Source code for gemseo.disciplines.taylor

# Copyright 2021 IRT Saint Exupéry,
# 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
# 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.
"""A discipline to create Taylor polynomials from another discipline."""

from __future__ import annotations

from copy import deepcopy
from types import MappingProxyType
from typing import TYPE_CHECKING

from gemseo.core.discipline import MDODiscipline

    from import Mapping

    from numpy.typing import NDArray

[docs] class TaylorDiscipline(MDODiscipline): r"""The first-order polynomial of a discipline. The first-order polynomial of a function :math:`f` at an expansion point :math:`a` is :math:`f(a)+\sum_{i=1}^d\frac{\partial f(a)}{\partial x_i}(x_i-a_i)`. """ __offset: Mapping[str, NDArray[float]] """The offset of the polynomial.""" def __init__( self, discipline: MDODiscipline, input_data: Mapping[str, NDArray[float]] = MappingProxyType({}), name: str = "", ) -> None: """ Args: discipline: The discipline to be approximated by a Taylor polynomial. input_data: The point of expansion. If empty, use the default inputs of ``discipline``. Raises: ValueError: If neither ``input_data`` nor ``discipline.default_inputs`` is specified. """ # noqa: D205 D212 input_names = set(discipline.get_input_data_names()) if ( (input_data and (input_data.keys() < input_names)) or not input_data and discipline.default_inputs.keys() < input_names ): raise ValueError( "All the discipline input values must be " "specified either in input_data or in discipline.default_inputs." ) discipline.linearize(compute_all_jacobians=True, input_data=input_data) super().__init__(name=name) self.input_grammar.update_from_names(input_names) self.output_grammar.update_from_names(discipline.get_output_data_names()) self.default_inputs = input_data or discipline.default_inputs self.__offset = {} for output_name in self.get_output_data_names(): self.__offset[output_name] = discipline.local_data[output_name] - sum( discipline.jac[output_name][input_name] @ input_value for input_name, input_value in self.default_inputs.items() ) self.jac = deepcopy(discipline.jac) self._is_linearized = True def _run(self) -> None: input_data = self.get_input_data() for output_name in self.get_output_data_names(): self.local_data[output_name] = self.__offset[output_name] + sum( self.jac[output_name][input_name] @ input_value for input_name, input_value in input_data.items() )