# Why ProPlot?¶

ProPlot’s core mission is to improve upon the parts of matplotlib that tend to be cumbersome or repetitive for power users. This page enumerates the stickiest of these limitations and describes how ProPlot addresses them.

## Less typing, more plotting¶

Problem

Power users often need to change lots of plot settings all at once. In matplotlib, 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 Axes setter (e.g. set_title, set_xlabel and set_xticks), an XAxis or YAxis setter (e.g. set_major_locator and set_major_formatter), a Spine setter (e.g. set_bounds), a random “bulk” setter (e.g. tick_params), or whether they require tinkering with several different objects. Also, one often needs to loop through lists of subplots to apply identical settings to each subplot.

Solution

ProPlot introduces the format method for changing arbitrary settings in bulk. Think of this as an expanded and thoroughly documented version of the Artist update method. For even more efficiency, format can be used to locally apply various rcParams and Bulk global settings to particular axes, The subplot container class can be used to identically apply settings to several axes at once, and Class constructor functions are used by format (and in several other places) to concisely generate complex, verbose class instances like Locators and Formatters.

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 proplot as plot
f, axs = plot.subplots(ncols=2)
axs.format(linewidth=1, color='gray')
axs.format(xticks=20, xtickminor=True, xlabel='x axis', ylabel='y axis')


…is much more succinct than

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from matplotlib import rcParams
rcParams['axes.linewidth'] = 1
rcParams['axes.color'] = 'gray'
fig, axs = plt.subplots(ncols=2)
for ax in axs:
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('x axis', color='gray')
ax.set_ylabel('y axis', color='gray')
plt.style.use('default')  # restore


## Class constructor functions¶

Problem

Matplotlib and cartopy introduce a bunch of classes with verbose names like MultipleLocator, FormatStrFormatter, and LambertAzimuthalEqualArea. Since plotting code has a half life of about 30 seconds, typing out all of these extra class names and import statements can be a major drag.

Certain parts of the matplotlib API were designed with this in mind. Backend classes, native axes projections, axis scales, box style classes, arrow style classes, and arc style classes are referenced with “registered” string names, as are basemap projection types. If these are already “registered”, why not “register” everything else?

Solution

In ProPlot, tick locators, tick formatters, axis scales, cartopy projections, colormaps, and property cyclers are all “registered”. This is done by creating several constructor functions and passing various keyword argument through the constructor functions. This may seem “unpythonic” but it is absolutely invaluable when writing plotting code.

Each constructor function accepts various other input types for your convenience. For example, scalar numbers passed to Locator returns a MultipleLocator instance, lists of strings passed to Formatter returns a FixedFormatter instance, and Colormap and Cycle accept colormap names, individual colors, and lists of colors. When a class instance is passed to the relevant constructor function, it is simply returned. See X and Y axis settings, Colormaps, and Color cycles for details.

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

Function

Returns

Used by

Keyword argument(s)

Locator

locator=, xlocator=, ylocator=, minorlocator=, xminorlocator=, yminorlocator=, ticks=, xticks=, yticks=, minorticks=, xminorticks=, yminorticks=

Formatter

formatter=, xformatter=, yformatter=, ticklabels=, xticklabels=, yticklabels=

Scale

format

xscale=, yscale=

Cycle

Property Cycler

1d plotting methods

cycle=

Colormap

Colormap instance

2d plotting methods

cmap=

Norm

Normalize instance

2d plotting methods

norm=

Proj

subplots

proj=

Note that set_xscale and set_yscale now accept instances of ScaleBase thanks to a monkey patch applied by ProPlot.

## Automatic dimensions and spacing¶

Problem

Matplotlib plots tend to require lots of “tweaking” when you have more than one subplot in the figure. This is partly because you must specify the physical dimensions of the figure, while the dimensions of the individual subplots are more important:

1. The subplot aspect ratio is usually more relevant than the figure aspect ratio, e.g. for map projections.

2. The subplot width and height control the evident thickness of text and other content plotted inside the axes.

Matplotlib has a tight layout algorithm to keep you from having to “tweak” the spacing, but the algorithm cannot apply different amounts of spacing between different subplot row and column boundaries. This is a silly limitation that often results in unnecessary whitespace, and can be a major problem when you want to put e.g. a legend on the outside of a subplot.

Solution

In ProPlot, you can specify the physical dimensions of a reference subplot instead of the figure by passing axwidth, axheight, and/or aspect to Figure. The default behavior is aspect=1 and axwidth=2 (inches). If the aspect ratio mode for the reference subplot is set to 'equal', as with Geographic and polar plots and imshow plots, the existing aspect will be used instead. Figure dimensions are constrained as follows:

• If axwidth or axheight are used, the figure width and height are calculated automatically.

• If width is used, the figure height is calculated automatically.

• If height is used, the figure width is calculated automatically.

• If width and height or figsize is used, the figure dimensions are fixed.

By default, ProPlot also uses a custom tight layout algorithm that automatically determines the left, right, bottom, top, wspace, and hspace GridSpec parameters. This algorithm is simpler and more precise because:

1. The new GridSpec class permits variable spacing between rows and columns. It turns out this is critical for putting Colorbars and legends on the outside of subplots.

2. Figures are restricted to have only one GridSpec per figure. This is done by requiring users to draw all of their subplots at once with subplots, and it considerably simplifies the algorithm (see GH#50 for details).

See Subplots features for details.

## Outer colorbars and legends¶

Problem

In matplotlib, it is difficult to draw colorbars and legends on the outside of subplots. It is very easy to mess up the subplot aspect ratios and the colorbar widths. It is even more difficult to draw colorbars and legends that reference more than one subplot:

• Matplotlib has no capacity for drawing colorbar axes that span multiple plots – you have to create the axes yourself. This requires so much tinkering that most users just add identical colorbars to every single subplot!

• Legends that span multiple plots tend to require manual positioning and tinkering with the GridSpec spacing, just like legends placed outside of individual subplots.

Solution

ProPlot introduces a brand new engine for drawing colorbars and legends along the outside of individual subplots and along contiguous subplots on the edge of the figure:

• Passing loc='l', loc='r', loc='b', or loc='t' to Axes colorbar or Axes legend draws the colorbar or legend along the outside of the axes.

• Passing loc='l', loc='r', loc='b', or loc='t' to Figure colorbar and legend draws the colorbar or legend along the edge of the figure, centered relative to the subplot grid rather than figure coordinates.

• Outer colorbars and legends don’t mess up the subplot layout or subplot aspect ratios, since GridSpec permits variable spacing between subplot rows and columns. This is critical e.g. if you have a colorbar between columns 1 and 2 but nothing between columns 2 and 3.

• Figure and Axes colorbar widths are specified in physical units rather than relative units. This makes colorbar thickness independent of figure size and easier to get just right.

The colorbar and legend commands also add several new features, like colorbars-from-lines and centered-row legends. And to make Axes colorbar consistent with Axes legend, you can also now draw inset colorbars. See Colorbars and legends for details.

## The subplot container class¶

Problem

In matplotlib, subplots returns a 2D ndarray, a 1D ndarray, or the axes itself. This inconsistent behavior can be confusing.

Solution

In ProPlot, subplots returns a subplot_grid filled with Axes instances. This container lets you call arbitrary methods on arbitrary subplots all at once, which can be useful when you want to style your subplots identically (e.g. axs.format(tickminor=False)). The subplot_grid class also unifies the behavior of the three possible matplotlib.pyplot.subplots return values:

• subplot_grid permits 2d indexing, e.g. axs[1,0]. Since subplots can generate figures with arbitrarily complex subplot geometry, this 2d indexing is useful only when the arrangement happens to be a clean 2d matrix.

• Since subplot_grid is a list subclass, it also supports 1d indexing, e.g. axs[1]. The default order can be switched from row-major to column-major by passing order='F' to subplots.

• subplot_grid behaves like a scalar when it is singleton. So if you just made a single axes with f, axs = plot.subplots(), calling axs[0].command is equivalent to axs.command.

See The basics for details.

## New and improved plotting methods¶

Problem

Certain plotting tasks are quite difficult to accomplish with the default matplotlib API. The seaborn, xarray, and pandas packages offer improvements, but it would be nice to have this functionality build right into matplotlib. There is also room for improvement that none of these packages address.

Solution

ProPlot adds various seaborn, xarray, and pandas features to the Axes plotting methods along with several brand new features designed to make your life easier.

## Xarray and pandas integration¶

Problem

When you pass the array-like xarray.DataArray, pandas.DataFrame, and pandas.Series containers to matplotlib plotting commands, the metadata is ignored. To create plots that are automatically labeled with this metadata, you must use the dedicated xarray.DataArray.plot, pandas.DataFrame.plot, and pandas.Series.plot tools instead.

This approach is fine for quick plots, but not ideal. It requires learning a different syntax from matplotlib, and tends to encourage using the pyplot API rather than the object-oriented API. These tools also introduce features that would be useful additions to matplotlib in their own right, without requiring special data containers and an entirely separate API.

Solution

ProPlot reproduces most of the xarray.DataArray.plot, pandas.DataFrame.plot, and pandas.Series.plot features on the Axes plotting methods themselves. Passing an xarray.DataArray, pandas.DataFrame, or pandas.Series through any plotting method automatically updates the axis tick labels, axis labels, subplot titles, and colorbar and legend labels from the metadata. This can be disabled by passing autoformat=False to the plotting method or to subplots.

Also, as described in New and improved plotting methods, ProPlot implements certain features like grouped bar plots, layered area plots, heatmap plots, and on-the-fly colorbars and legends from the xarray and pandas APIs directly on the Axes class.

## Cartopy and basemap integration¶

Problem

There are two widely-used engines for plotting geophysical data with matplotlib: cartopy and basemap. Using cartopy tends to be quite verbose and involve lots of boilerplate code, while basemap is outdated and requires you to use plotting commands on a separate Basemap object.

Also, cartopy and basemap plotting commands assume map projection coordinates unless specified otherwise. For most of us, this choice is very frustrating, since geophysical data are usually stored in longitude-latitude or “Plate Carrée” coordinates.

Solution

ProPlot integrates various cartopy and basemap features into the ProjAxes format method. This lets you apply all kinds of geographic plot settings, like coastlines, continents, political boundaries, and meridian and parallel gridlines. ProjAxes also overrides various plotting methods:

• transform=ccrs.PlateCarree() is the new default for all GeoAxes plotting methods.

• latlon=True is the new default for all BasemapAxes plotting methods.

• globe=True can be passed to any 2D plotting command to enforce global coverage over the poles and across the longitude boundaries.

See Geographic and polar plots for details. Note that active development on basemap will halt after 2020. For now, cartopy is missing several features offered by basemap – namely, flexible meridian and parallel gridline labels, drawing physical map scales, and convenience features for adding background images like the “blue marble”. But once these are added to cartopy, ProPlot may remove the basemap integration features.

## Colormaps and property cycles¶

Problem

In matplotlib, colormaps are implemented with the ListedColormap and LinearSegmentedColormap classes. They are hard to edit and hard to create from scratch.

Solution

In ProPlot, it is easy to manipulate colormaps and property cycles:

Importing ProPlot also makes all colormap names case-insensitive, and colormaps can be reversed or cyclically shifted by 180 degrees simply by appending '_r' or '_shifted' to the colormap name. This is powered by the CmapDict dictionary, which replaces matplotlib’s native colormap database.

## Smarter colormap normalization¶

Problem

In matplotlib, when extend='min', extend='max', or extend='neither' is passed to colorbar , the colormap colors reserved for “out-of-bounds” values are truncated. The problem is that matplotlib discretizes colormaps by generating a low-resolution lookup table (see LinearSegmentedColormap for details). This approach cannot be fine-tuned and creates an unnecessary copy of the colormap.

It is clear that the task discretizing colormap colors should be left to the normalizer, not the colormap itself. Matplotlib provides BoundaryNorm for this purpose, but it is seldom used and its features are limited.

Solution

In ProPlot, all colormap visualizations are automatically discretized with the BinNorm class. This reads the extend property passed to your plotting command and chooses colormap indices so that your colorbar levels always traverse the full range of colormap colors.

BinNorm also applies arbitrary continuous normalizer requested by the user, e.g. Normalize or LogNorm, before discretization. Think of BinNorm as a “meta-normalizer” – other normalizers perform the continuous transformation step, while this performs the discretization step.

## Bulk global settings¶

Problem

In matplotlib, there are several rcParams that you often want to set all at once, like the tick lengths and spine colors. It is also often desirable to change these settings for individual subplots or individual blocks of code rather than globally.

Solution

In ProPlot, you can use the rc object to change lots of settings at once with convenient shorthands. This is meant to replace matplotlib’s rcParams. dictionary. Settings can be changed with plot.rc.key = value, plot.rc[key] = value, plot.rc.update(...), with the format method, or with the context method.

For details, see Configuring proplot. The most notable bulk settings are described below.

Key

Description

Children

color

The color for axes bounds, ticks, and labels.

axes.edgecolor, geoaxes.edgecolor, axes.labelcolor, tick.labelcolor, hatch.color, xtick.color, ytick.color

linewidth

The width of axes bounds and ticks.

axes.linewidth, geoaxes.linewidth, hatch.linewidth, xtick.major.width, ytick.major.width

small

Font size for “small” labels.

font.size, tick.labelsize, xtick.labelsize, ytick.labelsize, axes.labelsize, legend.fontsize, geogrid.labelsize

large

Font size for “large” labels.

abc.size, figure.titlesize, axes.titlesize, suptitle.size, title.size, leftlabel.size, toplabel.size, rightlabel.size, bottomlabel.size

tickpad

xtick.major.pad, xtick.minor.pad, ytick.major.pad, ytick.minor.pad

tickdir

Tick direction.

xtick.direction, ytick.direction

ticklen

Tick length.

xtick.major.size, ytick.major.size, ytick.minor.size * tickratio, xtick.minor.size * tickratio

tickratio

Ratio between major and minor tick lengths.

xtick.major.size, ytick.major.size, ytick.minor.size * tickratio, xtick.minor.size * tickratio

margin

Margin width when limits not explicitly set.

axes.xmargin, axes.ymargin

## Physical units engine¶

Problem

Matplotlib requires users to use inches for the figure size figsize. This must be confusing for users outside of the U.S.

Matplotlib also 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 meaningless numbers until you find the right one… and then if your figure size changes, you have to adjust them again.

Solution

ProPlot introduces the physical units engine units for interpreting figsize, width, height, axwidth, axheight, left, right, top, bottom, wspace, hspace, and arguments in a few other places. Acceptable units include inches, centimeters, millimeters, pixels, points, picas, em-heights, and light years (because why not?). Em-heights are particularly useful, as labels already present can be useful rulers for figuring out the amount of space needed.

units is also used to convert settings passed to rc from arbitrary physical units to points – for example, rc.linewidth, rc.ticklen, rc[‘axes.titlesize’], and rc[‘axes.titlepad’]. See Configuring proplot for details.

## The .proplot folder¶

Problem

In matplotlib, it can be difficult to design your own colormaps and color cycles, and there is no builtin way to save them for future use. It is also quite difficult to get matplotlib to use custom .ttc, .ttf, and .otf font files, which may be desirable when you are working on Linux servers with limited font selections.

Solution

ProPlot automatically adds colormaps, color cycles, and font files saved in the .proplot/cmaps, .proplot/cycles, and .proplot/fonts folders in your home directory. You can save colormaps and color cycles to these folders simply by passing save=True to Colormap and Cycle. To manually load from these folders, e.g. if you have added files to these folders but you do not want to restart your ipython session, simply call register_cmaps, register_cycles, and register_fonts.