:orphan: .. Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Post-process an optimization problem ==================================== .. start-after-label .. raw:: html
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Customize with matplotlib
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Post-process a scenario
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Post-process an HDF5 file.
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Post-process an optimization dataset
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Post-process an optimization problem
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Save a scenario for post-processing
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Save an optimization problem for post-processing
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.. toctree:: :hidden: /examples/post_process/matplotlib_opt_post_processor /examples/post_process/post_process_scenario /examples/post_process/post_process_file /examples/post_process/plot_post_process_optimization_dataset /examples/post_process/post_process_opt_problem /examples/post_process/save_from_scenario /examples/post_process/save_from_opt_problem .. Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. Algorithms ~~~~~~~~~~ The data used to illustrate the post-processing algorithms in the following examples are from an MDO scenario on the Sobieski's SSBJ problem. The scenario uses the MDF formulation and was solved using the SciPy SLSQP algorithm. The data have been saved in an HDF5 file, which is passed to the high-level function :func:`.execute_post` along with the appropriate settings model. .. note:: To get the specific settings of a given post-processing, one should use the appropriate settings model accessible in :mod:`gemseo.settings.post`. Further details on the available post-processings and their settings can be found in the following dedicated page: :ref:`gen_post_algos`. Details on how to execute such a scenario can be found :ref:`here `. .. raw:: html
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Basic history
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Constraints history
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Correlations
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Gradient Sensitivity
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Objective and constraints history
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Optimization History View
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Parallel coordinates
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Pareto front
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Pareto front on a multi-objective problem
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Quadratic approximations
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Radar chart
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Robustness
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Scatter plot matrix
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Self-Organizing Map
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Variables influence
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.. toctree:: :hidden: /examples/post_process/algorithms/plot_basic_history /examples/post_process/algorithms/plot_constraints_history /examples/post_process/algorithms/plot_correlations /examples/post_process/algorithms/plot_gradient_sensitivity /examples/post_process/algorithms/plot_obj_constr_hist /examples/post_process/algorithms/plot_opt_hist_view /examples/post_process/algorithms/plot_para_coord /examples/post_process/algorithms/plot_pareto_front /examples/post_process/algorithms/plot_pareto_front_binhkorn /examples/post_process/algorithms/plot_quad_approx /examples/post_process/algorithms/plot_constraints_radar_chart /examples/post_process/algorithms/plot_robustness /examples/post_process/algorithms/plot_history_scatter_matrix /examples/post_process/algorithms/plot_som /examples/post_process/algorithms/plot_variable_influence .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-gallery .. container:: sphx-glr-download sphx-glr-download-python :download:`Download all examples in Python source code: post_process_python.zip ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download all examples in Jupyter notebooks: post_process_jupyter.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_