Python versions of what we have been doing:

New library:  SciPy (Scientific Python)
   Builds off Numpy
   Contains lots of data analysis functions
   Most of the functions in this library are translations of very old 
     Fortran functions.
   Python (and MATLAB) allows you to write a function in one language 
     (Fortran, C, C++, etc.) and call it from Python.
   
Goal
   Estimate the value of a definite integral
   
The approach taken depends on what we know about the function
   If we know the function, using the integral function is 
      generally going to be the best approach becase we don't need 
      to worry about whether the table of values has enough points 
      to gurantee a desired accuracy.
         
   If we only have a table of values, we have options, but the
      simplest approach would be to create the spline and integrate
      that.  
         
funints.py
   Demonstrates how to estimate integrals using the quad function
      when you know the function.
   
tableints.py
   Assumes you only know a table of values
   Demonstrates how to use the trapezoidal and Simpson's rule
      functions in the SciPy library.
      
splineints.py 
   Demonstrates how to build a spline, evaluate the spline at a given
     number of points, integrate the spline.
   There are also routines for derivatives and roots of splines to estimate
     these for the function in the table.