This lesson motivates the use of Python. The Python programming language is compared with other systems for geo-scientific data processing and analysis.

Software for geo-scientific data processing and analysis

Comparison of Programming Languages

Ideal programming language for geo-scientific data processing and analysis

Fast array operations
Image processing and numeric/scientific routines
Visualization
Various data formats
Processing of files and metadata
Short development cycles
Very high level of abstraction
Interactive

Ideal programming language for geo-scientific data processing and analysis?

Assembler
Fortran, C/C++, Java
Perl, Python
Matlab, IDL
Visual and menu driven environments, ENVI, GIS

Programming versus visual environments

Visual environments are very useful for specific tasks
Closed commerical software
Programming offers more flexibility
GIS scripting with python

Scripting verus Traditional Programming

Traditional programming refers to building usually large, monolithic systems
Fortran, C/C++, Java
Scripting means programming at a high and flexible abstraction level
Perl, Python, Ruby, Scheme, Tcl
Scientific computing environments
IDL, Matlab/Octave, Maple, Mathematica, R

Why Python?

Scalability

The ability to scale from easy to difficult problems and the ability for beginners and experts to be comfortable.
Python is easy enough to be a first language and powerful enough to write complex applications
Scientific computing is more than number crunching
- Converting data formats
- Extracting metadata from text
- Working with a large number of files and directories
Object oriented programming possible, but not required
Simple interfacing of C,C++ and Fortran code
Heterogeneous data structures are easy to use
Readable and compact code
Freely available, open source, and runs on Unix, Mac and Windows

Scientific Python Environment

Basic Python has limited instruction set
Extensions (modules)
Scientific modules (scipy/numpy)
Interactive environment (ipython)
Plots and visualization (pylab)

Getting started with IPython and Pylab

Invoking the IPython shell

ipython -pylab

loads matplotlib module and enables interactive plotting

Quit with CTRL-D

Getting help

help()
help modules

list available modules

Features of IPython:

Command history (up, down)
Word completion by typing TAB
System commands through magic functions cd, ls, env, pwd
Access to Unix shell by prefix !, e.g. !ls -s | sort -g
Debugging and profiling
Program control: run
Print interactive variables who, whos

Are you a Matlab user?

The exercise is to plot a sine wave:

Matlab/Octave:

>> x=linspace(0,2*pi,100)
>> y=sin(x)
>> plot(x,y)

Pylab:

In [1]: x=linspace(0,2*pi,100)
In [2]: y=sin(x)
In [3]: plot(x,y)

So, it works pretty much the same way!

Of course there are some differences but many similarities.

Exercise

Install Python (2.6.x), Scipy/Numpy, Ipython and Matplotlib on your machine or use a ZMAW system.
Read Think Python: How to Think Like a Computer Scientist pdf Chapter 1-3 and solve the exercises 2.1, 2.2, 2.3, 3.1, 3.2, 3.3, 3.4
Read the Python Tutorial, chapter 1-7 and try the examples.