This page offers a condensed overview of UltraPlot’s features. It is populated with links to the API reference and User Guide. For a more in-depth discussion, see Why UltraPlot?.
BackgroundUltraPlot is an object-oriented matplotlib wrapper. The “wrapper” part means that UltraPlot’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 UltraPlot’s features are implemented with subclasses of the Figure and Axes classes.
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 UltraPlot features may still work, we do not officially support the pyplot interface.
Importing UltraPlot 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 UltraPlot at the top of your script and do nothing else! We recommend importing UltraPlot as follows:
This differentiates UltraPlot from the usual plt abbreviation reserved for the pyplot module.
Creating figures with UltraPlot is very similar to matplotlib. You can either create the figure and all of its subplots at once:
fig, axs = uplt.subplots(...)
or create an empty figure then fill it with subplots:
fig = uplt.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.subplots and 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 cm and mm.
Instead of the native matplotlib.figure.Figure and matplotlib.axes.Axes classes, UltraPlot uses the ultraplot.figure.Figure, ultraplot.axes.Axes, and ultraplot.axes.PlotAxes subclasses. UltraPlot figures are saved with save() or savefig, and UltraPlot axes belong to one of the following three child classes:
ultraplot.axes.CartesianAxes: For ordinary plots with x and y coordinates.
ultraplot.axes.GeoAxes: For geographic plots with longitude and latitude coordinates.
ultraplot.axes.PolarAxes: For polar plots with azimuth and radius coordinates.
Most of UltraPlot’s features are implemented using these subclasses. They include several new figure and axes methods and added functionality to existing figure and axes methods.
The ultraplot.axes.Axes.format and ultraplot.figure.Figure.format commands fine-tunes various axes and figure settings. Think of this as a dedicated update method 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.
The ultraplot.axes.Axes.colorbar and ultraplot.axes.Axes.legend commands draw colorbars and legends inside of subplots or along the outside edges of subplots. The ultraplot.figure.Figure.colorbar and ultraplot.figure.Figure.legend commands draw colorbars or legends along the edges of figures (aligned by subplot boundaries). These commands considerably simplify the process of drawing colorbars and legends.
The ultraplot.axes.PlotAxes subclass (used for all UltraPlot axes) adds many, many useful features to virtually every plotting command (including plot(), scatter(), bar(), area(), box(), violin(), contour(), pcolor(), and imshow()). See the 1D plotting and 2D plotting sections for details.
UltraPlot 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.
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().
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 commands. See the sections on 1D plotting and 2D plotting for a demonstration.
Since these features are optional, UltraPlot can be used without installing any of these packages.
Additional featuresOutside of the features provided by the ultraplot.figure.Figure and ultraplot.axes.Axes subclasses, UltraPlot 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 ContinuousColormap and DiscreteColormap subclasses replace the default matplotlib colormap classes and add several methods. The new PerceptualColormap 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 SegmentedNorm 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 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_fmt() so that your inline figures look the same as your saved figures.
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