Source code for gemseo.formulations.bilevel_test_helper

# -*- 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.
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# along with this program; if not, write to the Free Software Foundation,
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"""Provide base test class stub for testing bilevel also for |g| plugins."""
from __future__ import division, unicode_literals

from copy import deepcopy
from typing import Callable, Dict

from gemseo.core.mdo_scenario import MDOScenario
from gemseo.problems.sobieski.wrappers import (
    SobieskiAerodynamics,
    SobieskiMission,
    SobieskiProblem,
    SobieskiPropulsion,
    SobieskiStructure,
)

# TODO: remove when PEP 484 type hints will be used
FixtureFunc = Callable[[Dict[str, float]], MDOScenario]


[docs]def create_sobieski_bilevel_scenario(): # type: (...) -> FixtureFunc """Create a function to generate a Sobieski BiLevel Scenario. Returns: A function which generates a Sobieski BiLevel Scenario with specific options. """ def func(**options): """Create a Sobieski BiLevel scenario. Args: **options: The options of the system scenario. Returns: A Sobieski BiLevel Scenario. """ propulsion = SobieskiPropulsion() aerodynamics = SobieskiAerodynamics() struct = SobieskiStructure() mission = SobieskiMission() ds = SobieskiProblem().read_design_space() sc_prop = MDOScenario( disciplines=[propulsion], formulation="DisciplinaryOpt", objective_name="y_34", design_space=deepcopy(ds).filter("x_3"), name="PropulsionScenario", ) # Maximize L/D sc_aero = MDOScenario( disciplines=[aerodynamics], formulation="DisciplinaryOpt", objective_name="y_24", design_space=deepcopy(ds).filter("x_2"), name="AerodynamicsScenario", maximize_objective=True, ) # Maximize log(aircraft total weight / (aircraft total weight - fuel # weight)) sc_str = MDOScenario( disciplines=[struct], formulation="DisciplinaryOpt", objective_name="y_11", design_space=deepcopy(ds).filter("x_1"), name="StructureScenario", maximize_objective=True, ) sub_scenarios = [sc_str, sc_aero, sc_prop] sub_disciplines = sub_scenarios + [mission] for sc in sub_scenarios: sc.default_inputs = {"max_iter": 5, "algo": "SLSQP"} ds = SobieskiProblem().read_design_space() sc_system = MDOScenario( sub_disciplines, formulation="BiLevel", objective_name="y_4", design_space=ds.filter(["x_shared", "y_14"]), maximize_objective=True, **options ) sc_system.set_differentiation_method("finite_differences", step=1e-6) return sc_system return func