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# Multi Image

Make a set of images with a single colormap, norm, and colorbar.

It also illustrates colorbar tick labelling with a multiplier.

    from matplotlib.pyplot import figure, show, axes, sci
    from matplotlib import cm, colors
    from matplotlib.font_manager import FontProperties
    import numpy as np

    Nr = 3
    Nc = 2

    fig = figure()
    cmap = cm.cool

    figtitle = 'Multiple images'
    t = fig.text(0.5, 0.95, figtitle,
                 horizontalalignment='center',
                 fontproperties=FontProperties(size=16))

    cax = fig.add_axes([0.2, 0.08, 0.6, 0.04])

    w = 0.4
    h = 0.22
    ax = []
    images = []
    vmin = 1e40
    vmax = -1e40
    for i in range(Nr):
        for j in range(Nc):
            pos = [0.075 + j * 1.1 * w, 0.18 + i * 1.2 * h, w, h]
            a = fig.add_axes(pos)
            if i > 0:
                a.set_xticklabels([])
            # Make some fake data with a range that varies
            # somewhat from one plot to the next.
            data = ((1 + i + j) / 10) * np.random.rand(10, 20) * 1e-6
            dd = data.ravel()
            # Manually find the min and max of all colors for
            # use in setting the color scale.
            vmin = min(vmin, np.min(dd))
            vmax = max(vmax, np.max(dd))
            images.append(a.imshow(data, cmap=cmap))

            ax.append(a)

    # Set the first image as the master, with all the others
    # observing it for changes in cmap or norm.

    class ImageFollower(object):
        'update image in response to changes in clim or cmap on another image'

        def __init__(self, follower):
            self.follower = follower

        def __call__(self, leader):
            self.follower.set_cmap(leader.get_cmap())
            self.follower.set_clim(leader.get_clim())

    norm = colors.Normalize(vmin=vmin, vmax=vmax)
    for i, im in enumerate(images):
        im.set_norm(norm)
        if i > 0:
            images[0].callbacksSM.connect('changed', ImageFollower(im))

    # The colorbar is also based on this master image.
    fig.colorbar(images[0], cax, orientation='horizontal')

    # We need the following only if we want to run this interactively and
    # modify the colormap:

    axes(ax[0])     # Return the current axes to the first one,
    sci(images[0])  # because the current image must be in current axes.

    show()

Total running time of the script: ( 0 minutes 0.261 seconds)

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