Created
June 21, 2018 14:40
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Decorator version of `timeit.repeat()`
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import functools | |
import gc | |
import itertools | |
from timeit import default_timer as _timer | |
_repeat = functools.partial(itertools.repeat, None) | |
def timeit(repeat=3, number=1000): | |
"""Decorator: prints time from best of `repeat` trials. | |
Mimics `timeit.repeat()`. | |
""" | |
def wrap(func): | |
@functools.wraps(func) | |
def _timeit(*args, **kwargs): | |
# Temporarily turn off garbage collection during the timing. | |
# Makes independent timings more comparable. | |
# If it was originally enabled, switch it back on afterwards. | |
gcold = gc.isenabled() | |
gc.disable() | |
try: | |
# Outer loop - the number of repeats. | |
trials = [] | |
for _ in _repeat(repeat): | |
# Inner loop - the number of calls within each repeat. | |
total = 0 | |
for _ in _repeat(number): | |
start = _timer() | |
result = func(*args, **kwargs) | |
end = _timer() | |
total += end - start | |
trials.append(total) | |
# We want the *average time* from the *best* trial. | |
# For more on this methodology, see the docs for | |
# Python's `timeit` module. | |
# | |
# "In a typical case, the lowest value gives a lower bound | |
# for how fast your machine can run the given code snippet; | |
# higher values in the result vector are typically not | |
# caused by variability in Python’s speed, but by other | |
# processes interfering with your timing accuracy." | |
best = min(trials) / number | |
print('Best of {} trials with {} function' | |
' calls per trial:'.format(repeat, number)) | |
print('Function `{}` ran in average' | |
' of {:0.3f} seconds.'.format(func.__name__, best)) | |
finally: | |
if gcold: | |
gc.enable() | |
return result | |
return _timeit | |
return wrap |
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