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

# Copyright 2021 IRT Saint Exupéry,
# 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
# Lesser General Public License for more details.
# 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
"""Class to create a normal distribution from the OpenTURNS library.

This class inherits from :class:`.OTDistribution`.
from __future__ import annotations

from gemseo.uncertainty.distributions.openturns.distribution import OTDistribution

[docs]class OTNormalDistribution(OTDistribution): """Create a normal distribution. Example: >>> from gemseo.uncertainty.distributions.openturns.normal import ( ... OTNormalDistribution >>> ) >>> distribution = OTNormalDistribution('x', -1, 2) >>> print(distribution) Normal(mu=-1, sigma=2) """ def __init__( self, variable: str, mu: float = 0.0, sigma: float = 1.0, dimension: int = 1, transformation: str | None = None, lower_bound: float | None = None, upper_bound: float | None = None, threshold: float = 0.5, ) -> 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. transformation: A transformation applied to the random variable, e.g. 'sin(x)'. If None, no transformation. lower_bound: A lower bound to truncate the distribution. If None, no lower truncation. upper_bound: An upper bound to truncate the distribution. If None, no upper truncation. threshold: A threshold in [0,1]. """ # noqa: D205,D212,D415 standard_parameters = {self._MU: mu, self._SIGMA: sigma} super().__init__( variable, "Normal", (mu, sigma), dimension, standard_parameters, transformation, lower_bound, upper_bound, threshold, )