Mercurial > cpython-lru_cache-weakref

annotate Lib/profile.py @ 4566:6403c94de50d legacy-trunk

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Added safeguard against failure in __del__.
author Guido van Rossum <guido@python.org>
date Wed, 27 Nov 1996 19:39:58 +0000
parents a5c29accc438
children 73758db2f464
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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1 #! /usr/local/bin/python
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2 #
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3 # Class for profiling python code. rev 1.0 6/2/94
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4 #
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5 # Based on prior profile module by Sjoerd Mullender...
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6 # which was hacked somewhat by: Guido van Rossum
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7 #
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8 # See profile.doc for more information
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9
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10
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11 # Copyright 1994, by InfoSeek Corporation, all rights reserved.
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12 # Written by James Roskind
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13 #
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14 # Permission to use, copy, modify, and distribute this Python software
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15 # and its associated documentation for any purpose (subject to the
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16 # restriction in the following sentence) without fee is hereby granted,
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17 # provided that the above copyright notice appears in all copies, and
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18 # that both that copyright notice and this permission notice appear in
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19 # supporting documentation, and that the name of InfoSeek not be used in
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20 # advertising or publicity pertaining to distribution of the software
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21 # without specific, written prior permission. This permission is
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22 # explicitly restricted to the copying and modification of the software
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23 # to remain in Python, compiled Python, or other languages (such as C)
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24 # wherein the modified or derived code is exclusively imported into a
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25 # Python module.
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26 #
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27 # INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
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28 # SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
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29 # FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY
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30 # SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER
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31 # RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF
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32 # CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
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33 # CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
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34
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35
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36
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37 import sys
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38 import os
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39 import time
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40 import string
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41 import marshal
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42
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43
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44 # Global variables
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45 func_norm_dict = {}
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46 func_norm_counter = 0
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47 if hasattr(os, 'getpid'):
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48 pid_string = `os.getpid()`
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49 else:
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50 pid_string = ''
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51
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52
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53 # Sample timer for use with
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54 #i_count = 0
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55 #def integer_timer():
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56 # global i_count
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57 # i_count = i_count + 1
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58 # return i_count
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59 #itimes = integer_timer # replace with C coded timer returning integers
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60
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61 #**************************************************************************
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62 # The following are the static member functions for the profiler class
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63 # Note that an instance of Profile() is *not* needed to call them.
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64 #**************************************************************************
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65
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66
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67 # simplified user interface
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68 def run(statement, *args):
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69 prof = Profile()
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70 try:
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71 prof = prof.run(statement)
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72 except SystemExit:
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73 pass
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74 if args:
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75 prof.dump_stats(args[0])
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76 else:
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77 return prof.print_stats()
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78
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79 # print help
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80 def help():
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81 for dirname in sys.path:
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82 fullname = os.path.join(dirname, 'profile.doc')
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83 if os.path.exists(fullname):
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84 sts = os.system('${PAGER-more} '+fullname)
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85 if sts: print '*** Pager exit status:', sts
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86 break
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87 else:
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88 print 'Sorry, can\'t find the help file "profile.doc"',
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89 print 'along the Python search path'
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90
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91
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92 #**************************************************************************
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93 # class Profile documentation:
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94 #**************************************************************************
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95 # self.cur is always a tuple. Each such tuple corresponds to a stack
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96 # frame that is currently active (self.cur[-2]). The following are the
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97 # definitions of its members. We use this external "parallel stack" to
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98 # avoid contaminating the program that we are profiling. (old profiler
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99 # used to write into the frames local dictionary!!) Derived classes
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100 # can change the definition of some entries, as long as they leave
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101 # [-2:] intact.
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102 #
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103 # [ 0] = Time that needs to be charged to the parent frame's function. It is
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104 # used so that a function call will not have to access the timing data
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105 # for the parents frame.
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106 # [ 1] = Total time spent in this frame's function, excluding time in
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107 # subfunctions
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108 # [ 2] = Cumulative time spent in this frame's function, including time in
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109 # all subfunctions to this frame.
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110 # [-3] = Name of the function that corresonds to this frame.
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111 # [-2] = Actual frame that we correspond to (used to sync exception handling)
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112 # [-1] = Our parent 6-tuple (corresonds to frame.f_back)
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113 #**************************************************************************
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114 # Timing data for each function is stored as a 5-tuple in the dictionary
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115 # self.timings[]. The index is always the name stored in self.cur[4].
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116 # The following are the definitions of the members:
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117 #
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118 # [0] = The number of times this function was called, not counting direct
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119 # or indirect recursion,
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120 # [1] = Number of times this function appears on the stack, minus one
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121 # [2] = Total time spent internal to this function
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122 # [3] = Cumulative time that this function was present on the stack. In
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123 # non-recursive functions, this is the total execution time from start
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124 # to finish of each invocation of a function, including time spent in
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125 # all subfunctions.
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126 # [5] = A dictionary indicating for each function name, the number of times
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127 # it was called by us.
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128 #**************************************************************************
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129 # We produce function names via a repr() call on the f_code object during
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130 # profiling. This save a *lot* of CPU time. This results in a string that
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131 # always looks like:
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132 # <code object main at 87090, file "/a/lib/python-local/myfib.py", line 76>
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133 # After we "normalize it, it is a tuple of filename, line, function-name.
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134 # We wait till we are done profiling to do the normalization.
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135 # *IF* this repr format changes, then only the normalization routine should
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136 # need to be fixed.
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137 #**************************************************************************
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138 class Profile:
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139
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140 def __init__(self, timer=None):
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141 self.timings = {}
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142 self.cur = None
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143 self.cmd = ""
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144
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145 self.dispatch = { \
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146 'call' : self.trace_dispatch_call, \
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147 'return' : self.trace_dispatch_return, \
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148 'exception': self.trace_dispatch_exception, \
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149 }
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150
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151 if not timer:
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152 if hasattr(os, 'times'):
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153 self.timer = os.times
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154 self.dispatcher = self.trace_dispatch
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155 else:
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156 self.timer = time.time
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157 self.dispatcher = self.trace_dispatch_i
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158 else:
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159 self.timer = timer
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160 t = self.timer() # test out timer function
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161 try:
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162 if len(t) == 2:
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163 self.dispatcher = self.trace_dispatch
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164 else:
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165 self.dispatcher = self.trace_dispatch_l
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166 except TypeError:
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167 self.dispatcher = self.trace_dispatch_i
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168 self.t = self.get_time()
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169 self.simulate_call('profiler')
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170
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171
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172 def get_time(self): # slow simulation of method to acquire time
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173 t = self.timer()
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174 if type(t) == type(()) or type(t) == type([]):
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175 t = reduce(lambda x,y: x+y, t, 0)
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176 return t
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177
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178
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179 # Heavily optimized dispatch routine for os.times() timer
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180
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181 def trace_dispatch(self, frame, event, arg):
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182 t = self.timer()
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183 t = t[0] + t[1] - self.t # No Calibration constant
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184 # t = t[0] + t[1] - self.t - .00053 # Calibration constant
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185
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186 if self.dispatch[event](frame,t):
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187 t = self.timer()
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188 self.t = t[0] + t[1]
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189 else:
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190 r = self.timer()
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191 self.t = r[0] + r[1] - t # put back unrecorded delta
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192 return
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193
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194
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195
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196 # Dispatch routine for best timer program (return = scalar integer)
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197
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198 def trace_dispatch_i(self, frame, event, arg):
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199 t = self.timer() - self.t # - 1 # Integer calibration constant
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200 if self.dispatch[event](frame,t):
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201 self.t = self.timer()
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202 else:
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203 self.t = self.timer() - t # put back unrecorded delta
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204 return
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205
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206
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207 # SLOW generic dispatch rountine for timer returning lists of numbers
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208
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209 def trace_dispatch_l(self, frame, event, arg):
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210 t = self.get_time() - self.t
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211
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212 if self.dispatch[event](frame,t):
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213 self.t = self.get_time()
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214 else:
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215 self.t = self.get_time()-t # put back unrecorded delta
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216 return
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217
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218
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219 def trace_dispatch_exception(self, frame, t):
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220 rt, rtt, rct, rfn, rframe, rcur = self.cur
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221 if (not rframe is frame) and rcur:
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222 return self.trace_dispatch_return(rframe, t)
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223 return 0
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224
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225
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226 def trace_dispatch_call(self, frame, t):
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227 fn = `frame.f_code`
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228
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229 # The following should be about the best approach, but
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230 # we would need a function that maps from id() back to
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231 # the actual code object.
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232 # fn = id(frame.f_code)
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233 # Note we would really use our own function, which would
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234 # return the code address, *and* bump the ref count. We
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235 # would then fix up the normalize function to do the
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236 # actualy repr(fn) call.
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237
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238 # The following is an interesting alternative
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239 # It doesn't do as good a job, and it doesn't run as
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240 # fast 'cause repr() is written in C, and this is Python.
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241 #fcode = frame.f_code
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242 #code = fcode.co_code
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243 #if ord(code[0]) == 127: # == SET_LINENO
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244 # # see "opcode.h" in the Python source
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245 # fn = (fcode.co_filename, ord(code[1]) | \
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246 # ord(code[2]) << 8, fcode.co_name)
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247 #else:
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248 # fn = (fcode.co_filename, 0, fcode.co_name)
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249
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250 self.cur = (t, 0, 0, fn, frame, self.cur)
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251 if self.timings.has_key(fn):
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252 cc, ns, tt, ct, callers = self.timings[fn]
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253 self.timings[fn] = cc, ns + 1, tt, ct, callers
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254 else:
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255 self.timings[fn] = 0, 0, 0, 0, {}
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256 return 1
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257
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258 def trace_dispatch_return(self, frame, t):
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259 # if not frame is self.cur[-2]: raise "Bad return", self.cur[3]
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260
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261 # Prefix "r" means part of the Returning or exiting frame
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262 # Prefix "p" means part of the Previous or older frame
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263
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264 rt, rtt, rct, rfn, frame, rcur = self.cur
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265 rtt = rtt + t
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266 sft = rtt + rct
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267
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268 pt, ptt, pct, pfn, pframe, pcur = rcur
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269 self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur
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270
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271 cc, ns, tt, ct, callers = self.timings[rfn]
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272 if not ns:
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273 ct = ct + sft
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274 cc = cc + 1
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275 if callers.has_key(pfn):
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276 callers[pfn] = callers[pfn] + 1 # hack: gather more
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277 # stats such as the amount of time added to ct courtesy
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278 # of this specific call, and the contribution to cc
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279 # courtesy of this call.
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280 else:
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281 callers[pfn] = 1
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282 self.timings[rfn] = cc, ns - 1, tt+rtt, ct, callers
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283
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284 return 1
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285
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286 # The next few function play with self.cmd. By carefully preloading
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287 # our paralell stack, we can force the profiled result to include
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288 # an arbitrary string as the name of the calling function.
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289 # We use self.cmd as that string, and the resulting stats look
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290 # very nice :-).
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291
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292 def set_cmd(self, cmd):
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293 if self.cur[-1]: return # already set
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294 self.cmd = cmd
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295 self.simulate_call(cmd)
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296
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297 class fake_code:
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298 def __init__(self, filename, line, name):
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299 self.co_filename = filename
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300 self.co_line = line
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301 self.co_name = name
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302 self.co_code = '\0' # anything but 127
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303
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304 def __repr__(self):
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305 return (self.co_filename, self.co_line, self.co_name)
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306
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307 class fake_frame:
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308 def __init__(self, code, prior):
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309 self.f_code = code
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310 self.f_back = prior
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311
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312 def simulate_call(self, name):
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313 code = self.fake_code('profile', 0, name)
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314 if self.cur:
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315 pframe = self.cur[-2]
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316 else:
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317 pframe = None
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318 frame = self.fake_frame(code, pframe)
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319 a = self.dispatch['call'](frame, 0)
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320 return
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321
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322 # collect stats from pending stack, including getting final
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323 # timings for self.cmd frame.
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324
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325 def simulate_cmd_complete(self):
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326 t = self.get_time() - self.t
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327 while self.cur[-1]:
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328 # We *can* cause assertion errors here if
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329 # dispatch_trace_return checks for a frame match!
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330 a = self.dispatch['return'](self.cur[-2], t)
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331 t = 0
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332 self.t = self.get_time() - t
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333
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334
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335 def print_stats(self):
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336 import pstats
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337 pstats.Stats(self).strip_dirs().sort_stats(-1). \
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338 print_stats()
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339
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340 def dump_stats(self, file):
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341 f = open(file, 'w')
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342 self.create_stats()
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343 marshal.dump(self.stats, f)
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344 f.close()
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345
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346 def create_stats(self):
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347 self.simulate_cmd_complete()
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348 self.snapshot_stats()
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349
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350 def snapshot_stats(self):
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351 self.stats = {}
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352 for func in self.timings.keys():
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353 cc, ns, tt, ct, callers = self.timings[func]
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354 nor_func = self.func_normalize(func)
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355 nor_callers = {}
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356 nc = 0
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357 for func_caller in callers.keys():
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358 nor_callers[self.func_normalize(func_caller)]=\
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359 callers[func_caller]
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360 nc = nc + callers[func_caller]
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361 self.stats[nor_func] = cc, nc, tt, ct, nor_callers
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362
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363
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364 # Override the following function if you can figure out
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365 # a better name for the binary f_code entries. I just normalize
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366 # them sequentially in a dictionary. It would be nice if we could
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367 # *really* see the name of the underlying C code :-). Sometimes
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368 # you can figure out what-is-what by looking at caller and callee
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369 # lists (and knowing what your python code does).
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370
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371 def func_normalize(self, func_name):
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372 global func_norm_dict
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373 global func_norm_counter
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374 global func_sequence_num
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375
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376 if func_norm_dict.has_key(func_name):
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377 return func_norm_dict[func_name]
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378 if type(func_name) == type(""):
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379 long_name = string.split(func_name)
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380 file_name = long_name[-3][1:-2]
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381 func = long_name[2]
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382 lineno = long_name[-1][:-1]
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383 if '?' == func: # Until I find out how to may 'em...
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384 file_name = 'python'
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385 func_norm_counter = func_norm_counter + 1
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386 func = pid_string + ".C." + `func_norm_counter`
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387 result = file_name , string.atoi(lineno) , func
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388 else:
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389 result = func_name
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390 func_norm_dict[func_name] = result
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391 return result
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392
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393
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394 # The following two methods can be called by clients to use
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395 # a profiler to profile a statement, given as a string.
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396
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397 def run(self, cmd):
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398 import __main__
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399 dict = __main__.__dict__
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400 return self.runctx(cmd, dict, dict)
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401
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402 def runctx(self, cmd, globals, locals):
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403 self.set_cmd(cmd)
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404 sys.setprofile(self.dispatcher)
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405 try:
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406 exec cmd in globals, locals
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407 finally:
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408 sys.setprofile(None)
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409 return self
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410
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411 # This method is more useful to profile a single function call.
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412 def runcall(self, func, *args):
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413 self.set_cmd(`func`)
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414 sys.setprofile(self.dispatcher)
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415 try:
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416 return apply(func, args)
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417 finally:
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418 sys.setprofile(None)
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419
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420
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421 #******************************************************************
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422 # The following calculates the overhead for using a profiler. The
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423 # problem is that it takes a fair amount of time for the profiler
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424 # to stop the stopwatch (from the time it recieves an event).
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425 # Similarly, there is a delay from the time that the profiler
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426 # re-starts the stopwatch before the user's code really gets to
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427 # continue. The following code tries to measure the difference on
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428 # a per-event basis. The result can the be placed in the
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429 # Profile.dispatch_event() routine for the given platform. Note
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430 # that this difference is only significant if there are a lot of
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431 # events, and relatively little user code per event. For example,
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432 # code with small functions will typically benefit from having the
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433 # profiler calibrated for the current platform. This *could* be
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434 # done on the fly during init() time, but it is not worth the
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435 # effort. Also note that if too large a value specified, then
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436 # execution time on some functions will actually appear as a
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437 # negative number. It is *normal* for some functions (with very
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438 # low call counts) to have such negative stats, even if the
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439 # calibration figure is "correct."
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440 #
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441 # One alternative to profile-time calibration adjustments (i.e.,
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442 # adding in the magic little delta during each event) is to track
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443 # more carefully the number of events (and cumulatively, the number
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444 # of events during sub functions) that are seen. If this were
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445 # done, then the arithmetic could be done after the fact (i.e., at
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446 # display time). Currintly, we track only call/return events.
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447 # These values can be deduced by examining the callees and callers
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448 # vectors for each functions. Hence we *can* almost correct the
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449 # internal time figure at print time (note that we currently don't
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450 # track exception event processing counts). Unfortunately, there
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451 # is currently no similar information for cumulative sub-function
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452 # time. It would not be hard to "get all this info" at profiler
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453 # time. Specifically, we would have to extend the tuples to keep
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454 # counts of this in each frame, and then extend the defs of timing
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455 # tuples to include the significant two figures. I'm a bit fearful
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456 # that this additional feature will slow the heavily optimized
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457 # event/time ratio (i.e., the profiler would run slower, fur a very
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458 # low "value added" feature.)
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459 #
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460 # Plugging in the calibration constant doesn't slow down the
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461 # profiler very much, and the accuracy goes way up.
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462 #**************************************************************
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463
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464 def calibrate(self, m):
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465 n = m
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466 s = self.timer()
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467 while n:
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468 self.simple()
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469 n = n - 1
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470 f = self.timer()
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471 my_simple = f[0]+f[1]-s[0]-s[1]
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472 #print "Simple =", my_simple,
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473
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474 n = m
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475 s = self.timer()
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476 while n:
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477 self.instrumented()
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478 n = n - 1
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479 f = self.timer()
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480 my_inst = f[0]+f[1]-s[0]-s[1]
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481 # print "Instrumented =", my_inst
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482 avg_cost = (my_inst - my_simple)/m
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483 #print "Delta/call =", avg_cost, "(profiler fixup constant)"
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484 return avg_cost
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485
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486 # simulate a program with no profiler activity
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487 def simple(self):
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488 a = 1
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489 pass
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490
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491 # simulate a program with call/return event processing
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492 def instrumented(self):
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493 a = 1
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494 self.profiler_simulation(a, a, a)
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495
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496 # simulate an event processing activity (from user's perspective)
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497 def profiler_simulation(self, x, y, z):
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498 t = self.timer()
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499 t = t[0] + t[1]
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500 self.ut = t
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501
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502
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503
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504 #****************************************************************************
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505 # OldProfile class documentation
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506 #****************************************************************************
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507 #
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508 # The following derived profiler simulates the old style profile, providing
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509 # errant results on recursive functions. The reason for the usefulnes of this
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510 # profiler is that it runs faster (i.e., less overhead). It still creates
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511 # all the caller stats, and is quite useful when there is *no* recursion
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512 # in the user's code.
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513 #
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514 # This code also shows how easy it is to create a modified profiler.
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515 #****************************************************************************
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516 class OldProfile(Profile):
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517 def trace_dispatch_exception(self, frame, t):
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518 rt, rtt, rct, rfn, rframe, rcur = self.cur
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519 if rcur and not rframe is frame:
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520 return self.trace_dispatch_return(rframe, t)
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521 return 0
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522
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523 def trace_dispatch_call(self, frame, t):
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524 fn = `frame.f_code`
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525
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526 self.cur = (t, 0, 0, fn, frame, self.cur)
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527 if self.timings.has_key(fn):
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528 tt, ct, callers = self.timings[fn]
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529 self.timings[fn] = tt, ct, callers
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530 else:
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531 self.timings[fn] = 0, 0, {}
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532 return 1
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533
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534 def trace_dispatch_return(self, frame, t):
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535 rt, rtt, rct, rfn, frame, rcur = self.cur
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536 rtt = rtt + t
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537 sft = rtt + rct
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538
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539 pt, ptt, pct, pfn, pframe, pcur = rcur
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540 self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur
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541
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542 tt, ct, callers = self.timings[rfn]
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543 if callers.has_key(pfn):
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544 callers[pfn] = callers[pfn] + 1
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545 else:
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546 callers[pfn] = 1
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547 self.timings[rfn] = tt+rtt, ct + sft, callers
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548
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549 return 1
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550
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551
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552 def snapshot_stats(self):
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553 self.stats = {}
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554 for func in self.timings.keys():
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555 tt, ct, callers = self.timings[func]
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556 nor_func = self.func_normalize(func)
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557 nor_callers = {}
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558 nc = 0
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559 for func_caller in callers.keys():
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560 nor_callers[self.func_normalize(func_caller)]=\
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561 callers[func_caller]
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562 nc = nc + callers[func_caller]
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563 self.stats[nor_func] = nc, nc, tt, ct, nor_callers
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564
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565
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566
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567 #****************************************************************************
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568 # HotProfile class documentation
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569 #****************************************************************************
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570 #
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571 # This profiler is the fastest derived profile example. It does not
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572 # calculate caller-callee relationships, and does not calculate cumulative
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573 # time under a function. It only calculates time spent in a function, so
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574 # it runs very quickly (re: very low overhead)
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575 #****************************************************************************
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576 class HotProfile(Profile):
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577 def trace_dispatch_exception(self, frame, t):
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578 rt, rtt, rfn, rframe, rcur = self.cur
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579 if rcur and not rframe is frame:
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580 return self.trace_dispatch_return(rframe, t)
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581 return 0
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582
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583 def trace_dispatch_call(self, frame, t):
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584 self.cur = (t, 0, frame, self.cur)
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585 return 1
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586
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587 def trace_dispatch_return(self, frame, t):
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588 rt, rtt, frame, rcur = self.cur
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589
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590 rfn = `frame.f_code`
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591
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592 pt, ptt, pframe, pcur = rcur
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593 self.cur = pt, ptt+rt, pframe, pcur
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594
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595 if self.timings.has_key(rfn):
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596 nc, tt = self.timings[rfn]
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597 self.timings[rfn] = nc + 1, rt + rtt + tt
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598 else:
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599 self.timings[rfn] = 1, rt + rtt
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600
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601 return 1
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602
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603
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604 def snapshot_stats(self):
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605 self.stats = {}
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606 for func in self.timings.keys():
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607 nc, tt = self.timings[func]
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608 nor_func = self.func_normalize(func)
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609 self.stats[nor_func] = nc, nc, tt, 0, {}
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610
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611
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612
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613 #****************************************************************************
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614 def Stats(*args):
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615 print 'Report generating functions are in the "pstats" module\a'
4347
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616
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617
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618 # When invoked as main program, invoke the profiler on a script
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619 if __name__ == '__main__':
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620 import sys
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621 import os
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622 if not sys.argv[1:]:
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623 print "usage: profile.py scriptfile [arg] ..."
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624 sys.exit(2)
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625
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626 filename = sys.argv[1] # Get script filename
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627
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628 del sys.argv[0] # Hide "profile.py" from argument list
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629
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630 # Insert script directory in front of module search path
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631 sys.path.insert(0, os.path.dirname(filename))
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632
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633 run('execfile(' + `filename` + ')')