#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Random Numbers Utilities
========================
Defines random numbers generator objects:
- :func:`random_triplet_generator`
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.utilities import warning
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2017 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['RANDOM_STATE',
'random_triplet_generator']
RANDOM_STATE = np.random.RandomState()
[docs]def random_triplet_generator(size,
limits=np.array([[0, 1], [0, 1], [0, 1]]),
random_state=RANDOM_STATE):
"""
Returns a generator yielding random triplets.
Parameters
----------
size : integer
Generator size.
limits : array_like, (3, 2)
Random values limits on each triplet axis.
random_state : RandomState
Mersenne Twister pseudo-random number generator.
Returns
-------
generator
Random triplets generator.
Notes
-----
- The doctest is assuming that :func:`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 pprint import pprint
>>> prng = np.random.RandomState(4)
>>> pprint( # doctest: +ELLIPSIS
... tuple(random_triplet_generator(10, random_state=prng)))
(array([ 0.9670298..., 0.5472322..., 0.9726843...]),
array([ 0.7148159..., 0.6977288..., 0.2160895...]),
array([ 0.9762744..., 0.0062302..., 0.2529823...]),
array([ 0.4347915..., 0.7793829..., 0.1976850...]),
array([ 0.8629932..., 0.9834006..., 0.1638422...]),
array([ 0.5973339..., 0.0089861..., 0.3865712...]),
array([ 0.0441600..., 0.9566529..., 0.4361466...]),
array([ 0.9489773..., 0.7863059..., 0.8662893...]),
array([ 0.1731654..., 0.0749485..., 0.6007427...]),
array([ 0.1679721..., 0.7333801..., 0.4084438...]))
"""
integer_size = int(size)
if integer_size != size:
warning(('"size" has been cast to integer: {0}'.format(
integer_size)))
for _ in range(integer_size):
yield np.array([random_state.uniform(*limits[0]),
random_state.uniform(*limits[1]),
random_state.uniform(*limits[2])])