Tool for measuring execution time of small code snippets.
This module 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.
Library usage: see the Timer class.
Command line usage:
python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [statement]
-n/--number N: how many times to execute 'statement' (default: see below)
-r/--repeat N: how many times to repeat the timer (default 3)
-s/--setup S: statement to be executed once initially (default 'pass')
-t/--time: use time.time() (default on Unix)
-c/--clock: use time.clock() (default on Windows)
-v/--verbose: print raw timing results; repeat for more digits precision
-h/--help: print this usage message and exit
statement: statement to be timed (default 'pass')
A multi-line statement may be given by specifying each line as a
separate argument; indented lines are possible by enclosing an
argument in quotes and using leading spaces. Multiple -s options are
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 difference in default timer function is because 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 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 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
||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
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.
||Methods defined here:|
- __init__(self, stmt='pass', setup='pass', timer=<built-in function time>)
- Constructor. See class doc string.
- print_exc(self, file=None)
- Helper to print a traceback from the timed code.
t = Timer(...) # outside the try/except
t.timeit(...) # or t.repeat(...)
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.
- repeat(self, 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(), defaulting to 3;
the second argument specifies the timer argument, defaulting
to one million.
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.
- timeit(self, number=1000000)
- Time '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.
- default_timer = time(...)
- time() -> floating point number
Return the current time in seconds since the Epoch.
Fractions of a second may be present if the system clock provides them.
- Main program, used when run as a script.
The optional argument specifies the command line to be parsed,
defaulting to sys.argv[1:].
The return value is an exit code to be passed to sys.exit(); it
may be None to indicate success.
When an exception happens during timing, a traceback is printed to
stderr and the return value is 1. Exceptions at other times
(including the template compilation) are not caught.
- reindent(src, indent)
- Helper to reindent a multi-line statement.
||__all__ = ['Timer']|
default_number = 1000000
default_repeat = 3
dummy_src_name = '<timeit-src>'
template = '\ndef inner(_it, _timer):\n %(setup)s\n _t0 =...%(stmt)s\n _t1 = _timer()\n return _t1 - _t0\n'