Notebook

PySeison - Tutorial 0: First steps into Python and Ipython¶

1. Launching Ipython¶

From your shell, *cd* to your python workspace/folder and type:

$ipython

This command will launch Ipython with the pylab option on. This option will provide an interactive environment, to generate plots.

2. Importing libraries¶

Importing libraries permits to extend python default functionality by calling exterior code packages. There are several ways to import libraries in python with different implications. In this tutorial, two different import statements will be use.

Let us import a very useful library, numpy. numpy has very similar capability to matlab and is therefore widely use in the scientific community.

In [1]:

import numpy as np

Here the entire package has been imported and renamed np. This format of import format is good-practise since it allows to keep track of the package and their attached functions and avoid confusions. for example:

In [2]:

np.round

Out[2]:

<function numpy.core.fromnumeric.round_>

is different from:

In [3]:

round

Out[3]:

<function round>

3. Ipython useful shortcuts¶

Here is a non-exhaustive list of useful shortcuts available in Ipython:

Tab: Tab completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type *object_name.Tab* to view the object’s attributes (see the readline section for more). Besides Python objects and keywords, tab completion also works on file and directory names.
Up & down arrows: IPython stores both the commands you enter, and the results it produces. You can easily go through previous commands with the up- and down-arrow keys, or access your history in more sophisticated ways. One can also use up- and down-arrow keys for auto-completion purposes based on one's command history.
?: ? permits a quick access to the documentation of any object or function. Typing *object_name?* will unravel the documentation related to the object. Sometimes, one has to type *q* to quit the documentation.

4. Numpy vs. Matlab¶

Despite their capability similitudes, there are few major differences between numpy and matlab. Here follows probably the most important ones:

*[ ] vs. ( ):* if M is a matrix, i th and j th element of M would be respectively called M[i,j] in numpy and M(j,i) in matlab.
*Row-major vs. Column-major:* as one may has notice the previous point, the order of row and column indices are inverse between python and matlab. Consequently, be extra cautious when translating matlab scripts to python and similarly when optimisizing double for loops.
*0 to N-1 vs. 1 to N:* Additionally to the index order, the number attributed to first element of a matrix is different between python and matlab. In matlab, the number attributed to first element of a matrix is one whereas, in python it is zero, for instance:

In [4]:

N = 10
M = np.ones((N)) #define a 1D array of N elements
print "Shape of M: ", M.shape
print "First element= ", M[0]
print "Last element= ", M[9]

Shape of M:  (10,)
First element=  1.0
Last element=  1.0

*Indentation: * Python uses whitespace indentation, rather than curly braces or keywords, to delimit blocks; this feature is also termed the off-side rule. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block...this features will be illustrated later on with exercises.

Fortunately, numpy and matlab have more similitudes than differences, so much that matlab users will rapidly make their heads around numpy

5. PEP convetions¶

PEP stands for Python Enhancement Proposals. They are Best-practises type documents for Python programming. It is recommended to follow as much as possible PEP 20, PEP 8 and PEP 257 (i.e.http://legacy.python.org/dev/peps/)