Plotting

Don't miss the update: "2014 Python lecture on Matplotlib" on this blog: http://python-astro.blogspot.mx/2014/09/2014-python-lecture.html

Plotting is certainly one of the most common task one would do in Python (at least for astronomers).
There is various libraries to draw plots in Python, but the mostly used and powerful is perhaps matplotlib.
The plotting part is pyplot, so before any use, one must import it, with the (widely used) alias plt:

import matplotlib.pyplot as plt

The best to see how it works is to dive into the website where there is a lot of examples of plots with the code used to generate them: http://matplotlib.sourceforge.net/, especially the gallery: http://matplotlib.sourceforge.net/gallery.html
In the following we will present a few examples to help you to start.

X-Y plot

Let's say you have 2 vectors x and y and wanna plot y vs. x. Here are some examples of how to do this.
First generate x and y (don't forget to import numpy as np):

x = np.linspace(0., 4 * np.pi, 100)

y = np.sin(x)

plt.plot(x, y)
plt.show()

This latest command is not always necessary, if you run ipython with the --pylab option.
As you can see, python automatically define the axis ranges and draw the plot using a default blue color.
If you want to overplot another function, just call plt.plot again:

plt.plot(x, y**2)

The figure can be cleaned before another plot using

plt.clf()

You may need to draw various figure in different windows at the same time. Every call to

plt.figure()

will start a new figure where the next plot(s) will be drawn. The figures (each one in a separate window) are identified with numbers, the first one being 1.
When calling plt.figure(N), a new figure is created if figure N doesn't exist, and focus will be on figure N otherwise.

The color of the line is defined using c or color keyword:

plt.plot(x, y**3, color = 'red')

plt.plot(x, y**4, c='b')

Abbreviation	Color
b	blue
g	green
r	red
c	cyan
m	magenta
y	yellow
k	black
w	white

Symbols and line style can also be easily defined:

plt.plot(x, y**3, color='r', marker='o', linestyle=':')

Symbol	Description
-	solid line
--	dashed line
-.	dash-dot line
:	dotted line
.	points
,	pixels
o	circle symbols
^	triangle up symbols
v	triangle down symbols
<	triangle left symbols
>	triangle right symbols
s	square symbols
+	plus symbols
x	cross symbols
D	diamond symbols
d	thin diamond symbols
1	tripod down symbols
2	tripod up symbols
3	tripod left symbols
4	tripod right symbols
h	hexagon symbols
H	rotated hexagon symbols
p	pentagon symbols
|	vertical line symbols
_	horizontal line symbols
steps	use gnuplot style ‘steps’ # kwarg only

Other useful line properties:

Property	Value
alpha	alpha transparency on 0-1 scale
antialiased	True or False - use antialised rendering
color	matplotlib color arg
data_clipping	whether to use numeric to clip data
label	string optionally used for legend
linestyle	one of - : -. -
linewidth	float, the line width in points
marker	one of + , o . s v x > <, etc
markeredgewidth	line width around the marker symbol
markeredgecolor	edge color if a marker is used
markerfacecolor	face color if a marker is used
markersize	size of the marker in points

If you need more control on the markers, better use scatter. For example, if one need ot change the size of the symbol according to a function:

plt.scatter(x, y**3, c=abs(y), marker='s', s=10+abs(y)*100, edgecolors='none')

The labels are set after the plot is done:

plt.xlabel('X')

plt.ylabel('Some trig function')

LaTex fans are welcome:

plt.title(r'Example of LaTex $\alpha_{\beta}$') #Notice the r before the string

One can change the axis ranges afterward:

plt.xlim((0, 10))

plt.ylim((-2, 2))

Multiple plots

To draw multiple plots of the same figure:

plt.subplot(N_y, N_x, N)

for i in np.arange(9):

    plt.subplot(3,3,i+1)

    plt.plot(x, y**i)
    plt.ylim((-2, 2))

log plots

using plt.semilogx, plt.semilogy and plt.loglog

plt.loglog(x)

plt.grid(True, which='minor')

Contours

Contour plots are done with plt.contour and plt.contourf.

x = np.linspace(-1, 1, 100)

y = x

X, Y = np.meshgrid(x, y) #this create 2D arrays containing x and y for each pixel

dist =  (X**2 + Y**2)**0.5

plt.contourf(X, Y, dist)

plt.colorbar()

CS = plt.contour(X, Y, dist, colors ='black', linewidths = 4)

plt.clabel(CS) #to print label on each contour

Saving the plot

The result of the plot can be save in PDF, EPS, JPG, BMP format, using:
plt.savefig('fig1.pdf')

More on plotting:

http://scipy-lectures.github.com/intro/matplotlib/matplotlib.html