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Why UltraPlot?

Matplotlib is an extremely versatile plotting package used by scientists and engineers far and wide. However, matplotlib can be cumbersome or repetitive for users who...

  • Make highly complex figures with many subplots.
  • Want to finely tune their annotations and aesthetics.
  • Need to make new figures nearly every day.

UltraPlot's core mission is to provide a smoother plotting experience for matplotlib's most demanding users. We accomplish this by expanding upon matplotlib's :ref:`object-oriented interface <usage_background>`. UltraPlot makes changes that would be hard to justify or difficult to incorporate into matplotlib itself, owing to differing design choices and backwards compatibility considerations.

This page enumerates these changes and explains how they address the limitations of matplotlib's default interface. To start using these features, see the :ref:`usage introduction <usage>` and the :ref:`user guide <ug_basics>`.

Less typing, more plotting

Limitation

Matplotlib users often need to change lots of plot settings all at once. With the default interface, this requires calling a series of one-liner setter methods.

This workflow is quite verbose -- it tends to require "boilerplate code" that gets copied and pasted a hundred times. It can also be confusing -- it is often unclear whether properties are applied from an :class:`~matplotlib.axes.Axes` setter (e.g. :func:`~matplotlib.axes.Axes.set_xlabel` and :func:`~matplotlib.axes.Axes.set_xticks`), an :class:`~matplotlib.axis.XAxis` or :class:`~matplotlib.axis.YAxis` setter (e.g. :func:`~matplotlib.axis.Axis.set_major_locator` and :func:`~matplotlib.axis.Axis.set_major_formatter`), a :class:`~matplotlib.spines.Spine` setter (e.g. :func:`~matplotlib.spines.Spine.set_bounds`), or a "bulk" property setter (e.g. :func:`~matplotlib.axes.Axes.tick_params`), or whether one must dig into the figure architecture and apply settings to several different objects. It seems like there should be a more unified, straightforward way to change settings without sacrificing the advantages of object-oriented design.

Changes

UltraPlot includes the :func:`~ultraplot.axes.Axes.format` command to resolve this. Think of this as an expanded and thoroughly documented version of the :func:`~matplotlib.artist.Artist.update` command. :func:`~ultraplot.axes.Axes.format` can modify things like axis labels and titles and apply new :ref:`"rc" settings <why_rc>` to existing axes. It also integrates with various :ref:`constructor functions <why_constructor>` to help keep things succinct. Further, the :func:`~ultraplot.figure.Figure.format` and :func:`~ultraplot.gridspec.SubplotGrid.format` commands can be used to :func:`~ultraplot.axes.Axes.format` several subplots at once.

Together, these features significantly reduce the amount of code needed to create highly customized figures. As an example, it is trivial to see that...

import ultraplot as uplt
fig, axs = uplt.subplots(ncols=2)
axs.format(color='gray', linewidth=1)
axs.format(xlim=(0, 100), xticks=10, xtickminor=True, xlabel='foo', ylabel='bar')

is much more succinct than...

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib as mpl
with mpl.rc_context(rc={'axes.linewidth': 1, 'axes.edgecolor': 'gray'}):
    fig, axs = plt.subplots(ncols=2, sharey=True)
    axs[0].set_ylabel('bar', color='gray')
    for ax in axs:
        ax.set_xlim(0, 100)
        ax.xaxis.set_major_locator(mticker.MultipleLocator(10))
        ax.tick_params(width=1, color='gray', labelcolor='gray')
        ax.tick_params(axis='x', which='minor', bottom=True)
        ax.set_xlabel('foo', color='gray')

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Class constructor functions

Limitation

Matplotlib and cartopy define several classes with verbose names like :class:`~matplotlib.ticker.MultipleLocator`, :class:`~matplotlib.ticker.FormatStrFormatter`, and :class:`~cartopy.crs.LambertAzimuthalEqualArea`. They also keep them out of the top-level package namespace. Since plotting code has a half life of about 30 seconds, typing out these extra class names and import statements can be frustrating.

Parts of matplotlib's interface were designed with this in mind. Backend classes, native axes projections, axis scales, colormaps, box styles, arrow styles, and arc styles are referenced with "registered" string names, as are basemap projections. So, why not "register" everything else?

Changes

In UltraPlot, tick locators, tick formatters, axis scales, property cycles, colormaps, normalizers, and cartopy projections are all "registered". This is accomplished by defining "constructor functions" and passing various keyword arguments through these functions.

The constructor functions also accept intuitive inputs alongside "registered" names. For example, a scalar passed to :class:`~ultraplot.constructor.Locator` returns a :class:`~matplotlib.ticker.MultipleLocator`, a lists of strings passed to :class:`~ultraplot.constructor.Formatter` returns a :class:`~matplotlib.ticker.FixedFormatter`, and :class:`~ultraplot.constructor.Cycle` and :class:`~ultraplot.constructor.Colormap` accept colormap names, individual colors, and lists of colors. Passing the relevant class instance to a constructor function simply returns it, and all the registered classes are available in the top-level namespace -- so class instances can be directly created with e.g. uplt.MultipleLocator(...) or uplt.LogNorm(...) rather than relying on constructor functions.

The below table lists the constructor functions and the keyword arguments that use them.

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Automatic dimensions and spacing

Limitation

Matplotlib plots tend to require "tweaking" when you have more than one subplot in the figure. This is partly because you must specify the physical dimensions of the figure, despite the fact that...

  1. The subplot aspect ratio is generally more relevant than the figure aspect ratio. A default aspect ratio of 1 is desirable for most plots, and the aspect ratio must be held fixed for :ref:`geographic and polar <ug_proj>` projections and most :func:`~matplotlib.axes.Axes.imshow` plots.
  2. The subplot width and height control the "apparent" size of lines, markers, text, and other plotted content. If the figure size is fixed, adding more subplots will decrease the average subplot size and increase the "apparent" sizes. If the subplot size is fixed instead, this can be avoided.

Matplotlib also includes "tight layout" and "constrained layout" algorithms that can help users avoid having to tweak :class:`~matplotlib.gridspec.GridSpec` spacing parameters like left, bottom, and wspace. However, these algorithms are disabled by default and somewhat cumbersome to configure. They also cannot apply different amounts of spacing between different subplot row and column boundaries.

Changes

By default, UltraPlot fixes the physical dimensions of a reference subplot rather than the figure. The reference subplot dimensions are controlled with the refwidth, refheight, and refaspect :class:`~ultraplot.figure.Figure` keywords, with a default behavior of refaspect=1 and refwidth=2.5 (inches). If the data aspect ratio of the reference subplot is fixed (as with :ref:`geographic <ug_geo>`, :ref:`polar <ug_polar>`, :func:`~matplotlib.axes.Axes.imshow`, and :func:`~ultraplot.axes.Axes.heatmap` plots) then this is used instead of refaspect.

Alternatively, you can independently specify the width or height of the figure with the figwidth and figheight parameters. If only one is specified, the other is adjusted to preserve subplot aspect ratios. This is very often useful when preparing figures for submission to a publication. To request figure dimensions suitable for submission to a :ref:`specific publication <journal_table>`, use the journal keyword.

By default, UltraPlot also uses :ref:`its own tight layout algorithm <ug_tight>` -- preventing text labels from overlapping with subplots. This algorithm works with the :class:`~ultraplot.gridspec.GridSpec` subclass rather than :class:`~matplotlib.gridspec.GridSpec`, which provides the following advantages:

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Working with multiple subplots

Limitation

When working with multiple subplots in matplotlib, the path of least resistance often leads to redundant figure elements. Namely...

  • Repeated axis tick labels.
  • Repeated axis labels.
  • Repeated colorbars.
  • Repeated legends.

These sorts of redundancies are very common even in publications, where they waste valuable page space. It is also generally necessary to add "a-b-c" labels to figures with multiple subplots before submitting them to publications, but matplotlib has no built-in way of doing this.

Changes

UltraPlot makes it easier to work with multiple subplots and create clear, concise figures.

Links

Simpler colorbars and legends

Limitation

In matplotlib, it can be difficult to draw :func:`~matplotlib.figure.Figure.legend`s along the outside of subplots. Generally, you need to position the legend manually and tweak the spacing to make room for the legend.

Also, :func:`~matplotlib.figure.Figure.colorbar`s drawn along the outside of subplots with e.g. fig.colorbar(..., ax=ax) need to "steal" space from the parent subplot. This can cause asymmetry in figures with more than one subplot. It is also generally difficult to draw "inset" colorbars in matplotlib and to generate outer colorbars with consistent widths (i.e., not too "skinny" or "fat").

Changes

UltraPlot includes a simple framework for drawing colorbars and legends that reference :ref:`individual subplots <ug_guides_loc>` and :ref:`multiple contiguous subplots <ug_guides_multi>`.

Since :class:`~ultraplot.gridspec.GridSpec` permits variable spacing between subplot rows and columns, "outer" colorbars and legends do not alter subplot spacing or add whitespace. This is critical e.g. if you have a colorbar between columns 1 and 2 but nothing between columns 2 and 3. Also, :class:`~ultraplot.figure.Figure` and :class:`~ultraplot.axes.Axes` colorbar widths are now specified in physical units rather than relative units, which makes colorbar thickness independent of subplot size and easier to get just right.

Links

Improved plotting commands

Limitation

A few common plotting tasks take a lot of work using matplotlib alone. The seaborn, xarray, and pandas packages offer improvements, but it would be nice to have this functionality built right into matplotlib's interface.

Changes

UltraPlot uses the :class:`~ultraplot.axes.PlotAxes` subclass to add various seaborn, xarray, and pandas features to existing matplotlib plotting commands along with several additional features designed to make things easier.

The following features are relevant for "1D" :class:`~ultraplot.axes.PlotAxes` commands like :func:`~ultraplot.axes.PlotAxes.line` (equivalent to :func:`~ultraplot.axes.PlotAxes.plot`) and :func:`~ultraplot.axes.PlotAxes.scatter`:

The following features are relevant for "2D" :class:`~ultraplot.axes.PlotAxes` commands like :func:`~ultraplot.axes.PlotAxes.pcolor` and :func:`~ultraplot.axes.PlotAxes.contour`:

Links

Cartopy and basemap integration

Limitation

There are two widely-used engines for working with geographic data in matplotlib: cartopy and basemap. Using cartopy tends to be verbose and involve boilerplate code, while using basemap requires plotting with a separate :class:`~mpl_toolkits.basemap.Basemap` object rather than the :class:`~matplotlib.axes.Axes`. They both require separate import statements and extra lines of code to configure the projection.

Furthermore, when you use cartopy and basemap plotting commands, "map projection" coordinates are the default coordinate system rather than longitude-latitude coordinates. This choice is confusing for many users, since the vast majority of geophysical data are stored with longitude-latitude (i.e., "Plate Carrée") coordinates.

Changes

UltraPlot can succinctly create detailed geographic plots using either cartopy or basemap as "backends". By default, cartopy is used, but basemap can be used by passing backend='basemap' to axes-creation commands or by setting :rcraw:`geo.backend` to 'basemap'. To create a geographic plot, simply pass the PROJ name to an axes-creation command, e.g. fig, ax = uplt.subplots(proj='pcarree') or fig.add_subplot(proj='pcarree'). Alternatively, use the :class:`~ultraplot.constructor.Proj` constructor function to quickly generate a :class:`~cartopy.crs.Projection` or :class:`~mpl_toolkits.basemap.Basemap` instance.

Requesting geographic projections creates a :class:`~ultraplot.axes.GeoAxes` with unified support for cartopy and basemap features via the :func:`~ultraplot.axes.GeoAxes.format` command. This lets you quickly modify geographic plot features like latitude and longitude gridlines, gridline labels, continents, coastlines, and political boundaries. The syntax is conveniently analogous to the syntax used for :func:`~ultraplot.axes.CartesianAxes.format` and :func:`~ultraplot.axes.PolarAxes.format`.

The :class:`~ultraplot.axes.GeoAxes` subclass also makes longitude-latitude coordinates the "default" coordinate system by passing transform=ccrs.PlateCarree() or latlon=True to :class:`~ultraplot.axes.PlotAxes` commands (depending on whether cartopy or basemap is the backend). And to enforce global coverage over the poles and across longitude seams, you can pass globe=True to 2D :class:`~ultraplot.axes.PlotAxes` commands like :func:`~ultraplot.axes.PlotAxes.contour` and :func:`~ultraplot.axes.PlotAxes.pcolormesh`.

Links

Pandas and xarray integration

Limitation

Scientific data is commonly stored in array-like containers that include metadata -- namely, :class:`~xarray.DataArray`s, :class:`~pandas.DataFrame`s, and :class:`~pandas.Series`. When matplotlib receives these objects, it ignores the associated metadata. To create plots that are labeled with the metadata, you must use the :func:`~xarray.DataArray.plot`, :func:`~pandas.DataFrame.plot`, and :func:`~pandas.Series.plot` commands instead.

This approach is fine for quick plots, but not ideal for complex ones. It requires learning a different syntax from matplotlib, and tends to encourage using the :obj:`~matplotlib.pyplot` interface rather than the object-oriented interface. The plot commands also include features that would be useful additions to matplotlib in their own right, without requiring special containers and a separate interface.

Changes

UltraPlot reproduces many of the :func:`~xarray.DataArray.plot`, :func:`~pandas.DataFrame.plot`, and :func:`~pandas.Series.plot` features directly on the :class:`~ultraplot.axes.PlotAxes` commands. This includes :ref:`grouped or stacked <ug_bar>` bar plots and :ref:`layered or stacked <ug_bar>` area plots from two-dimensional input data, auto-detection of :ref:`diverging datasets <ug_autonorm>` for application of diverging colormaps and normalizers, and :ref:`on-the-fly colorbars and legends <ug_guides_loc>` using colorbar and legend keywords.

UltraPlot also handles metadata associated with :class:`~xarray.DataArray`, :class:`~pandas.DataFrame`, :class:`~pandas.Series`, and :class:`~pint.Quantity` objects. When a plotting command receives these objects, it updates the axis tick labels, axis labels, subplot title, and colorbar and legend labels from the metadata. For :class:`~pint.Quantity` arrays (including :class:`~pint.Quantity` those stored inside :class:`~xarray.DataArray` containers), a unit string is generated from the pint.Unit according to the :rcraw:`unitformat` setting (note UltraPlot also automatically calls :func:`~pint.UnitRegistry.setup_matplotlib` whenever a :class:`~pint.Quantity` is used for x and y coordinates and removes the units from z coordinates to avoid the stripped-units warning message). These features can be disabled by setting :rcraw:`autoformat` to False or passing autoformat=False to any plotting command.

Links

Aesthetic colors and fonts

Limitation

A common problem with scientific visualizations is the use of "misleading" colormaps like 'jet'. These colormaps have jarring jumps in hue, saturation, and luminance that can trick the human eye into seeing non-existing patterns. It is important to use "perceptually uniform" colormaps instead. Matplotlib comes packaged with a few of its own, plus the ColorBrewer colormap series, but external projects offer a larger variety of aesthetically pleasing "perceptually uniform" colormaps that would be nice to have in one place.

Matplotlib also "registers" the X11/CSS4 color names, but these are relatively limited. The more numerous and arguably more intuitive XKCD color survey names can only be accessed with the 'xkcd:' prefix. As with colormaps, there are also external projects with useful color names like open color.

Finally, matplotlib comes packaged with DejaVu Sans as the default font. This font is open source and include glyphs for a huge variety of characters. However in our opinion, it is not very aesthetically pleasing. It is also difficult to switch to other fonts on limited systems or systems with fonts stored in incompatible file formats (see :ref:`below <why_dotUltraPlot>`).

Changes

UltraPlot adds new colormaps, colors, and fonts to help you make more aesthetically pleasing figures.

Links

Manipulating colormaps

Limitation

In matplotlib, colormaps are implemented with the :class:`~matplotlib.colors.LinearSegmentedColormap` class (representing "smooth" color gradations) and the :class:`~matplotlib.colors.ListedColormap` class (representing "categorical" color sets). They are somewhat cumbersome to modify or create from scratch. Meanwhile, property cycles used for individual plot elements are implemented with the :class:`~cycler.Cycler` class. They are easier to modify but they cannot be "registered" by name like colormaps.

The seaborn package includes "color palettes" to make working with colormaps and property cycles easier, but it would be nice to have similar features integrated more closely with matplotlib's colormap and property cycle constructs.

Changes

UltraPlot tries to make it easy to manipulate colormaps and property cycles.

UltraPlot also makes all colormap and color cycle names case-insensitive, and colormaps are automatically reversed or cyclically shifted 180 degrees if you append '_r' or '_s' to any colormap name. These features are powered by :class:`~ultraplot.colors.ColormapDatabase`, which replaces matplotlib's native colormap database.

Links

Physical units engine

Limitation

Matplotlib uses figure-relative units for the margins left, right, bottom, and top, and axes-relative units for the column and row spacing wspace and hspace. Relative units tend to require "tinkering" with numbers until you find the right one. And since they are relative, if you decide to change your figure size or add a subplot, they will have to be readjusted.

Matplotlib also requires users to set the figure size figsize in inches. This may be confusing for users outside of the United States.

Changes

UltraPlot uses physical units for the :class:`~ultraplot.gridspec.GridSpec` keywords left, right, top, bottom, wspace, hspace, pad, outerpad, and innerpad. The default unit (assumed when a numeric argument is passed) is em-widths. Em-widths are particularly appropriate for this context, as plot text can be a useful "ruler" when figuring out the amount of space you need. UltraPlot also permits arbitrary string units for these keywords, for the :class:`~ultraplot.figure.Figure` keywords figsize, figwidth, figheight, refwidth, and refheight, and in a few other places. This is powered by the physical units engine :func:`~ultraplot.utils.units`. Acceptable units include inches, centimeters, millimeters, pixels, points, and picas (a table of acceptable units is found :ref:`here <units_table>`). Note the :func:`~ultraplot.utils.units` engine also translates rc settings assigned to :func:`~ultraplot.config.rc_matplotlib` and :obj:`~ultraplot.config.rc_UltraPlot`, e.g. :rcraw:`subplots.refwidth`, :rcraw:`legend.columnspacing`, and :rcraw:`axes.labelpad`.

Links

Flexible global settings

Limitation

In matplotlib, there are several :obj:`~matplotlib.rcParams` that would be useful to set all at once, like spine and label colors. It might also be useful to change these settings for individual subplots rather than globally.

Changes

In UltraPlot, you can use the :obj:`~ultraplot.config.rc` object to change both native matplotlib settings (found in :obj:`~ultraplot.config.rc_matplotlib`) and added UltraPlot settings (found in :obj:`~ultraplot.config.rc_UltraPlot`). Assigned settings are always validated, and "meta" settings like meta.edgecolor, meta.linewidth, and font.smallsize can be used to update many settings all at once. Settings can be changed with uplt.rc.key = value, uplt.rc[key] = value, uplt.rc.update(key=value), using :func:`~ultraplot.axes.Axes.format`, or using :func:`~ultraplot.config.Configurator.context`. Settings that have changed during the python session can be saved to a file with :func:`~ultraplot.config.Configurator.save` (see :func:`~ultraplot.config.Configurator.changed`), and settings can be loaded from files with :func:`~ultraplot.config.Configurator.load`.

Links

Loading stuff

Limitation

Matplotlib :obj:`~matplotlib.rcParams` can be changed persistently by placing ref:matplotlibrc <ug_mplrc> files in the same directory as your python script. But it can be difficult to design and store your own colormaps and color cycles for future use. It is also difficult to get matplotlib to use custom .ttf and .otf font files, which may be desirable when you are working on Linux servers with limited font selections.

Changes

UltraPlot settings can be changed persistently by editing the default ultraplotrc file in the location given by :func:`~ultraplot.config.Configurator.user_file` (this is usually $HOME/.ultraplot/ultraplotrc) or by adding loose ultraplotrc files to either the current directory or an arbitrary parent directory. Adding files to parent directories can be useful when working in projects with lots of subfolders.

UltraPlot also automatically registers colormaps, color cycles, colors, and font files stored in subfolders named cmaps, cycles, colors, and fonts in the location given by :func:`~ultraplot.config.Configurator.user_folder` (this is usually $HOME/.ultraplot), as well as loose subfolders named ultraplot_cmaps, ultraplot_cycles, ultraplot_colors, and ultraplot_fonts in the current directory or an arbitrary parent directory. You can save colormaps and color cycles to :func:`~ultraplot.config.Configurator.user_folder` simply by passing save=True to :class:`~ultraplot.constructor.Colormap` and :class:`~ultraplot.constructor.Cycle`. To re-register these files during an active python session, or to register arbitrary input arguments, you can use :func:`~ultraplot.config.register_cmaps`, :func:`~ultraplot.config.register_cycles`, :func:`~ultraplot.config.register_colors`, or :func:`~ultraplot.config.register_fonts`.

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