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doc: Note that dbstop can be used with class methods as well (bug #40958). * debug.txi: Note that dbstop can be used with class methods as well (bug #40958).
author Rik <rik@octave.org>
date Wed, 01 Jan 2014 18:24:55 -0800
parents dae2230227a7
children 7e39cbefe299
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
rev   line source
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d63878346099 maint: Update copyright notices for release.
John W. Eaton <jwe@octave.org>
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1 @c Copyright (C) 1996-2013 John W. Eaton
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2 @c
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3 @c This file is part of Octave.
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4 @c
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5 @c Octave is free software; you can redistribute it and/or modify it
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6 @c under the terms of the GNU General Public License as published by the
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7 @c Free Software Foundation; either version 3 of the License, or (at
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8 @c your option) any later version.
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9 @c
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10 @c Octave is distributed in the hope that it will be useful, but WITHOUT
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11 @c ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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12 @c FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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13 @c for more details.
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14 @c
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15 @c You should have received a copy of the GNU General Public License
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16 @c along with Octave; see the file COPYING. If not, see
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17 @c <http://www.gnu.org/licenses/>.
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18
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19 @node Debugging
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20 @chapter Debugging
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21
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22 Octave includes a built-in debugger to aid in the development of
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23 scripts. This can be used to interrupt the execution of an Octave script
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24 at a certain point, or when certain conditions are met. Once execution
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25 has stopped, and debug mode is entered, the symbol table at the point
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26 where execution has stopped can be examined and modified to check for
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27 errors.
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28
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29 The normal command-line editing and history functions are available in
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30 debug mode.
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31
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32 @menu
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33 * Entering Debug Mode::
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34 * Leaving Debug Mode::
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35 * Breakpoints::
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36 * Debug Mode::
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37 * Call Stack::
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38 * Profiling::
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39 * Profiler Example::
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40 @end menu
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41
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42 @node Entering Debug Mode
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43 @section Entering Debug Mode
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44
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45 There are two basic means of interrupting the execution of an Octave
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46 script. These are breakpoints (@pxref{Breakpoints}), discussed in the next
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47 section and interruption based on some condition.
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48
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49 Octave supports three means to stop execution based on the values set in
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50 the functions @code{debug_on_interrupt}, @code{debug_on_warning} and
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51 @code{debug_on_error}.
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52
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53 @DOCSTRING(debug_on_interrupt)
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54
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55 @DOCSTRING(debug_on_warning)
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56
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57 @DOCSTRING(debug_on_error)
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58
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59 @node Leaving Debug Mode
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60 @section Leaving Debug Mode
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61
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62 To leave the debug mode, use either @code{dbcont}
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63 or @code{return}.
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64
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65 @DOCSTRING(dbcont)
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66
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67 To quit debug mode and return directly to the prompt @code{dbquit}
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68 should be used instead
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69
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70 @DOCSTRING(dbquit)
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71
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72 Finally, typing @code{exit} or @code{quit} at the debug prompt will
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73 result in Octave terminating normally.
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74
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75 @node Breakpoints
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76 @section Breakpoints
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77
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78 Breakpoints can be set in any Octave function, using the @code{dbstop}
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79 function.
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80
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81 @DOCSTRING(dbstop)
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82
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83 @noindent
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84 Breakpoints in class methods are also supported (e.g.,
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85 @code{dbstop("@@class/method")}). However, breakpoints cannot be set in
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86 built-in functions (e.g., @code{sin}, etc.) or dynamically loaded function
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87 (i.e., oct-files).
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88
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89 To set a breakpoint immediately on entering a function, the breakpoint
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90 should be set to line 1. The leading comment block will be ignored and
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91 the breakpoint will be set to the first executable statement in the
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92 function. For example:
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93
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94 @example
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95 @group
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96 dbstop ("asind", 1)
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97 @result{} 29
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98 @end group
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99 @end example
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100
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101 @noindent
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102 Note that the return value of @code{29} means that the breakpoint was
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103 effectively set to line 29. The status of breakpoints in a function can
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104 be queried with the @code{dbstatus} function.
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105
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106 @DOCSTRING(dbstatus)
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107
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108 @noindent
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109 Reusing the previous example, @code{dbstatus ("asind")} will return
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110 29. The breakpoints listed can then be cleared with the @code{dbclear}
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111 function.
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112
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113 @DOCSTRING(dbclear)
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114
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115 @noindent
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116 These functions can be used together to clear all the breakpoints in a
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117 particular function. For example:
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118
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119 @example
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120 dbclear ("asind", dbstatus ("asind"));
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121 @end example
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122
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123 A breakpoint may also be set in a subfunction. For example, if a file contains
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124 the functions
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125
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126 @example
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127 @group
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128 function y = func1 (x)
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129 y = func2 (x);
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130 endfunction
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131 function y = func2 (x)
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132 y = x + 1;
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133 endfunction
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134 @end group
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135 @end example
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136
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137 @noindent
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138 then a breakpoint can be set at the start of the subfunction directly
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139 with
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140
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141 @example
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142 @group
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143 dbstop (["func1", filemarker(), "func2"])
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144 @result{} 5
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145 @end group
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146 @end example
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147
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148 Note that @code{filemarker} returns a character that marks the
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149 subfunctions from the file containing them.
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150
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151 Another simple way of setting a breakpoint in an Octave script is the
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152 use of the @code{keyboard} function.
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153
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154 @DOCSTRING(keyboard)
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155
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156 @noindent
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157 The @code{keyboard} function is typically placed in a script at the
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158 point where the user desires that the execution be stopped. It
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159 automatically sets the running script into the debug mode.
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160
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161 @node Debug Mode
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162 @section Debug Mode
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163
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164 There are three additional support functions that allow the user to
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165 interrogate where in the execution of a script Octave entered the debug
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166 mode and to print the code in the script surrounding the point where
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167 Octave entered debug mode.
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168
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169 @DOCSTRING(dbwhere)
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170
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171 @DOCSTRING(dbtype)
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172
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173 @DOCSTRING(dblist)
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174
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175 You may also use @code{isdebugmode} to determine whether the debugger is
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176 currently active.
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177
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178 @DOCSTRING(isdebugmode)
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179
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180 Debug mode also allows single line stepping through a function using
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181 the command @code{dbstep}.
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182
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183 @DOCSTRING(dbstep)
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184
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185 When in debug mode the @key{RETURN} will execute the last entered command.
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186 This is useful, for example, after hitting a breakpoint and entering
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187 @code{dbstep}. After that one can advance line by line through the code
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188 with only a single key stroke.
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189
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190 @node Call Stack
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191 @section Call Stack
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192
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193 @DOCSTRING(dbstack)
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194
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195 @DOCSTRING(dbup)
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196
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197 @DOCSTRING(dbdown)
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198
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199 @node Profiling
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200 @section Profiling
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201 @cindex profiler
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202 @cindex code profiling
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203
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204 Octave supports profiling of code execution on a per-function level. If
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205 profiling is enabled, each call to a function (supporting built-ins,
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206 operators, functions in oct- and mex-files, user-defined functions in
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207 Octave code and anonymous functions) is recorded while running Octave
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208 code. After that, this data can aid in analyzing the code behavior, and
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209 is in particular helpful for finding ``hot spots'' in the code which use
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210 up a lot of computation time and are the best targets to spend
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211 optimization efforts on.
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212
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213 The main command for profiling is @code{profile}, which can be used to
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214 start or stop the profiler and also to query collected data afterwards.
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215 The data is returned in an Octave data structure which can then be
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216 examined or further processed by other routines or tools.
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217
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218 @DOCSTRING(profile)
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219
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220 An easy way to get an overview over the collected data is
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221 @code{profshow}. This function takes the profiler data returned by
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222 @code{profile} as input and prints a flat profile, for instance:
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223
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224 @example
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225 @group
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226 Function Attr Time (s) Calls
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227 ----------------------------------------
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228 >myfib R 2.195 13529
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229 binary <= 0.061 13529
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230 binary - 0.050 13528
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231 binary + 0.026 6764
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232 @end group
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233 @end example
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234
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235 This shows that most of the run time was spent executing the function
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236 @samp{myfib}, and some minor proportion evaluating the listed binary
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237 operators. Furthermore, it is shown how often the function was called
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238 and the profiler also records that it is recursive.
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239
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240 @DOCSTRING(profshow)
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241
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242 @DOCSTRING(profexplore)
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243
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244 @node Profiler Example
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245 @section Profiler Example
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246
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247 Below, we will give a short example of a profiler session.
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248 @xref{Profiling}, for the documentation of the profiler functions in
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249 detail. Consider the code:
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250
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251 @example
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252 global N A;
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253
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254 N = 300;
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255 A = rand (N, N);
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256
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257 function xt = timesteps (steps, x0, expM)
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258 global N;
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259
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260 if (steps == 0)
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261 xt = NA (N, 0);
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262 else
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263 xt = NA (N, steps);
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264 x1 = expM * x0;
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265 xt(:, 1) = x1;
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266 xt(:, 2 : end) = timesteps (steps - 1, x1, expM);
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267 endif
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268 endfunction
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269
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270 function foo ()
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271 global N A;
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272
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273 initial = @@(x) sin (x);
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274 x0 = (initial (linspace (0, 2 * pi, N)))';
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275
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276 expA = expm (A);
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277 xt = timesteps (100, x0, expA);
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278 endfunction
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279
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280 function fib = bar (N)
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281 if (N <= 2)
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282 fib = 1;
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283 else
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284 fib = bar (N - 1) + bar (N - 2);
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285 endif
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286 endfunction
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287 @end example
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288
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289 If we execute the two main functions, we get:
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290
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291 @example
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292 @group
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293 tic; foo; toc;
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294 @result{} Elapsed time is 2.37338 seconds.
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295
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296 tic; bar (20); toc;
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297 @result{} Elapsed time is 2.04952 seconds.
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298 @end group
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299 @end example
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300
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301 But this does not give much information about where this time is spent;
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302 for instance, whether the single call to @code{expm} is more expensive
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303 or the recursive time-stepping itself. To get a more detailed picture,
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304 we can use the profiler.
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305
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306 @example
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307 @group
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308 profile on;
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309 foo;
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310 profile off;
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311
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312 data = profile ("info");
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313 profshow (data, 10);
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314 @end group
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315 @end example
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316
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317 This prints a table like:
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318
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319 @example
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320 @group
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321 # Function Attr Time (s) Calls
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322 ---------------------------------------------
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323 7 expm 1.034 1
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324 3 binary * 0.823 117
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325 41 binary \ 0.188 1
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326 38 binary ^ 0.126 2
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327 43 timesteps R 0.111 101
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328 44 NA 0.029 101
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329 39 binary + 0.024 8
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330 34 norm 0.011 1
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331 40 binary - 0.004 101
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332 33 balance 0.003 1
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333 @end group
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334 @end example
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335
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336 The entries are the individual functions which have been executed (only
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337 the 10 most important ones), together with some information for each of
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338 them. The entries like @samp{binary *} denote operators, while other
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339 entries are ordinary functions. They include both built-ins like
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340 @code{expm} and our own routines (for instance @code{timesteps}). From
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341 this profile, we can immediately deduce that @code{expm} uses up the
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342 largest proportion of the processing time, even though it is only called
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343 once. The second expensive operation is the matrix-vector product in the
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344 routine @code{timesteps}. @footnote{We only know it is the binary
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345 multiplication operator, but fortunately this operator appears only at
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346 one place in the code and thus we know which occurrence takes so much
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347 time. If there were multiple places, we would have to use the
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348 hierarchical profile to find out the exact place which uses up the time
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349 which is not covered in this example.}
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350
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351 Timing, however, is not the only information available from the profile.
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352 The attribute column shows us that @code{timesteps} calls itself
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353 recursively. This may not be that remarkable in this example (since it's
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354 clear anyway), but could be helpful in a more complex setting. As to the
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355 question of why is there a @samp{binary \} in the output, we can easily
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356 shed some light on that too. Note that @code{data} is a structure array
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357 (@ref{Structure Arrays}) which contains the field @code{FunctionTable}.
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358 This stores the raw data for the profile shown. The number in the first
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359 column of the table gives the index under which the shown function can
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360 be found there. Looking up @code{data.FunctionTable(41)} gives:
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361
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362 @example
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363 @group
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364 scalar structure containing the fields:
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365
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366 FunctionName = binary \
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367 TotalTime = 0.18765
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368 NumCalls = 1
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369 IsRecursive = 0
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370 Parents = 7
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371 Children = [](1x0)
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372 @end group
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373 @end example
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374
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375 Here we see the information from the table again, but have additional
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376 fields @code{Parents} and @code{Children}. Those are both arrays, which
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377 contain the indices of functions which have directly called the function
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378 in question (which is entry 7, @code{expm}, in this case) or been called
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379 by it (no functions). Hence, the backslash operator has been used
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380 internally by @code{expm}.
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381
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382 Now let's take a look at @code{bar}. For this, we start a fresh
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383 profiling session (@code{profile on} does this; the old data is removed
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384 before the profiler is restarted):
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385
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386 @example
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387 @group
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388 profile on;
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389 bar (20);
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390 profile off;
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391
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392 profshow (profile ("info"));
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393 @end group
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394 @end example
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395
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396 This gives:
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397
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398 @example
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399 @group
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400 # Function Attr Time (s) Calls
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401 -------------------------------------------------------
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402 1 bar R 2.091 13529
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403 2 binary <= 0.062 13529
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404 3 binary - 0.042 13528
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405 4 binary + 0.023 6764
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406 5 profile 0.000 1
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407 8 false 0.000 1
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408 6 nargin 0.000 1
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409 7 binary != 0.000 1
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410 9 __profiler_enable__ 0.000 1
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411 @end group
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412 @end example
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413
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414 Unsurprisingly, @code{bar} is also recursive. It has been called 13,529
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415 times in the course of recursively calculating the Fibonacci number in a
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416 suboptimal way, and most of the time was spent in @code{bar} itself.
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417
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418 Finally, let's say we want to profile the execution of both @code{foo}
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419 and @code{bar} together. Since we already have the run-time data
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420 collected for @code{bar}, we can restart the profiler without clearing
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421 the existing data and collect the missing statistics about @code{foo}.
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422 This is done by:
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423
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424 @example
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425 @group
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426 profile resume;
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427 foo;
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428 profile off;
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429
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430 profshow (profile ("info"), 10);
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431 @end group
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432 @end example
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433
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434 As you can see in the table below, now we have both profiles mixed
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435 together.
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436
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437 @example
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438 @group
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439 # Function Attr Time (s) Calls
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440 ---------------------------------------------
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441 1 bar R 2.091 13529
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442 16 expm 1.122 1
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443 12 binary * 0.798 117
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444 46 binary \ 0.185 1
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445 45 binary ^ 0.124 2
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446 48 timesteps R 0.115 101
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447 2 binary <= 0.062 13529
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448 3 binary - 0.045 13629
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449 4 binary + 0.041 6772
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450 49 NA 0.036 101
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Jordi Gutiérrez Hermoso <jordigh@gmail.com>
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451 @end group
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452 @end example