gemseo.uncertainty.distributions.scipy.log_normal module#

The SciPy-based log-normal distribution.

class SPLogNormalDistribution(mu=1.0, sigma=1.0, location=0.0, set_log=False, settings=None)[source]#

Bases: SPDistribution

The SciPy-based log-normal distribution.

Examples

>>> from gemseo.uncertainty.distributions.scipy.distribution import (
...     SPDistribution,
... )
>>> distribution = SPDistribution("expon", {"loc": 3, "scale": 1 / 2.0})
>>> print(distribution)
expon(loc=3, scale=0.5)

Initialize self. See help(type(self)) for accurate signature.

Parameters:

  • mu (float) --

    Either the mean of the log-normal random variable or that of its logarithm when set_log is True.

    By default it is set to 1.0.

  • sigma (float) --

    Either the standard deviation of the log-normal random variable or that of its logarithm when set_log is True.

    By default it is set to 1.0.

  • location (float) --

    The location of the log-normal random variable.

    By default it is set to 0.0.

  • set_log (bool) --

    Whether mu and sigma apply to the logarithm of the log-normal random variable. Otherwise, mu and sigma apply to the log-normal random variable directly.

    By default it is set to False.

  • settings (SPLogNormalDistribution_Settings | None) -- The settings of the distributions. If set, the other arguments are ignored. If None, the other arguments are used instead.

Settings#

alias of SPLogNormalDistribution_Settings