cProfile based python profiling an omega network perfect shuffle simulation

omega.py

#simulate omega network
import math
import sys
import cProfile, pstats

class simulate:
	def __init__(self):
    self.a = range(int(input('Enter number of inputs:')))
		self.b = self.a[:]
		self.count = int(math.log(len(self.a),2))
		self.var = 1

	def shuffle(self,list):
		print('call', str(self.var))
		self.var += 1
		return_list = []
		for x in list:
			if x == len(list)-1 or x == 0:
				return_list.append(x)
			else:
				return_list.append((x<<1) % (len(self.a) - 1))
		return return_list

	def sim(self):
		while(self.count):
			self.a = self.shuffle(self.a)
			self.count -= 1
		for x,y in zip(self.b,self.a):
			print(str(x), '\t=>\t', str(y))

if __name__ == '__main__':
	sim = simulate()
	profiler = cProfile.Profile()
	profiler.enable()		            #start collecting profiling data
	sim.sim()
	profiler.disable()		          #stop collecting profile data
	stats = pstats.Stats(profiler)	#generate stats
	stats.print_stats()		          #print stats

apart from the normal program output, the above script outputs the following stats

         210 function calls in 0.002 seconds

   Random listing order was used

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       20    0.001    0.000    0.001    0.000 {built-in method print}
      120    0.000    0.000    0.000    0.000 {built-in method len}
        4    0.000    0.000    0.000    0.000 omega.py:13(shuffle)
       64    0.000    0.000    0.000    0.000 {method 'append' of 'list' objects}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
        1    0.000    0.000    0.002    0.002 omega.py:24(sim)

more features like profiling a function call with parameters etc. are documented here