6 1 Introduction
other competing methods for two-dimensional scientific visualization and today is a
very actively maintained project, even without John Hunter, who sadly passed away
in 2012.
John had a few basic requirements for Matplotlib:
• Plots should look publication quality with beautiful text.
• Plots should output Postscript for inclusion within L
A
T
E
X documents and publi-
cation quality printing.
• Plots should be embeddable in a Graphical User Interface (GUI) for application
development.
• The code should be mostly Python to allow for users to become developers.
• Plots should be easy to make with just a few lines of code for simple graphs.
Each of these requirements has been completely satisfied, judging from the
enthusiastic reception of Matplotlib by the scientific Python community. In the
beginning, to ease the transition from MATLAB to Python, many of the Matplotlib
functions were closely named after the corresponding MATLAB commands. The
community has moved away from this style and, even though you will still find the
old MATLAB-esque style used in the Matplotlib documentation. This book uses the
more powerful and explicit Matplotlib interfaces.
Listing 1.19 shows the quickest way to draw a plot using Matplotlib and the
plain Python interpreter. Later, we’ll see how to do this even faster using IPython.
The first line imports the requisite module as plt which is the recommended
convention. The next line plots a sequence of numbers generated using Python’s
range function. Note the output list contains a Line2D object. This is an artist
in Matplotlib parlance. Finally, the plt.show() function draws the plot in a GUI
(i.e. figure window).
If you try this in your own plain Python interpreter (and you should!), you will
see that you cannot type in anything further in the interpreter until the figure window
is closed. This is because the plt.show() function preoccupies the interpreter with
the controls in the GUI and blocks further interaction. As we discuss below, IPython
provides ways to get around this blocking so you can simultaneously interact with
the interpreter and the figure window.
The plot function returns a list containing the Line2D object as shown
in Listing 1.19. More complicated plots yield larger lists filled with artists in
Matplotlib terminology. The suggestion is that artists draw on the canvas contained
in the Matplotlib figure. The final line is the plt.show function that provokes the
embedded artists to render on the Matplotlib canvas. The reason this is a separate
function is that plots may have dozens of complicated artists and rendering may be a
time-consuming task to only be undertaken at the end, when all the artists have been
mustered. Matplotlib supports plotting types such as images, contours, and many
others that we cover in detail in the following chapters.
Even though Listing 1.19 is the quickest way to draw a plot in Matplotlib, it is
not recommended because there are no handles to the intermediate products of the
plot such as the plot’s axis. While this is okay for a simple plot like this, later on
we will see how to construct complicated plots using the recommended style. There
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