.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/post_process/plot_para_coord.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_para_coord.py: Parallel coordinates ==================== In this example, we illustrate the use of the :class:`~gemseo.post.para_coord.ParallelCoordinates` plot on the Sobieski's SSBJ problem. .. GENERATED FROM PYTHON SOURCE LINES 29-35 .. code-block:: default 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 .. GENERATED FROM PYTHON SOURCE LINES 36-40 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 40-43 .. code-block:: default configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 44-58 Description ----------- The :class:`~gemseo.post.para_coord.ParallelCoordinates` post-processing builds parallel coordinates plots among design variables, outputs functions and constraints. The :class:`~gemseo.post.para_coord.ParallelCoordinates` portrays the design variables history during the scenario execution. Each vertical coordinate is dedicated to a design variable, normalized by its bounds. A polyline joins all components of a given design vector and is colored by objective function values. This highlights the correlations between the values of the design variables and the values of the objective function. .. GENERATED FROM PYTHON SOURCE LINES 60-64 Create disciplines ------------------ At this point, we instantiate the disciplines of Sobieski's SSBJ problem: Propulsion, Aerodynamics, Structure and Mission .. GENERATED FROM PYTHON SOURCE LINES 64-73 .. code-block:: default disciplines = create_discipline( [ "SobieskiPropulsion", "SobieskiAerodynamics", "SobieskiStructure", "SobieskiMission", ] ) .. GENERATED FROM PYTHON SOURCE LINES 74-77 Create design space ------------------- We also read the design space from the :class:`.SobieskiProblem`. .. GENERATED FROM PYTHON SOURCE LINES 77-79 .. code-block:: default design_space = SobieskiProblem().design_space .. GENERATED FROM PYTHON SOURCE LINES 80-87 Create and execute scenario --------------------------- The next step is to build an MDO 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 SLSQP optimization algorithm and a maximum number of iterations equal to 100. .. GENERATED FROM PYTHON SOURCE LINES 87-99 .. code-block:: default scenario = create_scenario( disciplines, formulation="MDF", objective_name="y_4", maximize_objective=True, design_space=design_space, ) scenario.set_differentiation_method("user") for constraint in ["g_1", "g_2", "g_3"]: scenario.add_constraint(constraint, "ineq") scenario.execute({"algo": "SLSQP", "max_iter": 10}) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none INFO - 07:14:53: INFO - 07:14:53: *** Start MDOScenario execution *** INFO - 07:14:53: MDOScenario INFO - 07:14:53: Disciplines: SobieskiPropulsion SobieskiAerodynamics SobieskiStructure SobieskiMission INFO - 07:14:53: MDO formulation: MDF INFO - 07:14:53: Optimization problem: INFO - 07:14:53: minimize -y_4(x_shared, x_1, x_2, x_3) INFO - 07:14:53: with respect to x_1, x_2, x_3, x_shared INFO - 07:14:53: subject to constraints: INFO - 07:14:53: g_1(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 07:14:53: g_2(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 07:14:53: g_3(x_shared, x_1, x_2, x_3) <= 0.0 INFO - 07:14:53: over the design space: INFO - 07:14:53: +----------+-------------+-------+-------------+-------+ INFO - 07:14:53: | name | lower_bound | value | upper_bound | type | INFO - 07:14:53: +----------+-------------+-------+-------------+-------+ INFO - 07:14:53: | x_shared | 0.01 | 0.05 | 0.09 | float | INFO - 07:14:53: | x_shared | 30000 | 45000 | 60000 | float | INFO - 07:14:53: | x_shared | 1.4 | 1.6 | 1.8 | float | INFO - 07:14:53: | x_shared | 2.5 | 5.5 | 8.5 | float | INFO - 07:14:53: | x_shared | 40 | 55 | 70 | float | INFO - 07:14:53: | x_shared | 500 | 1000 | 1500 | float | INFO - 07:14:53: | x_1 | 0.1 | 0.25 | 0.4 | float | INFO - 07:14:53: | x_1 | 0.75 | 1 | 1.25 | float | INFO - 07:14:53: | x_2 | 0.75 | 1 | 1.25 | float | INFO - 07:14:53: | x_3 | 0.1 | 0.5 | 1 | float | INFO - 07:14:53: +----------+-------------+-------+-------------+-------+ INFO - 07:14:53: Solving optimization problem with algorithm SLSQP: INFO - 07:14:53: ... 0%| | 0/10 [00:00` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_para_coord.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_