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:

octave:1> x=linspace(0,2*pi,100)
octave:2> y=sin(x)
octave:3> 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.