colour.RGB_colourspace_volume_MonteCarlo

colour.RGB_colourspace_volume_MonteCarlo(colourspace: RGB_Colourspace, samples: int = 1000000, limits: ArrayLike = ([0, 100], [-150, 150], [-150, 150]), illuminant_Lab: ArrayLike = CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'], chromatic_adaptation_transform: LiteralChromaticAdaptationTransform | str | None = 'CAT02', random_generator: Callable = random_triplet_generator, random_state: np.random.RandomState | None = None) → float

Perform given RGB colourspace volume computation using Monte Carlo method and multiprocessing.

Parameters:

• of. (colourspace RGB colourspace to compute the volume)

• count. (samples Sample)

• volume. (limits CIE L*a*b* colourspace)

• coordinates. (illuminant_Lab CIE L*a*b* colourspace illuminant chromaticity)

• method. (chromatic_adaptation_transform Chromatic adaptation)

• the (random_generator Random triplet generator providing the random samples within) – CIE L*a*b* colourspace volume.

• random (random_state Mersenne Twister pseudo-random number generator to use in the) – number generator.

• colourspace (RGB_Colourspace)

• samples (int)

• limits (ArrayLike)

• illuminant_Lab (ArrayLike)

• chromatic_adaptation_transform (LiteralChromaticAdaptationTransform | str | None)

• random_generator (Callable)

• random_state (np.random.RandomState | None)

Returns:

RGB colourspace volume.

Return type:

float

Notes

• The doctest is assuming that np.random.RandomState() definition will return the same sequence no matter which OS or Python version is used. There is however no formal promise about the prng sequence reproducibility of either Python or Numpy implementations: Laurent. (2012). Reproducibility of python pseudo-random numbers across systems and versions? Retrieved January 20, 2015, from http://stackoverflow.com/questions/8786084/reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions

Examples

>>> from colour.models import RGB_COLOURSPACE_sRGB as sRGB
>>> from colour.utilities import disable_multiprocessing
>>> prng = np.random.RandomState(2)
>>> with disable_multiprocessing():
...     RGB_colourspace_volume_MonteCarlo(sRGB, 10e3, random_state=prng)
... 
...
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