colour.RGB_colourspace_volume_MonteCarlo¶
- colour.RGB_colourspace_volume_MonteCarlo(colourspace: colour.models.rgb.rgb_colourspace.RGB_Colourspace, samples: int = 1000000, limits: ArrayLike = np.array([[0, 100], [- 150, 150], [- 150, 150]]), illuminant_Lab: ArrayLike = CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'], chromatic_adaptation_transform: Union[Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'], str] = 'CAT02', random_generator: Callable = random_triplet_generator, random_state: Optional[numpy.random.mtrand.RandomState] = None) float[source]¶
Perform given RGB colourspace volume computation using Monte Carlo method and multiprocessing.
- Parameters
of. (colourspace RGB colourspace to compute the volume) –
count. (samples Samples) –
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 (colour.models.rgb.rgb_colourspace.RGB_Colourspace) –
samples (int) –
limits (ArrayLike) –
illuminant_Lab (ArrayLike) –
chromatic_adaptation_transform (Union[Literal['Bianco 2010', 'Bianco PC 2010', 'Bradford', 'CAT02 Brill 2008', 'CAT02', 'CAT16', 'CMCCAT2000', 'CMCCAT97', 'Fairchild', 'Sharp', 'Von Kries', 'XYZ Scaling'], str]) –
random_generator (Callable) –
random_state (Optional[numpy.random.mtrand.RandomState]) –
- Returns
RGB colourspace volume.
- Return type
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) ... 8...