.. _sphx_glr_gallery_images_contours_and_fields_pcolor_demo.py:


===========
Pcolor Demo
===========

Generating images with pcolor.

Pcolor allows you to generate 2-D image-style plots. Below we will show how
to do so in Matplotlib.



.. code-block:: python

    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib.colors import LogNorm
    from matplotlib.mlab import bivariate_normal








A simple pcolor demo
--------------------



.. code-block:: python


    Z = np.random.rand(6, 10)

    fig, (ax0, ax1) = plt.subplots(2, 1)

    c = ax0.pcolor(Z)
    ax0.set_title('default: no edges')

    c = ax1.pcolor(Z, edgecolors='k', linewidths=4)
    ax1.set_title('thick edges')

    fig.tight_layout()
    plt.show()




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_pcolor_demo_001.png
    :align: center




Comparing pcolor with similar functions
---------------------------------------

Demonstrates similarities between pcolor, pcolormesh, imshow and pcolorfast
for drawing quadrilateral grids.



.. code-block:: python


    # make these smaller to increase the resolution
    dx, dy = 0.15, 0.05

    # generate 2 2d grids for the x & y bounds
    y, x = np.mgrid[slice(-3, 3 + dy, dy),
                    slice(-3, 3 + dx, dx)]
    z = (1 - x / 2. + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
    # x and y are bounds, so z should be the value *inside* those bounds.
    # Therefore, remove the last value from the z array.
    z = z[:-1, :-1]
    z_min, z_max = -np.abs(z).max(), np.abs(z).max()

    fig, axs = plt.subplots(2, 2)

    ax = axs[0, 0]
    c = ax.pcolor(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
    ax.set_title('pcolor')
    # set the limits of the plot to the limits of the data
    ax.axis([x.min(), x.max(), y.min(), y.max()])
    fig.colorbar(c, ax=ax)

    ax = axs[0, 1]
    c = ax.pcolormesh(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
    ax.set_title('pcolormesh')
    # set the limits of the plot to the limits of the data
    ax.axis([x.min(), x.max(), y.min(), y.max()])
    fig.colorbar(c, ax=ax)

    ax = axs[1, 0]
    c = ax.imshow(z, cmap='RdBu', vmin=z_min, vmax=z_max,
                  extent=[x.min(), x.max(), y.min(), y.max()],
                  interpolation='nearest', origin='lower')
    ax.set_title('image (nearest)')
    fig.colorbar(c, ax=ax)

    ax = axs[1, 1]
    c = ax.pcolorfast(x, y, z, cmap='RdBu', vmin=z_min, vmax=z_max)
    ax.set_title('pcolorfast')
    fig.colorbar(c, ax=ax)

    fig.tight_layout()
    plt.show()





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_pcolor_demo_002.png
    :align: center




Pcolor with a log scale
-----------------------

The following shows pcolor plots with a log scale.



.. code-block:: python


    N = 100
    X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]

    # A low hump with a spike coming out of the top right.
    # Needs to have z/colour axis on a log scale so we see both hump and spike.
    # linear scale only shows the spike.
    Z1 = (bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0) +
          0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0))

    fig, (ax0, ax1) = plt.subplots(2, 1)

    c = ax0.pcolor(X, Y, Z1,
                   norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()), cmap='PuBu_r')
    fig.colorbar(c, ax=ax0)

    c = ax1.pcolor(X, Y, Z1, cmap='PuBu_r')
    fig.colorbar(c, ax=ax1)

    plt.show()



.. image:: /gallery/images_contours_and_fields/images/sphx_glr_pcolor_demo_003.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.745 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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