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

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

.. _sphx_glr_gallery_images_contours_and_fields_image_transparency_blend.py:


===========================================
Blend transparency with color in 2-D images
===========================================

Blend transparency with color to highlight parts of data with imshow.

A common use for :func:`matplotlib.pyplot.imshow` is to plot a 2-D statistical
map. While ``imshow`` makes it easy to visualize a 2-D matrix as an image,
it doesn't easily let you add transparency to the output. For example, one can
plot a statistic (such as a t-statistic) and color the transparency of
each pixel according to its p-value. This example demonstrates how you can
achieve this effect using :class:`matplotlib.colors.Normalize`. Note that it is
not possible to directly pass alpha values to :func:`matplotlib.pyplot.imshow`.

First we will generate some data, in this case, we'll create two 2-D "blobs"
in a 2-D grid. One blob will be positive, and the other negative.



.. code-block:: python

    # sphinx_gallery_thumbnail_number = 3
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import Normalize


    def normal_pdf(x, mean, var):
        return np.exp(-(x - mean)**2 / (2*var))


    # Generate the space in which the blobs will live
    xmin, xmax, ymin, ymax = (0, 100, 0, 100)
    n_bins = 100
    xx = np.linspace(xmin, xmax, n_bins)
    yy = np.linspace(ymin, ymax, n_bins)

    # Generate the blobs. The range of the values is roughly -.0002 to .0002
    means_high = [20, 50]
    means_low = [50, 60]
    var = [150, 200]

    gauss_x_high = normal_pdf(xx, means_high[0], var[0])
    gauss_y_high = normal_pdf(yy, means_high[1], var[0])

    gauss_x_low = normal_pdf(xx, means_low[0], var[1])
    gauss_y_low = normal_pdf(yy, means_low[1], var[1])

    weights_high = np.array(np.meshgrid(gauss_x_high, gauss_y_high)).prod(0)
    weights_low = -1 * np.array(np.meshgrid(gauss_x_low, gauss_y_low)).prod(0)
    weights = weights_high + weights_low

    # We'll also create a grey background into which the pixels will fade
    greys = np.empty(weights.shape + (3,), dtype=np.uint8)
    greys.fill(70)

    # First we'll plot these blobs using only ``imshow``.
    vmax = np.abs(weights).max()
    vmin = -vmax
    cmap = plt.cm.RdYlBu

    fig, ax = plt.subplots()
    ax.imshow(greys)
    ax.imshow(weights, extent=(xmin, xmax, ymin, ymax), cmap=cmap)
    ax.set_axis_off()




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_transparency_blend_001.png
    :class: sphx-glr-single-img




Blending in transparency
========================

The simplest way to include transparency when plotting data with
:func:`matplotlib.pyplot.imshow` is to convert the 2-D data array to a
3-D image array of rgba values. This can be done with
:class:`matplotlib.colors.Normalize`. For example, we'll create a gradient
moving from left to right below.



.. code-block:: python


    # Create an alpha channel of linearly increasing values moving to the right.
    alphas = np.ones(weights.shape)
    alphas[:, 30:] = np.linspace(1, 0, 70)

    # Normalize the colors b/w 0 and 1, we'll then pass an MxNx4 array to imshow
    colors = Normalize(vmin, vmax, clip=True)(weights)
    colors = cmap(colors)

    # Now set the alpha channel to the one we created above
    colors[..., -1] = alphas

    # Create the figure and image
    # Note that the absolute values may be slightly different
    fig, ax = plt.subplots()
    ax.imshow(greys)
    ax.imshow(colors, extent=(xmin, xmax, ymin, ymax))
    ax.set_axis_off()




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_transparency_blend_002.png
    :class: sphx-glr-single-img




Using transparency to highlight values with high amplitude
==========================================================

Finally, we'll recreate the same plot, but this time we'll use transparency
to highlight the extreme values in the data. This is often used to highlight
data points with smaller p-values. We'll also add in contour lines to
highlight the image values.



.. code-block:: python


    # Create an alpha channel based on weight values
    # Any value whose absolute value is > .0001 will have zero transparency
    alphas = Normalize(0, .3, clip=True)(np.abs(weights))
    alphas = np.clip(alphas, .4, 1)  # alpha value clipped at the bottom at .4

    # Normalize the colors b/w 0 and 1, we'll then pass an MxNx4 array to imshow
    colors = Normalize(vmin, vmax)(weights)
    colors = cmap(colors)

    # Now set the alpha channel to the one we created above
    colors[..., -1] = alphas

    # Create the figure and image
    # Note that the absolute values may be slightly different
    fig, ax = plt.subplots()
    ax.imshow(greys)
    ax.imshow(colors, extent=(xmin, xmax, ymin, ymax))

    # Add contour lines to further highlight different levels.
    ax.contour(weights[::-1], levels=[-.1, .1], colors='k', linestyles='-')
    ax.set_axis_off()
    plt.show()

    ax.contour(weights[::-1], levels=[-.0001, .0001], colors='k', linestyles='-')
    ax.set_axis_off()
    plt.show()




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_image_transparency_blend_003.png
    :class: sphx-glr-single-img




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

References
""""""""""

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



.. code-block:: python


    import matplotlib
    matplotlib.axes.Axes.imshow
    matplotlib.pyplot.imshow
    matplotlib.axes.Axes.contour
    matplotlib.pyplot.contour
    matplotlib.colors.Normalize
    matplotlib.axes.Axes.set_axis_off







.. _sphx_glr_download_gallery_images_contours_and_fields_image_transparency_blend.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: image_transparency_blend.ipynb <image_transparency_blend.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>`_
