openmc.stats.PowerLaw

class openmc.stats.PowerLaw(a: float = 0.0, b: float = 1.0, n: float = 0.0, bias: Univariate | None = None)[source]

Distribution with power law probability over a finite interval [a,b]

The power law distribution has density function \(p(x) dx = c x^n dx\).

Added in version 0.13.0.

Parameters:

a (float, optional) – Lower bound of the sampling interval. Defaults to zero.
b (float, optional) – Upper bound of the sampling interval. Defaults to unity.
n (float, optional) – Power law exponent. Defaults to zero, which is equivalent to a uniform distribution.
bias (openmc.stats.Univariate, optional) – Distribution for biased sampling.

Variables:

a (float) – Lower bound of the sampling interval
b (float) – Upper bound of the sampling interval
n (float) – Power law exponent
support (tuple of float) – A 2-tuple (lower, upper) defining the interval over which the distribution is nonzero-valued
bias (openmc.stats.Univariate or None) – Distribution for biased sampling

Evaluate the probability density at the provided value.

Parameters:: x (float or sequence of float) – Location to evaluate p(x)
Returns:: Value of p(x)
Return type:: float or numpy.ndarray

classmethod from_xml_element(elem: Element)[source]

Generate power law distribution from an XML element

property support

Return the support of the probability distribution.

Returns:: Returns the set of unique points assigned probability mass in a discrete distribution, the sampling interval for a continuous distribution, or a dictionary storing the discrete and continuous parts of the support of a mixed random variable
Return type:: set or tuple of float or dict

to_xml_element(element_name: str)[source]

Return XML representation of the power law distribution