# colour.colorimetry.whiteness_CIE2004¶

colour.colorimetry.whiteness_CIE2004(xy: ArrayLike, Y: FloatingOrNDArray, xy_n: ArrayLike, observer: Literal['CIE 1931 2 Degree Standard Observer', 'CIE 1964 10 Degree Standard Observer'] = 'CIE 1931 2 Degree Standard Observer') [source]

Return 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 (ArrayLike) – Chromaticity coordinates CIE xy of the sample.

• Y (FloatingOrNDArray) – Tristimulus $$Y$$ value of the sample.

• xy_n (ArrayLike) – Chromaticity coordinates xy_n of a perfect diffuser.

• observer (Literal['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

numpy.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...])