annotate Doc/lib/libdifflib.tex @ 17780:a1575dbf7d81 legacy-trunk

Replace "\begin{classdesc}{SomeClass}{\unspecified}" with "\begin{classdesc*}{SomeClass}" -- the rendering of \unspecified was identical to \moreargs, so this helps clarify things just a little.
author Fred Drake <fdrake@acm.org>
date Fri, 11 May 2001 01:08:13 +0000
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children 0ed8c5f3f530
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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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13 \begin{funcdesc}{get_close_matches}{word, possibilities\optional{,
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14 n\optional{, cutoff}}}
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15 Return a list of the best ``good enough'' matches. \var{word} is a
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16 sequence for which close matches are desired (typically a string),
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17 and \var{possibilities} is a list of sequences against which to
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18 match \var{word} (typically a list of strings).
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19
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20 Optional argument \var{n} (default \code{3}) is the maximum number
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21 of close matches to return; \var{n} must be greater than \code{0}.
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22
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23 Optional argument \var{cutoff} (default \code{0.6}) is a float in
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24 the range [0, 1]. Possibilities that don't score at least that
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25 similar to \var{word} are ignored.
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26
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27 The best (no more than \var{n}) matches among the possibilities are
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28 returned in a list, sorted by similarity score, most similar first.
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29
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30 \begin{verbatim}
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31 >>> get_close_matches('appel', ['ape', 'apple', 'peach', 'puppy'])
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32 ['apple', 'ape']
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33 >>> import keyword
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34 >>> get_close_matches('wheel', keyword.kwlist)
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35 ['while']
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36 >>> get_close_matches('apple', keyword.kwlist)
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37 []
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38 >>> get_close_matches('accept', keyword.kwlist)
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39 ['except']
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40 \end{verbatim}
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41 \end{funcdesc}
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42
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43 \begin{classdesc*}{SequenceMatcher}
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44 This is a flexible class for comparing pairs of sequences of any
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45 type, so long as the sequence elements are hashable. The basic
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46 algorithm predates, and is a little fancier than, an algorithm
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47 published in the late 1980's by Ratcliff and Obershelp under the
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48 hyperbolic name ``gestalt pattern matching.'' The idea is to find
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49 the longest contiguous matching subsequence that contains no
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50 ``junk'' elements (the Ratcliff and Obershelp algorithm doesn't
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51 address junk). The same idea is then applied recursively to the
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52 pieces of the sequences to the left and to the right of the matching
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53 subsequence. This does not yield minimal edit sequences, but does
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54 tend to yield matches that ``look right'' to people.
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55
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56 \strong{Timing:} The basic Ratcliff-Obershelp algorithm is cubic
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57 time in the worst case and quadratic time in the expected case.
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58 \class{SequenceMatcher} is quadratic time for the worst case and has
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59 expected-case behavior dependent in a complicated way on how many
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60 elements the sequences have in common; best case time is linear.
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61 \end{classdesc}
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62
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63
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64 \begin{seealso}
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65 \seetitle{Pattern Matching: The Gestalt Approach}{Discussion of a
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66 similar algorithm by John W. Ratcliff and D. E. Metzener.
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67 This was published in
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68 \citetitle[http://www.ddj.com/]{Dr. Dobb's Journal} in
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69 July, 1988.}
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70 \end{seealso}
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71
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72
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73 \subsection{SequenceMatcher Objects \label{sequence-matcher}}
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74
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75 The \class{SequenceMatcher} class has this constructor:
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76
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77 \begin{classdesc}{SequenceMatcher}{\optional{isjunk\optional{,
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78 a\optional{, b}}}}
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79 Optional argument \var{isjunk} must be \code{None} (the default) or
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80 a one-argument function that takes a sequence element and returns
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81 true if and only if the element is ``junk'' and should be ignored.
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82 \code{None} is equivalent to passing \code{lambda x: 0}, i.e.\ no
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83 elements are ignored. For example, pass
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84
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85 \begin{verbatim}
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86 lambda x: x in " \t"
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87 \end{verbatim}
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88
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89 if you're comparing lines as sequences of characters, and don't want
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90 to synch up on blanks or hard tabs.
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91
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92 The optional arguments \var{a} and \var{b} are sequences to be
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93 compared; both default to empty strings. The elements of both
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94 sequences must be hashable.
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95 \end{classdesc}
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96
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97
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98 \class{SequenceMatcher} objects have the following methods:
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99
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100 \begin{methoddesc}{set_seqs}{a, b}
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101 Set the two sequences to be compared.
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102 \end{methoddesc}
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103
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104 \class{SequenceMatcher} computes and caches detailed information about
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105 the second sequence, so if you want to compare one sequence against
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106 many sequences, use \method{set_seq2()} to set the commonly used
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107 sequence once and call \method{set_seq1()} repeatedly, once for each
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108 of the other sequences.
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109
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110 \begin{methoddesc}{set_seq1}{a}
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111 Set the first sequence to be compared. The second sequence to be
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112 compared is not changed.
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113 \end{methoddesc}
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114
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115 \begin{methoddesc}{set_seq2}{b}
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116 Set the second sequence to be compared. The first sequence to be
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117 compared is not changed.
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118 \end{methoddesc}
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119
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120 \begin{methoddesc}{find_longest_match}{alo, ahi, blo, bhi}
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121 Find longest matching block in \code{\var{a}[\var{alo}:\var{ahi}]}
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122 and \code{\var{b}[\var{blo}:\var{bhi}]}.
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123
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124 If \var{isjunk} was omitted or \code{None},
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125 \method{get_longest_match()} returns \code{(\var{i}, \var{j},
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126 \var{k})} such that \code{\var{a}[\var{i}:\var{i}+\var{k}]} is equal
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127 to \code{\var{b}[\var{j}:\var{j}+\var{k}]}, where
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128 \code{\var{alo} <= \var{i} <= \var{i}+\var{k} <= \var{ahi}} and
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129 \code{\var{blo} <= \var{j} <= \var{j}+\var{k} <= \var{bhi}}.
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130 For all \code{(\var{i'}, \var{j'}, \var{k'})} meeting those
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131 conditions, the additional conditions
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132 \code{\var{k} >= \var{k'}},
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133 \code{\var{i} <= \var{i'}},
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134 and if \code{\var{i} == \var{i'}}, \code{\var{j} <= \var{j'}}
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135 are also met.
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136 In other words, of all maximal matching blocks, return one that
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137 starts earliest in \var{a}, and of all those maximal matching blocks
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138 that start earliest in \var{a}, return the one that starts earliest
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139 in \var{b}.
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140
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141 \begin{verbatim}
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142 >>> s = SequenceMatcher(None, " abcd", "abcd abcd")
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143 >>> s.find_longest_match(0, 5, 0, 9)
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144 (0, 4, 5)
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145 \end{verbatim}
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146
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147 If \var{isjunk} was provided, first the longest matching block is
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148 determined as above, but with the additional restriction that no
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149 junk element appears in the block. Then that block is extended as
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150 far as possible by matching (only) junk elements on both sides.
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151 So the resulting block never matches on junk except as identical
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152 junk happens to be adjacent to an interesting match.
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153
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154 Here's the same example as before, but considering blanks to be junk.
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155 That prevents \code{' abcd'} from matching the \code{' abcd'} at the
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156 tail end of the second sequence directly. Instead only the
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157 \code{'abcd'} can match, and matches the leftmost \code{'abcd'} in
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158 the second sequence:
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159
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160 \begin{verbatim}
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161 >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
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162 >>> s.find_longest_match(0, 5, 0, 9)
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163 (1, 0, 4)
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164 \end{verbatim}
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165
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166 If no blocks match, this returns \code{(\var{alo}, \var{blo}, 0)}.
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167 \end{methoddesc}
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168
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169 \begin{methoddesc}{get_matching_blocks}{}
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170 Return list of triples describing matching subsequences.
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171 Each triple is of the form \code{(\var{i}, \var{j}, \var{n})}, and
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172 means that \code{\var{a}[\var{i}:\var{i}+\var{n}] ==
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173 \var{b}[\var{j}:\var{j}+\var{n}]}. The triples are monotonically
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174 increasing in \var{i} and \var{j}.
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175
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176 The last triple is a dummy, and has the value \code{(len(\var{a}),
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177 len(\var{b}), 0)}. It is the only triple with \code{\var{n} == 0}.
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178 % Explain why a dummy is used!
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179
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180 \begin{verbatim}
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181 >>> s = SequenceMatcher(None, "abxcd", "abcd")
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182 >>> s.get_matching_blocks()
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183 [(0, 0, 2), (3, 2, 2), (5, 4, 0)]
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184 \end{verbatim}
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185 \end{methoddesc}
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186
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187 \begin{methoddesc}{get_opcodes}{}
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188 Return list of 5-tuples describing how to turn \var{a} into \var{b}.
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189 Each tuple is of the form \code{(\var{tag}, \var{i1}, \var{i2},
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190 \var{j1}, \var{j2})}. The first tuple has \code{\var{i1} ==
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191 \var{j1} == 0}, and remaining tuples have \var{i1} equal to the
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192 \var{i2} from the preceeding tuple, and, likewise, \var{j1} equal to
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193 the previous \var{j2}.
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194
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195 The \var{tag} values are strings, with these meanings:
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196
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197 \begin{tableii}{l|l}{code}{Value}{Meaning}
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198 \lineii{'replace'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
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199 replaced by \code{\var{b}[\var{j1}:\var{j2}]}.}
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200 \lineii{'delete'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
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201 deleted. Note that \code{\var{j1} == \var{j2}} in
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202 this case.}
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203 \lineii{'insert'}{\code{\var{b}[\var{j1}:\var{j2}]} should be
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204 inserted at \code{\var{a}[\var{i1}:\var{i1}]}.
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205 Note that \code{\var{i1} == \var{i2}} in this
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206 case.}
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207 \lineii{'equal'}{\code{\var{a}[\var{i1}:\var{i2}] ==
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208 \var{b}[\var{j1}:\var{j2}]} (the sub-sequences are
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209 equal).}
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210 \end{tableii}
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211
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212 For example:
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213
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214 \begin{verbatim}
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215 >>> a = "qabxcd"
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216 >>> b = "abycdf"
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217 >>> s = SequenceMatcher(None, a, b)
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218 >>> for tag, i1, i2, j1, j2 in s.get_opcodes():
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219 ... print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
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220 ... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))
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221 delete a[0:1] (q) b[0:0] ()
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222 equal a[1:3] (ab) b[0:2] (ab)
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223 replace a[3:4] (x) b[2:3] (y)
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224 equal a[4:6] (cd) b[3:5] (cd)
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225 insert a[6:6] () b[5:6] (f)
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226 \end{verbatim}
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227 \end{methoddesc}
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228
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229 \begin{methoddesc}{ratio}{}
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230 Return a measure of the sequences' similarity as a float in the
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231 range [0, 1].
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232
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233 Where T is the total number of elements in both sequences, and M is
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234 the number of matches, this is 2.0*M / T. Note that this is \code{1.}
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235 if the sequences are identical, and \code{0.} if they have nothing in
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236 common.
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237
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238 This is expensive to compute if \method{get_matching_blocks()} or
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239 \method{get_opcodes()} hasn't already been called, in which case you
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240 may want to try \method{quick_ratio()} or
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241 \method{real_quick_ratio()} first to get an upper bound.
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242 \end{methoddesc}
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243
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244 \begin{methoddesc}{quick_ratio}{}
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245 Return an upper bound on \method{ratio()} relatively quickly.
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246
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247 This isn't defined beyond that it is an upper bound on
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248 \method{ratio()}, and is faster to compute.
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249 \end{methoddesc}
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250
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251 \begin{methoddesc}{real_quick_ratio}{}
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252 Return an upper bound on \method{ratio()} very quickly.
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253
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254 This isn't defined beyond that it is an upper bound on
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255 \method{ratio()}, and is faster to compute than either
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256 \method{ratio()} or \method{quick_ratio()}.
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257 \end{methoddesc}
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258
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259 The three methods that return the ratio of matching to total characters
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260 can give different results due to differing levels of approximation,
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261 although \method{quick_ratio()} and \method{real_quick_ratio()} are always
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262 at least as large as \method{ratio()}:
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263
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264 \begin{verbatim}
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265 >>> s = SequenceMatcher(None, "abcd", "bcde")
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266 >>> s.ratio()
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267 0.75
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268 >>> s.quick_ratio()
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269 0.75
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270 >>> s.real_quick_ratio()
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271 1.0
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272 \end{verbatim}
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273
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274
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275 \subsection{Examples \label{difflib-examples}}
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276
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277
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278 This example compares two strings, considering blanks to be ``junk:''
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279
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280 \begin{verbatim}
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281 >>> s = SequenceMatcher(lambda x: x == " ",
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282 ... "private Thread currentThread;",
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283 ... "private volatile Thread currentThread;")
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284 \end{verbatim}
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285
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286 \method{ratio()} returns a float in [0, 1], measuring the similarity
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287 of the sequences. As a rule of thumb, a \method{ratio()} value over
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288 0.6 means the sequences are close matches:
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289
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290 \begin{verbatim}
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291 >>> print round(s.ratio(), 3)
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292 0.866
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293 \end{verbatim}
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294
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295 If you're only interested in where the sequences match,
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296 \method{get_matching_blocks()} is handy:
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297
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298 \begin{verbatim}
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299 >>> for block in s.get_matching_blocks():
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300 ... print "a[%d] and b[%d] match for %d elements" % block
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301 a[0] and b[0] match for 8 elements
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302 a[8] and b[17] match for 6 elements
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303 a[14] and b[23] match for 15 elements
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304 a[29] and b[38] match for 0 elements
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305 \end{verbatim}
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306
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307 Note that the last tuple returned by \method{get_matching_blocks()} is
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308 always a dummy, \code{(len(\var{a}), len(\var{b}), 0)}, and this is
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309 the only case in which the last tuple element (number of elements
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310 matched) is \code{0}.
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311
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312 If you want to know how to change the first sequence into the second,
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313 use \method{get_opcodes()}:
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314
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315 \begin{verbatim}
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316 >>> for opcode in s.get_opcodes():
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317 ... print "%6s a[%d:%d] b[%d:%d]" % opcode
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318 equal a[0:8] b[0:8]
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319 insert a[8:8] b[8:17]
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320 equal a[8:14] b[17:23]
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321 equal a[14:29] b[23:38]
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322 \end{verbatim}
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323
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324 See \file{Tools/scripts/ndiff.py} from the Python source distribution
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325 for a fancy human-friendly file differencer, which uses
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326 \class{SequenceMatcher} both to view files as sequences of lines, and
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327 lines as sequences of characters.
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328
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329 See also the function \function{get_close_matches()} in this module,
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330 which shows how simple code building on \class{SequenceMatcher} can be
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331 used to do useful work.