.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "sphinx_gallery_output/plot_03_as_wordcloud.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_sphinx_gallery_output_plot_03_as_wordcloud.py>`
        to download the full example code.

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

.. _sphx_glr_sphinx_gallery_output_plot_03_as_wordcloud.py:


Word clouds
===========

To visualise subset contents (rather than sizes), use the
:code:`as_wordcloud` constructors, which generate a word cloud using
the word_cloud_ library.

.. _word_cloud: https://github.com/amueller/word_cloud

.. GENERATED FROM PYTHON SOURCE LINES 13-56



.. image-sg:: /sphinx_gallery_output/images/sphx_glr_plot_03_as_wordcloud_001.png
   :alt: plot 03 as wordcloud
   :srcset: /sphinx_gallery_output/images/sphx_glr_plot_03_as_wordcloud_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt

    from matplotlib_set_diagrams import (
        EulerDiagram,
        VennDiagram,
    )

    text_1 = """Lorem ipsum dolor sit amet, consectetur adipiscing elit,
    sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut
    enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi
    ut aliquip ex ea commodo consequat."""

    text_2 = """Duis aute irure dolor in reprehenderit in voluptate velit
    esse cillum dolore eu fugiat nulla pariatur. Lorem ipsum dolor sit
    amet."""


    def word_tokenize(text):
        """Break a string into its constituent words, and convert the words
        into their 'standard' form (tokens).

        The procedure below is a poor-man's tokenization.
        Consider using the Natural Language Toolkit (NLTK) instead:

        >>> import nltk; words = nltk.word_tokenize(text)

        """
        # get a word list
        words = text.split(' ')
        # remove non alphanumeric characters
        words = [''.join(ch for ch in word if ch.isalnum()) for word in words]
        # convert to all lower case
        words = [word.lower() for word in words]
        return words

    # Tokenize strings.
    sets = [set(word_tokenize(text)) for text in [text_1, text_2]]

    fig, (ax1, ax2) = plt.subplots(1, 2)
    EulerDiagram.as_wordcloud(sets, ax=ax1)
    VennDiagram.as_wordcloud(sets, ax=ax2)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.512 seconds)


.. _sphx_glr_download_sphinx_gallery_output_plot_03_as_wordcloud.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_03_as_wordcloud.ipynb <plot_03_as_wordcloud.ipynb>`

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

      :download:`Download Python source code: plot_03_as_wordcloud.py <plot_03_as_wordcloud.py>`

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

      :download:`Download zipped: plot_03_as_wordcloud.zip <plot_03_as_wordcloud.zip>`


.. only:: html

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

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