#acl AdminGroup:read,write EditorGroup:read All:read
#format wiki
#language en
#pragma section-numbers off

SiaProgrammingPython

This lesson motivates the use of Python. The Python  programming language is compared with
other systems for satellite image processing and analysis. 

= Software for Satellite Image processing and analysis =


== Programming languages ==

Ideal programming language for satellite image 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 satellite image processing and analysis?

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

=== Visual environments ===

Examples

 * http://www.informatik.uni-bremen.de/agki/www/grp/sima/e-home.html


== Programming versus visual environments ==

 * Visual environments are very useful for specific tasks
 * Closed commerical software
 * Programming offers more flexibility
 * ESRI's ArcGIS 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
 * Freely available and runs on Unix, Mac and Windows
 * Simple interfacing of C,C++ and Fortran code
 * Heterogeneous data structures are easy to use
 * Readable and compact code
== 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 ===
Invoking the IPython shell

{{{
ipython
ipython -pylab
}}}
loads pylab module and enables interactive plotting

Quit with CTRL-D

Getting help
{{{
help()
help modules
}}}
list available modules

'''Features of IPython''':

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

=== Importing modules ===
There are many ways to import modules. The resulting namespaces are different

 * Import module SciPy: {{{import scipy}}}
 * List SciPy subpackages {{{help(scipy)}}} or {{{scipy.info(scipy)}}}
 * Import SciPy subpackage n-dimensional image package {{{import scipy.ndimage}}}
 * List ndimage functions {{{help(scipy.ndimage)}}}
 * Help on ndimage function laplace {{{help(scipy.ndimage.laplace)}}}
 * Import SciPy module into local namespace {{{from scipy import *}}}
 * Import SciPy subpackage ndimage in namespace ndi {{{import scipy.ndimage as ndi}}}
 * Write source for this object to output {{{scipy.source(ndi.laplace)}}}

=== Defining functions and visualization ===
{{{
def tv(a):
 	imshow(a,interpolation='nearest')	
 	colorbar()

a=rand(10,10)
tv(a)
savefig('figure1.png',dpi=100)
}}}

{{{
x=linspace(0,2*pi,100)
plot(x,sin(x),x,cos(x))
grid()
axis('tight')
legend(('sin(x)','cos(x)'),'upper right')
xlabel('x')
ylabel('f(x)')
}}}