Demonstration program for Python docopt module. Here with fitting of a polynomial.

mydocopter

#!/usr/bin/env python
"""
mydocopter.

Usage: mydocopter [options] <filename>

Options:
  -v --verbose  Log messages
  -o OUTPUT --output=OUTPUT   Output file 
  -a <a>        Initial coefficient for second order term [default: 1.]
  -b <b>        Initial coefficient for first order term [default: 1.]
  -c <c>        Initial coefficient for constant term [default: 1.]

Example:
  $ echo -e "1 4\\n2 5\\n6 8\\n3 3.2" > datafile.txt
  $ ./mydocopter --verbose datafile.txt
  0.315471154631 -1.51271481921 5.64476836068

  $ ./mydocopter -o p.txt datafile.txt
  $ ipython --pylab --c="m=loadtxt('datafile.txt'); p=loadtxt('p.txt'); x=linspace(1,6); plot(m[:,0], m[:,1], 'o', x, p[0]*x**2+p[1]*x+p[2], '-'); raw_input()"


Description:
  Fit a polynomial to data. The datafile should have x y values in each row
"""
import docopt, logging, scipy.optimize

args = docopt.docopt(__doc__, version=1.0)

if args['--verbose']:
   logging.getLogger().setLevel(logging.INFO)

a, b, c = (float(args['-' + coef]) for coef in ['a', 'b', 'c'])
logging.info("Setting 'a' to %f" % a)

logging.info('Reading data from ' + args['<filename>'])       
data = [ map(float, line.split()) for line in open(args['<filename>']).readlines()]

def cost_function((a, b, c), data):
    return sum(map(lambda (x, y): (a*x**2 + b*x + c - y)**2, data))

parameters = scipy.optimize.fmin(cost_function, [a, b, c], 
                                 args=(data,), disp=False)

if args['--output'] is None: 
   print(" ".join(map(str, parameters)))
else:
   with open(args['--output'], 'w') as f:
      f.write(" ".join(map(str, parameters)))