Source code for gemseo.core.mdofunctions.mdo_discipline_adapter_generator
# 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.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Francois Gallard, Charlie Vanaret
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""A class to create :class:`.MDOFunction` objects from an :class:`.MDODiscipline`."""
from __future__ import annotations
from numbers import Number
from typing import TYPE_CHECKING
from typing import Callable
from typing import Union
from numpy import ndarray
from gemseo.core.mdofunctions.mdo_discipline_adapter import MDODisciplineAdapter
if TYPE_CHECKING:
from collections.abc import Mapping
from collections.abc import MutableMapping
from collections.abc import Sequence
from gemseo.core.discipline import MDODiscipline
OperandType = Union[ndarray, Number]
OperatorType = Callable[[OperandType, OperandType], OperandType]
[docs]
class MDODisciplineAdapterGenerator:
"""Generator of :class:`.MDOFunction` objects executing an :class:`.MDODiscipline`.
It creates an :class:`.MDODisciplineAdapter` evaluating some of the outputs of the
discipline from some of its
It uses closures to generate functions instances from a discipline execution.
"""
discipline: MDODiscipline
"""The discipline from which to generate functions."""
__names_to_sizes: MutableMapping[str, int] | None = None
"""The names of the inputs bound to their sizes, if known."""
def __init__(
self,
discipline: MDODiscipline,
names_to_sizes: MutableMapping[str, int] | None = None,
) -> None:
"""
Args:
discipline: The discipline from which the generator builds the functions.
names_to_sizes: The sizes of the input variables.
If ``None``, guess them from the default inputs and local data
of the discipline :class:`.MDODiscipline`.
""" # noqa: D205, D212, D415
self.discipline = discipline
self.__names_to_sizes = names_to_sizes
[docs]
def get_function(
self,
input_names: Sequence[str],
output_names: Sequence[str],
default_inputs: Mapping[str, ndarray] | None = None,
differentiable: bool = True,
) -> MDODisciplineAdapter:
"""Build a function executing a discipline for some inputs and outputs.
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):
msg = (
f"Some elements of {input_names} "
f"are not inputs of the discipline {self.discipline.name}; "
f"available inputs are: {self.discipline.get_input_data_names()}."
)
raise ValueError(msg)
if not self.discipline.is_all_outputs_existing(output_names):
msg = (
f"Some elements of {output_names} "
f"are not outputs of the discipline {self.discipline.name}; "
f"available outputs are: {self.discipline.get_output_data_names()}."
)
raise ValueError(msg)
# 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 MDODisciplineAdapter(
input_names,
output_names,
default_inputs,
self.discipline,
self.__names_to_sizes,
linear_candidate=self.__is_linear(input_names, output_names),
)
def __is_linear(
self, input_names: Sequence[str], output_names: Sequence[str]
) -> bool:
"""Check if the MDOFunction should be linear.
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.
Returns:
Whether the function should be linear.
"""
input_names = set(input_names)
for output_name in output_names:
linear_input_names = self.discipline.linear_relationships.get(output_name)
if linear_input_names is not None:
if not input_names.issubset(linear_input_names):
return False
else:
return False
return True