colour.characterisation.training_data_sds_to_XYZ#

colour.characterisation.training_data_sds_to_XYZ(training_data: MultiSpectralDistributions, cmfs: MultiSpectralDistributions, illuminant: SpectralDistribution, chromatic_adaptation_transform: LiteralChromaticAdaptationTransform | str | None = 'CAT02') → Range1[source]#

Convert training data to CIE XYZ tristimulus values using the specified illuminant and standard observer colour matching functions.

Parameters:

training_data (MultiSpectralDistributions) – Training data multi-spectral distributions.
cmfs (MultiSpectralDistributions) – Standard observer colour matching functions.
illuminant (SpectralDistribution) – Illuminant spectral distribution.
chromatic_adaptation_transform (LiteralChromaticAdaptationTransform | str | None) – Chromatic adaptation transform, if None no chromatic adaptation is performed.

Returns:

Training data CIE XYZ tristimulus values.

Return type:

numpy.ndarray

Notes

Range	Scale - Reference	Scale - 1
`XYZ`	1	1

Examples

>>> from colour import MSDS_CMFS
>>> path = os.path.join(
...     ROOT_RESOURCES_RAWTOACES,
...     "CANON_EOS_5DMark_II_RGB_Sensitivities.csv",
... )
>>> cmfs = MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
>>> sensitivities = sds_and_msds_to_msds(read_sds_from_csv_file(path).values())
>>> illuminant = normalise_illuminant(SDS_ILLUMINANTS["D55"], sensitivities)
>>> training_data = read_training_data_rawtoaces_v1()
>>> training_data_sds_to_XYZ(training_data, cmfs, illuminant)[:5]
...
array([[ 0.0174353...,  0.0179504...,  0.0196109...],
       [ 0.0855607...,  0.0895735...,  0.0901703...],
       [ 0.7455880...,  0.7817549...,  0.7834356...],
       [ 0.1900528...,  0.1995   ...,  0.2012606...],
       [ 0.5626319...,  0.5914544...,  0.5894500...]])

colour.characterisation.training_data_sds_to_XYZ#

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