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

# 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
"""The OpenTURNS-based uniform distribution."""

from __future__ import annotations

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


[docs] class OTUniformDistribution(OTDistribution): """The OpenTURNS-based uniform distribution. Examples: >>> from gemseo.uncertainty.distributions.openturns.uniform import ( ... OTUniformDistribution >>> ) >>> distribution = OTUniformDistribution("x", -1, 1) >>> print(distribution) Uniform(lower=-1, upper=1) """ def __init__( self, variable: str = OTDistribution.DEFAULT_VARIABLE_NAME, minimum: float = 0.0, maximum: 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: minimum: The minimum of the uniform random variable. maximum: The maximum of the uniform random variable. """ # noqa: D205,D212,D415 super().__init__( variable, "Uniform", (minimum, maximum), dimension, {self._LOWER: minimum, self._UPPER: maximum}, transformation, lower_bound, upper_bound, threshold, )