feat(color): bound legacy-correction accuracy from datasheet ranges (P1) - #12
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FastLED/FastLED#4035's second acceptance criterion -- quantify TypicalLEDStrip, Typical8mmPixel and UncorrectedColor -- had no number, because the datasheets publish dominant-wavelength and luminous-intensity ranges, not chromaticity. tools/bound_legacy_correction_accuracy.py propagates those ranges into an interval of admissible profiles and scores each legacy model across it under A1: linear-sRGB source, Bradford D65 -> full-drive white (Y=1), CIEDE2000 over a black-safe neutral ramp, primaries and secondaries. Stated assumptions, not datasheet facts: a Gaussian emitter spectrum whose dominant wavelength (about E) matches the datasheet, with FWHM red 15-25 / green 25-40 / blue 18-30 nm; luminous intensity proportional to Y. The CIE 1931 2-degree CMF payload is pinned by SHA-256, as in the SK9822 tool. Results (LEGACY-ACCURACY-BOUND.md): - under A1, every legacy model misses the datasheet budget (median <= 3.0, p95 <= 6.0) for every admissible profile in 26 of 27 part/model cells; - the admissible median alone spans more than the whole 3.0 budget in 14 of 27 cells, so datasheet ranges cannot pin a derived profile to the A1 budget -- the numerical reason there are zero admissible derived profiles and accuracy claims wait on P10; - a supplementary absolute-colorimetry table (not A1) scores the corrections' D65 design intent. Tests: CIEDE2000 against Sharma et al. reference pairs; an sRGB-primary device scores zero uncorrected; Bradford white mapping; dominant- wavelength round trip and the checked-in outputs, with the real CMF table. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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📝 WalkthroughWalkthroughThe PR adds a tool that computes datasheet-bounded ΔE2000 accuracy for three legacy correction models. It adds generated JSON and Markdown results, updates characterization findings, and adds unit and optional data-backed tests. ChangesLegacy correction accuracy
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant CLI as bound_legacy_correction_accuracy.py
participant CMF as Pinned CIE CMF data
participant Evaluator as Profile and ΔE2000 evaluator
participant Outputs as JSON and Markdown outputs
CLI->>CMF: Load and verify CMF CSV
CLI->>Evaluator: Build admissible emitter profiles
Evaluator->>Evaluator: Score correction models
Evaluator->>Outputs: Write accuracy bounds
Merge Risk: 🔵 Low · up to The report overstates what its sampled grid proves, and CI does not detect stale generated results. These are bounded documentation and validation risks that should be corrected or explicitly accepted. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 25.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 32 functions across 2 files. (3 skipped: 3 unsupported.) ✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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In `@tests/test_legacy_accuracy_bound.py`:
- Around line 79-107: Update the CI workflow to provision the pinned CIE CSV,
verify its CIE_SHA256, set DATASHEETS_CIE_CSV, and execute
tests/test_legacy_accuracy_bound.py in the CI test command so
RealColourMatchingTest runs and validates both checked-in outputs via
BOUND.main.
In `@tools/bound_legacy_correction_accuracy.py`:
- Around line 465-467: Update the explanatory wording in the tool around
evaluate and its generated Markdown/characterization outputs to describe the
finite sampled grid: dominant wavelength {min, mid, max} × FWHM {low, mid, high}
× intensity {min, max} per channel. State that nominal uses midpoint intensity,
which is not included in the sweep, and that reported ranges and verdicts apply
only to sampled profiles; change both verdicts to “every sampled profile.”
Regenerate the checked-in outputs and documentation.
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measured-profiles/LEGACY-ACCURACY-BOUND.mdmeasured-profiles/P1-CHARACTERIZATION.mdmeasured-profiles/legacy-correction-accuracy-bound-v1.jsontests/test_legacy_accuracy_bound.pytools/bound_legacy_correction_accuracy.py
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Review follow-up on #12: - The tool scores a grid (each range's ends and midpoint), not the continuous datasheet ranges. Wording is now "at every sampled profile" and says the grid is an inner bound, not a proof over the continuum. The spread claim is unaffected in direction: a sampled spread can only understate the continuous one. Intensity is now sampled at min/mid/max, so the nominal profile is one of the grid points. Headline counts are unchanged (14/27 spreads exceed the budget; 26/27 cells outside budget at every sample). - Add .github/workflows/tests.yml. No test in this repo ran in CI; it now fetches the CIE table, verifies its pinned SHA-256, runs the full suite with DATASHEETS_CIE_CSV set, and --checks every checked-in artifact. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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For FastLED/FastLED#4035 (colour pipeline P1). This implements option 2 from the last analysis on that issue: quantify the legacy corrections as a bound, since the datasheets publish dominant-wavelength and luminous-intensity ranges, not chromaticity.
Method
tools/bound_legacy_correction_accuracy.pypropagates each datasheet range into an interval of admissible profiles and scoresUncorrectedColor,TypicalLEDStripandTypical8mmPixelacross it under A1:Two assumptions are stated, not taken from the PDFs:
Sampling covers {min, mid, max} dominant wavelength × {low, mid, high} FWHM per channel × {min, max} intensity. The CIE 1931 2° CMF payload is pinned by SHA-256 and not redistributed, as with the SK9822 tool.
Results (
measured-profiles/LEGACY-ACCURACY-BOUND.md)UncorrectedColorunder A1 because they tint the device white, which relative colorimetry penalizes. A supplementary absolute-colorimetry table, clearly marked as not A1, scores their D65 design intent.Tests
tests/test_legacy_accuracy_bound.py:DATASHEETS_CIE_CSV): the dominant-wavelength round trip holds, a wider emitter is less saturated, and the checked-in outputs match (--check).python -m unittest discover -s tests: 42 passed, with the CIE table andjsonschemaavailable.check_profile_artifacts.pyand the intensity-sensitivity--checkpass.🤖 Generated with Claude Code
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