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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"""Generate golden test vectors for the ``tm30`` crate.
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Every case runs the production Python reference ``compute_tm30`` (HPCS
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``tm30.py`` on colour-science 0.4.7) and writes one JSON file into
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``crates/tm30/tests/golden`` holding the exact input (wavelengths, values)
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and every output field. NaN is encoded as ``null``. Floats are written with
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``repr`` so they round-trip exactly.
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Usage (from the repository root):
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python -W ignore crates/tm30/tools/golden.py [--tm30-dir DIR] [--lamps-dir DIR]
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``--tm30-dir`` is the folder containing ``tm30.py`` (defaults to the HPCS
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scratch copy), ``--lamps-dir`` the buildfor.life ``comparison-data/lamps``
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folder with ``L*/spd.csv`` files (read only).
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"""
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from __future__ import annotations
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import argparse
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import csv
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import glob
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import json
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import math
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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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HERE = os.path.dirname(os.path.abspath(__file__))
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OUT = os.path.normpath(os.path.join(HERE, "..", "tests", "golden"))
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DEFAULT_TM30_DIR = os.path.join(
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os.path.expanduser("~"), "AppData", "Local", "hermes", "cache", "scratch", "hpcs-py"
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)
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DEFAULT_LAMPS_DIR = "C:/dev/buildfor_life_web/apps/web/comparison-data/lamps"
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# HPCS 6500 grid: 350 points, 380..1050 nm.
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HPCS_GRID = np.linspace(380.0, 1050.0, 350)
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def clean(x):
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x = float(x)
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return None if math.isnan(x) else x
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def read_lamp_csv(path):
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wl, vals = [], []
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with open(path, newline="") as f:
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reader = csv.DictReader(f)
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for row in reader:
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wl.append(float(row["wavelength_nm"]))
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vals.append(float(row["value"]))
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return np.array(wl), np.array(vals)
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def build_cases(lamps_dir):
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import colour
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from colour.colorimetry import SDS_ILLUMINANTS, SpectralShape, sd_blackbody
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from colour.colorimetry import sd_CIE_illuminant_D_series
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from colour.temperature import CCT_to_xy_CIE_D
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cases = []
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# Real lamps measured with the HPCS 6500 (380-1050 nm, ~1.92 nm steps).
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for path in sorted(glob.glob(os.path.join(lamps_dir, "L*", "spd.csv"))):
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lamp = os.path.basename(os.path.dirname(path))
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wl, vals = read_lamp_csv(path)
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cases.append((f"lamp_{lamp}", f"{lamp}/spd.csv", wl, vals))
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# colour-science illuminant library.
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names = (
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[f"FL{i}" for i in range(1, 13)]
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+ ["A", "D50", "D65"]
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+ [k for k in SDS_ILLUMINANTS.keys() if k.startswith("LED-")]
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+ [f"HP{i}" for i in range(1, 6)]
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)
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for name in names:
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sd = SDS_ILLUMINANTS[name]
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cases.append(
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(
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"illuminant_" + name.replace("-", "_").replace(".", "_"),
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f"colour.SDS_ILLUMINANTS[{name!r}] {sd.shape}",
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sd.wavelengths,
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sd.values,
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)
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)
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# Planckian radiators, covering the < 4000 K (pure Planckian reference)
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# and 4000-5000 K (Planckian / daylight mixture) regimes.
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shape = SpectralShape(360, 830, 1)
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for T in (2700, 4300, 4700):
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sd = sd_blackbody(T, shape)
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cases.append((f"blackbody_{T}K", f"sd_blackbody({T}, {shape})", sd.wavelengths, sd.values))
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# CIE daylight (> 5000 K regime).
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for T in (6500, 10000):
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xy = CCT_to_xy_CIE_D(T * 1.4388 / 1.4380)
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sd = sd_CIE_illuminant_D_series(xy)
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cases.append((f"daylight_{T}K", f"sd_CIE_illuminant_D_series(CCT_to_xy_CIE_D({T}*1.4388/1.4380))", sd.wavelengths, sd.values))
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# Grid variations.
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# Coarse 10 nm grid.
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d50 = SDS_ILLUMINANTS["D50"]
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wl10 = np.arange(380.0, 781.0, 10.0)
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cases.append(("grid_10nm_D50", "D50 sampled every 10 nm", wl10, d50[wl10]))
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# HPCS 6500 grid (380-1050 nm, 670/349 nm steps), blackbody and LED.
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bb = sd_blackbody(3000, SpectralShape(360, 1100, 1))
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cases.append(("hpcs_grid_blackbody_3000K", "sd_blackbody(3000) on 380-1050 nm / 350 points", HPCS_GRID, bb[HPCS_GRID]))
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led = SDS_ILLUMINANTS["LED-B3"]
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cases.append(
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(
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"hpcs_grid_LED_B3",
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"LED-B3 linearly resampled on 380-1050 nm / 350 points (0 beyond 780)",
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HPCS_GRID,
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np.interp(HPCS_GRID, led.wavelengths, led.values, right=0.0),
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)
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)
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# Irregular, non-uniform grid.
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rng = np.random.default_rng(20180)
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wl_irr = np.sort(np.concatenate([[371.3], rng.uniform(372.0, 800.0, 240), [801.7]]))
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fl11 = SDS_ILLUMINANTS["FL11"]
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cases.append(
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(
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"irregular_grid_FL11",
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"FL11 linearly resampled on 242 random wavelengths 371-802 nm",
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wl_irr,
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np.interp(wl_irr, fl11.wavelengths, fl11.values),
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)
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)
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# Partial coverage (400-700 nm only): np.interp holds the end values.
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fl4 = SDS_ILLUMINANTS["FL4"]
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wl_p = np.arange(400.0, 701.0, 2.5)
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cases.append(
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(
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"partial_400_700_FL4",
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"FL4 on 400-700 nm / 2.5 nm",
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wl_p,
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np.interp(wl_p, fl4.wavelengths, fl4.values),
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)
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)
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# Negated spectrum: colour normalises by Y, so a negative scale must give
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# exactly the positive-scale result.
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fl2 = SDS_ILLUMINANTS["FL2"]
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cases.append(("negated_FL2", "-1 * FL2 (scale invariance incl. sign)", fl2.wavelengths, -fl2.values))
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# Noisy real lamp with negative values (dark-noise like).
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if cases and cases[0][0].startswith("lamp_"):
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_, _, wl, vals = cases[0]
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noise = rng.normal(0.0, 0.01 * float(np.max(vals)), vals.shape)
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cases.append(("noisy_" + cases[0][0], "first lamp + 1% gaussian noise (negative values)", wl, vals + noise))
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return cases
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--tm30-dir", default=DEFAULT_TM30_DIR)
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ap.add_argument("--lamps-dir", default=DEFAULT_LAMPS_DIR)
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args = ap.parse_args()
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sys.path.insert(0, args.tm30_dir)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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import colour # noqa: F401
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from tm30 import compute_tm30
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cases = build_cases(args.lamps_dir)
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os.makedirs(OUT, exist_ok=True)
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for old in glob.glob(os.path.join(OUT, "*.json")):
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os.remove(old)
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for name, source, wl, vals in cases:
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wl = [float(x) for x in np.asarray(wl, dtype=float)]
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vals = [float(x) for x in np.asarray(vals, dtype=float)]
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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res = compute_tm30(wl, vals)
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expected = {
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"Rf": clean(res["Rf"]),
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"Rg": clean(res["Rg"]),
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"CCT_K": clean(res["CCT_K"]),
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"Duv": clean(res["Duv"]),
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"bins": [
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{k: (v if k == "bin" else clean(v)) for k, v in b.items()}
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for b in res["bins"]
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],
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}
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doc = {
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"name": name,
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"source": source,
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"generator": "crates/tm30/tools/golden.py (tm30.py + colour-science "
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+ __import__("colour").__version__
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+ ", numpy "
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+ np.__version__
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+ ")",
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"wavelengths": wl,
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"values": vals,
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"expected": expected,
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}
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path = os.path.join(OUT, name + ".json")
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with open(path, "w", newline="\n") as f:
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json.dump(doc, f, indent=1, allow_nan=False)
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print(
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f"{name:32s} CCT={expected['CCT_K']:9.2f} Duv={expected['Duv']: .5f} "
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f"Rf={expected['Rf']:6.2f} Rg={expected['Rg']:6.2f}"
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)
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print(f"{len(cases)} cases -> {OUT}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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