timeit versus timing decorator
Use wrapping from functools
to improve Matt Alcock's answer.
from functools import wrapsfrom time import timedef timing(f): @wraps(f) def wrap(*args, **kw): ts = time() result = f(*args, **kw) te = time() print 'func:%r args:[%r, %r] took: %2.4f sec' % \ (f.__name__, args, kw, te-ts) return result return wrap
In an example:
@timingdef f(a): for _ in range(a): i = 0 return -1
Invoking method f
wrapped with @timing
:
func:'f' args:[(100000000,), {}] took: 14.2240 secf(100000000)
The advantage of this is that it preserves attributes of the original function; that is, metadata like the function name and docstring is correctly preserved on the returned function.
I would use a timing decorator, because you can use annotations to sprinkle the timing around your code rather than making you code messy with timing logic.
import timedef timeit(f): def timed(*args, **kw): ts = time.time() result = f(*args, **kw) te = time.time() print 'func:%r args:[%r, %r] took: %2.4f sec' % \ (f.__name__, args, kw, te-ts) return result return timed
Using the decorator is easy either use annotations.
@timeitdef compute_magic(n): #function definition #....
Or re-alias the function you want to time.
compute_magic = timeit(compute_magic)
Use timeit. Running the test more than once gives me much better results.
func_list=[locals()[key] for key in locals().keys() if callable(locals()[key]) and key.startswith('time')]alist=range(1000000)times=[]for f in func_list: n = 10 times.append( min( t for t,_,_ in (f(alist,31) for i in range(n)))) for (time,func_name) in zip(times, func_list): print '%s took %0.3fms.' % (func_name, time*1000.)
->
<function wrapper at 0x01FCB5F0> took 39.000ms.<function wrapper at 0x01FCB670> took 41.000ms.