colour.XYZ_to_ATD95¶

colour.XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2, sigma=300)[source]¶

Computes the ATD (1995) colour vision model correlates.

Parameters:	XYZ (array_like) – CIE XYZ tristimulus values of test sample / stimulus. XYZ_0 (array_like) – CIE XYZ tristimulus values of reference white. Y_0 (numeric or array_like) – Absolute adapting field luminance in \(cd/m^2\). k_1 (numeric or array_like) – Application specific weight \(k_1\). k_2 (numeric or array_like) – Application specific weight \(k_2\). sigma (numeric or array_like, optional) – Constant \(\sigma\) varied to predict different types of data.
Returns:	ATD (1995) colour vision model specification.
Return type:	ATD95_Specification

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]
`XYZ_0`	[0, 100]	[0, 1]

Range	Scale - Reference	Scale - 1
`ATD95_Specification.h`	[0, 360]	[0, 1]

For unrelated colors, there is only self-adaptation and \(k_1\) is set to 1.0 while \(k_2\) is set to 0.0. For related colors 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

[Fai13a], [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)  # doctest: +ELLIPSIS
ATD95_Specification(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...)