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 in domain [0, 100].
- XYZ_0 (array_like) – CIE XYZ tristimulus values of reference white in domain [0, 100].
- 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: Warning
The input domain of that definition is non standard!
Notes
- Input CIE XYZ tristimulus values are in domain [0, 100].
- Input CIE XYZ_0 tristimulus values are in domain [0, 100].
- 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
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) 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...)