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

.. only:: html

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

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

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

.. _sphx_glr_examples_dataset_visualization_matplotlib_dataset_plot.py:


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

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

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.. code-block:: Python


    from __future__ import annotations

    from matplotlib import pyplot as plt

    from gemseo.datasets.dataset import Dataset
    from gemseo.post.dataset.yvsx import YvsX


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First,
we build a simple :class:`.Dataset`
containing a cloud of points ``((1, 1), (2, 0), (3, 1))`` of the tuple ``("a", "b")``.

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.. code-block:: Python

    dataset = Dataset()
    dataset.add_variable("a", [[1], [2], [3]])
    dataset.add_variable("b", [[1], [0], [1]])


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Then,
we define a :class:`YvsX` chart, which is a particular :class:`.DatasetPlot`:

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.. code-block:: Python

    yvsx = YvsX(dataset, "a", "b")


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and draw the figure.

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.. code-block:: Python

    figures = yvsx.execute(save=False)


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By default,
the horizontal and vertical labels are the names of the variables,
namely ``"a"`` and ``"b"`` in this example.
As these labels are not so relevant,
we are going to modify this figure directly with matplotlib
instead of saving it.
For that,
we get the matplotlib ``Axes`` of the matplotlib ``Figure``:

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.. code-block:: Python

    figure = figures[0]
    ax = figure.axes[0]


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and change the labels:

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.. code-block:: Python

    ax.set_xlabel("A relevant x-label")
    ax.set_ylabel("A relevant y-label")


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We can also add a grid:

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.. code-block:: Python

    ax.grid()


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Lastly,
we can save the figure with matplotlib

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.. code-block:: Python

    plt.savefig("foo.png")


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and display it:

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.. code-block:: Python

    plt.show()


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Note that in this pedagogical example,
we modified basic properties of the chart
which could have been modified with the attributes of ``DatasetPlot``,
e.g. ``yvsx.xlabel = "A relevant x-label"``.
But it is impossible to add attributes or methods for all the features of matplotlib.
It is therefore advisable to check if an attribute of :class:`.DatasetPlot` can help
and if not, access and modify the matplotlib ``Figure`` as shown here.


.. _sphx_glr_download_examples_dataset_visualization_matplotlib_dataset_plot.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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