
``timeit`` --- Measure execution time of small code snippets
************************************************************

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 defines the following public class:

class timeit.Timer([stmt='pass'[, setup='pass'[, 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).  The statements may contain newlines,
   as long as they don't contain multi-line string literals.

   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 *stmt* and *setup* parameters can also take objects that are
   callable without arguments. This will embed calls to them in a
   timer function that will then be executed by ``timeit()``.  Note
   that the timing overhead is a little larger in this case because of
   the extra function calls.

Timer.print_exc([file=None])

   Helper to print a traceback from the timed code.

   Typical use:

      t = Timer(...)       # outside the try/except
      try:
          t.timeit(...)    # or t.repeat(...)
      except:
          t.print_exc()

   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
   ``sys.stderr``.

Timer.repeat([repeat=3[, number=1000000]])

   Call ``timeit()`` a few times.

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

Timer.timeit([number=1000000])

   Time *number* executions of the main statement. This executes the
   setup statement once, and then returns the time it takes to execute
   the main statement a number of times, measured in seconds as a
   float.  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.

   Note: By default, ``timeit()`` temporarily turns off *garbage
     collection* during the timing.  The advantage of this approach is
     that it makes independent timings more comparable.  This
     disadvantage is that GC may be an important component of the
     performance of the function being measured. If so, GC can be re-
     enabled as the first statement in the *setup* string. For
     example:

        timeit.Timer('for i in range(10): oct(i)', 'gc.enable()').timeit()

The module also defines two convenience functions:

timeit.repeat(stmt[, setup[, timer[, repeat=3[, number=1000000]]]])

   Create a ``Timer`` instance with the given statement, setup code
   and timer function and run its ``repeat()`` method with the given
   repeat count and *number* executions.

timeit.timeit(stmt[, setup[, timer[, number=1000000]]])

   Create a ``Timer`` instance with the given statement, setup code
   and timer function and run its ``timeit()`` method with *number*
   executions.


Command Line Interface
======================

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

   python -m timeit [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement ...]

where the following options are understood:

-n N/*--number=N*
   how many times to execute 'statement'

-r N/*--repeat=N*
   how many times to repeat the timer (default 3)

-s S/*--setup=S*
   statement to be executed once initially (default ``'pass'``)

-t/*--time*
   use ``time.time()`` (default on all platforms but Windows)

-c/*--clock*
   use ``time.clock()`` (default on Windows)

-v/*--verbose*
   print raw timing results; repeat for more digits precision

-h/*--help*
   print a short usage message and exit

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,
``time.clock()`` has microsecond granularity but ``time.time()``'s
granularity is 1/60th of a second; on Unix, ``time.clock()`` has
1/100th of a second granularity and ``time.time()`` is much more
precise.  On either platform, the default timer functions measure 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 ``time.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's *-O* option for the older versions to avoid timing
``SET_LINENO`` instructions.


Examples
========

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

   % timeit.py 'try:' '  str.__bool__' 'except AttributeError:' '  pass'
   100000 loops, best of 3: 15.7 usec per loop
   % timeit.py 'if hasattr(str, "__bool__"): pass'
   100000 loops, best of 3: 4.26 usec per loop
   % timeit.py 'try:' '  int.__bool__' 'except AttributeError:' '  pass'
   1000000 loops, best of 3: 1.43 usec per loop
   % timeit.py 'if hasattr(int, "__bool__"): pass'
   100000 loops, best of 3: 2.23 usec per loop

   >>> import timeit
   >>> s = """\
   ... try:
   ...     str.__bool__
   ... 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, '__bool__'): pass
   ... """
   >>> t = timeit.Timer(stmt=s)
   >>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
   4.85 usec/pass
   >>> s = """\
   ... try:
   ...     int.__bool__
   ... 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, '__bool__'): pass
   ... """
   >>> t = timeit.Timer(stmt=s)
   >>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
   3.15 usec/pass

To give the ``timeit`` module access to functions you define, you can
pass a ``setup`` parameter which contains an import statement:

   def test():
       "Stupid test function"
       L = [i for i in range(100)]

   if __name__=='__main__':
       from timeit import Timer
       t = Timer("test()", "from __main__ import test")
       print(t.timeit())
