

.. _sphx_glr_gallery_images_contours_and_fields_image_masked.py:


============
Image Masked
============

imshow with masked array input and out-of-range colors.

The second subplot illustrates the use of BoundaryNorm to
get a filled contour effect.




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_masked_001.png
    :align: center





.. code-block:: python

    from copy import copy

    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.colors as colors
    import matplotlib.mlab as mlab

    # compute some interesting data
    x0, x1 = -5, 5
    y0, y1 = -3, 3
    x = np.linspace(x0, x1, 500)
    y = np.linspace(y0, y1, 500)
    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 = 10*(Z2 - Z1)  # difference of Gaussians

    # Set up a colormap:
    # use copy so that we do not mutate the global colormap instance
    palette = copy(plt.cm.gray)
    palette.set_over('r', 1.0)
    palette.set_under('g', 1.0)
    palette.set_bad('b', 1.0)
    # Alternatively, we could use
    # palette.set_bad(alpha = 0.0)
    # to make the bad region transparent.  This is the default.
    # If you comment out all the palette.set* lines, you will see
    # all the defaults; under and over will be colored with the
    # first and last colors in the palette, respectively.
    Zm = np.ma.masked_where(Z > 1.2, Z)

    # By setting vmin and vmax in the norm, we establish the
    # range to which the regular palette color scale is applied.
    # Anything above that range is colored based on palette.set_over, etc.

    # set up the axes
    fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(6, 5.4))

    # plot using 'continuous' color map
    im = ax1.imshow(Zm, interpolation='bilinear',
                    cmap=palette,
                    norm=colors.Normalize(vmin=-1.0, vmax=1.0),
                    aspect='auto',
                    origin='lower',
                    extent=[x0, x1, y0, y1])
    ax1.set_title('Green=low, Red=high, Blue=masked')
    cbar = fig.colorbar(im, extend='both', shrink=0.9, ax=ax1)
    cbar.set_label('uniform')
    for ticklabel in ax1.xaxis.get_ticklabels():
        ticklabel.set_visible(False)

    # Plot using a small number of colors, with unevenly spaced boundaries.
    im = ax2.imshow(Zm, interpolation='nearest',
                    cmap=palette,
                    norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
                                             ncolors=palette.N),
                    aspect='auto',
                    origin='lower',
                    extent=[x0, x1, y0, y1])
    ax2.set_title('With BoundaryNorm')
    cbar = fig.colorbar(im, extend='both', spacing='proportional',
                        shrink=0.9, ax=ax2)
    cbar.set_label('proportional')

    fig.suptitle('imshow, with out-of-range and masked data')
    plt.show()

**Total running time of the script:** ( 0 minutes  0.261 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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