colour.XYZ_to_LLAB#

colour.XYZ_to_LLAB(XYZ: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], XYZ_0: Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], Y_b: Union[float, _SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], L: Union[float, _SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], surround: InductionFactors_LLAB = VIEWING_CONDITIONS_LLAB['Reference Samples & Images, Average Surround, Subtending < 4']) CAM_Specification_LLAB[source]#

Compute the :math:`LLAB(l:c)` colour appearance model correlates.

Parameters:
Returns:

:math:`LLAB(l:c)` colour appearance model specification.

Return type:

colour.CAM_Specification_LLAB

Notes

Domain

Scale - Reference

Scale - 1

XYZ

[0, 100]

[0, 1]

XYZ_0

[0, 100]

[0, 1]

Range

Scale - Reference

Scale - 1

CAM_Specification_LLAB.h

[0, 360]

[0, 1]

References

[Fai13h], [LLK96], [LM96]

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_0 = np.array([95.05, 100.00, 108.88])
>>> Y_b = 20.0
>>> L = 318.31
>>> surround = VIEWING_CONDITIONS_LLAB['ref_average_4_minus']
>>> XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)  
CAM_Specification_LLAB(J=37.3668650..., C=0.0089496..., h=270..., s=0.0002395..., M=0.0190185..., HC=None, a=..., b=-0.0190185...)