Source code for gemseo.formulations.disciplinary_opt

# -*- coding: utf-8 -*-
# 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 - API and implementation and/or documentation
#        :author: Francois Gallard
A formulation for uncoupled or weakly coupled problems
from __future__ import absolute_import, division, print_function, unicode_literals

from builtins import super

from future import standard_library

from gemseo.core.chain import MDOChain
from gemseo.core.execution_sequence import ExecutionSequenceFactory
from gemseo.core.formulation import MDOFormulation
from gemseo.utils.data_conversion import DataConversion


[docs]class DisciplinaryOpt(MDOFormulation): """ The disciplinary optimization formulation draws the architecture of a mono disciplinary optimization process from an ordered list of disciplines, an objective function and a design space. The objective function is minimized by default. """ def __init__( self, disciplines, objective_name, design_space, maximize_objective=False ): """ Constructor, initializes the objective functions and constraints :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 """ self.chain = None if len(disciplines) > 1: self.chain = MDOChain(disciplines) super(DisciplinaryOpt, self).__init__( disciplines, objective_name, design_space, maximize_objective=maximize_objective, ) self._filter_design_space() self._set_defaultinputs_from_ds() # Build the objective from its objective name self._build_objective_from_disc(objective_name)
[docs] def get_expected_workflow(self): """ Returns the expected execution sequence, used for xdsm representation """ if self.chain is None: return ExecutionSequenceFactory.serial(self.disciplines[0]) return self.chain.get_expected_workflow()
[docs] def get_expected_dataflow(self): """ Returns the expected data exchange sequence, used for xdsm representation """ if self.chain is None: return [] return self.chain.get_expected_dataflow()
[docs] def get_top_level_disc(self): """Returns the disciplines which inputs are required to run the associated scenario By default, returns all disciplines To be overloaded by subclasses :returns: the list of top level disciplines """ if self.chain is not None: return [self.chain] return self.disciplines
def _filter_design_space(self): """ Filters the design space to keep only available variables """ all_inpts = DataConversion.get_all_inputs(self.get_top_level_disc()) kept = set(self.design_space.variables_names) & set(all_inpts) self.design_space.filter(kept)