316 lines
12 KiB
TeX
316 lines
12 KiB
TeX
\section{\module{difflib} ---
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Helpers for computing deltas}
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\declaremodule{standard}{difflib}
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\modulesynopsis{Helpers for computing differences between objects.}
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\moduleauthor{Tim Peters}{tim.one@home.com}
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\sectionauthor{Tim Peters}{tim.one@home.com}
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% LaTeXification by Fred L. Drake, Jr. <fdrake@acm.org>.
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\begin{funcdesc}{get_close_matches}{word, possibilities\optional{,
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n\optional{, cutoff}}}
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Return a list of the best ``good enough'' matches. \var{word} is a
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sequence for which close matches are desired (typically a string),
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and \var{possibilities} is a list of sequences against which to
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match \var{word} (typically a list of strings).
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Optional argument \var{n} (default \code{3}) is the maximum number
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of close matches to return; \var{n} must be greater than \code{0}.
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Optional argument \var{cutoff} (default \code{0.6}) is a float in
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the range [0, 1]. Possibilities that don't score at least that
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similar to \var{word} are ignored.
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The best (no more than \var{n}) matches among the possibilities are
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returned in a list, sorted by similarity score, most similar first.
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\begin{verbatim}
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>>> get_close_matches('appel', ['ape', 'apple', 'peach', 'puppy'])
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['apple', 'ape']
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>>> import keyword
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>>> get_close_matches('wheel', keyword.kwlist)
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['while']
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>>> get_close_matches('apple', keyword.kwlist)
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[]
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>>> get_close_matches('accept', keyword.kwlist)
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['except']
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\end{verbatim}
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\end{funcdesc}
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\begin{classdesc}{SequenceMatcher}{\unspecified}
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This is a flexible class for comparing pairs of sequences of any
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type, so long as the sequence elements are hashable. The basic
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algorithm predates, and is a little fancier than, an algorithm
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published in the late 1980's by Ratcliff and Obershelp under the
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hyperbolic name ``gestalt pattern matching.'' The idea is to find
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the longest contiguous matching subsequence that contains no
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``junk'' elements (the Ratcliff and Obershelp algorithm doesn't
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address junk). The same idea is then applied recursively to the
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pieces of the sequences to the left and to the right of the matching
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subsequence. This does not yield minimal edit sequences, but does
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tend to yield matches that ``look right'' to people.
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\strong{Timing:} The basic Ratcliff-Obershelp algorithm is cubic
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time in the worst case and quadratic time in the expected case.
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\class{SequenceMatcher} is quadratic time for the worst case and has
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expected-case behavior dependent on how many elements the sequences
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have in common; best case time (no elements in common) is linear.
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\end{classdesc}
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\subsection{SequenceMatcher Objects \label{sequence-matcher}}
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\begin{classdesc}{SequenceMatcher}{\optional{isjunk\optional{,
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a\optional{, b}}}}
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Optional argument \var{isjunk} must be \code{None} (the default) or
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a one-argument function that takes a sequence element and returns
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true if and only if the element is ``junk'' and should be ignored.
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\code{None} is equivalent to passing \code{lambda x: 0}, i.e.\ no
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elements are ignored. For example, pass
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\begin{verbatim}
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lambda x: x in " \\t"
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\end{verbatim}
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if you're comparing lines as sequences of characters, and don't want
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to synch up on blanks or hard tabs.
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The optional arguments \var{a} and \var{b} are sequences to be
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compared; both default to empty strings. The elements of both
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sequences must be hashable.
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\end{classdesc}
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\class{SequenceMatcher} objects have the following methods:
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\begin{methoddesc}{set_seqs}{a, b}
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Set the two sequences to be compared.
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\end{methoddesc}
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\class{SequenceMatcher} computes and caches detailed information about
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the second sequence, so if you want to compare one sequence against
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many sequences, use \method{set_seq2()} to set the commonly used
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sequence once and call \method{set_seq1()} repeatedly, once for each
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of the other sequences.
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\begin{methoddesc}{set_seq1}{a}
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Set the first sequence to be compared. The second sequence to be
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compared is not changed.
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\end{methoddesc}
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\begin{methoddesc}{set_seq2}{b}
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Set the second sequence to be compared. The first sequence to be
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compared is not changed.
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\end{methoddesc}
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\begin{methoddesc}{find_longest_match}{alo, ahi, blo, bhi}
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Find longest matching block in \code{\var{a}[\var{alo}:\var{ahi}]}
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and \code{\var{b}[\var{blo}:\var{bhi}]}.
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If \var{isjunk} was omitted or \code{None},
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\method{get_longest_match()} returns \code{(\var{i}, \var{j},
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\var{k})} such that \code{\var{a}[\var{i}:\var{i}+\var{k}]} is equal
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to \code{\var{b}[\var{j}:\var{j}+\var{k}]}, where
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\code{\var{alo} <= \var{i} <= \var{i}+\var{k} <= \var{ahi}} and
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\code{\var{blo} <= \var{j} <= \var{j}+\var{k} <= \var{bhi}}.
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For all \code{(\var{i'}, \var{j'}, \var{k'})} meeting those
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conditions, the additional conditions
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\code{\var{k} >= \var{k'}},
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\code{\var{i} <= \var{i'}},
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and if \code{\var{i} == \var{i'}}, \code{\var{j} <= \var{j'}}
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are also met.
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In other words, of all maximal matching blocks, return one that
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starts earliest in \var{a}, and of all those maximal matching blocks
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that start earliest in \var{a}, return the one that starts earliest
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in \var{b}.
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\begin{verbatim}
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>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
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>>> s.find_longest_match(0, 5, 0, 9)
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(0, 4, 5)
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\end{verbatim}
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If \var{isjunk} was provided, first the longest matching block is
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determined as above, but with the additional restriction that no
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junk element appears in the block. Then that block is extended as
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far as possible by matching (only) junk elements on both sides.
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So the resulting block never matches on junk except as identical
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junk happens to be adjacent to an interesting match.
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Here's the same example as before, but considering blanks to be junk.
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That prevents \code{' abcd'} from matching the \code{ abcd} at the
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tail end of the second sequence directly. Instead only the
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\code{'abcd'} can match, and matches the leftmost \code{'abcd'} in
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the second sequence:
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\begin{verbatim}
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>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
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>>> s.find_longest_match(0, 5, 0, 9)
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(1, 0, 4)
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\end{verbatim}
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If no blocks match, this returns \code{(\var{alo}, \var{blo}, 0)}.
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\end{methoddesc}
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\begin{methoddesc}{get_matching_blocks}{}
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Return list of triples describing matching subsequences.
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Each triple is of the form \code{(\var{i}, \var{j}, \var{n})}, and
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means that \code{\var{a}[\var{i}:\var{i}+\var{n}] ==
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\var{b}[\var{j}:\var{j}+\var{n}]}. The triples are monotonically
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increasing in \var{i} and \var{j}.
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The last triple is a dummy, and has the value \code{(len(\var{a}),
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len(\var{b}), 0)}. It is the only triple with \code{\var{n} == 0}.
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% Explain why a dummy is used!
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\begin{verbatim}
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>>> s = SequenceMatcher(None, "abxcd", "abcd")
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>>> s.get_matching_blocks()
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[(0, 0, 2), (3, 2, 2), (5, 4, 0)]
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\end{verbatim}
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\end{methoddesc}
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\begin{methoddesc}{get_opcodes}{}
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Return list of 5-tuples describing how to turn \var{a} into \var{b}.
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Each tuple is of the form \code{(\var{tag}, \var{i1}, \var{i2},
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\var{j1}, \var{j2})}. The first tuple has \code{\var{i1} ==
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\var{j1} == 0}, and remaining tuples have \var{i1} equal to the
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\var{i2} from the preceeding tuple, and, likewise, \var{j1} equal to
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the previous \var{j2}.
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The \var{tag} values are strings, with these meanings:
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\begin{tableii}{l|l}{code}{Value}{Meaning}
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\lineii{'replace'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
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replaced by \code{\var{b}[\var{j1}:\var{j2}]}.}
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\lineii{'delete'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
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deleted. Note that \code{\var{j1} == \var{j2}} in
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this case.}
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\lineii{'insert'}{\code{\var{b}[\var{j1}:\var{j2}]} should be
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inserted at \code{\var{a}[\var{i1}:\var{i1}]}.
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Note that \code{\var{i1} == \var{i2}} in this
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case.}
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\lineii{'equal'}{\code{\var{a}[\var{i1}:\var{i2}] ==
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\var{b}[\var{j1}:\var{j2}]} (the sub-sequences are
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equal).}
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\end{tableii}
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For example:
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\begin{verbatim}
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>>> a = "qabxcd"
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>>> b = "abycdf"
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>>> s = SequenceMatcher(None, a, b)
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>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
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... print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
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... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))
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delete a[0:1] (q) b[0:0] ()
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equal a[1:3] (ab) b[0:2] (ab)
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replace a[3:4] (x) b[2:3] (y)
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equal a[4:6] (cd) b[3:5] (cd)
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insert a[6:6] () b[5:6] (f)
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\end{verbatim}
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\end{methoddesc}
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\begin{methoddesc}{ratio}{}
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Return a measure of the sequences' similarity as a float in the
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range [0, 1].
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Where T is the total number of elements in both sequences, and M is
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the number of matches, this is 2,0*M / T. Note that this is \code{1}
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if the sequences are identical, and \code{0} if they have nothing in
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common.
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This is expensive to compute if \method{get_matching_blocks()} or
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\method{get_opcodes()} hasn't already been called, in which case you
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may want to try \method{quick_ratio()} or
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\method{real_quick_ratio()} first to get an upper bound.
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\end{methoddesc}
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\begin{methoddesc}{quick_ratio}{}
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Return an upper bound on \method{ratio()} relatively quickly.
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This isn't defined beyond that it is an upper bound on
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\method{ratio()}, and is faster to compute.
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\end{methoddesc}
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\begin{methoddesc}{real_quick_ratio}{}
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Return an upper bound on \method{ratio()} very quickly.
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This isn't defined beyond that it is an upper bound on
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\method{ratio()}, and is faster to compute than either
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\method{ratio()} or \method{quick_ratio()}.
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\end{methoddesc}
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The three methods that return the ratio of differences to similarities
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can give different results due to differing levels of approximation:
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\begin{verbatim}
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>>> s = SequenceMatcher(None, "abcd", "bcde")
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>>> s.ratio()
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0.75
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>>> s.quick_ratio()
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0.75
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>>> s.real_quick_ratio()
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1.0
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\end{verbatim}
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\subsection{Examples \label{difflib-examples}}
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This example compares two strings, considering blanks to be ``junk:''
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\begin{verbatim}
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>>> s = SequenceMatcher(lambda x: x == " ",
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... "private Thread currentThread;",
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... "private volatile Thread currentThread;")
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\end{verbatim}
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\method{ratio()} returns a float in [0, 1], measuring the similarity
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of the sequences. As a rule of thumb, a \method{ratio()} value over
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0.6 means the sequences are close matches:
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\begin{verbatim}
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>>> print round(s.ratio(), 3)
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0.866
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\end{verbatim}
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If you're only interested in where the sequences match,
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\method{get_matching_blocks()} is handy:
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\begin{verbatim}
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>>> for block in s.get_matching_blocks():
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... print "a[%d] and b[%d] match for %d elements" % block
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a[0] and b[0] match for 8 elements
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a[8] and b[17] match for 6 elements
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a[14] and b[23] match for 15 elements
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a[29] and b[38] match for 0 elements
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\end{verbatim}
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Note that the last tuple returned by \method{get_matching_blocks()} is
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always a dummy, \code{(len(\var{a}), len(\var{b}), 0)}, and this is
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the only case in which the last tuple element (number of elements
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matched) is \code{0}.
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If you want to know how to change the first sequence into the second,
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use \method{get_opcodes()}:
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\begin{verbatim}
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>>> for opcode in s.get_opcodes():
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... print "%6s a[%d:%d] b[%d:%d]" % opcode
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equal a[0:8] b[0:8]
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insert a[8:8] b[8:17]
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equal a[8:14] b[17:23]
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equal a[14:29] b[23:38]
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\end{verbatim}
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See \file{Tools/scripts/ndiff.py} from the Python source distribution
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for a fancy human-friendly file differencer, which uses
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\class{SequenceMatcher} both to view files as sequences of lines, and
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lines as sequences of characters.
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See also the function \function{get_close_matches()} in this module,
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which shows how simple code building on \class{SequenceMatcher} can be
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used to do useful work.
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