The basics¶
Creating figures¶
ProPlot works by introducing the proplot.figure.Figure
subclass of the
matplotlib figure class Figure
, and the proplot.axes.Axes
subclass of the matplotlib axes class 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 subplots
command, which is modeled
after matplotlib.pyplot.subplots
.
ProPlot’s subplots
command can be used as follows:
Without any arguments,
subplots
returns a figure with a single subplot.With
ncols
ornrows
,subplots
returns a figure with a simple grid of subplots.With
array
,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 aGridSpec
slot that is occupied by the corresponding subplot and0
indicates an empty space.
In the below examples, we create subplot grids with subplots
and modify the axes labels. See the formatting guide
and subplots container sections for details.
Note
ProPlot sets the default display background color rc[‘figure.facecolor’]
to gray, the default background color for saved figures
rc[‘savefig.facecolor’]
to white, and makes saved figure backgrounds
transparent by default by setting rc[‘savefig.transparent’]
to True
.
It also switches the default rc[‘savefig.format’]
from PNG to PDF
for the following reasons:
Vector graphic formats are infinitely scalable.
Vector graphic formats are preferred by academic journals.
Most academic journals accept PDF figures alongside the traditional EPS format.
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 these
settings.
[1]:
# Generate sample data
import numpy as np
state = np.random.RandomState(51423)
data = 2 * (state.rand(100, 5) - 0.5).cumsum(axis=0)
[2]:
# 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')
[3]:
# 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'
)
[4]:
# 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)
[4]:
(<matplotlib.lines.Line2D at 0x7f2d98c5ca90>,
<matplotlib.lines.Line2D at 0x7f2d98c5ce20>,
<matplotlib.lines.Line2D at 0x7f2d98c5f1f0>,
<matplotlib.lines.Line2D at 0x7f2d98c5f580>,
<matplotlib.lines.Line2D at 0x7f2d98c5f910>)
[5]:
# 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)
[5]:
(<matplotlib.lines.Line2D at 0x7f2d982ce970>,
<matplotlib.lines.Line2D at 0x7f2d982ced00>,
<matplotlib.lines.Line2D at 0x7f2d982d10d0>,
<matplotlib.lines.Line2D at 0x7f2d982d1460>,
<matplotlib.lines.Line2D at 0x7f2d982d17f0>)
Plotting data¶
Matplotlib has
two different APIs:
an object-oriented API and a MATLAB-style
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 Axes
and the 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 plot
, scatter
,
pcolormesh
, and contourf
.
See the 1d plotting and 2d plotting
sections for details on the plotting features added by ProPlot.
[6]:
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 Axes
returned by subplots
has a
format
method. This is your one-stop-shop for changing axes settings.
Keyword arguments passed to format
are interpreted as follows:
Any keyword matching the name of an
rc
setting is used to update the axes. If the name has “dots”, you can omit them (e.g.titleloc='left'
to change therc[‘title.loc’]
property). See the configuration section for details.Valid keywords arguments are passed to
proplot.axes.CartesianAxes.format
,proplot.axes.PolarAxes.format
, orproplot.axes.GeoAxes.format
. These change settings that are specific to the axes type. For example:To change the x axis bounds on a
CartesianAxes
, use e.g.xlim=(0, 5)
.To change the radial bounds on a
PolarAxes
, use e.g.rlim=(0, 10)
.To change the meridional bounds on a
GeoAxes
, use e.g.lonlim=(-90, 0)
.
Remaining keyword arguments are passed to the base
proplot.axes.Axes.format
method.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 Locator
, Formatter
,
and 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.
[7]:
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 rc
is created whenever you import
ProPlot. rc
is similar to the matplotlib
rcParams
dictionary, but can be used to change both
matplotlib settings and
ProPlot settings. 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
Axes
format
method. To temporarily
modify setting(s) for a block of code, use
context
. To modify setting(s) for the
entire python session, just assign it to the rc
object or
use update
. To reset everything to the
default state, use reset
. See the below
example.
[8]:
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()
[9]:
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 ndarray
of axes, subplots
returns a
SubplotsContainer
instance. This container behaves like an
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
SubplotsContainer
returns another container (e.g. axs[:, 0]
),
and 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 SubplotsContainer
returned by
subplots
is used to cusomtize several axes at once with
proplot.axes.Axes.format
.
[10]:
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)