colour.OSA_UCS_to_XYZ#

colour.OSA_UCS_to_XYZ(Ljg: ArrayLike, optimisation_kwargs: dict | None = None) → NDArrayFloat[source]#

Convert from OSA UCS colourspace to CIE XYZ tristimulus values under the CIE 1964 10 Degree Standard Observer.

Parameters:

Ljg (ArrayLike) – OSA UCS \(Ljg\) lightness, jaune (yellowness), and greenness.
optimisation_kwargs (dict | None) – Parameters for scipy.optimize.fmin() definition.

Returns:

CIE XYZ tristimulus values under the CIE 1964 10 Degree Standard Observer.

Return type:

numpy.ndarray

Warning

There is no analytical inverse transformation from OSA UCS to \(Ljg\) lightness, jaune (yellowness), and greenness to CIE XYZ tristimulus values, the current implementation relies on optimization using scipy.optimize.fmin() definition and thus has reduced precision and poor performance.

Notes

Domain	Scale - Reference	Scale - 1
`Ljg`	`L` : [-100, 100] `j` : [-100, 100] `g` : [-100, 100]	`L` : [-1, 1] `j` : [-1, 1] `g` : [-1, 1]

Domain

Scale - Reference

Scale - 1

Ljg

L : [-100, 100]

j : [-100, 100]

g : [-100, 100]

L : [-1, 1]

j : [-1, 1]

g : [-1, 1]

Range	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]

OSA UCS uses the CIE 1964 10 Degree Standard Observer.

References

[CTS13], [Mor03]

Examples

>>> import numpy as np
>>> Ljg = np.array([-3.00499790, 2.99713697, -9.66784231])
>>> OSA_UCS_to_XYZ(Ljg)  
array([ 20.6540240...,  12.1972369...,   5.1369372...])