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

= (Free) 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 [[http://www.scipy.org/NumPy_for_Matlab_Users| differences]] but many similarities.

= Exercise =

 * Install Python (2.6.x), Scipy/Numpy, Ipython and Matplotlib on your machine or use a ZMAW system. 
 * Read [[http://www.greenteapress.com/thinkpython/html/| Think Python: How to Think Like a Computer Scientist]] [[http://www.greenteapress.com/thinkpython/thinkpython.pdf|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 [[http://docs.python.org/tutorial/ | Tutorial]], chapter 1-7 and try the examples.
 * Read [[http://www.euroscipy.org/file/3776?vid=download|EuroSciPy 2010 lecture notes]]

= Links =

 * http://python.org/
 * http://scipy.org/
 * http://matplotlib.sourceforge.net/
 * http://www.pyngl.ucar.edu/
 * http://gmt.soest.hawaii.edu/

 * http://techreport.com/discussions.x/16713