# 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 - API and implementation and/or documentation
# :author: Francois Gallard
# OTHER AUTHORS - MACROSCOPIC CHANGES
# :author: Benoit Pauwels - Stacked data management
# (e.g. iteration index)
# :author: Gilberto Ruiz Jimenez
"""The MDOFunction subclass to create a function from an MDODiscipline."""
from __future__ import annotations
from typing import TYPE_CHECKING
from numpy import empty
from gemseo.core.mdofunctions.linear_candidate_function import LinearCandidateFunction
from gemseo.core.mdofunctions.mdo_discipline_adapter_generator import (
MDODisciplineAdapterGenerator,
)
if TYPE_CHECKING:
from collections.abc import Iterable
from collections.abc import Sequence
from gemseo.core.base_formulation import BaseFormulation
from gemseo.core.discipline import MDODiscipline
from gemseo.typing import NumberArray
[docs]
class FunctionFromDiscipline(LinearCandidateFunction):
"""An :class:`.MDOFunction` object from an :class:`.MDODiscipline`."""
def __init__(
self,
output_names: Sequence[str],
mdo_formulation: BaseFormulation,
discipline: MDODiscipline | None = None,
top_level_disc: bool = True,
x_names: Sequence[str] | None = None,
all_data_names: Iterable[str] | None = None,
differentiable: bool = True,
) -> None:
"""
Args:
output_names: The names of the outputs.
mdo_formulation: The MDOFormulation object in which the function is
located.
discipline: The discipline computing these outputs.
If ``None``, the discipline is detected from the inner disciplines.
top_level_disc: If ``True``, search the discipline among the top level ones.
x_names: The names of the design variables.
If ``None``, use self.get_x_names_of_disc(discipline).
all_data_names: The reference data names for masking x.
If ``None``, use self.get_optim_variable_names().
differentiable: If ``True``, then inputs and outputs are added
to the list of variables to be differentiated.
""" # noqa: D205, D212, D415
self.__output_names = output_names
self.__mdo_formulation = mdo_formulation
self.__discipline = discipline
self.__top_level_disc = top_level_disc
self.__x_names = x_names
self.__all_data_names = all_data_names
self.__differentiable = differentiable
self.__x_mask = None
if self.__discipline is None:
self.__gen = self.__mdo_formulation._get_generator_from(
self.__output_names, top_level_disc=self.__top_level_disc
)
self.__discipline = self.__gen.discipline
else:
self.__gen = MDODisciplineAdapterGenerator(
self.__discipline, self.__mdo_formulation.design_space.variable_sizes
)
if self.__x_names is None:
self.__x_names = self.__mdo_formulation.get_x_names_of_disc(
self.__discipline
)
self.__out_x_func = self.__gen.get_function(
self.__x_names, self.__output_names, differentiable=self.__differentiable
)
super().__init__(
self._func_to_wrap,
jac=self._jac_to_wrap,
name=self.__out_x_func.name,
input_names=self.__x_names,
expr=self.__out_x_func.expr,
dim=self.__out_x_func.dim,
output_names=self.__out_x_func.output_names,
)
@property
def linear_candidate(self) -> bool: # noqa: D102
return self.__out_x_func.linear_candidate
@property
def input_dimension(self) -> int | None: # noqa: D102
return self.__out_x_func.input_dimension
def _func_to_wrap(self, x_vect: NumberArray) -> NumberArray:
"""Compute the outputs.
Args:
x_vect: The design variable vector.
Returns:
The value of the outputs.
"""
if self.__x_mask is None:
self.__x_mask = self.__mdo_formulation.get_x_mask_x_swap_order(
self.__x_names, self.__all_data_names
)
return self.__out_x_func(x_vect[self.__x_mask])
def _jac_to_wrap(self, x_vect: NumberArray) -> NumberArray:
"""Compute the gradient of the outputs.
Args:
x_vect: The design variable vector.
Returns:
The value of the gradient of the outputs.
"""
if self.__x_mask is None:
self.__x_mask = self.__mdo_formulation.get_x_mask_x_swap_order(
self.__x_names, self.__all_data_names
)
x_of_disc = x_vect[self.__x_mask]
loc_jac = self.__out_x_func.jac(x_of_disc)
if len(loc_jac.shape) == 1:
# This is surprising but there is a duality between the
# masking operation in the function inputs and the
# unmasking of its outputs
jac = self.__mdo_formulation.unmask_x_swap_order(
self.__x_names, loc_jac, self.__all_data_names
)
else:
n_outs = loc_jac.shape[0]
# TODO: The support of sparse Jacobians requires modifications here.
jac = empty((n_outs, x_vect.size), dtype=x_vect.dtype)
for func_ind in range(n_outs):
gr_u = self.__mdo_formulation.unmask_x_swap_order(
self.__x_names, loc_jac[func_ind, :], self.__all_data_names
)
jac[func_ind, :] = gr_u
return jac
