627 lines
22 KiB
TeX
627 lines
22 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@users.sourceforge.net}
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\sectionauthor{Tim Peters}{tim_one@users.sourceforge.net}
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% LaTeXification by Fred L. Drake, Jr. <fdrake@acm.org>.
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\versionadded{2.1}
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\begin{classdesc*}{SequenceMatcher}
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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 in a complicated way on how many
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elements the sequences have in common; best case time is linear.
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\end{classdesc*}
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\begin{classdesc*}{Differ}
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This is a class for comparing sequences of lines of text, and
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producing human-readable differences or deltas. Differ uses
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\class{SequenceMatcher} both to compare sequences of lines, and to
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compare sequences of characters within similar (near-matching)
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lines.
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Each line of a \class{Differ} delta begins with a two-letter code:
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\begin{tableii}{l|l}{code}{Code}{Meaning}
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\lineii{'- '}{line unique to sequence 1}
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\lineii{'+ '}{line unique to sequence 2}
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\lineii{' '}{line common to both sequences}
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\lineii{'? '}{line not present in either input sequence}
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\end{tableii}
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Lines beginning with `\code{?~}' attempt to guide the eye to
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intraline differences, and were not present in either input
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sequence. These lines can be confusing if the sequences contain tab
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characters.
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\end{classdesc*}
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\begin{funcdesc}{context_diff}{a, b\optional{, fromfile\optional{, tofile
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\optional{, fromfiledate\optional{, tofiledate\optional{, n
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\optional{, lineterm}}}}}}}
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Compare \var{a} and \var{b} (lists of strings); return a
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delta (a generator generating the delta lines) in context diff
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format.
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Context diffs are a compact way of showing just the lines that have
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changed plus a few lines of context. The changes are shown in a
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before/after style. The number of context lines is set by \var{n}
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which defaults to three.
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By default, the diff control lines (those with \code{***} or \code{---})
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are created with a trailing newline. This is helpful so that inputs created
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from \function{file.readlines()} result in diffs that are suitable for use
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with \function{file.writelines()} since both the inputs and outputs have
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trailing newlines.
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For inputs that do not have trailing newlines, set the \var{lineterm}
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argument to \code{""} so that the output will be uniformly newline free.
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The context diff format normally has a header for filenames and
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modification times. Any or all of these may be specified using strings for
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\var{fromfile}, \var{tofile}, \var{fromfiledate}, and \var{tofiledate}.
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The modification times are normally expressed in the format returned by
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\function{time.ctime()}. If not specified, the strings default to blanks.
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\file{Tools/scripts/diff.py} is a command-line front-end for this
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function.
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\versionadded{2.3}
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\end{funcdesc}
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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{funcdesc}{ndiff}{a, b\optional{, linejunk\optional{,
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charjunk}}}
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Compare \var{a} and \var{b} (lists of strings); return a
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\class{Differ}-style delta (a generator generating the delta lines).
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Optional keyword parameters \var{linejunk} and \var{charjunk} are
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for filter functions (or \code{None}):
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\var{linejunk}: A function that accepts a single string
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argument, and returns true if the string is junk, or false if not.
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The default is (\code{None}), starting with Python 2.3. Before then,
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the default was the module-level function
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\function{IS_LINE_JUNK()}, which filters out lines without visible
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characters, except for at most one pound character (\character{\#}).
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As of Python 2.3, the underlying \class{SequenceMatcher} class
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does a dynamic analysis of which lines are so frequent as to
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constitute noise, and this usually works better than the pre-2.3
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default.
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\var{charjunk}: A function that accepts a character (a string of
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length 1), and returns if the character is junk, or false if not.
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The default is module-level function \function{IS_CHARACTER_JUNK()},
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which filters out whitespace characters (a blank or tab; note: bad
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idea to include newline in this!).
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\file{Tools/scripts/ndiff.py} is a command-line front-end to this
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function.
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\begin{verbatim}
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>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
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... 'ore\ntree\nemu\n'.splitlines(1))
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>>> print ''.join(diff),
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- one
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? ^
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+ ore
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? ^
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- two
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- three
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? -
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+ tree
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+ emu
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\end{verbatim}
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\end{funcdesc}
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\begin{funcdesc}{restore}{sequence, which}
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Return one of the two sequences that generated a delta.
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Given a \var{sequence} produced by \method{Differ.compare()} or
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\function{ndiff()}, extract lines originating from file 1 or 2
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(parameter \var{which}), stripping off line prefixes.
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Example:
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\begin{verbatim}
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>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
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... 'ore\ntree\nemu\n'.splitlines(1))
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>>> diff = list(diff) # materialize the generated delta into a list
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>>> print ''.join(restore(diff, 1)),
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one
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two
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three
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>>> print ''.join(restore(diff, 2)),
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ore
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tree
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emu
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\end{verbatim}
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\end{funcdesc}
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\begin{funcdesc}{unified_diff}{a, b\optional{, fromfile\optional{, tofile
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\optional{, fromfiledate\optional{, tofiledate\optional{, n
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\optional{, lineterm}}}}}}}
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Compare \var{a} and \var{b} (lists of strings); return a
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delta (a generator generating the delta lines) in unified diff
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format.
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Unified diffs are a compact way of showing just the lines that have
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changed plus a few lines of context. The changes are shown in a
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inline style (instead of separate before/after blocks). The number
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of context lines is set by \var{n} which defaults to three.
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By default, the diff control lines (those with \code{---}, \code{+++},
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or \code{@@}) are created with a trailing newline. This is helpful so
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that inputs created from \function{file.readlines()} result in diffs
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that are suitable for use with \function{file.writelines()} since both
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the inputs and outputs have trailing newlines.
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For inputs that do not have trailing newlines, set the \var{lineterm}
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argument to \code{""} so that the output will be uniformly newline free.
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The context diff format normally has a header for filenames and
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modification times. Any or all of these may be specified using strings for
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\var{fromfile}, \var{tofile}, \var{fromfiledate}, and \var{tofiledate}.
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The modification times are normally expressed in the format returned by
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\function{time.ctime()}. If not specified, the strings default to blanks.
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\file{Tools/scripts/diff.py} is a command-line front-end for this
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function.
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\versionadded{2.3}
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\end{funcdesc}
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\begin{funcdesc}{IS_LINE_JUNK}{line}
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Return true for ignorable lines. The line \var{line} is ignorable
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if \var{line} is blank or contains a single \character{\#},
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otherwise it is not ignorable. Used as a default for parameter
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\var{linejunk} in \function{ndiff()} before Python 2.3.
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\end{funcdesc}
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\begin{funcdesc}{IS_CHARACTER_JUNK}{ch}
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Return true for ignorable characters. The character \var{ch} is
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ignorable if \var{ch} is a space or tab, otherwise it is not
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ignorable. Used as a default for parameter \var{charjunk} in
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\function{ndiff()}.
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\end{funcdesc}
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\begin{seealso}
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\seetitle[http://www.ddj.com/documents/s=1103/ddj8807c/]
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{Pattern Matching: The Gestalt Approach}{Discussion of a
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similar algorithm by John W. Ratcliff and D. E. Metzener.
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This was published in
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\citetitle[http://www.ddj.com/]{Dr. Dobb's Journal} in
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July, 1988.}
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\end{seealso}
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\subsection{SequenceMatcher Objects \label{sequence-matcher}}
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The \class{SequenceMatcher} class has this constructor:
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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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Passing \code{None} for \var{b} is equivalent to passing
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\code{lambda x: 0}; in other words, no elements are ignored. For
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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}{get_grouped_opcodes}{\optional{n}}
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Return a generator of groups with up to \var{n} lines of context.
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Starting with the groups returned by \method{get_opcodes()},
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this method splits out smaller change clusters and eliminates
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intervening ranges which have no changes.
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The groups are returned in the same format as \method{get_opcodes()}.
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\versionadded{2.3}
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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
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\code{1.0} if the sequences are identical, and \code{0.0} if they
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have nothing in 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 matching to total characters
|
|
can give different results due to differing levels of approximation,
|
|
although \method{quick_ratio()} and \method{real_quick_ratio()} are always
|
|
at least as large as \method{ratio()}:
|
|
|
|
\begin{verbatim}
|
|
>>> s = SequenceMatcher(None, "abcd", "bcde")
|
|
>>> s.ratio()
|
|
0.75
|
|
>>> s.quick_ratio()
|
|
0.75
|
|
>>> s.real_quick_ratio()
|
|
1.0
|
|
\end{verbatim}
|
|
|
|
|
|
\subsection{SequenceMatcher Examples \label{sequencematcher-examples}}
|
|
|
|
|
|
This example compares two strings, considering blanks to be ``junk:''
|
|
|
|
\begin{verbatim}
|
|
>>> s = SequenceMatcher(lambda x: x == " ",
|
|
... "private Thread currentThread;",
|
|
... "private volatile Thread currentThread;")
|
|
\end{verbatim}
|
|
|
|
\method{ratio()} returns a float in [0, 1], measuring the similarity
|
|
of the sequences. As a rule of thumb, a \method{ratio()} value over
|
|
0.6 means the sequences are close matches:
|
|
|
|
\begin{verbatim}
|
|
>>> print round(s.ratio(), 3)
|
|
0.866
|
|
\end{verbatim}
|
|
|
|
If you're only interested in where the sequences match,
|
|
\method{get_matching_blocks()} is handy:
|
|
|
|
\begin{verbatim}
|
|
>>> for block in s.get_matching_blocks():
|
|
... print "a[%d] and b[%d] match for %d elements" % block
|
|
a[0] and b[0] match for 8 elements
|
|
a[8] and b[17] match for 6 elements
|
|
a[14] and b[23] match for 15 elements
|
|
a[29] and b[38] match for 0 elements
|
|
\end{verbatim}
|
|
|
|
Note that the last tuple returned by \method{get_matching_blocks()} is
|
|
always a dummy, \code{(len(\var{a}), len(\var{b}), 0)}, and this is
|
|
the only case in which the last tuple element (number of elements
|
|
matched) is \code{0}.
|
|
|
|
If you want to know how to change the first sequence into the second,
|
|
use \method{get_opcodes()}:
|
|
|
|
\begin{verbatim}
|
|
>>> for opcode in s.get_opcodes():
|
|
... print "%6s a[%d:%d] b[%d:%d]" % opcode
|
|
equal a[0:8] b[0:8]
|
|
insert a[8:8] b[8:17]
|
|
equal a[8:14] b[17:23]
|
|
equal a[14:29] b[23:38]
|
|
\end{verbatim}
|
|
|
|
See also the function \function{get_close_matches()} in this module,
|
|
which shows how simple code building on \class{SequenceMatcher} can be
|
|
used to do useful work.
|
|
|
|
|
|
\subsection{Differ Objects \label{differ-objects}}
|
|
|
|
Note that \class{Differ}-generated deltas make no claim to be
|
|
\strong{minimal} diffs. To the contrary, minimal diffs are often
|
|
counter-intuitive, because they synch up anywhere possible, sometimes
|
|
accidental matches 100 pages apart. Restricting synch points to
|
|
contiguous matches preserves some notion of locality, at the
|
|
occasional cost of producing a longer diff.
|
|
|
|
The \class{Differ} class has this constructor:
|
|
|
|
\begin{classdesc}{Differ}{\optional{linejunk\optional{, charjunk}}}
|
|
Optional keyword parameters \var{linejunk} and \var{charjunk} are
|
|
for filter functions (or \code{None}):
|
|
|
|
\var{linejunk}: A function that accepts a single string
|
|
argument, and returns true if the string is junk. The default is
|
|
\code{None}, meaning that no line is considered junk.
|
|
|
|
\var{charjunk}: A function that accepts a single character argument
|
|
(a string of length 1), and returns true if the character is junk.
|
|
The default is \code{None}, meaning that no character is
|
|
considered junk.
|
|
\end{classdesc}
|
|
|
|
\class{Differ} objects are used (deltas generated) via a single
|
|
method:
|
|
|
|
\begin{methoddesc}{compare}{a, b}
|
|
Compare two sequences of lines, and generate the delta (a sequence
|
|
of lines).
|
|
|
|
Each sequence must contain individual single-line strings ending
|
|
with newlines. Such sequences can be obtained from the
|
|
\method{readlines()} method of file-like objects. The delta generated
|
|
also consists of newline-terminated strings, ready to be printed as-is
|
|
via the \method{writelines()} method of a file-like object.
|
|
\end{methoddesc}
|
|
|
|
|
|
\subsection{Differ Example \label{differ-examples}}
|
|
|
|
This example compares two texts. First we set up the texts, sequences
|
|
of individual single-line strings ending with newlines (such sequences
|
|
can also be obtained from the \method{readlines()} method of file-like
|
|
objects):
|
|
|
|
\begin{verbatim}
|
|
>>> text1 = ''' 1. Beautiful is better than ugly.
|
|
... 2. Explicit is better than implicit.
|
|
... 3. Simple is better than complex.
|
|
... 4. Complex is better than complicated.
|
|
... '''.splitlines(1)
|
|
>>> len(text1)
|
|
4
|
|
>>> text1[0][-1]
|
|
'\n'
|
|
>>> text2 = ''' 1. Beautiful is better than ugly.
|
|
... 3. Simple is better than complex.
|
|
... 4. Complicated is better than complex.
|
|
... 5. Flat is better than nested.
|
|
... '''.splitlines(1)
|
|
\end{verbatim}
|
|
|
|
Next we instantiate a Differ object:
|
|
|
|
\begin{verbatim}
|
|
>>> d = Differ()
|
|
\end{verbatim}
|
|
|
|
Note that when instantiating a \class{Differ} object we may pass
|
|
functions to filter out line and character ``junk.'' See the
|
|
\method{Differ()} constructor for details.
|
|
|
|
Finally, we compare the two:
|
|
|
|
\begin{verbatim}
|
|
>>> result = list(d.compare(text1, text2))
|
|
\end{verbatim}
|
|
|
|
\code{result} is a list of strings, so let's pretty-print it:
|
|
|
|
\begin{verbatim}
|
|
>>> from pprint import pprint
|
|
>>> pprint(result)
|
|
[' 1. Beautiful is better than ugly.\n',
|
|
'- 2. Explicit is better than implicit.\n',
|
|
'- 3. Simple is better than complex.\n',
|
|
'+ 3. Simple is better than complex.\n',
|
|
'? ++ \n',
|
|
'- 4. Complex is better than complicated.\n',
|
|
'? ^ ---- ^ \n',
|
|
'+ 4. Complicated is better than complex.\n',
|
|
'? ++++ ^ ^ \n',
|
|
'+ 5. Flat is better than nested.\n']
|
|
\end{verbatim}
|
|
|
|
As a single multi-line string it looks like this:
|
|
|
|
\begin{verbatim}
|
|
>>> import sys
|
|
>>> sys.stdout.writelines(result)
|
|
1. Beautiful is better than ugly.
|
|
- 2. Explicit is better than implicit.
|
|
- 3. Simple is better than complex.
|
|
+ 3. Simple is better than complex.
|
|
? ++
|
|
- 4. Complex is better than complicated.
|
|
? ^ ---- ^
|
|
+ 4. Complicated is better than complex.
|
|
? ++++ ^ ^
|
|
+ 5. Flat is better than nested.
|
|
\end{verbatim}
|