\section{\module{difflib} ---
         Helpers for computing deltas}

\declaremodule{standard}{difflib}
\modulesynopsis{Helpers for computing differences between objects.}
\moduleauthor{Tim Peters}{tim.one@home.com}
\sectionauthor{Tim Peters}{tim.one@home.com}
% LaTeXification by Fred L. Drake, Jr. <fdrake@acm.org>.

\versionadded{2.1}


\begin{classdesc*}{SequenceMatcher}
  This is a flexible class for comparing pairs of sequences of any
  type, so long as the sequence elements are hashable.  The basic
  algorithm predates, and is a little fancier than, an algorithm
  published in the late 1980's by Ratcliff and Obershelp under the
  hyperbolic name ``gestalt pattern matching.''  The idea is to find
  the longest contiguous matching subsequence that contains no
  ``junk'' elements (the Ratcliff and Obershelp algorithm doesn't
  address junk).  The same idea is then applied recursively to the
  pieces of the sequences to the left and to the right of the matching
  subsequence.  This does not yield minimal edit sequences, but does
  tend to yield matches that ``look right'' to people.

  \strong{Timing:} The basic Ratcliff-Obershelp algorithm is cubic
  time in the worst case and quadratic time in the expected case.
  \class{SequenceMatcher} is quadratic time for the worst case and has
  expected-case behavior dependent in a complicated way on how many
  elements the sequences have in common; best case time is linear.
\end{classdesc*}

\begin{classdesc*}{Differ}
  This is a class for comparing sequences of lines of text, and
  producing human-readable differences or deltas.  Differ uses
  \class{SequenceMatcher} both to compare sequences of lines, and to
  compare sequences of characters within similar (near-matching)
  lines.

  Each line of a \class{Differ} delta begins with a two-letter code:

\begin{tableii}{l|l}{code}{Code}{Meaning}
  \lineii{'- '}{line unique to sequence 1}
  \lineii{'+ '}{line unique to sequence 2}
  \lineii{'  '}{line common to both sequences}
  \lineii{'? '}{line not present in either input sequence}
\end{tableii}

  Lines beginning with `\code{?~}' attempt to guide the eye to
  intraline differences, and were not present in either input
  sequence. These lines can be confusing if the sequences contain tab
  characters.
\end{classdesc*}

\begin{funcdesc}{context_diff}{a, b\optional{, fromfile\optional{, tofile
	\optional{, fromfiledate\optional{, tofiledate\optional{, n
	\optional{, lineterm}}}}}}}

  Compare \var{a} and \var{b} (lists of strings); return a
  delta (a generator generating the delta lines) in context diff
  format.
  
  Context diffs are a compact way of showing just the lines that have
  changed plus a few lines of context.  The changes are shown in a
  before/after style.  The number of context lines is set by \var{n}
  which defaults to three.

  By default, the diff control lines (those with \code{***} or \code{---})
  are created with a trailing newline.  This is helpful so that inputs created
  from \function{file.readlines()} result in diffs that are suitable for use
  with \function{file.writelines()} since both the inputs and outputs have
  trailing newlines.

  For inputs that do not have trailing newlines, set the \var{lineterm}
  argument to \code{""} so that the output will be uniformly newline free.

  The context diff format normally has a header for filenames and
  modification times.  Any or all of these may be specified using strings for
  \var{fromfile}, \var{tofile}, \var{fromfiledate}, and \var{tofiledate}.
  The modification times are normally expressed in the format returned by
  \function{time.ctime()}.  If not specified, the strings default to blanks.

  \file{Tools/scripts/diff.py} is a command-line front-end for this
  function.

  \versionadded{2.3}
\end{funcdesc}  

\begin{funcdesc}{get_close_matches}{word, possibilities\optional{,
                 n\optional{, cutoff}}}
  Return a list of the best ``good enough'' matches.  \var{word} is a
  sequence for which close matches are desired (typically a string),
  and \var{possibilities} is a list of sequences against which to
  match \var{word} (typically a list of strings).

  Optional argument \var{n} (default \code{3}) is the maximum number
  of close matches to return; \var{n} must be greater than \code{0}.

  Optional argument \var{cutoff} (default \code{0.6}) is a float in
  the range [0, 1].  Possibilities that don't score at least that
  similar to \var{word} are ignored.

  The best (no more than \var{n}) matches among the possibilities are
  returned in a list, sorted by similarity score, most similar first.

\begin{verbatim}
>>> get_close_matches('appel', ['ape', 'apple', 'peach', 'puppy'])
['apple', 'ape']
>>> import keyword
>>> get_close_matches('wheel', keyword.kwlist)
['while']
>>> get_close_matches('apple', keyword.kwlist)
[]
>>> get_close_matches('accept', keyword.kwlist)
['except']
\end{verbatim}
\end{funcdesc}

\begin{funcdesc}{ndiff}{a, b\optional{, linejunk\optional{,
                 charjunk}}}
  Compare \var{a} and \var{b} (lists of strings); return a
  \class{Differ}-style delta (a generator generating the delta lines).

  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, or false if not.
  The default is (\code{None}), starting with Python 2.3.  Before then,
  the default was the module-level function
  \function{IS_LINE_JUNK()}, which filters out lines without visible
  characters, except for at most one pound character (\character{\#}).
  As of Python 2.3, the underlying \class{SequenceMatcher} class
  does a dynamic analysis of which lines are so frequent as to
  constitute noise, and this usually works better than the pre-2.3
  default.

  \var{charjunk}: A function that accepts a character (a string of
  length 1), and returns if the character is junk, or false if not.
  The default is module-level function \function{IS_CHARACTER_JUNK()},
  which filters out whitespace characters (a blank or tab; note: bad
  idea to include newline in this!).

  \file{Tools/scripts/ndiff.py} is a command-line front-end to this
  function.

\begin{verbatim}
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
...              'ore\ntree\nemu\n'.splitlines(1))
>>> print ''.join(diff),
- one
?  ^
+ ore
?  ^
- two
- three
?  -
+ tree
+ emu
\end{verbatim}
\end{funcdesc}

\begin{funcdesc}{restore}{sequence, which}
  Return one of the two sequences that generated a delta.

  Given a \var{sequence} produced by \method{Differ.compare()} or
  \function{ndiff()}, extract lines originating from file 1 or 2
  (parameter \var{which}), stripping off line prefixes.

  Example:

\begin{verbatim}
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
...              'ore\ntree\nemu\n'.splitlines(1))
>>> diff = list(diff) # materialize the generated delta into a list
>>> print ''.join(restore(diff, 1)),
one
two
three
>>> print ''.join(restore(diff, 2)),
ore
tree
emu
\end{verbatim}

\end{funcdesc}

\begin{funcdesc}{unified_diff}{a, b\optional{, fromfile\optional{, tofile
	\optional{, fromfiledate\optional{, tofiledate\optional{, n
	\optional{, lineterm}}}}}}}

  Compare \var{a} and \var{b} (lists of strings); return a
  delta (a generator generating the delta lines) in unified diff
  format.
  
  Unified diffs are a compact way of showing just the lines that have
  changed plus a few lines of context.  The changes are shown in a
  inline style (instead of separate before/after blocks).  The number
  of context lines is set by \var{n} which defaults to three.

  By default, the diff control lines (those with \code{---}, \code{+++},
  or \code{@@}) are created with a trailing newline.  This is helpful so
  that inputs created from \function{file.readlines()} result in diffs
  that are suitable for use with \function{file.writelines()} since both
  the inputs and outputs have trailing newlines.

  For inputs that do not have trailing newlines, set the \var{lineterm}
  argument to \code{""} so that the output will be uniformly newline free.

  The context diff format normally has a header for filenames and
  modification times.  Any or all of these may be specified using strings for
  \var{fromfile}, \var{tofile}, \var{fromfiledate}, and \var{tofiledate}.
  The modification times are normally expressed in the format returned by
  \function{time.ctime()}.  If not specified, the strings default to blanks.

  \file{Tools/scripts/diff.py} is a command-line front-end for this
  function.

  \versionadded{2.3}  
\end{funcdesc} 

\begin{funcdesc}{IS_LINE_JUNK}{line}
  Return true for ignorable lines.  The line \var{line} is ignorable
  if \var{line} is blank or contains a single \character{\#},
  otherwise it is not ignorable.  Used as a default for parameter
  \var{linejunk} in \function{ndiff()} before Python 2.3.
\end{funcdesc}


\begin{funcdesc}{IS_CHARACTER_JUNK}{ch}
  Return true for ignorable characters.  The character \var{ch} is
  ignorable if \var{ch} is a space or tab, otherwise it is not
  ignorable.  Used as a default for parameter \var{charjunk} in
  \function{ndiff()}.
\end{funcdesc}


\begin{seealso}
  \seetitle{Pattern Matching: The Gestalt Approach}{Discussion of a
            similar algorithm by John W. Ratcliff and D. E. Metzener.
            This was published in
            \citetitle[http://www.ddj.com/]{Dr. Dobb's Journal} in
            July, 1988.}
\end{seealso}


\subsection{SequenceMatcher Objects \label{sequence-matcher}}

The \class{SequenceMatcher} class has this constructor:

\begin{classdesc}{SequenceMatcher}{\optional{isjunk\optional{,
                                   a\optional{, b}}}}
  Optional argument \var{isjunk} must be \code{None} (the default) or
  a one-argument function that takes a sequence element and returns
  true if and only if the element is ``junk'' and should be ignored.
  Passing \code{None} for \var{b} is equivalent to passing
  \code{lambda x: 0}; in other words, no elements are ignored.  For
  example, pass:

\begin{verbatim}
lambda x: x in " \t"
\end{verbatim}

  if you're comparing lines as sequences of characters, and don't want
  to synch up on blanks or hard tabs.

  The optional arguments \var{a} and \var{b} are sequences to be
  compared; both default to empty strings.  The elements of both
  sequences must be hashable.
\end{classdesc}


\class{SequenceMatcher} objects have the following methods:

\begin{methoddesc}{set_seqs}{a, b}
  Set the two sequences to be compared.
\end{methoddesc}

\class{SequenceMatcher} computes and caches detailed information about
the second sequence, so if you want to compare one sequence against
many sequences, use \method{set_seq2()} to set the commonly used
sequence once and call \method{set_seq1()} repeatedly, once for each
of the other sequences.

\begin{methoddesc}{set_seq1}{a}
  Set the first sequence to be compared.  The second sequence to be
  compared is not changed.
\end{methoddesc}

\begin{methoddesc}{set_seq2}{b}
  Set the second sequence to be compared.  The first sequence to be
  compared is not changed.
\end{methoddesc}

\begin{methoddesc}{find_longest_match}{alo, ahi, blo, bhi}
  Find longest matching block in \code{\var{a}[\var{alo}:\var{ahi}]}
  and \code{\var{b}[\var{blo}:\var{bhi}]}.

  If \var{isjunk} was omitted or \code{None},
  \method{get_longest_match()} returns \code{(\var{i}, \var{j},
  \var{k})} such that \code{\var{a}[\var{i}:\var{i}+\var{k}]} is equal
  to \code{\var{b}[\var{j}:\var{j}+\var{k}]}, where
      \code{\var{alo} <= \var{i} <= \var{i}+\var{k} <= \var{ahi}} and
      \code{\var{blo} <= \var{j} <= \var{j}+\var{k} <= \var{bhi}}.
  For all \code{(\var{i'}, \var{j'}, \var{k'})} meeting those
  conditions, the additional conditions
      \code{\var{k} >= \var{k'}},
      \code{\var{i} <= \var{i'}},
      and if \code{\var{i} == \var{i'}}, \code{\var{j} <= \var{j'}}
  are also met.
  In other words, of all maximal matching blocks, return one that
  starts earliest in \var{a}, and of all those maximal matching blocks
  that start earliest in \var{a}, return the one that starts earliest
  in \var{b}.

\begin{verbatim}
>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
(0, 4, 5)
\end{verbatim}

  If \var{isjunk} was provided, first the longest matching block is
  determined as above, but with the additional restriction that no
  junk element appears in the block.  Then that block is extended as
  far as possible by matching (only) junk elements on both sides.
  So the resulting block never matches on junk except as identical
  junk happens to be adjacent to an interesting match.

  Here's the same example as before, but considering blanks to be junk.
  That prevents \code{' abcd'} from matching the \code{' abcd'} at the
  tail end of the second sequence directly.  Instead only the
  \code{'abcd'} can match, and matches the leftmost \code{'abcd'} in
  the second sequence:

\begin{verbatim}
>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
(1, 0, 4)
\end{verbatim}

  If no blocks match, this returns \code{(\var{alo}, \var{blo}, 0)}.
\end{methoddesc}

\begin{methoddesc}{get_matching_blocks}{}
  Return list of triples describing matching subsequences.
  Each triple is of the form \code{(\var{i}, \var{j}, \var{n})}, and
  means that \code{\var{a}[\var{i}:\var{i}+\var{n}] ==
  \var{b}[\var{j}:\var{j}+\var{n}]}.  The triples are monotonically
  increasing in \var{i} and \var{j}.

  The last triple is a dummy, and has the value \code{(len(\var{a}),
  len(\var{b}), 0)}.  It is the only triple with \code{\var{n} == 0}.
  % Explain why a dummy is used!

\begin{verbatim}
>>> s = SequenceMatcher(None, "abxcd", "abcd")
>>> s.get_matching_blocks()
[(0, 0, 2), (3, 2, 2), (5, 4, 0)]
\end{verbatim}
\end{methoddesc}

\begin{methoddesc}{get_opcodes}{}
  Return list of 5-tuples describing how to turn \var{a} into \var{b}.
  Each tuple is of the form \code{(\var{tag}, \var{i1}, \var{i2},
  \var{j1}, \var{j2})}.  The first tuple has \code{\var{i1} ==
  \var{j1} == 0}, and remaining tuples have \var{i1} equal to the
  \var{i2} from the preceeding tuple, and, likewise, \var{j1} equal to
  the previous \var{j2}.

  The \var{tag} values are strings, with these meanings:

\begin{tableii}{l|l}{code}{Value}{Meaning}
  \lineii{'replace'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
                     replaced by \code{\var{b}[\var{j1}:\var{j2}]}.}
  \lineii{'delete'}{\code{\var{a}[\var{i1}:\var{i2}]} should be
                    deleted.  Note that \code{\var{j1} == \var{j2}} in
                    this case.}
  \lineii{'insert'}{\code{\var{b}[\var{j1}:\var{j2}]} should be
                    inserted at \code{\var{a}[\var{i1}:\var{i1}]}.
                    Note that \code{\var{i1} == \var{i2}} in this
                    case.}
  \lineii{'equal'}{\code{\var{a}[\var{i1}:\var{i2}] ==
                   \var{b}[\var{j1}:\var{j2}]} (the sub-sequences are
                   equal).}
\end{tableii}

For example:

\begin{verbatim}
>>> a = "qabxcd"
>>> b = "abycdf"
>>> s = SequenceMatcher(None, a, b)
>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
...    print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
...           (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))
 delete a[0:1] (q) b[0:0] ()
  equal a[1:3] (ab) b[0:2] (ab)
replace a[3:4] (x) b[2:3] (y)
  equal a[4:6] (cd) b[3:5] (cd)
 insert a[6:6] () b[5:6] (f)
\end{verbatim}
\end{methoddesc}

\begin{methoddesc}{get_grouped_opcodes}{\optional{n}}
  Return a generator of groups with up to \var{n} lines of context.

  Starting with the groups returned by \method{get_opcodes()},
  this method splits out smaller change clusters and eliminates
  intervening ranges which have no changes.

  The groups are returned in the same format as \method{get_opcodes()}.
  \versionadded{2.3}			    
\end{methoddesc}

\begin{methoddesc}{ratio}{}
  Return a measure of the sequences' similarity as a float in the
  range [0, 1].

  Where T is the total number of elements in both sequences, and M is
  the number of matches, this is 2.0*M / T. Note that this is
  \code{1.0} if the sequences are identical, and \code{0.0} if they
  have nothing in common.

  This is expensive to compute if \method{get_matching_blocks()} or
  \method{get_opcodes()} hasn't already been called, in which case you
  may want to try \method{quick_ratio()} or
  \method{real_quick_ratio()} first to get an upper bound.
\end{methoddesc}

\begin{methoddesc}{quick_ratio}{}
  Return an upper bound on \method{ratio()} relatively quickly.

  This isn't defined beyond that it is an upper bound on
  \method{ratio()}, and is faster to compute.
\end{methoddesc}

\begin{methoddesc}{real_quick_ratio}{}
  Return an upper bound on \method{ratio()} very quickly.

  This isn't defined beyond that it is an upper bound on
  \method{ratio()}, and is faster to compute than either
  \method{ratio()} or \method{quick_ratio()}.
\end{methoddesc}

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}
