![]() ![]() Applying constrained layout to this set of subplots fixes the error and creates a nice looking set of plots.įig, axes = plt.subplots(2,2, figsize=(8,8), constrained_layout=True) Constrained layout should be called during the creation of the figure object, as demonstrated below. ![]() Luckily, there is an alternative to tight_layout, called constrained_layout, which uses a constrained solver to optimize subplot placement. The result will look like this (and produce an warning output): This will make things worse however, because the colorbar confuses the algorithm that tight_layout uses to arrange the axes objects. One way you may attempt to fix this could be to use the tight_layout() function, which can help align subplots. Unfortunately, with the default settings, this code will shrink two subplots disproportionately.įrom mpl_toolkits.axes_grid1 import make_axes_locatableįig, axes = plt.subplots(2,2, figsize=(8,8))Ĭbar = fig.colorbar(im1, ax=axes, shrink=0.8) The fig.colorbar() function, allows you to easily add a colorbar to the set of subplots. Here’s an example of creating a single colorbar for four different subplots. Making a colorbar in matplotlib is fairly easy, but unless you use the right tools, making the colorbar fit into the overall graphic can be unexpectedly difficult. Often, you may use a common color to link multiple views of the same data set, or contrast two data sets. Formatting colorbars with constrained_layoutĬolorbars play an important role in data visualization. This feature is still in its testing phase, but will likely be the new standard for making subplots in the future. Then I’ll discuss a brand new feature of Matplotlib, the subplot_mosaic interface. First, I’ll discuss working with colorbars, a seemly minor task that can be quite time consuming. In this post I’ll go over two more subplot tools that are helpful for designing informative and attractive subplots. While the information in that post can allow you to do quite a lot, there is in fact more that you might want to know. I got a little ahead of myself with the title of my last post, “Everything you want to know about subplots in Python’s Matplotlib”.
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