# -*- coding: utf-8 -*-
# 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
"""
The Multi-disciplinary Design Feasible formulation
**************************************************
"""
from __future__ import absolute_import, division, unicode_literals
from future import standard_library
from gemseo.core.formulation import MDOFormulation
from gemseo.mda.mda_factory import MDAFactory
standard_library.install_aliases()
[docs]class MDF(MDOFormulation):
"""
The Multidisciplinary Design Feasible formulation draws an
optimization architecture where the coupling of strongly
coupled disciplines is made consistent by means of a
Multidisciplinary Design Analysis (MDA), the optimization
problem w.r.t. local and global design variables is made
at the top level. Multidisciplinary analysis is made at
a each optimization iteration.
"""
def __init__(
self,
disciplines,
objective_name,
design_space,
maximize_objective=False,
main_mda_class="MDAChain",
sub_mda_class="MDAJacobi",
**mda_options
):
"""
Constructor, initializes the objective functions and constraints
:param main_mda_class: classname of the main MDA, typically the
MDAChain, but one can force to use MDAGaussSeidel for instance
:type main_mda_class: str
:param disciplines: the disciplines list.
:type disciplines: list(MDODiscipline)
:param objective_name: the objective function data name.
:type objective_name: str
:param design_space: the design space.
:type design_space: DesignSpace
:param maximize_objective: if True, the objective function
is maximized, by default, a minimization is performed.
:type maximize_objective: bool
:param sub_mda_class: the type of MDA to be used,
shall be the class name. (default MDAJacobi)
:type sub_mda_class: str
:param mda_options: options passed to the MDA at construction
"""
super(MDF, self).__init__(
disciplines,
objective_name,
design_space,
maximize_objective=maximize_objective,
)
self.mda = None
self._main_mda_class = main_mda_class
self._mda_factory = MDAFactory()
self._instantiate_mda(main_mda_class, sub_mda_class, **mda_options)
self._update_design_space()
self._build_objective()
[docs] def get_top_level_disc(self):
return [self.mda]
def _instantiate_mda(
self, main_mda_class="MDAChain", sub_mda_class="MDAJacobi", **mda_options
):
"""Create MDA discipline"""
if main_mda_class == "MDAChain":
mda_options["sub_mda_class"] = sub_mda_class
self.mda = self._mda_factory.create(
main_mda_class, self.disciplines, **mda_options
)
[docs] @classmethod
def get_sub_options_grammar(cls, **options):
"""
When some options of the formulation depend on higher level
options, a sub option schema may be specified here, mainly for
use in the API
:param options: options dict required to deduce the sub options grammar
:returns: None, or the sub options grammar
"""
main_mda = options.get("main_mda_class")
if main_mda is None:
raise ValueError(
"main_mda_class option required \n"
+ "to deduce the sub options of MDF !"
)
factory = MDAFactory().factory
return factory.get_options_grammar(main_mda)
[docs] @classmethod
def get_default_sub_options_values(cls, **options):
"""
When some options of the formulation depend on higher level
options, a sub option defaults may be specified here, mainly for
use in the API
:param options: options dict required to deduce the sub options grammar
:returns: None, or the sub options defaults
"""
main_mda = options.get("main_mda_class")
if main_mda is None:
raise ValueError(
"main_mda_class option required \n"
+ "to deduce the sub options of MDF !"
)
factory = MDAFactory().factory
return factory.get_default_options_values(main_mda)
def _build_objective(self):
"""Builds the objective function on the MDA"""
# Build the objective from the mda and the objective name
self._build_objective_from_disc(self._objective_name, discipline=self.mda)
[docs] def get_expected_workflow(self):
return self.mda.get_expected_workflow()
[docs] def get_expected_dataflow(self):
return self.mda.get_expected_dataflow()
def _update_design_space(self):
"""Update the design space by removing the coupling variables"""
self._set_defaultinputs_from_ds()
# No couplings in design space (managed by MDA)
self._remove_couplings_from_ds()
# Cleanup
self._remove_unused_variables()
def _remove_couplings_from_ds(self):
"""Removes the coupling variables from the design space"""
design_space = self.opt_problem.design_space
for coupling in self.mda.strong_couplings:
if coupling in design_space.variables_names:
design_space.remove_variable(coupling)
