colour.sd_to_aces_relative_exposure_values#

colour.sd_to_aces_relative_exposure_values(sd: colour.colorimetry.spectrum.SpectralDistribution, illuminant: Optional[colour.colorimetry.spectrum.SpectralDistribution] = None, chromatic_adaptation_transform: Optional[Union[Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'], str]] = 'CAT02', **kwargs) numpy.ndarray[source]#

Convert given spectral distribution to ACES2065-1 colourspace relative exposure values.

Parameters
Returns

ACES2065-1 colourspace relative exposure values array.

Return type

numpy.ndarray

Notes

Range

Scale - Reference

Scale - 1

XYZ

[0, 100]

[0, 1]

  • The chromatic adaptation method implemented here is a bit unusual as it involves building a new colourspace based on ACES2065-1 colourspace primaries but using the whitepoint of the illuminant that the spectral distribution was measured under.

References

[For18], [TheAoMPAaSciencesScienceaTCouncilAcademyCESACESPSubcommittee14b], [TheAoMPAaSciencesScienceaTCouncilAcademyCESACESPSubcommittee14c], [TheAoMPAaSciencesScienceaTCouncilAcademyCESACESPSubcommitteea]

Examples

>>> from colour import SDS_COLOURCHECKERS
>>> sd = SDS_COLOURCHECKERS['ColorChecker N Ohta']['dark skin']
>>> sd_to_aces_relative_exposure_values(
...     sd, chromatic_adaptation_transform=None)  
array([ 0.1171814...,  0.0866360...,  0.0589726...])
>>> sd_to_aces_relative_exposure_values(sd, apply_chromatic_adaptation=True)
... 
array([ 0.1180779...,  0.0869031...,  0.0589125...])