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# pcolormesh

Shows how to combine Normalization and Colormap instances to draw “levels” in pcolor, pcolormesh and imshow type plots in a similar way to the levels keyword argument to contour/contourf.

    import matplotlib.pyplot as plt
    from matplotlib.colors import BoundaryNorm
    from matplotlib.ticker import MaxNLocator
    import numpy as np

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

    # generate 2 2d grids for the x & y bounds
    y, x = np.mgrid[slice(1, 5 + dy, dy),
                    slice(1, 5 + dx, dx)]

    z = np.sin(x)**10 + np.cos(10 + y*x) * np.cos(x)

    # 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]
    levels = MaxNLocator(nbins=15).tick_values(z.min(), z.max())

    # pick the desired colormap, sensible levels, and define a normalization
    # instance which takes data values and translates those into levels.
    cmap = plt.get_cmap('PiYG')
    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

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

    im = ax0.pcolormesh(x, y, z, cmap=cmap, norm=norm)
    fig.colorbar(im, ax=ax0)
    ax0.set_title('pcolormesh with levels')

    # contours are *point* based plots, so convert our bound into point
    # centers
    cf = ax1.contourf(x[:-1, :-1] + dx/2.,
                      y[:-1, :-1] + dy/2., z, levels=levels,
                      cmap=cmap)
    fig.colorbar(cf, ax=ax1)
    ax1.set_title('contourf with levels')

    # adjust spacing between subplots so `ax1` title and `ax0` tick labels
    # don't overlap
    fig.tight_layout()

    plt.show()

Total running time of the script: ( 0 minutes 0.212 seconds)

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