So far, we have shown you bits and pieces of two of Matplotlib's topmost abstraction layers: pylab and pyplot. The layer below them is the object-oriented layer (the OO layer). To develop any type of application, you will want to use this layer. Mixing the pylab/pyplot layers with the OO layer will lead to very confusing behaviors when dealing with multiple plots and figures.
Below the OO layer is the backend interface. Everything above this interface level in Matplotlib is completely platform-agnostic. It will work the same regardless of whether it is in an interactive GUI or comes from a driver script running on a headless server. The backend interface abstracts away all those considerations so that you can focus on what is most important: writing code to visualize your data.
There are several backend implementations that are shipped with Matplotlib. These backends are responsible for taking the figures represented by the OO layer and interpreting it for whichever "display device" they implement. The backends are chosen automatically but can be explicitly set, if needed (see Chapter 5, Embedding Matplotlib).
There are two main classes of backends: ones that provide interactive figures and ones that don't. Interactive backends are ones that support a particular GUI, such as Tcl/Tkinter, GTK, Qt, Cocoa/Mac OS X, wxWidgets, and Cairo. With the exception of the Cocoa/Mac OS X backend, all interactive backends can be used on Windows, Linux, and Mac OS X. Therefore, when you make an interactive Matplotlib application that you wish to distribute to users of any of those platforms, unless you are embedding Matplotlib (again, see Chapter 5, Embedding Matplotlib), you will not have to concern yourself with writing a single line of code for any of these toolkits—it has already been done for you!
Non-interactive backends are used to produce image files. There are backends to produce Postscript/EPS, Adobe PDF, and Scalable Vector Graphics (SVG) as well as rasterized image files such as PNG, BMP, and JPEGs.
The open secret behind the high quality of Matplotlib's rasterized images is its use of the Anti-Grain Geometry (AGG) library (http://agg.sourceforge.net/antigrain.com/index.html). The quality of the graphics generated from AGG is far superior than most other toolkits available. Therefore, not only is AGG used to produce rasterized image files, but it is also utilized in most of the interactive backends as well. Matplotlib maintains and ships with its own fork of the library in order to ensure you have consistent, high quality image products across all platforms and toolkits. What you see on your screen in your interactive figure window will be the same as the PNG file that is produced when you call savefig().
When you install Matplotlib, a default backend is chosen for you based upon your OS and the available GUI toolkits. For example, on Mac OS X systems, your installation of the library will most likely set the default interactive backend to MacOSX or CocoaAgg for older Macs. Meanwhile, Windows users will most likely have a default of TkAgg or Qt5Agg. In most situations, the choice of interactive backends will not matter. However, in certain situations, it may be necessary to force a particular backend to be used. For example, on a headless server without an active graphics session, you would most likely need to force the use of the non-interactive Agg backend:
import matplotlib
matplotlib.use("Agg")When done prior to any plotting commands, this will avoid loading any GUI toolkits, thereby bypassing problems that occur when a GUI fails on a headless server. Any call to show() effectively becomes a no-op (and the execution of the script is not blocked). Another purpose of setting your backend is for scenarios when you want to embed your plot in a native GUI application. Therefore, you will need to explicitly state which GUI toolkit you are using (see Chapter 5, Embedding Matplotlib). Finally, some users simply like the look and feel of some GUI toolkits better than others. They may wish to change the default backend via the backend parameter in the matplotlibrc configuration file. Most likely, your rc file can be found in the .matplotlib directory or the .config/matplotlib directory under your home folder. If you can't find it, then use the following set of commands:
>>> import matplotlib >>> matplotlib.matplotlib_fname() u'/home/ben/.config/matplotlib/matplotlibrc'
Here is an example of the relevant section in my matplotlibrc file:
#### CONFIGURATION BEGINS HERE # the default backend; one of GTK GTKAgg GTKCairo GTK3Agg # GTK3Cairo CocoaAgg MacOSX QtAgg Qt4Agg TkAgg WX WXAgg Agg Cairo # PS PDF SVG # You can also deploy your own backend outside of matplotlib by # referring to the module name (which must be in the PYTHONPATH) # as 'module://my_backend' #backend : GTKAgg #backend : QT4Agg backend : TkAgg # If you are using the Qt4Agg backend, you can choose here # to use the PyQt4 bindings or the newer PySide bindings to # the underlying Qt4 toolkit. #backend.qt4 : PyQt4 # PyQt4 | PySide
This is the global configuration file that is used if one isn't found in the current working directory when Matplotlib is imported. The settings contained in this configuration serves as default values for many parts of Matplotlib. In particular, we see that the choice of backends can be easily set without having to use a single line of code.