colour.XYZ_to_ATD95#

Compute the ATD (1995) colour vision model correlates from the specified CIE XYZ tristimulus values.

Parameters:

XYZ (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 100]) – CIE XYZ tristimulus values of test sample / stimulus.
XYZ_0 (Annotated[_Buffer | _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | complex | bytes | str | _NestedSequence[complex | bytes | str], 100]) – CIE XYZ tristimulus values of reference white.
Y_0 (ArrayLike) – Absolute adapting field luminance in \(cd/m^2\).
k_1 (ArrayLike) – Application specific weight \(k_1\).
k_2 (ArrayLike) – Application specific weight \(k_2\).
sigma (ArrayLike) – Constant \(\sigma\) varied to predict different types of data.

Returns:

ATD (1995) colour vision model specification.

Return type:

colour.CAM_Specification_ATD95

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	100	1
`XYZ_0`	100	1

Range	Scale - Reference	Scale - 1
`specification.h`	360	1

For unrelated colours, there is only self-adaptation and \(k_1\) is set to 1.0 while \(k_2\) is set to 0.0. For related colours such as typical colorimetric applications, \(k_1\) is set to 0.0 and \(k_2\) is set to a value between 15 and 50 (Guth, 1995).

References

[Fai13e], [Gut95]

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_0 = np.array([95.05, 100.00, 108.88])
>>> Y_0 = 318.31
>>> k_1 = 0.0
>>> k_2 = 50.0
>>> XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2)
CAM_Specification_ATD95(h=1.9089869..., C=1.2064060..., Q=0.1814003..., A_1=0.1787931... T_1=0.0286942..., D_1=0.0107584..., A_2=0.0192182..., T_2=0.0205377..., D_2=0.0107584...)

colour.XYZ_to_ATD95#

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