# Source code for gemseo.formulations.disciplinary_opt

```
# -*- 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
"""
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
standard_library.install_aliases()
[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)
```