Lamp testbench in Rust around the HPCS 6500
lampbench station / export / label / tm30 / calibrate replace the Python lamp_station.py, lamp_export.py, label_printer.py, tm30.py and calibration.py that lived in HPCS6500-py. The driver is a submodule (HPCS6500/, like mppt-testbench). Workspace crates: tm30 (TM-30-18 port of colour-science 0.4.7), brother-ql (raster protocol, byte-identical to brother_ql) and lamp-label (QR + ID labels for the QL-820NWB). Bundles are byte-identical to lamp_export.py's (golden tests); the station runs end to end in a test against the simulated instrument.
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"""Extract the spectral tables used by the TM-30-18 path of colour-science.
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Writes plain CSV files into ``crates/tm30/data`` containing the *effective*
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arrays colour-science 0.4.7 uses when ``colour_fidelity_index_ANSIIESTM3018``
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is called on a 380-780 nm / 1 nm spectral distribution (which is what
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``tm30.py`` always builds). "Effective" matters: colour re-aligns its 1 nm
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CMFs and 5 nm D-series basis functions with a Sprague interpolator, which is
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not bit-exact even on the original nodes, so the arrays are dumped exactly as
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the pipeline sees them (same reshape calls, same order).
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Usage (from the repository root, with a Python that has colour-science 0.4.7):
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python -W ignore crates/tm30/tools/extract_data.py
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Values are written with ``repr`` (shortest round-trip), so Rust's
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``str::parse::<f64>`` recovers the identical doubles.
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"""
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from __future__ import annotations
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import os
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import sys
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import warnings
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import numpy as np
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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.appearance.ciecam02 import CAT_INVERSE_CAT02, MATRIX_XYZ_TO_HPE
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from colour.adaptation import CAT_CAT02
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from colour.colorimetry import (
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MSDS_CMFS,
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SDS_BASIS_FUNCTIONS_CIE_ILLUMINANT_D_SERIES,
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SDS_ILLUMINANTS,
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SPECTRAL_SHAPE_DEFAULT,
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SpectralShape,
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reshape_msds,
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reshape_sd,
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)
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from colour.quality.cfi2017 import load_TCS_CIE2017
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HERE = os.path.dirname(os.path.abspath(__file__))
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DATA = os.path.normpath(os.path.join(HERE, "..", "data"))
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SHAPE = SpectralShape(380, 780, 1)
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def fmt(x: float) -> str:
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return repr(float(x))
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def write_csv(name: str, header: list[str], rows) -> None:
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path = os.path.join(DATA, name)
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with open(path, "w", newline="\n") as f:
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f.write(",".join(header) + "\n")
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for row in rows:
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f.write(",".join(fmt(v) for v in row) + "\n")
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print(f"wrote {path}")
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def main() -> int:
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assert colour.__version__ == "0.4.7", colour.__version__
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os.makedirs(DATA, exist_ok=True)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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# --- CIE 1931 2 degree observer -------------------------------------
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# handle_spectral_arguments() reshapes the default observer to
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# SPECTRAL_SHAPE_DEFAULT (360-780 nm, 1 nm); this array feeds the
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# Ohno (2013) Planckian table (CCT_to_uv_Planck1900).
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cmfs_2_default = reshape_msds(
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MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
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SPECTRAL_SHAPE_DEFAULT,
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copy=False,
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)
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assert cmfs_2_default.shape == SpectralShape(360, 780, 1)
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# sd_to_XYZ_integration() then re-aligns *that* array to the test
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# shape (380-780 nm) for the test SPD CCT and the reference mixing
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# normalisation.
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cmfs_2_test = reshape_msds(cmfs_2_default, SHAPE, copy=False)
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# --- CIE 1964 10 degree observer ------------------------------------
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cmfs_10 = reshape_msds(
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MSDS_CMFS["CIE 1964 10 Degree Standard Observer"], SHAPE, copy=False
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)
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# --- Illuminant E (implicit weighting in the integration) -----------
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for shape in (SPECTRAL_SHAPE_DEFAULT, SHAPE):
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E = reshape_sd(SDS_ILLUMINANTS["E"], shape, copy=False)
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assert np.all(E.values == 100.0), "illuminant E is not exactly 100"
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E2 = reshape_sd(
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reshape_sd(SDS_ILLUMINANTS["E"], SPECTRAL_SHAPE_DEFAULT, copy=False),
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SHAPE,
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copy=False,
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)
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assert np.all(E2.values == 100.0)
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# --- CIE D-series basis functions -----------------------------------
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basis = [
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reshape_sd(SDS_BASIS_FUNCTIONS_CIE_ILLUMINANT_D_SERIES[n], SHAPE, copy=False)
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for n in ("S0", "S1", "S2")
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]
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# --- CIE 2017 test colour samples -----------------------------------
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tcs = load_TCS_CIE2017(SHAPE)
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assert tcs.shape == SHAPE and tcs.values.shape == (401, 99)
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wl_default = cmfs_2_default.wavelengths
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write_csv(
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"cmf_cie1931_2deg_360_780.csv",
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["wavelength_nm", "x_bar", "y_bar", "z_bar"],
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(
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[w, *v]
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for w, v in zip(wl_default, cmfs_2_default.values, strict=True)
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),
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)
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write_csv(
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"cmf_cie1931_2deg_380_780.csv",
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["wavelength_nm", "x_bar", "y_bar", "z_bar"],
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([w, *v] for w, v in zip(SHAPE.wavelengths, cmfs_2_test.values, strict=True)),
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)
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write_csv(
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"cmf_cie1964_10deg_380_780.csv",
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["wavelength_nm", "x_bar", "y_bar", "z_bar"],
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([w, *v] for w, v in zip(SHAPE.wavelengths, cmfs_10.values, strict=True)),
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)
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write_csv(
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"d_series_basis_380_780.csv",
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["wavelength_nm", "S0", "S1", "S2"],
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(
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[w, a, b, c]
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for w, a, b, c in zip(
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SHAPE.wavelengths,
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basis[0].values,
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basis[1].values,
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basis[2].values,
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strict=True,
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)
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),
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)
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write_csv(
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"tcs_cie2017_380_780.csv",
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["wavelength_nm", *[f"TCS{i:02d}" for i in range(99)]],
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([w, *v] for w, v in zip(SHAPE.wavelengths, tcs.values, strict=True)),
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)
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# CIECAM02 matrices exactly as colour uses them: CAT02 (literal) and the
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# numpy product MATRIX_XYZ_TO_HPE @ inv(CAT02) used by RGB_to_rgb().
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hpe_cat02_inv = np.matmul(MATRIX_XYZ_TO_HPE, CAT_INVERSE_CAT02)
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path = os.path.join(DATA, "ciecam02_matrices.csv")
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with open(path, "w", newline="\n") as f:
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f.write("name,m00,m01,m02,m10,m11,m12,m20,m21,m22\n")
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for name, m in (("CAT02", CAT_CAT02), ("HPE_CAT02_INV", hpe_cat02_inv)):
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f.write(name + "," + ",".join(fmt(v) for v in np.ravel(m)) + "\n")
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print(f"wrote {path}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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