Matplotlib
LINKS
Matplotlib tutorial
Introduction
matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We are going to explore matplotlib in interactive mode covering most common cases.
IPython and the pylab mode
IPython <http://ipython.org/>_ is an enhanced interactive Python shell that
has lots of interesting features including named inputs and outputs, access to
shell commands, improved debugging and much more. When we start it with the
command line argument -pylab (–pylab since IPython version 0.12), it allows
interactive matplotlib sessions that have Matlab/Mathematica-like functionality.
pyplot
pyplot provides a convenient interface to the matplotlib object-oriented plotting library. It is modeled closely after Matlab(TM). Therefore, the majority of plotting commands in pyplot have Matlab(TM) analogs with similar arguments. Important commands are explained with interactive examples.
Simple plot
In this section, we want to draw the cosine and sine functions on the same plot. Starting from the default settings, we’ll enrich the figure step by step to make it nicer.
The first step is to get the data for the sine and cosine functions:
import numpy as np
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C, S = np.cos(X), np.sin(X)
X is now a NumPy array with 256 values ranging from -π to +π (included). C is the cosine (256 values) and S is the sine (256 values).
To run the example, you can download each of the examples and run it using::
$ python exercice_1.py
You can get source for each step by clicking on the corresponding figure.
Using defaults
.. admonition:: Documentation
plot tutorial [plot() command] (http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.plot)
Matplotlib comes with a set of default settings that allow customizing all kinds of properties. You can control the defaults of almost every property in matplotlib: figure size and dpi, line width, color and style, axes, axis and grid properties, text and font properties and so on. While matplotlib defaults are rather good in most cases, you may want to modify some properties for specific cases.