Source code for gemseo.uncertainty.distributions.openturns.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
#
# 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 a normal distribution from the OpenTURNS library.

This class inherits from :class:.OTDistribution.
"""

from __future__ import division, unicode_literals

from typing import Optional

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,  # type: str
mu=0.0,  # type: float
sigma=1.0,  # type: float
dimension=1,  # type: int
transformation=None,  # type: Optional[str]
lower_bound=None,  # type: Optional[float]
upper_bound=None,  # type: Optional[float]
threshold=0.5,  # type: float
):  # 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.
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(OTNormalDistribution, self).__init__(
variable,
"Normal",
(mu, sigma),
dimension,
standard_parameters,
transformation,
lower_bound,
upper_bound,
threshold,
)