Application: gvSIG desktop

#! /usr/bin/env python

#

# Class for profiling python code. rev 1.0  6/2/94

#

# Based on prior profile module by Sjoerd Mullender...

#   which was hacked somewhat by: Guido van Rossum

#

# See profile.doc for more information

"""Class for profiling Python code."""

# Copyright 1994, by InfoSeek Corporation, all rights reserved.

# Written by James Roskind

#

# Permission to use, copy, modify, and distribute this Python software

# and its associated documentation for any purpose (subject to the

# restriction in the following sentence) without fee is hereby granted,

# provided that the above copyright notice appears in all copies, and

# that both that copyright notice and this permission notice appear in

# supporting documentation, and that the name of InfoSeek not be used in

# advertising or publicity pertaining to distribution of the software

# without specific, written prior permission.  This permission is

# explicitly restricted to the copying and modification of the software

# to remain in Python, compiled Python, or other languages (such as C)

# wherein the modified or derived code is exclusively imported into a

# Python module.

#

# INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS

# SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND

# FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY

# SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER

# RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF

# CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN

# CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

import sys

import os

import time

import marshal

__all__ = ["run","help","Profile"]

# Sample timer for use with

#i_count = 0

#def integer_timer():

#       global i_count

#       i_count = i_count + 1

#       return i_count

#itimes = integer_timer # replace with C coded timer returning integers

#**************************************************************************

# The following are the static member functions for the profiler class

# Note that an instance of Profile() is *not* needed to call them.

#**************************************************************************

def run(statement, filename=None):

    """Run statement under profiler optionally saving results in filename

    This function takes a single argument that can be passed to the

    "exec" statement, and an optional file name.  In all cases this

    routine attempts to "exec" its first argument and gather profiling

    statistics from the execution. If no file name is present, then this

    function automatically prints a simple profiling report, sorted by the

    standard name string (file/line/function-name) that is presented in

    each line.

    """

    prof = Profile()

    try:

        prof = prof.run(statement)

    except SystemExit:

        pass

    if filename is not None:

        prof.dump_stats(filename)

    else:

        return prof.print_stats()

# print help

def help():

    for dirname in sys.path:

        fullname = os.path.join(dirname, 'profile.doc')

        if os.path.exists(fullname):

            sts = os.system('${PAGER-more} '+fullname)

            if sts: print '*** Pager exit status:', sts

            break

    else:

        print 'Sorry, can\'t find the help file "profile.doc"',

        print 'along the Python search path'

class Profile:

    """Profiler class.

    self.cur is always a tuple.  Each such tuple corresponds to a stack

    frame that is currently active (self.cur[-2]).  The following are the

    definitions of its members.  We use this external "parallel stack" to

    avoid contaminating the program that we are profiling. (old profiler

    used to write into the frames local dictionary!!) Derived classes

    can change the definition of some entries, as long as they leave

    [-2:] intact.

    [ 0] = Time that needs to be charged to the parent frame's function.

           It is used so that a function call will not have to access the

           timing data for the parent frame.

    [ 1] = Total time spent in this frame's function, excluding time in

           subfunctions

    [ 2] = Cumulative time spent in this frame's function, including time in

           all subfunctions to this frame.

    [-3] = Name of the function that corresponds to this frame.

    [-2] = Actual frame that we correspond to (used to sync exception handling)

    [-1] = Our parent 6-tuple (corresponds to frame.f_back)

    Timing data for each function is stored as a 5-tuple in the dictionary

    self.timings[].  The index is always the name stored in self.cur[4].

    The following are the definitions of the members:

    [0] = The number of times this function was called, not counting direct

          or indirect recursion,

    [1] = Number of times this function appears on the stack, minus one

    [2] = Total time spent internal to this function

    [3] = Cumulative time that this function was present on the stack.  In

          non-recursive functions, this is the total execution time from start

          to finish of each invocation of a function, including time spent in

          all subfunctions.

    [5] = A dictionary indicating for each function name, the number of times

          it was called by us.

    """

    def __init__(self, timer=None):

        self.timings = {}

        self.cur = None

        self.cmd = ""

        self.dispatch = {  \

                  'call'     : self.trace_dispatch_call, \

                  'return'   : self.trace_dispatch_return, \

                  'exception': self.trace_dispatch_exception, \

                  }

        if not timer:

            if os.name == 'mac':

                import MacOS

                self.timer = MacOS.GetTicks

                self.dispatcher = self.trace_dispatch_mac

                self.get_time = self.get_time_mac

            elif hasattr(time, 'clock'):

                self.timer = time.clock

                self.dispatcher = self.trace_dispatch_i

            elif hasattr(os, 'times'):

                self.timer = os.times

                self.dispatcher = self.trace_dispatch

            else:

                self.timer = time.time

                self.dispatcher = self.trace_dispatch_i

        else:

            self.timer = timer

            t = self.timer() # test out timer function

            try:

                if len(t) == 2:

                    self.dispatcher = self.trace_dispatch

                else:

                    self.dispatcher = self.trace_dispatch_l

            except TypeError:

                self.dispatcher = self.trace_dispatch_i

        self.t = self.get_time()

        self.simulate_call('profiler')

    def get_time(self): # slow simulation of method to acquire time

        t = self.timer()

        if type(t) == type(()) or type(t) == type([]):

            t = reduce(lambda x,y: x+y, t, 0)

        return t

    def get_time_mac(self):

        return self.timer()/60.0

    # Heavily optimized dispatch routine for os.times() timer

    def trace_dispatch(self, frame, event, arg):

        t = self.timer()

        t = t[0] + t[1] - self.t        # No Calibration constant

        # t = t[0] + t[1] - self.t - .00053 # Calibration constant

        if self.dispatch[event](frame,t):

            t = self.timer()

            self.t = t[0] + t[1]

        else:

            r = self.timer()

            self.t = r[0] + r[1] - t # put back unrecorded delta

        return

    # Dispatch routine for best timer program (return = scalar integer)

    def trace_dispatch_i(self, frame, event, arg):

        t = self.timer() - self.t # - 1 # Integer calibration constant

        if self.dispatch[event](frame,t):

            self.t = self.timer()

        else:

            self.t = self.timer() - t  # put back unrecorded delta

        return

    # Dispatch routine for macintosh (timer returns time in ticks of 1/60th second)

    def trace_dispatch_mac(self, frame, event, arg):

        t = self.timer()/60.0 - self.t # - 1 # Integer calibration constant

        if self.dispatch[event](frame,t):

            self.t = self.timer()/60.0

        else:

            self.t = self.timer()/60.0 - t  # put back unrecorded delta

        return

    # SLOW generic dispatch routine for timer returning lists of numbers

    def trace_dispatch_l(self, frame, event, arg):

        t = self.get_time() - self.t

        if self.dispatch[event](frame,t):

            self.t = self.get_time()

        else:

            self.t = self.get_time()-t # put back unrecorded delta

        return

    def trace_dispatch_exception(self, frame, t):

        rt, rtt, rct, rfn, rframe, rcur = self.cur

        if (not rframe is frame) and rcur:

            return self.trace_dispatch_return(rframe, t)

        return 0

    def trace_dispatch_call(self, frame, t):

        fcode = frame.f_code

        fn = (fcode.co_filename, fcode.co_firstlineno, fcode.co_name)

        self.cur = (t, 0, 0, fn, frame, self.cur)

        if self.timings.has_key(fn):

            cc, ns, tt, ct, callers = self.timings[fn]

            self.timings[fn] = cc, ns + 1, tt, ct, callers

        else:

            self.timings[fn] = 0, 0, 0, 0, {}

        return 1

    def trace_dispatch_return(self, frame, t):

        # if not frame is self.cur[-2]: raise "Bad return", self.cur[3]

        # Prefix "r" means part of the Returning or exiting frame

        # Prefix "p" means part of the Previous or older frame

        rt, rtt, rct, rfn, frame, rcur = self.cur

        rtt = rtt + t

        sft = rtt + rct

        pt, ptt, pct, pfn, pframe, pcur = rcur

        self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur

        cc, ns, tt, ct, callers = self.timings[rfn]

        if not ns:

            ct = ct + sft

            cc = cc + 1

        if callers.has_key(pfn):

            callers[pfn] = callers[pfn] + 1  # hack: gather more

            # stats such as the amount of time added to ct courtesy

            # of this specific call, and the contribution to cc

            # courtesy of this call.

        else:

            callers[pfn] = 1

        self.timings[rfn] = cc, ns - 1, tt+rtt, ct, callers

        return 1

    # The next few function play with self.cmd. By carefully preloading

    # our parallel stack, we can force the profiled result to include

    # an arbitrary string as the name of the calling function.

    # We use self.cmd as that string, and the resulting stats look

    # very nice :-).

    def set_cmd(self, cmd):

        if self.cur[-1]: return   # already set

        self.cmd = cmd

        self.simulate_call(cmd)

    class fake_code:

        def __init__(self, filename, line, name):

            self.co_filename = filename

            self.co_line = line

            self.co_name = name

            self.co_firstlineno = 0

        def __repr__(self):

            return repr((self.co_filename, self.co_line, self.co_name))

    class fake_frame:

        def __init__(self, code, prior):

            self.f_code = code

            self.f_back = prior

    def simulate_call(self, name):

        code = self.fake_code('profile', 0, name)

        if self.cur:

            pframe = self.cur[-2]

        else:

            pframe = None

        frame = self.fake_frame(code, pframe)

        a = self.dispatch['call'](frame, 0)

        return

    # collect stats from pending stack, including getting final

    # timings for self.cmd frame.

    def simulate_cmd_complete(self):

        t = self.get_time() - self.t

        while self.cur[-1]:

            # We *can* cause assertion errors here if

            # dispatch_trace_return checks for a frame match!

            a = self.dispatch['return'](self.cur[-2], t)

            t = 0

        self.t = self.get_time() - t

    def print_stats(self):

        import pstats

        pstats.Stats(self).strip_dirs().sort_stats(-1). \

                  print_stats()

    def dump_stats(self, file):

        f = open(file, 'wb')

        self.create_stats()

        marshal.dump(self.stats, f)

        f.close()

    def create_stats(self):

        self.simulate_cmd_complete()

        self.snapshot_stats()

    def snapshot_stats(self):

        self.stats = {}

        for func in self.timings.keys():

            cc, ns, tt, ct, callers = self.timings[func]

            callers = callers.copy()

            nc = 0

            for func_caller in callers.keys():

                nc = nc + callers[func_caller]

            self.stats[func] = cc, nc, tt, ct, callers

    # The following two methods can be called by clients to use

    # a profiler to profile a statement, given as a string.

    def run(self, cmd):

        import __main__

        dict = __main__.__dict__

        return self.runctx(cmd, dict, dict)

    def runctx(self, cmd, globals, locals):

        self.set_cmd(cmd)

        sys.setprofile(self.dispatcher)

        try:

            exec cmd in globals, locals

        finally:

            sys.setprofile(None)

        return self

    # This method is more useful to profile a single function call.

    def runcall(self, func, *args):

        self.set_cmd(`func`)

        sys.setprofile(self.dispatcher)

        try:

            return apply(func, args)

        finally:

            sys.setprofile(None)

    #******************************************************************

    # The following calculates the overhead for using a profiler.  The

    # problem is that it takes a fair amount of time for the profiler

    # to stop the stopwatch (from the time it receives an event).

    # Similarly, there is a delay from the time that the profiler

    # re-starts the stopwatch before the user's code really gets to

    # continue.  The following code tries to measure the difference on

    # a per-event basis. The result can the be placed in the

    # Profile.dispatch_event() routine for the given platform.  Note

    # that this difference is only significant if there are a lot of

    # events, and relatively little user code per event.  For example,

    # code with small functions will typically benefit from having the

    # profiler calibrated for the current platform.  This *could* be

    # done on the fly during init() time, but it is not worth the

    # effort.  Also note that if too large a value specified, then

    # execution time on some functions will actually appear as a

    # negative number.  It is *normal* for some functions (with very

    # low call counts) to have such negative stats, even if the

    # calibration figure is "correct."

    #

    # One alternative to profile-time calibration adjustments (i.e.,

    # adding in the magic little delta during each event) is to track

    # more carefully the number of events (and cumulatively, the number

    # of events during sub functions) that are seen.  If this were

    # done, then the arithmetic could be done after the fact (i.e., at

    # display time).  Currently, we track only call/return events.

    # These values can be deduced by examining the callees and callers

    # vectors for each functions.  Hence we *can* almost correct the

    # internal time figure at print time (note that we currently don't

    # track exception event processing counts).  Unfortunately, there

    # is currently no similar information for cumulative sub-function

    # time.  It would not be hard to "get all this info" at profiler

    # time.  Specifically, we would have to extend the tuples to keep

    # counts of this in each frame, and then extend the defs of timing

    # tuples to include the significant two figures. I'm a bit fearful

    # that this additional feature will slow the heavily optimized

    # event/time ratio (i.e., the profiler would run slower, fur a very

    # low "value added" feature.)

    #

    # Plugging in the calibration constant doesn't slow down the

    # profiler very much, and the accuracy goes way up.

    #**************************************************************

    def calibrate(self, m):

        # Modified by Tim Peters

        n = m

        s = self.get_time()

        while n:

            self.simple()

            n = n - 1

        f = self.get_time()

        my_simple = f - s

        #print "Simple =", my_simple,

        n = m

        s = self.get_time()

        while n:

            self.instrumented()

            n = n - 1

        f = self.get_time()

        my_inst = f - s

        # print "Instrumented =", my_inst

        avg_cost = (my_inst - my_simple)/m

        #print "Delta/call =", avg_cost, "(profiler fixup constant)"

        return avg_cost

    # simulate a program with no profiler activity

    def simple(self):

        a = 1

        pass

    # simulate a program with call/return event processing

    def instrumented(self):

        a = 1

        self.profiler_simulation(a, a, a)

    # simulate an event processing activity (from user's perspective)

    def profiler_simulation(self, x, y, z):

        t = self.timer()

        ## t = t[0] + t[1]

        self.ut = t

class OldProfile(Profile):

    """A derived profiler that simulates the old style profile, providing

    errant results on recursive functions. The reason for the usefulness of

    this profiler is that it runs faster (i.e., less overhead).  It still

    creates all the caller stats, and is quite useful when there is *no*

    recursion in the user's code.

    This code also shows how easy it is to create a modified profiler.

    """

    def trace_dispatch_exception(self, frame, t):

        rt, rtt, rct, rfn, rframe, rcur = self.cur

        if rcur and not rframe is frame:

            return self.trace_dispatch_return(rframe, t)

        return 0

    def trace_dispatch_call(self, frame, t):

        fn = `frame.f_code`

        self.cur = (t, 0, 0, fn, frame, self.cur)

        if self.timings.has_key(fn):

            tt, ct, callers = self.timings[fn]

            self.timings[fn] = tt, ct, callers

        else:

            self.timings[fn] = 0, 0, {}

        return 1

    def trace_dispatch_return(self, frame, t):

        rt, rtt, rct, rfn, frame, rcur = self.cur

        rtt = rtt + t

        sft = rtt + rct

        pt, ptt, pct, pfn, pframe, pcur = rcur

        self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur

        tt, ct, callers = self.timings[rfn]

        if callers.has_key(pfn):

            callers[pfn] = callers[pfn] + 1

        else:

            callers[pfn] = 1

        self.timings[rfn] = tt+rtt, ct + sft, callers

        return 1

    def snapshot_stats(self):

        self.stats = {}

        for func in self.timings.keys():

            tt, ct, callers = self.timings[func]

            callers = callers.copy()

            nc = 0

            for func_caller in callers.keys():

                nc = nc + callers[func_caller]

            self.stats[func] = nc, nc, tt, ct, callers

class HotProfile(Profile):

    """The fastest derived profile example.  It does not calculate

    caller-callee relationships, and does not calculate cumulative

    time under a function.  It only calculates time spent in a

    function, so it runs very quickly due to its very low overhead.

    """

    def trace_dispatch_exception(self, frame, t):

        rt, rtt, rfn, rframe, rcur = self.cur

        if rcur and not rframe is frame:

            return self.trace_dispatch_return(rframe, t)

        return 0

    def trace_dispatch_call(self, frame, t):

        self.cur = (t, 0, frame, self.cur)

        return 1

    def trace_dispatch_return(self, frame, t):

        rt, rtt, frame, rcur = self.cur

        rfn = `frame.f_code`

        pt, ptt, pframe, pcur = rcur

        self.cur = pt, ptt+rt, pframe, pcur

        if self.timings.has_key(rfn):

            nc, tt = self.timings[rfn]

            self.timings[rfn] = nc + 1, rt + rtt + tt

        else:

            self.timings[rfn] =      1, rt + rtt

        return 1

    def snapshot_stats(self):

        self.stats = {}

        for func in self.timings.keys():

            nc, tt = self.timings[func]

            self.stats[func] = nc, nc, tt, 0, {}

#****************************************************************************

def Stats(*args):

    print 'Report generating functions are in the "pstats" module\a'

# When invoked as main program, invoke the profiler on a script

if __name__ == '__main__':

    import sys

    import os

    if not sys.argv[1:]:

        print "usage: profile.py scriptfile [arg] ..."

        sys.exit(2)

    filename = sys.argv[1]  # Get script filename

    del sys.argv[0]         # Hide "profile.py" from argument list

    # Insert script directory in front of module search path

    sys.path.insert(0, os.path.dirname(filename))

    run('execfile(' + `filename` + ')')