.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/post_process/matplotlib_opt_post_processor.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_matplotlib_opt_post_processor.py>`
        to download the full example code

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

.. _sphx_glr_examples_post_process_matplotlib_opt_post_processor.py:


Customize with matplotlib
=========================

In this example,
we will see how to modify the matplotlib figures
generated by an :class:`.OptPostProcessor`.
This can be useful to finely tune a graph for a presentation or a paper.

.. GENERATED FROM PYTHON SOURCE LINES 25-34

.. code-block:: Python


    from __future__ import annotations

    from matplotlib import pyplot as plt

    from gemseo import create_design_space
    from gemseo import create_discipline
    from gemseo import create_scenario


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We consider a minimization problem over the interval :math:`[0,1]`
of the :math:`f(x)=x^2` objective function:

.. GENERATED FROM PYTHON SOURCE LINES 37-44

.. code-block:: Python

    discipline = create_discipline("AnalyticDiscipline", expressions={"y": "x**2"})

    design_space = create_design_space()
    design_space.add_variable("x", l_b=0.0, u_b=1.0)

    scenario = create_scenario([discipline], "DisciplinaryOpt", "y", design_space)


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We solve this optimization problem with the gradient-free algorithm COBYLA:

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.. code-block:: Python

    scenario.execute({"algo": "NLOPT_COBYLA", "max_iter": 10})


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Then,
we can post-process this :class:`.MDOScenario` with an :class:`.OptHistoryView`.

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.. code-block:: Python

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


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Instead of saving or showing this :class:`.OptHistoryView`,
and so the associated :attr:`.OptHistoryView.figures`,
we will slightly modify the latter which are matplotlib figures by default.

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.. code-block:: Python

    figures = opt_post_processor.figures
    print(figures.keys())


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By default,
the color bar representing the evolution of the optimization variables
does not use labels.
To add a custom label indicating
that the optimization variables are scaled in :math:`[0,1]`,
we can get the matplotlib ``Axes`` of the matplotlib figures:

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.. code-block:: Python

    figure = figures["variables"]
    axes = figure.axes


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and change the y-label of the color bar:

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.. code-block:: Python

    axes[1].set_ylabel("Optimization variables scales in [0,1]")


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We can also change the y-label of the graph:

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.. code-block:: Python

    axes[0].set_ylabel("Optimization variables")


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Lastly,
we can plot all the figures:

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.. code-block:: Python

    plt.show()


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or save the modified figure:

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.. code-block:: Python

    figure.savefig("variables.png")


.. _sphx_glr_download_examples_post_process_matplotlib_opt_post_processor.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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