Source code for gemseo.core.mdofunctions.function_generator

# 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.
# Contributors:
#    INITIAL AUTHORS - initial API and implementation and/or initial
#                        documentation
#        :author: Francois Gallard, Charlie Vanaret
"""A class to creat MDOFunctions from MDODisciplines."""
from __future__ import annotations

import logging
from numbers import Number
from typing import Callable
from typing import Mapping
from typing import Sequence
from typing import TYPE_CHECKING
from typing import Union

from numpy import ndarray

from gemseo.core.discipline import MDODiscipline
from gemseo.core.mdofunctions.make_function import MakeFunction

    from gemseo.core.mdofunctions.mdo_function import MDOFunction

LOGGER = logging.getLogger(__name__)

OperandType = Union[ndarray, Number]
OperatorType = Callable[[OperandType, OperandType], OperandType]

[docs]class MDOFunctionGenerator: """Generator of :class:`.MDOFunction` objects from a :class:`.MDODiscipline`. It creates a :class:`.MDOFunction` evaluating some of the outputs of the discipline from some of its It uses closures to generate functions instances from a discipline execution. """ def __init__( self, discipline: MDODiscipline, ) -> None: """ Args: discipline: The discipline from which the generator builds the functions. """ # noqa: D205, D212, D415 self.discipline = discipline
[docs] def get_function( self, input_names: Sequence[str], output_names: Sequence[str], default_inputs: Mapping[str, ndarray] | None = None, differentiable: bool = True, ) -> MDOFunction: """Build a function from a discipline input and output lists. Args: input_names: The names of the inputs of the discipline to be inputs of the function. output_names: The names of outputs of the discipline to be returned by the function. default_inputs: The default values of the inputs. If None, use the default values of the inputs specified by the discipline. differentiable: If True, then inputs and outputs are added to the variables to be differentiated. Returns: The function. Raises: ValueError: If a given input (or output) name is not the name of an input (or output) variable of the discipline. """ if isinstance(input_names, str): input_names = [input_names] if isinstance(output_names, str): output_names = [output_names] if input_names is None: input_names = self.discipline.get_input_data_names() if output_names is None: output_names = self.discipline.get_output_data_names() if not self.discipline.is_all_inputs_existing(input_names): raise ValueError( "Some elements of {} are not inputs of the discipline {}; " "available inputs are: {}.".format( input_names,, self.discipline.get_input_data_names(), ) ) if not self.discipline.is_all_outputs_existing(output_names): raise ValueError( "Some elements of {} are not outputs of the discipline {}; " "available outputs are: {}.".format( output_names,, ", ".join(self.discipline.get_output_data_names()), ) ) # adds inputs and outputs to the list of variables to be # differentiated if differentiable: self.discipline.add_differentiated_inputs(input_names) self.discipline.add_differentiated_outputs(output_names) return MakeFunction(input_names, output_names, default_inputs, self)