colour.adaptation.chromatic_adaptation_CMCCAT2000¶

colour.adaptation.chromatic_adaptation_CMCCAT2000(XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, surround=CMCCAT2000_InductionFactors(F=1), direction='Forward')[source]¶

Adapts given stimulus CIE XYZ tristimulus values using given viewing conditions.

This definition is a convenient wrapper around colour.adaptation.chromatic_adaptation_forward_CMCCAT2000() and colour.adaptation.chromatic_adaptation_inverse_CMCCAT2000().

Parameters

XYZ (array_like) – CIE XYZ tristimulus values of the stimulus to adapt.
XYZ_w (array_like) – Source viewing condition CIE XYZ tristimulus values of the whitepoint.
XYZ_wr (array_like) – Target viewing condition CIE XYZ tristimulus values of the whitepoint.
L_A1 (numeric or array_like) – Luminance of test adapting field \(L_{A1}\) in \(cd/m^2\).
L_A2 (numeric or array_like) – Luminance of reference adapting field \(L_{A2}\) in \(cd/m^2\).
surround (CMCCAT2000_InductionFactors, optional) – Surround viewing conditions induction factors.
direction (unicode, optional) – {‘Forward’, ‘Inverse’}, Chromatic adaptation direction.

Returns

Adapted stimulus CIE XYZ tristimulus values.

Return type

ndarray

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]
`XYZ_w`	[0, 100]	[0, 1]
`XYZ_wr`	[0, 100]	[0, 1]

Range	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]

References

[LLRH02], [WRC12a]

Examples

>>> XYZ = np.array([22.48, 22.74, 8.54])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000(
...     XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, direction='Forward')
... 
array([ 19.5269832...,  23.0683396...,  24.9717522...])

Using the CMCCAT2000 inverse model:

>>> XYZ = np.array([19.52698326, 23.06833960, 24.97175229])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000(
...     XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, direction='Inverse')
... 
array([ 22.48,  22.74,   8.54])