matplotlib

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General Concepts

matplotlib has an extensive codebase that can be daunting to many new users. However, most of matplotlib can be understood with a fairly simple conceptual framework and knowledge of a few important points.

Plotting requires action on a range of levels, from the most general (e.g., ‘contour this 2-D array’) to the most specific (e.g., ‘color this screen pixel red’). The purpose of a plotting package is to assist you in visualizing your data as easily as possible, with all the necessary control – that is, by using relatively high-level commands most of the time, and still have the ability to use the low-level commands when needed.

Therefore, everything in matplotlib is organized in a hierarchy. At the top of the hierarchy is the matplotlib “state-machine environment” which is provided by the matplotlib.pyplot module. At this level, simple functions are used to add plot elements (lines, images, text, etc.) to the current axes in the current figure.

Note

Pyplot’s state-machine environment behaves similarly to MATLAB and should be most familiar to users with MATLAB experience.

The next level down in the hierarchy is the first level of the object-oriented interface, in which pyplot is used only for a few functions such as figure creation, and the user explicitly creates and keeps track of the figure and axes objects. At this level, the user uses pyplot to create figures, and through those figures, one or more axes objects can be created. These axes objects are then used for most plotting actions.

For even more control – which is essential for things like embedding matplotlib plots in GUI applications – the pyplot level may be dropped completely, leaving a purely object-oriented approach.

Coding Styles

When viewing this documentation and examples, you will find different coding styles and usage patterns. These styles are perfectly valid and have their pros and cons. Just about all of the examples can be converted into another style and achieve the same results. The only caveat is to avoid mixing the coding styles for your own code.

Note

Developers for matplotlib have to follow a specific style and guidelines. See The Matplotlib Developers’ Guide.

Of the different styles, there are two that are officially supported. Therefore, these are the preferred ways to use matplotlib.

For the preferred pyplot style, the imports at the top of your scripts will typically be:

import matplotlib.pyplot as plt
import numpy as np

Then one calls, for example, np.arange, np.zeros, np.pi, plt.figure, plt.plot, plt.show, etc. So, a simple example in this style would be:

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 10, 0.2)
y = np.sin(x)
plt.plot(x, y)
plt.show()

Note that this example used pyplot’s state-machine to automatically and implicitly create a figure and an axes. For full control of your plots and more advanced usage, use the pyplot interface for creating figures, and then use the object methods for the rest:

import matplotlib.pyplot as plt
import numpy as np
x = np.arange(0, 10, 0.2)
y = np.sin(x)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, y)
plt.show()

Next, the same example using a pure MATLAB-style:

from pylab import *
x = arange(0, 10, 0.2)
y = sin(x)
plot(x, y)
show()

So, why all the extra typing as one moves away from the pure MATLAB-style? For very simple things like this example, the only advantage is academic: the wordier styles are more explicit, more clear as to where things come from and what is going on. For more complicated applications, this explicitness and clarity becomes increasingly valuable, and the richer and more complete object-oriented interface will likely make the program easier to write and maintain.

What is a backend?

A lot of documentation on the website and in the mailing lists refers to the “backend” and many new users are confused by this term. matplotlib targets many different use cases and output formats. Some people use matplotlib interactively from the python shell and have plotting windows pop up when they type commands. Some people embed matplotlib into graphical user interfaces like wxpython or pygtk to build rich applications. Others use matplotlib in batch scripts to generate postscript images from some numerical simulations, and still others in web application servers to dynamically serve up graphs.

To support all of these use cases, matplotlib can target different outputs, and each of these capabilities is called a backend; the “frontend” is the user facing code, ie the plotting code, whereas the “backend” does all the hard work behind-the-scenes to make the figure. There are two types of backends: user interface backends (for use in pygtk, wxpython, tkinter, qt4, or macosx; also referred to as “interactive backends”) and hardcopy backends to make image files (PNG, SVG, PDF, PS; also referred to as “non-interactive backends”).

There are a two primary ways to configure your backend. One is to set the backend parameter in your matplotlibrc file (see Customizing matplotlib):

backend : WXAgg   # use wxpython with antigrain (agg) rendering

The other is to use the matplotlib use() directive:

import matplotlib
matplotlib.use('PS')   # generate postscript output by default

If you use the use directive, this must be done before importing matplotlib.pyplot or matplotlib.pylab.

Note

Backend name specifications are not case-sensitive; e.g., ‘GTKAgg’ and ‘gtkagg’ are equivalent.

With a typical installation of matplotlib, such as from a binary installer or a linux distribution package, a good default backend will already be set, allowing both interactive work and plotting from scripts, with output to the screen and/or to a file, so at least initially you will not need to use either of the two methods given above.

If, however, you want to write graphical user interfaces, or a web application server (Matplotlib in a web application server), or need a better understanding of what is going on, read on. To make things a little more customizable for graphical user interfaces, matplotlib separates the concept of the renderer (the thing that actually does the drawing) from the canvas (the place where the drawing goes). The canonical renderer for user interfaces is Agg which uses the Anti-Grain Geometry C++ library to make a raster (pixel) image of the figure. All of the user interfaces except macosx can be used with agg rendering, eg WXAgg, GTKAgg, QT4Agg, TkAgg. In addition, some of the user interfaces support other rendering engines. For example, with GTK, you can also select GDK rendering (backend GTK) or Cairo rendering (backend GTKCairo).

For the rendering engines, one can also distinguish between vector or raster renderers. Vector graphics languages issue drawing commands like “draw a line from this point to this point” and hence are scale free, and raster backends generate a pixel representation of the line whose accuracy depends on a DPI setting.

Here is a summary of the matplotlib renderers (there is an eponymous backed for each; these are non-interactive backends, capable of writing to a file):

Renderer Filetypes Description
AGG png raster graphics – high quality images using the Anti-Grain Geometry engine
PS ps eps vector graphicsPostscript output
PDF pdf vector graphicsPortable Document Format
SVG svg vector graphicsScalable Vector Graphics
Cairo png ps pdf svg ... vector graphicsCairo graphics
GDK png jpg tiff ... raster graphics – the Gimp Drawing Kit

And here are the user interfaces and renderer combinations supported; these are interactive backends, capable of displaying to the screen and of using appropriate renderers from the table above to write to a file:

Backend Description
GTKAgg Agg rendering to a GTK 2.x canvas (requires PyGTK)
GTK3Agg Agg rendering to a GTK 3.x canvas (requires PyGObject)
GTK GDK rendering to a GTK 2.x canvas (not recommended) (requires PyGTK)
GTKCairo Cairo rendering to a GTK 2.x canvas (requires PyGTK and pycairo)
GTK3Cairo Cairo rendering to a GTK 3.x canvas (requires PyGObject and pycairo)
WXAgg Agg rendering to to a wxWidgets canvas (requires wxPython)
WX Native wxWidgets drawing to a wxWidgets Canvas (not recommended) (requires wxPython)
TkAgg Agg rendering to a Tk canvas (requires TkInter)
Qt4Agg Agg rendering to a Qt4 canvas (requires PyQt4)
macosx Cocoa rendering in OSX windows (presently lacks blocking show() behavior when matplotlib is in non-interactive mode)

What is interactive mode?

Use of an interactive backend (see What is a backend?) permits–but does not by itself require or ensure–plotting to the screen. Whether and when plotting to the screen occurs, and whether a script or shell session continues after a plot is drawn on the screen, depends on the functions and methods that are called, and on a state variable that determines whether matplotlib is in “interactive mode”. The default Boolean value is set by the matplotlibrc file, and may be customized like any other configuration parameter (see Customizing matplotlib). It may also be set via matplotlib.interactive(), and its value may be queried via matplotlib.is_interactive(). Turning interactive mode on and off in the middle of a stream of plotting commands, whether in a script or in a shell, is rarely needed and potentially confusing, so in the following we will assume all plotting is done with interactive mode either on or off.

Note

Major changes related to interactivity, and in particular the role and behavior of show(), were made in the transition to matplotlib version 1.0, and bugs were fixed in 1.0.1. Here we describe the version 1.0.1 behavior for the primary interactive backends, with the partial exception of macosx.

Interactive mode may also be turned on via matplotlib.pyplot.ion(), and turned off via matplotlib.pyplot.ioff().

Note

Interactive mode works with suitable backends in ipython and in the ordinary python shell, but it does not work in the IDLE IDE.

Interactive example

From an ordinary python prompt, or after invoking ipython with no options, try this:

import matplotlib.pyplot as plt
plt.ion()
plt.plot([1.6, 2.7])

Assuming you are running version 1.0.1 or higher, and you have an interactive backend installed and selected by default, you should see a plot, and your terminal prompt should also be active; you can type additional commands such as:

plt.title("interactive test")
plt.xlabel("index")

and you will see the plot being updated after each line. This is because you are in interactive mode and you are using pyplot functions. Now try an alternative method of modifying the plot. Get a reference to the Axes instance, and call a method of that instance:

ax = plt.gca()
ax.plot([3.1, 2.2])

Nothing changed, because the Axes methods do not include an automatic call to draw_if_interactive(); that call is added by the pyplot functions. If you are using methods, then when you want to update the plot on the screen, you need to call draw():

plt.draw()

Now you should see the new line added to the plot.

Non-interactive example

Start a fresh session as in the previous example, but now turn interactive mode off:

import matplotlib.pyplot as plt
plt.ioff()
plt.plot([1.6, 2.7])

Nothing happened–or at least nothing has shown up on the screen (unless you are using macosx backend, which is anomalous). To make the plot appear, you need to do this:

plt.show()

Now you see the plot, but your terminal command line is unresponsive; the show() command blocks the input of additional commands until you manually kill the plot window.

What good is this–being forced to use a blocking function? Suppose you need a script that plots the contents of a file to the screen. You want to look at that plot, and then end the script. Without some blocking command such as show(), the script would flash up the plot and then end immediately, leaving nothing on the screen.

In addition, non-interactive mode delays all drawing until show() is called; this is more efficient than redrawing the plot each time a line in the script adds a new feature.

Prior to version 1.0, show() generally could not be called more than once in a single script (although sometimes one could get away with it); for version 1.0.1 and above, this restriction is lifted, so one can write a script like this:

import numpy as np
import matplotlib.pyplot as plt
plt.ioff()
for i in range(3):
    plt.plot(np.random.rand(10))
    plt.show()

which makes three plots, one at a time.

Summary

In interactive mode, pyplot functions automatically draw to the screen.

When plotting interactively, if using object method calls in addition to pyplot functions, then call draw() whenever you want to refresh the plot.

Use non-interactive mode in scripts in which you want to generate one or more figures and display them before ending or generating a new set of figures. In that case, use show() to display the figure(s) and to block execution until you have manually destroyed them.