from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(19680801)
Nr = 3
Nc = 2
cmap = "cool"

fig, axs = plt.subplots(Nr, Nc)
fig.suptitle('Multiple images')

images = []
for i in range(Nr):
    for j in range(Nc):
        # Generate data with a range that varies from one plot to the next.
        data = ((1 + i + j) / 10) * np.random.rand(10, 20) * 1e-6
        images.append(axs[i, j].imshow(data, cmap=cmap))
        axs[i, j].label_outer()

# Find the min and max of all colors for use in setting the color scale.
vmin = min(image.get_array().min() for image in images)
vmax = max(image.get_array().max() for image in images)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
for im in images:
    im.set_norm(norm)

fig.colorbar(images[0], ax=axs, orientation='horizontal', fraction=.1)


# Make images respond to changes in the norm of other images (e.g. via the
# "edit axis, curves and images parameters" GUI on Qt), but be careful not to
# recurse infinitely!
def update(changed_image):
    for im in images:
        if (changed_image.get_cmap() != im.get_cmap()
                or changed_image.get_clim() != im.get_clim()):
            im.set_cmap(changed_image.get_cmap())
            im.set_clim(changed_image.get_clim())


for im in images:
    im.callbacksSM.connect('changed', update)

images[1].set_clim(1e-9, 2e-8)
fig.savefig('ax1_bad.png')
images[0].set_clim(1e-9, 2e-8)
fig.savefig('ax0_good.png')