colour.whiteness¶
-
colour.
whiteness
(method='CIE 2004', **kwargs)[source]¶ Returns the whiteness \(W\) using given method.
Parameters: method (unicode, optional) – {‘CIE 2004’, ‘Berger 1959’, ‘Taube 1960’, ‘Stensby 1968’, ‘ASTM E313’, ‘Ganz 1979’}, Computation method.
Other Parameters: - XYZ (array_like) – {
colour.colorimetry.whiteness_Berger1959()
,colour.colorimetry.whiteness_Taube1960()
,colour.colorimetry.whiteness_ASTME313()
}, CIE XYZ tristimulus values of sample. - XYZ_0 (array_like) – {
colour.colorimetry.whiteness_Berger1959()
,colour.colorimetry.whiteness_Taube1960()
}, CIE XYZ tristimulus values of reference white. - Lab (array_like) – {
colour.colorimetry.whiteness_Stensby1968()
}, CIE L*a*b* colourspace array of sample. - xy (array_like) – {
colour.colorimetry.whiteness_Ganz1979()
,colour.colorimetry.whiteness_CIE2004()
}, Chromaticity coordinates xy of sample. - Y (numeric or array_like) – {
colour.colorimetry.whiteness_Ganz1979()
,colour.colorimetry.whiteness_CIE2004()
}, Tristimulus \(Y\) value of sample. - xy_n (array_like) – {
colour.colorimetry.whiteness_CIE2004()
}, Chromaticity coordinates xy_n of perfect diffuser. - observer (unicode, optional) – {
colour.colorimetry.whiteness_CIE2004()
}, {‘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 \(W\).
Return type: numeric or ndarray
Notes
Domain Scale - Reference Scale - 1 Lab
L
: [0, 100]a
: [-100, 100]b
: [-100, 100]L
: [0, 1]a
: [-1, 1]b
: [-1, 1]XYZ
[0, 100] [0, 1] XYZ_0
[0, 100] [0, 1] Y
[0, 100] [0, 1] Range Scale - Reference Scale - 1 W
[0, 100] [0, 1] References
[CIET14804i], [WS00k], [XRP12], [Wik04c]
Examples
>>> import numpy as np >>> xy = np.array([0.3167, 0.3334]) >>> Y = 100 >>> xy_n = np.array([0.3139, 0.3311]) >>> whiteness(xy=xy, Y=Y, xy_n=xy_n) # doctest: +ELLIPSIS array([ 93.85..., -1.305...]) >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> XYZ_0 = np.array([94.80966767, 100.00000000, 107.30513595]) >>> method = 'Taube 1960' >>> whiteness(XYZ=XYZ, XYZ_0=XYZ_0, method=method) # doctest: +ELLIPSIS 91.4071738...
- XYZ (array_like) – {