Customizing plots with style sheets

The style package adds support for easy-to-switch plotting “styles” with the same parameters as a matplotlibrc file.

There are a number of pre-defined styles provided by matplotlib. For example, there’s a pre-defined style called “ggplot”, which emulates the aesthetics of ggplot (a popular plotting package for R). To use this style, just add:

>>> import matplotlib.pyplot as plt
>>> plt.style.use('ggplot')

To list all available styles, use:

>>> print plt.style.available

Defining your own style

You can create custom styles and use them by calling style.use with the path or URL to the style sheet. Alternatively, if you add your <style-name>.mplstyle file to ~/.matplotlib/stylelib (you may need to create this directory), you can reuse your custom style sheet with a call to style.use(<style-name>). Note that a custom style sheet in ~/.matplotlib/stylelib will override a style sheet defined by matplotlib if the styles have the same name.

For example, you might want to create ~/.matplotlib/stylelib/presentation.mplstyle with the following:

axes.titlesize : 24
axes.labelsize : 20
lines.linewidth : 3
lines.markersize : 10
xtick.labelsize : 16
ytick.labelsize : 16

Then, when you want to adapt a plot designed for a paper to one that looks good in a presentation, you can just add:

>>> import matplotlib.pyplot as plt
>>> plt.style.use('presentation')

Composing styles

Style sheets are designed to be composed together. So you can have a style sheet that customizes colors and a separate style sheet that alters element sizes for presentations. These styles can easily be combined by passing a list of styles:

>>> import matplotlib.pyplot as plt
>>> plt.style.use(['dark_background', 'presentation'])

Note that styles further to the right will overwrite values that are already defined by styles on the left.

Temporary styling

If you only want to use a style for a specific block of code but don’t want to change the global styling, the style package provides a context manager for limiting your changes to a specific scope. To isolate the your styling changes, you can write something like the following:

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>>
>>> with plt.style.context(('dark_background')):
>>>     plt.plot(np.sin(np.linspace(0, 2*np.pi)), 'r-o')
>>>
>>> # Some plotting code with the default style
>>>
>>> plt.show()