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# Colormap Normalizations Lognorm

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

    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.
    '''
    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')

    plt.show()

Total running time of the script: ( 0 minutes 0.507 seconds)

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