colour.whiteness¶
-
colour.
whiteness
(XYZ, XYZ_0, method='CIE 2004', **kwargs)[source]¶ Returns the whiteness \(W\) using given method.
- Parameters
XYZ (array_like) – CIE XYZ tristimulus values of sample.
XYZ_0 (array_like) – CIE XYZ tristimulus values of reference white.
method (unicode, optional) – {‘CIE 2004’, ‘Berger 1959’, ‘Taube 1960’, ‘Stensby 1968’, ‘ASTM E313’, ‘Ganz 1979’}, Computation method.
- Other Parameters
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
XYZ
[0, 100]
[0, 1]
XYZ_0
[0, 100]
[0, 1]
Range
Scale - Reference
Scale - 1
W
[0, 100]
[0, 1]
References
[CIET14804i], [WS00k], [XRP12], [Wik04c]
Examples
>>> import numpy as np >>> from colour.models import xyY_to_XYZ >>> XYZ = xyY_to_XYZ(np.array([0.3167, 0.3334, 100])) >>> XYZ_0 = xyY_to_XYZ(np.array([0.3139, 0.3311, 100])) >>> whiteness(XYZ, XYZ_0) array([ 93.85..., -1.305...]) >>> XYZ = np.array([95.00000000, 100.00000000, 105.00000000]) >>> XYZ_0 = np.array([94.80966767, 100.00000000, 107.30513595]) >>> whiteness(XYZ, XYZ_0, method='Taube 1960') 91.4071738...