.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/post_process/algorithms/plot_para_coord.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_post_process_algorithms_plot_para_coord.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_post_process_algorithms_plot_para_coord.py:


Parallel coordinates
====================

In this example, we illustrate the use of the
:class:`.ParallelCoordinates` plot on the Sobieski's SSBJ
problem.

.. GENERATED FROM PYTHON SOURCE LINES 29-37

.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.problems.sobieski.core.design_space import SobieskiDesignSpace








.. GENERATED FROM PYTHON SOURCE LINES 38-42

Import
------
The first step is to import some high-level functions
and a method to get the design space.

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: Python


    configure_logger()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 46-60

Description
-----------

The :class:`.ParallelCoordinates` post-processing
builds parallel coordinates plots among design
variables, outputs functions and constraints.

The :class:`.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 62-66

Create disciplines
------------------
At this point, we instantiate the disciplines of Sobieski's SSBJ problem:
Propulsion, Aerodynamics, Structure and Mission

.. GENERATED FROM PYTHON SOURCE LINES 66-73

.. code-block:: Python

    disciplines = create_discipline([
        "SobieskiPropulsion",
        "SobieskiAerodynamics",
        "SobieskiStructure",
        "SobieskiMission",
    ])








.. GENERATED FROM PYTHON SOURCE LINES 74-77

Create design space
-------------------
We also create the :class:`.SobieskiDesignSpace`.

.. GENERATED FROM PYTHON SOURCE LINES 77-79

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. 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:: Python

    scenario = create_scenario(
        disciplines,
        "MDF",
        "y_4",
        design_space,
        maximize_objective=True,
    )
    scenario.set_differentiation_method()
    for constraint in ["g_1", "g_2", "g_3"]:
        scenario.add_constraint(constraint, constraint_type="ineq")
    scenario.execute({"algo": "SLSQP", "max_iter": 10})





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 13:57:15:  
        INFO - 13:57:15: *** Start MDOScenario execution ***
        INFO - 13:57:15: MDOScenario
        INFO - 13:57:15:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 13:57:15:    MDO formulation: MDF
        INFO - 13:57:15: Optimization problem:
        INFO - 13:57:15:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 13:57:15:    with respect to x_1, x_2, x_3, x_shared
        INFO - 13:57:15:    subject to constraints:
        INFO - 13:57:15:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:57:15:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:57:15:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:57:15:    over the design space:
        INFO - 13:57:15:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:57:15:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 13:57:15:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:57:15:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 13:57:15:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 13:57:15:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 13:57:15:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 13:57:15:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 13:57:15:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 13:57:15:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 13:57:15:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 13:57:15:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 13:57:15:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 13:57:15:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:57:15: Solving optimization problem with algorithm SLSQP:
        INFO - 13:57:15:     10%|█         | 1/10 [00:00<00:00, 10.66 it/sec, obj=-536]
        INFO - 13:57:15:     20%|██        | 2/10 [00:00<00:01,  7.36 it/sec, obj=-2.12e+3]
     WARNING - 13:57:15: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7130677857005655e-05 is still above the tolerance 1e-06.
        INFO - 13:57:15:     30%|███       | 3/10 [00:00<00:01,  6.19 it/sec, obj=-3.75e+3]
        INFO - 13:57:15:     40%|████      | 4/10 [00:00<00:01,  5.92 it/sec, obj=-3.96e+3]
        INFO - 13:57:16:     50%|█████     | 5/10 [00:00<00:00,  5.76 it/sec, obj=-3.96e+3]
        INFO - 13:57:16: Optimization result:
        INFO - 13:57:16:    Optimizer info:
        INFO - 13:57:16:       Status: 8
        INFO - 13:57:16:       Message: Positive directional derivative for linesearch
        INFO - 13:57:16:       Number of calls to the objective function by the optimizer: 6
        INFO - 13:57:16:    Solution:
        INFO - 13:57:16:       The solution is feasible.
        INFO - 13:57:16:       Objective: -3963.408265187933
        INFO - 13:57:16:       Standardized constraints:
        INFO - 13:57:16:          g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346  -0.05732607 -0.13720865
        INFO - 13:57:16:  -0.10279135]
        INFO - 13:57:16:          g_2 = 3.333278582928756e-06
        INFO - 13:57:16:          g_3 = [-7.67181773e-01 -2.32818227e-01  8.30379541e-07 -1.83255000e-01]
        INFO - 13:57:16:       Design space:
        INFO - 13:57:16:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:57:16:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 13:57:16:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:57:16:          | x_shared[0] |     0.01    | 0.06000083331964572 |     0.09    | float |
        INFO - 13:57:16:          | x_shared[1] |    30000    |        60000        |    60000    | float |
        INFO - 13:57:16:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 13:57:16:          | x_shared[3] |     2.5     |         2.5         |     8.5     | float |
        INFO - 13:57:16:          | x_shared[4] |      40     |          70         |      70     | float |
        INFO - 13:57:16:          | x_shared[5] |     500     |         1500        |     1500    | float |
        INFO - 13:57:16:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 13:57:16:          | x_1[1]      |     0.75    |         0.75        |     1.25    | float |
        INFO - 13:57:16:          | x_2         |     0.75    |         0.75        |     1.25    | float |
        INFO - 13:57:16:          | x_3         |     0.1     |  0.1562448753887276 |      1      | float |
        INFO - 13:57:16:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:57:16: *** End MDOScenario execution (time: 0:00:00.994995) ***

    {'max_iter': 10, 'algo': 'SLSQP'}



.. GENERATED FROM PYTHON SOURCE LINES 100-105

Post-process scenario
---------------------
Lastly, we post-process the scenario by means of the
:class:`.ParallelCoordinates` plot which parallel
coordinates plots among design variables, objective function and constraints.

.. GENERATED FROM PYTHON SOURCE LINES 107-115

.. tip::

   Each post-processing method requires different inputs and offers a variety
   of customization options. Use the high-level function
   :func:`.get_post_processing_options_schema` to print a table with
   the options for any post-processing algorithm.
   Or refer to our dedicated page:
   :ref:`gen_post_algos`.

.. GENERATED FROM PYTHON SOURCE LINES 115-116

.. code-block:: Python

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



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_para_coord_001.png
         :alt: Design variables history colored by '-y_4' value
         :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_para_coord_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_para_coord_002.png
         :alt: Objective function and constraints history colored by '-y_4' value.
         :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_para_coord_002.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <gemseo.post.para_coord.ParallelCoordinates object at 0x7f2d516f0460>




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.656 seconds)


.. _sphx_glr_download_examples_post_process_algorithms_plot_para_coord.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_para_coord.py <plot_para_coord.py>`


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