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 division, unicode_literals

from typing import List, Sequence, Tuple

from gemseo.algos.design_space import DesignSpace
from gemseo.core.chain import MDOChain
from gemseo.core.discipline import MDODiscipline
from gemseo.core.execution_sequence import ExecutionSequence, ExecutionSequenceFactory
from gemseo.core.formulation import MDOFormulation
from gemseo.utils.data_conversion import DataConversion

[docs]class DisciplinaryOpt(MDOFormulation): """The disciplinary optimization. This formulation draws the architecture of a mono-disciplinary optimization process from an ordered list of disciplines, an objective function and a design space. """ def __init__( self, disciplines, # type: Sequence[MDODiscipline] objective_name, # type: str design_space, # type: DesignSpace maximize_objective=False, # type: bool grammar_type=MDODiscipline.JSON_GRAMMAR_TYPE, # type: str ): # type: (...) -> None super(DisciplinaryOpt, self).__init__( disciplines, objective_name, design_space, maximize_objective=maximize_objective, grammar_type=grammar_type, ) self.chain = None if len(disciplines) > 1: self.chain = MDOChain(disciplines, grammar_type=grammar_type) 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, ): # type: (...) -> List[ExecutionSequence,Tuple[ExecutionSequence]] if self.chain is None: return ExecutionSequenceFactory.serial(self.disciplines[0]) return self.chain.get_expected_workflow()
[docs] def get_expected_dataflow( self, ): # type: (...) -> List[Tuple[MDODiscipline,MDODiscipline,List[str]]] if self.chain is None: return [] return self.chain.get_expected_dataflow()
[docs] def get_top_level_disc(self): # type: (...) -> List[MDODiscipline] if self.chain is not None: return [self.chain] return self.disciplines
def _filter_design_space(self): # type: (...) -> None """Filter 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)