Whitespace normalization.

1 files changed, 485 insertions, 485 deletions

diff --git a/Lib/profile.py b/Lib/profile.py
index feaf287..c32b3f8 100755
--- a/Lib/profile.py
+++ b/Lib/profile.py
@@ -11,7 +11,7 @@
 
 # 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,
@@ -24,7 +24,7 @@
 # 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
@@ -41,12 +41,12 @@ import time
 import marshal
 
 
-# Sample timer for use with 
+# Sample timer for use with
 #i_count = 0
 #def integer_timer():
-#	global i_count
-#	i_count = i_count + 1
-#	return i_count
+#       global i_count
+#       i_count = i_count + 1
+#       return i_count
 #itimes = integer_timer # replace with C coded timer returning integers
 
 #**************************************************************************
@@ -57,515 +57,515 @@ import marshal
 
 # simplified user interface
 def run(statement, *args):
-	prof = Profile()
-	try:
-		prof = prof.run(statement)
-	except SystemExit:
-		pass
-	if args:
-		prof.dump_stats(args[0])
-	else:
-		return prof.print_stats()
+    prof = Profile()
+    try:
+        prof = prof.run(statement)
+    except SystemExit:
+        pass
+    if args:
+        prof.dump_stats(args[0])
+    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('${PAGER-more} '+fullname)
-			if sts: print '*** Pager exit status:', sts
-			break
-	else:
-		print 'Sorry, can\'t find the help file "profile.doc"',
-		print 'along the Python search path'
+    for dirname in sys.path:
+        fullname = os.path.join(dirname, 'profile.doc')
+        if os.path.exists(fullname):
+            sts = os.system('${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
+    """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
-
-		
+    """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.
-	"""
+    """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_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_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
 
-	def trace_dispatch_return(self, frame, t):
-		rt, rtt, frame, rcur = self.cur
+        rfn = `frame.f_code`
 
-		rfn = `frame.f_code`
+        pt, ptt, pframe, pcur = rcur
+        self.cur = pt, ptt+rt, pframe, pcur
 
-		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
 
-		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
 
-		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 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'
+    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)
+    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
+    filename = sys.argv[1]  # Get script filename
 
-	del sys.argv[0]		# Hide "profile.py" from argument list
+    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))
+    # Insert script directory in front of module search path
+    sys.path.insert(0, os.path.dirname(filename))
 
-	run('execfile(' + `filename` + ')')
+    run('execfile(' + `filename` + ')')