Source code for gemseo_umdo.use_cases.beam_model.advanced_uncertain_space

# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
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"""The advanced uncertain space for the beam use case."""
from __future__ import annotations

from typing import ClassVar
from typing import Mapping

from gemseo.algos.parameter_space import ParameterSpace

from gemseo_umdo.use_cases.beam_model.core.variables import alpha
from gemseo_umdo.use_cases.beam_model.core.variables import b
from gemseo_umdo.use_cases.beam_model.core.variables import beta
from gemseo_umdo.use_cases.beam_model.core.variables import dy
from gemseo_umdo.use_cases.beam_model.core.variables import dz
from gemseo_umdo.use_cases.beam_model.core.variables import E
from gemseo_umdo.use_cases.beam_model.core.variables import h
from gemseo_umdo.use_cases.beam_model.core.variables import L
from gemseo_umdo.use_cases.beam_model.core.variables import t
from gemseo_umdo.use_cases.beam_model.core.variables import Variable


[docs]class AdvancedBeamUncertainSpace(ParameterSpace): """The advanced uncertain space for the beam use case.""" __DEFAULT_DISPERSION: ClassVar[float] = 1.0 def __init__( self, nominal_values: Mapping[str, float] | None = None, **dispersions: float ) -> None: """ Args: nominal_values: The nominal values of some uncertain variables. For missing ones, use the default values of the variables available in :mod:`~gemseo_umdo.use_cases.beam_model.core.variables.` **dispersions: The dispersions around the nominal values. """ # noqa: D205 D212 D415 super().__init__() variables = [ b, h, t, L, alpha, beta, dy, dz, E, Variable("Rd", 180.0), Variable("Ry", 600.0), ] self.__nominal_values = { variable.name: variable.value for variable in variables } if nominal_values is not None: self.__nominal_values.update(nominal_values) for variable in variables[:4]: name = variable.name nominal_value = variable.value delta = dispersions.get(name, self.__DEFAULT_DISPERSION) self.add_random_variable( name, "OTUniformDistribution", minimum=nominal_value - delta, maximum=nominal_value + delta, ) for variable in variables[4:]: self.__add_truncated_normal(variable.name, **dispersions) def __add_truncated_normal(self, name: str, **dispersions: float) -> None: r"""Add a truncated normal distribution to the parameter space. Use the ``.__nominal_values[name]`` as mean. Args: name: The name of the random variable. **dispersions: The dispersion $\delta$ around the mean $\mu$ to define the standard deviation $\sigma=\delta/3$; the distribution is truncated to the interval $[\mu-3\sigma,\mu+3\sigma]$; if the dispersion is missing, use ``.__DEFAULT_DISPERSION``. """ nominal_value = self.__nominal_values[name] sigma = dispersions.get(name, self.__DEFAULT_DISPERSION / 3.0) self.add_random_variable( name, "OTNormalDistribution", mu=nominal_value, sigma=sigma, lower_bound=nominal_value - 3 * sigma, upper_bound=nominal_value + 3 * sigma, )