern "C" + * guards keep the file safe if it is ever compiled as C++ anyway. + * + * Only the __atomic_* helpers the linked code currently needs are + * provided; any future use of others (e.g. compare-exchange) fails loudly + * at link time. + */ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#include +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +extern uint32_t __bss_start__, __bss_end__; +extern uint32_t __stack_top; +extern uint32_t __stack_limit; +extern char __heap_start__, __heap_end__; + +extern void __libc_init_array(void); +extern void initialise_monitor_handles(void); +extern int main(int argc, char** argv); +extern void exit(int) __attribute__((noreturn)); + +void* __dso_handle; + +/* Referenced by newlib's fini machinery; nothing to do without crti/crtn. */ +void _init(void) {} +void _fini(void) {} + +void* _sbrk(ptrdiff_t increment) { + static char* brk = &__heap_start__; + if (brk + increment > &__heap_end__) { + errno = ENOMEM; + return (void*)-1; + } + char* prev = brk; + brk += increment; + return prev; +} + +static inline uint32_t irqLock(void) { + uint32_t primask; + __asm volatile("mrs %0, PRIMASK\n cpsid i" : "=r"(primask)::"memory"); + return primask; +} + +static inline void irqRestore(uint32_t primask) { + __asm volatile("msr PRIMASK, %0" ::"r"(primask) : "memory"); +} + +uint64_t __atomic_load_8(const volatile void* ptr, int memorder) { + (void)memorder; + const uint32_t m = irqLock(); + const uint64_t v = *(const volatile uint64_t*)ptr; + irqRestore(m); + return v; +} + +void __atomic_store_8(volatile void* ptr, uint64_t value, int memorder) { + (void)memorder; + const uint32_t m = irqLock(); + *(volatile uint64_t*)ptr = value; + irqRestore(m); +} + +uint64_t __atomic_fetch_add_8(volatile void* ptr, uint64_t value, int memorder) { + (void)memorder; + const uint32_t m = irqLock(); + const uint64_t prev = *(volatile uint64_t*)ptr; + *(volatile uint64_t*)ptr = prev + value; + irqRestore(m); + return prev; +} + +uint64_t __atomic_exchange_8(volatile void* ptr, uint64_t value, int memorder) { + (void)memorder; + const uint32_t m = irqLock(); + const uint64_t prev = *(volatile uint64_t*)ptr; + *(volatile uint64_t*)ptr = value; + irqRestore(m); + return prev; +} + +void Reset_Handler(void) { + /* MSPLIM exists on Armv8-M Mainline only (both targets are M33/M55 + * class): a main-stack overflow past __stack_limit raises a fault + * instead of silently corrupting whatever sits below the stack. */ + __asm volatile("msr msplim, %0" ::"r"(&__stack_limit)); + + /* Grant full access to CP10/CP11 (scalar FPU + MVE) first: code below + * may legitimately use FP registers once newlib is involved. */ + volatile uint32_t* const cpacr = (volatile uint32_t*)0xE000ED88u; + *cpacr |= 0xFu << 20; + __asm volatile("dsb\n isb" ::: "memory"); + + memset(&__bss_start__, 0, (size_t)((char*)&__bss_end__ - (char*)&__bss_start__)); + + initialise_monitor_handles(); /* semihosting stdin/stdout/stderr */ + __libc_init_array(); /* C++ static constructors */ + exit(main(0, (char**)0)); +} + +void Default_Handler(void) { + for (;;) { + /* Faults park here; the test harness times out and fails the run. */ + } +} + +void HardFault_Handler(void) { + /* Distinct park loop so a HardFault (e.g. MSPLIM violation escalation) + * is distinguishable from other parked vectors under a debugger. */ + __asm volatile("bkpt #0"); + for (;;) { + } +} + +__attribute__((section(".vectors"), used)) static const uintptr_t vectors[16] = { + (uintptr_t)&__stack_top, + (uintptr_t)&Reset_Handler, + (uintptr_t)&Default_Handler, /* NMI */ + (uintptr_t)&HardFault_Handler, /* HardFault */ + (uintptr_t)&Default_Handler, /* MemManage */ + (uintptr_t)&Default_Handler, /* BusFault */ + (uintptr_t)&Default_Handler, /* UsageFault */ + (uintptr_t)&Default_Handler, /* SecureFault */ + 0, + 0, + 0, + (uintptr_t)&Default_Handler, /* SVCall */ + (uintptr_t)&Default_Handler, /* DebugMonitor */ + 0, + (uintptr_t)&Default_Handler, /* PendSV */ + (uintptr_t)&Default_Handler, /* SysTick */ +}; + +#ifdef __cplusplus +} /* extern "C" */ +#endif diff --git a/bridge/platform/mps2_an505/mps2_an505.ld b/bridge/platform/mps2_an505/mps2_an505.ld new file mode 100644 index 0000000..433390f --- /dev/null +++ b/bridge/platform/mps2_an505/mps2_an505.ld @@ -0,0 +1,89 @@ +/* Linker script for the Arm MPS2+ AN505 FPGA image (Cortex-M33) as modeled + * by QEMU's mps2-an505 machine. The board boots secure with the initial + * vector table at the secure alias 0x1000_0000, so everything is placed in + * the secure aliases. QEMU's -kernel loader places the ELF directly into + * RAM (VMA == LMA, no load-time copy). + * Ported from SampleRateTap's platform tree (shared embedded story). + * + * Memory map (QEMU model, secure aliases): + * SSRAM1 4 MB @ 0x10000000 - vector table + code + rodata + * SSRAM2/3 4 MB @ 0x38000000 - data + bss + heap + stack + */ +MEMORY +{ + CODE (rx) : ORIGIN = 0x10000000, LENGTH = 4M + DATA (rw) : ORIGIN = 0x38000000, LENGTH = 4M +} + +__stack_top = ORIGIN(DATA) + LENGTH(DATA); + +ENTRY(Reset_Handler) + +SECTIONS +{ + .vectors : { + KEEP(*(.vectors)) + } > CODE + + .text : { + *(.text*) + *(.rodata*) + KEEP(*(.init)) + KEEP(*(.fini)) + } > CODE + + .ARM.extab : { + *(.ARM.extab* .gnu.linkonce.armextab.*) + } > CODE + + .ARM.exidx : { + __exidx_start = .; + *(.ARM.exidx* .gnu.linkonce.armexidx.*) + __exidx_end = .; + } > CODE + + .preinit_array : { + PROVIDE_HIDDEN(__preinit_array_start = .); + KEEP(*(.preinit_array*)) + PROVIDE_HIDDEN(__preinit_array_end = .); + } > CODE + + .init_array : { + PROVIDE_HIDDEN(__init_array_start = .); + KEEP(*(SORT(.init_array.*))) + KEEP(*(.init_array*)) + PROVIDE_HIDDEN(__init_array_end = .); + } > CODE + + .fini_array : { + PROVIDE_HIDDEN(__fini_array_start = .); + KEEP(*(SORT(.fini_array.*))) + KEEP(*(.fini_array*)) + PROVIDE_HIDDEN(__fini_array_end = .); + } > CODE + + .data : { + *(.data*) + } > DATA + + .bss (NOLOAD) : { + __bss_start__ = .; + *(.bss*) + *(COMMON) + __bss_end__ = .; + } > DATA + + /* Stack lives at the top of DATA; cap the heap 64 KB below it. */ + .heap (NOLOAD) : ALIGN(8) { + __heap_start__ = .; + . = ORIGIN(DATA) + LENGTH(DATA) - 64K; + __heap_end__ = .; + } > DATA + + /* MSPLIM (set in Reset_Handler): the stack may descend to the heap cap + * but no further — overflow into the heap faults instead of corrupting. */ + __stack_limit = __heap_end__; + + /* librdimon's (unused, weak) _sbrk references `end`; satisfy it. */ + PROVIDE(end = __heap_start__); +} diff --git a/bridge/platform/mps3_an547/mps3_an547.ld b/bridge/platform/mps3_an547/mps3_an547.ld new file mode 100644 index 0000000..9b2ba68 --- /dev/null +++ b/bridge/platform/mps3_an547/mps3_an547.ld @@ -0,0 +1,89 @@ +/* Linker script for the Arm MPS3 AN547 FPGA image (Cortex-M55) as modeled + * by QEMU's mps3-an547 machine. The ELF is loaded directly into RAM by + * QEMU's -kernel loader, so .data needs no load-time copy (VMA == LMA). + * Ported from SampleRateTap's platform tree (shared embedded story). + * + * Memory map (QEMU model): + * ITCM 512 KB @ 0x00000000 - vector table (VTOR resets to 0) + * SRAM 2 MB @ 0x01000000 - code + read-only data + * DTCM 512 KB @ 0x20000000 - stack + * ISRAM 2 MB @ 0x21000000 - data + bss + heap + */ +MEMORY +{ + ITCM (rx) : ORIGIN = 0x00000000, LENGTH = 512K + CODE (rx) : ORIGIN = 0x01000000, LENGTH = 2M + DTCM (rw) : ORIGIN = 0x20000000, LENGTH = 512K + DATA (rw) : ORIGIN = 0x21000000, LENGTH = 2M +} + +__stack_top = ORIGIN(DTCM) + LENGTH(DTCM); +/* MSPLIM (set in Reset_Handler): the stack owns all of DTCM, so the lowest + * address it may legally reach is the region base. */ +__stack_limit = ORIGIN(DTCM); + +ENTRY(Reset_Handler) + +SECTIONS +{ + .vectors : { + KEEP(*(.vectors)) + } > ITCM + + .text : { + *(.text*) + *(.rodata*) + KEEP(*(.init)) + KEEP(*(.fini)) + } > CODE + + .ARM.extab : { + *(.ARM.extab* .gnu.linkonce.armextab.*) + } > CODE + + .ARM.exidx : { + __exidx_start = .; + *(.ARM.exidx* .gnu.linkonce.armexidx.*) + __exidx_end = .; + } > CODE + + .preinit_array : { + PROVIDE_HIDDEN(__preinit_array_start = .); + KEEP(*(.preinit_array*)) + PROVIDE_HIDDEN(__preinit_array_end = .); + } > CODE + + .init_array : { + PROVIDE_HIDDEN(__init_array_start = .); + KEEP(*(SORT(.init_array.*))) + KEEP(*(.init_array*)) + PROVIDE_HIDDEN(__init_array_end = .); + } > CODE + + .fini_array : { + PROVIDE_HIDDEN(__fini_array_start = .); + KEEP(*(SORT(.fini_array.*))) + KEEP(*(.fini_array*)) + PROVIDE_HIDDEN(__fini_array_end = .); + } > CODE + + .data : { + *(.data*) + } > DATA + + .bss (NOLOAD) : { + __bss_start__ = .; + *(.bss*) + *(COMMON) + __bss_end__ = .; + } > DATA + + .heap (NOLOAD) : ALIGN(8) { + __heap_start__ = .; + . = ORIGIN(DATA) + LENGTH(DATA); + __heap_end__ = .; + } > DATA + + /* librdimon's (unused, weak) _sbrk references `end`; satisfy it. */ + PROVIDE(end = __heap_start__); +} diff --git a/bridge/scripts/icount.py b/bridge/scripts/icount.py new file mode 100644 index 0000000..e7ee931 --- /dev/null +++ b/bridge/scripts/icount.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +"""Deterministic instruction-count ratchet (PLAN.md section 7). + +Runs every ratio_icount_* binary in a build directory under QEMU with the +instruction-counting plugin, then compares against bench/baselines.json. + + icount.py --target {hexagon,m55,m33} --build-dir DIR --plugin LIB [--update] + [--baselines bench/baselines.json] [--tolerance 0.03] + +The gate is two-sided: exit nonzero if any scenario regresses beyond +tolerance, improves beyond tolerance (the baseline must be re-recorded so +the gate stays tight), or has no recorded baseline. --update rewrites the +target's entry to exactly the measured scenarios instead. + +Ported from SampleRateTap's scripts/icount.py (marker strings and binary +prefix renamed for this repo). +""" +import argparse +import glob +import json +import os +import pathlib +import re +import subprocess +import sys + + +def qemu_cmd(target: str, plugin: str, binary: str) -> list[str]: + # "-d plugin" routes qemu_plugin_outs() to stderr; without it the count + # line is silently dropped. + if target == "hexagon": + return ["qemu-hexagon", "-d", "plugin", "-plugin", plugin, binary] + if target == "m55": + return ["qemu-system-arm", "-M", "mps3-an547", "-nographic", + "-semihosting", "-d", "plugin", "-plugin", plugin, + "-kernel", binary] + if target == "m33": + return ["qemu-system-arm", "-M", "mps2-an505", "-nographic", + "-semihosting", "-d", "plugin", "-plugin", plugin, + "-kernel", binary] + raise SystemExit(f"unknown target {target}") + + +def measure(target: str, plugin: str, binary: str) -> int: + try: + proc = subprocess.run(qemu_cmd(target, plugin, binary), timeout=600, + capture_output=True, text=True) + except subprocess.TimeoutExpired: + raise SystemExit(f"{binary}: timed out after 600 s under QEMU") + out = proc.stdout + proc.stderr + if "RATIO_ICOUNT_DONE ok=1" not in out: + print(out, file=sys.stderr) + raise SystemExit(f"{binary}: workload did not complete cleanly") + m = re.search(r"RATIO_INSN_COUNT (\d+)", out) + if not m: + print(out, file=sys.stderr) + raise SystemExit(f"{binary}: no RATIO_INSN_COUNT (plugin not loaded?)") + return int(m.group(1)) + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--target", required=True, choices=["hexagon", "m55", "m33"]) + ap.add_argument("--build-dir", required=True) + ap.add_argument("--plugin", required=True) + ap.add_argument("--baselines", default="bench/baselines.json") + ap.add_argument("--tolerance", type=float, default=0.03) + ap.add_argument("--update", action="store_true") + args = ap.parse_args() + + binaries = sorted(glob.glob(os.path.join(args.build_dir, "**", "ratio_icount_*"), + recursive=True)) + binaries = [b for b in binaries if os.access(b, os.X_OK) and os.path.isfile(b)] + if not binaries: + raise SystemExit(f"no ratio_icount_* binaries under {args.build_dir}") + + path = pathlib.Path(args.baselines) + baselines = json.loads(path.read_text()) if path.exists() else {} + base = baselines.get(args.target, {}) + + failures = [] + measured = {} + for binary in binaries: + scenario = os.path.basename(binary).removeprefix("ratio_icount_") + count = measure(args.target, args.plugin, binary) + measured[scenario] = count + recorded = base.get(scenario) + if recorded is None: + print(f"{scenario}: {count} insns (NO BASELINE — commit this value)") + if not args.update: + failures.append(scenario) + elif recorded == 0: + print(f"{scenario}: {count} insns vs baseline 0 (INVALID BASELINE)") + failures.append(scenario) + else: + delta = (count - recorded) / recorded + verdict = "ok" + if delta > args.tolerance: + verdict = "REGRESSION" + failures.append(scenario) + elif delta < -args.tolerance: + # Two-sided: a stale (too-high) baseline would let future + # regressions hide inside the slack, so improvements must be + # committed too. + verdict = ("IMPROVED beyond tolerance — run icount.py --update " + "and commit bench/baselines.json") + failures.append(scenario) + print(f"{scenario}: {count} insns vs baseline {recorded} " + f"({delta:+.2%}) {verdict}") + + if args.update: + # Exactly the measured scenarios: stale keys for renamed/removed + # workloads must not linger as dead gate entries. + baselines[args.target] = measured + path.write_text(json.dumps(baselines, indent=2, sort_keys=True) + "\n") + print(f"updated {path}") + return 0 + return 1 if failures else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index 5d00f5d..a5db822 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -1,9 +1,22 @@ include(FetchContent) -find_package(Threads QUIET) +# Threads are optional: bare-metal targets (Cortex-M33/M55) have no +# std::thread, so GoogleTest is built single-threaded there. The probe is +# skipped entirely on bare metal: newlib ships pthread.h/regex.h stub +# headers that make POSIX feature detection succeed spuriously. +if(NOT TAP_RATIO_BARE_METAL) + find_package(Threads QUIET) +endif() if(NOT Threads_FOUND) set(gtest_disable_pthreads ON CACHE BOOL "" FORCE) endif() +if(TAP_RATIO_BARE_METAL) + # Value-checked feature macros only: GTEST_HAS_DEATH_TEST is tested with + # #ifdef in gtest 1.14, so it must stay undefined (no GTEST_OS_* is + # detected on bare metal, which disables it). + add_compile_definitions(GTEST_HAS_POSIX_RE=0 GTEST_HAS_STREAM_REDIRECTION=0 + GTEST_HAS_FILE_SYSTEM=0) +endif() FetchContent_Declare( googletest @@ -30,7 +43,26 @@ target_link_libraries(tap_ratio_tests PRIVATE tap::ratio srt_headers tap_ratio_warnings) +if(Threads_FOUND) + target_link_libraries(tap_ratio_tests PRIVATE Threads::Threads) +endif() -target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) -include(GoogleTest) -gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) +if(TAP_RATIO_BARE_METAL) + # One-shot mode (Cortex-M33/M55 under qemu-system-arm): a custom main + # bakes in the emulation-sized filter (no argv on the target), and the + # run is judged on gtest's summary text because semihosting does not + # reliably propagate exit codes through the emulator. + target_sources(tap_ratio_tests PRIVATE bare_metal_main.cpp) + target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest) + add_test(NAME tap_ratio_tests_emulated COMMAND tap_ratio_tests) + set_tests_properties(tap_ratio_tests_emulated PROPERTIES + PASS_REGULAR_EXPRESSION "TAP_RATIO_TESTS_COMPLETE rc=0" + FAIL_REGULAR_EXPRESSION "\\[ FAILED \\]" + TIMEOUT 1800) +else() + target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) + # Generous timeouts: discovery and the DSP-measurement tests run orders + # of magnitude slower under instruction-set emulation (e.g. qemu-hexagon). + include(GoogleTest) + gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) +endif() diff --git a/bridge/tests/bare_metal_main.cpp b/bridge/tests/bare_metal_main.cpp new file mode 100644 index 0000000..79381ad --- /dev/null +++ b/bridge/tests/bare_metal_main.cpp @@ -0,0 +1,37 @@ +// Test runner main for bare-metal emulated targets (Cortex-M33/M55 under +// qemu-system-arm): there is no argv on the target, so the +// emulation-appropriate filter is baked in. Excluded are the measurement +// suites — seconds of soft-double virtual audio (sine fits, stopband +// sweeps, the SampleRateTap cross-validation) that prove target-independent +// DSP math already covered on every host platform — keeping the on-target +// run focused on datapath correctness: exact accounting from every phase, +// impulse/table identity, the committed scipy vectors sample-for-sample, +// fixed-point DC exactness and wrap safety, flush/reset/pull contracts. +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#include + +#include + +int main() { + ::testing::GTEST_FLAG(filter) = "-CrossValidation.*:Design.*MeetsSpec:" + "Converter.PassbandSine*:Converter.StopbandTone*:" + "FixedPoint.*SineQuality*"; + ::testing::InitGoogleTest(); + const int rc = RUN_ALL_TESTS(); + // A filter typo selects zero tests and RUN_ALL_TESTS() returns 0 — an + // empty run must not pass green. Checked after the run because gtest + // only applies the filter inside RUN_ALL_TESTS (the count reads 0 + // before it). The on-target selection is ~42 tests; 25 leaves headroom + // for legitimate removals without masking a typo. + const int selected = ::testing::UnitTest::GetInstance()->test_to_run_count(); + if (selected < 25) { + std::printf("only %d tests selected (expected >= 25): filter is broken\n", selected); + std::printf("TAP_RATIO_TESTS_COMPLETE rc=1\n"); + return 1; + } + // CTest's pass criterion: printed only if we get all the way here, so a + // crash after gtest's summary cannot register as a pass. + std::printf("TAP_RATIO_TESTS_COMPLETE rc=%d\n", rc); + return rc; +} diff --git a/bridge/tools/qemu_insn_plugin/insn_count.c b/bridge/tools/qemu_insn_plugin/insn_count.c new file mode 100644 index 0000000..337ace8 --- /dev/null +++ b/bridge/tools/qemu_insn_plugin/insn_count.c @@ -0,0 +1,49 @@ +/* Minimal QEMU TCG plugin: counts executed guest instructions and prints + * one line at exit: + * + * RATIO_INSN_COUNT + * + * Used by scripts/icount.py for the deterministic performance ratchet + * (PLAN.md section 7). Counting uses the inline-add fast path; the single + * counter is exact for our single-vCPU deterministic workloads. Ported from + * SampleRateTap's tools/qemu_insn_plugin/ with the marker renamed. + * + * Build (qemu-plugin.h fetched for the matching QEMU 8.2.x; API v2): + * gcc -shared -fPIC $(pkg-config --cflags glib-2.0) \ + * -I insn_count.c -o libinsncount.so + */ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +#include +#include +#include +#include + +QEMU_PLUGIN_EXPORT int qemu_plugin_version = QEMU_PLUGIN_VERSION; + +static uint64_t insn_count; + +static void tb_trans(qemu_plugin_id_t id, struct qemu_plugin_tb* tb) { + (void)id; + size_t n = qemu_plugin_tb_n_insns(tb); + for (size_t i = 0; i < n; i++) { + struct qemu_plugin_insn* insn = qemu_plugin_tb_get_insn(tb, i); + qemu_plugin_register_vcpu_insn_exec_inline(insn, QEMU_PLUGIN_INLINE_ADD_U64, &insn_count, 1); + } +} + +static void at_exit(qemu_plugin_id_t id, void* userdata) { + (void)id; + (void)userdata; + g_autofree gchar* msg = g_strdup_printf("RATIO_INSN_COUNT %" PRIu64 "\n", insn_count); + qemu_plugin_outs(msg); +} + +QEMU_PLUGIN_EXPORT int qemu_plugin_install(qemu_plugin_id_t id, const qemu_info_t* info, int argc, char** argv) { + (void)info; + (void)argc; + (void)argv; + qemu_plugin_register_vcpu_tb_trans_cb(id, tb_trans); + qemu_plugin_register_atexit_cb(id, at_exit, NULL); + return 0; +} From 240fb38dc167624fb97117f922df36999684e176 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 01:08:40 +0000 Subject: [PATCH 14/44] =?UTF-8?q?M7b:=20superblock=20codegen=20=E2=80=94?= =?UTF-8?q?=20the=20process()=20hot=20path=20as=20a=20register=20walk?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lever 1 of the optimization campaign (PLAN section 7), measured by the M7a ratchet and re-recorded in bench/baselines.json: - process() dispatches to a superblock walk: outputs_for() settles the trip count before the first sample moves (no exhaustion checks in the loop), the schedule cursor / history end / pending gap live in locals the compiler keeps in registers, and input/output advance by pointer bumps instead of per-frame index multiplies. - Mono and stereo are stamped out as specializations (CH = 1 / 2) with the per-channel loops dissolved and the planar history pointers hoisted (compaction memmoves in place, so they stay valid); any other channel count takes the generic instantiation. - Same append/emit order, same tap::dsp dot kernels: outputs are bit-exact. 57/57 host (gcc + clang -Werror), M55 and M33 bare-metal suites green (and faster: 3.5 -> 2.4 s, 38.5 -> 27.3 s wall), Hexagon 55/55, bluetooth_bridge output identical, clang-tidy clean. Measured (2 s stereo virtual audio per workload): - M55: -31% to -60%. up_q15 118.8M -> 58.5M; Q15 now beats float on M55 (58.5M vs 62.2M), resolving half of M7a's named anomaly — Helium just needed a register-resident loop around the dots. - M33: fixed point -12% to -25% (up_q15 245M -> 216M, q31 -24%); float only -2.8% (soft-double MACs dominate by design — the golden model's double accumulation). - Hexagon: flat to -3.8% — hexagon-clang already generated tight code around the old loop, confirming the overhead was an Arm codegen story. pull() deliberately keeps the generic per-frame loop: its granularity is the pop callback, a different lever. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 21 ++++- bridge/bench/baselines.json | 48 +++++----- bridge/include/tap/ratio/converter.h | 131 +++++++++++++++++++++++---- 3 files changed, 156 insertions(+), 44 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 5dd91e5..faf96a4 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -217,10 +217,29 @@ executed (it measures the shipping C++, not a Python re-implementation). (97 M vs 119 M) — so the fixed-point dot kernels are a named M7 target on that core, not just the float loop. Toolchains, QEMU source, and the plugin header are SHA256-pinned, mirroring SampleRateTap's CI. + - **M7b — superblock codegen (landed).** Lever 1: process()'s hot path + became the superblock walk — trip count settled by outputs_for() + arithmetic before the first sample moves, schedule cursor / history + end / pending gap in registers, pointer bumps instead of per-frame + index multiplies, mono and stereo stamped out as specializations with + the planar history pointers hoisted. Same append/emit order, same + tap::dsp dot kernels: outputs bit-exact (57/57 host, both bare-metal + suites, Hexagon suite all green, scipy vectors unmoved). Measured + (baselines re-recorded 2026-07-24): **M55 −31…−60%** (up_q15 118.8 M → + 58.5 M — Q15 now beats float on M55, 58.5 M vs 62.2 M, half of M7a's + named anomaly resolved by letting Helium see a register-resident + loop); **M33 fixed point −12…−25%** (up_q15 245 M → 216 M, q31 + −24%), M33 float only −2.8% (soft-double MACs dominate by design — + the golden model's double accumulation); **Hexagon ≈ flat** + (−0.1…−3.8% — hexagon-clang already generated tight code around the + old loop, confirming the overhead was an Arm codegen story). pull() + keeps the generic per-frame loop deliberately: its granularity is the + pop callback, a different lever. v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — v0.1 shipped (2026-07-23). M7a measurement harness landed (2026-07-23); -next lever: superblock codegen.** +M7b superblock codegen landed (2026-07-24); next lever: baked/committed +tables, or the M33 float story if a consumer needs it.** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json index 10ba284..fd02f0c 100644 --- a/bridge/bench/baselines.json +++ b/bridge/bench/baselines.json @@ -1,32 +1,32 @@ { "hexagon": { - "down_float_eco": 414246008, - "down_float_tr": 969773486, - "down_q15_eco": 77017845, - "down_q31_eco": 75022038, - "up_float_eco": 258120188, - "up_float_tr": 555333007, - "up_q15_eco": 50225518, - "up_q31_eco": 51414419 + "down_float_eco": 412765555, + "down_float_tr": 968469433, + "down_q15_eco": 76584622, + "down_q31_eco": 73096891, + "up_float_eco": 256643344, + "up_float_tr": 554048147, + "up_q15_eco": 49816030, + "up_q31_eco": 49475927 }, "m33": { - "down_float_eco": 2474264539, - "down_float_tr": 5949658686, - "down_q15_eco": 376921195, - "down_q31_eco": 559873777, - "up_float_eco": 1525404747, - "up_float_tr": 3380248412, - "up_q15_eco": 245489524, - "up_q31_eco": 357269041 + "down_float_eco": 2404904318, + "down_float_tr": 5786806465, + "down_q15_eco": 328164430, + "down_q31_eco": 421470597, + "up_float_eco": 1481996692, + "up_float_tr": 3287116501, + "up_q15_eco": 215691420, + "up_q31_eco": 271896906 }, "m55": { - "down_float_eco": 152072803, - "down_float_tr": 348789510, - "down_q15_eco": 185041635, - "down_q31_eco": 185380666, - "up_float_eco": 96844767, - "up_float_tr": 201986285, - "up_q15_eco": 118784384, - "up_q31_eco": 119885904 + "down_float_eco": 96234552, + "down_float_tr": 218157659, + "down_q15_eco": 73816005, + "down_q31_eco": 126751452, + "up_float_eco": 62212956, + "up_float_tr": 127229818, + "up_q15_eco": 58456571, + "up_q31_eco": 83029327 } } diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index 1c36d11..778030d 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -70,21 +70,24 @@ namespace tap::ratio { /// /// \pre out has room for outputs_for(in_frames) frames — the exact /// count this call will produce from the current position. + /// + /// The hot path is the superblock walk (M7 lever 1, PLAN section 7): + /// the output count is settled by arithmetic up front, the schedule + /// cursor / history end / pending gap live in locals the compiler + /// keeps in registers, input and output move by pointer bumps + /// instead of per-frame index multiplies, and the mono and stereo + /// shapes are stamped out as their own specializations so the + /// channel loops vanish. The dots themselves are the unchanged + /// tap::dsp kernels, and the append/emit order is identical, so + /// outputs stay bit-exact (pinned by the scipy-vector tests). std::size_t process(const S* in, std::size_t in_frames, S* out) noexcept { - std::size_t consumed = 0; - std::size_t produced = 0; - for (;;) { - while (m_pending != 0 && consumed < in_frames) { - append_frame(in + consumed * m_channels); - ++consumed; - --m_pending; - } - if (m_pending != 0) { // input exhausted mid-gap - return produced; - } - emit(out + produced * m_channels); - ++produced; + if (m_channels == 1) { + return process_walk<1>(in, in_frames, out); + } + if (m_channels == 2) { + return process_walk<2>(in, in_frames, out); } + return process_walk<0>(in, in_frames, out); } // ANCHOR_END: rt_process @@ -197,6 +200,89 @@ namespace tap::ratio { const S* window(std::size_t c) const noexcept { return m_hist[c].data() + m_end - m_table.taps(); } + // ANCHOR: rt_superblock_walk + /// The superblock walk behind process() (M7 lever 1). CH = 1 and 2 + /// are stamped-out specializations — the deployment shapes — with the + /// per-channel loops dissolved and the planar history pointers + /// hoisted (compaction memmoves in place, so they stay valid for the + /// whole call); CH = 0 is the any-channel-count generic. The schedule + /// cursor, history end and pending gap run in locals so the state + /// machine lives in registers, and the outputs_for() arithmetic + /// settles the trip count before the first sample moves — the walk + /// itself has no exhaustion checks. Same append/emit order and the + /// same tap::dsp dot kernels as the naive loop: bit-exact by + /// construction, pinned by the scipy-vector and pull-parity tests. + template + std::size_t process_walk(const S* in, std::size_t in_frames, S* out) noexcept { + const std::size_t taps = m_table.taps(); + const std::size_t total = static_cast(outputs_for(in_frames)); + const std::size_t ch = CH != 0 ? CH : m_channels; + + S* const h0 = m_hist[0].data(); + S* const h1 = CH == 2 ? m_hist[1].data() : nullptr; + + const S* src = in; + std::size_t remain = in_frames; + std::size_t end = m_end; + std::size_t pos = m_pos; + std::size_t pending = m_pending; + + const auto append = [&]() noexcept { + if (end == m_hist_cap) { + end = compact(end); + } + if constexpr (CH == 1) { + h0[end] = src[0]; + } + else if constexpr (CH == 2) { + h0[end] = src[0]; + h1[end] = src[1]; + } + else { + for (std::size_t c = 0; c < ch; ++c) { + m_hist[c][end] = src[c]; + } + } + src += ch; + --remain; + ++end; + }; + + for (std::size_t produced = 0; produced < total; ++produced) { + while (pending != 0) { // never outruns in_frames: total is exact + append(); + --pending; + } + const schedule_entry step = k_schedule[pos]; + const coeff* row = m_table.row(step.phase); + if constexpr (CH == 1) { + out[0] = tap::dsp::dot_row(row, h0 + end - taps, taps); + } + else if constexpr (CH == 2) { + out[0] = tap::dsp::dot_row(row, h0 + end - taps, taps); + out[1] = tap::dsp::dot_row(row, h1 + end - taps, taps); + } + else { + for (std::size_t c = 0; c < ch; ++c) { + out[c] = tap::dsp::dot_row(row, m_hist[c].data() + end - taps, taps); + } + } + out += ch; + pending = step.advance; + pos = pos + 1 == k_phases ? 0 : pos + 1; + } + while (remain != 0) { // leftover input smaller than the next gap + append(); + --pending; + } + + m_end = end; + m_pos = static_cast(pos); + m_pending = static_cast(pending); + return total; + } + // ANCHOR_END: rt_superblock_walk + /// One output frame at the current schedule position; advances state. void emit(S* out) noexcept { const schedule_entry step = k_schedule[m_pos]; @@ -226,13 +312,20 @@ namespace tap::ratio { } void compact_if_full() noexcept { - if (m_end == m_hist_cap) { // keep the newest T-1 frames at the front - const std::size_t keep = m_table.taps() - 1; - for (auto& h : m_hist) { - std::memmove(h.data(), h.data() + (m_end - keep), keep * sizeof(S)); - } - m_end = keep; + if (m_end == m_hist_cap) { + m_end = compact(m_end); + } + } + + /// Slide the newest T-1 frames to the front of every channel's + /// history (in place — data() pointers stay valid); returns the new + /// end index. + std::size_t compact(std::size_t end) noexcept { + const std::size_t keep = m_table.taps() - 1; + for (auto& h : m_hist) { + std::memmove(h.data(), h.data() + (end - keep), keep * sizeof(S)); } + return keep; } basic_phase_table m_table; From ebae70c5dfa4727b5e8ac7e96da6714bcf309365 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 12:15:53 +0000 Subject: [PATCH 15/44] =?UTF-8?q?M7c:=20commit=20the=20trip=20counts=20?= =?UTF-8?q?=E2=80=94=20constexpr=20profiles,=20compile-time=20dot=20length?= =?UTF-8?q?s?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lever 2 of the optimization campaign (PLAN section 7), the codegen half of "baked tables": the two canonical profiles become constexpr, and process() dispatches once per call on the four pinned taps-per-phase counts (44/78 economy, 96/184 transparent), handing the superblock walk a compile-time trip count that the inlined tap::dsp dot kernels unroll and vectorize against. Custom-taps profiles take the runtime-length instantiation, pinned by a new impulse test of its own (Converter.ImpulseReproducesTableCustomTaps). Measured (baselines re-recorded, all three targets): - Hexagon: fixed point -7% to -12% on all four scenarios (up_q15 49.8M -> 44.0M, up_q31 -11%) — hexagon-clang pipelines the exact-count scalar loops. Float ~-1%. - M55: up_q15 -15% (58.5M -> 49.6M; the 44-tap dot fully unrolls under Helium). The 78/96/184-tap scenarios stay loop-shaped: flat within tolerance. - M33: Q15 -2.6% / -3.4%; float flat (soft-double MAC bound). Coefficient BAKING (committed rodata tables) stays un-pulled: construction is <0.3% of every workload, so its value is MCU boot time and RAM, not instruction counts — deferred until a consumer needs it. Verification: 58/58 host (gcc + clang -Werror), M55 and M33 bare-metal suites green, Hexagon 56/56 under emulation, bluetooth_bridge output identical, clang-tidy clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 22 ++++++++-- bridge/bench/baselines.json | 48 +++++++++++----------- bridge/include/tap/ratio/converter.h | 61 ++++++++++++++++++++-------- bridge/include/tap/ratio/design.h | 8 ++-- bridge/tests/test_converter.cpp | 10 +++++ 5 files changed, 103 insertions(+), 46 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index faf96a4..9bf7d35 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -235,11 +235,27 @@ executed (it measures the shipping C++, not a Python re-implementation). old loop, confirming the overhead was an Arm codegen story). pull() keeps the generic per-frame loop deliberately: its granularity is the pop callback, a different lever. + - **M7c — committed trip counts (landed).** Lever 2, the codegen half of + "baked tables": the canonical profiles became constexpr, so process() + dispatches once per call on the four pinned taps-per-phase counts + (44/78 economy, 96/184 transparent) and the walk hands the inlined dot + kernels a compile-time trip count; custom-taps profiles take the + runtime-length instantiation (pinned by its own impulse test). + Measured: **Hexagon fixed point −7…−12%** (all four scenarios — + hexagon-clang pipelines the exact-count scalar loops), **M55 up_q15 + −15%** (44 taps fully unrolls under Helium; the 78/96/184-tap + scenarios stay loop-shaped and flat), **M33 Q15 −2.6/−3.4%**; float + everywhere within noise of flat (soft-double MAC bound on M33, FP64 + chain bound on M55). Coefficient *baking* (committed tables in rodata) + remains un-pulled: construction is <0.3% of every workload, so its + value is boot time and RAM on MCUs, not instruction counts — deferred + until a consumer needs it. v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — -v0.1 shipped (2026-07-23). M7a measurement harness landed (2026-07-23); -M7b superblock codegen landed (2026-07-24); next lever: baked/committed -tables, or the M33 float story if a consumer needs it.** +v0.1 shipped (2026-07-23). M7a measurement harness + M7b superblock +codegen + M7c committed trip counts landed (2026-07-23/24); next lever: +polyphase symmetry storage halving, or the M33 float story if a consumer +needs it.** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json index fd02f0c..4458ec2 100644 --- a/bridge/bench/baselines.json +++ b/bridge/bench/baselines.json @@ -1,32 +1,32 @@ { "hexagon": { - "down_float_eco": 412765555, - "down_float_tr": 968469433, - "down_q15_eco": 76584622, - "down_q31_eco": 73096891, - "up_float_eco": 256643344, - "up_float_tr": 554048147, - "up_q15_eco": 49816030, - "up_q31_eco": 49475927 + "down_float_eco": 408799302, + "down_float_tr": 967322580, + "down_q15_eco": 69795923, + "down_q31_eco": 67717362, + "up_float_eco": 253482716, + "up_float_tr": 551268731, + "up_q15_eco": 43962582, + "up_q31_eco": 43918255 }, "m33": { - "down_float_eco": 2404904318, - "down_float_tr": 5786806465, - "down_q15_eco": 328164430, - "down_q31_eco": 421470597, - "up_float_eco": 1481996692, - "up_float_tr": 3287116501, - "up_q15_eco": 215691420, - "up_q31_eco": 271896906 + "down_float_eco": 2405598841, + "down_float_tr": 5786890466, + "down_q15_eco": 319593940, + "down_q31_eco": 421757767, + "up_float_eco": 1483417327, + "up_float_tr": 3287871636, + "up_q15_eco": 208462743, + "up_q31_eco": 272069248 }, "m55": { - "down_float_eco": 96234552, - "down_float_tr": 218157659, - "down_q15_eco": 73816005, - "down_q31_eco": 126751452, - "up_float_eco": 62212956, - "up_float_tr": 127229818, - "up_q15_eco": 58456571, - "up_q31_eco": 83029327 + "down_float_eco": 96760015, + "down_float_tr": 218246138, + "down_q15_eco": 74152380, + "down_q31_eco": 126978624, + "up_float_eco": 62737997, + "up_float_tr": 127311142, + "up_q15_eco": 49644781, + "up_q31_eco": 82296302 } } diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index 778030d..2331199 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -47,6 +47,12 @@ namespace tap::ratio { static constexpr std::size_t k_phases = ratio_traits::k_phases; ///< L static constexpr std::size_t k_decimation = ratio_traits::k_decimation; ///< M + /// The canonical trip counts (taps per phase = MACs per output) the + /// hot path hard-commits to at compile time; any other profile runs + /// the runtime-length walk. + static constexpr std::size_t k_taps_economy = profile::economy().taps(); + static constexpr std::size_t k_taps_transparent = profile::transparent().taps(); + /// Allocates histories and designs the table; setup time only. explicit basic_converter(std::size_t channels = 1, const profile& p = profile::economy()) : m_table(p) @@ -80,14 +86,22 @@ namespace tap::ratio { /// channel loops vanish. The dots themselves are the unchanged /// tap::dsp kernels, and the append/emit order is identical, so /// outputs stay bit-exact (pinned by the scipy-vector tests). + /// + /// M7 lever 2 commits the trip counts: the two canonical profiles' + /// taps-per-phase are compile-time facts (constexpr profile), so one + /// dispatch per call hands the walk a constant dot length — the + /// inlined kernels unroll and vectorize against it instead of a + /// runtime bound. A custom-taps profile takes the runtime-length + /// instantiation (pinned by the custom-profile parity test). std::size_t process(const S* in, std::size_t in_frames, S* out) noexcept { - if (m_channels == 1) { - return process_walk<1>(in, in_frames, out); + const std::size_t taps = m_table.taps(); + if (taps == k_taps_economy) { + return process_taps(in, in_frames, out); } - if (m_channels == 2) { - return process_walk<2>(in, in_frames, out); + if (taps == k_taps_transparent) { + return process_taps(in, in_frames, out); } - return process_walk<0>(in, in_frames, out); + return process_taps<0>(in, in_frames, out); } // ANCHOR_END: rt_process @@ -200,21 +214,36 @@ namespace tap::ratio { const S* window(std::size_t c) const noexcept { return m_hist[c].data() + m_end - m_table.taps(); } + /// Channel-shape dispatch for one compile-time trip count T (0 = + /// runtime taps): mono and stereo get stamped-out walks, anything + /// else the generic. + template + std::size_t process_taps(const S* in, std::size_t in_frames, S* out) noexcept { + if (m_channels == 1) { + return process_walk<1, T>(in, in_frames, out); + } + if (m_channels == 2) { + return process_walk<2, T>(in, in_frames, out); + } + return process_walk<0, T>(in, in_frames, out); + } + // ANCHOR: rt_superblock_walk - /// The superblock walk behind process() (M7 lever 1). CH = 1 and 2 - /// are stamped-out specializations — the deployment shapes — with the - /// per-channel loops dissolved and the planar history pointers + /// The superblock walk behind process() (M7 levers 1 + 2). CH = 1 and + /// 2 are stamped-out specializations — the deployment shapes — with + /// the per-channel loops dissolved and the planar history pointers /// hoisted (compaction memmoves in place, so they stay valid for the - /// whole call); CH = 0 is the any-channel-count generic. The schedule - /// cursor, history end and pending gap run in locals so the state - /// machine lives in registers, and the outputs_for() arithmetic - /// settles the trip count before the first sample moves — the walk - /// itself has no exhaustion checks. Same append/emit order and the - /// same tap::dsp dot kernels as the naive loop: bit-exact by + /// whole call); CH = 0 is the any-channel-count generic. T is the + /// compile-time dot length for the canonical profiles (0 = runtime). + /// The schedule cursor, history end and pending gap run in locals so + /// the state machine lives in registers, and the outputs_for() + /// arithmetic settles the trip count before the first sample moves — + /// the walk itself has no exhaustion checks. Same append/emit order + /// and the same tap::dsp dot kernels as the naive loop: bit-exact by /// construction, pinned by the scipy-vector and pull-parity tests. - template + template std::size_t process_walk(const S* in, std::size_t in_frames, S* out) noexcept { - const std::size_t taps = m_table.taps(); + const std::size_t taps = T != 0 ? T : m_table.taps(); const std::size_t total = static_cast(outputs_for(in_frames)); const std::size_t ch = CH != 0 ? CH : m_channels; diff --git a/bridge/include/tap/ratio/design.h b/bridge/include/tap/ratio/design.h index d8ae5b3..9d56560 100644 --- a/bridge/include/tap/ratio/design.h +++ b/bridge/include/tap/ratio/design.h @@ -80,15 +80,17 @@ namespace tap::ratio { std::size_t taps_down_to_44k1 = 78; ///< taps per phase, 48 -> 44.1 /// The speed-first default: ~70 dB stopband, 19 kHz passband. - static profile economy() noexcept { return {}; } + /// constexpr so the converter's hot path can hard-commit to the + /// canonical trip counts at compile time (M7 lever 2). + static constexpr profile economy() noexcept { return {}; } /// Pristine tier: 120 dB stopband, flat to 20 kHz. - static profile transparent() noexcept { + static constexpr profile transparent() noexcept { return {.passband_hz = 20000.0, .stopband_atten_db = 120.0, .taps_up_to_48k = 96, .taps_down_to_44k1 = 184}; } template - std::size_t taps() const noexcept { + constexpr std::size_t taps() const noexcept { return D == direction::up_to_48k ? taps_up_to_48k : taps_down_to_44k1; } }; diff --git a/bridge/tests/test_converter.cpp b/bridge/tests/test_converter.cpp index a7d5cc7..b0289f8 100644 --- a/bridge/tests/test_converter.cpp +++ b/bridge/tests/test_converter.cpp @@ -64,6 +64,16 @@ namespace { TEST(Converter, ImpulseReproducesTableUp) { check_impulse_reproduces_table(profile::economy()); } + // Non-canonical taps take the runtime-length walk (process() only + // hard-commits to the two constexpr profile trip counts, M7 lever 2); + // this pins that instantiation with the same bit-exact sweep. + TEST(Converter, ImpulseReproducesTableCustomTaps) { + profile p = profile::economy(); + p.taps_up_to_48k = 52; + p.taps_down_to_44k1 = 86; + check_impulse_reproduces_table(p); + check_impulse_reproduces_table(p); + } TEST(Converter, ImpulseReproducesTableDownTransparent) { check_impulse_reproduces_table(profile::transparent()); } From 0ae08adef43711a72ccb31969ddc1e4df3b31f8f Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 15:04:38 +0000 Subject: [PATCH 16/44] =?UTF-8?q?M7d:=20symmetry=20storage=20halving=20?= =?UTF-8?q?=E2=80=94=20ceil(L/2)=20stored=20rows,=20mirrored=20dots?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Lever 3 of the optimization campaign (PLAN section 7), the RatioTap half of the two-PR lever (DspTap grew dot_row_reversed first; this bumps the pin to c2bfbdb and consumes it). The linear-phase prototype gives b_p[t] = b_{L-1-p}[T-1-t]: branch p is branch L-1-p tap-reversed. The table now stores only ceil(L/2) rows (74 of 147 down, 80 of 160 up) and a mirrored phase dots its partner's stored row backward via tap::dsp::dot_row_reversed — same products, same accumulation order, bit-identical outputs. Quantization is canonical over the stored half, so the mirror is exact by construction and the fixed-point exact-unity row sums carry over (reversal preserves the multiset). Storage, pinned by test: economy f32 44.8 -> 22.5 KiB (down), 27.5 -> 13.8 KiB (up); transparent 105.7 -> 53.2 / 60.0 -> 30.0 KiB; Q15 halves those again. Compute cost: ZERO baseline changes on all three targets — with one codegen subtlety the ratchet caught and the diagnostics isolated: inlining both dot arms in the walk broke Arm's unrolled forward codegen (M55 up_q15 +3.3%, reversed kernel itself measured free), while outlining the mirrored arm broke Hexagon's (down_q31 +3.3% called, +2.7% inlined). The mirrored-arm out-lining is therefore gated per target (TAP_RATIO_MIRRORED_DOT_ATTR), the same measured-per-target pattern as the tap::dsp kernel gates. Worst residual rides inside the two-sided gate (Hexagon down_q31 +2.7%); Arm came out slightly ahead (M33 Q31 -2.5%, M55 up_q15 -1.1%). Verification: 58/58 host (gcc + clang -Werror); M55 and M33 bare-metal suites green — the M33 leg is the on-target bit-exactness proof of the SMLALDX swapped-lane pairing (impulse-reproduces-table sweeps every mirrored phase with the intrinsic active); Hexagon 56/56; every-phase table battery extended with the exhaustive mirror-identity sweep; bluetooth_bridge identical; clang-tidy clean. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/PLAN.md | 30 +++++++++++-- bridge/include/tap/ratio/converter.h | 59 +++++++++++++++++++++++--- bridge/include/tap/ratio/phase_table.h | 57 ++++++++++++++++++------- bridge/submodules/dsptap | 2 +- bridge/tests/test_converter.cpp | 2 +- bridge/tests/test_phase_table.cpp | 38 ++++++++++++----- 6 files changed, 148 insertions(+), 40 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 9bf7d35..aee7555 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -106,8 +106,11 @@ Two quality tiers behind one design path, named in the family vocabulary: | Profile | Stopband | Passband edge | Taps/phase (=MACs/out) down / up | Storage f32 down / up | Role | |---|---|---|---|---|---| -| `economy()` — **default** | 70 dB | 19 kHz | **78 / 44** | 44.8 / 27.5 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | -| `transparent()` | 120 dB | 20 kHz | **184 / 96** | 105.7 / 60.0 KiB | Pristine/offline tier | +| `economy()` — **default** | 70 dB | 19 kHz | **78 / 44** | 22.5 / 13.8 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | +| `transparent()` | 120 dB | 20 kHz | **184 / 96** | 53.2 / 30.0 KiB | Pristine/offline tier | + +Storage figures are with the M7d symmetry halving (ceil(L/2) stored rows; +pinned by `PhaseTable.StorageBudgetsArePinned`); Q15 halves them again. `economy` as default is a deliberate positioning choice consistent with the speed-first charter; the README must state the reasoning (the §4 argument: @@ -250,11 +253,30 @@ executed (it measures the shipping C++, not a Python re-implementation). remains un-pulled: construction is <0.3% of every workload, so its value is boot time and RAM on MCUs, not instruction counts — deferred until a consumer needs it. + - **M7d — polyphase symmetry storage halving (landed).** Lever 3, in two + PRs per the substrate discipline: `tap::dsp::dot_row_reversed` landed + in DspTap first (hist forward × row backward, SMLALDX swapped-lane + dual-MAC on DSP-extension Arm — bit-identical to dotting the + materialized mirror), then the table dropped to ceil(L/2) stored rows + (74 of 147 down, 80 of 160 up; economy f32 44.8→22.5 / 27.5→13.8 KiB, + transparent 105.7→53.2 / 60.0→30.0 KiB, Q15 halves again) with + mirrored phases dotting their partner's stored row reversed. + Quantization is canonical over the stored half, so the mirror is exact + by construction and every row-sum guarantee carries over. Compute cost + measured at **zero baseline changes on all three targets** — with one + codegen subtlety the ratchet caught: inlining both dot arms in the + walk broke Arm's unrolled forward codegen (M55 up_q15 +3.3%), while + outlining the mirrored arm broke Hexagon's (down_q31 +3.3% called, + +2.7% inlined) — so the mirrored-arm out-lining is gated per target + (TAP_RATIO_MIRRORED_DOT_ATTR), the same measured-per-target pattern as + the tap::dsp kernel gates. Worst residual rides inside the ±3% gate + (Hexagon down_q31 +2.7%); Arm came out slightly ahead (M33 Q31 −2.5%). v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — v0.1 shipped (2026-07-23). M7a measurement harness + M7b superblock -codegen + M7c committed trip counts landed (2026-07-23/24); next lever: -polyphase symmetry storage halving, or the M33 float story if a consumer +codegen + M7c committed trip counts + M7d symmetry halving landed +(2026-07-23/24); next lever candidates: multistage decomposition, +minimum-phase economy variant, or the M33 float story if a consumer needs it.** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index 2331199..7fd08b7 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -16,6 +16,21 @@ #include "tap/ratio/phase_table.h" #include "tap/ratio/schedule.h" +// Out-of-lining attribute for the mirrored-phase dot (see dot_mirrored): +// measured per target, gated per target, the same pattern as the tap::dsp +// kernel gates. On Arm, two inlined dot expansions in the walk's loop body +// break the forward arm's unrolled codegen (M55 up_q15 +3.3%), so the +// mirrored arm is a call; hexagon-clang keeps both expansions tight inline +// and pays for the call instead (down_q31: +3.3% outlined, +2.7% inlined), +// so there the attribute is empty and the helper inlines away. +#if defined(__hexagon__) +#define TAP_RATIO_MIRRORED_DOT_ATTR +#elif defined(_MSC_VER) +#define TAP_RATIO_MIRRORED_DOT_ATTR __declspec(noinline) +#else +#define TAP_RATIO_MIRRORED_DOT_ATTR __attribute__((noinline)) +#endif + namespace tap::ratio { // ANCHOR: rt_converter_doc @@ -282,18 +297,34 @@ namespace tap::ratio { append(); --pending; } + // Symmetry-halved table (M7 lever 3): a mirrored phase dots + // its partner's stored row tap-reversed — same products, + // same accumulation order, bit-identical to a full table. + // The mirrored arm is a CALL (dot_mirrored), not an inline + // expansion: two inlined dot bodies in this loop measurably + // break the forward arm's unrolled codegen (M55 up_q15, + // +3.3%), while a call against a ~300-insn dot is noise. const schedule_entry step = k_schedule[pos]; - const coeff* row = m_table.row(step.phase); + const coeff* row = m_table.stored_row(step.phase); + const bool mir = basic_phase_table::is_mirrored(step.phase); if constexpr (CH == 1) { - out[0] = tap::dsp::dot_row(row, h0 + end - taps, taps); + out[0] = + mir ? dot_mirrored(row, h0 + end - taps) : tap::dsp::dot_row(row, h0 + end - taps, taps); } else if constexpr (CH == 2) { - out[0] = tap::dsp::dot_row(row, h0 + end - taps, taps); - out[1] = tap::dsp::dot_row(row, h1 + end - taps, taps); + if (mir) { + out[0] = dot_mirrored(row, h0 + end - taps); + out[1] = dot_mirrored(row, h1 + end - taps); + } + else { + out[0] = tap::dsp::dot_row(row, h0 + end - taps, taps); + out[1] = tap::dsp::dot_row(row, h1 + end - taps, taps); + } } else { for (std::size_t c = 0; c < ch; ++c) { - out[c] = tap::dsp::dot_row(row, m_hist[c].data() + end - taps, taps); + out[c] = mir ? dot_mirrored(row, m_hist[c].data() + end - taps) + : tap::dsp::dot_row(row, m_hist[c].data() + end - taps, taps); } } out += ch; @@ -312,13 +343,27 @@ namespace tap::ratio { } // ANCHOR_END: rt_superblock_walk + /// The mirrored-phase dot, forced out of line (T = compile-time trip + /// count, 0 = runtime): the walk's loop body keeps the forward + /// tap::dsp::dot_row as its only inlined dot expansion — inlining + /// both arms measurably regressed the forward arm's unrolled codegen + /// — while this instantiation gets the same compile-time trip count + /// on its own. Bit-exactness is the reversed kernel's contract: + /// identical bits to dotting the materialized mirrored row. + template + TAP_RATIO_MIRRORED_DOT_ATTR S dot_mirrored(const coeff* row, const S* hist) const noexcept { + return tap::dsp::dot_row_reversed(row, hist, T != 0 ? T : m_table.taps()); + } + /// One output frame at the current schedule position; advances state. void emit(S* out) noexcept { const schedule_entry step = k_schedule[m_pos]; - const coeff* row = m_table.row(step.phase); + const coeff* row = m_table.stored_row(step.phase); + const bool mir = basic_phase_table::is_mirrored(step.phase); const std::size_t taps = m_table.taps(); for (std::size_t c = 0; c < m_channels; ++c) { - out[c] = tap::dsp::dot_row(row, window(c), taps); + out[c] = mir ? tap::dsp::dot_row_reversed(row, window(c), taps) + : tap::dsp::dot_row(row, window(c), taps); } m_pending = step.advance; m_pos = m_pos + 1 == k_phases ? 0 : m_pos + 1; diff --git a/bridge/include/tap/ratio/phase_table.h b/bridge/include/tap/ratio/phase_table.h index a3798dd..36a430d 100644 --- a/bridge/include/tap/ratio/phase_table.h +++ b/bridge/include/tap/ratio/phase_table.h @@ -17,30 +17,47 @@ namespace tap::ratio { // ANCHOR: rt_phase_table /// Immutable polyphase coefficient table, designed at construction. /// - /// Phase-major layout: exactly L rows (no interpolation between phases, - /// so no extra wrap row and no power-of-two rounding — the schedule - /// indexes branches exactly), each row taps() contiguous coefficients, - /// stored tap-reversed so the dot product runs forward over an - /// oldest-first history window (the tap::dsp::dot_row convention). - /// Branch p holds prototype taps h[p + t*L], quantized per row with the - /// shared row-sum-preserving utility, so every branch's DC gain survives - /// fixed point within one coefficient LSB. + /// Phase-major layout, symmetry-halved (M7 lever 3): the linear-phase + /// prototype satisfies h[n] = h[LT-1-n], which per branch reads + /// b_p[t] = b_{L-1-p}[T-1-t] — branch p is branch L-1-p tap-reversed. So + /// only the low half of the branches is stored (k_stored = ceil(L/2) + /// rows, each taps() contiguous coefficients, tap-reversed for the + /// forward tap::dsp::dot_row convention), and a mirrored phase dots its + /// partner's stored row backward via tap::dsp::dot_row_reversed — same + /// products, same accumulation order, bit-identical outputs to a full + /// table, at half the bytes. L odd (down: 147) has a self-symmetric + /// middle branch, stored normally. + /// + /// Quantization is canonical over the stored half only: each stored row + /// is quantized with the shared row-sum-preserving utility, and a + /// mirrored row IS its partner's reversal by construction — reversal + /// preserves the coefficient multiset, so every branch's DC gain (and + /// the fixed-point exact-unity row sum) carries over unchanged. template class basic_phase_table { public: using coeff = typename tap::dsp::sample_traits::coeff; static constexpr std::size_t k_phases = ratio_traits::k_phases; + static constexpr std::size_t k_stored = (k_phases + 1) / 2; ///< rows actually held + + /// True when phase ph reads its partner's stored row tap-reversed. + static constexpr bool is_mirrored(std::size_t ph) noexcept { return ph >= k_stored; } + + /// The stored row index serving phase ph (identity for the low half). + static constexpr std::size_t stored_index(std::size_t ph) noexcept { + return ph < k_stored ? ph : k_phases - 1 - ph; + } /// Designs the prototype (double precision, via the shared tap::dsp - /// designer) and quantizes the table. Allocates; may throw. Setup - /// time only, off the audio path. + /// designer) and quantizes the stored half. Allocates; may throw. + /// Setup time only, off the audio path. explicit basic_phase_table(const profile& p = profile::economy()) : m_taps(p.taps()) - , m_table(k_phases * m_taps) { + , m_table(k_stored * m_taps) { const std::vector proto = design_prototype(p); std::vector row_d(m_taps); - for (std::size_t ph = 0; ph < k_phases; ++ph) { + for (std::size_t ph = 0; ph < k_stored; ++ph) { for (std::size_t t = 0; t < m_taps; ++t) { row_d[m_taps - 1 - t] = proto[t * k_phases + ph]; } @@ -48,9 +65,17 @@ namespace tap::ratio { } } - /// Row pointer for branch ph in [0, k_phases); taps() contiguous - /// coefficients, ready for tap::dsp::dot_row. - const coeff* row(std::size_t ph) const noexcept { return m_table.data() + ph * m_taps; } + /// Stored-row pointer serving phase ph in [0, k_phases): taps() + /// contiguous coefficients for tap::dsp::dot_row when + /// !is_mirrored(ph), or for tap::dsp::dot_row_reversed when it is. + const coeff* stored_row(std::size_t ph) const noexcept { return m_table.data() + stored_index(ph) * m_taps; } + + /// Logical coefficient (ph, t) — the cold-path accessor for tests and + /// tools; the hot path pairs stored_row() with is_mirrored(). + coeff at(std::size_t ph, std::size_t t) const noexcept { + const coeff* r = stored_row(ph); + return is_mirrored(ph) ? r[m_taps - 1 - t] : r[t]; + } std::size_t taps() const noexcept { return m_taps; } ///< T: MACs per output sample @@ -63,7 +88,7 @@ namespace tap::ratio { private: std::size_t m_taps; - std::vector m_table; // L x T, rows tap-reversed + std::vector m_table; // ceil(L/2) x T, rows tap-reversed }; // ANCHOR_END: rt_phase_table diff --git a/bridge/submodules/dsptap b/bridge/submodules/dsptap index 98abb04..c2bfbdb 160000 --- a/bridge/submodules/dsptap +++ b/bridge/submodules/dsptap @@ -1 +1 @@ -Subproject commit 98abb04733c3f171276d07059bb3fe25a1fe7474 +Subproject commit c2bfbdb67fdaf01b48f89d3bd3eddf312994ab07 diff --git a/bridge/tests/test_converter.cpp b/bridge/tests/test_converter.cpp index b0289f8..665f941 100644 --- a/bridge/tests/test_converter.cpp +++ b/bridge/tests/test_converter.cpp @@ -53,7 +53,7 @@ namespace { for (std::size_t n = 0; n < made; ++n) { const std::size_t k = n * m / l; // newest-input index for output n const std::size_t phase = (n * m) % l; - const float expected = k < taps ? c.table().row(phase)[taps - 1 - k] : 0.0f; + const float expected = k < taps ? c.table().at(phase, taps - 1 - k) : 0.0f; ASSERT_EQ(y[n], expected) << "n=" << n; // bit-exact, transient included } } diff --git a/bridge/tests/test_phase_table.cpp b/bridge/tests/test_phase_table.cpp index b2170fc..1df9c39 100644 --- a/bridge/tests/test_phase_table.cpp +++ b/bridge/tests/test_phase_table.cpp @@ -29,23 +29,28 @@ namespace { using tr = tap::dsp::sample_traits; const basic_phase_table table(p); EXPECT_EQ(table.taps(), p.template taps()); - EXPECT_EQ(table.storage_bytes(), table.k_phases * table.taps() * sizeof(typename tr::coeff)); + // Symmetry-halved storage (M7 lever 3): only ceil(L/2) rows held. + EXPECT_EQ(table.storage_bytes(), table.k_stored * table.taps() * sizeof(typename tr::coeff)); EXPECT_NEAR(table.group_delay_input_samples(), static_cast(table.taps()) / 2.0, 0.51); - // Every phase, exhaustively: fixed-point rows sum to the format's - // unity exactly (the row-sum guarantee), and a full-scale DC window - // through the shared kernel lands within one output LSB of full - // scale for every branch — DC gain is phase-independent. + // Every phase, exhaustively — through the same stored_row/is_mirrored + // pairing the converter's hot path uses, so the reversed-kernel leg + // is exercised for every mirrored branch: fixed-point rows sum to + // the format's unity exactly (the row-sum guarantee; reversal + // preserves the multiset), and a full-scale DC window lands within + // one output LSB of full scale — DC gain is phase-independent. std::vector dc(table.taps(), std::is_floating_point_v ? S(1) : std::numeric_limits::max()); for (std::size_t ph = 0; ph < table.k_phases; ++ph) { if constexpr (!std::is_floating_point_v) { std::int64_t sum = 0; for (std::size_t t = 0; t < table.taps(); ++t) { - sum += table.row(ph)[t]; + sum += table.at(ph, t); } ASSERT_EQ(sum, static_cast(tr::k_coeff_scale)) << "phase " << ph; } - const S y = tap::dsp::dot_row(table.row(ph), dc.data(), table.taps()); + const S y = table.is_mirrored(ph) + ? tap::dsp::dot_row_reversed(table.stored_row(ph), dc.data(), table.taps()) + : tap::dsp::dot_row(table.stored_row(ph), dc.data(), table.taps()); if constexpr (std::is_floating_point_v) { ASSERT_NEAR(y, 1.0f, 1e-3f) << "phase " << ph; } @@ -53,6 +58,15 @@ namespace { ASSERT_NEAR(y, std::numeric_limits::max(), 2) << "phase " << ph; } } + + // The mirror identity itself, exhaustively: phase ph IS phase + // L-1-ph tap-reversed, coefficient for coefficient. + for (std::size_t ph = 0; ph < table.k_phases; ++ph) { + for (std::size_t t = 0; t < table.taps(); ++t) { + ASSERT_EQ(table.at(ph, t), table.at(table.k_phases - 1 - ph, table.taps() - 1 - t)) + << "phase " << ph << " tap " << t; + } + } } TYPED_TEST(phase_table_test, DownEconomyEveryPhase) { @@ -69,14 +83,16 @@ namespace { } // The storage numbers the plan quotes, pinned: economy is the compact - // profile the speed-first charter defaults to. + // profile the speed-first charter defaults to, and the symmetry halving + // stores ceil(L/2) rows — 74 of 147 down, 80 of 160 up (the odd L keeps + // its self-symmetric middle branch as a stored row). TEST(PhaseTable, StorageBudgetsArePinned) { const basic_phase_table de(profile::economy()); - EXPECT_EQ(de.storage_bytes(), 147u * 78u * 4u); // 44.8 KiB + EXPECT_EQ(de.storage_bytes(), 74u * 78u * 4u); // 22.5 KiB (was 44.8 full) const basic_phase_table dt(profile::transparent()); - EXPECT_EQ(dt.storage_bytes(), 147u * 184u * 4u); // 105.7 KiB + EXPECT_EQ(dt.storage_bytes(), 74u * 184u * 4u); // 53.2 KiB (was 105.7 full) const basic_phase_table ue(profile::economy()); - EXPECT_EQ(ue.storage_bytes(), 160u * 44u * 2u); // 13.8 KiB — Q15 halves it + EXPECT_EQ(ue.storage_bytes(), 80u * 44u * 2u); // 6.9 KiB — Q15 halves it again } } // namespace From 2138c05caa0d30df640d1595d51e0ba7358131f1 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 15:23:53 +0000 Subject: [PATCH 17/44] Re-pin dsptap to the merged main commit The DspTap merge rewrote the branch commit (c2bfbdb -> dfbe18d, identical tree); the pin must be reachable from dsptap main or recursive submodule checkouts break once the branch ref is pruned. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/submodules/dsptap | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bridge/submodules/dsptap b/bridge/submodules/dsptap index c2bfbdb..dfbe18d 160000 --- a/bridge/submodules/dsptap +++ b/bridge/submodules/dsptap @@ -1 +1 @@ -Subproject commit c2bfbdb67fdaf01b48f89d3bd3eddf312994ab07 +Subproject commit dfbe18d79565b8ce11c2232789c1ccd79871b27b From 14e7cd65eec95766a880d329cc6cb2f560d4fc13 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 24 Jul 2026 16:15:25 +0000 Subject: [PATCH 18/44] v0.2: wrap the M7 codegen campaign MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Declares the output-preserving phase of the optimization campaign complete and bumps the version (CMake project + TAP_RATIO_VERSION_*). Four levers landed, each measured by the instruction-count ratchet, outputs bit-identical throughout: the M7a harness itself, the M7b superblock walk, the M7c committed trip counts, and the M7d symmetry-halved tables. Cumulative vs the M7a baselines: M55 Q15 -59%/-60% and float -35%/-37%; M33 Q31 -26%/-27%, Q15 -15%/-16%; Hexagon Q15 -13%/-10%, Q31 -15%/-7%; table storage halved (economy Q15 up: 6.9 KiB). README and PLAN now state the deferral rationale for the remaining levers (multistage, minimum-phase, IIR pre-filter, FFT offline): each changes the output contract or serves a currently-unpressured need, so they wait for a consumer to pull them — a latency need pulls minimum-phase, a storage need pulls multistage, an MCU float consumer pulls the accumulation-contract discussion in DspTap. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01Ldeq57sBySx2nFTQsGcQB6 --- bridge/CLAUDE.md | 20 +++++++++++--------- bridge/CMakeLists.txt | 2 +- bridge/PLAN.md | 17 ++++++++++++----- bridge/README.md | 28 +++++++++++++++++----------- bridge/include/tap/ratio/ratio.h | 2 +- 5 files changed, 42 insertions(+), 27 deletions(-) diff --git a/bridge/CLAUDE.md b/bridge/CLAUDE.md index 005543d..f99e4cb 100644 --- a/bridge/CLAUDE.md +++ b/bridge/CLAUDE.md @@ -14,15 +14,17 @@ which of its decisions were superseded. Read PLAN.md before implementing anythin re-derive decisions it has already settled (compile-time direction, profile vocabulary, the three-leg test strategy, the pinned-eps cross-validation design). -Current state: **v0.1 (M6 complete), M7a measurement harness landed.** Design/schedule/tables -(M2), the streaming converter for float/Q15/Q31 with committed scipy reference vectors (M3/M4), -the golden cross-validation against SampleRateTap at pinned eps (M5, test-only submodule), the -bluetooth_bridge example + C ABI + executed demo notebook (M6), and the embedded CI matrix + -instruction-count ratchet (M7a: Cortex-M33/M55 + Hexagon under QEMU, eight fixed workloads gated -two-sided ±3% against `bench/baselines.json` — `scripts/icount.py`). Next: the M7 levers, one -measured PR each, superblock codegen first — see PLAN.md section 7. Any change that moves a -workload's count beyond ±3% must re-record baselines (`icount.py --update` per target) in the -same PR; an *improvement* beyond tolerance fails the gate too, by design. +Current state: **v0.2 — M7 codegen phase complete.** v0.1 (M0–M6): design/schedule/tables, the +streaming converter for float/Q15/Q31 with committed scipy reference vectors, the golden +cross-validation against SampleRateTap at pinned eps (test-only submodule), bluetooth_bridge + +C ABI + executed notebook. M7 (v0.2): the embedded CI matrix + instruction-count ratchet +(Cortex-M33/M55 + Hexagon under QEMU, eight workloads gated two-sided ±3% against +`bench/baselines.json` — `scripts/icount.py`), then three measured codegen levers — superblock +walk, committed trip counts, symmetry-halved tables — outputs bit-identical throughout; PLAN.md +section 7 records each lever's numbers. Remaining levers are deferred until a consumer pulls +them (they change the output contract). Any change that moves a workload's count beyond ±3% +must re-record baselines (`icount.py --update` per target) in the same PR; an *improvement* +beyond tolerance fails the gate too, by design. ## The charter constraints (load-bearing) diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index 3a88d61..428a62b 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.24) -project(RatioTap VERSION 0.1.0 LANGUAGES CXX) +project(RatioTap VERSION 0.2.0 LANGUAGES CXX) # ============================================================================== # RatioTap — synchronous 44.1 <-> 48 kHz sample rate conversion, as fast as diff --git a/bridge/PLAN.md b/bridge/PLAN.md index aee7555..44fdfe3 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -273,11 +273,18 @@ executed (it measures the shipping C++, not a Python re-implementation). (Hexagon down_q31 +2.7%); Arm came out slightly ahead (M33 Q31 −2.5%). v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — -v0.1 shipped (2026-07-23). M7a measurement harness + M7b superblock -codegen + M7c committed trip counts + M7d symmetry halving landed -(2026-07-23/24); next lever candidates: multistage decomposition, -minimum-phase economy variant, or the M33 float story if a consumer -needs it.** +v0.1 shipped (2026-07-23). M7 codegen phase complete — v0.2 (2026-07-24): +M7a measurement harness, M7b superblock codegen, M7c committed trip +counts, M7d symmetry halving, all measured, outputs bit-identical +throughout. Cumulative vs the M7a baselines: M55 Q15 −59%/−60% and float +−35%/−37%; M33 Q31 −26%/−27%, Q15 −15%/−16%; Hexagon Q15 −13%/−10%, +Q31 −15%/−7%; table storage halved. The remaining levers (multistage +decomposition, minimum-phase economy, IIR pre-filter, FFT offline path) +all change the output contract or serve currently-unpressured needs +(storage, latency) — deferred until a consumer pulls them: a latency +need pulls minimum-phase; a storage need pulls multistage; an MCU float +consumer pulls the accumulation-contract discussion (a DspTap decision, +since double accumulation is the float golden model's identity).** ## 8. Acceptance criteria (v0.1) diff --git a/bridge/README.md b/bridge/README.md index 81d9e1c..067a9db 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,18 +13,24 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: v0.1 (milestone M6).** The converter is in for all three sample -> formats: float (the golden model, pinned against committed scipy -> reference vectors sample-for-sample), Q31 (tracks float within −147 dB; -> measures 146 dB at 997 Hz — exceeding float, whose float32 I/O is its -> own bound), and Q15 (format-limited: pair it with `economy`, which is -> both cheaper *and* quieter than `transparent` at 16 bits). The golden -> cross-validation against SampleRateTap's async engine at pinned -> eps = L/M−1 agrees to −109 dB (down) / −99 dB (up) over every phase. -> The `bluetooth_bridge` example, the C ABI (`tools/capi/`), and the -> executed demo notebook (`notebooks/ratio_demo.ipynb`) complete v0.1. +> **Status: v0.2 (M7 codegen campaign).** v0.1 shipped the converter for +> all three sample formats: float (the golden model, pinned against +> committed scipy reference vectors sample-for-sample), Q31 (tracks float +> within −147 dB), and Q15 (format-limited: pair it with `economy`, which +> is both cheaper *and* quieter than `transparent` at 16 bits), plus the +> golden cross-validation against SampleRateTap's async engine (−109 dB +> down / −99 dB up over every phase), the `bluetooth_bridge` example, the +> C ABI, and the executed demo notebook. v0.2 is the measured optimization +> campaign on top — four levers, each gated by the instruction-count +> ratchet, outputs bit-identical throughout: the superblock walk, +> committed compile-time trip counts, and symmetry-halved tables. Since +> the campaign's baselines: **Q15 −59%/−60% and float −35%/−37% on +> Cortex-M55, Q31 −26%/−27% on Cortex-M33, Q15 −13%/−10% on Hexagon — +> with table storage halved** (economy Q15 up: 6.9 KiB). The remaining +> PLAN §7 levers (multistage, minimum-phase, IIR, FFT) change the output +> contract and stay deferred until a consumer needs them. > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture -> decisions, milestones, acceptance criteria); +> decisions, milestones, acceptance criteria, per-lever measurements); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. ## Quick start diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index 4d0be5d..0d496eb 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -35,7 +35,7 @@ #include "tap/ratio/schedule.h" // IWYU pragma: export #define TAP_RATIO_VERSION_MAJOR 0 -#define TAP_RATIO_VERSION_MINOR 1 +#define TAP_RATIO_VERSION_MINOR 2 #define TAP_RATIO_VERSION_PATCH 0 namespace tap::ratio { From 8ff36d6a8916a17176324ce8bc0d981427a467c2 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 27 Jul 2026 18:43:40 +0000 Subject: [PATCH 19/44] Add the shared pull-request template (taphouse sync) Synced from the canonical TapHouse copy: the family's shared review prompts -- what changed and why, a Verification section asking what was actually built and run versus what CI will gate (plus the measured-not-remembered rule for performance claims), and a delete-what-does-not-apply list of the recurring cross-repo concerns: contract changes, submodule pin flow, notebook re-execution, package docs/help and universal binaries, and documented per-repo exceptions. Created only-if-missing and deliberately NOT drift-guarded, following the .claude/settings.json precedent: it is prose for humans rather than a machine-enforced config, so this repo is free to tailor it -- trim the bullets that do not apply here. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01JhhQ93r2E1QTnCx46YfX8j --- bridge/.github/pull_request_template.md | 40 +++++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 bridge/.github/pull_request_template.md diff --git a/bridge/.github/pull_request_template.md b/bridge/.github/pull_request_template.md new file mode 100644 index 0000000..8723f79 --- /dev/null +++ b/bridge/.github/pull_request_template.md @@ -0,0 +1,40 @@ +## What this changes + + + +## Why + + + +## Verification + + + +## Notes for the reviewer + + From a808a0f13af75d3ed018822c58dcaa710a776af1 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 28 Jul 2026 11:38:29 +0000 Subject: [PATCH 20/44] Bump the DspTap pin to 28a34a1 Refreshes submodules/dsptap from dfbe18d to 28a34a1, the current DspTap main. This repo was the least stale consumer in the family, one commit behind, and that commit is documentation only -- the shared pull-request template synced from taphouse. No substrate change reaches this repo. Pinned to a commit on DspTap's main rather than to any in-flight branch, per the release flow: consumer pins must reference a tree that stays reachable after branch cleanup. Verified against the substrate discipline this repo depends on -- shared code lands in DspTap first and here second, so a pin bump is the only correct way to pick it up: configure and build clean with zero errors, ctest 58/58 passing in Release, which includes the exhaustive phase sweeps and the pinned-eps cross-validation against SampleRateTap. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01JhhQ93r2E1QTnCx46YfX8j --- bridge/submodules/dsptap | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bridge/submodules/dsptap b/bridge/submodules/dsptap index dfbe18d..28a34a1 160000 --- a/bridge/submodules/dsptap +++ b/bridge/submodules/dsptap @@ -1 +1 @@ -Subproject commit dfbe18d79565b8ce11c2232789c1ccd79871b27b +Subproject commit 28a34a18c40bda74b5d8cea0c54e09f31fbdc94b From bbcfb875b23a4e6ac90df51c40da793d1fef4b77 Mon Sep 17 00:00:00 2001 From: Claude Date: Tue, 28 Jul 2026 15:39:04 +0000 Subject: [PATCH 21/44] Compile the C ABI in CI so the verification layer cannot rot This repo ships a C ABI under tools/capi that the executed notebook drives via ctypes, but TAP_RATIO_BUILD_CAPI defaults to OFF and CI never turned it on -- so nothing in the pipeline compiled it. A change to a kernel signature could break the ABI and the notebook with it, and CI would stay green. AmbiTap and DspTap already build theirs in CI; this brings RatioTap in line. Enabled on the Linux and macOS legs only. tools/capi carries no __declspec(dllexport) (unlike DspTap's), so an MSVC build would link a DLL that exports nothing -- it would pass without gating anything. That is recorded in the matrix comment and in taphouse's known-divergences list, to be flipped on in the same change that gives the C ABI an export decoration. Verified locally: configure and build succeed with TAP_RATIO_WERROR=ON, the shared library links, it exports its 11 ratio_* entry points, and the full suite passes 58/58. Co-Authored-By: Claude Opus 5 Claude-Session: https://claude.ai/code/session_01JhhQ93r2E1QTnCx46YfX8j --- bridge/.github/workflows/ci.yml | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index 291e161..62ae8fb 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -12,16 +12,24 @@ jobs: fail-fast: false matrix: include: - - { os: ubuntu-latest, name: linux } - - { os: macos-latest, name: macos } - - { os: windows-latest, name: windows } + # capi stays OFF on Windows: tools/capi carries no __declspec(dllexport) + # (unlike DspTap's), so an MSVC build would link a DLL exporting nothing + # — it would pass without gating anything. Turn this ON in the same + # change that gives the C ABI an export decoration. + - { os: ubuntu-latest, name: linux, capi: ON } + - { os: macos-latest, name: macos, capi: ON } + - { os: windows-latest, name: windows, capi: OFF } steps: - uses: actions/checkout@v4 with: submodules: recursive + # TAP_RATIO_BUILD_CAPI is ON here so the verification layer (the C ABI the + # executed notebook drives via ctypes) cannot rot unnoticed. - name: Configure - run: cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON + run: > + cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON + -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} - name: Build run: cmake --build build --config Release From 4b4bc088829f32c68d37e9d1a8e77dfc2a0e0b80 Mon Sep 17 00:00:00 2001 From: Claude Date: Thu, 6 Aug 2026 23:47:52 +0000 Subject: [PATCH 22/44] Fix the audit findings: create-path leak, pull() contract, doc rot, CI nits - ratio_create no longer leaks the wrapper when the converter constructor throws (unique_ptr owns it until success), and the C ABI header states the NULL contract explicitly (ratio_destroy(NULL) is the free() no-op). - pull() now enforces its stated noexcept-PopFn requirement with a static_assert instead of terminating at runtime on a throwing callback. - ratio.h's status comment catches up from M3 to the shipped v0.2 state. - test_schedule.cpp includes for itself instead of leaning on gtest's transitive includes. - The Hexagon toolchain download+verify moves into a shared script so both writers of the digest-keyed cache run identical checks, and the style workflow's clang-tidy loop reads its file list line-wise. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/.github/workflows/ci.yml | 35 +++++------------------ bridge/.github/workflows/style.yml | 7 +++-- bridge/include/tap/ratio/converter.h | 6 +++- bridge/include/tap/ratio/ratio.h | 9 +++--- bridge/scripts/fetch_hexagon_toolchain.sh | 35 +++++++++++++++++++++++ bridge/tests/test_schedule.cpp | 1 + bridge/tools/capi/ratio_capi.cpp | 10 ++++--- bridge/tools/capi/ratio_capi.h | 4 +++ 8 files changed, 67 insertions(+), 40 deletions(-) create mode 100755 bridge/scripts/fetch_hexagon_toolchain.sh diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index 62ae8fb..1ec8cc9 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -96,27 +96,12 @@ jobs: # writer can poison the trusted entry. key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 + # Download + verification live in the shared script so this job and + # icount-ratchet (the other writer of the digest-keyed cache) can + # never drift onto different checks. - name: Download toolchain if: steps.cache.outputs.cache-hit != 'true' - run: | - mkdir -p ~/hexagon && cd ~/hexagon - curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" - # Integrity check against the published SHA256SUMS, plus the hard - # pin. The SUMS file catches corruption and cache-poisoning; only - # the pin catches an origin compromise. - curl -sfLo SHA256SUMS "$(dirname "$HEXAGON_TOOLCHAIN_URL")/SHA256SUMS" - expected=$(grep "$(basename "$HEXAGON_TOOLCHAIN_URL")" SHA256SUMS | awk '{print $1}' | head -1) - actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) - echo "toolchain sha256: $actual (pin this in HEXAGON_TOOLCHAIN_SHA256)" - if [ -z "$expected" ] || [ "$actual" != "$expected" ]; then - echo "::error::toolchain does not match published SHA256SUMS"; exit 1 - fi - if [ -n "${HEXAGON_TOOLCHAIN_SHA256:-}" ] && \ - [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then - echo "::error::toolchain checksum mismatch against pinned value"; exit 1 - fi - tar --zstd -xf toolchain.tar.zst - rm toolchain.tar.zst SHA256SUMS + run: scripts/fetch_hexagon_toolchain.sh - name: Set up toolchain and QEMU paths run: | @@ -341,15 +326,9 @@ jobs: if: ${{ !cancelled() }} run: | if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then - mkdir -p ~/hexagon && cd ~/hexagon - curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" - actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) - if [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then - echo "::error::toolchain checksum mismatch against pinned value" - exit 1 - fi - tar --zstd -xf toolchain.tar.zst && rm toolchain.tar.zst - cd "$GITHUB_WORKSPACE" + # Same shared download+verify as the hexagon-qemu job: both + # writers of the digest-keyed cache run identical checks. + scripts/fetch_hexagon_toolchain.sh fi # No -type f: the compiler may be a symlink in the restored tree. clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml index a811718..53f7332 100644 --- a/bridge/.github/workflows/style.yml +++ b/bridge/.github/workflows/style.yml @@ -25,10 +25,11 @@ jobs: run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DTAP_RATIO_BUILD_TESTS=ON -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - name: clang-tidy (project TUs; the submodule and fetched deps excluded) run: | - files=$(python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))") + python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))" > /tmp/tidy-files.txt fail=0 - for f in $files; do + # Line-wise read, not word splitting: robust to paths with spaces. + while IFS= read -r f; do out=$(clang-tidy-18 -p build "$f" 2>/dev/null || true) if echo "$out" | grep -qE "warning:|error:"; then echo "$out"; fail=1; fi - done + done < /tmp/tidy-files.txt [ "$fail" -eq 0 ] && echo "clang-tidy clean." || { echo "::error::clang-tidy found violations"; exit 1; } diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index 7fd08b7..a9ed220 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -8,6 +8,7 @@ #include #include #include +#include #include #include "tap/dsp/fir_kernels.h" @@ -127,9 +128,12 @@ namespace tap::ratio { /// than out_frames means the source ran dry — the partial consumption /// is retained, so delivering more input later resumes exactly where /// the stream left off. Bit-identical to process() on the same input. - /// PopFn must be noexcept. + /// PopFn must be noexcept (enforced at compile time: pull() is + /// noexcept, so a throwing callback could only terminate). template std::size_t pull(S* out, std::size_t out_frames, PopFn&& pop) noexcept { + static_assert(std::is_nothrow_invocable_r_v, + "pull() requires a noexcept PopFn: std::size_t(S*, std::size_t) noexcept"); for (std::size_t n = 0; n < out_frames; ++n) { while (m_pending != 0) { const std::size_t pending = m_pending; diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index 0d496eb..cac5cf9 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -23,10 +23,11 @@ // profiles, dot kernels, row-sum quantization, measurement instruments), // consumed as the submodules/dsptap submodule. // -// Status: M3 — the streaming converter (converter.h: process/pull/flush, -// float aliases) over the M2 design/schedule/table layer. Q15/Q31 aliases -// land with their parity battery in M4. PLAN.md is the authoritative -// roadmap; HANDOFF.md is the original design brief. +// Status: v0.2 — M7 codegen phase complete. The streaming converter +// (converter.h: process/pull/flush) ships float, Q15 and Q31 aliases with +// the superblock walk, committed trip counts and symmetry-halved tables, +// outputs bit-identical throughout. PLAN.md is the authoritative roadmap; +// HANDOFF.md is the original design brief. #pragma once #include "tap/ratio/converter.h" // IWYU pragma: export diff --git a/bridge/scripts/fetch_hexagon_toolchain.sh b/bridge/scripts/fetch_hexagon_toolchain.sh new file mode 100755 index 0000000..fde4d50 --- /dev/null +++ b/bridge/scripts/fetch_hexagon_toolchain.sh @@ -0,0 +1,35 @@ +#!/usr/bin/env bash +# Download and verify the pinned Hexagon cross toolchain into ~/hexagon. +# +# Shared by the hexagon-qemu and icount-ratchet CI jobs (.github/workflows/ +# ci.yml sets the env below): both jobs write the same digest-keyed cache +# entry, so both must verify the same pin before anything lands under it — +# one script keeps the two writers from drifting apart. +# +# HEXAGON_TOOLCHAIN_URL release artifact (CodeLinaro) +# HEXAGON_TOOLCHAIN_SHA256 hard pin; the download must match exactly +set -euo pipefail + +: "${HEXAGON_TOOLCHAIN_URL:?set by the CI workflow}" +: "${HEXAGON_TOOLCHAIN_SHA256:?set by the CI workflow}" + +mkdir -p ~/hexagon && cd ~/hexagon +curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" +actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) +echo "toolchain sha256: $actual (pin this in HEXAGON_TOOLCHAIN_SHA256)" + +# Integrity check against the published SHA256SUMS, plus the hard pin. The +# SUMS file catches corruption and cache poisoning; only the pin catches an +# origin compromise. +curl -sfLo SHA256SUMS "$(dirname "$HEXAGON_TOOLCHAIN_URL")/SHA256SUMS" +expected=$(grep "$(basename "$HEXAGON_TOOLCHAIN_URL")" SHA256SUMS | awk '{print $1}' | head -1) +if [ -z "$expected" ] || [ "$actual" != "$expected" ]; then + echo "::error::toolchain does not match published SHA256SUMS" + exit 1 +fi +if [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then + echo "::error::toolchain checksum mismatch against pinned value" + exit 1 +fi +tar --zstd -xf toolchain.tar.zst +rm toolchain.tar.zst SHA256SUMS diff --git a/bridge/tests/test_schedule.cpp b/bridge/tests/test_schedule.cpp index ef767cb..d0c5c1a 100644 --- a/bridge/tests/test_schedule.cpp +++ b/bridge/tests/test_schedule.cpp @@ -8,6 +8,7 @@ #include #include +#include #include diff --git a/bridge/tools/capi/ratio_capi.cpp b/bridge/tools/capi/ratio_capi.cpp index 7b461ad..64b9a7e 100644 --- a/bridge/tools/capi/ratio_capi.cpp +++ b/bridge/tools/capi/ratio_capi.cpp @@ -5,7 +5,7 @@ #include "ratio_capi.h" -#include +#include #include "tap/ratio/ratio.h" @@ -37,15 +37,17 @@ ratio_converter* ratio_create(int direction, int profile, unsigned channels) { } const tap::ratio::profile p = profile == 0 ? tap::ratio::profile::economy() : tap::ratio::profile::transparent(); try { - auto* c = new ratio_converter; - c->dir = direction; + // unique_ptr owns the wrapper until the converter constructor has + // succeeded, so a throw below cannot leak it. + auto c = std::make_unique(); + c->dir = direction; if (direction == 0) { c->up = new conv(channels, p); } else { c->down = new conv(channels, p); } - return c; + return c.release(); } catch (...) { return nullptr; diff --git a/bridge/tools/capi/ratio_capi.h b/bridge/tools/capi/ratio_capi.h index bf2cc50..ffe5ff3 100644 --- a/bridge/tools/capi/ratio_capi.h +++ b/bridge/tools/capi/ratio_capi.h @@ -18,6 +18,10 @@ extern "C" { typedef struct ratio_converter ratio_converter; +/// Every function below requires a valid converter from a successful +/// ratio_create; passing NULL is undefined behavior. The one exception is +/// ratio_destroy, where NULL is a safe no-op (the free() convention). + /// direction: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1). /// profile: 0 = economy (default tier), 1 = transparent. /// Returns NULL on invalid arguments. From af369bfa27b87576bd073394af826bdc2f24383d Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 00:45:42 +0000 Subject: [PATCH 23/44] =?UTF-8?q?v0.3:=20re-pin=20the=20profile=20ladder?= =?UTF-8?q?=20=E2=80=94=20economy=20at=2018=20kHz,=20balanced=20keeps=2019?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The economy18 spec-relaxation experiment graduates: the default economy profile moves to an 18 kHz passband at 58/38 taps per phase (minimal even counts meeting the 70 dB stopband with >= 1 dB margin on a 12.5 Hz sweep grid; up-direction 40 taps fails while 38 passes — sidelobe peaking is non-monotonic near threshold, recorded in PLAN section 4). That is 26%/14% fewer MACs per output and -25%/-14% table storage against the previous default, whose design continues bit-for-bit unchanged as balanced(). The 18-19 kHz shelf moves into the transition band (-1.4 dB at 19 kHz going down) — the same species of inaudible speed-first trade that set economy at 19 kHz rather than transparent's 20. Re-measured through every leg: scipy reference vectors regenerated for the six direction x profile cases; cross-validation floors re-pinned at 1.2e-5 down / 3.1e-5 up (-98/-90 dB), still equal at async L=512 and L=1024 (the floor remains the deliberate per-branch DC-normalization difference, which scales with the shorter designs' branch-sum spread); Q15 flagship at 76.5 dB (was 76.1) — the format floor, not the filter; 997 Hz float imaging floor 91.3 dB (was 89.2); bluetooth_bridge locks and recovers at 72.8 dB SNR, total latency 1.93 ms. The converter dispatches on three committed trip counts now (economy/balanced/transparent); the C ABI gains profile tag 2 = balanced; both notebooks re-executed against the shipping engine. Host suite 68/68 green. Contract change: the default output changes for every consumer that does not name a profile; balanced() is the bit-exact escape hatch. Embedded icount baselines for the six economy workloads are re-recorded in the follow-up commit harvested from this push's CI measurements. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/CMakeLists.txt | 2 +- bridge/PLAN.md | 49 ++- bridge/README.md | 40 +- bridge/include/tap/ratio/converter.h | 6 +- bridge/include/tap/ratio/design.h | 38 +- bridge/include/tap/ratio/ratio.h | 11 +- bridge/notebooks/design_spike.ipynb | 83 ++-- bridge/notebooks/ratio_demo.ipynb | 148 ++++--- bridge/notebooks/ratiotap_py.py | 2 +- bridge/tests/reference/reference_vectors.h | 408 ++++++++++++++++++ bridge/tests/test_converter.cpp | 23 +- bridge/tests/test_converter_fixed_point.cpp | 6 +- bridge/tests/test_cross_validation.cpp | 28 +- bridge/tests/test_design.cpp | 8 + bridge/tests/test_phase_table.cpp | 14 +- bridge/tools/capi/ratio_capi.cpp | 6 +- bridge/tools/capi/ratio_capi.h | 2 +- .../tools/reference/make_reference_vectors.py | 6 +- 18 files changed, 716 insertions(+), 164 deletions(-) diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index 428a62b..a60328b 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.24) -project(RatioTap VERSION 0.2.0 LANGUAGES CXX) +project(RatioTap VERSION 0.3.0 LANGUAGES CXX) # ============================================================================== # RatioTap — synchronous 44.1 <-> 48 kHz sample rate conversion, as fast as diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 44fdfe3..dba8fe3 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -102,11 +102,14 @@ side of the ASRC: ## 4. Profiles -Two quality tiers behind one design path, named in the family vocabulary: +Three quality tiers behind one design path, named in the family vocabulary +(ladder re-pinned 2026-08-07, v0.3: the 18 kHz design became `economy` and +the default; the former economy design continues unchanged as `balanced`): | Profile | Stopband | Passband edge | Taps/phase (=MACs/out) down / up | Storage f32 down / up | Role | |---|---|---|---|---|---| -| `economy()` — **default** | 70 dB | 19 kHz | **78 / 44** | 22.5 / 13.8 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) | +| `economy()` — **default** | 70 dB | 18 kHz | **58 / 38** | 16.8 / 11.9 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) — at 26%/14% fewer MACs than balanced | +| `balanced()` | 70 dB | 19 kHz | **78 / 44** | 22.5 / 13.8 KiB | The v0.1–v0.2 economy design, unchanged: top of the audible band stays inside the flat passband | | `transparent()` | 120 dB | 20 kHz | **184 / 96** | 53.2 / 30.0 KiB | Pristine/offline tier | Storage figures are with the M7d symmetry halving (ceil(L/2) stored rows; @@ -116,17 +119,25 @@ pinned by `PhaseTable.StorageBudgetsArePinned`); Q15 halves them again. speed-first charter; the README must state the reasoning (the §4 argument: nothing *can* fold below 20.1 kHz going down; images land ≥ 22.05 kHz going up) rather than just the number, and the program-weighted measurement style -from SampleRateTap's `economy` preset applies here too. +from SampleRateTap's `economy` preset applies here too. The 18 kHz edge is +the same species of inaudible trade that put economy at 19 kHz rather than +transparent's 20: the 18–19 kHz shelf moves into the transition band +(measured −1.4 dB at 19 kHz going down, −0.5 dB going up). Content that +must keep that shelf flat pairs with `balanced`. Numbers pinned by the M2 design spike (`notebooks/design_spike.ipynb`, -executed and committed; enforced in CI by `test_design.cpp`): taps are the -minimal even counts meeting the stopband with ≥ 1 dB margin. Measured -worst-case stopband on the shipping designs: economy −72.1 dB (down) / -−72.8 dB (up); transparent −121.7 dB (both). Passband ripple ±0.003 dB -(economy) / ±0.00001 dB (transparent). Q15 tables halve the storage. The -designs additionally normalize every polyphase branch's DC sum to exactly -1.0 (kills fs_out/L-harmonic spurs from DC/LF energy; lets fixed-point -row-sum quantization land on format unity exactly). +executed and committed; enforced in CI by `test_design.cpp`) for +balanced/transparent, and by the 2026-08-07 economy18 re-pin for economy: +taps are the minimal even counts meeting the stopband with ≥ 1 dB margin on +a fine (12.5 Hz) sweep grid. Measured worst-case stopband on the shipping +designs: economy −71.5 dB (down) / −71.7 dB (up); balanced −72.1 / −72.8; +transparent −121.7 (both). Passband ripple ±0.003 dB (economy/balanced) / +±0.00001 dB (transparent). One re-pin quirk worth recording: in the up +direction 40 taps *fails* the margin criterion while 38 passes (Kaiser +sidelobe peaking is non-monotonic near threshold) — 38 is a genuine sweet +spot, not a typo. The designs additionally normalize every polyphase +branch's DC sum to exactly 1.0 (kills fs_out/L-harmonic spurs from DC/LF +energy; lets fixed-point row-sum quantization land on format unity exactly). ## 5. The async composition (`bluetooth_bridge`) @@ -272,7 +283,13 @@ executed (it measures the shipping C++, not a Python re-implementation). the tap::dsp kernel gates. Worst residual rides inside the ±3% gate (Hexagon down_q31 +2.7%); Arm came out slightly ahead (M33 Q31 −2.5%). -v0.1 ships at M6. Nothing in M7+ blocks it. **Status: M0–M6 complete — +v0.1 ships at M6. Nothing in M7+ blocks it. **v0.3 (2026-08-07): the +profile-ladder re-pin.** economy moved to the 18 kHz/58/38 design (the +"economy18" spec-relaxation experiment, measured through every leg: scipy +vectors regenerated, cross-validation floors re-pinned at −98/−90 dB, +Q15 flagship unchanged at 76.5 dB, storage −25%/−14%); the former economy +became `balanced`, unchanged; icount baselines re-recorded for the six +economy workloads. **Status: M0–M6 complete — v0.1 shipped (2026-07-23). M7 codegen phase complete — v0.2 (2026-07-24): M7a measurement harness, M7b superblock codegen, M7c committed trip counts, M7d symmetry halving, all measured, outputs bit-identical @@ -288,7 +305,13 @@ since double accumulation is the float golden model's identity).** ## 8. Acceptance criteria (v0.1) -All numbers pinned (M2 design spike, 2026-07-23). +All numbers pinned (M2 design spike, 2026-07-23). *These are the v0.1 +acceptance records: "economy" below refers to the 19 kHz design that is +`balanced()` as of the v0.3 ladder re-pin (§4). The v0.3 economy's numbers: +worst stopband −71.5/−71.7 dB, 997 Hz imaging floor ~91 dB (float) / +76.5 dB (Q15), cross-validation floors 1.2e-5 down / 3.1e-5 up, latency +29 smp (0.60 ms) down / 19 smp (0.43 ms) up — every contract bound below +still holds on the new default, re-measured in the same test batteries.* - `economy`, both directions: every spurious product ≥ **71 dB below the source content** (design floors: −72.1 dB down, −72.8 dB up). Two species, diff --git a/bridge/README.md b/bridge/README.md index 067a9db..f27ef60 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -13,22 +13,26 @@ on the Tap family's shared FIR substrate float/Q15/Q31 sample-format traits, measured dot-product kernels, row-sum quantization, measurement instruments). -> **Status: v0.2 (M7 codegen campaign).** v0.1 shipped the converter for -> all three sample formats: float (the golden model, pinned against -> committed scipy reference vectors sample-for-sample), Q31 (tracks float -> within −147 dB), and Q15 (format-limited: pair it with `economy`, which -> is both cheaper *and* quieter than `transparent` at 16 bits), plus the -> golden cross-validation against SampleRateTap's async engine (−109 dB -> down / −99 dB up over every phase), the `bluetooth_bridge` example, the -> C ABI, and the executed demo notebook. v0.2 is the measured optimization -> campaign on top — four levers, each gated by the instruction-count -> ratchet, outputs bit-identical throughout: the superblock walk, -> committed compile-time trip counts, and symmetry-halved tables. Since -> the campaign's baselines: **Q15 −59%/−60% and float −35%/−37% on -> Cortex-M55, Q31 −26%/−27% on Cortex-M33, Q15 −13%/−10% on Hexagon — -> with table storage halved** (economy Q15 up: 6.9 KiB). The remaining -> PLAN §7 levers (multistage, minimum-phase, IIR, FFT) change the output -> contract and stay deferred until a consumer needs them. +> **Status: v0.3 (profile-ladder re-pin).** The default `economy` profile +> moved to an 18 kHz passband at **58/38 taps — 26%/14% fewer MACs and +> −25%/−14% storage** than the previous default, with every 70 dB contract +> bound re-measured and held (the 18–19 kHz shelf moves into the +> transition band; the former economy design continues unchanged as +> `balanced` for content that needs that shelf flat). v0.1 shipped the +> converter for all three sample formats: float (the golden model, pinned +> against committed scipy reference vectors sample-for-sample), Q31 +> (tracks float within −147 dB), and Q15 (format-limited: pair it with +> `economy`, which is both cheaper *and* quieter than `transparent` at 16 +> bits), plus the golden cross-validation against SampleRateTap's async +> engine (every phase, floor at the one deliberate design difference), the +> `bluetooth_bridge` example, the C ABI, and the executed demo notebook. +> v0.2 was the measured optimization campaign — superblock walk, committed +> compile-time trip counts, symmetry-halved tables, each gated by the +> instruction-count ratchet, outputs bit-identical throughout: **Q15 +> −59%/−60% and float −35%/−37% on Cortex-M55, Q31 −26%/−27% on +> Cortex-M33, Q15 −13%/−10% on Hexagon**. The remaining PLAN §7 levers +> (multistage, minimum-phase, IIR, FFT) change the output contract and +> stay deferred until a consumer needs them. > [PLAN.md](PLAN.md) is the authoritative roadmap (charter, architecture > decisions, milestones, acceptance criteria, per-lever measurements); > [HANDOFF.md](HANDOFF.md) is the original design brief it grew from. @@ -39,6 +43,8 @@ quantization, measurement instruments). #include tap::ratio::converter_to_44k1 down(2); // 48 -> 44.1, stereo, economy +// profiles: economy() (default, 18 kHz passband) | balanced() (19 kHz, +// the pre-v0.3 default) | transparent() (120 dB pristine tier) std::vector out(down.outputs_for(n_in) * 2); std::size_t made = down.process(in, n_in, out.data()); // noexcept, alloc-free // ... and at end of stream: @@ -52,7 +58,7 @@ callback-driven shape, and `frames_needed(n)` is exact arithmetic. For 44.1↔48 across *independent clocks* (a Bluetooth chip on its own crystal), compose with SampleRateTap — `examples/bluetooth_bridge.cpp` is the documented recipe: +200 ppm crystal, servo locked, 997 Hz recovered -exactly, 2.0 ms total latency. +exactly, 1.9 ms total latency. ## The boundaries are identity, not policy diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index a9ed220..19bd6e5 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -67,6 +67,7 @@ namespace tap::ratio { /// hot path hard-commits to at compile time; any other profile runs /// the runtime-length walk. static constexpr std::size_t k_taps_economy = profile::economy().taps(); + static constexpr std::size_t k_taps_balanced = profile::balanced().taps(); static constexpr std::size_t k_taps_transparent = profile::transparent().taps(); /// Allocates histories and designs the table; setup time only. @@ -103,7 +104,7 @@ namespace tap::ratio { /// tap::dsp kernels, and the append/emit order is identical, so /// outputs stay bit-exact (pinned by the scipy-vector tests). /// - /// M7 lever 2 commits the trip counts: the two canonical profiles' + /// M7 lever 2 commits the trip counts: the canonical profiles' /// taps-per-phase are compile-time facts (constexpr profile), so one /// dispatch per call hands the walk a constant dot length — the /// inlined kernels unroll and vectorize against it instead of a @@ -114,6 +115,9 @@ namespace tap::ratio { if (taps == k_taps_economy) { return process_taps(in, in_frames, out); } + if (taps == k_taps_balanced) { + return process_taps(in, in_frames, out); + } if (taps == k_taps_transparent) { return process_taps(in, in_frames, out); } diff --git a/bridge/include/tap/ratio/design.h b/bridge/include/tap/ratio/design.h index 9d56560..653d386 100644 --- a/bridge/include/tap/ratio/design.h +++ b/bridge/include/tap/ratio/design.h @@ -57,33 +57,45 @@ namespace tap::ratio { // ANCHOR_END: rt_direction // ANCHOR: rt_profile - /// Quality profile. Two tiers behind one design path; taps-per-phase are - /// pinned numbers from the M2 design spike (notebooks/design_spike.ipynb, - /// verified by test_design.cpp): the minimal even counts whose Kaiser - /// designs meet the stopband spec with >= 1 dB margin. + /// Quality profile. Three tiers behind one design path; taps-per-phase + /// are pinned numbers (M2 design spike for balanced/transparent, the + /// 2026-08-07 economy18 re-pin for economy; verified by test_design.cpp): + /// the minimal even counts whose Kaiser designs meet the stopband spec + /// with >= 1 dB margin on a fine (12.5 Hz) sweep grid. /// /// | profile | stopband | passband | taps down | taps up | measured worst stop | /// |-------------|----------|----------|-----------|---------|---------------------| - /// | economy | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.8 dB | + /// | economy | 70 dB | 18 kHz | 58 | 38 | -71.5 / -71.7 dB | + /// | balanced | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.8 dB | /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.7 / -121.7 dB | /// /// economy is the default, per the speed-first charter: going down, every /// alias product is confined above 20.1 kHz by arithmetic (see - /// ratio_traits), so its 70 dB stopband buys ultrasonic cleanliness at - /// half the compute and storage of transparent — the relaxation trades - /// nothing audible. transparent exists for pristine/offline use and for - /// consumers who post-process the ultrasonic band. + /// ratio_traits), so the 70 dB stopband buys ultrasonic cleanliness at a + /// quarter the compute of transparent — and the 18 kHz edge widens the + /// transition band for another 26%/14% off balanced's tap counts. The + /// trade is the 18-19 kHz shelf moving into the transition band (measured + /// -1.4 dB at 19 kHz going down); content there is where balanced (the + /// v0.2 economy design, unchanged) remains the right pairing. transparent + /// exists for pristine/offline use. struct profile { - double passband_hz = 19000.0; ///< edge of the flat passband + double passband_hz = 18000.0; ///< edge of the flat passband double stopband_atten_db = 70.0; ///< prototype stopband target - std::size_t taps_up_to_48k = 44; ///< taps per phase, 44.1 -> 48 - std::size_t taps_down_to_44k1 = 78; ///< taps per phase, 48 -> 44.1 + std::size_t taps_up_to_48k = 38; ///< taps per phase, 44.1 -> 48 + std::size_t taps_down_to_44k1 = 58; ///< taps per phase, 48 -> 44.1 - /// The speed-first default: ~70 dB stopband, 19 kHz passband. + /// The speed-first default: ~70 dB stopband, 18 kHz passband. /// constexpr so the converter's hot path can hard-commit to the /// canonical trip counts at compile time (M7 lever 2). static constexpr profile economy() noexcept { return {}; } + /// The v0.2 economy design, unchanged: 70 dB, flat to 19 kHz. For + /// content where the top of the audible band must stay in the flat + /// passband at half of transparent's cost. + static constexpr profile balanced() noexcept { + return {.passband_hz = 19000.0, .stopband_atten_db = 70.0, .taps_up_to_48k = 44, .taps_down_to_44k1 = 78}; + } + /// Pristine tier: 120 dB stopband, flat to 20 kHz. static constexpr profile transparent() noexcept { return {.passband_hz = 20000.0, .stopband_atten_db = 120.0, .taps_up_to_48k = 96, .taps_down_to_44k1 = 184}; diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index cac5cf9..0170a3d 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -23,10 +23,11 @@ // profiles, dot kernels, row-sum quantization, measurement instruments), // consumed as the submodules/dsptap submodule. // -// Status: v0.2 — M7 codegen phase complete. The streaming converter -// (converter.h: process/pull/flush) ships float, Q15 and Q31 aliases with -// the superblock walk, committed trip counts and symmetry-halved tables, -// outputs bit-identical throughout. PLAN.md is the authoritative roadmap; +// Status: v0.3 — the profile ladder re-pin. economy (the default) moved to +// an 18 kHz passband at 58/38 taps; the former economy design continues +// unchanged as balanced (19 kHz, 78/44); transparent is untouched. On top +// of v0.2's M7 codegen campaign (superblock walk, committed trip counts, +// symmetry-halved tables). PLAN.md is the authoritative roadmap; // HANDOFF.md is the original design brief. #pragma once @@ -36,7 +37,7 @@ #include "tap/ratio/schedule.h" // IWYU pragma: export #define TAP_RATIO_VERSION_MAJOR 0 -#define TAP_RATIO_VERSION_MINOR 2 +#define TAP_RATIO_VERSION_MINOR 3 #define TAP_RATIO_VERSION_PATCH 0 namespace tap::ratio { diff --git a/bridge/notebooks/design_spike.ipynb b/bridge/notebooks/design_spike.ipynb index dc03009..7a8a5e5 100644 --- a/bridge/notebooks/design_spike.ipynb +++ b/bridge/notebooks/design_spike.ipynb @@ -20,9 +20,13 @@ "\n", "- **down** 48→44.1: L=147, stopband edge forced to 22.05 kHz (output Nyquist)\n", "- **up** 44.1→48: L=160, stopband edge 24 kHz (output Nyquist)\n", - "- **economy** (default): 70 dB stopband, 19 kHz passband — the spec\n", - " relaxation: going down, aliasing maps f → 44100−f, so nothing can fold\n", - " below 20.1 kHz *arithmetically*; 70 dB buys ultrasonic cleanliness only\n", + "- **economy** (default, v0.3 ladder re-pin): 70 dB stopband, 18 kHz\n", + " passband — the spec relaxation twice over: going down, aliasing maps\n", + " f → 44100−f, so nothing can fold below 20.1 kHz *arithmetically* (70 dB\n", + " buys ultrasonic cleanliness only), and the 18 kHz edge widens the\n", + " transition band for 26%/14% fewer taps than balanced\n", + "- **balanced**: 70 dB stopband, 19 kHz passband (the v0.1–v0.2 economy\n", + " design, unchanged)\n", "- **transparent**: 120 dB stopband, 20 kHz passband\n", "- taps per phase: minimal **even** count meeting the stopband with ≥ 1 dB margin\n" ] @@ -33,10 +37,10 @@ "id": "c97a9e67", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T15:31:26.350513Z", - "iopub.status.busy": "2026-07-23T15:31:26.350257Z", - "iopub.status.idle": "2026-07-23T15:31:27.873960Z", - "shell.execute_reply": "2026-07-23T15:31:27.872550Z" + "iopub.execute_input": "2026-08-07T00:44:09.338794Z", + "iopub.status.busy": "2026-08-07T00:44:09.338575Z", + "iopub.status.idle": "2026-08-07T00:44:11.610246Z", + "shell.execute_reply": "2026-08-07T00:44:11.607830Z" } }, "outputs": [], @@ -82,9 +86,11 @@ " return out\n", "\n", "CASES = [ # name, L, M, fs_in, pass_hz, stop_hz, atten_db, taps (pinned)\n", - " (\"down economy\", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78),\n", + " (\"down economy\", 147, 160, 48000.0, 18000.0, 22050.0, 70.0, 58),\n", + " (\"down balanced\", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78),\n", " (\"down transparent\", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184),\n", - " (\"up economy\", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44),\n", + " (\"up economy\", 160, 147, 44100.0, 18000.0, 24000.0, 70.0, 38),\n", + " (\"up balanced\", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44),\n", " (\"up transparent\", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96),\n", "]\n" ] @@ -98,7 +104,9 @@ "\n", "For each case: design at the pinned taps-per-phase, measure worst-case\n", "stopband and passband ripple by direct DFT, and confirm the pinned count is\n", - "*minimal* (two fewer taps fails the ≥ 1 dB-margin criterion).\n" + "*minimal* (two fewer taps fails the ≥ 1 dB-margin criterion; up economy\n", + "has the recorded quirk that 40 taps *also* fails while 38 passes — Kaiser\n", + "sidelobe peaking is non-monotonic near threshold).\n" ] }, { @@ -107,10 +115,10 @@ "id": "b87fc098", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T15:31:27.876508Z", - "iopub.status.busy": "2026-07-23T15:31:27.876097Z", - "iopub.status.idle": "2026-07-23T15:31:43.198224Z", - "shell.execute_reply": "2026-07-23T15:31:43.197635Z" + "iopub.execute_input": "2026-08-07T00:44:11.613867Z", + "iopub.status.busy": "2026-08-07T00:44:11.613519Z", + "iopub.status.idle": "2026-08-07T00:44:39.686307Z", + "shell.execute_reply": "2026-08-07T00:44:39.685839Z" } }, "outputs": [ @@ -120,9 +128,11 @@ "text": [ "case L taps worst stop ripple f32 KiB Q15 KiB delay smp delay ms\n", "------------------------------------------------------------------------------------\n", - "down economy 147 78 -72.10dB ±0.0025dB 44.8 22.4 39.0 0.812\n", + "down economy 147 58 -71.49dB ±0.0028dB 33.3 16.7 29.0 0.604\n", + "down balanced 147 78 -72.10dB ±0.0025dB 44.8 22.4 39.0 0.812\n", "down transparent 147 184 -121.39dB ±0.0000dB 105.7 52.8 92.0 1.917\n", - "up economy 160 44 -72.66dB ±0.0026dB 27.5 13.8 22.0 0.499\n", + "up economy 160 38 -71.73dB ±0.0024dB 23.8 11.9 19.0 0.431\n", + "up balanced 160 44 -72.66dB ±0.0026dB 27.5 13.8 22.0 0.499\n", "up transparent 160 96 -121.10dB ±0.0000dB 60.0 30.0 48.0 1.088\n" ] } @@ -163,18 +173,18 @@ "id": "d3db6503", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T15:31:43.207190Z", - "iopub.status.busy": "2026-07-23T15:31:43.206987Z", - "iopub.status.idle": "2026-07-23T15:31:52.902877Z", - "shell.execute_reply": "2026-07-23T15:31:52.901849Z" + "iopub.execute_input": "2026-08-07T00:44:39.695435Z", + "iopub.status.busy": "2026-08-07T00:44:39.695176Z", + "iopub.status.idle": "2026-08-07T00:44:54.427256Z", + "shell.execute_reply": "2026-08-07T00:44:54.425084Z" } }, "outputs": [ { "data": { - "image/png": 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", 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EyWTCrFmzsGbNGmzcuBHXX3891q1bh6ysrIjVfQYMGIBLL71U+lmj0eDCCy/E3r17Y7qmfv36hX3nsCyLv/3tb7j++uuxYcMGqfcKz/P44YcfsHHjRtTU1MDr9cLr9QIQhj1fdNFFzb5GPGVjuZ5169ahrKwMb731VkTPgu7duwMQVi0qLi7GG2+8IQ1PBgCVSoU77rgDs2fPhtvtjus97YwefvjhmOdGUyqVbXqNBx98ECzLRp0DriV1dXVYuXIlxo0bF/b9frK0R1u0Vue8vDx88MEHGD58OCZPngyGYeDz+fDBBx9g/PjxGDt2bELXQDoXSq6QLqOoqAg5OTkR43jFbqWhiouL0a9fv4g/lNnZ2UhLS0NRUREAYMSIEejevTvWrl0rJVduvPFGXHnllbBarcjLy4NCoYDJZIr6n1txThaRRqOBVqtFdXV1i9dy9OhRqFQq3H333QCEIEP819DQgNLSUgBAQUEBcnJyWuw2Geu1xlPneM952mmnhf0srtrS3Paamhppm0KhwLRp0/Dmm2+ivLwcffr0wYcffgiO48LmwommuWV8YzFt2rSEJ2a9/vrrEyrfmvvvvx9KpVIajhaPjz76CBqNJmxs/snU1rYITawAQhD/9NNP44cffsB3330nBTKrVq3Cyy+/jLfeeitsWNGNN96IESNG4LnnnpOGmxFCyKkifh8OHDgwYt/AgQNRVlYm/VxcXAwAOP300yOOPeOMM7Bx40Y4nU5oNBpcccUVWLx4MbxeL9atW4fTTz8dI0aMwJgxY7Bu3TopuXL55ZdHDENu+j0PCH9r8/LyYrqmaNcixlri9fI8j+uuuw6//PILHnzwQUyaNAl6vR42mw0rV65s8bs53rKxXI84t0XTeblCHT16FIAwxGrlypVS3AUAhYWF8Hg8qK6ujus9jWbRokVxDeENNXToULz22mttKisaOXIkRo4cmdA5WvL5559jxYoVWLFiBdLS0uIu6/F4TtmNn/Zoi5bq7HQ6MXnyZPTp0webN2+Who098MADGDduHCZPnozCwsKTPvyJnDqUXCFdhlKpjPpl7XA4oh4bbf4HnufhdDrDekNMnDgR69atQ2VlJQ4dOoS5c+eG3TVSKpWQyWS47LLLor5OUwzDgOO4Fq9FoVAgJSUl6kRef/3rX6Vl71QqFex2e4vniudaY61zoucUA73mtjdtnwcffBBvvvkmFixYgOeffx4fffQRzj///FYn37vhhhvgdrtbPKY50YLbeJ3sOVfy8vKQmZkZNma3oqICAPDuu+9i2bJlmD9/fsQyiYcOHcK2bdtw1113xR34tFV7toXYqyl0klpx1YKmSZwhQ4Zg8ODBMY+pJoSQ9iR+z0WLRZpuE4+N9rfSbreDYRhpzocrrrgC7733HnJzc7Fu3TrpBs8VV1yBzz77DAUFBSgpKcGLL77YbJ1CxRKbNFfv0G3i9//mzZvxww8/4LPPPsMdd9whHXfw4MFWzx9v2ViuR6xXSzFTaNs2vfkjysrKius9jWbkyJFt7kmZlZXVpnKhTvacK3l5edBoNPj888/x+eefS9s5jsOOHTswefJkTJ06Fddee21E2Y8++ghpaWm4+eab43rNtmqPtmipzocPH0Z1dTUefvjhsPl4WJbFtddei3/+8584fPgwzjvvvLZfBOlUKLlCuoxzzz0XCxYsQH19vdQDAgB27NgR9djc3FxUVlaiV69e0va9e/fC7Xbj3HPPlbZdccUV+OKLL/Duu+8iJSVFGu5yxRVXYO3atVCpVBg1alS7DjkYM2YMcnNzMX78+Ig7902P27p1K06cOBH1zg0Q37XG6mScsyV9+/bFtddeiwULFuC0005DXV1dTHduon1xn0orVqxoNfkFIKbhMtEsXrw4InmUl5eHvLw8jB07FhdeeGHYsCuROEzuVK5s0J5tsXv3bgDhvdLEwNlsNkccb7FYaEgQIaRDiN+HO3fuDBue43A4cPjwYWlISuix27ZtCxtay/M8duzYgbPOOksarnLppZdCoVBgxYoVyM3NxRNPPAFAiE1efvllzJ8/X/q5vR08eBButzvsRooYa4nXICb6zzrrrLCysSzfm0jZ5ojtuWXLFlx44YUtHqPX61tcpSae9zSac845J2Ko1qm0c+dOLF++PKZj/X5/3MmVadOmhQ3TEq1YsQK9e/fGLbfcEjEpPSAMAz5w4AD+9re/RY1dToZE26K1OrcUm4jbqNdK10LJFdJlPPTQQ5g3bx6efvppLFiwACzLoq6uLixrLnr44Ycxf/58PPHEE/j888+hUChgs9kwY8YMpKWl4d5775WOFQOT999/H1dccYX0h/XKK6/Ef//7X8hkMjz33HPtei2PPfYYPvroI9x99934/PPPkZ6eLu377bff4PF4cNlll+HRRx/FggULcP/992Pp0qVSD4Xy8nIcOnQIkyZNiutaY3Uyztma6dOn47vvvsNjjz0GjUaDW2+9td1fo72d7DlXWkoejR49Ompw6PV68fnnn2PYsGHNBpgA8PLLL+PQoUNYtmxZm+rWVFvaYvPmzVCr1WHLSx87dgzPPPMMevToEfYZuPHGGzFr1iy8/vrr+Oabb6Rg5cMPP0RpaSlmzpzZLtdBCCHxGDduHM477zy8+eab+POf/yz9jXvppZfCvtvFY0eNGoX/+7//wzXXXCMNO5kzZw7y8/Px8ccfS8fq9XqMGTMGH374IXiex4QJEwAIvf/S0tLw3//+F2eccQb69evX7tdkMBgwc+ZMzJo1CwBQWlqKt99+G2PHjsUFF1wAQEggMAyD7777DmeffTYA4S5+LN8piZRtznnnnYerr74ab731Vtg8F16vF1999RXuuusujBw5En/5y1/w0ksvYdSoUWHDRSorK7Fq1Srcd999cb2nndHJnnOlueQRwzDo1atXs4krcWnxllbQWbduHebNm4cnnniixRgmVom2RWt1Hj58OIYOHYpPPvkEDz/8sHRT6Pjx4/joo48wbNiwFoeqkeRDyRXSZQwfPhyffvopHnjgAWzatAmDBg1CRUUFFi1ahBUrVoQdO3ToUCxduhT3338/BgwYgGHDhmHv3r3Q6XT46aefwu469OrVC8OGDcPhw4fD7gBdeumlkMvl8Hg87X5nqE+fPvjll19w7733IicnB+eccw5UKhWOHj2KnJwcvPvuuwCEHh3r16/HXXfdhQEDBuDcc8+Fy+VCfX095s6dG/e1xupknLM1l19+OQYPHoyjR4/izjvv7BQ9EdatWxc1SLjsssswffr0Ns8zIi75CAAmkwn5+fnS69x8880JdZdduXIlamtr8fzzz7d43KZNm2Iefx+LtrRFWloaHnvsMZSVleH000+H2WzG7t27ce6550rdcEXnnXcevvzySzz11FMYOHAgzj77bFRUVODo0aN48sknW71eQgg5GRiGwfLly3Httddi2LBhGDVqFCoqKnDfffdhxIgRyM/PDzv+22+/xa233oqzzz4bo0aNQm1tLY4fP45Zs2Zh6tSpYcdeccUV2LJlC8aNGweDwQAA0jDlFStWnJReK4Dw9zY1NRXDhw9Hz549sX37dgwcOBBLliyRjhk+fDhmzpyJmTNn4qeffoJGo0FDQwMWLVoUljCPJpGyLfnqq6/w0EMP4ZJLLsGIESOQmZmJI0eOhLXrl19+iSeeeEKanLRnz54oLS2Fw+GQhqzG+56eag6HI2psolQq8eWXX7Z5npFdu3ZJy3rv27cPgDBsW6PRoHfv3vjPf/7T5jo7nU58/fXXGD16dItDvgsKCrB8+fKoi0i0RSJzrsRSZzFJ+NBDD+Gss87CmDFjwPM8tm3bhnHjxmHevHkRcyKR5Mbw4kxNhHQRZrMZeXl5kMvlGDduHFQqFb799lsMGzYMw4YNCzvW4/Fg586dqKurQ48ePTBy5MiICXEBodtfaWkpLr300rDxrmvXroXFYsH1118fNrN8Y2MjNmzYgLFjx0b0Svjuu++khEksCgoKcOzYMWg0Gpxxxhno06dPxDE8z+PAgQMoKSlBz549MWLEiIgMe2vX2pY6t/WcZrMZ69atw5gxY8Kux2KxYO3atRg1alTUu22PPPII5s6di19++QXjx4+Pqf1OBo/Hg5UrVza7PycnB+eff36bz79jxw6UlJRE3RftcxyqoqICubm5EW0r2rt3L44fP44rr7xSCsab8vv9yMzMxG233RbzJLAnTpzA7t27cfHFF7d7cq28vByHDx+G3+/H4MGDmx0CBwjvzZEjR1BeXo6MjAwMGTIkKe4kEkK6No7jsH37dtTX1+Pss89Gnz59kJubC6vViiuvvDLi+Pz8fBQUFECj0eCCCy6IekOhsrISv//+OwYOHBg2HPfIkSM4dOgQRo4cGTF3yOrVq5GWlhY27AgQ/uNcWVnZ6nDa0PJlZWXYv38/srKycMEFF4TNKSEqLy/HwYMHpWHVHMfh+++/x1lnnSUNDdm4cSOUSmXE6kGxlG3L9VRVVWHfvn1QKpU4++yzw4aSixobG7Fnzx44nU7k5ORg8ODBYXEeEP97eips2bIlbFGAUDKZDDfccEObz11dXY2tW7dG3WcwGFq95m+//RY9e/aMujpOfX09Nm3ahKFDh7a4StB9992H1atX4/jx4zENHfJ6vfj+++8TjsuiibXOoqKiIhQWFgIQJj5ubl4fktwouUIISQo8z6N///5QqVQ4duwYZfpPory8PEyYMAGFhYVJsawkIYQQQrq+nJwcPPfcc3jwwQc7uiqEREXDggghSWHNmjUoKyvD+++/T4mVk6x79+5Yt24dJVYIIYQQ0in4fD688847uO666zq6KoQ0i3quEEI6tXXr1uG///0vNm7ciHPPPRcbNmyIe+Z6QgghhBBCCDmZKLlCCOnUTpw4gT179qBHjx4YO3Zs1DHdhBBCCCGEENKRKLlCCCGEEEIIIYQQkgC6BUwIIYQQQgghhBCSAEquEEIIIYQQQgghhCSAVgtqI47jUFlZiZSUFFq5hBBCCIkTz/OwWq3o1asXzaXUTig2IYQQQtou0diEkittVFlZib59+3Z0NQghhJCkVlZWhj59+nR0NboEik0IIYSQxLU1NqHkShulpKQAEBreYDC0yzk5jkNtbS2ys7PpLl4UFrcFfef0RdmMMhhUkW3eavt5LcCKvsANZYCifd6zroQ+f4mjNkwMtV9ikq39LBYL+vbtK32fksRRbNLxmsYqMbUfxSctos9gYqj9EkPtl7hkasNEY5M/bHIlPz8fCxYsQE1NDc466yxMnz4der0+5vJid1uDwdCuAYzL5YLBYOj0H7yOkMKnwPyKGSnK6N2dW20/PgWYagbkKQB1l45An7/EURsmhtovMcnafjR8Jchut+ODDz7A/v370a1bN0ybNg3Dhg2LuTzFJh2vaawSU/tRfNIi+gwmhtovMdR+iUvGNmxrbJIcV9fOdu/ejZEjR6KhoQEXX3wxli1bhosvvhhut7ujq0ZawIOHxW0Bj7auHs4Ld4faXJ4QQgg5OTweDy655BIsWbIEF198MUwmE84//3zs3Lmzo6tG4tC2WIXiE0II6Qr+kMmV5557DpdeeikWL16MBx98EKtXr8bRo0exePHijq4aaYHNY0Pff/eFzWNr2wl8NuC7vsIjIYQQ0ol88sknOHLkCNasWYMHH3wQH330ES677DI8++yzHV01Eoc2xSoUnxBCSJfwh0uuuN1ubNy4EZMnT5a2ZWZm4vLLL8fPP//cgTUjrTGoDOBf5qPOtxIThQG4jafxzIQQQjqdn3/+GZdddhkyMzOlbTfffDM2bdoEp9PZgTUj8WhTrELxCSGEdAl/uDlXSktL4fP50K9fv7Dt/fr1w+bNm5st53a7w4YNWSwWAMIYMo7j2qVu3+woxdGKeui0jWAYBmEjvULGfTHRNzfZ3vrxJ+OcYbVu9jzRj2/tdTnejyp7IXrqBiJTp8a4gZnI1Kuk4ziOA8/zzb8fnB+w5gMpQwBWFv2YP7BW24+0itowMdR+iUm29kuWep4qJ06cwLhx48K29evXDxzHoaSkBEOGDIkocypik90lDfhxT0WLsUm7xBBtjEnaGo+0NRZB2LbIYznejyrHCfTSDUSfdB0uHJDR+u8lxSctSra/bZ0NtV9iqP0Sl0xtmGgd/3DJFTEI0Wq1Ydv1ej1cLlez5WbPno2ZM2dGbK+trW2xXDwqahtR3WiH0u4N+8bm+ehjcEM3881sb7q3ueOaL89H3d70JZqpYtiY49jrK24Pr7eHs2OF8Ubc0O1bmBxKPL18P+4Z1RNTL+ghTRpnNpvB83zUyZIYnxXZv41D7YW7wctpdYqmWms/0jpqw8RQ+yUm2drParV2dBU6FbfbHTU2AdBsnHEqYpPqWnPMsUk8cUVb45G4Y4kocUhbYpBYy3n8NnxXdxOuz1qOkgYW2XoFZl7WrcXfS4pPWpZsf9s6G2q/xFD7JS6Z2jDR2OQPl1xJTU0FADQ2NoZtr6+vR1paWrPlnnvuOcyYMUP6WVymKTs7u91m5H/8T1lJs0xVxwl+4PeXm/HIl7vBKjX4x5VngOM4MAzTQvt1A6aYkX3qKptUWm8/0hpqw8RQ+yUm2dpPrVZ3dBU6ldTU1KixCYBm45NTEZv8KSsLI/sakuZz1TkIsYrb68eDn+/G3J0mzJs6uIX2o/ikJcn2t62zofZLDLVf4pKpDRONTf5wyZU+ffogPT0d+/fvx1VXXSVt379/P0aMGNFsOZVKBZVKFbGdZdl2/ZAwDNPu5+wqfJwPOyp24ILeF0DOynFOv3R8eu8oXPff33DJGdkYlZPecvtxPqB+B5B5AcD+4T76MaHPX+KoDRND7ZeYZGq/ZKjjqTRixAjs2bMnbNv+/fuRmpoaMZRZRLFJ5xMaq2hUCvzf5BG47F+/4ECFBef2z4heiOKTVtFnMDHUfomh9ktcsrRhovXr3Fd3EjAMg9tvvx2LFi2CyWQCAGzevBk7duzAHXfc0bGVIy1yep2YsnQKnN7gxH6DuqVg+oRBeP2nw80On5L4ncDWKcIjIYQQ0onccccd2LVrF3755RcAgNlsxoIFC3Dbbbd1+mCUBDWNVbJTVLhqWCY+31bafCGKTwghpEv4Q35bv/HGG+jWrRuGDh2KCRMm4KqrrsLzzz+PCRMmdHTVSAtSVCkon1GOFFX4eOR7LzwNFY1O5J1oaPkEihTghnLhkRBCCOlExo8fj5deeglXX301LrvsMgwdOhRZWVmYNWtWR1eNxCFarHLN8CysPlgNj6+ZiRIpPiGEkC7hD9n30GAwYOvWrdixYwdqampw5pln4rTTTuvoapFW+DgfNpzYgMsHXA55SLdZtUKGv17QD5/lleCVK/o0fwLOB1RvAHpcTt1uCSGEdDozZ87EPffcgwMHDqBbt24YNWpU1FVqSOcVLVYZnK2BXi3HjuIGXDgoK7IQxSeEENIl/CF7rgDC8KBRo0bh2muvpcRKknD5XJixdgZcvsgVECaP7I1NR2vh8PibP4HfBeyZITwSQgghnVBOTg6uvfZajB49mhIrSSharMIwDMafkY1fjhqjF6L4hBBCuoQ/bHKFJB+9Uo9DjxyCXqmP2DeoWwr6ZWjxe7G5+RMo9MDVh4RHQgghhJB21lysMionA7tKGqMXoviEEEK6BEqukKTh9Xux9NBSeP3eqPuvGNYdW0+0kFzhvEDpUuGREEIIIaSdNRernNM3FQcrLdHnXaH4hBBCugRKrpCk4fF7MCdvDjx+T9T94wZmYle5tflVgzgPcGSO8EgIIYQQ0s6ai1VyMnVQy1nkV1siC1F8QgghXQIlV0jS0Cl1yL0vFzqlLur+8/qlweT0objeEf0Ech0wKVd4JIQQQghpZ83FKizLYHivVORXWSMLUXxCCCFdAiVXSNLw+D1YuHthsz1X1AoZzuyhQ96J+ugn8HuA4wuFR0IIIYSQdtZSrDKomx4FxijJFYpPCCGkS6DkCkkaXr8XSw83P+cKAJzVU4995c3Mu8IHxjTzNKaZEEIIIe2vpVjl9O56HDfaIgtRfEIIIV0CJVdI0tApdVhzx5pmhwUBwJDuWhysaCa5ItcBE9ZQt1tCCCGEnBQtxSpCz5UoyRWKTwghpEug5ApJGm6fG3Ny58Dtczd7zJBuWhyrscHl9Ufu9LuFCeP8zZcnhBBCCGmrlmKVQd30KG90wuHxhe+g+IQQQroESq6QpOHn/cgtz4Wfj5I4CehpUEKvkiO/OsqYZt4P1OUKj4QQQggh7aylWCVbr4JOKUNZgzN8B8UnhBDSJVByhSQNrUKLpVOWQqvQNnsMwzAY3tuAw5VRljqUa4GLlwqPhBBCCCHtrKVYhWEY9E7XoLyxyaqGFJ8QQkiXQMkVkjTcPjde+eWVFocFAcCg7GYmjPO7gf2vULdbQgghhJwUrcUqfdK1qDA16blC8QkhhHQJlFwhSYPjOZRbysHxXIvHDczW4XhttNn4OcBZLjwSQgghhLSz1mKVPukalDc2HRZE8QkhhHQFlFwhSUOj0GDhXxZCo9C0eNygbnoURp2NXwOMXig8EkIIIYS0s9ZilT5RhwVRfEIIIV0BJVdI0nD5XJixZgZcPleLxw3M1qPC5ITd3XQ2fhewa4bwSAghhBDSzlqLVXqnaVHRtOcKxSeEENIlUHKFdDlZeiVSNQqcqLV3dFUIIYQQQiS90zWRc64QQgjpEii5QpKGWq7GnElzoJarWzyOYRjkZOlQ0tAkuSJTAyPnCI+EEEIIIe2stVilW4oK9XYPfP6Q+VUoPiGEkC6BkiskaTi9TkxbOQ1Ob+t3fPqma1DW0OQ4nxPYNk14JIQQQghpZ63FKpl6JXgeaLB7ghspPiGEkC6BkiskabAMiz6GPmCZ1j+2fTO0KGs6YRzDApo+wiMhhBBCSDtrLVZRyWVI0ypgtIYsu0zxCSGEdAnyjq4AIbFSyVV45dJXYjq2T7oGByvM4RtlKmBEbOUJIYQQQuIVS6zSLUWF2tDkCsUnhBDSJVCKnCQNh9eBKUunwOF1tHps33QtypvOxu9zAFumCI+EEEIIIe0sllglu2lyheITQgjpEii5QpKGjJFhbJ+xkDGyVo/tmyEsdchxfHAjIwOyxgqPhBBCCCHtLJZYJVuvgtEasuwyxSeEENIl0LAgkjRUchVmjJ0R07G90tTwchyMVjd6pAZm35epgKGxlSeEEEIIiVcssUpEzxWKTwghpEugniskadg9dkz6fBLsHnurx6rkMmTpVagyhwwN8tmBjZOER0IIIYSQdhZLrNItRR0+oS3FJ4QQ0iVQcoUkDYVMgSnDpkAhU8R0fHeDCjWW0Nn4FUC/KcIjIYQQQkg7iyVWSdcp0egIWYqZ4hNCCOkSaFgQSRpKmRLTzpsW8/HdU9ThY5plSmBQ7OUJIYQQQuIRS6ySrlXA5PAGN1B8QgghXUKXS67U19fjww8/xO+//w65XI6LLroI06dPh1arlY55++238e2334aVGzhwIL744otTXV0SB7vHjomfTcT6O9dDp9S1enw3gxo1lpDkis8ObJgIXL4ekLdenhBCCGkPfr8f33zzDX788UfU1tZi6NCheOyxxzBw4EDpmB07duDRRx+NKPvNN9+gf//+p7K6JAGxxCppTZMrFJ8QQkiX0KWSK36/H+effz7uuOMOTJ8+HQ6HAy+//DJWrlyJTZs2QS4XLvfEiRPQarWYNWuWVDY0+UI6J6VMiRljZkApU8Z0fHeDKnw5ZlYpTBjHxlaeEEIIaQ/Tpk2D1+vFNddcg+zsbHzyySc455xzsH37dgwdOhQAYDabsW3bNuTm5oaV7d69e0dUmbRRLLFKmrbJsCCKTwghpEvoUskVmUyGgwcPQqcLZv3POOMMnH322di+fTvGjRsnbc/IyMCYMWM6opqkjRQyBaYMnxLz8T0MauwqaQxuYANjmgkhhJBT6P3334der5d+njhxIoYMGYIFCxZgzpw5YcdSbJLcYolV0jQKuH0cXF4/1AoZxSeEENJFdLkJbUMTKwCkYMbj8YRt37FjByZMmIAbbrgBc+bMidhPOh+bx4bh/xsOm8cW0/HdDWoYQye09dqAn4YLj4QQQsgpEppYAQCGYaDT6aLGHtdeey2uvPJKPPHEEygtLT1VVSTtJJZYJVUjTFwr9V6h+IQQQrqELtVzJZo33ngDPXv2xKhRo6RtWq0Wd9xxB8aPH4+Kigq8/vrrWL58OX799VfIZLKo53G73XC7g/9Rt1gsAACO48BxXLvUleM48DzfbufrapSsEv+64l9QssqobdS0/bL1StRYXMFjGSVwzr+ER2rjCPT5Sxy1YWKo/RKTbO2XLPU8GVatWoU9e/bgrbfeCtt+ww034I477oBcLseiRYswfPhw7NixA0OGDIl6HopNOp+msUq09mMZIEUtR4PNje4pKopPWkGfwcRQ+yWG2i9xydSGidax0ydXXnvtNfz0008tHrNs2TL06dMnYvt7772Hzz77DKtWrQqbU2X27NlQqVTSzxdffDGGDBmCpUuX4pZbbon6GrNnz8bMmTMjttfW1sLlckUpET+O42A2m8HzPFi2y3UqahfnppyLhrqGqPuath/r8cLk9KK0shpqeaA9ZecCzZT/o6PPX+KoDRND7ZeYZGs/q9Xa0VVos99++w1PPvlki8dMnz4dd955Z8T2Q4cO4fbbb8ff//53XHHFFdL2iy++GBMnTpR+vuaaazBmzBj885//xLJly6K+BsUmnVNorNJc+xlULIora5EpC7xPFJ80iz6DiaH2Swy1X+KSqQ0TjU06fXLltttuCws+osnOzo7YNn/+fDz11FNYsmQJLr/88rB9oYkVABg0aBBycnKwb9++ZpMrzz33HGbMmCH9bLFY0LdvX2RnZ8NgMMR6OS3iOA4MwyA7O7vTf/A6gtVtxfC5w3Ho4UNIUaVE7G/afpkcD4bZD5k2Fd3SNIDXCubn4eCvOgQoIsv/0dHnL3HUhomh9ktMsrWfWq3u6Cq02fDhw/Huu++2eEy/fv0ituXn5+Pyyy/HDTfcEFG+aWzCsiwmTJiA5cuXN/saFJt0Pk1jlebaL0OvAVQ6dOvWjeKTVtBnMDHUfomh9ktcMrVhorFJp0+uDBw4MGypwlgsXLgQjz76KL766itcf/31rR7v8/lQV1cXMV9LKJVKFRH4AELw054fEoZh2v2cXYVOpcPSKUuhU+mabZ/Q9mNZIF2rhNnpQ98MFlDogIuWglHoAGrfqOjzlzhqw8RQ+yUmmdovGerYnLS0tLgnnj169Cguu+wy/PnPf8aCBQvAMEyrZaqrqyk2STLRYpVo7ZemVcDi8gnbKD5pFX0GE0Ptlxhqv8QlSxsmWr/OfXVt8NFHH2H69On46quvcOONN0bs93g8eOutt6RJ5Hw+H5555hk4HA7cdNNNp7q6JA5yVo6xfcdCzsaeE0zXKlBvD0wYx8qB7LHCIyGEEHKKFBQUSImVRYsWRQ3e5s2bh7KyMunn9evX46uvvmq2Ry3pnGKNVdJDl2Om+IQQQrqELpVcMZlMuP/++6HT6fD2229jzJgx0r+VK1cCABQKBWw2G3r16oWzzjoLPXr0wPfff4/vv/8eQ4cO7eArIC2xuC0wzDbA4rbEXCZTp0KjmFzxWoAlBuGREEIIOUWmT5+O6upqHDx4EOPGjZNikyeeeEI6pm/fvpg4cSIGDRqEgQMH4rrrrsNzzz2Hp59+ugNrTuIVa6xi0MhhdfmEHyg+IYSQLqFLpcj1ej1+++23qPvEoUUMw+C1117Diy++iIKCAqSnp6Nnz54xdc8lHUun0CH3vlzoFM13kW4qXRfSc0WmA67MFR4JIYSQU+Tdd9+VVvIJlZaWJj2/6qqrcNVVV6G4uBgejwennXYaFArFKawlaQ+xxip6lQI2MblC8QkhhHQJXSq5IpfLYx4DrVQqMXz48JNcI9KeZKwMw7vF955lhPZcYWVAGr3nhBBCTq1hw4bFfGxOTs7Jqwg56WKNVfQqGWosgZWCKD4hhJAuoUsNCyJdm8VtATOTiWtYUEZozxWvBfiSoW63hBBCCDkpYo1V9Co5bO6QYUEUnxBCSNKj5ApJGnqlHmVPlEGv1MdcJl2rRIPdLfwg1wPXlwmPhBBCCCHtLNZYRaeSB4cFUXxCCCFdAiVXSNJgwMCgMoBB7PPjZOqVaLR7pTNAYRAeCSGEEELaWayxSopaDrvHJ5Wi+IQQQpIfJVdI0rB6rEh9MxVWjzXmMulaJerFnis+K7A0VXgkhBBCCGlnscYqYRPaUnxCCCFdAiVXSNJIUabA/KwZKcqUmMukaZUwO8VutynAFLPwSAghhBDSzmKNVXQqWXDOFYpPCCGkS6DkCkkaPHhY3Bbw4GMuY1DLYXGJw4L4wGRxsZcnhBBCCIlVrLFKijpkQluKTwghpEug5ApJGjaPDX3/3Rc2jy3mMgaNAh4fB5fXD/hswHd9hUdCCCGEkHYWa6yiU8nh8Pjh53iKTwghpIuQd3QFCImVQWUA/3J8d3VS1MJH3OryQZ1iAG6ju0KEEEIIOTlijVX0KiE+sXt8MKgpPiGEkK6Aeq6QpOHn/DhkPAQ/54+5jEoug1rBCkODOD9gOiQ8EkIIIYS0s1hjFZ1SSK7YXD6KTwghpIug5ApJGnavHWMXjYXda4+rnEGtgMXpBfx2YO1Y4ZEQQgghpJ3FGquwLAOdUga720fxCSGEdBE0LIgkDYPKAMtzlvjLaRSwuHyAIh24Of7yhBBCCCGxiCdW0aoCk9pSfEIIIV0C9VwhScPH+ZBblgsf52v94BAGtVzoucL5gNpc4ZEQQgghpJ3FE6toFDK4vBzFJ4QQ0kVQcoUkDafXiSlLp8DpdcZVTui54gX8TmDrFOGREEIIIaSdxROrCMkVP8UnhBDSRdCwIJI0UlQpKJ9RHnc5Yc4VH6BIAW6IvzwhhBBCSCziiVXUClZIrlB8QgghXQL1XCFJw8f5sOb4mviHBWnkgdWCfEDlGup2SwghhJCTIp5YRa2Qwen1U3xCCCFdBCVXSNJw+VyYsXYGXD5XXOX0KgWsLi/gdwF7ZgiPhBBCCCHtLJ5YRUquUHxCCCFdAg0LIklDr9Tj0COH4i6nU8pgtLoAhR64Ov7yhBBCCCGxiCdWkSa0pfiEEEK6BOq5QpKG1+/F0kNL4fV74yqnVcnhcPsBzguULhUeCSGEEELaWTyxikYZmNCW4hNCCOkSKLlCkobH78GcvDnw+D1xldMqZXB4/QDnAY7MER4JIYQQQtpZPLGKWsHC6aH4hBBCugoaFkSShk6pQ+59uXGX0yplcLh9gFwHTIq/PCGEEEJILOKJVdTiUswUnxBCSJdAPVdI0vD4PVi4e2HcPVd0SjnsHj/g9wDHFwqPhBBCCCHtLJ5YJTihLcUnhBDSFVByhSQNr9+LpYfbMueKDA6PD+ADY5p5GtNMCCGEkPYXT6wiTWhL8QkhhHQJNCyIJA2dUoc1d6yJu5xWKYfDE+h2OyH+8oQQQgghsYgnVtGEDgui+IQQQpIe9VwhScPtc2NO7hy4fe64yunEOVf8bmHCOH985QkhhBBCYhFPrKJWsIFhQRSfEEJIV0DJFZI0/LwfueW58PP+uMppVXI4vH5wfh9QlwvEWZ4QQgghJBbxxCrShLa8n+ITQgjpArpccuW5555Dnz59wv5NnDgx4rglS5bgwgsvxKBBg3DDDTfgyJEjHVBbEg+tQoulU5ZCq9DGVU6nlIHnARdUwMVLAXl85QkhhJBEbN26NSI26dOnDwoLC8OOO3r0KG688UYMGjQI48aNw1dffdVBNSZtFU+sIk1oK9dSfEIIIV1Al0uuNDY24uyzz0ZeXp7074svvgg7ZsWKFbj99ttx5513Yvny5UhJScH48eNRW1vbQbUmsXD73Hjll1fiHhakVQpTCzmcDmD/K9TtlhBCyCnlcrlQUVERFpvk5eWhf//+0jH19fW45JJLoNPpsHz5ctx999246667sGzZsg6sOYlXPLGKWpzQ1u+m+IQQQrqALjmhrUajQZ8+fZrd/9prr2Hq1Kl46KGHAAAfffQRevbsiblz5+Kll146VdUkceJ4DuWWcnA8F1c5pZyFnGXgdHsBZzkQZ3lCCCGkPbQUm8ybNw9+vx+LFy+GXC7H2Wefje3bt+PVV1/F5MmTT2EtSSLiiVUUMgZePyfEJRSfEEJI0utyPVcAYPPmzTjjjDNwwQUXYMaMGWhsbJT2WSwW7NmzB1dccYW0TS6X4/LLL8evv/7aEdUlMdIoNFj4l4XQKDTxl1XKYOcUwOiFgDz+8oQQQkiizj33XAwbNgxTpkzB7t27w/Zt3rwZl112GeTy4H2vP/3pTzhw4EBYHEM6t3hiFaWMFZIrcg3FJ4QQ0gV0uZ4r2dnZePXVVzF+/HhUVFTgueeew4UXXojdu3dDrVajoqICANCjR4+wct27d8e+ffuaPa/b7YbbHeyuabFYAAAcx4Hj2udOA8dx4Hm+3c7X1bh8Lryw8QW8MeENqOXqiP0ttZ9aIYPLZQO/62XwI94AZJHl/+jo85c4asPEUPslJtnaL1nq2R5YlsWjjz6KO+64A3K5HAsWLMDo0aPx22+/YdSoUQCA8vJyTJgwIaxc9+7dAQCVlZVIT0+POC/FJp1P01ilpfaTyxh4fBw4rwPM/hcoPmkGfQYTQ+2XGGq/xCVTGyZax06fXJkxYwaWLFnS4jFbt25FTk4OAODVV18FwzAAgGHDhuHMM89E//798fXXX+Puu++WGiz0zhAAKBQK+P3Nz9I+e/ZszJw5M2J7bW0tXC5XPJfULI7jYDabwfM8WLZLdipKiMvngsPhgNFobDa50lz7yRkextoGOFwOWI1GCl6ioM9f4qgNE0Ptl5hkaz+r1drRVWizDRs2YOrUqS0e8/zzz+ORRx4BAFx22WVhiZO5c+ciPz8fr776Kn788UcAwvsXLTYB0Gx8QrFJ59M0Vmmp/WxmO9xeH4xGI1IcFJ80hz6DiaH2Swy1X+KSqQ0TjU06fXLlxRdfxIwZM1o8pmfPntJzMbESuq9///7SakDZ2dkAgLq6urDj6urqpH3RPPfcc2H1sFgs6Nu3L7Kzs2EwGGK7mFZwHAeGYZCdnd3pP3gdZe71c5vd11L7aVUKqAzZ0IycC+p0Gx19/hJHbZgYar/EJFv7qdXJ+5/ICy+8EHl5eS0ek5qaKj1vGpsAwJgxY8JuHmVnZ0eNTcR90VBs0jmFxiottV+93wI/X4huPfsBPSk+aQ59BhND7ZcYar/EJVMbJhqbdPrkSnp6etSusLFyOp2oqqpCRkYGAKBbt27o378/fvvtN1x33XXScVu3bsW1117b7HlUKhVUKlXEdpZl2/VDwjBMu5+zq3B6nXh01aN4/8/vNzuWubn2U8ll8HmdYHc8AIx8n8Y1N4M+f4mjNkwMtV9ikqn9kqGOzVGr1S1OThuLEydOSLEJAIwaNQorVqwIO2bLli3o06dP2E2kUBSbdD7RYpVmYxOFDF4/B5ZzA7sepfikBfQZTAy1X2Ko/RKXLG2YaP0699XFye1249FHH0VlZSUAwGw24/777wcA3HLLLdJx06dPx6JFi7Bv3z5wHIf33nsPpaWleOCBBzqk3iQ2LMOij6EPWCb+j61KwcLt4wFNH6AN5QkhhJC2mjVrFrZt2ybNhfLJJ59g2bJlmDZtmnTM/fffj7KyMvznP/8Bx3HYv38/Fi5ciOnTp3dgzUm84olVFOKEtgxL8QkhhHQBnb7nSjxUKhXOPPNMXHTRRWhsbITT6cTo0aOxefNm9O/fXzruySefRFVVFcaMGQOZTAaDwYCvv/4aQ4cO7cDak9ao5Cq8cukrbSqrlsvg9CuAc9pWnhBCCGmriRMn4plnnsGuXbvg8/nQvXt3zJs3Lyy5MnjwYCxduhTTp0/HCy+8AL/fjwceeABPP/10B9acxCueWEVIrvDgGCXYEbGVIYQQ0nl1uRT5gw8+iBMnTqC4uBh2ux2bN2/GyJEjw45hWRZz5syB2WxGUVERKioqcOONN3ZQjUmsHF4HpiydAofXEXdZlYKFz2MHtkwBfPGXJ4QQQtpq1KhR2LRpE4xGI+rq6lBUVBSWWBFdd911KCsrQ1FREUwmE/7zn/90+i7UJFw8sYpCJry3Xo+N4hNCCOkCulTPlVChE8k1R6lUtjiJLelcZIwMY/uMhYyRxV1WJWfh9PFA1ligDeUJIYSQRMUyUZ446R9JTvHEKkoxucKxUFF8QgghSa/LJldI16OSqzBjbMsrRzVfNjAsaGjbyhNCCCGEtCaeWEUpDyRXeIpPCCGkK6C+piRp2D12TPp8Euwee9xl1QoWfq8V2DgJ8MVfnhBCCCGkNfHEKgqZsES3103xCSGEdAWUXCFJQyFTYMqwKVDIFHGXVcllcPpYoN8UgIm/PCGEEEJIa+KJVWQsA4YBPLyM4hNCCOkCaFgQSRpKmRLTzoucADAWKjkLh48BBrWtPCGEEEJIa+KJVRiGEVYM4hUUnxBCSBdAPVdI0rB77Bi7aGybhgUp5CzgswFrxlK3W0IIIYScFPHGKkoZC5/HSvEJIYR0AZRcIUlDKVNixpgZUMqUcZdVsAzcfrkwYRwbf3lCCCGEkNbEG6soZBSfEEJIV0HDgkjSUMgUmDJ8SpvKymUs3JxcGNNMCCGEEHISxBuryGUsvOKcK4QQQpIa9VwhScPmsWH4/4bD5rHFXVYuY8D6bcBPwwFv/OUJIYQQQloTb6wiYxjAZ6X4hBBCugBKrpCkoZarMefKOVDL1XGXlbMMXJwCOHcOIIu/PCGEEEJIa+KNVWQsAy/UFJ8QQkgXQMOCSNKQs3JMGjSpjWVZuP0s0Ktt5QkhhBBCWhNvrCJjGfh4ik8IIaQroJ4rJGlY3Vb0mdMHVrc17rIKGQM5ZwNW9AG88ZcnhBBCCGlNvLGKjGXAeCk+IYSQroCSKyRpaBQaLJ2yFBqFJu6ychkLu18BXLQUkMVfnhBCCCGkNfHGKjKWgYdRUXxCCCFdAA0LIklDzsoxtu/YNpZl4PHLgOwx7VwrQgghhBBBvLGKjGHg51kgu23xDSGEkM6Deq6QpGFxW2CYbYDFbYm7rELGQsnZgCUGwBt/eUIIIYSQ1sQbq8hYRhgORPEJIYQkPUqukKShU+iQe18udApd3GXlMgY2TglcmQvI4i9PCCGEENKaeGMVGcvAAzXFJ4QQ0gXQsCCSNGSsDMO7DW9TWTnLwuNngbS2lSeEEEIIaU28sQrLMvDzMopPCCGkC6CeKyRpWNwWMDOZNg4LYqDgrMCXDHW7JYQQQshJEW+sImcZMD4LxSeEENIFUHKFJA29Uo+yJ8qgV+rjLiuXsbD61cD1ZYA8/vKEEEIIIa2JN1aRMQzc0FB8QgghXQAlV0jSYMDAoDKAARN3WQXLwOvnAYUBaEN5QgghhJDWxBuryFgGfo6h+IQQQroASq6QpGH1WJH6ZiqsHmvcZeUyFgrODixNBXzxlyeEEEIIaU28sYpMHBZE8QkhhCQ9Sq6QpJGiTIH5WTNSlClxl5WxgJ3TAFPMgDz+8oQQQgghrYk3VmFZBm7oKT4hhJAugJIriXI6hUeXS/gnbnO7hecOR/C53Q54PMHnXq/w3GYDfD4AABPyHBYL4PcHn3McwPPCc54XfrYEJj/z+4PPfT7Aag0+t9mE516v8LqAUA/xudst1FN83s7XBKu1Xa6Jt1pgcVvA+7zNXhMTeh0hz1m3CxzPAVYj4HJ2mmvqiu8TXRNdUyLXxFitXe6auuL71G7XRE4Oik067HeUBw+LrR683SZdU3OxCZxOyBiAdzmE+AR8p7ymrvg+0TXRNVFs0vXep04Rm/CkTcxmMw+AN995p7DhiSeEfzzP8/fdx/Mvvyw8nzyZ5995R3h+5ZU8v2CB8HzMGJ5fskR4PmwYz69ezfv9ft7Xsyfv37pV2J6SwvMHDwrPAZ4vK+N5s1l4bjYLP4tv4cGDwvE8z/O//87zvXsLz1evFs7P88LrjRkjPF+wQKgPzwv1mzxZeP7yy0L92+maeJ4X6vL77wlfkzmnJ49XwJt/XB71mvzz5vGu8eN5v98fcU11t9zJD39uCc9/AZ5/8pFOc02d6X3ievfm6374QWi/LnJNp/p98vv9vPnll3nuppu6zDWdyvfJ7/fzfr2e9+/f32Wu6VS+T/5//Yt3XnON8DucBNdkXr5c+B41m3nSPig26fjfUbPLLMQqF57P83zLsQl/3338X97fwn887iohPvGYO+U1dfT75N+6la+qquK5LnRNFJskz/tEsUni18TddJPwO/zSS53+mhKNTSi50kZSAFNdLWxwOoV/PM/zDgfPu1zCc7s9+Nxm43m3O/jc4xGeW6087/Xyfr+fry4o4P3iMWYzz/t8wed+P89znPCc44SfxTfe5ws+93p53mIJPrdahecej/C6PC/UQ3zucgn1FJ87HO12TTzPC3URn5/Ea/I7nXx1YaEQwDS5poMFVXz/Z37kz3jy26S6plP5PvlNJr6qrExovy5yTaf6ffL7/XxVcTHvF4/pAtd0Kt8nv9/PVx87xvvFY7rANZ3K98nvcPBVoX8DO/k1mevrKbnSzig26Xy/oy3FJrzDIcUmNpM1aa7pVL9Pfrebr6qq4v2NjV3mmig2SZ73iWKTxK/Jb7UKv8MOR6e/pkRjE4bneT6xvi9/TBaLBampqTCbzTAYDO1yTo7jYDQa0a1bN7Asjdhqys/5kV+XjyFZQyBjZRH7W2q/w5UWXPPeLxioKseEUZcgXa/BQ+MHnqqqJwX6/CWO2jAx1H6JSbb2Oxnfo390FJt0vKaxSmvtl/PsT2Dhx+p7umHAgJGQKxQdUOvOjT6DiaH2Swy1X+KSqQ0T/R6N++rq6+uRm5sLa2Bc0u7du/Hwww9j2rRp+PHHH+OuQHvT6/VgGCbi39///nfpmIceeihi/5lnntmBtSaxsHvtGLtoLOxee9xlZSwDLevGt4P+gS9+O4I3V+WfhBoSQgjpCB6PBzt37kRpaSkAoKGhAS+//DLuuOMOvPPOO/CIY7Y7yN133x01NunVq5d0zPr166Mek59P31fJJN5YRYxPem6fgDmr95zk2hFCCDmZ4kqubNy4Ef369cO4ceNwxhlnYP369bjooouwadMmbN26Fddeey2+/PLLk1XXmNhsNvDCcCfwPI8NGzYAACZPnhx23E033RR23MGDBzuiuiQOBpUBlucsMKjakEVkABunxVmHlsLGaQEAfo7H78fr2ruahBBCTiGTyYTzzjsPF1xwAQYMGIAvv/wSV155JT788EMcOnQIL7zwAm699dYOrePHH38cFnN4PB507949IjYBEHYcz/MYMmRIB9SYtFW8sQrP81J8UmSO7JVLCCEkecSVXHnxxRdx991348CBA7jxxhtx8803480330R+fj7y8/MxZ84cvPPOOyerrm2yaNEinHHGGbjkkks6uiokQT7Oh9yyXPi4+GdxZlkGMvhxnvYIZBBmkf69sA63LdyGgxXm9q4qIYSQU2Tx4sVgWRY7d+7EV199hSeeeALp6ekoKyvDnj17sG/fPqxevRpFRUUdXVXJjz/+iJqaGtx///0dXRXSztoSq4jxidvrxlNL96HK7DyJNSSEEHKyxJVcOXDgAF599VWceeaZePXVV9HY2Ii7775b2n/vvffi2LFj7V3HNjOZTPj222+jBi+rV6+GVqtFz549cfPNN6O4uPjUV5DExel1YsrSKXB64w86ZAwDNevGB/3ehJoVlvfaVdIIADhSZWnXehJCCDl1Dhw4gOnTp2PkyJGYMmUKhg0bhltvvRVKpRIAMHjwYIwbN65TxSeLFi3C6NGjcdZZZ0Xsy87OhsFgwNixY/Hzzz93QO1IIuKNVXhAik+MjSYs3VWODUeMJ7eShBBCTgp5PAdbrVZkZmYCgPQYOtFLamoqbOJa153AF198AY7jMHXq1LDtgwYNwldffYXx48ejoqICjz/+OC6++GIcPHgQqampUc/ldrvhFtfchjDZDSBM0MNxXLvUl+M48DzfbufranQKHUofF8bTR2ujltqP5znYOS3G5n8ibTtcKbyHpQ0O7ClpwMKtRXj3r+dAxjIn6Qo6N/r8JY7aMDHUfolJtvZrr3paLBYpJgGE+KTpJHSpqanSXHEdrbKyEqtXr8a8efPCtqvVarz11lu44447IJfLsWDBAlxzzTVYtWoVJk2aFPVcFJt0Pk1jlVjaT4xPlHIGAI9aqwu5x2sxtJcBBjVNcEufwcRQ+yWG2i9xydSGidYxruRKR7j77rvxySeftHhMQUEBBg0aFLF90aJFuO6665CdnR22/R//+If03GAw4JtvvkHPnj3xzTff4IEHHoj6GrNnz8bMmTMjttfW1sLlcsVyKa3iOA5msxk8z3f6mZQ7go/zYUvFFlzc+2LI2ciPbkvt12B2QwY/LtTvxW+2c8CwcpTWWaGSMyiuacSXDWb8dKAWt59TigGZmlN1SZ0Kff4SR22YGGq/xCRb+3WWZEdb/Pjjj7j22mtbPGb27Nl49tlnI7Z//PHH0Gq1uOWWW8K2X3TRRbjoooukn1944QXk5ubinXfeaTa5QrFJ59M0Vmmt/XgeYfEJIENJjQn/2XAcUy/ogYcv7H3Kr6Gzoc9gYqj9EkPtl7hkasNEY5O4kytNV9U52avsfPzxx/j444/jLrdnzx7s2bMHb731VqvHpqWloU+fPigoKGj2mOeeew4zZsyQfrZYLOjbt6/Ufbc9cBwHhmGQnZ3d6T94HcHmseH1Fa8j975c6JX6iP0ttZ9b7oCK9eCfvRbh+uPvQKZUwWjzYkC2Hh7IYbIL87DUe5U4XZuKtYdrcOuofqfkujoL+vwljtowMdR+iUm29lOr1e12rhkzZuCVV14BAJSVlWH79u3Sz+K2psmMRFxzzTXgeT7ucjzP46OPPsKtt94KnU7X6vFnnXUWvv7662b3U2zS+TSNVWJpv9D4xMFpUGkTYhIHJ4eZ10AhZ5GT2frnpauiz2BiqP0SQ+2XuGRqw0Rjk7iSK08++WTYz3/6058ijom2rSMsWrQIp512GiZOnNjqsSaTCeXl5WFLIjalUqmgUqkitrMs264fEoZh2v2cXYVBbcCh6YdaPKa59mNZFg5OgyuP/Q8A0F0pQ43FjVGnZcLs9MLi8kIpY1FlceHzbWX4z4YCjB2YhQHZkUmcrow+f4mjNkwMtV9ikqn92quOf/nLX5CTk9PqcYMHD26X10vE5s2bUVhY2GLCJNSBAwcoNkky0WKV1tovND4BgBN1wjLOjQ4Prv3gd/TL0GL9jPEnr9JJgD6DiaH2Swy1X+KSpQ0TrV9cyZV//etfCb3YqeJyufDll1/iySefBMOEz5/hdrtxyy234LnnnsPw4cNRWlqKxx9/HCkpKbj99ts7qMYkFl6/F9/lf4frh1wPhSy+McgMA8jhw5WpeVhrHgOtUgfAjR6papQ2OGByeDCwmx71dg/KGhwAgEOVFvRK02Dl3kpMHtkH7B90LhZCCOnM7rrrro6uQswWLVqEc845B+eff37EvhkzZuDCCy/E+PHjwfM85s+fj59++glLlizpgJqStmpLrBIan/ggR53NAwAwO73w+DgcN9pQYXLil6NG3D66/8msPiGEkAR07tRRG3377bewWCy45557IvapVCpMmzYN//jHP9CjRw9MmjQJWVlZ2LZtG7p169YBtSWx8vg9mJM3Bx6/J+6yDMNAwfgwLWsFVKwPaoUMANDNoILZ6UWDw4PTu+lRb/OgpN4BhYxBpcmJnw9U4enl+5F7or69L4cQQsgfiNlsxvLly5tdfvlvf/sbvvvuO5x55pkYPHgwVq9ejZ9//hlTpkw5xTUliWhLrCLGJwrGB4M6eN/T5vZLzz/+rQgvrDgIp8cf7RSEEEI6gZh7rlx//fUxn/S7775rQ1Xaz2233Ybbbrut2f1XX301rr766lNYI9IedEodcu/LbVNZBoCTV+PGwncgZxlolUJyJUOrRKPDA5eXw8BsPfaVm2ByeDG8VyoqTE7UWoVVGA5UmDF2QCbeXnMU916Yg26G9psrgBBCSNu8/fbb+P3332M69plnnsHYsWNPco2al5qaCofD0ez+AQMG4LPPPjuFNSInQ1tiFTE+AYAMtQIWlw8AUG0OLudcbxeSNceNNkz/cjc+vGMkhvVqn3l1CCGEtI+Ye67k5ORI/wwGA77//nsUFhYiPT0d6enpOH78OL7//vt2m0CNkKY8fg8W7l7Yxp4rgILx4q8Za6BkvdAEeq6kahRwBO4C9UpTw+72CUOEsvVosHtQVGeHWsGiotGJ3aWN+HBzIb7eUdau10UIIaRtunXrFhafbNmyBVu2bIFarUaPHj3gcDjwww8/wGg0QqvVdnR1yR9AW2IVMT5RMF6oFUJonqVXodHhlY4pDszDknuiDqUNDmw9Xos9pY3YXdrYvhdACCGkzWLuufLuu+9Kz2+77Ta88sorePnll8OOmTlzJo4dO9ZulSMklNfvxdLDS3HrmbdCKVPGVZYBAzn8uDp1K1ZbL4NcJsyfYtAI46FVchYpgbtFdo8f/TK02FXaCKvLixF90lDe6MCRamFpriNVFgDAXR9tx4TB2bj7wtPa8SoJIYTE6u6775aer1ixAnl5eVi/fj30+uBk5Nu3b8eUKVMwYMCADqgh+aNpS6wixicrG8dDE+hZm6VXos7mRrpWAbvbj8JaIblyqFKIQSpNLty5aDu8fg75r/0JRqsb3alXLSGEdKg2zbmyfv16PPbYYxHbH3vsMaxfvz7hShESjU6pw5o71kCnjH85QoYRut3eVfQa3AgGH6mB5Io4VEjsgts3QwOz0wuzw4uB2To0OLwoqbOju0GF8kYnjFYXfj1WK/ViWbS1CK+sbHklI0IIISfPunXrMG3atLDECgCMGjUKp59+Ovbu3dsxFSN/KPHGKilquRSfOHk1FLJgzxUAkMtYaJQymJ1Cr9vCWhsAwGh1web2we3jkHuiHqNnbUCdzQ2Oi3+JcEIIIe2jTckVj8eDw4cPR2w/dOgQPJ74h2wQEgu3z405uXPg9rnjLssAUDJe3Je1AnJ44fMLwYeYXGEYBjqVDI0OL1RyFtkpKlidXjQ6POifqYPF6UWD3YPhvVJhtLpwInAHqdIkJGP+s/4YPv69GC5v5ERzNPkcIYScfM3FJm63G4WFhRSfkFMi3lhl9eOXSPGJkvFCGUiupGmDKw2JQ5l7pKpRWi/M22MNzMsCAPvKzACEXi2T3v0VC7ecaJdrIYQQEp82JVemTp2KyZMn43//+x927NiB7du343//+x+mTJkS1kWXkPbk5/3ILc+Fn29DsoIBWPhxnjYfLPzwcRwAwKAOBi9apTBKLkWtgEGtEHquOL3IydRKzwd106PO5kFJvR1DeqTA4vLB6vLC5RXOV1hrA8/z0nLO1WYXhr60GusO1yR49YQQQlpy55134v3338eMGTOwefNm7Nu3D9999x0mTZoEuVyOiy66qKOrSP4A4o1VeqdpwuITpVwIzVMC8QnLQBoqlKYVhi9n6JSoNrukcxQYhWHL5Y0OFBht+HF/FbadqMeTS/YBABrsHurRQgghp0CbkivvvPMOHnnkEbz44osYNWoURo8ejZdeegnTp0/H//3f/7V3HQkBAGgVWiydshRaRfyTEjJg4OLVmF76HJycCoHcCvSBJQ95nocukFxRyYUuuPV2Dzge6Jehg9nphcnpRf9MLfwcj/JGJ07L0kEhY7CvzAyO5zGkRwrKG534LK8EF7+9CXU2N7YU1AIAfjlqjKiTz8/B7PRGbCeEEBK/8ePH4/vvv8eGDRtw6aWX4pxzzsGUKVPQs2dPbNy4ESqVqqOrSP4A2hKriPGJK2RYkF4lJFQYMFAHeq6ka4U5XLob1ChtcEAhY6BRyHCsRkiu5FcJjzyApbvKsXx3Oeptbpz32jos3UWT8RNCyMnWpuSKXC7HP//5T9TX16O6uhrV1dWoq6vDCy+8ALk85jlyCYmL2+fGK7+80rZhQYwwLOjx7l9ADi8YYT5byFhGOkacoV8uY6CSB381eqSq4ed41Fhc6JWqAcMAFY1OpGmVyNKrcKTKgnSdEj1S1ai1urG7RJi5/0C5GSX1DqjkLPIDk+GGeuPnIzjn1bXw090kQghpF1dddRX27dsHq9WKkpISOJ1OfPXVV+jbt29HV438QbQlVhHjEyXjleIPeSDJwjKQerOIQ4Wy9Eq4fRzUchl0KhnKG51QK1gcDcQaDXY3LIGbN78cFW7yHKmyYvOxWnyw6Th4nsfyXeVw+2jYMiGEtKc2JVdCde/eHd27d2+PuhDSIo7nUG4pB8dzcZdlADDg0ENRD573g2WYiGPEu0UMAJVcuEskYxlprLPR4kaqVgG9Uo7yRidSNQqkahQob3QgVaNAtl6FWqsbJwLLJR432lDS4MC4gZmoCszNcqzGijqbEHBtKagDzwe784badNQIo8UVsZ0QQkjr9Ho9+vXrRzd8yCnXllhFjE8YcNJNHz5w34VhGPCBH8SeK5k64VEuE3q1mBxe9EzVoLTBARnLoNbqRoNdmGNof7kJAFBlduLt1fn4vzVHUVhrx5NL92HjESM4jqchQ4QQ0k4STq4QcqpoFBos/MtCaBSauMsyDAM3r8Kz5X+Hi1OBZSP3KwJ3hhiGkXqxCIkW4bnHz8GgVkCvlqPCJCRX9Krg8wy9Eg12D4wWN87qLUx8W2124py+6TBahRn8r/z3r/jbl7vB8zzKGx0wqOXSnSZRvc2NexbvwKs/Rk7MCICCIEIIIaSTakusIsYnbl4V1qMWAFgWUg9XcZ44cTJ+GctKN4DStQpUW1zonaaBy8uhNnAjpyQwB5zJ4ZUmwd1dKvSwPW604enl+/HQ57tQ3ujAOa+uhdFKN3YIIaStKLlCkobL58KMNTPg8sX/xc8AUDEe/LPnAqgYT0TPFbmMgULGBI8N9FwBAJZlpNn7tUqZlFBJ0wqJFrEXS4pKDrvbB5PTg4HZOtTbPLA4fTi9ux4+jsfxwPKJR6qsqLW54fJyGHVaJipNwvV4fMJdroOVFgDA4SpLxHVsKajFwBd+Rg31aiGEEEI6nbbEKqHxiSwQn/AQEioMGCm5ogvMwyJOditnGWiV4fOxZAR6tVSZXMjSq1Da4IBSxsLs9MLrF+KMA+XC6kKVZieW7SrH2sM12FNqgsnhxa7iRqw9VI0dxQ2otbrxxk+HafgyIYTEiJIr5A+h6SigpskVGcNAIXZnCRnfLIYT4i6lnIVOJXQz16vkYT1XdCo56u0euLwcBmbrUWf3wOLyoluKCkoZi71lJgCA1eVFWYMDWXolBmTrUGV24lClGWf8cxVO1NpQUm/H6d30KKl3RAQ0Px+oAs8Dm4/VRlzjZ3klWPxbUdsbiRBCCCEdShyiLAYgLBPsuaIK9FKRJuMHL012m9YkueLxc8jSK1He4ETfDA2sLp80iX5RYPhy6KT6BYFJcastLjzw2S489Nku/HLUiAVbinC02oqyBgdsbh8a7R6s3Fd5si6fEEKSGiVXSNJQy9WYM2kO1HJ13GUZMHDzSrxedT/cvDIi2QIIPVSEYxHRLVe8k6SUs9KQIbVChhS1HFaXL2yIEAD0z9LB7PDA4vTCoFHAoJFLyzfzAApqbEjTKpGpE4YS/X68HgCQe6IexXUOjB6QAY7nUWsNnxCv0GiHXiWXgiARx/F48buDmPnD4YhhQz4/h/9bky/VjRBCCCEnR1tildD4RCEXe65AevQH5lxRBnrYpgSSKxwfXKY5Qyf0ZtGr5JAHYph0rRIeP4eeqRrU2dxwePzI0qtQVGeHQS2HyRFMrpQGhg9VB3rGNjg8KDDapH2X/usXPP/tASzfXY6/f7UHjXYPPD4OPM+j3ubG1oK6eJuKEEK6HEqukKTh9DoxbeU0OL1tSxKoGDfe7PMeVIwbUXIrEiYk8yJOIicmXpQyFsrAkCGlnIU2sHyzmGipaHRCp5TBEEi62D1+YciQWoHSBiey9Cpk6pQorLUhRS1HqkYBs9OL44EA5rjRhnq7Gz1TNcjSq1BlduLVHw7j7sXbAQDljQ5cNCgLxfWOsDobQ5IwTZMo24sb8MGmQnzye3HEtR43WqWVAwghhBCSmLbEKqHxidRzJYDnIcUsclZcpjnQc4Xng3OuBHqsqBWslHBJDyRcslNUcAeGHvdKU6PG4kKfdC3KGoOxRFmjE0oZi6Jau/S6dYHYoqxB6El7uMqCwsAQ56J6Oy5+eyM++q0Y8349gTsWbZOGN4tlthRE9rIlhJCujJIrJGmwDIs+hj5gmbZ9bHmwqPZmggcblkAR9gUxUbaLw4iUMlaa4Db0uSowXMjp9UtDhMS7Pwa1Aga1HKX1dqTrlDCoFagwOWFQK5CmFZIr9XY3+qRrUGNxwez0IlWjQLpWAZPDi8/yivHL0Vq4vH5UW1w4t1+atOJQo90DnudR1uhAz1Q1BmTrpMBHJA5HEsdYh/rfL4X4vzVHcbQmcsWieZsLsepAVSxNSwghhBC0LVYJjU/E5Ip404PjeSkGESfeD+25Ig5jzggMC9IoZFLCRRe4ASROgAsIMYmP49E7XYOyBidUchYZOiVKGxzok65BWWMwKSROiivOAcdxPGoswraCGitqLG78frwO5YEkTVmjA6PeWI8NR2rw5qp83LloO3iel67lUKUZ6w7XxNwuhBCSbCi5QpKGSq7CK5e+ApVcFXdZHjw8vALv1twOD6+I6LkS2nMjNO8iDgcSt7EsE0yoKFgpqFHKgzP2q5UyaJUyODx+yFlh5SG9Wo7SBgfStQqph0uKWg6DRkigNNg9GNRNjwa7R0qupGoUaLB7pLHW+8vN4HhgeK9U1FrdsLl9OPe1dfhiWynKG4WgqIdBDaMlfChRWYMTYwZkSBPqhtoXSLzsb5J4cXh8mL0qH08v3x9RptrswjXvb8GxKAkZn5+Dze2L2E4IIYT8EbQlVgmLT5oEKDwfHKqsCDyqAz1oOZ6XYoQ0rZBAUStkUoJGnCPOEJJcEedoyU4R6peilsOglqPW6kaPVDUqGh1S8qa80YneaRqU1Au9WSwuL0wOYYnn3SUmAIDN7UOVWbiZtKu4EUarG78eq0V5oBet0erGkBdXY9uJekz/Yjfu/3QnAMDl9QMAvttTgc/ySppcM089agkhSYmSKyRpOLwOTFk6BQ6vo/WDm+B5QM248EG/2VAzrqhzrohCJ7uVB8Y3h04sK64kJPRckQW2hT6XSXeLVHKhl4xaLkOjw4u0wBChCpNLmItFrYDVJSRXTo+SXCmstYHjgT7pGuRXW6BWsOiRqkadzY2DFUJCZEdxA6rNbnQ3qNEtRQWj1QWTw4MVe8oBCEOJRuVkoM7mhs8f7LIrLAftxLiBmSg0hidejlQJiROb2xeRLFlzqBoHKyxYsaciou1mr8rHOTPXwu3zR+zLO1GPsob43ztCCCEkWbQlVgmNT4I3dQJzr4T2XAkkTeSBR47jIeYgxKSJSiGTkjHiSkKhPVfEIUPiss4quUxafShTr4LF5UN3gxpylkFZg9ArtsLkhJxlYHH6YHIKE/UX1wcnxTUH5m7ZV24CIPR4sQQmy918tBZuH4cdxQ3SBLqVJieGvLgaB8rNePybvXjxu4MAgpPqTl28A08vC7+5U9zgipiHjhBCOhtKrpCkIWNkGNtnLGSMrPWDo+Agw27HEHCQtTgsKJQY0IROEqsMGQoUOiwotBeLNrBcojhXiyowCa5WJYdBI0edzQ2DWgG1QgaXl0OD3YOB2Xo02L2wBJIrBo0CpQ0O6FVyZKeoUFLvQEpgiJHLy0nDf8obnbC4hDLZKSrUWt34YlspnvhmH8oaHKg2u3BWnzRh/LTNI11HrdUNt4/D+f3TpSFMovJGB87rlwaDWoHiwKoCosOVFqRrFVJyJ9Smo0b4OB4HK8KXkba5fbhlfh6eidITptLkxJ2LtqHKHDk+3eb20bLThBBCkkZbYpXQ+ESMO8QohUdwxULxho8YwjAMIz0PjU1EYs8VnTJYF40ifGiRShGcqD890PtFo5DBoBGGD2XpVaixuNEvQwuPn4PR4kavNI00v5vF6YXJ6UW6ViFNittoD/ZwERMuRqsbPr8QS206agQA7C5tlOq1v9yEK/79K45UWfDrsVos3VUOj4/Dd3sqwPM8bvvsEJ5Ysi+s3eZtLsSO4oawbdTrhRDSkSi5QpKGSq7CjLEz2jQsKF2nxPt3jMGiuhuaGRYUfM6F/CA+9YdsE2frV8llUtJEGZpokQUnuhUDCbFXi1rOSr1atEoZ1AoWTq8fFpcPvdM1cHh8Us+VlMDqQwa1HBlaYTx0ilou3WEqrXegV6oaVSYnrC5vSE8YHw5VComPo9VWWFxeZOmVyNApUWt14+XvD2LOumOoMruQqVOiX6YO1eamyRUn+mZo0TNVHZHcKGt04JIzsiN6oXh8HIrrhGWkxfHZoj2BAOpAhTliNaPlu8qxpaAOP0RZ2vEfS/Zh9KwN8Ib0uBHeFx5vrsrHziZBlajB7om6nRBCCDmZ2hKreHiFFJ+wIYkTQIhJmEDUIk5oGzwmeJwYg8hYBnzglpFG6s0SDPfFHi6GQHIltBeuuJyzMI9c+ES53QzC9djcPnRLUaHS5ESfdA3MTiGR0jdDK8UFJqcXZqcXmYG5XADhho410BNWnAOuNCSOyC0UVk0MTbjknqjH49/sxaFKCzgeOFhhRqXJiWeW7QfH8Zi9Kh+zfj4S1pbicOlQaw5Vo5HiAkLIKUDJFZI07B47Jn0+CXaPvfWDo5g0OAWfnvYiNExkT4jQuxyh//cXAxQu5P/2TMiyzEpZSHIlELwo5Iy0XRScp0UmBTah87QAQIZOCYfHD6+fFxIvShmMFjcMGgXSdUqUBnquqBUsZCyD4no7BvdIgdHqhsXpCyRe5LC4fKg0CddYXG+HxemDITDMyOz04pPcEry3oUBI4mgDvV1sbvA8j1VH6uHy+lFldqJnqgY9UtWojJJ4uSAnA5VmV1i7VZtdYBkG5+ekS5PbiYrrHRg7IBNWl0+ajFe0r9yMDJ0yYt4XjuORVyQEW0erw+d3Ka534MPNhZiz7hia+ml/Fc57bV3UCXx/2l+F+b8WRmwHhJWTxDHghBBCSFu0JVbRMC4pPmGazPXG8SHzvjVJvDAh28QhQ2zI3SMxQSLO0QIEEy7ijRogGKOIPVdUimDCRdymVymk4zJ0SnA80CtVA7vHLz0va3Sih0GNSpMTHI+whEtRoBdstxSVlFQJnYBffH4k5OaMeGNmR3GjdI3rj9Tgm51l2HpcWPrZ5+exMb8Gw15ajXqbGyaHFxvzjbC4vNhV0gi724cHP9uFRVuLpPO6fX6c99o67CoJJnIAYPXBKooDCCEJoeQKSRoKmQJThk2BQqZo/eBoGAV+Ml8EH2SRE8aFPG+t54q4XykPzuqvlMmkhAoDBoomXXdDhw+J3W+VMlZKtABCsCJSyWVQy2UwWl3CECFphSE5GIaBXiVHSb0DOVk6+DgeRqsLBrUwn4vV5UWjw4OB2TpUmV1wev3SikUn6oKBTKPDI83tIvR2sWDmmmL8sL8KZqcPaVoFMnUqaUUil9cPnudRbXbhnL5p8Pg4NAbGWQPCUKJeaRr0zdCiojF8iE9FoxMDsnXolqIKW/pRLHfZ4G7S+G1RjVVYOem8fmkRPWF2FDVAo5DhQIU5ovvvL4HuxmK341DPLN+PWT/nR8wjU212YeKcX/HO2qMRZfaUNuLmD3OlMeWh9paZwu6yhYo27wwhhJCurS2xig8yKT6JGBYUshQz02QfwzDB1QzlYnIlGOBoAj1llSFDhVRNVhwCgj1bxElxVXJZWCJFPEY8t9ibJVMfjFuyUpTwB1YhMju9YBigZ6oaZY1CD5fiejsUMgY9UtVScqXSJKxWlKVX4URgCeiiOrv02uI2MQYwOb3SNvG71+T0YHtRIxweP345Kiz9bHZ6MW9zIW6a+zsKjOIQagdyC+vxztqjOFFrR4Pdg83HauHxcbC7fSisteGhz3djbchqRrVWN0a9sT6sp67Pz+H7vRVhc/EJ7xMNRSKEUHKFJBGlTIlp502DUqZs/eBoZEp80zAJXl4hdbGNprXvR68/mFyR5lQJmXOFBy8FQGIgolIEJ7uVJsSVhydX0jQhyRWFsM/r52HQKKBRsrC5fVIwJCZX+mdoAUBIvGiElYisLh8a7B4MyNZLAYE4nOhErR06pZBcOm60SUkXi9OLI4HeIcdqrLA4vTCog71dFm0twvmvr4fD44fHz6FnqhoqOSuNqQaE8dTdDSpk61Wot4V3v60wOdE7XYNeaRqpV03ovgty0lHVZHulyYnuKWoMyNajvEmypqjejj+d2QNWlw/1Tbr67i8344Kc9LC7X0L9XLB7fNCr5BG9WrYFesj8drweTX25rRTbixuw/kjk8pG3L8jDjf/7PSKoMlpcGPzP1fh6e2lEmYMVZjy1dF/U5MuBcjMOV1oitgOgVZgIISQJtCVW8fIKKT5hpV4qIbOuSDFF+D4GwZs4wZ4rwfhG2WQC3NDnYs8VhmGkuEQctqxWsJAHXix0qJBI7M0SOlGuGMNk61XS8SlqOfwcj16pGri8HFICcUV5oxM9U9WoDEzun6qRo6hOSL5UmYQhyzqlTOrtUlzvQJZOAT/HS9uOVlvBMECNxS3N/3IgMBdco92D0gZh27YTwvd6tcWF/24qwPsbj+O4mHBpcOCt1fm46K2N0sT+x402bC9qwLrDNdhXZoLR6kbuiXrwPA+O4/FbYT0e+3qv1KsGEFZePO25n8NuwtRa3XhvQ0F4z2iOR6Upcn45QkjXQckVkjTsHjvGLhrb5mFB8Nnx7cAnoWVbXi0orOdK4FEWpYCcZaTtSjkrjYUWH4Hg3SUxwAmdBFcpY6UZ/QFIPVrEfdKkcyq51KVXTMaIc7Vk6FVQK1hUmlxSAsXk8MDq8mFAlg6lDY5AbxkZDBo5yhocyEpRwaBWoKTeIczTolHA7eOkwKK4zgGLywuDRo7UwFLR24oaYHP7kF8t/MffoFEgTauAyenFu+uPYfXBamlS3XStEo2O8IRHvc2NbL0KmTplWELG6vLC6vLh/Jx01Ns9Yd1xK0wu9EpTo3eaJqInTJXJiYHZOmTplRFzv1SanLhwUFbYWG4AKDTa0TtNg3P7pUX0kjlQbsb4M7JxrMYacTdqX7kJ/TO12BtYtlpUY3HB7hHqW9hkmetNx4S7Zz/sj5xHZu4vhVi6qxy/Bbo0i3x+Dtf+dytuX5gXUaaozo4zX16D7/dGrtAEIKLOhBBCOkZbYhUN48K3A5+EhnFJN23E8IAL6bkS3BfsHSv1XIkyLEje5FwAIlYSYhBMnIgT4KpChhHpo2wTEy7i/HJAcBUiMeHCMowUs6TrAstEy1npdXumqmFz+2BQy2HQKFBv9wg3YMzOQIyhxIlaG7RKGcobHeimF5apFpMTZY0OYZJdHwdjYG64kno7tEoZLC6vtE3suWJ2+uDxCWO8xZ4wjQ4PVh+sRqPDi5J6cW4YF574Zi/u/3Sn9N1ebXZhxpJ9mLFkL44FbkQVGG3YWlCH/eUm7AwML9pfYZKSKd/vrcCcdcek8wLA59tKMO7NjWE3V7adqMcLKw4glNHiwvaiyIl6m84/RwjpfCi5QpKGUqbEjDEz2t5zhVViYd0N8PLyyORKMxPaittDjw+dSE4MUhQyNjibf2gU03RYkIINm3MlVOgKRizLhCyryEpBS3COl+AEuSlq4W6ORiFMkFsVCChysnQoa3BId6f0KjlKGxxI0yqRFpjVP1Ujl5ZjLGlwoJdBiXq7O2Q5aDnMTi9KA8HB3jIztEoZFDIWaRolGmwevLu+AK//dFjq7ZKuExIyHh+HeZsL4fL6pfOl65RhPU1MDi9kLIP+mToAkJZpBIDqwLwv3Qwq1NvD52mpNLmkOWFqLMF9VpcXVrcPo3IypDtZ0vksTvRK1aBnYBLgUBUmJ0YPyICfF4ZYiTiOx4laoZdM04TMoUozBnXT48zeBhTUhCdXDldaMConI2ovlP0VJvRKVUeM9RbvxjU6vBErJ605VA0AUSf9/e/GAoyetUEKGgkhhHSctsQqXl4eGZ9E6Z0SGn8EnknbxJWE2JAYRBbYJgvp+SIL6XELAApZcMUhMbkSOm+cOG9L2CpEyuBKQyJxv0ETTLiIcYzYqyV03rmsQA8XtUIm3UDK1CkDPVzk0ChlsHv8gYn13dAqZdAqZNIQ6bIG4TsdACoD35mlDULCxeL0SfHEiVobuhtUsDi9sDiFHqAFNTao5CwaHV4pdhN7sxhDesKIk+NXW1xYsacC3+2tlF6ryuTEHYu24ZEvdqMoMOS6tMGBv87Lw79DkirF9Xb8uL8SZQ0OHAssNV1UZ4fbJwy1/iyvBF9sKw3r9fL8igO4eV5uWK+X51ccxIR3fkGon/ZX4ZPfi8O25VdbcNwYPk8dz/PhsS0h5KSh5ApJGgqZAlOGJzDnCqvAz+aL4IM8YlhQ2JwrXOh2YU9oD5Ngd1xG2s4yweAl9FjxmRiAqOSysCWbWyIGIApZZEJGfFQrZEhRBcdUq+Qy8LwQ5KRrlbB7/NAog8dWW1xI1yqQplFIiRdxYt4qswu901QwOYQAxKAWerVYXN7A0tFyHK22SMmYVI0CBwOrEnl8HCwuYeLcdK0SDQ4Pfiusw+xV+fjlqDHQE0aBTJ0SDXYPGuwe7C83wez0wqCWQyFjkaKSh/V4ESfcTdcqI2b5r7W50d2gRro2vCdMldkFrVKGnCwdzE4vfCF3eSpNLvRIVaNnqiZikt4qswv9MrTonqIO6yVTZ3fDx/EYlZMR0UOmotGJvuka5GTqUNQk8VJYa8eVw7uj0eENq7vN7UNZgxN/Oae3FMiJDldZcHbfNAzI0iG/yQS++VUWXDgoM2qy5ts9FaizuSOSNYQQQk69tsQqPsil+KTpnCvhwudzk7NMxEpCoSvyiTd7mJD4RNwmxhFyGSsNd9YHEilyGSO9iJhwUStkUkykkIUnaEKfizFCaI+YtEDPFYWMkRIp4lwuSnnwBlJmSMJF0yQJowoc5/D40StNmNclQ68Ewwjfx90NKpQFVjr0+DkYrW4oZAwqTcL3u9nplW7UlDc60D9T2OZwC71IiuuF3q2NDo/URsX1duhVctRZgzdxxCSImLwpb3RKSZgGmwfbixuwZGeZlKAxWt3425d78Oy3+1EWGKpUY3Hj/NfW4/O8EmkYdWmDA+9vKEBpvUO6CVVn8wiTA3M8NuUbUdbgDOup+sQ3e/HyykMINfWj7bhlfngP2Ov+9zve3BA+TPmDTcexMT98uPPWgrqIBQka7J6wWIoQ0jJKrpCkYfPYMPx/w2Hz2Fo/OBqvDWvPeAQ61hkRtTQ3EZm4OTy5EnwUb+6wbGiiJTIkCh0WJN5dUshiS64IQ4SCiRZAWO5ZPEYZMsxITOKoA71Ymr621eVDulaJVK3QgyR0mcYaiwt9UgN3d1xe4c6RQib1POmXqUVZg1Pq8qtVyaRAodHhgdkhJEr0ajk8Pg75VUKCIL/aGpassbp8ePG7g/jLf38TersEzpcW6PFSZXbC5fVLZdK0irCJcwHAEugJk6ZVhu1rsHuQGVh2mufDe8LUWMTkSuTy0tVmF3qmqtEzTR3W46Xa7EKGTok+6VrUNZlHpjpwvh4GNYyW8J41RosLg7rpYVDLURWSyKk2O6FRyHBO39SInjXFdQ4MzNJJPY5CHaux4aqzeqLS7AobOuXy+lFcZ8fwXoaIOWYIIYScem2JVbSsE2vPeARa1indqBFDCYYJGRbUZM4VGctE9JoNjWai3vRpMgEuEBxaKg7zYRkG/sCdptAJ+UVSLBIyVEh8rg/MDceHlBFvArEMEzJ5biC5IgtO9C8mUkLjnqyUQMIlZEhRdmCbRiH0ZuF4oFeaBh4fh+6BJaMb7B70TNWg2uJC33QtbG4f6mwepGkVqDQ50S9Diwa7R7qpU2V2oZtBBavLB1+gPcoahMl4G+zh88v1MKjDvtvNTi90Spk0YT8T2MYywdWPPD4OtYEkzdFqC6xuH34vrJcSPkdrrHhn3TEs210uJVwKjFaMe3Mj1h2pkepUaXLigU93otrsgieQ9PBzPLYU1ILjeNRY3FK8wvM8eJ7HwQoLVh4MDkXmeR7/t+Yo/rniYNi2OxZtw6s/HJa2+Tke5722DvN+PRF23F/n5Upz2YjbPtxciPomq0HuKmmkxAz5w0nK5Irb7UZ5eTncbnezx3g8HtTV1bU4e3csx5DOQy1XY86Vc6CWq9t2Apkar1feBw+virgjFPoJCM2NSHOuRAlMGIaBjBVXCApu50POxjQJbBQyNmyelpaIAUjoxLfKkOFFgDD3ijR8KGRpaJU8dHv4o04lBCPiceK+GosbfdLUaHQKQ3o0geWgG+we+Dge/TN0KGt0SJPqahQyVJqc6JuhgdfPo6xRmMNFDM6OBuZnqbG4YQ3M4aJTymB3+6QhNifq7NJdrjSN0Atl7OyNmPXzEWnel9CeK06P0I1WTP6kaxVhPVfEoUlqhQxapSyyJ0wgIWMKScjwPI86mxvZejUytMomQ5Nc6G5QI0OnhM3tCxsnXW0Wes9k6lURk+rWWN3okapGVooqbOnpGktg0t+UKAkZqwvdU9XoboiS/LG4cGavVChlLKpDArrCWht0KjkuGpQVMe8LIeTUq6yshMlkanY/z/Ooq6uDx+NJ6BjSebUlVnFzSrxeeR/cnDJkCJA0PihiknzpGFbYDwSHAIVOwSXGHuK9HB/HRyRoQmNgMaEBCEscAyGJFAUrnVsereeKooWES0hyRYxNxF4ySjkr9WYRVytSyoM3izKkCXUZKb4IDilioQ2cW5xIV6eUS9fYI1V4D0JXY+yWooLF5UN3gxpmpzewjLSwfHTPwKPYFhUmJ3qlacISKUV1dvTL1EpDeQGg3uZBv0ydtM3h9cPi9KJ/pk5a3cjs9MLq9qJbigr7yoRevza3D40OLxQyBgcDk/FWm51SXCPOu3KowgyzU9iWe6Ieaw/XYF3IJPu5hfW4c9F2aXlqACioseL0F1ahMPD6Mka4ETZn7VHUBuISHkJPp+I6u5T4CU0kVQdikdCbNyX1DmwrasBPB6qkbcX1Dry5Kh8r9gTnhSuqs+Omub9jU2AFJ0D4rK0/XBPWu4qQriapkitFRUX4xz/+gX79+qFv377Izc2NOIbjODz55JNIS0tDTk4O+vTpgxUrVsR9DOl85KwckwZNgpyVt35wNKwcv9pGwg8Zrj6rJ/qka6RdreXXQnujhI59loUkWkSh3xniF4iYhJGF9HBRttJzRcGK46FZaWhPcOnnYA8VVchQITEYiTaUSB0S+ASDoPB9PQ1KqS2k5aADSYC+GVrUWFxS912NUoZKsxN90rRQK1gU1dml3i6A0FW2h0GNkno7OF6YBFerksPh8UvzpBypskjjs3UqGYoDY5SF3i7ewPLSctg8PhwoN2PoS6tRYLRJqyila5VSIODneFhdwRWV0rXKsFWLxF4yqRoFTM7gdqfXDx/HIyUwqV7ouGeTw4sMnUJaGSE06Ki1uZGdokKmXhl2t8bp9cPq8qF7ihpZ+qbJFRe6GdToliIkZEK79xqtbnRLUaG7QRU2j4zHx6HB7kGPVDW6GVRSsCOeT5xHpsYSmWzedqI+oosvIaR9ORwO/O9//8NZZ52Ffv364Z///GfU41auXIm+ffsiJycHqampePzxx8FxXNzHkM6tLbHKPRcNgrrfVfBHWYo5FNskySLEIHzgdYPJEr5JEkS6+cOHDBUKnJMPea4IxAT+kKBIPF7BslIvBHEIUuicKwpZeFzB88H94tAihhF6oIRuU8lZaUVF8WaLIqTnividrpQFe64Y1MFJdrVNJtJVyYNLRouJGUPIqkaZOiEJk64NJlyyU1TwcTy6pahh9/ihkrPI1As3W9K0ClSYnNCr5EhRy1FhcqJvupBcEV+70uRE/wytlEixu4U5X3oYgr1hrS4fLE4feqSqpXlbTA4vzI5AEiaQmKm2uGFx+dAzVS0NHy6qdwhxj1ourVIk3sDSKGTYmG8EAOwoDk6CK/Z2+eWoMfCeAusO1+C9jcelCfWdXj/WHq7Gpf/6RRqObHZ6kXeiHrfOz5Pm26uzuVHW4MCKPeXSYgE1Fhc8Pg51NnewniEJp6OB8+WHJGb2lZsx7dOd2FHcgLwT9fi9sA5eP4clO8vg53gcqbKgrMEBjuOlczo9fpq0nySVpEqurFixAt27d8eaNWuaPeadd97B4sWL8fvvv8NiseCZZ57BX//6Vxw5ciSuY0jnY3Vb0WdOH1jd1tYPjsZrRe6QqdCxDvz5rJ7Y+syEmIuGJlfYkISK2B03rLdL5Hy4weFDTDDYUcqjj6gWyULGRStl4T1NpIRJkzlcVLLQO0bhxwZ7sATvEimbrEKUoZWHldcoZfD4OaSohF4iXj8vJXbEnisZOiVS1ArU2zxQK2SQsYwwsa7ZhQHZOhQHvmz1Sjl0Sjmsbh9MDg9SNQocrbZKwZRWKcehwJwiDXaPlCjRKeXgeWDLceHux+bAXZAUtVxasej5FQdw1X+2CL1dxJ4wgeFE4pey1SWsSpDaJIFidQkT3OnFfSE9VywuL1JUCshlLNK0iohkTapGgSx9eBLH5PSBZYTXz27Sc8VoFRIy2Skq+Dk+vKuxRegl0y0lvOdKnc0NhhEm+uthUIf1XKm1igme8NcBAK+fw1/n5+GRL3YDAPJO1OOZZfvB8zx8fg7zfy2ExSVca361Rbpr5fVzYW0Qq+Z6AH69vRQPf74r6r5FW4uwv9wUsd3P8dhR3BD1nOJymIR0FocOHcKBAwfwxRdf4Pzzz496zNGjRzF58mQ89dRTsFgs2LZtGz777DO8/fbbcR1DOr+2xCr/nNQX81OuxwW95bh0cDaA8LiiyRy3wd4nLCPFHKFzyYl/I5mwJEywTCg+JLsifr+H/o2VlnhmGSmmEedcCb1JJMZGYkIldPJcfUhyRTyfmFxRyIKrKEqJFDkrxS6GkKSJGMeIE/WrFax0PnGbShFMUImrFImvDwCZeiGpErqMtPgaYg8XjVImnU88zhCIR4TjFPBxvHQuoSeMcNMkO0UFr1/oEdsj0BNGGRiWbXUFEi6N4nwswtCe/hna4KS4gZ69/TODyZoTgZ6p/TK1Uk+U40YblDIW2SkqnAiUPVxpQapGAY1ChuOBifYPV1mkazhYIcRY+8rMUMpYmJ1e7A30ohETM9UWFzYcqUHuiXqpN02V2YX3NhTgiW/2STFIg92DWT8fwfmvr0d1IFlUa3VjyY4yvP7jYZQ2CPWssbrww75KfPxbEQ4F5uk7ZrRh6kfbMfUjobfN08v2Y2+ZCde8vxUPfb4LvxXWYeKczTBaXPjTf37Faz8exs7iBpz/+jp4fBxeWHEAm/KNyK+2YOYPwpwzS3aWobzRgVqrW5pLxurygud5HK60YMmOMoQqrXfg98LwVRvFOQZDiUOrCIlVUiVXZsyYgaeeegpZWVnNHvPBBx9g2rRpOOecc8CyLP7+97+jX79+mD9/flzHkM5Ho9Bg6ZSl0Cg0rR8cjUyD6aXPws2pInbxiP6HU/yDGtrJJHyZQzGIYCLKAMGVh6ThQyFDiVqbc0UeErwEhxWFrwagVrDB8dMhc64I+5oM/Ymh54pOGUzOKOXBO0fCcB9Z2Hm0Shm8fh6pWmGfx89J59Mq5cIcLuka1NrcUMpZsCwDrUoGo8UFH8cjJ1OLKrNLCrA0geUW1Qph6Is4Ca420HVYnMz1UGVwxSKtUganx4+1h2pwtMYKk8MrBUQ6lRwOjw8XvLEeS3aWScmaVK0CVrcPfk74T7rV5YVGIZxPnMBXFNoTJkMXvsS0ze1DilroPVMfNmmtH3qVHAzDILPJ6kgWpxdpGmHYki4w5ErU4PAgQ6eMeB2j1Y1MnRJymXAnLXRfbaC3S9MeMoCwJCUAlDU4wPM8Fm4pwjc7y1DW4MSWgjrM+jkf3++thJ/j8ad3t0gJkH+tOYqzZ66Fx8ehrMGB0bPWSysc/GvNUaw/LAQtpfUOrD5YJb3Wac/9LAVioeZuLsSqg9URKyAZrS689uNhvLkqP6LMTweqMOXDXOQW1kfse+n7Q7j6/a0R2zmOx1NL94WNAxeZnV58vb00alLG7IgMpgiJxwUXXIC5c+dixIgRzR6zYMEC9O3bF4899hhYlsWIESMwbdo0fPDBB3EdQzq/NsUqMg1w0VIsnX45BmTrAYQOQUbEUKHQpImUXJHmaGGkniehc7NILyUdKDzwIceJMYaf46XXkHq/IKSHi4wN2xf6GmICRC5jpV4v2pBhQeLwpdChQuJxYpJDKQ9NmgR7uIhhU0pIzxUxzgo9TrxEcV6X0NhISpaErGok1kUcliROsg8Ee9OoFbKI5aa1CrkUN6UGXkscssTxQo8Yh8ePPmka2Nw+cLywBLXR6kaWPthLtUeqGmUNQu+YSrMLcpZBD4NaGkJdZXZBr5IjU6eSblhVmlzCcGuVXBrKVNLgQLpW6KFbWBdMwnQ3qKCSMVLvmGM1VvTP1ILnIQ0pPlptRU6mFlaXD5WmwHCgagu6G4SFDsQbUXvLTUhRy2FyeLE70Ium2uICwwg9cd5ZdxQLtxahwS7EUkaLG88u349XfjiMmkBiptbigtvHwevnpSRQodEGP8fjcJUFe0tNAIBDlRaU1Duw6mAVfi+sR53Ng/xqC77YVor/bjqO7/ZUYvFvxagyO/H0sv14f8NxvLP2KO79eCdcXj/OemUtVuypwCsrD+Hp5fvBcTzmbS5Eg92D6V/uxm0LtgEA9pUJS2jf9/EOXDFnMwBIy17PWLIPTy8PXyp7/q+F+C5kCBQArD5YFbHowO7Sxoh5aOptbuqJ08UlVXKlNUajESUlJbjwwgvDtl900UXYvn17zMeQzknOyjG279iEhgXtdgyFH7KIXc0lpaNtDh0CJH7Rh94H4qImV8Tjg0FEtIlvQ4X2XJFHuUsECIGFeBZx1R+hjog6oS0Qvhy0NHeLPHzCXEDoCqwOSaQ0ncNFTLzolMFZ/UP3+TgePVKF+VjECXh1SmGCVznLoEeqGvV2d/A1FDLUBFbtsbl9sLp80CpkUnKpvDEYPEjzvijlcHr90pfgcaNN2qdVylBgtKHB7sFvx+uEeV/UCuhVQk+Y3MJ6DHj+Z1SYXFIZseeK2DvC6vJJk/PpVXLY3T6pfawur9RNOHS73cNJZVLUcthcwX02ty9knwI2dzCRYwskcgxquRTEAAib9DdFrZCWkgSCPVeyU5Sos7nB8zxqLC5wHI+Sekdg5QMvTA6vNDzoYKUZhwPddPeXmaSg7HggwNpTZpLacktBHWosbqw7XAOb24f/bjqON1cLyZAXvjuAhz7fDafHjw1HhG7H60PGgANCwqPa7AqMJw8POg6Um6XXaUpMquwoDl8Bied5LNtVjiNVlrAlswFhKNnSXeVYsOUEmvpiWwme/fYA8ooiEy+3L8qTgqlQNrcPo95Yj59DxpWLDldaMGPJ3qjLX+cW1je7clNRnb3ZXjc06V/Xtn37dowbNy5s28UXX4zy8nJUVVXFfAzp/NoUq7ByIHus8BjQJAciHNYk5pCxTMTNIZYBxD8nkUs3h694CADg+ZDjAskVnpdeQ1qFiI9MuLBR4iFxn4xlgr1Z5CE3mJqsQgQmOF9MSshS0OKpxeSGSs5I1xXam0UMuUJ7vYhlxfnlQmMnpdRLRhGxTUya8DwvxUepIQkfMQ4St4XO+SJuSw1J2ojJGnFSXiCYfOmdpg60SbBsll4pzHmnkEGjFIZRdzeo0GD3QK+SQ61gYbS6kRJIqBjUCuiUMikZUhO4YZWqUUi9XcoaHEjVKGBQy6VhO6UNDvRIVUMpZ6XjSuod6BlY2locylRUZ0fPVA3sbh8aAjd2TtTakJOpgymkh2tBjQ2nZerQ6PBIQ+NrLC70zdCg3u6B3SPMVyfebApdsVF8LXFYklouQ03g+11M3ihkrJRoEuMCP8dLEwhvLRB6oDQ6PNJkvuK2/eVmab68/RVmzF6Vj5/2V0oxRFmDA9d98Bt+OVqLHcWNaHR4YbS4cPoLq1BcZ8eKPRVYvrsC9XYvbl+4HVaXF7N+zsc/lu6DyeHBfzcWgON4PPT5bjy5dB+8fg4HK8zgeR43/u93zPo5HzzPS71oRr6+Hou2BuMUP8fjsn/9EtaLxs/xeHrZvrDFDTiOx8ItJ8JuAPI8j435kXPYmB3x9z4m7adDkyuNjY0oLy9v8Z/f72/9RAG1tcJwgaY9W7KysqR9sRwTjdvthsViCfsHCPO3tOc/4T917XvOrvLP5DTBMNsAk9PUtvZzm3Bg+BToWUfYdgBSOUAIWoLbg88hbeel5yH9VaTjuJBzieWlXzSeC/Z8CTkueO7gc7GMnAXYkDHVoftYhpdm9BdiA166BoW0coDwOuIdKQXLQBnynOO44FwtIX8ReJ6HShqvHTLMSNakTMg4aEVgn/hzN31wqUWO46AODIUyqOXQq+RweTkoZMI+jUKGKosLvdOEL/d6uwdKOQOe56EN9Grpl6FBpUlYcUc8n93tk75sShrsSFHJpPMdD/S4qLO5YXEJs/mL17T5mJAQ2HKsFilqOTiOg0Etg9nhxSsrD2Hq4u2wuISAhuM4aBUy2Fw+uDw++P3CvCp6JQuNghUSPD4/OI4TerQEyuiUMlhdXumzZnF6oVcK9UtRy2F2CPv8fj9sbh90SmH8uM3lk8qISRypjNMT8jshTOybqhbassbsxOhZG7BkZymK6+wY2jMFOqUMVWYniuvtGJStQ0WjA8V1dvQwqFDe6EBJvR06pQwmhxc2lwfHa6yQsQyOVltQYAyOmRbbstrsgt/vl7o151ebpeDsWLUVHMfh6+0lOFRhQo3FCY+fw5gBmShrsIf93pXU2zGyfzqMVjdMDnfY73BJvQMDsnQ4Wm0J/xvg8MDp9SNDq8CRSnPYvn3lQsB1pMoa8bu/M9DdeVdJY9h2p8eLgxUWNDq8qLO6wvblHq+F0erGd3sqIs73WV4xvt1dgdzCurDtfr8fty7Iwy3zcyPKHKu24LJ//YJPc4sj9n2RV4yhL62GNeS9Ff+9+sMhLN56ImK70+3Fm6uOoNbijPgbWGd1Ia9J3cR/NWYnHG5v1H1+v/+k/f1u7l9n5HK5Wo1NrNb4hqfW1tZGjTvEfbEe0xTFJp3vX7RYpdX2c5vALzGAc5uCvxfR7vpI28SeKcGVfqR9fDAuEHvShiVopJxKYKWZ0BtC0msHEzIyRtgvnlPY1rQ+wf9QiPEKeF46TiElhYKJHG1gI8MHe9Oo5MEeNOJ1iRPfCtuEOqSogj1zuSjHNe2JowzpYROMhRCxLbT3rrgtdM47sc5iIketCC4QYFAHbzJJPWfE4Uba0CFI4ZPyitcBhC9P3XQFJWFYlLCtR6oaPo6HVhWIGdw+9E7TwOr2QS1noVfJYHX5kBEYHi0ka4Th2uKw7RSVPCwxU2FySistiTddqkxO9DAI89GI28oahIl/zQ6PtNjAcaMN/TK1YT1rC4xW9DSo4fD4pPf8RK0Qe5wImYBfTJqIvW1dPr8015+YDDI5vNJriXO4NNo9qA8MZxZvaNRa3dKw6m2Bmyn1tuDqSVsKagOv5YAjkPDZELgplF8dvAG0NTAMfXvIDZnd5VbknqjH74H5angAm/KN+NfaY9hTKsQYZqcHy3aV4Zr3t+JY4HxlDXZ8s6MUZ72yFuWBZMmeUhPWHa7GPYu3o7TehqI6O9Yeqsb2onrM31yI4zUWLNlZjh/3V6LQaEVeYR0OVJjw+k9HsP5wNRpsLjTYXNhfbsK9H+/EthN18Pv98Pn82FfWiLNfXYviOpv092VvaSNynv0JlpAYI7/KjAc+3QmfL/i9f9xoxX/WHwv721RndWHLMWPYNrvLGxHT+Xx+OJuJLZLxeyQRbewC0D5ee+01LFmypMVjtm7dipycnJjOxway6z6fL2y71+uFLDAXRSzHRDN79mzMnDkzYnttbS1cLleUEvHjOA5ms5DtFOtJgvycHz9c9wPsjXa42Mg2b7X9eD/uPP4vODgVjEZjeFke0ja/3x+232g0wh+4XWI0GuFyOKXnFpPwh7+xsRFGhfBH3uXySOU5nofRaIQtEIg3NjTAagksX9zYAKPcFfY6oc8tJuGPsMthh8XsDzy3wWg0wusRXqu+vg4ut/ClYaqvC9518vthNdUH6uMW6m0Xvsw8Tju8gZ4WTpsFRiMD3ickJ5z24H8YjEYjLIEeFF6vD26HsM/vFc7ndQr187mdYHmhfg6rGUajHyyE9mJ9wrUqWOF8tsC1K2UM5Jzwmn63C0ajEZzXBZ4H0pRC8OXxcbBbzDAaPVDLGdTaPBibY0BusQUDM9WBa7Ki2uwEzwPZOgUqGx3wuZ0wGo1gOS8KaoT3p7zeDrvbD5fNhEbGARkD7CsRviD3ldZBLUPgmuywOFz4JFf4orxsUBp66RgYjUbI4EdVXSOGvbwfL03KgcPjh9tuhswj/N0oqagGz3MwNlqhYoX3nfe4UG9xSO9tvcWOgWkyGI1GqGU8Koz1MKbzUvdYl9UEj0tIFollqmoboGQ44Zp8bhhtwc9Xg8UOpMvgsAiBxbp9xQCAvGPVSNXIkSLnkKmTY8exCri8HIZ2U6O4phGFNXaM6KlFvtGOg8XVGNZDi4NVduQdKUWDw4tR/VJQWFmHoxVWjOilQ2mdFXsKKzEoS4PjdU4cLalESYMdWToF9p2owtGqRozuZ0BxrQVF5VV49tuDuKBfCqaN6YUsnQI9dAwKKutRVK7CR9uqcP/YXjhWUY+B6QocqmBxpLgSPVKUsLt9kHkdKK23YXR/AwpqrWG/F8eMDuiVMpyepcGRUiMGpwYD+4KKeozpb8C2EgsqqqrDemEVGa04s4cOR8rqYDSmSNtLG4VeNekaObYfLcN5fYL7dhdWI1Utw6GKxoi/F3uKhd+tbccqMTg1+CVcYxV+F71+HsdKKpEWcgdz4wEhWNt0uBJ/Ghg+XOC73aXw+nms21uEcaelStsdHj8++k14T/88SBtWZmNBIz7cfALwunD3qJ4Agn8D311dhDVHG7H6wbPD6sDxPMb9ZzfGD0zDW9cODDvfiXon7vnyCOb/dQgGdwt/rdX59dhbbsOzE/ujPcWboDhVfvvtN0ydOrXFY55//nk88sgjMZ+TZdmocQeAsPiktWOaotik82kaq8TUfrwf8vN+gK/BDjDC++awC99fHMfBF/gcNDY0hD3ynB9Op3B8XSABZ7XZpJjF1NgYeAxOdGqzCf/pq6sTvgM9Hq/0WRH/1jldTumz2FAv/L2zWm0QkzoWU2NgW/A/pBaL0BvRZDIH2oGTYh+r2SS9ltMhXJfbLpR1u11wB0aNOgLnczkdcAZ6RngCsYfH7YI7sIKW3xX4j7jDBm+gnl5n4LyO4PW7A9tCYxuvS3gxhy24zR+IqcTXAs8DfuH1vYH6+n1eMKzwe8i7hW0M5wMfiH94d2AZZs4LGcPAx/OAJ7hNei2XsE3F+KSX8gXK6uWBFZpYgPO4AtuEcjKGA+8Trj8lMBcvw/khh3CeTC2LChPA8MGb0ga1DA0OL+D3Qh5IkmVq5agwewC/B3JWmNQ2W69Arc0LBbxQyRgYrW5olSyqLW6kKYVy5Y1OqGQM6qwuGBR6ePy8tIpSpcmJs3sKPVzEJaPLGxwY09+AknqX1JulpM6KbJ0C5Q12aALJo8IaCwwqGYprLUhVy2B2+XG0ygyNgkWR0QIZK0wQbDQLsdvRKuGzZ3Z6oGD5wDlMAIAGm0tKyhUEjqtqtMEcSPocCMR9RTUmqcfx7iLhM3+4LNhz5PejQk/BXYXB3rh7SoTfobxjlYEtPPYVC/vX7i8FAHi8fhwuFX4P1+wrAQCY7C7sOC4ct3pvEQCgutGGr3ILsanAhAkDhGGAJUYz/m9VA3aUWpEiGwAAOFZRj5/3leNAlR2v/fk0oZ6ltfg6rwgljS7cN7pXoJ5VWLzlOOQsg+E9dACArYdLUVTvQvcUpZRI2nKwBOVmNwZkqLGjzIq1h2uw42gpGhw+DMzS4JMdVfhqtxGTBmrh9nHI0Mrxf5vKsPJgHbY+eh5YVuit9tKqE1h7tBF5j4+U2mfW+hJsKzHj+/uCw2Pn51aiwe7FsxP7S38Hv95TA41ChuvPypaO+7XQBINahnN6B2Ow/2fvvOPcKM7//9lV1510XedyPvdug41tMGDcDQbTwRCaIZRA4oSW5EsLhPxIwAkJJSEkEEiooRgCjpPQYwgkBtNsmm1ccff1XtTm98dqZmd3R+V0ujvJN+/Xy9Zpd9qOVppnn3nKZ/taUJbvwEC/roTcVd+BkjyHQQna1BFGvsuW1BugK3RXNulT5co999yDe+65J2PtDR48GABw4MABw/GDBw+yc6mUEXHTTTfh+uuvZ++bmpowZMgQlJWVwe/3Z2T80WgUiqKgrKxMCjBxGDhgYNxzqczfls6hcNoUBAIBw3ECsGN2u81wPhAIQIm1FwgEkJdXz/6uCWuCQGlxMQIB7T6wOx2sPiFaueJqbaEvKytFQ1RTchRzdWh7/N/1Ue3LXVJUgLIS7ce3uKgAgUAAHo/2wz8gEIBq2wkAKC8vZ/UddjsGDdDeu90uBAIBlNaS2FgLYGsLAdiH8tJiBAIlcDi1RaC0qBDALv1aY4uPw25HeWkxgG0o9OUjEAigqFATUIoLfChsCANoxoCyEgQCBfC6tgFow9ABpQC2w+W0IxAIoM2mCSQelwOBIh+AKhQVaO2VFjYCOIiyIh/yXQ1o6ghjQKAUgdI85Lm0hX9EeSHW7myCN3ZNgzocaGjfAodNwZCSPHyyqwHFhX5tzv3VOLilAQML3KhuDYEAGDwggNJ8FzxOO2raYgqzlggGF3kQCAQQqAMiir5D3BQEBpYUatfr24edTVFECfDpAW2hHj54ADMTfn9fCD9Z9SWuPm4winxaewPLQgjtaIa3oBiqAoSIDQNLi7Tx+b6B4vRq91HMJ3fo4HI0tYcRinyJguISbQdsSxuKfR0IBAIYUNKKPS317F4JkR0YUFKEioHlsKsKdsc2gw62EeTlOREo8qK8OYLaoBb4eMzgEmw52Iy2sIIpw8rw3o5tCKkuVJT4sL85jPqIEzZVwbhBxWiHgpawgslDSvDe1hoEVRfGDizAvqYgDgadiBLgiKHFCCpuNHYQLBhfhpfX70MTPGzuiDMPAb8HFaWF+Ka2DRuqo3j644M4ZuwgNIdVTB5cgMCuZkQd+bj7nV1Ys7kab373MFS17MDRYwZi3e6tCAQC+PELn+HUwweh0+ZBRbEXQ8p8aCNOFBaX4u2vq7FofACNoQOYMaIMH+5uBtwFKGMm1wqqW9dj3vgB+Gpfk+F7trmxGoMLPSj3u9EGt+FcQ7gKx40O4JUvD6C0tMwQBLKq5TPMHVuG2k7jb8n2lloMLvQgHI2iGR6MCRSxc7tbqjF2gA+7GkOW359dDV+g3O/C/g7VcO6TXfVwO1R0hKKw5xUa0olu+0h74NlaH2Z16G/gV1Xa78PuNhvGDNXbo+k0P9vfahnDc19sRWeE4MP9QRw3aZjh3F+f2YyvD7bg+hMnoqLIqHjpDm536qlqe5MFCxZgz549GW2zoqJCKHcAwKBBg1IuY0bKJtkJL6ukPn9G+cbn09Z9VVXhdMbieZSUGF5dTgdcbu3hIxDQHlb8Ph+oXW1pSTEAoCxWHgCKCjQFblnMKsputyPP44m1of0uuN1uOBza+l8eazcvPx+qoiACgrJSrb3CAl0ZXFKk/d4VFxVqBxQVxUXa+eJibRyKzY5Cv/YAVTFAa9fldsOXrz0QDgxo7ebl5SEvoj24DyyLHfN6YbNr67Ymj2jykaJWx/7W+i0rLkIU38TG549dv/5brPV/AAHuWEF+HoAaDAqUAvgadpsKX54XQAPKS4sA7IDL6YQrZh0zdGAZgK3w53lwoEWbpyHlJQC2wZ/nAaApmAaVFQPYHmu/jo0ZAEoLfADqtID1hQUA9mJgsQ/Y3gi73YaSAm2cA4rzgV1N8LqcKPTlAajFgMJ8YE8L8j0uFPk8ABowoDAfn+1rhc9Lf1ebEfB7sLOuA/48D/LawwCCKPN7sLcxiIL8PHhdHUBLCOV+D6pbQijy5SPf3YjO1iAqCr34uqoFg0sLYFOrEYkSlPhc2NfQgcGlBQCqEY4SlOY7UdMSxMCSArSHNAVFvsuOhvYwKssK8Oom7bq9Thtq28KYPKQIn+8/iIEx65uqlhBGBfKxo6YVFYUetIVaUd0aQmVJHg40d6KiyItvattwsEXLqnSgOYwBfhdqWoLwOO1a9sW2KIq8DrSGoghHCAo8DlS1RuC0KWjoiKIl5l5V067dP/WdhMW/2dukfX417THlU54Te5u1cgfbCRw2BeEoQU0sdMrBmKdOJArUdGjfs73NMaVjZwR1ndqxXbE26tojaArRY1q5lhDgiWr30t427VxrWEF7RPt7X6v22ha1oapVG19D2B47Zse6XdrzQUtUs4hqJ068vlkL2DuoRLvnm6NOPPrBDqgKcN6Rldox4sRdb36NSYP9GD9AK9emerD8xY9xwsRyOGJK/HabF2c8tBbXLxyN5tjYg858nHDfu3jgvKk42KpdmyO/ED94Zj1+evJ4rP2mGdXNIRSXlOJXr32N788biT9/EIvl963peHn9Xhw1sAD3/+dTAMAV8yfgy31NmDS4AP9338co87nwwU3z0R6MwOO04Tv3fYwZw4rw3HdmAtCs8Gbe9yrOmV6BFWdO1o/d8ip+smQcLj12ODJFd2WTQ2qV9Pv9mDJlCt544w12LBwO46233sLs2bNTLiPC5XLB7/cb/gHaopfJf1oGmsy2eaj8awm1wHaHDS2hlvTmL9KCnYedjDy13XAcgGlHSTEIQOa/6UOWqqqw21XDfWCuQ2J/60FsbSzVoc0mrkP/djpsXB39b3qdgBYwjv5tuJ5YWQBQFe24naVv1tMlu5x2qKrK/Jip37F5DKqqsHgqboc2Bj7TkNepx0BRVT0QXSH3MKiqKtwOPeCcx0XNarU67lgbbrsepZ+2R/suibXnsmvX5HXq0fx1H2htfF6XFo8l4Hczrb071h5NI+1z2VHT0snqeJzGGCkHmjrhdWl18l12Fh9kW3ULFAXIdzvgsGsxZ/4XC6T62f5W+NwOqKoKn9uJ5s4ILvnLh7j08Y/REgzD59HO5bsdaAtFsLu+Ha3BqBZA2OlgQfFag1GoqorWYIS1V+B1oqUzzD7r1mAE+W4HbDYb8t127G3oQJnPhf2N7WjuCKPQ64Tf7cC+xg743Q4Uep2xjAVhVJbkoS0YQV2bFoumyOvEN7XtKPA4UF7gRm1LEI3tIYwp96GmuRON7Vp7ZT4XtlW3wueyY2CBGzWtQTR2hDC63Iealk7sqtPa2FnTiqaOMAo8DhTnu1DfFsTXVZpybUtVC5o6aHtaG5/tbUR7KIK9jUGEowQTBhWgulkz533xk714Yu03qGoOotzvxgC/B1XNnXjlywO46qlP8OX+Zhxo6sCQ4jyU5btwsLkTP/vHRlz6+MdoDUbQ0hnBtKHFsaB7Ch58exu+qWvHvsZODCn2otzvRnVLEAQKNh/Ufl/2NnTgiKFFWlan9hCb884wQV1bCNOHFuFAU6fht+RAUycGFboxsMCD/eZzjR04ekQJ9jW0G36nQlGCg02dOHZkKfY2dBjq7GvsxPiBfpTmu7DHdG5vfQdmjSrFN7VthuORKLCnoR1TKwuxvcZ47pu6dowoy0N9Wwh1bSHDua+rWjDA78ZX+5sNx6NEM+WeN7aM3ZOZ/NdfmD17NtasWWOwTHn11VcxefJkFMUeSlMpY0bKJtn3TySrJJ2/SAvUZ23aq0oD5esx1MYP1D5Xm001vqoqi51CrZtsqu5SQ8vZ7fpOL22flidQwMs1gBaPhbnWsLoK68tu04PWUuymsRFC2DHabpTo7dFXPtC/KyYj2FVdtvE49cC3imlMNlVlrk92Fl/Oxq5fjwOn7yVT2cUVa9emKiwVtNtJ5SZFD9DPZTqi3gJ8ZiJqP+nzOFlf9Bi9HroJQ+sAQF7MtUgBmAxVHHMBikQJu27eVYgeK4odc9itQXY9Tv1YSaw9t9PGroemonY59Hh5tF+Xw8aSCNA4Ma5YAH5+LCWcSxNtj8++FIhZG9B23Q4VxXlOzRoiVl6L5aeiMxzF4EIP2oIR5Lk1+VSLNeNGbWuQuYrXtGjrf1VM4RKOEhxo6sDgIg/2N3agosiLxvYQmjpCqCjyYHd9OypL8ph1TWWxF7vq2pkrlKIAAZ8Le2LZnPY3dsDrtMHntrMA/Hvq21HkdcLnsmN/o7aptqO2DRVF2ph2x9yjdzdorlLBcBRVMVelb2rbMMDvRl1rkMWn2VnbhnyXHU0dIZaVcUdNK5w2FU0dYbR2arLqlpis1NgeYvI5PVbNZaekAZEPNutBc2lf1TFLWrtNZckTqAtYS0cYB2LuU5tjAYWb2sNs7J/HYuRtrW5lxz7a2YBQhOCDHfVs7P/ZUoP/bavF21/XoDOkjf3DbxrwyHs78NYmfaPy831N+NELn2PDPt0dbP2eJpz6+//h65hcXd8axIc76zHx9tfZNe5r6MAX+5qw8J7/oD4Wb3BnbRs2HWjBbX//Egdi19gRyi7ZJKckm7a2NuzZs4ft7FRXV2PPnj3MxxgAbr31Vjz++ON46KGH8Nlnn+HSSy8FAHz3u9/tUhlJ9pHvzMfu63Yj35mfXgP2fMzc+BjaokaN5F++PQMvfveYOJWs8G7QNqbY0I/xrno0oC1TgCh6oFrF4AUtGK5Kg8JxdViAOz1gnMgSTgs8x0Yc61sPkMsi/cfapYKIOWAuDWSnKAonuBjruhw2S7pnc6R+1j5VRimKJdAuHZPLYWPzaU4nbY78TwUIj1NX8JgD7gZ8Vr9mj8OGjlAUlSVetAYjbCxuh8qCq3kcNlQ3dxoyINGF9GBTZyzgHg3MZ2PB2L6p62AxZzwOGzpDEXyyqwHrdtRpQWtduhKpvi2EOXe/jb9+8A077rKrcNgU1LcG8eW+xlhWIpq1wBjslj+X57RjT0M7hpfkobFdW7gLPA5N6VLfjgKPFiy3pTOMpvYQE1j2NbTD73Gg0OvAjppWFMYyDTS0a4Fwh5XmoTUYQXVzJwo8mhJGK+dkWYoa2kIYXpqHUESLlzJugA+d4Sj2N3TE6mh+37vqWuG0x7JBxcZX6HGgoS3EUkFvqW6HqgCDCz0IRqL4JJY14ECTnkGq0KvV+TImAHy1rwn1rSEU5TlRlOdEQ1sIf/1gF975uhr7GjrgtKsYVupFXWsQ+xs78OvXv8aj721HXWsQJXlOBHwuHGzqwEuf7sWJ97+LfQ3tqGrSgisXeR042NiJX766CXf9ayP2N2qC2IRBfhxo7AAhBH9+bweqmzuxv7EDAws8GFToxv6GdkSjBFUxAaa6pROHVRSgMxw1ZImi45s0uIBlUqDsrW/H4EK9PZ59je2YNrQI+xraDRnKqluDUABMHVLE7mXK7ro2jB/gR3Gek/nP830dM6qEBT6mHGzuRJQQPLxsOnvAkxghhLBYLMFgEK2trdizZw+zOgGAK6+8EjabDd/+9rexYcMGPPzww3j88cdx6623dqmMJPtJS1ax5wOn79ZeYwwp1qzEFCj4yZLx+Pz249k6zrIAKXp6ZLbkc9mCqKzAZzk0lycGeUGHHjKci/2tMpmED2hrlG0I0TMl0vESQpjcRGtGo3r/fFB+PW4KVSTpcocuH+nXT/u32RTmhkLr8htH5jTSNkVh5fi000629utKIDqvVOHiUBUm59EYKXxmItqXy9A/jV+nzw2tQ4P3qorC6lJFjt2mwh1rhwbGd9p0eY4ec9ttLElBMduQsrHxFXObVFTRQ2PCOG16lkhazsFlrCyKyWB8amua7ppXrhSwWDNO1i6tU8KNifZVFpPT3JyyqCymmMlz6RmZaF+DYvILIUC5z432UAQVRVoChSjR5IfmjjDK8l1oDUZgUxUMKPCgpqUTgwo9ONAUiznjsuNgUycGF3qwL5bcwOu0a9Y5hR49GLDHgb1NnRjgd2FHTQuK8zSFy566tpiypp0pXHbVtWmWxHXtGFToRpSAKTJ217WhsljLyNTQpitXhhR70NSuK1z2NGjlGtpCLI7NnnpNqcNnN/ymrhXlfpdhPa9p0QIe01TYkShBY3sILrvKMki1hyJo6gjDaVOxcb8muza0a3KYXVVYtqba1k42zg17Glj79bFjNDvSvoZ2tMeUK/+NxaShMXQA4N1YcOEv9+vHaCy8dzZrShhVUZi899qXB9jY12yqxvaaVqzZpLlvtQXDePGTPXjq/V3YsLsBQ4o9+P780cgmckq58s9//hMzZ87E6aefjsGDB+O6667DzJkz8cgjj7AyZ555Jp566ik8/vjjOOOMM9DU1IR33nkHZWVlXSojyT4UKPC7/EmVEolaaIl4QUxSxLyxAUyt1HcFu+K2p5gEHcCYLYilSIy9V1WFU1gkbtvGCS/mDEN8ykWRnyEh+tjoGPh0zjZTe1RgMGeHZhkEwAWBMyk8nDZVD0bn0C1nACDfqe/48HUMAXK5QLm0PYq5DF1YqQLFxY2B7gw5TXXKOOUKbZsu6HQxNCuH8l12FOc5EYxE2Tm3Q9P+O+0qqps7DcKa06Yy5cCB5qCuOIrtylDaghG2M+ZyqCwI24bdjWy3SFEUeJ12PLBmK5b89j3UtHQyocTlsKEjFMHabbX4Yq+meKEZF/Jdduytb8PQEm33pqE9BL9HCxy8N6ZAyXfZ0dgeQkswjHK/G3ZV0dI5xqxadtS0osjrRL5LU+LQHSBAe5gv9Drgc2tZCqjiprYlyHaeAG2RHVLshU1VsKOmhSlkGtqCaGgLYfxAf0xREtbGFGuvMxzFsBIvtte2w+d2MKXRtuoW+N127K5rQ3NHGH63HX6PA82dIeys1RQBO2pa0dypBf71u+2oaw2y7+GWqmbdaqczzNJF76xpY8oaTSETxIZYpqQv9jaiuSOMAm9s7O1B/OHtbXjoP9tR3xZCUex4fVsQ26pb8f/+8RWeWbeLpcbW0mmH8PS6XTjyzrfQ1BFCTbNmJeNx2FDfFsQvX92Eh/+zDTUtnSjLd2FQoRsHmzVlzV/+uwPNHSHsb2zHoJjb0oEmzZecpnXc16ApV1qDERYbCQAONodQ7ndjSLGH7VJRdseEs3K/GwdMipw99e04clgx2+Wj7K1vR7nfnTR1fH+mvb0dM2fOxMyZM1FVVYU33ngDM2fOxAUXXMDKlJaW4p133kFrayvOPPNM/OUvf8ETTzyBpUuXdqmMJPtJT1ZRAIcfVFrY8NPjsXjSAHbWblPhczu4tV97VVV+Eyd2TAEL8qqyY9axiDIIUQhXwMadY7KMYnyvjcWoyCHgMioq1nL0GJ+FyGHXC9J2qMzBW9PwMgxTOAkyMfKZiyhmuYKAGOQZ2obTZsyCqECfVypDKIrCNtSobOd22ATWNALlCicP0fFRBYmq6nXpxouq6FYvxs0YYzmXw6YrTXhFio3KUdwx08aV066y+aNKHaddZRtkVDHi5WJe0D545Yo5tXWU6EGAWf8OXZHDsj85VLZRRttz2VU2L3SceS69/5J8p+EV0OU+KjO67SoLPDwwJqvku+wGRVIwEoXP7YDXaUMwEsWgQjc6w1F4YtYsbcEoBhZ40BGKarKGx4Ha1iAGFXpQ3dyJwpjsdLBJU+AcbO5g49jX0IGKIm1NHlykWenUtHRiUIEbO2paUVnsZZtiWiYobXOnqllL2+x2qNjX0IEB3NqtKFpwYU0xpCtXNGWNF7vrtGORKEFNSyeGlnixvVpTbjR3hNHcHkJFsYcpQZraQ2hqD6Gy2MuyMDW2h9DQFkSZz4VdsWC8dKOrKLYhB2jWJKEIQaHXga9jljB0wzHPaWNKmC017ex7vzmWJIGWD0aibHNHV/gE2bVR2a2qSQ9a/NmeRqbAyyb6NOZKV1m6dGlKQsa5556Lc889t9tlJNlFc7AZBSsK0HhjI/yuNHZQw834YtI5OGLTC12uGi9VsyhVojh9s/aqKgq3u5MYO2cOY65jEGhS1Aax3SJFsaRRpAunYmpLdznidoWoS5KqKxCocGfnpSoYLVUA466UrtCIWcJwAgcx1aftFnK7IFodXXCiEf1dJiGplHNNotdDd3Dowuey60IRffV7HNjb0M4ELDqWocVebKlqYQIXPdfcGIZNVdAe0jMpOW0qalv1XYb6Nl3x4rbbWOT4nbWtTLig7dFFa8vBFowsy2d1OsNRnPen91FRpAWPoxkSPE4bag4GMaw0D1ECgwJkb4PmDuJzO7C/QQscXODRFBh7G9rhc9tZVqGplUXIc9mxv0ELFDywwA1V0R6yCz2a8LCtugUBnxt5McUNoAlMWlandowf6ENxnqasOWpECbxOzcyXKle+PtiMxnbNHcnn1ixubKqCYaV5mn+42w57LAvVrto2jAzk49NdDWhoC6LA44TfbUdTexidoShGluWhtjXIUlkXeBzYXtOKKNHSWm6ramXHCdFTTh+IpYksyXPBF7MIomkid9S0aimw3dqOFa+I2N+oKX/8sbTdX8cEhM0Hm+G22zC0xAtXWEVdq26N88WeRlTHlCjUiucPb28DADyybHrMGkezuPl8byN+tvor2G0qGts1RQmt8+CarfjNG19j0x2LUdcaxPDSPNhVBY1tIdz92iZMGOhHtDOIcr+m4GloCyIcieKR93Zg2dFDUd+qCU7lfs11qiMUQW1rEAP8btS0dOKwikI0d4TRFgwzAZcqeCTx8Xq9KcVoGTduHP72t791u4wku0lLVgk3AysLgKWNgMNveFDl0S1ROMsNuonDpViOmBQuvHLBTJQQq3KFS8VM6xIQgyxjhm0C0fJcu7Q4IVaFD98/VRRECWH92rmxU7GIl2GYHMBZCFOYdQp30Gw5EonqGYyEFi6cEoRdq02/RsI2p3Q5h8Rpg+/fxSmNqDxFH/ZtBssVOytH6/CKD1qOWa449AxKtK7Lrluf8Js1tD16jJe/aF2HTWWWQEWcNQuF1s3jrFmo8iWfjV1XUhXn6UoTc18uQbprp11l80OzL/GbcLo1DafccVMFDXWf4rIvMeWObiVTFFMCeRw2ZpXEZ2mi2TepAsft0C2BBhS4saWqBXlOG9wOLXPToEI3dsWsXhRFu8cG+N3YU9/OrKk7w1EMLPRgX2MHBhR4WLrqwUUebDrQjMGFHrwXswIp97vxTW0bpg0rwkffaLHY/G4H9je2Y/LgAnyxtwlFXgezHj5hYjn+83UNkx32NXRg5ohivLe1BgUx2aW+LYgJg/zYtL8ZboeK5o4QgpEoRpblMyVHXYuWRnvcQD9LC61Z7GpuVlRZQxUggwo8rNyumFVPcb6TyYn7GjtRkudEezDCym2rbsHgQg/2NrSz9jQZ04Wq5k7si7loba9p1eLrtAaZNc/XB5vZc0E2IbeiJDmDz+lD442N8Dl9yQuLsPsw6Yvn0RpN/KDQxb0m7VWws6MdN77aFEXf8UnRckXzW45fWNQO7yZA/+J3dZipbuwXgKVyjNcHFIO5LKALFy67HqWbLoAERjcgOkSmXIEew4XusDhEyhXqux2rX8jtbgCAgzM5ZnFkuBTRgC6E8DBLGG63BtCVLtrCZTeco68DCtyx61Yt7Q2OBVHlLVc6QlH2WYYihNVzOVRmKlrV3Gmx2KGL1c6aVkOdzpC2Tbanvh1twQjnDqWVqSjyQFW0lIRepx0+lx2RKGHWIM2dmhJI8y12oK41CL/HAbdDc1PyOm3Ic2lpsW2qNq95zPpFM6PdF3P3yXdpVidepw2OmNkvVepQxU2BxwGv0472YASN7ZolDHVN0tpwxNyWHPC77TjYHGSKpnyXHd/UtWJ4aR67Zp/bDr/bgeYOTTgYXpqPutYgmmPKFb/Hgd2xRX1wkRdbq1uQHzMrtqsKdta0YuIgP/Y3tKOpPQy/x86UK3WtQVQUaSbDLUG9vW3VrbCrCvKcNnx9oJkpazrDUaZcOdjYwdJmF3qcaGwPshSTW6pa2K5OgddpSENZ3xaEP9ZeU3uI7fZsOdisWerE4gk1tYewYY+2c7NhdwOiBDGLHAdqWzvx1Pu7cMc/NqKlM8IsderbgvhgRx1WvLIJr395kLmKFcbaW/HKJhy74t/MFHlIsfbb2NgewuWPf4jnPtyFhrYQ+55IJJLkpCWr2H2aYsVurSN02eEUFObVW1GsG0JCyxVQ5UbioZk3XeK1p5qUGwS6TMDcgkAsm0VRTuFC13RtTEarEwLCyRr0HCxKIKPlii5zwFSOvkYJWFyLRModQxwaVS+nK1J0JQydf12RYk3xTNdsAmJxd+KtnHlrFlqHKjJsqi6bUeWCy25jMh6Vf9wOG2ubKVJsupVKHrOEsTH5LZ+zjqEyIrOiMVgC6ZtSFCdnBUznhMpJukWKrtzwcf3TLEIFHqNsA1hlNn5++HTb1C2KWs4Qosua/CYdVQL5meuVwixcaF2XXbcEKuCUO/R6qRKGT59d7tdkQY/D6vrEK07p9RRyqboDsbqFMSWUTVWYdRBVzHidmtwSJUBxvtHqhvbfHtLi1qiK5kI+oMCNjlCUpduubwsZ4to0dYTREYqizOfCvkZt82lfbGOposiDXXVtCMRi+tlUBYMKPdhR24rSfM0tKd9lZ5t5/phFst/jQJ5T+9tpV7GvKcjcrHbXae3sa2hHSb4TeU4bttdoshG1gAb0VN17Y3FtIrE4dYDmZlXkFSui+xKpXJHkDAQETZ1NIELbkNRayLe1wSqKdLUVHbZTlERRQhd2RTUeTQQTUIhRCDD3J3QLErTHCzRmN6NkqAonzFAhiNuRYWa+pl8UXSiKtcMpZujiSHcERL7JdH5FuzCAbi6sKoruFmQzKmvcnPkqxc4ED6MChbZhU/X2LJYwsYXUwS3ktK8BsUXRbGJc6HGwBZb1ZbchSqwuU/TvmlhAr+bOsGGnq7lDT+loGB8XG4YfO/83XXi9DhsUTiGV77IzIcftsCHfZQchmimtoijId9kRimgB9vJdWtyW/Ji/Mq/gyXdpVigFXs2KpaYlCK/TplmuhCKob9OUFzXNnQhHCfLddoMSJt9lx4HmIFNs+dx27Kptw6ACDzwOG3bVtcHvtsPndqAp5rM8MpCHfQ3tCHMKpN31bSj0OlHocWBvvRZATlEU5Lvt2FnbhlGBfLTGzHILPA74XA40dYRQH7MG2VOvWe34Ywqf3XVtKPRq7kPf1LVplisxYWxXbRvGlOfjYHMHi4FTEIsJU9+qmdNSKySv046imNksnfut1S1aPx4HmjvDzER3Z21bzHrGznaa6G4SVcDkx85Rc1m7TYkFQNb6aWgLsQxBW6qa0dQRYsGfG9tD+HKfVm/zgWbYVe1zpm5fb26swiPvau5J9POQSCTJSU9WIUCoCeLV29g2oMsUEwcVGFyRtXM6VGbg48KxzRfFWp4bjUWuIcQaw4XvWbcO4SxSVP6I0WWZHuRjrtBNGz5unMgtycbFgzO7Z/PjFrkliax46BzSdT0S5RUe+kWIFDM2Tq7h29LqGs9pfRjX7HCUwEYtcbix0Tp5Tt1yRTUpSPj2qJLBrupWL3TN1+QEYijHx4bJ52Qhes/kcUoYagnFghYLXK+Mrk/G/hXOEocqgZx2lSlB8jnZjs4ni7Fn192sCmIP0g6byj5TXdHEbVAxBU3MLSlKLPFq6OYRAOaWbY9tPAFc0GLOfaqIi/vnNilhnAKrGzcXk5BeD6+EonX93CYgXW/9XP90LIUep+VYMWtXtQQ3zovJafwxPusglWd59/nSmHXOQL++EV2S59KyKxVqcW28sU03QrS6VO7wOjXLnYEFmrLGHbMOqm0NYkiRBw3tmlWs3+3AgaYODCrUAhT73ZpccrBJc5Xa39iBkpg19O66di1RQ0MHU1rRTTzN8loqVySStGkJtmDIvUPQEmxJXlhEuAXvj78EebaOhMVEuzTxBB5akldSCKtzQkKqChleUKALWcS8HRWnHf4QXZT4QHjmHZ4EFsOxPqyWKw5ukVW569P6NNY3C392VQ9oa25PU9aI65sXUX2Xx+imRNvhy/DQ69cXL6Pig88UYHZfEplr0zJ8VH9tLLpgQ32E9XPaa2VMO88rV8yBgfmYMNR0lOLgztGyunuTHrDOadezLlFBjgXgs+u+z26Hqu9i0awG3A6WblKsGna2AE1oCYaj8Lvt8Dr0nSi3Q8ve0BaMoKLIi+ZYim+3Q0WeUwsc7Pc44HM7UNMaYsqaPJcd+xo7WJDdPfXaTojHaUN7MIKG9hCGlWjKEEATON0OGw42dbB4LgebOpkSzePQXJ+GxuZ8X2M7c/1p7gijvi2EEaV52F3XBlXRfIV9bgcz7y3Oc+Kb2jb43Ha2S7W7XlPWVDd3MusZn8uO1k7NzWjcAB921rbCEVMo5rm0AMNlPhdT3PjdmhKHEM06Z1iJF3WtnVpMGLeuDKlrDaLI68C2as0E2aYqKOAsa5o7wqhrCzPrmYb2EDPH/aa2jbliUUsY6pO9YU9DzHxZa+/TWFC5tlg8F7/gnpdIJGLSklXCLcDLQ7RXE/GW5y9+dgJ+fMJYa3muQjgiDlZvLidCKMrEDoo2ZnS5QnsfJXxQfHqMD2jLKVLMViqEixdn04+ZFS42ReEULorhnNavdSMpUfwZ2kYkSixWurybE5UrCFeHWrPw4o4ejDe+coUQfsML3DGjFS4vCzKLEOifLV3nVE4JpLtdK6Dh33wCi5R8Tj6hMqObswLWY8gYr58fnyGQr2qUS/gx0bHbVYX14aNWNw5ducO7BZmtlw3ykslNHNycUBkvQoglXo2i6JmovILMUVTJ4ebcp5hFs01XuDAXLZtupUKVHC6HjX1menv62On1+LgNjHyTMsQWs5oFeGsaa+BhJ3fMz8akMuUcbc/rtHNJJ7RjfPYnqnAp9elKGOp2X5av98XcrPJ1hQ9VTPEu97R/anWjyZgxCxufmx2jG6GlPhciUYI8LiZOwOeKZf90QVE02WRQLMOUR7CB2tdI5YokZ/C7/CA/JenFWwEAhx/DPvsHWqPelIo/smw6/nrFUZbj/MIpirkiQqiESVaHK2AWBngz3mTWJ+ZI+rwVClOKJNGu8AHXWBpoZu5rFUzCJjtjPoMShaWkNvlaO20qW5hZfZObEGuDCz5HF1Zahu6S2ATzQwUKv2mhdqh6XZfJ0sRpWlz5K6R9Un9kcyYklVMm6TFXjJYwIjcjZgnDZTMCjJ+X0yTY8LssTpsNvOWNixPmtLI0Jaa+KPMR/PnsSvQ9r5Bhu2LUnYozD/ZwO2Z88DvqVsXGxO30mC2TeKHR49QUNJ6YoNMeiiASJRhY4EZLZxhOu8p2wg42daLQ40C+U0upmM8JSfsaNP9mu6rgYFNnLNOUDQ1tQbSHIhhemodddbq1i8dhw4GmDs0SxuuMWc/ou1I0LWRHKIqG9iB8bgfcThuaO8NobA9haIlX64cKXQ4tDXiR16G3Fws8rCiameuw0jw0tIWY21K+W0sRXt8WxLgBfmytamECXZ7Ljj0xBY9NVbCrvkMzD3Zp81XV3IlhJV6WWptayTS2h1DXFkRpvgtbq1rYd4HGrLGpCqpbOtHUHjIIfhKJJDFpySoOP3A+iQW1jQ/v3pLvsmtrgWkzg98g6gxryviuPoAYLEzYMb0jlVOCsGNmRQbhg+Trygiza3SUcBY59FiUwJzpkB+OQeZgf+tjM28gGeQpVdAe4pfnsyrqKat1xZDZciXCHaMyCj9Peqw4fc2n8hTbSIsSLq4MWBu0Xw+zCAFnEWKPjYmL/8LJWfSa6OaLw6YiHBPOaKpqp83qls0HuTXH1eGP8a5P9NL0eDV6bCD6YM3LMbrlig3hmI+W3yMYk0keM1wj76rEbRbR+TS7VGlta3WojGLjrH5YkF27at1o4ixXqGLKYVeEMhOdd709fZ68pg0q/m+qDFEVhbN+iR1T9WN8ogfm5kTnzm51ffI4bOy+ZBYu3O8DtQ7iNxJ1NytOuWK3ukXR+4iPTUP7ZynAuY2+orwE7QlcrzwOG3Pb4pU62Ub2jUgiiUMkGsGXVV8iEo0kLywiGsFo1zdQkVr9hRPKcczIUgDGhZE39bUIEyboUd5axXwsHroQpViUOEaBRlCXa5vt/rAxKJao+skUNAqMuzfg3vMZi2gz5hguvFBGx8RbnfDtOe2qRfNEa9uZoGIMIMebnPJB5fj3PNQShje75MvaVdWiDKGLAV0g+UuKF8OFt4QxZ1liuyhc0DgKi+pvao+OoZBb9NiuEScI8TtJLF6LXdWzOcUGzwfepbsGbodqDUTM9a9natLNXUUKHrqg8rspgNEk1W7TlTXemDuScY74fvXr43fI6ILtMQlTNJhvlOhCJ7WgyXPZmJUN3ZWiFkEVRV7DbojLoWWCKoplSqprDbLPzG234UBjBwbFFEbUSsbjsLG0ypXFXlQ1dbCdMa9TU/AUeJ0o9Gp+x76YIocqhoaVaMqV5o6Qpqyx21Dd0olQhGBEWV7Mgke3xtnb0IGSfCcK3Hbsawoi36ULNPsb2jG0JA8NsUwA1P2qpqUTwViGpl21bUxpqClr2jGm3IdgOBrzn5aWKxJJqqQlq0QjQMOX2qsJkazAH0rkFkR3kIWWK4K24rVjPiaSO8wKDT5GCisvUMZECWEbKPxmCN2U4SQhvS6nIDHLVKoCS7pnXsYRbbiYFT6AvsZTuSNCdLdovZDeh52zsKHt2Tk3I/N1803x8hSgzYnD5CoUjkb1zIy81YspC6IWeNio8FBV3cKYl3foHPNyAh0p23SyqRYXKZElDm/Nom/gqOxaVZMSxqYqiBlWGaxgaf+8NS6Vu2n//P0sslJ2meQ2lbO+5jMNUQUXta7g+2CbPZwcRd2HnDYbGwNTuHAxbLxMnrKxzz6P23Bic0fnhJOR9Bg6MWVZlFisk/h2mNUPZ7XMLFe4vlisG4PljO56RfEw2VRf92kdGt9Ey2BktZyh113IKabMCh8tho3WXjHn+kVltxLeEsdpVeCYFS68m1W2IJUrkpyhNdSKox89Gq2h1uSFRURa8bdRP4JX7UxYLLGaIU6dJJXoaX5BStqPYFeFwit4kilp9HgnsVdO8BAFgIvXBgvgZknxyGUu4IQqHlHAPLNihy6M/C4Jhb6jwoZZmNSscYyLvsgcVx+PMYaL0yQcOOwq2wUzB8jldz3M12Jx/WG7NnoZc6YA3nyTQvss5PyL+eMi/3FeUULP8zsEvFuQ2XKFV4DwShMKrzzhr81socMrQHjLFX6XK8+0g2oYn8mli7fY8Tit1wEYfcW1srpZLhVOHFw7tE9ekcPGYFOZ8MSC9Nl1YcVtNwpH7liq7QEFmm9yMBxlMW9agxG47CqKvE7UtgZZu26HDTUtmrKGuvvwu2wHmzowrMSLls4wWoMReJ02uJ22WOYqFQGflpaZjt8Ty/JU5HUiz2VHdXOQKWQAYH+j1l51cyeiRLt/Ndcp7XeQpl00WvdorlNOm5a1igpFEokkOWnJKpFW4PWjtdcUMGz4CCxX6FpWUeTFzhVL4ihodGWACKHLsUl24LUl+gaOPi59Y0lhpWkNvnk6BqGlichyhfap6q7Wxg0kY13VsGYarwXglDUCyYzFUhEIMoaAtpyMZJZvRGu26BiL5RIlFpkmErVayfDneSsZer38Rgm9NrrO8nIWLy/RzReDq7Zi7NeoXLEeMwf05e9JXrnAUls7dWsasysXX563yDH3YbDoNckkNs6aiY9XQ6+H9h+J6kot3m3cnO5ai01DDO3xchN1N3I7dNcr5ibOu0+Zxskfo+1FCWHHqGWInbNcYbF2ONmPd6nS5V3d4pbetLpcwvVvUtbROeDb4OvQeXLwAYK5mDMsaLEohg2XFlykrOE3y2gf9HM0WzpnE1JikuQMfpcfTTc1pd+Aw4/JX640uCh0F2ZRkkARwp9XOdPMpAoZvpypbHLLFb6s0cpDi0JvXNyTugWB83tmfeiCjLkdqxuTyXIF1kWaT/FsHo25/YjJzYhPW8inqIx3bdRtiQ9gC/AZlTirDbZwI1YnJsBwQiX9i+5+MCsSTmFCPwezRQ1vvkmhZQo8Rssat0k5wsNiztj1+eMXJye3KNHPgz/HZ0rS4+Ho7dC2RRYkZhNk3h/YZdpVsNuMC6GhPZOgwVvJuDkFCL+Y0n7oPc1fE+/bzZfVYr3oCzO9DruNC4xstp5x2Ng9oCtr9B2ZPKeuUIlEdUUKFQBYMGGaotJlR0coYrne+mgIA7nUx9RcNhiOosjrQJ5L+9vDtUcz+uS57Nhd3448lw1qzJ99f2MHKkvyUNPSyfqiSpx8lx2lPhcONHVg4iB/bI5U7K1vx5HDi5Hn0tyevE4pKkgkqZKWrOLwA+ckryOSG0SWK2t+NNfinksxxbMVl4lTj9ZJFLdEd/chnNWJ/qqnjub7i23a8A/oJlchhftft/6wxlxROWtakUWKyHJEP6dfK51XO7dumpVU/NzzY08Uz84cc4bvg5aPEKtbEN+HbvVjtZLhzxuVC4gd0605YKqryahG2cnJpWQyuy+JxsQf4xVJZjlBy9IUU644dDcn8/XwspHZ/Zs/b0zZbayrbcDROeGtRLRjdGMsGtXXdz8X8yS2VBviz7FYgE5OhosNgbquOG2ccsUk1/BjFqXqZjF5uOul4+QtcaicoypAiMbVoYoMm24JROu6+etXrWMSxdChclUe60uXlfRU4XyWTl25ZLYEcjtsLH4kn1SCbTgKUnDzcjK9zXTLImm5IpGkTTgaxtrdaxGOhtNrIBrGEd6NsCVzC0qi9DAUNe3KxDvPL7Qsc1CSjnhhxCy0GHyfk5gMU2zcGCxuQcmUK7zrj6lfXhERbz7MMh7vpywaQ1zLFRYwzmwZQ/Qo9rFftUTKFbpbIhJetPZFChfjwiyKI2O2XOGFFD1rg65EAowpACl6tHyjaafI39lcx2W3sX6dNtWyUPPj4i116HjcDt3c1ZwZgrdqcdlVuGw2Q3u8MsRlsvKIh8iyxuxW5bJYrliVKxGTwozGYOHnRg/0azO4HOnXC4MSB9Bj47jsuk+w2UpGSympW8mIggNTBYWHG4M5to3ZLFZvW6/Dp9Hkx6Cl0NbNbvk+hxR5uPY0//HOcJSlvyZEr+Nx2NAe0tI5e52a61Q27gxJJNlKWrJKNAxUr9VeTRg2bwTKArMiRFGAIcVelsI+Hsk2eoQySoI65rWS33hJlAoaEFvfmOUGReE3qvRjZisR1TBfVplEJBOIxknXFLo2RjnlEkWkv+IVBMI01qpejmJJWU2MMeUAbb5spmsknMKHD7Jrtlzhx8IrHOgQ+A03eoz1pXLlBFYqKldOvx69PXo99PNm6a6jhCkAebdos4zp4NxtzAoavi8+ho05lh9vuaLLZ3odJkdwsik9piq6zJjHWalEzMe4a6CbKHabwhQJZmuauMdMCiRC9HIe04YgwCtX9L7yDAGKEevDaqWiy0ZWBRavcNGzUepKMDrvVBmiKHrsQ0MKcHaM29CKtce7SpkVM06b1epGU1bBcCwb5ZPsG5FEEof2UDuWrlyK9lB7eg1E2vH7yhVwJXELEkHivtHgl0+R0MEUJdzJ5JYrnIDC2tEXz8T9WYUW3UdZtNOTZCyCMomEB6sAYp00czpog8uUqQGmpKKWKyJLGO76+PbEgpRRGDErKwisAps5lotZwQMYI87ziPzdddNGazwLPVq+MeaKrpiJLxw6OcsVO2etwgsj5upOzqfaaVMN/uyAfr8Z3IK4FIO0Od4KRZRaWwTvzmQOZMvv9DCLFLtquOfcTir4GneHeOWK1XLFBt5KRne3Ugz90Ouk5ZiCx+JmpF+v066yMdlV1WKxwrtLeZxGZYiuKDH6VetWRTYWAM8sbGmZnKhptfF6i0xxbjzcjpPZUkdX5FgzXEkkkuSkJatE2oH3lmqvCRhc6MGflk1n8QYAkWKiK6OlbSSOkwbE0jPH/hbFLdHlHP6YcS0xCku0vGLpT4HIVYiXERRWLpHlLF3rRBkdDQoqgTUNSz/MuepYZBNY5R1+M4rPfkQRySR2U3wVfuysDRCLS1OUz2DEPYybNzwIIeyBj49VZ90YE8+nRWbilVWK4JilHLG0G4laj2kuTda+mExDN4Q4mcomkItYcGOufTrHDm4jy2FSJCgQK3CoEohXBtB4Mfo6aWPjpDHsVEUP5CuKV2NPYDmiW67oMVf4DSPaitdlVQLRddymKswCjJdRzMcMihST/KW1Y5TNVEWBzWQ5o3BjZ5uGXPxCPsYhvQ5+Q4jOMR8vhmY74y1cqKxnTDOeXUhbX0nO4HP5sOf6Pek34PDh9TEfYcWRnoTFRHKJSNAw1EkizIwu9xnib6SCUWFjFFD43RLR0Iy7WjG3IG4BTDUdNN+e2cKEt2pIJtyZu+EtQ1KN+8Jj3i3id3nM8WREQiALzmbaGdLb0xd9JpxxVh6AUcFlTl1oFp7sNsUyJ8z/V5CJhUbwZ36zJuVMImsc84OwcKeH7hzFLsJpU5nbF++2w8QitvNhDPRric3CKUdEJr0ieLNYc/pr3UJEtViUsD5j70MRo1+zkxurTTUKCy67vhPGX5PWl1iBwgdSM1uu8Aoth02BI2bREyUETptRAHDx1i6OaOyY6Ry3i+S0qQYFSh7nmw7A4H5lME+GLmCZA9K6BQojs8myFuXf6rImkUgSk5as4vABZySvoygKFk0oNx01xyBLbS1NZj0rckcWpTaO1y+v+DC0w7IA6eUscowidgsSKXAo+gO1WAmjN22UYQzdcnVZfLk4mzrmvgzHE2xcsdh0AoULs0iBeK7Nmz68lYpuuaIrXPQ4ddb+VYEiRYFVMcQrXKh8YHBBEshwZksg3uqHjjfCpeW2Ga5H1L/xmN2goDCe0+oY5VXtvElBoejWLCLLYTafRL8XPZzSJMrcffRNDXrdVF5R+T4E40yU2pp3nxLJU/Qa3Q5uTKZ4OXZVD1oskontpr74MfHHWCBlLu4fHTMfaoEeo8oQwrm3MQWJQwWJ+S/lJ7AE4lOF53GWwfSY2Xo3m5ASkyRnCEfDeG3ra91yC7p42GYsHFfSrXGI1CzxhBR6tMznwuafn9ilfniBwjKGZMoeQ1ntlV+82YKaqhDGL5qm8fE/6BSLkGUW0sALCcbxCeubxmMOLMfHcLHEXLGE99cRWYAAxt0Tc7Be3RxXL0+162bTXopNVa0KF7owCRaGUMzuke6I2G3iuoY6nF8vPztmixt+fPRz03xb6bzpdc3CJK80cTlUg/DDt+vi/GyT7Srw0f/N6a8NbkYOo1KBYrac4q1fmOUKUxwZhRF27QLlChWm+GC3FsWL3TomB+eKxadQpNepZwdQ4XGKLXUMn5Wq+zY7bEb3KH68Issa3WzXqMDjFUaWtNvUcsXOpdq2Z5/wIpFkK2nJKtEwsO81oVtQMkYG8rtU3qy0EJcRY7b6SBT/LZ6FrW4logjLmg/yD+XmtYYXDkTuKSLLFXPA/XiYY7tpcldi2YYOydwv3yOfxpmvAxhlMv26rQ/+9Aifdpn/TJgyRDVal/Lt8TEA+WDEZhmKtxxxcMcSjd18jt+w0gMp6y44imqtpHLXyrJK0uvhFU4iBYlJXgtHiSXjERSFuZrr7RrdkQCjvErX1igh7CGfDyhMZ4q3MGHzabPOnSirpcMsB3A3j4Prn1bh5QWzIkdVdaswJp8mUUyJZDezHMkrjURWRFR2DXOBmalyxaboLn98Kmo6BN4i2OxSxbsKmTfhsonsG5FEEoeOcAeuf/16dIQ70msg0gF8er322oN0xRpEBL+DQMuZixqtJkRtWxvndxdEAkfCsQrKmpUPhvGJtqGMBXQhSaBNN9dOJgoRQriFQzuWyHKFopuiGnuIGixXrIKGNia9jq5JjzMn3A6NuW/6MMtfJN1pELlMid7z12C3GVV9tF+RW5C+y6EIFUNmU2nejNjOSbC0HL+j4LBZy4ngg+YyxYPAcoWOIZ6yhinpuOsV+azT9ihmxQELWEyM710O1eIyJNrh4ePc2G26ckUXAFVLOT0jlfH69THqgp9ZEaMru/SMBrwABhgD2PHveWscqrziLWG8XNBfiUSSGmnJKgnkk2TL9I+PH4sNtx3PlU9RCEmCqBV6TNhHgm7FMVd0LNavUPRMh0IrGb0chV+jzQoc4XC5Y6INK5E7s7kd3qqCG3zCfs2bG8ZxUpkkjrLIJIfwQXZ1ixxO4cEpXmhvuuzFWSBzVj9m2ccgTwgextlnwY3T7A6mWa7El0uZfMQpDYwxZMxymN6GSEHAuz4BxkxL+gZZvOxL2qsx+5JRkSByaVIEY+LlbYdAxtJTcOt1mQKLs0SiG4p6/2LrIMvmosK5dHHyqTnTkkHhY7fKbubAuwSEu0awa9CzYeqx8MwWLgT6xp3uysxbE1stgmm/DrtqsdJOdZO4N5FuQZKcId+Zjy+/92X6DTjygSXJ63dFMOmOu5ConzU/mmsItkVfzZs0yfo1jlF7Nf4AmRQFSZrTtPnGcfNB2Sz+2qb6oh0ti0VIovaskpfpvHWh0bMPxf8Q9CwA1vbMOx+JduyYbzZbqEztQeAqZFKuGAUt7TWey5RIYWEwIeaKi/3QY9cdU+LYOYsUkXJFtHNmSLsYew1zi148lyszzKKDi/ViSY1tUxGJiN2ezOOycwoKpoAw7RSKMg1o10uY4oGW5X2NzQF36XVr6a91gYjP0MUHD9SuJTY+hwpHyCiIiMakXZN+HQ5OOcOfM7hscebEovaoQsWuGrNJaePTFVpmZZJEIklOWrJKAvkkmfuO3aaiwKt9R287eQIWjTe7DSWHXwJ/s/RwzBxZguufW28o43XaEspHiX7rkyk3RIoM+jtqtnjg/9bkBWP/qiJwPRL1b+jL1Df0zYJEJPtsEimGRGPR11VikWX4v5nSIkoEMoqumOJj0dFj/AaeRZGiJHbLMcdDoW2bx2k+x1uuUDQ5yzgmo1Wzvm7TqubYenxdUbYgXsYVpQoXueXogXd15YZIkWLewBONSVWsMpNRMWSVk3iLHTpO3oUbiMX/gbHdcDQqlM8cpmskIHomLBagWDQm/Ri1utH712UP/n4SbTiZ5booIUye4+MU6nGCdLnD7N7m5JRgohg22YKUmCQ5QygSwsovVyIUCaXXQDQE7FqpvXYRo6UI4f62luUX20RpmUU/B8NL8zC40BoTxtxOKsoQVtYkZPAkMSrQy3Hab9FinEwEES2qzNJA9LAuEHQM7cF8Xi+RKAuReTypWK6YF1B+l8A8HvpgahZA+RgzFDoul+lB3TA+k4LHXNdwjLuv+LOiXRW6sPICbcIAwHx7gjHp7ei7K7pZqqU5A3xMGKYQY/NIr02xmIJaxsgEQk7ZYDct/txnav586d/mFJK6r7Geitkc1NhpUw33EC9s6gKFdo4PYGfeDYpExdfIxwgy+2Pz1j4OOxVEjJYrZssTPkuW2QSYpbS227hdKCkqSCSpkpas0g35hOfSWcN1a8gkxNOFnDWtwiKH/O17x+DiY4YltKI1n1MMf+sPjGaLFJEMoShgAUMVbs0xW0SI1zpOBjOt20By+YkiirFiuUbBHPLymthiR7R+W9dVtv4JLDL4Y/zc0npsc8DGzTuhsqC1f97dyCwLKNz6KxpTomul8Btg7BjXDq8EoeX4zS9zzBXDgz8bL+eeYprPSJToc8EpbfjNHQrrX2C5wgfetWw0cnX5OdQDDltlrESbX0wJxP3NlGqEcNZGornQXiNcwGNRcGVqTcN/Z0RyrFmBFIpEhYouh0nhoc2xUa4D0WV2utGjKfW00y4Wu1Dl7gVO4WJSpnUlXmNvISUmSc4QjARxz/v3IBgJptdANAhsvEd7TUA6X9OufLdFUemTtZnIakJYV9gf/+NNX2M/tombM1qucIFsaRsWZYjAxNdwnss7oCjWMmafaGvAXMVy3urCor0mslzhd0vi9Wdd1Gl7eiF98aLv4/dFMS8Mos9UJCwC4nuUX/z48mwB5GpFBUKW2cRTGxMVqLh7RyDc6WPXP9NUFz5ewWDOUsQL1Xrmhq67BZmtclRVEQq4org/KqdQMQfEi6cM4dvQy2pmQrzrj91khWIO1GYel01RWH2HyXKFT6HNsiFQhY6pPd6Mmo+ho5XV34uCIUskksSkJaukKJ9kArZWJgjsCgBXHDcCJ00eAAA4orIITruKhePLUZrvFJY3u18Sw7kUB8URDEfiFud/y6m8IMyiR19F/fNrneC0ObYbn1qaP2ZpVuEVGYn7peXNa57IGpeW5ds1yol0/vXpdPBP/jH4JdSsfNJcf41t8+1ReCsVJgsmWCaihAhlHMuDP/RyzC2Gt1wRxNATWdPwbk6A0T2FSXBEl9lEm0qG9gTzbba6EdVVBRs5RgsXY/uGvsBdv+lzihBehubvGToXuoULvWA7tymUKA5MItcrCm+NbVCkUAUSNyZzvBbeOoltLgqUagpgka3tqmqQX/jXbEK6BUlyhjxnHtZetjb9Bux5wAndqB+jCx45CUlmTioWfPQfMTYegVjAp3LVd4m4VgS7OYkQWa7wbViUIab6om7Mlit8mS5brhC9lNn0U7TDwlylBJp8fczGeubo/4Y6JiEomX853zdb6A1lFWEZitgXXFxWNL/mQGyA2DyV9sLXFV2jWZBUoKS88LGgaKrChBWzrze/+xOvOXOWKIdNtQhOvEWJMG6N4G/Djomitw3oAQ/N6aH1NggTHiImn2k+XTXbZYpSU12xMsRcnz/n4pRJtE/RfcL3pyq6mxGfXQDQLFdEblQSiSQxackqCeSTntqYVRTgzCMGY86YMnz8Tb3l/MIJ5Vhoykz0y7MPM7wXPfiLO6NlFJjdfQDxGk8zwPHtm5ULfDwp/mHc/NOXzIJE9FOZmltQ4mO8W0wqbYgU/PyDqr65FV/G4JU7YtlLsdQRyQkKV562FzXJTnylRPdClFjXI0KI0BKWFuNTYNPBmNdK0TUAVjkwQohh3dPGpCstHAIlg8p9diK5x2zdalAmGvoyluPHaY61w/dBD0U5S29WVWAJxM+FaqirveEtgbRjhFm+8venefPVPGaKeUNONMeaS5VxjjWrm9iYbPrnaZ5jRdGVZHRjy65aZcss1K1IyxVJ7hCMBPHIJ4+kb7kSCQJbH9Feu0imFCo8yQQmdl6xlk00nCOHFeOSY4ZZCgt9f9P4BeAXXO21a5Y0gMnMUvhQai6fuAN+V8T8A50woC23yBrGK5hzs1mkyBw6ngJApAAzm/eKYq6IUhwCYhNbXjgSmYnyxalgwrchirkSNSlNtPZgORa1zD0sVijx4PuNt1iqqiL0a+ahs8cvvHRBNgtavFuQoQ2BdkXfMbGmbeRjq4gDIloVeHZmeaIHEWYCYBxhnjdjdthNyhXOkoWe092ChM0Z5kjPoECvLdaGXWVzIC1XJJLUSUtW6YZ8ki4KgHvOmYLTpgxOS8Z5ZNl0LJpQLnQPsPZlPSeypuQ5Y+pgTB5cwN6rAmUIH5ycf3hMZYMnGWK3IGNDwgfcVGU79p6PfaK98rv7RksTaxvWlNWKRR5SwG0qGR58wc7zr3RctF06E1FuzYOpTqLNOpHlCuGujbe+oNdj4z5PfXz6vWbe6OM3IcwP+ZEoEcZcoTVE8TsU7rMwfz58v7xSj21cCeRPkXxjVqTwfxs+iwRKNR7znEQ4qx/ewoSXhaxtWMckVK6Y4upFCRcgl3oAcZ+5w67LJXpMRF0GssT6gWKR4xVOWcVbB2UbUmKS5AyhSAgrv+pGzBUS82km3fNpvmL2CPxkyfi45xXjL2L8ckn6ES922iv/g2VesJ6/6micNa1CP89irugticwOE45FsCOhcj/iltoWZUV85YBwxz/JeBL9lpof6sWLR/xzdExmIUoUaT5ee6kERbZYrgiu2RznhRLldlIo/OchyqDAFxftPiWKfSPyIRfunCl6mVSUW3x/UcGuB5tXRY+FEq81kdBnNh9l3ykl+X1n/t4onPKH1uSFb5Eig9+Z08ek+zObfbBFwryxPc7lx9SequiKEnPMFTO8Ys/sX81/BmYTY4lEkpy0ZJUE8klpviuDo8vcZtHCCeWwqQr+83/z8O7/zbMqDbi+dJmDO69Yy/HnTjl8EFb/YJbhGK2f57LjgfOnYhSXhppfS6x9xF+v4mF2CxLVifewm8oUG2LJCNZVfj2l7+lZswsW/7ei8Bseel/EVFerY+3LakWkK2vYhkICmVB8rYndr9kxWDdrDBmRuD70udBeDVY/AnnG6gJlfaDn1z1jHBjr50Ob5gPmm93T+aVTHF8FgmPGL4t2fxjbJUjtexzlshqxgLbEet18W123XKFtEMv9yQ/RaJUVO2aYY2Nd/n4XybGiTKPZgnQLkuQMec48vHbha+k3YM8D5qdXn3/oKve7cflxIwAAhV4HAD1/e2ptxf5I8ntgUGiYChsfAlODT5NX5tOEtVR9FflSNpP7iGa5YjL3TNIevytjHoNoRMlirvB9mheGRNfI75bwiKqYlQoGIwcqDMTRpBNibdRstZAozov5crUgZQDvkR7PJFd0/XR3RRRzxbB4JdjB4I9ZFRGp7WYBvPWQtW1+t0YXEsXtUBcdo5+09rc53aFNETvliT5T/nMSzRFFdCxKrPcCb34sMkVOBAHR47PETOYdXHBa+j1M1q6ds+gxK9V4VyUb21VMaXgSiQRpyipx5JN//3AOirziGCfdhV9HfzB/FMYO8KXVzqBY8NumjvjKJNGabbSSTN6PuYmTDxsEwOrmS7Pg8ef4n2F90wCYNrQIeS67MM5bJJUxwSqzJHf5jn+Mf1BN5L5sdMs1NqIpUgj7m57S5RS9nllJI1JeKNzawjZmBLFPkm0qpaIMIASc1aiutGBj4V5VRTEoXgzplDn5VB9f/H51iwvForQhXOYm4WdHZZiovkHCyy5m5YZIkSK0ZhFYqyiG+0NkWWUkwmUVMshnVBai95YghiA/XyJ5VRRzhWII/Gu5Ll5Wtl63aCPNqPyiY0ou4/cVcjtKkjN0hjtxz9p70BnuTK+BSKcWMC6SuH5XHiR8bgd2rlhiiHGSKvQH74KjKnH9ojGC83Q8nPlj7Fi8HWkRZvNQABhY4MHOFUuEP0pnTh2M84+qNI6FK+YwPagqipLUN9m88c2n5UvFLSjZ9Wp+3LH2TD/MiQLa6v2ZLW2s94HFl5S3HqJlYnVS+a3nd5TM7Zn7NI+FF4r0MYs75U1rKaKAtuY4L3y/IoWLSLjj7wnzQhkPumjzUe3Ngp6qGi1IRPAuOrScbhliVAjGcwuC4DPlA+Pp1xJrR7TbxGGMjQTD+FTFGufGlsRChBdoLOm/VW7HLYnQoSsgOWWKaQy84iVV5Y9EIklTVokjn4woy0dRXmaVK6L9nYDfjQtnDs1I+0Lls+AcX8yyBsdxI1o4oRz+OJtZfJwHsxAR7zfsxe8egycuPVK4/qZiuSJ62hYFXRVBS/HrkWiYoodr01JkqKtAYDGkAIBg3VfMdcXn6EwwC9Eka58ZzfrFuglndRUiumKIq8vLFnRMTD5gigIuW5DAipg95HPror6OmuYVRgVBIvmDnydzVqVk1sH6NfDzGf8cJZ7linl8EU7W5j/jxJYr1nVfJJqw+Yz9EY1yvy1C2VE/JupD9Eygy03GNvjy2SifSOWKJGeIkAjW7lmLCIkfQT4hJALUrNVeexB+lynRV57+HvzijMm4esHouOcVrh1RvI9kmC06kvHrpYfhzjMmG46JzAN5jXskaiieMLI8xZzGLlF9S13BMZH1hNZ+8gu3WMZAscyXeRc/KtCuxHOF4bX3rA/u8+XHzx+zcQ/2PBHO1JNiXHS4z4vuqnDjFSnDRAsbL9yw9gQP3CL3Id6kNRF0Bywa1d2C6FzwPuLJdiciZuUK97d5x4hX/vAYLVes7ZkDuInc5QztCe5jXqgwt5dMJufdwXTLGr09s9VWvOaM2aSMO1IOJpSKd9UkEkli0pJVekk+4emNZxJzPBBArIxPRahRFGDptAp8dvsJpj403A4b7v/WFIwf4DfUMfevn7M+xPGY10lNFlNMx4zvz50+BOfOGJLkOox17KpqkVs0Aw4q0/B1xW3Q8dEy+kOu6EE1/gdvVH7pawv9zJjLDh8HRtSO4Fiqcqv1/uA2XCA6Zlzn6Zj5V0N77L3CFGjMCkKxKnIIVzeeBRQlaopnpypGJZq5DXMcGPE4BUooYlVICueX8AopvX+zfMPXFSktRK7B5s0cg+WKQAbhN88SbdzQrqLEujmsKMbPjy+fTUi3IEnO4HV4sXLpyvQbsHuB45LXT2bSmYg3rpuNgTET2XiYtfJxx2HQ0BtLTx9ahLc3Vycdz0+WjMeMYcUAuqfd5Wv63A5DewoUaxR408+8yE2GLkIOQSYSa1T5BAMyYd5hEKdnNI3HfF6xlrH43IoenAW7JfExtmd0C4rTZwxRzBVryxqi6PqEvXIKHaHCwLTKg9990Y+JFHgKO5Z4Luzc+MxzwcdRSTalZiGJnyNh3B1BGyJ3Oxu3gJt3w0SmvIYx8e2ZLasU3epEVwIllhIMAXLp+DiBzZIxKtmcEWsgY35M0nJFIuk6ackqKcon2QxN++t12nDtwtEYVKDLQsaHfGI5JgoqbyaVn6HTpgw29CHuP3YsSVvmQN4EwILxAazZXMWOab+7ehmaTSkV42LRAzi/BrZ2aoq2PJf1UY0p+A3tccoGYtzxElmkiGUOsXKDfj50jUy2Vpk/K74NijC2HuGtP6xjNjZqPCeyEhHpkfj1zGwBKo7FBssGGiCWG6Ns7vS10zw+Xh6Ol7TA3IYoKUJKBuyK9T7nu+JjnpjHxJcTzyN91dsQyTnmfg0bN9RFDZzLmaLLcOZ7QZRVKRtjrmShvkciEdMZ7sTtb9/ePbegz25P6hYkIlWN++hyH/IFC6GI1B7AY2VN75fPG4Xtd56kjS3B4C4/bgQOH1IY6y9+uWQ/0jNHlAAA/v79YzF7dCkA449tUrcgU+cE1uw+PGGTs7NF8LLsHgnMYOP0TftP2L6gjk2wGJjbE+1C0LIiYYN/5afQPHbzwsYvTiJEu08i81j+IsxxXgxj5NoWWW2wVMyCnaNkFieGhd5kosrvviVbQPUdNV5ZE2tXKLhY2+A/U3PQX363RXRvidozfkbmHS3FYlGTbK4IeEHGqEziLVcg+NxERAhhyk167bwfM2su+2QXiSRrSUtW6YZ8ki5dkUFSweO04cNbFqKiyItrF47BOZwFR6KulBTG8q0ZQzC9Ir2YMMn6ByAUgv7faROx8qqjDcfOPKICX/2/xYZ2u+ClrdUxvbfbVOFv9pjyfCyeOABuhw1mErmpqIpRngAAl10VxlpjY+IVMzF4i1TanCiDYMoxw0S6FLNFBhLL27pMYl17jdmCrGOzKI2gy676uiccuaUvY3ucsoZarsDav2izQlTObBGjCu4xIlS3iLHKntYxGWOuGPsXtcEf4+Ves+JUFF+G78N43UZrGkNgakEb7FgWmtZKyxVJzhAlUexp2oOoSSOfMiQKtO+xaPR7EuEDnMCUMOX2WLvJd/KtdY0Prqmyc8US9vdhFYV6e9wAJg324+8b9rH3qbgFRUwPhzxmZUfI5HdkzpgCWHfyRUHO4mF1C7J+dtaYK1arBHopVhNO66Trz8H0rwRlRMKQ5Qh3zrAqJjkfg4/DoR+zCnAic1tRV2blUdyxMuWPYKEXBEhLBp+Ok8WRsVk/M9HEGDy9LDFcrLsxomBsPIpgLvlXc3tJr5EQiwKKj69idgdKHmDQGqhPlDUh0w9hEsmhTFqySi/KJ8my8XUHGixf70t7TfYLsnzuKEyJbQSJyt95xiRUVVUhVcyXKArSaXhQFrRR6HUyy994dMfSGQAGF3owf1zAsA4C2mfkczvwx4umCeuJLCj4h03zo3dFsQejSr14fXM9l4qZvw6w/plbLueWxALaRmk5xVo5CancdXwgXx6R8oditrrkjyVSCgC6rMlvUpiraC44xnEY2uP+Nsez09rT12itPesFipZ+fiPGXCMaFcvy5mbiZckyX48o5kryz9U6xwktVwRjFN7HSRRi/LwA2Wm5IpUrkpzB4/DgkVMfSb8Buwc4Knn93vqeptqP12lP8qCamqBEf+xohiNjC10XtvixXHHcCFxyzHC9PVNzw0q97O+ZI4px4qSBiMRWabPWWVGAkMlyJWhSrpjNdRUFCIWjsb+NC1g6P7yKYl1XdPNN7b1oxlTBLgBr09KHWZji2jE9iFt2HkSKgSTnRbMgqiMSPvm6InNb0Q6OeVcjGQrXptlyxdx2vPoA5/8rSEFosA4SNMd/blZfX4XzI7YKeqLR8cfMAoGqWC1Wku7AcNYpImspNgcpznk0ylsiGV+zMQK/RJILpCWrpCif5Br0F9UcrNxMgdeBkyYP1A90Yd1ORVmkKprbtsOmYktVi2Us1iZSk4kUJd4mGjG9N9ah/PfG+QCAmhZqsaSdHF2e2EonmdKA7++TWxeh0GPHAEcnrj9xcsK6wjYU8Zp8WEUBnDZVvPaZOlGQeE74Ywmz4Aie0EWyhjA2HKuqr5lUtOTXYPPY43gvCcaowBzQlr8/REot/Rri3++KIh6EUM4U3ovWcYoshhK1odfVa4hkBf37TttSLLIPFGs5wmuwoNc1f2Yia+FsdFvOSeVKVVUVtm/fjgkTJsDv91vOR6NRbNu2DXa7HZWVlbDZjCZ1O3bswMGDBw3HvF4vDjvssB4dt6R7dIQ7cPNbN+POBXfCbXd3vYFIB7D+ZmDKnYCti/UzuNFj/rFIxL+uPg4TBlmDs6WDTVWw+eeL4bJbTUy7A4GWCs9pFw/ug5sXwO/WFTrPfkczs61rDQIA3A6rHeaAAhcnbACHDS7E7rp29l6UncnsmsS7dQzwu1HV3MHOJUptrZ2HZbL19YMqb7j60PvqKky5IjiWaPFIxXTW/LelDd7s0iK9iNsQZgsSBC0ULYbJxkr9uOmourTBalYE8UKNQOklVDYZPtPYjhaf3lGwG5NwSFxBs4uXolgD0CYzsuLjqpjdloyWJrE+koyPz9AUb5yS1AkGg1i/fj0CgQCGDRsmLFNVVYXq6moMHz4cXq/XcK6pqQlfffWVpc6UKVPgdqex5kn6hLRkle7IJzlAvAf/uOUz3T8UprD4+mCz5Xw6G0xAcqV/vLFYj2moiqYMcdmtchE/Qt7ikK7jvNKAL1uc50Q0GtXWHIHywFA3jmUIbY+3Kn75e8eCAPhoZ53wOi3jF7i2WMrAKtsoioL8WIYooWKIyhqq4JigAr+26QFo45fn3ditozWOicVcMYxPQ5SZh5VJeF1x7rFUvkeCqqoimHtuUF1VVojnWL8/zWMxunQlliOJQFllbk8GtO0mn376KX75y1/irbfeQk1NDdasWYO5c+cayvzyl7/EfffdB5/Ph/b2dqiqij/+8Y848cQTDWVWrlyJ0aP1DC0jR47E008/3VuXIuknJFSgpPD7xStWMkGmFSvx4H+2y/1iQbE4z2lwOeKn46nLjkJLZ5i9v+9bU/DrpYcDAJ687EgcUVlkae/0qYNRXqD3xbt1rLzq6ISKD7N/ssg0VBQ3xtxXomC3op0cUbsiRDFXEiFa2MWWGqLx8P1aF8hUFUj6wpdEucL9rUeJN5olpwszF6ZKm6SWK9zfVHnBWUKZlSuGe0DQv+HaqPDGW/6YlStJ5oq/V8zmx4Z0ngIrpGRtihR6WbghlJXU19fj7rvvxhNPPIG6ujpceumleOCBBwxl3nrrLdx000345ptvUFpaip07d+Kaa67BnXfeycqsW7cOixYtwlFHHWWo+9xzz2Ho0KG9ci0SSU9gePC37FB3r23Rb6/ISkEwqG7TnbGLXTYUFKeQejuRG4koyUDSsSRYD3kFDr8hQ9eyo0aUYNMdi5EMsTLFvCkmVmT8/vwjsK+hXehaYs4UCSS2xOE3ISKmzkQbHMmmkv8cEylrdFnIuqkllAMTnCMknnQkUg6ZSiSRfRLJscYxxMqzTJqKRRYWJkiAeE5TcSEUbeBlo5VtzilXTjvtNNx5550YOXKk5XwkEkF9fT0+++wzlJWVgRCC2267DUuXLsW2bdtQXl7Oys6bNw8vvPBCbw5f0k3cdjfuOeGe9BuwuYFp6dVP9xEvoXldFv0epOOC3ZPDL/Q6UciltHbYVNCYbseNLrOORdFcheaM0c/xqXmHFHstdSjDS/Mwf1zAcEz0YGl+zytkLCaOMJeNb77J1rEE8UBS2R0zVo+/M2X+29yHKPCtKABa3L4FdRLB92uOXJ8kTrKxHUG/uuWKtT2h4pP/TE2CGOHaSzl+kWiXhRO69N01QYXEzbGxity0VNM4E7ZnVshkscCSrezevRt5eXn4+OOPcdpppwnLbNq0CQ8++CCmT58OAHj//fcxd+5cjBkzBpdccomh7Pvvv9/TQ5b0IGnJKt2QT7pKBg1xk/dl2nlOlW4rXCxB6rvWR2m+y3KsK/qZVC1LzeUTPlhyfycNaN/FD9mgrBBsNtG1MxInJJAo6K5xTFZXFJEFMiFieanM50KZz8U23YyZprTXZCmm9Qf/2DEoCJuEDJHsF28qRUpCsVsQXVvF9fgx8f3pMoN1DAQpZl8SIHQL4t4ncn23jkJ8L5qD/xv6j/MxWRNX8HIYHRunoDG5RmcTWWhME59LL70U5513HpxOsVbXZrNhxYoVKCvTHrAURcHy5cvR2tqKTz75xFA2GAzis88+wzfffNOjwb0kmaM91I7L/3452kPtyQuLCLcDH1yuvSagp4M36iacXUcYayMLb99s+E5FTb608Vjzo7mYNLjAeFCxzrX+A04ML4YycfoixPp5JzLDNSMUxhLMMV880f0ssuQw1rUeS5Ry2NBe3F6NiNoWCSFdtWJRINo9se4Y8YgsUfjI+dagg1zdRH7i4JUq+ntd+WMdnwij+TFVHurt8T7y5vLxMMdcYQKLVK6kzGGHHYZbbrnFsIFjZvny5UyxAgAzZ87E9OnT8e6771rKbtu2DV999RU6O3svc4wkc6Qlq6Qon2SEPlieDRYEvd+9AaGlC3fwnR/PxV+vmJlSW8NL85Ku4RcfPRSnThlkOS62qojfjmj/ROQmoUCsoDCjGrXrgjGJ3F7S//T4qn///rG4++zDUsogpAj+Vri/dWuRJP2zuvo6HjUpVxSB1XJSyxWuQsQk3/PnqAIqILDmThQoV6SYIsSa6MHcH99GojJae7xslNrab06dzCtDzHJFsjEB/HfAqkAxpLY2bW5lY7agnFKupANVqgwfPtxw/F//+he+9a1v4YgjjsDw4cPx2muv9cXwJF1AVVRU+CugKmnetooKeCq0114i4S5EhpQ4S6dXsHTL6ZLOekkfvrLhIUwk3JjdJrrWHnDJMcPw89MnsWOJ3YLEZcxjMfTB7UrEO0dPmdtNdkVG08nUEAkDNtU0ECT2G443hlTKaTs8xnMGhUrS/qw7Jeb5S+oWJFCWiCxhzIqIZGPi6/BuOIqgXKLxiUxsRTFwUs1goSiCezbJvSzJDG1tbdi8ebNFNgE0y9qTTz4ZhYWFuO2227JCWS1JnbRklT6QT3oTBQoWji/HZbOs93u88plgdHk+zpleAacgxy7fA/8NG1qSl5JrzvrbFuHSY5Nfz89OmxTXPZr1b4qbEo+BMdfnRHFTDIr2ODx80TTMHatb7IqC+5qVEQAwJkmgXb09K7wcdFhFocE6We878aoqWpbYRoNgvRW5mvBfMXOsPpE8VpIvvhdE4zRnU+LXUYdNxc4VS1Dg4ZNKGDddDGPmNmREsxKOCORK0TgtVlzW7EP8+0Qivci1Rxx/T5fHhGMyjYCPZ6uYjvPHRArJbLRc6VO3oG3btqG6ujphmalTp8LlsprnpUJtbS2+//3v4+yzz8a4cePY8UWLFuH222/HgAEDEA6HccMNN+Css87CZ599hhEjRgjb6uzsNOwiNTU1AdCC50ajmUmdF41GQQjJWHuHGg7Vgdtm3wYAwjlKOn+KA5h0Gy2coCdrG3oauq5/NuY69D1J494RXd/xE8px/ITybt431PIg9fvP77bjue8chUEF7rh1ujImNi9pfAeI4DOjcXIV0eeJxJ+nAsDntuH8I4dwZWgd7dXvdhjGHPsDABAxfbb82qbXMdaNEv0c25Fh7Zra48ZKj9O0n9FolNvRiRr6468lGo2y9rXj+jXQcqqiWNpTYb1GQvTvH2svzneGf2/8mxj8urWx623E+w7yc2a8XsIJrFp7kSg/PusYiGB8/OfEzsX+JoQvZ21PFYwP3JiI6fPl21AE41H497Ex6GIS0VO/mu6XeH/Tdvjri0T1NuLWyRDZutY1NDRg06ZNCctUVlZi0CDrbnSq/OAHP4CqqvjOd77DjgUCAbzzzjuYPXs2AODNN9/EkiVLUF5ejuXLlwvbkbJJ9mGWVVKav5Tlk+7DrxU9jf6bHsXDFx0BAHjxkz2W/q0yl/U3LN4cin576a+i06ZgxZmTDeuBeZ0BgHHl+cJxmOHP+912AARzx5ZhT31bXLnUMl6BrMN+d5Hge0sIBhW4sf3OEw1lIqy92CsIs6Bg8oFp/haODwBc+FhiuCeIcZzQ+5o+tNDSv3io1vVadIyXQR44bwqOHlmC8x9ZZyjHj5tEjZ8hQD8HwM0FAablItGI3hcne1OOGl6EkljAX0B7eKdJFqLRKN649jhUFHlw9+ubrWMixnkCd4yunyQaxeEVBdjb0B7n3tXrmo/RdrV1X5+nSYP8mDOmDF/t13/rzXOS8F4kxCCfzRlThvljy/Cz1dDPw/z9NB6jMl/sLJs7mrWTn0/z5+6wKegwldM+Y1O/hDDrHNH9GY5on62qZH5t6m57fapc+etf/4p//vOfCcu88MILqKio6HLbTU1NOOmkk1BaWopHH33UcO6ss85if9vtdvzyl7/Eo48+ilWrVuG6664TtnfXXXfhZz/7meV4dXU1Ojo6BDW6TjQaRWNjIwghULMx/HEf0xZqwzVvX4P7594Pr8MaQyPp/EXaUPjVNWiYcD9gix+DY2iBA1VVVYZj9MfBfDwZhBBLnfp6LQ1gTU01WpP4qZppbGxAVVXmdzGDwRAA7aGiK/ffUK94TkJhzS+2K/NVF8sGVF9Xjyp3KOV6ABAJhy19FSjAY+ePR31tjXV8oVDC8YXDIcu55g7tmmrravHssonwOlRWhrZXW1OtX4NHD8gbjoQRCimGPmsatQeiuro6AEAw2MnO0YelhoZ6rWxNjUFwiEYilnuyrbWNveevrz2kLUBNTU2oqtLvt6qqKvbbVVVVhcbY9dXX16PK0RnrR5vXi6YUY0SBDVVVVWgNRti4q5xaOaeq3eetra2sPXe0XTjH/Hv+75aWZva+uVn7u7lFb6+1rc1Sh39PSBRVVVVoC+rXW1urzXlD7Jra2/Ux0Tnm2+O/r+HYPUXnpaGhAe6ItoNVX1eHKqXd0EY4Yr3nIxH9vgx2ap9vfXOQzV9rUFvA62pr0O6woTOot0cVWnx7oVCQvW9ta9Pai91H9XW1aG7Wfluqq6vhsNsQFnwP+b+DnZ2oid2zzU1NqKqyo76hKTa+WrTHmfNM0dxszdqRDXz55Zf44Q9/mLDM8uXLcdFFF6XV/q233ornn38er7/+OgIBfffYnLFw4cKFOO+88/DXv/41rnJFyibZh1lWSWn+UpRPMgH93vXU95qnri4m71RXM6vHxsYmQ/+dnZ2WsQz3K4Zjiebw5PFFeHur+bcchj54GhoaAGjfkTantiYeNdCO/159RMI5CQvkDAD49hFF+PYRRZbxxuufbio3Njaiqkq7ls6QVj7c2oiqUIuw/2DIKpd0dHQwhWp1dTWumDkQs4e48Jd9dYb+483f2ZOKkG+LorZGk5Pq6+rYulFXp8kftdW6DJXqPWNerwmJol2w5vIyyPRyG0ItDQib5LOWlhb2N30ob2hoYB9yZ3M93r92Gpobalm7odaG2LkGdqy+XvubXms4FEKJrQP/vGKy1v8QHxaMyMOkAQ78celYVFVVwQegsb4NbabraWlpQTB2PfTzbGpqQjisyR/VsT7q6utw07yB+NHsAcK5q6dzXKPPMf1+0LWZkKih/0fO0ZKxbBnsxPhSpy5jBIOwkYhhnB0d7RY5oLW1hSkfq6qq8MuTKoGwfs+xe6FBn7vautrYvGvjjUajqKs3jj0UCmHmQDvuP2O0/t3u6ERzSwvr67JpxThxpBdPf1JlGFNzSwuTf+h1NzU1oSMYk7tra2PzVce+W9W1dbFjtWgXZNfqDt2VTfpUuXLrrbfi1ltvzXi7zc3NWLx4MSKRCN58801humYeu92O0tJS7N27N26Zm266Cddffz1739TUhCFDhqCsrCxp+6kSjWqp0srKyqQAI6Az3Im5I+diYPlAuOxWa6ak8xfpBBrnIhAYCNjE1lDrb10It8MGp+mLSk3ceEE4FRRFsdQp7NBMAgNlAXicXVOuFBYWIhCwBnTtLg7HDtZ+Ju4/m03T8ndlvhqJ9mNWVFyEQKCoS/3Z7Q5hX/G6dzi2Jxyf0+m0nHO3awt+aXEJRgbyjf07tgIABpRrdcrLShAIFLLzimqD0+kw9BlyaA/6paUlsT5d7NySKUF8uLsZhYXaPJQHygyZnmw2G6CEDe15vXXsvdO5HUArAoEA2mPKBr/fr19T7L50u/axOq7Y9ZUUFyMQ0H7TXLF5CASAw0ZpSu6OmLKmsKgIgUAh/vH9Y1mwOW+ePob5gQA2jB0Cn5s3gTXOOf+33+dj7/Njf7vcNawcf32i9myqikAggLbYYlxQ4EcgFn+rtES7Jpf7AKvjce+ztEe493a73TAvRYWFKCn0aHNUUoJAIB9O527WhqraLO3ZYm0AgMfjRiAQAHFrwmRxcTFcsc+mPBCA22GDi2svtuVjaM/F3SN5Xi8CgQAiLnoflcJ3MMzas9ttsNm/TjjnLpeL/Z4UFBQgEAigoFGJtVEGr7dROOeZIlvTCx977LE9FlT29ttvx3333YdXXnkFRx99dNLyAwYMwNtvvx33vJRNsg+zrJLS/KUgn2SKfJ/m2tFT32seKu+UBwIsNoJvd9DQv/Y7pI9l+50nwkyiObzuxADE26Liayyo1h7OAmVlyHOl/hhk537Pk6EoKoCoofzNJ42D266izPSbSxFdN4/TYZVz3G43/D7te14eCOCmU7W4TwO2tgKoYuXjzd/xgQCOnzoCtS3ag21JSQnsdm0dKirW5A9e5kz1+vNiaeZpeUVRWbxMvg03tyZTzPKjz5fP/u6MyR9FRYWgkXtFYxpWMRDb7xwYe7cegCbf8tdjN83n89/V/x44wNiex6PLIgCQn58Ph9N4H/v9fpx+RAXe3lyN0tJSAEBZSQkGlxnlRZ7iYl3Go9Df7UCZ1q7DbofHNJ/a38CxE/W2XC4XXA6jHOJ2e2Czm8bp83FtWOeOjqWwsBBvXj8bNkV3QaIyqaqqKCgoBKDLvXa7HRWDBqCCM+j0eNzw5eezvi6fpd2D3k0thv59+flwxjbr6HUXFPgxflABDmyuRllsPktKSmCzbQcQgj/W/6DyAOwCt7/u0F3ZJKeyBaUCVawEg0G88cYb7MtEIYSgs7PTMHFbt27Fzp07MXHiRMTD5XIJ3ZNUVc2osKHloc9sm4cKHqcHPzwm8W5iwvlTPcCExPUL8xILNV39XOh4jMe09zZb1z/nMp+7Z+4NFksiM/cf8/3tQju0rKJ0vX9F6VpfzJc1Th1V9LnR8anWcxSbzYbHzhuHyRWFrMyvzjoMo8vzcde/Nhn6VGOLgY1dt37unBmVOGdGJf63tSZWxmbok/d7Zce5a7r/W1Ow5WBL7LMkrB82x7FyRNHbsNms92XA57JcK9Xx0HtlUkWhZVy0ToFX/Jv5g/mj0BGKGK+JGx+df+oOrapq3M+Mv37V0IbK5pZeE+HqKKq1PUKM7w3tif7mx8TNJYWfc1vsvqHzrCgqbLHPxm6zWfpVYG1PVbnvSaw9u81mmSN6veI2+L8Vrn7svo79PjlsNi59dM+sR/1tnft//+//4Te/+Q3+9a9/YdasWZbzbW1t8Hp1qwVCCP79739L2STHEMkqSecvBfkkU/T095qHl3fMGen0NT/+mmpsq+v3oKhsd2SwVMuLfnu/M1vLchoM6263XepfUF5VFLaWaeuN9vf35o3CCRMHWuSGePNn49YBAuPnZLOpOHxIIXZUtyQd77PfmQmHTcHrXx2MtafLNxDIhaJ1/bSpg/G3T/ZY5AEAUFQaS0+8psU7pnDzRNe8VO870TgVLiA9LxfcsmQCblkCpqxKdr9SuZLOv3ZMn3etL0A0dyKospCfO/O9yMdJTHgvKCpGBTRFzNaqFkt5/v6g74X3Jzd3tIxoPs3XqCgq/nDhNIQiUbZJqKr69dD71GG3ZTwRSXd/G3NKuVJdXW2I0/LVV1/B7XajoqICFRUVCIVCWLJkCbZu3YrHH38cmzdvZnVHjhyJsrIyhEIhTJs2DVdeeSUmTpyIXbt24ec//zkOP/xwnHfeeX11aZIUaA224sznz8Tfzvkb8px5XW8g3Ar850xg9t8Ae9fqZ0M4wfW3LRIGAMsEmY6X2NXMLt0l0+GsRL/T1A+3WKCA4+dvXLnx3jpnxpBYPeNnp5hexQPRXswBxpJdb0WRFxVF2kNayoFl6Wvsjzevn4OA33qtLKCtqJEUP/YfHj82bv88kS7cmPp8coIDXfz1CLSMEaWp/QaIA8vGmjMEwLWWc9qsYzEEno3z+cYdS4JPng8EyNJHpzB9eoYmWocYjkuSE41GsW6dFiegpaUFBw8exPvvvw+v18tcfX7zm9/g9ttvxy9/+Us4HA5mGVNYWMhiwn3ve99DWVkZ5syZA0IIHnnkEXz11VdYs2ZN31yYJC3SklW6IZ9kM6PK8lFR5Mn4w0+2k8rK1ZNT4rLbMGFQ6pZriuAv/YiC56+cmdJ6MnOEZolblu+2BFydNLgA/95kdrm3trF83igsnzdKPE5Ffz3vyEp8ua8x+aBYZ8Y2ukJXg+ya1+JkiALaUiYM9KcsU99+ykRcOLPSOBZz1TgBj90OFQGfO0lAW67d2GuigLJaogLreWtAXSJMY+122OB22Jh7O39/jh/ow8RB/qz8bckp5cq6detwxx13AACOOuooPPHEE3jiiSdw+eWX4/LLL0dbWxuCwSCGDx+O22+/3VD35ptvxqmnngqn04nXXnsNv/3tb7F69WoUFRXh2muvxZVXXhk3xbMkO3DYHFg6YSkcNkfywiIUB1C5VHvtU4w/IKnSU4oVIPPKo66Y2mYjoh9rl92GnSuWCMunsvD9aulhuP74MVwfxtc4DQMQPOgqGVKICdqgfY0KiE1Z6cJbWWyNC5Cx+yjWx7dmDEFTzC2nOxlTmLKBO3b1gtG4+JhhhnIVRZ54QzFkM0pV8eCwCXbouLp0DkXtJVLqiMbHxahjJM1mBMWSMpEKOA6b0qPC/6FEZ2cnrr32WgCaufju3btx7bXXYuTIkXj66acBaAH8jzzySLz44ot48cUXWd2jjz4a9957LwDg4Ycfxp/+9Cc88sgjCAaDmDhxIh588EEMHjy4169Jkj5pySpZI59klgKvA+/dMD9hmb76mclURqIu95tGt2U+F06YGD/Ve7rtiurqAX/1c7xbcipUlnhx68kT9PYBXLtgNL43d2T6g4TxM7v91PgWfam00Z1Pn19vRe14Yu45hZ7Uvs/xMgZu+OnxcDtU3PP61ym1U+B1YNrQYkMbZuIpTz64eaFmYZvijWTOFiSWT1KXIxKJeHz6bEqh14l/Xn1cao33Mjn1BLRkyRIsWSJ+uAE0/8VU/KQrKirwq1/9KpNDk/QCTpsTlx9xefoN2JzAqPTqp/Ncd/5RlRgT5wEV6LuFvTd4+KLpONiUXjDFtASEDD4FLp1WgdOnZv5hxu92wM/FHzF//vZEpqMpWK4kUz4kmyK205K4GBRFiatk6g6i8R1WUYgHzj+iS/X5dliKxpg0waeCtKmKwZrog5sXCNN1GlMcG1+TqZPGD9R3DqlAY2OKHoLSfJdhLpN9RoZUzMyXL3GdZNA2qTAYiuhzlo07QtmIx+NJKns8+OCDSdtxOp1Yvnx53OC1ktwgLVmlG/JJV+nzzN593X+aG1yZIp1uP7xlYdK2uvN7LZJHdeuLtJvV21cUqKoCt2pU0qSyMWVY99Jc9hTo15OqpShPKps7/DzluewpyUmlMUto0ZAUKKa0zYlJ9XutwJqKGQDriynXEnw2BLBYmgj76sJcJ7rf+M89F+SSnFKuSPo3rcFWLHxyId686M303YLeWggseLNXzG7vPGOy8Di/G5A1ZFjYoQFOu0JvzsedZ0zC+t1ic9K7lx7e5fbSEVb1xULBX684CmPKfQnKigSf1DpN6NbCtZGV9yVH8jk2DlxRdFXPYoIAAQAASURBVLciKkxFE7RR7hcHMOM/Jz1mgHVM5vGtv22RwYKL7e50w5VX9NE4VBojJY2GuQYrSzQrGpquOhcEGIkkG0lLVull+aQv6W234ewlM7+xmVSWGS1YtNdschFVLH+k0UYCS4uu0FXXHxGVJV5sumMxi5VjbNfaV1dRFOP37RdnTMLpUwfhJy9/nqBOipYrpvIiBV28tszKKs0SKP5V8q3cdeZkbDrQlNIY+wqpXJHkDE6bE9fPvB5OW5ruMaoTGH+99poFZM9y1f8YFfCxQF2ZoDvCjaIAx4wsjXMy/Xb1JgQLHouvoR+jVhvm7D5doVuuO0kuNp2W/W4HKoo8CPg0xUk64+PHpQiOxcPsxkdlp1QFVcWw12Zsg6coz4l//GAWBhR0Pbo9bW7bnScx655QJNrldiQSiU5askqWySc9yRCBS+mhRsIHxUwqK3pBkOzRLrq4JCd6kE9cj1cWda1PAMJx0s+4u/Pjdohdrvh2uyNb8Vxw1NCMtAOkbs1z4qSB2HwgfmrjO06fhFMPH4R3t2gJHBK5bSmKgtljyjB7TOazpmYSqVyR5AwOmwNLJy5NvwE15tOcBpncacmEtltiJBdnMpUxTxxUgJkjii3HhZYsXbTsMNfxOG348mcn5Ey8nHOmVyDfxblZCSbUaVcN/v7pfItF7kaC+LgpuGVplRIJdsk+Q5F5NKAFCtTGY90NSgU+e8DC8eX48QnWgMMSiSQ10pJVuiGfdJW+ths5ZmQpNt2xmL3vbVEoXSvNTI8zU+1l4rk7kYybCVk1U/FgMjFluoIms2Ta1T9Tzwii+yPZLTO40GNwaxaRzDr9rjMnY97YAMp8LvzijMmIRsUbNxfNHBobUwpuRgl7zB5kTj1JztASbMHEByeiJdiSXgOhFuCfE7XXLCCbfiSkmW73SGf2UqlT4HHg2e8cnUbrOokWKvPn3peKla7KEb86+3DcdsqE5AU5urv7k1IQ4jhQQURkNSTuzHoo2Y5bVy9PJLzluewsU0M2/UZJJLlCWrJKlsknPU283fr+QDb/roqsJdKy9OghurMGd0fONdccP1C3fO7OmIR9CSxiPM7kshndZDGS3qD+e+N8lOZblSd8axMHFeDz24/Xz5m6Ou/IyrgWtUKFT4KPJ9eeUHJji1IiAeC2u3HP8ffAbe+6+TsAwOYGpt6jvWYB0nBFTDrT0tdzmSmTzVRR0JXgZb1Ht9yjYq8njS/G/LGBLrfNXHYSCDo/WTIBx08YkNJ4RLubesR6qiDhYtYkaGvNj+ayTETdu1e7Vrm7d+XCCeVYtX5fN1uRSPoXackqvSif9HlA216gyOtAfVsoYZmeTCqQyhRnxAojTnDS7mC+P7LJyjrdsSiwph9Oty0aqPbR93aY+sgs/PC+N3ck5iRwhdl0x2JhQP54dPc3oCSWDKA7buRmzNmXRPF/suhWTIhUrkhyBrtqxwmjTki/AdUODOpG/Qyh/0hkz69EfxC2epJlRw/D6g1dewjN9JwnNYQQubVk4ed+2wnDuxwMGUht0R1U6EkpE9T935rCAgwbYq6w4LYaqU7f8FI9QKXDpsKmKhhSlDjuwN+/fyzqW40PB8V5fLap7pOsjRnDivH+zQsy0JNE0n9IS1bJEvmkL+gJJccr18xGZzgiPJfJLDjpkEWiX0Ic9sw5N8S75HSVUBkKm5J63URpgtP4QO88YzLq24IJy/Dtuh02TBtaFLds3LgtSmasPvhLXPOjufC5reqDrsyCaEwzR5Rg7fZa7rpz5IsiQCpXJDlDc2czxv9+PDYu3wifK41gpKFm4B/jgZM3Ao6u1c/kQ6joR0mSPr89byomJPEN7WnOP6oS5x9VGdenVEQq/qXx6EqdbFLiJSJTw9SDoaXf4GlTdAWMalKoAGLlVFfim2y786Sk5cYNMN7Tr107m1m/xCMLdWUSSb8jLVmlG/KJxEo6Ab5zlUxYztJsNU67ymSTqUMK8ex3Zna77Z5AzYCvUndb6M60n39UZTd77zv4zaJMcs3C0bhm4WiWsZAn10IXyKc8Sc7gcXiwculKeByJHzDiYvMAs1Zqr10kk1/r8QP9ePf/5mWwxf7NqYcP6ushdIt0lR+p3pOJWs/0cjV/fAAvr9+b4VY1ki2uPWXizX8+utlqzC2oR3oUM3aA8YFLeNskGNCL3z0G+aaYOjmid5NIcoq0ZJVuyCeS9OjJn79MWzrEbysz7fjdDrx67XGo4CwqFUXBzBElmekgDqkohkpMsT8eumgaplXGt+KIx2GDC3BYhRaX5I8XHmHZwEjEKYcPxEc7qg3HzDJJv19PMzQBolZolkVbNgUASoBUrkhyBrtqx9FDuhHcU7UDZd0LDpopsi0VYW7phA8NzP6lmWovHr2xJB0zshQf/WSR8Nx/b5yfOD1lhkaY8YwOlj/EAf56e2cllfni51tkUpwbYopEklukJav0onySa7vAmSZbXLP7un8zXVE2dJV0r/Xd/5tnsdg8YWJqcdOMAwCK8pz4+/dnAQAWTxrYperThhbhz+eNNxzrKbfqTLarxefLXIO9keFKFDevNN+Fpy47yqD8y2ZktiBJztDU2QT/XX40dTal10CoCXjer71KJDmMgtQdaUWLIT2ULO5HJhlc6OnRhdF8nRlzM0rg8N3bgYwzTbYJ9xLJoUBasko/lk/kz5AkHkOKvXKd6mfE+7xnjS7t5ZGkj7RckeQMeY48rL1sLfIcafr72fKA49dqrxID2fSQ2F8W0oxH+I/TIp1P0bzeeOI4XDVnRIZH0jP0lWUOmz8+sK2gt2z4CpnvgSwYkkTS70hLVpHySa9zKEgaPXENPbGWHQpzbeZbR1YiFOHj7GXmKjNpWaYoukuNpPeQyhVJzmBTbZgYmJh+A6oNKEyzvnxKkfQQmQpom47ywWlXEfBnSeC/bq7/+bFA0T3qR58gCHFv/0RkQl6SIpdEknnSklW6I5/kOIdi1p5UHpAz5hKcoXb6gt4ae0981KcePignYv49evEM7KlvMxyrKPJgT317H43o0HdNlG5BkpyhqbMJys+U7rkF/VVJy+x2UGGWPIAe0vSvR71sshbKBrrz6b9yzXF46rKjMjYWEYpiVWClky0oEd1tostj6F9fOYmkV0hLVumGfNJV5NJz6NATCiJ5exw6KFBQWeLFMaOMLjWrvz8L625ekOG+MksuiyfSckWSM+Q787H7ut3Id+an14A9Hzh9t/baRf72vWPRHoqk128OIBfT3mdQgQc3nzQO5b70FHfd0fxno+tVsjElUkaN51Jx98S1XTizEodXFOJgU4fWR+z4MaNK8N7Wmoz3ly6WGZJfbImk10lLVumGfNJV+vvPgm6B2NcBbTPUUA58oPGutbcUfb3xUfdGsNdMUZTnTLNmhlyfUrjGvv5+dgdpuSLJGRQo8Lv83cgqogAOP9L5cSjOc2Jw4aGbIvGINNLaSbqHqir4zuyRUHsptRy/TmXbkuW0q5haWdjXw4jLz0+fDI/Txt7TRf97c0dh54olsaO9nS0oOTOGFSdpI9vuBIkk90lPVklfPpH0Dl35PHtNaSDvF0mWkMO6kIwjlSuSnKE52IyCFQVoDjan10C4GVhZoL1KDNxy0nhs+n8n9PUwJCmSyxp9EV///ESMLMvMji2dmZ6Yo0Ty8tyxAfjd2WUMeueZk/H1z0/s62FIJP2KtGSVfiyfSAVB+ihKbsevyN2R9xwZTcWcqayJmWkm6/vMFNklCUokCfA5fWi8sRE+py+9Buw+YGmj9ioxoKoKnPbs0LXm8g9qb6FAFP/j0BZTsv3qfr30cGDp4X09DAM2VYEtgWXUjGHSYk0iyTRpySq9KJ9MH1rUr3eZD6Wl0mHLvNzWM7JEH7tg9UL/3e3huoVjUJznyMhYKL056/34J8WCVK5IcgYCgqbOJuQ789P8oSRasDh7PuTPgKS/0V+E6V7xre75LpIjuNCuCMW6O5NEIskk6ckqvSefjB/ox4675Pe/r+nuWvX05UdhckUBXvl8f2YGJOkW3bWWvWbhaADAix/vycRwABx6Vs65QnZsVUskKdASbMGQe4egJdiSXgPhFuDlIdqrRNKfkettWmSTdZAoRk32jE4i6b+kJav0Z/nkEFyPeuO3+NhRpfC7HRm3xJHrSN8i5z/3kZYrkpzB7/KD/LQbPzsOP3C+/NmS5D6izYhD/c7uqgB5CMrrDGl1IpFkL2nJKlI+6Xf0l1gz1y8agzHlYne3bNqw6C794dNMZAjTFSuZVD73XDa6kZYrkpwhEo3gy6ovEYmmmRI5GgEavtReJZIcp6siCS/I5eKalWrQvp40g6UjyOVFXyKR9CxpySpSPpGkScbVExlu8OoFo7F40oDMNtpFemPNLvRmJl7KoaRw6q9I5YokZ2gNteLoR49Ga6g1vQYircDrR2uvEkkOIx/u+5Zs3XF09kBwQ4lE0jXSklWkfNJr9Mqjq3w+7lf878b5mD6suK+H0ad0RSpK5euh5rCgK92CJDmD3+VH001N6Tfg8APndKO+pFfI4d/TPuVQ3+zosltQD9xHZT4XAMDtzE4lxoUzKzHALXe+JZK+JC1ZpR/LJ/11yfd75CPYoSK2DCr09PUQhOSqPP3A+VNx3OjSvh5G2mSnhCiRCAhHw1i7ey3C0XB6DUTDQPVa7VWSdeTqIpAtfPvYYbhyzoi454eX5vXiaA5N/G4Hdq5YApfd1iPtd9cc2GW3YUalP0OjkUgk6ZCWrCLlk37F2z+ai2lDM2PpsGBcAEsmD8xIW4cquSRe5qrCKZMy/MmHDYI9hy1xc3fkkn5He6gdS1cuRXuoPb0GIu3Ae0u1V4kkh1GgWB7ER5Tl46YTxwvL71yxBMM45UoupuebNLigS+Wz1XVHIpEc2qQlq/Rj+cTRyw9R2RDTYlgGNzsCfjd+f8ERGWuv72dHkikyLQdlqrUs+Ar2KDlpk7Zx40Zs3rwZs2bNQmmp0Wxow4YN2LFjh+GY3+/H/PnzLe18+umnOHjwICZOnIghQ4b06Jgl3cfn8mHP9d3I/+7wAWdkLn+8RNKXHOJrk4VvzRiCpdMq+noYEklc6uvr8e6772LYsGE47LDDDOeqq6vx3//+11Jn4cKFyM/PNxzbs2cPvvjiCwQCAUydOjUnlaH9mbRklX4qnzx52ZGYNKhrivNcINUA7JLM47JblXW59BuayZGW5Dsz0k4uzV82kFPKlXfeeQc//elPsXPnTnzzzTdYs2YN5s6dayjzhz/8AatXr8aMGTPYsaFDhxqUK83NzTj55JOxceNGjB07Fh9//DH+7//+D7fffnsvXYkkHcLRMN7a/hYWjFgAu5rGrRsNAwfeAgYsANKpL5FkAYsmlGPJ5IG47vn1fT2UXkVRFNhtcoGXZB8HDx7ETTfdhFdffRWtra246KKL8MADDxjKbNiwAWeccQZOO+00w/EZM2YYlCt33HEH7rrrLkybNg1ff/01xowZg3/+85/w+6W7V66QlqzST+WT40aX9fUQJCZ61bInw119cPOCnHcxP+XwQRmx5vr3D+dgSLE3AyPKPF5nz7hWZws59QteXV2Nn/70pxg9enRCS5Ojjz4aL7zwQtzzP/nJT7B3715s3rwZRUVF+Pe//40FCxZg7ty5FmWNJHvoCHfg+tevxweXf4B8Z37yCmYiHcCn1wPHfwCoadSXSLKAPy2bDgD9TrnSH5C7Q7lJfX09jj32WDzwwANCK1mel19+Oe65d999F7fddhveeustzJ8/H/X19ZgxYwZuueUW/O53v8vwqCU9RVqyipRPsh7585z9lPvdfT2EbuN22HD61MHdbmdEWfb+jlyzcDQWTSjv62H0GDkVc+Xss8/GvHnzkpZramrC66+/jg8++AAtLS2Gc4QQPPXUU7jssstQVFQEAJg/fz6mTZuGJ598skfGLckM+c58fPm9L9NTrACAIx9Y8qX2KslaZKyMnqc/CIm5eI3ZEAtA0nXGjRuHyy67DF5v8l3C//73v/j3v/+N/fv3W849+eSTOOKII5iCpqioCJdffjmeeuopRKPRjI9b0jOkJatI+eSQQv6Up0ZvuU/loDiQc3RFdvc67Yd06uqcslxJlQ8++AArVqzA3r17UV1djQceeADnn38+AM2Xua6uDocffrihzpQpU7Bhw4a4bXZ2dqKzs5O9b2rSUuZFo9GMCT3RaBSEEClExSEUCeHlzS/j9LGnw2FzWM4nnb9oCNjzMlBxOqBa6/d3+vr+I7F+oyRz36nepjfnkApv6fSlpFmvp8nk/GXyt7m3oGJmuuPu6+9wV8mVcWYKm82G6667Dna7HR9//DEuvvhiPPjgg7DbNVFsw4YNQtmkoaEBu3fvxtChQy1tStkk+zDLKinNXy/KJ7n4OWZ2bSCsza5A0qiTLXPdlfm7ev4orN1e1ytjD/hcAHp+ntL57Hj6+28gu+4Ec0CQeH5yaQ67O8Y+Va588skn2LVrV8IyixYtQl5e6lG1zzrrLPz6179mPswrVqzAt7/9bUyZMgUTJkxAY2MjADCrFUpJSQkaGhritnvXXXfhZz/7meV4dXU1Ojo6Uh5fIqLRKBobG0EIgarmlFFRr9AWasPd796NGQUz4HVYdwiTzZ8SaUPRF3ej3jEDxJadfoh9SV/ff7V12veovq4OVY7OJKWzk76Yw6qqqi7XiUajadXraTI5f01NTaiqyi2/3mDsITndz6avv8Ndpbm5ua+HIOTgwYNYu3ZtwjITJ07E6NGjU25zyJAh+PzzzzF+vJbRa/369Zg1axZGjBiBG2+8EQDQ2NgolE0AoKGhQahckbJJ9mGWVVKZv96UT7Lxtz8ZmV4bgK7PQzgcTrkOtcjIlrnuyvwdM9iBYwaX98rYLz2iGKeO9fV4X4R0T+bp77+BtfXaWlJXX48qV9By/rRJpZg7qjDhHOfSHHZXNulT5cp7772Hf//73wnLzJw5s0vKlUWLFhne33DDDbj77rvxz3/+ExMmTIDTqUVObmtrM5RraWmBy+WK2+5NN92E66+/nr1vamrCkCFDUFZWlrFAc9FoFIqioKysLOtvvL5i3ZXr4p5Laf4GroMMnyamr++/FkVz4SsqLkYgkJvZA/piDgOBQJfrqKqaVr2eJpPz5/f7s/IaE+F0aZsN6Y67r7/DXcXtzk7/+N27d+Oxxx5LWGbZsmVdUq6MHTvW8H7KlCk4//zz8dJLLzHlitPpFMomAOLKJ1I2yU54WSXl+esl+STXfheBzN6Dvn0hAF2fB7vd3oU6CgCSNXOdzd/h3sgBqCjdk3myef56g1a1FQBQXFSEQKDQcv7e85PPbS7NYXdlkz5Vrlx99dW4+uqre7QPRVFQUFDAtGmVlZWw2WzYvXu3odzu3bsxfPjwuO24XC6hcKOqakZvEkVRMt7moUIwEsQTG57AssOXwWkTpxdLOH+RILDjCWD4MiBO/f5OX95/SqxPVcnt+7+35zDdfrJ1jjM1f7Yc/B2lHsvdGXcurSHZOsbp06cnDDybKQoLjTt9I0aMEMomqqqisrJS2IaUTbIPkaySdP56UT7J1c8wU/egqsRkjS62o6RRJ5vmuj9/hxWl+59Ff54/es1KN68/V+aw278xGRpHVhCNRlFXV2c4tmHDBuzcuRNHHHEEAE0bNW/ePLz44ousTH19Pd566y2ceOKJvTpeSdcIRUJY+dVKhCKh9BogIWDXSu1VIunH9IesNP3gEiU5RE1NjeF9KBTCv/71LyabAMCJJ56INWvWoL6+nh1buXIl5syZk1KwXEl2kJasIuUTiaTHkOKApDfJqYC2u3btwieffILa2loAmltRQ0MDxo0bh3HjxiESieCYY47BmWeeiYkTJ2LXrl247777sHDhQixdupS1c9ddd+G4447DFVdcgaOPPhoPPfQQRo4ciUsvvbSvLk2SAnnOPLx24WvpN2DPA+Z3o76kR6GLn3wolvRXZIKJ3CQSiWD16tUAtM2aHTt24OWXX4bf72eZf26++Wa0t7djzpw5IITgL3/5C6qrq7Fy5UrWziWXXII//vGPOOGEE3DllVfigw8+wBtvvIF33nmnT65Lkh5pySpSPuk1eitDjUQi6Z/klOXK1q1b8dhjj2H16tU47bTT8NFHH+Gxxx7D+vXrAQAOhwPr1q1DaWkpXn31Vezfvx8PPPAAXnvtNRaNH9BMfj/66CN4PB689tprOOWUU/Dee+9lrf+3RKMz3Il71t6DznCawU4jncDGe7RXiUQikUgyQCgUwmOPPYbHHnsM48ePh8PhwGOPPYZVq1axMg899BBOP/10fPTRR1i7di3OOussfP311yzALaC5+PznP//B6aefjtdffx0ulwsffvghjjrqqL64LEmapCWrSPnkkILIXMySQwjF9CpJTE5ZrsyfP5/tAsXD7/cbgrvFY+LEifjtb3+bqaFJeoEIiWDtnrW4avpV6TVAIkDNWmB0mvUlEomkB5GCS27idruTxmhRFAVnnXUWzjrrrITlfD4fbr755gyOTtLbpCWrSPlEIpFIDglySrki6d94HV6sXLoyecF42L3Acd2oL5EcIvSPh/j+cZUSiSS7SEtWkfLJIcWz3zkada3WlLWSvqE/xJmTZA855RYk6d90hjtx+9u3d88t6LPbpdmtRCKRSCSSHiEtWUXKJ4cURw4vxuJJA/p6GBKJpA+QyhVJzhAlUexp2oMoiabXAIkC7Xu0V4mkH1Oab03dKul7pJe+RJL7pCWrSPmk15DhUCQSSU8i3YIkOYPH4cEjpz6SfgN2D3BUN+pLJIcAa340F363/OmXSCSSniAtWUXKJxJJj1DkdWD2mLK+HsYhgfSuSg1puSLJGTrCHbj+tevREe5Ir4FIB/Dx9dqrRNJPGV6ah5J+YLkihQCJRNIXpCWrSPmk15g/LoALZ1b29TAkvcQnty7CfedO6ethSPoRcvtSIpFIJBKJRCKRHPIUep34+emT+3oYkl5CBrOV9DZSuSLJGdx2N+454Z70G7C5gWndqC+RSHIGKU5JJJK+IC1ZRconEolEckgglStpQmIRsZqamjLWZjQaRXNzM9xuN1RVemyZaQ+148dv/Bh3L7obHofHcj7p/IXbgfU/Bqbcrfk3Swz09f3X3NyCaGcbWpqb0NSUm4/GvTmHN8wfgqrmYEZ/g/qaTM3fJTPKMTngzLm5CbZr34F0x93X3+GuQq+TyAiTGUPKJn2PWVZJaf56QT6JdrYByOy90Vv09T0Y7WxDuEPNybkD+n7+cp3+Pn/Nza2cfJ7e9efSHHZXNlGIlGrSYs+ePRgyZEhfD0MikUgkkpxm9+7dqKio6OthHBJI2UQikUgkku6TrmwilStpEo1GsW/fPvh8voz58zU1NWHIkCHYvXs3/H5/RtrsT8j56x5y/rqPnMPuIeeve+Ta/BFC0NzcjEGDBmX9TlauIGWT7EPOX/eRc9g95Px1Dzl/3SeX5rC7sol0C0oTVVV7bKfN7/dn/Y2Xzcj56x5y/rqPnMPuIeeve+TS/BUUFPT1EA4ppGySvcj56z5yDruHnL/uIeev++TKHHZHNpFbRRKJRCKRSCQSiUQikUgk3UAqVyQSiUQikUgkEolEIpFIuoFUrmQRLpcLP/3pT+Fyufp6KDmJnL/uIeev+8g57B5y/rqHnD9JTyDvq+4h56/7yDnsHnL+uoecv+7Tn+ZQBrSVSCQSiUQikUgkEolEIukG0nJFIpFIJBKJRCKRSCQSiaQbSOWKRCKRSCQSiUQikUgkEkk3kMoViUQikUgkEolEIpFIJJJuYO/rAUgAQghWrVqFDz74AIWFhTj33HMxbNiwvh5W1rJ+/Xq8+eabaGlpwZQpU3DqqadCVY16wvb2djz99NPYsmULhg4digsuuKBbOcsPRQghuOOOO1BXV4df/epXcDqdhvOvvPIK3n33XeTn52Pp0qUYPXp0H400+wiHw3jppZfw6aeforKyEhdddBHy8vIMZb788kv87W9/Q0dHBxYsWID58+f30Wizj02bNuEf//gHamtrUVFRgaVLlyIQCBjK1NbW4umnn8a+ffswfvx4nHfeeZZ7tL9w4MABPPHEE9i3bx9+8YtfWO41ANiyZQtWrlyJlpYWHHfccTjxxBPTKiOR8Hz++ed4+eWX0dnZiYULF2Lu3Ll9PaSspbq6GqtWrcKOHTtQWVmJc845B0VFRZZyb775JtasWQOPx4OzzjoL48eP74PRZjcvvfQS3nnnHZx//vk48sgjDec2b96MF154Aa2trZgzZw5OOOGEPhpldvLhhx/i1VdfhaqqWLp0KcaMGWM439DQgKeffhq7du3CmDFjcMEFF8DtdvfRaLOLpqYmPPfcc9ixYwf8fj8WLlyI6dOnG8pEo1G8+OKL+Pjjj1FaWopvfetbqKio6KMR9y3BYBAvvvgi1q1bh6VLl+KYY46xlGlpacHTTz+N7du3Y8SIEbjggguQn5/f5TK5hLRcyQKWLVuG733ve1AUBevWrcOkSZPwwQcf9PWwspLLLrsMl112GQ4cOAAAuO666zBnzhx0dnayMi0tLTjmmGPwwAMPwO1248knn8QRRxyB6urqvhp2VvLLX/4SDzzwAO6//34Eg0HDue9///tYtmwZotEovvrqK0yePBlvvfVWH400u6ipqcFRRx2F22+/HR6PB1u2bMGsWbMQjUZZmZdffhlHHHEEdu7cic7OTpxxxhm49dZb+3DU2cNTTz2FyZMnY+PGjSgoKMDq1asxatQobNiwgZXZtWsXDjvsMLz00ktwOp1YsWIF5s2bZ7lP+wM33HADZsyYgXfffRf3338/2tvbLWXeeustTJ48GV999RUikQguuugi/OAHP+hyGYmE54UXXsD06dOxa9cudHR04JRTTsHPfvazvh5WVvLCCy/gyCOPxAcffIC8vDy8+OKLGDFihOF3DQBuvPFGLF26FKFQCNu2bcOUKVPwj3/8o49GnZ188cUXuPbaa3H//ffjq6++Mpx7/fXXcfjhh2Pz5s2IRCI4//zzcd111/XRSLOP66+/HgsXLkRdXR0A4LzzzsN///tfdv7AgQOYOnUqnnnmGbjdbtx333049thj0dbW1ldDzhp27dqFsWPH4oknnkB+fj52796NWbNm4Ve/+pWh3FlnnYUf/ehHsNvt+M9//oNJkybhs88+66NR9x2vv/46RowYgZdffhkPPvigcA4aGhowY8YMPPLII3C73fjTn/6EGTNmoKGhoUtlcg4i6VPeeustAoB88skn7NhZZ51FjjzyyD4cVfbCzxMhhOzbt484nU7y5z//mR37xS9+QQKBAGloaCCEENLW1kZGjRpFrr766l4dazbz/vvvk6FDh5I//vGPBABpbm5m5z7++GMCgKxZs4Ydu+yyy8jYsWP7YKTZx9lnn00mT55M2tra2LHt27eTaDRKCCEkHA6TgQMHkhtuuIGdX7lyJVFVlWzZsqXXx5ttTJs2jVx66aXsfTQaJRMnTiTLly9nxy666CIybdo0EgqFCCGEHDhwgOTl5ZEHH3yw18fb17z//vskGAySV155hQAg1dXVljJjxowhV155JXv/xhtvWNaVVMpIJJRgMEgCgQC55ZZb2LFnnnmG2Gw2smPHjr4bWJayadMm0traajg2b948ctJJJ7H3GzduJIqikNWrV7Nj11xzDRkyZAiJRCK9NtZspq2tjUycOJGsWrWKACB/+ctf2LloNEpGjBhhWCvo7+KGDRv6YLTZxbPPPktsNhv58MMP2bGOjg6ye/du9v673/0uGT9+POno6CCEEFJbW0uKiorIr371q14fb7Zx1113kdLSUtLZ2cmO3XrrraS8vJy9X7VqFVEUhWzevJkdW7x4MZk/f36vjjUb2Lx5Mzl48CAhhJC8vDzyhz/8wVLmpptuIpWVlaSlpYUQQkhzczMZPHgwufnmm7tUJteQlit9zKpVqzBx4kRMnTqVHVu2bBnWrVuH/fv39+HIshN+ngBg4MCBKC0txd69e9mxVatW4ZRTTmFuQB6PB0uXLsXLL7/cm0PNWhobG3H++efjT3/6E0pKSiznV61ahYqKCoP597Jly7B582Zs3LixF0eafRw4cAB/+9vf8OMf/xgej4cdHz58OBRFAQD23b3wwgvZ+dNPPx35+flYvXp1r4852wgEAmhpaWHvw+Ew2tvbMWDAAACau9rf//53fOtb34LdrnmulpeX4/jjj++X3+GjjjoKDocj7vmNGzfi66+/NtxvCxcuxODBg9l8pVJGIuF5//33UVVVZbhnzjzzTHg8Hvk7JmDs2LHwer2GY5MnTzbIJqtXr0ZRUZHBHW/ZsmXYvXs3Pv74414bazZz9dVX47jjjsOpp55qOffFF19g+/bthnvyhBNOQHl5OVatWtWbw8xKHnzwQSxZssTgxuJyuQwuK6tWrcI555wDl8sFACguLsbJJ58s1wFoskkoFDJYyLa0tDDZBNDm78gjjzS4Wl100UV4++230djY2Kvj7WvGjBljcec2s2rVKpxxxhnMlTk/Px9nnnmm4X5LpUyuIZUrfczXX3+NkSNHGo7R91u2bOmLIeUUb7zxBvbt24fZs2ezY/HmdNeuXQb3of7KFVdcgdNOOw2LFi0Snv/6668xYsQIwzF5T2p8+umniEajmDZtGh588EHcfPPNeOyxxwyuGl9//TUAGO5Bu92OysrKfj9/APDHP/4RTU1NWLRoEa666irMnDkTJ5xwAn74wx8CAKqqqtDY2Cj8Dsv5syK63wBN4UfnK5UyEgkPvWf4tcDpdKKiokLeMynQ3NyMF1980SKbDBs2DDabjR2Ta6vO888/j3fffRe/+c1vhOdFv2OKosjfsRgfffQRZs2ahVdeeQU33XQT7r33Xmzbto2db21txb59++TaGodly5bhqquuwqxZs3DllVfilFNOwUcffYS//vWvrEy854toNGqYa4lGvPni77dUyuQaUrnSx7S1tcHn8xmO+f1+dk4Sn507d2LZsmW45JJLDAJMojkVxSvoTzz00EPYtGkT7rrrrrhl5D0ZH7ozccEFF+CTTz6B1+vF7373O0yaNAm1tbUAtDmy2WwGyxZAm8P+Pn8AsHXrVnz55ZcYNGgQKisrUVpaig8++ABVVVUA9HtMdA/K+bOSynzJOZV0lba2NrhcLksQaXnPJIfGNLLb7fjpT3/KjovWVvq+v8/pjh07sHz5cjz99NMWCyCK/B2LTzgcRltbG5544gmsWLEC+fn5LIbjP//5TwBy/pJRX1+PdevWwev1orKyEhUVFdi6davBYlvKx6kTCoUQDoeF80XPpVImF5HZgvoYn89nCdpTX1/PzknE7NmzBwsWLMCRRx6Jhx9+2HAu3pwqiiLMstGfuPXWWzF9+nTccMMNALQHXUALmnnKKadg8eLF8Pl82Llzp6GevCc16PWfeuqpLLDj9ddfj5EjR+K3v/0tfvazn8Hn8yESiaClpcUQ7by+vr7fzx8hBBdccAEuueQSpuC7+eabMW/ePFx77bV46aWX2ByJvsP9ff5E8PNlvt/oblAqZSQSHp/Ph87OTnR0dBgyicjvYWKi0SguueQSfPjhh3j77bcNrrci2YS+7+9zevfdd6OwsBBPPvkknnzySXb8r3/9Kw4ePIgbbrjB8DvGu2rU19dj0KBBvT7mbMJut8PtdkNRFKxZs4Zl0Lzsssvwf//3f1iyZAn77Zdrq5jbb78d+/fvx+eff85ckidNmoSLL74YixYtgt/vl89sXcDhcMDlcgnny+12szlOpUyuIS1X+pgJEyZg06ZNhmObNm2CoigYN25cH40qu9m7dy/mzZuHCRMmYOXKlZZ4BPHmdPTo0QljF/QHfvazn+H444/HsGHDMGzYMJSVlQEAhg4disLCQgDa/G3ZssWQ/YbOZ39PGTlx4kQAwLRp09gxr9eL8ePHY/v27QC0+QNguAfb29vxzTffsHP9lYaGBhw4cAAzZswwHJ8+fTrbHSotLUVZWZnwO9zf50+E6H6LRCLYsmULO5dKGYmER3TPtLa2Yvfu3fKeiUM0GsW3v/1tvPXWW1izZg1Gjx5tOD9hwgRs377dENOBzm9/n9MzzzwTy5cvZ7LJsGHDAGjrweDBgwGI78lwOIytW7f2+/kDNPlkypQpTLECaLIKlU08Hg+GDx8u19Y4bNq0CVOnTjU80E+fPh2tra3YtWsXgPjPFw6HA6NGjerV8eYC8eaLv99SKZNz9HVE3f7OunXrCADyxhtvEEIIiUQiZP78+WThwoV9PLLsZO/evWTUqFHklFNOMUT05vnd735HCgoKyJ49ewghWjT0QYMGkZ/85Ce9OdScYOXKlZZsQZs2bSKqqpKVK1eyY6effjqZPn16Xwwx65g5cyb5wQ9+wN5XV1eToqIicvfddxNCtIwGo0aNIpdddhkr84c//IG4XC6yd+/eXh9vtjFo0CBD1ppQKESmTZtGzjzzTHZs+fLlZOzYsSx6/ObNm4nT6SRPPfVUr483W0iULWjatGnk7LPPZu9pVhc+o0EqZSQSSiQSIcOHDzd8V3/3u98Rt9tN9u/f34cjy06i0Si55JJLyMCBA8mmTZuEZb755hvicDgMGXAuvPBCMn78+F4aZW4BU7YgQgg5/PDDybnnnsveP/nkk8Rms5GtW7f28uiyj1//+tdkxIgRhkyGZ5xxBjnqqKPY+xtuuIEMHTqUZdPcuXMnycvLI3/84x97fbzZxtVXX02GDh3K5A5CCPl//+//EbfbzeZ0zZo1BABZu3YtIUSTX44++mhy+umn98mYs4V42YLuuusuUlZWxrIKHThwgJSUlJAVK1Z0qUyuIZUrWcBNN91EfD4fOf/888mMGTMSLs79naOOOoo4nU7y3e9+l1xzzTXs3/PPP8/KBINBcsIJJ5DBgweTZcuWkREjRpAjjzzSoECQaIiUK4QQsmLFCpKXl0fOPfdcMmvWLFJSUiJTtsb44osvyIABA8iCBQvI5ZdfTgYPHkzmz5/PUhsSQsi7775L/H4/WbBgATn77LOJx+Ppl2mERaxevZr4fD4ye/Zs8p3vfIdMmDCBDBkyxJCmuqamhkyePJmMGzeOLFu2jAQCAXL22Wf3y3Slq1atItdccw05/fTTCQBy5ZVXkmuuuYasX7+elfnkk09ISUkJmTVrFjn33HOJ1+u1CCaplJFIeN5++23i8/nIwoULyVlnnUXcbjd5+OGH+3pYWcndd99NAJAlS5YYZBM+lTUhmoLK4/GQpUuXknnz5pGCggLyv//9r49Gnd2IlCsfffQRKS4uJrNnzybnnHMO8Xq95Ne//nXfDDDL6OzsJCeccAIZPXo0ueKKK8jRRx9NBgwYYJDdGhsbyfTp08moUaPIsmXLyKBBg8iSJUtIKBTqw5FnB1VVVWTChAlk+PDh5PLLLyeLFy8mbrebPProo4ZyV199NSksLCQXXHABmTp1KqmsrOyX6el37tzJfuccDgeZP38+ueaaa8jjjz/OyrS1tZE5c+aQoUOHkmXLlpGhQ4eSOXPmkPb29i6VyTUUQgjpM7MZCePTTz/FBx98gMLCQpx00kksQJLEyJ///Gc0NTVZjh922GGYP38+e08IwVtvvYUtW7agsrISJ5xwQs767vUkW7duxT/+8Q8sX77c4jL1xRdf4L///S/y8/Nx4oknori4uI9GmX00NTXhlVdeQVNTE8aNG4fjjjvOUqaqqgqvvvoqOjs7MWfOHEPqvv5OTU0N/vOf/6C6uhpDhgzBggULWGpISmdnJ1599VXs27cP48ePN6QG70+8++67wjStJ598ssEMua6uDq+88gpaWlpw7LHHYtKkSZY6qZSRSHgOHjyI1157DZ2dnZg7d67F1UWiEe976vF4cOWVVxqObdq0Cf/5z3/g8XiwePFi5p4rMXLffffh+OOPt7gH1NbW4pVXXkFbWxtmzZqV2+4DGYYQgjVr1mDz5s0YNGgQFixYYIizBWiBRl977TXs2rULY8eOxfz586EoSh+NOLuIRCJYs2YNtm/fjsLCQhx77LHMLY1n3bp1+Pjjj1FSUoKTTjrJMsf9gQMHDuDZZ5+1HB87dqwh3XwkEsEbb7yB7du3Y8SIEVi0aJEhY1qqZXIJqVyRSCQSiUQikUgkEolEIukGMqCtRCKRSCQSiUQikUgkEkk3kMoViUQikUgkEolEIpFIJJJuIJUrEolEIpFIJBKJRCKRSCTdQCpXJBKJRCKRSCQSiUQikUi6gVSuSCQSiUQikUgkEolEIpF0A6lckUgkEolEIpFIJBKJRCLpBlK5IpFIJBKJRCKRSCQSiUTSDaRyRSKR5Aznnnsu1q9f39fDkEgkEolEIgEA/Pa3v8Xvf//7vh6GRCLJAqRyRSKR5AwvvvgiDhw4EPf89773PTz//PPCeldccUVPDk0ikUgkEkk/ZN26dfjwww/jnn/66adxzTXXWI4fOHAAZ599Nr788sueHJ5EIulFpHJFIpEcMvzrX//CV199ZTm+ceNGrF69ug9GJJFIJBKJpD/z+eef47XXXrMcb2lpwYsvvojq6uo+GJVEIukJ7H09AIlEIkmXtWvX4t5778XNN9+MKVOmpFzvxRdfxDPPPGM5fs455+Ccc87J4AglEolEIpH0J5qamvDDH/4QU6dOxfe+972U6x04cADf//73LccHDx6M+++/P5NDlEgkPYRUrkgkkpzk1Vdfxbnnnot77723S4oVAJg4cSK+9a1vsffbt2/HDTfcgIULF2Z4lBKJRCKRSPoLVVVVWLx4MYYOHYrLLrusS3V9Pp9BNolGo/jhD3+IkSNHZnqYEomkh5DKFYlEknM888wzuPLKK/H444/jjDPOMJx7/vnn8cUXXxiObdq0yfB+3LhxGDduHADNLPfoo4/GySefjO985zs9O3CJRCKRSCSHJDt37sSiRYswe/ZsPPzww7DZbOzcvn37cPbZZxvKt7a2Gt7n5eUZytxwww1ob2/HX/7yl54duEQiyRhSuSKRSHKKRx55BG+++SZWrVqFefPmWc5PmjTJ4trz4osvYs2aNZayhBBcdNFFiEQiePrpp6GqMgyVRCKRSCSSrrFlyxYce+yxOP/883H33XdbzputUgDNDejVV18Vtvfss8/i3nvvxeuvv47hw4f3yJglEknmkcoViUSSU2zcuBEFBQUYMWKE8PyECRMsu0ObNm0SKlduv/12vPPOO1i3bh38fn+PjFcikUgkEsmhTXV1NWpqajB16lTheZ/PZ5FNtm7dKiz7ySef4NJLL8V9992HuXPnZnqoEomkB5HbtBKJJKe4++67MWfOHMydOxfffPNN2u387W9/w5133onnnnsOo0aNyuAIJRKJRCKR9CeOOeYYPPPMM7jsssuEAfNTpaqqCqeffjouvPDCLgXDlUgk2YFUrkgkkpxCVVU89thjmDVrFubNm4ddu3Z1uY3PP/8cy5Ytw913341Fixb1wCglEolEIpH0J84880w89dRTuPTSS9NSsIRCIZx11lkYOnQofv/73/fACCUSSU8j3YIkEknOoaoqHn/8cSxbtgxz587F22+/jcrKypTr33333YhEInjvvffw3nvvseMyFbNEIpFIJJJ0Oeuss1g8N0VRLHFWEvHGG2/gvffew5w5c3Deeeex4zIVs0SSOyiEENLXg5BIJJJU+Nvf/oZjjz0W5eXlAIBIJILVq1dj0KBBOPLII/HKK69g6NChmDBhgqHepk2bsHXrVpx88skAgA8//FDoUjRhwgRLXYlEIpFIJJJ4fPjhh1AUBdOnT2fH1q1bh3379uGUU07Bxo0bsW/fPhx//PGGeq2trXjllVcwZ84clJWV4cCBA4YNH4rf77fUlUgk2YlUrkgkEolEIpFIJBKJRCKRdAMZc0UikUgkEolEIpFIJBKJpBtI5YpEIpFIJBKJRCKRSCQSSTeQyhWJRCKRSCQSiUQikUgkkm4glSsSiUQikUgkEolEIpFIJN1AKlckEolEIpFIJBKJRCKRSLqBVK5IJBKJRCKRSCQSiUQikXQDqVyRSCQSiUQikUgkEolEIukGUrkikUgkEolEIpFIJBKJRNINpHJFIpFIJBKJRCKRSCQSiaQbSOWKRCKRSCQSiUQikUgkEkk3kMoVSdYzbtw4nH766X09DIlEIpFIJBKJRCKRSIRI5YpE0gPY7Xb86Ec/6uth9BueffZZKIqC9evX9+k43n77bSiKggceeKBH2t+wYQNuueUWjB49Goqi4Kqrrkqp3quvvgpFUaAoCr744gvDuRtvvJGdE/37/e9/36Uxut3uhO3Rfz/5yU+61K6ZrszFpk2bcOGFF2Lo0KFwu90YNmwYLrnkEmzdujVhH4nmTSKRSCSSnmbhwoUoLCzskbZbW1vx7LPP4owzzoDH44GiKNizZ0/c8tFoFA899BCmT5+OvLw8VFZW4pprrkFtba2l7Pr167F48WL4/X74fD4sXrwYGzZs6PIYf/3rX6ckUyiKgo6Oji63T9m9ezd+8pOf4PDDD0deXh4GDx6MJUuW4O2337aU5eehsLAQhYWFmD59Oh5++GEQQtIeg+TQQCpXJBKJJAdobW3FsmXL4PV68eSTT6Zcr66uDpdeeikGDBggPL9ixQoQQiz/jj76aKiqipNPPrlL4+zo6DC0s3z5cgCa4MIf//nPf96ldnm6Mhe7d+/GUUcdha+++gqrVq1CfX09XnjhBXz44YeYOXMmqqqqhPWSzZtEIpFIJLnMvffei1WrVmHZsmW4+OKLE5YlhOCCCy7AL37xC9x2222oqqrC+vXrMWbMGDz22GOGshs3bsTs2bNRXFyMrVu3Ytu2bSgpKcHs2bPx9ddfd2mMP/rRjwyyQ319PQDg4osvtsgtbre7S23z/PznP0cgEMAzzzyDmpoa/Pvf/4bL5cL8+fOxatUqQ9kbbrgBV111Fc4991zs2LEDO3bswNlnn40rr7wSN998c9pjkBwa2Pt6ABKJRCJJTl5eHtv1qampSbneVVddhQEDBuCkk07CL37xi5TqbNq0CWvXrsXixYsxdOjQtMbbk3RlLl566SU0NTXhzjvvxJQpUwAA06dPx89//nOceeaZ+Mc//oFLL73UUi+deZNIJBKJJFfgLUjfeeedhGUfffRRPP/881i/fj0mT54MQFuL6QYKzy233AKXy4U///nPTOHxyCOPoLKyErfccgtWrlyZwavIDA899JDh/dixY/HUU0+hoKAAf/rTn3Daaaexc48//jimTZuGH//4x+zYjTfeiJUrV+Lxxx/HXXfd1WvjlmQf0nJFkjW0tLRg+fLlKCsrg8/nw6mnnoq9e/cKy3Z2duK2227DqFGj4HQ6UV5ejosvvthgznjSSSfhiCOOMNQ777zzoCgKfvvb37JjTU1NcDgcuOOOOwAA4XCYuS288847mD59OtxuN0aNGpV0l3zPnj1QFAWRSAS/+c1vmKki3f3n237jjTcwffp0uFwu5kYyatQoVsdut6OiogJXXHEFqqurWR9dHd/TTz+NGTNmoKCgAGVlZVi0aBHWrFkjbO+VV17B4YcfDrfbjXHjxll2I9IZo+g6q6ursXz5clRWVsLpdKKiogLXX3892traWBuPPfYYFEXBxo0b8fOf/xyDBg2Cx+PBwoULDTsfK1aswHnnnQcAmDp1Khvbs88+m/Cz6g88/fTTeOmll/Doo4/Cbk9dl/7oo48CAL7zne/01NB6jYKCgi6fT3feJBKJJBO43W5ce+21luMnn3wyJk2aZDg2atQonH322fj0008xa9YseDweDB06FL/4xS8QjUZT6m/VqlWYPXs2fD4fvF4vZs2ahbfeestS7u9//zvmzZuHgoICFBYW4vjjj8fatWvZeUIIHnjgAUyePBlutxvFxcU47bTT8PnnnwvHvHHjRsyfPx9erxcVFRW48847LX12tc3169fjuOOOg9frxaRJk9h1fPLJJ5g9eza8Xi+GDx+O5557zlD/lFNOwcCBAxEKhSxjOOuss1BaWopgMJjSfB6K3HvvvZg3bx5TrMSjo6MD//znP7F48WKDJYnH48GJJ56I1atXo7Ozs6eHmxFsNhtUVUVeXp7heCK5IpnMIekHEIkkC4hGo2ThwoWktLSUrF69mjQ2NpI1a9aQJUuWkBEjRpDTTjvNUP6kk04ifr+fPPfcc6ShoYF88MEHZOLEiWTw4MHk4MGDhBBC7rnnHqIoCnsfjUZJIBAgHo+HLFmyhLX18ssvEwBk7dq1hBBCQqEQAUBOO+00cuGFF5KtW7eSmpoactVVVxFFUciGDRuSXo/NZiM//OEPLcdp2yeffDJZunQp2bJlC9m2bRt59dVXLWVbW1vJO++8QyZMmEDmzp1LotFol8f36quvEgDkt7/9LamrqyMNDQ3k3//+NznppJMsY1qyZAk555xzyLZt20hVVRW57bbbCADy8MMPx73OZGMUXWdVVRUZPnw4Oeyww8i7775Lmpubyf/+9z8yevRoMm/ePBKJRAghhPzlL38hAMiyZcvIgw8+SGpra8nnn39OxowZQw4//HDWFyGEPPPMMwQA+fTTT5N+Nj3JmjVrCADyu9/9Lm6ZHTt2EAAp/bvllluEbVRXVxMA5Morr4zbz+7du0lhYSG58cYbCSGE/PSnPyUAyOeff57wGkKhECkvLycDBw4koVAohatOzPLlywkAsnv3bsu53piLtrY2Mm3aNDJ16lSyfv160t7eTj766CMyYcIEcuyxx5JgMGgon+68SSQSSaZwuVzkmmuusRxfsmQJmThxouHYyJEjyXHHHUdOPPFEsmHDBlJXV0ceeugh4nQ6yfXXX5+0r/vuu48oikJuv/12smfPHlJdXU1uv/12YrPZDLLJr3/9a6IoCvnxj39Mtm3bRhobG8kbb7xBzjnnHFbmuuuuI3a7ndx///2ktraWbNy4kSxYsIDk5eWRzz77zDDmOXPmkDPOOINs2LCBNDQ0kF/+8pcEAFm5cqVhfF1pc/bs2eSMM84gX331FamtrSWXXXYZ8Xq95H//+x856aSTyBdffEHq6urIlVdeSWw2G9m2bRur/8orrxAA5LnnnjP0v2fPHmK328m1116bdC57ggULFpCCgoKEZS677LKU19L29nZhG9dcc03ctZqus9/97nfJ8uXLyYABA4jT6SRjxowhv/71r5ncRggh69evJwDIHXfcYWnnzjvvJAAMn1tXqa+vJwDIxRdfLDyfibmIRCJk+/bt5MILLyRFRUXk448/Npx/7rnniMPhIL/61a9IXV0dqaurIytWrCAOh4O88MILaV+b5NBAKlckWcFrr71GAJC//OUvhuNUOcArV15//XWmMOD58ssviaIoTKnx+eefEwDkqaeeIoQQ8umnnxIA5KabbiL5+fnsoWr58uWkoKCAhMNhQoiuGKioqCCdnZ2s/dbWVpKfn09+8IMfJL2eZMqVsrKyuD/qZv7xj38QAGTTpk1dHt+NN95IvF5vwvZpe+Xl5ZYxLV68mJSWlloeQFMdo+g6f/CDHxCXy0V27NhhOP7ee+8RAGT16tWEEF25YhYOn376aQKAfPDBB+yYVK4YiUajZNGiRWTMmDFs/lNVErz00ksEALn55ptTvOrE9LVy5f+zd+dxTlXn/8A/d82ezL7BsK+yKYiKWsUFcK8b1qqtVqnWam1FbevXaqHqT9uqVVvXAm21WhXrUq2KooIFURBEZN9mmH2fyZ7c3Nz7++NOMknuzTCQDLPwvF8vX5nce3JzciYyT5485xxVVdX6+nr1O9/5TtL1Zs+erTY1NSW1y2TcCCEkWw41uSIIglpVVZV0/Be/+IXKcZzhv70xjY2NqtlsVm+44QbduXPOOUedNm2aqqqqWldXp4qiqP7whz9Me62KigqVZVn1pz/9adLxjo4O1eVyJX2pNXr0aNVsNqt1dXVJbSdOnKjOmTMno2vW19fHj7W2tqo8z6v5+flqbW1t0uMFQVDvvffe+DFFUdTRo0ers2fPTnquvv4b0B+SK7H4meM49YwzzlB37typtrW1xb/ETIw7P/7447Rx0NNPP60CUD/55JNDG4QEvZ1cMZlM8fMlJSVp+7ps2TLVarXG21osFnXZsmWH/brI4EHTgki/ECvbvOiii5KOz5s3DxaLxbDtpZdemnT8mGOOwTHHHBM/P3nyZJSWluLDDz8EAHz00UcYNmwYbr31Vvh8Pnz++efx42eeeSY4jku63pw5cyCKYvx+rJR0//79mb5czJ0713Dhra+++grf/e53UVRUBI7jkqYUpe5s0pP+TZs2DYFAANdccw3Wrl1rWO7aXZ8uvvhitLS0YMuWLYfVR6NrvvPOO5g5cyZGjBiRdPzkk0+G1WrVzfs9//zzk+7HSqKz8XvoCyNGjDBcQNbov8Nd9PUvf/kLVq5cib/+9a+HvMDbsmXLwDAMFixYcFjPfSiOxFjEFrQNh8PYuHEj/H4/NmzYAK/Xi1mzZqGxsTHeNpNxI4SQvnLssceivLw86djFF1+MaDTa7VoaH330EUKhEObPn687d/bZZ+Obb76B2+3GypUrIUkSrrrqqrTXWrVqFRRF0cVmLpcLZ599Nj755JOknVSOO+44lJaWJrWdPHly0t/2w7lm4iLkeXl5KCoqwqhRo1BWVpb0+NLS0qTnYhgGN998M1atWoUdO3YA0KY4//Wvf8UJJ5ygm47VnyxZsqTHf0sP529bbHqZIAj417/+hfHjxyM3Nxe333475s+fj6eeegp1dXVJj2EYJu31ujuXqUzHIhQKQZKk+JS1efPm4d///ndSmwcffBALFizA/fffj+bmZjQ3N+P//b//hwULFuDhhx/utddGBgZKrpB+obW1FTzPIy8vT3euqKhI1xYAiouLdW1LSkqSFrg8++yz8dFHHwHQgog5c+agrKwMxxxzDD766CNUVVVh9+7dmDNnju5aqX/0AcDpdKKjo+OQXpuRIUOG6I7t2bMHp512GgDgk08+gc/ng6qq8W3gUhMjPenflVdeiSeffBIbN27EqaeeipycHJx33nn45JNPdI81Gs/YsdiYHmofjV5nQ0MD1q5dC57nwfN8fE4ry7IIBAK6Lf1SX6fT6QSAw/49NDQ09HhbP6P/jLbl6088Hk98JfvY76qnGhoa8P777+Pss8/GyJEje6mHR9bDDz+MqqoqvPzyy5g+fTqsViuOP/54vPTSS9i3bx/++Mc/Ashs3Agh5EhITCQk6snfbyMNDQ0AtC8xUv8e33nnnQC0mCu2q5rR3/SY2N9uox3WSkpKEAwGk9ZV60kMk41rOhyOtMdT44gf/ehHsFgseOaZZwBoa9HU1dXhhhtu0D0+0a233nrYMcWpp57a7bX7g4KCAgDaIq+p77XZs2dDURRs3LgRAJCfnw8A8V19EsXG2yjW708EQcCECRPwwgsvYPTo0Vi4cGH8XFNTExYtWoQrrrgCCxcuREFBAQoKCvCLX/wCV111Fe67776kNQjJ0YeSK6RfyM/PhyzLaGtr051L3So19o+y0RaqjY2N8T8CgFbdUV9fjw0bNuB///sf5s6dC0CrqPjwww/jiRej5EpvZtYFQdAdW758OYLBIJ5//nlMnjw5XrFTUVFheI2e9u9nP/sZduzYgbq6Oixbtgytra2YO3du/A9hTOI3+KnHYn8sD7WPRq+zoKAA8+bNgyzLkGUZ0WgUiqLEv0lYtmzZYb3OniopKenxtxpG/82ePTuj56+srOxx0JW4kn9PBQIBBINBPPPMM0nXWrx4MQBgypQpyMnJMXzsP/7xD8iyfMQWsu3tsQC0nY8sFgtGjx6ddHz8+PEQRRE7d+4EkNm4EUJINrlcLni9Xt3xdIv89+Tvt5FYvLR69eqkv8eJf5NHjRqFwsLCbp8f6IrN0vXFYrHAarXGj/Xkb3u2rtnTOCIvLw9XXnklXnjhBfj9fjz99NOwWq248soru33cX/7yl8OOKdasWdOjvnVnwYIFPf5bGgqFDvn65eXlaRMisYRfbIxjf1t37dqla7tjxw6YTCaMHz/+kPvQU9kcC47jMGnSJFRVVUGWZQDAvn37IMuy4cK+kydPRiQSwb59+3rltZGBgZIrpF8488wzAWhTRhJ9+OGHCAaDScfOOussANoWq4l27tyJ7du3x88DWuUKANx3332QJCl+bs6cOdi4cSNeffVVjBgxAmPGjMnq67HZbIe9GrrJZEq6/8ILL2SjSygtLcX3vvc9LF26FNFoFGvXrk06/9FHH+n6/Pbbb6OgoADTpk3LWh8vvPBCrFmzRldCmonYSu4DZQX63pQuefTb3/4WAPDtt9+mrfpZtmwZiouLk7YcHOiGDx+OYDCom0a2c+dOSJIU32o6k3EjhJBsGj16NLZu3Zp0rKKiAtu3bzdsv3nz5qTdEgHt7zfHcTj99NPTPs/cuXNhMpl0O+ekik1Dfvnll9O2OeOMM8CyrC4283g8WLlyJc4888xD/rKkN655MLfccgvcbjfuu+8+fPLJJ5g/f368YvZoxTAMLr/8cuzatUv3xeZnn30Gnudx/PHHA9B2ujr33HPxwQcfJCUvgsEg3n//fVxwwQW6GLK/ikQi2Lx5M8aOHRvfOXDYsGEAoPv/E0B8B6tYG3J0ouQK6Rfmzp2LM844A3fddRfee+89eL1efPbZZ/jzn/+s+8Z5zpw5OOecc3DPPffg9ddfh8fjwVdffYUrrrgCxcXFSfvOl5aWYvLkyfjggw8wffr0+Dc4s2fPBs/z8alC2TZ58mSsXbvWsLomnfPOOw8cx2HhwoVoaWlBdXU1brvttm6/dTqYu+66Cw8++CC2bt2KYDCIhoYGPPvss+A4Dt/5zneS2k6fPh3XXXcdKioq0NzcjMWLF+P999/H/fffH69AyUYfFy1ahOLiYpx77rn46KOP4Ha70dLSgtWrV+Oaa64x3P7xYI455hiwLIv33nuv3ydYjsQ6I4djzZo12L17N6677jrDiqOYgoKCrM09PxJjcdttt8FsNuPqq6/G119/jWAwiI0bN+Kaa66B3W7Hz372s6y8FkIIyZYf//jHWL9+PZ555hl4vV5s2rQJt99+O0444QTD9ieddBJuuummeBJ4yZIlePrpp3Hrrbfq1mJJVFpait///vd46qmncM8992D//v0IBoPYtWsXnn322fgaK6WlpXjwwQfx4osv4pe//CUqKirg8Xjw8ccf43vf+x4A7d/zW2+9Fc899xyeeuoptLW1YdeuXbj88sshSRIefPDBQx6H3rjmwcyYMQMnnHACHnvsMQA46JSg/qC311wBgN/+9rfIycnB1VdfjT179qCjowNPPPEEli9fjrvuuitp6taDDz6IUCiE66+/Hk1NTWhqasKCBQsQDod1f8ufffZZMAyDV155JaMxiDmcsaitrcWFF16IlStXorGxEYFAAJs2bcLll1+OyspK/OEPf4hff8iQIbjmmmvwyiuv4IknnkBraytaWlrw+OOP41//+heuvfbapPV9yFFIJaSf8Hg86k9+8hM1Pz9ftdls6nnnnadWV1er48eP123FHAwG1XvuuUcdNWqUKgiCWlhYqF5zzTXqgQMHdNe9/fbb47sEJZo9e7bhtnuxnW6Mdic55ZRT1FNOOeWgr+Wrr75SZ86cqZrNZhVAfEX77q6tqqr6xhtvqFOnTlXNZrM6cuRI9ZFHHlHXrVunAlDffPPNQ+5fQ0ODet9996lTpkxRLRaLWlRUpJ577rnqqlWrDF/vu+++q06ePDm+xd6SJUuy3kdVVdW2tjb1zjvvVMeOHauKoqgWFxerZ555pvryyy/Ht/+N7Ra0Y8eOpMfGdph55plnko4/+uij6rBhw1SO41QA6r/+9S/D5+5Nsd2C0v33xz/+MaPrT5s2Le21U7evTHWwHQ+uu+46lWEYde/evWmvIcuyarVak3ZzOJjudgvKxKGMxbfffqteccUV6vDhw1WTyaSOGDFCveqqq+K7W3Wnr3eKIIQcfRRFUe+99161pKRENZvN6plnnqnu3r077W5Bl112mfrVV1+ps2bNUs1mszp06FD1d7/7XdIWud354IMP1Llz56q5ubmq2WxWJ0yYoN56663qnj17ktq98cYb6qmnnqrabDY1NzdXnTt3rvr5558n9fuJJ55QJ02apIqiqLpcLvWCCy5QN2/ebNjnVDfccIOan5+vG4tMrmkUQ6qqqk6aNClpt6FE//jHP1QA6rhx4wzPH0lnnXVW2r91JpMpo2u/8847aa89fvx4Xft9+/ap8+fPV3Nzc1VRFNXJkyfrYrGYjRs3qnPmzFHtdrtqt9vVuXPnGu7o+Mc//lEFoK5evbpHfT7YbkGH68MPP1QvvvhidciQISrP82ppaal6+eWXq+vWrdO1lSRJ/ctf/qJOnz5ddblcqsvlUqdPn64+/fTT8RiWHL0YVU2zOhYh5KggyzIEQcA999xzRCslyMDz5Zdf4qSTTsLq1atp0VdCCOkHxowZg2OPPRavv/56X3dl0HjrrbdwySWX4OGHH8avfvWrvu7OoHbhhRfC7Xbjs88+6+uuEJIVfF93gBBCyMDw8ccf45xzzqHECiGEkEHr1VdfhSiKuPbaa/u6K4OaLMv47LPP8N577/V1VwjJGkquEEII6ZH/+7//6+suEEIIIb1m3bp1eOONN3D99dcbbgFNsofnebjd7r7uBiFZRckVQgghhBBCyFGro6MDubm5sFqtuOCCC5IWMSWEkJ6iNVcIIYQQQgghhBBCMkBbMRNCCCGEEEIIIYRkgJIrhBBCCCGEEEIIIRmgNVcOk6IoqKurg8PhAMMwfd0dQgghZEBRVRVerxdlZWVgWfquJxsoNiGEEEIOX6axCSVXDlNdXR3Ky8v7uhuEEELIgFZdXY2hQ4f2dTcGBYpNCCGEkMwdbmxCyZXD5HA4AGgD73Q6s3JNRVHQ3NyMwsJC+hbvMESjUVTWVWJE2QhwHKdvoKqA7AV4B0Df6OnQ+y9zNIaZofHLzEAbP4/Hg/Ly8vjfU5I5ik36F1VV4ZW8cIjpK4myMr4U3xii927vobHtXTS+vedgY5tpbELJlcMU+yPpdDqzGsCEQiE4nU76H+kwdAQ7MP316Wj/ZTucFoPfScQDLC8H5rsBITu/s8GE3n+ZozHMDI1fZgbq+NH0leyh2KR/8YQ9KH+8HO5fu+E0Gf8+sjK+FN8Yovdu76Gx7V00vr2np2N7uLHJUfvb+uMf/4ghQ4ZAEAQcd9xxWL16dV93iWTIITqw+0e74RDTZBp5hxZ48PQtKSGEkP5nzZo1mD59OgRBQFlZGR5++OG+7hLJgEN0wP1rd/q4JFsoviGEkH7hqEyuPP/881i8eDGWLl2KlpYWnHfeeTjvvPNQWVnZ110jGVChld+qUNO2QMSj3RJCCCH9SFVVFc455xycc845aGlpwd///nc88MADeOaZZ/q6a+QwqVDhCXu6iUuy90wU3xBCSN87KpMrjz76KG644Qacc845cLlceOCBB5CXl4dnn322r7tGMuCTfJjx0gz4JJ9xA9kHvFWu3RJCCCH9yHPPPYecnBw8+OCDcLlcmDt3Lm644QY8+uijfd01cph8kg/lfypPH5dkC8U3hBDSLxx1yZW2tjbs3r0bp59+evwYwzCYPXs2Pv/88z7sGcmU0+RE/U31aec1Q3ACV6k0H5kQQki/8/nnn+O0005Lmud95plnYt++fWhqaurDnpHD5TQ5of5WTR+XZAvFN4QQ0i8cdQvaNjQ0AAAKCwuTjhcVFWH9+vVpHxcOhxEOh+P3PR4PAG1RHEVRstK3ha99g00HWsFz2q8ldR2dxLupi+zoltxJONBd2+6eI/Wx3T5HylmGMWyW5jmZbs6lf2Dq61CVKNrC+1DqGIufnTEeJ4zMS76YEgW8OwHHBIA12E3oKKcoClRVzdr7+WhEY5gZGr/MDLTxGyj9PFIaGxsxderUpGNFRUXxc7GfEx2J2OStr2vw+MrdhrFJurikpzFJunikR9dNE4Okv05qlw7+GGTYV0WNwisfgFMYDpbR4o4SlxmTy1yYN6kYZTmW7Px/S/GNoYH2b+JAQmPbu2h8e8/BxjbTMT/qkivpKIrS7arADz30EBYvXqw73tzcjFAolJU+XDjegZmFCmw2GxgmuahIVbvm0abOqFW7mWKrb3uY10k5lzh/OGvXOaTH6a8RkLy448tbsLD8PVz/jw14/brJyLMK8XaM7EXh2pPRfMomqLTom46iKHC73VBVlVYmP0w0hpmh8cvMQBs/r9fb113o92JBXrr45EjEJiPtCn5yfC5sNhvAGL+v0sUW6f6uJ7c5xMcmHE8XixzyYw3aJ7ft+eMSjwdlH+5cfxP+eMLHsPB2qKqKeo+EFd/W4KH3d2LehDzcekoZIPkz+v+W4htjA+3fxIGExrZ30fj2noONbaaxyVGXXCktLQUAXYltc3MzSkpK0j7u7rvvxsKFC+P3Y3tgFxYWZm27w4KCAtrTPAOKouCSKXtRWFiIyo6vsLoqjJtOG5LQogiY70Zh2isc3WIJRnr/HT4aw8zQ+GVmoI2f2Wzu6y70K6WlpYaxCYC08cmRiU0UlDhNA+Z91d/cfLbH8HhVWwC/e3c7frx8L/544QiMLSrKYHwpvjEy0P5NHEhobHsXjW/vOdjYZhqbHHXJldzcXEycOBGffvopLrvsMgDatyaffvoprr322rSPM5lMMJlMuuMsy2b1Tc8wTNavebSQFRkbGzdibuFcXDC1DG9sqsHNs8d0NVBkoHUDkD8TYI+6t36P0PsvczSGmaHxy8xAGr+B0Mcj6ZRTTsGSJUugqmq8UuXjjz/G2LFjUVBQYPgYik36N1mRsaF2A2YOmQk+Je4YUWDHkh/OxB9W7MRtb+7FGz8tRnme7fCeiOKbtOi923tobHsXjW/v6W5sMx3vo/K3ddddd2HZsmV4++230dTUhDvvvBMejwc333xzX3eNZCAYCeLGlTciGAni+OG5+LbWDTmaMG8uGgTWzNduCSGEkH7kpptugt/vxx133IHGxkb85z//wdKlS/HLX/6yr7tGDlMwEsT85fMRjBjHHSzL4K6543DScCd+8eo3yTHLoaD4hhBC+oWjMr39ox/9CF6vF7fffjsaGxsxZcoUfPjhhygvL+/rrpEMOEwObLpmExwmB+yFDDiGwZ4mHyaWdpZGCw7gkpq+7SQhhBBiYMiQIfjwww9x++23Y9SoUSgqKsKDDz6IBQsW9HXXyGFymByoWdh93MEwDO6YPQw3vLYbS9ZU4Cenjz70J6L4hhBC+oWjsnIFAG677Tbs378ffr8fX3zxBWbNmtXXXSIZkhUZn1Z/ClmRwbEMxhQ7sLfJ19VAkYG6FdotIYQQ0s+ceOKJ+Pzzz+H3+1FRUYHbb7+9r7tEMiArMlbsXQH5IHGHWWDxu4sm4alP9qLVF+62rSGKbwghpF84apMrZPAJySEsWrcIIVnbIWF4nhVVbYGuBtEQ8PVC7ZYQQgghpBeF5BAWfrgwHpd0Z9bofJwwMg9Pfbrv0J+I4htCCOkXKLlCBg27aMfqK1bDLtoBAMPzrTjQ6u9qINiB87dpt4QQQgghvcgu2rHtp9viccnB/OyssXhlQxXcwcihPRHFN4QQ0i9QcoUMGpFoBO/seweRqBaUDMuz4kBrQuWKEgGqlmu3hBBCCCG9KBKNYPm25fG45GCOLc/BpDInXllfdWhPRPENIYT0C5RcIYOGFJXw3JbnIEUlAECJy4xmb8LcZUUCdjym3RJCCCGE9CIpKuGxLx6LxyU9ce3JI/Cv9VVQVbXnT0TxDSGE9AuUXCGDhk204d1L3oVNtAEACh2m5OQKbwPmrdNuCSGEEEJ6kU20Yd0N6+JxSU+cPbEYrX4JX1d39PyJKL4hhJB+gZIrZNCQohJe2vFS/BuiQrsJ3rCMoBTVGkQlYO8S7ZYQQgghpBdJUQlLNi05pMoVs8DhgqmleGPTIWytTPENIYT0C5RcIYNGJBrBO/u71lzJtYrgWaarekXtnJOs0pxkQgghhPSuSDSC5dt7vuZKzEXThuCDrY1QlB5ODaL4hhBC+gVKrpBBwyba8Mr5r8TLb1mWQYHdhGZf59aEvA04cwWVzRJCCCGk19lEG1Zcs+KQpgUBwPEjchGJKvimpqNnD6D4hhBC+gVKrpBBIyyH8eyWZxGWu9ZZKXSY0OTpvB8Nawu+RcNprkAIIYQQkh1hOYzH1j2WFJf0hMCxmD2+ECt3NPbsARTfEEJIv0DJFTJoRNUoNjZuRFSNxo/l2US0BTrnIKtRoGWddksIIYQQ0ouiahTratYlxSU9ddbEYny8o6lnjSm+IYSQfoGSK2TQsApW/HXOX2EVrPFjLosAd7BzDjJvBb6zXLslhBBCCOlFVsGK5fOXJ8UlPXXqmALsavSi1deDahSKbwghpF+g5AoZNMJyGI989UhS+W1SciUaBrYsorJZQgghhPS6sBzGolWLDnlaEKBV3o4vdmB9RdvBG1N8Qwgh/QIlV8igoagK6v31UFQlfsxlEeCJJVdUBQjWaLeEEEIIIb1IURXUeGqS4pJDcdKofHyxv/XgDSm+IYSQfoGSK2TQsAgWPHr6o7AIlvix5GlBFuDEJdotIYQQQkgvsggWLLloSVJccihOGpWHL/b3oHKF4htCCOkXKLlCBo2QHMJvP/8tQnIofsxlEdARiE0LCgEbF2q3hBBCCCG9KCSHsHDFwqS45FCcMDIfu5u8aPdL3Tek+IYQQvoFSq6QQc1lTahcIYQQQggZIPJsIoblWbGl1t3XXSGEENIDlFwhg4aZN2PxyYth5s3xY0nTgjgzMOMx7ZYQQgghpBeZeTMem/dYUlxyqKYOzcGW6o7uG1F8Qwgh/QIlV8igEYwEccfqOxCMBOPHkpIrchD4coF2SwghhBDSi4KRIBb8Z0FSXHKopg5xHbxyheIbQgjpFyi5QgYNlmFRaisFy3S9rR1mHr6wDFVVAYYFLEO1W0IIIYSQXsQyLIY6hybFJYdq6lAXttR0dN+I4htCCOkX6F9hMmiYeBPuPP5OmHhT/JhN5KGqQDASBTgTMHWRdksIIYQQ0otMvAmLZi9KiksO1eQhLjR5w2j0dLNYLcU3hBDSL1ByhQwagUgAP/7oxwhEAvFjNhMPAPCHo4AcAP43X7slhBBCCOlFgUgA85fPT4pLDpXNxGN0oR3f1nQzNYjiG0II6RcouUIGDY7hMKN4BjiGix8TeRYix8IflgGGAwpmabeEEEIIIb2IYzjMGjorKS45HONLHNjd5E3fgOIbQgjpF/i+7gAh2WLiTfjJ1J/oym+tJg5+SQY4GzBxYR/1jhBCCCFHExNvwsJZmccd44sd2N3QTXKFM1F8Qwgh/QBVrpBBwy/5ceV/r4Rf8icdt4l857QgP/DJPO2WEEIIIaQX+SU/5v1zni4uOVTjih3Y1ehL34DiG0II6RcouUIGDYETcOGoCyFwQtJxm4nrnBYkAMPma7eEEEIIIb1I4ATMP2a+Li45VOOK7djX7IMcVYwbUHxDCCH9wqCdFuTxeMDzPKxWq+5ca2srvN7k8kpRFFFWVnakukd6gciJuHri1RA5Mem4zcR3TgsSgTEL+qh3hBBCjnaRSAQejwf5+fm6c6FQCA0NDbrjQ4YMgSDQh+aBSORELJieedwxPN8GADjQFsDoQru+AcU3hBDSLwy6ypV//OMfmDJlCoYNG4bCwkLMmDEDX3zxRVKbe+65B5MmTcLs2bPj//3oRz/qox6TbPFLflzw5gVppgXJWrnsillUNksIIeSI2rhxIy666CLk5uZi3LhxKCoqwpNPPpnUZs2aNRg5cmRSbDJ79mxUVVX1Ua9JpvySH7OWzsp4WhDHMhhbZE+/7grFN4QQ0i8MqsqVaDSKTz/9FK+88gomTZoESZLw85//HOeffz727NmDvLy8eNtzzz0Xr7/+eh/2lmSbyIm4aepNBpUrnLbmCitqC76xYporEEIIIdn3/vvv48Ybb8Trr78OURTxzjvv4NJLL0VZWRkuv/zypLaVlZV900mSdSInYuFJC3VxyeEYVWhHRWua5AnFN4QQ0i8MqsoVjuPw97//HZMmTQKgTfW599570dbWhvXr1+vaNzU1IRgMHulukl4icAIuHG205kpn5QrbOSeZpfJqQgghR85vfvMbXHDBBRBF7cPvhRdeiJkzZ+KDDz7QtfV6vWhrazvSXSS9QOAEzJ+U+ZorADAi34rKlnTJFYpvCCGkPxhUyRUje/bsAQDdeipvvPEGJkyYAJfLhRNOOAEbNmzoi+6RLPJJPpz+2unwSckr6ttEHn4pCkR8wH8nabeEEEJIH5EkCQcOHDBc6620tBTDhg1DaWkpnn766T7oHckWn+TDpKcn6eKSwzE834bK1oDxSYpvCCGkX+j304JaWlrg83X/x2Lo0KHgef1L8fv9+NnPfoazzz4bU6dOjR8/8cQTccstt2DKlCnw+Xy45ZZbMG/ePGzbtg2lpaWGzxEOhxEOh+P3PR4PAEBRFChKmtXbD5GiKFBVNWvXO9qIrIhFJy2CyIpJY2gRWfjDESiMCBz7CMCIAI2xDr3/MkdjmBkav8wMtPEbKP00EgwG0djY2G2b3NxcuFwuw3O/+c1v4PP5sGBB1yKkOTk5ePXVV3HppZeC4zi8/PLL+OEPfwiXy4Wrr77a8DoUm/RvIivikTmP6OKSRD0d32F5Fhxo9Ru3o/jGEL13ew+Nbe+i8e09BxvbTMecUVVVzegKvezOO+886Nooq1evxvDhw5OOhcNhXHTRRThw4AA+++wzFBUVpX18KBRCQUEBHn74Ydx6662GbRYtWoTFixfrju/evRsOh6MHr+TgFEWB2+2Gy+UCyw76oqKsSzd+f11XhyafhHvmjOi7zg0A9P7LHI1hZmj8MjPQxs/r9WLcuHFwu91wOp193Z1D8sknn+D666/vts2vfvUr3Hzzzbrjf/7zn3HnnXfirbfewrnnntvtNa688ko0NDRg1apVhucpNhn4ejq+rf4Izv/rFnx6y7GwCNwR7OHARe/d3kNj27tofHvPwcY209ik31euPPLII3jkkUcO6TGSJOHSSy9FRUUFVq1a1W1iBQDMZjNKS0u7XUTu7rvvxsKFC+P3PR4PysvLUVhYmLWgUFEUMAyDwsJC+h/pMLiDbhz/8vHYdvM2uCxd3xbm53jRFPSiKNcC5r1JUM/bBgjZCToHE3r/ZY7GMDM0fpkZaONnNpv7uguH7cwzzzyshWefeeYZ3HnnnVi+fPlBEysAMHLkSN2Oh4koNunfvGEvJj0zCdtu3gaHyTju6On4Fqoq7KZtCLA2DC9K+d1GvBTfGKD3bu+hse1dNL6952Bjm2ls0u+TK4cqlljZs2cPVq1aZTifWVGUpMGsq6vDgQMHMHr06LTXNZlMMJlMuuMsy2b1Tc8wTNavebSwmWx4/uznYTPZksbPKvIIyQpYwQacuhyMYANofA3R+y9zNIaZofHLzEAav4HQx2x67rnn8Itf/AKvvfYaLrroIt35aDQKjkuuSPj8888pNhnAbCYbls9frotLUvV0fIfn21DdHsSkITnJJyi+SYveu72HxrZ30fj2nu7GNtPxHlTJlWg0ivnz52PDhg148803IUlS/JulgoIC2O12hMNhnHbaabjjjjswadIkVFVV4Z577sGwYcNwzTXX9O0LIBnhWR7HlxwPnk1+W5sFDqFIFGB5oHBWH/WOEELI0epvf/sbfvrTn+JPf/oTpk2bFo9NLBYLiouLAQA33ngjjjnmGJx++ulQVRXPP/88Pv/8c6xYsaIPe04ywbM8ZpVnL+4Ynm81XtSW4htCCOkXBlUqzOPx4JtvvoHFYsFVV12F2bNnx//773//C0D7lmfJkiV49913ceWVV+Khhx7Cueeei40bN2ZtfjLpG56wB2OXjYUn7Ek6bhY4hCMKEPEArzm1W0IIIeQIWbFiBcrLy/HYY48lxSa//OUv420ee+wxdHR04NZbb8VNN92EUCiEr7/+GmeeeWYf9pxkwhP2wPmQUxeXHK4hORbUdQT1Jyi+IYSQfmFQVa7k5ub2aA70lClT8MILL/R+h8gRZRNsePfid2ETbEnHzQKHYCQKcDZg7jrtlhBCCDlCXnnllYO2cblcuP/++3H//fcfgR6RI8Em2LDuhnW6uORwleVYsHZvq/4ExTeEENIvDKrkCjm6cSyH8XnjwbHJc9bNAts5LYgDcib1Ue8IIYQQcjThWA6TirIXd5S60lSuUHxDCCH9wqCaFkSObp6wB6XPlRpOCwrJUa1c9mWGymYJIYQQ0us8YQ+YxUxWpwXVu9NMC6L4hhBC+hwlV8igYRft2Hj1RthFe9Jxs8AhKCkAbwcurtZuCSGEEEJ6kV20o/r2al1ccrjKcsxoD0QQkOTkExTfEEJIv0DJFTJoMGDgEB1gwCQdtwgcwpEoAAYQnNotIYQQQkgvYsDAaXLq4pLDlWcTYeJZ1HWEdM9E8Q0hhPQ9Sq6QQcMreTHub+PglbxJx80Cq00Lkr3Acpd2SwghhBDSi7ySF66HXbq45HAxDIMyo6lBFN8QQki/QMkVMmg4RAd2/2g3HGLyltpmgUMkqkJmbMB8N8DTltuEEEII6V0O0QH3r926uCQTZTlm/aK2vIPiG0II6QcouUIGDRUqvJIXKtSk42ZB2z0ovqhtynlCCCGEkGxTocIT9ujikkwUO81o9IR1z0TxDSGE9D1KrpBBwyf5MOOlGfBJvqTjZkF7m4eDbuCtckD2GT2cEEIIISRrfJIP5X8q18UlmSh0mNDsTUmuyD6KbwghpB/g+7oDhGSL0+RE/U31cJqcScdFjgXDAEHYgKvoWx1CCCGE9D6nyQn1t9mNOwrtJnzV0p58UHBSfEMIIf0AVa6QQSOqRLGrbReiSjTpOMMwsAgcQpIEdGwDUs4TQgghhGRbVIliW9M2XVySiUKHCc2+lMoVJUrxDSGE9AOUXCGDhj/ixwVvXQB/xK87J/IsImEv8OEsIKo/TwghhBCSTf6IH7OWzjKMSw6X4bSgqJ/iG0II6QdoWhAZNJwmJ/Zcv0c3LQjQpgaFYAeu8PRBzwghhBBytHGanPDcnd24o9CuJVdUVQXDMNpBwUnxDSGE9ANUuUIGDVmR8VXDV5AVWXdO5FlIEQloXgcYnCeEEEIIySZZkbGuep1hXHK4Ch0mBCNR+KWEKUCKTPENIYT0A5RcIYNGMBLEjStvRDAS1J0TORZKJAismQ9E9ecJIYQQQrIpGAli/vL5hnHJ4XJZBAgcg5bEqUFRim8IIaQ/oGlBZNBwmBzYdM0mOEwO3TmRZxGEBbikpg96RgghhJCjjcPkQM3C7MYdDMNoU4N8YYwosGkHBQfFN4QQ0g9Q5QoZNGRFxqfVn6adFhSJSEDdCiqbJYQQQkivkxUZK/auyOq0IAAoSF3UVpEpviGEkH6Akitk0AjJISxatwghOaQ7J3AsIpEg8PVCIKo/TwghhBCSTSE5hIUfLjSMSzKRYxXRHpC6DkRDFN8QQkg/QNOCyKBhF+1YfcVq2EW77pzIsQgqFuD8bX3QM0IIIYQcbeyiHdt+mv24I9cqoCMQ6Tog2Cm+IYSQfoAqV8igEYlG8M6+dxCJRnTnRJ6FLEtA1XJA0Z8nhBBCCMmmSDSC5duWG8Ylmci1iuhIrFxRIhTfEEJIP0DJFTJoSFEJz215DlJU0p0TeRbRSAjY8Rig6M8TQgghhGSTFJXw2BePGcYlmXBZBLQnVq4oEsU3hBDSD9C0IDJo2EQb3r3kXdhEm+6cyLEIqGZg3ro+6BkhhBBCjjY20YZ1N2Q/7si1CthW5+k6wNsoviGEkH6AKlfIoCFFJby046W0lStyJATsXQJk+RskQgghhJBUUlTCkk1Lsl65kmtLmRYUlSi+IYSQfoCSK2TQiEQjeGd/mjVXOBZytHNOskpzkgkhhBDSuyLRCJZvz/6aK7rdglSKbwghpD+gaUFk0LCJNrxy/ivG04J4FoGoCMxd0Qc9I4QQQsjRxibasOKa7McdOZaU3YJ4G3AmxTeEENLXqHKFDBphOYxntzyLsBzWnRO4hAVto/rzhBBCCCHZFJbDeGzdY4ZxSSZyrSI6ghGoqqodiIYpviGEkH5g0FWuVFRUoLGxMemY1WrF1KlTdW3r6urQ1NSEMWPGwG63H6kukl4SVaPY2LgRUTWqOyfyLMIhCWhZB4z9SR/0jhBCyNHK4/Fg+/btuuPHHnsszGZz0jGfz4e9e/eiqKgIZWVlR6qLpBdE1SjW1azDT47PbtyRYxMQVVR4wzKcZgFQoxTfEEJIPzDokiu///3vsXz5cowdOzZ+bPTo0XjppZfi98PhMH74wx/iP//5D4YOHYq6ujo88sgjuPnmm/uiyyRLrIIVf53zV1gFq+6cyLNoi5qA7yzvg54RQgg5mq1fvx5z5szBiSeemHT81VdfxfDhw+P3n3/+edx+++0oKytDTU0Nzj//fPzzn//UJWDIwGAVrFg+P/txh13kwTCAN9SZXOGtFN8QQkg/MCinBZ1xxhn44osv4v8lJlYA4P7778eaNWuwd+9e7NmzBy+88AJuueUWbNiwoY96TLIhLIfxyFePGJbfmngWihwGtiyisllCCCF9IjE2+eKLL5ISK5s2bcJPfvIT/OMf/8CePXuwb98+rFu3DosWLeq7DpOMhOUwFq1alPVpQSzLwC7y8IVk7UCU4htCCOkPBmVyRZIkbNmyBQcOHOiaj5pg6dKlWLBgAYYMGQIAuOyyy3DMMcdg2bJlR7qrJIsUVUG9vx6KqujOCRwDOSoDwRrA4DwhhBDS2/bt24ft27cjHNZ/CF62bBkmTpyIyy+/HABQVlaGH//4x1i2bJlhLEP6P0VVUOOpMYxLMmUz8fCFOxe1VRWKbwghpB8YdNOCAOC9997D3r170djYCIfDgeeeew7z5s0DoK2z0tDQgJkzZyY95sQTT8TXX3+d9prhcDgpGPJ4PAAARVGgKNn5Y6YoClRVzdr1jjYmzoRHTnsEJs6kG0OBZeCTBSgzn9cO0Bjr0PsvczSGmaHxy8xAG7+B0s9sOuOMM8DzPOrr63HXXXdh8eLFYBgGgFa5YhSbLF68GLW1tRg6dKjuehSb9G8mzoTnL9DijnTjd7jjazdx8AYj2uNYE0DxjQ69d3sPjW3vovHtPQcb20zHvN8nV/bt24fm5uZu2xx33HEwmUwAgDlz5mDRokUoKSmBLMv41a9+hcsuuwxbtmzBqFGj0NbWBgDIy8tLukZ+fj5aW1vTPsdDDz2ExYsX6443NzcjFAod6ssypCgK3G43VFUFyw7KoqJeFZAC+N3a3+G+U+6DVUxedyUc9CMccCO49mZ4R90NcDR/PRW9/zJHY5gZGr/MDLTx83q9fd2Fw9bR0YGdO3d222bYsGHxBWmLioqwevVqnHbaaQCAlStX4vzzz0dxcTFuueUWAEBbW5thbAIAra2thskVik36t5AcwkPrH8LdJ9wNM28cdxzu+Jo4oLapDU05KhANwbH/IYpvUtB7t/fQ2PYuGt/ec7CxzTQ26ffJlZdffhn//e9/u23z+uuvx4OOyy67LH6c53n8/ve/x9KlS/H222/j9ttvhyAIAKAryQ0Gg/FzRu6++24sXLgwft/j8aC8vByFhYVwOp2H/LqMKIoChmFQWFhI/yMdhoAUgMlkQlFRkS65kp8rAWwrrFYrLEVFFHwYoPdf5mgMM0Pjl5mBNn4DeZHWbdu24Y477ui2zS233IIf/OAHAKDbsfDss8/G97//fbz88svx5IogCIaxSeycEYpN+reQHILVakVRUVG3yZXDGd8ceyVYs3ZtRENgGii+SUXv3d5DY9u7aHx7z8HGNtPYpN8nV+69917ce++9h/14nudRUFCA2tpaAEB5eTlYlkVdXV1Su7q6uqSF5VKZTKZ4dUwilmWz+qZnGCbr1zxaWEUrFp+8GFbRqhs/kefgjwpgZvwJTB/1byCg91/maAwzQ+OXmYE0fgOhj+mccsop+OKLLzK6RklJCVatWhW/P3z4cMPYhGEYlJeXG16DYpP+zSpa8adz/nTQdoczvg6TAH84qj2GtQIU3xii927vobHtXTS+vae7sc10vAfVb0tVVV0Z7N69e1FZWYlJkyYBAKxWK0466SS888478TaBQAArV67EWWeddUT7S7IrGAnijtV3IBgJ6s4JHAtOCQFfLgBk/XlCCCGktwQCgaT7qqrik08+iccmAHDWWWfhk08+SWr79ttv44QTToDD4ThifSXZE4wEseA/CwzjkkzZzTz84ah2Rw5SfEMIIf1Av69cORSRSAQzZszATTfdhEmTJqGqqgoPPPAApk2bhu9///vxdg888ADmzp2Le++9F7NmzcKTTz6JvLw83HjjjX3Ye5IplmFRaisFy+hzhjzLIBwFYBkKGJwnhBBCestPf/pTFBYW4vTTT4eqqliyZAm2b9+OTz/9NN7mxz/+Mf7yl7/gkksuwW233YYvv/wS//73v/H+++/3Yc9JJliGxVDnUMO4JFP2xN2CGJbiG0II6QcG1b/CoihixYoVqKmpwcMPP4z3338fv/jFL7Bu3bqk+VNnnHEGPv74Y+zatQu///3vMWbMGKxduzZr85NJ3zDxJtx5/J0w8foSaYFjEVYEYOoigNOfJ4QQQnrL888/jxEjRmDJkiV45plnMG7cOOzatStpdyC73Y61a9di/Pjx+MMf/oAdO3bgo48+wtlnn92HPSeZMPEmLJq9yDAuyZSWXJG1O5yJ4htCCOkHBlXlCgAMHToUf/jDHw7a7rTTTouv2k8Gh0AkgB9/9GP864p/wW6yJ53jWAasEgD+Nx+Y9Q+At6a5CiGEEJJdoijilltuiS9em05JSQmefPLJI9Qr0tsCkQCufeta/OPif8AqZDfusJt5HGjrnEImB4B111J8QwghfWxQVa6QoxvHcJhRPAMcw+nO8RwDKcoABbMAg/OEEEIIIdnEMRxmDZ1lGJdkymbi4QvFpgVxFN8QQkg/MOgqV8jRy8Sb8JOpP0k7LSikCMDEhQaPJIQQQgjJLhNvwsJZvRN32EQOfqlzQVvORPENIYT0A1S5QgYNv+THlf+9En7JrzvHsww4xQ98Mg+Q9ecJIYQQQrLJL/kx75/zDOOSTJkFDuFIbLcgim8IIaQ/oOQKGTQETsCFoy6EwAm6czzLIhTlgGHzAUZ/nhBCCCEkmwROwPxj5hvGJZkyCyxCEUW7wwgU3xBCSD9A04LIoCFyIq6eeDVETtSd4zlGS66MWdAHPSOEEELI0UbkRCyY3jtxh5nnEJJj04JEim8IIaQfoMoVMmj4JT8uePMCw/JbgWMgqEFgxSwqmyWEEEJIr/NLfsxaOqtXpgWZBA5BKWFaEMU3hBDS5yi5QgYNkRNx09SbDCtXOJZFUOa0Bd9Y/XlCCCGEkGwSORELT1poGJdkSpsW1JlcYUWKbwghpB+gaUFk0BA4AReOTrfmCoOQwmpzkgkhhBBCepnACZg/qXfiDovAISR3rrnCChTfEEJIP0CVK2TQ8Ek+nP7a6fBJPt05gWNhZoJQ350ERPTnCSGEEEKyySf5MOnpSYZxSabMAgdJVqAoqhbX/JfiG0II6WuUXCGDhpk3Y9GsRTDzZt05jmUQVkREjv0jwOnPE0IIIYRkk5k347G5jxnGJRlfW+AAAGFZ0eKa4x6j+IYQQvoYJVfIoMGzPM4oPwM8q5/tJnAMouAgF80FDM4TQgghhGQTz/KYN2aeYVySKbOghfChSFSLa8rmUXxDCCF9jJIrZNDwhr2Y/s/p8Ia9unM8x8LGBmB+dwQQ0Z8nhBBCCMkmb9iLoY8NNYxLMmXmtcqVkBzV4po3h1J8QwghfYySK2TQsAgWPH/287AIFt05bUFbE7zHvwRw+vOEEEIIIdlkESxYPn+5YVySKZZlIHIsQhFFi2tOXU7xDSGE9DGqHySDBs/yOL7keMPyW57VpgWFc06ksllCCCGE9Dqe5TGrfFavXd8U246Z5YHC3nseQgghPUOVK2TQ8IQ9GLtsLDxhj+4cxzKwswEUfFAERPTnCSGEEEKyyRP2wPmQ0zAuyQaRYyFHVS2uec1J8Q0hhPQxSq6QQcMm2PDuxe/CJth05xiGQYQxo+HEjwFOf54QQgghJJtsgg3rblhnGJdkA88xkKKKFtfMXUfxDSGE9DGaH0EGDY7lMD5vPDiWMzzPsDyC1olAmvOEEEIIIdnCsRwmFU3qtesLHAs5qmhxTU7vPQ8hhJCeocoVMmh4wh6UPleatvw2hw9i9EcOKpslhBBCSK/zhD1gFjO9Oi0oEpsW9DJD8Q0hhPQxSq6QQcMu2rHx6o2wi3bD8xJjxc6TtgG88XlCCCGEkGyxi3ZU316dNi7JFM8xiCiKFtdcXE3xDSGE9DFKrpBBgwEDh+gAA8bwPMtyiDB2IM15QgghhJBsYcDAaXKmjUsyJXAsIrICgAEEJyi+IYSQvkXJFTJoeCUvxv1tHLyS1/C8iw9iyrrhgGx8nhBCCCEkW7ySF66HXWnjkkwJsWlBshdY7qL4hhBC+hglV8ig4RAd2P2j3XCIDsPzEmvDhuMrAN74PCGEEEJItjhEB9y/dqeNSzIlcAy8oQgijA2Y76b4hhBC+hglV8igoUKFV/JChWp4XmAZQPYAac4TQgghg0IwqN2GQtp/sWPhsPZzIND1s98PSFLXz5GI9rPPB8gyAIBJ+BkeDxCNdv2sKICqaj+rqnbf07mwajTa9bMsA15v188+n/ZzJKI9L6D1I/ZzOKz1M/Zzll8TvN5ef00qVHi8LVD9vl55TQLH4nevrMfCl9dri9l6PfR7otd0xF4T4/cPutc0GH9P9JoO4zVlgJIrZNDwST7MeGkGfJLP8LydC2Hm5mmAbHyeEEIIGRTuuku7/b//0/4DgJ/9DHjoIe3na68FnnpK+/nSS4EXXtB+Pvts4K23tJ9PPBH4+GMAQMFppwEbNmjHhw4Fdu7Ufna5gLo6LSh1ubTbujrtZ0BrN3So9vOGDcDEidrPH3+sXR/Qnu/ss7WfX3hB6w+g9e/aa7WfH3pI638WXxMmTuz11+STfCh/ajR8V1zc7WtyPPQQmHvuOeTXxHMs3nrhDoQ/eAd4qxw4rvdf00D6PTHDhg2619Sffk9555036F5Tf/o92f/8ZzC33TaoXlN/+D2xubndv6ZVq5AJRlVV+hr/MHg8HrhcLrjdbjidzqxcU1EUNDU1oaioCCxLea9DdbDxm/enz/DrcyfgjAlFfdC7/o/ef5mjMcwMjV9mBtr49cbf0aNdfEwbGuAsLu76VtBs1r4ZZFnAZNK+GeQ47We/HxAEQBS1n0VRu+/zAWYzFJZF8/79KBw2DKwoat/02Wza4z0ewG4HGEYLXB0O7dtBnw9wOrVvBv1+7WdZ1vrgcGg/h0LaYyMR7ZtJm027jUS0n8Nh7fFWq/azogAWS1ZeE3he66/Fov3ch69JMZnQVFWl/X9rtR7Sa7r+pc34YssBhHkR+/5wUb95Tf3l96R4vWgKhVCUlwc2HB4Ur6m//J4UjkNzRQUKy8u1fxcGwWvqT78nxWxGU3U1igoKwNpsg+I19Zffk+LxaP8u5OeDDQZ1r8nT1gZXfv5hxyZZS66oqgqG6ftVyt966y3IBuU8I0eOxIwZMwAAX3/9Nfbt25d03uVyYc6cOT1+Hkqu9D8ROYLPd3+Ok8edDIEXdOfPf2IVfnMqg1nHnQqwXB/0sH+j91/maAwzQ+OXmYE2fkciudJfYpOvvvoKlZWVuuOiKOKiiy4CADQ1NeGzzz7TtZk3bx4cjp6tpUGxSf8SVaLY2bITEwomgEsTd2QyvtcuW4/Vu5vBIop9vx6FK19pxwXTyvGDWSOy0PuBj967vYfGtnfR+Paeg41tpn9H+UNprKoq/v73v2Pz5s04++yzceGFF2LRokV44oknEI1G8d3vfhfPPPMM7Hb7IXckW/79738jGJvLBSAQCOD999/HokWL4smV5557Du+++y5OOumkeLthw4YdUnKF9D/+iB8XvHUBqm+vRg6foztv5UKYuWc+MLUWYOlbUkIIGSzWrVuHf//73ygtLcUtt9yCNWvW4JZbbkFFRQWmTZuGJUuWYNq0aX3Wvy+//BKffvpp0rGPP/4YQ4cOjSdXtmzZgvnz5+Oyyy5LanfyySf3OLlC+hd/xI9ZS2ehZmENnKbsxx1hWVtbwMqGoaw4GduqliIkM/jBrBGIRBU8/P5OXDZ9KI4po5iHEEKOhENKrvy///f/sHjxYowbNw7PPvssbrvtNrz88sv4+c9/DgB4/vnnsWjRIjzyyCO90tmeePHFF5PuL1u2DCtWrMB1112XdPykk07C66+/fgR7Rnqb0+TEnuv3pA1gJNjx0aTdOFegIIMQQgaLrVu34vTTT8fw4cPR1taGPXv2YPny5Zg/fz6uv/56vPfee7jsssuwc+dO8PwhhT1Zc8stt+CWW26J329pacGQIUNw/fXX69pSbDJ4OE1OeO729Nr1QxEFAOBXrXhx6Dfwfb0d+5v9UFUVn+1uxtI1FahuC+D5Hx7fa30ghBDS5ZDqjP72t7/h9ddfx9atW7F8+XI8+uijeP3117Fo0SIsWrQIy5cvxxtvvNFbfT0sS5cuxZw5czB8+PCk4263G++99x7Wrl0Lj6f3/vCRI0dWZHzV8BVkxXiVZ46JwuHfAKQ5TwghZOD55z//iSuvvBJ79uzBvn378Oabb+K6667Ds88+i1/96ldYuXIlJEnCli1b+rqrcS+++CIYhsEPf/hD3bnVq1fjww8/RHV1dR/0jGSTrMhYV70ubVySqbCsJVdcIoNww1qcNNIJb1iGJyhjfWUbJpU5sW5/K2h5RUIIOTIO6Suc6upqzJ07FwAwd+5cqKqK44/vyobPnDmzXwUDO3fuxOeff274LdDGjRvx+OOPo7a2FnV1dXjyySfxgx/8IO21wuEwwrFtoYB4QkZRFCiKkpX+KooCVVWzdr2jjT/sx40rb8S2sdvAs/q3toWRMKPqJijH7QQEKrFORe+/zNEYZobGLzMDbfyy1c/q6mqce+65AICcnBzMnDkzKTYRBAHHHnssqqurMX369Kw8Z6aWLl2KSy65BPn5+UnHeZ7H3XffDZ7nsX79elx11VV4/vnn01bcUGzSv/nDfsxfPh/bbt4Gh8k47shkfJXOpEmOScblwYWoy38bW+s41HYEcKDFj3nHFOPxj/egriOAUpclo9cyENF7t/fQ2PYuGt/ec7CxzXTMDym5IkkSzGYzAMRvBaFr4VBRFA0Xk81EukXgEp177rmw2Wy640uXLkVRUVF8PnPMFVdcgcceewxWqxUA8Mgjj+CGG27Acccdh8mTJxs+x0MPPYTFixfrjjc3NyMUW+04Q4qiwO12Q1VVWrzoMCiKgo/P/xhBdxBhb1h33h8V8WrRSpzTHgQQ1F/gKEfvv8zRGGaGxi8zA238vF5vVq4TDofjMQmgxSeJsQmgxSeRSCQrzwcADQ0NWLNmTbdtpkyZgvHjx+uOf/nll9i2bRuefPLJpOPDhg3Dtm3bMG7cOADAt99+i5NPPhmjR4/GPbFtelNQbNL/fXXVVwi6gwimiTsyGd9YzC2zVlxS+zLOGCOiwCZg54EG7G/yYPZIG0qdIjbtqcXMYUfflGh67/YeGtveRePbew42tpnGJoc8+Xjr1q3d3s82o0XgUp166qm65EokEsELL7yA6667ThdknXnmmUn377jjDjz88MN4//330yZX7r77bixcuDB+3+PxoLy8HIWFhVldkZ9hGBQWFtL/SIdBkiWsrlmNS0ZfApEXdeet4h6MZr5GUcEVgEFly9GO3n+ZozHMDI1fZgba+CUmRDJVXV0dj0c8Hk/S/dixbKqtrcUrr7zSbRue5w2TK0uWLMHo0aNxxhlnJB2PJVVipkyZgu9///v4z3/+kza5QrFJ/yYrMj6u+BhnjTzLsKIWyGx8eX43AMBpZjE0vA7FuZei0BkGTFY0eCOYNKIEQ7a2Q+IsKCoqwn+31KPFF8a1J48AoC2I+/D7u3DxsWWYVp6TyUvtl+i923tobHsXjW/vOdjYZhqbHPInzClTpnR7P9tSF4HrqXfeeQfNzc1YsGDBQdsyDAOHw4Gmpqa0bUwmE0wmk+44y7JZfdMzDJP1ax4tJEXCoi8W4fyp58PM6v/HMHMRTG3+HVj1YoDVJ18Ivf+ygcYwMzR+mRlI45fNPiYmGADgo48+0rW58cYbs/Z8M2bMOKyFZ/1+P1599VX83//9X4+2iHa5XBSbDGCSLOHOj+7Elwu+NPzSJ+Zwxze2lkqOGMWdzufxleliuCwC3EEZnpCMAocZxS4Lmn0SVDC4699bEIoouGT6UORYRbz3bR3+se4AdjR48dpNszJ6rf0VvXd7D41t76Lx7T3djW2m431IyZUNGzZk9GRH0tKlSzF79myMHTs26biiKGhra0NBQUH82Ndff40DBw4kzdEmA49dtGP1FathF423Ao8wNrw/bCWuFPpuq3BCCCHZ9Yc//AG//vWvD9pu9OjRR6A33XvttdcQDAbxox/9SHeusbERxcXF8fuRSATvvfcexSYDmF20Y9tPt/Xa9WPr1DKiA3N3P41Hp7ngNEdQ065NQXJZBBQ7TGj0hFDTHkBUUTEsz4qNB9px1sRifLG/FZceNwRvbq5FQJJhFamqlxBCMnFI/4oOlD/wtbW1WLFihW5bZgCIRqM49dRTcdFFF2HSpEmoqqrCE088gXnz5uHyyy/vg96SbIlEI3hn3zv4Yf4PYWL13+QJTBRDvO8Cyo8BVjC4AiGEkIFm1KhRGDVqVF93o0eWLl2Kiy66KCmJEnPffffB4/Hg9NNPh6qq+Pvf/462tjY88MADfdBTkg2RaARv7XwLF0+4GAKX/bgjtqCtXQDOc62BwzQVTouAmvYAeJaBReBQ5DThm2o39jb5MCLfhvElDuxq9OKsicX4pqYDd84dj9W7m7GzwYvpw3Kz3kdCCDma9LjuJRQK9fi/vrZ161ZcfvnluPTSS3XnBEHAhg0bMHToUKxatQrt7e3461//ivfffx8cx/VBb0m2SFEJz215DlJUMjwvsjImdCwBFOPzhBBCBpZIJNLj2KSvd11wu90oLS3F7bffbnj+ueeew5VXXomtW7fi66+/xlVXXYXdu3cbrttCBgYpKuGxLx5LG5dkKla5YhcULCh4Ew5BgdMioLotCKdFAMMwyLGIcAcj2N/sx+hCO4bnW1HVGoCqqjjQGsCoQjsmljqxsz79Io4rtzfi2mXr0RGg+IkQQrrT48oVi6XnW7jF5oD2lXnz5mHevHlpzzscDtx2221HsEfkSLCJNrx7ybuwifqdowBAZqx4r/QNXMsbnyeEEDKwfP/738e///3vHrVdvnx5n1aoulwuLF++vNs23/3ud/Hd7373CPWI9DabaMO6G9b12vVj0bbKW3Hpvkfx+hwHbKKMBk8ITrMW4jvMPLyhCFr8YRQ7TRiWZ8Xm6g60+SWEZQVlOWaU51lQ2xGIX9cbisBh7qq0efiDndjb5MNrX1XjxtP6fnodIYT0Vz2uXFm3bl38v0cffRS5ubm4//778cEHH+CDDz7A/fffj9zcXDz66KO92V9C0pKiEl7a8VI3lSsRjPa8AvTSN0iEEEKOrIceeigem6xcuRL5+fn44Q9/iDfffBOffvopli5dimOPPRYXXHCBbqdAQnqbFJWwZNOSXqtciU0LMjNRfC9vBQRGhkXk0OaX4LJoyRGHWYAnJKPdLyHHKqLAbkKrT0K9O4QcqwCryKPEaUF9h1Z5/v639Ziy6EN8ub8VANDsDWNfsw93zRuPNXtbe+V1EELIYNHjypWTTjop/vPPf/5zLF++HGeddVb82Lx583DSSSfhN7/5jW7VfkKOhEg0gnf2v4MbZ90Is6DfLUhkoxgReA9QfwaAdgsihJCBbuzYsfGF6//4xz9i/vz5eOaZZ+LnZ8+eje9973uYOHEi/H4/8vLy+qqr5CgUiUawfPtyfH/y9yFy2Y87YskVgZNxjmsNeEaGWdCmuNs7K1ecFq1ypc0fwaQyF/LtJrR0JldKnFqsVJpjxhedyZS3N9dpt9/U4cRR+dhS04HRhXacMqYAz3+2H6qqpt3patmaCnyxvxV/uWo6RJ52OCGEHH0O61++bdu2GS5uO3PmTGzdujXjThFyOGyiDa+c/0q304L+m/83gKYFEULIoJMuNrHZbBg/fjy2beu9XVsIMWITbVhxzYq0cUmm4rPwORt+WHE/WMEeT64InBbiO8wCPEEZbf4wcm0i8m0i2vxhtAck5Fi16pYSpxkNHq1yZUeDB98/YRi+rXEDAPY3+zGm0I4JJQ64gxE0+8KGffGHZfxxxS58uL0RH21v7JXXSwgh/d1hJVdKSkrwt7/9TXf8b3/7G0pLSzPuFCGHIyyH8eyWZxGWjf/wi4hgsncpEDU+TwghZOAqKSnBSy+9hEgkknR8586d+OKLLyg+IUdcWA7jsXWPpY1LMhVLroiMjBsK3gQPCZbO5ArPatUlNhMHKaqgyRtGrlVArk2EogK17UE4O9dVybOJ6AhICMtRVLcFcM7kEuxp8kJRVNR2BDEk1wKzwKHQYUJ1W9CwL+sr2lDsNOGm00Zhzd7mbvu9tdaNlZSAIYQMQoe1of3vf/97fO9738Mbb7yBmTNnQlVVfPXVV1i3bt1BF2sjpLdE1Sg2Nm5EVI0anudZBcXhr4E05wkhhAxcP//5z3HiiSdi7NixOPfcc5GTk4OKigq8/fbb+N73vodp06b1dRfJUSaqRrGuZh1+cvxPeuX6sWlBIqfgGOtOcIwCs6B9b8qz2q2J15Itzd4wXBYBVoEDwwAN7lB80VqXRYA7GEF1WwACx+KEEXkIRRS0BSTUdgQxa1Q+AKA814Ka9gBmDM9FdVsAX1d34KJpZQC0hMnUoTmYMTwXj3y4K22fI1EF1/1tPVp8Elb84jSML3H0ytgQQkhfOKzKlcsuuwxbt27FlClTsGnTJnz99deYMmUKtm7diosvvjjLXSSkZ6yCFX+d81dYBavh+ShrxX9dTwK88XlCCCEDV2lpKbZt24abbroJ1dXVWL16NRiGwSuvvIK///3vfd09chSyClYsn788bVySqVhyBbwVt1TdDU6wxacDcZxWuWLqXPskLCuwCBxYloFN5FHnDsJp0b5jzbEKUFRtClChwwSLyCHHKqDBHUK9O4iyHG1tlqG5VtS0a5Urd7/xLW7719fY2+QDAOxs8GJCqQPjih2oaPEjqhjvHLqhog0Mw+DCaWV455u6bl/f11XteH1jTQYjRAghR9ZhVa4AwIQJE/DUU09lsy+EZCQsh/HIV4/g/rn3wyLqtw4XGQkn+JYA0UcAztQHPSSEENKbHA4H7r777r7uBiEAtLjkoTUP4e5T74aJz37coSRMC/pF8Uvg1JMhdCZVYtOCTAkLy8bWY7GZODS4QzhuWC4AwG7iwbEMKlv9yLVqC++WOM1ocIfQ7o/Ej+XZRLT7JUiygvUVbSjPs+B/e5oxpsiO2o4gzplcgqG5FqgqUNcRRHmePqn01YF2nDw6HyeNyu82uRKJKljwj6/Q6pcwPN+KmSNoMWpCSP9HS3mTQUNRFdT766GoiuF5nlVhjzYAac4TQgghhGSLoiqo8dSkjUsyFStc4RgVJUIreLZrOlDslmEYcLFES+eUIbuJR4M7BGfnjkIMw8Bp5lHZGkCurSuR0haQ4AlG4LJ2rc3SHoigqi0AhgEunFqGXQ1eAFoypSzHAp5jMTTXgspWv2GfdzZ4MLHUicllLmytdUNVjStcPt/XCpFncfWJw/Cfzd1XuDz16V5c/NRauIORbtsRQkhvo+QKGTQsggWPnv4oLIK+agUAFNaC9233A7zxeUIIIYSQbLEIFiy5aEnauCRTsWlBCmvGr2tuAytYwKdUrgCIJzDi2zSbeHjDcnxBWwCwijzqO4LItXatw9Lul+ANy3BZtGO5VgHtAQkHWv0YkW/D6EI79rf4IckKmn3h+PShYqcZjR5tEd/KFn98m2cA2NPow7hiO8YW2+EJyWjxSYavbdOBdpw0Kh+njCnApqr2tGPgDkbwxMd70OAO4aUvD3Q7XvuafVi2pgJylL5kI4T0DkqukEEjJIfw289/i5AcMjwvQsJs/8NA1Pg8IYQQQki2hOQQFq5YmDYuyVRQ0hbo59UwflP6V/Cq1FW5wiUkVzpvzXxsWpBWsRKrZAEAs8CiriMUnwLksgiobg/EfwaAXJuINr+EA60BDMu3ojzPipq2AFp8YagqUGjXpj4VOExo6dyy+acvbcKVz38Bd0CrKql3hzAkxxrffaim8zlSbatzY/IQF6YMcWFXgxehiPFmBGv2tGBUgQ23zxmLT3Y0pR0rVVVxy0ub8Lt3t+Pl9VVp2wHauH62uxlKmnVjCCEkHUqukKMGxzJpy08JIYQQQgaS0s5KEaYzj8KxTJrKFe02th6LyCdPHQI6K1fcwXgixWURUNUWhMAx8e2d86zals2t/jAKHSYUOkxo8UnoCETgMPHgOxfTLbSb0OoLwx2IYEeDB3k2Eev2t8AbisAXllHiii2Qa0F1u/HWzhUtfowpsmNorgUCx6K6zTgJs7XOjWPLczBjeB6+rXVDko2rUvY2+VDZ6seiC4/BG5tquxlV4N63t+KHy9Zj2dqKbtvJUQWf721J+5yEkKMPJVfIoGHmzVh88mKYebPheYU1Y4XpVwBnfJ4QQgghJFvMvBmPzXssbVySqTduPhnr/+8sRGDCA/U/Bi9YIHQmTDhWH+IzTPIit4nVLRaBgyckwypqiRSnRUB1WwAuixB/nLNzy+aOQAQ5FgGFDhOkqIKa9gCclq4pRvk2ES0+CftbfMi3mXD6uELsavChwR2CVeTia70MybGg1iC5oqoq6jpCGJJjBsMwKM+zoCpdcqVWq3AZVWADzzLY3+IzbPfF/lbMHJGHs48pxtZaN/xh2bCdNxTBW1/X4q554/HSl91XuDz5yV5cteRLPPT+jm7bRRUV73xTh2ZvuNt2hJCBj5IrZNAIRoK4Y/UdCEaMvwUxMSFcELwXkI3PE0IIIYRkSzASxIL/LEgbl2QqxyqiyGkGokE8PPRJcGqoq3IlIXGSSuycHpRY3WLpTKrE1mWxihyaPCHYTV0bi1pFDgEpio5gBDlWATaRg1lgsbfZF694AbqmBR1oDWBEvhVjiuzY1+xDkzeMIocpnqzJt4loD2hrrry+sQYPvLsdANARiCAYiaLUpa1VMyzPmrZyZX+zH2OL7GBZBsPzbahsMV5Id0eDF8eUOjE014o8m4jt9R7Ddl/ub0N5nhXXnjwC1W2BtM+rKCpe/vIAfn3uBLyyvjrttCUAWLamAj/719e4+Z8bu62glmQFj6zYhU93pZ/eRAjp3yi5QgYNlmFRaisFyxi/rRmWg5spBtKcJ4QQQgjJFpZhMdQ5NG1cki2qyqAhkg+O5eIJk/SpFUDkYtUtyZUrgLb2inbLwS9F48kWQJs6FJYVtPkk5FjEzl2GBNS2B5OSK06zAG9Ijq/NUuoyo8ETgjsYgatzTRegaw0XVVXxhw92YsmaClS3BVDboV0vtjZMWY4FdW79ujVRRUWjJ4SyHC0JM7LAhspW42TIrgYvJpY6AUBL9jQZV7h8W+vGceU5sJt4TCx14puaDsN22+s9CEcU3HDqSLgsAjYeSL/o7ktfHsAfLp+Kb2vd2Nds/LwA8OIXB/DPLw/g1pc2xdeoMRKQZHzvuXW45eVNB10XprYjiL1N3m7bEEKyhz5lkkHDxJtw5/F3wsSbjBuwJqzkbwa4NOcJIYQQQrLExJuwaPai9HFJlkQg4vHGq8Hx5vi6J9195I6tuSJwiWuuaEkUE5+cZIlNIQK6qlvq3cH49sx2Mx9PhsTYzTx8YRlt/jAK7SYUOcxo9oa15EpCuzybiHa/hGZfGE3eMCaUOLChsg0N7hBKXV1TqfJtJrQa7CrU4gtDVlQUO7W25d1WuPgwutAOQEuu7E2TXNnb5MPYYgcAYHyJI77VdKpNVe2YPjwXAsdi5sg8fJ1mR6O6jiCq24M4b0opThlTgE93Nhu2A7TqnfsuOAZjiux4f2t92nb/Wl+NNr+E9RVt3Va5tPklnPv4Z5j7p8+wu7H7BMsr66tw+6ubD7qdtSQrB70WIUczSq6QQSMQCeDHH/0YgYjxH1YRIVwZvgOQjc8TQgghhGRLIBLA/OXz08Yl2cIqATw17CGwSjA+Hai76SexhEli5YoptXKFT062AF0JmDp3KJ4kcZh4XeWK3cTDF5LREYzAaRFQ5DShyROCJxiJr7cCALlWEW0BbfehEqcZ04bmoKLFj/aAhDxbV4VLvl1Eq19br6TdL2FrrVvrR0cQBXZTPFlU5DAZrmsSjkTRHojEF9IdlmdFTZqFdPc0eTG2SEvCjC92YE+jcRImtqU0AEwsdWBHvXHCYUNlGyaVOWE38ZgxPBeb01TCuAMR7Gzw4DtjC3HmhGKs3ddq2A4A3vmmDjecOhKXzxiK/3xTl7bda19VY8pQF648YRiW/i/94rzVbQHc959t2FbnxlOf7k3bDgBufXkT5v7pM7ywrvttrzceaMcPln6JL/enfx2EDEaUXCGDBsdwmFE8AxzDGZ5nWB6VzFQgzXlCCCGEkGzhGA6zhs5KG5dkS1TlsCkwAWC4pHVUYpiUQ/EFbRPaxnYS6kqydCZXErZrFjgWIsdCkpX4WiwOs4AmbxhWU9drdHRWrnQEtLVZcq0i/FIUzd5w0sK3scqVihY/RhRYMSzfiqq2ANzBCJzm5AVy2/xa5cpdr2/BBX9eA28oggZ3CGU5XRUuhQ4Tmn365EqjNwyeZZDfmbAp6ZymlEpVVVS2BjCiwAYAKM+zoKbDODGmJWG0CpcJJQ7sSlPNsbtRW+sFACYPccUTQ6k2VrVhZL4NhQ4TZo7IxaY004wCkoxva904bVwhvjO2AF/ub0ubSFu5vRHnTynDRdPK8PHOxrRTiP7zTR1OH1eIRRdOwusbaxBN025HvQef7WnGI/On4elV+yCnaacoKu54bTPCEQV3LP8GkWj63ZRUVZvaRbt5ksGCkitk0DDxJvxk6k+6nRa0ivkBTQsihBBCSK8z8SYsnLXwCEwLErC05RKAM4HtzKSwCYmT1HRLbDoQnzAtKLYtc7xiJT4tKDkxZIlPH9LO20083MFIUju7KZZc0dZmcXRWq9R2BJOSJq7O3YfqO7R1U8pyzKjv0CpcEith8u3atCBVVfFlhVYJsb6iDa1+KZ4wATqTKwaVK42eEIocpviYlDjNaDRIrriDEUiygmKn9vsakmM13M0I0BbSHVWoJWG0SpiAYYKgoqWr3ehCG2rag4bJht2NPkws05IwE0udqO0IwhvST9HZVqdtbV3qMuO48lw0eUOoN1iPJixHsbm6A6eOKcCM4bnwhGRUthov9vvpziacNaEIJ4zMgyQr2NlgvNjv+9/WY+4xJbjkuCGIKiq21htX9WyobIM7GMELN5wAOari826qcP60cg9O/H8f49nV+9O2IWQgoeQKGTT8kh9X/vdK+CXjPx4mBPBj+WZANj5PCCGEEJItfsmPef+clzYuyRYu6scLI+8FZH+8SoVLKFdhUkpX4jsKGVSuJC5oCyRXrgD6tVlii84mrs0SO1bboU0fMgscRI5FXUcQTot+9yF3MIJcq4g8mwltAQmekJzULs8moK1zbRZvSMaZE4qwu9EHTyg5CVNg15IrsSRH7LbRE0ZxwhouxU4zmrxhXYVGszcMi8DFq3KG5FrQHojotm2WowqafWGUdi6kOzTXilBEQatfvy7M/mY/RhZo04dKXRawDAwTNvubfRjVWTGTaxORZxOxv1n/vtla68aUIS4wDAOLyGFEvg17DNaP2d3gg1XkUJ5ngcCxmFjqxLcGVTORqIIttW6cMDIPPMdixvBcfLm/TdcOANbtb8UpY/LBsQxmjcrHxmrjap1PdzXj7InFMAsc5k4qxic7Gg3beUMRPP/ZPtxz3kQ8u3pftzsuETJQUHKFDBoCJ+DCURdC4ATjBpyAr3E2wKQ5TwghhBCSJQInYP4x89PHJVkSUXn8130qwAhgOutUWCZ95UosqZK45gofT650TgvikytUYmLXNcWTMGzSfaBr56EWXxg5CQvf1rlDcCRs7Wwzde4+5A/DZRHi039SF761mwQEI1HsafRhSI4Fx5Q6Udnihyco66YZhWUFoYiCF9dVYvoDHyMYiaLJq1WuxBQ6TIgqKjoCycmQJm8YRc6uraJzrQJEjtVVw7T6JagqUGg3xceswG7SLaarqioOtAYwPN8aH+/yXCsOGCy6W9Hix8jO5AoAjCqwGe4sdKA1kNRuTJEdewymJG2vd+OYMmf8tUwZ4jSckrS/2Q+eZTAiX7vmtKEubKvTV67IUQXfVLtx/Ig8AMDMkbnYUmecNPy6qh3Hj8jV2o3Iw6aqDsN2K3c0YmSBHQu+MxJ2E48vK4yTOoQMJJRcIYOGyIm4euLVEDnR8DzDmrAWFwNpzhNCCCGEZIvIiVgwfUHauCRbZAh4tW0ewImI5UtYg7VXYmLnEitXYtOCUitYUqcFxcSSLvHdhRLaCRwT70es0sVh5tHqC8fXdAESdx/SKlxybSLaAxLaA1JScsXWuZ7L/mYfynLMKM3p2to5cZpRbPqRNxTBW5vr4A5GsKWuMwmT0M4scDDxrG5nnNQkDMMwyLUJuoqURk8IeTYxvpAuABQ7TWhKScL4wjKCkSiKHV1VM+V52royqSpbAxie35U0GZFmW+kDrf54sgbo3FbaIAmzv9mPMZ0L8wLAqAI7Klr0yZDt9W5MKHHE3xNjix2GWzdXtQUABhiepz33xFIn9rXqK3DkqIItNW4cN0xLrkwfnovt9R4EJX1VyobKdpw6Jh8Mw+CUMflY1830IUIGCkqukEHDL/lxwZsXpC2/FdUgFqrX0bQgQgghhPQ6v+THrKWzen9akOLHG6PvAGR/vLIkcVpQ6kogsaRK4porsaRK7PFiPHmS/FFB6ZxqY0pdmyWhcoVhmPjjY5UwDjMPRU2+nlXoSq7kWAXkWASoKtDgDsWnFgGATdR+rmkPwmURkW8zoc0vwROKJE0fEjgWFoGDJxTBnkYvRuRbsbPRD29YhsOcXD3ksgjoSEmuNHvDKEpIhADajkbtKcmVJk84KQkDdK0Lk6jFJ0Hk2KQ+FjtNaE5Z7yWqqGj1heO7GQHaujBNBuvCHGgLYFheV3KlPM+K2g59u+r2AMpzu9oNzzdO6lS0BDCqsCsJM67YgT1NPt3it3uatGlLsffMuGI7WvwRtKdU/1S2BqCoanzb6zKXGTaRM0wAbTrQjumdSZgpQ1zYUW+81gshAwklV8igIXIibpp6U9pviFROxEfKVQBLlSuEEEII6V0iJ2LhSQt7vXIlovJY0nIJwIpda64kRPipC61yrH63oNiHZi5lylAs6RITW6ckljwROeMKFy4lSRObKpSYXOE5FiY+thaLEG/jDkbii+4CWqWNTeRQ0xFErlXQtmb2hTu3dk5OmjgtPPY2aQmVMyYUod4jwZuShAG6FtPd3ejFL1//BmE5Gt/dKFG+vWunophGbwiFKcmVApvWp0TN3jAK7GLSmjfajkbJ12v1h6GoQIG9631S7DTpFt1VVRU17UGUJyRXSlxmNLj1FSSp7WLJldT3Qm17EENzLfH7IwtsnVOpkl/L3iYfxhU74vedZgFFdkG3VXVlix8j8m3x9w/DMJ3VMMntglIUuxu98QqXccUO7E6z4xIhAwklV8igIXACLhydfs0VhhWwUT0bYGnNFUIIIYT0LoETMH9S76+5IkPAe+5TAVaIf5DvbloQF0/AJE4LSq5ciSVHUhfDjVWuxB7btauQ8UeK1OlDqUkYm4mHrKhwmgWwLAOzwMITjOiSOjYTj5q2AHJtIvJtIlr82sK3qRUpTrOAfc0+5FpFjMy3otEnwWvQzmUR4AlG8MK6Srz2VQ0+39cKX1iG3ZychMm1imhLqc7oCESQZ0tOmOXbRd30oRZfWJ+Eset3NGr2amvOJI5NsdOMRk9yO29YhiQrSdcsdWk7LKWqSUmaxBbdTU2a1HYEMCSnq53Isyi0m1CXkrDRtsu2JR0b4jKhtiO5XWWrtq12ojGFdl1ypaLFD5vIx3dmGl/iQL07pJuqdTg2V3fgm+qOjK9DyOGg5AoZNHySD6e/djp8kvHWcILix2+5K4CI8XlCCCGEkGzxST5MenpS2rgkWwTFjw/H/TQpvmG7mRYUS5jwnEFyJeU2tdIhZbZIQtIk+SNF7DlSz6e2i1WrxG6tIg+/FI1X18TYTTxq2oPIsQpwmAVIsgJPMAKrKTlZYzfzqG4LIMcioMRlRrMv0plcMa5c2VqrTUX5tsYNX0hOWnAX0BbJTZ0W5AlFdNfLs5l0yRWtckWfXGkxqHBJTcJoOxolJ01aO6cZJfax1GmBNywnbdscikTR5pdQlpA0MQsc8mwimlISNjXtQQxJSMIAQFmOBXUpSZMGdwhlruQpU6VOUbeIr1ESZmShTbcNtJaEscXfJzlWES6LoLtejKKoWLqmAt/W6BflTdTml3DxU2tx2TOf635vqe5c/g3OfeJ/CEhyt+1q2gN4e3Ot4VbbhKQacMkVRVHwwQcf4PHHH0dNTY1hm2AwiDfffBPPPvss1q5de9htyMBi5s1YNGsRzLzZ8LzKmfFa9BcAZ3yeEEIIOVw7d+7Ek08+iVWrVqVt8/nnn+PZZ5/FG2+8gWBQX8rf0zZkYDDzZjw297G0cUm2SIqIB+puSIpvktZcSflMGJvawyckMOLTgVIqV1Ifq6QciK/NIiQnOWLPHqtAMVqbJfHxsdvYArh8SuWKWeDQ6peQYxHjC9y2+aWkhXQBbWHdBk/nGi5WEd6wrK3NYpRcCURQ1RbAWROKUNUWgDcsx7dhjrGbeHjDMuSogg+3NUBRVMNKmFyroNt9qNUvpalwMUiupCRhipwmtPgkRKJK/FiLTz/NyGnhYRG4pClErX4JDKNV3SQqtJvQ7OtqF1VUNLhDGJqTXGkyxCi54gklbWcNAKVOE2pS2h1oDcR3Hoq3c5lR705OFKXujgQYJ3ViPtzeiPvf3Y5fvPp1t0mO/26pwwkj8jBlqAsfbm9I225/sw9vb66FJxjBm1/Xpm0HAD99aRN+/spmvLOlvtt2/91Sj1kPfYyNB7rf9SgUieKFdZW65JmR1O3CSf83oJIrb7zxBsaOHYv7778ft99+O/bu3atr09DQgGOPPRb33Xcf1q5di+9+97u47rrrDrkNGXh4lscZ5WeAZ3njBqyAb5VZQLrzhBBCyCE6cOAAzjzzTFx88cV44IEH8Prrrxu2u+GGG3DhhRdi7dq1WLRoEaZOnYq6urpDbkMGDp7lMW/MvPRxSZZEwOEz34yk+Ka7aUGpU3sAJEwn0u5zncmN1I92qZ9rhTRrs8idHwr1FSzJyZB48iUluSKkVK7EkjI2ExdPqLiDEV2yxiywaHCHkGsV4TTz8IajWkVKSjLEInLwhWW0ByRMKnOi0ROCLyTDntLOZuLhD8t4f2sDbnxxI1bvboY3JOvWerGZePjCyRUQvlDyVtGANm3JG0pu1+qXkGdPToTEEiOehGkyrb4w8lOSMAzDIM8mJi2m2+aTkGsVk36/AFDgEJOmJLX6wpAVFcWu5GuW5ZhR266vXCk1qFzRtfPo2xklawynGeXokzAx731bjxtPG4W6jhB2N6avBPu6qgMnjc7HKaMLut3a+dNdzThlTAF+MGs4Vu9qTttuX7MPOxu8uO2ssXhjk/GX+oBW4fXoR7tgFjj85RP959NET3+6F/e9vQ33vrW123ZV7SEc+7uPcN/b3bdTVRXPrNqHtzd3nyQCtARdTbtxdRDJjgGVXMnJycFHH32EV199NW2bX//617BYLNiwYQNefPFFfPLJJ3jxxRfx9ttvH1IbMvB4w15M/+d0eMPGC2IJig+PCOcDEVowixBCSHaoqop77rkHO3bswKhRowzbvPvuu/j73/+OTz/9FC+++CI2bNgAm82GX/3qV4fUhgws3rAXQx8bmjYuyRZB8WHdhGuT4hsufW4lnlxJTIjEciYHq1zhUz6wxxbCZVPWZkn9xj3dtCCWSU6uWDp3BkqtXElcu4VlmXgSJvV6ZoHr3H1IhNMsICAp8EvR+K5FMRaBQ507BFUFjilzocEd0tZcMahc8YdlfFWpfVDfVNUOn8G0oFi7RL5wJGnXI6AruZJYfeEN6RfmNQscxJTtopt9EvLt+sWRc20C2gMJSRh/WFcxA2iVKy2JSZiABIeJ1yW8Ch3J7byhCHxhGSXO5KRJgU3QreHS5NEv9luaY0GjJwQ5oQqntj2I8pTpSKWu9JUrW2o6cOqYAkwrd3W7nso3NR2YNtSFY8tzup1CtLXWjWlDc3BceQ6+qUl/vfUVbZgxLBdzjynGxsp23S5KMZWtAdS0B/Hn7x+HtXtbDbeeBrS/F29ursV9FxyDT3Y2dbvGzCtfN2F8iQMvf1nV7RSnz/a04LGPduHO5d90Ww0TlKI494n/4YxHVh20auapT/fi0qfXHnRq1YFWP55etRehiPHrjVFVFfubfUfF1KoBlVw588wz0wYugDZl6N///jeuu+46mM3aPwBTp07FKaecgtdee63HbcjAZBEseP7s52ERLIbnVdaCv0gPAZzxeUIIIeRQjRgxAmeddZZu4c9Er732GmbNmoWpU6cCAEwmE66//nq88cYbkGW5x23IwGIRLFg+f3nauCRbZo4egluqfp0U33RXuRL7jJtY2aCmVLPEbtWU2pXU6/Ip7WNkRUm6L6YsbJsqPi2oMwmSmsRJ3frZ2pmESU2amAUO7qC2609sh6BWX1i/1ovIoaY9AIeJR6nLjPaAlkBITZrEKlIqWgMocZpR0x40XMPFbubhDyd/wPSHo7o1XBxmHlFFRTDhw6jP4HpA17owMW0+Cfk2k65drlWEO5iQNDGYjgR07lSUkAxp80vINWinrR/T1a7RE4JZYOFKqcLJswpJSZhQJApPSNZtZ13cmWxpTHjuRm8IxSnJGqOdmQDAF5ZR2RrAxFInJpQ4saPBeMvmSFRBRYsfE0qdGF1kx4HWQNppNVtr3ZgyxIXJQ1xo9IR1Oz3F7Kj34JgyJyaUOBBRFOxvMa6a+bbWjYklDkwqc8Ju5rEzTR9r2oOo7wjh+ycMQ3muFZsOtBu2A4DPK9y47cwxmFjqxKrdTWnbvftNHX50ykgcW56DFdsa07Zbsa0BuVYBJ43Kx5ub0le5NHpCeOLjPWjxSXhh3YG07QDgF69uxh8+2IWnV+3rtt2ytZU489HVB213oNWPc5/4H15ZX9Vtu6ii4ulVe/G/PemrjvrKoJofUVVVBZ/PhwkTJiQdnzBhAjZs2NDjNkbC4TDC4a7/8Twe7X8aRVGgpPwBOVyKokBV1axd72jDgsWM4hlgwRqPIcNhjzIZCliAxliH3n+ZozHMDI1fZgba+A2UfmbD9u3bceyxxyYdmzBhAgKBAA4cOIDRo0f3qE0qik36NxYsThxyIoD07/dsjO/F04fh4ukLoWgXBKCteZJ6zdj9WOKDTTjW9Y18Z186ky2pfWNSrhUvMElpF7tc/FjnfZ5N7lfsWWPHY9U0HJPcLpYcEVkGiqLAKibfT21nFViYOAYcqyWTBFZ/vdr2IPLtImwiC184AkCFVUiOIa0CC19YRtAbxkmj8rS1WUIR2E2cYbs2XwiXP/sF/vz9Y+ENRWBLbdfZb3dAgrmzr55QBCNtou735TLzcAek+HFPSILDzBm0E9Dm72rX4g0jzyro2uXbRGypccePt3rDyLPp2+VaebT5uq7X4A6hyGGCqqrxJJyiKMi18nAHIwhKEZh4Dk2eIDiWQY6FT7omy2gL9Na0+VHauTtQkyeEQnvya86xCNhS06Hrz+4GD/JtIvJtAsYU2vDBtoZ4m3p3EDkWERaRQ0Nn1UuhTUsCKaqK6jY/huUlrymjKCoOtPoxssAKi8CiwC6issWHXKt+V68d9R5ccfxQsAwwqsCGvU0+jEqZzgQA2+vcmFjqhKqqmFTmxLedFTSpdjZ4MKrABhPPxNudPq5A167FG0KDV8K0oS5MH5aDb6o68N1pZbp2ALC+sg2LLzwGNpHD+v2tuPqEcsN2q3c34eyJxSi0i/hkVzN+/J2Rhu0+2dmIKWVOXH3iMPx93QH87Ez93x5Am9q1rdaNx66YisdX7sEvzhpj2E5VVSz5335cN2s4lv5vP278zkhdMjbmiZV7EFUUPPjeDlw4tRQW0TgZ+5/NdXh85R6IHIM1vzxDN/2uOwf7NzfTv3V9mlz58MMPsX379m7b/OhHP4LLpX9zGvF6tXLInJycpOO5ubnxcz1pY+Shhx7C4sWLdcebm5sRCh18QaKeUBQFbrcbqqqCZQdUUVG/4A65MeOlGdh49Ua4zPr3TMhdj+fMZ6G5bjNU3tEHPezf6P2XORrDzND4ZWagjV93f3P7UkVFxUGnCZ966qk4/vjje3xNr9drGHfEzvW0TSqKTfo3r6RNV950zSY4ROO4Ixvjy8heFK6djuZTNsXjG7/Pi6am5G+7Y/e9Xu3b99aWrm99PZ3vsbaWFoRELv6NfyAQTLqO2vnBI3bM59MSeu6ODjRZ9FMcYu0CQW2dh472VrDhrg9CckR7TEdbK3wsg6is3fe4O9Bk6qpiUGXt54DXjaamKJjOD/mejjYIka7rKREt2RgJB9Hc3Awzx8KvKPB5OtDEdE05iYYDaPKGMbHYipDPjVBEgapGOq/f9byRoA+eQBjeUBRD7Aw2VgYQiaqIBLxIHN6QLwxfKIIVmyuwv8WPf6/fj3ZfENGQX/d7MPMsKusawYS0SqNWtx8jXZyunYUHqhtb0ZSjvdbmDh/yrbyunYmRUdvcET9e3dwOCxvVtWPkEFo8Xf050NgKGw9dO4T9aPGF4serGtphF5ikdoqigAn7wADYfaAexQ4Ru+p8yLXwSe+rGJeJxb7aZgy3yghIUfjCUbCSF01NXb8TPhpEU0dA158dB9pRbNdet40Jo7pVew3ekIw5z36D00fn4PcXjsa2Wi8KbALaWlsAAMUOAdsq6mGWk//fa/VHIEVVcJIXTU1+lDoEfFvZgCFm/ft3X5MXuZyEpqYmFNs4bDvQhGML9ImBb6taceJwJ5qamlBmY7G9ugVNo/QVa1/va0C5S0BTUxOGOVl8c6AFTU1OXbsNB9wosvEIetowzMHgP1tb9L8nAAEpiqrWAAoFCeU2Ff+ubjNsBwCbD7ThlFOHoMgOPHKgDY2NjYYVl2t21mNKiRljXCq21rpRUVMPm0GSY8WWZkwptePYAha1HUFs3lONMpe+sqqiNYg2fxjXTs/F8o3VWLutEhOK9QmqqKLi4x0N+ONFY/DbDyrw0eb9OGmEcQ5g+foK3DSrFCt2tuGtDXtxzoR8w3ZGDvZvbqaxSZ8mV1paWlBZWdltm0ik5/udW61aZjJ1UDweT/xcT9oYufvuu7Fw4cKk9uXl5SgsLITTqf+f4nAoigKGYVBYWEgBzGHIlXPx7sXvYkTZCAi8PoNpywnit1V/xe9LRgCscSb0aEbvv8zRGGaGxi8zA238YlNz+5tgMHjQ2GTKlCmHdE2r1WoYd8TO9bRNKopN+rd8JR+fX/85RhSMAJcm7sjK+Cr5wNzPUegYEY9vclxOFBUVJTWL3bdY3Un3AcBh15IfxUVFsIhcvELBYrEktWM711iJHSto1doV5OehqEj/QSjWzmbVPvAWFRYmTVnhuF0AgLKSYq2dpQaAG0UF+Sgq6vpQ7LQ3AGhHSVE+iopc4PmdAIAhpUVJ65XkOtsBNCEvR3v9Is/CH1FQWlSIooSFVgtyQwBqYDGJGDmkBMAWhGUVJUUFKEqoTCgLCogo1fCEozhmWBH+takJDMOgpLAg6fXyNglRdSuqOv8XrvMpCEcZDCnKR1FRYdKYOC0CRKsTRUVa8lRSK1BakKP7feU7q6AKXeOvMHUoztP/XsvyO9DoDcePy2wTSvIEXbshhVGEdnYktHOjJNeuaxfmA3CHdqGgoBAsy0CtDKHAaU1qF3vf5lhroJocKCpyIdKkoMRl0V0PAIpzDkDhtXMVLX6IPIsx5aVJH+5HeDl4I426x4f2BDAkT+vnBMWC1kAFioqKsKFz9571VV648vIRrJNRnmeLP74kxwaJ0/enocaNPKuAYWUlAIDRxfXwyPrxikQVtAdlTBheiqIcC8aVtaM1FDF8fW2h3ZhQXoiioiKMHeLH+op2w3b1gXpMHpaPoqIiTCyP4rOKCsN2zXv8GFOojfmxkgnPrqs3bLe5ugN5NhETRw5BTl4Q1f/Zi5y8gvg0u8TXUt0RxsxxQ1HkMCEY2QHF7ESpS58AqnLvxTlTyzF5VClyrbvgVi0YWZSja1fja8L0kQUYMbQU44v3oSEs4FiDPn5cWYXpw/IwfEgpZo6sxX4vg9Om6Nttq3MjqjKYPWUETt3nx442BRedoG8XiSr4pm4z/u+CKWAECxqDiuHYpHOwf3MzjU36NLly1VVX4aqrrsra9YYNGwaTyYR9+/Zhzpw58eP79u3D2LFje9zGiMlkgsmkz8axLJvVYINhmKxf82gh8AIm5E+AwAuG48dyAmqUUWANEi9EQ++/zNEYZobGLzMDafz6ax+POeYYPP7441m95rhx47BvX/Jc83379kEQBIwYMaLHbVJRbNK/sSyLKSUHT8RlPL4sC+QmPw/Pcbrrxe7H1pRMOt/5IZfnk/sR65v+KdnO9rGtk437HzsW+xAtpPRLTWknxKb/CMntBK5rwVuWZeOvwSomx3yxaQTmzsfHPrvHHhdjNcUWzmVhNfHgWAZRRYU5pZ1F5NHqlxBVVAzPt8ETkmERON31HJ3TEvY2+1GeZ0GLT4JfisJh0cekDjMPn6QgGFFgFTn4JBlOi6hr5zQL8EnR+HGfJBtez24WUNEaiB8PSlEMzbXq2uVYTfCG5PjxjqCMPJv+efMdZkQVFT4pihyrCHdIRo5V345hGBTaTWgLSGBZFq2BCAocJsP3Qb7NhPZABCzLotknochhAsclJxzz7Sa0+yXd45u8YZS4LGBZFmW5VvjCUfilKLY3eHHlzHK8800dDrQFUe8JoTTHEn98ced21izLoqo1gLAcxdhiBxo84aR2RU4zWg2et9WjVUEVx547x4L/7fEZvj5tNyVtzMvzbHhjU12admHMGl0AlmUxLN+GmvaAYbuajhCG5pjBsiyGF9jR7A0jLKu6aTIVLQGMLrSDZVmUuqwQOBZ17hBGFdqT2lU1+8FzDMrzbGBZBsPzbdjbHMCQ3OQKEkVRsbfJh/GlTrAsi7HFduxt8mP68DxdH/c0+XDlzGFgWRbjS5zY0+THeQavZW+zH+NLHGBZFhNLndjTZDyGe5r8mFjqgCjwmDLUhU92Nhm229fgA8MwOKbMhUlDXN2ud5ZOd//mZvp3blD9lRQEAeeddx5efvnl+HypqqoqrFq1ChdffHGP25CByRP2oPS5UnjCxotICVEv/mWfBUSMzxNCCCG94bvf/S4+++wzVFVpi/QpioJ//vOfOPfccyGKYo/bkIHFE/aAWcykjUuyJuIBXmaS4ptjy9NPqY8a7NgRO8SlfFA52O4esfapuwWlw6R88lBSrh/b2jl1QdvYGg2xhW1jryF1C+jYArextVdiZ1O/ybd0thM4BgzDxNc/Sb2eiWchyQpYBijLsUBWVHjDsu56Jp6DwDGobgtgfLETzb4wfGEZNlH/PbZV5OELyZj02xV4ZUO1tkCuyagdh5CUvPBt6m5GgJYoCiS2C0cN2zktfNLWzu5gRLdILQDYRG2notjisu6AtkCwkRyrgI7OnYo8wQhy0qx9kWcT0dp5vSZvGEUOfVI4xyrAE5KhKCokWYnvQNPoCaG4c60Wp5mHReDQ6AmhssWPkQU2jC6yY1+TH+1+CQUJW1UX2k3xXXG+/9cvMOdPnyGqqGhwB5O2i863ifGFeQOSjAOtfgDattIFdlM8sVdgN6E5YQHfmFAkivZABCWd1xySY0Gd23jXo6aEhXzLc63xhZR17TxhFNqFeP+sIodqgy2UGzyh+POyLIPyXAuq2vTtKlsDGJFviy9IPSzPimqDdvWeEMJyFCPytaTLqEI79rf4DV/L7kYfxhVr1WVjiuzY02Q8nWZvkw9jirRkz/hiB3Y1GLfb0+TDmM5qtXHdtNvX7MPoQu21HE5ipbcNqAVtt2/fjg8//BBut1bO+Prrr2Pz5s046aSTcNJJJwEAfv/73+Pkk0/GueeeixNPPBEvv/wyTjvttKQKmZ60IQOPXbRj49UbYRfthucV3oaf+N7Cs7zxeUIIIeRQRSIRPPXUUwCAhoYGfPPNN3j88cdRUFCAa665BgBw5ZVX4h//+AfOOOMMXHXVVdiwYQO2b9+OtWvXxq/TkzZkYLGLdlTfXp02Lska3g5cXK3dAqh8+PxumxttoBLfijklqXGwjVNjSZDUL3tjlSAxsc9AB9uyOba1c+w2tR+pu/6kfrjiU5IwsfOpj4ud5zs7Hk/epFRTxNrZRD4pEZF6vVjbBk8IZ00swtq9LVChxnc3SmQWWGyp7QAArNunbdtrNUiGWEQuJWlinFyxiVzSNtD+sBzfqjpR4jbQDMNoyR+D6zEMA6eZj3/odwcjhskQQJviFEvYeNIkawAgzy5iZ732YbkjICHXqk8Yx/ril2Rc//cNkBUVb/70FDR6wjh5TEG8b7mdCZ3K1gAuPm4IyvOsqGoLwB2MoCRhmkuhw4S9TT40eUOo7Vzsdm+TD22BSNLUtAK7CS2duwXd9/Y2vL6xBt/8di6aEpI6WjsRLQk7HsWSU42eEASOQX7nNfPtIjoCEchRRfc+bvJ0JZZyrAIEjkGLN6z7vTZ6Q5hR5oq/5hKXGY2eUDyZEW+XkFwBgPI8K2ra9YkdLUHV1a7UZUaDW78uV4M7iCKHOZ48HJJjwe5GfZLDH5bhDkYwtHM77aG5Fny8w3inov3N/nhyZVi+FdUG/QOAPY0+nDxaWztldKEd9e4QQhH9Nur7m/26ypz+ZEBVrvh8PlRWVqK9vR0///nPwfM8Kisr0dHREW8zduxYbN26FfPmzUM4HMaiRYvwwQcfJJWe9aQNGXgYMHCIDjAwzmKyDIuAYgXSnCeEEEIOlaqqqKysRGVlJS6++GLMmDEDlZWVqK3t2uqSZVm89957uP/++yFJEubMmYOtW7di/Pjxh9SGDCwMGDhNzrRxSTafCYIT6eKbN396Mt786cnx+4pBdiVWoZKarDhI4UrX1s0pj0u3E3Rqu9SuxM4KKReIRrWGsWRHukqZruRKcuVK6ofcWL+FlOO6ihQhluxhwLFMQgWNUXKFRUcgghH5NgQjUYQiCkTOOAkT+1a+uj2AsKwYXs8icMlbNqdJrljF5HZ+ybid0yJAiioIy1rlfkAyTq4AgN2kVdcAQEcgApdBMgTQtov2dLbzhCJpd23Jt4lJlTAug0qYWJVPi0/Chsp2fF3VAX9YRntAQl7C87usWvKi0RNCmcuCIoeWHPEE5aTkjssqwh2MYH+zH0NyLDh+eC621bl1SaB8uxhPrqzapS0Gu/FAG5q94aRtpQscJjT7wlBVFa9uqMK0xR+issWPRk8YhXZTvCoklrhp76zoiVXgBCQZ3rAcT3IwDJNU0ZOo0ROKV64AQIHNhFaDqpl6d3LSpMBu3C61WqjEZTasrmn0hJMSSiVOM+o7DJIwnhBEno1XNJXlWFBvkKxRFBWNnhCG5GhJmCE5ls4pTlFd29qOIMo7d3bKt4kQeTaeFEu0v8V4x6b+YkBVrpxwwgk44YQTDtquuLg4aYG3w21DBhav5MW4v41D+y/bkWPJ0Z3nFR9ecM4FZHdnEEIIIYRkRhTFHq3RwnHcQdea60kbMnB4JS9cD7vg/rUbTlMvxh2yF1juAuYbxzfHDctNup9aLQKkT6KoB6ld4eKVK6nJFQaJdS+xZEhqTiTd/dRkSHwaEG98nXh/Oh9nVDGSiOdiyZXY9ZLvx+gqZWLHBf0XsrHETOIioanJmtg1q9sDsAgc2vwSwnLUsJ1V5JKqC/xpKk2sIp9UuZKuIsVh1o55ghGYBQ6+cNRw2hIA2M08vJ3X7AhKaaf7OMx8QuWKjFEFxhUFudauJEK66Ugcy8AssNhe54HdxINltO1+fWE53ncAcFl4tAckdAQk5FgFFDpM2FnvhTsYgTOhndPMwxuSUdE5fajIYUJtexCeYAQjEz6cF9hNaPNLcAciaPFJOHNCEbbWesCxTNL1CuwmSLICb1jG+1sbAACrdzejLMeCnITkj4nnYDfxaPNLqGkP4JKnP8drN81CkcMEgWOStnzOt5nQ2pnYURRVW0BYVbWETUJ1TWICKFGjJ5Q8xSlNu2ZvCEUJSZMylwVf7m8zvF5hQkKpNCddEiaEEqc5/v9NWY4FjZ4QIlElKWHZFpAgKyoKOxM7hXZtDBrcIQzPT06QJFYKsSyDITkW1LYHMTqlSqXeHcLp45IXie5PBlTlCiHdcYgO7P7R7vTbHXIOXNWxAqBtmAkhhBDSyxyiA+5fu9PGJVnDO7TESg/jG+NpQcZJlNSkS2oVTjy5whgf73ocDNulrUDhUtslt0+tgIk/b3wakJb8kNNkjYQ0049S78euE7tK7GmNK1K0Y7m2rg/PRu3MAoe6jhBGFdrQEYikrVwxd1auPL5yNz7d2YSgwRQJALCZkqcPpUvCCBwLkWPjbQNhGTaTcdV+YuWKJ5ic3EjkNAvwhLTkijsYgdNi3C7fLqLNr33o7whGkGMxroSxm3hsr3ejPM+KkYV2VLT44Q3JsCc8f45FRHV7EIoK5NpEFNpNaPaG4QklJ22cFq1vNe0BlOdZUNa5Foo7mFw54+icAlXR6ke+TcTkIS7UtAfgCUXgSNiJymHiwTBacmpXgxcnjszD1lqtEib1dWsVKWF8sE1Lwny4rQFtndOhEqvDtHGR0OaXMPY37+PtzbXwhWWEZQW5Vj6pnVGFS7M3HE9cAJ0VLn59ckWbjpSchGkPGFXMJFeuFDnMaPYaJ3VKEipmihwmKCp0iZ1mbxgOMx9/37Isg2KnGXUp1TCRqIJWv5RUhVOWY0adQeVKS8pr7m8ouUIGDRUqvJI3bYDAMCqsjB8Hn0FMCCGEEJIZFSo8Yc9Bqz+y8UzaYrY9e57URWQPcuVuxdcsSU2upNzvWnMFhsf11zVOwqRbuyX1cUaVIEbtUqcfpYpVsqT2J7XCJfE5LQkJECFN5YovLGNUoR3uYASq2pXESWQVtQ/9j6/cg0c/2gUpTRImtXIlkGZBW0Bb7yU2hcifZpoRANhNQnzNlWAkCmuaChenhYc7VrkSiiRti50op3MqDxCrXDG+ns3EY3+zHyVOE4odJjR5tYWBE6/rtPA40OqHyLGwiRycFgHecKQzuZPQzizAE5TREYgg1yqiuHOdkdQkjN3EIxLVdskpz7Oi1GVGvTukLTSckNRhWQZ2E48Gdwj1bm1tnZr2oC4JA2hJnza/hN0NXkwe4sS3tW74QvokVWxa0KpdTYgqKt7dUg9vZ1LLlrBuTqzCRVFUnPP4Z/jzx3u0MU+pAipwiGjxHnxaUOJCxEntPKGkJEyuVUBYVhCUkqfxNLjDSZUwAsfCaebR7k++ptHixfk2fWKn2RsGwyC+bg2gVbn0JKHU31ByhQwaPsmHGS/NgE/yGZ7nFT+WuC4FZOPzhBBCCCHZ4pN8KP9Tedq4JGtkH/BWeY/jG6NpQVHFuO3B8jCxhWxTcx2p04RiUita0idJjBeuTTe9KPX6sYqRdKmT2PV5rmfXi93GKneMdimJJVdMAhdPIhlWuHROWYotBpp4LJFFZOPf3PvDUSiq8VoviWuuqKoKv2S8oK3WtmtnIb+UPmniSFjQNiDJsKapcHFZtAQG0P2CtrlWEeHOHYC03YeMK1dsIo+6jiByrSLy7SJq24OIKmpSEsgicKjvCCHHKoBhGDg6q2w8weTkjtPMwxOKoKNztyOXRYA72JmESWgXq4qpbgsg3yaipDMJoyVXkl+Pw8Rjf4sfIs9qFS4dAXiCsi6p5DRrfdrd6MOZE4pR0x7U1sxJaefqXBB4d6MPowpt2Frr1ip1TFzS/yu5VgFt/gi213uws8GLNzfXQo4q8EvRpOfWpl9p1SMLX9uMu9/YAkBLaCWOucsioiOoT1x0BCPIS6i8iv0+U9t2BKSkRAigJZQ6UpImqcmaWLu2lKRJY+fOTImVY0btglIU3rCMQjslVwjpdU6TE/U31aed1xzlnLi4fQ2tt0IIIYSQXuc0OaH+Vu3d9VYALa65Su1xfGNUuRKU9NvB9kRq8iFGv5aKcVIiNQcT61pqZUisXexo6vPFxJIl8SRMmn7H2qVOA0oV62882dNNoUss8WJOSIAYVbjEqlQSP5ymW9A2tvNLbGqG8dosWuWFJCuIRFUoKgynDwHaDkShSFRLwnRbucLHKygC4WjaZE3itCBPSE67oG1s7ZKOQCTtmiuAVlnT4AnBZRWQZxPj2yInTgsyixzqPcH4jkP2zkRQKKIk9TO2RXV7QEKOVYwnVzxBOWkaj4nnIHIsatqDcFkE5FpFdAQj8IUiukoTh1lAVWsAeVZtOlKrT4I3pJ8WZOusOmr2hnH88Fw0eELoCER0W27bTVq7vU1enDWhCA2eENoDki6pYzcL8Idl7G3yYUiOBZUt/vj20YnP7TBrFUcBScYbm2rxr/XViCqqbt2aHKuAUERLdj23eh+uXbYegJYgS3xuPk1FitHixTlWEW0pyZVWv4R8e3ISJs8qoj0ladLqS95GO127Fl8YPMsY7jbVX1ByhQwaUSWKXW27EFX0K1ADAIMohrL7gTTnCSGEEEKyJapEsa1pW9q4JGuUKNCxrcfxzXlTSjG2KHmRyBNH5cNsUD2hHqR0JZZQSJfs6GrXM7GqGn0SJjlpku754hUmB5k+FEt6CGz3SZjU63b3MuXOvicmNowqXGJVKomL4hpVuAgci7CsQOCYeBWJUXLF0plQCEaiiHSWIBldL9a3oBSFFFUgK2raihSbiYcvHIGqqghEorAK6StcPCGtXWrlSCKeY+Ew8+gISugISoa7BQFaoqPRE0aORUSezYQDbdrCv4mLpFoF7cO+ufN12008PEEZUlRJqgCKJbkaPSHkWATkWAS4gzJCsn7tGruZR21HAK6ECpfUaUGxdgfaAsizicixighIUbT4wrrXbTPxaPaFIUUVTCh1IKqoqGoL6K5n60yuNHhCOLY8FyzDYE+TL20SZk+TF6eMyQfPstjV6AXPMknT0BIrZpxmHiae7Vy3JpKUSIstUOwORvDX/+3H6t3NqHcHDV9zjlWrcvlwWwMufmotwnLUsFon1yrEd0iK8RokYXJtoq6dNqUsZcqUwTozzb4w8u1i2sq4/oCSK2TQ8Ef8uOCtC+CP+A3P80oAf3D+BIganyeEEEIIyRZ/xI9ZS2eljUuyJuoHPpzV4/jmlDEF+Gjh6bpjO+8/V9f2YOuzxHIHqTmEQ1jWJUk0zQPZlOdJd33dQrdpp/skL2hrtMhvonilC5v+o5Pcmdgw8axhUiXG3Fm5kljhYtQ+llCIbU8LGCdNYscqW/zY3aht8ZxuzZnYFKLYGhqWdBUuAoegpECKKogqajyBo2snasmaYCQKWVHTLnwLdK3z0V3lSiw54rLwsJs4NHpCusV5LaK2Zk0sMWY385A6x96csHZNLIHS6Akj1xarXJEQMlgY2GbiUNsRRI5FhNPMQ5IVtPjC+uSKiUdVq78zuaK9BuOkCYeatiA4lkGBzQSLoF0/tVLIbtLWy2n3R5Bv16ph9jZ6DZ/XH5ZR7w5hSI4VJS4zdjd44bQISe8du5mHX4qissWPscUODMuzYm+TD5Fo8u+G51g4TDxqO4Jo8UlwWQTsqPfAa7R+TOfv7bWvarC5ugMbK9s7K1dSkiFWER1+CfuaffjuX9agyRvS1plJec1aEiY5aWI0BSvPql+bpbupZ/0FJVfIoOE0ObHn+j3dTAty4PvtK2haECGEEEJ6ndPkhOduz5GZFnSFp1fim9Skg25tlc776Xbv6Xqg8eHUJInRejDa8yZPK0q3SLC+csX4eeML2nYmJg72RTifcl0jscqVg001ilXNpJu609VOu07idAmja8eu992n1uKSpz8HkD65YkmoXOmunVlgEZKjCIS1JEy6XYWsgrZTkT/erpvkikVEoyeEUERJu7VzLDliNfGwiNrUpNQpU7GEUCzhlZiwSKpc6WwXWxDXYdamT4Ui+oWBzTyHRncYOVYh/iG/0ROGJaVixyywqHOHkGcTIXAs7CZeayemJmt4VLcHkGsVwLIMcqwCatoDSdObYn33hWV0dO4klGMVtCSMblqQtjW2OxBBrk1AidOMXQZJmFjfD7Rq68cUO83Y1+yLXyP1mjvqtW2vZ47IRVVrwLByxWkR4A1FsKvRA7PAYk+TTzd9KNbOE4rgjU01+KbGjZXbmwyv57Jqa7P4wjKufH4ddtR7OqueUtvpF931dTOVrb+g5AoZNGRFxlcNX0FWjOcNM6qMcfxWIM15QgghhJBskRUZ66rXpY1LskaRgeZ1vRLfHLwCxXgrZn0r4/Opl09XKXOw68fEF6hF8m2qWOIilqvortIk8fm760ck3arAKWJVM0brrCQSeX3ywAjDMEkVLSyTftqUWeAQkGRIsgKG0e+ClNguHIki0LlQrtlgNyNAW+8lVgnDsYzhGjMxOVZtvRIAaddmiU+Z4llYO5MjukRI5/HY7zBxp6XEnxP7YxbYpARIamLLJLCQogpcFgEiz2pJqEhUl3wy8Rza/FI8ieSyCGjyhnS7PdlNPGrau9aFcVkE1LQHdVUcNhOPjkAEfimKXKsAp9m4XWxr7I7Oyg2XVUBtu74SxipwYBjgQKsf+XYRRU4T9jX5IHKsro8WUVvTJ98uotBhRoMnDJ+kT4ZYBA7ekIy6jhBOHVOIA60BbX2dlOSKVdQSbbsatGTOniYvvAbJEFtnu493NOKL/W14a3MtvGGDKVid1TqqquKnL23E//Y0wxcy3ma8P6HkChk0gpEgblx5I4IR/Z7oAMCpIfzKdh8QNT5PCCGEEJItwUgQ85fPTxuXZE00CKyZ3yvxzcHWXOnp43qYG0lbuZJu4dtUscRFuulKMalJmINVrsSSPt2uuRLt2VjF8iAHq1yJTUEyWgsnVWISoLttqE0CG1/8VuTST18yCyxCEQVBSYZF4NKucWEROagq0B6QYBG4bpNULouAA20B2MTkNVSS+sd3JUxii9Omvp7YDkexxFBiIik1qZSYgDGaMhS/H5uq1Xk89typiR0TzyKqqPGkjVXkEImqunZWUUvCxKawOC0Cmr3h+DoxMTYTh9rORYtzrCKcFr6zHatrF4xE0e7XFud1mgU0+8K6aV0sy8AqcKhqCyDfZkK+TTSctgRoSZPa9iDybCKKHCZUtvihqtBVpFhFDpWtfiiqiuNH5KLeHeysXNEnigJSFJWtfpw2rhDVbQFtrReD9Wj8kow9jZ1JmEaf4XSkWLudDV68920Dlq6p0C3M2x9RcoUMGg6TA5uu2QSHyWF4Pso58KOONwDB+DwhhBBCSLY4TA7ULKxJG5dkjeAALqnplfhmylBXt+djn2UPujZLmuOpSZh0xR+pH+7TPV3q9J10z8sxyUmVg1WuxKb8dFe5kjhVqbsFfmPX6Om0oNSKAyNJCYZu+ihyWoWGFFXSLnob61sook33SbdTENCVhGjzSwd9PTlWAQda/d2umRHfzprvqjRJTa7Ekk0Hm34FdI21yLNJ7yFd0iS+yHAsoWX83LH7scSXJU0SJtYudj5WiZI65iaegzcswyZyEHkWTouAVr+kaxdL/jR6/j979x0eRbX+Afy7Pcmmk4SWUKT3HoogVUCaIgKKYkURy1XBBvq7oAjoRbEjKlyliRBFLlhAeg299xIhCYGQXjbJ1vP7Y3Ym20t2N7vZvJ/n4Zlk9syZMyfD7pl3T6lATBg3xCmnRG1zCW+FTIJbxqFL4QoZbhVZz1sDcH+7m4XliA2TIy6CmzwYsO4pFSqXIquwApEhMsSFK1BQpkGZRm+VZ5hcApVah9xSNbokReNmYYXNYTxKuRRlaj3+yVUhuUksMgu45axt9Vyp0Bpw8XYxAG5uG26ZagquEFItdAYddmbsdDgsqIvsEA0LIoQQQojP6Qw6bLm6pXqGBWVt8Xr75uq8+/BknyYO0/APygqLB2vL2Ie9oIRlOr3BdnTF8mh7wRxhzhWYB1ksCcN8XFgFiCuX7VWMbOUJOA5w8AGgEJnYYY8efliQKz1XTHv8qHX2hyfJJCJodAau54qjHi5SCSp0eqh1BodBE27yXm7J3VC543JGh8pxI6/M7pAgoDJoYRpcsezlwgdVHA1B4vEpLIMflnnywQuFxDzIYisYYnpuvueI5f0vl1ReB2A/WMO/HmYMGPBDbSzz48uj0uiFSXeLyrU2A28KKbecdYxShvAQKfJUapv3UKhcKvRciVBIka/iljm2DAyGySXIKixHdBi34lJhmRYVOr3N3jolFToUlmnRLCEcRWUalFTorOZSCVNIoNJwQZjOjaKRVVhhHBZk3XMFAM7dLEaHhlG4VViBEhoWREj1qdBVYE7qHFToKmy+LmFqPBP2FaC3/TohhBBCiLdU6Cow/e/pdtslXqOvAE5M93r7Rupg2AivTrgCV+fd58K8IK6d08WRNXbxAQ6Rsx4pxt3C0BInBdQbnA8LMn3JUTr+4VUuFTtcAtqdnis6k6CUzsHSR3KpGFq9AVq94+AKPyzIWTqRiBuGUqDS2F15iBcdJsOdErXDniv830MhEwvLP1sGOPg0jlZuqiwgt3E2v40QTJGa17llzxCF0GvGGFyxN3zIIugiBFcsgzUyO0EYO0EdgJuIlg9E2LoufuhSqEyKCGPvD1vDsEJlXBAmVilHmFyCApXWZn5hcq4nTFSoDDFKbngTY9Y9r8Lk3OpDAHBXnBJF5VqUafTWk/0ae64UlGnQLF6JUrUOJRVaq+AcP+fOjfwydEqKQrlWj8yCMqv5aAINBVdI0AiXh2P3hN0Il4fbfN0gUeKFwhWAzPbrhBBCCCHeEi4Px7kXztltl3iNLBwYea5a2je2ggGuDM+wG0SwiAMYnK2J7Ow8FsOB7J2XD36IhWFErgVXHCUzzcPR8CF+XhiJSOQwncyi94Mr5XNGJuGCK2onPVdCjcOCNDqD0x4ioXIp8sucB1f4oIqj4IrEeC6FVCLMO2IZQxGCKy70XBGOcXKP8sEMvk745NZBDothQXYm3eWP43vE8IEDyyCHEMQRgivm5eBJLIY08b03bAdX+ICOWFghyNbfmp+7JkwuRbiCW87asscM/zq/fHZ0mBx5Km55ZMvAE7+cdZhcgvgIBVQaPcrUOqtzK409V/JVGjSuowQA5JaqrYKIYrEIYcZJdxtGhyFEJkZ6fhn1XCGkumj1Wmy6tglavdbm6yKmxd2ynYDB9uuEEEIIId6i1WuRci7FbrvEawxaID0lsNo3Vks4u/YgbC9IYLnX3pwrQlDFyVLMfFDD6RLSFuWyt/qQ6TkB6zliTJmuUOQouGK5XLQjWhe7/MglYqw6mI5J3x9ymC835wo3N4uz84fJJcgvdWXOFW7lHMfDgip79fABCss6EoYFudBzxdWgEx/w4gMBpnO1mOJ/5wNOlj1deFZzuEgdDwuSC0EW2+ksyxAis31e02NDZRKhR5mt+XVCZJVBGKWDdPy8OuEKKZRyk2WvbazipDcwhMmlQgBNpbEePhQik8DAgNxSDepHhUAuESO3RGMzUBQqk+B2UblxnhkZckutl70ONBRcIUFDo9fg29PfQqPX2HxdzLQYG7YOMNh+nRBCCCHEWzR6DRYdXGS3XeI1Bg1wYVFAt2/sxRCs5lxxcXUiZnWk1RkttrbLw/cIcLYqkis9V8RmPVfsp+Mf5MUiJ/nZWA3HU3KpGOXG5ZUdTWgrl4qh0emdDgsCuIdvbs4V58OCACc9V0zmXJHY6VXEBzZc6bni6gpOwnAkY7CCvx1sLcXMndu8B4vV8CGLniuWy0dX5sf9zl9JiMUwIXtllVv0dLGVZ4jM/opLpunMesLYyE9ukp9p7yR7vXpC5WKEyCSVvYEkEpvpAG5elchQbjlve/PHFJRpER0mQ4RCijyVxqVgoz8FdukIcYNSrsTvY3+HUq60+TqTKDG98BtAavt1QgghhBBvUcqVSH0m1W67xGukSmBYakC1bywfae31+LAMatgbFmSZzm7PFZHtrSU+EMI/VDt7BNcJPVfsM42BOAqImPZccRRckViU0RtMH0wdBU1kEhF0BmYcFuT4cTFULkG+ynpZYEvRLgwLqpyAViIELiwvn9/vykO2zs4EyZbEJj1mgMpgmv1hQXyvIpHZfp7Qo0VmXlariXX54IrxGu3NuWJKJBKZzAljIyBhsuIRXw5b+fFlDzHp4WLrXjMtu2kAzTLoxQdRQoVAkuPeP/y5KyfxtREAMuYVEcKtkMSY47oJBIFdOkLcoNFrsPrCarvfEIkMGgwL+R3w9TdIhBBCCKn1NHoNlh5f6vueK3oNcHVpQLdvXI0P2Ou5Yrnbs34r1j1XXOXqakGupBOLHA8zcjS0qKpMAxKO6pCfm0WrNzid8yVMLnFpKeYoY88VyyV3TfGrQMmlYqFniuXQLf6h3ZW/netT+JgHU+ytDlU5LIifD0ZkdhyPL1uI0NPF9hCvyvljzHuiOJ2AV2o/nemcK/z5bAXSZJLKIAx/XlvVZRpAcjT/jkxqvoKSZcCKZ1pXIVIxwhS2560xTRsiEwuT+DrrSeVvgV06Qtyg1WuxKc3+nCtiaNFPvhNgATQmmRBCCCFBSavXIuV8Ncy5woxzrgRw+2ZichJeHtTcar/lw5yrE9rq9PZ6JLg2QS0f1OCHobg4Gskx0zlXHJy+skeG4zLyD+gezvFrce7Kc6qNw4NsppOIodW72HNFxg3XcDrnSig350pcuMJuGpFJ4ElqZ1iQRAhIeC/4JPRUMT6421vq23IyXT7wYxkIE4Ir/OpCdgINwrVYrIDkanDFVi8O/r4KNe25YiM/qUmPFCFYZOOeFJaVlkkc/r+SmwRrTPOyvBbTPKQSscmkvvZ74SikEiEoR8OCCKkmSrkSP4/82W73W4NEiXeKPg6obrOEEEIICU5KuRJbHttSPcOCBm2plvaNqxPTWg7jSYgIwYyhrWykM//d0TLCpiq0toMrlqsF2cM/C/PPac7mcHHlsk2TOF4tqDJPR+flH9ydzy/jOtPeHo4me+V7KJRr9S6sFiRBSYXO6bAguVSM5U8nY0SH+vbLxy+lDZHJvWZeTv7h2pUJbV3Fx+r4QIC9vx9ff/y5+d8t0wvDzvi5WfgyW9Qlf72WQRZnvTOEVY1s/G34/3sKs3lPbPUKqRwWxNeprQmeZQ7ysFUmfugQf6s5uxbHvXAqe644ShdIArt0hLhBrVNjyeklUOvUNl8XMzUeDF0L6G2/TgghhBDiLWqdGotSF9ltl3iNXs1NaFsN7ZuPxnXEuyPb+Cz/ER3qo2F0qNV+yzBA/5bxiI9w1APCfGupcmiOu8OC7L/mal58bxmRSOSwxwwfO/BqzxWTh3FHgSw+eKBS64WVbOwJk5kvN+xI/5bxrs1HY5KVZTH5a3Bl2JSrf14+IGE53McS37NEKIOdVaf4a+Sr21nPFcuhTs6GPAnBFRsXyPe6UUgrAxK28jPvucL3ErI+l9TOvDJWZRICSOYBKmdzBglzuticnLeyV4ujXjiBJLBLR4gb9EyPY9nHoGe2uzmKYUBr2TnAzuuEEEIIId6iZ3qkZqbabZd4DdMDuanV0r7p2yIOU/rd5bxILuZn2SvjxYHNsf/tQU6P++ihjjjyzhCr/ZVzrTgZFsQ/9BofHF0dFuQogGL6mqP8+ACCWOS4nqTCkCXf9FzR2h1aVTnRqqs9VwA47bniCpHQc6WSZQ8bqRs9VlwNeJkORzLdWqocksQHLYzHi22nE1sMI7Ls/SEy6aljepyz4IrQ08RGOpFJbxhHQ2iEFZJcHO5jK/hhlk7o9cOXw7w89vDpbQ0L4ilklcOHaFgQIdUkTBaG7+/9HmGyMJuvG8RhmFf0PiC1/TohhBBCiLeEycKQMj7FbrvEa6RhQL8Uat+YcPZMLbLoueJq+MJRvq4O3zE7t6OeK8Zz2Zv/oypMezo4WqaYf/BWqXVOh4PwwRVnc664VD4b86xYXn9lrxDngRNX5wQ2XcHJ9ByWhFWCLIInlmWx7BllOYzIEn/v2Ft+2rq89nte8Xskosolm23dQnIbw2xsDRWTCemcD/sy5WjYmVl5+TqyEcTjV3uiniu1SXk5t62o4P7x+9TGrpllZZU/q1SARlP5s9Y48VhpKaDTAQBEJj+juBjQ6yt/Nhi4/x3FxdzWYOB+Brh0/M86HVBSUvlzaSn3s1bLnRfgysH/rFZz5eR/9vI1oaSkWq5JXVaCj1MXcN1vbVzTnaIijDJ8DaiKasw1BePfia6JrsnRNYlKSoLumoLx7+S1ayIkiKl1aszZNad6hgWdnlMjhz17MW4AwPU5YXjurhbkiOm1OAq0iCy2dtO5eS2uML1eR8sU870DyjR6pw+zlcOCvBBcsdFzxTK4YjlfjiOu1qHlfWDvOKueK3aGl1kGPxwFQwDuI9M0nbNSOwrW8KcQm/RcsRWgk9rIw2Zwxc4y0pb4fPhTuRpccbT6Ex8AVEjFDpeVDiSBXbqa4I03uO2sWdw/AHj5ZWDBAu7nJ54Avv6a+/nBB4EVK7ifhwwBNmzgfu7ZE9i+HQAQd889wJEj3P7ERODiRe7nqCggK4trlEZFcdusLO5ngEuXmMj9fOQI0MY4Hnb7di5/gDvfEGMXyhUruPIAXPmeeIL7ecECrvxevCa0aVMt12RYvRJ5m9fBwAw2ryntTgmG5h5E2guv1Zhrqs6/k6hdO8hOngyqa/LH3ynshx8gevLJoLqm6vw7xXftGnTXVG1/p8WLEf3KKzXnmnbtAiHBzMAMyCzO5NolvsQMQHkmtw0QrgZNvJ1OCFy4GJeQWjwQOs3XtWxdIhKJXOrt4myIkztcnXNFIhZBLALKNDqnAShvDgsS2whCWD6kCz1/XAiMudK7xTRPoRxOhwWZz1FiOVKpMugCm+kt8cEPZ0EYIX+Jef7mKncKAQ8bqSznjQFs3xOuTrIrDGNzmMqao2vWGkyCK8Ky0t4POnqT/YXGiWsWLuS28+dX7vvyy8r/ZcuXAxLjm8369YCMW6Mb27YBcm5JMhw6BISEAABy9+xBfKNG3P7MTEBpnPm9qAgID+f+VxYVARER3O9Fxl4YrVtz6QGgRw/gwgXu58GDufwB4IEHgBEjuJ8ffxx45BHu5xdfBJ5/nvt55szK8KmXrgkXLgChoT6/ptAnpuDDe0cgVBZq85rYzusYo/0MiAcu1ZBrqs6/Ezt3Dlr+G+UguSZ//J3KnnoK4XXqcB9tQXJN1fl3yjl+HPFNmgTVNVXb3+mFF1A4diwSaso1DRgAQoJZqCwUS8cs9f2JpKFAz2o4jx+5u2KO6z0WXPuemT+7N3qT8HmJRa4FjbzZgcXsQdrBsCCA652h0RmcBii8GVzhmZ7SsoONMF+OC8EVl4cFWfY8sXPNlsN8+MCX1WpBYn5rDMY4WX6bvw8q0zkur6NrNz2FMLTMZo8U6wCNrd4mlr1v7OFf5q/FleAXYL8Ogcol10UmQ5zkEu/dZ75Q44IrKpUKa9aswcWLFzFt2jQ0a9bMKs3u3btx4MABSKVS9O3bF7179zZ7/ddff0VqaqrZvvr162PGjBnuF4hvaPKNT9N9ABBmMv6Vb4ha/hwezm0NBrDwcEBq/LNERlamsfWzSFT5s0RS+bNUyjVu+Z/5/GWyysazXF7ZeFaYzLZu+rM3rgmoLIuPr6lCbMC/T36ETxM/RZjCpIzGa5JCjTeSlmPh7SfAGIOoBlxTtf+d+GEEwXRNjq7D29dkMHDXwZczGK7J8jp8eU0GA1hEROUDfzBck73r8NU11bR7L0gwxrBjxw5s2bIFffr0wQMPPGCVJi0tDZs3b0ZOTg7atGmDsWPHQsb/HQFcuXIF3377rdVxb731FuLj431ZfOIjFboKzNo+C/MHz0eINMT5AVWlrwBOzgI6zwckPjyPG7y5fLA7XF2KmVfZk8PF+SFczNdh0IR/+BSJvDqfiisc9QixJBGJoDMwpw/JYV6cc8XW5L3Ww4Jc693hahouncXvdq7ZXk8Ue6sF8Xud9VzRCz1XYEwngqN7ks/f1p/Q9BR8MNDWfcbXjcjJPeHq0Dnr3j+uHmf/PKbl4XuuSJ1MsOxvNWpY0H//+1+0aNECmzdvxieffIKMjAyz1w0GA5KTkzFnzhyUlJTg5s2bGDZsGP71r3+Zpdu6dSv+/vtv1KtXT/hXp06d6rwU4gemb3z3fb7XjyUhhBASLC5fvow2bdrggw8+wJo1a7Bt2zarNHPnzsWIESNw9uxZGAwGvP/+++jcuTMKCgqENDdu3MAnn3xi1japV68eJAH+LR0hnvDmSjimXAmyfPlIF/RrHmcsh6sZ23/J3SvxwZQqTpmtaOSkxBKxCBqdwelDMt9jJcwLc67wTE9peXqxg4dx64xcO59lTdjN2rhfWLqY322Rnn/m4POV2RiCY6pyWJBrPVeEuWlcvD5bnZQqg1SV+2zdE+5PccLM8nfGVjksXwMcB2ECSY362qhz5864cOECSkpK8Ouvv1q9LhKJsHjxYnTv3l3YN2zYMIwYMQLPPfcc2rdvL+xv2bIlXn/99WopN6keIdIQvNfnPbvfDjFxCD649SwA4OLtEmj1hoBfzosQQkhgi4iIwP/+9z+0atUKvXr1splm5MiRmDVrlhAoef3119GiRQt8/vnnmDNnjllaapsEjxBpCBYNW+T7E0lCgG7VcB4/cjcG48o8JaM7NXC7HP83qi2OXS+w+Zq7gSJuzhUX0oF7sPRGHMr0eddZfowxXM4uQbfGMQ7Thcq5x0mlwnuPlY7+fvxrrg0Lcu1B3LIu7D3A8/kJy0Hb6P0BOOr5Yi+4AmO+tvOzl4+tVLb+rI56rjirI1frUDi/0DPLtfSVSzdbH2A6as/RNQeSGvVk2bVrV0TxE+TZIBKJzAIrANClSxcAQCY/3tsoLS0N//73v/HJJ5/g8OHD3i8sqXbl2nLM2D0D5dpym6/LUYEPE7+AQsTNpv96yqnqLB4hhJAgVL9+fbRq1cphmq5du5r1QImIiEDz5s2t2iYA8NFHH+H999/Hr7/+Ch2tqFSjlWvLMWXjFLvtEq/RlQOHpnDbAOHyRLU+Oi//nBYfLvdKvvwD3cBWCXh9mOP/74Dj62LCN/su1pMXu7iYPmQ7O7VKo0dBmdbpwzXfY8WbwRXTB3PLQIvpajjOVLXq7F2zEFyROH7QF3rLGyvZWcCBD8w56sVhSsQ/wdsop80gn41d/PfLTifPrWJPEXd7rjh7TShHgEdXalTPlapYsWIFQkJCzIIuIpEIEcYx3+fPn8c777yD559/Hp999pndfNRqNdT8kpUAio3LShoMBhgcLGXmDoPBAMaY1/KrdRhQL6wewGCzDhnEuK2tA2aMKf7vZBYWjG3vlTGiwYDuP89RHXqG6s8zNa3+ako5feHSpUs4fPgwnn32WbP9jRs3RnZ2NqRSKWbMmIH3338fu3btQkyM7W+OqW0S4BjQMKKh3XYJ4KX6ZYAopCH3ABdgfydXr8tZOv6B0Vk6fmUmZvw/8MF9TSEJi/RK/lW5FlvHCJOLmjwEO8qbuZjOXhlsntt4flfyE8E6nel9y0+BESYTe/w+wZfP9P8Eszw/H4iA9XVanZ/Z3m/1u8Xf3979wIz3l0TEv2Y7Hd8nSc8MZu/H9v6vGwzcfrGo8t6wn9ZQee/YSGN5LQC37La9v43p35fZeK8Swfa57N5jxnR8XMTZ/ylmpw65c5vmYbw3DJ69Xzp7z/X0HvZrcGXt2rU4wi/raMfMmTOrPB/Knj178H//939YuHAh4uLihP1vvvkmmjZtKvz+yCOP4N5778WYMWMwaNAgm3ktWLAA7733ntX+nJwcVFRUVKl8lgwGA4qKisAYg9jF2ctJJYPBgCnNp6Aovwgl4hKr11UVOnyX/ajZvv/8cRrP92lYXUUMaHT/eY7q0DNUf56pafVXUmL9Ph0ILly4gGXLljlMM3LkSAwcOLBK+RcWFmLcuHG4++678dhjjwn7O3TogAsXLiDUOOnwu+++iw4dOuCdd97B4sWLbeZFbZPAN63NNBTlF9l93Wv1W3cakGf/PNWNfzi9c+eOw3Q6vd6ldGXlZS6ly89XAQDy8vJgKJNAW16CMLkYd+6UOTzO4KS8BoPB+bXoKq/F4OC6iowrqeXm5gj7HOVdplKBn97UWRmc5cefG27kV1yqskpnet9KNVwPO21pIe6Ue9atoKS0FACQk3NH6LWg02rNzs+vcqQqLbEql61y2tpv+Xu5cUEHfr9Go7GZrqiQK19xYQHuiMpRXmb7vlRpuL9/STFXxoJCrt7z83LBymWwpNZw11hYwL1vFxUVolCnt/m+cOfOHZRrufxLS6zrQK22LntFhdoqXbHxXsjPz4NMy/Xw0uv1VukKS7j8CouKcedOZejA3r2j1Wq415jture6lvIKu+n0xt6bd+7cgcp4b9irQ1c5e8/1tG3i1+BKVFQU6tWr5zBNVSdyO3ToEEaPHo0ZM2ZYTWhrGlgBgCFDhiAxMRF79+61G1yZOXMmpk+fLvxeXFyMpKQkxMfHI9J0pQQPcFE+EeLj46kBUwWl6lJM3TYVq8evRrgi3Or1GGU2vm60ADMyXkMF4+Zl+fHwbbw8tB1iwrzTbbQmo/vPc1SHnqH680xNq7+QkMBY1cSSQqFw2jZRmq6o5IaSkhLcd999CA0NxYYNG8zaOHXr1jVLGxkZiQcffNDmBLk8apsEtjJtGZ7835P48f4fESYLs5nGK/WrK4Po0JNgPX8EpLbPU+2Mq50kJCQ4TMZfs7N0YaG5LqW7pS4EAMTFxaGOUuZy3fLzPdjLXywWOz23RHJRyEPk4Loi87lt3YQEjO3SACUVOod5m77fOCuDs3QRt3QupTOlCAm1Smd639YTi5E2P9GlcjkTEc4Fx+omJAh/E7lcbnZ+fgWZmOgoq3JZXY+dv6vl7yEhd8z2y+TXbaarU8E91NeNj0NCbBjClcU205UbgysRERFISEhAdB5X5vj4eMQqrZ85ZDIpEhISUCbhrr9ObAyiQ3U2792EhARUaM3zN8tLnm5VJoVCYZUuhi9TXBwSorjPY5Gt+zyEC36EW5zL3r0TajyXTCpxmI5/LSTklt10Cvk14bWoSK4c9urQVc7ecz1tm/g1uDJ8+HAMHz7c6/kePnwYw4YNw/PPP4/58+e7dIxOpzPrWmtJoVBAYbq0pZFYLPZqY0MkEnk9z9pCJpGhW71ukElktutPLMXxstYwwDxgt/JgOl4d0rKaShnY6P7zHNWhZ6j+PFOT6i9Qy3jXXXf5ZFLZkpISDBs2DFqtFtu2bXM4hxyP2iY1m0wiQ5+kPvbbJUYe169EBsT1gUgiM58BMgA4u6b72tfH1vPZLl+703TCnBhcfbpat/xgGUfpqlJGW8fwc4hIJGJ8OrGLC/mJTH72tJ5MVgtiruWnM9i5Dl+8L/Arwlh8uW56DpFx6Az/N7aXztF+q3T8XCfG/fzfyDod97tcJjFeu+10UikzZisy3of8MsL26otLJzNet6N7l9tnXF3Ibn7Or9l2mURW6Sr/FiKn97bpNYtFdurQTplspTOtX34lJYkX7jlH966neQfWO7AXHDlyBEOHDsXzzz+Pjz76yOp1nU6Hffv2me1bt24dbt++jXvvvbe6ikl8QCFV4PmOz0MhtW5oAoBIEoJluWOhYeZdyT7bdgWXswOzezohhJCar7S0FPfddx80Gg22bt2K6OhoqzT79u2D3jiUAABu3bqFdevWUdukBlNIFZjee7rddonXSBRAm+ncNlC4OFPtnDHtsP9t273GTfVuFuc0jelpna224i5383NlolpXVjTi03nrekyX2nV1MmF9Nc7jY6veLC+drwt3V7Fxqxx2aocfPsavFmTv72K5FDM/TM5eemHpcH6yXg9WCzK4UIfmr3m3Hl1dhYjn6D40LZu7+fpLjZrQ9ujRo/j5559Rahxz9c033+D333/H0KFDMXToUKhUKgwbNgwKhQI6nc7sm6cJEyYgOTkZIpEIs2fPhlarRbt27ZCeno7t27dj9uzZVR4/TQKDSqPCw388jE2PbkJESITV6zJDGVY0/T9Mvf4Oypl5l6+hn+7BxbnDaXJbQgghbtFoNJg1axYAID09HWq1Gq+//jrq16+PGTNmAACee+45HDhwAE8//TTmzZsnHNu6dWtMmTIFAHDw4EFMnToVPXr0AGMMmzZtQteuXfHBBx9U/0URr1BpVHhw3YNYP2E9lPKqDSVziU4F7HkQuGc9IPXhefxoePt6uP7hSJfTu/v45cqyxN5WxUVYPGI+L6xr16Sz9bQeAKq6io0tllVhr2r4IUkyieNz82XjgzFCzyhnqwC5uNyw2CIYY8rW39VWIK8ykGN2tIOyOSmUZb5e+PuIbP0S2LGVmhVcCQ0NFcZBL1y4UNgfHs7NryEWi4UGjq1jAa5r0/bt23H48GGcOHECAwcOxOLFi9G4cWMfl574mkwiw+i7RkMmsT3JkUEkxR9FfaGD7QBK34924vCswV55MyCEEFI7iEQioW1iOv+J6WT8999/P7p27Wp1rOkqQK+//jomTpyI3bt3Q6PR4IUXXkDPnj19WHLiazKJDOPbjrfbLvEakQxoNJ7bBgh73/z7/LwWSzG7fpzn5XU1h8oHbdcL6a2WqWkZXY2ZVOdf0ta57PXwMa2/Hk1iUC8q1KX8PMEHV6QSvueK7XR8kMRkUR8A9v/mVoEOJ3/wyp4rnt8ZpmXyRgzRUa8aWxyd06y+qvh/u7rVqOBKu3bt0K5dO7uvh4aGujxOOjk5GcnJyd4qGgkAcokcj7Z5FHKJ7UmODCI51uYPs3t8bqkas347gwUPdvB6FzlCCCHBSSaTOW17TJw40aW8kpKSzFYQIjWbXCLHlK5TfH8iiRxoXg3nqUG88dDpCVeeUd3tCeBIp6RoXM9Voahc67hcVXh69kWvHW+QmnwZmvJ8Hw9zc+0aheCK2LXghuUgLFeH+zgjchCEcWVoldk5vf2lsjE7d4ONrgr0YUFBN+cKqb1UGhVG/TYKKo3K5usSQxnWN5uBUJH95Sl/PpKB+X9eCNgPEkIIIYTUDCqNCr2X9bbbLvEanQrY0pvbBgi/N6Pc7bnim1I45OoXea6kWvtcLxycOdhpuqr8XapxyhW3AgNxES7MMeRq7xxXhwUxfliQ454rlfmYZ+Qsvau3raPhQ+72GvN2bMVXwQ/+ugI7tFLDeq4Q4ohcIsfUjlPt9lzRQ4aluWOhZY5v++/3/oN/csvw/ePdqAcLIYQQQqpELpFjeq/pdtslXiOWcxPain18nhqBnzjUu7l6sz1YlS/wnJ3e1TkDNXr3IyUGv0fKrB19dwjiwp0HV7w+LEhvXKXHGJEY2aE+zmQW2T+/RQHsDiMyhgxEbg73kUut+0no3Zwjx2xYkFtH2sbn5up97iidrUBRoD+aUc8VEjRkEhlGN7M/54peJMWfRX2hcyGmuO1CNprO/BM3C8u9XUxCCCGE1AIyiQzj21XDnCti45wr4sCZc8Xf3H3+6pwU7fE5TR8SHT8wusebD5PaKgRXqnfOFdcepl0JrLh1XhcvUm+RMCk2DF8/aj2fFk+Y0NbJnCs8d//WfA8aUxqd9d/YVr62ViZydN+6WjZ3e8LQsCBCAlSpphT91/VHqabU5utSvQp/t3wBYWLXAyZ3f7gDK1Ov0zAhQgghhLilVFOKdovb2W2XeI22FPijHbcNEP5qNVVOaOveA9jSJ7rj3Hv25+VzxbP33IWhbet6lIc9T9/dFP1bxnuczyPJjfDDUz3cOiYQe654m2VQx97Qmmbx7q3GxXcicTaJsWWgw9XbV2ojkqG2FVxxEG60MWesR/hrcDkvBwlNb72achvSsCASNEKkIZjTew5CpCE2X4+LisIHWc9AbXCv2+z//e8c/u9/57DqmZ7o2yLOG0UlhBBCSJALkYZg0dBFdtslXiMJAbos4rYEgPs9VxRSCRQePhU92rMxHu3JrT7qzedAkUiEmSPaeCWvEJkEA1sluHWMvx9q/T05sanmCREuLwlePyoEdzfnVo2r7Lni+Bh3rnRkx/ro2bSO1f5HezbG76eznB7P16urvWlcvg/c7rni4vAhi/IEKgqukKAhFUsxMGkgpGLbt/XYbo0x/ZduVc7/sWWHEKuU47cX+qBxHfci14QQQgipXaRiKYY196w3hEvEUqBBNZzHDf7u8RvID2D+Dla4qzp7rri70o3z/MwznDagGbQ2enb44hJTTSYYFiZjtddzxbh1Z8jL15NsD0ea1LMRJvVs5PR4vkw+m9DWxTpVqfV2X7OVBQ0LIqSalKhL0HVVV5SoS2y+LtKV4lK3Z6EUl1X5HPkqDfov3IVFf1/CVzuuoEKrx9Hr+W5PHkUIIYSQ4FaiLkHiokS77RKv0ZYAvyVy21pO+HY7gHo7WJJJ/F+2htGhQq8KZ2pyE9ey6G8Nb413R7V1fpy/rtlXt4ajpZjN5lzx6alserpvU0zsnuSz/Ksb9VwhQSNUForvhnyHUFmo7QSSUCgG/IqKYzken+uLHVcBAJ2TYvDYskP45tGu2HLuNv41uAXuig/3OH9CCCGE1GyhslCkjE+x3y7xFkko0DeF2xIA/u+54ughdUCrBLfnPfG23W8McHleGn/3QqoOllfo7St2WoUifoiO8Vcvn9/mKflhQWLT4Io3JrR1r/T9W8a7NJ9QVedTqm4UXCFBQyqWonu97naHBUEsBeJ7Q48/vHbOx5YdAsAtbbfhZBaSYsOw8VQWXhrYHO0aRKFhTCiiQmn2fkIIIaS2kYql6J3U2/cnMrZvAknwP45XnUQscmveE188S0ptrDJjT02Orbhadl9fo6vZuz0ZrItcuYVCZRL0buZabyaH53LhZHveGOjaXCumE9r6aBiTt9GwIBI0itXFaPHfFihWF9tOoC0G1kUi3INhQfbsu5ILgIum3sgrw7YL2RjxxV68nnIKn227jJSjGdDoDKjQ2h9XSAghhJDgUawuRuSCSPvtEm8xtm+g9fF5aoDKb7f9Ww5v8vcQp+qdc8XWUsx+uP5qDijxV1itl2pxrtNzhmLxo1Wfm1LI1oUAUaM6YVWev5J6rhBSTZQyJX5/4HcoZXb+s0qUwNBUlB2/5vVzpxzLBAD8doLbhsm5/1r5Kg0+23YFErEIqw+lo6hci0UTOqGoXIs+zeJQodMjMoR6thBCCCHBRilTIvWZVPvtEm8xtm8gCZzJ9v3d28HfAYlgGkrj9wltq+O8Po6muHo/CEsxe/n8tgISsWHmq6fK3OjN5Ig3e5aY/l1qyn8pCq6QoCERS9AqthUkYontBGIJEN0OBlz3WRky8ssBAL+duAkAwkS3SrkEJzMKAQDPrjiK3FINJvVshJ8OpWPb9HuQciwTM+9rg5wSNeIjFD4rHyGEEEKqh0QsQbuEdr4/kbF9QyofYr395XZgf1fuW4Nb1/Xr+T1aLaiGDFDzR2eMTknRODxrsPOEbmoQzc39FEwBRnfQsCASNIrVxaj/bX3Hw4J+EvlkWJA9fEBFq698gyko03KvpXOvLdv3D77dnYZt57PRY9425JWq8a81J2AwMGw9nw3GGK1GRAghhNQwxepiiN4TVc+woJ9EATUsqHW9CL+eP5iCIf4cBZHcJBYTeri+koun/NbaZZa/1o6lxBMiQ5ymEYYsGX/665V+2DGjv820B94ehKn33OWt4tWY3iqmKLhCgka4PBzHHj2GcLmd1Xqk4cADGVAZnL+ReFu5yVwrfKCEf+Pk13e/mlMKADhwLQ8bT2XhbFYRnl1xFDfyytBs1p+4kl2ChVsu4lZRObIKy6HW0fwthBBCSKAKl4cj47UM++0SbzG2byANnNUKf5nWByf/fa/fzu/veRm8+UzozysJhOktPCmCyxPaenAOV4TK7PSqtyAxjqmpo/RuL3Zv/hnb1I+0uzJqg+hQYbLkGhgX8QoaFkSChggiRMgjHIyzFQGySLAA+T7jXBb3DRP/5qPTGwBUBmJuFnBDjP7JUwEAsooq8PXOa5CKxfh8+xU8268prt4pxcDWCYgMkSE6TIZm8eHQGxgaxYbBwJhbs8ETQgghxHtEECFSEVkN839w7ZtA6q8RrvDPIwbfpgqcmqjZ3F1Wl9h2b9u62PRSX6fpZBIxrn84EgaDAXfueK8nmjf+jP7ozcPs/BzIKLhCgkaJpgQtf2iJgjcLEB0abZ1AVwKkRCFcvA6lhrBqL589m05lAQC2XrgDACgu54YN3SlRAwByjNtc4zbSuLRzXqkGOy/loLBcixPGIUbhCilK1To83rsx1h3NwJpne2HtkQzMvK8NTt8sRN/mccgsKEdSbOBcPyGEEBKMSjQliPowCkVvFyFSEem7ExnbNxhfZAyyEJHIz0MKvHhudx6MVzydDJ3B4LVzi6v5OzqbE9p6EBn44pEuyCosd+G8zOL3Kp/SJpFIhA6JUfZf9+7pglMNGSNEX2uToBEhj8Dlpy4jQm5nnK80AhhfhFJDKCJDAi+ueMo4P8sHf1wAAOy9kgMAyC3lgioFZRoAlW/AUgn3Ez8di1wqRqlaBwDYfuEOKrQGLN51DT8fycCqQzcwedlhpF7LQ7//7MStonI0efsPlFRoMXbxfhSWabDhxE2oNHpkFZZDo/PeBzMhhBBSG0XII1D0dpH9dom3GNs3kPp3npNAULkUc+18XL2nZTwGeXEC2uruueLt3hHD2tXDU3c3deG8jn8PBP1bxmN4u3p+O78/VuCqiZPiBt4TJiFVxMBQoilx8MbMAG0xRGB4sGsifj2WiRJjMCIQbTP2ZPnP5ksAgH9yueFBKmOZ+fcbfjiRVCyCxngsP7SoXMNt+d4wt4srAABXsrn5Xc7eLMaJ9EJcvF2C6Smn8a97EvHFnpN4YUAzHP4nH6M7NcDt4go0iApBYmwY9HqGlnUjkFOqRoeGUcgurqBeMIQQQogNDAzF6mKEy8N9/GDCtW+4OVdqZ1Ah0Hh3zhX//U39HaTqfVcdPFwNE+q+M6IN7m3rv1WRXKnn5U8nVz3/Kh9Zdd6Oi9SUMAsFV0jQKNWUotvqbg6GBZUCG5KgFK+DSMRNunQpu6Tay1lVqw+lAwA+2XoZAHCriAuU8BPkqk16m/DfNFi+V/NBFr43TJ6K22Ybgy4hUq4zW75Kg6M3CiARi3Don3xhuBEAtKwbjsvZpXiid2MsT72BH5/qgSd/OII9bwzEnE3nsPjRrvh651W8MrgFfjmWiXHdEnE6sxCdEqNRVK5FdJhcmLCLEEIICValmlIkfZpUDcOCuPYNDQsKHN78xt1f8Y0fnuyBNvX9ez+tea5XtZwnITIEozo2EH6viT0mSGCg4AoJGpGKSNyaest+A0YWCUxiKH37DwCAoYa/ce67mgsAuHibCxCZLtfMz2PLR8L5V4rKuQBJXqnGbJtr3PJ1wgc/+DxNP2RKKrg8Mo0T7h76Jx8AcDarCDsu3sGxGwX4csdVDG5TF2+vP4OmcUpM/O4gFj7UEW/8choz72uNFak38HjvxtAzhqSYMESHyaA3MDSMDkV2sRrtG0bin1wVOiZGIyO/DE3ilCjT6BAmp7csQgghNUOkIhJsdjW0NYztG+L/JXSDycDWCdV+zhreNK8yn0957YcInTf+L5pNaFtD7g16UiFBQ2/Q41L+JdSJqwOxrRm4DHqg+CLE4IbK1JD/o1WSXcz1SLl2hxv+U2EcJqTScIGRwnI+uGKcz0XF/c4HX/i60RiHHGlNAjeVvWK4LT8siR+uxPeC4ScQKzYGY/igzKXsEtwsLMf64zdxKbsEErEIUrEIap0BHROjcDqzCA/3SMLPRzLwwQPt8e6Gs9jw4t144Ov9OPLOEPSYtw1n5gzFcyuO4bOHO+OXY5mY0D0J+6/motdddXCzsAwNo8OgZwxyiRjhCim0BgMiFFIwBoip1wwhhJBqoDfocTH3IlrHtYZE7NpSrFVibN8gsjXgy/PUAAkR3l3CNhCEuLiMbzAI5ra5I3Hhvr1vPWn5+ntoGK+m9CaiCW1J0FBpVRi1YRRUWpXtBHoV8Hdv1FPqMah1Qo3vueKKm8YAx4rUGwCAH/b/AwAoLOOGB/GBD36y3KIKY+DJWDdCzxeTuuIn0uXfa/k0fFClyDj0SFjlyBjAqewFwx3HR7QZY8KQplzhGI3ZsXze529xy9KdzixCaloejl4vwMItl/C/kzfx6tqT+PjvSxj3TSpeXnMcd3+4Aw8tOYCnfjyM7h9sw3+2XELfj3Zg/9VcDPp4F67nqvBGyikUlWvxzm9noNUb8Pm2K9DqDfjlWCYqtHoc/icf5cZJfiu0eqh1eiGYRAghhDii0qrQe1lv++0SbzG2b6D38XlqgOYJEbg4d7jX83X3+dJbLcx1U3tjYjXMOULMVecTwo4Z/fHxhE7VeMaqcfv/gBcqsSY+qlHPFRI0IhWRuPL0FcfDgiYU44Dx1+8md8O1HBWmrjxWbWX0N62ee5fi52/58cB1AMDZm0UAgCJjsIUPdkiNvTykYjG0ei7wIjG+u/IdQPiVhfiADR9cyTP2huGDLHwvGMuglkwiFs6nMH47I5Pw5+W2/Fwxt4zBIj5oxE/cqzMGbviy5BvPfSOvDDfyygAAey7nIKuoAr8cy0Rargp/nLmFlGOZuK9DPaw+lI4HuzbEp9suo1/LOLyecgrhCimeX3UMs0a0xvw/L2Ji9yT8ff42ejerg8gQGRgDGseF4ezNIgxrVw8bT2bh5UHNsXh7GuY/FI0vd1zF/41qi+WpN/D03U2w7cIdDGqdgFOZhcJkwPUiQ5BfpkFMmBw6PYNUIhKuOVC+KSCEEFI1kYpIFM8s9v2JjO0bwgmmnh7JTWP9XYRa6a64cJxIL6yec8WHV8t5SPWg4AoJGjqDDkdvH8XQuKGQi+XWCQw6IO8IUKcHIJaieUIEmifQsoUAcCqTC65sucjNn7L++E0AlUENqclQGj3fqwXmgQ9+yBEfXOGHCfG/80OPTOeGAcy/HZAbJ4uRGbd80CTH2IOFn8S30NjTht81wKUyAAEAAElEQVTyqyKpdebBFlN8rIIP7vDXxAeFsgq5vG8bz6E1BoMKjK/nlKpRUKbFsRsFwrArsYhbCjuzoBynM4uQEKnA35cKMOhCNpan3sCYzg0x9/fz6H1XHTy74ih+erYnJn1/CEse64rnVx3HzPtaY8FfF/F478b46+xttG8QCbFIhIIyDQa3qYttF7Ix/d6W+H7vP5g/tj2+25OG14a0xLJ9/+Dlwc3x9Y6reGVIS6xMvY5HejbCzot3cE/LeBSWaREXroBcSp0TCSHEX3QGHY7cPIIeDXtAKvZhk9uifUP8ryZ+4x4QAqTi5j/YHjNHtPZ3MWq0OaPb4VRmoUd5BMbd4B5qeZOgUa4tx3PbnkO5ttx2An05sG88tyUuuWKcs8V0yWq+JwgfyOCDKio193uRMRhRWsHvN59zhcd/fmpMVjniAx98LKfYOAdMbolxjhjj6kYFFr1kdAYuD52xZ47WbOUk82uSWEzyyy9PnW2xLTTmbTDwwSQ+v8oMFVK+pw33VsoHbMqMwZ60HK7+LhiHM/G9eEqNdXXH+PuV7FLklKhx+J98bL94B8fTC/Hjges4kV6IpXv/wZ7LOVh+4DpWpN7A7ss5+GrnVZzKKMIXO67iwq1izNl0HqnX8vD8quNYfTAdfT7cgU+2XgIhhBD/KdeWY3zKePvtEm+h9g0JEoHyMK2QSnw+D0q18kJnaHfjXkPa1sWMoa08OufdzepUnt+jnKpPjQtvZ2ZmYunSpbh48SL+/e9/o23btmavL1u2DFu3bjXbl5SUhIULF5rtu3LlCpYuXYrs7Gx06NABzz//PJRKpc/LT3wnQhGB448dR4TCTm8UWQQwNrN6CxXEdl7KAQBsOZcNANh4KgsAkGkcssMv3cwHZvjf+QCIVGId2+V7xfA9T0rV/Nww3LbMGJTge6zwc8XwgRyxjSE1CqlEGD5kmkaYZ0ZYnpr7nQ945BSbL3XND2sy7RUjl4pRrtULPW74VZZUxuAKPzSKPxe/OpPQ48ZYLv66ZVIxYDxWZzGMii83H9TKLOCCXHzghs+bL0O+8XdCiO9VVFRg7dq12LJlC+6991489dRTZq+fPXsWH3zwgdVxixYtQoMGlct/lpWVYcmSJTh9+jQSEhLwzDPPoFUrzxqnxH8iFBHInF4N7Q5q3wScjolRwmqKxHV1lDZ6nhOPiXy+HpFvvDm8Nd4czvUgCpBOTU7VqJ4rixYtQt++fZGXl4e1a9fizp07VmmOHDmC69ev44EHHhD+DR482CzNyZMn0aVLF2RlZaFHjx5YvXo17rnnHmg09DBSk+kMOuzM2AmdQWc7gUEHZG3htk50axzj5dLVHoeNjYk/ztwCAGw9zwVf+AAJH2SxNWTFsjdLibDSEHcsPzcL3/ODX92ID+DwvVQkJt1V+Al4+bz5nih8DxvLcwjBFpVFAMR4nOWwJrPzGV/i53zht3kWv/PBFr6HDV8mtbayx41lbxi+l8ydYvMhUvyW74HDD2cSiYDpa0/itxPU4CbEl86cOYNmzZph27ZtOHToEI4ds57H6/bt21i7dq1Z2+SBBx5ARETllwEajQYDBgzAypUr0aNHD9y+fRtdu3bF8ePHq/NyiBfpDDpsubrFfrvEW9xo35DqsfzpZJx9b5i/i1HjTO7dBDtm9Pd3MYgNNBWga2pUz5WxY8filVdewa1bt/DVV1/ZTZeYmIiHH37Y7utvv/02+vXrh5UrVwIAJk6ciEaNGuGHH37A1KlTvV5uUj0qdBWYkzoHIzuMhFxqI/KtrwBOTAeGHgLElZNHbX3tHtz76R6zpL9O64NFf1/CFzuu+rrYQY8PlOy9kgsA2HeV257KKLRKywcu+IBGqcWQIr7XRrlxKFKZxev8Qj58QMUUH3Tg8+aDKCUWS0Xzc8jwW36ID18209WCDEJPG2PQR2M+FIrvocL3IslTmfeW4XvD8MEZPh+gMrgiMS4rzgd/+CBVgUXAhl9ZiT+HVs/w24mbSMtVYWyXRKv6IIR4R8OGDXHmzBnExsaiV69eDtM6apusWLECZ86cQUZGBuLi4vDiiy8iJycHb731llWPXFIzVOgqMP3v6Tg05RDC5T6ctNJO+4b4TzBNqludJGIRTfBKbKohHVdqVs+Vpk2bQiJx/mZ17tw5PP3003jttdewfv16s9fUajW2b9+O8ePHC/vi4uIwaNAg/PHHH14vM6k+4fJw7J6w234DRhYOjDzHbU20qGt7GNF0D8cJEvel53NDXfhAzJVsbs4SlTBhLRd84Od34ed74YcF8cOIJCbhdf4nfjgPHyzh87QMWvDLUxdbzBnDB0BMe8UYLHqz8HPE8Mfw5ygVJvvVmKXje6oIy1qbdNsUCb1wuG2BRWDGcuJgvg74HjkaWjKakGoRGxuL2FjXVvR4+eWX8dxzz+Hzzz9HSUmJ2Wt//vknBg4ciLi4OGHfxIkTsXPnTpSX01waNVG4PBznXjjn28AKYLd9QwghAPU6qU41queKKyQSCbp27YpevXrh5s2bmDJlCtasWYOUlBQAQHp6OnQ6HRo1amR2XOPGjbF79267+arVaqjVauH34mJungODwQCDwTsPMQaDAYwxr+VX26i1amy8thGTYyZDIbMxCZVBC2RuABIfAMQyu/k82rMR/Q0CBD/Uhe/lwgddUtPyAABpOSoAlQGSEou5V0zxe4TAB9/rhe/BIvRY4edxMV8FiQ+gSGysnMT3iuGDJ5YBD8stHxgp15rnbTok1mCRN39t/OpGfK+YEovhVvzvIlaZz1u/nMIfZ27hvvb10TQuDHoDQ4PoUIhFIsQq5aijlEOjN6BhdCjUOj0iQ2RQSMWQiEU258Yxte5oBhRSCco0OlzJLsWYzg2w+3IOJvdqjG0XsjGuayL2X81FvxZxOHK9AMlNY3EivQCdk6JxLUeFZvFK5JZqUEcph1qnh96L76m1TU37DKkp5fSW7t27o2nTppBKpVixYgUWLlyIQ4cOoWHDhgCAa9euoXfv3mbHNG7cGHq9Hjdu3EDr1tYrV1DbJLBp9VpsuLQBD7R6ADKJ7XaHV+rXxfZNbePNe5ex2vee5Qi9L/iWt+vXk7wYqxzC7s+/d2U5PCuDs7r1NH+/Ble++eYbhwENAPj8889Rt25dl/N8//33UadO5czCY8aMQY8ePbBp0yaMHj0aFRXcw1p4uHl0Pzw8XHjNlgULFuC9996z2p+Tk+PwOHcYDAYUFRWBMQaxuEZ1KgoIpepSLD6xGIMSByFcYf3tjUhfhpizC1Eg6wEmCbObz8u944X5fH6a3BaTVp73WZmJZ/jJZ89mcQ8U54zbvBKT/5PGIIVBz/UiKS7jXitWcdsCi21JmXFFn3JuW1TK7+e2IpMZtfigSFmFcS4VY9qCUu5b5kKLLb8/v6TMrAwVao1ZWQFAZxwCxZ+fPya3WGXccr8XlBjzNr7Opyur4PafzizCaeNS2ynHqj7/SlK0AjeL1GgaG4Lm8WHYcjEfT/Soh+VHbgMAlHIxVBoDTmfk4XhmKXIKSvDT8WzoK8rwzp9pWDqxNaasvYh1T7TDhOXnsPLRNpi8+gI+GtUMb/1+DdMHJGHRrgxM6hSNy/lX0CgmFPUi5Mgv06JHo0hczy9H54YR2JdWiDHt4/D7uTxM6lYX60/nYFLXuth0Lhdj2sfh8I1i9GgUifTCCiRFh6C4QoeoECm0egaFVASdgUEkEkEi4rqYioPo65ya9hli2XMjUJw4cQIfffSRwzSTJk3CmDFjXM6ze/fuOHjwoND79vnnn0enTp0wa9YsLF++HAA3Ka6ttgn/mi3UNglsZdoyLNy7ED2ieiBMZrvd4Y36dbV9U9t48941MIPNuR5rK3pf8C1v16+6oqLK9y8/v2FxcTHu3PFf6KCkhOvN7un/Q2d162nbxK/Bla5duyImxvHEoe6u4GMaWAGAbt26oXHjxjh8+DBGjx6NqKgoAEB+vvkM3nl5eYiOjrab78yZMzF9+nTh9+LiYiQlJSE+Ph6RkZFuldEeg8EAkUiE+Ph4eqOqgjhDHP4a95fj+qt/GPE2dofJJcJwkYSEBGE/9yMFV2qaXJVW+LnYOITon3zuQeNyDhd0uJLLbSuMCwnpmHHeE+NoSb1xqzGIzLZSqQSAcZlmfgJe42s6ZjxG2Iosttx+4+cUdOAetJiY23KrHPG9WLi0astj9SKzrRaW5zTO1WJr3iEPZBRywaZreRW4lsfVJR9YAYBy4xAn/vx647enOmkIAKBCzG1LwG11Mu69XRLCbe+Uc9dz9o4Wp2+pcPKmShjKte96CTILytH7rjpITcuDRB6KZYduoUOTBHy97yaGdGyED7enY2jnJnh1w3H894nueHrFecwZ3RZzNp3HG8Na4qsd1zC2SwMcTMtHozphUEjFuFlYjvva18PaIxl4b0w7zP39Av77ZHe8+ctp/PhUDyz8+zLeHt4KS/f9g2f7NsWKgzfwSI9G2HL+Nga1SsCZm0VoVS8C+SoNokNlkIhFYAAiQqTQ6AwIV0ihMzDIJGIYDMxmjypvqmmfISEhIf4ugk1169bFAw884DBNixYt3MrTsn0hl8sxatQo/P7778K+qKgom20TW8fzqG0S+A5PPezwda/Vr532TW3mzXtXIhabtQ9rO3pf8C1v129oaGiV71+Zsad0VFSkX/8PPH5PNESyEI/L4KxuPW2b+DW40rNnT/Ts2dPn5ykpKRGWZk1KSkJ0dDTOnj2LESNGCGnOnDmDDh062M1DoVBAobAeaiIWi736piISibyeZ22h0Wvw08Wf8GLciwgR2/iPodcA/6wAmj4OSMwfPI+9ey/a/HszAFDd1zJZhVygID2fC7bcyON6fgjzuxiDbnzwzXQpOIlIBD2YkJbfCvO6qM0n5q2cG4bbX6E1z1tkNiwIZnnwQ42EeV2EyXPNz8H/rtZX79RffHnP3uR6D609yvWSmb2RC06++79zAIDFu9IAVC7hnSesymTshmmsA6VCKgyf4utVmJtG2HI/5JVy6a7eUZml57fX7qhQrtXjzM1ipOWq8E+eSvg76vQM6fnl+P30baTlqrDjYg4OXy/Aycwi/LD/OkZ1rI+FWy6jX4t4fPDHRSTGKDF93Wm8NqQlPt12GWM6NcDGU1no0igaN/LKoNUZ0Kd5HWw5l403hrXCT4fS8e7INpi2+jjWv9AHDy4+gIMzB6PXgu04+94wtJ+9BUfeGYLJyw7hq0ld8NOhDIzr1hAZ+eVoEheGnBI14sIVwkTM0aEy5JZq0DwhHFeyS9ClUQzOZhWhS1I00nLLECkSoaBMi/jIUGh0BsillYEdxpjZMuX+FqjvtQ0aNHA48ay3FBcXm/09OnbsiNOnT5ulOXPmDCIjI62GMvOobRLYNHoNVpxagcc7PQ65xH7A2+P6ddC+qe28d++K6P63QO8LvuXN+u2YGFXlfMTGL/v48vhLnfAQvHqvd+bDdFS3nl5jUP1v0Gq1WLt2rdm+zz77DHl5eRg9ejQArjInTZqEZcuWCWOT9+3bh8OHD+Oxxx6r9jIT79HqtdiUtglavdZ2AqYF0lO4rYVQuXuzuocrgm66ImIhs4ALttws5LZZFkseA5WTxvIPvvxqRnwgpExY1cj89Qp+1SOteXpTen4FIovJcfljyjXmr1cGbsznlAkU/Fwxx24UAAB+PpIBAFjw10UAwK/HuWDM6SwuQMIHVgAgxzj868A17lv85ak3AACbTnHLfW+7wAVq9lzJAQCcuckNg+LroHJVJ25rGiCrDISZ/834c940Bt/4IByPz7PQWM6swnLkqzQoUeuw53KuUK6bheXYeYnrwnrWWK603FKz36/nqXDxdgn2XM7Ff/f/g2V7/8Hzq47h1Z9PYvKyw3jqhyN4cPEBPLj4AKauPIYJ36ZiwZ8XMPG7g1hzOB0PLj6A4+kFGPrZXm4Y1fwdSM8rQ8t3/8K5rCLcNetP7Lx4B01n/omfDqVjwMKd+GzbZby4+jje33QeS3Zfw5u/nMLuyzn4vw1nkZFfhi+3X0FeqRpL96ahTKPD1zuvgrGaMld/4NmwYQNKS0uF38+fP481a9aYDS169NFHceTIEezduxcAF3xZunQpJk2aRA8vNZRWr0XK+RT77RJvcdC+IYTUblfn3YfHejX2dzFqjRr1hLh792588803wqz577//PpYsWYKHHnoIDz30ECQSCX7//Xe8++67aN26NdLT05Geno7vv/8ePXr0EPKZN28ejh07hrZt26Jt27Y4cOAA3nzzTQwePNhfl0a8QClX4ueRP0MptzOUTKoEBm1xO9829SNx4Vax2b5m8UqcMs5jQQgfOMgt1ZhtC8rMe17wW52xV4nl6kemz678EtYV/EpJxqCKRsjLfLUjywltAy244k18HfBBFT5Qs+pgOgBg8a5rACAspb7hZBYAWP0/BipXqNp8jhvi9McZbrvWmOeWs9zvh/7JM6bngj98oEFrLIvp3C38XDz8Hn45bb6XQuV9YgzgGAN54SHcR3JkKDekKsL4Ox/wASqDOfyxfBDoTjG3PX+bK9+l7BKzvNNyuf1Hr+fjel4ZNp7KEiaEloq5uWgu3i7B6cwiaPUG/HwkA2EKKT744wJa14vEwi2XMCm5EWKU9K24JbVajSeeeAIAcPXqVeTn5+Phhx9GUlISFi5cCAAoLCxEp06d0LRpUzDGcODAAUycOBGzZ88W8hk4cCBmzZqF4cOHo0+fPrh48SLq16+P+fPn++W6iOeUciW2POZ+u8NtVWzfEEKCn7OFCYh31ajgSqNGjYRx0I888oiwn59BXywWY+XKlcjMzMSpU6cQExODDh06ICLCfKnd6OhoHDhwAAcPHkR2djYWL16M5s2bV9t1EN9Q69RYcnoJ3hrwFkLlodYJ9Grg8tdAyxcBiY3VhOz465V+aPK2+TLdq6b0ROf3t1au8gIgbf4IPL/qGP4+n13layDBhb8/KgMf5r1L+BV/Knu+VM5QXsEHUYy9YzQWW61FrxnLHi4qTfAGV3yJD8DwvWT+OMP1jvlh/3UAwPw/uZ42fOCGX7nqVpF1j6bj6YUAgJSjXKBmnTFg88dpLthz0vj6tRyuRwMfKLFcRltrMsSLn7aF38en5YMu+WXc373AeG/x+/llwy1XoQKslynn7yl+2JppAI+CK9YkEonQNjGdq8V0npQnn3wSDz30EI4ePQqNRoO2bdsiMTHRKq958+bh6aefxunTp5GQkIDevXtTr5UaTK1T4+sjX+PFHi9CIXW93eG2KrZvCCGEeFeNCq40bdoUTZs2dZouMTHRZqPFlFgsRp8+fbxVNBIA9EyPY9nHoGd62wmYHshNBVo87/G5IkJkiFXKhYchgFv+Vy6lRjBxHf+AbBqk4/H7+F4a/JZ/KOZ7v/B58Fnwx2UY55Ah/scHL/jhSvx8M0v3/QMA+NIYqFm45RIA4KdDXA+cI9e5IVR8sAMAso09VPZd5YYebTUGc1cah0ptOMMNjdp0igvgHDQGibKKuPtBCNLpzAM4QGVPG34rN37bxfeSKQ3i3lCekEqlLs3REh4ejgEDBjhN16xZMzRr1swLJSP+pmd6pGam4vnunrc7HPJi+4YQQkjV0ZMgCRphsjB8f+/3dpc7hDQM6JfCbX3E3owEc+9v57NzktqBD54IE7HaCMiQ2ucf43Cf88YeN+ezuWFOe69wwZf1J24CqOx586dx2BMfpDF15Q7Xg4YfRjXvzwsAgP8zTkYczEPNCPGFMFkYUsan2G+XeEs1tG8IIbVTAM2DXyNQcIUEDbVOjY+Pfgy1zvqhAQDXbfb0HG7rK3aedzsmRvvunIQQUg3iwmm4ASHuUOvUmLNrjv12ibdUR/uGEEKIUxRcIUHDwAy4pboFA7Pu7g4AYAagPJPbeoE7C2fYSvruyDZeKQchhPhar7ti0STOzmThhBCbDMyAzOJM++0Sb/Fy+4YQQkjV1Kg5VwhxJFQWik/6f4JQmY3JbAFAGgr0XOrxecRC9zjXoitrnu3l8TkJIYQQUrOEykKxdIzn7Q6nvNS+IYQQ4hnquUKCRoWuArMPzEaFrsJ2An0FcGw6t/XAnjcH2n2NmQRc3hjWCgDQo0mM0zz/PaqtR2UihBBfMtAX4oS4rUJXgelbpttvl3iLl9o3xD6ad4LUdiL6T+AS6rlCiNHUe+5C3cgQp+kSY7gJ45wNC+qYGAUAEItEYE4S23u/iguXI7dU47RMhBDiSzqKrhBCCCGEOEQ9V0jQCJGG4L0+7yFEaidAIgkBui3itjbMHNEGT/e1vdT3iA713C4PH0+xFzgxjQDzP7WuF+Fy/p0So7B6Sk+3y0UIIe6i1akIcV+INASLhi2y3y7xFiftG0IIIdWDeq5UEd8Tobi42Gt5GgwGlJSUICQkBGIxxb3cpVKr8Mpfr+DzUZ9DqbAx8aKuHDj5BtB5ITc+2Q0fjm6B34+mAaj8m+srVDCotUKa4uJiaMpKYVBzS6GqSktgUJehpKQEpSXFMKjLEB4iQWmFHgBQVlospC1XcWmTwiNw3rgPAPRSrdk5AKD3XXWQmpaHlY/3Q+q1PCEPQgjxlXKVxKufd0Dle6mznn3EddQ2CSzl2nK8sfUNLLx3od354LxSvx60b4KZt+5dg7oMOqnO6++BNRm9L/hWINVvcZkGBnUZVCXFKC4O92tZvMFZ3XraNhExatVUSWZmJpKSkvxdDEIIIaRGy8jIQGJior+LERSobUIIIYR4rqptEwquVJHBYEBWVhYiIiK8NsFPcXExkpKSkJGRgcjISK/kWZtQ/XmG6s9zVIeeofrzTE2rP8YYSkpK0KBBA79/MxcsqG1S81D9+g7Vre9Q3foW1a/vOKtbT9smNCyoisRisc++aYuMjKT/SB6g+vMM1Z/nqA49Q/XnmZpUf1FRUf4uQlChtknNRfXrO1S3vkN161tUv77jqG49aZvQV0WEEEIIIYQQQgghHqDgCiGEEEIIIYQQQogHKLgSQBQKBWbPng2FQuHvotRIVH+eofrzHNWhZ6j+PEP1R3yB7ivfovr1Hapb36G69S2qX9/xdd3ShLaEEEIIIYQQQgghHqCeK4QQQgghhBBCCCEeoOAKIYQQQgghhBBCiAcouEIIIYQQQgghhBDiAQquBICMjAw89thjaNGiBXr06IElS5b4u0gBKycnB//3f/+HXr16oX379njsscdw4cIFq3SHDh3C8OHD0axZMwwaNAhbt271Q2kDW25uLjp27IjExESUlZWZvZadnY1nnnkGLVu2RNeuXbFo0SLQ9EyVTp06hYkTJ6Jly5YYMmQI9uzZY/a6Xq/H/Pnz0alTJ7Rq1QovvPACCgoK/FTawKLRaDBv3jz06tULLVq0wMCBA7Fy5UqrdP/73/9wzz33oHnz5hg9ejROnTrlh9L6X2lpKb777jskJycjMTHR5n3EGMOiRYvQtWtXtGzZEs888wzu3LnjdhpCTOl0OnzwwQfo1KkTWrdujZdeegmFhYX+LlaNwxjDxo0bcf/996NVq1bo378/vvvuOxgMBrN05eXleOutt9CuXTu0a9cOb731FsrLy/1U6prp22+/RWJiIt58802r1+gzpepWr16NgQMHolWrVnjyySdx69Yts9ezsrLwxBNPoEWLFujevTu+/PJLP5W0ZsnJycGrr76Krl27olWrVhg1ahR27txple7LL79E9+7d0aJFCzzxxBPIysryQ2kDW2FhIb744gt07twZiYmJNtO48pnmlc89RvyqrKyMNW/enI0cOZIdO3aM/fTTTyw0NJR9+eWX/i5aQBo+fDibN28eS01NZWfOnGGTJ09m0dHRLC0tTUhz8eJFFhYWxmbMmMFOnz7N5s6dy2QyGTtw4IAfSx54Ro4cyfr27csAsJKSEmG/VqtlnTp1YgMHDmRHjhxhv/32G4uKimJz5871Y2kDx969e1loaCh7/fXX2fnz59mBAwfYkCFDmE6nE9LMmDGDJSQksD///JOlpqay7t27s759+zKDweDHkgeGl19+mdWvX5/9+eef7MqVK+y7775jUqmUrV69WkizZcsWJpVK2WeffcZOnTrFnnvuORYdHc0yMjL8WHL/uP/++9mUKVPYhx9+yACwnJwcqzRz585l0dHR7LfffmOHDx9m/fv3Z506dWJardatNISYeuWVV1i9evXY5s2bWWpqKuvatSsbMGCAv4tV4/z888/s/vvvZ//73//YxYsX2c8//8yio6PZO++8Y5Zu4sSJrEWLFmzXrl1s9+7drFmzZmzixIl+KnXNc/r0aZaUlMRatmzJnnnmGbPX6DOl6t59910WHR3NVqxYwa5evcrWrl3Lpk2bJryuVqtZmzZt2NChQ9nRo0dZSkoKCw8PZwsXLvRjqWuGfv36sW7durH9+/ezixcvsrfffpvJZDJ28uRJIc3ChQtZREQES0lJYUePHmVDhgxhbdq0YWq12o8lDzz9+/dnL730Env33XeZvfCGK59p3vjco+CKny1btozJ5XJWWFgo7HvnnXdYvXr1mF6v92PJApPpAyz/e2xsrNmb+LPPPss6depklm7w4MFs9OjR1VHEGmHRokVs8ODBbN26dVbBlV9//ZWJRCJ28+ZNYR//5l5eXu6P4gaUtm3bskceecRsn2nQpKCggMlkMvbDDz8I+86cOcMAsO3bt1dXMQNW+/bt2YwZM8z29enTh02ZMkX4vV+/fmYPFQaDgSUlJbE33nij2soZKPj3vL/++stmcKW8vJxFRESwTz/9VNiXnp7ORCIR+/XXX11OQ4ip3NxcJpVK2cqVK4V9J06cYADY7t27/Viymsey3cIYY++//z6rW7eu8PvFixcZALZ161ZhH/9//tKlS9VSzppMpVKxtm3bsv/973/s7rvvtgqu0GdK1Vy4cIGJxWK2du1as/2mzyerV69mEonE7LNp7ty5LDY2loL3Dmg0GiYSiczeYxljTKlUsq+++kpIExsbyxYsWCC8fvv2bSYWi82+kCKV77MrV660GVxx5TPNW597NCzIz3bv3o3k5GRERUUJ+4YPH47bt2/j8uXLfixZYJJIJGa/GwwG6PV6yGQyYd/u3btx7733mqUbPny41dCN2urYsWP4z3/+g+XLl0MkElm9vnv3brRr1w4NGjQQ9g0fPhwlJSU4efJkNZY08Jw9exbnz5/HU089ZbbftB5TU1Oh1WrN7sH27dujYcOGdA8CuPfee7F9+3bk5eUB4Or03LlzGDZsGABu2FBqaqpZ/YlEIgwdOrRW1p/le56lEydOoKSkxKy+kpKS0LZtW6G+XElDiKkDBw5Ap9OZ3TOdO3dG3bp16Z5xk63/w1qt1qzdsmfPHshkMgwYMEDYN3jwYEilUqpvF7z88svo168fxowZY/UafaZU3S+//ILw8HA8+OCDZvvF4srHx927d6NLly6Ii4sT9g0fPhz5+fk4e/ZstZW1puH/v2/cuBEVFRUAgE2bNkGn0wnvA2fPnkV+fr7ZvVu3bl106dKF7l0LztpKrnymeetzj4IrfpaZmYl69eqZ7atbty4A4ObNm/4oUo3y4YcfQqPR4IEHHhD22avToqIiqFSqai5hYCktLcXDDz+Mr776Cg0bNrSZhu5J+/iAp06nw6BBg9CyZUuMGjUKu3fvFtJkZmYCqKwzXt26dWt9/QHAxx9/jK5du6Ju3bqIi4tD165dMX/+fDz00EMAuPl+dDqdzXuQ6s8af785qi9X0hBiKjMzE2KxGPHx8Wb76Z7xXGZmJr7++mtMmjTJbF+dOnUglUqFfTKZDLGxsVTfTvz888/Yv38/Fi1aZPN1+kypusuXL6N9+/b473//i86dO6Njx4549tlnzeqN2oxVt2HDBty5cwfR0dGIjY3F448/jk2bNqFdu3YA6LPbm1z5TPPW557UeRLiSwaDwezDFIDwbYZer/dHkWqMdevW4f3338fy5cvRuHFjYT/VqX0vvPAC+vXrh3HjxtlNQ/Vnn1arBQC88sor+OKLL9CkSROsWrUKQ4YMwb59+9CzZ08YDAaIxWKzb3YArg5re/0BwJw5c7Blyxb89ttvaNWqFXbv3o3XXnsNjRo1wqhRo4RJHm3dg1R/1lypL6pT4i56H/ONwsJCjB49Gq1bt8b7778v7Lf1uQtQfTvzzz//4OWXX8bmzZsRFhZmMw29/1WdVqvF0aNHERsbix9//BE6nQ5vvvkmBg0ahJMnTyI0NBQGgwFyudzsOGozuuaxxx5DaWkptm7diri4OKxatQoPP/ww9u3bhzZt2ji8dzUajT+KXGO58pnmrc896rniZ/Hx8cjNzTXbx/9uGTkjlX777TdMnjwZixcvNvv2B7BfpyEhIYiIiKjOYgacjRs34vfff0diYiISExMxdepUAECrVq2Eb33onrQvISEBADBz5kwMHz4crVu3xgcffIA2bdpg1apVALg6MhgMVqu65Obm1vr6U6vVWLBgAf79739j9OjRaNmyJZ599lmMHz8ec+fOBQDExcVBJBLZvAdre/3ZwteJo/pyJQ0hpuLj46HT6VBUVGS2n+6ZqisqKsKwYcOgUCjw119/QaFQCK/Fx8cLQyVN5eXlUX07sHv3bhQVFeH+++8X2jVHjhzBmjVrkJiYiOLiYvpM8UBCQgI0Gg2WL1+Ozp07o3v37li6dCkuX76MAwcOAKA2Y1UdP34cmzZtwuLFi9GvXz+0adMG8+bNQ6NGjfDZZ58BoM9ub3LlM81bn3sUXPGz5ORkHDlyRPhGHAD27t2L8PBwtG3b1o8lC1wbNmzAww8/jC+//BLPPvus1evJycnYv3+/2b69e/eie/fuNucYqU3Onz+P48eP4+DBgzh48CAWLFgAANi5cyemTJkCgKu/M2fOoKSkRDhu7969kMlk6Ny5sz+KHTC6du0KmUyG8PBws/0RERHCkpk9evQAALN7MCsrC2lpaUhOTq6+wgYgnU4HvV5vFeSMjIwUxhwrlUq0bdvW5v/h2l5/tnTp0gVSqdSsvoqKinDmzBmhvlxJQ4gp/r4wvWcyMjKQnp5O90wVFBcXC/NKbdmyBZGRkWavJycno7y8HMePHxf2HTlyBBUVFVTfDkyYMAFpaWlCm+bgwYPo1KkT7r//fhw8eBARERH0meKBXr16QSwWQ6lUCvv4z2++zZOcnIwTJ06YLRu+d+9ehISEoEOHDtVb4BpErVYDgMP2UMeOHaFQKMzu3bKyMpw4cYLuXTe58pnmtc+9qs7KS7zj9u3bLDIyks2aNYtptVqWlpbGkpKS2CuvvOLvogWkjRs3MoVCwb799lu7abZv384kEglLSUlhjDG2bds2plAo2Jo1a6qrmDVGSkqK1WpBhYWFLD4+nr344otMo9GwmzdvspYtW7InnnjCfwUNIM8++yzr06cPy83NZYwxtmnTJiaRSNjGjRuFNGPGjGFdu3ZlOTk5rKKigj366KOscePGtNoS41Zt6NGjB7t9+zZjjLFTp06xOnXqsDfffFNIs3jxYhYREcEOHjzIDAYDW7p0KROLxezo0aP+Krbf2VstiDHGHn/8cda6dWuWlZXF1Go1mzZtGouPj2dFRUVupSHE1IgRI1j37t1ZXl4eKy8vZw8//DBr2rQpLQHqppKSEta7d2+WnJxstjKkKYPBwLp168aGDh3KSkpKWElJCRsyZAjr3r272Wp0xDlbqwXRZ0rVlJWVsUaNGrGZM2cyvV7PtFote/HFF1lcXBzLy8tjjHErrMTExLAZM2YwjUbDbty4wZo2bcqmTp3q59IHttLSUlavXj328MMPM5VKxRjjnnEkEgn76aefhHTPPfcca9asGUtPT2cajYa9+uqrLCYmRqh/Ys7eakGMufaZ5o3PPQquBIBdu3axZs2asbCwMCaTydjjjz/OKioq/F2sgNS0aVMmlUpZw4YNzf69/fbbZum+/fZbFhsby5RKJYuIiGDz58/3U4kDm63gCmOMHTp0iLVp04aFhoYymUzGxo8fb5WmtiorK2OTJ09mISEhLCoqitWtW5d9/fXXZmny8vLYqFGjmEwmYyEhIaxz587szJkzfipxYElPT2ejRo1iCoVC+D86bdo0q8DTrFmzWFhYGFMqlSwhIaHWLjs4d+5c1rBhQxYXF8cAsPr167OGDRuyP/74Q0hTUlLCxo8fz2QyGQsNDWVt2rRhhw4dMsvHlTSEmMrJyWEjRowQ3se6du3Kzp075+9i1Thff/01A8Dq1Klj1XbhH6oYY+zGjRusX79+TC6XM7lczvr168du3Ljhx5LXTLaCK4zRZ0pVnT17lnXv3p2FhYWxsLAw1rVrV3bw4EGzNPv372ctW7YUnmMeeeQRs3ub2Hb48GGWnJzMFAoFi4qKYnFxcWbLLjPGLTP+yCOPMJlMxsLCwljLli3Z/v37/VTiwPXaa6+xhg0bspiYGAZAeI89cOCAkMaVzzRvfO6JGGPMk242xHtyc3OhVCoRGhrq76IErNu3b0On01ntDw8PR3R0tNk+vV6P/Px8xMTE2JwojnDdOvPy8tCwYUObQ6by8vIQGhpqd6K42qyiogLl5eWIiYmxm6a0tBRardZhmtpKr9ejsLAQderUsZtGo9GgqKgIderUsZpgrLYoKioyG6LHq1OnjtVnRVlZGcrLyx3WqStpCDFF72OeUalUVnNw8Wx99hYVFUEkElkNHSKuycnJgVQqtXm/0mdK1RUWFkKhUDh8RsnLy0NYWBg9x7hJo9FApVI5fI8tLy9HWVkZfXbbUVBQYHNF2Pj4eLP5rQDXPtM8+dyj4AohhBBCCCGEEEKIByhsSwghhBBCCCGEEOIBCq4QQgghhBBCCCGEeICCK4QQQgghhBBCCCEeoOAKIYQQQgghhBBCiAcouEIIIYQQQgghhBDiAQquEEIIIYQQQgghhHiAgiuEEEIIIYQQQgghHqDgCiGkxpBKpdi8ebO/i0EIIYQQAgB47LHH8OSTT/q7GISQAEDBFUJI0GjSpAnmzJljtf+DDz5AvXr1qr9AhBBCCKnV3n77bbRu3dpq/9WrVyESibBr167qLxQhxCcouEIIIYQQQgghhBDiAQquEEJqrH//+9+oV68eTp486dZxH3zwAUQikdU/W71eCCGEEEJctX//fsTGxmLRokVuHcf3ZLH8Z6vXCyEkMFFwhRBS4xgMBkybNg0rV67Evn370LlzZ7eOf/fdd8EYE/6tW7cOUqkU/fv3902BCSGEEBL0/vzzT9x333349NNPMX36dLeObd68uVnb5NatW2jYsCEGDx7so9ISQrxN6u8CEEKIOzQaDR555BGcO3cO+/fvR4MGDcxef++99/Dee+9ZHVe3bl2b+Z06dQpPPvkkFi5ciIEDB/qkzIQQQggJbqtWrcILL7yAlStX4v777zd77dKlSxCJRC7npdFoMG7cODRt2hSfffaZl0tKCPEV6rlCCKlRXnzxRZw+fRp79+61CqwAwOzZs82++WGMYe7cuTbzys3Nxf33348JEybg1Vdf9XHJCSGEEBKMtm7diieffBK//fabVWAFAFq1amXVNrly5Yrd/F544QVkZmbi119/hUwm82XRCSFeRMEVQkiNMnz4cFy/fh1bt271KB+dToeHHnoI9evXx5IlS7xUOkIIIYTUNm3btkWTJk2wbNky6PV6j/L64osvsGbNGmzYsAEJCQleKiEhpDpQcIUQUqOMGzcOK1aswBNPPIGUlJQq5/Ovf/0LV69exfr166FQKLxYQkIIIYTUJg0bNsSuXbtw+PBhTJ48ucoBlp07d+L111/HDz/8gC5duni5lIQQX6M5VwghNc748ePBGMPjjz8OkUiEhx56yK3jf/jhB/zwww/Ys2cP6tev76NSEkIIIaS2SExMxM6dOzFgwAA8/vjjWLFiBSQSicvHZ2RkYPz48XjzzTcxYcIEH5aUEOIr1HOFEFIjTZgwAT/++CMmT56MX375xa1jt2/fjoqKCiQnJ9NSzIQQQgjxiqSkJOzatQsHDx7EE0884VYPllOnTiEvLw/z5s2jpZgJqaFEjDHm70IQQgghhBBCCCGE1FTUc4UQQgghhBBCCCHEAxRcIYQQQgghhBBCCPEABVcIIYQQQgghhBBCPEDBFUIIIYQQQgghhBAPUHCFEEIIIYQQQgghxAMUXCGEEEIIIYQQQgjxAAVXCCGEEEIIIYQQQjxAwRVCCCGEEEIIIYQQD1BwhRBCCCGEEEIIIcQDFFwhhBBCCCGEEEII8QAFVwipBgUFBfjll19w8+ZNr+WpUqnwyy+/4MaNG17L018MBgN++eUXnD9/3t9FIYQQQgghhBC3iRhjzN+FICTYHT16FD169EBKSgoeeughr+R59epVtGjRAj/88AOefPJJr+TpLxUVFQgNDcXs2bMxZ86caj+/RqPBxo0b0axZM3Tp0sVn5/nnn39w/Phx1KlTBwMGDHCY1mAw4Pjx48jOzkbr1q3RrFkzu2kvXryIq1evIjY2FsnJyZBKpW6XbceOHcjPz3earmHDhujdu7fb+dvz119/QaVSoVevXkhMTPRaWkIIqY1KSkqwZcsW9OjRA40bN/Z3cWqFc+fO4cKFCxg3bhxEIpHfynHt2jWcOHEC/fv3R3x8vE/OYTAYkJqailu3bqFLly4O2yYAUFxcjGPHjkGv16Nbt26IiYmxmU6r1eLw4cMoKChAy5Yt0bJlS7fLplKp8Ndff7mU1hvtiLy8POHaOnTo4DQ/V+uC1Gzut8AJISTIFBcXY/z48Zg6dSqWLFni9fwXL16MH3/8Ebm5ucjKykL37t2xb98+u+k3bdqEF154AUqlEi1btsSNGzeQmJiIlStXIjY2VkhXVFSECRMm4ODBg+jbty8uX74MrVaLlJQU9OjRw60ybtmyBdeuXRN+T0tLw4kTJ9C3b1/UrVtX2J+cnOy14Mq6deswceJEAMCaNWvw8MMPeyUtIYTUVjdv3sT48eOD4ouXmmLNmjWYN28etFptlb7c8Ja//voLL7/8Mnbu3On0Cxx35ebmYu7cuVi/fj3CwsJw+fJlfPnll3jppZdsptfpdJg5cya++eYbdO7cGTExMThz5gymTp2KmTNnmqXdt28fHn74YSiVSjRv3hx79+7FgAEDsGbNGiiVSpfLWFJSgp9//tls3+7du1FUVIQxY8aY7a9fv36VgyuMMbzzzjv45JNP0KFDB4SHh+PgwYN47LHHsGTJEqt7wJ26IDUfBVcIIcTHVCoVvvrqKyQnJ6NevXoO0/7xxx948MEH8e233+Lpp58W9m/ZsgUqlcosuPLUU0/h1KlTOHfuHBITE6HX6zFu3DiMHDkSly9fRnR0tMtl/Oijj8x+X7JkCaZNm4Z33nkHw4cPdzkfV926dQvTpk1D//79sXv3bq+lJYQQQoh3FRUVoVmzZjh8+DAuXbqEgQMHOkw/ZcoU7Ny5E8eOHUOrVq0AcL2UU1JSzNLduXMHo0ePxr333ouff/4ZYrEYN27cQLdu3TBt2jSsWLHC5TLWq1cPv/zyi9m+AQMG4OTJk1b7PfHFF19gwYIF+P777zFlyhQAwOHDh9GvXz/ExMRg4cKFZuldrQsSHCi4QmqtrKwsHDhwAIMGDTJ7YNXr9fjtt9/Qvn17tG7dGgA3Z8r27dvRu3dvNGzYEMeOHcOtW7fQoUOHKnW7dXQ8f36eQqFAo0aN0LFjR6fdTV091vJ6Tp06hYyMDLRt2xZ33XWX3fzPnz+PtLQ01K1bFx07doRCoTB7nTGGs2fP4vr164iMjERycjJCQ0Ot8ikuLkZqaipkMhn69Onj8JqCwRtvvOFSOp1Oh+effx4TJkwwC6wAwLBhw8x+v3TpEn777Td88MEHwrcvEokE8+bNQ/v27bF06VK8/vrr3rkAH3j66afRunVrvPDCC04DJu6kJYSQQHP79m3s27cPAwcORJ06dYT9BoMB69evR9u2bdG2bVsA3EPs1q1bhWELx48fR1ZWFtq3b48mTZo4PE92djb+/vtvANxw5PDwcABA69at0b59e6u8z5w5g2vXruHuu+9GnTp1sH79eiEvuVyORo0aoVOnTmbtB8s8Tp8+jfT0dLRu3RrNmze3Wa7z58/jxo0biImJQbt27RAREWE3P0fXy9eXu2U0vU5+uEx5eTmOHj2KgoICNGnSBB07djQ715kzZ3DlyhU8+OCDUKvV2L9/P3Q6HXr16oXIyEgh3ZEjR3Dx4kUAwPr16yEWc9NZDh061CxdTdesWTP861//AsC1PxzZtWsXli9fjo0bNwrBBAAICQnB5MmTzdJ+8803KCwsxIIFC4S6a9y4MaZNm4Z58+Zh3rx5SEpK8vLVeObrr79Gq1athMAKwPXqnThxIr788ku88847wpdb7tQFCQ4UXCG11uHDhzF+/HikpqaiV69ewv7y8nKMHz8ec+fOxbvvvguAG8fKd7Ndt24diouLUVFRgZMnT+Kll17Cp59+6tI429LSUowYMcLh8Vqt1qxbY2FhIY4ePYoGDRpg06ZNDse3unosfz0rV67Ehg0bcOvWLeh0Ohw5cgTvvPMO5s6da5bv8ePH8eSTTyI9PR1dunRBeXk58vPz8c0332Dw4MEAgBMnTmDy5Mm4ffs2unbtips3byI7Oxvff/89xo4dK+S1bt06PPvss4iNjUXz5s1x69YtLFu2zGndBYINGzZAp9M5TVfVeUl27NiBzMxMTJw4EXl5eThy5AgAoEuXLmZDcwBg27ZtAGDV9bddu3ZISEjA33//7dPgiid18c0332Dnzp04ceIEzp075/B4d9ISQkggOnr0KMaPH4+9e/eib9++wn6NRoPx48ebzTf2zz//YPz48Vi2bBnWr1+PgoICaDQanDhxAtOmTcPnn38uPIRaunXrFv744w8AwKFDh3D79m0AwNixY9G+fXuzvDdu3Ijs7Gzk5ubi+++/x913323WfigqKsLRo0dRt25dbNy4UZgDg8/jxx9/xJ9//omMjAwYDAYcPnwYb7/9NubPny/kcefOHYwaNQrXrl1Djx49UF5ejrS0NLz++ut45ZVX3L5evV7vVhltXeeAAQPw1Vdf4Z133kH9+vXRpEkTHD9+HE2bNsWvv/4qfFmxfPlyfPbZZzh+/Dgef/xxJCQk4OLFiygtLcXvv/8ufDGUmpoqfDatXbtWaMv17NkzIIMrfKDPFVWdl2TFihUIDw/HiBEjcOHCBVy9ehUxMTHo0aOH1Zdy27ZtQ2JiolXbdtCgQfjggw+wdetWqy+bvKWqdXHt2jWMGjXKKk3r1q2hVquxa9cuPPDAAwDcqwsSJBghtdRvv/3GALDU1FSz/SUlJQwAmzt3rrDvyJEjDABr164d279/v7B/9erVDAD78ssvHZ7L0+OLi4tZz5492T333CPsu3LlCgPAfvjhB7eP5cvTuXNndvDgQWH/u+++y8RiMTt//rywLyMjg8XExLCBAwey3Nxcs/1//fUXY4yxmzdvstjYWDZ06FBWVFQkpJk9ezaTy+Xs7NmzjDHGzp07x+RyOXvyySeZTqdjjDGWnZ3N7r//fgaAzZ492+G1+EpOTg4DwKZOneownVKpZACc/rv//vvt5lG3bl12991323xt/vz5DAB74oknWGRkJBswYADr0qULk0ql7LXXXmMGg0FI+8orrzAALCMjwyqfHj16sCZNmrh28XZ88803DIDwN7ZU1bq4cuUKUyqVwv+vlJQUBoCtWbPG6hzupCWEkEC1adMmBoDt3bvXbH95ebnVZ9+JEycYANamTRu2Z88eYf/atWsZAPbpp586PNeFCxfstg34vNu2bct2797NGGOsoqKCpaen28yrpKSE9enTh/Xp08cqj44dO5q1Z+bMmcNEIhE7c+aMsG/atGmsQYMGZu2CkpIStnLlSq9dr6My2rrOH3/8kQFgn332mZC+sLCQJScns+7duwufszNmzGBisZhNnjyZFRQUMMYYU6lUrFOnTqxr165mZXjnnXcYAKbVah2W1de+/PJLBoDt3LnTbhr+XnTln73P2p07dzpsu3bs2JHdddddbPDgwaxevXps6NChrEGDBiwhIYH98ccfZmnttYmuXbvGALC3337b9QqwoX///iwqKsrma1Wti+joaJtlnj59OgPAPvroI2GfO3VBggP1XCHEDb169TIbxjJp0iR88803+PTTT+1O6lXV4+/cuYMzZ86gqKgIBoMBrVu3xqpVq1BRUYGQkBCH53H12N69e6Nnz57C7y+++CI++OAD/P3332jTpg0AbmxpYWEhli5datadOTExUYjif/nll8jPz8f3339v9k3Nu+++i2+++QZLlizBl19+KUwW+/HHH0MikQAAEhISMHnyZPzvf/9zWn/+NnbsWJSXlztNl5ycXKX8+dV6UlJScODAAXTq1AkA8Nlnn+G1115DUlISXnvtNQBcLygACAsLs8pHqVQiLS2tSmVwVVXqQq/X4/HHH0ezZs3w1ltvOTzOnbSEEBJskpOT0a9fP+H3CRMmCO2FV1991aO8u3btinvuuQcAN3zYdNhFTk4OTp8+LbQfWrVqhR9//BFlZWVmnzc9e/Y0a8+8+OKLmDNnDrZs2YL27dsD4CbXDQ8PNzsuPDwcjz32mEfX62oZbV3ne++9h969ews9ZwAgKioKc+fOxbBhw3DgwAHcfffdALhhSFOmTBGGeISFheHJJ5/Ea6+9huzsbKsepTVB/fr1MW7cOJfSVnU4Tn5+PjIzM5GYmIirV69CqVSirKwMo0aNwrhx43Du3DlhCHppaanddgzATVLrK1Wti+HDh+OXX37B1atXhaFw5eXlwhwqpmV2py5IcKDgCiFu6N69u9W+5ORkLFq0CMXFxU67gLpyvFqtxrPPPos1a9YIS7vJ5XKkpaVBr9cjOzvb7jwv7h7brVs3s+Pr1asHuVyOjIwMYd+hQ4fQsGFDh2/+qampiImJwfHjx3H8+HEwxoR/MTExOH36NAAIXW9NgzR8Hbhi06ZNUKvVLqW15MqSgc6sXLnSo+OdkcvlAIDJkycLgRUAeOWVV/DZZ59hyZIlQnCFT2traI5Wq3UagPNUVeriww8/xOHDh4X5dryVlhBCgo299sKuXbuQn59vNlecu2ytJqfRaDB16lSsWrUK7du3R1JSEuRyOa5fvw7GGG7dumX2GWrZfoiLi0NoaKhZ++HRRx/Fww8/jPbt22P8+PHo378/+vbta/PzyZXrdbeMltd5+/Zt/PPPP+jQoQM2bNgAxhgAbr64nJwcAMDp06eF4ArABWhMNWrUCACQkZFRpeDKnTt3sGfPHreP440ZM0b4/K+Kbt26eXVyV1v48r333ntCkCQsLAwffPAB7r77bixfvhzvvfeekNZeOwaAT9syVa2L//znP0hNTUX//v0xffp0KJVKLF261Oxaee7UBQkOFFwhtRY/hpf/cOU5eni3FV3n97ny0O/K8Z9++ilWr16NXbt2mX2Ls2DBApw4ccKqvKbcPTYqKsoqD5lMhoqKCuH3iooKp0vhlZeXw2AwYNWqVVavtW/fXmjsaDQah3XgzG+//Ybi4mKX0loKDw/3OLji6zlX+J5AppOeAYBIJELLli3NJnNt2LAhAG4Cw4SEBLP02dnZwuu+UpW6+Pzzz9GrVy/cuHEDN27cAAAcPHgQABfEk0qlGDlyJEJDQ91KSwghgcwf7Q1H+EldTX3xxRdYvnw5duzYYTaX18KFC3Hs2DGX2g9yudys/TBhwgQ0a9YMq1atwu+//4758+cjLCwMc+fOteqN4sr1ultGy+vke1umpaXZbK+MGzfObI4RiUQiTApseo0AzK7THdnZ2VbLBbtj2LBhHgVXqmPOlcTERKSlpVm1ZfhFIq5fvy7sa9iwIbKzs63y4Pf5si1T1bpISkrC6dOn8eOPP+Lo0aMQi8WYNm0aEhMTMXz4cLPrdqcuSHCg4AqptfhvHHJzc832X7t2ze4xtl67evUqIiIirHpjVPX4ffv2oXnz5mbBEQA4e/as0/w9OdaeFi1a4JdffnE4HKlZs2Y4f/481q1bZ3eiPQBo2rQptm7dCr1eLwwLArg6cMV///tf9wrvZY899hhUKpXTdPfffz82bNjgdv58F+u8vDyr1/Ly8hATEyP8zk/CfOzYMXTo0EHYX1BQgLS0NNx3331un98dVamLUaNGobi42KxhefPmTQBc76eMjAwMGjQIoaGhbqUlhJBA5s32hlKptBkc4bkyub6tNPv27UPTpk2tJkn3pP0AcL0D+F4uubm5mDZtGl577TUMHz5ceMAEXLted8toeZ0NGjRAaGgoevTo4dX2hCt1zuvQoYPPe444wk+u7Io1a9bg4Ycfdvscffr0wZ49e5CXl4f69esL+/m2jWVb5scff0RpaalZIOvo0aMAYDZ03ds8qYvIyEhh9STezJkzERYWhkGDBgn73KkLEhwouEJqrVatWiE0NBTbt2/H6NGjhf1Lly61e8zPP/+Mt956S/g2JTMzExs2bMAjjzziMKjgzvH169fH/v37zcYOX758WVgBwBFPjrXn6aefxurVq/HRRx9h9uzZZq/xY46nTJmCNWvW4IsvvrD6Nkqv1+POnTuoX78+xo8fj3Xr1mHNmjVmY64d1Xkg8fWcK506dULPnj2xbt06zJ49WxgOc+bMGZw8edJs2b/+/fvjrrvuwvfff48nnnhCaNx99913YIxZza6/e/du5OTk4KGHHqpS2SxVpS5sNWZ/+eUXjB8/Hq+++qpZw8WdtIQQEshatmyJsLAwbN++3Wz1PEeffevWrcOsWbOEnqNZWVlYv349xo0bB6nUfvOd/6KmqKjIrTLWr18fO3fuhEqlEs559epVbNy40a18TKWnpwvDaABu6NDYsWPxyy+/ICcnxyy44sr1elpGhUKByZMn46effsKsWbOslo7OyclBRESE20NRTOvclS/a/Kk65lx56qmn8Mknn2D16tVYsGCBsH/FihUAuC9deE8//TSWLl2KZcuWCfPgGAwGfP/992jXrp1ZL2C1Wo1NmzYhKSnJK0GXqtZFcXEx5HK52X2SlpaGb775BjNmzBDm6AHcqwsSHCi4Qmqt6OhovPLKK1i4cCEUCgVatmyJv/76C0OHDsX3339v85hHHnkE9913HyZMmAC1Wo3PP/8cDRs2xIcffujSOV05/qWXXsLKlSsxYsQIPPHEE7h58yZSUlLw0ksvYd68eQ7z9+RYewYNGoR58+bh3XffxdmzZzFkyBBUVFRg06ZNuO+++zBjxgwMGjQIH3/8Md544w3s378fgwYNgkKhwKVLl7B+/Xq8//77eOSRR/DQQw9hwoQJeOaZZ3DmzBm0atUKmzdvNlsK25/S0tJsfqPUpEkTdO/evcpzrhw5ckQY2qJWq5GXlyecp23btmjbtq2Q9scff8SAAQNwzz334KmnnkJRURE+/vhjdOjQweyDWSqVYvny5Rg+fDjGjBmDCRMm4Pz58/jkk0/w3nvvoXPnzmZlmD17Nnbv3u1wWJk7fD3/DCGEBIuIiAhMnz4d8+fPR2hoKNq0aYO//voL9957r91jJk2aJLQXtFotvvjiC9StWxf/+c9/HJ4rLi4OXbp0wZIlSxAZGYmIiAi0bt1amGTWnhdffBE//vgj7rvvPjz11FPIysrCunXr8PLLL2Pu3LlVuu5//etfKC0txeDBg9GoUSPcvHkTn3/+Oe655x6robOuXK83yrho0SJcu3YN3bt3x9SpU9G2bVsUFhbixIkT2LVrF44dO+Z2cGXw4MGQSCR47bXXMGLECEilUgwdOtRvSzHv3r3bqpcUAPTr18+jOVfWr18Pg8EgLD198uRJIa9hw4YhIiICABdM/PTTT/HKK68gPz8fvXv3xuHDh7FkyRK8/vrrGDhwoJBn79698dZbb+HNN99ETk4OWrVqhZ9++glXr17Ftm3bzHoFFRQUYPz48Zg4caJHQ6t4Va2LmzdvCuVo1qwZLl68iMWLF2Ps2LH497//bZbWnbogwYGCK6RWmzdvHtq0aYPt27ejqKgIr7zyCnr06GG2Wo6p9u3bY9KkSfj2229x+/ZtvPDCC3j++edtjj02FRsbi3HjxmH48OFOj+/UqRNOnz6NZcuWYceOHWjRogW2bNmCM2fO4OLFi8K3NeHh4Rg3bhyaNGni9rF8eWyNpR07diy6dOlitm/WrFkYOXIk1q5diz179qB+/fp45513zD4UZsyYgdGjR2Pt2rU4cOAAQkND0bJlS+zYscMs4r9mzRr8/PPP2Lp1K/Lz8/HUU09hyJAhOHDggFmQoTopFArh2wtbH9gDBw60Odmeq1JTU4UJ7AYPHmx2ngkTJphdd+vWrXHu3DksW7YMe/fuhUwmw/z58/Hoo49aNfj69u2Lc+fO4b///S82b96M2NhYbNu2TVgdwVR6errZJH3ONGvWDOPGjUO9evXcvl53JCYmYty4cS59Q+ZOWkIICSTvv/8+WrVqhW3btqG4uBgvvfQS+vTpg7///tvmZ1+bNm3w2GOP4bvvvsOtW7cwdepUPP/882bfituzadMmfPvtt9i6dSs0Gg3Gjh2L9u3bIzo62u57aPv27XHmzBksXboUO3bsQPPmzbF582acP38e58+fF4ZsOMrj/vvvN5sAdsOGDdi9ezf+/vtv/PXXX4iLi8NXX32FMWPGmA0NdvV6vVFGpVKJrVu3YvPmzfj777+xZcsW1KtXD0OGDMG3334LhUIBAOjYsaPNXg18b4e4uDhhX4cOHfD333/j119/FQIQPXv2rPbgSvPmzTFu3DicOXMGZ86csfm6JyscrV27Fnq9HgA3P01hYaHQlunTp48QXAG4QFjv3r3x008/4e+//0Z8fDx27tyJ/v37W+X74Ycf4t5778X69euFL9yWLl1qNd9Keno6AK7t46r+/fvbXQSiqtq0aYPNmzfjv//9L/7880/Uq1cPGzZssFsud+qC1Hwi5q2vMQkJYkePHkWPHj2QkpLitWEVhFSXf/75B3fddRf27NljNR8PIYSQwHHy5El06dKlyvNd1DS17XpJ1S1YsADfffcdLl265NGkvoT4kvNJIgghhNRoN2/exJtvvkmBFUIIIYTUSGKxGJ9//jkFVkhAo2FBhBAS5Pr27etWN1pCCCGEkEDy1ltv+bsIhDhFPVcIcYGjOUoIIYQQQrzB0Xwhwai2XS8hJLjRnCuEEEIIIYQQQgghHqCeK4QQQgghhBBCCCEeoOAKIYQQQgghhBBCiAdoQtsqMhgMyMrKQkREBEQikb+LQwghhNQojDGUlJSgQYMGEIvpux5voLYJIYQQUnWetk0ouFJFWVlZNPkWIYQQ4qGMjAyaLNxLqG1CCCGEeK6qbRMKrlRRREQEAK7iIyMjvZKnwWBATk4O4uPj6Vs8DxWri5G0KAkZ0zMQqYj0Xt1qi4HfkoCxGYDMO3/3YED3ru9Q3foO1a1vOavf4uJiJCUlCZ+nxHPUNql5CssL0fizxrjx6g1Eh0ZXLRNqm9hE967vUN36FtWv7/i6bVJrgysGgwGHDh1CdnY22rdvj+bNm7t1PN/dNjIy0qsNmIqKCkRGRtJ/JA9FsAgUzSlChJzrGu21umURwBNFgDQCoC7XArp3fYfq1neobn3L1fql4Svmrl27hjNnziAhIQG9evVy696ktknNo1QqcXnaZTSMawiJRFK1TKhtYhPdu75DdetbVL++4+u2Sa38axUVFeHuu+/GQw89hK+//hqdOnXC22+/7e9iES9iYChWF4PB2yuNM+4bIq/nSwghpLZ799130bFjR3z99deYMGECevXqhYKCAn8Xi/gQA0OJpsTD9gq1TQghJBDUyuDKO++8g/z8fJw/fx5bt27Fli1b8NFHH2H79u3+LhrxklJNKZI+TUKpptS7GetKgQ1J3JYQQgjxkl27dmHevHnYvHkztm7divPnz6OoqAizZs3yd9GID5VqStFtdTfP2ivUNiGEkIBQ64IrjDGsXr0azzzzDKKiogAAffv2RXJyMlatWuXn0hFviVREgs1miFR4eeyxLBKYxGhMMyGEEK9atWoVevTogX79+gHghvZMmTIFP/30EwwGg59LR3wlUhGJW1NvedZeobYJIYQEhFo350pmZiYKCwvRvn17s/0dOnTAyZMn7R6nVquhVquF34uLiwFw47a81ei5nluKmzllyNMVQSQWw3Skl+WwL/PXRDb3Wx3nYjp7eVu/ZrrffgEdX4fI5mvW5bM97s1evegNelwtuIwWsa0gEUsQoZCAMeb538qgB0ouAhGtAXEVx0YHIYPB4J36JVaobn2H6ta3nNUv1bu5M2fO2GybFBcXIz09HU2aNLE6pjraJnmlFbhi0jYBnH2uW/zuahvFYoeztK62VSyPddResc7X8jWL81Sh3WJ5nE6nxdGbZ9Fd0RMyiRThCikUMjfbF9Q2sYne432H6ta3qH59x9dtk1oXXCkqKgIAxMbGmu2vU6cOCgsL7R63YMECvPfee1b7c3JyUFFR4ZWy/d+GKziVVQqRSARmMmzWdByu1WhaZvNHu8dbZsDs/VyV4+0nq3YGlCEz5AkkViyHGGEIkYrRJl6BCV0LMKB5TJUnKRLpShC/vw9y7j4OJqUVLngGgwFFRUVgjNHEW15Gdes7VLe+5ax+S0pK/FCqwFVUVGSzbQLAbvukOtom/zuTgy/3Zgqfm3bbB7D47HexvWCVj4vtFGdlcZBlQLdX5BIR+t0VjVf7JyI+XO5SHtQ2sY3e432H6ta3qH59x9dtk1oXXFEoFACA0lLzcamlpaUICQmxe9zMmTMxffp04Xd+mab4+Hivzci/fEpc0C+7xSxaVPYaOKbpXG5MWf3IBdIMjCE9T4U/jl/HJ7tvYkeaCp9O7IQweVVu/wRgfBHiq3BkMDMYDBCJREF97/oL1a3vUN36lrP6dfSZWxspFAqbbRPAfl1VR9vkmYFxGNM+rlb+P3G1zWKZ1lG7hXu9codBb0BO7hXEx8VBLBbjZmE5Ptt2FS+tv4bfXuiN6DBXAizUNrGF3uN9h+rWt6h+fcfXbZNaF1xp1KgRpFIp0tPTzfbfuHEDd911l93jFAqFEJgxJRaLvXrTi0Qir+dZG+kMOhy5eQQ9GvaAVCxD6/pSxPaoj2cGtsG01SfwzPJjWDWlJ2QSN+vZoAPyjgB1egDiWvffxyG6d32H6tZ3qG59y1H9Up2ba9asmc22iUQiQePGjW0eQ22Tmk+j0+Bs3gkMrT8UcqkMzevK8MUjXfDM8iP4aPNlfPRQR+eZUNvELrp3fYfq1reofn3Hl22TWvfXUigUGDx4MFJSUoR9ubm52LFjB0aOHOnHkhFvKteWY3zKeJRry832R4fJ8eNTySiu0OHDvy66n7G+HNg3ntsSQgghXjJixAjs3LkTubm5wr61a9di4MCBCA0N9WPJiC+Va8vx3LbnzNorYrEIc8a0w28nbiKzoMx5JtQ2IYSQgFDrgisA8OGHH2Lv3r2YPHkyFi9ejKFDh6JNmzZ48skn/V004iURighkTs9EhMJ67HGoXIKvJ3XB6kM3cPZmkXsZyyKAsZnclhBCCPGSxx9/HB06dMC9996Lr7/+Go8//jj27NmDDz/80N9FIz4UoYjA8ceOW7VXGtdRYmDreKw9kuE8E2qbEEJIQKiVwZXOnTvj+PHjqF+/Pg4dOoRJkyZhz549NrvWkppJZ9Bhy9Ut0Bl0Nl+/Kz4cz/Rtirm/n3cvY4MOyNrCbQkhhBAvkcvl2LVrFyZPnozDhw+jbt26OH78OLp16+bvohEf0hl02Jmx02Z75f7ODfHH6VtWc79YobYJIYQEhFo7MLNly5b4z3/+4+9iEB+p0FVg+t/TcWjKIYTLw22mee6eZvhx/3acSC9Al0YxrmWsrwBOTAeGHgLEtvMlhBBCqiIsLMxsgloS/Cp0FZiTOgcjO4yEXGo+ee3AVgl4be1JpOWq0CzeQZuD2iaEEBIQamXPFRL8wuXhOPfCObuBFQCICpXhkeRG+H5vmusZy8KBkee4LSGEEEKIB8Ll4dg9YbfN9kqoXIKujWKQei3PcSbUNiGEkIBAwRUSlLR6LVLOpUCr1zpM91ivxth2/g4KVBrXMjZogfQUbksIIYQQ4gGtXotN1zbZba/0blYHqWlOgivUNiGEkIBAwRUSlDR6DRYdXASN3nHQpEmcEu0bRuL301muZWzQABcWcVtCCCGEEA9o9Bp8e/pbu+2V5KaxOHo933Em1DYhhJCAQMEVEpSUciVSn0mFUq50mnZs10RsOOlicEWqBIalcltCCCGEEA8o5Ur8PvZ3u+2Vdg0ikV2sRk6J2n4m1DYhhJCAQMEVEpQ0eg2WHl/qtOcKAAxtWxcn0guQV+qg4cLTa4CrS7ktIYQQQogHNHoNVl9Ybbe9EhEiQ9M4Jc5lFdnPhNomhBASECi4QoKSVq9Fynnnc64AQN3IELRrEIVdl3KcZ8yM45oZjWsmhBBCiGe0ei02pdmfcwUA2jaIxLmsYvuZUNuEEEICAgVXSFBSypXY8tgWl4YFAcDgNgnYcfGO84RSJTBoC3W9JYQQQojHlHIlfh75s8P2Stv6kbh4u8R+JtQ2IYSQgEDBFRKU1Do1FqUuglrnwlAfAP1axCE1LQ+MMccJ9Wpu0ji9a/kSQgghhNij1qmx5PQSh+2Vu+KU+Ce31H4m1DYhhJCAQMEVEpT0TI/UzFTomd6l9B0aRkOl1uFajoPGCwAwPZCbym0JIYQQQjygZ3ocyz7msL1yV3w4/slR2f8CiNomhBASECi4QoJSmCwMKeNTECYLcym9XCpG10YxOPSPk+UOpWFAvxRuSwghhBDigTBZGL6/93uH7ZXGdcJQptXjjr0Vg6htQgghAYGCKyQoqXVqzNk1x+VhQQCQ3DQWh50FV/Rq4PQc6npLCCGEEI+pdWp8fPRjh+2VEJkEDaJCkZajsp2A2iaEEBIQKLhCgpKBGZBZnAkDM7h8THLTWBy9XuA4ETMA5ZnclhBCCCHEAwZmwC3VLaftlbvilUizN+8KtU0IISQgSP1dAEJ8IVQWiqVjlrp1TPsGUbhZWI4ClQYxSrntRNJQoKd7+RJCCCGE2BIqC8Un/T9BqCzUYbrGdcKQkV9u+0VqmxBCSECgniskKFXoKjB9y3RU6CpcPiYqTIZGsWE4c7PIfiJ9BXBsOrclhBBCCPFAha4Csw/MdtpeaRAdiqxCO8EVapsQQkhAoOAKISY6NIzC2SwHwRVCCCGEkGrW0FFwhRBCSECgYUEkKIVIQ7Bo2CK3j2vXMBJnHfVckYQA3dzPlxBCCCHEUog0BO/1eQ8h0hCH6Rz2XKG2CSGEBATquUKCUrm2HFM2TkG51r1veTo0jHI8LEhXDhyawm0JIYQQQjxQri3HjN0znLZXGkSH4nZxBXR6G5PWUtuEEEICAgVXSFASi8RIjEyEWOTeLd66XiQy8stRptHZTiASA6GJ3JYQQgghxANikRj1lfWdtlfqRigAANklNpZbprYJIYQEBHoXJkFJIVVgzoA5UEgVbh0XFy5HVKgM1+6obCeQKICOc7gtIYQQQogHFFIFXu/+utP2ilQiRt3IENtDg6htQgghAYGCKyQolWnLMD5lPMq0ZW4dJxKJ0DwhHFdzSmwn0JUBe8dzW0IIIYQQD5Rpy/Ds1mddaq/Uj7ITXKG2CSGEBAQKrpCgJBFJ0DuxNyQiidvHNo8Px9U7pbZfFEmAuN7clhBCCCHEAxKRBN3qdnOpvZIQEYIcm8OCqG1CCCGBgFYLIkFJIVVgeu/pVTq2eUI4jt7It/2iRAG0qVq+hBBCCCGmFFIFnu/4vEvDmOMjFMgptRFcobYJIYQEBOq5QoKSSqPCsFXDoNLYmTvFgeYJ4bhir+eKTgXsGMZtCSGEEEI8oNKo8PAfD7vUXomPUNjuuUJtE0IICQgUXCFBSSaRYXzb8ZBJZG4f2zwhHDfyyqC1tdyhSAY0Gs9tCSGEEEI8IJPIMPqu0S61V+LC7QRXqG1CCCEBgYIrJCjJJXJM6ToFconc7WMbRIdCBNiZkV8ONJ/CbQkhhBBCPCCXyPFom0ddaq/ERyiQW6qxfoHaJoQQEhCCcs6VixcvIjU1FVKpFL1790bz5s3NXt+2bRtOnjxpti8uLg5PPvlk9RWS+JRKo8KQlUOwbfI2KOVKt46ViEVIjAlFRn45GtexOFanArYPAQZvA6Tu5UsIIaT2ys/Px65du5CTk4M2bdrgnnvuMXs9PT0d69atszru6aefRmxsbHUVk1QzlUaFUb+Nws6ndiIiJMJhWofDgqhtQgghfhdUPVcYYxg9ejTGjRuHvXv34o8//kCHDh0wb948s3S//PILvv/+e9y+fVv4l5eX56dSE1+QS+SY3mt6lXquAEBSbBjS820saSiWc5PGienbIUIIIa756quv0LVrV6xatQrHjx/HI488gv79+6OsrPJz5vLly3jjjTfM2ia3b9+GXq/3Y8mJr8klckztONWl9kpcuBz5KjX0Bmb+ArVNCCEkIARVzxXGGKZOnYpRo0YJ+1JSUjBhwgSMHz8eLVu2FPZ36NABH3/8sT+KSaqBTCLD+Hbjq3x8I7vBFeO4ZkIIIcRFbdu2xblz56BUcr0KcnNz0apVK3z++eeYOXOmWVpqm9QuMokMo5u5PueKgQF5KjUSIkIqX6C2CSGEBISg6rkiFovNAisA0L9/fwDA1atXzfZnZWVh8eLFWL16Na5du1ZtZSTVo1RTinaL26FUY2fVHycaxYYhw1ZwRVsK/NGO2xJCCCEuGDRokBBYAbihyO3atcOVK1es0i5fvhzfffcd9u/fX51FJH5SqilF/3X9XWqvhMgkiAyRWg8NorYJIYQEhKDquWLL+vXrIZVK0aVLF7P9BQUFOHnyJG7evImnn34ac+bMsfr2yJRarYZaXflhVlxcDAAwGAwwGGysKlMFBoMBjDGv5VebycVyfHzvx5CL5cLfyJ26bRgdgt9P37JOL5IDnT/mtvR3EtC96ztUt75Ddetbzuq3Ntd7RkYGjhw5gkmTJpntj4mJwZYtWyCVSvH222+jS5cu2Lhxo1lgxhS1TWo+uViOOb3mCO0VZ2KVcuSVqs3TUtvEJrp3fYfq1reofn3H122TgA+ubN68GWfPnnWYZsqUKYiOjrbaf+rUKbzxxhuYNWsW6tevL+yfNm0avvnmG4hEIgDA2rVr8cgjj2DgwIHo1auXzXMsWLAA7733ntX+nJwcVFRUuHFF9hkMBhQVFYExBrE4qDoV+UWXiC7Iz80H4H7dhovUuJFXijt37li/KOkCGPMlHLp3fYfq1neobn3LWf2WlJT4oVTekZaWhvXr1ztMc8899yA5Odlqf0VFBSZOnIi2bdviqaeeEva3bNkSV65cQZ06dQAAt27dQufOnTFnzhwsXLjQ5jmobVLzGQwGdI7ojPzcfNfaJzIRbtzKRctIiwcAaptYoXvXd6hufYvq13d83TYJ+OBKUVERbt++7TCNrcneLly4gKFDh2LixImYM2eO2WudOnUy+33ixIl49dVXsX37drvBlZkzZ2L69OnC78XFxUhKSkJ8fDwiIyNdvBrHDAYDRCIR4uPj6T+Sh0rUJWj3TTucm3YOEYoIt+tWHq5BccUFKKNioVSY/DfRlkD0ZzuwEecAmeNZ/WsTund9h+rWd6hufctZ/YaEhNg4qmZQq9VO2yYqlcrmcWPHjkVeXh527doFhUIhvNaoUSOztPXr18eECROwbds2u+egtknNV1RehO4/dce5aecQFRrlNH18VDoMslAkJCRU7qS2iU107/oO1a1vUf36jq/bJgEfXJk4cSImTpzo1jGXLl3CoEGDMHLkSHz33XdCDxVHpFKp0J3WFoVCYdYI4onFYq/e9CKRyOt51kZKhRIp41OgVCiFunSnbmOUCiikYtwp1aBZqMns+zIl0DcFIpkSoL+RGbp3fYfq1neobn3LUf3W5Dpv06aN2xPPajQajBs3DteuXcOuXbvMetTaQ22T4KdUKPHdkO/M2iuOxCjlKCrXmaeltolddO/6DtWtb1H9+o4v2yZB99e6fPkyBg4ciPvuuw9Lly61qiC9Xo+LFy+a7du2bRsyMzNxzz33VGdRiQ9JxVL0TuoNqbhq8UORSIR6USG4XWTRrVosBeJ7c1tCCCHEBXxg5fLly9i5cycaNGhglebcuXNmvxcXF2P9+vXUNglyUrEU3et1d7m9EhMmR0GZxnwntU0IISQgBNW7cHl5OQYNGgS9Xo/WrVtj0aJFwmvDhg1Dhw4dwBjDpEmT0Lx5c7Rr1w7p6en46aefMHXqVIwcOdKPpSfeVKwuRuKiRGROz0Skompdo+tF2giuaIuB3xKBsZmAzDtdrgkhhAS3l156CX/88QdeffVVrFmzRtjftGlTjBs3DgDw008/YdeuXejXrx8YY1i7di2io6Mxb948fxWbVINidTFa/LcFMl7LQHRotNP0MWEyXMuxGHJGbRNCCAkIQRVcYYzh4YcfBgCriUjLy8sBcF1sjx49it9//x0nTpxAp06d8OKLL6Jr167VXl7iO0qZEqnPpEIps73CgivqRYXgdrFFcEWiBIamcltCCCHEBV26dBHmRjGdq8V0Mv558+bh5MmT2LZtGzQaDT7//HOMGjUKEomkuotLqpFSpsTvD/zucnslOkyOgrIC853UNiGEkIAQVMGVsLAwl8ZAi8VijBkzBmPGjKmGUhF/kIglaJfQzqM8bPZcEUuAaM/yJYQQUrtMmzbNpXSdO3dG586dfVsYElAkYglaxbaCROxaEI0bFqQ130ltE0IICQhBN+cKIQDXzVb0ngjFavsTATpjs+eKthj4ScRtCSGEEEI8UKwuRv1v67vcXokJk6HQcs4VapsQQkhAoOAKCUrh8nBkvJaBcHl4lfOoFxmCbMvgijQceCCD2xJCCCGEeCBcHo5jjx5zub0SFSZDgcoiuEJtE0IICQgUXCFBSQQRIhWREMH5Mtz21I0KwS3LYUEQGSeLq3q+hBBCCCEA116JkEe43F6JCZOjuEIHnd5glgu1TQghxP8ouEKCUommBFEfRqFEU1LlPOpGhiC3VA29gVXu1JUAKVHclhBCCCHEAyWaErT8oaXL7ZWYMDkAoKjcZN4VapsQQkhAoOAKCUoR8ggUvV2ECHlElfOoo5SDMaDAdGyzNAIYX8RtCSGEEEI8ECGPwOWnLrvcXgmVSyCXiFFcoavcSW0TQggJCBRcIUGJgaFYXQwG5jyxHSEyCcIVUuSVmo5tZsYJ46qeLyGEEEIIwLVXSjQlbrVXIkKkKKkwXTGI2iaEEBIIKLhCglKpphRJnyahVFPqUT51wuXIU6krd+hKgQ1J3JYQQgghxAOlmlJ0W93NrfYKF1wx6blCbRNCCAkIUn8XgBBfiFREgs32/BucOkq5ec8VWSQwib4ZIoQQQojnIhWRuDX1FiIVkS4fExEiM++5Qm0TQggJCNRzhQQlvUGPc3fOQW/Qe5RPrFKBvFKTnisGPVB4jtsSQgghhHhAb9DjUv4lt9orESFS8zlXqG1CCCEBgYIrJCiptCr0XtYbKq3Ko3ziwuXIV5n0XNGrgL97c1tCCCGEEA+otCqM2jDKrfaK1bAgapsQQkhAoGFBJChFKiJRPLPY43zqhMuRq7IYFjTB83wJIYQQQiIVkbjy9BXPhwVR24QQQvyOeq6QoKQz6JCakQqdQec8sQN1rIYF6YCcVG5LCCGEEOIBnUGHo7ePutVeseq5Qm0TQggJCBRcIUGpXFuO8SnjUa4t9yifOlbDgsqBfeO5LSGEEEKIB8q15Xhu23NutVeseq5Q24QQQgICDQsiQSlCEYHM6Zke5xNrtVpQBDDW83wJIYQQQiIUETj+2HFEKCJcPiYyRIor2Sa9VKhtQgghAYF6rpCgpDPosOXqFo+HBUWHylFUbvLtkEEHZG2hrreEEEII8ZjOoMPOjJ2eDwuitgkhhPgdBVdIUKrQVWD639NRoavwKJ+oUBmKyrVgjHE79BXAienclhBCCCHEAxW6CsxJneNWe8V6WBC1TQghJBDQsCASlMLl4Tj3wjmP84kMlUJnYCjX6hEmlwKycGCk5/kSQgghhITLw7F7wm6Ey8NdPiYiRIoStemwIGqbEEJIIKCeKyQoafVapJxLgVavdZ7YgYgQGQBUDg0yaIH0FG5LCCGEEOIBrV6LTdc2udVe4XqumA4LorYJIYQEAgqukKCk0Wuw6OAiaPQa54kdkIhFiFBITYIrGuDCIm5LCCGEEOIBjV6Db09/61Z7hZtzxXQ+OGqbEEJIIKBhQSQoKeVKpD6T6pW8IkNlKC43fkMkVQLDvJMvIYQQQmo3pVyJ38f+DqVc6cYxUlRoDdAbGCRiEbVNCCEkQFDPFRKUNHoNlh5f6nHPFYALrgg9V/Qa4OpSbksIIYQQ4gGNXoPVF1a71V4JU0gAAGUa4xc/1DYhhJCAQMEVEpS0ei1Szns+5woARIVKUcwHV5hxXDOjcc2EEEII8YxWr8WmNPfmXAmT8cEVPbeD2iaEEBIQaFgQCUpKuRJbHtvilbwiQ0x6rkiVwCDv5EsIIYSQ2k0pV+LnkT+7NSxIKhFDIRVDpTYZskxtE0II8TvquUKCklqnxqLURVDr1B7nFRUqQzE/cZxezU0ap/c8X0IIIYTUbmqdGktOL3G7vRIml1T2XKG2CSGEBAQKrpCgpGd6pGamQs/0HudlNucK0wO5qdyWEEIIIcQDeqbHsexjbrdXwuRSk2FB1DYhhJBAEHTDglJSUrB//36zffXr18dbb71lti87Oxs//fQTsrOz0aFDB0ycOBFSadBVR60VJgtDyvgUr+QVGSJDen4Z94s0DOjnnXwJIYTUDpcvX8bixYut9s+aNQsJCQnC73q9HmvXrsXp06eRkJCASZMmoV69etVZVFLNwmRh+P7e7xEmC3PrOKVCAhU/oS21TQghJCAEXc+V7du3Y9euXWjSpInwr2HDhmZprly5gg4dOmDz5s2QyWSYPXs2hg8fDr2eIv7BQq1T4//ZO+94Ocp6/3+mbt89Pb2QBEgI0kIxtABGEERBvEEUGyICclEBRRCuwkUN/hS8ihVBkSZFBaRJ7wkGQgmphJTTe9m+M7s78/vjmZndnZ3ds2fPnpTD9/165TXtmaftnM0zn/2W6168riZuQT6XkBeRXwHWXUemtwRBEETFtLW14Ve/+lXB2mTu3LmQJMkqo2kaTjvtNPzP//wPJEnC008/jQMPPBBbtmzZjT0nJholo+AXb/5izOsVjywioeS5Ba27jtYmBEEQu5lJaaqxYMECfOc73yl5/fvf/z4WLlyIJ598EjzP44ILLsCCBQtw77334ktf+tKu6ygxYWi6ho5IBzRdG3ddPpeIuGV6qwHJDrYlCIIgiDFQbm1y33334YUXXsAHH3yA2bNnQ9M0nHDCCbjyyivxyCOP7LpOErsUTdfQHe8e83rFJ+dZrtDahCAIYo9g0lmuAMC2bdvwgx/8AD/72c+watWqgmvpdBpPPPEEvvCFL4Dn2fBnz56NE044AQ8//PBu6C0xEXgkD2779G3wSJ5x1+WVBSSsiPwe4Kjb2JYgCIIgxsBPfvIT/PCHP8T999+PdLowbe4jjzyC448/HrNnzwYA8DyPL37xi3jyySehquru6C6xC/BIHty07KYxr1e8soik+cMPrU0IgiD2CCad5QrHcWhoaIDb7cb27dtx3XXX4etf/zpuueUWAMw0V1EUzJ8/v+C++fPnF8VqyUdRFChKztwyEokAYGa8mlabXwo0TYOu6zWr78NMKpPCNc9fg5+c9BO4Rfe45tYr8YirGXZvNgVu3TXQD/oJILgnoOd7J/TsThw0txMHze3EMtr8ftjmfd68eQiHwxBFEVdffTVuuOEGvPzyy2hoaADA4rIceeSRBffMnz8f6XQaO3fuxH777VdUJ61N9n4SagI/WvUj3HTaTfDKlcdd8co8Ykqa1iZloGd34qC5nVhofieOiV6b7PHiyr333os1a9aULXPttdeiqakJAHDVVVdhzpw51rVzzjkHJ510Es444wwsX74ciQQLTBoIBArqCAaD1jUnVq5cieuvv77ofH9/P1KpVMXjKYemaQiHw9B13bKqIaojlUkhkUigr6/PEleqnVs1EUM0oaKvrw/IphBIJBDt66MFTB707E4cNLcTB83txDLa/Eaj0d3Qq9qwceNG3HrrrWXLfOpTn8LHPvYxAMBBBx2EDRs2wO1m/29cffXV+MhHPoJrrrkGv//97wEAiUTCcW1iXnOC1iZ7Pwk1AUVR0NfXNyZxhcum0T8cobVJGejZnThobicWmt+JY6LXJnu8uNLU1IS5c+eWLZMfEC5fWAGAE088EbNmzcKrr76K5cuXWwuXkZGRgnLDw8NFi5p8rr76alx++eXWcSQSwaxZs9Dc3GwtfsaLpmngOA7Nzc30h1QDfn/m76398cztDNWFVHZnLqPDtN+DDG8LoWd34qC5nThobieW0ebXFBr2Rjwez6hrk1AoZO3nZwQC2A88Z511Fp5++umCc05rE/OaE7Q22fvRNA0rT1g55vltDA1C03Vam5SBnt2Jg+Z2YqH5nTgmem2yx4srJ598Mk4++eRx1aGqquXbPHv2bPj9fmzevBmf+MQnrDKbN2/GAQccULIOl8sFl8tVdJ7n+Zo+9BzH1bzODyPJdBKXPnkpbjn1FsuPudq59bslxJUsuy+TBNZeCiy5hXybbdCzO3HQ3E4cNLcTS7n53ZvnfJ999ikbnLYS8tcmAHDAAQdg8+bNBWU2b94Mr9db9MORCa1N9n6S6SS++/J3ceuZt8In+iq+z+8S0RdVaG0yCvTsThw0txMLze/EMZFrk0n1aWUyGbz00ksF5+6991709vZaAg3P8/jsZz+LO+64wzKZXbduHV577TWcffbZu7zPxMTAczxmBmeC58b/iPtcApLpLLKaDnA84JnJtgRBEARRAS+99BIymYx13NnZifvvv7/gx6Ozzz4bq1evxrp16wCweCp//vOfcdZZZ0EU9/jfwogq4Tke03zTxrxe8ch5mQxpbUIQBLFHMKn+t+Y4Dj/96U9x9dVXY/HixWhra8PLL7+MG264AcuWLbPK3XjjjVi2bBmOOOIIHHrooXjiiSfwxS9+EWecccZu7D1RS1yiC9edcF1N6vLK7M8kmc7C73IBB9WmXoIgCOLDwVtvvYWLLroIS5Ysga7reOKJJ3DUUUfhhhtusMqcfvrp+OpXv4qTTjoJp556Kt59910kk0n87Gc/2409JyYal+jCdw//LlxisQVSOXyuvEyGAq1NCIIg9gQmlbgiCAKeeuopvPXWW3j77bdx6qmn4vbbb8fMmTMLyk2dOhXvvPMOnnzySfT29uIb3/gGjj322N3Ua2IiSKQT+MrDX8Ffz/wrvFLlAeKc8MoCq1PJwC+owOqvAEv/Cojjq5cgCIL4cHDZZZfhc5/7HF5++WWoqorLL78cS5YsKSp3++234/zzz8e7776LM888E6eddho8HnLzmMwk0glc8MwF+NvZf4Pf5a/4Po8kIK4a4komQWsTgiCIPYBJJa6YHHbYYTjssMPKlvF4PDjrrLN2UY+IXY3ACVg6cykEThh3XZLAQxZ5xJQMWnwS0LQUqEG9BEEQxIeH6dOn45xzzhm13NFHH42jjz56F/SI2BMQOAFLpiwZ83rFLQlQMkbKUE6gtQlBEMQewKQUVwjCJbpw+dLLRy9YIR5JQCqtMdPbRbWrlyAIgiCIDy8u0YWLDrpozG5BbnNdAtDahCAIYg+BIl8Rk5K4Gscpd5+CuBqvSX0ukYeSyQKZOPD8KWxLEARBEAQxDuJqHOc8fs6Y1ytuiYeSNgLa0tqEIAhij4DEFWJSIgkSVhywApIg1aQ+l8Qz81tOAmavYFuCIAiCIIhxIAkSPjXvU2NerzDLFTNbEK1NCIIg9gTILYiYlMiCjK8f9vWa1ecSDd9mQQYW1K5egiAIgiA+vMiCjHMXnQtZkMd0n1sUkDJjrtDahCAIYo+ALFeISUlcjWPp7Utr6xaUNtyCnlpKprcEQRAEQYybuBrH6Q+dXpVbUCrfLYjWJgRBELsdEleISYksyLj8o5eP+ZegUrCYKxrAyyxoHF+begmCIAiC+PAiCzIuPOjCsVuuGG5Buq7T2oQgCGIPgdyCiEmJJEhYsXhFzeqz3IJ4w6+ZIAiCIAhinEiChE/NH3vMFZfEQ9OBjKaze2ltQhAEsdshyxViUhJTY1j8u8WIqbGa1OeSeKgZDUjHgMcXsy1BEARBEMQ4iKkxLHtg2ZjXKy5RAADmGkRrE4IgiD0CEleISYlbdOPmk2+GW3TXpD4rFbPgBg69mW0JgiAIgiDGgVt047ql1415veKW2BI+ldZobUIQBLGHQG5BxKRE5EWcsuCUmtWXcwsSgem1q5cgCIIgiA8vIi/ixFknQuTHtiSXBR4cZ1iu8C5amxAEQewBkOUKMSmJKlHMvHkmokq0JvWxbEEakI4CD81kW4IgCIIgiHEQVaI47O7Dxrxe4TgOblFgVrW0NiEIgtgjIHGFmJR4JA8eXPEgPJKnJvW5JNMtyAMc+yDbEgRBEARBjAOP5MGty2+tar3C0jFrtDYhCILYQyC3IGJSIvIils5aWrP6CtyCmmtXL0EQBEEQH15EXsThUw8fs1sQkEvHTGsTgiCIPQOyXCEmJRElguDKICJKpCb1WQFt0xHggSDbEgRBEARBjIOIEsG+f963qvUKE1c0WpsQBEHsIZC4QkxKfJIPq89fDZ/kq0l9kmCkYhZ8wMmr2ZYgCIIgCGIc+CQfHjvzsarWK7lMhrQ2IQiC2BMgtyBiUiLwAha3LK5ZfZLAIaPpAC8AdbWrlyAIgiCIDy8CL2D/hv0h8MKY77UsV2htQhAEsUdAlivEpCSiRMBdz9XMLUjgeWSyOjO5vZcj01uCIAiCIMZNRIlg2h+nVekWxLOYK7Q2IQiC2CMgcYWYlPhlP9ova4df9tekPkngkNV0QPQDZ7azLUEQBEEQxDjwy36sPXdtVesVtyQglcnS2oQgCGIPgcQVYlLCgUPQFQQHrib1CTyHdFYDwAFSkG0JgiAIgiDGAQcOATlQ1XrFLRpuQbQ2IQiC2CMgcYWYlETVKEI3hhBVozWpTxR4ZrmSiQIPhtiWIAiCIAhiHETVKPb7y35VrVdcplsQrU0IgiD2CEhcISYlATmA8FVhBORATeoTeQ5pTQfEALAizLYEQRAEQRDjICAH8P5571e1XnGLApSMRmsTgiCIPQQSV4hJiQ4dESUCHXpN6hN4DllNA2AEta1RvQRBEARBfHjRoSOqRqtar8gib7gs09qEIAhiT4DElfGSTLJtKsX+mecUhe0nErn9eBxQ1dx+Os32YzEgkwEAcHn7iESAbDa3r2mArrN9XWfHESMyfDab289kgGg0tx+Lsf10mrULsH6Y+4rC+mnu13hMiEZ3+Zhi0UHM+uUsxNRYTcYkCRze3tiBH93/CvDwLGC4mz4nGtMuGxMXj0+6MU3Gz4nGVMWYCOJDTkyNYck9S9h6ZYxIAg81owGZGFubZMZeB0EQBFE7SFwZL9/7Htv+4AfsHwBceimwciXb/8pXgN/+lu2fdRZw551sf/ly4OGH2f5RRwHPPQcAaDr+eOCNN9j5mTOBzZvZfigEdHWxRWkoxLZdXWwfYOVmzmT7b7wBLFrE9p97jtUPsPaWL2f7d97J+gOw/n3lK2x/5UrW/xqOCYsW7fIxBW+7C/r6/0LQFQRWrgT3rW8BALhrrqlqTALP4+E7r8COf74EfEEHDjqKPqe8MXGzZ0+6Me1Jn1PDaadNujHtCZ8T99WvAgC4G2+cNGPakz4nvr6+/JhefBEE8WEn6Aqi+8Jutl4ZI5JoBNuXgmxtIo29DoIgCKJ2cLqukw1hFUQiEYRCIYR7ehCcMiX3q6DbzX4Z5HnA5WK/DAoC24/HAUkCZJntyzI7jsUAtxsaz6N/+3Y0z54NXpbZL30+H7s/EgH8foDj2MI1EGC/DsZiQDDIfhmMx9l+JsP6EAiw/VSK3ZtOs18mfT62TafZvqKw+71etq9pgMdTkzFBFFl/PR62v4vGlE0msLl/IxbOPBRCOgMtk0FfNIqWYBA8z495TE9uHsAVf3kNaVHAuu/Ng8c1B/D6d+mY9uTPSYtG0ZdKoaWhAbyiTIox7SmfkyYI6N+xA82zZrHvhUkwpj3lc9LSafTFYmgJhdgvDZNgTHvS56RFIux7obERfDJZNKbI0BBCjY0Ih8MIBumlsBZYa5Mazqmmaejr60NLSwv7/5OoKelMGqveX4Wj9zsakiiN6d6bn96C/piClWcuBiKbgeBCgBcmqKd7H/TsThw0txMLze/EMdrcjvf/0XGLK5lMBi+99BLS6TQ++tGPoq6ubjzVjZsvfelLSJqm0HksX74cF110EQDg1ltvxdNPP11wffbs2bj55psrbocWMHs2ESWCmTfPRMflHQi6guOe26c39OAbd62Fn09g3SHng/9MB/1ClAc9uxMHze3EQXM7sUz0AmY03nvvPWzbtg0HHXQQ5s2bV/P6x8Jvf/tbvPDCC0Xn/X4/7rjjDgCsv9dff31RmV//+teYPn16Re3Q2mTvYyQ5glm/nIX2y9pR56kb0723PLcVOwbjuPkz84CHZgK0NimAnt2Jg+Z2YqH5nTgmem0ijqVwT08Pzj//fLzzzjv4+Mc/jl/84hdYvnw53n33XQDA1KlT8cwzz+DAAw8cc0dqxWc/+1lk8vy4d+zYgSuvvBKf+tSnrHNvvfUWOjs7ccUVV1jnQqZJNjEpCLqCiFwdqVl9ksD++GKaF2sO34ErbnoTt355CRZPp+eGIAhid6JpGq699lrce++9mDZtGv7yl7/g9ttvxy9+8QsAAM/zuOWWW/DNb35zt/XxqKOOwpQpUwrO/fd//zeOOOII67i3txf/+Mc/8OCDDxaUCwQoA8xkJugKYuvXtlblFsQC2hruQGfXbs1DEARBVMeYxJUrrrgC7e3tuOCCC/DQQw/htNNOw5w5c3D33XcDAK6++mpcc801eOSRRyaks5Vw5plnFhxfe+21CIVCWLFiRcH5GTNm4L/+6792Yc+IXUlGy+CNzjdwxIwjIPJjeswd4Ti2FZDF+xueRs+ID09t6MXi6SE89HYHZjf4sGRO/bjbIQiCIMbGI488gltuuQX//d//je3bt+NLX/oS+vr68Mwzz2CfffbBww8/jCuvvBKf//znUV+/e76nDz/8cBx++OHW8TvvvIPe3l5ccMEFRWVpbfLhIqNl8GbPmzi56WTIvDyme1lA2yygZYDBN4DGI4AarHkIgiCI6hjTN/Czzz6Ll156CQsXLsQ555yDRYsWobW1FbONYJa/+c1vcOSRR05IR6shm83ijjvuwLnnnguv11twbf369fjyl7+MUCiE4447DmefffZu6iUxESTTSax4cAU2XbIJAdf4f/UTeKauuHkFp0e+g5/xv8H2/hgSagaX3f8uFk4N4N/fOX7c7RAEQRBj49lnn8X3v/99XHvttQCAAw88EN/+9rex3Aj6e8UVV+C+++7D+vXrcdxxx+3OrlrcfvvtmD59Oj75yU8WXbv44ouhKAoWL16Mr3/962RZO8lJppP4xrPfwKb9N0EWxyiumJYr2STw6grg9E0AT5ZOBEEQu4sxiSv9/f3Yb7/9AMDazpo1y7o+e/Zs9PX11bB74+Opp55CZ2dn0S9DoijiqKOOwrJly9DZ2YlvfvOb+Nvf/oaHHnqoZF2KokAxU1aC+WMBzBxZ07Sa9FfTNOi6XrP6Psz4JB/avtMGIPcZjWtujdBEcc2Lc3ofQEKPoT+qYENnGACwczAONZ2BKHw4/SLp2Z04aG4nDprbiWW0+a3VvPf392PZsmXW8X777VewNgH2rPVJKpXCPffcg29+85sQhMLgox/96EexaNEiiKKIv/71r7jpppuwZs0azDSzLdmgtcnej0/yYe25a+GTfGOeY4kH1EwWmuADzmBrHtDnZEHP7sRBczux0PxOHBO9NhmTuKLruhX4xdxypr+Ebb9WlAoCl89vfvMbTJ06tej8bbfdhsMPPxyHHHJIwfkbbrihwDT49NNPx5IlS/DII4/gjDPOcGxj5cqVjoHm+vv7kTIzMYwTTdMQDocL5pmojoyWwSudr+C4GcdB5MVxz20kHAUAuAUNs5VXwDUehZ6RODa09mDxVB829caxcUcXpgZl9EZVtPilCfl72FOhZ3fioLmdOGhuJ5bR5jcajdasnfz6eZ4v+v7lOA61TI64du1arDRTWpfgi1/8YpGrMgD885//xMjICM4///yC80cccQRee+01aywXXHABDjnkEFx99dW46667HNugtcnej5pR8dTWp3BK9pQxW64k4zHEkwr6erogD78Ctf44cgvKg57diYPmdmKh+Z04JnptMuZv4Ouuu67sca1xCgJnxynYW19fHx577DH85je/Kbpm97k+9NBDMWfOHLzxxhslxZWrr74al19+uXUciUQwa9YsNDc31zQiP8dxaG5upj+kcRJTY/jxQz/G6vNXwy/7xz23DTH262KjW8OVU/6E2zwn4onNMUSzEhZMDWEomUVK8GIwK+KM29fiN58/BKd9ZBo6hhO48u/vYeVZB2JOo6/Ww9xjoGd34qC5nThobieW0ebX7XbXrK0HHngA69evBwBs3Lix4Ng8V0tmzJiBc845p2yZRYsWOZ6//fbbsXz5cuyzzz4F5+3uP5Ik4bTTTsOjjz5asg1am+z9RFIR/OK9X2DFUSsQdI/tM2vuzQL8CFoag+De+jH0BasByT9BPd37oGd34qC5nVhofieOiV6bjElcmTNnjpUy0OnYPFdL7EHgKuXOO++ELMv4/Oc/P2pZXdcRjUaLzHPzcblccLlcRed5nq/pQ89xXM3r/DASdAex4ZINBefGM7fms8HJAZz8/u/wzRMaEVNGMBhPo8nvwvQ6D3qjCt7YOQwAePWDQZx+8Az88+0uvL5jCH9/qxPfO2Xh+Ae2B0PP7sRBcztx0NxOLOXmt1Zz3tLSgieeeAJr1qyxzq1Zs6bgGAB8vtoJ3FOnTq0q8OyOHTvwwgsv4P7776+ofDgcprXJJCfoDuKls19C0B0c8/zKogA1q4F3BYHTN+DDYy9bOfTsThw0txMLze/EMZFrkzGJKzt37hxXY7uSP//5zzjnnHOKrFrS6TQeeOABnHvuuda5m266CUNDQwXpmom9m3Q2jYc3P4wzF54JSZDGXZ8ZSsUn6Tgt9CoavQsAAN3hJBZPD6HBJ2M4rmL7QBz7TwlgYzfze1/XEcbCqQGsbR0edx8IgiCIYn73u9/t7i5UzJ///Gc0NTU5Wsn+4x//wMknn2ytW9577z387W9/w6WXXrqru0nsQtLZNB7d9ii+3PhluPhioawcLKCtBmhpoONhYOaZAD/+NQ9BEARRHZNSClu1ahU2bdrkmOJQEAQ8++yzmDdvHk499VQceOCBWLlyJW6//faqLGSIPRM1q+Lm12+GmlVrUp/pv+8Ts/h600No8rLjrpEU6rwS6jwShhNptA7Gcey+TegcTgIAdg7EcfLiqdgxEAcAaJqOh97uQFzJAACUTBZfuv0/eOjtjpr0kyAIgtgz0TQNd9xxB77yla9AlotjayQSCRx88ME44YQTsGzZMhx55JH43Oc+hx/+8Ie7obfErkLNqvjjuj9WtV5xCUa2IE0FNt3MtgRBEMRuo2LLlbvvvrviSr/4xS9W1Zla0dDQgIceeghHHXVU0TWe5/GXv/wF3d3dWLduHerr67F48eKamgsTux+f7MPq81fXrD7eEFc4yY+ztt2EPx4dhEvk0TmSRMgjoc4rIZxMYyiu4uBZdbj91R2IptJoH07g+H2b8OvntiKVzuL17YO47P538b1TUrjkxAV45f0BvLJ1AAMxFZ851DkbBEEQBOHMK6+8gtbW1orKHn/88Zg9e/YE96g08Xgcv/zlL0umg/7Sl76Ez372s3jrrbegqioWLVqEadOm7eJeErsan+zDY595DD557OtQSeShZjRA9AGn1G7NQxAEQVRHxeLKVVddZe3ruo6uri4AgN/vB8dxVmTd6dOn73ZxZeHChVi4sHx8i2nTptGiZRKjZlXc+e6d+PLBX4YsjC36vhO84cjsFbP4XMNTcPMHw+cSMRRXEXRLqPPK2NobxUgyjXlNPvAcsLUvhnRWx0dmhiALPLpGkli9fRAAsGbHEC45EVjbNowT92/GC1v6kVAz8MrFf5KbeyJ4desAzj92nw9VBiKCIIjRuPvuu/H4449bxwMDA1AUBZIkIRgMYmhoCLquo76+HnffffduFVcCgcCocVq8Xi+OPfbYXdQjYk9Azaq4Z9M9uKTxErj5sQVSlAUealYDsiqw405gny8DNVjzEARBENVRsVtQR0eH9e+yyy7D8ccfj02bNiEajSISiWDTpk047rjjCqLWE8TuIp1N48GNDyKdTdekPtNyxSfp+GToVXhEDT4XCzLokngE3SJGkmmEk2nU+2SEPBJaB+PwygJcooAGn4yhuIqdA3GcsngKNvewmCxtgwkcNa8RIY+E7f3MdWhjVwRbenJpwP730Y348eOb8F5nuCZjIQiCmCz88Y9/tNYmTz/9NJqamvCvf/0LqVQKAwMDGB4exlVXXYUDDjgAp5xyyu7uLkEUkc6m8ej2R6tar0iCEXNFTwNtD7ItQRAEsduoKubKH//4R9x5550F1iELFy7EnXfeiT/+8Y816xxBVItP9uGpLz5VlZmtE5ZbkOjDl3fcANkdhM+wMnGJAtySgL6IAl0H6jwS6r0ydg4kUOdhgeUa/TIG4yp2DiSwbL8W9EYUKJksOoYTmFXvxdwmH3YMxJHVdJz261fwuVuZeW8qncXa1mHsN8WPVz8YcOybruv4+VOb8eKWvpqMlSAIYm/krrvuwmWXXYZPfepTVrT/UCiElStXIpVKYcOGDaPUQBC7Hp/sw32fvK+q9Yoscjm3oJOeYluCIAhit1GVuNLe3g5RLHZfkCQJ7e3t4+4UQYwXJaPg5tU3Q8koNanPzMrlFTI4v+khuPk0ZJGddIk83JKAnkgKssDDKwsIeSW0DSUQNMQV03KlaySJg2eFIPAc+iIKOoaTmFnvwdSgC31RBa2DzHplJJFG10gS2/vjkAUepx80He/nWbPks64jjN++sA3/++jGsmMIJ+kXLYIgJi+l1iYArU+IPRclo+AP6/5Q1XpFFgRmuZJVWEDbbG3WPARBEER1VCWuHHPMMbjwwgvR3d1tnevu7saFF16IY445pmadI4hqyepZrO5YjayerUl9puWKVwIO826GW2S+zgDglnh4JIHFX/FI4DgOIQ8TV0KGuFLnZeJKVMmgwSej2e9C50gSg3EVU4JuTAm60RdJ4YO+GBZODWBuoxcf9MXQMZzAjHoPFrT48UF/DADQOhjHqb96BR/0MbFlzY4hHLlPA7YPxNEfdV5YPbauCwdf/zRWbxusyXwQBEHsaRxzzDG48cYbsWrVKuucqqq46aab8M4772DJkiW7sXcE4UxWz2Jt79qq1iuSyCGd1aFlM8DAaqBGax6CIAiiOqoSV/70pz+hs7MTs2bNwpw5czB79mzMmjUL3d3duO2222rdR4IYM17JiwdXPAiv5K1JfWZAW17y4ZK2qyHKPkiGuCILAjwyi7/izdv2hFmaZgDwuwR0h1l65pBHQoNPRttgwjpuCTDLlW39ccxv9mOfJh92DsbROZLEzHovZtR50BNOAQD+sbYDm7oj+PvaTgDA+q4wjl3QhFkNnoJYLfk88k6Xse0sOca7X2/FVf9YB13Xq54ngiCI3cUFF1yA008/Hccddxyam5ux7777oq6uDitXrsSdd96JqVOn7u4uEkQRXsmLP338T1WtV8wfedK8GzjuQUCszZqHIAiCqI6qxJV58+bhrbfewjPPPIMrr7wS3//+9/HMM8/gzTffxNy5c2vcRYIYO0pGwXUvXlcztyAzS4+Lz+A7U+6BoKuWq5BL4uGWDKHFcBVyiwJ6IykE3Uxc8coiukaY25BHEhD0iOgYTkASOLglHiEPS+XcF01hasiNaYaY0jmcxIw6ZtkyEFORzmrY2B3F/GYfNnSxALddI0nMavBgXpMfOwy3osfXdeNn/94MgMVkWds6jAuPn4d3O5yD4mqajp88vgn3vdGODV0RxzK6ruOW57bila39NZhRgiCI2iKKIv70pz9h48aN+PnPf46LL74Yd911F7Zv344VK1bs7u4RhCNKRsEv3vxFVesV0RRX1BSw7jpyCyIIgtjNVJyK2Q7HcTjxxBNx4okn1rI/BFETNF1DR6QDmq7VpD7BEFdEXsdUaRAcdJgGHmbMFXMfANyygIymWxYtPpeIrpGk5TYUdEvoGE4i6DaOPRIiyTRGEmnMb/bBl2ExXLIaMLvBiya/DI4D+qMKtvXHcPpB0/Dgmx0AgK6RFGbUeTG9zo3uEWYdc92jG9AfVXDe0XMhizyG4io+fch0/Pm1HchqOgS+MKXz1r4YOA5Ytl8z3tg5hANnhIrm4L3OMG565n3MafTixe+e4JgWejih4pbnt+GiZfMxNTS2lJIEQRC1YP/998f++++/u7tBEBWh6Rq6491VrVckgf0/nM1mgWQHUKM1D0EQBFEdVVmuEMSejkfy4LZP3waP5KlJfaaOoAseXNXxLfCiB5qhrrhEAR5DXDEtVzw2scXvEtA1kkTIw/TMkMcQV4yYLEGPhEgqjaG4inqfjKaACwMxFZFUGiGPCFHgUeeRMBhT0TaUwNL5jeiLpqBksuiJpDC9zo1pIQ+6wylEU2n0RxVMC7nxZuswOoaTCHkk7NsSQDqrYzCmQNN0fPnPa/DPt5hA815nGIunB3HQzFBJy5XV2wZx7IImtA8l0BNJOZa5c3Ur7li1E7e+vL3kXL78fj+ueOBdluGAIAiCID7EeCQPblp2U1XrFfOHkgznAo66DRBrs+YhCIIgqoPEFWJSksqkcPlTlyOVcRYBxgoHwy2IU3HttD+B0xTLckXOs1yR8oLcmtcA5hYUSWUKxJSO4QSCbia2BN0SIskMRhIq6r0yGn0uDMYUhJPponuymo7F00LQdGBrbwxZTUeT34XmgAsDMRa3pcEnY8mceuwYYHFbZtR5IIs8mvwudIdTeLt9GC+/348/vbIDANA5nMSsBi9mNXjROZy0xm3GiQGYALN0fiPmN/uxodNZgPnP9iEs268Zr5VIGw0AP31iE/7xVgee31w6dfQl97yF79z3dsnrANA+lCCBhiAIgtirSWVS+NGqH1W1XhEN/+RsOgmsvRzI1mbNQxAEQVQHiSsEMQbMX4k4Dpa4IvCcJaKY3jY5yxW29bvEgvM+l4iucMoSTkIekVmuGOJKwC0ipmQQSaatuC1Bt4TWoQRkgUfQI6LRJ2Nbf8wSd+q9EkYSaewciGOfJh/2afKhbTCB7pEkphkuOtNCbnSHk9jYFcG8Jh+29cWQzmroMgSYmXUedBmCytMberB05fOWUNI+nMTsBi/2afKhdShRNDe6rmNdZxhfPXou3u+LQskUZy0YjqvY3BPFWYfOwKptzgLMtv4YHn+vGw+/04WukaRjmQ/6Yjju/72AnzxeOv30QEzBb1/4AHElU7IMQRAEQeytmGuOjEaB6AmCIPYESFwhJiVu0Y2bT7kZbrHGcT94N37cfQF40QPkhRwReTMmi2m5Uugm5LJtTZHFzC7klgQk01mMJNKo80rwuQTElSyiqQxCXtNyRUTrYAJ1XiPdszcXtwVg6Z6HEyoG4yqa/DJaAi70xxSEkxnUeWUAwJSgCz3hFLb1x3H8fs3gOKB1MIGuMBNXptd50D2SgqbpeG4TsywxLUw6hhKY1eA1yjDR44XNfVbg3KFEBgk1i6PmNUAS+AILGJOtfTFMC7lxzIImbOp2tn55u20Eh8+px8Gz6vDGziHHMo+v60ZLwIV/vdsFrcSi8vcvbsPPn9qCu15vdbwOAH98aRs+/ZtXkVRLp6/c3BPBB32xktcJgiAIolrcohvXH319VesVjuMg8hyynAtYcjMgUKwzgiCI3QmJK8SkJJlO4uv/+jqSaWfLh2oRkcKNM38NLptEfkxY06KFN7b2ALeybZtzG8qJK7oORFMZeGUBPpeIuIPlSttQHPWGUMKC4iYQNOK41HklhBNpDMdVNPhkNPpzrkUhy0JGRjiZQetgHPObfZgacqM3kkJ/VEFzwIWpITfUrIbhhIqtfVEsndeIzT0RJNUsBuMqZtZ7ML3ObVm3/L+ntuD3L25D62AcvVEVjT4ZXlnEzDoP2g1xZX1nGJ2GGLO1L4oFLX7s0+zDzsFi6xez/IEzQjhgWhCbup1TS6/rGMH5x+6DmJJB+7BzPa9s7cdJC1vwQgn3o6ym4w8vbcO6jjCe29zrWCaaSuMT//cKVvxhVUkRJ5xI4+w/rMa/13c7XgeAVDqL5zb1Iku/LhIEQRB5JNNJXPHSFVWvVwSeQzadAP7zdSBT2zUPQRAEMTZIXCEmJTzHY2ZwJniuNo+4DuOlmOPRk24Ezwng87LlmBYrpgVLzlKl0IIlX0wBAFkotGgxr/lkEVElg6iSQcCIy+J3iWgfSqLOmx+DJc9yxSMjqmQwEFOMuC2yFRQ3mBdI1wyc2+R3YUrQjZ5wilnIeCS4JQEukcdIMo0P+mI4aWEL2oYSGEqo4Dig3itjWsiDrpEUMlkN2/piqPdKeLttBD1RFdPrWDC9mQ1eKybK6be8igvvehMASxs9s96L6SEPBmKKFTMllue60z6UwD5NPsxt9JYUTjZ1R3DIrDrMb/Y7CjBxJYOtfTF8aekcbOyKOAojm7ojyGR1fPXouVizw9lC5pWtA5jX5ENW0/Fep3Ma64ff6cSanUP49XMfOF4HgD++tB3n//VN/MMIIOzEH17ahs/+fhVS6dJWNC9s6cPaVue+mmQ1HbpOIg5BEMTeAM/xmOabVvV6ReQ5ZHQAnplAjdY8BEEQRHXQtzAxKXGJLlx3wnVwia7aVsy78X+95wKiC5yD5Yq5FY30iKaoYga6NcUUe3YhU4QBALfILFesYyknyPRHlVyAW7eIzuGkJb54Xaxc50gSDT4ZDT7mJpRvuRL0iAgn0xhOpBHySpgSdKM3mioInFvvldETTiGSyuDIfRrQNZLCUExF0C1B4Dk0+mSMJFTsHEyA44ATF7Zg52AC/bE0pgbZfDf7XRiMqVjfxQSJzd1RqBnNspBpDrjAcxx6Iym0DyVw4I+ewr/e7QIAdIdTmBZyY1qe+1E+mayGnkgKM80AvA5ldgzEEXRLOHp+I2JqBn1RpajMB30x7DvFj4/MKJ0haUNXGIfPZS5K6zpGHMus2jaAi5bNx8buCMKJtGOZR9d14dDZdXjiPWfrFiWTxe9e+ABrW4fx7CZnK5rhhIrz/vIGvvLnN0oG8m0djGP+D57A717c5ngdYPO3scR4CYIgiF2LS3Thu4d/t+r1isBzyEAGDroOEGq85iEIgiDGBIkrxKQkkU5gxYMrkEg7Wz5Ui6Qn8dvZK8FnEzbLFUNc4UyRpdAipZRbkMsSX3J1uSQePldObMmP25JMZy1hJuiR0D6csEQR83zXSBJ1Xhk+l4iEmkU4kXMtCnkkRJJpKytRyCNiJJFGTMnkxW6RsH0gDo4D5rf4kdV0tA0lCixmRpJpdAwnMLPegzkNPnSOJBFVcvFhGv0yhuIKdg7EsWROPSSBx46BuCWuCDyHKQEXukaSeHojExNMt5rucBLT6zyYHnKjO8wyH7y6dQDH/78XMJJQ0WsIJVMCLswoIcCYAXpdooBmv8tyY8pnW38M85v9mNvkQ7tDgF4A2NQdxaJpQSyaFsT7vc5xVzZ2R3DsgibMqPNgQ3exdUtCzWBbfwwXLZuPd9tHHK1KNndHwfMcvnbMPvjPdmfLlOc39+HgmSF4ZAFvtw07lvnH2g54JAF3rW4tab3yh5e24bRfv1LSXYogCILYdSTSCVzwzAVVr1dEgUdWjQOvrAAytV3zEARBEGODxBViUiJwApbOXAqBE0YvXAGzG7z4+X8dhDqvG28lFoLjRXB54ooZa8V0HzJFFktUsbn/mJYqpqiSX5dL5AssWWQrvbNpwWKkd5YEpLM6fHIuDbQkcOiLKAi6Rcv6pSeSylmuuFlGoUgqgzqvhKBbsiw/8mO3bO+Poc4jwe8S4ZZ47Bhgx+b1SDKNwZiKRp8L0+rc6BpJIqZkLYGm3itjKC9z0ZxG5ibUH1PQ7Ge/rDX6XRhOqNjWH8PCqQG83xtDKp3FcCKNKUG3FQ8mq+n42xttaBtK4KX3+9E1kkRLwA1R4DEtlIv/csdrO/A/D68HwMQV00Vpep3HMetQ21ACcxq9mFHnQV9Uccxu1D6UwNxGH2bWexwtZFLpLNqHkthvqh/7lBBpNvdE0eCVcdy+TRhOpDEYV4vKbOqOYNHUIA6aGcKGLmf3ow2dERw6ux6Hza7DO+0jjmXW7BzC1actxGBcQVsJweifb3diXrMPj7zT6XidIAiC2HUInIAlU5ZUvV4ReA5ZnQealgI1WvMQBEEQ1UHiCjEpcYkuXL708pq5BXEchxWHz4LGu3D7wGfAi66CgLYmWcNbw3QPslusmFv7dXtb+ZgCjF2YsWckApj1SlTJwC0JViai3kjKElo8soDeKLMGqfPIVtwWkedyaaJl5m5U72OBcxu8Mrb3xxEyAunWeWVoOtA6lGDuR14ZI8k0IqmsJeI0+pjlSudICrPqvWg2MhcNRFkmI1ZPLnX08kVTsHMgjuEEEx7qvRIafS5oOhBJprGhM4zZDV6s7wyjP6pgiuF+xAQYBbqu48ePb8Jdr7diKK6iK5zC9DqWNWFGncfKXHT1P9dZ7kcDMWZF0xJwQRI49BhWMvn0RFKYEnQX1FFwPZyCLPBo9rsws96DDocy7UMJzG3ywSuLaPLLjmW29sWw/9QA5jR60TbkHJBwU08UB0wLYt+WALb3x4uu67qOTd1RHDKrDnMbfY4ZjiKpNLb3x3HpSQvwdgmBxoSC7xIEQUw8LtGFiw66qOr1ishzSEMGFl1ObkEEQRC7GRJXiElJXI3jlLtPQVwtfgkdD4KWwJ37/A+4TAIO2go0wxXD7iZkt1yxYrIIo//KZIotdssVK+NQXh2evNTOksBDFnkoGa3gHlMQcEs8Am4RnYZrkdmORxbQOZK0shLVeWXsGIxbwolPFsBzwM6BOOp9siWSMNciJuIE3CJiqQzCSRV1XgktATf6IgriSgaBPBelcDKNtqEEDp9bj4ymo3UwAa8sQDT6Jws8hhIqOoaTOGlhC1oHEwXxYeq8LP7LQExFRtPR4JOxuSeCISNjEgA0+WUMxVXsGIjjb2va8evntgKAIfS4wPMcWgJudI2kMBxXce5tr2NrbxQJNYNoKoOpITdmlLBc6Q6nMCXkAsdxJcWVnnAKU0OG0FPvdbRu6QmnMKPOg9kNXgzEFCTUTFGZ9mFmaTO70etolTIYZ/F19m0JYEGL31Fc2dwdxZSgC8fMb0LrYKJk8NyfPrEJx/7s+bLBdde2DmF9iSC/JhRYlyAIojxxNY5zHj+n6vWKwHPQ0zHg+VOATG3XPARBEMTYIHGFmJRIgoQVB6yAJEg1rVfjBDwePhacIBXEXDExf+0XLHcfoz829yAzu5CT5UopXJLdPajYcsUri8Y1ds50GbJivYgClIwGr0sAx3EIuiUMxFTLagVg1i894ZTlBuR3iRiIKfAbcWA4joNXFtE1kkRjnrgSVbKW6OF1iYirWYSTadR5JTQFZPTHUoirGSueTMiI3TKSSGNayIOAW8S2/pjlWsRxHEJeCTsH4shoOj4yI4TeSIqlpzbFFUOg2d4fw/SQGwfOCGF7f7ww/bSXBfbd0BVG0C2ibTCBrKZblisA0GAE6X1yfQ9e+2AQD67tYCKUyKPeK6HJ70JMySCVzuLVrQP47O9XIalm0RtJYWqQCSdm5iU73eFcmZn1zi5K3eEkpoTcaPDJ8MpCkUiT1XT0RRRMCzEBxklc6Qmn0OCT4ZEFzG3yodWhzM5B5qbVHHDBJfKOgpGm6bhvTRu6wyms3j4IgMWNufnpLegzrJ6UTBaf/f1qnP3H1QUCSn6g3cGYgkNveAZ3rd5Z1IbJcFzFz/69uSBblJ2splvtEgRBTDYkQcKn5n2q6vWKyHNI6wIwewXA1XbNQxAEQYwNEleISYksyPj6YV+HLMg1rVfjZNw/dAo4QS5y3wGKLVdMAUa0sgjZLFfGIq6Io4sr9mumO5DpSuQyzpt1ma5DpnADMMuVwbhqWcF4ZAEj8XRhRiNJQHc4hTqvhDqvjGQ6i8FETvTwyQLiSgYjxrmgW8JAVIWmM7cjgIkrQzEVMYWlgW4OuPBBX8zKfgQw8eT93hgCLhH7NPvQHU4hksoJJ/VeGSOJNHqjCrMwqfOgJ5wqEFfqDfFnW18cy/ZvgabraB2MYyjBLFcA5qI0nEhjQ1cYAZeITd3M+qXRxz5nU2gaTqi4d00r1rYOY9W2AfRGUmgxhJN6Q8QBgOv+tQEX3MnST/fkiSsNXhlDRpmEmrGEiR4jQxLHcWgOuDAQK8xuNJzMIKPpaAm6MM2IRWO3CskXcVoCLvQ7ZEjqCacwPeQBx3GYkWdp0xdNWQLHB/0xZDQdnz54Ot41XIcefrsLv37+A/zltZ0AgHfaRhB0i8hqOrYZLkr3v9GG/a59Et1GDJznNvdhJJHG/W+2W+2/srUff1+bS0d9+6s78PsXt+G+NW3WuYSaQTqbE2muffg9HPmT5zAYKx6Pyc1Pb8Gvnt1a8jrAAib3RcqLNE4WQwRBEBOJLMg4d9G5Va9XWLYgCVjwdaDGax6CIAhibJC4QkxK4mocS29fWnO3ID6bwD/nXwE+6xws1LJcMcQUU34xRRYzTotYJuZKKdw2YcR+DAAeKWehwo7t95SI2yIUiiv51/LjuOTK8BiMK/C5RPgNAWcgnrYsZXwuEXElY4kcfpdoxXoxU0b7XCJ6jJfdkEdCnRH/xRRoAJaZ6IO+GJqDLiOOC3N9sbIfeSVkNB3tRvyXJr+MwbjCrFvysh8xASaFWfUeTA258X5vFLqOApFmOMFch05a1IKdg3FEUxlL6BEFHkG3iOF4Gpu7owi6RWzpjRYKPT5mRaPrOu5YtRPPbOzFSELFUEJFoxFnpt4nYziuIp3VsHTl8/jx45ug6zr6ogpaDCuaei8bJwBs6YlC03T0x1Q0eCW4JQENPhkZTUckVSgE9ISTmGa4HzWXEFe6812U6jzoGGZWPEevfB4X370WAEtRvaDFjwOmB/F+bxQA8Pr2Qcxr8mHVNmbJsq4jjI/Oa8TCaUFs6mZpnU3R5NlNLAvRmh1D+OxhM7GhK4KkmoWu67j0b2/juw++i0iKpaxevX0Qi6YFrXpT6SwOuu5pXPn3dQCYW9FTG1g2qRe39AMAwsk0rnjgXWzrZ25PQ3EVv37+A/zy2fcRNwQiJZPFex05l6V1HSO46O638L+PbSyaE5NXtvbjgB8+hVUfDJQt890H3y0bj2Z9Z3hUd6l8YY0giA83cTWO0x86ver1isjz0NIx4Kml5BZEEASxmyFxhZiUyIKMyz96+QRYrki4beAz4Hjnei1xxbRqsYkpJqZbkEsYu+WKaX1iBrrNT+Oci8/CypqiTilBxhJbbG5B+de8cuGxWSaVZrFczLoSqpazmJGNNNB54kpfRIHIcwXZj3ojufgvPpeI7nCywHKFucgk0ORzIehmQkpfRLEyG5nCTvtQAvVeGY0+Gf1RJsCYaaHN2C7DRhyWRr8L7UNJcFxurMy6RUVPJIUj5jage4RZyJjxYQDmOtQfY1l4TlrYgh39hQJMyMMEmq5wCjzHRJ1t/bECoafespCJIJxM44UtfUioWWQ0HXUe2WpnOK7infYRnPJ/L+Ox97oxksxYMWT8LhGSwGE4rmIoruJRI0BvXzTn5tQScJewXMkTYPwuDMVUbOqOIKPpeH37ILKajs5hlsZ6ToPXsmzZMRDH6QdPx7a+GHRdR8dwArMbvJjf5MP2/jg0Tce6jjBO+8hUvNM2AoDF5Dlu3yb4ZBE7BuLoGE4irmQwLeTG2p3D0HUdG7rC+PyRs7ClJyfiZDQdz27qRVbTsXMwgZiSwRc/OhvvGYLFY+u68I+3OvDnV3cAANa2DmN+sw8z6jyWpc1vX9iGT/3mVazrYMcvbunH7AYvXnq/3xI1fv3cVlxy79vW8SPvsHk0Ax4DzNolXyi5+Zn38fe1HVi1LSfADMdVq46spuMzv3sNp9/yakkBZiiu4qDrnsbPn9rieB1g2a4uvOtNtA6WflHqjaQK+upEXMmUdbkiCGL3IwsyLjzowvFZrugSC2hbYm1CEARB7BpIXCEmJZIgYcXiiYi5IuGJ8LHgSyyCLLcgodAtyEzVbL5vmddFodi1qBSWtYkZFNeM25In0GSMBkyxxOyPbBNXZFvmoXyRxxJTxJxbUP69QE6U8EgsdkvOIsYQZFwCMpoOJaMxccUtojeSglcWcoFzJQE9kZQVTDfgFtETThUIGm5JYPFe3CL8hojRMZy0rEUEnoNsxA4xhRPTcsUs45VFJNIZDCdU1BkCTPtwAl5JsD4Xv5vFiBlJpLGgxY+MpqNzpFDoCXpy8V+WzG1Ajxn/JU84SaU1tA7E0RxwYb+WAHYMJAoEmHojAO/7vVFMC7nRPpSwXIn8eWWG4mm8upVZaqzeNsjSXHtysWgafDIG4ypufXk7Lv3b23i/N1owZtNyRdd1PL2hB/f8pxUAe7FvNFyh6n3MRWlbfwwHzwyB5zjsGIijc4SJK1NCbnQbMWRaB+M4Yf9mxJQM+mMK2oeTmNXgxcwGLzpHWIptJaNh2X7NliDQPpzArAYP5jZ5sXMwjg/6Y5jd4MWhs+uwtS+KgZiKVFrDCfu1oHMkiVQ6iy09UZx8wBQoaQ1tQwnsHIxjdoMXB0wLWQF639w5jMXTg3hj5xAAlsb6IzNC2H9qwLK0eW4Ts3Z5fjOzonm/N4rPHDoDCTWLzpEkspqO3zz/AZ5c34P2ESZCvbFzCF/66By8bYhDAzEFF939Fr5939sAgJiSwbqOMI5Z0IjXjVg0W3ujOPSGZ3D/G+1WX0Seh08WrJTa6zvD+O9737KsdV5+vx8ZTcfDb3daokzHcAJvGuMBgLtfb8VTG3px9+ut1rmsphdYu1z/6AZ8629vW4KSE5/9/Sqc8suXS1rJKJksvvCn163nw4m4ksFPn9jkKNYRBDF+JEHCp+aPI+aKwCGtiyzmCk8xVwiCIHYnJK4Qk5KYGsPi3y1GTC3OmDIeRC2Op/f7JrhMDEDxC4v5DiMYwocpnZhuQjoK3YYEp3zOJTDLSkVxW3KiR1ZjsSpMscPsoV2Y4W0WLi6bVQq7Vuha5C4T28UUYkxxxYyrAjCxJuASkdF0y9IEYK5FI4k0QoYVik8WMRBT4bWJOINxFV6ZZT8yBRl7/Z0jLHV0wC0inEwjrmYtQcMrC0iqWQzH02jwSWj0yWgbSsDryq9DNGLEqJhR54Es8mgdSBQIPV5ZQFc4Cb9LxJSAC4Mx1Wa5wspu7oliStCNaXUsNko0lZ/diFmudAwlsHR+I3SdZfDxu0Tr823wSRhOqPigL4a5jV7sGIgjpmRtVjQuDMVVS2B4p20EkVTGaqfeK0HNakims/jO/e/gmofWI5PVEEllrH6aFjI7BuKY3+I30kDH0R1OYlqdB9NCbgzEFAzGFERSGezb4kedV0JfREHncBIz6z1oDrjQF1XQPpTA1KAbC1oCaB1KQMlk0RtRMKPOi6lBN/oiKXQMMWuXOY0+7BxMoHMkiSa/jJn1HkgCh95ICjsG4th3ih+zG73YORC3rGhmNeSyNW0fiOPUA6di5wBzaeoaSWJmvRf7NLF6NU3HB30xfP7IWdjczcSWbf1xHDA9iDkNXmzrj2PHQBw8Dxw2uw7vdceRzmroGE7i5MVTsGMwjqym4z/bh9Dkl7F9II5IKo0d/XEE3SJO3L8FW3vZ98qT63sKtu92jODwufU4aGad5S7151d34LF13fj3e6zMGzuH8IWjZqMrnMJAjAlr/33v2/ivP6y2BJg3dg7huH2b8Pp29vlqmo6Tf/kSvvvgOuv4tQ8GMbvBa7lLJdUsrvz7u9jYxdrtDiexuSeKzhG2BZjb1Qub+yyxZc2OIazaNojfv7jNerY0TS8ITHzfG+249eXtuN2wFCIIorbE1BiWPbCs6vWKwHNAOgY8vphtCYIgiN0GiSvEpMQtunHzyTfDLbprWm8GLvy463xwosfxOmdzA+JsAW1N8UUyxReHoLilsILk8oV15sdtMeOAmtYspSxXTJHHFF3yrV8ky23H7hbknPI5v6xVX16fZIG3hIx8QcMUbcygu+ZWFguFnpFE2hJlgh4RQ3HVFmdGQG84hZBHglcWMZJgL6j5cWcSatayXAl6JPSEUwVCj1dmGZI0nVl0NPpk7ByMF1iu+GQRXSOsnQYfcwGyx2XxyQK29sUwJcgy/wzGVESVQgEmnExjKKGiJeBGk9+F7QMxK4U1kEsv3TGcxHH7NqNtKMEyMeWVYe5FKtqGElg4NYCdg3HDiqZwLnsjChIqS6e8fYCVybeiGYyraB9KYnaD18h2pGA4kUa9l41R15kwwdJ2S1ag3KEEc7EyjzsMsWVK0IXBmILesAKOYxY0zQF3gbXLjDoPOoeT6DIsZHieQ7Pfhd6Igp2Dccxp9GGOkRGpcySJ6XUeTA3mgvjuHIjj2H2boelMWLGX6Y2moGY1HLdvM7YPsBeN9iGWxnp6nQc94SRaDYuYfVv8aBtOoTucAgfgiLkNyGQ1dIeT2NYfw3H7NqPJ78LW3phhiePFvGYfdgww65z1nWEW+NdwPzLnMj8V9lttw/jIjJAlhO0YiOPgmSHMrPdga2/UsIgZgVcWsGb7EHRdx6buKP5ryUx80BeDpunY2B3Btv44Hl3XBTWjoXUogaSaxdmHz8TmHiamPLOpFw+82YE/vbKdtds6go/MCOHIfRosl6rbXtmO8+54Ay++n7OKOvXAqegcSVoZmW54fCMO//EzUDLsuVn1wQA+MiOE18rEoiEIonrcohvXLb2u6vWKyHNQIQGH3gwItV3zEARBEGNjrxNX3nzzTXz961/Hsccei7ffftuxzKpVq3Duuedi+fLluOyyy9Db21tVGWLvReRFnLLgFIi8OHrhMaBzIl6OLQFGqdcKaGtoJ6aliGZP1TyGts1MQ6axi+AgrpjXLNFGLyxrWruYMWFMixU+z4JGFAqFGPcobkH595h9EWz12bMT5ddnCjt+J3HFKG+mmDbL2LMbxdUsvLIAryxYwWAtyxtZQDLNXH7qvSzV8UBMLeiLaZUiCRx8soCQEVy3IP6Li6WfrjOEh8G4ikgq5xbE2hLRNhRHS8CFBi9zP9J15FJUy2KBFU29T0brYLGFjCkG7Tc1gME4E2jyA/36XCJGEir6owqWzm9E+3CS9cUoY1r5bOgKw+8SsXh6ENv74wX9Dbhz1joNPhlTDHHCdC9yiQLcEo/WwbgVD4ZZquSyMTX5WWaj4YRqiFIuaDrQOhRH0C1B4Dm0BFzoiyjojaQwJehGk58F7B00YuAAQIvR9kBMRUvAZWVM6jWyKLUE3UiozBomnExjbqMXTX7Wl66RJKbXuTEl5EZPJIXWwQSmhzyY1+xD90gKqXQWMSWDJr8LU4LM1al1MIHZDT7MbfKhM8zEoel1HrglAY1+Jhi1DTExZU6DF+1DCXZc78W0kMdKud0+nMTx+zVjJJFGOJlGx3ACM+u9RsrtFNSMhvbhJD5x4FRLkOk0LG3mNHrRPpzAzoE4Am4JH1s0hQVJTrI4Kcft24xUJoveaAobusJYOq8RssBjW38M7UMJzKz3YNG0oCXivLFjCIumBbFmBxNxzLTb+08JYKvhLvXy+wNwiTxeMqxdtvXHsGROPWbUebCtj8XOefjtTkRSGSt2zsbuCD5/5Gxs7Yta319EMT09PfjJT36C448/Hr/85S8dy/T19eHyyy/H8uXL8YUvfAGvvvpqVWWIyYXIizhx1olVr1dYzBUBmH7KqGsTgiAIYmLZq8SVH/7wh7joooswb948vPbaawiHizMyvPzyyzjhhBMwa9YsXHrppVi/fj2OOeYYxGKxMZUh9m6iShQzb56JqBKtab2iFsPqhV8B0lGUS/Zhigt2IcS8RRqDO5C9TlMYMS1M8q1O7IYw9i6acVqslNDmcV5/zAC5VuBcvtCSJb9tU8Awb3eVyH5kni/ISmSL/2JmEXISV3yuQoEnPy20J++cL88axapXFqHrgJrV4JUFeGQWx6XQtYhZpdR5WepljyygP6Yg4Mq3XBEscSXkkaBmNAzE1AJhxCPzRsBdUziJg+MAv5znopTOYjCuoMHnQoNPQutgwgrQa5ZJGPFf5jf5kM7q6I2mCyxXvLKAnYMJ8BywcGoAw3EVkWSmQOgJuEW0DibQ5GcWJu1DCaSzuiXA+F0i4kou6LApaESSOZEm5JHQNpSwXInqvDJ6IwpUI5ZOyCMiksxlhfIYAteOgXjePcxaJ5xMo84rsbg4scK4OA0+Zq0zklBZYGK/jIEYE6/qvBKCbhbE94O+GHgOCLolNAVY8OLhRBpNfpdlRdMfVdASdKHJ70JUyaBrhAUvrvfKmBJkFjJM4PCg2e/CYDyNwVguGHBznrgyu4EJJe1DCStddkuA1ZtUs+gYSmDx9CDqvBLahxLoGE5iRr0HU4Ju9EVTaB9OQOQ5fHReI3YOMvGia8S08nGjN6Jgx0Acc5t8mNvoRetgHB0jCQTcIosh5HOhJ5zCzsEE9mn2sfg1A3FDUPIwSxwj49bOwThOWTwFnSNJJNUs2vP63zXCymzti2LF4TOt2DTtQ8yaaJ8mH7YPxNA5kkQklcHHFrbgnfYRKJkseiIpHLdvE9JZ3XLNIgp58803ccQRRyAWi6G/vx/btm0rKhOPx3Hsscdi3bp1uPTSSzF37lyceOKJeOGFF8ZUhph8RJUoDrv7sKrXKyLPA5ko8NBMIF3bNQ9BEAQxNvYqceWyyy7Dm2++iS9/+csly1xzzTU488wzceONN+KMM87Aww8/jL6+Ptx6661jKkPs3XgkDx5c8SA8krP7TrVkOTcuabsKEDwOEVdy5LSKQgsVewyWcild7djdgnIxWHLCCD+Km5Fd7BFsdbJ2CoPTOlnImH0xhQ3OlpXIjnlvfjumcCLbhBcnAcYUTZzST+dnMzJTQXNcrt/5FioukbdiuphijlkmmxcTxisLUDNagYjjlUV0h1Oo88hW34cTqtVHs7+DcdWyfukOpwoC53qMdvoiCrNc8cpoHYrbYtGIiKssAO+sBi8EnkNnWLFlURLRPcLSVpvxV5jlSq5MwC1i50DccN9xWy4y+bFo4ioTRoIeCQG3iGgqUxCXJeRh4o+ZeSngEtExzNKQs3skJNNZDMVVS9hp9MvY3p8TVwJuidVrBP9t9MkYiKsFIk7QLSKSymA4kTZSapsCDBOMOI5jfRmKo84rg+c5NPryxCA3E7wiyTRGksxCqd4rQ+A5bOmJWvv1XhnhpIphw1qnOSBjKMHuqTP60hJkcWT6owqmGCLNoJECvN7H6hV5Dtv6Y4gqGcyo9+TcpeIqE7MMEccUZGbUeTAQU9EXVZDO6pgSdFuWQm1DCcxp8GJWPcvO1DWSwow69r01NeRCbySF1sE45jZ6MafBh7ahhGWtMy3kRjSVQVzJoG0ogcPnNMAnC2gdilsBhafVedAVZpZNw4k0TtivBdv7jaDDhgAzLcSEnvbhBKbXubHvFBY7p2skBYnnMcOIwUPiijOLFi3C9u3bsXLlSoRCIccyt912G3p6evDII4/gjDPOwE9/+lOcddZZuOaaa8ZUhph8eCQPbl1+a9XrFYHnoOou4NgHAaG2ax6CIAhibOxV4kp9fX3Z64lEAqtWrcKnP/1p65zP58Py5cvxzDPPVFyG2PsReRFLZy2tuVuQxgl4K7GoAtPbQrcg+3Y0EcQJewYiU1TJFyzsMVzsAXPN6+ZZ0WYNk9+OPdVzfp95m+BiXpFHEVfys5bYRZVcquliccW6ZstyBBQG1zVFj3yrIpctEK8Z9yXfcsW8zyxrtpsvXJnWM36XaMVzyWp6UYyYobgKryzCIwmIpjKOaa47RpJW/Jf+qFLgcuU1YshoOrPoqPdK6IkolmsU67uAbiPOjBl/JZbKFFju+N1MGGn0u9AUkLGtL24FBmbjYemyRwwLkqDbqEfJWcDYLVcCbhGdI0n4jHpMoaYzL4NTwCUVZHQKuEVElbQl4phWP/0xxWonaLhhZTUddV7JyKqULhCMgm7WlzpvTsTpGE4io+kIekSEPBKiSgbDcRV1HuaS1OCTsaU3ikbD/ciMeRMxrGiYcJJBOJFL3c0EmLRljdNguDGNJFQEPRJ4nkO9T8a2/hhEnkPAJVpuYvnuUoMxJrbU+2TL/WnHAItf45YENPlzgkyjn1nrDMZUDBvHADDFSKndE05hashjWRf1RRVMDboR8kiQRR49kRQ6h3Oxc/oiCobiaTT6XEw4CafQPpRA0C1i4bQA+qIpxJUMokoGLQGXkbqblZlZ58VswxXKFHF4nsPfLvgoDplVB6IYn88HSSqfpeWZZ57BSSedBJ/PZ50744wz8Prrr1tWs5WUISYfIi/i8KmHV71eEXkOaV0AmpeSWxBBEMRuZlJ9C7e3t0PTNMyYMaPg/IwZM/D8889XXMYJRVGgKLlUlJEICyKoaRo0TSt125jQNA26rtesvg8zESWC2f83G23faUPQFazZ3IqZCN5bvAKa0mUJBfY6NU2DbpzjbG2afdB1reDYfr/Tcc76hd1jvdLn1cHb7jHFFXudZrucYX/Dc3n3mEKQcZ8VP6agHr2gL2bg3FLjkY06MtncdVNOkAUemqZZQo/Ec3n9R0H/TPFD5HN9sQQXgSvIaOT0WUs84MkTaswy5n2ycc6dJ66YZeQCgUeHW+KRSmsFfTHv88o8XKIhQOXV4TImV81o8Ig8PEYdcl5fPBKPrnAKssDBLbIU1W2DiYJ6PIa4MqvBg5BbxHBChabpBWXcRorqfZq88LtyqbBzfeURVzLQwSxH/C4BncPMMiHgYuU8koC2oSgWtPigaRr8LhEdQ8xiRtM0uAQOAs+hYySJYxc0QtM0IzgwczfRNA1+WUAkmUYyrSHoEuAxxLPukSQWTglA0zQEXCLebh+BJHDwSjy8Mo+YIcj4XSIr4xbROhBHndG2XxbRNcKsaLwSD55jolrbUBxBj2jV2zbIBBlWh4BwIo2UpCHoFhFwCYipzE0rZNbrEhBOMqEk4BJQ75EwFGeBgUPuXL3thuik6zoavDL6oykmBhmZn+JqFgPRFOo8EkSeiUzb+6NWO0E3m5fhuIo5jV40+CQMxBSMJFUEjDEHPSy2znAijZBbRKNPwrb+OJSMhvnNPui6jjqPhLZBliK8ziuiyc/6wsQgESG3iJFkGt0jSUwNutHolaDpwAd9UeuzbvLL2NgdRsdQAjPq3WgJyOiNpDAUU6y5m1HnLvl3VYrRvnc/TP/Xtba24vjjjy84N2PGDOi6jvb2dixatKiiMnZobbL3M5Icwb5/3het325FnaduzPcLPAdOHYH+wEegn9EGSMHad3IvhZ7diYPmdmKh+Z04JnptslvFlZ/+9Kd44oknypa5//77i4SQUqTTRpYQd2G0dI/HA1VVKy7jxMqVK3H99dcXne/v70cqlaqof6OhaRrC4TB0XQfP71VGRXscWS2LR894FPHhOFJ8qmZzOxTL4qwPfoG7huLWgravr8+6nk6n0dfXh6Eh9kwMDw+jz5V7ruLxOPr6+hBJZQAA0UgEfX2Ff4b59eUfhw2T/GgkjL4+IBxmL5aRcBh9fUzcyGTSBfdomYxjnarRz0yW3ZdIpqwysQiLZRSLsr4lYlGjnRH09bEvHMV45ocGB6AnJSjpXDv2+e3r60PKSO2qqKrVzogRB0LLKKxMgv0yqyTjVplkPG5sY+xcNm30cRh9PJuPbJrNbzwaxuBAbq7tYwbY36uaZO2k1dyYo8Zc8noWfX194LKsnlQi1xc1ycpkjPtcAodUGoiODKNPixv3s3nIpOJIxVhfBU4v6ItL5KBkdMQiw0CaPUNaWrHKpIwxeyQe/f39kDgdWR1IJWJWGV1NIpxMYz/BjVRsBKk0m9/YyDD6sux+TsugJ5yCjDQ0JYnBmIKAW8jNbUyFYnwu6dgItBSzVBA4IDI8gCjHgdMyGIgq4LPseUE6he5wEjPrXFY9PolH+1AcSCfR19cHEVl0Dicwv0FGX18f0skEwgkVybSGdCKC8JACgUPBPVwmhdaBKPwuAf39/UgnYogkmMCRSUTQ15eGm9exvS+CZr9sfEYK2gZi8Eo8ew51HQIHfNATxpGzg+jr64Nb0NE+GIHEc+jr64OWimMoloIs8tCVOFJR9vzv6A1j4RQfe36zKlr7VGQ1Hel4GHw6ib5wAkpGg67ErXq3dQ/BK7F6PXwWWzoGWWyf2IhlobalcxAenj1TdW4B69v64RXZPbqSwFAsCQFZzKvjgVQUwwkVnf0jkJBhc6mn0T0YwXBcgZ6KQtIUdA1FmbSZZp+BT+KwobUXAg/ERwYRkICdPYMYjqvIJiNIa8xCaUf3ALwSEB4eRMAl4K0PuuCXBQwNDkDIJNEfTqBLZs8dp8YxEE2ho28IHkF3/FuqhNG+d6PRPTM+xKpVq3DllVeWLXPxxRfj3HPPrbjOdDrtuO4AULA+Ga2MHVqb7P2kM2ncc9I9iA/HoUZLr0NLkUkrGIjyGDzsUWSG4gBXm899MkDP7sRBczux0PxOHBO9Ntmt4sqKFSuKfqWx09jYWHF9DQ0NAIDBwcGC84ODg9a1Sso4cfXVV+Pyyy+3jiORCGbNmoXm5mYEg7X5lUDTNHAch+bmZvpDqgHTpk6z9ms1t/5gCluVOWiZMg2yqxMA0NLSYl2XJQktLS2I8ewFt6GhAS0tOR98n9+PlpYWuFPsxTsUChbcb68v/zhu1llfh5aWZgxp7I+/vr4OLS1NrH15O4C4dY8kve9YpyiyfprZP2SXyyrTFGXWF/V1dWhpaUFDJ+trY0M9WlrY36PP2wdgEFNamo0YGAKANFpaWormN78d8ILVTlpi4kjQ70NLSwuaeljq18b6kFWmoU4pmKeArwvACKZPaUZLyGP0xTzXhJZGX0G7dlpaWjAtZrj3eL1WmRGdzaXfw+ahITgAYLCwLyEmrtQH2WfokkQglcW0Kc1WMNSQrwNABNObGzEl6AKwBR5ZKuiLSxSgZDKYMaUFTT0ZAN0IGXOQ3xePLKKlpQVB7zYASTQ31FllmhuSADpR7/di1vQpAN4DAEyf2mK5oAS87choETTVBTG13oOY2oaWoNuqwxNMW/fNnj4VMS6CVOYD+GQBU6ZMYZ+NrwtZfcQa87QmFWq2HT537nnxeyR0jaQwvbkBLS0tqA90Yrg1gsYQuychxBFObQIAzJ0xBU1+F7wuEf2xNKY316OlpQVTG1MYTHSj2c/qnaG6EE/vQELVMHf6FLSE3GgMdmJL/yAOmMHuaa6PYuCDMIKe3PwG3BJ6YxlMb2JzFfLtxGBMwb5T2PzOybgRT+9AWtcxd1oz5kwLAFiHgaRm3TOlIYr1ff3gOGDezGnoVQehaN2IqTrmTGtGS0sIDYGdGEwBDX42ny31w9jQFYHAc5g7cyoA9mvyQAqY3cCe3Xq/G8MKh4aABy0tLZgdF5HIdCGR4TB7SiPmzWyCpq9HJMNjqnHP1IYwusMsyOy8mVOQ4CJIbB4BBw4zjPluDGzHkCqgziNjypQpmNYwgJjGI5XRMH/mVMP1ah3CGRFNQfbMN/pd6FcE1PtkNt/DHFLZfqQ5Efs0BjB/5hSEU1uQEd1oDvkc/5YqYbTvXbuQsKewaNEi3HjjjWXLzJ07d0x1NjQ0OK47zGuVlrFDa5O9H03TIApi1fPr9XTB7QuiYd4hte/cXg49uxMHze3EQvM7cUz02mS3iiv77rsv9t1335rVN336dEyZMgVr167F6aefbp1fs2YNjjnmmIrLOOFyueByuYrO8zxf04ee47ia1/lhJKJEELoxhPBVYQRdbIFZi7mV9Bh2HnQ6kM1lqiqoz2hD4M1gsIXt8bbrnEN/Sh3Lopn2WADP83lpj3N1cEV1cLZjhg7z2TWOdad2WL2SrV1Wn5HaWWTnzHgsTvNb0A4467pZr2S0I+fFTjHLmFmNJKNtM2aMR5asMrnAtblzHFc8ZrMv9vEBYEJJXtteV+FxfhmXcc5s1y2LVhmPmRXIJcLrkqz5zO+LOQavS7QC2ea3Y9ZhtmPGWslvJz/OjN9VmAo6199cKmqfUcZV0E7uPrcsWmOWxdy8WG0b91l9yyuTS3nN2va57P3Pz2Akg+d5+F0iulMp+FySVW8qrVnt+N0ShhNM1At4cmVGkml4ZFYmaAQMnt/sK+jLYJxlcOJ5FhNmU08EB8+qY3W4RKTSWSTVLOp8LkiiAK/Eozei5O7xyOgcTiLgEiGKAvxuCUk1a2QuYv0PeWRs6olgRp3H6q8ZA0cQBGOsItqGEjjEaNsni+iOKGj0GXV4ZUSSacgCj3qfDL+biWJsTH7wPI86r4y32oah6UC9z4WAR0JCyULTdasvdV4JbUNJhLyS1ZcdRvDiep8LksDDKwusjEe25q7LiPvD+iJZwYzrvDKaAm5kNR2dw0nUG2Wqpdz37p76/1x9fT2OPfbYmtZ56KGH4uWXXy44t2bNGjQ1NWHmzJkVl7FDa5O9n4gSwfRbp2P4yuGq3IIkgQefjYK/TwBWhMktyAY9uxMHze3EQvM7cUzk2mTSfVrnnXcebrvtNnR3dwMAHn74Yaxfvx7nnXfemMoQezd+2Y/2y9rhl/01rTfN+fDRTXcAot8xFbM9K1CpuLX2wLKVYAoappBhCTR5ldi7VOr7Qbd1Pj9rkRnQNpfy2Wwvr15bYN5K4/Pmt2u1YwuOKwu54K6SlTI6F7Mlv2x+3/MD15YLGGwKNvnBfs36zUC5VuaivDqtFNVm5iOHDElW3BiBcwyKC8Cy4nFLQq69gnYKA/3ag+3ml3eJvDUeexmzbibAiEXX8/sl8JxVvjBYcLGYAxQGHXbbxuCz9Tc/hXcuPXbhPeYYzW1+9iSzPx65MJixTxaR1XRL8DHbUjO5mDl+l4iRRNoSfDySACWjWWm5zf4OxlXrnqBbRE8kZWUy8soiIqk00lnduifgFguC9nplAT2RVEFGp4CbZZcy2/a5WCyawkC/GSNTEctm5JFYLJ1gXpn2oSQEnoPfJcLnEhFTMoimMnmZliR0DCesbEd+l1AQdDjX31zKb7+L9d8MDhx0S4ik0lYGJ58sgOeA9uFcMGNifJx33nnYsGEDHnroIQBAT08PbrvtNpx33nnW/weVlCEmH37Zj7Xnrq16vcJzHFK6FzizHRBru+YhCIIgxsZeFdD2ySefxE9+8hPL9/jSSy9FKBTC1772NXzta18DAPzoRz/Cli1bsGDBAsyZMwc7d+7Er371Kxx11FFWPZWUIfZuOHAIuoLgxiRfVIAOxLJejCaL2LMDFZ2H8/VyiHxhYctKJa8SzSaalBq/PQN0Nu8+yWYRkhNx8rISoVCA4auYZtEmFjkJGqItI5JuyEf5c2H2S8wTC+zikVO7+eKK2WZxpqRiYSCXVhoF/Wb354QXU1zJ2vqSyRODJLFQ1Mnvg2AJPoViS36/7NmZCoQWUxgRBSvtdL64Yn9Zc4nFddpTXztlazLbsTItWdYthYJM/ph8NrHHnddXdj1fYCtM+23WZ9VRIC4V9tcSVeTivrjyhZ54Oq8vQkF7XlmwYtrkj0nNaAWpu6OpDKaHcmlQPZKA9lSyIJ14f1Qp6IuuA+Fk2hKOfC4myJhZlNySgIEYS8PNcUxgiSsZqFnNEnJcEhN2DjUy+fhdIvoiimWJZPalN6JYQonPJaJ1MIKDZzGXxYBbQkLNssC5Hpb62kw9HiRxZVSSySQ+/vGPAwA2btyItrY2vPPOO5g3bx7uvPNOAMDhhx+OW265Beeeey7mzJmDtrY2LF++vCBeSiVliMkHBw4BOVD1ekXgjf/TpSDG9pMNQRAEUWv2KnFlyZIljn7Qs2fPtvbdbjf++c9/oq2tDb29vdhvv/0QCoUKyldShti7iarRIregWiDpMaw/8GwgEx69MIrFDfM4J7JUvhASbAKDU1pnu6ZQSryxSw/5YoTZjuX6Y7NkYW0aW+O9tppFodWOcatoO84/Z245S8wp7ku+WGIXj/KRHCxXpDxRhF0rFjTs4o8ZTJx3sIARed4SHexBx02xheM4y0onX9Aw2+EsyyHTzad0X5ywxAJZsCwu8tNC2zFFhfz5d9ksVewiC1BsuWIXaVwOfbSXMevItZPrpzkPOeuZQkGmUAyyuyrlrFPyr+e3Yb/H3rf89NZW/2z3mO5T9rTc9jKanjdWo2xW062++F1MTMmJTTwSatYSW3wuEXE1W9Avt8QXWOf4XCIG4yqm1+V8ht2SgI7hWF66bJG1Y7ZrptQeSSLozglGA1GlYPyEMy6Xy3Ftkp9SGQAuueQSfOlLX8KWLVtYDKA5c4ruqaQMMbmIqlHs95f9qnYL4jkOYjYGPBgityCCIIjdzF61amppaak4sN7s2bMLRJdqyxB7JwE5gPBVYQTkQE3rVTk/Dlz/ANZ/PlAkUADFoklpy5WxudMAeeKGXroOe59KmpKXcQsyBRPLLchmyZJfL283xRkDkiUioKh+E9GM8WJzo8ovKjgILuWwW4awvhTOpSmSOLnrmC/CWQcFJ79uJxHHfp/d1Si/HVPXkYViwUFysCCxY4kHIp/X9zJijPFCnz8qt03scNkEiPz77C5OZhmnZ9BuBeSyCRF2V6r8a+bWXkdB/0zhxXKtMoSIfIsbI6W2bH4GJUQWU5jKP1fkCuVgGeSyiUE+mytUvhVNKZHGPh6/nO8uZe9vzhUqq+kFY/XIAjKabgk+frcIJaPl+mjUpWY0BNw5V6e+qFIgSBHO8DxfcYyWYDCII444YtxliMlDQA7g/fPer3q9wvMcUvAxYUWs7ZqHIAiCGBu0aiImJTp0RJSIZeVRy5r9QgLFMkYhTlYlTtfHokkIgmm5Ym8jv3t2tyBnit2Ccvu54LRGu3yxeGFZeIxR2MjHrNdKJOQkrgiFliu8Q3tO1izlsNyC8srnu//kt1vOLcju7pNfXuA5R4sawCauOAgEVh12y5X8F/exWK5IQkWWLubLeL5rmd3Kwi62OJUxhZFybdmFKrtQ4CTI5ISNwvnIF2LsljW5OD6F1lj5++bc2MfhZHljt6Ix7/VaQYjLWK7YXKHyXdssAcZVKJS4bWJOvrtUKaufnEWLw2dkK2P1ReDz4g7ZxlRGwCMIYvzo0BFVo1WvV3gO0HQNSEcw2tqEIAiCmFhIXCEmJTE1hlm/nIWYGqtpvaIWx+uLvgpknOstcgOyW66UOF9R26ZbUJHrT55bkL0/Jd2CCktqeS/85i3my70l4uR9W+TOFVqUjAXREld0x/ZYGUNosFmW5JcpJWKUbNd0AXJwCzLnwf6i6XROc7BcycWp4azPpVy/ygXOtceSKShTxuXGxHyR9uQFNpWF0uWtdvM+zSJXHwcXJas9S+wYvW/2mDHmsaeMlYQ1/5awYVqCFFuA2K1PhDKR36WSliulhR67S1JunkoHA7aEH6nYosceTNhu3WLekx9TxxKFbC5buXvzhB65cM7sYhabh8J6vA6uVARB1J6YGsOSe5ZUvV7hOQ6CHgcenlVybUIQBEHsGmjVRExKgq4g9B/pNY23AgBpPoC56x4DpGDZoKmlLFOqibViUirmSn5NxQFtnbHHAcm/zy4K2MUWoFjgqSagrdmOKVI4ugXZ4r1wtntZ26blSmXtSg6CkHmvqZc4ZRSyWzo4CVdm3flWCfbnJP8+ezYkdr1wPHbrnfw+lBNL8svY3Z6csMScvHYEm6BhD7IL5AL02oWScuKK2YLZR9FyjyptJSHaxARZLJ6DUpYrZaapyNXHKa6MnSLXJIcxF7kxlfnMJFsg5ZxwUuxuZGK3oillBQTkrGjsYlC+cGJ+7FZGJxdZrhDEriDoCqL7wu6q1ys8x0GBH/iCTvFWCIIgdjMkrhCTkqyWxYa+Dchq2dpWrGWwr6sVqLBe+6/eRQFtxyBKmAKD+a7u5CJjFz1KyT/lDIdzwWo5xy3gIOJUOBAn1x3TvcZJXJFKuAU5UWkfzJd03UFQMs84uUKJtnlwFoOKRRm7gUuhO9Lo47LHnWHtFFpbOJFvRWNaulRiMZ7fd7Ovsm1chamwCzPpOMWIsWN2w7LIKDOnJuZc2eOdOAUDtluAlLNcMZvMBastDjJc3JdCocRJUDI/N3tKbqegwubzV0r8cDvcY1LKcqVcRientNu8rQ8eqXhMBEHUnqyWxZahLVWvV3iOg57NACMbKl6bEARBEBMDrZqISUk8HcfS25cino7XtN4jZ7vwzwXfBbLO9eZEk/KWApzD3mOXHotnLju+ZNs5yxV7W7kyZYxpCihnTWGPp+JsuVKZhYwdp3dcK62zw4u1ZcVhXqvCQsaOaItdk485LifRw+4CJToMRnAQCexClJM7ktNzYsbBERw+Z0vkKGOSkS9YmEGJM+XSKJn9y2vIPh57eu78flqWK2KxAGPHnJKc5UqxgFQ8HmeBR86LuWKJNjaLEnsacycsFyJbrBvnvhSKW5KD5Y0lBlmWNoagUdY1y1kgKdcXu+uQPT4OUJnlivlg2wWjcsIOQRDjJ56O4/SHT696vcJzAK8ngKeXllybEARBELuGvSpbEEFUStAVROTqSM3rXTR7FvClxKjlONvWOl9GfDlwRvl04JZ1hZnK17SCQfHLpXVcodiSX4c9e49TwNii2C4V+uQ4vTzbg/9yDtKTlYrZQV0ZaxBAU2iwix6sLoYpHhQIGmVi0Fj9NV15yogr+SKVWM5yxbjPshxysKIpJ2AIeWKE+aLvFIS3qNm8+bSny7ZEo7x6dJvlUaksSfZWgGLxo9xnac/yZE9Znd8Xe6Bgp2DJJua82q2Oygkyos19xy6KFNQj2ASYMtZGdveiSuKd2C2GzLgq+aKIPXaOk+uQ+ZGKZaxxCIKoPUFXEFu/trVqtyCB56BofuDs2q95CIIgiLFBqyZiUpLRMljdvhoZLVPbirUM0L8a0DKOwoXdmqSUq8p4DDHMLB5OlitOQVadKOdCY3c3chITioLqVtSqc7uWZYzDy6xlRWN7yc+nUgHJqpM323WqjG2suCwO1iJ2y558nKxw7PFt8nEK4mvrSl5GprwxVBAsN9+VysrMVMHzkT+fdpcwsy/51diFIakCKxTzU+dtQka5z9Kq1xJxHKyLbHPlFNOmFObYKnFRsotbToKSYJszp+xDduzuQbm5LN3vokC/Ngscp/46xZWxpxbPBXAmyxWCmEgyWgZv9rxZ9XqF4zjo2bS1NiEIgiB2HySuEJOSZDqJFQ+uQDKdrG3F2STw6gq2LcNoAWsrjQ9i59/fOQ5HzK032igmU+5NPo9KgvHaXy7LurpUOJxy5ZyuFVvRFJcZa+LJcgFwTcsJp5gr9gC/5awhys1V/hjKpZE277NnZgKc47CU6kN+5qJ0dvTZyrdusce6sQdVZtcK78/dU7oNu5eLUxwcO/YU2pYwk+cWZJ9PJzctO3bxUHBI21zc/0JxIj8dtEkpoaesu5Qxry6hMIBwJZ+zPeZKvgsYbxNK7JmdAAcLK5vrE0EQE0MyncQ3nv1G1esVgQc4PVXR2oQgCIKYWMgtiJiUBFwBdFzeUfuKpQDwmdL1FgWsHaW6sYosC6fmmQ073GqPqTFW4QHIfzE16rBif+QaLE75XNk4nN2CbOfKWGg4ugWNcZBmDU59NrUpp2DB1gu6KfiUEUTKiit5+1bw4HIWOQ59saxvylk02DItGbWWvsHeLooFAktcKWO5wpcRwkrdk0vLXfoeu3WI6NBObj5tVihlLbVQUCYXOHp0oScXZ6bY1Yy3iUC5/o8uVuQC8VZgcVNCXMnmCa32NOL2+DX2vudTLt4LQRDjJ+AK4K0vvoWAK1DV/Rw4KPCVXZsQBEEQuwZaNRGTkoyWwVMfPDUxbkFdTzG3oApeVIt0A9uJ6uxXzHuLXy4zFVgmONbFFe/n+qrbjscuaJhUauFix/oF3+Ebq9xLsHMfRrcGMHEK9FvOJcfsS36wW7tg4JRG2lHoKZPu2imYrp3cy/2oRQvIn88iVygHEcRu4SGUGZO9byamEFTqBT+/TNH8Owg9RQF4HSxLTEpZu5R157IF9jXjneSLm5bVlRUgd3Shx/ybtgtj5Z5w+2eUi19TjCXaOLhU2d2CcrF+yjROEMS4yWgZvND+wjjcggBeT1trE4IgCGL3QeIKMSlJZVK4/OnLkcqkaltxNgW8fTmQTZUVGEq9j9jPV+kdVHBvvjXHYXPqCwtVoYKY9eUCv7Lz+WLCWAUNq+4yAW2dsL8w1iJdUC6AbvE1UzCzZ2TK3zdPOYkrTnNVLs5JOaEnF7gYRWV4m1WRE07uXZV8bE5uQfa6CoLe2i1XKhCv7JdMsahc9+zWFrn4L8WChjlk855yMVes+bVZxFQm9JjtsJ1snrhpWdFY9ZbODGW/x96ncp9bKQHM6R7RNsZKUoaXc48iCGL8pDIpXLf6uqrXKxwAQVettQlBEASx+yC3IGJS4pf92PDNDbWvWPIDnyxdr/XiVOJ9pNiSpfquON36y7MPxs//66DqK0Ve3BGh8CWtMBVzdXVX8p6WXyT3Ul/6xXSsXSlK75yH+YJpChv5AoH9hdepL2N1Cyon9BT3O7dfidWNU9lK5irfYsOeqSjnMpNfxtaeFTC4sj4B+YJA6R5alh9mbBQHEcFudWUXYpywXzLvKZdZyR7zplwacbvVVbmPrrQbUOm+FGV0crB2sVsT5fo/el/G8qwRBDF2/LIfL539Evyyv7oKOA4KvGXXJgRBEMSugSxXiElJOpvGgxseRDqbrm3FWhpoexDQ0qNYrnAF29z52uGUzlkU+IL0qqPd64TpWmT/Bb8gW1BV0VxKxFwpMyvlMvNYfanaiqb0NbPGAnHFnO8ydZriTP5La7k4Ik5plq0+FLkFcUX7ZcegF5athlymokKhQCtwf+HK3uNYb4m3+nKuOHb3HfO4cH4L67Wnb66EXMrz0mXswY2te5A/LzD6a9xjPT9lnvcS/ayoLzbRMP/vgrf1V3QI2mv/uCr52yMIYvyks2k8uu3RqtcrHABOV621CUEQBLH7IHGFmJSoWRU3v34z1Kxa24o1Fdh0M9uWYVe8j+Re7EqXqVR2yH9BNuNG2C0DyrkQVIrzL/zl+mXcVyZmRpVdKSH0lD/O75PTC6/lylNgLVI6b3U5byfdinVT3N9KHi8nK5pKhKj82Bt2qwgnQcP+8l0us5NJqWsVPQu2Z8jJLcjEctsp9/yUmJKybkE2QcMJu8hUSQwT+zxbfSx9i2Oq7lJl7HOY/9kVf44ouIcgiIlBzar447o/Vr1e4TkOvJ6paG1CEARBTCzkFkRMSnyyD6vPX137ikUfcMro9ZrvZcVuQIUnWgLuqrtSy5ee/KpmN3hx9akL0eCVAeRe8gvcS6pUNBxjrlRwX+5X/2Im0kXJSdAo90JtDwrqRP7d5aw7ii1X8uooeq5Kz0O5LE9O5IsKdquIXD3FZax+VpEtyKSSILj2ewvi4hTXWLY9oPSclPsoxyIg2d1rysUwKRWouJwoVvozypGzXOGMdootelj/ii2SxmL1QxDE2PHJPjz2mcfgk31V3c9xgAJPRWsTgiAIYmIhyxViUqJmVdz21m21t1zJqsAHtwFZtaxrTKlrB8+qs/bXX38KDpwRqrorpVyPCvpRhfAg8BwuXDa/KJhmobhSrVtQ8blyApST9YWdWlquWHWa4ljeN6QVz8NWJp9KLHryx1eJQOYk6tg/+3KiU4E7VwX9K4jVYdxrn/6COCe2+ytxCyp1pRKrDvuzkP/8l7RcKZMyqaTlSpkP00xPbO9ufl32WEV2VyInSs1ZZdmCSt9kTyPuJFSV+rgo5gpBTCxqVsU9m+6per3CAeD13NqEIAiC2H2QuEJMStLZNB7cOAExV3Qj5oqexs8+exBu+fyhBZfL/fK+88ZP4pA8ccXvGqfhmOVCML5qRsPJLcj+sldpFxxdccoMwIzBUS7bi5rJVtj66OS64hTQdnRXkGrTQpd7e3Zsr4LP3gpMPEbLA63ALagwcKtVd5lxVhIPphSVWLuUG09RjKOKrC+cx1LOCsmeCcgJK/6OzQWnvHUO25YTbeyINssYi7xDu1uQk4tVsWhV2CeCICaGdDaNR7ePI+YKBwh6xlqbEARBELsPcgsiJiU+2YenvvhU7SsWfcBJrN45jcCcxurMeGuB3ZJiTPeOoWzOLSjvXC0tV8qUzwXTLV0qoRaKK1/86GxkywRGzfVl9FnIL2EPUOqEU5yO4hf+3P5Ystg4XSuwarG1XW1AW6f+FYkrFdxfjcVDOSssk3JuQcVl2bas5VMVMVcq+duzW6+NJ1tQub6UcsPinMpY10zRKa9MiQrKiUEEQYwfn+zDfZ+8bxxuQRwUzmOtTQiCIIjdB1muEJMSJaPg5tU3Q8kota04q7CgcVnnem2GD7udSrP6lHt/Mn+BL3TXqa4/Za0wHFxczHdKscQLJAB862P74sLj51nHPz7zI1h51kdG7Yuz0MMVtDvW7EZjnZexCjz2c+Ve8iuxOBqNSgSBIiumCkSoku1VYblSiZtNNW5l5efK2QpFL3lQWWrjaoSMUpYr+cdmPN9cRqHiMnbBtBKhiyCI8aNkFPxh3R/GtV7htfJrE4IgCGLXQOIKMSnJ6lms7liNrF47lxEAgJ4FBlazbQXsqT/6NvjkguOyv8A7/GperXY0LTS2AL7ZCmKuHDG3AVeftmjMfSlnDWOOz1ELKuPJ42i5UsaigCvzDVzu5db+Eu7UT7MnQpkX6NGoJAirvc7cC/vYBY2yLjPGXNmtfQoC2pa4vZxbWakpKfc3YXevcazXdlxJVh97/ZVQSaBfu+WKk+hUSiQjCGJiyepZrO1dW/V6hec4cBjb2oQgCIKYGMgtiJiUeCUvHlzxYO0rFr3AcaXrrSCERs0p+4Ln0JFnL1+GZr+r4voPnBHCvOZCc+Vq3IJe/t6JaAkWt1veEqS6uCGVUD52Cts6Bfwc63yXa9esv1ILI6sO2x6bw8I6KnGpGrX2KqxQKrmnGq+ynNBTpu0S99T6+RldPioOiJuzMhp7X8rGXCmRZto5vpF5rXQZq2zFvSMIYjx4JS/+9PE/wSt5q7pfzWj4+7vD+MXnJ2DNQxAEQYwJslwhJiVKRsF1L143MW5B666r2PR2IuMVVJuCeEGLHyGvVHH5Jr8Lz19xQtm2Z9R7Rq1ndqMXbkkoOl+cLSi/HUNcmYB5rCxzUf5+vpDhLDA5BUG1N1MQC6PMsMqJOUXXHP2CStddKeZYxxI/pZp7rHvLzYexLXILKhDAnK03qhFXyse8GX2MxY+C6UpUut5SIls58U0oIdAV/h2ZPSh8YMoGByZ1Ze8mmWTbVIr9M88pxv9diURuPx4HVDW3nzaCosZiQCYDAODy9hGJANlsbl/T2EMWibCtprF9gJUz9zMZIBrN7cdibD+dZu0CrB/mvqKwfpr7NR4TotE9YkxKRsEvXr8RSixc1ZheWLsNHj3F1iYjA3vEmCbj50RjKh4TF49PujFNxs+JxlTFmMYBiSvEpETTNXREOqDpFUQ2HQu6BiQ72LZcsV1gumK+GLnEYsFirIxVBJpeV+jec9OKg/DI+aPHOXFsu8y1BS0BLJoWrML6opB/f+c4PHLJMQXnyqZidswWZNxX5luzXOBRJ6pNc1suBbNJyiGLUrXPZTUxV6oREcp/Jowit6DSzRTF7BlLX8phSRRjGGMl85IrW1io3OdW6m/XyULGbrlS4BZka4NiruzlfO97bPuDH7B/AHDppcDKlWz/K18Bfvtbtn/WWcCdd7L95cuBhx9m+0cdBTz3HACg6fjjgTfeYOdnzgQ2b2b7oRDQ1cUWpaEQ23Z1sX2AlZs5k+2/8QawyHDhfO45Vj/A2lu+nO3feSfrD8D695WvsP2VK1n/azgmLFq0R4xJ0zUMrPo39B/9sKox/fr/LsbRre9AT7QDhxy8R4xpT/mcuNmzJ92Y9qTPqeG00ybdmPakz8l/yy3gvvWtSTWmPeFz4uvry4/pxRcxHsgtiJiUeCQPbvv0bbWvWPQAR5Wu13wd8chM8PDJ4xc+SuF3iXj6suMxq6G0KXGlL9NjfY363ikL8bVj97GOvbKIKQG5zB3VEfJIePLbx1nH1b7wLZwaLDpXidtKfplK3DqcAtraX35PXNiCZzf1FtRZjnIvyTlrmmKk/FQwY8CpT2VFD9uYK3E5KUW5zyRtpIAqGpfDZ2RSiWvUeITQ4hTVpestFRulEsqLK6X6ln+/c1ycgoC2ZSxfiL2Qn/+cbX/609y5W27JqcN//SsgGP8//fOfgGRYMz77LCAb3+X/+Q/gZkL6wMsvo9l8Ue3oAHyGq2g4DPj97IEJh4FAgB2HDSuMhQtZeQA44ghg0ya2/7GPsfoB4MwzgdNOY/tf/jLw+c+z/UsuAS66iO1ffTX7xbGGY8KmTYDHs9vH5JFcuPGb/4a7paWqMV3z/T9hVVcCrQs+hpEnBnHIftN3+5j2lM9Jb2tjv4xPojHtSZ/T0BNPoHnWrEk1pj3pc4pdeim8TU1smTNJxrQnfE7a8DD7Xig1phNOwHggcYWYlKQyKfzguR/gpx/7Kdzi2IKoliWbAt75AXDITwGhdL0hj4TNN3zC0Q2mluw3JTCh9ZdCFnlMC43uClQJY3mJ+8xhM7B9IDbuNk8/aBpOXNgyal8KXE4qEHYqiUXzixUHW/tVW64U9bO4zIolM/GRGaGx1+1wbmxuWaO7v5S8s8xNpsuVLFYuGlVmuVKiL2UDClfStk2sMLZlP/MSnSmbirmU5YrD6ZzlCtsp6xZU8gqxV2AuNN3u4nMA4M0T5c2FqH3f72dbTYPu9wOisWQM5onVTvscl9sXhNy+KLLFrblv1i9JucWzLOcWz668GF35+7UYE5Dry24eU0pN4EdvrcQvT/8lvFWMKeX2QhSiePaec/Hznq9gy8rZu31MAPaczymVYuMw+zsZxmSOY3d+TpoG3efLfS9MhjEBe87npGlsv5bf5bt7TPb93TmmVKr8mMbBXuUWlM1m8fDDD+MTn/gE5s6di9dff72ozLZt23DppZdiyZIlOOqoo3DFFVdgYGCgoMw111yDuXPnFvw75ZRTdtUwiElM/svhRAsrlXDt6YvwneX77u5ulMX+Elvupfaw2fW45+sfHXebv/nCYWgJVCe6lXs3PnxuQ3H5MnWN1y3I5LLl++Gzh80sOCcKPA60iSvVuMAADployvXNZlXjSBUZejKGuGK3XMmfC/u8VBIQOZUeu+sgV4GAVI27VDWYQ6vk78je70LrFnthklfGwrp163DxxRdj3rx5+NGPflR0PR6P4+abb8ZJJ52Ej3zkIzj77LPxhmnGbPDaa68VrU3mzp2Lbdu27aphEHshTtrr9v4YtvWP/4cIgiAIYmzsVZYrV111Fd5//3187nOfw9e+9jWkzCA4BtlsFp/85Cdx6aWX4rzzzkMikcCVV16Jj33sY1izZg1chlo2ODiIAw44AL/73e+se2VTXSMmBW7RjZtPubn2FQtuYMkE1DtBHD2/CUfPb9rd3SjJxSfMxxeOmr27u1GE07t/zi2oNP+1ZCb+a8nMMiUKKR/QtnSD9ksXLptfUXtVx1ypQoavJkxOOUEmkzXFFZuIUIFbUDlxZf8WL15vjYyxn2xb5BaU9+TYrU1ygX7H1JRRb5m+lHgi82MDmffb50fME6qKtJWKe0e89tpruPjii3HhhRdi1apVGBwcLCpzySWXoKmpCddeey2am5txxx134Nhjj8Vrr72Gww8/HACQTCbR2tqKHTt2FNw7Y8aMXTIOYvfgFt24/ujrx2Vlq+gyftx9AQDgjtd24E+v7ICSyeLNaz9eq24SBEEQFbBXiSsrV66EKIroMP2jbAiCgA0bNkAQchYDd9xxB/bff3+8/vrrWLZsmXXe6/Vi7ty5E91lYjeRTCdx6ZOX4pZTb4FHqo37CgAgkwTWXgosuYXFX5kkTMSP1F89ei4afOVFy+9/YmHtGx4H5TIB2aeoYqGiAgGlXF1Bd/HXtFnlWD+3j85rHLXMmGOulKqnitfzcuM5eFYIly3frzgjUN7+l5fOKbhmxsApJ65cdPR0/ODTBxedryzwbOlrRQFirbK1VVdM4au4L06WKwzLxUpwUGCIMXPUUUdh3bp1AIC77rrLscxtt90GMc/U+KabbsIzzzyDv/71r5a4YkJrkw8XyXQSV7x0BW4981b4XL7Rb7DBcYCLU3D9jD/iR50X4rpHN05ALwmCIIhK2KvEFbECH6h8YQVg1ixO51955RUccMABCIVCOO6443DNNdcgFCo0oSf2XniOx8zgTPDV/OReDo4HPDNL/pS/9/7aW/ueX/fpxWPrwQS5TYytE6Nfm4j+OYkrZjOfPWwmZngKM/9YgWzH0JedN36yyt6Vp1ScmbIBg0ucLzcclyjg26O4uM1p9OF/Tj/AOjatR8QyKZ44jhs1+O/Bs+oQTqh597BtkeikO+7a7smdm9fkg9c1uvtgOXeuUsKXc6pxdjLjJK4UlR21W4RBJWsTpzLZbLZobQIARxxxBBRFweLFi3HVVVfh4IOLxT9i8sBzPKb5plW9XtF1QAePnnQjdAdvfyWTxQNvtOOLH50z5syABEEQxNjYq8SVarj22msxb948HHnkkda5xsZGXHvttVi2bBk6OztxzTXX4IknnsDatWst1yE7iqJAMXNuA4gYebE1TYOmjd1n3wlN06Dres3q+zAj8RJ+eDxLa2h+RjWZW04CDjTSJTrUpaP2n9+ueR7G1+9azK95by3/psaMMQbNePnM74dundOt8drLOMFVUEbXncbM2uGgY0GTp2xfxou9DvNY150/E13Pu0cvvEfLmvcW9y1Xr/PcOc1VJeMrVSabNfuoQdNs8VhG+V7IP//AN45y7L9un5e8vyP7M2Tdk1fvk98+1rFM8RyUmcsSfzf5c2n2Rdc0aOCgGmm6eS5XryngmMemtc9Efi98mP+vu/fee7Fp0ybcdlsu+xzP87j44ovxxS9+EaIo4k9/+hMOP/xwrFq1CkcccYRjPbQ22fuReAlXLLkCEi9VNcdZTYeqS/i/3nOLrv3rnU4k01n8zyMbcML+zZhRN3ksbiuBnt2Jg+Z2YqH5nTgmem2yW8WV7373u/j73/9etsxLL72EOXPmlC1TimuvvRZPPfUUXnzxxYKYKjfccAN445fMgw46CAcffDDmzp2L++67D18x83DbWLlyJa6//vqi8/39/UWxX6pF0zSEw2Houm71j6iORDqBb7/4bfzqhF/BK3lrN7fZBOo2fhsjB/wKEIpTIKuqir6+vnH0PMcx+zBLqlrVVw5FUcbVTi3mdzDMXhBGRkbQ17d7/jNJpdg8hCNhAIVzP5RIs+3QEPqQQCabKSrjRCaTHbXMSDiMvr7Cl/9MJoO+vj7HuTVfwiPhMGrxeOT3T8tq1nE0zL7bBgf6kcgL0JxIJqwyKSVVUMfgkHHP4AD0pOTYjvmdaZ+XRCJedG60uXO6x8QP9pn19/cXXSs1tyaqWvpvYjDCrFiGhofQxyWt86lUyronkUwW9H8oyu4ZHhqCT4s71htPJAruAYBpQRnH7+MvOS9mQF77300qlbTKxGJRYx76wHEcfJqGk/dvQHRkEHHjl2zTyse85/R9vWiR50/o90I0Gq267onk+eefx9e+9rWyZb7//e/j4osvrqr+VatW4YILLsD//u//YunSpdb5E044ASeddJJ1fOSRR+L999/H//7v/+LRRx91rIvWJns/cTWObz7zTfzu47+DTx67W1BKVeHmUrhp1i9xRftlSOm52C3fuu8dTA2w9e/mnd2QpvtLVTMpoWd34qC5nVhofieOiV6b7FZx5aqrrsJ///d/ly1TbSC3H//4x/i///s/PPHEE0X+zPaJnD59OubMmYONG0v7qV599dW4/PLLreNIJIJZs2ahubkZwfxUT+NA0zRwHIfm5mb6QxonSkbBCfNPwLQp0+ASXbWb26wChE9AS8s0QCi2cnLJLrS0FKf4rYa7LqhNPZXgdo2v37WYX0VkL5b19fVoaRk9NshE4HG70dLSgmAneynPnxM+xsSfpsYGtDT7IQpbiso4IYrCqGWmNDWgpaUw8LAoimhpaSk7t3V1dWhpaa5scGXI758g8NZxSwvw8DdDmGPLOuT1eK0yLldnQR1RsAwVLc3NRTF3rHvc3UXtAoDf7y86N9rc+X2+kmVaAGz/qfP/IaPNrdvlLllvRmbCSXNjI1qacy8rrrx73O7egv5nXeyepqZGtDQUC7MA4PUOFdwDAK9d9bGS/QcAJc2sUNizkHuGfN7cZ+TzMzFnypQp1vU/fGWqrUYOgF7Q9pwZ0xzbrpTRvhfc7uoDeE4kS5cuxYsvvli2TH19fVV1/+c//8Gpp56Kb33rW7j22msLrjnN0dFHH40HHnigZH20Ntn7SapJLJ25FFNbpsIjj92yhOPfhwYBbyUWQkOxm1lUYd8RGYl9J/zjrQ7EUhl85ei54+36Hg89uxMHze3EQvM7cUz02mS3iitNTU1oaqp9JpOf/OQnWLlyJR5//HEcf/zxo5ZPpVLo7u5GQ0NxGlUTl8vl6DLE83xNH3qO42pe54cRj+zBFUdfUXCuJnPLe4ADrih52Wxjb6MW/R7v/Jr38btzDjnWD85wicjvh2Dsc+YYzRgao/R1tLl9+JJjcPDMUHGg1rz7Ss0tz9dmrvLrsPf3kNnFL5HH7NuUK8MVzpU5d6Lg1F+jjEO7gPNnP975LcVoc8uVmVvBiFViv8+pL+axwAvGtvTfCMcVP3ej9Z8X9FH7wnF8wT2V1Fsryn0v7KnflR6PZ0KCyq5ZswannHIKLrroIqxcubKie3bs2EFrk0mOR/bgooMvgkf2VDW/N519CE791Su4feAzjtfjatbYauB5Ht/7+3sAgPOOnVd9p/ci6NmdOGhuJxaa34ljItcmk+7TuvHGG7Fy5Uo88cQTOOGEE4quq6qKyy67LM9cOoYLL7wQuq7jc5/73C7uLTFRxNU4Trn7FMRVZ/P7qsnEgedPYVsHfBUEpyT2XJoD7CXFMcCsPe1ujbKrHDKrzjHIYGXZanZ9cMKdN34SpyzOs3ooMQ9jzRb0y88djPOP22fM/dkd4RkrGZtmm5dqUjBXghnQ1l79WB+NUoGJidqwdu1anHzyybjooovws5/9zLHMz372M6xduxa6rkPXddxzzz148MEHR3VRIvZu4moc5zx+TtXrlUXTgvBwKdy5z//Aw5V2Bbv/jTYs+/kLReffbR9BJJWuqm2CIAiikL0qoO3f//53fPe737UyAJ1zzjlwu934zne+g+985zsYGhrC1VdfDZ/PVxQ75Wc/+xk+97nPQZIkLFiwAIcffjiSySQikQgOP/xwvPDCC5T+cBIhCRJWHLACkiCNXngscBIwewXb2njkkmOwT/PY/aWJPYMXv3sCpoaYKeDS+Y04eFZdwXXOtq2UD1tuhkre0Z2KfObQmdU1OEYV4ZnLjkc6Oz4hIZfZKtf2//uvg3DsgpwlpkeyCa0VdLMagaN0tqDceRJOJpZEIoEDDmBZqrq7u7Fx40Y89thj2H///fHUU08BAL73ve8hEongvvvuw3333Wfdu2zZMvz1r38FABx//PH4zne+g/feew+ZTAb19fX47W9/i4suumjXD4rYZUiChE/N+9S41isZCHg8fCwyDm5BJm/sHHY8f8ZvX8NXj5475gx/BEEQRDF7lbjyiU98oih+CsB8zc3tjh07HO813Y84jsMll1yCSy65BIODgwgGg5CkGr+AE7sdWZDx9cO+XvuKBRlY4Fyv/WWcqJLdpEbMbcoJY9NCHjxyyTFly0/062ol1hETMVVjrbNkmuByqZhrOHlj7e++UwLjrtfp2tmHzyo4/v6pC3HmodXFDBsLTn25+tSF+PQh08dUD8kv1ePxeBxjtOQH0r/33nsdA8x6PLkYG0uXLsUrr7yCeDyObDZbs5gpxJ6NLMg4d9G5kAV59MIlSOsS7h86ZUz3KEbWMADYOZizmlnfGcYB04LgJ8rcjiAIYhKzV4krfr8ffn/pSOc8z4/J+qSxcfcEzSQmnrgax/K7luPZLz1bVfT9kmTiwHPLgY89C4iTx0plN3iX7HWYL59jdcWZyLkVhb3zg6vVi/zNZx+M5QdMGb1greEKNo74XSKWzMmLVzNB6oXT83XhsvkT0xjhCMdxo649pk61BxAujc83ef5vIUYnrsZx+kOn44XzXkDAXZn4a8fDpXDPvGtw7vafIKlXFozxxS39cBsWdqbgPRhTcPotr+KO847ACfvvuqD6BEEQk4W9SlwhiEqRBRmXf/Tycf0S5AgvA4suZ9tJxFjjY3yYMWdqd7ta3HX+kVg6b3wC8bc+tq8VZ6ZW7MpZOeuwKl2JakQ1wlmtxbZaxd0hzyGC2D3IgowLD7pwnJYrIm4b+AzSuvOy3u8SEVMyBecuvGuttR81Yq60D7OsZtv64zhhfyCVzuKe/7Thq0fPhUCWLARBEKNC4goxKZEECSsWr6h9xbwRc2WSccQ+pbNR7Gr2VKFnV4spM+rKp+Q8bt/xp2C+/OP7FRyLPIejF4wtg1upadnbraHKiRZ74jNa551cgi9BfFiQBAmfmj/emCsinggfW/K6zyUUiCsizyGTF3U7nGTiStcIE1f6owoAYM2OIdzw2EYsndeIA6YzNzVN08lliCAIogSTLlsQQQBATI1h8e8WI6bGaltxOgY8vphtJwk7b/wkzj927FlaPqyMVTTwyGPXsF+76iTc/LmDx3zfeNny41Px/z570JjusYsr85v9+NGnDkDAVXrcu9vqZ7xYAW33EJFl7bXLrRcfJ/by6SaISU1MjWHZA8vGtV7x8kk8vd834eWTjtd9tu/jgLvwOK5k8UFf1BJVwkkVANATZnGCtg+wvr2xcwjzfvAE1IxWdV8JgiAmM2S5QkxK3KIbN598M9xiZb7HFSO4gUNvZluCGIVHLjkG0+rG/qyMZrUyUdTC7FvgOZx3zOQW66oRKyq5pVoRpNFfW9cugiB2HW7RjeuWXjeu9Yqiyfhx1/lQNGcLNr9NXPFIAobBrFVkkUcyncXym1/GnEYvAGAkwa51G+LKUJyJLW/sHAIA7BiIY/+pAWzoCuOfb3Xif04/oOq+EwRBTCbIcoWYlIi8iFMWnAKRr7F+yIvA9FPYlpgQ9lR3EiugrWGtUMl78MGz6tASICFub6SSx3Asz6opnNQqRgpBEJMDkRdx4qwTx7VeyULAy7ElyJZIxeyzWVBm85Tceq9kuQV1GjFX4irLJDQQU4wtE1cGjW1XmJW79z9tuP3VHYgbLkdbe6O4/422qsdBEASxt0PiCjEpiSpRzLx5JqJKtLYVp6PAQzPZlvhQkXs53r39mAzs7V4qJdNPV8DueHzG01+CICaWqBLFYXcfNq71io9PYPXCr8DHJ5yv2yxX0tncd0KdJ2ftktF0BN0iUmoWr28fRFxlookpnphiy7BhyWKebx1k7f78qS34/j/es1w/13eG8fDbnVWPiyAIYm+Dfn4nJiUeyYMHVzwIj1Rj9wrBAxz7INsSHypCHgnzm3257Dr0vgrgQ/riXsWQG3wylsypR6O/dODZSqr1yQL87rH9112Ju9FVpy604isQBLHr8Ege3Lr81nGtV1KaC5e0XYWU5uwi6JELLVrS2VzMlJC3MJBug0/Glt4ozrn1dcxt9MIt8UgYIotpuWK6CY0YFi/DCXacTDOLl96IgqkhN659eD3eaR/BmYfOAAC8sLkPHcMJfGnp3KrHShAEsSdD4goxKRF5EUtnLa19xbwINE9AvcQejyzyeO6KE3Z3N/Y4Jmuw1EoslMZixSSLPP5x8dHVd8hg1VUfG7P5SyUf0UXL5lfVH4IgxofIizh86uHjdgt6K7Go5HWXWGionimwXCkUV+p9MnYaliitQwlMD3kQV5hoElMy8EiCdWzGZhk0xJZYnoXL1JDbOo4pGfhdIq7+53voiaQsceX3L27DSFLF1aeyvifUDLKajoC7+sxJBEEQuxNyCyImJRElguDKICJKpLYVpyPAA0G2JSYE8rqZfHz16Ln4/icW5k7s5YLM7ux+yCsh5KEXD4KYLESUCPb9877jWq/4+QTeW7wC/hJuQbJNXBHzgpfbLeEafTnrOl0HGv0yesIp/O+jG5FKZ9Hgk5FIM9FkJKFCFnkrZovpLmRatmhGuucBIwuRaQFjZhv6xdNb8MeXtlvtXXjXWnzp9jXW8eptg1izY6iiOSAIgtgTIMsVYlLik3xYff5q+CRfbSsWfMDJq9mWIIiKuO7Ti3d3F8ZMZQFtSQokCGJ8+CQfHjvzsXGtVxKaC2d98AskSrgF2S1X8r/gZKHwWp230HWxwSfjxS39WLNzCAGXiDlNXiSNgLfhZBrTQ24kDdFkOJGGW8oTWwx3oYGYgrlNPvCGqDMQUzC9zoOsIb5kshpEgccrWwcK2v78n16HRxKw6YZPAACueeg9NPlduOzj+wFgAXRdooDZRpYjgiCI3Q1ZrhCTEoEXsLhlMQTeOXJ+1fACULeYbYkPNXu58cUez/3f+Cgeu/TY3db+ZBJOJqvrFkFMBgRewP4N+49rvaJBwFZlDrQS2YLsAkq5bzd72uZ8S7mokkG9V0ZcySKmZJBKa6j3yUioWei6bogtHsu9ZySZhk8WEFUy0DQd0RQTYYbiqmXVArBsRHreF1U275oZxwUA7vlPG3713Fbr+OO/fBlf/vN/rOMfPrIev33hA+v43fYRtA7lYknlt0kQBDERkLhCTEoiSgTc9dzEuAXdy5Fb0AQymV5qPwxUs1StJAjuUfMaceCMUBW1j59PHjQNX/zo7JLXTZN6iadnlSCI8RFRIpj2x2njdgvaedDpJd2CJLu4kvf/rGZTX+1WLkF3ccDbR97pxIE/egpqVkO9l4kriuHq0+g3xJdUBroOTKvzIKlmEU0xwaXeKyGmZBAzrF0AIJJKW+mfAWYRk8oTVZRMtuAYgCXGmPFhAODO1a24+Zn3rePP/H41rnki53b02T+swnX/2mAd37emDa9vH7SOt/fHMGJY2xAEQVQDiSvEpMQv+9F+WTv8sr+2FYt+4Mx2tiU+1OhkDgBgclpF/PYLh2HJnIaS1xv9Ljz+rWPREnTXtN3JOJcEQZTHL/ux9ty141qvxDU3PrrpDsQ15++kYnElt5+XOAhAsbgSsMVkCbolZAwLkKymo84rIaFmoBoVMbElg1SGiSENhvgSTqYh8BymBN1IqBlEUxlwHNAScCGayqA/qkAWeLglHkNx1Ur7DLAsRWYcF4DFbuk34rjwHPv/OJpirkjm0EwxpmNEsfr6dtsIHnmHpYbWdR1X/fO9ArHlpJtewlX/eM86/todb+C2V3LizF2rd+KNnbkYMOs7w+iL5ixjUuksWccQxIccEleISQkHDkFXEFzNw6NygBQEhV2tPWSwsndy0sKWomCJozEZRITF03ePVU01fCjTZRPEXgIHDgE5MK71ig4OsawXeok6RKHwfP6R3XLF/n3utaVxdhJbNndH8fN/bwHALFtiShZKWoPIcwi4RSQNscUjCfC5RMSULKKpNPyyiKCHWbLElQz8bhGNPheG4ioGYyoafTJ8soC4kkE4mbZcliLJDAaMtNCaDsTVLDpHkgCAjKZDzWjoGGbHpoFhT4SJIIJxYtAWfNe0WNnSGwUAJNUsnt/chztW7QTAxJn/eWSDJcbouo7Tb3kVP/jnemsuTvrFi/h/T22xjq/6xzo8tq7LOv7nWx1436gfAN5uG7ZEIoBlVcqSOEMQezUkrhCTkqgaRejGEKJqdPTCYyETBR4MsS0xIewtIgstfxhfOGo23v/xqWO6ZzKIKwRBELUgqkax31/2G9d6xc8nsf7As+Hnk47XRZsLY75bkP1l3i6uuKVCccWeXcjnEvBm6zDuer0VPAd4ZRFqRoOSycItCfC6RMRV5tbjlnh4DbEkmsog4Bbhd4mIpZi44pEEBNwiYkoaccW47hYRSTFxpd4nwe8SEU6mEUmlMbPeA1ng0R9VMBxPY6phTRhJpRFOqnCJPBJpDQk1gy5DfBmKq0hnNXQa4ks4mYau6+gOM/ElYgTjbR9m7kZpwyLHvN8M5ttllN85GGf1JNLoCqfw0vv9AJhQct8b7VY2JCWTxeUPvIv/eZiJMZqm4zO/W4UfPsKOdV3HgT96Cjc8ttGa2y//eQ3uer3VOr7hsY14blOvdfzIO50FYs277SPoCecsadqHEogrOfcrJZMlq1uCmGBIXCEmJQE5gPBVYQTkQG0rFgPAijDbEgRB1JCJsjChtTRB7LkE5ADeP+/9ca1XYpoHB65/ADHN43hdGEN8KHvwW5dNXAm47OJK7ljkecgij5Gkik09UbhEHl5JsGKyuEQBfpdoiCtp+N2iJaYk0ll4ZQEuSUAqrSGhZuGRRQTczLJlJJFGyCMh6BYRSaURSRrHHhHRFBNbGv0yXEZq6EgygzkNXog8h/6ogkgyjQUtfmg6E1h6IinMqPNAybC2esIpNPpkDMZVpNJZ9EUUCDyHgRgLvmuKKF3hJHRdt8SWQcN9adtADEAuHXW+eAMAbUZsmF7DgmaHUd+OAbY1LWtWbxu06n35/X7cY4gr4WQat7+6wwrYm1Az+PZ97+Cah5gbk5LJ4ozfvmaJNVlNx3H/74UCS5sTfv4ibvz3Zuvz+sqf1+CvhmUOAFz3rw148r1u6/jPr+7Af/Ji0vzr3S5s6Apbx+u6YgViTutg3Bo/APRHlaLYOSTuEJMdEleISYkOHRElMgEvK7oRzJb+cyAIgiAIYnzo0BFVo+Nar3DQ4RcS4ErUwXOl3YLs2MUUt82SxWcTVzx55dWsBlnk8c+3OvGtv70Nl8jDIwvMLSidhUvi4ZVFxJUs1DyxJZrKIKlm4XWJ8Eg8kmrWEltMy5ZIKo2gW0LQIyGcSCOSyiDolqz6TEuYkEeyLFuCHgk+mUc0xSxlGrwyPJKAaCqNWCqDGfUeCDyHwZiK/piCBS0s7s1wQkVvJIXF04PIaiwL0lBcxcKpAaTSGsLJNHrCKcxp9GI4kbYsY6aH3OiJpKBmNHSOJOCWeHSNJJHJatg+EIdL5NEdThWIMx3DTKz5oI+JM2YMl63Gsem2ZIozfYYbkVnedCtqNa5vN8QaUwx6q20YANA5kkR3OIWXtjDLmh7DyuZva9oAMLeoO1bttAICx5QM/vexjfjJE5sAsHgy3/rb25bljZLJ4hsPbMEVD74LgIk5y37+Ir5rHGuajqN++ix+8E8m/ui6juU3v4TrH91oHZ9z62r837O5AMQ/eOg93Gf0hyD2VkhcISYlMTWGWb+chZgaq23FmRjw8Cy2JQiiKigGCEEQBCOmxrDkniXjWq/4+BReX/RV+PiU43W75YpeYh/IWa6YgW2LxBbbsd2NSM6L7+KWBCaupLNIqlm4RAEemYeSySKj6RB4Di6Rt6xHvJIAj2SWz8Ar59yElIwGt+E2FFWY5UrQI8IrC0aAXCa+WOJK3nXTUiZgWMpEUhnEFCbONPhkDMYVxFIZhDwSAi4RkWQGQ3EVM+o88MoCBmLM8mV6nQcizyGcTKMvqmD/KQHwHAu4O5JIY14zE2dGEio6h5NYMqceGU3HcCKNwZiKA2eEoGQ0RFIZ9EYULJ4eREzJIGIE9J3f7MNwIo2kmkVvJIW5jV70GdYfrUNxTAm60DmShJLJonUwgZaAC61DCaSzGloHE/BIAnYOxJHJatjaG4Ms8ugcYeLNlp4oOI6JLpqmW+5EnYa4s7GbZasyLWs2dDILFVP82dDFrrcbFjntQ2y7rZ+JOFv7WH3rDcuWnYNxaDrw6gcDVj3tQ0k8taEHABONXt8+ZIk7AzEF9/6nDb/ME1uefK8b7UPOGbAIYk+FxBViUhJ0BaH/SEfQFaxtxVIQ+IJuBLUlJoK9JOQKuVoQNaf2AbgJgtjTCbqC6L6we1zrlZjmxdx1j+ELxx6I/aYUZx2yewXlB7G1u2mYYomZYchuuWLPJmTPRJQvtsgiD4nncPfrbfjGXWvhlnhIAo90VkNW0yEJHGSRh5rRLDHFLQlIpbPMLUgS4JUFxJQsMlkWINctCVCMOCpeWYTPiOnCLFckJsakMnmWLQJiahYRw7Il4GaWMjEld5wfUDdoiDMxJQO/S0S9V8ZwIo2RRBp1Hgl13px4U++VrfIjCRXNARcCbhFDCRX9UQUz6jzwyQKGEyqGEypm1TOxpi+SQl80hXnNfsgCy47UH1Ww/9QAZIFHdziJvgg75jlmndI+xMQaXWdiTtdIEofMqrOOWwfjOGpeAzKabtSXwhFz65FKa+iPKRiIKThsNjseSabRNpTAUfs0IKpkrADCC1r8LL5NIo2ucBIHTAtiKK4ikvz/7b15nBxVuf//qb332WeyEpIQAUUSQJCEGyEiwcgS8bKEXS+yXEDwohC4giA/ECWAfIkiilwNiERyWRI2g1cgMiwBsi8kkGDIOktm6b2qazm/P05VdVfPZCEzk5kJz/v1yqvz1Dl9+vSZ6jrVn34WCzuSeYyti6I1bSCZM9Gc0pEISWhJG0jrJnYkddTGNDSnuP3hjjSGVYTQkjaQNSys2Z5CfVzDjqSOrGFheycXcZpTvH31tiRCCs+fk3erS/3nk0vxi1fWgSAGEySuEAcktmNjTcsa2I69586fBccGOtfwR4IgiF6EPHoI4vOH7dhY376+R/crImyM0z7FVw+uwMiqSJd2SQze7u+uXLAnjniCTLlniiaXea6Uiysltu0wyCW2JotQJS6mWLbnuSLBsBxkCzbCKvdc8cSViCpBkyUULMcdi3u6FGzH9WThCXJzrjgS1YriTCrPPVmiqoiswcOK4m5YkZejJabJiLphRZ6YkggrSOVNdzwZUa1Yragiwj1jOnOu+BKSUemLKyYqI9wTpj1bQNrgYk9lREVHtoCObAGVEdUXa1pSBhriGqqiCtqzBlrTBurjIVRFFXS44sXQijCqoxp2Zgx05gqojWmoiihozxaQzJuojWuoiapoSetoSRs4qDqCREhGa8ZAZ85EQyKEmqiKmPkwfgAASu9JREFUpqSO9iz3vIlpMlrSOjqyBQyvCiOuyWhJG2jLGDi4JoqKsIItHTm0ZQo4uDaCqCqhKaWjNW1gdG0MMU327TE1XCzakdTRmjLwhYaYb+9I5nHkiErEQzI+bctheye3w4qE7Z15X7yJqBK2deaxpT2H48fUIKRI2NqR8z1nPmxK7fJcJYiBCIkrxAFJ1sxi4mMTkTWzvTuwnQVencgfic819EV43yGvH4IgCE7WzOL050/v0f1KRDTw7CE/hsxy3e5Mnr7hhQft7hLsiSleRaFy8UQpK+tc7rmilIgxDMEy0CFFgiKJeGlVEx5+Y4OfALdgOX4OllBJGFFYlf1202aQRZGLMWaxf1Tlnite2FBIkaC7oTexkIyIEqxOFA8p3HPFtcNuWJEvroR4NaJswebii8Y9WzrdBLqVERUduQIyOu/vhSF15ExUhlVXTDH91/PEko4c93ThnjPc06UqqqI6qqEtU8DOjIHamIoKV9zZmTFQF9dQG1OxM1PwPW+q3aS7Xg6aurjmJ+ytCCuojfPxOvN8PlWu2NORK6A6oqA+zr1L2nMFVEVU1CX489uyBdTGVNTFuZizM1NATVRDg5tHhs9HRUNCQ3NKR3PaQE1EwdCKELZ35tGc0tGQCGFoRQg7klzsGVIRwpBECM1pHU0pHcMqQxheFcbWzjx2dOYxrDKEYZVhbOvg+WCGVoS53ZnHR81pjKmLYmt7frdiIEEMNEhcIQ5IEloCqVtSfRMWdG6KwoL6kMFSipnYdyaNrenvKXyuoOoMBDFwSWgJfPwfH/c4LOjLa+bBlrqvOOQJJZ43SuklofzqoIieuOLaZZ4r8h7EllIxRigZDwBCsgRVFrEzY2BjaxaS6IYF2TwHiywKrueK45du5jlZbNgODwvybF59SETE9SwxLAeqJCKkiDBMG4ZlQ5NFRFWpJOGtgogiIV+wfc+TqCohW2prMnKm7XuueNWNkq54EdVk5ApBTxcvLIiXipaQLXg5XhRUhlUk8664EVWQ8MQdP+xIQWfeRDJvojKi+mINzwkj+2FI3ng1UQ3tWQOpvIVEuNjuza82xsWRjlyBe9K4YpDnOVOf0NCS4p4r1VEVdTENLWlXTImpvGJSpoC2jIGamMrFkZSOnWnuOdOQ4GJLS9pAXUwJiCn1CQ3DKsPY4YotdXEN9QkNrSkD2zvzGFoRxvDKMLa6YsqwStfuzKMpqWNYRcgVa3TsSOr46uhqmI6D5nT3uYQIYiBC4gpxQGI5Ft7Z8g4sx+rdgR0LaH2HPxIEsU9894TR2PSL0/p7GgMO0kAI4vOH5Vj4oOmDHt2vrL79ZBwd+RACs7oVUyVfXHE9V3ajrngCjCeZyGUJW8rzt+wu54rNWMBzRRKFQH9PLNnQksG/dmYhigJCioi8WRRbvLAh03bDghSeANewbKiyyHOwWEUxJSTzsCBPfAkpInJutaKwIvrPz+gW4pqMiCYjX3BzrmgyF3cKth9mFHM9V3IFLrZ4paXTrhjjl5Z2nx9RZeRKPGV4WBHPIZIIK77nSsYPO+LPzxZsRDUJiZCClM49X6Ka52lT9ISpdsUPv3pSiHu6+J4srriSzJmoiihu2JHpijuqK74U0O560tQnQmhJ61xMiWqojWloy3JPlhpXTGlxPVc8caU5qaMlpaMmqnDbzSFTH+eeKjuSuu/JwsUbHU1JHUMrQhhWGUJTMo9trtjiiTE7kjqGVIQwvDKM7a49sjqCyrCCtkwBBDFYIHGFOCDJm3mcM+8c5M187w5s54HGc/gj0asI5LJCEH0CiTYEMXDJm3lc8X9X9Oh+RbDz+M1Bv4DkGP6xow6qxFdHVwMohgP54spuxhLdvt6eXC6elJd1FruIL0WbJ60Vy+yg2KLKIt79pB0vrNgOWRQgiyIs24HDGCQ3bIh7rnDbE1sM0/FzuJg2Q8HiZaA1RYTuhg15CXWL7ZLv+ZItWAircsCzJabJ7vO5Z0rUS5hr2NBNHnYUcUtLp92cLbzUNC8t7bV7CXYTIS625N0cMlG1WK0oa9iIueJN1hVvIqobZpQzkS144orslpLmnitVUZ7zJeWKNYmwwpPQunZlhLd35AqoiKiojro5X0pywnTmCuh0PWlqoiraMwW0Z13PlVjRc6U2qvphSZ644oUNtaQN1EaLYUnNKQMNCVeMSRs8p0xCc8UbA02u2NKQCKEpabieKyE3zMjwc8x4njE7knkMrQi5OWq4uPJJawaGRTkPiYENiSvEAUlci2PrDVsR17p3kd1nlDhw1lb+SPQRg0NkoS+sBEEQRE+Ja3EsvWhpz+5XlDgmrpsDW4r5wslzV5+Ae77zZQClgglvc3azgZWXbd6TuCKV2aWeLpYdFFNsxgKeLbIkBMKIJFGALApujhUHksiT4BYsB6bjQHET2hq+Z4rk52TxPFU013Ol4IYJKbIA0y6KLaWeMKosIqJ64oaNiCoj5JaC9sQNHjZk+Z4xPEeLzXOuhLinS86djyrx8fIF2xdDvFLQuskT9sZcsaQ07Chj2K7YUlKtyPWsSZR5rnieLSm3dLTnuZJ0S09XumFGPOeKgqoIz9HihQFVRXgOlvYsz7lS4+ZwacvyHCs1Uc0VU7jnSk1Mc6sN8ZwsNVEVO7OForgS4+KKl5C3wQ078jxX6uMaWlJue0Lzw4x2uJ4rvp3UMaRCw5CKoj20IozKCM9ZwxjD1+9fhDsWrN3luUsQA4FBJa4kk0n8+te/xhFHHAFZlrFo0aIufa655hrIshz4N2HChC79Zs+ejbFjxyIWi2HixIl455139sM7IPYXlmNh4YaFfRMWtH0hhQURBEEQAADHcfDyyy/jjDPOgKqquP7667v0ee2117rcm8iyjPXr1wf6LV68GJMmTUIsFsOYMWPw4IMP7qd3QfQXlmPh9S2v9+h+RWAWvhZbAgFWt/lUjj6oEpf922i/zQn0CQot5aFD5TlVyp1MywoRBTxZbIcnofVwyjxZJFEMiDWSKECWRDy/fDueem8LJEFwPVcc2H51IS8syIGmuNWHfPFEcsOESj1X3IS5dqk44/jii6Z41YdsaIro53zJuWJHSOHVijzPlbAbFpTxw4C454r3/IgrxnilniOuGJMrWAirkh/m0yUsyOClqIs5V+wyzxVeWtqrZsSrIclIhGUutuS52FIZVpHMmW5YEBdTvJwvVZ4nS64ottTENLRluWeK77mSNdCW5XZtTMPOtIHWjIHauObbLSkDtTHuudKSNtCS1n1PlX/tzCJbsFEX4/aG1gwMy+FhRV4C3LTBPVcqQvioOY28aWOIK7Z4YUVDEiHf82Zzew4AsGknFZQgBjaDSlyZNWsWPvzwQ9x3332wbbvbuFLbtjF9+nTouu7/W7JkSaDPH//4R8ycORP3338/Nm7ciEmTJmHq1KnYsmXL/norRB+jWzpuePUG6FYvJ8GydWDZDfyR+FxDnivEYIFO1b7lrbfewuzZs/H9738f48ePh213dVt3HAe2bQfuTXRdx6GHHur32bZtG6ZOnYrjjz8eGzduxIMPPohbbrkFf/jDH/bn2yH2M7ql44537ujZ/Yqj49Zhj0F0jMDn3dunIqqM207/YklD1z4eftJb1+6R54oTzLliMxZol0Uh4Cnjea4UbRGSKMB2GCxXmNFc8cTzJFEkz3PF9nOs6KaNgu2KK5Lgiy+a7OVccT1bZMENK3I9WdyEuHnT9hPqqn4CXT5+pCQMiHuycE8VHqYkIeq266aDsCLxHCwFi1c/UiRfLPFyvMQ0yc3pUhRTMkbRs6U0LIiLKTwMKKW7YUEhN0xI5wltKyIKOnJutaAIt73qQVVRFZURBU0pHdmCjUpXbNnRmUdat1wxRcWW9hx000FtlFcr2tSWRcFyUBfXUBNT8WlbDnnTRm1EQV1MxcfNaZg2cz1XQvhkZxaKJKAyomBIIoQNLRmosohESMYQt91hDA0JnqOlJW0gqvIQqQa3v2kzDKkI+dWZdiT556M5VfycLPqoFeub0r69rTOP9mwxP4tu2rBsBwSxPxlU4spdd92F3/zmNzjiiCN2208QhMAvQ5IkBdpnzZqF//iP/8C3v/1tNDQ04L777kMikcBvf/vbvpw+sR+JqTGsuXoNYmqsdwdWYsBpa/gjQRDEIGAiVWfqUyZPnoxXXnkF06dP73K/UU6550opv/vd7xCNRnH//fejoaEBZ555Jr7//e/j3nvv7cvpE/1MTI1h0bmLenS/IshxTP3oYThStNsfHsuT0H6WsKDSMB6gq6dKef+g54rTJedKaX9BCPaXuogtXIB5dW0z3vy41fdc0cvCgkrDfrxSzEXxRSjxbPHChnjYkSpJrjhTzNkSViQYpo2CGzbkebr4nisqryZk2vy9RVQeFuSJORGNl4bWTRuaXMzBkjdtRNRijpWsK6ZENVdsKXDPlXhIRmfORN60/dLRnfkCsgUb8ZCCirCCnRkDuun4niw7kjoYAxJuGNCWjhxsh6Ei4trtOTAGVLk5Vz5p5d4flREFtTEVG1ozAIDqCC8NvbE1C1kUkAjLqI3xnCiqLCKucbsppSOkiIhpEuriGp+bm3+mIaEBACrCKgRBwJBECABQEw3aMVWGIolo8OwQvx4OqQj5511IkXg1pZyJlrSBkCKiPVcUTy79n/fw43krfPubD/4T189d5ttnPfw2bpu/2rd/9PQKPLJoo2//4c1PMH/5Nt9+aeUOvL+p3beXb+nERndtAGBnxkBnyes7DiPxhujCoBJX9paFCxcikUhg5MiRuOCCC7B582a/raOjAx9++CGmTJniHxMEAVOmTMHbb7/dH9Ml+gDTNjFvzTyYttm7AzsmsHkefyT6BMprSxC9y7EHV1N1pgHC0KFDUV1djcmTJ2PhwoWBtrfeegsnnnhiILn3ySefjI8//hg7d+7c31Ml9hOmbeKFjS/06H5FYCa+VdEIgZWPwUWU8oTxu01o64cFcbtrtaDdJ7Qt91wpFUt4ktqizVjQ80UShLLqQjxsqGA52NiaheJ6tpg24zlOPPHEDfvRZBGyKMB2uklo64oppaWcVc/zxfbChkQ/50rBsrmnjCzBsB0YZtBzxbSZL67kC1agPa2bsBzGw4Q0Gam8CdNmvifLzgxPPBxTec6VtqwBxuB6sih+6WHPc2VHJ7d5zhUZWzt48uNEmNtbOnIQBT5eZUTBlvY8JFFAXOP29qTOvWwUCVURFbmCjUSIixvVURWMcaFFlkTUxFQAQLUrhtTFuVhSGVYgCAJqY1qgvdZtD6lcWPbavT9rvSu2eOdNZUQJtFd5tptvr2jDX4OsYaElpePQhjiSeROOw/zEtl64kGU7SOsWln7a4Z5bDB/uSOGZJUXx5JmlW3Hv39b59l0vfYifvVDM4XLNX5bi6ieX+va3f/MWLn/8A9+eMusNXPzYe759+uxG/NfTRXHnxnkr8NMSMecPb36Cx9/Z5Nt/W92EV9c0+fY7G9uw+JM23/6kNYMNLUVPnIxhoy1TTFKdL9jIF4qekYyxbsVUon+R99xlcDF69Gg8/vjjOPHEE7Ft2zb813/9FyZPnoxVq1YhkUhgx44dAID6+vrA8+rq6vDBBx90NyQAwDAMGEbxBE+lUgC4q6/j9I5q6TgOGGO9Nt7nGd3U8cA7D+CbY78JSZB6b20tHcKHD4AN+SYg7/4Xys8TvbG+3nNZL36m+hIvTr2v50rXhb5joK2td5M0UObTU/a0vgfK+9wbNE3D3XffjYsuugiyLOPRRx/FtGnTsHDhQpxyyikAgB07dnTxzK2rqwMANDU1oba2tsu4dG8y+NFNHb9b+Tuce9S5UCRln8ZwLB3fr30OafvywHXEdn9VZ4yfD96+VfqFrNyLReiSgyX4WqzsPBC6PL+kLwPEkvFsmwXaHcYCrycIwV99RYEFXt/7v8P4exMF+OKKYfKcKpIAmG5OFkUUoEgCDJMnpJVFQJUE1zPFgSwCsggU3DAhxU2gm3PFE9kd3zBt6JYDVRLc1+OeK5LA/P6GxV8/rIjocENTVElAWBb9L8iazNu90JawIiCiSmhyQ15CsoiYJhZtSUBMlbA9ycWSkCwgrklozxYQUrhwlHA9XSrCCgCGhMbvTSvDChgr2lURbleG+Ve/irACx3FQ7YoZIVkM2GGV3zt7/QWBn1NVEW5LggDGGKKK6P9tHMeBp405Dr9mqJIQeL5HLCQH7CEVWsBm4P3Dbint5pSOQ+pjWLE1ic6cgbTOcxTJogDHcUrWmL8Pr8KQpnA7rXPhMeK+L9P9bGR0k39W3ERErWnDvX5y+5PWrD+vtGFh1bakb6/dkcLaHSn8v/PGAwDmLdkKALjjDB6Cd9dLHwIALvrqQQCAq/7M01R88vNpAIDzH303YJ/560YwBqy6Yyocx8GFT6yBqshYdONJAIArn/gAnXkTz189CQBw0zMrsTNTwP9c+hUAwG8XbURnzsQt0w7z55PRLXzvhIMB8DCqjmwB3z5qOABg2eYOdORMfP0w/p14Y0sGedPGEcMr/LXIFiwcXBPla+UmZvYENC5yOQirg+v7UF/fm/SruHLZZZdhzpw5u+2zfv16jB07dq/HnDlzpv//6upqzJs3D0OHDsXcuXNxxRVX7PJ5oijuVv2755578LOf/azL8dbWVuh67+TfcBwHyWQSjDGI5X6XxGfmudOfQ7Yziyyyvbu2458D2rMAKKmWR2+sb1uKb4QdHR1oCQ18z6AHp49FU7qAlpaWPn0dui70HQNtbXN5/gtcX59T+4s9rW86ne7mWf3Pyy+/jDPPPHO3fX7+85/jpptu2usxJ0+ejMmTJ/v27bffjvfeew+zZs3yxZXu8NZtV/cndG8y+HEcB0+c9ASynVnkxX0rx2xYDr6z8X48+GUThQLfS1taWtDWzsdra90JXZOKwkvJ6aTrRmCszg73l3/3C0Z7W9BrqsNt90h2Bu1UsrP43piDtCv4AYBeKCCTLtr5fB7ZTPE6oOeyyInF/T+fzcIpCUsy8jnkWAGvrWsFAKRTSeg5C5mcjnzBRDadRC5rIKcb0F3bNHRkchaMgoVsOgkjn0M6p8MwLWSSnTDyWWRyOgqWjVSyA3oui2xeR8G2kU52wMhnkcrmUbAcZJIdyGczyOR1WA5DqrMDejaDbN7g7Z0dMPNZ7EzzdU92tMHUM2h1xZR0ZzvMXBotaQOaJKC9bSesfAataQOyKKCzfSfMXMatXCRi585WWPkMDwHSJLS2tsLK8bFiqoSWlhZY+bxrC9zO8fULyQjYmuS2m8UfslpaWvxzwi63bTuwFwmMBWwRDjo7O4uJj4Xg3nVYfdi3R1Zq+NqYhG9ffvxQHN4Q9e3HZhyGqrDs25ceOwRjavjzbSOPjnQOomOiPqZCkQRs2NKEnPs+2rMFNDU3Y1M7X5ekbqKpuRmfunauYKG5uRnbkvxzoZsOmpub0Zrl6yKAz7slzdslAWhubkZHnos3omtnjKLHSEtLC3KFoG2VfKhaWloComXpunp2KZ7tvUZLSwscx0FzxgRg+u3//HhnoP//ul45nj1r4UcAgMuO4SXYZz6zCgBw2rgIAOA//7wMuuVg0nAuoF31xEq0Zk28+8NjXHsNNrbpRXveeizflvHtmxZswOqmLF6+gotJv/i/T7FqRwZPXvwlAMAf3t2O9zan8Ptzubjz/KpWvL85jbtPG8Pnv7ETK7dncO3kEQCA9zan8HFrHhce0wAAWNecxfrWPKYfwX9I2NppYMPOHE46pAoA0JQqoCVTwJHDeAhlUrfQnCrgC/URfBb6+t6kX8WVRx99FL/73e9226c8JvmzUlVVhYMOOggff/wxAGDIkCEA+I1HKS0tLWhoaNjlOLfccgtuuOEG306lUhg5ciTq6uqQSCR6NEcPx3G4C15dHd3A9JCCXcDjKx/HJUdeAlVSe29t7QKw6XHg4EsASe29CQ9yemN9TZXfIFRXV6O+vrIXZ9c3lDm/9Rl0Xeg7BtrahkP8xqncs3Kwsqf1DYVC/TCrPTNt2rQ9ChN7yq2yN4wfPx5PPfWUbzc0NHR7b+K1dQfdmwx+dFPHX9b/BVePuRohZd8+E0Yhj/OqF6IqcSSGVseBzWnU19ejw+FfEurq6xDTZDc8KCjUaZoXtsFFl9oa/sXMi9sYUnY9qq0N5nCqrQna1VVV/v8FCKiprvRtUZJRVVnh26oaQmVF0Y7FYqiuDPt2RSKOkCIF7ES46N1TXVUJphQgKRlAMFFbXY20k4Uk52A5Ahpqa1DRYUDoyMJiAobU1aDdUgAxA8sBhtTXYltegijnYdoMQ+vrsCMvA2IKtgMMqa9DXUaE4XQCAIYPqcemjAiTdbhrU4favIwC2wkGYGhDHXZaKjIFXiBjxJAGDM1I6NT/BVUSMGxIA4YZKhy2AVFNRn19PYbrCiznY1SEFdTX1+MgRACsRzwUtBMRFfX19RDCBoA1iLjPdzQdwFokIhrq6+tRYdkAVkKWeHul5QBYCVWRA3uLUmbLkhSwI5oSsIdXR307okqY/IV6VFZWoq6uDj+YMhbHjKpCfT33tHv+6kmoj2uod/On/P2Gk6BIgh+edsuZwXOqfMu7/azigSE1JqxP0nBEBfXVCcS1nQjFKmDkTVRHFLTnTMQqayDmUkiEZKR0C5FENRQj45fpTlTVotnMQJF4iFissgbNJv+B1LAZKqpr0O7wHzdsBiSqapER+D2pw4BYZQ0yyeKeUFldC6MksW6iqgZtJYl045U1SOlFkbC6phbJvBnoXyrGJKpqAomlq2tqfc8aAKioqgnkLqqorgmUMK+prQt4eFXX1EIuaa+sroUqi9AtJ9C+0xXePHtL59KA/XHr8oC9ZOtyZAs2amrrIIkC3ti4Ep1507efX72Kl/B27TkfrMGOpI5HXPs3j3+IT9tzuP2soyCJAh7560dYuyON6089AqIo4Nrn3sUHn3bgP046HJIo4Ecvvoe3NrRhw13fhCgK+OmrS/Hq2mbfnvW/K/HM0m2+vbf09b1Jv4oroij2+UadSqWwZcsWDB06FAD/4vaFL3wBixYtwne+8x0A/BehN954AxdeeOEux9E0zd+ASunt9yAIwn5ZlwMd27LxzIfP4MIvX+ivZa+srWMDW54BRl/YNavb55yerm/534koQteFvmMgra0Xgz4Q5tJb7G59B+r79JLi9zWrV6/2700AYNKkSZgzZw4YY/6XkNdeew1jx47dpeBG9yaDH5vZePGTF3HlxCv3ff+EjdMqGiEKN+DnZ30ZP/j6OIiiCMH9wiHt5m/nXXcEQQAYg1RWHajclsXdt5e+DgP/0l58LQFKic3bS58vQCnxVJFlKdCuyFLg9SVRhCKLaE0bSOVNyJIIWRJhMwbTdqApPGGtZfOEtdyW4DD4tirz6kM8R4oERZZ8r4SQKiOkyn4ISkSToSm8+g8AaIoETebVfgAgrMmIaDKSeROqJEKWJUQ0BabNkAjJEEUR8ZAXdsPtmGvHNG4nwvyHu7AiQRRFVES4HVG5XRlV/bUURRHVMe/zz+2wKnomRFFEyLVlKXgOKFLwXqs6qvp2XJMx9YtDfPv/zZiAQ+pjvv3OLScjJAvoaNsJURTxo1MPC5wDEw6qCtjeHPaFaEhBzrSRLdiIhRReGttmSBs2hlaG0Z4zoZsOUrqFYZVhpJrS0C3ePqwihE1tOWRNG2nDwtCKMDa355B3+4+qieDTthySeRsZw0ZdXOOhMKaNlGGjNqZiZ6aAnOkgU7BR6SbXTeoW0gZP4ps2LGQKNlK6FbDThu2LO515CxnD9gXMjMFLecuiAMthSOoWLLuornTqlh/SBwApw4YgFD1lOnIWwiWiY0feDNidbtluj53Zgp87BwBasyaGJkK+oNOpW6iLab7s2po1Mawi5AtArVkTwyvD/r7UkTNRnwj5gkZLphBob80UMKwy7I/v9XfcV2jLmhhSEYLhij0tbn/vc+T1T7neQ63ZAoZWhNGcNgL2Fjf3kPf8z0Jf3pscULukYRg455xzsHTpUhiGgY8//hgzZsxANBoNCCc/+tGP8Nhjj2HhwoVIJpO49dZb0d7ejquuuqofZ0/0JlE1ioUXLURUjfbuwHIU+PpC/kgQBEEQe8GNN96I+fPno7OzE+3t7fjlL3+JF154Adddd53f58orr0RnZyd+8pOfIJlM4tVXX8Vjjz0W8EwhDjyiahRzT5vbo/sVQY7hkn/9f3CkKKKajEPqudt8TZR/ofJ+9e4uusw7Vkws6ya09ayyH4TLE9qWt5fajAX7C0CXBLelvzgzBBPeSkKwepAsCt1UFxLxcUsGKd3iwqgkwLKZ/9qqJKBQktBWEnn1IMvh1YAUWfTFElXiCW49W5F4qWbPC4FXJxJ88UWReKnmjCu+aG6FG4Dn+gDgf+mNqFywjbqPIbfds728FfFQUNj1bC/hq+bm+/P+bt7rlf9wX56IuLzq04lfqPP/P/v8o/Cr8yb49pLbTsGPpn7Bt6dPGI4vDSt6GFWElS4luvuKqCojZ9h+6eqQwqs9JfMmqqMqNFlExrACdq5guaWlNd6uW13686S+CuKajM58Aam8iRq3Pa1bSOYLqIlqCCm8f8awUB1RkQjJaM8WkNJNVMdUhBQRqbyFjG6hIqL4CY3TOh8vEZKxM1NASrdQHdUQUSWkdBMZw0JlREFUlZB2S2tXhBUkQjJa0waSeRMRRURUlZDMm0jlTUTVYilvT8CrCCt+f0HgwtjOdAGtaYPbIRmpvIVO10slrsnoyBZ8TxtNFrEzXUBnjiddVmWeMyhjWDzps8RzBumm7YsfLWkeBufltWnLGLDsYt6b5pQOxhhafdsVRVxxpCXN21vc4zuSefdRDzxu78wHnr/Tfb6X0Nl73hY3qfFAYVCJK48//jhkWcbBBx8MgGfRl2UZd955JwD+C85FF12Ea6+9FlVVVTjxxBMRj8exePHigEvtFVdcgZ/+9Kf43ve+h5qaGrz00kt4+eWX/XGJwY9hGXjgnQdgWMaeO38WbAP48AH+SPQq/m0dlQsiPqdQzv/BSTab9UsrL168GA8//DBkWcaECRP8PldccQXmzp2LQw45BKNHj8bzzz+PBQsWYMaMGX6fgw46CK+88gr+9re/oaamBpdeeil+8pOf4Oqrr+6Hd0XsLwzLwCMrH+nZ/Ypt4LLa5yA4wTHq4ho2/eK0Ll+sS/GEGN9zztuC3QuSgN2LKeUEEtqWiSWiEBRHREEIVAtiLCgKSGJQjJElAaXf6aWy53tii+0wLtwI/DkFiycslSVebUg3S8QRSUDOzXWhuNWHsiXiieZ+eVYkLvSokoSsYRWf734ZB7g444kpngjieZJ44knETTDreeDEtGKCWIB7qPC145R6JJRSLp6Ul8QeXlXMQ3HR8QfhB1MO8e3VPzsVM79Z9DY5Y/wwfKEh7tuqLA6Ye7GIJiFb4J4fUVWGpkjQTS62RFXZrSbES1+HFQlRTUauYCOVN91S1lyMSOUtJMKKWwrbRq5gIaxKCKmS6/liIhFWEA8pvnhREVYQ0xRkdC7WxENyyfiWK4YovljCqz25r6dzOx5SkC1448lIhBQk8yYyJe1cjOHjV0QUpPL8+fGQhESYe8ukdAuxkOy/Xkrn788rVZ3Mm0iEFNTGNbRlDX8+lRH+emmde7dUx1SkdBPJfAFhRcKIqjBaMwZSuglFEjCsIsTFHN2CIADDKrndli1AEPj1oiWtozNXAGPAqJqIPz+HASOqwkjmTWQLNmyHYXhlGE0pHYZlQzcd1Ma4d5BhOUgbFuriGjpzJmyHIZk3URtTsSOpw3EYOnIFVIQVX6xpSumIu+IWwEWXuCb7Is9AYVBVC7r44otxwQUXdDle6r4zffp0TJ8+fY9j3XTTTZ8pGR0xuLCZjXe2voOrvtLL3kjMBna+A4wjLyeCIAgCiEaj3eZoKf1yMm7cuEB+lV0xefJkLF26dI/9iAMHm9lY0rwENrP33HkXCLBxdGQdROx+jHIBd8VPpyKqSXj4jY3B8KDSscs9V8q+xHcVX0o9U4CyqJ+Ah4UgBNsZeKiPhySKXT1XSj1hyjxbRIH3MR0GmzGIAhdTCm6Ihej2z5eIK4okIlvmueKVu1UlEapbqtkTORSp1HNF4GFHrheMIAhFzxXZE1U8TxV+3PNU8RKeRrXgVzFv/TzPFM8u/zuUlqwGgFE1Rc+nP373WIyti/n2Xd/+cqBvTBs8X/+iKhczVNlEVJMRUkReKtt2oCkioq74Yljcjqhc/MoVLERdscOrchNWRERdO1ewEVUlhBVeWjutW0i4pa7TuomsYSOqcU+RjGEhrZu+8KKbXLxJhBRkDS68ZAwujhRsx+3PbYcxX+xJhBVIooBU3kLBdhALyVBlHqJk2QzxkALHYcibFmyHIabJUGQeZqbJIrclEam8iZDChZewwsPYBAGuGMRLV4cUCYmQgoowF1dUWXRLd3PxJuIKU4lwqXikoCKi+p4yXJxRfc+cmMrFHE88CikiamMaOnIFpF1xZkgi5Is5ADC8KoxU3vQFyDG1UbRlCr4XzMiqMDpzpu/9NbI6gmS+gLTBxZpx9TG0pg3kXLFmdG0UnbkCDLfC17j6GDpyA6sIxuD5dGH/xUETg5+IEsG8c+b1/sByBJjcB+MSBPG5Z2D8TkjsC3RvQuwrESWCR095FBHls1W8KEWQI7hm8y14QvpsY1REupZ+Lr8OFb1KvTCfsvbd2axrGFEpUrlYgjLxBUEPDblMbJFEIdDuiScrtnRyW+SeLabliStFTxa4YyuSiKxRFFNkSUC2YPkhSJ6I4T2qMhdTZJEnaPVEFF9McUUUtcz2pumFA3lCl9ePlUlf5cumlXkfeR5HAPDK9ZMxJFFMwjnlsAMjKTrAPXlyBRuKxMWFsOu5YpgONFlEVOVigmE5CMmSL8YULAeaJCKiyn6IiyZLvriSL9iIqFys0S3u+aIpEmKh4niaLPmeKJ5YorliTNb1fCn1PIlpMs8HoxfFGNNmvngT02RIgoCUbsKyuXhiygypPPfcSIRkGJbD52/aiGsSNFVBKm8irEqIhRRoMg9TM22Hz8cNg7IdB4mw7K+XqnNxpCKsuGKMiERIQSIs+14v8ZDM188No/L6d+ZMXzxKuOJMxuBilbeeXExSfM+ZVJ7nefE8ZTK6hajKBZ6MYflzqIxwO6NbUCUR9fEQOnI8zEoUgIZ4iHsO5UzIooCGihCy7usB3DOmPVsUa0ZWRfyQp4HCoAoLIoi9xbAM3PHGHX0TFrTyDgoLIgii16GwIIL4/GFYBu774L4ehwX9sOFJiM7eucffeOqhuOzfRgeOeZ4SnthRzLkiBI53yblSNnZpXhcG1kU8KfV0Kc+pIgrCbnO8yFJ5GBAgScHnKwGbCzBe5RXRFXM8W/LElYLFhR6R51hhrCh6eOE7nojj5Ropf/TED188cReiPKzHW0+nLAFOuQdQ+TqPH1np/3/RjSfhgXMn+PbhQxOoih6YFSwjqgTbYWjPFhDVJJ5zxXJ4Dh1ZRNgVEwzT5p4rmuSLK6rMw7oKlsPFFpnnMMkVuOdK2PVc0UvEmJCb4Niw7IAnjGE5CCkSwp7njDteSBGhmw4PU9JkxDQJGd1CzvV8CasS8qYFw7L581XJz18S0xTfs8bzfIlqEnKGjbzracPfr42MmzA3URLmFA9xsSNr8IS8Ma0ornhiUFzzwoi4zT1XuDgR1STe3339mFb03PHDlErElVhI9j1j0rrpzoeP5wk2FWHVDxOKhxS+Hgb3dEmEip413nhVUS9syX39MH9/HbkCKiM8J47nCRRWJNTGNHTmCkjrFlRZRH1CQ2eewoIIos9xmIOtqa1wmLPnzp8F5gD5rfyR6BPo13uCIAji84LDHOzI7ujR/YoAhiFKGwTsfoxZZx+JrR15XFOSf6PLWOWeKO6jKAB2d+1dNu2iaFCeQFcQgv0FIei5IgrBsCJRDIa/yKIYCEsqf76Xc6XUFgX4VU94mJDoe64IAqDKAhgr5iyRy0QT77g3rieiKCWeLPw4F1G88B/vrXv9y9ei3C4Ptyo137xpSkA8KQ0BOtApDZuKuWFBhu+5IkGVRFiO43ua8DAZC4YrvqgS/3sbFq/e41XwyRUsRFSJ53CxbF+sUWURhun4njGaIqFgc3FGkQRXLCmKNyFFgmHZvvgSkiUYtiv+uDl4cgXbnR8X7nTTQbbAxQ3dBAyT5wTi55CAXMGCwxhUSYTmzkeHg6jGq2fppg3GgLAiuwmWLTBw4Smi8mpWggDEQny9CnZR/PHEGC9BcFSTkS3YrqdNMWFw2jBdMURCvsA9TaIaF3+yRlG8Cal8/dJuzhovTMsbL+aGVSXzXruMTImnjDcfz9MnpikBz5hS8Snmi0l8/ERIxmFDEn4C6oECiSvEAUlYCeMPZ/6h9weWw8BX+2BcgiAIgiA+d4SVMO4/8X6Elc9WSjSAHMbNW6/Dn8XdjzF9wvBdtpVXB/I8Lzz7zPHD8XFLerdhPt2NGRBTyvwzRO7KUrTF8vZuxJNSWxACIoQkBsOIPPHFqz7iecqYdlFs8UQTb1hPNCn3VPFyw3i2J6qUP5aLKZ5oUu6ZWO65UjrvWWcfiS8OS/j2yOp9Dxkb7JSGQ0U1GSGZe34UbBvxkAJVFmFaDLrJPUMUSYTplt7mCYm5J0rBFV9UmYsNeTcBLs+54hQ9XWTRF0s88aZQ6injhgV5Yokmc88VL1SMizNF8SWi8v7e6wPg83cr8QCAYTlcTJF52FvOtCEAUCXBnY8DBoaQwkuR66YDAbwtrHKxQ3RD1DzPFVEQEHZLhetm8f1rioiCbfueKp5njie+eGFSpsXc9eCeQlmDe85EPc8TN8FuyB0/a/D1DCkSDNMJJADOumFBiRLbe/2QIqIjV/SUiYVkNCd15Av87xnVZGzvzPueOxFVQmva8HPEXPDVg/rjtNwtFBZEHJDolo4bFt4A3eqaZLBH2Dqw5Ab+SBAE0Yt0VyaVIIgDG93Scfvbt/fofkWwddw69FGIrAf3JmUJVMvDgo4fU40F1/5bN+JK0A6EBZWHvnQT8tNltLKEtXKJJ4pXscejS86WsjAjLqaUjO16wvhhQSXVhrxxvNcTfY+V4HFPRJF88cV7fnD+5TlUyikVV0bXRvGNw4tVTc/5yshA6ePPM6XnA/c04WKD51miSCIea/wXXlq1w7ebUjqSObPEc8X2E956YonlVo/iYT22X3bYH9+yffHEsGyYrlgTUqRiQl2ZixeGZcN2HEhiUQwpDVviYozte7ropgPTdqD44kzx9T0xxjBtP6zJcMUO/vqiKy7x8b0wJ08M8sUc2wk83wtz8jxh8qaNsFoMI/LEIk+8Mh0HsifuuGFMEVXyc7Tolo2QLCGs8jCpoucOn19atxALKYipXgLhYtgUF1dMxEKyH5aV0k2/NHbaMKFbPCwqHpLdalEl/c2ip81AZGDOiiCIzy0DpPofQRAEQQwKemPfbKjQcMFxozDUTYzaNaQnKCLs6rXZLv7f3bGuoTBdPVuCnilCmfgSfP2uOVwQ8HTxEt56YUKCwEOPvLFL35/3KJeJLIoU/F1akyR3rOB7cfYQ5VX6Tl/70YkDpvTxQMYTz2a/tgEAcNM3D4UiCVjfnAYAaIoERRLw0D8+BgBcf/I4aLKIBSu2oy1bwLcnDIcii8joFmybQRJdTxRXrIioiiuW8DCjqojihxF5FaM8z5ViWBAXKzzPFe/53DNFAlMQyNEiiYBu8co3qiRCEgQYbpia77lSsKFKAlRJ8HPAiAIC4owscvEn5IopYVcMCrthM2GVe914niReThlV5tWwTF/M4Z4hnhgTUiS0ZQu8dLkoQFMkdOZMtzqThIgmYXN7yfuXJeimXhSjXPHDq84U0WTkjOL79xPiup4onliVLdiIaJK/nqWeK5myMKRcSVjRQIQ8V4gDkpAcwgOnPoCQHNpz58+CFAKOeYA/EgRBEARB9ICQHMLPJv2sZ/crUgh37bgcEPd9DFEQ8J8njfUFj+NGV3fbr0up5rL2oOdKWQLbbvKKBHKsCGXiiyAEXkBxv4yWjieWiScBccZNUuv3F4rVhbzX9ubkDSP67cEqQd7zPM8V75UUWeh2HXbHwTURnH3MCN8mYWXvKfVk0mQpIHZ5niseXpjP0s2d+LQtxz03JBH3//0j/PWDLZAELoa8vGoH3v2krSQsyAnkdClY3NPEy8li2m4OF8kLm7F9scbzFPHEBx6m5Pilo/2wJjeHS6isvyaLsGynGHbkig2mzVyxhIftlOaUMb3+SolYZBU9YzyxiHuuSO54DlRZcMOEHD9MyctpY9kMsljuucPXq+CO53nmeJ43mhsWpJue544EzQ3DCvS3uOeMl+C3VHzywrZ0i4tZqsTnkzV4+eiI+/50t/9AZGBKPoMAz9UxlUr12piO4yCdTiMUCkEUB+YJM1jIm3nc+PcbMeuUWQgr4d5bWysPLL8RmDCL518hAPTOuZtO5eEYOaRTKaRSdP570HWh7xhoa2vkMnCMXK/uK/3JntbXe5/loQPEvkP3JoOPrJHF9a9cj/93+v9DVNvHRKVWHj+teQD51GykUp+9aoxj5GDpzD9vHCOHL9UqmH32ZKRSKThGDrlMGqlUChndhGPk/OdmMumgnU4F7GymaDtGFtl0sb+ZzyJb8nw9l0Y6Dd/OZ9LQHLXYns3AYSzQnssV55PNpJDNGEU7nYaeLY6fTqeQz/LrrCgKSKVSyLvtTJCRSqWQy2ThGDk4hsPbMzpfH4N/rvSC7a6XhVQqBct24Bg52IYQWD9LdwK2mRd9e8GVxwDo3c/p/mZ/Xhe8v18qlYKpZ33bNrJgRi5wfjkltq1nwQq7tgv5DCxDx6p/NfH2wyrACgYefHk9AOC/ThkHmDoa17agYDv42rg6OEYBGVtEKmOgSo7CKZhIGQxgRSEulec5YMIIwbFsZCzXEyOuwXYYMoYJxoBESIEoCEjmCpBEIKYpsAUB6VyBe23ZBpgsIKmbUCURiiyCQUYymQUrKJAEAbagIJXKQjBVCAJgGRay6QzS0MBsBke2kEpmoToamGnDUW2kUhmEhTBsw4KtM2TSKaQUG6xgghWAVCqJTEKAbWThGPxzkdYcoGDANmRkMimkkgKYkQcrKEilUkh2ivxvUVCQSieRSopwjCxsXUMmnUJnUgYzcrD1LDKpFFIpBayQAzPCSCVT/LpVyMPWc8ik0+joCEE087B0fo1IpSJAIQdWyPH+yShg7tv9Ul/fmwiM7mr2ia1bt2LkyJH9PQ2CIAiCGNRs2bIFI0aM2HNHYo/QvQlBEARB9Jx9vTchcWUfcRwH27dvRzwe7zV3vlQqhZEjR2LLli1IJBJ7fgKx19Da9i20vn0HrW3fQWvbt+xpfRljSKfTGDZsGHlE9BJ0bzL4oPXtO2ht+w5a276F1rfv6Ot7EwoL2kdEUeyzX9oSiQR9kPoIWtu+hda376C17TtobfuW3a1vRQVVxOhN6N5k8ELr23fQ2vYdtLZ9C61v39FX9yb0UxFBEARBEARBEARBEEQPIHGFIAiCIAiCIAiCIAiiB5C4MoDQNA233347NE3r76kccNDa9i20vn0HrW3fQWvbt9D6HhjQ37FvofXtO2ht+w5a276F1rfv6Ou1pYS2BEEQBEEQBEEQBEEQPYA8VwiCIAiCIAiCIAiCIHoAiSsEQRAEQRAEQRAEQRA9gMQVgiAIgiAIgiAIgiCIHkDiygAhl8thyZIl2LhxY39PZdCzbds2rFixAplMZpd9kskkPvjgA2zZsmU/zuzAYdOmTWhsbERnZ2eXNsYY1qxZgxUrVsCyrP0/uUEMYwzr16/HunXrsKt0WM3NzXj//fexc+fO/Ty7wU0qlcLy5cuxbt06FAqFbvsUCgUsXboU69ev38+zG1xYloXFixdj3bp1u+yzN3sa7XsDH8YYPvzwQyxfvhymafb3dAY1mUwGy5Ytw44dO3bZx3EcrFq1CqtWrYLjOPtxdgcGmUwGjY2N2LBhQ7ft27ZtwwcffICOjo79PLPBT1NTE5YtWwZd17ttz+fzWLJkyS7Xnugey7Lw0UcfYdmyZbs9Lzds2IAlS5Ygn8/vx9kNPtatW4fGxsZdtu/NntbjfY8R/c7cuXNZIpFg48aNY/F4nJ144omsvb29v6c16HjllVfY+PHj2bBhw9iRRx7JIpEIu/nmm7v0e+ihh1g4HGaHH344C4fD7Dvf+Q7Tdb0fZjw4aW5uZsOGDWMA2CuvvBJoW79+PTvssMNYfX09GzlyJBs+fDh7++23+2mmg4t//vOfbOzYsWz48OHsqKOOYl/5ylfYxo0b/XbHcdjVV1/NNE1jX/ziF5mmaWzmzJn9OOPBw8yZM1k4HGbjx49no0ePZg0NDey5554L9Fm4cCGrra1lo0ePZtXV1eyoo45iW7du7Z8JD1Cy2Sy7/fbb2UEHHcTi8Tj793//92777c2eRvvewGfjxo3siCOOYLW1tWzUqFFsyJAhbNGiRf09rUHH5s2b2fnnn88qKirYhAkTWEVFBZsyZQrbtm1boN/y5cvZ6NGj2dChQ9mwYcPY6NGj2fLly/tp1oOTc889l4miyC677LLA8UKhwC644AIWCoXY4YcfzkKhEPvFL37RT7McXOzcuZOdfvrpLBaLsa985Sts1KhRbN68eYE+zz77LKuoqGCHHHIIq6ioYJMmTWKtra39NOPBw8KFC9mIESPYQQcdxCZMmMBCoRC75pprmOM4fp/W1lY2adKkwPo+++yz/TjrgcnTTz/NJk6cyKqqqtiu5I292dN6Y98jcaWf+eSTT5iqquzhhx9mjDGWTCbZF7/4RXbxxRf388wGH7/5zW/YihUrfPudd95hmqaxOXPm+MfeffddJggCW7BgAWOMsW3btrFhw4axW265Zb/PdzDiOA775je/yWbOnNmtuHLUUUexM844g1mWxRhj7Morr2TDhg1j+Xy+P6Y7aPjoo49YJBJhP/3pT/1NdcWKFezNN9/0+zzyyCMsHo+z1atXM8YYW7x4MVNVlf31r3/tlzkPFl577TUGgL3++uv+sRtvvJFFo1FmmiZjjLG2tjZWUVHBfvrTnzLGGNN1nZ1wwgnslFNO6Y8pD1g2b97Mbr/9drZlyxY2ffr0bsWVvdnTaN8bHEycOJFNnTrV/5xcf/31rL6+nqXT6X6e2eBi0aJF7KmnnmK2bTPGGOvs7GTHHnssmzZtmt/HNE02btw4duGFFzLHcZjjOGzGjBls3Lhx/n5K7J7f/e53bPLkyWzixIldxJW77rqL1dfXs02bNjHGGHv11VeZIAjsH//4R39MddBg2zabNGkSmzRpki9+p1Ip9vjjj/t9tmzZwsLhMHvggQcYY4yl02k2fvx4ds455/TLnAcTDQ0N7KqrrvLv+95//30GIPDjz9lnn82OPvpolslkGGOMzZo1i4XDYfrxp4w77riDNTY2sieeeGKX4sre7Gm9se+RuNLP3Hnnnay+vt7fdBnjX6I0TfM/SMS+c/zxx7PLL7/ct6+44go2YcKEQJ9bb72VNTQ07O+pDUp++ctfslNOOYVt3769i7iydOlSBoC9++67/rEtW7YwQRC6eAkQQb73ve+xww47LPBrRTnHHXcc++53vxs4dvrpp7NTTz21r6c3qPnrX//KBEFguVzOP/bcc88xQRBYZ2cnY4yx3//+9ywUCgU2z+eff54BYJs3b97vcx4M7Epc2Zs9jfa9gc/atWsZAPbGG2/4x1paWpgkSeypp57qx5kdGDz44IMsHo/7ticCr1u3zj+2evXqLsIw0T2rV69mQ4cOZZs2bWInnHBCF3FlzJgx7Mc//nHg2PHHH88uvPDC/TnNQcdLL73EALCVK1fuss+9997Lqqqq/C+jjDH2pz/9icmyzDo6OvbDLAcnpmkyVVXZn/70J/+YbdssEomw3//+94wxxtrb25kkSezPf/6z38cwDFZRUcFmzZq13+c8GNiVuLI3e1pv7XuUc6WfWbZsGY466iiIYvFPcdxxx8EwDKxdu7YfZzb4SafTWL9+PQ455BD/2LJly3DMMccE+h133HFobm7ebRw0Abz33nv41a9+hT/96U8QBKFL+7JlywAARx99tH9sxIgRGDp0qN9GdM8//vEPnHbaaTAMA0uWLMHmzZsDOVcYY1ixYkW35y6t7e4544wzMGnSJFx66aVYuHAh5s2bh1tvvRU/+clPUFFRAYCfu4ceeihisZj/vOOOOw4AsHz58v6Y9qBlb/Y02vcGPt51pfSaU1dXh1GjRtE1pxd4//33u9ybRKNRHHroof6xL33pS4hEIrTeeyCfz+O8887Dfffdh1GjRnVpT6VS+OSTT2j/3Af+8Y9/YNSoUfjyl7+MdevWYe3atTAMI9Bn2bJlOPLIIyHLsn/suOOOg2VZWLVq1f6e8qBBlmXceeeduPvuu/HUU0/h1VdfxXe/+10cfvjhmDFjBgBg1apVsG07cO6qqorx48fTufsZ2Zs9rbf2PXnPXYi+pL29HcOHDw8cq6mp8duIfec///M/EQqFcNlll/nH2tvb/fX1KF3voUOH7tc5DhZSqRTOP/98PPzwwxg2bBiampq69Glvb0cikYCiKIHjNTU1dC7vge3bt6OpqQmHHXYYqqqqsHnzZowZMwZ//etfMWbMGGSzWRiG0e25S2u7e8LhMK677jpcd911+PDDD5FOp1FfX4/zzjvP77On6wKx9+zNnkb73sCnvb0dqqoGBEeArjm9wYIFC/CXv/wFzzzzjH+su2sQQOu9N/zwhz/EhAkTcMEFF3Tb7q0f7Z+fne3bt6Oqqgonn3wytm3bBsuykEwm8fDDD+Occ84BQPtnTzjrrLPw0ksv4aabbkJ1dTW2b9+O++67D/F4HACdu73J3uxpvbXvkedKP6MoSpfM214maFVV+2NKBwQ33ngjXnzxRSxYsCBwUaL13jduvvlmDBs2DHV1dWhsbMR7770HAFizZg1Wr14NoPu1Bfj60truHkVR8OKLL+Lvf/87li1bhi1btiAcDvvCoCdYdXfu0trunpdeegkXXngh5s2bh1WrVmHTpk341re+hRNPPNHfLOm60HvszVrSeg98FEWBaZqwbTtwnK45PeOf//wnzj//fNx5550466yz/OO0f+4br732Gp588kmcf/75aGxsRGNjI1KpFJqbm9HY2Ajbtmn/7AGKomD58uU4++yzsW7dOmzYsAE33HADLrnkEmzbts3vQ9fzz04mk8GJJ56IY445Bps3b8aKFSuwcOFCXHXVVZg7dy4AuvfrTfZmT+utfY/ElX5m1KhR/gXKw7MPOuig/pjSoOeWW27B73//eyxcuBBf+cpXAm27Wm9RFDFixIj9Oc1BRTweB2MMN998M26++WbcfffdAIA//vGPeOyxxwDwtS0UCoESwbZto7m5mc7lPXDwwQfj5JNPxrhx4wAAkUgEF198Md5++204jgNN09DQ0NDtuUtru3tefPFFHHnkkZg8ebJ/7JprrkF7e7tfro+uw73H3qwlrffAZ9SoUWCMBcJlPZv+RvtGY2MjTjvtNNx444249dZbA22jRo1CW1tb4EtUPp9HR0cHrfduMAwDEyZMwD333OPfn3z66ad4//33cfPNNyOfz2PIkCHQNI32z33g4IMPhiRJuPzyy/1jV111FXRdx/vvvw+Aruf7ypIlS9DU1ISrr77aD7U/+uijMWnSJCxYsAAA/DA3Ond7zt7sab2175G40s+ccsop+OCDDwJ/yPnz52P06NEYO3ZsP85scPLf//3fePjhh7Fw4UJ89atf7dJ+yimn4P/+7/+Qy+X8Y/Pnz8cJJ5yAcDi8P6c6qPjlL3/p/yrU2NiI+fPnAwDuu+8+/OpXvwIAfO1rX4Oqqv6mAPBfldLpNE455ZR+mfdg4dRTT+2yeW7duhVVVVV+XopTTjkFL7zwgt9u2zZefPFFWts9UFdXh+bmZliW5R/bsmWL3wbwtf3000+xcuVKv8/8+fNRWVmJY489dv9OeJCzN3sa7XsDH29PLL2eNzY2oq2tja45+8Bbb72FadOm4YYbbsAdd9zRpf3kk08GYwwvv/yyf+zFF18EYwxf//rX9+NMBxfTpk0L3Js0Njbiy1/+Mk4//XQ0NjYiFotBkiRMmTIlcC4bhoG//e1vdC7vgVNPPRW2bQdCwbdu3QoguH+uXLkSn376qd9n/vz5GD58OA4//PD9O+FBhLd+3noC/Iv8tm3b/LYvfvGLGDZsWODc/eSTT7Bq1So6dz8je7On9dq+9xmT8BK9jGVZ7Nhjj2XHHnsse/bZZ9k999zDZFmm8qr7wN13380EQWD33Xcfe/PNN/1/a9as8fukUik2ZswYNnXqVDZ//nw2c+ZMJsvyZ65h/nlnx44d3ZZivu2221hlZSV79NFH2ZNPPslGjBjBLrnkkn6a5eBh+/btrKGhgV199dVs4cKF7MEHH2TRaJT96le/8vusW7eOxeNxdtlll7EFCxawGTNmsJqaGqpmswc2btzI4vE4+853vsNeeukl9tRTT7HDDz+cffWrXw1UN5g2bRo77LDD2NNPP81mz57NQqEQe+ihh/px5gOTt99+m7355pts8uTJ7KSTTmJvvvlmoELY3uxptO8NDu6++24Wj8fZI488wp566ik2evRodu655/b3tAYdy5YtY/F4nH3rW98K3Ju8+eabgYpZP/jBD1h9fT2bM2cOmzNnDqurq2PXXXddP858cNJdtaD33nuPaZrGfvjDH7IFCxaw0047jY0YMYK1tbX10ywHD2eddRY77rjj2HPPPcfmzZvHjjzySPa1r33NLxFu2zY74YQT2FFHHcWeeeYZNmvWLCbLMpszZ04/z3xg4zgO+8Y3vsHGjBnDnnjiCfbKK6+wiy66iIVCoUB1pjlz5jBFUdh9993HnnnmGTZhwgT2b//2b4FrB8HYRx99xN5880122223MQD+NdarCsnY3u1pvbHvCYyVlKQg+oVkMol7770XixcvRmVlJS677DJMmzatv6c16Lj66qsDvzx7nHDCCfjlL3/p201NTfjFL36BVatWoaGhAddccw1OOOGE/TnVQU97ezvOPPNM3H///QEPIcYY/vSnP+GZZ56BZVk49dRTce2113ZJckt0ZfPmzbj33nuxbt06DBkyBDNmzMDpp58e6LNmzRrcf//9+PTTTzF27FjcdNNNgYoTRPd88sknmD17NtatW4dQKITjjz8e11xzTSBpWT6fxwMPPIBFixYhEong/PPPDyS9JThTp04NeP4BQGVlJV588UXf3ps9jfa9wcETTzyBefPmwTAMfOMb38D1119Psf6fkWeffRYPPPBAt21///vffa9Zx3HwyCOP+J+l008/HVdddVWgqhaxZ66++mqMGjUKM2fODBx///338dBDD2H79u04/PDDcfPNN1M4+F5QKBQwe/Zs/P3vf0coFMIJJ5yAa6+9NuDtnU6nMWvWLLzzzjtIJBK49NJLceaZZ/bjrAcH+XwejzzyCBobG5HJZDBu3Dhce+21OOywwwL9FixYgDlz5iCVSmHixIm48cYb/aS3BOfnP/95wPPPY/bs2TjqqKN8e2/2tJ7ueySuEARBEARBEARBEARB9ACSwwmCIAiCIAiCIAiCIHoAiSsEQRAEQRAEQRAEQRA9gMQVgiAIgiAIgiAIgiCIHkDiCkEQBEEQBEEQBEEQRA8gcYUgCIIgCIIgCIIgCKIHkLhCEARBEARBEARBEATRA0hcIQiCIAiCIAiCIAiC6AEkrhAEMWh4+umn0dTU1N/TIAiCIAiCAAC8++67WLx4cX9PgyCIAQCJKwRBDBouuOACLF++fJftL7zwAlavXt3l+Nq1azF//vw+nBlBEARBEJ9Hfv3rX+O3v/3tLttXrlyJl19+ucvxTCaDuXPnoqWlpS+nRxDEfoTEFYIgDhh+8IMf4H//93+7HH/22Wdx5ZVX9sOMCIIgCIL4PPOXv/wFN9xwQ5fjTU1NOP/887F27dp+mBVBEH2B3N8TIAiC2FfS6TRefvllTJgwAYceeuheP2/t2rVYuXJll+NHHHEEjjjiiN6cIkEQBEEQnzMWLlwIQRAwderUvX5OJpPBiy++2OV4IpHAt771rd6cHkEQfQSJKwRBDEpaW1sxbdo0DB8+HNOnT/9Mz12/fj2ef/553y4UCnjuuedw5513krhCEARBEMQ+M3PmTPz5z3/GwoULP9Pzstls4N4EABYtWoTq6moSVwhikEDiCkEQg45PP/0UU6dOxcSJE/GHP/wBsly8lK1evRpz584N9F+1alXAPuuss3DWWWf59uWXX46RI0fiiiuu6NuJEwRBEARxQGLbNq644gr885//xFtvvYWDDz7Yb0un013uTZqbmwN2Q0NDoM/ChQvxzDPP4Ne//nWfzpsgiN6DxBWCIAYVa9euxfe//32ce+65uP/++yEIQqB93bp1XX75+fDDD3c53uzZs/Hkk0+isbERDQ0NfTFlgiAIgiAOYAzDwDnnnIN//etf3d5PpNPpLvcmmUxml+N9/PHHmDFjBn7yk5/g3//93/tiygRB9AEkrhAEMai49dZbMWbMGMyaNauLsAIAZ599Nu64447AsbvuuqvbX35ef/11/OhHP8Ljjz+Oo48+uq+mTBAEQRDEAcz8+fOh6zqWLl3a7Q81w4YN6+K5smHDBrz00ktd+qZSKUyfPh1TpkzB7bff3mdzJgii96FqQQRBDCoeeugh6LqOiy++GLZt7/M4mzZtwrnnnosf//jHmDFjRi/OkCAIgiCIzxNnn302Lr30UkyfPh3/+te/9nkcx3Fw0UUXQZZlPP74493+iEQQxMCFxBWCIAYVI0aMwOuvv47Fixfvs8CSzWYxffp0HH/88bjrrrv6YJYEQRAEQXxeEEURjz32GKZMmYIpU6Zg06ZN+zTOrbfeirfffhvz589HLBbr3UkSBNHnkLhCEMSgY+TIkXjjjTewePFiXHLJJZ9ZYLntttvw0Ucf4YwzzsDTTz+NuXPnYu7cuVi9enUfzZggCIIgiAMZURTxP//zPzjxxBNx0kknfWaBZfHixbjnnntwzjnnYPHixf69ycsvv9w3EyYIotehnCsEQQwazjvvPAwdOhQAF1hef/11/Pd//zcWLFiAs846C2eccUa3pZS/9KUv4dvf/rZvjx07FtOnT8drr70W6CfLMpViJgiCIAhir5k4cSJEkf9eLYoi/vjHP+K2227DI488grvvvhvjx4+HaZpdnhePx3Heeeehvr4eAKCqKs477zx0dHQEkt8OHz6cSjETxCBBYIyx/p4EQRAEQRAEQRAEQRDEYIXCggiCIAiCIAiCIAiCIHoAiSsEQRAEQRAEQRAEQRA9gMQVgiAIgiAIgiAIgiCIHkDiCkEQBEEQBEEQBEEQRA8gcYUgCIIgCIIgCIIgCKIHkLhCEARBEARBEARBEATRA0hcIQiCIAiCIAiCIAiC6AEkrhAEQRAEQRAEQRAEQfQAElcIgiAIgiAIgiAIgiB6AIkrBEEQBEEQBEEQBEEQPYDEFYIgCIIgCIIgCIIgiB5A4gpBEARBEARBEARBEEQP+P8BpuFOBA5lhgMAAAAASUVORK5CYII=", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -182,7 +192,7 @@ } ], "source": [ - "fig, axes = plt.subplots(2, 2, figsize=(11, 6.5), constrained_layout=True)\n", + "fig, axes = plt.subplots(3, 2, figsize=(11, 9.5), constrained_layout=True)\n", "for ax, (name, (h, L, M, fs, pas, stop, atten, taps)) in zip(axes.flat, designs.items()):\n", " f = np.arange(0.0, 2.2 * fs, 25.0)\n", " ax.plot(f / 1e3, response_db(h, L, fs, f), lw=0.8)\n", @@ -216,10 +226,10 @@ "id": "6f2f5522", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T15:31:52.905103Z", - "iopub.status.busy": "2026-07-23T15:31:52.904904Z", - "iopub.status.idle": "2026-07-23T15:31:53.060805Z", - "shell.execute_reply": "2026-07-23T15:31:53.059565Z" + "iopub.execute_input": "2026-08-07T00:44:54.430955Z", + "iopub.status.busy": "2026-08-07T00:44:54.430664Z", + "iopub.status.idle": "2026-08-07T00:44:54.656063Z", + "shell.execute_reply": "2026-08-07T00:44:54.654535Z" } }, "outputs": [ @@ -227,13 +237,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "worst spur below 20 kHz : -100.0 dBFS\n", - "worst product >= 20.1 kHz: -115.7 dBFS\n" + "worst spur below 20 kHz : -94.3 dBFS\n", + "worst product >= 20.1 kHz: -114.6 dBFS\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -289,17 +299,22 @@ "\n", "| case | L | taps/phase (=MACs/out) | worst stopband | passband ripple | storage f32 / Q15 | group delay |\n", "|---|---|---|---|---|---|---|\n", - "| down economy | 147 | **78** | −72.1 dB | ±0.0025 dB | 44.8 / 22.4 KiB | 39.0 smp = 0.81 ms |\n", + "| down economy | 147 | **58** | −71.5 dB | ±0.0028 dB | 33.3 / 16.6 KiB | 29.0 smp = 0.60 ms |\n", + "| down balanced | 147 | **78** | −72.1 dB | ±0.0025 dB | 44.8 / 22.4 KiB | 39.0 smp = 0.81 ms |\n", "| down transparent | 147 | **184** | −121.4 dB | ±0.00001 dB | 105.7 / 52.8 KiB | 92.0 smp = 1.92 ms |\n", - "| up economy | 160 | **44** | −72.7 dB | ±0.0026 dB | 27.5 / 13.8 KiB | 22.0 smp = 0.50 ms |\n", + "| up economy | 160 | **38** | −71.7 dB | ±0.0023 dB | 23.8 / 11.9 KiB | 19.0 smp = 0.43 ms |\n", + "| up balanced | 160 | **44** | −72.7 dB | ±0.0026 dB | 27.5 / 13.8 KiB | 22.0 smp = 0.50 ms |\n", "| up transparent | 160 | **96** | −121.1 dB | ±0.00001 dB | 60.0 / 30.0 KiB | 48.0 smp = 1.09 ms |\n", "\n", + "*(Storage here is the full L×T table; the shipping engine stores the\n", + "symmetry-halved ceil(L/2) rows — see `PhaseTable.StorageBudgetsArePinned`.)*\n", + "\n", "Notes:\n", "\n", "- The ~2× direction asymmetry the handoff doc predicted is measured\n", " (78 vs 44, 184 vs 96): never share one transposed prototype.\n", - "- economy is ~2.4× cheaper than transparent in both MACs and storage —\n", - " the §4 spec relaxation, delivered.\n", + "- economy is ~3.2× cheaper than transparent in both MACs and storage\n", + " (balanced ~2.4×) — the §4 spec relaxation, delivered twice.\n", "- Group delay is quoted in *input* samples at the direction's input rate;\n", " every figure is inside the plan's 45–90-sample linear-phase budget except\n", " up-economy, which beats it.\n", diff --git a/bridge/notebooks/ratio_demo.ipynb b/bridge/notebooks/ratio_demo.ipynb index 7370f43..4e4339a 100644 --- a/bridge/notebooks/ratio_demo.ipynb +++ b/bridge/notebooks/ratio_demo.ipynb @@ -5,11 +5,11 @@ "id": "01802365", "metadata": {}, "source": [ - "# RatioTap demo — the shipping converter, measured\n", + "# RatioTap demo \u2014 the shipping converter, measured\n", "\n", "Family convention: this notebook drives the **actual shipping C++** through\n", "the C ABI (`tools/capi/`, ctypes bridge `ratiotap_py.py`, which builds\n", - "`build_capi/` on first import) — nothing here is a Python re-implementation.\n", + "`build_capi/` on first import) \u2014 nothing here is a Python re-implementation.\n", "It demonstrates the three things RatioTap promises: exact rational\n", "conversion, deterministic accounting, and the economy profile's measured\n", "spectral contract.\n" @@ -21,10 +21,10 @@ "id": "7674e466", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T22:10:44.042273Z", - "iopub.status.busy": "2026-07-23T22:10:44.042078Z", - "iopub.status.idle": "2026-07-23T22:10:44.423943Z", - "shell.execute_reply": "2026-07-23T22:10:44.422269Z" + "iopub.execute_input": "2026-08-07T00:44:57.465692Z", + "iopub.status.busy": "2026-08-07T00:44:57.465397Z", + "iopub.status.idle": "2026-08-07T00:45:03.354627Z", + "shell.execute_reply": "2026-08-07T00:45:03.352344Z" } }, "outputs": [ @@ -32,10 +32,56 @@ "name": "stdout", "output_type": "stream", "text": [ - "RatioTap 0.1.0\n", - "down economy taps= 78 latency= 39.0 smp = 0.81 ms\n", + "-- The CXX compiler identification is GNU 13.3.0\n", + "-- Detecting CXX compiler ABI info\n", + "-- Detecting CXX compiler ABI info - done\n", + "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", + "-- Detecting CXX compile features\n", + "-- Detecting CXX compile features - done\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- The C compiler identification is GNU 13.3.0\n", + "-- Detecting C compiler ABI info\n", + "-- Detecting C compiler ABI info - done\n", + "-- Check for working C compiler: /usr/bin/cc - skipped\n", + "-- Detecting C compile features\n", + "-- Detecting C compile features - done\n", + "-- Configuring done (0.6s)\n", + "-- Generating done (0.0s)\n", + "-- Build files have been written to: /home/user/RatioTap/build_capi\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 20%] \u001b[32mBuilding C object ratiotap/submodules/dsptap/CMakeFiles/tap_dsp_fft.dir/third_party/ooura/fftsg.c.o\u001b[0m\n", + "[ 40%] \u001b[32mBuilding C object ratiotap/submodules/dsptap/CMakeFiles/tap_dsp_fft.dir/third_party/ooura/fftsg_float.c.o\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 60%] \u001b[32m\u001b[1mLinking C static library libtap_dsp_fft.a\u001b[0m\n", + "[ 60%] Built target tap_dsp_fft\n", + "[ 80%] \u001b[32mBuilding CXX object CMakeFiles/ratio_capi.dir/ratio_capi.cpp.o\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[100%] \u001b[32m\u001b[1mLinking CXX shared library libratio_capi.so\u001b[0m\n", + "[100%] Built target ratio_capi\n", + "RatioTap 0.3.0\n", + "down economy taps= 58 latency= 29.0 smp = 0.60 ms\n", "down transparent taps=184 latency= 92.0 smp = 1.92 ms\n", - "up economy taps= 44 latency= 22.0 smp = 0.50 ms\n", + "up economy taps= 38 latency= 19.0 smp = 0.43 ms\n", "up transparent taps= 96 latency= 48.0 smp = 1.09 ms\n" ] } @@ -64,7 +110,7 @@ "\n", "`frames_needed` / `outputs_for` are exact arithmetic, not estimates: a\n", "steady-state superblock of 147 outputs costs exactly 160 inputs (the fresh\n", - "start costs one less — the pre-advance convention the cell below\n", + "start costs one less \u2014 the pre-advance convention the cell below\n", "demonstrates), and feeding the converter in arbitrary ragged chunks produces\n", "exactly the predicted totals. This is the\n", "capability the asynchronous engine can never offer, and what the Bluetooth\n", @@ -77,10 +123,10 @@ "id": "dc5c88f7", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T22:10:44.426351Z", - "iopub.status.busy": "2026-07-23T22:10:44.426047Z", - "iopub.status.idle": "2026-07-23T22:10:44.446159Z", - "shell.execute_reply": "2026-07-23T22:10:44.444825Z" + "iopub.execute_input": "2026-08-07T00:45:03.359691Z", + "iopub.status.busy": "2026-08-07T00:45:03.359009Z", + "iopub.status.idle": "2026-08-07T00:45:03.394074Z", + "shell.execute_reply": "2026-08-07T00:45:03.391591Z" } }, "outputs": [ @@ -98,7 +144,7 @@ "rng = np.random.default_rng(1)\n", "c = RatioConverter(direction=\"down\", profile=\"economy\")\n", "# Pre-advance convention: from the fresh zero-primed state the first 147\n", - "# outputs cost 159 inputs — the 160th is the down-payment on the NEXT\n", + "# outputs cost 159 inputs \u2014 the 160th is the down-payment on the NEXT\n", "# superblock's first output. Steady state costs exactly M=160 per L=147:\n", "print(\"fresh: frames_needed(147) =\", c.frames_needed(147))\n", "print(\" frames_needed(294) - frames_needed(147) =\",\n", @@ -125,9 +171,9 @@ "source": [ "## The economy contract, measured on the shipping engine\n", "\n", - "A 3-tone program (997 Hz, 6 kHz, 18.5 kHz) plus a deliberately hostile\n", - "23 kHz ultrasonic component, converted 48 → 44.1 through the C ABI. The\n", - "acceptance criteria from PLAN §8: every spurious product at least the\n", + "A 3-tone program (997 Hz, 6 kHz, 17.5 kHz) plus a deliberately hostile\n", + "23 kHz ultrasonic component, converted 48 \u2192 44.1 through the C ABI. The\n", + "acceptance criteria from PLAN \u00a78: every spurious product at least the\n", "stopband (71 dB) below its source; decimation aliases confined above\n", "20 kHz by arithmetic; the imaging products (e.g. the 23 kHz tone's image at\n", "25 kHz folding to 19.1 kHz) bounded by the stopband.\n" @@ -139,16 +185,16 @@ "id": "addd1d53", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T22:10:44.448798Z", - "iopub.status.busy": "2026-07-23T22:10:44.448390Z", - "iopub.status.idle": "2026-07-23T22:10:44.861834Z", - "shell.execute_reply": "2026-07-23T22:10:44.860377Z" + "iopub.execute_input": "2026-08-07T00:45:03.397707Z", + "iopub.status.busy": "2026-08-07T00:45:03.397320Z", + "iopub.status.idle": "2026-08-07T00:45:03.835089Z", + "shell.execute_reply": "2026-08-07T00:45:03.833592Z" } }, "outputs": [ { "data": { - "image/png": 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", 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AQAAA4N69e4rWY23i0SYtFzQ5VuRYlsW4ceMUny0tLbF//36wLIvvv/9eKe3kyZNhbW0NPz8/rWM6ePAgAGDq1KlK0ydNmoTs7Ox8l+nVqxfKlCmj+Ny0aVM4ODjkux/a0mTd//zzDyQSCebNm6e0bM2aNdGmTRvFPuVHKBQiKSkpT6uip6dnoWJRp2fPnkqt/U5OThg5ciTOnDmDpKSkQqfNjyaxFqasc9M234sjJm22pU38Xl5e8PHxUXpkRB1tyq979+5KaS0tLfHvv/+iXbt2aNy4sWK6SCTCjBkzEBMTg0uXLgGQtUh269YNNWrUUKSzsbHBsGHDNIpTle3bt6NVq1bo0qWL0nQLC4s83QnzIxQK8eDBgzyv1PDw8NBo++PGjVM6Zw8ZMgRlypRRajXTNI8K48suuV27dkVmZiZev36tmKZN3m/duhVeXl5o166d0vSJEyciKytLMV6Cvsrz48ePWLt2LSZMmIDhw4dj9OjRyMzMxNOnT/W2Pk2OAU3zpajHkz6NGTNGcaxaWloqpusqj7Q9zjU5dvNT1O88oWdaSSHJX01x6dIlvH//HizLApBd+EdGRgKQnVASEhIAALVq1VK5rg8fPgCQdRX5UvXq1ZGTk4OwsDDFj0z58uXzPAvn4uKClJQUZGZmwtLSUifrdHJyQrly5SAUCvNMj4iIUJo2duxYLFmyBFu2bMGvv/6KuLg4HD58GH369FH73GFAQAB+/fVXrFy5Uuk5WVUGDRqE6OhovHr1CnZ2dgCAKVOmoG/fvhgwYADev3+P0qVLo3HjxtizZ0+B6zty5AiOHj2K6dOno1KlSlrFo23sXNDkWJHz8PBQetYZkB2bNjY2cHNzU5pubm6OChUqKI4zbYSEhMDZ2Rn29vZK093d3ZXiyS2/AR1cXFyUurkXlibrfvXqFfh8PhYvXgxA9j2Xf+dDQkKQk5Ojcv0zZ87EhQsX0KpVK1StWhXt2rVDly5d0KdPnzzfraLuZ37HYbVq1cCyLEJDQ9GgQYNCpc2PJrEWpqxz0zbfiyMmbbZVlOOmIEUp68zMTHz+/DnfUZHlvxkfPnxQpFO1rcLKyspCeHg4unfvXuh1bNy4EWPGjIGbmxuaN2+Otm3bom/fvmjYsKFGy3+5TzweD1WqVFGc0zTNo8L68viR39SWHz/a5L1YLEZoaCiEQiG+/fZbpesR+d8fP37UW3mePn0a3t7eaNWqFbp37w5nZ2cIBAK8e/euUK/Q0XR9BR0DmuaLJuviUn7lpas8KsxxXtCxmx9dfOcJVVpJIckvNhs0aKB0d0pu1KhRqFatmqJFMjMzU+W6RCIRAOT73jj5cvI0APJULAAoRsyVPyOlq3Wqmi5/FlfO1dUVAwYMwK5du7Bs2TLs2LEDYrG4wAEeAgICwLIsnjx5otQiGx0djYSEBPj4+KBLly4YMWIEEhIS8PjxY3z77beKCquct7c3jh07hoCAAPTt2xceHh4FtvAePXoUJ0+exKhRo7Bu3Tqt49EmLVc0OVbkvsxTQHaMSCQSsCybpyU5MzMTtra2Stv5sqVKKpXmeUZPJBLl+yyqVCpV2dKlaj/UPUuqKU3WLRQKIRKJ0KlTpzxpu3Tporay4+7ujsDAQNy7dw/Xrl3DtWvXMHjwYNStWxc3b95U6glR1P3U9Puubdr8aBJrYco6N23zvThi0mZbRTluCqJN+X353Zb/fhW0Dk3S5WZubo6cnJw854vk5OQ82+fxeGp/FwsycOBAdOvWDZcuXcKtW7dw6NAhLFu2DD/99JPiJoE6qvZJnnea5hGg+X7n9uXx8+V5WZu85/P54PF48PT0zPdY69q1K2rVqqV1eWpq7ty5aNiwIS5evKi0/3/99Zde11fQMaBpvmiyLlUKU/bayu+3WVd5pM1xLlfQsZsfXXznCVVaSSE1atQIfD4fVlZWaitH8nS3b9/OM8iDXL169QDIHugfNGiQ0rwHDx7A3t5e65HV9LHOgkyePBl+fn74999/8ddff6FcuXL5/lDk1rNnT6UudnKXLl2Cubk5unXrprjbJz/p5feyannFSJOLbUA2IMC8efPQp08fbNu2TXHS1yYebdLqi1AozHMDQZfq16+PAwcOICgoCPXr11dMj46ORlhYmKKbj7yr0Zct8EFBQXlak+rVq4cDBw4gODhY6Y7ts2fPitTylB/5D3JR86hp06Y4cOAAGjZsqLbXhCp8Ph8tWrRAixYt4Ovriz179mDEiBG4cOEC+vXrV6TYcsuvG/GjR49gZWWFqlWrFjptYRW1rIua7/qISRv6iF+uKOUnEolQq1YtPHz4MM+8Bw8eAJDdkJWnU7WtL3l4eEAqleLz589Ko45+ubxQKMRXX32Fu3fvQiqV5juKtjwdoPr7a2dnh379+qFfv35Yu3Ytunfvjl9//RU//fRTgY9rPH78WOn8nJaWhrdv3yq+j5rmkTb7rQ1t8l4kEqFBgwZISkoq8GatNuWpqbCwMAwfPlwpz5OSkvD06dM8b1LQ9frUHQPa5EtB61J1PBW17Av7G6XLPNL0OC8KTb/zRD16ppUUStmyZTFhwgSsX78e169fV5qXmpqKbdu2AQDKlCmDsWPHYvv27bhw4YJSugMHDoBhGJQrVw59+vTB1q1bERQUpJh/8uRJnDt3DlOnTtX6C66PdRbEy8sLDRo0wPfff4+QkBCMGjWqwHem1qxZEz4+Pnn+2djYwNHRET4+PopXqNjZ2aFDhw44deoUnjx5olhHQkICNmzYACcnJ41fNyMUCnHlyhX4+fkp5YM28WiTFgBOnToFHx8fpfIoKnd3d3z+/FnnF9ty8lbtOXPmKO6Q5uTkYPbs2eDxeJg8eTIAwNHREU2aNMHevXsV6TIyMrBu3bo8rUkjR46EtbU1fvjhB0XLllgsxrp162Btba3T+G1sbGBvb4/w8PAirWfMmDHw8PDA+PHjERsbqzTv1atXOHv2rMplDxw4oHhkQE5+R9rBwaFIcX3p9evXSueZgIAAHDhwAOPHj4eZmVmh0xZWUcu6KPmur5i0oU38mzdvho+PT56RzFUpavlNmzYNz58/x5YtWxTTQkNDsWbNGjRu3BheXl4AZCON3r9/H/v27VOke/bsmdKoonKdOnUCn89X/P4BslHs80s7f/58vHv3DosWLVK6YA8MDFSMvi9/DvfL729aWhr+/vtvSCQSpekMw8DOzk6j8QX8/PyUnvNbuHAh0tPTFec0QPM80ma/taFN3v/000948uQJli9frpSfDMPg6NGjCAsL03qdAODj44Ply5erjbNevXq4cuWK4twvlUoxe/ZstY8GFXV9mh4DmuRLUY6nopa9qmO8ILrMI02P86LS5DtP1KOWVlJoGzZsgIWFBbp27Yo6deqgQoUKiIqKQnh4uFKX0I0bN4LH46Fnz55o0KAB3N3d8eLFCzRu3BgDBw4EAOzcuRNDhw5F8+bN0bJlS2RlZeHu3bsYN24cFi1aVKj49LHOgkyePBnjx48Hj8fL815OXdi9ezfGjh2LZs2aoU2bNrC0tERAQADc3Nxw8uRJRXdVTXz5LkJ9e/78Ofz8/ODj46NoCY+IiMCPP/4IAIpBDJYuXQoXFxeYmZnleffbl8aNG4ddu3ahSZMmqFmzJkQiUb7vaS2ssmXL4vjx4/Dx8UHVqlXRqFEjvHjxAklJSdi3b5/S62PWr1+PHj16oE6dOqhTpw5CQkKwZcsWxUAXcu7u7jhw4AB8fHxQvXp11K1bF8HBwfjtt99w6tQpnd9MmTZtGlatWoWwsDA4Ojrm+57Wgtjb2+Pq1av49ttvUblyZTRp0gS2trZ4//49+Hy+4n15+cnKykLbtm1hb2+PihUrIi4uDg8fPsTcuXPzfe1NUUycOBGbN2/G8uXLIRKJcPPmTfTo0QMrVqwoUtrCKmpZFyXf9RWTNrSJX/7Km1WrVql9T6tcUctvwoQJCAsLw7Rp07Bz5044Ozvj9u3bqFWrFg4dOqS4oJ00aRJevnwJHx8f/Pnnn7C2tkZGRgbmzp2bp/WqZs2amDt3LhYvXozLly/DzMwMQqEQP/zwA4YMGaKUtl+/fti2bRvmzJmDffv2oV69eoiMjASPx8OBAwcAyLpQ16xZEwMHDkTLli1hZmYGX19fVKlSBTdv3sT8+fNRrVo1ODs7IygoCJmZmRqNZQAAs2fPRvv27VGhQgWEhIQgODgYmzdvVnqHtaZ5pM1+a0ObvP/mm2+wZ88ezJ49G9u3b0e9evWQkZGB169fo1mzZmjVqpXW62QYBn5+fujUqRPmz5+vMs6NGzeiV69eqF69uuI3YsyYMUhPT8fdu3e13m9N1ic/5gs6BjTJF03XlZ+ilr2qY/zLd8jqM480Pc6LSpPvPFGPx8qfxiakkBITE/HgwQMkJyejXLlyqFOnTr537D9//oyHDx+CYRjUq1cv3+65r1+/xosXLyAQCNC4ceM8I9ddvnwZEokkz8Psz58/R2BgIAYPHpxncJfCrvPKlSvIyspCjx49lKZfu3YNaWlp6NWrV574w8LCUL58eXTu3DlPy7I2jh8/DpFIlGfbch8+fMDbt28hFotRoUIF1K1bt8BW3aIoKB5N0r548QJPnjxBx44dFRXm5OTkfN9vC8hG/tXkRy8uLg53795FcnIyeDyeohu6NseKqrRyEolEMcKys7MzWrRoke8op0lJSbh58yZ4PB7atGkDOzs7HDx4EBUqVFC6GASAlJQU3Lx5E1KpFK1atYKNjQ0sLCwwZ84crF69GoCswnf48GE0btxYabRLQNZrwM7OTqPW9RcvXuDly5cQi8WoXLkyWrRoUeh1v337Fi9fvgSfz0fVqlUV79lTRyqVIigoCB8+fICjoyMaNGig1IVLm1gYhsG///6LBg0aoE6dOgCg6Jq2bt06zJgxAw8ePEBoaCiqVq2ap2uXNmnz21Zh8k2Tsi6IunzXV0y62v+C4gdkLaUfPnxAv3791I4grE35qYtVLjY2Fvfu3UNmZma+65B79+4dnj59ChcXF7Ru3RrR0dG4fPkyOnfunOc93K9fv8azZ8/g7u6OFi1aICoqClevXkXXrl3zjKKfkZGBu3fvIiEhAVWqVEH9+vWVLpLFYjECAgLw+fNnSKVSdOrUSfFYRmJiIp4+fYqEhAS4u7ujcePGBd50ePXqFR49eoQBAwZAKpXi+vXrEIvF8PLygqura5HySJP9zr393M8GJiQk4MyZM2jTpg3KlStX6LyXSCR48OABIiMj4eLigtq1a+e7X5qs89GjR2jcuDH8/PxUPt4kl5aWhrt37yItLU1xnXHr1i3ExcUpBvkJDg7GnTt30LdvX8U1Un7TNF0foPkxoEm+FOZ4ktOk7FXtq6pjXNWxoq88Kug41+bYVbWvQMHfeaIaVVoJ0aEtW7Zg0qRJ2L9/f55naQlR586dO/Dy8tLoAoko+7Iio6u0+mKIZW2IMeXHEMqPlAy//PIL/Pz88OTJE73eFCaEaIa+hYToCMuy2LlzJzw9PXU6uAwxPVevXkVKSoric1JSEubOnQtXV1f07t2bw8iIrhliWRtiTIQYmlatWuHff/+lCishBqLEP9PKMAydkEiRTZgwAU+ePEFgYCCOHDmi8Si+pGRKSkpCnTp1ULVqVZibm+P+/fuwsrLC0aNHlV4BQ4yfIZa1IcZEiKHR1QA8hBDdKLHdg1euXIn169cjLi4OtWrVwoYNG3Q+KAgpOQ4ePAg+n4+mTZvmeR6HkPykp6fjyZMniIqKgpubG5o1a0Y3OwpJk2cXC5NWVwyxrA0xJk1wUX6EEEK4VyIrrVu2bMGcOXNw9OhRNG/eHGvWrMGaNWvw4sULVKxYkevwCCGEEEIIIYT8p0RWWqtVq4YePXpg/fr1AGTPIpYvXx5DhgzReCRHQgghhBBCCCH6V+Ie5oyPj8e7d++UhuTn8Xho27Yt7ty5w2FkhBBCCCGEEEK+VOIGYoqOjgaAPO9Kc3V1xf3791UuJxaLIRaLFZ8ZhkFCQgKcnJzo/UqEEEIIIYQQogWWZZGamgo3N7cCB8YtcZVWOYZh8nxWV/lcuXIllixZou+wCCGEEEIIIaTECA8Ph4eHh9o0Ja7SWrZsWQBATEyM0vSYmBiUKVNG5XK+vr6YNWuW4nNycjLKlSuHkJAQODg46CVWohrDMIiLi4OzszO9sogjVAbcozLgFuU/96gMuEdlwC3Kf25JpBKsurkKGZkZWNxpMSxEFlyHZFRSUlJQvnx52NraFpi2xFVaHR0dUatWLVy9ehX9+/cHIPvCX716FaNGjVK5nLm5OczNzfNMd3BwoEorBxiGgUQigYODA52kOUJlwD0qA25R/nOPyoB7VAbcovzn3q+9f0VMTAxcnVypDLQkzy9NHrUskTn7ww8/YOfOnfD390dUVBRmzZqFtLQ0TJo0ievQCCGEEEIIIYTkUuJaWgFgxIgRSEtLg6+vL6Kjo1G3bl1cvHixwL7UhBBClEmlUmRnZ3MdBicYhkF2djaysrLo7jpHjL0MRCIRBAIB12EQQgqJZVmkS9KRkZ2BEvgW0WJVIiutADB58mRMnjyZ6zAIIcRopaWlISIiosT+ULMsC4ZhkJqaSqPIc8TYy4DH48HDwwM2NjZch0IIKYSM7AzYrbYDAKT8kAJbQcHPZpLCKbGVVkIIIYUnlUoREREBKysruLi4GGWFoahYlkVOTg6EQmGJ3H9DYMxlwLIsYmNjERERgapVq1KLKyGEqEGVVkIIIVrLzs4Gy7JwcXGBpaUl1+FwwpgrTKbC2MvAxcUFoaGhyM7OpkorIUbISmSFlB9SEBsbCyuRFdfhmDTjewCEEEKIwTDGigIhhoK+P4QYNx6PB2sza1iJrOj7rGdUaSWEEEJyefHiBUJDQ7kOgxBCCCH/oUorIYQQk5Gamorr16/j3bt3eeZJJBLcuHEDDx48UDt41ObNm3Hq1Kk809++fYtnz54pTQsODkZQUFDRAyeEEGJ0JFIJFlxZgJX3V0IilXAdjkmjZ1oJIYSYhH/++QcLFixApUqV8OrVK/Ts2RM7d+4Ej8dDYmIiWrZsCaFQiLS0NFSvXh2nT5/W6jUp//77Lz5//owtW7Yopvn7++PDhw/YsWOHPnaJEEKIAcuWZmPl7ZUAgGVdlsFCZMFxRKaLKq2EEEJMgrm5OV6+fAlbW1vEx8ejcuXKmDZtGr766its3rwZVapUwYkTJyAWi9GoUSOcOXMGvXr1KnC9N2/ehLu7e4Hpzp49C6lUqvjs6uqKpk2bFmmfCCGEGC4hX4hpTachIyMDQj5Vq/SJcpcQQohOpEvSAUBpQAqJVIJsaTaEfCHMheZ50lqKLMHnyVo7s6XZkEglEPAFsBBaKKW1NrMucPuDBw9W/O3g4ACRSAQrK9lojpcvX8bYsWMByCq33t7euHz5stpKK8uy+PHHH/H27Vv8+++/AICwsDBF12GWZfHmzRuIRCIAwLZt2yCRyLqHvXnzBl5eXlRpJYQQE2YuNMe6rusQExOj9BtHdI+eaSWEEKITNittYLPSBnEZcYppa26vgc1KG0w5M0UpreuvrrBZaYOw5DDFtD8f/AmblTYYc2KMUtoKGypoHcuCBQvQsWNHVK9eHQAQHR2NMmXKKOaXLl0anz59Urm8RCLBsGHDkJ6eDn9/f8VrfV68eIEtW7Zgy5Yt+Ouvv3D//n3FMkeOHMGpU6ewatUquLu7Y+PGjVrHTQghhJC8qKWVEEKISVm6dCmePHmCY8eOKabZ2NggPT1d8Tk9PR12dnYq17Fy5Uo0bdpU0cIq1717d8UzrSzLYsWKFfjw4YNifnh4OCZOnIhDhw7B3t5eR3tECCGElGzU0koIIUQn0nzTkOabBmcrZ8W0OS3nIM03DX/0+EMpbcz3MUjzTUM5+3KKad81+Q5pvmnY0Vt5UKPQ6aEax+Dr64s7d+7g+PHjsLD4fxfjGjVq4MGDB4rPDx8+RI0aNVSuZ9asWUhNTcXq1as13nZiYiKGDRuGHTt2oGzZshovRwghxDilS9IhWCpA2b/KKh57IfpBLa2EEEJ0Ir/nTs0EZjATmGmUViQQQSQQaZQ2PwsWLMCePXuwYcMGXLx4EQDQpEkTlC5dGhMnTkTXrl3h6uqKhIQEXLhwAX/++afKddna2uLcuXPo06cPMjIysGTJErXblkgk+Prrr9GuXTu8e/cO7969o4GYCCGEEB2hSishhBCTkJ2djQYNGmDXrl2Kac7OzihdujSaN2+O/fv3Y8+ePTA3N8eVK1fg7Oyc73rq1KkDT09PWFlZ4eTJk5gxYwbOnz+PatWqwdXVVSlt5cqVYWtri9TUVDg4OCAwMBCBgYEAgEaNGlGllRBCTJiVyAqfZ31GbFwsrERWXIdj0nisujesE5VSUlJgb2+PxMREODg4cB1OicMwDGJiYuDq6qrVexaJ7hhyGSRnZsPeMm+LnanhsgyysrIQEhKCihUrKnXDLUlYlkVOTg6EQqFitGRSvIy9DEzhe2TIvwUlQZYkB0/eh6NZjfKU/xyh70DhyetTycnJaseZAOiZVpPzLjoVW64Hcx0GIZyqv+QC1yEQQgghehcQHI8hu19yHQYhekeVVhPzOCwRq86+5joMQgghhBBCTJpEKsGKmyuw/vF6SKQSrsMxaVRpJYQQQgghhBAtZUuzsfDaQqx+sBrZ0myuwzFpNBATIYQQQgghhGhJyBdizFdjkJmZCSGfqlX6RLlLCCGEEEIIIVoyF5pja6+tiImJgbnQnOtwTBp1DyaEEEIIIYQQYrCo0koIIYRoID09HVlZWZxtozi2r05B22dZFklJScW6TWOQmpoKqVTKdRiEEGLUTLLSmp6ejjt37uDBgwdIT09Xme7Zs2e4dOkSoqKiijE6QgghxUlXFZ8ffvgB27dv10FE6rfx999/c7Z9ddTFBgD79u3DihUrlKapy/esrCwU9Kp4VfuckZGR5/c9MzMTaWlpatf3peKoFO/evRu//PKLXrdBSi4W6r9DRL/SJemwXWWLSjsqIV2ius5Bis6kKq0sy2LOnDmoXLkyZs+ejXHjxqFcuXLYv3+/Urq0tDR07NgR7du3x8KFC1G5cmUsW7aMo6h1iwfje7k6IYTo0/z587FlyxauwzB5y5Ytw4wZMwAA9+/fR4sWLVCqVCk4Oztj8+bNinRhYWH4+uuv4erqChsbG3z33Xdat0T+8ssvmD17ttK0DRs2KLavqeI4NsaOHYu//voLGRkZet0OIYQbGdkZyMzJ5DoMk2dyldbSpUsjODgYAQEBCAwMxPz58zFy5EhEREQo0i1cuBAhISF48+YN7ty5gxMnTmDhwoW4ceMGh9HrBt1xI4SUZCzLIjExEXFxccjOzkZ2djaysrKQnp6OuLg4ZGb+/8JCLBbnaenL3fImFotVbufLeZmZmYiLi8u3e6wm61S3LW3S57dPaWlpkEj+//7A9PR0xfK6ii0gIABOTk5wc3MDAJw5cwbr169Heno6zpw5g1mzZiE4OBgAcP36dcycORNJSUkIDg7GqVOncOnSpQK3Aci62qrrQSUXHx+PuLg4xb+UlJQ8afRxbOTk5CAnJ0dpmrm5Odq0aYNjx44VGDchxLhYiiwRPDUY94feh6XIkutwTJpJVVr5fD6+//57WFtbK6YNGzYMEokEQUFBAGQXNHv27MHYsWPh5OQEAOjcuTMaNmyI3bt3cxI3IYSQotu2bRscHR1RuXJl1KhRA1evXsXNmzfxzz//YOXKlahRowa2bduGyMhItGvXDo6OjnBwcMDKlSsV6/jhhx8wceJEVKpUCQ4ODujVq5dSl9OHDx8q5n399deKeZs3b0aNGjVQsWJFODk5YefOnUrrnDx5MqpVqwYHBwd07txZUYkMCwtDkyZN4ODggEaNGiE8PFztPubefu7Y1O3TxIkTceTIEcXn2bNnY8+ePTqN7dq1a2jWrJni8+LFi9GsWTPweDw0bdoUzs7Oigr98OHD0bJlS8THxyMkJAQsy6JChQpq9xsA/P390aZNG8TGxgKQdS/OXTHN3ZJZv3591KhRAzVq1ECFChXg4+OTZ32FOTZU5VVCQgL69esHJycnODg4YODAgUrdjps3b46rV68WuI+EaIt62HGLz+OjgkMFeNp6gs8zqWqVwTH5V97cunULAFC9enUAsh/2+Ph41K9fXyldgwYN8PTpU5XrEYvFSndW5XdtGYYBwzC6DrvQmP/uDBtSTPrAMAxYljX5/TRkhl4GhhqXLnFZBvJty/8BAMRigGEAS0tAfsFuYQFkZgJ8PmBuDmRkAAKB7O/0dEAkAszMZH+bmck+p6XJlhMKgdRU2fqEBf9cLV26FHfu3EGNGjWUpo8fPx4VKlRQdB0dNGgQatWqhUuXLuHjx49o1aoVWrZsidatW4NlWdy/fx937tyBnZ0dBg4ciLVr12LRokX5zlu/fj0WL16MmTNnYubMmUhPT8fbt2/Ro0cPDBkyBBYWFmBZFs+fP0dAQABsbGzQqVMnnDhxAt7e3pg9ezZat26Nu3fv4u3bt/Dy8kK3bt3yfdZTXWwzZ85UuU/yZeXrzF1uuootLCwMVapUyXfevn37ULp0adSvX18x/8SJE5gwYQKSkpKwaNEiVKtWTeU+syyL9evX4+jRo7h06RJKlSoFlmVx4MABnDp1SpE2MzMTgwcPBsuyigp2QkIC+vbtizVr1uRZf/v27bU+NtTlVZMmTbBlyxZkZ2djxowZ2Lx5s2K9bm5uOHbsWJ4Y5PtnaNcS2jD03wJTx7KyfKf85w59BwpPmzwz+Err06dPC7zz3KFDB1hZWeWZHhkZialTp2LUqFGoXLkyACju9JYqVUoprZOTk9pRD1euXIklS5bkmR4bG6vU7YprqSmpAICYmBiOI9EvhmGQnJwMlmXB59OdLS4YehmY+ncA4LYMsrOzwTCMUndI/vLl4EVGQvrXX+D7+spiXLMGgilTwLq7g1m0CIIRI8A2bw5mxgwI+vUD4+0NdvRoCDp1AjN9Otj+/SFs1gzSNWvAdukCYa1akO7bB7Z58wJjatmyJXx8fNC5c2f07t0bTZo0AQDFxYQ8zhs3buDatWtgWRblypXDgAEDcO3aNbRo0QIsy2LIkCGK34iJEyfit99+Q05OTp55EyZMwG+//Ybs7GxcuXIFkydPxufPn2FlZYWkpCSEhISgatWqYFkWAwYMgL29PQCgWbNmCA0NRU5ODm7evIm7d++CYRhUqVIFPXr0UIo1N3WxqdsnhmEglUoV68ydH7qKjcfj5ds19syZM1i+fDlOnToFlmUV8/v06YM+ffogPDwcXbt2Re3atdGnT5989/n333+Hm5sbTp06BQsLC+Tk5IBhGAwfPhx//PEHpFIpBAIB1qxZg+DgYMU25JXYlStXolKlSirzVJtjQ1VeXbhwASdOnMDatWsV6/bw8FCsVywWg8/n54lBvi/x8fEQiUR54jMGhv5bYOrk164xMTGU/xzIlmZj5/OdyMrKwsRGE+ldrVpKTU3VOK3BV1qvXr1a4LMujRo1ylNpjY2NRZcuXVCrVi1s2rRJMd3MzAwA8gyIkJGRoZiXH19fX8yaNUvxOSUlBZ6ennBxcYGDg4Omu6N3tnay1mBXV1eOI9EvhmHA4/Hg4uJCJ2mOGHoZmPp3AOC2DLKyspCamgqhUAihvBV0/nyAYWSf/+tWyRcKgT/+AI/Pl/29ezd4AoHs7yNHIBCJZK2oly5BYGYm+/vePQjkLa0vX0KgYUurn58fbt26hStXrsDHxwcLFizAqFGjwOfzwefzFXGKRCJIpVLFZ7FYDHNzcwiFQvB4PEgkEsW87OxslfNycnJgZmYGkUiEOXPm4LfffsM333wDQFZh4fF4iuUsLCwUywmFQrAsC6FQmCcWiUSiFGtu6mJTt09mZmaK7QGyypx8G7qKrWrVqggJCVGa5+/vj59++gnnz5+Hu7u7YjrLsuDxZF0aK1asiPbt2+PFixfo379/vvvctWtXXLhwAQEBAejSpQsA2eNAPB5PUdETiUSKaUKhEFKpFKNGjcKMGTPg5eWl8pjR9thQlVdCoRAXL17EV199le92IiMjUaVKlTx5JxQKwefz4eTkBAsLC5VxGjJD/y0wdQ4JstZ7V1dXyn8OpEvSsfjuYgDAj51/hK2FLbcBGRltznsGX2mdMWOG1qMBxsXFoUOHDihdujROnDihlCHlypWDQCBQGpgJAMLDw1GxYkWV6zQ3N4e5ed67J/IfPEPB/+9CwJBi0hcej2dw+V/SGHIZGGJM+sBVGcgrCPJ/AGRdeuUscw1IkfumYq4xB2Bjk//ftrl+9O3sNI4pISEBtWrVQqVKlRAaGop3796Bx+PByckJL1++RGxsLGxtbdGrVy/4+vrit99+Q3BwMA4ePIjr168r9mXnzp3o1q0bHB0dsWLFCowYMULlvGHDhim2HxMTg/DwcOzcuRNRUVFK+ZM7n3J/7tmzJ3788UesXr0az549w6lTp9CxY8f/52ku6mJTt0/Vq1fHoUOH0KZNG7x8+RLHjx9H27ZtdRpb586d8e233yrm7du3D76+vjh+/DgsLCwQHx8POzs7mJmZYdq0aejRowfq1KmDoKAgHDt2DAcOHFC5z9WrV8esWbPQo0cPrF69Gr17984T85exT548Gc2bN0ezZs0QHx8PMzMz2OVzLGl7bKjKq8GDB2Pq1KnYuHEjypcvDwCwtbVVXDfcvn1bKX9y758hn0c1ZQr7YLRyXfdR/hc/kVCEIXWGICsrCyKhiMpAS9rkl8nlrLzC6uLiglOnTuVpgbWwsEDbtm2VBqVISkrC5cuX0a1bt+IOV+fogXxCSElVu3Zt1KhRAy1btoRYLMacOXMAyAb+ef36NerWrYtt27bhl19+gZubG7p27Yr58+dj69atqFevnmI9w4YNw9KlS/H111+jTZs2mDhxIgDAxsYGY8aMUcxr3bo1xo8fDwD4/fffsWnTJnTt2hVZWVnw8vJStKrZ2NjAMlcl3traWvHbtHr1alhYWKBTp044cOAARo0apZQ2ty+3nzs2dfs0adIkCIVCtGvXDgcPHsSwYcMUN3N1FVvdunVhZmaGN2/eAJC1estfLycfEOny5csAgFmzZmH79u1o1aoVli9fjg0bNqB9+/Yq99nS0hIVK1bExYsXsWzZMpw/fx5WVlawyX2jA4CVlRVsbW3x6dMnHDlyBKtXr1Zse9y4cfmuX5tjQ11eLV26FF26dMGoUaNQs2ZN1KhRA0ePHgUAJCcnIygoCN27d883BkKI8bIQWmBv373Y1HETLITG2VvCWPDYgt7sbUQkEgmaNWuGqKgo/P7770oV1vr168PT0xOA7P1xbdq0wahRo9CiRQts2bIFKSkpePDggcof5C+lpKTA3t4eiYmJBtU9+OCDcMz1D0Loqp5ch6JXDMMgJiaGusNwyJDLoMKPp03+OwBwWwZZWVkICQlBxYoVjbZbY36mTJmCGjVqYMqUKQWmlT+jKe86WtJdvnwZZ86cUXquU9+MoQw2bNgAOzs7jBo1Ks88U/geGfJvQUlw+dVnjPnnET6s6E75zxH6DhSevD6VnJycb2+Y3Ay+e7A2srKy4O7uDnd39zyvr5k2bZqi0tq0aVPcv38fW7ZswfHjx9GlSxfMmDFD4worIYQQ0/RlaxrRXMeOHdGxY0euwzA406dP5zoEQggxeiZVabWzs1Ma/l6devXqKQ3QZCpYmEzDOSGEFLtVq1ZxHQIhhBAjkS5JR4UNFcAwDEKnh9JATHpkUpVWQgghhBBCSgoay4R7cRlxXIdQIlCllRBCCCGEEEK0ZCmyRNCEICQkJMBSRI+W6BNVWk0M3XEjhBQnExrLj5BiR98fUlT0WBi3+Dw+arvWRgxiwOfRIEz6RJVWQgghWhOJRODxeIiNjYWLi4vBjtyqT8Ywcq2pM+YyYFkWsbGx4PF4EIlEXIdDCCEGjSqthBBCtCYQCODh4YGIiAiEhoZyHQ4nWJYFwzDg8/lGV2EyFcZeBjweDx4eHhAIBFyHQggphGxpNnY+2YnU1FRMdZoKc7451yGZLKq0EkIIKRQbGxtUrVoV2dnZXIfCCYZhEB8fDycnJ3o3H0eMvQxEIhFVWAkxYhKpBBNPTwQATGgxAeYiqrTqC1VaCSGEFJpAICixF90Mw0AkEsHCwsIoK0ymgMqAEMIlAV+A3tV6QywRQ8Avmb+FxYUqrSaGHsgnhBBCCCFE/yyEFjg66ChiYmJgIbTgOhyTRrclCSGEEEIIIYQYLKq0mhh65Q0hhBBCCCHElFCllRBCCCGEEEK0lJGdgUobK6GJXxNkZGdwHY5Jo2daCSGEEEIIIURLLMviY/JHxd9Ef6jSSgghhBBCCCFashBa4O6Yu0hMSKSBmPSMKq2EEEIIIYQQoiUBX4Ambk0QI4yhV97oGT3TSgghhBBCCCHEYFGllRBCCCGEEEK0lMPkwO+ZH/zf+SOHyeE6HJNG3YMJIYQQQgghREviHDFGHBsBABjRZATMhGYcR2S6qNJKCCGEEEKIEaIBa7nF5/HRsWJHZEuywedRB1Z9okorIYQQQgghhGjJUmSJCz4XEBMTA0uRJdfhmDS6JUAIIYQQQogR4vG4joCQ4kGVVkIIIYQQQgghBsukuwffvXsXe/fuRfv27eHt7a0079OnT9i7dy+io6NRt25dDB06FCKRiKNICSGEEEIIIcYkIzsDTbY2QY40B48mPIKNuQ3XIZksk21pTUhIwNChQ/Hvv//i5s2bSvPevn2LunXr4urVq7C1tcXy5cvRpUsXSKVSjqLVIeomQgghhBBCiN6xLIuXcS/xNvEtWBoVS69MtqV11KhRGDt2LA4ePJhn3g8//IA6derg9OnT4PF4GDNmDCpXrgw/Pz+MGDGCg2gJIYQQQgghxsRCaIHLwy8jKSkJFkILrsMxaSbZ0rpx40YkJibixx9/zDMvOzsbZ8+exdChQ8H77+l1Dw8PtGvXDsePHy/uUAkhhBBCCCkUatzjloAvQLsK7eDl5gUBX8B1OCbN5FpaAwMDsXz5cty/fx98ft46eVhYGMRiMSpWrKg0vVKlSrh9+7bK9YrFYojFYsXnlJQUAADDMGAYRkfRF508FkOKSR8YhgHLsia/n4bM0MvAUOPSJUMvA1NH+c89KgPuURlwS94llfKfO/QdKDxt8szgK6179+7F3bt31aZZvHgxnJ2dkZ6ejkGDBmHdunUoX758vmkzMzMBALa2tkrT7ezskJGRoXIbK1euxJIlS/JMj42NhUQiKWg3ik1qaioAICYmhuNI9IthGCQnJ4Nl2XxvThD9M/QyMPXvAGD4ZWDqKP+5R2XAPSoDbiUlJQGQ/eZR/he/HCYHF0IvID0jHd/U+AZmQjOuQzIq8nqLJgy+0lq2bFnUqFFDbRr5qL/y0YADAgIQEBAAAIiMjMS1a9cwZcoUbNy4ETY2slG95F9yucTERNjZ2anchq+vL2bNmqX4nJKSAk9PT7i4uMDBwaEQe6YftmGy1mBXV1eOI9EvhmHA4/Hg4uJCJ2mOGHoZmPp3ADD8MjB1lP/cozLgHpUBtxziZS2trq6ulP8cSJekY8y2MQCAEV4jYGthW8ASJDcLC82fAzb4SmvHjh3RsWNHjdJ6eXlh2bJlStPMzc3h6OiIGjVqgMfjoVy5crCxscGrV6/QrVs3RbpXr16hVq1aKtdtbm4Oc3PzPNP5fL5BnST4PFkshhSTvvB4PIPL/5LGkMvAEGPSB0Mug5KA8p97VAbcozLgDp/P++9/yn8uCAVCeHl4ITs7G0KBkMpAS9rkl8FXWrVRt25d1K1bV2na9u3bUb9+fUyZMgWA7MQ6YMAA/P3335g4cSIsLS0RGBiIgIAAzJ07l4uwdYteeUMIIYQQQojeWYoscXPUTcTExMBSZMl1OCatRN4OWLVqFbKzs9GoUSMMHToUHTp0wLfffouvv/6a69AIIYQQQgghhORiUi2t+fH19YW7u7vSNFdXVzx58gTnz59HdHQ0pk2bhubNm3MUISGEEEIIIdqjV96QksLkK62DBg3Kd7q5uTl69+5dzNEQQgghhBBCTEFmdiba7GqD7Jxs3B5zG9bm1lyHZLJMvtJa4tAdN0IIIYQQQvSOYRk8/PRQ8TfRH6q0EkIIIYQQQoiWzIXmODH4BJKTkmEuzPuWEaI7VGklhBBCCCGEEC0J+UL0rNoTMTExEPKpWqVPJXL0YJNGr7whhBBCCCGEmBCqtBJCCCGEEGKEeNRYwSkpI8XFDxdxPeI6pIyU63BMGrVjE0IIIYQQYoTolTfcysrJQje/bgCAlDopEAlFHEdkuqjSSgghhBBCCCFa4vP4qF+6PnJycsDnUQdWfaJKKyGEEEIIIYRoyVJkicfjHyMmJgaWIkuuwzFpdEuAEEIIIYQQQojBokorIYQQQgghhBCDRZVWQgghhBBCCNFSZnYmOuzugH4n+iEzO5PrcEwaPdNKCCGEEEIIIVpiWAbXP15X/E30hyqthBBCCCGEGCF64w23zIXm2O+9HynJKTAXmnMdjkmjSishhBBCCCGEaEnIF2JArQGIiYmBkE/VKn2iZ1oJIYQQQkxAREQEMjMN67m66OhoREdHF8u2wsPDkZWVVSzbMhS8IixbEvOLGC+qtBJCCCGEmIDmzZvj+vXris9cVmJjY2NRv3591KtXD3Pnzi2WbX711Ve4e/dusWzLFFB+FZ2UkeJ2+G3c/3wfUkbKdTgmjdqxTUxR7rgRQgghxHQMGDAAq1atQtu2bYt929u2bUOFChXw9OnTAtPGx8fD3t4eQmH+l6VJSUmwsLCAhYVFkWKKioqCjY0N7OzslKZHRETAwcEBNjY2RVp/Yana/5iYGGRkZAAAhEIhPDw81K5H2/QFMdT8MiRZOVlo83cbAEBKjRSIhCKOIzJd1NJKCCGEEGKC7ty5w0mFFQBevXqFBg0aqJwvlUqxceNGlCtXDtWrV4eNjQ2GDBmC1NRURZozZ86gfv36qFKlChwdHdGpUyeEh4cXOqYuXbpg69ateaY3b94c+/fvL/R6C0OT/V+wYAHatWuHRo0aoVWrVgWuU9v0BTGk/DJUPB4PVUpVQUW7iuDxqOlIn6jSamJoFDlCCCHENEVFRSE0NBQRERGQSgvuivhl92BNlheLxYiPj9c4puTkZIjF4jxxxsTEID09HaGhoUoVMbnExER8+PABAQEBiIuLw4cPHxAYGAhfX19Fmnv37uHgwYOIi4tDbGwsRCIRJk6cqHFsGRkZCA0NRU5OjsbLpKWlITQ0NM+/7OxsjdcBoMBtarL/W7duRWhoKJYvX67RNrVN/yUu88tYWYms8Oa7NwgYEgArkRXX4Zg0qrQSQgghhBiBsWPHol27dmjevDns7Ozw448/qk3/5TOu6pbPycnBiBEj4ODggJo1a6JGjRq4c+eOynU/fPgQ9erVQ4UKFVCjRg188803iI2NVWzn5s2b2LFjB9q1a4cTJ07kWd7Z2Rnr169XdGF1c3NDz5498ezZM0WaJUuWoHr16gAAGxsb9OjRA2/fvtUgp4Dg4GA0bNgQf//9t8pux/k5ffo02rVrp/jn5eWFihUrIiQkRON1MAyDFi1a4J9//lGZRpP9L05c5hchmqBnWgkhhBBCjMCZM2cUf4eFhSkqCb179y7y8hcvXsTFixcREREBJycnBAcH48KFC2jRokWe9WRkZKBv374YPnw4li5dipCQEIwfPx6TJk3C4cOHcebMGfTp0weNGzfGggULNIotOzsbZ8+eRZ8+fZSmJycnIyEhASEhIdiyZQsmTJhQ4Lru3LmD/v37Y8WKFRg5cqTSvMTERISGhipNy93qPGjQIAwaNEgxvW/fvihVqhSqVauWZzvp6emKivqXFi9eDB8fH0RFRSm1nqqiav8LoosedsWVX4QUhUlWWuPj47F69WpcuXIFVlZWGD9+PHx8fJTSHD9+HBs3bkR0dDTq1q2LpUuXokqVKhxFTAghhBBSMIZhEBcXB4Zh0LVrV1y5ckXjSqu65d3c3CCRSPD8+XO0bt0alStXxqRJk/Jdx507d5CQkIAlS5aAx+PBxsYGCxcuROfOnZGdnQ2RSLvBaBiGwahRo2BmZoZ58+YpzduzZw9++eUXxMTEoFmzZhg8eLDadfn7++PQoUPYu3cvOnXqlGf+5s2b4efnpzRNVcVzypQpyMrKwrZt2/Kdf/HiRcyYMUNlLGKxGIsXL8bIkSPh5uamMp26/de34swvU5SVk4V+B/pBIpHgxLATsDKjLsL6YnLdg+Pj49GsWTMEBgZi3bp1WLduHS5fvoxbt24p0pw6dQr9+/fH119/jR07doBlWbRu3RoJCQkcRk4IIYQQopqvry8cHBxQu3ZttGvXDvv378enT590snz9+vVx5MgR/P3332jatCl69uyJe/fu5bue6OhouLi4KFVO3dzcIJVKERcXp9U+ybslv3r1CpcuXYK1tbXS/ClTpiAsLAxJSUmoX78+2rRpA4ZhVK7v0qVLKF++PJo2bZrv/Hnz5uV5/rJMmTJ50q1YsQJ3796Fv7+/ykp4nz598n2eU/5cqY2NDc6dO6e2wlrQ/utbceaXKZIyUpx9fxaXwy7TK2/0zOQqrQsXLoRUKsWJEyfQunVrNGrUCLt27VLq3iLvsjFjxgw0a9YMu3fvhkQiwebNmzmMXDdo3DJCCCHE9AQEBGDTpk0ICgpCbGwsQkNDMWzYMI0GZNJkealUirZt22LXrl14+PAhhgwZgl69euW7rkqVKiEqKkrpZn9QUBCsrKzyrdCoIhaLMWDAAISEhODKlStwcnJSzPuyYmphYYEhQ4YgJCQEycnJKtf5xx9/oHr16ujYsWOhGyP27NmDrVu34syZM7C1tdV6eYZhcPHiRVy6dAnt27dXmU7d/msiOjoaKYmaD5qVH0PIL2NmJjDDjt47sL7depgJzLgOx6SZVKWVZVkcOHAAPj4+ed7lJRAIAACpqal4/PgxunbtqphnZmaGTp064dq1a8UZLiGEEEKIRszNzSEWi/HkyRO8fv0a27Ztw65du3S2/O7duzFp0iTcuHEDr169wv3791W+h7NZs2b46quvMHz4cDx69AjXrl3D3LlzMW3aNI1f+yEWi9G7d28EBwdj69atimcnIyMjAQCRkZHo3r07zp49izdv3uDChQuYPXs22rZtC0dHR5XrFQgE+Oeff9CkSRO0bdsWnz9/1jiPAODq1av47rvvsGnTJojF4kKNhsvn8/H333+jXr16KtMUtP+A7P20oaGhiI+PR05ODkJDQ/Hx40fF/JEjR+KvNT9rnD4/hpBfxkwkEOHb+t9iUPVBEAlKTgszF0zqmda4uDgkJCTA09MT3377LR49egQ3NzeMHDkSQ4cOBSAb/p1l2Tx3AsuUKYOgoCCV6xaLxUpDuqekpACQ3U1T102luLGs7JF8Q4pJHxiGAcuyJr+fhszQy8BQ49IlQy8DU0f5z72SVAZfffUV1q5di+XLlyM9PR1eXl6YM2cOoqOjFfvv6ekJc3PzfD8XtPzw4cMhkUiwcOFCxMfHo06dOjh58qTKvD19+jR++uknjBs3DiKRCJMmTcLMmTMV6V1cXGBvb69y+bCwMLx58wYA0LNnT8X0ChUq4MqVK3B3d8ePP/6IdevW4c2bN3B2dkaXLl0wffp0lessV64czMzMwLIs/vjjD8yfPx8+Pj44cOAAHB0d4ebmBltb2zzLe3p6wtraGgzDICAgAKVKlcLkyZOV0ly8eBGVK1cuqJg0VtD+A8CuXbuwYcMGAIBQKES7du1gbm6OV69eAQBcXV2RyZc9Q8kwTIHpv2RM+WXIStJ5SNe0yTMeK6/lGKgffvgB/v7+atNcuXIF5cqVQ2RkJDw8PGBnZ4eVK1eiVatWuHfvHqZOnYp169Zh0qRJePHiBerUqYNbt26hZcuWinXMmTMHx48fVzmU+uLFi7FkyZI801+/fg17e/ui7aQOnXoRh2UXP+LujEZch6JXDMMgOTkZ9vb24PNNqsOA0TDkMmi+/pHJfwcAwy6DkoDyn3tUBtyjMuDWzeBEzDn5AQHTvqL854CUkeJF/AukpaahSbkmEAmptVUbqampqFatGpKTk2FnZ6c2rcG3tM6ePRvjxo1Tm6Zs2bIAACcnJ/D5fPTt21dxx6devXp49OgRtm3bhkmTJsHZ2RkA8rw4Oy4uTjEvP76+vpg1a5bic0pKCjw9PeHi4gIHB4fC7Jpe2IZLAMjuvpkyhmHA4/Hg4uJCJ2mOGHoZmPp3ADD8MjB1lP/cozLgHpUBt+zjZC1Vrq6ulP8cSJeko+s22SOHSXOSYGtRsp7pLaovH+dUx+Arra6urhpffFpYWKBevXp5KpEODg5IT08HAJQuXRqenp64c+eO0hDxt2/fRrdu3VSu29zcHObm5nmm8/l8gzpJyJ8lMaSY9IXH4xlc/pc0hlwGhhiTPhhyGZQElP/cozLgHpUBd3j/5TnlPzcEAgHcbN3ASBkIBAIqAy1pk18ml7NTpkzB/v378f79ewBASEgI9u7dqzQC3sSJE7F9+3a8fv0aALB161Z8+PChwBZdQgghhBBCDIZhP+Vn8qxEVgifEY4nw5/ASkTvaNUng29p1daYMWMQFhaGhg0bwsbGBomJiRg+fDiWL1+uSPPDDz8gIiIC9evXh7W1NQQCAfbu3Yu6detyGLlu0CtvCCGEEEIIIabE5CqtALBkyRLMmzcPsbGxKFOmDIRC5d0UCATYtGkT1qxZg4SEBJQtWzZPGkIIIYQQQvRJIpEgPT1d7Wt8CCEm2D1YztzcHB4eHmoro9bW1vD09KQKKyGEEEIIKXaLFy+Gs7MzOnTogD///BNRUVFch0S0kJWThYGHB2LcxXHIysniOhyTZrKVVkIIIYQQQgyZr68vtmzZAjMzM8yYMQMeHh5o1aoV1q1bh7CwMK7DIwWQMlL4v/LHqQ+nIGWkXIdj0qjSSgghhBBCCAdsbW0xbtw4nDt3DtHR0di1axccHR3x448/onz58mjatCl++eUXBAcH57s8DcPELTOBGX7v9jtWtFwBM4EZ1+GYNKq0EkIIIYQQwrFSpUph5MiROHnyJGJjY+Hn5wdPT08sXrwYVapUwVdffYVly5bh1atXXIdK/iMSiDC5yWSMqjMKIoGI63BMGlVaCSGEEEIIMSB2dnYYOnQo/P39ERsbi0OHDqF69epYvXo1atWqhdq1a2PRokUIfv0SLL32hpQANAIRIYQQQgghBsra2hr9+/dH//79kZWVhQsXLuDw4cPYuHEjkpOXQuhYFvN4I9C/f380atQIPB69ALG4MCyDd/HvEJ8cD2cXZ/CpPVBvKGcJIYQQQggxAhYWFujduzd2796NmJgY/LxpLyw862L79u1o0qQJKlasiNmzZyMgIAAMw3AdrsnLzM5EjU010HJ/S2RmZ3IdjkmjSishhBBCCCFGxszMDE1ad4BT92mIiorCpUuX0L17d/j5+aFly5bw9PTE1KlTcf36dUilNLKtvtib28POzI7rMEweVVoJIYQQQggxQvLnWYVCITp27IjNmzcjMjIS169fR//+/XH06FG0a9cObm5umDBhAi5evIjs7GyOozYd1mbWSJibgDej3sDazJrrcExakSutLMvi6tWrOHLkCGJiYnQREyGEEEIIIaQQBAIB2rRpgw0bNiAsLAx37tzBiBEjcOHCBXTp0gVlypTB6NGjcfr0aYjFYq7DJUQjWlVa3759i2+//VZp2sCBA9GhQwd4e3ujdu3aePbsmS7jI4QQQgghhBQCn89H8+bNsWbNGnz48AGPHj3ChAkTcPv2bfTq1Quurq7w8fHB0aNHkZlJz2QSw6VVpXXVqlXo2LGj4vPVq1fh7++Pffv2ITw8HB06dMDPP/+s8yCJ5vg0YhwhhBBCCPkCj8dDw4YNsWLFCrx+/RrPnj3DzJkz8fTpU/Tr1w8uLi4YOHAgDh48iLS0NK7DNQriHDFGHR+F6VenQ5xDrdb6pFWl9dKlS+jUqZPi87lz59C6dWsMHjwYHh4eWL58Oe7evavzIInmqM5KCCGEEELU4fF4qFOnDhYvXoxnz57h9evXmDdvHt6/f49BgwbBxcUFffv2xd69e5GcnMx1uAYrh8nB7qDdOPj2IHKYHK7DMWlaVVrj4+Nha2ur+Hz37l20atVK8dnDwwNxcXG6i44QQgghhBCSr//GYSqy6tWrY968eXj8+DGCg4Px888/49OnTxg+fDhcXFzQs2dP7Ny5E/Hx8brZoIkQCURY3XE1FjZbCJFAxHU4Jk2rSmulSpVw4cIFAEBsbCzu3buHtm3bKuaHhoaiYsWKuo2QaIVaWgkhhBBCSgYd1VmVVKpUCXPmzMHdu3cRFhaGNWvWIDU1FWPHjkXp0qXRuXNn/PXXX4iOjtbD1o2LmcAM33t9j8kNJsNMYMZ1OCZNq0rrhAkTMGLECAwdOhStW7eGu7s72rVrp5h/4cIFdO/eXdcxEkKI1lhd3X4mJuHDhw/YvXs3Tp06VSzb2717NyIjI4tlW6aA8ouQwnkXnarX9Xt6emL69Om4ceMGoqKi8Pvvv4NlWXz33XcoW7Ys2rZti99//52+v0TvtKq0TpkyBatXr0ZsbCzq16+P06dPw8zs/3cVnjx5gmnTpuk8SEII0RbVWQvHz88PYWFhXIehU8+fP0fjxo1x6dIlBAcHF8s2586di1evXhXLtkwB5RchhcMU429dmTJlMGnSJFy6dAmfP3/Gtm3bYGVlhdmzZ8PDwwNeXl5Yu3YtQkNDiy8ojjEsg8iUSHxK/wSGZbgOx6QJtV3gu+++w3fffZfvvF27dhU5IFI0PFD/YEIA/XSZKglevnyJevXqcR2GTu3duxfe3t7Ytm2b2nSvXr3C48ePUapUKbRr1w6WlpZK88PDwxEQEABzc3O0bt0ajo6OhY7p8OHDqFKlCho0aKA03c/PD1999RVq1apV6HUXxsWLFxUVehsbG/j4+ORJExoainv37oHP56NTp05q91+T9WlDXX6VK1cOrq6uRVo/IUQ7zs7OGDNmDMaMGYPExEScPHkS/v7+mD9/Pr7//ns0atQI3t7e8Pb2RrVq1bgOV28yszNRbkM5AEDKDymwFdgWsAQpLK1aWnMPugTIfkSIYaFnWgmRoe7BhbN8+XLUrVuX6zB0Kjw8HGXLllU5PysrC7169cKAAQNw9uxZLFy4EBUrVlR67/iiRYvQqVMnnDhxAn/88QcqVaqEs2fPFjqmZcuW4dy5c3mmz58/Hzdu3Cj0egsrJCQEgYGB2L9/P3788cc88zdv3oy6devi8OHD2Lp1K6pWrYrHjx8Xen3aUpVfCxcupLcWkBLNzcGC6xDg6OiIESNG4Pjx44iNjcW+fftQsWJFLFu2DNWrV0e9evXw888/48WLFyb52yzkCyHka90OSLSkVQ7fvn1b6fOAAQNM8uAjhBg/OjMVjp+fH1q3bo1y5WR3jnfv3o2OHTsiOjoaL168QPXq1dG0aVOkp6fjwoULyM7ORufOnZVa3Y4cOYKYmBgIBAJUqlQJbdq0gUikPKriy5cv8eTJE1SqVAlVq1bFkSNHMH78eMX82NhY3L17F1lZWfDy8oK7u7vauD9+/Ij79+/DwsICLVu2RKlSpRSxvHr1CsnJydiyZQtat26N2rVrKy0rlUoxefJk9OjRQzHt66+/xoIFC3D8+HEAQOvWrbF48WLw+bJ7vXPmzMH8+fPzrUjl5/HjxwgMDISPj4/SYzXqvHnzBlevXs0zfdCgQVq18j579gx16tQBT81dTXneb9++HYsXL1aaFxcXhxkzZmDfvn3o168fAOD777/HxIkTcf/+fa3Xpwku84sQY+JkY851CEpsbW0xePBgDB48GBkZGTh37hwOHz6MX3/9FT/99BNq1KihaIFt0KCB2vOSMbA2s4Z4vhgxMTGwNrPmOhyTplVLKyGEGAu6n1Y48+fPR1BQkOLz3Llz0aNHD8ycORNnzpxBmzZtMH/+fDRv3hwHDx7EunXr0LBhQ6UX0b958waBgYG4d+8eZs2ahWbNmiEjI0Mxf8OGDWjevDlOnDiBuXPnolevXpg8ebJi/rFjx1CzZk1s3boVhw4dQr169RSVx/xs27YNderUwYEDB7B+/XrUqFEDd+7cUcSSnJyMz58/IzAwELGxsXmWt7a2VqqwArLRM1NT/z/ASefOnRUVVgBwdXWFVCrVJEtx+PBh9OzZExUrVtS4AgYACQkJCAwMVPw7evQopkyZgpSUFI3XwTAMpk6diiFDhkAikWi8XG6vX7+GRCJB586dFdO6dOmCBw8eICoqqlDrVIfL/CKE6I6VlRX69euHf//9FzExMTh58iSaNWuGP//8Ew0bNkSVKlUwd+5c3L9/nxrBSIFMti1bLBYjKSkJLi4uShcauWVlZSEpKQmurq4q0xgbY79jRYiusNTWqjOtW7fGH3/8AQBYuXIl5s+fj8DAQNSrVw8sy6JatWo4ceIEhg4dCgDw9fVVLMuyLDp37ozt27dj2rRp+Pz5M3788UecO3dO8cq00aNH4+HDhwCAxMREjBo1Cnv37kXPnj0BAGfOnMHo0aPRs2dPCIXKP1vR0dGYPn069u3bh2+++QaArBV0/PjxePbsGXx9ffHo0SPUqVNH4xa/qKgo/Pvvv1ixYoXS9MDAQNy5cwcfP37E0aNHFXmizpo1a7Bp0yZcvnw5z3Oq9+7dw5YtW5Sm5a4ot2jRAi1atAAga3n28vLCpk2bUL58+TzbCQ4OxsWLF/ON4ZtvvsHSpUvRrVs3nDp1ClZWVgXGnZuHhwcA4MWLF2jevDkAKLpOh4SEwM3NTav1qVNc+UWIqTCWqz4LCwv06tULvXr1gkQiwdWrV+Hv749du3ZhzZo18PT0VLTAenl5mcx1OdEdrSutX3aFyq9rVLdu3QofURGFhoZi7NixuHXrFuzt7ZGWloaxY8fit99+g0AgACC78zxz5kz89ddfMDc3h6WlJf744w/079+fs7gJIbpFN211J3crZI0aNeDh4aEYrInH46FatWqIiIhQpGEYBgEBAfjw4QMyMjJgaWmJp0+fAgAePXoER0dHpXd8Dxs2DLt37wYAPHz4EJmZmQgPD1dUUBiGQUxMDEJCQlC1alWl2B49egRzc3NFhRUARo4ciV9//RWJiYladwuNjY1F165d8c0332DcuHFK86KjoxEYGIjw8HAIBAKlClN+5s+fj6ysLAQEBOT7TO2nT58QGBioNC2/1tCMjAz06tULgwcPVupCnVtiYmKedX0pNDQUmZmZWldaK1SogJEjR2LAgAGYNGkSkpOTFa8O0mXrSHHmFyGmwhjbKszMzNC1a1d07doVmzZtws2bN+Hv74/9+/dj/fr1KFOmDPr16wdvb2+0adMmz81KQyLOEWPm+ZnIzMjEpm82wdLMsuCFSKFofRR8+R7W/N7LymUT/7hx45CZmYno6GjY29vj8ePHaN26NapXr67ofrZmzRr4+fnh4cOHqF27NjZv3owhQ4agZs2aeZ51MjZGeO4ihBi43F00eTxeni6bPB4PDCMb6j8jIwNt27ZFeno6GjVqBGtra8TExCguOiQSSZ7lc3/OyMiAQCDIUzkZP358vhcu6tanbXfYqKgodOrUCW3btsWmTZvyzJdfZAHAvn374OPjg+fPn6tcX+XKlXHp0iVERUXlWwnr06dPnkGKvrwRLJVKMXjwYNSsWRNLly5Vua3GjRujcePGeaYzDINx48ahYsWKOHPmDJycnFSuQ51du3bB398f9+7dg4ODAzZs2IDOnTujcuXKhVpffoozvwghhkEoFKJ9+/Zo3749Nm7ciDt37uDw4cPw9/fHpk2b4OzsjD59+sDb2xsdOnTQ6pGB4pDD5GDzw80AgI1fb+Q4GtOmVaX106dP+opDZ4KDgzFixAjY29sDgKLPfO53823atAljx45FnTp1AACTJ0/GunXrsG3bNqxfv56LsHXGGO+4EaIP1NLKjfv37yM4OBixsbGK3i39+/dHTk4OAKBevXqIiIjA+/fvUaVKFQDA5cuXFcs3bNgQOTk5mDlzJqpXr66YHhwcjIoVK+bZXr169RAXF4enT5+ifv36AIDz58+jbNmyKF26tMZxh4aGomPHjujTpw/Wrl2rNC8jIwPp6elwcXFRTCtVqhTEYrGisp6f0aNHo1evXujatSuOHTuWZwR+TUyZMgVZWVkFvq5HFT6fj86dO2PDhg2wsbEp1DoAWYW+f//+ih5Jo0ePRvv27RWVy7Nnz8Lc3BwdOnQo9DYMIb8IMTam9KpDPp+Pli1bomXLlvjtt9/w4MED+Pv7w9/fH9u3b4eDgwN69+6N/v37o3PnzrCw4H7kZJFAhEVtFiE9PR0igajgBUihaVVpLVOmjL7i0JmZM2di7dq1aNGiBcqXL48LFy4gKioKo0ePBgDExMQgLCwMXl5eSsu1bNkSDx484CJknTKlkxchRUHPtHKjatWqkEgkmDhxImrXro3bt2/jxo0binNu5cqVMXr0aHTp0gXjxo1DVFQUjh8/rnge39PTE8uWLUP79u0xYsQIuLi44MmTJ/jw4QMCAgLybK9SpUqYOnUqevTogUmTJiExMRFbtmzB9u3bNY45MTERrVu3hp2dHapWrarolmxra4thw4ZBLBajY8eOaNeuHSpXroywsDD8/fffmDp1aoEVwaFDh8La2hq9e/fG/v370aVLF43j2r59O3bu3Illy5Zhx44diunajoY7ePDgAtM8fvwY9+/fx+3bt5Geno4tW7aAz+cruteuW7cOcXFxqF27Nq5du4bAwEBcv35dsfyff/4JBwcHRaW1oPWpYgj5RQjhHo/HQ9OmTdG0aVOsWrUKT58+hb+/Pw4fPozdu3fDxsYGX3/9Nby9vdG9e3etH3vQFTOBGX5q+xNiYmJgJjCsVmBTo5NO4qmpqZBKpXBwcNDF6pTExcUpjUqZHw8PD0W3sdGjR+P27dvo1asXHB0dkZKSgnXr1im6/cpHjvyyi5Szs3O+F0RyYrEYYrFY8Vk+GiHDMGrvtBc3lpXFYkgx6QPDMGBZ1uT305AZehlIpYb13dQHfZTB0KFD4enpqVjniBEj4Obmpvhcvnx5DBkyRGmb3bt3R6VKlcAwDMqWLYs7d+5g//79CA0NRf/+/TFq1CiEhoYqltm8eTP27duHwMBA1KhRA506dcLo0aMV82fPno0OHTrg/Pnz+Pz5M3r06IH+/fur3M/ffvsNnTp1wu3bt2FnZ4fr16+jYcOGivSdOnVS2ocvSSQSxXO7T548UUx3cnICwzCwt7fHrVu38O+//+L169dwcnLCyZMn0bRpU8TGxua73tz59vXXX+PgwYM4ceIEGjZsiFKlSqF///6oX79+nmWHDh2KmjVrgmEYlCpVCt9++y3ev3+vlKZXr16K3kS68unTJzx58gRWVlYYOHAgnjx5AoFAoIhv9erV2L9/Px48eIBu3bph7969sLe3V8zv3r07LCwsFJ8LWp+u8mvIkCGoXr16secX+T9D/y0wdSXluq9evXqoV68elixZgpcvX8Lf3x9HjhzBvn37YGlpie7du6Nfv37o2bMn7OzsijU2+g4UnjZ5xmO1eAA1Ozsbq1atQmBgIDp37ozx48dj/Pjx2LlzJ1iWRbt27XDw4EGlLlRF9f333+Pw4cNq01y/fl0xOmDHjh3BMAyOHDkCR0dHPHnyBO3bt8eKFSswefJkvHr1CrVq1cK1a9eUBgKZPn06Lly4gFevXuW7jcWLF2PJkiV5pr9+/dqgfgyvvEvEvNMfcHdGI65D0SuGYZCcnAx7e3saYY4jhlwGzdc/wuXJDWBtJuA6FL0y5DJQJzY2Vul3YvHixXj16hUOHDjAYVTaM9b8NyVUBtyjMuBWwIckzDoRjIBpX5XI/P/w4QNOnz6N06dP4+nTpzA3N0fbtm3Rs2dPdOnSRS8NarmxLIukrCSkpKTAw9lD8VgM0UxqaiqqVauG5OTkAm82aNXSOm/ePGzbtg3NmzfHjz/+iNu3b+Px48fYunUrAGDt2rVYsGAB/vrrr8JH/4Vff/0Vv/76q0ZpP336hCtXruDkyZOKrkBfffUV+vfvj927d2Py5MmKofs/f/6stOznz5/Vvrze19cXs2bNUnxOSUmBp6cnXFxc9P6F0IZdtOy9ga6urhxHol8Mw4DH46l9pRHRL0MvA2dnZ9hamPbzJYZeBqocPnwYFy9eRKtWrfDs2TMcOXIEJ06cMLrzlrHmvymhMuAelQG3HBJl/5vS6xu14erqiubNm2Pp0qUIDQ3F0aNH4e/vj+nTp0MoFKJDhw7w9vbGN998o9NGNbl0STrcVste+5U0Jwm2FrY634Yp0+a5ZK0qrfLuOm3atMGNGzfQtm1bBAYGKga/aNSokdJrB4qbra0teDwekpKSlKYnJiYqau+2trZo2LAhzp8/j0GDBgGQtSBfvnwZ06dPV7luc3NzmJub55nO5/MN6iQh+C8WQ4pJX3g8nsHlf0ljyGXAM9C4dM2Qy0CVKVOmoFKlSrh+/Tpq166NhQsX5nmVjbEwxvw3NVQG3KMy4A7/v/EAKP9lYxzMnj0bs2fPRmRkJI4ePYrDhw9j0qRJmDRpEtq1awdvb2/07ds33xHKCyN3nlMZaE+b/NKq0hoVFaV4sbj8/9yviKlbty6ioqK0WaVO2djYwNvbGwsWLICDgwMqVaqECxcu4NixY4p3AALAokWL0L9/fzRq1AgtWrTA2rVrIRAIMGnSJM5i1xUaPZgQGRo92LD16NFD6f2vhBBCtEfXfflzd3fHlClTMGXKFERHR+PYsWPw9/fHtGnTMGXKFLRs2RLe3t7w9vaGp6dnobdjJbJC1rwsxMTEwErEzWBQJYVWtwNycnIU70eS/5/7vXlCoRBSqVSH4Wnv77//xpgxY7Bq1Sp4e3vj3LlzOHjwIIYNG6ZI880332D//v04cOAAhgwZArFYjBs3bsDZ2ZnDyAkhOkWVVkJILly+Q54Q/aFaa0FKly6NCRMm4MKFC4iOjsaOHTtgb2+PuXPnoly5cmjWrBnWrFmDDx8+aL1uHo8HkUAEkUCkGAWf6IfWowd/+SLvLz9zzdraGgsXLsTChQvVppPfXTE99IUhBKBX3hBC/u/333/HnDlz4ObmBg8PD7i7u8PDwyPPv9KlSyvdjCeEmBYnJyeMGjUKo0aNQnJyMk6dOoXDhw9j0aJFmDt3Lho0aID+/fvD29sbNWrU4DpckovWZ+bu3bur/UwIIYaAGlUIIXIDBw4Ej8dDZGQkIiIiEBERgYcPHyIiIgJZWVmKdHw+H2XLls1Tmc1dyXVzc8t3jAtCuECNe4Vnb2+PYcOGYdiwYUhLS8PZs2dx+PBhrFy5EgsWLEDt2rXh7e2N/v37o06dOvm2pEqkEsy7PA8Z6Rn4rddvsOBrPrAQ0Y5WldZPnz7pKw6iI3TyIkSG6qyEELnSpUtjypQpeaazLIvExERFRVb+T165vXjxIiIiIhTvZpdzdXVV22Lr7u4Oa2vr4to9QkgR2djYYMCAARgwYAAyMzNx/vx5+Pv7Y/369fj5559RtWpVRQtsw4YNFRXYbGk21t5ZCwBY3X01LERUadUXrSqtZcqU0VcchBCiU/T8GiGkIDweD6VKlUKpUqVQr149lelSUlIUFdncrbUREREICAhAREQE4uPjlZZxcHBQWaGV/21vb0/PwZEioaNH9ywtLdGnTx/06dMHYrEYly9fhr+/P/766y+sXLkSFSpUUDxm+FXjrzC7xWxkpGdAJDDt1+xxTeNK6/v37zVeaZUqVQoVDCk6uk4nRIa+CoQQXbGzs4OdnR1q1qypMk1mZiaioqLybbV9+vQpTp8+jc+fPyvdULO2ts63Mpv7n7OzM1VsCeGIubm5YrT7LVu24Pr16zh8+DD27NmDtWvXwt3dHX379kXnDp0hgIDrcE2axpVWbd6hRy0cXKK8JwSgGziEkOJlaWmJypUro3LlyirTZGdn49OnT/m22L5//x7Xrl1DVFQUcnJyFMuYmZmp7Ybs4eGBMmXKQCCgC+aSiH7qio9IJEKnTp3QqVMn/Pnnn7h16xb8/f1x5MgR/PHHH3B1dUXfvn3Rv39/tG3bFiIRtbzqksaV1vDwcMXfp06dwooVK/Dzzz+jSZMmAIAHDx5g0aJFmD9/vu6jNGSZmYCDAyAfyMHCQjaNzwfMzYGMDEAgkP2dng6IRICZmexvMzPZ57Q02XJCIZCaClhayv5OSQGsrWXLp6QANjayh1ZTUwFbW9lVeVoaYGcHSKVAejpYFhAw0v+nycmRxWZjA2RnAxKJbJ0SieyztTUgFsuWt7KS/c0wshgMZJ9gZyfbj8zM/+9TRoYstuxs2WdT2CdjKyf5M15S6f/3z0D2ScBIZaMHl4Rykr9mzJT2yVjKSSgELyNDtk5zc9PYJ2MrJzMz2frEYtl0A98nkbU1ypUujXLOzoCXV777xJiZIebjR0R8+oSImBhEfPgg+/vTJ0SGheHB/fuIiIxUGkBKIBD8fwCpMmXgUa4c3D094VGqFDwqV4ZH+fJws7GBWalSui8nS0vZfmRkyNKUlGPPQPaJn5omiycnRxa/CeyTMZSTID0dbVu3RpvWrbF64XycDwjAjes3ceS/bsSlSpVCnx494D1kCDq1awczqdTg94nTctIEWwj169dn79+/n2f6vXv32Pr16xdmlUYnOTmZBcAm+fjIJsycKfvHsiw7ZgzL/vST7O/+/Vl27VrZ3126sOy2bbK/mzdn2YMHZX/XqsWy587J/nZ3Z9mAANnftrYs+/y57G+AZcPDWTY5WfZ3crLss7wInz9nWVtb9nRQFNvXZ41sPSwrW2+tWrK/Dx6UbZdlZXF06SL7e+1aWZwsK4t7zBiD2ieWZWXL59onplYt9tOnT6x0/36T2SdjLKdPnz6x0qAgg9un4QOWsNHJmSZfToytLRt77RorlUpNZp+MqZykUikrbtRIdh4ykX0ytnKSSqVs+tChLLNokcnskyblxDAMG9eoERv4yy/sqVOn2L/KlmUXDh3Kjho1iu1sbs7WrFCBtbGxYSFriFP8K+3iwjasX5/tDbCTx45lV/zwA7sbYK9cucK+OXWKTbex0XqfpFIpm/zTTyzj7V2kfTLFciqOfcoqXZYt/8MpVnrmjMnskzGVU1psFMsCrO2PYFPfvWIZgH348CH745gxbFUejwXA2tvYsD6WluzRo0fZjOPHDX6firOckidPZgGwycnJbEF4shi1Y2VlhcjISDg6OipNT0xMhLu7OzLkrWAmLCUlBfb29kiMioJD2bIGc/fjzMd0TN3zAMEL25v0HR0mIwMxGRlwdXQEn1paOdknJiUFMVlZcHVyAj8z06D2qfLPVxCwoAtK87JNupyYpCTEpKfDtWxZ8NPSTGKfjKmcGKEQsaGhcHF3B59aWjnZJ3mrpGuZMuAbQUtrcZdTSkQEIpOSZC21794hIi5O1h3540dERkfnO4CUo6MjPNzd4VG2LDwqVIB72bLwcHWFR5Uq8ChdGh6urrArWxa87GwgOxuMpSViwsNlvwXU0lrs+3Tj6UeMOPQaH37uDD61tBb7PqVnZ6DsYlukmgMpc5Ngm8NT7BOblobnYWE4fPAg/P398eLVK1hbW6Nn167oP3gwunfqBBszM4Pbp+Isp5SYGNiXLo3k5GTY2dmprXsVqtJap04dfP3111ixYoVicACWZTFv3jycPn0aQUFB2q7S6CgqrYmJcHBw4DochdNBn/Ddv48Ruqon16HoFcMwiImJgaurK/h8PtfhlEiGXAYVfjyNu74dUcbetIeeN+QyKAko/7lHZVB0mZmZeZ6v/fJzdHS00nglNjY2imdq3d3d4ejoiGrVqqFcuXKK6TSAVPG48ioao/95iA8rutN3gAMsyyIxMxGxsbGo4lFF7bPlr1+/hr+/Pw4fPozAwEBYWFigW7du6N+/P3r16gV7e/tijNwwyOtTmlRatXrljdwff/yBr7/+GocOHUKjRo3AsiweP36MmJgYnDp1qlBBE91g6ZF8QgDQd4EQQjRhaWmJKlWqqH3zg0QiwadPn/JUZiMiIvDu3TuEhYXh8+fPkMqfsYds1NUvB5D68nPp0qVpAKkiot86bvF4PDhYOEBiLinwJk2NGjUwf/58zJ8/H8HBwThy5AgOHz4MHx8fmJmZoXPnzvD29sY333yDUqVKFdMeGI9CVVrbtWuHDx8+YPv27Xj58iUAYMyYMRg3bhycnZ11GiDRDo2YSogMfRcIIUQ3zMzMUL58eZQvXz7PPHlrt5OTE+Lk3Y/zabG9d+8eIiIiIBaLFcsKBAK4ubmpHB3Zw8MDZcuWhZmZWXHurlGh3zrjVLlyZcyZMwdz5sxBWFgYjhw5An9/f4wZMwbjx49H+/bt4e3tjb59+8LV1VVn253s9wibhjXS2fqKU6EqrQDg4uICX19fXcZCdIDOXYTI0HeBEEKKj3wE47JlyyreLPEllmURHx+fbxfkiIgIPH/+HBEREUhLS1NarnTp0ipf9yP/28rKqjh20+AU4ik/okMSqQTLbyxHeno6lnVdBgu+9o8llStXDjNmzMCMGTPw6dMnHD16FP7+/pg8eTImT56M1q1bo3///ujbty/c3d2LFO+ZZ5+LtDyXCl1pJYaJTl6EyNB3gRBCDAuPx4OzszOcnZ3RoEEDlelSUlLyVGjlldybN28iIiICCQkJSsuUKlUq3y7Iuf8V9MycMaJfOm5lS7Px842fAQA/dfoJFqKijaVRtmxZRWU1NjYWx48fx+HDhzFz5kxMnToVXl5e8Pb2hre3d56eD0FBQfj555+xefNmuLi4FCkOQ0SVVkKISaI6KyGEGCc7OzvUqlULtWrVUpkmIyMDkZGRispseHi44u/Hjx/jxIkTiI6OVlpGPoCUulZbJycnoxpAin7ruCXkCzGp8SRkZmRCyNdttcrFxQVjx47F2LFjkZiYiBMnTsDf3x/z5s3D7Nmz0bhxY0UFtmrVqnB0dMStW7fQvn17XL58GaVLl9ZpPFyjSishhBBCCDEqVlZWqFq1KqpWraoyjXwAqS9bbSMiIvDmzRtcvnwZUVFReQaQUjd4lIeHB1xdXQ1mACmqs3LLXGiOP7r/gZiYGJgLzfW2HUdHR4wcORIjR45ESkoKTp8+DX9/f/z888/w9fVFvXr14O3tje3btyueib1y5QrKlCmjt5iKG1VaTQzdcSNEhr4LhBBSsqkbQEpOKpUi+r931n75rG14eDju3LmDiIgISCQSxTJCoVDtAFLu7u5wc3ODSCTS+z7SozAlj52dHYYMGYIhQ4YgPT0d586dw+HDh7FmzRqkpaWhUqVKiIiIQIsWLXDr1q0iPwdrKKjSamJo6HNCZOi7QAghpCDyEYzd3NzQtGnTfNPkHkAqv3/Pnj1DeHg40tPTFcvweDyNBpCytLQsUvxUZy3ZrK2t4e3tjR49euDp06c4cuQILly4gI8fPyI1NRV16tRBYmIi12HqBFVaTQydvAiRoe8CIYQQXdBkACmWZfMMIJW71fb69euIiIjIU4FwcnJSO4CUu7u72gGkGPqx41S6JB0Oqx0AAAlzEmBrYctJHG3atMHDhw8ByJ6Fbd68OaytrVG/fn1O4tEHqrSaGDp3ESJDXwVCCCHFhcfjwd7eHvb29qhdu7bKdOnp6UoDSOX+9/DhQxw7dgwxMTFKy9ja2qqs0Iakm0GamULdhDmUw+RwHQL+/vtvpKenKwZkMkVUaTUxdMoiRIZ+wAkhhBgaa2trVKtWDdWqVVOZRiwW5zuAVGRkJF69eoWLFy8iKioKDMMolrHZOlqjAaT4fH5x7GaJYSmyRNj0MMTFx8FSVLSu3kWh7kaJqaBKq4mhC3VCZOibQAghxBiZm5ujQoUKqFChgso0OTk5iI6Oxr9XHmPNkbuY385V0Xr78eNH3L59G5GRkfkOIKXqPbbu7u4oW7ZssQwgZSr4PD7c7dwhyhKBz6MbAvpklJXWa9eu4fXr1+jduzfc3NzyzJdIJLh8+TKio6NRt25dNGrUqFBpjBHVWQmRoe8CIYQQUyUUCuHu7o5qdVhYveBj6tTueVpRWZZFXFxcvoNHRUZG4unTpwgPD0dGRoZiGR6PhzJlyqhttXV3d4eFhUVx7zIp4Yyq0nry5EnMnTsX1tbWePToEWrUqJGn0hobG4sOHTpAIpGgXr16mDlzJgYNGoQtW7ZolcZY0YiphMjRd4EQQohpY9T81PF4PLi4uMDFxQVfffVVvmlYlkVycrLKAaSuXr2KiIgIJCUlKS3n7OyscvAoecXW1rZwgxLt3LkT58+fxy+//KL2dUWGQCKVYP2d9UhLS8O8jvNgwafKvL4YVaXV3NwcR44cga2tLTw9PfNN4+vrCwB48uQJrKys8OjRIzRp0gRff/01evbsqXEaY6Xu5EVISUItrYQQQkxdURsreDweHBwc4ODggDp16qhMJx9AKr9W2/v37+Po0aN5BpCys7NT+8ofDw8PODo6gsfjKS1Xp04d/PTTT6hduzZWrFiB7777DgKBoEj7qS/Z0mz8cPkHAMCcdnNgIaJKq74YVaW1S5cuAICIiIh85zMMg4MHD2Lx4sWwsrICADRq1AheXl7Yv38/evbsqVEaY8awLL747hNSIlGdlRBCiMkrph87TQeQioqKyrfV9sWLFzh//jw+ffqkNICUpaVlvhXaX375BUePHsX06dOxb98+7NixA7Vq1SqOXdWKkC/EiHojkJWVBSHfqKpVRsekcjc8PBypqal5DupatWop3l2kSZr8iMViiMVixeeUlBQAsopy7i8f1xiGBZ/HM6iY9IFhGLAsa/L7acgMvQykUsP6buqDoZeBqaP85x6VAfeoDLgl/a+LnSHkv0gkQvny5dV26c3JycHnz58VldncrbehoaG4desWIiMjkZ2drVjm7t27qF27Nv766y+MHTu2OHZFYyK+CDu+3oHY2FiI+CKDKIeCGFKM2sTCaaX16tWrePPmjdo0Q4cOVftS5dzkFUkHBwel6Y6Ojop5mqTJz8qVK7FkyZI802NjY5VGZuNackoKeECeLhqmhmEYJCcng2VZGr6dI4ZeBvEJCYjhZ3Idhl4ZehmYOsp/7lEZcI/KgFspKckAZNd9xpL/ZmZmqFixIipWrJjv/KysLBw/fhwHDx7EvXv3wLIsKlWqhPLlyxvk9a2xfQcMKQ9TU1M1TstppTUsLAyBgYFq03h7e2u8PktL2fuRvsyAlJQURVdgTdLkx9fXF7NmzVJK7+npCRcXlzwVYC7Z2GSCzwNcXV25DkWvGIZRDDBgDCcIU2ToZeDo6AhXV81ueBkrQy8DU0f5zz0qA+5RGXDLJkzWC9BU3sGakpKCJk2aICIiAg0bNsTatWsxePBguLi4cB2aSsb2HTCkOoI2o1BzWmkdOXIkRo4cqbP1lS9fHmZmZggJCVGaHhISgipVqmicJj/m5uYwNzfPM53P5xvcAcrj8QwuJn2Q72dJ2FdDZchlwOMZZly6ZshlUBJQ/nOPyoB7VAYc+m8gE1PJfzs7OyxZsgRNmzZVOzCUoUiXpMP9N3ewLIuImRGwtSjciMnFyZCOE21iMZyodUAkEqFbt27Yv38/2P+GDo2KisLVq1fRu3dvjdMYM4YF+DQSEyH0+idCCCEmjzWxofL5fD5Gjx5tFBVWuWRxMlIkqh8xJLphVAMxvXnzBlevXlW8K+rEiRN4/fo1GjdujMaNGwMAVq9eDS8vL3zzzTdo3rw5du/ejWbNmsHHx0exHk3SGCsaPZiUdPIfcBP7HSeEEELyoJ86blmKLPF68mvEJ8TDUmTJdTgmzahaWhMSEhAYGIjQ0FBMmDABGRkZCAwMxOfPnxVpatSogaCgIDRv3hzR0dH4/vvvcenSJQiFQq3SGDNqaSUlGb2rmBBCSIlBv3mc4vP4qOpUFZXsK4HPM6pqldExqlpaixYt0KJFiwLTeXh4YN68eUVOY4yopZWUdAy1tBJCCCkhWAB02UdKArolYGIYlk5eRD/+vh2Ciy+juQ6jQPLKKj3TSvTB1J4fI4QYN4ahxgouZUuzsenBJux6vgvZ0uyCFyCFRpVWE8OyAJ9PZy+iewcfRuDG21iuwygQtbQSfaroe4brEAghRIFaWrklkUow9dxUzLs9DxKphOtwTJpRdQ8mBWNYlp5pJQT0mA8hhBDTx7LUxY5LAr4A3jW9IRaLIeALuA7HpFGl1cSwLEvnLqIXxlIJ/H9Lq7FETAghhBSOrKWVrvy4YiG0wMH+BxETEwMLoQXX4Zg06h5sYlhW9pJvQvTBGA6t/z/TSgghhJg2lgVK4lNhe+9+xI/+QVyHQYoRVVpNDFNCT16EyNEzrYQQQkqKktrD7n1MGp6EJXEdBilGVGk1MfTKG1LS/f89rVRrJYQQYtpYgJ5p5VBGdgY813viqz1fISM7g+twdGrX7RAcfBDOdRgKVGk1MSxAAzGRkk3ePZjqrIQQQkycbBwmuu7jCsuyiEqNwueMzyY3lsblVzG4+T6O6zAUaCAmE8PS6MGkhFN0D+Y4DkIIIUTfWFAPOy5ZCC3waNwjJCQkmNxATIb2vnuqtJoYxsTu8hDDYSx3EOVRGkm4hBBCSKHRG2+4JeAL0KBMA8TwY+iVN3pG3YNNDMsCfCpVoifG8MNIr7whhBiimNQsrkMgJoiFcYzsT0hRUfXGxMhGD6azFym5qHswIcQQNV1+mesQiAmSjR5M131cyZZm4++nf+PAmwPIlmZzHY5Jo+7BJqakDn1OiMJ/tVXqKk8IIcTUsWzJbWk1hP2WSCUYc2IMAGB0s9EwF5lzHJHpokqriaHRg0lJx9DowYQQQkoIetUhtwR8AbpX6Q6JRGJyz7Qa2nUUVVpNDMPQyYuUbPLR7qSMgZ1tCSGEEB2TsoCghF74GUKlykJogVNDTiEmJsbkRg82NPRMq4lhWIBXQk9ehADy7wAgNYRfM0IIIUSPWIYFny77SAlAlVYTw4JOXkR/jOGGCMOwEPJ5NHqwEfnhcBBSs2gAC0II0ZaUZcGnCz+iJ4Z0ZFGl1cSwNHowIRDweZAyXEdBNHXgYTgS0iVch0EIZ97HpGHPnVCuwyBGiKGWVk5lZGeg+p/V4bXPCxnZGVyHo3OGdPufKq0mhkZMJSUdw7IQ8fn0TCshxGjc+RCPJSdfch0GMUIMC3rlDYdYlsX7hPcISQmhHl56RgMxmRhjaml9EZWMyi42sBCZ1mhrhFssCwgEPLqBQwghxORJWRaCEtoEZQiXuxZCC9z49gYSExNpICY9K6GHuekypqHPe268hYDgOK7DICaGYVkIqaWVEEJICcCwrNE0VpgiAV+Alp4t0bRMU5N75Q1gWM+0Gl1L68ePH7Ft2za8fv0aS5YsQe3atZXmi8Vi7N+/HwEBARAKhWjVqhUGDRoEPl+5fv769Wts27YN0dHRqFu3Lr777jvY2NgU567oBcOyENDDDUQPjKXhkmEBIZ9aWgkhhGtxaWJ8u+s+Tk1tzXUoJothjKeHHTE+hnQlZVQtrb/++ivat2+PtLQ0+Pv7IzY2Vmk+wzCoVasWrl27hkaNGqFq1aqYM2cO+vXrp9TP/PHjx2jUqBESEhLQunVrHD58GK1bt4ZYLC7uXdI5KSOrtFK/elJSMSwLkZAqrYQQwrXkzGw8j0zhOgyTJmVNZyCm44GRXIegtRwmB4deHsLJ4JPIYXK4DkenDO0yyqhaWgcMGIBZs2YhKioKGzZsyDOfx+Phxo0bcHd3V0xr1qwZvLy88OjRIzRu3BgA4Ovri3bt2mHXrl0AgP79+8PT0xO7du3CxIkTi2dn9ETKACI+HyxrGH39CSluUoaFSMCn0YMJIYSYPJYxnVfeTN8fiG8auBec8D+GUKkS54gx2H8wAGBwo8EwE5pxHJFuGdKRZVQtreXLl8/TzTc3Ho+nVGEFAA8PDwBAYmIiAFn34StXrqB///6KNE5OTujYsSPOnDmjh6iLl5RhIKRBaEgJJmVYmAn4YOiZVqNCpyxSkvFgWN3wiPGQsqzai/mZBwKx81ZIscVT0vB5fLQt3xYtyrYAn2dU1aoC8XiGdV4yqpbWwvj999/h4OCAZs2aAQDCwsKQk5ODcuXKKaUrV64crl+/rnI9YrFYqftwSoqsuwvDMGAYw2nSyfmve7CUYYyiuwjLsoXKP4ZhCr0sKRwWLJArzw21DLKlUogEfOQY2HdTHwy1DAqDYY2nvAz9O1CSFFQGGy+/w7SOVYs5KtVUxZn7mDI26sqAkRrvfhkLKSMbgFNVHr+ISoa9hdBoykDTOFmWBQvuz7/mAnNc8rmE2NhYmAvMOY9HE5rnceGv03UdC8BxpfXPP//E1atX1ab5448/UKZMmUKt/+DBg1i7di327dsHOzs7AFBUPK2srJTS2tjYICsrS+W6Vq5ciSVLluSZHhsbC4lEUqj49CEzMwtMDoPo6BiYCQ3/jk9yUjJiYrRfjmEYJCcng2VZta3vRHekOTnIyMxEzH8FZqhlEB+fBjA5SEpOUcRqqgy1DAojPj4eVtJ0rsPQiKF/B0qSgspg/eX3GFzXnoPI8qfqnJSamgaANcpzlroyiE+QXVcZ434Zi/SMDIBhEB0dDYEg7+i1X/52GzpN48zIzIBUKjWI/TK23wJN80wikUCcxeg1j1NTUzVOy2mltVmzZihdurTaNLa2toVa9/HjxzF8+HD8+eefGDhwoGK6vb3sx0veXVguPj4eDg4OKtfn6+uLWbNmKT6npKTA09MTLi4uapcrbkKzCFiLABcXF5gbwftP7ezt4erqqvVyDMOAx+PBxcXFKE4QpkAgeAMrS0tFeRlqGdhlimBtEQsra5tCHVvGxFDLoDCcnJzg6mTNdRgaMfTvQEmiSRkY0nlAVSy2tpnggWdQsWpKXRmkIg2AYZWBqTEz/wwLsyw4ObvATJT3sl4gVP7tNnSaxmllGQehMMsg9svYfgs0zTMzsxBYWJjrNY8tLDR/ty2nldbGjRsrBkfSpZMnT2LQoEFYt25dnoGVPDw84OjoiKCgIPTo0UMxPSgoCPXq1VO5TnNzc5ibm+eZzufzDeoAlTKAuZAP8Io/rk/JmTj4IALTO2neFYvP5xU6Th6PZ3D5b9J4AO+L8jLEMmBZQCTkgwUMKi59McQyKAyBEe2DoX8HSpqCysCQykZVLLz/phtSrNpQVQbGvl/GgAUgEvDB8vK/nuKBB6iYZ4g0jZPH44GnRXp9yczORIsdLZCTk4N74+7BWmj4N1+1yWN9HzvarNs4jmAtnD59GgMGDMBvv/2GyZMn55nP4/EwbNgw7NixA0lJSQCA69ev48GDB/Dx8SnmaPVBNoocy8Gj068/p2LdpbdaLcMzqHHJiCmQsv8NxGRIowcQYuCSMgznMRdi2KrMO4OsbCnXYXAqW8rg3PPPXIcBQPaeViGfByN4lNIkMSyDp9FP8SL+BRjWtAqhoAES/R9FQJJTfPtsVAMxXbt2DX/88QcyMzMBAD/99BNcXFwwcOBADBw4ECkpKfD29oadnR2uXLmCK1euKJadNGkSOnbsCABYvnw5Hj9+jJo1a6JmzZq4d+8e5s2bhw4dOnCyX7rGA+iCnZRY8lfe0OjBhGiuwc8XEbqqJ9dhlFjGdPs2h2GRU8LPr9EpWZi495FBfGcYloVQwIOUhmDnhIXQAueGnUNSUhIshJp3dTUW6s5Nsw89RYvKTnBzsCyWWIyq0lqxYkUMHix7F9KoUaMU02vVqgVA1i967969+S5buXJlxd92dna4desWHjx4gOjoaNSpUwcVK1bUY+TFi8+TjfZFSEkkZViIhHz6ATcyVFykpKOvACkMKcP+19JKRxAXBHwBOlfqjJiYGAj4hj+WjDEzqkpr+fLlUb58eZXzzczMlN6/qg6Px0PTpk11FZpB4fF41NJKTEqGJAd8Hg8WGgwuxrAsRAIepPQlIIQQYuIY9r9Kq4o7f1w8LlYcqHFG/wztPa0m90wr+a8p35COMmISuPx9mLE/EItPvNAorZSB7JlWqrQSDbyMSinWZ3IIyY/pnq1Md8/0acTO+xqnZVgUeKOWZ0z9z41MDpOD0+9O49LHS8hhcrgOR6cM7b4AVVpNEI/HzUBMxPRxNXBWmjgH6RLNBv6QMgzMqHsw0VCPjTcRGJ7EdRiEGNVzrUS/bryN1TitonuwEfzkTdjzEMkZ2TpZF89AauLiHDF67++N4eeGQ5wjLvL6nkcm48rraB1EphuGkcsyVGk1QTweNwMxGdKBTUouKQMI+dTSSkq2cbsf4tDDcK7DIIToGftf92B1La2Gcg/3/ItoJGWa1kjlfB4fjcs2Rn2X+uDzil6tOvwoAqvPvtFBZLphIIcOAKq0miQBj57nIyWXlGUhEhrHXWdtvYtOpco40UhQRBIikzK5DqNEqzLvDNchFMr1t7F4Hpmso7XR7Wx9YwEI+KpHD6ZXC+qXpcgS98bew7l+52ApKp5RdIuLJo3ZxXlFQpVWEySkQWiIidHmLjHDyN7TylX34AyJ/p5p6bzuBlVESrCS/m5MY6PNa2F4MJwWjTXnX+PAA/Wt9JoPgmMoe2W6WBbg80E3NHWsyfJLXIfAOUNpoZejSqsJEvJ5yJYaycAiJngDMDUrmy4uOcT1e1prLTrPyXaJ6aux8BzXIZBcQuPSIc6hcz0pXlKGRYuVl5WmCXj0nlZdi00t+vOpxiYrW4qY1CylaYZ0mU6VVhMkFPA5efE3nS5lfHbcxx9X3nMdhs5xWb7ajLcgZVjZQExGcNc5OTMbIXHpXIdBCCmEdr9ew5VXMVyHUWQxKVnYe/cj12EUyoJjz7gOodgxLItPyf+vWPB4AL8EvurQUF55k5mdida7WqP3sd7IzDbunlC774Six4ZbXIehElVaTZCse7CRtLSaoDQTbmk1kMH61JKyspZWY7jrvOdOKAZsCeA6DGKEnkcm40f/IJXzjeDwJwYiMDwJC44912oZQxm5de/dMK5D4BzLAnwedQ/mCsMyCIgIwIPoB2BY4772ljJAjgHXH6jSyoF0cQ7i0vTX7UDWPdhITl5GEiYxHlKGhZmAZzA/4Gli03pvGzEM72PSsL+A5w6J8TCMs1XhdN+oumXGVG+eGEqlXY6vZgBOY30FopRhMebvB1yHUSBzoTn8B/hjZ5edMBeacx1OsSvObwJVWjnw+5X3GLbtnt7WL+TzkcNBpdWwTuG6I2VYDN+hv/IyJlxdgGg1ENN/La0GUmdFnZ/oGVdDZ2DXnzrx5T4N3noH4QkZ3ARDNGKsh+Gbz6lch1DsDKVrqhyfL/vtU4Wrc1yOlEFOIcdYyWEYXH5t+N3vhXwh+tTog+4Vu0PIF3IdjkmjSisHGJbVa/O7SMAziOb9x2GJOPk0Sn0iA/mVDo5NUzkvh2Fw812c4vOH2DT8cFh1tzxTZgwX9zlS4+ke/KX4NLHOXryurWtvYnA66BMn29anV59S8CA0geswOHf3QwJiOBpYpOfGmwiKSOJk28bCCE6thWIMvxnGjscz3Fcdzj0chOVnXull3YbW2l1S5T7qKvx4Wq/bokqrAQqJS4ckp/CVTgGfm4GYvnTq6SdsvPxO8fnRxwR8UFM55FLHtdc1TvshNh0HHhpmt7zV517jcViiyvlX3xj+Xcv8fPnbdD8kQeXIfgzLQiTkQ2osXeRzmbrvCRad0O7ZMl05+iQSe+6GFnr5629jdReMDu26HYIVerpoIpp5EZWi10diTIUxnbEMraWxMLKypYVuBeTSl1U1+TOthnij9nNKFuLTJFyHoVdSRoprodcQEBUAKWOa45kYCqq0GqD2v17DjSJcAIoEPE66Bxdk3pHnNGhCEWVLGTxRUyndcSsEzyJUvxR+1C7Dfz5EEz7b7+Hci8/5zpMyLCyEfGQbQG+Dgnx5jaGuexfXCrqDOnLn/WKKRPcMONsLzRT3yRBRNhuvcbsfYkOuG+vGIr9jjs/XfByHqfuecPqqJp6J9SvIyslCxz0d4X3SG1k5slGdA97HGeUNEUNHlVYjsPDYc7UHf+6TD8vKBmIyhO7BRPdef0pF302qR5tlWbZEdsdafOIFUrNk3WqlLAtzkcAgb9wQ1Qy1tLqtv8F1CPnSZHAVU7g4fBGVjMUnXnAdRrET50gNZjC5ojDkmyexqWKkZHLzOIYu8Xjy6z7VmZ27HE4+jUKWpHiuEYtS/gUtayit/TweD7Wca6GaYzVFl+Wh2+8pvZaI6AZVWvVk6La7OlvXnrsf1Z6Mqi/4/wvneTxAIOBmIKbCMP5LKu0l6/FHkmW1e84jXk2XvafhSfjmj+J5X5ckh8Gjj6pbkAv6bfo7IBRx/3VBkkplLa3GcOOmJN5gMDav1Qwyk5AuwdsYGtyoqDaqae169SkVfweEFl8wBmLgljvYY6TvTjUUWdlSPI9U3fPIVMgbK1S9NYLrm1df/s4Z62jGqliJrPBs0jNcH3gdViIrnazTUPLIUOKQo0qrngQEx6udr8/DQFTAHTd9KcxD8Yb1dSge9Zdc4DoEhUbLLqmcF52Shadquhrr0pvPqfDerJv3lWYzLKzMhMbz2iditA4/isB3/m852bYmF6KGdMGh7sbTbxe5yUMuJWdkY7Sa13lEJWfp9QZnSXAvJAG9fi+eG69cE/J5ih55t9/HYS/d8NCrE0+jcO55/o8oFZUh38yOTxMjm8Nuz1RpNUFCAV9vfenV3bU0lK4aJZmploA2J/FsKQMzIZ+OR46pG8ly1+0QHHsSqTSNy99pQ75IKCxT3CdTkpQpwRUjeJ0HIBv4Tv74RUmy+txrgxyRN7+vtpDPQ/Z/sV5+FYN/cvVOMKSbV6bi0MNwnHgaWXDCQjCkS5cvb462/uUqTgQW8FYQPaJKqwmRMiz4PJ7SyUvX9HnXUt9DZWtjwp6HXIeQL33++GRLGUzc80j99g3kZKouH3KkDESC/59oWZbFSgMZPZZhWITGpRdpHaoqIwzD4nigfn5EtfXmcyqqzj+j+ByekIGA4P+/Nurs88+48c4wRxs2pPOQNhLTJYhJ+f8zVLr8rr7+nIKDRRwxXV+V6LfRqUjKzFF83n7zQ7G+NkqXu2Wo9xkG/nUHt98r9x4rCa8b2XwtmNNWJW3IBuBUHashFVfuitDma8Gq0xlQzOpkZmeiy94uGHRqEDKzMzVapvGyi2rnc92lW5UMiZTTR6+o0mqEVLUgZUsZiAR8CAU8SIvpoPr1/Bu187+M1Fju+J1/Ec11CMUuW8qoHJHXEKk6pWf/955WOYYF/rrxoXiCKsCzyGS0+/Wa4rMuKxYZ2VJM3x+ouxUWQZo4B7nvmx19EslZbIZyo0Xflp5+ie/+faw07csLn8JeBF59HYtVZ18XNjS9Grb9Po4++/8NkGWnX+FDXPG9Wk2Xh1cJOVSVvP6conZsBUOSe6wSLivt+R0nAjXPtHKpoGu+1edUn1eM5dzNsAwuh1zGjcgbYNj/X3uriz/OSF4DZGjX7FRp5UhRTncVfc/kOz37vxYmIZ9fbCevP66+VzlPk3O6Yd5LMlya3H3TZ55y9Tud38lf1REu+e/mjakylh9yQ5L7sM3KliJTUjLfpWeMx86XN2lzpIzRtH6ZMl09fjF02z3sf2CY7z3/UkFjlXAp9zOtpHiZC82xu89u/NHhD5gLzbkOx6SZ7pWdidHk9yFbykIo4Bvse1q1rfDsuROqlzgAoOfGm7j40vhaU7m+61VcF73BsWmot/i80jRND58vuwcbMmPp/lQc9Hlo5V734hMvMPNAoB63Vjy4PhdoS1fnjpVnX2OGCZQfMTwHH4QjJtU4X1MiEujvsTBdK65zV+ffriMrWzc3KD8lZyLgfVy+84R8IYbVHQbvqt4Q8oU62V5R8qj3H7cQUsTHkOQMrZuyUVVa09PTsX37djRu3Bg2Nja4efOm2vRLliyBjY0Nfvjhhzzztm7ditq1a8PZ2Rnt27fHo0fqn+XTNX18ZeXdgwX84usenFt+3WVyT9H2omXhcf29m+9FVArijKRLkjZMZfChlMxspGTlKE3TdM/kN2/UiUg0zNeUaFJ8+qzoGtoPlC58mV+p4hykiXPyT2xijK1im58vf1cS0iWITTXMc3e6OEfjc7CJnKoLVNy7WZSBk+b6ByEwLEmjtMX9W5uu4pwlj4NaWvN6F5MGiY7y5NTTT5i4Vz/1hHRxjtJYCkX9jQ+KSDaaLvfaMqpK69KlS3Hnzh0sWLAA6enpkEpV30G5fv069u7dizJlykAsVi48Pz8/TJs2DQsXLsSjR49Qu3ZtdOzYEVFR3I2IpQvZUgZmAh5EguLrHqxOfud0U7woNjR6rdToad0FrffL+eqOI9n3QPWpLV2cg1arr2oTXrHi8htSbJUcFjq9mn0Spvodv6aKzqX69zgsEV4rL2ucvs7i87jzoXBdSKnXRdGxLIvK884ofVaHy+dSV559hd8uqB8TJLfaP53Pd3oOw0LA56l9TyvXTPXQlh9eUkaKB1EPEBgTCCnz/3pJ7sNL3c0UrntGqhuvwNBufBpVpXXVqlXYsWMHGjdurDZdfHw8RowYgX/++Qc2NjZ55q9cuRKjRo3C4MGDUb58eWzcuBGWlpbYvHmzvkIvsv+1d+bhUVXnH//OlskGJISEHRRQEWvFFZEiFqWtbRXBIvxaF6riUrRaxbWlgmuFilUfW2tFsIIsFhFBUUAWE9Gwb7IFCISEJJNtlsy+nN8fk5nMXWbmzs2dmTvh/TyPknPnbO8957z3rO+RoltDK0x6rUb2bKPXH+g0q3VExxCrBWqpGrEUqdcfgD7G9mCViABA+D7V1HF9eMlunG5W54o0nwn/VOaO347i8vphdqjHwIaaBraRdfv298rTl5EEMDs8OGORvl2UMcDtk7ayo6a2riSBAMPBM9Z0Z0MS6ezrHK614Wh9xw2G+QMMhvBihfpWWjvr4kXkhIfL58LV86/GjStvhMsnri8iJ1Pikepq+c6W6BacAXXpqowatEpl6tSpmDp1Kq655hrBb2azGT/88AOuv/768DOtVouxY8eirCw1l1BLKX85lcQXYT3YK3N78M9f/wZr9tXKCksQsfiqA5aJxT984vgDDHqtirRsXLh5VcugevXeM6gxSzPfz0dMf3HKUINOM/0eKcb8skpMTNcAWi0VRwJlUc6GiXG2TKJ2FjFNNjd++Wb70a303r/cSZRMDLz+AHRaDXRaDXyJLFZk4KvZfrIZK3ZWpzsbAjQaDbI1PdEjuw+nznWkTatpYM+XQ+hOnfJS5sSwivjHP/6BhoYGzJw5U/T30BbgkpISzvOSkpKY51rdbjdnm7HVGpxJDAQCCEQZIIaeV7c48J/SSsy++aLgc8YAhqjhxOINujUcN9+/2+uHQQtoNYDXFz1fsTjZZIfF4YmbNzE3a/s37G7rRUW6A4ybL8aYQM5YaYXYeqwRC0qP49939ZAklxhS0+b/yxjX/cX+WvTIN+Kqc7u3xw2W0DuUSiDAfaf7qi0AgB/369aediCxtKPLyU0rqKkYxx//Hcbi/g934sTLN4r+5o+XNhjAS4ufNmurWwHGwBgDY6F2w42L/y+fQ7VWdM02oG9hjiS5xIjaRnhyMd47DYrZ7v7Lpwfw5C8uQNdsg6jckWXAb3+JwkT0EhPRQ7GIpgv4dQciacXSpVKIFrYtaU7a/PYppm9jxR1ZfizC7fH54fT6o+Zlx6kWXNq/ALoOTKqEy56nhxja63wIJtC37e7z/vIlKl78hWjcgjRZYm1dPN/ydL1om4nICws+5IaPo9sTyXO88Py6w9e/svUthDouWQh1fXT9GkIgd5t/vlx83S6SuAJ1K4p+5aW9as8Z7D1txl9vGhaZfNw+iVjfi0moG7ER6qFocvHdfn+7XB6fH3qtBnot4GnTPfy+l1h5dlTfJkKkjgQg6AdKbSNfHqjFN0cbMeHSPsF4mXh4flzJqFuIcGfrstHbOR9PjhyAbF02R0dLkpOny8W+UQnnO06biv5NC/C+l2K6gOeOo/Pi5jUB/2kdtD7wwANYtGhRTD/79u3DoEGDJMW3d+9evPjiiygvL4deH1s0rVYrcMeaLXjllVcwe/ZswfOGhgZ4POLbwUwmEwBg10kLPvy+CtOvLgYAOBwO+P2+8O9imC0WmEztitJkakC2QRvh5oY1mUyob7TD7XLCZjbDamuNGX8sbDZbzLBiaQOA2WzhuJ0OB7y+djn9Ph+cDicnvNlshsnEBHFFc4fYV2nCpmNmmEwmQVlKxWq1cuKPlranbbtXWE4LV863Nx7F0JJcnJMXNJTg8/nhcDjCvz++6hgeGt0X53bPEcSdKC0tdk74uWuPQasB5t48BEBQj0aWX9kJM4b2zEOPvPZBTzQ5nW1W9kJuK09On58rVyAQgMViAWNMchlEk7ulmStXc3Mrx+3xeOFysbCbLycANDY2Iddvh9vtgclkgtvjhslkCm+VD/m1e7hy8nlkyUEM75uPGT8dIEkmKXK2y8WVs9Vuhz8QCLvdHg9crvbfP9p2Grdc2BUDCrPDcTU1NsHgyQLALQOXjytnorhcbni8Hk74FrMZJlP7GZ14cYd+b2nhll+r3Y5AhJxerxcuF1cXNDU1IS8g3+JhtLw5nU54vd7w7y6XGx4PV/+2tJhhym03dBJPD4Xlam0FYyysh0K2FqLl5bZ/78TKu3+E3l3lX4sQittisXLcbpeLI2cgEIDdbufq25YWmLK9AII7EqTqW3srt/zkENT10dOKFndogjisp3hyskAATpeL8y1obmmBydj+XZabbwtPB4r7scJk0oXdUr9pTWY3x22z2QDWXiYBv59TfnvPtKJ3lyyUdMmSJUss+HJ6fT44ndz2abFYYDK16/nGhkY4jVy5GWNodPg4cTU3OzluPgHG0Noqv78SGXeoDxetT7LjeB1KT5jxwIjgZLfP54PD6RCRM6Lv1dCAbL2w79Vo5ZZforjdHmgCWll9rdA2YJPJhCa7F16PG067BhZbUPc4HQ74IvpePr+w79XQ0AB3dvwhgC/AsLemFZf375KwjADg4X3TgOC3OtvXvjU6mpxuXt/LwZPL4eS6xWhsaIQrop4mSrh9Ruh6IFh+enDTDvWPQnqoqUmanCGjk+1yOePKFY+WlhaYcrxx5eK7W1vtCLB2Xe/xeuB2c/1b+X2vpkYYvEZBXFKx2WyS/aZ10PrGG2/g73//e0w/eXl5kuMrLS2F2WzGJZdcEn7mdDpx8OBBvPfee7BYLOEV1oaGBk7YhoYGweprJM888wwee+yxsNtqtaJ///4oLi5GQUGBaJhQfAVmDcedm9sMra417Pb6g6tBRkN7wyro1o2Tn+LiYuRktf8utlJc5WxBQdcASoqLYKhxx5QnFl26dIkZVixtQEzOJuh19rBbpz+CnNwcTvjCggKUlBTHjVuYR2f4d7mD1q5du3Lij5a22+fnuAuaGMet1x9DTk5OhPswcnNzw+5vK3di2pjzYsoplVqPmRPeaKyCVqNpj08DdO3aXn4z/rETr956MSadG19Oh8fHcXdr5MmpO4ycCLkCgQA0Gg2Ki4sll0E0ueu9Fs7vhS4Dx52VdRLG7KywW6MB8vPzOfEVFRWhpEceso2nUVJSAmNW8N+QRcWQ35Dl2Gh50euPIjcnV3YZicUdcvPLr0u+FVqNNkLOSuTkZIvKxXG3rQKv2XcGV/YsQHFxMRxerpyJkp19Blkebvhg+ywSlev2+duw6J6rROUs4JVfXp4VWm27nAbDCWRnc3VBjx5FKCmSru/5RJM7O6cOWVn+8O/Z2TVwMS1XzsIClJS075SIp4dC7vx8GzRt7U+r1SIvzwKdriVmGfQo6hEuPzmE22eNl+POzq6DwcHa24hWi7y8PJ6chSgpKYwrFwCMenUTvn3qpwCAvHwbp/zk0K2gQJK+ZYzBZHOjZ9fgRE3X0x6enLXIcre7tVotcrKzOd+CoJwFUdOSnOfm+OG7deN+RwokftMcOjvH3aWLC4jQ5VqdDvkR5ffMu/tw90/OxR+u6yeaD3+bQR458HW9QV/B+aYF5eT2SXoU90CXiB0gBQVBPQQbt7zMzMZx89Fptcjj6fJEiaw7kW6xvpcuou8lpuv5cpYUFyPbIOx7eQ1OjjtRjMZTMBp0svpaoYn0kpIS+C1OdMlrQWG3fGRZXSgpKUFObhP0ekfEt1vY9youLka3nGD5XTt3MzY+dq2o1f3TzQ5MX7Er6i6peGRlVYbbZ4iiou4cXR+17+Xl9r1yeXLl5DRAr3fF1rfFPTg7lRKl/VttD+t6AMgynoLRqOekHer3hfRQUVERSrrnxpXT6OTq8tzcRuh1jg61Cb6ujyYX352fb+P2SQyVyM42cvxH9jEB4TdNar5bHB44PH4UF2fH99xGWgetRqMRRqNyF/Hef//9mDp1KufZNddcg9GjR+PVV1+FTqdDjx49MGTIEHzzzTeYMGFC2N+WLVtw2223JZxXrVYbtcMeeq5p+zfs1gR3q4fcb6w7igNnrPjv3e0dQI2GGy8/HbGVYl+AIUuvQ06WHl6/9NUvPhqtJmZYsbSD4YRyQtMel1ajgUbDjVsjQS7RPLadG4j1/uPBz0u0tLVaxpOLL6cwLg0Sk1MqWn7abYcDOfHx8yJZTpHyi/Sv0UAbJW6p8kTzp4uSh5jvWOSdarVaaDRo+7ctbyy2nGKE4pBL1HcsqDsaQVoaXnj++41sn39cuhffP3p5mx/xtKUSbK+I+45DbD3eFF0XiMoZWX68ugWhzkuUqLqCl1ZQ/wrbRCJ6SKyNhOpcrLwA8fVrPNr1Lb99Cuut4J2KfFfE4gaAWouLU2/57S/hfEvUQ9UtDvzk1U04+bdftcnAKz+x8Dw9FE/nSc4z/x2L+uG1T6n6ltdGRNOKjEvk+xnJoGc/D78zIGj45fjLv4wpX2SeOWmL6ds4fRL+tyDavx5fAK1uH7rnBVeMGYR1I1FCYUOD1mh9km0nW1DZaOflm99mpOkGrS7+dyQ2Qj0UTS5B2hG6PsCCRpgMOi38rC2//O+KSHlqI8qzusUJFqUMxL6Xt79XjkX3jpAkJYMwbZ1WJ6mNaPh9LzFdHhG32+eHTqPhDL4T6Z+IEfkO+XkNvWOXz4V6w4t453AO7hyzBrn64EBVK1HfivUL+N/ihPMdp03F/HZr+HLz6w7PrZPXv33z62Mor2zG8ruHS/IPoHMZYjIYDMjPz+f8p9Vqw89DPProo5g/fz62bNkCl8uFF154ASaTCQ888EDqMhsxIWpxetFs7/idSj4/g0GnhVGvC68Oqh2lDnA7PX58tveMInF1NlrsylkzTaS4lLwnLJ4hgCihFEn77U3HMOuz5NwZLMd6cCYYF1Gb8RwW5e90Y3Z4wucmlUZttURqtRW2CWFANZWhWunIfaVKwi/PFbuq8dO/b05JWnw9JMWyd6qujQkO5joeT8havkGngdcXOpeYXMNeiRhSSzaRZTz1/e345+bYlnDlxBvdT/Bff8APh+577G3exLnyRi3XxUTeAZtqNh2Wv8VZjIwatC5evBj5+fm44IILAAA33ngj8vPz8fLLLycUz/Tp0zFjxgxMmDABeXl5WLx4MT777DMMHjw4GdmOy+LyKhyosXKeyVFm3jbrwUa9Fm6vtIPNP5yxJNVMeqQYyVSixxta8cclu5OXQAzqLC5UmDpuul4KUl4h3+pciyP6uYZkcvmLGxSNj98kpDQRJQZPx0ytOFhrje9RNuoZXihlsXD5jtOKxKMEkiYCkp8NUa58aQPWHayXFVZN1iWTCb8NH66zodGm3IRYIjyydDfe+rpCkbgyYO4pKXj9ATg96p1Ut7nS872Ui9sXgFGvC97TqgLrwVVNjqRelcY3Qh+JyeaC1Zma8oucTMvSZaG75yF09zyELF3Hz5yrbM43rq6K1c/6/cLtiuYlowatkydPRl1dHerr62Gz2dDQ0IC6ujrMmDEjapjvvvsOc+bMETz/61//iubmZrhcLhw+fBjjxo1LZtY5SOtsJx5vaMYtS6+VfFfcr94sC1ugFWPZ9qrEM5ICNBr1NGyTzY1tlc3pzgYAZd+JWFRKdrR2VbUk5J/zsRLJB/+RXqtN7AoAFSBWfmpbtZRC6NxwJEpdeePzB/D6+qNRf//hjAVP/m+vvMhTjNfPlJs05FUTk82tKv2tVDU+XGfDiUb5Brtisf5gPZ5esS/q75WNdsG9rYrq3BS1dZ8/gJNJeod81Dw4T+WulSc+3osNvAmqyOIe+crXMFml3QkcuYLn8vqRrdcGr7xRwT2tz312AC99fijd2RBl+8lmuLzyJkxiTRIadAZ08f8CXfy/gEFnkBSmI+mdzWTUoFWv1wu2/+bn5yMrK/rMRk5OTszfDQb5B7Qj8UgcJCYTr58hK7TSqtD24KdW7FcknmhkwlZHNcHv1CR7YClMX7m4Yt1nyU9Gikz8MHqdBj5//A06JxvtmPvV4fgJKABfDqfXj0aFtlGLvaKZnx5QJO6OomSVdHj9eCPGatcxUyuW70jfXX6RbSR4zUtyBiJiNZvf0eEPsJTC5vJiy9GG+B5ThGAXhkwleKTOig2H5K18R82MRFI5N1Ve2YzrOrhF94w5OXWrtKIBZRXq2X6qFOsP1XN2ZPGrSa3FBZvIZF88XN4Asg06GHQJ3tOawvqmlP7vaN9m0jvfxVykyVR2nmpO6eR2OufRM2rQqmbO/8tayX5Z+H/K4vEFtwfrdVrZ51mUHETOL6tUdNvsi2sOhv/OwMUnWdjdPpxoSM3WYzUjpbwDPE9ZOi08EmaeD9Va8fYm7jmYVE2m/HAm/rbjnacSW5GO5MPvT0X9zecPJG1AdTYRq6Y8tnwP5vFWhZOpu5J1hip4B2x73DtOteCu97dF9f/K2kMp3QKaibVYrB7w9Q73eA03QDKP9UjhmMRve6L1feG3J/Hf704mniER+O8zE1fF4+Hy+WE0aKHXaqIuniSa1dmrk2PDoaPIeeXJ/JKHqleABeDRnIJHcyp852pHSUSX3/qv78JXFnZ2aNDaiXD5/BF3uQZbU7Pdg4dTdNYznnI41WyX1EmPxntlldz0MmiRNtYHbuxrm6P+9snuGvzs9W+i/p7qV5DOdx6ZtNfPUGN2cn7/8kAdgPYPW5Zem5SO3c5TLZwtRokYORAYCpEQ5mRTcrbx3fvfHfjXFu5gPWWGIxhSNtJIZ9/ydIsTdRK3/InxbSIGTyQZ8ZKdlYT495YTaJFg9CYd3P/hjuQmILO+JVo25/15LY6ZpN9v+O8tHTNQI9zlk56GlczzkpmIy+NHjiF4pjW00mqyCe1sJLLddMG3JyX7La1oiH0cI6mTdMrRYHPjR899JSus0+tEbfZ01GZPh9PrjB8gDnxdsPe0OWX9+GTT0clMGrSmAbvbF3dfvZyDz6FtIm0+AASV1+oIq7rHTK04Uhf9Q5fM2UOXN5Cys5+tbh/nXJ3D41ONNUU+JxqkD0rSLUFk9Xhr4zG8vKF9Je/NrytQ34EOeqLwlV/IHWo7Rr1W9vmVWNz6r62yVz9X7eFauOa3t1ROCtS0ONHUmpyBhZzzOJHv4i+f7sfBDkxwCfMTJ23FUuoYkZYWGWP43XvlacyNsiSrbjfZPahskq53vvqBu/23o0cDfIHUrHiK7f5weqSn/cradjnlTOY1KWiFPpJEq8XoOZtkp8UfaCdiIVYKYxPccq1EmwguVuig12nC5Sr2fYo3yVDTIm+wdaTOhs/31Ybdm440oLQiOUcH9lVbOCv8UsomlpfIPqHD4xO1xdAeDzcmm8vLsTStZV2hZV1jhpFLZaOd049XkqqmxCeB1rYtECSKxeHFhX/9UlbYEDRoTQOLy6s6fNZI7OyCyxu50hqEb0X4718dwctfqPOQvJI8+8l+zFjePvs37K9f4aPy6FsllaTW4uqQoRClt2wma0a8qtmBU83t9Xje+qM4Wi995j+SL/bXctxi3/J4UgSNc7X7yjboJBskE6QlZ0lUAkpcbSXXgMoZsxMfKrTtLlECDAmd3V30fRUqElhFSgSlx06RVcPp9Se8shrZcY20tGhJ0Armt8caUdmYnlWoeeuOYMm22EafonUwLQpYN193RP5kKP9oQKI0J2kwB3DrVrPdg7lfHVEk3g+2nlQkHrXBr2JyJlH5A8ntJ6XXrcjvfvmJpphXpSn1VQ4uVgTvZg3HLbg2SiQg79lpmYNWMexJOhrQwPuGfHu8EYd5izCJvNfBz34R9bd4A+LdVWasbzOslZeVh/6uj9Df9RHysvISyIE8Nh024XCdvEld/g41qwxr2XLtGSgxwUeD1k6E2+uHUa/jPFu5uyZuOKVmwJMxNIq2OsrPM9/t8voFxqg6cjYwERwePzby7qZK5IzkoA4oUiWJl+MVu2qw94wy521Nca6wEHt9gu1qLLhtWN92sXXo6qeOvrNU7oiWkte91WZZce+uMmPmqvScVSqvbIrrJ1Yb6ciWzo93VmNXlTnsLjvWiErewF+pMt55Mr6OkXtfqSAeXq7rrW7FjHolSumxRuw9bZYVNlUrlaki2mpNOndV8HGo+MoZPq+vPyp7YmNxOXciRcoOEL4fuUdMfjhjxbLtyb/6yxXR7+vIty6pZ3STVNfNvHrRWQx7xiuKF9YcxCe74vftxUjG7rNUQoNWlSJHf7h8kduDg/CN0yhJuo24qMQOQloRtR4sNWyCLzCdnwOpWY0812006OBSwIp2uquZYNY8A03hC/RQglfe8Ld0dgSzwyuYbT5bkKszNdAkrUOYSR1NKTntyDc3VcZ9lEhFKT0ULy9vfF2B0y3qO8eajHortfgj/Tm9fuRk6aJ7jhpJ4kFkI2XlVyxYgnlUi3EsJUjWdz7TX5E+3Rk4G1Ci6knZ4im2PVhS3DIrsV9FtV9FWUk5qerzpfIVf33IJHjGF1MsP8E2EPyAh1Za5SDokCj0jgXWQWUUXib08ZXYkh6t8x9P/EwaBAHydVe6DOF0fqLXHyXf+Nn8zYpFMl+LtKvTMqtgXN4AsvXBfl9IPjWpQLH3KX/yTBk/SuPyudBgmNv290+Rm5Xb4TgzrR6mChq0diKcHr9gpTWVKKEsfP4A9DraABCdxBWZ3Bm7VKpMfg7lnI1lYHBHGCMzGnSK3VecLNQ2M5yyVVwJ1oOb7eJbAuO9sVS+01a3Dza3uuuY2lFR/7qNJF0bJCHaWFfeEMojNsElV33YZJwNVAJ3xEStyj4n7ajk1oFYJHocIxJ/wA+Hfkv4bylhYqYlI9jZsjJLg9YUIKfMhQs98e8bE9serFR+koWU1TM5qEnGZJMqpZIJHSgnb6XVJXOlNV2Int3lubUKFkSyZuQzcQuzHGJZYo+G2joBUuDf0yqHaHUtrqX8DqXauZHbflN6Tj+NJSjoXyQxK4kafRK1jSSjYMI77GKMmcXkbnF40C3XkHiCaYQvhlomfbN0WSj0TAv/HSKVdT8TVmaV2AVFg9ZOhMvrD28TiYZYnYl8lklb6+JlVU2yxFOujDF5W0XlZkgG6VaJca0HQ8PZIp/dttKqJmUevx7Ej0PJAaFKvvmiqKf1Jhe5gzkl00oFaq5raoD/fhpsblTGuaNZ+hnIOHpHWjS8MMkp0LOl3StFaKI2EGehl//d2HPajHN6JN/SrZLIGaSmQu0YdAZ09Y8P/6007wjuU08dKupGAyBDTJ0Kxlh4a61Oq4FPxOqd2joO8QciRKLwlYyaBm1SkXT2SGy3QeT24A6caRUmpkw0UuBPXijVZtVeDwTWoNOUj1ikcmZfbbo6FowJ8ys1+5m+Kq9kOfHfhFwLofHgZ/n9ssqkpCMFSecUJRvuia1DpOjATLMREfnN60jeUznJL7k8eW9JSh6TtVsvnd9PMcOBqSottX2HaNCqUuLur49TY4366HdUZnYXoZ3Id5QkuzmdmkSVUTLfqZSPmJT0I3cbGPVaRc60proupWpgVGFqxfvfpq+zqibUNpssBbUP9jYdFhpTk4O6pZTeSeb7U1Od499/qTZSe9Vb+gpG1q0RvB12cr8fyZI6pWWXpkYVYAH4NPXwaeoRYB2fKI9/vrZDsSccIl56qSxj2h6cISQ6y5Ol10YdtKqJdH63+Xc1qhLG4Iu4WkhsNYP/DpN2ID8pscqH3yYYGMd6cHB7cOJtIKnX1SnwUU3ldlLZxDmTL+XKm2idr85mPVgual85d3jE7ysVkIxt0EkKrWRW07qCoeKqo+KscVDLeUp/oH2HXZZOer8vlfpDik0WafGoE6fXiZrse9r+noQuui5pzpGyqKOmB6FBawpIRkOL1+hjrTLFCpqIIhYMlhQQNJXfAbmXhqeSY6bWuH46xXZghVqJyxcI31kXNMSU+ZZdBbsIkjgoi1d35J69FklIoIi8/vTVW5X0PzMGlyd17YqKhkgWahl4KkW2QQe3NyD6NY2r25OTJUWRZdRUZlqtLomTbqF0mFHwTPbgXEXWg9U2B0yDVpXS0Ypi1Isrr2RWwGTof7lRZoICloI3wF9NTB/J1F1SBtpSZHd5/eiaHTSEENoi38n6JVHLgS9nqzuxj24ykVK+n+87gwt6XRB2ny0rpsncjphvTM4n/t/fnFAsLuGkTMfjTOd1XdGQYgwpUdnPkibSaVGi/CLjMBq0cIksVoga4BSsfmbehzKZOT7d4pDsNy8rDwNcK8J/J5tUlpTaqgUNWpOAGhp/bpYOjiTPhqd1AJUkYzViKBF3So0cKNQJTtZ1RGIItj1LSEzMj9vrR3aX4IxntkHeFnnhynVqiVe3pVYlP2/lMl69WLXnjLSIpZB+FahapO6M6KjKuLB3V1x5TiE3Ldnb8jSceunnTaZpNPHzG20SJZXXksSDMaAxxvlOsaxl5MBRicGSQt8ZKcWt1DuWkmepO5XUOKmWKbuLpBvWSixeKX1vX0D9O+y8/gBONUkfNAOA2elBTlZOXH9y9Gv8IzmJxykXMsSUBAL8Dnga8pBn1MPh8UlKm98ByQTUMDHQEQIKvvOOXIodM15ZoXhxKFhOcRUnNNx7Wg06RT7gZodHEYNOSqG+rlJyiLqiHCec2nVDKrOnVFr1Vhea7Z4OxZEJ35ntp1rSmr7ajWvJIdE6uPGwCesO1icnMzzEBp7Jap9mhxer9nCtQSuRVmQc2Qad6N3kKleJkpGi26X0E+QgJ5zcwdzmIw0JT7jvq7bIS0wCaqo+tNKaISTaYPKMOthFVlrF2vySbVW48pzuAIID7npLYpdkK0kijTxZszvJ6PTGNcXP5F3zIuUVKHXGVW3dKTGpIs3/Z8u88uZovQ06bbu0m440xM+LxLOe6RxQZdpZ58zKrfJ0tKqI1Ua5OvOUhPtC5Z/fUo9m8fNWYaSsdijWpOXcQSkxSHKO7igTabPdg91VZkXiijuhleYrbw7X2cJ/+/wBMKZTNO3sGLZM4vUhpbbDRCefUrlDSyy9Y6ZWXDqgUOgxwbhj1R23z40mw5ttf4+VtOKZbDrr95MGrSolXoPh65fgyl37w9wsPRxun3jHhefmr0aZnXFuqVYJke/os73crY3q6QYlFzHrwVKFT1SpJVMJKnGurd16cNuVNwYdnDJWWgMM+OGMNfEMROalAwaL1L5SmCqkvgclVtNTOW46Wm9D/+65YbctQYMfZxvp1OXLd1TH9SO1taqqVasqM8CGQ7FXVjvjlTdbjzcJnsmZhOCeaRVfaRWNg9+nlJY0Kky2+J54CI5EKDTBxf9GiH1zT7c4ZQ1aE8EX8KFVvy78d0dJ5pU3yTBmJbc8GZDwijJtD04BqbAe7PYFYDS0F2delg6tbp/49heF8pBO68E2F3frs5U30JZ0TiYDh7ZiHXkVLVTIRtpZozi/s+BKa07bSmuOzEFrMpEzkJV6wbqq6kG8K29EkKqX+DE5eTtK1LRyJ0bkaguQPCvmKhuXyCaVFkPTFW9nRuXNsVORnSFnWlOJZENpHUjDoDOgwHsHCrx3wKAztMeZJCUserY+OUkllY2HTQlfPZmRg9ba2lqUlZXBYom+h9vv9+PAgQM4cSK6lcPTp09jx44dsFo7vqqiNIkq+uAF0+1bTXKNejg8fpGZKJG0MrC6SzlflXlSqRu1v0+NBnD52s+06rSajDzLIxh08WTQqr0goExdUfvgUymiSam0ZdGOoMz5dmn+zpJiV4RMeFfKGDLseBydET9vh112AnYc0tnvk2yIqYO/i4dRvlOQpctCN99kdPNNRpYuS/H4RZFZfHLaY7IWuuwybjjIqEHrjh078Jvf/AaXXHIJRo8ejd27d4v6++STT9CvXz+MHz8e48ePx7hx49DY2Bj+3eVy4dZbb8UFF1yAO+64A7169cJbb72lWD7ToQqcEdsiASDfqINd6uXuaYQ+RplDusd/UqyM8idv0p/rJECNhiCSQjI78pI6i9S0iQRwef3he8mB4JU3cizmZyyd8PMO0BGhWGTUoPXAgQOYPHkyysvLo/r55ptvMGnSJLz00ks4fvw49u/fjz//+c+or28/MzF79mxs27YNx48fx6FDh/DRRx/hj3/8Y8x41Y7d7ePcyRc80+qXtlpBH8qMgVRZbNxe7jb5zkhnba6dVS6loI5M56azli5V2+QRnKRt/94Z9cEjMcncpZLoxE4y9VYqr0uKBWMMfljgh0UReZNqvEpG7Gr6NmeUIaapU6cCAKqroxtHmDVrFm644Qbcfffd4WfXXXcdx8+CBQvw4IMPonfv3gCAW265BT/60Y+wYMECjBgxQvF8p4JWtw95EYPWvCx9Rqy0Espz9pznEipfty8Aoz5y0Kq+XHcUWmgliM5JZ2zamWa1PJNw+dqt5QNBWyZNHbyaKhkkq16rZSLP4XWgOud3bX/fhC66LmnOUeclowat8XC73SgrK8O8efNgs9lw9OhR9OnTJzw4BYAzZ86gvr4el19+OSfsVVddFXW7cShut7v90vHQeVqz2QzGGAJuB8xmM4CgJd9Id6vNwv3d7QiHjea2WS0wm41hd0uLGZ62QWnA7UCLuQXGtm2QAbcD9Y3N0Hjt4Ti8Tjeams3Iy9Jx0nY7bHB7Apy03HYbx+1s5bpDf/PdPn+A47ZZrTLlNESVq6WlBXqdVpC2y2ELu7VaLdyOVs7vHocNvgDjpOXiy2m3isrJL0+3169AeVphNmdFlavFbA5fs8JJ227jpm2xCMpTp9Vw0mq1ceWKVp5+Xj11eHw8Obnl6XPZ4bQbReXUarXi5dlihlZELoGcDq6cHie3PL3OVri0nphyOVptcDtaOWfd3Y6gH45c7thySilPq9UCszmiPfLkjCY3X06v0y5wOw0+rly8euvgla/VakVWVhZcPm57tDi9HLddTvu0WGA2R7ZHaeXpcfDLzy4oX2cWT05eedptFtF6a3Nx5bI4PDw5E2+fFosVZrNQz/DdfN0QWU+1Wq2wPB2tcNm5+tbOk5PfXkN/m3ly8dOOV54+VyucrXpuXbFYYDbHLz++W9ge+fW2FU47953aeXK12iwwm/MFcfPbo9Xi5MqZQPsM6SGrxQxzl/hy8d1enpw+F++74rTDZdfGrKdBHZgnfIf89mm1C+T0JVxvzTDnsbhy8fskfDn9LhE9ZNfHlLPVaoXZnBt2h/SQxcattzaLVSCn321PSE6rxQxzbnw5vbw+iU9UThe3jfDktNksMLdVnvbyNArSspi59dRq4ablaLXCJ0fOLF/Y3dLSEl41jYzb6Qn2SUyNTWCe4HOr1YqAS4+GJgt8AjmF3+5WXj3lf7vDclocPDltCckZrEvcvpjZYkYXrSduefJ1g49XTwMeOwLudv3qc7bCYc+OWU+DfTG9IC2LhRu3lfcdiSWn3WMH2m6KNJvN8Gf7w3Ka9d64cvK/1UE53RFp2RFw+znt19may6u3VpjN2WG31WyB2DeNX342MTmdkWMJO1x6b1w9JKU8+d/qgMuOgLu9HyDpHl6WxqmKiooKzrZdMa644gpkZ2dznlVXV6N///7YtGkTZxW1pqYG/fr1w7Rp07BmzRr06tULx44dw8iRI/HRRx+hqKgIBw4cwMUXX4ytW7di5MiR4bBPPvkkVq5ciYqKCtF8zJo1C7Nnz5YvLEEQBEEQBEEQBMHh9OnT6NevX0w/aV1p/fjjj/HFF1/E9LNs2TL07dtXUnwGQ3CWaP369dizZw9KSkrQ1NSEkSNHYsaMGViwYEHYj8vl4oR1Op3Iyopu9euZZ57BY489FnabzWYMHDgQVVVV6Natm6T8EcphtVrRv39/nD59Gl27dk13ds5KqAzSD5VBeqH3n36oDNIPlUF6ofeffqgM5MMYg81mQ58+feL6Teug9dlnn8Wzzz6rWHw9evRAXl4eJk6ciJKSEgBAUVERJk2ahGXLlgEA+vfvD61Wi5qaGk7YmpoaDBgwIGrcRqMRRqNR8Lxbt25UQdNI165d6f2nGSqD9ENlkF7o/acfKoP0Q2WQXuj9px8qA3lIXfzrVGY2tVotxo0bJxiQVldXo7i4GACQm5uLa665Bp999ln4d7vdjg0bNmDcuHEpzS9BEARBEARBEAQRm4wyxGQymXD06FE0NDQAAPbv3w+9Xo8BAwaEV0mff/55jBo1Cs899xxGjRqF8vJyfPTRR1i+fHk4nhdffBHjxo3DM888g5EjR+Ktt95CSUkJ7rvvvrTIRRAEQRAEQRAEQYiTUSutO3fuxNNPP43XXnsNo0aNwrJly/D0009jw4YNYT8hI0vV1dWYM2cOKisrsWXLFkyYMCHsZ8yYMdi0aRNOnTqFN954AxdddBHKysqQn58vOS9GoxHPPfec6JZhIvnQ+08/VAbph8ogvdD7Tz9UBumHyiC90PtPP1QGqSGt1oMJgiAIgiAIgiAIIhYZtdJKEARBEARBEARBnF3QoJUgCIIgCIIgCIJQLTRoJQiCIAiCIAiCIFRLRlkPTiWMMRw8eBA+nw8XXXQR9Pr4r0pOGCI6dXV1qKurw6BBg+Lee2Wz2bB3717B8+HDhydkYIsIUllZKbg6KicnB5dffnncsNXV1airq8OQIUNQUFCQpBx2biwWC/bv3y/620UXXYTCwkLR37Zt2waPx8N5FmldnYjP8ePHUVtbixEjRsBgMIj6qaiogM1mw7Bhw5CdnS0pXjlhzkYCgQB27tyJ7OxsXHzxxaJ+mpubcerUKQwcOBDdu3ePGZ/X60V5ebng+YUXXoiioiJF8tzZMJvN+OGHHzB48GD06tWL89uZM2dw4sQJzjONRoNRo0bFjddkMoXLraSkRNE8dzaOHTuGuro6jBw5EjqdLvzc5XJhx44domEGDx6M3r17i/62b98+WK1WzrOSkhKcf/75ymW6E9HU1ISqqiqcc845Ub+3LpcLBw8eRJcuXXDeeedJildOGCICRgg4cuQIGzp0KCspKWH9+/dnffv2ZVu3blU8DCHO5s2b2YgRI1jPnj3ZJZdcwnJyctjDDz/M/H5/1DDfffcdA8BGjhzJRo0aFf7v8OHDKcx55+GRRx5hhYWFnHc5efLkmGHcbjebPHkyy8nJYRdeeCHLzs5mc+fOTVGOOxfbt2/nvPtRo0axQYMGMQCsvLw8ariePXuy888/nxPu3XffTWHOM5e1a9eysWPHsu7duzMArLa2VuCnvr6eXX311aygoIANGTKEFRYWslWrVsWMV06YsxGPx8NeeeUVNmjQINatWzc2ZswYgZ89e/awn/3sZ6x79+5s+PDhLDc3l02ZMoXZ7fao8dbW1jIA7NJLL+W0i02bNiVPmAzl+PHj7J577mG9e/dmWq2W/etf/xL4ef3111lubi7nXYqVFZ9HH32UGY1GNmzYMGY0Gtmjjz6aBAkyn9WrV7MxY8awwsJCBoC1tLRwfq+urhZ8G4YNG8YAsGXLlkWNd8SIEWzAgAGccM8//3ySpck8duzYwcaOHct69OgR1jG33347c7lcHH+rVq1ihYWFbMiQIaygoIBdffXVrL6+PmbccsIQXGjQKsKll17KbrrpJubz+RhjjN1///2sT58+zOl0KhqGEOe9995j27ZtC7v37dvH8vPz2RtvvBE1TGjQarPZUpHFTs8jjzzCfvWrXyUUZtasWaxXr16sqqqKMcbYF198wTQaDduyZUsysnjWcdttt7ELL7wwpp+ePXuyDz/8MEU56lzMnTuXrV+/nq1bty7qoPWWW25hV1xxRXiQ9Morr7Dc3FxRvx0JczZiNpvZU089FR44iQ2EPv74Y7Zu3bqw+/Tp06xv377sT3/6U9R4Q4PW/fv3JyPbnYovvviCvfvuu6y1tZXl5eVFHbRedNFFCcW7cOFClpuby/bs2cMYY2zXrl0sJyeHffDBB4rkuzPxt7/9jW3cuJGtXr1adNAqxpNPPskKCwtj9jdHjBjBXnjhBQVz2jlZsmQJ27hxY9hdWVnJevbsyZ555pnwszNnzrDc3Fw2Z84cxhhjdrudXXbZZWzChAlR45UThhBCg1Yeu3btYgDY999/H352+vRpptFo2MqVKxULQyTGL37xCzZp0qSov4cGrbt27WK7d+9mra2tKcxd5+ORRx5h48aNYzt37mQVFRXhyZhYDBgwgD399NOcZ1dccQW76667kpTLs4fGxkZmNBrZvHnzYvrr2bMnmzdvHtu+fTvN4Mpk/fr1ooPWhoYGptVq2dKlS8PPnE4n69KlC3v99ddF45IThmBRB61iPPDAA+zKK6+M+nto0Pr555+znTt3MrPZrFAuOzexBq1Dhw5le/fuZQcPHmQejyduXNdeey2bMmUK59lvfvMbyWV8NiJ10Or1elmvXr3YH//4x5j+RowYwWbMmMG2bdvGqqurFcxp52fq1Kls9OjRYfe8efNY165dmdvtDj9btGgR0+l0rLGxUTQOOWEIIWSIicfu3bsBAJdddln4Wb9+/dC7d+/wb0qEIaTjdruxf/9+DBkyJK7f8ePHY8qUKejevTsee+wx+P3+FOSwc7Jx40bcdddd+MlPfoL+/ftj1apVUf02NzejqqpKcOb1qquuojagAIsWLQJjDHfeeWdcv7NmzcK9996Lc889F2PHjsWpU6dSkMPOz759+xAIBDh1PHTuMlodlxOGkA5jDDt37pT0bbj77rtx5513ori4GHfddRdaW1tTkMPOyZEjRzBlyhT8/Oc/R3FxMf7zn//E9L979276NiSJzz//HHV1dbj33nvj+v3nP/+JadOmYdiwYbjsssuwb9++FOQwswkEAti1axdHx+zevRsXX3wxsrKyws+uuuoq+P3+qO9UThhCCA1aeTQ3N6Nr164CAxxFRUVobm5WLAwhnccffxxOpxPTp0+P6qd79+7YuHEjqqqqcPjwYWzZsgXvvvsu5syZk8Kcdh7Gjh2L6upq7N+/H2fOnME999yDyZMn49ChQ6L+Q/Wcb9iE2oAyzJ8/HxMnToxrOObll19GU1MT9uzZg5MnT8LhcGDKlClgjKUop50XOXWc2kVyee2117B37148+eSTUf0YjUYsX74cdXV1OHDgAPbv348NGzbg8ccfT2FOOw8//vGPcfToURw8eBBVVVWYO3cu7r//fmzcuFHUv8/ng81mE20DVquVJpY7yPz583H11VdHNVoWYvr06WhsbMSePXtQU1OD/v3745ZbboHD4UhRTjOTl19+GRUVFZgxY0b4WXNzs2h9Dv0mhpwwhBAatPIwGAxwuVyC506nkzND0tEwhDReeuklLFiwAJ988gn69u0b1d/555+Pn/70p2H31VdfjalTp2Lp0qWpyGan4+abbw5bjdRqtZg9eza6du2KTz/9VNR/aMKG3w6oDXSc7du3Y//+/Zg2bVpcv3fffXfYanlxcTFefPFFfP/99zh58mSSc9n5kVPHqV0kjw8//BDPPvssPvjgAwwfPjyqv8LCQkyaNCnsvuCCC/CnP/0Jy5YtS0EuOx9jx47lrDpNmzYNl112WdT3qdPpoNVqRduAVqvlWMYlEqO2thZr166V9G244447kJOTAwDIz8/Ha6+9hsrKSlHL2kSQ9957D88//zwWL16MYcOGhZ+L9fmdTicAJDROiBeGEEKDVh4DBw6Ex+NBY2Nj+Jnf70d9fX3UayPkhCHi87e//Q0vvfQSVq9ejTFjxiQcvmfPnoJrWwh5aLVaFBcXR32fffr0gcFgEPxeU1NDbaCDzJ8/H4MHD+ZMykilZ8+eAEDtQAEGDhwIQPguY9VxOWGI+CxevBj33HMPFixYgClTpiQcvmfPnrBYLLRFWCFifWs1Gg369+9PbSAJfPDBB8jJycHkyZMTDkvfhti8//77mD59OhYvXowJEyZwfhs4cKBofQYQ81uQaBhCCA1aeVx77bXIysrCZ599Fn62ceNG2Gw2jBs3Lvxs+/bt4dULqWEI6cyZMwfPP/88Vq9ejbFjxwp+t1qtKCsrC3c67Ha7wM/69evxox/9KOl57Yzw3+fJkydRUVHBeZ8nTpzAzp07AQRnEceMGcNpAy6XC1999RW1gQ7gcDiwZMkS3HvvvdBoNILfy8vLUVVVBUC8Daxbtw56vR5Dhw5Nel47OxdffDF69uzJqeNHjx7FoUOHOHV8//79OHz4cEJhCOksWbIEd999N95//3387ne/E/zu8XhQVlaGpqYmANHbxcCBA+kObxnw36fFYsG2bds434aamhp8++23Yfe4ceOwZs2a8DEFxhhWr15NbaCDvP/++/jtb3+LvLw8wW979+7F0aNHAQRX9AKBAOf3devWAQD1kURYuHAhHnzwQSxatIizSyPEuHHj8MMPP+D48ePhZ6tWrUKvXr3C27RdLhfKysrQ0tIiOQwhgbSagVIpM2fOZAUFBew///kPW7x4MevXrx+78847OX769u3LHn/88YTCENJ4++23GQA2e/ZsVlpaGv5v3759YT+lpaUMANu+fTtjjLE//OEPbPr06WzFihVs5cqVbOLEiSwnJ4eVlZWlS4yMZujQoWzu3Lls7dq1bP78+WzIkCFs+PDhnPsQp0+fzgYPHhx2f/fddywrK4s9/vjjbNWqVezGG29kAwYMkGSynxBn4cKFTK/XR70epaioiP35z39mjDG2Zs0adsMNN7D333+fffnll2zmzJksOzubzZw5M5VZzlhOnjzJSktL2euvv84AsFWrVrHS0lLW0NAQ9jN//nyWlZXF5s2bx/73v/+xiy++mI0ZM4YFAoGwn1GjRrFbb701oTBEkO3bt7PS0lL261//mg0fPjys+0OsWrWK6XQ69uCDD3K+DZFXpJ0+fZoBYB9//DFjjLGXX36Z3X777WzJkiVszZo1bNq0aUyv18e80/JsxWq1ht9pTk4OmzFjBistLeXcd37ttdey2bNns88//5wtXryYXXbZZWzAgAEcHTV37lym0+nC7uPHj7OCggJ25513ss8++4zdcccdrKCggB0/fjyl8mUCJ06cYKWlpezVV19lANjatWtZaWkpa2pq4vjbvHkzA8B27NghGs/ll1/Ofve73zHGGNu7dy+76qqr2DvvvMO++uorNmfOHFZQUMDuuOOOpMuTaSxfvpxptVr2yCOPcHRM5HsOBAJszJgx7JJLLmH/+9//2GuvvcYMBgObP39+2E9FRQUDwFavXi05DBEfDWNkoYMPYwwLFy7EihUr4PP58POf/xwPPfQQx9DSxIkTcf3114eNA0kJQ0jjqaee4szShvjxj3+Mf/7znwCCqxkPPvgg5s+fjwsuuAB+vx8ffPABvvzySzidTlx44YV4+OGH0b9//1Rnv1NgMpnw1ltvYefOnejWrRtGjRqF++67j3P24vXXX0d5eTnn3HB5eTneeust1NbW4qKLLsJTTz0V8ywyEZsnnngCfr8f8+bNE/3917/+NcaPHx8+07R161YsWLAAVVVVGDBgAH7729/K2lZ8NvLOO+9g0aJFguezZ8/G9ddfH3Z/+umn+O9//wubzYZRo0bhiSee4Kx0/OEPf0BRURFeeOEFyWGIIJMmTUJtba3geVlZGYCgzlmxYoXg95KSEnzyyScAgIaGBkyYMAEvvvgirrvuOgDAihUrsHLlSjQ1NeG8887DAw88wDmjRgQ5ePAg7rvvPsHzG264AbNmzQIQ3OX09ttvY+vWrTAajbj88svx0EMPoUuXLmH/S5cuxTvvvIPNmzeHnx0+fBh///vfceLECQwaNAgzZsygHSAivPnmm1i+fLng+SuvvILRo0eH3a+99hp27NiBJUuWiMZz7733YuDAgZg5cyYA4MCBA3jnnXdw9OhR9OnTBzfffDMmTpyYHCEymFdffRWrV68WPO/Xrx+nr2O32zF37lx8++236NKlC+68807ccsst4d9ramowefJkzJkzB9dcc42kMER8aNBKEARBEARBEARBqBY600oQBEEQBEEQBEGoFhq0EgRBEARBEARBEKqFBq0EQRAEQRAEQRCEaqFBK0EQBEEQBEEQBKFaaNBKEARBEARBEARBqBYatBIEQRAEQRAEQRCqhQatBEEQBEEQBEEQhGqhQStBEARBZAirV6/GiRMn0p0NgiAIgkgpNGglCIIgiAzh4YcfxsaNG6P+/vXXX2P79u2C55WVlVi6dCkYY8nMHkEQBEEkBRq0EgRBEEQn4bnnnsO///1vwfMtW7bg//7v/+D3+9OQK4IgCILoGPp0Z4AgCIIgCHl4PB6sWrUKQ4YMwaWXXio5XGVlJcrLywXPBw8ejCuvvFLJLBIEQRBEh6FBK0EQBEFkIK2trZg4cSLsdjs+//zzhMJWVVXh008/DbsZY1i5ciV+//vf06CVIAiCUB00aCUIgiCIDKOpqQm//OUvUVRUhPXr1yM3Nzf824kTJ7B06VKOf/6q6pgxYzBmzJiwe9asWVi3bh0ef/zx5GacIAiCIGRAg1aCIAiCyCCqq6sxevRoXHrppVi4cCEMBgPn91OnTnFWUYHgduBorFy5Ei+99BLWrFmD888/PxlZJgiCIIgOoWFkSpAgCIIgMoJzzjkHJpMJ3bp1w7Fjx5CXl8f5/Sc/+QmGDh2K9957j/N84cKF+P3vfw+v1wu9vn2++sCBAxg5ciRmzZpFq6wEQRCEaiHrwQRBEASRQTz//PPo27cvxo8fD6fTKTuepqYmjB8/HhMmTKABK0EQBKFqaNBKEARBEBlEQUEB1q9fj5aWFtx0002yBq4+nw+33XYbevTogXfffTcJuSQIgiAI5aBBK0EQBEFkGIWFhdiwYQNaWlpw8803JzxwffPNN7F582ZMnjwZn376KZYuXYqlS5di+/btScoxQRAEQciHDDERBEEQRIZw0003YfDgwQCCA9f169fjiSeewEcffYR77rkHN9xwA/r06SMId+6552Ly5MnQaoNz1b1798akSZOwbds2jr+xY8fSlTcEQRCE6iBDTARBEARBEARBEIRqoe3BBEEQBEEQBEEQhGqhQStBEARBEARBEAShWmjQShAEQRAEQRAEQagWGrQSBEEQBEEQBEEQqoUGrQRBEARBEARBEIRqoUErQRAEQRAEQRAEoVpo0EoQBEEQBEEQBEGoFhq0EgRBEARBEARBEKqFBq0EQRAEQRAEQRCEaqFBK0EQBEEQBEEQBKFaaNBKEARBEARBEARBqBYatBIEQRAEQRAEQRCq5f8Br+urhb6BWs8AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -160,7 +206,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "worst audible-band product: -95.9 dBFS\n" + "worst audible-band product: -104.4 dBFS\n" ] } ], @@ -168,7 +214,7 @@ "fs_in, fs_out = 48000.0, 44100.0\n", "n_in = 1 << 17\n", "t = np.arange(n_in) / fs_in\n", - "tones = [(997.0, 0.25), (6000.0, 0.25), (18500.0, 0.25), (23000.0, 0.15)]\n", + "tones = [(997.0, 0.25), (6000.0, 0.25), (17500.0, 0.25), (23000.0, 0.15)]\n", "x = sum(a * np.sin(2 * np.pi * f * t) for f, a in tones).astype(np.float32)\n", "\n", "c = RatioConverter(direction=\"down\", profile=\"economy\")\n", @@ -184,13 +230,13 @@ "plt.plot(f / 1e3, spec, lw=0.5)\n", "plt.axvline(20, color='g', ls=':', label='20 kHz')\n", "plt.axhline(-16.5 - 71, color='r', ls=':', lw=0.8, label='stopband bound (23 kHz tone)')\n", - "plt.annotate('alias of 23 kHz → 21.1 kHz', xy=(21.1, -95), xytext=(14, -60),\n", + "plt.annotate('alias of 23 kHz \u2192 21.1 kHz', xy=(21.1, -95), xytext=(14, -60),\n", " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", - "plt.annotate('image of 23 kHz → 19.1 kHz', xy=(19.1, -100), xytext=(8, -80),\n", + "plt.annotate('image of 23 kHz \u2192 19.1 kHz', xy=(19.1, -100), xytext=(8, -80),\n", " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", "plt.ylim(-160, 0); plt.xlim(0, 22.05)\n", "plt.xlabel('kHz'); plt.ylabel('dBFS'); plt.legend(fontsize=8); plt.grid(alpha=0.3)\n", - "plt.title('economy 48→44.1 through the shipping engine: products bounded, aliases ultrasonic')\n", + "plt.title('economy 48\u219244.1 through the shipping engine: products bounded, aliases ultrasonic')\n", "plt.show()\n", "\n", "# The numeric contract, asserted:\n", @@ -210,8 +256,9 @@ "## Measured passband response\n", "\n", "Sine probes through the shipping engine (fit amplitude per frequency): flat\n", - "to the 19 kHz economy passband edge within the design's ±0.003 dB, rolling\n", - "into the transition exactly where the design says.\n" + "to the 18 kHz economy passband edge within the design's \u00b10.003 dB, rolling\n", + "into the transition exactly where the design says (the pre-v0.3 economy\n", + "design keeps its 19 kHz edge as `balanced`).\n" ] }, { @@ -220,10 +267,10 @@ "id": "ccf696a7", "metadata": { "execution": { - "iopub.execute_input": "2026-07-23T22:10:44.864471Z", - "iopub.status.busy": "2026-07-23T22:10:44.864209Z", - "iopub.status.idle": "2026-07-23T22:10:45.028420Z", - "shell.execute_reply": "2026-07-23T22:10:45.026839Z" + "iopub.execute_input": "2026-08-07T00:45:03.838314Z", + "iopub.status.busy": "2026-08-07T00:45:03.838092Z", + "iopub.status.idle": "2026-08-07T00:45:03.983553Z", + "shell.execute_reply": "2026-08-07T00:45:03.981686Z" } }, "outputs": [ @@ -232,21 +279,22 @@ "output_type": "stream", "text": [ " 100 Hz +0.002 dB\n", - " 1000 Hz -0.000 dB\n", + " 1000 Hz +0.001 dB\n", " 5000 Hz +0.000 dB\n", - " 10000 Hz +0.000 dB\n", - " 15000 Hz +0.000 dB\n", - " 17000 Hz +0.000 dB\n", - " 18000 Hz +0.000 dB\n", - " 19000 Hz +0.002 dB\n", - " 19800 Hz -0.658 dB\n", - " 20500 Hz -5.694 dB\n", - " 21200 Hz -20.999 dB\n" + " 10000 Hz -0.000 dB\n", + " 15000 Hz -0.000 dB\n", + " 17000 Hz +0.002 dB\n", + " 17800 Hz -0.000 dB\n", + " 18000 Hz +0.002 dB\n", + " 19000 Hz -0.557 dB\n", + " 19800 Hz -4.061 dB\n", + " 20500 Hz -12.168 dB\n", + " 21200 Hz -28.789 dB\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -266,16 +314,16 @@ " a = 2 / len(y) * np.abs(np.sum(y * np.exp(-1j * w * i)))\n", " return 20 * np.log10(a / 0.5)\n", "\n", - "freqs = [100, 1000, 5000, 10000, 15000, 17000, 18000, 19000, 19800, 20500, 21200]\n", + "freqs = [100, 1000, 5000, 10000, 15000, 17000, 17800, 18000, 19000, 19800, 20500, 21200]\n", "gains = [measured_gain_db(fq) for fq in freqs]\n", "for fq, g in zip(freqs, gains):\n", " print(f\"{fq:6d} Hz {g:+8.3f} dB\")\n", - "assert all(abs(g) < 0.02 for fq, g in zip(freqs, gains) if fq <= 19000)\n", + "assert all(abs(g) < 0.02 for fq, g in zip(freqs, gains) if fq <= 18000)\n", "\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(np.array(freqs) / 1e3, gains, 'o-')\n", - "plt.axvline(19, color='g', ls=':'); plt.ylabel('dB'); plt.xlabel('kHz')\n", - "plt.title('measured passband (economy down): flat to the 19 kHz edge')\n", + "plt.axvline(18, color='g', ls=':'); plt.ylabel('dB'); plt.xlabel('kHz')\n", + "plt.title('measured passband (economy down): flat to the 18 kHz edge')\n", "plt.grid(alpha=0.3); plt.show()\n" ] }, @@ -293,7 +341,7 @@ "\n", "The Bluetooth composition (RatioTap for the number, SampleRateTap for the\n", "clock) is demonstrated by `examples/bluetooth_bridge.cpp`: +200 ppm crystal,\n", - "servo locks at +200.1 ppm, tone recovered at 997.000 Hz, 2.0 ms total\n", + "servo locks at +200.1 ppm, tone recovered at 997.000 Hz, 1.9 ms total\n", "latency.\n" ] } @@ -319,4 +367,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/bridge/notebooks/ratiotap_py.py b/bridge/notebooks/ratiotap_py.py index 49681b2..338db2f 100644 --- a/bridge/notebooks/ratiotap_py.py +++ b/bridge/notebooks/ratiotap_py.py @@ -71,7 +71,7 @@ def _load(): _LIB = _load() _DIRS = {"up": 0, "down": 1} -_PROFILES = {"economy": 0, "transparent": 1} +_PROFILES = {"economy": 0, "transparent": 1, "balanced": 2} class RatioConverter: diff --git a/bridge/tests/reference/reference_vectors.h b/bridge/tests/reference/reference_vectors.h index dc8761d..8899d7f 100644 --- a/bridge/tests/reference/reference_vectors.h +++ b/bridge/tests/reference/reference_vectors.h @@ -215,6 +215,193 @@ namespace ratio_ref { }; inline constexpr std::array k_down_economy = { + 8.910730685e-06f, -2.701886842e-05f, 6.103283158e-05f, -1.167573500e-04f, 1.999283704e-04f, + -3.164291265e-04f, 4.907615366e-04f, -6.406812463e-04f, 8.266436635e-04f, -9.384463192e-04f, + 8.771301364e-04f, -6.713036564e-04f, 2.128021733e-04f, 5.597842974e-04f, -1.660779584e-03f, + 3.062783275e-03f, -4.674788564e-03f, 6.319532637e-03f, -7.885663770e-03f, 9.011914022e-03f, + -9.391137399e-03f, 8.585615084e-03f, -5.975623149e-03f, 6.408552290e-04f, 9.351792745e-03f, + -2.954430133e-02f, 9.346589446e-02f, 8.536423743e-02f, 7.332964242e-02f, -1.926064491e-02f, + -2.845678944e-03f, 5.277129263e-02f, -2.781954706e-01f, -2.567010522e-01f, -2.919430435e-01f, + 1.653705351e-02f, 4.295780957e-01f, 6.547377259e-02f, 1.988839172e-02f, 1.314782444e-02f, + 1.933668852e-01f, 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-6.820257753e-02f, + 2.541698702e-02f, -2.846562862e-01f, -2.243813127e-01f, 1.008729115e-01f, 2.819507122e-01f, + 4.012686014e-01f, 3.867348284e-02f, -3.637934327e-01f, -1.690583527e-01f, 1.122367606e-01f, + -5.444098730e-03f, 7.784196734e-02f, 2.885732055e-01f, 4.417825490e-02f, -3.559975922e-01f, + -1.718737036e-01f, 2.969900668e-01f, -2.077926546e-01f, 8.472156525e-02f, 1.782695390e-02f, + 7.268377393e-02f, 5.226581097e-01f, 3.049477041e-01f, 2.222939432e-01f, -3.006328642e-01f, + 1.076984107e-01f, 2.884767652e-01f, 3.333790302e-01f, -2.186296321e-02f, -3.290601373e-01f, + -1.584249139e-01f, -5.544849634e-01f, -1.800164580e-01f, -7.951329648e-02f, -1.546196342e-01f, + -1.217017416e-02f, 8.153094351e-02f, 7.001200318e-02f, 2.865265906e-01f, -1.591437906e-01f, + -2.676718533e-01f, 3.621762097e-01f, -1.956398785e-01f, 9.376740456e-02f, 2.615632713e-01f, + 2.376307994e-01f, -1.809084564e-01f, -2.129136175e-01f, 3.792290092e-01f, -4.910968989e-02f, + 1.568886787e-01f, 2.813882530e-01f, 2.282258272e-01f, 5.654140562e-02f, 2.644413412e-01f, + -1.222711243e-02f, -6.270495653e-01f, -2.063506544e-01f, 2.938789874e-02f, 3.888420165e-01f, + 5.122586712e-02f, -7.602394372e-02f, 3.073031008e-01f, -4.380511045e-01f, -4.785902798e-02f, + 5.108188987e-01f, 1.754178852e-01f, -2.185119502e-02f, -3.435895443e-01f, -1.813377887e-01f, + -2.037927955e-01f, -3.274573386e-01f, -3.629397228e-02f, -1.011109576e-01f, -5.512815714e-02f, + 3.572831750e-01f, 3.771714568e-01f, 1.254508048e-01f, 2.916769385e-01f, 1.138835028e-01f, + -1.221820638e-01f, -5.334211513e-02f, -3.374506235e-01f, 2.153844237e-01f, 2.175326943e-01f, + 1.112612486e-01f, 3.648651838e-01f, 1.265221834e-01f, 3.884064853e-01f, 3.249467537e-02f, + -2.357494533e-01f, 1.334787309e-01f, -4.724608734e-02f, -2.158771455e-01f, 3.863004968e-02f, + 1.163178235e-01f, -2.117978409e-02f, 6.512800604e-02f, 6.933923811e-02f, -3.659440875e-01f, + 2.081400529e-02f, 1.268786937e-01f, -8.486334234e-02f, -6.080020219e-03f, 3.981895745e-02f, + 2.910838425e-01f, -1.771028191e-01f, -4.875285178e-02f, 5.726027489e-02f, 9.808046371e-02f, + 1.597384810e-01f, 3.084250726e-02f, 1.637664586e-01f, -1.047536209e-01f, -6.165748835e-02f, + 3.910065591e-01f, 3.300817907e-01f, -1.186069399e-01f, 9.721723944e-02f, -1.703517288e-01f, + 6.841188297e-03f, 5.055619478e-01f, -2.730417252e-02f, 1.604388207e-01f, 9.638918191e-02f, + -1.442179922e-02f, 3.013131917e-01f, 4.258711338e-01f, 5.116463080e-02f, -3.429048657e-01f, + 3.782555759e-01f, -6.722379941e-03f, 5.392624810e-02f, 1.519088149e-01f, -5.236828923e-01f, + -2.184269577e-02f, -3.774182871e-02f, -1.693475991e-01f, 1.409826875e-01f, 4.094244242e-01f, + -5.594679341e-02f, -2.663115263e-01f, 2.597021125e-02f, -1.196393929e-02f, -4.968722537e-02f, + 1.315204352e-01f, 3.103920519e-01f, 9.915567189e-02f, -1.695101559e-01f, -2.529810667e-01f, + -7.372769713e-02f, -1.703893393e-01f, -2.502328157e-01f, -1.581052095e-01f, 3.240737021e-01f, + 2.126459964e-02f, -5.375116467e-01f, 3.532287851e-02f, 3.298478425e-01f, 3.559778333e-01f, + 3.642790616e-01f, 3.156924844e-01f, -3.547229618e-02f, -2.421355397e-01f, 2.340071797e-01f, + -4.265449569e-02f, -5.777911544e-01f, -3.524464369e-02f, -3.067139089e-01f, -2.713879347e-01f, + 2.791727483e-01f, -1.301613450e-01f, 2.932202071e-02f, -1.470600963e-01f, -1.068015397e-01f, + 2.399521917e-01f, 1.624004245e-01f, 2.687681615e-01f, -2.250813134e-02f, 5.601781607e-02f, + -1.054409984e-02f, -4.109199643e-01f, -1.414823383e-01f, 8.544462174e-02f, 7.999484241e-02f, + -2.625741363e-01f, -3.830757737e-01f, -2.765292525e-01f, 1.303986181e-02f, 4.069864750e-01f, + -1.181969866e-01f, 1.732545793e-01f, 1.862813160e-02f, 1.847944222e-02f, 4.742450118e-01f, + -1.917335838e-01f, 1.394033730e-01f, -1.493587494e-01f, 1.252374798e-02f, 8.910587430e-02f, + -3.252667189e-01f, 3.166500926e-01f, 4.109439552e-01f, 4.681721330e-01f, 2.532285452e-01f, + 3.924045563e-01f, 2.805994451e-01f, -3.220072389e-01f, -3.394772410e-01f, 6.592188030e-02f, + -5.845461786e-02f, -4.467928708e-01f, -7.113572210e-03f, 2.931979857e-02f, -4.018233418e-01f, + -3.048707247e-01f, 3.441884220e-01f, -5.224706233e-02f, -2.262521684e-01f, 2.383745462e-01f, + 4.559903964e-02f, 1.519621760e-01f, 5.424887687e-02f, -1.814341694e-01f, 1.875206712e-03f, + -1.553083509e-01f, -1.414207369e-01f, 1.019384116e-01f, -2.295486927e-01f, -2.045723647e-01f, + 4.617778957e-02f, 1.558824331e-01f, -1.546127945e-01f, -3.428036571e-01f, -2.673056126e-01f, + -3.855946660e-01f, -2.532715909e-02f, -2.065508068e-01f, -3.341349363e-01f, -1.933673956e-02f, + 2.922244966e-01f, 5.543836355e-01f, -2.234610729e-02f, 8.680351824e-02f, 9.711202234e-03f, + -3.973563313e-01f, -1.662561148e-01f, -9.175079316e-02f, -5.755416304e-02f, -9.862808883e-02f, + 6.584737450e-02f, 1.619397290e-02f, -6.592135131e-02f, -2.235071361e-01f, -2.181846946e-01f, + 1.997656673e-01f, 1.099107489e-01f, -2.304994315e-01f, -3.410610855e-01f, -4.818221927e-01f, + 6.838808954e-02f, 4.909379035e-02f, -1.102115680e-02f, 1.285609603e-01f, -4.423808753e-01f, + 2.346835732e-01f, 5.628217384e-02f, -2.001432031e-01f, 2.352138311e-01f, -4.143257067e-02f, + 1.923954636e-01f, 1.249761283e-01f, 5.965692457e-03f, 1.109091863e-01f, -2.498130351e-01f, + -1.220119223e-01f, 1.508086026e-01f, -5.106155947e-02f, -8.881353587e-02f, -9.546887130e-02f, + -1.862939000e-01f, 1.884343103e-02f, -2.216851860e-01f, -4.052824080e-01f, 3.657518923e-01f, + 2.187283188e-01f, 3.959757462e-02f, 9.312948585e-02f, -4.902515411e-01f, -3.689847142e-02f, + 9.661672264e-02f, -1.932503283e-01f, -4.694114625e-02f, -4.447239041e-01f, 1.472376883e-01f, + 3.447058499e-01f, -2.764361203e-01f, 2.823427878e-02f, 3.278430924e-02f, + }; + + inline constexpr std::array k_down_balanced = { -1.031435477e-05f, 2.191725434e-05f, -4.393685231e-05f, 7.615395589e-05f, -1.198024765e-04f, 1.758044818e-04f, -2.517092798e-04f, 3.142122005e-04f, -4.037040926e-04f, 4.691039794e-04f, -4.746864142e-04f, 4.803411721e-04f, -4.358982551e-04f, 3.050960077e-04f, -8.902783156e-05f, @@ -589,6 +776,227 @@ namespace ratio_ref { }; inline constexpr std::array k_up_economy = { + 6.789742201e-06f, -3.785208173e-05f, 1.134053164e-04f, -2.731592394e-04f, 5.101181450e-04f, + -7.676394889e-04f, 9.002895094e-04f, -6.338239764e-04f, -1.953862957e-04f, 1.894126064e-03f, + -4.465742037e-03f, 7.685088087e-03f, -1.097695529e-02f, 1.331870444e-02f, -1.330862381e-02f, + 9.346500970e-03f, 1.202827625e-04f, -1.653897949e-02f, 4.131402448e-02f, -7.811734080e-02f, + 1.561057866e-01f, 6.469760090e-02f, 5.286132172e-02f, 1.031685397e-01f, -1.614794880e-01f, + 1.702904701e-01f, -8.541693538e-02f, -2.159755677e-01f, -2.649696171e-01f, -3.500379324e-01f, + -8.512882888e-02f, 7.440065593e-02f, 5.343287587e-01f, -1.288204081e-02f, -2.661504969e-02f, + 1.521590203e-01f, -9.569982439e-02f, 3.792250454e-01f, -2.616969645e-01f, -3.778686002e-02f, + 4.295474291e-01f, 2.916665077e-01f, 4.699739218e-01f, -2.063517869e-01f, 1.206139103e-01f, + -2.369867079e-02f, -3.369408250e-01f, 6.696245074e-02f, -4.368779659e-01f, -1.296108216e-01f, + 1.871555001e-01f, -3.949487805e-01f, -4.194065332e-01f, 1.925706118e-01f, -7.248366624e-02f, + -2.986861467e-01f, 2.541424707e-02f, -2.939255238e-01f, -1.640871912e-01f, 1.190436482e-01f, + 2.502176911e-02f, 2.448604554e-01f, -3.089765459e-02f, -3.179369867e-01f, 1.895103902e-01f, + 4.394874275e-01f, 4.655020237e-01f, -7.771699131e-02f, -1.432814151e-01f, 4.115757346e-01f, + -5.425790325e-02f, -1.274345070e-01f, 3.732277751e-01f, 2.716337740e-01f, -1.575140208e-01f, + -2.512241602e-01f, -3.142564893e-01f, -8.364627510e-02f, 2.346829325e-01f, -2.099664360e-01f, + 3.587685525e-02f, 2.946539782e-02f, -5.612038970e-01f, -8.427114785e-02f, -6.895851344e-02f, + 7.038474828e-02f, 3.910298944e-01f, -3.290968239e-01f, -3.430276811e-01f, 1.448116452e-01f, + -2.282192558e-01f, -1.361554265e-01f, 4.809495211e-01f, -4.116250202e-02f, 3.323456645e-02f, + 3.437979221e-01f, 2.242577523e-01f, 2.692515552e-01f, -2.374058962e-01f, -7.020162418e-03f, + -2.908204198e-01f, -2.334786206e-01f, 2.688030303e-01f, -2.473072559e-01f, -4.153571092e-03f, + -2.083300352e-01f, -1.110980734e-01f, 2.092007697e-01f, -2.848283350e-01f, -3.722383678e-01f, + -3.308308721e-01f, 1.264518052e-01f, 2.151871771e-01f, -2.876003385e-01f, -1.416314095e-01f, + 1.456015408e-01f, -1.244507059e-01f, -3.246280830e-03f, 1.829945743e-01f, -6.918897480e-02f, + -2.346871942e-01f, -4.200062715e-03f, 1.101755574e-01f, -2.805771530e-01f, -4.188568890e-02f, + -2.408149093e-01f, -3.557111919e-01f, -1.032960266e-01f, -3.574880064e-01f, 1.112292260e-01f, + 5.884601176e-02f, 1.136650443e-01f, 2.923135459e-01f, -5.980736017e-01f, -3.456958532e-01f, + 4.665653706e-01f, 1.732609123e-01f, -1.420997828e-01f, 1.927690208e-01f, 1.037433073e-01f, + -7.652560622e-02f, 1.821455508e-01f, -1.365070492e-01f, -2.030644566e-01f, 6.091908738e-02f, + -1.856517494e-01f, -2.638795674e-01f, -3.862205148e-01f, -3.659365177e-01f, 1.358411312e-01f, + 6.988750398e-02f, 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-2.646074593e-01f, + 1.351717561e-01f, 4.060145840e-02f, 7.689962536e-02f, -4.852114618e-02f, 1.464247853e-01f, + -3.308453411e-02f, -3.734732866e-01f, -2.379298583e-02f, 1.759155393e-01f, -2.473441698e-02f, + -1.011974886e-01f, 6.887283921e-02f, -5.398451351e-03f, 3.490312397e-01f, -8.874262869e-02f, + -2.604095936e-01f, 2.259597182e-01f, -1.335768998e-01f, 2.617424428e-01f, 1.107035130e-01f, + -2.792232856e-02f, 2.796204388e-01f, -1.204686537e-01f, -3.565400839e-02f, -2.325415798e-02f, + 4.275503755e-01f, 4.181041718e-01f, -1.879160255e-01f, 1.137951985e-01f, -2.949909866e-02f, + -2.269003093e-01f, 1.725119501e-01f, 4.385961592e-01f, 1.095251888e-01f, -3.013306484e-02f, + 2.716839015e-01f, -3.097756207e-02f, 1.692328788e-02f, 3.896323442e-01f, 3.567391038e-01f, + 2.452567518e-01f, -3.408384621e-01f, -7.095868140e-02f, 4.825390875e-01f, -1.658345908e-01f, + 9.500158578e-02f, 2.399618328e-01f, -4.397568107e-01f, -2.912822962e-01f, 5.480709299e-02f, + -8.256043494e-02f, -1.817879677e-01f, 1.312221289e-01f, 3.552378118e-01f, 2.501181066e-01f, + -3.034413755e-01f, -1.717583984e-01f, 7.888518274e-02f, -9.454320371e-02f, 5.439636856e-02f, + -5.703875422e-02f, 3.159112632e-01f, 2.860032320e-01f, -3.559577465e-02f, -6.127898023e-02f, + -3.766237497e-01f, -4.097387195e-02f, -1.063493416e-01f, -2.742340267e-01f, -1.658950299e-01f, + -2.115569711e-01f, 3.624798059e-01f, 1.725696027e-01f, -4.889111221e-01f, -2.906651199e-01f, + 1.583112478e-01f, 3.660521507e-01f, 3.412950933e-01f, 3.690597117e-01f, 3.467896581e-01f, + 1.737676859e-01f, -2.023424804e-01f, -1.685196906e-01f, 2.783116996e-01f, -1.831796579e-02f, + -5.022118688e-01f, -3.338463306e-01f, 3.839769214e-02f, -4.632320702e-01f, -1.936513186e-01f, + 3.543454707e-01f, -1.764994264e-01f, 4.837168753e-02f, -7.817288488e-02f, -1.677240729e-01f, + -3.856194764e-02f, 2.255731374e-01f, 2.190479040e-01f, 1.656597853e-01f, 2.496371418e-01f, + -2.052258253e-01f, 2.431971282e-01f, -1.620114893e-01f, -3.630192578e-01f, -1.975239217e-01f, + -5.405466259e-02f, 2.617257833e-01f, -1.741746813e-01f, -1.642743647e-01f, -4.866168201e-01f, + -2.558319271e-01f, -8.489210904e-02f, 2.221152484e-01f, 3.795877397e-01f, -2.841754258e-01f, + 2.992697358e-01f, 2.905659936e-02f, -1.362176239e-01f, 5.083590746e-01f, 1.056713983e-01f, + -1.398272216e-01f, 1.327030510e-01f, -1.092962846e-01f, -1.264041811e-01f, 2.611001134e-01f, + -2.823575437e-01f, -1.276625246e-01f, 4.184574783e-01f, 3.757928014e-01f, 4.974670410e-01f, + 3.055411577e-01f, 2.578849196e-01f, 4.889555573e-01f, 7.822677493e-02f, -3.604898453e-01f, + -3.217412829e-01f, -5.508538708e-02f, 1.430649310e-01f, -3.679400384e-01f, -3.600751162e-01f, + 1.041231528e-01f, -3.425583243e-02f, -3.138539791e-01f, -4.468860924e-01f, 8.752880991e-02f, + 3.621194065e-01f, -2.446249574e-01f, -1.767742187e-01f, 2.675776482e-01f, 3.008134849e-02f, + 1.332363933e-01f, 1.534745097e-01f, -8.492947370e-02f, -8.820226789e-02f, -1.126494184e-01f, + 5.549424328e-03f, -3.027623594e-01f, 7.348921895e-02f, 5.395352095e-02f, -3.753554523e-01f, + -6.920564175e-02f, -7.696381956e-02f, 2.234771550e-01f, -4.811770050e-04f, -3.406315446e-01f, + -2.467299402e-01f, -3.437298536e-01f, -3.517209888e-01f, -9.036117047e-02f, -9.084025025e-02f, + -3.276815116e-01f, -2.586481273e-01f, 3.801833838e-02f, 3.438420296e-01f, 4.942566454e-01f, + 2.464168370e-01f, -1.836322546e-01f, 2.605374753e-01f, -1.498313993e-01f, -4.706735909e-01f, + -3.042328171e-02f, -2.721029818e-01f, 9.033654630e-02f, -1.678744853e-01f, -7.469047606e-02f, + 1.844895184e-01f, -1.404950917e-01f, 9.834367782e-02f, -2.732599676e-01f, -2.455533892e-01f, + -3.851595521e-02f, 1.826516688e-01f, 1.561371982e-01f, -2.832369208e-01f, -2.606805861e-01f, + -5.032322407e-01f, -2.714347243e-01f, 1.566662043e-01f, 3.152116016e-02f, -6.195690483e-02f, + 2.173653990e-01f, -3.240406513e-01f, -2.611772418e-01f, 4.949095845e-01f, -2.434067875e-01f, + -4.968932644e-02f, 1.572406590e-01f, 2.166620456e-02f, 1.110188365e-01f, 1.356157362e-01f, + 1.785236150e-01f, -9.985750169e-02f, 2.111628056e-01f, -2.332306355e-01f, -2.450841218e-01f, + 1.039915010e-01f, 6.220710650e-02f, -2.909099683e-02f, -1.400184929e-01f, -3.072408587e-02f, + -2.339032590e-01f, -3.974583745e-02f, -1.831052266e-02f, -3.286301494e-01f, -3.615392745e-01f, + 3.061154485e-01f, 3.863283694e-01f, -7.150588930e-02f, 2.295820862e-01f, -1.416477859e-01f, + -4.890299439e-01f, -1.081212331e-02f, 9.724198282e-02f, -1.106861681e-01f, -1.313704997e-01f, + -1.530033648e-01f, -4.207980931e-01f, 1.535309553e-01f, 4.744601250e-01f, -2.783285677e-01f, + -8.545870334e-02f, 7.945273817e-02f, -1.688120328e-02f, -4.647631664e-03f, -2.932426333e-01f, + -3.065643907e-01f, -3.359408379e-01f, -4.337041378e-01f, 4.007133096e-02f, 3.643221259e-01f, + -1.614156365e-01f, -1.156840622e-01f, 2.408827394e-01f, 6.880822033e-02f, + }; + + inline constexpr std::array k_up_balanced = { -2.024352216e-05f, 4.592908226e-05f, -8.434985648e-05f, 1.413756545e-04f, -1.714049140e-04f, 1.128912263e-04f, 1.191139309e-04f, -6.055129343e-04f, 1.394894323e-03f, -2.469108440e-03f, 3.737050574e-03f, -5.014420021e-03f, 5.910654552e-03f, -6.010395009e-03f, 4.765956197e-03f, diff --git a/bridge/tests/test_converter.cpp b/bridge/tests/test_converter.cpp index 665f941..0ac69d4 100644 --- a/bridge/tests/test_converter.cpp +++ b/bridge/tests/test_converter.cpp @@ -98,12 +98,18 @@ namespace { TEST(Converter, MatchesScipyDownEconomy) { check_reference(profile::economy(), ratio_ref::k_down_economy); } + TEST(Converter, MatchesScipyDownBalanced) { + check_reference(profile::balanced(), ratio_ref::k_down_balanced); + } TEST(Converter, MatchesScipyDownTransparent) { check_reference(profile::transparent(), ratio_ref::k_down_transparent); } TEST(Converter, MatchesScipyUpEconomy) { check_reference(profile::economy(), ratio_ref::k_up_economy); } + TEST(Converter, MatchesScipyUpBalanced) { + check_reference(profile::balanced(), ratio_ref::k_up_balanced); + } TEST(Converter, MatchesScipyUpTransparent) { check_reference(profile::transparent(), ratio_ref::k_up_transparent); } @@ -259,13 +265,14 @@ namespace { // IMAGING LEAKAGE: the upsampling images at k*48000 +/- 997 Hz // survive at stopband depth and fold in-band (e.g. 47003 -> 2903 Hz). // That floor is bounded by the stopband (>= 71 dB below the tone, - // deeper where the Kaiser sidelobes have decayed; measured ~89 dB - // SNR here). This is economy's honest in-band contract — and exactly - // what the k*fs image-zeros lever (PLAN M7) would deepen. + // deeper where the Kaiser sidelobes have decayed; measured ~91 dB + // SNR on the 18 kHz economy design). This is economy's honest + // in-band contract — and exactly what the k*fs image-zeros lever + // (PLAN M7) would deepen. const auto tail = run_sine_down(profile::economy(), 997.0, 0.5, 1 << 16); const auto fit = an::fit_sine_tracked(tail, 997.0 / 44100.0); EXPECT_NEAR(fit.amplitude, 0.5, 1e-4); - EXPECT_GT(an::snr_db(fit), 85.0); // measured ~89.2 dB + EXPECT_GT(an::snr_db(fit), 85.0); // measured ~91.3 dB (balanced: ~89.2) } TEST(Converter, PassbandSineIsTransparentTransparent) { @@ -297,8 +304,10 @@ namespace { for (double f = 100.0; f < 20000.0; f += 100.0) { ASSERT_LT(probe_dbfs(y, f / 44100.0), -6.0 - 71.0) << f << " Hz"; } - // And the worst in-band product is the predicted 19.1 kHz image. - EXPECT_LT(probe_dbfs(y, 19100.0 / 44100.0), -80.0); // measured ~-85 dBFS + // And the worst in-band product is the predicted 19.1 kHz image + // (deeper on the 18 kHz economy design than balanced's ~-85: the + // image at 25 kHz sits further into the narrower filter's stopband). + EXPECT_LT(probe_dbfs(y, 19100.0 / 44100.0), -80.0); // measured ~-96 dBFS } // ------------------------------------------------------------------ @@ -385,7 +394,7 @@ namespace { TEST(Converter, LatencyAndValidation) { conv c(1); - EXPECT_NEAR(c.latency_input_frames(), 78.0 / 2.0, 0.51); + EXPECT_NEAR(c.latency_input_frames(), 58.0 / 2.0, 0.51); EXPECT_EQ(c.channels(), 1u); EXPECT_THROW(conv(0), std::invalid_argument); } diff --git a/bridge/tests/test_converter_fixed_point.cpp b/bridge/tests/test_converter_fixed_point.cpp index 05bd2ba..c9ba8b5 100644 --- a/bridge/tests/test_converter_fixed_point.cpp +++ b/bridge/tests/test_converter_fixed_point.cpp @@ -113,14 +113,14 @@ namespace { TEST(FixedPoint, Q15SineQualityEconomyDown) { // Q15's floor is the format (input quantization + output requant + - // Q1.14 coefficient noise over 78 taps), of the same order as - // economy's imaging floor. Measured 76.1 dB. + // Q1.14 coefficient noise over 58 taps), of the same order as + // economy's imaging floor. Measured 76.5 dB (balanced's 78 taps: 76.1). EXPECT_GT((measure_sine_snr_db(profile::economy(), 997.0, 0.5)), 72.0); } TEST(FixedPoint, Q15SineQualityTransparentDown) { // LOWER than economy, on purpose pinned: Q1.14 coefficient noise // stacks with tap count, so transparent's 184 taps cost ~3.7 dB over - // economy's 78 while the 120 dB filter buys nothing a 16-bit format + // economy's 58 while the 120 dB filter buys nothing a 16-bit format // can express. At Q15, economy is the better pairing in both compute // AND noise — the profile guidance the README states. Measured 72.8 dB. EXPECT_GT((measure_sine_snr_db(profile::transparent(), 997.0, 0.5)), diff --git a/bridge/tests/test_cross_validation.cpp b/bridge/tests/test_cross_validation.cpp index a04d12c..2c233b2 100644 --- a/bridge/tests/test_cross_validation.cpp +++ b/bridge/tests/test_cross_validation.cpp @@ -122,25 +122,29 @@ namespace { 20.0 * std::log10(worst + 1e-18), phases_seen, l); } - // Measured floors: down 3.5e-6 (-109 dB), up 1.2e-5 (-99 dB) — and the - // SAME at async L=512 and L=1024. That equality is itself evidence: the - // async table's mu-interpolation residual (its documented -12 dB per - // doubling of L) is already below the one deliberate filter difference - // between the machines — RatioTap's per-branch DC normalization, a - // ~5e-6-level perturbation the async bank does not apply. The exact and - // interpolated machines agree to the last systematic difference we chose - // to introduce, on every phase. + // Measured floors on the 18 kHz economy designs (v0.3 re-pin): down + // 1.2e-5 (-98 dB), up 3.1e-5 (-90 dB) — and the SAME at async L=512 and + // L=1024. That equality is itself evidence: the async table's + // mu-interpolation residual (its documented -12 dB per doubling of L) is + // already below the one deliberate filter difference between the + // machines — RatioTap's per-branch DC normalization, a perturbation the + // async bank does not apply. The perturbation scales with the branch-sum + // spread of the raw windowed-sinc, which is larger for the shorter + // wider-transition economy designs (the v0.2 economy = today's balanced + // measured 3.5e-6 / 1.2e-5 here). The exact and interpolated machines + // agree to the last systematic difference we chose to introduce, on + // every phase. TEST(CrossValidation, DownEconomyAgainstAsync512) { - check_cross_validation(512, 1e-5); + check_cross_validation(512, 3e-5); } TEST(CrossValidation, DownEconomyAgainstAsync1024) { - check_cross_validation(1024, 1e-5); + check_cross_validation(1024, 3e-5); } TEST(CrossValidation, UpEconomyAgainstAsync512) { - check_cross_validation(512, 3e-5); + check_cross_validation(512, 8e-5); } TEST(CrossValidation, UpEconomyAgainstAsync1024) { - check_cross_validation(1024, 3e-5); + check_cross_validation(1024, 8e-5); } } // namespace diff --git a/bridge/tests/test_design.cpp b/bridge/tests/test_design.cpp index 6809bb8..24ad6ee 100644 --- a/bridge/tests/test_design.cpp +++ b/bridge/tests/test_design.cpp @@ -83,12 +83,18 @@ namespace { TEST(Design, DownEconomyMeetsSpec) { check_meets_spec(profile::economy(), "down economy"); } + TEST(Design, DownBalancedMeetsSpec) { + check_meets_spec(profile::balanced(), "down balanced"); + } TEST(Design, DownTransparentMeetsSpec) { check_meets_spec(profile::transparent(), "down transparent"); } TEST(Design, UpEconomyMeetsSpec) { check_meets_spec(profile::economy(), "up economy"); } + TEST(Design, UpBalancedMeetsSpec) { + check_meets_spec(profile::balanced(), "up balanced"); + } TEST(Design, UpTransparentMeetsSpec) { check_meets_spec(profile::transparent(), "up transparent"); } @@ -100,6 +106,8 @@ namespace { TEST(Design, DirectionsAreAsymmetric) { const profile eco = profile::economy(); EXPECT_GE(eco.taps_down_to_44k1, (eco.taps_up_to_48k * 3) / 2); + const profile bal = profile::balanced(); + EXPECT_GE(bal.taps_down_to_44k1, (bal.taps_up_to_48k * 3) / 2); const profile tr = profile::transparent(); EXPECT_GE(tr.taps_down_to_44k1, (tr.taps_up_to_48k * 3) / 2); } diff --git a/bridge/tests/test_phase_table.cpp b/bridge/tests/test_phase_table.cpp index 1df9c39..1c457bb 100644 --- a/bridge/tests/test_phase_table.cpp +++ b/bridge/tests/test_phase_table.cpp @@ -75,6 +75,12 @@ namespace { TYPED_TEST(phase_table_test, UpEconomyEveryPhase) { check_table(profile::economy()); } + TYPED_TEST(phase_table_test, DownBalancedEveryPhase) { + check_table(profile::balanced()); + } + TYPED_TEST(phase_table_test, UpBalancedEveryPhase) { + check_table(profile::balanced()); + } TYPED_TEST(phase_table_test, DownTransparentEveryPhase) { check_table(profile::transparent()); } @@ -88,11 +94,15 @@ namespace { // its self-symmetric middle branch as a stored row). TEST(PhaseTable, StorageBudgetsArePinned) { const basic_phase_table de(profile::economy()); - EXPECT_EQ(de.storage_bytes(), 74u * 78u * 4u); // 22.5 KiB (was 44.8 full) + EXPECT_EQ(de.storage_bytes(), 74u * 58u * 4u); // 16.8 KiB + const basic_phase_table db(profile::balanced()); + EXPECT_EQ(db.storage_bytes(), 74u * 78u * 4u); // 22.5 KiB (was 44.8 full) const basic_phase_table dt(profile::transparent()); EXPECT_EQ(dt.storage_bytes(), 74u * 184u * 4u); // 53.2 KiB (was 105.7 full) const basic_phase_table ue(profile::economy()); - EXPECT_EQ(ue.storage_bytes(), 80u * 44u * 2u); // 6.9 KiB — Q15 halves it again + EXPECT_EQ(ue.storage_bytes(), 80u * 38u * 2u); // 5.9 KiB — Q15 halves it again + const basic_phase_table ub(profile::balanced()); + EXPECT_EQ(ub.storage_bytes(), 80u * 44u * 2u); // 6.9 KiB } } // namespace diff --git a/bridge/tools/capi/ratio_capi.cpp b/bridge/tools/capi/ratio_capi.cpp index 64b9a7e..fe622bd 100644 --- a/bridge/tools/capi/ratio_capi.cpp +++ b/bridge/tools/capi/ratio_capi.cpp @@ -32,10 +32,12 @@ struct ratio_converter { extern "C" { ratio_converter* ratio_create(int direction, int profile, unsigned channels) { - if ((direction != 0 && direction != 1) || (profile != 0 && profile != 1) || channels == 0) { + if ((direction != 0 && direction != 1) || profile < 0 || profile > 2 || channels == 0) { return nullptr; } - const tap::ratio::profile p = profile == 0 ? tap::ratio::profile::economy() : tap::ratio::profile::transparent(); + const tap::ratio::profile p = profile == 0 ? tap::ratio::profile::economy() + : profile == 1 ? tap::ratio::profile::transparent() + : tap::ratio::profile::balanced(); try { // unique_ptr owns the wrapper until the converter constructor has // succeeded, so a throw below cannot leak it. diff --git a/bridge/tools/capi/ratio_capi.h b/bridge/tools/capi/ratio_capi.h index ffe5ff3..6965001 100644 --- a/bridge/tools/capi/ratio_capi.h +++ b/bridge/tools/capi/ratio_capi.h @@ -23,7 +23,7 @@ typedef struct ratio_converter ratio_converter; /// ratio_destroy, where NULL is a safe no-op (the free() convention). /// direction: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1). -/// profile: 0 = economy (default tier), 1 = transparent. +/// profile: 0 = economy (default tier), 1 = transparent, 2 = balanced. /// Returns NULL on invalid arguments. ratio_converter* ratio_create(int direction, int profile, unsigned channels); void ratio_destroy(ratio_converter* c); diff --git a/bridge/tools/reference/make_reference_vectors.py b/bridge/tools/reference/make_reference_vectors.py index 895321c..cf620e6 100644 --- a/bridge/tools/reference/make_reference_vectors.py +++ b/bridge/tools/reference/make_reference_vectors.py @@ -58,9 +58,11 @@ def xorshift_f32(count, seed): CASES = [ # tag, L, M, fs_in, pass_hz, stop_hz, atten, taps - ("down_economy", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78), + ("down_economy", 147, 160, 48000.0, 18000.0, 22050.0, 70.0, 58), + ("down_balanced", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78), ("down_transparent", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184), - ("up_economy", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44), + ("up_economy", 160, 147, 44100.0, 18000.0, 24000.0, 70.0, 38), + ("up_balanced", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44), ("up_transparent", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96), ] From 9139032be8df002315c57bd74a6e17ffffa7e15a Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 01:01:34 +0000 Subject: [PATCH 24/44] Add super_economy: the 16 kHz voice/comms tier MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fourth rung of the v0.3 ladder, from the same fine-grid criterion as the economy re-pin: 40/28 taps per phase (minimal even counts at 70 dB with >= 1 dB margin; 38 and 26 both fail) — half of balanced's MACs, 11.6/8.8 KiB stored f32 tables, 0.42/0.32 ms latency. Unlike economy's inaudible trade, this tier audibly shelves the top octave (-1.4 dB at 18 kHz, -5.9 dB at 19 kHz going down), so it is never a default: opt-in by name, positioned for voice/comms/Bluetooth-class links where the top octave is already gone. In Q15 it is quieter than transparent (77.7 dB measured at 997 Hz) at a fifth of the taps — the format floors near 76 dB regardless of tier, so cheap tiers are the right 16-bit pairing. Wired through every layer the other tiers get: constexpr profile with a committed trip count in the process() dispatch, scipy reference vectors (both directions), the design/phase-table/converter batteries (78 -> 82 host tests, all green), C ABI profile tag 3, ctypes bridge name, two Q15 icount scenarios (up_q15_se / down_q15_se) pinning the tier in its deployment format, and the design-spike notebook re-executed over all eight designs. Baselines for the new scenarios land with the harvest commit. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/PLAN.md | 29 +- bridge/README.md | 3 +- bridge/bench/icount/CMakeLists.txt | 7 +- bridge/bench/icount/icount_main.cpp | 9 +- bridge/include/tap/ratio/converter.h | 10 +- bridge/include/tap/ratio/design.h | 32 +- bridge/include/tap/ratio/ratio.h | 5 +- bridge/notebooks/design_spike.ipynb | 46 +- bridge/notebooks/ratiotap_py.py | 2 +- bridge/tests/reference/reference_vectors.h | 408 ++++++++++++++++++ bridge/tests/test_converter.cpp | 6 + bridge/tests/test_design.cpp | 6 + bridge/tests/test_phase_table.cpp | 8 + bridge/tools/capi/ratio_capi.cpp | 5 +- bridge/tools/capi/ratio_capi.h | 3 +- .../tools/reference/make_reference_vectors.py | 14 +- 16 files changed, 532 insertions(+), 61 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index dba8fe3..3812c48 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -102,12 +102,14 @@ side of the ASRC: ## 4. Profiles -Three quality tiers behind one design path, named in the family vocabulary +Four quality tiers behind one design path, named in the family vocabulary (ladder re-pinned 2026-08-07, v0.3: the 18 kHz design became `economy` and -the default; the former economy design continues unchanged as `balanced`): +the default; the former economy design continues unchanged as `balanced`; +`super_economy` added as the explicit voice/comms tier): | Profile | Stopband | Passband edge | Taps/phase (=MACs/out) down / up | Storage f32 down / up | Role | |---|---|---|---|---|---| +| `super_economy()` | 70 dB | 16 kHz | **40 / 28** | 11.6 / 8.8 KiB | Voice/comms tier: audibly shelves the top octave (−1.4 dB at 18 kHz, −5.9 dB at 19 kHz going down) for half of balanced's MACs. Never a default — opt-in by name | | `economy()` — **default** | 70 dB | 18 kHz | **58 / 38** | 16.8 / 11.9 KiB | The speed-first default. All alias products land above 20 kHz at ≤ −71 dBFS — arithmetically confined to the ultrasonic band (see HANDOFF §4) — at 26%/14% fewer MACs than balanced | | `balanced()` | 70 dB | 19 kHz | **78 / 44** | 22.5 / 13.8 KiB | The v0.1–v0.2 economy design, unchanged: top of the audible band stays inside the flat passband | | `transparent()` | 120 dB | 20 kHz | **184 / 96** | 53.2 / 30.0 KiB | Pristine/offline tier | @@ -127,15 +129,16 @@ must keep that shelf flat pairs with `balanced`. Numbers pinned by the M2 design spike (`notebooks/design_spike.ipynb`, executed and committed; enforced in CI by `test_design.cpp`) for -balanced/transparent, and by the 2026-08-07 economy18 re-pin for economy: -taps are the minimal even counts meeting the stopband with ≥ 1 dB margin on -a fine (12.5 Hz) sweep grid. Measured worst-case stopband on the shipping -designs: economy −71.5 dB (down) / −71.7 dB (up); balanced −72.1 / −72.8; -transparent −121.7 (both). Passband ripple ±0.003 dB (economy/balanced) / +balanced/transparent, and by the 2026-08-07 ladder re-pin for +super_economy/economy: taps are the minimal even counts meeting the +stopband with ≥ 1 dB margin on a fine (12.5 Hz) sweep grid. Measured +worst-case stopband on the shipping designs: super_economy −71.7 dB (both); +economy −71.5 dB (down) / −71.7 dB (up); balanced −72.1 / −72.8; +transparent −121.7 (both). Passband ripple ±0.003 dB (the 70 dB tiers) / ±0.00001 dB (transparent). One re-pin quirk worth recording: in the up -direction 40 taps *fails* the margin criterion while 38 passes (Kaiser -sidelobe peaking is non-monotonic near threshold) — 38 is a genuine sweet -spot, not a typo. The designs additionally normalize every polyphase +direction at the 18 kHz passband, 40 taps *fails* the margin criterion +while 38 passes (Kaiser sidelobe peaking is non-monotonic near threshold) — +38 is a genuine sweet spot, not a typo. The designs additionally normalize every polyphase branch's DC sum to exactly 1.0 (kills fs_out/L-harmonic spurs from DC/LF energy; lets fixed-point row-sum quantization land on format unity exactly). @@ -288,8 +291,10 @@ profile-ladder re-pin.** economy moved to the 18 kHz/58/38 design (the "economy18" spec-relaxation experiment, measured through every leg: scipy vectors regenerated, cross-validation floors re-pinned at −98/−90 dB, Q15 flagship unchanged at 76.5 dB, storage −25%/−14%); the former economy -became `balanced`, unchanged; icount baselines re-recorded for the six -economy workloads. **Status: M0–M6 complete — +became `balanced`, unchanged; `super_economy` (16 kHz, 40/28) joined as +the explicit voice/comms tier with its own scipy vectors and two Q15 +icount scenarios; icount baselines re-recorded for the six economy +workloads and recorded for the two new super_economy ones. **Status: M0–M6 complete — v0.1 shipped (2026-07-23). M7 codegen phase complete — v0.2 (2026-07-24): M7a measurement harness, M7b superblock codegen, M7c committed trip counts, M7d symmetry halving, all measured, outputs bit-identical diff --git a/bridge/README.md b/bridge/README.md index f27ef60..2941284 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -44,7 +44,8 @@ quantization, measurement instruments). tap::ratio::converter_to_44k1 down(2); // 48 -> 44.1, stereo, economy // profiles: economy() (default, 18 kHz passband) | balanced() (19 kHz, -// the pre-v0.3 default) | transparent() (120 dB pristine tier) +// the pre-v0.3 default) | transparent() (120 dB pristine tier) | +// super_economy() (16 kHz voice/comms tier — audible top-octave shelf) std::vector out(down.outputs_for(n_in) * 2); std::size_t made = down.process(in, n_in, out.data()); // noexcept, alloc-free // ... and at end of stream: diff --git a/bridge/bench/icount/CMakeLists.txt b/bridge/bench/icount/CMakeLists.txt index b5cace2..71ef405 100644 --- a/bridge/bench/icount/CMakeLists.txt +++ b/bridge/bench/icount/CMakeLists.txt @@ -2,7 +2,8 @@ # targets have no argv, and per-binary instruction totals are what the # ratchet compares. Economy is the speed-first default the M7 levers are # judged on, in every format and both directions; the two transparent legs -# keep the pristine profile honest without tripling the matrix. +# keep the pristine profile honest without tripling the matrix, and the two +# Q15 super_economy legs pin the voice tier in its deployment format. set(_ratio_icount_scenarios up_float_eco:0:0:0:2 down_float_eco:1:0:0:2 @@ -11,7 +12,9 @@ set(_ratio_icount_scenarios up_q31_eco:0:2:0:2 down_q31_eco:1:2:0:2 up_float_tr:0:0:1:2 - down_float_tr:1:0:1:2) + down_float_tr:1:0:1:2 + up_q15_se:0:1:3:2 + down_q15_se:1:1:3:2) foreach(_sc IN LISTS _ratio_icount_scenarios) string(REPLACE ":" ";" _parts "${_sc}") diff --git a/bridge/bench/icount/icount_main.cpp b/bridge/bench/icount/icount_main.cpp index cedfb27..bd16c95 100644 --- a/bridge/bench/icount/icount_main.cpp +++ b/bridge/bench/icount/icount_main.cpp @@ -8,7 +8,8 @@ // // RATIO_SC_DIR: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1) // RATIO_SC_TYPE: 0 = float, 1 = Q15, 2 = Q31 -// RATIO_SC_PROFILE: 0 = economy, 1 = transparent +// RATIO_SC_PROFILE: 0 = economy, 1 = transparent, 3 = super_economy +// (matching the C ABI tags; 2 = balanced unused here) // RATIO_SC_CH: channel count (default 2) // SPDX-License-Identifier: MIT // Copyright 2026 Timothy Place and the RatioTap contributors. @@ -66,8 +67,12 @@ namespace { #endif #if RATIO_SC_PROFILE == 0 const tap::ratio::profile k_prof = tap::ratio::profile::economy(); -#else +#elif RATIO_SC_PROFILE == 1 const tap::ratio::profile k_prof = tap::ratio::profile::transparent(); +#elif RATIO_SC_PROFILE == 2 + const tap::ratio::profile k_prof = tap::ratio::profile::balanced(); +#else + const tap::ratio::profile k_prof = tap::ratio::profile::super_economy(); #endif constexpr std::size_t k_ch = RATIO_SC_CH; constexpr std::size_t k_block = 32; diff --git a/bridge/include/tap/ratio/converter.h b/bridge/include/tap/ratio/converter.h index 19bd6e5..35e4ffd 100644 --- a/bridge/include/tap/ratio/converter.h +++ b/bridge/include/tap/ratio/converter.h @@ -66,9 +66,10 @@ namespace tap::ratio { /// The canonical trip counts (taps per phase = MACs per output) the /// hot path hard-commits to at compile time; any other profile runs /// the runtime-length walk. - static constexpr std::size_t k_taps_economy = profile::economy().taps(); - static constexpr std::size_t k_taps_balanced = profile::balanced().taps(); - static constexpr std::size_t k_taps_transparent = profile::transparent().taps(); + static constexpr std::size_t k_taps_economy = profile::economy().taps(); + static constexpr std::size_t k_taps_super_economy = profile::super_economy().taps(); + static constexpr std::size_t k_taps_balanced = profile::balanced().taps(); + static constexpr std::size_t k_taps_transparent = profile::transparent().taps(); /// Allocates histories and designs the table; setup time only. explicit basic_converter(std::size_t channels = 1, const profile& p = profile::economy()) @@ -115,6 +116,9 @@ namespace tap::ratio { if (taps == k_taps_economy) { return process_taps(in, in_frames, out); } + if (taps == k_taps_super_economy) { + return process_taps(in, in_frames, out); + } if (taps == k_taps_balanced) { return process_taps(in, in_frames, out); } diff --git a/bridge/include/tap/ratio/design.h b/bridge/include/tap/ratio/design.h index 653d386..46083c2 100644 --- a/bridge/include/tap/ratio/design.h +++ b/bridge/include/tap/ratio/design.h @@ -57,17 +57,18 @@ namespace tap::ratio { // ANCHOR_END: rt_direction // ANCHOR: rt_profile - /// Quality profile. Three tiers behind one design path; taps-per-phase + /// Quality profile. Four tiers behind one design path; taps-per-phase /// are pinned numbers (M2 design spike for balanced/transparent, the - /// 2026-08-07 economy18 re-pin for economy; verified by test_design.cpp): - /// the minimal even counts whose Kaiser designs meet the stopband spec - /// with >= 1 dB margin on a fine (12.5 Hz) sweep grid. + /// 2026-08-07 ladder re-pin for super_economy/economy; verified by + /// test_design.cpp): the minimal even counts whose Kaiser designs meet + /// the stopband spec with >= 1 dB margin on a fine (12.5 Hz) sweep grid. /// - /// | profile | stopband | passband | taps down | taps up | measured worst stop | - /// |-------------|----------|----------|-----------|---------|---------------------| - /// | economy | 70 dB | 18 kHz | 58 | 38 | -71.5 / -71.7 dB | - /// | balanced | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.8 dB | - /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.7 / -121.7 dB | + /// | profile | stopband | passband | taps down | taps up | measured worst stop | + /// |---------------|----------|----------|-----------|---------|---------------------| + /// | super_economy | 70 dB | 16 kHz | 40 | 28 | -71.7 / -71.7 dB | + /// | economy | 70 dB | 18 kHz | 58 | 38 | -71.5 / -71.7 dB | + /// | balanced | 70 dB | 19 kHz | 78 | 44 | -72.1 / -72.8 dB | + /// | transparent | 120 dB | 20 kHz | 184 | 96 | -121.7 / -121.7 dB | /// /// economy is the default, per the speed-first charter: going down, every /// alias product is confined above 20.1 kHz by arithmetic (see @@ -77,7 +78,11 @@ namespace tap::ratio { /// trade is the 18-19 kHz shelf moving into the transition band (measured /// -1.4 dB at 19 kHz going down); content there is where balanced (the /// v0.2 economy design, unchanged) remains the right pairing. transparent - /// exists for pristine/offline use. + /// exists for pristine/offline use. super_economy is the voice/comms + /// tier: it audibly shelves the top octave for listeners who can hear it + /// (-1.4 dB at 18 kHz, -5.9 dB at 19 kHz going down), which is a + /// different promise from economy's inaudible trade — it is never the + /// default, chosen only by name. struct profile { double passband_hz = 18000.0; ///< edge of the flat passband double stopband_atten_db = 70.0; ///< prototype stopband target @@ -89,6 +94,13 @@ namespace tap::ratio { /// canonical trip counts at compile time (M7 lever 2). static constexpr profile economy() noexcept { return {}; } + /// Voice/comms tier: 70 dB, 16 kHz passband — half of balanced's + /// MACs, at the cost of an audible top-octave shelf (see the table + /// above). Never a default; an explicit opt-in by name. + static constexpr profile super_economy() noexcept { + return {.passband_hz = 16000.0, .stopband_atten_db = 70.0, .taps_up_to_48k = 28, .taps_down_to_44k1 = 40}; + } + /// The v0.2 economy design, unchanged: 70 dB, flat to 19 kHz. For /// content where the top of the audible band must stay in the flat /// passband at half of transparent's cost. diff --git a/bridge/include/tap/ratio/ratio.h b/bridge/include/tap/ratio/ratio.h index 0170a3d..fa0cab0 100644 --- a/bridge/include/tap/ratio/ratio.h +++ b/bridge/include/tap/ratio/ratio.h @@ -25,8 +25,9 @@ // // Status: v0.3 — the profile ladder re-pin. economy (the default) moved to // an 18 kHz passband at 58/38 taps; the former economy design continues -// unchanged as balanced (19 kHz, 78/44); transparent is untouched. On top -// of v0.2's M7 codegen campaign (superblock walk, committed trip counts, +// unchanged as balanced (19 kHz, 78/44); super_economy (16 kHz, 40/28) is +// the explicit voice/comms tier; transparent is untouched. On top of +// v0.2's M7 codegen campaign (superblock walk, committed trip counts, // symmetry-halved tables). PLAN.md is the authoritative roadmap; // HANDOFF.md is the original design brief. #pragma once diff --git a/bridge/notebooks/design_spike.ipynb b/bridge/notebooks/design_spike.ipynb index 7a8a5e5..09210a8 100644 --- a/bridge/notebooks/design_spike.ipynb +++ b/bridge/notebooks/design_spike.ipynb @@ -20,6 +20,8 @@ "\n", "- **down** 48→44.1: L=147, stopband edge forced to 22.05 kHz (output Nyquist)\n", "- **up** 44.1→48: L=160, stopband edge 24 kHz (output Nyquist)\n", + "- **super_economy** (v0.3): 70 dB stopband, 16 kHz passband — the\n", + " voice/comms tier; audibly shelves the top octave, never a default\n", "- **economy** (default, v0.3 ladder re-pin): 70 dB stopband, 18 kHz\n", " passband — the spec relaxation twice over: going down, aliasing maps\n", " f → 44100−f, so nothing can fold below 20.1 kHz *arithmetically* (70 dB\n", @@ -37,10 +39,10 @@ "id": "c97a9e67", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:44:09.338794Z", - "iopub.status.busy": "2026-08-07T00:44:09.338575Z", - "iopub.status.idle": "2026-08-07T00:44:11.610246Z", - "shell.execute_reply": "2026-08-07T00:44:11.607830Z" + "iopub.execute_input": "2026-08-07T00:53:09.553988Z", + "iopub.status.busy": "2026-08-07T00:53:09.553765Z", + "iopub.status.idle": "2026-08-07T00:53:10.785497Z", + "shell.execute_reply": "2026-08-07T00:53:10.783927Z" } }, "outputs": [], @@ -86,6 +88,8 @@ " return out\n", "\n", "CASES = [ # name, L, M, fs_in, pass_hz, stop_hz, atten_db, taps (pinned)\n", + " (\"down super_economy\", 147, 160, 48000.0, 16000.0, 22050.0, 70.0, 40),\n", + " (\"up super_economy\", 160, 147, 44100.0, 16000.0, 24000.0, 70.0, 28),\n", " (\"down economy\", 147, 160, 48000.0, 18000.0, 22050.0, 70.0, 58),\n", " (\"down balanced\", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78),\n", " (\"down transparent\", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184),\n", @@ -115,10 +119,10 @@ "id": "b87fc098", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:44:11.613867Z", - "iopub.status.busy": "2026-08-07T00:44:11.613519Z", - "iopub.status.idle": "2026-08-07T00:44:39.686307Z", - "shell.execute_reply": "2026-08-07T00:44:39.685839Z" + "iopub.execute_input": "2026-08-07T00:53:10.788955Z", + "iopub.status.busy": "2026-08-07T00:53:10.788508Z", + "iopub.status.idle": "2026-08-07T00:53:35.385446Z", + "shell.execute_reply": "2026-08-07T00:53:35.382826Z" } }, "outputs": [ @@ -128,6 +132,8 @@ "text": [ "case L taps worst stop ripple f32 KiB Q15 KiB delay smp delay ms\n", "------------------------------------------------------------------------------------\n", + "down super_economy 147 40 -71.70dB ±0.0023dB 23.0 11.5 20.0 0.417\n", + "up super_economy 160 28 -71.68dB ±0.0023dB 17.5 8.8 14.0 0.317\n", "down economy 147 58 -71.49dB ±0.0028dB 33.3 16.7 29.0 0.604\n", "down balanced 147 78 -72.10dB ±0.0025dB 44.8 22.4 39.0 0.812\n", "down transparent 147 184 -121.39dB ±0.0000dB 105.7 52.8 92.0 1.917\n", @@ -173,18 +179,18 @@ "id": "d3db6503", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:44:39.695435Z", - "iopub.status.busy": "2026-08-07T00:44:39.695176Z", - "iopub.status.idle": "2026-08-07T00:44:54.427256Z", - "shell.execute_reply": "2026-08-07T00:44:54.425084Z" + "iopub.execute_input": "2026-08-07T00:53:35.392155Z", + "iopub.status.busy": "2026-08-07T00:53:35.391889Z", + "iopub.status.idle": "2026-08-07T00:53:51.082698Z", + "shell.execute_reply": "2026-08-07T00:53:51.080201Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -192,7 +198,7 @@ } ], "source": [ - "fig, axes = plt.subplots(3, 2, figsize=(11, 9.5), constrained_layout=True)\n", + "fig, axes = plt.subplots(4, 2, figsize=(11, 12.5), constrained_layout=True)\n", "for ax, (name, (h, L, M, fs, pas, stop, atten, taps)) in zip(axes.flat, designs.items()):\n", " f = np.arange(0.0, 2.2 * fs, 25.0)\n", " ax.plot(f / 1e3, response_db(h, L, fs, f), lw=0.8)\n", @@ -226,10 +232,10 @@ "id": "6f2f5522", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:44:54.430955Z", - "iopub.status.busy": "2026-08-07T00:44:54.430664Z", - "iopub.status.idle": "2026-08-07T00:44:54.656063Z", - "shell.execute_reply": "2026-08-07T00:44:54.654535Z" + "iopub.execute_input": "2026-08-07T00:53:51.086657Z", + "iopub.status.busy": "2026-08-07T00:53:51.086231Z", + "iopub.status.idle": "2026-08-07T00:53:51.307366Z", + "shell.execute_reply": "2026-08-07T00:53:51.304574Z" } }, "outputs": [ @@ -299,6 +305,8 @@ "\n", "| case | L | taps/phase (=MACs/out) | worst stopband | passband ripple | storage f32 / Q15 | group delay |\n", "|---|---|---|---|---|---|---|\n", + "| down super_economy | 147 | **40** | −71.7 dB | ±0.0023 dB | 23.0 / 11.5 KiB | 20.0 smp = 0.42 ms |\n", + "| up super_economy | 160 | **28** | −71.7 dB | ±0.0023 dB | 17.5 / 8.8 KiB | 14.0 smp = 0.32 ms |\n", "| down economy | 147 | **58** | −71.5 dB | ±0.0028 dB | 33.3 / 16.6 KiB | 29.0 smp = 0.60 ms |\n", "| down balanced | 147 | **78** | −72.1 dB | ±0.0025 dB | 44.8 / 22.4 KiB | 39.0 smp = 0.81 ms |\n", "| down transparent | 147 | **184** | −121.4 dB | ±0.00001 dB | 105.7 / 52.8 KiB | 92.0 smp = 1.92 ms |\n", diff --git a/bridge/notebooks/ratiotap_py.py b/bridge/notebooks/ratiotap_py.py index 338db2f..e57c442 100644 --- a/bridge/notebooks/ratiotap_py.py +++ b/bridge/notebooks/ratiotap_py.py @@ -71,7 +71,7 @@ def _load(): _LIB = _load() _DIRS = {"up": 0, "down": 1} -_PROFILES = {"economy": 0, "transparent": 1, "balanced": 2} +_PROFILES = {"economy": 0, "transparent": 1, "balanced": 2, "super_economy": 3} class RatioConverter: diff --git a/bridge/tests/reference/reference_vectors.h b/bridge/tests/reference/reference_vectors.h index 8899d7f..9979dee 100644 --- a/bridge/tests/reference/reference_vectors.h +++ b/bridge/tests/reference/reference_vectors.h @@ -214,6 +214,193 @@ namespace ratio_ref { -1.200256348e-01f, -2.962188721e-01f, -2.485519350e-01f, -4.943847656e-02f, 4.244292974e-01f, }; + inline constexpr std::array k_down_super_economy = { + -1.015259204e-05f, 5.546667126e-06f, 2.594562284e-05f, -1.178572347e-04f, 3.068805672e-04f, + -6.255468470e-04f, 1.129787182e-03f, -1.660679351e-03f, 2.126511186e-03f, -2.202021657e-03f, + 1.477087848e-03f, 4.502626543e-04f, -3.837757278e-03f, 8.547047153e-03f, -1.393882185e-02f, + 1.847196929e-02f, -1.898563653e-02f, 6.766628940e-03f, 9.758564830e-02f, 8.413082361e-02f, + 6.051670387e-02f, -5.075610802e-02f, 5.630544573e-02f, -4.054699838e-02f, -2.955985963e-01f, + -2.588064671e-01f, -2.718614042e-01f, 1.870456189e-01f, 3.643345237e-01f, 2.386139706e-02f, + -1.723825466e-03f, 7.976371795e-02f, 1.562482268e-01f, -8.495485038e-02f, -2.590205707e-02f, + 4.558668137e-01f, 3.831655681e-01f, 2.752818400e-03f, 5.571629480e-02f, -1.226320043e-01f, + -1.404701322e-01f, -1.893997490e-01f, -3.030709624e-01f, 1.777855307e-01f, -3.998723626e-01f, + -2.708303332e-01f, 1.742201746e-01f, -2.725160122e-01f, -7.040565461e-02f, -2.167613655e-01f, + -1.409084350e-01f, 9.910707921e-02f, 1.460096836e-01f, 7.366690040e-02f, -2.480370551e-01f, + 1.132675856e-01f, 5.835009813e-01f, 1.829723120e-01f, -1.834706068e-01f, 2.976960838e-01f, + -3.069406841e-03f, -6.037463620e-02f, 4.315304160e-01f, 1.222239155e-02f, -2.353785187e-01f, + -3.478101492e-01f, 3.989083692e-02f, 4.484979063e-02f, -9.422811121e-02f, 5.335760117e-02f, + -4.041724801e-01f, -1.818912029e-01f, -3.957364336e-02f, 2.662242651e-01f, 1.209114585e-02f, + -4.232506752e-01f, 9.006602317e-02f, -2.365026623e-01f, 8.428870887e-02f, 3.138769567e-01f, + -9.699444473e-02f, 2.892277241e-01f, 3.348120451e-01f, 2.447160147e-02f, -5.884958431e-02f, + -2.477722019e-01f, -1.742458493e-01f, 7.112656534e-02f, -6.559199840e-02f, -1.662593186e-01f, + -1.218198091e-01f, 6.627901644e-02f, -1.083000824e-01f, -4.781877100e-01f, -1.753766090e-01f, + 2.454894632e-01f, -1.129795462e-01f, -2.151307017e-01f, 1.265293509e-01f, -1.252094954e-01f, + 8.680757135e-02f, 1.056702808e-01f, -2.793166935e-01f, 4.466328025e-02f, -2.586034499e-02f, + -1.538571715e-01f, -1.650481820e-01f, -2.749477029e-01f, -2.554119229e-01f, -1.916070580e-01f, + 4.463365301e-02f, 7.963307947e-02f, 2.777312100e-01f, -3.627716005e-01f, -4.104351997e-01f, + 5.071405172e-01f, -2.160710283e-02f, 2.387059294e-02f, 1.769237220e-01f, -5.460076779e-02f, + 1.386133581e-01f, -1.897455752e-01f, -7.544410974e-02f, -5.144948140e-02f, -2.706493735e-01f, + -3.957918584e-01f, -2.717655003e-01f, 2.066213787e-01f, -2.383013070e-01f, -3.552723825e-01f, + -2.153333724e-01f, 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-1.353237927e-01f, 1.607261449e-01f, + 3.129783571e-01f, 2.867553532e-01f, 1.392505765e-01f, 3.935252130e-02f, -2.954190373e-01f, + -4.602202475e-01f, 2.612287365e-02f, -4.261559993e-02f, -3.365992904e-01f, -1.221119016e-01f, + 1.494317502e-01f, 3.379650116e-01f, 3.548559248e-01f, -1.035221145e-01f, -3.761890233e-01f, + -5.208128691e-02f, 9.389011562e-02f, -1.467510313e-02f, 1.478941441e-01f, 2.686503530e-01f, + -5.840929225e-02f, -4.296294153e-01f, 5.916706100e-02f, 1.541664600e-01f, -1.763072759e-01f, + 1.208736226e-01f, -3.470372036e-02f, 2.272935212e-01f, 4.787322581e-01f, 3.271855712e-01f, + 3.655274212e-02f, -2.307684869e-01f, 1.715684086e-01f, 3.350936472e-01f, 2.746520340e-01f, + -1.643293947e-01f, -2.587650418e-01f, -2.676864564e-01f, -5.069847107e-01f, -1.147344261e-01f, + -8.006554842e-02f, -1.754377037e-01f, 7.689779997e-02f, 3.062689677e-02f, 1.472563744e-01f, + 2.535805702e-01f, -3.430871069e-01f, -1.504944079e-02f, 2.308135927e-01f, -1.787392497e-01f, + 1.543650031e-01f, 2.977307737e-01f, 1.505583674e-01f, -2.967940271e-01f, -3.509657923e-03f, + 2.962929308e-01f, -4.020042717e-02f, 1.904154569e-01f, 3.226560950e-01f, 1.395183355e-01f, + 9.710964561e-02f, 2.933893502e-01f, -2.533008456e-01f, -5.548298359e-01f, -1.409095973e-01f, + 1.434721351e-01f, 3.785174489e-01f, -9.443001449e-02f, 9.378276020e-02f, 1.505495310e-01f, + -4.884847403e-01f, 1.876827031e-01f, 4.665426314e-01f, 1.063231304e-01f, -1.118549556e-01f, + -3.435035050e-01f, -1.425112784e-01f, -2.623419464e-01f, -2.724660635e-01f, -4.310430028e-03f, + -1.333885640e-01f, 3.278180584e-02f, 4.451449811e-01f, 2.752700448e-01f, 1.498057842e-01f, + 3.113774657e-01f, -1.486052014e-02f, -5.625017360e-02f, -1.711971164e-01f, -2.340540290e-01f, + 3.065921068e-01f, 1.292233318e-01f, 2.087968141e-01f, 3.011962771e-01f, 1.729973108e-01f, + 3.732199371e-01f, -1.125229970e-01f, -1.561176330e-01f, 1.580802351e-01f, -1.414895058e-01f, + -1.706008464e-01f, 9.816110879e-02f, 8.553349972e-02f, -4.430579767e-02f, 1.412950158e-01f, + -8.225315064e-02f, -3.200939000e-01f, 1.183822602e-01f, 5.390960723e-02f, -6.395121664e-02f, + -3.206050768e-02f, 1.605397016e-01f, 1.753463298e-01f, -1.929106414e-01f, -5.258009769e-03f, + 6.968005002e-02f, 1.235913336e-01f, 1.226625592e-01f, 6.390107423e-02f, 1.195885539e-01f, + -1.405617148e-01f, 2.197215520e-02f, 4.989067912e-01f, 1.436646432e-01f, -7.123325765e-02f, + 7.941399515e-02f, -2.425128222e-01f, 2.325573266e-01f, 3.784996867e-01f, -3.245289251e-02f, + 1.877853721e-01f, 4.109739512e-02f, 4.238085449e-02f, 3.612280488e-01f, 4.192551374e-01f, + -1.854119599e-01f, -1.381034106e-01f, 3.606429100e-01f, -9.130523354e-02f, 1.775066853e-01f, + -5.376206338e-02f, -4.722721577e-01f, 5.622735992e-02f, -9.029121697e-02f, -1.547871828e-01f, + 2.920623720e-01f, 3.184903264e-01f, -1.735342741e-01f, -2.028997689e-01f, 5.521108583e-02f, + -4.013951495e-02f, -2.802685834e-02f, 2.122878134e-01f, 2.760613859e-01f, 3.297073767e-02f, + -2.405436039e-01f, -1.885655373e-01f, -9.199631959e-02f, -1.838259995e-01f, -2.757438421e-01f, + -3.120280989e-02f, 3.636845350e-01f, -2.089970261e-01f, -4.542588592e-01f, 1.764543802e-01f, + 3.431119621e-01f, 3.563241661e-01f, 3.676918447e-01f, 2.532213032e-01f, -1.478522569e-01f, + -1.579188854e-01f, 3.054328263e-01f, -2.887795866e-01f, -4.664389193e-01f, 1.307193190e-03f, + -4.316175580e-01f, -5.264725164e-02f, 1.952322870e-01f, -1.323166490e-01f, 3.130342811e-02f, + -2.027205527e-01f, 1.057821792e-02f, 2.285608947e-01f, 1.995590925e-01f, 2.075518966e-01f, + -4.660631344e-02f, 9.681756049e-02f, -1.400614232e-01f, -3.893547058e-01f, -6.570001692e-02f, + 1.299939752e-01f, -6.285632029e-03f, -3.342238069e-01f, -3.431790173e-01f, -2.765033543e-01f, + 2.240796238e-01f, 2.545069158e-01f, -9.327946603e-02f, 2.159102261e-01f, -9.773913026e-02f, + 2.382648289e-01f, 2.861312032e-01f, -1.414553970e-01f, 1.065449864e-01f, -1.726171821e-01f, + 1.088154912e-01f, -6.188229099e-02f, -2.084082961e-01f, 3.960780501e-01f, 4.482112527e-01f, + 3.932774961e-01f, 2.809540629e-01f, 4.029757380e-01f, 1.486434042e-01f, -4.242221713e-01f, + -2.303070426e-01f, 1.251918226e-01f, -2.253722847e-01f, -3.645775318e-01f, 6.302907318e-02f, + -5.125465989e-02f, -4.971092343e-01f, -9.271737933e-02f, 3.395662010e-01f, -2.095221877e-01f, + -8.247038722e-02f, 2.288805097e-01f, 2.861037664e-02f, 1.829090416e-01f, -4.154159874e-02f, + -1.519978195e-01f, 1.126276329e-02f, -2.210655510e-01f, -3.323466703e-02f, 3.467239812e-02f, + -2.690400481e-01f, -1.494581103e-01f, 1.137333736e-01f, 1.073592752e-01f, -2.601491213e-01f, + -2.937719822e-01f, -3.344715536e-01f, -2.926048040e-01f, -1.578885503e-02f, -2.922272086e-01f, + -2.591825426e-01f, 3.690079227e-02f, 4.410995245e-01f, 4.142830372e-01f, -6.252654642e-02f, + 1.343632489e-01f, -1.223091781e-01f, -3.815192282e-01f, -1.211435720e-01f, -6.987414509e-02f, + -8.945634216e-02f, -4.890016094e-02f, 6.773811579e-02f, -6.020176690e-03f, -9.508309513e-02f, + -2.685576081e-01f, -1.202434003e-01f, 2.614101768e-01f, -2.086576261e-02f, -2.356748283e-01f, + -4.325933754e-01f, -3.667441607e-01f, 1.611007750e-01f, -5.393641815e-02f, 1.212315634e-01f, + -7.288121432e-02f, -3.316741586e-01f, 3.217907250e-01f, -1.059971526e-01f, -7.294581831e-02f, + 2.016043514e-01f, -3.613336012e-02f, 2.352437824e-01f, 5.932068080e-02f, 4.366912693e-02f, + 5.003922433e-02f, -2.982797623e-01f, -1.164021809e-02f, 1.331618130e-01f, -1.063455269e-01f, + -6.015203893e-02f, -1.377695352e-01f, -1.549842656e-01f, 4.458856955e-02f, -3.787532151e-01f, + -2.178318501e-01f, 4.487124979e-01f, 8.377071470e-02f, 1.230177134e-01f, -7.446999848e-02f, + -4.757368565e-01f, 1.183272079e-01f, -3.447525203e-02f, -1.261973381e-01f, -1.529085040e-01f, + -3.967022002e-01f, 3.453234434e-01f, 1.510764211e-01f, -2.558788657e-01f, 7.341340929e-02f, + 2.497880720e-02f, -9.142975509e-02f, -2.924621701e-01f, -3.228149712e-01f, -4.212294519e-01f, + -3.063657135e-02f, 3.295589089e-01f, -2.260433286e-01f, 5.988408625e-02f, + }; + inline constexpr std::array k_down_economy = { 8.910730685e-06f, -2.701886842e-05f, 6.103283158e-05f, -1.167573500e-04f, 1.999283704e-04f, -3.164291265e-04f, 4.907615366e-04f, -6.406812463e-04f, 8.266436635e-04f, -9.384463192e-04f, @@ -775,6 +962,227 @@ namespace ratio_ref { -2.293668836e-01f, -3.268848956e-01f, 3.325173631e-02f, 3.267023861e-01f, }; + inline constexpr std::array k_up_super_economy = { + 2.634748307e-05f, -4.436748713e-05f, -1.777423131e-05f, 2.848513832e-04f, -9.302373510e-04f, + 2.001351444e-03f, -3.222349333e-03f, 3.742250614e-03f, -2.352857729e-03f, -2.396933967e-03f, + 1.120977197e-02f, -2.316494472e-02f, 3.500474617e-02f, -4.002422094e-02f, 2.411533706e-02f, + 1.497844309e-01f, 2.133260109e-02f, 1.238013357e-01f, -5.367132649e-02f, -2.223475277e-02f, + 1.139542907e-01f, -1.753792614e-01f, -2.346331328e-01f, -3.254980147e-01f, -2.353756428e-01f, + -6.733570248e-02f, 3.609594405e-01f, 3.387112916e-01f, -1.247396544e-01f, 1.302334070e-01f, + -2.987245098e-02f, 1.632765234e-01f, 1.495896131e-01f, -3.108578026e-01f, 2.481004745e-01f, + 3.571985364e-01f, 4.308666289e-01f, 1.639488041e-01f, -1.506300569e-01f, 1.788598746e-01f, + -2.597291470e-01f, -1.350813806e-01f, -9.639916569e-02f, -4.443267882e-01f, 1.169321537e-01f, + 3.633458866e-03f, -5.812541246e-01f, -1.006804630e-01f, 1.895619333e-01f, -2.925718427e-01f, + -1.122333482e-01f, -7.945729047e-02f, -3.323457837e-01f, 2.223831788e-02f, 6.504739076e-02f, + 1.137902513e-01f, 2.114637792e-01f, -2.526870668e-01f, -1.330605298e-01f, 3.435117602e-01f, + 5.028823018e-01f, 2.679096162e-01f, -2.479939759e-01f, 1.489876211e-01f, 3.074903488e-01f, + -2.224165946e-01f, 9.707268327e-02f, 4.449829161e-01f, 4.032189399e-02f, -2.180550992e-01f, + -2.759001553e-01f, -2.949394286e-01f, 1.590926051e-01f, 4.256152734e-02f, -1.994286627e-01f, + 1.789891422e-01f, -3.038357198e-01f, -4.254911840e-01f, 2.092250250e-02f, -1.056352407e-01f, + 3.015221059e-01f, 1.524191052e-01f, -5.218742490e-01f, -5.133182928e-02f, 5.707608908e-02f, + -3.576194644e-01f, 2.415331453e-01f, 3.086466491e-01f, -1.286289841e-01f, 2.282500714e-01f, + 2.838413417e-01f, 2.966623604e-01f, 1.453476399e-02f, -1.626610309e-01f, -7.017721236e-02f, + -3.909262419e-01f, 7.802481204e-02f, 5.780551955e-02f, -1.949440539e-01f, -3.573394939e-02f, + -2.541554868e-01f, 9.664402157e-02f, 3.644140065e-02f, -3.835006058e-01f, -3.823392093e-01f, + -1.568042040e-01f, 2.678047121e-01f, -4.257509485e-03f, -3.310013413e-01f, 5.398565903e-02f, + 4.986251891e-02f, -1.438082308e-01f, 1.236222759e-01f, 1.180547997e-01f, -1.983532608e-01f, + -1.737563014e-01f, 1.402535737e-01f, -1.036656052e-01f, -1.988891810e-01f, -6.058474258e-02f, + -3.769614398e-01f, -1.918203980e-01f, -2.432668507e-01f, -1.891777813e-01f, 1.718445867e-01f, + -2.824933035e-03f, 2.976170778e-01f, -5.911633745e-02f, -7.048393488e-01f, 8.813716471e-02f, + 4.770867825e-01f, -8.161143959e-02f, -5.222602282e-03f, 2.393048257e-01f, -5.338808894e-02f, + 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-2.518458962e-01f, 6.754755229e-02f, 2.546039522e-01f, -1.525511444e-01f, -2.448524982e-01f, + 2.044855505e-01f, -3.851740062e-02f, -2.836390138e-01f, -3.950823098e-02f, + }; + inline constexpr std::array k_up_economy = { 6.789742201e-06f, -3.785208173e-05f, 1.134053164e-04f, -2.731592394e-04f, 5.101181450e-04f, -7.676394889e-04f, 9.002895094e-04f, -6.338239764e-04f, -1.953862957e-04f, 1.894126064e-03f, diff --git a/bridge/tests/test_converter.cpp b/bridge/tests/test_converter.cpp index 0ac69d4..c2a52a5 100644 --- a/bridge/tests/test_converter.cpp +++ b/bridge/tests/test_converter.cpp @@ -98,6 +98,12 @@ namespace { TEST(Converter, MatchesScipyDownEconomy) { check_reference(profile::economy(), ratio_ref::k_down_economy); } + TEST(Converter, MatchesScipyDownSuperEconomy) { + check_reference(profile::super_economy(), ratio_ref::k_down_super_economy); + } + TEST(Converter, MatchesScipyUpSuperEconomy) { + check_reference(profile::super_economy(), ratio_ref::k_up_super_economy); + } TEST(Converter, MatchesScipyDownBalanced) { check_reference(profile::balanced(), ratio_ref::k_down_balanced); } diff --git a/bridge/tests/test_design.cpp b/bridge/tests/test_design.cpp index 24ad6ee..da2378d 100644 --- a/bridge/tests/test_design.cpp +++ b/bridge/tests/test_design.cpp @@ -80,6 +80,12 @@ namespace { traits::k_phases, p.taps(), worst, static_cast(h.size() * sizeof(float)) / 1024.0); } + TEST(Design, DownSuperEconomyMeetsSpec) { + check_meets_spec(profile::super_economy(), "down super_economy"); + } + TEST(Design, UpSuperEconomyMeetsSpec) { + check_meets_spec(profile::super_economy(), "up super_economy"); + } TEST(Design, DownEconomyMeetsSpec) { check_meets_spec(profile::economy(), "down economy"); } diff --git a/bridge/tests/test_phase_table.cpp b/bridge/tests/test_phase_table.cpp index 1c457bb..01db411 100644 --- a/bridge/tests/test_phase_table.cpp +++ b/bridge/tests/test_phase_table.cpp @@ -75,6 +75,12 @@ namespace { TYPED_TEST(phase_table_test, UpEconomyEveryPhase) { check_table(profile::economy()); } + TYPED_TEST(phase_table_test, DownSuperEconomyEveryPhase) { + check_table(profile::super_economy()); + } + TYPED_TEST(phase_table_test, UpSuperEconomyEveryPhase) { + check_table(profile::super_economy()); + } TYPED_TEST(phase_table_test, DownBalancedEveryPhase) { check_table(profile::balanced()); } @@ -103,6 +109,8 @@ namespace { EXPECT_EQ(ue.storage_bytes(), 80u * 38u * 2u); // 5.9 KiB — Q15 halves it again const basic_phase_table ub(profile::balanced()); EXPECT_EQ(ub.storage_bytes(), 80u * 44u * 2u); // 6.9 KiB + const basic_phase_table us(profile::super_economy()); + EXPECT_EQ(us.storage_bytes(), 80u * 28u * 2u); // 4.4 KiB — the voice tier } } // namespace diff --git a/bridge/tools/capi/ratio_capi.cpp b/bridge/tools/capi/ratio_capi.cpp index fe622bd..c8385be 100644 --- a/bridge/tools/capi/ratio_capi.cpp +++ b/bridge/tools/capi/ratio_capi.cpp @@ -32,12 +32,13 @@ struct ratio_converter { extern "C" { ratio_converter* ratio_create(int direction, int profile, unsigned channels) { - if ((direction != 0 && direction != 1) || profile < 0 || profile > 2 || channels == 0) { + if ((direction != 0 && direction != 1) || profile < 0 || profile > 3 || channels == 0) { return nullptr; } const tap::ratio::profile p = profile == 0 ? tap::ratio::profile::economy() : profile == 1 ? tap::ratio::profile::transparent() - : tap::ratio::profile::balanced(); + : profile == 2 ? tap::ratio::profile::balanced() + : tap::ratio::profile::super_economy(); try { // unique_ptr owns the wrapper until the converter constructor has // succeeded, so a throw below cannot leak it. diff --git a/bridge/tools/capi/ratio_capi.h b/bridge/tools/capi/ratio_capi.h index 6965001..e49a801 100644 --- a/bridge/tools/capi/ratio_capi.h +++ b/bridge/tools/capi/ratio_capi.h @@ -23,7 +23,8 @@ typedef struct ratio_converter ratio_converter; /// ratio_destroy, where NULL is a safe no-op (the free() convention). /// direction: 0 = up (44.1 -> 48), 1 = down (48 -> 44.1). -/// profile: 0 = economy (default tier), 1 = transparent, 2 = balanced. +/// profile: 0 = economy (default tier), 1 = transparent, 2 = balanced, +/// 3 = super_economy (the voice/comms tier). /// Returns NULL on invalid arguments. ratio_converter* ratio_create(int direction, int profile, unsigned channels); void ratio_destroy(ratio_converter* c); diff --git a/bridge/tools/reference/make_reference_vectors.py b/bridge/tools/reference/make_reference_vectors.py index cf620e6..953dfe3 100644 --- a/bridge/tools/reference/make_reference_vectors.py +++ b/bridge/tools/reference/make_reference_vectors.py @@ -58,12 +58,14 @@ def xorshift_f32(count, seed): CASES = [ # tag, L, M, fs_in, pass_hz, stop_hz, atten, taps - ("down_economy", 147, 160, 48000.0, 18000.0, 22050.0, 70.0, 58), - ("down_balanced", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78), - ("down_transparent", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184), - ("up_economy", 160, 147, 44100.0, 18000.0, 24000.0, 70.0, 38), - ("up_balanced", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44), - ("up_transparent", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96), + ("down_super_economy", 147, 160, 48000.0, 16000.0, 22050.0, 70.0, 40), + ("down_economy", 147, 160, 48000.0, 18000.0, 22050.0, 70.0, 58), + ("down_balanced", 147, 160, 48000.0, 19000.0, 22050.0, 70.0, 78), + ("down_transparent", 147, 160, 48000.0, 20000.0, 22050.0, 120.0, 184), + ("up_super_economy", 160, 147, 44100.0, 16000.0, 24000.0, 70.0, 28), + ("up_economy", 160, 147, 44100.0, 18000.0, 24000.0, 70.0, 38), + ("up_balanced", 160, 147, 44100.0, 19000.0, 24000.0, 70.0, 44), + ("up_transparent", 160, 147, 44100.0, 20000.0, 24000.0, 120.0, 96), ] N_IN = 1000 From 7dd3ae7d0612646c602ff80f82d90eaf93346d46 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 01:01:35 +0000 Subject: [PATCH 25/44] Add the profile-ladder comparison notebook notebooks/profile_ladder.ipynb puts the four v0.3 tiers side by side, all measured against the shipping C++ through the C ABI (family convention): overlaid prototype responses both directions, the top-octave zoom that is the tier-choice decision plot, engine-measured passband sweeps (each tier asserted flat within +/-0.02 dB inside its own passband), the four-way program-material spectra with the alias/image species annotated, grouped MACs/storage/latency cost bars, and a measured quality-vs-compute scatter (997 Hz float SNR: 85.3 / 91.3 / 89.2 / 144.9 dB for super_economy/economy/balanced/transparent). Tier colors are fixed across every figure and CVD-validated (adjacent-pair delta-E >= 8.6); every series is direct-labeled so identity never rides on color alone. Committed executed. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/notebooks/profile_ladder.ipynb | 594 ++++++++++++++++++++++++++ 1 file changed, 594 insertions(+) create mode 100644 bridge/notebooks/profile_ladder.ipynb diff --git a/bridge/notebooks/profile_ladder.ipynb b/bridge/notebooks/profile_ladder.ipynb new file mode 100644 index 0000000..b253177 --- /dev/null +++ b/bridge/notebooks/profile_ladder.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "da66f733", + "metadata": {}, + "source": [ + "# The profile ladder, compared\n", + "\n", + "Four quality tiers behind one design path (PLAN §4, v0.3 ladder). This\n", + "notebook puts them side by side: frequency responses, the top-octave trade\n", + "each tier makes, cost in MACs/storage/latency, and quality measured through\n", + "the **actual shipping C++** via the C ABI (`ratiotap_py.py` ctypes bridge —\n", + "family convention: nothing here is a Python re-implementation except the\n", + "prototype *designs*, which mirror `tap::dsp::design_prototype` exactly and\n", + "are cross-checked against the engine by the design-spike notebook).\n", + "\n", + "| tier | stopband | passband | taps down/up | role |\n", + "|---|---|---|---|---|\n", + "| `super_economy` | 70 dB | 16 kHz | 40 / 28 | voice/comms — audible top-octave shelf, half of balanced's MACs |\n", + "| `economy` (default) | 70 dB | 18 kHz | 58 / 38 | speed-first default — inaudible trade |\n", + "| `balanced` | 70 dB | 19 kHz | 78 / 44 | the pre-v0.3 default, unchanged |\n", + "| `transparent` | 120 dB | 20 kHz | 184 / 96 | pristine/offline |\n", + "\n", + "Tier colors are fixed across every plot (colorblind-safe, validated:\n", + "adjacent-pair CVD ΔE ≥ 8.6); every series is also direct-labeled, so\n", + "identity never rides on color alone." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ea9fcb56", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:20.166084Z", + "iopub.status.busy": "2026-08-07T01:00:20.165877Z", + "iopub.status.idle": "2026-08-07T01:00:20.585311Z", + "shell.execute_reply": "2026-08-07T01:00:20.583920Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8 designs built\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ratiotap_py import RatioConverter\n", + "\n", + "# Fixed tier order and colors — identical in every figure below.\n", + "TIERS = { # name: (passband_hz, taps_down, taps_up, atten_db, color)\n", + " \"super_economy\": (16000.0, 40, 28, 70.0, \"#E69F00\"),\n", + " \"economy\": (18000.0, 58, 38, 70.0, \"#009E73\"),\n", + " \"balanced\": (19000.0, 78, 44, 70.0, \"#0072B2\"),\n", + " \"transparent\": (20000.0, 184, 96, 120.0, \"#CC79A7\"),\n", + "}\n", + "DIRS = {\"down\": (147, 160, 48000.0, 22050.0), \"up\": (160, 147, 44100.0, 24000.0)}\n", + "\n", + "def kaiser_beta(a):\n", + " if a > 50.0:\n", + " return 0.1102 * (a - 8.7)\n", + " if a > 21.0:\n", + " return 0.5842 * (a - 21.0) ** 0.4 + 0.07886 * (a - 21.0)\n", + " return 0.0\n", + "\n", + "def design(L, taps, cutoff_norm, beta):\n", + " # Mirrors tap::dsp::design_prototype + RatioTap's per-branch DC norm.\n", + " n = L * taps\n", + " i = np.arange(n)\n", + " center = 0.5 * (n - 1)\n", + " t = (i - center) / L\n", + " u = (i - center) / center\n", + " w = np.i0(beta * np.sqrt(np.maximum(0.0, 1.0 - u * u))) / np.i0(beta)\n", + " h = cutoff_norm * np.sinc(cutoff_norm * t) * w\n", + " h *= L / h.sum()\n", + " b = h.reshape(taps, L).sum(axis=0)\n", + " return (h.reshape(taps, L) / b).reshape(-1)\n", + "\n", + "def response_db(h, L, fs, freqs, chunk=512):\n", + " m = np.arange(len(h))\n", + " out = np.empty(len(freqs))\n", + " for k in range(0, len(freqs), chunk):\n", + " f = np.asarray(freqs[k:k + chunk])[:, None]\n", + " out[k:k + chunk] = 20 * np.log10(\n", + " np.abs(np.exp(-2j * np.pi * f * m / (L * fs)) @ h) / L)\n", + " return out\n", + "\n", + "H = {} # (dir, tier) -> prototype\n", + "for d, (L, M, fs, stop) in DIRS.items():\n", + " for name, (pas, td, tu, atten, _c) in TIERS.items():\n", + " taps = td if d == \"down\" else tu\n", + " H[d, name] = design(L, taps, (pas + stop) / fs, kaiser_beta(atten))\n", + "\n", + "def label_at_crossing(ax, f, r, level, name, color, dx=-0.15, ha=\"right\"):\n", + " # Anchor a series label where its own curve crosses `level` dB — each\n", + " # tier crosses at a different frequency, so labels never collide.\n", + " i = int(np.argmax(r <= level))\n", + " ax.annotate(name, xy=(f[i] / 1e3, r[i]), xytext=(f[i] / 1e3 + dx, r[i]),\n", + " color=color, fontsize=9, fontweight=\"bold\", ha=ha, va=\"center\")\n", + "\n", + "def tier_legend(ax):\n", + " ax.legend([plt.Line2D([], [], color=TIERS[n][4], lw=2) for n in TIERS],\n", + " list(TIERS), fontsize=8, frameon=False, loc=\"lower left\")\n", + "\n", + "print(f\"{len(H)} designs built\")" + ] + }, + { + "cell_type": "markdown", + "id": "f3a784ea", + "metadata": {}, + "source": [ + "## Frequency response: the whole ladder\n", + "\n", + "Both directions, all four tiers. Every 70 dB tier holds the same stopband\n", + "contract — what changes is *where the passband ends* and how steep the\n", + "transition must therefore be (which is exactly what the tap counts buy)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "130df8c1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:20.588381Z", + "iopub.status.busy": "2026-08-07T01:00:20.588086Z", + "iopub.status.idle": "2026-08-07T01:00:29.217240Z", + "shell.execute_reply": "2026-08-07T01:00:29.215916Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(2, 1, figsize=(11, 7), constrained_layout=True)\n", + "LEVELS = {\"super_economy\": -20.0, \"economy\": -35.0, \"balanced\": -50.0, \"transparent\": -90.0}\n", + "for ax, d in zip(axes, (\"down\", \"up\")):\n", + " L, M, fs, stop = DIRS[d]\n", + " f = np.arange(0.0, 30000.0, 12.5)\n", + " for name, (pas, td, tu, atten, color) in TIERS.items():\n", + " r = response_db(H[d, name], L, fs, f)\n", + " ax.plot(f / 1e3, r, color=color, lw=1.4)\n", + " label_at_crossing(ax, f, r, LEVELS[name], name, color)\n", + " ax.axvline(pas / 1e3, color=color, ls=\":\", lw=0.7, alpha=0.5)\n", + " ax.axhline(-70, color=\"0.4\", ls=\":\", lw=0.8)\n", + " ax.axhline(-120, color=\"0.4\", ls=\":\", lw=0.8)\n", + " ax.axvline(stop / 1e3, color=\"0.2\", ls=\"--\", lw=0.8)\n", + " ax.text(stop / 1e3 + 0.25, -30, f\"stopband edge {stop/1e3:g} kHz\",\n", + " fontsize=8, color=\"0.2\")\n", + " ax.set_ylim(-140, 5)\n", + " ax.set_xlim(0, 30)\n", + " ax.set_ylabel(\"dB\")\n", + " ax.grid(alpha=0.3)\n", + " tier_legend(ax)\n", + " ax.set_title(f\"{d}: 48\\u219244.1\" if d == \"down\" else f\"{d}: 44.1\\u219248\")\n", + "axes[1].set_xlabel(\"kHz\")\n", + "fig.suptitle(\"prototype magnitude responses \\u2014 dotted verticals mark each tier's passband edge\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "bc2e562a", + "metadata": {}, + "source": [ + "## The top octave — the decision plot\n", + "\n", + "This is the only place the 70 dB tiers differ audibly: where the shelf into\n", + "the transition band begins. `economy` gives up 18–19 kHz (inaudible for\n", + "almost all listeners and program material); `super_economy` gives up\n", + "16–19 kHz, which *is* audible to young ears on wideband material — that is\n", + "why it is opt-in by name and never a default." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b6d0b1d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:29.219913Z", + "iopub.status.busy": "2026-08-07T01:00:29.219685Z", + "iopub.status.idle": "2026-08-07T01:00:32.302153Z", + "shell.execute_reply": "2026-08-07T01:00:32.300789Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "super_economy down shelf: -1.42 dB @ 18 kHz, -5.85 dB @ 19 kHz\n" + ] + } + ], + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(11, 4), constrained_layout=True)\n", + "ZLEVELS = {\"super_economy\": -2.0, \"economy\": -3.5, \"balanced\": -5.0, \"transparent\": -7.0}\n", + "for ax, d in zip(axes, (\"down\", \"up\")):\n", + " L, M, fs, stop = DIRS[d]\n", + " f = np.arange(12000.0, 22500.0, 12.5)\n", + " for name, (pas, td, tu, atten, color) in TIERS.items():\n", + " r = response_db(H[d, name], L, fs, f)\n", + " ax.plot(f / 1e3, r, color=color, lw=1.6)\n", + " label_at_crossing(ax, f, r, ZLEVELS[name], name, color)\n", + " for probe in (16.0, 18.0, 19.0, 20.0):\n", + " ax.axvline(probe, color=\"0.85\", lw=0.6, zorder=0)\n", + " ax.set_ylim(-10, 0.6)\n", + " ax.set_xlim(12, 22.5)\n", + " ax.grid(alpha=0.3)\n", + " ax.set_ylabel(\"dB\")\n", + " ax.set_xlabel(\"kHz\")\n", + " ax.set_title(\"down: 48\\u219244.1\" if d == \"down\" else \"up: 44.1\\u219248\")\n", + "fig.suptitle(\"the audible-band shelf, tier by tier (zoom of the plot above)\")\n", + "plt.show()\n", + "# The numbers quoted in PLAN section 4, asserted from the same designs:\n", + "r18 = response_db(H[\"down\", \"super_economy\"], 147, 48000.0, np.array([18000.0]))[0]\n", + "r19 = response_db(H[\"down\", \"super_economy\"], 147, 48000.0, np.array([19000.0]))[0]\n", + "print(f\"super_economy down shelf: {r18:+.2f} dB @ 18 kHz, {r19:+.2f} dB @ 19 kHz\")\n", + "assert -1.6 < r18 < -1.2 and -6.3 < r19 < -5.5" + ] + }, + { + "cell_type": "markdown", + "id": "6d6fadeb", + "metadata": {}, + "source": [ + "## Measured through the shipping engine\n", + "\n", + "Swept sine probes 48 → 44.1 through the C ABI, fit at the output rate.\n", + "Every tier must be flat (±0.02 dB) inside **its own** passband; the shelf\n", + "begins exactly where the design says. This is the engine, not the design\n", + "math." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "848ed7a6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:32.304951Z", + "iopub.status.busy": "2026-08-07T01:00:32.304685Z", + "iopub.status.idle": "2026-08-07T01:00:32.886666Z", + "shell.execute_reply": "2026-08-07T01:00:32.885361Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "all tiers flat within ±0.02 dB inside their own passbands\n" + ] + } + ], + "source": [ + "fs_in, fs_out = 48000.0, 44100.0\n", + "\n", + "def measured_gain_db(profile, freq_hz):\n", + " c = RatioConverter(direction=\"down\", profile=profile)\n", + " n = 1 << 15\n", + " x = (0.5 * np.sin(2 * np.pi * freq_hz / fs_in * np.arange(n))).astype(np.float32)\n", + " y = c.process(x)[int(c.latency_input_frames) * 2:]\n", + " w = 2 * np.pi * freq_hz / fs_out\n", + " a = 2 / len(y) * np.abs(np.sum(y * np.exp(-1j * w * np.arange(len(y)))))\n", + " return 20 * np.log10(a / 0.5)\n", + "\n", + "freqs = np.array([100, 1000, 4000, 8000, 12000, 14000, 15000, 15800, 16000,\n", + " 17000, 17800, 18000, 18500, 19000, 19500, 19800, 20200, 20600, 21000])\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "for k, (name, (pas, td, tu, atten, color)) in enumerate(TIERS.items()):\n", + " g = np.array([measured_gain_db(name, fq) for fq in freqs])\n", + " ax.plot(freqs / 1e3, g, \"o-\", color=color, lw=1.4, ms=4)\n", + " # Label above each tier's own passband edge; alternate heights so the\n", + " # 1-kHz-apart economy/balanced pair cannot collide.\n", + " ax.annotate(name, xy=(pas / 1e3, 0.0), xytext=(pas / 1e3 - 0.1, 0.55 + 0.75 * (k % 2)),\n", + " color=color, fontsize=9, fontweight=\"bold\", ha=\"right\",\n", + " arrowprops=dict(arrowstyle=\"-\", color=color, lw=0.7))\n", + " inside = freqs <= pas\n", + " assert np.all(np.abs(g[inside]) < 0.02), name # flat in own passband\n", + "ax.set_ylim(-12, 2.1)\n", + "ax.set_xlim(0, 22)\n", + "ax.grid(alpha=0.3)\n", + "tier_legend(ax)\n", + "ax.set_xlabel(\"kHz\")\n", + "ax.set_ylabel(\"measured gain (dB)\")\n", + "ax.set_title(\"shipping engine, 48\\u219244.1: flat to each tier's edge, shelf where designed\")\n", + "plt.show()\n", + "print(\"all tiers flat within \\u00b10.02 dB inside their own passbands\")" + ] + }, + { + "cell_type": "markdown", + "id": "f5de96f5", + "metadata": {}, + "source": [ + "## Program material through each tier\n", + "\n", + "The demo notebook's program — 997 Hz + 6 kHz + 17.5 kHz tones with a\n", + "hostile 23 kHz ultrasonic component — down-converted through all four\n", + "tiers. The two spurious species behave exactly as the acceptance contract\n", + "says: the decimation **alias** of 23 kHz folds to 21.1 kHz (ultrasonic by\n", + "arithmetic, bounded by the stopband), and the upsampling **image** at\n", + "25 kHz folds in-band to 19.1 kHz at stopband depth. Note transparent's\n", + "−120 dB floor, and that every 70 dB tier keeps the audible band clean." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c26cf5cb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:32.889590Z", + "iopub.status.busy": "2026-08-07T01:00:32.889342Z", + "iopub.status.idle": "2026-08-07T01:00:33.583175Z", + "shell.execute_reply": "2026-08-07T01:00:33.581615Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_in = 1 << 17\n", + "t = np.arange(n_in) / fs_in\n", + "tones = [(997.0, 0.25), (6000.0, 0.25), (17500.0, 0.25), (23000.0, 0.15)]\n", + "x = sum(a * np.sin(2 * np.pi * f0 * t) for f0, a in tones).astype(np.float32)\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(11, 6.5), constrained_layout=True)\n", + "for ax, (name, (pas, td, tu, atten, color)) in zip(axes.flat, TIERS.items()):\n", + " c = RatioConverter(direction=\"down\", profile=name)\n", + " y = np.concatenate([c.process(x), c.flush()])[int(c.latency_input_frames) * 2:]\n", + " win = np.blackman(len(y))\n", + " spec = 20 * np.log10(np.maximum(np.abs(np.fft.rfft(y * win)) / (win.sum() / 2), 1e-12))\n", + " fbin = np.fft.rfftfreq(len(y), 1 / fs_out)\n", + " ax.plot(fbin / 1e3, spec, lw=0.5, color=color)\n", + " ax.axvline(20, color=\"0.3\", ls=\":\", lw=0.8)\n", + " ax.axhline(-16.5 - (atten + 1), color=\"0.5\", ls=\":\", lw=0.8)\n", + " ax.set_ylim(-160, 0)\n", + " ax.set_xlim(0, 22.05)\n", + " ax.grid(alpha=0.3)\n", + " tone_mask = np.zeros(len(fbin), bool)\n", + " for f0, _ in tones[:3]:\n", + " tone_mask |= np.abs(fbin - f0) < 80\n", + " audible = ~tone_mask & (fbin > 200) & (fbin < 20000)\n", + " worst = spec[audible].max()\n", + " ax.set_title(f\"{name}: worst audible product {worst:.0f} dBFS\", fontsize=10)\n", + " ax.set_ylabel(\"dBFS\")\n", + " ax.set_xlabel(\"kHz\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "dbca00e2", + "metadata": {}, + "source": [ + "## Cost: MACs, storage, latency\n", + "\n", + "One tier, one color, three costs. Solid bars are the down direction\n", + "(48 → 44.1, the expensive one), hatched bars are up. Storage is the\n", + "shipping symmetry-halved table (`ceil(L/2)` rows, f32; Q15 halves it\n", + "again)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8be1816e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:33.586006Z", + "iopub.status.busy": "2026-08-07T01:00:33.585788Z", + "iopub.status.idle": "2026-08-07T01:00:34.147196Z", + "shell.execute_reply": "2026-08-07T01:00:34.145881Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "names = list(TIERS)\n", + "xd = np.arange(len(names))\n", + "W = 0.38\n", + "fig, axes = plt.subplots(1, 3, figsize=(11, 3.6), constrained_layout=True)\n", + "def bars(ax, vals_down, vals_up, title, unit):\n", + " for i, name in enumerate(names):\n", + " color = TIERS[name][4]\n", + " ax.bar(i - W / 2, vals_down[i], W, color=color)\n", + " ax.bar(i + W / 2, vals_up[i], W, color=color, hatch=\"///\",\n", + " edgecolor=\"white\", lw=0)\n", + " for dx, v in ((-W / 2, vals_down[i]), (W / 2, vals_up[i])):\n", + " ax.text(i + dx, v, f\"{v:g}\", ha=\"center\", va=\"bottom\", fontsize=8)\n", + " ax.set_xticks(xd, names, rotation=20, fontsize=8)\n", + " ax.set_title(title, fontsize=10)\n", + " ax.set_ylabel(unit)\n", + " ax.grid(alpha=0.3, axis=\"y\")\n", + " ax.margins(y=0.15)\n", + "bars(axes[0], [TIERS[n][1] for n in names], [TIERS[n][2] for n in names],\n", + " \"MACs per output frame\", \"taps/phase\")\n", + "bars(axes[1], [round(74 * TIERS[n][1] * 4 / 1024, 1) for n in names],\n", + " [round(80 * TIERS[n][2] * 4 / 1024, 1) for n in names],\n", + " \"table storage (f32, stored half)\", \"KiB\")\n", + "bars(axes[2], [round(TIERS[n][1] / 2 / 48000 * 1e3, 2) for n in names],\n", + " [round(TIERS[n][2] / 2 / 44100 * 1e3, 2) for n in names],\n", + " \"latency (group delay)\", \"ms\")\n", + "axes[0].legend(handles=[plt.Rectangle((0, 0), 1, 1, fc=\"0.55\"),\n", + " plt.Rectangle((0, 0), 1, 1, fc=\"0.55\", hatch=\"///\", ec=\"white\")],\n", + " labels=[\"down 48\\u219244.1\", \"up 44.1\\u219248\"],\n", + " loc=\"upper left\", fontsize=8, frameon=False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3d0fabac", + "metadata": {}, + "source": [ + "## Quality vs cost, measured\n", + "\n", + "997 Hz half-scale through the shipping engine (float, down direction):\n", + "fit the fundamental, everything left is the residual. The 70 dB tiers all\n", + "sit on their imaging floors in the high 80s/low 90s; transparent buys\n", + "~25 dB more for 3–4.6× the MACs. (Q15 numbers, pinned by the C++ suite,\n", + "tell the embedded story: the *format* floors near 76 dB regardless of\n", + "tier — which is why economy-class tiers are the right Q15 pairing.)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "66972308", + "metadata": { + "execution": { + "iopub.execute_input": "2026-08-07T01:00:34.149940Z", + "iopub.status.busy": "2026-08-07T01:00:34.149667Z", + "iopub.status.idle": "2026-08-07T01:00:34.345638Z", + "shell.execute_reply": "2026-08-07T01:00:34.344487Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "super_economy 40 MACs/out SNR 85.3 dB\n", + "economy 58 MACs/out SNR 91.3 dB\n", + "balanced 78 MACs/out SNR 89.2 dB\n", + "transparent 184 MACs/out SNR 144.9 dB\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def measured_snr_db(profile, freq_hz=997.0):\n", + " c = RatioConverter(direction=\"down\", profile=profile)\n", + " n = 1 << 16\n", + " x = (0.5 * np.sin(2 * np.pi * freq_hz / fs_in * np.arange(n))).astype(np.float32)\n", + " y = c.process(x)[int(c.latency_input_frames) * 2:].astype(np.float64)\n", + " i = np.arange(len(y))\n", + " w = 2 * np.pi * freq_hz / fs_out\n", + " A = np.column_stack([np.sin(w * i), np.cos(w * i)])\n", + " coef, *_ = np.linalg.lstsq(A, y, rcond=None)\n", + " resid = y - A @ coef\n", + " return 10 * np.log10((coef @ coef) / 2 / np.mean(resid ** 2))\n", + "\n", + "OFFS = {\"super_economy\": (8, 8, \"left\"), \"economy\": (-8, 6, \"right\"),\n", + " \"balanced\": (8, -12, \"left\"), \"transparent\": (-10, -4, \"right\")}\n", + "plt.figure(figsize=(7.5, 4.2))\n", + "for name, (pas, td, tu, atten, color) in TIERS.items():\n", + " snr = measured_snr_db(name)\n", + " dx, dy, ha = OFFS[name]\n", + " plt.scatter(td, snr, s=90, color=color, zorder=3)\n", + " plt.annotate(f\"{name}\\n{snr:.1f} dB\", xy=(td, snr), xytext=(dx, dy),\n", + " textcoords=\"offset points\", ha=ha, va=\"center\",\n", + " color=color, fontsize=9, fontweight=\"bold\")\n", + " print(f\"{name:14s} {td:3d} MACs/out SNR {snr:.1f} dB\")\n", + "plt.xlabel(\"MACs per output frame (down)\")\n", + "plt.ylabel(\"measured SNR at 997 Hz (dB)\")\n", + "plt.title(\"shipping engine, float: quality vs compute\")\n", + "plt.grid(alpha=0.3)\n", + "plt.xlim(0, 215)\n", + "plt.ylim(78, 152)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b591c5c1", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| tier | MACs down/up | storage f32 (stored) | latency down | measured 997 Hz float | Q15 997 Hz (C++ suite) | the trade |\n", + "|---|---|---|---|---|---|---|\n", + "| `super_economy` | 40 / 28 | 11.6 / 8.8 KiB | 0.42 ms | ~85 dB | 77.7 dB | audible top-octave shelf (−1.4 dB @ 18 k, −5.9 dB @ 19 k down) |\n", + "| `economy` (default) | 58 / 38 | 16.8 / 11.9 KiB | 0.60 ms | ~91 dB | 76.5 dB | 18–19 kHz into the transition — inaudible |\n", + "| `balanced` | 78 / 44 | 22.5 / 13.8 KiB | 0.81 ms | ~89 dB | 76.1 dB | none vs the pre-v0.3 default (it *is* that design) |\n", + "| `transparent` | 184 / 96 | 53.2 / 30.0 KiB | 1.92 ms | ~115+ dB | 72.8 dB | 2–5× the compute; Q15 *worse* (coefficient noise stacks with taps) |\n", + "\n", + "Guidance: `economy` unless you have a reason; `balanced` when the\n", + "18–19 kHz shelf must stay flat; `transparent` for pristine float/Q31\n", + "offline work; `super_economy` for voice/comms/Bluetooth-class links where\n", + "the top octave is already gone — in Q15 it is *quieter* than transparent\n", + "and a quarter of balanced's compute." + ] + } + ], + "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": "3.11.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 879dcbd45f5310587eeaba9d6377712b8a376520 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 01:55:08 +0000 Subject: [PATCH 26/44] Re-record icount baselines for the v0.3 profile ladder MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Harvested from the ratchet job's measurements on 924b737 (QEMU counts are deterministic; the job measures all targets even after a gate failure, the designed serial-harvest path). The six economy workloads land at the new 58/38-tap design: M33 -23.5/-25.5% (down q15/float) and -11.6/-13.6% (up), M55 -21.4/-22.6% and -8.7/-10.4%, Hexagon -21.8/-23.3% and -3.7/-10.6% — the tap-count cut, delivered on silicon paths. The two new super_economy Q15 scenarios record their first baselines (e.g. M55 down 47.7M vs economy's 58.3M). The transparent legs moved only inside the gate (+/-1.3% worst) and are re-recorded at measured per --update semantics. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/bench/baselines.json | 54 ++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 24 deletions(-) diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json index 4458ec2..7050f56 100644 --- a/bridge/bench/baselines.json +++ b/bridge/bench/baselines.json @@ -1,32 +1,38 @@ { "hexagon": { - "down_float_eco": 408799302, - "down_float_tr": 967322580, - "down_q15_eco": 69795923, - "down_q31_eco": 67717362, - "up_float_eco": 253482716, - "up_float_tr": 551268731, - "up_q15_eco": 43962582, - "up_q31_eco": 43918255 + "down_float_eco": 313564771, + "down_float_tr": 979598163, + "down_q15_eco": 54576857, + "down_q15_se": 39378689, + "down_q31_eco": 54524022, + "up_float_eco": 226581827, + "up_float_tr": 553818763, + "up_q15_eco": 42340774, + "up_q15_se": 32106712, + "up_q31_eco": 42358271 }, "m33": { - "down_float_eco": 2405598841, - "down_float_tr": 5786890466, - "down_q15_eco": 319593940, - "down_q31_eco": 421757767, - "up_float_eco": 1483417327, - "up_float_tr": 3287871636, - "up_q15_eco": 208462743, - "up_q31_eco": 272069248 + "down_float_eco": 1791425947, + "down_float_tr": 5772164016, + "down_q15_eco": 244450483, + "down_q15_se": 179742418, + "down_q31_eco": 315684738, + "up_float_eco": 1282189491, + "up_float_tr": 3280145822, + "up_q15_eco": 184359117, + "up_q15_se": 140538142, + "up_q31_eco": 235400465 }, "m55": { - "down_float_eco": 96760015, - "down_float_tr": 218246138, - "down_q15_eco": 74152380, - "down_q31_eco": 126978624, - "up_float_eco": 62737997, - "up_float_tr": 127311142, - "up_q15_eco": 49644781, - "up_q31_eco": 82296302 + "down_float_eco": 74885193, + "down_float_tr": 219532760, + "down_q15_eco": 58251264, + "down_q15_se": 47710153, + "down_q31_eco": 97748073, + "up_float_eco": 56229455, + "up_float_tr": 128890219, + "up_q15_eco": 45305070, + "up_q15_se": 39607853, + "up_q31_eco": 73399258 } } From cf5a4a27d1cf0112394de5af8bfbcf1dd7fabe17 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 02:02:01 +0000 Subject: [PATCH 27/44] Dedup CI: one surviving run per head SHA across push and pull_request MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A branch push and its open PR fired the full matrix twice for the same commit (both run sets are visible on any PR of this branch). Concurrency groups keyed on the head SHA collapse the pair to one surviving run — the later-starting PR run cancels the push run — while a branch with no PR keeps its push-triggered CI, which the pre-PR baseline-harvest flow (PLAN section 7) depends on. Keyed per workflow so CI and the style gate never cancel each other. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/.github/workflows/ci.yml | 9 +++++++++ bridge/.github/workflows/style.yml | 5 +++++ 2 files changed, 14 insertions(+) diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index 1ec8cc9..3087c7d 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -4,6 +4,15 @@ on: push: pull_request: +# Dedup: a branch push and its open PR fire simultaneous runs of the same +# commit. Keying the group on the head SHA collapses that pair to one +# surviving run (the later-starting PR run cancels the push run), while a +# branch with no PR keeps its push-triggered CI — the pre-PR +# baseline-harvest flow (scripts/icount.py, PLAN section 7) depends on it. +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.head.sha || github.sha }} + cancel-in-progress: true + jobs: build-test: name: ${{ matrix.name }} diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml index 53f7332..1376510 100644 --- a/bridge/.github/workflows/style.yml +++ b/bridge/.github/workflows/style.yml @@ -6,6 +6,11 @@ name: Tap House Style # repo's own translation units (scripts/tidy.sh is the local mirror). on: [push, pull_request] +# Same push+PR dedup as ci.yml: one surviving run per head SHA per workflow. +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.head.sha || github.sha }} + cancel-in-progress: true + jobs: drift: uses: tap/taphouse/.github/workflows/drift-check.yml@v5 From f9bd873a7d59b99fe01d2b36d0014a07f7e33d25 Mon Sep 17 00:00:00 2001 From: Claude Date: Fri, 7 Aug 2026 02:21:38 +0000 Subject: [PATCH 28/44] CI dedup, take two: branch-filter push instead of SHA-keyed cancellation MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The SHA-keyed cancel-in-progress scheme left the cancelled push run's checks attached to the PR head commit, so the merge box read "10 cancelled, 10 successful — some checks haven't completed yet" on a fully green head. Push runs are now filtered to main: a PR branch gets exactly one run set (pull_request events) and a clean merge box; main keeps push coverage; a branch with no PR yet runs via workflow_dispatch (the pre-PR baseline-harvest path) or by opening the PR first. Concurrency now only cancels superseded in-flight runs per PR/ref — those cancellations attach to the old commit, never the current head. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01C1fs1FmoRxYgVLwATD3HB9 --- bridge/.github/workflows/ci.yml | 18 ++++++++++++------ bridge/.github/workflows/style.yml | 11 ++++++++--- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index 3087c7d..0665366 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -1,16 +1,22 @@ name: CI +# Dedup: push runs only on main, so a PR branch gets exactly one run set +# (the pull_request events) and its merge box never shows the cancelled +# twin a SHA-keyed cancellation scheme would leave attached to the head +# commit. A branch with no PR yet runs CI via workflow_dispatch — the +# pre-PR baseline-harvest flow (scripts/icount.py, PLAN section 7) — or +# simply by opening the PR first and harvesting from its run. on: push: + branches: [main] pull_request: + workflow_dispatch: -# Dedup: a branch push and its open PR fire simultaneous runs of the same -# commit. Keying the group on the head SHA collapses that pair to one -# surviving run (the later-starting PR run cancels the push run), while a -# branch with no PR keeps its push-triggered CI — the pre-PR -# baseline-harvest flow (scripts/icount.py, PLAN section 7) depends on it. +# Superseded-run cancellation: a new push to the same PR (or to main) +# cancels the previous in-flight run. Those cancelled checks attach to the +# old commit, so the current head stays clean. concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.head.sha || github.sha }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} cancel-in-progress: true jobs: diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml index 1376510..06bcf13 100644 --- a/bridge/.github/workflows/style.yml +++ b/bridge/.github/workflows/style.yml @@ -4,11 +4,16 @@ name: Tap House Style # pre-commit hook; this adds (1) a drift check against the canonical TapHouse # configs and (2) clang-tidy naming + mandatory-braces enforcement over this # repo's own translation units (scripts/tidy.sh is the local mirror). -on: [push, pull_request] +# Same dedup scheme as ci.yml: PR branches run on pull_request events only, +# main runs on push, superseded in-flight runs get cancelled per PR/ref. +on: + push: + branches: [main] + pull_request: + workflow_dispatch: -# Same push+PR dedup as ci.yml: one surviving run per head SHA per workflow. concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.head.sha || github.sha }} + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} cancel-in-progress: true jobs: From 1c3ef7e4d828ee2ff38f5e4f92122a8e114c0e62 Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 26 Sep 2026 16:11:20 +0000 Subject: [PATCH 29/44] Bump DspTap to 0eb09fa and SampleRateTap to 2b4dff1; re-record icount DspTap #38 shares the Kaiser window's Bessel series across the prototype design (bit-identical coefficients). RatioTap designs at construction for every profile, so every workload's construction got cheaper: per profile and direction, identical across float/Q15/Q31 -- M33 -37..-299 M, Hexagon -4.7..-35 M, M55 -0.6..-4.6 M. Ten M33 and eight Hexagon scenarios left the two-sided gate (M33 down_q15_eco -28.9%); baselines re-recorded on all three targets. The test-only sampleratetap pin moves in step (no header changes in 5315689..2b4dff1) so both repos keep the identical dsptap tree the dev-only include path relies on. PLAN.md: M7e ledger entry, and a correction. M7c deferred coefficient baking because "construction is <0.3% of every workload"; a construct-only measurement of every scenario shows 3-5% on M55, 5-37% on Hexagon and up to 63% of the M33 Q15 workloads (74% before this change), which dilutes the M33 gate for hot-path regressions ~2-3x. Host: 78/78 under GCC and clang with TAP_RATIO_WERROR; scripts/tidy.sh clean. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01G3HxzEGiZp7jStoMuYNuBK --- bridge/PLAN.md | 37 ++++++++++++++++++-- bridge/bench/baselines.json | 60 ++++++++++++++++----------------- bridge/submodules/dsptap | 2 +- bridge/submodules/sampleratetap | 2 +- 4 files changed, 66 insertions(+), 35 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 3812c48..60d62ad 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -264,9 +264,13 @@ executed (it measures the shipping C++, not a Python re-implementation). scenarios stay loop-shaped and flat), **M33 Q15 −2.6/−3.4%**; float everywhere within noise of flat (soft-double MAC bound on M33, FP64 chain bound on M55). Coefficient *baking* (committed tables in rodata) - remains un-pulled: construction is <0.3% of every workload, so its - value is boot time and RAM on MCUs, not instruction counts — deferred - until a consumer needs it. + remains un-pulled — deferred until a consumer needs it. (This entry + first justified the deferral with "construction is <0.3% of every + workload"; measured directly on 2026-09-26 it is not — see the M7e + entry: 3–5% on M55, 5–37% on Hexagon, and on M33 up to 63% of the Q15 + workloads, 74% before the shared-window change. Baking would therefore + move the M33/Hexagon fixed-point counts a lot, while the audio path it + leaves untouched is what the ratchet is meant to watch.) - **M7d — polyphase symmetry storage halving (landed).** Lever 3, in two PRs per the substrate discipline: `tap::dsp::dot_row_reversed` landed in DspTap first (hist forward × row backward, SMLALDX swapped-lane @@ -285,6 +289,33 @@ executed (it measures the shipping C++, not a Python re-implementation). (TAP_RATIO_MIRRORED_DOT_ATTR), the same measured-per-target pattern as the tap::dsp kernel gates. Worst residual rides inside the ±3% gate (Hexagon down_q31 +2.7%); Arm came out slightly ahead (M33 Q31 −2.5%). + - **M7e — DspTap pin 28a34a1 → 0eb09fa: shared Kaiser-window Bessel + series (re-record, not a lever).** DspTap #38 evaluates the window's + `bessel_i0` series once for half the taps and mirrors it — the design + evaluated it per tap. Coefficients bit-identical (FNV-1a-64 over the + whole prototype on M33 and on x86 under GCC and clang; scipy vectors + and the cross-validation floors unmoved, 78/78 host tests), so only + construction moved: per profile × direction and identical across + float/Q15/Q31 (down economy −70.7 M on M33 in all three formats), + M33 −37…−299 M, Hexagon −4.7…−35 M, M55 −0.6…−4.6 M. That took ten + of ten M33 scenarios and eight of ten Hexagon scenarios past the + two-sided gate (M33 down_q15_eco −28.9%); baselines re-recorded on all + three targets (M55 moved −1.0…−2.1%, re-recorded to keep the gate + tight). The sampleratetap test pin moves with it (5315689 → 2b4dff1, + no header changes in that range) so both repos keep the identical + dsptap tree the dev-only include path relies on. + Measuring the delta exposed how much of each workload construction is. + A construct-only build of every scenario, after the change (before in + parentheses): M55 3–5% (4–6%); Hexagon float 5–6% (8–10%), fixed + point 32–37% (42–47%); M33 float 6–8% (10–12%), Q31 42–45% (55–57%), + **Q15 55–63% (67–74%)**. So on M33 the fixed-point baselines mostly + price the constructor, and a hot-path regression there is diluted + ~2–3× before the ±3% gate sees it; the per-lever M33 percentages + above were measured through the same dilution (the audio-path + improvements were correspondingly larger). A construct-only ratchet + scenario, or measuring a second workload length and differencing as + SampleRateTap now does (steady state = 4 s − 2 s), would restore the + gate's sensitivity. v0.1 ships at M6. Nothing in M7+ blocks it. **v0.3 (2026-08-07): the profile-ladder re-pin.** economy moved to the 18 kHz/58/38 design (the diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json index 7050f56..e5c5cea 100644 --- a/bridge/bench/baselines.json +++ b/bridge/bench/baselines.json @@ -1,38 +1,38 @@ { "hexagon": { - "down_float_eco": 313564771, - "down_float_tr": 979598163, - "down_q15_eco": 54576857, - "down_q15_se": 39378689, - "down_q31_eco": 54524022, - "up_float_eco": 226581827, - "up_float_tr": 553818763, - "up_q15_eco": 42340774, - "up_q15_se": 32106712, - "up_q31_eco": 42358271 + "down_float_eco": 304640233, + "down_float_tr": 944369106, + "down_q15_eco": 45680933, + "down_q15_se": 33239067, + "down_q31_eco": 45626536, + "up_float_eco": 220218304, + "up_float_tr": 533814335, + "up_q15_eco": 35994330, + "up_q15_se": 27428012, + "up_q31_eco": 36011772 }, "m33": { - "down_float_eco": 1791425947, - "down_float_tr": 5772164016, - "down_q15_eco": 244450483, - "down_q15_se": 179742418, - "down_q31_eco": 315684738, - "up_float_eco": 1282189491, - "up_float_tr": 3280145822, - "up_q15_eco": 184359117, - "up_q15_se": 140538142, - "up_q31_eco": 235400465 + "down_float_eco": 1720707553, + "down_float_tr": 5473297976, + "down_q15_eco": 173755176, + "down_q15_se": 130967926, + "down_q31_eco": 244985862, + "up_float_eco": 1231730349, + "up_float_tr": 3110380470, + "up_q15_eco": 133914472, + "up_q15_se": 103390933, + "up_q31_eco": 184954171 }, "m55": { - "down_float_eco": 74885193, - "down_float_tr": 219532760, - "down_q15_eco": 58251264, - "down_q15_se": 47710153, - "down_q31_eco": 97748073, - "up_float_eco": 56229455, - "up_float_tr": 128890219, - "up_q15_eco": 45305070, - "up_q15_se": 39607853, - "up_q31_eco": 73399258 + "down_float_eco": 73794800, + "down_float_tr": 214977684, + "down_q15_eco": 57208782, + "down_q15_se": 46983269, + "down_q31_eco": 96697327, + "up_float_eco": 55451902, + "up_float_tr": 126303581, + "up_q15_eco": 44556679, + "up_q15_se": 39050794, + "up_q31_eco": 72647042 } } diff --git a/bridge/submodules/dsptap b/bridge/submodules/dsptap index 28a34a1..0eb09fa 160000 --- a/bridge/submodules/dsptap +++ b/bridge/submodules/dsptap @@ -1 +1 @@ -Subproject commit 28a34a18c40bda74b5d8cea0c54e09f31fbdc94b +Subproject commit 0eb09fa1edf5ce4f83043a05ce04ed8f3a653dbc diff --git a/bridge/submodules/sampleratetap b/bridge/submodules/sampleratetap index 5315689..2b4dff1 160000 --- a/bridge/submodules/sampleratetap +++ b/bridge/submodules/sampleratetap @@ -1 +1 @@ -Subproject commit 53156897a5afe5de4967dd33a9db5fba159dfeaf +Subproject commit 2b4dff17abd095dd01d72055a69a4385a64b2304 From 6f916644887dcb093cec1886326bc4b217fdf772 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 15:52:36 +0000 Subject: [PATCH 30/44] Harden the ratchet and CI so the migration gates have data to read - icount.py (ported in step with SampleRateTap's): --exact, --json-out and --compare-json for same-job A/B measurement, a failure when a recorded workload has no binary, a fatal error on a missing baselines file, and the workload checksum printed and recorded. Hexagon workloads run from one fixed path with a fixed argv[0] and an empty environment, because qemu-hexagon copies those onto the guest stack and static musl's startup walks them; qemu-hexagon is resolved to an absolute path first. Hexagon baselines are re-recorded separately. - Tests carry a "ratio." ctest prefix and a ratio label (the bare-metal entry too), so names stay unique once they share a tree with SampleRateTap's. - New OutputHash suite: FNV-1a hashes of the converter's output for both directions, every format and every profile, printed for same-job comparison and never pinned. About 8 s on Cortex-M33. - The cross-validation lines now print their tolerance, so a loosened limit is visible in the output and not only in the source. - CI: contents: read permissions; cancellation spares main and the migration PR; every action SHA-pinned (checkout v6, cache v5, as in SampleRateTap); ubuntu-24.04 pinned for QEMU and ratchet jobs, with the image OS in the plugin-qemu cache key and image/toolchain versions logged; --no-tests=error on every ctest; QEMU legs keep and upload full test logs. Step P.2 of the monorepo migration plan (SampleRateTap docs/MONOREPO_PLAN.md). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- bridge/.github/workflows/ci.yml | 94 +++++++++++++---- bridge/.github/workflows/style.yml | 7 +- bridge/scripts/icount.py | 121 +++++++++++++++++---- bridge/tests/CMakeLists.txt | 11 +- bridge/tests/test_cross_validation.cpp | 8 +- bridge/tests/test_output_hash.cpp | 139 +++++++++++++++++++++++++ 6 files changed, 332 insertions(+), 48 deletions(-) create mode 100644 bridge/tests/test_output_hash.cpp diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml index 0665366..3e62a20 100644 --- a/bridge/.github/workflows/ci.yml +++ b/bridge/.github/workflows/ci.yml @@ -12,12 +12,17 @@ on: pull_request: workflow_dispatch: -# Superseded-run cancellation: a new push to the same PR (or to main) -# cancels the previous in-flight run. Those cancelled checks attach to the -# old commit, so the current head stays clean. +permissions: + contents: read + +# Superseded-run cancellation: a new push to the same PR cancels the +# previous in-flight run. Those cancelled checks attach to the old commit, +# so the current head stays clean. Never on main (every main commit keeps +# its ratchet evidence), and never on the monorepo migration branch, whose +# plan requires a complete run for every pushed commit. concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true + cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} jobs: build-test: @@ -35,7 +40,7 @@ jobs: - { os: macos-latest, name: macos, capi: ON } - { os: windows-latest, name: windows, capi: OFF } steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive @@ -50,13 +55,13 @@ jobs: run: cmake --build build --config Release - name: Test - run: ctest --test-dir build --build-config Release --output-on-failure + run: ctest --test-dir build --build-config Release --output-on-failure --no-tests=error sanitizers: name: ASan + UBSan runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive @@ -74,7 +79,7 @@ jobs: run: cmake --build build -j 4 - name: Test - run: ctest --test-dir build --output-on-failure + run: ctest --test-dir build --output-on-failure --no-tests=error # ------------------------------------------------------------------------ # Embedded matrix (PLAN.md section 7): the M33/M55 eurorack/pedal cores and @@ -86,7 +91,7 @@ jobs: hexagon-qemu: name: Hexagon cross (QEMU) - runs-on: ubuntu-latest + runs-on: ubuntu-24.04 timeout-minutes: 45 env: # Prebuilt open-source toolchain (BSD-3) published by Qualcomm/Quicinc; @@ -97,13 +102,13 @@ jobs: # published SHA256SUMS). HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive - name: Cache toolchain id: cache - uses: actions/cache@v4 + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 with: path: ~/hexagon # Keyed on the pinned digest: every job that can write this key @@ -152,14 +157,25 @@ jobs: # -j 4: each test is an independent qemu-user process and the # per-test soft-double table construction dominates, so the suite # parallelizes cleanly (and serial would crowd the job timeout). + # -V --output-log keeps every test's own output ([ RUN ] lines, + # [ measured ] numbers), which --output-on-failure prints only for + # failures; the log is uploaded below as evidence. run: > - ctest --test-dir build -j 4 --output-on-failure + ctest --test-dir build -j 4 --output-on-failure --no-tests=error + -V --output-log ctest-hexagon.log -E 'BadProfilesThrow|LatencyAndValidation' # This static-musl toolchain cannot unwind across frames — the # constructor throws correctly but EXPECT_THROW never catches and # libc++abi terminates (same known debt as SampleRateTap's leg). # Validation is target-independent and covered on every other leg. + - name: Upload test log + if: ${{ !cancelled() }} + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: ctest-hexagon + path: ctest-hexagon.log + # Cross-compile for Arm Cortex-M55 (bare metal, newlib + semihosting) and # run the emulation-sized test subset on QEMU's MPS3 AN547 board model. # Validates the converter on a 32-bit MCU-class target with no OS, no @@ -167,10 +183,10 @@ jobs: # performance-appropriate formats here. cortex-m55-qemu: name: Cortex-M55 cross (QEMU) - runs-on: ubuntu-latest + runs-on: ubuntu-24.04 timeout-minutes: 30 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive @@ -190,7 +206,16 @@ jobs: run: cmake --build build -j 4 - name: Test under emulation - run: ctest --test-dir build --output-on-failure + run: > + ctest --test-dir build --output-on-failure --no-tests=error + -V --output-log ctest-m55.log + + - name: Upload test log + if: ${{ !cancelled() }} + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: ctest-m55 + path: ctest-m55.log # Cortex-M33 (Raspberry Pi Pico 2 / RP2350 class: single-precision FPU, # no FP64, no MVE) on QEMU's MPS2+ AN505 model. Shares the Armv8-M @@ -198,10 +223,10 @@ jobs: # anchors the Q15/Q31 budgets for Pico-class parts. cortex-m33-qemu: name: Cortex-M33 cross (QEMU) - runs-on: ubuntu-latest + runs-on: ubuntu-24.04 timeout-minutes: 30 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive @@ -221,7 +246,16 @@ jobs: run: cmake --build build -j 4 - name: Test under emulation - run: ctest --test-dir build --output-on-failure + run: > + ctest --test-dir build --output-on-failure --no-tests=error + -V --output-log ctest-m33.log + + - name: Upload test log + if: ${{ !cancelled() }} + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: ctest-m33 + path: ctest-m33.log # Deterministic instruction-count ratchet (PLAN.md section 7): fixed # workloads under QEMU with a counting plugin, gated two-sided (±3%) @@ -230,7 +264,9 @@ jobs: # measurement harness every M7 lever must move before it merges. icount-ratchet: name: Instruction-count ratchet - runs-on: ubuntu-latest + # Pinned image: the counts are a function of the apt toolchain, the + # plugin build and QEMU, so the job must not straddle an image rollout. + runs-on: ubuntu-24.04 timeout-minutes: 45 env: # Commit the v8.2.2 tag pointed at when pinned (tags are movable; @@ -244,7 +280,7 @@ jobs: # shared toolchain cache, so it must verify against the same digest. HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive @@ -264,6 +300,15 @@ jobs: gcc -shared -fPIC $(pkg-config --cflags glib-2.0) -I/tmp \ -o /tmp/libinsncount.so tools/qemu_insn_plugin/insn_count.c + # What produced the counts: the runner image and the toolchain packages + # (the counts move when either does; PLAN.md section 7). + - name: Record image and toolchain versions + id: image + run: | + echo "image: ${ImageOS:-unknown} ${ImageVersion:-unknown}" + dpkg-query -W gcc-arm-none-eabi qemu-system-arm + echo "os=${ImageOS:-unknown}" >> "$GITHUB_OUTPUT" + # Release (-O2), matching how the baselines were recorded. - name: Build M55 workloads run: > @@ -305,10 +350,13 @@ jobs: - name: Cache plugin-enabled qemu-hexagon if: ${{ !cancelled() }} id: qemu-hex - uses: actions/cache@v4 + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 with: path: ~/qemu-hexagon-plugins - key: qemu-hexagon-plugins-${{ env.QEMU_SRC_URL }}-1 + # The image OS is part of the key: the cached binary links the + # image's glib, so a new Ubuntu release must not restore an old + # build. (Weekly image updates keep the same glib ABI.) + key: qemu-hexagon-plugins-${{ steps.image.outputs.os }}-${{ env.QEMU_SRC_URL }}-1 - name: Build plugin-enabled qemu-hexagon if: ${{ !cancelled() && steps.qemu-hex.outputs.cache-hit != 'true' }} @@ -330,7 +378,7 @@ jobs: - name: Cache Hexagon toolchain if: ${{ !cancelled() }} id: cache - uses: actions/cache@v4 + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 with: path: ~/hexagon # Same digest-keyed name as the hexagon-qemu job; the download diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml index 06bcf13..4937b5c 100644 --- a/bridge/.github/workflows/style.yml +++ b/bridge/.github/workflows/style.yml @@ -12,9 +12,12 @@ on: pull_request: workflow_dispatch: +permissions: + contents: read + concurrency: group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true + cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} jobs: drift: @@ -26,7 +29,7 @@ jobs: runs-on: ubuntu-latest timeout-minutes: 30 steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive - name: Install tools diff --git a/bridge/scripts/icount.py b/bridge/scripts/icount.py index e7ee931..c5c58dc 100644 --- a/bridge/scripts/icount.py +++ b/bridge/scripts/icount.py @@ -4,13 +4,23 @@ Runs every ratio_icount_* binary in a build directory under QEMU with the instruction-counting plugin, then compares against bench/baselines.json. - icount.py --target {hexagon,m55,m33} --build-dir DIR --plugin LIB [--update] + icount.py --target {hexagon,m55,m33} --build-dir DIR --plugin LIB [--baselines bench/baselines.json] [--tolerance 0.03] + [--exact] [--update] [--json-out FILE] [--compare-json FILE] The gate is two-sided: exit nonzero if any scenario regresses beyond tolerance, improves beyond tolerance (the baseline must be re-recorded so -the gate stays tight), or has no recorded baseline. --update rewrites the -target's entry to exactly the measured scenarios instead. +the gate stays tight), or has no recorded baseline. The measured scenarios +must also be exactly the recorded ones: a baseline with no binary is a +failure, so a workload cannot silently drop out of the gate. --update +rewrites the target's entry to exactly the measured scenarios instead. + +--exact demands integer equality (tolerance 0). --json-out records each +scenario's count and the workload's printed checksum; --compare-json gates +exactly against such a file (counts and checksums) instead of the committed +baselines. That is the same-job A/B mode: measure a reference tree and a +candidate tree with one toolchain, then compare the two measurements, so +toolchain drift on the runner cannot masquerade as a code change. Ported from SampleRateTap's scripts/icount.py (marker strings and binary prefix renamed for this repo). @@ -21,15 +31,34 @@ import os import pathlib import re +import shutil import subprocess import sys +BINARY_PREFIX = "ratio_icount_" +DONE_MARKER = "RATIO_ICOUNT_DONE" +COUNT_MARKER = "RATIO_INSN_COUNT" + +# qemu-hexagon is a user-mode emulator: the guest's argv[0], its exec path +# (AT_EXECFN) and the host environment are copied onto the guest stack, and +# static musl's startup walks them, so their lengths leak into the count. +# Every workload therefore runs from one fixed path, with a fixed argv[0] and +# an empty environment. The system-mode Arm targets get none of these +# (main(0, NULL) under -nostartfiles). +HEXAGON_RUN_DIR = "/tmp/tap-icount" +HEXAGON_ARGV0 = "w" + def qemu_cmd(target: str, plugin: str, binary: str) -> list[str]: # "-d plugin" routes qemu_plugin_outs() to stderr; without it the count # line is silently dropped. if target == "hexagon": - return ["qemu-hexagon", "-d", "plugin", "-plugin", plugin, binary] + # Resolve before the environment is cleared: with env={} there is no + # PATH, and a bare name would fail (or find a plugin-less qemu). + qemu = shutil.which("qemu-hexagon") + if qemu is None: + raise SystemExit("qemu-hexagon not found on PATH") + return [qemu, "-0", HEXAGON_ARGV0, "-d", "plugin", "-plugin", plugin, binary] if target == "m55": return ["qemu-system-arm", "-M", "mps3-an547", "-nographic", "-semihosting", "-d", "plugin", "-plugin", plugin, @@ -41,21 +70,29 @@ def qemu_cmd(target: str, plugin: str, binary: str) -> list[str]: raise SystemExit(f"unknown target {target}") -def measure(target: str, plugin: str, binary: str) -> int: +def measure(target: str, plugin: str, binary: str) -> tuple[int, str]: + env = None + if target == "hexagon": + os.makedirs(HEXAGON_RUN_DIR, exist_ok=True) + fixed = os.path.join(HEXAGON_RUN_DIR, HEXAGON_ARGV0) + shutil.copyfile(binary, fixed) + os.chmod(fixed, 0o755) + binary, env = fixed, {} try: proc = subprocess.run(qemu_cmd(target, plugin, binary), timeout=600, - capture_output=True, text=True) + capture_output=True, text=True, env=env) except subprocess.TimeoutExpired: raise SystemExit(f"{binary}: timed out after 600 s under QEMU") out = proc.stdout + proc.stderr - if "RATIO_ICOUNT_DONE ok=1" not in out: + done = re.search(DONE_MARKER + r" ok=1 checksum=(\S+)", out) + if not done: print(out, file=sys.stderr) raise SystemExit(f"{binary}: workload did not complete cleanly") - m = re.search(r"RATIO_INSN_COUNT (\d+)", out) + m = re.search(COUNT_MARKER + r" (\d+)", out) if not m: print(out, file=sys.stderr) - raise SystemExit(f"{binary}: no RATIO_INSN_COUNT (plugin not loaded?)") - return int(m.group(1)) + raise SystemExit(f"{binary}: no {COUNT_MARKER} (plugin not loaded?)") + return int(m.group(1)), done.group(1) def main() -> int: @@ -65,25 +102,49 @@ def main() -> int: ap.add_argument("--plugin", required=True) ap.add_argument("--baselines", default="bench/baselines.json") ap.add_argument("--tolerance", type=float, default=0.03) + ap.add_argument("--exact", action="store_true", + help="require identical counts (tolerance 0)") ap.add_argument("--update", action="store_true") + ap.add_argument("--json-out", help="write measured counts and checksums here") + ap.add_argument("--compare-json", + help="gate exactly against a --json-out file instead of the baselines") args = ap.parse_args() + if args.update and args.compare_json: + raise SystemExit("--update and --compare-json are mutually exclusive") + tolerance = 0.0 if (args.exact or args.compare_json) else args.tolerance - binaries = sorted(glob.glob(os.path.join(args.build_dir, "**", "ratio_icount_*"), + binaries = sorted(glob.glob(os.path.join(args.build_dir, "**", BINARY_PREFIX + "*"), recursive=True)) binaries = [b for b in binaries if os.access(b, os.X_OK) and os.path.isfile(b)] if not binaries: - raise SystemExit(f"no ratio_icount_* binaries under {args.build_dir}") + raise SystemExit(f"no {BINARY_PREFIX}* binaries under {args.build_dir}") path = pathlib.Path(args.baselines) - baselines = json.loads(path.read_text()) if path.exists() else {} - base = baselines.get(args.target, {}) + ref_checksums = {} + if args.compare_json: + ref = json.loads(pathlib.Path(args.compare_json).read_text()).get(args.target, {}) + base = {k: v["insns"] for k, v in ref.items()} + ref_checksums = {k: v["checksum"] for k, v in ref.items()} + if not base: + raise SystemExit(f"{args.compare_json} has no {args.target} measurements") + elif path.exists(): + baselines = json.loads(path.read_text()) + base = baselines.get(args.target, {}) + elif args.update: + baselines, base = {}, {} + else: + # A missing file must not read as "no baselines, nothing to check". + raise SystemExit(f"baselines file {path} not found") failures = [] measured = {} + checksums = {} for binary in binaries: - scenario = os.path.basename(binary).removeprefix("ratio_icount_") - count = measure(args.target, args.plugin, binary) + scenario = os.path.basename(binary).removeprefix(BINARY_PREFIX) + count, checksum = measure(args.target, args.plugin, binary) measured[scenario] = count + checksums[scenario] = checksum + print(f"{scenario}: checksum={checksum}") recorded = base.get(scenario) if recorded is None: print(f"{scenario}: {count} insns (NO BASELINE — commit this value)") @@ -95,10 +156,13 @@ def main() -> int: else: delta = (count - recorded) / recorded verdict = "ok" - if delta > args.tolerance: + if tolerance == 0.0 and count != recorded: + verdict = "MISMATCH (exact)" + failures.append(scenario) + elif delta > tolerance: verdict = "REGRESSION" failures.append(scenario) - elif delta < -args.tolerance: + elif delta < -tolerance: # Two-sided: a stale (too-high) baseline would let future # regressions hide inside the slack, so improvements must be # committed too. @@ -106,7 +170,26 @@ def main() -> int: "and commit bench/baselines.json") failures.append(scenario) print(f"{scenario}: {count} insns vs baseline {recorded} " - f"({delta:+.2%}) {verdict}") + f"({count - recorded:+d}, {delta:+.4%}) {verdict}") + if scenario in ref_checksums and ref_checksums[scenario] != checksum: + print(f"{scenario}: checksum {checksum} vs reference " + f"{ref_checksums[scenario]} MISMATCH") + failures.append(scenario) + + # The measured set must be exactly the recorded set: a workload whose + # binary vanished would otherwise simply stop being gated. + missing = sorted(set(base) - set(measured)) + for scenario in missing: + print(f"{scenario}: recorded but NOT MEASURED (binary missing)") + if not args.update: + failures.extend(missing) + + if args.json_out: + out_path = pathlib.Path(args.json_out) + doc = json.loads(out_path.read_text()) if out_path.exists() else {} + doc[args.target] = {k: {"insns": measured[k], "checksum": checksums[k]} + for k in sorted(measured)} + out_path.write_text(json.dumps(doc, indent=2, sort_keys=True) + "\n") if args.update: # Exactly the measured scenarios: stale keys for renamed/removed diff --git a/bridge/tests/CMakeLists.txt b/bridge/tests/CMakeLists.txt index a5db822..28f9585 100644 --- a/bridge/tests/CMakeLists.txt +++ b/bridge/tests/CMakeLists.txt @@ -35,6 +35,7 @@ add_executable(tap_ratio_tests test_design.cpp test_phase_table.cpp test_schedule.cpp + test_output_hash.cpp test_skeleton.cpp) # The tests' own headers (the committed reference vectors) resolve relative # to this directory. @@ -58,11 +59,17 @@ if(TAP_RATIO_BARE_METAL) set_tests_properties(tap_ratio_tests_emulated PROPERTIES PASS_REGULAR_EXPRESSION "TAP_RATIO_TESTS_COMPLETE rc=0" FAIL_REGULAR_EXPRESSION "\\[ FAILED \\]" - TIMEOUT 1800) + TIMEOUT 1800 + LABELS ratio) else() target_link_libraries(tap_ratio_tests PRIVATE GTest::gtest_main) # Generous timeouts: discovery and the DSP-measurement tests run orders # of magnitude slower under instruction-set emulation (e.g. qemu-hexagon). include(GoogleTest) - gtest_discover_tests(tap_ratio_tests DISCOVERY_TIMEOUT 120 PROPERTIES TIMEOUT 900) + # The "ratio." prefix and label keep ctest names unique once this suite + # shares a tree with SampleRateTap's (FixedPoint.* exists in both, and + # CTest applies a duplicated name's properties to every copy); the + # monorepo plan's gates key tests by these names. + gtest_discover_tests(tap_ratio_tests TEST_PREFIX "ratio." DISCOVERY_TIMEOUT 120 + PROPERTIES TIMEOUT 900 LABELS ratio) endif() diff --git a/bridge/tests/test_cross_validation.cpp b/bridge/tests/test_cross_validation.cpp index 2c233b2..2d2a6ff 100644 --- a/bridge/tests/test_cross_validation.cpp +++ b/bridge/tests/test_cross_validation.cpp @@ -117,9 +117,13 @@ namespace { } } EXPECT_EQ(phases_seen, l); // all phases exercised - std::printf("[ measured ] cross-validation %s, async L=%zu: worst |diff| = %.3e (%.1f dB), %zu/%zu phases\n", + // The limit is printed with the measurement so a loosened tolerance + // shows in the output, not only in the source (the monorepo + // migration's gates compare these lines). + std::printf("[ measured ] cross-validation %s, async L=%zu: worst |diff| = %.3e (%.1f dB), %zu/%zu phases," + " limit %.1e\n", D == direction::down_to_44k1 ? "down" : "up ", async_phases, worst, - 20.0 * std::log10(worst + 1e-18), phases_seen, l); + 20.0 * std::log10(worst + 1e-18), phases_seen, l, tolerance); } // Measured floors on the 18 kHz economy designs (v0.3 re-pin): down diff --git a/bridge/tests/test_output_hash.cpp b/bridge/tests/test_output_hash.cpp new file mode 100644 index 0000000..8534f2a --- /dev/null +++ b/bridge/tests/test_output_hash.cpp @@ -0,0 +1,139 @@ +// SPDX-License-Identifier: MIT +// Copyright 2026 Timothy Place and the RatioTap contributors. +// +// Output fingerprints of the converter, for same-job A/B comparison. +// +// Each test runs the streaming converter over a fixed two-channel multitone +// for both directions, every sample format (float, Q15, Q31) and one +// profile, and prints an FNV-1a-64 hash of the raw output bytes (process() +// output followed by flush()): +// +// [ measured ] hash // <16 hex digits> +// +// The hashes are deliberately NOT pinned here. The integer paths are +// bit-exact by contract, but the prototype design runs through each +// platform's libm, and a float hash also depends on the compiler's +// multiply-add contraction; a hash is meaningful only against another hash +// from the same toolchain on the same host. The monorepo migration's gate +// G5 builds a reference tree and a candidate tree in one job and diffs +// these lines. What the test asserts is only that the output is +// non-trivial, so a silently zeroed datapath cannot hash stably. +#include +#include +#include +#include +#include +#include +#include + +#include + +#include "tap/ratio/converter.h" + +namespace { + + using tap::ratio::basic_converter; + using tap::ratio::direction; + using tap::ratio::profile; + + constexpr std::size_t k_channels = 2; + constexpr std::size_t k_in_frames = 2940; // a whole number of 147-frame schedule periods + + std::uint64_t fnv1a64(const void* data, std::size_t bytes) { + std::uint64_t h = 0xcbf29ce484222325ULL; + const auto* p = static_cast(data); + for (std::size_t i = 0; i < bytes; ++i) { + h ^= p[i]; + h *= 0x100000001b3ULL; + } + return h; + } + + template + S to_sample(double v) { + if constexpr (std::is_floating_point_v) { + return static_cast(v); + } + else { + const double scaled = std::nearbyint(v * static_cast(std::numeric_limits::max())); + return static_cast(scaled); + } + } + + template + const char* format_name() { + if constexpr (std::is_same_v) { + return "float"; + } + else if constexpr (std::is_same_v) { + return "q15"; + } + else { + return "q31"; + } + } + + // Two channels with different tone sets, so a channel swap or a + // cross-channel leak changes the hash. Amplitudes stay well inside full + // scale, so to_sample() never needs to saturate. + template + std::vector multitone(double fs) { + std::vector x(k_in_frames * k_channels); + for (std::size_t n = 0; n < k_in_frames; ++n) { + const double t = static_cast(n) / fs; + const double l = 0.30 * std::sin(2.0 * std::numbers::pi * 997.0 * t) + + 0.20 * std::sin(2.0 * std::numbers::pi * 6001.0 * t) + + 0.10 * std::sin(2.0 * std::numbers::pi * 17503.0 * t); + const double r = 0.35 * std::sin(2.0 * std::numbers::pi * 441.0 * t + 0.3) + + 0.25 * std::sin(2.0 * std::numbers::pi * 12007.0 * t); + x[n * k_channels] = to_sample(l); + x[n * k_channels + 1] = to_sample(r); + } + return x; + } + + template + void hash_one(const char* profile_name, const profile& p) { + basic_converter conv(k_channels, p); + const double fs = D == direction::up_to_48k ? 44100.0 : 48000.0; + const std::vector x = multitone(fs); + std::vector y(static_cast(conv.outputs_for(k_in_frames) + conv.flush_output_frames()) + * k_channels); + std::size_t n = conv.process(x.data(), k_in_frames, y.data()); + n += conv.flush(y.data() + n * k_channels); + y.resize(n * k_channels); + ASSERT_GT(n, 0u); + + double energy = 0.0; + for (const S v : y) { + energy += static_cast(v) * static_cast(v); + } + EXPECT_GT(energy, 0.0) << "silent output would hash stably"; + + std::printf("[ measured ] hash %s/%s/%s %016llx\n", D == direction::up_to_48k ? "up" : "down", format_name(), + profile_name, static_cast(fnv1a64(y.data(), y.size() * sizeof(S)))); + } + + void hash_profile(const char* profile_name, const profile& p) { + hash_one(profile_name, p); + hash_one(profile_name, p); + hash_one(profile_name, p); + hash_one(profile_name, p); + hash_one(profile_name, p); + hash_one(profile_name, p); + } + + TEST(OutputHash, SuperEconomy) { + hash_profile("super_economy", profile::super_economy()); + } + TEST(OutputHash, Economy) { + hash_profile("economy", profile::economy()); + } + TEST(OutputHash, Balanced) { + hash_profile("balanced", profile::balanced()); + } + TEST(OutputHash, Transparent) { + hash_profile("transparent", profile::transparent()); + } + +} // namespace From f0651442a128b0242e8a61f3720ceb1a49a3745b Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 16:03:25 +0000 Subject: [PATCH 31/44] Re-record Hexagon icount baselines under the isolated harness The previous commit runs Hexagon workloads from a fixed path with a fixed argv[0] and an empty environment. The first isolated CI run (PR #18) measured every scenario 3,724 to 3,809 instructions lower (-0.0004% to -0.0136%): the path and environment strings static musl's startup used to walk. Inside the gate, but re-recorded so exact comparisons start from the new harness; PLAN.md section 7 ledger entry. M33/M55 are unaffected. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- bridge/PLAN.md | 12 ++++++++++++ bridge/bench/baselines.json | 20 ++++++++++---------- 2 files changed, 22 insertions(+), 10 deletions(-) diff --git a/bridge/PLAN.md b/bridge/PLAN.md index 60d62ad..02f2057 100644 --- a/bridge/PLAN.md +++ b/bridge/PLAN.md @@ -317,6 +317,18 @@ executed (it measures the shipping C++, not a Python re-implementation). SampleRateTap now does (steady state = 4 s − 2 s), would restore the gate's sensitivity. + - **Hexagon harness isolation (monorepo migration step P.2; re-record, + not a lever).** `scripts/icount.py` now runs every Hexagon workload + from one fixed path with a fixed `argv[0]` and an empty environment: + qemu-hexagon (user mode) copies argv, the exec path and the host + environment onto the guest stack, and static musl's startup walks + them, so the build path and the runner's environment were part of + every Hexagon count. First isolated CI run: −3,724…−3,809 instructions + per scenario (−0.0004…−0.0136%), inside the gate; M33/M55 (system + mode, `main(0, NULL)`) unaffected and matching their baselines to the + instruction. Hexagon baselines re-recorded so exact (`--exact`) + comparisons start from the isolated harness. + v0.1 ships at M6. Nothing in M7+ blocks it. **v0.3 (2026-08-07): the profile-ladder re-pin.** economy moved to the 18 kHz/58/38 design (the "economy18" spec-relaxation experiment, measured through every leg: scipy diff --git a/bridge/bench/baselines.json b/bridge/bench/baselines.json index e5c5cea..579ffd4 100644 --- a/bridge/bench/baselines.json +++ b/bridge/bench/baselines.json @@ -1,15 +1,15 @@ { "hexagon": { - "down_float_eco": 304640233, - "down_float_tr": 944369106, - "down_q15_eco": 45680933, - "down_q15_se": 33239067, - "down_q31_eco": 45626536, - "up_float_eco": 220218304, - "up_float_tr": 533814335, - "up_q15_eco": 35994330, - "up_q15_se": 27428012, - "up_q31_eco": 36011772 + "down_float_eco": 304636424, + "down_float_tr": 944365314, + "down_q15_eco": 45677158, + "down_q15_se": 33235309, + "down_q31_eco": 45622761, + "up_float_eco": 220214529, + "up_float_tr": 533810577, + "up_q15_eco": 35990589, + "up_q15_se": 27424288, + "up_q31_eco": 36008031 }, "m33": { "down_float_eco": 1720707553, From 7211dccdced2cc7dadc51f8e7ecaa3b52ceeb62a Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 16:05:29 +0000 Subject: [PATCH 32/44] Pin the notebook environment and re-execute every notebook - requirements.in / requirements.lock at the repository root: the notebook environment pinned with hashes (numpy 2.4.6, scipy 1.17.1, matplotlib 3.11.2, jupyter/nbconvert; samplerate and soxr for SampleRateTap's comparison notebook, since the file is identical in both repositories). It replaces notebooks/requirements.txt, which named packages without versions. - ratiotap_py rebuilds build_capi/ incrementally on every import instead of only when the library is missing, so a library left over from an older checkout can no longer be measured silently, and the build is quiet (its log used to land in ratio_demo's committed output; it now prints only on failure). - notebooks/figure_digest.py wraps plt.show() to print a digest of each figure's plotted data, quantized to 9 significant digits of each array's peak, so a changed curve shows up as changed text; stable across re-executions. - All three notebooks re-executed in the pinned environment: every committed number reproduces exactly; the only output changes are the added digest lines and the removed build log. Step P.3 of the monorepo migration plan. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- bridge/notebooks/design_spike.ipynb | 48 +- bridge/notebooks/figure_digest.py | 77 + bridge/notebooks/profile_ladder.ipynb | 108 +- bridge/notebooks/ratio_demo.ipynb | 116 +- bridge/notebooks/ratiotap_py.py | 24 +- bridge/notebooks/requirements.txt | 4 - bridge/requirements.in | 11 + bridge/requirements.lock | 1884 +++++++++++++++++++++++++ 8 files changed, 2130 insertions(+), 142 deletions(-) create mode 100644 bridge/notebooks/figure_digest.py delete mode 100644 bridge/notebooks/requirements.txt create mode 100644 bridge/requirements.in create mode 100644 bridge/requirements.lock diff --git a/bridge/notebooks/design_spike.ipynb b/bridge/notebooks/design_spike.ipynb index 09210a8..779b54e 100644 --- a/bridge/notebooks/design_spike.ipynb +++ b/bridge/notebooks/design_spike.ipynb @@ -39,16 +39,18 @@ "id": "c97a9e67", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:53:09.553988Z", - "iopub.status.busy": "2026-08-07T00:53:09.553765Z", - "iopub.status.idle": "2026-08-07T00:53:10.785497Z", - "shell.execute_reply": "2026-08-07T00:53:10.783927Z" + "iopub.execute_input": "2026-09-27T16:01:58.551761Z", + "iopub.status.busy": "2026-09-27T16:01:58.551427Z", + "iopub.status.idle": "2026-09-27T16:02:00.277896Z", + "shell.execute_reply": "2026-09-27T16:02:00.275844Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import figure_digest # prints a data digest per figure (A/B gates)\n", + "figure_digest.install()\n", "from scipy import signal\n", "\n", "def kaiser_beta(atten_db):\n", @@ -119,10 +121,10 @@ "id": "b87fc098", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:53:10.788955Z", - "iopub.status.busy": "2026-08-07T00:53:10.788508Z", - "iopub.status.idle": "2026-08-07T00:53:35.385446Z", - "shell.execute_reply": "2026-08-07T00:53:35.382826Z" + "iopub.execute_input": "2026-09-27T16:02:00.281324Z", + "iopub.status.busy": "2026-09-27T16:02:00.280858Z", + "iopub.status.idle": "2026-09-27T16:02:31.159405Z", + "shell.execute_reply": "2026-09-27T16:02:31.158361Z" } }, "outputs": [ @@ -179,16 +181,23 @@ "id": "d3db6503", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:53:35.392155Z", - "iopub.status.busy": "2026-08-07T00:53:35.391889Z", - "iopub.status.idle": "2026-08-07T00:53:51.082698Z", - "shell.execute_reply": "2026-08-07T00:53:51.080201Z" + "iopub.execute_input": "2026-09-27T16:02:31.166406Z", + "iopub.status.busy": "2026-09-27T16:02:31.165934Z", + "iopub.status.idle": "2026-09-27T16:02:51.424657Z", + "shell.execute_reply": "2026-09-27T16:02:51.421916Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest 06985aaa5439b9f0 (32 arrays)\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -232,10 +241,10 @@ "id": "6f2f5522", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:53:51.086657Z", - "iopub.status.busy": "2026-08-07T00:53:51.086231Z", - "iopub.status.idle": "2026-08-07T00:53:51.307366Z", - "shell.execute_reply": "2026-08-07T00:53:51.304574Z" + "iopub.execute_input": "2026-09-27T16:02:51.427596Z", + "iopub.status.busy": "2026-09-27T16:02:51.427368Z", + "iopub.status.idle": "2026-09-27T16:02:51.680962Z", + "shell.execute_reply": "2026-09-27T16:02:51.679802Z" } }, "outputs": [ @@ -244,12 +253,13 @@ "output_type": "stream", "text": [ "worst spur below 20 kHz : -94.3 dBFS\n", - "worst product >= 20.1 kHz: -114.6 dBFS\n" + "worst product >= 20.1 kHz: -114.6 dBFS\n", + "[ figure ] digest a93f8f515054cc21 (3 arrays)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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1N06BNlpr167t8rT4tddeY0ql0iWPZ8+e7bHR+sorr/CWPXPmDAPg6DEhFL1ez44dO8Z27NjBtm/fzkaMGMESEhIc+2ugjVZf4fXs2ZN16tTJa5zGjx/PALCLFy/y5peWlrIqVaqw9957z++w3Bk7diyTSqXs9OnTHpd5/fXXmUwmY5cvX+bN37hxIwPAvvvuO8ZY4OUbaKPVn3D9za9A6q03vsrYHvfnn3+etx7Hcax169asffv2jnn+5vOuXbsYADZr1izHMlarldWrV4898sgjAeeFv9544w2351T7+aB87xK7Ll26sHr16nkNt3yjdd68eUyhULBRo0a5XDvYG6116tRxeXqalZXFmjVr5rXRqtPpWJcuXVhcXBzbsmVLQGknxB16p5WQENu2bRsAIDU1Fbt37waz3RwCAFSvXh3r1q2DxWJxvFfUp08fj2Ht3bsXAHDPPffw5iclJaFDhw6O3+3uuusul/dhb7vtNly5ciXoMDt27MgLs3r16qhSpQo6dOgAufzWISQlJQWpqam8bQHAqFGj8Oabb+Lo0aNo27Ytrl27hl9//RXDhg3z+o7RsWPH8P777+Odd97BHXfc4XE5AGCM4aGHHsKJEyewZ88exzuHf/31Fx5++GGcOnUK3333HQCgQYMGyMrK8hoeYHsXKicnx/EeaiDxCWTZcJDL5Rg2bBhmzJiBGzduoFatWtDpdFi2bBkGDRrkNd8BYMmSJRg7diy0Wi0aN26MxMREGAwGAEBOTo7H9R544AFMmDABXbp0wXPPPYd7770XnTt3hkKhcCzjT733plOnTrzwAKBbt26YOnUq9u3b53gfLdh6F844Bequu+5yeR947969aNq0qcvoud26dfMYTteuXXnTDRs2hEQicdlXne3atQtWqzXAWNt4egcQsL2fOHbsWMyaNQupqamoV68eFAoFrl27Br1ej4KCAr/ebw00vMceewyjR49Gr1698NRTT6FHjx4u7z1v27YNtWrVwo0bN3Djxg3H8Zsxhtq1a+PgwYMA4FdY7mRnZ6NBgwZe33Pcu3cvGjZs6Hh/2e6ee+6BRCLB3r17MXLkSMf8YMvXF3/C9Te/gqm35QVaZ5zDlUgkyMrKwuzZs2EymRAXF+d3Pnfp0gWtWrXCvHnz8OKLLwIANmzYgNzcXHz11VcB54U/vvjiC3z11Vfo378/3n33Xd5v9mONu33TYrF4HEPA2bRp07Bjxw5Mnz4d//73vz0uN3DgQHzxxRcu85cuXYp//etfbtdhjGHo0KHYvXs3Fi5ciJ49e/oVJ0K8oUYrISGm1WoBAF9//TWvUWd39913w2AwoLS0FICtceuJfUCDlJQUl99SUlJcPuVSfoAku/j4eN7ACKEIU6lUepzv/G25Z599FhMmTMD333+P77//HrNmzYLFYvE5UMS0adOQmJiIXr16ORo69jzLy8tDdnY2mjZtioyMDOzbtw/btm3DpEmTeIPk3HfffXjqqacwc+ZMfPLJJ6hatSpefPFFx4WHJ0ePHsW9996Lbt26Yfny5QHHJ5Blw2XEiBGYNm0aFi5ciHfeeQcrVqyARqPxme8nTpzAkCFD8Nprr2H69OmOOnzo0CG0b98eHMd5XLd169Y4dOgQvv32WyxevBjvv/8+qlSpglGjRmHKlCmQSqV+1Xtv3I3qbK/L5et5sPXO20A6ZrMZ8fHxQccpUO4afTqdzu1gOO72ZzvnfVWhUEAqlfqM28SJE4OO/7hx4/Doo4+6/e3zzz/HV199hdWrV6Nv376O+R9++CHef/99r3WsIuG99dZbaNSoERYvXoyJEydCpVKhYcOG+Oyzz/DEE08AsB2/y8rKMGbMGJftZGRk4LbbbvM7LHdKS0t91n2dTue2POPi4pCQkODzGO1v+friT7j+5lcw9ba8QOuMp32S4zgYDAbExcUFlM/Dhw/H6NGjHQMizZ07FxkZGbw67m9e+DJ79myMHj0ajzzyCJYtWwaplP91SvtgRvn5+S7pzM/P9/smmcFgAGMs4P3NH2PHjsXKlSsxadIkDB06NOThk8qJGq2EhFiDBg0AAN988w06duzocTn7Zy/OnTvnMvqpnX1EyUuXLqF27dq83y5evOgyuqk/whGmNykpKXjmmWewaNEifPTRR5g9ezaaN2+Ou+++2+t6jRs3RsuWLTFx4kTHPPud5X379mHcuHF477338NBDD0GlUgFwf5GfkZEBxhjy8/P9GjH16tWr6N27Nxo2bIh169YhMTEx4PgEsmy43H777bjnnnswb948vPPOO5g7d65f+f7XX3+B4zi89dZbvJsu//zzj1/bbdmypePzQiqVCtOmTcO0adPQsmVLPPvss37Ve28uX77sMu/SpUsAwKu7wda7pKQkJCYmuv024uXLl9G6deug4xQo54tVwLb/Hj9+3OP2QmnDhg0hDxMANm7ciBYtWvAaH4D/dawi4fXr1w/9+vUDYOsR8eqrr+Jf//oXrly5glq1aqFBgwZQqVR+fQbGV1juNGzYENu3bwfHcW7LF7CV8d69e8EY491EycvLg06nC/kxuiL8za+K1ttA64ynfTIlJcXR0Askn4cNG4Zx48Zh7ty5mDBhAn799Ve88cYbvB4WgdQdTxYuXIiXX34ZDz74IFauXOn2qam998+RI0fQqFEjx/yioiJcuXIF/fv392tb77//Pg4cOIC3334bxcXF+PDDD4OOd3nff/89pk+fjmHDhvHOgYRUlPsjJiEkaI8//jiSkpJ43YbKU6vVAID+/fsjMTERM2bMcFnG/jTq0UcfhVwux+zZs3m/Hzx4EPv378fjjz8ecPzCEaYvr776KkpLSzF48GBcu3bNr+H4J02ahOzsbN6/9evXO9KQnZ3taPS1bdsWUqkUmzZt4oVhtVqxefNmVK9e3XEzwZe6devi3//+N/7880/eXfhA4hPIsgBw6tQpZGdnw2Kx+BVHfw0fPhynT5/GggULsH37dr/y3f50paCgwDHParXi+++/97muvW7bZWRkYOzYsQBsN0QA/+q9N7t378a5c+d48+bNm4datWqhc+fOvPnB1DvA1vB2vvBcvXq1S/qCiVNF9e/fH7m5udi4cSNv/g8//BDS7YRT9erVUVxczOveePnyZaxduzas4TmXX5s2bfDss8/CbDYjNzcXADB06FDk5eU5elg4s38Kyp+w3HnmmWdQUlKCuXPnuvxmr//9+/dHYWEhVq1axft91qxZABCWY3Sw/M2vitbbQOvMkiVLeMuqVCqsW7cO/fr1czRQA8nntLQ09OvXDz/++CPmzp0Lk8nkcjzxNy88Wb58OV544QU88MADWLVqlcdP5D344INIT0/H/PnzefMXLFjgiIe/Jk6ciK+++gofffQRXnvtNcerTMH6/fff8frrr+Pee+/FnDlzKhQWIc7oSSshIZaRkYFFixbh6aefRmFhIZ5++mlkZGTg4sWL+PPPPxEfH4/FixcjIyMDCxYswJAhQ/Dggw9i2LBhqFq1Knbu3IlDhw5h/fr1qF+/PqZOnYq3334bMpkM/fr1Q05ODiZOnIh27drhvffeCzh+4QjTl7Zt2yIrKwsbN26EXC4PeXeh+vXr491338WUKVPw/PPP44knnoDRaMScOXNw7NgxzJs3z21XbU8mTJgQ0vj58p///AcrV65Efn6+49uTKpXK8V3P/Px8AMDOnTsRFxeHlJQUXjdoTwYOHIg33ngDo0aNgkKh8Pjd2fIeffRR1K1bF88//7zjLvmsWbPQu3dvn08QvvjiC2zZsgVPPvkkbr/9duh0OsyaNQtVq1Z1vPvkT733ZsCAARgxYgSGDRuGGjVqYOHChdi+fbvbpxLB1rt3330XAwcOxMiRI/HYY4/h2LFjOHr0KDp06FDhOFXUCy+8gMWLF+Opp57CBx98gMaNG2Pt2rWOehMLXnrpJSxfvhxDhgzBs88+i5ycHHz77bcYPnw4vvzyy7CF9/DDD6Np06bo0aMHMjMzceXKFXz00Ufo1KkT7rzzTgDAU089hezsbAwbNgy7du1Cjx49oFAocOrUKSxevBhvvfUWhg4d6ldY7gwaNAh//fUXRo4ciZMnT6JXr14oLS3Fr7/+ijvvvBPvvPMOXnjhBaxcuRLDhg3D+fPn0bp1a2zduhWfffYZ3nzzTb/fA40Ef/OrovU20Dpz3333oV+/fnjhhReg0+nw0UcfoVq1avj4448dywSazyNGjMCKFSswadIkZGVluXzL1N+8cGfjxo0YMmQIGjRogDFjxmDfvn2838t/RzcxMREzZ87EU089hZEjR+KJJ57A0aNH8Z///Advv/22x+OUJ6+//jpSU1PxwgsvQK1WY/78+QGdL+2MRqNjzITRo0djz549vN/pO62koqjRSkgY9O/fHydPnsTcuXOxZMkSmM1m3HbbbXj22Wd578AMHDgQbdq0wezZs7FgwQIkJSWha9eu+Pnnnx3LjB49Gh07dsQPP/yAGTNmIDk5Ge+99x5efPFFR9dVwDZoRpMmTVzi0rJlSyQlJfHmVTTMLl264Pbbb3eZ37lzZ48DjLz88svYsWMH+vbt63GAFl/kcjmysrLcbvvjjz9G3759sWjRInz33XdQKBRo2bIlvvzyS78+7h7q+ASybIsWLZCVlcXranbgwAF89NFHjumsrCy8//77AGyNMfvAUt4kJCRgzJgx+OOPP9CpUye379DeddddaNasmWM6NTUVe/bswWeffYZZs2YhLS0NY8eORcuWLbFx40avFx0TJ07Efffdh+XLl2Pt2rVITExE586dMWfOHN57Vv7Ue+d42VWtWhWfffYZpk6dihMnTqBOnTrIzs5Gly5d3MYpmHr35JNP4ueff8aiRYvwxRdf4N5778W8efPw+uuvu22E+hunZs2aOd55L8/dfuNp31MoFPjzzz/xzTff4LfffkNcXByeeuop9O/fH1u3bnXpIp2VleX2/fNu3br5/Y5dqPXq1Qvbtm3D7NmzMX36dDRv3hy//PILDhw4gP379zvyOCkpCVlZWbzBe9zN8ze8LVu2YPny5fjrr7+Qk5OD9PR0TJgwAc888wxvsLmvvvoKTzzxBJYuXYpvv/0WiYmJaN68OX744QfHzSJ/w3Jn1qxZ6N+/P5YvX44vvvgCderUwYABAzBw4EAAtmPFb7/9hoULF+L333/H77//jrp162LVqlV47LHHHOEEWr5du3Z1Of4kJycjKyuL13gMNFx/8iuQeuuOv2Vsj3v//v3Rr18/fPvtt1CpVHjkkUcwZswY3vHL33y26927Nx555BGUlJTgrbfechtPf/LCnfPnz6NTp04A4LZL7f/+9z/eYEYDBgzAnj17MHPmTEyZMgU1atTAokWLMGDAAK/5WD6Pypfv0KFDkZKSgk8//RSff/45xo4dC6VSiaysLF4X5PLS09N5dYfjOMfAg5988onL8mPHjnV0pyckGBJW0b4AhBDihylTpmDChAlYt24dHnnkEaGjQ2JQamoqBg8ejJkzZ/q9TrjrXTBxIoQQQkhg6J1WQkhELF26FPXr18eDDz4odFRIJUL1jhBCCIl91D2YEBI2BoMB+/btw4YNG3D06FHMnz/fZ9c5QiqK6h0hhBAiLpW20Xrp0iXMnz8feXl5aNOmDYYPH+72+3uEkOAVFhZi/PjxSE5OxqxZs/Dcc88JHSUSwzy95+kskvXO3zgRQgghJHiV8p3W48ePIysrC/fffz+6dOniGAhk+/btvIFQCCGEEEIIIYQIq1I2Wh955BGYTCbH98Ly8vLQoEEDx/DphBBCCCGEEEKiQ6UbiMneWB00aJBjXs2aNdGrV6+gP2xOCCGEEEIIISQ8Kt07rVeuXIHZbEbDhg158xs2bIjt27d7XM9oNMJoNDqmOY5DUVER0tLSIJFIwhVdQgghhBBCCBEdxhi0Wi3q1KkDqdT7s9RK12jV6/UAbB8oLy85OdnxmztTpkzB5MmTwxo3QgghhBBCCKlMcnJyUK9ePa/LVLpGa9WqVQEAJSUlvPlFRUVISUnxuN748eMxevRox7RarUb9+vVx8eJFpKamhiOqxAuO41BQUIAaNWr4vDNDwoPKQHhUBsKi/BcelYHwqAyERfkvrDJTGep9bmtsXXnzCpLjkwWOUWzRaDRo0KABkpN951ula7RmZmYiJSUFJ06cwEMPPeSYf/z4cbRu3drjekqlEkql0mV+amoqNVoFwHEcTCYTUlNT6SAtECoD4VEZCIvyX3hUBsKjMhAW5b+wFCYFcPOLmampqdRoDZC9zvrzqmWlq91SqRSDBg3CvHnzUFpaCgDYs2cP9uzZg6efflrg2BFCCCGEEEJiQaIiETdG38CxYceQqEgUOjqiVukarYDt/dT4+Hi0adMGjz32GPr06YPXX38dDzzwgNBRI4QQQgghhMQAiUSC9KR01EioQQOzhlml6x4MANWrV8fevXvx999/Iy8vDx9//LHXrsGEEEIIIYQQQoRRKRutACCXy9GrVy+ho0EIIYQQQgiJQSarCdOyp6G0rBST+kxCvDRe6CiJVqXsHkwIIYQQQgghFWG2mvHfrf/F1H1TYbaahY6OqFXaJ62EEEIIIYQQEiy5VI7hdw6HXq+HXErNqnCi3CWEEEIIIYSQACnlSsx6dBZUKhWUctdPY5LQoe7BhBBCCCGEEEKiFjVaCSGEEEIIIYRELWq0EkIIIYQQQkiAykxlSP4kGY3mNkKZqUzo6IgavdNKCCGEEEIIIUHQmXVCR6FSoEYrIYQQQgghhAQoQZGA86+fR2FhIRIUCUJHR9So0UoIIYQQQgghAZJKpGiY2hCJpkRIJfTWZThR7opM9tkCPP7dDqGjQQghhLj195l8oaNACCEkxlCjVWRuaAw4dKVE6GgQQgghbg2bt1foKBBCIuBsnhZqvVnoaISV2WrGl3u+xKyjs2C2ijutQqNGKyGEEEIIISSkHvtmB1YeyBU6GmFlspowesNoTNw1ESarSejoiBq900oIIYQQQgghAZJJZfhX63/BYDBAJpUJHR1Ro0YrIYQQQgghhAQoXh6PRY8vgkqlQrw8XujoiBp1DyaEEEIIIYQQErWo0UoIIYQQQgghJGpRo5UQQgghhBBCAlRmKkPN6TXR6odWKDOVCR0dUaN3WkWGMSZ0FAghhBBCSCXHwCCRCB2L8CvQFQgdhUqBGq2EEEIIIYSQkBP7s5QERQKOvnwURUVFSFAkCB0dURNd9+CrV69i/Pjx6NGjB+677z5MnDgRarXaZbk//vgDjzzyCDp27Ijnn38ely5dinxkCSGEEEIIESEJxP+YVSqRolVGKzSr3gxSieiaVVFFVLlrtVpxzz33IDU1FR9++CHGjh2LtWvXok+fPjCZbn3w948//kDfvn3RvXt3TJ8+HSUlJejWrRtKSkqEi3yISCpDPwxCCCGEEEJIpSGq7sEymQwnT56EUql0zKtfvz5atWqFvXv3olu3bgCAiRMnYvDgwRg3bhwAoEuXLqhVqxZmzpzpmEcIIYQQQgghnpitZsw7NA9arRavp70OpVTpeyUSFFE9aQXAa7ACgFxua5dzHAcAKC0txb59+/Dwww/z1unduze2bNkSuYgSQgghhBBCYpbJasIrv72CsX+Phclq8r0CCZqonrS6M2nSJGRmZqJTp04AbO+8MsZQu3Zt3nK1a9fGiRMnPIZjNBphNBod0xqNBoCtMWxvEEcDxmxxiaY4hQPHcWCMiT6d0YzKQHhUBsKi/A9eqPKMykB4VAbCiub8Z2AAojNuoSKBBI81fQxGoxESSESd1nAIJL+ivtH6n//8B6tXr/a6zPr165GZmekyf8qUKVi1ahU2bdqE+Ph4AIDFYgEAxMXF8ZZVKpUwm80etzFlyhRMnjzZZX5+fj7vfVmhaTRaAIBKpRI4JuHFcRzUajUYY5BKRddhICZQGQiPykBYlP/BC9U5ispAeFQGworm/GeMQastFf016fc9vodarYamSINSaanQ0YkpWq3W72WjvtE6cuRIDB482OsyNWvWdJn3+eef44MPPsDq1auRlZXlmJ+WlgYAKCws5C1fWFiIGjVqeNzG+PHjMXr0aMe0RqNBZmYm0tPTkZqa6k9SIqJqrq0BnZGRIXBMwovjOEgkEqSnp0fdQbqyoDIQHpWBsCj/gxeqcxSVgfCoDIQVzfkvkUiQnFyFrkmJR/aHiv6I+kZr3bp1Ubdu3YDW+fLLLzFhwgSsWrUKDzzwAO+3WrVqoU6dOtizZw/69u3rmL9r1y7cd999HsNUKpUu78sCgFQqjbIKahs9OLriFB4SiSQK879yoTIQHpWBsCj/gxPK/KIyEB6VgbCiO/8lURqv0IruMohegeSX6HL266+/xrhx47Bq1So8+OCDbpd5+eWXMWfOHFy4cAEAsHDhQpw5cwYjRoyIZFQJIYQQQggRpcrwnVadWYdGXzXCXYvvgs6sEzo6ohb1T1oDUVxcjDfffBPJyckYM2YMxowZ4/jtgw8+wIABAwAAEyZMwKVLl9C8eXPUqFEDOp0O8+bNQ7t27QSKOSGEEEIIISSWMMZwWX3Z8TcJH1E1WqtWrYqjR4+6/a18F2O5XI558+Zh+vTpKCgoQP369d12/SWEEEIIIYQEjoFBIvKHrfHyeOwevhvFRcWIl/v/fiYJnKgarTKZDK1bt/Z7+WrVqqFatWphjFHkScR+dCCEEEIIISQKyKQy3FXnLqjkKsikMqGjI2qie6eVEEIIIYQQQoh4UKOVEEIIIYQQQgJk4SxYfGwxVp5dCQtnETo6oiaq7sGEEEIIIYQQEglGixHDVg8DAAy7axji5HECx0i8qNEqMjRyGSGEEEIIIeEnlUhx3233wWwyQyqhDqzhRI1WQgghhBBCSEhVhu+0JigSsGHIBqhUKiQoEoSOjqjRLQFCCCGEEEJISDFQ7z8SOtRoJYQQQgghhBAStajRSgghhBBCCCEB0pl1aPN9G/RY3gM6s07o6IgavdNKCCGEEEIIIQFijOFkwUnH3yR8qNEqMhKJ+F96J4QQQgghRGjx8nj8NfQvlJSUIF4eL3R0RI0arYQQQgghhJCQE/ujFJlUhp4Ne0KlUkEmlQkdHVGjd1pFhromEEIIIYSQaEBXpSRUqNFKCCGEEEIIIQGycBasPrUa6y+uh4WzCB0dUaPuwYQQQgghhBASIKPFiCdWPAEAePLOJxEnjxM4RuJFjVZCCCGEEEIICZBUIsXd9e6G2WyGVEIdWMOJGq2EEEIIIYQQEqAERQK2P78dKpUKCYoEoaMjanRLgBBCCCGEEBJyYh89mEQONVoJIYQQQgghIUUftCChJOpGK2MMBoMBFovn0bw4jotgjAghhBBCCCFioDfr0XlOZzz4y4PQm/VCR0fURN1ofeutt5CQkIAxY8a4/DZlyhTUrFkTCoUCbdq0webNmwWIYehJJNQRgxBCCCGECE/sD1s5xmH/9f04kn8EHKMHYeEk2kbrunXrsGnTJjRr1szlt5kzZ+Ljjz/G4sWLoVarMWDAADz66KO4ePGiADENLUZ9MQghhBBCiMAqw3MUpVyJXwf/ih8f/BFKuVLo6IiaKButV69excsvv4zFixcjPj7e5fcZM2Zg+PDh6N27N6pUqYJJkyahRo0amDlzpgCxJYQQQgghhMQauVSOR25/BL0b9IZcSh9lCSfRNVo5jsOQIUMwevRo3HHHHS6/FxYW4uzZs+jRo4djnkQiQY8ePbBr165IRpUQQgghhBDRqgQPW0mERP0tAbPZDKvV6nUZpVLpeJfzww8/hEwmw+jRo90um5eXBwBIT0/nzc/IyMDevXs9bsNoNMJoNDqmNRoNAFsjOZoGc7J3D46mOIUDx3FgjIk+ndGMykB4VAbCovwPXqjyjMpAeFQGworm/GcMURu3ULFyVvx14S+UqEvwePXHoZArhI5STAmkbkR9o3XUqFH48ccfvS5z8uRJNGrUCLt378bXX3+NPXv2OBqYjDFYrVYYDAZeV2HnTOI4zusgRlOmTMHkyZNd5ufn58NkMgWSpLCyN6ZVKpXAMQkvjuOgVqvBGINUKroOAzGBykB4VAbCovwPXqjOUVQGwqMyEFZU5z9j0JaWivqaVGfW4aElDwEAzmacRRVlFYFjFFu0Wq3fy0Z9o3X27NmYPXu2X8sePnwYpaWlaNWqlWOeyWTCiRMnMHv2bJSVlaF27doAXE+YKpUKtWrV8hj2+PHjeU9vNRoNMjMzkZ6ejtTU1ABSFF5Vc2wN6IyMDIFjEl72mwzp6enRd5CuJKgMhEdlICzK/+CF6hxFZSA8KgNhRXX+SyRIrlJF1NekerMebTPawmK1oGZGTSQpk4SOUkxxN/aQJ1HfaA3EK6+8gldeeYU3r127dujZsye++OILAEC1atXQsmVLbNmyBU8++SQA2w6/ZcsWPP/88x7DViqVUCpdRwWTSqVRdZCwPy2OpjiFi0Qiibr8r2yoDIRHZSAsyv/ghDK/qAyER2UgrGjOf3vcxCpJmYRDLx+CSqVCkjJJ1GkNh0Dyq1Lm7Lvvvot58+Zh5cqVuHbtGkaPHo3S0lKMHDlS6KgRQgghhBBCCClHVE9a3VEqlVAo+C9FDxs2DKWlpRg/fjzy8vLQpk0bbNy4EfXq1RMoloQQQgghhIhHZfhOK4kc0Tda9+zZ43b+qFGjMGrUqAjHhhBCCCGEECIGerMeDy1+CCaTCRuf3UjvtIaR6ButhBBCCCGEkMi6+RVGUeMYh22Xtzn+JuFDjVZCCCGEEEIICZBSrsTSJ5ZCo9ZAKXcdsJWEDjVaCSGEEEIIISRAcqkcA1sOhEqlglxKzapwqpSjBxNCCCGEEELCS0KjMZEQoUYrIYQQQgghhATIylmxI2cH9t7YCytnFTo6okbPsQkhhBBCCCEhx0Q+GpPBYsA9C+4BAGiaa6CQK3ysQYJFjVZCCCGEEEIICZBEIkGT6k1gtVipK3SYUaNVZMR9P4sQQgghhJDokKhIxOlXT0OlUiFRkSh0dESN3mklhBBCCCGEEBK1qNFKCCGEEEIICTnqMktChRqtIkOHBkIIIYQQQsLPYDHg0SWPYsj6ITBYDEJHR9TonVZCCCGEEEIICZCVs2L9ufWOv0n4UKOVEEIIIYQQElKVYXDQOFkc5j42F1qNFnGyOKGjI2rUaCWEEEIIIYSEnNi/06qQKfDcHc9BpVJBIaNvtIYTvdNKCCGEEEIICSkaZ4WEEjVaRUbc97MIIYQQQkisEPvowVbOisM3DuN4wXF6pzXMqHswIYQQQgghhATIYDGgw+wOAADNuxoo5NRFOFyo0UoIIYQQQgghAZJIJKiTXAeclRP9U2WhUaOVEEIIIYQQQgKUqEhEzr9zoFKpkKhIFDo6oibad1qtViuOHz+OCxcueFwmJycH+/fvh0ajiWDMCCGEEEIIETcaZ4WEkigbrb/88gvq1auHfv36oV+/fujTpw8KCgocvxsMBjzxxBNo1qwZhg4dilq1auHrr78WMMahQx0TCCGEEEIIIWIiuu7Bf//9NwYOHIjZs2fjhRdeAABs3boVeXl5qFGjBgBg8uTJ2Lt3L86fP4/atWtj9erVePzxx9GpUyd07txZyOgTQgghhBBCYoDBYsCQX4bAaDRi2aBlSIyjLsLhIrpG66RJk9C7d29HgxUAevbsyVtm/vz5GDlyJGrXrg0A6N+/P1q3bo358+dTo5UQQgghhJAKqgy9/6ycFSv/Wen4m4SPqBqtRqMR2dnZmDFjBrRaLc6cOYM6deo4GqcAcO3aNeTl5aFDhw68dTt16oRDhw55DdtoNDqm7e/BchwHjuNCnJLgMWZ7gyCa4hQOHMeBMSb6dEYzKgPhURkIi/I/eKHKMyoD4VEZCCva8z+a4xYKcokcXz7wJUpLSyGXyEWd1nAIJL+ivtF69uxZ5OXleV2mY8eOiI+PR0FBAcxmM44ePYpmzZqhVq1aOHfuHLp27YqffvoJaWlpKCoqAgCkpaXxwij/mztTpkzB5MmTXebn5+fDZDIFkbLwsDemVSqVwDEJL47joFarwRiDVCrKV7OjHpWB8KgMhEX5H7xQnaOoDIRHZSCsqM5/xqDVakV/Tfpk/SehVqtRXFgcfWUQ5bRard/LRn2jdcWKFfj999+9LrNs2TLUrVsXCoXtg74bN27E4cOHkZGRgcLCQnTt2hVjxozB/PnzHcsYDAZeGHq9HnFxcR63MX78eIwePdoxrdFokJmZifT0dKSmpgaZutBLzrE1oDMyMgSOSXhxnO17WOnp6XSAEAiVgfCoDIRF+R+8UJ2jqAyER2UgrGjOfyaRIDk5ma5JiUfx8fF+Lxv1jdYJEyZgwoQJfi1bo0YNJCUlYcCAAY4dJC0tDQMHDsSyZcsAAJmZmZBKpbh69Spv3atXr6J+/foew1YqlVAqlS7zpVJpVFVQ+4eNoylO4SKRSKIu/ysbKgPhURkIi/I/OKHMLyoD4VEZCCua898eN7HiGIfzxedRqClEegY1WgMVSH6JKmelUin69Onj0iDNzc1Feno6ACAxMRF33303fv31V8fvZWVl2LRpE/r06RPR+BJCCCGEECJWEpGPxqQ369H8u+bIWpoFvVkvdHRELeqftAbqgw8+QFZWFiZOnIisrCzs2bMHP/30E5YvX+5Y5n//+x/69OmD8ePHo2vXrvj666+RkZGBl156ScCYE0IIIYQQQmJJijLFMRAqCR9RPWkFgDZt2mDnzp3Izc3FtGnTcPHiRWzbtg2PP/64Y5kePXpgy5YtuHz5Mr788ku0atUK2dnZqFKlioAxJ4QQQgghRDzE3pZLiktC0TtFOP38aSTFJQkdHVET3ZNWAGjdujXmzp3rdZmsrCxkZWVFKEaRI/JeGIQQQgghJAbQNSkJJdE9aSWEEEIIIYQIS+QPWUmEUaNVZOgAQQghhBBCSPgZLUY8v+Z5vLnlTRgtRqGjI2qi7B5MCCGEEEIIEZbYRw+2cBYsPLoQADCHmyNwbMSNGq2EEEIIIYQQEiCFTIGp901FaWkpFDKF0NERNWq0EkIIIYQQQkiA4mRxGHP3GKhUKsTJ4oSOjqjRO62EEEIIIYQQQqIWNVoJIYQQQgghocXE/51WjnG4qrmK62XXwTFO6OiIWsCN1sLCQt701atX8fbbb+OFF17AmjVrQhYxQgghhBBCSOwS+0BMerMe9b+sj/aL2kNv1gsdHVELqNG6fv16PPvss45pvV6P7t27Y968eTh48CAGDBiAn3/+OeSRjGr6mxXUYLD9s88z3hz2Wqe79XdZGWAy3frbbLb9XVoKWCy2v7XaW39rNIDVeutvjrPdstJobP9znO1vwLbczb9lnNUWDmALq7TU9rfZbNsuYIuH/W+j0RZP+99RmCZYLJQmShOlidIUdWmS6HSiS1O4yynJqBNdmsRYTpQmSlNF0yTlxJcmd+VUzSSDXCITVZoiWk7+YgHo06cP27x5s2N68eLFLCkpiV25coUxxtiMGTNYVlZWIEHGLLVazQCwkiFDbDPeesv2jzHGhg9nbOJE299PPsnY9Om2v++/n7HZs21/d+nC2PLltr9btmTsjz9sf9ety9jOnba/k5MZO37c9jfAWE4OY2q17W+12jZtL8LjxxlLTmbL9l1hjw/51BYOY7ZwW7a0/b18uW27jNnicf/9tr+nT7fFkzFbvIcPj6o0McZs65dLE9eyJbt+/TqzLl0qmjTFYjldv36dWY8eFVWaYqmcuORklr91K7NaraJJUyyVk9VqZcYOHWzHIZGkKRLldK1KWsjSZLVaWdnTTzPu/fcFTZMYy8nfNFmtVqaeOJFxTzwhmjTFUjlxdevazsW//x51aXrope/ZDzsvVopyunHmDLNeviyqNEWinNSjRjEATK1WM18CarRWq1aNaTQax/SIESPYgAEDHNP5+fksLS0tkCBjlr3RWnztmm2GXm/7xxhjOh1jBoPt77KyW3+XljJmNN7622Sy/a3VMmY22/7WaG79rVYzZrHc+ttqZYzjbH9znG3aXsgWC2NqNVu27wprNHaNLRzGbGFptba/TSbbdhmzxcP+t8Fgi6f9b50uqtLkSEe5NFnVattB2mAQTZpirZysxcW2MjCZRJOmWCsna3Exu56ba2u0iiRNsVROVquV3Th/3nYcEkmaIlFOLf+9PGRpslqt7PqFC8xqn19J6l40pclqtbLrly4xq30ZEaQplsrJWlJiOxcbjVGXpmbj19oarSIvJ2txMbt+7Rqzms2iSVOkykmdl+d3o1XCmP+vSFevXh2nTp1CRkYGAKBNmzYYNmwYxo4de/NpsxZ169aFxv7oWMQ0Gg1SUlJQXFyM1NRUoaPjsHxfDt5ZeRSXPnlE6KiEFcdxUKlUyMjIgFRK44kJgcpAeFQGwqL8D07Dcb+F7BxFZSA8KgNhRXP+N31vPf7zaAsM69pQ6KiEVTSXQbSzt6fUajWqVq3qddmAcrZdu3b44osvAAA7duzA8ePH0adPH8fvp06dQsuWLQOPMQkZBpEP00YIIYQQQmKCyMdhgtFixGvrX8P47eNhtBiFjo6oBdRonThxIj7//HNUrVoV99xzDx555BG0a9fO8fvixYsxZMiQUMeREEIIIYQQQqKKhbPg+/3fY8HJBbBwFqGjI2ryQBbu0aMHDh06hD///BPVq1fH4MGDeb+3aNGCGq0CE/v3sAghhBBCCIkGCpkC79/zPsrKyqCQKYSOjqgF1GgtKSlB8+bN0bx5c7e/v/zyyyGJFAketVkJIYQQQojQKsMra3GyOEzsMREqlQpxsjihoyNqAXUPrlatGm/6ueeeC2VcSAhw9KiVEEJIlAtgDEhCSCyTiP2tVhIpFRri6ocffghVPEiI7L1YJHQUCCGEEK+ozUoIEQPGGEoMJVAb1XQzLswC6h5Mop+Vox2GEEJIdKMzFSFEDHRmHdI+TQMAaN7VIFmWLHCMxEuUjdbCwkJs27YNxcXFqF+/Pu69917I5fykGo1GbNiwAXl5eWjTpg06d+4sUGxDS0LdMAghhEQ5jjHIRP8xDEIqN3rwSEIp4Ebra6+95nUaAL755pvgY1RBa9euxb/+9S907twZDRs2xNSpUyGRSLB161bUrl0bAKBSqdCzZ08AwB133IF33nkHAwYMwJw5cwSLNyGEEFJZ0MUsIZWD2G9NJSoSYZhggEqlQqIiUejoiFpAjdYOHTpg9+7dHqejwXvvvYeBAwdi/vz5AACdTofGjRtj5syZmDx5MgBg3LhxUCgU2L17NxISEnD48GF06NAB/fr1Q9++fYWMPiGEECJ6lWFUUUKI+EkkEihkCihkCurtGGYBNVr3798frniEjFwuR2pqqmM6Pj4eiYmJju7BHMfh559/xuTJk5GQkAAAaNeuHe6++24sW7aMGq2EEEJImNGTVkIIIYEQ3Tut3333HUaMGIHXX38dDRo0wObNm9GyZUu88cYbAICcnBxotVq0aNGCt16LFi28NsqNRiOMRqNjWqPRALA1gjmOC0NKgmMfuSya4hQOHMeBMSb6dEYzKgPhURkIi/I/eFYrB05W8acSVAbCozIQVjTnPwOiNm6hYrKa8N7m96DT6TD94emIV8QLHaWYEkjdqHCjtaysDOvWrYPZbMa9996LunXrVjRInr/++gv//POP12WGDh2KlJQUAIBSqURCQgKOHj2KsrIyXLx4ER07doRUavu6j1arBQDe01jA9g1ae0PUnSlTpji6F5eXn58Pk8kUSJLCymgwALC9tytmHMdBrbYNL24vWxJZVAbCozIQFuV/8FT5KiQoZBUOh8pAeFQGworq/GcMpVqtqK9JdWYdZuyeAQB4u/3bqKKsInCMYou9XeaPgBqtFy9exODBg3HkyBH07t0b33//PXr27IkLFy4AAFJSUrBhwwZ06tQpsBh7cf36dZw6dcrrMmazGQBgtVrRr18/PPnkk5gxw1aBjEYj2rVrh/Hjx+Obb75xdAl2ziSNRoPERM8vUI8fPx6jR4/mLZ+ZmYn09HSXBrCQlPHXAAAZGRkCxyS8OI6DRCJBenp69B2kKwkqA+FRGQiL8j94aTXSUUVZ8c5eVAbCozIQVlTnv0SC5ORkUV+TmqwmjO4yGjqdDnVq1aEnrQGKj/c/vwI6Y7z99tuQy+X47LPPsHTpUjz44IPIysrCjh07HL//97//xZ9//hlYjL0YMmQIhgwZ4tey165dQ05ODh588EHHPKVSiZ49e2LXrl0AgPr16yMuLg4XL17krXvhwgXcfvvtHsNWKpVQKpUu86VSaVQdJOwvgUdTnMJFIpFEXf5XNlQGwqMyEBblf3Ds+RbKsKgMhENlIKyozv8Q7uvRKF4aj0/7fAqVSoV4Rbyo0xoOgeRXQI3W7du3Y8+ePWjUqBEefvhhNG7cGJs2bUKtWrUAAJ9++inuuOOOwGIbQrVr10Z8fDwOHDiA+++/H4CtL/2hQ4fQqFEjAIBCocBDDz2En376CS+++CIkEglyc3OxdetWzJo1S7C4E0IIIZUFjcNEiPgxGnGNhFBAjdaCggLcdtttAICGDRsCgOPbpwBQp04dFBQUhC52AZLL5fj4448xbtw4XL58GY0aNcLGjRtx8uRJXoN06tSpuPvuu/Hoo4+ic+fOWLRoEe6++24888wzgsU9VGomuz4NJoQQQqIJE++4LISQcsT+FRjGGMxWM8xWMzXSwyzgZ9jR3v30rbfewq5du9CwYUPk5+ejX79+OH/+PNq2betYplmzZjh+/Dh69OgBtVqNCRMmYMOGDY7P4sSylnWqCh0FQgghxCv6TishRAx0Zh3iP45H/Tn1oTPrhI6OqAXcSnvttde8TkeD9u3bo3379l6XqV27Nt55550IxYgQQgghdvRAgpDKgfZ1EioBNVo7dOiA3bt3e5y2zyPCEXs3DEIIIbGPoytZQioFsV+XJioSUTi2EPn5+UhUeP4KCam4gBqt+/fvD1c8SIhIIPKjAyGEkJhHTVZCKgex35+SSCRIjU+FSWlyvEJJwiM6X0wlhBBCiGiJ/UKWEAJqxJGQ8vtJ6xdffOF3oP/+97+DiAohhBBCKgMaZZOQykHse7rJasJHf3+EsrIy/O+B/yFeGi90lETL70brggULHH9brVYcP34ccrkc9erVAwDk5ubCYrGgdevW1GgVEN3UIoQQEu3EfiFLCEGleGHNbDXjg78/AABM7D0R8QpqtIaL343Ww4cPO/6eOHEiGjZsiNmzZ6NWrVoAgOvXr+Oll16igZgIIYQQ4hU9aCWkkhD5zi6XyjGy40jodXrIpbH/6cxoFlTuLly4EDt27HA0WAHbJ2T+7//+D926dcOkSZNCFT9CCCGEiAyNHkyI+FWG3n9KuRLfPPQNVCoVlHKl0NERtaAGYrpx4wZMJpPLfKPRiBs3blQ4UoQQQggRL2qyEiJ+EkhoXychE1Sj9b777sOwYcNw8uRJx7yTJ0/i2WefRe/evUMWOUIIIYSIDw3EREglUAmetJLICarROnv2bMTFxaFVq1ZISkpCYmIiWrVqBaVSidmzZ4c6joQQQggREWqzElI5iH1fLzOVQfmREpmzM1FmKhM6OqIW1DuttWvXxqZNm3Ds2DHH09aWLVuiTZs2IY0cIYQQQsRH7BeyhJDK86DVwlmEjkKlUKFhrtq0aUMN1ShDH3ImhBAS7Ri96UYIEYEERQKuvHkFBYUFSFAkCB0dUaOxmQkhhBASURy1WQmpFMT+/rpUIkXdqnWhMCgglQT11iXxE+UuIYQQQiJK7BeyhBDbJ29oTyehQo1WkaHOwYQQQqIdXcgSUjmI/f6UyWrCZzs/w3eHv4PJ6vo5UBI61GglhBBCSESJ/UI2lvy4+zJe/emg0NEgIiSpBI9SzFYz3v3rXXy450OYrWahoyNq9E4rIYQQQiKKugdHj5wiHf65phE6GkSkxL6ny6VyDGs7DAaDAXIpNavCiXJXZMR+cCCEEBL76FxFiPhVhg9aKOVKzO83HyqVCkq5UujoiBp1DxYZuntNCCEk2tGpipDKga5LSajE3JPWvLw8zJ8/H6dOncK4cePQvHlzl2UuXbqE+fPnIy8vD23atMHw4cMRHx8f8DKxiI4NhJBQuFhQhsxqCZDL6N4mCT2OTlaEiF4leNBKIiimrka+/vprdOjQARcuXMAPP/yAGzduuCxz/Phx3HHHHTh58iRuv/12zJw5Ez179oTZbA5omVhFH2wnhITCvZ9txb5LxUJHg4gUtVkJET+JRCL6fb3MVIbq06qj2fxmKDOVCR0dUYupRusDDzyACxcu4P333/e4zLvvvotOnTphxYoVePvtt7Fp0yYcPnwYCxcuDGiZWMVxQseAECIWleF9JCIMusEaXag0SDjYvtMq/tqlNqqhMdFgZuEWU43Wpk2bIi4uzuPvJpMJGzduxKBBgxzzatasiV69emHt2rV+LxPLxH9oIIQQEuvE/vQlltC9KRIuEgCcyPf1BEUCTo06hR2DdyBBkSB0dEQt5t5p9ebKlSswm81o2LAhb37Dhg2xfft2v5dxx2g0wmg0OqY1GtsdFY7jwEXR403rzbhEU5zCgeM4MMZEn85oRmUgvHCXAYuy41u0oX0gcPZBWawhqltUBhXHGKtQHlIZCCua818qkUTddXI4NK7WGFUtVQEm/uvvUAskvwRttM6ZMwfZ2dlel5k2bRoyMjL8Ck+v1wMAkpKSePOTk5Mdv/mzjDtTpkzB5MmTXebn5+fDZDL5Fb9IsDemVSqVwDEJL47joFarwRiDVBpTHQZEg8pAeOEug+KSEqhU1pCHKxa0DwTOevOxS1FREVQKo4+lfaMyqLgynQ5WqzXo6wYqA2FFc/4zxkGrLaVrUuKRVqv1e1lBG63NmzeHXO49CgkJ/j9qr1q1KgCgpKSEN7+oqAgpKSl+L+PO+PHjMXr0aMe0RqNBZmYm0tPTkZqa6nccw63KFdtFgL8N/VjFcRwkEgnS09PpACEQKgPhhbsMqqWmIiMjLeThigXtA4EzW2131VNTqyEjI7XC4VEZVFxiYhFkMm3Q1w1UBsKK5vyXSaVIrFJF1NekZqsZ/3fg/1BaWoq3Gr0FpYK+1RqIQL7cImijtVu3bujWrVvIwsvMzERKSgpOnDiBhx56yDH/+PHjaN26td/LuKNUKqFUulZEqVQaZQcJCSQSRFmcwkMikURh/lcuVAbCC2cZSKhsfaJ9IED2F9xu5lsoUBlUjEQigbSC5UFlIKxozX+pVAIwcV+TWiwWvPnnmwCA17u9jgQpvdcaiEDqhqhqkVQqxaBBgzBv3jyUlpYCAPbs2YM9e/bg6aef9nuZWMYYDapACCEkOtkHYBL52CyEENz85I3QkQgzmVSGJ1o8gUcbPQqZVCZ0dEQtpgZiys7Oxpw5c6DT6QAAn3zyCRYsWID+/fujf//+AGzvnvbu3Rtt2rRBmzZtsHXrVrz++ut44IEHHOH4s0ys4hiDlL5TQcLgvVXH0Lx2VQzt0kDoqJAIoRFeSahxNysVo8oVVag0SDjYRg8Wd+2Kl8dj+ZPLoVKpEC/3v6srCVxMNVpr1aqFnj17AgAefvhhx/zyIwFXr14de/fuxd9//428vDx8/PHHLt1+/VkmVjHQtxVJeBy4XAy5lCoXISR49t7BIr+OjSl0VCfhIpHQvk5CJ6YarU2aNEGTJk18LieXy9GrV68KLxOLGGOQ0CmIEEJIFHI8aRU4HoSQ8JNWgu7BJHJE9U4ruflOK7VZCSGERCF285N8HEeXsoSInQTifxVAZ9Yh84tM3PnjndCZdUJHR9Ri6kkr8Y0xRo1WQgghUYmetBJSeUgkEtF3D2aM4Zr2muNvEj7UaBUZjoEGYiKEEBKVbg3EJHBECCFhJ5FUjoGYDrx4AEVFRTQQU5hRo1VkGGhQBUJIaND9LxJqtwZiEveFbKyh8iDhUBneaZVJZWhXqx1UUhV98ibM6J1WkWH0yRsSRmI/+RBCwotR92BCKo3K8KSVRA41WkWG0aNWEiYSuhlCCKkg+uRNdKLjOwkHqUQi+jtUZqsZC44swLLTy2C2moWOjqhR92CR4RijNishJCSoYUFCzf7UhZ6+ECJ+Eoh/XzdZTRj+63AAwAudX4BSoRQ4RuJFjVaRYQCkUmq2VhZ6kxUAkBBH71EQQqIfjR5MSOUhkYj/5qdMKsNDTR6CyWSid1rDjBqtIsNo9OBKZdwvRyEB8MXgO8O+LUZP8QkRlJVjkMX4TUnGAJlUQgP/EFIJSCQSiP2TzPHyeKz71zqoVCoaPTjM6J1WkaHuwZVLqcGCUqNV6GgQQsKsuMyExhN+FzoaFcYxW8Nb5NexMYduIpBwkABgtLeTEKFGqwjRgAokXOjUQ4gwzFZO6CiEBMcAmYSetEYVumQgYSKVSETfPZhEDjVaRYbjGH1bkYQF3QwhhFSU40krXcgSInq2d1rFvbPrzDo0+7YZ7l5yN3RmndDRETV6p1Vk6Is3hBBCopVt3AXxD85ChHG1RI+6qQlCR4PcJJGI/1UAxhjOFZ1z/E3Ch560igzHGA3ERAiJKd9uOecYCZuIG7v5pFXsn8EgkccYQ9Ynm4WOBimnMnzyJl4ej7+f+xtr+q2hgZjCjBqtIsMYqHtwJSLuUwGpLD798zQKSo1CR4NEAGcfPVjoiJBbqDBImMhlEojkdXyPZFIZsjKz0KlWJ/rkTZhRozUCGGMo0Zkisy3QJ28qGypuEi406iMJNXqnNTrRmAUkHKQSCaycyFutxOGp/9sV1vCp0RoBO88Xot0HGyOyLepPTwghJFpxjNHowVGIyoOEg1wq/ietFs6CFSdXYO35tbBwFqGjI6i9F4vCGj41WiPAFME9ljFASqVaaUhAA5pEm+X7coSOQshIaFg3EmK2cxR1D44qtJuTMJGK5P31LadVGL3ssNvfjBYjBq8cjJc2vQSjhV5zCSdq3ogMxxhdaJKwoFrln3dWHhU6CkSEYv+yz4ZjDHLqHkyIYApKjeC4yOyAcqkElghtK5zO5ZVi48k8t79JJVL0aNADXWt3hVRCzapwiqncZYxh06ZNePLJJ9G6dWvs27fPZZmrV69i/Pjx6NGjB+677z5MnDgRarXaZbk//vgDjzzyCDp27Ijnn38ely5dikAKwo+jgZiizm9Hr6O4LDLvNIdT7J92SDSjRkzlwDHxPH0h0YWqlH86/m8Ttp8riMi2ZFJJxBrIQklQJGDzsM345bFfkKCgzy2FU0w1WseNG4cpU6agZ8+eOHHiBMrKyni/W61W3HPPPUhNTcWHH36IsWPHYu3atejTpw9MpluNhj/++AN9+/ZF9+7dMX36dJSUlKBbt24oKSmJcIpCz/6+EIker/50EIdyisMStrhPBYQQsXG80xqh7TUc91uEtkRI7IjU4EhSiQQWGoiJhIhc6AgEYtKkSUhISEBubi5ef/11l99lMhlOnjwJpVLpmFe/fn20atUKe/fuRbdu3QAAEydOxODBgzFu3DgAQJcuXVCrVi3MnDnTMS9WWTkGqZQarb7sOl+Ijg2rQSGLqfs2btE9CkLETyy7OXOMHky33AgRO1klGIipoqwcw8Jdl/B81m1CRyXqxdQVe0KC78fu5RusACCX29rl3M07PaWlpdi3bx8efvhh3jq9e/fGli1bQhhbYVg5etLqj3/N3o0rRTqho0EIuYkOW5WDlQN98iYKUXGQcLA9QxF37dKb9Wg/qz16/9wberM+iPWtmLz2ZBhiJj4x9aQ1GJMmTUJmZiY6deoEwPbOK2MMtWvX5i1Xu3ZtnDhxwmM4RqMRRuOtUcE0Gg0AW2OY89X14ebZ2edyIWDlOEilkdmWkDiOA2OsQulk/pRdiHBcxeLqCWMMYJEqb8bL81CUgVhFKk/u/+Jv/PivZmHbnl/HtxjcVqhEch8ov9/FMovVCqVcCos1NOXtTxnEYp5pDWZwDEhJUIR/YwwVOo9Ey7nA/u6k0PGItGDyP1zXJM7sN6divUwYs3213F06LFYLjuQdcfwdaFrFcmy3Czb9/hC00fqf//wHq1ev9rrM+vXrkZmZGVT4U6ZMwapVq7Bp0ybEx8cDACwW2zeU4uLieMsqlUqYzWavYU2ePNllfn5+Pu99WXeKi20DQalUqoDiH4zSMh04qzUi2xISx3FQq9VgjEEa5Dd+CguLUIVF5mmrWq1GOIrEZDLBKolM3bKYLdDp9I5thaIMxCpS+985VRlKSkrCVgYlJSVQqawhD9edwsJCKC2lEdlWqERyHygos52fYv3YXlCkgYSzolithkpV8QaZP2UQi3k2+c+LKCqz4MsBt4d9W2VlZbBW4LohWs4F9sG9IlHejDEUlJmRXiXO98JhFkz+h+uaxJnBaIQkQtco4aQtLQVjnNt0WDkrljy0BGVlZdAUaaCXB/a0tcxkO8fGeh7ZBZoOrVbr97KCNlpHjhyJwYMHe12mZs2aQYX9+eef44MPPsDq1auRlZXlmJ+WlgbAdoFUXmFhIWrUqOExvPHjx2P06NGOaY1Gg8zMTKSnpyM1NdVrXFKLbf3eMjIyAk1GwJTxKijjzBHZlpA4joNEIkF6enrQJ8m0tOrISK8S4pi5l5KSEpYyiYu7ArlUEpHyVijOICEhwbGtUJSBWEVy/0tNTQ1bGaSmpiIjIy3k4bpTPS0NGdUTI7KtUInoPqAxAIhs3QqH5BIJqiSWIDGpSkjS4k8ZxGKeyRTXIJVH5tiemFQEmVwb9Lai5VxgvfmkNVx5tuJALgZ2qAcAuFaiR98vt+LCxw+FZVuBCCb/w3VNUh5jDPFK23fLY3EfLC+5ShkkEqnHdDyZ8STy8/OD2gdKjbaHabGeR3aBpsP+UNEfgjZa69ati7p164Y83C+//BITJkzAqlWr8MADD/B+q1WrFurUqYM9e/agb9++jvm7du3Cfffd5zFMpVLp8r4sAEilUp8VVHJzYKRIHMy5m4NcVIZGhEQi8Sv/Pa5fgXUDJQ1TmUglEkASqfKWOPLcMaeCZSBWkcyPcJaBJILHEqkk9urRxYIyvLb0FH57s2bY424PP9byyBnHgHiF9Oanb0KTlvL7QEGpEWlJcZCUe0k6FvNMIpFAIolM3CUS29fdK7KtaDgXsJvvToYrDu+uPIZBd9W3TUgid13nj0DzP1zXJOVZrBzkMsnNAUKjI5+C5c8+Euw+IJZju12w6fdr2UAjE+2+/vprjBs3DqtWrcKDDz7odpmXX34Zc+bMwYULFwAACxcuxJkzZzBixIhIRjUsrBxoIKZKJNLDG1DVIuSWfK0R/+SJc0C35+bvDUu4Fo5BKZfBEqZvN3b83yacvK4JS9iEEP9ZOAa5SBpi3lg4C347+xs2Xd4EC2cROjqiFlO1ac2aNWjdujV69+4NAHj++efRunVrfPfddwCA4uJivPnmm4iLi8OYMWPQunVrx79ffvnFEc6ECRPw8MMPo3nz5qhTpw7eeOMNzJs3D+3atRMiWSHFMfrkjb9o9MrAnLyuwcJdl4WORlC6TvlL6CiQcqwcc3TlI/6JdG5tPZ0flnCtHINSLg1r+YtkPBNR2nm+AK8uPih0NEgEWDlbzz+xM1qMeGzpYxj6x1AYLUbfK5CgxdTowT169MDSpUtd5tv7T1etWhVHjx51u275bshyuRzz5s3D9OnTUVBQgPr167vt+hsqkdxl6ZM3hLi6rjYIHQVSzpgVR5CWFIf/PNpS6KiQCLNyDHFyadietJLgRKo0LhaUYf3x6xHaGvFEEoErU9uT1shdj1qsHKQSSfge3HgIViqRomPtjjBbzJBKAn8W6PzN6sM5Jbih1uPB1rU9rFF5xVSjNTU11eugRzKZDK1bt/Y7vGrVqqFatWohiFn0sLLKcWcr1kTiBEFIrLiu1jtG+rTLLdGhflpsDcREAme92T2YnrR753whG050fqp8WARuU0T6SevzC/bh7sY1MLJn4/BswEOWJSgSsGfEHqhUKiQoEiq8mdWHrmLX+UJqtLoRU92DiW8cZ+seHMkTXqg0Gv8bDObIfF4DoPczCfElkhezO84VRGxbsUgshysLx6BUSGGxxt45KtLoHBWYWLzuETPbQEyRa2ZcLdajWOf9E5TRSEI7ut+o0SoC32897/jbyjHEySSI1E1sLoQb4hhcnr6IRSTuaobaoSvFuPezrUJHgwgoFustiW5Wjrv5Tiu9eOqNRCKhcRdITLvVPTg2G2Ut3/9D6CiExOXCMqGjEDLUaI2AcJ93pv5xyvE3xwC5NLyDXDi2xTE0mvB72LcTCcv2XRE6ClGnsNSEiwXCHOwMZisKSmlAAxLd6AZ54MI9ejBAN1sqE43BLHQUQmLGhtPQiiQtdrE+EJPO5F/PP71Zj+7zu+Ox1Y9Bb9aHOVaB6/HpVqGjEDLUaBUhmVQSkSeWsX5ZUD6L3l15TLiIVFDsnhI8+2nPFTz4xXaho0EICTEuAqMHh8vMbeex5vBVoaMRcowxaPSx2WBqO2mD0FEIia82n0OeRlwDBkZ6ICahcIzDztyd2Je3DxyjHiThRI3WCIj0LiuVSmLygoAQOyvHYLJE7v3mcLok0NNq4t7ZPC0K6Sm+YBzvtMbgOWr98RvYfaFI6GiE3LYz+SjWxWajlUQvKxfZd1qFopQrsXLgSsy7fx6U8tB8iYR6i7gn/tpUCckktlGESeVAJR3detJ7wRF1JKcEL/+43+PvQ+buwY+7Q/O9YSEPs1dL9Fi6NzZea1i46xKuq23d5mj0YP9EclAhk4X/dEhrMIdtUESxXJrQ4Dm+ma2x+6Q1kKfecqkc/Zv3x0O3PQS5tOIfZTl0pRhn8korHI4YUaNVhKRSScgGSHpjyaGQhENiT7ivLfrM2BbmLZBYEqqRii8VluHPE3khCSuaOO+P+y4WYdwvsfFaw/trTuBIjhqA7Umr7Tut4utGl1usC2ljU6h20fAf9uOzP0+HLfxwNfgi2R6O9EjFV0ui711JX2690xq5vApVuRSWRm4UYuc4F0Rw27GGGq0iJJOErnvwr0euhSQcf3m7cD2SUxK5iJCwO6uiO4nRRGeyoNRoEToaRKTs7RRrDL/T6ku3qVtQIoJutjqTBUZLeG4q0APK4GR9sjmk4UXic2bu3ml95ccDUIdrHxGoblk5K7Ze2oqd13bCysXea03GGHoVixqtIiSTSiLSPTjSdxr7fbsjotsLpVCdIApLjfhg7cmQhOULXVtULq8sOogXf/DcrTbcYvEdHl8X4BqDGWV0I4DHYrU1Wuk7rZUXfU81shbtvozss/zvYEdisE4rx0Em43/y5o8TN1AUg99S9cZgMeC+H+/DE2ufgMESW4Npnc3Totl/gv+0z69Hrrm8XhBO1GgNEyEPyrbuwYJtvkJi8cI1kjaezMO8HRcd03Tyr7h/rmswavEBoaMhuL/P5GPXhUKhoyEqoxYdxIfrInOTKdqpb45Oa2X0Tqs7/1zXYPqGW11yfXWh/eP49bC9d1pR0RqvyuqHnZew+ZSKN09rCP/NNIuVQSGVQjQjb3jYJSUSCVrWaImm1ZoG1fVdyPejDeaKNRbeWHIoop8npEZrmNw2Xrjvl8okkXnSWtEdTWswU6MrQDvPuzYq6Imoq3d/Pur3spcLdfj92I0wxiaGiXD3DOUhx9fFudFihckanXcQIz2Ik/3JqpXjYnb04HA6mluCrzefc0z/dvQaTt/w/ArFK4sO4kJ+dI5M3vy/wT+5IZERiQcEt95pDdF4BQVlmLDKy3v8Ah1SEhWJODbyGLY9tQ2JikRhIhEjcot1FVqfGq0iJAvhQEzh1O6Djcg+V+B7QZHZeloVE4316I+hZ8v25wgdBUHE4mAdsabR+N8cf/+4O3INP+fLvopedEZ6ECd7fC0ifqc1lDiGiD7BIMRu1aFcv5f94/gNDJmzx+1vZo5BLrMNxBSKa54LBaX4aY/3Y26onlrSu9ehpzWY0W3qlgqFQY3WKKDSGhxdp0JBGsKBmAKhNZix/Wy+38tbOQZjBbsmRIOcIh2OX1X7vfxz8/eF7OlLZRl2f+ofp/D5xjNBrRvqfeGNJYdwuTAyTzi+23oOp25o/FrWYuVCPliHGDnvMvd/vg3n8/0fFKx8dQr1e2GVoSux1WrrHizG0YOjmddBFUVyGomBe8Ex4a1lR/xe9lqJHgcuF7v9zcpxkEkltnFWInRNGgsPBCqrUJQMNVqjwPPz94VweHkGmdTzd1r1Jit0pvC8y7DnQhGGzt1boTBCNWDRr0euYfWhqyEJy5c52y9gzAr/D/Ji9cqPBwK6+A/EyWsanL6hDWrdUDcsfj1yDbnFkXmi+dmfp3HoSolfy4alu2WUXMyGbbRJAGfySlFcFh0Dg8zNvuh7oSCZK3CjbNm+K2g47jffC3ph3w1j+kkrY/B16VX+psg7P0fPecHr5+siWBTUpqg4xpjPJ47RwP5Oa5xcBnOQA68FdNxyOl8VlZlC2+vQQ1B6sx73L7ofg9YNgt4c272d/jxxA69H8acuqdEaBZwP4gcuF1fo0xPevtP63zXHMTqAu2ixat2Ra1hzODKNVn8INcBU8/+uh6rcR7K3nfH/SXig7ZU/TtwIaWOuMr7ztu7oNWw8GRvfGI3kMPl3fLAhYtsSq9vfW+/3U3tnv/l45/uvf/Kw71KRX2FZOO5ml0EPv1s5bD4VG/uAO+XP58v3+9/NMpZdVwd23C/fQ+j/tp332otm78WiCt1w8aaw1Bi2z3z9fuw6Hv16e4Br+XfWtXDM+7udUcL+TqtCJuH1LvP32sJoseL299YHvf32H26MyOCCHOPw18W/8PfVv8GxwOtqoE+Hp/1xCn+e8HxMziny/71R555HF/LLAuoxGWnUaI1CT3y/02V48kB4G4hJZ7KgLERPWitrNwzGGLSG8D35afof/w/SvsrAYOagLzdYzLPzKvYkPFJO3dBCZ6p8I1Au3ZuD1WG62XJDbcCSEA6+4zxMfqBPQ50vXCLx3UCgcj/p0Ydpn/pu63ms8PM9crOFIU7m+dJDpTXihQW3Pr2071IRXlwY2KeYAiljb0+Qi8pMuBCm3iOhELKboU67HmOBhZ01dWvQm56y/hSW7fNcd576v124WBCa1zHO5mmxs9w4Gs/O3xt0Lzdfr+YUlBpxxstAWu6J6+Bkf6c1Tibl3XjwN5WhOFZ7+xzL0dySim8AgFKuxML+C/FNr2+glCsB2G5ahOtTMOuOXsfhnBKPv3eftiUmxrUJBjVaRUcChYy+geef4PJo25l8tP9wY4jjckskv3kVrcLVhV3MfJ3gD1wuxvgwDr4T6NPQUB2hyoyWgN/lDVUDOVINbcA1vyK57VAyWzko5P5felwu1AnW+2D29gt4bv6+gNaJyTOvwJG+oYnMty2X7M3BxF9POKYtVv4AQYWlxohd7C/cdQln8oJ75SUUInH8MFs4KGRSKGTSkFzXBNOI1XsZ4f2xb3ZUIDa3yKVyPNPmGTxx+xOQS+UAgFGLDyKvXL0eEeFvoJ+4FlyvmmhHjdYwcH76FdnBcphLV4xAjP/lKEpi4MPPpUYL5u72MrCEH45fDW6nNpi5gN/PCNUJItSnU28n6FBvqyxM3bBIdIpUeRvM1oBGTVZpjfh8U3CDekWTWPumtT22RiuHOJk0Np94S3x/vmPn+eB6ScVkfgQhmscO7PC/Tdh6RuV7wRB4f80JzAvjO+yR5OlYZLq5ryucnrSK0ZC5e/H3+RKPv2/6J7I33gx+vr4Ta8edmGy0MsZgMBjA+Rh90L6cxeL54slXGLGoIk9al+zNgUob/cPs31AbMHv39QqF8U+Q73c5i7F9nqfRhMh9T7jVxD8jtq2KKDNaoubGTSB1K5ouBo9fVQdc3rHWCAuGwWyttK9V2K+O7E9fgq2vZ/K0IevW58vZvFJcCeD9MCB8XbB92XKqAo2tKDp2VEQojiH+XjuFYj9e6qVbdChJJMKcH0wWDnFyKRRyiagbrVbOiuzLu7HvxkFYucD3/8ryFYhQnPpiqtGan5+PTz75BI0aNUJCQgL+/vtvr8u/9dZbSEhIwJgxY1x+mzJlCmrWrAmFQoE2bdpg8+bwfSqioge31346GMCgJxKvd7XCeb0UzH4XC5dvHMfw/prjQkcDQGSvLaLpMBrJejJl/T8Bv0MXrPXHrmPkogNel/G3HMKybwcZprGSdHEP9JjXfdoW/HHc+6BG0SiUDW2O2b4lHqzvt57H/377J2Tx8eZKUeDvUgZ7AVrR69bnFwTWjdkbiST43kHhHO9B/MJz1mWsYueH8t9tZYy5DPTjqa7YG61xMilMloofQ3ad9z2okhD3BA0WA3IUb+Gz48/AYIlMV/dQibX2ckw1Wr///nsUFxfjhx9+8LnsunXrsGnTJjRr1szlt5kzZ+Ljjz/G4sWLoVarMWDAADz66KO4eDE6u2qsO3o9oItAX92DfZ1Uo+XzD95sdxqoijH+uykMobt7ZWUMC3ddDklYoeZPGsXyYCd8B1d+BhVoTdDob/XOUOvNMHh5L6YiLhXqsOei91FXRVJ8ohRolczXGlEWoSdxz8/fi18ORtHotRG8Ooq1C7FwGDJnj9/LBppdRV6uEcqMvuu3kL0NxHA8jXT2lf9u6zlVKbpP2+LXeibrrUar2c/Rg9uU66HjnM7Np733JhBqt5dIJJCzDFSPqyO6p6ZFZaaw9Ty7WqLHnO0XAlonphqt77//PqZOnYpGjRp5Xe7q1at4+eWXsXjxYsTHx7v8PmPGDAwfPhy9e/dGlSpVMGnSJNSoUQMzZ84MV9S9UuvNIRyIgFV4IKY/vAylzd+S03QED6TX1fz8Wnf0Oh79OjtyEShn4a7LOBXkN0QrKpq7GmpCeMc9kqeBP07cwOlyA2QM+r9d+L9tgR1Yg+XcvY0xwBimBnMsOxnGQSbGBvDN5YrufeF8KnXsqhrXAnjXN9yC3YeDOcZF62Gx00ebIrat7HPBf4HAl/Yfbgy67gZTNqEqz+hvT0RpxS0nkBgaLTffaZVL/R5nRettLAQfGw9l7rmtKx7qT6IiEbeZ52Ny+/VIVCSGMBbCe2vZYby3Kjw9DY/mlATcayamGq3+4DgOQ4YMwejRo3HHHXe4/F5YWIizZ8+iR48ejnkSiQQ9evTArl27IhlVh6slejejIwa++1k5BqlEAnkAL72v2J+Dcyr+sOyhOkGsOXwVU36/VSFHLjqA/9t2nrdMqM4hhaVGXjr8CjfIdEbbiS9c8Qkme8pHpe2kDbCGcCTG8vVSrTPDEqF3ZHQmK0zWyDUcnYvT+QZNsMT0zujDXwX6/UP/rTgQ3qeT5RthbSZt8PtTQVF22OGx9XTxsYyfYQl5fLVyLKBBvdzx1sguP16ElWN+f9s23II5MlTkaCK2p1HuOFcDs5UL2Xudztl3Jk+L//4enhurhaXBj3HieKdVJoU5Qq+MCFW1KrI/VPTG3Kd/nsKNEF0nOOMYC9m1QyjKRtBGq9lshsFg8Pov0ML88MMPIZPJMHr0aLe/5+XZGofp6em8+RkZGY7f3DEajdBoNLx/gK2RzHEcLheUOv623jww2afttcs+7fzPrvw0xzGnaffrlv/dZLZALpVALrU9nfG4LXYr7PfXnMD2MyqPv/u7bY7jwBh/+nBOCTafuhX2+fxSXC/R89ZlzH1YvrfFvE4Dtp3Na1he0qk3mT1u23naufzcbyu4dFosVlzI13pOt624vIZVUGrwKx3O/5zL0106mZc8DHTaOVz+tm0HTvt0r+lbseJAjmO67aQ/ka/Rh2hbzvsj4+VxIOXnq24xxvH2N8YAOG2L+ajHnsrAYLLg/dXHb4XNBXEc8rUPeZh2rjsu6zLXbcFHPfZ/2xVLp/O6Xaf8FfS23JWn8zHQwlk9btt7PvjetnPd8La/etuWc54t3XsZLy3czw8b/tUV3FzO13HLUx6zmy/o8dPqlK4gj7e7zxcg65PNHuup/TUUb2H5e0zMLSrDwJm7bv3u4zjkdlterhN8TfN+c94fnerOoStF0OpNXsP2t275Ot6aLVZYrcHuE35s22WfCOx46wjL5/7HAMmt399ffRzjVh71mG7b38Edby8WlGHjmWLH9PYzKizde5mfLgS372v0Jq/bZox53PdNFivkUtiuSS1Wx7rWcss8N3+vl7Cdpn3tE8y1PMv/3nDcbzCZLX7mg/drTHd55nys97deOv/uXDec/0mc6um3W87jbJ4mJNu2XZM47xPBn5u9LZ+vNTim/SX3e8kwGDVqFH788Uevy5w8edJnd2C73bt34+uvv8aePXtgNNruDjHGYLVaYTAYeF2FnTOJ4zivd/+mTJmCyZMnu8zPz8+HyWRCjy8OYPe/O9jCulm5VSpb//uSEjVv2pPyv6s1aqhUt+4pFOQXwKCUeV23zGSFxWyCrlQLvZmDSuVavEajAUYj59gWYwza0lLetnV6PW/aU7ztXZAd6VTz06nX6WCxWBzTVovFJewStRoq1a18z8/PR7zCy4fnb66r0+kc01KpFKoiNYyWW+kyGo0wWzmvea7T6Tyms0u58nROp1rtvjy9bUtdokb5n/NV+Yi7+a1CjtmekLsL61yBHkMWnXTExWg08H43GY1gMqnXba/ZfxH14m89zfEUb8YY9l7RonODqgA811v7dKnBDF2pFowxSKXSm+uUQKVivGXLD7rib57Z999b5WmChLuVTo3BjKJiTblpC3JvqGCtqnQJ27n8nPkqT6vVirKyMn69LSmBSuX5QOsprAuFejz9463yLC0tA8fYrfI0maCX8uttmVM9dVZSUgLGGOyvSpbPk4W7L2NUlxq26Zs32uy/czefjjkPisNPZzFUKs9PmT2ls7iolDe994oG+69oMapbXVs6zSYYDHBKZ5nXdHrc1s0nlY50qzVul/cWXnFxMVQJ7veR62qDx22bTCbHtH0fKM92Y5Z/zCt1qkunLl1H4xoJHtNpV1DmlE6N93RyjHPZVlFRMVTKW+8n+ZtH5nLpBIDTuQU4mlN863ezGQb9rXz67WQhOmYmo2ZynCMsrVYLlUoFo9EIlUoFk8nodvsFWv62nNOpNxhgNptv1WOOg1qt5h2HiouKoYpzn04LxyD3UOeLivnbslgt/GmzGXqnc5gzjUbD+91T3SlU849xGo3W6/LuFBUVQSU3eFy2/LHdW1hWp3SaLRZeOh//7gA+fqQRet1ezbGO8zWJ/ThUoOOH5Uyr1QLljnnOcR22+CT+1b4mHmqRxk+n1PYEvPy52Xld+9gfnrZ9SVWCc/m39gmLxQKdnn98VTtdkzizL1ug4Zef3mzFz0fyMbRjLUc6Wbl0XivSur0m4e+fRVDB82jVns5p6pISx7RUKsWaA1dw+GopetZXOtKpdzqPqDUaqFQKn9sqLOGns6hIz5suLS3lpbM8tbYM2pJiGHRlyGdGx7VBYWEhkjjbIGdbT+d7zBODU3lyVu91y2q1uFzX2fbHW/VUpcqHXOb+msRstY1s7i6d2tJSMM59Og0WA67KPsT7eyV4uOUiJMYlOtKptNzqAVh+3a3nitGziW1/sr8HXv6aw1s6LVYLdDrv1yTFxcVQJd7qau0prOJiHe932zXJrXpqMppgxK3r+Ml/XkTfVjXQvl6yI4zCwkLITbeO9Z62VeqUzi9ufnouPz/f7fLuCNponT17NmbPnh2y8A4fPozS0lK0atXKMc9kMuHEiROYPXs2ysrKULt2bQDuD+61atXyGPb48eN5T281Gg0yMzORnp6O1NRUALantQAcd2js06klEt70nguFuFiow+C7MnnbsP8OAKkpKbzpGuk1UDXedoDJmroFO96912XdYp0JyUn5qFE9FQWlJmRkZGDDyTx8v/U8Vo26GwAQF5cLM6yOsA0WDnqm4G0rMSGBN13+74W7LmNY1wYA4Ojq4khnEX/5hMRCyOU6x7RMftolbOd0ZmSkI17huXFuXzYxsdAxLZVKoTLc4P2uVOZCYuV4YTtLSkz0mM7y0/aPYtunU/I5r8tPWX8KLWtXRb92dRy/paTy05mekQ6l3JbORhPW48LHD7kNq4jTOqXrGm86TnkFcTKp13Qm+JlOg9mKN7886IhLtULmdfmXv9uJu+olYtyjGY6LxdTUVGRkpPOWLd8o8hRWmdGCdceuY1DHzJvpzHVK92UoFTLHtARAcnIV/j6SVgMZ1RJcwnaup4wxnLqhRYvatsa5r/K8qjbhh303MPHxOx2/2dJpawwOmbsXi4Z3cruu8/R1UwlvukoVLaQSSbl0XkJ8PH9/TEpK9Fq+qampSE9Ph9HCLy+l3sybTsmz8qY//fM0cor0+Opf7TzGPTW1GjIybBePbSdvwNGJ9/uVzn0HinjTl09o8Ns/RZg0wJaHcYqLiI+P56czMYlXZvaLB+ewnY+vspvd1+zTVa9b3MbNWfnfq1WrhoyMam5/8zatVOY4pqVSKfZfLsbR3BK8kHUbAEChuID4eP4xr0pSEm/6glaCri3d75/ljw3s5rgHjnRe5Zfv0n050BrMeLG77SavVCLl5SkAVK9eDRkZqW63lacxoGZV/hgQ9t8VcZd500lJJZDKSm4dhxTnkVDu2P7hFwcw46m2aNP4VvhVk5ORkZEBpTKH979zOi1xen46c0y86X9UJ8HYrenRy4+ga714PHH7reNQNS/p9Ha8TdFIedNy2Wn+tOIcL51Xi/VIT1Y6bkACQEpKVb+Ot0a5jp/OZKPX5d2pXr06MjKq+tyWvdHqKSy5XA7AeCud8rOIdzpXp3i5JgFuHYeg5ZdXic6Efy87ggXP3wUASE42AOWOec5xzSk5DE7O33ZaWnVkZCS7LOs8bX//31M6/1Gd4P0ul59BYgL/+CpPuLXPjFx8EF8OascrX8cxyqmeXsgvxbfZh/H2w21vplMPCcofK9xfk/DSWb06MjKqAADeXnEE0wfe4XZZ53Na+XOYVCpFYkIB5HJDuXSedrkOSKl6q56WGS1IUvKbA/bf9LIy3nQJ41+TVKmig6RcedrrmkQigSwuD7VrpiNNLUG84tZ1So0aachIS3KbB+WnDU7lKZPx66n9XCC9eY0hk8mR6JTOqlX5+2N6Rjrv3OLp2OB87ZVcpQwS6a10fvXXWchlUozq2RhlpjLoZbuhZ0BajTQkx9vqalpaGjKqJ7rd1rgvDji2ZX8//FY6ZbzpXw5eRU6xDm/ed7vj9ySn80j5axLbdDVkZFT3mMd2+RY173fbNcmtsopTXoZSKXdM/3nqALKa1nbaP9OQkep67QUA9362DVvG2F7HjHe6JrEfr517vnojqndaX3nlFZfuxW3btsVrr70Gg8EAmUyGatWqoWXLltiy5dboZxzHYcuWLcjKyvIYtlKpRNWqVXn/AFum2zPe/rfztMRpesuZAszNvshb1jksiYQflqxc2NfVBrfbsjLYRmqTy2zvt0qlyC814cQ1za24SCSQSCS8dVVaEz8uTr+X39aktSc9ptP+tNCxLbiZdgrbOZ3lwy4fL5c8ddoW4DwNSMtt6+cDV3GpUOe0bc/p9FqeEqnLtspPbz6l4uW5LS7+he067ZQulzzmb3vvpWIUlPLL01M6nfMw0HSW6M0wmDn+tvxOF3/6cpEe43857jFuDBJIUD4dEpdtSaT+lafewvDI1zu81CX4nC4f9s7zhf6Xp1Oeao1WFOnM3veRcukeOm8vykxWt+UrcaorzttyLs/8UhOuawze01kuLqVGq9/pzL35XmD5PJZIytdjN3lc7vdm//0TGoPFbdgGK/O6befyLDNZXcLyls5A6q3z/rfzfCFmb79YLi78dNmW5W/L2/HW+zQ/nbsuFGHbmQKnPHc+3nreVtdPtnjclnOeSSRO+6Nz+cL1mCdx/C0pV06SgNN9pUiPnGK9Y3rr6XxcLjZ63ba/YS/fn8tPt1M9ldiKyzHd/dOt2PiPyimPg9u2r+PQJ3+cxp6LxSGpO2dVZRg0a7fXfcK1PL1vy1PdUhus+PtsQbnlfR9vgz43O00fvapBj8+2BpROk4U5pv88kQcLC+46z5ZO52O5j3SXC3vVoWt+p9vl2svN8VUCz3naZvJGmKzMv225qbflw/7wt1P48LdTkEqlMFsZlAo54hQyWNmtRkqw+6dznn2/7QJeW3L41u9u0u0cFsf8rVvOeSoBytWFI7lqHL+qgVQqRbwiHtVNr6G66TXEK+IDTpevdB64UoKt5Y7tgLtju//bemLmrnLpcnOd4LRPOO8j/l5rSaVSXC66dd3tvI+UT6e//F8yCti7+dq7DppMJhgMBlgsXkYbc+Pdd9/FvHnzsHLlSly7dg2jR49GaWkpRo4cGY5oR4zFyiCX3vwmVgCjB0fTAC3lX2HuMuWv4MNxmv7P6uPY4fyNryBfCo/mMSSenbcXvx+7HpKwoqdW2ERzvgfrtNOo0772xR3nCv36pITQQjGYm7kCI6CX98Hakxi1+GBIwgqFaB3ZtjznsSScX52xBDNgkVOYtvOU/+8yRUK+NvhBZ3yp6EjvS/ZcwfGr6pDE5YbGgH2XiisUxomrmpDExZmVYy4D+AWbdUVlRuQURccI2ieuaVAYos8JBpofEknkzp+5xXrkFtvy3GThoLz5yZtgvt3tK50qrRG5JTrHtD9JPHC5YvXeHYVMgWTrg0i2PgiFzHOXa09CMTCZ0eL/dcGhKyUV3p5QYqrR+tNPPyE1NRWtWrWCUqnEY489htTUVHzyySce11EqlVAo+JVo2LBhmD59OsaPH48WLVpg//792LhxI+rVqxfuJATNn2OU2cpBLgts9OAKxclHpNYdvYaz5Ub0tXCuo+cJev0WAxePm0/xu7GLsN0WlGi60RJqwZaxpw+82znn2dHcEuQW6zwsLR4WjsESwlGsKypSMSkoNeLbrefCEvbOEHxOJU7u/kLW+fotqJE1g41UGPlqsAh5Uy6Y/MqvwKiy3pisHD76PbDPYMSCqyV6HM31ddMhuJq78uDVoNYLN/snb+LkktBck0bRBVA0HmPCYfvZApeb60IS9J3WQA0dOhRDhw4NaJ09e9x/YHvUqFEYNWpUKKLlQqjKbLbavtGqkEkcnwNhLHIXbM7XFiqnu9bn88twPr8MHz3eJiLx8SUWPsHgzzDmvuLnqzETi8xWBpXTt42Df5ARufyxBDBKXridySv1vRDxSshPd/hT3w3m0NQ354ZjKE4pcTKpY7yAmGHv7x2EWHjCHgmUD6F3Pp9/LPfnuBTKQ5fnm8gMUqkEihjY13OKAr+Ba89DjnEwSS47/pbG1vNAn87nlzn+5hhQVBqaHgPBHEvFlbMxQqUxoMTnt/kCP7JbOA4K2c0DxM2uddF0hyScnA/AzruCycqFcEcTp0AvJi4X6rDoAP8zUcGeBzU+PlTvLlytgf9awC7n7t9RaPm+8H0DNJqfPmsNFhT42P+cb66cVcXesYsx23d9vfF3H9GZAnvtZe2Ra2Hp+uYPd99p9fV94Ti5rXtwRbvNEt+cs9i5nvhTJ0N58ztSt3qc7xEGs12N3v25yVe9Da5a+xdD194I/OkyowWnArz2C7ZrvGvj91Zk7PFSymVR02j11Fh3flXBZ8+lcnmuN+txPf5VXI9/FXpz4N3RfdWl8/mlOJJT4nH77qbDafrGMyEJJ5gbJ9RoFcDqw9cq/G6DxU1XC7PF9k6rolz34EheDoTizl0ouoY5wnKaztMG9/HlQA8O5/PLMCf7YlDbqiijhYPGENjFrieRfIa05tC1CoeRG+g7dg6R20sCee/EE87PfULIRqzzfnz8mmu3OF/16+S10Lwzt+rQVey9WBSSsHw5cLnY5WZKsIJpJATz3li4WN30KuA45qgbcXIpzBbOjxu43unNVujN/P0qFvuW+NqttUZL4O8Re/D3Gf4nJpyfzEkkrhftRSF6HxOI3BH3h12XeNPB9Iw47NRYCIhAFXHLaf5rRTc/T+syr7wzee4buYH21DqfX+bS0I9XSF32UX/4U1xC3fNS6828Ls9SVhVSVtXLGsFzPhdW5Fo7mCfK4RJM2VGjNUY411F3ZW12PGmVuG3UegqrsojFG/rBlNV1dWgubGItu/zNq3A82fE3zFB0J/3zxA33YfvIATF2E482hiAuzKKRr+oc7MWkyWp7xw249aS1oowWDvP3ut8nIsH5NZhwWrDzUsS25SwWB8IrdOrdEcyx3//zSsBBC8r5pqan8vV189M53RcLyrD3UhEvzASFLKhGqy9XS/Q4Ua5BF/YiKJdHh3NKsOkf282BpLgkZBp+QqbhJyTFJXlYOTqEoxwiiRqtImKxMshl9ietAey+Ah5sw3UeZPCdrKAHvInBk7c74TjJBhukrzyNseuBsPLV/TQahLOBLJLdL2DRlO5gjx0Gs9XxHe6YfKeVuZ5Z3D1RjjX+NOaCLXMh661YzxvOZSH0NYmn4709nvEKGYwher++POcus0T8qNEqIkaLFfFy6c13Wm0HCHeHEl8Hcn/vRkbTO3SxdpfTmac891lWoY9KzIjFd+HCGWeXO+dOe38k99doOjaQigumh4C7VfRmKxLibj1pDVd35oi+FhNVtxNCgzF/nrDRPu4TZREAW6NVH8TN1oA/7RPwFmKTP/nif++vCkYmwmJq9OBYEZqDeeA1yWjmoFTIeN2DY61CRpKQ55Ngq0g44yzoJxcEPblH904i1uueYNMluvzw+yZh9HAZ+E4icTvPmcHMIV5ue9KqlMfgk1Y36AaNd7GeO57i7+49YG/T0cblpmYFCsrXPpAQJ4MhBOM5RCuDxYB8xac3/74XiXGJYd9mqOpXrN17okariBgsVijlUsjLdbsKZoSxSH7GwTk6sbYD+S3KT2BA7OV9sPVUyM+UhEIkox/JKuH6jlVsl5MnMbabAXC9ERvMnX4GQG+yIiHuZqNVEX0XsiKtclFV58KZx7F2DgsVlzFP3HQfDlej2ls49t/i5dKgnrTGCitnhU6+zfF3oEJxrhPrscsZNVpFxGjmkBAns93B9jJ6cCWp227SGftnNOc0+VOWwT4JEEs98X0hE931QqwDgYhBSC4UovBqI1xVyWC59U5rgkIGg8AXsuHo4hqLYyUE813PYC+0hTxOBZXOMMVF7MrvW3KZlDfqfRQe8iokThaHaqYXHX8T/wRTD6jRGrWC+9xBaqKC1+1KZMcGj+hEU55Tt6UgcyOYawvXO77MzdyKh0t8i/Vui5X9nblYSr6777S6YzDdarQmxsmiblCxYPLc9d1x4kukjueCDgIVwYrgvKlgRvcOdUPSZOUQJ3c/bE6o8iZa9jWFTIGq1n6OvwMVzLlOyO+0CokGYhIRo8UKpVzGu5sYa+9ZOKssO2I4xVqZ+yvYRk14PnkT8iBDvq1oGzDGtddAdMXPnVDUHZdUxuBBLthjit5sRbzCdtkRrs9gRFqobg7FYDUQpWAbc27LT7BDWhADp3lYx9dx2VO91Ze7QRUssV67VFRF8sXlvBvJPA7BMY6etIqI0cJBqaj4fYhYfMLhz3dsfa0TVhH9XEDsD+zizK/yjIETnK/uae52vWgqJ3/RJ288c3mPX5BYhJ+ngZgS7N2D44IbUZSEXjD7lN+jkwYRtsu2gtxLYm3f8ruBHGUpc3e815utjn09WD6/F+1unQptsVzYAQTOMQ4WSZ7jb6kAzwOjq0aEDzVaw0CoymMsNzJjJMRg25YnmqLPWHCNrmhKQ7j5bNiKJDPCNQBRMBc6sd5AFLNYq++M2S5kE+NuvdOqN1vdvNpR8e62kbzxGgs9BHyJsaokWrH4LVzA/blFb7q1r0eKX+8sh2G7erMeV+OH3/x7IJJlyWHYCgGoe3BEROqAYrBYfT5pFcuTnFCIxe/RuhNs9x2X5aI8nc7EOrosiR1irYG+jo1+DQDnJgxDuacv9ndahWx8B9wVVCJBLJZ6tPWeCldsIvot6ijKU9dzoeto3+F6h9VTOKF40uqyrZCGFloSpoSEKYWOhk/RU2uDQ09aY4TLN8HcLGM0c1B6ePGdHxZ/2t2nCYIh6Hc+/Vkm1vfWGBDOLK6s5RfNJ2qPYjLSkeHvCODRfE8mqEHaJLZGq/3GarxCBr3JEtqIObYVpsxjDL5SH8XF5jeJRBxPkINJQTTvd6EU7pvZepMV8R6etAabx75erfDnRkKFLiM8xDspLgn1DSsdfxP/BHNNR09aRcQ+EJON56NCNF38h+r8EMk0Cfneurtkup5UYu+sG0z5RdOdbn/5dVIN1U0kIeuBP9/xdJmOvXexoyku0cZdw1Fv4j9pdTcQU6z1+BAjxkI4wFRIQhFOhU4zMZT4UO93rk9aI3M+8rWVGLxscOEuDcGM6xJxIagC1GiNgOAqj9N7PX7saUYL5xiZMaAt+fEU1x9BNTxcpsO3q4XqO3PBbTxymwpWJBo54WxoeipO5zoViXIPNp3+RC2S1TbY0grFfizW7t9hHSgkQly7Gkr8ig/vO603uwf7Wk+ctSC6RHseR+NTX5frpij+UoNEEvn4lb9BBQByqcTxKUbCF8y5Lprrm7/oO62i4v3Sxt2vRgtX7kmrTTAH+2i6WPT3Ai+U32n11eAQ8g6Wv10LhRKuu32hTGdYPnkT8hC9bEvAd5QZYxE7PkTjk3SXxloIwvCUyihMvkf+fqdVb7o1enC8XAaDmYuqdAZ34zU0PSNiTUWOA9F23iov2PoYiu9mBt1tNgTf+QyWx0/emK1IKNc9OEkph+7m6wCxMOBUIHE0WowoVHx18+9eSIhLCHBbFS+/aDqOehSC8yc1WsNAqMpjMFsdH3OWSQGLlfPrboxLQyMmaj9fTHSNKMffC1efXV38mOvxCWQUlXMoPnkTRcnxKJhP3sQif26Wud6A8e8UFs0XvP4IVZs/nHXFV9BBjXTOAJ3J4hhRVCqV+LGl6B89WAzCmcex/smbCu2vAa7r9w1Jn8sF8bAiyO+0eqI3WVE9Kc4xXUUpQ6kxtO+wh/OTN4GwcBaUyjc4/g43d3XS7wc8zuvF2KGSGq0REKmLLMYYZFLb1uIVMhgsrnex/Rk9OMbqsN9c88LPd+iiPENC1XUq2t8lE7R7dwSJNV1EPIIdPVhjMKNqgoIflo9P3oSTy2gAQWxayK6r/ve6qByi/VwdLqHoXhr8tt3PLzVaUD8t0TFdJV5e4Uarr2tVvz55E4bdVSFTINU81PE38U8wu2tMvtN6/fp1ZGdnQ61We1zGarXi+PHjuHDhgsdlcnJysH//fmg0mnBEM6QCPSjZul5Z3TZEfI0eHKxou9Z2TWeIwg1NMGEU/TEMBXqaEsUqRxWkGwxeuMsbo5lzvNMqFqF8NSWaxWK6QjN6cCymPAghTqbWYEGy8lYDropSgVKDrdEqtsNmnCwOKZZBSLEMQpwszvcKUSTWyiKmGq379+/Hk08+iTvuuAPdu3fHoUOH3C73yy+/oF69eujXrx/69euHPn36oKCgwPG7wWDAE088gWbNmmHo0KGoVasWvv7660glI4xu1T6lQgqD2RqVAxgQEg6xdvAlsY+qHBGDc6pSoaMQEdF+jgg2ftGYrFKjBVXib3XmrKKUQVvBJ62RTKewA91VjhvywWRxTDVajx8/jkGDBmHPnj0el/n7778xcOBAfPTRRzh//jyOHTuG9957D3l5eY5lJk+ejL179+L8+fP4559/8NNPP+GNN97wGm5suFXR4xW2QS6i/SBNCCGkcqok12aEVDqlBguSyzda4+UoC/E7rdGCMQYr1LBCXWkanEKJqXdan3vuOQBAbm6ux2UmTZqE3r1744UXXnDM69mzJ2+Z+fPnY+TIkahduzYAoH///mjdujXmz5+Pzp07hzzeQoiX2560EkIIIYGg6y5CSEWUGi2ooiz/pFWBwlKjgDEKH51Zh9yEZ27+3RfJsmSBYyReMdVo9cVoNCI7OxszZsyAVqvFmTNnUKdOHUfjFACuXbuGvLw8dOjQgbdup06dPHY3todtNN7a4ezv05aUlAAAOKPO8bfJwvGmS7Vq3jRn1Lms6zyt1WhQUpLgmC4uLoYlXlFuusQxUrA9bKOu9FYYJh1UhcUwlpXytm3Ua2E0c7xtGcq0vGl9qdZtupynDWYrb1qr0QSRTjVKSpS8dJluHujs6ZbL+OkEAINO65iWSqUw6vjpNOm0sDLGT5dTOnVl7tPJGOPnmVM6S7XBpjPuVrpKimGM851Od+ly/h1yqdfy1HkoT4uVX091JkvQ9VYqlZZLp+d6WlxccnPUUB/p1DunuxQGCz+dLuWpdZ9Oo8WpnhrM/HQGUW81GjVKSjyXn+d0ap3S6TStK4MRJq/lV+Y0rdFoEBcXB72ZX54lOhNvuiwk6XSfLudps9Nxx6wv40/rymCQmX2kU+O2PMuM/HqqLjVWvDzVapSUuO5/ztMcx5zK71Y6pVKpSzpNujIYlFanespPV5nWfTo1en49VWsM/HwI4jhkS6fv8nM+h5mcy8/NtL6Mf7x1rqelWjWMei1vm8xUhvyCIn461cGn034c8jedVqfytDily2Ioc8kHfanEa/mVatUoKanisi21ll9PNRodP52lgZenWl2CkirMZzrNTsd653Q6h202lEFfKnNKl1M6NRqUlCQ5pu3HIbWWf9xRa/jb0pVqYA00nSUlKInnfKbT+dhgMfK3bTWUgTMaeOWrL1P4SKcaJSXxLttSl+j55xV1qWs6DWWB7Z8lJShRWHym0/mapHw6bftAGTjjrfOIRV8GXVk8b1u2c3WS03Scy7bUTvVUo9a6pNNicC1PjUYNfakGBnt3P2MZ8gpv1nF1CVJkZp/p1Jusbsqv3LYMZeCM5nLpLHVJp638bl1LO5/TbqWr1I90ui/PMlMZYIBj2hpvdaQzWWrymU7naxJbeRpvpdtYBs5oLZfOMujK+PXU+Vpaq1ajpETmJp38dGnVGtd06gOrtyUlJUiE0Wc61U7nNOd66s9TagkT8Fn22bNned123enYsSPi4+N583Jzc5GZmYktW7bwnqJevXoV9erVw4svvoh169ahVq1aOHfuHLp27YqffvoJaWlpOH78ONq0aYOdO3eia9eujnXfeecdrFq1CmfPnnUbj0mTJmHy5MnBJ5YQQgghhBBCCE9OTg7q1avndRlBn7SuWLECv//+u9dlli1bhrp16/oVnkJhe8KzceNGHD58GBkZGSgsLETXrl0xZswYzJ8/37GMwWDgravX6xEX53nUr/Hjx2P06NGO6ZKSEjRo0ABXrlxBSkqKX/EjoaPRaJCZmYmcnBxUrVpV6OhUSlQGwqMyEBblv/CoDIRHZSAsyn/hURkEjzEGrVaLOnXq+FxW0EbrhAkTMGHChJCFV6NGDSQlJWHAgAHIyMgAAKSlpWHgwIFYtmwZACAzMxNSqRRXr17lrXv16lXUr1/fY9hKpRJKpdJlfkpKClVQAVWtWpXyX2BUBsKjMhAW5b/wqAyER2UgLMp/4VEZBMffh38xNXqwL1KpFH369HFpkObm5iI9PR0AkJiYiLvvvhu//vqr4/eysjJs2rQJffr0iWh8CSGEEEIIIYR4F1MDMalUKpw5cwb5+fkAgGPHjkEul6N+/fqOp6QffPABsrKyMHHiRGRlZWHPnj346aefsHz5ckc4//vf/9CnTx+MHz8eXbt2xddff42MjAy89NJLgqSLEEIIIYQQQoh7MfWk9cCBAxg3bhymT5+OrKwsLFu2DOPGjcOmTZscy9gHWcrNzcW0adNw8eJFbNu2DY8//rhjmR49emDLli24fPkyvvzyS7Rq1QrZ2dmoUqWK33FRKpWYOHGi2y7DJPwo/4VHZSA8KgNhUf4Lj8pAeFQGwqL8Fx6VQWQIOnowIYQQQgghhBDiTUw9aSWEEEIIIYQQUrlQo5UQQgghhBBCSNSiRishhBBCCCGEkKgVU6MHRxJjDCdPnoTFYkGrVq0gl/vOqmDWIZ7duHEDN27cQKNGjXx+90qr1eLIkSMu89u1axfQAFvE5uLFiy6fjkpISECHDh18rpubm4sbN26gSZMmSE1NDVMMxU2tVuPYsWNuf2vVqhWqVavm9re9e/fCZDLx5pUfXZ34dv78eVy/fh2dO3eGQqFwu8zZs2eh1WrRsmVLxMfH+xVuMOtURhzH4cCBA4iPj0ebNm3cLlNUVITLly+jQYMGqF69utfwzGYz9uzZ4zK/RYsWSEtLC0mcxaakpAQnTpxA48aNUatWLd5v165dw4ULF3jzJBIJsrKyfIarUqkc5ZaRkRHSOIvNuXPncOPGDXTt2hUymcwx32AwYP/+/W7Xady4MWrXru32t6NHj0Kj0fDmZWRkoGnTpqGLtIgUFhbiypUraNiwocfzrcFgwMmTJ5GcnIzbb7/dr3CDWYeUw4iL06dPs+bNm7OMjAyWmZnJ6taty3bu3BnydYh7W7duZZ07d2Y1a9Zkd9xxB0tISGCvv/46s1qtHtfZtWsXA8C6du3KsrKyHP9OnToVwZiLx5tvvsmqVavGy8tBgwZ5XcdoNLJBgwaxhIQE1qJFCxYfH88+/fTTCMVYXPbt28fL+6ysLNaoUSMGgO3Zs8fjejVr1mRNmzblrTdr1qwIxjx2rV+/nvXq1YtVr16dAWDXr193WSYvL4916dKFpaamsiZNmrBq1aqxNWvWeA03mHUqI5PJxKZMmcIaNWrEUlJSWI8ePVyWOXz4MLv//vtZ9erVWbt27VhiYiIbPHgwKysr8xju9evXGQB255138vaLLVu2hC8xMer8+fNs+PDhrHbt2kwqlbLvv//eZZnPP/+cJSYm8vLSXVk5+/e//82USiVr2bIlUyqV7N///ncYUhD71q5dy3r06MGqVavGALDi4mLe77m5uS7nhpYtWzIAbNmyZR7D7dy5M6tfvz5vvQ8++CDMqYk9+/fvZ7169WI1atRwHGOGDBnCDAYDb7k1a9awatWqsSZNmrDU1FTWpUsXlpeX5zXsYNYhfNRodePOO+9kffv2ZRaLhTHG2Msvv8zq1KnD9Hp9SNch7s2ZM4ft3bvXMX306FFWpUoV9uWXX3pcx95o1Wq1kYii6L355pvskUceCWidSZMmsVq1arErV64wxhj7/fffmUQiYdu2bQtHFCudp556irVo0cLrMjVr1mQ//vhjhGIkLp9++inbuHEj27Bhg8dGa//+/VnHjh0djaQpU6awxMREt8tWZJ3KqKSkhL377ruOhpO7htCKFSvYhg0bHNM5OTmsbt267K233vIYrr3ReuzYsXBEW1R+//13NmvWLFZaWsqSkpI8NlpbtWoVULgLFixgiYmJ7PDhw4wxxg4ePMgSEhLYDz/8EJJ4i8knn3zCNm/ezNauXeu20erOO++8w6pVq+b1erNz587sww8/DGFMxWnJkiVs8+bNjumLFy+ymjVrsvHjxzvmXbt2jSUmJrJp06YxxhgrKytj7du3Z48//rjHcINZh7iiRquTgwcPMgBs9+7djnk5OTlMIpGwVatWhWwdEpgHH3yQDRw40OPv9kbrwYMH2aFDh1hpaWkEYyc+b775JuvTpw87cOAAO3v2rONmjDf169dn48aN483r2LEje/bZZ8MUy8qjoKCAKZVKNmPGDK/L1axZk82YMYPt27eP7uAGaePGjW4brfn5+UwqlbKlS5c65un1epacnMw+//xzt2EFsw5hHhut7rzyyivsrrvu8vi7vdH622+/sQMHDrCSkpIQxVLcvDVamzdvzo4cOcJOnjzJTCaTz7DuueceNnjwYN68J5980u8yroz8bbSazWZWq1Yt9sYbb3hdrnPnzmzMmDFs7969LDc3N4QxFb/nnnuOde/e3TE9Y8YMVrVqVWY0Gh3zFi1axGQyGSsoKHAbRjDrEFc0EJOTQ4cOAQDat2/vmFevXj3Url3b8Vso1iH+MxqNOHbsGJo0aeJz2X79+mHw4MGoXr06Ro8eDavVGoEYitPmzZvx7LPPolu3bsjMzMSaNWs8LltUVIQrV664vPPaqVMn2gdCYNGiRWCMYdiwYT6XnTRpEkaMGIHbbrsNvXr1wuXLlyMQQ/E7evQoOI7j1XH7e5ee6ngw6xD/McZw4MABv84NL7zwAoYNG4b09HQ8++yzKC0tjUAMxen06dMYPHgwHnjgAaSnp2P27Nlelz906BCdG8Lkt99+w40bNzBixAify3733Xd48cUX0bJlS7Rv3x5Hjx6NQAxjG8dxOHjwIO8Yc+jQIbRp0wZxcXGOeZ06dYLVavWYp8GsQ1xRo9VJUVERqlat6jIAR1paGoqKikK2DvHf22+/Db1ej1dffdXjMtWrV8fmzZtx5coVnDp1Ctu2bcOsWbMwbdq0CMZUPHr16oXc3FwcO3YM165dw/DhwzFo0CD8888/bpe313PngU1oHwiNuXPnYsCAAT4Hjvn4449RWFiIw4cP49KlS9DpdBg8eDAYYxGKqXgFU8dpvwiv6dOn48iRI3jnnXc8LqNUKrF8+XLcuHEDx48fx7Fjx7Bp0ya8/fbbEYypeLRt2xZnzpzByZMnceXKFXz66ad4+eWXsXnzZrfLWywWaLVat/uARqOhG8sVNHfuXHTp0sXjoGV2r776KgoKCnD48GFcvXoVmZmZ6N+/P3Q6XYRiGps+/vhjnD17FmPGjHHMKyoqcluf7b+5E8w6xBU1Wp0oFAoYDAaX+Xq9nneHpKLrEP989NFHmD9/Pn755RfUrVvX43JNmzbFvffe65ju0qULnnvuOSxdujQS0RSdxx57zDFqpFQqxeTJk1G1alWsXr3a7fL2GzbO+wHtAxW3b98+HDt2DC+++KLPZV944QXHqOXp6en43//+h927d+PSpUthjqX4BVPHab8Inx9//BETJkzADz/8gHbt2nlcrlq1ahg4cKBjulmzZnjrrbewbNmyCMRSfHr16sV76vTiiy+iffv2HvNTJpNBKpW63QekUilvZFwSmOvXr2P9+vV+nRuGDh2KhIQEAECVKlUwffp0XLx40e3I2sRmzpw5+OCDD7B48WK0bNnSMd/dNb9erweAgNoJvtYhrqjR6qRBgwYwmUwoKChwzLNarcjLy/P42Yhg1iG+ffLJJ/joo4+wdu1a9OjRI+D1a9as6fLZFhIcqVSK9PR0j/lZp04dKBQKl9+vXr1K+0AFzZ07F40bN+bdlPFXzZo1AYD2gxBo0KABANe89FbHg1mH+LZ48WIMHz4c8+fPx+DBgwNev2bNmlCr1dRFOES8nWslEgkyMzNpHwiDH374AQkJCRg0aFDA69K5wbt58+bh1VdfxeLFi/H444/zfmvQoIHb+gzA67kg0HWIK2q0OrnnnnsQFxeHX3/91TFv8+bN0Gq16NOnj2Pevn37HE8v/F2H+G/atGn44IMPsHbtWvTq1cvld41Gg+zsbMdFR1lZmcsyGzduROvWrcMeVzFyzs9Lly7h7NmzvPy8cOECDhw4AMB2F7FHjx68fcBgMODPP/+kfaACdDodlixZghEjRkAikbj8vmfPHly5cgWA+31gw4YNkMvlaN68edjjKnZt2rRBzZo1eXX8zJkz+Oeff3h1/NixYzh16lRA6xD/LVmyBC+88ALmzZuHZ555xuV3k8mE7OxsFBYWAvC8XzRo0IC+4R0E5/xUq9XYu3cv79xw9epV7NixwzHdp08frFu3zvGaAmMMa9eupX2ggubNm4enn34aSUlJLr8dOXIEZ86cAWB7osdxHO/3DRs2AABdI7mxYMECjBw5EosWLeL10rDr06cPTpw4gfPnzzvmrVmzBrVq1XJ00zYYDMjOzkZxcbHf6xA/CDoMVJT673//y1JTU9ns2bPZ4sWLWb169diwYcN4y9StW5e9/fbbAa1D/PPtt98yAGzy5Mls+/btjn9Hjx51LLN9+3YGgO3bt48xxtioUaPYq6++ylauXMlWrVrFBgwYwBISElh2drZQyYhpzZs3Z59++ilbv349mzt3LmvSpAlr164d73uIr776KmvcuLFjeteuXSwuLo69/fbbbM2aNeyhhx5i9evX92vIfuLeggULmFwu9/h5lLS0NPbee+8xxhhbt24d6927N5s3bx77448/2H//+18WHx/P/vvf/0YyyjHr0qVLbPv27ezzzz9nANiaNWvY9u3bWX5+vmOZuXPnsri4ODZjxgz2888/szZt2rAePXowjuMcy2RlZbEnnngioHWIzb59+9j27dvZo48+ytq1a+c49tutWbOGyWQyNnLkSN65ofwn0nJychgAtmLFCsYYYx9//DEbMmQIW7JkCVu3bh178cUXmVwu9/pNy8pKo9E48jQhIYGNGTOGbd++nfe983vuuYdNnjyZ/fbbb2zx4sWsffv2rH79+rxj1KeffspkMplj+vz58yw1NZUNGzaM/frrr2zo0KEsNTWVnT9/PqLpiwUXLlxg27dvZ1OnTmUA2Pr169n27dtZYWEhb7mtW7cyAGz//v1uw+nQoQN75plnGGOMHTlyhHXq1InNnDmT/fnnn2zatGksNTWVDR06NOzpiTXLly9nUqmUvfnmm7xjTPl85jiO9ejRg91xxx3s559/ZtOnT2cKhYLNnTvXsczZs2cZALZ27Vq/1yG+SRijETqcMcawYMECrFy5EhaLBQ888ABee+013kBLAwYMwH333ecYHMifdYh/3n33Xd5dWru2bdviu+++A2B7mjFy5EjMnTsXzZo1g9VqxQ8//IA//vgDer0eLVq0wOuvv47MzMxIR18UVCoVvv76axw4cAApKSnIysrCSy+9xHv34vPPP8eePXt47w3v2bMHX3/9Na5fv45WrVrh3Xff9fouMvFu7NixsFqtmDFjhtvfH330UfTr18/xTtPOnTsxf/58XLlyBfXr18fTTz8dVLfiymjmzJlYtGiRy/zJkyfjvvvuc0yvXr0aCxcuhFarRVZWFsaOHct70jFq1CikpaXhww8/9HsdYjNw4EBcv37dZX52djYA2zFn5cqVLr9nZGTgl19+AQDk5+fj8ccfx//+9z/07NkTALBy5UqsWrUKhYWFuP322/HKK6/w3lEjNidPnsRLL73kMr93796YNGkSAFsvp2+//RY7d+6EUqlEhw4d8NprryE5Odmx/NKlSzFz5kxs3brVMe/UqVP47LPPcOHCBTRq1AhjxoyhHiBufPXVV1i+fLnL/ClTpqB79+6O6enTp2P//v1YsmSJ23BGjBiBBg0a4L///S8A4Pjx45g5cybOnDmDOnXq4LHHHsOAAQPCk4gYNnXqVKxdu9Zlfr169XjXOmVlZfj000+xY8cOJCcnY9iwYejfv7/j96tXr2LQoEGYNm0a7r77br/WIb5Ro5UQQgghhBBCSNSid1oJIYQQQgghhEQtarQSQgghhBBCCIla1GglhBBCCCGEEBK1qNFKCCGEEEIIISRqUaOVEEIIIYQQQkjUokYrIYQQQgghhJCoRY1WQgghhBBCCCFRixqthBBCSIxYu3YtLly4IHQ0CCGEkIiiRishhBASI15//XVs3rzZ4+9//fUX9u3b5zL/4sWLWLp0KRhj4YweIYQQEhbUaCWEEEJEYuLEifi///s/l/nbtm3Dv/71L1itVgFiRQghhFSMXOgIEEIIISQ4JpMJa9asQZMmTXDnnXf6vd7FixexZ88el/mNGzfGXXfdFcooEkIIIRVGjVZCCCEkBpWWlmLAgAEoKyvDb7/9FtC6V65cwerVqx3TjDGsWrUKzz//PDVaCSGERB1qtBJCCCExprCwEA8//DDS0tKwceNGJCYmOn67cOECli5dylve+alqjx490KNHD8f0pEmTsGHDBrz99tvhjTghhBASBGq0EkIIITEkNzcX3bt3x5133okFCxZAoVDwfr98+TLvKSpg6w7syapVq/DRRx9h3bp1aNq0aTiiTAghhFSIhNFQgoQQQkhMaNiwIVQqFVJSUnDu3DkkJSXxfu/WrRuaN2+OOXPm8OYvWLAAzz//PMxmM+TyW/erjx8/jq5du2LSpEn0lJUQQkjUotGDCSGEkBjywQcfoG7duujXrx/0en3Q4RQWFqJfv354/PHHqcFKCCEkqlGjlRBCCIkhqamp2LhxI4qLi9G3b9+gGq4WiwVPPfUUatSogVmzZoUhloQQQkjoUKOVEEIIiTHVqlXDpk2bUFxcjMceeyzghutXX32FrVu3YtCgQVi9ejWWLl2KpUuXYt++fWGKMSGEEBI8GoiJEEIIiRF9+/ZF48aNAdgarhs3bsTYsWPx008/Yfjw4ejduzfq1Knjst5tt92GQYMGQSq13auuXbs2Bg4ciL179/KW69WrF33yhhBCSNShgZgIIYQQQgghhEQt6h5MCCGEEEIIISRqUaOVEEIIIYQQQv6//ToWAAAAABjkbz2NHWURW9IKAADAlrQCAACwJa0AAABsSSsAAABb0goAAMCWtAIAALAlrQAAAGxJKwAAAFvSCgAAwJa0AgAAsBWCYdHqcdilwQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] diff --git a/bridge/notebooks/figure_digest.py b/bridge/notebooks/figure_digest.py new file mode 100644 index 0000000..7d1f230 --- /dev/null +++ b/bridge/notebooks/figure_digest.py @@ -0,0 +1,77 @@ +"""Printed digests of every figure's plotted data, for A/B comparison. + +A committed notebook's figures are PNGs, which a text comparison cannot +see and which are not byte-stable across matplotlib versions. So that a +changed curve still shows up as a changed *text* output, install() wraps +plt.show(): before each figure renders, it prints one line + + [ figure ] digest <16 hex digits> ( arrays) + +hashing the data drawn on the figure (line data, collection offsets and +values, images, bar geometry), each array quantized to 9 significant +digits of its largest magnitude so last-bit floating-point noise does not +register. The digest is only meaningful against another run in the same +environment (requirements.lock pins it): the monorepo migration's notebook +gate re-executes a reference and a candidate tree in one job and compares +these lines. +""" +import hashlib + +import matplotlib.pyplot as plt +import numpy as np + + +def _rounded(values) -> np.ndarray: + # Quantize to 9 significant digits of the array's largest magnitude, so + # noise far below the plotted scale (including tiny values next to an + # exact zero) does not change the digest. Non-finite values pass through. + a = np.asarray(np.ma.filled(np.ma.asarray(values, dtype=float), np.nan), dtype=float).ravel() + finite = np.isfinite(a) + out = a.copy() + peak = np.max(np.abs(a[finite])) if finite.any() else 0.0 + if peak > 0.0: + step = 10.0 ** (np.floor(np.log10(peak)) - 8) + out[finite] = np.round(a[finite] / step) * step + 0.0 # + 0.0 folds -0.0 + return out + + +def _arrays(fig): + for ax in fig.axes: + for line in ax.get_lines(): + yield line.get_xydata() + for coll in ax.collections: + offsets = coll.get_offsets() + if len(offsets): + yield offsets + values = coll.get_array() + if values is not None: + yield values + for image in ax.get_images(): + yield image.get_array() + for patch in ax.patches: + yield np.asarray(patch.get_bbox().bounds) + + +def digest(fig): + """Return (hex digest, number of arrays) for one figure.""" + h, n = hashlib.blake2b(digest_size=8), 0 + for values in _arrays(fig): + h.update(_rounded(values).tobytes()) + n += 1 + return h.hexdigest(), n + + +def install(): + """Make plt.show() print a digest line for every open figure first.""" + if getattr(plt.show, "_figure_digest", False): + return + original = plt.show + + def show(*args, **kwargs): + for num in plt.get_fignums(): + hexdigest, n = digest(plt.figure(num)) + print(f"[ figure ] digest {hexdigest} ({n} arrays)") + return original(*args, **kwargs) + + show._figure_digest = True + plt.show = show diff --git a/bridge/notebooks/profile_ladder.ipynb b/bridge/notebooks/profile_ladder.ipynb index b253177..c6493d6 100644 --- a/bridge/notebooks/profile_ladder.ipynb +++ b/bridge/notebooks/profile_ladder.ipynb @@ -33,10 +33,10 @@ "id": "ea9fcb56", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:20.166084Z", - "iopub.status.busy": "2026-08-07T01:00:20.165877Z", - "iopub.status.idle": "2026-08-07T01:00:20.585311Z", - "shell.execute_reply": "2026-08-07T01:00:20.583920Z" + "iopub.execute_input": "2026-09-27T16:02:55.237031Z", + "iopub.status.busy": "2026-09-27T16:02:55.236598Z", + "iopub.status.idle": "2026-09-27T16:02:59.106195Z", + "shell.execute_reply": "2026-09-27T16:02:59.104781Z" } }, "outputs": [ @@ -51,6 +51,8 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import figure_digest # prints a data digest per figure (A/B gates)\n", + "figure_digest.install()\n", "from ratiotap_py import RatioConverter\n", "\n", "# Fixed tier order and colors — identical in every figure below.\n", @@ -129,16 +131,23 @@ "id": "130df8c1", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:20.588381Z", - "iopub.status.busy": "2026-08-07T01:00:20.588086Z", - "iopub.status.idle": "2026-08-07T01:00:29.217240Z", - "shell.execute_reply": "2026-08-07T01:00:29.215916Z" + "iopub.execute_input": "2026-09-27T16:02:59.109215Z", + "iopub.status.busy": "2026-09-27T16:02:59.108723Z", + "iopub.status.idle": "2026-09-27T16:03:12.037508Z", + "shell.execute_reply": "2026-09-27T16:03:12.036378Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest a2c8dd19ba27ccbe (22 arrays)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -194,16 +203,23 @@ "id": "b6d0b1d8", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:29.219913Z", - "iopub.status.busy": "2026-08-07T01:00:29.219685Z", - "iopub.status.idle": "2026-08-07T01:00:32.302153Z", - "shell.execute_reply": "2026-08-07T01:00:32.300789Z" + "iopub.execute_input": "2026-09-27T16:03:12.041277Z", + "iopub.status.busy": "2026-09-27T16:03:12.041055Z", + "iopub.status.idle": "2026-09-27T16:03:16.784740Z", + "shell.execute_reply": "2026-09-27T16:03:16.783249Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest 2688c772c24d6deb (16 arrays)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -265,16 +281,23 @@ "id": "848ed7a6", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:32.304951Z", - "iopub.status.busy": "2026-08-07T01:00:32.304685Z", - "iopub.status.idle": "2026-08-07T01:00:32.886666Z", - "shell.execute_reply": "2026-08-07T01:00:32.885361Z" + "iopub.execute_input": "2026-09-27T16:03:16.787512Z", + "iopub.status.busy": "2026-09-27T16:03:16.787285Z", + "iopub.status.idle": "2026-09-27T16:03:17.458242Z", + "shell.execute_reply": "2026-09-27T16:03:17.457162Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest ab0db7486499886e (4 arrays)\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -348,16 +371,23 @@ "id": "c26cf5cb", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:32.889590Z", - "iopub.status.busy": "2026-08-07T01:00:32.889342Z", - "iopub.status.idle": "2026-08-07T01:00:33.583175Z", - "shell.execute_reply": "2026-08-07T01:00:33.581615Z" + "iopub.execute_input": "2026-09-27T16:03:17.460342Z", + "iopub.status.busy": "2026-09-27T16:03:17.460145Z", + "iopub.status.idle": "2026-09-27T16:03:18.156731Z", + "shell.execute_reply": "2026-09-27T16:03:18.155828Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest 04e64770e3034166 (12 arrays)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -415,16 +445,23 @@ "id": "8be1816e", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:33.586006Z", - "iopub.status.busy": "2026-08-07T01:00:33.585788Z", - "iopub.status.idle": "2026-08-07T01:00:34.147196Z", - "shell.execute_reply": "2026-08-07T01:00:34.145881Z" + "iopub.execute_input": "2026-09-27T16:03:18.159826Z", + "iopub.status.busy": "2026-09-27T16:03:18.159543Z", + "iopub.status.idle": "2026-09-27T16:03:18.607577Z", + "shell.execute_reply": "2026-09-27T16:03:18.606419Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest ab1306c53ed327da (24 arrays)\n" + ] + }, { "data": { - "image/png": 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", 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", 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" ] @@ -487,10 +524,10 @@ "id": "66972308", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T01:00:34.149940Z", - "iopub.status.busy": "2026-08-07T01:00:34.149667Z", - "iopub.status.idle": "2026-08-07T01:00:34.345638Z", - "shell.execute_reply": "2026-08-07T01:00:34.344487Z" + "iopub.execute_input": "2026-09-27T16:03:18.610749Z", + "iopub.status.busy": "2026-09-27T16:03:18.610409Z", + "iopub.status.idle": "2026-09-27T16:03:18.827409Z", + "shell.execute_reply": "2026-09-27T16:03:18.826049Z" } }, "outputs": [ @@ -501,12 +538,13 @@ "super_economy 40 MACs/out SNR 85.3 dB\n", "economy 58 MACs/out SNR 91.3 dB\n", "balanced 78 MACs/out SNR 89.2 dB\n", - "transparent 184 MACs/out SNR 144.9 dB\n" + "transparent 184 MACs/out SNR 144.9 dB\n", + "[ figure ] digest f8106bd6653d5693 (4 arrays)\n" ] }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/bridge/notebooks/ratio_demo.ipynb b/bridge/notebooks/ratio_demo.ipynb index 4e4339a..ba2e501 100644 --- a/bridge/notebooks/ratio_demo.ipynb +++ b/bridge/notebooks/ratio_demo.ipynb @@ -5,11 +5,11 @@ "id": "01802365", "metadata": {}, "source": [ - "# RatioTap demo \u2014 the shipping converter, measured\n", + "# RatioTap demo — the shipping converter, measured\n", "\n", "Family convention: this notebook drives the **actual shipping C++** through\n", "the C ABI (`tools/capi/`, ctypes bridge `ratiotap_py.py`, which builds\n", - "`build_capi/` on first import) \u2014 nothing here is a Python re-implementation.\n", + "`build_capi/` on first import) — nothing here is a Python re-implementation.\n", "It demonstrates the three things RatioTap promises: exact rational\n", "conversion, deterministic accounting, and the economy profile's measured\n", "spectral contract.\n" @@ -21,10 +21,10 @@ "id": "7674e466", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:44:57.465692Z", - "iopub.status.busy": "2026-08-07T00:44:57.465397Z", - "iopub.status.idle": "2026-08-07T00:45:03.354627Z", - "shell.execute_reply": "2026-08-07T00:45:03.352344Z" + "iopub.execute_input": "2026-09-27T16:03:21.902420Z", + "iopub.status.busy": "2026-09-27T16:03:21.902195Z", + "iopub.status.idle": "2026-09-27T16:03:22.504401Z", + "shell.execute_reply": "2026-09-27T16:03:22.502533Z" } }, "outputs": [ @@ -32,52 +32,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "-- The CXX compiler identification is GNU 13.3.0\n", - "-- Detecting CXX compiler ABI info\n", - "-- Detecting CXX compiler ABI info - done\n", - "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n", - "-- Detecting CXX compile features\n", - "-- Detecting CXX compile features - done\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- The C compiler identification is GNU 13.3.0\n", - "-- Detecting C compiler ABI info\n", - "-- Detecting C compiler ABI info - done\n", - "-- Check for working C compiler: /usr/bin/cc - skipped\n", - "-- Detecting C compile features\n", - "-- Detecting C compile features - done\n", - "-- Configuring done (0.6s)\n", - "-- Generating done (0.0s)\n", - "-- Build files have been written to: /home/user/RatioTap/build_capi\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 20%] \u001b[32mBuilding C object ratiotap/submodules/dsptap/CMakeFiles/tap_dsp_fft.dir/third_party/ooura/fftsg.c.o\u001b[0m\n", - "[ 40%] \u001b[32mBuilding C object ratiotap/submodules/dsptap/CMakeFiles/tap_dsp_fft.dir/third_party/ooura/fftsg_float.c.o\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 60%] \u001b[32m\u001b[1mLinking C static library libtap_dsp_fft.a\u001b[0m\n", - "[ 60%] Built target tap_dsp_fft\n", - "[ 80%] \u001b[32mBuilding CXX object CMakeFiles/ratio_capi.dir/ratio_capi.cpp.o\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[100%] \u001b[32m\u001b[1mLinking CXX shared library libratio_capi.so\u001b[0m\n", - "[100%] Built target ratio_capi\n", "RatioTap 0.3.0\n", "down economy taps= 58 latency= 29.0 smp = 0.60 ms\n", "down transparent taps=184 latency= 92.0 smp = 1.92 ms\n", @@ -89,6 +43,8 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import figure_digest # prints a data digest per figure (A/B gates)\n", + "figure_digest.install()\n", "from ratiotap_py import RatioConverter, version\n", "\n", "print(\"RatioTap\", \".\".join(map(str, version())))\n", @@ -110,7 +66,7 @@ "\n", "`frames_needed` / `outputs_for` are exact arithmetic, not estimates: a\n", "steady-state superblock of 147 outputs costs exactly 160 inputs (the fresh\n", - "start costs one less \u2014 the pre-advance convention the cell below\n", + "start costs one less — the pre-advance convention the cell below\n", "demonstrates), and feeding the converter in arbitrary ragged chunks produces\n", "exactly the predicted totals. This is the\n", "capability the asynchronous engine can never offer, and what the Bluetooth\n", @@ -123,10 +79,10 @@ "id": "dc5c88f7", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:45:03.359691Z", - "iopub.status.busy": "2026-08-07T00:45:03.359009Z", - "iopub.status.idle": "2026-08-07T00:45:03.394074Z", - "shell.execute_reply": "2026-08-07T00:45:03.391591Z" + "iopub.execute_input": "2026-09-27T16:03:22.508011Z", + "iopub.status.busy": "2026-09-27T16:03:22.507659Z", + "iopub.status.idle": "2026-09-27T16:03:22.528070Z", + "shell.execute_reply": "2026-09-27T16:03:22.526821Z" } }, "outputs": [ @@ -144,7 +100,7 @@ "rng = np.random.default_rng(1)\n", "c = RatioConverter(direction=\"down\", profile=\"economy\")\n", "# Pre-advance convention: from the fresh zero-primed state the first 147\n", - "# outputs cost 159 inputs \u2014 the 160th is the down-payment on the NEXT\n", + "# outputs cost 159 inputs — the 160th is the down-payment on the NEXT\n", "# superblock's first output. Steady state costs exactly M=160 per L=147:\n", "print(\"fresh: frames_needed(147) =\", c.frames_needed(147))\n", "print(\" frames_needed(294) - frames_needed(147) =\",\n", @@ -172,8 +128,8 @@ "## The economy contract, measured on the shipping engine\n", "\n", "A 3-tone program (997 Hz, 6 kHz, 17.5 kHz) plus a deliberately hostile\n", - "23 kHz ultrasonic component, converted 48 \u2192 44.1 through the C ABI. The\n", - "acceptance criteria from PLAN \u00a78: every spurious product at least the\n", + "23 kHz ultrasonic component, converted 48 → 44.1 through the C ABI. The\n", + "acceptance criteria from PLAN §8: every spurious product at least the\n", "stopband (71 dB) below its source; decimation aliases confined above\n", "20 kHz by arithmetic; the imaging products (e.g. the 23 kHz tone's image at\n", "25 kHz folding to 19.1 kHz) bounded by the stopband.\n" @@ -185,16 +141,23 @@ "id": "addd1d53", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:45:03.397707Z", - "iopub.status.busy": "2026-08-07T00:45:03.397320Z", - "iopub.status.idle": "2026-08-07T00:45:03.835089Z", - "shell.execute_reply": "2026-08-07T00:45:03.833592Z" + "iopub.execute_input": "2026-09-27T16:03:22.530479Z", + "iopub.status.busy": "2026-09-27T16:03:22.530227Z", + "iopub.status.idle": "2026-09-27T16:03:22.969292Z", + "shell.execute_reply": "2026-09-27T16:03:22.967876Z" } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ figure ] digest 8c47d74311ceaec1 (3 arrays)\n" + ] + }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -230,13 +193,13 @@ "plt.plot(f / 1e3, spec, lw=0.5)\n", "plt.axvline(20, color='g', ls=':', label='20 kHz')\n", "plt.axhline(-16.5 - 71, color='r', ls=':', lw=0.8, label='stopband bound (23 kHz tone)')\n", - "plt.annotate('alias of 23 kHz \u2192 21.1 kHz', xy=(21.1, -95), xytext=(14, -60),\n", + "plt.annotate('alias of 23 kHz → 21.1 kHz', xy=(21.1, -95), xytext=(14, -60),\n", " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", - "plt.annotate('image of 23 kHz \u2192 19.1 kHz', xy=(19.1, -100), xytext=(8, -80),\n", + "plt.annotate('image of 23 kHz → 19.1 kHz', xy=(19.1, -100), xytext=(8, -80),\n", " arrowprops=dict(arrowstyle='->'), fontsize=9)\n", "plt.ylim(-160, 0); plt.xlim(0, 22.05)\n", "plt.xlabel('kHz'); plt.ylabel('dBFS'); plt.legend(fontsize=8); plt.grid(alpha=0.3)\n", - "plt.title('economy 48\u219244.1 through the shipping engine: products bounded, aliases ultrasonic')\n", + "plt.title('economy 48→44.1 through the shipping engine: products bounded, aliases ultrasonic')\n", "plt.show()\n", "\n", "# The numeric contract, asserted:\n", @@ -256,7 +219,7 @@ "## Measured passband response\n", "\n", "Sine probes through the shipping engine (fit amplitude per frequency): flat\n", - "to the 18 kHz economy passband edge within the design's \u00b10.003 dB, rolling\n", + "to the 18 kHz economy passband edge within the design's ±0.003 dB, rolling\n", "into the transition exactly where the design says (the pre-v0.3 economy\n", "design keeps its 19 kHz edge as `balanced`).\n" ] @@ -267,10 +230,10 @@ "id": "ccf696a7", "metadata": { "execution": { - "iopub.execute_input": "2026-08-07T00:45:03.838314Z", - "iopub.status.busy": "2026-08-07T00:45:03.838092Z", - "iopub.status.idle": "2026-08-07T00:45:03.983553Z", - "shell.execute_reply": "2026-08-07T00:45:03.981686Z" + "iopub.execute_input": "2026-09-27T16:03:22.972417Z", + "iopub.status.busy": "2026-09-27T16:03:22.972082Z", + "iopub.status.idle": "2026-09-27T16:03:23.128157Z", + "shell.execute_reply": "2026-09-27T16:03:23.126666Z" } }, "outputs": [ @@ -289,12 +252,13 @@ " 19000 Hz -0.557 dB\n", " 19800 Hz -4.061 dB\n", " 20500 Hz -12.168 dB\n", - " 21200 Hz -28.789 dB\n" + " 21200 Hz -28.789 dB\n", + "[ figure ] digest c64fc112a05dc1c8 (2 arrays)\n" ] }, { "data": { - "image/png": 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ixX3j7j8UbKWi75GqUNF+VFHW7Gtr32v36tSpU4iJicHzzz+Pt99+21jerVs3REdHY+LEiWWu5FDsm2++gV6vx/Lly8tc5kyr1VpcdaPkd8nd4uLi8NBDD8Hd3R1bt25FzZo1y4zFFt9Dt2/fhlqtRq1atUzKlUqlWd3qOnaOgMmuk1OpVBUqF3edBFD8BTJp0iSzvyKLhYaGIiEhAatWrcLcuXPx0ksvGR+TJMnsC+qxxx7DY489htOnT2PPnj34/vvvMWjQICxYsACvvvoqgKK/Oi19sZW2FIylD6fi2FesWGHxi1ChUAAoSh4KCwuRmZlpVi81NdXi61liqW5xWXG7q1atQkJCAvbt22eSkB47dszkeRqNBp999hkWLlyIv/76C7t27cKXX36JpUuX4tixY2jYsKFVdWwRWzFr+s+97ls3NzcEBgZaPO7WHt/is+xTUlJKTbCLn3/nzh3odDqTx1JSUszar8h7517btOa1AKBHjx5o0qQJlixZgtGjR+Pzzz9HSEgIHnzwQYvPL1aRY1SRberTpw9++uknXLhwAX/++Sc++ugjY/lvv/2GevXqQQiBPn36VHqbgX8/E0JDQ8vczqpQ0fdIVahoP6qoivTl8t5r9+rIkSMQQqBLly4m5Wq1Gh07dsSKFSuQkZFR7soZzz//PC5fvowpU6agRo0aZidLVlZWVhYGDhyIpKQk/PHHH4iMjCz3Obb4Hiqum5WVZRwQKHbnzp1Kvb4r4DQGKlXz5s2h0+lw/PjxUs9ErVGjhvGvw5LJxObNm1FQUGCx7WbNmuGZZ57B9u3b0aRJE6xfv974WHh4OK5evYrCwkJjmRDCbAWIsvTs2RMAcPXqVYtxN27cGADwwAMPACg6W/VuR44cQWZmptWvt2vXLkiSZFK2Y8cOeHl5oUWLFgBgbK/kX+93b/vdtFotevTogZiYGPz000/Izs42W2vSmjq2iM0aVbFvO3bsaDGZsPb4FtcrueKHpTh///13k/K4uDicPXsWnTp1sipWW7dZnmeffRZ79uzBZ599hlOnTmH8+PFmv+qUFqc1x6gi21ScxM6bNw/p6enG+3369MG+ffuwYcMGBAUF4b777qv4ht7l8OHDAGCT/VlSRd4jWq3W5POrPKXVv9d+VNE4LLH2vXavildhuHHjhtlj169fh4eHBzw9Pcttx83NDWvXrsW4ceMwatQoLFu27J5jMxgMGDlyJP7++298++236Nixo1XPs8X3UPFr79y506T8+PHjyMjIqNTruwImu1QqtVqNhQsXYtOmTYiJiTH+TCeEwMmTJ/Hkk08CAOrXr4+6deviyy+/RFpaGgDgxIkT+Oijj1C7dm1je/v378c777xjnPoAFM1zi4+PR9OmTY1lo0aNQmpqqvGyj4WFhZg1a1aFLiowYMAA9O/fH9OmTTNJ/vR6PX766Sd8+OGHAIDBgwejadOmmDFjhnGZtNu3b2PevHlmPyGXJSAgALNmzTJ+saxduxZr1qzBiy++aPyA7tatGwAYX1uSJCxevNhkfxQ//uOPPxqXMxJCGD/YmjRpYnUdW8RWEVWxb/v3749Lly6ZxWHt8X3wwQfRu3dvvP766ybz086cOWP8CX3w4MG477778Nprrxmv1JSWloaJEydCrVZjxowZldr2qm6zPOPGjYO3tzdefPFFAMDEiROtitPaY1SRbYqMjET9+vWNyz4Vt9W7d28UFhbihx9+QO/eve95ZGnXrl0IDg5Gq1atTMqtWXqsoiryHomMjMSJEyfM/sgsTWn177UfVTQOS6x9r92rXr16oX79+liwYAH+/vtvY/nSpUvxxx9/GLfZGiqVCsuXL8f06dPx9NNP4/3337+n2F5++WVs2bIFCxYswNChQ61+ni2+h4YMGYImTZpgxowZuHLlCoCiXzgs1a2uY+cQqu1UOKp2xctylTRmzBgRGhpqVj5+/HhRq1Yts/KNGzeKli1bCrVaLerWrSu8vb1Fy5YtTVZAOHDggGjUqJHQarUiJCREtGzZUpw+fVpERkYaV3hIS0sTMTExonbt2iIwMFDUqVNHeHl5iaefftpkmSIhilYbcHNzEzqdToSEhIhly5aVufSYJXl5eWLWrFkiMDBQeHt7i7CwMOHl5SWGDRtmXNpGCCGuX78uunfvLhQKhahdu7Zo1KiROHz4cIWWHps9e7b4+OOPRVBQkAgICBBarVbMmDHDbLWD//73v8LT01MEBgaKmjVriieffFL8+eefJmevHz16VAwfPlx4eXmJunXrCn9/fxERESGWLVtmbMeaOraIzdLyPsUsraJxL/tWiKI+4+XlJRYuXGj2mLXHNzs7Wzz//POiRo0awsfHR+h0OtG8eXOxbds2Y53bt2+Lxx57TGg0GhESEiLUarVo27at2L9/v8X9ac2232ubACyuQFDWmf6TJ08WAETXrl0tPm5JRY6RNdtU7OmnnxYAxPPPP29S3q5dOwFAfP311yblFd2/er1e+Pv7m63EIIR1S48Vq8hqDNa8R4QQYsuWLcLf318EBgaKxo0bi8mTJ5cZQ1n1K7LPrW23ovva2veaJV999ZVxpQMvLy+hUCiM95977jmTulevXhUjR44Ubm5uIiQkRPj4+IjAwEDxzjvvmKw0Y0lp2/Tuu+8KAGLmzJnGsoouPdasWTOTuO/+V97SY7b4Hrp27Zro1q2bsW6TJk3EkSNHRGRkpBg6dGilXt/ZKYSo5ErNZPfi4uKQm5trNrcoPj4eOTk5Fsuzs7PRoEEDi+2lpaXhzp07CAkJKXUSf/FC1cWT5y9fvgytVmsywgsAycnJyM/PR61atUqdN5aTk4OEhASEhobCzc0NycnJSE5ORuPGjY0jQqVt492EEIiLi4MQAqGhoaWOJiUnJyMvL89Y58qVK3BzcytzPmBhYSE0Gg1mz56NefPmIS8vD/Hx8QgODi51MfXCwkLcunULgYGB8PLyQl5eHq5evYo6deqYzMGSJAk3b95EjRo1Sh3VLq/OuXPnEBgYiMDAwCqL7e4273b9+nUAlk+mqcy+LTZ9+nRs3boVZ86cgVJp/mOUtce3eNt8fX0trvAAFPW527dvw8/Pz+LJJ5XZ9sq2ee7cOQQEBJjN1Tx//jxq1qxpVg4An3zyCZ5//nl88803Fb7qXEWOUXnbBBTNH0xISEBwcLDJ/o6Pj0d6ejrq1atn9jlSkf373Xff4ZlnnsHFixfNPl8SExORmpqK+vXrl7siw82bN1FQUID69eublJe2n619/xoMBty8eRN5eXnw8vIq99eM8upbs88r0m5l+rK177W7paamlnq+RY0aNSz2L0mSEBcXBw8PDwQEBJT7GsVK26YrV65Ar9cjIiICbm5uiI2NhVqtRnh4uFkbBQUFiI2NNXnvFT/fEl9fX6tOuLXF91DJup6enhg/fjyWLFlS6dd3Vkx2ie5ByWSXqt6dO3fQoEEDfPzxxxg7dqzc4di1rl274sSJE4iLi7NqfqOjMhgMaNKkCcaMGYOYmBi5wyGS3d69e9G5c2esWLEC48ePlzscu8PVGIjIrvn7++PkyZNyh2H3Dh48iD179mDmzJlOnegCRcnupk2bzJbZI3IF33//PYKDg9GlSxcoFAqcP38eU6ZMQXh4eIXmFLsSnqBGRHavdu3aZj9VU5Hjx4+jUaNG6Ny5M3r27InZs2fLHZLNubm5ISoqymytcCJX0Lx5c8yZMwd+fn4IDg5G06ZNodPpsG3bNnh5eckdnl3iNAaie1TaPDGi6lA8Z7T4i4+IXINer0dcXBwCAwOr5cIqjozJLhERERE5LU5jICIiIiKnxRPULChe9qRGjRoutzwHERERkSMQQiAzMxO1a9e2uDRlMSa7FsTFxSEsLEzuMIiIiIioHDdu3ChzLWsmuxYUT/S+ceMGfHx8bPIakiQhKSkJOp2uzL9GyLWwX1BJ7BNkCfsFleSKfSIjIwNhYWHlnqDHZNeC4qkLPj4+Nk128/Ly4OPj4zKdksrHfkElsU+QJewXlVdgKMBnhz4DADzX7jloVBqZI6oartwnyptyymSXiIiIXEa+IR8v/foSAOCp1k85TbJLpWOyS0RERC5DpVRhdPPRxtvk/JjsEhERkcvQqrVY9egqucOgauSUye6JEyfwxRdfICEhAc2bN8cLL7wAPz8/ucOyyCAJHLiSgsTMPATV0KJ9/ZpQKatuuTODJHDwSqrN2qeqY5AEDlxOwaWbqWiQpUKHiEAeKxfHPkGWOGO/sNV3laV2AfB70cU4XbJ78OBBdOvWDU888QQGDRqEL774Aj/88AMOHToEDw8PucMzsePSHSz66jRuZ+QZy0J8tYgZ1BT9o0Puuf1fTsVj7v/OID7dNu1T1TE/Vld4rFwc+wRZ4oz9wlbfVZba9fMsmp+bllNQpa9F9s3pLhfcq1cveHt7Y+PGjQCAtLQ01KlTB++99x6mTp1qVRsZGRnw9fVFenq6zVZj2HoiDs+uPmZWXvy35ZLHW9/zm3zKd0dR8uBWVftUdXisqCT2CbLEGfuFrbaptHYBQEIebmknAgBC876CCtp7ei17IUkSEhMTERQU5DKrMVibrznVyG5eXh527tyJL7/80ljm5+eHXr164ZdffrE62bU1gyTw9uazFh8TKHqTz9l0Bp0alP7TlAKl/+RikATmbDpt8U1ubP9/Z9CloY4/3cjM2mPVtRGPlatgnyBLqutz3drhL2Exkoq1aZAEYsrbJgvfhZa+/+5eeaqsfVVMUmSYvdbc/51Bn6bBfF85Iaca2b1w4QIaN26M7du3o1evXsbyqVOnYseOHTh9+rTF5+n1euj1euP94kWK79y5Y5OR3f2XUzB6+cEqb5eIiIjKJiChQHEDAKARYVDg31HQdx5uikEta8NHWzXLkRkkgUNXU5GYqUdQDXe0q2e7+cGuelEJf39/1xrZzc/PBwB4enqalHt6ehofs2T+/PmYO3euWXlSUhLy8vIsPOPeXLqZWuVtEhERUfkUUMJNhFt87M1NZ/DmpjOo6alGuL8Wdf/5F+7vjrr+WtT2dYfaymR1x6U7+OjPG0jM+nd+cJC3Bi91D0OPBv5Vsi13kyQJ6enpEEK4TLKbmZlpVT2nSnaLV1xITTVNJlNSUspcjWHmzJmYPn268X7xyK5Op7PJyG6DLBWAK+XW+3p8W+OZo3cr7+ejg1dSMfGbI+W2/9X4NmhXz7x9R+EMP0kcupqKSVYcq+XjWjv0sSLrHbqaiidXHi233jL2CZdy6GoqnrKiX5T2WVHeFaZM6lpbrwIDlJamHhy8kooJ3xwu97lfjW9j/C609Ft0yaKDV1Kseg9Z4uehQVpuAVJzCpGak4Vjt7JMHteoFAiv6YkInTfqB3ohQueFiH/+9/d0M9b75dRtzNp82Sy2pKwCzNp8GZ+NboX+0cGVirE0kiRBoVC41MiuVqu1qp5TJbuhoaEICAjA8ePH8dBDDxnL//77b7Rq1arU57m7u8Pd3d2sXKlU2qTDdIgIRLCP1mQVhrspAAT7atG1cVClfu7o1rgWQny1uJ2eZzEhLG6/W+NanJsks+5WHqseUZxH5ip6RAVb1Sd6sk+4lJ5W9gtH+qzo2jjIJt9V5b2HBAqRpdoOAPA29IYCauNr7XmtJ3LyC3ElORuXk7JxOSkLscnZiE3MwpXkbOgLJVxKysalpGyzdv09NUVJcIAnfj2dUOZc5He2nEW/6JAqP1YKhcJmuYs9snY7nWpvKBQKjB07FsuXL0dKSgoAYNu2bTh69CjGjRsnc3T/UikVeGtgEwDmf0EX348Z1LTSbwKVUoGYQU1t1j5VHR4rKol9gixxxn5hq20qq12gKNlNdfsUqW6fQqDQ7LVqaDW4r44fhrQKxfS+jfHZ6Nb45cWuOPt2f+x5rQdWTmyPOYOaYlzHcHRuEIjavkWji3dyCnDk2h2sO3oLmfrCUuMTAOLT83DwCqc0VhenOkENALKysvDwww/jxIkTaNSoEY4dO4ZZs2bhzTfftLqN6lh6TJIkrN13Hot2xXGdXeKxIjPsE2SJM/aL6l5nVxL5uGT4DwBAl/8qavv63PNr3T0avOVkPH45dbvc5ywa2RKDW4ZW+jVL4tJjpedrTpfsFjtx4gQSEhLQrFkz1K5du0LPra5kNzExEQGBOhy+lsYrqNE/V0VKxqWbSWhQR+cUV0Wie8M+QZY4Y79wpiuo/RWbglHL9pdb7/89dT86RgZU2esy2XXBZPdeVGey60qdksrHfkElsU+QJewX9ssgCXRe8Eepc4aBopHrPa/1rNIk2xX7hLX5mmvsDSIiIqJqUN6cYQB4tV9jhx+NdyRMdomIiMhl5BTkoN7H9VDv43rIKcixyWv0jw7BksdbI9jXdGms4vx24/E4GCT+sF5dnGrpMSIiIqKyCCFwLf2a8bat9I8OQZ+mwSbzg7UaJUYt248/zyfh3a1n8ebApjZ7ffoXk10iIiJyGVq1FgefPGi8bUsqpcLsJLSFw1riudVH8eWeK2gQ5I1R7evaNAbiNAYiIiJyISqlCu1C26FdaDuolKpqf/2H7gvB9D6NAABvbjiFv2JTqj0GV8Nkl4iIiKgaTevZAINa1EahJDBl1RFcTTa/IhtVHSa7RERE5DIKpUKsOrEKq06sQqFU+pXObEmhUOD9ofehRZgf0nIKMPGbQ0jPLZAlFlfAZJeIiIhchr5Qj8fXP47H1z8OfaFetji0GhWWjW2DEF8tLidlY+rqoyg0SLLF48yY7BIREZHLUCqU6B3RG70jekOpkDcNCvLRYtm4tvDQqLD7YjLe2XxG1nicFZNdIiIichkeGg9sG7sN28Zug4fGQ+5wEB3qi49GtAQAfPPXNXz711VZ43FGTHaJiIiIZNQ/Ohiv9GsMAJjzvzPYczFZ5oicC5NdIiIiIpk92z0Sj7YKhUESeHbVEcQmZckdktNgsktEREQuI6cgB80WN0Ozxc1sdrngylAoFJj/WHO0CfdHRl4hnvzmMNJy8uUOyykw2SUiIiKXIYTAmaQzOJN0xqaXC64Md7UKX4xtg1A/D1xJzsazq46igCs03DMmu0REROQytGotdozfgR3jd9j8csGVEejtjuXj28LLTYV9sSmI2XTa7pJyR8Nkl4iIiFyGSqlC93rd0b1ed1kuF2yNJiE+WDSyFRQKYPWB6/hm31W5Q3JoTHaJiIiI7EzvprUw88EoAMDbm8/gz/OJMkfkuJjsEhERkcsolAqx4dwGbDi3QbbLBVvrqS4RGNamDiQBTFt9DBcTMuUOySEx2SUiIiKXoS/U45G1j+CRtY/IerlgaygUCsx7JBrt69VEpr4Qk745jKRMPf6KTcHGv2/hr9gUGCTO5y2PWu4AiIiIiKqLUqHEA2EPGG/bO3e1Ckseb40hi/fiemoOOr33O/IN/ya4Ib5axAxqir5Na8kYpX2z/6NMREREVEU8NB7YO3Ev9k7caxeXC7ZGgLc7JjxQHwBMEl0AuJ2ehynfHcUvp27LEZpDYLJLREREZMcMksCy3ZctPlac+r6z5SynNJSCyS4RERGRHTt4JRXx6XmlPi4AxKfn4e9bvMSwJUx2iYiIyGXkFuSi3bJ2aLesHXILcuUOxyqJmaUnundLyS6wcSSOyelOUIuNjUV8fLxJmZeXF1q1aiVTRERERGQvJCHhcNxh421HEFTDuiu9BXhpbByJY3K6ZPf999/HunXrEBUVZSyLiIjAypUrZYyKiIiI7IG72h2bR2023nYE7evXRIivFrfT82BpVq4CQLCvFi1Dvas7NIfgdMkuAHTv3h3r1q2TOwwiIiKyM2qlGg81ekjuMCpEpVQgZlBTTPnuKBSAxYT3zYeaQKVUVHdoDsEp5+zm5eXh6NGjiI2NhSQ5xk8URERERKXpHx2CJY+3RrCv6ZQGrUaJJY+3Rv/oYJkis39OObL766+/4vr167h9+zbc3d3xxRdfYMCAAaXW1+v10Ov/vYpKRkYGAECSJJsly5IkQQjBZJxMsF9QSewTZAn7ReUZJAP+uPoHAKBnvZ5QKVUyR2S9vk1roVdUEA5dTcXha3fw4baLUALo1jDQJfuEtdtq98nuxYsXkZCQUGadtm3bQqst+kunX79+ePvttxEUFASDwYCZM2di2LBhOHHiBCIjIy0+f/78+Zg7d65ZeVJSEvLyrDsDsqIkSUJ6ejqEEFAqnXKAnSqB/YJKYp8gS9gvKi+nIAf9V/UHAMROjIWnxlPmiCouwhuo37QGvvtLg8SsAmw5EosH6vm4XJ/IzMy0qp5CCGHXKxC/++672Lp1a5l11q5di9DQUIuPGQwGBAQEICYmBi+99JLFOpZGdsPCwnDnzh34+PhUPvgySJKEpKQk6HQ6l+mUVD72CyqJfYIsYb+ovNyCXHT6uhMAYO8Ex7mKmiVzNp3Byv3XMKxNHcx/pJnL9YmMjAz4+/sjPT29zHzN7kd2Z82ahVmzZlX6+SqVCgEBAbh161apddzd3eHubn5GplKptGmHUSgUNn8NcjzsF1QS+wRZwn5ROV7uXvh78t9yh1El+kcHY+X+a/j9XCIk0czl+oS12+lUe0MIgdxc0wWiL168iGvXriE6OlqmqIiIiIiqXvv6NeHnqUFqdj6OXLsjdzh2y6mS3YKCArRp0wYffvghfv31Vyxbtgx9+/ZF69atMWrUKLnDIyIiIqoyapUSvaJqAQB+PVP2+U2uzKmSXTc3N/z+++9ITk7Gxx9/jB07duDVV1/F3r17LU5TICIiIteSW5CL7iu6o/uK7g5zueCy9GtWlOz+diYBdn4almzsfs5uRYWEhODdd9+VOwwiIiKyQ5KQsPPaTuNtR9e1kQ4eGhXi0vJwPikXtWrJHZH9caqRXSIiIqKyuKvd8f3Q7/H90O8d5nLBZdFqVOjWSAcA2HmJ83YtYbJLRERELkOtVGNYs2EY1mwY1Ern+IG7X3TRcO7O2DR5A7FTTHaJiIiIHFjPxrWgVipwOSUPV5Kz5Q7H7jDZJSIiIpdhkAzYe30v9l7fC4NkkDucKuHrqcH9ETUBFJ2oRqaY7BIREZHLyCvMQ+evO6Pz152RV5gndzhVpm/Tf1ZlOM1ktyQmu0REROQyFAoFGtRsgAY1G0ChUMgdTpXp80+ye+xGGhIynCeJrwpMdomIiMhleGo8cXHaRVycdhGeGk+5w6kytXy0iA72AsCpDCUx2SUiIiJyAt0a+AEAfjt9W95A7AyTXSIiIiIn0C3SDwDwV2wK0nMK5A3GjjDZJSIiIpeRV5iHh1Y/hIdWP+RUJ6gBQF1/LRoGeaNQEvjjPKcyFGOyS0RERC7DIBmw9eJWbL241WmWHrtb8aoMv55islvMOS4dQkRERGQFN5Ubvh78tfG2s+nbrBY++zMWOy8kIa/AAK1GJXdIsmOyS0RERC5Do9LgiZZPyB2GzUTX9kGonwdupeVi98Vk45JkrozTGIiIiIichEKhMCa4v3JVBgBMdomIiMiFGCQD/r79N/6+/bdTztkFgH7NggEAv59NQKFBkjka+THZJSIiIpeRV5iHVl+0QqsvWjndagzF2tXzh7+nBndyCnDwaqrc4ciOyS4RERG5DIVCgdo1aqN2jdpOdbngu6lVSvRuUjSV4bfTXJWByS4RERG5DE+NJ25Nv4Vb02851eWCSyqeyvDb6dsQQsgcjbyY7BIRERE5mc4NA+HppkJceh5O3kqXOxxZMdklIiIicjJajQrdG+sAcFUGJrtERETkMvIK8zDsh2EY9sMwpz1BrVjxVIZfXXzeLpNdIiIichkGyYB1Z9Zh3Zl1Trv0WLEeUUHQqBS4lJiF2KQsucORDZNdIiIichluKjd8+uCn+PTBT53ycsF389Fq0DEyEIBrT2VgsktEREQuQ6PS4Ln2z+G59s9Bo9LIHY7N9WtWfDU1153KwGSXiIiIyEn1aVoLCgVw/EYabqc79xzl0jhcsnv48GE8+eST6Ny5M44dO2axzr59+zBmzBj07t0bL730EhISXPevGSIiIvqXJCRcTLmIiykXIQnnv5RuUA0tWoX5AQB+O+OaUxkcKtl96623MHnyZERERGDv3r1ITzdfN27Xrl3o3r07wsLCMG3aNJw6dQqdOnVCVpbrTswmIiKiIrkFuWj0aSM0+rQRcgty5Q6nWvy7KgOTXbv30ksv4fDhwxg3blypdWbPno0hQ4bgvffew+DBg7FhwwYkJiZi6dKl1RgpERER2Stfd1/4uvvKHUa1KU52919ORVpOvszRVD+HSnb9/f3LfDwnJwf79u3Dww8/bCzz8vJC7969sW3bNluHR0RERHbOy80Laa+nIe31NHi5eckdTrWoF+iFxrVqwCAJ/H42Ue5wqp1a7gCq0o0bNyBJEkJDQ03KQ0ND8ccff5T6PL1eD71eb7yfkZEBAJAkCZJkm/k8kiRBCGGz9skxsV9QSewTZAn7BZVUXp/o2zQI5xMy8evp23ikVe1qjs42rO3/sia77777LrZu3VpmnbVr15olr6UpKCgAAGi1WpNyDw8P5OeXPmw/f/58zJ0716w8KSkJeXm2OXNRkiSkp6dDCAGl0qEG2MmG2C+oJPYJsoT9gkoqr0+0DSlaU3jnhURcv3UbWo3j95vMzEyr6sma7A4bNgxdu3Yts05AQIDV7dWsWRMAkJKSYlKekpJifMySmTNnYvr06cb7GRkZCAsLg06ng4+Pj9WvXxGSJEGhUECn0/GDiozYL6gk9gmyhP2i8vSFekzeMhkA8PlDn8Nd7S5zRFWjvD6h0wmE+l3FrbRcnE0D+jULqv4gq1jJwc3SyJrsNmzYEA0bNqyy9mrXro1atWrhyJEjGDhwoLH84MGD6NSpU6nPc3d3h7u7eWdXKpU2/RBRKBQ2fw1yPOwXVBL7BFnCflE5EiSsPLESALD4ocVOtf/K6xP9mgXjq71XsO1MIh5s7vhTGaw9ds5zhP8xYcIELF++HPHx8QCADRs24NSpU5gwYYLMkREREZHcNCoN/tv7v/hv7/+6xBXU7lZ8NbXtZxNQYHCd+d4OdYLazz//jP/85z/G+bfTpk2Dr68vJk6ciIkTJwIAYmJicP78eTRo0ADh4eG4evUqFi1ahA4dOsgZOhEREdkBN5UbXun0itxhyKJtvZoI8HJDSnY+DlxOReeGgXKHVC0cKtlt06YN3nvvPbPyunXrGm9rtVr89NNPuH79OhISEtCoUSP4+rrOWnpERERElqiUCvRuUgtrD9/Ar6dvM9m1R0FBQQgKsm5Cdd26dU2SYCIiIiJJSIjPLJrqGFIjBEqF083oLFO/6KJkd9uZBMx9uBmUSoXcIdmcax1hIiIicmm5Bbmo81Ed1PmojstcLvhuD0QGwstNhdsZeThxK13ucKoFk10iIiJyKWqlGmqlQ/24XWW0GhW6RxX9Sv7r6dsyR1M9mOwSERGRy/By80LBmwUoeLPAZS4XXFK/ZsEAmOwSERERkRPq0VgHjUqBy0nZuJRo3VXIHBmTXSIiIiIXUkOrwQORRSsxLNt9BRv/voW/YlNgkITMkdmGa05YISIiIpekL9Rj+q/TAQAf9vvQaS4XXFGhfh4AgLWHbmDtoRsAgBBfLWIGNUX/6BA5Q6tyHNklIiIil1EoFWLx4cVYfHgxCqVCucORxS+n4rH64HWz8tvpeZjy3VH8cipehqhshyO7RERE5DI0Kg1iusUYb7sagyQw939nLD4mACgAzP3fGfRpGgyVk6zBy2SXiIiIXIabyg1zus+ROwzZHLySivj0vFIfFwDi0/Nw8EoqOkYGVF9gNsRpDEREREQuIjGz9ES3MvUcAUd2iYiIyGUIIZCuL7pymK+7LxQK5/ip3lpBNbRVWs8RcGSXiIiIXEZOQQ78F/jDf4E/cgpy5A6n2rWvXxMhvlqUluIrULQqQ/v6NaszLJtisktERETkIlRKBWIGNQUAs4S3+H7MoKZOc3IawGSXiIiIXIinxhP5b+Qj/418eGo85Q5HFv2jQ7Dk8dYI9jWdqhDsq8WSx1s73Tq7nLNLRERELkOhULjkkmMl9Y8OQZ+mwYjZdArf7b+O9vX88f+e7uhUI7rFOLJLRERE5IJUSgV6NakFAEjPLXTKRBeoomRXr9cjJ8f1JnkTERGRY8k35OOV317BK7+9gnxDvtzhyC4y0BsAcCUlGwZJyByNbVQo2TUYDHj//fcxZswYrFixAkIITJ06FV5eXqhRowYefPBBpKSk2CpWIiIiontSYCjAB399gA/++gAFhgK5w5FdqL8H3NRK5BdKuHUnV+5wbKJCc3bffPNNfPLJJ2jTpg2mTZuGPXv2YNeuXfj4448BAIsWLcKbb76JxYsX2yJWIiIionuiUWkwo+MM421Xp1IqUD/AC+cTMhGbnIW6Ac530l6Fkt3Vq1dj48aN6NmzJ/744w/06tULR48eRatWrQAAHTt2xKOPPmqTQImIiIjulZvKDe/3fV/uMOxKZNA/yW5iFno0DpI7nCpXoWkMt27dQufOnQEAnTp1AgA0b97c+HiLFi1w69atKgyPiIiIiGwp4p95u5eTs2WOxDYqNLJbWFgINzc3AIC7u3tRA+p/m1Cr1TAYDFUYHhEREVHVEUKgUCoEAKiVape7XLAlkUFeAIDYxCyZI7GNCq+z++eff5Z5n4iIiMhe5RTkwHt+0Uhm1swseLl5yRyR/DiyW0KPHj3KvE9EREREjiNCV5TwJ2XqkZFXAB+tc524V6Fk98qVK7aKo0IuXLiACxcuoGPHjggICDB57OTJk7h27ZpJmY+PD7p27VqdIRIREZEd8tR44s5rd4y3Caih1SCohjsSM/W4nJSNlmF+codUpSqU7NarV89GYVhn9+7dmDt3Ls6fP4+bN29ix44d6N69u0mdzz77DBs3bkSbNm2MZeHh4Ux2iYiICAqFAn5aP7nDsDuROm8kZuoRm5jlusnuzZs3rW60Tp06lQqmPHFxcXjttdcQFRWFunXrllqvU6dOWLdunU1iICIiInI2ETov/HU5BZeTne8kNauT3bCwMKsbFcI2l5sbMWIEgPIT78zMTPzxxx/w9fVF06ZN4eHhYZN4iIiIyLHkG/Lx7u53AQCzusyCm8pN5ojsQ6Su6CS12ETnO0nN6mT34sWLxtu//vor3n33Xbz55pto164dAODQoUN4++238cYbb1R9lBW0b98+zJkzB3FxcUhLS8PixYsxfPjwUuvr9Xro9Xrj/YyMDACAJEmQJMkmMUqSBCGEzdonx8R+QSWxT5Al7BeVpy/QY+7OuQCAl+9/GWpFhc/Vt0v32ifqBxbNX76clOUw/craOK0+wg0aNDDeHj58ONatW4eOHTsay9q0aYP77rsP06ZNw7PPPmtVm8ePH8eNGzfKrNOzZ094elo/gfyRRx7Bf//7X/j4+AAA/vOf/2Ds2LFo3rw5mjRpYvE58+fPx9y5c83Kk5KSkJeXZ/VrV4QkSUhPT4cQAkplha7tQU6M/YJKYp8gS9gvKk9v0OOJpk8AAFJTUpGtco6RzHvtE76KokG/KynZiL+dAJXS/tcfzszMtKpepf6cOXfunMXEsUmTJjh37pzV7ezYsQPbt28vs06bNm0qlOz269fP5P6sWbOwcOFCbNmypdRkd+bMmZg+fbrxfkZGBsLCwqDT6YxJc1WTJAkKhQI6nY4fVGTEfkElsU+QJewX9+bLx76UO4Qqd699IjBQwF19BvpCCfkab4QH2P/6w1qt1qp6lUp269Wrh48//hhz5swxKV+0aFGFVmx48cUX8eKLL1YmBKspFAr4+fnh9u3bpdZxd3c3XhHubkql0qYfIgqFwuavQY6H/YJKYp8gS9gvqKR76RNKJVA/0AvnbmfiSkoO6utq2CDCqmXtdlYq2V20aBEGDx6MH374AW3btoUQAocPH8a1a9ewadOmyjRZJSRJQkZGBvz8/Ixlxevutm7dWra4iIiIiOxdpM4b525n4nJSNnpGyR1N1alUstunTx9cunQJX3zxBc6cOQMAGDZsGCZPnoyQkJAqDfBuN2/exN9//42UlBQAwF9//YWsrCw0atQIjRo1gsFgwP3334/hw4ejWbNmuH79Oj788EN069YNw4YNs1lcRERE5Biy87Pht8APAJD2WhovF3yXyH+upBab5FzLj1X6FMTatWtbPKnLls6fP4/PP/8cAPDQQw9h79692Lt3L0aPHo1GjRpBo9Fg//79WLp0KTZs2AB/f398+OGHGDlyJBQK+59oTURERLZXKBXKHYJdiihefizJOU7aK+ZQ62306tULvXr1KrOOn58fXn311WqKiIiIiByJh8YDN1+6abxN/ypea/cyR3aJiIiIHJNSoUSoT6jcYdil+v9MY0jOykd6TgF8PTUyR1Q1eAonEREREcHbXY1gn6LlvGKd6LLBTHaJiIjIZeQb8vH+3vfx/t73kW/IlzscuxPxz+juZSeat8tkl4iIiFxGgaEAr25/Fa9ufxUFhgK5w7E7kcaT1JxnZJdzdomIiMhlqJVqjG8x3nibTP07sstkl4iIiMjhuKvdsWLICrnDsFuRTrj8GKcxEBERERGAf0d2r6Vko9AgyRxN1WCyS0REREQAgNq+HtBqlCgwCNy4kyt3OFWCyS4RERG5jOz8bPi95we/9/yQne88P9VXFaVSgfqBznVxCSa7RERE5FLS9elI16fLHYbdivxnKoOzrMjAE9SIiIjIZXhoPHBh6gXjbTIXYbxssHOMfDPZJSIiIpehVCjRMKCh3GHYNWcb2eU0BiIiIiIyinSykV0mu0REROQyCgwF+OzgZ/js4Ge8glop6gcWjeymZOcjLcfxL6nMZJeIiIhcRr4hH1N/noqpP09FvsHxEzlb8HJXI8RXC8A5Li7BObtERETkMlRKFYY2HWq8TZZF6LwQn56H2KQstAn3lzuce8Jkl4iIiFyGVq3FD8N+kDsMuxep88beSylOMW+X0xiIiIiIyEREoPOsyMBkl4iIiIhMRAY5z1XUmOwSERGRy8gpyEHoh6EI/TAUOQU5codjt4ovLHEtJQcFBknmaO4Nk10iIiJyGUIIxGXGIS4zDkIIucOxWyE+WnhoVCiUBG6kOvYfBTxBjYiIiFyGVq3FsWeOGW+TZUqlAvUDvXAmPgOxSdnGkV5HxGSXiIiIXIZKqULL4JZyh+EQIoO8cSY+4595u7XkDqfSOI2BiIiIiMw4y4oMHNklIiIil1FgKMCqk6sAAGOaj4FGpZE5Ivv174oMjr3WrkON7N66dQszZ85Et27d0KtXL8TExCA9Pd2s3i+//IKHHnoIbdu2xYQJE3D16tXqD5aIiIjsTr4hHxM2TsCEjRN4ueByOMvIrsMkuwaDAV27doWfnx/eeecdvPLKK/jf//6HPn36ID//3876yy+/YNCgQejSpQsWLlyItLQ0dO7cGWlpafIFT0RERHZBpVRhQMMBGNBwAC8XXI4IXVGyeyenAHeyHfcPA4eZxqBSqXDmzBm4u7sby+rWrYtmzZrh4MGD6Ny5MwAgJiYGI0eOxOuvvw4AuP/++xEcHIzPP//cWEZERESuSavWYsvoLXKH4RA83dSo7atFXHoeLidnoY1XTblDqhSHGdkFYJLoAoBaXZSrS1LRYsdZWVk4dOgQBgwYYPKc3r17Y8eOHdUXKBEREZETKJ63G5vouPN2HWZk15I5c+YgLCwM7du3B1A0p1cIgZCQEJN6ISEhOH36dKnt6PV66PV64/2MjAwARUl0cSJd1SRJghDCZu2TY2K/oJLYJ8gS9gsqyVZ9on6AF3ZfTMalpEy762/WxiNrsvvGG29gw4YNZdb5+eefERYWZlY+f/58rF+/Htu3b4dWW7QodGFhIQDAzc3NpK67uzsKCgpKfY358+dj7ty5ZuVJSUnIy8srbzMqRZIkpKenQwgBpdKhBtjJhtgvqCT2CbKE/aLycgpy0HtdbwDA9qHb4anxlDmiqmGrPhHkUZRQnr2ZisTExCprtypkZmZaVU/WZHfKlCkYOXJkmXVq1TJfxPijjz7C22+/jQ0bNqBTp07G8oCAAABASkqKSf2UlBQEBgaW+hozZ87E9OnTjfczMjIQFhYGnU4HHx8fq7aloiRJgkKhgE6n4wcVGbFfUEnsE2QJ+0XlZedn40rGFQCATqeDl5uXzBFVDVv1ifvqK4EdN3AroxBBQUFV1m5VKB7sLI+syW5oaChCQ0Mr9JxFixZh1qxZWL9+Pfr162fyWHBwMGrXro0DBw5g0KBBxvK//voLvXr1KrVNd3d3s/nAAKBUKm36IaJQKGz+GuR42C+oJPYJsoT9onI83TyxZ8Ie421n2n+26BMNa9UAAFxPzYFBABqV/ewva7fTfiK2wieffILXX38d69evR//+/S3WeeaZZ7B8+XJcvnwZALBy5UpcuHABTz75ZHWGSkRERHZIpVShU91O6FS3E5ces0KwjxaebioUSgLXU3PkDqdSHCbZvXPnDl544QW4ublhxowZiI6ONv776aefjPVmzZqFAQMGICoqCrVr18bzzz+Pr776Ci1btpQveCIiIiIHpFAojOvtxiY65sUlHGY1Bh8fH5w4ccLiY3dPhVCr1fjqq6+wcOFCJCcno27duhanKBAREZHrKZQKsf7segDAI00egVrpMKmQbCICvXHqVgYuJzvm8mMOc4RVKhWio6Otru/v7w9/f38bRkRERESORl+ox/B1wwEAWTOzoHZzmFRINpG64rV2ObJLREREZNeUCiW6hXcz3qbyFU9j4MguERERkZ3z0Hjgzyf+lDsMh2Ic2U1yzJFd/klDRERERKWqH1g0spuWU4DU7HyZo6k4JrtEREREVCoPNxVC/TwAOOboLpNdIiIichm5Bblo+XlLtPy8JXILcuUOx2EY5+0y2SUiIiKyX5KQcDzhOI4nHIckJLnDcRj/ztt1vJPUeIIaERERuQytWovfHv/NeJusE+nAI7tMdomIiMhlqJQq9InsI3cYDseRR3Y5jYGIiIiIyhTxT7J7PTUH+YWONf2DyS4RERG5jEKpEFsubMGWC1tQKBXKHY7DqOXjDi83FQySwPVUxxrdZbJLRERELkNfqMfA/zcQA//fQOgL9XKH4zAUCoVxdNfRpjJwzi4RERG5DKVCiba12xpvk/UidV44eSvd4dbaZbJLRERELsND44FDTx2SOwyHVDyye9nBRnb5Jw0RERERlevfFRkca2SXyS4RERERlevfq6hlQwghczTWY7JLRERELiO3IBedvuqETl914uWCK6h+oBcUCiA9twAp2flyh2M1ztklIiIilyEJCftu7DPeJutpNSqE+nng5p1cXE7KRqC3u9whWYXJLhEREbkMd7U71o9Yb7xNFROp88bNO7mITcpC+/o15Q7HKkx2iYiIyGWolWoMiRoidxgOK0LnhZ0XknDZgU5S45xdIiIiIrJKpANeWIIju0REROQyDJIBu6/vBgB0qdsFKqVK5ogcy78rMjjOyC6TXSIiInIZeYV56PFNDwBA1swseLl5yRyRY2nwz8ju9dQc6AsNcFfb/x8LTHaJiIjIZSgUCjTVNTXeporR1XCHt7saWfpCXE/JQcNaNeQOqVxMdomIiMhleGo8cfrZ03KH4bAUCgUidV44fjMdsUlZTHZtISkpCYcPH4ZarUarVq0QGBho8vjBgwdx4cIFkzI/Pz8MHDiwOsMkIiIickoROu9/kl3HOEnNYZJdIQSefvpp/Pzzz7jvvvuQk5ODw4cP46OPPsJTTz1lrPfVV1/h559/RpcuXYxlYWFhTHaJiIiIqkDkPyepxTrISWoOlex26NABixcvhkajAQAsXboUU6ZMQd++fREeHm6s265dO3z33XdyhUpERER2KrcgFw+veRgAsGnkJnhoPGSOyPFE/HOS2mUHGdl1mHV2lUolnnzySWOiCwBDhgyBwWDA6dOmc29SU1Px008/4ffff0dqamp1h0pERER2ShIStl/eju2Xt/NywZX071q7WRBCyBxN+RxmZNeS3377DQqFAs2aNTMpP336NFasWIFbt27hwoUL+Oijj/Dkk0+W2o5er4derzfez8jIAABIkgRJss0bQZIkCCFs1j45JvYLKol9gixhv6g8jVKDlUNWGm87yz6szj5R118LhQLIzCtEYkYedDXkueyytdsqa7L7119/ITY2tsw6Q4YMgbe3t1n55cuX8eKLL2Lq1KkmUxhGjx6NRYsWwd29aMd/8sknmDJlCtq1a4cWLVpYfI358+dj7ty5ZuVJSUnIy8uryCZZTZIkpKenQwgBpdJhBtjJxtgvqCT2CbKE/eLe9KnVBwCQmuw8v/5Wd5+o7eOGW+n5OHLxJlrXkWdFhszMTKvqyZrsnjx5Ert27SqzTp8+fcyS3Zs3b6J3797o3LkzPvzwQ5PHunbtanJ/2rRpePvtt/HLL7+UmuzOnDkT06dPN97PyMhAWFgYdDodfHx8KrJJVpMkCQqFAjqdjh9UZMR+QSWxT5Al7BdUUnX3iQa1fHErPQl3CjUICgqy+etZotVqraona7L79NNP4+mnn67Qc27duoXu3bsjOjoa33//PdTq8jfBy8sLycnJpT7u7u5uHAm+m1KptGmHUSgUNn8NcjzsF1QS+wRZwn5ROQbJgKPxRwEArUNaO9XlgquzT0TqvLHzQhIuJ+fI1getfV2HeocUJ7pNmzbFunXr4ObmZvK4JEm4ffu2SdmhQ4dw/fp1dOjQoTpDJSIiIjuUV5iH9svbo/3y9sgrtM1URVcQGVS0/NhlB1h+zGFOUNPr9ejVqxcyMjIwZMgQfP/998bHOnbsiMjISBgMBvTs2RO9e/dGs2bNcP36dSxevBiDBw/Go48+KmP0REREZA8UCgXCfcONt6lyIgKLV2Sw/+XHHCbZLSgoQNu2bQEAf/zxh8ljderUQWRkJDQaDY4cOYJvv/0Wx44dg7+/P1avXo0HH3xQjpCJiIjIznhqPHH1xatyh+Hwikd2b97Jgb7QAHe1/U4HcZhk19vb26oLRXh4eFR4HjARERERWU/n7Y4a7mpk6gtxLSUHjWrJsyKDNRxqzi4RERERyU+hUCAi6J+pDIn2PW+XyS4RERG5jLzCPAxZMwRD1gzhCWr3KDLwn5PUku173q7DTGMgIiIiulcGyYCN5zcab1PlRTrIyC6TXSIiInIZbio3LB241HibKi/in5HdWI7sEhEREdkHjUqDp9o8JXcYTqF4ZPdyYhaEEHa7lBvn7BIRERFRhYUHeEKpADL1hUjK0ssdTqmY7BIREZHLkISE04mncTrxNCQhyR2OQ3NXqxBW0xMAEJtov1MZmOwSERGRy8gtyEX0kmhEL4lGbkGu3OE4vAjjigz2e5Iak10iIiJyKYGegQj0DJQ7DKcQqStekcF+R3Z5ghoRERG5DC83LyS9kiR3GE4j4p9klyO7REREROR0InX/LD+WxGSXiIiIiJxM8cjuzTu5yCuwz4t0MNklIiIil5FXmIcxP43BmJ/G8HLBVSDQ2w0+WjWEAK6m2Oe8XSa7RERE5DIMkgGrT67G6pOrebngKqBQKP6dt5tkn8kuT1AjIiIil+GmcsNH/T4y3qZ7F6nzxt830hCbaJ/zdpnsEhERkcvQqDR48f4X5Q7DqUToitfatc+RXU5jICIiIqJKqx9QlOwevpaKv2JTYJCEzBGZYrJLRERELkMSEq6mXcXVtKu8XHAV+OVUPN7adAoAcCM1F6OW7UfnBX/gl1PxMkf2Lya7RERE5DJyC3JRf1F91F9Un5cLvke/nIrHlO+OIjkr36T8dnoepnx31G4SXia7RERE5FI8NZ7w1HjKHYZDM0gCc/93BpYmLBSXzf3fGbuY0sAT1IiIiMhleLl5IXuWfZ5I5UgOXklFfHrp6xQLAPHpeTh4JRUdIwOqLzALOLJLRERERBWSmGndBTmsrWdLTHaJiIiIqEKCamirtJ4tMdklIiIil6Ev1OOpTU/hqU1PQV+olzsch9W+fk2E+GqhKOVxBYAQXy3a169ZnWFZxGSXiIiIXEahVIjlx5Zj+bHlKJQK5Q7HYamUCsQMagoAZglv8f2YQU2hUpaWDlcfh0p279y5g7feegsdOnRAs2bNMHz4cBw+fNis3vfff49OnTqhQYMGeOSRR3D27FkZoiUiIiJ7o1FpMK/HPMzrMQ8alUbucBxa/+gQLHm8NYJ9TacqBPtqseTx1ugfHSJTZKYcajWGF198Ec2bN8fnn38OtVqNzz//HN26dcPff/+Nhg0bAgDWr1+PMWPG4JNPPkHHjh2xcOFCdOvWDadPn4ZOp5N5C4iIiEhObio3zO46W+4wnEb/6BD0aRqMg1dSkZiZh6AaRVMX7GFEt5hCCCH/AmhWkiQJSuW/g9EGgwFarRZffPEFJk6cCABo3bo1WrdujeXLlwMACgsLERISgmnTpuGtt96y6nUyMjLg6+uL9PR0+Pj4VP2GoGhbEhMTERQUZLJN5NrYL6gk9gmyhP2CSnLFPmFtvuZQe6PkwVu7di1UKhU6duwIoGijjx07hj59+hjrqNVq9OrVC7t27arWWImIiMj+CCGQlJ2EpOwkONB4H90Dh5rGAAB//vknHn/8cWRkZEClUmHTpk1o0qQJAODWrVsAgODgYJPn1KpVC8ePHy+1Tb1eD73+3zMyMzIyABT9lSRJtrlutiRJEELYrH1yTOwXVBL7BFnCflF52fnZCPogCACQ8VoGvNy8ZI6oarhin7B2W2VNdqdPn47vv/++zDp79uxBvXr1jPc7duyI/fv3Izk5GUuXLsXIkSOxZ88eNG3a1LjRarXpZmk0GhgMhlJfY/78+Zg7d65Z+ZUrV+Dt7V2BLbKeJEnIyMhARkaGy/zcQOVjv6CS2CfIEvaLysspyAH+uc7B5cuXneaywa7YJ7KysgCg3BF6Wefs3rlzB9nZZV+yLyQkBCqVqtTHo6Oj0b17d3z66adITExErVq1sGHDBgwePNhY54knnsDFixexd+9ei22UHNm9desWmjZtWsGtISIiIqLqduPGDdSpU6fUx2Ud2fX394e/v/89teHp6YmcnBwAQFBQEMLDw7F3716TZHfPnj0YNGhQqW24u7vD3d3deN/b2xs3btxAjRo1oFDY5mzCjIwMhIWF4caNGzY7CY4cD/sFlcQ+QZawX1BJrtgnhBDIzMxE7dq1y6znMHN2c3JyMHv2bLz22msIDg5GYWEhli5disOHD2POnDnGes899xzee+89jBkzBs2bN8enn36K69ev4+mnn7b6tZRKZZl/IVQlHx8fl+mUZD32CyqJfYIsYb+gklytT/j6+pZbx2GSXQ8PDzRu3BgPPPAA0tLSkJOTg4YNG2LNmjUYMGCAsd7LL7+M+Ph43H///VCpVPDx8cGaNWuMJ7ERERERketwqHV2i6Wnp8PT0xMaTelXPsnPz0d6ejoCAwNtNhXhXlTHWr7keNgvqCT2CbKE/YJKYp8oncOM7N7NmiFrNzc3u75imru7O2JiYkzmChOxX1BJ7BNkCfsFlcQ+UTqHHNklIiIiIrKGayzERkREREQuickuERERETktJrtERERE5LQc8gQ1RyeEwNmzZ5Gfn4/o6GizyxuTa7lz5w5Onz5tVt6uXTueaOBi8vPzceTIEeh0OjRo0MBinczMTJw/fx6BgYEml1In53X9+nVcv34drVu3hqen6aVtz507h+TkZJMyX19fNG/evDpDpGqWmpqKa9euITw8HDVr1rRYJy8vD2fOnIG3tzcaNWpUzRHaF56gVs0uXbqEwYMHIykpCR4eHigoKMAPP/yATp06yR0ayWTz5s14+OGH8cADD5iUr1u3DsHBwTJFRdUpPT0dCxYswMqVK5GWloaRI0di+fLlZvWWL1+OF154AWFhYbh58ya6du2KH374AV5eXjJETba2e/duLFiwAPv370dKSgpOnjyJ6OhokzpDhw7F3r17ERkZaSxr1aoVPvnkk+oOl6rBsWPH8Oqrr+LYsWMICwvDhQsXMGTIEHz55ZfQarXGeps3b8b48ePh7++PlJQUNGrUCJs2bUKtWrVkjF4+nMZQzUaPHo169eohPj4e165dw6OPPoqhQ4caL3lMrsnNzQ179uwx+cdE13XEx8fD29sbR44cQdu2bS3WOXnyJJ555hksX74c586dw5UrV3D27FnMnDmzmqOl6nLy5ElMnjwZv/32W5n1Bg8ebPLZwUTXeV28eBGvv/46kpOTcezYMZw9exY7duzAW2+9ZayTkJCAUaNGYcaMGbh06RLi4uIghMBTTz0lY+TyYrJbjU6ePIlDhw5h1qxZUKlUAIDZs2cjMTERW7dulTk6ktvZs2dx8uRJ5OXlyR0KVbOoqCjMmjWrzFGXFStWICIiAqNGjQIA6HQ6TJ48GStXroTBYKiuUKkaPfvssxg4cCCUyrK/qnNycnD48GFcu3YN/LHWuQ0fPhy9evUy3q9bty4eeugh7Nmzx1j2/fffAwBeeuklAEVXoH355ZexZcsWJCYmVm/AdoLJbjU6duwYAKBNmzbGspCQENSpU8f4GLkmvV6PgQMH4tFHH4W/vz/mzJkjd0hkZ44dO2by2QEA7du3R3p6Oi5fvixTVGQP1q5di0mTJqF169Zo1KgRdu3aJXdIVE2EEDhy5IjJHP9jx46hWbNmJtMa2rdvD0mScPz4cTnClB2T3WqUmpoKT09Pkw4IAAEBAUhNTZUpKpJbaGgoDh48iNjYWFy8eBEbN27Eu+++i2XLlskdGtmR1NRUBAQEmJQV3+fnh+saPnw4EhIScPz4ccTHx6N79+545JFHkJCQIHdoVA0WLFiAs2fP4pVXXjGW8bPCHJPdaqTRaKDX681+ZsrNzYWbm5tMUZHcWrVqhXbt2hnv9+3bF48++ijWrFkjY1RkbzQajdkUl9zcXADg54cLGz58OPz8/AAU9YOPP/4Y6enp5c7zJcf39ddf46233sK3335rsvoGPyvMMdmtRuHh4TAYDCZ/cUuShNu3b6Nu3boyRkb2platWrh165bcYZAdCQ8PN+sTxff5+UHFvLy84O3tzc8PJ7dy5Uo888wzWLlyJYYOHWryGD8rzDHZrUZdunSBu7s7Nm3aZCzbuXMn0tLS0KdPHxkjIzllZ2eb3DcYDNixY4fZEkPk2vr06YNdu3YhPT3dWLZx40a0atXK7CdLcg0FBQXIz883KTtw4ADS09P5+eHEvv32Wzz11FP45ptvMHLkSLPH+/Tpg/Pnz+PChQvGso0bN0Kn06FFixbVGard4NUMqpGvry9mzZqFV155BQqFAt7e3nj99dcxevRo3HfffXKHRzJ56qmnULt2bXTp0gWFhYVYunQprl+/jlWrVskdGlWj4rOp09PTkZCQgD179sDDw8N4Utr48ePxf//3f3j44Ycxffp0HD16FKtWrcLmzZvlDJts6NatW7hy5QouXboEoOjEo7S0NERGRiIkJAR37txB79698dRTT6Fx48a4ePEi5s2bh169emHAgAEyR0+2sH79ekyYMAFTpkxBWFiY8XNDq9Ualy3s27cvevbsiaFDh+Ktt97CzZs38d577+GTTz5x2YtY8aISMlixYgV+/PFH5Ofno2/fvpg2bZrLzqOhoqtmLV++HNu3b4fBYEB0dDSef/55l1382xUZDAZ069bNrLxOnTomc7eTk5OxYMECHDt2DAEBAZg8eTJ69OhRnaFSNVqzZg0+/fRTs/Lp06fj0UcfBQBcvXoVn376KU6dOoXAwED07t0b48aNK3e5MnJMH3zwATZs2GBWHhISgh9++MF4Pzs7GwsXLsTu3bvh7e2NsWPHGvuMK2KyS0REREROi3/6EREREZHTYrJLRERERE6LyS4REREROS0mu0RERETktJjsEhEREZHTYrJLRERERE6LyS4REREROS0mu0REDig7Oxtr1qxBVlaW3KEQEdk1JrtERA4oKSkJo0aNwu3bty0+LoTAmjVrcO3aNbPHDhw4gN9//93WIRIR2QUmu0RETshgMGDUqFHYvXu32WNLlixBTEyMDFEREVU/tdwBEBFR1YiLi8OuXbvQpUsX1KpVy+rnHThwAFeuXDEr79ixI8LDw6syRCKiasdkl4jICVy4cAF9+/bFwIEDMWLECBgMBqufe/ToUezcudN4/86dO/jtt9+wevVqJrtE5PCY7BIRObijR4/iwQcfxOTJkzF37lyTx/bv3w+12vSjvuQo7pQpUzBlyhQARdMf+vXrhzZt2mDIkCE2jZuIqDow2SUicmB//vknZsyYgXfeeQfTpk0ze/zIkSNITk42Kbt+/TpCQ0Mttjd9+nScOnUKhw8fhoeHh01iJiKqTkx2iYgc2PPPP49evXpZTHQB4LnnnsPjjz9uUvbEE0/g0qVLZnVXrFiBzz//HDt27ECdOnVsEi8RUXXjagxERA5s5cqV2LdvH2bNmnVP7ezfvx+TJ0/G4sWL8cADD1RRdERE8uPILhGRA2vZsiV+//139OrVCwqFAv/5z38q3EZcXBweffRRPPPMM5g0aZINoiQikg+TXSIiB9eyZUts374dvXv3hkKhwLx58yr0/MmTJ6OwsBDt2rXDmjVrjOVceoyInAGTXSIiB+Tl5YURI0agRo0aAIBWrVph27ZteP/99/HXX3+hQ4cOGDFiBOrVq2f23A4dOpiUt2jRAp6enti8ebNJvTp16jDZJSKHpxBCCLmDICIiIiKyBZ6gRkREREROi8kuERERETktJrtERERE5LSY7BIRERGR02KyS0REREROi8kuERERETktJrtERERE5LSY7BIRERGR02KyS0REREROi8kuERERETktJrtERERE5LSY7BIRERGR0/r/oBp9if6Q0PEAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -367,4 +331,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/bridge/notebooks/ratiotap_py.py b/bridge/notebooks/ratiotap_py.py index e57c442..477f3ff 100644 --- a/bridge/notebooks/ratiotap_py.py +++ b/bridge/notebooks/ratiotap_py.py @@ -1,6 +1,8 @@ """ctypes bridge to the shipping RatioTap C++ through the C ABI (tools/capi/ratio_capi.h). Family convention: the notebooks measure the real -library, never a Python re-implementation. Builds build_capi/ on first import. +library, never a Python re-implementation. (Re)builds build_capi/ on import: +the build is incremental, and loading a library left over from an older +checkout would silently measure old code. from ratiotap_py import RatioConverter conv = RatioConverter(direction="down", profile="economy") @@ -28,18 +30,24 @@ def _lib_path(): return BUILD / "libratio_capi.so" +def _run(cmd): + # Quiet on success: the build log would otherwise land in the executed + # notebook's outputs, where it varies with the machine and toolchain. + r = subprocess.run(cmd, capture_output=True, text=True) + if r.returncode != 0: + print(r.stdout) + print(r.stderr, file=sys.stderr) + raise RuntimeError("command failed: " + " ".join(cmd)) + + def _build(): - subprocess.run( - ["cmake", "-S", str(ROOT / "tools" / "capi"), "-B", str(BUILD), "-DCMAKE_BUILD_TYPE=Release"], - check=True, - ) - subprocess.run(["cmake", "--build", str(BUILD), "-j"], check=True) + _run(["cmake", "-S", str(ROOT / "tools" / "capi"), "-B", str(BUILD), "-DCMAKE_BUILD_TYPE=Release"]) + _run(["cmake", "--build", str(BUILD), "-j"]) def _load(): + _build() path = _lib_path() - if not path.exists(): - _build() lib = ctypes.CDLL(str(path)) lib.ratio_create.restype = ctypes.c_void_p lib.ratio_create.argtypes = [ctypes.c_int, ctypes.c_int, ctypes.c_uint] diff --git a/bridge/notebooks/requirements.txt b/bridge/notebooks/requirements.txt deleted file mode 100644 index bc07e75..0000000 --- a/bridge/notebooks/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -numpy -scipy -matplotlib -jupyter diff --git a/bridge/requirements.in b/bridge/requirements.in new file mode 100644 index 0000000..fad936d --- /dev/null +++ b/bridge/requirements.in @@ -0,0 +1,11 @@ +# Notebook environment for the executed notebooks (see requirements.lock). +# Edit this file, then regenerate the lock with: +# pip-compile --generate-hashes --allow-unsafe -o requirements.lock requirements.in +numpy +scipy +matplotlib +jupyter +nbconvert +ipykernel +samplerate==0.2.4 +soxr==1.1.0 diff --git a/bridge/requirements.lock b/bridge/requirements.lock new file mode 100644 index 0000000..dcd00cc --- /dev/null +++ b/bridge/requirements.lock @@ -0,0 +1,1884 @@ +# +# This file is autogenerated by pip-compile with Python 3.11 +# by the following command: +# +# pip-compile --allow-unsafe --generate-hashes --no-index --output-file=requirements.lock requirements.in +# +anyio==4.15.1 \ + --hash=sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101 \ + --hash=sha256:9f28306018cbd6d329e64a36d58256edff76dd996fe423bc957326e578b82a94 + # via + # httpx + # jupyter-server +argon2-cffi==25.1.0 \ + --hash=sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1 \ + --hash=sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741 + # via jupyter-server +argon2-cffi-bindings==26.1.0 \ + --hash=sha256:061a6919145bbf282ebf1f9c59d3135d4833c25313c8595c0d68cf7712ddfce2 \ + --hash=sha256:0cc40f7b4050bb93eb67de95d2d759322fc7ce4930b9d645581ecf4913ec651e \ + --hash=sha256:151dfaad9de753f4af2a7854e707e4784f2acc434340ade64239c5b104b2d605 \ + --hash=sha256:19423e5d7ac1cc354baab59eaabf18db2ec04ef6593b5abe5a34f323c4a8f87a \ + --hash=sha256:19b562b1de4b9052ef1214a2821c44b6e6f22945daa102c32ae4eff929d8b6d8 \ + --hash=sha256:1a0a29ed86960e44eaace7e081bdfab4f08b012fd96ec8edba71e2ad020939e4 \ + --hash=sha256:1af817e84578ef8b7295ad17de0f9896e4c8520dbf2233c7aa5aa3d487256fc4 \ + --hash=sha256:1b0bcac4d490a237e18cf91f57352920c29f77f2fa39efd0813fb81298bf17ba \ + --hash=sha256:1d98e33bd8bd67d7206c124e200bf2229c4cfa8c9c19f7b44a897f0fc71837eb \ + --hash=sha256:21ca0396fe5ec995dd54431c32698189666f9224810acfa752e50d2bd94d9df2 \ + --hash=sha256:224865cbbcb7a2bd1356741dff12b0134df726b6d44bb7b500df8e303cbd9e81 \ + --hash=sha256:242bb0cda2ae3650764fc194593d9ea45fc9e72729acd89778c7cfe184cec2a5 \ + --hash=sha256:27f1821903e2ceadcb88ec2b45ef190897b7682449c772f4d9b53e42c520cf29 \ + --hash=sha256:28524438cd3e723f25412f63d4fd516ff5bae9ae5aa56acbe2a1404398a0cf31 \ + --hash=sha256:2b741888c93147444fdfc851abd81cc207f37f7f7da42062a00deb3888e57da8 \ + --hash=sha256:2c36ff87b5dfaa477d0bd51e9d7f6abdae7c8955d2983c97419085d842154b3e \ + --hash=sha256:34b7d9c24a4165a2c61cc8ae11d44d48c9ce2830fb536cb7914e11fdd9962728 \ + --hash=sha256:49d525938467d52c923a890153c99087c9d5a937d1f6b585dbdba34ec82e397a \ + --hash=sha256:4f84cdd868978d7b7350a566c254042d44216d9e37f241f3a6d3b1dfebeede35 \ + --hash=sha256:62ff20cd130c956c7c9144d5fe35228f98b51c579b2439e988b27ef93e16c02a \ + --hash=sha256:63505c71542a44b68b1e38060450fb006404170da375feb31af153e7f9c6205d \ + --hash=sha256:6376d4b3aca039375ca8bf92f770da0ec424a1ce3a37077a8d3c557411aa56ca \ + --hash=sha256:6a4e68eed961a8de6928d1c17ff3dc2a547e0e923c17f8f1cd79fb7bc9502f98 \ + --hash=sha256:6ab674f668d5962a3a4136ae0812519b0f1586874263723a32181d60d64137e1 \ + --hash=sha256:7014ab7e6f5d8511af92544667a0346ea6dfc314ea9a7cad1dba9fdb5c9a6e33 \ + --hash=sha256:76ae29acace5d33355344612844d588e19deaaba4639d8bb01601e4b1418ef36 \ + --hash=sha256:78de2d65e0b9ea7ce9d1b1c3e87297b2d7305a02c266ee2a2d6910daddd7ee69 \ + --hash=sha256:9bacedc04b0402837586a17f0919e3dfdd95291f441f1f56bd80ec274c2840a1 \ + --hash=sha256:a86c069c91a747a2c4e5c51473590aeb48172fff9b2130d23729a42d98665ecb \ + --hash=sha256:ac82fc756a446b6ccd7139ce70efa9d8bbe541e7ad579a12dcb52764b7175c5f \ + --hash=sha256:af11ac37a7c53dc16cb7950a6190851b0870fe218b6c60c0bb7ac355234e3083 \ + --hash=sha256:b70225b5fd1e0d2ef4f7fd30d24658454535f0924dff0caca5dc08efbbbadfbb \ + --hash=sha256:c49e853a3bef9dd10329f31f702e7fa9b5c58229ff9c2ff6d069efaf09177c08 \ + --hash=sha256:ccaf0a46cbb380f1fd102a874e32aa629fd3cb0c0e94f4943fa1f6d5edc5dac6 \ + --hash=sha256:d157ddfab1e8b21f2f1dedda9c09645d98b5ed0b667b0626be600a345d426440 \ + --hash=sha256:d88e5f7e60f28ae0b0cc6b2f16c43e87cd642a196a86f85e0d8bb6fe016fc16d \ + --hash=sha256:db0fcd827ca61622a01b220aadfbece01939acf53888f2cb98cd93e9b1e2c97e \ + --hash=sha256:df612391feca41c44d20118f3b88d1b86419465cd1f5496859f715ca60ec2210 \ + --hash=sha256:f0c3103fcff20183e593459cfea6e012281c0e76ae3ed8b5565ad1b92eac3990 \ + --hash=sha256:f9c4420a7a864fe1b86ce35befc95b8e39fb852493b81cf798671ddc265de638 \ + --hash=sha256:ffff613aaa9ce6236766e2fc6dc560bb5abde7a2e2416e3db1f9ae395a2b4dd4 + # via argon2-cffi +arrow==1.4.0 \ + --hash=sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205 \ + --hash=sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7 + # via isoduration +asttokens==3.0.2 \ + --hash=sha256:3ecdbd8f2cc195f53ccada3a613538bb5f9ef6f6869129f13e03c30a677b8fe2 \ + --hash=sha256:9da13157f5b28becde0bd374fc677dcd3c290614264eff096f167c469cd9f933 + # via stack-data +async-lru==2.3.0 \ + --hash=sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6 \ + --hash=sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315 + # via jupyterlab +attrs==26.1.0 \ + --hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \ + --hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32 + # via + # jsonschema + # referencing +babel==2.18.0 \ + --hash=sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d \ + --hash=sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35 + # via jupyterlab-server +beautifulsoup4==4.15.0 \ + --hash=sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7 \ + --hash=sha256:d6f88de62e1d4e38ecb1077eb9724cd0eff29d2a08ca16a401e9b9e93f117cf9 + # via nbconvert +bleach[css]==6.4.0 \ + --hash=sha256:4202482733d85cedd04e59fcb2f89f4e4c7c385a78d3c3c23c30446843a37452 \ + --hash=sha256:4b6b6a54fff2e69a3dde9d21cc6301220bee3c3cb792187d11403fd795031081 + # via nbconvert +certifi==2026.7.22 \ + --hash=sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775 \ + --hash=sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55 + # via + # httpcore + # httpx + # requests +cffi==2.1.1 \ + --hash=sha256:046bfc24911b37851ee1b51aab8bffe713d89c68c6a057b09484ce9fd5f69b4e \ + --hash=sha256:06c72bb76605a4b0cd0aad6930b69d4baf7dd5d806cfc409b824191099700e66 \ + --hash=sha256:0beceaabe56af686895136a2de78db54ecd8e4046b236b8fd6d6cb61389e9bf2 \ + --hash=sha256:154852545011f779917b11c78db2358d095da62a9a172b78ad0a583ee5adc0d0 \ + --hash=sha256:194cffa889098ced9976c3fc6340305e43f6303657d298da55366907c05c22d6 \ + --hash=sha256:19ee6127ee34de7d83ce3d371ebc5ed91addbdcc39f9ab15ce4eb35a4e534971 \ + --hash=sha256:1a18a57b58cfb21fc28d72e876acf10eaed67a1ed96226f92af4df681d571c4c \ + --hash=sha256:1aa5645c30469b09530c4ebca77ebf8f17618293c58f8549cb1a543a50236e7d \ + --hash=sha256:1dea0e4d7d4f11f619fe8c1d76caf49e24405b4b5743c0e3be16a500ecd930c9 \ + --hash=sha256:208f941bb9d18e768138677f0a6d2ce01f590df56043dda1df1535ac57c88517 \ + --hash=sha256:210019b6c7cf07f081b4c54635c8cf744377001350e29cc0f81c4377b4797735 \ + --hash=sha256:246fa40ce8645a614ff682e0b70f37134e460eaf93a775e0cbe3cca585a67a80 \ + --hash=sha256:25792eac27877609e7bb06d42ff88278a6624fff2ba9bbb523c09616b117e80f \ + --hash=sha256:27350daa11d4f10c540e6e89dada4c54feb7256ad03e9a4dc075ebad7ba360d1 \ + --hash=sha256:28907ab9bfb6aa13184cfc17c6b8e1023c5ab6fd7076d8c20a35e59fe04f8f29 \ + --hash=sha256:2ae64be792b8966f2c69538199728b290e34726562896df1e5dc8ffd8d8188e8 \ + --hash=sha256:31348097ff5bbe827ccc41795d4dd099d9f0625e7def00ee653c137a490c2a6c \ + --hash=sha256:3143d81e29e1e20a9ce10901ec369012947876596f75a222235965f2b7ae832e \ + --hash=sha256:3222ba5d678f80a030e6afbcc33dc1ae5cb45facabb61cee2c7016b8432fde48 \ + --hash=sha256:3311ed60d36f83378794e1009ac6258bafbf81f7888b4caa7b35a521e3f95813 \ + --hash=sha256:334644fbac4eff73d985a17a91226df55d0f394160c4cfb880e084c8f7161cac \ + --hash=sha256:34e261f78cb6ceaaa36f42f2613f4380d94d9c759a9c73c769ee6e0247364632 \ + --hash=sha256:363e05fa78e15116c3c32c210ee36884fd6b9afa6d440e47112c3bd511d64cb6 \ + --hash=sha256:398aff33cee2767e3e781d2554c54bd0dff386bb437581e0d8011fde1a942ec1 \ + --hash=sha256:3d22a20b1fb1632cc72c22f95f7b0d2961c3e1c235f245ba4c606c4771035659 \ + --hash=sha256:42a494cee34437f05546455144f2b5d9ac09b1face62bcfce597d2e521066688 \ + --hash=sha256:42e2f76b9455f5a9a844f770bf3e200ed3da0e15f5df3db9c31fe80b04b3d004 \ + --hash=sha256:42f6930c31dc7f50732c9ae793c2786c7b6b044195967bbdde40bb9be81c4cc0 \ + --hash=sha256:456a61fa52d579ebf9df2e9552ead5129855dbaff6c1e5a9b1bc408809bdc062 \ + --hash=sha256:471cee653ae88de62096552e6d24ccb4a5adb8c8c9f10b5054d0122c15bf2779 \ + --hash=sha256:49cbc70e6542d4ccccb936558d1064a8012541e78f821f955cff24e357776c94 \ + --hash=sha256:4a7c934f7360e8cd64fe9efadcbd10c7c6364f531e432b9a4bf5ccbc9e0e8b50 \ + --hash=sha256:4be96343e422f2dfcd12ab5c9f5aebe03f82f737c6bffeca6830b3875cb44aab \ + --hash=sha256:4f42141fc14250de6dde5ee7ea4432be017252d91f19c5ad043c084cea629cac \ + --hash=sha256:507a24c282e0f42f8ed737cf048572cbf580468da5555764a8331735e9c736b6 \ + --hash=sha256:51b31d1c98274844cfd7838ce00bfc27c7423a4dc00fc0772fc3331c2cc90676 \ + --hash=sha256:58acb8ab8e295e6c5ea12f888cbb13cf21511ef2a3303a23f4325c29d17fe5c1 \ + --hash=sha256:5a59cc1c4442bc3d5c703bf720b51138d0bfc173618807c9ee2490a7541dd3d9 \ + --hash=sha256:5bb4e7ea95dcd6a014a6fef62e62467d67d8e582326443f3d68e71d6320a9fcf \ + --hash=sha256:5c58fe613dc5e5336357eff555824a314d8e43282600435c8d1cb6a7a2fedd13 \ + --hash=sha256:5e7cecbaadb83884793e05828cee59b210b24583b9c7425d0ba6a754fe22eb4e \ + --hash=sha256:616f097f2fe415bc92a247f02e11f634e1f9e9a83d327e3c915c15089c87869e \ + --hash=sha256:63bbfd5ded17c4840ac07cd8f1c21ba9d9708141f840b324f422f41b207e3973 \ + --hash=sha256:64faea20f4e2613363a1a9b9c7dd73058f3ecd00133a511e72ad7c511658f527 \ + --hash=sha256:661c298b4821edebead0c91edd2b00374d67ad7c5a1f7a91d4442633b79d6a72 \ + --hash=sha256:68e62fe11f30d5ca8289242866f0a5291402d8529ca2178ab8afc5c9694ae890 \ + --hash=sha256:6a8dddef476fab96d066d578fc88526767b836ab5ab21754e1d5bf3879c31c7c \ + --hash=sha256:6e192623c49c94421616a5778fba35cf0d5a8d000650c1967ef4448ee5cdd990 \ + --hash=sha256:7225e4514edb64eb6740324353e0da0711954fd8d7da4576755b1c6e09b697cd \ + --hash=sha256:75f80557d1389eddbd0de2681f6a390a0c5338c31ddaa821381c203fc3fd50d9 \ + --hash=sha256:770de9db11e84213beec501cfcaa013b019820ca881e03344dea5844f7876d94 \ + --hash=sha256:7750c6449dff7864bb9bb27ddfb0267756189201a3afc911d82b3caacd70dfc3 \ + --hash=sha256:7bde5e4cc5c10140859842b9d383af292b22639a4dffb725314baf45968cef80 \ + --hash=sha256:7ce713ace7c0e4520535b42b77eaa742c16dab813978064913e5a3cf82973b41 \ + --hash=sha256:7da0c5eff80f0197f3b3d1232ec5a682a9325f4ae9016a78f5f5ca35f9ced1f5 \ + --hash=sha256:7dbb61fe3a7699468030f71bbe5f8a0e326a151daa91beb11a6fc1f980c55e1c \ + --hash=sha256:811bd1e21d32de12efca32393a0ab3f5133b54fce9bd44b8bd77ab07da14bf6a \ + --hash=sha256:8ef53b2de9bcb9197d31854256575d59dbac0cba72ac627bb291ef5eceb74be4 \ + --hash=sha256:937c0052c05a31ca1daf18de3158eed4dbfcb9cc107adbea227728d647be701e \ + --hash=sha256:9d2055050ea716bd38b7f7f1579c275386646b4894c155a3e2f3cd62ed41b7c6 \ + --hash=sha256:9f8d177621de5cb38ee3e731eda45d421db093ec0739f46a5594babda7987a98 \ + --hash=sha256:a2d7755bef5a12ed488f4ef1f1b69ee9191d7396083b755a5d2295f6edb4768b \ + --hash=sha256:a48d62ab9d6f4f98c983223a547af44be6ca3691074c31cecced6facd3ba2dc1 \ + --hash=sha256:a4f00aa42f75d6e4595e8866e748cc1705adc0cddfeb2ca86d0d03993d63ba03 \ + --hash=sha256:a6e721d4b0e45d5b65e87534470e67b18dcd092c83f68fba09f152b9cbc061af \ + --hash=sha256:a730a083190634c65cca36ba5f489531576ebd79bcd5c8e172130f6453127231 \ + --hash=sha256:a931079504ecc49efed7744c476a5c343a92fabf66dec2db95edb1b2fdc770e2 \ + --hash=sha256:aa9511c62d14da7aacc9b4bf51f3f697a621e83b2d6919008243c3aad168eea3 \ + --hash=sha256:ab36d55f9ed2d067327667c2fea18dda018eb628dd6347aa01dda6cf1f5d3836 \ + --hash=sha256:ad2c86c495b899d862ea0f4b42891b8713a3bd45dd4105c7fd51c2a72f39f3a5 \ + --hash=sha256:aeae0e330c9f6acd681f647d46cefd30c29f93e3392882e792e82080c9691399 \ + --hash=sha256:b0431303acaea1089ad4b3e9ce4e6518193def1118d4073ca848635ee4ea2e96 \ + --hash=sha256:b5bdfd1c873d4e093aabc0ca84c4ca6dbc4f752afb5c86f146d9742580c9da2e \ + --hash=sha256:baed1e86cc735622097354b9d1281406caf42ff42a886d29faa8e8d1630333be \ + --hash=sha256:c1453022f490d2459a11819d83ad1d586e9ff65a12ac3e705ffebd46d3685dcf \ + --hash=sha256:c26608d2222fb1e94487e4a387d85f13eb55d5ed725cb25a0c589ac4ee60e7bc \ + --hash=sha256:c7659f22557c5a0bc4855cd635f55edec690cc008a40768527762cb9fb263455 \ + --hash=sha256:c8c69575568085ba0b1b10c0249d779a214aea6f6522e949a0fc9fb0fcb449d0 \ + --hash=sha256:c8d2c9fd1f2d16f780d15127abb050d13d1a76c03a4bd87d7e4980e45e511e12 \ + --hash=sha256:ca82be1a1d406ecfe1d25dc16cb33488e5a16bf4438c9fb590484ea29d92478b \ + --hash=sha256:cc572dace3f60ef98d7b12ff411d20f5362feb31a0439eab0085bbfd349982d7 \ + --hash=sha256:d18e5ac0f2f03f4f518d3e23db0f0cad7faa1da8620e9c09461d443bbf6e6692 \ + --hash=sha256:d28630f5854ab07ab1fd4aba756de52326c82e6be15d414b12793f1975048b54 \ + --hash=sha256:d9c275eaacd24aa73f94ffd6de08fc3f932424d8b6c376f4bed7cde376fe7bc3 \ + 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--hash=sha256:9ac4444d8d4fd4c4bd08bf451ed3167aa9e7ec6cdb41b648794f1d1103652e36 \ + --hash=sha256:9b5db6052055d34d41230fb78d7c439c23dc536a9896f6cb039e8dd92cfc1263 \ + --hash=sha256:9d9a0dc7cbe9bec24c3f767c9122c41fe5a1bc43f47cd099d00d393e09769de4 \ + --hash=sha256:9dbdd9205662134957cf0c324f639bdc5031c0ca056e2369e238db75187c0f11 \ + --hash=sha256:9eea3ab2597a5e65fe65296e2d6a84570845a6b55532d90333d740d48bbc850a \ + --hash=sha256:a2028475ba855475b8b4d3cfeb4994269c967aea8b9892dfba907f4263a863a3 \ + --hash=sha256:a3a370082ce34d0612f421e15fe011c53bb1feff21a26d06ad4fb244dab5a375 \ + --hash=sha256:a545775cfe815855ea32d7c27731d79da358ef2055b4a25830231b1622dd18aa \ + --hash=sha256:a5cbd90ecf0fc62e64726917ad083b73001f0563657a87ec3c0b504e277dc90d \ + --hash=sha256:a6d095662e73e74f0a49988e0593373e243e3a52e27bfeea0a859e88acf4a0f5 \ + --hash=sha256:a6dac12ff6b846103483683f60c5f8fee205121adc58ffd87e90a90a3af69e99 \ + --hash=sha256:a951ad59cad9145664a730d3036b40b844e74d2d3683da40111463cd3a83845d \ + --hash=sha256:aa1099b956fb795e686d073568f6dc002a0bb89765ea6d5b055dd7d9bf1b116c \ + --hash=sha256:aa2bb0b37202dca27175591f761108b5d34096ade1191ffe4808bdf6b1571488 \ + --hash=sha256:aae2ee51122d3ae968a3837d97dc24a0aeebb0dea23694422cd172bd30017cd6 \ + --hash=sha256:ab743e9bc90c1f73552ec33e10e3331315acd2c397b36065b591b0181de533cc \ + --hash=sha256:ac00177c4831ffa650f8609e4bdddd5fe09c03b1c0c47acece7e6ea20421598b \ + --hash=sha256:ac13b004224fb341e1e25a1ed5e19d32f57cdb2a403e01f003b46f051a550f6f \ + --hash=sha256:acaf604462bf330b0d07e7a07c1d6e4adac79e5fb13e9c5140590542cafacc00 \ + --hash=sha256:ae31a1a1db2ee6cc2942fccaf695c934bc7f3db9f2133a3fef1f367cf1a4ab10 \ + --hash=sha256:ae4a097991662cd4fff0ddc74e0fe7874f82e00042fa0ea00855645ed0c79598 \ + --hash=sha256:aea996a6aba25260827c9ea511d1addfde2da9eb686ac961838509086188b7e6 \ + --hash=sha256:b39b69b347e5e47a3b5b8cfc005c68c1ba347474e3960236c4944a8ecd174962 \ + --hash=sha256:b54e7e13267d49ffbfe68e25b3cbd774dab38fa37238f71265e91b36146eb21c \ + --hash=sha256:b9af956078716df40d985fb0dfeb2c2120c5ca92ba4ff4b388acfd01cdc14d08 \ + --hash=sha256:ba2f37ee79e6338845261a3c5b1784e5d1acdff2c0785b284f1b633033d136ab \ + --hash=sha256:ba501e667c17d8411f98e67a022d9604ef179aff0e459b7e292c796837c13573 \ + --hash=sha256:baf3775a2635e5a11fbd5e4e64ee69c7e86875d224a5c72aca4c141064589a90 \ + --hash=sha256:bb57753e36e4855b8ca375069482250a6246372331a3e4f3407eaebb007443f5 \ + --hash=sha256:bd6c173f04743d483881bffa1478d5a4624475b8cd1d2194956a75548e191c18 \ + --hash=sha256:be47f99644b208bff7766314013f9acf57b056b04191d570d68ad14022cf5b1d \ + --hash=sha256:c010f5581d9c612804cc59fcf7b524b707fbcb72828551237ab545bb5c7034af \ + --hash=sha256:c1dcc36dcb96abc02236e182d17e0f71430152a6c2c7447421da2d2dc144edea \ + --hash=sha256:c428c6c31eb5f4277d7f8eccaf767fbd548ddd5ce3c8b4f4cbbfab3d96b5904c \ + --hash=sha256:c658c50ac0c98cd755a2dd50b7977d3bca7df401dcc47fbdfa87db53ef7d4e8b \ + --hash=sha256:c71fb0d56c920c269cd3e2e3fe7c610e3f1fdb21a6ce60efa6430ff63676cea6 \ + --hash=sha256:c7b742bf31c88566b4bb6335a7f393bb322e580b6bb98df7bd0c25e6e3519ce8 \ + --hash=sha256:cc0329df4caaceb950d2f580b5ac716a377f7059624a0bafaeaf8a218c6ed774 \ + --hash=sha256:cc5d36d96478aa9c60654bd932525bf32964c62a7281eafdf16d85003a8d6004 \ + --hash=sha256:ce854f5f478050ade5a238731c4ca985a7d3b3cb53ff600a9b5c3b689b5f0a7a \ + --hash=sha256:ced3fdd71aaa83ce593746c2edb42b7a59cb4c19c8b5c407781c72e493aae55a \ + --hash=sha256:cee5dd7c6fb5dd52a0fe2a740f9bc6e3593f5f8b1788bde49de02086f30182b2 \ + --hash=sha256:cfa1c0cc3a8f9f53f1243a5a99ac36fd003880199383b37672e86ddda9cb07e2 \ + --hash=sha256:d1ee1e296209fdce05b81b663250eefa02213a2da7b41bf26f7829b8ba3545aa \ + --hash=sha256:d59b75732e9b6f27388e10c14b0259cc5f2e48c78627d185e6a177b58ad3cffe \ + --hash=sha256:d63600d620ad0064c3a748b950ac5ea38a80190e5498532efefa4b7b3f1da1f3 \ + --hash=sha256:dd732602a7009217f658d5863d12d79d373a4de0eebc111094bcdd3bb8e0a6cc \ + --hash=sha256:e06efa066f7dbadbc84ebc126a97c452a6451dfcf589d89d788484949e1cf795 \ + --hash=sha256:e199fb99720074809a7720f1c0b4d919eea8b87e88713e0f8f602f7bef543d9d \ + --hash=sha256:e4b018dc5a0eee4676e38fe84a47a427816c590b93b55d9025274ec4d6ffc2dc \ + --hash=sha256:e6621fb2a4988d6e53eedc455e5903e2679f3967b8acb3d639f1b63c14a2e893 \ + --hash=sha256:e71c909f353863b2b89c83de2ebed71ea6d0df8a6ef65a128193c5e650766bef \ + --hash=sha256:e90251c0c7bdd54a100a0dce3c07b7e637278c93af29dbf78ebb89a58c4bac7d \ + --hash=sha256:e9fbdce1e47394b09bc9f26ab117dfc8d6491977a11d86f592bb42c779db2fda \ + --hash=sha256:eb12fb2ba69ffa05f8695f61c69e591dc4b4a12ac3757ac8af8adb259bf56d17 \ + --hash=sha256:eda059b6bc8bc0812d626fd91a7ce01bf583df0a61296eff390fd94141a34e30 \ + --hash=sha256:f03ac127268b43ef4fe9e6ab6794a6794b49485a0cc0c1db79876d2f33f75bc7 \ + --hash=sha256:f298e218441525d3794428b4c8b8fb8662c6d3ea79925d4807ee6b9a96a3bca5 \ + --hash=sha256:f5542f9b941279d82d41eb0aa9f98eba36fe4df5c7086c651df7944935b37182 \ + --hash=sha256:f6f7deae3feb4edfa2efaf7c574fe88cbf055038a6abdb40188e4fff66d5699f \ + --hash=sha256:f9b1e28d0e8dbfa858abdba91d6b547beaf2df1a59bec6da6faae7b96a4991a9 \ + --hash=sha256:f9f8405c2c758532c74fed975dbee57be1f31a6e865c031870c79a6ed3212ada \ + --hash=sha256:fa48b1b63d639f9483e0633e092f5851e2348c352f1f9bb6c8182f87884ef876 \ + --hash=sha256:fb78f6e7fcd8ad785d28cd577168bc1aaee827b25bb8755638f694794ea98f0a \ + --hash=sha256:fbc597639158fd7c14d55e808718848319540f51b0e6746e3eefa59723a4a348 \ + --hash=sha256:fce8cbd4997efeb450bd298b54f755dcdff18d496f7a5ddbb4867c6d7c88fdc3 \ + --hash=sha256:fd0350afdc3aabd5576f60ea109228bd5538139713c7b094c5cd27c73a98bc6f \ + --hash=sha256:fd0a274c0e5f9a21565cd9d3dd749b61f96b7aa1e20a93aa1ba4029518f2e5c0 \ + --hash=sha256:fdb8a068947befafba9952162645dc2fecaeb400e64584829ed5e9b2fbe21a7f + # via requests +comm==0.2.3 \ + --hash=sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971 \ + --hash=sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417 + # via + # ipykernel + # ipywidgets +contourpy==1.3.3 \ + --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ + --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ + --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ + --hash=sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a \ + --hash=sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8 \ + --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \ + --hash=sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470 \ + --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \ + --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \ + --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \ + --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \ + --hash=sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381 \ + --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \ + --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \ + --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \ + --hash=sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f \ + --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \ + --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \ + --hash=sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42 \ + --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \ + --hash=sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77 \ + --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ + --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ + --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ + --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \ + --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \ + --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \ + --hash=sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db \ + --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ + --hash=sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620 \ + --hash=sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989 \ + --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ + --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \ + --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \ + --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \ + --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \ + --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \ + --hash=sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1 \ + --hash=sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e \ + --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ + --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \ + --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \ + --hash=sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7 \ + --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ + --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \ + --hash=sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1 \ + --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \ + --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \ + --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \ + --hash=sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20 \ + --hash=sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36 \ + --hash=sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb \ + --hash=sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d \ + --hash=sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8 \ + --hash=sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0 \ + --hash=sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b \ + --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \ + --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \ + --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \ + --hash=sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497 \ + --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \ + --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \ + --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \ + --hash=sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13 \ + --hash=sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae \ + --hash=sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae \ + --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \ + --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \ + --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \ + --hash=sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff \ + --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ + --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a + # via matplotlib +cycler==0.12.1 \ + --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ + --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c + # via matplotlib +debugpy==1.8.22 \ + --hash=sha256:0c1104233340196e5cbf5514e7dfdbb98968e730cfd7fdd6f3d72a082be838a9 \ + --hash=sha256:12bc7f368182b517cf26c76a2393fff65354e365fa1552b6241e66edff17997b \ + --hash=sha256:192b73e8d53bbd60225220c0943627bf249ac93d0ea3090e23c45f3e0ceb6a35 \ + --hash=sha256:1bd0c6df3c68c0a3f71db8baa3780a953abb65537ec4b3bc6b935ad5b3b3d45c \ + --hash=sha256:1e76339d5510bc17e9181dba9577508afcb21aad5728f1a55ef74d7d97d255f3 \ + --hash=sha256:225d063f81708c2546999e7edfac0198b2d5c2f144797dc64858f867948633e0 \ + --hash=sha256:371a4ba4a5975eb958393903f3254cf983da7b1c7c178b3f987ee427f42515e3 \ + --hash=sha256:3b7c328cb47b4e2f2b40801936daf8150f88dcb4ffef5080ff119270f0d23626 \ + --hash=sha256:4ad076f4f66cb8acb79e4384d48b1ea60a0b1957aa0b1112d4887ea3a4df60e0 \ + --hash=sha256:56b877b37ed73f0bf53ba7afc394816ff1eb5d701bae24a24ed9e1ea6f7ce34f \ + --hash=sha256:66e4ac3d6e7026e83e7d93d7ee2f51dd4a4e8dff673578d424e60796893e5b2c \ + --hash=sha256:745e1800ec2961e5660c1a317c0a20e28c0fef4de36c04f17a21c33f2b37a92a \ + --hash=sha256:7bf29e0d8ce80b100d37fb333e1b193b790d962739f3067796c2b03ffdc9afee \ + --hash=sha256:8a697acec45dbc70d17fb5d9f4f61989fc294d273c69de487fd10cb35fdd75eb \ + --hash=sha256:92fc425308a08f601f3c69afb4719767f9b898276f44b97e6942a713c4c32740 \ + --hash=sha256:a9e9d3550e15ca479c59333e90845029190531f0cacfedab3b815a57bd913947 \ + --hash=sha256:a9eca6ab09a61534e923064400081e016f7e1d8430cc6e8b7a9cecd2f59e2b07 \ + --hash=sha256:b17a4896520f1c6da09ce76ec8215df0b85b0b6f3617526a4c65c2315340875a \ + --hash=sha256:b7dbde1fb822d100802d505b2aed6d0813b1a0a2015d495cb8798b2215d5d1e5 \ + --hash=sha256:ba810a66b437e3c43ca0ce0892404f010338286263ca8e5108442d76a9485337 \ + --hash=sha256:c1ffb9953708b648ecf6acd2e5ad2c002bf68785ed618197dd4463a2a7226f39 \ + --hash=sha256:c21dd7e1ec22556bb41dc3bf6b86c603e440ec889c4acb27d7206354b2106c54 \ + --hash=sha256:cba7b99573ee41f9510c6deeff1cdd0c333748b9dd63d5016413dd57ba9a4593 \ + --hash=sha256:d593a330297332ec435f3965c448b6d500e03f33675cf2063178bc76377a788e \ + --hash=sha256:e489c7268e1c7b41e13b438d9c533d2a7af73fb59bf8cd30fead8286c1c39c4e \ + --hash=sha256:e6744ac1850c73c2ba7b29a126cf9ab74efd1831190720e8fee17ef5389c8f5d \ + --hash=sha256:f49b1d6cecf326b63c06d7f517e4a6642777f758c58cb799e7bc5a6dd74721c2 \ + --hash=sha256:fa47099176d1d612bef69e9f74658a5687a9f05bd80fb5710f99959bace85735 \ + --hash=sha256:feea785c7bbeb8cfd5b01a63f9061c899dc468c8be81671141a29305774fa294 \ + --hash=sha256:ffeeaf4dfe1534375902040df32a4c48f721fd35a4d4d337ac72a44d4176075b + # via ipykernel +defusedxml==0.7.1 \ + --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \ + --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 + # via nbconvert +executing==2.2.1 \ + --hash=sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4 \ + --hash=sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017 + # via stack-data +fastjsonschema==2.22.2 \ + --hash=sha256:0fb3915616adac85ccfdd737d26be1089845d2019819505b42d39888458f74d4 \ + --hash=sha256:72064e12356a7d6ef02165be2946b9abadbdf238536e07eb587e3dbaa33099cf + # via nbformat +fonttools==4.66.0 \ + --hash=sha256:02117d05dddb51e39b5c5a6eed0702ed8e0e1c341cb138101b2446eb479858f8 \ + --hash=sha256:03922992966a1830b94a750961d1ccfe1a7375d792ca61077a5afb719811788a \ + --hash=sha256:056641fcedeaad24b92e343e35e9fd1337200c102450678894b17928482d010d \ + --hash=sha256:0640e69b00e089d6b82e0a54e3a03af84b775f1f0b2cbecc1b0d7fe3980a194a \ + --hash=sha256:0cf33d1041364ebb7a9958db757e213c6d2a238eb41755fbf935db028c2db20f \ + --hash=sha256:107aae7c38f05561fef5103baa98a44400b5f4c69d87839c9a5f8125405e7d9a \ + --hash=sha256:135ce8738a6341fbefeedc061e20c2889350e84405abea7e61b1c9d87e1a1f2f \ + --hash=sha256:15315302c620e6ce8582934e07932dcf442fc3f59ab208d30564200b9a2f7ea4 \ + --hash=sha256:1d120ea0f5260b04e9b5ac0d9239efde4c23d990347d5d5889cdd37221726fec \ + --hash=sha256:221defad3b1949c1fdde2558d1fe780d42f926b683a71a46a4495a0e6a6614bd \ + --hash=sha256:2b278e2abe596872b3c9fe611f956ded85588537974e48e2adbe2d6ee159e099 \ + --hash=sha256:35684b562df7154d7a0ebfa512db9199c7fb0a93b5abfb59dac9d022dbee7aaa \ + --hash=sha256:379b94fe10651d26caf398eb646b3ecde886a9c504d3cce5e46164d58a78e32d \ + --hash=sha256:3889fdbe63e85dbf2a0d9d7c9d90746827ce60af2cdd24f0bb4738380a9540eb \ + --hash=sha256:398bfc5ce9055e0b91a6189f8cf462d4230b38f522b2ceaecb4f34cbf4fb5480 \ + --hash=sha256:3ea59aaec135c96c4cff20d79fdf1d10623993712a77990585a42cfd6a42bc5c \ + --hash=sha256:4513753259f06316bdc2039c9bf3129e74923c1cfad0722b4728a9ce19beeeb2 \ + --hash=sha256:4bdaf80590fcccb1b9d8e3b8ca72225defb14d03b79788f59781ce1bb66049b9 \ + --hash=sha256:5dda4087a7f8f0b985995489b3c0115bd117f18c0fc236013a0e78e837bb9ac2 \ + --hash=sha256:6371e7ed43cc9e23550fe4a1b72af3205275f28dcecac8c09b80a4582e326802 \ + --hash=sha256:6627f48130cdbe04c7e1189bff4192886ecda757f575626ee8468f6580eaecf3 \ + --hash=sha256:67ea5af3ca60e1e5c9b2841b1f6dba5ef16aab581bf463abb68515447154ab61 \ + --hash=sha256:6b11180c4166a4fc353bb02b147322b7c454927bba4f5040ccdef109a73f9051 \ + --hash=sha256:700c448f190cc7bbd9f059bd96131f4770e90938c9692550b142153bea73b14c \ + --hash=sha256:8009e736e1491c569392b2e9782c30dcb6ac879def102c8c2e06add8d2adc10f \ + --hash=sha256:850d50b347f31667d3a277a6e66a4219236db9d98afadb2993b8addf9bd4f593 \ + --hash=sha256:85e2960a29e6882567b981ba408b5a5ca2d8865b8f67240e1b420348997a13be \ + --hash=sha256:8739d77810aeb6d2ecd55cc2e4864d98a00c317fb6131a6d22ca58b3f50cf22a \ + --hash=sha256:886f3029ea10362c592d2e1a5326a44645b85a2a0be6fdb7c9edbdb60cedb0ab \ + --hash=sha256:8af85dfc2ad564f95b3e650024578bb2268bc4d827d418aab9650bbde4b6926a \ + --hash=sha256:8ea6265535b25f8254868f5a212a1c9dbd88c2c4050f3df18d9174949df0d6d9 \ + --hash=sha256:9b0198962bbba64a88bf15dc5d95e4bab8c5208dede22a10fb06280406ed9874 \ + --hash=sha256:a5a0624aaefb0a29e4ed7500a98732a1b0ec2d3040fe86cc8837f82e3fe4f446 \ + --hash=sha256:a9a45a23b020e667499fb2dbce0f1373b1b1b8cf88e47f3031e69ef3ea21f3ee \ + --hash=sha256:ada87643b20a8fa5763e6dd4fa0f5e2901cbdbe0833deb6d5c5a9c3186782b94 \ + --hash=sha256:aea0cc5609a5f2a2500f91bd6e1989fdd22da0f225be00dc1c4cec775838a7d0 \ + --hash=sha256:b02686b2e47115b2378ab6156abd984c776c6faa4f56898fbcc1c30ee387937d \ + --hash=sha256:b432b6b54a0f3be118c6243312c826aa086aa9bcb150160be4f0bc8a21ea3aaa \ + --hash=sha256:b46500e4d6f5708a9127ea12902ba54cb0ec594fc6bec649afc53154a1125b2b \ + --hash=sha256:ba65eb3e2c46ad0922d84ef78e287c2f48b7a8cae4420728bf3b8e583282d6d7 \ + --hash=sha256:bc7b7ddc1a1f46c363354304e9a8dd93722e4a6a24f785015650898dacf40df9 \ + --hash=sha256:c144e68572af1dfcb05fe065827d0920d4118b265504928e1d2262e3701a25a1 \ + --hash=sha256:c3a66c749b69e9abd92e519ccbdea7210522b0fffdba3f6492a8fab461d4708a \ + --hash=sha256:c519433e8632284dbe64ee4c81b609a17e205bc1afff285553723964880d1c89 \ + --hash=sha256:ce818581070527883e3678b92b1ac7ed3569e48c80f51b89b264f97d84be2608 \ + --hash=sha256:cf0033f343cb1592fb24ae3eaf8b58156f208884ac16991094293ee94c585654 \ + --hash=sha256:cffa168522a0e057d3a5628c8ebd43ddf66a34613f42e116bffb85b0b32ce5a0 \ + --hash=sha256:d2d10f89c4bf7cd42b84695da617f9130fa594647ee74673bf8c8d001fa04ef9 \ + --hash=sha256:dbee639a23c4e067aedfbcf84d2e466a71a1a1156ad04d6d09023791ed167495 \ + --hash=sha256:e4677025ee40b3b1420387ca85f55545b8095f7596c3d5d59552dbee19c91f07 \ + --hash=sha256:ee4f85d1341af630d787514eef1a0e1ad883f9d6c11a4c1465faeb9cd0bb5ced \ + --hash=sha256:ee7b9f6c835ea1a9beb5a35c8eb0c62b6a3290f8105d3d0c29a0a18c90f9b8ba \ + --hash=sha256:ef0610dfe7bb5bf574d9bdad6f597403ebc9807d124ac6f148d7604b2609be98 \ + --hash=sha256:f014bbf05f30731bf8b9f5c7d2b4c0e4f28f375926f95b187f85e0bbceb16029 \ + --hash=sha256:f2032ac005e14c56e774fd19f2bc1c22c796c0c3b39d91741c0e3f9575944d38 \ + --hash=sha256:fa995d96ced196388d8ed7a67ee001b181459d230e24bb68b193ae859f5609da \ + --hash=sha256:fd3dcb69d65c05a2fffa5d9b743b77e0cdc13832665ba435065493dad2b57f83 \ + --hash=sha256:fd9297a5f670f3e59ae697289ddad5902d3c4a86188e83e7823bde06334a560c + # via matplotlib +fqdn==1.5.1 \ + --hash=sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f \ + --hash=sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014 + # via jsonschema +h11==0.16.0 \ + --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ + --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 + # via httpcore +httpcore==1.0.9 \ + --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ + --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 + # via httpx +httpx==0.28.1 \ + --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ + --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad + # via jupyterlab +idna==3.20 \ + --hash=sha256:a7db850025b95ded1eae8a46181a1a6c56c92c96f0e2b005d9ff8dc0210cab44 \ + --hash=sha256:ab7ae7122974553370f0bdb919e1a960b2cd1bc1ef0276416d896db81c14582c + # via + # anyio + # httpx + # jsonschema + # requests +ipykernel==7.3.0 \ + --hash=sha256:897eb64da762549ef610698fca5e9675195ec6ac8ec7f19d81ce1ca20c876057 \ + --hash=sha256:9acaaaf97d16355166e4085afe9d225bfbdf2b7ef520f9df3be8f2b248275e09 + # via + # -r requirements.in + # jupyter + # jupyter-console + # jupyterlab +ipython==9.17.1 \ + --hash=sha256:6d1645743cfd1a07eb695d85aa2b5fa66721f8cbae9431d4049f7084bbf06509 \ + --hash=sha256:8919be8c27f20a6f4423145028063f6637b42a03ce57665bb12015ee1f073529 + # via + # ipykernel + # ipywidgets + # jupyter-console +ipython-pygments-lexers==1.1.1 \ + --hash=sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81 \ + --hash=sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c + # via ipython +ipywidgets==8.1.9 \ + --hash=sha256:bcccba38a6ec3253f7a39c943cea5b9ad01999ce071396171adbc51c6a6a8613 \ + --hash=sha256:f2b8cbcaae10252b809fbe4d7470db75c09b769a32cbf816d20e5ca6d3c5a79d + # via jupyter +isoduration==20.11.0 \ + --hash=sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9 \ + --hash=sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042 + # via jsonschema +jedi==0.20.0 \ + --hash=sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67 \ + --hash=sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011 + # via ipython +jinja2==3.1.6 \ + --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ + --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 + # via + # jupyter-server + # jupyterlab + # jupyterlab-server + # nbconvert +json5==0.15.0 \ + --hash=sha256:56636a30c0e8a4665fe2179c0212f32eae3796dea89ea6f649b9436ecdb39618 \ + --hash=sha256:7424d1f1eb1d56da6e3d70643f53619862b4ce81440bdb8ecfd6f875e5ba4a71 + # via jupyterlab-server +jsonpointer==3.1.1 \ + --hash=sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900 \ + --hash=sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca + # via jsonschema +jsonschema[format-nongpl]==4.26.0 \ + --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \ + --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce + # via + # jupyter-events + # jupyterlab-server + # nbformat +jsonschema-specifications==2025.9.1 \ + --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \ + --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d + # via jsonschema +jupyter==1.1.1 \ + --hash=sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83 \ + --hash=sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a + # via -r requirements.in +jupyter-builder==1.2.3 \ + --hash=sha256:01aba6794eb9b19e0e29ae21137ca60ba4135c70347d4b9f664a586822b8c809 \ + --hash=sha256:c5ea5a7190c2a7b082494abade98eece1b2b5bd5dbd7d610606cbcd10a1d08b3 + # via + # jupyterlab + # notebook +jupyter-client==8.10.0 \ + --hash=sha256:5f73f24f22fa25192cfff6b23c051932a2473a797b05734aff495b392103e14e \ + --hash=sha256:9f7116294dca55f1785be880057d44544db9b1567718d92cb33c58886afb9497 + # via + # ipykernel + # jupyter-console + # jupyter-server + # nbclient +jupyter-console==6.6.3 \ + --hash=sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485 \ + --hash=sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539 + # via jupyter +jupyter-core==5.9.1 \ + --hash=sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508 \ + --hash=sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407 + # via + # ipykernel + # jupyter-builder + # jupyter-client + # jupyter-console + # jupyter-server + # jupyterlab + # nbclient + # nbconvert + # nbformat +jupyter-events==0.12.1 \ + --hash=sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf \ + --hash=sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3 + # via jupyter-server +jupyter-lsp==2.3.1 \ + --hash=sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81 \ + --hash=sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6 + # via jupyterlab +jupyter-server==2.21.1 \ + --hash=sha256:2a6467606af7dbae2e7e31640030025969e15db6a649eae334af90415dc71dca \ + --hash=sha256:a8960aa29263f6041283e97d4756b099fb49b767baab6371891ecb1bd40a63df + # via + # jupyter-lsp + # jupyterlab + # jupyterlab-server + # notebook + # notebook-shim +jupyter-server-terminals==0.5.4 \ + --hash=sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14 \ + --hash=sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5 + # via jupyter-server +jupyterlab==4.6.4 \ + --hash=sha256:15b13f991d3985129c797eb84d9949eeb8b6615e14b444868e642411f2c418b2 \ + --hash=sha256:404f49b081819378524886c9db66dba57a5565981eff885830df1baba3a17df5 + # via + # jupyter + # notebook +jupyterlab-pygments==0.3.0 \ + --hash=sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d \ + --hash=sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780 + # via nbconvert +jupyterlab-server==2.28.1 \ + --hash=sha256:0c3c2418d51021ce280916e63dfe4cba8386b4e2787be5c25433c98f560ddb31 \ + --hash=sha256:4bd36c7c11d872e15cefa4951e12380a47e6901687165332dd50a007cb367ad9 + # via + # jupyterlab + # notebook +jupyterlab-widgets==3.0.17 \ + --hash=sha256:40ac1e9955acf116c4d995d9bfa082d86ad9ec6d91c4f134827cf5e0a5eb75e0 \ + --hash=sha256:6e61fe21ca8a66039180a5cc52a433e07279d2fee79c8be963e00d55193f17a8 + # via ipywidgets +kiwisolver==1.5.1 \ + --hash=sha256:007a5553dfc4f4e8d184f588a0200e2cd4b63a59cc8796df3c39909e679dc7a0 \ + --hash=sha256:0324cd2567259b7a095f6cf18a52b0ffc6f3de9e69528ff1bc0e7a37bd43ff1a \ + --hash=sha256:0627b9bceb9c3cdcf12b8a18655eedfed2692b038df27423383c120d0b7dc2d6 \ + --hash=sha256:06a6917674de9e0fe3f66f5430787f59a9f2ddb64af9b714eaec547e29ef5c19 \ + --hash=sha256:072bdb15a3c19a5b5dbc8f8fb1f4e1884bf4f3507eeb4cc6334401274d37a5c0 \ + --hash=sha256:0a4faea5c6db201c6a21391d2ac926ea97acf7dacdbc3c417189e1adb1a00837 \ + --hash=sha256:0ba9527afc80ae3d7814ed98b6572d02bf85eaf48065678342c5f0c6dab7a8c7 \ + --hash=sha256:0d8924877ce22e17326a99a418c3c82037da078df3c6a260b13eca677444e6e7 \ + --hash=sha256:0ebdef3eae5336568147c39a55be6a2036ffde53faa9ca2d978989ae7c2da12c \ + --hash=sha256:1209042a623ddfda5497e4066c7b77651dde8e1d3a9dd97599dc7e97f3b9b78c \ + --hash=sha256:16895f553ee6620a827d2da56b871f835fb70b9216cca5d188e885caf6e3bd23 \ + --hash=sha256:17851e5dad4484be0cbccbde3b15331deae036de9aebd45eed964487802b172f \ + --hash=sha256:1798e83840c3f627246104c4d8a9639c60fa068adf9ce92b61791781fa8a68c1 \ + --hash=sha256:18170a77ddfecf40ec60d0928268dc95880c881864e015a8f34094ed18b9b9ad \ + --hash=sha256:186884a58486651e3c217b6acea0a53eaa9498fdd472057c46f2f0fb5c25aad5 \ + --hash=sha256:18a0cfb124546a4c2e6087c5f3029c7f44b37c85b142e0ced71f73a7599ac208 \ + --hash=sha256:1983f0974a750a6f6556f368ba11105d1d8369c735b944747c9f12ae5aea7aae \ + --hash=sha256:1a7587dc335f2c0f5bd577fd0540bd16c66006bdb60f759a1059f025e6c4f071 \ + --hash=sha256:1acc7e5b7ef05e9da8bb70cd6c7c4513090213d2e1ad9720f599f0bf6c52aec5 \ + --hash=sha256:1d852545c4d0e35a72728d072cbaa59e2fa7dd84bdf01e068d670dd0ceb58eb6 \ + --hash=sha256:1ed0f5e49d0ceff8b72190824d9e59c062fbbc02c231b853112c78474b3f5ec2 \ + --hash=sha256:1fff05e239575b1481b6ed1a782f6fad616efbf1f0b1f44e6e85c4dfe426e483 \ + --hash=sha256:21e46b23a2da695c364124817bc01d970effd5483147f8d66a6a7167e3f6b851 \ + --hash=sha256:22d5e5aaad6be121f2515765e3b1c444352cb8eb4c86510801db8f2e50757316 \ + --hash=sha256:2551cf9917af48ee7c4b29cc82320489508cf96fd26a51f6fc124de661cd44c7 \ + --hash=sha256:255605693a483db7bd5c79f60437f7bf658f7f520d61aa42722e32257c941951 \ + --hash=sha256:26e8268480be5061d509e29669d59103c067a26377a56491630ece11762e3858 \ + --hash=sha256:27add358abe374ebaa3b8763ef380bc99051b5a4b18d94878366a9e4f59efef0 \ + --hash=sha256:2ae70bc59790d2af72a3f76f24b272403e135070340281108b447cb77ea70819 \ + --hash=sha256:2e10ae1bba1899188b33557c10d73affcc12033edd18adddb57d209039976a4c \ + --hash=sha256:3221f78211074f561c44ca42eac0619828171bec15a2c4cf6f7747d07df76e8e \ + --hash=sha256:34633ecf50d16187ab8e5528b7a2530f2feb4e23f300db4672538b51cfc5cd38 \ + --hash=sha256:34ec467940442c9943016fb2d4c81d1ba84351eeca2f1a78f8bc87f1ba0d414c \ + --hash=sha256:37f801b5d7cc0e5a548921308e059fd2b057bb42972b591cfa3049f95423c4ed \ + --hash=sha256:38f6e0deb4d0a4615efe0c4efc5990b06ae450ab50a0b321c0b078b6d238c083 \ + --hash=sha256:3c24cd69455e1b00ddf770c13b6e2c33e07d6dc3f2d34add0bf9277c5c6bbd46 \ + --hash=sha256:3cc210010fd2f438a3ed430b45f1b501fd13a8618bf984dc2c5ce5b69b78752e \ + --hash=sha256:3fa5855898f6d3d01b72ccd48a2d65cbdee301251603fefe34e2025bddba219c \ + --hash=sha256:416ba7ff9f233b7036689bb5a3783537e838ad483f63558d2a800f75afe738b1 \ + --hash=sha256:431dc224a1a92a5c8f582d96e505196a3b5997a7271076678da2dfde67b77e9a \ + --hash=sha256:43844c1a7ad6d723d5b5b4c4fc7f5bd399c40e288120d16257c7c9e8765c6e85 \ + --hash=sha256:44b8faef94f1857e77fa0238f3390ff1ac51d2ea20a487e2e452a59fd2b5f5ca \ + --hash=sha256:470d420f98d368d6f010633a20659b544c5fdfa5329e6b70219f2ef08fd4a7ef \ + --hash=sha256:482676e5bd48d70ac99d9fc78863469845421e01184fa83f1f9366dc49f7e974 \ + --hash=sha256:4d4ca09bf13cff792b1884f64b98ee6c2467930d632233be25c56b442d99f10e \ + --hash=sha256:5025e36fb4fb275cef0a4e30dbb11cb4ae61d1c83deb90189cb5d7e4cafd6b55 \ + --hash=sha256:509735237ae0d849e8a843551d423d2500d2e0a9ac1611a145658b29c0fb9f85 \ + --hash=sha256:534f02c1abb31ed6dbd3515545285c330b2f12d00fdb1fdb71658b9ca5a13a6a \ + --hash=sha256:5978c3340f16a35c30f8ab2fa7bcf559973c55f1a5ef6970e1f621acf3c4db13 \ + --hash=sha256:5b973887ff782cfd6b67c9904ad8ca542e0bc5e4961503408b423b5a688b4d38 \ + --hash=sha256:5c490db2168a508088f59140dd392556a54b8bd1048fc6383c8baff13c359673 \ + --hash=sha256:5d142e352eb13facc7dd047489aebdff6ba78576c239f1ea04931979caaf0567 \ + --hash=sha256:5daa1f19e097050b9c4d9a78fcc9263cb96c9dfae08037ddc1b7c4ad1889f2a2 \ + --hash=sha256:61e9a64c7635095a6bfe483e2ff055d437c59bd45f3617a228b37277f0185d62 \ + --hash=sha256:63fb7294b768f444eb4b068965f2662f28c2fd4161e23bd60fcf3ff27b74c046 \ + --hash=sha256:685929988b208a911f1285e2f8ed54210b0d681a3dc0f03e00d599d291986e7e \ + --hash=sha256:6a797a1cefc8b9c93170db580337e1fe3d011ad18b1299943231279406342048 \ + --hash=sha256:6b92f60017dda7d877fdc546438b5e28f31c523264f49cf5a48c1d0ce1a0dfbc \ + --hash=sha256:70ed9a45c7484d2b30cdacf60d220f494a1763b9fec1ad03285c6553fa0889f2 \ + --hash=sha256:719a35fa1156db3640555f95ebb94f60a444e64d1c69626b0edef5df78eba225 \ + --hash=sha256:74ad5c3dad54a4641b4c28cd15ded70899d04459c6c7aeacafea716be97cce6d \ + --hash=sha256:74ea337e0ec3f6f342a36a4f1b5cd94dd9affddcd28ba9aae2905af932ee8c6b \ + --hash=sha256:75d9b1cf8258462dbdc1eeda718c96ea7f079324c09067f6daabfcf37712b7fe \ + --hash=sha256:77a4c8187a5948d7f8795adb765a3c7b553d07d86d88e43038fc32fc1fb9a3f3 \ + --hash=sha256:7824b5e8bdbf0bccb4ccd37bbb115849a1dc45437fb4de8351385ed07c437ee0 \ + --hash=sha256:7d38b0c279c3032e8c9cc013b405c6df8e1668dbf15465779aa7f15f61201812 \ + --hash=sha256:7e9c01d3dd7ceba4d1d436cc021d40d592466e40b9bc7f5d83dc4e98a5c9cd8c \ + --hash=sha256:7fd82debf43c6acd0a94359d232f6bb516ee13f269a7993736a9ac9f988bb5d9 \ + --hash=sha256:824c3d763a05ea9e9003610145186b0e9848c7584a5575c79bac5a8e7cd80bad \ + --hash=sha256:828f75af2b0080c8a972e75f649ab46af008e92c6104a57a759157200b835b75 \ + --hash=sha256:83f78128fa28705fa85d01c59771c72fe81c11bd0e6155edbb9f818983a7d761 \ + --hash=sha256:876bbfd276473d3daffe30e8c975df4ed9429967b41a6cb362dbb5155b6f13ad \ + --hash=sha256:886fc26012f0e8b5f69d1cfe6d711f6b11f194621539bf8e6bb1c25c5dc82724 \ + --hash=sha256:8a34616dc2521cc8dc1d7d081734da63539f021ac0450ce950908340c6e7aa2f \ + --hash=sha256:8a708a47ade1fe19e8371d5da076bac0dd4b0a5a7985ad6c637f7f7e361b6baa \ + --hash=sha256:8af9b142ad719ae3a911ebf616bc4b78b32bbab84d6a40d3ad2f129670509957 \ + --hash=sha256:8bf4df63592c2a66b4f8edc5df2544998c288aa02f96ce0acd880cd1de8c8127 \ + --hash=sha256:8de6f2a4ce7e7bd27d23dd94abf0ccafe0e0e5cc9c764b0577191f2c25f08f26 \ + --hash=sha256:8f8fddb8e323bd6eee4e54e69a39243beab22689070f4c66b472c4cc88bb89d8 \ + --hash=sha256:8fca690b00c4c48f6c2a547b0160ed511357093a4e4c9b47e0fadf3128066d89 \ + --hash=sha256:9506e892bcc3b409831d363c6f53e5985e1c8d1f6f6b0256d00358684ff85378 \ + --hash=sha256:958254518717542d02d0688d0d20cbf771da5e415e6f49543f92481c850a4540 \ + --hash=sha256:95a02752aa032eef4aed01cda6d9b687c669bd0396bf4519eef8bba22a286720 \ + --hash=sha256:96c30002424670b5e1e46495c2b8cbffef39cf77c1d79e76462029d50339785b \ + --hash=sha256:98b208a7cc42c803445ef551d6753cc42a5ea13e9cab1ee66cd8b9cb70195330 \ + --hash=sha256:9b3092d8992a1d69b7a59c3e39f35e1b9be327a17f68a7c35fc17329e337d6f2 \ + --hash=sha256:9e51c119992ea8820706871c30a4642ec76de20ae82f9b50b9a45517d8e9f810 \ + --hash=sha256:a5716a33bfabb2c6ce27b6cf03253467b3804f83e215f4d202685cf93c6c9874 \ + --hash=sha256:a5a00665d1a0e26763a7338d7e911d4598fbc1d50dd0d6b7919b7dc6c5d6569f \ + --hash=sha256:a5ca5aebae78a0bc13c1943af4af615d4966c5b650b05d5aa83b50e427196fee \ + --hash=sha256:a7b85b2cc6ea45e5f7e8c9a30bc9fabd47cda09106cbb4b967335c3e6c43b69d \ + --hash=sha256:a83ee7107df13abe42a54a6654670eef9bb39425cf2e27f65e0007465e1286ab \ + --hash=sha256:aa7d00b1700966d2917e54d278aba86897890ca9276dd8b76cf6446b6c181b92 \ + --hash=sha256:ab620eb663952455271ac37f9aaad86b73c969c02f11f53cea405b38e96a4300 \ + --hash=sha256:ad8b9671348d7c8716715652ae11f85ed0eb99e265a2df2ca490577d69860b2c \ + --hash=sha256:aefe930d113798330e9462f7874542977869c0613cba3262e2de3a8d5dee8f3a \ + --hash=sha256:b03af77d77e50edba2030fd5f7c352ff209314b09030a3cba7c14edf9a09a444 \ + --hash=sha256:b390aec180a7c054919c04898835e1c77bced23ea8383eb2c570213bf25d1a86 \ + --hash=sha256:b3d78f7bb2b9d9a30345be1474b9aaa8685430b54afb51ba3639b5c6c11e9ed6 \ + --hash=sha256:b5664603a253efd3a75716d793d1d3a6a82723b61dc6db767b2460bbbeec4c0f \ + --hash=sha256:b69602970994a2ed8bbfa78c2f0394a7435226c6040489702d9f0a0ad0c07052 \ + --hash=sha256:b6ae6a0328f0bc035741820fdeecdcd67bf4694eee03972e843663107122f450 \ + --hash=sha256:bad20d4c69c851c982a1e3606f4c293edfd5a87885786c50082412240c4b1ffd \ + --hash=sha256:bb7c99f0673c03017a3ee01e54a5c2617a05468b11eabe513b0080e063ed95b1 \ + --hash=sha256:bebb89489b279b2f5661bbbb2abcc87bcd4a46607bb4a5c966f04f1db6b8df9a \ + --hash=sha256:bfd1de989b3330420e29de39352f5c049905c9e3ee67233a50d550e3d652c148 \ + --hash=sha256:c2306e8bb53601979fcb3fa09cc65e031876d9ae01eff2fcbcd7a84ef94d5bc1 \ + --hash=sha256:c3a4e41e3096bf1f0f1b76e2ffd6d828d6547f574f702d59bdbef7acfa59db9c \ + --hash=sha256:c6834b92dd2428e2dd85ef3d85f723d3c12f20aaf43a2ddd4f944ca25d833408 \ + --hash=sha256:c90d3022d8a94778939cda8638c6c8da8fa757b8958dad7ec868ce29c87681b8 \ + --hash=sha256:ca307d6c259e5c98d3cb9ade55342b47a6839762caf2536f3d7b46ee660cc82e \ + --hash=sha256:ca7f6fe0f37ca978a1e5eb7a3a68e6413f417e78e838324947ffd420202b198b \ + --hash=sha256:cb6fae641357ed2f6e533c0d3c6504a4a5703621a50c89459e46051d56b61140 \ + --hash=sha256:cdaeeb6c350106df6bf9d873395973e5f066a9713200b72cd64f55d0a3eafab6 \ + --hash=sha256:cea20da04494e662b83c872683bf4ff2345206043d036315ed0e924b652e7294 \ + --hash=sha256:cea90547bfd93807e0013a004dc76552be44fad3bc1cc2b38610a9e889ed098f \ + --hash=sha256:d09037ca068d784ebc4aec290ef952ca27ac15dd9c0b5801a88c6e1096b83e6b \ + --hash=sha256:d27c2123977cb9269c30a49ba45f03a4323017ef693e19db4ec9dbe1299a3002 \ + --hash=sha256:d50de98e8d807dc31822fff96f50293163a62418eb65487a21b42713d72ed0b7 \ + --hash=sha256:d66a64dd5dec136040ec2ae94aa026a912ee60fdd45bc28d3db30037fd809e88 \ + --hash=sha256:d79308fa689fac89cbcfbd4dbfc80b5f95c54c5a7fd4d194be221f9d33d026e6 \ + --hash=sha256:da3275833be0edbaf4830fae08bae3dc7219f40ce0c37eaa6c25825957e06612 \ + --hash=sha256:dc1a26b8e53395a01c2c611e58602fa47461f136fba7cd5542e6db6d64be1839 \ + --hash=sha256:dc23390afe9f4ef9ac3bcc72a03a56eebbde03f4c571a32cb38f859cff9a6524 \ + --hash=sha256:e05c2f7925f1d88778e53cb44f14e0223204a3bdd09a41664750363acfb1f2ef \ + --hash=sha256:e12dfea7f5fc2a34a9080efbf79c4c44eb380ec5b9c6fea09407e08f0d1e941d \ + --hash=sha256:e4e4523d6f336708d732516e6cfca7796cf3d96c9474eb5aecf6165f2f1fefc3 \ + --hash=sha256:e4e49f7e1a4e7191bdf9dc67a974db714501b1fc52c24324103d06a86abd5c08 \ + --hash=sha256:e68e151428b5384f766cd25739bf77c7e4a3dc93b5ded7a12118d9fbfdf78ab6 \ + --hash=sha256:e8e4d953faaded9ec7ede36824e9814082d22d4c7b1eafbfa079ecba8cd0d076 \ + --hash=sha256:ee9df1f0d77b9c6e94f4ac0fec533fbddd5ea3a327807f18d7b069ae019ded80 \ + --hash=sha256:f0a887b6565bbfe80efde2b7f6e8890d7d9bbdb11bdb17028a3690c32fe0621f \ + --hash=sha256:f0f4a42db92d6ec7677ab9d12830a2a8ec145a9c6d15db2b593466bc875c78d7 \ + --hash=sha256:f1303ef2eec81262a4b708c3e858afe58d7c75ad91c1c05266eda7673369859a \ + --hash=sha256:f1d56ec54d257d05e0b50f5780d967540cd07beeaf9e5f645b26d50cce79f4d8 \ + --hash=sha256:f4167e87b397f273dc2356fcf1eaf50a6bac51e6105f45103ef7129c8efb0255 \ + --hash=sha256:f76fc85bd054c806960f917ec0f329e24e436f1712267d90588e4c39890caa63 \ + --hash=sha256:f942903fde7363d1d879057ec5de01310efda2597161784d752fa9953a01a71a \ + --hash=sha256:f9b1c4900736e489a812c529100de4b8fb617d4db075e931e213c57424b83d9b \ + --hash=sha256:fc271a6f0a2126958f4090e5507b9da5848927dae331f8f763bd4aa642b3d2cd \ + --hash=sha256:febcce10f2bcdbb80b4ea919238a6a4ac13dbc4c7cadbe8d5d75c3682f8b5404 + # via matplotlib +lark==1.3.1 \ + --hash=sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905 \ + --hash=sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12 + # via rfc3987-syntax +markupsafe==3.0.3 \ + --hash=sha256:0303439a41979d9e74d18ff5e2dd8c43ed6c6001fd40e5bf2e43f7bd9bbc523f \ + --hash=sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a \ + --hash=sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf \ + --hash=sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19 \ + --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ + --hash=sha256:0f4b68347f8c5eab4a13419215bdfd7f8c9b19f2b25520968adfad23eb0ce60c \ + --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \ + --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \ + --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \ + --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \ + --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \ + --hash=sha256:15d939a21d546304880945ca1ecb8a039db6b4dc49b2c5a400387cdae6a62e26 \ + --hash=sha256:177b5253b2834fe3678cb4a5f0059808258584c559193998be2601324fdeafb1 \ + --hash=sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce \ + --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \ + --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \ + --hash=sha256:1ba88449deb3de88bd40044603fafffb7bc2b055d626a330323a9ed736661695 \ + --hash=sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad \ + --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ + --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ + --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ + --hash=sha256:2a15a08b17dd94c53a1da0438822d70ebcd13f8c3a95abe3a9ef9f11a94830aa \ + --hash=sha256:2f981d352f04553a7171b8e44369f2af4055f888dfb147d55e42d29e29e74559 \ + --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ + --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ + --hash=sha256:3537e01efc9d4dccdf77221fb1cb3b8e1a38d5428920e0657ce299b20324d758 \ + --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ + --hash=sha256:38664109c14ffc9e7437e86b4dceb442b0096dfe3541d7864d9cbe1da4cf36c8 \ + --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ + --hash=sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c \ + --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ + --hash=sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a \ + --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \ + --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ + --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ + --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ + --hash=sha256:591ae9f2a647529ca990bc681daebdd52c8791ff06c2bfa05b65163e28102ef2 \ + --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ + --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ + --hash=sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50 \ + --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ + --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \ + --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ + --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ + --hash=sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115 \ + --hash=sha256:7c3fb7d25180895632e5d3148dbdc29ea38ccb7fd210aa27acbd1201a1902c6e \ + --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ + --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ + --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ + --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ + --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ + --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ + --hash=sha256:949b8d66bc381ee8b007cd945914c721d9aba8e27f71959d750a46f7c282b20b \ + --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ + --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ + --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ + --hash=sha256:a320721ab5a1aba0a233739394eb907f8c8da5c98c9181d1161e77a0c8e36f2d \ + --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ + --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ + --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ + --hash=sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f \ + --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ + --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ + --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ + --hash=sha256:c0c0b3ade1c0b13b936d7970b1d37a57acde9199dc2aecc4c336773e1d86049c \ + --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ + --hash=sha256:c4ffb7ebf07cfe8931028e3e4c85f0357459a3f9f9490886198848f4fa002ec8 \ + --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ + --hash=sha256:d2ee202e79d8ed691ceebae8e0486bd9a2cd4794cec4824e1c99b6f5009502f6 \ + --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ + --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ + --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ + --hash=sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01 \ + --hash=sha256:df2449253ef108a379b8b5d6b43f4b1a8e81a061d6537becd5582fba5f9196d7 \ + --hash=sha256:e1c1493fb6e50ab01d20a22826e57520f1284df32f2d8601fdd90b6304601419 \ + --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ + --hash=sha256:e2103a929dfa2fcaf9bb4e7c091983a49c9ac3b19c9061b6d5427dd7d14d81a1 \ + --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ + --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ + --hash=sha256:e8fc20152abba6b83724d7ff268c249fa196d8259ff481f3b1476383f8f24e42 \ + --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ + --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ + --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ + --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ + --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ + --hash=sha256:f42d0984e947b8adf7dd6dde396e720934d12c506ce84eea8476409563607591 \ + --hash=sha256:f71a396b3bf33ecaa1626c255855702aca4d3d9fea5e051b41ac59a9c1c41edc \ + --hash=sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a \ + --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 + # via + # jinja2 + # nbconvert +matplotlib==3.11.2 \ + --hash=sha256:01dc8eaaab5a9fce9ff615eca82345728f289e4715b186ee10c6d85272fc26bb \ + --hash=sha256:02329432ae5c6af87cf208ee575b701d698bdf0b1a3bb28cc6d53c36e967e575 \ + --hash=sha256:07d9b9fa60cd4c393692f50d0bb03123242ddf61c99bb0e95e75feb354e7c1a8 \ + --hash=sha256:116cdb0eb0eb5644fc98eb2975d4b4dd4ad35e5c8e6b851c22e6976f371f7ab5 \ + --hash=sha256:1944895967f87c84c9b4bad29a707b31f5b36df4a3a2339ea1f8ea3ce5105539 \ + --hash=sha256:1a3040b209f3968b4e84161df7b174f07a9fad33b0f2d7e48ea3bbd3075e2863 \ + --hash=sha256:1b9a7ad579856284135e401ecc918c5f8a017ee30539298862a109f51b971710 \ + --hash=sha256:254d4ddb2fa8df3b4c689c0c306063aee10521df82cfb438185e499c75fe37c1 \ + --hash=sha256:2a8285cea8ef4d92aa041d1c33788bcae82248503300f93f1ca2136b9049452f \ + --hash=sha256:30ec15d7eefee71de16b7c689b42ba49715a4644650b76a6c7c70d79daf24e91 \ + --hash=sha256:399fef672f7046ef7d6a57572b2a6f9845f3f5afcff04e7b2df7a363a9f42190 \ + --hash=sha256:3aa4b8516fd26659e4363abbf317c703d9116496c5db2e9d0609a2866dd39dd2 \ + --hash=sha256:3da3bc0cbf7245e7db72cc6d29d12c5abef72cb73059c73b945f14e3545f3eb2 \ + --hash=sha256:3e8576f7c47e02fd4f21f44171302d2d1d58d4471d46da3d71fe8899d19539d9 \ + --hash=sha256:57b9ea60a835937c2012861923cbb91f47db8565775d326d1c42fc926aa10351 \ + --hash=sha256:5e1e923a3fc3326b99ec0a6ff1ab1338ac6c6cc62ad9d8a9c944197c7f8c6221 \ + --hash=sha256:643ff850d8e0f5b8319337f87ed3cb59506afb3df3cc48de777d85871233be7b \ + --hash=sha256:75b6d88402770e181b5d06a67dda4129c31da7d05004d63a21c310ec78d1b83c \ + --hash=sha256:79a258f58253dfa025af80a9e9bb228d75fced007f0e93ae7423fefbde81a74d \ + --hash=sha256:7d43ff8cebb50840648cb6429b2228621dbd709010f3117b3740abb20abf21c0 \ + --hash=sha256:7e5a90f8a707ebb6004a713b1cff091a40cc5df4c7c1cb165e5a505ebc11c292 \ + --hash=sha256:7ef7a53b66780e5d942923724f08577fbc5be1f7322da0f0f3f9dcaa45dd803d \ + --hash=sha256:854df8d7dfe9fdffcbaa6f39e44a6c24b409cb4d7561fbc09213b157d833f6a6 \ + --hash=sha256:894a9cbbecbe30ae6787d464df2e8fc7a8d475cfc68f87c3029f7c11152899b3 \ + --hash=sha256:8c8255de28f986d935a64c9ca71c0ec2d2f41d355691f5ea684725dc91413f71 \ + --hash=sha256:930efb28f59fda124e39265d177bab302625297bb147d2910de725a2fc2aef54 \ + --hash=sha256:a24d5fd36e4f0e742c3851dcd20810e56a95633a342e4bf6cb591c678e8fe61f \ + --hash=sha256:a6939df7567114b6bac7f4c5e06c84a67f197c1b2f2e4b234d4eecb3bfec9482 \ + --hash=sha256:a8756cc73d9af9a7fe0deb54ea2e75ef73b01d9e575877e72acad5458e660943 \ + --hash=sha256:af2661f6ac6bbd1d081996f54fdd9385715c625ace8cec0869055c0cfbf38981 \ + --hash=sha256:bbf1062991d826ed27e2144f3afa4461afbb8ff56e8f703043e191a8163b1ee9 \ + --hash=sha256:bc067c462a86f0e57bf52fc6e058d90171a5420007dac00c46e90153050c69a7 \ + --hash=sha256:bf3fe71fbfb8ec0e310e0bc8537c3405a01f38f25f9394ed2135e6202fed542b \ + --hash=sha256:c0b83f044ce10a98027b105b3931548719a6e8c7ef986b4362651e0b5367c8dc \ + --hash=sha256:c27e577ece613ea12a0790e00b4eb80d901c59a3e16cad474f31d8b1529690b9 \ + --hash=sha256:c5c1c68ee401fc98271263410f0e5ce88285abacf7627132914e8adf3d70ff43 \ + --hash=sha256:c9721f81275499da1feeb36a2cf8192ea086283b3bd16b7dc4c9d7aedb7396d6 \ + --hash=sha256:cc82dde2a0d3e3ad472edce04897ad7146b8d8bfd1df8a32992eebb81af18fdc \ + --hash=sha256:cec596316640f2b394b8f0daa0ea61a8eae82d017b620b9f202befb972a59ea4 \ + --hash=sha256:cf41ecd1b0c0b6f7177ed965a54c2afbe888715c7cf6054dc12d53bc1494002c \ + --hash=sha256:d3304eb5a59442a8867f6920d484591c0fa09ffc29e9260be2feec3351e25869 \ + --hash=sha256:d480038c83691532ed52ff3147db51fa902fc78cb2d8349993a1cdb684435bff \ + --hash=sha256:df4f7784aca81a94f254c0a2767d592ee25f407e488f5fa7203e51093fb6ca27 \ + --hash=sha256:e43b188f0a5b75447bcc197728258166aa64365770ed1caa36595a5e1ca4bbba \ + --hash=sha256:e60cf3047a51edecdc4535a9196bbd6a732936b8e9f8184aabf4d16165884aaf \ + --hash=sha256:eac4b07d4e3743b172451e122ea964f72f152879d4f9adcf3f3d33e518f12ead \ + --hash=sha256:eb3712dc9b464793de0a4e42a7313d50293c751f94bddaf7332a1bc71bccdda9 \ + --hash=sha256:ecea603dd2fbf8242fd31a305a8b12a4ece2de28096870c65fdd0d1e35b8d9a6 \ + --hash=sha256:ef31985c4dedb5f1424e1aec6849a47dd37689cb7fa3c20b1b82187f26806261 \ + --hash=sha256:ef752769cd962f39ea0b6ffc82d1ea43a0012c5a6157c7a075212fa509cfcff2 \ + --hash=sha256:f25446b2981717dca9786bac841cb3fd7efb568e3e3c755dd980481c5cb9228d \ + --hash=sha256:f2ac30cf5eb5dff1b584627ae0b0e1186551a4f69ae3c75073911da497a29170 \ + --hash=sha256:fea03cf56568cc1cba08b470be6a0559e71c3a5b688d54b7179bb35ba23d0821 + # via -r requirements.in +matplotlib-inline==0.2.2 \ + --hash=sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6 \ + --hash=sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79 + # via + # ipykernel + # ipython +mistune==3.3.4 \ + --hash=sha256:58b5c96d6fcb61190dfe5fae498d2b2065f99cf61e9649418fd54cf1ada86dfe \ + --hash=sha256:ee015381e955e370962968befe1d729ab60fafb6a715ac6751763fbce38c8d4a + # via nbconvert +nbclient==0.11.0 \ + --hash=sha256:04a134a5b087f2c5887f228aca155db50169b8cd9334dee6942c8e927e56081a \ + --hash=sha256:ef7fa0d59d6e1d41103933d8a445a18d5de860ca6b613b87b8574accdb3c2895 + # via nbconvert +nbconvert==7.17.1 \ + --hash=sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2 \ + --hash=sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8 + # via + # -r requirements.in + # jupyter + # jupyter-server +nbformat==5.11.1 \ + --hash=sha256:32d4521c68c6e7d5b29c76defaeed9f42ea733142b9b19f88277ce10390b9c4d \ + --hash=sha256:cc6698fa75f4fab8755ead786317815f13a6fee3b53311c0abb1a8b51d52f7ec + # via + # jupyter-server + # nbclient + # nbconvert +nest-asyncio2==1.7.3 \ + --hash=sha256:2bc87bdca654e719425145f5e84eaeb0080b013fdd47d65729a7d66243f4987c \ + --hash=sha256:2e9a84d5d1efe6d020c72988d21aec569bac42d98af2ff6b9de24640c5d22a34 + # via ipykernel +notebook==7.6.3 \ + --hash=sha256:ad7e0eb765fba836cd4a2ab0c7a3a26cde1d91665fbf6f533b6ae7b2de6d88d2 \ + --hash=sha256:e2c08e469c0ae20bb0b3214f0ab77e79653317a2f8e5b34c10361c66874a5b50 + # via jupyter +notebook-shim==0.2.4 \ + --hash=sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef \ + --hash=sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb + # via + # jupyterlab + # notebook +numpy==2.4.6 \ + --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ + --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ + --hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \ + --hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \ + --hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \ + --hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \ + --hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \ + --hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \ + --hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \ + --hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \ + --hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \ + --hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \ + --hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \ + --hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \ + --hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \ + --hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \ + --hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \ + --hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \ + --hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \ + --hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \ + --hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \ + --hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \ + --hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \ + --hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \ + --hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \ + --hash=sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662 \ + --hash=sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91 \ + --hash=sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0 \ + --hash=sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f \ + --hash=sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3 \ + --hash=sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f \ + --hash=sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67 \ + --hash=sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6 \ + --hash=sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997 \ + --hash=sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b \ + --hash=sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e \ + --hash=sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538 \ + --hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \ + --hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \ + --hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \ + --hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \ + --hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \ + --hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \ + --hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \ + --hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \ + --hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \ + --hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \ + --hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \ + --hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \ + --hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \ + --hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \ + --hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \ + --hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \ + --hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \ + --hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \ + --hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \ + --hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \ + --hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \ + --hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \ + --hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \ + --hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \ + --hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \ + --hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \ + --hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \ + --hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \ + --hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \ + --hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \ + --hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \ + --hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \ + --hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \ + --hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \ + --hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20 + # via + # -r requirements.in + # contourpy + # matplotlib + # samplerate + # scipy + # soxr +overrides==7.7.0 \ + --hash=sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a \ + --hash=sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49 + # via jupyter-server +packaging==26.3 \ + --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \ + --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c + # via + # ipykernel + # jupyter-events + # jupyter-server + # jupyterlab + # jupyterlab-server + # matplotlib + # nbconvert +pandocfilters==1.5.1 \ + --hash=sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e \ + --hash=sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc + # via nbconvert +parso==0.8.7 \ + --hash=sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c \ + --hash=sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1 + # via jedi +pexpect==4.9.0 \ + --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \ + --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f + # via ipython +pillow==12.3.0 \ + --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ + --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ + --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ + --hash=sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45 \ + --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3 \ + --hash=sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df \ + --hash=sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139 \ + --hash=sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b \ + --hash=sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39 \ + --hash=sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e \ + --hash=sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8 \ + --hash=sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1 \ + --hash=sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8 \ + --hash=sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89 \ + --hash=sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5 \ + --hash=sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130 \ + --hash=sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd \ + --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ + --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ + --hash=sha256:25b9b82bb22e6e2b3cd07b39c68b7b862001226cb3dff7130d1cb914121b39ed \ + --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ + --hash=sha256:300557495eb45ebb8aec96c2da9c4be642fbf7cd937278b4013ba894ea8eb0eb \ + --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ + --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ + --hash=sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6 \ + --hash=sha256:37dc8f7bbb66efe481bb60defacef820c950c24713fb44962ed6aa2a50966de1 \ + --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ + --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ + --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ + --hash=sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c \ + --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ + --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ + --hash=sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c \ + --hash=sha256:514435a37670e3e5e08f3945b68718b6ed329bb84367777e16f9f4dfe1e61a0f \ + --hash=sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64 \ + --hash=sha256:5594fc43d548a7ed94949d139aa1341b270f1863f11cfd37f5a6c8b778a6b67f \ + --hash=sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a \ + --hash=sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827 \ + --hash=sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17 \ + --hash=sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4 \ + --hash=sha256:6c0016e7b354317c4e9e525b937ac8596c38d2d232b419529b9cd7a1cd46e39a \ + --hash=sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701 \ + --hash=sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e \ + --hash=sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91 \ + --hash=sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66 \ + --hash=sha256:85f998ea1848bc6757289e739cfbdda3a04adfd58b02fc018ce54d754a5ce468 \ + --hash=sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217 \ + --hash=sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658 \ + --hash=sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418 \ + --hash=sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a \ + --hash=sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c \ + --hash=sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330 \ + --hash=sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402 \ + --hash=sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09 \ + --hash=sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930 \ + --hash=sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f \ + --hash=sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec \ + --hash=sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a \ + --hash=sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94 \ + --hash=sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468 \ + --hash=sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b \ + --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ + --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ + --hash=sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd \ + --hash=sha256:bcc33feacfaefce60c12fd500a277533bdc02b10a19f7f6d348763d8140bbba7 \ + --hash=sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c \ + --hash=sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777 \ + --hash=sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35 \ + --hash=sha256:d9c7f76c0673154f044e9d78c8655fb4213f6ca31a836df48b40fe5d187717b9 \ + --hash=sha256:dbce0b29841537a2fa4a214c2bbf14de3587c9680caa9b4e217568472490b28f \ + --hash=sha256:dc624f6bc473dacdf7ef7eb8678d0d08edf15cd94fad6ae5c7d6cc67a4e4902f \ + --hash=sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0 \ + --hash=sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c \ + --hash=sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71 \ + --hash=sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3 \ + --hash=sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838 \ + --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ + --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ + --hash=sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26 \ + --hash=sha256:f0606c8bf2cdefea14a43530f7657cbbb7ecf1c4222512492ef4a4434a9501ec \ + --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ + --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ + --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ + --hash=sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e \ + --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ + --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ + --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 + # via matplotlib +platformdirs==4.12.0 \ + --hash=sha256:095be5c143382b1bee917c4f3e9987a0d8d6a582261f1d061ad0c403b7695b5b \ + --hash=sha256:f6fb2960f2f2eb0870f820f7e49e49e6c0f0589c637f34d33b260b07591327c9 + # via jupyter-core +prometheus-client==0.26.0 \ + --hash=sha256:04a91bcf94e2cf74a44a1a874d651a2e853ed354b6e822f3b7487751465d5c2b \ + --hash=sha256:fa93d06737aa02bacd05794768508bb97d2fbee28cb3bca04eaae92f0ca953d6 + # via jupyter-server +prompt-toolkit==3.0.53 \ + --hash=sha256:01c0891d7f9237d5e339f7d3e42cdae80b7534abb1c7c0e3352efba6231492f2 \ + --hash=sha256:9ec8a0ad96d5c56148b3f914aa79c1564c3fde5d2e6b876e7bc327e353cf8fa6 + # via + # ipython + # jupyter-console +psutil==7.2.2 \ + --hash=sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372 \ + --hash=sha256:076a2d2f923fd4821644f5ba89f059523da90dc9014e85f8e45a5774ca5bc6f9 \ + --hash=sha256:11fe5a4f613759764e79c65cf11ebdf26e33d6dd34336f8a337aa2996d71c841 \ + --hash=sha256:1a571f2330c966c62aeda00dd24620425d4b0cc86881c89861fbc04549e5dc63 \ + --hash=sha256:1a7b04c10f32cc88ab39cbf606e117fd74721c831c98a27dc04578deb0c16979 \ + --hash=sha256:1fa4ecf83bcdf6e6c8f4449aff98eefb5d0604bf88cb883d7da3d8d2d909546a \ + --hash=sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b \ + --hash=sha256:7b6d09433a10592ce39b13d7be5a54fbac1d1228ed29abc880fb23df7cb694c9 \ + --hash=sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee \ + --hash=sha256:917e891983ca3c1887b4ef36447b1e0873e70c933afc831c6b6da078ba474312 \ + --hash=sha256:ab486563df44c17f5173621c7b198955bd6b613fb87c71c161f827d3fb149a9b \ + --hash=sha256:ae0aefdd8796a7737eccea863f80f81e468a1e4cf14d926bd9b6f5f2d5f90ca9 \ + --hash=sha256:b0726cecd84f9474419d67252add4ac0cd9811b04d61123054b9fb6f57df6e9e \ + --hash=sha256:b58fabe35e80b264a4e3bb23e6b96f9e45a3df7fb7eed419ac0e5947c61e47cc \ + --hash=sha256:c7663d4e37f13e884d13994247449e9f8f574bc4655d509c3b95e9ec9e2b9dc1 \ + --hash=sha256:e452c464a02e7dc7822a05d25db4cde564444a67e58539a00f929c51eddda0cf \ + --hash=sha256:e78c8603dcd9a04c7364f1a3e670cea95d51ee865e4efb3556a3a63adef958ea \ + --hash=sha256:eb7e81434c8d223ec4a219b5fc1c47d0417b12be7ea866e24fb5ad6e84b3d988 \ + --hash=sha256:ed0cace939114f62738d808fdcecd4c869222507e266e574799e9c0faa17d486 \ + --hash=sha256:eed63d3b4d62449571547b60578c5b2c4bcccc5387148db46e0c2313dad0ee00 \ + --hash=sha256:fd04ef36b4a6d599bbdb225dd1d3f51e00105f6d48a28f006da7f9822f2606d8 + # via + # ipykernel + # ipython +ptyprocess==0.7.0 \ + --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \ + --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220 + # via + # pexpect + # terminado +pure-eval==0.2.4 \ + --hash=sha256:260c2774686e651b79f8b8e7fc9d80b3599ea6a66334b47d5f4abb69fc2c0ea1 \ + --hash=sha256:96cae060a313cfaad51bb761278bfb0e62dc0248d9315a81173752dc546cd37a + # via stack-data +pycparser==3.0 \ + --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \ + --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992 + # via cffi +pygments==2.21.0 \ + --hash=sha256:2363c69b61c4a97c838da3b130dcd6468f4848992b21a82f2a63ec34377137d9 \ + --hash=sha256:610ca751c9bc2492b38eb9a38a7fbc93edbbb2d7182edaf34e66ae493dee5c8c + # via + # ipython + # ipython-pygments-lexers + # jupyter-console + # nbconvert +pyparsing==3.3.3 \ + --hash=sha256:928ae7e20211f3b6f3915a72f06a0cfd29ab9d24279dd6346b6b1a7146397d36 \ + --hash=sha256:ece8c00a69cf01b45d0b1dedabb469c90d8caf996d4fda40f147627a122849a4 + # via matplotlib +python-dateutil==2.9.0.post0 \ + --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ + --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 + # via + # arrow + # jupyter-client + # matplotlib +python-json-logger==4.2.0 \ + --hash=sha256:158a52126fcd6869e09574d2b66272666f3dc8f468c62637ef9a1fa883719cb9 \ + --hash=sha256:e371ebe22ec01e289850102091a2b1f6fc9e655c7f1f5f29073936756c290afa + # via jupyter-events +pyyaml==6.0.3 \ + --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \ + --hash=sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a \ + --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \ + --hash=sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956 \ + --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \ + --hash=sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c \ + --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \ + --hash=sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a \ + --hash=sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0 \ + --hash=sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b \ + --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \ + --hash=sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6 \ + --hash=sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7 \ + --hash=sha256:28c8d926f98f432f88adc23edf2e6d4921ac26fb084b028c733d01868d19007e \ + --hash=sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007 \ + --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \ + --hash=sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4 \ + --hash=sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9 \ + --hash=sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295 \ + --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ + --hash=sha256:418cf3f2111bc80e0933b2cd8cd04f286338bb88bdc7bc8e6dd775ebde60b5e0 \ + --hash=sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e \ + --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \ + --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \ + --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \ + --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \ + --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \ + --hash=sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b \ + --hash=sha256:5e0b74767e5f8c593e8c9b5912019159ed0533c70051e9cce3e8b6aa699fcd69 \ + --hash=sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5 \ + --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \ + --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \ + --hash=sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369 \ + --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \ + --hash=sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824 \ + --hash=sha256:66291b10affd76d76f54fad28e22e51719ef9ba22b29e1d7d03d6777a9174198 \ + --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \ + --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \ + --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \ + --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \ + --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ + --hash=sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b \ + --hash=sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00 \ + --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \ + --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \ + --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ + --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ + --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \ + --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ + --hash=sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4 \ + --hash=sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b \ + --hash=sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf \ + --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \ + --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \ + --hash=sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8 \ + --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \ + --hash=sha256:b865addae83924361678b652338317d1bd7e79b1f4596f96b96c77a5a34b34da \ + --hash=sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d \ + --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \ + --hash=sha256:bdb2c67c6c1390b63c6ff89f210c8fd09d9a1217a465701eac7316313c915e4c \ + --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \ + --hash=sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f \ + --hash=sha256:c3355370a2c156cffb25e876646f149d5d68f5e0a3ce86a5084dd0b64a994917 \ + --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \ + --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \ + --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \ + --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \ + --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \ + --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \ + --hash=sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3 \ + --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \ + --hash=sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926 \ + --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0 + # via jupyter-events +pyzmq==27.2.0 \ + --hash=sha256:00e73942ef12cecbc7951c4a9104bb8ffaed742abb13af2da6833d90dd368cef \ + --hash=sha256:010db74a1dd67c7cd8b8b30916355735db7d633a070510bb34e41ab679ab2c0e \ + --hash=sha256:0e1af01858d6dc0c09cea57f9cb1ddf4601f04897b6bb1efc3a2038123c87d79 \ + --hash=sha256:0f4bd6743e8bf854c3bfce892dd6578a514aabf128e37a4b2eafcf01856f7e44 \ + --hash=sha256:1132805970045adb9f5f05dd57040978286a8e21a5475f2c2ddf1bc983b9a2c7 \ + --hash=sha256:1ecbdd131b9669f62d3a45afee5527c7ae9f141e4301267f21714c90bd21725f \ + --hash=sha256:1f8079d0521fe94bbb401fe9407578b28f3701627c8be2c9f7e0c5b77dcb0109 \ + --hash=sha256:211350c3ccd4746bc5a85e8fe961bad1f7f2f274f67cf1f785fad7f96f562eea \ + --hash=sha256:288cc790da0e3064a14a38ddc56ba169dada8c8af4cb86518db2bcbd380eedbb \ + --hash=sha256:2c218c6ab8bc447ba62054b581fd30209689d199c6ecb253f79615ca74a38e12 \ + --hash=sha256:3146385b94a760236c5eceff468a66a296a716ca98a2e0f9217b1518118466b1 \ + --hash=sha256:348d6fd3e4b81ae4580622ea8c2ea60224e84b2ac1b3be4482e6edc7de06e7a3 \ + --hash=sha256:376981d106598beb70be384f44d8f589832fd0051d184d38d10043da3cc3b080 \ + --hash=sha256:39755dc4a923021bd0677990ffdbc21cff0e1ee1cf07fe3817acea153ef4cb67 \ + --hash=sha256:3ab6eb88590e510ab16715c32dbba12000da9bee989fdadd9ee19a234c492eb7 \ + --hash=sha256:3d45189c0c3c99f817b7fefff0d32eeef684cf33e1e3c0fc4281515357c54702 \ + --hash=sha256:3ee556ed1cf836f96de9d5e545563116426d4a94f21b8041fdc79408eff18ebb \ + --hash=sha256:3ee8dd7031d5e23f632e0e7eee67183ca7d2536e0de35dc1e5d69f3471a791e8 \ + --hash=sha256:40124779c3a56ad5d91902df1ff89159cb414b6c1a0ee697abcc66cf5e6db62d \ + --hash=sha256:40d96cb7a8f6a43aa9617c00215c2b73e1b5e4a1d6cbc9f5860ed7ac682599f0 \ + --hash=sha256:44f261eca7dfb9904ea2b56428f59ab693bbe2715c0413a701f17b067ebf877c \ + --hash=sha256:468139ddb2e494d06e586bd3a6835077e8b3764560c8db552fe685c5867fc24e \ + --hash=sha256:480dba27b145373b5e103890f17969d891bc9e86746d6b8b29dd70b0d4addc62 \ + --hash=sha256:4ebc7889b31bc11c72e9f17ba3ebb0a8b0911cce413f41b498e55383a94819a3 \ + --hash=sha256:507c0b33f95502723d325487e8e50c2cdd3b37444143f05423a3861327f69bf7 \ + --hash=sha256:54d4259d1bfae24ecdb5ca79f7acc2eac6c286a02d6a0ae617797cb45f0726d3 \ + --hash=sha256:56b48fa9d478a3af7254f397697a62f5ad3e1bb677e200b2701f0c290d97e5af \ + --hash=sha256:591c8de5851c5ea372194469fe97587b97c3b641e9a70f31bb3474acbfde0241 \ + --hash=sha256:650c6cd7cb39a069e7048261efe66fce8bf2e0052c831a7a099b7a0f2ea860d7 \ + --hash=sha256:679b5b1dde326a921ea2c9ec1f9ea3115bfe1b4735779bbc6eb0473a0ed93f71 \ + --hash=sha256:6eb63cc61ab93b01b9afc887a160255e2fbe703fdbacfe5feaef87214f51bd6c \ + --hash=sha256:714f8cbd66c7e405338d668f79d2fe83fe923defe348e843be998603cf92eeff \ + --hash=sha256:722f0a6940be1a483c81029a271d950e04dc2ff113a42e21b3d2b7a0d8e59638 \ + --hash=sha256:76afba06ae698f2b8fe4fb34b32c760a650f168c2e622f370f2c528035b7f650 \ + --hash=sha256:770a37f28ddfbe1d2c40a2e3ce37e5fd10831daa6ae9634105aa8a5d23507b00 \ + --hash=sha256:7e2579c5de82ddf4544d723c1bc8b44c3b806d157acc9fb2a2d18e10ef28e202 \ + --hash=sha256:82a09aa67871d4f2fcafd47bf670fb93210b232a7c2d4b8a54676314edf04033 \ + --hash=sha256:88c0fac061bac269076edeb3a209acefc96cd6167c239daf1c2b404ac48d7012 \ + --hash=sha256:8a5c04ad2e368142aea52d1abdf6631cb2534864e3c16ab78268ab957060b2a6 \ + --hash=sha256:8b86e04f55af0f4d8cd8ecf14c0b8b81ebc8fd66fa20126b753514628ecadc7e \ + --hash=sha256:917d601e9540098f580d2723d0ce6402cdb6f02bc8dc2de74e0dca6e13bffd1b \ + --hash=sha256:9216132843d139a123f243c07fe70f7487dce5041093dd77040f9adb5dc91872 \ + --hash=sha256:94242bd4de6af7e74665e14a88630bccd615057f6acfaf08a3a432551d604645 \ + --hash=sha256:95369ed6626afcfe2ac89832fb1b917c077fbeb905fbbe5d918349ce0222b89b \ + --hash=sha256:95f52b877149b06bbdeec2e8ea6230aad14950bbfbcfa16e7eb88951f07d6b28 \ + --hash=sha256:97d4c6622f129b514a4f5939af1b5f434c97f47085d9311b5f7f36e24b3bd447 \ + --hash=sha256:9846e881620dd62566ca76a53e384c3f37490faf4b9240aebc7498810dfca853 \ + --hash=sha256:9ab72ee77b313d0658447204c8201f9b315146e923b48c56ea7dbd005d464a91 \ + --hash=sha256:a070a9cdad1f8f8a85ea153afcc4654f11b10895d14c0acabe10f1df0e0892ea \ + --hash=sha256:a0ee3c49be2aa15abd12cbbd14d4ea2892f872c688e1e487af39ec1972ed549d \ + --hash=sha256:a7c1144dc61777938e932a2c9011b980b89fd8ff3733033b34c44c299187a6e1 \ + --hash=sha256:a843094b4d3d633bc3623e47a2ff50742d6af02bc1f7606aa2e67e971e21878d \ + --hash=sha256:ac126d48cf18aa955daabef43bf0009ff76ad4deee437d09ecf15388214b5beb \ + --hash=sha256:ae6ebbc0bfe5a21ce21e32ba567bf73df2d93888109c65acbd42506cf9395759 \ + --hash=sha256:b26f2d0493b79ce3c3112c8a12649418915582ba4707b8ed9f44febf2be71f42 \ + --hash=sha256:b398c5fe102b41e1559f7ffdae760aabd5f432d73b047b4ae0eac4e01cb594d2 \ + --hash=sha256:b8d5f66e4a8246cf77f7b8f7902af64f00553368fa0373c89d99b78f0ad79394 \ + --hash=sha256:baa2ce3485145653194d6c8c5beedd1e9f0bf46a0919c9fa2fe2204fc35b74d9 \ + --hash=sha256:bad4813f270592cedf56977e31ac1fc374fb0f6f67ea5134a5e37c19cb429a8e \ + --hash=sha256:bf0b6e4ce1bb089751c504c5493d6b0557eabd02dd21b76e9086cf964234b103 \ + --hash=sha256:c218b816220d05acf6ab1bafca58926d95cbcc5fec5024724666030466308f0c \ + --hash=sha256:c5129a8fe43ecc49b99eb75616603d483a3c2fcaef504988fafe8ea392aea98b \ + --hash=sha256:c551b9e2f86dc625fcb1a032c0d68042678caf96a8dd7c28796766b673bd5b52 \ + --hash=sha256:c7cfb75caa83f5153c687e9d2107f64b5ef0ef0d6edd260d3ff920baaaa69101 \ + --hash=sha256:c9322f9c87b0935870516c2876e1e29497fdc50439c785ece63e3fbbab06c821 \ + --hash=sha256:d1526b42a2e725b84ed226f37becedc250c6347594e5ed304e4e9aff68c9aec3 \ + --hash=sha256:d1bc1d380a91d954ed5fc9f12915dba014eed0978d2de05ee7ca688bdaac144a \ + --hash=sha256:d41ebb260b69329b7d4a2936d44c872c86dd785355b51366c8b14e07ed7e9373 \ + --hash=sha256:d61910b52be5b2cd8b248dbcbe3a1b0275556a7d99fb613fc43323b546e273b8 \ + --hash=sha256:d61a0169ba05ab7ebc48dc793f092df12f789bf378dac8321ccd966fd93d94e8 \ + --hash=sha256:d64da42cae09e6b0c61368b4cc8ca80f23ce3af17584d08053f3dc957433d5ed \ + --hash=sha256:d9527e3dbaef1edaeeb2446fa7379446814a43ade8adc7c4a5ebe69437815ddd \ + --hash=sha256:dcc99ca132b667a4ed750afd42db4ea73288f18425a9b2e3c0af095665c491f5 \ + --hash=sha256:dde5e291548ca0f397623b5e523db5c90172b32aa4fd3ba464a79ea31a580b43 \ + --hash=sha256:dea74fd65f1fc5f7fe167916a473ebe6ed6174e5e5d9de11ea6583661be6cf43 \ + --hash=sha256:dfcd024eade5870b25f890c4df0ba9421ed8167d8d3d82334237512c1158dada \ + --hash=sha256:e0fa0bc6b1a184aee59b32efcd1b7f0e6d5b8f9387799e4c16a4cb66a86747d6 \ + --hash=sha256:e1ed46048d1920cabc96d952a0d5cfe4127ad8db572c335aae4e3c57b9278d7f \ + --hash=sha256:ec8a318dfc27c7d946651b3d9e8025d5734f30c168a822195601827207bac09b \ + --hash=sha256:edce90a1e588ec63adbf612cc0ad582de4169cd216c7ae53c15f42a2ee902f35 \ + --hash=sha256:f52f08101907609cc08db6a1f9f2a7a9afd54e9b2ca16178c9c38e99fb593cef \ + --hash=sha256:f5c6d8744d10b5e1eadd90a7c58f8546acf6bf680ee463f7e6ada09ad6c9f802 \ + --hash=sha256:f707bcf2c1d007d14d70531d4dd7b41060881c73efa845580bf6faaf9ea24d42 \ + --hash=sha256:fba8afcf265c6e9fbe1594cb045d4765c6c9a7d607653a8196067ef23566b843 \ + --hash=sha256:fdaaa4ea3242f6ad298eb5177eb042aea5c73c30e76d20caee7b15af20d24ec2 \ + --hash=sha256:ff60f0f7ccfda0e303ac43bec7096007b7cdf2c41b3739d1ec667febe67acab3 + # via + # ipykernel + # jupyter-client + # jupyter-console + # jupyter-server +referencing==0.37.0 \ + --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \ + --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8 + # via + # jsonschema + # jsonschema-specifications + # jupyter-events +requests==2.34.2 \ + --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \ + --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed + # via jupyterlab-server +rfc3339-validator==0.1.4 \ + --hash=sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b \ + --hash=sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa + # via + # jsonschema + # jupyter-events +rfc3986-validator==0.1.1 \ + --hash=sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 \ + --hash=sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055 + # via + # jsonschema + # jupyter-events +rfc3987-syntax==1.1.0 \ + --hash=sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f \ + --hash=sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d + # via jsonschema +rpds-py==2026.6.3 \ + --hash=sha256:0be972be84cfcaf46c8c6edf690ca0f154ac17babf1f6a955a51579b34ad2dc5 \ + --hash=sha256:127565fead0a10943b282957bd5447804ff3160ad79f2ad2635e6d249e380680 \ + --hash=sha256:127e08c0642d880cf32ca47ec2a4a77b901f7e2dd1ad9762adb13955d72ffcc9 \ + --hash=sha256:166cf54d9f44fc6ceb53c7860258dde44a81406646de79f8ed3234fca3b6e538 \ + --hash=sha256:168c733a7112e071bb7a66460e667edfcff06c017a3c523f7a8a8e08d0140804 \ + --hash=sha256:1967debc37f64f2c4dc90a7f563aec558b471966e12adcac4e1c4240496b6ebf \ + --hash=sha256:1cebd1337c242e4ec2293e541f712b2da849b29f48f0c293684b71c0632625d4 \ + --hash=sha256:1cf01971c4f2c5553b772a542e4aaf191789cd331bc2cd4ff0e6e65ba49e1e97 \ + --hash=sha256:1e5822dfc2f0d4ab7e745eaa6d85945069329beeccef965af3f3bb26058fcab6 \ + --hash=sha256:22bffe6042b9bcb0822bcd1955ec00e245daf17b4344e4ed8e9551b976b63e96 \ + --hash=sha256:23a439f31ccbeff1574e24889128821d1f7917470e830cf6544dced1c662262a \ + --hash=sha256:24e9c5386e16669b674a69c156c8eeefcb578f3b3397b713b08e6d60f3c7b187 \ + --hash=sha256:270b293dae9058fc9fcedab50f13cebf46fb8ed1d1d54e0521a9da5d6b211975 \ + --hash=sha256:29dfa0533a5d4c94d4dfa1b694fcb56c9c63aad8330ffdd816fd225d0a7a162f \ + --hash=sha256:2a9c6f195058cb45335e8cc3802745c603d716eb96bc9625950c1aac71c0c703 \ + --hash=sha256:2bfd04c19ddbd6640de0b51894d764bd2758854d5b75bd102d2ef10cb9c293a9 \ + --hash=sha256:2c54a076ca4d370980ab57bc0e31df57bbe8d41340436a90ef8b1219a3cbb127 \ + --hash=sha256:2c958bf94822e9290a40aaf2a822d4bc5c88099093e3948ad6c571eca9272e5f \ + --hash=sha256:2c99f7e8ccb3dd6e3e4bfeac657a7b208c9bac8075f4b078c02d7404c34107fa \ + --hash=sha256:2f7c26fbc5acd2522b95d4177fe4710ffd8e9b20529e703ffbf8db4d93903f05 \ + --hash=sha256:30c6dc199b24a5e3e81d50da0f00858c5bbdb2617a750395687f4339c5818171 \ + --hash=sha256:38a2fea2787428f811719ceb9114cb78964a3138838320c29ac39526c79c16ba \ + --hash=sha256:3a83ae6c67b7676b9878378547ca8e93ed77a580037bcbcd1d32f739e1e6089c \ + --hash=sha256:3cfe765c1da0072636ca06628261e0ea05688e160d5c8a03e0217c3854037223 \ + --hash=sha256:421aba32367055614287a4292b6a17f1939c9452299f7a0209c117e990b646d4 \ + --hash=sha256:425560c6fa0415f27261727bb20bd097568485e5eb0c121f1949417d1c516885 \ + --hash=sha256:4470ce197d4090875cf6affbf1f853338387428df97c4fb7b7106317b8214698 \ + --hash=sha256:4cf2d36a2357e4d07bb5a4f98801265327b48256867816cfd2ceb001e9754a8f \ + --hash=sha256:4f4bca01b63096f606e095734dd56e74e175f94cfbf24ff3d63281cec61f7bb7 \ + --hash=sha256:501f9f04a588d6a09179368c57071301445191767c64e4b52a6aa9871f1ef5ed \ + --hash=sha256:536bceea4fa4acf7e1c61da2b5786304367c816c8895be71b8f537c480b0ea1f \ + --hash=sha256:538949e262e46caa31ac01bdb3c1e8f642622922cacbabbae6a8445d9dc33eaf \ + --hash=sha256:539d75de9e0d536c84ff18dfeb805398e58227001ce09231a26a08b9aed1ee0e \ + --hash=sha256:54f45a148e28767bf343d33a684693c70e451c6f4c0e9904709a723fafbdfc1f \ + --hash=sha256:55927d532399c2c646100ff7feb48eaa940ad70f42cd68e1328f3ded9f81ca24 \ + --hash=sha256:58eadac9cd119677b60e1cf8ac4052f35949d71b8a9e5556efccbe82533cf22a \ + --hash=sha256:5e8d07bddee435a2ff6f1920e18feff28d0bc4533e42f4bf6927fbd073312c41 \ + --hash=sha256:62698275682bf121181861295c9181e789030a2d516071f5b8f3c23c170cd0fc \ + --hash=sha256:639c8929aa0afe81be836b04de888460d6bed38b9c54cfc18da8f6bfabf5af5d \ + --hash=sha256:67e3a721ffc5d8d2210d3671872298c4a84e4b8035cfe42ffd7cde35d772b146 \ + --hash=sha256:6de4744d05bd1aa1be4ed7ea1189e3979196808008113bbbf899a460966b925e \ + --hash=sha256:6e84adbcf4bf841aed8116a8264b9f50b4cb3e7bd89b516122e616ac56ca269e \ + --hash=sha256:7491ee23305ac3eb59e492b6945881f5cd77a6f731061a3f25b77fd40f9e99a4 \ + --hash=sha256:79486287de1730dbaff3dbd124d0ca4d2ef7f9d29bf2544f1f93c09b5bcbbd12 \ + --hash=sha256:7b689145a1485c335569bd056464f3243a29af7ed3871c7be31ad624ba239bc7 \ + --hash=sha256:7f88d653e7b3b779d71ae7454e20dcc9b6bae903f33c269db9f2be41bda3f261 \ + --hash=sha256:8020133a74bd81b4572dd8e4be028a6b1ebcd70e6726edc3918008c08bee6ee6 \ + --hash=sha256:808345f53cb952433ca2816f1604ff3515608a81784954f38d4452acfe8e61d5 \ + --hash=sha256:83e35b57523816c8613fd0776b40cd8bb9f596b37ddd2692eb4a6bb5ab2f8c93 \ + --hash=sha256:842e7b070435622248c7a2c44ae53fa1440e073cc3023bc919fed570884097a7 \ + --hash=sha256:847927daf4cffbd4e90e42bc890069897101edd015f956cb8721b3473372edda \ + --hash=sha256:882076c00c0a608b131187055ddc5ae29f2e7eaf870d6168980420d58528a5c8 \ + --hash=sha256:8b95977e7211527ab0ba576e286d023389fbeeb32a6b7b771665d333c60e5342 \ + --hash=sha256:8bb68f03f395eb793220b45c097bd4d8c32944393da0fad8b999efac0868fc8c \ + --hash=sha256:8c2642a7603ec0b16ed77da4555db3b4b472341904873788327c0b0d7b95f1bb \ + --hash=sha256:8c3d1e9c15b9d51ca0391e13da1a25a0a4df3c58a37c9dc368e0736cf7f69df0 \ + --hash=sha256:8c6e5a2f750cc71c3e3b11d71661f21d6f9bc6cebc6564b1466417a1ec03ec77 \ + --hash=sha256:8d2294a31386bfa251d8c8a39472beee17db67d4f1a6eabea665d35c9a4461c3 \ + --hash=sha256:8e4320744c1ffdd95a603def63344bfab2d33edeab301c5007e7de9f9f5b3885 \ + --hash=sha256:8e65860d238379ed982fd9ba690579b5e95af2f4840f99c772816dbe573cb826 \ + --hash=sha256:8f2e5c5ee828d42cb11760761c0af6507927bec42d0ad5458f97c9203b054617 \ + --hash=sha256:900a67df3fd1660b035a4761c4ce73c382ea6b35f90f9863c36c6fd8bf8b09bb \ + --hash=sha256:913ca42ccad3f8cc6e292b587ae8ae49c8c823e5dce51a736252fc7c7cdfa577 \ + --hash=sha256:9250a9a0a6fd4648b3f868da8d91a4c52b5811a62df58e753d50ae4454a36f80 \ + --hash=sha256:931908d9fc855d8f74783377822be318edb6dcb19e47169dc038f9a1bf60b06e \ + --hash=sha256:9826217f048f620d9a712672818bf231442c1b35d96b227a07eabd11b4bb6945 \ + --hash=sha256:9891e594296ab9dada6551c8e7b387b2721f27a67eecd528412e8906247a7b90 \ + --hash=sha256:9c1255b302953c86a486b81d330d5ee1d5bd937691ce271b6be0ef0e299eaab7 \ + --hash=sha256:a0811d33247c3d6128a3001d763f2aa056bb3425204335400ac54f89eec3a0d0 \ + --hash=sha256:a136d453475ac0fcbda502ef1e6504bd28d6d904700915d278deeab0d00fe140 \ + --hash=sha256:a214c993455f99a89aaeadc9b21241900037adc9d97203e374d75513c5911822 \ + --hash=sha256:a3086b538543802f84c843911242db20447de00d8752dd0efc936dbcf02218ba \ + --hash=sha256:a3450b693fde92133e9f51060568a4c31fcca76d5e53bbd611e689ca446517e9 \ + --hash=sha256:a550fb4950a06dde3beb4721f5ad4b25bf4513784665b0a8522c792e2bd822a4 \ + --hash=sha256:a9f4645593036b81bbdb36b9c8e0ea0d1c3fee968c4d59db0344c14087ef143a \ + --hash=sha256:aca6c1ef08a82bfe327cc156da694660f599923e2e6665b6d81c9c2d0ac9ffc8 \ + --hash=sha256:acac386b453c2516111b50985d60ce46e7fadb5ea71ae7b25f4c946935bf27cf \ + --hash=sha256:acc992ab27b15f852c76755eb2ab7dce86585ddadba6fa5946e58556088845b4 \ + --hash=sha256:ae3d4fe8c0b9213624fdce7279d70e3b148b682ca20719ebd193a23ebfa47324 \ + --hash=sha256:ae50181a047c871561212bb97f7932a2d45fb53e947bd9b57ebad85b529cbc53 \ + --hash=sha256:ae6dd8f10bd17aad820876d24caec9efdafd80a318d16c0a48edb5e136902c6b \ + --hash=sha256:af05d726809bff6b141be124d4c7ce998f9c9c7f30edb1f46c07aa103d540b41 \ + --hash=sha256:afd70d95892096cdb26f15a00c45907b17817577aa8d1c76b2dcc2788391f9e9 \ + --hash=sha256:b5c2dc92304aa48a4a60443b548bb12f12e119d4b72f314015e67b9e1be97fca \ + --hash=sha256:bc0011654b91cc4fb2ae701bec0a0ba1e552c0714247fa7af6c59e0ccfa3a4e1 \ + --hash=sha256:bcfbcf66006befb9fd2aeaa9e01feaf881b4dc330a02ba07d2322b1c11be7b5d \ + --hash=sha256:bdbd97738551fca3917c1bd7188bec1920bb520104f28e7e1007f9ceb17b7690 \ + --hash=sha256:c60924535c75f1566b6eb75b5c31a48a43fef04fa2d0d201acbad8a9969c6107 \ + --hash=sha256:c7b9a2f8f4d8e90af72571d3d495deebdd7e3c75451f5b41719aee166e940fc2 \ + --hash=sha256:ca6546b66be9dc4738b1b043d5ebd5488c66c578c5ff0fd0e8065313fe3afb76 \ + --hash=sha256:ccffae9a092a00deb7efd545fe5e2c33c33b88e7c054337e9a74c179347d0b7d \ + --hash=sha256:cdc7e35386f3847df728fbcb5e887e2d79c19e2fa1eba9e51b6621d23e3243af \ + --hash=sha256:d15fde0e6fb0d88a60d221204873743e5d9f0b7d29165e62cd86d0413ad74ba6 \ + --hash=sha256:d34c20167764fbcf927194d532dd7e0c56772f0a5f943fa5ef9e9afbba8fb9db \ + --hash=sha256:d483fe17f01ad64b7bf7cc38fcefff1ca9fb83f8c2b2542b68f97ffe0611b369 \ + --hash=sha256:d7469697dce35be237db177d42e2a2ee26e6dcc5fc052078a6fefabd288c6edd \ + --hash=sha256:db08f45aecde626498fb3df07bcf6d2ec040af42e859a4f5040d79c200342911 \ + --hash=sha256:dc319e5a1de4b6913aac94bf6a2f9e847371e0a140a43dd4991db1a09bc2d504 \ + --hash=sha256:de3eceba0b683bcbb1ab93da016d0270df1f9ae7be716b40214c5dafac6ea45a \ + --hash=sha256:dfcc8b909769d19db55c7cc9541eb64b9b774b1057ffffb4f1048070475bb9f9 \ + --hash=sha256:e059c5dde6452b44424bd1834557556c226b57781dee1227af23518459722b13 \ + --hash=sha256:e4316bf32babbed84e691e352faf967ce2f0f024174a8643c37c94a1080374fc \ + --hash=sha256:e52655eaf81e32593abedaa4bfe33170c8cfedf3365ed9be6e11e07f148f0278 \ + --hash=sha256:e55d236be29255554da47abe5c577637db7c24a02b8b46f0ca9524c855801868 \ + --hash=sha256:ea7bb13b7c9a29791f87a0387ba7d3ad3a6d783d827e4d3f27b40a0ff44495e2 \ + --hash=sha256:ea964164cc9afa72d4d9b23cc28dafae93693c0a53e0b42acbff15b22c3f9ddd \ + --hash=sha256:ec829541c45bca16e61c7ae50c20501f213605beb75d1aba91a6ee37fbbb56a4 \ + --hash=sha256:ecabd69db66de867690f9797f2f8fa27ba501bbc24540cbdbdc649cd15888ba6 \ + --hash=sha256:ed0c1e5d10cdc7135537988c74a0188da68e2f3c30813ba3744ab1e42e0480f9 \ + --hash=sha256:f0840b5b17057f7fd918b76183a4b5a0635f43e14eb2ce60dce1d4ee4707ea00 \ + --hash=sha256:f4d78253f6996be4901669ad25319f842f740eccf4d58e3c7f3dd39e6dde1d8f \ + --hash=sha256:f56f1695bc5c0871cbc33dc0130fcf503aab0c57dcc5a6700a4f49eba4f2652e \ + --hash=sha256:f826877d462181e5eb1c26a0026b8d0cab05d99844ecb6d8bf3627a2ca0c0442 \ + --hash=sha256:f8f23ead891a3b762f35ab3b04623da7056545b48aa60d59957e6789914545da \ + --hash=sha256:f90938e92afda60266da758ee7d363447f7f0138c9559f9e1811629580582d90 \ + --hash=sha256:faa679d19a6696fd54259ad321251ad77a13e70e03dd834daa762a44fb6196ef + # via + # jsonschema + # referencing +samplerate==0.2.4 \ + --hash=sha256:089a19db605be2385558294480169afdcb1e89dcf1fdb72bd5a2e0849dd783b5 \ + --hash=sha256:2625a21eeffb81c896887659d6dffc01eedfdd60caaf653126a42871eee77aa6 \ + --hash=sha256:26cea1b3ba494a9632ec46a43c31bcca2e24b341f41ea42f333a1183f25773a5 \ + --hash=sha256:316736283db9c24a7058caaa56d9782d8446361ceb343930a0be5eebbd8d09fd \ + --hash=sha256:3a0bbbf088894208386b0dc30ebacd51bab7406931cd9c6c31c1cf99baf026ee \ + --hash=sha256:41249f544776a4b133443e811df3e1e364d3f66706fc6b45ea9a450ea2d6259f \ + --hash=sha256:4555da77c9b85c94688a31cde24a974ca467d45896f4b31dd16c185545d49198 \ + --hash=sha256:4a6b9fc423687dd84323cea7a2629bf18b9b102f2df430b5654631f134fe8fcf \ + --hash=sha256:558ad3b4416559a9b3a0e30a5315fd750d8d814162415531e961098a37976ddc \ + --hash=sha256:5a4c2bd7f4fa3a1d638857adf8fb6f364383b18c5f5d877dc1b9eb1b6e45405d \ + --hash=sha256:610a33a2d7e2a68b94dffc5918cb8354de1b2f2ab1f03394190b58aa8a5da320 \ + --hash=sha256:68316335e3710de4991d31a715268c6c7bdc95a931858b8e4dc6e721859a2473 \ + --hash=sha256:98f13c280f9e220eeb7512896eedeab45bf8fcb7914a9507f79b3f88add72bef \ + --hash=sha256:ad01c5f49181dd50ac2e36e7590619e743985a1a783540c7f4eb138482091da0 \ + --hash=sha256:ae92e3bcea4f74b2a66dc68b2cccef939250fca749982a45a614d10fe9f71440 \ + --hash=sha256:af5ec978ed22567042ad188db622409d3c059245fe32a19202b9281b1352b962 \ + --hash=sha256:b6a2c5cda090f3d5007a443c0f22635efe87b2fc0d2ea18dbdbd933cc2e560d2 \ + --hash=sha256:be2159e7119647214f93da735d3f09753dd9f7969ff8be061c23f8eb218b1a2a \ + --hash=sha256:c44dcb6fe680246f8f36588ba1f0fc7a0c5fbce710ad5e9b3812d88e8c39ac7d \ + --hash=sha256:c5db609a3e54484882a18536ef7e7d6216f3f29944bdda440771c066607e1beb \ + --hash=sha256:d10c3088f63a55923cfda33dfc55498df0f92bf2afeeeca4a90751589e3b6af0 \ + --hash=sha256:e692d453ae6952a9fe7c66b451027370333553d31ca4690375f34f725ba6a6cd \ + --hash=sha256:f8093c12d698ae821501efeb0796f189cb354d48ea8f6299ad9d79468df22eec + # via -r requirements.in +scipy==1.17.1 \ + --hash=sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0 \ + --hash=sha256:02ae3b274fde71c5e92ac4d54bc06c42d80e399fec704383dcd99b301df37458 \ + --hash=sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118 \ + --hash=sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39 \ + --hash=sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e \ + --hash=sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6 \ + --hash=sha256:1f95b894f13729334fb990162e911c9e5dc1ab390c58aa6cbecb389c5b5e28ec \ + --hash=sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21 \ + --hash=sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1 \ + --hash=sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6 \ + --hash=sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce \ + --hash=sha256:35c3a56d2ef83efc372eaec584314bd0ef2e2f0d2adb21c55e6ad5b344c0dcb8 \ + --hash=sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448 \ + --hash=sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19 \ + --hash=sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b \ + --hash=sha256:41b71f4a3a4cab9d366cd9065b288efc4d4f3c0b37a91a8e0947fb5bd7f31d87 \ + --hash=sha256:43af8d1f3bea642559019edfe64e9b11192a8978efbd1539d7bc2aaa23d92de4 \ + --hash=sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9 \ + --hash=sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b \ + --hash=sha256:4eb6c25dd62ee8d5edf68a8e1c171dd71c292fdae95d8aeb3dd7d7de4c364082 \ + --hash=sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464 \ + --hash=sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87 \ + --hash=sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c \ + --hash=sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369 \ + --hash=sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad \ + --hash=sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f \ + --hash=sha256:744b2bf3640d907b79f3fd7874efe432d1cf171ee721243e350f55234b4cec4c \ + --hash=sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475 \ + --hash=sha256:766e0dc5a616d026a3a1cffa379af959671729083882f50307e18175797b3dfd \ + --hash=sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866 \ + --hash=sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d \ + --hash=sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6 \ + --hash=sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb \ + --hash=sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca \ + --hash=sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0 \ + --hash=sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca \ + --hash=sha256:9ecb4efb1cd6e8c4afea0daa91a87fbddbce1b99d2895d151596716c0b2e859d \ + --hash=sha256:a3472cfbca0a54177d0faa68f697d8ba4c80bbdc19908c3465556d9f7efce9ee \ + --hash=sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4 \ + --hash=sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717 \ + --hash=sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49 \ + --hash=sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2 \ + --hash=sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a \ + --hash=sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350 \ + --hash=sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950 \ + --hash=sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b \ + --hash=sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086 \ + --hash=sha256:cd96a1898c0a47be4520327e01f874acfd61fb48a9420f8aa9f6483412ffa444 \ + --hash=sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068 \ + --hash=sha256:d30e57c72013c2a4fe441c2fcb8e77b14e152ad48b5464858e07e2ad9fbfceff \ + --hash=sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a \ + --hash=sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50 \ + --hash=sha256:e18f12c6b0bc5a592ed23d3f7b891f68fd7f8241d69b7883769eb5d5dfb52696 \ + --hash=sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21 \ + --hash=sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c \ + --hash=sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484 \ + --hash=sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118 \ + --hash=sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3 \ + --hash=sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea \ + --hash=sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293 \ + --hash=sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76 + # via -r requirements.in +send2trash==2.1.0 \ + --hash=sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c \ + --hash=sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459 + # via jupyter-server +six==1.17.0 \ + --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ + --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 + # via + # python-dateutil + # rfc3339-validator +soupsieve==2.10 \ + --hash=sha256:49e9380d7d2905463583bafe285e818c7366a9ed7b3aee221c1ac79c905d8bc0 \ + --hash=sha256:8596eb8967d744174820280fa62b4542a2e955bfaccca73ed8a13c6eb8e9b502 + # via beautifulsoup4 +soxr==1.1.0 \ + --hash=sha256:1577865e993f98ffb261257c3060fa76ec3db44ed3f181b16464268000424464 \ + --hash=sha256:26925618945f1a44dfbd783cc572874f0685e9ecdf46b96f4000f6b8c9c8b825 \ + --hash=sha256:318925f7281df61dfa7f17fe343952eb10cefd3954f2423a733fabe3a517bab2 \ + --hash=sha256:33525740fb7dbed8b09970bf0cd4219b365538845053987b11cc235b20562e09 \ + --hash=sha256:34cc92208c3c412c046813e69da639c04a792c6a41fbfd7d909d359cd3e97a2d \ + --hash=sha256:3b033078e86f3c4a658e5697fac8995764fad9e799563616b630136b613167f1 \ + --hash=sha256:3da87e3ffa3e41823d873b051c7ecb2acebd8d1b6b46b752f5facf10a0d84ab9 \ + --hash=sha256:474aabb9283f177e899747510d60661730538052fca0ed93a943d4686d6655b1 \ + --hash=sha256:52c9ca84e3dc656d83acc424574770e20ea8e0704dc3842d4e27b0fe9d3ba449 \ + --hash=sha256:588c7de1abafe59e66face9a074514658ac0398c85a774cdbb8efac131192692 \ + --hash=sha256:6ae2a174bffea94e8ead857dad85999d3f49f091774dbad5b046c0417d7092f4 \ + --hash=sha256:868a24d864c25024f60ca964f851a759f2ada5352608fc194d927b7facc2e28b \ + --hash=sha256:8e11e26f1718b5c2e5b96f2f71b9f00e31d247b065289661e3a6996c758669d9 \ + --hash=sha256:9443e5eb82152d8952422b7285692192cc7dcffa5218bb511b096203018bc273 \ + --hash=sha256:9564d82f7fa6bf548e5f18bb86235dff20eea8bd30727b64d49783c95c34fb8d \ + --hash=sha256:9f228ae21c78fa9359ca98d8a5e8e91f30639e438e574133dace62c5b5309e44 \ + --hash=sha256:a941f5aaa0b8abced24318105c1ea22576afcc1138c19f625716ce4e2f76ad64 \ + --hash=sha256:ae30c48ac795378cf23ba3c7c640b8ff794af714ac388b9fd6b31a40b39e6e86 \ + --hash=sha256:b2e94c713b7d96fb92841947b785bcee6606124bc852273fab70454b51bfe270 \ + --hash=sha256:bd30f7201eac896ebf5db7b09156e6f1a1b82601900d29d9c8449bdad8365b11 \ + --hash=sha256:bf98c0d7b7d5ef5bf072fee8d3020e8b664f2d195933ea7bc5089267c2e22a06 \ + --hash=sha256:d6a7ad82b8d5f3fcc04b1d2ca055562b96af571e1d4fa7c6c61d0fb509ac43b4 \ + --hash=sha256:e0e09fa633ce2e67df08b298afced4d184f6e753fc330f241022250f1d0d61da \ + --hash=sha256:e17d4ef9b0185214b2c0935605ae63f827ea423bc74964be44763d68d2b6c21e \ + --hash=sha256:f4977323ef9c3aa3c2a26ff5fe0191c84b8fd759daf7afb1f25a91a55ad8b730 \ + --hash=sha256:feebcba99ac99adb8009d46c8f4c1956b8c167576b0ae8a6fb47502e9a6f78e7 + # via -r requirements.in +stack-data==0.6.3 \ + --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \ + --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695 + # via ipython +terminado==0.18.1 \ + --hash=sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 \ + --hash=sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e + # via + # jupyter-server + # jupyter-server-terminals +tinycss2==1.5.1 \ + --hash=sha256:3415ba0f5839c062696996998176c4a3751d18b7edaaeeb658c9ce21ec150661 \ + --hash=sha256:d339d2b616ba90ccce58da8495a78f46e55d4d25f9fd71dfd526f07e7d53f957 + # via bleach +tornado==6.5.10 \ + --hash=sha256:302eb1e0e3e159314eb591920529fdea80acca92df5510a2cec5bbd4f099ec72 \ + --hash=sha256:37ae8f150cecfdbf747fc4e12f5e9a97ecd8cf1d4cdb3f119e2de84b11196918 \ + --hash=sha256:4bd192b959f9128fb99b8898148070ba4574c9589b78bce42d1851131fe85828 \ + --hash=sha256:66aaa3f57d30c6e6becee83ff28055d5930ac724214bde99393eefda83d5e015 \ + --hash=sha256:69acca6501eed74582b76dbbceee2a91613f54728e3e418346000d7103101676 \ + --hash=sha256:83e6cf438b106c6b3852d70960967bb1b70c87438050dca0981e4b9aa751a4c1 \ + --hash=sha256:9261783640e23258694a9ff0795df430a5a7b0a651d3dd53dd0969ad6be16da7 \ + --hash=sha256:a6b1ccd08c04b4a06fb5aeb381be99de5ad1e5375c1785e31d78c880feb57687 \ + --hash=sha256:bdf942448169e5336451d0494d7e3d81cfa726d5aa312affdc4682dd62a62f6d \ + --hash=sha256:ce045d3c298fddd30e89a2777f97039d1b641eb9518ac7b26a4721903539c694 + # via + # ipykernel + # jupyter-client + # jupyter-server + # jupyterlab + # notebook + # terminado +traitlets==5.16.1 \ + --hash=sha256:ed900c2b631aa3a112811139fa97b8d2c3bad5e989656bba4b7e52c7852c18c1 \ + --hash=sha256:f775618166caa0396c8e337099240f2bd3e5e917d203b2e6fbe21a58d3cb1f6b + # via + # ipykernel + # ipython + # ipywidgets + # jupyter-builder + # jupyter-client + # jupyter-console + # jupyter-core + # jupyter-events + # jupyter-server + # jupyterlab + # matplotlib-inline + # nbclient + # nbconvert + # nbformat +typing-extensions==4.16.0 \ + --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ + --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5 + # via + # anyio + # beautifulsoup4 + # ipython + # jupyter-client + # jupyterlab + # referencing +tzdata==2026.4 \ + --hash=sha256:c2169a8b0a7a5e9674da5a135ccdfb2b3e671b333ed9fed17b41f73c34476e81 \ + --hash=sha256:f1b8bd365d8d210c55353f4d7f8d6d8561c0ba50d704b700d195a9424bba0d79 + # via arrow +uri-template==1.3.0 \ + --hash=sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7 \ + --hash=sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 + # via jsonschema +urllib3==2.8.0 \ + --hash=sha256:0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3 \ + --hash=sha256:63bf2ead4c879426ebf22ef2a781eeb4aa3b4ae798a0435506f8687fd5bb9b63 + # via requests +wcwidth==0.9.1 \ + --hash=sha256:03cfca3dcbffa86564290fe3c9978a6191ba003e8ced7f7dbda315fcb3fbe725 \ + --hash=sha256:0665ee822ea04e25801e6e82e5407528be0863e88a17b0e7ca038843a4a3ac0c \ + --hash=sha256:0d68a30d504c68cfdff2a5f804675c1e7ab4c0bbe878024c8b680ec5579cde67 \ + --hash=sha256:10b00ba23482e352f874d2e8135e7ace9da838646c7dd800566246bbd46125ff \ + --hash=sha256:356376852357b8fca71fe5415808ec421679e04b4a98eb7c9cb6a7984b911a05 \ + --hash=sha256:40d936d72c9bdc10df43f93a8be502bc5024b487259139f66a328722c07f34a9 \ + --hash=sha256:5823209b0d43af322ce698c689380d7c15ca31fa8e6e3be8459f27031bef0af5 \ + --hash=sha256:61bd7aef9cafb6cb77a37a169998d7928ce82a51522146d11f60db9e7d1cb43a \ + --hash=sha256:69bb970cf5652b88cfdb9d3fdd1764fc15e5f8ad531643e7bb3e894cd969740e \ + --hash=sha256:6e1272b7986cefe79783737e38bdb9eaae0682b333c7bd132024441193dd5ce7 \ + --hash=sha256:708158c082364af442f9983de7b6ec9ac0d2e1b825ada25f0911b1f138d55405 \ + --hash=sha256:747fb724223f417a17541a95a17c1dac3a8ef9a0cf41684950f0eab191a35f65 \ + --hash=sha256:991d1c8834f548e9c1f16432075ee84638e122312556bbf1ed595ea8fffc4673 \ + --hash=sha256:9f1636c5075ffd5c2e835b4561874f6e5dd2bbeba3c6c2c99f067d0d16883af8 \ + --hash=sha256:b5da43d6967668982e44a52fb551967d293f86d26cd86036ac95bbdd34394ed9 \ + --hash=sha256:bcb9ed4a367cc025bf1092ac679a156759605182d60b7ed3c28f7221a42ddf55 \ + --hash=sha256:c4cead196551112cb8f43cdd1f80c235ef2456e34b1a9955c424537ec99961b2 \ + --hash=sha256:dc10e262c3ac0abbfd0a2a51e45a848b1b7f500b21ff512630277973ba25674d \ + --hash=sha256:e0c3a1c45c5b9550c6919a4449e95f5b177f6e165786376981db8f1addae9b21 \ + --hash=sha256:eab587e18e7cadf1a750b0098fc8bebfb62c125eb9306f2268f0443a282a3d78 \ + --hash=sha256:fe021c4d8de9d36c31a0cb41d0d2546dadd3b0708a301f1a0c66ce200851831f + # via prompt-toolkit +webcolors==25.10.0 \ + --hash=sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d \ + --hash=sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf + # via jsonschema +webencodings==0.6.1 \ + --hash=sha256:565f9ad031c702dae404e27a099e3e09186a3ab1b9520f06d215502b651fd910 \ + --hash=sha256:7fab6269c8bf237c657876b52058ccb182e861518d1c695c1a9aaa8c1c105d5b + # via + # bleach + # tinycss2 +websocket-client==1.9.2 \ + --hash=sha256:0fcb57545848be86992e128218fd96dd87a6769ffdb1a968dff79632b85604d0 \ + --hash=sha256:e1a673830a9c7bfa47b1cd3d5e4178f4c9651d80a4eab02c9c23a1c3ec6250ce + # via jupyter-server +widgetsnbextension==4.0.16 \ + --hash=sha256:a31a8774885b96fe825462f5d6496166f0c7cae111195b6465c801d230eb5a4e \ + --hash=sha256:adeea0ae78f0856ee4945f413299801b82a0a01416303301f39a704282a37b73 + # via ipywidgets From 654659ea272fa62d6458c2ef1f95eaeb7c4ce276 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 20:42:01 +0000 Subject: [PATCH 33/44] Re-pin the SampleRateTap test dependency to its post-step-P main Moves submodules/sampleratetap 2b4dff1 -> 5e2057f, SampleRateTap's main after its step-P PR (#48). That range changes no header under include/ and keeps the same DspTap pin (0eb09fa), so the tree the cross-validation compiles against is unchanged: 82/82 tests pass and the four cross-validation lines are byte-identical to before. The monorepo migration's step 0 snapshots against this pin, so the cross-validation lines it records come from the same async tree the import compiles (step P.4 of the plan). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- bridge/submodules/sampleratetap | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bridge/submodules/sampleratetap b/bridge/submodules/sampleratetap index 2b4dff1..5e2057f 160000 --- a/bridge/submodules/sampleratetap +++ b/bridge/submodules/sampleratetap @@ -1 +1 @@ -Subproject commit 2b4dff17abd095dd01d72055a69a4385a64b2304 +Subproject commit 5e2057f192cee1eeb61286c466e5f50293580c09 From bb9f8fc8b9db81e37f7aa168682db7d6fa9d0c2e Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 26 Sep 2026 20:32:01 +0000 Subject: [PATCH 34/44] Add monorepo migration plan for the tap::sr family (draft for review) Proposes merging RatioTap into SampleRateTap as one repository with per-engine directories (async/, ratio/), the tap::sr:: namespace, and a history-preserving import. Records the decisions taken, a measured inventory of both repositories, a gated five-step migration, risks, open questions and an adversarial audit checklist. Nothing is executed yet. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 451 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 451 insertions(+) create mode 100644 docs/MONOREPO_PLAN.md diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md new file mode 100644 index 0000000..15af382 --- /dev/null +++ b/docs/MONOREPO_PLAN.md @@ -0,0 +1,451 @@ +# Monorepo plan: the `tap::sr` sample-rate family + +Status: **DRAFT for adversarial review** — nothing below has been executed. +Written 2026-09-26. Once this plan is reviewed and approved, it becomes the +family-level `PLAN.md`. Until then it is the only document that describes +the change, and nothing in the repository depends on it. + +This document has two jobs: + +1. Record the **decisions** already taken (section 1), so the review can + challenge them one by one rather than re-deriving them. +2. Specify the **migration** in enough detail (sections 4–7) that each step + has a mechanical acceptance gate, and the review can find what is wrong + or missing *before* any code moves. + +Section 9 is the audit checklist: the specific questions the adversarial +review should try to break. + +--- + +## 1. Decisions (proposed as settled; the review may reopen any) + +| # | Decision | Rationale | +|---|---|---| +| D1 | **Merge SampleRateTap and RatioTap into one repository**; each engine keeps its own charter, CMake target, CI job and ratchet baselines | The engines are built to be composed and cross-checked against each other. Today that needs a test-only submodule of the sibling repo (`RatioTap/submodules/sampleratetap`), plus two DspTap pins to keep in step and a duplicated embedded toolchain. There are no external consumers yet, so the rename is free | +| D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" describes the whole family once it is namespaced. The published book (tap.github.io/SampleRateTap) keeps its URL. SampleRateTap has more history (64 commits) than RatioTap (29) | +| D3 | **RatioTap's history is preserved** through a `git filter-repo` path rewrite and an unrelated-histories merge | `git log --follow` and `git blame` must keep working for every file of both engines | +| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace; today's `srt/` include path breaks it | +| D5 | **Engine names:** `async` (today's SampleRateTap), `ratio` (today's RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term (ASRC) and the family's own clock-topology vocabulary. It is the only async engine, and async at other ratios is reached by composition. Every other engine is sync, so those are named by what they convert | +| D6 | **One top-level directory per engine** (`async/`, `ratio/`, …), each with its own `include/ tests/ bench/ examples/ capi/ PLAN.md` | Keeps each charter's boundary physical, where a single shared `include/` tree would not | +| D7 | **Clean renames, no compatibility aliases** | There are no consumers yet; aliases would be permanent debt | +| D8 | **Unified C ABI prefix** `tap_sr__*` and one shared library | One ctypes bridge serves all the notebooks | +| D9 | **CMake options** `TAP_SR_*` (e.g. `TAP_SR_BUILD_TESTS`, `TAP_SR_WERROR`), plus per-engine enables `TAP_SR_ENGINE_` | Replaces `SRT_*` / `TAP_RATIO_*` | +| D10 | **DspTap stays a separate repository**, pinned once at `submodules/dsptap` | It has consumers outside the rate family (TapTools and others) | +| D11 | **Charter rule for new engines:** a capability gets a directory here when it has its own charter, optimization campaign and instruction-count ratchet. Building blocks (filter design, kernels, the `chain<>` composition template, `fractional_resampler`) go into DspTap | This was the repo-vs-substrate rule; it now decides engine directory vs. DspTap | + +--- + +## 2. The family after the merge + +| Engine | Namespace | Origin | Charter | +|---|---|---|---| +| `async` | `tap::sr::async` | SampleRateTap v0.1.0 | Asynchronous, near-unity (±`max_deviation_ppm`, default 1000 ppm): absorbs the clock | +| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous, 44.1 ↔ 48 kHz only (160/147, 147/160): converts the number | +| `integer` | `tap::sr::integer` | new (step 5) | Synchronous integer factors 2^a·3^b, up, down, and oversampling pairs; half-band/third-band (Nyquist L-th band) stages. Absorbs DspTap's `decimate.h` | +| `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM (MEMS mics, DSD): CIC → compensation FIR → `integer` stages | +| `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio (varispeed, scrubbing, Doppler); bandlimited interpolation (Smith, CCRMA) | + +Not engines, deliberately: + +- **Timestamp-driven clock recovery** is a feature of `async`. +- **Minimum-phase and IIR low-latency tiers** are *profiles* of `ratio` and + `integer`. +- An **offline FFT tier** would be a `transparent+` profile, built only if + someone asks for it. + +### 2.1 Rate coverage with `ratio` + `integer` + +Every standard rate is 44.1k·2^a or 48k·2^a·3^b (b ∈ {0, −1}). Target coverage: + +| From → To | Chain (proposed) | Intermediate rates | +|---|---|---| +| 48 ↔ 44.1 | `ratio` | — | +| 96 → 44.1 | `integer` ↓2 → `ratio` ↓ | 48 | +| 192 → 44.1 | `integer` ↓4 → `ratio` ↓ | 96, 48 | +| 44.1 → 96 / 192 | `ratio` ↑ → `integer` ↑2 / ↑4 | 48 | +| 48 → 16 / 8 | `integer` ↓3 / ↓6 | — | +| 44.1 → 16 | `ratio` ↑ → `integer` ↓3 | 48 | +| 16 → 48 | `integer` ↑3 | — | +| 48 → 32 | `integer` ↑2 → ↓3 | 96 | +| 44.1 → 32 | `ratio` ↑ → `integer` ↑2 → ↓3 | 48, 96 | +| 44.1 ↔ 22.05 / 11.025 | `integer` ↓2 / ↓4 (and ↑) | — | +| 88.2 ↔ 96, 176.4 ↔ 192 | **open — see Q3** | — | + +**Chain invariant (proposed):** no intermediate rate may be lower than +min(input, output), so the chain never throws away band it must deliver. +48 → 32 is therefore ↑2 then ↓3, never ↓3 then ↑2, which would cut the band +at 8 kHz. + +The coverage matrix is generated and pinned by tests, not written by hand. +For each pair it records the MACs per output, total latency and measured +floor. **It is not produced in this migration.** It arrives with `integer` +(step 5). + +--- + +## 3. Inventory of the two repositories (measured 2026-09-26) + +Branch `claude/sample-rate-expansion-strategies-ezqzu6` in both repositories, +at SampleRateTap `2b4dff1` and RatioTap `349ab7b`. + +### 3.1 Shipped headers + +| Today | After | +|---|---| +| `SampleRateTap/include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…` | +| `SampleRateTap/include/srt/detail/kaiser.h` (26 lines: a re-export shim of `tap::dsp` kaiser) | **deleted**; callers include `tap/dsp/kaiser.h` directly | +| `RatioTap/include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `ratio/include/tap/sr/ratio/…` | + +`srt/sample_traits.h` layers on `tap/dsp/sample_traits.h` rather than +duplicating it. It moves unchanged. Folding it into DspTap is out of scope. + +### 3.2 Pins + +- Both repositories record DspTap at **`0eb09fa`**. The pins agree, so step 1 + needs no pin reconciliation. +- RatioTap also pins `submodules/sampleratetap` at `2b4dff1`, which equals + SampleRateTap's HEAD. After the merge the pin disappears, and the + cross-validation compiles against the in-tree `async` engine **at the same + tree**. +- Note: in this session's checkouts the working-tree submodules sit at + `28a34a1` / `5315689`, not the recorded pins. The migration must build + against the **recorded** pins (`git submodule update --init` before every + gate). + +### 3.3 Duplicated infrastructure: measured divergence + +RatioTap's copies were ported from SampleRateTap. The measured differences +are **cosmetic only**: comments, mdBook `ANCHOR` markers (present only on +the SampleRateTap side) and name prefixes. + +| File | Changed lines | Nature | +|---|---|---| +| `.clang-format`, `.clang-tidy`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/hooks/session-start.sh`, `.claude/settings.json`, `scripts/tidy.sh` | 0 | identical | +| `platform/armv8m_startup.c` | 21 | comments, copyright line, book anchors | +| `platform/mps2_an505/*.ld`, `platform/mps3_an547/*.ld` | 5 each | book anchors, provenance comment | +| `cmake/arm-cortex-m33-mps2.cmake`, `…-m55-mps3.cmake`, `hexagon-linux-musl.cmake` | 10 / 21 / 12 | `SRT_`/`TAP_RATIO_` option names, comments, anchors | +| `scripts/icount.py` | 19 | binary prefix `srt_icount_*` vs `ratio_icount_*`; markers `SRT_INSN_COUNT` / `SRT_ICOUNT_DONE` vs `RATIO_*` | +| `tools/qemu_insn_plugin/insn_count.c` | 13 | to be diffed line by line in step 2 (**audit item A6**) | + +Keep the SampleRateTap copies, because they carry the book anchors, and +parameterize the prefixes. + +Present in only one repository: + +- **SampleRateTap only:** `cmake/r8brain.cmake`, `tools/compare_shim`, + `bench/compare`, `compare.yml`, `ci-arm64.yml` (native weak-memory + TSan), + `book-pages.yml`, `book/`, `docs/`, `scripts/update_*_docs.py`, + `scripts/book_figures*`, `examples/pico2_*`. +- **RatioTap only:** `scripts/fetch_hexagon_toolchain.sh`, + `tools/reference/make_reference_vectors.py`, `tests/reference/`, + `HANDOFF.md`, and the CI dedup scheme (branch-filtered `push` + + `pull_request`, commits `f1e566a` and `94775b0`). + +### 3.4 The book + +`book/src` pulls in live code with `{{#include path:ANCHOR}}`, and CI fails +on a stale anchor. By path: + +- `include/srt/*` — 42 includes (`polyphase_filter.h` 17, `sample_traits.h` 8, `pi_servo.h` 8, `asrc.h` 5, `spsc_ring.h` 4) +- `submodules/dsptap/…` — 21 (unaffected by the move) +- `platform/`, `cmake/`, `tools/`, `tests/` — 20 + +Every non-DspTap include path changes in step 3. This is a mechanical +rewrite, but it is gated by the book build (`mdbook build`, warnings are +errors). + +### 3.5 CI and the ratchet + +| Job | SampleRateTap | RatioTap | +|---|---|---| +| Host matrix (GCC, Clang, AppleClang, MSVC) | `build-and-test` | `build-test` | +| Sanitizers (ASan+UBSan, TSan) | yes | yes | +| Linux arm64 native + TSan | `ci-arm64.yml` | — | +| Hexagon / M55 / M33 under QEMU | yes | yes | +| `icount-ratchet` | 7 workloads × {m33, m55, hexagon}; README table drift check | 10 workloads × {m33, m55, hexagon} (RatioTap's CLAUDE.md still says eight) | +| Resampler comparison | `compare-smoke`, `compare.yml` | — | +| Style (clang-format, tidy, drift) | yes | yes | +| Book | `book`, `book-pages.yml` | — | + +Baselines: `SampleRateTap/bench/baselines.json`, `RatioTap/bench/baselines.json`. +Both are keyed by target, then by workload. They are **not** merged into +one file (see step 2). + +### 3.6 C ABI + +`srt_capi.h` (≈8 exported functions) and `ratio_capi.h` (≈11) are +consumed through ctypes: RatioTap's notebooks via `notebooks/ratiotap_py.py`, +and SampleRateTap's via ctypes code inline in each notebook (three of its +four notebooks). + +### 3.7 Outside references to fix + +- **DspTap:** `README.md`, `CLAUDE.md`, `STYLE.md`, `platform/README.md`, + `docs/audit-fft-and-code-smells.md` and five test files mention + SampleRateTap or RatioTap by name. +- **RatioTap:** `CLAUDE.md`, `PLAN.md`, `HANDOFF.md`, `README.md` URLs. +- **SampleRateTap:** `README.md` ("Position in the Tap family"), `book/`, + `docs/COMPARISON.md`, `docs/PERFORMANCE.md`, `Doxyfile`. + +--- + +## 4. Target layout + +``` +SampleRateTap/ +├── CMakeLists.txt project(SampleRateTap); umbrella target tap::sr +├── CLAUDE.md family-level guidance (new; SampleRateTap has none today) +├── PLAN.md this document, promoted after review +├── README.md family front page: which engine, the coverage matrix +├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ (unchanged, identical today) +├── submodules/dsptap the ONE pin +├── cmake/ platform/ shared embedded toolchains (SampleRateTap copies) +├── tools/qemu_insn_plugin/ shared +├── scripts/ icount.py (engine-aware), tidy.sh, fetch_hexagon_toolchain.sh, +│ book/doc updaters +├── book/ one book; ratio chapters are follow-up work +├── notebooks/sr_py.py one ctypes bridge over the unified C ABI +├── capi/ tap_sr C ABI: one shared library, per-engine sources +├── async/ +│ ├── PLAN.md the async charter (drawn from today's README/docs) +│ ├── include/tap/sr/async/ +│ ├── tests/ bench/ examples/ notebooks/ docs/ +│ └── CMakeLists.txt target tap::sr::async +└── ratio/ + ├── PLAN.md HANDOFF.md (RatioTap's, history preserved) + ├── include/tap/sr/ratio/ + ├── tests/ bench/ examples/ notebooks/ tools/reference/ + └── CMakeLists.txt target tap::sr::ratio +``` + +Dependency rule, enforced by CMake: + +- `ratio` → `tap::dsp` only. +- `async` → `tap::dsp` only. +- An engine may depend on another **only in tests and examples** + (`ratio/tests` → `tap::sr::async` for cross-validation; the + `bluetooth_bridge` example composes both). +- A shipped header that includes a sibling engine is a CI failure: a + header-isolation test compiles each engine's headers against `tap::dsp` + alone. + +--- + +## 5. Migration steps + +Each step is one or more commits on +`claude/sample-rate-expansion-strategies-ezqzu6` in SampleRateTap. Each has +a gate that must be green before the next begins. Nothing reaches `main` +until a PR is reviewed. + +### Step 0 — Freeze and snapshot + +- Record the green state of both repositories at the SHAs above: + - `ctest` pass lists per host; + - `icount.py` output per target (these must match the committed + baselines); + - C ABI function lists. +- Merge nothing into either repository's `main` until step 4 lands. If + anything must land, it lands in SampleRateTap only, and step 1 is re-cut. + +**Gate:** both repositories are green on CI at the recorded SHAs, and the +snapshot artifacts are committed under `docs/migration/` (removed in step 4). + +### Step 1 — History-preserving import (no content changes) + +1. `git mv` SampleRateTap's engine files (`include/`, `tests/`, `bench/`, + `examples/`, `tools/capi`, `tools/compare_shim`, `notebooks/`, `docs/`) + into `async/`. Shared files stay at the root. +2. In a scratch clone of RatioTap, run + `git filter-repo --to-subdirectory-filter ratio/`. +3. Merge with `git merge --allow-unrelated-histories`. +4. Remove the duplicate shared files from `ratio/`: `.clang-*`, `STYLE.md`, + `.pre-commit-config.yaml`, `.claude/`, `scripts/tidy.sh`, `LICENSE` (see + Q5), `.gitmodules`, `submodules/`. +5. Keep, for now: `ratio/cmake`, `ratio/platform`, + `ratio/tools/qemu_insn_plugin`, `ratio/scripts/icount.py`. These are + deduplicated in step 2, so this step stays content-free. +6. Minimal CMake glue: a root `CMakeLists.txt` that `add_subdirectory`s + `async/` and `ratio/`, with each keeping its **current** target and + option names. Point `ratio`'s `srt_headers` at `async/include`, which + replaces the `submodules/sampleratetap` path. + +**Gate:** + +- `git log --follow` works on a sample of files from both engines. +- Host build and `ctest` pass lists equal the step-0 snapshot (same test + names, same count). +- Every QEMU leg is green. +- `icount.py` for **both** engines reproduces its committed baselines + **exactly** (0 %). Nothing that reaches codegen has changed, so any drift + is a bug in the import. + +### Step 2 — Shared infrastructure + +- Delete `ratio/cmake`, `ratio/platform` and + `ratio/tools/qemu_insn_plugin`; `ratio` uses the root copies. Merge any + non-cosmetic difference in `insn_count.c` first (**A6**). +- Make `scripts/icount.py` engine-aware: `--engine async|ratio`, a binary + prefix `tap_sr__icount_*`, and uniform markers `TAP_SR_INSN_COUNT` / + `TAP_SR_ICOUNT_DONE`. Baselines move to `/bench/baselines.json`, + with the same keys. +- One CI workflow: + - The host matrix and sanitizers build every engine. + - `icount-ratchet` runs as a matrix over engine × target, each engine + against its own baselines. + - `ci-arm64`, `compare`, `book` and style stay as they are. + - Adopt RatioTap's CI dedup scheme family-wide. +- One DspTap pin: already at root since step 1; verify there is no second + checkout. + +**Gate:** as for step 1, plus **0 % icount drift** for both engines on all +three targets. The toolchain files and startup code are now shared, so any +drift means the deduplication changed codegen. + +### Step 3 — Renames + +In one commit per rename class, each gated separately: + +1. **Include paths:** `srt/…` → `tap/sr/async/…`; `tap/ratio/…` → + `tap/sr/ratio/…`. Delete `srt/detail/kaiser.h` and point its callers at + `tap/dsp/kaiser.h`. +2. **Namespaces:** `tap::samplerate` → `tap::sr::async`; `tap::ratio` → + `tap::sr::ratio`. Optionally, `async_sample_rate_converter` → + `tap::sr::async::converter` (**Q2**). +3. **CMake:** + - Targets `tap::sr::async`, `tap::sr::ratio`, umbrella `tap::sr`. + - Options `TAP_SR_*`. + - Remove `SampleRateTap::SampleRateTap`, `tap::samplerate`, `tap::ratio` + (D7). +4. **C ABI:** + - `srt_*` → `tap_sr_async_*`; ratio's → `tap_sr_ratio_*`. + - One library, `capi/`. + - One bridge, `notebooks/sr_py.py`. + - `srt_version()` → `tap_sr_version()`, plus a per-engine version (**Q4**). +5. **Book:** rewrite the include paths (section 3.4), move anchors to the + new paths, and fix figure scripts. +6. **Ratchet names:** workload and binary renames. Baseline **keys stay + unchanged**, so history remains comparable. + +**Gate:** + +- Tidy gate and a local clang `-Werror` build pass. +- `mdbook build` passes with warnings as errors. +- `ctest` lists equal the snapshot, modulo mechanical renames (a mapping + table is committed). +- icount drift is 0 %. A symbol rename must not move codegen; if a + namespace-length change shifts the instruction count of anything + (it should not), it is investigated rather than re-recorded. +- Every notebook re-executes clean against the new bridge, with outputs + equal to the committed ones (numbers identical, only paths and names + differ). + +### Step 4 — Documentation and archival + +- Promote this document to `PLAN.md`. Write the family `CLAUDE.md` + (combining both repositories' guidance, and the dependency rule of + section 4). Rewrite `README.md` as the family front page. Add per-engine + `PLAN.md`s. +- DspTap: update the references in section 3.7, as a DspTap PR. +- Delete `docs/migration/`. +- **By the user, on GitHub:** archive RatioTap with a README pointer. The + repository rename is not needed (D2). + +**Gate:** a PR to SampleRateTap `main` that is green on every job; the DspTap +docs PR merged or approved. + +### Step 5 — First new engine: `integer` (separate plan) + +`integer` is out of scope for this migration and gets its own PLAN.md +reviewed the same way. The sequence already decided: + +1. The L-th band design lands in DspTap. +2. `decimate.h` moves out of DspTap into `integer/` (a DspTap PR first; its + only in-repo users are its tests and the C ABI). +3. The `chain<>` template lands in DspTap. +4. The coverage matrix of section 2.1 becomes a test. + +--- + +## 6. What stays unchanged (explicit non-goals) + +- No algorithm, coefficient, filter design or process-loop change anywhere. + Every output stays bit-identical, and every icount stays within 0 %. +- No new engine, profile or API function. +- DspTap content is untouched; only its docs change. +- Versioning is not unified in this migration (**Q4**). + +--- + +## 7. Risks + +| # | Risk | Mitigation | +|---|---|---| +| R1 | A step silently drops a test (e.g. a CMake glob moves) | The gate compares **named** test lists against the step-0 snapshot, not just "green" | +| R2 | Infrastructure dedup changes codegen (a startup or linker-script difference) | Divergence measured as cosmetic (section 3.3); gate at 0 % icount drift on every target | +| R3 | Book anchors break across the move | `mdbook build` with warnings as errors is part of the step 3 gate; the paths are enumerated (section 3.4) | +| R4 | Cross-validation passes against a different SampleRateTap than before | RatioTap pins `2b4dff1` == SampleRateTap HEAD; step 1 must start from exactly that tree | +| R5 | `filter-repo` rewrites RatioTap commit SHAs, breaking references in its docs and commit messages ("M7d", PR numbers) | SHAs cited in `ratio/PLAN.md` are listed and annotated with a pointer to the archived RatioTap repository | +| R6 | Two ratchets in one CI multiply QEMU time and cache contention | The matrix runs engine × target in parallel; the existing digest-keyed toolchain cache is shared | +| R7 | Merged `main` history interleaves two engines' commits and confuses `git bisect` | Accepted; `--first-parent` bisect on `main` is unaffected | +| R8 | The monorepo erodes charter boundaries over time | The dependency rule is enforced by CMake plus the header-isolation test (section 4); per-engine PLAN.md files | + +--- + +## 8. Open questions for the review + +- **Q1.** Should `ratio` keep `HANDOFF.md` in-tree (history), or retire it to + the archived repository? +- **Q2.** Rename `async_sample_rate_converter` → `tap::sr::async::converter` + for symmetry with `ratio`'s `converter`, or keep the descriptive name? +- **Q3.** 88.2 ↔ 96 and 176.4 ↔ 192. Either widen `ratio`'s charter to the + 2× and 4× rates (its designs are specified in Hz, so this is a + re-specification), or chain `integer` ↓2 → `ratio` → `integer` ↑2, which + breaks the chain invariant of section 2.1 by passing through 44.1 on a + 48-family path. Decide before `integer`'s plan. +- **Q4.** Versioning: SampleRateTap is 0.1.0 and RatioTap is 0.3.0. Options + are one family version, per-engine versions, or both. +- **Q5.** License and copyright lines differ ("SampleRateTap contributors" + vs "Timothy Place and the RatioTap contributors"). Unify to one holder + line family-wide? +- **Q6.** Does the family README absorb the "Position in the Tap family" + material from both repositories, or link to per-engine READMEs? + +## 9. Adversarial audit checklist + +The review should try to **break** each item, not confirm it. + +- **A1 — Decisions:** is any of D1–D11 wrong, or inconsistent with the + DspTap, RatioTap or SampleRateTap CLAUDE.md/PLAN.md rules + ("substrate lands in DspTap first", "never route by rate", "correctness + before optimization")? +- **A2 — Chain invariant:** is "no intermediate rate below min(in, out)" + the right rule? Find a standard rate pair it forbids that must be + supported, or one it allows that loses band. +- **A3 — History:** does the `filter-repo` + unrelated-merge recipe keep + `--follow`/`blame` for *every* file? (RatioTap's history contains no + renames today (`git log --diff-filter=R` is empty); re-check at cut time.) +- **A4 — Gates:** is each gate actually sufficient to detect a regression + the step could introduce? Name a regression that passes a gate. +- **A5 — Bit-identity claims:** are "0 % icount drift" gates achievable? + Consider whether any symbol-name length, section order or string table + reaches the measured instruction stream (the icount counts executed + instructions, not size). +- **A6 — Infrastructure:** diff `insn_count.c`, the startup code and the + linker scripts line by line. Is any difference functional? +- **A7 — Cross-validation:** after the merge, is `ratio`'s cross-validation + still an *independent* check, or does sharing one tree with `async` create + a common-mode failure the separate repositories prevented? +- **A8 — Dependency rule:** can the header-isolation test be bypassed? Does + `bluetooth_bridge` pull `async` into a shipped target anywhere? +- **A9 — Inventory completeness:** is anything in either repository missing + from section 3 (files, workflows, secrets, Pages configuration, badges, + issue references, the Pico 2 examples' build paths)? +- **A10 — Reversibility:** if the migration is abandoned after step 2, is + RatioTap still intact and usable? (It should be: nothing touches the + RatioTap repository until step 4.) +- **A11 — Consumers:** is it really true that nothing outside these + repositories consumes `srt/`, `tap::samplerate`, `tap::ratio` or the C + ABIs (TapTools, TapTools-Max, MuTap, AmbiTap, any Pages or notebook + links)? From 3f61daa5ac196de3d2e9407fea85e785db585b5d Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 26 Sep 2026 20:53:58 +0000 Subject: [PATCH 35/44] Revise the monorepo plan after adversarial audit (v2) Folds in the five-reviewer audit (78 findings, 6 blockers). Main changes: - Correct the step-1 recipe: filter-repo rewrites .gitmodules throughout history; a pure-move commit before the merge keeps blame; build glue (root enable_testing, single dsptap, forced test options) and ratio's CI port happen in step 1, not later. - Define gates once (G1-G13): test multisets, exact icount with workload set equality, disassembly and compile-flag identity, full-output hashes, all as same-job A/B against the step-0 SHA. - Add a pre-work step fixing existing breakage (arm64 TSan filter, Pico 2 builds, blame-ignore file) and hardening the harness (Hexagon argv/env, icount --exact) before the snapshot. - Require a merge commit for the final PR; document first-parent bisect. - Replace the chain invariant with a passband rule; restate ratio's charter at 44.1*2^k <-> 48*2^k; keep decimate.h and the async datapath out of the move; forbid rate routing through chain<>. - Correct the inventory (136 commits, functional infra diffs, CI table, exact C ABI counts, 25 DspTap references) and add a file disposition table and an appendix mapping every finding to its resolution. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 1072 +++++++++++++++++++++++++++-------------- 1 file changed, 707 insertions(+), 365 deletions(-) diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index 15af382..91677af 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -1,38 +1,46 @@ # Monorepo plan: the `tap::sr` sample-rate family -Status: **DRAFT for adversarial review** — nothing below has been executed. -Written 2026-09-26. Once this plan is reviewed and approved, it becomes the -family-level `PLAN.md`. Until then it is the only document that describes -the change, and nothing in the repository depends on it. +Status: **DRAFT v2. Revised after adversarial audit; nothing executed.** -This document has two jobs: +- v1 (2026-09-26, `11f2a94`): the first draft. +- v2 (2026-09-26): folds in the five-reviewer adversarial audit. It had 78 + findings, of which 6 were blockers. +- Appendix A maps every finding ID (DEC-, GIT-, GATE-, INF-, INV-) to where + it landed in this document, or to why it was rejected. -1. Record the **decisions** already taken (section 1), so the review can - challenge them one by one rather than re-deriving them. -2. Specify the **migration** in enough detail (sections 4–7) that each step - has a mechanical acceptance gate, and the review can find what is wrong - or missing *before* any code moves. +Once this plan is approved, it becomes the family-level `PLAN.md`. Until +then it is the only document that describes the change, and nothing depends +on it. -Section 9 is the audit checklist: the specific questions the adversarial -review should try to break. +How to read it: + +- Section 1 holds the decisions. +- Section 2 is the family, the charters and the coverage rules. +- Section 3 is the measured inventory. +- Section 4 is the target layout and a file-by-file disposition table. +- Section 5 defines the gates once. +- Section 6 is the migration: pre-work step P, then steps 0–5, each + referencing those gates. +- Sections 7–9 are the non-goals, risks and open questions. --- -## 1. Decisions (proposed as settled; the review may reopen any) +## 1. Decisions -| # | Decision | Rationale | +| # | Decision | Rationale / supersession | |---|---|---| -| D1 | **Merge SampleRateTap and RatioTap into one repository**; each engine keeps its own charter, CMake target, CI job and ratchet baselines | The engines are built to be composed and cross-checked against each other. Today that needs a test-only submodule of the sibling repo (`RatioTap/submodules/sampleratetap`), plus two DspTap pins to keep in step and a duplicated embedded toolchain. There are no external consumers yet, so the rename is free | -| D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" describes the whole family once it is namespaced. The published book (tap.github.io/SampleRateTap) keeps its URL. SampleRateTap has more history (64 commits) than RatioTap (29) | -| D3 | **RatioTap's history is preserved** through a `git filter-repo` path rewrite and an unrelated-histories merge | `git log --follow` and `git blame` must keep working for every file of both engines | -| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace; today's `srt/` include path breaks it | -| D5 | **Engine names:** `async` (today's SampleRateTap), `ratio` (today's RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term (ASRC) and the family's own clock-topology vocabulary. It is the only async engine, and async at other ratios is reached by composition. Every other engine is sync, so those are named by what they convert | -| D6 | **One top-level directory per engine** (`async/`, `ratio/`, …), each with its own `include/ tests/ bench/ examples/ capi/ PLAN.md` | Keeps each charter's boundary physical, where a single shared `include/` tree would not | -| D7 | **Clean renames, no compatibility aliases** | There are no consumers yet; aliases would be permanent debt | -| D8 | **Unified C ABI prefix** `tap_sr__*` and one shared library | One ctypes bridge serves all the notebooks | -| D9 | **CMake options** `TAP_SR_*` (e.g. `TAP_SR_BUILD_TESTS`, `TAP_SR_WERROR`), plus per-engine enables `TAP_SR_ENGINE_` | Replaces `SRT_*` / `TAP_RATIO_*` | -| D10 | **DspTap stays a separate repository**, pinned once at `submodules/dsptap` | It has consumers outside the rate family (TapTools and others) | -| D11 | **Charter rule for new engines:** a capability gets a directory here when it has its own charter, optimization campaign and instruction-count ratchet. Building blocks (filter design, kernels, the `chain<>` composition template, `fractional_resampler`) go into DspTap | This was the repo-vs-substrate rule; it now decides engine directory vs. DspTap | +| D1 | **Merge SampleRateTap and RatioTap into one repository**; each engine keeps its own charter, CMake target, CI coverage and ratchet baselines | The engines are built to be composed and cross-checked against each other. **This supersedes** HANDOFF.md preamble item 1 ("separate repo") and RatioTap PLAN.md §2 (SampleRateTap as a test-only dependency). Those were decided when the family had two engines and no plan for more. With five engines planned, the cost of pins, duplicated harnesses and cross-repository test dependencies grows with every addition. There are no external consumers, so the rename is free | +| D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" names the whole family once it is namespaced. The published book (tap.github.io/SampleRateTap) keeps its URL. It is also the larger history: **136** commits on `main` (full clone), against RatioTap's 29 | +| D3 | **RatioTap's history is preserved** through a `git filter-repo` path rewrite that also rewrites `.gitmodules` throughout history (section 6, step 1), plus an unrelated-histories merge. **The final PR is merged with a merge commit, never squash or rebase** | `git log --follow` and `git blame` keep working for every file. A squash would erase all 29 imported commits (GIT-1) | +| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace. **This supersedes** the rename agreed in RatioTap PLAN.md (`include/srt/` → `include/tap/samplerate/`) and extends the taphouse convention of one `tap::` sub-namespace per repository to two levels. That extension needs **confirming with taphouse** (Q7) | +| D5 | **Engine names:** `async` (today's SampleRateTap), `ratio` (today's RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term (ASRC) and the family's own clock-topology vocabulary. It is the only async engine, and async at other ratios is reached by composition. Every other engine is sync and is named by what it converts. Known wrinkle: in code with `using namespace tap::sr`, these names sit beside `std::ratio` and `std::async`. The house style already avoids namespace-wide `using` directives | +| D6 | **One top-level directory per engine** (`async/`, `ratio/`, …), each with its own `include/ tests/ bench/ examples/ capi/ notebooks/ README.md PLAN.md` | Keeps each charter's boundary physical, where a single shared `include/` tree would not | +| D7 | **Clean renames, no compatibility aliases.** Removed user-facing override macros get an `#error` **tripwire**, not an alias | Aliases would be permanent debt. A tripwire makes a stale `-DSRT_CP_MIN_CHANNELS=…` a loud error rather than a silent no-op (GATE-14) | +| D8 | **C ABI prefix `tap_sr__*`, one shared library per engine** (`tap_sr_async_capi`, `tap_sr_ratio_capi`), one bridge module per engine | Per-engine libraries keep `capi/` inside the engine boundary, so no shipped artifact links two engines (DEC-14). A combined notebook library can come later if a notebook needs both engines | +| D9 | **CMake options `TAP_SR_*`**: `TAP_SR_BUILD_TESTS`, `…_EXAMPLES`, `…_CAPI`, `…_ICOUNT_BENCH`, and per-engine `TAP_SR__WERROR`. There is **no per-engine enable switch in the migration** | A per-engine WERROR keeps ratio's MSVC `/WX` gate, which async has not triaged yet (INF-11). Per-engine enables could silently drop the cross-validation (DEC-12), so they are deferred. If they are ever added, ratio tests ON with async OFF is a `FATAL_ERROR` | +| D10 | **DspTap stays a separate repository**, pinned once at `submodules/dsptap` | It has consumers outside the rate family: TapTools, and MuTap through the `LogMel`/`Decimator` C ABI | +| D11 | **Charter rule for new engines:** a capability gets an engine directory here when it has its own charter, optimization campaign and ratchet. Building blocks (filter design math, kernels, the `chain<>` template) go into DspTap. **Engine-owned datapaths stay with their engine.** In particular, `fractional_resampler`, the polyphase bank and the blend stratum are `async`'s and never move to DspTap | Agrees with RatioTap PLAN.md Appendix A ("the blend stratum does not move"). Keeping async's datapath out of DspTap is also what keeps `ratio`'s cross-validation oracle outside `ratio`'s reach (DEC-6) | +| D12 | **No routing by rate, ever, including through composition.** `chain<>` is a caller-named, compile-time composition of **synchronous** stages. There is no `(in_hz, out_hz) → engine` lookup, `async` is never selected by a chain, and the coverage matrix *documents* chains without dispatching them | Keeps HANDOFF preamble item 4 ("factory dropped; clock topology is routed by type choice") intact as the family grows (DEC-8) | --- @@ -41,152 +49,240 @@ review should try to break. | Engine | Namespace | Origin | Charter | |---|---|---|---| | `async` | `tap::sr::async` | SampleRateTap v0.1.0 | Asynchronous, near-unity (±`max_deviation_ppm`, default 1000 ppm): absorbs the clock | -| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous, 44.1 ↔ 48 kHz only (160/147, 147/160): converts the number | -| `integer` | `tap::sr::integer` | new (step 5) | Synchronous integer factors 2^a·3^b, up, down, and oversampling pairs; half-band/third-band (Nyquist L-th band) stages. Absorbs DspTap's `decimate.h` | +| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous **160/147 pair at 44.1·2^k ↔ 48·2^k** (k = 0, 1, 2): converts the number. See 2.2 | +| `integer` | `tap::sr::integer` | new, separate plan | Synchronous **rational L/M with L, M ∈ {2^a·3^b}**: integer up, down, oversampling pairs, and 2/3 · 3/2 steps. Nyquist (L-th band) stages. Does **not** absorb DspTap's `decimate.h` (DEC-7) | | `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM (MEMS mics, DSD): CIC → compensation FIR → `integer` stages | -| `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio (varispeed, scrubbing, Doppler); bandlimited interpolation (Smith, CCRMA) | +| `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio: bandlimited interpolation (Smith, CCRMA) | -Not engines, deliberately: +Not engines: -- **Timestamp-driven clock recovery** is a feature of `async`. -- **Minimum-phase and IIR low-latency tiers** are *profiles* of `ratio` and +- **Timestamp-driven clock recovery** is an `async` feature. +- **Minimum-phase and IIR low-latency tiers** are profiles of `ratio` and `integer`. -- An **offline FFT tier** would be a `transparent+` profile, built only if - someone asks for it. - -### 2.1 Rate coverage with `ratio` + `integer` - -Every standard rate is 44.1k·2^a or 48k·2^a·3^b (b ∈ {0, −1}). Target coverage: - -| From → To | Chain (proposed) | Intermediate rates | -|---|---|---| -| 48 ↔ 44.1 | `ratio` | — | -| 96 → 44.1 | `integer` ↓2 → `ratio` ↓ | 48 | -| 192 → 44.1 | `integer` ↓4 → `ratio` ↓ | 96, 48 | -| 44.1 → 96 / 192 | `ratio` ↑ → `integer` ↑2 / ↑4 | 48 | -| 48 → 16 / 8 | `integer` ↓3 / ↓6 | — | -| 44.1 → 16 | `ratio` ↑ → `integer` ↓3 | 48 | -| 16 → 48 | `integer` ↑3 | — | -| 48 → 32 | `integer` ↑2 → ↓3 | 96 | -| 44.1 → 32 | `ratio` ↑ → `integer` ↑2 → ↓3 | 48, 96 | -| 44.1 ↔ 22.05 / 11.025 | `integer` ↓2 / ↓4 (and ↑) | — | -| 88.2 ↔ 96, 176.4 ↔ 192 | **open — see Q3** | — | - -**Chain invariant (proposed):** no intermediate rate may be lower than -min(input, output), so the chain never throws away band it must deliver. -48 → 32 is therefore ↑2 then ↓3, never ↓3 then ↑2, which would cut the band -at 8 kHz. - -The coverage matrix is generated and pinned by tests, not written by hand. -For each pair it records the MACs per output, total latency and measured -floor. **It is not produced in this migration.** It arrives with `integer` -(step 5). +- **An offline FFT tier** would be a `transparent+` profile, built only if + someone asks. +- **`decimate.h`** stays in DspTap as the speech front end MuTap consumes. + `integer` builds on the same L-th-band design math beside it. Moving it + would need its own consumer plan, with a MuTap pin. + +### 2.1 Coverage rule: passband, not Nyquist + +v1's invariant "no intermediate rate below min(in, out)" is withdrawn +(DEC-3). It allowed band loss the profiles already take (`economy` is flat +to 18 kHz), and it forbade harmless chains: 176.4 → 44.1 → 48 would have +had to run `ratio` at 4× the rate. + +**The rule instead, per chain and profile:** + +1. The chain's passband edge is the **minimum over its stages** of each + stage's passband edge (in Hz at that stage's rate). +2. That edge must be **≥ the passband the profile declares for the pair**. +3. Every intermediate Nyquist frequency must be **> the declared passband + plus the next stage's transition band**. +4. The coverage-matrix test pins the resulting edge, MACs per output and + latency for every pair. + +**Supported rates:** 8, 11.025, 12, 16, 22.05, 24, 32, 44.1, 48, 88.2, 96, +176.4, 192 and 384 kHz. That is every rate of the form 44.1k·2^a or +48k·2^a·3^b that the family supports. + +**Explicitly excluded, with the reason:** + +- 37.8 and 50.4 kHz (ratios of 7). +- The 1000/1001 video pull-down rates (44.056 and 47.952 kHz). These are + *synchronous* ratios whose 999 ppm offset happens to fall inside + `async`'s ±1000 ppm. They must **never** be served by `async`, because + that would be routing by rate. If they are ever supported, it is as a + sync engine. + +**The full 14 × 14 matrix is generated, not hand-picked.** It arrives with +`integer`'s plan, with every pair marked supported, excluded or +not-yet-supported. Illustrative rows (all respect 1–3): + +| From → To | Chain | +|---|---| +| 48 ↔ 44.1 | `ratio` | +| 96 ↔ 88.2, 192 ↔ 176.4 | `ratio` at k = 1, 2 (section 2.2) | +| 96 → 44.1 | `integer` ↓2 → `ratio` | +| 176.4 → 48 | `integer` ↓4 → `ratio` (the passband rule allows 44.1 intermediate) | +| 48 → 88.2 | `integer` ↑2 → `ratio` k = 1 | +| 44.1 → 16 | `ratio` → `integer` ↓3 | +| 48 → 32 | `integer` 2/3 (one rational stage, not ↑2 then ↓3) | + +### 2.2 `ratio` at 2× and 4× rates: resolves v1's Q3 + +The Hz values in `ratio`'s profiles feed only a normalized cutoff +(`design.h:138`). The converter is a pure sample-count transformer, and the +charter bans other **ratios** ("not 2:1, not 96→44.1"), not the same ratio +at a multiple of the rate. Fed 88.2 kHz, today's tables give a 36 kHz +passband, and alias products land above 40.2 kHz. + +**Decision:** + +- Restate the charter as "160/147 at 44.1·2^k ↔ 48·2^k". +- Document profile edges as fractions of the rate, with the Hz figures as + the k = 0 labels. +- Add a contract test that the table designed at k = 1 labels is + **bit-identical** to the k = 0 table. + +This is a charter and documentation change in `ratio`, made after the +migration (it is not part of it). The `direction::up_to_48k` / +`down_to_44k1` names are reconsidered then. --- -## 3. Inventory of the two repositories (measured 2026-09-26) +## 3. Inventory (measured 2026-09-26; corrected in v2) + +The migration runs on **fresh, full GitHub clones**. The session checkouts +are unfit for it: -Branch `claude/sample-rate-expansion-strategies-ezqzu6` in both repositories, -at SampleRateTap `2b4dff1` and RatioTap `349ab7b`. +- SampleRateTap's session checkout is **shallow** (8 grafts), which is why + v1 reported 64 commits. +- Both checkouts have **stale local `main`** branches: SampleRateTap + `0922541`, RatioTap `94775b0`. +- Remote truth: SampleRateTap `main` = `2b4dff1` (136 commits); RatioTap + `main` = `349ab7b` (29 commits, no renames in history, no tags). Neither + repository has tags. +- RatioTap's leftover remote branch `claude/sample-rate-solutions-comparison-mqc190` + (merged PR #17) is not imported. It should be deleted. ### 3.1 Shipped headers | Today | After | |---|---| -| `SampleRateTap/include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…` | -| `SampleRateTap/include/srt/detail/kaiser.h` (26 lines: a re-export shim of `tap::dsp` kaiser) | **deleted**; callers include `tap/dsp/kaiser.h` directly | -| `RatioTap/include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `ratio/include/tap/sr/ratio/…` | +| `include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…` | +| `include/srt/detail/kaiser.h` (26 lines: re-exports `tap::dsp` kaiser into `tap::samplerate::detail`) | **Deleted in step 3.** `polyphase_filter.h:153,157` qualifies the calls as `tap::dsp::`. `tests/test_kaiser.cpp` (9 tests, `using namespace tap::samplerate::detail`) repoints to `tap::dsp`, keeping test names. The path citations in the book, the bibliography, `book_figures.py:7` and `asrc_rbj_analysis.ipynb` are rewritten (INV-7) | +| `include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `ratio/include/tap/sr/ratio/…` | -`srt/sample_traits.h` layers on `tap/dsp/sample_traits.h` rather than -duplicating it. It moves unchanged. Folding it into DspTap is out of scope. +`srt/sample_traits.h` layers on `tap/dsp/sample_traits.h` and moves +unchanged. ### 3.2 Pins -- Both repositories record DspTap at **`0eb09fa`**. The pins agree, so step 1 - needs no pin reconciliation. -- RatioTap also pins `submodules/sampleratetap` at `2b4dff1`, which equals - SampleRateTap's HEAD. After the merge the pin disappears, and the - cross-validation compiles against the in-tree `async` engine **at the same - tree**. -- Note: in this session's checkouts the working-tree submodules sit at - `28a34a1` / `5315689`, not the recorded pins. The migration must build - against the **recorded** pins (`git submodule update --init` before every - gate). +- Both repositories record DspTap at **`0eb09fa`**. +- RatioTap pins `submodules/sampleratetap` at `2b4dff1` = SampleRateTap + `main`. +- Every gate runs `git submodule update --init --recursive` first. Session + hooks move checkouts off their recorded pins (observed in this session), + so the gates must not trust the checkout. -### 3.3 Duplicated infrastructure: measured divergence +### 3.3 Duplicated infrastructure -RatioTap's copies were ported from SampleRateTap. The measured differences -are **cosmetic only**: comments, mdBook `ANCHOR` markers (present only on -the SampleRateTap side) and name prefixes. +There are **three** copies of the embedded harness: SampleRateTap, +RatioTap, **and DspTap**. DspTap has `cmake/arm-cortex-m33-mps2.cmake`, +`platform/`, `tools/qemu_insn_plugin/` and `scripts/icount.py`, which +differ from SampleRateTap's by 40–54 lines. The merge removes one of the +three. Consuming the harness from `submodules/dsptap` instead is the natural +next deduplication. It is **out of scope** here because DspTap's copy has +diverged, and adopting it would change async's and ratio's codegen inputs +(DEC-9). -| File | Changed lines | Nature | -|---|---|---| -| `.clang-format`, `.clang-tidy`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/hooks/session-start.sh`, `.claude/settings.json`, `scripts/tidy.sh` | 0 | identical | -| `platform/armv8m_startup.c` | 21 | comments, copyright line, book anchors | -| `platform/mps2_an505/*.ld`, `platform/mps3_an547/*.ld` | 5 each | book anchors, provenance comment | -| `cmake/arm-cortex-m33-mps2.cmake`, `…-m55-mps3.cmake`, `hexagon-linux-musl.cmake` | 10 / 21 / 12 | `SRT_`/`TAP_RATIO_` option names, comments, anchors | -| `scripts/icount.py` | 19 | binary prefix `srt_icount_*` vs `ratio_icount_*`; markers `SRT_INSN_COUNT` / `SRT_ICOUNT_DONE` vs `RATIO_*` | -| `tools/qemu_insn_plugin/insn_count.c` | 13 | to be diffed line by line in step 2 (**audit item A6**) | +SampleRateTap vs RatioTap, line by line: -Keep the SampleRateTap copies, because they carry the book anchors, and -parameterize the prefixes. +| File | Class | Detail | +|---|---|---| +| `.clang-*`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/**`, `scripts/tidy.sh`, `.github/pull_request_template.md` | identical | | +| `platform/armv8m_startup.c`, `platform/*/**.ld` | cosmetic | Comments, copyright line, book `ANCHOR`s. Memory map, heap, MSPLIM, vectors, `_sbrk` and atomics are byte-identical | +| `cmake/arm-cortex-m33/m55-*.cmake`, `hexagon-linux-musl.cmake` | **functional (one variable)** | Flags are identical. `set(SRT_BARE_METAL ON)` vs `set(TAP_RATIO_BARE_METAL ON)` selects each engine's one-shot test mode and the gtest `GTEST_HAS_*` definitions (INF-1) | +| `tools/qemu_insn_plugin/insn_count.c` | **functional (host-side marker)** | Prints `SRT_INSN_COUNT` vs `RATIO_INSN_COUNT`, which `icount.py` parses. Host-side, so it never affects the guest count | +| `scripts/icount.py` | **functional (prefix, marker)** | `srt_icount_*`/`SRT_*` vs `ratio_icount_*`/`RATIO_*`. Tolerance logic identical | +| `tests/bare_metal_main.cpp` | **per-engine, never deduplicated** | Engine-specific filter, floor (15 vs 25) and completion marker | +| `.github/workflows/style.yml` | **functional** | RatioTap's configures tests and icount ON, excludes `submodules` and `_deps`, and reads file lists line-wise. SampleRateTap's configures nothing and would lint **zero** TUs in a monorepo (INF-10). RatioTap's body wins | +| `scripts/fetch_hexagon_toolchain.sh` (RatioTap only) | **functional** | Checks the pin and `SHA256SUMS` unconditionally. SampleRateTap's inline copies skip `SHA256SUMS` or make the pin check conditional (INF-13). RatioTap's script wins | Present in only one repository: -- **SampleRateTap only:** `cmake/r8brain.cmake`, `tools/compare_shim`, - `bench/compare`, `compare.yml`, `ci-arm64.yml` (native weak-memory + TSan), - `book-pages.yml`, `book/`, `docs/`, `scripts/update_*_docs.py`, - `scripts/book_figures*`, `examples/pico2_*`. -- **RatioTap only:** `scripts/fetch_hexagon_toolchain.sh`, - `tools/reference/make_reference_vectors.py`, `tests/reference/`, - `HANDOFF.md`, and the CI dedup scheme (branch-filtered `push` + - `pull_request`, commits `f1e566a` and `94775b0`). +- **SampleRateTap:** `cmake/r8brain.cmake`, `tools/compare_shim`, + `bench/compare`, `compare.yml`, `ci-arm64.yml`, `book-pages.yml`, + `book/`, `docs/`, `scripts/update_*_docs.py`, `scripts/book_figures*`, + `examples/pico2_*`, `.git-blame-ignore-revs`. +- **RatioTap:** `scripts/fetch_hexagon_toolchain.sh`, `tools/reference/`, + `tests/reference/`, `HANDOFF.md`, `CLAUDE.md`, `notebooks/requirements.txt`, + the `build_capi/` rule in `.gitignore`, and the CI dedup scheme (RatioTap + `f1e566a`, `94775b0`; rewritten SHAs are recorded in `ratio/docs/HISTORY.md`, + step 1b). -### 3.4 The book - -`book/src` pulls in live code with `{{#include path:ANCHOR}}`, and CI fails -on a stale anchor. By path: - -- `include/srt/*` — 42 includes (`polyphase_filter.h` 17, `sample_traits.h` 8, `pi_servo.h` 8, `asrc.h` 5, `spsc_ring.h` 4) -- `submodules/dsptap/…` — 21 (unaffected by the move) -- `platform/`, `cmake/`, `tools/`, `tests/` — 20 +GoogleTest: both repositories use the same pin (`f8d7d77`, v1.14.0) and the +same FetchContent name. A merged tree configures, and all 151 tests pass +(measured in a scratch tree). -Every non-DspTap include path changes in step 3. This is a mechanical -rewrite, but it is gated by the book build (`mdbook build`, warnings are -errors). +### 3.4 The book -### 3.5 CI and the ratchet +- **84** `{{#include}}` directives: + - `include/srt` 42 + - `submodules/dsptap` 21 + - `tools/capi` 6 + - `tests` 4 + - `platform/` 7, `cmake/` 2, `tools/qemu_insn_plugin` 1 +- **52 of them break at step 1**, not step 3, because step 1 moves their + targets. +- The staleness check is inline in `ci.yml` (job `book`): `mdbook build` + with a `warning|error` grep, plus an image-reference check. + `book-pages.yml` repeats the build and runs `doxygen docs/Doxyfile` + (`INPUT = include README.md`). +- The prose also carries **23 files** of `srt/…`, `tap::samplerate` and + `SRT_*` identifiers that mdbook cannot check (GATE-16), and **5 links** to + RatioTap as a live repository, including `git clone …/RatioTap` build + commands (`part5/scaling.md:194,352-355`). + +### 3.5 CI (corrected) | Job | SampleRateTap | RatioTap | |---|---|---| -| Host matrix (GCC, Clang, AppleClang, MSVC) | `build-and-test` | `build-test` | -| Sanitizers (ASan+UBSan, TSan) | yes | yes | -| Linux arm64 native + TSan | `ci-arm64.yml` | — | -| Hexagon / M55 / M33 under QEMU | yes | yes | +| Host matrix | GCC, Clang, AppleClang, MSVC (MSVC `werror: OFF`) | same, **MSVC with `-DTAP_RATIO_WERROR=ON`** | +| Sanitizers | ASan+UBSan, TSan | **ASan+UBSan only**, WERROR ON | +| Linux arm64 native + TSan | `ci-arm64.yml`: **currently tests nothing** (`-R 'SpscRing'`; the suite is `spsc_ring`) | — | +| Hexagon / M55 / M33 correctness | yes. Hexagon `-E 'AsrcQuality\|AsrcLock\|TwoThreadStress\|TransparentPrototypeMeetsSpec\|MultiChannel\.\|Feasibility\|Reset\.\|ConfigValidation'`, serial | yes. Hexagon `-E 'BadProfilesThrow\|LatencyAndValidation'`, `-j 4` | | `icount-ratchet` | 7 workloads × {m33, m55, hexagon}; README table drift check | 10 workloads × {m33, m55, hexagon} (RatioTap's CLAUDE.md still says eight) | -| Resampler comparison | `compare-smoke`, `compare.yml` | — | -| Style (clang-format, tidy, drift) | yes | yes | -| Book | `book`, `book-pages.yml` | — | - -Baselines: `SampleRateTap/bench/baselines.json`, `RatioTap/bench/baselines.json`. -Both are keyed by target, then by workload. They are **not** merged into -one file (see step 2). - -### 3.6 C ABI - -`srt_capi.h` (≈8 exported functions) and `ratio_capi.h` (≈11) are -consumed through ctypes: RatioTap's notebooks via `notebooks/ratiotap_py.py`, -and SampleRateTap's via ctypes code inline in each notebook (three of its -four notebooks). - -### 3.7 Outside references to fix - -- **DspTap:** `README.md`, `CLAUDE.md`, `STYLE.md`, `platform/README.md`, - `docs/audit-fft-and-code-smells.md` and five test files mention - SampleRateTap or RatioTap by name. -- **RatioTap:** `CLAUDE.md`, `PLAN.md`, `HANDOFF.md`, `README.md` URLs. -- **SampleRateTap:** `README.md` ("Position in the Tap family"), `book/`, - `docs/COMPARISON.md`, `docs/PERFORMANCE.md`, `Doxyfile`. +| bench-smoke, compare-smoke, clang-format job, book | yes | **no** (clang-format by pre-commit only) | +| Triggers / concurrency | push + PR, cancel-in-progress by ref | push to `main` + PR + dispatch (dedup scheme) | +| Actions pinning | by SHA | by tag | + +Branch protection: `main` is unprotected in both repositories (checked +through the GitHub API; rulesets were not checked), so job renames break +nothing today. + +### 3.6 C ABI (exact) + +- **async:** 8 functions, `srt_{version,create,destroy,push,pull,status,designed_latency_seconds,reset_from_consumer}`, + with opaque `SrtHandle`, library `libsrt_capi.so`. `srt_version` + encodes `M*10000+m*100+p`. +- **ratio:** 11 functions, opaque `ratio_converter`, library + `libratio_capi.so`. `ratio_version` encodes `(M<<16)|(m<<8)|p`, pinned by + `test_skeleton.cpp`. +- **Separately:** the r8brain comparison shim exports `srt_r8b_oneshot` and + `srt_r8b_latency_frames` from its own library. +- **Bridges:** RatioTap's notebooks use `notebooks/ratiotap_py.py`. + SampleRateTap's use inline ctypes in 3 of 4 notebooks; `asrc_rbj_analysis` + uses `scripts/book_figures.py`. Both bridges build only when the library + is missing, so a stale library would be measured silently (GATE-10). + +### 3.7 Outside references + +- **DspTap:** 25 files, **comments and docs only**. No code, test, CMake or + submodule dependency. + - Most stay true after the merge, because the SampleRateTap name survives. + - Only path citations (`include/srt/…`, `tests/support/`, + `docs/PERFORMANCE.md`) and mentions of RatioTap as a live repository + change. + - These change through a DspTap PR after the merge. `STYLE.md` changes + only through **taphouse**, because it is a drift-checked copy. + - Close DspTap's open item on `TAP_DSP_CP_MIN_CHANNELS` + (`docs/audit-fft-and-code-smells.md:392,458,668`) at the same time as + `SRT_CP_MIN_CHANNELS`. +- **SampleRateTap:** RatioTap URLs in `README.md:407,432`, + `book/src/part0/two-crystals.md:173`, `part5/scaling.md:194,352-355` and + `docs/COMPARISON.md:234`. The README quotes stale cross-validation figures + (−109/−99 dB; the current v0.3 floors are about −98/−90 dB). +- **Outside this session: the user checks these** before step 1: + - **TapTools, TapTools-Max, MuTap, AmbiTap, OscTap:** submodules pointing + at either repository, and uses of `srt/`, `tap/ratio`, + `tap::samplerate`, `tap::ratio`, `SampleRateTap::SampleRateTap`, the C + ABI symbols and Pages links. + - **taphouse:** `sync.sh` target list (drop RatioTap, or syncs to the + archive fail), `drift-check.yml`, and the catalog README. + - **GitHub:** open RatioTap issues and PRs, and the Pages source setting. + Note: the repository is private while its Pages site is public. --- @@ -194,258 +290,504 @@ four notebooks). ``` SampleRateTap/ -├── CMakeLists.txt project(SampleRateTap); umbrella target tap::sr -├── CLAUDE.md family-level guidance (new; SampleRateTap has none today) -├── PLAN.md this document, promoted after review -├── README.md family front page: which engine, the coverage matrix -├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ (unchanged, identical today) -├── submodules/dsptap the ONE pin -├── cmake/ platform/ shared embedded toolchains (SampleRateTap copies) -├── tools/qemu_insn_plugin/ shared -├── scripts/ icount.py (engine-aware), tidy.sh, fetch_hexagon_toolchain.sh, -│ book/doc updaters +├── CMakeLists.txt NEW root: project(SampleRateTap), enable_testing(), +│ add_subdirectory(submodules/dsptap) ONCE, then engines +├── CLAUDE.md PLAN.md README.md NEW family-level files (step 4) +├── LICENSE SampleRateTap's; ratio/LICENSE kept until Q5 is decided +├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ .github/ (shared) +├── .git-blame-ignore-revs repaired (step P.1), extended after step 4 +├── submodules/dsptap the one pin +├── cmake/ platform/ tools/qemu_insn_plugin/ shared embedded harness +├── scripts/ icount.py (engine-aware), tidy.sh, +│ fetch_hexagon_toolchain.sh (RatioTap's) ├── book/ one book; ratio chapters are follow-up work -├── notebooks/sr_py.py one ctypes bridge over the unified C ABI -├── capi/ tap_sr C ABI: one shared library, per-engine sources +├── docs/ family docs: PLAN (this file), Doxyfile (both engines) ├── async/ -│ ├── PLAN.md the async charter (drawn from today's README/docs) -│ ├── include/tap/sr/async/ -│ ├── tests/ bench/ examples/ notebooks/ docs/ -│ └── CMakeLists.txt target tap::sr::async +│ ├── CMakeLists.txt README.md PLAN.md +│ ├── include/tap/sr/async/ tests/ bench/ (icount, compare) examples/ +│ ├── capi/ notebooks/ docs/ (PERFORMANCE, COMPARISON, HARDWARE_TESTING) +│ └── tools/compare_shim/ cmake/r8brain.cmake └── ratio/ - ├── PLAN.md HANDOFF.md (RatioTap's, history preserved) - ├── include/tap/sr/ratio/ - ├── tests/ bench/ examples/ notebooks/ tools/reference/ - └── CMakeLists.txt target tap::sr::ratio + ├── CMakeLists.txt README.md PLAN.md HANDOFF.md CLAUDE.md LICENSE + ├── include/tap/sr/ratio/ tests/ (+reference/) bench/ examples/ + ├── capi/ notebooks/ (+requirements.txt) tools/reference/ docs/HISTORY.md ``` -Dependency rule, enforced by CMake: +### 4.1 File disposition -- `ratio` → `tap::dsp` only. -- `async` → `tap::dsp` only. -- An engine may depend on another **only in tests and examples** - (`ratio/tests` → `tap::sr::async` for cross-validation; the - `bluetooth_bridge` example composes both). -- A shipped header that includes a sibling engine is a CI failure: a - header-isolation test compiles each engine's headers against `tap::dsp` - alone. +| Path today | Destination | Step | +|---|---|---| +| SRT `CMakeLists.txt`, `README.md` | `async/CMakeLists.txt`, `async/README.md` (pure move; new root files in a *later* commit, see GIT-5) | step 1a | +| SRT `include/ tests/ bench/ examples/ notebooks/` | `async/…` | step 1a | +| SRT `tools/capi/`, `tools/compare_shim/` | `async/capi/`, `async/tools/compare_shim/` | step 1a | +| SRT `cmake/r8brain.cmake` | `async/cmake/r8brain.cmake` (async-only) | step 1a | +| SRT `docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md` | `async/docs/` | step 1a | +| SRT `docs/Doxyfile`, `docs/MONOREPO_PLAN.md` | **stay** in root `docs/` | — | +| SRT `book/`, `scripts/`, `cmake/arm-*`, `cmake/hexagon-*`, `platform/`, `tools/qemu_insn_plugin/`, dotfiles, `LICENSE`, `STYLE.md`, `.github/` | **stay** at root | — | +| RatioTap, whole tree | `ratio/…` via filter-repo | step 1b | +| `ratio/.gitmodules`, `ratio/submodules/*` | rewritten into the root `.gitmodules` throughout history, then removed at the merge | step 1b | +| `ratio/.clang-*`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/`, `scripts/tidy.sh`, `.github/pull_request_template.md` | deleted (identical to root) | step 1b merge commit | +| `ratio/.github/workflows/ci.yml` | ported into root `.github/workflows/ci.yml` as ratio jobs | step 1c | +| `ratio/.github/workflows/style.yml` | its body replaces root `style.yml` | step 1c | +| `ratio/scripts/fetch_hexagon_toolchain.sh` | root `scripts/` | step 1c | +| `ratio/.gitignore` | `build_capi/` rule merged into root `.gitignore`; file deleted | step 1c | +| `ratio/tools/capi/` | `ratio/capi/` | step 3 | +| `ratio/cmake/`, `ratio/platform/`, `ratio/tools/qemu_insn_plugin/`, `ratio/scripts/icount.py` | deleted (root copies) | step 2 | +| `ratio/CLAUDE.md` | kept, build commands corrected at step 1c; reduced to the ratio charter at step 4 | step 1c, step 4 | +| `ratio/LICENSE` | kept until Q5 is decided | — | +| `ratio/notebooks/requirements.txt` | superseded by the root lockfile | step P.3 | +| `docs/migration/` (snapshots) | created at root `docs/migration/` (not moved, since `docs/` stays) and deleted at the end | step 0, step 4 | + +### 4.2 Dependency rule and its enforcement + +- `async` and `ratio` each depend on `tap::dsp` only. +- An engine may depend on another **only** in `tests/` and `examples/`: + `ratio/tests` uses `tap::sr::async` for the cross-validation, and the + `bluetooth_bridge` example composes both. +- `capi/` is per-engine (D8) and never links a sibling. + +Enforcement, since v1's "enforced by CMake" named no mechanism (DEC-13): + +1. **Configure-time assertion:** `INTERFACE_LINK_LIBRARIES` of each engine + target is exactly `tap::dsp`. +2. **Install-tree isolation test:** install each engine alone, together with + dsptap, into a staging prefix. Then compile every one of its public + headers in its own TU from that prefix. Sibling source paths do not + exist there. +3. **Grep gate on `*/include/**`:** rejects `srt/`, `tap/sr//`, + `../` and `__has_include` of a sibling. +4. **Header glob with pinned count:** a header added outside the list fails + the test. --- -## 5. Migration steps +## 5. Gates (defined once; the steps reference them) + +Every gate is measured **against the step-0 snapshot** and not against +committed files. This separates migration effects from toolchain drift on +unpinned `ubuntu-latest` apt packages (GATE-3). Where a gate compares +builds, **step 0's SHA and step N's SHA are built in the same job, with the +same toolchain and the same binary paths**. -Each step is one or more commits on -`claude/sample-rate-expansion-strategies-ezqzu6` in SampleRateTap. Each has -a gate that must be green before the next begins. Nothing reaches `main` -until a PR is reviewed. +| ID | Gate | Catches | +|---|---|---| +| G1 | **Test multiset:** `ctest --show-only=json-v1` → multiset of (engine label, test name), equal to the snapshot. Every ctest call carries `--no-tests=error`, and each engine's tests carry a `LABELS` value | Dropped tests, including the duplicate `FixedPoint.FullScaleSineDoesNotWrapQ15` that exists in both engines (GATE-7, INF-12, GIT-12, INV-2) | +| G2 | **On-target test multiset:** the multiset of `[ RUN ] Suite.Name` lines from each QEMU leg's gtest log (M33, M55, Hexagon), equal to the snapshot | M33/M55 register one ctest per binary, and the floors (15/25) hide losses of up to 5/17 tests (GATE-5). Also the Hexagon exclusion regexes (GATE-8) | +| G3 | **Exact icount:** `icount.py --exact` means integer equality, and the **set** of measured workloads equals the set of baseline keys. A missing baselines file is fatal. `--update` is forbidden during the migration | v1's "0 %" was undefined, since the default tolerance is ±3 % and the output rounds to `+0.00%` (GATE-1). Missing workloads passed silently (GATE-2) | +| G4 | **Codegen identity:** `objdump -d --no-show-raw-insn` of every icount and test binary, addresses stripped (plus a symbol map for renames), equal to step 0 | Stronger than icount and independent of QEMU | +| G5 | **Output identity:** new per-engine host tests (added at step P.2) FNV-hash the **full** output of every direction × format × profile workload and pin the hash. QEMU `checksum=` lines equal the snapshot exactly | icount is data-independent, so a changed coefficient passes G3. The existing checksum is weak and is never read (GATE-4) | +| G6 | **Cross-validation lines:** the printed `[ measured ] cross-validation …` lines are byte-identical to the snapshot | Loosened tolerances (DEC-11) | +| G7 | **Compile flags:** per-TU flags in `compile_commands.json` equal to the snapshot, modulo path prefixes | E.g. `-std=gnu++20` → `-std=c++20` flips GCC's `-ffp-contract`, which changes FMA on M33/M55 while the host tests stay green (GATE-15) | +| G8 | **Book:** `mdbook build` clean, plus the image check | Broken anchors | +| G9 | **Retired-identifier grep:** zero hits for the retired names (`srt/`, `tap::samplerate`, `tap/ratio`, `tap::ratio`, `SRT_`, `TAP_RATIO_`, `srt_`, `ratio_capi`, …) outside an allowlist (history docs, `HISTORY.md`, the plan) | Stale prose and code that mdbook and the compiler cannot see (GATE-16). Applies from step 3 commit 8 | +| G10 | **C ABI symbols:** `nm -D` of each capi library equals the snapshot under the committed name map | The step-0 ABI snapshot was otherwise never used (GATE-17) | +| G11 | **Notebooks:** re-executed in the pinned environment (step P.3) from a fresh clone, with bridges that always rebuild. Compared through a normalizer that keeps text outputs only and drops timing lines, PNGs and paths | Wall-clock cells, unpinned numpy/scipy and stale-library loading made "identical outputs" unattainable or vacuous (GATE-10) | +| G12 | **History:** `git log --follow` and `git blame` spot checks on a fixed file list from both engines. Blame of a moved file does **not** attribute all lines to a migration commit | Lost blame when a move and a recreate share a commit (GIT-5) | +| G13 | **Every CI job ran:** each job in the workflow reports on the gated SHA (run IDs recorded in `docs/migration/runs.md`). None is skipped or cancelled | Cancel-in-progress and trigger filters that test only tips (GATE-11, INF-9) | + +A step's gate is a subset of G1–G13, listed with the step. + +--- + +## 6. Migration + +All work happens on `claude/sample-rate-expansion-strategies-ezqzu6` in +SampleRateTap, in **fresh full clones**. It proceeds **one commit per +push**, with a **draft PR open from the start** so every push runs CI +(G13). Nothing reaches `main` until step 4's PR. RatioTap is not written +to until step 5. + +### Step P — Pre-work: harden both repositories *before* the snapshot + +The snapshot must not record existing breakage as "green" (GATE-6, INV-8). +Each item below is a normal PR to the affected repository, merged before +step 0. + +- **P.1 Repair existing breakage:** + - `ci-arm64.yml:61`: fix `-R 'SpscRing'` → `spsc_ring`. + - Pico 2 examples: add the dsptap include path (broken since `5315689`), + and build them in CI once. + - `scripts/book_figures_trace.cpp`: update it to the current API, or + retire its "before" panel as a committed image. + - `.git-blame-ignore-revs`: replace the dangling `34bb89e…` with + `b84020e738f771c7689ffc1e592f8448b9ce063f` and `e2f5a48`. + - README cross-validation figures: update to the current floors. +- **P.2 Harden the harness,** in both repositories, with identical changes: + - `icount.py --exact`, plus the workload-set equality and the fatal + missing-baselines check (G3). + - Run Hexagon under a **fixed `argv[0]` and an empty environment** + (`qemu-hexagon -0 w` with `env -i`). Static musl's startup walks + `argv[0]` and `envp`, so the binary path and job environment otherwise + enter the count (GATE-1). **Re-record the Hexagon baselines once**, + with the delta stated. + - `--no-tests=error` on every ctest call. + - Per-engine `LABELS` on the tests. + - The G5 full-output hash tests. +- **P.3 Pin a notebook environment:** + - Add a lockfile with numpy, scipy, matplotlib, jupyter, samplerate and + soxr. + - Make the bridges always rebuild. + - Re-execute every notebook in that environment and confirm its text + outputs equal the committed ones before they become the baseline. + - Mark `asrc_rbj_analysis` cell 18 (wall-clock timings) as excluded. ### Step 0 — Freeze and snapshot -- Record the green state of both repositories at the SHAs above: - - `ctest` pass lists per host; - - `icount.py` output per target (these must match the committed - baselines); - - C ABI function lists. -- Merge nothing into either repository's `main` until step 4 lands. If - anything must land, it lands in SampleRateTap only, and step 1 is re-cut. - -**Gate:** both repositories are green on CI at the recorded SHAs, and the -snapshot artifacts are committed under `docs/migration/` (removed in step 4). - -### Step 1 — History-preserving import (no content changes) - -1. `git mv` SampleRateTap's engine files (`include/`, `tests/`, `bench/`, - `examples/`, `tools/capi`, `tools/compare_shim`, `notebooks/`, `docs/`) - into `async/`. Shared files stay at the root. -2. In a scratch clone of RatioTap, run - `git filter-repo --to-subdirectory-filter ratio/`. -3. Merge with `git merge --allow-unrelated-histories`. -4. Remove the duplicate shared files from `ratio/`: `.clang-*`, `STYLE.md`, - `.pre-commit-config.yaml`, `.claude/`, `scripts/tidy.sh`, `LICENSE` (see - Q5), `.gitmodules`, `submodules/`. -5. Keep, for now: `ratio/cmake`, `ratio/platform`, - `ratio/tools/qemu_insn_plugin`, `ratio/scripts/icount.py`. These are - deduplicated in step 2, so this step stays content-free. -6. Minimal CMake glue: a root `CMakeLists.txt` that `add_subdirectory`s - `async/` and `ratio/`, with each keeping its **current** target and - option names. Point `ratio`'s `srt_headers` at `async/include`, which - replaces the `submodules/sampleratetap` path. - -**Gate:** - -- `git log --follow` works on a sample of files from both engines. -- Host build and `ctest` pass lists equal the step-0 snapshot (same test - names, same count). -- Every QEMU leg is green. -- `icount.py` for **both** engines reproduces its committed baselines - **exactly** (0 %). Nothing that reaches codegen has changed, so any drift - is a bug in the import. +- Record the tips: SampleRateTap `main` and RatioTap `main` after step P. +- Assert that the full clones are not shallow + (`git rev-parse --is-shallow-repository` = `false`) and that the RatioTap + tip is the expected SHA. +- Take G1–G11 snapshots and commit them under root `docs/migration/`. +- No merges to either `main` until step 4. If one is unavoidable, it lands in + SampleRateTap only and step 1 is re-cut. The filter-repo output is + deterministic, so a re-cut is cheap. + +### Step 1 — Import (three commits) + +**1a — Pure move** (one commit; `git show -M --stat` shows zero +insertions and deletions): + +- `git mv` SampleRateTap's engine files per section 4.1, including + `CMakeLists.txt` → `async/CMakeLists.txt` and `README.md` → + `async/README.md`. +- Nothing else in this commit, so blame survives (GIT-5). + +**1b — Import RatioTap** (one merge commit): + +```sh +git clone https://github.com/tap/RatioTap rt && cd rt +test "$(git rev-parse main)" = "" +git filter-repo --path-rename :ratio/ --path-rename ratio/.gitmodules:.gitmodules \ + --blob-callback ' +if blob.data.startswith(b"[submodule \"submodules/"): + blob.data = (blob.data + .replace(b"path = submodules/", b"path = ratio/submodules/") + .replace(b"[submodule \"submodules/", b"[submodule \"ratio/submodules/"))' +cd ../SampleRateTap +git fetch ../rt main:ratio-import +git merge --allow-unrelated-histories --no-commit ratio-import +git checkout --ours .gitmodules # the one expected conflict (add/add) +git rm -r --cached ratio/submodules # the engine uses the root dsptap pin +git rm -f # -f: they are staged by the merge +git commit # message records the RatioTap tip SHA +``` + +This command was verified in a dry run. The rewritten history keeps +`.gitmodules` at the root with rewritten paths, so every imported RatioTap +commit can still initialize its submodules. The naive +`--to-subdirectory-filter` recipe orphans the gitlinks and breaks +`git submodule update` everywhere (GIT-2). + +Also: + +- Generate `ratio/docs/HISTORY.md` from `.git/filter-repo/commit-map`: old + SHA → new SHA → `https://github.com/tap/RatioTap/pull/N` for all 29 + commits. This preserves PR linkage lost to the squash merges and the PR + number collisions (GIT-8). +- Optionally, use `--message-callback` to rewrite bare `DspTap #38` to + `tap/DspTap#38`. +- If tags exist at cut time, add `--tag-rename '':'ratio/'`. + +**1c — Build glue and path fix-ups.** No codegen-reaching changes. G4 +and G7 prove that. + +- **Root `CMakeLists.txt`:** + - `project(SampleRateTap)` and `enable_testing()`. + - `add_subdirectory(submodules/dsptap)` once. + - Force the engines' `*_BUILD_TESTS`/`*_EXAMPLES` ON when the root is top + level. Their `PROJECT_IS_TOP_LEVEL` defaults evaluate false under a root + project, and v1's glue ran **0 tests while ctest exited 0** (INF-3). + - `add_subdirectory(async)` and `add_subdirectory(ratio)`. +- **Engine CMakeLists:** + - Wrap `add_subdirectory(submodules/dsptap)` in `if(NOT TARGET tap::dsp)`. + This also keeps standalone engine builds working. + - Ratio's `srt_headers` → `async/include` (kept `SYSTEM`). + - `${PROJECT_SOURCE_DIR}/cmake/r8brain.cmake` → `async/cmake/`. + - Standalone capi entry points fixed (ratio's `add_subdirectory(../..)`). +- **Bare-metal variables:** the root toolchain files set **both** + `SRT_BARE_METAL` and `TAP_RATIO_BARE_METAL` until step 3 unifies them. + The `GTEST_HAS_*` definitions move to root scope under that condition, + so the gtest library and both engines' test TUs agree (INF-1). +- **CI** (INF-4, GIT-4): + - Port RatioTap's `ci.yml` jobs into the root workflow as ratio jobs, with + ratio's options, its Hexagon `-E` list and `-j 4`, ratio's MSVC and + sanitizer WERROR, and ratio's icount against `ratio/bench/baselines.json`. + - Replace root `style.yml` with RatioTap's body, configuring **both** + engines with tests and icount ON, and failing on an empty TU list. + - Adopt `scripts/fetch_hexagon_toolchain.sh` for every cache writer. + - Move the RatioTap-only files per 4.1. +- **Paths:** + - The book's 52 includes (G8). + - `book-pages.yml` path filters and Doxyfile `INPUT`. + - The `bench-smoke` binary path, `compare.yml` build paths, + `icount.py --baselines` per engine, `update_icount_docs.py` → + `async/bench/baselines.json` and `async/README.md`. + - Notebook `REPO` roots and `sys.path` entries. + - The 11 README relative links. + - `ratio/CLAUDE.md` build commands. +- **LICENSE:** keep `ratio/LICENSE`, which MIT requires to be retained + (GIT-13). + +**Gate 1:** + +- G1, G2, G3 (both engines, all targets), G4, G5, G6, G7, G8, G11, G12, + G13. +- Every notebook bridge builds standalone. ### Step 2 — Shared infrastructure -- Delete `ratio/cmake`, `ratio/platform` and - `ratio/tools/qemu_insn_plugin`; `ratio` uses the root copies. Merge any - non-cosmetic difference in `insn_count.c` first (**A6**). -- Make `scripts/icount.py` engine-aware: `--engine async|ratio`, a binary - prefix `tap_sr__icount_*`, and uniform markers `TAP_SR_INSN_COUNT` / - `TAP_SR_ICOUNT_DONE`. Baselines move to `/bench/baselines.json`, - with the same keys. -- One CI workflow: - - The host matrix and sanitizers build every engine. - - `icount-ratchet` runs as a matrix over engine × target, each engine - against its own baselines. - - `ci-arm64`, `compare`, `book` and style stay as they are. - - Adopt RatioTap's CI dedup scheme family-wide. -- One DspTap pin: already at root since step 1; verify there is no second - checkout. - -**Gate:** as for step 1, plus **0 % icount drift** for both engines on all -three targets. The toolchain files and startup code are now shared, so any -drift means the deduplication changed codegen. - -### Step 3 — Renames - -In one commit per rename class, each gated separately: - -1. **Include paths:** `srt/…` → `tap/sr/async/…`; `tap/ratio/…` → - `tap/sr/ratio/…`. Delete `srt/detail/kaiser.h` and point its callers at - `tap/dsp/kaiser.h`. +- Delete ratio's copies of `cmake/`, `platform/`, `tools/qemu_insn_plugin/` + and `scripts/icount.py` (all cosmetic after step 1c). +- `icount.py` becomes engine-aware: `--engine async|ratio` sets its + `_icount_*` glob, baselines path and marker regex. + - **Guest-printed strings do not change.** `SRT_ICOUNT_DONE` and + `RATIO_ICOUNT_DONE` stay byte-identical, because guest `printf` scans + format text per character and a longer marker moves the count (GATE-1, + INF-7). + - The host-side plugin marker becomes `TAP_SR_INSN_COUNT`, which is + harmless. +- **Ratchet CI:** one job **per target** measures both engines. This shares + the toolchain, the plugin build and the qemu-hexagon cache, and uses + `fail-fast: false` so each target stays independent evidence (INF-8). + - Build the plugin once, as an artifact. + - Docs freshness runs as its own async-only job. + - `compare.yml` is updated in the same commit, and runs once by + `workflow_dispatch` as part of this gate. +- CI dedup: adopt RatioTap's scheme with `workflow_dispatch` and + `cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}`. Actions are + SHA-pinned (SampleRateTap's pins). + +**Gate 2:** G1–G7 and G13, plus one manual `compare.yml` run. + +### Step 3 — Renames (one commit per class, each gated) + +1. **Paths:** `srt/…` → `tap/sr/async/…`; `tap/ratio/…` → `tap/sr/ratio/…`; + `ratio/tools/capi` → `ratio/capi`. Delete `srt/detail/kaiser.h` per 3.1. 2. **Namespaces:** `tap::samplerate` → `tap::sr::async`; `tap::ratio` → - `tap::sr::ratio`. Optionally, `async_sample_rate_converter` → - `tap::sr::async::converter` (**Q2**). -3. **CMake:** - - Targets `tap::sr::async`, `tap::sr::ratio`, umbrella `tap::sr`. - - Options `TAP_SR_*`. - - Remove `SampleRateTap::SampleRateTap`, `tap::samplerate`, `tap::ratio` - (D7). -4. **C ABI:** - - `srt_*` → `tap_sr_async_*`; ratio's → `tap_sr_ratio_*`. - - One library, `capi/`. - - One bridge, `notebooks/sr_py.py`. - - `srt_version()` → `tap_sr_version()`, plus a per-engine version (**Q4**). -5. **Book:** rewrite the include paths (section 3.4), move anchors to the - new paths, and fix figure scripts. -6. **Ratchet names:** workload and binary renames. Baseline **keys stay - unchanged**, so history remains comparable. - -**Gate:** - -- Tidy gate and a local clang `-Werror` build pass. -- `mdbook build` passes with warnings as errors. -- `ctest` lists equal the snapshot, modulo mechanical renames (a mapping - table is committed). -- icount drift is 0 %. A symbol rename must not move codegen; if a - namespace-length change shifts the instruction count of anything - (it should not), it is investigated rather than re-recorded. -- Every notebook re-executes clean against the new bridge, with outputs - equal to the committed ones (numbers identical, only paths and names - differ). - -### Step 4 — Documentation and archival - -- Promote this document to `PLAN.md`. Write the family `CLAUDE.md` - (combining both repositories' guidance, and the dependency rule of - section 4). Rewrite `README.md` as the family front page. Add per-engine - `PLAN.md`s. -- DspTap: update the references in section 3.7, as a DspTap PR. -- Delete `docs/migration/`. -- **By the user, on GitHub:** archive RatioTap with a README pointer. The - repository rename is not needed (D2). - -**Gate:** a PR to SampleRateTap `main` that is green on every job; the DspTap -docs PR merged or approved. - -### Step 5 — First new engine: `integer` (separate plan) - -`integer` is out of scope for this migration and gets its own PLAN.md -reviewed the same way. The sequence already decided: - -1. The L-th band design lands in DspTap. -2. `decimate.h` moves out of DspTap into `integer/` (a DspTap PR first; its - only in-repo users are its tests and the C ABI). -3. The `chain<>` template lands in DspTap. -4. The coverage matrix of section 2.1 becomes a test. + `tap::sr::ratio`; test namespaces `srt_test` / `ratio_ref` as decided. + Optionally `async_sample_rate_converter` → `converter` (Q2). +3. **clang-format reflow:** its own commit. The namespace rename reflows + 22 files (+112/−114) through alignment columns (GIT-6). +4. **Macros:** + - `SRT_VERSION_*`, `TAP_RATIO_VERSION_*` → `TAP_SR__VERSION_*`. + - `SRT_RESTRICT`, `SRT_Q15_SMLALD`, `SRT_CHANNEL_PARALLEL`, + `TAP_RATIO_MIRRORED_DOT_ATTR`: renamed, or replaced by their + `TAP_DSP_*` originals where they are pure aliases. + - `SRT_CP_MIN_CHANNELS` (a user override, documented in the book): + renamed with an `#error` tripwire (D7). + - `SRT_SC_*`, `RATIO_SC_*`, `SRT_CMP_*`, `*_TESTS_COMPLETE`, + `*_BARE_METAL` (unified to `TAP_SR_BARE_METAL`), `SRT_PICO2_*`. + - **Guest-printed icount markers stay unchanged** (step 2). +5. **CMake:** + - Targets `tap::sr::async`, `tap::sr::ratio` and umbrella `tap::sr`. + - Internal targets (`srt_warnings`, `srt_tests`, `srt_bench*`, + `srt_alsa_bridge`, `srt_r8brain`, `srt_r8b_shim`, `tap_ratio*`, + `srt_headers`, …). + - Options → `TAP_SR_*` per D9. Old public targets removed (D7). +6. **C ABI:** + - `srt_*` → `tap_sr_async_*`, `ratio_*` → `tap_sr_ratio_*`. + - Handle types, header names and library names. + - The shim's exports. + - Reconcile the version encodings (Q4). Bridges renamed per D8. +7. **Ratchet workload binaries:** prefix only (`tap_sr__icount_*`). + Workload names do not change, because the key is the basename minus the + prefix (GATE-12, INV-5). +8. **Book and docs prose:** the 23 files and the RatioTap URLs, including + rewriting the `git clone …/RatioTap` instructions. + +**Gate for each commit:** G1 (the name-map table must be **empty** unless +a suite rename is listed explicitly; GATE-17), G2, G3, G4 (with the symbol +map), G5, G6, G7, G8, G10, G11 and G13. G9 applies from commit 8. + +After the PR merges (step 4), append the SHAs of commits 1–3 to +`.git-blame-ignore-revs`, together with the rewritten RatioTap reformat +commit (`c0894cf`, looked up in the commit map). + +### Step 4 — Documentation and the PR + +- **Root files:** + - `PLAN.md` promoted from this document. + - Family `CLAUDE.md`, which carries the dependency rule, D12, **`git bisect + start --first-parent`** (GIT-9), and the cross-validation separation + rule (section 8, R4). + - Family `README.md`. +- **Per-engine files:** `PLAN.md` and `README.md`. `ratio/CLAUDE.md` is + reduced to the ratio charter. +- **Delete `docs/migration/`,** keeping `runs.md` as history if wanted. +- **Mark the PR ready.** It is merged with **"Create a merge commit"** + (enable it in repository settings if disabled). **Never squash or + rebase** (D3, GIT-1). +- **Post-merge gate:** + - `git rev-list --count origin/main` ≥ 136 + 29 + N. + - `git log --follow ratio/include/tap/sr/ratio/converter.h` reaches + RatioTap's M7d commit. + +### Step 5 — Outside the repository + +- A DspTap PR for the comment and doc references (3.7). +- A taphouse PR for `STYLE.md`'s macro example and `sync.sh`. +- The user archives RatioTap with a README pointer and deletes its leftover + branch. +- The user completes the outside-consumer checks in 3.7 before the RatioTap + archive. + +### Next — First new engine: `integer` (separate plan) + +`integer` is out of scope and gets its own reviewed PLAN.md. That plan +covers: + +- L-th-band design math in DspTap. +- `chain<>` in DspTap under D12. +- The `integer` engine. +- The generated 14 × 14 coverage matrix test (2.1). + +`ratio`'s 2^k charter restatement (2.2) is a small separate change, +sequenced before it. --- -## 6. What stays unchanged (explicit non-goals) +## 7. Non-goals -- No algorithm, coefficient, filter design or process-loop change anywhere. - Every output stays bit-identical, and every icount stays within 0 %. +- No algorithm, coefficient, design or process-loop change. Outputs stay + bit-identical (G5). Codegen stays identical (G4), and so do instruction + counts (G3), except the one Hexagon re-record in step P.2, which is a + harness change made before the snapshot. - No new engine, profile or API function. -- DspTap content is untouched; only its docs change. -- Versioning is not unified in this migration (**Q4**). - ---- +- DspTap **code** is untouched. DspTap docs and comments change only + through a DspTap PR (step 5), and `STYLE.md` only through taphouse. +- Versioning is not unified (Q4). +- The third harness copy (DspTap's) is not adopted (3.3). -## 7. Risks +## 8. Risks | # | Risk | Mitigation | |---|---|---| -| R1 | A step silently drops a test (e.g. a CMake glob moves) | The gate compares **named** test lists against the step-0 snapshot, not just "green" | -| R2 | Infrastructure dedup changes codegen (a startup or linker-script difference) | Divergence measured as cosmetic (section 3.3); gate at 0 % icount drift on every target | -| R3 | Book anchors break across the move | `mdbook build` with warnings as errors is part of the step 3 gate; the paths are enumerated (section 3.4) | -| R4 | Cross-validation passes against a different SampleRateTap than before | RatioTap pins `2b4dff1` == SampleRateTap HEAD; step 1 must start from exactly that tree | -| R5 | `filter-repo` rewrites RatioTap commit SHAs, breaking references in its docs and commit messages ("M7d", PR numbers) | SHAs cited in `ratio/PLAN.md` are listed and annotated with a pointer to the archived RatioTap repository | -| R6 | Two ratchets in one CI multiply QEMU time and cache contention | The matrix runs engine × target in parallel; the existing digest-keyed toolchain cache is shared | -| R7 | Merged `main` history interleaves two engines' commits and confuses `git bisect` | Accepted; `--first-parent` bisect on `main` is unaffected | -| R8 | The monorepo erodes charter boundaries over time | The dependency rule is enforced by CMake plus the header-isolation test (section 4); per-engine PLAN.md files | +| R1 | A step silently drops a test or workload | G1 and G2 compare multisets. G3 requires set equality of workloads. `--no-tests=error` everywhere | +| R2 | Infrastructure deduplication or build glue changes codegen | G4 (disassembly), G7 (flags) and G3 (exact), all as same-job A/B against step 0 | +| R3 | Book and docs rot | G8 from step 1; G9 from step 3 commit 8 | +| R4 | The cross-validation loses its independence | Independence comes from leg 2 (scipy vectors) and from the structural difference between the engines, never from repository separation. Both already share DspTap. What the merge removes is the pin bump as a separately reviewed event. Replacements: G6 during the migration, and a **permanent family rule** in CLAUDE.md. A PR that changes cross-validation tolerances must leave `ratio/tests/reference/` untouched, must keep the scipy leg green, and must not also change async's datapath (`polyphase_filter.h`, `sample_traits.h`). `ratio` never includes async's bank or blend (D11) | +| R5 | Rewritten SHAs and colliding PR numbers | `ratio/docs/HISTORY.md` (step 1b). Rewriting `DspTap #38` in messages is optional | +| R6 | CI load: about 21 jobs, which exceeds the 20-concurrent limit of GitHub Free, and macOS minutes cost 10× | The ratchet runs per target rather than engine × target (step 2). Raise the M33/M55 correctness timeouts, or split them per engine if two one-shot suites exceed 30 min | +| R7 | The merge interleaves histories for `git bisect` | `git bisect start --first-parent` is required and documented. Pre-merge ratio regressions are bisected in-tree (possible thanks to the `.gitmodules` rewrite) or in the archive | +| R8 | Charter boundaries erode | The four-part enforcement in 4.2, D11, D12 and per-engine PLAN.md files | +| R9 | The final PR is squash-merged | D3; the merge-commit instruction in step 4; the post-merge history gate | +| R10 | Toolchain drift mid-migration is blamed on a step | All gates are same-job A/B against the step-0 SHA (section 5) | + +## 9. Open questions + +- **Q2.** Rename `async_sample_rate_converter` → `tap::sr::async::converter` + for symmetry with `ratio`, or keep the descriptive name? +- **Q4.** Versioning: SampleRateTap is 0.1.0 and RatioTap is 0.3.0, and the + two version functions encode differently. Options are one family version, + per-engine versions, or both, plus one encoding. +- **Q5.** License holder line: unify to one line family-wide, or keep both + notices? Until this is decided, `ratio/LICENSE` stays. +- **Q7.** taphouse: is a two-level `tap::sr::` namespace acceptable + under the one-sub-namespace-per-repository convention? +- **Q8.** Where does the async icount table live once the root README + becomes the family front page? Proposed: `async/README.md`, with + `update_icount_docs.py` pointed there at step 1c. + +Resolved since v1: + +- **Q1:** `HANDOFF.md` stays in `ratio/`. +- **Q3:** section 2.2. +- **Q6:** per-engine READMEs plus a family README. --- -## 8. Open questions for the review +## Appendix A — Audit disposition -- **Q1.** Should `ratio` keep `HANDOFF.md` in-tree (history), or retire it to - the archived repository? -- **Q2.** Rename `async_sample_rate_converter` → `tap::sr::async::converter` - for symmetry with `ratio`'s `converter`, or keep the descriptive name? -- **Q3.** 88.2 ↔ 96 and 176.4 ↔ 192. Either widen `ratio`'s charter to the - 2× and 4× rates (its designs are specified in Hz, so this is a - re-specification), or chain `integer` ↓2 → `ratio` → `integer` ↑2, which - breaks the chain invariant of section 2.1 by passing through 44.1 on a - 48-family path. Decide before `integer`'s plan. -- **Q4.** Versioning: SampleRateTap is 0.1.0 and RatioTap is 0.3.0. Options - are one family version, per-engine versions, or both. -- **Q5.** License and copyright lines differ ("SampleRateTap contributors" - vs "Timothy Place and the RatioTap contributors"). Unify to one holder - line family-wide? -- **Q6.** Does the family README absorb the "Position in the Tap family" - material from both repositories, or link to per-engine READMEs? - -## 9. Adversarial audit checklist - -The review should try to **break** each item, not confirm it. - -- **A1 — Decisions:** is any of D1–D11 wrong, or inconsistent with the - DspTap, RatioTap or SampleRateTap CLAUDE.md/PLAN.md rules - ("substrate lands in DspTap first", "never route by rate", "correctness - before optimization")? -- **A2 — Chain invariant:** is "no intermediate rate below min(in, out)" - the right rule? Find a standard rate pair it forbids that must be - supported, or one it allows that loses band. -- **A3 — History:** does the `filter-repo` + unrelated-merge recipe keep - `--follow`/`blame` for *every* file? (RatioTap's history contains no - renames today (`git log --diff-filter=R` is empty); re-check at cut time.) -- **A4 — Gates:** is each gate actually sufficient to detect a regression - the step could introduce? Name a regression that passes a gate. -- **A5 — Bit-identity claims:** are "0 % icount drift" gates achievable? - Consider whether any symbol-name length, section order or string table - reaches the measured instruction stream (the icount counts executed - instructions, not size). -- **A6 — Infrastructure:** diff `insn_count.c`, the startup code and the - linker scripts line by line. Is any difference functional? -- **A7 — Cross-validation:** after the merge, is `ratio`'s cross-validation - still an *independent* check, or does sharing one tree with `async` create - a common-mode failure the separate repositories prevented? -- **A8 — Dependency rule:** can the header-isolation test be bypassed? Does - `bluetooth_bridge` pull `async` into a shipped target anywhere? -- **A9 — Inventory completeness:** is anything in either repository missing - from section 3 (files, workflows, secrets, Pages configuration, badges, - issue references, the Pico 2 examples' build paths)? -- **A10 — Reversibility:** if the migration is abandoned after step 2, is - RatioTap still intact and usable? (It should be: nothing touches the - RatioTap repository until step 4.) -- **A11 — Consumers:** is it really true that nothing outside these - repositories consumes `srt/`, `tap::samplerate`, `tap::ratio` or the C - ABIs (TapTools, TapTools-Max, MuTap, AmbiTap, any Pages or notebook - links)? +Reviewers: **DEC** (decisions and charters), **GIT** (history mechanics, +dry-run on scratch clones), **GATE** (gates and bit-identity, host builds), +**INF** (infrastructure and CI, simulated merged tree), **INV** (inventory +completeness). Severity: B blocker, M major, m minor, n nit. + +**Rejected:** no finding was rejected outright. + +**Partly deferred:** + +- DEC-9's harness-from-DspTap is deferred (3.3). +- DEC-10's `std::` shadowing is noted, not acted on (D5). +- INF-14's per-engine timeout split is conditional (R6). + +| ID | Sev | Finding (short) | Disposition | +|---|---|---|---| +| DEC-1 | M | Q3 premise wrong: `ratio` is rate-normalized | 2.2; Q3 resolved | +| DEC-2 | M | Invariant forbids more pairs than listed | 2.1: generated full matrix | +| DEC-3 | M | Nyquist criterion wrong | 2.1: passband rule | +| DEC-4 | m | "Every standard rate" false (37.8, 50.4, pull-down) | 2.1 exclusions | +| DEC-5 | n | Table rows; 2/3 needs a rational stage | 2.1 rows; `integer` charter L/M | +| DEC-6 | M | `fractional_resampler` to DspTap contradicts settled M0 | D11 | +| DEC-7 | M | Moving `decimate.h` breaks MuTap / D10 | 2; the `integer` plan (section 6, Next) | +| DEC-8 | M | `chain<>` risks a rate-routing factory | D12 | +| DEC-9 | m | D1 supersession unrecorded; third harness copy | D1; 3.3 | +| DEC-10 | n | D4 supersedes agreed rename; `std::` shadowing | D4, D5; Q7 | +| DEC-11 | M | Independence misattributed; R4 guards the wrong thing | R4; G6 | +| DEC-12 | M | Per-engine enables can drop leg 3 | D9 | +| DEC-13 | M | Header-isolation bypassable | 4.2 | +| DEC-14 | m | One shared capi links both engines | D8 | +| DEC-15 | M | Step-1 CMake glue drops ratio tests | step 1c | +| DEC-16 | n | Stale pin checkout; stale README figures | 3.2; step P.1 | +| GIT-1 | B | Squash merge erases imported history | D3; step 4; R9 | +| GIT-2 | M | Orphan submodule gitlinks under subdir filter | step 1b corrected recipe | +| GIT-3 | M | Inventory from a shallow clone | D2; 3; step 0 | +| GIT-4 | M | Step 1 not content-free | step 1c | +| GIT-5 | M | Move+recreate in one commit loses blame | step 1a; G12 | +| GIT-6 | M | Namespace rename reflow needs own commit + ignore-revs | step 3.3 | +| GIT-7 | m | `.git-blame-ignore-revs` dangling | step P.1 | +| GIT-8 | M | PR linkage lost; `#NN` collisions | step 1b HISTORY.md; R5 | +| GIT-9 | M | Bisect needs `--first-parent` | step 4; R7 | +| GIT-10 | m | Import branch; stale local refs | 3; step 0 | +| GIT-11 | m | Tags | 3; step 1b | +| GIT-12 | m | Duplicate ctest names | G1 | +| GIT-13 | m | Deleting ratio LICENSE drops notice | 4.1; step 1c; Q5 | +| GIT-14 | n | Plan swept into `async/docs` | 4.1 | +| GATE-1 | B | 0 % icount unattainable (marker, Hexagon argv/env) | G3; step P.2; step 2 | +| GATE-2 | M | icount.py passes on missing workloads | G3; step P.2 | +| GATE-3 | M | Toolchain drift vs committed baselines | Section 5 A/B; G4 | +| GATE-4 | M | icount ≠ bit-identity | G5; step P.2 | +| GATE-5 | M | Bare-metal: one ctest name, loose floors | G2 | +| GATE-6 | M | arm64 TSan runs zero tests | step P.1; `--no-tests=error` | +| GATE-7 | M | Duplicate test name defeats set compare | G1 | +| GATE-8 | M | Hexagon exclusion lists unguarded | G2; step 1c | +| GATE-9 | M | Book/notebooks/docs break at step 1 | step 1c; G8, G11 | +| GATE-10 | M | Notebook gate unattainable | step P.3; G11 | +| GATE-11 | M | CI does not gate each commit | Section 6 preamble; G13 | +| GATE-12 | m | v1 step 3.6 contradicts key derivation | step 3.7 | +| GATE-13 | m | BARE_METAL variable functional | 3.3; step 1c; step 3.4 | +| GATE-14 | m | Override macro silently ignored | D7; step 3.4 | +| GATE-15 | m | Glue may change compile flags | G7 | +| GATE-16 | m | Stale prose unchecked | G9 | +| GATE-17 | n | Mapping-table loophole; ABI snapshot unused | step 3 gate; G10 | +| INF-1 | M | BARE_METAL coupling; gtest ODR mismatch | step 1c; step 3.4 | +| INF-2 | B | Merged tree does not configure (dsptap twice) | step 1c | +| INF-3 | B | 0 tests, ctest exits 0 | step 1c; G1 | +| INF-4 | B | ratio has no CI between steps 1 and 2 | step 1c | +| INF-5 | M | Book/Doxyfile/book-pages break at step 1 | step 1c; G8 | +| INF-6 | M | Hard-coded paths in jobs and compare.yml | step 1c; step 2 | +| INF-7 | M | Marker rename moves guest count | step 2 | +| INF-8 | M | Matrix fail-fast; duplicated setup | step 2 | +| INF-9 | M | Dedup scheme stops branch CI | Section 6 preamble; step 2 | +| INF-10 | M | style.yml functional; tidy would lint 0 TUs | 3.3; step 1c | +| INF-11 | M | CI table wrong; lost WERROR/Hexagon gates | 3.5; D9; step 1c | +| INF-12 | m | Duplicate test name | G1 | +| INF-13 | m | Toolchain fetch hardening | 3.3; step 1c; step 2 | +| INF-14 | m | Job count vs concurrency; timeouts | R6 | +| INF-15 | n | Stale branch/pin facts; cancel-in-progress on main | 3; step 2 | +| INV-1 | B | Step 1 cannot build/test without CMake edits | step 1c | +| INV-2 | M | Duplicate ctest name | G1 | +| INV-3 | M | Root-level consumers break at step 1 | step 1c | +| INV-4 | M | "Cosmetic only" false | 3.3 | +| INV-5 | M | Marker rename breaks 0 % gate; key contradiction | step 2; step 3.7 | +| INV-6 | M | Rename list missing classes | step 3.4–step 3.6 | +| INV-7 | M | kaiser.h deletion more than an include swap | 3.1 | +| INV-8 | M | Existing breakage invisible | step P.1 | +| INV-9 | M | Unaccounted files | 4.1 | +| INV-10 | m | 3.4 conclusion wrong | 3.4 | +| INV-11 | m | 3.5 table errors | 3.5 | +| INV-12 | m | C ABI counts exact; version encodings | 3.6; Q4 | +| INV-13 | m | DspTap list 10 of 25; non-goal conflict; STYLE via taphouse | 3.7; 7; step 5 | +| INV-14 | m | RatioTap URLs dangle | 3.7; step 3.8 | +| INV-15 | n | 3.2 / R4 stale | 3.2; R4 | +| INV-16 | m | Outside consumers unchecked | 3.7 (user checklist) | From 8d241b0d25db5c91da1d7796d62e6d12c6a5a29c Mon Sep 17 00:00:00 2001 From: Claude Date: Sat, 26 Sep 2026 22:08:14 +0000 Subject: [PATCH 36/44] Record plan decisions on naming, versioning and licensing (v2.1) Adds D13 (one family version, 0.4.0, bit-packed tap_sr_version), D14 (one copyright holder line; ratio/LICENSE retired in the commit that adds it to the root) and D15 (async_sample_rate_converter -> converter), accepts the two-level tap::sr namespace, places icount tables in each engine's README, and records the confirmed outside-repository facts (no external consumers, taphouse syncs RatioTap, merge commits allowed). Every open question is now resolved. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 91 +++++++++++++++++++++++-------------------- 1 file changed, 49 insertions(+), 42 deletions(-) diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index 91677af..96a71c8 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -1,10 +1,12 @@ # Monorepo plan: the `tap::sr` sample-rate family -Status: **DRAFT v2. Revised after adversarial audit; nothing executed.** +Status: **DRAFT v2.1. Revised after adversarial audit; open questions answered; nothing executed.** - v1 (2026-09-26, `11f2a94`): the first draft. - v2 (2026-09-26): folds in the five-reviewer adversarial audit. It had 78 findings, of which 6 were blockers. +- v2.1 (2026-09-26): records the user's answers to Q2, Q4, Q5, Q7 and Q8 + (D13–D15) and the confirmed outside-repository facts. - Appendix A maps every finding ID (DEC-, GIT-, GATE-, INF-, INV-) to where it landed in this document, or to why it was rejected. @@ -32,7 +34,7 @@ How to read it: | D1 | **Merge SampleRateTap and RatioTap into one repository**; each engine keeps its own charter, CMake target, CI coverage and ratchet baselines | The engines are built to be composed and cross-checked against each other. **This supersedes** HANDOFF.md preamble item 1 ("separate repo") and RatioTap PLAN.md §2 (SampleRateTap as a test-only dependency). Those were decided when the family had two engines and no plan for more. With five engines planned, the cost of pins, duplicated harnesses and cross-repository test dependencies grows with every addition. There are no external consumers, so the rename is free | | D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" names the whole family once it is namespaced. The published book (tap.github.io/SampleRateTap) keeps its URL. It is also the larger history: **136** commits on `main` (full clone), against RatioTap's 29 | | D3 | **RatioTap's history is preserved** through a `git filter-repo` path rewrite that also rewrites `.gitmodules` throughout history (section 6, step 1), plus an unrelated-histories merge. **The final PR is merged with a merge commit, never squash or rebase** | `git log --follow` and `git blame` keep working for every file. A squash would erase all 29 imported commits (GIT-1) | -| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace. **This supersedes** the rename agreed in RatioTap PLAN.md (`include/srt/` → `include/tap/samplerate/`) and extends the taphouse convention of one `tap::` sub-namespace per repository to two levels. That extension needs **confirming with taphouse** (Q7) | +| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace. **This supersedes** the rename agreed in RatioTap PLAN.md (`include/srt/` → `include/tap/samplerate/`) and extends the taphouse convention of one `tap::` sub-namespace per repository to two levels. The two-level form is **accepted** (user decision, 2026-09-26). The convention note goes into taphouse's `STYLE.md` through a taphouse PR (step 5) | | D5 | **Engine names:** `async` (today's SampleRateTap), `ratio` (today's RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term (ASRC) and the family's own clock-topology vocabulary. It is the only async engine, and async at other ratios is reached by composition. Every other engine is sync and is named by what it converts. Known wrinkle: in code with `using namespace tap::sr`, these names sit beside `std::ratio` and `std::async`. The house style already avoids namespace-wide `using` directives | | D6 | **One top-level directory per engine** (`async/`, `ratio/`, …), each with its own `include/ tests/ bench/ examples/ capi/ notebooks/ README.md PLAN.md` | Keeps each charter's boundary physical, where a single shared `include/` tree would not | | D7 | **Clean renames, no compatibility aliases.** Removed user-facing override macros get an `#error` **tripwire**, not an alias | Aliases would be permanent debt. A tripwire makes a stale `-DSRT_CP_MIN_CHANNELS=…` a loud error rather than a silent no-op (GATE-14) | @@ -41,6 +43,9 @@ How to read it: | D10 | **DspTap stays a separate repository**, pinned once at `submodules/dsptap` | It has consumers outside the rate family: TapTools, and MuTap through the `LogMel`/`Decimator` C ABI | | D11 | **Charter rule for new engines:** a capability gets an engine directory here when it has its own charter, optimization campaign and ratchet. Building blocks (filter design math, kernels, the `chain<>` template) go into DspTap. **Engine-owned datapaths stay with their engine.** In particular, `fractional_resampler`, the polyphase bank and the blend stratum are `async`'s and never move to DspTap | Agrees with RatioTap PLAN.md Appendix A ("the blend stratum does not move"). Keeping async's datapath out of DspTap is also what keeps `ratio`'s cross-validation oracle outside `ratio`'s reach (DEC-6) | | D12 | **No routing by rate, ever, including through composition.** `chain<>` is a caller-named, compile-time composition of **synchronous** stages. There is no `(in_hz, out_hz) → engine` lookup, `async` is never selected by a chain, and the coverage matrix *documents* chains without dispatching them | Keeps HANDOFF preamble item 4 ("factory dropped; clock topology is routed by type choice") intact as the family grows (DEC-8) | +| D13 | **One family version**, starting at **0.4.0**: `project(SampleRateTap VERSION 0.4.0)`, macros `TAP_SR_VERSION_{MAJOR,MINOR,PATCH}`, one C function `tap_sr_version()` encoded `(M<<16)\|(m<<8)\|p`, and tags `vX.Y.Z` | User decision, 2026-09-26. 0.4.0 sits above both current versions (async 0.1.0, ratio 0.3.0), so neither engine appears to go backwards. The bit-packed encoding is RatioTap's, already pinned by `test_skeleton.cpp`, and has room above 99. Any engine change bumps the family version | +| D14 | **One copyright holder line family-wide:** "Copyright 2026 Timothy Place and the SampleRateTap contributors" in the root `LICENSE` and in every file banner | User decision, 2026-09-26. Both repositories' notices name the same author, and RatioTap's contributors become SampleRateTap contributors when the histories merge, so this is a restatement, not a relicensing. `ratio/LICENSE` is kept until the root `LICENSE` carries the unified line (step 1c), then deleted in the same commit | +| D15 | **`async_sample_rate_converter` → `tap::sr::async::converter`** | User decision, 2026-09-26. Matches `tap::sr::ratio::converter`; the namespace already says "async" | --- @@ -274,15 +279,18 @@ nothing today. `book/src/part0/two-crystals.md:173`, `part5/scaling.md:194,352-355` and `docs/COMPARISON.md:234`. The README quotes stale cross-validation figures (−109/−99 dB; the current v0.3 floors are about −98/−90 dB). -- **Outside this session: the user checks these** before step 1: - - **TapTools, TapTools-Max, MuTap, AmbiTap, OscTap:** submodules pointing - at either repository, and uses of `srt/`, `tap/ratio`, - `tap::samplerate`, `tap::ratio`, `SampleRateTap::SampleRateTap`, the C - ABI symbols and Pages links. - - **taphouse:** `sync.sh` target list (drop RatioTap, or syncs to the - archive fail), `drift-check.yml`, and the catalog README. - - **GitHub:** open RatioTap issues and PRs, and the Pages source setting. - Note: the repository is private while its Pages site is public. +- **Outside this session** (confirmed by the user, 2026-09-26): + - **TapTools, TapTools-Max, MuTap, AmbiTap, OscTap** do not submodule or + reference either repository's headers, targets or C ABIs. D7's "no + consumers" premise holds. + - **taphouse's `sync.sh` includes RatioTap.** It must be removed from the + target list before RatioTap is archived (step 5), or syncs to the + archive fail. `drift-check.yml` and the catalog README are updated in the + same taphouse PR. + - **"Create a merge commit" is allowed** on SampleRateTap (D3, step 4). + - Still to check at step 5: open RatioTap issues and PRs, and the Pages + source setting. The repository is private while its Pages site is + public. --- @@ -293,7 +301,7 @@ SampleRateTap/ ├── CMakeLists.txt NEW root: project(SampleRateTap), enable_testing(), │ add_subdirectory(submodules/dsptap) ONCE, then engines ├── CLAUDE.md PLAN.md README.md NEW family-level files (step 4) -├── LICENSE SampleRateTap's; ratio/LICENSE kept until Q5 is decided +├── LICENSE unified holder line (D14) ├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ .github/ (shared) ├── .git-blame-ignore-revs repaired (step P.1), extended after step 4 ├── submodules/dsptap the one pin @@ -308,7 +316,7 @@ SampleRateTap/ │ ├── capi/ notebooks/ docs/ (PERFORMANCE, COMPARISON, HARDWARE_TESTING) │ └── tools/compare_shim/ cmake/r8brain.cmake └── ratio/ - ├── CMakeLists.txt README.md PLAN.md HANDOFF.md CLAUDE.md LICENSE + ├── CMakeLists.txt README.md PLAN.md HANDOFF.md CLAUDE.md ├── include/tap/sr/ratio/ tests/ (+reference/) bench/ examples/ ├── capi/ notebooks/ (+requirements.txt) tools/reference/ docs/HISTORY.md ``` @@ -334,7 +342,7 @@ SampleRateTap/ | `ratio/tools/capi/` | `ratio/capi/` | step 3 | | `ratio/cmake/`, `ratio/platform/`, `ratio/tools/qemu_insn_plugin/`, `ratio/scripts/icount.py` | deleted (root copies) | step 2 | | `ratio/CLAUDE.md` | kept, build commands corrected at step 1c; reduced to the ratio charter at step 4 | step 1c, step 4 | -| `ratio/LICENSE` | kept until Q5 is decided | — | +| `ratio/LICENSE` | deleted in the same commit that puts D14's unified line in the root `LICENSE` | step 1c | | `ratio/notebooks/requirements.txt` | superseded by the root lockfile | step P.3 | | `docs/migration/` (snapshots) | created at root `docs/migration/` (not moved, since `docs/` stays) and deleted at the end | step 0, step 4 | @@ -521,11 +529,13 @@ and G7 prove that. - `book-pages.yml` path filters and Doxyfile `INPUT`. - The `bench-smoke` binary path, `compare.yml` build paths, `icount.py --baselines` per engine, `update_icount_docs.py` → - `async/bench/baselines.json` and `async/README.md`. + `async/bench/baselines.json` and `async/README.md` (Q8). ratio gains the + same table in `ratio/README.md`, and the script takes `--engine`. - Notebook `REPO` roots and `sys.path` entries. - The 11 README relative links. - `ratio/CLAUDE.md` build commands. -- **LICENSE:** keep `ratio/LICENSE`, which MIT requires to be retained +- **LICENSE:** in one commit, the root `LICENSE` takes D14's unified line + and `ratio/LICENSE` is deleted. The notice MIT requires is never absent (GIT-13). **Gate 1:** @@ -565,11 +575,12 @@ and G7 prove that. `ratio/tools/capi` → `ratio/capi`. Delete `srt/detail/kaiser.h` per 3.1. 2. **Namespaces:** `tap::samplerate` → `tap::sr::async`; `tap::ratio` → `tap::sr::ratio`; test namespaces `srt_test` / `ratio_ref` as decided. - Optionally `async_sample_rate_converter` → `converter` (Q2). + `async_sample_rate_converter` → `converter` (D15). 3. **clang-format reflow:** its own commit. The namespace rename reflows 22 files (+112/−114) through alignment columns (GIT-6). 4. **Macros:** - - `SRT_VERSION_*`, `TAP_RATIO_VERSION_*` → `TAP_SR__VERSION_*`. + - `SRT_VERSION_*`, `TAP_RATIO_VERSION_*` → `TAP_SR_VERSION_*`, set to + 0.4.0 (D13). - `SRT_RESTRICT`, `SRT_Q15_SMLALD`, `SRT_CHANNEL_PARALLEL`, `TAP_RATIO_MIRRORED_DOT_ATTR`: renamed, or replaced by their `TAP_DSP_*` originals where they are pure aliases. @@ -588,7 +599,8 @@ and G7 prove that. - `srt_*` → `tap_sr_async_*`, `ratio_*` → `tap_sr_ratio_*`. - Handle types, header names and library names. - The shim's exports. - - Reconcile the version encodings (Q4). Bridges renamed per D8. + - `srt_version` and `ratio_version` → one `tap_sr_version()`, bit-packed + (D13); `test_skeleton.cpp` re-pins it at 0.4.0. Bridges renamed per D8. 7. **Ratchet workload binaries:** prefix only (`tap_sr__icount_*`). Workload names do not change, because the key is the basename minus the prefix (GATE-12, INV-5). @@ -655,7 +667,8 @@ sequenced before it. - No new engine, profile or API function. - DspTap **code** is untouched. DspTap docs and comments change only through a DspTap PR (step 5), and `STYLE.md` only through taphouse. -- Versioning is not unified (Q4). +- No release is cut during the migration. 0.4.0 (D13) is set in step 3 and + tagged `v0.4.0` after step 4 merges. - The third harness copy (DspTap's) is not adopted (3.3). ## 8. Risks @@ -675,24 +688,18 @@ sequenced before it. ## 9. Open questions -- **Q2.** Rename `async_sample_rate_converter` → `tap::sr::async::converter` - for symmetry with `ratio`, or keep the descriptive name? -- **Q4.** Versioning: SampleRateTap is 0.1.0 and RatioTap is 0.3.0, and the - two version functions encode differently. Options are one family version, - per-engine versions, or both, plus one encoding. -- **Q5.** License holder line: unify to one line family-wide, or keep both - notices? Until this is decided, `ratio/LICENSE` stays. -- **Q7.** taphouse: is a two-level `tap::sr::` namespace acceptable - under the one-sub-namespace-per-repository convention? -- **Q8.** Where does the async icount table live once the root README - becomes the family front page? Proposed: `async/README.md`, with - `update_icount_docs.py` pointed there at step 1c. - -Resolved since v1: - -- **Q1:** `HANDOFF.md` stays in `ratio/`. -- **Q3:** section 2.2. -- **Q6:** per-engine READMEs plus a family README. +None. All were resolved; the decisions are recorded where they apply. + +| Q | Resolution | +|---|---| +| Q1 | `HANDOFF.md` stays in `ratio/` (v2) | +| Q2 | Rename to `converter`: D15 (user, 2026-09-26) | +| Q3 | `ratio` at 2^k rates: section 2.2 (v2) | +| Q4 | One family version, 0.4.0, bit-packed encoding: D13 (user) | +| Q5 | One unified holder line: D14 (user) | +| Q6 | Per-engine READMEs plus a family README (v2) | +| Q7 | Two-level namespace accepted: D4 (user) | +| Q8 | Icount tables in each engine's README: step 1c (user) | --- @@ -722,7 +729,7 @@ completeness). Severity: B blocker, M major, m minor, n nit. | DEC-7 | M | Moving `decimate.h` breaks MuTap / D10 | 2; the `integer` plan (section 6, Next) | | DEC-8 | M | `chain<>` risks a rate-routing factory | D12 | | DEC-9 | m | D1 supersession unrecorded; third harness copy | D1; 3.3 | -| DEC-10 | n | D4 supersedes agreed rename; `std::` shadowing | D4, D5; Q7 | +| DEC-10 | n | D4 supersedes agreed rename; `std::` shadowing | D4, D5 | | DEC-11 | M | Independence misattributed; R4 guards the wrong thing | R4; G6 | | DEC-12 | M | Per-engine enables can drop leg 3 | D9 | | DEC-13 | M | Header-isolation bypassable | 4.2 | @@ -741,7 +748,7 @@ completeness). Severity: B blocker, M major, m minor, n nit. | GIT-10 | m | Import branch; stale local refs | 3; step 0 | | GIT-11 | m | Tags | 3; step 1b | | GIT-12 | m | Duplicate ctest names | G1 | -| GIT-13 | m | Deleting ratio LICENSE drops notice | 4.1; step 1c; Q5 | +| GIT-13 | m | Deleting ratio LICENSE drops notice | D14; 4.1; step 1c | | GIT-14 | n | Plan swept into `async/docs` | 4.1 | | GATE-1 | B | 0 % icount unattainable (marker, Hexagon argv/env) | G3; step P.2; step 2 | | GATE-2 | M | icount.py passes on missing workloads | G3; step P.2 | @@ -786,8 +793,8 @@ completeness). Severity: B blocker, M major, m minor, n nit. | INV-9 | M | Unaccounted files | 4.1 | | INV-10 | m | 3.4 conclusion wrong | 3.4 | | INV-11 | m | 3.5 table errors | 3.5 | -| INV-12 | m | C ABI counts exact; version encodings | 3.6; Q4 | +| INV-12 | m | C ABI counts exact; version encodings | 3.6; D13 | | INV-13 | m | DspTap list 10 of 25; non-goal conflict; STYLE via taphouse | 3.7; 7; step 5 | | INV-14 | m | RatioTap URLs dangle | 3.7; step 3.8 | | INV-15 | n | 3.2 / R4 stale | 3.2; R4 | -| INV-16 | m | Outside consumers unchecked | 3.7 (user checklist) | +| INV-16 | m | Outside consumers unchecked | 3.7 (confirmed by user) | From b805c019416206a4113dfa03798ae49c1dacb851 Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 15:25:40 +0000 Subject: [PATCH 37/44] Revise the monorepo plan after the second audit round (v3) Folds in the round-2 audit (78 findings from an end-to-end dry run of steps 1a-1c, gate prototypes, a CI design review and a coherence check): - Correct the step-1 recipe: resolve the .gitmodules conflict with git add, spell out the duplicate removal, filter only main, and guard engine CMake with a ../submodules/dsptap path so standalone builds and notebook bridges keep working. Root options become defaults, never forced; GTEST_HAS_* stays in each engine's test tree. - Split gates into same-job A/B (icount, codegen, output hashes, flags, notebooks) run by a dedicated migration-gates workflow, and snapshot gates. Scope codegen identity to icount and C ABI binaries, never pin output hashes as constants, and add G14, a rename-only residual diff that catches edits no other gate sees. - Give tests an engine prefix and label so duplicate names no longer alias, select engines at ctest time, and push one gated commit at a time with cancellation disabled on the migration PR. - Rewrite the coverage rule in terms of stopband edge, attenuation and summed ripple; restate the ratio 2^k follow-up as an API change. - Specify D13 version mechanics, correct D14's rationale (the author holds SampleRateTap's copyright) and add banners to every source, and give D15 its full mapping. - Commit the audit's draft workflows and root CMake under docs/migration/drafts/ for review. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 1337 ++++++++++------- docs/migration/drafts/ci-after-1c.yml | 175 +++ docs/migration/drafts/migration-gates.yml | 43 + .../migration/drafts/root-CMakeLists-1c.cmake | 35 + 4 files changed, 1041 insertions(+), 549 deletions(-) create mode 100644 docs/migration/drafts/ci-after-1c.yml create mode 100644 docs/migration/drafts/migration-gates.yml create mode 100644 docs/migration/drafts/root-CMakeLists-1c.cmake diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index 96a71c8..7dee121 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -1,29 +1,30 @@ # Monorepo plan: the `tap::sr` sample-rate family -Status: **DRAFT v2.1. Revised after adversarial audit; open questions answered; nothing executed.** +Status: **DRAFT v3. Two adversarial audit rounds folded in; nothing executed.** -- v1 (2026-09-26, `11f2a94`): the first draft. -- v2 (2026-09-26): folds in the five-reviewer adversarial audit. It had 78 - findings, of which 6 were blockers. -- v2.1 (2026-09-26): records the user's answers to Q2, Q4, Q5, Q7 and Q8 - (D13–D15) and the confirmed outside-repository facts. -- Appendix A maps every finding ID (DEC-, GIT-, GATE-, INF-, INV-) to where - it landed in this document, or to why it was rejected. +| Version | Commit | What changed | +|---|---|---| +| v1 | `11f2a94` | First draft | +| v2 | `4884b01` | Round 1: 5 reviewers, 78 findings (Appendix A) | +| v2.1 | `c78ab93` | User decisions D13–D15; outside-repository facts confirmed | +| v3 | this commit | Round 2: 4 reviewers, including an end-to-end dry run of steps 1a–1c and gate prototypes; 78 findings (Appendix B). The user reconfirmed D14 and chose banners everywhere | -Once this plan is approved, it becomes the family-level `PLAN.md`. Until -then it is the only document that describes the change, and nothing depends -on it. +When this plan is approved, it becomes the family-level `PLAN.md`. Draft +workflow and CMake files that the audit produced live in +`docs/migration/drafts/`. They are untested sketches, and GitHub does not +run them from that directory. How to read it: -- Section 1 holds the decisions. -- Section 2 is the family, the charters and the coverage rules. -- Section 3 is the measured inventory. -- Section 4 is the target layout and a file-by-file disposition table. -- Section 5 defines the gates once. -- Section 6 is the migration: pre-work step P, then steps 0–5, each - referencing those gates. -- Sections 7–9 are the non-goals, risks and open questions. +- **Section 1:** decisions. +- **Section 2:** family, charters and coverage rules. +- **Section 3:** measured inventory. +- **Section 4:** layout, file disposition and dependency enforcement. +- **Section 5:** gates. +- **Section 6:** the migration: pre-work step P, then steps 0–5, then what + comes next. +- **Sections 7–9:** non-goals, risks and open questions. +- **Appendices A and B:** audit disposition. --- @@ -31,21 +32,22 @@ How to read it: | # | Decision | Rationale / supersession | |---|---|---| -| D1 | **Merge SampleRateTap and RatioTap into one repository**; each engine keeps its own charter, CMake target, CI coverage and ratchet baselines | The engines are built to be composed and cross-checked against each other. **This supersedes** HANDOFF.md preamble item 1 ("separate repo") and RatioTap PLAN.md §2 (SampleRateTap as a test-only dependency). Those were decided when the family had two engines and no plan for more. With five engines planned, the cost of pins, duplicated harnesses and cross-repository test dependencies grows with every addition. There are no external consumers, so the rename is free | -| D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" names the whole family once it is namespaced. The published book (tap.github.io/SampleRateTap) keeps its URL. It is also the larger history: **136** commits on `main` (full clone), against RatioTap's 29 | -| D3 | **RatioTap's history is preserved** through a `git filter-repo` path rewrite that also rewrites `.gitmodules` throughout history (section 6, step 1), plus an unrelated-histories merge. **The final PR is merged with a merge commit, never squash or rebase** | `git log --follow` and `git blame` keep working for every file. A squash would erase all 29 imported commits (GIT-1) | -| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | Follows DspTap's rule that the path mirrors the namespace. **This supersedes** the rename agreed in RatioTap PLAN.md (`include/srt/` → `include/tap/samplerate/`) and extends the taphouse convention of one `tap::` sub-namespace per repository to two levels. The two-level form is **accepted** (user decision, 2026-09-26). The convention note goes into taphouse's `STYLE.md` through a taphouse PR (step 5) | -| D5 | **Engine names:** `async` (today's SampleRateTap), `ratio` (today's RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term (ASRC) and the family's own clock-topology vocabulary. It is the only async engine, and async at other ratios is reached by composition. Every other engine is sync and is named by what it converts. Known wrinkle: in code with `using namespace tap::sr`, these names sit beside `std::ratio` and `std::async`. The house style already avoids namespace-wide `using` directives | -| D6 | **One top-level directory per engine** (`async/`, `ratio/`, …), each with its own `include/ tests/ bench/ examples/ capi/ notebooks/ README.md PLAN.md` | Keeps each charter's boundary physical, where a single shared `include/` tree would not | -| D7 | **Clean renames, no compatibility aliases.** Removed user-facing override macros get an `#error` **tripwire**, not an alias | Aliases would be permanent debt. A tripwire makes a stale `-DSRT_CP_MIN_CHANNELS=…` a loud error rather than a silent no-op (GATE-14) | -| D8 | **C ABI prefix `tap_sr__*`, one shared library per engine** (`tap_sr_async_capi`, `tap_sr_ratio_capi`), one bridge module per engine | Per-engine libraries keep `capi/` inside the engine boundary, so no shipped artifact links two engines (DEC-14). A combined notebook library can come later if a notebook needs both engines | -| D9 | **CMake options `TAP_SR_*`**: `TAP_SR_BUILD_TESTS`, `…_EXAMPLES`, `…_CAPI`, `…_ICOUNT_BENCH`, and per-engine `TAP_SR__WERROR`. There is **no per-engine enable switch in the migration** | A per-engine WERROR keeps ratio's MSVC `/WX` gate, which async has not triaged yet (INF-11). Per-engine enables could silently drop the cross-validation (DEC-12), so they are deferred. If they are ever added, ratio tests ON with async OFF is a `FATAL_ERROR` | -| D10 | **DspTap stays a separate repository**, pinned once at `submodules/dsptap` | It has consumers outside the rate family: TapTools, and MuTap through the `LogMel`/`Decimator` C ABI | -| D11 | **Charter rule for new engines:** a capability gets an engine directory here when it has its own charter, optimization campaign and ratchet. Building blocks (filter design math, kernels, the `chain<>` template) go into DspTap. **Engine-owned datapaths stay with their engine.** In particular, `fractional_resampler`, the polyphase bank and the blend stratum are `async`'s and never move to DspTap | Agrees with RatioTap PLAN.md Appendix A ("the blend stratum does not move"). Keeping async's datapath out of DspTap is also what keeps `ratio`'s cross-validation oracle outside `ratio`'s reach (DEC-6) | -| D12 | **No routing by rate, ever, including through composition.** `chain<>` is a caller-named, compile-time composition of **synchronous** stages. There is no `(in_hz, out_hz) → engine` lookup, `async` is never selected by a chain, and the coverage matrix *documents* chains without dispatching them | Keeps HANDOFF preamble item 4 ("factory dropped; clock topology is routed by type choice") intact as the family grows (DEC-8) | -| D13 | **One family version**, starting at **0.4.0**: `project(SampleRateTap VERSION 0.4.0)`, macros `TAP_SR_VERSION_{MAJOR,MINOR,PATCH}`, one C function `tap_sr_version()` encoded `(M<<16)\|(m<<8)\|p`, and tags `vX.Y.Z` | User decision, 2026-09-26. 0.4.0 sits above both current versions (async 0.1.0, ratio 0.3.0), so neither engine appears to go backwards. The bit-packed encoding is RatioTap's, already pinned by `test_skeleton.cpp`, and has room above 99. Any engine change bumps the family version | -| D14 | **One copyright holder line family-wide:** "Copyright 2026 Timothy Place and the SampleRateTap contributors" in the root `LICENSE` and in every file banner | User decision, 2026-09-26. Both repositories' notices name the same author, and RatioTap's contributors become SampleRateTap contributors when the histories merge, so this is a restatement, not a relicensing. `ratio/LICENSE` is kept until the root `LICENSE` carries the unified line (step 1c), then deleted in the same commit | -| D15 | **`async_sample_rate_converter` → `tap::sr::async::converter`** | User decision, 2026-09-26. Matches `tap::sr::ratio::converter`; the namespace already says "async" | +| D1 | **Merge SampleRateTap and RatioTap into one repository.** Each engine keeps its own charter, CMake target, CI coverage and ratchet baselines | The engines are built to be composed and cross-checked. **This supersedes** HANDOFF.md preamble item 1 ("separate repo") and RatioTap PLAN.md §2 (SampleRateTap as a test-only dependency). Those decisions predate a five-engine roadmap: every added engine multiplies pins, harness copies and cross-repository test dependencies. Nothing outside these two repositories consumes them (confirmed, 3.7) | +| D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" names the family once it is namespaced. The Pages book keeps its URL. It also has the larger history: 136 commits on `main` in a full clone, against RatioTap's 29 at the time of writing (step 0 re-counts both) | +| D3 | **RatioTap's history is preserved.** `git filter-repo` rewrites the paths, and `.gitmodules` throughout history, on `main` only (step 1b). The rewritten history is joined by an unrelated-histories merge. **The final PR is merged with a merge commit, never squash or rebase** | `--follow`, `blame` and submodule checkout keep working for every imported commit. A squash would erase all imported commits (GIT-1). Merge commits are allowed on SampleRateTap (confirmed) | +| D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | The path mirrors the namespace, as in DspTap. **This supersedes** RatioTap PLAN.md's agreed `include/tap/samplerate/`. The two-level form is **accepted** (user, 2026-09-26). The convention note goes to taphouse's `STYLE.md` (step 5) | +| D5 | **Engines:** `async` (was SampleRateTap), `ratio` (was RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term and the family's clock-topology word. It is the only async engine, and async at other ratios comes from composition. The sync engines are named for what they convert. The names sit next to `std::ratio`/`std::async` only under namespace-wide `using`, which the house style avoids | +| D6 | **One top-level directory per engine**, each with `include/ tests/ bench/ examples/ capi/ notebooks/ README.md PLAN.md` | Keeps each charter's boundary physical | +| D7 | **Clean renames, no aliases.** Retired user-facing override macros get an `#error` **tripwire**. Retired **CMake options** get a `FATAL_ERROR` tripwire (step 3.4) | Aliases would be permanent debt. A tripwire turns a stale `-DSRT_CP_MIN_CHANNELS=…` or `-DSRT_WERROR=ON` into a loud failure instead of a silent no-op. An unknown `-D` otherwise only warns and drops a gate (R2-CI-6) | +| D8 | **C ABI prefix `tap_sr__*`, one shared library per engine** (`tap_sr_async_capi`, `tap_sr_ratio_capi`), and one bridge module per engine | No shipped artifact links two engines. The version function follows the rule as well: `tap_sr_async_version()` and `tap_sr_ratio_version()`, which return the same family value (D13) | +| D9 | **CMake options `TAP_SR_*`** after step 3.4: `BUILD_TESTS`, `BUILD_EXAMPLES`, `BUILD_CAPI` (builds both engine libraries), `BUILD_ICOUNT_BENCH`, `BUILD_BENCHMARKS` and `BUILD_COMPARE_BENCH` (async-only), plus per-engine `TAP_SR__WERROR`. **Every configure builds both engines. CI picks an engine only when running tests**, through ctest names and labels (step P.2) | Per-engine WERROR keeps ratio's MSVC `/WX` alongside async's untriaged `/W4`. The warning flags sit on separate INTERFACE targets, and the dry run showed they coexist. There are no per-engine enables, which could silently drop the cross-validation (DEC-12). Selecting tests at ctest time settles the D9 versus per-engine-job conflict (R2-CI-3, R2-COH-11) | +| D10 | **DspTap stays separate**, pinned once at `submodules/dsptap` | It has consumers outside the family: TapTools, and MuTap through the `LogMel`/`Decimator` C ABI | +| D11 | **Engine directory vs. DspTap:** a capability gets an engine directory when it has its own charter, campaign and ratchet. Building blocks go into DspTap. **Engine-owned datapaths stay with their engine.** `fractional_resampler`, the polyphase bank and the blend stratum belong to `async` | Consistent with RatioTap PLAN.md Appendix A. It also keeps `ratio`'s cross-validation oracle out of `ratio`'s reach | +| D12 | **No routing by rate, including through composition.** `chain<>` is a caller-named, compile-time chain of **synchronous** stages. There is no `(in_hz, out_hz)` lookup, `async` is never chained, and the coverage matrix documents chains without dispatching them | Keeps HANDOFF preamble item 4 | +| D13 | **One family version, 0.4.0**, with tags `vX.Y.Z` and bit-packed encoding `(M<<16)\|(m<<8)\|p`. **Mechanics (step 3):** root `project(SampleRateTap VERSION 0.4.0)`; engine subprojects renamed `tap_sr_async` / `tap_sr_ratio` with no VERSION; macros `TAP_SR_VERSION_{MAJOR,MINOR,PATCH}` defined **token-identically** in each engine's umbrella header, checked by a static_assert test (no shared header, so 4.2 check 1 holds); each C ABI library exports its own `tap_sr__version()`; a new C ABI test `CApi.VersionIsBitPacked` pins the encoding, **which nothing pins today** (`test_skeleton.cpp` checks only MAJOR = 0) | User decision. 0.4.0 is above both current versions. The encoding is RatioTap's (`ratio_capi.cpp:97`) | +| D14 | **One copyright line family-wide:** `Copyright (c) 2026 Timothy Place and the SampleRateTap contributors` in the root `LICENSE`, and the same holder in a **banner on every C/C++/Python source file** | User decision, **reconfirmed**: the user holds SampleRateTap's copyright. SampleRateTap's notice today names only "SampleRateTap contributors"; RatioTap's names the user. So this **adds the author's name** to SampleRateTap's notice, which is accurate on the user's word, and restates RatioTap's. There are 35 banner lines today; about 25 of SampleRateTap's C/C++ files have none. Banners are added everywhere in step 3.8. `STYLE.md`'s banner template ("Copyright 2025-2026 Timothy Place.") is reconciled through taphouse (step 5) | +| D15 | **`async` renames its converter family to match `ratio`'s `basic_converter` family:** `basic_async_sample_rate_converter` → `basic_converter`, `async_sample_rate_converter` → `converter`, `…_q15`/`…_q31` → `converter_q15`/`converter_q31`, exception prefixes `"async_sample_rate_converter: "` → `"tap::sr::async::converter: "`, and `asrc.h` → `converter.h` | User decision. v2.1's rationale cited a `tap::sr::ratio::converter` that does not exist. The real parallel is ratio's `basic_converter` with its `converter_to_48k` family. Scale: about 80 hits in 22 files, plus the book's naming-decision prose (R2-COH-19) | +| D16 | **Tests carry an engine prefix:** `gtest_discover_tests(… TEST_PREFIX "async." / "ratio.")`, plus a `LABELS` value of `async` / `ratio` on every test, including the bare-metal `*_tests_emulated` entries. Lands in step P.2, so the snapshot already has it | CTest applies a duplicate name's properties to both tests. `FixedPoint.FullScaleSineDoesNotWrapQ15` exists in both engines, so labels alias and `ctest -L ratio` selects async's copy (reproduced, R2-GATE-4). Unique names fix G1 and engine selection | --- @@ -54,243 +56,239 @@ How to read it: | Engine | Namespace | Origin | Charter | |---|---|---|---| | `async` | `tap::sr::async` | SampleRateTap v0.1.0 | Asynchronous, near-unity (±`max_deviation_ppm`, default 1000 ppm): absorbs the clock | -| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous **160/147 pair at 44.1·2^k ↔ 48·2^k** (k = 0, 1, 2): converts the number. See 2.2 | -| `integer` | `tap::sr::integer` | new, separate plan | Synchronous **rational L/M with L, M ∈ {2^a·3^b}**: integer up, down, oversampling pairs, and 2/3 · 3/2 steps. Nyquist (L-th band) stages. Does **not** absorb DspTap's `decimate.h` (DEC-7) | -| `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM (MEMS mics, DSD): CIC → compensation FIR → `integer` stages | -| `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio: bandlimited interpolation (Smith, CCRMA) | +| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous 160/147 pair, 44.1 ↔ 48 kHz: converts the number. **After the 2.2 follow-up:** 44.1·2^k ↔ 48·2^k, k ≤ 2 | +| `integer` | `tap::sr::integer` | new, separate plan | Synchronous rational L/M with L, M ∈ {2^a·3^b}. Nyquist (L-th band) stages. Does not absorb DspTap's `decimate.h` | +| `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM: CIC → compensation FIR → `integer` stages | +| `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio (Smith, CCRMA bandlimited interpolation) | Not engines: -- **Timestamp-driven clock recovery** is an `async` feature. -- **Minimum-phase and IIR low-latency tiers** are profiles of `ratio` and - `integer`. -- **An offline FFT tier** would be a `transparent+` profile, built only if - someone asks. -- **`decimate.h`** stays in DspTap as the speech front end MuTap consumes. - `integer` builds on the same L-th-band design math beside it. Moving it - would need its own consumer plan, with a MuTap pin. - -### 2.1 Coverage rule: passband, not Nyquist - -v1's invariant "no intermediate rate below min(in, out)" is withdrawn -(DEC-3). It allowed band loss the profiles already take (`economy` is flat -to 18 kHz), and it forbade harmless chains: 176.4 → 44.1 → 48 would have -had to run `ratio` at 4× the rate. - -**The rule instead, per chain and profile:** - -1. The chain's passband edge is the **minimum over its stages** of each - stage's passband edge (in Hz at that stage's rate). -2. That edge must be **≥ the passband the profile declares for the pair**. -3. Every intermediate Nyquist frequency must be **> the declared passband - plus the next stage's transition band**. -4. The coverage-matrix test pins the resulting edge, MACs per output and - latency for every pair. - -**Supported rates:** 8, 11.025, 12, 16, 22.05, 24, 32, 44.1, 48, 88.2, 96, -176.4, 192 and 384 kHz. That is every rate of the form 44.1k·2^a or -48k·2^a·3^b that the family supports. - -**Explicitly excluded, with the reason:** - -- 37.8 and 50.4 kHz (ratios of 7). -- The 1000/1001 video pull-down rates (44.056 and 47.952 kHz). These are - *synchronous* ratios whose 999 ppm offset happens to fall inside - `async`'s ±1000 ppm. They must **never** be served by `async`, because - that would be routing by rate. If they are ever supported, it is as a - sync engine. - -**The full 14 × 14 matrix is generated, not hand-picked.** It arrives with -`integer`'s plan, with every pair marked supported, excluded or -not-yet-supported. Illustrative rows (all respect 1–3): +- **Timestamp clock recovery** is an `async` feature. +- **Minimum-phase and IIR tiers** are profiles. +- **An offline FFT tier** is built only on request. +- **`decimate.h`** stays in DspTap, where MuTap consumes it. + +### 2.1 Coverage rule (rewritten in v3) + +v2's rules 1–3 contradicted v2's own 176.4 → 48 row, and had no stopband or +image condition (R2-COH-7). The replacement is stated per **chain**. The +chain declares a passband `f_pass`, a stopband attenuation `A` and a +passband ripple `δ`, and these are pinned per rate pair by the coverage +matrix test. For **every rate-changing stage** whose lower rate is `r`: + +- **(a) Aliases and images stay out of the passband.** The stage's stopband + edge is ≤ `r − f_pass`, so every alias (decimating) or image + (interpolating) of passband content lands above `f_pass`. +- **(b) Attenuation.** The stage's stopband attenuation is ≥ `A`. +- **(c) Ripple.** The chain's passband ripple is the **sum** of its stages' + ripples, and must be ≤ `δ`. Droop compounds, so this is stated as ripple, + not as a design-parameter edge. +- **(d) What gets pinned.** The declared `(f_pass, A, δ)` belongs to the + chain, not to any one engine's profile. The matrix test measures and pins + it for each pair, next to MACs per output and latency. + +Checks against the rows below: + +- **176.4 → 48** (`integer` ↓4 → `ratio` ↑): `ratio`'s stopband edge, + 24 kHz, is ≤ 44.1 − `f_pass` for `f_pass` ≤ 20.1 kHz. The ↓4 stage needs a + stopband ≤ 24.1 kHz at 20 kHz passband. +- **16 → 44.1** through ↑3 (lower rate 16): needs stopband ≤ 16 − `f_pass` + at attenuation `A`. That rules out an L-th-band ↑3 stage whose transition + band leaves images at 8–9 kHz, which v2's rules let through. + +**Supported rates:** exactly these 14: 8, 11.025, 12, 16, 22.05, 24, 32, +44.1, 48, 88.2, 96, 176.4, 192 and 384 kHz. + +**Excluded, with reasons:** + +- **352.8 kHz (DXD):** `ratio`'s k stops at 2 and no consumer has asked. +- **37.8 and 50.4 kHz:** ratios of 7. +- **1000/1001 pull-down rates:** these are synchronous ratios that happen to + sit inside `async`'s ±1000 ppm. They must never be served by `async`, since + that would be routing by rate. + +The **full 14 × 14 matrix is generated** in `integer`'s plan. Illustrative +rows: | From → To | Chain | |---|---| | 48 ↔ 44.1 | `ratio` | -| 96 ↔ 88.2, 192 ↔ 176.4 | `ratio` at k = 1, 2 (section 2.2) | +| 96 ↔ 88.2, 192 ↔ 176.4 | `ratio` at k = 1, 2 (after 2.2) | | 96 → 44.1 | `integer` ↓2 → `ratio` | -| 176.4 → 48 | `integer` ↓4 → `ratio` (the passband rule allows 44.1 intermediate) | +| 176.4 → 48 | `integer` ↓4 → `ratio` | | 48 → 88.2 | `integer` ↑2 → `ratio` k = 1 | | 44.1 → 16 | `ratio` → `integer` ↓3 | -| 48 → 32 | `integer` 2/3 (one rational stage, not ↑2 then ↓3) | +| 48 → 32 | `integer` 2/3 (one rational stage) | + +### 2.2 `ratio` at 2× and 4× rates (a follow-up after the migration) + +**What the Hz values reach.** `ratio`'s profile Hz values feed: + +- the normalized cutoff (`design.h:138`); +- the validation `p.passband_hz >= traits::k_stopband_edge_hz` + (`design.h:133-134`). + +`ratio_traits` hard-codes `k_input_rate_hz` and `k_stopband_edge_hz` +(`design.h:37-55`). So a 2×-rate design cannot even be *expressed* today: +a 36 kHz passband throws "bad profile". -### 2.2 `ratio` at 2× and 4× rates: resolves v1's Q3 +**The follow-up:** -The Hz values in `ratio`'s profiles feed only a normalized cutoff -(`design.h:138`). The converter is a pure sample-count transformer, and the -charter bans other **ratios** ("not 2:1, not 96→44.1"), not the same ratio -at a multiple of the rate. Fed 88.2 kHz, today's tables give a 36 kHz -passband, and alias products land above 40.2 kHz. +- Add a rate-scale parameter `k` to `ratio_traits`. This is an API change. +- Document the profile edges as fractions of the rate. +- Pin with a test that `design_prototype` is bit-identical to + ``. -**Decision:** +**The claim holds exactly.** Power-of-two scaling is exact in IEEE double, +and `kaiser_beta` depends only on dB (R2-COH-9). -- Restate the charter as "160/147 at 44.1·2^k ↔ 48·2^k". -- Document profile edges as fractions of the rate, with the Hz figures as - the k = 0 labels. -- Add a contract test that the table designed at k = 1 labels is - **bit-identical** to the k = 0 table. +**At 2× rates:** -This is a charter and documentation change in `ratio`, made after the -migration (it is not part of it). The `direction::up_to_48k` / -`down_to_44k1` names are reconsidered then. +- 88.2 → 96 (up) places images at ≥ 44.1 kHz. +- 96 → 88.2 (down) folds aliases above 40.2 kHz. + +`ratio/PLAN.md` keeps "no other ratios" until then. The follow-up is +sequenced before `integer`. --- -## 3. Inventory (measured 2026-09-26; corrected in v2) +## 3. Inventory (measured 2026-09-26/27; corrected in v3) + +**Clones.** Use fresh, full GitHub clones only. The session checkouts are +shallow (SampleRateTap) or have a stale local `main`. -The migration runs on **fresh, full GitHub clones**. The session checkouts -are unfit for it: +**Remote state:** -- SampleRateTap's session checkout is **shallow** (8 grafts), which is why - v1 reported 64 commits. -- Both checkouts have **stale local `main`** branches: SampleRateTap - `0922541`, RatioTap `94775b0`. -- Remote truth: SampleRateTap `main` = `2b4dff1` (136 commits); RatioTap - `main` = `349ab7b` (29 commits, no renames in history, no tags). Neither - repository has tags. -- RatioTap's leftover remote branch `claude/sample-rate-solutions-comparison-mqc190` - (merged PR #17) is not imported. It should be deleted. +- **SampleRateTap `main`:** `2b4dff1` (136 commits). The migration branch + carries the plan commits on top. +- **RatioTap `main`:** `349ab7b` (29 commits, no renames, no tags). Its PRs + were **rebase-merged**, not squashed: #16 has 6 commits, and #2, #3, #6 and + #10 have several each. Its root commit has no PR. Commit messages carry no + `#NN`. +- **Leftover RatioTap branches:** `claude/sample-rate-solutions-comparison-mqc190` + (merged PR #17) and `claude/sample-rate-expansion-strategies-ezqzu6`. + Neither is imported (`--refs main`); both are deleted at step 5. +- **Visibility:** all three repositories are **public** + (`"visibility": "public"`, 0 billable CI minutes). v2's "private + repository" came from a stale comment in `book-pages.yml:4`. RatioTap has + no Pages site. ### 3.1 Shipped headers | Today | After | |---|---| -| `include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…` | -| `include/srt/detail/kaiser.h` (26 lines: re-exports `tap::dsp` kaiser into `tap::samplerate::detail`) | **Deleted in step 3.** `polyphase_filter.h:153,157` qualifies the calls as `tap::dsp::`. `tests/test_kaiser.cpp` (9 tests, `using namespace tap::samplerate::detail`) repoints to `tap::dsp`, keeping test names. The path citations in the book, the bibliography, `book_figures.py:7` and `asrc_rbj_analysis.ipynb` are rewritten (INV-7) | +| `include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…`, with `asrc.h` → `converter.h` (D15) and `srt.h` → `async.h` (umbrella) | +| `include/srt/detail/kaiser.h` (a 26-line re-export into `tap::samplerate::detail`) | **Deleted in step 3.1.** `polyphase_filter.h:152,153,157` requalify as `tap::dsp::`. `tests/test_kaiser.cpp` (9 tests) repoints to `tap::dsp` with its test names unchanged. Path citations in the book, the bibliography, `book_figures.py:7` and `asrc_rbj_analysis.ipynb` are rewritten | | `include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `ratio/include/tap/sr/ratio/…` | -`srt/sample_traits.h` layers on `tap/dsp/sample_traits.h` and moves -unchanged. - ### 3.2 Pins -- Both repositories record DspTap at **`0eb09fa`**. -- RatioTap pins `submodules/sampleratetap` at `2b4dff1` = SampleRateTap - `main`. -- Every gate runs `git submodule update --init --recursive` first. Session - hooks move checkouts off their recorded pins (observed in this session), - so the gates must not trust the checkout. +- Both repositories record DspTap at `0eb09fa`. +- RatioTap pins `submodules/sampleratetap` at `2b4dff1`. +- **Step P changes SampleRateTap `main`**, so step P.4 re-pins RatioTap's + copy to post-P `main`. That way step 0's cross-validation lines (G6) are + measured against the same async tree that step 1c compiles + (R2-COH-1). +- Every gate runs `git submodule update --init --recursive` first. ### 3.3 Duplicated infrastructure -There are **three** copies of the embedded harness: SampleRateTap, -RatioTap, **and DspTap**. DspTap has `cmake/arm-cortex-m33-mps2.cmake`, -`platform/`, `tools/qemu_insn_plugin/` and `scripts/icount.py`, which -differ from SampleRateTap's by 40–54 lines. The merge removes one of the -three. Consuming the harness from `submodules/dsptap` instead is the natural -next deduplication. It is **out of scope** here because DspTap's copy has -diverged, and adopting it would change async's and ratio's codegen inputs -(DEC-9). +**Three harness copies exist:** SampleRateTap, RatioTap and DspTap. The +merge removes one. Adopting DspTap's copy is out of scope, because it has +diverged and would change codegen inputs. -SampleRateTap vs RatioTap, line by line: +SampleRateTap vs RatioTap: | File | Class | Detail | |---|---|---| -| `.clang-*`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/**`, `scripts/tidy.sh`, `.github/pull_request_template.md` | identical | | -| `platform/armv8m_startup.c`, `platform/*/**.ld` | cosmetic | Comments, copyright line, book `ANCHOR`s. Memory map, heap, MSPLIM, vectors, `_sbrk` and atomics are byte-identical | -| `cmake/arm-cortex-m33/m55-*.cmake`, `hexagon-linux-musl.cmake` | **functional (one variable)** | Flags are identical. `set(SRT_BARE_METAL ON)` vs `set(TAP_RATIO_BARE_METAL ON)` selects each engine's one-shot test mode and the gtest `GTEST_HAS_*` definitions (INF-1) | -| `tools/qemu_insn_plugin/insn_count.c` | **functional (host-side marker)** | Prints `SRT_INSN_COUNT` vs `RATIO_INSN_COUNT`, which `icount.py` parses. Host-side, so it never affects the guest count | -| `scripts/icount.py` | **functional (prefix, marker)** | `srt_icount_*`/`SRT_*` vs `ratio_icount_*`/`RATIO_*`. Tolerance logic identical | -| `tests/bare_metal_main.cpp` | **per-engine, never deduplicated** | Engine-specific filter, floor (15 vs 25) and completion marker | -| `.github/workflows/style.yml` | **functional** | RatioTap's configures tests and icount ON, excludes `submodules` and `_deps`, and reads file lists line-wise. SampleRateTap's configures nothing and would lint **zero** TUs in a monorepo (INF-10). RatioTap's body wins | -| `scripts/fetch_hexagon_toolchain.sh` (RatioTap only) | **functional** | Checks the pin and `SHA256SUMS` unconditionally. SampleRateTap's inline copies skip `SHA256SUMS` or make the pin check conditional (INF-13). RatioTap's script wins | +| `.clang-*`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/**`, `scripts/tidy.sh`, `.github/pull_request_template.md` | identical | Verified with `cmp` / `diff -r` in the dry run | +| `platform/armv8m_startup.c`, `platform/*/**.ld` | cosmetic | Comments, copyright line and book `ANCHOR`s only | +| `cmake/arm-cortex-m33-mps2.cmake`, `…-m55-mps3.cmake` | **functional (one variable)** | `set(SRT_BARE_METAL ON)` vs `set(TAP_RATIO_BARE_METAL ON)`. `hexagon-linux-musl.cmake` sets **no** such variable (corrected, R2-RUN-17) | +| `tools/qemu_insn_plugin/insn_count.c` | functional (host-side marker) | `SRT_INSN_COUNT` vs `RATIO_INSN_COUNT`. Never affects the guest count | +| `scripts/icount.py` | functional (prefix, marker) | | +| `tests/bare_metal_main.cpp` | per-engine, never deduplicated | Filters and floors. Ratio's M33 selection is **59** tests against a floor of 25 | +| `.github/workflows/style.yml` | functional | RatioTap's body wins | +| `scripts/fetch_hexagon_toolchain.sh` (RatioTap only) | functional | RatioTap's copy wins, and every Hexagon cache writer uses it, **including `compare.yml`** | Present in only one repository: - **SampleRateTap:** `cmake/r8brain.cmake`, `tools/compare_shim`, - `bench/compare`, `compare.yml`, `ci-arm64.yml`, `book-pages.yml`, - `book/`, `docs/`, `scripts/update_*_docs.py`, `scripts/book_figures*`, + `bench/compare`, `compare.yml`, `ci-arm64.yml`, `book-pages.yml`, `book/`, + `docs/`, `scripts/update_*_docs.py`, `scripts/book_figures*`, `examples/pico2_*`, `.git-blame-ignore-revs`. - **RatioTap:** `scripts/fetch_hexagon_toolchain.sh`, `tools/reference/`, `tests/reference/`, `HANDOFF.md`, `CLAUDE.md`, `notebooks/requirements.txt`, - the `build_capi/` rule in `.gitignore`, and the CI dedup scheme (RatioTap - `f1e566a`, `94775b0`; rewritten SHAs are recorded in `ratio/docs/HISTORY.md`, - step 1b). + and the CI dedup scheme. -GoogleTest: both repositories use the same pin (`f8d7d77`, v1.14.0) and the -same FetchContent name. A merged tree configures, and all 151 tests pass -(measured in a scratch tree). +GoogleTest is the same pin in both (`f8d7d77`, v1.14.0). ### 3.4 The book -- **84** `{{#include}}` directives: - - `include/srt` 42 - - `submodules/dsptap` 21 - - `tools/capi` 6 - - `tests` 4 - - `platform/` 7, `cmake/` 2, `tools/qemu_insn_plugin` 1 -- **52 of them break at step 1**, not step 3, because step 1 moves their - targets. -- The staleness check is inline in `ci.yml` (job `book`): `mdbook build` - with a `warning|error` grep, plus an image-reference check. - `book-pages.yml` repeats the build and runs `doxygen docs/Doxyfile` - (`INPUT = include README.md`). -- The prose also carries **23 files** of `srt/…`, `tap::samplerate` and - `SRT_*` identifiers that mdbook cannot check (GATE-16), and **5 links** to - RatioTap as a live repository, including `git clone …/RatioTap` build - commands (`part5/scaling.md:194,352-355`). - -### 3.5 CI (corrected) - -| Job | SampleRateTap | RatioTap | +There are 84 `{{#include}}` matches: + +| Target | Count | +|---|---| +| `include/srt` | 42 | +| `submodules/dsptap` | 21 | +| `tools/capi` | 6 | +| `tests` | 4 | +| `platform/` | 7 | +| `cmake/` | 2 | +| `tools/qemu_insn_plugin` | 1 | +| escaped example in prose (`bibliography.md:104`) | 1 | + +- **Moves at step 1:** 52 of the 84 break when step 1 moves their targets. + The dry run rewrote them all; mdbook 0.4.40 then reported 0 warnings, and + the rendered book was byte-identical to the baseline. +- **Prose:** 23 book files carry retired identifiers that mdbook cannot + check, plus 5 links to RatioTap as a live repository. + +### 3.5 CI (measured) + +| Job | async (SampleRateTap) | ratio (RatioTap) | |---|---|---| -| Host matrix | GCC, Clang, AppleClang, MSVC (MSVC `werror: OFF`) | same, **MSVC with `-DTAP_RATIO_WERROR=ON`** | -| Sanitizers | ASan+UBSan, TSan | **ASan+UBSan only**, WERROR ON | -| Linux arm64 native + TSan | `ci-arm64.yml`: **currently tests nothing** (`-R 'SpscRing'`; the suite is `spsc_ring`) | — | -| Hexagon / M55 / M33 correctness | yes. Hexagon `-E 'AsrcQuality\|AsrcLock\|TwoThreadStress\|TransparentPrototypeMeetsSpec\|MultiChannel\.\|Feasibility\|Reset\.\|ConfigValidation'`, serial | yes. Hexagon `-E 'BadProfilesThrow\|LatencyAndValidation'`, `-j 4` | -| `icount-ratchet` | 7 workloads × {m33, m55, hexagon}; README table drift check | 10 workloads × {m33, m55, hexagon} (RatioTap's CLAUDE.md still says eight) | -| bench-smoke, compare-smoke, clang-format job, book | yes | **no** (clang-format by pre-commit only) | -| Triggers / concurrency | push + PR, cancel-in-progress by ref | push to `main` + PR + dispatch (dedup scheme) | -| Actions pinning | by SHA | by tag | - -Branch protection: `main` is unprotected in both repositories (checked -through the GitHub API; rulesets were not checked), so job renames break -nothing today. - -### 3.6 C ABI (exact) - -- **async:** 8 functions, `srt_{version,create,destroy,push,pull,status,designed_latency_seconds,reset_from_consumer}`, - with opaque `SrtHandle`, library `libsrt_capi.so`. `srt_version` - encodes `M*10000+m*100+p`. -- **ratio:** 11 functions, opaque `ratio_converter`, library - `libratio_capi.so`. `ratio_version` encodes `(M<<16)|(m<<8)|p`, pinned by - `test_skeleton.cpp`. -- **Separately:** the r8brain comparison shim exports `srt_r8b_oneshot` and - `srt_r8b_latency_frames` from its own library. -- **Bridges:** RatioTap's notebooks use `notebooks/ratiotap_py.py`. - SampleRateTap's use inline ctypes in 3 of 4 notebooks; `asrc_rbj_analysis` - uses `scripts/book_figures.py`. Both bridges build only when the library - is missing, so a stale library would be measured silently (GATE-10). +| Host matrix | GCC, Clang, AppleClang, MSVC (MSVC `werror: OFF`) | GCC, AppleClang, MSVC with `WERROR=ON` | +| Sanitizers | ASan+UBSan, TSan | ASan+UBSan only, WERROR ON | +| arm64 native + TSan | `ci-arm64.yml` (scheduled/dispatch). **Currently tests nothing:** `-R 'SpscRing'` vs suite `spsc_ring` | — | +| Hexagon correctness | 22.5 min, serial, `-E` list of 8 | 11.0 min at `-j 4`, `-E` list of 2 | +| M33 correctness | 22.4 min (30-min timeout) | 0.4 min | +| `icount-ratchet` | 7 workloads × 3 targets | 10 × 3 | +| bench-smoke, compare-smoke, clang-format, book | yes | no | +| Triggers | push (all branches) + PR, cancel-in-progress by ref. **Every push to a PR branch runs twice** | push to `main` + PR + dispatch | +| Actions | SHA-pinned | tag-pinned | + +Timings are from runs 36253867157 and 36256431538. + +`main` is unprotected in both repositories. + +### 3.6 C ABI + +| Engine | Functions | Opaque type | Library | Version encoding | +|---|---|---|---|---| +| async | 8: `srt_{version,create,destroy,push,pull,status,designed_latency_seconds,reset_from_consumer}` | `SrtHandle` | `libsrt_capi.so` | decimal `srt_version()` | +| ratio | 11 | `ratio_converter` | `libratio_capi.so` | bit-packed `ratio_version()`. Pinned **by nothing** except the ctypes bridge | + +The r8brain shim exports `srt_r8b_oneshot` and `srt_r8b_latency_frames`. + +Bridges build **only when the library is missing**, so they can measure a +stale library. They also print CMake logs into notebook outputs. ### 3.7 Outside references -- **DspTap:** 25 files, **comments and docs only**. No code, test, CMake or - submodule dependency. - - Most stay true after the merge, because the SampleRateTap name survives. - - Only path citations (`include/srt/…`, `tests/support/`, - `docs/PERFORMANCE.md`) and mentions of RatioTap as a live repository - change. - - These change through a DspTap PR after the merge. `STYLE.md` changes - only through **taphouse**, because it is a drift-checked copy. - - Close DspTap's open item on `TAP_DSP_CP_MIN_CHANNELS` - (`docs/audit-fft-and-code-smells.md:392,458,668`) at the same time as - `SRT_CP_MIN_CHANNELS`. -- **SampleRateTap:** RatioTap URLs in `README.md:407,432`, - `book/src/part0/two-crystals.md:173`, `part5/scaling.md:194,352-355` and - `docs/COMPARISON.md:234`. The README quotes stale cross-validation figures - (−109/−99 dB; the current v0.3 floors are about −98/−90 dB). -- **Outside this session** (confirmed by the user, 2026-09-26): - - **TapTools, TapTools-Max, MuTap, AmbiTap, OscTap** do not submodule or - reference either repository's headers, targets or C ABIs. D7's "no - consumers" premise holds. - - **taphouse's `sync.sh` includes RatioTap.** It must be removed from the - target list before RatioTap is archived (step 5), or syncs to the - archive fail. `drift-check.yml` and the catalog README are updated in the - same taphouse PR. - - **"Create a merge commit" is allowed** on SampleRateTap (D3, step 4). - - Still to check at step 5: open RatioTap issues and PRs, and the Pages - source setting. The repository is private while its Pages site is - public. +- **DspTap:** 25 files, all of them comments or docs. + - Path citations change through a DspTap PR (step 5). + - `STYLE.md` changes only through taphouse. + - Close DspTap's `TAP_DSP_CP_MIN_CHANNELS` item (audit doc lines 126, + 392, 458 and 668) when `SRT_CP_MIN_CHANNELS` is retired. +- **SampleRateTap:** + - RatioTap URLs: `README.md:407,432`, `book/src/part0/two-crystals.md:173`, + `part5/scaling.md:194,352-355` and `docs/COMPARISON.md:234`. + - Stale cross-validation figures in the README. +- **Outside this session** (confirmed by the user): + - No other Tap repository consumes either one. + - **taphouse's `sync.sh` includes RatioTap.** Drop it before archiving. + - Merge commits are allowed. + - Open RatioTap issues and PRs are checked at step 5. --- @@ -298,174 +296,241 @@ nothing today. ``` SampleRateTap/ -├── CMakeLists.txt NEW root: project(SampleRateTap), enable_testing(), -│ add_subdirectory(submodules/dsptap) ONCE, then engines -├── CLAUDE.md PLAN.md README.md NEW family-level files (step 4) -├── LICENSE unified holder line (D14) -├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ .github/ (shared) -├── .git-blame-ignore-revs repaired (step P.1), extended after step 4 -├── submodules/dsptap the one pin -├── cmake/ platform/ tools/qemu_insn_plugin/ shared embedded harness -├── scripts/ icount.py (engine-aware), tidy.sh, -│ fetch_hexagon_toolchain.sh (RatioTap's) -├── book/ one book; ratio chapters are follow-up work -├── docs/ family docs: PLAN (this file), Doxyfile (both engines) +├── CMakeLists.txt root: project(SampleRateTap VERSION 0.4.0 from step 3), enable_testing(), +│ option() defaults, dsptap once, add_subdirectory(async|ratio) +├── PLAN.md CLAUDE.md README.md family files (step 4; PLAN.md is this document, moved) +├── LICENSE D14 line +├── requirements.lock notebook environment (step P.3) +├── STYLE.md .clang-* .pre-commit-config.yaml .claude/ .github/ .gitmodules .gitignore +├── .git-blame-ignore-revs repaired at P.1; extended after step 4 +├── submodules/dsptap +├── cmake/ platform/ tools/qemu_insn_plugin/ +├── scripts/ icount.py, tidy.sh, fetch_hexagon_toolchain.sh, +│ update_icount_docs.py, update_perf_docs.py, book_figures* +├── book/ (ratio chapters are follow-up work) +├── docs/ Doxyfile (both engines); migration/ (removed at step 4) ├── async/ -│ ├── CMakeLists.txt README.md PLAN.md +│ ├── CMakeLists.txt README.md (+ icount table) PLAN.md │ ├── include/tap/sr/async/ tests/ bench/ (icount, compare) examples/ │ ├── capi/ notebooks/ docs/ (PERFORMANCE, COMPARISON, HARDWARE_TESTING) -│ └── tools/compare_shim/ cmake/r8brain.cmake +│ └── tools/compare_shim/ cmake/r8brain.cmake └── ratio/ - ├── CMakeLists.txt README.md PLAN.md HANDOFF.md CLAUDE.md + ├── CMakeLists.txt README.md (+ icount table) PLAN.md HANDOFF.md CLAUDE.md ├── include/tap/sr/ratio/ tests/ (+reference/) bench/ examples/ - ├── capi/ notebooks/ (+requirements.txt) tools/reference/ docs/HISTORY.md + └── capi/ notebooks/ tools/reference/ docs/HISTORY.md ``` ### 4.1 File disposition | Path today | Destination | Step | |---|---|---| -| SRT `CMakeLists.txt`, `README.md` | `async/CMakeLists.txt`, `async/README.md` (pure move; new root files in a *later* commit, see GIT-5) | step 1a | -| SRT `include/ tests/ bench/ examples/ notebooks/` | `async/…` | step 1a | -| SRT `tools/capi/`, `tools/compare_shim/` | `async/capi/`, `async/tools/compare_shim/` | step 1a | -| SRT `cmake/r8brain.cmake` | `async/cmake/r8brain.cmake` (async-only) | step 1a | -| SRT `docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md` | `async/docs/` | step 1a | -| SRT `docs/Doxyfile`, `docs/MONOREPO_PLAN.md` | **stay** in root `docs/` | — | -| SRT `book/`, `scripts/`, `cmake/arm-*`, `cmake/hexagon-*`, `platform/`, `tools/qemu_insn_plugin/`, dotfiles, `LICENSE`, `STYLE.md`, `.github/` | **stay** at root | — | -| RatioTap, whole tree | `ratio/…` via filter-repo | step 1b | -| `ratio/.gitmodules`, `ratio/submodules/*` | rewritten into the root `.gitmodules` throughout history, then removed at the merge | step 1b | -| `ratio/.clang-*`, `STYLE.md`, `.pre-commit-config.yaml`, `.claude/`, `scripts/tidy.sh`, `.github/pull_request_template.md` | deleted (identical to root) | step 1b merge commit | -| `ratio/.github/workflows/ci.yml` | ported into root `.github/workflows/ci.yml` as ratio jobs | step 1c | -| `ratio/.github/workflows/style.yml` | its body replaces root `style.yml` | step 1c | -| `ratio/scripts/fetch_hexagon_toolchain.sh` | root `scripts/` | step 1c | -| `ratio/.gitignore` | `build_capi/` rule merged into root `.gitignore`; file deleted | step 1c | -| `ratio/tools/capi/` | `ratio/capi/` | step 3 | -| `ratio/cmake/`, `ratio/platform/`, `ratio/tools/qemu_insn_plugin/`, `ratio/scripts/icount.py` | deleted (root copies) | step 2 | -| `ratio/CLAUDE.md` | kept, build commands corrected at step 1c; reduced to the ratio charter at step 4 | step 1c, step 4 | -| `ratio/LICENSE` | deleted in the same commit that puts D14's unified line in the root `LICENSE` | step 1c | -| `ratio/notebooks/requirements.txt` | superseded by the root lockfile | step P.3 | -| `docs/migration/` (snapshots) | created at root `docs/migration/` (not moved, since `docs/` stays) and deleted at the end | step 0, step 4 | - -### 4.2 Dependency rule and its enforcement +| SRT `CMakeLists.txt`, `README.md` | `async/…` (pure move; the new root files come later) | 1a | +| SRT `include/ tests/ bench/ examples/ notebooks/` | `async/…` | 1a | +| SRT `tools/capi/`, `tools/compare_shim/`, `cmake/r8brain.cmake` | `async/capi/`, `async/tools/compare_shim/`, `async/cmake/r8brain.cmake` | 1a | +| SRT `docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md` | `async/docs/` | 1a | +| SRT `docs/Doxyfile`, `docs/MONOREPO_PLAN.md`, `docs/migration/` | stay in root `docs/` (the plan moves to root `PLAN.md` at step 4) | —, 4 | +| SRT `book/`, `scripts/`, `cmake/{arm,hexagon}-*`, `platform/`, `tools/qemu_insn_plugin/`, dotfiles, `LICENSE`, `STYLE.md`, `.github/` | stay at root | — | +| RatioTap `main` | `ratio/…` through filter-repo | 1b | +| `ratio/.gitmodules`, `ratio/submodules/*` | rewritten into root `.gitmodules` throughout history; gitlinks removed at the merge | 1b | +| `ratio/{.clang-format,.clang-tidy,STYLE.md,.pre-commit-config.yaml,.claude,scripts/tidy.sh,.github/pull_request_template.md}` | deleted (byte-identical to root) | 1b | +| `ratio/.github/workflows/{ci,style}.yml` | ported into root workflows, then deleted | 1c | +| `ratio/scripts/fetch_hexagon_toolchain.sh` | root `scripts/` | 1c | +| `ratio/.gitignore` | deleted (root `build*/` already covers `build_capi/`) | 1c | +| `ratio/LICENSE` | deleted in the commit that writes D14's line into root `LICENSE` | 1c | +| `ratio/requirements.lock`, `ratio/notebooks/requirements.txt` | deleted (identical root lockfile from P.3) | 1c | +| `ratio/cmake/`, `ratio/platform/`, `ratio/tools/qemu_insn_plugin/`, `ratio/scripts/icount.py` | deleted (root copies) | 2 | +| `ratio/tools/capi/` | `ratio/capi/` | 3.1 | +| `ratio/CLAUDE.md` | build commands fixed at 1c; reduced to the charter at 4 | 1c, 4 | +| `docs/migration/` | created at 0; deleted at 4 (`runs.md` optionally kept) | 0, 4 | + +### 4.2 Dependency rule and its enforcement (scheduled: step 3.4) - `async` and `ratio` each depend on `tap::dsp` only. -- An engine may depend on another **only** in `tests/` and `examples/`: - `ratio/tests` uses `tap::sr::async` for the cross-validation, and the - `bluetooth_bridge` example composes both. -- `capi/` is per-engine (D8) and never links a sibling. - -Enforcement, since v1's "enforced by CMake" named no mechanism (DEC-13): - -1. **Configure-time assertion:** `INTERFACE_LINK_LIBRARIES` of each engine - target is exactly `tap::dsp`. -2. **Install-tree isolation test:** install each engine alone, together with - dsptap, into a staging prefix. Then compile every one of its public - headers in its own TU from that prefix. Sibling source paths do not - exist there. -3. **Grep gate on `*/include/**`:** rejects `srt/`, `tap/sr//`, - `../` and `__has_include` of a sibling. -4. **Header glob with pinned count:** a header added outside the list fails - the test. +- An engine may depend on a sibling only in `tests/` and `examples/`: ratio's + cross-validation, and `bluetooth_bridge`. +- `capi/` is per-engine. + +Enforcement: + +1. **Link interface.** A configure-time assertion that each engine target's + `INTERFACE_LINK_LIBRARIES` is exactly `tap::dsp`. +2. **Header isolation.** Each public header compiles in its own TU with only + `tap::dsp` and its own engine's include directory on the path. This + replaces v2's install-tree test, which needed install rules the repository + does not have (R2-COH-14). +3. **Include grep.** `*/include/**` rejects `srt/`, `tap/sr//`, `../` + and a sibling `__has_include`. +4. **Header count.** The header glob count is pinned. --- -## 5. Gates (defined once; the steps reference them) +## 5. Gates -Every gate is measured **against the step-0 snapshot** and not against -committed files. This separates migration effects from toolchain drift on -unpinned `ubuntu-latest` apt packages (GATE-3). Where a gate compares -builds, **step 0's SHA and step N's SHA are built in the same job, with the -same toolchain and the same binary paths**. +Round 2 showed that one uniform "same-job A/B for everything" design breaks +two ways: it pushes the M33 and Hexagon jobs past their timeouts +(R2-GATE-2), and several gates had no input data or were red on a no-op. +v3 splits the gates into two classes: -| ID | Gate | Catches | -|---|---|---| -| G1 | **Test multiset:** `ctest --show-only=json-v1` → multiset of (engine label, test name), equal to the snapshot. Every ctest call carries `--no-tests=error`, and each engine's tests carry a `LABELS` value | Dropped tests, including the duplicate `FixedPoint.FullScaleSineDoesNotWrapQ15` that exists in both engines (GATE-7, INF-12, GIT-12, INV-2) | -| G2 | **On-target test multiset:** the multiset of `[ RUN ] Suite.Name` lines from each QEMU leg's gtest log (M33, M55, Hexagon), equal to the snapshot | M33/M55 register one ctest per binary, and the floors (15/25) hide losses of up to 5/17 tests (GATE-5). Also the Hexagon exclusion regexes (GATE-8) | -| G3 | **Exact icount:** `icount.py --exact` means integer equality, and the **set** of measured workloads equals the set of baseline keys. A missing baselines file is fatal. `--update` is forbidden during the migration | v1's "0 %" was undefined, since the default tolerance is ±3 % and the output rounds to `+0.00%` (GATE-1). Missing workloads passed silently (GATE-2) | -| G4 | **Codegen identity:** `objdump -d --no-show-raw-insn` of every icount and test binary, addresses stripped (plus a symbol map for renames), equal to step 0 | Stronger than icount and independent of QEMU | -| G5 | **Output identity:** new per-engine host tests (added at step P.2) FNV-hash the **full** output of every direction × format × profile workload and pin the hash. QEMU `checksum=` lines equal the snapshot exactly | icount is data-independent, so a changed coefficient passes G3. The existing checksum is weak and is never read (GATE-4) | -| G6 | **Cross-validation lines:** the printed `[ measured ] cross-validation …` lines are byte-identical to the snapshot | Loosened tolerances (DEC-11) | -| G7 | **Compile flags:** per-TU flags in `compile_commands.json` equal to the snapshot, modulo path prefixes | E.g. `-std=gnu++20` → `-std=c++20` flips GCC's `-ffp-contract`, which changes FMA on M33/M55 while the host tests stay green (GATE-15) | -| G8 | **Book:** `mdbook build` clean, plus the image check | Broken anchors | -| G9 | **Retired-identifier grep:** zero hits for the retired names (`srt/`, `tap::samplerate`, `tap/ratio`, `tap::ratio`, `SRT_`, `TAP_RATIO_`, `srt_`, `ratio_capi`, …) outside an allowlist (history docs, `HISTORY.md`, the plan) | Stale prose and code that mdbook and the compiler cannot see (GATE-16). Applies from step 3 commit 8 | -| G10 | **C ABI symbols:** `nm -D` of each capi library equals the snapshot under the committed name map | The step-0 ABI snapshot was otherwise never used (GATE-17) | -| G11 | **Notebooks:** re-executed in the pinned environment (step P.3) from a fresh clone, with bridges that always rebuild. Compared through a normalizer that keeps text outputs only and drops timing lines, PNGs and paths | Wall-clock cells, unpinned numpy/scipy and stale-library loading made "identical outputs" unattainable or vacuous (GATE-10) | -| G12 | **History:** `git log --follow` and `git blame` spot checks on a fixed file list from both engines. Blame of a moved file does **not** attribute all lines to a migration commit | Lost blame when a move and a recreate share a commit (GIT-5) | -| G13 | **Every CI job ran:** each job in the workflow reports on the gated SHA (run IDs recorded in `docs/migration/runs.md`). None is skipped or cancelled | Cancel-in-progress and trigger filters that test only tips (GATE-11, INF-9) | - -A step's gate is a subset of G1–G13, listed with the step. +- **A/B gates** are the toolchain-sensitive ones. They run in the dedicated + **`migration-gates`** workflow (draft: `docs/migration/drafts/migration-gates.yml`), + on a pinned `ubuntu-24.04` with `cancel-in-progress: false`. Each job checks + out the gated SHA **and** the two step-0 tips: `tap/SampleRateTap@S0` and + `tap/RatioTap@R0`, both public, so no token is needed. It builds all three + with one toolchain and compares them. The jobs are cheap: under 3 minutes + per engine per target for icount. +- **Snapshot gates** compare against the step-0 snapshot committed in + `docs/migration/`. They do not depend on the toolchain. + +Each run records in `docs/migration/runs.md`: the run IDs, +`ImageVersion`, and `dpkg-query -W gcc-arm-none-eabi qemu-system-arm`. + +| ID | Class | Gate | Catches | +|---|---|---|---| +| G1 | snapshot | **Test multiset per (job, engine).** `ctest --show-only=json-v1`; names are unique through D16's prefixes. The collector asserts that the list is non-empty itself, because `--no-tests=error` is ignored under `-N` and `--show-only`. New rows need an entry in `docs/migration/allow.txt`. Ratio newly runs under Linux Clang, where it passes clang `-Werror` 78/78; TSan runs `-L async` only | Dropped tests; label aliasing | +| G2 | snapshot | **On-target test multiset.** `[ RUN ]` lines from each QEMU leg's `Testing/Temporary/LastTest.log`, which every QEMU job uploads. `--output-on-failure` prints nothing on success, so the CI log alone is not enough. Keyed by (target, engine) | Losses hidden by the floors (up to 34 for ratio on M33); Hexagon exclusion drift | +| G3 | A/B | **Exact icount.** `icount.py --compare-json`: the gated SHA's measured counts equal step 0's counts **measured in the same job**, exactly. The measured workload **set** equals the baseline key set. Committed `baselines.json` files must be **byte-unchanged**. Everyday CI keeps ±3 % against the committed files, since unpinned apt toolchains drift (R2-CI-10) | Codegen and harness changes. Verified deterministic: ratio's M33 counts match the baselines to the instruction, and the namespace rename leaves all 10 unchanged | +| G4 | A/B | **Codegen identity of icount and C ABI binaries only.** Split the disassembly at `STT_FUNC` bounds and skip non-function bytes. Key functions by demangled name after the rename map, and compare them as a sorted multiset. Strip addresses and RIP displacements. Symbolize literal-pool words **through relocations** (a gate-only link with `-Wl,--emit-relocs`, or per-TU `objdump -dr`), and resolve string-literal pointers to their text after the name and path maps. Normalizer prototype: `audit2-gates/norm3.py`. **Test binaries are excluded:** a pure namespace rename changes their codegen (stack-slot swaps in `check_cross_validation`), and gtest embeds `__FILE__` | Real codegen changes in shipped code | +| G5 | A/B | **Output identity.** P.2's per-engine tests print `[ measured ] hash ` for every direction × format × profile. The gate compares these lines between A and B **per host, in one job**, and compares the QEMU `checksum=` lines, which `icount.py` now prints. **Hashes are never pinned as constants:** async's float `interpolate()` hashes differently when FMA is available, so a pinned hash would fail on arm64 and macOS (R2-GATE-3) | Coefficient, table and datapath changes that leave icount unchanged | +| G6 | snapshot | **Cross-validation lines**, including the tolerance arguments. P.2 makes the test print its limit | A loosened tolerance. Also covered by G14 | +| G7 | A/B | **Compile and link flags.** Every gated configure sets `-DCMAKE_EXPORT_COMPILE_COMMANDS=ON`. Each entry is keyed by source path under the 4.1 map and tokenized with shlex. `-o/-c/-MD/-MT/-MF` and their arguments are dropped. Paths become ``/``, then the 4.1 path map and the macro map apply; FetchContent's `_deps` relocation is mapped away. The **ordered** token lists are compared. The same normalization applies to each target's `link.txt`, which holds the startup file, `-T`, specs and `--gc-sections`. `$CXX --version` is recorded. Linux and cross builds only | `-std`/`-ffp-contract` flips, leaked `-D`s, link changes | +| G8 | snapshot | **Book and API docs.** `mdbook build` clean, the image check, and `doxygen docs/Doxyfile` producing non-empty output, all in the `ci.yml` book job. `book-pages` itself is never dispatched from the branch, because it deploys | Broken anchors; an empty API reference | +| G9 | snapshot | **Retired identifiers.** Zero hits for `srt/`, `tap::samplerate`, `tap/ratio`, `tap::ratio`, `SRT_`, `TAP_RATIO_`, `srt_`, `ratio_capi`, `async_sample_rate_converter`, `basic_async_sample_rate_converter`, and retired `-D` option names in `.github/`. Exceptions are an explicit **(file, pattern) allowlist**: the guest icount markers `SRT_ICOUNT_DONE` and `RATIO_ICOUNT_DONE` (kept on purpose), each D7 tripwire line, `STYLE.md` until step 5, `ratio/HANDOFF.md` and `ratio/docs/HISTORY.md` (history). `ratio/PLAN.md` is **rewritten**, not allowlisted. Applies from step 3.7 | Stale prose, code and CI flags | +| G10 | snapshot | **C ABI symbols.** `nm -D --defined-only` on Linux equals the snapshot under the name map, plus the D13 version functions | ABI drift | +| G11 | A/B | **Notebooks.** Pinned environment (`pip install --require-hashes`, Python version from `setup-python`). Bridges always rebuild **quietly**, printing the CMake log only on failure. Every figure cell also prints an FNV hash or `%.6g` summary of its plotted arrays. A and B are executed in one CI job. The normalizer applies the name and version map and drops timing lines and PNGs | Changed numbers and curves; stale libraries | +| G12 | snapshot | **History.** `--follow` and `blame` on a fixed file list. Blame of a moved file is not attributed wholesale to a migration commit. The new root `CMakeLists.txt` is checked with `git log --`, since its history starts at 1c | Lost blame | +| G13 | snapshot | **Every named workflow ran on each gated SHA.** `ci.yml`, `style.yml` and `migration-gates.yml` run automatically. `ci-arm64` and `compare` are dispatched on the SHA. `book-pages` is replaced by G8. Gated SHAs are 1c, 2 and 3.1–3.8 (1a and 1b are not buildable on their own) | Skipped or cancelled evidence | +| G14 | snapshot | **Rename-only residual.** Apply the committed mechanical rename script (`docs/migration/rename.py`: paths, namespaces, macros, targets, banners) to the step-0 trees, then `git diff --no-index` against HEAD. Every residual hunk must appear in a reviewed allowlist (`docs/migration/residual/.txt`) | Anything the other gates miss: **a loosened `EXPECT_NEAR`, MSVC-only paths, docs, CI.** Round 2 showed that a tolerance change passes G1–G13 (R2-GATE-8) | + +A step's gate lists the IDs it requires. --- ## 6. Migration -All work happens on `claude/sample-rate-expansion-strategies-ezqzu6` in -SampleRateTap, in **fresh full clones**. It proceeds **one commit per -push**, with a **draft PR open from the start** so every push runs CI -(G13). Nothing reaches `main` until step 4's PR. RatioTap is not written -to until step 5. - -### Step P — Pre-work: harden both repositories *before* the snapshot - -The snapshot must not record existing breakage as "green" (GATE-6, INV-8). -Each item below is a normal PR to the affected repository, merged before -step 0. - -- **P.1 Repair existing breakage:** - - `ci-arm64.yml:61`: fix `-R 'SpscRing'` → `spsc_ring`. - - Pico 2 examples: add the dsptap include path (broken since `5315689`), - and build them in CI once. - - `scripts/book_figures_trace.cpp`: update it to the current API, or - retire its "before" panel as a committed image. - - `.git-blame-ignore-revs`: replace the dangling `34bb89e…` with - `b84020e738f771c7689ffc1e592f8448b9ce063f` and `e2f5a48`. - - README cross-validation figures: update to the current floors. -- **P.2 Harden the harness,** in both repositories, with identical changes: - - `icount.py --exact`, plus the workload-set equality and the fatal - missing-baselines check (G3). - - Run Hexagon under a **fixed `argv[0]` and an empty environment** - (`qemu-hexagon -0 w` with `env -i`). Static musl's startup walks - `argv[0]` and `envp`, so the binary path and job environment otherwise - enter the count (GATE-1). **Re-record the Hexagon baselines once**, - with the delta stated. - - `--no-tests=error` on every ctest call. - - Per-engine `LABELS` on the tests. - - The G5 full-output hash tests. -- **P.3 Pin a notebook environment:** - - Add a lockfile with numpy, scipy, matplotlib, jupyter, samplerate and - soxr. - - Make the bridges always rebuild. - - Re-execute every notebook in that environment and confirm its text - outputs equal the committed ones before they become the baseline. - - Mark `asrc_rbj_analysis` cell 18 (wall-clock timings) as excluded. +**Branch and PR.** + +- After step P, the migration branch `claude/sample-rate-expansion-strategies-ezqzu6` + is **rebased onto post-P SampleRateTap `main`**, keeping the plan commits. + The plan is part of the PR, and becomes `PLAN.md` at step 4. +- A **draft PR** is open from the first migration push. +- Apart from step P's PRs, nothing reaches either `main` until step 4. +- RatioTap is written to only by step P's PRs and by the archive at step 5. + +**Push discipline** (R2-CI-1, R2-GATE-9, R2-RUN-15): + +- 1a, 1b and 1c are pushed **together**, because 1a and 1b are not buildable + alone. The gated SHA is 1c. +- After that, **one gated commit per push**. The next push waits until every + workflow for the previous SHA has concluded and its run IDs are in + `runs.md`. +- Concurrency is set in step P so that the migration PR never cancels + in-flight runs: + + ```yaml + cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} + ``` + + v2's `github.ref != 'refs/heads/main'` was backwards: on a PR the ref is + `refs/pull/N/merge`, so it cancelled on the migration PR and never on + `main`. On this branch, run 36269857609 was already cancelled by a later + push. +- That makes roughly 12 gated pushes, at 25–50 minutes each. + +### Step P — Pre-work (normal PRs in both repositories, before step 0) + +The snapshot must not record existing breakage as green, and the harness +must produce the data the gates read. + +**P.1 Repair existing breakage:** + +- `ci-arm64.yml`: `SpscRing` → `spsc_ring`. +- Pico 2 examples: add the dsptap include path, and build them in CI once. +- `scripts/book_figures_trace.cpp`: move to the current API, or commit its + "before" panel as an image. +- `.git-blame-ignore-revs`: `34bb89e…` → `b84020e738f771c7689ffc1e592f8448b9ce063f` and `e2f5a48`. +- README cross-validation figures updated to the current floors. + +**P.2 Harness hardening (both repositories, identical where shared):** + +- **`icount.py`:** + - `--exact`; + - `--compare-json A.json`; + - workload-set equality; + - a missing baselines file is fatal; + - print the guest's `checksum=` line. +- **Hexagon runs:** + - Resolve `shutil.which("qemu-hexagon")` **before** clearing the + environment. `env -i` empties PATH, and the run then fails or silently + picks apt's plugin-less qemu (R2-GATE-11). + - Copy each workload to one fixed path. + - Run `[abs_qemu, "-0", "w", "-d", "plugin", "-plugin", …]` with + `env={}`. `-0 argv0` is supported in QEMU 8.2.2. + - **Re-record the Hexagon baselines once**, with each repository's + documentation duties: + - SampleRateTap: the README icount table and a `docs/PERFORMANCE.md` + ledger row; + - RatioTap: a PLAN.md §7 ledger row. +- **Tests:** + - D16's test prefixes and `LABELS`, including on the `*_tests_emulated` + entries. + - `--no-tests=error` on every executing ctest call. + - `-V --output-log` plus artifact upload on the QEMU legs (the G2 input). + - G5 hash tests that print `[ measured ] hash …`. On async M33 they are + budgeted through `MAIN_FILTER`, and the M33 timeout rises to 40 minutes. + - The cross-validation test prints its tolerance (G6). +- **CI hygiene:** + - Adopt RatioTap's triggers (push `main` + PR + dispatch) in + SampleRateTap, which ends the double runs, with the concurrency + expression above. + - `permissions: contents: read` on `ci.yml` and `style.yml`. + `ci-arm64` keeps `issues: write` in its own workflow. + - SHA-pin every action. + - Pin `runs-on: ubuntu-24.04` for QEMU and ratchet jobs. + - Add `ImageOS` to the `qemu-hexagon-plugins` cache key. + - Build the plugin per job, not as an artifact: it takes under 1 s, and a + shared artifact would couple jobs across images. + +**P.3 Notebook environment.** + +- Commit an identical `requirements.lock` at both repository roots: numpy, + scipy, matplotlib, jupyter, samplerate, soxr, hash-pinned. +- Bridges always rebuild, quietly. +- Every figure cell gets an array-hash cell. +- Re-execute all notebooks in that environment. Confirm that their text + outputs equal the committed ones, apart from the added hash cells. +- Mark `asrc_rbj_analysis` cell 18 (wall-clock timings) as excluded. + +**P.4 Re-pin RatioTap's `submodules/sampleratetap`** to post-P SampleRateTap +`main` (R2-COH-1). ### Step 0 — Freeze and snapshot -- Record the tips: SampleRateTap `main` and RatioTap `main` after step P. -- Assert that the full clones are not shallow - (`git rev-parse --is-shallow-repository` = `false`) and that the RatioTap - tip is the expected SHA. -- Take G1–G11 snapshots and commit them under root `docs/migration/`. -- No merges to either `main` until step 4. If one is unavoidable, it lands in - SampleRateTap only and step 1 is re-cut. The filter-repo output is - deterministic, so a re-cut is cheap. +- Record the two tips, `S0` (SampleRateTap `main`) and `R0` (RatioTap + `main`), in `docs/migration/tips.txt`. Record their commit counts: every + later count check uses these, not the literals 136 or 29. +- Assert that the full clones are not shallow. +- Commit the snapshot-class baselines (G1, G2, G6, G8–G10, G12) under + `docs/migration/`. +- Commit `docs/migration/rename.py` (the G14 map) and its first residual + allowlist. +- If `main` must move, it moves in SampleRateTap only, and step 1 is re-cut. + filter-repo is deterministic (tip `daceb8d` on two fresh clones). -### Step 1 — Import (three commits) +### Step 1 — Import (1a, 1b and 1c are pushed together) -**1a — Pure move** (one commit; `git show -M --stat` shows zero -insertions and deletions): +**1a — Pure move.** One commit. `git show -M --stat` shows **0 insertions +and 0 deletions**; the dry run moved 59 files. -- `git mv` SampleRateTap's engine files per section 4.1, including - `CMakeLists.txt` → `async/CMakeLists.txt` and `README.md` → - `async/README.md`. -- Nothing else in this commit, so blame survives (GIT-5). +- Use `git mv` for exactly the 1a rows of 4.1. -**1b — Import RatioTap** (one merge commit): +**1b — Import RatioTap.** One merge commit. Verified in the dry run, with +the two corrections that round found: ```sh git clone https://github.com/tap/RatioTap rt && cd rt -test "$(git rev-parse main)" = "" -git filter-repo --path-rename :ratio/ --path-rename ratio/.gitmodules:.gitmodules \ +test "$(git rev-parse main)" = "$R0" +git filter-repo --refs main \ + --path-rename :ratio/ --path-rename ratio/.gitmodules:.gitmodules \ --blob-callback ' if blob.data.startswith(b"[submodule \"submodules/"): blob.data = (blob.data @@ -474,236 +539,305 @@ if blob.data.startswith(b"[submodule \"submodules/"): cd ../SampleRateTap git fetch ../rt main:ratio-import git merge --allow-unrelated-histories --no-commit ratio-import -git checkout --ours .gitmodules # the one expected conflict (add/add) -git rm -r --cached ratio/submodules # the engine uses the root dsptap pin -git rm -f # -f: they are staged by the merge -git commit # message records the RatioTap tip SHA +git checkout --ours .gitmodules && git add .gitmodules # the one add/add conflict +git rm -r --cached ratio/submodules +git rm -rf ratio/.clang-format ratio/.clang-tidy ratio/STYLE.md \ + ratio/.pre-commit-config.yaml ratio/.claude ratio/scripts/tidy.sh \ + ratio/.github/pull_request_template.md +git commit # message records R0 and the rewritten tip +git branch -D ratio-import # never pushed ``` -This command was verified in a dry run. The rewritten history keeps -`.gitmodules` at the root with rewritten paths, so every imported RatioTap -commit can still initialize its submodules. The naive -`--to-subdirectory-filter` recipe orphans the gitlinks and breaks -`git submodule update` everywhere (GIT-2). - -Also: - -- Generate `ratio/docs/HISTORY.md` from `.git/filter-repo/commit-map`: old - SHA → new SHA → `https://github.com/tap/RatioTap/pull/N` for all 29 - commits. This preserves PR linkage lost to the squash merges and the PR - number collisions (GIT-8). -- Optionally, use `--message-callback` to rewrite bare `DspTap #38` to - `tap/DspTap#38`. -- If tags exist at cut time, add `--tag-rename '':'ratio/'`. - -**1c — Build glue and path fix-ups.** No codegen-reaching changes. G4 -and G7 prove that. - -- **Root `CMakeLists.txt`:** - - `project(SampleRateTap)` and `enable_testing()`. - - `add_subdirectory(submodules/dsptap)` once. - - Force the engines' `*_BUILD_TESTS`/`*_EXAMPLES` ON when the root is top - level. Their `PROJECT_IS_TOP_LEVEL` defaults evaluate false under a root - project, and v1's glue ran **0 tests while ctest exited 0** (INF-3). - - `add_subdirectory(async)` and `add_subdirectory(ratio)`. -- **Engine CMakeLists:** - - Wrap `add_subdirectory(submodules/dsptap)` in `if(NOT TARGET tap::dsp)`. - This also keeps standalone engine builds working. - - Ratio's `srt_headers` → `async/include` (kept `SYSTEM`). - - `${PROJECT_SOURCE_DIR}/cmake/r8brain.cmake` → `async/cmake/`. - - Standalone capi entry points fixed (ratio's `add_subdirectory(../..)`). -- **Bare-metal variables:** the root toolchain files set **both** - `SRT_BARE_METAL` and `TAP_RATIO_BARE_METAL` until step 3 unifies them. - The `GTEST_HAS_*` definitions move to root scope under that condition, - so the gtest library and both engines' test TUs agree (INF-1). -- **CI** (INF-4, GIT-4): - - Port RatioTap's `ci.yml` jobs into the root workflow as ratio jobs, with - ratio's options, its Hexagon `-E` list and `-j 4`, ratio's MSVC and - sanitizer WERROR, and ratio's icount against `ratio/bench/baselines.json`. - - Replace root `style.yml` with RatioTap's body, configuring **both** - engines with tests and icount ON, and failing on an empty TU list. - - Adopt `scripts/fetch_hexagon_toolchain.sh` for every cache writer. - - Move the RatioTap-only files per 4.1. +- `--refs main` keeps the leftover branches out, so the commit map has + exactly as many entries as `main`. +- `git submodule update --init --recursive` works on the merge commit and on + the rewritten RatioTap commits. The dry run checked the root commit, M1, + M7c (which recurses into `ratio/submodules/sampleratetap/submodules/dsptap`) + and the tip. +- The rewritten reformat commit `c0894cf` becomes `4c3562c`. It is recorded + for `.git-blame-ignore-revs`. + +**1c — Build glue and path fix-ups.** No change reaches codegen: G4 and G7 +prove it. **Gate 1:** G1, G2, G3, G4, G5, G6, G7, G8, G11, G12, G13 and +G14; every notebook bridge and every standalone engine build configures and +builds. + +- **Root `CMakeLists.txt`** (draft: `docs/migration/drafts/root-CMakeLists-1c.cmake`): + - `project(SampleRateTap LANGUAGES CXX)` and `enable_testing()`. + - `option()` **defaults** ON for `SRT_BUILD_TESTS`, `SRT_BUILD_EXAMPLES`, + `TAP_RATIO_BUILD_TESTS` and `TAP_RATIO_BUILD_EXAMPLES`, declared before + the engines, **never FORCE**. CI's `-D…=OFF` must still win: every + bare-metal job disables examples, because async's examples need Threads. + Jobs that disable one engine's tests pass both engines' OFF flags. + - `add_subdirectory(submodules/dsptap)`, then `async` and `ratio`. + - Hoist the gtest settings (`INSTALL_GTEST OFF`, the Threads probe) to the + root, so the result does not depend on the order of `add_subdirectory`. +- **Engine `CMakeLists.txt`:** + - Guard with + `if(NOT TARGET tap::dsp) add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../submodules/dsptap ${CMAKE_CURRENT_BINARY_DIR}/submodules/dsptap) endif()`. + The dry run verified that this keeps `cmake -S async`, `cmake -S ratio` + and the notebook bridges working. The plain relative guard in v2 broke + all three (R2-RUN-3). + - async: `add_subdirectory(tools/capi)` → `add_subdirectory(capi)`. + - ratio: `srt_headers` becomes + `cmake_path(… NORMALIZE)` of `${CMAKE_CURRENT_SOURCE_DIR}/../async/include`, + kept `SYSTEM`. + - Engines **keep their `project()`** until step 3.4, so + `${PROJECT_SOURCE_DIR}/cmake/r8brain.cmake` resolves unchanged. No + r8brain edit is needed. +- **Bare-metal:** + - `arm-cortex-m33/m55` toolchain files set **both** `SRT_BARE_METAL` and + `TAP_RATIO_BARE_METAL`. + - `GTEST_HAS_*` stays **in each engine's `tests/CMakeLists.txt`**. Moving + it to the root leaked it into the icount TUs and failed G7 in the dry + run (R2-CI-5, R2-RUN-8). Setting both variables is what makes gtest and + both test trees agree. +- **CI** (draft: `docs/migration/drafts/ci-after-1c.yml`): + - Every job configures the root once and builds both engines. + - Correctness jobs run **per (target, engine)** with + `ctest -L '^$'`, each engine's own `-E` list and `-j`: + - Hexagon: async serial, ratio `-j 4`. + - M33: async gets a 40-minute timeout. + - Host jobs: per-engine WERROR as in the draft (MSVC: async OFF, ratio ON). + - Sanitizers: ASan for both engines; TSan with `-L async` only. + - Ratchet: `icount-async` and `icount-ratio` jobs until step 2, each with + its own baselines and README freshness check. + - Add `migration-gates.yml` from the draft. + - Replace root `style.yml` with RatioTap's body. It configures both + engines with tests and icount ON, fails on an empty TU list, and is + SHA-pinned. + - Every Hexagon cache writer uses `fetch_hexagon_toolchain.sh`. + - Delete `ratio/.github/workflows/`. - **Paths:** - - The book's 52 includes (G8). - - `book-pages.yml` path filters and Doxyfile `INPUT`. - - The `bench-smoke` binary path, `compare.yml` build paths, - `icount.py --baselines` per engine, `update_icount_docs.py` → - `async/bench/baselines.json` and `async/README.md` (Q8). ratio gains the - same table in `ratio/README.md`, and the script takes `--engine`. - - Notebook `REPO` roots and `sys.path` entries. - - The 11 README relative links. - - `ratio/CLAUDE.md` build commands. -- **LICENSE:** in one commit, the root `LICENSE` takes D14's unified line - and `ratio/LICENSE` is deleted. The notice MIT requires is never absent - (GIT-13). - -**Gate 1:** - -- G1, G2, G3 (both engines, all targets), G4, G5, G6, G7, G8, G11, G12, - G13. -- Every notebook bridge builds standalone. + - The 52 book includes. + - `book-pages.yml` path filters. + - Doxyfile: `INPUT = async/include ratio/include async/README.md` and + `USE_MDFILE_AS_MAINPAGE = async/README.md` until step 4. + - `bench-smoke` → `build/async/bench/srt_bench`. + - `compare.yml` build paths. + - `icount.py --baselines /bench/baselines.json`. + - `update_icount_docs.py --engine` (async and ratio tables; README + freshness per engine). + - `update_perf_docs.py` default → `async/README.md`. + - `book_figures.py`: `ROOT/"async"/"include"`. + - Notebooks: + - `asrc_demo` and `asrc_block_size_study`: `CAPI_DIR = REPO/"build"/"capi"`. + - `asrc_comparison`: `TOOLS_DIR = REPO/"build"`. + - `asrc_rbj_analysis`: `sys.path` → `"../../scripts"`. + - README links, four in total: `async/README` `LICENSE`, and + `ratio/README` `LICENSE` ×2 and `STYLE.md` → `../`. + - `ratio/README`: build commands, and "eight workloads" → ten. + - `ratio/CLAUDE.md`: build commands. +- **HISTORY.md:** + - `ratio/docs/HISTORY.md` maps old SHA → new SHA → RatioTap PR for + `git rev-list R0` (29 commits, skipping the commit-map header). + - PR numbers come from `GET /repos/tap/RatioTap/commits//pulls`, + queried once at cut time. The repository is public; the dry run built + the map as `audit2-dryrun/history_map.tsv`. + - PRs were rebase-merged, so several commits share one PR, and the root + commit has none. + - Optionally rewrite bare `DspTap #38` in messages at 1b with + `--message-callback`. +- **LICENSE:** one commit writes D14's line into the root `LICENSE` and + deletes `ratio/LICENSE`, so the notice is never absent. +- **Notebook environment:** delete `ratio/requirements.lock` and + `ratio/notebooks/requirements.txt`; they are identical to the root lock. ### Step 2 — Shared infrastructure -- Delete ratio's copies of `cmake/`, `platform/`, `tools/qemu_insn_plugin/` - and `scripts/icount.py` (all cosmetic after step 1c). -- `icount.py` becomes engine-aware: `--engine async|ratio` sets its - `_icount_*` glob, baselines path and marker regex. - - **Guest-printed strings do not change.** `SRT_ICOUNT_DONE` and - `RATIO_ICOUNT_DONE` stay byte-identical, because guest `printf` scans - format text per character and a longer marker moves the count (GATE-1, - INF-7). - - The host-side plugin marker becomes `TAP_SR_INSN_COUNT`, which is - harmless. -- **Ratchet CI:** one job **per target** measures both engines. This shares - the toolchain, the plugin build and the qemu-hexagon cache, and uses - `fail-fast: false` so each target stays independent evidence (INF-8). - - Build the plugin once, as an artifact. - - Docs freshness runs as its own async-only job. - - `compare.yml` is updated in the same commit, and runs once by - `workflow_dispatch` as part of this gate. -- CI dedup: adopt RatioTap's scheme with `workflow_dispatch` and - `cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}`. Actions are - SHA-pinned (SampleRateTap's pins). - -**Gate 2:** G1–G7 and G13, plus one manual `compare.yml` run. +**Changes:** + +- Delete ratio's `cmake/`, `platform/`, `tools/qemu_insn_plugin/` and + `scripts/icount.py`. +- `icount.py --engine async|ratio` sets its glob, baselines path and marker + regex. **Guest-printed markers stay byte-identical** + (`SRT_ICOUNT_DONE` / `RATIO_ICOUNT_DONE`, allowlisted in G9). Only the + host-side plugin marker becomes `TAP_SR_INSN_COUNT`. +- **Ratchet:** one job **per target** measures both engines, with + `fail-fast: false` and the plugin built in each job. The combined M33 run + is about 45 s of QEMU, well within the timeout. +- Docs freshness runs as its own job, per engine. +- `compare.yml`: update it in the same commit, then dispatch it once. + +**Gate 2:** G1–G7, G13 and G14, plus the `compare.yml` run. ### Step 3 — Renames (one commit per class, each gated) -1. **Paths:** `srt/…` → `tap/sr/async/…`; `tap/ratio/…` → `tap/sr/ratio/…`; - `ratio/tools/capi` → `ratio/capi`. Delete `srt/detail/kaiser.h` per 3.1. -2. **Namespaces:** `tap::samplerate` → `tap::sr::async`; `tap::ratio` → - `tap::sr::ratio`; test namespaces `srt_test` / `ratio_ref` as decided. - `async_sample_rate_converter` → `converter` (D15). -3. **clang-format reflow:** its own commit. The namespace rename reflows - 22 files (+112/−114) through alignment columns (GIT-6). -4. **Macros:** - - `SRT_VERSION_*`, `TAP_RATIO_VERSION_*` → `TAP_SR_VERSION_*`, set to - 0.4.0 (D13). - - `SRT_RESTRICT`, `SRT_Q15_SMLALD`, `SRT_CHANNEL_PARALLEL`, - `TAP_RATIO_MIRRORED_DOT_ATTR`: renamed, or replaced by their - `TAP_DSP_*` originals where they are pure aliases. - - `SRT_CP_MIN_CHANNELS` (a user override, documented in the book): - renamed with an `#error` tripwire (D7). - - `SRT_SC_*`, `RATIO_SC_*`, `SRT_CMP_*`, `*_TESTS_COMPLETE`, - `*_BARE_METAL` (unified to `TAP_SR_BARE_METAL`), `SRT_PICO2_*`. - - **Guest-printed icount markers stay unchanged** (step 2). -5. **CMake:** - - Targets `tap::sr::async`, `tap::sr::ratio` and umbrella `tap::sr`. - - Internal targets (`srt_warnings`, `srt_tests`, `srt_bench*`, - `srt_alsa_bridge`, `srt_r8brain`, `srt_r8b_shim`, `tap_ratio*`, - `srt_headers`, …). - - Options → `TAP_SR_*` per D9. Old public targets removed (D7). -6. **C ABI:** - - `srt_*` → `tap_sr_async_*`, `ratio_*` → `tap_sr_ratio_*`. - - Handle types, header names and library names. - - The shim's exports. - - `srt_version` and `ratio_version` → one `tap_sr_version()`, bit-packed - (D13); `test_skeleton.cpp` re-pins it at 0.4.0. Bridges renamed per D8. -7. **Ratchet workload binaries:** prefix only (`tap_sr__icount_*`). - Workload names do not change, because the key is the basename minus the - prefix (GATE-12, INV-5). -8. **Book and docs prose:** the 23 files and the RatioTap URLs, including - rewriting the `git clone …/RatioTap` instructions. - -**Gate for each commit:** G1 (the name-map table must be **empty** unless -a suite rename is listed explicitly; GATE-17), G2, G3, G4 (with the symbol -map), G5, G6, G7, G8, G10, G11 and G13. G9 applies from commit 8. - -After the PR merges (step 4), append the SHAs of commits 1–3 to -`.git-blame-ignore-revs`, together with the rewritten RatioTap reformat -commit (`c0894cf`, looked up in the commit map). +Every commit is formatted by the pre-commit hook as it is made, so the +**rename and its clang-format reflow are one commit**. Measured reflow: + +- the namespace rename: 20 files, +101/−103; +- D15: 10 files, +61/−61. + +Every step-3 commit's SHA goes into `.git-blame-ignore-revs` after step 4. +There is no separate reflow commit, since the hook would absorb it anyway +(R2-COH-6). + +1. **Paths:** + - `srt/…` → `tap/sr/async/…` (with `asrc.h` → `converter.h` and + `srt.h` → `async.h`). + - `tap/ratio/…` → `tap/sr/ratio/…`. + - `ratio/tools/capi` → `ratio/capi`. Its standalone + `add_subdirectory(../..)` becomes `add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/.. ratio)`. + - Delete `srt/detail/kaiser.h` per 3.1. +2. **Namespaces and names:** + - `tap::samplerate` → `tap::sr::async`; `tap::ratio` → `tap::sr::ratio`. + - The D15 mapping. + - Test namespace `srt_test` → `async_test`. `ratio_ref` is unchanged; it + is not a retired name. +3. **Macros:** + - `SRT_VERSION_*` / `TAP_RATIO_VERSION_*` → `TAP_SR_VERSION_*` = 0.4.0 in + both umbrella headers (D13). + - `SRT_RESTRICT`, `SRT_Q15_SMLALD` and `SRT_CHANNEL_PARALLEL` → their + `TAP_DSP_*` originals (they are pure aliases). + - `TAP_RATIO_MIRRORED_DOT_ATTR` → `TAP_SR_RATIO_MIRRORED_DOT_ATTR`. + - `SRT_CP_MIN_CHANNELS` → `TAP_SR_ASYNC_CP_MIN_CHANNELS`, with an + `#error` tripwire. + - `SRT_SC_*`, `RATIO_SC_*`, `SRT_CMP_*` → `TAP_SR_{ASYNC,RATIO}_SC_*` / + `…_CMP_*`. + - `*_TESTS_COMPLETE` → `TAP_SR_TESTS_COMPLETE`. + - `*_BARE_METAL` → `TAP_SR_BARE_METAL`, in the toolchain files and both + test trees. + - `SRT_PICO2_*` → `TAP_SR_PICO2_*`. + - Guest icount markers are unchanged. +4. **CMake, dependency enforcement and workflows:** + - Targets: `tap::sr::async`, `tap::sr::ratio`, umbrella `tap::sr`. + - Internal targets renamed, including the ctest entries + `srt_tests_emulated` / `tap_ratio_tests_emulated` → + `tap_sr_{async,ratio}_tests_emulated`. They are listed in the G1 name + map. + - Engine `project()` → `tap_sr_async` / `tap_sr_ratio`, with no VERSION. + - Root `project(SampleRateTap VERSION 0.4.0)`. + - Options → `TAP_SR_*` per D9, with the D7 `FATAL_ERROR` tripwire for + every retired option. + - **All five workflows** (`ci`, `style`, `ci-arm64`, `compare`, + `book-pages`) updated in the same commit. + - The four 4.2 enforcement checks land here with their own tests. +5. **C ABI:** + - `srt_*` → `tap_sr_async_*` and `ratio_*` → `tap_sr_ratio_*`, including + handle types, header names and library names. + - The shim's exports → `tap_sr_async_r8b_*`. + - The version functions per D13, and a new `CApi.VersionIsBitPacked` + test, listed in the G1 name map. + - Bridges renamed per D8. +6. **Ratchet binaries:** prefix only, `tap_sr__icount_*`. Workload + names and baseline keys do not change. +7. **Docs and prose:** + - The book: 23 files. This includes `part4/c-abi.md`'s decimal-encoding + prose (lines 161-162 and 305-306) and the D15 naming-decision prose. + - Non-book docs: + - `async/docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md`; + - `examples/pico2_*/README.md`; + - `ratio/{README,PLAN,CLAUDE}.md`; + - notebook markdown (`asrc_comparison` 42 hits, `asrc_demo` 30, + `asrc_block_size_study` 24). + - RatioTap URLs; the `git clone …/RatioTap` instructions. + - **G9 applies from this commit.** +8. **Banners (D14):** + - Rewrite the 35 existing banner lines. + - **Add** banners to every C/C++/Python source file that has none. + - Update `ratio/tools/reference/make_reference_vectors.py`, and + regenerate `tests/reference/reference_vectors.h`. That header's only + residual must be its banner (G14). + +**Gate for each commit:** G1 (the name map is empty except for the +renames listed in 3.4 and 3.5), G2, G3, G4, G5, G6, G7, G8, G10, G11, +G13 and G14, plus G9 from 3.7. ### Step 4 — Documentation and the PR -- **Root files:** - - `PLAN.md` promoted from this document. - - Family `CLAUDE.md`, which carries the dependency rule, D12, **`git bisect - start --first-parent`** (GIT-9), and the cross-validation separation - rule (section 8, R4). - - Family `README.md`. -- **Per-engine files:** `PLAN.md` and `README.md`. `ratio/CLAUDE.md` is - reduced to the ratio charter. -- **Delete `docs/migration/`,** keeping `runs.md` as history if wanted. -- **Mark the PR ready.** It is merged with **"Create a merge commit"** - (enable it in repository settings if disabled). **Never squash or - rebase** (D3, GIT-1). -- **Post-merge gate:** - - `git rev-list --count origin/main` ≥ 136 + 29 + N. +- `git mv docs/MONOREPO_PLAN.md PLAN.md`. +- Write the family `CLAUDE.md`. It records the dependency rule, D12, + `git bisect start --first-parent`, and the cross-validation separation + rule (R4). +- Write the family `README.md`. +- Per-engine `PLAN.md` and `README.md` (each with its icount table). +- `ratio/CLAUDE.md` reduced to the charter. +- Doxyfile main page → `README.md`. +- Delete `docs/migration/`, keeping `runs.md` if wanted. +- Mark the PR ready. **Merge with "Create a merge commit".** +- **Post-merge checks:** + - `git rev-list --count origin/main` ≥ count(S0) + count(R0) + N. - `git log --follow ratio/include/tap/sr/ratio/converter.h` reaches - RatioTap's M7d commit. + RatioTap M3 (`06769f2` before rewriting). +- **Follow-up commit:** append to `.git-blame-ignore-revs` the step-3 + commit SHAs and the rewritten RatioTap reformat commit `4c3562c`. +- Tag `v0.4.0`. ### Step 5 — Outside the repository - A DspTap PR for the comment and doc references (3.7). -- A taphouse PR for `STYLE.md`'s macro example and `sync.sh`. -- The user archives RatioTap with a README pointer and deletes its leftover - branch. -- The user completes the outside-consumer checks in 3.7 before the RatioTap - archive. - -### Next — First new engine: `integer` (separate plan) - -`integer` is out of scope and gets its own reviewed PLAN.md. That plan -covers: - -- L-th-band design math in DspTap. -- `chain<>` in DspTap under D12. -- The `integer` engine. -- The generated 14 × 14 coverage matrix test (2.1). - -`ratio`'s 2^k charter restatement (2.2) is a small separate change, -sequenced before it. +- A taphouse PR: + - drop RatioTap from `sync.sh`, `drift-check.yml` and the catalog; + - `STYLE.md`: the two-level namespace note, the macro example + (`SRT_VERSION_MAJOR`), and the banner template (D14). +- The user checks open RatioTap issues and PRs. +- The user archives RatioTap, with a README pointer to the monorepo. +- Delete RatioTap's leftover branches (`…mqc190` and + `claude/sample-rate-expansion-strategies-ezqzu6`). + +### Next — `integer` (separate plan) + +- The 2.2 follow-up comes first (`ratio_traits` k). +- Then `integer`'s own reviewed plan: L-th-band design math and `chain<>` + (under D12) in DspTap, the engine, and the generated 14 × 14 matrix + under 2.1's rule. --- ## 7. Non-goals -- No algorithm, coefficient, design or process-loop change. Outputs stay - bit-identical (G5). Codegen stays identical (G4), and so do instruction - counts (G3), except the one Hexagon re-record in step P.2, which is a - harness change made before the snapshot. -- No new engine, profile or API function. -- DspTap **code** is untouched. DspTap docs and comments change only - through a DspTap PR (step 5), and `STYLE.md` only through taphouse. -- No release is cut during the migration. 0.4.0 (D13) is set in step 3 and - tagged `v0.4.0` after step 4 merges. -- The third harness copy (DspTap's) is not adopted (3.3). +- **No algorithm, coefficient, design or process-loop change.** + - Outputs are identical (G5), and codegen of shipped code is identical + (G4). + - Instruction counts are identical (G3), except P.2's one Hexagon + re-record, which is a harness change made before the snapshot. +- **No new engine, profile or API function**, apart from D13's version + function and its test. +- **DspTap code is untouched.** Its docs change through a DspTap PR, and + `STYLE.md` through taphouse. +- **No install or package rules.** 4.2's check 2 does not need them. +- **The third harness copy (DspTap's) is not adopted.** ## 8. Risks | # | Risk | Mitigation | |---|---|---| -| R1 | A step silently drops a test or workload | G1 and G2 compare multisets. G3 requires set equality of workloads. `--no-tests=error` everywhere | -| R2 | Infrastructure deduplication or build glue changes codegen | G4 (disassembly), G7 (flags) and G3 (exact), all as same-job A/B against step 0 | -| R3 | Book and docs rot | G8 from step 1; G9 from step 3 commit 8 | -| R4 | The cross-validation loses its independence | Independence comes from leg 2 (scipy vectors) and from the structural difference between the engines, never from repository separation. Both already share DspTap. What the merge removes is the pin bump as a separately reviewed event. Replacements: G6 during the migration, and a **permanent family rule** in CLAUDE.md. A PR that changes cross-validation tolerances must leave `ratio/tests/reference/` untouched, must keep the scipy leg green, and must not also change async's datapath (`polyphase_filter.h`, `sample_traits.h`). `ratio` never includes async's bank or blend (D11) | -| R5 | Rewritten SHAs and colliding PR numbers | `ratio/docs/HISTORY.md` (step 1b). Rewriting `DspTap #38` in messages is optional | -| R6 | CI load: about 21 jobs, which exceeds the 20-concurrent limit of GitHub Free, and macOS minutes cost 10× | The ratchet runs per target rather than engine × target (step 2). Raise the M33/M55 correctness timeouts, or split them per engine if two one-shot suites exceed 30 min | -| R7 | The merge interleaves histories for `git bisect` | `git bisect start --first-parent` is required and documented. Pre-merge ratio regressions are bisected in-tree (possible thanks to the `.gitmodules` rewrite) or in the archive | -| R8 | Charter boundaries erode | The four-part enforcement in 4.2, D11, D12 and per-engine PLAN.md files | -| R9 | The final PR is squash-merged | D3; the merge-commit instruction in step 4; the post-merge history gate | -| R10 | Toolchain drift mid-migration is blamed on a step | All gates are same-job A/B against the step-0 SHA (section 5) | +| R1 | Silently dropped tests or workloads | G1 and G2 multisets with unique names (D16); G3 workload-set equality; non-empty assertions | +| R2 | Infrastructure or build glue changes codegen | G3, G4 and G7 as same-job A/B; G14 | +| R3 | Docs rot | G8 from 1c; G9 from 3.7; G14 | +| R4 | The cross-validation loses its independence | Independence comes from the scipy leg and from the engines' structural difference, never from repository separation. G6 now prints tolerances, and G14 sees any tolerance edit. **Permanent family rule** (CLAUDE.md): a PR that changes cross-validation tolerances leaves `ratio/tests/reference/` untouched, keeps the scipy leg green, and does not also change async's datapath. `ratio` never includes async's bank or blend (D11) | +| R5 | Rewritten SHAs and PR numbers | `ratio/docs/HISTORY.md` built from the API (1c) | +| R6 | CI load | The repositories are public: no minutes cost. The Free concurrency caps (20 jobs, 5 macOS) mean queueing, not failure. About 20 `ci`+`style` jobs plus 4 gate jobs per push; at most 4 macOS jobs | +| R7 | `git bisect` across the merge | `--first-parent` is required and documented. Imported commits remain buildable in `ratio/` thanks to the `.gitmodules` rewrite | +| R8 | Charter erosion | 4.2 checks, D11, D12, per-engine PLAN.md files | +| R9 | Squash-merge of the final PR | D3; step 4; the post-merge history check | +| R10 | Toolchain drift blamed on a step | A/B gates in one job on a pinned image; image and package versions in `runs.md` | +| R11 | The migration outlives the snapshot's assumptions: many gated pushes over days, while `main` or the image moves | Re-cut rule (step 0); A/B gates always compare against S0/R0 built fresh in the same job | ## 9. Open questions -None. All were resolved; the decisions are recorded where they apply. +None. The decisions are recorded where they apply. | Q | Resolution | |---|---| -| Q1 | `HANDOFF.md` stays in `ratio/` (v2) | -| Q2 | Rename to `converter`: D15 (user, 2026-09-26) | -| Q3 | `ratio` at 2^k rates: section 2.2 (v2) | -| Q4 | One family version, 0.4.0, bit-packed encoding: D13 (user) | -| Q5 | One unified holder line: D14 (user) | -| Q6 | Per-engine READMEs plus a family README (v2) | -| Q7 | Two-level namespace accepted: D4 (user) | -| Q8 | Icount tables in each engine's README: step 1c (user) | +| Q1 | `HANDOFF.md` stays in `ratio/` (allowlisted as history) | +| Q2 | Rename to the `converter` family: D15 | +| Q3 | `ratio` at 2^k rates: 2.2 (follow-up) | +| Q4 | One family version 0.4.0, bit-packed: D13 | +| Q5 | Unified holder line; user reconfirmed ownership; banners everywhere: D14 | +| Q6 | Per-engine READMEs plus a family README | +| Q7 | Two-level namespace: D4 | +| Q8 | Icount tables in each engine's README: 1c | --- -## Appendix A — Audit disposition +## Appendix A — Round-1 audit disposition (v1 → v2) + +Citations in this table use **v2's** step numbering (v2 had a separate +reflow commit, so its step 3 sub-numbers differ from v3's). Rows that round +2 found only partly resolved are listed at the top of Appendix B and completed +there. Reviewers: **DEC** (decisions and charters), **GIT** (history mechanics, dry-run on scratch clones), **GATE** (gates and bit-identity, host builds), @@ -798,3 +932,108 @@ completeness). Severity: B blocker, M major, m minor, n nit. | INV-14 | m | RatioTap URLs dangle | 3.7; step 3.8 | | INV-15 | n | 3.2 / R4 stale | 3.2; R4 | | INV-16 | m | Outside consumers unchecked | 3.7 (confirmed by user) | + +--- + +## Appendix B — Round-2 audit disposition (v2.1 → v3) + +Reviewers: **RUN** (end-to-end dry run of steps 1a–1c on fresh full +clones: GCC/clang builds, M33 under QEMU, mdbook), **GATE** (gate +prototypes: disassembly normalizers, FMA hash experiments, CTest toy +projects), **CI** (workflow design against live Actions data), **COH** +(plan coherence; every Appendix A row re-checked). + +Severity: B blocker, M major, m minor, n nit. **Rejected:** none. + +**Round-2 corrections to Appendix A.** COH found these 19 round-1 rows only +partly resolved in v2: + +- DEC-1, DEC-3, DEC-4, DEC-13; +- GIT-6, GIT-8, GIT-13; +- GATE-3, GATE-10, GATE-11, GATE-14, GATE-16; +- INF-2, INF-9, INF-11; +- INV-3, INV-6, INV-9, INV-12. + +Each is completed in v3 under the COH, CI, GATE or RUN row that names it +below. + +| ID | Sev | Finding (short) | Disposition in v3 | +|---|---|---|---| +| RUN-1 | M | `checkout --ours` leaves `.gitmodules` unmerged | 1b: `git add .gitmodules` | +| RUN-2 | M | `git rm -f` aborts on directory | 1b: explicit `git rm -rf` list | +| RUN-3 | M | `NOT TARGET` guard breaks standalone builds and bridges | 1c: `../submodules/dsptap` with a binary dir | +| RUN-4 | M | `add_subdirectory(tools/capi)` missed | 1c | +| RUN-5 | M | "Force ON" ignores CI's `-D…=OFF` | 1c: `option()` defaults, never FORCE | +| RUN-6 | M | Root `ctest` runs both engines under one `-E` | D9; 1c per-(target, engine) jobs with `-L` | +| RUN-7 | M | G4 unmeetable for gtest binaries | G4 scoped to icount and C ABI binaries | +| RUN-8 | M | Root-scope `GTEST_HAS_*` leaks into icount TUs | 1c: left in engine test trees | +| RUN-9 | m | HISTORY.md needs the API; PRs rebase-merged; 30 map entries | 3 (inventory); 1b `--refs main`; 1c HISTORY.md | +| RUN-10 | m | `--no-tests=error` ignored with `-N` / `--show-only` | G1 collector asserts non-empty | +| RUN-11 | m | Notebook and script paths under-specified | 1c: five paths listed | +| RUN-12 | m | G7 path shapes (`_deps`, `../async`) | G7 normalization; 1c `cmake_path` | +| RUN-13 | m | README link count wrong | 1c: four links | +| RUN-14 | m | Doxyfile `INPUT` and main page | 1c; step 4 | +| RUN-15 | m | 1a/1b red by construction under G13 | Push discipline; G13 gated SHAs | +| RUN-16 | n | r8brain edit unnecessary | 1c: engines keep `project()` until 3.4 | +| RUN-17 | n | Hexagon toolchain sets no BARE_METAL | 3.3 | +| RUN-18 | n | D14 wording; generator banner; unscheduled | D14; 3.8 | +| RUN-19 | n | New root `CMakeLists.txt` history check | G12 | +| RUN-20 | n | `ratio/.github/workflows` deletion timing | 4.1; 1c | +| GATE-1 (R2) | B | G4 red on a pure rename | G4 redefined (per-function, relocations, icount/C ABI only) | +| GATE-2 (R2) | B | A/B exceeds M33/Hexagon timeouts | Section 5: A/B only for G3/G4/G5/G7/G11 in `migration-gates` | +| GATE-3 (R2) | M | Pinned hashes fail with FMA | G5: per-host A/B, never pinned | +| GATE-4 (R2) | M | Duplicate-name label aliasing | D16 prefixes in P.2 | +| GATE-5 (R2) | M | G2 input not in logs; floor numbers | G2 `LastTest.log`; P.2 upload; 3.3 (59 tests) | +| GATE-6 (R2) | M | G7 inputs missing; undefined normalization | G7 fully specified, with `link.txt` | +| GATE-7 (R2) | M | G6 misses loosened tolerances | P.2 prints tolerances; G14 | +| GATE-8 (R2) | M | Regression passing all gates | **G14** (rename-only residual) | +| GATE-9 (R2) | M | G13 unmeetable (book-pages, dispatch-only, 1a/1b) | G13 names workflows and gated SHAs | +| GATE-10 (R2) | M | G11 leaks volatile text; compares little | G11 redefined; P.3 quiet bridges and array hashes | +| GATE-11 (R2) | M | `env -i` breaks qemu lookup | P.2: resolve absolute path first | +| GATE-12 (R2) | M | Forced options break cross/icount jobs | 1c `option()` defaults | +| GATE-13 (R2) | m | G9 vs kept markers | G9 (file, pattern) allowlist | +| GATE-14 (R2) | m | G10 per platform | G10 Linux-only | +| CI-1 | B | cancel-in-progress defeats per-SHA runs | Section 6 push discipline and concurrency expression (from P.2) | +| CI-2 | B | No A/B workflow; no G2 capture | `migration-gates` (draft); P.2 `--output-log` and upload | +| CI-3 | B | D9 vs per-engine jobs; Hexagon timeout | D9; 1c per-(target, engine) matrix | +| CI-4 | M | FORCE breaks bare-metal configures | 1c | +| CI-5 | M | Root `GTEST_HAS_*` fails G7 | 1c | +| CI-6 | M | Renamed options silently drop gates | D7 option tripwire; 3.4 updates all five workflows; G9 covers `.github/` | +| CI-7 | M | Merged jobs change coverage | G1 per (job, engine) plus `allow.txt`; TSan `-L async` | +| CI-8 | M | Repositories are public | 3; R6 | +| CI-9 | M | Double runs until step 2 | P.2 adopts triggers | +| CI-10 | M | `--exact` vs committed baselines turns daily CI red | G3: exact only in A/B; ±3 % stays for daily CI | +| CI-11 | m | Plugin artifact adds risk | P.2 per-job build; pinned image; `ImageOS` key | +| CI-12 | m | Docs freshness async-only | 1c/2 per engine | +| CI-13 | m | G4 on test binaries | G4 scope | +| CI-14 | m | Preamble contradicts step P | Section 6 preamble | +| CI-15 | m | Doxygen unchecked on PRs | G8 | +| CI-16 | m | Permissions and pinning; `compare.yml` cache writer | P.2; 3.3 | +| CI-17 | m | Ratio tests must see async as SYSTEM | 1c `srt_headers` kept `SYSTEM` | +| CI-18 | m | G5 tests in the tight M33 leg | P.2 `MAIN_FILTER` budget; 40-minute timeout | +| CI-19 | m | P.2 re-record documentation duties | P.2 | +| CI-20 | n | `INSTALL_GTEST` order dependence | 1c: gtest settings hoisted | +| COH-1 | M | Step P writes RatioTap; stale 29/136; pin | Section 6 preamble; step 0 counts; P.4 re-pin; 4.1 lockfile | +| COH-2 | M | Encoding pinned by nothing | D13 `CApi.VersionIsBitPacked`; 3.6 | +| COH-3 | M | D13 mechanics unspecified | D13 mechanics; 3.3–3.5 | +| COH-4 | M | G11 fails on rename commits | G11 name and version map | +| COH-5 | M | G9 fails by construction | G9 allowlist; 3.7 widened; `ratio/PLAN.md` rewritten | +| COH-6 | M | Rename/reflow split vs pre-commit | Step 3: rename and reflow in one commit, ignore-revs | +| COH-7 | M | Passband rule contradicts its example; no stopband rule | 2.1 rewritten (a)–(d); rows re-checked | +| COH-8 | m | Rate list circular; 352.8 missing | 2.1: exactly 14 rates; 352.8 excluded with reason | +| COH-9 | m | 2.2 facts; test needs API; direction sentence | 2.2 rewritten; section 2 table marks the follow-up | +| COH-10 | M | D14 false premise; banners unscheduled | D14 (user reconfirmed); 3.8 | +| COH-11 | M | Per-engine jobs run both engines | D9; 1c | +| COH-12 | m | Standalone builds; capi step | 1c guard; 3.1 capi fix; r8brain untouched | +| COH-13 | m | D9 option list incomplete | D9 | +| COH-14 | M | 4.2 enforcement unscheduled; install rules | 4.2 check 2 replaced; scheduled at 3.4 | +| COH-15 | M | G3 vs committed baselines; two tips; ratio A | G3; section 5 (S0/R0) | +| COH-16 | m | cancel-in-progress | Section 6 push discipline | +| COH-17 | m | Layout vs 4.1 (PLAN location, lockfile, scripts) | Section 4 tree; 4.1; step 4 `git mv` | +| COH-18 | m | 1c path list gaps | 1c paths | +| COH-19 | m | D15 scope and rationale | D15 full mapping; G9 | +| COH-20 | m | Rename-list gaps (test namespaces, emulated tests, `project()`, CP macro) | 3.2–3.4 | +| COH-21 | m | Ratio README table unguarded | 1c/2 freshness per engine | +| COH-22 | m | Plan tier vs concurrency | CI-8: public; R6 | +| COH-23 | m | Branch housekeeping | Section 6 preamble (rebase); 1b `ratio-import`; step 5 branches | +| COH-24 | n | Small inaccuracies (84 includes, line 152, audit line 126, HISTORY commit, `.gitignore`, reading guide) | 3.4; 3.1; 3.7; 1c; 4.1; reading guide | diff --git a/docs/migration/drafts/ci-after-1c.yml b/docs/migration/drafts/ci-after-1c.yml new file mode 100644 index 0000000..9d872e4 --- /dev/null +++ b/docs/migration/drafts/ci-after-1c.yml @@ -0,0 +1,175 @@ +# DRAFT (audit round 2, R2-CI): root .github/workflows/ci.yml as it should +# stand right after step 1c. This is a sketch, not a tested workflow. +# +# Design choices it makes, and why: +# - ONE configure of the root tree per job, both engines built. D9 forbids +# per-engine enables, and from step 3.5 on there is one TAP_SR_BUILD_TESTS, +# so "ratio jobs" that build only ratio cannot survive step 3. Per-engine +# behaviour is selected at *ctest* time by LABELS (added in P.2), and +# per-engine warnings by the per-engine WERROR options, which already live +# on separate INTERFACE targets (srt_warnings / tap_ratio_warnings), so +# async-OFF / ratio-ON on MSVC coexists in one tree. +# - Options are DEFAULTED at the root (option() before add_subdirectory), never +# FORCEd: every bare-metal/Hexagon job passes *_BUILD_EXAMPLES=OFF, and +# async's examples do find_package(Threads REQUIRED), which fails a +# bare-metal configure. +# - Dedup/concurrency is adopted HERE (not at step 2), with cancel-in-progress +# OFF for the migration PR, so every pushed SHA gets exactly one full run +# and G13 can hold without waiting-before-push discipline being the only +# guard. +# - Every ctest call: --no-tests=error, --output-log (G2 needs the [ RUN ] +# lines, which --output-on-failure never prints on success), log uploaded. +name: CI + +on: + push: + branches: [main] + pull_request: + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + # Never cancel on main, and never on the migration PR (G13 needs a complete + # run per SHA). Other PRs keep superseded-run cancellation. + cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} + +env: + CTEST_COMMON: --no-tests=error --output-on-failure + +jobs: + host: + name: ${{ matrix.name }} + runs-on: ${{ matrix.os }} + timeout-minutes: 30 + strategy: + fail-fast: false + matrix: + include: + - { name: Linux GCC, os: ubuntu-latest, cc: gcc, cxx: g++, async_werror: ON, ratio_werror: ON, capi: ON } + - { name: Linux Clang, os: ubuntu-latest, cc: clang, cxx: clang++, async_werror: ON, ratio_werror: ON, capi: ON } # ratio: NEW coverage (measured 78/78 locally, clang 18 -Werror) + - { name: macOS AppleClang, os: macos-latest, async_werror: ON, ratio_werror: ON, capi: ON } + - { name: Windows MSVC, os: windows-latest, async_werror: OFF, ratio_werror: ON, capi: OFF } # async /W4 untriaged (INF-11); ratio keeps /WX + steps: + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: { submodules: recursive } + - name: Configure + env: { CC: "${{ matrix.cc }}", CXX: "${{ matrix.cxx }}" } + run: > + cmake -B build -DCMAKE_BUILD_TYPE=Release + -DSRT_WERROR=${{ matrix.async_werror }} -DTAP_RATIO_WERROR=${{ matrix.ratio_werror }} + -DSRT_BUILD_CAPI=${{ matrix.capi }} -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} + - run: cmake --build build --config Release -j 4 + - run: ctest --test-dir build -C Release $CTEST_COMMON --output-log ctest-${{ matrix.name }}.log + shell: bash + - uses: actions/upload-artifact@ # pin + if: ${{ !cancelled() }} + with: { name: "ctest-${{ matrix.name }}", path: "ctest-*.log" } + + sanitizers: + name: ${{ matrix.name }} + runs-on: ubuntu-latest + timeout-minutes: 30 + strategy: + fail-fast: false + matrix: + include: + # async ran ASan without WERROR, ratio with it; per-engine options keep both. + - { name: ASan + UBSan, flags: "-fsanitize=address,undefined -fno-sanitize-recover=all", labels: "" } + # TSan: async only (ratio is single-threaded; running it costs ~3 s + # but adds rows G1 has no step-0 counterpart for). Build is shared. + - { name: TSan, flags: "-fsanitize=thread", labels: "-L async" } + steps: + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: { submodules: recursive } + - name: Configure + env: { CC: clang, CXX: clang++ } + run: > + cmake -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo + -DSRT_BUILD_EXAMPLES=OFF + -DSRT_WERROR=OFF -DTAP_RATIO_WERROR=ON + -DCMAKE_CXX_FLAGS="${{ matrix.flags }}" + # NB ratio's sanitizer job built ratio examples (bluetooth_bridge); keep + # TAP_RATIO_BUILD_EXAMPLES at its default (ON) so G7's TU set matches. + - run: cmake --build build -j 4 + - env: { TSAN_OPTIONS: halt_on_error=1, UBSAN_OPTIONS: print_stacktrace=1 } + run: ctest --test-dir build $CTEST_COMMON ${{ matrix.labels }} --output-log ctest.log + + # One job per (target, engine) for correctness: both engines are BUILT + # (one tree), each job RUNS one engine's label with that engine's -E list + # and parallelism. Measured today: async Hexagon 22.5 min serial, ratio + # Hexagon 11.0 min at -j 4, async M33 22.4 min, ratio M33 0.4 min + # (runs 36253867157, 36256431538). Serialising both in one Hexagon job is + # ~36 min of a 45 min timeout; a single un-labelled ctest at one -j is + # either 67 min (serial) or changes async's leg (-j 4). + qemu: + name: ${{ matrix.target }} ${{ matrix.engine }} (QEMU) + runs-on: ubuntu-24.04 # pinned: plugin header, qemu, glib and caches must agree + timeout-minutes: ${{ matrix.timeout }} + strategy: + fail-fast: false + matrix: + include: + - { target: hexagon, engine: async, timeout: 45, j: 1, exclude: 'AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec|MultiChannel\.|Feasibility|Reset\.|ConfigValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } + - { target: hexagon, engine: ratio, timeout: 30, j: 4, exclude: 'BadProfilesThrow|LatencyAndValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } + - { target: m55, engine: async, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } + - { target: m55, engine: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } + - { target: m33, engine: async, timeout: 40, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } + - { target: m33, engine: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } + env: + HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst + HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" + steps: + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: { submodules: recursive } + - if: matrix.target != 'hexagon' + run: sudo apt-get update -q && sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + - if: matrix.target == 'hexagon' + id: cache + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 + with: { path: ~/hexagon, key: "hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1" } + - if: matrix.target == 'hexagon' && steps.cache.outputs.cache-hit != 'true' + run: scripts/fetch_hexagon_toolchain.sh + # (hexagon PATH / qemu-user setup as today) + - name: Configure (both engines, one tree) + run: > + cmake -B build -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + -DCMAKE_TOOLCHAIN_FILE=${{ matrix.toolchain }} + -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF + - run: cmake --build build -j 4 + - name: Test under emulation (${{ matrix.engine }} only) + shell: bash + run: | + args=(--test-dir build $CTEST_COMMON -L '^${{ matrix.engine }}$' -j ${{ matrix.j }} -V --output-log ctest.log) + [ -n '${{ matrix.exclude }}' ] && args+=(-E '${{ matrix.exclude }}') + ctest "${args[@]}" + - uses: actions/upload-artifact@ + if: ${{ !cancelled() }} + with: { name: "gtest-${{ matrix.target }}-${{ matrix.engine }}", path: ctest.log } # G2 input + + # Ratchet stays per ENGINE until step 2 (step 2 then folds to per target). + # Two jobs x three targets inside, exactly as today, each pointing icount.py + # at its engine's baselines. Plugin built in-job (1 s; an artifact would + # couple jobs across runner images). + icount-async: # = today's SampleRateTap job, --baselines async/bench/baselines.json, docs freshness kept + runs-on: ubuntu-24.04 + timeout-minutes: 45 + steps: [ { run: "# as today; configure root with -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF -DSRT_BUILD_ICOUNT_BENCH=ON" } ] + icount-ratio: # = RatioTap's job, --baselines ratio/bench/baselines.json, plus ratio README freshness (1c adds the table) + runs-on: ubuntu-24.04 + timeout-minutes: 45 + steps: [ { run: "# as RatioTap today, SHA-pinned; -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON, tests/examples OFF for both engines" } ] + + bench-smoke: { runs-on: ubuntu-latest, timeout-minutes: 15, steps: [ { run: "# ./build/async/bench/srt_bench" } ] } + compare-smoke: { runs-on: ubuntu-latest, timeout-minutes: 20, steps: [ { run: "# as today; paths under async/; ALSO run one cmp_icount binary under qemu so compare.yml's markers are exercised per push" } ] } + clang-format: { runs-on: ubuntu-latest, timeout-minutes: 10, steps: [ { run: "pre-commit run --all-files" } ] } + book: { runs-on: ubuntu-latest, timeout-minutes: 10, steps: [ { run: "# as today" } ] } + + # NEW: the jobs that actually evaluate G1-G7 (section 5 demands same-job + # A/B, but no workflow in v2 performs it). See migration-gates.yml. + +# Job count per push after this sketch: host 4 + sanitizers 2 + qemu 6 + +# icount 2 + bench/compare/format/book 4 = 18 in ci.yml, + style (drift + +# clang-tidy) 2 = 20, + migration-gates. macOS: 1. diff --git a/docs/migration/drafts/migration-gates.yml b/docs/migration/drafts/migration-gates.yml new file mode 100644 index 0000000..c2b08f2 --- /dev/null +++ b/docs/migration/drafts/migration-gates.yml @@ -0,0 +1,43 @@ +# DRAFT (audit round 2): the workflow v2's section 5 presupposes but never +# defines. Same-job A/B: step-0 trees (SampleRateTap@S0 and RatioTap@R0, both +# PUBLIC repositories, so no token is needed) and the gated SHA are built in +# ONE job with one toolchain. Lives only on the migration branch; deleted in +# step 4 with docs/migration/. +name: migration-gates +on: + pull_request: + workflow_dispatch: +permissions: { contents: read } +concurrency: + group: migration-gates-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: false +env: + S0: # from docs/migration/tips.txt + R0: +jobs: + ab: + name: A/B ${{ matrix.target }} + runs-on: ubuntu-24.04 # pinned: G3/G4/G7 must not straddle an image rollout + timeout-minutes: 90 # builds 3 trees; Hexagon A/B re-runs both suites' icount + strategy: + fail-fast: false + matrix: + target: [host, m33, m55, hexagon] + steps: + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: { submodules: recursive, path: new, fetch-depth: 1 } + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 + with: { repository: tap/SampleRateTap, ref: "${{ env.S0 }}", submodules: recursive, path: old-async } + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 + with: { repository: tap/RatioTap, ref: "${{ env.R0 }}", submodules: recursive, path: old-ratio } + # 1. Same install step for all three (one toolchain). + # 2. Build old-async, old-ratio, new with identical flags; export + # compile_commands.json (G7), ctest --show-only=json-v1 (G1), + # icount measurements to JSON (G3; compare new vs old MEASURED values + # with --exact, NOT new vs committed baselines), objdump of icount + # binaries (G4: restrict to icount binaries and library symbols; test + # binaries embed __FILE__ via gtest, and the moved paths shift .rodata), + # G5 hash tests, G6 lines. + # 3. Diff; upload all artefacts; fail on any difference not in the + # committed allowlist (docs/migration/allow.txt, e.g. new ratio rows + # in Linux Clang and new async-label rows nowhere). diff --git a/docs/migration/drafts/root-CMakeLists-1c.cmake b/docs/migration/drafts/root-CMakeLists-1c.cmake new file mode 100644 index 0000000..105c016 --- /dev/null +++ b/docs/migration/drafts/root-CMakeLists-1c.cmake @@ -0,0 +1,35 @@ +# DRAFT (audit round 2): root CMakeLists.txt at step 1c. +cmake_minimum_required(VERSION 3.24) +project(SampleRateTap LANGUAGES CXX) # languages must equal both engines' (G7) +enable_testing() + +# DEFAULT, never FORCE: CI passes *_BUILD_EXAMPLES=OFF on every bare-metal / +# Hexagon job, and async/examples does find_package(Threads REQUIRED). +# option() here creates the cache entry first, so the engines' own +# PROJECT_IS_TOP_LEVEL-dependent option() calls become no-ops, while -D wins. +option(SRT_BUILD_TESTS "async tests" ON) +option(SRT_BUILD_EXAMPLES "async examples" ON) +option(TAP_RATIO_BUILD_TESTS "ratio tests" ON) +option(TAP_RATIO_BUILD_EXAMPLES "ratio examples" ON) + +# Stale-option tripwire (extends D7 to CMake options, needed from step 3.5): +# an unknown -D is only a "Manually-specified variables were not used" +# warning, so a CI line still passing -DSRT_WERROR=ON after the rename would +# silently DROP the warnings gate. Enable this block in the step-3.5 commit. +# foreach(_old SRT_WERROR SRT_BUILD_TESTS SRT_BUILD_EXAMPLES SRT_BUILD_CAPI +# SRT_BUILD_ICOUNT_BENCH SRT_BUILD_BENCHMARKS TAP_RATIO_WERROR +# TAP_RATIO_BUILD_TESTS TAP_RATIO_BUILD_EXAMPLES TAP_RATIO_BUILD_CAPI +# TAP_RATIO_BUILD_ICOUNT_BENCH) +# if(DEFINED ${_old} OR DEFINED CACHE{${_old}}) +# message(FATAL_ERROR "${_old} was renamed (MONOREPO_PLAN step 3.5)") +# endif() +# endforeach() + +add_subdirectory(submodules/dsptap) +add_subdirectory(async) +add_subdirectory(ratio) + +# gtest is made available ONCE, by whichever engine's tests/ runs first +# (async). ratio/tests' set(INSTALL_GTEST OFF ...) and its Threads probe are +# then no-ops: hoist both gtest settings here so the result does not depend +# on add_subdirectory order (and so 4.2's install test does not install gtest). From 0939dd332f9e37713e74a4ebf095699c1297214f Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 22:55:05 +0000 Subject: [PATCH 38/44] Rename the planned engines to bridge and rational (plan v3.1) RatioTap's engine becomes `bridge` (tap::sr::bridge, bridge/ from the step-1b import on) and the future 2^a*3^b engine becomes `rational`, replacing `ratio` and `integer`. The Python ctypes modules are now called bindings so "bridge" names only the engine. D16 records that step P landed RatioTap's prefix as `ratio.`, renamed at step 3.4 through the G1/G2 name map, and the CI draft carries a `label` key until then. Step 0 also gains the note that the G2 collector strips ctest's test-number prefix. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 239 +++++++++--------- docs/migration/drafts/ci-after-1c.yml | 39 +-- docs/migration/drafts/migration-gates.yml | 2 +- .../migration/drafts/root-CMakeLists-1c.cmake | 8 +- 4 files changed, 153 insertions(+), 135 deletions(-) diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index 7dee121..8df35a0 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -1,13 +1,14 @@ # Monorepo plan: the `tap::sr` sample-rate family -Status: **DRAFT v3. Two adversarial audit rounds folded in; nothing executed.** +Status: **DRAFT v3.1. Two adversarial audit rounds folded in; step P executed, steps 0–5 not started.** | Version | Commit | What changed | |---|---|---| | v1 | `11f2a94` | First draft | | v2 | `4884b01` | Round 1: 5 reviewers, 78 findings (Appendix A) | | v2.1 | `c78ab93` | User decisions D13–D15; outside-repository facts confirmed | -| v3 | this commit | Round 2: 4 reviewers, including an end-to-end dry run of steps 1a–1c and gate prototypes; 78 findings (Appendix B). The user reconfirmed D14 and chose banners everywhere | +| v3 | `b34681c` | Round 2: 4 reviewers, including an end-to-end dry run of steps 1a–1c and gate prototypes; 78 findings (Appendix B). The user reconfirmed D14 and chose banners everywhere | +| v3.1 | this commit | Engine names (D5, user decision 2026-09-27): RatioTap's engine becomes **`bridge`** and the future 2^a·3^b engine **`rational`**, replacing `ratio` and `integer`. The Python ctypes modules are now called **bindings**, leaving "bridge" to the engine. Step 0's G2 parser note | When this plan is approved, it becomes the family-level `PLAN.md`. Draft workflow and CMake files that the audit produced live in @@ -36,18 +37,18 @@ How to read it: | D2 | **SampleRateTap is the host repository** and keeps its name | "Sample rate" names the family once it is namespaced. The Pages book keeps its URL. It also has the larger history: 136 commits on `main` in a full clone, against RatioTap's 29 at the time of writing (step 0 re-counts both) | | D3 | **RatioTap's history is preserved.** `git filter-repo` rewrites the paths, and `.gitmodules` throughout history, on `main` only (step 1b). The rewritten history is joined by an unrelated-histories merge. **The final PR is merged with a merge commit, never squash or rebase** | `--follow`, `blame` and submodule checkout keep working for every imported commit. A squash would erase all imported commits (GIT-1). Merge commits are allowed on SampleRateTap (confirmed) | | D4 | **Namespace `tap::sr::`**, include path `include/tap/sr//` | The path mirrors the namespace, as in DspTap. **This supersedes** RatioTap PLAN.md's agreed `include/tap/samplerate/`. The two-level form is **accepted** (user, 2026-09-26). The convention note goes to taphouse's `STYLE.md` (step 5) | -| D5 | **Engines:** `async` (was SampleRateTap), `ratio` (was RatioTap); future `integer`, `pdm`, `varispeed` | `async` is the industry term and the family's clock-topology word. It is the only async engine, and async at other ratios comes from composition. The sync engines are named for what they convert. The names sit next to `std::ratio`/`std::async` only under namespace-wide `using`, which the house style avoids | +| D5 | **Engines:** `async` (was SampleRateTap), `bridge` (was RatioTap); future `rational`, `pdm`, `varispeed` | `async` is the industry term and the family's clock-topology word. It is the only async engine, and async at other ratios comes from composition. The sync engines are named for what they convert: **`rational`** converts *within* a rate family (small-factor L/M, L, M ∈ {2^a·3^b}); **`bridge`** crosses *between* the 44.1 and 48 kHz families, the one large-factor ratio (147/160) and hand-optimized for it. Both are rational in the mathematical sense; the charters (section 2), not the names, draw the line. **User decision, 2026-09-27**, replacing v3's `ratio` (which read too close to `rational`) and `integer` (which read as the integer-sample Q15/Q31 profiles). `async` sits next to `std::async` only under namespace-wide `using`, which the house style avoids. The `bluetooth_bridge` example keeps its name: it composes `async` and `bridge` | | D6 | **One top-level directory per engine**, each with `include/ tests/ bench/ examples/ capi/ notebooks/ README.md PLAN.md` | Keeps each charter's boundary physical | | D7 | **Clean renames, no aliases.** Retired user-facing override macros get an `#error` **tripwire**. Retired **CMake options** get a `FATAL_ERROR` tripwire (step 3.4) | Aliases would be permanent debt. A tripwire turns a stale `-DSRT_CP_MIN_CHANNELS=…` or `-DSRT_WERROR=ON` into a loud failure instead of a silent no-op. An unknown `-D` otherwise only warns and drops a gate (R2-CI-6) | -| D8 | **C ABI prefix `tap_sr__*`, one shared library per engine** (`tap_sr_async_capi`, `tap_sr_ratio_capi`), and one bridge module per engine | No shipped artifact links two engines. The version function follows the rule as well: `tap_sr_async_version()` and `tap_sr_ratio_version()`, which return the same family value (D13) | -| D9 | **CMake options `TAP_SR_*`** after step 3.4: `BUILD_TESTS`, `BUILD_EXAMPLES`, `BUILD_CAPI` (builds both engine libraries), `BUILD_ICOUNT_BENCH`, `BUILD_BENCHMARKS` and `BUILD_COMPARE_BENCH` (async-only), plus per-engine `TAP_SR__WERROR`. **Every configure builds both engines. CI picks an engine only when running tests**, through ctest names and labels (step P.2) | Per-engine WERROR keeps ratio's MSVC `/WX` alongside async's untriaged `/W4`. The warning flags sit on separate INTERFACE targets, and the dry run showed they coexist. There are no per-engine enables, which could silently drop the cross-validation (DEC-12). Selecting tests at ctest time settles the D9 versus per-engine-job conflict (R2-CI-3, R2-COH-11) | +| D8 | **C ABI prefix `tap_sr__*`, one shared library per engine** (`tap_sr_async_capi`, `tap_sr_bridge_capi`), and one Python binding module per engine | No shipped artifact links two engines. The version function follows the rule as well: `tap_sr_async_version()` and `tap_sr_bridge_version()`, which return the same family value (D13) | +| D9 | **CMake options `TAP_SR_*`** after step 3.4: `BUILD_TESTS`, `BUILD_EXAMPLES`, `BUILD_CAPI` (builds both engine libraries), `BUILD_ICOUNT_BENCH`, `BUILD_BENCHMARKS` and `BUILD_COMPARE_BENCH` (async-only), plus per-engine `TAP_SR__WERROR`. **Every configure builds both engines. CI picks an engine only when running tests**, through ctest names and labels (step P.2) | Per-engine WERROR keeps bridge's MSVC `/WX` alongside async's untriaged `/W4`. The warning flags sit on separate INTERFACE targets, and the dry run showed they coexist. There are no per-engine enables, which could silently drop the cross-validation (DEC-12). Selecting tests at ctest time settles the D9 versus per-engine-job conflict (R2-CI-3, R2-COH-11) | | D10 | **DspTap stays separate**, pinned once at `submodules/dsptap` | It has consumers outside the family: TapTools, and MuTap through the `LogMel`/`Decimator` C ABI | -| D11 | **Engine directory vs. DspTap:** a capability gets an engine directory when it has its own charter, campaign and ratchet. Building blocks go into DspTap. **Engine-owned datapaths stay with their engine.** `fractional_resampler`, the polyphase bank and the blend stratum belong to `async` | Consistent with RatioTap PLAN.md Appendix A. It also keeps `ratio`'s cross-validation oracle out of `ratio`'s reach | +| D11 | **Engine directory vs. DspTap:** a capability gets an engine directory when it has its own charter, campaign and ratchet. Building blocks go into DspTap. **Engine-owned datapaths stay with their engine.** `fractional_resampler`, the polyphase bank and the blend stratum belong to `async` | Consistent with RatioTap PLAN.md Appendix A. It also keeps `bridge`'s cross-validation oracle out of `bridge`'s reach | | D12 | **No routing by rate, including through composition.** `chain<>` is a caller-named, compile-time chain of **synchronous** stages. There is no `(in_hz, out_hz)` lookup, `async` is never chained, and the coverage matrix documents chains without dispatching them | Keeps HANDOFF preamble item 4 | -| D13 | **One family version, 0.4.0**, with tags `vX.Y.Z` and bit-packed encoding `(M<<16)\|(m<<8)\|p`. **Mechanics (step 3):** root `project(SampleRateTap VERSION 0.4.0)`; engine subprojects renamed `tap_sr_async` / `tap_sr_ratio` with no VERSION; macros `TAP_SR_VERSION_{MAJOR,MINOR,PATCH}` defined **token-identically** in each engine's umbrella header, checked by a static_assert test (no shared header, so 4.2 check 1 holds); each C ABI library exports its own `tap_sr__version()`; a new C ABI test `CApi.VersionIsBitPacked` pins the encoding, **which nothing pins today** (`test_skeleton.cpp` checks only MAJOR = 0) | User decision. 0.4.0 is above both current versions. The encoding is RatioTap's (`ratio_capi.cpp:97`) | +| D13 | **One family version, 0.4.0**, with tags `vX.Y.Z` and bit-packed encoding `(M<<16)\|(m<<8)\|p`. **Mechanics (step 3):** root `project(SampleRateTap VERSION 0.4.0)`; engine subprojects renamed `tap_sr_async` / `tap_sr_bridge` with no VERSION; macros `TAP_SR_VERSION_{MAJOR,MINOR,PATCH}` defined **token-identically** in each engine's umbrella header, checked by a static_assert test (no shared header, so 4.2 check 1 holds); each C ABI library exports its own `tap_sr__version()`; a new C ABI test `CApi.VersionIsBitPacked` pins the encoding, **which nothing pins today** (`test_skeleton.cpp` checks only MAJOR = 0) | User decision. 0.4.0 is above both current versions. The encoding is RatioTap's (`ratio_capi.cpp:97`) | | D14 | **One copyright line family-wide:** `Copyright (c) 2026 Timothy Place and the SampleRateTap contributors` in the root `LICENSE`, and the same holder in a **banner on every C/C++/Python source file** | User decision, **reconfirmed**: the user holds SampleRateTap's copyright. SampleRateTap's notice today names only "SampleRateTap contributors"; RatioTap's names the user. So this **adds the author's name** to SampleRateTap's notice, which is accurate on the user's word, and restates RatioTap's. There are 35 banner lines today; about 25 of SampleRateTap's C/C++ files have none. Banners are added everywhere in step 3.8. `STYLE.md`'s banner template ("Copyright 2025-2026 Timothy Place.") is reconciled through taphouse (step 5) | -| D15 | **`async` renames its converter family to match `ratio`'s `basic_converter` family:** `basic_async_sample_rate_converter` → `basic_converter`, `async_sample_rate_converter` → `converter`, `…_q15`/`…_q31` → `converter_q15`/`converter_q31`, exception prefixes `"async_sample_rate_converter: "` → `"tap::sr::async::converter: "`, and `asrc.h` → `converter.h` | User decision. v2.1's rationale cited a `tap::sr::ratio::converter` that does not exist. The real parallel is ratio's `basic_converter` with its `converter_to_48k` family. Scale: about 80 hits in 22 files, plus the book's naming-decision prose (R2-COH-19) | -| D16 | **Tests carry an engine prefix:** `gtest_discover_tests(… TEST_PREFIX "async." / "ratio.")`, plus a `LABELS` value of `async` / `ratio` on every test, including the bare-metal `*_tests_emulated` entries. Lands in step P.2, so the snapshot already has it | CTest applies a duplicate name's properties to both tests. `FixedPoint.FullScaleSineDoesNotWrapQ15` exists in both engines, so labels alias and `ctest -L ratio` selects async's copy (reproduced, R2-GATE-4). Unique names fix G1 and engine selection | +| D15 | **`async` renames its converter family to match `bridge`'s `basic_converter` family:** `basic_async_sample_rate_converter` → `basic_converter`, `async_sample_rate_converter` → `converter`, `…_q15`/`…_q31` → `converter_q15`/`converter_q31`, exception prefixes `"async_sample_rate_converter: "` → `"tap::sr::async::converter: "`, and `asrc.h` → `converter.h` | User decision. v2.1's rationale cited a `tap::sr::ratio::converter` that does not exist. The real parallel is RatioTap's (now `bridge`'s) `basic_converter` with its `converter_to_48k` family. Scale: about 80 hits in 22 files, plus the book's naming-decision prose (R2-COH-19) | +| D16 | **Tests carry an engine prefix:** `gtest_discover_tests(… TEST_PREFIX "async." / "bridge.")`, plus a `LABELS` value of `async` / `bridge` on every test, including the bare-metal `*_tests_emulated` entries. Lands in step P.2, so the snapshot already has it, **with RatioTap's pre-rename spelling `ratio.` / `ratio`**; step 3.4 renames it to `bridge.` / `bridge`, and G1/G2 compare through `rename.py`'s name map | CTest applies a duplicate name's properties to both tests. `FixedPoint.FullScaleSineDoesNotWrapQ15` exists in both engines, so labels alias and `ctest -L ratio` selects async's copy (reproduced, R2-GATE-4). Unique names fix G1 and engine selection | --- @@ -56,9 +57,9 @@ How to read it: | Engine | Namespace | Origin | Charter | |---|---|---|---| | `async` | `tap::sr::async` | SampleRateTap v0.1.0 | Asynchronous, near-unity (±`max_deviation_ppm`, default 1000 ppm): absorbs the clock | -| `ratio` | `tap::sr::ratio` | RatioTap v0.3.0 | Synchronous 160/147 pair, 44.1 ↔ 48 kHz: converts the number. **After the 2.2 follow-up:** 44.1·2^k ↔ 48·2^k, k ≤ 2 | -| `integer` | `tap::sr::integer` | new, separate plan | Synchronous rational L/M with L, M ∈ {2^a·3^b}. Nyquist (L-th band) stages. Does not absorb DspTap's `decimate.h` | -| `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM: CIC → compensation FIR → `integer` stages | +| `bridge` | `tap::sr::bridge` | RatioTap v0.3.0 | Synchronous 160/147 pair, 44.1 ↔ 48 kHz: converts the number. **After the 2.2 follow-up:** 44.1·2^k ↔ 48·2^k, k ≤ 2 | +| `rational` | `tap::sr::rational` | new, separate plan | Synchronous rational L/M with L, M ∈ {2^a·3^b}. Nyquist (L-th band) stages. Does not absorb DspTap's `decimate.h` | +| `pdm` | `tap::sr::pdm` | new, when a consumer asks | 1-bit sigma-delta → PCM: CIC → compensation FIR → `rational` stages | | `varispeed` | `tap::sr::varispeed` | new, when a consumer asks | Time-varying ratio (Smith, CCRMA bandlimited interpolation) | Not engines: @@ -89,7 +90,7 @@ matrix test. For **every rate-changing stage** whose lower rate is `r`: Checks against the rows below: -- **176.4 → 48** (`integer` ↓4 → `ratio` ↑): `ratio`'s stopband edge, +- **176.4 → 48** (`rational` ↓4 → `bridge` ↑): `bridge`'s stopband edge, 24 kHz, is ≤ 44.1 − `f_pass` for `f_pass` ≤ 20.1 kHz. The ↓4 stage needs a stopband ≤ 24.1 kHz at 20 kHz passband. - **16 → 44.1** through ↑3 (lower rate 16): needs stopband ≤ 16 − `f_pass` @@ -101,28 +102,28 @@ Checks against the rows below: **Excluded, with reasons:** -- **352.8 kHz (DXD):** `ratio`'s k stops at 2 and no consumer has asked. +- **352.8 kHz (DXD):** `bridge`'s k stops at 2 and no consumer has asked. - **37.8 and 50.4 kHz:** ratios of 7. - **1000/1001 pull-down rates:** these are synchronous ratios that happen to sit inside `async`'s ±1000 ppm. They must never be served by `async`, since that would be routing by rate. -The **full 14 × 14 matrix is generated** in `integer`'s plan. Illustrative +The **full 14 × 14 matrix is generated** in `rational`'s plan. Illustrative rows: | From → To | Chain | |---|---| -| 48 ↔ 44.1 | `ratio` | -| 96 ↔ 88.2, 192 ↔ 176.4 | `ratio` at k = 1, 2 (after 2.2) | -| 96 → 44.1 | `integer` ↓2 → `ratio` | -| 176.4 → 48 | `integer` ↓4 → `ratio` | -| 48 → 88.2 | `integer` ↑2 → `ratio` k = 1 | -| 44.1 → 16 | `ratio` → `integer` ↓3 | -| 48 → 32 | `integer` 2/3 (one rational stage) | +| 48 ↔ 44.1 | `bridge` | +| 96 ↔ 88.2, 192 ↔ 176.4 | `bridge` at k = 1, 2 (after 2.2) | +| 96 → 44.1 | `rational` ↓2 → `bridge` | +| 176.4 → 48 | `rational` ↓4 → `bridge` | +| 48 → 88.2 | `rational` ↑2 → `bridge` k = 1 | +| 44.1 → 16 | `bridge` → `rational` ↓3 | +| 48 → 32 | `rational` 2/3 (one stage) | -### 2.2 `ratio` at 2× and 4× rates (a follow-up after the migration) +### 2.2 `bridge` at 2× and 4× rates (a follow-up after the migration) -**What the Hz values reach.** `ratio`'s profile Hz values feed: +**What the Hz values reach.** `bridge`'s profile Hz values feed: - the normalized cutoff (`design.h:138`); - the validation `p.passband_hz >= traits::k_stopband_edge_hz` @@ -147,8 +148,8 @@ and `kaiser_beta` depends only on dB (R2-COH-9). - 88.2 → 96 (up) places images at ≥ 44.1 kHz. - 96 → 88.2 (down) folds aliases above 40.2 kHz. -`ratio/PLAN.md` keeps "no other ratios" until then. The follow-up is -sequenced before `integer`. +`bridge/PLAN.md` keeps "no other ratios" until then. The follow-up is +sequenced before `rational`. --- @@ -179,7 +180,7 @@ shallow (SampleRateTap) or have a stale local `main`. |---|---| | `include/srt/{asrc,pi_servo,polyphase_filter,sample_traits,spsc_ring,srt}.h` (1 525 lines) | `async/include/tap/sr/async/…`, with `asrc.h` → `converter.h` (D15) and `srt.h` → `async.h` (umbrella) | | `include/srt/detail/kaiser.h` (a 26-line re-export into `tap::samplerate::detail`) | **Deleted in step 3.1.** `polyphase_filter.h:152,153,157` requalify as `tap::dsp::`. `tests/test_kaiser.cpp` (9 tests) repoints to `tap::dsp` with its test names unchanged. Path citations in the book, the bibliography, `book_figures.py:7` and `asrc_rbj_analysis.ipynb` are rewritten | -| `include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `ratio/include/tap/sr/ratio/…` | +| `include/tap/ratio/{converter,design,phase_table,ratio,schedule}.h` (820 lines) | `bridge/include/tap/sr/bridge/…` | ### 3.2 Pins @@ -206,7 +207,7 @@ SampleRateTap vs RatioTap: | `cmake/arm-cortex-m33-mps2.cmake`, `…-m55-mps3.cmake` | **functional (one variable)** | `set(SRT_BARE_METAL ON)` vs `set(TAP_RATIO_BARE_METAL ON)`. `hexagon-linux-musl.cmake` sets **no** such variable (corrected, R2-RUN-17) | | `tools/qemu_insn_plugin/insn_count.c` | functional (host-side marker) | `SRT_INSN_COUNT` vs `RATIO_INSN_COUNT`. Never affects the guest count | | `scripts/icount.py` | functional (prefix, marker) | | -| `tests/bare_metal_main.cpp` | per-engine, never deduplicated | Filters and floors. Ratio's M33 selection is **59** tests against a floor of 25 | +| `tests/bare_metal_main.cpp` | per-engine, never deduplicated | Filters and floors. RatioTap's M33 selection is **59** tests against a floor of 25 | | `.github/workflows/style.yml` | functional | RatioTap's body wins | | `scripts/fetch_hexagon_toolchain.sh` (RatioTap only) | functional | RatioTap's copy wins, and every Hexagon cache writer uses it, **including `compare.yml`** | @@ -245,7 +246,7 @@ There are 84 `{{#include}}` matches: ### 3.5 CI (measured) -| Job | async (SampleRateTap) | ratio (RatioTap) | +| Job | async (SampleRateTap) | bridge (RatioTap) | |---|---|---| | Host matrix | GCC, Clang, AppleClang, MSVC (MSVC `werror: OFF`) | GCC, AppleClang, MSVC with `WERROR=ON` | | Sanitizers | ASan+UBSan, TSan | ASan+UBSan only, WERROR ON | @@ -266,11 +267,11 @@ Timings are from runs 36253867157 and 36256431538. | Engine | Functions | Opaque type | Library | Version encoding | |---|---|---|---|---| | async | 8: `srt_{version,create,destroy,push,pull,status,designed_latency_seconds,reset_from_consumer}` | `SrtHandle` | `libsrt_capi.so` | decimal `srt_version()` | -| ratio | 11 | `ratio_converter` | `libratio_capi.so` | bit-packed `ratio_version()`. Pinned **by nothing** except the ctypes bridge | +| bridge (RatioTap) | 11 | `ratio_converter` | `libratio_capi.so` | bit-packed `ratio_version()`. Pinned **by nothing** except the ctypes binding | The r8brain shim exports `srt_r8b_oneshot` and `srt_r8b_latency_frames`. -Bridges build **only when the library is missing**, so they can measure a +Bindings build **only when the library is missing**, so they can measure a stale library. They also print CMake logs into notebook outputs. ### 3.7 Outside references @@ -297,7 +298,7 @@ stale library. They also print CMake logs into notebook outputs. ``` SampleRateTap/ ├── CMakeLists.txt root: project(SampleRateTap VERSION 0.4.0 from step 3), enable_testing(), -│ option() defaults, dsptap once, add_subdirectory(async|ratio) +│ option() defaults, dsptap once, add_subdirectory(async|bridge) ├── PLAN.md CLAUDE.md README.md family files (step 4; PLAN.md is this document, moved) ├── LICENSE D14 line ├── requirements.lock notebook environment (step P.3) @@ -307,16 +308,16 @@ SampleRateTap/ ├── cmake/ platform/ tools/qemu_insn_plugin/ ├── scripts/ icount.py, tidy.sh, fetch_hexagon_toolchain.sh, │ update_icount_docs.py, update_perf_docs.py, book_figures* -├── book/ (ratio chapters are follow-up work) +├── book/ (bridge chapters are follow-up work) ├── docs/ Doxyfile (both engines); migration/ (removed at step 4) ├── async/ │ ├── CMakeLists.txt README.md (+ icount table) PLAN.md │ ├── include/tap/sr/async/ tests/ bench/ (icount, compare) examples/ │ ├── capi/ notebooks/ docs/ (PERFORMANCE, COMPARISON, HARDWARE_TESTING) │ └── tools/compare_shim/ cmake/r8brain.cmake -└── ratio/ +└── bridge/ ├── CMakeLists.txt README.md (+ icount table) PLAN.md HANDOFF.md CLAUDE.md - ├── include/tap/sr/ratio/ tests/ (+reference/) bench/ examples/ + ├── include/tap/sr/bridge/ tests/ (+reference/) bench/ examples/ └── capi/ notebooks/ tools/reference/ docs/HISTORY.md ``` @@ -330,23 +331,23 @@ SampleRateTap/ | SRT `docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md` | `async/docs/` | 1a | | SRT `docs/Doxyfile`, `docs/MONOREPO_PLAN.md`, `docs/migration/` | stay in root `docs/` (the plan moves to root `PLAN.md` at step 4) | —, 4 | | SRT `book/`, `scripts/`, `cmake/{arm,hexagon}-*`, `platform/`, `tools/qemu_insn_plugin/`, dotfiles, `LICENSE`, `STYLE.md`, `.github/` | stay at root | — | -| RatioTap `main` | `ratio/…` through filter-repo | 1b | -| `ratio/.gitmodules`, `ratio/submodules/*` | rewritten into root `.gitmodules` throughout history; gitlinks removed at the merge | 1b | -| `ratio/{.clang-format,.clang-tidy,STYLE.md,.pre-commit-config.yaml,.claude,scripts/tidy.sh,.github/pull_request_template.md}` | deleted (byte-identical to root) | 1b | -| `ratio/.github/workflows/{ci,style}.yml` | ported into root workflows, then deleted | 1c | -| `ratio/scripts/fetch_hexagon_toolchain.sh` | root `scripts/` | 1c | -| `ratio/.gitignore` | deleted (root `build*/` already covers `build_capi/`) | 1c | -| `ratio/LICENSE` | deleted in the commit that writes D14's line into root `LICENSE` | 1c | -| `ratio/requirements.lock`, `ratio/notebooks/requirements.txt` | deleted (identical root lockfile from P.3) | 1c | -| `ratio/cmake/`, `ratio/platform/`, `ratio/tools/qemu_insn_plugin/`, `ratio/scripts/icount.py` | deleted (root copies) | 2 | -| `ratio/tools/capi/` | `ratio/capi/` | 3.1 | -| `ratio/CLAUDE.md` | build commands fixed at 1c; reduced to the charter at 4 | 1c, 4 | +| RatioTap `main` | `bridge/…` through filter-repo | 1b | +| `bridge/.gitmodules`, `bridge/submodules/*` | rewritten into root `.gitmodules` throughout history; gitlinks removed at the merge | 1b | +| `bridge/{.clang-format,.clang-tidy,STYLE.md,.pre-commit-config.yaml,.claude,scripts/tidy.sh,.github/pull_request_template.md}` | deleted (byte-identical to root) | 1b | +| `bridge/.github/workflows/{ci,style}.yml` | ported into root workflows, then deleted | 1c | +| `bridge/scripts/fetch_hexagon_toolchain.sh` | root `scripts/` | 1c | +| `bridge/.gitignore` | deleted (root `build*/` already covers `build_capi/`) | 1c | +| `bridge/LICENSE` | deleted in the commit that writes D14's line into root `LICENSE` | 1c | +| `bridge/requirements.lock`, `bridge/notebooks/requirements.txt` | deleted (identical root lockfile from P.3) | 1c | +| `bridge/cmake/`, `bridge/platform/`, `bridge/tools/qemu_insn_plugin/`, `bridge/scripts/icount.py` | deleted (root copies) | 2 | +| `bridge/tools/capi/` | `bridge/capi/` | 3.1 | +| `bridge/CLAUDE.md` | build commands fixed at 1c; reduced to the charter at 4 | 1c, 4 | | `docs/migration/` | created at 0; deleted at 4 (`runs.md` optionally kept) | 0, 4 | ### 4.2 Dependency rule and its enforcement (scheduled: step 3.4) -- `async` and `ratio` each depend on `tap::dsp` only. -- An engine may depend on a sibling only in `tests/` and `examples/`: ratio's +- `async` and `bridge` each depend on `tap::dsp` only. +- An engine may depend on a sibling only in `tests/` and `examples/`: bridge's cross-validation, and `bluetooth_bridge`. - `capi/` is per-engine. @@ -386,17 +387,17 @@ Each run records in `docs/migration/runs.md`: the run IDs, | ID | Class | Gate | Catches | |---|---|---|---| -| G1 | snapshot | **Test multiset per (job, engine).** `ctest --show-only=json-v1`; names are unique through D16's prefixes. The collector asserts that the list is non-empty itself, because `--no-tests=error` is ignored under `-N` and `--show-only`. New rows need an entry in `docs/migration/allow.txt`. Ratio newly runs under Linux Clang, where it passes clang `-Werror` 78/78; TSan runs `-L async` only | Dropped tests; label aliasing | -| G2 | snapshot | **On-target test multiset.** `[ RUN ]` lines from each QEMU leg's `Testing/Temporary/LastTest.log`, which every QEMU job uploads. `--output-on-failure` prints nothing on success, so the CI log alone is not enough. Keyed by (target, engine) | Losses hidden by the floors (up to 34 for ratio on M33); Hexagon exclusion drift | -| G3 | A/B | **Exact icount.** `icount.py --compare-json`: the gated SHA's measured counts equal step 0's counts **measured in the same job**, exactly. The measured workload **set** equals the baseline key set. Committed `baselines.json` files must be **byte-unchanged**. Everyday CI keeps ±3 % against the committed files, since unpinned apt toolchains drift (R2-CI-10) | Codegen and harness changes. Verified deterministic: ratio's M33 counts match the baselines to the instruction, and the namespace rename leaves all 10 unchanged | +| G1 | snapshot | **Test multiset per (job, engine).** `ctest --show-only=json-v1`; names are unique through D16's prefixes. The collector asserts that the list is non-empty itself, because `--no-tests=error` is ignored under `-N` and `--show-only`. New rows need an entry in `docs/migration/allow.txt`. `bridge` newly runs under Linux Clang, where it passes clang `-Werror` 78/78; TSan runs `-L async` only | Dropped tests; label aliasing | +| G2 | snapshot | **On-target test multiset.** `[ RUN ]` lines from each QEMU leg's `Testing/Temporary/LastTest.log`, which every QEMU job uploads. `--output-on-failure` prints nothing on success, so the CI log alone is not enough. Keyed by (target, engine) | Losses hidden by the floors (up to 34 for bridge on M33); Hexagon exclusion drift | +| G3 | A/B | **Exact icount.** `icount.py --compare-json`: the gated SHA's measured counts equal step 0's counts **measured in the same job**, exactly. The measured workload **set** equals the baseline key set. Committed `baselines.json` files must be **byte-unchanged**. Everyday CI keeps ±3 % against the committed files, since unpinned apt toolchains drift (R2-CI-10) | Codegen and harness changes. Verified deterministic: RatioTap's M33 counts match the baselines to the instruction, and the namespace rename leaves all 10 unchanged | | G4 | A/B | **Codegen identity of icount and C ABI binaries only.** Split the disassembly at `STT_FUNC` bounds and skip non-function bytes. Key functions by demangled name after the rename map, and compare them as a sorted multiset. Strip addresses and RIP displacements. Symbolize literal-pool words **through relocations** (a gate-only link with `-Wl,--emit-relocs`, or per-TU `objdump -dr`), and resolve string-literal pointers to their text after the name and path maps. Normalizer prototype: `audit2-gates/norm3.py`. **Test binaries are excluded:** a pure namespace rename changes their codegen (stack-slot swaps in `check_cross_validation`), and gtest embeds `__FILE__` | Real codegen changes in shipped code | | G5 | A/B | **Output identity.** P.2's per-engine tests print `[ measured ] hash ` for every direction × format × profile. The gate compares these lines between A and B **per host, in one job**, and compares the QEMU `checksum=` lines, which `icount.py` now prints. **Hashes are never pinned as constants:** async's float `interpolate()` hashes differently when FMA is available, so a pinned hash would fail on arm64 and macOS (R2-GATE-3) | Coefficient, table and datapath changes that leave icount unchanged | | G6 | snapshot | **Cross-validation lines**, including the tolerance arguments. P.2 makes the test print its limit | A loosened tolerance. Also covered by G14 | | G7 | A/B | **Compile and link flags.** Every gated configure sets `-DCMAKE_EXPORT_COMPILE_COMMANDS=ON`. Each entry is keyed by source path under the 4.1 map and tokenized with shlex. `-o/-c/-MD/-MT/-MF` and their arguments are dropped. Paths become ``/``, then the 4.1 path map and the macro map apply; FetchContent's `_deps` relocation is mapped away. The **ordered** token lists are compared. The same normalization applies to each target's `link.txt`, which holds the startup file, `-T`, specs and `--gc-sections`. `$CXX --version` is recorded. Linux and cross builds only | `-std`/`-ffp-contract` flips, leaked `-D`s, link changes | | G8 | snapshot | **Book and API docs.** `mdbook build` clean, the image check, and `doxygen docs/Doxyfile` producing non-empty output, all in the `ci.yml` book job. `book-pages` itself is never dispatched from the branch, because it deploys | Broken anchors; an empty API reference | -| G9 | snapshot | **Retired identifiers.** Zero hits for `srt/`, `tap::samplerate`, `tap/ratio`, `tap::ratio`, `SRT_`, `TAP_RATIO_`, `srt_`, `ratio_capi`, `async_sample_rate_converter`, `basic_async_sample_rate_converter`, and retired `-D` option names in `.github/`. Exceptions are an explicit **(file, pattern) allowlist**: the guest icount markers `SRT_ICOUNT_DONE` and `RATIO_ICOUNT_DONE` (kept on purpose), each D7 tripwire line, `STYLE.md` until step 5, `ratio/HANDOFF.md` and `ratio/docs/HISTORY.md` (history). `ratio/PLAN.md` is **rewritten**, not allowlisted. Applies from step 3.7 | Stale prose, code and CI flags | +| G9 | snapshot | **Retired identifiers.** Zero hits for `srt/`, `tap::samplerate`, `tap/ratio`, `tap::ratio`, `SRT_`, `TAP_RATIO_`, `srt_`, `ratio_capi`, `async_sample_rate_converter`, `basic_async_sample_rate_converter`, and retired `-D` option names in `.github/`. Exceptions are an explicit **(file, pattern) allowlist**: the guest icount markers `SRT_ICOUNT_DONE` and `RATIO_ICOUNT_DONE` (kept on purpose), each D7 tripwire line, `STYLE.md` until step 5, `bridge/HANDOFF.md` and `bridge/docs/HISTORY.md` (history). `bridge/PLAN.md` is **rewritten**, not allowlisted. The retired test prefix and label (`"ratio."`, `LABELS ratio`, `-L ratio`) are checked in CMake files and workflows. Applies from step 3.7 | Stale prose, code and CI flags | | G10 | snapshot | **C ABI symbols.** `nm -D --defined-only` on Linux equals the snapshot under the name map, plus the D13 version functions | ABI drift | -| G11 | A/B | **Notebooks.** Pinned environment (`pip install --require-hashes`, Python version from `setup-python`). Bridges always rebuild **quietly**, printing the CMake log only on failure. Every figure cell also prints an FNV hash or `%.6g` summary of its plotted arrays. A and B are executed in one CI job. The normalizer applies the name and version map and drops timing lines and PNGs | Changed numbers and curves; stale libraries | +| G11 | A/B | **Notebooks.** Pinned environment (`pip install --require-hashes`, Python version from `setup-python`). Bindings always rebuild **quietly**, printing the CMake log only on failure. Every figure cell also prints an FNV hash or `%.6g` summary of its plotted arrays. A and B are executed in one CI job. The normalizer applies the name and version map and drops timing lines and PNGs | Changed numbers and curves; stale libraries | | G12 | snapshot | **History.** `--follow` and `blame` on a fixed file list. Blame of a moved file is not attributed wholesale to a migration commit. The new root `CMakeLists.txt` is checked with `git log --`, since its history starts at 1c | Lost blame | | G13 | snapshot | **Every named workflow ran on each gated SHA.** `ci.yml`, `style.yml` and `migration-gates.yml` run automatically. `ci-arm64` and `compare` are dispatched on the SHA. `book-pages` is replaced by G8. Gated SHAs are 1c, 2 and 3.1–3.8 (1a and 1b are not buildable on their own) | Skipped or cancelled evidence | | G14 | snapshot | **Rename-only residual.** Apply the committed mechanical rename script (`docs/migration/rename.py`: paths, namespaces, macros, targets, banners) to the step-0 trees, then `git diff --no-index` against HEAD. Every residual hunk must appear in a reviewed allowlist (`docs/migration/residual/.txt`) | Anything the other gates miss: **a loosened `EXPECT_NEAR`, MSVC-only paths, docs, CI.** Round 2 showed that a tolerance change passes G1–G13 (R2-GATE-8) | @@ -494,7 +495,7 @@ must produce the data the gates read. - Commit an identical `requirements.lock` at both repository roots: numpy, scipy, matplotlib, jupyter, samplerate, soxr, hash-pinned. -- Bridges always rebuild, quietly. +- Bindings always rebuild, quietly. - Every figure cell gets an array-hash cell. - Re-execute all notebooks in that environment. Confirm that their text outputs equal the committed ones, apart from the added hash cells. @@ -512,9 +513,13 @@ must produce the data the gates read. - Commit the snapshot-class baselines (G1, G2, G6, G8–G10, G12) under `docs/migration/`. - Commit `docs/migration/rename.py` (the G14 map) and its first residual - allowlist. + allowlist. The map also serves G1/G2 as the test-name map + (`ratio.` → `bridge.`, D16). +- The G2 collector strips the test-number prefix that ctest's verbose log + puts on each output line (`1: [ RUN ] …`) before it compares. - If `main` must move, it moves in SampleRateTap only, and step 1 is re-cut. - filter-repo is deterministic (tip `daceb8d` on two fresh clones). + filter-repo is deterministic (tip `daceb8d` on two fresh clones, with + the dry run's `ratio/` prefix). ### Step 1 — Import (1a, 1b and 1c are pushed together) @@ -524,26 +529,29 @@ and 0 deletions**; the dry run moved 59 files. - Use `git mv` for exactly the 1a rows of 4.1. **1b — Import RatioTap.** One merge commit. Verified in the dry run, with -the two corrections that round found: +the two corrections that round found. The dry run used the prefix +`ratio/`; v3.1's `bridge/` changes only the prefix string, so the +rewritten tip differs from the dry run's `daceb8d` and is re-verified on +two fresh clones at 1b: ```sh git clone https://github.com/tap/RatioTap rt && cd rt test "$(git rev-parse main)" = "$R0" git filter-repo --refs main \ - --path-rename :ratio/ --path-rename ratio/.gitmodules:.gitmodules \ + --path-rename :bridge/ --path-rename bridge/.gitmodules:.gitmodules \ --blob-callback ' if blob.data.startswith(b"[submodule \"submodules/"): blob.data = (blob.data - .replace(b"path = submodules/", b"path = ratio/submodules/") - .replace(b"[submodule \"submodules/", b"[submodule \"ratio/submodules/"))' + .replace(b"path = submodules/", b"path = bridge/submodules/") + .replace(b"[submodule \"submodules/", b"[submodule \"bridge/submodules/"))' cd ../SampleRateTap git fetch ../rt main:ratio-import git merge --allow-unrelated-histories --no-commit ratio-import git checkout --ours .gitmodules && git add .gitmodules # the one add/add conflict -git rm -r --cached ratio/submodules -git rm -rf ratio/.clang-format ratio/.clang-tidy ratio/STYLE.md \ - ratio/.pre-commit-config.yaml ratio/.claude ratio/scripts/tidy.sh \ - ratio/.github/pull_request_template.md +git rm -r --cached bridge/submodules +git rm -rf bridge/.clang-format bridge/.clang-tidy bridge/STYLE.md \ + bridge/.pre-commit-config.yaml bridge/.claude bridge/scripts/tidy.sh \ + bridge/.github/pull_request_template.md git commit # message records R0 and the rewritten tip git branch -D ratio-import # never pushed ``` @@ -552,14 +560,14 @@ git branch -D ratio-import # never pushed exactly as many entries as `main`. - `git submodule update --init --recursive` works on the merge commit and on the rewritten RatioTap commits. The dry run checked the root commit, M1, - M7c (which recurses into `ratio/submodules/sampleratetap/submodules/dsptap`) + M7c (which recurses into `bridge/submodules/sampleratetap/submodules/dsptap`) and the tip. - The rewritten reformat commit `c0894cf` becomes `4c3562c`. It is recorded for `.git-blame-ignore-revs`. **1c — Build glue and path fix-ups.** No change reaches codegen: G4 and G7 prove it. **Gate 1:** G1, G2, G3, G4, G5, G6, G7, G8, G11, G12, G13 and -G14; every notebook bridge and every standalone engine build configures and +G14; every notebook binding and every standalone engine build configures and builds. - **Root `CMakeLists.txt`** (draft: `docs/migration/drafts/root-CMakeLists-1c.cmake`): @@ -569,17 +577,17 @@ builds. the engines, **never FORCE**. CI's `-D…=OFF` must still win: every bare-metal job disables examples, because async's examples need Threads. Jobs that disable one engine's tests pass both engines' OFF flags. - - `add_subdirectory(submodules/dsptap)`, then `async` and `ratio`. + - `add_subdirectory(submodules/dsptap)`, then `async` and `bridge`. - Hoist the gtest settings (`INSTALL_GTEST OFF`, the Threads probe) to the root, so the result does not depend on the order of `add_subdirectory`. - **Engine `CMakeLists.txt`:** - Guard with `if(NOT TARGET tap::dsp) add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../submodules/dsptap ${CMAKE_CURRENT_BINARY_DIR}/submodules/dsptap) endif()`. - The dry run verified that this keeps `cmake -S async`, `cmake -S ratio` - and the notebook bridges working. The plain relative guard in v2 broke + The dry run verified that this keeps `cmake -S async`, `cmake -S bridge` + and the notebook bindings working. The plain relative guard in v2 broke all three (R2-RUN-3). - async: `add_subdirectory(tools/capi)` → `add_subdirectory(capi)`. - - ratio: `srt_headers` becomes + - bridge: `srt_headers` becomes `cmake_path(… NORMALIZE)` of `${CMAKE_CURRENT_SOURCE_DIR}/../async/include`, kept `SYSTEM`. - Engines **keep their `project()`** until step 3.4, so @@ -596,27 +604,27 @@ builds. - Every job configures the root once and builds both engines. - Correctness jobs run **per (target, engine)** with `ctest -L '^$'`, each engine's own `-E` list and `-j`: - - Hexagon: async serial, ratio `-j 4`. + - Hexagon: async serial, bridge `-j 4`. - M33: async gets a 40-minute timeout. - - Host jobs: per-engine WERROR as in the draft (MSVC: async OFF, ratio ON). + - Host jobs: per-engine WERROR as in the draft (MSVC: async OFF, bridge ON). - Sanitizers: ASan for both engines; TSan with `-L async` only. - - Ratchet: `icount-async` and `icount-ratio` jobs until step 2, each with + - Ratchet: `icount-async` and `icount-bridge` jobs until step 2, each with its own baselines and README freshness check. - Add `migration-gates.yml` from the draft. - Replace root `style.yml` with RatioTap's body. It configures both engines with tests and icount ON, fails on an empty TU list, and is SHA-pinned. - Every Hexagon cache writer uses `fetch_hexagon_toolchain.sh`. - - Delete `ratio/.github/workflows/`. + - Delete `bridge/.github/workflows/`. - **Paths:** - The 52 book includes. - `book-pages.yml` path filters. - - Doxyfile: `INPUT = async/include ratio/include async/README.md` and + - Doxyfile: `INPUT = async/include bridge/include async/README.md` and `USE_MDFILE_AS_MAINPAGE = async/README.md` until step 4. - `bench-smoke` → `build/async/bench/srt_bench`. - `compare.yml` build paths. - `icount.py --baselines /bench/baselines.json`. - - `update_icount_docs.py --engine` (async and ratio tables; README + - `update_icount_docs.py --engine` (async and bridge tables; README freshness per engine). - `update_perf_docs.py` default → `async/README.md`. - `book_figures.py`: `ROOT/"async"/"include"`. @@ -625,11 +633,11 @@ builds. - `asrc_comparison`: `TOOLS_DIR = REPO/"build"`. - `asrc_rbj_analysis`: `sys.path` → `"../../scripts"`. - README links, four in total: `async/README` `LICENSE`, and - `ratio/README` `LICENSE` ×2 and `STYLE.md` → `../`. - - `ratio/README`: build commands, and "eight workloads" → ten. - - `ratio/CLAUDE.md`: build commands. + `bridge/README` `LICENSE` ×2 and `STYLE.md` → `../`. + - `bridge/README`: build commands, and "eight workloads" → ten. + - `bridge/CLAUDE.md`: build commands. - **HISTORY.md:** - - `ratio/docs/HISTORY.md` maps old SHA → new SHA → RatioTap PR for + - `bridge/docs/HISTORY.md` maps old SHA → new SHA → RatioTap PR for `git rev-list R0` (29 commits, skipping the commit-map header). - PR numbers come from `GET /repos/tap/RatioTap/commits//pulls`, queried once at cut time. The repository is public; the dry run built @@ -639,17 +647,17 @@ builds. - Optionally rewrite bare `DspTap #38` in messages at 1b with `--message-callback`. - **LICENSE:** one commit writes D14's line into the root `LICENSE` and - deletes `ratio/LICENSE`, so the notice is never absent. -- **Notebook environment:** delete `ratio/requirements.lock` and - `ratio/notebooks/requirements.txt`; they are identical to the root lock. + deletes `bridge/LICENSE`, so the notice is never absent. +- **Notebook environment:** delete `bridge/requirements.lock` and + `bridge/notebooks/requirements.txt`; they are identical to the root lock. ### Step 2 — Shared infrastructure **Changes:** -- Delete ratio's `cmake/`, `platform/`, `tools/qemu_insn_plugin/` and +- Delete bridge's `cmake/`, `platform/`, `tools/qemu_insn_plugin/` and `scripts/icount.py`. -- `icount.py --engine async|ratio` sets its glob, baselines path and marker +- `icount.py --engine async|bridge` sets its glob, baselines path and marker regex. **Guest-printed markers stay byte-identical** (`SRT_ICOUNT_DONE` / `RATIO_ICOUNT_DONE`, allowlisted in G9). Only the host-side plugin marker becomes `TAP_SR_INSN_COUNT`. @@ -676,12 +684,12 @@ There is no separate reflow commit, since the hook would absorb it anyway 1. **Paths:** - `srt/…` → `tap/sr/async/…` (with `asrc.h` → `converter.h` and `srt.h` → `async.h`). - - `tap/ratio/…` → `tap/sr/ratio/…`. - - `ratio/tools/capi` → `ratio/capi`. Its standalone - `add_subdirectory(../..)` becomes `add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/.. ratio)`. + - `tap/ratio/…` → `tap/sr/bridge/…`. + - `bridge/tools/capi` → `bridge/capi`. Its standalone + `add_subdirectory(../..)` becomes `add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/.. bridge)`. - Delete `srt/detail/kaiser.h` per 3.1. 2. **Namespaces and names:** - - `tap::samplerate` → `tap::sr::async`; `tap::ratio` → `tap::sr::ratio`. + - `tap::samplerate` → `tap::sr::async`; `tap::ratio` → `tap::sr::bridge`. - The D15 mapping. - Test namespace `srt_test` → `async_test`. `ratio_ref` is unchanged; it is not a retired name. @@ -690,10 +698,10 @@ There is no separate reflow commit, since the hook would absorb it anyway both umbrella headers (D13). - `SRT_RESTRICT`, `SRT_Q15_SMLALD` and `SRT_CHANNEL_PARALLEL` → their `TAP_DSP_*` originals (they are pure aliases). - - `TAP_RATIO_MIRRORED_DOT_ATTR` → `TAP_SR_RATIO_MIRRORED_DOT_ATTR`. + - `TAP_RATIO_MIRRORED_DOT_ATTR` → `TAP_SR_BRIDGE_MIRRORED_DOT_ATTR`. - `SRT_CP_MIN_CHANNELS` → `TAP_SR_ASYNC_CP_MIN_CHANNELS`, with an `#error` tripwire. - - `SRT_SC_*`, `RATIO_SC_*`, `SRT_CMP_*` → `TAP_SR_{ASYNC,RATIO}_SC_*` / + - `SRT_SC_*`, `RATIO_SC_*`, `SRT_CMP_*` → `TAP_SR_{ASYNC,BRIDGE}_SC_*` / `…_CMP_*`. - `*_TESTS_COMPLETE` → `TAP_SR_TESTS_COMPLETE`. - `*_BARE_METAL` → `TAP_SR_BARE_METAL`, in the toolchain files and both @@ -701,12 +709,12 @@ There is no separate reflow commit, since the hook would absorb it anyway - `SRT_PICO2_*` → `TAP_SR_PICO2_*`. - Guest icount markers are unchanged. 4. **CMake, dependency enforcement and workflows:** - - Targets: `tap::sr::async`, `tap::sr::ratio`, umbrella `tap::sr`. + - Targets: `tap::sr::async`, `tap::sr::bridge`, umbrella `tap::sr`. - Internal targets renamed, including the ctest entries `srt_tests_emulated` / `tap_ratio_tests_emulated` → - `tap_sr_{async,ratio}_tests_emulated`. They are listed in the G1 name + `tap_sr_{async,bridge}_tests_emulated`. They are listed in the G1 name map. - - Engine `project()` → `tap_sr_async` / `tap_sr_ratio`, with no VERSION. + - Engine `project()` → `tap_sr_async` / `tap_sr_bridge`, with no VERSION. - Root `project(SampleRateTap VERSION 0.4.0)`. - Options → `TAP_SR_*` per D9, with the D7 `FATAL_ERROR` tripwire for every retired option. @@ -714,12 +722,12 @@ There is no separate reflow commit, since the hook would absorb it anyway `book-pages`) updated in the same commit. - The four 4.2 enforcement checks land here with their own tests. 5. **C ABI:** - - `srt_*` → `tap_sr_async_*` and `ratio_*` → `tap_sr_ratio_*`, including + - `srt_*` → `tap_sr_async_*` and `ratio_*` → `tap_sr_bridge_*`, including handle types, header names and library names. - The shim's exports → `tap_sr_async_r8b_*`. - The version functions per D13, and a new `CApi.VersionIsBitPacked` test, listed in the G1 name map. - - Bridges renamed per D8. + - Bindings renamed per D8. 6. **Ratchet binaries:** prefix only, `tap_sr__icount_*`. Workload names and baseline keys do not change. 7. **Docs and prose:** @@ -728,7 +736,7 @@ There is no separate reflow commit, since the hook would absorb it anyway - Non-book docs: - `async/docs/{PERFORMANCE,COMPARISON,HARDWARE_TESTING}.md`; - `examples/pico2_*/README.md`; - - `ratio/{README,PLAN,CLAUDE}.md`; + - `bridge/{README,PLAN,CLAUDE}.md`; - notebook markdown (`asrc_comparison` 42 hits, `asrc_demo` 30, `asrc_block_size_study` 24). - RatioTap URLs; the `git clone …/RatioTap` instructions. @@ -736,7 +744,7 @@ There is no separate reflow commit, since the hook would absorb it anyway 8. **Banners (D14):** - Rewrite the 35 existing banner lines. - **Add** banners to every C/C++/Python source file that has none. - - Update `ratio/tools/reference/make_reference_vectors.py`, and + - Update `bridge/tools/reference/make_reference_vectors.py`, and regenerate `tests/reference/reference_vectors.h`. That header's only residual must be its banner (G14). @@ -752,13 +760,13 @@ G13 and G14, plus G9 from 3.7. rule (R4). - Write the family `README.md`. - Per-engine `PLAN.md` and `README.md` (each with its icount table). -- `ratio/CLAUDE.md` reduced to the charter. +- `bridge/CLAUDE.md` reduced to the charter. - Doxyfile main page → `README.md`. - Delete `docs/migration/`, keeping `runs.md` if wanted. - Mark the PR ready. **Merge with "Create a merge commit".** - **Post-merge checks:** - `git rev-list --count origin/main` ≥ count(S0) + count(R0) + N. - - `git log --follow ratio/include/tap/sr/ratio/converter.h` reaches + - `git log --follow bridge/include/tap/sr/bridge/converter.h` reaches RatioTap M3 (`06769f2` before rewriting). - **Follow-up commit:** append to `.git-blame-ignore-revs` the step-3 commit SHAs and the rewritten RatioTap reformat commit `4c3562c`. @@ -776,10 +784,10 @@ G13 and G14, plus G9 from 3.7. - Delete RatioTap's leftover branches (`…mqc190` and `claude/sample-rate-expansion-strategies-ezqzu6`). -### Next — `integer` (separate plan) +### Next — `rational` (separate plan) - The 2.2 follow-up comes first (`ratio_traits` k). -- Then `integer`'s own reviewed plan: L-th-band design math and `chain<>` +- Then `rational`'s own reviewed plan: L-th-band design math and `chain<>` (under D12) in DspTap, the engine, and the generated 14 × 14 matrix under 2.1's rule. @@ -806,10 +814,10 @@ G13 and G14, plus G9 from 3.7. | R1 | Silently dropped tests or workloads | G1 and G2 multisets with unique names (D16); G3 workload-set equality; non-empty assertions | | R2 | Infrastructure or build glue changes codegen | G3, G4 and G7 as same-job A/B; G14 | | R3 | Docs rot | G8 from 1c; G9 from 3.7; G14 | -| R4 | The cross-validation loses its independence | Independence comes from the scipy leg and from the engines' structural difference, never from repository separation. G6 now prints tolerances, and G14 sees any tolerance edit. **Permanent family rule** (CLAUDE.md): a PR that changes cross-validation tolerances leaves `ratio/tests/reference/` untouched, keeps the scipy leg green, and does not also change async's datapath. `ratio` never includes async's bank or blend (D11) | -| R5 | Rewritten SHAs and PR numbers | `ratio/docs/HISTORY.md` built from the API (1c) | +| R4 | The cross-validation loses its independence | Independence comes from the scipy leg and from the engines' structural difference, never from repository separation. G6 now prints tolerances, and G14 sees any tolerance edit. **Permanent family rule** (CLAUDE.md): a PR that changes cross-validation tolerances leaves `bridge/tests/reference/` untouched, keeps the scipy leg green, and does not also change async's datapath. `bridge` never includes async's bank or blend (D11) | +| R5 | Rewritten SHAs and PR numbers | `bridge/docs/HISTORY.md` built from the API (1c) | | R6 | CI load | The repositories are public: no minutes cost. The Free concurrency caps (20 jobs, 5 macOS) mean queueing, not failure. About 20 `ci`+`style` jobs plus 4 gate jobs per push; at most 4 macOS jobs | -| R7 | `git bisect` across the merge | `--first-parent` is required and documented. Imported commits remain buildable in `ratio/` thanks to the `.gitmodules` rewrite | +| R7 | `git bisect` across the merge | `--first-parent` is required and documented. Imported commits remain buildable in `bridge/` thanks to the `.gitmodules` rewrite | | R8 | Charter erosion | 4.2 checks, D11, D12, per-engine PLAN.md files | | R9 | Squash-merge of the final PR | D3; step 4; the post-merge history check | | R10 | Toolchain drift blamed on a step | A/B gates in one job on a pinned image; image and package versions in `runs.md` | @@ -821,19 +829,24 @@ None. The decisions are recorded where they apply. | Q | Resolution | |---|---| -| Q1 | `HANDOFF.md` stays in `ratio/` (allowlisted as history) | +| Q1 | `HANDOFF.md` stays in `bridge/` (allowlisted as history) | | Q2 | Rename to the `converter` family: D15 | -| Q3 | `ratio` at 2^k rates: 2.2 (follow-up) | +| Q3 | `bridge` at 2^k rates: 2.2 (follow-up) | | Q4 | One family version 0.4.0, bit-packed: D13 | | Q5 | Unified holder line; user reconfirmed ownership; banners everywhere: D14 | | Q6 | Per-engine READMEs plus a family README | | Q7 | Two-level namespace: D4 | | Q8 | Icount tables in each engine's README: 1c | +| Q9 | Engine names `bridge` and `rational`: D5 (v3.1) | --- ## Appendix A — Round-1 audit disposition (v1 → v2) +Both appendices are the audit record. Where a finding's wording still +says `ratio` or `integer`, read `bridge` or `rational` (D5, v3.1); paths +and resolutions use the current names. + Citations in this table use **v2's** step numbering (v2 had a separate reflow commit, so its step 3 sub-numbers differ from v3's). Rows that round 2 found only partly resolved are listed at the top of Appendix B and completed @@ -858,9 +871,9 @@ completeness). Severity: B blocker, M major, m minor, n nit. | DEC-2 | M | Invariant forbids more pairs than listed | 2.1: generated full matrix | | DEC-3 | M | Nyquist criterion wrong | 2.1: passband rule | | DEC-4 | m | "Every standard rate" false (37.8, 50.4, pull-down) | 2.1 exclusions | -| DEC-5 | n | Table rows; 2/3 needs a rational stage | 2.1 rows; `integer` charter L/M | +| DEC-5 | n | Table rows; 2/3 needs a rational stage | 2.1 rows; `rational` charter L/M | | DEC-6 | M | `fractional_resampler` to DspTap contradicts settled M0 | D11 | -| DEC-7 | M | Moving `decimate.h` breaks MuTap / D10 | 2; the `integer` plan (section 6, Next) | +| DEC-7 | M | Moving `decimate.h` breaks MuTap / D10 | 2; the `rational` plan (section 6, Next) | | DEC-8 | M | `chain<>` risks a rate-routing factory | D12 | | DEC-9 | m | D1 supersession unrecorded; third harness copy | D1; 3.3 | | DEC-10 | n | D4 supersedes agreed rename; `std::` shadowing | D4, D5 | @@ -961,7 +974,7 @@ below. |---|---|---|---| | RUN-1 | M | `checkout --ours` leaves `.gitmodules` unmerged | 1b: `git add .gitmodules` | | RUN-2 | M | `git rm -f` aborts on directory | 1b: explicit `git rm -rf` list | -| RUN-3 | M | `NOT TARGET` guard breaks standalone builds and bridges | 1c: `../submodules/dsptap` with a binary dir | +| RUN-3 | M | `NOT TARGET` guard breaks standalone builds and bindings | 1c: `../submodules/dsptap` with a binary dir | | RUN-4 | M | `add_subdirectory(tools/capi)` missed | 1c | | RUN-5 | M | "Force ON" ignores CI's `-D…=OFF` | 1c: `option()` defaults, never FORCE | | RUN-6 | M | Root `ctest` runs both engines under one `-E` | D9; 1c per-(target, engine) jobs with `-L` | @@ -978,7 +991,7 @@ below. | RUN-17 | n | Hexagon toolchain sets no BARE_METAL | 3.3 | | RUN-18 | n | D14 wording; generator banner; unscheduled | D14; 3.8 | | RUN-19 | n | New root `CMakeLists.txt` history check | G12 | -| RUN-20 | n | `ratio/.github/workflows` deletion timing | 4.1; 1c | +| RUN-20 | n | `bridge/.github/workflows` deletion timing | 4.1; 1c | | GATE-1 (R2) | B | G4 red on a pure rename | G4 redefined (per-function, relocations, icount/C ABI only) | | GATE-2 (R2) | B | A/B exceeds M33/Hexagon timeouts | Section 5: A/B only for G3/G4/G5/G7/G11 in `migration-gates` | | GATE-3 (R2) | M | Pinned hashes fail with FMA | G5: per-host A/B, never pinned | @@ -988,7 +1001,7 @@ below. | GATE-7 (R2) | M | G6 misses loosened tolerances | P.2 prints tolerances; G14 | | GATE-8 (R2) | M | Regression passing all gates | **G14** (rename-only residual) | | GATE-9 (R2) | M | G13 unmeetable (book-pages, dispatch-only, 1a/1b) | G13 names workflows and gated SHAs | -| GATE-10 (R2) | M | G11 leaks volatile text; compares little | G11 redefined; P.3 quiet bridges and array hashes | +| GATE-10 (R2) | M | G11 leaks volatile text; compares little | G11 redefined; P.3 quiet bindings and array hashes | | GATE-11 (R2) | M | `env -i` breaks qemu lookup | P.2: resolve absolute path first | | GATE-12 (R2) | M | Forced options break cross/icount jobs | 1c `option()` defaults | | GATE-13 (R2) | m | G9 vs kept markers | G9 (file, pattern) allowlist | @@ -1017,7 +1030,7 @@ below. | COH-2 | M | Encoding pinned by nothing | D13 `CApi.VersionIsBitPacked`; 3.6 | | COH-3 | M | D13 mechanics unspecified | D13 mechanics; 3.3–3.5 | | COH-4 | M | G11 fails on rename commits | G11 name and version map | -| COH-5 | M | G9 fails by construction | G9 allowlist; 3.7 widened; `ratio/PLAN.md` rewritten | +| COH-5 | M | G9 fails by construction | G9 allowlist; 3.7 widened; `bridge/PLAN.md` rewritten | | COH-6 | M | Rename/reflow split vs pre-commit | Step 3: rename and reflow in one commit, ignore-revs | | COH-7 | M | Passband rule contradicts its example; no stopband rule | 2.1 rewritten (a)–(d); rows re-checked | | COH-8 | m | Rate list circular; 352.8 missing | 2.1: exactly 14 rates; 352.8 excluded with reason | diff --git a/docs/migration/drafts/ci-after-1c.yml b/docs/migration/drafts/ci-after-1c.yml index 9d872e4..c938b74 100644 --- a/docs/migration/drafts/ci-after-1c.yml +++ b/docs/migration/drafts/ci-after-1c.yml @@ -4,11 +4,15 @@ # Design choices it makes, and why: # - ONE configure of the root tree per job, both engines built. D9 forbids # per-engine enables, and from step 3.5 on there is one TAP_SR_BUILD_TESTS, -# so "ratio jobs" that build only ratio cannot survive step 3. Per-engine +# so "bridge jobs" that build only bridge cannot survive step 3. Per-engine # behaviour is selected at *ctest* time by LABELS (added in P.2), and # per-engine warnings by the per-engine WERROR options, which already live # on separate INTERFACE targets (srt_warnings / tap_ratio_warnings), so -# async-OFF / ratio-ON on MSVC coexists in one tree. +# async-OFF / bridge-ON on MSVC coexists in one tree. +# - Engine names follow D5 (v3.1): the imported RatioTap engine is `bridge`, +# in the bridge/ directory from 1b. Its options (TAP_RATIO_*), warning target +# and ctest label (`ratio`) keep RatioTap's spelling until step 3.4/3.5 +# rename them; matrix keys below already say bridge. # - Options are DEFAULTED at the root (option() before add_subdirectory), never # FORCEd: every bare-metal/Hexagon job passes *_BUILD_EXAMPLES=OFF, and # async's examples do find_package(Threads REQUIRED), which fails a @@ -48,10 +52,10 @@ jobs: fail-fast: false matrix: include: - - { name: Linux GCC, os: ubuntu-latest, cc: gcc, cxx: g++, async_werror: ON, ratio_werror: ON, capi: ON } - - { name: Linux Clang, os: ubuntu-latest, cc: clang, cxx: clang++, async_werror: ON, ratio_werror: ON, capi: ON } # ratio: NEW coverage (measured 78/78 locally, clang 18 -Werror) - - { name: macOS AppleClang, os: macos-latest, async_werror: ON, ratio_werror: ON, capi: ON } - - { name: Windows MSVC, os: windows-latest, async_werror: OFF, ratio_werror: ON, capi: OFF } # async /W4 untriaged (INF-11); ratio keeps /WX + - { name: Linux GCC, os: ubuntu-latest, cc: gcc, cxx: g++, async_werror: ON, bridge_werror: ON, capi: ON } + - { name: Linux Clang, os: ubuntu-latest, cc: clang, cxx: clang++, async_werror: ON, bridge_werror: ON, capi: ON } # bridge: NEW coverage (measured 78/78 locally, clang 18 -Werror) + - { name: macOS AppleClang, os: macos-latest, async_werror: ON, bridge_werror: ON, capi: ON } + - { name: Windows MSVC, os: windows-latest, async_werror: OFF, bridge_werror: ON, capi: OFF } # async /W4 untriaged (INF-11); bridge keeps /WX steps: - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: { submodules: recursive } @@ -59,7 +63,7 @@ jobs: env: { CC: "${{ matrix.cc }}", CXX: "${{ matrix.cxx }}" } run: > cmake -B build -DCMAKE_BUILD_TYPE=Release - -DSRT_WERROR=${{ matrix.async_werror }} -DTAP_RATIO_WERROR=${{ matrix.ratio_werror }} + -DSRT_WERROR=${{ matrix.async_werror }} -DTAP_RATIO_WERROR=${{ matrix.bridge_werror }} -DSRT_BUILD_CAPI=${{ matrix.capi }} -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} - run: cmake --build build --config Release -j 4 - run: ctest --test-dir build -C Release $CTEST_COMMON --output-log ctest-${{ matrix.name }}.log @@ -76,9 +80,9 @@ jobs: fail-fast: false matrix: include: - # async ran ASan without WERROR, ratio with it; per-engine options keep both. + # async ran ASan without WERROR, bridge with it; per-engine options keep both. - { name: ASan + UBSan, flags: "-fsanitize=address,undefined -fno-sanitize-recover=all", labels: "" } - # TSan: async only (ratio is single-threaded; running it costs ~3 s + # TSan: async only (bridge is single-threaded; running it costs ~3 s # but adds rows G1 has no step-0 counterpart for). Build is shared. - { name: TSan, flags: "-fsanitize=thread", labels: "-L async" } steps: @@ -91,7 +95,7 @@ jobs: -DSRT_BUILD_EXAMPLES=OFF -DSRT_WERROR=OFF -DTAP_RATIO_WERROR=ON -DCMAKE_CXX_FLAGS="${{ matrix.flags }}" - # NB ratio's sanitizer job built ratio examples (bluetooth_bridge); keep + # NB RatioTap's sanitizer job built its examples (bluetooth_bridge); keep # TAP_RATIO_BUILD_EXAMPLES at its default (ON) so G7's TU set matches. - run: cmake --build build -j 4 - env: { TSAN_OPTIONS: halt_on_error=1, UBSAN_OPTIONS: print_stacktrace=1 } @@ -99,8 +103,8 @@ jobs: # One job per (target, engine) for correctness: both engines are BUILT # (one tree), each job RUNS one engine's label with that engine's -E list - # and parallelism. Measured today: async Hexagon 22.5 min serial, ratio - # Hexagon 11.0 min at -j 4, async M33 22.4 min, ratio M33 0.4 min + # and parallelism. Measured today: async Hexagon 22.5 min serial, bridge + # Hexagon 11.0 min at -j 4, async M33 22.4 min, bridge M33 0.4 min # (runs 36253867157, 36256431538). Serialising both in one Hexagon job is # ~36 min of a 45 min timeout; a single un-labelled ctest at one -j is # either 67 min (serial) or changes async's leg (-j 4). @@ -113,11 +117,11 @@ jobs: matrix: include: - { target: hexagon, engine: async, timeout: 45, j: 1, exclude: 'AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec|MultiChannel\.|Feasibility|Reset\.|ConfigValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } - - { target: hexagon, engine: ratio, timeout: 30, j: 4, exclude: 'BadProfilesThrow|LatencyAndValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } + - { target: hexagon, engine: bridge, label: ratio, timeout: 30, j: 4, exclude: 'BadProfilesThrow|LatencyAndValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } - { target: m55, engine: async, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } - - { target: m55, engine: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } + - { target: m55, engine: bridge, label: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } - { target: m33, engine: async, timeout: 40, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } - - { target: m33, engine: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } + - { target: m33, engine: bridge, label: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } env: HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" @@ -140,9 +144,10 @@ jobs: -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF - run: cmake --build build -j 4 - name: Test under emulation (${{ matrix.engine }} only) + # label: bridge's ctest label is still `ratio` until step 3.4 renames it (D16); drop the key then. shell: bash run: | - args=(--test-dir build $CTEST_COMMON -L '^${{ matrix.engine }}$' -j ${{ matrix.j }} -V --output-log ctest.log) + args=(--test-dir build $CTEST_COMMON -L '^${{ matrix.label || matrix.engine }}$' -j ${{ matrix.j }} -V --output-log ctest.log) [ -n '${{ matrix.exclude }}' ] && args+=(-E '${{ matrix.exclude }}') ctest "${args[@]}" - uses: actions/upload-artifact@ @@ -157,7 +162,7 @@ jobs: runs-on: ubuntu-24.04 timeout-minutes: 45 steps: [ { run: "# as today; configure root with -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF -DSRT_BUILD_ICOUNT_BENCH=ON" } ] - icount-ratio: # = RatioTap's job, --baselines ratio/bench/baselines.json, plus ratio README freshness (1c adds the table) + icount-bridge: # = RatioTap's job, --baselines bridge/bench/baselines.json, plus bridge README freshness (1c adds the table) runs-on: ubuntu-24.04 timeout-minutes: 45 steps: [ { run: "# as RatioTap today, SHA-pinned; -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON, tests/examples OFF for both engines" } ] diff --git a/docs/migration/drafts/migration-gates.yml b/docs/migration/drafts/migration-gates.yml index c2b08f2..79db0f7 100644 --- a/docs/migration/drafts/migration-gates.yml +++ b/docs/migration/drafts/migration-gates.yml @@ -39,5 +39,5 @@ jobs: # binaries embed __FILE__ via gtest, and the moved paths shift .rodata), # G5 hash tests, G6 lines. # 3. Diff; upload all artefacts; fail on any difference not in the - # committed allowlist (docs/migration/allow.txt, e.g. new ratio rows + # committed allowlist (docs/migration/allow.txt, e.g. new bridge rows # in Linux Clang and new async-label rows nowhere). diff --git a/docs/migration/drafts/root-CMakeLists-1c.cmake b/docs/migration/drafts/root-CMakeLists-1c.cmake index 105c016..77e5bf3 100644 --- a/docs/migration/drafts/root-CMakeLists-1c.cmake +++ b/docs/migration/drafts/root-CMakeLists-1c.cmake @@ -9,8 +9,8 @@ enable_testing() # PROJECT_IS_TOP_LEVEL-dependent option() calls become no-ops, while -D wins. option(SRT_BUILD_TESTS "async tests" ON) option(SRT_BUILD_EXAMPLES "async examples" ON) -option(TAP_RATIO_BUILD_TESTS "ratio tests" ON) -option(TAP_RATIO_BUILD_EXAMPLES "ratio examples" ON) +option(TAP_RATIO_BUILD_TESTS "bridge tests" ON) +option(TAP_RATIO_BUILD_EXAMPLES "bridge examples" ON) # Stale-option tripwire (extends D7 to CMake options, needed from step 3.5): # an unknown -D is only a "Manually-specified variables were not used" @@ -27,9 +27,9 @@ option(TAP_RATIO_BUILD_EXAMPLES "ratio examples" ON) add_subdirectory(submodules/dsptap) add_subdirectory(async) -add_subdirectory(ratio) +add_subdirectory(bridge) # gtest is made available ONCE, by whichever engine's tests/ runs first -# (async). ratio/tests' set(INSTALL_GTEST OFF ...) and its Threads probe are +# (async). bridge/tests' set(INSTALL_GTEST OFF ...) and its Threads probe are # then no-ops: hoist both gtest settings here so the result does not depend # on add_subdirectory order (and so 4.2's install test does not install gtest). From f2b7d045239b721503d3fbd6e92c19add40e879e Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 27 Sep 2026 23:15:38 +0000 Subject: [PATCH 39/44] Step 0: freeze the tips and commit the migration snapshot S0 = SampleRateTap 5e2057f (140 commits) and R0 = RatioTap 8f19e8b (33), from fresh full clones (docs/migration/tips.txt). docs/migration/ now holds what the gates compare against: - rename.py: the mechanical rename map (paths, namespaces, macros, CMake, C ABI, icount prefixes, D14 banners) and gate G14. Applied through 3.8 to S0 and R0 it builds with -Werror, passes 77/77 and 82/82, reproduces both tips' output hashes and cross-validation lines, and reports a loosened tolerance as one residual hunk. residual/1c.txt seeds 1c's allowlist from the plan. - collect.py: collectors for G1, G2, G6, G9, G10 and G12. - snapshot/: G1 test lists per CI job and labels, G2 on-target [ RUN ] lists from the QEMU artifacts, G6 cross-validation lines, G9 rules, G10 C ABI symbols, G12 history; provenance in runs.md. The plan's step 0 records the result and three map details it left implicit; step 1c's HISTORY.md count follows R0 (33, not 29). Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/MONOREPO_PLAN.md | 11 +- docs/migration/README.md | 38 ++ docs/migration/allow.txt | 11 + docs/migration/collect.py | 200 +++++++ docs/migration/rename.py | 487 ++++++++++++++++++ docs/migration/residual/1c.txt | 29 ++ docs/migration/residual/README.md | 23 + docs/migration/runs.md | 41 ++ .../snapshot/g1/async-asan-ubsan.txt | 77 +++ docs/migration/snapshot/g1/async-hexagon.txt | 49 ++ docs/migration/snapshot/g1/async-labels.txt | 77 +++ .../snapshot/g1/async-linux-clang.txt | 77 +++ .../migration/snapshot/g1/async-linux-gcc.txt | 77 +++ docs/migration/snapshot/g1/async-m33.txt | 1 + docs/migration/snapshot/g1/async-m55.txt | 1 + .../snapshot/g1/async-macos-appleclang.txt | 77 +++ docs/migration/snapshot/g1/async-tsan.txt | 77 +++ .../snapshot/g1/async-windows-msvc.txt | 77 +++ .../snapshot/g1/bridge-asan-ubsan.txt | 82 +++ docs/migration/snapshot/g1/bridge-hexagon.txt | 80 +++ docs/migration/snapshot/g1/bridge-labels.txt | 82 +++ docs/migration/snapshot/g1/bridge-linux.txt | 82 +++ docs/migration/snapshot/g1/bridge-m33.txt | 1 + docs/migration/snapshot/g1/bridge-m55.txt | 1 + docs/migration/snapshot/g1/bridge-macos.txt | 82 +++ docs/migration/snapshot/g1/bridge-windows.txt | 82 +++ docs/migration/snapshot/g10/async.txt | 8 + docs/migration/snapshot/g10/bridge.txt | 11 + docs/migration/snapshot/g12/async.txt | 240 +++++++++ docs/migration/snapshot/g12/bridge.txt | 116 +++++ docs/migration/snapshot/g2/async-hexagon.txt | 49 ++ docs/migration/snapshot/g2/async-m33.txt | 41 ++ docs/migration/snapshot/g2/async-m55.txt | 41 ++ docs/migration/snapshot/g2/bridge-hexagon.txt | 80 +++ docs/migration/snapshot/g2/bridge-m33.txt | 63 +++ docs/migration/snapshot/g2/bridge-m55.txt | 63 +++ docs/migration/snapshot/g6.txt | 4 + docs/migration/snapshot/g9.txt | 38 ++ docs/migration/tips.txt | 5 + 39 files changed, 2680 insertions(+), 1 deletion(-) create mode 100644 docs/migration/README.md create mode 100644 docs/migration/allow.txt create mode 100755 docs/migration/collect.py create mode 100755 docs/migration/rename.py create mode 100644 docs/migration/residual/1c.txt create mode 100644 docs/migration/residual/README.md create mode 100644 docs/migration/runs.md create mode 100644 docs/migration/snapshot/g1/async-asan-ubsan.txt create mode 100644 docs/migration/snapshot/g1/async-hexagon.txt create mode 100644 docs/migration/snapshot/g1/async-labels.txt create mode 100644 docs/migration/snapshot/g1/async-linux-clang.txt create mode 100644 docs/migration/snapshot/g1/async-linux-gcc.txt create mode 100644 docs/migration/snapshot/g1/async-m33.txt create mode 100644 docs/migration/snapshot/g1/async-m55.txt create mode 100644 docs/migration/snapshot/g1/async-macos-appleclang.txt create mode 100644 docs/migration/snapshot/g1/async-tsan.txt create mode 100644 docs/migration/snapshot/g1/async-windows-msvc.txt create mode 100644 docs/migration/snapshot/g1/bridge-asan-ubsan.txt create mode 100644 docs/migration/snapshot/g1/bridge-hexagon.txt create mode 100644 docs/migration/snapshot/g1/bridge-labels.txt create mode 100644 docs/migration/snapshot/g1/bridge-linux.txt create mode 100644 docs/migration/snapshot/g1/bridge-m33.txt create mode 100644 docs/migration/snapshot/g1/bridge-m55.txt create mode 100644 docs/migration/snapshot/g1/bridge-macos.txt create mode 100644 docs/migration/snapshot/g1/bridge-windows.txt create mode 100644 docs/migration/snapshot/g10/async.txt create mode 100644 docs/migration/snapshot/g10/bridge.txt create mode 100644 docs/migration/snapshot/g12/async.txt create mode 100644 docs/migration/snapshot/g12/bridge.txt create mode 100644 docs/migration/snapshot/g2/async-hexagon.txt create mode 100644 docs/migration/snapshot/g2/async-m33.txt create mode 100644 docs/migration/snapshot/g2/async-m55.txt create mode 100644 docs/migration/snapshot/g2/bridge-hexagon.txt create mode 100644 docs/migration/snapshot/g2/bridge-m33.txt create mode 100644 docs/migration/snapshot/g2/bridge-m55.txt create mode 100644 docs/migration/snapshot/g6.txt create mode 100644 docs/migration/snapshot/g9.txt create mode 100644 docs/migration/tips.txt diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index 8df35a0..b9340cb 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -517,6 +517,15 @@ must produce the data the gates read. (`ratio.` → `bridge.`, D16). - The G2 collector strips the test-number prefix that ctest's verbose log puts on each output line (`1: [ RUN ] …`) before it compares. +- **Done (v3.1):** S0 `5e2057f` (140 commits) and R0 `8f19e8b` (33), from + CI runs 36348916668 and 36356088654 (`docs/migration/runs.md`). + `rename.py` applied through 3.8 builds with `-Werror`, passes 77/77 and + 82/82, reproduces both tips' output hashes and cross-validation lines, + and flags a loosened tolerance as residual. The map fixed three things the + plan left implicit: the `SRT_RESTRICT`/`…_Q15_SMLALD`/`…_CHANNEL_PARALLEL` + alias `#define`s are deleted rather than renamed; the kaiser re-export's + two users requalify to `tap::dsp` at 3.1; and `SrtHandle` becomes + `tap_sr_async_converter`, matching `tap_sr_bridge_converter`. - If `main` must move, it moves in SampleRateTap only, and step 1 is re-cut. filter-repo is deterministic (tip `daceb8d` on two fresh clones, with the dry run's `ratio/` prefix). @@ -638,7 +647,7 @@ builds. - `bridge/CLAUDE.md`: build commands. - **HISTORY.md:** - `bridge/docs/HISTORY.md` maps old SHA → new SHA → RatioTap PR for - `git rev-list R0` (29 commits, skipping the commit-map header). + `git rev-list R0` (33 commits at R0, `tips.txt`; skipping the commit-map header). - PR numbers come from `GET /repos/tap/RatioTap/commits//pulls`, queried once at cut time. The repository is public; the dry run built the map as `audit2-dryrun/history_map.tsv`. diff --git a/docs/migration/README.md b/docs/migration/README.md new file mode 100644 index 0000000..def1bda --- /dev/null +++ b/docs/migration/README.md @@ -0,0 +1,38 @@ +# docs/migration — the monorepo migration's working set + +Everything the migration gates need (MONOREPO_PLAN.md sections 5 and 6), +committed at step 0 and deleted at step 4 (`runs.md` may be kept). + +| Path | What it is | +|---|---| +| `tips.txt` | S0 and R0, the step-0 tips every A/B gate rebuilds, with their commit counts | +| `rename.py` | the mechanical rename map (paths, namespaces, macros, targets, C ABI, banners) and gate **G14** (`check`) | +| `residual/.txt` | reviewed allowlists of G14 residual hunks, one per gated step | +| `collect.py` | collectors for the snapshot gates (G1, G2, G6, G9, G10, G12) | +| `snapshot/` | the step-0 snapshot those collectors compare against | +| `allow.txt` | G1 allowlist: test rows a job may gain (never lose) | +| `runs.md` | provenance of the snapshot, and the run record of every gated SHA (G13) | +| `drafts/` | the audit's untested workflow and CMake sketches for step 1c | + +## How the pieces fit + +- **Snapshot gates** re-collect from the gated SHA and diff against + `snapshot/`: `collect.py ctest-log job.log | diff - snapshot/g1/async-linux-gcc.txt`. + Bridge test names carry RatioTap's `ratio.` prefix at step 0 and `bridge.` + after step 3.4 (D16); collect the snapshot side with `--map` to compare + across that rename. Hosted in the `migration-gates` workflow from 1c. +- **G14** rebuilds the tree a purely mechanical migration would have at a + step from S0 and R0 and diffs it against HEAD: + + git clone https://github.com/tap/SampleRateTap old-async # at S0 + git clone https://github.com/tap/RatioTap old-ratio # at R0 + python3 docs/migration/rename.py check --through 1c + + Measured at step 0: the map applied through 3.8 to S0 and R0 builds with + `-Werror` (GCC 13), passes 77/77 and 82/82 tests, prints output hashes + identical to S0's and R0's (24 each) and the same four cross-validation + lines, and leaves four G9 hits, all `srt_headers`, which 1c's glue + replaces. A loosened cross-validation tolerance in that tree is reported + as one residual hunk. +- **A/B gates** (G3, G4, G5, G7, G11) never read files here except + `tips.txt`: they rebuild S0/R0 and the gated SHA in one job. diff --git a/docs/migration/allow.txt b/docs/migration/allow.txt new file mode 100644 index 0000000..8a02506 --- /dev/null +++ b/docs/migration/allow.txt @@ -0,0 +1,11 @@ +# G1 allowlist: test rows that may appear in, or disappear from, a job's +# test multiset relative to snapshot/g1/ (after the D16 name map). Each row: +# +# +|- -- +# +# The plan anticipates these additions; they are listed when the step that +# makes them lands (never in advance of the evidence): +# - bridge tests in the Linux Clang job (new coverage at 1c, 78/78 at audit) +# - CApi.VersionIsBitPacked (3.5, D13) +# - the four 4.2 dependency-enforcement tests (3.4) +# No test may disappear. diff --git a/docs/migration/collect.py b/docs/migration/collect.py new file mode 100755 index 0000000..e0a3e4a --- /dev/null +++ b/docs/migration/collect.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +# SPDX-License-Identifier: MIT +# Copyright 2026 Timothy Place and the SampleRateTap contributors +"""Collectors for the migration's snapshot gates (MONOREPO_PLAN.md section 5). + +Each subcommand reads one kind of evidence and prints it in the canonical, +sorted text form that snapshot/ stores, so a gate is `collect.py ... | +diff - snapshot/` after the name map. Nothing here depends on the +toolchain; the A/B gates live in the migration-gates workflow. + + ctest-log FILE G1: tests ctest ran, from a CI job log or a + --output-log file ("Test #N: name" / "Start N: name") + ctest-json BUILD_DIR G1: registered tests and their labels + (ctest --show-only=json-v1); fails if empty + gtest-runs FILE G2: "[ RUN ]" test names in an on-target log, + with ctest's "N: " line prefix stripped + xval FILE G6: the cross-validation lines, limits included + symbols LIB G10: exported C ABI symbols (nm -D --defined-only, + unmangled only) + history REPO REV PATH... G12: per file, the commits `git log --follow` + reaches and the lines blame attributes to each + retired [TREE] G9: retired identifiers outside snapshot/g9.txt's + allowlist (applies from step 3.7) + +--map applies rename.py's test-name map (ratio. -> bridge., D16) so a +step-0 snapshot compares directly against a post-3.4 tree. +""" +import argparse +import collections +import fnmatch +import json +import re +import subprocess +import sys + +TEST_NAME_MAP = [(re.compile(r"^ratio\."), "bridge.")] +TIMESTAMP = re.compile(r"^\d{4}-\d\d-\d\dT[\d:.]+Z ") +CTEST_PREFIX = re.compile(r"^\d+: ") + + +def clean(line: str) -> str: + # GitHub job logs prefix a timestamp; ctest -V prefixes "N: ". + return CTEST_PREFIX.sub("", TIMESTAMP.sub("", line.rstrip("\n"))) + + +def mapped(name: str, use_map: bool) -> str: + if use_map: + for pat, rep in TEST_NAME_MAP: + name = pat.sub(rep, name) + return name + + +def emit_multiset(names, use_map): + counts = collections.Counter(mapped(n, use_map) for n in names) + if not counts: + sys.exit("no entries found: an empty list must never pass as a snapshot") + for name in sorted(counts): + print(name if counts[name] == 1 else f"{name}\t×{counts[name]}") + + +def ctest_log(args): + names = [] + for line in open(args.file, encoding="utf-8", errors="replace"): + line = clean(line) + m = re.match(r"^\s*\d+/\d+ Test +#\d+: (\S+) ", line) + if m: + names.append(m.group(1)) + emit_multiset(names, args.map) + + +def ctest_json(args): + out = subprocess.run(["ctest", "--test-dir", args.build, "--show-only=json-v1"], + check=True, capture_output=True, text=True).stdout + tests = json.loads(out).get("tests", []) + if not tests: # --no-tests=error is ignored under --show-only + sys.exit("ctest --show-only listed no tests") + rows = [] + for t in tests: + labels = [] + for p in t.get("properties", []): + if p.get("name") == "LABELS": + labels = p.get("value", []) + rows.append(f"{mapped(t['name'], args.map)}\t{','.join(sorted(labels))}") + for row in sorted(rows): + print(row) + + +def gtest_runs(args): + names = [] + for line in open(args.file, encoding="utf-8", errors="replace"): + m = re.match(r"^\[ RUN \] (\S+)", clean(line)) + if m: + names.append(m.group(1)) + emit_multiset(names, False) + + +def xval(args): + lines = sorted({m.group(0) for line in open(args.file, encoding="utf-8", errors="replace") + if (m := re.search(r"\[ measured \] cross-validation .*", clean(line)))}) + if not lines: + sys.exit("no cross-validation lines") + print("\n".join(lines)) + + +def symbols(args): + out = subprocess.run(["nm", "-D", "--defined-only", args.lib], + check=True, capture_output=True, text=True).stdout + # The C ABI is the unmangled symbols. Weak C++ template and inline + # instantiations (_Z...) are incidental to it and re-mangle under the + # namespace rename; G4 covers their code. + syms = sorted({f"{p[1]} {p[2]}" for p in (l.split() for l in out.splitlines()) + if len(p) == 3 and not p[2].startswith("_Z")}) + if not syms: + sys.exit("no exported symbols") + print("\n".join(syms)) + + +def history(args): + # Keyed by author date and subject, never by SHA: RatioTap's SHAs are + # rewritten by filter-repo at 1b, and both survive the rewrite. + for path in args.paths: + log = subprocess.run(["git", "-C", args.repo, "log", "--follow", "--format=%aI %s", + args.rev, "--", path], check=True, capture_output=True, + text=True).stdout.splitlines() + blame = subprocess.run(["git", "-C", args.repo, "blame", "--line-porcelain", args.rev, + "--", path], check=True, capture_output=True, + text=True).stdout.splitlines() + per_commit, meta, cur = collections.Counter(), {}, None + for line in blame: + if re.match(r"^[0-9a-f]{40} ", line): + cur = line.split()[0] + meta.setdefault(cur, {}) + elif line.startswith("author-time "): + meta[cur]["t"] = line.split()[1] + elif line.startswith("summary "): + meta[cur]["s"] = line[len("summary "):] + elif line.startswith("\t"): + per_commit[(meta[cur].get("t"), meta[cur].get("s"))] += 1 + print(f"== {path} commits={len(log)} lines={sum(per_commit.values())}") + for entry in log: + print(f"log {entry}") + for (t, s), n in sorted(per_commit.items(), key=lambda kv: (kv[0][0] or "", kv[0][1] or "")): + print(f"blame {n:5d} {t} {s}") + + +def retired(args): + rules = open(args.rules).read().splitlines() + patterns = [re.compile(l.split(None, 1)[1]) for l in rules if l.startswith("pattern ")] + allows = [] + for l in rules: + if l.startswith("allow "): + glob, rx = l.split("--")[0].split(None, 2)[1:] + allows.append((glob, re.compile(rx.strip()))) + files = subprocess.run(["git", "-C", args.tree, "ls-files", "-z"], check=True, + capture_output=True).stdout.decode().split("\0") + hits = 0 + for path in filter(None, files): + if path.startswith("docs/migration/"): + continue + try: + text = open(f"{args.tree}/{path}", encoding="utf-8").read() + except (UnicodeDecodeError, IsADirectoryError, FileNotFoundError): + continue + for n, line in enumerate(text.splitlines(), 1): + for pat in patterns: + for m in pat.finditer(line): + ok = any(fnmatch.fnmatch(path, g) and rx.search(line) + for g, rx in allows) + if not ok: + hits += 1 + print(f"{path}:{n}: {m.group(0)!r}: {line.strip()[:120]}") + print(f"G9: {hits} retired-identifier hit(s)") + sys.exit(1 if hits else 0) + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + sub = ap.add_subparsers(dest="cmd", required=True) + for name, fn, arg in (("ctest-log", ctest_log, "file"), ("ctest-json", ctest_json, "build"), + ("gtest-runs", gtest_runs, "file"), ("xval", xval, "file"), + ("symbols", symbols, "lib")): + p = sub.add_parser(name) + p.add_argument(arg) + p.add_argument("--map", action="store_true", help="apply the D16 test-name map") + p.set_defaults(fn=fn) + p = sub.add_parser("retired") + p.add_argument("tree", nargs="?", default=".") + p.add_argument("--rules", default=str(__import__("pathlib").Path(__file__).parent / "snapshot" / "g9.txt")) + p.set_defaults(fn=retired) + p = sub.add_parser("history") + p.add_argument("repo") + p.add_argument("rev") + p.add_argument("paths", nargs="+") + p.set_defaults(fn=history) + args = ap.parse_args() + args.fn(args) + + +if __name__ == "__main__": + main() diff --git a/docs/migration/rename.py b/docs/migration/rename.py new file mode 100755 index 0000000..0f1993f --- /dev/null +++ b/docs/migration/rename.py @@ -0,0 +1,487 @@ +#!/usr/bin/env python3 +# SPDX-License-Identifier: MIT +# Copyright 2026 Timothy Place and the SampleRateTap contributors +"""The monorepo migration's mechanical rename map, and gate G14. + +This file is the specification of every *mechanical* change the migration +makes (MONOREPO_PLAN.md sections 4.1 and 6, steps 1-3). Applied to the +step-0 trees (SampleRateTap@S0 and RatioTap@R0, from tips.txt) it produces +the tree a purely mechanical migration would have at a given step. G14 then +diffs that against the real tree: every hunk left over is a *residual*, and +each must be listed in the reviewed allowlist residual/.txt. That is +how a loosened EXPECT_NEAR or a stray CI edit is caught even when every +other gate is green (R2-GATE-8). + + rename.py build --through STEP --out DIR [--s0-repo P] [--r0-repo P] + [--no-format] + rename.py check --through STEP [--tree DIR] [--allow FILE] [...] + +STEP is one of 1c, 2, 3.1 ... 3.8 (4 behaves as 3.8). A step's classes +apply cumulatively: --through 3.3 applies paths, 3.1, 3.2 and 3.3. + +Conventions: +- Inputs come from `git archive` of the tips, so submodule contents and + untracked files never enter the comparison; the tree under test is read + the same way (`git archive HEAD`), and docs/migration/ is excluded. +- Text substitutions are whole-token regexes applied to every text file, in + the order listed. Order matters where one name is a prefix of another. +- After the substitutions, C/C++ files that changed are run through + clang-format with the tree's .clang-format, because step 3 formats each + rename commit with the pre-commit hook (one commit per class, rename and + reflow together). The hook pins clang-format 18.1.3; so must this. +- Engine names follow D5 (v3.1): RatioTap's engine is `bridge`. +""" +import argparse +import fnmatch +import hashlib +import io +import os +import pathlib +import re +import shutil +import subprocess +import sys +import tarfile +import tempfile + +HERE = pathlib.Path(__file__).resolve().parent +STEPS = ["1c", "2", "3.1", "3.2", "3.3", "3.4", "3.5", "3.6", "3.7", "3.8"] +CXX_SUFFIXES = {".h", ".hpp", ".c", ".cc", ".cpp"} +FAMILY_HOLDER = "Timothy Place and the SampleRateTap contributors" + + +def at_least(through: str, step: str) -> bool: + through = "3.8" if through == "4" else through + return STEPS.index(through) >= STEPS.index(step) + + +# -------------------------------------------------------------------------- +# Paths (4.1). Each rule is (step, source repo, old prefix, new prefix); +# new prefix None deletes. The first matching rule of the latest applicable +# step wins, so a later step can re-map an earlier destination. + +S0_MOVES_1A = [ + ("CMakeLists.txt", "async/CMakeLists.txt"), + ("README.md", "async/README.md"), + ("include/", "async/include/"), + ("tests/", "async/tests/"), + ("bench/", "async/bench/"), + ("examples/", "async/examples/"), + ("notebooks/", "async/notebooks/"), + ("tools/capi/", "async/capi/"), + ("tools/compare_shim/", "async/tools/compare_shim/"), + ("cmake/r8brain.cmake", "async/cmake/r8brain.cmake"), + ("docs/PERFORMANCE.md", "async/docs/PERFORMANCE.md"), + ("docs/COMPARISON.md", "async/docs/COMPARISON.md"), + ("docs/HARDWARE_TESTING.md", "async/docs/HARDWARE_TESTING.md"), +] + +# RatioTap files that do not survive under bridge/, with the step that +# removes them. Everything else in R0 moves to bridge/ at 1b. +R0_DROPS = [ + ("1c", ".gitmodules"), # 1b: merged into root .gitmodules (--ours) + ("1c", ".clang-format"), # 1b: byte-identical to root + ("1c", ".clang-tidy"), + ("1c", "STYLE.md"), + ("1c", ".pre-commit-config.yaml"), + ("1c", ".claude/"), + ("1c", "scripts/tidy.sh"), + ("1c", ".github/pull_request_template.md"), + ("1c", ".github/workflows/"), # 1c: ported into the root workflows + ("1c", ".gitignore"), + ("1c", "LICENSE"), # 1c: root LICENSE carries D14's line + ("1c", "requirements.lock"), # 1c: identical root lockfile (P.3) + ("1c", "requirements.in"), + ("2", "cmake/"), # 2: root copies + ("2", "platform/"), + ("2", "tools/qemu_insn_plugin/"), + ("2", "scripts/icount.py"), +] +R0_RELOCATE_1C = [("scripts/fetch_hexagon_toolchain.sh", "scripts/fetch_hexagon_toolchain.sh")] + + +def map_s0_path(path: str, through: str) -> str | None: + for old, new in S0_MOVES_1A: + if path == old or (old.endswith("/") and path.startswith(old)): + path = new + path[len(old):] + break + return map_later_paths(path, through) + + +def map_r0_path(path: str, through: str) -> str | None: + if path.startswith("submodules/"): + return None # 1b: gitlinks removed at the merge + for step, old in R0_DROPS: + if at_least(through, step) and (path == old or (old.endswith("/") and path.startswith(old))): + return None + for old, new in R0_RELOCATE_1C: + if path == old: + return map_later_paths(new, through) + return map_later_paths("bridge/" + path, through) + + +def map_later_paths(path: str, through: str) -> str | None: + if not at_least(through, "3.1"): + return path + # 3.1: header paths, asrc.h -> converter.h (D15), srt.h -> async.h. + if path == "async/include/srt/detail/kaiser.h": + return None + if path.startswith("async/include/srt/"): + rest = path[len("async/include/srt/"):] + rest = {"asrc.h": "converter.h", "srt.h": "async.h"}.get(rest, rest) + return "async/include/tap/sr/async/" + rest + if path.startswith("bridge/include/tap/ratio/"): + return "bridge/include/tap/sr/bridge/" + path[len("bridge/include/tap/ratio/"):] + if path.startswith("bridge/tools/capi/"): + path = "bridge/capi/" + path[len("bridge/tools/capi/"):] + if at_least(through, "3.5"): + path = re.sub(r"\bsrt_capi\.", "tap_sr_async_capi.", path) + path = re.sub(r"\bratio_capi\.", "tap_sr_bridge_capi.", path) + return path + + +# -------------------------------------------------------------------------- +# Text substitutions per class. (step, pattern, replacement); patterns are +# regexes, applied in order. + +# Token start: not inside another identifier, except right after a CMake +# "-D" (so -DSRT_WERROR=ON renames with SRT_WERROR). +LB = r"(?:(?<=-D)|(? str: + return LB + re.escape(name) + r"(?![A-Za-z0-9_])" + + +SUBS = [ + # 3.1 paths inside files: include directives and path citations. + ("3.1", r"srt/detail/kaiser\.h", "tap/dsp/kaiser.h"), + # 3.1 deletes the kaiser.h re-export; its two users requalify. + ("3.1", r"(? bool: + name = pathlib.PurePosixPath(path).name + return name == "CMakeLists.txt" or name.endswith(".cmake") + + +def apply_subs(text: str, path: str, through: str) -> str: + # A rule's optional fourth field scopes it: "code" skips CMake files, + # "cmake" applies only to them (CMake targets rename at 3.4, C++ at 3.2), + # and "path:" limits it to matching files. + for step, pat, rep, *scope in SUBS: + if not at_least(through, step): + continue + if scope and scope[0].startswith("path:"): + if not fnmatch.fnmatch(path, scope[0][5:]): + continue + elif scope and (scope[0] == "cmake") != is_cmake(path): + continue + text = re.sub(pat, rep, text) + return text + + +# -------------------------------------------------------------------------- +# 3.8 banners (D14). C/C++ and Python sources outside vendored code get +# exactly these two lines at the top (after a shebang); an existing +# Copyright line in the first five lines is rewritten in place. + +BANNER_SKIP = ["third_party/*", "*/third_party/*", "submodules/*", "*/reference_vectors.h"] + + +def banner(text: str, path: str) -> str: + suffix = pathlib.PurePosixPath(path).suffix + if suffix in CXX_SUFFIXES: + c = "//" + elif suffix == ".py": + c = "#" + else: + return text + if any(fnmatch.fnmatch(path, g) for g in BANNER_SKIP): + return text + lines = text.split("\n") + head = 1 if lines and lines[0].startswith("#!") else 0 + spdx = f"{c} SPDX-License-Identifier: MIT" + copy = f"{c} Copyright 2026 {FAMILY_HOLDER}" + window = lines[head:head + 5] + idx = next((i for i, l in enumerate(window) if re.match(re.escape(c) + r"\s*Copyright\b", l)), None) + if idx is not None: + lines[head + idx] = copy + if not any(l.strip() == spdx for l in window): + lines.insert(head + idx, spdx) + elif any(l.strip() == spdx for l in window): + at = head + next(i for i, l in enumerate(window) if l.strip() == spdx) + lines.insert(at + 1, copy) + else: + lines[head:head] = [spdx, copy] + return "\n".join(lines) + + +# -------------------------------------------------------------------------- + +def read_tips() -> dict: + tips = {} + for line in (HERE / "tips.txt").read_text().splitlines(): + m = re.match(r"^(S0|R0)\s+([0-9a-f]{40})\b", line) + if m: + tips[m.group(1)] = m.group(2) + return tips + + +def archive(repo: str, rev: str) -> dict[str, bytes]: + data = subprocess.run(["git", "-C", repo, "archive", "--format=tar", rev], + check=True, capture_output=True).stdout + files = {} + with tarfile.open(fileobj=io.BytesIO(data)) as tar: + for m in tar.getmembers(): + if m.isfile(): + files[m.name] = tar.extractfile(m).read() + elif m.issym(): + files[m.name] = b"@symlink " + m.linkname.encode() + return files + + +def is_text(b: bytes) -> bool: + return b"\0" not in b[:8192] + + +def build(args) -> pathlib.Path: + tips = read_tips() + out = pathlib.Path(args.out) + if out.exists(): + shutil.rmtree(out) + tree: dict[str, bytes] = {} + for key, repo, mapper in (("S0", args.s0_repo, map_s0_path), ("R0", args.r0_repo, map_r0_path)): + for path, blob in archive(repo, tips[key]).items(): + new = mapper(path, args.through) + if new is None: + continue + if new in tree and tree[new] != blob: + sys.exit(f"path collision at {new} ({key}:{path})") + tree[new] = blob + changed_cxx = [] + for path, blob in tree.items(): + if path.startswith("docs/migration/") or not is_text(blob): + continue + text = blob.decode("utf-8", errors="surrogateescape") + new = apply_subs(text, path, args.through) + if at_least(args.through, "3.8"): + new = banner(new, path) + if new != text: + tree[path] = new.encode("utf-8", errors="surrogateescape") + if pathlib.PurePosixPath(path).suffix in CXX_SUFFIXES: + changed_cxx.append(path) + for path, blob in tree.items(): + dst = out / path + dst.parent.mkdir(parents=True, exist_ok=True) + dst.write_bytes(blob) + if changed_cxx and not args.no_format: + fmt = shutil.which("clang-format") + if fmt is None: + sys.exit("clang-format 18.1.3 is required (or pass --no-format)") + subprocess.run([fmt, "-i", "--style=file"] + [str(out / p) for p in changed_cxx], + check=True, cwd=out) + return out + + +# -------------------------------------------------------------------------- +# check: diff the renamed step-0 tree against the tree under test, then +# match each residual hunk against residual/.txt. Entry forms: +# file -- every hunk in matching files is allowed +# hunk -- +# The hash is over the hunk's -/+ lines only (not its line numbers), so it +# survives unrelated edits elsewhere in the file. + +def hunks(diff: str): + path, body = None, [] + for line in diff.splitlines(): + if line.startswith("diff --git"): + if path and body: + yield path, body + path, body = None, [] + # "a/renamed/

b/tree/

"; one side only for an added or + # deleted file, where git names that side twice. + m = re.match(r"diff --git a/(\S+) b/(\S+)", line) + path = re.sub(r"^(renamed|tree)/", "", m.group(1)) if m else line + elif line.startswith("@@"): + if path and body: + yield path, body + body = [] + elif line[:1] in "+-" and not line.startswith(("+++", "---")): + body.append(line) + if path and body: + yield path, body + + +def check(args) -> int: + with tempfile.TemporaryDirectory() as tmp: + tmp = pathlib.Path(tmp) + args.out = str(tmp / "renamed") + build(args) + tree = tmp / "tree" + tree.mkdir() + for path, blob in archive(args.tree, "HEAD").items(): + if path.startswith("docs/migration/"): + continue + (tree / path).parent.mkdir(parents=True, exist_ok=True) + (tree / path).write_bytes(blob) + proc = subprocess.run(["git", "diff", "--no-index", "--no-color", "--no-renames", + "renamed", "tree"], cwd=tmp, capture_output=True, text=True, + errors="surrogateescape") + allow_files, allow_hunks = [], set() + allow = pathlib.Path(args.allow or HERE / "residual" / f"{args.through}.txt") + if allow.exists(): + for line in allow.read_text().splitlines(): + parts = line.split("--")[0].split() + if len(parts) >= 2 and parts[0] == "file": + allow_files.append(parts[1]) + elif len(parts) >= 3 and parts[0] == "hunk": + allow_hunks.add((parts[1], parts[2])) + bad = 0 + for path, body in hunks(proc.stdout): + h = hashlib.sha256("\n".join(body).encode()).hexdigest()[:12] + if any(fnmatch.fnmatch(path, g) for g in allow_files) or (path, h) in allow_hunks: + continue + bad += 1 + print(f"RESIDUAL hunk {path} {h}") + for line in body[:20]: + print(" " + line) + if len(body) > 20: + print(f" ... {len(body) - 20} more lines") + print(f"G14 {args.through}: {bad} unlisted residual hunk(s)") + return 1 if bad else 0 + + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + sub = ap.add_subparsers(dest="cmd", required=True) + for name in ("build", "check"): + p = sub.add_parser(name) + p.add_argument("--through", required=True, choices=STEPS + ["4"]) + p.add_argument("--s0-repo", default=os.environ.get("S0_REPO", "old-async")) + p.add_argument("--r0-repo", default=os.environ.get("R0_REPO", "old-ratio")) + p.add_argument("--no-format", action="store_true") + if name == "build": + p.add_argument("--out", required=True) + else: + p.add_argument("--tree", default=".") + p.add_argument("--allow") + args = ap.parse_args() + if args.cmd == "build": + build(args) + return 0 + return check(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/docs/migration/residual/1c.txt b/docs/migration/residual/1c.txt new file mode 100644 index 0000000..4750075 --- /dev/null +++ b/docs/migration/residual/1c.txt @@ -0,0 +1,29 @@ +# G14 residual allowlist for the step-1c gated SHA. Seeded at step 0 from +# MONOREPO_PLAN.md step 1c; narrowed to hunk entries by the 1c commit. + +# New and rewritten build glue. +file CMakeLists.txt -- new root CMakeLists (1c; draft root-CMakeLists-1c.cmake) +file async/CMakeLists.txt -- tap::dsp guard with explicit binary dir (R2-RUN-3) +file bridge/CMakeLists.txt -- tap::dsp guard; srt_headers -> ../async/include (SYSTEM) +file cmake/arm-cortex-m33-mps2.cmake -- sets both SRT_ and TAP_RATIO_BARE_METAL +file cmake/arm-cortex-m55-mps3.cmake -- sets both SRT_ and TAP_RATIO_BARE_METAL + +# CI: one configure per job, per-(target, engine) correctness legs. +file .github/workflows/*.yml -- 1c CI port (draft ci-after-1c.yml); style.yml from RatioTap +file scripts/fetch_hexagon_toolchain.sh -- RatioTap's copy at root; all cache writers use it + +# Path fix-ups that follow the 1a/1b moves. +file book/src/** -- the 52 moved book includes +file book/book.toml -- include roots, if the moves touch them +file docs/Doxyfile -- INPUT/USE_MDFILE_AS_MAINPAGE for async/ and bridge/ +file scripts/*.py -- icount.py --baselines, update_*_docs.py, book_figures.py paths +file async/notebooks/*.ipynb -- CAPI_DIR / TOOLS_DIR / sys.path after the move +file async/notebooks/*.py -- binding paths after the move +file async/README.md -- LICENSE link -> ../ +file bridge/README.md -- build commands; LICENSE x2, STYLE.md -> ../; "eight workloads" -> ten +file bridge/CLAUDE.md -- build commands +file bridge/notebooks/*.py -- binding paths after the move +file bridge/docs/HISTORY.md -- new: old SHA -> new SHA -> RatioTap PR + +# Licence: D14's line lands in the root LICENSE as bridge/LICENSE goes. +file LICENSE -- D14 holder line diff --git a/docs/migration/residual/README.md b/docs/migration/residual/README.md new file mode 100644 index 0000000..541e5a8 --- /dev/null +++ b/docs/migration/residual/README.md @@ -0,0 +1,23 @@ +# Residual allowlists (G14) + +`rename.py check --through ` applies the mechanical rename map to the +step-0 trees and diffs the result against the tree under test. Every hunk +left over is a *residual*: a change the map does not explain. Each must be +listed in `.txt` here, and each entry is reviewed in the PR that adds +it. + +Entry forms (one per line; `--` starts the reason, which is required): + + file -- every hunk in matching files + hunk -- one hunk, by the hash `check` prints + +Prefer `hunk` entries: a `file` entry lets any later edit to that file +through, so it is for files that are rewritten wholesale (the root +`CMakeLists.txt`, workflows) or that are expected to be hand-edited +throughout a step (prose at 3.7). Allowlists are cumulative in content but +not in effect: each step's file is complete on its own, because the check at +a step compares the whole tree. + +`1c.txt` is the seed written at step 0 from the plan's 1c/2 change lists; +the 1c commit replaces its `file` entries with `hunk` entries wherever a +file changes only in a few places. diff --git a/docs/migration/runs.md b/docs/migration/runs.md new file mode 100644 index 0000000..c9cfca0 --- /dev/null +++ b/docs/migration/runs.md @@ -0,0 +1,41 @@ +# Gate runs + +One row per gated SHA (MONOREPO_PLAN.md section 5): the run IDs of every +named workflow (G13), the runner image, and the Arm toolchain and QEMU +package versions the QEMU legs installed. + +## Step 0 — the snapshot's evidence + +The snapshot-class baselines under `snapshot/` come from these runs of the +two step-0 tips (`tips.txt`), all green, plus local builds of the same tips +where a job does not print what a gate needs. + +| Tip | Workflow | Run | Result | +|---|---|---|---| +| S0 `5e2057f` | CI | 36348916668 | success, 15/15 jobs | +| S0 `5e2057f` | Tap House Style | 36348917285 | success | +| S0 `5e2057f` | book-pages | 36348916693 | success | +| R0 `8f19e8b` | CI | 36356088654 | success, 8/8 jobs | +| R0 `8f19e8b` | Tap House Style | 36356089470 | success | + +QEMU legs (both tips): runner image `ubuntu-24.04` version 20260920.314.1, +`gcc-arm-none-eabi` 15:13.2.rel1-2, `qemu-system-arm` 1:8.2.2+ds-0ubuntu1.18. + +| Snapshot | Source | +|---|---| +| `g1/-.txt` | the ctest summary of each CI job's log (host jobs), or the job's uploaded `--output-log` (QEMU jobs); `collect.py ctest-log` | +| `g1/-labels.txt` | `ctest --show-only=json-v1` of a local Linux GCC Release build of each tip with the C ABI ON (77 and 82 tests; the C ABI adds none); `collect.py ctest-json` | +| `g2/-.txt` | the `ctest-` artifacts of the runs above; `collect.py gtest-runs` | +| `g6.txt` | the local R0 build's cross-validation test (GCC 13, x86-64), byte-identical to R0's P.4 measurement; `collect.py xval` | +| `g10/.txt` | `libsrt_capi.so` / `libratio_capi.so` from the local builds; `collect.py symbols` | +| `g12/.txt` | fresh full clones at S0/R0; `collect.py history` | +| `g9.txt` | the retired-identifier rule itself (applies from 3.7) | + +G8 (book and API docs) has no file: its baseline is the S0 CI run's green +"Book build" job (mdBook with warnings as errors, image check). The Doxygen +check joins the book job at 1c. + +## Gated SHAs + +| Step | SHA | ci.yml | style.yml | migration-gates | ci-arm64 | compare | Image | Notes | +|---|---|---|---|---|---|---|---|---| diff --git a/docs/migration/snapshot/g1/async-asan-ubsan.txt b/docs/migration/snapshot/g1/async-asan-ubsan.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-asan-ubsan.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-hexagon.txt b/docs/migration/snapshot/g1/async-hexagon.txt new file mode 100644 index 0000000..c11443a --- /dev/null +++ b/docs/migration/snapshot/g1/async-hexagon.txt @@ -0,0 +1,49 @@ +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-labels.txt b/docs/migration/snapshot/g1/async-labels.txt new file mode 100644 index 0000000..347d198 --- /dev/null +++ b/docs/migration/snapshot/g1/async-labels.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset async +async.AsrcLock.RecoversFromConsumerStall async +async.AsrcLock.TracksDriftRampWithoutUnlocking async +async.AsrcLock.WholeSampleSlipsAreGlitchFree async +async.AsrcQuality.Balanced12kHz async +async.AsrcQuality.Balanced19_5kHz async +async.AsrcQuality.Balanced6kHz async +async.AsrcQuality.Balanced997Hz async +async.AsrcQuality.Transparent19_5kHz async +async.AsrcQuality.Transparent997Hz async +async.AsrcQuality16k.Balanced2kHz async +async.AsrcQuality16k.Balanced333Hz async +async.AsrcQuality16k.Balanced4kHz async +async.AsrcQuality16k.Balanced6_5kHz async +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly async +async.ConfigValidation.RejectsSilentMisbehavior async +async.EdgeCalls.ZeroLengthAndOversized async +async.Fade.OutputRampsAfterFill async +async.FadeQ15.OutputRampsAfterFill async +async.Feasibility.Pull128LocksCleanly async +async.Feasibility.Pull240LocksCleanly async +async.Feasibility.Pull64LocksCleanly async +async.Feasibility.SmallPullsKeepConfiguredSetpoint async +async.FixedPoint.AsrcQualityQ15_997Hz async +async.FixedPoint.AsrcQualityQ31_19_5kHz async +async.FixedPoint.AsrcQualityQ31_997Hz async +async.FixedPoint.CoefficientConversionRoundsAndSaturates async +async.FixedPoint.DcGainIsUnityQ15 async +async.FixedPoint.DcGainIsUnityQ31 async +async.FixedPoint.FinalizeSaturates async +async.FixedPoint.FullScaleSineDoesNotWrapQ15 async +async.FixedPoint.RowSumsAreExactQ15 async +async.FixedPoint.RowSumsAreExactQ31 async +async.Kaiser.BalancedPrototypeMeetsSpec async +async.Kaiser.BesselI0ReferenceValues async +async.Kaiser.BetaReferenceValues async +async.Kaiser.CompensatedBranchSumsAreUniform async +async.Kaiser.CompensatedSpecsHoldAt16k async +async.Kaiser.EconomyPrototypeMeetsSpec async +async.Kaiser.FastPrototypeMeetsSpec async +async.Kaiser.TapEstimateMatchesHarrisFormula async +async.Kaiser.TransparentPrototypeMeetsSpec async +async.Latency.DesignedLatencyConsistency async +async.Latency.ImpulseDelayMatchesDesignedLatency async +async.MultiChannel.Independence12chFloat async +async.MultiChannel.Independence16chQ15 async +async.MultiChannelShort.Independence12chQ15 async +async.MultiChannelShort.Independence5chFloat async +async.MultiChannelShort.Independence7chFloat async +async.OutputHash.Balanced async +async.OutputHash.Economy async +async.OutputHash.Fast async +async.OutputHash.Transparent async +async.Polyphase.DcGainIsUnityAcrossMu async +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap async +async.Polyphase.FractionalDelayAccuracyBalanced async +async.Polyphase.FractionalDelayAccuracyTransparent async +async.Polyphase.MuWrapIsContinuousWithWindowShift async +async.ProgramWeighted.BalancedBaseline async +async.ProgramWeighted.EconomyNearBalanced async +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented async +async.ProgramWeighted.InstrumentFloor async +async.QuickQuality.FullScaleQ15Short async +async.QuickQuality.Q15Tone997 async +async.Reset.ConsumerResetRelocks async +async.Resync.SmallSetpointRecovers async +async.Servo.BandwidthSwitchIsTransientFree async +async.Servo.ClampsToMaxDeviation async +async.Servo.DropoutResetKeepsPpmEstimate async +async.Servo.LocksFromConstantOffsetAndNullsError async +async.Servo.TracksSlowDriftRampWithBoundedLag async +async.spsc_ring.CapacityRoundsUpToPowerOfTwo async +async.spsc_ring.DiscardAdvancesConsumer async +async.spsc_ring.FillDrainExactness async +async.spsc_ring.PartialWriteWhenNearlyFull async +async.spsc_ring.TwoThreadStressPreservesSequence async +async.spsc_ring.WrapAroundPreservesData async diff --git a/docs/migration/snapshot/g1/async-linux-clang.txt b/docs/migration/snapshot/g1/async-linux-clang.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-linux-clang.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-linux-gcc.txt b/docs/migration/snapshot/g1/async-linux-gcc.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-linux-gcc.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-m33.txt b/docs/migration/snapshot/g1/async-m33.txt new file mode 100644 index 0000000..3f151dc --- /dev/null +++ b/docs/migration/snapshot/g1/async-m33.txt @@ -0,0 +1 @@ +srt_tests_emulated diff --git a/docs/migration/snapshot/g1/async-m55.txt b/docs/migration/snapshot/g1/async-m55.txt new file mode 100644 index 0000000..3f151dc --- /dev/null +++ b/docs/migration/snapshot/g1/async-m55.txt @@ -0,0 +1 @@ +srt_tests_emulated diff --git a/docs/migration/snapshot/g1/async-macos-appleclang.txt b/docs/migration/snapshot/g1/async-macos-appleclang.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-macos-appleclang.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-tsan.txt b/docs/migration/snapshot/g1/async-tsan.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-tsan.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/async-windows-msvc.txt b/docs/migration/snapshot/g1/async-windows-msvc.txt new file mode 100644 index 0000000..3368dda --- /dev/null +++ b/docs/migration/snapshot/g1/async-windows-msvc.txt @@ -0,0 +1,77 @@ +async.AsrcLock.LocksAndHoldsAtConstantOffset +async.AsrcLock.RecoversFromConsumerStall +async.AsrcLock.TracksDriftRampWithoutUnlocking +async.AsrcLock.WholeSampleSlipsAreGlitchFree +async.AsrcQuality.Balanced12kHz +async.AsrcQuality.Balanced19_5kHz +async.AsrcQuality.Balanced6kHz +async.AsrcQuality.Balanced997Hz +async.AsrcQuality.Transparent19_5kHz +async.AsrcQuality.Transparent997Hz +async.AsrcQuality16k.Balanced2kHz +async.AsrcQuality16k.Balanced333Hz +async.AsrcQuality16k.Balanced4kHz +async.AsrcQuality16k.Balanced6_5kHz +async.AsrcQuality16k.ForSampleRateScalesHzFieldsOnly +async.ConfigValidation.RejectsSilentMisbehavior +async.EdgeCalls.ZeroLengthAndOversized +async.Fade.OutputRampsAfterFill +async.FadeQ15.OutputRampsAfterFill +async.Feasibility.Pull128LocksCleanly +async.Feasibility.Pull240LocksCleanly +async.Feasibility.Pull64LocksCleanly +async.Feasibility.SmallPullsKeepConfiguredSetpoint +async.FixedPoint.AsrcQualityQ15_997Hz +async.FixedPoint.AsrcQualityQ31_19_5kHz +async.FixedPoint.AsrcQualityQ31_997Hz +async.FixedPoint.CoefficientConversionRoundsAndSaturates +async.FixedPoint.DcGainIsUnityQ15 +async.FixedPoint.DcGainIsUnityQ31 +async.FixedPoint.FinalizeSaturates +async.FixedPoint.FullScaleSineDoesNotWrapQ15 +async.FixedPoint.RowSumsAreExactQ15 +async.FixedPoint.RowSumsAreExactQ31 +async.Kaiser.BalancedPrototypeMeetsSpec +async.Kaiser.BesselI0ReferenceValues +async.Kaiser.BetaReferenceValues +async.Kaiser.CompensatedBranchSumsAreUniform +async.Kaiser.CompensatedSpecsHoldAt16k +async.Kaiser.EconomyPrototypeMeetsSpec +async.Kaiser.FastPrototypeMeetsSpec +async.Kaiser.TapEstimateMatchesHarrisFormula +async.Kaiser.TransparentPrototypeMeetsSpec +async.Latency.DesignedLatencyConsistency +async.Latency.ImpulseDelayMatchesDesignedLatency +async.MultiChannel.Independence12chFloat +async.MultiChannel.Independence16chQ15 +async.MultiChannelShort.Independence12chQ15 +async.MultiChannelShort.Independence5chFloat +async.MultiChannelShort.Independence7chFloat +async.OutputHash.Balanced +async.OutputHash.Economy +async.OutputHash.Fast +async.OutputHash.Transparent +async.Polyphase.DcGainIsUnityAcrossMu +async.Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +async.Polyphase.FractionalDelayAccuracyBalanced +async.Polyphase.FractionalDelayAccuracyTransparent +async.Polyphase.MuWrapIsContinuousWithWindowShift +async.ProgramWeighted.BalancedBaseline +async.ProgramWeighted.EconomyNearBalanced +async.ProgramWeighted.EconomyWorstCaseSineIsDocumented +async.ProgramWeighted.InstrumentFloor +async.QuickQuality.FullScaleQ15Short +async.QuickQuality.Q15Tone997 +async.Reset.ConsumerResetRelocks +async.Resync.SmallSetpointRecovers +async.Servo.BandwidthSwitchIsTransientFree +async.Servo.ClampsToMaxDeviation +async.Servo.DropoutResetKeepsPpmEstimate +async.Servo.LocksFromConstantOffsetAndNullsError +async.Servo.TracksSlowDriftRampWithBoundedLag +async.spsc_ring.CapacityRoundsUpToPowerOfTwo +async.spsc_ring.DiscardAdvancesConsumer +async.spsc_ring.FillDrainExactness +async.spsc_ring.PartialWriteWhenNearlyFull +async.spsc_ring.TwoThreadStressPreservesSequence +async.spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g1/bridge-asan-ubsan.txt b/docs/migration/snapshot/g1/bridge-asan-ubsan.txt new file mode 100644 index 0000000..1120706 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-asan-ubsan.txt @@ -0,0 +1,82 @@ +ratio.Converter.AccountingExactFromEveryPositionDown +ratio.Converter.AccountingExactFromEveryPositionUp +ratio.Converter.FlushDrainsTailToSilence +ratio.Converter.ImpulseReproducesTableCustomTaps +ratio.Converter.ImpulseReproducesTableDown +ratio.Converter.ImpulseReproducesTableDownTransparent +ratio.Converter.ImpulseReproducesTableUp +ratio.Converter.LatencyAndValidation +ratio.Converter.MatchesScipyDownBalanced +ratio.Converter.MatchesScipyDownEconomy +ratio.Converter.MatchesScipyDownSuperEconomy +ratio.Converter.MatchesScipyDownTransparent +ratio.Converter.MatchesScipyUpBalanced +ratio.Converter.MatchesScipyUpEconomy +ratio.Converter.MatchesScipyUpSuperEconomy +ratio.Converter.MatchesScipyUpTransparent +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor +ratio.Converter.PassbandSineIsTransparentTransparent +ratio.Converter.PullMatchesProcessBitExact +ratio.Converter.PullShortReturnsOnDryThenResumes +ratio.Converter.ResetReproducesBitExactly +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec +ratio.Converter.TwoChannelsAreIndependent +ratio.CrossValidation.DownEconomyAgainstAsync1024 +ratio.CrossValidation.DownEconomyAgainstAsync512 +ratio.CrossValidation.UpEconomyAgainstAsync1024 +ratio.CrossValidation.UpEconomyAgainstAsync512 +ratio.Design.BadProfilesThrow +ratio.Design.DirectionsAreAsymmetric +ratio.Design.DownBalancedMeetsSpec +ratio.Design.DownEconomyMeetsSpec +ratio.Design.DownSuperEconomyMeetsSpec +ratio.Design.DownTransparentMeetsSpec +ratio.Design.UpBalancedMeetsSpec +ratio.Design.UpEconomyMeetsSpec +ratio.Design.UpSuperEconomyMeetsSpec +ratio.Design.UpTransparentMeetsSpec +ratio.FixedPoint.DcEveryPhaseQ15Down +ratio.FixedPoint.DcEveryPhaseQ15Up +ratio.FixedPoint.DcEveryPhaseQ31Down +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 +ratio.FixedPoint.PullMatchesProcessBitExactQ15 +ratio.FixedPoint.Q15SineQualityEconomyDown +ratio.FixedPoint.Q15SineQualityTransparentDown +ratio.FixedPoint.Q31MatchesFloatDownEconomy +ratio.FixedPoint.Q31MatchesFloatUpTransparent +ratio.FixedPoint.Q31SineQualityEconomyUp +ratio.FixedPoint.Q31SineQualityTransparentDown +ratio.OutputHash.Balanced +ratio.OutputHash.Economy +ratio.OutputHash.SuperEconomy +ratio.OutputHash.Transparent +ratio.PhaseTable.StorageBudgetsArePinned +ratio.Schedule.DownExhaustive +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks +ratio.Schedule.UpExhaustive +ratio.Skeleton.IdentityConstants +ratio.Skeleton.SubstrateIsWiredEndToEnd +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g1/bridge-hexagon.txt b/docs/migration/snapshot/g1/bridge-hexagon.txt new file mode 100644 index 0000000..095f9a6 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-hexagon.txt @@ -0,0 +1,80 @@ +ratio.Converter.AccountingExactFromEveryPositionDown +ratio.Converter.AccountingExactFromEveryPositionUp +ratio.Converter.FlushDrainsTailToSilence +ratio.Converter.ImpulseReproducesTableCustomTaps +ratio.Converter.ImpulseReproducesTableDown +ratio.Converter.ImpulseReproducesTableDownTransparent +ratio.Converter.ImpulseReproducesTableUp +ratio.Converter.MatchesScipyDownBalanced +ratio.Converter.MatchesScipyDownEconomy +ratio.Converter.MatchesScipyDownSuperEconomy +ratio.Converter.MatchesScipyDownTransparent +ratio.Converter.MatchesScipyUpBalanced +ratio.Converter.MatchesScipyUpEconomy +ratio.Converter.MatchesScipyUpSuperEconomy +ratio.Converter.MatchesScipyUpTransparent +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor +ratio.Converter.PassbandSineIsTransparentTransparent +ratio.Converter.PullMatchesProcessBitExact +ratio.Converter.PullShortReturnsOnDryThenResumes +ratio.Converter.ResetReproducesBitExactly +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec +ratio.Converter.TwoChannelsAreIndependent +ratio.CrossValidation.DownEconomyAgainstAsync1024 +ratio.CrossValidation.DownEconomyAgainstAsync512 +ratio.CrossValidation.UpEconomyAgainstAsync1024 +ratio.CrossValidation.UpEconomyAgainstAsync512 +ratio.Design.DirectionsAreAsymmetric +ratio.Design.DownBalancedMeetsSpec +ratio.Design.DownEconomyMeetsSpec +ratio.Design.DownSuperEconomyMeetsSpec +ratio.Design.DownTransparentMeetsSpec +ratio.Design.UpBalancedMeetsSpec +ratio.Design.UpEconomyMeetsSpec +ratio.Design.UpSuperEconomyMeetsSpec +ratio.Design.UpTransparentMeetsSpec +ratio.FixedPoint.DcEveryPhaseQ15Down +ratio.FixedPoint.DcEveryPhaseQ15Up +ratio.FixedPoint.DcEveryPhaseQ31Down +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 +ratio.FixedPoint.PullMatchesProcessBitExactQ15 +ratio.FixedPoint.Q15SineQualityEconomyDown +ratio.FixedPoint.Q15SineQualityTransparentDown +ratio.FixedPoint.Q31MatchesFloatDownEconomy +ratio.FixedPoint.Q31MatchesFloatUpTransparent +ratio.FixedPoint.Q31SineQualityEconomyUp +ratio.FixedPoint.Q31SineQualityTransparentDown +ratio.OutputHash.Balanced +ratio.OutputHash.Economy +ratio.OutputHash.SuperEconomy +ratio.OutputHash.Transparent +ratio.PhaseTable.StorageBudgetsArePinned +ratio.Schedule.DownExhaustive +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks +ratio.Schedule.UpExhaustive +ratio.Skeleton.IdentityConstants +ratio.Skeleton.SubstrateIsWiredEndToEnd +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g1/bridge-labels.txt b/docs/migration/snapshot/g1/bridge-labels.txt new file mode 100644 index 0000000..6addb69 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-labels.txt @@ -0,0 +1,82 @@ +ratio.Converter.AccountingExactFromEveryPositionDown ratio +ratio.Converter.AccountingExactFromEveryPositionUp ratio +ratio.Converter.FlushDrainsTailToSilence ratio +ratio.Converter.ImpulseReproducesTableCustomTaps ratio +ratio.Converter.ImpulseReproducesTableDown ratio +ratio.Converter.ImpulseReproducesTableDownTransparent ratio +ratio.Converter.ImpulseReproducesTableUp ratio +ratio.Converter.LatencyAndValidation ratio +ratio.Converter.MatchesScipyDownBalanced ratio +ratio.Converter.MatchesScipyDownEconomy ratio +ratio.Converter.MatchesScipyDownSuperEconomy ratio +ratio.Converter.MatchesScipyDownTransparent ratio +ratio.Converter.MatchesScipyUpBalanced ratio +ratio.Converter.MatchesScipyUpEconomy ratio +ratio.Converter.MatchesScipyUpSuperEconomy ratio +ratio.Converter.MatchesScipyUpTransparent ratio +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor ratio +ratio.Converter.PassbandSineIsTransparentTransparent ratio +ratio.Converter.PullMatchesProcessBitExact ratio +ratio.Converter.PullShortReturnsOnDryThenResumes ratio +ratio.Converter.ResetReproducesBitExactly ratio +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec ratio +ratio.Converter.TwoChannelsAreIndependent ratio +ratio.CrossValidation.DownEconomyAgainstAsync1024 ratio +ratio.CrossValidation.DownEconomyAgainstAsync512 ratio +ratio.CrossValidation.UpEconomyAgainstAsync1024 ratio +ratio.CrossValidation.UpEconomyAgainstAsync512 ratio +ratio.Design.BadProfilesThrow ratio +ratio.Design.DirectionsAreAsymmetric ratio +ratio.Design.DownBalancedMeetsSpec ratio +ratio.Design.DownEconomyMeetsSpec ratio +ratio.Design.DownSuperEconomyMeetsSpec ratio +ratio.Design.DownTransparentMeetsSpec ratio +ratio.Design.UpBalancedMeetsSpec ratio +ratio.Design.UpEconomyMeetsSpec ratio +ratio.Design.UpSuperEconomyMeetsSpec ratio +ratio.Design.UpTransparentMeetsSpec ratio +ratio.FixedPoint.DcEveryPhaseQ15Down ratio +ratio.FixedPoint.DcEveryPhaseQ15Up ratio +ratio.FixedPoint.DcEveryPhaseQ31Down ratio +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 ratio +ratio.FixedPoint.PullMatchesProcessBitExactQ15 ratio +ratio.FixedPoint.Q15SineQualityEconomyDown ratio +ratio.FixedPoint.Q15SineQualityTransparentDown ratio +ratio.FixedPoint.Q31MatchesFloatDownEconomy ratio +ratio.FixedPoint.Q31MatchesFloatUpTransparent ratio +ratio.FixedPoint.Q31SineQualityEconomyUp ratio +ratio.FixedPoint.Q31SineQualityTransparentDown ratio +ratio.OutputHash.Balanced ratio +ratio.OutputHash.Economy ratio +ratio.OutputHash.SuperEconomy ratio +ratio.OutputHash.Transparent ratio +ratio.PhaseTable.StorageBudgetsArePinned ratio +ratio.Schedule.DownExhaustive ratio +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks ratio +ratio.Schedule.UpExhaustive ratio +ratio.Skeleton.IdentityConstants ratio +ratio.Skeleton.SubstrateIsWiredEndToEnd ratio +ratio.phase_table_test.DownBalancedEveryPhase ratio +ratio.phase_table_test.DownBalancedEveryPhase ratio +ratio.phase_table_test.DownBalancedEveryPhase ratio +ratio.phase_table_test.DownEconomyEveryPhase ratio +ratio.phase_table_test.DownEconomyEveryPhase ratio +ratio.phase_table_test.DownEconomyEveryPhase ratio +ratio.phase_table_test.DownSuperEconomyEveryPhase ratio +ratio.phase_table_test.DownSuperEconomyEveryPhase ratio +ratio.phase_table_test.DownSuperEconomyEveryPhase ratio +ratio.phase_table_test.DownTransparentEveryPhase ratio +ratio.phase_table_test.DownTransparentEveryPhase ratio +ratio.phase_table_test.DownTransparentEveryPhase ratio +ratio.phase_table_test.UpBalancedEveryPhase ratio +ratio.phase_table_test.UpBalancedEveryPhase ratio +ratio.phase_table_test.UpBalancedEveryPhase ratio +ratio.phase_table_test.UpEconomyEveryPhase ratio +ratio.phase_table_test.UpEconomyEveryPhase ratio +ratio.phase_table_test.UpEconomyEveryPhase ratio +ratio.phase_table_test.UpSuperEconomyEveryPhase ratio +ratio.phase_table_test.UpSuperEconomyEveryPhase ratio +ratio.phase_table_test.UpSuperEconomyEveryPhase ratio +ratio.phase_table_test.UpTransparentEveryPhase ratio +ratio.phase_table_test.UpTransparentEveryPhase ratio +ratio.phase_table_test.UpTransparentEveryPhase ratio diff --git a/docs/migration/snapshot/g1/bridge-linux.txt b/docs/migration/snapshot/g1/bridge-linux.txt new file mode 100644 index 0000000..1120706 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-linux.txt @@ -0,0 +1,82 @@ +ratio.Converter.AccountingExactFromEveryPositionDown +ratio.Converter.AccountingExactFromEveryPositionUp +ratio.Converter.FlushDrainsTailToSilence +ratio.Converter.ImpulseReproducesTableCustomTaps +ratio.Converter.ImpulseReproducesTableDown +ratio.Converter.ImpulseReproducesTableDownTransparent +ratio.Converter.ImpulseReproducesTableUp +ratio.Converter.LatencyAndValidation +ratio.Converter.MatchesScipyDownBalanced +ratio.Converter.MatchesScipyDownEconomy +ratio.Converter.MatchesScipyDownSuperEconomy +ratio.Converter.MatchesScipyDownTransparent +ratio.Converter.MatchesScipyUpBalanced +ratio.Converter.MatchesScipyUpEconomy +ratio.Converter.MatchesScipyUpSuperEconomy +ratio.Converter.MatchesScipyUpTransparent +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor +ratio.Converter.PassbandSineIsTransparentTransparent +ratio.Converter.PullMatchesProcessBitExact +ratio.Converter.PullShortReturnsOnDryThenResumes +ratio.Converter.ResetReproducesBitExactly +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec +ratio.Converter.TwoChannelsAreIndependent +ratio.CrossValidation.DownEconomyAgainstAsync1024 +ratio.CrossValidation.DownEconomyAgainstAsync512 +ratio.CrossValidation.UpEconomyAgainstAsync1024 +ratio.CrossValidation.UpEconomyAgainstAsync512 +ratio.Design.BadProfilesThrow +ratio.Design.DirectionsAreAsymmetric +ratio.Design.DownBalancedMeetsSpec +ratio.Design.DownEconomyMeetsSpec +ratio.Design.DownSuperEconomyMeetsSpec +ratio.Design.DownTransparentMeetsSpec +ratio.Design.UpBalancedMeetsSpec +ratio.Design.UpEconomyMeetsSpec +ratio.Design.UpSuperEconomyMeetsSpec +ratio.Design.UpTransparentMeetsSpec +ratio.FixedPoint.DcEveryPhaseQ15Down +ratio.FixedPoint.DcEveryPhaseQ15Up +ratio.FixedPoint.DcEveryPhaseQ31Down +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 +ratio.FixedPoint.PullMatchesProcessBitExactQ15 +ratio.FixedPoint.Q15SineQualityEconomyDown +ratio.FixedPoint.Q15SineQualityTransparentDown +ratio.FixedPoint.Q31MatchesFloatDownEconomy +ratio.FixedPoint.Q31MatchesFloatUpTransparent +ratio.FixedPoint.Q31SineQualityEconomyUp +ratio.FixedPoint.Q31SineQualityTransparentDown +ratio.OutputHash.Balanced +ratio.OutputHash.Economy +ratio.OutputHash.SuperEconomy +ratio.OutputHash.Transparent +ratio.PhaseTable.StorageBudgetsArePinned +ratio.Schedule.DownExhaustive +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks +ratio.Schedule.UpExhaustive +ratio.Skeleton.IdentityConstants +ratio.Skeleton.SubstrateIsWiredEndToEnd +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g1/bridge-m33.txt b/docs/migration/snapshot/g1/bridge-m33.txt new file mode 100644 index 0000000..0612ee2 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-m33.txt @@ -0,0 +1 @@ +tap_ratio_tests_emulated diff --git a/docs/migration/snapshot/g1/bridge-m55.txt b/docs/migration/snapshot/g1/bridge-m55.txt new file mode 100644 index 0000000..0612ee2 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-m55.txt @@ -0,0 +1 @@ +tap_ratio_tests_emulated diff --git a/docs/migration/snapshot/g1/bridge-macos.txt b/docs/migration/snapshot/g1/bridge-macos.txt new file mode 100644 index 0000000..1120706 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-macos.txt @@ -0,0 +1,82 @@ +ratio.Converter.AccountingExactFromEveryPositionDown +ratio.Converter.AccountingExactFromEveryPositionUp +ratio.Converter.FlushDrainsTailToSilence +ratio.Converter.ImpulseReproducesTableCustomTaps +ratio.Converter.ImpulseReproducesTableDown +ratio.Converter.ImpulseReproducesTableDownTransparent +ratio.Converter.ImpulseReproducesTableUp +ratio.Converter.LatencyAndValidation +ratio.Converter.MatchesScipyDownBalanced +ratio.Converter.MatchesScipyDownEconomy +ratio.Converter.MatchesScipyDownSuperEconomy +ratio.Converter.MatchesScipyDownTransparent +ratio.Converter.MatchesScipyUpBalanced +ratio.Converter.MatchesScipyUpEconomy +ratio.Converter.MatchesScipyUpSuperEconomy +ratio.Converter.MatchesScipyUpTransparent +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor +ratio.Converter.PassbandSineIsTransparentTransparent +ratio.Converter.PullMatchesProcessBitExact +ratio.Converter.PullShortReturnsOnDryThenResumes +ratio.Converter.ResetReproducesBitExactly +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec +ratio.Converter.TwoChannelsAreIndependent +ratio.CrossValidation.DownEconomyAgainstAsync1024 +ratio.CrossValidation.DownEconomyAgainstAsync512 +ratio.CrossValidation.UpEconomyAgainstAsync1024 +ratio.CrossValidation.UpEconomyAgainstAsync512 +ratio.Design.BadProfilesThrow +ratio.Design.DirectionsAreAsymmetric +ratio.Design.DownBalancedMeetsSpec +ratio.Design.DownEconomyMeetsSpec +ratio.Design.DownSuperEconomyMeetsSpec +ratio.Design.DownTransparentMeetsSpec +ratio.Design.UpBalancedMeetsSpec +ratio.Design.UpEconomyMeetsSpec +ratio.Design.UpSuperEconomyMeetsSpec +ratio.Design.UpTransparentMeetsSpec +ratio.FixedPoint.DcEveryPhaseQ15Down +ratio.FixedPoint.DcEveryPhaseQ15Up +ratio.FixedPoint.DcEveryPhaseQ31Down +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 +ratio.FixedPoint.PullMatchesProcessBitExactQ15 +ratio.FixedPoint.Q15SineQualityEconomyDown +ratio.FixedPoint.Q15SineQualityTransparentDown +ratio.FixedPoint.Q31MatchesFloatDownEconomy +ratio.FixedPoint.Q31MatchesFloatUpTransparent +ratio.FixedPoint.Q31SineQualityEconomyUp +ratio.FixedPoint.Q31SineQualityTransparentDown +ratio.OutputHash.Balanced +ratio.OutputHash.Economy +ratio.OutputHash.SuperEconomy +ratio.OutputHash.Transparent +ratio.PhaseTable.StorageBudgetsArePinned +ratio.Schedule.DownExhaustive +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks +ratio.Schedule.UpExhaustive +ratio.Skeleton.IdentityConstants +ratio.Skeleton.SubstrateIsWiredEndToEnd +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g1/bridge-windows.txt b/docs/migration/snapshot/g1/bridge-windows.txt new file mode 100644 index 0000000..1120706 --- /dev/null +++ b/docs/migration/snapshot/g1/bridge-windows.txt @@ -0,0 +1,82 @@ +ratio.Converter.AccountingExactFromEveryPositionDown +ratio.Converter.AccountingExactFromEveryPositionUp +ratio.Converter.FlushDrainsTailToSilence +ratio.Converter.ImpulseReproducesTableCustomTaps +ratio.Converter.ImpulseReproducesTableDown +ratio.Converter.ImpulseReproducesTableDownTransparent +ratio.Converter.ImpulseReproducesTableUp +ratio.Converter.LatencyAndValidation +ratio.Converter.MatchesScipyDownBalanced +ratio.Converter.MatchesScipyDownEconomy +ratio.Converter.MatchesScipyDownSuperEconomy +ratio.Converter.MatchesScipyDownTransparent +ratio.Converter.MatchesScipyUpBalanced +ratio.Converter.MatchesScipyUpEconomy +ratio.Converter.MatchesScipyUpSuperEconomy +ratio.Converter.MatchesScipyUpTransparent +ratio.Converter.PassbandSineEconomyHitsTheImagingFloor +ratio.Converter.PassbandSineIsTransparentTransparent +ratio.Converter.PullMatchesProcessBitExact +ratio.Converter.PullShortReturnsOnDryThenResumes +ratio.Converter.ResetReproducesBitExactly +ratio.Converter.StopbandToneProductsAreBoundedByTheSpec +ratio.Converter.TwoChannelsAreIndependent +ratio.CrossValidation.DownEconomyAgainstAsync1024 +ratio.CrossValidation.DownEconomyAgainstAsync512 +ratio.CrossValidation.UpEconomyAgainstAsync1024 +ratio.CrossValidation.UpEconomyAgainstAsync512 +ratio.Design.BadProfilesThrow +ratio.Design.DirectionsAreAsymmetric +ratio.Design.DownBalancedMeetsSpec +ratio.Design.DownEconomyMeetsSpec +ratio.Design.DownSuperEconomyMeetsSpec +ratio.Design.DownTransparentMeetsSpec +ratio.Design.UpBalancedMeetsSpec +ratio.Design.UpEconomyMeetsSpec +ratio.Design.UpSuperEconomyMeetsSpec +ratio.Design.UpTransparentMeetsSpec +ratio.FixedPoint.DcEveryPhaseQ15Down +ratio.FixedPoint.DcEveryPhaseQ15Up +ratio.FixedPoint.DcEveryPhaseQ31Down +ratio.FixedPoint.FullScaleSineDoesNotWrapQ15 +ratio.FixedPoint.PullMatchesProcessBitExactQ15 +ratio.FixedPoint.Q15SineQualityEconomyDown +ratio.FixedPoint.Q15SineQualityTransparentDown +ratio.FixedPoint.Q31MatchesFloatDownEconomy +ratio.FixedPoint.Q31MatchesFloatUpTransparent +ratio.FixedPoint.Q31SineQualityEconomyUp +ratio.FixedPoint.Q31SineQualityTransparentDown +ratio.OutputHash.Balanced +ratio.OutputHash.Economy +ratio.OutputHash.SuperEconomy +ratio.OutputHash.Transparent +ratio.PhaseTable.StorageBudgetsArePinned +ratio.Schedule.DownExhaustive +ratio.Schedule.FramesNeededIsPositionInvariantOverSuperblocks +ratio.Schedule.UpExhaustive +ratio.Skeleton.IdentityConstants +ratio.Skeleton.SubstrateIsWiredEndToEnd +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownBalancedEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownSuperEconomyEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.DownTransparentEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpBalancedEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpSuperEconomyEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase +ratio.phase_table_test.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g10/async.txt b/docs/migration/snapshot/g10/async.txt new file mode 100644 index 0000000..36c877c --- /dev/null +++ b/docs/migration/snapshot/g10/async.txt @@ -0,0 +1,8 @@ +T srt_create +T srt_designed_latency_seconds +T srt_destroy +T srt_pull +T srt_push +T srt_reset_from_consumer +T srt_status +T srt_version diff --git a/docs/migration/snapshot/g10/bridge.txt b/docs/migration/snapshot/g10/bridge.txt new file mode 100644 index 0000000..a240e60 --- /dev/null +++ b/docs/migration/snapshot/g10/bridge.txt @@ -0,0 +1,11 @@ +T ratio_create +T ratio_destroy +T ratio_flush +T ratio_flush_output_frames +T ratio_frames_needed +T ratio_latency_input_frames +T ratio_outputs_for +T ratio_process +T ratio_reset +T ratio_taps +T ratio_version diff --git a/docs/migration/snapshot/g12/async.txt b/docs/migration/snapshot/g12/async.txt new file mode 100644 index 0000000..bf04522 --- /dev/null +++ b/docs/migration/snapshot/g12/async.txt @@ -0,0 +1,240 @@ +== include/srt/asrc.h commits=13 lines=415 +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-09T15:06:54+00:00 Finish the style migration's two loose ends +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-07T00:18:09+00:00 Enforce mandatory, expanded braces on control flow +log 2026-07-07T00:17:21+00:00 Migrate identifiers to Tap House Rules snake_case +log 2026-07-06T17:11:48+00:00 Apply clang-format under the new shared style +log 2026-07-01T21:30:40+00:00 book: Part 0 - the problem and its budgets +log 2026-07-01T21:29:39+00:00 book: the composition chapter (asrc.hpp) with the feasibility-bug case study +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T20:40:38+00:00 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +log 2026-06-10T11:44:39+00:00 Address self-review findings: RT contract, validation, CI hardening +log 2026-06-10T02:16:52+00:00 Add Q15/Q31 fixed-point sample type support +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 16 1781054936 Add near-unity ASRC library core with test suite +blame 3 1781091879 Address self-review findings: RT contract, validation, CI hardening +blame 2 1781296838 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +blame 5 1781303209 Core hardening from the package audit (PR A) +blame 170 1783357908 Apply clang-format under the new shared style +blame 180 1783383441 Migrate identifiers to Tap House Rules snake_case +blame 20 1783383489 Enforce mandatory, expanded braces on control flow +blame 9 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 7 1783609614 Finish the style migration's two loose ends +blame 3 1784643949 Rename the C++ namespace srt -> tap::samplerate +== include/srt/polyphase_filter.h commits=23 lines=519 +log 2026-07-23T12:42:45+00:00 Adopt the DspTap FIR substrate via submodule +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-09T15:06:54+00:00 Finish the style migration's two loose ends +log 2026-07-09T14:32:00+00:00 Row-sum-preserving quantization + Appendix D, ported to Tap House Rules +log 2026-07-09T13:42:06+00:00 Propagate the snake_case API rename into bench/ and a gated header path +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-07T00:18:09+00:00 Enforce mandatory, expanded braces on control flow +log 2026-07-07T00:17:21+00:00 Migrate identifiers to Tap House Rules snake_case +log 2026-07-06T17:11:48+00:00 Apply clang-format under the new shared style +log 2026-07-04T20:40:50+00:00 Compensated prototype design (k*fs zeros), economy preset, program-weighted metric +log 2026-07-01T21:37:09+00:00 book: the optimization campaign chapters (C1-C6) +log 2026-07-01T21:36:16+00:00 book: the servo and the fractional resampler chapters +log 2026-07-01T21:35:05+00:00 book: filter design chapters (kaiser.hpp and the polyphase bank) +log 2026-07-01T21:30:40+00:00 book: Part 0 - the problem and its budgets +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T20:40:38+00:00 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +log 2026-06-12T17:20:39+00:00 Channel-parallel float dot for high channel counts (perf C6) +log 2026-06-12T11:38:58+00:00 SMLALD Q15 dot product for DSP-extension Arm cores (perf C4) +log 2026-06-12T00:42:31+00:00 Q0.64 fixed-point phase accumulator (perf C3) +log 2026-06-12T00:18:54+00:00 Vectorization audit: restrict-qualify kernel hot-loop pointers (C2) +log 2026-06-11T22:55:53+00:00 Precompute the blended coefficient row once per multichannel frame +log 2026-06-10T02:16:52+00:00 Add Q15/Q31 fixed-point sample type support +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 20 1781054936 Add near-unity ASRC library core with test suite +blame 1 1781224951 Q0.64 fixed-point phase accumulator (perf C3) +blame 3 1781264338 SMLALD Q15 dot product for DSP-extension Arm cores (perf C4) +blame 7 1781284839 Channel-parallel float dot for high channel counts (perf C6) +blame 1 1781303209 Core hardening from the package audit (PR A) +blame 229 1783357908 Apply clang-format under the new shared style +blame 161 1783383441 Migrate identifiers to Tap House Rules snake_case +blame 41 1783383489 Enforce mandatory, expanded braces on control flow +blame 7 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 10 1783607520 Row-sum-preserving quantization + Appendix D, ported to Tap House Rules +blame 5 1783609614 Finish the style migration's two loose ends +blame 2 1784643949 Rename the C++ namespace srt -> tap::samplerate +blame 32 1784810565 Adopt the DspTap FIR substrate via submodule +== include/srt/pi_servo.h commits=9 lines=266 +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-07T00:18:09+00:00 Enforce mandatory, expanded braces on control flow +log 2026-07-07T00:17:21+00:00 Migrate identifiers to Tap House Rules snake_case +log 2026-07-06T17:11:48+00:00 Apply clang-format under the new shared style +log 2026-07-01T21:36:16+00:00 book: the servo and the fractional resampler chapters +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T20:40:38+00:00 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 60 1781054936 Add near-unity ASRC library core with test suite +blame 1 1781296838 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +blame 1 1781303209 Core hardening from the package audit (PR A) +blame 80 1783357908 Apply clang-format under the new shared style +blame 109 1783383441 Migrate identifiers to Tap House Rules snake_case +blame 8 1783383489 Enforce mandatory, expanded braces on control flow +blame 5 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 2 1784643949 Rename the C++ namespace srt -> tap::samplerate +== include/srt/sample_traits.h commits=13 lines=165 +log 2026-07-23T12:42:45+00:00 Adopt the DspTap FIR substrate via submodule +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-09T14:32:00+00:00 Row-sum-preserving quantization + Appendix D, ported to Tap House Rules +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-07T00:18:09+00:00 Enforce mandatory, expanded braces on control flow +log 2026-07-07T00:17:21+00:00 Migrate identifiers to Tap House Rules snake_case +log 2026-07-06T17:11:48+00:00 Apply clang-format under the new shared style +log 2026-07-01T21:37:49+00:00 book: sample traits and the C ABI chapters +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T00:42:31+00:00 Q0.64 fixed-point phase accumulator (perf C3) +log 2026-06-10T11:44:39+00:00 Address self-review findings: RT contract, validation, CI hardening +log 2026-06-10T02:16:52+00:00 Add Q15/Q31 fixed-point sample type support +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 9 1781054936 Add near-unity ASRC library core with test suite +blame 13 1781057812 Add Q15/Q31 fixed-point sample type support +blame 2 1782941869 book: sample traits and the C ABI chapters +blame 67 1783357908 Apply clang-format under the new shared style +blame 36 1783383441 Migrate identifiers to Tap House Rules snake_case +blame 4 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 2 1784643949 Rename the C++ namespace srt -> tap::samplerate +blame 32 1784810565 Adopt the DspTap FIR substrate via submodule +== include/srt/spsc_ring.h commits=9 lines=140 +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-09T15:06:54+00:00 Finish the style migration's two loose ends +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-07T00:17:21+00:00 Migrate identifiers to Tap House Rules snake_case +log 2026-07-06T17:11:48+00:00 Apply clang-format under the new shared style +log 2026-07-01T21:09:15+00:00 Book foundation: mdBook skeleton, live-excerpt CI gate, first chapters +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-10T11:44:39+00:00 Address self-review findings: RT contract, validation, CI hardening +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 22 1781054936 Add near-unity ASRC library core with test suite +blame 69 1783357908 Apply clang-format under the new shared style +blame 37 1783383441 Migrate identifiers to Tap House Rules snake_case +blame 5 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 5 1783609614 Finish the style migration's two loose ends +blame 2 1784643949 Rename the C++ namespace srt -> tap::samplerate +== tests/CMakeLists.txt commits=11 lines=80 +log 2026-09-27T15:46:27+00:00 Harden the ratchet and CI so the migration gates have data to read +log 2026-07-04T20:40:50+00:00 Compensated prototype design (k*fs zeros), economy preset, program-weighted metric +log 2026-06-12T22:39:34+00:00 Harden CI infrastructure per audit +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T17:13:58+00:00 Add measured 16 kHz quality suite (AsrcQuality16k) +log 2026-06-12T12:56:55+00:00 Multichannel hardening: 12/16-channel tests, bench and icount coverage +log 2026-06-10T11:44:39+00:00 Address self-review findings: RT contract, validation, CI hardening +log 2026-06-10T02:35:47+00:00 Add Arm Cortex-M55 bare-metal target with QEMU CI coverage +log 2026-06-10T02:16:52+00:00 Add Q15/Q31 fixed-point sample type support +log 2026-06-10T02:00:14+00:00 Add Hexagon cross-compile + QEMU emulation CI job +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 19 1781054936 Add near-unity ASRC library core with test suite +blame 13 1781057812 Add Q15/Q31 fixed-point sample type support +blame 30 1781058947 Add Arm Cortex-M55 bare-metal target with QEMU CI coverage +blame 2 1781091879 Address self-review findings: RT contract, validation, CI hardening +blame 1 1781269015 Multichannel hardening: 12/16-channel tests, bench and icount coverage +blame 1 1781284438 Add measured 16 kHz quality suite (AsrcQuality16k) +blame 1 1781303209 Core hardening from the package audit (PR A) +blame 2 1781303974 Harden CI infrastructure per audit +blame 1 1783197650 Compensated prototype design (k*fs zeros), economy preset, program-weighted metric +blame 10 1790523987 Harden the ratchet and CI so the migration gates have data to read +== CMakeLists.txt commits=9 lines=87 +log 2026-09-25T21:19:11-05:00 Add r8brain-free-src to the resampler comparison (#45) +log 2026-07-28T15:39:14+00:00 Compile the C ABI in CI, and export the tap::samplerate alias +log 2026-07-23T12:42:45+00:00 Adopt the DspTap FIR substrate via submodule +log 2026-06-12T03:28:41+00:00 Computational comparison vs libsamplerate and soxr +log 2026-06-10T22:37:21+00:00 Add ctypes demo notebook and C ABI shared library +log 2026-06-10T12:22:58+00:00 Add deterministic instruction-count ratchet (perf plan PR B) +log 2026-06-10T12:06:29+00:00 Add performance plan, benchmark infrastructure and host baselines +log 2026-06-10T01:38:43+00:00 Add GitHub Actions CI workflow +log 2026-06-10T01:28:56+00:00 Add near-unity ASRC library core with test suite +blame 32 1781054936 Add near-unity ASRC library core with test suite +blame 6 1781055523 Add GitHub Actions CI workflow +blame 6 1781093189 Add performance plan, benchmark infrastructure and host baselines +blame 7 1781094178 Add deterministic instruction-count ratchet (perf plan PR B) +blame 6 1781131041 Add ctypes demo notebook and C ABI shared library +blame 10 1781234921 Computational comparison vs libsamplerate and soxr +blame 7 1784810565 Adopt the DspTap FIR substrate via submodule +blame 5 1785253154 Compile the C ABI in CI, and export the tap::samplerate alias +blame 8 1790389151 Add r8brain-free-src to the resampler comparison (#45) +== README.md commits=44 lines=452 +log 2026-09-27T16:05:16+00:00 Pin the notebook environment and re-execute every notebook +log 2026-09-27T15:53:33+00:00 Re-record Hexagon icount baselines under the isolated harness +log 2026-09-27T15:37:15+00:00 Repair pre-existing breakage ahead of the monorepo snapshot +log 2026-09-26T10:59:56-05:00 Bump DspTap to 0eb09fa: shared Kaiser-window Bessel series; re-record icount (#47) +log 2026-09-25T22:24:16-05:00 Correct the M33 budget (stereo Q15 fits one core); refresh stale figures; fix clang sign-conversion (#46) +log 2026-09-25T21:19:11-05:00 Add r8brain-free-src to the resampler comparison (#45) +log 2026-08-07T02:00:02+00:00 Reference RatioTap from the README, book, and comparison doc +log 2026-07-21T14:25:49+00:00 Rename the C++ namespace srt -> tap::samplerate +log 2026-07-07T01:43:32+00:00 Update book and docs prose for the snake_case identifier migration +log 2026-07-07T00:21:51+00:00 Adopt .h extension, #pragma once, and SPDX banners +log 2026-07-04T21:07:41+00:00 icount: ratify all three targets for the sized compensated design +log 2026-07-04T20:51:54+00:00 icount: ratify M55 baselines for the compensated design (round 1 of 3) +log 2026-07-04T20:40:50+00:00 Compensated prototype design (k*fs zeros), economy preset, program-weighted metric +log 2026-07-04T19:01:07+00:00 Notebook: verify the music-dsp (RBJ) design feedback against shipped filters +log 2026-07-01T23:51:34+00:00 book: publish to GitHub Pages from main +log 2026-07-01T22:27:11+00:00 README: point at the book +log 2026-06-12T22:50:19+00:00 Docs truth sweep from the package audit (PR C) +log 2026-06-12T22:26:49+00:00 Core hardening from the package audit (PR A) +log 2026-06-12T20:40:38+00:00 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +log 2026-06-12T17:20:39+00:00 Channel-parallel float dot for high channel counts (perf C6) +log 2026-06-12T17:13:58+00:00 Add measured 16 kHz quality suite (AsrcQuality16k) +log 2026-06-12T13:06:14+00:00 Pin Hexagon pipeline12_q15 baseline; default-init genCh for -Werror +log 2026-06-12T12:56:55+00:00 Multichannel hardening: 12/16-channel tests, bench and icount coverage +log 2026-06-12T11:38:58+00:00 SMLALD Q15 dot product for DSP-extension Arm cores (perf C4) +log 2026-06-12T03:28:41+00:00 Computational comparison vs libsamplerate and soxr +log 2026-06-12T01:37:12+00:00 Add hardware test setups doc (real-clock validation on Pi-class gear) +log 2026-06-12T00:58:50+00:00 Pin Hexagon C3 baselines from the PR gating run +log 2026-06-12T00:54:13+00:00 Add Cortex-M33 target (Raspberry Pi Pico 2 / RP2350 class) +log 2026-06-12T00:42:31+00:00 Q0.64 fixed-point phase accumulator (perf C3) +log 2026-06-12T00:18:54+00:00 Vectorization audit: restrict-qualify kernel hot-loop pointers (C2) +log 2026-06-12T00:08:55+00:00 Pin Hexagon baselines after the blend-precompute change +log 2026-06-11T22:55:53+00:00 Precompute the blended coefficient row once per multichannel frame +log 2026-06-11T22:06:03+00:00 Add converter comparison: notebook with AES17-style metrics + docs table +log 2026-06-11T13:18:48+00:00 Add block-size study notebook (32/64/240 frames) with latency comparison +log 2026-06-11T00:54:42+00:00 Publish instruction-count baselines in README with a CI freshness gate +log 2026-06-11T00:53:26+00:00 Note the demo notebook's Python prerequisites in README +log 2026-06-10T22:37:21+00:00 Add ctypes demo notebook and C ABI shared library +log 2026-06-10T12:06:29+00:00 Add performance plan, benchmark infrastructure and host baselines +log 2026-06-10T11:44:39+00:00 Address self-review findings: RT contract, validation, CI hardening +log 2026-06-10T02:35:47+00:00 Add Arm Cortex-M55 bare-metal target with QEMU CI coverage +log 2026-06-10T02:16:52+00:00 Add Q15/Q31 fixed-point sample type support +log 2026-06-10T02:06:59+00:00 Document platform support and DSP-target CI in README +log 2026-06-10T02:05:37+00:00 Add CI, license and C++20 badges to README +log 2026-06-10T01:28:57+00:00 Add README and Doxygen config +blame 121 1781054937 Add README and Doxygen config +blame 4 1781057137 Add CI, license and C++20 badges to README +blame 24 1781057219 Document platform support and DSP-target CI in README +blame 10 1781057812 Add Q15/Q31 fixed-point sample type support +blame 11 1781058947 Add Arm Cortex-M55 bare-metal target with QEMU CI coverage +blame 7 1781091879 Address self-review findings: RT contract, validation, CI hardening +blame 11 1781093189 Add performance plan, benchmark infrastructure and host baselines +blame 4 1781131041 Add ctypes demo notebook and C ABI shared library +blame 1 1781139206 Note the demo notebook's Python prerequisites in README +blame 9 1781139282 Publish instruction-count baselines in README with a CI freshness gate +blame 7 1781183928 Add block-size study notebook (32/64/240 frames) with latency comparison +blame 4 1781215563 Add converter comparison: notebook with AES17-style metrics + docs table +blame 8 1781224951 Q0.64 fixed-point phase accumulator (perf C3) +blame 7 1781225653 Add Cortex-M33 target (Raspberry Pi Pico 2 / RP2350 class) +blame 5 1781228232 Add hardware test setups doc (real-clock validation on Pi-class gear) +blame 1 1781234921 Computational comparison vs libsamplerate and soxr +blame 15 1781269015 Multichannel hardening: 12/16-channel tests, bench and icount coverage +blame 7 1781284438 Add measured 16 kHz quality suite (AsrcQuality16k) +blame 15 1781284839 Channel-parallel float dot for high channel counts (perf C6) +blame 3 1781296838 Config::forSampleRate: rate-scaled defaults for non-48 kHz deployments +blame 9 1781303209 Core hardening from the package audit (PR A) +blame 17 1781304619 Docs truth sweep from the package audit (PR C) +blame 21 1782944831 README: point at the book +blame 2 1782949894 book: publish to GitHub Pages from main +blame 5 1783191667 Notebook: verify the music-dsp (RBJ) design feedback against shipped filters +blame 13 1783197650 Compensated prototype design (k*fs zeros), economy preset, program-weighted metric +blame 1 1783198314 icount: ratify M55 baselines for the compensated design (round 1 of 3) +blame 3 1783383711 Adopt .h extension, #pragma once, and SPDX banners +blame 18 1783388612 Update book and docs prose for the snake_case identifier migration +blame 5 1784643949 Rename the C++ namespace srt -> tap::samplerate +blame 51 1786068002 Reference RatioTap from the README, book, and comparison doc +blame 9 1790389151 Add r8brain-free-src to the resampler comparison (#45) +blame 10 1790393056 Correct the M33 budget (stereo Q15 fits one core); refresh stale figures; fix clang sign-conversion (#46) +blame 1 1790438396 Bump DspTap to 0eb09fa: shared Kaiser-window Bessel series; re-record icount (#47) +blame 2 1790523435 Repair pre-existing breakage ahead of the monorepo snapshot +blame 7 1790524413 Re-record Hexagon icount baselines under the isolated harness +blame 4 1790525116 Pin the notebook environment and re-execute every notebook diff --git a/docs/migration/snapshot/g12/bridge.txt b/docs/migration/snapshot/g12/bridge.txt new file mode 100644 index 0000000..fcea154 --- /dev/null +++ b/docs/migration/snapshot/g12/bridge.txt @@ -0,0 +1,116 @@ +== include/tap/ratio/converter.h commits=8 lines=443 +log 2026-08-07T01:01:34+00:00 Add super_economy: the 16 kHz voice/comms tier +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-08-06T23:47:52+00:00 Fix the audit findings: create-path leak, pull() contract, doc rot, CI nits +log 2026-07-24T15:04:38+00:00 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +log 2026-07-24T12:15:53+00:00 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +log 2026-07-24T01:08:40+00:00 M7b: superblock codegen — the process() hot path as a register walk +log 2026-07-23T18:12:53+00:00 Add M4: fixed-point converters and their parity battery +log 2026-07-23T16:59:55+00:00 Add M3: the streaming converter, pinned to scipy sample-for-sample +blame 229 1784825995 Add M3: the streaming converter, pinned to scipy sample-for-sample +blame 13 1784830373 Add M4: fixed-point converters and their parity battery +blame 91 1784855320 M7b: superblock codegen — the process() hot path as a register walk +blame 42 1784895353 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +blame 52 1784905478 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +blame 5 1786060072 Fix the audit findings: create-path leak, pull() contract, doc rot, CI nits +blame 4 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 7 1786064494 Add super_economy: the 16 kHz voice/comms tier +== include/tap/ratio/design.h commits=5 lines=166 +log 2026-08-07T01:01:34+00:00 Add super_economy: the 16 kHz voice/comms tier +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-24T12:15:53+00:00 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +log 2026-07-23T15:34:36+00:00 Refresh the profile doc table to the post-normalization measurements +log 2026-07-23T15:33:42+00:00 Add M2: design spike, profiles, schedule, and phase tables +blame 116 1784820822 Add M2: design spike, profiles, schedule, and phase tables +blame 5 1784820876 Refresh the profile doc table to the post-normalization measurements +blame 5 1784895353 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +blame 18 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 22 1786064494 Add super_economy: the 16 kHz voice/comms tier +== include/tap/ratio/schedule.h commits=2 lines=62 +log 2026-07-23T15:34:59+00:00 Apply clang-format reflow the pre-commit hook produced post-staging +log 2026-07-23T15:33:42+00:00 Add M2: design spike, profiles, schedule, and phase tables +blame 60 1784820822 Add M2: design spike, profiles, schedule, and phase tables +blame 2 1784820899 Apply clang-format reflow the pre-commit hook produced post-staging +== include/tap/ratio/phase_table.h commits=3 lines=95 +log 2026-07-24T15:04:38+00:00 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +log 2026-07-23T15:34:59+00:00 Apply clang-format reflow the pre-commit hook produced post-staging +log 2026-07-23T15:33:42+00:00 Add M2: design spike, profiles, schedule, and phase tables +blame 53 1784820822 Add M2: design spike, profiles, schedule, and phase tables +blame 1 1784820899 Apply clang-format reflow the pre-commit hook produced post-staging +blame 41 1784905478 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +== tests/test_converter.cpp commits=5 lines=408 +log 2026-08-07T01:01:34+00:00 Add super_economy: the 16 kHz voice/comms tier +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-24T15:04:38+00:00 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +log 2026-07-24T12:15:53+00:00 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +log 2026-07-23T16:59:55+00:00 Add M3: the streaming converter, pinned to scipy sample-for-sample +blame 375 1784825995 Add M3: the streaming converter, pinned to scipy sample-for-sample +blame 10 1784895353 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +blame 1 1784905478 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +blame 16 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 6 1786064494 Add super_economy: the 16 kHz voice/comms tier +== tests/test_cross_validation.cpp commits=3 lines=154 +log 2026-09-27T15:52:36+00:00 Harden the ratchet and CI so the migration gates have data to read +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-23T20:08:43+00:00 Add M5: the golden cross-validation against SampleRateTap +blame 132 1784837323 Add M5: the golden cross-validation against SampleRateTap +blame 16 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 6 1790524356 Harden the ratchet and CI so the migration gates have data to read +== CMakeLists.txt commits=6 lines=84 +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-24T16:15:25+00:00 v0.2: wrap the M7 codegen campaign +log 2026-07-24T00:07:09+00:00 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +log 2026-07-23T22:32:45+00:00 Mark the SampleRateTap dev headers as SYSTEM includes +log 2026-07-23T22:12:23+00:00 Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 +log 2026-07-23T13:23:54+00:00 Add the M1 skeleton: build, substrate, style, CI +blame 46 1784813034 Add the M1 skeleton: build, substrate, style, CI +blame 25 1784844743 Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 +blame 4 1784845965 Mark the SampleRateTap dev headers as SYSTEM includes +blame 8 1784851629 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +blame 1 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +== PLAN.md commits=15 lines=524 +log 2026-09-27T16:03:25+00:00 Re-record Hexagon icount baselines under the isolated harness +log 2026-09-26T16:11:20+00:00 Bump DspTap to 0eb09fa and SampleRateTap to 2b4dff1; re-record icount +log 2026-08-07T01:01:34+00:00 Add super_economy: the 16 kHz voice/comms tier +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-24T16:15:25+00:00 v0.2: wrap the M7 codegen campaign +log 2026-07-24T15:04:38+00:00 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +log 2026-07-24T12:15:53+00:00 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +log 2026-07-24T01:08:40+00:00 M7b: superblock codegen — the process() hot path as a register walk +log 2026-07-24T00:07:09+00:00 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +log 2026-07-23T22:12:23+00:00 Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 +log 2026-07-23T20:08:43+00:00 Add M5: the golden cross-validation against SampleRateTap +log 2026-07-23T18:12:53+00:00 Add M4: fixed-point converters and their parity battery +log 2026-07-23T16:59:55+00:00 Add M3: the streaming converter, pinned to scipy sample-for-sample +log 2026-07-23T15:33:42+00:00 Add M2: design spike, profiles, schedule, and phase tables +log 2026-07-23T03:32:38+00:00 Add the v0.1 plan and the amended design-brief handoff +blame 316 1784777558 Add the v0.1 plan and the amended design-brief handoff +blame 11 1784820822 Add M2: design spike, profiles, schedule, and phase tables +blame 12 1784825995 Add M3: the streaming converter, pinned to scipy sample-for-sample +blame 10 1784830373 Add M4: fixed-point converters and their parity battery +blame 8 1784837323 Add M5: the golden cross-validation against SampleRateTap +blame 16 1784851629 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +blame 18 1784855320 M7b: superblock codegen — the process() hot path as a register walk +blame 12 1784895353 M7c: commit the trip counts — constexpr profiles, compile-time dot lengths +blame 22 1784905478 M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots +blame 12 1784909725 v0.2: wrap the M7 codegen campaign +blame 24 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 17 1786064494 Add super_economy: the 16 kHz voice/comms tier +blame 34 1790439080 Bump DspTap to 0eb09fa and SampleRateTap to 2b4dff1; re-record icount +blame 12 1790525005 Re-record Hexagon icount baselines under the isolated harness +== README.md commits=9 lines=139 +log 2026-08-07T01:01:34+00:00 Add super_economy: the 16 kHz voice/comms tier +log 2026-08-07T00:45:42+00:00 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +log 2026-07-24T16:15:25+00:00 v0.2: wrap the M7 codegen campaign +log 2026-07-24T00:07:09+00:00 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +log 2026-07-23T22:12:23+00:00 Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 +log 2026-07-23T20:08:43+00:00 Add M5: the golden cross-validation against SampleRateTap +log 2026-07-23T18:12:53+00:00 Add M4: fixed-point converters and their parity battery +log 2026-07-23T16:59:55+00:00 Add M3: the streaming converter, pinned to scipy sample-for-sample +log 2026-07-23T13:23:54+00:00 Add the M1 skeleton: build, substrate, style, CI +blame 74 1784813034 Add the M1 skeleton: build, substrate, style, CI +blame 20 1784844743 Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 +blame 20 1784851629 M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) +blame 1 1784909725 v0.2: wrap the M7 codegen campaign +blame 22 1786063542 v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 +blame 2 1786064494 Add super_economy: the 16 kHz voice/comms tier diff --git a/docs/migration/snapshot/g2/async-hexagon.txt b/docs/migration/snapshot/g2/async-hexagon.txt new file mode 100644 index 0000000..7caf273 --- /dev/null +++ b/docs/migration/snapshot/g2/async-hexagon.txt @@ -0,0 +1,49 @@ +EdgeCalls.ZeroLengthAndOversized +Fade.OutputRampsAfterFill +FadeQ15.OutputRampsAfterFill +FixedPoint.CoefficientConversionRoundsAndSaturates +FixedPoint.DcGainIsUnityQ15 +FixedPoint.DcGainIsUnityQ31 +FixedPoint.FinalizeSaturates +FixedPoint.FullScaleSineDoesNotWrapQ15 +FixedPoint.RowSumsAreExactQ15 +FixedPoint.RowSumsAreExactQ31 +Kaiser.BalancedPrototypeMeetsSpec +Kaiser.BesselI0ReferenceValues +Kaiser.BetaReferenceValues +Kaiser.CompensatedBranchSumsAreUniform +Kaiser.CompensatedSpecsHoldAt16k +Kaiser.EconomyPrototypeMeetsSpec +Kaiser.FastPrototypeMeetsSpec +Kaiser.TapEstimateMatchesHarrisFormula +Latency.DesignedLatencyConsistency +Latency.ImpulseDelayMatchesDesignedLatency +MultiChannelShort.Independence12chQ15 +MultiChannelShort.Independence5chFloat +MultiChannelShort.Independence7chFloat +OutputHash.Balanced +OutputHash.Economy +OutputHash.Fast +OutputHash.Transparent +Polyphase.DcGainIsUnityAcrossMu +Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +Polyphase.FractionalDelayAccuracyBalanced +Polyphase.FractionalDelayAccuracyTransparent +Polyphase.MuWrapIsContinuousWithWindowShift +ProgramWeighted.BalancedBaseline +ProgramWeighted.EconomyNearBalanced +ProgramWeighted.EconomyWorstCaseSineIsDocumented +ProgramWeighted.InstrumentFloor +QuickQuality.FullScaleQ15Short +QuickQuality.Q15Tone997 +Resync.SmallSetpointRecovers +Servo.BandwidthSwitchIsTransientFree +Servo.ClampsToMaxDeviation +Servo.DropoutResetKeepsPpmEstimate +Servo.LocksFromConstantOffsetAndNullsError +Servo.TracksSlowDriftRampWithBoundedLag +spsc_ring.CapacityRoundsUpToPowerOfTwo +spsc_ring.DiscardAdvancesConsumer +spsc_ring.FillDrainExactness +spsc_ring.PartialWriteWhenNearlyFull +spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g2/async-m33.txt b/docs/migration/snapshot/g2/async-m33.txt new file mode 100644 index 0000000..d529435 --- /dev/null +++ b/docs/migration/snapshot/g2/async-m33.txt @@ -0,0 +1,41 @@ +ConfigValidation.RejectsSilentMisbehavior +EdgeCalls.ZeroLengthAndOversized +Fade.OutputRampsAfterFill +FadeQ15.OutputRampsAfterFill +FixedPoint.CoefficientConversionRoundsAndSaturates +FixedPoint.DcGainIsUnityQ15 +FixedPoint.DcGainIsUnityQ31 +FixedPoint.FinalizeSaturates +FixedPoint.RowSumsAreExactQ15 +FixedPoint.RowSumsAreExactQ31 +Kaiser.BesselI0ReferenceValues +Kaiser.BetaReferenceValues +Kaiser.CompensatedBranchSumsAreUniform +Kaiser.CompensatedSpecsHoldAt16k +Kaiser.TapEstimateMatchesHarrisFormula +Latency.DesignedLatencyConsistency +Latency.ImpulseDelayMatchesDesignedLatency +MultiChannelShort.Independence12chQ15 +MultiChannelShort.Independence5chFloat +MultiChannelShort.Independence7chFloat +OutputHash.Balanced +OutputHash.Economy +OutputHash.Fast +OutputHash.Transparent +Polyphase.DcGainIsUnityAcrossMu +Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +Polyphase.FractionalDelayAccuracyBalanced +Polyphase.FractionalDelayAccuracyTransparent +Polyphase.MuWrapIsContinuousWithWindowShift +ProgramWeighted.BalancedBaseline +ProgramWeighted.EconomyNearBalanced +ProgramWeighted.EconomyWorstCaseSineIsDocumented +ProgramWeighted.InstrumentFloor +QuickQuality.FullScaleQ15Short +QuickQuality.Q15Tone997 +Resync.SmallSetpointRecovers +spsc_ring.CapacityRoundsUpToPowerOfTwo +spsc_ring.DiscardAdvancesConsumer +spsc_ring.FillDrainExactness +spsc_ring.PartialWriteWhenNearlyFull +spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g2/async-m55.txt b/docs/migration/snapshot/g2/async-m55.txt new file mode 100644 index 0000000..d529435 --- /dev/null +++ b/docs/migration/snapshot/g2/async-m55.txt @@ -0,0 +1,41 @@ +ConfigValidation.RejectsSilentMisbehavior +EdgeCalls.ZeroLengthAndOversized +Fade.OutputRampsAfterFill +FadeQ15.OutputRampsAfterFill +FixedPoint.CoefficientConversionRoundsAndSaturates +FixedPoint.DcGainIsUnityQ15 +FixedPoint.DcGainIsUnityQ31 +FixedPoint.FinalizeSaturates +FixedPoint.RowSumsAreExactQ15 +FixedPoint.RowSumsAreExactQ31 +Kaiser.BesselI0ReferenceValues +Kaiser.BetaReferenceValues +Kaiser.CompensatedBranchSumsAreUniform +Kaiser.CompensatedSpecsHoldAt16k +Kaiser.TapEstimateMatchesHarrisFormula +Latency.DesignedLatencyConsistency +Latency.ImpulseDelayMatchesDesignedLatency +MultiChannelShort.Independence12chQ15 +MultiChannelShort.Independence5chFloat +MultiChannelShort.Independence7chFloat +OutputHash.Balanced +OutputHash.Economy +OutputHash.Fast +OutputHash.Transparent +Polyphase.DcGainIsUnityAcrossMu +Polyphase.ExtraRowEqualsPhaseZeroAdvancedOneTap +Polyphase.FractionalDelayAccuracyBalanced +Polyphase.FractionalDelayAccuracyTransparent +Polyphase.MuWrapIsContinuousWithWindowShift +ProgramWeighted.BalancedBaseline +ProgramWeighted.EconomyNearBalanced +ProgramWeighted.EconomyWorstCaseSineIsDocumented +ProgramWeighted.InstrumentFloor +QuickQuality.FullScaleQ15Short +QuickQuality.Q15Tone997 +Resync.SmallSetpointRecovers +spsc_ring.CapacityRoundsUpToPowerOfTwo +spsc_ring.DiscardAdvancesConsumer +spsc_ring.FillDrainExactness +spsc_ring.PartialWriteWhenNearlyFull +spsc_ring.WrapAroundPreservesData diff --git a/docs/migration/snapshot/g2/bridge-hexagon.txt b/docs/migration/snapshot/g2/bridge-hexagon.txt new file mode 100644 index 0000000..3429c96 --- /dev/null +++ b/docs/migration/snapshot/g2/bridge-hexagon.txt @@ -0,0 +1,80 @@ +Converter.AccountingExactFromEveryPositionDown +Converter.AccountingExactFromEveryPositionUp +Converter.FlushDrainsTailToSilence +Converter.ImpulseReproducesTableCustomTaps +Converter.ImpulseReproducesTableDown +Converter.ImpulseReproducesTableDownTransparent +Converter.ImpulseReproducesTableUp +Converter.MatchesScipyDownBalanced +Converter.MatchesScipyDownEconomy +Converter.MatchesScipyDownSuperEconomy +Converter.MatchesScipyDownTransparent +Converter.MatchesScipyUpBalanced +Converter.MatchesScipyUpEconomy +Converter.MatchesScipyUpSuperEconomy +Converter.MatchesScipyUpTransparent +Converter.PassbandSineEconomyHitsTheImagingFloor +Converter.PassbandSineIsTransparentTransparent +Converter.PullMatchesProcessBitExact +Converter.PullShortReturnsOnDryThenResumes +Converter.ResetReproducesBitExactly +Converter.StopbandToneProductsAreBoundedByTheSpec +Converter.TwoChannelsAreIndependent +CrossValidation.DownEconomyAgainstAsync1024 +CrossValidation.DownEconomyAgainstAsync512 +CrossValidation.UpEconomyAgainstAsync1024 +CrossValidation.UpEconomyAgainstAsync512 +Design.DirectionsAreAsymmetric +Design.DownBalancedMeetsSpec +Design.DownEconomyMeetsSpec +Design.DownSuperEconomyMeetsSpec +Design.DownTransparentMeetsSpec +Design.UpBalancedMeetsSpec +Design.UpEconomyMeetsSpec +Design.UpSuperEconomyMeetsSpec +Design.UpTransparentMeetsSpec +FixedPoint.DcEveryPhaseQ15Down +FixedPoint.DcEveryPhaseQ15Up +FixedPoint.DcEveryPhaseQ31Down +FixedPoint.FullScaleSineDoesNotWrapQ15 +FixedPoint.PullMatchesProcessBitExactQ15 +FixedPoint.Q15SineQualityEconomyDown +FixedPoint.Q15SineQualityTransparentDown +FixedPoint.Q31MatchesFloatDownEconomy +FixedPoint.Q31MatchesFloatUpTransparent +FixedPoint.Q31SineQualityEconomyUp +FixedPoint.Q31SineQualityTransparentDown +OutputHash.Balanced +OutputHash.Economy +OutputHash.SuperEconomy +OutputHash.Transparent +PhaseTable.StorageBudgetsArePinned +Schedule.DownExhaustive +Schedule.FramesNeededIsPositionInvariantOverSuperblocks +Schedule.UpExhaustive +Skeleton.IdentityConstants +Skeleton.SubstrateIsWiredEndToEnd +phase_table_test/0.DownBalancedEveryPhase +phase_table_test/0.DownEconomyEveryPhase +phase_table_test/0.DownSuperEconomyEveryPhase +phase_table_test/0.DownTransparentEveryPhase +phase_table_test/0.UpBalancedEveryPhase +phase_table_test/0.UpEconomyEveryPhase +phase_table_test/0.UpSuperEconomyEveryPhase +phase_table_test/0.UpTransparentEveryPhase +phase_table_test/1.DownBalancedEveryPhase +phase_table_test/1.DownEconomyEveryPhase +phase_table_test/1.DownSuperEconomyEveryPhase +phase_table_test/1.DownTransparentEveryPhase +phase_table_test/1.UpBalancedEveryPhase +phase_table_test/1.UpEconomyEveryPhase +phase_table_test/1.UpSuperEconomyEveryPhase +phase_table_test/1.UpTransparentEveryPhase +phase_table_test/2.DownBalancedEveryPhase +phase_table_test/2.DownEconomyEveryPhase +phase_table_test/2.DownSuperEconomyEveryPhase +phase_table_test/2.DownTransparentEveryPhase +phase_table_test/2.UpBalancedEveryPhase +phase_table_test/2.UpEconomyEveryPhase +phase_table_test/2.UpSuperEconomyEveryPhase +phase_table_test/2.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g2/bridge-m33.txt b/docs/migration/snapshot/g2/bridge-m33.txt new file mode 100644 index 0000000..c681373 --- /dev/null +++ b/docs/migration/snapshot/g2/bridge-m33.txt @@ -0,0 +1,63 @@ +Converter.AccountingExactFromEveryPositionDown +Converter.AccountingExactFromEveryPositionUp +Converter.FlushDrainsTailToSilence +Converter.ImpulseReproducesTableCustomTaps +Converter.ImpulseReproducesTableDown +Converter.ImpulseReproducesTableDownTransparent +Converter.ImpulseReproducesTableUp +Converter.LatencyAndValidation +Converter.MatchesScipyDownBalanced +Converter.MatchesScipyDownEconomy +Converter.MatchesScipyDownSuperEconomy +Converter.MatchesScipyDownTransparent +Converter.MatchesScipyUpBalanced +Converter.MatchesScipyUpEconomy +Converter.MatchesScipyUpSuperEconomy +Converter.MatchesScipyUpTransparent +Converter.PullMatchesProcessBitExact +Converter.PullShortReturnsOnDryThenResumes +Converter.ResetReproducesBitExactly +Converter.TwoChannelsAreIndependent +Design.BadProfilesThrow +Design.DirectionsAreAsymmetric +FixedPoint.DcEveryPhaseQ15Down +FixedPoint.DcEveryPhaseQ15Up +FixedPoint.DcEveryPhaseQ31Down +FixedPoint.FullScaleSineDoesNotWrapQ15 +FixedPoint.PullMatchesProcessBitExactQ15 +FixedPoint.Q31MatchesFloatDownEconomy +FixedPoint.Q31MatchesFloatUpTransparent +OutputHash.Balanced +OutputHash.Economy +OutputHash.SuperEconomy +OutputHash.Transparent +PhaseTable.StorageBudgetsArePinned +Schedule.DownExhaustive +Schedule.FramesNeededIsPositionInvariantOverSuperblocks +Schedule.UpExhaustive +Skeleton.IdentityConstants +Skeleton.SubstrateIsWiredEndToEnd +phase_table_test/0.DownBalancedEveryPhase +phase_table_test/0.DownEconomyEveryPhase +phase_table_test/0.DownSuperEconomyEveryPhase +phase_table_test/0.DownTransparentEveryPhase +phase_table_test/0.UpBalancedEveryPhase +phase_table_test/0.UpEconomyEveryPhase +phase_table_test/0.UpSuperEconomyEveryPhase +phase_table_test/0.UpTransparentEveryPhase +phase_table_test/1.DownBalancedEveryPhase +phase_table_test/1.DownEconomyEveryPhase +phase_table_test/1.DownSuperEconomyEveryPhase +phase_table_test/1.DownTransparentEveryPhase +phase_table_test/1.UpBalancedEveryPhase +phase_table_test/1.UpEconomyEveryPhase +phase_table_test/1.UpSuperEconomyEveryPhase +phase_table_test/1.UpTransparentEveryPhase +phase_table_test/2.DownBalancedEveryPhase +phase_table_test/2.DownEconomyEveryPhase +phase_table_test/2.DownSuperEconomyEveryPhase +phase_table_test/2.DownTransparentEveryPhase +phase_table_test/2.UpBalancedEveryPhase +phase_table_test/2.UpEconomyEveryPhase +phase_table_test/2.UpSuperEconomyEveryPhase +phase_table_test/2.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g2/bridge-m55.txt b/docs/migration/snapshot/g2/bridge-m55.txt new file mode 100644 index 0000000..c681373 --- /dev/null +++ b/docs/migration/snapshot/g2/bridge-m55.txt @@ -0,0 +1,63 @@ +Converter.AccountingExactFromEveryPositionDown +Converter.AccountingExactFromEveryPositionUp +Converter.FlushDrainsTailToSilence +Converter.ImpulseReproducesTableCustomTaps +Converter.ImpulseReproducesTableDown +Converter.ImpulseReproducesTableDownTransparent +Converter.ImpulseReproducesTableUp +Converter.LatencyAndValidation +Converter.MatchesScipyDownBalanced +Converter.MatchesScipyDownEconomy +Converter.MatchesScipyDownSuperEconomy +Converter.MatchesScipyDownTransparent +Converter.MatchesScipyUpBalanced +Converter.MatchesScipyUpEconomy +Converter.MatchesScipyUpSuperEconomy +Converter.MatchesScipyUpTransparent +Converter.PullMatchesProcessBitExact +Converter.PullShortReturnsOnDryThenResumes +Converter.ResetReproducesBitExactly +Converter.TwoChannelsAreIndependent +Design.BadProfilesThrow +Design.DirectionsAreAsymmetric +FixedPoint.DcEveryPhaseQ15Down +FixedPoint.DcEveryPhaseQ15Up +FixedPoint.DcEveryPhaseQ31Down +FixedPoint.FullScaleSineDoesNotWrapQ15 +FixedPoint.PullMatchesProcessBitExactQ15 +FixedPoint.Q31MatchesFloatDownEconomy +FixedPoint.Q31MatchesFloatUpTransparent +OutputHash.Balanced +OutputHash.Economy +OutputHash.SuperEconomy +OutputHash.Transparent +PhaseTable.StorageBudgetsArePinned +Schedule.DownExhaustive +Schedule.FramesNeededIsPositionInvariantOverSuperblocks +Schedule.UpExhaustive +Skeleton.IdentityConstants +Skeleton.SubstrateIsWiredEndToEnd +phase_table_test/0.DownBalancedEveryPhase +phase_table_test/0.DownEconomyEveryPhase +phase_table_test/0.DownSuperEconomyEveryPhase +phase_table_test/0.DownTransparentEveryPhase +phase_table_test/0.UpBalancedEveryPhase +phase_table_test/0.UpEconomyEveryPhase +phase_table_test/0.UpSuperEconomyEveryPhase +phase_table_test/0.UpTransparentEveryPhase +phase_table_test/1.DownBalancedEveryPhase +phase_table_test/1.DownEconomyEveryPhase +phase_table_test/1.DownSuperEconomyEveryPhase +phase_table_test/1.DownTransparentEveryPhase +phase_table_test/1.UpBalancedEveryPhase +phase_table_test/1.UpEconomyEveryPhase +phase_table_test/1.UpSuperEconomyEveryPhase +phase_table_test/1.UpTransparentEveryPhase +phase_table_test/2.DownBalancedEveryPhase +phase_table_test/2.DownEconomyEveryPhase +phase_table_test/2.DownSuperEconomyEveryPhase +phase_table_test/2.DownTransparentEveryPhase +phase_table_test/2.UpBalancedEveryPhase +phase_table_test/2.UpEconomyEveryPhase +phase_table_test/2.UpSuperEconomyEveryPhase +phase_table_test/2.UpTransparentEveryPhase diff --git a/docs/migration/snapshot/g6.txt b/docs/migration/snapshot/g6.txt new file mode 100644 index 0000000..33bd954 --- /dev/null +++ b/docs/migration/snapshot/g6.txt @@ -0,0 +1,4 @@ +[ measured ] cross-validation down, async L=1024: worst |diff| = 1.255e-05 (-98.0 dB), 147/147 phases, limit 3.0e-05 +[ measured ] cross-validation down, async L=512: worst |diff| = 1.195e-05 (-98.5 dB), 147/147 phases, limit 3.0e-05 +[ measured ] cross-validation up , async L=1024: worst |diff| = 3.222e-05 (-89.8 dB), 160/160 phases, limit 8.0e-05 +[ measured ] cross-validation up , async L=512: worst |diff| = 3.031e-05 (-90.4 dB), 160/160 phases, limit 8.0e-05 diff --git a/docs/migration/snapshot/g9.txt b/docs/migration/snapshot/g9.txt new file mode 100644 index 0000000..4f701eb --- /dev/null +++ b/docs/migration/snapshot/g9.txt @@ -0,0 +1,38 @@ +# G9 — retired identifiers (MONOREPO_PLAN.md section 5). Applies from step +# 3.7: `collect.py retired` must then report zero hits outside the allowlist. +# At S0 every pattern hits by construction; this file is the rule, not a +# measurement. +# +# pattern a retired identifier (Python regex) +# allow -- why hits of in matching files pass +# +# docs/migration/ is never scanned (it describes the old names). + +pattern (? new SHA map) +allow .git-blame-ignore-revs .* -- SHAs only diff --git a/docs/migration/tips.txt b/docs/migration/tips.txt new file mode 100644 index 0000000..91cc0b7 --- /dev/null +++ b/docs/migration/tips.txt @@ -0,0 +1,5 @@ +# Step-0 tips (MONOREPO_PLAN.md step 0). Every A/B gate builds these two +# commits fresh in the same job; every later commit-count check uses the +# counts recorded here. Measured on fresh, full (non-shallow) clones. +S0 5e2057f192cee1eeb61286c466e5f50293580c09 tap/SampleRateTap main 140 commits +R0 8f19e8b967b8d3e9eb22393e881396d418c0584f tap/RatioTap main 33 commits From a16d499afae9396ca9f767cb2bdfaeadb1241a38 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 28 Sep 2026 02:01:55 +0000 Subject: [PATCH 40/44] Move SampleRateTap's engine under async/ (migration step 1a) A pure move: every row 1a of MONOREPO_PLAN.md 4.1 with git mv, nothing edited, so git show -M reports no insertions or deletions. The tree does not build on its own; 1c adds the root build glue. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- CMakeLists.txt => async/CMakeLists.txt | 0 README.md => async/README.md | 0 {bench => async/bench}/CMakeLists.txt | 0 {bench => async/bench}/baselines.json | 0 {bench => async/bench}/bench_asrc.cpp | 0 {bench => async/bench}/compare/CMakeLists.txt | 0 {bench => async/bench}/compare/bench_compare.cpp | 0 {bench => async/bench}/icount/CMakeLists.txt | 0 {bench => async/bench}/icount/cmp_main.cpp | 0 {bench => async/bench}/icount/icount_main.cpp | 0 {bench => async/bench}/icount/r8b_single_thread_mutex.h | 0 {tools => async}/capi/CMakeLists.txt | 0 {tools => async}/capi/srt_capi.cpp | 0 {tools => async}/capi/srt_capi.h | 0 {cmake => async/cmake}/r8brain.cmake | 0 {docs => async/docs}/COMPARISON.md | 0 {docs => async/docs}/HARDWARE_TESTING.md | 0 {docs => async/docs}/PERFORMANCE.md | 0 {examples => async/examples}/CMakeLists.txt | 0 {examples => async/examples}/alsa_bridge.cpp | 0 {examples => async/examples}/drifting_clocks.cpp | 0 {examples => async/examples}/pico2_cyccnt/CMakeLists.txt | 0 {examples => async/examples}/pico2_cyccnt/README.md | 0 {examples => async/examples}/pico2_cyccnt/main.cpp | 0 {examples => async/examples}/pico2_dualcore/CMakeLists.txt | 0 {examples => async/examples}/pico2_dualcore/README.md | 0 {examples => async/examples}/pico2_dualcore/main.cpp | 0 {include => async/include}/srt/asrc.h | 0 {include => async/include}/srt/detail/kaiser.h | 0 {include => async/include}/srt/pi_servo.h | 0 {include => async/include}/srt/polyphase_filter.h | 0 {include => async/include}/srt/sample_traits.h | 0 {include => async/include}/srt/spsc_ring.h | 0 {include => async/include}/srt/srt.h | 0 {notebooks => async/notebooks}/asrc_block_size_study.ipynb | 0 {notebooks => async/notebooks}/asrc_comparison.ipynb | 0 {notebooks => async/notebooks}/asrc_demo.ipynb | 0 {notebooks => async/notebooks}/asrc_rbj_analysis.ipynb | 0 {notebooks => async/notebooks}/figure_digest.py | 0 {tests => async/tests}/CMakeLists.txt | 0 {tests => async/tests}/bare_metal_main.cpp | 0 {tests => async/tests}/support/multitone_analysis.h | 0 {tests => async/tests}/support/sine_analysis.h | 0 {tests => async/tests}/support/two_clock_sim.h | 0 {tests => async/tests}/test_asrc_lock.cpp | 0 {tests => async/tests}/test_asrc_program.cpp | 0 {tests => async/tests}/test_asrc_quality.cpp | 0 {tests => async/tests}/test_asrc_quality_16k.cpp | 0 {tests => async/tests}/test_fade.cpp | 0 {tests => async/tests}/test_fixed_point.cpp | 0 {tests => async/tests}/test_hardening.cpp | 0 {tests => async/tests}/test_kaiser.cpp | 0 {tests => async/tests}/test_latency.cpp | 0 {tests => async/tests}/test_multichannel.cpp | 0 {tests => async/tests}/test_output_hash.cpp | 0 {tests => async/tests}/test_polyphase.cpp | 0 {tests => async/tests}/test_servo.cpp | 0 {tests => async/tests}/test_spsc_ring.cpp | 0 {tests => async/tests}/test_spsc_ring_threads.cpp | 0 {tools => async/tools}/compare_shim/CMakeLists.txt | 0 {tools => async/tools}/compare_shim/srt_r8b_shim.cpp | 0 61 files changed, 0 insertions(+), 0 deletions(-) rename CMakeLists.txt => async/CMakeLists.txt (100%) rename README.md => async/README.md (100%) rename {bench => async/bench}/CMakeLists.txt (100%) rename {bench => async/bench}/baselines.json (100%) rename {bench => async/bench}/bench_asrc.cpp (100%) rename {bench => async/bench}/compare/CMakeLists.txt (100%) rename {bench => async/bench}/compare/bench_compare.cpp (100%) rename {bench => async/bench}/icount/CMakeLists.txt (100%) rename {bench => async/bench}/icount/cmp_main.cpp (100%) rename {bench => async/bench}/icount/icount_main.cpp (100%) rename {bench => async/bench}/icount/r8b_single_thread_mutex.h (100%) rename {tools => async}/capi/CMakeLists.txt (100%) rename {tools => async}/capi/srt_capi.cpp (100%) rename {tools => async}/capi/srt_capi.h (100%) rename {cmake => async/cmake}/r8brain.cmake (100%) rename {docs => async/docs}/COMPARISON.md (100%) rename {docs => async/docs}/HARDWARE_TESTING.md (100%) rename {docs => async/docs}/PERFORMANCE.md (100%) rename {examples => async/examples}/CMakeLists.txt (100%) rename {examples => async/examples}/alsa_bridge.cpp (100%) rename {examples => async/examples}/drifting_clocks.cpp (100%) rename {examples => async/examples}/pico2_cyccnt/CMakeLists.txt (100%) rename {examples => async/examples}/pico2_cyccnt/README.md (100%) rename {examples => async/examples}/pico2_cyccnt/main.cpp (100%) rename {examples => async/examples}/pico2_dualcore/CMakeLists.txt (100%) rename {examples => async/examples}/pico2_dualcore/README.md (100%) rename {examples => async/examples}/pico2_dualcore/main.cpp (100%) rename {include => async/include}/srt/asrc.h (100%) rename {include => async/include}/srt/detail/kaiser.h (100%) rename {include => async/include}/srt/pi_servo.h (100%) rename {include => async/include}/srt/polyphase_filter.h (100%) rename {include => async/include}/srt/sample_traits.h (100%) rename {include => async/include}/srt/spsc_ring.h (100%) rename {include => async/include}/srt/srt.h (100%) rename {notebooks => async/notebooks}/asrc_block_size_study.ipynb (100%) rename {notebooks => async/notebooks}/asrc_comparison.ipynb (100%) rename {notebooks => async/notebooks}/asrc_demo.ipynb (100%) rename {notebooks => async/notebooks}/asrc_rbj_analysis.ipynb (100%) rename {notebooks => async/notebooks}/figure_digest.py (100%) rename {tests => async/tests}/CMakeLists.txt (100%) rename {tests => async/tests}/bare_metal_main.cpp (100%) rename {tests => async/tests}/support/multitone_analysis.h (100%) rename {tests => async/tests}/support/sine_analysis.h (100%) rename {tests => async/tests}/support/two_clock_sim.h (100%) rename {tests => async/tests}/test_asrc_lock.cpp (100%) rename {tests => async/tests}/test_asrc_program.cpp (100%) rename {tests => async/tests}/test_asrc_quality.cpp (100%) rename {tests => 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bench/icount/CMakeLists.txt rename to async/bench/icount/CMakeLists.txt diff --git a/bench/icount/cmp_main.cpp b/async/bench/icount/cmp_main.cpp similarity index 100% rename from bench/icount/cmp_main.cpp rename to async/bench/icount/cmp_main.cpp diff --git a/bench/icount/icount_main.cpp b/async/bench/icount/icount_main.cpp similarity index 100% rename from bench/icount/icount_main.cpp rename to async/bench/icount/icount_main.cpp diff --git a/bench/icount/r8b_single_thread_mutex.h b/async/bench/icount/r8b_single_thread_mutex.h similarity index 100% rename from bench/icount/r8b_single_thread_mutex.h rename to async/bench/icount/r8b_single_thread_mutex.h diff --git a/tools/capi/CMakeLists.txt b/async/capi/CMakeLists.txt similarity index 100% rename from tools/capi/CMakeLists.txt rename to async/capi/CMakeLists.txt diff --git a/tools/capi/srt_capi.cpp b/async/capi/srt_capi.cpp similarity index 100% rename from tools/capi/srt_capi.cpp rename to async/capi/srt_capi.cpp diff --git a/tools/capi/srt_capi.h b/async/capi/srt_capi.h similarity index 100% rename from tools/capi/srt_capi.h rename to async/capi/srt_capi.h diff --git a/cmake/r8brain.cmake b/async/cmake/r8brain.cmake similarity index 100% rename from cmake/r8brain.cmake rename to async/cmake/r8brain.cmake diff --git a/docs/COMPARISON.md b/async/docs/COMPARISON.md similarity index 100% rename from docs/COMPARISON.md rename to async/docs/COMPARISON.md diff --git a/docs/HARDWARE_TESTING.md b/async/docs/HARDWARE_TESTING.md similarity index 100% rename from docs/HARDWARE_TESTING.md rename to async/docs/HARDWARE_TESTING.md diff --git a/docs/PERFORMANCE.md b/async/docs/PERFORMANCE.md similarity index 100% rename from docs/PERFORMANCE.md rename to async/docs/PERFORMANCE.md diff --git a/examples/CMakeLists.txt b/async/examples/CMakeLists.txt similarity index 100% rename from examples/CMakeLists.txt rename to async/examples/CMakeLists.txt diff --git a/examples/alsa_bridge.cpp b/async/examples/alsa_bridge.cpp similarity index 100% rename from examples/alsa_bridge.cpp rename to async/examples/alsa_bridge.cpp diff --git a/examples/drifting_clocks.cpp b/async/examples/drifting_clocks.cpp similarity index 100% rename from examples/drifting_clocks.cpp rename to async/examples/drifting_clocks.cpp diff --git a/examples/pico2_cyccnt/CMakeLists.txt b/async/examples/pico2_cyccnt/CMakeLists.txt similarity index 100% rename from examples/pico2_cyccnt/CMakeLists.txt rename to async/examples/pico2_cyccnt/CMakeLists.txt diff --git a/examples/pico2_cyccnt/README.md b/async/examples/pico2_cyccnt/README.md similarity index 100% rename from examples/pico2_cyccnt/README.md rename to async/examples/pico2_cyccnt/README.md diff --git a/examples/pico2_cyccnt/main.cpp b/async/examples/pico2_cyccnt/main.cpp similarity index 100% rename from examples/pico2_cyccnt/main.cpp rename to async/examples/pico2_cyccnt/main.cpp diff --git a/examples/pico2_dualcore/CMakeLists.txt b/async/examples/pico2_dualcore/CMakeLists.txt similarity index 100% rename from examples/pico2_dualcore/CMakeLists.txt rename to async/examples/pico2_dualcore/CMakeLists.txt diff --git a/examples/pico2_dualcore/README.md b/async/examples/pico2_dualcore/README.md similarity index 100% rename from examples/pico2_dualcore/README.md rename to async/examples/pico2_dualcore/README.md diff --git a/examples/pico2_dualcore/main.cpp b/async/examples/pico2_dualcore/main.cpp similarity index 100% rename from examples/pico2_dualcore/main.cpp rename to async/examples/pico2_dualcore/main.cpp diff --git a/include/srt/asrc.h b/async/include/srt/asrc.h similarity index 100% rename from include/srt/asrc.h rename to async/include/srt/asrc.h diff --git a/include/srt/detail/kaiser.h b/async/include/srt/detail/kaiser.h similarity index 100% rename from include/srt/detail/kaiser.h rename to async/include/srt/detail/kaiser.h diff --git a/include/srt/pi_servo.h b/async/include/srt/pi_servo.h similarity index 100% rename from include/srt/pi_servo.h rename to async/include/srt/pi_servo.h diff --git a/include/srt/polyphase_filter.h b/async/include/srt/polyphase_filter.h similarity index 100% rename from include/srt/polyphase_filter.h rename to async/include/srt/polyphase_filter.h diff --git a/include/srt/sample_traits.h b/async/include/srt/sample_traits.h similarity index 100% rename from include/srt/sample_traits.h rename to async/include/srt/sample_traits.h diff --git a/include/srt/spsc_ring.h b/async/include/srt/spsc_ring.h similarity index 100% rename from include/srt/spsc_ring.h rename to async/include/srt/spsc_ring.h diff --git a/include/srt/srt.h b/async/include/srt/srt.h similarity index 100% rename from include/srt/srt.h rename to async/include/srt/srt.h diff --git a/notebooks/asrc_block_size_study.ipynb b/async/notebooks/asrc_block_size_study.ipynb similarity index 100% rename from notebooks/asrc_block_size_study.ipynb rename to async/notebooks/asrc_block_size_study.ipynb diff --git a/notebooks/asrc_comparison.ipynb b/async/notebooks/asrc_comparison.ipynb similarity index 100% rename from notebooks/asrc_comparison.ipynb rename to async/notebooks/asrc_comparison.ipynb diff --git a/notebooks/asrc_demo.ipynb b/async/notebooks/asrc_demo.ipynb similarity index 100% rename from notebooks/asrc_demo.ipynb rename to async/notebooks/asrc_demo.ipynb diff --git a/notebooks/asrc_rbj_analysis.ipynb b/async/notebooks/asrc_rbj_analysis.ipynb similarity index 100% rename from notebooks/asrc_rbj_analysis.ipynb rename to async/notebooks/asrc_rbj_analysis.ipynb diff --git a/notebooks/figure_digest.py b/async/notebooks/figure_digest.py similarity index 100% rename from notebooks/figure_digest.py rename to async/notebooks/figure_digest.py diff --git a/tests/CMakeLists.txt b/async/tests/CMakeLists.txt similarity index 100% rename from tests/CMakeLists.txt rename to async/tests/CMakeLists.txt diff --git a/tests/bare_metal_main.cpp b/async/tests/bare_metal_main.cpp similarity index 100% rename from tests/bare_metal_main.cpp rename to async/tests/bare_metal_main.cpp diff --git a/tests/support/multitone_analysis.h b/async/tests/support/multitone_analysis.h similarity index 100% rename from tests/support/multitone_analysis.h rename to async/tests/support/multitone_analysis.h diff --git a/tests/support/sine_analysis.h b/async/tests/support/sine_analysis.h similarity index 100% rename from tests/support/sine_analysis.h rename to async/tests/support/sine_analysis.h diff --git a/tests/support/two_clock_sim.h b/async/tests/support/two_clock_sim.h similarity index 100% rename from tests/support/two_clock_sim.h rename to async/tests/support/two_clock_sim.h diff --git a/tests/test_asrc_lock.cpp b/async/tests/test_asrc_lock.cpp similarity index 100% rename from tests/test_asrc_lock.cpp rename to async/tests/test_asrc_lock.cpp diff --git a/tests/test_asrc_program.cpp b/async/tests/test_asrc_program.cpp similarity index 100% rename from tests/test_asrc_program.cpp rename to async/tests/test_asrc_program.cpp diff --git a/tests/test_asrc_quality.cpp b/async/tests/test_asrc_quality.cpp similarity index 100% rename from tests/test_asrc_quality.cpp rename to async/tests/test_asrc_quality.cpp diff --git a/tests/test_asrc_quality_16k.cpp b/async/tests/test_asrc_quality_16k.cpp similarity index 100% rename from tests/test_asrc_quality_16k.cpp rename to async/tests/test_asrc_quality_16k.cpp diff --git a/tests/test_fade.cpp b/async/tests/test_fade.cpp similarity index 100% rename from tests/test_fade.cpp rename to async/tests/test_fade.cpp diff --git a/tests/test_fixed_point.cpp b/async/tests/test_fixed_point.cpp similarity index 100% rename from tests/test_fixed_point.cpp rename to async/tests/test_fixed_point.cpp diff --git a/tests/test_hardening.cpp b/async/tests/test_hardening.cpp similarity index 100% rename from tests/test_hardening.cpp rename to async/tests/test_hardening.cpp diff --git a/tests/test_kaiser.cpp b/async/tests/test_kaiser.cpp similarity index 100% rename from tests/test_kaiser.cpp rename to async/tests/test_kaiser.cpp diff --git a/tests/test_latency.cpp b/async/tests/test_latency.cpp similarity index 100% rename from tests/test_latency.cpp rename to async/tests/test_latency.cpp diff --git a/tests/test_multichannel.cpp b/async/tests/test_multichannel.cpp similarity index 100% rename from tests/test_multichannel.cpp rename to async/tests/test_multichannel.cpp diff --git a/tests/test_output_hash.cpp b/async/tests/test_output_hash.cpp similarity index 100% rename from tests/test_output_hash.cpp rename to async/tests/test_output_hash.cpp diff --git a/tests/test_polyphase.cpp b/async/tests/test_polyphase.cpp similarity index 100% rename from tests/test_polyphase.cpp rename to async/tests/test_polyphase.cpp diff --git a/tests/test_servo.cpp b/async/tests/test_servo.cpp similarity index 100% rename from tests/test_servo.cpp rename to async/tests/test_servo.cpp diff --git a/tests/test_spsc_ring.cpp b/async/tests/test_spsc_ring.cpp similarity index 100% rename from tests/test_spsc_ring.cpp rename to async/tests/test_spsc_ring.cpp diff --git a/tests/test_spsc_ring_threads.cpp b/async/tests/test_spsc_ring_threads.cpp similarity index 100% rename from tests/test_spsc_ring_threads.cpp rename to async/tests/test_spsc_ring_threads.cpp diff --git a/tools/compare_shim/CMakeLists.txt b/async/tools/compare_shim/CMakeLists.txt similarity index 100% rename from tools/compare_shim/CMakeLists.txt rename to async/tools/compare_shim/CMakeLists.txt diff --git a/tools/compare_shim/srt_r8b_shim.cpp b/async/tools/compare_shim/srt_r8b_shim.cpp similarity index 100% rename from tools/compare_shim/srt_r8b_shim.cpp rename to async/tools/compare_shim/srt_r8b_shim.cpp From 5f6769764bafaba33076640bd110e4fe361212f5 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 28 Sep 2026 02:16:54 +0000 Subject: [PATCH 41/44] Build glue and path fix-ups for the two-engine tree (migration step 1c) The root CMakeLists composes async/ and bridge/ over one tap::dsp; each engine keeps its project() and options until step 3 and still configures on its own (cmake -S async|bridge, bridge/tools/capi) through a guarded dsptap add. bridge's dev-only view of the async headers now points at ../async/include. The M33/M55 toolchain files set both engines' BARE_METAL variables. CI configures the root once per job. Host legs keep each engine's warning policy (MSVC: async /W4, bridge /WX); the QEMU legs run per (target, engine) by ctest label with each engine's own exclusions and -j; the ratchet is one matrix job per engine, each with its own plugin marker and icount.py until step 2, and each engine README carries its icount table. style.yml is RatioTap's body over both engines; the book job also builds the API reference. migration-gates.yml runs docs/migration/gates.py against S0 and R0 rebuilt in the same job. Paths follow the 1a/1b moves: the 52 book includes (the rendered book is byte-identical to S0's), Doxyfile, book-pages filters, compare.yml, the scripts, the notebook bindings, README links and bridge build commands. LICENSE carries D14's holder line as bridge/LICENSE goes; bridge's duplicate lockfile, .gitignore and workflows go; bridge/docs/HISTORY.md maps RatioTap's SHAs to the imported ones. Gates measured locally against S0 and R0 built in the same session: G1, G3+G5 (M33, M55: counts and checksums exact), G4 (C ABI and 17 icount binaries), G5 host hashes, G6, G7, G10, G11 (7 notebooks), G12 and G14 all pass; Hexagon A/B and the macOS/Windows legs run first in CI. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- .github/workflows/book-pages.yml | 7 +- .github/workflows/ci-arm64.yml | 4 +- .github/workflows/ci.yml | 436 ++-- .github/workflows/compare.yml | 22 +- .github/workflows/migration-gates.yml | 190 ++ .github/workflows/style.yml | 35 +- CMakeLists.txt | 37 + LICENSE | 2 +- async/CMakeLists.txt | 11 +- async/README.md | 4 +- async/examples/pico2_cyccnt/CMakeLists.txt | 2 +- async/examples/pico2_dualcore/CMakeLists.txt | 2 +- async/notebooks/asrc_block_size_study.ipynb | 2 +- async/notebooks/asrc_comparison.ipynb | 2 +- async/notebooks/asrc_demo.ipynb | 4 +- async/notebooks/asrc_rbj_analysis.ipynb | 2 +- book/src/epilogue/letter.md | 6 +- book/src/part0/budgets.md | 4 +- book/src/part1/asrc.md | 8 +- book/src/part1/fractional-resampler.md | 18 +- book/src/part1/pi-servo.md | 16 +- book/src/part1/polyphase-bank.md | 10 +- book/src/part1/sample-traits.md | 16 +- book/src/part1/spsc-ring.md | 8 +- book/src/part2/tests.md | 4 +- book/src/part4/c-abi.md | 12 +- book/src/part4/cortex-m.md | 2 +- bridge/.github/workflows/ci.yml | 417 ---- bridge/.github/workflows/style.yml | 48 - bridge/.gitignore | 11 - bridge/CLAUDE.md | 5 +- bridge/CMakeLists.txt | 26 +- bridge/LICENSE | 21 - bridge/README.md | 48 +- bridge/docs/HISTORY.md | 89 + bridge/requirements.in | 11 - bridge/requirements.lock | 1884 ----------------- cmake/arm-cortex-m33-mps2.cmake | 3 + cmake/arm-cortex-m55-mps3.cmake | 3 + docs/Doxyfile | 6 +- docs/MONOREPO_PLAN.md | 40 +- docs/migration/README.md | 3 +- docs/migration/drafts/ci-after-1c.yml | 180 -- docs/migration/drafts/migration-gates.yml | 43 - .../migration/drafts/root-CMakeLists-1c.cmake | 35 - docs/migration/gates.py | 582 +++++ docs/migration/rename.py | 4 + docs/migration/residual/1c.txt | 64 +- docs/migration/runs.md | 1 + docs/migration/step.txt | 1 + scripts/book_figures.py | 2 +- .../fetch_hexagon_toolchain.sh | 0 scripts/icount.py | 8 +- scripts/tidy.sh | 10 +- scripts/update_icount_docs.py | 26 +- scripts/update_perf_docs.py | 4 +- 56 files changed, 1403 insertions(+), 3038 deletions(-) create mode 100644 .github/workflows/migration-gates.yml create mode 100644 CMakeLists.txt delete mode 100644 bridge/.github/workflows/ci.yml delete mode 100644 bridge/.github/workflows/style.yml delete mode 100644 bridge/.gitignore delete mode 100644 bridge/LICENSE create mode 100644 bridge/docs/HISTORY.md delete mode 100644 bridge/requirements.in delete mode 100644 bridge/requirements.lock delete mode 100644 docs/migration/drafts/ci-after-1c.yml delete mode 100644 docs/migration/drafts/migration-gates.yml delete mode 100644 docs/migration/drafts/root-CMakeLists-1c.cmake create mode 100644 docs/migration/gates.py create mode 100644 docs/migration/step.txt rename {bridge/scripts => scripts}/fetch_hexagon_toolchain.sh (100%) diff --git a/.github/workflows/book-pages.yml b/.github/workflows/book-pages.yml index a30d14b..52b5021 100644 --- a/.github/workflows/book-pages.yml +++ b/.github/workflows/book-pages.yml @@ -13,9 +13,12 @@ on: paths: - "book/**" - "docs/**" - - "include/**" + - "async/include/**" + - "bridge/include/**" + - "async/README.md" + - "async/tests/**" + - "async/capi/**" - "platform/**" - - "tests/support/**" - "tools/**" - "cmake/**" - ".github/workflows/book-pages.yml" diff --git a/.github/workflows/ci-arm64.yml b/.github/workflows/ci-arm64.yml index 0a72386..c31d857 100644 --- a/.github/workflows/ci-arm64.yml +++ b/.github/workflows/ci-arm64.yml @@ -35,8 +35,10 @@ jobs: - name: Build run: cmake --build build -j 4 + # The async engine's suite, as before the monorepo import; the root + # also builds the bridge engine, which this workflow does not target. - name: Full test suite - run: ctest --test-dir build --output-on-failure --no-tests=error + run: ctest --test-dir build --output-on-failure --no-tests=error -L '^async$' # The ring stress under TSan on weakly-ordered silicon is the point # of this workflow: run it several times for schedule diversity. diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e7bbc80..64b8bc3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -21,10 +21,20 @@ concurrency: group: ci-${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} +env: + CTEST_COMMON: --no-tests=error --output-on-failure + +# Monorepo migration step 1c: one tree, two engines (async/, bridge/). Every +# job configures the ROOT once and builds both engines; a job that tests +# only one engine selects it at ctest time by label (async / ratio — the +# bridge engine keeps RatioTap's `ratio` label until step 3.4 renames it, +# docs/MONOREPO_PLAN.md D16). Per-engine options keep each engine's own +# warning policy. jobs: build-and-test: name: ${{ matrix.name }} runs-on: ${{ matrix.os }} + timeout-minutes: 30 strategy: fail-fast: false matrix: @@ -33,27 +43,31 @@ jobs: os: ubuntu-latest cc: gcc cxx: g++ - werror: ON + async_werror: ON + bridge_werror: ON capi: ON + # The bridge engine's first Clang coverage on Linux. - name: Linux Clang os: ubuntu-latest cc: clang cxx: clang++ - werror: ON + async_werror: ON + bridge_werror: ON capi: ON - name: macOS AppleClang os: macos-latest - werror: ON + async_werror: ON + bridge_werror: ON capi: ON - # Warnings stay non-fatal on MSVC until /W4 output has been - # triaged (docs/PERFORMANCE.md "Known debt"). capi stays OFF here too: - # tools/capi carries no __declspec(dllexport) (unlike DspTap's), so an - # MSVC build would link a DLL exporting nothing — it would pass without - # gating anything. Turn it ON in the same change that gives the C ABI - # an export decoration. + # async's warnings stay non-fatal on MSVC until its /W4 output has + # been triaged (async/docs/PERFORMANCE.md "Known debt"); bridge keeps + # /WX, as RatioTap's CI did. The C ABI stays OFF: neither library + # carries __declspec(dllexport), so an MSVC build would link DLLs + # exporting nothing and gate nothing. - name: Windows MSVC os: windows-latest - werror: OFF + async_werror: OFF + bridge_werror: ON capi: OFF steps: - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 @@ -67,31 +81,40 @@ jobs: run: > cmake -B build -DCMAKE_BUILD_TYPE=Release - -DSRT_WERROR=${{ matrix.werror }} + -DSRT_WERROR=${{ matrix.async_werror }} + -DTAP_RATIO_WERROR=${{ matrix.bridge_werror }} -DSRT_BUILD_CAPI=${{ matrix.capi }} + -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} - name: Build run: cmake --build build --config Release -j 4 - name: Test - run: ctest --test-dir build -C Release --output-on-failure --no-tests=error + run: ctest --test-dir build -C Release ${{ env.CTEST_COMMON }} sanitizers: name: ${{ matrix.name }} runs-on: ubuntu-latest + timeout-minutes: 30 strategy: fail-fast: false matrix: include: - name: ASan + UBSan flags: -fsanitize=address,undefined -fno-sanitize-recover=all + labels: "" + # TSan runs the async engine only: bridge is single-threaded, and + # RatioTap never ran a TSan job. - name: TSan flags: -fsanitize=thread + labels: -L ^async$ steps: - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive + # Each engine keeps its old sanitizer configuration: async without + # WERROR and without examples, bridge with WERROR and its examples. - name: Configure env: CC: clang @@ -100,6 +123,7 @@ jobs: cmake -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo -DSRT_BUILD_EXAMPLES=OFF + -DTAP_RATIO_WERROR=ON -DCMAKE_CXX_FLAGS="${{ matrix.flags }}" - name: Build @@ -109,65 +133,121 @@ jobs: env: TSAN_OPTIONS: halt_on_error=1 UBSAN_OPTIONS: print_stacktrace=1 - run: ctest --test-dir build --output-on-failure --no-tests=error - - # Cross-compile for Qualcomm Hexagon (Linux/musl) with the open-source - # toolchain and run a subset of the suite under qemu-hexagon user-mode - # emulation. Validates ISA-level correctness on a 32-bit audio DSP target - # (size_t width, atomics lowering, musl libc, soft-float doubles); the - # long-running quality/lock simulations and the 10M-element thread stress - # are excluded — they prove DSP math and concurrency, which emulation - # neither speeds up nor measures meaningfully. - hexagon-qemu: - name: Hexagon cross (QEMU) + run: ctest --test-dir build ${{ env.CTEST_COMMON }} ${{ matrix.labels }} + + # Cross-compiled correctness under emulation, one job per (target, engine): + # both engines are built in one tree and each job runs one engine's label + # with that engine's own exclusions and parallelism, as each repository ran + # them before (async serial, bridge -j 4 on Hexagon). The -V --output-log + # file keeps every test's own output ([ RUN ] lines, [ measured ] numbers) + # and is uploaded as evidence (monorepo gate G2). + # Hexagon (Linux/musl, qemu-hexagon user mode): 32-bit audio DSP, soft- + # float doubles. Excluded: the long quality/lock simulations and the + # thread stress (emulation neither speeds up nor measures them), and the + # EXPECT_THROW tests — this static-musl toolchain cannot unwind across + # frames, so a correct throw terminates (Known debt, PERFORMANCE.md). + # Cortex-M55 (MPS3 AN547) and Cortex-M33 (MPS2+ AN505, Pico 2 class): + # bare metal, newlib + semihosting, no threads, no FP64; each engine's + # one-shot binary bakes in its emulation-sized filter. + qemu: + name: ${{ matrix.title }} ${{ matrix.engine }} (QEMU) + # Pinned image: toolchain and qemu packages must not move under a run. runs-on: ubuntu-24.04 - timeout-minutes: 45 + timeout-minutes: ${{ matrix.timeout }} + strategy: + fail-fast: false + matrix: + include: + - target: hexagon + title: Hexagon cross + engine: async + label: async + timeout: 45 + jobs: 1 + build_type: Release + toolchain: cmake/hexagon-linux-musl.cmake + exclude: AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec|MultiChannel\.|Feasibility|Reset\.|ConfigValidation + - target: hexagon + title: Hexagon cross + engine: bridge + label: ratio + timeout: 30 + jobs: 4 + build_type: Release + toolchain: cmake/hexagon-linux-musl.cmake + exclude: BadProfilesThrow|LatencyAndValidation + - target: m55 + title: Cortex-M55 cross + engine: async + label: async + timeout: 30 + jobs: 1 + build_type: MinSizeRel + toolchain: cmake/arm-cortex-m55-mps3.cmake + exclude: "" + - target: m55 + title: Cortex-M55 cross + engine: bridge + label: ratio + timeout: 20 + jobs: 1 + build_type: MinSizeRel + toolchain: cmake/arm-cortex-m55-mps3.cmake + exclude: "" + # ~23 min of emulated suite against a 40-minute budget. + - target: m33 + title: Cortex-M33 cross + engine: async + label: async + timeout: 40 + jobs: 1 + build_type: MinSizeRel + toolchain: cmake/arm-cortex-m33-mps2.cmake + exclude: "" + - target: m33 + title: Cortex-M33 cross + engine: bridge + label: ratio + timeout: 20 + jobs: 1 + build_type: MinSizeRel + toolchain: cmake/arm-cortex-m33-mps2.cmake + exclude: "" env: # Prebuilt open-source toolchain (BSD-3) published by Qualcomm/Quicinc; # binary artifacts are hosted on CodeLinaro and linked from the - # quic/toolchain_for_hexagon release notes. + # quic/toolchain_for_hexagon release notes. Hard pin verified against + # the published SHA256SUMS by scripts/fetch_hexagon_toolchain.sh. HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst - # Hard pin, taken from the "toolchain sha256:" line of run #13 (which - # also matched the published SHA256SUMS). HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" steps: - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: submodules: recursive - - name: Cache toolchain + - name: Install Arm toolchain and QEMU + if: matrix.target != 'hexagon' + run: > + sudo apt-get update -q && + sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + + - name: Cache Hexagon toolchain + if: matrix.target == 'hexagon' id: cache uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 with: path: ~/hexagon # Keyed on the pinned digest: every job that can write this key - # verifies its download against the same pin, so no unverified - # writer can poison the trusted entry. + # verifies its download against the same pin (one shared script), + # so no unverified writer can poison the trusted entry. key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 - - name: Download toolchain - if: steps.cache.outputs.cache-hit != 'true' - run: | - mkdir -p ~/hexagon && cd ~/hexagon - curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" - # Integrity check against the published SHA256SUMS, plus the hard - # pin when set. The SUMS file catches corruption and - # cache-poisoning; only the pin catches an origin compromise. - curl -sfLo SHA256SUMS "$(dirname "$HEXAGON_TOOLCHAIN_URL")/SHA256SUMS" - expected=$(grep "$(basename "$HEXAGON_TOOLCHAIN_URL")" SHA256SUMS | awk '{print $1}' | head -1) - actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) - echo "toolchain sha256: $actual (pin this in HEXAGON_TOOLCHAIN_SHA256)" - if [ -z "$expected" ] || [ "$actual" != "$expected" ]; then - echo "::error::toolchain does not match published SHA256SUMS"; exit 1 - fi - if [ -n "${HEXAGON_TOOLCHAIN_SHA256:-}" ] && \ - [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then - echo "::error::toolchain checksum mismatch against pinned value"; exit 1 - fi - tar --zstd -xf toolchain.tar.zst - rm toolchain.tar.zst SHA256SUMS + - name: Download Hexagon toolchain + if: matrix.target == 'hexagon' && steps.cache.outputs.cache-hit != 'true' + run: scripts/fetch_hexagon_toolchain.sh - - name: Set up toolchain and QEMU paths + - name: Set up Hexagon toolchain and QEMU paths + if: matrix.target == 'hexagon' run: | # No -type f (symlinks count); dirname of an empty find result is # ".", so assert on the find output itself. @@ -182,124 +262,41 @@ jobs: sudo apt-get update -q && sudo apt-get install -y -q qemu-user fi - - name: Verify tools + - name: Record image and toolchain versions run: | - hexagon-unknown-linux-musl-clang++ --version - qemu-hexagon --version - - - name: Configure - run: > - cmake -B build - -DCMAKE_BUILD_TYPE=Release - -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake - -DSRT_BUILD_EXAMPLES=OFF - - - name: Build - run: cmake --build build -j 4 - - # -V --output-log keeps every test's own output ([ RUN ] lines, - # [ measured ] numbers), which --output-on-failure prints only for - # failures; the log is uploaded below as evidence. - - name: Test under emulation - run: > - ctest --test-dir build --output-on-failure --no-tests=error - -V --output-log ctest-hexagon.log - -E 'AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec|MultiChannel\.|Feasibility|Reset\.|ConfigValidation' - # ConfigValidation: this static-musl toolchain cannot unwind across - # frames — the constructor throws correctly but EXPECT_THROW never - # catches and libc++abi terminates. Validation is target-independent - # and covered on every other leg; limitation tracked in - # docs/PERFORMANCE.md "Known debt". - - - name: Upload test log - if: ${{ !cancelled() }} - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 - with: - name: ctest-hexagon - path: ctest-hexagon.log - - # Cross-compile for Arm Cortex-M55 (bare metal, newlib + semihosting) and - # run the emulation-sized test subset on QEMU's MPS3 AN547 board model. - # Validates the library on a 32-bit MCU-class target with no OS, no - # threads and no double-precision FPU; the fixed-point datapaths are the - # performance-appropriate formats here. - cortex-m55-qemu: - name: Cortex-M55 cross (QEMU) - runs-on: ubuntu-24.04 - timeout-minutes: 30 - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Install toolchain and QEMU - run: > - sudo apt-get update -q && - sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm - - - name: Configure - run: > - cmake -B build - -DCMAKE_BUILD_TYPE=MinSizeRel - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake - -DSRT_BUILD_EXAMPLES=OFF - - - name: Build - run: cmake --build build -j 4 - - - name: Test under emulation - run: > - ctest --test-dir build --output-on-failure --no-tests=error - -V --output-log ctest-m55.log - - - name: Upload test log - if: ${{ !cancelled() }} - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 - with: - name: ctest-m55 - path: ctest-m55.log - - # Cortex-M33 (Raspberry Pi Pico 2 / RP2350 class: single-precision FPU, - # no FP64, no MVE) on QEMU's MPS2+ AN505 model. Shares the Armv8-M - # startup with the M55 target; quantifies the soft-double float path and - # anchors the Q15/Q31 budgets for Pico-class parts. - cortex-m33-qemu: - name: Cortex-M33 cross (QEMU) - runs-on: ubuntu-24.04 - # ~23 min of emulated suite (plus ~1 min of output hashes) against the - # old 30: headroom for a slower runner. - timeout-minutes: 40 - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Install toolchain and QEMU - run: > - sudo apt-get update -q && - sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm + echo "image: ${ImageOS:-unknown} ${ImageVersion:-unknown}" + if [ "${{ matrix.target }}" = hexagon ]; then + hexagon-unknown-linux-musl-clang++ --version + qemu-hexagon --version + else + dpkg-query -W gcc-arm-none-eabi qemu-system-arm + fi - - name: Configure + - name: Configure (both engines, one tree) run: > cmake -B build - -DCMAKE_BUILD_TYPE=MinSizeRel - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake + -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + -DCMAKE_TOOLCHAIN_FILE=${{ matrix.toolchain }} -DSRT_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_EXAMPLES=OFF - name: Build run: cmake --build build -j 4 - - name: Test under emulation - run: > - ctest --test-dir build --output-on-failure --no-tests=error - -V --output-log ctest-m33.log + - name: Test under emulation (${{ matrix.engine }} only) + shell: bash + run: | + args=(--test-dir build ${{ env.CTEST_COMMON }} -L '^${{ matrix.label }}$' + -j ${{ matrix.jobs }} -V --output-log ctest-${{ matrix.target }}-${{ matrix.engine }}.log) + if [ -n '${{ matrix.exclude }}' ]; then args+=(-E '${{ matrix.exclude }}'); fi + ctest "${args[@]}" - name: Upload test log if: ${{ !cancelled() }} uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 with: - name: ctest-m33 - path: ctest-m33.log + name: ctest-${{ matrix.target }}-${{ matrix.engine }} + path: ctest-${{ matrix.target }}-${{ matrix.engine }}.log # ------------------------------------------------------------------------ # Template: genuine Tensilica HiFi4/HiFi5 coverage. The HiFi audio ISA, @@ -323,25 +320,39 @@ jobs: # -DCMAKE_C_COMPILER=xt-clang # -DCMAKE_CXX_COMPILER=xt-clang++ # "-DCMAKE_CROSSCOMPILING_EMULATOR=xt-run;--xtensa-core=$XTENSA_CORE" - # -DSRT_BUILD_EXAMPLES=OFF + # -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF # - name: Build # run: cmake --build build -j # - name: Test on ISS # run: > - # ctest --test-dir build --output-on-failure + # ctest --test-dir build --output-on-failure -L '^async$' # -E 'AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec' # ------------------------------------------------------------------------ - # Deterministic instruction-count ratchet (docs/PERFORMANCE.md): fixed - # workloads under QEMU with a counting plugin, gated against - # bench/baselines.json. Unlike wall-clock numbers these are noise-free, - # so a hard >3% gate is safe on shared runners. + # Deterministic instruction-count ratchet (async/docs/PERFORMANCE.md, + # bridge/PLAN.md section 7): fixed workloads under QEMU with a counting + # plugin, gated two-sided at ±3% against each engine's + # bench/baselines.json. Noise-free, so a hard gate is safe on shared + # runners. One job per engine until migration step 2 unifies the harness: + # each engine still has its own plugin marker and icount.py. icount-ratchet: - name: Instruction-count ratchet + name: Instruction-count ratchet (${{ matrix.engine }}) # Pinned image: the counts are a function of the apt toolchain, the # plugin build and QEMU, so the job must not straddle an image rollout. runs-on: ubuntu-24.04 timeout-minutes: 45 + strategy: + fail-fast: false + matrix: + include: + - engine: async + plugin_src: tools/qemu_insn_plugin/insn_count.c + icount: scripts/icount.py + bench: -DSRT_BUILD_ICOUNT_BENCH=ON + - engine: bridge + plugin_src: bridge/tools/qemu_insn_plugin/insn_count.c + icount: bridge/scripts/icount.py + bench: -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON env: # Commit the v8.2.2 tag pointed at when pinned (tags are movable; # commit SHAs are not), with the header's digest verified on download. @@ -351,9 +362,13 @@ jobs: # Hard pin from the "qemu source sha256:" line of run #24. QEMU_SRC_SHA256: "847346c1b82c1a54b2c38f6edbd85549edeb17430b7d4d3da12620e2962bc4f3" HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst - # Same hard pin as the hexagon-qemu job: this job also writes the - # shared toolchain cache, so it must verify against the same digest. + # Same hard pin as the qemu job: this job also writes the shared + # toolchain cache, so it verifies against the same digest. HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" + # Both engines' tests and examples OFF: workloads only. + WORKLOADS_ONLY: >- + -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF steps: - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 with: @@ -373,10 +388,10 @@ jobs: echo "::error::qemu-plugin.h checksum mismatch"; exit 1 fi gcc -shared -fPIC $(pkg-config --cflags glib-2.0) -I/tmp \ - -o /tmp/libinsncount.so tools/qemu_insn_plugin/insn_count.c + -o /tmp/libinsncount.so ${{ matrix.plugin_src }} # What produced the counts: the runner image and the toolchain packages - # (the counts move when either does; see docs/PERFORMANCE.md). + # (the counts move when either does). - name: Record image and toolchain versions id: image run: | @@ -390,14 +405,14 @@ jobs: cmake -B build-m55 -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake - -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF - -DSRT_BUILD_ICOUNT_BENCH=ON + $WORKLOADS_ONLY ${{ matrix.bench }} && cmake --build build-m55 -j 4 - name: Ratchet M55 run: > - python3 scripts/icount.py --target m55 + python3 ${{ matrix.icount }} --target m55 --build-dir build-m55 --plugin /tmp/libinsncount.so + --baselines ${{ matrix.engine }}/bench/baselines.json - name: Build M33 workloads if: ${{ !cancelled() }} @@ -405,23 +420,22 @@ jobs: cmake -B build-m33 -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake - -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF - -DSRT_BUILD_ICOUNT_BENCH=ON + $WORKLOADS_ONLY ${{ matrix.bench }} && cmake --build build-m33 -j 4 - name: Ratchet M33 if: ${{ !cancelled() }} run: > - python3 scripts/icount.py --target m33 + python3 ${{ matrix.icount }} --target m33 --build-dir build-m33 --plugin /tmp/libinsncount.so + --baselines ${{ matrix.engine }}/bench/baselines.json # Neither Debian's nor the CodeLinaro toolchain's qemu-hexagon enables # TCG plugins, so the Hexagon leg builds its own from the pinned QEMU # release (linux-user target only, ~4 min, cached thereafter). # The remaining ratchet steps run even if an earlier target failed - # (each target's numbers are independent evidence; stopping at the - # first failure forces a serial harvest when baselines legitimately - # move). The job still fails if any step failed. + # (each target's numbers are independent evidence). The job still + # fails if any step failed. - name: Cache plugin-enabled qemu-hexagon if: ${{ !cancelled() }} id: qemu-hex @@ -456,30 +470,17 @@ jobs: uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 with: path: ~/hexagon - # Same digest-keyed name as the hexagon-qemu job; the download - # below verifies the same pin before anything is saved under it. key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 - name: Ratchet Hexagon if: ${{ !cancelled() }} run: | if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then - mkdir -p ~/hexagon && cd ~/hexagon - curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" - actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) - if [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then - echo "::error::toolchain checksum mismatch against pinned value" - exit 1 - fi - tar --zstd -xf toolchain.tar.zst && rm toolchain.tar.zst - cd "$GITHUB_WORKSPACE" + scripts/fetch_hexagon_toolchain.sh fi # No -type f: the compiler may be a symlink in the restored tree. clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) if [ -z "$clangxx" ]; then - echo "contents of ~/hexagon:" - ls -la "$HOME/hexagon" 2>&1 | head -20 - find "$HOME/hexagon" -maxdepth 3 | head -40 echo "::error::hexagon cross compiler not found under ~/hexagon" exit 1 fi @@ -494,22 +495,23 @@ jobs: fi cmake -B build-hex -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake \ - -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF \ - -DSRT_BUILD_ICOUNT_BENCH=ON + $WORKLOADS_ONLY ${{ matrix.bench }} cmake --build build-hex -j 4 - python3 scripts/icount.py --target hexagon \ - --build-dir build-hex --plugin /tmp/libinsncount.so + python3 ${{ matrix.icount }} --target hexagon \ + --build-dir build-hex --plugin /tmp/libinsncount.so \ + --baselines ${{ matrix.engine }}/bench/baselines.json - # The README instruction-count table derives 1:1 from the committed - # baselines; regenerating it must produce no diff. + # Each engine README's instruction-count table derives 1:1 from its + # committed baselines; regenerating it must produce no diff. - name: Docs freshness + if: ${{ !cancelled() }} run: | - python3 scripts/update_icount_docs.py - git diff --exit-code README.md || { - echo "::error::README icount table is stale; run scripts/update_icount_docs.py"; exit 1; } + python3 scripts/update_icount_docs.py --engine ${{ matrix.engine }} + git diff --exit-code ${{ matrix.engine }}/README.md || { + echo "::error::${{ matrix.engine }}/README.md icount table is stale; run scripts/update_icount_docs.py --engine ${{ matrix.engine }}"; exit 1; } # Keeps the benchmarks compiling and runnable; never a performance gate - # (shared runners are noise — see docs/PERFORMANCE.md). + # (shared runners are noise — see async/docs/PERFORMANCE.md). bench-smoke: name: Benchmark smoke runs-on: ubuntu-latest @@ -524,18 +526,18 @@ jobs: cmake -B build -DCMAKE_BUILD_TYPE=Release -DSRT_BUILD_BENCHMARKS=ON - -DSRT_BUILD_TESTS=OFF - -DSRT_BUILD_EXAMPLES=OFF + -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF - name: Build run: cmake --build build -j 4 - name: Run (smoke) - run: ./build/bench/srt_bench --benchmark_min_time=0.01s + run: ./build/async/bench/srt_bench --benchmark_min_time=0.01s # Keeps the manually-triggered comparison paths (compare.yml, - # bench/compare) compiling per push; build-only — the measured numbers - # come from the manual workflow, never from here. + # async/bench/compare) compiling per push; build-only — the measured + # numbers come from the manual workflow, never from here. compare-smoke: name: Comparison build smoke runs-on: ubuntu-latest @@ -558,6 +560,7 @@ jobs: -DSRT_BUILD_BENCHMARKS=ON -DSRT_BUILD_COMPARE_BENCH=ON -DSRT_BUILD_COMPARE_SHIM=ON + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF && cmake --build build-host -j 4 --target srt_bench_compare srt_r8b_shim - name: Build M55 comparison workload @@ -566,16 +569,14 @@ jobs: -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF -DSRT_BUILD_ICOUNT_BENCH=ON -DSRT_ICOUNT_COMPARE=ON && cmake --build build-m55 -j 4 --target cmp_icount_lsr_medium cmp_icount_srt_q15 cmp_icount_r8b_120 # The Pico 2 firmware examples are standalone projects (Pico SDK fetched at - # configure time), deliberately outside the root build. Building them here - # keeps them from silently rotting again: they had stopped compiling after - # the snake_case rename and the DspTap substrate move, with nothing - # noticing. Build-only; running them needs the hardware - # (docs/HARDWARE_TESTING.md). + # configure time), deliberately outside the root build. Build-only; running + # them needs the hardware (async/docs/HARDWARE_TESTING.md). pico2-build: name: Pico 2 firmware build runs-on: ubuntu-24.04 @@ -590,12 +591,12 @@ jobs: - name: Build pico2_cyccnt run: > - cmake -S examples/pico2_cyccnt -B build-cyccnt -DPICO_BOARD=pico2 + cmake -S async/examples/pico2_cyccnt -B build-cyccnt -DPICO_BOARD=pico2 && cmake --build build-cyccnt -j 4 - name: Build pico2_dualcore run: > - cmake -S examples/pico2_dualcore -B build-dualcore -DPICO_BOARD=pico2 + cmake -S async/examples/pico2_dualcore -B build-dualcore -DPICO_BOARD=pico2 && cmake --build build-dualcore -j 4 clang-format: @@ -647,6 +648,17 @@ jobs: exit 1 fi + # The API reference must build and be non-empty (both engines' headers + # are its input); book-pages.yml publishes it from main. + - name: API reference (Doxygen) + run: | + sudo apt-get update -q && sudo apt-get install -y -q doxygen graphviz + doxygen docs/Doxyfile + test -s docs/html/index.html || { echo "::error::Doxygen produced no index.html"; exit 1; } + n=$(find docs/html -name '*.html' | wc -l) + echo "doxygen: $n pages" + [ "$n" -ge 20 ] || { echo "::error::Doxygen produced only $n pages"; exit 1; } + # mdBook does not fail on a missing image, so check every relative # image reference resolves. (The SVGs are committed, generated by # scripts/book_figures.py; regeneration is not gated because diff --git a/.github/workflows/compare.yml b/.github/workflows/compare.yml index c2c1673..0ebb00e 100644 --- a/.github/workflows/compare.yml +++ b/.github/workflows/compare.yml @@ -1,4 +1,4 @@ -# Cross-resampler instruction-count comparison (docs/COMPARISON.md). +# Cross-resampler instruction-count comparison (async/docs/COMPARISON.md). # Manual trigger only: competitor counts are measured once per # toolchain/version pin and recorded in the docs, not gated — the ratchet # (ci.yml) gates only our own code. Prints one "CMP_COUNT " @@ -56,15 +56,16 @@ jobs: cmake -B build-$tgt -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_TOOLCHAIN_FILE=$tc \ -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF \ + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ -DSRT_BUILD_ICOUNT_BENCH=ON -DSRT_ICOUNT_COMPARE=ON cmake --build build-$tgt -j 4 # Each engine at 2 s and 4 s: the difference is steady state, the - # remainder construction (bench/icount/cmp_main.cpp). + # remainder construction (async/bench/icount/cmp_main.cpp). for bin in $(for e in srt_float srt_q15 lsr_medium lsr_best r8b_120 r8b_120_tb8; do echo cmp_icount_$e cmp_icount_${e}_4s; done); do out=$(qemu-system-arm -M $machine -nographic -semihosting \ -d plugin -plugin /tmp/libinsncount.so \ - -kernel build-$tgt/bench/icount/$bin 2>&1) + -kernel build-$tgt/async/bench/icount/$bin 2>&1) echo "$out" | grep -q 'SRT_ICOUNT_DONE ok=1' || { echo "$out"; echo "::error::$tgt $bin did not complete"; exit 1; } n=$(echo "$out" | grep -o 'SRT_INSN_COUNT [0-9]*' | cut -d' ' -f2) @@ -106,16 +107,10 @@ jobs: - name: Measure Hexagon run: | + # The shared download+verify script, as every writer of the + # digest-keyed cache uses. if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then - mkdir -p ~/hexagon && cd ~/hexagon - curl -sfLo toolchain.tar.zst "$HEXAGON_TOOLCHAIN_URL" - actual=$(sha256sum toolchain.tar.zst | cut -d' ' -f1) - if [ "$actual" != "$HEXAGON_TOOLCHAIN_SHA256" ]; then - echo "::error::toolchain checksum mismatch against pinned value" - exit 1 - fi - tar --zstd -xf toolchain.tar.zst && rm toolchain.tar.zst - cd "$GITHUB_WORKSPACE" + scripts/fetch_hexagon_toolchain.sh fi clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) [ -n "$clangxx" ] || { echo "::error::hexagon compiler not found"; exit 1; } @@ -123,12 +118,13 @@ jobs: cmake -B build-hex -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake \ -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF \ + -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ -DSRT_BUILD_ICOUNT_BENCH=ON -DSRT_ICOUNT_COMPARE=ON cmake --build build-hex -j 4 for bin in $(for e in srt_float srt_q15 lsr_medium lsr_best r8b_120 r8b_120_tb8; do echo cmp_icount_$e cmp_icount_${e}_4s; done); do out=$(qemu-hexagon -d plugin -plugin /tmp/libinsncount.so \ - build-hex/bench/icount/$bin 2>&1) + build-hex/async/bench/icount/$bin 2>&1) echo "$out" | grep -q 'SRT_ICOUNT_DONE ok=1' || { echo "$out"; echo "::error::hexagon $bin did not complete"; exit 1; } n=$(echo "$out" | grep -o 'SRT_INSN_COUNT [0-9]*' | cut -d' ' -f2) diff --git a/.github/workflows/migration-gates.yml b/.github/workflows/migration-gates.yml new file mode 100644 index 0000000..41c1acc --- /dev/null +++ b/.github/workflows/migration-gates.yml @@ -0,0 +1,190 @@ +name: migration-gates + +# The monorepo migration's gates (docs/MONOREPO_PLAN.md section 5), run on +# every gated push of the migration PR. Each job checks out the gated tree +# AND the two step-0 tips (docs/migration/tips.txt; both public, no token +# needed), builds all three with one toolchain, and runs +# docs/migration/gates.py: +# host G1 G4(C ABI) G5 G6 G7 G9 G10 G12 G14 +# m33/m55/hexagon G3 G4(icount) G5(checksums) G7 +# notebooks G11 +# Lives only on the migration branch; deleted at step 4 with docs/migration/. +on: + pull_request: + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: migration-gates-${{ github.event.pull_request.number || github.ref }} + # Every gated SHA needs a complete run (G13). + cancel-in-progress: false + +env: + S0: 5e2057f192cee1eeb61286c466e5f50293580c09 + R0: 8f19e8b967b8d3e9eb22393e881396d418c0584f + +jobs: + gates: + name: gates ${{ matrix.target }} + # Pinned image: G3, G4 and G7 compare builds made in this job, so the + # job must not straddle an image rollout. + runs-on: ubuntu-24.04 + timeout-minutes: 90 + strategy: + fail-fast: false + matrix: + target: [host, m33, m55, hexagon] + env: + QEMU_PLUGIN_HEADER_URL: https://raw.githubusercontent.com/qemu/qemu/11aa0b1ff115b86160c4d37e7c37e6a6b13b77ea/include/qemu/qemu-plugin.h + QEMU_PLUGIN_HEADER_SHA256: "c53a2af163e80e3f4bc6c60dbdfc84003db329d757e37cd8a16a77e1d82606ff" + QEMU_SRC_URL: https://download.qemu.org/qemu-8.2.2.tar.xz + QEMU_SRC_SHA256: "847346c1b82c1a54b2c38f6edbd85549edeb17430b7d4d3da12620e2962bc4f3" + HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst + HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" + steps: + # Full history for G12 (--follow and blame across the import). + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + path: new + submodules: recursive + fetch-depth: 0 + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + repository: tap/SampleRateTap + ref: ${{ env.S0 }} + path: old-async + submodules: recursive + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + repository: tap/RatioTap + ref: ${{ env.R0 }} + path: old-ratio + submodules: recursive + + - name: The tips match docs/migration/tips.txt + run: | + grep -q "^S0 $S0" new/docs/migration/tips.txt + grep -q "^R0 $R0" new/docs/migration/tips.txt + echo "step: $(cat new/docs/migration/step.txt)" + + - name: Install tools + run: | + sudo apt-get update -q + sudo apt-get install -y -q python3-pyelftools clang-format-18 libglib2.0-dev \ + gcc-arm-none-eabi qemu-system-arm ninja-build meson flex bison + # rename.py formats like the pre-commit hook (clang-format 18.1.3). + sudo ln -sf "$(command -v clang-format-18)" /usr/local/bin/clang-format + echo "image: ${ImageOS:-unknown} ${ImageVersion:-unknown}" + dpkg-query -W gcc-arm-none-eabi qemu-system-arm g++ clang-format-18 + echo "os=${ImageOS:-unknown}" >> "$GITHUB_OUTPUT" + id: image + + - name: Counting plugin header + if: matrix.target != 'host' + run: | + curl -sfLo /tmp/qemu-plugin.h "$QEMU_PLUGIN_HEADER_URL" + actual=$(sha256sum /tmp/qemu-plugin.h | cut -d' ' -f1) + [ "$actual" = "$QEMU_PLUGIN_HEADER_SHA256" ] || { echo "::error::qemu-plugin.h checksum mismatch"; exit 1; } + + - name: Cache plugin-enabled qemu-hexagon + if: matrix.target == 'hexagon' + id: qemu-hex + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 + with: + path: ~/qemu-hexagon-plugins + key: qemu-hexagon-plugins-${{ steps.image.outputs.os }}-${{ env.QEMU_SRC_URL }}-1 + + - name: Build plugin-enabled qemu-hexagon + if: matrix.target == 'hexagon' && steps.qemu-hex.outputs.cache-hit != 'true' + run: | + curl -sfLo /tmp/qemu-src.tar.xz "$QEMU_SRC_URL" + actual=$(sha256sum /tmp/qemu-src.tar.xz | cut -d' ' -f1) + [ "$actual" = "$QEMU_SRC_SHA256" ] || { echo "::error::qemu source checksum mismatch"; exit 1; } + tar -xJf /tmp/qemu-src.tar.xz -C /tmp + cd /tmp/qemu-*/ + ./configure --target-list=hexagon-linux-user --enable-plugins \ + --disable-docs --disable-tools --disable-system + ninja -C build qemu-hexagon + mkdir -p ~/qemu-hexagon-plugins + cp build/qemu-hexagon ~/qemu-hexagon-plugins/ + + - name: Cache Hexagon toolchain + if: matrix.target == 'hexagon' + id: cache + uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 + with: + path: ~/hexagon + key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 + + - name: Hexagon toolchain on PATH + if: matrix.target == 'hexagon' + run: | + if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then + new/scripts/fetch_hexagon_toolchain.sh + fi + clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) + test -n "$clangxx" + echo "$(dirname "$clangxx")" >> "$GITHUB_PATH" + echo "$HOME/qemu-hexagon-plugins" >> "$GITHUB_PATH" + + - name: Gates (${{ matrix.target }}) + run: | + if [ "${{ matrix.target }}" = host ]; then + python3 new/docs/migration/gates.py host --work work 2>&1 | tee gates-host.log + else + python3 new/docs/migration/gates.py cross --target ${{ matrix.target }} \ + --work work --plugin-header-dir /tmp 2>&1 | tee gates-${{ matrix.target }}.log + fi + exit "${PIPESTATUS[0]}" + + - name: Upload gate log + if: ${{ !cancelled() }} + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: gates-${{ matrix.target }} + path: gates-${{ matrix.target }}.log + + notebooks: + name: gates notebooks (G11) + runs-on: ubuntu-24.04 + timeout-minutes: 120 + steps: + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + path: new + submodules: recursive + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + repository: tap/SampleRateTap + ref: ${{ env.S0 }} + path: old-async + submodules: recursive + - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 + with: + repository: tap/RatioTap + ref: ${{ env.R0 }} + path: old-ratio + submodules: recursive + # The pinned notebook environment (step P.3): Python from setup-python, + # every package hash-pinned by the lockfile. + - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7.0.0 + with: + python-version: "3.11" # the lockfile was compiled for 3.11 + - name: Install the pinned environment + run: | + python3 -m pip install --require-hashes -r new/requirements.lock + python3 --version && python3 -m pip freeze | grep -iE '^(numpy|scipy|matplotlib|samplerate|soxr)==' + - name: Gates (notebooks) + run: | + python3 new/docs/migration/gates.py notebooks --work work 2>&1 | tee gates-notebooks.log + exit "${PIPESTATUS[0]}" + - name: Upload gate log and executed notebooks + if: ${{ !cancelled() }} + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: gates-notebooks + path: | + gates-notebooks.log + work/notebooks/ diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml index 02d40f1..e21a10c 100644 --- a/.github/workflows/style.yml +++ b/.github/workflows/style.yml @@ -1,8 +1,11 @@ name: Tap House Style -# Enforces the shared Tap House Rules. clang-format is already checked in -# ci.yml; this adds (1) a drift check against the canonical TapHouse configs -# and (2) clang-tidy naming + mandatory-braces enforcement. +# Enforces the shared Tap House Rules. clang-format is checked in ci.yml (and +# locally by the pre-commit hook); this adds (1) a drift check against the +# canonical TapHouse configs and (2) clang-tidy naming + mandatory-braces +# enforcement over both engines' own translation units (scripts/tidy.sh is +# the local mirror). Same dedup scheme as ci.yml: PR branches run on +# pull_request events only, main runs on push. on: push: branches: [main] @@ -12,6 +15,10 @@ on: permissions: contents: read +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} + jobs: drift: uses: tap/taphouse/.github/workflows/drift-check.yml@v5 @@ -26,15 +33,25 @@ jobs: with: submodules: recursive - name: Install tools - run: sudo apt-get update && sudo apt-get install -y clang-tidy-18 libgtest-dev cmake python3 + run: sudo apt-get update && sudo apt-get install -y clang-tidy-18 cmake python3 + # Both engines with their tests and examples, plus the bridge engine's + # icount workloads: the coverage each repository's gate had. The async + # workloads (async/bench/icount) were never under this gate and do not + # pass it yet; bringing them in is follow-up work, not a migration step. - name: Configure (compile database) - run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON - - name: clang-tidy (project TUs; third_party excluded) + run: > + cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON + - name: clang-tidy (project TUs; the submodule, third_party and fetched deps excluded) run: | - files=$(python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'third_party' not in e['file']))") + python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))" > /tmp/tidy-files.txt + # An empty list would pass silently. + test -s /tmp/tidy-files.txt || { echo "::error::no translation units to check"; exit 1; } + echo "checking $(wc -l < /tmp/tidy-files.txt) translation units" fail=0 - for f in $files; do + # Line-wise read, not word splitting: robust to paths with spaces. + while IFS= read -r f; do out=$(clang-tidy-18 -p build "$f" 2>/dev/null || true) if echo "$out" | grep -qE "warning:|error:"; then echo "$out"; fail=1; fi - done + done < /tmp/tidy-files.txt [ "$fail" -eq 0 ] && echo "clang-tidy clean." || { echo "::error::clang-tidy found violations"; exit 1; } diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..6716374 --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,37 @@ +cmake_minimum_required(VERSION 3.24) +# The tap::sr sample-rate family: one tree, two engines. async/ is the +# near-unity asynchronous converter (was SampleRateTap), bridge/ the +# synchronous 44.1 <-> 48 kHz converter (was RatioTap). Each engine keeps its +# own project() and options until the monorepo migration's renames (step 3, +# docs/MONOREPO_PLAN.md); this file only composes them. +project(SampleRateTap LANGUAGES CXX) +enable_testing() + +# Defaults, never FORCE: every bare-metal and Hexagon job passes +# *_BUILD_EXAMPLES=OFF (async's examples need Threads), and a -D on the +# command line must win. Declared before the engines, so their own +# PROJECT_IS_TOP_LEVEL-dependent option() calls find the cache entry set. +option(SRT_BUILD_TESTS "Build the async engine's tests" ON) +option(SRT_BUILD_EXAMPLES "Build the async engine's examples" ON) +option(TAP_RATIO_BUILD_TESTS "Build the bridge engine's tests" ON) +option(TAP_RATIO_BUILD_EXAMPLES "Build the bridge engine's examples" ON) + +# GoogleTest is fetched once, by whichever engine's tests/ is configured +# first; the other engine's FetchContent_MakeAvailable is then a no-op. Its +# settings are hoisted here so the result does not depend on that order. +set(INSTALL_GTEST OFF CACHE BOOL "" FORCE) +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) +# The Threads probe is skipped on bare metal: newlib ships pthread.h stubs +# that make POSIX feature detection succeed spuriously. +if(NOT SRT_BARE_METAL AND NOT TAP_RATIO_BARE_METAL) + find_package(Threads QUIET) +endif() +if(NOT Threads_FOUND) + set(gtest_disable_pthreads ON CACHE BOOL "" FORCE) +endif() + +# The shared substrate (tap::dsp), once for both engines; each engine's +# guard skips its own copy when this target exists. +add_subdirectory(submodules/dsptap) +add_subdirectory(async) +add_subdirectory(bridge) diff --git a/LICENSE b/LICENSE index e830616..792d0a6 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2026 SampleRateTap contributors +Copyright (c) 2026 Timothy Place and the SampleRateTap contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/async/CMakeLists.txt b/async/CMakeLists.txt index 6041b4c..cf29f9e 100644 --- a/async/CMakeLists.txt +++ b/async/CMakeLists.txt @@ -3,9 +3,12 @@ project(SampleRateTap VERSION 0.1.0 LANGUAGES CXX) # Shared Tap-family substrate (tap::dsp): kaiser design math, sample-format # traits, FIR dot kernels, row-sum quantization, measurement instruments. -# Pinned as a submodule per the DspTap release flow (changes land there first, -# consumers bump the pin). -add_subdirectory(submodules/dsptap) +# Pinned once at the repository root (submodules/dsptap); added here only +# when this engine is configured on its own (cmake -S async). +if(NOT TARGET tap::dsp) + add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../submodules/dsptap + ${CMAKE_CURRENT_BINARY_DIR}/submodules/dsptap) +endif() add_library(SampleRateTap INTERFACE) add_library(SampleRateTap::SampleRateTap ALIAS SampleRateTap) @@ -75,7 +78,7 @@ endif() # C ABI shared library for FFI consumers (see notebooks/asrc_demo.ipynb). option(SRT_BUILD_CAPI "Build the C ABI shared library" OFF) if(SRT_BUILD_CAPI) - add_subdirectory(tools/capi) + add_subdirectory(capi) endif() # ctypes shim over r8brain-free-src for the comparison notebook (fetched at a diff --git a/async/README.md b/async/README.md index 9c8a6c5..a9a919a 100644 --- a/async/README.md +++ b/async/README.md @@ -1,7 +1,7 @@ # SampleRateTap [![CI](https://github.com/tap/SampleRateTap/actions/workflows/ci.yml/badge.svg)](https://github.com/tap/SampleRateTap/actions/workflows/ci.yml) -[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](../LICENSE) [![C++20](https://img.shields.io/badge/C%2B%2B-20-blue.svg)](https://en.cppreference.com/w/cpp/20) Header-only C++20 **asynchronous sample rate converter** (ASRC) for the @@ -323,7 +323,7 @@ wall-clock and embedded instruction counts, steady state and construction [docs/COMPARISON.md](docs/COMPARISON.md). -Executed instructions per fixed workload (`bench/icount/`), measured under QEMU with a counting plugin — deterministic, and gated in CI at ±3% against `bench/baselines.json`: +Executed instructions per fixed workload (`async/bench/icount/`), measured under QEMU with a counting plugin — deterministic, and gated in CI at ±3% against `async/bench/baselines.json`: | Workload | Cortex-M33 | Cortex-M55 | Hexagon | |---|---:|---:|---:| diff --git a/async/examples/pico2_cyccnt/CMakeLists.txt b/async/examples/pico2_cyccnt/CMakeLists.txt index 989afce..3b0f34a 100644 --- a/async/examples/pico2_cyccnt/CMakeLists.txt +++ b/async/examples/pico2_cyccnt/CMakeLists.txt @@ -49,7 +49,7 @@ option(PICO2_MEASURE_FLOAT "Measure the float (soft FP64) datapath too" ON) add_executable(pico2_cyccnt main.cpp) # Self-contained except for the header-only library itself. target_include_directories(pico2_cyccnt PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../../include - ${CMAKE_CURRENT_SOURCE_DIR}/../../submodules/dsptap/include) + ${CMAKE_CURRENT_SOURCE_DIR}/../../../submodules/dsptap/include) target_link_libraries(pico2_cyccnt PRIVATE pico_stdlib cmsis_core) if(PICO2_MEASURE_FLOAT) target_compile_definitions(pico2_cyccnt PRIVATE PICO2_MEASURE_FLOAT=1) diff --git a/async/examples/pico2_dualcore/CMakeLists.txt b/async/examples/pico2_dualcore/CMakeLists.txt index a31b8cd..d119d29 100644 --- a/async/examples/pico2_dualcore/CMakeLists.txt +++ b/async/examples/pico2_dualcore/CMakeLists.txt @@ -44,7 +44,7 @@ pico_sdk_init() add_executable(pico2_dualcore main.cpp) # Self-contained except for the header-only library itself. target_include_directories(pico2_dualcore PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../../include - ${CMAKE_CURRENT_SOURCE_DIR}/../../submodules/dsptap/include) + ${CMAKE_CURRENT_SOURCE_DIR}/../../../submodules/dsptap/include) target_link_libraries(pico2_dualcore PRIVATE pico_stdlib pico_multicore cmsis_core) # Telemetry prints from the producer core; when the USB host stops draining diff --git a/async/notebooks/asrc_block_size_study.ipynb b/async/notebooks/asrc_block_size_study.ipynb index 24359c1..35fe084 100644 --- a/async/notebooks/asrc_block_size_study.ipynb +++ b/async/notebooks/asrc_block_size_study.ipynb @@ -62,7 +62,7 @@ "\n", "FS = 48000.0\n", "REPO = pathlib.Path.cwd().parent if pathlib.Path.cwd().name == \"notebooks\" else pathlib.Path.cwd()\n", - "CAPI_DIR = REPO / \"build\" / \"tools\" / \"capi\"\n", + "CAPI_DIR = REPO / \"build\" / \"capi\"\n", "\n", "def _find_dso():\n", " for name in (\"libsrt_capi.so\", \"libsrt_capi.dylib\", \"srt_capi.dll\"):\n", diff --git a/async/notebooks/asrc_comparison.ipynb b/async/notebooks/asrc_comparison.ipynb index ca8a81e..e306023 100644 --- a/async/notebooks/asrc_comparison.ipynb +++ b/async/notebooks/asrc_comparison.ipynb @@ -71,7 +71,7 @@ "EPS = 200e-6\n", "FS_IN = FS * (1 + EPS)\n", "REPO = pathlib.Path.cwd().parent if pathlib.Path.cwd().name == \"notebooks\" else pathlib.Path.cwd()\n", - "TOOLS_DIR = REPO / \"build\" / \"tools\"\n", + "TOOLS_DIR = REPO / \"build\"\n", "\n", "def _find_dso(stem):\n", " for name in (f\"lib{stem}.so\", f\"lib{stem}.dylib\", f\"{stem}.dll\"):\n", diff --git a/async/notebooks/asrc_demo.ipynb b/async/notebooks/asrc_demo.ipynb index b6402bc..dc2149b 100644 --- a/async/notebooks/asrc_demo.ipynb +++ b/async/notebooks/asrc_demo.ipynb @@ -58,7 +58,7 @@ "\n", "FS = 48000.0\n", "REPO = pathlib.Path.cwd().parent if pathlib.Path.cwd().name == \"notebooks\" else pathlib.Path.cwd()\n", - "CAPI_DIR = REPO / \"build\" / \"tools\" / \"capi\"\n", + "CAPI_DIR = REPO / \"build\" / \"capi\"\n", "\n", "def _find_dso():\n", " # Platform-dependent name (and Release/ subdir under multi-config generators).\n", @@ -785,7 +785,7 @@ "| 50 ppm/s drift ramp | tracked, locked, zero underruns |\n", "| 60 ms dropout | silence + 64-frame fade-in, estimate retained |\n", "\n", - "The C ABI used here (`tools/capi/`) is ~80 lines; the same pattern works for\n", + "The C ABI used here (`capi/`) is ~80 lines; the same pattern works for\n", "any FFI host. Quality and embedded performance claims are continuously\n", "enforced in CI — see `docs/PERFORMANCE.md` and the test suite.\n" ] diff --git a/async/notebooks/asrc_rbj_analysis.ipynb b/async/notebooks/asrc_rbj_analysis.ipynb index a418687..7f85aab 100644 --- a/async/notebooks/asrc_rbj_analysis.ipynb +++ b/async/notebooks/asrc_rbj_analysis.ipynb @@ -49,7 +49,7 @@ "import sys\n", "import numpy as np\n", "\n", - "sys.path.insert(0, \"../scripts\")\n", + "sys.path.insert(0, \"../../scripts\")\n", "from book_figures import design_prototype, kaiser_beta # kaiser.h, ported verbatim\n", "\n", "FS = 48000.0\n", diff --git a/book/src/epilogue/letter.md b/book/src/epilogue/letter.md index 190c5c5..ae4e3c0 100644 --- a/book/src/epilogue/letter.md +++ b/book/src/epilogue/letter.md @@ -115,7 +115,7 @@ thing diagnoses the bug for you. Every preset except `fast` now ships this design: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:pw_image_zeros}} +{{#include ../../../async/include/srt/polyphase_filter.h:pw_image_zeros}} ``` At *equal tap count*, `balanced`'s passband stays flat to ±0.003 dB, its @@ -196,13 +196,13 @@ With the instrument in place, the suggestion could finally become a shippable preset: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:pw_economy}} +{{#include ../../../async/include/srt/polyphase_filter.h:pw_economy}} ``` And the promise could be measured instead of asserted: ```cpp -{{#include ../../../tests/test_asrc_program.cpp:pw_measure}} +{{#include ../../../async/tests/test_asrc_program.cpp:pw_measure}} ``` The numbers, end to end through the full converter at +200 ppm: diff --git a/book/src/part0/budgets.md b/book/src/part0/budgets.md index 79c937a..3c6b610 100644 --- a/book/src/part0/budgets.md +++ b/book/src/part0/budgets.md @@ -111,7 +111,7 @@ this is the Q0.64 phase accumulator the README describes, live from `include/srt/polyphase_filter.h`: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:p0_phase_step}} +{{#include ../../../async/include/srt/polyphase_filter.h:p0_phase_step}} ``` The fractional position lives in an unsigned 64-bit integer interpreted as @@ -157,7 +157,7 @@ accident. Here is where every frame of it is decided — the converter's entire configuration surface, live from `include/srt/asrc.h`: ```cpp -{{#include ../../../include/srt/asrc.h:p0_config}} +{{#include ../../../async/include/srt/asrc.h:p0_config}} ``` The README's latency equation prices the defaults: diff --git a/book/src/part1/asrc.md b/book/src/part1/asrc.md index 073fe6e..d32e37b 100644 --- a/book/src/part1/asrc.md +++ b/book/src/part1/asrc.md @@ -65,11 +65,11 @@ Acquiring or Locked — plus two exceptional transitions. Here is the filling and resync machinery as it ships: ```cpp -{{#include ../../../include/srt/asrc.h:asrc_filling}} +{{#include ../../../async/include/srt/asrc.h:asrc_filling}} ``` ```cpp -{{#include ../../../include/srt/asrc.h:asrc_resync}} +{{#include ../../../async/include/srt/asrc.h:asrc_resync}} ``` Filling exists because the resampler cannot produce its first output until @@ -160,7 +160,7 @@ to demonstrate it. The fix is the first thing `pull()` now does: ```cpp -{{#include ../../../include/srt/asrc.h:asrc_feasibility}} +{{#include ../../../async/include/srt/asrc.h:asrc_feasibility}} ``` The design choices inside those lines carry the interesting reasoning: @@ -237,7 +237,7 @@ is written down. ## The underrun tail, end to end ```cpp -{{#include ../../../include/srt/asrc.h:asrc_underrun}} +{{#include ../../../async/include/srt/asrc.h:asrc_underrun}} ``` Read this excerpt slowly and you can see the whole chapter in ten lines: diff --git a/book/src/part1/fractional-resampler.md b/book/src/part1/fractional-resampler.md index 55dd26d..8e98c21 100644 --- a/book/src/part1/fractional-resampler.md +++ b/book/src/part1/fractional-resampler.md @@ -117,7 +117,7 @@ The C3 redesign eliminates the per-sample double entirely by changing what the phase *is*: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_class_doc}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_class_doc}} ``` The fractional position lives in `phase_`, an unsigned 64-bit integer @@ -135,7 +135,7 @@ Per `process()` call — once per block, not per sample — the servo's double ε̂ is converted to fixed point: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_slip}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_slip}} ``` Walk the slip logic carefully; it is the subtlest six lines in the @@ -190,7 +190,7 @@ resets and re-primes before processing again. Downstream, the phase bits feed the kernel directly: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_blend_row_phase}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_blend_row_phase}} ``` The top log₂ L bits *are* the phase-row index; the bits below, shifted @@ -203,7 +203,7 @@ the floating-point phase math. The fused mono form is the same bit surgery around the same blend-and-mac loop: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_interpolate_phase}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_interpolate_phase}} ``` **Is 2⁻⁶⁴ enough?** Part 0 derived the timing-jitter budget for 120 dB @@ -238,7 +238,7 @@ records the trade explicitly. x86 same-minute A/B: float −5.4%, Q15 With phase in hand, each output frame takes one of three routes: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_dispatch}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_dispatch}} ``` Mono takes the fused `interpolate_phase` — no scratch-row traffic for a @@ -262,7 +262,7 @@ oldest-first, per channel. Input arrives interleaved, in whatever chunks the FIFO happens to hold. Between those two facts sits `append_one`: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_append}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_append}} ``` Three mechanisms, each with an RT-safety argument: @@ -297,7 +297,7 @@ is allowed to throw precisely because it runs at setup time. **Two storage shapes.** The member block records the fork: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_members}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_members}} ``` Planar — one delay line per channel — below the channel-parallel @@ -362,7 +362,7 @@ its safety is a documented protocol that the converter — its only in-tree caller — upholds. The documentation is the code's own: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_process_doc}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_process_doc}} ``` **Prime before process.** `prime()` fills the window with T real frames @@ -387,7 +387,7 @@ servo keeping its ppm estimate and a fade-in masking the splice. Finally, the small read-side API that closes the control loop: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:rs_mu}} +{{#include ../../../async/include/srt/polyphase_filter.h:rs_mu}} ``` `mu()` converts the phase to double **once per pull, not per sample** — diff --git a/book/src/part1/pi-servo.md b/book/src/part1/pi-servo.md index c0f573a..ad41a94 100644 --- a/book/src/part1/pi-servo.md +++ b/book/src/part1/pi-servo.md @@ -146,7 +146,7 @@ tabulates — and read off the gains: The code computes exactly this, nothing more: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_gains}} +{{#include ../../../async/include/srt/pi_servo.h:sv_gains}} ``` Note the division by `fs_` in both gains: the plant's gain is fs, so the @@ -164,7 +164,7 @@ Here is the full tuning surface, with the defaults that suit a 48 kHz near-unity converter: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_config}} +{{#include ../../../async/include/srt/pi_servo.h:sv_config}} ``` Three bandwidths, three smoother corners, and a small state machine's @@ -228,7 +228,7 @@ measurement is smoothed before the loop sees it. The update begins by maintaining *both* kinds of smoothed error on every call: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_update_smooth}} +{{#include ../../../async/include/srt/pi_servo.h:sv_update_smooth}} ``` Two details here repay attention. The smoothing coefficient @@ -265,7 +265,7 @@ is a step input injected into your own loop. Here is the whole state machine: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_update_stages}} +{{#include ../../../async/include/srt/pi_servo.h:sv_update_stages}} ``` Reading it as a protocol: promotion out of Acquire requires the *fast* @@ -291,7 +291,7 @@ Quiet." The physics writes it. Both promotions share their hold logic, and it does double duty: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_hold}} +{{#include ../../../async/include/srt/pi_servo.h:sv_hold}} ``` While the hold window runs, the servo is not just waiting — it is @@ -320,7 +320,7 @@ data. The last lines of `update()` are the PI itself: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_update_out}} +{{#include ../../../async/include/srt/pi_servo.h:sv_update_out}} ``` The clamp appears twice, and the first one — on the integrator, not just @@ -343,7 +343,7 @@ requires the output to saturate exactly at 1.5× the configured range. ## Knowing when not to chase: `seed()` and `reset()` ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_reset}} +{{#include ../../../async/include/srt/pi_servo.h:sv_reset}} ``` A feedback loop's reflex is to chase every step in its input. Some steps @@ -398,7 +398,7 @@ absolute-Hz constants said, against a disturbance that had moved. The rule that fixes it is now a method, so it cannot be half-remembered: ```cpp -{{#include ../../../include/srt/pi_servo.h:sv_scaled_to}} +{{#include ../../../async/include/srt/pi_servo.h:sv_scaled_to}} ``` Every field with units of Hz scales with the rate — keeping the loop diff --git a/book/src/part1/polyphase-bank.md b/book/src/part1/polyphase-bank.md index c65d79a..b46a6c3 100644 --- a/book/src/part1/polyphase-bank.md +++ b/book/src/part1/polyphase-bank.md @@ -56,7 +56,7 @@ quality tier: pick the two rows adjacent to μ·L and interpolate the the quality knob the spec exposes: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:bank_spec}} +{{#include ../../../async/include/srt/polyphase_filter.h:bank_spec}} ``` The comment's two slopes are the design law for choosing L, and they are @@ -96,7 +96,7 @@ Here is the file's cleverest line, and it is a line of *allocation*, not of algorithm: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:bank_layout}} +{{#include ../../../async/include/srt/polyphase_filter.h:bank_layout}} ``` The problem it dissolves: blending needs rows `p` and `p + 1`. For @@ -121,7 +121,7 @@ The bank's fix: **store row L explicitly, as branch 0 advanced by one input sample**. It falls out of the construction loop with no special case: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:bank_build}} +{{#include ../../../async/include/srt/polyphase_filter.h:bank_build}} ``` Follow the index math for `p == phases_`: the prototype index is @@ -288,7 +288,7 @@ of storing it. **The accessor surface is four functions, and their shapes are load-bearing:** ```cpp -{{#include ../../../include/srt/polyphase_filter.h:bank_accessors}} +{{#include ../../../async/include/srt/polyphase_filter.h:bank_accessors}} ``` `phase(p)` returns a raw `const Coeff*`, not a `std::span` — the kernels @@ -301,7 +301,7 @@ the extra row is a first-class citizen of the API, which is exactly how `interpolate()` gets to be branch-free: ```cpp -{{#include ../../../include/srt/polyphase_filter.h:bank_interpolate}} +{{#include ../../../async/include/srt/polyphase_filter.h:bank_interpolate}} ``` Note the one guard that *does* exist — clamping `p` when μ rounds up to diff --git a/book/src/part1/sample-traits.md b/book/src/part1/sample-traits.md index 929ba22..93c9053 100644 --- a/book/src/part1/sample-traits.md +++ b/book/src/part1/sample-traits.md @@ -31,7 +31,7 @@ exactly as wide as they are, and two places where the file's own comments record hard-won corrections. The two stories are one file: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_overview}} +{{#include ../../../async/include/srt/sample_traits.h:st_overview}} ``` Three sample types, and a division of labor worth pausing on: the clock @@ -45,7 +45,7 @@ would be effort spent where the profile isn't. The customization point is a class template with no primary definition: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_primary}} +{{#include ../../../async/include/srt/sample_traits.h:st_primary}} ``` Leaving the primary template *undefined* is deliberate. A defined primary @@ -60,7 +60,7 @@ simplest and shows the complete vocabulary — three associated types and seven operations: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_float}} +{{#include ../../../async/include/srt/sample_traits.h:st_float}} ``` Every operation the datapath performs on samples is named here: convert a @@ -277,7 +277,7 @@ The full specialization, for reference — note the doc comment carries the same overflow argument, so the file survives without the book: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_q31}} +{{#include ../../../async/include/srt/sample_traits.h:st_q31}} ``` ## The blend, and the comment that was wrong by three orders of magnitude @@ -287,7 +287,7 @@ rows (the polyphase chapter explains why; the residual falls ~12 dB per doubling of the phase count). In Q15 it looks like this: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_q15_blend}} +{{#include ../../../async/include/srt/sample_traits.h:st_q15_blend}} ``` That comment has a history, and the history is this book's whole @@ -329,13 +329,13 @@ Q15 version is a single shift — the top 15 bits of the fraction *are* the Q15 blend factor: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_q15_q64}} +{{#include ../../../async/include/srt/sample_traits.h:st_q15_q64}} ``` The float version is subtler: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_blend_q64_float}} +{{#include ../../../async/include/srt/sample_traits.h:st_blend_q64_float}} ``` Why reduce to 24 bits first? Because a `float` significand holds exactly @@ -359,7 +359,7 @@ Everything above defines the customization point; the last twenty lines of the file *enforce* it: ```cpp -{{#include ../../../include/srt/sample_traits.h:st_concept}} +{{#include ../../../async/include/srt/sample_traits.h:st_concept}} ``` The datapath templates constrain themselves with it — diff --git a/book/src/part1/spsc-ring.md b/book/src/part1/spsc-ring.md index 5d3f02c..f1dddd1 100644 --- a/book/src/part1/spsc-ring.md +++ b/book/src/part1/spsc-ring.md @@ -29,7 +29,7 @@ biased frequency estimate. Here is the entire contract: ```cpp -{{#include ../../../include/srt/spsc_ring.h:contract}} +{{#include ../../../async/include/srt/spsc_ring.h:contract}} ``` Forty lines of comment and assertion before any logic. Three things deserve @@ -83,7 +83,7 @@ in the file: Read the producer side with that lens: ```cpp -{{#include ../../../include/srt/spsc_ring.h:write}} +{{#include ../../../async/include/srt/spsc_ring.h:write}} ``` The two `memcpy` calls happen *before* the `release` store of the new head. @@ -91,7 +91,7 @@ That ordering — data first, then the index that publishes it — is the entire correctness argument for the data path. Symmetrically: ```cpp -{{#include ../../../include/srt/spsc_ring.h:read}} +{{#include ../../../async/include/srt/spsc_ring.h:read}} ``` The consumer `acquire`-loads `head_` (inside the cache-refresh branch, @@ -157,7 +157,7 @@ store. The member layout enforces the same philosophy at the hardware level: ```cpp -{{#include ../../../include/srt/spsc_ring.h:layout}} +{{#include ../../../async/include/srt/spsc_ring.h:layout}} ``` Producer-owned state (`head_`, `tailCache_`), consumer-owned state diff --git a/book/src/part2/tests.md b/book/src/part2/tests.md index a9ce45e..2c8ec5a 100644 --- a/book/src/part2/tests.md +++ b/book/src/part2/tests.md @@ -98,13 +98,13 @@ want the clocks without the threads. The rig is a struct of knobs: ```cpp -{{#include ../../../tests/support/two_clock_sim.h:pf_knobs}} +{{#include ../../../async/tests/support/two_clock_sim.h:pf_knobs}} ``` and one loop: ```cpp -{{#include ../../../tests/support/two_clock_sim.h:pf_run}} +{{#include ../../../async/tests/support/two_clock_sim.h:pf_run}} ``` This is discrete-event simulation reduced to its minimum. Two virtual diff --git a/book/src/part4/c-abi.md b/book/src/part4/c-abi.md index dd642d2..aaf8476 100644 --- a/book/src/part4/c-abi.md +++ b/book/src/part4/c-abi.md @@ -34,7 +34,7 @@ throw an exception at all. The entire foreign-function interface: ```c -{{#include ../../../tools/capi/srt_capi.h:abi_surface}} +{{#include ../../../async/capi/srt_capi.h:abi_surface}} ``` Create, destroy, push, pull, status, latency, reset, version. The shim @@ -58,7 +58,7 @@ Here is the other side of the wall, and the file structure is itself a fossil of a compile error: ```cpp -{{#include ../../../tools/capi/srt_capi.cpp:abi_impl}} +{{#include ../../../async/capi/srt_capi.cpp:abi_impl}} ``` The handle is simply the converter pointer in disguise — @@ -88,7 +88,7 @@ you are promising to the world, and *nothing else* belongs inside it. The shim's entire error vocabulary is one value: ```cpp -{{#include ../../../tools/capi/srt_capi.cpp:abi_create}} +{{#include ../../../async/capi/srt_capi.cpp:abi_create}} ``` `srt_create` returns `NULL` on invalid configuration or allocation @@ -103,14 +103,14 @@ unconditionally. The hardening audit changed every entry point to this shape: ```cpp -{{#include ../../../tools/capi/srt_capi.cpp:abi_null}} +{{#include ../../../async/capi/srt_capi.cpp:abi_null}} ``` The reasoning is stated in the file's own header comment, and it is worth reading as a small essay on API design: ```cpp -{{#include ../../../tools/capi/srt_capi.cpp:abi_doc}} +{{#include ../../../async/capi/srt_capi.cpp:abi_doc}} ``` A "check create for NULL" convention *concentrates* failure on precisely @@ -134,7 +134,7 @@ nothing for anyone else. The audit shipped the header, and its top comment is the ABI's real substance — the part no binary interface can encode: ```c -{{#include ../../../tools/capi/srt_capi.h:abi_contract}} +{{#include ../../../async/capi/srt_capi.h:abi_contract}} ``` Three promises deserve emphasis, because each answers a real foreign-caller diff --git a/book/src/part4/cortex-m.md b/book/src/part4/cortex-m.md index 0cbf2a3..608f0a0 100644 --- a/book/src/part4/cortex-m.md +++ b/book/src/part4/cortex-m.md @@ -219,7 +219,7 @@ The replacement is a one-shot protocol. A dedicated `main` bakes the filter in at compile time, and the *pass criterion is a printed string*: ```cpp -{{#include ../../../tests/bare_metal_main.cpp}} +{{#include ../../../async/tests/bare_metal_main.cpp}} ``` CTest registers a single test whose `PASS_REGULAR_EXPRESSION` is diff --git a/bridge/.github/workflows/ci.yml b/bridge/.github/workflows/ci.yml deleted file mode 100644 index 3e62a20..0000000 --- a/bridge/.github/workflows/ci.yml +++ /dev/null @@ -1,417 +0,0 @@ -name: CI - -# Dedup: push runs only on main, so a PR branch gets exactly one run set -# (the pull_request events) and its merge box never shows the cancelled -# twin a SHA-keyed cancellation scheme would leave attached to the head -# commit. A branch with no PR yet runs CI via workflow_dispatch — the -# pre-PR baseline-harvest flow (scripts/icount.py, PLAN section 7) — or -# simply by opening the PR first and harvesting from its run. -on: - push: - branches: [main] - pull_request: - workflow_dispatch: - -permissions: - contents: read - -# Superseded-run cancellation: a new push to the same PR cancels the -# previous in-flight run. Those cancelled checks attach to the old commit, -# so the current head stays clean. Never on main (every main commit keeps -# its ratchet evidence), and never on the monorepo migration branch, whose -# plan requires a complete run for every pushed commit. -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} - -jobs: - build-test: - name: ${{ matrix.name }} - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - include: - # capi stays OFF on Windows: tools/capi carries no __declspec(dllexport) - # (unlike DspTap's), so an MSVC build would link a DLL exporting nothing - # — it would pass without gating anything. Turn this ON in the same - # change that gives the C ABI an export decoration. - - { os: ubuntu-latest, name: linux, capi: ON } - - { os: macos-latest, name: macos, capi: ON } - - { os: windows-latest, name: windows, capi: OFF } - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - # TAP_RATIO_BUILD_CAPI is ON here so the verification layer (the C ABI the - # executed notebook drives via ctypes) cannot rot unnoticed. - - name: Configure - run: > - cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON - -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} - - - name: Build - run: cmake --build build --config Release - - - name: Test - run: ctest --test-dir build --build-config Release --output-on-failure --no-tests=error - - sanitizers: - name: ASan + UBSan - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Configure - env: - CC: clang - CXX: clang++ - run: > - cmake -S . -B build - -DCMAKE_BUILD_TYPE=RelWithDebInfo - -DTAP_RATIO_WERROR=ON - -DCMAKE_CXX_FLAGS="-fsanitize=address,undefined -fno-sanitize-recover=all" - - - name: Build - run: cmake --build build -j 4 - - - name: Test - run: ctest --test-dir build --output-on-failure --no-tests=error - - # ------------------------------------------------------------------------ - # Embedded matrix (PLAN.md section 7): the M33/M55 eurorack/pedal cores and - # the Hexagon DSP are deployment targets, so every M7 optimization lever is - # gated on them — correctness under emulation here, instruction counts in - # the icount-ratchet job below. Toolchain provenance and pins mirror - # SampleRateTap's CI (the family's embedded story lives there first). - # ------------------------------------------------------------------------ - - hexagon-qemu: - name: Hexagon cross (QEMU) - runs-on: ubuntu-24.04 - timeout-minutes: 45 - env: - # Prebuilt open-source toolchain (BSD-3) published by Qualcomm/Quicinc; - # binary artifacts are hosted on CodeLinaro and linked from the - # quic/toolchain_for_hexagon release notes. - HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst - # Hard pin, matching SampleRateTap's (verified there against the - # published SHA256SUMS). - HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Cache toolchain - id: cache - uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 - with: - path: ~/hexagon - # Keyed on the pinned digest: every job that can write this key - # verifies its download against the same pin, so no unverified - # writer can poison the trusted entry. - key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 - - # Download + verification live in the shared script so this job and - # icount-ratchet (the other writer of the digest-keyed cache) can - # never drift onto different checks. - - name: Download toolchain - if: steps.cache.outputs.cache-hit != 'true' - run: scripts/fetch_hexagon_toolchain.sh - - - name: Set up toolchain and QEMU paths - run: | - # No -type f (symlinks count); dirname of an empty find result is - # ".", so assert on the find output itself. - clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) - test -n "$clangxx" - echo "$(dirname "$clangxx")" >> "$GITHUB_PATH" - # Prefer a qemu-hexagon bundled with the toolchain; else use distro qemu. - qemu=$(find "$HOME/hexagon" -name 'qemu-hexagon' -type f | head -1 || true) - if [ -n "$qemu" ]; then - echo "$(dirname "$qemu")" >> "$GITHUB_PATH" - else - sudo apt-get update -q && sudo apt-get install -y -q qemu-user - fi - - - name: Verify tools - run: | - hexagon-unknown-linux-musl-clang++ --version - qemu-hexagon --version - - - name: Configure - run: > - cmake -B build - -DCMAKE_BUILD_TYPE=Release - -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake - -DTAP_RATIO_BUILD_EXAMPLES=OFF - - - name: Build - run: cmake --build build -j 4 - - - name: Test under emulation - # -j 4: each test is an independent qemu-user process and the - # per-test soft-double table construction dominates, so the suite - # parallelizes cleanly (and serial would crowd the job timeout). - # -V --output-log keeps every test's own output ([ RUN ] lines, - # [ measured ] numbers), which --output-on-failure prints only for - # failures; the log is uploaded below as evidence. - run: > - ctest --test-dir build -j 4 --output-on-failure --no-tests=error - -V --output-log ctest-hexagon.log - -E 'BadProfilesThrow|LatencyAndValidation' - # This static-musl toolchain cannot unwind across frames — the - # constructor throws correctly but EXPECT_THROW never catches and - # libc++abi terminates (same known debt as SampleRateTap's leg). - # Validation is target-independent and covered on every other leg. - - - name: Upload test log - if: ${{ !cancelled() }} - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 - with: - name: ctest-hexagon - path: ctest-hexagon.log - - # Cross-compile for Arm Cortex-M55 (bare metal, newlib + semihosting) and - # run the emulation-sized test subset on QEMU's MPS3 AN547 board model. - # Validates the converter on a 32-bit MCU-class target with no OS, no - # threads and no double-precision FPU; the fixed-point datapaths are the - # performance-appropriate formats here. - cortex-m55-qemu: - name: Cortex-M55 cross (QEMU) - runs-on: ubuntu-24.04 - timeout-minutes: 30 - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Install toolchain and QEMU - run: > - sudo apt-get update -q && - sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm - - - name: Configure - run: > - cmake -B build - -DCMAKE_BUILD_TYPE=MinSizeRel - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake - -DTAP_RATIO_BUILD_EXAMPLES=OFF - - - name: Build - run: cmake --build build -j 4 - - - name: Test under emulation - run: > - ctest --test-dir build --output-on-failure --no-tests=error - -V --output-log ctest-m55.log - - - name: Upload test log - if: ${{ !cancelled() }} - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 - with: - name: ctest-m55 - path: ctest-m55.log - - # Cortex-M33 (Raspberry Pi Pico 2 / RP2350 class: single-precision FPU, - # no FP64, no MVE) on QEMU's MPS2+ AN505 model. Shares the Armv8-M - # startup with the M55 target; quantifies the soft-double design path and - # anchors the Q15/Q31 budgets for Pico-class parts. - cortex-m33-qemu: - name: Cortex-M33 cross (QEMU) - runs-on: ubuntu-24.04 - timeout-minutes: 30 - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Install toolchain and QEMU - run: > - sudo apt-get update -q && - sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm - - - name: Configure - run: > - cmake -B build - -DCMAKE_BUILD_TYPE=MinSizeRel - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake - -DTAP_RATIO_BUILD_EXAMPLES=OFF - - - name: Build - run: cmake --build build -j 4 - - - name: Test under emulation - run: > - ctest --test-dir build --output-on-failure --no-tests=error - -V --output-log ctest-m33.log - - - name: Upload test log - if: ${{ !cancelled() }} - uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 - with: - name: ctest-m33 - path: ctest-m33.log - - # Deterministic instruction-count ratchet (PLAN.md section 7): fixed - # workloads under QEMU with a counting plugin, gated two-sided (±3%) - # against bench/baselines.json. Unlike wall-clock numbers these are - # noise-free, so a hard gate is safe on shared runners — this is the - # measurement harness every M7 lever must move before it merges. - icount-ratchet: - name: Instruction-count ratchet - # Pinned image: the counts are a function of the apt toolchain, the - # plugin build and QEMU, so the job must not straddle an image rollout. - runs-on: ubuntu-24.04 - timeout-minutes: 45 - env: - # Commit the v8.2.2 tag pointed at when pinned (tags are movable; - # commit SHAs are not), with the header's digest verified on download. - QEMU_PLUGIN_HEADER_URL: https://raw.githubusercontent.com/qemu/qemu/11aa0b1ff115b86160c4d37e7c37e6a6b13b77ea/include/qemu/qemu-plugin.h - QEMU_PLUGIN_HEADER_SHA256: "c53a2af163e80e3f4bc6c60dbdfc84003db329d757e37cd8a16a77e1d82606ff" - QEMU_SRC_URL: https://download.qemu.org/qemu-8.2.2.tar.xz - QEMU_SRC_SHA256: "847346c1b82c1a54b2c38f6edbd85549edeb17430b7d4d3da12620e2962bc4f3" - HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst - # Same hard pin as the hexagon-qemu job: this job also writes the - # shared toolchain cache, so it must verify against the same digest. - HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - - name: Install toolchains and QEMU - run: > - sudo apt-get update -q && - sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm - libglib2.0-dev ninja-build meson flex bison - - - name: Build counting plugin - run: | - curl -sfLo /tmp/qemu-plugin.h "$QEMU_PLUGIN_HEADER_URL" - actual=$(sha256sum /tmp/qemu-plugin.h | cut -d' ' -f1) - if [ "$actual" != "$QEMU_PLUGIN_HEADER_SHA256" ]; then - echo "::error::qemu-plugin.h checksum mismatch"; exit 1 - fi - gcc -shared -fPIC $(pkg-config --cflags glib-2.0) -I/tmp \ - -o /tmp/libinsncount.so tools/qemu_insn_plugin/insn_count.c - - # What produced the counts: the runner image and the toolchain packages - # (the counts move when either does; PLAN.md section 7). - - name: Record image and toolchain versions - id: image - run: | - echo "image: ${ImageOS:-unknown} ${ImageVersion:-unknown}" - dpkg-query -W gcc-arm-none-eabi qemu-system-arm - echo "os=${ImageOS:-unknown}" >> "$GITHUB_OUTPUT" - - # Release (-O2), matching how the baselines were recorded. - - name: Build M55 workloads - run: > - cmake -B build-m55 - -DCMAKE_BUILD_TYPE=Release - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake - -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF - -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - && cmake --build build-m55 -j 4 - - - name: Ratchet M55 - run: > - python3 scripts/icount.py --target m55 - --build-dir build-m55 --plugin /tmp/libinsncount.so - - - name: Build M33 workloads - if: ${{ !cancelled() }} - run: > - cmake -B build-m33 - -DCMAKE_BUILD_TYPE=Release - -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m33-mps2.cmake - -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF - -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - && cmake --build build-m33 -j 4 - - - name: Ratchet M33 - if: ${{ !cancelled() }} - run: > - python3 scripts/icount.py --target m33 - --build-dir build-m33 --plugin /tmp/libinsncount.so - - # Neither Debian's nor the CodeLinaro toolchain's qemu-hexagon enables - # TCG plugins, so the Hexagon leg builds its own from the pinned QEMU - # release (linux-user target only, ~4 min, cached thereafter). - # The remaining ratchet steps run even if an earlier target failed - # (each target's numbers are independent evidence; stopping at the - # first failure forces a serial harvest when baselines legitimately - # move). The job still fails if any step failed. - - name: Cache plugin-enabled qemu-hexagon - if: ${{ !cancelled() }} - id: qemu-hex - uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 - with: - path: ~/qemu-hexagon-plugins - # The image OS is part of the key: the cached binary links the - # image's glib, so a new Ubuntu release must not restore an old - # build. (Weekly image updates keep the same glib ABI.) - key: qemu-hexagon-plugins-${{ steps.image.outputs.os }}-${{ env.QEMU_SRC_URL }}-1 - - - name: Build plugin-enabled qemu-hexagon - if: ${{ !cancelled() && steps.qemu-hex.outputs.cache-hit != 'true' }} - run: | - curl -sfLo /tmp/qemu-src.tar.xz "$QEMU_SRC_URL" - actual=$(sha256sum /tmp/qemu-src.tar.xz | cut -d' ' -f1) - echo "qemu source sha256: $actual (pin this in QEMU_SRC_SHA256)" - if [ -n "${QEMU_SRC_SHA256:-}" ] && [ "$actual" != "$QEMU_SRC_SHA256" ]; then - echo "::error::qemu source checksum mismatch"; exit 1 - fi - tar -xJf /tmp/qemu-src.tar.xz -C /tmp - cd /tmp/qemu-*/ - ./configure --target-list=hexagon-linux-user --enable-plugins \ - --disable-docs --disable-tools --disable-system - ninja -C build qemu-hexagon - mkdir -p ~/qemu-hexagon-plugins - cp build/qemu-hexagon ~/qemu-hexagon-plugins/ - - - name: Cache Hexagon toolchain - if: ${{ !cancelled() }} - id: cache - uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 - with: - path: ~/hexagon - # Same digest-keyed name as the hexagon-qemu job; the download - # below verifies the same pin before anything is saved under it. - key: hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1 - - - name: Ratchet Hexagon - if: ${{ !cancelled() }} - run: | - if [ "${{ steps.cache.outputs.cache-hit }}" != "true" ]; then - # Same shared download+verify as the hexagon-qemu job: both - # writers of the digest-keyed cache run identical checks. - scripts/fetch_hexagon_toolchain.sh - fi - # No -type f: the compiler may be a symlink in the restored tree. - clangxx=$(find "$HOME/hexagon" -name 'hexagon-unknown-linux-musl-clang++' | head -1) - if [ -z "$clangxx" ]; then - echo "::error::hexagon cross compiler not found under ~/hexagon" - exit 1 - fi - bindir=$(dirname "$clangxx") - export PATH="$HOME/qemu-hexagon-plugins:$bindir:$PATH" - test -x "$HOME/qemu-hexagon-plugins/qemu-hexagon" || { - echo "::error::plugin-enabled qemu-hexagon missing"; exit 1; } - # qemu exits 1 with or without plugin support here (no guest binary - # was given), so probe by the error text, not the exit code. - if qemu-hexagon -plugin help 2>&1 | grep -q "unknown option"; then - echo "::error::built qemu-hexagon lacks plugin support"; exit 1 - fi - cmake -B build-hex -DCMAKE_BUILD_TYPE=Release \ - -DCMAKE_TOOLCHAIN_FILE=cmake/hexagon-linux-musl.cmake \ - -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ - -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - cmake --build build-hex -j 4 - python3 scripts/icount.py --target hexagon \ - --build-dir build-hex --plugin /tmp/libinsncount.so diff --git a/bridge/.github/workflows/style.yml b/bridge/.github/workflows/style.yml deleted file mode 100644 index 4937b5c..0000000 --- a/bridge/.github/workflows/style.yml +++ /dev/null @@ -1,48 +0,0 @@ -name: Tap House Style - -# Enforces the shared Tap House Rules. clang-format is run locally via the -# pre-commit hook; this adds (1) a drift check against the canonical TapHouse -# configs and (2) clang-tidy naming + mandatory-braces enforcement over this -# repo's own translation units (scripts/tidy.sh is the local mirror). -# Same dedup scheme as ci.yml: PR branches run on pull_request events only, -# main runs on push, superseded in-flight runs get cancelled per PR/ref. -on: - push: - branches: [main] - pull_request: - workflow_dispatch: - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} - -jobs: - drift: - uses: tap/taphouse/.github/workflows/drift-check.yml@v5 - with: - ref: v5 - - clang-tidy: - runs-on: ubuntu-latest - timeout-minutes: 30 - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: - submodules: recursive - - name: Install tools - run: sudo apt-get update && sudo apt-get install -y clang-tidy-18 cmake python3 - - name: Configure (compile database) - run: cmake -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DTAP_RATIO_BUILD_TESTS=ON -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON - - name: clang-tidy (project TUs; the submodule and fetched deps excluded) - run: | - python3 -c "import json; print('\n'.join(e['file'] for e in json.load(open('build/compile_commands.json')) if 'submodules' not in e['file'] and 'third_party' not in e['file'] and '_deps' not in e['file']))" > /tmp/tidy-files.txt - fail=0 - # Line-wise read, not word splitting: robust to paths with spaces. - while IFS= read -r f; do - out=$(clang-tidy-18 -p build "$f" 2>/dev/null || true) - if echo "$out" | grep -qE "warning:|error:"; then echo "$out"; fail=1; fi - done < /tmp/tidy-files.txt - [ "$fail" -eq 0 ] && echo "clang-tidy clean." || { echo "::error::clang-tidy found violations"; exit 1; } diff --git a/bridge/.gitignore b/bridge/.gitignore deleted file mode 100644 index 6590f84..0000000 --- a/bridge/.gitignore +++ /dev/null @@ -1,11 +0,0 @@ -build*/ -.cache/ -compile_commands.json -CMakeUserPresets.json -.vscode/ -.idea/ -.claude/* -!.claude/settings.json -!.claude/hooks/ -build_capi/ -__pycache__/ diff --git a/bridge/CLAUDE.md b/bridge/CLAUDE.md index f99e4cb..ca14b84 100644 --- a/bridge/CLAUDE.md +++ b/bridge/CLAUDE.md @@ -18,7 +18,7 @@ Current state: **v0.2 — M7 codegen phase complete.** v0.1 (M0–M6): design/sc streaming converter for float/Q15/Q31 with committed scipy reference vectors, the golden cross-validation against SampleRateTap at pinned eps (test-only submodule), bluetooth_bridge + C ABI + executed notebook. M7 (v0.2): the embedded CI matrix + instruction-count ratchet -(Cortex-M33/M55 + Hexagon under QEMU, eight workloads gated two-sided ±3% against +(Cortex-M33/M55 + Hexagon under QEMU, ten workloads gated two-sided ±3% against `bench/baselines.json` — `scripts/icount.py`), then three measured codegen levers — superblock walk, committed trip counts, symmetry-halved tables — outputs bit-identical throughout; PLAN.md section 7 records each lever's numbers. Remaining levers are deferred until a consumer pulls @@ -60,9 +60,10 @@ the tidy job and a local clang `-Werror` build as second compilers before pushin ## Build & test ```sh +# from the repository root (this engine lives in bridge/; the root builds both) cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DTAP_RATIO_WERROR=ON cmake --build build -ctest --test-dir build --output-on-failure +ctest --test-dir build --output-on-failure -L '^ratio$' scripts/tidy.sh # local mirror of the CI clang-tidy gate ``` diff --git a/bridge/CMakeLists.txt b/bridge/CMakeLists.txt index a60328b..b6cec6b 100644 --- a/bridge/CMakeLists.txt +++ b/bridge/CMakeLists.txt @@ -9,10 +9,13 @@ project(RatioTap VERSION 0.3.0 LANGUAGES CXX) # and milestones; this is the M1 skeleton. # ============================================================================== -# Shared Tap-family substrate (tap::dsp), pinned as a submodule per the DspTap -# release flow (changes land there first, consumers bump the pin). Declares -# its own C language support for the vendored Ooura sources. -add_subdirectory(submodules/dsptap) +# Shared Tap-family substrate (tap::dsp), pinned once at the repository root +# (submodules/dsptap); added here only when this engine is configured on its +# own (cmake -S bridge). +if(NOT TARGET tap::dsp) + add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/../submodules/dsptap + ${CMAKE_CURRENT_BINARY_DIR}/submodules/dsptap) +endif() add_library(tap_ratio INTERFACE) add_library(tap::ratio ALIAS tap_ratio) @@ -38,19 +41,18 @@ option(TAP_RATIO_BUILD_CAPI "Build the C ABI shared library" OFF) # buildable for any target including bare metal. See PLAN.md section 7. option(TAP_RATIO_BUILD_ICOUNT_BENCH "Build instruction-count ratchet workloads" OFF) -# SampleRateTap, dev-only (never part of the shipped tap::ratio target): the -# golden cross-validation test and the bluetooth_bridge example compose -# against its near-unity ASRC. Consumed headers-only via include path — its -# own CMake would add_subdirectory a second dsptap and collide with ours; -# both repos pin the identical dsptap tree, so our tap::dsp serves its -# "tap/dsp/..." includes. +# The sibling async engine, dev-only (never part of the shipped tap::ratio +# target): the golden cross-validation test and the bluetooth_bridge example +# compose against its near-unity ASRC. Consumed headers-only via include path, +# so this engine's code never links async's target (MONOREPO_PLAN.md 4.2); +# tap::dsp serves its "tap/dsp/..." includes. if(TAP_RATIO_BUILD_TESTS OR TAP_RATIO_BUILD_EXAMPLES) add_library(srt_headers INTERFACE) # SYSTEM: dependency headers are exempt from this repo's warning gates # (MSVC /W4 otherwise fires benign C4324 on the ASRC ring's deliberate # cache-line alignment padding, and -Werror turns it fatal). - target_include_directories(srt_headers SYSTEM INTERFACE - ${CMAKE_CURRENT_SOURCE_DIR}/submodules/sampleratetap/include) + cmake_path(SET _tap_async_include NORMALIZE "${CMAKE_CURRENT_SOURCE_DIR}/../async/include") + target_include_directories(srt_headers SYSTEM INTERFACE ${_tap_async_include}) endif() # Warning flags for this project's own tests; never exported to consumers of diff --git a/bridge/LICENSE b/bridge/LICENSE deleted file mode 100644 index e57addf..0000000 --- a/bridge/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2026 Timothy Place and the RatioTap contributors - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/bridge/README.md b/bridge/README.md index 2941284..84cf084 100644 --- a/bridge/README.md +++ b/bridge/README.md @@ -1,7 +1,7 @@ # RatioTap [![CI](https://github.com/tap/RatioTap/actions/workflows/ci.yml/badge.svg)](https://github.com/tap/RatioTap/actions/workflows/ci.yml) -[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](../LICENSE) [![C++20](https://img.shields.io/badge/C%2B%2B-20-blue.svg)](https://en.cppreference.com/w/cpp/20) **Synchronous 44.1 ↔ 48 kHz sample rate conversion, as fast as possible.** @@ -103,37 +103,61 @@ exactly, 1.9 ms total latency. ## Build ```sh -git clone --recurse-submodules https://github.com/tap/RatioTap -cmake -S RatioTap -B build -DCMAKE_BUILD_TYPE=Release +git clone --recurse-submodules https://github.com/tap/SampleRateTap +cmake -S SampleRateTap -B build -DCMAKE_BUILD_TYPE=Release cmake --build build -ctest --test-dir build --output-on-failure +ctest --test-dir build --output-on-failure -L '^ratio$' ``` -Consume with `add_subdirectory` (or FetchContent) and link `tap::ratio`; -the DspTap submodule rides along automatically. +This engine lives in `bridge/` of the SampleRateTap family repository; the +root build configures both engines, and the `ratio` label selects this one's +tests. Consume with `add_subdirectory` (or FetchContent) and link +`tap::ratio`; the DspTap submodule at the repository root rides along +automatically. ### Embedded targets and the instruction-count ratchet The deployment cores are CI targets, not aspirations: every push runs the emulation-sized test suite on **Cortex-M33** (QEMU mps2-an505 — Raspberry Pi Pico 2 class), **Cortex-M55** (mps3-an547) and **Hexagon** -(qemu-hexagon, static musl), and gates eight fixed conversion workloads -(direction × float/Q15/Q31) against committed per-target instruction -counts (`bench/baselines.json`, two-sided ±3% — see `scripts/icount.py`). +(qemu-hexagon, static musl), and gates ten fixed conversion workloads +(direction × float/Q15/Q31 at the economy profile, plus four profile +variants) against committed per-target instruction +counts (`bench/baselines.json`, two-sided ±3% — see `scripts/icount.py`), +run from the repository root: The counts are deterministic, so the M7 optimization campaign in [PLAN.md](PLAN.md) lands one measured lever at a time: ```sh cmake -B build-m55 -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_TOOLCHAIN_FILE=cmake/arm-cortex-m55-mps3.cmake \ + -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF \ -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF \ -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON cmake --build build-m55 -j -python3 scripts/icount.py --target m55 --build-dir build-m55 --plugin libinsncount.so +python3 bridge/scripts/icount.py --target m55 --build-dir build-m55 \ + --baselines bridge/bench/baselines.json --plugin libinsncount.so ``` + +Executed instructions per fixed workload (`bridge/bench/icount/`), measured under QEMU with a counting plugin — deterministic, and gated in CI at ±3% against `bridge/bench/baselines.json`: + +| Workload | Cortex-M33 | Cortex-M55 | Hexagon | +|---|---:|---:|---:| +| `down_float_eco` | 1,720,707,553 | 73,794,800 | 304,636,424 | +| `down_float_tr` | 5,473,297,976 | 214,977,684 | 944,365,314 | +| `down_q15_eco` | 173,755,176 | 57,208,782 | 45,677,158 | +| `down_q15_se` | 130,967,926 | 46,983,269 | 33,235,309 | +| `down_q31_eco` | 244,985,862 | 96,697,327 | 45,622,761 | +| `up_float_eco` | 1,231,730,349 | 55,451,902 | 220,214,529 | +| `up_float_tr` | 3,110,380,470 | 126,303,581 | 533,810,577 | +| `up_q15_eco` | 133,914,472 | 44,556,679 | 35,990,589 | +| `up_q15_se` | 103,390,933 | 39,050,794 | 27,424,288 | +| `up_q31_eco` | 184,954,171 | 72,647,042 | 36,008,031 | + + ## License -MIT (see [LICENSE](LICENSE)), consistent with the family. Style is the -shared [Tap House Rules](STYLE.md), enforced by pre-commit clang-format, +MIT (see [LICENSE](../LICENSE)), consistent with the family. Style is the +shared [Tap House Rules](../STYLE.md), enforced by pre-commit clang-format, the drift check, and clang-tidy in CI. diff --git a/bridge/docs/HISTORY.md b/bridge/docs/HISTORY.md new file mode 100644 index 0000000..7db125c --- /dev/null +++ b/bridge/docs/HISTORY.md @@ -0,0 +1,89 @@ +# RatioTap history map + +This engine was RatioTap (github.com/tap/RatioTap) until its history was +imported into this repository (monorepo migration step 1b). The import +rewrote every commit into `bridge/` with `git filter-repo`, so each SHA +changed; author, date and message did not. RatioTap's own issues, pull +requests and commit links cite the old SHAs, and this table maps them. + +- Imported at R0 = RatioTap `main` `8f19e8b` (33 commits), rewritten tip + `654659e`, joined by the merge commit that imports it. +- PR numbers are RatioTap's (`GET /repos/tap/RatioTap/commits//pulls`, + queried once at import). PRs were rebase-merged, so several commits share + one PR; the root commit has none. +- `c0894cf` → `89c7eba` is RatioTap's reformat commit; it belongs in + `.git-blame-ignore-revs` (added after the migration merges). + +| Old SHA (RatioTap) | New SHA (here) | RatioTap PR | Date | Subject | +|---|---|---|---|---| +| `b3b088dd8e` | `37b3a34903` | — | 2026-07-23 | Add the v0.1 plan and the amended design-brief handoff | +| `f1d5013cff` | `a5a727ad6f` | #1 | 2026-07-23 | Add the M1 skeleton: build, substrate, style, CI | +| `34d97d0b59` | `bfb8eb66c3` | #2 | 2026-07-23 | Add M2: design spike, profiles, schedule, and phase tables | +| `b4aac51791` | `5a2ee3f626` | #2 | 2026-07-23 | Refresh the profile doc table to the post-normalization measurements | +| `c0894cfcd5` | `89c7eba254` | #2 | 2026-07-23 | Apply clang-format reflow the pre-commit hook produced post-staging | +| `a83d6d85ae` | `b8bad2e02f` | #3 | 2026-07-23 | Add M3: the streaming converter, pinned to scipy sample-for-sample | +| `81c09e8f18` | `d93c0d5373` | #3 | 2026-07-23 | Deduplicate the tests include-dir line | +| `c80a872405` | `29223e9da7` | #4 | 2026-07-23 | Add M4: fixed-point converters and their parity battery | +| `c3f19672cc` | `15061f4366` | #5 | 2026-07-23 | Add M5: the golden cross-validation against SampleRateTap | +| `4bec30cef0` | `4ee4c4cef5` | #6 | 2026-07-23 | Add M6: bluetooth_bridge, the C ABI, and the demo notebook — v0.1 | +| `01de4aae7e` | `9354dacae9` | #6 | 2026-07-23 | Ignore __pycache__ and drop a committed .pyc | +| `58e3e1fdea` | `454671a3c7` | #6 | 2026-07-23 | Mark the SampleRateTap dev headers as SYSTEM includes | +| `79ce9f1529` | `c89e674bc8` | #7 | 2026-07-24 | M7a: embedded CI matrix + instruction-count ratchet (M33/M55/Hexagon) | +| `179f52558c` | `240fb38dc1` | #8 | 2026-07-24 | M7b: superblock codegen — the process() hot path as a register walk | +| `580ea47c16` | `ebae70c5df` | #9 | 2026-07-24 | M7c: commit the trip counts — constexpr profiles, compile-time dot lengths | +| `d1045b82d0` | `0ae08adef4` | #10 | 2026-07-24 | M7d: symmetry storage halving — ceil(L/2) stored rows, mirrored dots | +| `e56f7041df` | `2138c05caa` | #10 | 2026-07-24 | Re-pin dsptap to the merged main commit | +| `9f871b5302` | `14e7cd65ee` | #11 | 2026-07-24 | v0.2: wrap the M7 codegen campaign | +| `f0f459a25c` | `8ff36d6a89` | #12 | 2026-07-27 | Add the shared pull-request template (taphouse sync) | +| `606587e57e` | `a808a0f13a` | #13 | 2026-07-28 | Bump the DspTap pin to 28a34a1 | +| `1d7ba8d2e6` | `bbcfb875b2` | #14 | 2026-07-28 | Compile the C ABI in CI so the verification layer cannot rot | +| `9459ae3e61` | `4b4bc08882` | #15 | 2026-08-06 | Fix the audit findings: create-path leak, pull() contract, doc rot, CI nits | +| `ff19b220dc` | `af369bfa27` | #16 | 2026-08-07 | v0.3: re-pin the profile ladder — economy at 18 kHz, balanced keeps 19 | +| `52f37ac074` | `9139032be8` | #16 | 2026-08-07 | Add super_economy: the 16 kHz voice/comms tier | +| `3ba6f1393c` | `7dd3ae7d06` | #16 | 2026-08-07 | Add the profile-ladder comparison notebook | +| `1389e6bea2` | `879dcbd45f` | #16 | 2026-08-07 | Re-record icount baselines for the v0.3 profile ladder | +| `f1e566a1e8` | `cf5a4a27d1` | #16 | 2026-08-07 | Dedup CI: one surviving run per head SHA across push and pull_request | +| `94775b0416` | `f9bd873a7d` | #16 | 2026-08-07 | CI dedup, take two: branch-filter push instead of SHA-keyed cancellation | +| `349ab7b1df` | `1c3ef7e4d8` | #17 | 2026-09-26 | Bump DspTap to 0eb09fa and SampleRateTap to 2b4dff1; re-record icount | +| `65aa2902e8` | `6f91664488` | #18 | 2026-09-27 | Harden the ratchet and CI so the migration gates have data to read | +| `bc3f5eaab0` | `f0651442a1` | #18 | 2026-09-27 | Re-record Hexagon icount baselines under the isolated harness | +| `dcf2abcb45` | `7211dccdce` | #18 | 2026-09-27 | Pin the notebook environment and re-execute every notebook | +| `8f19e8b967` | `654659ea27` | #19 | 2026-09-27 | Re-pin the SampleRateTap test dependency to its post-step-P main | + +Full 40-character SHAs, one pair per line (old new), oldest first: + +``` +b3b088dd8e583dff75265d40a195fe6316d38e4f 37b3a349037e1e09d0be8a2061b72c4002084515 +f1d5013cffbba90170a90d9f5167a2a692a9f612 a5a727ad6f7a7d181a43c2173c539d1273a55df4 +34d97d0b595384e7188ef8fd3bec29ef2e4d7280 bfb8eb66c38437e2ef34964e8a95e6949ed49c15 +b4aac5179178a4546af78f0aa8542fa912f94a95 5a2ee3f626a8e90f919a41db69836c1876442b34 +c0894cfcd5c622113dcbed9e2a841540450f15a2 89c7eba2545e6d9e9619437fe242e66390a9b393 +a83d6d85ae39a2d3cd2123c5a2df9112c403e50a b8bad2e02f8f12b564d2e46884cf2d535ebb1af3 +81c09e8f18dd9468d73bd9cc2c2fb5458764739f d93c0d537311754c4ebe521447f1a3f410289c37 +c80a87240502a87ef406e6644b63da248a13a8c0 29223e9da708b2b89914bdb404ef01e472300184 +c3f19672cc874a65eac95bf23d52540cd3276d44 15061f4366ce182fe411abc149a3823f27735f5e +4bec30cef066894eda919771ff001a1c0ca60afa 4ee4c4cef5e85e6c2c02411f3ceff5051cf5f63c +01de4aae7ef523593b934a02f7fa306aa8223a05 9354dacae94873399911b4253261013965416c8e +58e3e1fdea0df184105ee95a5231a175d71e4129 454671a3c70c031e91e543bb7e169354b1b81620 +79ce9f1529638e487383cb9aec6764aac2a2daa4 c89e674bc81b40816bc785e9f65b8bd8ecadc95e +179f52558c96aeea8d987092ce6e6989986e8937 240fb38dc167624fb97117f922df36999684e176 +580ea47c1619bb7bb88f947c234155fa07ff6a03 ebae70c5dfa4727b5e8ac7e96da6714bcf309365 +d1045b82d0ebcd9a843a8a5af411d778f245df00 0ae08adef43711a72ccb31969ddc1e4df3b31f8f +e56f7041dfad444ee75f0304e26bceb9a07ca261 2138c05caa0d30df640d1595d51e0ba7358131f1 +9f871b5302f73a5dc6c85991310206c1e835fb72 14e7cd65eec95766a880d329cc6cb2f560d4fc13 +f0f459a25c84a136d26463167ca364917dfba0a6 8ff36d6a8916a17176324ce8bc0d981427a467c2 +606587e57e45d712c7452459437ef047c79556b0 a808a0f13af75d3ed018822c58dcaa710a776af1 +1d7ba8d2e6a1872909533226966641bf25832f10 bbcfb875b23a4e6ac90df51c40da793d1fef4b77 +9459ae3e61d17a99ac5de7bbb44087e3abe6b13b 4b4bc088829f32c68d37e9d1a8e77dfc2a0e0b80 +ff19b220dc41bdab585d90d3fe23e172650e0cb1 af369bfa27b87576bd073394af826bdc2f24383d +52f37ac07428695c3ab58a5ce3fba36a627cf7b6 9139032be8df002315c57bd74a6e17ffffa7e15a +3ba6f1393c20a96114208cbb2fbeec6c9e95a4f9 7dd3ae7d0612646c602ff80f82d90eaf93346d46 +1389e6bea25d4e197733e70f9924a872bdd99afc 879dcbd45f5310587eeaba9d6377712b8a376520 +f1e566a1e89d3b4e491d58b806f7984e6b4abf8d cf5a4a27d1cf0112394de5af8bfbcf1dd7fabe17 +94775b04161e7c0d8c28aea4360c1c7db57be340 f9bd873a7d59b99fe01d2b36d0014a07f7e33d25 +349ab7b1df63d75e5dac9f823674e40e865b40e9 1c3ef7e4d828ee2ff38f5e4f92122a8e114c0e62 +65aa2902e85d77ae8959a2d3278b559f715a48e2 6f916644887dcb093cec1886326bc4b217fdf772 +bc3f5eaab072e526917b85e88ad13fab4b43e544 f0651442a128b0242e8a61f3720ceb1a49a3745b +dcf2abcb450a148411d4de3e3d23dbb420a3b2b4 7211dccdced2cc7dadc51f8e7ecaa3b52ceeb62a +8f19e8b967b8d3e9eb22393e881396d418c0584f 654659ea272fa62d6458c2ef1f95eaeb7c4ce276 +``` diff --git a/bridge/requirements.in b/bridge/requirements.in deleted file mode 100644 index fad936d..0000000 --- a/bridge/requirements.in +++ /dev/null @@ -1,11 +0,0 @@ -# Notebook environment for the executed notebooks (see requirements.lock). -# Edit this file, then regenerate the lock with: -# pip-compile --generate-hashes --allow-unsafe -o requirements.lock requirements.in -numpy -scipy -matplotlib -jupyter -nbconvert -ipykernel -samplerate==0.2.4 -soxr==1.1.0 diff --git a/bridge/requirements.lock b/bridge/requirements.lock deleted file mode 100644 index dcd00cc..0000000 --- a/bridge/requirements.lock +++ /dev/null @@ -1,1884 +0,0 @@ -# -# This file is autogenerated by pip-compile with Python 3.11 -# by the following command: -# -# pip-compile --allow-unsafe --generate-hashes --no-index --output-file=requirements.lock requirements.in -# -anyio==4.15.1 \ - --hash=sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101 \ - --hash=sha256:9f28306018cbd6d329e64a36d58256edff76dd996fe423bc957326e578b82a94 - # via - # httpx - # jupyter-server -argon2-cffi==25.1.0 \ - --hash=sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1 \ - --hash=sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741 - # via jupyter-server -argon2-cffi-bindings==26.1.0 \ - --hash=sha256:061a6919145bbf282ebf1f9c59d3135d4833c25313c8595c0d68cf7712ddfce2 \ - --hash=sha256:0cc40f7b4050bb93eb67de95d2d759322fc7ce4930b9d645581ecf4913ec651e \ - --hash=sha256:151dfaad9de753f4af2a7854e707e4784f2acc434340ade64239c5b104b2d605 \ - --hash=sha256:19423e5d7ac1cc354baab59eaabf18db2ec04ef6593b5abe5a34f323c4a8f87a \ - --hash=sha256:19b562b1de4b9052ef1214a2821c44b6e6f22945daa102c32ae4eff929d8b6d8 \ - --hash=sha256:1a0a29ed86960e44eaace7e081bdfab4f08b012fd96ec8edba71e2ad020939e4 \ - --hash=sha256:1af817e84578ef8b7295ad17de0f9896e4c8520dbf2233c7aa5aa3d487256fc4 \ - --hash=sha256:1b0bcac4d490a237e18cf91f57352920c29f77f2fa39efd0813fb81298bf17ba \ - --hash=sha256:1d98e33bd8bd67d7206c124e200bf2229c4cfa8c9c19f7b44a897f0fc71837eb \ - --hash=sha256:21ca0396fe5ec995dd54431c32698189666f9224810acfa752e50d2bd94d9df2 \ - --hash=sha256:224865cbbcb7a2bd1356741dff12b0134df726b6d44bb7b500df8e303cbd9e81 \ - --hash=sha256:242bb0cda2ae3650764fc194593d9ea45fc9e72729acd89778c7cfe184cec2a5 \ - --hash=sha256:27f1821903e2ceadcb88ec2b45ef190897b7682449c772f4d9b53e42c520cf29 \ - --hash=sha256:28524438cd3e723f25412f63d4fd516ff5bae9ae5aa56acbe2a1404398a0cf31 \ - --hash=sha256:2b741888c93147444fdfc851abd81cc207f37f7f7da42062a00deb3888e57da8 \ - --hash=sha256:2c36ff87b5dfaa477d0bd51e9d7f6abdae7c8955d2983c97419085d842154b3e \ - --hash=sha256:34b7d9c24a4165a2c61cc8ae11d44d48c9ce2830fb536cb7914e11fdd9962728 \ - --hash=sha256:49d525938467d52c923a890153c99087c9d5a937d1f6b585dbdba34ec82e397a \ - --hash=sha256:4f84cdd868978d7b7350a566c254042d44216d9e37f241f3a6d3b1dfebeede35 \ - --hash=sha256:62ff20cd130c956c7c9144d5fe35228f98b51c579b2439e988b27ef93e16c02a \ - --hash=sha256:63505c71542a44b68b1e38060450fb006404170da375feb31af153e7f9c6205d \ - --hash=sha256:6376d4b3aca039375ca8bf92f770da0ec424a1ce3a37077a8d3c557411aa56ca \ - --hash=sha256:6a4e68eed961a8de6928d1c17ff3dc2a547e0e923c17f8f1cd79fb7bc9502f98 \ - --hash=sha256:6ab674f668d5962a3a4136ae0812519b0f1586874263723a32181d60d64137e1 \ - --hash=sha256:7014ab7e6f5d8511af92544667a0346ea6dfc314ea9a7cad1dba9fdb5c9a6e33 \ - --hash=sha256:76ae29acace5d33355344612844d588e19deaaba4639d8bb01601e4b1418ef36 \ - --hash=sha256:78de2d65e0b9ea7ce9d1b1c3e87297b2d7305a02c266ee2a2d6910daddd7ee69 \ - --hash=sha256:9bacedc04b0402837586a17f0919e3dfdd95291f441f1f56bd80ec274c2840a1 \ - --hash=sha256:a86c069c91a747a2c4e5c51473590aeb48172fff9b2130d23729a42d98665ecb \ - --hash=sha256:ac82fc756a446b6ccd7139ce70efa9d8bbe541e7ad579a12dcb52764b7175c5f \ - --hash=sha256:af11ac37a7c53dc16cb7950a6190851b0870fe218b6c60c0bb7ac355234e3083 \ - --hash=sha256:b70225b5fd1e0d2ef4f7fd30d24658454535f0924dff0caca5dc08efbbbadfbb \ - --hash=sha256:c49e853a3bef9dd10329f31f702e7fa9b5c58229ff9c2ff6d069efaf09177c08 \ - --hash=sha256:ccaf0a46cbb380f1fd102a874e32aa629fd3cb0c0e94f4943fa1f6d5edc5dac6 \ - --hash=sha256:d157ddfab1e8b21f2f1dedda9c09645d98b5ed0b667b0626be600a345d426440 \ - --hash=sha256:d88e5f7e60f28ae0b0cc6b2f16c43e87cd642a196a86f85e0d8bb6fe016fc16d \ - --hash=sha256:db0fcd827ca61622a01b220aadfbece01939acf53888f2cb98cd93e9b1e2c97e \ - --hash=sha256:df612391feca41c44d20118f3b88d1b86419465cd1f5496859f715ca60ec2210 \ - --hash=sha256:f0c3103fcff20183e593459cfea6e012281c0e76ae3ed8b5565ad1b92eac3990 \ - --hash=sha256:f9c4420a7a864fe1b86ce35befc95b8e39fb852493b81cf798671ddc265de638 \ - --hash=sha256:ffff613aaa9ce6236766e2fc6dc560bb5abde7a2e2416e3db1f9ae395a2b4dd4 - # via argon2-cffi -arrow==1.4.0 \ - --hash=sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205 \ - --hash=sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7 - # via isoduration -asttokens==3.0.2 \ - --hash=sha256:3ecdbd8f2cc195f53ccada3a613538bb5f9ef6f6869129f13e03c30a677b8fe2 \ - --hash=sha256:9da13157f5b28becde0bd374fc677dcd3c290614264eff096f167c469cd9f933 - # via stack-data -async-lru==2.3.0 \ - --hash=sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6 \ - --hash=sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315 - # via jupyterlab -attrs==26.1.0 \ - --hash=sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309 \ - --hash=sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32 - # via - # jsonschema - # referencing -babel==2.18.0 \ - --hash=sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d \ - --hash=sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35 - # via jupyterlab-server -beautifulsoup4==4.15.0 \ - --hash=sha256:288e3ca7d54b06f2ac191970bc275c1939cb46d450b255bf6718b04aa37ab4f7 \ - --hash=sha256:d6f88de62e1d4e38ecb1077eb9724cd0eff29d2a08ca16a401e9b9e93f117cf9 - # via nbconvert -bleach[css]==6.4.0 \ - --hash=sha256:4202482733d85cedd04e59fcb2f89f4e4c7c385a78d3c3c23c30446843a37452 \ - --hash=sha256:4b6b6a54fff2e69a3dde9d21cc6301220bee3c3cb792187d11403fd795031081 - # via nbconvert -certifi==2026.7.22 \ - --hash=sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775 \ - --hash=sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55 - # via - # httpcore - # httpx - # requests -cffi==2.1.1 \ - --hash=sha256:046bfc24911b37851ee1b51aab8bffe713d89c68c6a057b09484ce9fd5f69b4e \ - --hash=sha256:06c72bb76605a4b0cd0aad6930b69d4baf7dd5d806cfc409b824191099700e66 \ - --hash=sha256:0beceaabe56af686895136a2de78db54ecd8e4046b236b8fd6d6cb61389e9bf2 \ - --hash=sha256:154852545011f779917b11c78db2358d095da62a9a172b78ad0a583ee5adc0d0 \ - --hash=sha256:194cffa889098ced9976c3fc6340305e43f6303657d298da55366907c05c22d6 \ - --hash=sha256:19ee6127ee34de7d83ce3d371ebc5ed91addbdcc39f9ab15ce4eb35a4e534971 \ - --hash=sha256:1a18a57b58cfb21fc28d72e876acf10eaed67a1ed96226f92af4df681d571c4c \ - --hash=sha256:1aa5645c30469b09530c4ebca77ebf8f17618293c58f8549cb1a543a50236e7d \ - --hash=sha256:1dea0e4d7d4f11f619fe8c1d76caf49e24405b4b5743c0e3be16a500ecd930c9 \ - --hash=sha256:208f941bb9d18e768138677f0a6d2ce01f590df56043dda1df1535ac57c88517 \ - --hash=sha256:210019b6c7cf07f081b4c54635c8cf744377001350e29cc0f81c4377b4797735 \ - --hash=sha256:246fa40ce8645a614ff682e0b70f37134e460eaf93a775e0cbe3cca585a67a80 \ - --hash=sha256:25792eac27877609e7bb06d42ff88278a6624fff2ba9bbb523c09616b117e80f \ - --hash=sha256:27350daa11d4f10c540e6e89dada4c54feb7256ad03e9a4dc075ebad7ba360d1 \ - --hash=sha256:28907ab9bfb6aa13184cfc17c6b8e1023c5ab6fd7076d8c20a35e59fe04f8f29 \ - --hash=sha256:2ae64be792b8966f2c69538199728b290e34726562896df1e5dc8ffd8d8188e8 \ - --hash=sha256:31348097ff5bbe827ccc41795d4dd099d9f0625e7def00ee653c137a490c2a6c \ - --hash=sha256:3143d81e29e1e20a9ce10901ec369012947876596f75a222235965f2b7ae832e \ - --hash=sha256:3222ba5d678f80a030e6afbcc33dc1ae5cb45facabb61cee2c7016b8432fde48 \ - --hash=sha256:3311ed60d36f83378794e1009ac6258bafbf81f7888b4caa7b35a521e3f95813 \ - --hash=sha256:334644fbac4eff73d985a17a91226df55d0f394160c4cfb880e084c8f7161cac \ - --hash=sha256:34e261f78cb6ceaaa36f42f2613f4380d94d9c759a9c73c769ee6e0247364632 \ - --hash=sha256:363e05fa78e15116c3c32c210ee36884fd6b9afa6d440e47112c3bd511d64cb6 \ - --hash=sha256:398aff33cee2767e3e781d2554c54bd0dff386bb437581e0d8011fde1a942ec1 \ - --hash=sha256:3d22a20b1fb1632cc72c22f95f7b0d2961c3e1c235f245ba4c606c4771035659 \ - --hash=sha256:42a494cee34437f05546455144f2b5d9ac09b1face62bcfce597d2e521066688 \ - --hash=sha256:42e2f76b9455f5a9a844f770bf3e200ed3da0e15f5df3db9c31fe80b04b3d004 \ - --hash=sha256:42f6930c31dc7f50732c9ae793c2786c7b6b044195967bbdde40bb9be81c4cc0 \ - --hash=sha256:456a61fa52d579ebf9df2e9552ead5129855dbaff6c1e5a9b1bc408809bdc062 \ - --hash=sha256:471cee653ae88de62096552e6d24ccb4a5adb8c8c9f10b5054d0122c15bf2779 \ - --hash=sha256:49cbc70e6542d4ccccb936558d1064a8012541e78f821f955cff24e357776c94 \ - --hash=sha256:4a7c934f7360e8cd64fe9efadcbd10c7c6364f531e432b9a4bf5ccbc9e0e8b50 \ - --hash=sha256:4be96343e422f2dfcd12ab5c9f5aebe03f82f737c6bffeca6830b3875cb44aab \ - --hash=sha256:4f42141fc14250de6dde5ee7ea4432be017252d91f19c5ad043c084cea629cac \ - --hash=sha256:507a24c282e0f42f8ed737cf048572cbf580468da5555764a8331735e9c736b6 \ - --hash=sha256:51b31d1c98274844cfd7838ce00bfc27c7423a4dc00fc0772fc3331c2cc90676 \ - --hash=sha256:58acb8ab8e295e6c5ea12f888cbb13cf21511ef2a3303a23f4325c29d17fe5c1 \ - --hash=sha256:5a59cc1c4442bc3d5c703bf720b51138d0bfc173618807c9ee2490a7541dd3d9 \ - --hash=sha256:5bb4e7ea95dcd6a014a6fef62e62467d67d8e582326443f3d68e71d6320a9fcf \ - --hash=sha256:5c58fe613dc5e5336357eff555824a314d8e43282600435c8d1cb6a7a2fedd13 \ - --hash=sha256:5e7cecbaadb83884793e05828cee59b210b24583b9c7425d0ba6a754fe22eb4e \ - --hash=sha256:616f097f2fe415bc92a247f02e11f634e1f9e9a83d327e3c915c15089c87869e \ - --hash=sha256:63bbfd5ded17c4840ac07cd8f1c21ba9d9708141f840b324f422f41b207e3973 \ - --hash=sha256:64faea20f4e2613363a1a9b9c7dd73058f3ecd00133a511e72ad7c511658f527 \ - --hash=sha256:661c298b4821edebead0c91edd2b00374d67ad7c5a1f7a91d4442633b79d6a72 \ - --hash=sha256:68e62fe11f30d5ca8289242866f0a5291402d8529ca2178ab8afc5c9694ae890 \ - --hash=sha256:6a8dddef476fab96d066d578fc88526767b836ab5ab21754e1d5bf3879c31c7c \ - --hash=sha256:6e192623c49c94421616a5778fba35cf0d5a8d000650c1967ef4448ee5cdd990 \ - --hash=sha256:7225e4514edb64eb6740324353e0da0711954fd8d7da4576755b1c6e09b697cd \ - --hash=sha256:75f80557d1389eddbd0de2681f6a390a0c5338c31ddaa821381c203fc3fd50d9 \ - --hash=sha256:770de9db11e84213beec501cfcaa013b019820ca881e03344dea5844f7876d94 \ - --hash=sha256:7750c6449dff7864bb9bb27ddfb0267756189201a3afc911d82b3caacd70dfc3 \ - --hash=sha256:7bde5e4cc5c10140859842b9d383af292b22639a4dffb725314baf45968cef80 \ - --hash=sha256:7ce713ace7c0e4520535b42b77eaa742c16dab813978064913e5a3cf82973b41 \ - --hash=sha256:7da0c5eff80f0197f3b3d1232ec5a682a9325f4ae9016a78f5f5ca35f9ced1f5 \ - --hash=sha256:7dbb61fe3a7699468030f71bbe5f8a0e326a151daa91beb11a6fc1f980c55e1c \ - --hash=sha256:811bd1e21d32de12efca32393a0ab3f5133b54fce9bd44b8bd77ab07da14bf6a \ - --hash=sha256:8ef53b2de9bcb9197d31854256575d59dbac0cba72ac627bb291ef5eceb74be4 \ - --hash=sha256:937c0052c05a31ca1daf18de3158eed4dbfcb9cc107adbea227728d647be701e \ - --hash=sha256:9d2055050ea716bd38b7f7f1579c275386646b4894c155a3e2f3cd62ed41b7c6 \ - --hash=sha256:9f8d177621de5cb38ee3e731eda45d421db093ec0739f46a5594babda7987a98 \ - --hash=sha256:a2d7755bef5a12ed488f4ef1f1b69ee9191d7396083b755a5d2295f6edb4768b \ - --hash=sha256:a48d62ab9d6f4f98c983223a547af44be6ca3691074c31cecced6facd3ba2dc1 \ - --hash=sha256:a4f00aa42f75d6e4595e8866e748cc1705adc0cddfeb2ca86d0d03993d63ba03 \ - --hash=sha256:a6e721d4b0e45d5b65e87534470e67b18dcd092c83f68fba09f152b9cbc061af \ - --hash=sha256:a730a083190634c65cca36ba5f489531576ebd79bcd5c8e172130f6453127231 \ - --hash=sha256:a931079504ecc49efed7744c476a5c343a92fabf66dec2db95edb1b2fdc770e2 \ - --hash=sha256:aa9511c62d14da7aacc9b4bf51f3f697a621e83b2d6919008243c3aad168eea3 \ - --hash=sha256:ab36d55f9ed2d067327667c2fea18dda018eb628dd6347aa01dda6cf1f5d3836 \ - --hash=sha256:ad2c86c495b899d862ea0f4b42891b8713a3bd45dd4105c7fd51c2a72f39f3a5 \ - --hash=sha256:aeae0e330c9f6acd681f647d46cefd30c29f93e3392882e792e82080c9691399 \ - --hash=sha256:b0431303acaea1089ad4b3e9ce4e6518193def1118d4073ca848635ee4ea2e96 \ - --hash=sha256:b5bdfd1c873d4e093aabc0ca84c4ca6dbc4f752afb5c86f146d9742580c9da2e \ - --hash=sha256:baed1e86cc735622097354b9d1281406caf42ff42a886d29faa8e8d1630333be \ - --hash=sha256:c1453022f490d2459a11819d83ad1d586e9ff65a12ac3e705ffebd46d3685dcf \ - --hash=sha256:c26608d2222fb1e94487e4a387d85f13eb55d5ed725cb25a0c589ac4ee60e7bc \ - --hash=sha256:c7659f22557c5a0bc4855cd635f55edec690cc008a40768527762cb9fb263455 \ - --hash=sha256:c8c69575568085ba0b1b10c0249d779a214aea6f6522e949a0fc9fb0fcb449d0 \ - --hash=sha256:c8d2c9fd1f2d16f780d15127abb050d13d1a76c03a4bd87d7e4980e45e511e12 \ - --hash=sha256:ca82be1a1d406ecfe1d25dc16cb33488e5a16bf4438c9fb590484ea29d92478b \ - --hash=sha256:cc572dace3f60ef98d7b12ff411d20f5362feb31a0439eab0085bbfd349982d7 \ - --hash=sha256:d18e5ac0f2f03f4f518d3e23db0f0cad7faa1da8620e9c09461d443bbf6e6692 \ - --hash=sha256:d28630f5854ab07ab1fd4aba756de52326c82e6be15d414b12793f1975048b54 \ - --hash=sha256:d9c275eaacd24aa73f94ffd6de08fc3f932424d8b6c376f4bed7cde376fe7bc3 \ - --hash=sha256:da0e573f9f97159390c89d9f1a9e41908b66d408cc5b58d08cf3847d844c531b \ - --hash=sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be \ - --hash=sha256:dddad92b554513a31f272570678ba307fb9f618f05e3d4a5eacafff9eae03e1d \ - --hash=sha256:df423d40ee8654634421812bc3b196da3f9bd7d32929da813f8394c4348a5358 \ - --hash=sha256:df913725b79db7bcf03448f36b7bf8815363417d5b58deecf9305e3e30f0f21a \ - --hash=sha256:e0bcb7e0f677f543555d2adff3bf19c05f66cdb4796e5ff602442ab2fe3c4ef7 \ - --hash=sha256:e2d65b31f36619cda3999b78b2aa9632e76b78448e7a56fc4240824200e7c4fc \ - --hash=sha256:e6e8cff14d6fb0be70a09c0bdc58096f501952d04624ebf867e0e56da2df8960 \ - --hash=sha256:f16c709686a78c727bbbf059f92b0bf41c6fc60deec706d2dc19f529175a6125 \ - --hash=sha256:f24fb43132a4c6b4cb4eb029492919b2db645be6808d738f244fd146c03c32cb \ - --hash=sha256:f53e442b08449d42821fa4a4fba000095af9f62742a500f978a9f557ec44339a \ - --hash=sha256:f5cfbc5fe74540d335175b656c725d74d90e3730c626d92575eea35029d9afaa \ - --hash=sha256:f81b3b8f3d4e343550fa4baa0e479bba9f2d29ce9c2e9b51d1ce1718d7442fcf \ - --hash=sha256:f8ec5e643a9a937f64e1999eb9f75d072263751912dc5cd06d3c85f8f44be7c3 \ - --hash=sha256:fb92203a88b3d3053034db775110081c49d28be6551923805e039924093761e4 \ - --hash=sha256:fcd22650c908d7b7da162bbfaab594a1227a15d1643a98c68b122ac642fa2264 - # via argon2-cffi-bindings -charset-normalizer==3.5.1 \ - --hash=sha256:00668ebb0609751758682eb0b5857e7c35b9f00e84dfdef062e103244ec94d45 \ - 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--hash=sha256:fd0350afdc3aabd5576f60ea109228bd5538139713c7b094c5cd27c73a98bc6f \ - --hash=sha256:fd0a274c0e5f9a21565cd9d3dd749b61f96b7aa1e20a93aa1ba4029518f2e5c0 \ - --hash=sha256:fdb8a068947befafba9952162645dc2fecaeb400e64584829ed5e9b2fbe21a7f - # via requests -comm==0.2.3 \ - --hash=sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971 \ - --hash=sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417 - # via - # ipykernel - # ipywidgets -contourpy==1.3.3 \ - --hash=sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69 \ - --hash=sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc \ - --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \ - --hash=sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a \ - --hash=sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8 \ - --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \ - --hash=sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470 \ - --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \ - --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \ - --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \ - --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \ - --hash=sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381 \ - --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \ - --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \ - --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \ - --hash=sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f \ - --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \ - --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \ - --hash=sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42 \ - --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \ - --hash=sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77 \ - --hash=sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7 \ - --hash=sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411 \ - --hash=sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1 \ - --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \ - --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \ - --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \ - --hash=sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db \ - --hash=sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6 \ - --hash=sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620 \ - --hash=sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989 \ - --hash=sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea \ - --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \ - --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \ - --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \ - --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \ - --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \ - --hash=sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1 \ - --hash=sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e \ - --hash=sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b \ - --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \ - --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \ - --hash=sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7 \ - --hash=sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7 \ - --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \ - --hash=sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1 \ - --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \ - --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \ - --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \ - --hash=sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20 \ - --hash=sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36 \ - --hash=sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb \ - --hash=sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d \ - --hash=sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8 \ - --hash=sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0 \ - --hash=sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b \ - --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \ - --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \ - --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \ - --hash=sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497 \ - --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \ - --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \ - --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \ - --hash=sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13 \ - --hash=sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae \ - --hash=sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae \ - --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \ - --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \ - --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \ - --hash=sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff \ - --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \ - --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a - # via matplotlib -cycler==0.12.1 \ - --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \ - --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c - # via matplotlib -debugpy==1.8.22 \ - --hash=sha256:0c1104233340196e5cbf5514e7dfdbb98968e730cfd7fdd6f3d72a082be838a9 \ - --hash=sha256:12bc7f368182b517cf26c76a2393fff65354e365fa1552b6241e66edff17997b \ - --hash=sha256:192b73e8d53bbd60225220c0943627bf249ac93d0ea3090e23c45f3e0ceb6a35 \ - --hash=sha256:1bd0c6df3c68c0a3f71db8baa3780a953abb65537ec4b3bc6b935ad5b3b3d45c \ - --hash=sha256:1e76339d5510bc17e9181dba9577508afcb21aad5728f1a55ef74d7d97d255f3 \ - --hash=sha256:225d063f81708c2546999e7edfac0198b2d5c2f144797dc64858f867948633e0 \ - --hash=sha256:371a4ba4a5975eb958393903f3254cf983da7b1c7c178b3f987ee427f42515e3 \ - --hash=sha256:3b7c328cb47b4e2f2b40801936daf8150f88dcb4ffef5080ff119270f0d23626 \ - --hash=sha256:4ad076f4f66cb8acb79e4384d48b1ea60a0b1957aa0b1112d4887ea3a4df60e0 \ - --hash=sha256:56b877b37ed73f0bf53ba7afc394816ff1eb5d701bae24a24ed9e1ea6f7ce34f \ - --hash=sha256:66e4ac3d6e7026e83e7d93d7ee2f51dd4a4e8dff673578d424e60796893e5b2c \ - --hash=sha256:745e1800ec2961e5660c1a317c0a20e28c0fef4de36c04f17a21c33f2b37a92a \ - --hash=sha256:7bf29e0d8ce80b100d37fb333e1b193b790d962739f3067796c2b03ffdc9afee \ - --hash=sha256:8a697acec45dbc70d17fb5d9f4f61989fc294d273c69de487fd10cb35fdd75eb \ - --hash=sha256:92fc425308a08f601f3c69afb4719767f9b898276f44b97e6942a713c4c32740 \ - --hash=sha256:a9e9d3550e15ca479c59333e90845029190531f0cacfedab3b815a57bd913947 \ - --hash=sha256:a9eca6ab09a61534e923064400081e016f7e1d8430cc6e8b7a9cecd2f59e2b07 \ - --hash=sha256:b17a4896520f1c6da09ce76ec8215df0b85b0b6f3617526a4c65c2315340875a \ - --hash=sha256:b7dbde1fb822d100802d505b2aed6d0813b1a0a2015d495cb8798b2215d5d1e5 \ - --hash=sha256:ba810a66b437e3c43ca0ce0892404f010338286263ca8e5108442d76a9485337 \ - --hash=sha256:c1ffb9953708b648ecf6acd2e5ad2c002bf68785ed618197dd4463a2a7226f39 \ - --hash=sha256:c21dd7e1ec22556bb41dc3bf6b86c603e440ec889c4acb27d7206354b2106c54 \ - --hash=sha256:cba7b99573ee41f9510c6deeff1cdd0c333748b9dd63d5016413dd57ba9a4593 \ - --hash=sha256:d593a330297332ec435f3965c448b6d500e03f33675cf2063178bc76377a788e \ - --hash=sha256:e489c7268e1c7b41e13b438d9c533d2a7af73fb59bf8cd30fead8286c1c39c4e \ - --hash=sha256:e6744ac1850c73c2ba7b29a126cf9ab74efd1831190720e8fee17ef5389c8f5d \ - --hash=sha256:f49b1d6cecf326b63c06d7f517e4a6642777f758c58cb799e7bc5a6dd74721c2 \ - --hash=sha256:fa47099176d1d612bef69e9f74658a5687a9f05bd80fb5710f99959bace85735 \ - --hash=sha256:feea785c7bbeb8cfd5b01a63f9061c899dc468c8be81671141a29305774fa294 \ - --hash=sha256:ffeeaf4dfe1534375902040df32a4c48f721fd35a4d4d337ac72a44d4176075b - # via ipykernel -defusedxml==0.7.1 \ - --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \ - --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 - # via nbconvert -executing==2.2.1 \ - --hash=sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4 \ - --hash=sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017 - # via stack-data -fastjsonschema==2.22.2 \ - --hash=sha256:0fb3915616adac85ccfdd737d26be1089845d2019819505b42d39888458f74d4 \ - --hash=sha256:72064e12356a7d6ef02165be2946b9abadbdf238536e07eb587e3dbaa33099cf - # via nbformat -fonttools==4.66.0 \ - --hash=sha256:02117d05dddb51e39b5c5a6eed0702ed8e0e1c341cb138101b2446eb479858f8 \ - --hash=sha256:03922992966a1830b94a750961d1ccfe1a7375d792ca61077a5afb719811788a \ - --hash=sha256:056641fcedeaad24b92e343e35e9fd1337200c102450678894b17928482d010d \ - --hash=sha256:0640e69b00e089d6b82e0a54e3a03af84b775f1f0b2cbecc1b0d7fe3980a194a \ - --hash=sha256:0cf33d1041364ebb7a9958db757e213c6d2a238eb41755fbf935db028c2db20f \ - --hash=sha256:107aae7c38f05561fef5103baa98a44400b5f4c69d87839c9a5f8125405e7d9a \ - --hash=sha256:135ce8738a6341fbefeedc061e20c2889350e84405abea7e61b1c9d87e1a1f2f \ - --hash=sha256:15315302c620e6ce8582934e07932dcf442fc3f59ab208d30564200b9a2f7ea4 \ - --hash=sha256:1d120ea0f5260b04e9b5ac0d9239efde4c23d990347d5d5889cdd37221726fec \ - --hash=sha256:221defad3b1949c1fdde2558d1fe780d42f926b683a71a46a4495a0e6a6614bd \ - --hash=sha256:2b278e2abe596872b3c9fe611f956ded85588537974e48e2adbe2d6ee159e099 \ - --hash=sha256:35684b562df7154d7a0ebfa512db9199c7fb0a93b5abfb59dac9d022dbee7aaa \ - --hash=sha256:379b94fe10651d26caf398eb646b3ecde886a9c504d3cce5e46164d58a78e32d \ - --hash=sha256:3889fdbe63e85dbf2a0d9d7c9d90746827ce60af2cdd24f0bb4738380a9540eb \ - --hash=sha256:398bfc5ce9055e0b91a6189f8cf462d4230b38f522b2ceaecb4f34cbf4fb5480 \ - --hash=sha256:3ea59aaec135c96c4cff20d79fdf1d10623993712a77990585a42cfd6a42bc5c \ - --hash=sha256:4513753259f06316bdc2039c9bf3129e74923c1cfad0722b4728a9ce19beeeb2 \ - --hash=sha256:4bdaf80590fcccb1b9d8e3b8ca72225defb14d03b79788f59781ce1bb66049b9 \ - --hash=sha256:5dda4087a7f8f0b985995489b3c0115bd117f18c0fc236013a0e78e837bb9ac2 \ - --hash=sha256:6371e7ed43cc9e23550fe4a1b72af3205275f28dcecac8c09b80a4582e326802 \ - --hash=sha256:6627f48130cdbe04c7e1189bff4192886ecda757f575626ee8468f6580eaecf3 \ - --hash=sha256:67ea5af3ca60e1e5c9b2841b1f6dba5ef16aab581bf463abb68515447154ab61 \ - --hash=sha256:6b11180c4166a4fc353bb02b147322b7c454927bba4f5040ccdef109a73f9051 \ - --hash=sha256:700c448f190cc7bbd9f059bd96131f4770e90938c9692550b142153bea73b14c \ - --hash=sha256:8009e736e1491c569392b2e9782c30dcb6ac879def102c8c2e06add8d2adc10f \ - --hash=sha256:850d50b347f31667d3a277a6e66a4219236db9d98afadb2993b8addf9bd4f593 \ - --hash=sha256:85e2960a29e6882567b981ba408b5a5ca2d8865b8f67240e1b420348997a13be \ - --hash=sha256:8739d77810aeb6d2ecd55cc2e4864d98a00c317fb6131a6d22ca58b3f50cf22a \ - --hash=sha256:886f3029ea10362c592d2e1a5326a44645b85a2a0be6fdb7c9edbdb60cedb0ab \ - --hash=sha256:8af85dfc2ad564f95b3e650024578bb2268bc4d827d418aab9650bbde4b6926a \ - --hash=sha256:8ea6265535b25f8254868f5a212a1c9dbd88c2c4050f3df18d9174949df0d6d9 \ - --hash=sha256:9b0198962bbba64a88bf15dc5d95e4bab8c5208dede22a10fb06280406ed9874 \ - --hash=sha256:a5a0624aaefb0a29e4ed7500a98732a1b0ec2d3040fe86cc8837f82e3fe4f446 \ - --hash=sha256:a9a45a23b020e667499fb2dbce0f1373b1b1b8cf88e47f3031e69ef3ea21f3ee \ - --hash=sha256:ada87643b20a8fa5763e6dd4fa0f5e2901cbdbe0833deb6d5c5a9c3186782b94 \ - --hash=sha256:aea0cc5609a5f2a2500f91bd6e1989fdd22da0f225be00dc1c4cec775838a7d0 \ - --hash=sha256:b02686b2e47115b2378ab6156abd984c776c6faa4f56898fbcc1c30ee387937d \ - --hash=sha256:b432b6b54a0f3be118c6243312c826aa086aa9bcb150160be4f0bc8a21ea3aaa \ - --hash=sha256:b46500e4d6f5708a9127ea12902ba54cb0ec594fc6bec649afc53154a1125b2b \ - --hash=sha256:ba65eb3e2c46ad0922d84ef78e287c2f48b7a8cae4420728bf3b8e583282d6d7 \ - --hash=sha256:bc7b7ddc1a1f46c363354304e9a8dd93722e4a6a24f785015650898dacf40df9 \ - --hash=sha256:c144e68572af1dfcb05fe065827d0920d4118b265504928e1d2262e3701a25a1 \ - --hash=sha256:c3a66c749b69e9abd92e519ccbdea7210522b0fffdba3f6492a8fab461d4708a \ - --hash=sha256:c519433e8632284dbe64ee4c81b609a17e205bc1afff285553723964880d1c89 \ - --hash=sha256:ce818581070527883e3678b92b1ac7ed3569e48c80f51b89b264f97d84be2608 \ - --hash=sha256:cf0033f343cb1592fb24ae3eaf8b58156f208884ac16991094293ee94c585654 \ - --hash=sha256:cffa168522a0e057d3a5628c8ebd43ddf66a34613f42e116bffb85b0b32ce5a0 \ - --hash=sha256:d2d10f89c4bf7cd42b84695da617f9130fa594647ee74673bf8c8d001fa04ef9 \ - --hash=sha256:dbee639a23c4e067aedfbcf84d2e466a71a1a1156ad04d6d09023791ed167495 \ - --hash=sha256:e4677025ee40b3b1420387ca85f55545b8095f7596c3d5d59552dbee19c91f07 \ - --hash=sha256:ee4f85d1341af630d787514eef1a0e1ad883f9d6c11a4c1465faeb9cd0bb5ced \ - --hash=sha256:ee7b9f6c835ea1a9beb5a35c8eb0c62b6a3290f8105d3d0c29a0a18c90f9b8ba \ - --hash=sha256:ef0610dfe7bb5bf574d9bdad6f597403ebc9807d124ac6f148d7604b2609be98 \ - --hash=sha256:f014bbf05f30731bf8b9f5c7d2b4c0e4f28f375926f95b187f85e0bbceb16029 \ - --hash=sha256:f2032ac005e14c56e774fd19f2bc1c22c796c0c3b39d91741c0e3f9575944d38 \ - --hash=sha256:fa995d96ced196388d8ed7a67ee001b181459d230e24bb68b193ae859f5609da \ - --hash=sha256:fd3dcb69d65c05a2fffa5d9b743b77e0cdc13832665ba435065493dad2b57f83 \ - --hash=sha256:fd9297a5f670f3e59ae697289ddad5902d3c4a86188e83e7823bde06334a560c - # via matplotlib -fqdn==1.5.1 \ - --hash=sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f \ - --hash=sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014 - # via jsonschema -h11==0.16.0 \ - --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ - --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 - # via httpcore -httpcore==1.0.9 \ - --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ - --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 - # via httpx -httpx==0.28.1 \ - --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ - --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad - # via jupyterlab -idna==3.20 \ - --hash=sha256:a7db850025b95ded1eae8a46181a1a6c56c92c96f0e2b005d9ff8dc0210cab44 \ - --hash=sha256:ab7ae7122974553370f0bdb919e1a960b2cd1bc1ef0276416d896db81c14582c - # via - # anyio - # httpx - # jsonschema - # requests -ipykernel==7.3.0 \ - --hash=sha256:897eb64da762549ef610698fca5e9675195ec6ac8ec7f19d81ce1ca20c876057 \ - --hash=sha256:9acaaaf97d16355166e4085afe9d225bfbdf2b7ef520f9df3be8f2b248275e09 - # via - # -r requirements.in - # jupyter - # jupyter-console - # jupyterlab -ipython==9.17.1 \ - --hash=sha256:6d1645743cfd1a07eb695d85aa2b5fa66721f8cbae9431d4049f7084bbf06509 \ - --hash=sha256:8919be8c27f20a6f4423145028063f6637b42a03ce57665bb12015ee1f073529 - # via - # ipykernel - # ipywidgets - # jupyter-console -ipython-pygments-lexers==1.1.1 \ - --hash=sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81 \ - --hash=sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c - # via ipython -ipywidgets==8.1.9 \ - --hash=sha256:bcccba38a6ec3253f7a39c943cea5b9ad01999ce071396171adbc51c6a6a8613 \ - --hash=sha256:f2b8cbcaae10252b809fbe4d7470db75c09b769a32cbf816d20e5ca6d3c5a79d - # via jupyter -isoduration==20.11.0 \ - --hash=sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9 \ - --hash=sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042 - # via jsonschema -jedi==0.20.0 \ - --hash=sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67 \ - --hash=sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011 - # via ipython -jinja2==3.1.6 \ - --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ - --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 - # via - # jupyter-server - # jupyterlab - # jupyterlab-server - # nbconvert -json5==0.15.0 \ - --hash=sha256:56636a30c0e8a4665fe2179c0212f32eae3796dea89ea6f649b9436ecdb39618 \ - --hash=sha256:7424d1f1eb1d56da6e3d70643f53619862b4ce81440bdb8ecfd6f875e5ba4a71 - # via jupyterlab-server -jsonpointer==3.1.1 \ - --hash=sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900 \ - --hash=sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca - # via jsonschema -jsonschema[format-nongpl]==4.26.0 \ - --hash=sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326 \ - --hash=sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce - # via - # jupyter-events - # jupyterlab-server - # nbformat -jsonschema-specifications==2025.9.1 \ - --hash=sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe \ - --hash=sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d - # via jsonschema -jupyter==1.1.1 \ - --hash=sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83 \ - --hash=sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a - # via -r requirements.in -jupyter-builder==1.2.3 \ - --hash=sha256:01aba6794eb9b19e0e29ae21137ca60ba4135c70347d4b9f664a586822b8c809 \ - --hash=sha256:c5ea5a7190c2a7b082494abade98eece1b2b5bd5dbd7d610606cbcd10a1d08b3 - # via - # jupyterlab - # notebook -jupyter-client==8.10.0 \ - --hash=sha256:5f73f24f22fa25192cfff6b23c051932a2473a797b05734aff495b392103e14e \ - --hash=sha256:9f7116294dca55f1785be880057d44544db9b1567718d92cb33c58886afb9497 - # via - # ipykernel - # jupyter-console - # jupyter-server - # nbclient -jupyter-console==6.6.3 \ - --hash=sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485 \ - --hash=sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539 - # via jupyter -jupyter-core==5.9.1 \ - --hash=sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508 \ - --hash=sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407 - # via - # ipykernel - # jupyter-builder - # jupyter-client - # jupyter-console - # jupyter-server - # jupyterlab - # nbclient - # nbconvert - # nbformat -jupyter-events==0.12.1 \ - --hash=sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf \ - --hash=sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3 - # via jupyter-server -jupyter-lsp==2.3.1 \ - --hash=sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81 \ - --hash=sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6 - # via jupyterlab -jupyter-server==2.21.1 \ - --hash=sha256:2a6467606af7dbae2e7e31640030025969e15db6a649eae334af90415dc71dca \ - --hash=sha256:a8960aa29263f6041283e97d4756b099fb49b767baab6371891ecb1bd40a63df - # via - # jupyter-lsp - # jupyterlab - # jupyterlab-server - # notebook - # notebook-shim -jupyter-server-terminals==0.5.4 \ - --hash=sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14 \ - --hash=sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5 - # via jupyter-server -jupyterlab==4.6.4 \ - --hash=sha256:15b13f991d3985129c797eb84d9949eeb8b6615e14b444868e642411f2c418b2 \ - --hash=sha256:404f49b081819378524886c9db66dba57a5565981eff885830df1baba3a17df5 - # via - # jupyter - # notebook -jupyterlab-pygments==0.3.0 \ - --hash=sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d \ - --hash=sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780 - # via nbconvert -jupyterlab-server==2.28.1 \ - --hash=sha256:0c3c2418d51021ce280916e63dfe4cba8386b4e2787be5c25433c98f560ddb31 \ - --hash=sha256:4bd36c7c11d872e15cefa4951e12380a47e6901687165332dd50a007cb367ad9 - # via - # jupyterlab - # notebook -jupyterlab-widgets==3.0.17 \ - --hash=sha256:40ac1e9955acf116c4d995d9bfa082d86ad9ec6d91c4f134827cf5e0a5eb75e0 \ - --hash=sha256:6e61fe21ca8a66039180a5cc52a433e07279d2fee79c8be963e00d55193f17a8 - # via ipywidgets -kiwisolver==1.5.1 \ - --hash=sha256:007a5553dfc4f4e8d184f588a0200e2cd4b63a59cc8796df3c39909e679dc7a0 \ - --hash=sha256:0324cd2567259b7a095f6cf18a52b0ffc6f3de9e69528ff1bc0e7a37bd43ff1a \ - --hash=sha256:0627b9bceb9c3cdcf12b8a18655eedfed2692b038df27423383c120d0b7dc2d6 \ - --hash=sha256:06a6917674de9e0fe3f66f5430787f59a9f2ddb64af9b714eaec547e29ef5c19 \ - --hash=sha256:072bdb15a3c19a5b5dbc8f8fb1f4e1884bf4f3507eeb4cc6334401274d37a5c0 \ - --hash=sha256:0a4faea5c6db201c6a21391d2ac926ea97acf7dacdbc3c417189e1adb1a00837 \ - --hash=sha256:0ba9527afc80ae3d7814ed98b6572d02bf85eaf48065678342c5f0c6dab7a8c7 \ - --hash=sha256:0d8924877ce22e17326a99a418c3c82037da078df3c6a260b13eca677444e6e7 \ - --hash=sha256:0ebdef3eae5336568147c39a55be6a2036ffde53faa9ca2d978989ae7c2da12c \ - --hash=sha256:1209042a623ddfda5497e4066c7b77651dde8e1d3a9dd97599dc7e97f3b9b78c \ - --hash=sha256:16895f553ee6620a827d2da56b871f835fb70b9216cca5d188e885caf6e3bd23 \ - --hash=sha256:17851e5dad4484be0cbccbde3b15331deae036de9aebd45eed964487802b172f \ - --hash=sha256:1798e83840c3f627246104c4d8a9639c60fa068adf9ce92b61791781fa8a68c1 \ - --hash=sha256:18170a77ddfecf40ec60d0928268dc95880c881864e015a8f34094ed18b9b9ad \ - --hash=sha256:186884a58486651e3c217b6acea0a53eaa9498fdd472057c46f2f0fb5c25aad5 \ - --hash=sha256:18a0cfb124546a4c2e6087c5f3029c7f44b37c85b142e0ced71f73a7599ac208 \ - --hash=sha256:1983f0974a750a6f6556f368ba11105d1d8369c735b944747c9f12ae5aea7aae \ - --hash=sha256:1a7587dc335f2c0f5bd577fd0540bd16c66006bdb60f759a1059f025e6c4f071 \ - --hash=sha256:1acc7e5b7ef05e9da8bb70cd6c7c4513090213d2e1ad9720f599f0bf6c52aec5 \ - --hash=sha256:1d852545c4d0e35a72728d072cbaa59e2fa7dd84bdf01e068d670dd0ceb58eb6 \ - --hash=sha256:1ed0f5e49d0ceff8b72190824d9e59c062fbbc02c231b853112c78474b3f5ec2 \ - --hash=sha256:1fff05e239575b1481b6ed1a782f6fad616efbf1f0b1f44e6e85c4dfe426e483 \ - --hash=sha256:21e46b23a2da695c364124817bc01d970effd5483147f8d66a6a7167e3f6b851 \ - --hash=sha256:22d5e5aaad6be121f2515765e3b1c444352cb8eb4c86510801db8f2e50757316 \ - --hash=sha256:2551cf9917af48ee7c4b29cc82320489508cf96fd26a51f6fc124de661cd44c7 \ - --hash=sha256:255605693a483db7bd5c79f60437f7bf658f7f520d61aa42722e32257c941951 \ - --hash=sha256:26e8268480be5061d509e29669d59103c067a26377a56491630ece11762e3858 \ - --hash=sha256:27add358abe374ebaa3b8763ef380bc99051b5a4b18d94878366a9e4f59efef0 \ - --hash=sha256:2ae70bc59790d2af72a3f76f24b272403e135070340281108b447cb77ea70819 \ - --hash=sha256:2e10ae1bba1899188b33557c10d73affcc12033edd18adddb57d209039976a4c \ - --hash=sha256:3221f78211074f561c44ca42eac0619828171bec15a2c4cf6f7747d07df76e8e \ - --hash=sha256:34633ecf50d16187ab8e5528b7a2530f2feb4e23f300db4672538b51cfc5cd38 \ - --hash=sha256:34ec467940442c9943016fb2d4c81d1ba84351eeca2f1a78f8bc87f1ba0d414c \ - --hash=sha256:37f801b5d7cc0e5a548921308e059fd2b057bb42972b591cfa3049f95423c4ed \ - --hash=sha256:38f6e0deb4d0a4615efe0c4efc5990b06ae450ab50a0b321c0b078b6d238c083 \ - --hash=sha256:3c24cd69455e1b00ddf770c13b6e2c33e07d6dc3f2d34add0bf9277c5c6bbd46 \ - --hash=sha256:3cc210010fd2f438a3ed430b45f1b501fd13a8618bf984dc2c5ce5b69b78752e \ - --hash=sha256:3fa5855898f6d3d01b72ccd48a2d65cbdee301251603fefe34e2025bddba219c \ - --hash=sha256:416ba7ff9f233b7036689bb5a3783537e838ad483f63558d2a800f75afe738b1 \ - --hash=sha256:431dc224a1a92a5c8f582d96e505196a3b5997a7271076678da2dfde67b77e9a \ - --hash=sha256:43844c1a7ad6d723d5b5b4c4fc7f5bd399c40e288120d16257c7c9e8765c6e85 \ - 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--hash=sha256:e12dfea7f5fc2a34a9080efbf79c4c44eb380ec5b9c6fea09407e08f0d1e941d \ - --hash=sha256:e4e4523d6f336708d732516e6cfca7796cf3d96c9474eb5aecf6165f2f1fefc3 \ - --hash=sha256:e4e49f7e1a4e7191bdf9dc67a974db714501b1fc52c24324103d06a86abd5c08 \ - --hash=sha256:e68e151428b5384f766cd25739bf77c7e4a3dc93b5ded7a12118d9fbfdf78ab6 \ - --hash=sha256:e8e4d953faaded9ec7ede36824e9814082d22d4c7b1eafbfa079ecba8cd0d076 \ - --hash=sha256:ee9df1f0d77b9c6e94f4ac0fec533fbddd5ea3a327807f18d7b069ae019ded80 \ - --hash=sha256:f0a887b6565bbfe80efde2b7f6e8890d7d9bbdb11bdb17028a3690c32fe0621f \ - --hash=sha256:f0f4a42db92d6ec7677ab9d12830a2a8ec145a9c6d15db2b593466bc875c78d7 \ - --hash=sha256:f1303ef2eec81262a4b708c3e858afe58d7c75ad91c1c05266eda7673369859a \ - --hash=sha256:f1d56ec54d257d05e0b50f5780d967540cd07beeaf9e5f645b26d50cce79f4d8 \ - --hash=sha256:f4167e87b397f273dc2356fcf1eaf50a6bac51e6105f45103ef7129c8efb0255 \ - --hash=sha256:f76fc85bd054c806960f917ec0f329e24e436f1712267d90588e4c39890caa63 \ - --hash=sha256:f942903fde7363d1d879057ec5de01310efda2597161784d752fa9953a01a71a \ - --hash=sha256:f9b1c4900736e489a812c529100de4b8fb617d4db075e931e213c57424b83d9b \ - --hash=sha256:fc271a6f0a2126958f4090e5507b9da5848927dae331f8f763bd4aa642b3d2cd \ - --hash=sha256:febcce10f2bcdbb80b4ea919238a6a4ac13dbc4c7cadbe8d5d75c3682f8b5404 - # via matplotlib -lark==1.3.1 \ - --hash=sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905 \ - --hash=sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12 - # via rfc3987-syntax -markupsafe==3.0.3 \ - --hash=sha256:0303439a41979d9e74d18ff5e2dd8c43ed6c6001fd40e5bf2e43f7bd9bbc523f \ - --hash=sha256:068f375c472b3e7acbe2d5318dea141359e6900156b5b2ba06a30b169086b91a \ - --hash=sha256:0bf2a864d67e76e5c9a34dc26ec616a66b9888e25e7b9460e1c76d3293bd9dbf \ - --hash=sha256:0db14f5dafddbb6d9208827849fad01f1a2609380add406671a26386cdf15a19 \ - --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \ - 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--hash=sha256:1cc7ea17a6824959616c525620e387f6dd30fec8cb44f649e31712db02123dad \ - --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \ - --hash=sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c \ - --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \ - --hash=sha256:2a15a08b17dd94c53a1da0438822d70ebcd13f8c3a95abe3a9ef9f11a94830aa \ - --hash=sha256:2f981d352f04553a7171b8e44369f2af4055f888dfb147d55e42d29e29e74559 \ - --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \ - --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \ - --hash=sha256:3537e01efc9d4dccdf77221fb1cb3b8e1a38d5428920e0657ce299b20324d758 \ - --hash=sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f \ - --hash=sha256:38664109c14ffc9e7437e86b4dceb442b0096dfe3541d7864d9cbe1da4cf36c8 \ - --hash=sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d \ - --hash=sha256:3b562dd9e9ea93f13d53989d23a7e775fdfd1066c33494ff43f5418bc8c58a5c \ - --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \ - --hash=sha256:4bd4cd07944443f5a265608cc6aab442e4f74dff8088b0dfc8238647b8f6ae9a \ - --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \ - --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \ - --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \ - --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \ - --hash=sha256:591ae9f2a647529ca990bc681daebdd52c8791ff06c2bfa05b65163e28102ef2 \ - --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \ - --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \ - --hash=sha256:6b5420a1d9450023228968e7e6a9ce57f65d148ab56d2313fcd589eee96a7a50 \ - --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \ - --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \ - --hash=sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b \ - --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \ - --hash=sha256:7be7b61bb172e1ed687f1754f8e7484f1c8019780f6f6b0786e76bb01c2ae115 \ - --hash=sha256:7c3fb7d25180895632e5d3148dbdc29ea38ccb7fd210aa27acbd1201a1902c6e \ - --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \ - --hash=sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f \ - --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \ - --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \ - --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \ - --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \ - --hash=sha256:949b8d66bc381ee8b007cd945914c721d9aba8e27f71959d750a46f7c282b20b \ - --hash=sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a \ - --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \ - --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \ - --hash=sha256:a320721ab5a1aba0a233739394eb907f8c8da5c98c9181d1161e77a0c8e36f2d \ - --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \ - --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \ - --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \ - --hash=sha256:bc51efed119bc9cfdf792cdeaa4d67e8f6fcccab66ed4bfdd6bde3e59bfcbb2f \ - --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \ - --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \ - --hash=sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b \ - --hash=sha256:c0c0b3ade1c0b13b936d7970b1d37a57acde9199dc2aecc4c336773e1d86049c \ - --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \ - --hash=sha256:c4ffb7ebf07cfe8931028e3e4c85f0357459a3f9f9490886198848f4fa002ec8 \ - --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \ - --hash=sha256:d2ee202e79d8ed691ceebae8e0486bd9a2cd4794cec4824e1c99b6f5009502f6 \ - --hash=sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e \ - --hash=sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d \ - --hash=sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d \ - --hash=sha256:de8a88e63464af587c950061a5e6a67d3632e36df62b986892331d4620a35c01 \ - --hash=sha256:df2449253ef108a379b8b5d6b43f4b1a8e81a061d6537becd5582fba5f9196d7 \ - --hash=sha256:e1c1493fb6e50ab01d20a22826e57520f1284df32f2d8601fdd90b6304601419 \ - --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \ - --hash=sha256:e2103a929dfa2fcaf9bb4e7c091983a49c9ac3b19c9061b6d5427dd7d14d81a1 \ - --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \ - --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \ - --hash=sha256:e8fc20152abba6b83724d7ff268c249fa196d8259ff481f3b1476383f8f24e42 \ - --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \ - --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \ - --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \ - --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \ - --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \ - --hash=sha256:f42d0984e947b8adf7dd6dde396e720934d12c506ce84eea8476409563607591 \ - --hash=sha256:f71a396b3bf33ecaa1626c255855702aca4d3d9fea5e051b41ac59a9c1c41edc \ - --hash=sha256:f9e130248f4462aaa8e2552d547f36ddadbeaa573879158d721bbd33dfe4743a \ - --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50 - # via - # jinja2 - # nbconvert -matplotlib==3.11.2 \ - --hash=sha256:01dc8eaaab5a9fce9ff615eca82345728f289e4715b186ee10c6d85272fc26bb \ - --hash=sha256:02329432ae5c6af87cf208ee575b701d698bdf0b1a3bb28cc6d53c36e967e575 \ - --hash=sha256:07d9b9fa60cd4c393692f50d0bb03123242ddf61c99bb0e95e75feb354e7c1a8 \ - --hash=sha256:116cdb0eb0eb5644fc98eb2975d4b4dd4ad35e5c8e6b851c22e6976f371f7ab5 \ - --hash=sha256:1944895967f87c84c9b4bad29a707b31f5b36df4a3a2339ea1f8ea3ce5105539 \ - --hash=sha256:1a3040b209f3968b4e84161df7b174f07a9fad33b0f2d7e48ea3bbd3075e2863 \ - --hash=sha256:1b9a7ad579856284135e401ecc918c5f8a017ee30539298862a109f51b971710 \ - --hash=sha256:254d4ddb2fa8df3b4c689c0c306063aee10521df82cfb438185e499c75fe37c1 \ - --hash=sha256:2a8285cea8ef4d92aa041d1c33788bcae82248503300f93f1ca2136b9049452f \ - --hash=sha256:30ec15d7eefee71de16b7c689b42ba49715a4644650b76a6c7c70d79daf24e91 \ - --hash=sha256:399fef672f7046ef7d6a57572b2a6f9845f3f5afcff04e7b2df7a363a9f42190 \ - --hash=sha256:3aa4b8516fd26659e4363abbf317c703d9116496c5db2e9d0609a2866dd39dd2 \ - --hash=sha256:3da3bc0cbf7245e7db72cc6d29d12c5abef72cb73059c73b945f14e3545f3eb2 \ - --hash=sha256:3e8576f7c47e02fd4f21f44171302d2d1d58d4471d46da3d71fe8899d19539d9 \ - --hash=sha256:57b9ea60a835937c2012861923cbb91f47db8565775d326d1c42fc926aa10351 \ - --hash=sha256:5e1e923a3fc3326b99ec0a6ff1ab1338ac6c6cc62ad9d8a9c944197c7f8c6221 \ - --hash=sha256:643ff850d8e0f5b8319337f87ed3cb59506afb3df3cc48de777d85871233be7b \ - --hash=sha256:75b6d88402770e181b5d06a67dda4129c31da7d05004d63a21c310ec78d1b83c \ - --hash=sha256:79a258f58253dfa025af80a9e9bb228d75fced007f0e93ae7423fefbde81a74d \ - --hash=sha256:7d43ff8cebb50840648cb6429b2228621dbd709010f3117b3740abb20abf21c0 \ - --hash=sha256:7e5a90f8a707ebb6004a713b1cff091a40cc5df4c7c1cb165e5a505ebc11c292 \ - --hash=sha256:7ef7a53b66780e5d942923724f08577fbc5be1f7322da0f0f3f9dcaa45dd803d \ - --hash=sha256:854df8d7dfe9fdffcbaa6f39e44a6c24b409cb4d7561fbc09213b157d833f6a6 \ - --hash=sha256:894a9cbbecbe30ae6787d464df2e8fc7a8d475cfc68f87c3029f7c11152899b3 \ - --hash=sha256:8c8255de28f986d935a64c9ca71c0ec2d2f41d355691f5ea684725dc91413f71 \ - --hash=sha256:930efb28f59fda124e39265d177bab302625297bb147d2910de725a2fc2aef54 \ - --hash=sha256:a24d5fd36e4f0e742c3851dcd20810e56a95633a342e4bf6cb591c678e8fe61f \ - --hash=sha256:a6939df7567114b6bac7f4c5e06c84a67f197c1b2f2e4b234d4eecb3bfec9482 \ - --hash=sha256:a8756cc73d9af9a7fe0deb54ea2e75ef73b01d9e575877e72acad5458e660943 \ - --hash=sha256:af2661f6ac6bbd1d081996f54fdd9385715c625ace8cec0869055c0cfbf38981 \ - --hash=sha256:bbf1062991d826ed27e2144f3afa4461afbb8ff56e8f703043e191a8163b1ee9 \ - --hash=sha256:bc067c462a86f0e57bf52fc6e058d90171a5420007dac00c46e90153050c69a7 \ - --hash=sha256:bf3fe71fbfb8ec0e310e0bc8537c3405a01f38f25f9394ed2135e6202fed542b \ - --hash=sha256:c0b83f044ce10a98027b105b3931548719a6e8c7ef986b4362651e0b5367c8dc \ - --hash=sha256:c27e577ece613ea12a0790e00b4eb80d901c59a3e16cad474f31d8b1529690b9 \ - --hash=sha256:c5c1c68ee401fc98271263410f0e5ce88285abacf7627132914e8adf3d70ff43 \ - --hash=sha256:c9721f81275499da1feeb36a2cf8192ea086283b3bd16b7dc4c9d7aedb7396d6 \ - --hash=sha256:cc82dde2a0d3e3ad472edce04897ad7146b8d8bfd1df8a32992eebb81af18fdc \ - --hash=sha256:cec596316640f2b394b8f0daa0ea61a8eae82d017b620b9f202befb972a59ea4 \ - --hash=sha256:cf41ecd1b0c0b6f7177ed965a54c2afbe888715c7cf6054dc12d53bc1494002c \ - --hash=sha256:d3304eb5a59442a8867f6920d484591c0fa09ffc29e9260be2feec3351e25869 \ - --hash=sha256:d480038c83691532ed52ff3147db51fa902fc78cb2d8349993a1cdb684435bff \ - --hash=sha256:df4f7784aca81a94f254c0a2767d592ee25f407e488f5fa7203e51093fb6ca27 \ - --hash=sha256:e43b188f0a5b75447bcc197728258166aa64365770ed1caa36595a5e1ca4bbba \ - --hash=sha256:e60cf3047a51edecdc4535a9196bbd6a732936b8e9f8184aabf4d16165884aaf \ - --hash=sha256:eac4b07d4e3743b172451e122ea964f72f152879d4f9adcf3f3d33e518f12ead \ - --hash=sha256:eb3712dc9b464793de0a4e42a7313d50293c751f94bddaf7332a1bc71bccdda9 \ - --hash=sha256:ecea603dd2fbf8242fd31a305a8b12a4ece2de28096870c65fdd0d1e35b8d9a6 \ - --hash=sha256:ef31985c4dedb5f1424e1aec6849a47dd37689cb7fa3c20b1b82187f26806261 \ - --hash=sha256:ef752769cd962f39ea0b6ffc82d1ea43a0012c5a6157c7a075212fa509cfcff2 \ - --hash=sha256:f25446b2981717dca9786bac841cb3fd7efb568e3e3c755dd980481c5cb9228d \ - --hash=sha256:f2ac30cf5eb5dff1b584627ae0b0e1186551a4f69ae3c75073911da497a29170 \ - --hash=sha256:fea03cf56568cc1cba08b470be6a0559e71c3a5b688d54b7179bb35ba23d0821 - # via -r requirements.in -matplotlib-inline==0.2.2 \ - --hash=sha256:3c821cf1c209f59fb2d2d64abbf5b23b67bcb2210d663f9918dd851c6da1fcf6 \ - --hash=sha256:72f3fe8fce36b70d4a5b612f899090cd0401deddc4ea90e1572b9f4bfb058c79 - # via - # ipykernel - # ipython -mistune==3.3.4 \ - --hash=sha256:58b5c96d6fcb61190dfe5fae498d2b2065f99cf61e9649418fd54cf1ada86dfe \ - --hash=sha256:ee015381e955e370962968befe1d729ab60fafb6a715ac6751763fbce38c8d4a - # via nbconvert -nbclient==0.11.0 \ - --hash=sha256:04a134a5b087f2c5887f228aca155db50169b8cd9334dee6942c8e927e56081a \ - --hash=sha256:ef7fa0d59d6e1d41103933d8a445a18d5de860ca6b613b87b8574accdb3c2895 - # via nbconvert -nbconvert==7.17.1 \ - --hash=sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2 \ - --hash=sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8 - # via - # -r requirements.in - # jupyter - # jupyter-server -nbformat==5.11.1 \ - --hash=sha256:32d4521c68c6e7d5b29c76defaeed9f42ea733142b9b19f88277ce10390b9c4d \ - --hash=sha256:cc6698fa75f4fab8755ead786317815f13a6fee3b53311c0abb1a8b51d52f7ec - # via - # jupyter-server - # nbclient - # nbconvert -nest-asyncio2==1.7.3 \ - --hash=sha256:2bc87bdca654e719425145f5e84eaeb0080b013fdd47d65729a7d66243f4987c \ - --hash=sha256:2e9a84d5d1efe6d020c72988d21aec569bac42d98af2ff6b9de24640c5d22a34 - # via ipykernel -notebook==7.6.3 \ - --hash=sha256:ad7e0eb765fba836cd4a2ab0c7a3a26cde1d91665fbf6f533b6ae7b2de6d88d2 \ - --hash=sha256:e2c08e469c0ae20bb0b3214f0ab77e79653317a2f8e5b34c10361c66874a5b50 - # via jupyter -notebook-shim==0.2.4 \ - --hash=sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef \ - --hash=sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb - # via - # jupyterlab - # notebook -numpy==2.4.6 \ - --hash=sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1 \ - --hash=sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4 \ - --hash=sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f \ - --hash=sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079 \ - --hash=sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096 \ - --hash=sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47 \ - --hash=sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66 \ - --hash=sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d \ - --hash=sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1 \ - --hash=sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e \ - --hash=sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147 \ - --hash=sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd \ - --hash=sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75 \ - --hash=sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063 \ - --hash=sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73 \ - --hash=sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab \ - --hash=sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4 \ - --hash=sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41 \ - --hash=sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402 \ - --hash=sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698 \ - --hash=sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7 \ - --hash=sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8 \ - --hash=sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b \ - --hash=sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8 \ - --hash=sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0 \ - --hash=sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662 \ - --hash=sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91 \ - --hash=sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0 \ - --hash=sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f \ - --hash=sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3 \ - --hash=sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f \ - --hash=sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67 \ - --hash=sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6 \ - --hash=sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997 \ - --hash=sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b \ - --hash=sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e \ - --hash=sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538 \ - --hash=sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627 \ - --hash=sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93 \ - --hash=sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02 \ - --hash=sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853 \ - --hash=sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c \ - --hash=sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43 \ - --hash=sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd \ - --hash=sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8 \ - --hash=sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089 \ - --hash=sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778 \ - --hash=sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1 \ - --hash=sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb \ - --hash=sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261 \ - --hash=sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb \ - --hash=sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a \ - --hash=sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8 \ - --hash=sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359 \ - --hash=sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5 \ - --hash=sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7 \ - --hash=sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751 \ - --hash=sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8 \ - --hash=sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605 \ - --hash=sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e \ - --hash=sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45 \ - --hash=sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2 \ - --hash=sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895 \ - --hash=sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe \ - --hash=sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb \ - --hash=sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a \ - --hash=sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577 \ - --hash=sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d \ - --hash=sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a \ - --hash=sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda \ - --hash=sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6 \ - --hash=sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20 - # via - # -r requirements.in - # contourpy - # matplotlib - # samplerate - # scipy - # soxr -overrides==7.7.0 \ - --hash=sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a \ - --hash=sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49 - # via jupyter-server -packaging==26.3 \ - --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \ - --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c - # via - # ipykernel - # jupyter-events - # jupyter-server - # jupyterlab - # jupyterlab-server - # matplotlib - # nbconvert -pandocfilters==1.5.1 \ - --hash=sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e \ - --hash=sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc - # via nbconvert -parso==0.8.7 \ - --hash=sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c \ - --hash=sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1 - # via jedi -pexpect==4.9.0 \ - --hash=sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523 \ - --hash=sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f - # via ipython -pillow==12.3.0 \ - --hash=sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756 \ - --hash=sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a \ - --hash=sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59 \ - --hash=sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45 \ - --hash=sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3 \ - --hash=sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df \ - --hash=sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139 \ - --hash=sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b \ - --hash=sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39 \ - --hash=sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e \ - --hash=sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8 \ - --hash=sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1 \ - --hash=sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8 \ - --hash=sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89 \ - --hash=sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5 \ - --hash=sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130 \ - --hash=sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd \ - --hash=sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d \ - --hash=sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b \ - --hash=sha256:25b9b82bb22e6e2b3cd07b39c68b7b862001226cb3dff7130d1cb914121b39ed \ - --hash=sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace \ - --hash=sha256:300557495eb45ebb8aec96c2da9c4be642fbf7cd937278b4013ba894ea8eb0eb \ - --hash=sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931 \ - --hash=sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510 \ - --hash=sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6 \ - --hash=sha256:37dc8f7bbb66efe481bb60defacef820c950c24713fb44962ed6aa2a50966de1 \ - --hash=sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce \ - --hash=sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385 \ - --hash=sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e \ - --hash=sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c \ - --hash=sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7 \ - --hash=sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace \ - --hash=sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c \ - --hash=sha256:514435a37670e3e5e08f3945b68718b6ed329bb84367777e16f9f4dfe1e61a0f \ - --hash=sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64 \ - --hash=sha256:5594fc43d548a7ed94949d139aa1341b270f1863f11cfd37f5a6c8b778a6b67f \ - --hash=sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a \ - --hash=sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827 \ - --hash=sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17 \ - --hash=sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4 \ - --hash=sha256:6c0016e7b354317c4e9e525b937ac8596c38d2d232b419529b9cd7a1cd46e39a \ - --hash=sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701 \ - --hash=sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e \ - --hash=sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91 \ - --hash=sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66 \ - --hash=sha256:85f998ea1848bc6757289e739cfbdda3a04adfd58b02fc018ce54d754a5ce468 \ - --hash=sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217 \ - --hash=sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658 \ - --hash=sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418 \ - --hash=sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a \ - --hash=sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c \ - --hash=sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330 \ - --hash=sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402 \ - --hash=sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09 \ - --hash=sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930 \ - --hash=sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f \ - --hash=sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec \ - --hash=sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a \ - --hash=sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94 \ - --hash=sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468 \ - --hash=sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b \ - --hash=sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965 \ - --hash=sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8 \ - --hash=sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd \ - --hash=sha256:bcc33feacfaefce60c12fd500a277533bdc02b10a19f7f6d348763d8140bbba7 \ - --hash=sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c \ - --hash=sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777 \ - --hash=sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35 \ - --hash=sha256:d9c7f76c0673154f044e9d78c8655fb4213f6ca31a836df48b40fe5d187717b9 \ - --hash=sha256:dbce0b29841537a2fa4a214c2bbf14de3587c9680caa9b4e217568472490b28f \ - --hash=sha256:dc624f6bc473dacdf7ef7eb8678d0d08edf15cd94fad6ae5c7d6cc67a4e4902f \ - --hash=sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0 \ - --hash=sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c \ - --hash=sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71 \ - --hash=sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3 \ - --hash=sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838 \ - --hash=sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf \ - --hash=sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321 \ - --hash=sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26 \ - --hash=sha256:f0606c8bf2cdefea14a43530f7657cbbb7ecf1c4222512492ef4a4434a9501ec \ - --hash=sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9 \ - --hash=sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65 \ - --hash=sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5 \ - --hash=sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e \ - --hash=sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d \ - --hash=sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198 \ - --hash=sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7 - # via matplotlib -platformdirs==4.12.0 \ - --hash=sha256:095be5c143382b1bee917c4f3e9987a0d8d6a582261f1d061ad0c403b7695b5b \ - --hash=sha256:f6fb2960f2f2eb0870f820f7e49e49e6c0f0589c637f34d33b260b07591327c9 - # via jupyter-core -prometheus-client==0.26.0 \ - --hash=sha256:04a91bcf94e2cf74a44a1a874d651a2e853ed354b6e822f3b7487751465d5c2b \ - --hash=sha256:fa93d06737aa02bacd05794768508bb97d2fbee28cb3bca04eaae92f0ca953d6 - # via jupyter-server -prompt-toolkit==3.0.53 \ - --hash=sha256:01c0891d7f9237d5e339f7d3e42cdae80b7534abb1c7c0e3352efba6231492f2 \ - --hash=sha256:9ec8a0ad96d5c56148b3f914aa79c1564c3fde5d2e6b876e7bc327e353cf8fa6 - # via - # ipython - # jupyter-console -psutil==7.2.2 \ - --hash=sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372 \ - --hash=sha256:076a2d2f923fd4821644f5ba89f059523da90dc9014e85f8e45a5774ca5bc6f9 \ - --hash=sha256:11fe5a4f613759764e79c65cf11ebdf26e33d6dd34336f8a337aa2996d71c841 \ - --hash=sha256:1a571f2330c966c62aeda00dd24620425d4b0cc86881c89861fbc04549e5dc63 \ - --hash=sha256:1a7b04c10f32cc88ab39cbf606e117fd74721c831c98a27dc04578deb0c16979 \ - --hash=sha256:1fa4ecf83bcdf6e6c8f4449aff98eefb5d0604bf88cb883d7da3d8d2d909546a \ - --hash=sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b \ - --hash=sha256:7b6d09433a10592ce39b13d7be5a54fbac1d1228ed29abc880fb23df7cb694c9 \ - --hash=sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee \ - --hash=sha256:917e891983ca3c1887b4ef36447b1e0873e70c933afc831c6b6da078ba474312 \ - --hash=sha256:ab486563df44c17f5173621c7b198955bd6b613fb87c71c161f827d3fb149a9b \ - --hash=sha256:ae0aefdd8796a7737eccea863f80f81e468a1e4cf14d926bd9b6f5f2d5f90ca9 \ - --hash=sha256:b0726cecd84f9474419d67252add4ac0cd9811b04d61123054b9fb6f57df6e9e \ - --hash=sha256:b58fabe35e80b264a4e3bb23e6b96f9e45a3df7fb7eed419ac0e5947c61e47cc \ - --hash=sha256:c7663d4e37f13e884d13994247449e9f8f574bc4655d509c3b95e9ec9e2b9dc1 \ - --hash=sha256:e452c464a02e7dc7822a05d25db4cde564444a67e58539a00f929c51eddda0cf \ - --hash=sha256:e78c8603dcd9a04c7364f1a3e670cea95d51ee865e4efb3556a3a63adef958ea \ - --hash=sha256:eb7e81434c8d223ec4a219b5fc1c47d0417b12be7ea866e24fb5ad6e84b3d988 \ - --hash=sha256:ed0cace939114f62738d808fdcecd4c869222507e266e574799e9c0faa17d486 \ - --hash=sha256:eed63d3b4d62449571547b60578c5b2c4bcccc5387148db46e0c2313dad0ee00 \ - --hash=sha256:fd04ef36b4a6d599bbdb225dd1d3f51e00105f6d48a28f006da7f9822f2606d8 - # via - # ipykernel - # ipython -ptyprocess==0.7.0 \ - --hash=sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35 \ - --hash=sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220 - # via - # pexpect - # terminado -pure-eval==0.2.4 \ - --hash=sha256:260c2774686e651b79f8b8e7fc9d80b3599ea6a66334b47d5f4abb69fc2c0ea1 \ - --hash=sha256:96cae060a313cfaad51bb761278bfb0e62dc0248d9315a81173752dc546cd37a - # via stack-data -pycparser==3.0 \ - --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \ - --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992 - # via cffi -pygments==2.21.0 \ - --hash=sha256:2363c69b61c4a97c838da3b130dcd6468f4848992b21a82f2a63ec34377137d9 \ - --hash=sha256:610ca751c9bc2492b38eb9a38a7fbc93edbbb2d7182edaf34e66ae493dee5c8c - # via - # ipython - # ipython-pygments-lexers - # jupyter-console - # nbconvert -pyparsing==3.3.3 \ - --hash=sha256:928ae7e20211f3b6f3915a72f06a0cfd29ab9d24279dd6346b6b1a7146397d36 \ - --hash=sha256:ece8c00a69cf01b45d0b1dedabb469c90d8caf996d4fda40f147627a122849a4 - # via matplotlib -python-dateutil==2.9.0.post0 \ - --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ - --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 - # via - # arrow - # jupyter-client - # matplotlib -python-json-logger==4.2.0 \ - --hash=sha256:158a52126fcd6869e09574d2b66272666f3dc8f468c62637ef9a1fa883719cb9 \ - --hash=sha256:e371ebe22ec01e289850102091a2b1f6fc9e655c7f1f5f29073936756c290afa - # via jupyter-events -pyyaml==6.0.3 \ - --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \ - --hash=sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a \ - --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \ - --hash=sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956 \ - --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \ - --hash=sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c \ - --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \ - --hash=sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a \ - --hash=sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0 \ - --hash=sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b \ - --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \ - --hash=sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6 \ - --hash=sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7 \ - --hash=sha256:28c8d926f98f432f88adc23edf2e6d4921ac26fb084b028c733d01868d19007e \ - --hash=sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007 \ - --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \ - --hash=sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4 \ - --hash=sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9 \ - --hash=sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295 \ - --hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \ - --hash=sha256:418cf3f2111bc80e0933b2cd8cd04f286338bb88bdc7bc8e6dd775ebde60b5e0 \ - --hash=sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e \ - --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \ - --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \ - --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \ - --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \ - --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \ - --hash=sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b \ - --hash=sha256:5e0b74767e5f8c593e8c9b5912019159ed0533c70051e9cce3e8b6aa699fcd69 \ - --hash=sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5 \ - --hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \ - --hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \ - --hash=sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369 \ - --hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \ - --hash=sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824 \ - --hash=sha256:66291b10affd76d76f54fad28e22e51719ef9ba22b29e1d7d03d6777a9174198 \ - --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \ - --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \ - --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \ - --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \ - --hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \ - --hash=sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b \ - --hash=sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00 \ - --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \ - --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \ - --hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \ - --hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \ - --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \ - --hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \ - --hash=sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4 \ - --hash=sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b \ - --hash=sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf \ - --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \ - --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \ - --hash=sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8 \ - --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \ - --hash=sha256:b865addae83924361678b652338317d1bd7e79b1f4596f96b96c77a5a34b34da \ - --hash=sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d \ - --hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \ - --hash=sha256:bdb2c67c6c1390b63c6ff89f210c8fd09d9a1217a465701eac7316313c915e4c \ - --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \ - --hash=sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f \ - --hash=sha256:c3355370a2c156cffb25e876646f149d5d68f5e0a3ce86a5084dd0b64a994917 \ - --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \ - --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \ - --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \ - --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \ - --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \ - --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \ - --hash=sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3 \ - --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \ - --hash=sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926 \ - --hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0 - # via jupyter-events -pyzmq==27.2.0 \ - --hash=sha256:00e73942ef12cecbc7951c4a9104bb8ffaed742abb13af2da6833d90dd368cef \ - --hash=sha256:010db74a1dd67c7cd8b8b30916355735db7d633a070510bb34e41ab679ab2c0e \ - --hash=sha256:0e1af01858d6dc0c09cea57f9cb1ddf4601f04897b6bb1efc3a2038123c87d79 \ - --hash=sha256:0f4bd6743e8bf854c3bfce892dd6578a514aabf128e37a4b2eafcf01856f7e44 \ - --hash=sha256:1132805970045adb9f5f05dd57040978286a8e21a5475f2c2ddf1bc983b9a2c7 \ - --hash=sha256:1ecbdd131b9669f62d3a45afee5527c7ae9f141e4301267f21714c90bd21725f \ - --hash=sha256:1f8079d0521fe94bbb401fe9407578b28f3701627c8be2c9f7e0c5b77dcb0109 \ - --hash=sha256:211350c3ccd4746bc5a85e8fe961bad1f7f2f274f67cf1f785fad7f96f562eea \ - --hash=sha256:288cc790da0e3064a14a38ddc56ba169dada8c8af4cb86518db2bcbd380eedbb \ - --hash=sha256:2c218c6ab8bc447ba62054b581fd30209689d199c6ecb253f79615ca74a38e12 \ - --hash=sha256:3146385b94a760236c5eceff468a66a296a716ca98a2e0f9217b1518118466b1 \ - --hash=sha256:348d6fd3e4b81ae4580622ea8c2ea60224e84b2ac1b3be4482e6edc7de06e7a3 \ - --hash=sha256:376981d106598beb70be384f44d8f589832fd0051d184d38d10043da3cc3b080 \ - --hash=sha256:39755dc4a923021bd0677990ffdbc21cff0e1ee1cf07fe3817acea153ef4cb67 \ - --hash=sha256:3ab6eb88590e510ab16715c32dbba12000da9bee989fdadd9ee19a234c492eb7 \ - --hash=sha256:3d45189c0c3c99f817b7fefff0d32eeef684cf33e1e3c0fc4281515357c54702 \ - --hash=sha256:3ee556ed1cf836f96de9d5e545563116426d4a94f21b8041fdc79408eff18ebb \ - --hash=sha256:3ee8dd7031d5e23f632e0e7eee67183ca7d2536e0de35dc1e5d69f3471a791e8 \ - --hash=sha256:40124779c3a56ad5d91902df1ff89159cb414b6c1a0ee697abcc66cf5e6db62d \ - --hash=sha256:40d96cb7a8f6a43aa9617c00215c2b73e1b5e4a1d6cbc9f5860ed7ac682599f0 \ - --hash=sha256:44f261eca7dfb9904ea2b56428f59ab693bbe2715c0413a701f17b067ebf877c \ - --hash=sha256:468139ddb2e494d06e586bd3a6835077e8b3764560c8db552fe685c5867fc24e \ - --hash=sha256:480dba27b145373b5e103890f17969d891bc9e86746d6b8b29dd70b0d4addc62 \ - --hash=sha256:4ebc7889b31bc11c72e9f17ba3ebb0a8b0911cce413f41b498e55383a94819a3 \ - --hash=sha256:507c0b33f95502723d325487e8e50c2cdd3b37444143f05423a3861327f69bf7 \ - --hash=sha256:54d4259d1bfae24ecdb5ca79f7acc2eac6c286a02d6a0ae617797cb45f0726d3 \ - --hash=sha256:56b48fa9d478a3af7254f397697a62f5ad3e1bb677e200b2701f0c290d97e5af \ - --hash=sha256:591c8de5851c5ea372194469fe97587b97c3b641e9a70f31bb3474acbfde0241 \ - --hash=sha256:650c6cd7cb39a069e7048261efe66fce8bf2e0052c831a7a099b7a0f2ea860d7 \ - --hash=sha256:679b5b1dde326a921ea2c9ec1f9ea3115bfe1b4735779bbc6eb0473a0ed93f71 \ - --hash=sha256:6eb63cc61ab93b01b9afc887a160255e2fbe703fdbacfe5feaef87214f51bd6c \ - --hash=sha256:714f8cbd66c7e405338d668f79d2fe83fe923defe348e843be998603cf92eeff \ - --hash=sha256:722f0a6940be1a483c81029a271d950e04dc2ff113a42e21b3d2b7a0d8e59638 \ - --hash=sha256:76afba06ae698f2b8fe4fb34b32c760a650f168c2e622f370f2c528035b7f650 \ - --hash=sha256:770a37f28ddfbe1d2c40a2e3ce37e5fd10831daa6ae9634105aa8a5d23507b00 \ - --hash=sha256:7e2579c5de82ddf4544d723c1bc8b44c3b806d157acc9fb2a2d18e10ef28e202 \ - --hash=sha256:82a09aa67871d4f2fcafd47bf670fb93210b232a7c2d4b8a54676314edf04033 \ - --hash=sha256:88c0fac061bac269076edeb3a209acefc96cd6167c239daf1c2b404ac48d7012 \ - --hash=sha256:8a5c04ad2e368142aea52d1abdf6631cb2534864e3c16ab78268ab957060b2a6 \ - --hash=sha256:8b86e04f55af0f4d8cd8ecf14c0b8b81ebc8fd66fa20126b753514628ecadc7e \ - --hash=sha256:917d601e9540098f580d2723d0ce6402cdb6f02bc8dc2de74e0dca6e13bffd1b \ - --hash=sha256:9216132843d139a123f243c07fe70f7487dce5041093dd77040f9adb5dc91872 \ - --hash=sha256:94242bd4de6af7e74665e14a88630bccd615057f6acfaf08a3a432551d604645 \ - --hash=sha256:95369ed6626afcfe2ac89832fb1b917c077fbeb905fbbe5d918349ce0222b89b \ - --hash=sha256:95f52b877149b06bbdeec2e8ea6230aad14950bbfbcfa16e7eb88951f07d6b28 \ - --hash=sha256:97d4c6622f129b514a4f5939af1b5f434c97f47085d9311b5f7f36e24b3bd447 \ - --hash=sha256:9846e881620dd62566ca76a53e384c3f37490faf4b9240aebc7498810dfca853 \ - --hash=sha256:9ab72ee77b313d0658447204c8201f9b315146e923b48c56ea7dbd005d464a91 \ - --hash=sha256:a070a9cdad1f8f8a85ea153afcc4654f11b10895d14c0acabe10f1df0e0892ea \ - --hash=sha256:a0ee3c49be2aa15abd12cbbd14d4ea2892f872c688e1e487af39ec1972ed549d \ - --hash=sha256:a7c1144dc61777938e932a2c9011b980b89fd8ff3733033b34c44c299187a6e1 \ - --hash=sha256:a843094b4d3d633bc3623e47a2ff50742d6af02bc1f7606aa2e67e971e21878d \ - --hash=sha256:ac126d48cf18aa955daabef43bf0009ff76ad4deee437d09ecf15388214b5beb \ - --hash=sha256:ae6ebbc0bfe5a21ce21e32ba567bf73df2d93888109c65acbd42506cf9395759 \ - --hash=sha256:b26f2d0493b79ce3c3112c8a12649418915582ba4707b8ed9f44febf2be71f42 \ - --hash=sha256:b398c5fe102b41e1559f7ffdae760aabd5f432d73b047b4ae0eac4e01cb594d2 \ - --hash=sha256:b8d5f66e4a8246cf77f7b8f7902af64f00553368fa0373c89d99b78f0ad79394 \ - --hash=sha256:baa2ce3485145653194d6c8c5beedd1e9f0bf46a0919c9fa2fe2204fc35b74d9 \ - --hash=sha256:bad4813f270592cedf56977e31ac1fc374fb0f6f67ea5134a5e37c19cb429a8e \ - --hash=sha256:bf0b6e4ce1bb089751c504c5493d6b0557eabd02dd21b76e9086cf964234b103 \ - --hash=sha256:c218b816220d05acf6ab1bafca58926d95cbcc5fec5024724666030466308f0c \ - --hash=sha256:c5129a8fe43ecc49b99eb75616603d483a3c2fcaef504988fafe8ea392aea98b \ - --hash=sha256:c551b9e2f86dc625fcb1a032c0d68042678caf96a8dd7c28796766b673bd5b52 \ - --hash=sha256:c7cfb75caa83f5153c687e9d2107f64b5ef0ef0d6edd260d3ff920baaaa69101 \ - --hash=sha256:c9322f9c87b0935870516c2876e1e29497fdc50439c785ece63e3fbbab06c821 \ - --hash=sha256:d1526b42a2e725b84ed226f37becedc250c6347594e5ed304e4e9aff68c9aec3 \ - --hash=sha256:d1bc1d380a91d954ed5fc9f12915dba014eed0978d2de05ee7ca688bdaac144a \ - --hash=sha256:d41ebb260b69329b7d4a2936d44c872c86dd785355b51366c8b14e07ed7e9373 \ - --hash=sha256:d61910b52be5b2cd8b248dbcbe3a1b0275556a7d99fb613fc43323b546e273b8 \ - --hash=sha256:d61a0169ba05ab7ebc48dc793f092df12f789bf378dac8321ccd966fd93d94e8 \ - --hash=sha256:d64da42cae09e6b0c61368b4cc8ca80f23ce3af17584d08053f3dc957433d5ed \ - --hash=sha256:d9527e3dbaef1edaeeb2446fa7379446814a43ade8adc7c4a5ebe69437815ddd \ - --hash=sha256:dcc99ca132b667a4ed750afd42db4ea73288f18425a9b2e3c0af095665c491f5 \ - --hash=sha256:dde5e291548ca0f397623b5e523db5c90172b32aa4fd3ba464a79ea31a580b43 \ - --hash=sha256:dea74fd65f1fc5f7fe167916a473ebe6ed6174e5e5d9de11ea6583661be6cf43 \ - --hash=sha256:dfcd024eade5870b25f890c4df0ba9421ed8167d8d3d82334237512c1158dada \ - --hash=sha256:e0fa0bc6b1a184aee59b32efcd1b7f0e6d5b8f9387799e4c16a4cb66a86747d6 \ - --hash=sha256:e1ed46048d1920cabc96d952a0d5cfe4127ad8db572c335aae4e3c57b9278d7f \ - --hash=sha256:ec8a318dfc27c7d946651b3d9e8025d5734f30c168a822195601827207bac09b \ - --hash=sha256:edce90a1e588ec63adbf612cc0ad582de4169cd216c7ae53c15f42a2ee902f35 \ - --hash=sha256:f52f08101907609cc08db6a1f9f2a7a9afd54e9b2ca16178c9c38e99fb593cef \ - --hash=sha256:f5c6d8744d10b5e1eadd90a7c58f8546acf6bf680ee463f7e6ada09ad6c9f802 \ - --hash=sha256:f707bcf2c1d007d14d70531d4dd7b41060881c73efa845580bf6faaf9ea24d42 \ - --hash=sha256:fba8afcf265c6e9fbe1594cb045d4765c6c9a7d607653a8196067ef23566b843 \ - --hash=sha256:fdaaa4ea3242f6ad298eb5177eb042aea5c73c30e76d20caee7b15af20d24ec2 \ - --hash=sha256:ff60f0f7ccfda0e303ac43bec7096007b7cdf2c41b3739d1ec667febe67acab3 - # via - # ipykernel - # jupyter-client - # jupyter-console - # jupyter-server -referencing==0.37.0 \ - --hash=sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231 \ - --hash=sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8 - # via - # jsonschema - # jsonschema-specifications - # jupyter-events -requests==2.34.2 \ - --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \ - --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed - # via jupyterlab-server -rfc3339-validator==0.1.4 \ - --hash=sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b \ - --hash=sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa - # via - # jsonschema - # jupyter-events -rfc3986-validator==0.1.1 \ - --hash=sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 \ - --hash=sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055 - # via - # jsonschema - # jupyter-events -rfc3987-syntax==1.1.0 \ - --hash=sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f \ - --hash=sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d - # via jsonschema -rpds-py==2026.6.3 \ - --hash=sha256:0be972be84cfcaf46c8c6edf690ca0f154ac17babf1f6a955a51579b34ad2dc5 \ - --hash=sha256:127565fead0a10943b282957bd5447804ff3160ad79f2ad2635e6d249e380680 \ - --hash=sha256:127e08c0642d880cf32ca47ec2a4a77b901f7e2dd1ad9762adb13955d72ffcc9 \ - --hash=sha256:166cf54d9f44fc6ceb53c7860258dde44a81406646de79f8ed3234fca3b6e538 \ - --hash=sha256:168c733a7112e071bb7a66460e667edfcff06c017a3c523f7a8a8e08d0140804 \ - --hash=sha256:1967debc37f64f2c4dc90a7f563aec558b471966e12adcac4e1c4240496b6ebf \ - --hash=sha256:1cebd1337c242e4ec2293e541f712b2da849b29f48f0c293684b71c0632625d4 \ - --hash=sha256:1cf01971c4f2c5553b772a542e4aaf191789cd331bc2cd4ff0e6e65ba49e1e97 \ - --hash=sha256:1e5822dfc2f0d4ab7e745eaa6d85945069329beeccef965af3f3bb26058fcab6 \ - --hash=sha256:22bffe6042b9bcb0822bcd1955ec00e245daf17b4344e4ed8e9551b976b63e96 \ - --hash=sha256:23a439f31ccbeff1574e24889128821d1f7917470e830cf6544dced1c662262a \ - --hash=sha256:24e9c5386e16669b674a69c156c8eeefcb578f3b3397b713b08e6d60f3c7b187 \ - --hash=sha256:270b293dae9058fc9fcedab50f13cebf46fb8ed1d1d54e0521a9da5d6b211975 \ - --hash=sha256:29dfa0533a5d4c94d4dfa1b694fcb56c9c63aad8330ffdd816fd225d0a7a162f \ - --hash=sha256:2a9c6f195058cb45335e8cc3802745c603d716eb96bc9625950c1aac71c0c703 \ - --hash=sha256:2bfd04c19ddbd6640de0b51894d764bd2758854d5b75bd102d2ef10cb9c293a9 \ - --hash=sha256:2c54a076ca4d370980ab57bc0e31df57bbe8d41340436a90ef8b1219a3cbb127 \ - --hash=sha256:2c958bf94822e9290a40aaf2a822d4bc5c88099093e3948ad6c571eca9272e5f \ - --hash=sha256:2c99f7e8ccb3dd6e3e4bfeac657a7b208c9bac8075f4b078c02d7404c34107fa \ - --hash=sha256:2f7c26fbc5acd2522b95d4177fe4710ffd8e9b20529e703ffbf8db4d93903f05 \ - --hash=sha256:30c6dc199b24a5e3e81d50da0f00858c5bbdb2617a750395687f4339c5818171 \ - --hash=sha256:38a2fea2787428f811719ceb9114cb78964a3138838320c29ac39526c79c16ba \ - --hash=sha256:3a83ae6c67b7676b9878378547ca8e93ed77a580037bcbcd1d32f739e1e6089c \ - --hash=sha256:3cfe765c1da0072636ca06628261e0ea05688e160d5c8a03e0217c3854037223 \ - --hash=sha256:421aba32367055614287a4292b6a17f1939c9452299f7a0209c117e990b646d4 \ - --hash=sha256:425560c6fa0415f27261727bb20bd097568485e5eb0c121f1949417d1c516885 \ - 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--hash=sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866 \ - --hash=sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d \ - --hash=sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6 \ - --hash=sha256:8a604bae87c6195d8b1045eddece0514d041604b14f2727bbc2b3020172045eb \ - --hash=sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca \ - --hash=sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0 \ - --hash=sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca \ - --hash=sha256:9ecb4efb1cd6e8c4afea0daa91a87fbddbce1b99d2895d151596716c0b2e859d \ - --hash=sha256:a3472cfbca0a54177d0faa68f697d8ba4c80bbdc19908c3465556d9f7efce9ee \ - --hash=sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4 \ - --hash=sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717 \ - --hash=sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49 \ - --hash=sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2 \ - --hash=sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a \ - --hash=sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350 \ - --hash=sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950 \ - --hash=sha256:c80be5ede8f3f8eded4eff73cc99a25c388ce98e555b17d31da05287015ffa5b \ - --hash=sha256:cc90d2e9c7e5c7f1a482c9875007c095c3194b1cfedca3c2f3291cdc2bc7c086 \ - --hash=sha256:cd96a1898c0a47be4520327e01f874acfd61fb48a9420f8aa9f6483412ffa444 \ - --hash=sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068 \ - --hash=sha256:d30e57c72013c2a4fe441c2fcb8e77b14e152ad48b5464858e07e2ad9fbfceff \ - --hash=sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a \ - --hash=sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50 \ - --hash=sha256:e18f12c6b0bc5a592ed23d3f7b891f68fd7f8241d69b7883769eb5d5dfb52696 \ - --hash=sha256:e19ebea31758fac5893a2ac360fedd00116cbb7628e650842a6691ba7ca28a21 \ - --hash=sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c \ - --hash=sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484 \ - --hash=sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118 \ - --hash=sha256:f4115102802df98b2b0db3cce5cb9b92572633a1197c77b7553e5203f284a5b3 \ - --hash=sha256:f590cd684941912d10becc07325a3eeb77886fe981415660d9265c4c418d0bea \ - --hash=sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293 \ - --hash=sha256:fcb310ddb270a06114bb64bbe53c94926b943f5b7f0842194d585c65eb4edd76 - # via -r requirements.in -send2trash==2.1.0 \ - --hash=sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c \ - --hash=sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459 - # via jupyter-server -six==1.17.0 \ - --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ - --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 - # via - # python-dateutil - # rfc3339-validator -soupsieve==2.10 \ - --hash=sha256:49e9380d7d2905463583bafe285e818c7366a9ed7b3aee221c1ac79c905d8bc0 \ - --hash=sha256:8596eb8967d744174820280fa62b4542a2e955bfaccca73ed8a13c6eb8e9b502 - # via beautifulsoup4 -soxr==1.1.0 \ - --hash=sha256:1577865e993f98ffb261257c3060fa76ec3db44ed3f181b16464268000424464 \ - --hash=sha256:26925618945f1a44dfbd783cc572874f0685e9ecdf46b96f4000f6b8c9c8b825 \ - --hash=sha256:318925f7281df61dfa7f17fe343952eb10cefd3954f2423a733fabe3a517bab2 \ - --hash=sha256:33525740fb7dbed8b09970bf0cd4219b365538845053987b11cc235b20562e09 \ - --hash=sha256:34cc92208c3c412c046813e69da639c04a792c6a41fbfd7d909d359cd3e97a2d \ - --hash=sha256:3b033078e86f3c4a658e5697fac8995764fad9e799563616b630136b613167f1 \ - --hash=sha256:3da87e3ffa3e41823d873b051c7ecb2acebd8d1b6b46b752f5facf10a0d84ab9 \ - --hash=sha256:474aabb9283f177e899747510d60661730538052fca0ed93a943d4686d6655b1 \ - --hash=sha256:52c9ca84e3dc656d83acc424574770e20ea8e0704dc3842d4e27b0fe9d3ba449 \ - --hash=sha256:588c7de1abafe59e66face9a074514658ac0398c85a774cdbb8efac131192692 \ - --hash=sha256:6ae2a174bffea94e8ead857dad85999d3f49f091774dbad5b046c0417d7092f4 \ - --hash=sha256:868a24d864c25024f60ca964f851a759f2ada5352608fc194d927b7facc2e28b \ - --hash=sha256:8e11e26f1718b5c2e5b96f2f71b9f00e31d247b065289661e3a6996c758669d9 \ - --hash=sha256:9443e5eb82152d8952422b7285692192cc7dcffa5218bb511b096203018bc273 \ - --hash=sha256:9564d82f7fa6bf548e5f18bb86235dff20eea8bd30727b64d49783c95c34fb8d \ - --hash=sha256:9f228ae21c78fa9359ca98d8a5e8e91f30639e438e574133dace62c5b5309e44 \ - --hash=sha256:a941f5aaa0b8abced24318105c1ea22576afcc1138c19f625716ce4e2f76ad64 \ - --hash=sha256:ae30c48ac795378cf23ba3c7c640b8ff794af714ac388b9fd6b31a40b39e6e86 \ - --hash=sha256:b2e94c713b7d96fb92841947b785bcee6606124bc852273fab70454b51bfe270 \ - --hash=sha256:bd30f7201eac896ebf5db7b09156e6f1a1b82601900d29d9c8449bdad8365b11 \ - --hash=sha256:bf98c0d7b7d5ef5bf072fee8d3020e8b664f2d195933ea7bc5089267c2e22a06 \ - --hash=sha256:d6a7ad82b8d5f3fcc04b1d2ca055562b96af571e1d4fa7c6c61d0fb509ac43b4 \ - --hash=sha256:e0e09fa633ce2e67df08b298afced4d184f6e753fc330f241022250f1d0d61da \ - --hash=sha256:e17d4ef9b0185214b2c0935605ae63f827ea423bc74964be44763d68d2b6c21e \ - --hash=sha256:f4977323ef9c3aa3c2a26ff5fe0191c84b8fd759daf7afb1f25a91a55ad8b730 \ - --hash=sha256:feebcba99ac99adb8009d46c8f4c1956b8c167576b0ae8a6fb47502e9a6f78e7 - # via -r requirements.in -stack-data==0.6.3 \ - --hash=sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9 \ - --hash=sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695 - # via ipython -terminado==0.18.1 \ - --hash=sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 \ - --hash=sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e - # via - # jupyter-server - # jupyter-server-terminals -tinycss2==1.5.1 \ - --hash=sha256:3415ba0f5839c062696996998176c4a3751d18b7edaaeeb658c9ce21ec150661 \ - --hash=sha256:d339d2b616ba90ccce58da8495a78f46e55d4d25f9fd71dfd526f07e7d53f957 - # via bleach -tornado==6.5.10 \ - --hash=sha256:302eb1e0e3e159314eb591920529fdea80acca92df5510a2cec5bbd4f099ec72 \ - --hash=sha256:37ae8f150cecfdbf747fc4e12f5e9a97ecd8cf1d4cdb3f119e2de84b11196918 \ - --hash=sha256:4bd192b959f9128fb99b8898148070ba4574c9589b78bce42d1851131fe85828 \ - --hash=sha256:66aaa3f57d30c6e6becee83ff28055d5930ac724214bde99393eefda83d5e015 \ - --hash=sha256:69acca6501eed74582b76dbbceee2a91613f54728e3e418346000d7103101676 \ - --hash=sha256:83e6cf438b106c6b3852d70960967bb1b70c87438050dca0981e4b9aa751a4c1 \ - --hash=sha256:9261783640e23258694a9ff0795df430a5a7b0a651d3dd53dd0969ad6be16da7 \ - --hash=sha256:a6b1ccd08c04b4a06fb5aeb381be99de5ad1e5375c1785e31d78c880feb57687 \ - --hash=sha256:bdf942448169e5336451d0494d7e3d81cfa726d5aa312affdc4682dd62a62f6d \ - --hash=sha256:ce045d3c298fddd30e89a2777f97039d1b641eb9518ac7b26a4721903539c694 - # via - # ipykernel - # jupyter-client - # jupyter-server - # jupyterlab - # notebook - # terminado -traitlets==5.16.1 \ - --hash=sha256:ed900c2b631aa3a112811139fa97b8d2c3bad5e989656bba4b7e52c7852c18c1 \ - --hash=sha256:f775618166caa0396c8e337099240f2bd3e5e917d203b2e6fbe21a58d3cb1f6b - # via - # ipykernel - # ipython - # ipywidgets - # jupyter-builder - # jupyter-client - # jupyter-console - # jupyter-core - # jupyter-events - # jupyter-server - # jupyterlab - # matplotlib-inline - # nbclient - # nbconvert - # nbformat -typing-extensions==4.16.0 \ - --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \ - --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5 - # via - # anyio - # beautifulsoup4 - # ipython - # jupyter-client - # jupyterlab - # referencing -tzdata==2026.4 \ - --hash=sha256:c2169a8b0a7a5e9674da5a135ccdfb2b3e671b333ed9fed17b41f73c34476e81 \ - --hash=sha256:f1b8bd365d8d210c55353f4d7f8d6d8561c0ba50d704b700d195a9424bba0d79 - # via arrow -uri-template==1.3.0 \ - --hash=sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7 \ - --hash=sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363 - # via jsonschema -urllib3==2.8.0 \ - --hash=sha256:0cf3cae568d36aa9576b28dfb35f11328f1cb974ca7647d9475ebb86c75ac6e3 \ - --hash=sha256:63bf2ead4c879426ebf22ef2a781eeb4aa3b4ae798a0435506f8687fd5bb9b63 - # via requests -wcwidth==0.9.1 \ - --hash=sha256:03cfca3dcbffa86564290fe3c9978a6191ba003e8ced7f7dbda315fcb3fbe725 \ - --hash=sha256:0665ee822ea04e25801e6e82e5407528be0863e88a17b0e7ca038843a4a3ac0c \ - --hash=sha256:0d68a30d504c68cfdff2a5f804675c1e7ab4c0bbe878024c8b680ec5579cde67 \ - --hash=sha256:10b00ba23482e352f874d2e8135e7ace9da838646c7dd800566246bbd46125ff \ - --hash=sha256:356376852357b8fca71fe5415808ec421679e04b4a98eb7c9cb6a7984b911a05 \ - --hash=sha256:40d936d72c9bdc10df43f93a8be502bc5024b487259139f66a328722c07f34a9 \ - --hash=sha256:5823209b0d43af322ce698c689380d7c15ca31fa8e6e3be8459f27031bef0af5 \ - --hash=sha256:61bd7aef9cafb6cb77a37a169998d7928ce82a51522146d11f60db9e7d1cb43a \ - --hash=sha256:69bb970cf5652b88cfdb9d3fdd1764fc15e5f8ad531643e7bb3e894cd969740e \ - --hash=sha256:6e1272b7986cefe79783737e38bdb9eaae0682b333c7bd132024441193dd5ce7 \ - --hash=sha256:708158c082364af442f9983de7b6ec9ac0d2e1b825ada25f0911b1f138d55405 \ - --hash=sha256:747fb724223f417a17541a95a17c1dac3a8ef9a0cf41684950f0eab191a35f65 \ - --hash=sha256:991d1c8834f548e9c1f16432075ee84638e122312556bbf1ed595ea8fffc4673 \ - --hash=sha256:9f1636c5075ffd5c2e835b4561874f6e5dd2bbeba3c6c2c99f067d0d16883af8 \ - --hash=sha256:b5da43d6967668982e44a52fb551967d293f86d26cd86036ac95bbdd34394ed9 \ - --hash=sha256:bcb9ed4a367cc025bf1092ac679a156759605182d60b7ed3c28f7221a42ddf55 \ - --hash=sha256:c4cead196551112cb8f43cdd1f80c235ef2456e34b1a9955c424537ec99961b2 \ - --hash=sha256:dc10e262c3ac0abbfd0a2a51e45a848b1b7f500b21ff512630277973ba25674d \ - --hash=sha256:e0c3a1c45c5b9550c6919a4449e95f5b177f6e165786376981db8f1addae9b21 \ - --hash=sha256:eab587e18e7cadf1a750b0098fc8bebfb62c125eb9306f2268f0443a282a3d78 \ - --hash=sha256:fe021c4d8de9d36c31a0cb41d0d2546dadd3b0708a301f1a0c66ce200851831f - # via prompt-toolkit -webcolors==25.10.0 \ - --hash=sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d \ - --hash=sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf - # via jsonschema -webencodings==0.6.1 \ - --hash=sha256:565f9ad031c702dae404e27a099e3e09186a3ab1b9520f06d215502b651fd910 \ - --hash=sha256:7fab6269c8bf237c657876b52058ccb182e861518d1c695c1a9aaa8c1c105d5b - # via - # bleach - # tinycss2 -websocket-client==1.9.2 \ - --hash=sha256:0fcb57545848be86992e128218fd96dd87a6769ffdb1a968dff79632b85604d0 \ - --hash=sha256:e1a673830a9c7bfa47b1cd3d5e4178f4c9651d80a4eab02c9c23a1c3ec6250ce - # via jupyter-server -widgetsnbextension==4.0.16 \ - --hash=sha256:a31a8774885b96fe825462f5d6496166f0c7cae111195b6465c801d230eb5a4e \ - --hash=sha256:adeea0ae78f0856ee4945f413299801b82a0a01416303301f39a704282a37b73 - # via ipywidgets diff --git a/cmake/arm-cortex-m33-mps2.cmake b/cmake/arm-cortex-m33-mps2.cmake index 250b32b..f761aae 100644 --- a/cmake/arm-cortex-m33-mps2.cmake +++ b/cmake/arm-cortex-m33-mps2.cmake @@ -32,3 +32,6 @@ set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) # One-shot CTest mode (no argv on bare metal; see tests/CMakeLists.txt). set(SRT_BARE_METAL ON) +# Both engines' test trees read their own variable until step 3.3 of the +# monorepo migration unifies them as TAP_SR_BARE_METAL. +set(TAP_RATIO_BARE_METAL ON) diff --git a/cmake/arm-cortex-m55-mps3.cmake b/cmake/arm-cortex-m55-mps3.cmake index 23a8991..a8ffec1 100644 --- a/cmake/arm-cortex-m55-mps3.cmake +++ b/cmake/arm-cortex-m55-mps3.cmake @@ -45,3 +45,6 @@ set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY) # running the whole (emulation-sized) suite, judged by gtest's summary text # rather than the exit code, which semihosting does not reliably propagate. set(SRT_BARE_METAL ON) +# Both engines' test trees read their own variable until step 3.3 of the +# monorepo migration unifies them as TAP_SR_BARE_METAL. +set(TAP_RATIO_BARE_METAL ON) diff --git a/docs/Doxyfile b/docs/Doxyfile index 5b498c4..5201259 100644 --- a/docs/Doxyfile +++ b/docs/Doxyfile @@ -2,9 +2,11 @@ # root to generate HTML API documentation in docs/html. PROJECT_NAME = SampleRateTap PROJECT_BRIEF = "Near-unity asynchronous sample rate converter (C++20, header-only)" -INPUT = include README.md +# Both engines' public headers; the async README stays the main page until +# the family README lands (monorepo migration step 4). +INPUT = async/include bridge/include async/README.md RECURSIVE = YES -USE_MDFILE_AS_MAINPAGE = README.md +USE_MDFILE_AS_MAINPAGE = async/README.md OUTPUT_DIRECTORY = docs GENERATE_LATEX = NO EXTRACT_ALL = YES diff --git a/docs/MONOREPO_PLAN.md b/docs/MONOREPO_PLAN.md index b9340cb..62a4fc0 100644 --- a/docs/MONOREPO_PLAN.md +++ b/docs/MONOREPO_PLAN.md @@ -10,10 +10,10 @@ Status: **DRAFT v3.1. Two adversarial audit rounds folded in; step P executed, s | v3 | `b34681c` | Round 2: 4 reviewers, including an end-to-end dry run of steps 1a–1c and gate prototypes; 78 findings (Appendix B). The user reconfirmed D14 and chose banners everywhere | | v3.1 | this commit | Engine names (D5, user decision 2026-09-27): RatioTap's engine becomes **`bridge`** and the future 2^a·3^b engine **`rational`**, replacing `ratio` and `integer`. The Python ctypes modules are now called **bindings**, leaving "bridge" to the engine. Step 0's G2 parser note | -When this plan is approved, it becomes the family-level `PLAN.md`. Draft -workflow and CMake files that the audit produced live in -`docs/migration/drafts/`. They are untested sketches, and GitHub does not -run them from that directory. +When this plan is approved, it becomes the family-level `PLAN.md`. The +audit's draft workflow and CMake files were replaced at step 1c by the real +ones (root `CMakeLists.txt`, `.github/workflows/`), and the gates they +sketched are implemented in `docs/migration/gates.py`. How to read it: @@ -373,7 +373,7 @@ two ways: it pushes the M33 and Hexagon jobs past their timeouts v3 splits the gates into two classes: - **A/B gates** are the toolchain-sensitive ones. They run in the dedicated - **`migration-gates`** workflow (draft: `docs/migration/drafts/migration-gates.yml`), + **`migration-gates`** workflow (`.github/workflows/migration-gates.yml`, running `docs/migration/gates.py`), on a pinned `ubuntu-24.04` with `cancel-in-progress: false`. Each job checks out the gated SHA **and** the two step-0 tips: `tap/SampleRateTap@S0` and `tap/RatioTap@R0`, both public, so no token is needed. It builds all three @@ -571,15 +571,35 @@ git branch -D ratio-import # never pushed the rewritten RatioTap commits. The dry run checked the root commit, M1, M7c (which recurses into `bridge/submodules/sampleratetap/submodules/dsptap`) and the tip. -- The rewritten reformat commit `c0894cf` becomes `4c3562c`. It is recorded +- The rewritten reformat commit `c0894cf` becomes `89c7eba` (`4c3562c` in the + dry run, under the old prefix). It is recorded for `.git-blame-ignore-revs`. **1c — Build glue and path fix-ups.** No change reaches codegen: G4 and G7 -prove it. **Gate 1:** G1, G2, G3, G4, G5, G6, G7, G8, G11, G12, G13 and +prove it. + +**Done (v3.1).** 1b's rewritten tip is `654659e`, identical on two fresh +clones; the branch holds 182 commits (140 + 33 + 9). Measured locally before +the push, against S0 and R0 built in the same session (`gates.py`): +G7 exact for every host, M33 and M55 TU; G3 and G5 exact on M33 and M55 (the +new icount ELFs are byte-identical to S0's and R0's); G4 exact for the C ABI +libraries and all 17 icount binaries; G1, G6 and G10 equal to the snapshot; +G12 reaches every step-0 commit; G14 has no unlisted residual; the rendered +book is byte-identical to S0's. Hexagon A/B, the macOS/Windows legs and G11 +run first in CI. Choices the plan left open: + +- clang-tidy keeps each repository's old coverage: both engines' tests and + examples, and bridge's icount workloads. async's workloads were never + under the gate and fail it; bringing them in is follow-up work. +- ci-arm64 runs `-L '^async$'` (its old scope). TSan builds both engines and + runs async only. +- The ratchet is one matrix job per engine, each with its own plugin marker + and `icount.py`, until step 2. +- The book's anchor includes are a `rename.py` rule, not residual. **Gate 1:** G1, G2, G3, G4, G5, G6, G7, G8, G11, G12, G13 and G14; every notebook binding and every standalone engine build configures and builds. -- **Root `CMakeLists.txt`** (draft: `docs/migration/drafts/root-CMakeLists-1c.cmake`): +- **Root `CMakeLists.txt`**: - `project(SampleRateTap LANGUAGES CXX)` and `enable_testing()`. - `option()` **defaults** ON for `SRT_BUILD_TESTS`, `SRT_BUILD_EXAMPLES`, `TAP_RATIO_BUILD_TESTS` and `TAP_RATIO_BUILD_EXAMPLES`, declared before @@ -609,7 +629,7 @@ builds. it to the root leaked it into the icount TUs and failed G7 in the dry run (R2-CI-5, R2-RUN-8). Setting both variables is what makes gtest and both test trees agree. -- **CI** (draft: `docs/migration/drafts/ci-after-1c.yml`): +- **CI**: - Every job configures the root once and builds both engines. - Correctness jobs run **per (target, engine)** with `ctest -L '^$'`, each engine's own `-E` list and `-j`: @@ -778,7 +798,7 @@ G13 and G14, plus G9 from 3.7. - `git log --follow bridge/include/tap/sr/bridge/converter.h` reaches RatioTap M3 (`06769f2` before rewriting). - **Follow-up commit:** append to `.git-blame-ignore-revs` the step-3 - commit SHAs and the rewritten RatioTap reformat commit `4c3562c`. + commit SHAs and the rewritten RatioTap reformat commit `89c7eba`. - Tag `v0.4.0`. ### Step 5 — Outside the repository diff --git a/docs/migration/README.md b/docs/migration/README.md index def1bda..3fa4194 100644 --- a/docs/migration/README.md +++ b/docs/migration/README.md @@ -12,7 +12,8 @@ committed at step 0 and deleted at step 4 (`runs.md` may be kept). | `snapshot/` | the step-0 snapshot those collectors compare against | | `allow.txt` | G1 allowlist: test rows a job may gain (never lose) | | `runs.md` | provenance of the snapshot, and the run record of every gated SHA (G13) | -| `drafts/` | the audit's untested workflow and CMake sketches for step 1c | +| `gates.py` | the gates as the `migration-gates` workflow runs them: `host`, `cross --target`, `notebooks` | +| `step.txt` | the step the current commit is gated as; selects the rename classes every gate expects | ## How the pieces fit diff --git a/docs/migration/drafts/ci-after-1c.yml b/docs/migration/drafts/ci-after-1c.yml deleted file mode 100644 index c938b74..0000000 --- a/docs/migration/drafts/ci-after-1c.yml +++ /dev/null @@ -1,180 +0,0 @@ -# DRAFT (audit round 2, R2-CI): root .github/workflows/ci.yml as it should -# stand right after step 1c. This is a sketch, not a tested workflow. -# -# Design choices it makes, and why: -# - ONE configure of the root tree per job, both engines built. D9 forbids -# per-engine enables, and from step 3.5 on there is one TAP_SR_BUILD_TESTS, -# so "bridge jobs" that build only bridge cannot survive step 3. Per-engine -# behaviour is selected at *ctest* time by LABELS (added in P.2), and -# per-engine warnings by the per-engine WERROR options, which already live -# on separate INTERFACE targets (srt_warnings / tap_ratio_warnings), so -# async-OFF / bridge-ON on MSVC coexists in one tree. -# - Engine names follow D5 (v3.1): the imported RatioTap engine is `bridge`, -# in the bridge/ directory from 1b. Its options (TAP_RATIO_*), warning target -# and ctest label (`ratio`) keep RatioTap's spelling until step 3.4/3.5 -# rename them; matrix keys below already say bridge. -# - Options are DEFAULTED at the root (option() before add_subdirectory), never -# FORCEd: every bare-metal/Hexagon job passes *_BUILD_EXAMPLES=OFF, and -# async's examples do find_package(Threads REQUIRED), which fails a -# bare-metal configure. -# - Dedup/concurrency is adopted HERE (not at step 2), with cancel-in-progress -# OFF for the migration PR, so every pushed SHA gets exactly one full run -# and G13 can hold without waiting-before-push discipline being the only -# guard. -# - Every ctest call: --no-tests=error, --output-log (G2 needs the [ RUN ] -# lines, which --output-on-failure never prints on success), log uploaded. -name: CI - -on: - push: - branches: [main] - pull_request: - workflow_dispatch: - -permissions: - contents: read - -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - # Never cancel on main, and never on the migration PR (G13 needs a complete - # run per SHA). Other PRs keep superseded-run cancellation. - cancel-in-progress: ${{ github.event_name == 'pull_request' && github.head_ref != 'claude/sample-rate-expansion-strategies-ezqzu6' }} - -env: - CTEST_COMMON: --no-tests=error --output-on-failure - -jobs: - host: - name: ${{ matrix.name }} - runs-on: ${{ matrix.os }} - timeout-minutes: 30 - strategy: - fail-fast: false - matrix: - include: - - { name: Linux GCC, os: ubuntu-latest, cc: gcc, cxx: g++, async_werror: ON, bridge_werror: ON, capi: ON } - - { name: Linux Clang, os: ubuntu-latest, cc: clang, cxx: clang++, async_werror: ON, bridge_werror: ON, capi: ON } # bridge: NEW coverage (measured 78/78 locally, clang 18 -Werror) - - { name: macOS AppleClang, os: macos-latest, async_werror: ON, bridge_werror: ON, capi: ON } - - { name: Windows MSVC, os: windows-latest, async_werror: OFF, bridge_werror: ON, capi: OFF } # async /W4 untriaged (INF-11); bridge keeps /WX - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: { submodules: recursive } - - name: Configure - env: { CC: "${{ matrix.cc }}", CXX: "${{ matrix.cxx }}" } - run: > - cmake -B build -DCMAKE_BUILD_TYPE=Release - -DSRT_WERROR=${{ matrix.async_werror }} -DTAP_RATIO_WERROR=${{ matrix.bridge_werror }} - -DSRT_BUILD_CAPI=${{ matrix.capi }} -DTAP_RATIO_BUILD_CAPI=${{ matrix.capi }} - - run: cmake --build build --config Release -j 4 - - run: ctest --test-dir build -C Release $CTEST_COMMON --output-log ctest-${{ matrix.name }}.log - shell: bash - - uses: actions/upload-artifact@ # pin - if: ${{ !cancelled() }} - with: { name: "ctest-${{ matrix.name }}", path: "ctest-*.log" } - - sanitizers: - name: ${{ matrix.name }} - runs-on: ubuntu-latest - timeout-minutes: 30 - strategy: - fail-fast: false - matrix: - include: - # async ran ASan without WERROR, bridge with it; per-engine options keep both. - - { name: ASan + UBSan, flags: "-fsanitize=address,undefined -fno-sanitize-recover=all", labels: "" } - # TSan: async only (bridge is single-threaded; running it costs ~3 s - # but adds rows G1 has no step-0 counterpart for). Build is shared. - - { name: TSan, flags: "-fsanitize=thread", labels: "-L async" } - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: { submodules: recursive } - - name: Configure - env: { CC: clang, CXX: clang++ } - run: > - cmake -B build -DCMAKE_BUILD_TYPE=RelWithDebInfo - -DSRT_BUILD_EXAMPLES=OFF - -DSRT_WERROR=OFF -DTAP_RATIO_WERROR=ON - -DCMAKE_CXX_FLAGS="${{ matrix.flags }}" - # NB RatioTap's sanitizer job built its examples (bluetooth_bridge); keep - # TAP_RATIO_BUILD_EXAMPLES at its default (ON) so G7's TU set matches. - - run: cmake --build build -j 4 - - env: { TSAN_OPTIONS: halt_on_error=1, UBSAN_OPTIONS: print_stacktrace=1 } - run: ctest --test-dir build $CTEST_COMMON ${{ matrix.labels }} --output-log ctest.log - - # One job per (target, engine) for correctness: both engines are BUILT - # (one tree), each job RUNS one engine's label with that engine's -E list - # and parallelism. Measured today: async Hexagon 22.5 min serial, bridge - # Hexagon 11.0 min at -j 4, async M33 22.4 min, bridge M33 0.4 min - # (runs 36253867157, 36256431538). Serialising both in one Hexagon job is - # ~36 min of a 45 min timeout; a single un-labelled ctest at one -j is - # either 67 min (serial) or changes async's leg (-j 4). - qemu: - name: ${{ matrix.target }} ${{ matrix.engine }} (QEMU) - runs-on: ubuntu-24.04 # pinned: plugin header, qemu, glib and caches must agree - timeout-minutes: ${{ matrix.timeout }} - strategy: - fail-fast: false - matrix: - include: - - { target: hexagon, engine: async, timeout: 45, j: 1, exclude: 'AsrcQuality|AsrcLock|TwoThreadStress|TransparentPrototypeMeetsSpec|MultiChannel\.|Feasibility|Reset\.|ConfigValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } - - { target: hexagon, engine: bridge, label: ratio, timeout: 30, j: 4, exclude: 'BadProfilesThrow|LatencyAndValidation', build_type: Release, toolchain: cmake/hexagon-linux-musl.cmake } - - { target: m55, engine: async, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } - - { target: m55, engine: bridge, label: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m55-mps3.cmake } - - { target: m33, engine: async, timeout: 40, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } - - { target: m33, engine: bridge, label: ratio, timeout: 20, j: 1, exclude: '', build_type: MinSizeRel, toolchain: cmake/arm-cortex-m33-mps2.cmake } - env: - HEXAGON_TOOLCHAIN_URL: https://artifacts.codelinaro.org/artifactory/codelinaro-toolchain-for-hexagon/19.1.5/clang+llvm-19.1.5-cross-hexagon-unknown-linux-musl.tar.zst - HEXAGON_TOOLCHAIN_SHA256: "55b41922318f6331590ab7baa7f5dbdd99c109327a9c44a52c5e9878fab148c1" - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: { submodules: recursive } - - if: matrix.target != 'hexagon' - run: sudo apt-get update -q && sudo apt-get install -y -q gcc-arm-none-eabi qemu-system-arm - - if: matrix.target == 'hexagon' - id: cache - uses: actions/cache@27d5ce7f107fe9357f9df03efb73ab90386fccae # v5 - with: { path: ~/hexagon, key: "hexagon-toolchain-${{ env.HEXAGON_TOOLCHAIN_SHA256 }}-1" } - - if: matrix.target == 'hexagon' && steps.cache.outputs.cache-hit != 'true' - run: scripts/fetch_hexagon_toolchain.sh - # (hexagon PATH / qemu-user setup as today) - - name: Configure (both engines, one tree) - run: > - cmake -B build -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} - -DCMAKE_TOOLCHAIN_FILE=${{ matrix.toolchain }} - -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF - - run: cmake --build build -j 4 - - name: Test under emulation (${{ matrix.engine }} only) - # label: bridge's ctest label is still `ratio` until step 3.4 renames it (D16); drop the key then. - shell: bash - run: | - args=(--test-dir build $CTEST_COMMON -L '^${{ matrix.label || matrix.engine }}$' -j ${{ matrix.j }} -V --output-log ctest.log) - [ -n '${{ matrix.exclude }}' ] && args+=(-E '${{ matrix.exclude }}') - ctest "${args[@]}" - - uses: actions/upload-artifact@ - if: ${{ !cancelled() }} - with: { name: "gtest-${{ matrix.target }}-${{ matrix.engine }}", path: ctest.log } # G2 input - - # Ratchet stays per ENGINE until step 2 (step 2 then folds to per target). - # Two jobs x three targets inside, exactly as today, each pointing icount.py - # at its engine's baselines. Plugin built in-job (1 s; an artifact would - # couple jobs across runner images). - icount-async: # = today's SampleRateTap job, --baselines async/bench/baselines.json, docs freshness kept - runs-on: ubuntu-24.04 - timeout-minutes: 45 - steps: [ { run: "# as today; configure root with -DSRT_BUILD_TESTS=OFF -DSRT_BUILD_EXAMPLES=OFF -DTAP_RATIO_BUILD_TESTS=OFF -DTAP_RATIO_BUILD_EXAMPLES=OFF -DSRT_BUILD_ICOUNT_BENCH=ON" } ] - icount-bridge: # = RatioTap's job, --baselines bridge/bench/baselines.json, plus bridge README freshness (1c adds the table) - runs-on: ubuntu-24.04 - timeout-minutes: 45 - steps: [ { run: "# as RatioTap today, SHA-pinned; -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON, tests/examples OFF for both engines" } ] - - bench-smoke: { runs-on: ubuntu-latest, timeout-minutes: 15, steps: [ { run: "# ./build/async/bench/srt_bench" } ] } - compare-smoke: { runs-on: ubuntu-latest, timeout-minutes: 20, steps: [ { run: "# as today; paths under async/; ALSO run one cmp_icount binary under qemu so compare.yml's markers are exercised per push" } ] } - clang-format: { runs-on: ubuntu-latest, timeout-minutes: 10, steps: [ { run: "pre-commit run --all-files" } ] } - book: { runs-on: ubuntu-latest, timeout-minutes: 10, steps: [ { run: "# as today" } ] } - - # NEW: the jobs that actually evaluate G1-G7 (section 5 demands same-job - # A/B, but no workflow in v2 performs it). See migration-gates.yml. - -# Job count per push after this sketch: host 4 + sanitizers 2 + qemu 6 + -# icount 2 + bench/compare/format/book 4 = 18 in ci.yml, + style (drift + -# clang-tidy) 2 = 20, + migration-gates. macOS: 1. diff --git a/docs/migration/drafts/migration-gates.yml b/docs/migration/drafts/migration-gates.yml deleted file mode 100644 index 79db0f7..0000000 --- a/docs/migration/drafts/migration-gates.yml +++ /dev/null @@ -1,43 +0,0 @@ -# DRAFT (audit round 2): the workflow v2's section 5 presupposes but never -# defines. Same-job A/B: step-0 trees (SampleRateTap@S0 and RatioTap@R0, both -# PUBLIC repositories, so no token is needed) and the gated SHA are built in -# ONE job with one toolchain. Lives only on the migration branch; deleted in -# step 4 with docs/migration/. -name: migration-gates -on: - pull_request: - workflow_dispatch: -permissions: { contents: read } -concurrency: - group: migration-gates-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: false -env: - S0: # from docs/migration/tips.txt - R0: -jobs: - ab: - name: A/B ${{ matrix.target }} - runs-on: ubuntu-24.04 # pinned: G3/G4/G7 must not straddle an image rollout - timeout-minutes: 90 # builds 3 trees; Hexagon A/B re-runs both suites' icount - strategy: - fail-fast: false - matrix: - target: [host, m33, m55, hexagon] - steps: - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6 - with: { submodules: recursive, path: new, fetch-depth: 1 } - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 - with: { repository: tap/SampleRateTap, ref: "${{ env.S0 }}", submodules: recursive, path: old-async } - - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 - with: { repository: tap/RatioTap, ref: "${{ env.R0 }}", submodules: recursive, path: old-ratio } - # 1. Same install step for all three (one toolchain). - # 2. Build old-async, old-ratio, new with identical flags; export - # compile_commands.json (G7), ctest --show-only=json-v1 (G1), - # icount measurements to JSON (G3; compare new vs old MEASURED values - # with --exact, NOT new vs committed baselines), objdump of icount - # binaries (G4: restrict to icount binaries and library symbols; test - # binaries embed __FILE__ via gtest, and the moved paths shift .rodata), - # G5 hash tests, G6 lines. - # 3. Diff; upload all artefacts; fail on any difference not in the - # committed allowlist (docs/migration/allow.txt, e.g. new bridge rows - # in Linux Clang and new async-label rows nowhere). diff --git a/docs/migration/drafts/root-CMakeLists-1c.cmake b/docs/migration/drafts/root-CMakeLists-1c.cmake deleted file mode 100644 index 77e5bf3..0000000 --- a/docs/migration/drafts/root-CMakeLists-1c.cmake +++ /dev/null @@ -1,35 +0,0 @@ -# DRAFT (audit round 2): root CMakeLists.txt at step 1c. -cmake_minimum_required(VERSION 3.24) -project(SampleRateTap LANGUAGES CXX) # languages must equal both engines' (G7) -enable_testing() - -# DEFAULT, never FORCE: CI passes *_BUILD_EXAMPLES=OFF on every bare-metal / -# Hexagon job, and async/examples does find_package(Threads REQUIRED). -# option() here creates the cache entry first, so the engines' own -# PROJECT_IS_TOP_LEVEL-dependent option() calls become no-ops, while -D wins. -option(SRT_BUILD_TESTS "async tests" ON) -option(SRT_BUILD_EXAMPLES "async examples" ON) -option(TAP_RATIO_BUILD_TESTS "bridge tests" ON) -option(TAP_RATIO_BUILD_EXAMPLES "bridge examples" ON) - -# Stale-option tripwire (extends D7 to CMake options, needed from step 3.5): -# an unknown -D is only a "Manually-specified variables were not used" -# warning, so a CI line still passing -DSRT_WERROR=ON after the rename would -# silently DROP the warnings gate. Enable this block in the step-3.5 commit. -# foreach(_old SRT_WERROR SRT_BUILD_TESTS SRT_BUILD_EXAMPLES SRT_BUILD_CAPI -# SRT_BUILD_ICOUNT_BENCH SRT_BUILD_BENCHMARKS TAP_RATIO_WERROR -# TAP_RATIO_BUILD_TESTS TAP_RATIO_BUILD_EXAMPLES TAP_RATIO_BUILD_CAPI -# TAP_RATIO_BUILD_ICOUNT_BENCH) -# if(DEFINED ${_old} OR DEFINED CACHE{${_old}}) -# message(FATAL_ERROR "${_old} was renamed (MONOREPO_PLAN step 3.5)") -# endif() -# endforeach() - -add_subdirectory(submodules/dsptap) -add_subdirectory(async) -add_subdirectory(bridge) - -# gtest is made available ONCE, by whichever engine's tests/ runs first -# (async). bridge/tests' set(INSTALL_GTEST OFF ...) and its Threads probe are -# then no-ops: hoist both gtest settings here so the result does not depend -# on add_subdirectory order (and so 4.2's install test does not install gtest). diff --git a/docs/migration/gates.py b/docs/migration/gates.py new file mode 100644 index 0000000..53e1eb4 --- /dev/null +++ b/docs/migration/gates.py @@ -0,0 +1,582 @@ +#!/usr/bin/env python3 +# SPDX-License-Identifier: MIT +# Copyright 2026 Timothy Place and the SampleRateTap contributors +"""The migration gates, as the migration-gates workflow runs them. + +Every check compares the gated tree ("new") against the two step-0 tips +(old-async = SampleRateTap@S0, old-ratio = RatioTap@R0) built in the SAME +job with the same toolchain (A/B gates), or against docs/migration/snapshot/ +(snapshot gates). Which renames to expect comes from docs/migration/step.txt +through rename.py's map, so one script serves every gated step. + + gates.py host --work DIR G1 G4(C ABI) G5 G6 G7 G9 G10 G12 G14 + gates.py cross --work DIR --target m33|m55|hexagon + G3 G4(icount) G5(checksums) G7 + gates.py notebooks --work DIR G11 + +Trees default to ./new, ./old-async and ./old-ratio (the workflow's +checkout paths); override with --new/--old-async/--old-ratio. Each gate +prints PASS or FAIL lines; the command exits nonzero if any gate failed. +""" +import argparse +import bisect +import collections +import difflib +import glob +import json +import os +import pathlib +import re +import shlex +import shutil +import subprocess +import sys + +HERE = pathlib.Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) +import collect # noqa: E402 +import rename # noqa: E402 + +FAILS = [] + + +def report(gate: str, ok: bool, detail: str = ""): + print(f"{'PASS' if ok else 'FAIL'} {gate}{': ' + detail if detail else ''}", flush=True) + if not ok: + FAILS.append(gate) + + +def run(cmd, **kw): + print("+ " + " ".join(map(str, cmd)), flush=True) + return subprocess.run(cmd, check=True, **kw) + + +def out(cmd, **kw) -> str: + return subprocess.run(cmd, check=True, capture_output=True, text=True, **kw).stdout + + +def step() -> str: + return (HERE / "step.txt").read_text().split()[0] + + +def at_least(s: str) -> bool: + return rename.at_least(step(), s) + + +# -- configuration per tree ------------------------------------------------ + +def opts_new(kind: str) -> list[str]: + """CMake options for the gated tree; the option names follow the step.""" + unified = at_least("3.4") + if kind == "host": + return (["-DTAP_SR_BUILD_CAPI=ON"] if unified + else ["-DSRT_BUILD_CAPI=ON", "-DTAP_RATIO_BUILD_CAPI=ON"]) + if unified: + return ["-DTAP_SR_BUILD_TESTS=OFF", "-DTAP_SR_BUILD_EXAMPLES=OFF", + "-DTAP_SR_BUILD_ICOUNT_BENCH=ON"] + return ["-DSRT_BUILD_TESTS=OFF", "-DSRT_BUILD_EXAMPLES=OFF", + "-DTAP_RATIO_BUILD_TESTS=OFF", "-DTAP_RATIO_BUILD_EXAMPLES=OFF", + "-DSRT_BUILD_ICOUNT_BENCH=ON", "-DTAP_RATIO_BUILD_ICOUNT_BENCH=ON"] + + +OPTS_OLD = { + "host": {"async": ["-DSRT_BUILD_CAPI=ON"], "ratio": ["-DTAP_RATIO_BUILD_CAPI=ON"]}, + "cross": {"async": ["-DSRT_BUILD_TESTS=OFF", "-DSRT_BUILD_EXAMPLES=OFF", "-DSRT_BUILD_ICOUNT_BENCH=ON"], + "ratio": ["-DTAP_RATIO_BUILD_TESTS=OFF", "-DTAP_RATIO_BUILD_EXAMPLES=OFF", + "-DTAP_RATIO_BUILD_ICOUNT_BENCH=ON"]}, +} +TOOLCHAIN = {"m33": "cmake/arm-cortex-m33-mps2.cmake", "m55": "cmake/arm-cortex-m55-mps3.cmake", + "hexagon": "cmake/hexagon-linux-musl.cmake"} + + +def configure_build(src, bld, opts, target=None, build_type="Release"): + cmd = ["cmake", "-S", str(src), "-B", str(bld), f"-DCMAKE_BUILD_TYPE={build_type}", + "-DCMAKE_EXPORT_COMPILE_COMMANDS=ON"] + opts + if target: + cmd.append(f"-DCMAKE_TOOLCHAIN_FILE={pathlib.Path(src).resolve() / TOOLCHAIN[target]}") + run(cmd, stdout=subprocess.DEVNULL) + run(["cmake", "--build", str(bld), "-j", str(os.cpu_count() or 4)], stdout=subprocess.DEVNULL) + + +# -- name and path maps (from rename.py, at the current step) -------------- + +def map_old_path(repo: str, rel: str) -> str | None: + """Where a step-0 path lives in the gated tree.""" + if repo == "ratio": + # RatioTap's test-only copy of SampleRateTap is the async engine here. + if rel.startswith("submodules/sampleratetap/"): + return map_old_path("async", rel[len("submodules/sampleratetap/"):]) + if rel.startswith("submodules/dsptap"): + return rel + mapped = rename.map_r0_path(rel + "/", step()) + return mapped.rstrip("/") if mapped is not None else None + if rel.startswith("submodules/"): + return rel + # Directory paths (include dirs) arrive without the trailing slash the + # map's prefixes carry. + mapped = rename.map_s0_path(rel + "/", step()) + if mapped is not None and mapped.rstrip("/") != rel: + return mapped.rstrip("/") + return rename.map_s0_path(rel, step()) + + +def map_text(text: str, path: str = "x.cpp") -> str: + return rename.apply_subs(text, path, step()) + + +# -- G7: compile and link flags -------------------------------------------- + +def load_flags(bld: pathlib.Path, src: pathlib.Path, repo: str | None): + """{mapped source path: ordered, normalized tokens}. repo None = new tree.""" + src_s, bld_s = str(src.resolve()), str(bld.resolve()) + res = {} + for e in json.load(open(bld / "compile_commands.json")): + toks = shlex.split(e["command"]) if "command" in e else list(e["arguments"]) + norm, skip = [], False + for t in toks[1:]: + if skip: + skip = False + continue + if t in ("-o", "-c", "-MF", "-MT", "-MQ"): + skip = True + continue + if t == "-MD" or t.startswith("-o"): + continue + norm.append(norm_token(t, src_s, bld_s, repo)) + f = norm_token(e["file"], src_s, bld_s, repo) + if "" in f: + continue + res[f] = norm + return res + + +def norm_token(t: str, src: str, bld: str, repo: str | None) -> str: + # FetchContent's _deps directory moves with the build layout. + t = re.sub(re.escape(bld) + r"(/[^ ]*?)?/_deps/", "/", t) + t = t.replace(bld, "") + + def path_map(m): + rel = m.group(1) + if repo is not None: + mapped = map_old_path(repo, rel) if rel else rel + rel = mapped if mapped is not None else "/" + rel + return "/" + rel if rel else "" + t = re.sub(re.escape(src) + r"/?([^\s\"']*)", path_map, t) + if repo is not None: + t = map_text(t, "x.cmake") if t.startswith("-D") else t + # Build-tree paths differ by the engine's subdirectory in the root tree. + t = re.sub(r"/(async|bridge)/", "/", t) + return t + + +def g7(old: dict, new: dict, label: str): + bad = [] + for f, toks in sorted(old.items()): + if f not in new: + bad.append(f"{f}: missing from the gated tree") + elif new[f] != toks: + d = [l for l in difflib.unified_diff(toks, new[f], lineterm="", n=0) + if l[:1] in "+-" and not l.startswith(("+++", "---"))] + bad.append(f"{f}: {' '.join(d)[:300]}") + for b in bad[:20]: + print(" ", b) + report(f"G7 {label}", not bad, f"{len(old)} TUs compared, {len(bad)} differ") + + +# -- G4: per-function disassembly ------------------------------------------ + +def disasm(elf: str, objdump: str, nm: str, mapper) -> dict: + """Normalized instruction lists keyed by mapped demangled function name + (after the MONOREPO_PLAN G4 normalizer: addresses and RIP displacements + stripped, literal-pool words symbolized, string literals resolved).""" + from elftools.elf.elffile import ELFFile + syms = [] + for line in out([nm, "-C", "-n", elf]).splitlines(): + p = line.split(" ", 2) + if len(p) == 3 and p[1] not in "aUwN": + syms.append((int(p[0], 16), mapper(p[2]))) + syms.sort() + addrs = [a for a, _ in syms] + lo, hi = (addrs[0], addrs[-1]) if addrs else (0, 0) + segs = [] + with open(elf, "rb") as fh: + for s in ELFFile(fh).iter_sections(): + if s["sh_addr"] and s["sh_type"] == "SHT_PROGBITS": + segs.append((s["sh_addr"], s.data())) + + def sym(v): + i = bisect.bisect_right(addrs, v) - 1 + if i < 0: + return hex(v) + a, n = syms[i] + return f"{n}+{v - a:#x}" + + def cstr(v): + for a, d in segs: + if a <= v < a + len(d): + o = v - a + e = d.find(b"\0", o) + if e < 0 or e - o > 400: + return None + b = d[o:e] + if b and all(32 <= c < 127 or c in (9, 10) for c in b): + return mapper(b.decode()) + return None + + funcs, cur = {}, None + for line in out([objdump, "-d", "--no-show-raw-insn", "-C", elf]).splitlines(): + m = re.match(r"^[0-9a-f]+ <(.*)>:$", line) + if m: + cur = mapper(m.group(1)) + funcs.setdefault(cur, []) + continue + m = re.match(r"^\s*[0-9a-f]+:\s+(.*)$", line) + if not m or cur is None: + continue + ins = mapper(m.group(1)) + ins = re.sub(r"\s+#.*$", "", ins) + ins = re.sub(r"\s*[@;].*$", "", ins) + ins = re.sub(r"-?0x[0-9a-f]+\(%rip\)", "REL(%rip)", ins) + ins = re.sub(r"\b[0-9a-f]+ <([^>]*)>", r"<\1>", ins) + w = re.match(r"^\.word\s+0x([0-9a-f]+)$", ins) + if w: + v = int(w.group(1), 16) + if lo <= v <= hi + 0x100000: + s = cstr(v) + ins = ".word " + (repr(s) if s is not None else "<" + sym(v) + ">") + funcs[cur].append(ins) + return funcs + + +def demangled_mapper(s: str) -> str: + return map_text(s) + + +def g4(pairs, objdump, nm, label): + bad = [] + for name, old_elf, new_elf in pairs: + a = disasm(old_elf, objdump, nm, demangled_mapper) + b = disasm(new_elf, objdump, nm, lambda s: s) + diffs = sorted(k for k in set(a) | set(b) if a.get(k) != b.get(k)) + if diffs: + bad.append(f"{name}: {len(diffs)} function(s) differ, e.g. {diffs[:3]}") + for b in bad[:20]: + print(" ", b) + report(f"G4 {label}", not bad, f"{len(pairs)} binaries compared") + + +# -- host ------------------------------------------------------------------ + +def hash_lines(bld, regex): + log = subprocess.run(["ctest", "--test-dir", str(bld), "-R", regex, "-V"], + capture_output=True, text=True).stdout + return sorted(set(re.findall(r"\[ measured \] hash .*", log))) + + +def host(args): + w = pathlib.Path(args.work).resolve() + new, oa, orat = map(lambda p: pathlib.Path(p).resolve(), (args.new, args.old_async, args.old_ratio)) + configure_build(oa, w / "old-async", OPTS_OLD["host"]["async"]) + configure_build(orat, w / "old-ratio", OPTS_OLD["host"]["ratio"]) + configure_build(new, w / "new", opts_new("host")) + + # G7 + new_flags = load_flags(w / "new", new, None) + for repo, tree, bld in (("async", oa, w / "old-async"), ("ratio", orat, w / "old-ratio")): + g7(load_flags(bld, tree, repo), new_flags, f"host {repo}") + + # G1: registered tests and labels, against the snapshot (+ allow.txt) + rows = out(["ctest", "--test-dir", str(w / "new"), "--show-only=json-v1"]) + tests = json.loads(rows)["tests"] + report("G1 non-empty", bool(tests), f"{len(tests)} tests") + got = collections.defaultdict(set) + for t in tests: + labels = next((p["value"] for p in t.get("properties", []) if p["name"] == "LABELS"), []) + got[",".join(sorted(labels))].add(f"{t['name']}\t{','.join(sorted(labels))}") + allow = [l.split("--")[0].split() for l in (HERE / "allow.txt").read_text().splitlines() + if l.strip() and not l.startswith("#")] + for engine, label in (("async", "async"), ("bridge", "bridge" if at_least("3.4") else "ratio")): + want = set() + for line in (HERE / "snapshot" / "g1" / f"{engine}-labels.txt").read_text().splitlines(): + name, lab = line.split("\t") + if at_least("3.4"): + name = collect.mapped(name, True) + lab = "bridge" if lab == "ratio" else lab + want.add(f"{name}\t{lab}") + have = got.get(label, set()) + added = {a[2] for a in allow if len(a) >= 3 and a[0] == f"{engine}-labels.txt" and a[1] == "+"} + missing = sorted(want - have) + extra = sorted(n for n in have - want if n.split("\t")[0] not in added) + for m in missing[:10]: + print(" missing:", m) + for m in extra[:10]: + print(" unlisted new row:", m) + report(f"G1 {engine}", not missing and not extra, f"{len(have)} tests") + + # G5: output hashes, A vs B in this job + async_re = "async.OutputHash" + bridge_re = ("bridge." if at_least("3.4") else "ratio.") + "OutputHash" + for label, old_b, old_re, new_re in (("async", w / "old-async", "OutputHash", async_re), + ("bridge", w / "old-ratio", "OutputHash", bridge_re)): + a, b = hash_lines(old_b, old_re), hash_lines(w / "new", new_re) + report(f"G5 host {label}", bool(a) and a == b, f"{len(a)} hashes") + + # G6: cross-validation lines against the snapshot + xlog = subprocess.run(["ctest", "--test-dir", str(w / "new"), "-R", "CrossValidation", "-V"], + capture_output=True, text=True).stdout + got6 = sorted({m.group(0) for m in re.finditer(r"\[ measured \] cross-validation [^\n]*", xlog)}) + want6 = (HERE / "snapshot" / "g6.txt").read_text().splitlines() + report("G6", got6 == want6, f"{len(got6)} lines") + + # G10 and G4 for the C ABI libraries + pairs = [] + for engine, old_glob, new_glob in (("async", "old-async/**/libsrt_capi.so", "new/**/lib*async*capi*.so" if at_least("3.5") else "new/**/libsrt_capi.so"), + ("bridge", "old-ratio/**/libratio_capi.so", "new/**/lib*bridge*capi*.so" if at_least("3.5") else "new/**/libratio_capi.so")): + olds = glob.glob(str(w / old_glob), recursive=True) + news = glob.glob(str(w / new_glob), recursive=True) + if len(olds) != 1 or len(news) != 1: + report(f"G10 {engine}", False, f"libraries found: old {olds} new {news}") + continue + want = [map_text(l) for l in (HERE / "snapshot" / "g10" / f"{engine}.txt").read_text().splitlines()] + have = out(["python3", str(HERE / "collect.py"), "symbols", news[0]]).splitlines() + if at_least("3.5"): + want += [l for l in have if re.search(r"_version$", l) and l not in want] # D13 + report(f"G10 {engine}", sorted(have) == sorted(want), f"{len(have)} symbols") + pairs.append((engine, olds[0], news[0])) + g4(pairs, "objdump", "nm", "host C ABI") + + # G14: the rename-only residual + r = subprocess.run(["python3", str(HERE / "rename.py"), "check", "--through", step(), + "--tree", str(new), "--s0-repo", str(oa), "--r0-repo", str(orat)], + capture_output=True, text=True) + print(r.stdout[-6000:], r.stderr[-2000:]) + report("G14", r.returncode == 0, r.stdout.strip().splitlines()[-1] if r.stdout.strip() else "no output") + + # G9: retired identifiers, from step 3.7 + if at_least("3.7"): + r = subprocess.run(["python3", str(HERE / "collect.py"), "retired", str(new)], + capture_output=True, text=True) + print(r.stdout[-4000:]) + report("G9", r.returncode == 0, r.stdout.strip().splitlines()[-1]) + else: + print(f"SKIP G9 (applies from step 3.7; this is {step()})") + + # G12: history of a fixed file list (needs a full clone of the gated tree) + g12(new) + + +def g12(new: pathlib.Path): + if out(["git", "-C", str(new), "rev-parse", "--is-shallow-repository"]).strip() == "true": + report("G12", False, "the gated tree is a shallow clone; G12 needs full history") + return + tips = rename.read_tips() + for engine, repo, tip_key in (("async", "async", "S0"), ("bridge", "ratio", "R0")): + text = (HERE / "snapshot" / "g12" / f"{engine}.txt").read_text() + for block in text.split("== ")[1:]: + lines = block.splitlines() + old_path = lines[0].split()[0] + new_path = map_old_path(repo, old_path) + want_log = [l[6:] for l in lines if l.startswith("log ")] + want_blame = {} + for l in lines: + if l.startswith("blame "): + n, rest = l[6:].strip().split(" ", 1) + want_blame[rest] = int(n) + r = subprocess.run(["python3", str(HERE / "collect.py"), "history", str(new), "HEAD", new_path], + capture_output=True, text=True) + if r.returncode: + report(f"G12 {new_path}", False, r.stderr.strip()[-200:]) + continue + got_lines = r.stdout.splitlines() + got_log = [l[6:] for l in got_lines if l.startswith("log ")] + got_blame = {} + for l in got_lines: + if l.startswith("blame "): + n, rest = l[6:].strip().split(" ", 1) + got_blame[rest] = int(n) + # --follow must still reach every step-0 commit, in order. + it = iter(got_log) + follows = all(any(g == w_ for g in it) for w_ in want_log) + total = sum(got_blame.values()) or 1 + new_owned = sum(n for k, n in got_blame.items() if k not in want_blame) + wholesale = new_owned > total / 2 + report(f"G12 {new_path}", follows and not wholesale, + f"{len(want_log)}/{len(got_log)} commits reached; " + f"{new_owned}/{total} lines owned by post-step-0 commits") + + +# -- cross ------------------------------------------------------------------- + +def build_plugin(src_c: pathlib.Path, dst: pathlib.Path, header_dir: str): + cflags = out(["pkg-config", "--cflags", "glib-2.0"]).split() + run(["gcc", "-shared", "-fPIC", *cflags, f"-I{header_dir}", "-o", str(dst), str(src_c)]) + + +def cross(args): + t = args.target + w = pathlib.Path(args.work).resolve() / t + w.mkdir(parents=True, exist_ok=True) + new, oa, orat = map(lambda p: pathlib.Path(p).resolve(), (args.new, args.old_async, args.old_ratio)) + configure_build(oa, w / "old-async", OPTS_OLD["cross"]["async"], t) + configure_build(orat, w / "old-ratio", OPTS_OLD["cross"]["ratio"], t) + configure_build(new, w / "new", opts_new("cross"), t) + + new_flags = load_flags(w / "new", new, None) + for repo, tree, bld in (("async", oa, w / "old-async"), ("ratio", orat, w / "old-ratio")): + g7(load_flags(bld, tree, repo), new_flags, f"{t} {repo}") + + # G3 (+ G5 checksums): each tree measured with its own harness, then + # the gated tree compared EXACTLY against the step-0 measurements. + plugins = {} + for key, c in (("old-async", oa / "tools/qemu_insn_plugin/insn_count.c"), + ("old-ratio", orat / "tools/qemu_insn_plugin/insn_count.c"), + ("new-async", new / "tools/qemu_insn_plugin/insn_count.c"), + ("new-bridge", new / "bridge/tools/qemu_insn_plugin/insn_count.c")): + if not c.exists(): + c = new / "tools/qemu_insn_plugin/insn_count.c" + plugins[key] = w / f"lib{key}.so" + build_plugin(c, plugins[key], args.plugin_header_dir) + + def icount(tree_script, build, plugin, extra): + cmd = ["python3", str(tree_script), "--target", t, "--build-dir", str(build), + "--plugin", str(plugin)] + extra + print("+ " + " ".join(cmd), flush=True) + return subprocess.run(cmd, capture_output=True, text=True) + + for label, old_tree, old_bld, old_plugin, new_script, new_extra, new_plugin in ( + ("async", oa, w / "old-async", plugins["old-async"], + new / "scripts/icount.py", ["--engine", "async"] if not (new / "bridge/scripts/icount.py").exists() and at_least("2") else [], + plugins["new-async"]), + ("bridge", orat, w / "old-ratio", plugins["old-ratio"], + (new / "bridge/scripts/icount.py") if (new / "bridge/scripts/icount.py").exists() else (new / "scripts/icount.py"), + [] if (new / "bridge/scripts/icount.py").exists() else ["--engine", "bridge"], + plugins["new-bridge"])): + ref = w / f"{label}-old.json" + ref.unlink(missing_ok=True) + r = icount(old_tree / "scripts/icount.py", old_bld, old_plugin, + ["--baselines", str(old_tree / "bench/baselines.json"), "--tolerance", "1e9", + "--json-out", str(ref)]) + print(r.stdout[-3000:], r.stderr[-2000:]) + if not ref.exists(): + report(f"G3 {t} {label}", False, "step-0 measurement failed") + continue + r = icount(new_script, w / "new", new_plugin, new_extra + ["--compare-json", str(ref)]) + print(r.stdout[-3000:], r.stderr[-2000:]) + n = len(json.loads(ref.read_text()).get(t, {})) + report(f"G3+G5 {t} {label}", r.returncode == 0, f"{n} workloads, counts and checksums exact") + + # G4: icount binaries, paired by workload name. + if t == "hexagon": + bindir = pathlib.Path(shutil.which("hexagon-unknown-linux-musl-clang++")).parent + objdump = str(bindir / "llvm-objdump") if (bindir / "llvm-objdump").exists() else "llvm-objdump" + nm = str(bindir / "llvm-nm") if (bindir / "llvm-nm").exists() else "llvm-nm" + else: + objdump, nm = "arm-none-eabi-objdump", "arm-none-eabi-nm" + pairs = [] + for old_bld, prefix, new_prefix in ((w / "old-async", "srt_icount_", "tap_sr_async_icount_" if at_least("3.6") else "srt_icount_"), + (w / "old-ratio", "ratio_icount_", "tap_sr_bridge_icount_" if at_least("3.6") else "ratio_icount_")): + for f in sorted(glob.glob(str(old_bld / "**" / (prefix + "*")), recursive=True)): + if not (os.path.isfile(f) and os.access(f, os.X_OK)): + continue + wl = os.path.basename(f)[len(prefix):] + cand = glob.glob(str(w / "new" / "**" / (new_prefix + wl)), recursive=True) + if len(cand) != 1: + report(f"G4 {t} {wl}", False, f"gated-tree binary not found ({cand})") + continue + pairs.append((wl, f, cand[0])) + g4(pairs, objdump, nm, f"{t} icount") + + +# -- notebooks (G11) --------------------------------------------------------- + +# Timing cells are tagged "nondeterministic" in the notebooks themselves +# (step P.3); these catch IPython's own timing magics. +VOLATILE = [re.compile(p) for p in (r"^CPU times:", r"^Wall time:")] + + +def notebook_text(path: pathlib.Path, mapper) -> list[str]: + nb = json.loads(path.read_text()) + lines = [] + for i, cell in enumerate(nb.get("cells", [])): + if cell.get("cell_type") != "code": + continue + if "nondeterministic" in cell.get("metadata", {}).get("tags", []): + lines.append(f"[cell {i}: nondeterministic, skipped]") + continue + for o in cell.get("outputs", []): + if o.get("output_type") == "stream": + text = "".join(o.get("text", "")) + elif "data" in o and "text/plain" in o["data"]: + text = "".join(o["data"]["text/plain"]) + elif o.get("output_type") == "error": + text = f"ERROR {o.get('ename')}: {o.get('evalue')}" + else: + continue + for line in text.splitlines(): + if re.match(r"^

"), (str(orat), ""), (str(new / "async"), ""), + (str(new / "bridge"), ""), (str(new), "")] + + def unroot(line): + for a_, b_ in roots: + line = line.replace(a_, b_) + return line + a = notebook_text(results[0], lambda s: map_text(unroot(s))) + b = notebook_text(results[1], unroot) + d = list(difflib.unified_diff(a, b, "step-0", "gated", lineterm="", n=1)) + for line in d[:40]: + print(" ", line) + report(f"G11 {new_nb.name}", not d, f"{len(a)} output lines") + + +def main(): + ap = argparse.ArgumentParser(description=__doc__.split("\n")[0]) + sub = ap.add_subparsers(dest="cmd", required=True) + for name, fn in (("host", host), ("cross", cross), ("notebooks", notebooks)): + p = sub.add_parser(name) + p.add_argument("--work", required=True) + p.add_argument("--new", default="new") + p.add_argument("--old-async", default="old-async") + p.add_argument("--old-ratio", default="old-ratio") + if name == "cross": + p.add_argument("--target", required=True, choices=["m33", "m55", "hexagon"]) + p.add_argument("--plugin-header-dir", default="/tmp") + p.set_defaults(fn=fn) + args = ap.parse_args() + print(f"migration gates, step {step()}") + args.fn(args) + print(f"\n{len(FAILS)} gate(s) failed: {', '.join(FAILS)}" if FAILS else "\nall gates passed") + return 1 if FAILS else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/docs/migration/rename.py b/docs/migration/rename.py index 0f1993f..166cc82 100755 --- a/docs/migration/rename.py +++ b/docs/migration/rename.py @@ -171,6 +171,10 @@ def w(name: str) -> str: ("3.1", r"(? ../async/include (SYSTEM) -file cmake/arm-cortex-m33-mps2.cmake -- sets both SRT_ and TAP_RATIO_BARE_METAL -file cmake/arm-cortex-m55-mps3.cmake -- sets both SRT_ and TAP_RATIO_BARE_METAL - -# CI: one configure per job, per-(target, engine) correctness legs. -file .github/workflows/*.yml -- 1c CI port (draft ci-after-1c.yml); style.yml from RatioTap -file scripts/fetch_hexagon_toolchain.sh -- RatioTap's copy at root; all cache writers use it - -# Path fix-ups that follow the 1a/1b moves. -file book/src/** -- the 52 moved book includes -file book/book.toml -- include roots, if the moves touch them -file docs/Doxyfile -- INPUT/USE_MDFILE_AS_MAINPAGE for async/ and bridge/ -file scripts/*.py -- icount.py --baselines, update_*_docs.py, book_figures.py paths -file async/notebooks/*.ipynb -- CAPI_DIR / TOOLS_DIR / sys.path after the move -file async/notebooks/*.py -- binding paths after the move -file async/README.md -- LICENSE link -> ../ -file bridge/README.md -- build commands; LICENSE x2, STYLE.md -> ../; "eight workloads" -> ten -file bridge/CLAUDE.md -- build commands -file bridge/notebooks/*.py -- binding paths after the move +file CMakeLists.txt -- new root CMakeLists (plan 1c; draft root-CMakeLists-1c.cmake) +file .github/workflows/*.yml -- the 1c CI port: one configure per job, per-(target, engine) legs, per-engine ratchet; style.yml from RatioTap; migration-gates.yml new file bridge/docs/HISTORY.md -- new: old SHA -> new SHA -> RatioTap PR +file docs/MONOREPO_PLAN.md -- the plan itself, not part of either step-0 tree -# Licence: D14's line lands in the root LICENSE as bridge/LICENSE goes. -file LICENSE -- D14 holder line +hunk LICENSE c40d3a39d41b -- D14 holder line (bridge/LICENSE deleted in the same commit) +hunk async/CMakeLists.txt de3b34f036c8 -- tap::dsp guard with an explicit binary dir (R2-RUN-3) +hunk async/README.md 5e560f48b735 -- LICENSE link -> ../; icount table cites async/ paths (update_icount_docs.py) +hunk async/README.md a714a9f697ac -- LICENSE link -> ../; icount table cites async/ paths (update_icount_docs.py) +hunk async/examples/pico2_cyccnt/CMakeLists.txt 867694912bf1 -- dsptap is one level further up after the move +hunk async/examples/pico2_dualcore/CMakeLists.txt 867694912bf1 -- dsptap is one level further up after the move +hunk async/notebooks/asrc_block_size_study.ipynb 7225f1b599c2 -- CAPI_DIR = build/capi after the move +hunk async/notebooks/asrc_comparison.ipynb fa4056a81847 -- TOOLS_DIR = build after the move +hunk async/notebooks/asrc_demo.ipynb 7225f1b599c2 -- CAPI_DIR = build/capi; prose names capi/ +hunk async/notebooks/asrc_demo.ipynb 01ac85487697 -- CAPI_DIR = build/capi; prose names capi/ +hunk async/notebooks/asrc_rbj_analysis.ipynb 226cc8587ac3 -- sys.path -> ../../scripts +hunk bridge/CLAUDE.md 775f8e09bca8 -- build commands from the repository root; ten workloads +hunk bridge/CLAUDE.md ff961518b4e3 -- build commands from the repository root; ten workloads +hunk bridge/CMakeLists.txt 8e356a531740 -- tap::dsp guard; srt_headers -> ../async/include, kept SYSTEM +hunk bridge/CMakeLists.txt 7698d7dc2634 -- tap::dsp guard; srt_headers -> ../async/include, kept SYSTEM +hunk bridge/README.md 5e560f48b735 -- family-repo build commands; ten workloads; LICENSE/STYLE.md -> ../; icount table added (Q8) +hunk bridge/README.md 0c810d79966f -- family-repo build commands; ten workloads; LICENSE/STYLE.md -> ../; icount table added (Q8) +hunk cmake/arm-cortex-m33-mps2.cmake 20598113d144 -- sets TAP_RATIO_BARE_METAL too (both test trees) +hunk cmake/arm-cortex-m55-mps3.cmake 20598113d144 -- sets TAP_RATIO_BARE_METAL too (both test trees) +hunk docs/Doxyfile f6fbec413ea3 -- INPUT and main page for async/ and bridge/ +hunk scripts/book_figures.py 7d52c4284a6e -- trace tool compiles against async/include +hunk scripts/icount.py 828920a2db17 -- default baselines path async/bench/baselines.json +hunk scripts/icount.py b480cbbe3301 -- default baselines path async/bench/baselines.json +hunk scripts/icount.py 33e83dab43ec -- default baselines path async/bench/baselines.json +hunk scripts/tidy.sh 4aad1bac113d -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded +hunk scripts/tidy.sh 47ad2dfdbc55 -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded +hunk scripts/tidy.sh 0c5d4b9f1c39 -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded +hunk scripts/update_icount_docs.py 6040f56f6190 -- --engine async|bridge: per-engine README table +hunk scripts/update_icount_docs.py 50a1737f72b0 -- --engine async|bridge: per-engine README table +hunk scripts/update_icount_docs.py 0c4949cfd53d -- --engine async|bridge: per-engine README table +hunk scripts/update_perf_docs.py fbd463752ac4 -- default README -> async/README.md +hunk scripts/update_perf_docs.py 88543a248c62 -- default README -> async/README.md diff --git a/docs/migration/runs.md b/docs/migration/runs.md index c9cfca0..2a1a64c 100644 --- a/docs/migration/runs.md +++ b/docs/migration/runs.md @@ -39,3 +39,4 @@ check joins the book job at 1c. | Step | SHA | ci.yml | style.yml | migration-gates | ci-arm64 | compare | Image | Notes | |---|---|---|---|---|---|---|---|---| +| 0 | `f2b7d04` | 36368216558 (15/15) | 36368217316 (2/2) | — (added at 1c) | — | — | ubuntu-24.04 | no-change baseline: S0's tree plus docs/migration | diff --git a/docs/migration/step.txt b/docs/migration/step.txt new file mode 100644 index 0000000..2b4b501 --- /dev/null +++ b/docs/migration/step.txt @@ -0,0 +1 @@ +1c diff --git a/scripts/book_figures.py b/scripts/book_figures.py index d0f9b33..3bbb5f6 100644 --- a/scripts/book_figures.py +++ b/scripts/book_figures.py @@ -439,7 +439,7 @@ def main(): with tempfile.TemporaryDirectory() as tmp: head_exe = os.path.join(tmp, "trace_head") - build_trace_tool(os.path.join(ROOT, "include"), head_exe) + build_trace_tool(os.path.join(ROOT, "async", "include"), head_exe) prefix_tree = os.path.join(tmp, "prefix") os.makedirs(prefix_tree) archive = subprocess.run(["git", "-C", ROOT, "archive", PREFIX_COMMIT, "include"], diff --git a/bridge/scripts/fetch_hexagon_toolchain.sh b/scripts/fetch_hexagon_toolchain.sh similarity index 100% rename from bridge/scripts/fetch_hexagon_toolchain.sh rename to scripts/fetch_hexagon_toolchain.sh diff --git a/scripts/icount.py b/scripts/icount.py index 8383bc9..f8b6576 100644 --- a/scripts/icount.py +++ b/scripts/icount.py @@ -2,10 +2,10 @@ """Deterministic instruction-count ratchet (see docs/PERFORMANCE.md). Runs every srt_icount_* binary in a build directory under QEMU with the -instruction-counting plugin, then compares against bench/baselines.json. +instruction-counting plugin, then compares against async/bench/baselines.json. icount.py --target {hexagon,m55,m33} --build-dir DIR --plugin LIB - [--baselines bench/baselines.json] [--tolerance 0.03] + [--baselines async/bench/baselines.json] [--tolerance 0.03] [--exact] [--update] [--json-out FILE] [--compare-json FILE] The gate is two-sided: exit nonzero if any scenario regresses beyond @@ -97,7 +97,7 @@ def main() -> int: ap.add_argument("--target", required=True, choices=["hexagon", "m55", "m33"]) ap.add_argument("--build-dir", required=True) ap.add_argument("--plugin", required=True) - ap.add_argument("--baselines", default="bench/baselines.json") + ap.add_argument("--baselines", default="async/bench/baselines.json") ap.add_argument("--tolerance", type=float, default=0.03) ap.add_argument("--exact", action="store_true", help="require identical counts (tolerance 0)") @@ -164,7 +164,7 @@ def main() -> int: # regressions hide inside the slack, so improvements must be # committed too. verdict = ("IMPROVED beyond tolerance — run icount.py --update " - "and commit bench/baselines.json") + "and commit the baselines file") failures.append(scenario) print(f"{scenario}: {count} insns vs baseline {recorded} " f"({count - recorded:+d}, {delta:+.4%}) {verdict}") diff --git a/scripts/tidy.sh b/scripts/tidy.sh index 45fc009..547fc0d 100755 --- a/scripts/tidy.sh +++ b/scripts/tidy.sh @@ -11,7 +11,7 @@ # # Usage: # scripts/tidy.sh # sweep every project TU (full CI mirror) -# scripts/tidy.sh tests/test_foo.cpp … # only the given TU(s) — fast, for a change +# scripts/tidy.sh async/tests/test_foo.cpp … # only the given TU(s) — fast, for a change # # Env: # CLANG_TIDY=clang-tidy-18 # binary to use (default: clang-tidy-18, then clang-tidy) @@ -43,11 +43,13 @@ build="${TIDY_BUILD:-build-tidy}" # missing or a reconfigure is forced. This does not touch your ./build dir. if [ "${TIDY_RECONFIGURE:-0}" = "1" ] || [ ! -f "$build/compile_commands.json" ]; then echo "== configuring compile database in $build/ (one-time; reuses cached deps) ==" - cmake -B "$build" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON >/dev/null + cmake -B "$build" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \ + -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON >/dev/null fi # File list: the given TUs, or — matching CI exactly — every project TU in the -# database with third_party/ and fetched deps (_deps) excluded. +# database (both engines) with the submodule, third_party/ and fetched deps +# (_deps) excluded. if [ "$#" -gt 0 ]; then files=("$@") else @@ -55,7 +57,7 @@ else import json for e in json.load(open('$build/compile_commands.json')): f = e['file'] - if 'third_party' not in f and '_deps' not in f: + if 'submodules' not in f and 'third_party' not in f and '_deps' not in f: print(f)") fi diff --git a/scripts/update_icount_docs.py b/scripts/update_icount_docs.py index d65460b..6a09315 100644 --- a/scripts/update_icount_docs.py +++ b/scripts/update_icount_docs.py @@ -1,12 +1,17 @@ #!/usr/bin/env python3 -"""Regenerate the README instruction-count table from bench/baselines.json. +"""Regenerate an engine README's instruction-count table from its baselines. -Usage: scripts/update_icount_docs.py [README.md] +Usage: scripts/update_icount_docs.py [--engine async|bridge] + +Each engine's table lives between the ICOUNT markers in /README.md +and is generated from /bench/baselines.json. Run from the +repository root. The table derives 1:1 from the committed baselines, so CI regenerates it and fails on any diff — the published numbers cannot drift from the gated ones (docs/PERFORMANCE.md, "Docs freshness"). """ +import argparse import json import pathlib import re @@ -16,13 +21,13 @@ TARGET_NAMES = {"m33": "Cortex-M33", "m55": "Cortex-M55", "hexagon": "Hexagon"} -def table(baselines: dict) -> str: +def table(baselines: dict, engine: str) -> str: targets = [t for t in ("m33", "m55", "hexagon") if baselines.get(t)] scenarios = sorted({s for t in targets for s in baselines[t]}) lines = [ - "Executed instructions per fixed workload (`bench/icount/`), measured " - "under QEMU with a counting plugin — deterministic, and gated in CI " - "at ±3% against `bench/baselines.json`:", + f"Executed instructions per fixed workload (`{engine}/bench/icount/`), " + "measured under QEMU with a counting plugin — deterministic, and gated " + f"in CI at ±3% against `{engine}/bench/baselines.json`:", "", "| Workload | " + " | ".join(TARGET_NAMES[t] for t in targets) + " |", "|---|" + "---:|" * len(targets), @@ -35,13 +40,16 @@ def table(baselines: dict) -> str: def main() -> int: - readme = pathlib.Path(sys.argv[1] if len(sys.argv) > 1 else "README.md") - baselines = json.loads(pathlib.Path("bench/baselines.json").read_text()) + ap = argparse.ArgumentParser() + ap.add_argument("--engine", choices=["async", "bridge"], default="async") + args = ap.parse_args() + readme = pathlib.Path(args.engine) / "README.md" + baselines = json.loads((pathlib.Path(args.engine) / "bench" / "baselines.json").read_text()) text = readme.read_text() if BEGIN not in text or END not in text: print(f"markers not found in {readme}", file=sys.stderr) return 1 - block = f"{BEGIN}\n{table(baselines)}\n{END}" + block = f"{BEGIN}\n{table(baselines, args.engine)}\n{END}" readme.write_text(re.sub(re.escape(BEGIN) + r".*?" + re.escape(END), block, text, flags=re.S)) print(f"updated {readme}") diff --git a/scripts/update_perf_docs.py b/scripts/update_perf_docs.py index 43389b6..e514ee2 100644 --- a/scripts/update_perf_docs.py +++ b/scripts/update_perf_docs.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 """Run the host benchmarks and refresh the README performance table. -Usage: scripts/update_perf_docs.py path/to/srt_bench [README.md] +Usage: scripts/update_perf_docs.py path/to/srt_bench [async/README.md] Rewrites the block between and with a machine- and date-annotated table. See docs/PERFORMANCE.md. @@ -65,7 +65,7 @@ def table(rows: list[dict]) -> str: def main() -> int: bench = sys.argv[1] - readme = pathlib.Path(sys.argv[2] if len(sys.argv) > 2 else "README.md") + readme = pathlib.Path(sys.argv[2] if len(sys.argv) > 2 else "async/README.md") text = readme.read_text() if BEGIN not in text or END not in text: print(f"markers not found in {readme}", file=sys.stderr) From e2d2381b6f885400698dc4f11436b0de59510fa1 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 28 Sep 2026 02:45:19 +0000 Subject: [PATCH 42/44] Restore scripts/tidy.sh to the TapHouse canonical copy The drift check guards scripts/tidy.sh as well as the style configs, and 1c had edited it (compile-database flags and the submodule filter). The CI gate in style.yml carries the monorepo-specific configuration; the local mirror stays the shared file and sweeps the default configure. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/migration/residual/1c.txt | 3 --- scripts/tidy.sh | 10 ++++------ 2 files changed, 4 insertions(+), 9 deletions(-) diff --git a/docs/migration/residual/1c.txt b/docs/migration/residual/1c.txt index d0794cf..e4ff10c 100644 --- a/docs/migration/residual/1c.txt +++ b/docs/migration/residual/1c.txt @@ -31,9 +31,6 @@ hunk scripts/book_figures.py 7d52c4284a6e -- trace tool compiles against async/ hunk scripts/icount.py 828920a2db17 -- default baselines path async/bench/baselines.json hunk scripts/icount.py b480cbbe3301 -- default baselines path async/bench/baselines.json hunk scripts/icount.py 33e83dab43ec -- default baselines path async/bench/baselines.json -hunk scripts/tidy.sh 4aad1bac113d -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded -hunk scripts/tidy.sh 47ad2dfdbc55 -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded -hunk scripts/tidy.sh 0c5d4b9f1c39 -- compile database covers both engines and bridge's icount TUs (the old gates' coverage); submodule excluded hunk scripts/update_icount_docs.py 6040f56f6190 -- --engine async|bridge: per-engine README table hunk scripts/update_icount_docs.py 50a1737f72b0 -- --engine async|bridge: per-engine README table hunk scripts/update_icount_docs.py 0c4949cfd53d -- --engine async|bridge: per-engine README table diff --git a/scripts/tidy.sh b/scripts/tidy.sh index 547fc0d..45fc009 100755 --- a/scripts/tidy.sh +++ b/scripts/tidy.sh @@ -11,7 +11,7 @@ # # Usage: # scripts/tidy.sh # sweep every project TU (full CI mirror) -# scripts/tidy.sh async/tests/test_foo.cpp … # only the given TU(s) — fast, for a change +# scripts/tidy.sh tests/test_foo.cpp … # only the given TU(s) — fast, for a change # # Env: # CLANG_TIDY=clang-tidy-18 # binary to use (default: clang-tidy-18, then clang-tidy) @@ -43,13 +43,11 @@ build="${TIDY_BUILD:-build-tidy}" # missing or a reconfigure is forced. This does not touch your ./build dir. if [ "${TIDY_RECONFIGURE:-0}" = "1" ] || [ ! -f "$build/compile_commands.json" ]; then echo "== configuring compile database in $build/ (one-time; reuses cached deps) ==" - cmake -B "$build" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \ - -DTAP_RATIO_BUILD_ICOUNT_BENCH=ON >/dev/null + cmake -B "$build" -DCMAKE_EXPORT_COMPILE_COMMANDS=ON >/dev/null fi # File list: the given TUs, or — matching CI exactly — every project TU in the -# database (both engines) with the submodule, third_party/ and fetched deps -# (_deps) excluded. +# database with third_party/ and fetched deps (_deps) excluded. if [ "$#" -gt 0 ]; then files=("$@") else @@ -57,7 +55,7 @@ else import json for e in json.load(open('$build/compile_commands.json')): f = e['file'] - if 'submodules' not in f and 'third_party' not in f and '_deps' not in f: + if 'third_party' not in f and '_deps' not in f: print(f)") fi From a2d7eb436548f41a1d604048342aec15d777398b Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 28 Sep 2026 02:47:44 +0000 Subject: [PATCH 43/44] gates.py: print the unreached entries when G12's --follow check fails G12 fails in CI for every file while passing locally on the same history (git 2.55 there, 2.43 here); print what the gated log lacks so the difference is visible in the job log. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/migration/gates.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/migration/gates.py b/docs/migration/gates.py index 53e1eb4..35cd59f 100644 --- a/docs/migration/gates.py +++ b/docs/migration/gates.py @@ -397,6 +397,10 @@ def g12(new: pathlib.Path): # --follow must still reach every step-0 commit, in order. it = iter(got_log) follows = all(any(g == w_ for g in it) for w_ in want_log) + if not follows: + unreached = [w_ for w_ in want_log if w_ not in got_log] + print(f" not reached ({len(unreached)}): {unreached[:3]!r}") + print(f" gated log head: {got_log[:4]!r}") total = sum(got_blame.values()) or 1 new_owned = sum(n for k, n in got_blame.items() if k not in want_blame) wholesale = new_owned > total / 2 From 5297394a7ac1bd3717804c2de04f6a72f9f1e3af Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 28 Sep 2026 02:50:55 +0000 Subject: [PATCH 44/44] gates.py: compare G12 log entries by epoch, not by ISO text git 2.55 (the runner) prints a UTC author date in %aI as 'Z'; git 2.43, which recorded the snapshot, prints '+00:00'. Every entry therefore differed in CI while the history itself was intact (the diagnostic from the previous commit shows the same commits in the same order). Compare ' ' instead. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015VR1VC4SDGxHZQQsQvPBaA --- docs/migration/gates.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/docs/migration/gates.py b/docs/migration/gates.py index 35cd59f..65798d4 100644 --- a/docs/migration/gates.py +++ b/docs/migration/gates.py @@ -365,6 +365,14 @@ def host(args): g12(new) +def log_key(entry: str) -> str: + """' ' with the date as epoch seconds: git + 2.55 prints UTC in %aI as 'Z' where 2.43 prints '+00:00'.""" + import datetime + date, _, subject = entry.partition(" ") + return f"{int(datetime.datetime.fromisoformat(date).timestamp())} {subject}" + + def g12(new: pathlib.Path): if out(["git", "-C", str(new), "rev-parse", "--is-shallow-repository"]).strip() == "true": report("G12", False, "the gated tree is a shallow clone; G12 needs full history") @@ -376,7 +384,7 @@ def g12(new: pathlib.Path): lines = block.splitlines() old_path = lines[0].split()[0] new_path = map_old_path(repo, old_path) - want_log = [l[6:] for l in lines if l.startswith("log ")] + want_log = [log_key(l[6:]) for l in lines if l.startswith("log ")] want_blame = {} for l in lines: if l.startswith("blame "): @@ -388,7 +396,7 @@ def g12(new: pathlib.Path): report(f"G12 {new_path}", False, r.stderr.strip()[-200:]) continue got_lines = r.stdout.splitlines() - got_log = [l[6:] for l in got_lines if l.startswith("log ")] + got_log = [log_key(l[6:]) for l in got_lines if l.startswith("log ")] got_blame = {} for l in got_lines: if l.startswith("blame "):