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Commitbb0a4eb

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DOC: Clarify/simplify example of multiple images with one colorbar
1 parent199c31f commitbb0a4eb

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Lines changed: 44 additions & 37 deletions
Original file line numberDiff line numberDiff line change
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"""
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===============
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Multiple images
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===============
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=================================
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Multiple images with one colorbar
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=================================
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Make a set of images with a single colormap, norm, and colorbar.
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Use a single colorbar for multiple images.
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Currently, a colorbar can only be associated with one image. To ensure that
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the colorbar is representative for multiple images, we have to ensure that
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all images use the same color mapping. The most important aspect is the
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data normalization. By explicitly creating a norm and using that for all
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images, we ensure that all images are scaled consistently.
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"""
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importmatplotlib.pyplotasplt
@@ -12,47 +18,50 @@
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frommatplotlibimportcolors
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np.random.seed(19680801)
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Nr=3
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Nc=2
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fig,axs=plt.subplots(Nr,Nc)
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datasets= [
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(i+1)/10*np.random.rand(10,20)
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foriinrange(4)
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]
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fig,axs=plt.subplots(2,2)
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fig.suptitle('Multiple images')
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images= []
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foriinrange(Nr):
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forjinrange(Nc):
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# Generate data with a range that varies from one plot to the next.
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data= ((1+i+j)/10)*np.random.rand(10,20)
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images.append(axs[i,j].imshow(data))
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axs[i,j].label_outer()
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# create a single norm to be shared across all images
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norm=colors.Normalize(vmin=np.max(datasets),vmax=np.min(datasets))
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# Find the min and max of all colors for use in setting the color scale.
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vmin=min(image.get_array().min()forimageinimages)
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vmax=max(image.get_array().max()forimageinimages)
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norm=colors.Normalize(vmin=vmin,vmax=vmax)
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foriminimages:
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im.set_norm(norm)
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images= []
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forax,datainzip(axs.flat,datasets):
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images.append(ax.imshow(data,norm=norm))
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fig.colorbar(images[0],ax=axs,orientation='horizontal',fraction=.1)
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# Make images respond to changes in the norm of other images (e.g. via the
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# "edit axis, curves and images parameters" GUI on Qt), but be careful not to
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# recurse infinitely!
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defupdate(changed_image):
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foriminimages:
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if (changed_image.get_cmap()!=im.get_cmap()
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orchanged_image.get_clim()!=im.get_clim()):
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im.set_cmap(changed_image.get_cmap())
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im.set_clim(changed_image.get_clim())
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foriminimages:
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im.callbacks.connect('changed',update)
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plt.show()
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# %%
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# The colors are now kept consistent across all images when changing the
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# scaling, e.g. through zooming in the colorbar or via the "edit axis,
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# curves and images parameters" GUI of the Qt backend.
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#
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# While the norm is shared, the colormaps are not. This is often ok
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# because colormaps are usually not changed dynamically. However, a user
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# could change the colormap of an individual image through the
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# "edit axis, curves and images parameters" GUI of the Qt backend.
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# Unlike with a norm, it does not help to share colormaps between images.
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# Changes to the norm limits modify the norm object in place and thus
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# propagate to all images. But changing a colormap sets a new colormap
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# object to the images and thus does not propagate to the other images.
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# To make all other images follow, you could additionally sync the
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# colormaps using the following code::
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#
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# def sync_cmaps(changed_image):
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# for im in images:
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# if changed_image.get_cmap() != im.get_cmap():
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# im.set_cmap(changed_image.get_cmap())
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#
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# for im in images:
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# im.callbacks.connect('changed', sync_cmaps)
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#
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#
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# .. admonition:: References
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#
@@ -63,6 +72,4 @@ def update(changed_image):
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# - `matplotlib.figure.Figure.colorbar` / `matplotlib.pyplot.colorbar`
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# - `matplotlib.colors.Normalize`
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# - `matplotlib.cm.ScalarMappable.set_cmap`
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# - `matplotlib.cm.ScalarMappable.set_norm`
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# - `matplotlib.cm.ScalarMappable.set_clim`
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# - `matplotlib.cbook.CallbackRegistry.connect`

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