colour.algebra.random_triplet_generator#

colour.algebra.random_triplet_generator(size: int, limits: ArrayLike = ([0, 1], [0, 1], [0, 1]), random_state: RandomState = RANDOM_STATE) → NDArrayFloat[source]#

Generate random triplets using a pseudo-random number generator.

Generate an array of random triplets with values constrained within specified limits for each dimension. The triplets are generated using a Mersenne Twister pseudo-random number generator.

Parameters:

size (int) – Number of random triplets to generate.
limits (ArrayLike) – Random value limits for each axis of the triplets, specified as a sequence of [min, max] pairs. Default limits are [0, 1] for each axis.
random_state (RandomState) – Mersenne Twister pseudo-random number generator instance used for generating random values.

Returns:

Array of shape (size, 3) containing the generated random triplets.

Return type:

numpy.ndarray

Notes

The test 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, see [Laurent12].

Examples

>>> from pprint import pprint
>>> prng = np.random.RandomState(4)
>>> random_triplet_generator(10, random_state=prng)
...
array([[0.9670298..., 0.7793829..., 0.4361466...],
       [0.5472322..., 0.1976850..., 0.9489773...],
       [0.9726843..., 0.8629932..., 0.7863059...],
       [0.7148159..., 0.9834006..., 0.8662893...],
       [0.6977288..., 0.1638422..., 0.1731654...],
       [0.2160895..., 0.5973339..., 0.0749485...],
       [0.9762744..., 0.0089861..., 0.6007427...],
       [0.0062302..., 0.3865712..., 0.1679721...],
       [0.2529823..., 0.0441600..., 0.7333801...],
       [0.4347915..., 0.9566529..., 0.4084438...]])

colour.algebra.random_triplet_generator#

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