Graphics with Python

The Python matplotlib module provides tools for interactive 2-D and 3-D graphics, and for saving plots in file formats you can easily display on the web or in other programs, print, and incorporate in documents.

Installation of matplotlib

The current version 1.2 may be included in some Linux distributions. Version 1.1 has most of the features you will need now, and it is in Ubuntu and OpenSuse packages that can be added to your core Python system after you also install numpy. For example, under Ubuntu you would use

sudo apt-get install python-matplotlib

to get the most recent version available for your system and resolve missing components.

For Windows and MacOS users, if you installed the Enthought version of Python you will have it "out of the box". For others, look at the matplotlib installation website for directions on how to install it. You will need numpy too, and it also comes in the Enthought collection.

Once you have it installed, programs that use this library will have to import it with lines such as

import numpy as np
import matplotlib as plt

to make the functions available. With these, numpy functions will start with np. and mathplotlib functions will have plt. in front of the function name, which shortens the code you would write. You can check that your computer has numpy and matplotlib by trying these commands in interactive Python or Idle. The version numbers will be available too with

print np.__version__
print plt.__version__

Learning the basics of 2D data and function plotting

The matplotlib user's guide offers a tutorial with many examples, some of which we will look at here. There is also a helpful but unfinished quick start guide written by an astrophysics graduate student.

Let's look at a simple program that generates its own data and creates a plot you can view on the screen:

import matplotlib.pyplot as plt
import math

f0 = 30.
a0 = 100.
tdecay = 2.

time = []
amplitude = []

for i in range(0, 10000, 1):
  t = 0.001 * float(i) 
  a = a0 * math.exp(-t/tdecay)*math.cos(2. * math.pi * f0 * t)
  time.append(t)
  amplitude.append(a)

plt.plot(time, amplitude)
plt.xlabel('Angle')
plt.ylabel('Value')
plt.title('A Damped Oscillator')

plt.show()