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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/post_process/algorithms/plot_constraints_radar_chart.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_constraints_radar_chart.py>`
        to download the full example code.

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

.. _sphx_glr_examples_post_process_algorithms_plot_constraints_radar_chart.py:


Radar chart
===========

In this example, we illustrate the use of the
:class:`.RadarChart` 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.mdo.sobieski.core.design_space import SobieskiDesignSpace








.. GENERATED FROM PYTHON SOURCE LINES 38-42

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 42-45

.. code-block:: Python


    configure_logger()





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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 46-55

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

The :class:`.RadarChart` post-processing
plots on a radar style chart a list of constraint functions
at a given iteration.

By default, the last iteration is used. This plot scales better with the number
of constraints than the constraint plot provided by the :ref:`opt_history_view`.

.. GENERATED FROM PYTHON SOURCE LINES 57-61

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

.. GENERATED FROM PYTHON SOURCE LINES 61-68

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 69-72

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

.. GENERATED FROM PYTHON SOURCE LINES 72-74

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. GENERATED FROM PYTHON SOURCE LINES 75-82

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 82-94

.. code-block:: Python

    scenario = create_scenario(
        disciplines,
        "y_4",
        design_space,
        formulation_name="MDF",
        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_name="SLSQP", max_iter=10)





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

 .. code-block:: none

     WARNING - 08:38:50: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
     WARNING - 08:38:50: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
        INFO - 08:38:50:  
        INFO - 08:38:50: *** Start MDOScenario execution ***
        INFO - 08:38:50: MDOScenario
        INFO - 08:38:50:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 08:38:50:    MDO formulation: MDF
        INFO - 08:38:50: Optimization problem:
        INFO - 08:38:50:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 08:38:50:    with respect to x_1, x_2, x_3, x_shared
        INFO - 08:38:50:    subject to constraints:
        INFO - 08:38:50:       g_1(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:50:       g_2(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:50:       g_3(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:50:    over the design space:
        INFO - 08:38:50:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:50:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:38:50:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:50:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 08:38:50:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 08:38:50:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 08:38:50:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 08:38:50:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 08:38:50:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 08:38:50:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 08:38:50:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:50:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:50:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 08:38:50:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:50: Solving optimization problem with algorithm SLSQP:
        INFO - 08:38:50:     10%|█         | 1/10 [00:00<00:00, 18.98 it/sec, obj=-536]
        INFO - 08:38:51:     20%|██        | 2/10 [00:00<00:00, 14.07 it/sec, obj=-2.12e+3]
     WARNING - 08:38:51: MDAJacobi has reached its maximum number of iterations but the normed residual 5.741449586530469e-06 is still above the tolerance 1e-06.
        INFO - 08:38:51:     30%|███       | 3/10 [00:00<00:00, 11.41 it/sec, obj=-3.46e+3]
        INFO - 08:38:51:     40%|████      | 4/10 [00:00<00:00, 10.91 it/sec, obj=-3.96e+3]
        INFO - 08:38:51:     50%|█████     | 5/10 [00:00<00:00, 11.14 it/sec, obj=-4.61e+3]
        INFO - 08:38:51:     60%|██████    | 6/10 [00:00<00:00, 11.88 it/sec, obj=-4.5e+3]
        INFO - 08:38:51:     70%|███████   | 7/10 [00:00<00:00, 12.29 it/sec, obj=-4.26e+3]
        INFO - 08:38:51:     80%|████████  | 8/10 [00:00<00:00, 12.62 it/sec, obj=-4.11e+3]
        INFO - 08:38:51:     90%|█████████ | 9/10 [00:00<00:00, 12.89 it/sec, obj=-4.02e+3]
        INFO - 08:38:51:    100%|██████████| 10/10 [00:00<00:00, 13.12 it/sec, obj=-3.99e+3]
        INFO - 08:38:51: Optimization result:
        INFO - 08:38:51:    Optimizer info:
        INFO - 08:38:51:       Status: None
        INFO - 08:38:51:       Message: Maximum number of iterations reached. GEMSEO stopped the driver.
        INFO - 08:38:51:       Number of calls to the objective function by the optimizer: 12
        INFO - 08:38:51:    Solution:
        INFO - 08:38:51:       The solution is feasible.
        INFO - 08:38:51:       Objective: -3463.120411437138
        INFO - 08:38:51:       Standardized constraints:
        INFO - 08:38:51:          g_1 = [-0.01112145 -0.02847064 -0.04049911 -0.04878943 -0.05476349 -0.14014207
        INFO - 08:38:51:  -0.09985793]
        INFO - 08:38:51:          g_2 = -0.0020925663903177405
        INFO - 08:38:51:          g_3 = [-0.71359843 -0.28640157 -0.05926796 -0.183255  ]
        INFO - 08:38:51:       Design space:
        INFO - 08:38:51:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:51:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 08:38:51:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:51:          | x_shared[0] |     0.01    | 0.05947685840242058 |     0.09    | float |
        INFO - 08:38:51:          | x_shared[1] |    30000    |   59246.692998739   |    60000    | float |
        INFO - 08:38:51:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 08:38:51:          | x_shared[3] |     2.5     |   2.64097355362077  |     8.5     | float |
        INFO - 08:38:51:          | x_shared[4] |      40     |  69.32144380869019  |      70     | float |
        INFO - 08:38:51:          | x_shared[5] |     500     |  1478.031626737187  |     1500    | float |
        INFO - 08:38:51:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 08:38:51:          | x_1[1]      |     0.75    |  0.7608797907508461 |     1.25    | float |
        INFO - 08:38:51:          | x_2         |     0.75    |  0.7607584987262048 |     1.25    | float |
        INFO - 08:38:51:          | x_3         |     0.1     |  0.1514057659459843 |      1      | float |
        INFO - 08:38:51:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:51: *** End MDOScenario execution (time: 0:00:00.771547) ***




.. GENERATED FROM PYTHON SOURCE LINES 95-99

Post-process scenario
---------------------
Lastly, we post-process the scenario by means of the
:class:`.RadarChart`.

.. GENERATED FROM PYTHON SOURCE LINES 101-109

.. tip::

   Each post-processing method requires different inputs and offers a variety
   of customization options. Use the API 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 109-117

.. code-block:: Python


    scenario.post_process(
        post_name="RadarChart",
        constraint_names=["g_1", "g_2", "g_3"],
        save=False,
        show=True,
        fig_size=(5, 5),
    )



.. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_constraints_radar_chart_001.png
   :alt: Constraints at iteration 3 (optimum)
   :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_constraints_radar_chart_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/6.0.0/lib/python3.9/site-packages/gemseo/post/dataset/plots/_matplotlib/plot.py:87: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
      figure.tight_layout()

    <gemseo.post.radar_chart.RadarChart object at 0x7f6dc104b6a0>




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

   **Total running time of the script:** (0 minutes 1.071 seconds)


.. _sphx_glr_download_examples_post_process_algorithms_plot_constraints_radar_chart.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_constraints_radar_chart.ipynb <plot_constraints_radar_chart.ipynb>`

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

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