openmc.stats.Discrete¶

class openmc.stats.Discrete(x, p)[source]¶

Distribution characterized by a probability mass function.

The Discrete distribution assigns probability values to discrete values of a random variable, rather than expressing the distribution as a continuous random variable.

Parameters

x (Iterable of float) – Values of the random variable
p (Iterable of float) – Discrete probability for each value

Variables

x (numpy.ndarray) – Values of the random variable
p (numpy.ndarray) – Discrete probability for each value

clip(tolerance: float = 1e-06, inplace: bool = False) → Discrete[source]¶

Remove low-importance points from discrete distribution.

Given a probability mass function \(p(x)\) with \(\{x_1, x_2, x_3, \dots\}\) the possible values of the random variable with corresponding probabilities \(\{p_1, p_2, p_3, \dots\}\), this function will remove any low-importance points such that \(\sum_i x_i p_i\) is preserved to within some threshold.

New in version 0.14.0.

Parameters

tolerance (float) – Maximum fraction of \(\sum_i x_i p_i\) that will be discarded.
inplace (bool) – Whether to modify the current object in-place or return a new one.

Return type

Discrete distribution with low-importance points removed

classmethod from_xml_element(elem: Element)[source]¶

Generate discrete distribution from an XML element

Parameters: elem (lxml.etree._Element) – XML element
Returns: Discrete distribution generated from XML element
Return type: openmc.stats.Discrete

integral()[source]¶

Return integral of distribution

New in version 0.13.1.

Returns: Integral of discrete distribution
Return type: float

classmethod merge(dists: Sequence[Discrete], probs: Sequence[int])[source]¶

Merge multiple discrete distributions into a single distribution

New in version 0.13.1.

Parameters

dists (iterable of openmc.stats.Discrete) – Discrete distributions to combine
probs (iterable of float) – Probability of each distribution

Returns

Combined discrete distribution

Return type

openmc.stats.Discrete

normalize()[source]¶: Normalize the probabilities stored on the distribution

sample(n_samples=1, seed=None)[source]¶

Sample the univariate distribution

Parameters

n_samples (int) – Number of sampled values to generate
seed (int or None) – Initial random number seed.

Returns

A 1-D array of sampled values

Return type

numpy.ndarray

to_xml_element(element_name)[source]¶

Return XML representation of the discrete distribution

Parameters: element_name (str) – XML element name
Returns: element – XML element containing discrete distribution data
Return type: lxml.etree._Element