Filename: ///lib64/python2.7/timeit.py

#! /usr/bin/env python """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] Options: -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 --: separate options from statement, use when statement starts with - 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 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 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 instructions. """ import gc import sys import time try: import itertools except ImportError: # Must be an older Python version (see timeit() below) itertools = None __all__ = ["Timer"] dummy_src_name = "<timeit-src>" default_number = 1000000 default_repeat = 3 if sys.platform == "win32": # On Windows, the best timer is time.clock() default_timer = time.clock else: # On most other platforms the best timer is time.time() default_timer = time.time # Don't change the indentation of the template; the reindent() calls # in Timer.__init__() depend on setup being indented 4 spaces and stmt # being indented 8 spaces. template = """ def inner(_it, _timer): %(setup)s _t0 = _timer() for _i in _it: %(stmt)s _t1 = _timer() return _t1 - _t0 """ def reindent(src, indent): """Helper to reindent a multi-line statement.""" return src.replace("\n", "\n" + " "*indent) def _template_func(setup, func): """Create a timer function. Used if the "statement" is a callable.""" def inner(_it, _timer, _func=func): setup() _t0 = _timer() for _i in _it: _func() _t1 = _timer() return _t1 - _t0 return inner class Timer: """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. """ def __init__(self, stmt="pass", setup="pass", timer=default_timer): """Constructor. See class doc string.""" self.timer = timer ns = {} if isinstance(stmt, basestring): stmt = reindent(stmt, 8) if isinstance(setup, basestring): setup = reindent(setup, 4) src = template % {'stmt': stmt, 'setup': setup} elif hasattr(setup, '__call__'): src = template % {'stmt': stmt, 'setup': '_setup()'} ns['_setup'] = setup else: raise ValueError("setup is neither a string nor callable") self.src = src # Save for traceback display code = compile(src, dummy_src_name, "exec") exec code in globals(), ns self.inner = ns["inner"] elif hasattr(stmt, '__call__'): self.src = None if isinstance(setup, basestring): _setup = setup def setup(): exec _setup in globals(), ns elif not hasattr(setup, '__call__'): raise ValueError("setup is neither a string nor callable") self.inner = _template_func(setup, stmt) else: raise ValueError("stmt is neither a string nor callable") def print_exc(self, 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. """ import linecache, traceback if self.src is not None: linecache.cache[dummy_src_name] = (len(self.src), None, self.src.split("\n"), dummy_src_name) # else the source is already stored somewhere else traceback.print_exc(file=file) def timeit(self, number=default_number): """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. """ if itertools: it = itertools.repeat(None, number) else: it = [None] * number gcold = gc.isenabled() gc.disable() try: timing = self.inner(it, self.timer) finally: if gcold: gc.enable() return timing def repeat(self, repeat=default_repeat, number=default_number): """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. """ r = [] for i in range(repeat): t = self.timeit(number) r.append(t) return r def timeit(stmt="pass", setup="pass", timer=default_timer, number=default_number): """Convenience function to create Timer object and call timeit method.""" return Timer(stmt, setup, timer).timeit(number) def repeat(stmt="pass", setup="pass", timer=default_timer, repeat=default_repeat, number=default_number): """Convenience function to create Timer object and call repeat method.""" return Timer(stmt, setup, timer).repeat(repeat, number) def main(args=None): """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. """ if args is None: args = sys.argv[1:] import getopt try: opts, args = getopt.getopt(args, "n:s:r:tcvh", ["number=", "setup=", "repeat=", "time", "clock", "verbose", "help"]) except getopt.error, err: print err print "use -h/--help for command line help" return 2 timer = default_timer stmt = "\n".join(args) or "pass" number = 0 # auto-determine setup = [] repeat = default_repeat verbose = 0 precision = 3 for o, a in opts: if o in ("-n", "--number"): number = int(a) if o in ("-s", "--setup"): setup.append(a) if o in ("-r", "--repeat"): repeat = int(a) if repeat <= 0: repeat = 1 if o in ("-t", "--time"): timer = time.time if o in ("-c", "--clock"): timer = time.clock if o in ("-v", "--verbose"): if verbose: precision += 1 verbose += 1 if o in ("-h", "--help"): print __doc__, return 0 setup = "\n".join(setup) or "pass" # Include the current directory, so that local imports work (sys.path # contains the directory of this script, rather than the current # directory) import os sys.path.insert(0, os.curdir) t = Timer(stmt, setup, timer) if number == 0: # determine number so that 0.2 <= total time < 2.0 for i in range(1, 10): number = 10**i try: x = t.timeit(number) except: t.print_exc() return 1 if verbose: print "%d loops -> %.*g secs" % (number, precision, x) if x >= 0.2: break try: r = t.repeat(repeat, number) except: t.print_exc() return 1 best = min(r) if verbose: print "raw times:", " ".join(["%.*g" % (precision, x) for x in r]) print "%d loops," % number, usec = best * 1e6 / number if usec < 1000: print "best of %d: %.*g usec per loop" % (repeat, precision, usec) else: msec = usec / 1000 if msec < 1000: print "best of %d: %.*g msec per loop" % (repeat, precision, msec) else: sec = msec / 1000 print "best of %d: %.*g sec per loop" % (repeat, precision, sec) return None if __name__ == "__main__": sys.exit(main())

./Ninja\.