我想测量执行一个函数所花费的时间。我没时间工作:
import timeit
start = timeit.timeit()
print("hello")
end = timeit.timeit()
print(end - start)
我想测量执行一个函数所花费的时间。我没时间工作:
import timeit
start = timeit.timeit()
print("hello")
end = timeit.timeit()
print(end - start)
当前回答
作为lambda,获取经过的时间和时间戳:
import datetime
t_set = lambda: datetime.datetime.now().astimezone().replace(microsecond=0)
t_diff = lambda t: str(t_set() - t)
t_stamp = lambda t=None: str(t) if t else str(t_set())
在实践中:
>>>
>>> t_set()
datetime.datetime(2021, 3, 21, 1, 25, 17, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=61200), 'PDT'))
>>> t = t_set()
>>> t_diff(t)
'0:00:14'
>>> t_diff(t)
'0:00:23'
>>> t_stamp()
'2021-03-21 01:25:57-07:00'
>>> t_stamp(t)
'2021-03-21 01:25:22-07:00'
>>>
其他回答
使用探查器模块。它提供了非常详细的概况。
import profile
profile.run('main()')
它输出类似于:
5 function calls in 0.047 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 :0(exec)
1 0.047 0.047 0.047 0.047 :0(setprofile)
1 0.000 0.000 0.000 0.000 <string>:1(<module>)
0 0.000 0.000 profile:0(profiler)
1 0.000 0.000 0.047 0.047 profile:0(main())
1 0.000 0.000 0.000 0.000 two_sum.py:2(twoSum)
我发现它很有启发性。
(仅使用Ipython)您可以使用%timeit来测量平均处理时间:
def foo():
print "hello"
然后:
%timeit foo()
结果如下:
10000 loops, best of 3: 27 µs per loop
这里有一个很好的文档记录和完全类型提示的装饰器,我将其用作通用工具:
from functools import wraps
from time import perf_counter
from typing import Any, Callable, Optional, TypeVar, cast
F = TypeVar("F", bound=Callable[..., Any])
def timer(prefix: Optional[str] = None, precision: int = 6) -> Callable[[F], F]:
"""Use as a decorator to time the execution of any function.
Args:
prefix: String to print before the time taken.
Default is the name of the function.
precision: How many decimals to include in the seconds value.
Examples:
>>> @timer()
... def foo(x):
... return x
>>> foo(123)
foo: 0.000...s
123
>>> @timer("Time taken: ", 2)
... def foo(x):
... return x
>>> foo(123)
Time taken: 0.00s
123
"""
def decorator(func: F) -> F:
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
nonlocal prefix
prefix = prefix if prefix is not None else f"{func.__name__}: "
start = perf_counter()
result = func(*args, **kwargs)
end = perf_counter()
print(f"{prefix}{end - start:.{precision}f}s")
return result
return cast(F, wrapper)
return decorator
示例用法:
from timer import timer
@timer(precision=9)
def takes_long(x: int) -> bool:
return x in (i for i in range(x + 1))
result = takes_long(10**8)
print(result)
输出:耗时:4.942629056秒真的
可以通过以下方式检查doctest:
$ python3 -m doctest --verbose -o=ELLIPSIS timer.py
类型提示:
$ mypy timer.py
print_elapsed_time函数如下
def print_elapsed_time(prefix=''):
e_time = time.time()
if not hasattr(print_elapsed_time, 's_time'):
print_elapsed_time.s_time = e_time
else:
print(f'{prefix} elapsed time: {e_time - print_elapsed_time.s_time:.2f} sec')
print_elapsed_time.s_time = e_time
用这种方式
print_elapsed_time()
.... heavy jobs ...
print_elapsed_time('after heavy jobs')
.... tons of jobs ...
print_elapsed_time('after tons of jobs')
结果是
after heavy jobs elapsed time: 0.39 sec
after tons of jobs elapsed time: 0.60 sec
这个函数的优点和缺点是你不需要经过开始时间
仅Python 3:
由于从Python 3.3开始,time.cclock()已被弃用,因此您将希望使用time.perf_counter()进行系统范围的计时,或使用time.process_time()进行进程范围的计时(就像您以前使用time.cclok()的方式一样):
import time
t = time.process_time()
#do some stuff
elapsed_time = time.process_time() - t
新函数process_time将不包括睡眠期间经过的时间。