\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}

\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}

\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}

\end{verbatim}

\begin{verbatim}

\end{verbatim}