colour.XYZ_to_ATD95

colour.XYZ_to_ATD95(XYZ: ArrayLike, XYZ_0: ArrayLike, Y_0: FloatingOrArrayLike, k_1: FloatingOrArrayLike, k_2: FloatingOrArrayLike, sigma: FloatingOrArrayLike = 300) → colour.appearance.atd95.CAM_Specification_ATD95[source]

Compute the ATD (1995) colour vision model correlates.

Parameters

  • XYZ (ArrayLike) – CIE XYZ tristimulus values of test sample / stimulus.

  • XYZ_0 (ArrayLike) – CIE XYZ tristimulus values of reference white.

  • Y_0 (FloatingOrArrayLike) – Absolute adapting field luminance in \(cd/m^2\).

  • k_1 (FloatingOrArrayLike) – Application specific weight \(k_1\).

  • k_2 (FloatingOrArrayLike) – Application specific weight \(k_2\).

  • sigma (FloatingOrArrayLike) – 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

[0, 100]

[0, 1]

XYZ_0

[0, 100]

[0, 1]

Range

Scale - Reference

Scale - 1

CAM_Specification_ATD95.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)  
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...)

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