NSF Research awards py parser

gistfile1.py

data = """
Title       : CAREER: Markov Chain Monte Carlo Methods
Type: Award
NSF Org     : CCR
Latest
Amendment
Date        : May 5, 2003
File        : a0237834

Award Number: 0237834
Award Instr.: Continuing grant
Prgm Manager: Ding-Zhu Du
          CCR  DIV OF COMPUTER-COMMUNICATIONS RESEARCH
          CSE  DIRECT FOR COMPUTER & INFO SCIE & ENGINR
Start Date  : August 1,  2003
Expires     : May 31,  2008 (Estimated)
Expected
Total Amt.  : $400000             (Estimated)
Investigator: Eric Vigoda [email protected]  (Principal Investigator current)
Sponsor     : University of Chicago
          5801 South Ellis Avenue
          Chicago, IL  606371404    773/702-8602

NSF Program : 2860      THEORY OF COMPUTING
Fld Applictn:
Program Ref : 1045,1187,9216,HPCC,
Abstract    :

     Markov chain Monte Carlo (MCMC) methods are an important algorithmic
     device in a variety of fields.  This project studies techniques for rigorous
     analysis of the convergence properties of Markov chains.   The emphasis is on
     refining probabilistic, analytic and combinatorial tools (such as coupling,
     log-Sobolev, and canonical paths) to improve existing algorithms and develop
     efficient algorithms for important open problems.

     Problems arising in
     computer science, discrete mathematics, and physics are of particular interest,
     e.g., generating random colorings and independent sets of bounded-degree
     graphs, approximating the permanent, estimating the volume of a convex body,
     and sampling contingency tables.  The project also studies inherent connections
     between phase transitions in statistical physics models and convergence
     properties of associated Markov chains.

     The investigator is developing a
     new graduate course on MCMC methods."""

result = {}
current_key = []
current_value = []

def reset(result, current_key, current_value):
	result[' '.join(current_key)] = (' '.join(current_value)).strip()
	return [], []

for line in data.split("\n"):

	# Value continues on this line
	if line.startswith(" ") or line == "":
		current_value.append(line.strip())
		continue

	# Oh, the value ended here, lets reset the machine
	if len(current_key) > 0:
		current_key, current_value = reset(result, current_key, current_value)	

	# Key spans multiple lines, this is a preceding line
	if not ':' in line:
		current_key.append(line.strip())

	# Oh, this is a line with both a key and a value....
	else:
		split_on_colon = line.split(":")
		current_key.append(split_on_colon[0].strip())
		current_value += map(lambda x: x.strip(), split_on_colon[1:])

reset(result, current_key, current_value)

import pprint	
print pprint.pprint(result)