colour.XYZ_to_RLAB¶

colour.XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma=0.4347826086956522, D=1)[source]¶

Computes the RLAB model color appearance correlates.

Parameters:	XYZ (array_like) – CIE XYZ tristimulus values of test sample / stimulus. XYZ_n (array_like) – CIE XYZ tristimulus values of reference white. Y_n (numeric or array_like) – Absolute adapting luminance in \(cd/m^2\). sigma (numeric or array_like, optional) – Relative luminance of the surround, see `colour.RLAB_VIEWING_CONDITIONS` for reference. D (numeric or array_like, optional) – Discounting-the-Illuminant factor normalised to domain [0, 1].
Returns:	RLAB colour appearance model specification.
Return type:	RLAB_Specification

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]
`XYZ_n`	[0, 100]	[0, 1]

Range	Scale - Reference	Scale - 1
`RLAB_Specification.h`	[0, 360]	[0, 1]

References

[Fai96], [Fai13h]

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = RLAB_VIEWING_CONDITIONS['Average']
>>> D = RLAB_D_FACTOR['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D)  # doctest: +ELLIPSIS
RLAB_Specification(J=49.8347069..., C=54.8700585..., h=286.4860208..., s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)