Doc/lib/libtimeit.tex

\section{\module{timeit} ---
         Measure execution time of small code snippets}

\declaremodule{standard}{timeit}
\modulesynopsis{Measure the execution time of small code snippets.}

\index{Benchmarking}
\index{Performance}

\versionadded{2.3}

This module provides a simple way to time small bits of Python code.  It has
both command line as well as callable interfaces.  It avoids a number of
common traps for measuring execution times.  See also Tim Peters'
introduction to the Algorithms chapter in the ``Python Cookbook'', published
by O'Reilly.

The module interface defines the following public class:

\begin{classdesc}{Timer}{\optional{stmt='pass'
			 \optional{, setup='pass'
			 \optional{, timer=<timer function>}}}}
Class for timing execution speed of small code snippets.

The constructor takes a statement to be timed, an additional statement used
for setup, and a timer function.  Both statements default to 'pass'; the
timer function is platform-dependent (see the module doc string).

To measure the execution time of the first statement, use the timeit()
method.  The repeat() method is a convenience to call timeit() multiple
times and return a list of results.

The statements may contain newlines, as long as they don't contain
multi-line string literals.

\begin{methoddesc}{print_exc}{\optional{file=None}}
Helper to print a traceback from the timed code.

Typical use:

\begin{verbatim}
    t = Timer(...)       # outside the try/except
    try:
        t.timeit(...)    # or t.repeat(...)
    except:
        t.print_exc()
\end{verbatim}

The advantage over the standard traceback is that source lines in the
compiled template will be displayed.

The optional file argument directs where the traceback is sent; it defaults
to \code{sys.stderr}.
\end{methoddesc}

\begin{methoddesc}{repeat}{\optional{repeat=3\optional{, number=1000000}}}
Call \method{timeit()} a few times.

This is a convenience function that calls the \method{timeit()} repeatedly,
returning a list of results.  The first argument specifies how many times to
call \function{timeit()}.  The second argument specifies the \code{number}
argument for \function{timeit()}.

Note: it's tempting to calculate mean and standard deviation from the result
vector and report these.  However, this is not very useful.  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.  So the \function{min()} of the result is
probably the only number you should be interested in.  After that, you
should look at the entire vector and apply common sense rather than
statistics.
\end{methoddesc}

\begin{methoddesc}{timeit}{\optional{number=1000000}}
Time \code{number} executions of the main statement.

To be precise, this executes the setup statement once, and then returns the
time it takes to execute the main statement a number of times, as a float
measured in seconds.  The argument is the number of times through the loop,
defaulting to one million.  The main statement, the setup statement and the
timer function to be used are passed to the constructor.
\end{methoddesc}
\end{classdesc}

\subsection{Command Line Interface}

When called as a program from the command line, the following form is used:

\begin{verbatim}
    python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement ...]
\end{verbatim}

where the following options are understood:

\begin{description}
\item[-n N/--number=N] how many times to execute 'statement'
\item[-r N/--repeat=N] how many times to repeat the timer (default 3)
\item[-s S/--setup=S] statement to be executed once initially (default
'pass')
\item[-t/--time] use time.time() (default on all platforms but Windows)
\item[-c/--clock] use time.clock() (default on Windows)
\item[-v/--verbose] print raw timing results; repeat for more digits
precision 
\item[-h/--help] print a short usage message and exit
\end{description}

A multi-line statement may be given by specifying each line as a separate
statement argument; indented lines are possible by enclosing an argument in
quotes and using leading spaces.  Multiple -s options are treated similarly.

If -n is not given, a suitable number of loops is calculated by trying
successive powers of 10 until the total time is at least 0.2 seconds.

The default timer function is platform dependent.  On Windows, clock() has
microsecond granularity but time()'s granularity is 1/60th of a second; on
Unix, clock() has 1/100th of a second granularity and time() is much more
precise.  On either platform, the default timer functions measures wall
clock time, not the CPU time.  This means that other processes running on
the same computer may interfere with the timing.  The best thing to do when
accurate timing is necessary is to repeat the timing a few times and use the
best time.  The -r option is good for this; the default of 3 repetitions is
probably enough in most cases.  On Unix, you can use clock() to measure CPU
time.

Note: there is a certain baseline overhead associated with executing a pass
statement.  The code here doesn't try to hide it, but you should be aware of
it.  The baseline overhead can be measured by invoking the program without
arguments.

The baseline overhead differs between Python versions!  Also, to fairly
compare older Python versions to Python 2.3, you may want to use python -O
for the older versions to avoid timing SET_LINENO instructions.

\subsection{Examples}

Here are two example sessions (one using the command line, one using the
module interface) that compare the cost of using \function{hasattr()}
vs. try/except to test for missing and present object attributes.

\begin{verbatim}
\% timeit.py 'try:' '  str.__nonzero__' 'except AttributeError:' '  pass'
100000 loops, best of 3: 15.7 usec per loop
\% timeit.py 'if hasattr(str, "__nonzero__"): pass'
100000 loops, best of 3: 4.26 usec per loop
\% timeit.py 'try:' '  int.__nonzero__' 'except AttributeError:' '  pass'
1000000 loops, best of 3: 1.43 usec per loop
\% timeit.py 'if hasattr(int, "__nonzero__"): pass'
100000 loops, best of 3: 2.23 usec per loop
\end{verbatim}

\begin{verbatim}
>>> import timeit
>>> s = """\
... try:
...   str.__nonzero__
... except AttributeError:
...   pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
17.09 usec/pass
>>> s = """\
... if hasattr(str, '__nonzero__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
4.85 usec/pass
>>> s = """\
... try:
...   int.__nonzero__
... except AttributeError:
...   pass 
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
1.97 usec/pass
>>> s = """\
... if hasattr(int, '__nonzero__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
3.15 usec/pass
\end{verbatim}