

.. _sphx_glr_gallery_userdemo_colormap_normalizations.py:


=======================
Colormap Normalizations
=======================

Demonstration of using norm to map colormaps onto data in non-linear ways.



.. code-block:: python


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







Lognorm: Instead of pcolor log10(Z1) you can have colorbars that have
the exponential labels using a norm.



.. 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, ax = plt.subplots(2, 1)

    pcm = ax[0].pcolor(X, Y, Z1,
                       norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()),
                       cmap='PuBu_r')
    fig.colorbar(pcm, ax=ax[0], extend='max')

    pcm = ax[1].pcolor(X, Y, Z1, cmap='PuBu_r')
    fig.colorbar(pcm, ax=ax[1], extend='max')





.. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_001.png
    :align: center




PowerNorm: Here a power-law trend in X partially obscures a rectified
sine wave in Y. We can remove the power law using a PowerNorm.



.. code-block:: python


    X, Y = np.mgrid[0:3:complex(0, N), 0:2:complex(0, N)]
    Z1 = (1 + np.sin(Y * 10.)) * X**(2.)

    fig, ax = plt.subplots(2, 1)

    pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1./2.),
                           cmap='PuBu_r')
    fig.colorbar(pcm, ax=ax[0], extend='max')

    pcm = ax[1].pcolormesh(X, Y, Z1, cmap='PuBu_r')
    fig.colorbar(pcm, ax=ax[1], extend='max')




.. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_002.png
    :align: center




SymLogNorm: two humps, one negative and one positive, The positive
with 5-times the amplitude. Linearly, you cannot see detail in the
negative hump.  Here we logarithmically scale the positive and
negative data separately.

Note that colorbar labels do not come out looking very good.



.. code-block:: python


    X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
    Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
        - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
    Z1 = Z1/0.03

    fig, ax = plt.subplots(2, 1)

    pcm = ax[0].pcolormesh(X, Y, Z1,
                           norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
                                                  vmin=-1.0, vmax=1.0),
                           cmap='RdBu_r')
    fig.colorbar(pcm, ax=ax[0], extend='both')

    pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
    fig.colorbar(pcm, ax=ax[1], extend='both')





.. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_003.png
    :align: center




Custom Norm: An example with a customized normalization.  This one
uses the example above, and normalizes the negative data differently
from the positive.



.. code-block:: python


    X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
    Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
        - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
    Z1 = Z1/0.03

    # Example of making your own norm.  Also see matplotlib.colors.
    # From Joe Kington: This one gives two different linear ramps:


    class MidpointNormalize(colors.Normalize):
        def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
            self.midpoint = midpoint
            colors.Normalize.__init__(self, vmin, vmax, clip)

        def __call__(self, value, clip=None):
            # I'm ignoring masked values and all kinds of edge cases to make a
            # simple example...
            x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
            return np.ma.masked_array(np.interp(value, x, y))
    #####
    fig, ax = plt.subplots(2, 1)

    pcm = ax[0].pcolormesh(X, Y, Z1,
                           norm=MidpointNormalize(midpoint=0.),
                           cmap='RdBu_r')
    fig.colorbar(pcm, ax=ax[0], extend='both')

    pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
    fig.colorbar(pcm, ax=ax[1], extend='both')




.. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_004.png
    :align: center




BoundaryNorm: For this one you provide the boundaries for your colors,
and the Norm puts the first color in between the first pair, the
second color between the second pair, etc.



.. code-block:: python


    fig, ax = plt.subplots(3, 1, figsize=(8, 8))
    ax = ax.flatten()
    # even bounds gives a contour-like effect
    bounds = np.linspace(-1, 1, 10)
    norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
    pcm = ax[0].pcolormesh(X, Y, Z1,
                           norm=norm,
                           cmap='RdBu_r')
    fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')

    # uneven bounds changes the colormapping:
    bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
    norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
    pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
    fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')

    pcm = ax[2].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
    fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')

    plt.show()



.. image:: /gallery/userdemo/images/sphx_glr_colormap_normalizations_005.png
    :align: center




**Total running time of the script:** ( 0 minutes  3.232 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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