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

# 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.
#
# 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 an exponential 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 OTExponentialDistribution(OTDistribution): """Create an exponential distribution. Example: >>> from gemseo.uncertainty.distributions.openturns.exponential import ( ... OTExponentialDistribution ... ) >>> distribution = OTExponentialDistribution('x', 2, 3) >>> print(distribution) Exponential(loc=3, rate=2) """ def __init__( self, variable: str, rate: float = 1.0, loc: float = 0.0, dimension: int = 1, transformation: str | None = None, lower_bound: float | None = None, upper_bound: float | None = None, threshold: float = 0.5, ) -> None: """.. # noqa: D205,D212,D415 Args: variable: The name of the exponential random variable. rate: The rate of the exponential random variable. loc: The location of the exponential random variable. dimension: The dimension of the exponential 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]. """ standard_parameters = {self._RATE: rate, self._LOC: loc} super().__init__( variable, "Exponential", (rate, loc), dimension, standard_parameters, transformation, lower_bound, upper_bound, threshold, )