This book is about Matplotlib, a Python package for 2D plotting that generates production quality graphs. Its variety of output formats, several chart types, and capability to run either interactively (from Python or IPython consoles) and non-interactively (useful, for example, when included into web applications), makes Matplotlib suitable for use in many different situations.
Matplotlib is a big package with several dependencies and having them all installed and running properly is the first step that needs to be taken. We provide some ways to have a system ready to explore Matplotlib. Then we start describing the basic functions required for plotting lines, exploring any useful or advanced commands for our plots until we come to the core of Matplotlib: the object-oriented interface. This is the root for the next big section of the book—embedding Matplotlib into GUI libraries applications. We cannot limit it only to desktop programs, so we show several methods to include Matplotlib into web sites using low level techniques for two well known web frameworks—Pylons and Django. Last but not the least, we present a number of real world examples of Matplotlib applications.
The core concept of the book is to present how to embed Matplotlib into Python applications, developed using the main GUI libraries: GTK+, Qt 4, and wxWidgets. However, we are by no means limiting ourselves to that. The step-by-step introduction to Matplotlib functions, the advanced details, the example with web frameworks, and several real-life use cases make the book suitable for anyone willing to learn or already working with Matplotlib.
Chapter 1—Introduction to Matplotlib introduces what Matplotlib is, describing its output formats and the interactions with graphical environments. Several ways to install Matplotlib are presented, along with its dependencies needed to have a correctly configured environment to get along with the book.
Chapter 2—Getting started with Matplotlib covers the first examples of Matplotlib usage. While still being basic, the examples show important aspects of Matplotlib like how to plot lines, legends, axes labels, axes grids, and how to save the finished plot. It also shows how to configure Matplotlib using its configuration files or directly into the code, and how to work profitable with IPython.
Chapter 3—Decorate Graphs with Plot Styles and Types discusses the additional plotting capabilities of Matplotlib: lines and points styles and ticks customizations. Several types of plots are discussed and covered: histograms, bars, pie charts, scatter plots, and more, along with the polar representation. It is also explained how to include textual information inside the plot.
Chapter 4—Advanced Matplotlib examines some advanced (or not so common) topics like the object-oriented interface, how to include more subplots in a single plot or how to generate more figures, how to set one axis (or both) to logarithmic scale, and how to share one axis between two graphs in one plot. A consistent section is dedicated to plotting date information and all that comes with that. This chapter also shows the text properties that can be tuned in Matplotlib and how to use the LaTeX typesetting language. It also presents a section about contour plot and image plotting.
Chapter 5—Embedding Matplotlib in GTK+ guides us through the steps to embed Matplotlib inside a GTK+ program. Starting from embedding just the Figure and the Navigation toolbar, it will present how to use Glade to design a GUI and then embed Matplotlib into it. It also describes how to dynamically update a Matplotlib plot using the GTK+ capabilities.
Chapter 6—Embedding Matplotlib in Qt 4 explores how to include a Matplotlib figure into a Qt 4 GUI. It includes an example that uses Qt Designer to develop a GUI and how to use Matplotlib into it. What Qt 4 library provides for a real-time update of a Matplotlib plot is described here too.
Chapter 7—Embedding Matplotlib in wxWidgets shows what is needed to embed Matplotlib into a wxWidget graphical application. An important example is the one for a real-time plot update using a very efficient technique (borrowed from computer graphics), allowing for a high update rate. WxGlade is introduced, which guides us step-by-step through the process of wxWidgets GUI creation and where to include a Matplotlib plot.
Chapter 8—Matplotlib for the Web describes how to expose plots generated with Matplotlib on the Web. The first examples start from the lower ground, using CGI and the Apache mod_python module, technologies recommended only for limited or simple tasks. For a full web experience, two web frameworks are introduced, Pylons and Django, and a complete guide for the inclusion of Matplotlib with these frameworks is given.
Chapter 9—Matplotlib in the Real World takes Matplotlib and brings it into the real world examples field, guiding through several situations that might occur in the real life. The source code to plot the data extracted from a database, a web page, a parsed log file, and from a comma-separated file are described in full detail here. A couple of third-party tools using Matplotlib, NetworkX, and Mpmath, are described presenting some examples of their usage. A considerable section is dedicated to Basemap, a Matplotlib toolkit to draw geographical data.
In order to be able to have the best experience with this book, you have to start with an already working Python environment, and then follow the advice in Chapter 1 on how to install Matplotlib and its most important dependencies. Some examples require additional tools, libraries, or modules to be installed: consult the distribution or project documentation for installation details.
Python, Matplotlib, and all other tools are cross-platform, so the book examples can be executed on Linux, Windows, or Mac OS X.
The book and the example code was developed using Python 2.5 and Matplotlib 0.98.5.3, but due to recent developments, Python 2.6 (Python 3.x is still not well supported by NumPy, Matplotlib, and several other modules) and Matplotlib 0.99.x can be used as well.
This book is essentially for Python developers who have a good knowledge of Python; no knowledge of Matplotlib is required. You will be creating 2D plots using Matplotlib in no time at all.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text are shown as follows: "This is used for enhanced handling of the datetime Python objects."
A block of code is set as follows:
In [1]: import matplotlib.pyplot as plt In [2]: import numpy as np In [3]: y = np.arange(1, 3) In [4]: plt.plot(y, 'y'); In [5]: plt.plot(y+1, 'm'); In [6]: plt.plot(y+2, 'c'); In [7]: plt.show()
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
c[0]*x**deg + c[1]*x**(deg 1) + ... + c[deg]
Any command-line input or output is written as follows:
$ easy_install matplotlib-<version>-py<py version>-win32.egg
New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "There are several aspects we might want to tune in a widget, and this can be done using the Properties window."
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