scipy.stats.Normal.

sample#

Normal.sample(shape=(), *, method=None, rng=None)[source]#

Random sample from the distribution.

Parameters:
shapetuple of ints, default: ()

The shape of the sample to draw. If the parameters of the distribution underlying the random variable are arrays of shape param_shape, the output array will be of shape shape + param_shape.

method{None, ‘formula’, ‘inverse_transform’}

The strategy used to produce the sample. By default (None), the infrastructure chooses between the following options, listed in order of precedence.

  • 'formula': an implementation specific to the distribution

  • 'inverse_transform': generate a uniformly distributed sample and return the inverse CDF at these arguments.

Not all method options are available for all distributions. If the selected method is not available, a NotImplementedError` will be raised.

rngnumpy.random.Generator or scipy.stats.QMCEngine, optional

Pseudo- or quasi-random number generator state. When rng is None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator and scipy.stats.QMCEngine are passed to numpy.random.default_rng to instantiate a Generator.

If rng is an instance of scipy.stats.QMCEngine configured to use scrambling and shape is not empty, then each slice along the zeroth axis of the result is a “quasi-independent”, low-discrepancy sequence; that is, they are distinct sequences that can be treated as statistically independent for most practical purposes. Separate calls to sample produce new quasi-independent, low-discrepancy sequences.

References

[1]

Sampling (statistics), Wikipedia, https://en.wikipedia.org/wiki/Sampling_(statistics)

Examples

Instantiate a distribution with the desired parameters:

>>> import numpy as np
>>> from scipy import stats
>>> X = stats.Uniform(a=0., b=1.)

Generate a pseudorandom sample:

>>> x = X.sample((1000, 1))
>>> octiles = (np.arange(8) + 1) / 8
>>> np.count_nonzero(x <= octiles, axis=0)
array([ 148,  263,  387,  516,  636,  751,  865, 1000])  # may vary

>>> X = stats.Uniform(a=np.zeros((3, 1)), b=np.ones(2))
>>> X.a.shape,
(3, 2)
>>> x = X.sample(shape=(5, 4))
>>> x.shape
(5, 4, 3, 2)
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