The basics

Creating figures

ProPlot works by creating a proplot.figure.Figure subclass of the matplotlib figure class ~matplotlib.figure.Figure, and a proplot.axes.Axes subclass of the matplotlib axes class ~matplotlib.axes.Axes. All plotting in ProPlot begins by generating an instance of the new figure class filled with instances of the new axes classes using the ~proplot.ui.subplots command, which is modeled after matplotlib.pyplot.subplots. ProPlot’s ~proplot.ui.subplots command can be used as follows:

• Without any arguments, ~proplot.ui.subplots returns a figure with a single subplot.

• With ncols or nrows, ~proplot.ui.subplots returns a figure with a simple grid of subplots.

• With array, ~proplot.ui.subplots returns an arbitrarily complex grid of subplots. This is a 2D array representing a “picture” of the subplot layout, where each unique integer indicates a ~matplotlib.gridspec.GridSpec slot that is occupied by the corresponding subplot and 0 indicates an empty space.

In the below examples, we create subplot grids with ~proplot.ui.subplots and modify the axes labels. See the formatting guide and subplots container sections for details.

Note

ProPlot figure backgrounds are only gray when displayed by the matplotlib backend – the default background color is white when the figure is saved. This is done by setting rc[‘figure.facecolor’] to gray, in order to improve contrast when working with figures. ProPlot also makes the default saved figure background transparent by setting rc[‘savefig.transparent’] to True and changes the default rc[‘savefig.format’] from PNG to PDF for the following reasons:

1. Vector graphic formats are infinitely scalable.

2. Vector graphic formats are preferred by academic journals.

3. Most academic journals accept PDF figures alongside the traditional EPS format.

4. The EPS format does not support transparent graphic elements.

In case you do need raster graphics, ProPlot sets the default rc[‘savefig.dpi’] to 1200 dots per inch, which is recommended by most journals as the minimum resolution for rasterized figures containing lines and text. See the configuration section for how to change any of these settings.

# Generate sample data
import numpy as np
state = np.random.RandomState(51423)
data = 2 * (state.rand(100, 5) - 0.5).cumsum(axis=0)

# Single subplot
import proplot as plot
fig, ax = plot.subplots()
ax.plot(data, lw=2)
ax.format(suptitle='Single subplot', xlabel='x axis', ylabel='y axis')

# Simple subplot grid
import proplot as plot
fig, axs = plot.subplots(ncols=2)
axs[0].plot(data, lw=2)
axs[0].format(xticks=20, xtickminor=False)
axs.format(
    suptitle='Simple subplot grid', title='Title',
    xlabel='x axis', ylabel='y axis'
)

# Complex grid
import proplot as plot
array = [  # the "picture" (0 == nothing, 1 == subplot A, 2 == subplot B, etc.)
    [1, 1, 2, 2],
    [0, 3, 3, 0],
]
fig, axs = plot.subplots(array, axwidth=1.8)
axs.format(
    abc=True, abcloc='ul', suptitle='Complex subplot grid',
    xlabel='xlabel', ylabel='ylabel'
)
axs[2].plot(data, lw=2)

(<matplotlib.lines.Line2D at 0x7f55861f7cd0>,
 <matplotlib.lines.Line2D at 0x7f55861f90a0>,
 <matplotlib.lines.Line2D at 0x7f55861f9430>,
 <matplotlib.lines.Line2D at 0x7f55861f97c0>,
 <matplotlib.lines.Line2D at 0x7f55861f9b50>)

# Really complex grid
import proplot as plot
array = [  # the "picture" (1 == subplot A, 2 == subplot B, etc.)
    [1, 1, 2],
    [1, 1, 6],
    [3, 4, 4],
    [3, 5, 5],
]
fig, axs = plot.subplots(array, width=5, span=False)
axs.format(
    suptitle='Really complex subplot grid',
    xlabel='xlabel', ylabel='ylabel', abc=True
)
axs[0].plot(data, lw=2)

(<matplotlib.lines.Line2D at 0x7f5586341c70>,
 <matplotlib.lines.Line2D at 0x7f55b2463880>,
 <matplotlib.lines.Line2D at 0x7f558e4d07f0>,
 <matplotlib.lines.Line2D at 0x7f558e4d0250>,
 <matplotlib.lines.Line2D at 0x7f55864225b0>)

Plotting data

Matplotlib has two different APIs: an object-oriented API and a MATLAB-style ~matplotlib.pyplot API (which uses the object-oriented API internally). Plotting in ProPlot is just like plotting in matplotlib with the object-oriented API. Rather than creating a brand new interface, ProPlot simply builds upon the existing matplotlib constructs of the ~matplotlib.axes.Axes and the ~matplotlib.figure.Figure by adding new commands and new options to existing commands, without changing the usage or syntax. This means a shallow learning curve for the average matplotlib user.

In the below example, we create a 4-panel figure with the familiar matplotlib commands ~matplotlib.axes.Axes.plot, ~matplotlib.axes.Axes.scatter, ~matplotlib.axes.Axes.pcolormesh, and ~matplotlib.axes.Axes.contourf. See the 1d plotting and 2d plotting sections for details on the plotting features added by ProPlot.

import proplot as plot
import numpy as np

# Sample data
N = 20
state = np.random.RandomState(51423)
data = (state.rand(N, N) - 0.5).cumsum(axis=0).cumsum(axis=1)

# Example plots
cycle = plot.Cycle('greys', left=0.2, N=5)
fig, axs = plot.subplots(ncols=2, nrows=2, share=0, width=5)
axs[0].plot(data[:, :5], linewidth=2, linestyle='--', cycle=cycle)
axs[1].scatter(data[:, :5], marker='x', cycle=cycle)
axs[2].pcolormesh(data, cmap='greys')
axs[3].contourf(data, cmap='greys')
axs.format(abc=True, xlabel='xlabel', ylabel='ylabel', suptitle='Quick plotting demo')

Formatting plots

Every ~matplotlib.axes.Axes returned by ~proplot.ui.subplots has a format method. This is your one-stop-shop for changing axes settings. Keyword arguments passed to format are interpreted as follows:

1. Any keyword matching the name of an ~proplot.config.rc setting is used to update the axes. If the name has “dots”, you can omit them (e.g. titleloc='left' to change the rc[‘title.loc’] property). See the configuration section for details.

2. Valid keywords arguments are passed to proplot.axes.CartesianAxes.format, proplot.axes.PolarAxes.format, or proplot.axes.GeoAxes.format. These change settings that are specific to the axes type. For example:

   • To change the x axis bounds on a ~proplot.axes.CartesianAxes, use e.g. xlim=(0, 5).

   • To change the radial bounds on a ~proplot.axes.PolarAxes, use e.g. rlim=(0, 10).

   • To change the meridional bounds on a ~proplot.axes.GeoAxes, use e.g. lonlim=(-90, 0).

3. Remaining keyword arguments are passed to the base proplot.axes.Axes.format method. ~proplot.axes.Axes is the base class for all other axes classes. This changes things that are the same for all axes types, like titles and a-b-c subplot labels (e.g. title='Title').

The format methods let you use simple shorthands for changing all kinds of settings at once, instead of one-liner setter methods like ax.set_title() and ax.set_xlabel(). They are also integrated with the ~proplot.constructor.Locator, ~proplot.constructor.Formatter, and ~proplot.constructor.Scale constructor functions (see the Cartesian axis settings section for details).

The below example shows the many different keyword arguments accepted by format, and demonstrates how format can be used to succinctly and efficiently customize your plots.

import proplot as plot
import numpy as np
fig, axs = plot.subplots(ncols=2, nrows=2, share=0, tight=True, axwidth=2)
state = np.random.RandomState(51423)
N = 60
x = np.linspace(1, 10, N)
y = (state.rand(N, 5) - 0.5).cumsum(axis=0)
axs[0].plot(x, y, linewidth=1.5)
axs.format(
    suptitle='Format command demo',
    abc=True, abcloc='ul', abcstyle='A.',
    title='Main', ltitle='Left', rtitle='Right',  # different titles
    urtitle='Title A', lltitle='Title B', lrtitle='Title C',  # extra titles
    collabels=['Column label 1', 'Column label 2'],
    rowlabels=['Row label 1', 'Row label 2'],
    xlabel='x-axis', ylabel='y-axis',
    xscale='log',
    xlim=(1, 10), xticks=1,
    ylim=(-3, 3), yticks=plot.arange(-3, 3),
    yticklabels=('a', 'bb', 'c', 'dd', 'e', 'ff', 'g'),
    ytickloc='both', yticklabelloc='both',
    xtickdir='inout', xtickminor=False, ygridminor=True,
)

Changing rc settings

A special object named ~proplot.config.rc is created whenever you import ProPlot. ~proplot.config.rc is similar to the matplotlib ~matplotlib.rcParams dictionary, but can be used to change both matplotlib settings and ProPlot settings. ~proplot.config.rc also provides a style parameter that can be used to switch between matplotlib stylesheets. See the configuration section for details.

To modify a setting for just one subplot, you can pass it to the ~proplot.axes.Axes ~proplot.axes.Axes.format method. To temporarily modify setting(s) for a block of code, use ~proplot.config.RcConfigurator.context. To modify setting(s) for the entire python session, just assign it to the ~proplot.config.rc object or use ~proplot.config.RcConfigurator.update. To reset everything to the default state, use ~proplot.config.RcConfigurator.reset. See the below example.

import proplot as plot
import numpy as np

# Update global settings in several different ways
plot.rc.cycle = 'colorblind'
plot.rc.color = 'gray6'
plot.rc.update({'fontname': 'Source Sans Pro', 'fontsize': 11})
plot.rc['figure.facecolor'] = 'gray3'
plot.rc.axesfacecolor = 'gray4'
# plot.rc.save()  # save the current settings to ~/.proplotrc

# Apply settings to figure with context()
with plot.rc.context({'suptitle.size': 13}, toplabelcolor='gray6', linewidth=1.5):
    fig, axs = plot.subplots(ncols=2, aspect=1, width=6, span=False, sharey=2)

# Plot lines
N, M = 100, 6
state = np.random.RandomState(51423)
values = np.arange(1, M + 1)
for i, ax in enumerate(axs):
    data = np.cumsum(state.rand(N, M) - 0.5, axis=0)
    lines = ax.plot(data, linewidth=3, cycle='Grays')

# Apply settings to axes with format()
axs.format(
    grid=False, xlabel='x label', ylabel='y label',
    collabels=['Column label 1', 'Column label 2'],
    suptitle='Rc settings demo',
    suptitlecolor='gray7',
    abc=True, abcloc='l', abcstyle='A)',
    title='Title', titleloc='r', titlecolor='gray7'
)
ay = axs[-1].twinx()
ay.format(ycolor='red', linewidth=1.5, ylabel='secondary axis')
ay.plot((state.rand(100) - 0.2).cumsum(), color='r', lw=3)

# Reset persistent modifications from head of cell
plot.rc.reset()

import proplot as plot
import numpy as np
# plot.rc.style = 'style'  # set the style everywhere

# Set up figure
styles = ('ggplot', 'seaborn', '538', 'bmh')
state = np.random.RandomState(51423)
data = state.rand(10, 5)
fig, axs = plot.subplots(ncols=2, nrows=2, span=False, share=False)

# Apply different styles to different axes with format()
axs.format(suptitle='Stylesheets demo')
for ax, style in zip(axs, styles):
    ax.format(style=style, xlabel='xlabel', ylabel='ylabel', title=style)
    ax.plot(data, linewidth=3)

Subplots containers

Instead of an ~numpy.ndarray of axes, ~proplot.ui.subplots returns a ~proplot.ui.SubplotsContainer instance. This container behaves like an ~matplotlib.axes.Axes object when it contains just one axes, and behaves like a list otherwise. It supports both 1D indexing (e.g. axs[1]) and 2D indexing (e.g. axs[0, 1]), and is row-major by default. Slicing a ~proplot.ui.SubplotsContainer returns another container (e.g. axs[:, 0]), and ~proplot.axes.Axes methods can be called simultaneously for all axes in the container by calling the method from the container (e.g. axs.format(abc=True)).

In the below example, the ~proplot.ui.SubplotsContainer returned by ~proplot.ui.subplots is used to cusomtize several axes at once with proplot.axes.Axes.format.

import proplot as plot
import numpy as np
state = np.random.RandomState(51423)
fig, axs = plot.subplots(ncols=4, nrows=4, axwidth=1.2)
axs.format(
    xlabel='xlabel', ylabel='ylabel', suptitle='SubplotsContainer demo',
    grid=False, xlim=(0, 50), ylim=(-4, 4)
)

# Various ways to select subplots in the container
axs[:, 0].format(facecolor='blush', color='gray7', linewidth=1)
axs[0, :].format(facecolor='sky blue', color='gray7', linewidth=1)
axs[0].format(color='black', facecolor='gray5', linewidth=1.4)
axs[1:, 1:].format(facecolor='gray1')
for ax in axs[1:, 1:]:
    ax.plot((state.rand(50, 5) - 0.5).cumsum(axis=0), cycle='Grays', lw=2)