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

# -*- 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 an uniform 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 OTUniformDistribution(OTDistribution):
"""Create an uniform distribution.

Example:
>>> from gemseo.uncertainty.distributions.openturns.uniform import (
...     OTUniformDistribution
>>> )
>>> distribution = OTUniformDistribution('x', -1, 1)
>>> print(distribution)
Uniform(lower=-1, upper=1)
"""

def __init__(
self,
variable,  # type: str
minimum=0.0,  # type: float
maximum=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 uniform random variable.
minimum: The minimum of the uniform random variable.
maximum: The maximum of the uniform random variable.
dimension: The dimension of the uniform 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._LOWER: minimum, self._UPPER: maximum}
super(OTUniformDistribution, self).__init__(
variable,
"Uniform",
(minimum, maximum),
dimension,
standard_parameters,
transformation,
lower_bound,
upper_bound,
threshold,
)