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

.. only:: html

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

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

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

.. _sphx_glr_examples_doe_plot_sobieski_doe_disc_example.py:


Simple disciplinary DOE example on the Sobieski SSBJ test case
==============================================================

.. GENERATED FROM PYTHON SOURCE LINES 24-35

.. 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

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 36-38

Instantiate the discipline
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 38-40

.. code-block:: Python

    discipline = create_discipline("SobieskiMission")








.. GENERATED FROM PYTHON SOURCE LINES 41-43

Create the design space
-----------------------

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: Python

    design_space = SobieskiDesignSpace()
    design_space.filter(["y_24", "y_34"])






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='margin: 1em;'>Sobieski design space:<br/><table>
        <thead>
            <tr>
                <th>Name</th>
                <th>Lower bound</th>
                <th>Value</th>
                <th>Upper bound</th>
                <th>Type</th>
            </tr>
        </thead>
        <tbody>
            <tr>
                <td>y_24</td>
                <td>0.44</td>
                <td>4.15006276</td>
                <td>11.13</td>
                <td>float</td>
            </tr>
            <tr>
                <td>y_34</td>
                <td>0.44</td>
                <td>1.10754577</td>
                <td>1.98</td>
                <td>float</td>
            </tr>
        </tbody>
    </table></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 47-51

Create the scenario
-----------------------
Build scenario which links the disciplines with the formulation and
The DOE algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 51-60

.. code-block:: Python

    scenario = create_scenario(
        [discipline],
        "y_4",
        design_space,
        maximize_objective=True,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )








.. GENERATED FROM PYTHON SOURCE LINES 61-64

Execute the scenario
-----------------------
Here we use a latin hypercube sampling algorithm with 30 samples.

.. GENERATED FROM PYTHON SOURCE LINES 64-66

.. code-block:: Python

    scenario.execute(algo_name="PYDOE_LHS", n_samples=30)





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

 .. code-block:: none

        INFO - 20:38:57: *** Start DOEScenario execution ***
        INFO - 20:38:57: DOEScenario
        INFO - 20:38:57:    Disciplines: SobieskiMission
        INFO - 20:38:57:    MDO formulation: DisciplinaryOpt
        INFO - 20:38:57: Optimization problem:
        INFO - 20:38:57:    minimize -y_4(y_24, y_34)
        INFO - 20:38:57:    with respect to y_24, y_34
        INFO - 20:38:57:    over the design space:
        INFO - 20:38:57:       +------+-------------+------------+-------------+-------+
        INFO - 20:38:57:       | Name | Lower bound |   Value    | Upper bound | Type  |
        INFO - 20:38:57:       +------+-------------+------------+-------------+-------+
        INFO - 20:38:57:       | y_24 |     0.44    | 4.15006276 |    11.13    | float |
        INFO - 20:38:57:       | y_34 |     0.44    | 1.10754577 |     1.98    | float |
        INFO - 20:38:57:       +------+-------------+------------+-------------+-------+
        INFO - 20:38:57: Solving optimization problem with algorithm PYDOE_LHS:
        INFO - 20:38:57:      3%|▎         | 1/30 [00:00<00:00, 374.49 it/sec, obj=-1.53e+3]
        INFO - 20:38:57:      7%|▋         | 2/30 [00:00<00:00, 666.98 it/sec, obj=-1.66e+3]
        INFO - 20:38:57:     10%|█         | 3/30 [00:00<00:00, 916.92 it/sec, obj=-832]
        INFO - 20:38:57:     13%|█▎        | 4/30 [00:00<00:00, 1138.44 it/sec, obj=-1.62e+3]
        INFO - 20:38:57:     17%|█▋        | 5/30 [00:00<00:00, 1337.38 it/sec, obj=-994]
        INFO - 20:38:57:     20%|██        | 6/30 [00:00<00:00, 1515.37 it/sec, obj=-601]
        INFO - 20:38:57:     23%|██▎       | 7/30 [00:00<00:00, 1678.39 it/sec, obj=-180]
        INFO - 20:38:57:     27%|██▋       | 8/30 [00:00<00:00, 1817.19 it/sec, obj=-755]
        INFO - 20:38:57:     30%|███       | 9/30 [00:00<00:00, 1952.25 it/sec, obj=-691]
        INFO - 20:38:57:     33%|███▎      | 10/30 [00:00<00:00, 2064.53 it/sec, obj=-393]
        INFO - 20:38:57:     37%|███▋      | 11/30 [00:00<00:00, 2169.74 it/sec, obj=-362]
        INFO - 20:38:57:     40%|████      | 12/30 [00:00<00:00, 2263.83 it/sec, obj=-748]
        INFO - 20:38:57:     43%|████▎     | 13/30 [00:00<00:00, 2357.37 it/sec, obj=-719]
        INFO - 20:38:57:     47%|████▋     | 14/30 [00:00<00:00, 2446.68 it/sec, obj=-293]
        INFO - 20:38:57:     50%|█████     | 15/30 [00:00<00:00, 2529.53 it/sec, obj=-931]
        INFO - 20:38:57:     53%|█████▎    | 16/30 [00:00<00:00, 2606.07 it/sec, obj=-264]
        INFO - 20:38:57:     57%|█████▋    | 17/30 [00:00<00:00, 2663.35 it/sec, obj=-1.17e+3]
        INFO - 20:38:57:     60%|██████    | 18/30 [00:00<00:00, 2729.09 it/sec, obj=-495]
        INFO - 20:38:57:     63%|██████▎   | 19/30 [00:00<00:00, 2785.84 it/sec, obj=-189]
        INFO - 20:38:57:     67%|██████▋   | 20/30 [00:00<00:00, 2848.42 it/sec, obj=-2.23e+3]
        INFO - 20:38:57:     70%|███████   | 21/30 [00:00<00:00, 2897.29 it/sec, obj=-344]
        INFO - 20:38:57:     73%|███████▎  | 22/30 [00:00<00:00, 2946.10 it/sec, obj=-799]
        INFO - 20:38:57:     77%|███████▋  | 23/30 [00:00<00:00, 2996.86 it/sec, obj=-55.9]
        INFO - 20:38:57:     80%|████████  | 24/30 [00:00<00:00, 3047.54 it/sec, obj=-123]
        INFO - 20:38:57:     83%|████████▎ | 25/30 [00:00<00:00, 3097.26 it/sec, obj=-875]
        INFO - 20:38:57:     87%|████████▋ | 26/30 [00:00<00:00, 3135.39 it/sec, obj=-726]
        INFO - 20:38:57:     90%|█████████ | 27/30 [00:00<00:00, 3178.39 it/sec, obj=-69.6]
        INFO - 20:38:57:     93%|█████████▎| 28/30 [00:00<00:00, 3220.37 it/sec, obj=-1.51e+3]
        INFO - 20:38:57:     97%|█████████▋| 29/30 [00:00<00:00, 3259.15 it/sec, obj=-1.15e+3]
        INFO - 20:38:57:    100%|██████████| 30/30 [00:00<00:00, 3296.46 it/sec, obj=-2.73e+3]
        INFO - 20:38:57: Optimization result:
        INFO - 20:38:57:    Optimizer info:
        INFO - 20:38:57:       Status: None
        INFO - 20:38:57:       Message: None
        INFO - 20:38:57:       Number of calls to the objective function by the optimizer: 0
        INFO - 20:38:57:    Solution:
        INFO - 20:38:57:       Objective: -2726.3660548732214
        INFO - 20:38:57:       Design space:
        INFO - 20:38:57:          +------+-------------+--------------------+-------------+-------+
        INFO - 20:38:57:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 20:38:57:          +------+-------------+--------------------+-------------+-------+
        INFO - 20:38:57:          | y_24 |     0.44    | 9.094543945649603  |    11.13    | float |
        INFO - 20:38:57:          | y_34 |     0.44    | 0.4769766573300308 |     1.98    | float |
        INFO - 20:38:57:          +------+-------------+--------------------+-------------+-------+
        INFO - 20:38:57: *** End DOEScenario execution (time: 0:00:00.011560) ***




.. GENERATED FROM PYTHON SOURCE LINES 67-73

Note that both the formulation settings passed to func:`.create_scenario` and the
algorithm settings passed to :meth:`~.DriverLibrary.execute` can be provided via a Pydantic model. For
more information, see :ref:`formulation_settings` and :ref:`algorithm_settings`.

Plot optimization history view
------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 73-75

.. code-block:: Python

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




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


    *

      .. image-sg:: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_001.png
         :alt: Evolution of the optimization variables
         :srcset: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_002.png
         :alt: Evolution of the objective value
         :srcset: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_003.png
         :alt: Evolution of the distance to the optimum
         :srcset: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_003.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none


    <gemseo.post.opt_history_view.OptHistoryView object at 0x785917d774a0>



.. GENERATED FROM PYTHON SOURCE LINES 76-82

Note that post-processor settings passed to :meth:`.BaseScenario.post_process` can be
provided via a Pydantic model (see the example below). For more information,
see :ref:`post_processor_settings`.

Plot scatter plot matrix
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 82-91

.. code-block:: Python

    from gemseo.settings.post import ScatterPlotMatrix_Settings  # noqa: E402

    settings_model = ScatterPlotMatrix_Settings(
        variable_names=["y_4", "y_24", "y_34"],
        save=False,
        show=True,
    )

    scenario.post_process(settings_model)



.. image-sg:: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_004.png
   :alt: plot sobieski doe disc example
   :srcset: /examples/doe/images/sphx_glr_plot_sobieski_doe_disc_example_004.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x78592d42e180>




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

   **Total running time of the script:** (0 minutes 0.622 seconds)


.. _sphx_glr_download_examples_doe_plot_sobieski_doe_disc_example.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_sobieski_doe_disc_example.zip <plot_sobieski_doe_disc_example.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_