

.. _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
imshow. The following examples demonstrate much of the
functionality of imshow and the many images you can create.




.. code-block:: python

    from __future__ import print_function

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







First we'll generate a simple bivariate normal distribution.



.. code-block:: python


    delta = 0.025
    x = y = np.arange(-3.0, 3.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
    Z = Z2 - Z1  # difference of Gaussians

    im = plt.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
    :align: center




It is also possible to show images of pictures.



.. code-block:: python


    # 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- and y-axes


    # And another image

    w, h = 512, 512

    with cbook.get_sample_data('ct.raw.gz', asfileobj=True) as datafile:
        s = datafile.read()
    A = np.fromstring(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
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_demo_003.png
            :scale: 47




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.

Earlier versions of matplotlib (<0.63) tried to hide the edge effects
from you by setting the view limits so that they would not be visible.
A recent bugfix in antigrain, and a new implementation in the
matplotlib._image module which takes advantage of this fix, no longer
makes this necessary.  To prevent edge effects, when doing
interpolation, the matplotlib._image module now pads the input array
with identical pixels around the edge.  e.g., 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

the _image module creates 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

does the interpolation/resizing, and then extracts the central region.
This allows you to plot the full range of your array w/o edge effects,
and for example to layer multiple images of different sizes over one
another with different interpolation methods - see
examples/layer_images.py.  It also implies a performance hit, as this
new temporary, padded array must be created.  Sophisticated
interpolation also implies a performance hit, so if you need maximal
performance or have very large images, interpolation='nearest' is
suggested.



.. code-block:: python


    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
    :align: center




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>`



.. code-block:: python


    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
    :align: center




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



.. code-block:: python


    delta = 0.025
    x = y = np.arange(-3.0, 3.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
    Z = Z2 - Z1  # difference of Gaussians

    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
    :align: center




**Total running time of the script:** ( 0 minutes  0.585 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
