.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/formulations/plot_doe_sobieski_mdf_example.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_formulations_plot_doe_sobieski_mdf_example.py: MDF-based DOE on the Sobieski SSBJ test case ============================================ .. GENERATED FROM PYTHON SOURCE LINES 24-34 .. code-block:: default from os import name as os_name from gemseo.api import configure_logger from gemseo.api import create_discipline from gemseo.api import create_scenario from gemseo.problems.sobieski.core.problem import SobieskiProblem from matplotlib import pyplot as plt configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 35-42 Instantiate the disciplines ---------------------------- First, we instantiate the four disciplines of the use case: :class:`~gemseo.problems.sobieski.disciplines.SobieskiPropulsion`, :class:`~gemseo.problems.sobieski.disciplines.SobieskiAerodynamics`, :class:`~gemseo.problems.sobieski.disciplines.SobieskiMission` and :class:`~gemseo.problems.sobieski.disciplines.SobieskiStructure`. .. GENERATED FROM PYTHON SOURCE LINES 42-51 .. code-block:: default disciplines = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiMission", "SobieskiStructure", ] ) .. GENERATED FROM PYTHON SOURCE LINES 52-60 Build, execute and post-process the scenario -------------------------------------------- Then, we build the scenario which links the disciplines with the formulation and the optimization algorithm. Here, we use the :class:`.BiLevel` formulation. We tell the scenario to minimize -y_4 instead of minimizing y_4 (range), which is the default option. We need to define the design space. .. GENERATED FROM PYTHON SOURCE LINES 60-62 .. code-block:: default design_space = SobieskiProblem().design_space .. GENERATED FROM PYTHON SOURCE LINES 63-65 Instantiate the scenario ^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 65-74 .. code-block:: default scenario = create_scenario( disciplines, formulation="MDF", objective_name="y_4", design_space=design_space, maximize_objective=True, scenario_type="DOE", ) .. GENERATED FROM PYTHON SOURCE LINES 75-77 Set the design constraints ^^^^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 77-80 .. code-block:: default for constraint in ["g_1", "g_2", "g_3"]: scenario.add_constraint(constraint, "ineq") .. GENERATED FROM PYTHON SOURCE LINES 81-84 Execute the scenario ^^^^^^^^^^^^^^^^^^^^ Use provided analytic derivatives .. GENERATED FROM PYTHON SOURCE LINES 84-86 .. code-block:: default scenario.set_differentiation_method("user") .. GENERATED FROM PYTHON SOURCE LINES 87-92 Multiprocessing ^^^^^^^^^^^^^^^ It is possible to run a DOE in parallel using multiprocessing, in order to do this, we specify the number of processes to be used for the computation of the samples. .. GENERATED FROM PYTHON SOURCE LINES 92-103 .. code-block:: default # The multiprocessing option has some limitations on Windows. # Due to problems with sphinx, we disable it in this example. # For Python versions < 3.7 and Numpy < 1.20.0, subprocesses may get hung # randomly during execution. It is strongly recommended to update your # environment to avoid this problem. # The features :class:`.MemoryFullCache` and :class:`.HDF5Cache` are not # available for multiprocessing on Windows. # As an alternative, we recommend the method # :meth:`.DOEScenario.set_optimization_history_backup`. .. GENERATED FROM PYTHON SOURCE LINES 104-106 We define the algorithm options. Here the criterion = center option of pyDOE centers the points within the sampling intervals. .. GENERATED FROM PYTHON SOURCE LINES 106-117 .. code-block:: default algo_options = { "criterion": "center", # Evaluate gradient of the MDA # with coupled adjoint "eval_jac": True, # Run in parallel on 1 or 4 processors "n_processes": 1 if os_name == "nt" else 4, } scenario.execute({"n_samples": 30, "algo": "lhs", "algo_options": algo_options}) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 10:07:39: INFO - 10:07:39: *** Start DOEScenario execution *** INFO - 10:07:39: DOEScenario INFO - 10:07:39: Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiMission SobieskiStructure INFO - 10:07:39: MDO formulation: MDF INFO - 10:07:39: Optimization problem: INFO - 10:07:39: minimize -y_4(x_shared, x_1, x_2, x_3) INFO - 10:07:39: with respect to x_1, x_2, x_3, x_shared INFO - 10:07:39: subject to constraints: INFO - 10:07:39: g_1(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 10:07:39: g_2(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 10:07:39: g_3(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 10:07:39: over the design space: INFO - 10:07:39: +----------+-------------+-------+-------------+-------+ INFO - 10:07:39: | name | lower_bound | value | upper_bound | type | INFO - 10:07:39: +----------+-------------+-------+-------------+-------+ INFO - 10:07:39: | x_shared | 0.01 | 0.05 | 0.09 | float | INFO - 10:07:39: | x_shared | 30000 | 45000 | 60000 | float | INFO - 10:07:39: | x_shared | 1.4 | 1.6 | 1.8 | float | INFO - 10:07:39: | x_shared | 2.5 | 5.5 | 8.5 | float | INFO - 10:07:39: | x_shared | 40 | 55 | 70 | float | INFO - 10:07:39: | x_shared | 500 | 1000 | 1500 | float | INFO - 10:07:39: | x_1 | 0.1 | 0.25 | 0.4 | float | INFO - 10:07:39: | x_1 | 0.75 | 1 | 1.25 | float | INFO - 10:07:39: | x_2 | 0.75 | 1 | 1.25 | float | INFO - 10:07:39: | x_3 | 0.1 | 0.5 | 1 | float | INFO - 10:07:39: +----------+-------------+-------+-------------+-------+ INFO - 10:07:39: Solving optimization problem with algorithm lhs: INFO - 10:07:39: ... 0%| | 0/30 [00:00 .. GENERATED FROM PYTHON SOURCE LINES 131-138 .. tip:: Each post-processing method requires different inputs and offers a variety of customization options. Use the API function :meth:`~gemseo.api.get_post_processing_options_schema` to print a table with the attributes for any post-processing algo. Or refer to our dedicated page: :ref:`gen_post_algos`. .. GENERATED FROM PYTHON SOURCE LINES 140-142 Plot the scatter matrix ^^^^^^^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 142-146 .. code-block:: default scenario.post_process( "ScatterPlotMatrix", show=False, save=False, variable_names=["y_4", "x_shared"] ) .. image-sg:: /examples/formulations/images/sphx_glr_plot_doe_sobieski_mdf_example_006.png :alt: plot doe sobieski mdf example :srcset: /examples/formulations/images/sphx_glr_plot_doe_sobieski_mdf_example_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 147-149 Plot correlations ^^^^^^^^^^^^^^^^^ .. GENERATED FROM PYTHON SOURCE LINES 149-152 .. code-block:: default scenario.post_process("Correlations", show=False, save=False) # Workaround for HTML rendering, instead of ``show=True`` plt.show() .. image-sg:: /examples/formulations/images/sphx_glr_plot_doe_sobieski_mdf_example_007.png :alt: R=0.98987, R=0.97511, R=0.99671, R=0.96235, R=0.99119, R=0.99866, R=0.95205, R=0.98584, R=0.99618, R=0.99936 :srcset: /examples/formulations/images/sphx_glr_plot_doe_sobieski_mdf_example_007.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 10:07:46: Detected 10 correlations > 0.95 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 7.399 seconds) .. _sphx_glr_download_examples_formulations_plot_doe_sobieski_mdf_example.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_doe_sobieski_mdf_example.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_doe_sobieski_mdf_example.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_