colour.XYZ_to_RLAB#

colour.XYZ_to_RLAB(XYZ: ArrayLike, XYZ_n: ArrayLike, Y_n: ArrayLike, sigma: ArrayLike = VIEWING_CONDITIONS_RLAB['Average'], D: ArrayLike = D_FACTOR_RLAB['Hard Copy Images']) → CAM_Specification_RLAB[source]#

Compute the RLAB model color appearance correlates.

Parameters:

XYZ (ArrayLike) – CIE XYZ tristimulus values of test sample / stimulus.
XYZ_n (ArrayLike) – CIE XYZ tristimulus values of reference white.
Y_n (ArrayLike) – Absolute adapting luminance in \(cd/m^2\).
sigma (ArrayLike) – Relative luminance of the surround, see colour.VIEWING_CONDITIONS_RLAB for reference.
D (ArrayLike) – Discounting-the-Illuminant factor normalised to domain [0, 1].

Returns:

RLAB colour appearance model specification.

Return type:

CAM_Specification_RLAB

Notes

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

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

References

[Fai96], [Fai13d]

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = VIEWING_CONDITIONS_RLAB["Average"]
>>> D = D_FACTOR_RLAB["Hard Copy Images"]
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D)  
CAM_Specification_RLAB(J=49.8347069..., C=54.8700585..., h=286.4860208..., s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)