Chapter 6. Customization and Configuration

This chapter marks a conceptual dividing line for the book. We've focused on topics such as matplotlib internals and APIs, plot interaction, high-level plotting, and the use of third-party libraries. We will continue in that vein in the first part of this chapter as we discuss advanced customization techniques for matplotlib. We will finish the chapter by discussing the elements of the advanced and lesser-known matplotlib configuration. The configuration theme will continue into the next chapter and then go beyond that into the realm of deployment. As such, this chapter will mark a transition to our exploration of matplotlib in the real world and its usage in computationally intensive tasks.

This chapter will provide an overview of the following, giving you enough confidence to tackle these in more depth at your own pace:

• Customization

  • matplotlib styles

  • Subplots

  • Further exploration

• Configuration

  • The matplotlib run control

  • Options in IPython

To follow along...

Customization

On the journey through the lands of matplotlib, one of the signposts for intermediate territories is an increased need for fine-grained control over the libraries in the ecosystem. In our case, this means being able to tweak matplotlib for particular use cases such as specialty scales or projections, complex layouts, or a custom look and feel.

Creating a custom style

The first customization topic that we will cover is that of the new style support introduced in matplotlib 1.4. In the previous notebook, we saw how to get a list of the available styles:

In [2]: print(plt.style.available)
        ['bmh', 'ggplot', 'fivethirtyeight', 'dark_background',
        'grayscale']

Now, we're going to see how we can create and use one of our own custom styles.

You can create custom styles and use them by calling style.use with the path or URL to the style sheet. Alternatively, if you save the <style-name>.mplstyle file to the ~/.matplotlib/stylelib directory (you may need to create it...

Configuration

We've just covered some examples of matplotlib customization. Hand in hand with this topic is that of configuration—the tweaking of predefined values to override default behaviors. The matplotlib module offers two ways to override the default values for the configuration settings—you can either run the control files, or run the control parameters that are stored in-memory to make changes to a running instance.

The run control for matplotlib

While commonly expanded to the run control, the .rc extension and -rc suffix trace their origins to 1965 and the Multics (short for Multiplexed Information and Computing Service) operating system, where rc stood for the run command. Like many software systems that were developed on UNIX- or BSD-based machines, matplotlib has an rc file where the control of matplotlib may be configured. This control is not limited to configuration files; one may also access an rc object via the matplotlib API. Each of these is covered in the following few...

Summary

In this chapter, we covered two areas of detailed customization—the creation of custom styles, as well as complex subplots. In the previous chapters, you have been exposed to the means by which you can discover more of matplotlib's functionality through its sources. It was in this context that the additional topics in customization were mentioned. With this, we transitioned into the topic of matplotlib configuration via files as well as rcParams. This is a transitional topic that will be picked up again at the beginning of the next chapter, where we will cover matplotlib deployments.