Mercurial > cpython-lru_cache-weakref
annotate Lib/profile.py @ 25838:8540887bcec6 2.2
Backport of 2.183:
Fix memory leak in add_subclass() found by NealN with valgrind.
| author | Guido van Rossum <guido@python.org> |
|---|---|
| date | Fri, 18 Oct 2002 13:42:21 +0000 |
| parents | 3e8460fb2fbe |
| children | a1de0bab5475 |
| rev | line source |
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1 #! /usr/bin/env python |
| 839 | 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 |
| 839 | 7 # |
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8 # See profile.doc for more information |
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9 |
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10 """Class for profiling Python code.""" |
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11 |
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12 # Copyright 1994, by InfoSeek Corporation, all rights reserved. |
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13 # Written by James Roskind |
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14 # |
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15 # Permission to use, copy, modify, and distribute this Python software |
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16 # and its associated documentation for any purpose (subject to the |
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17 # restriction in the following sentence) without fee is hereby granted, |
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18 # provided that the above copyright notice appears in all copies, and |
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19 # that both that copyright notice and this permission notice appear in |
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20 # supporting documentation, and that the name of InfoSeek not be used in |
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21 # advertising or publicity pertaining to distribution of the software |
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22 # without specific, written prior permission. This permission is |
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23 # explicitly restricted to the copying and modification of the software |
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24 # to remain in Python, compiled Python, or other languages (such as C) |
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25 # wherein the modified or derived code is exclusively imported into a |
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26 # Python module. |
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27 # |
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28 # INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS |
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29 # SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND |
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30 # FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY |
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31 # SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER |
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32 # RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF |
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33 # CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN |
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34 # CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. |
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35 |
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36 |
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| 38 import sys | |
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39 import os |
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40 import time |
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41 import marshal |
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43 __all__ = ["run","help","Profile"] |
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45 # Sample timer for use with |
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46 #i_count = 0 |
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47 #def integer_timer(): |
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48 # global i_count |
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49 # i_count = i_count + 1 |
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50 # return i_count |
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51 #itimes = integer_timer # replace with C coded timer returning integers |
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53 #************************************************************************** |
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54 # The following are the static member functions for the profiler class |
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55 # Note that an instance of Profile() is *not* needed to call them. |
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56 #************************************************************************** |
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57 |
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58 def run(statement, filename=None): |
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59 """Run statement under profiler optionally saving results in filename |
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60 |
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61 This function takes a single argument that can be passed to the |
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62 "exec" statement, and an optional file name. In all cases this |
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63 routine attempts to "exec" its first argument and gather profiling |
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64 statistics from the execution. If no file name is present, then this |
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65 function automatically prints a simple profiling report, sorted by the |
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66 standard name string (file/line/function-name) that is presented in |
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67 each line. |
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68 """ |
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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 filename is not None: |
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75 prof.dump_stats(filename) |
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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 if os.name == "mac": |
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93 import MacOS |
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94 def _get_time_mac(timer=MacOS.GetTicks): |
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95 return timer() / 60.0 |
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96 |
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97 if hasattr(os, "times"): |
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98 def _get_time_times(timer=os.times): |
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99 t = timer() |
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100 return t[0] + t[1] |
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101 |
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102 |
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103 class Profile: |
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104 """Profiler class. |
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105 |
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106 self.cur is always a tuple. Each such tuple corresponds to a stack |
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107 frame that is currently active (self.cur[-2]). The following are the |
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108 definitions of its members. We use this external "parallel stack" to |
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109 avoid contaminating the program that we are profiling. (old profiler |
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110 used to write into the frames local dictionary!!) Derived classes |
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111 can change the definition of some entries, as long as they leave |
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112 [-2:] intact (frame and previous tuple). In case an internal error is |
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113 detected, the -3 element is used as the function name. |
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114 |
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115 [ 0] = Time that needs to be charged to the parent frame's function. |
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116 It is used so that a function call will not have to access the |
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117 timing data for the parent frame. |
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118 [ 1] = Total time spent in this frame's function, excluding time in |
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119 subfunctions (this latter is tallied in cur[2]). |
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120 [ 2] = Total time spent in subfunctions, excluding time executing the |
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121 frame's function (this latter is tallied in cur[1]). |
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122 [-3] = Name of the function that corresponds to this frame. |
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123 [-2] = Actual frame that we correspond to (used to sync exception handling). |
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124 [-1] = Our parent 6-tuple (corresponds to frame.f_back). |
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125 |
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126 Timing data for each function is stored as a 5-tuple in the dictionary |
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127 self.timings[]. The index is always the name stored in self.cur[-3]. |
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128 The following are the definitions of the members: |
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129 |
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130 [0] = The number of times this function was called, not counting direct |
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131 or indirect recursion, |
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132 [1] = Number of times this function appears on the stack, minus one |
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133 [2] = Total time spent internal to this function |
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134 [3] = Cumulative time that this function was present on the stack. In |
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135 non-recursive functions, this is the total execution time from start |
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136 to finish of each invocation of a function, including time spent in |
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137 all subfunctions. |
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138 [4] = A dictionary indicating for each function name, the number of times |
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139 it was called by us. |
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140 """ |
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141 |
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142 bias = 0 # calibration constant |
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143 |
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144 def __init__(self, timer=None, bias=None): |
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145 self.timings = {} |
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146 self.cur = None |
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147 self.cmd = "" |
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148 |
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149 if bias is None: |
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150 bias = self.bias |
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151 self.bias = bias # Materialize in local dict for lookup speed. |
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152 |
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153 if not timer: |
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154 if os.name == 'mac': |
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155 self.timer = MacOS.GetTicks |
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156 self.dispatcher = self.trace_dispatch_mac |
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157 self.get_time = _get_time_mac |
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158 elif hasattr(time, 'clock'): |
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159 self.timer = self.get_time = time.clock |
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160 self.dispatcher = self.trace_dispatch_i |
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161 elif hasattr(os, 'times'): |
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162 self.timer = os.times |
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163 self.dispatcher = self.trace_dispatch |
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164 self.get_time = _get_time_times |
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165 else: |
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166 self.timer = self.get_time = time.time |
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167 self.dispatcher = self.trace_dispatch_i |
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168 else: |
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169 self.timer = timer |
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170 t = self.timer() # test out timer function |
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171 try: |
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172 length = len(t) |
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173 except TypeError: |
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174 self.get_time = timer |
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175 self.dispatcher = self.trace_dispatch_i |
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176 else: |
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177 if length == 2: |
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178 self.dispatcher = self.trace_dispatch |
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179 else: |
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180 self.dispatcher = self.trace_dispatch_l |
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181 # This get_time() implementation needs to be defined |
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182 # here to capture the passed-in timer in the parameter |
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183 # list (for performance). Note that we can't assume |
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184 # the timer() result contains two values in all |
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185 # cases. |
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186 import operator |
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187 def get_time_timer(timer=timer, |
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188 reduce=reduce, reducer=operator.add): |
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189 return reduce(reducer, timer(), 0) |
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190 self.get_time = get_time_timer |
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191 self.t = self.get_time() |
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192 self.simulate_call('profiler') |
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193 |
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194 # Heavily optimized dispatch routine for os.times() timer |
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195 |
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196 def trace_dispatch(self, frame, event, arg): |
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197 timer = self.timer |
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198 t = timer() |
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199 t = t[0] + t[1] - self.t - self.bias |
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200 |
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201 if self.dispatch[event](self, frame,t): |
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202 t = timer() |
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203 self.t = t[0] + t[1] |
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204 else: |
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205 r = timer() |
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206 self.t = r[0] + r[1] - t # put back unrecorded delta |
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207 |
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208 # Dispatch routine for best timer program (return = scalar, fastest if |
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209 # an integer but float works too -- and time.clock() relies on that). |
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210 |
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211 def trace_dispatch_i(self, frame, event, arg): |
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212 timer = self.timer |
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213 t = timer() - self.t - self.bias |
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214 if self.dispatch[event](self, frame,t): |
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215 self.t = timer() |
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216 else: |
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217 self.t = timer() - t # put back unrecorded delta |
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218 |
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219 # Dispatch routine for macintosh (timer returns time in ticks of |
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220 # 1/60th second) |
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221 |
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222 def trace_dispatch_mac(self, frame, event, arg): |
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223 timer = self.timer |
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224 t = timer()/60.0 - self.t - self.bias |
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225 if self.dispatch[event](self, frame, t): |
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226 self.t = timer()/60.0 |
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227 else: |
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228 self.t = timer()/60.0 - t # put back unrecorded delta |
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229 |
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230 # SLOW generic dispatch routine for timer returning lists of numbers |
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231 |
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232 def trace_dispatch_l(self, frame, event, arg): |
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233 get_time = self.get_time |
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234 t = get_time() - self.t - self.bias |
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235 |
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236 if self.dispatch[event](self, frame, t): |
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237 self.t = get_time() |
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238 else: |
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239 self.t = get_time() - t # put back unrecorded delta |
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240 |
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241 # In the event handlers, the first 3 elements of self.cur are unpacked |
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242 # into vrbls w/ 3-letter names. The last two characters are meant to be |
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243 # mnemonic: |
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244 # _pt self.cur[0] "parent time" time to be charged to parent frame |
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245 # _it self.cur[1] "internal time" time spent directly in the function |
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246 # _et self.cur[2] "external time" time spent in subfunctions |
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247 |
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248 def trace_dispatch_exception(self, frame, t): |
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249 rpt, rit, ret, rfn, rframe, rcur = self.cur |
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250 if (rframe is not frame) and rcur: |
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251 return self.trace_dispatch_return(rframe, t) |
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252 self.cur = rpt, rit+t, ret, rfn, rframe, rcur |
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253 return 1 |
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254 |
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255 |
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256 def trace_dispatch_call(self, frame, t): |
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257 if self.cur and frame.f_back is not self.cur[-2]: |
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258 rpt, rit, ret, rfn, rframe, rcur = self.cur |
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259 if not isinstance(rframe, Profile.fake_frame): |
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260 assert rframe.f_back is frame.f_back, ("Bad call", rfn, |
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261 rframe, rframe.f_back, |
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262 frame, frame.f_back) |
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263 self.trace_dispatch_return(rframe, 0) |
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264 assert (self.cur is None or \ |
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265 frame.f_back is self.cur[-2]), ("Bad call", |
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266 self.cur[-3]) |
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267 fcode = frame.f_code |
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268 fn = (fcode.co_filename, fcode.co_firstlineno, fcode.co_name) |
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269 self.cur = (t, 0, 0, fn, frame, self.cur) |
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270 timings = self.timings |
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271 if timings.has_key(fn): |
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272 cc, ns, tt, ct, callers = timings[fn] |
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273 timings[fn] = cc, ns + 1, tt, ct, callers |
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274 else: |
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275 timings[fn] = 0, 0, 0, 0, {} |
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276 return 1 |
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277 |
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278 def trace_dispatch_return(self, frame, t): |
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279 if frame is not self.cur[-2]: |
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280 assert frame is self.cur[-2].f_back, ("Bad return", self.cur[-3]) |
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281 self.trace_dispatch_return(self.cur[-2], 0) |
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282 |
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283 # Prefix "r" means part of the Returning or exiting frame. |
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284 # Prefix "p" means part of the Previous or Parent or older frame. |
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285 |
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286 rpt, rit, ret, rfn, frame, rcur = self.cur |
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287 rit = rit + t |
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288 frame_total = rit + ret |
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289 |
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290 ppt, pit, pet, pfn, pframe, pcur = rcur |
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291 self.cur = ppt, pit + rpt, pet + frame_total, pfn, pframe, pcur |
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292 |
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293 timings = self.timings |
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294 cc, ns, tt, ct, callers = timings[rfn] |
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295 if not ns: |
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296 # This is the only occurrence of the function on the stack. |
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297 # Else this is a (directly or indirectly) recursive call, and |
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298 # its cumulative time will get updated when the topmost call to |
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299 # it returns. |
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300 ct = ct + frame_total |
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301 cc = cc + 1 |
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302 |
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303 if callers.has_key(pfn): |
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304 callers[pfn] = callers[pfn] + 1 # hack: gather more |
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305 # stats such as the amount of time added to ct courtesy |
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306 # of this specific call, and the contribution to cc |
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307 # courtesy of this call. |
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308 else: |
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309 callers[pfn] = 1 |
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310 |
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311 timings[rfn] = cc, ns - 1, tt + rit, ct, callers |
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312 |
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313 return 1 |
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314 |
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315 |
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316 dispatch = { |
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317 "call": trace_dispatch_call, |
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318 "exception": trace_dispatch_exception, |
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319 "return": trace_dispatch_return, |
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320 } |
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321 |
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322 |
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323 # The next few functions play with self.cmd. By carefully preloading |
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324 # our parallel stack, we can force the profiled result to include |
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325 # an arbitrary string as the name of the calling function. |
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326 # We use self.cmd as that string, and the resulting stats look |
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327 # very nice :-). |
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328 |
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329 def set_cmd(self, cmd): |
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330 if self.cur[-1]: return # already set |
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331 self.cmd = cmd |
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332 self.simulate_call(cmd) |
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333 |
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334 class fake_code: |
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335 def __init__(self, filename, line, name): |
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336 self.co_filename = filename |
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337 self.co_line = line |
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338 self.co_name = name |
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339 self.co_firstlineno = 0 |
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340 |
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341 def __repr__(self): |
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342 return repr((self.co_filename, self.co_line, self.co_name)) |
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343 |
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344 class fake_frame: |
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345 def __init__(self, code, prior): |
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346 self.f_code = code |
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347 self.f_back = prior |
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348 |
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349 def simulate_call(self, name): |
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350 code = self.fake_code('profile', 0, name) |
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351 if self.cur: |
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352 pframe = self.cur[-2] |
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353 else: |
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354 pframe = None |
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355 frame = self.fake_frame(code, pframe) |
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356 self.dispatch['call'](self, frame, 0) |
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357 |
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358 # collect stats from pending stack, including getting final |
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359 # timings for self.cmd frame. |
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360 |
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361 def simulate_cmd_complete(self): |
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362 get_time = self.get_time |
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363 t = get_time() - self.t |
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364 while self.cur[-1]: |
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365 # We *can* cause assertion errors here if |
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366 # dispatch_trace_return checks for a frame match! |
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367 self.dispatch['return'](self, self.cur[-2], t) |
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368 t = 0 |
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369 self.t = get_time() - t |
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370 |
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371 |
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372 def print_stats(self): |
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373 import pstats |
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374 pstats.Stats(self).strip_dirs().sort_stats(-1). \ |
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375 print_stats() |
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376 |
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377 def dump_stats(self, file): |
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378 f = open(file, 'wb') |
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379 self.create_stats() |
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380 marshal.dump(self.stats, f) |
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381 f.close() |
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382 |
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383 def create_stats(self): |
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384 self.simulate_cmd_complete() |
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385 self.snapshot_stats() |
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386 |
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387 def snapshot_stats(self): |
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388 self.stats = {} |
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389 for func in self.timings.keys(): |
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390 cc, ns, tt, ct, callers = self.timings[func] |
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391 callers = callers.copy() |
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392 nc = 0 |
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393 for func_caller in callers.keys(): |
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394 nc = nc + callers[func_caller] |
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395 self.stats[func] = cc, nc, tt, ct, callers |
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396 |
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397 |
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398 # The following two methods can be called by clients to use |
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399 # a profiler to profile a statement, given as a string. |
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400 |
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401 def run(self, cmd): |
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402 import __main__ |
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403 dict = __main__.__dict__ |
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404 return self.runctx(cmd, dict, dict) |
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405 |
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406 def runctx(self, cmd, globals, locals): |
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407 self.set_cmd(cmd) |
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408 sys.setprofile(self.dispatcher) |
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409 try: |
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410 exec cmd in globals, locals |
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411 finally: |
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412 sys.setprofile(None) |
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413 return self |
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414 |
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415 # This method is more useful to profile a single function call. |
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416 def runcall(self, func, *args, **kw): |
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417 self.set_cmd(`func`) |
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418 sys.setprofile(self.dispatcher) |
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419 try: |
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420 return apply(func, args, kw) |
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421 finally: |
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422 sys.setprofile(None) |
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423 |
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424 |
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425 #****************************************************************** |
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426 # The following calculates the overhead for using a profiler. The |
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427 # problem is that it takes a fair amount of time for the profiler |
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428 # to stop the stopwatch (from the time it receives an event). |
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429 # Similarly, there is a delay from the time that the profiler |
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430 # re-starts the stopwatch before the user's code really gets to |
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431 # continue. The following code tries to measure the difference on |
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432 # a per-event basis. |
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433 # |
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434 # Note that this difference is only significant if there are a lot of |
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435 # events, and relatively little user code per event. For example, |
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436 # code with small functions will typically benefit from having the |
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437 # profiler calibrated for the current platform. This *could* be |
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438 # done on the fly during init() time, but it is not worth the |
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439 # effort. Also note that if too large a value specified, then |
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440 # execution time on some functions will actually appear as a |
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441 # negative number. It is *normal* for some functions (with very |
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442 # low call counts) to have such negative stats, even if the |
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443 # calibration figure is "correct." |
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444 # |
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445 # One alternative to profile-time calibration adjustments (i.e., |
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446 # adding in the magic little delta during each event) is to track |
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447 # more carefully the number of events (and cumulatively, the number |
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448 # of events during sub functions) that are seen. If this were |
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449 # done, then the arithmetic could be done after the fact (i.e., at |
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450 # display time). Currently, we track only call/return events. |
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451 # These values can be deduced by examining the callees and callers |
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452 # vectors for each functions. Hence we *can* almost correct the |
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453 # internal time figure at print time (note that we currently don't |
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454 # track exception event processing counts). Unfortunately, there |
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455 # is currently no similar information for cumulative sub-function |
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456 # time. It would not be hard to "get all this info" at profiler |
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457 # time. Specifically, we would have to extend the tuples to keep |
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458 # counts of this in each frame, and then extend the defs of timing |
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459 # tuples to include the significant two figures. I'm a bit fearful |
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460 # that this additional feature will slow the heavily optimized |
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461 # event/time ratio (i.e., the profiler would run slower, fur a very |
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462 # low "value added" feature.) |
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|
463 #************************************************************** |
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464 |
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|
465 def calibrate(self, m, verbose=0): |
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466 if self.__class__ is not Profile: |
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467 raise TypeError("Subclasses must override .calibrate().") |
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468 |
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469 saved_bias = self.bias |
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470 self.bias = 0 |
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471 try: |
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472 return self._calibrate_inner(m, verbose) |
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473 finally: |
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474 self.bias = saved_bias |
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475 |
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|
476 def _calibrate_inner(self, m, verbose): |
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477 get_time = self.get_time |
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|
478 |
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|
479 # Set up a test case to be run with and without profiling. Include |
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480 # lots of calls, because we're trying to quantify stopwatch overhead. |
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481 # Do not raise any exceptions, though, because we want to know |
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|
482 # exactly how many profile events are generated (one call event, + |
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|
483 # one return event, per Python-level call). |
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|
484 |
|
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485 def f1(n): |
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486 for i in range(n): |
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A brand new implementation of Profile.calibrate(). This measures an
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487 x = 1 |
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A brand new implementation of Profile.calibrate(). This measures an
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488 |
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489 def f(m, f1=f1): |
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490 for i in range(m): |
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A brand new implementation of Profile.calibrate(). This measures an
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491 f1(100) |
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492 |
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493 f(m) # warm up the cache |
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494 |
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495 # elapsed_noprofile <- time f(m) takes without profiling. |
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496 t0 = get_time() |
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497 f(m) |
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498 t1 = get_time() |
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499 elapsed_noprofile = t1 - t0 |
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500 if verbose: |
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501 print "elapsed time without profiling =", elapsed_noprofile |
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502 |
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503 # elapsed_profile <- time f(m) takes with profiling. The difference |
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504 # is profiling overhead, only some of which the profiler subtracts |
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505 # out on its own. |
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506 p = Profile() |
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507 t0 = get_time() |
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508 p.runctx('f(m)', globals(), locals()) |
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509 t1 = get_time() |
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510 elapsed_profile = t1 - t0 |
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511 if verbose: |
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512 print "elapsed time with profiling =", elapsed_profile |
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513 |
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514 # reported_time <- "CPU seconds" the profiler charged to f and f1. |
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515 total_calls = 0.0 |
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516 reported_time = 0.0 |
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517 for (filename, line, funcname), (cc, ns, tt, ct, callers) in \ |
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518 p.timings.items(): |
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519 if funcname in ("f", "f1"): |
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520 total_calls += cc |
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521 reported_time += tt |
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522 |
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523 if verbose: |
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524 print "'CPU seconds' profiler reported =", reported_time |
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525 print "total # calls =", total_calls |
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526 if total_calls != m + 1: |
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527 raise ValueError("internal error: total calls = %d" % total_calls) |
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528 |
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529 # reported_time - elapsed_noprofile = overhead the profiler wasn't |
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530 # able to measure. Divide by twice the number of calls (since there |
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531 # are two profiler events per call in this test) to get the hidden |
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532 # overhead per event. |
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533 mean = (reported_time - elapsed_noprofile) / 2.0 / total_calls |
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534 if verbose: |
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535 print "mean stopwatch overhead per profile event =", mean |
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536 return mean |
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537 |
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538 #**************************************************************************** |
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539 def Stats(*args): |
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540 print 'Report generating functions are in the "pstats" module\a' |
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541 |
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542 |
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543 # When invoked as main program, invoke the profiler on a script |
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544 if __name__ == '__main__': |
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545 if not sys.argv[1:]: |
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546 print "usage: profile.py scriptfile [arg] ..." |
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547 sys.exit(2) |
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548 |
|
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549 filename = sys.argv[1] # Get script filename |
|
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550 |
|
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551 del sys.argv[0] # Hide "profile.py" from argument list |
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diff
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552 |
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553 # Insert script directory in front of module search path |
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554 sys.path.insert(0, os.path.dirname(filename)) |
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555 |
|
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556 run('execfile(' + `filename` + ')') |
