Interactive Applications using Matplotlib:

There are many ways to install Matplotlib on your system. While the library used to have a reputation for being difficult to install on non-Linux systems, it has come a long way since then, along with the rest of the Python ecosystem. Refer to the following command:

$ pip install matplotlib

Most likely, the preceding command would work just fine from the command line. Python Wheels (the next-generation Python package format that has replaced "eggs") for Matplotlib are now available from PyPi for Windows and Mac OS X systems. This method would also work for Linux users; however, it might be more favorable to install it via the system's built-in package manager.

While the core Matplotlib library can be installed with few dependencies, it is a part of a much larger scientific computing ecosystem known as SciPy. Displaying your data is often the easiest part of your application. Processing it is much more difficult, and the SciPy ecosystem most likely has the packages you need to do that. For basic numerical processing and N-dimensional data arrays, there is NumPy. For more advanced but general data processing tools, there is the SciPy package (the name was so catchy, it ended up being used to refer to many different things in the community). For more domain-specific needs, there are "Sci-Kits" such as scikit-learn for artificial intelligence, scikit-image for image processing, and statsmodels for statistical modeling. Another very useful library for data processing is pandas.

This was just a short summary of the packages available in the SciPy ecosystem. Manually managing all of their installations, updates, and dependencies would be difficult for many who just simply want to use the tools. Luckily, there are several distributions of the SciPy Stack available that can keep the menagerie under control. The following are Python distributions that include the SciPy Stack along with many other popular Python packages or make the packages easily available through package management software:

With Matplotlib installed, you are now ready to make your first simple plot. Matplotlib has multiple layers. Pylab is the topmost layer, often used for quick one-off plotting from within a live Python session. Start up your favorite Python interpreter and type the following:

>>> from pylab import *
>>> plot([1, 2, 3, 2, 1])

Nothing happened! This is because Matplotlib, by default, will not display anything until you explicitly tell it to do so. The Matplotlib library is often used for automated image generation from within Python scripts, with no need for any interactivity. Also, most users would not be done with their plotting yet and would find it distracting to have a plot come up automatically. When you are ready to see your plot, use the following command:

>>> show()

A figure window should now appear, and the Python interpreter is not available for any additional commands. By default, showing a figure will block the execution of your scripts and interpreter. However, this does not mean that the figure is not interactive. As you mouse over the plot, you will see the plot coordinates in the lower right-hand corner. The figure window will also have a toolbar:

From left to right, the following are the tools:

The figure window would also be responsive to the keyboard. The default keymap is fairly extensive (and will be covered fully later), but some of the basic hot keys are the Home key for resetting the plot view, the left and right keys for back and forward actions, p for pan/zoom mode, o for zoom-to-rectangle mode, and Ctrl + s to trigger a file save. When you are done viewing your figure, close the window as you would close any other application window, or use Ctrl + w.

When we did the previous example, no plots appeared until show() was called. Furthermore, no new commands could be entered into the Python interpreter until all the figures were closed. As you will soon learn, once a figure is closed, the plot it contains is lost, which means that you would have to repeat all the commands again in order to show() it again, perhaps with some modification or additional plot. Matplotlib ships with its interactive plotting mode off by default.

There are a couple of ways to turn the interactive plotting mode on. The main way is by calling the ion() function (for Interactive ON). Interactive plotting mode can be turned on at any time and turned off with ioff(). Once this mode is turned on, the next plotting command will automatically trigger an implicit show() command. Furthermore, you can continue typing commands into the Python interpreter. You can modify the current figure, create new figures, and close existing ones at any time, all from the current Python session.

Python is known for more than just its interactive interpreters; it is also a fully fledged programming language that allows its users to easily create programs. Having a script to display plots from daily reports can greatly improve your productivity. Alternatively, you perhaps need a tool that can produce some simple plots of the data from whatever mystery data file you have come across on the network share. Here is a simple example of how to use Matplotlib's pyplot API and the argparse Python standard library tool to create a simple CSV plotting script called plotfile.py.

Code: chp1/plotfile.py

#!/usr/bin/env python

from argparse import ArgumentParser
import matplotlib.pyplot as plt

if __name__ == '__main__':
    parser = ArgumentParser(description="Plot a CSV file")
    parser.add_argument("datafile", help="The CSV File")
    # Require at least one column name
    parser.add_argument("columns", nargs='+',
                        help="Names of columns to plot")
    parser.add_argument("--save", help="Save the plot as...")
    parser.add_argument("--no-show", action="store_true",
                        help="Don't show the plot")
    args = parser.parse_args()

    plt.plotfile(args.datafile, args.columns)
    if args.save:
        plt.savefig(args.save)
    if not args.no_show:
        plt.show()

Note the two optional command-line arguments: --save and --no-show. With the --save option, the user can have the plot automatically saved (the graphics format is determined automatically from the filename extension). Also, the user can choose not to display the plot, which when coupled with the --save option might be desirable if the user is trying to plot several CSV files.

When calling this script to show a plot, the execution of the script will stop at the call to plt.show(). If the interactive plotting mode was on, then the execution of the script would continue past show(), terminating the script, thus automatically closing out any figures before the user has had a chance to view them. This is why the interactive plotting mode is turned off by default in Matplotlib.

Also note that the call to plt.savefig() is before the call to plt.show(). As mentioned before, when the figure window is closed, the plot is lost. You cannot save a plot after it has been closed.

We have covered how to install Matplotlib and went over how to make very simple plots from a Python session or a Python script. Most likely, this went very smoothly for you. The rest of this book will focus on how to use Matplotlib to make an interactive application, rather than the many ways to display data. You may be very curious and want to learn more about the many kinds of plots this library has to offer, or maybe you want to learn how to make new kinds of plots.

Help comes in many forms. The Matplotlib website (http://matplotlib.org) is the primary online resource for Matplotlib. It contains examples, FAQs, API documentation, and, most importantly, the gallery.