# Source code for gemseo.uncertainty.distributions.scipy.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 SciPy library.

This class inherits from :class:.SPDistribution.
"""

from __future__ import division, unicode_literals

from gemseo.uncertainty.distributions.scipy.distribution import SPDistribution

[docs]class SPUniformDistribution(SPDistribution):
"""Create an uniform distribution.

Example:
>>> from gemseo.uncertainty.distributions.scipy.uniform import (
...     SPUniformDistribution
... )
>>> distribution = SPUniformDistribution('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
):  # 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.
"""
parameters = {"loc": minimum, "scale": maximum - minimum}
standard_parameters = {self._LOWER: minimum, self._UPPER: maximum}
super(SPUniformDistribution, self).__init__(
variable, "uniform", parameters, dimension, standard_parameters
)