colour.colorimetry.whiteness Module¶

Whiteness Index \(W\)¶

Defines whiteness index \(W\) computation objects:

whiteness_Berger1959()
whiteness_Taube1960()
whiteness_Stensby1968()
whiteness_ASTM313()
whiteness_Ganz1979()
whiteness_CIE2004()

See also

References

[1]	Wikipedia. (n.d.). Whiteness. Retrieved September 17, 2014, from http://en.wikipedia.org/wiki/Whiteness

[2]	(1, 2, 3, 4, 5, 6) X-Rite, & Pantone. (2012). Color iQC and Color iMatch Color Calculations Guide. Retrieved from http://www.xrite.com/documents/literature/en/09_Color_Calculations_en.pdf

[3]	Wyszecki, G., & Stiles, W. S. (2000). Table I(6.5.3) Whiteness Formulae (Whiteness Measure Denoted by W). In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 837–839). Wiley. ISBN:978-0471399186

colour.colorimetry.whiteness.whiteness_Berger1959(XYZ, XYZ_0)[source]¶

Returns the whiteness index \(WI\) of given sample CIE XYZ colourspace matrix using Berger (1959) method. [2]

Parameters:	XYZ (array_like) – CIE XYZ colourspace matrix of sample. XYZ_0 (array_like) – CIE XYZ colourspace matrix of reference white.
Returns:	Whiteness \(WI\).
Return type:	numeric

Notes

Input CIE XYZ and CIE XYZ_0 colourspace matrices are in domain [0, 100].
Whiteness \(WI\) values larger than 33.33 indicate a bluish white, and values smaller than 33.33 indicate a yellowish white.

Warning

The input domain of that definition is non standard!

Examples

>>> XYZ = np.array([95., 100., 105.])
>>> XYZ_0 = np.array([94.80966767, 100., 107.30513595])
>>> whiteness_Berger1959(XYZ, XYZ_0)  
30.3638017...

colour.colorimetry.whiteness.whiteness_Taube1960(XYZ, XYZ_0)[source]¶

Returns the whiteness index \(WI\) of given sample CIE XYZ colourspace matrix using Taube (1960) method. [2]

Parameters:	XYZ (array_like) – CIE XYZ colourspace matrix of sample. XYZ_0 (array_like) – CIE XYZ colourspace matrix of reference white.
Returns:	Whiteness \(WI\).
Return type:	numeric

Notes

Input CIE XYZ and CIE XYZ_0 colourspace matrices are in domain [0, 100].
Whiteness \(WI\) values larger than 100 indicate a bluish white, and values smaller than 100 indicate a yellowish white.

Examples

>>> XYZ = np.array([95., 100., 105.])
>>> XYZ_0 = np.array([94.80966767, 100., 107.30513595])
>>> whiteness_Taube1960(XYZ, XYZ_0)  
91.4071738...

colour.colorimetry.whiteness.whiteness_Stensby1968(Lab)[source]¶

Returns the whiteness index \(WI\) of given sample CIE Lab colourspace matrix using Stensby (1968) method. [2]

Parameters:	Lab (array_like) – CIE Lab colourspace matrix of sample.
Returns:	Whiteness \(WI\).
Return type:	numeric

Notes

Input CIE Lab colourspace matrix are in domain [0, 100].
Whiteness \(WI\) values larger than 100 indicate a bluish white, and values smaller than 100 indicate a yellowish white.

Examples

>>> Lab = np.array([100., -2.46875131, -16.72486654])
>>> whiteness_Stensby1968(Lab)  
142.7683456...

colour.colorimetry.whiteness.whiteness_ASTM313(XYZ)[source]¶

Returns the whiteness index \(WI\) of given sample CIE XYZ colourspace matrix using ASTM 313 method. [2]

Parameters:	XYZ (array_like) – CIE XYZ colourspace matrix of sample.
Returns:	Whiteness \(WI\).
Return type:	numeric

Notes

Input CIE XYZ colourspace matrix is in domain [0, 100].

Warning

The input domain of that definition is non standard!

Examples

>>> XYZ = np.array([95., 100., 105.])
>>> whiteness_ASTM313(XYZ)  
55.7400000...

colour.colorimetry.whiteness.whiteness_Ganz1979(xy, Y)[source]¶

Returns the whiteness index \(W\) and tint \(T\) of given sample xy chromaticity coordinates using Ganz and Griesser (1979) method. [2]

Parameters:	xy (tuple) – Chromaticity coordinates xy of sample. Y (numeric) – Tristimulus \(Y\) value of sample.
Returns:	Whiteness \(W\) and tint \(T\).
Return type:	tuple

Notes

Input tristimulus \(Y\) value is in domain [0, 100].
The formula coefficients are valid for CIE Standard Illuminant D Series D65 and CIE 1964 10 Degree Standard Observer.
Positive output tint \(T\) values indicate a greener tint while negative values indicate a redder tint.
Whiteness differences of less than 5 Ganz units appear to be indistinguishable to the human eye.
Tint differences of less than 0.5 Ganz units appear to be indistinguishable to the human eye.

Warning

The input domain of that definition is non standard!

Examples

>>> whiteness_Ganz1979((0.3167, 0.3334), 100.)  
(85.6003766..., 0.6789002...)

colour.colorimetry.whiteness.whiteness_CIE2004(xy, Y, xy_n, observer=u'CIE 1931 2 Degree Standard Observer')[source]¶

Returns the whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample xy chromaticity coordinates using CIE 2004 method.

Parameters:	xy (tuple) – Chromaticity coordinates xy of sample. Y (numeric) – Tristimulus \(Y\) value of sample. xy_n (tuple) – 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:	tuple

Notes

Input tristimulus \(Y\) value is in domain [0, 100].
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. [2]
Positive output tint \(T\) or \(T_{10}\) values indicate a greener tint while negative values indicate a redder tint.

Warning

The input domain of that definition is non standard!

References

[4]	CIE TC 1-48. (2004). The evaluation of whiteness. In CIE 015:2004 Colorimetry, 3rd Edition (p. 24). ISBN:978-3-901-90633-6

Examples

>>> xy_n = (0.3139, 0.3311)
>>> whiteness_CIE2004((0.3167, 0.3334), 100., xy_n)  
(93.8500000..., -1.3049999...)

colour.colorimetry.whiteness.WHITENESS_METHODS = CaseInsensitiveMapping({u'ASTM 313': <function whiteness_ASTM313 at 0x2b437a9bb500>, u'Ganz 1979': <function whiteness_Ganz1979 at 0x2b437a9bb578>, u'Stensby 1968': <function whiteness_Stensby1968 at 0x2b437a9bb488>, u'Taube 1960': <function whiteness_Taube1960 at 0x2b437a9bb410>, u'Berger 1959': <function whiteness_Berger1959 at 0x2b437a9bb140>, u'CIE 2004': <function whiteness_CIE2004 at 0x2b437a9bb5f0>, u'cie2004': <function whiteness_CIE2004 at 0x2b437a9bb5f0>})¶

Supported whiteness computations methods.

WHITENESS_METHODS : CaseInsensitiveMapping: {‘CIE 2004’, ‘Berger 1959’, ‘Taube 1960’, ‘Stensby 1968’, ‘ASTM 313’, ‘Ganz 1979’, ‘CIE 2004’}

Aliases:

‘cie2004’: ‘CIE 2004’

colour.colorimetry.whiteness.whiteness(method=u'CIE 2004', **kwargs)[source]¶

Returns the whiteness \(W\) using given method.

Parameters:	method (unicode, optional) – {‘CIE 2004’, ‘Berger 1959’, ‘Taube 1960’, ‘Stensby 1968’, ‘ASTM 313’, ‘Ganz 1979’, ‘CIE 2004’} Computation method. kwargs () – Keywords arguments.
Returns:	whiteness \(W\).
Return type:	numeric

Examples

>>> xy = (0.3167, 0.3334)
>>> Y = 100
>>> xy_n = (0.3139, 0.3311)
>>> whiteness(xy=xy, Y=Y, xy_n=xy_n)  
(93.8500000..., -1.3049999...)
>>> XYZ = np.array([95., 100., 105.])
>>> XYZ_0 = np.array([94.80966767, 100., 107.30513595])
>>> method = 'Taube 1960'
>>> whiteness(XYZ=XYZ, XYZ_0=XYZ_0, method=method)  
91.4071738...