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
pcolor like you always
have. The “object-oriented” part means that proplot’s features are implemented with
subclasses of the
If you tend to use
pyplot and are not familiar with the figure and axes
classes, check out this guide.
Directly working with matplotlib classes 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 many proplot features may still work, we do not officially
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
Figure and axes classes¶
Creating figures with proplot is very similar to matplotlib. You can either create the figure and all of its subplots at once:
fig, axs = pplt.subplots(...)
or create an empty figure then fill it with subplots:
fig = pplt.figure(...) axs = fig.add_subplots(...) # add several subplots ax = fig.add_subplot(...) # add a single subplot # axs = fig.subplots(...) # shorthand # ax = fig.subplot(...) # shorthand
These commands are modeled after
matplotlib.pyplot.figure and are packed with new features.
One highlight is the
auto_layout algorithm that
automatically adjusts the space between subplots (similar to
matplotlib’s tight layout)
and automatically adjusts the figure size to preserve subplot
sizes and aspect ratios (particularly useful for grids of map projections
and images). All sizing arguments take arbitrary units,
including metric units like
Instead of the native
classes, proplot uses the
proplot.axes.PlotAxes subclasses. Proplot figures are saved with
and proplot axes 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 azimuth and radius coordinates.
Most of proplot’s features are implemented using these subclasses. They include several new figure and axes methods and added functionality to existing figure and axes methods.
proplot.figure.Figure.formatcommands fine-tunes various axes and figure settings. Think of this as a dedicated
updatemethod for axes and figures. See formatting subplots for a broad overview, along with the individual sections on formatting Cartesian plots, geographic plots, and polar plots.
proplot.axes.Axes.legendcommands draw colorbars and legends inside of subplots or along the outside edges of subplots. The
proplot.figure.Figure.legendcommands draw colorbars or legends along the edges of figures (aligned by subplot boundaries). These commands considerably simplify the process of drawing colorbars and legends.
proplot.axes.PlotAxessubclass (used for all proplot axes) adds many, many useful features to virtually every plotting command (including
imshow). See the 1D plotting and 2D plotting sections for details.
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.
GeoAxesclass uses the cartopy or basemap packages to plot geophysical data, add geographic features, and format projections.
GeoAxesprovides provides a simpler, cleaner interface than the original cartopy and basemap interfaces. Figures can be filled with
GeoAxesby passing the
If you pass a
xarray.DataArrayto 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
pandas.DataFrame.plotcommands. See the sections on 1D plotting and 2D plotting for a demonstration.
Since these features are optional, proplot can be used without installing any of these packages.
DiscreteColormapsubclasses replace the default matplotlib colormap classes and add several methods. The new
PerceptualColormapclass is used to make colormaps with perceptually uniform transitions.
Normconstructor function generates colormap normalizers from shorthand names. The new
SegmentedNormnormalizer scales colors evenly w.r.t. index for arbitrarily spaced monotonic levels, and the new
DiscreteNormmeta-normalizer is used to break up colormap colors into discrete levels.
Scaleconstructor 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.
rcobject, an instance of
Configurator, 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_fmtso that your inline figures look the same as your saved figures.