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

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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# Lesser General Public License for more details.
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# 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 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: """.. # noqa: D205,D212,D415 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]. """ standard_parameters = {self._MU: mu, self._SIGMA: sigma} super().__init__( variable, "Normal", (mu, sigma), dimension, standard_parameters, transformation, lower_bound, upper_bound, threshold, )