Summary

This article provides a comprehensive guide on mastering inset axes in Matplotlib, detailing their creation, customization, and advanced usage to enhance data visualization.

Abstract

The article "Mastering Inset Axes in Matplotlib" delves into the utility and versatility of inset axes within Matplotlib plots. It explains the basics of creating inset axes to zoom in on specific plot regions or to utilize empty space effectively. The author emphasizes the underutilization of this feature and provides detailed instructions on positioning inset axes, customizing their content, and improving plot aesthetics. The article also explores creative applications of inset axes, such as using multiple insets for different data aspects or presenting alternative views within the same figure. Practical tips on maintaining aspect ratios, positioning using different coordinate systems, and controlling the layering order of plot elements are discussed to ensure accurate and visually appealing data representation. The article concludes with a link to a Python Notebook containing the code used to generate the illustrative examples, inviting readers to apply these techniques in their own data plotting endeavors.

Opinions

The author believes that inset axes are an underutilized feature in Matplotlib that can significantly enhance the quality of data visualizations.
Inset axes are considered a powerful tool for providing additional detail and context within a plot without overwhelming the main figure.
The article suggests that the creative use of inset axes can make plots more interesting and informative, potentially making the inset content more engaging than the primary plot.
The author stresses the importance of considering aspect ratios when using inset axes to avoid misrepresenting data due to stretching or squishing.
The preference for axes relative positioning over data relative positioning is highlighted, with a caution that the latter can lead to undesirable shifts in the inset axis when plot limits change.
The author recommends adjusting the zorder property for precise control over the layering of plot elements, ensuring that inset axes are displayed appropriately on top of or beneath other plot features.

Mastering Inset Axes in Matplotlib

Inset axes are a fantastic (and often underutilised) tool in Matplotlib. They can be used to:

Zoom in on specific parts of plots to show them in greater detail.
Replace empty space in a plot with additional information.
Give your figures a little extra zing!

In this article, I go through the basics of using inset axes, and then give extra details on how to customize and improve your plots. The code used to generate of all the plots shown is included in a Python Notebook at the end of this article.

The basics

There are three main components to creating inset axes. First, you need an existing surrounding axis to add the inset axis to. Then, you need to create the inset axis and define where it sits in the surrounding axis. Finally, you need to plot the inset data to the inset axis. An optional step is to add lines to indicate the inset zoom. This can be achieved as follows:

# Code snippet to create inset axes.
# axis is the surrounding axis into which the inset axis will be added

# Create an inset axis in the bottom right corner
axin = axis.inset_axes([0.55, 0.02, 0.43, 0.43])

# Plot the data on the inset axis and zoom in on the important part
plot_data(axin)
axin.set_xlim(-10, 12)
axin.set_ylim(0.75, 1.4)

# Add the lines to indicate where the inset axis is coming from
axis.indicate_inset_zoom(axin)

In the above example, axis.inset_axes creates the inset axis axin and defines where it is positioned in the original axis (more on this positioning later). The region that is shown in the inset axis is controlled by setting the axis limits, i.e., axin.set_xlim and axin.set_ylim — think of this as cropping the original plot so we can zoom in on what’s important. The zoom lines and bounding box are neatly added by just using indicate_inset_zoom.

This gives a result that looks something like this:

An example of using a single inset axis to zoom in on part of a plot. Image created by the author.

The positioning of the inset axis is axes relative. This means the position is independent of the axis limits. The coordinates for the inset axis should be within [0,0] (bottom left) and [1,1] (top right). axis.inset_axes expects four arguments to define the bounds: [x0, y0, width, height]. So, for example, [0.4, 0.4, 0.2, 0.2] gives an inset axis centred in the middle of the plot, regardless of what the axis limits are:

The inset axis does not move when the external axis limits change as (by default) the position is axes relative. Image created by the author.

Getting fancy

We can get more creative with our use of inset axes:

There is no reason why we can’t have multiple inset axes in our plots. Perhaps there are two regions that we want to zoom in on?
Sometimes, the content in the inset axis might actually be more interesting that the overall plot. We can swap the content so that the surrounding plot contains the zoomed in version, and the inset axis shows the wider view.
The inset axis doesn’t even need to show the same data as the surrounding plot. Zooming in is only one use case. Showing additional information is also perfectly fine.

Below we give an example of these different use cases using the same plot that we had above. Look how much better the plots with inset axes look than the boring original plots!

A range of options for using inset axes to make more interesting plots. Image created by the author.

Further Tips

There are a few further things to note when creating inset axes.

Aspect Ratios — It is important to consider the aspect ratio of the inset axis versus the aspect ratio of the original region it is showing. This can lead to stretching or squishing of the original data in the inset axis, which could misrepresent it. For example:

Be wary of differing aspect ratios when using inset axes. Image created by the author.

Axis Positioning — Above, I mentioned axes relative coordinates for positioning the inset axes. As an alternative, it is possible to use data relative coordinates. Set transform=axis.transData when creating the inset axes. Watch out though! This makes the position of the inset axes sensitive to the axes limits:

Axes vs Data relative positioning. When the axis limits are changed, the inset axis moves when using data relative positioning. Image created by the author.

Z Ordering — By default, the inset axis should appear on top of anything you’ve already plotted. However, for manual control, you can adjust the zorder to control what's on top:

Changing the z order value of inset axes allows you to place them on top of existing data, or even each other. Image created by the author.

Below is the Python Notebook that I used to generate the plots in this article:

And that’s my guide on using Matplotlib Inset Axes. If you have any questions, please post a comment or contact me. Thanks for reading, and happy plotting!