Source code for

import numpy as np
import scipy.stats as stats

_future_warn = """\
Passing `None` as the seed currently return the NumPy singleton RandomState
(np.random.mtrand._rand). After release 0.13 this will change to using the
default generator provided by NumPy (np.random.default_rng()). If you need
reproducible draws, you should pass a seeded np.random.Generator, e.g.,

import numpy as np
seed = 32839283923801
rng = np.random.default_rng(seed)"

[docs]def check_random_state(seed=None): """ Turn `seed` into a random number generator. Parameters ---------- seed : {None, int, array_like[ints], `numpy.random.Generator`, `numpy.random.RandomState`, `scipy.stats.qmc.QMCEngine`}, optional If `seed` is None fresh, unpredictable entropy will be pulled from the OS and `numpy.random.Generator` is used. If `seed` is an int or ``array_like[ints]``, a new ``Generator`` instance is used, seeded with `seed`. If `seed` is already a ``Generator``, ``RandomState`` or `scipy.stats.qmc.QMCEngine` instance then that instance is used. `scipy.stats.qmc.QMCEngine` requires SciPy >=1.7. It also means that the generator only have the method ``random``. Returns ------- seed : {`numpy.random.Generator`, `numpy.random.RandomState`, `scipy.stats.qmc.QMCEngine`} Random number generator. """ if hasattr(stats, "qmc") and \ isinstance(seed, stats.qmc.QMCEngine): return seed elif isinstance(seed, np.random.RandomState): return seed elif isinstance(seed, np.random.Generator): return seed elif seed is not None: return np.random.default_rng(seed) else: import warnings warnings.warn(_future_warn, FutureWarning) return np.random.mtrand._rand