Mercurial > cpython-lru_cache-weakref
annotate Doc/lib/libdifflib.tex @ 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 | 3827f136e0d6 |
| children | a36dffd75300 b5f6e9f6e7f4 |
| rev | line source |
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1 \section{\module{difflib} --- |
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2 Helpers for computing deltas} |
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3 |
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4 \declaremodule{standard}{difflib} |
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5 \modulesynopsis{Helpers for computing differences between objects.} |
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6 \moduleauthor{Tim Peters}{tim.one@home.com} |
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7 \sectionauthor{Tim Peters}{tim.one@home.com} |
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8 % LaTeXification by Fred L. Drake, Jr. <fdrake@acm.org>. |
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9 |
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10 \versionadded{2.1} |
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11 |
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12 |
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13 \begin{classdesc*}{SequenceMatcher} |
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14 This is a flexible class for comparing pairs of sequences of any |
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15 type, so long as the sequence elements are hashable. The basic |
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16 algorithm predates, and is a little fancier than, an algorithm |
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17 published in the late 1980's by Ratcliff and Obershelp under the |
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18 hyperbolic name ``gestalt pattern matching.'' The idea is to find |
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19 the longest contiguous matching subsequence that contains no |
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20 ``junk'' elements (the Ratcliff and Obershelp algorithm doesn't |
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21 address junk). The same idea is then applied recursively to the |
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22 pieces of the sequences to the left and to the right of the matching |
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23 subsequence. This does not yield minimal edit sequences, but does |
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24 tend to yield matches that ``look right'' to people. |
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25 |
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26 \strong{Timing:} The basic Ratcliff-Obershelp algorithm is cubic |
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27 time in the worst case and quadratic time in the expected case. |
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28 \class{SequenceMatcher} is quadratic time for the worst case and has |
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29 expected-case behavior dependent in a complicated way on how many |
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30 elements the sequences have in common; best case time is linear. |
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31 \end{classdesc*} |
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32 |
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33 \begin{classdesc*}{Differ} |
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34 This is a class for comparing sequences of lines of text, and |
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35 producing human-readable differences or deltas. Differ uses |
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36 \class{SequenceMatcher} both to compare sequences of lines, and to |
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37 compare sequences of characters within similar (near-matching) |
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38 lines. |
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39 |
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40 Each line of a \class{Differ} delta begins with a two-letter code: |
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41 |
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42 \begin{tableii}{l|l}{code}{Code}{Meaning} |
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43 \lineii{'- '}{line unique to sequence 1} |
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44 \lineii{'+ '}{line unique to sequence 2} |
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45 \lineii{' '}{line common to both sequences} |
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46 \lineii{'? '}{line not present in either input sequence} |
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47 \end{tableii} |
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48 |
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49 Lines beginning with `\code{?~}' attempt to guide the eye to |
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50 intraline differences, and were not present in either input |
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51 sequence. These lines can be confusing if the sequences contain tab |
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52 characters. |
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53 \end{classdesc*} |
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54 |
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55 \begin{funcdesc}{get_close_matches}{word, possibilities\optional{, |
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56 n\optional{, cutoff}}} |
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57 Return a list of the best ``good enough'' matches. \var{word} is a |
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58 sequence for which close matches are desired (typically a string), |
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59 and \var{possibilities} is a list of sequences against which to |
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60 match \var{word} (typically a list of strings). |
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61 |
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62 Optional argument \var{n} (default \code{3}) is the maximum number |
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63 of close matches to return; \var{n} must be greater than \code{0}. |
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64 |
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65 Optional argument \var{cutoff} (default \code{0.6}) is a float in |
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66 the range [0, 1]. Possibilities that don't score at least that |
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67 similar to \var{word} are ignored. |
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68 |
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69 The best (no more than \var{n}) matches among the possibilities are |
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70 returned in a list, sorted by similarity score, most similar first. |
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71 |
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72 \begin{verbatim} |
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73 >>> get_close_matches('appel', ['ape', 'apple', 'peach', 'puppy']) |
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74 ['apple', 'ape'] |
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75 >>> import keyword |
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76 >>> get_close_matches('wheel', keyword.kwlist) |
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77 ['while'] |
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78 >>> get_close_matches('apple', keyword.kwlist) |
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79 [] |
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80 >>> get_close_matches('accept', keyword.kwlist) |
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81 ['except'] |
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82 \end{verbatim} |
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83 \end{funcdesc} |
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84 |
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85 \begin{funcdesc}{ndiff}{a, b\optional{, linejunk\optional{, |
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86 charjunk}}} |
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87 Compare \var{a} and \var{b} (lists of strings); return a |
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88 \class{Differ}-style delta (a generator generating the delta lines). |
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89 |
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90 Optional keyword parameters \var{linejunk} and \var{charjunk} are |
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91 for filter functions (or \code{None}): |
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92 |
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93 \var{linejunk}: A function that should accept a single string |
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94 argument, and return true if the string is junk (or false if it is |
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95 not). The default is module-level function |
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96 \function{IS_LINE_JUNK()}, which filters out lines without visible |
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97 characters, except for at most one pound character (\character{\#}). |
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98 |
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99 \var{charjunk}: A function that should accept a string of length 1. |
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100 The default is module-level function \function{IS_CHARACTER_JUNK()}, |
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101 which filters out whitespace characters (a blank or tab; note: bad |
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102 idea to include newline in this!). |
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103 |
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104 \file{Tools/scripts/ndiff.py} is a command-line front-end to this |
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105 function. |
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106 |
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107 \begin{verbatim} |
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108 >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), |
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109 ... 'ore\ntree\nemu\n'.splitlines(1))) |
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110 >>> print ''.join(diff), |
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111 - one |
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112 ? ^ |
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113 + ore |
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114 ? ^ |
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115 - two |
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116 - three |
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117 ? - |
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118 + tree |
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119 + emu |
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120 \end{verbatim} |
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121 \end{funcdesc} |
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122 |
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123 \begin{funcdesc}{restore}{sequence, which} |
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124 Return one of the two sequences that generated a delta. |
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125 |
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126 Given a \var{sequence} produced by \method{Differ.compare()} or |
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127 \function{ndiff()}, extract lines originating from file 1 or 2 |
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128 (parameter \var{which}), stripping off line prefixes. |
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129 |
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130 Example: |
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131 |
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132 \begin{verbatim} |
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133 >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), |
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134 ... 'ore\ntree\nemu\n'.splitlines(1)) |
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135 >>> diff = list(diff) # materialize the generated delta into a list |
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136 >>> print ''.join(restore(diff, 1)), |
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137 one |
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138 two |
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139 three |
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140 >>> print ''.join(restore(diff, 2)), |
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141 ore |
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142 tree |
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143 emu |
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144 \end{verbatim} |
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145 |
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146 \end{funcdesc} |
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147 |
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148 |
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149 \begin{funcdesc}{IS_LINE_JUNK}{line} |
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150 Return true for ignorable lines. The line \var{line} is ignorable |
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151 if \var{line} is blank or contains a single \character{\#}, |
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152 otherwise it is not ignorable. Used as a default for parameter |
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153 \var{linejunk} in \function{ndiff()}. |
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154 \end{funcdesc} |
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155 |
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156 |
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157 \begin{funcdesc}{IS_CHARACTER_JUNK}{ch} |
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158 Return true for ignorable characters. The character \var{ch} is |
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159 ignorable if \var{ch} is a space or tab, otherwise it is not |
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160 ignorable. Used as a default for parameter \var{charjunk} in |
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161 \function{ndiff()}. |
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162 \end{funcdesc} |
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163 |
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164 |
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165 \begin{seealso} |
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166 \seetitle{Pattern Matching: The Gestalt Approach}{Discussion of a |
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167 similar algorithm by John W. Ratcliff and D. E. Metzener. |
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168 This was published in |
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169 \citetitle[http://www.ddj.com/]{Dr. Dobb's Journal} in |
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170 July, 1988.} |
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171 \end{seealso} |
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172 |
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173 |
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174 \subsection{SequenceMatcher Objects \label{sequence-matcher}} |
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175 |
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176 The \class{SequenceMatcher} class has this constructor: |
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177 |
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178 \begin{classdesc}{SequenceMatcher}{\optional{isjunk\optional{, |
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179 a\optional{, b}}}} |
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180 Optional argument \var{isjunk} must be \code{None} (the default) or |
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181 a one-argument function that takes a sequence element and returns |
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182 true if and only if the element is ``junk'' and should be ignored. |
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183 Passing \code{None} for \var{b} is equivalent to passing |
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184 \code{lambda x: 0}; in other words, no elements are ignored. For |
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185 example, pass: |
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186 |
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187 \begin{verbatim} |
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188 lambda x: x in " \t" |
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189 \end{verbatim} |
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190 |
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191 if you're comparing lines as sequences of characters, and don't want |
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192 to synch up on blanks or hard tabs. |
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193 |
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194 The optional arguments \var{a} and \var{b} are sequences to be |
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195 compared; both default to empty strings. The elements of both |
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196 sequences must be hashable. |
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197 \end{classdesc} |
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198 |
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199 |
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200 \class{SequenceMatcher} objects have the following methods: |
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201 |
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202 \begin{methoddesc}{set_seqs}{a, b} |
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203 Set the two sequences to be compared. |
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204 \end{methoddesc} |
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205 |
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206 \class{SequenceMatcher} computes and caches detailed information about |
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207 the second sequence, so if you want to compare one sequence against |
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208 many sequences, use \method{set_seq2()} to set the commonly used |
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209 sequence once and call \method{set_seq1()} repeatedly, once for each |
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210 of the other sequences. |
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211 |
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212 \begin{methoddesc}{set_seq1}{a} |
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213 Set the first sequence to be compared. The second sequence to be |
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214 compared is not changed. |
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215 \end{methoddesc} |
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216 |
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217 \begin{methoddesc}{set_seq2}{b} |
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218 Set the second sequence to be compared. The first sequence to be |
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219 compared is not changed. |
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220 \end{methoddesc} |
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221 |
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222 \begin{methoddesc}{find_longest_match}{alo, ahi, blo, bhi} |
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223 Find longest matching block in \code{\var{a}[\var{alo}:\var{ahi}]} |
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224 and \code{\var{b}[\var{blo}:\var{bhi}]}. |
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225 |
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226 If \var{isjunk} was omitted or \code{None}, |
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227 \method{get_longest_match()} returns \code{(\var{i}, \var{j}, |
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228 \var{k})} such that \code{\var{a}[\var{i}:\var{i}+\var{k}]} is equal |
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229 to \code{\var{b}[\var{j}:\var{j}+\var{k}]}, where |
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230 \code{\var{alo} <= \var{i} <= \var{i}+\var{k} <= \var{ahi}} and |
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231 \code{\var{blo} <= \var{j} <= \var{j}+\var{k} <= \var{bhi}}. |
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232 For all \code{(\var{i'}, \var{j'}, \var{k'})} meeting those |
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233 conditions, the additional conditions |
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234 \code{\var{k} >= \var{k'}}, |
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235 \code{\var{i} <= \var{i'}}, |
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236 and if \code{\var{i} == \var{i'}}, \code{\var{j} <= \var{j'}} |
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237 are also met. |
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238 In other words, of all maximal matching blocks, return one that |
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239 starts earliest in \var{a}, and of all those maximal matching blocks |
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240 that start earliest in \var{a}, return the one that starts earliest |
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241 in \var{b}. |
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242 |
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243 \begin{verbatim} |
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244 >>> s = SequenceMatcher(None, " abcd", "abcd abcd") |
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245 >>> s.find_longest_match(0, 5, 0, 9) |
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246 (0, 4, 5) |
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247 \end{verbatim} |
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248 |
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249 If \var{isjunk} was provided, first the longest matching block is |
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250 determined as above, but with the additional restriction that no |
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251 junk element appears in the block. Then that block is extended as |
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252 far as possible by matching (only) junk elements on both sides. |
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253 So the resulting block never matches on junk except as identical |
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254 junk happens to be adjacent to an interesting match. |
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255 |
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256 Here's the same example as before, but considering blanks to be junk. |
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257 That prevents \code{' abcd'} from matching the \code{' abcd'} at the |
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258 tail end of the second sequence directly. Instead only the |
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259 \code{'abcd'} can match, and matches the leftmost \code{'abcd'} in |
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260 the second sequence: |
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261 |
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262 \begin{verbatim} |
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263 >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd") |
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264 >>> s.find_longest_match(0, 5, 0, 9) |
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265 (1, 0, 4) |
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266 \end{verbatim} |
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267 |
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268 If no blocks match, this returns \code{(\var{alo}, \var{blo}, 0)}. |
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269 \end{methoddesc} |
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270 |
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271 \begin{methoddesc}{get_matching_blocks}{} |
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272 Return list of triples describing matching subsequences. |
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273 Each triple is of the form \code{(\var{i}, \var{j}, \var{n})}, and |
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274 means that \code{\var{a}[\var{i}:\var{i}+\var{n}] == |
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275 \var{b}[\var{j}:\var{j}+\var{n}]}. The triples are monotonically |
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276 increasing in \var{i} and \var{j}. |
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277 |
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278 The last triple is a dummy, and has the value \code{(len(\var{a}), |
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279 len(\var{b}), 0)}. It is the only triple with \code{\var{n} == 0}. |
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280 % Explain why a dummy is used! |
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281 |
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282 \begin{verbatim} |
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283 >>> s = SequenceMatcher(None, "abxcd", "abcd") |
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284 >>> s.get_matching_blocks() |
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285 [(0, 0, 2), (3, 2, 2), (5, 4, 0)] |
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286 \end{verbatim} |
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287 \end{methoddesc} |
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288 |
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289 \begin{methoddesc}{get_opcodes}{} |
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290 Return list of 5-tuples describing how to turn \var{a} into \var{b}. |
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291 Each tuple is of the form \code{(\var{tag}, \var{i1}, \var{i2}, |
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292 \var{j1}, \var{j2})}. The first tuple has \code{\var{i1} == |
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293 \var{j1} == 0}, and remaining tuples have \var{i1} equal to the |
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294 \var{i2} from the preceeding tuple, and, likewise, \var{j1} equal to |
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295 the previous \var{j2}. |
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296 |
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297 The \var{tag} values are strings, with these meanings: |
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298 |
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299 \begin{tableii}{l|l}{code}{Value}{Meaning} |
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300 \lineii{'replace'}{\code{\var{a}[\var{i1}:\var{i2}]} should be |
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301 replaced by \code{\var{b}[\var{j1}:\var{j2}]}.} |
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302 \lineii{'delete'}{\code{\var{a}[\var{i1}:\var{i2}]} should be |
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303 deleted. Note that \code{\var{j1} == \var{j2}} in |
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304 this case.} |
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305 \lineii{'insert'}{\code{\var{b}[\var{j1}:\var{j2}]} should be |
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306 inserted at \code{\var{a}[\var{i1}:\var{i1}]}. |
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307 Note that \code{\var{i1} == \var{i2}} in this |
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308 case.} |
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309 \lineii{'equal'}{\code{\var{a}[\var{i1}:\var{i2}] == |
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310 \var{b}[\var{j1}:\var{j2}]} (the sub-sequences are |
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311 equal).} |
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312 \end{tableii} |
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313 |
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314 For example: |
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315 |
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316 \begin{verbatim} |
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317 >>> a = "qabxcd" |
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318 >>> b = "abycdf" |
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319 >>> s = SequenceMatcher(None, a, b) |
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320 >>> for tag, i1, i2, j1, j2 in s.get_opcodes(): |
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321 ... print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" % |
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322 ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])) |
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323 delete a[0:1] (q) b[0:0] () |
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324 equal a[1:3] (ab) b[0:2] (ab) |
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325 replace a[3:4] (x) b[2:3] (y) |
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326 equal a[4:6] (cd) b[3:5] (cd) |
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327 insert a[6:6] () b[5:6] (f) |
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328 \end{verbatim} |
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329 \end{methoddesc} |
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330 |
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331 \begin{methoddesc}{ratio}{} |
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332 Return a measure of the sequences' similarity as a float in the |
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333 range [0, 1]. |
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334 |
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335 Where T is the total number of elements in both sequences, and M is |
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336 the number of matches, this is 2.0*M / T. Note that this is |
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337 \code{1.0} if the sequences are identical, and \code{0.0} if they |
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338 have nothing in common. |
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339 |
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340 This is expensive to compute if \method{get_matching_blocks()} or |
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341 \method{get_opcodes()} hasn't already been called, in which case you |
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342 may want to try \method{quick_ratio()} or |
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343 \method{real_quick_ratio()} first to get an upper bound. |
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344 \end{methoddesc} |
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345 |
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346 \begin{methoddesc}{quick_ratio}{} |
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347 Return an upper bound on \method{ratio()} relatively quickly. |
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348 |
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349 This isn't defined beyond that it is an upper bound on |
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350 \method{ratio()}, and is faster to compute. |
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351 \end{methoddesc} |
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352 |
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353 \begin{methoddesc}{real_quick_ratio}{} |
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354 Return an upper bound on \method{ratio()} very quickly. |
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355 |
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356 This isn't defined beyond that it is an upper bound on |
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357 \method{ratio()}, and is faster to compute than either |
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358 \method{ratio()} or \method{quick_ratio()}. |
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359 \end{methoddesc} |
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360 |
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361 The three methods that return the ratio of matching to total characters |
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362 can give different results due to differing levels of approximation, |
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363 although \method{quick_ratio()} and \method{real_quick_ratio()} are always |
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364 at least as large as \method{ratio()}: |
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365 |
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366 \begin{verbatim} |
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367 >>> s = SequenceMatcher(None, "abcd", "bcde") |
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368 >>> s.ratio() |
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369 0.75 |
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370 >>> s.quick_ratio() |
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371 0.75 |
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372 >>> s.real_quick_ratio() |
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373 1.0 |
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374 \end{verbatim} |
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375 |
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376 |
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377 \subsection{SequenceMatcher Examples \label{sequencematcher-examples}} |
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378 |
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379 |
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380 This example compares two strings, considering blanks to be ``junk:'' |
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381 |
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382 \begin{verbatim} |
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383 >>> s = SequenceMatcher(lambda x: x == " ", |
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384 ... "private Thread currentThread;", |
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385 ... "private volatile Thread currentThread;") |
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386 \end{verbatim} |
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387 |
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388 \method{ratio()} returns a float in [0, 1], measuring the similarity |
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389 of the sequences. As a rule of thumb, a \method{ratio()} value over |
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390 0.6 means the sequences are close matches: |
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391 |
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392 \begin{verbatim} |
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393 >>> print round(s.ratio(), 3) |
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394 0.866 |
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395 \end{verbatim} |
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396 |
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397 If you're only interested in where the sequences match, |
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398 \method{get_matching_blocks()} is handy: |
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399 |
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400 \begin{verbatim} |
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401 >>> for block in s.get_matching_blocks(): |
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402 ... print "a[%d] and b[%d] match for %d elements" % block |
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403 a[0] and b[0] match for 8 elements |
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404 a[8] and b[17] match for 6 elements |
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405 a[14] and b[23] match for 15 elements |
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406 a[29] and b[38] match for 0 elements |
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407 \end{verbatim} |
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408 |
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409 Note that the last tuple returned by \method{get_matching_blocks()} is |
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410 always a dummy, \code{(len(\var{a}), len(\var{b}), 0)}, and this is |
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411 the only case in which the last tuple element (number of elements |
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412 matched) is \code{0}. |
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413 |
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414 If you want to know how to change the first sequence into the second, |
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415 use \method{get_opcodes()}: |
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416 |
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417 \begin{verbatim} |
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418 >>> for opcode in s.get_opcodes(): |
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419 ... print "%6s a[%d:%d] b[%d:%d]" % opcode |
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420 equal a[0:8] b[0:8] |
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421 insert a[8:8] b[8:17] |
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422 equal a[8:14] b[17:23] |
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423 equal a[14:29] b[23:38] |
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424 \end{verbatim} |
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425 |
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426 See also the function \function{get_close_matches()} in this module, |
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427 which shows how simple code building on \class{SequenceMatcher} can be |
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428 used to do useful work. |
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429 |
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430 |
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431 \subsection{Differ Objects \label{differ-objects}} |
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432 |
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433 Note that \class{Differ}-generated deltas make no claim to be |
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434 \strong{minimal} diffs. To the contrary, minimal diffs are often |
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435 counter-intuitive, because they synch up anywhere possible, sometimes |
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436 accidental matches 100 pages apart. Restricting synch points to |
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437 contiguous matches preserves some notion of locality, at the |
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438 occasional cost of producing a longer diff. |
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439 |
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440 The \class{Differ} class has this constructor: |
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441 |
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442 \begin{classdesc}{Differ}{\optional{linejunk\optional{, charjunk}}} |
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443 Optional keyword parameters \var{linejunk} and \var{charjunk} are |
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444 for filter functions (or \code{None}): |
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445 |
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446 \var{linejunk}: A function that should accept a single string |
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447 argument, and return true if the string is junk. The default is |
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448 module-level function \function{IS_LINE_JUNK()}, which filters out |
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449 lines without visible characters, except for at most one pound |
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450 character (\character{\#}). |
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451 |
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452 \var{charjunk}: A function that should accept a string of length 1. |
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453 The default is module-level function \function{IS_CHARACTER_JUNK()}, |
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454 which filters out whitespace characters (a blank or tab; note: bad |
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455 idea to include newline in this!). |
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456 \end{classdesc} |
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457 |
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458 \class{Differ} objects are used (deltas generated) via a single |
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459 method: |
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460 |
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461 \begin{methoddesc}{compare}{a, b} |
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462 Compare two sequences of lines, and generate the delta (a sequence |
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463 of lines). |
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464 |
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465 Each sequence must contain individual single-line strings ending |
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466 with newlines. Such sequences can be obtained from the |
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467 \method{readlines()} method of file-like objects. The delta generated |
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468 also consists of newline-terminated strings, ready to be printed as-is |
| 21106 | 469 via the \method{writelines()} method of a file-like object. |
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470 \end{methoddesc} |
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471 |
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472 |
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473 \subsection{Differ Example \label{differ-examples}} |
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474 |
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475 This example compares two texts. First we set up the texts, sequences |
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476 of individual single-line strings ending with newlines (such sequences |
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477 can also be obtained from the \method{readlines()} method of file-like |
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478 objects): |
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479 |
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480 \begin{verbatim} |
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481 >>> text1 = ''' 1. Beautiful is better than ugly. |
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482 ... 2. Explicit is better than implicit. |
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483 ... 3. Simple is better than complex. |
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484 ... 4. Complex is better than complicated. |
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485 ... '''.splitlines(1) |
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486 >>> len(text1) |
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487 4 |
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488 >>> text1[0][-1] |
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489 '\n' |
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490 >>> text2 = ''' 1. Beautiful is better than ugly. |
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491 ... 3. Simple is better than complex. |
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492 ... 4. Complicated is better than complex. |
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493 ... 5. Flat is better than nested. |
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494 ... '''.splitlines(1) |
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495 \end{verbatim} |
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496 |
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497 Next we instantiate a Differ object: |
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498 |
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499 \begin{verbatim} |
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500 >>> d = Differ() |
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501 \end{verbatim} |
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502 |
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503 Note that when instantiating a \class{Differ} object we may pass |
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504 functions to filter out line and character ``junk.'' See the |
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505 \method{Differ()} constructor for details. |
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506 |
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507 Finally, we compare the two: |
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508 |
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509 \begin{verbatim} |
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510 >>> result = list(d.compare(text1, text2)) |
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511 \end{verbatim} |
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512 |
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513 \code{result} is a list of strings, so let's pretty-print it: |
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514 |
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515 \begin{verbatim} |
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516 >>> from pprint import pprint |
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517 >>> pprint(result) |
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518 [' 1. Beautiful is better than ugly.\n', |
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519 '- 2. Explicit is better than implicit.\n', |
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520 '- 3. Simple is better than complex.\n', |
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521 '+ 3. Simple is better than complex.\n', |
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522 '? ++ \n', |
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523 '- 4. Complex is better than complicated.\n', |
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524 '? ^ ---- ^ \n', |
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525 '+ 4. Complicated is better than complex.\n', |
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526 '? ++++ ^ ^ \n', |
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527 '+ 5. Flat is better than nested.\n'] |
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528 \end{verbatim} |
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529 |
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530 As a single multi-line string it looks like this: |
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531 |
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532 \begin{verbatim} |
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533 >>> import sys |
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534 >>> sys.stdout.writelines(result) |
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535 1. Beautiful is better than ugly. |
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536 - 2. Explicit is better than implicit. |
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537 - 3. Simple is better than complex. |
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538 + 3. Simple is better than complex. |
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539 ? ++ |
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540 - 4. Complex is better than complicated. |
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541 ? ^ ---- ^ |
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542 + 4. Complicated is better than complex. |
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543 ? ++++ ^ ^ |
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544 + 5. Flat is better than nested. |
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545 \end{verbatim} |
