7.4. "difflib" — Helpers for computing deltas
*********************************************

New in version 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
"filecmp" module.

class difflib.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.

   **Timing:** The basic Ratcliff-Obershelp algorithm is cubic time in
   the worst case and quadratic time in the expected case.
   "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.

   **Automatic junk heuristic:** "SequenceMatcher" supports a
   heuristic that automatically treats certain sequence items as junk.
   The heuristic counts how many times each individual item appears in
   the sequence. If an item’s duplicates (after the first one) account
   for more than 1% of the sequence and the sequence is at least 200
   items long, this item is marked as “popular” and is treated as junk
   for the purpose of sequence matching. This heuristic can be turned
   off by setting the "autojunk" argument to "False" when creating the
   "SequenceMatcher".

   New in version 2.7.1: The *autojunk* parameter.

class difflib.Differ

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

   Each line of a "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 difflib.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:

   __init__(tabsize=8, wrapcolumn=None, linejunk=None, charjunk=IS_CHARACTER_JUNK)

      Initializes instance of "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 "HtmlDiff" to generate the side by side
      HTML differences).  See "ndiff()" documentation for argument
      default values and descriptions.

   The following methods are public:

   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).

   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
      "make_file()" method.

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

   New in version 2.4.

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

   Compare *a* and *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 *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 "file.readlines()" result in diffs that are suitable
   for use with "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 ISO 8601
   format. If not specified, the strings default to blanks.

   >>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
   >>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
   >>> for line in context_diff(s1, s2, fromfile='before.py', tofile='after.py'):
   ...     sys.stdout.write(line)  # doctest: +NORMALIZE_WHITESPACE
   *** before.py
   --- after.py
   ***************
   *** 1,4 ****
   ! bacon
   ! eggs
   ! ham
     guido
   --- 1,4 ----
   ! python
   ! eggy
   ! hamster
     guido

   See A command-line interface to difflib for a more detailed
   example.

   New in version 2.3.

difflib.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']

difflib.ndiff(a, b[, linejunk][, charjunk])

   Compare *a* and *b* (lists of strings); return a "Differ"-style
   delta (a *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 "IS_LINE_JUNK()", which filters out lines
   without visible characters, except for at most one pound character
   ("'#'"). As of Python 2.3, the underlying "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 "IS_CHARACTER_JUNK()", which
   filters out whitespace characters (a blank or tab; note: bad idea
   to include newline in this!).

   "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

difflib.restore(sequence, which)

   Return one of the two sequences that generated a delta.

   Given a *sequence* produced by "Differ.compare()" or "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

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

   Compare *a* and *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 an
   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 "file.readlines()" result in diffs that are
   suitable for use with "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 ISO 8601
   format. If not specified, the strings default to blanks.

   >>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
   >>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
   >>> for line in unified_diff(s1, s2, fromfile='before.py', tofile='after.py'):
   ...     sys.stdout.write(line)   # doctest: +NORMALIZE_WHITESPACE
   --- before.py
   +++ after.py
   @@ -1,4 +1,4 @@
   -bacon
   -eggs
   -ham
   +python
   +eggy
   +hamster
    guido

   See A command-line interface to difflib for a more detailed
   example.

   New in version 2.3.

difflib.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 "ndiff()"
   before Python 2.3.

difflib.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 "ndiff()".

See also:

  Pattern Matching: The Gestalt Approach
     Discussion of a similar algorithm by John W. Ratcliff and D. E.
     Metzener. This was published in Dr. Dobb’s Journal in July, 1988.


7.4.1. SequenceMatcher Objects
==============================

The "SequenceMatcher" class has this constructor:

class difflib.SequenceMatcher(isjunk=None, a='', b='', autojunk=True)

   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 *hashable*.

   The optional argument *autojunk* can be used to disable the
   automatic junk heuristic.

   New in version 2.7.1: The *autojunk* parameter.

   "SequenceMatcher" objects have the following methods:

   set_seqs(a, b)

      Set the two sequences to be compared.

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

   set_seq1(a)

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

   set_seq2(b)

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

   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", "find_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)
      Match(a=0, b=4, size=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)
      Match(a=1, b=0, size=4)

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

      Changed in version 2.6: This method returns a *named tuple*
      "Match(a, b, size)".

   get_matching_blocks()

      Return list of triples describing non-overlapping 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.

      Changed in version 2.5: The guarantee that adjacent triples
      always describe non-adjacent blocks was implemented.

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

   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)

   get_grouped_opcodes([n])

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

      Starting with the groups returned by "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 "get_opcodes()".

      New in version 2.3.

   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 "get_matching_blocks()" or
      "get_opcodes()" hasn’t already been called, in which case you
      may want to try "quick_ratio()" or "real_quick_ratio()" first to
      get an upper bound.

   quick_ratio()

      Return an upper bound on "ratio()" relatively quickly.

   real_quick_ratio()

      Return an upper bound on "ratio()" very quickly.

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

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


7.4.2. SequenceMatcher Examples
===============================

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

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

"ratio()" returns a float in [0, 1], measuring the similarity of the
sequences.  As a rule of thumb, a "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,
"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 21 elements
a[29] and b[38] match for 0 elements

Note that the last tuple returned by "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 "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[8:29] b[17:38]

See also:

  * The "get_close_matches()" function in this module which shows
    how simple code building on "SequenceMatcher" can be used to do
    useful work.

  * Simple version control recipe for a small application built with
    "SequenceMatcher".


7.4.3. Differ Objects
=====================

Note that "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 "Differ" class has this constructor:

class difflib.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.

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

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


7.4.4. 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 "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 "Differ" object we may pass functions
to filter out line and character “junk.”  See the "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.


7.4.5. A command-line interface to difflib
==========================================

This example shows how to use difflib to create a "diff"-like utility.
It is also contained in the Python source distribution, as
"Tools/scripts/diff.py".

   """ Command line interface to difflib.py providing diffs in four formats:

   * ndiff:    lists every line and highlights interline changes.
   * context:  highlights clusters of changes in a before/after format.
   * unified:  highlights clusters of changes in an inline format.
   * html:     generates side by side comparison with change highlights.

   """

   import sys, os, time, difflib, optparse

   def main():
        # Configure the option parser
       usage = "usage: %prog [options] fromfile tofile"
       parser = optparse.OptionParser(usage)
       parser.add_option("-c", action="store_true", default=False,
                         help='Produce a context format diff (default)')
       parser.add_option("-u", action="store_true", default=False,
                         help='Produce a unified format diff')
       hlp = 'Produce HTML side by side diff (can use -c and -l in conjunction)'
       parser.add_option("-m", action="store_true", default=False, help=hlp)
       parser.add_option("-n", action="store_true", default=False,
                         help='Produce a ndiff format diff')
       parser.add_option("-l", "--lines", type="int", default=3,
                         help='Set number of context lines (default 3)')
       (options, args) = parser.parse_args()

       if len(args) == 0:
           parser.print_help()
           sys.exit(1)
       if len(args) != 2:
           parser.error("need to specify both a fromfile and tofile")

       n = options.lines
       fromfile, tofile = args # as specified in the usage string

       # we're passing these as arguments to the diff function
       fromdate = time.ctime(os.stat(fromfile).st_mtime)
       todate = time.ctime(os.stat(tofile).st_mtime)
       with open(fromfile, 'U') as f:
           fromlines = f.readlines()
       with open(tofile, 'U') as f:
           tolines = f.readlines()

       if options.u:
           diff = difflib.unified_diff(fromlines, tolines, fromfile, tofile,
                                       fromdate, todate, n=n)
       elif options.n:
           diff = difflib.ndiff(fromlines, tolines)
       elif options.m:
           diff = difflib.HtmlDiff().make_file(fromlines, tolines, fromfile,
                                               tofile, context=options.c,
                                               numlines=n)
       else:
           diff = difflib.context_diff(fromlines, tolines, fromfile, tofile,
                                       fromdate, todate, n=n)

       # we're using writelines because diff is a generator
       sys.stdout.writelines(diff)

   if __name__ == '__main__':
       main()
