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

.. only:: html

    .. meta::
        :keywords: codex

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

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

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

.. _sphx_glr_gallery_images_contours_and_fields_interpolation_methods.py:


=========================
Interpolations for imshow
=========================

This example displays the difference between interpolation methods for
`~.axes.Axes.imshow`.

If *interpolation* is None, it defaults to the :rc:`image.interpolation`.
If the interpolation is ``'none'``, then no interpolation is performed for the
Agg, ps and pdf backends. Other backends will default to ``'antialiased'``.

For the Agg, ps and pdf backends, ``interpolation='none'`` works well when a
big image is scaled down, while ``interpolation='nearest'`` works well when
a small image is scaled up.

See :doc:`/gallery/images_contours_and_fields/image_antialiasing` for a
discussion on the default ``interpolation='antialiased'`` option.

.. GENERATED FROM PYTHON SOURCE LINES 20-43

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    methods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',
               'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',
               'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']

    # Fixing random state for reproducibility
    np.random.seed(19680801)

    grid = np.random.rand(4, 4)

    fig, axs = plt.subplots(nrows=3, ncols=6, figsize=(9, 6),
                            subplot_kw={'xticks': [], 'yticks': []})

    for ax, interp_method in zip(axs.flat, methods):
        ax.imshow(grid, interpolation=interp_method, cmap='viridis')
        ax.set_title(str(interp_method))

    plt.tight_layout()
    plt.show()




.. image-sg:: /gallery/images_contours_and_fields/images/sphx_glr_interpolation_methods_001.png
   :alt: None, none, nearest, bilinear, bicubic, spline16, spline36, hanning, hamming, hermite, kaiser, quadric, catrom, gaussian, bessel, mitchell, sinc, lanczos
   :srcset: /gallery/images_contours_and_fields/images/sphx_glr_interpolation_methods_001.png, /gallery/images_contours_and_fields/images/sphx_glr_interpolation_methods_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 44-50

.. admonition:: References

   The use of the following functions, methods, classes and modules is shown
   in this example:

   - `matplotlib.axes.Axes.imshow` / `matplotlib.pyplot.imshow`


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

   **Total running time of the script:** (0 minutes 2.064 seconds)


.. _sphx_glr_download_gallery_images_contours_and_fields_interpolation_methods.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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