cpython/Doc/library/difflib.rst

:mod:`difflib` --- Helpers for computing deltas
===============================================

.. module:: difflib
   :synopsis: Helpers for computing differences between objects.
.. moduleauthor:: Tim Peters <tim_one@users.sourceforge.net>
.. sectionauthor:: Tim Peters <tim_one@users.sourceforge.net>


.. % LaTeXification by Fred L. Drake, Jr. <fdrake@acm.org>.

.. versionadded:: 2.1

This module provides classes and functions for comparing sequences. It
can be used for example, for comparing files, and can produce difference
information in various formats, including HTML and context and unified
diffs. For comparing directories and files, see also, the :mod:`filecmp` module.

.. class:: SequenceMatcher

   This is a flexible class for comparing pairs of sequences of any type, so long
   as the sequence elements are :term:`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.

   **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.


.. class:: 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:

   +----------+-------------------------------------------+
   | Code     | Meaning                                   |
   +==========+===========================================+
   | ``'- '`` | line unique to sequence 1                 |
   +----------+-------------------------------------------+
   | ``'+ '`` | line unique to sequence 2                 |
   +----------+-------------------------------------------+
   | ``'  '`` | line common to both sequences             |
   +----------+-------------------------------------------+
   | ``'? '`` | line not present in either input sequence |
   +----------+-------------------------------------------+

   Lines beginning with '``?``' 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.


.. class:: HtmlDiff

   This class can be used to create an HTML table (or a complete HTML file
   containing the table) showing a side by side, line by line comparison of text
   with inter-line and intra-line change highlights.  The table can be generated in
   either full or contextual difference mode.

   The constructor for this class is:


   .. function:: __init__([tabsize][, wrapcolumn][, linejunk][, charjunk])

      Initializes instance of :class:`HtmlDiff`.

      *tabsize* is an optional keyword argument to specify tab stop spacing and
      defaults to ``8``.

      *wrapcolumn* is an optional keyword to specify column number where lines are
      broken and wrapped, defaults to ``None`` where lines are not wrapped.

      *linejunk* and *charjunk* are optional keyword arguments passed into ``ndiff()``
      (used by :class:`HtmlDiff` to generate the side by side HTML differences).  See
      ``ndiff()`` documentation for argument default values and descriptions.

   The following methods are public:


   .. function:: make_file(fromlines, tolines [, fromdesc][, todesc][, context][, numlines])

      Compares *fromlines* and *tolines* (lists of strings) and returns a string which
      is a complete HTML file containing a table showing line by line differences with
      inter-line and intra-line changes highlighted.

      *fromdesc* and *todesc* are optional keyword arguments to specify from/to file
      column header strings (both default to an empty string).

      *context* and *numlines* are both optional keyword arguments. Set *context* to
      ``True`` when contextual differences are to be shown, else the default is
      ``False`` to show the full files. *numlines* defaults to ``5``.  When *context*
      is ``True`` *numlines* controls the number of context lines which surround the
      difference highlights.  When *context* is ``False`` *numlines* controls the
      number of lines which are shown before a difference highlight when using the
      "next" hyperlinks (setting to zero would cause the "next" hyperlinks to place
      the next difference highlight at the top of the browser without any leading
      context).


   .. function:: make_table(fromlines, tolines [, fromdesc][, todesc][, context][, numlines])

      Compares *fromlines* and *tolines* (lists of strings) and returns a string which
      is a complete HTML table showing line by line differences with inter-line and
      intra-line changes highlighted.

      The arguments for this method are the same as those for the :meth:`make_file`
      method.

   :file:`Tools/scripts/diff.py` is a command-line front-end to this class and
   contains a good example of its use.

   .. versionadded:: 2.4


.. function:: context_diff(a, b[, fromfile][, tofile][, fromfiledate][, tofiledate][, n][, lineterm])

   Compare *a* and *b* (lists of strings); return a delta (a :term:`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 *n* which defaults to three.

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

   For inputs that do not have trailing newlines, set the *lineterm* argument to
   ``""`` 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 *fromfile*,
   *tofile*, *fromfiledate*, and *tofiledate*. The modification times are normally
   expressed in the format returned by :func:`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


.. function:: get_close_matches(word, possibilities[, n][, cutoff])

   Return a list of the best "good enough" matches.  *word* is a sequence for which
   close matches are desired (typically a string), and *possibilities* is a list of
   sequences against which to match *word* (typically a list of strings).

   Optional argument *n* (default ``3``) is the maximum number of close matches to
   return; *n* must be greater than ``0``.

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

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

      >>> 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']


.. function:: ndiff(a, b[, linejunk][, charjunk])

   Compare *a* and *b* (lists of strings); return a :class:`Differ`\ -style
   delta (a :term:`generator` generating the delta lines).

   Optional keyword parameters *linejunk* and *charjunk* are for filter functions
   (or ``None``):

   *linejunk*: A function that accepts a single string argument, and returns true
   if the string is junk, or false if not. The default is (``None``), starting with
   Python 2.3.  Before then, the default was the module-level function
   :func:`IS_LINE_JUNK`, which filters out lines without visible characters, except
   for at most one pound 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.

   *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 :func:`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. ::

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


.. function:: restore(sequence, which)

   Return one of the two sequences that generated a delta.

   Given a *sequence* produced by :meth:`Differ.compare` or :func:`ndiff`, extract
   lines originating from file 1 or 2 (parameter *which*), stripping off line
   prefixes.

   Example::

      >>> 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


.. function:: unified_diff(a, b[, fromfile][, tofile][, fromfiledate][, tofiledate][, n][, lineterm])

   Compare *a* and *b* (lists of strings); return a delta (a :term:`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 *n* which
   defaults to three.

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

   For inputs that do not have trailing newlines, set the *lineterm* argument to
   ``""`` 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 *fromfile*,
   *tofile*, *fromfiledate*, and *tofiledate*. The modification times are normally
   expressed in the format returned by :func:`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


.. function:: IS_LINE_JUNK(line)

   Return true for ignorable lines.  The line *line* is ignorable if *line* is
   blank or contains a single ``'#'``, otherwise it is not ignorable.  Used as a
   default for parameter *linejunk* in :func:`ndiff` before Python 2.3.


.. function:: IS_CHARACTER_JUNK(ch)

   Return true for ignorable characters.  The character *ch* is ignorable if *ch*
   is a space or tab, otherwise it is not ignorable.  Used as a default for
   parameter *charjunk* in :func:`ndiff`.


.. seealso::

   `Pattern Matching: The Gestalt Approach <http://www.ddj.com/184407970?pgno=5>`_
      Discussion of a similar algorithm by John W. Ratcliff and D. E. Metzener. This
      was published in `Dr. Dobb's Journal <http://www.ddj.com/>`_ in July, 1988.


.. _sequence-matcher:

SequenceMatcher Objects
-----------------------

The :class:`SequenceMatcher` class has this constructor:


.. class:: SequenceMatcher([isjunk[, a[, b]]])

   Optional argument *isjunk* must be ``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 ``None`` for *isjunk* is
   equivalent to passing ``lambda x: 0``; in other words, no elements are ignored.
   For example, pass::

      lambda x: x in " \t"

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

   The optional arguments *a* and *b* are sequences to be compared; both default to
   empty strings.  The elements of both sequences must be :term:`hashable`.

:class:`SequenceMatcher` objects have the following methods:


.. method:: SequenceMatcher.set_seqs(a, b)

   Set the two sequences to be compared.

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


.. method:: SequenceMatcher.set_seq1(a)

   Set the first sequence to be compared.  The second sequence to be compared is
   not changed.


.. method:: SequenceMatcher.set_seq2(b)

   Set the second sequence to be compared.  The first sequence to be compared is
   not changed.


.. method:: SequenceMatcher.find_longest_match(alo, ahi, blo, bhi)

   Find longest matching block in ``a[alo:ahi]`` and ``b[blo:bhi]``.

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

      >>> s = SequenceMatcher(None, " abcd", "abcd abcd")
      >>> s.find_longest_match(0, 5, 0, 9)
      (0, 4, 5)

   If *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 ``' abcd'`` from matching the ``' abcd'`` at the tail end of the second
   sequence directly.  Instead only the ``'abcd'`` can match, and matches the
   leftmost ``'abcd'`` in the second sequence::

      >>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
      >>> s.find_longest_match(0, 5, 0, 9)
      (1, 0, 4)

   If no blocks match, this returns ``(alo, blo, 0)``.


.. method:: SequenceMatcher.get_matching_blocks()

   Return list of triples describing matching subsequences. Each triple is of the
   form ``(i, j, n)``, and means that ``a[i:i+n] == b[j:j+n]``.  The triples are
   monotonically increasing in *i* and *j*.

   The last triple is a dummy, and has the value ``(len(a), len(b), 0)``.  It is
   the only triple with ``n == 0``.  If ``(i, j, n)`` and ``(i', j', n')`` are
   adjacent triples in the list, and the second is not the last triple in the list,
   then ``i+n != i'`` or ``j+n != j'``; in other words, adjacent triples always
   describe non-adjacent equal blocks.

   .. % Explain why a dummy is used!

   .. versionchanged:: 2.5
      The guarantee that adjacent triples always describe non-adjacent blocks was
      implemented.

   ::

      >>> s = SequenceMatcher(None, "abxcd", "abcd")
      >>> s.get_matching_blocks()
      [(0, 0, 2), (3, 2, 2), (5, 4, 0)]


.. method:: SequenceMatcher.get_opcodes()

   Return list of 5-tuples describing how to turn *a* into *b*. Each tuple is of
   the form ``(tag, i1, i2, j1, j2)``.  The first tuple has ``i1 == j1 == 0``, and
   remaining tuples have *i1* equal to the *i2* from the preceding tuple, and,
   likewise, *j1* equal to the previous *j2*.

   The *tag* values are strings, with these meanings:

   +---------------+---------------------------------------------+
   | Value         | Meaning                                     |
   +===============+=============================================+
   | ``'replace'`` | ``a[i1:i2]`` should be replaced by          |
   |               | ``b[j1:j2]``.                               |
   +---------------+---------------------------------------------+
   | ``'delete'``  | ``a[i1:i2]`` should be deleted.  Note that  |
   |               | ``j1 == j2`` in this case.                  |
   +---------------+---------------------------------------------+
   | ``'insert'``  | ``b[j1:j2]`` should be inserted at          |
   |               | ``a[i1:i1]``. Note that ``i1 == i2`` in     |
   |               | this case.                                  |
   +---------------+---------------------------------------------+
   | ``'equal'``   | ``a[i1:i2] == b[j1:j2]`` (the sub-sequences |
   |               | are equal).                                 |
   +---------------+---------------------------------------------+

   For example::

      >>> 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)


.. method:: SequenceMatcher.get_grouped_opcodes([n])

   Return a :term:`generator` of groups with up to *n* lines of context.

   Starting with the groups returned by :meth:`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 :meth:`get_opcodes`.

   .. versionadded:: 2.3


.. method:: SequenceMatcher.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 ``1.0`` if the sequences are
   identical, and ``0.0`` if they have nothing in common.

   This is expensive to compute if :meth:`get_matching_blocks` or
   :meth:`get_opcodes` hasn't already been called, in which case you may want to
   try :meth:`quick_ratio` or :meth:`real_quick_ratio` first to get an upper bound.


.. method:: SequenceMatcher.quick_ratio()

   Return an upper bound on :meth:`ratio` relatively quickly.

   This isn't defined beyond that it is an upper bound on :meth:`ratio`, and is
   faster to compute.


.. method:: SequenceMatcher.real_quick_ratio()

   Return an upper bound on :meth:`ratio` very quickly.

   This isn't defined beyond that it is an upper bound on :meth:`ratio`, and is
   faster to compute than either :meth:`ratio` or :meth:`quick_ratio`.

The three methods that return the ratio of matching to total characters can give
different results due to differing levels of approximation, although
:meth:`quick_ratio` and :meth:`real_quick_ratio` are always at least as large as
:meth:`ratio`::

   >>> s = SequenceMatcher(None, "abcd", "bcde")
   >>> s.ratio()
   0.75
   >>> s.quick_ratio()
   0.75
   >>> s.real_quick_ratio()
   1.0


.. _sequencematcher-examples:

SequenceMatcher Examples
------------------------

This example compares two strings, considering blanks to be "junk:" ::

   >>> s = SequenceMatcher(lambda x: x == " ",
   ...                     "private Thread currentThread;",
   ...                     "private volatile Thread currentThread;")

:meth:`ratio` returns a float in [0, 1], measuring the similarity of the
sequences.  As a rule of thumb, a :meth:`ratio` value over 0.6 means the
sequences are close matches::

   >>> print round(s.ratio(), 3)
   0.866

If you're only interested in where the sequences match,
:meth:`get_matching_blocks` is handy::

   >>> 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

Note that the last tuple returned by :meth:`get_matching_blocks` is always a
dummy, ``(len(a), len(b), 0)``, and this is the only case in which the last
tuple element (number of elements matched) is ``0``.

If you want to know how to change the first sequence into the second, use
:meth:`get_opcodes`::

   >>> 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]

See also the function :func:`get_close_matches` in this module, which shows how
simple code building on :class:`SequenceMatcher` can be used to do useful work.


.. _differ-objects:

Differ Objects
--------------

Note that :class:`Differ`\ -generated deltas make no claim to be **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:


.. class:: Differ([linejunk[, charjunk]])

   Optional keyword parameters *linejunk* and *charjunk* are for filter functions
   (or ``None``):

   *linejunk*: A function that accepts a single string argument, and returns true
   if the string is junk.  The default is ``None``, meaning that no line is
   considered junk.

   *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 ``None``,
   meaning that no character is considered junk.

:class:`Differ` objects are used (deltas generated) via a single method:


.. method:: Differ.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 :meth:`readlines` method of file-like
   objects.  The delta generated also consists of newline-terminated strings, ready
   to be printed as-is via the :meth:`writelines` method of a file-like object.


.. _differ-examples:

Differ Example
--------------

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 :meth:`readlines` method of file-like objects)::

   >>> 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)

Next we instantiate a Differ object::

   >>> d = Differ()

Note that when instantiating a :class:`Differ` object we may pass functions to
filter out line and character "junk."  See the :meth:`Differ` constructor for
details.

Finally, we compare the two::

   >>> result = list(d.compare(text1, text2))

``result`` is a list of strings, so let's pretty-print it::

   >>> 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']

As a single multi-line string it looks like this::

   >>> 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.