mirror of https://github.com/python/cpython
235 lines
8.7 KiB
ReStructuredText
235 lines
8.7 KiB
ReStructuredText
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:mod:`timeit` --- Measure execution time of small code snippets
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===============================================================
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.. module:: timeit
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:synopsis: Measure the execution time of small code snippets.
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.. index::
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single: Benchmarking
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single: Performance
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This module provides a simple way to time small bits of Python code. It has both
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command line as well as callable interfaces. It avoids a number of common traps
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for measuring execution times. See also Tim Peters' introduction to the
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"Algorithms" chapter in the Python Cookbook, published by O'Reilly.
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The module defines the following public class:
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.. class:: Timer([stmt='pass' [, setup='pass' [, timer=<timer function>]]])
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Class for timing execution speed of small code snippets.
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The constructor takes a statement to be timed, an additional statement used for
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setup, and a timer function. Both statements default to ``'pass'``; the timer
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function is platform-dependent (see the module doc string). The statements may
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contain newlines, as long as they don't contain multi-line string literals.
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To measure the execution time of the first statement, use the :meth:`timeit`
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method. The :meth:`repeat` method is a convenience to call :meth:`timeit`
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multiple times and return a list of results.
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The *stmt* and *setup* parameters can also take objects that are callable
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without arguments. This will embed calls to them in a timer function that
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will then be executed by :meth:`timeit`. Note that the timing overhead is a
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little larger in this case because of the extra function calls.
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.. method:: Timer.print_exc([file=None])
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Helper to print a traceback from the timed code.
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Typical use::
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t = Timer(...) # outside the try/except
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try:
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t.timeit(...) # or t.repeat(...)
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except:
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t.print_exc()
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The advantage over the standard traceback is that source lines in the compiled
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template will be displayed. The optional *file* argument directs where the
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traceback is sent; it defaults to ``sys.stderr``.
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.. method:: Timer.repeat([repeat=3 [, number=1000000]])
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Call :meth:`timeit` a few times.
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This is a convenience function that calls the :meth:`timeit` repeatedly,
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returning a list of results. The first argument specifies how many times to
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call :meth:`timeit`. The second argument specifies the *number* argument for
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:func:`timeit`.
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.. note::
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It's tempting to calculate mean and standard deviation from the result vector
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and report these. However, this is not very useful. In a typical case, the
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lowest value gives a lower bound for how fast your machine can run the given
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code snippet; higher values in the result vector are typically not caused by
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variability in Python's speed, but by other processes interfering with your
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timing accuracy. So the :func:`min` of the result is probably the only number
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you should be interested in. After that, you should look at the entire vector
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and apply common sense rather than statistics.
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.. method:: Timer.timeit([number=1000000])
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Time *number* executions of the main statement. This executes the setup
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statement once, and then returns the time it takes to execute the main statement
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a number of times, measured in seconds as a float. The argument is the number
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of times through the loop, defaulting to one million. The main statement, the
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setup statement and the timer function to be used are passed to the constructor.
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.. note::
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By default, :meth:`timeit` temporarily turns off garbage collection during the
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timing. The advantage of this approach is that it makes independent timings
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more comparable. This disadvantage is that GC may be an important component of
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the performance of the function being measured. If so, GC can be re-enabled as
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the first statement in the *setup* string. For example::
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timeit.Timer('for i in range(10): oct(i)', 'gc.enable()').timeit()
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Starting with version 2.6, the module also defines two convenience functions:
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.. function:: repeat(stmt[, setup[, timer[, repeat=3 [, number=1000000]]]])
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Create a :class:`Timer` instance with the given statement, setup code and timer
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function and run its :meth:`repeat` method with the given repeat count and
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*number* executions.
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.. function:: timeit(stmt[, setup[, timer[, number=1000000]]])
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Create a :class:`Timer` instance with the given statement, setup code and timer
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function and run its :meth:`timeit` method with *number* executions.
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Command Line Interface
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----------------------
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When called as a program from the command line, the following form is used::
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python -m timeit [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement ...]
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where the following options are understood:
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-n N/:option:`--number=N`
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how many times to execute 'statement'
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-r N/:option:`--repeat=N`
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how many times to repeat the timer (default 3)
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-s S/:option:`--setup=S`
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statement to be executed once initially (default ``'pass'``)
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-t/:option:`--time`
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use :func:`time.time` (default on all platforms but Windows)
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-c/:option:`--clock`
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use :func:`time.clock` (default on Windows)
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-v/:option:`--verbose`
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print raw timing results; repeat for more digits precision
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-h/:option:`--help`
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print a short usage message and exit
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A multi-line statement may be given by specifying each line as a separate
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statement argument; indented lines are possible by enclosing an argument in
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quotes and using leading spaces. Multiple :option:`-s` options are treated
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similarly.
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If :option:`-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 default timer function is platform dependent. On Windows,
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:func:`time.clock` has microsecond granularity but :func:`time.time`'s
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granularity is 1/60th of a second; on Unix, :func:`time.clock` has 1/100th of a
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second granularity and :func:`time.time` is much more precise. On either
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platform, the default timer functions measure wall clock time, not the CPU time.
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This means that other processes running on the same computer may interfere with
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the timing. The best thing to do when accurate timing is necessary is to repeat
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the timing a few times and use the best time. The :option:`-r` option is good
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for this; the default of 3 repetitions is probably enough in most cases. On
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Unix, you can use :func:`time.clock` to measure CPU time.
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.. note::
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There is a certain baseline overhead associated with executing a pass statement.
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The code here doesn't try to hide it, but you should be aware of it. The
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baseline overhead can be measured by invoking the program without arguments.
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The baseline overhead differs between Python versions! Also, to fairly compare
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older Python versions to Python 2.3, you may want to use Python's :option:`-O`
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option for the older versions to avoid timing ``SET_LINENO`` instructions.
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Examples
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--------
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Here are two example sessions (one using the command line, one using the module
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interface) that compare the cost of using :func:`hasattr` vs.
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:keyword:`try`/:keyword:`except` to test for missing and present object
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attributes. ::
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% timeit.py 'try:' ' str.__bool__' 'except AttributeError:' ' pass'
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100000 loops, best of 3: 15.7 usec per loop
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% timeit.py 'if hasattr(str, "__bool__"): pass'
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100000 loops, best of 3: 4.26 usec per loop
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% timeit.py 'try:' ' int.__bool__' 'except AttributeError:' ' pass'
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1000000 loops, best of 3: 1.43 usec per loop
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% timeit.py 'if hasattr(int, "__bool__"): pass'
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100000 loops, best of 3: 2.23 usec per loop
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::
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>>> import timeit
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>>> s = """\
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... try:
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... str.__bool__
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... except AttributeError:
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... pass
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... """
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>>> t = timeit.Timer(stmt=s)
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>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
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17.09 usec/pass
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>>> s = """\
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... if hasattr(str, '__bool__'): pass
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... """
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>>> t = timeit.Timer(stmt=s)
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>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
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4.85 usec/pass
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>>> s = """\
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... try:
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... int.__bool__
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... except AttributeError:
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... pass
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... """
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>>> t = timeit.Timer(stmt=s)
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>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
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1.97 usec/pass
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>>> s = """\
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... if hasattr(int, '__bool__'): pass
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... """
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>>> t = timeit.Timer(stmt=s)
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>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
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3.15 usec/pass
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To give the :mod:`timeit` module access to functions you define, you can pass a
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``setup`` parameter which contains an import statement::
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def test():
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"Stupid test function"
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L = [i for i in range(100)]
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if __name__=='__main__':
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from timeit import Timer
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t = Timer("test()", "from __main__ import test")
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print(t.timeit())
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