.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_images_contours_and_fields_image_demo.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_gallery_images_contours_and_fields_image_demo.py:


==========
Image Demo
==========

Many ways to plot images in Matplotlib.

The most common way to plot images in Matplotlib is with
`~.axes.Axes.imshow`. The following examples demonstrate much of the
functionality of imshow and the many images you can create.


.. code-block:: default


    import numpy as np
    import matplotlib.cm as cm
    import matplotlib.pyplot as plt
    import matplotlib.cbook as cbook
    from matplotlib.path import Path
    from matplotlib.patches import PathPatch


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








First we'll generate a simple bivariate normal distribution.


.. code-block:: default


    delta = 0.025
    x = y = np.arange(-3.0, 3.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = np.exp(-X**2 - Y**2)
    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
    Z = (Z1 - Z2) * 2

    fig, ax = plt.subplots()
    im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
                   origin='lower', extent=[-3, 3, -3, 3],
                   vmax=abs(Z).max(), vmin=-abs(Z).max())

    plt.show()





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_001.png
    :alt: image demo
    :class: sphx-glr-single-img





It is also possible to show images of pictures.


.. code-block:: default


    # A sample image
    with cbook.get_sample_data('ada.png') as image_file:
        image = plt.imread(image_file)

    fig, ax = plt.subplots()
    ax.imshow(image)
    ax.axis('off')  # clear x-axis and y-axis


    # And another image

    w, h = 512, 512

    with cbook.get_sample_data('ct.raw.gz') as datafile:
        s = datafile.read()
    A = np.frombuffer(s, np.uint16).astype(float).reshape((w, h))
    A /= A.max()

    fig, ax = plt.subplots()
    extent = (0, 25, 0, 25)
    im = ax.imshow(A, cmap=plt.cm.hot, origin='upper', extent=extent)

    markers = [(15.9, 14.5), (16.8, 15)]
    x, y = zip(*markers)
    ax.plot(x, y, 'o')

    ax.set_title('CT density')

    plt.show()





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


    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_002.png
          :alt: image demo
          :class: sphx-glr-multi-img

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png
          :alt: CT density
          :class: sphx-glr-multi-img





Interpolating images
--------------------

It is also possible to interpolate images before displaying them. Be careful,
as this may manipulate the way your data looks, but it can be helpful for
achieving the look you want. Below we'll display the same (small) array,
interpolated with three different interpolation methods.

The center of the pixel at A[i, j] is plotted at (i+0.5, i+0.5).  If you
are using interpolation='nearest', the region bounded by (i, j) and
(i+1, j+1) will have the same color.  If you are using interpolation,
the pixel center will have the same color as it does with nearest, but
other pixels will be interpolated between the neighboring pixels.

To prevent edge effects when doing interpolation, Matplotlib pads the input
array with identical pixels around the edge: if you have a 5x5 array with
colors a-y as below::

  a b c d e
  f g h i j
  k l m n o
  p q r s t
  u v w x y

Matplotlib computes the interpolation and resizing on the padded array ::

  a a b c d e e
  a a b c d e e
  f f g h i j j
  k k l m n o o
  p p q r s t t
  o u v w x y y
  o u v w x y y

and then extracts the central region of the result.  (Extremely old versions
of Matplotlib (<0.63) did not pad the array, but instead adjusted the view
limits to hide the affected edge areas.)

This approach allows plotting the full extent of an array without
edge effects, and for example to layer multiple images of different
sizes over one another with different interpolation methods -- see
:doc:`/gallery/images_contours_and_fields/layer_images`.  It also implies
a performance hit, as this new temporary, padded array must be created.
Sophisticated interpolation also implies a performance hit; for maximal
performance or very large images, interpolation='nearest' is suggested.


.. code-block:: default


    A = np.random.rand(5, 5)

    fig, axs = plt.subplots(1, 3, figsize=(10, 3))
    for ax, interp in zip(axs, ['nearest', 'bilinear', 'bicubic']):
        ax.imshow(A, interpolation=interp)
        ax.set_title(interp.capitalize())
        ax.grid(True)

    plt.show()





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_004.png
    :alt: Nearest, Bilinear, Bicubic
    :class: sphx-glr-single-img





You can specify whether images should be plotted with the array origin
x[0, 0] in the upper left or lower right by using the origin parameter.
You can also control the default setting image.origin in your
:ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`. For more on
this topic see the :doc:`complete guide on origin and extent
</tutorials/intermediate/imshow_extent>`.


.. code-block:: default


    x = np.arange(120).reshape((10, 12))

    interp = 'bilinear'
    fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(3, 5))
    axs[0].set_title('blue should be up')
    axs[0].imshow(x, origin='upper', interpolation=interp)

    axs[1].set_title('blue should be down')
    axs[1].imshow(x, origin='lower', interpolation=interp)
    plt.show()





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_005.png
    :alt: blue should be up, blue should be down
    :class: sphx-glr-single-img





Finally, we'll show an image using a clip path.


.. code-block:: default


    delta = 0.025
    x = y = np.arange(-3.0, 3.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = np.exp(-X**2 - Y**2)
    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
    Z = (Z1 - Z2) * 2

    path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
    patch = PathPatch(path, facecolor='none')

    fig, ax = plt.subplots()
    ax.add_patch(patch)

    im = ax.imshow(Z, interpolation='bilinear', cmap=cm.gray,
                   origin='lower', extent=[-3, 3, -3, 3],
                   clip_path=patch, clip_on=True)
    im.set_clip_path(patch)

    plt.show()




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_006.png
    :alt: image demo
    :class: sphx-glr-single-img





------------

References
""""""""""

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


.. code-block:: default


    import matplotlib
    matplotlib.axes.Axes.imshow
    matplotlib.pyplot.imshow
    matplotlib.artist.Artist.set_clip_path
    matplotlib.patches.PathPatch








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

   **Total running time of the script:** ( 0 minutes  3.345 seconds)


.. _sphx_glr_download_gallery_images_contours_and_fields_image_demo.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

    Keywords: matplotlib code example, codex, python plot, pyplot
    `Gallery generated by Sphinx-Gallery
    <https://sphinx-gallery.readthedocs.io>`_