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# Colormap Normalizations Power

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

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

    '''
    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.
    '''
    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')

    plt.show()

Total running time of the script: ( 0 minutes 0.105 seconds)

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