colour.adaptation.matrix_chromatic_adaptation_VonKries(XYZ_w: ArrayLike, XYZ_wr: ArrayLike, transform: Union[Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'], str] = 'CAT02') [source]

Compute the chromatic adaptation matrix from test viewing conditions to reference viewing conditions.

Parameters
• XYZ_w (ArrayLike) – Test viewing conditions CIE XYZ tristimulus values of whitepoint.

• XYZ_wr (ArrayLike) – Reference viewing conditions CIE XYZ tristimulus values of whitepoint.

• transform (Union[Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'], str]) – Chromatic adaptation transform.

Returns

Chromatic adaptation matrix $$M_{cat}$$.

Return type

numpy.ndarray

Notes

Domain

Scale - Reference

Scale - 1

XYZ_w

[0, 1]

[0, 1]

XYZ_wr

[0, 1]

[0, 1]

References

[Fai13b]

Examples

>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
...
array([[ 1.0425738...,  0.0308910..., -0.0528125...],
[ 0.0221934...,  1.0018566..., -0.0210737...],
[-0.0011648..., -0.0034205...,  0.7617890...]])


>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])