Simple disciplinary DOE example on the Sobieski SSBJ test case

from __future__ import absolute_import, division, print_function, unicode_literals

from future import standard_library

from gemseo.api import configure_logger, create_discipline, create_scenario
from gemseo.problems.sobieski.core import SobieskiProblem

configure_logger()

standard_library.install_aliases()

Instantiate the discipline

discipline = create_discipline("SobieskiMission")

Create the design space

design_space = SobieskiProblem().read_design_space()
design_space.filter(["y_24", "y_34"])

Out:

<gemseo.algos.design_space.DesignSpace object at 0x7fc29dadf490>

Create the scenario

Build scenario which links the disciplines with the formulation and The DOE algorithm.

scenario = create_scenario(
    [discipline],
    formulation="DisciplinaryOpt",
    objective_name="y_4",
    design_space=design_space,
    maximize_objective=True,
    scenario_type="DOE",
)

Execute the scenario

Here we use a latin hypercube sampling algorithm with 30 samples.

scenario.execute({"n_samples": 30, "algo": "lhs"})

Out:

{'eval_jac': False, 'algo': 'lhs', 'n_samples': 30}

Plot optimization history view

scenario.post_process("OptHistoryView", save=False, show=True)

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Out:

/home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.0.3/lib/python3.8/site-packages/gemseo/post/opt_history_view.py:312: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax1.set_yticklabels(y_labels)

<gemseo.post.opt_history_view.OptHistoryView object at 0x7fc29dec5130>

Plot parallel coordinates

scenario.post_process(
    "ScatterPlotMatrix", save=False, show=True, variables_list=["y_4", "y_24", "y_34"]
)

Out:

<gemseo.post.scatter_mat.ScatterPlotMatrix object at 0x7fc29e16a250>