colour.corresponding_chromaticities_prediction

colour.corresponding_chromaticities_prediction(experiment=1, model='Von Kries', **kwargs)[source]

Returns the corresponding chromaticities prediction for given chromatic adaptation model.

Parameters:
  • experiment (integer or CorrespondingColourDataset, optional) – {1, 2, 3, 4, 6, 8, 9, 11, 12} Breneman (1987) experiment number or colour.CorrespondingColourDataset class instance.
  • model (unicode, optional) – {‘Von Kries’, ‘CIE 1994’, ‘CMCCAT2000’, ‘Fairchild 1990’}, Chromatic adaptation model.
Other Parameters:
 

transform (unicode, optional) – {colour.corresponding.corresponding_chromaticities_prediction_VonKries()}, {‘CAT02’, ‘XYZ Scaling’, ‘Von Kries’, ‘Bradford’, ‘Sharp’, ‘Fairchild’, ‘CMCCAT97’, ‘CMCCAT2000’, ‘CAT02_BRILL_CAT’, ‘Bianco’, ‘Bianco PC’}, Chromatic adaptation transform.

Returns:

Corresponding chromaticities prediction.

Return type:

tuple

References

[Breneman1987b], [CIETC1-321994b], [Fairchild1991a], [Fairchild2013s], [Fairchild2013t], [Li2002a], [Westland2012k]

Examples

>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction(2, 'CMCCAT2000')
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr)  # doctest: +SKIP
[((0.207, 0.486), (0.2083210..., 0.4727168...)),
 ((0.449, 0.511), (0.4459270..., 0.5077735...)),
 ((0.263, 0.505), (0.2640262..., 0.4955361...)),
 ((0.322, 0.545), (0.3316884..., 0.5431580...)),
 ((0.316, 0.537), (0.3222624..., 0.5357624...)),
 ((0.265, 0.553), (0.2710705..., 0.5501997...)),
 ((0.221, 0.538), (0.2261826..., 0.5294740...)),
 ((0.135, 0.532), (0.1439693..., 0.5190984...)),
 ((0.145, 0.472), (0.1494835..., 0.4556760...)),
 ((0.163, 0.331), (0.1563172..., 0.3164151...)),
 ((0.176, 0.431), (0.1763199..., 0.4127589...)),
 ((0.244, 0.349), (0.2287638..., 0.3499324...))]