Source code for gemseo_umdo.use_cases.beam_model.uncertain_space

# 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.
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# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
"""The uncertain space for the beam use case."""
from __future__ import annotations

from gemseo.algos.parameter_space import ParameterSpace

from gemseo_umdo.use_cases.beam_model.core.variables import E
from gemseo_umdo.use_cases.beam_model.core.variables import F
from gemseo_umdo.use_cases.beam_model.core.variables import sigma_all


class BeamUncertainSpace(ParameterSpace):
    r"""The advanced uncertain space for the beam use case.

    math:`F`, :math:`E` and :math:`\sigma_{\text{all}}` are random variables
    with nominal values -200000, 73500 and 300
    and deviation values 10\%, 5\% and 5\%.

    Their probability distribution are centered in these values
    denoted :math:`\mu_F`, :math:`\mu_E` and :math:`\mu_{\sigma_{\text{all}}}`.

    Precisely,
    a uniform distribution is defined
    by the minimum :math:`\mu (1 - \delta)` and the maximum :math:`\mu (1 + \delta)`
    and a Gaussian distribution is defined
    by the mean :math:`\mu` and the standard deviation :math:`|\mu|\delta/3`,
    where :math:`\delta` is an aforementioned deviation value.
    """

    __DEFAULT_DELTA = {
        F.name: 10.0,
        E.name: 5.0,
        sigma_all.name: 5.0,
    }

    def __init__(self, uniform: bool = True, **deltas: float) -> None:
        r"""
        Args:
            uniform: If ``True``, use uniform distributions;
                otherwise, use Gaussian ones.
            **deltas: The percentage variations :math:`\delta` around the nominal values
                of the random variables.
        """  # noqa: D205 D212 D415
        super().__init__()
        for variable in [F, E, sigma_all]:
            nominal = variable.value
            name = variable.name
            delta = deltas.pop(name, self.__DEFAULT_DELTA[name]) / 100
            if uniform:
                minimum, maximum = sorted(
                    [nominal * (1 - delta), nominal * (1 + delta)]
                )
                self.add_random_variable(
                    name,
                    "OTUniformDistribution",
                    minimum=minimum,
                    maximum=maximum,
                )
            else:
                self.add_random_variable(
                    name,
                    "OTNormalDistribution",
                    mu=nominal,
                    sigma=abs(nominal) * delta / 3,
                )