grabowski
488c9a399c
lamp_export.py writes the per-lamp bundle consumed by the buildfor.life comparison pages: spd.csv (full 380-1050 nm spectrum), tm30.csv (ANSI/IES TM-30-18 hue-bin data for the color vector graphic), and metrics.json (photometric, colorimetric, CRI R1-R15, TM-30 Rf/Rg, electrical, supply settings). - tm30.py computes TM-30-18 via colour-science from the measured spectrum, resampled to a uniform 1 nm grid; validated against CIE FL2 (Rf 70, Rg 86) - Optional built-in PSU control (--mode/--voltage/--frequency/--current), switched on for the measurement and off afterwards, with --settle warm-up and --interval between readings - Readings use the vendor single-shot cycle verified from USB captures: test config -> trigger (8C 0E 02) -> poll -> read -> reset per reading - Also supports --passive and --parse <pcap> sources
126 lines
4.8 KiB
Python
126 lines
4.8 KiB
Python
"""
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ANSI/IES TM-30-18 computation from an HPCS 6500 spectrum.
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Wraps colour-science to derive the fidelity index Rf, gamut index Rg, and the
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per-hue-bin data needed to draw a TM-30 color vector graphic (CVG): the test
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and reference chromaticity averages in CAM02-UCS a'b' (raw and normalized so
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the reference gamut is the unit circle), plus per-bin chroma shift, hue shift,
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and local fidelity.
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The instrument spectrum is relative; TM-30 is scale-invariant, so no absolute
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calibration is required. Only the 380-780 nm range is used.
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"""
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import warnings
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import numpy as np
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try:
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# colour warns at import time about optional scipy/matplotlib features;
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# the TM-30 path used here needs neither.
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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import colour
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from colour.quality import colour_fidelity_index_ANSIIESTM3018
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except ImportError as e: # pragma: no cover
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raise ImportError(
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"TM-30 computation requires colour-science; run `uv sync`"
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) from e
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VISIBLE_MIN_NM = 380
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VISIBLE_MAX_NM = 780
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N_BINS = 16
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def compute_tm30(wavelengths_nm, values):
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"""Compute TM-30-18 quantities from a measured spectrum.
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Args:
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wavelengths_nm: sequence of wavelengths in nm (any step; 380-1050
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from the HPCS 6500 is fine, the IR tail is discarded).
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values: spectral power at each wavelength (relative units).
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Returns dict:
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Rf, Rg: floats
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CCT_K, Duv: floats (as derived by the TM-30 reference selection)
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bins: list of 16 dicts, hue bin 1..16, each with
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ref_a, ref_b, test_a, test_b raw CAM02-UCS bin averages
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ref_a_norm ... test_b_norm normalized (reference = unit circle)
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Rcs_pct chroma shift, percent
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Rhs hue shift (rad, CAM02-UCS)
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Rf_h local fidelity for the bin
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"""
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wl = np.asarray(wavelengths_nm, dtype=float)
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vals = np.asarray(values, dtype=float)
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mask = (wl >= VISIBLE_MIN_NM) & (wl <= VISIBLE_MAX_NM)
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if mask.sum() < 10:
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raise ValueError("spectrum does not cover the visible range")
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# Resample onto a uniform 1 nm grid: the instrument's ~1.92 nm grid is
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# non-uniform in colour's eyes and triggers interpolator paths that need
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# scipy; a uniform grid keeps the pipeline on plain numpy.
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grid = np.arange(VISIBLE_MIN_NM, VISIBLE_MAX_NM + 1, 1, dtype=float)
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resampled = np.interp(grid, wl[mask], vals[mask])
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sd = colour.SpectralDistribution(dict(zip(grid, resampled)))
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# colour emits informational "aligning shape" runtime warnings while it
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# adapts observers/illuminants to our 1 nm grid; not actionable.
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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spec = colour_fidelity_index_ANSIIESTM3018(sd, additional_data=True)
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averages_test = np.asarray(spec.averages_test)
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averages_reference = np.asarray(spec.averages_reference)
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average_norms = np.asarray(spec.average_norms)
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R_cs = np.asarray(spec.R_cs)
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R_hs = np.asarray(spec.R_hs)
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# Local fidelity per hue bin: mean of the per-CES R_f over the samples
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# assigned to the bin (spec.bins holds the bin index of each CES).
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sample_bins = np.asarray(spec.bins)
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R_s = np.asarray(spec.R_s)
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Rf_h = np.full(N_BINS, np.nan)
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for j in range(N_BINS):
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members = R_s[sample_bins == j]
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if members.size:
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Rf_h[j] = members.mean()
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bins = []
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for j in range(N_BINS):
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norm = average_norms[j] if average_norms[j] else 1.0
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bins.append(
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{
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"bin": j + 1,
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"ref_a": float(averages_reference[j, 0]),
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"ref_b": float(averages_reference[j, 1]),
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"test_a": float(averages_test[j, 0]),
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"test_b": float(averages_test[j, 1]),
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"ref_a_norm": float(averages_reference[j, 0] / norm),
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"ref_b_norm": float(averages_reference[j, 1] / norm),
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"test_a_norm": float(averages_test[j, 0] / norm),
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"test_b_norm": float(averages_test[j, 1] / norm),
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"Rcs_pct": float(R_cs[j]),
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"Rhs": float(R_hs[j]),
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"Rf_h": float(Rf_h[j]),
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}
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)
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return {
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"Rf": float(spec.R_f),
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"Rg": float(spec.R_g),
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"CCT_K": float(spec.CCT),
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"Duv": float(spec.D_uv),
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"bins": bins,
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}
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if __name__ == "__main__":
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# Sanity check against a known illuminant.
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sd = colour.SDS_ILLUMINANTS["FL2"]
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wl = sd.wavelengths
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result = compute_tm30(wl, sd.values)
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print(f"FL2: Rf={result['Rf']:.1f} Rg={result['Rg']:.1f} "
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f"CCT={result['CCT_K']:.0f} K Duv={result['Duv']:.4f}")
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for b in result["bins"][:4]:
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print(b)
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