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.
#
# 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.
"""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.

    $F$, $E$ and $\sigma_{\text{all}}$ 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 $\mu_F$, $\mu_E$ and $\mu_{\sigma_{\text{all}}}$.

    Precisely,
    a uniform distribution is defined
    by the minimum $\mu (1 - \delta)$ and the maximum $\mu (1 + \delta)$
    and a Gaussian distribution is defined
    by the mean $\mu$ and the standard deviation $|\mu|\delta/3$,
    where $\delta$ 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 $\delta$ 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,
                )