blocks|key|4059922|text|我通常使用这个装饰器来计时我的功能：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4059923|import+time++++++++++++++++++++++++++++++++++++++++++++++++
def+timeit(method):
++++def+timed(*args,+**kw):
++++++++ts+=+time.time()
++++++++result+=+method(*args,+**kw)
++++++++te+=+time.time()
++++++++print+'%25r+(%25r,+%25r)+%252.2f+sec'+%25+\
++++++++++++++(method.__name__,+args,+kw,+te-ts)
++++++++return+result

++++return+timed

@timeit
def+timeme():
++++time.sleep(3)|code-block|syntax|javascript|4059924|time.time()为基准测试提供了比time.clock()更精确的时间，这主要是因为time.clock()测量的是CPU时间。time.time()将返回自时代(即墙壁时间)以来的秒数，这正是您所需要的。|offset|length|style|CODE|4059925|或者您也可以使用timeit+https://docs.python.org/3/library/timeit.html。|4059926|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3/library/timeit.html^0|0|0|0|B|K|C|19|C|1V|B|0|8|6|F|19|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@$I|Z|J|10|K|L]|$I|11|J|12|K|L]|$I|13|J|14|K|L]|$I|15|J|16|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|17|8|@$I|18|J|19|K|L]]|9|@$I|1A|J|1B|1|1C]]|A|$]]|$1|O|3|-4|5|6|7|1D|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]]]

I generally use this decorator to time my functions:

<pre><code>import time 
def timeit(method):
 def timed(*args, **kw):
 ts = time.time()
 result = method(*args, **kw)
 te = time.time()
 print '%r (%r, %r) %2.2f sec' % \
 (method.__name__, args, kw, te-ts)
 return result

 return timed

@timeit
def timeme():
 time.sleep(3)
</code></pre>

<code>time.time()</code> gives more precise time for benchmarks than <code>time.clock()</code> primarily because <code>time.clock()</code> measures CPU time. <code>time.time()</code> will return seconds passed since epoch (i.e. wall time), which is what you need.

Or you can also use <code>timeit</code> <a href="https://docs.python.org/3/library/timeit.html" rel="nofollow">https://docs.python.org/3/library/timeit.html</a>

blocks|key|4059960|text|为了进行快速性能分析，我使用了以下两行(加上导入)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4059961|import+time
import+numpy+as+np

t+=+time.time()
#+...
print+np.round_(time.time()+-+t,+3),+'sec+elapsed'|code-block|syntax|javascript|4059962|它很短，很简单，也是我通常需要的。|4059963|(在大多数情况下，我还是导入了numpy。因此，这对我来说没有开销。)|offset|length|style|CODE|4059964|entityMap^0|0|0|0|F|5|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

For quick performance analyses I use the following two lines (plus imports):

<pre class="lang-py prettyprint-override"><code>import time
import numpy as np

t = time.time()
# ...
print np.round_(time.time() - t, 3), 'sec elapsed'
</code></pre>

It's short, simple and all I usually need.

(In most cases I've imported <code>numpy</code> anyway. So thats no overhead for me.)

blocks|key|4356456|text|我承认我不太熟悉Python的异步，但我相信问题不在于您的时间，而在于您对asyncio的使用。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4356457|我认为您只是在创建一个具有方法()价值的未来，然而，这就是您所要做的一切:实际创建这个承诺。|4356458|您没有对未来价值的实际评估进行计时。这就是为什么定时睡眠(3)和method()所花费的时间大致相同。|4356459|我建议尝试用yield+from+asyncio.wait_for((method()),+3)来改变yield+from+asyncio.wait_for((method()),+3)，如果可以的话只修改timing+method()。|4356460|entityMap^0|11|7|0|0|0|6|16|1F|16|2V|F|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|Q|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|R|8|@]|D|@]|E|$]]|$1|J|3|K|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|L|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|M|$]]

I'll admit that I'm not very familiar with Python's asyncio, but I believe the issue is not in your timing, but in your useage of <code>asyncio</code>. 

I think you are just creating a future with the value of method(), however that is all that you are timing: the actual creation of this promise.

You are not timing the actual evaluation of the future value. This is why timing sleep(3) and method() take roughly the same amount of time. 

I suggest trying to change <code>asyncio.wait_for((method()), 5)</code> with <code>yield from asyncio.wait_for((method()), 3)</code> or just <code>timing method()</code> if you can.

In Python 3.4.1, I'm trying to measure how long it takes for a function to run and complete then recording it. I'm currently doing it this like so:

<pre><code>starttime = time.clock()
asyncio.wait_for((method()), 5)
endtime = time.clock()
print(endtime - starttime)
</code></pre>

This usually results in Python spitting out something around 6.29989986222767E-06 (or 0.00000629989986222767E). Then I tried it with a time.sleep:

<pre><code>starttime = time.clock()
asyncio.wait_for((time.sleep(3)), 5)
endtime = time.clock()
print(endtime - starttime)
</code></pre>

This again resulted in 6.87261802845284E-06, even though (at least I think) it should take 3 seconds. I have tried this using threads, with the same result. What do you think? How can I measure how long it takes for a function to run and complete?

Measuring the time it takes for a function to run and complete in Python

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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在Python3.4.1中，我试图测量函数运行和完成所需的时间，然后记录它。我现在是这样做的starttime = time.clock()asyncio.wait_for((method()), 5)endtime = time.clock()print(endtime - starttime)这通常导致Python...

问度量函数在Python中运行和完成所需的时间
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