Using ProPlot

This page offers a condensed overview of ProPlot’s features. It is populated with links to the API reference and User Guide. For a more in-depth discussion, see Why ProPlot?

Background

ProPlot is an object-oriented matplotlib wrapper. The “wrapper” part means that ProPlot’s features are largely a superset of matplotlib. You can use plotting commands like plot, scatter, contour, and pcolor like you always have. The “object-oriented” part means that ProPlot’s features are implemented with subclasses of the Figure and Axes classes.

If you tend to use pyplot and are not familiar with figure and axes classes, check out this guide Directly working with objects tends to be more clear and concise than pyplot, makes things easier when working with multiple figures and axes, and is certainly more “pythonic”. Therefore, although some ProPlot features may still work, we do not officially support the pyplot interface.

Importing proplot

Importing ProPlot immediately adds several new colormaps, property cycles, color names, and fonts to matplotlib. If you are only interested in these features, you may want to import ProPlot at the top of your script and do nothing else! We recommend importing ProPlot as follows:

import proplot as pplt

This differentiates ProPlot from the usual plt abbreviation reserved for the pyplot module. The abbreviation pplt is a popular and less generic alternative.

Figure and axes classes

Creating plots with ProPlot always begins with a call to the subplots command:

fig, axs = pplt.subplots(...)

The subplots command is modeled after matplotlib’s native matplotlib.pyplot.subplots command and is packed with new features.

Instead of instances of the native matplotlib.figure.Figure and matplotlib.axes.Axes classes, subplots returns a proplot.figure.Figure instance populated with proplot.axes.Axes instances. ProPlot figures are saved with save (or, equivalently, savefig). All ProPlot axes also belong to one of the following three child classes:

• proplot.axes.CartesianAxes: For ordinary plots with x and y coordinates.

• proplot.axes.GeoAxes: For geographic plots with longitude and latitude coordinates.

• proplot.axes.PolarAxes: For polar plots with radius and azimuth coordinates.

Most of ProPlot’s features are added via the proplot.figure.Figure and proplot.axes.Axes classes. They include several brand new methods and add new functionality to the existing methods.

• The new format method is used to fine-tune various axes settings. Think of this as a dedicated update method for axes. See formatting subplots for a broad overview, along with the individual sections on formatting Cartesian plots, geographic plots, and polar plots.

• The proplot.axes.Axes.colorbar and proplot.axes.Axes.legend commands draw colorbars and legends inside of subplots or along the outside edges of subplots. The proplot.figure.Figure.colorbar and proplot.figure.Figure.legend commands draw colorbars or legends along the edges of figures (aligned by subplot boundaries). These commands simplify the process of drawing colorbars and legends.

• ProPlot adds a variety of useful features to the plot, bar, area, contour, contourf, pcolormesh, heatmap, and parametric plotting commands by “wrapping” them. See the 1D plotting and 2D plotting sections for details.

Integration features

ProPlot includes optional integration features with four external packages: the pandas and xarray packages, used for working with annotated tables and arrays, and the cartopy and basemap geographic plotting packages.

• If you pass a pandas.Series, pandas.DataFrame, or xarray.DataArray to any plotting command, the axis labels, tick labels, titles, colorbar labels, and legend labels are automatically applied from the metadata. If you did not supply the x and y coordinates, they are also inferred from the metadata. This works just like the native xarray.DataArray.plot and pandas.DataFrame.plot methods. A demonstration of this feature is given in the sections on 1D plotting and 2D plotting. This feature can be disabled by setting rc.autoformat to False.

• The GeoAxes class uses the cartopy or basemap packages to plot geophysical data, add geographic features, and format projections. GeoAxes provides provides a simpler, cleaner interface than the original cartopy and basemap interfaces. Figures can be filled with GeoAxes by passing the proj keyword to subplots.

Since these features are optional, ProPlot can be used without installing any of these packages.

Additional features

Outside of the features provided by the proplot.figure.Figure and proplot.axes.Axes subclasses, ProPlot includes several useful classes and constructor functions.

• The Colormap and Cycle constructor functions can be used to slice, and merge existing colormaps and color cycles. It can also make new colormaps and color cycles from scratch.

• The LinearSegmentedColormap and ListedColormap subclasses replace the default matplotlib colormap classes and add several methods. The new PerceptuallyUniformColormap class is used to make colormaps with perceptually uniform transitions.

• The show_cmaps, show_cycles, show_colors, show_fonts, show_channels, and show_colorspaces functions are used to visualize your color scheme and font options and inspect individual colormaps.

• The Norm constructor function generates colormap normalizers from shorthand names. The new LinearSegmentedNorm normalizer scales colors evenly w.r.t. index for arbitrarily spaced monotonic levels, and the new DiscreteNorm meta-normalizer is used to break up colormap colors into discrete levels.

• The Locator, Formatter, and Scale constructor functions return corresponding class instances from flexible input types. These are used to interpret keyword arguments passed to format, and can be used to quickly and easily modify x and y axis settings.

• The rc object, an instance of RcConfigurator, is used for modifying individual settings, changing settings in bulk, and temporarily changing settings in context blocks. It also introduces several new setings and sets up the inline plotting backend with inline_backend_fmt so that your inline figures look the same as your saved figures.