Python Forum

Just remember premature optimizing is the root of all evil.
Now if this was finish code. Using struct to optimize make since.
Me I probably be altering data in numpy. So numpy would be faster for me.

@Larc60 why use time.clock() over timeit.

import numpy as np
import struct
import timeit
import time

def windspar_build_up():
	array = np.array((0, 0, 64, 65)) << np.array((0, 8, 16, 24))
	return np.sum(array, dtype=np.int32).view(np.float32)

def gribouillis_build_up():
	return struct.unpack('>f', struct.pack('>BBBB', 65, 64, 0, 0))[0]

def gribouillis_build_up2():
	data = [0, 0, 64, 65]
	return struct.unpack('>f', struct.pack('>BBBB', *data[::-1]))[0]

def Larz_timeit(func, loops):
	time0 = time.clock()
	for i in range(loops):
		func()
	time1 = time.clock()
	print('time build_up:', (time1 - time0) / loops)


def main():
	loop = 1000
	print(timeit.timeit(windspar_build_up, number=loop))
	print(timeit.timeit(gribouillis_build_up, number=loop))
	print(timeit.timeit(gribouillis_build_up2, number=loop))
	Larz_timeit(windspar_build_up, loop)
	Larz_timeit(gribouillis_build_up, loop)
	Larz_timeit(gribouillis_build_up2, loop)

main()