colour.adaptation.chromatic_adaptation_Fairchild1990#

Adapt the specified stimulus CIE XYZ tristimulus values from test viewing conditions to reference viewing conditions using the Fairchild (1990) chromatic adaptation model.

Parameters:

XYZ_1 (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 100]) – CIE XYZ_1 tristimulus values of test sample / stimulus.
XYZ_n (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 100]) – Test viewing condition CIE XYZ_n tristimulus values of the whitepoint.
XYZ_r (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 100]) – Reference viewing condition CIE XYZ_r tristimulus values of the whitepoint.
Y_n (ArrayLike) – Luminance \(Y_n\) of test adapting stimulus in \(cd/m^2\).
discount_illuminant (bool) – Truth value indicating if the illuminant should be discounted.

Returns:

CIE XYZ tristimulus values of the stimulus corresponding colour.

Return type:

numpy.ndarray

Notes

Domain	Scale - Reference	Scale - 1
`XYZ_1`	100	1
`XYZ_n`	100	1
`XYZ_r`	100	1

Range	Scale - Reference	Scale - 1
`XYZ_2`	100	1

References

[Fai91], [Fai13f]

Examples

>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n)
...
array([ 23.3252634...,  23.3245581...,  76.1159375...])

colour.adaptation.chromatic_adaptation_Fairchild1990#

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