.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/post_process/plot_history_scatter_matrix.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_post_process_plot_history_scatter_matrix.py: Scatter plot matrix =================== In this example, we illustrate the use of the :class:`.ScatterPlotMatrix` plot on the Sobieski's SSBJ problem. .. GENERATED FROM PYTHON SOURCE LINES 30-34 .. code-block:: default from __future__ import division, unicode_literals from matplotlib import pyplot as plt .. GENERATED FROM PYTHON SOURCE LINES 35-39 Import ------ The first step is to import some functions from the API and a method to get the design space. .. GENERATED FROM PYTHON SOURCE LINES 39-44 .. code-block:: default from gemseo.api import configure_logger, create_discipline, create_scenario from gemseo.problems.sobieski.core import SobieskiProblem configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 45-53 Description ----------- The **ScatterPlotMatrix** post-processing builds the scatter plot matrix among design variables and outputs functions. Each non-diagonal block represents the samples according to the x- and y- coordinates names while the diagonal ones approximate the probability distributions of the variables, using a kernel-density estimator. .. GENERATED FROM PYTHON SOURCE LINES 55-59 Create disciplines ------------------ At this point, we instantiate the disciplines of Sobieski's SSBJ problem: Propulsion, Aerodynamics, Structure and Mission .. GENERATED FROM PYTHON SOURCE LINES 59-68 .. code-block:: default disciplines = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiStructure", "SobieskiMission", ] ) .. GENERATED FROM PYTHON SOURCE LINES 69-72 Create design space ------------------- We also read the design space from the :class:`.SobieskiProblem`. .. GENERATED FROM PYTHON SOURCE LINES 72-74 .. code-block:: default design_space = SobieskiProblem().read_design_space() .. GENERATED FROM PYTHON SOURCE LINES 75-81 Create and execute scenario --------------------------- The next step is to build a DOE scenario in order to maximize the range, encoded 'y_4', with respect to the design parameters, while satisfying the inequality constraints 'g_1', 'g_2' and 'g_3'. We can use the MDF formulation, the Monte Carlo DOE algorithm and 30 samples. .. GENERATED FROM PYTHON SOURCE LINES 81-94 .. code-block:: default scenario = create_scenario( disciplines, formulation="MDF", objective_name="y_4", maximize_objective=True, design_space=design_space, scenario_type="DOE", ) scenario.set_differentiation_method("user") for constraint in ["g_1", "g_2", "g_3"]: scenario.add_constraint(constraint, "ineq") scenario.execute({"algo": "OT_MONTE_CARLO", "n_samples": 30}) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 14:42:30: INFO - 14:42:30: *** Start DOE Scenario execution *** INFO - 14:42:30: DOEScenario INFO - 14:42:30: Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiStructure SobieskiMission INFO - 14:42:30: MDOFormulation: MDF INFO - 14:42:30: Algorithm: OT_MONTE_CARLO INFO - 14:42:30: Optimization problem: INFO - 14:42:30: Minimize: -y_4(x_shared, x_1, x_2, x_3) INFO - 14:42:30: With respect to: x_shared, x_1, x_2, x_3 INFO - 14:42:30: Subject to constraints: INFO - 14:42:30: g_1(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 14:42:30: g_2(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 14:42:30: g_3(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 14:42:30: Generation of OT_MONTE_CARLO DOE with OpenTurns INFO - 14:42:30: DOE sampling: 0%| | 0/30 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_history_scatter_matrix.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_