colour.adaptation.matrix_chromatic_adaptation_VonKries#

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

[Fai13a]

Examples

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

Using Bradford method:

>>> XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
>>> XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
>>> method = "Bradford"
>>> matrix_chromatic_adaptation_VonKries(XYZ_w, XYZ_wr, method)
... 
array([[ 1.0479297...,  0.0229468..., -0.0501922...],
       [ 0.0296278...,  0.9904344..., -0.0170738...],
       [-0.0092430...,  0.0150551...,  0.7518742...]])