141 lines
6.6 KiB
Markdown
141 lines
6.6 KiB
Markdown
# 如何用 timeit 为小块 Python 代码计时
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> 原文：<https://www.blog.pythonlibrary.org/2014/01/30/how-to-time-small-pieces-of-python-code-with-timeit/>
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有时候当你在编码的时候，你想知道一个特定的函数运行需要多长时间。这个主题称为性能分析或性能调优。Python 的标准库中内置了几个分析器，但是对于小段代码，使用 Python 的 **timeit** 模块会更容易。因此， **timeit** 将是本教程的重点。 **timeit** 模块使用特定于平台的方法来获得最准确的运行时间。基本上， **timeit** 模块将进行一次设置，运行代码 n 次，并返回运行所用的时间。通常它会输出一个“3 分中最好”的分数。奇怪的是，默认运行代码的次数是 1，000，000 次循环。 **timeit** 在 Linux / Mac 上用 time.time()计时，在 Windows 上用 time.clock()计时，以获得最准确的读数，这是大多数人不会想到的。
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您可以从命令行或通过导入来运行 **timeit** 模块。我们将查看这两个用例。
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* * *
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### 在控制台中计时
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在命令行上使用 timeit 模块非常简单。这里有几个例子:
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```py
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python -m timeit -s "[ord(x) for x in 'abcdfghi']"
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100000000 loops, best of 3: 0.0115 usec per loop
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python -m timeit -s "[chr(int(x)) for x in '123456789']"
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100000000 loops, best of 3: 0.0119 usec per loop
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```
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这是怎么回事？当你在命令行上调用 Python 并给它传递“-m”选项时，你是在告诉它查找一个模块并把它作为主程序使用。“-s”告诉 **timeit** 模块运行一次设置。然后，它将代码运行 n 次循环，并返回 3 次运行的最佳平均值。对于这些愚蠢的例子，你不会看到太大的区别。让我们快速看一下 **timeit's** help，这样我们可以了解更多关于它是如何工作的:
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```py
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C:\Users\mdriscoll>python -m timeit -h
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Tool for measuring execution time of small code snippets.
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This module avoids a number of common traps for measuring execution
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times. See also Tim Peters' introduction to the Algorithms chapter in
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the Python Cookbook, published by O'Reilly.
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Library usage: see the Timer class.
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Command line usage:
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python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [--] [statement]
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Options:
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-n/--number N: how many times to execute 'statement' (default: see below)
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-r/--repeat N: how many times to repeat the timer (default 3)
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-s/--setup S: statement to be executed once initially (default 'pass')
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-t/--time: use time.time() (default on Unix)
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-c/--clock: use time.clock() (default on Windows)
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-v/--verbose: print raw timing results; repeat for more digits precision
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-h/--help: print this usage message and exit
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--: separate options from statement, use when statement starts with -
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statement: statement to be timed (default 'pass')
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A multi-line statement may be given by specifying each line as a
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separate argument; indented lines are possible by enclosing an
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argument in quotes and using leading spaces. Multiple -s options are
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treated similarly.
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If -n is not given, a suitable number of loops is calculated by trying
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successive powers of 10 until the total time is at least 0.2 seconds.
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The difference in default timer function is because on Windows,
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clock() has microsecond granularity but time()'s granularity is 1/60th
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of a second; on Unix, clock() has 1/100th of a second granularity and
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time() is much more precise. On either platform, the default timer
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functions measure wall clock time, not the CPU time. This means that
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other processes running on the same computer may interfere with the
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timing. The best thing to do when accurate timing is necessary is to
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repeat the timing a few times and use the best time. The -r option is
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good for this; the default of 3 repetitions is probably enough in most
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cases. On Unix, you can use clock() to measure CPU time.
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Note: there is a certain baseline overhead associated with executing a
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pass statement. The code here doesn't try to hide it, but you should
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be aware of it. The baseline overhead can be measured by invoking the
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program without arguments.
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The baseline overhead differs between Python versions! Also, to
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fairly compare older Python versions to Python 2.3, you may want to
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use python -O for the older versions to avoid timing SET_LINENO
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instructions.
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```
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这告诉使用所有我们可以通过的奇妙的标志，以及它们做什么。它也告诉我们一些关于 **timeit** 如何在幕后运作的事情。让我们写一个简单的函数，看看我们能否从命令行计时:
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```py
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# simple_func.py
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def my_function():
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try:
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1 / 0
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except ZeroDivisionError:
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pass
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```
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这个函数所做的只是导致一个被忽略的错误。是的，这是一个愚蠢的例子。为了让 **timeit** 在命令行上运行这段代码，我们需要将代码导入到它的名称空间中，所以请确保您已经将当前的工作目录更改为该脚本所在的文件夹。然后运行以下命令:
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```py
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python -m timeit "import simple_func; simple_func.my_function()"
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1000000 loops, best of 3: 1.77 usec per loop
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```
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这里导入函数，然后调用它。注意，我们用分号分隔导入和函数调用，Python 代码用引号括起来。现在我们准备学习如何在实际的 Python 脚本中使用 **timeit** 。
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* * *
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### 导入 timeit 进行测试
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在代码中使用 timeit 模块也很容易。我们将使用之前相同的愚蠢脚本，并在下面向您展示如何操作:
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```py
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def my_function():
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try:
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1 / 0
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except ZeroDivisionError:
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pass
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if __name__ == "__main__":
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import timeit
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setup = "from __main__ import my_function"
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print timeit.timeit("my_function()", setup=setup)
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```
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在这里，我们检查脚本是否正在直接运行(即没有导入)。如果是，那么我们导入 **timeit** ，创建一个设置字符串将函数导入到 **timeit 的**名称空间，然后我们调用 **timeit.timeit** 。您会注意到，我们用引号将对函数的调用传递出去，然后是设置字符串。这真的就是全部了！
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* * *
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### 包扎
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现在你知道如何使用 **timeit** 模块了。它非常擅长计时简单的代码片段。您通常会将它用于您怀疑运行时间过长的代码。如果您想要更详细地了解代码中正在发生的事情，那么您可能希望切换到分析器。开心快乐编码！
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* * *
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* 关于 timeit 的 Python [文档](http://docs.python.org/2/library/timeit.html)
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* py motw-Time it "[对小部分 Python 代码的执行进行计时。](http://pymotw.com/2/timeit/)
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* 在 timeit 上深入研究 Python [部分](http://www.diveintopython.net/performance_tuning/timeit.html)
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* 关于 timeit 的梦想代码论坛教程 |