colour.colorimetry.whiteness_CIE2004¶
-
colour.colorimetry.
whiteness_CIE2004
(xy, Y, xy_n, observer='CIE 1931 2 Degree Standard Observer')[source]¶ Returns the whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample CIE xy chromaticity coordinates using CIE 2004 method.
- Parameters
xy (array_like) – Chromaticity coordinates CIE xy of sample.
Y (numeric or array_like) – Tristimulus \(Y\) value of sample.
xy_n (array_like) – Chromaticity coordinates xy_n of perfect diffuser.
observer (unicode, optional) – {‘CIE 1931 2 Degree Standard Observer’, ‘CIE 1964 10 Degree Standard Observer’}, CIE Standard Observer used for computations, tint \(T\) or \(T_{10}\) value is dependent on viewing field angular subtense.
- Returns
Whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample.
- Return type
ndarray
Notes
Domain
Scale - Reference
Scale - 1
Y
[0, 100]
[0, 1]
Range
Scale - Reference
Scale - 1
WT
[0, 100]
[0, 1]
This method may be used only for samples whose values of \(W\) or \(W_{10}\) lie within the following limits: greater than 40 and less than 5Y - 280, or 5Y10 - 280.
This method may be used only for samples whose values of \(T\) or \(T_{10}\) lie within the following limits: greater than -4 and less than +2.
Output whiteness \(W\) or \(W_{10}\) values larger than 100 indicate a bluish white while values smaller than 100 indicate a yellowish white.
Positive output tint \(T\) or \(T_{10}\) values indicate a greener tint while negative values indicate a redder tint.
References
Examples
>>> import numpy as np >>> xy = np.array([0.3167, 0.3334]) >>> xy_n = np.array([0.3139, 0.3311]) >>> whiteness_CIE2004(xy, 100, xy_n) array([ 93.85..., -1.305...])