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# Colormap Normalizations Symlognorm

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

    """
    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.
    """

    N = 100
    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')

    plt.show()

Total running time of the script: ( 0 minutes 0.125 seconds)

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