Source code for gemseo.uncertainty.distributions.scipy.normal

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
# Lesser General Public License for more details.
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# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                           documentation
#        :author: Matthias De Lozzo
#    OTHER AUTHORS   - MACROSCOPIC CHANGES

"""Class to create a normal distribution from the SciPy library.

This class inherits from :class:`.SPDistribution`.
"""

from __future__ import division, unicode_literals

from gemseo.uncertainty.distributions.scipy.distribution import SPDistribution


[docs]class SPNormalDistribution(SPDistribution): """Create a normal distribution. Example: >>> from gemseo.uncertainty.distributions.scipy.normal import ( ... SPNormalDistribution ... ) >>> distribution = SPNormalDistribution('x', -1, 2) >>> print(distribution) norm(mu=-1, sigma=2) """ def __init__( self, variable, # type: str mu=0.0, # type: float sigma=1.0, # type: float dimension=1, # type: int ): # noqa: D205,D212,D415 # type: (...) -> None """ Args: variable: The name of the normal random variable. mu: The mean of the normal random variable. sigma: The standard deviation of the normal random variable. dimension: The dimension of the normal random variable. """ standard_parameters = {self._MU: mu, self._SIGMA: sigma} parameters = {"loc": mu, "scale": sigma} super(SPNormalDistribution, self).__init__( variable, "norm", parameters, dimension, standard_parameters )