lxml.html._difflib module — lxml documentation

# lxml.html._difflib module

Module difflib – helpers for computing deltas between objects.

Function get_close_matches(word, possibilities, n=3, cutoff=0.6):

  Use SequenceMatcher to return list of the best “good enough” matches.

Function context_diff(a, b):

  For two lists of strings, return a delta in context diff format.

Function ndiff(a, b):

  Return a delta: the difference between a and b (lists of strings).

Function restore(delta, which):

  Return one of the two sequences that generated an ndiff delta.

Function unified_diff(a, b):

  For two lists of strings, return a delta in unified diff format.

Class SequenceMatcher:

  A flexible class for comparing pairs of sequences of any type.

Class Differ:

  For producing human-readable deltas from sequences of lines of text.

Class HtmlDiff:

  For producing HTML side by side comparison with change highlights.

class lxml.html._difflib.Differ(linejunk=None, charjunk=None)

  Bases: `object`

  Differ 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:

  > ‘- ‘ 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.

  Note that Differ makes no claim to produce a minimal diff. 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.

  Example: Comparing 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(keepends=True)
      >>> 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(keepends=True)

  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 Differ.__init__ 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 as _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:

      >>> print(''.join(result), end="")
          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.

  _dump(tag, x, lo, hi)

    Generate comparison results for a same-tagged range.

  _fancy_helper(a, alo, ahi, b, blo, bhi)
  _fancy_replace(a, alo, ahi, b, blo, bhi)

    When replacing one block of lines with another, search the blocks for similar lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it.

    Example:

        >>> d = Differ()
        >>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,
        ...                            ['abcdefGhijkl\n'], 0, 1)
        >>> print(''.join(results), end="")
        - abcDefghiJkl
        ?    ^  ^  ^
        + abcdefGhijkl
        ?    ^  ^  ^

  _plain_replace(a, alo, ahi, b, blo, bhi)
  _qformat(aline, bline, atags, btags)

    Format “?” output and deal with tabs.

    Example:

        >>> d = Differ()
        >>> results = d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
        ...                      '  ^ ^  ^      ', '  ^ ^  ^      ')
        >>> for line in results: print(repr(line))
        ...
        '- \tabcDefghiJkl\n'
        '? \t ^ ^  ^\n'
        '+ \tabcdefGhijkl\n'
        '? \t ^ ^  ^\n'

  compare(a, b)

    Compare two sequences of lines; generate the resulting delta.

    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.

    Example:

        >>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),
        ...                                'ore\ntree\nemu\n'.splitlines(True))),
        ...       end="")
        - one
        ?  ^
        + ore
        ?  ^
        - two
        - three
        ?  -
        + tree
        + emu

class lxml.html._difflib.HtmlDiff(tabsize=8, wrapcolumn=None, linejunk=None, charjunk=<cyfunction IS_CHARACTER_JUNK>)

  Bases: `object`

  For producing HTML side by side comparison with change highlights.

  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 following methods are provided for HTML generation:

  make_table – generates HTML for a single side by side table make_file – generates complete HTML file with a single side by side table

  See Doc/includes/diff.py for an example usage of this class.

  _collect_lines(diffs)

    Collects mdiff output into separate lists

    Before storing the mdiff from/to data into a list, it is converted into a single line of text with HTML markup.

  _convert_flags(fromlist, tolist, flaglist, context, numlines)

    Makes list of “next” links

  _format_line(side, flag, linenum, text)

    Returns HTML markup of “from” / “to” text lines

    side – 0 or 1 indicating “from” or “to” text flag – indicates if difference on line linenum – line number (used for line number column) text – line text to be marked up

  _line_wrapper(diffs)

    Returns iterator that splits (wraps) mdiff text lines

  _make_prefix()

    Create unique anchor prefixes

  _split_line(data_list, line_num, text)

    Builds list of text lines by splitting text lines at wrap point

    This function will determine if the input text line needs to be wrapped (split) into separate lines. If so, the first wrap point will be determined and the first line appended to the output text line list. This function is used recursively to handle the second part of the split line to further split it.

  _tab_newline_replace(fromlines, tolines)

    Returns from/to line lists with tabs expanded and newlines removed.

    Instead of tab characters being replaced by the number of spaces needed to fill in to the next tab stop, this function will fill the space with tab characters. This is done so that the difference algorithms can identify changes in a file when tabs are replaced by spaces and vice versa. At the end of the HTML generation, the tab characters will be replaced with a nonbreakable space.

  make_file(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5, *, charset='utf-8')

    Returns HTML file of side by side comparison with change highlights

    Arguments: fromlines – list of “from” lines tolines – list of “to” lines fromdesc – “from” file column header string todesc – “to” file column header string context – set to True for contextual differences (defaults to False

    > which shows full differences).

    numlines – number of context lines. When context is set True,

      controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the “next” link anchors before the next change (so click of “next” link jumps to just before the change).

    charset – charset of the HTML document

  make_table(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5)

    Returns HTML table of side by side comparison with change highlights

    Arguments: fromlines – list of “from” lines tolines – list of “to” lines fromdesc – “from” file column header string todesc – “to” file column header string context – set to True for contextual differences (defaults to False

    > which shows full differences).

    numlines – number of context lines. When context is set True,

      controls number of lines displayed before and after the change. When context is False, controls the number of lines to place the “next” link anchors before the next change (so click of “next” link jumps to just before the change).

  _default_prefix = 0
  _file_template = '\n<!DOCTYPE html>\n<html lang="en">\n<head>\n <meta charset="%(charset)s">\n <meta name="viewport" content="width=device-width, initial-scale=1">\n <title>Diff comparison</title>\n <style>%(styles)s\n </style>\n</head>\n\n<body>\n %(table)s%(legend)s\n</body>\n\n</html>'
  _legend = '\n <table class="diff" summary="Legends">\n <tr> <th colspan="2"> Legends </th> </tr>\n <tr> <td> <table border="" summary="Colors">\n <tr><th> Colors </th> </tr>\n <tr><td class="diff_add">&nbsp;Added&nbsp;</td></tr>\n <tr><td class="diff_chg">Changed</td> </tr>\n <tr><td class="diff_sub">Deleted</td> </tr>\n </table></td>\n <td> <table border="" summary="Links">\n <tr><th colspan="2"> Links </th> </tr>\n <tr><td>(f)irst change</td> </tr>\n <tr><td>(n)ext change</td> </tr>\n <tr><td>(t)op</td> </tr>\n </table></td> </tr>\n </table>'
  _styles = '\n :root {color-scheme: light dark}\n table.diff {\n font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace;\n border: medium;\n }\n .diff_header {\n background-color: #e0e0e0;\n font-weight: bold;\n }\n td.diff_header {\n text-align: right;\n padding: 0 8px;\n }\n .diff_next {\n background-color: #c0c0c0;\n padding: 4px 0;\n }\n .diff_add {background-color:palegreen}\n .diff_chg {background-color:#ffff77}\n .diff_sub {background-color:#ffaaaa}\n table.diff[summary="Legends"] {\n margin-top: 20px;\n border: 1px solid #ccc;\n }\n table.diff[summary="Legends"] th {\n background-color: #e0e0e0;\n padding: 4px 8px;\n }\n table.diff[summary="Legends"] td {\n padding: 4px 8px;\n }\n\n @media (prefers-color-scheme: dark) {\n .diff_header {background-color:#666}\n .diff_next {background-color:#393939}\n .diff_add {background-color:darkgreen}\n .diff_chg {background-color:#847415}\n .diff_sub {background-color:darkred}\n table.diff[summary="Legends"] {border-color:#555}\n table.diff[summary="Legends"] th{background-color:#666}\n }'
  _table_template = '\n <table class="diff" id="difflib_chg_%(prefix)s_top"\n cellspacing="0" cellpadding="0" rules="groups" >\n <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>\n <colgroup></colgroup> <colgroup></colgroup> <colgroup></colgroup>\n %(header_row)s\n <tbody>\n%(data_rows)s </tbody>\n </table>'

class lxml.html._difflib.SequenceMatcher

  Bases: `object`

  SequenceMatcher 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 basic idea is to find the longest contiguous matching subsequence that contains no “junk” elements (R-O 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.

  SequenceMatcher tries to compute a “human-friendly diff” between two sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the longest contiguous & junk-free matching subsequence. That’s what catches peoples’ eyes. The Windows(tm) windiff has another interesting notion, pairing up elements that appear uniquely in each sequence. That, and the method here, appear to yield more intuitive difference reports than does diff. This method appears to be the least vulnerable to syncing up on blocks of “junk lines”, though (like blank lines in ordinary text files, or maybe “<P>” lines in HTML files). That may be because this is the only method of the 3 that has a concept of “junk” <wink>.

  Example, comparing two strings, and 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 the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines.

  See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work.

  Timing: Basic R-O is cubic time worst case and quadratic time 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.

  __chain_b()
  get_grouped_opcodes(n=3)

    Isolate change clusters by eliminating ranges with no changes.

    Return a generator of groups with up to n lines of context. Each group is in the same format as returned by get_opcodes().

        >>> from pprint import pprint
        >>> a = list(map(str, range(1,40)))
        >>> b = a[:]
        >>> b[8:8] = ['i']     # Make an insertion
        >>> b[20] += 'x'       # Make a replacement
        >>> b[23:28] = []      # Make a deletion
        >>> b[30] += 'y'       # Make another replacement
        >>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))
        [[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],
         [('equal', 16, 19, 17, 20),
          ('replace', 19, 20, 20, 21),
          ('equal', 20, 22, 21, 23),
          ('delete', 22, 27, 23, 23),
          ('equal', 27, 30, 23, 26)],
         [('equal', 31, 34, 27, 30),
          ('replace', 34, 35, 30, 31),
          ('equal', 35, 38, 31, 34)]]

  ratio()

    Return a measure of the sequences’ similarity (float in [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 if the sequences are identical, and 0 if they have nothing in common.

    .ratio() is expensive to compute if you haven’t already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound.

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

  set_seq1(a)

    Set the first sequence to be compared.

    The second sequence to be compared is not changed.

        >>> s = SequenceMatcher(None, "abcd", "bcde")
        >>> s.ratio()
        0.75
        >>> s.set_seq1("bcde")
        >>> s.ratio()
        1.0
        >>>

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

    See also set_seqs() and set_seq2().

  set_seq2(b)

    Set the second sequence to be compared.

    The first sequence to be compared is not changed.

        >>> s = SequenceMatcher(None, "abcd", "bcde")
        >>> s.ratio()
        0.75
        >>> s.set_seq2("abcd")
        >>> s.ratio()
        1.0
        >>>

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

    See also set_seqs() and set_seq1().

  set_seqs(a, b)

    Set the two sequences to be compared.

        >>> s = SequenceMatcher()
        >>> s.set_seqs("abcd", "bcde")
        >>> s.ratio()
        0.75

  a
  b

lxml.html._difflib.IS_CHARACTER_JUNK(ch, ws=' \t')

  Return True for ignorable character: iff ch is a space or tab.

  Examples:

      >>> IS_CHARACTER_JUNK(' ')
      True
      >>> IS_CHARACTER_JUNK('\t')
      True
      >>> IS_CHARACTER_JUNK('\n')
      False
      >>> IS_CHARACTER_JUNK('x')
      False

lxml.html._difflib.IS_LINE_JUNK(line, pat=None)

  Return True for ignorable line: if line is blank or contains a single ‘#’.

  Examples:

      >>> IS_LINE_JUNK('\n')
      True
      >>> IS_LINE_JUNK('  #   \n')
      True
      >>> IS_LINE_JUNK('hello\n')
      False

lxml.html._difflib.__pyx_unpickle_SequenceMatcher(__pyx_type, __pyx_checksum, __pyx_state)

lxml.html._difflib._check_types(a, b, *args)

lxml.html._difflib._format_range_context(start, stop)

  Convert range to the “ed” format

lxml.html._difflib._format_range_unified(start, stop)

  Convert range to the “ed” format

lxml.html._difflib._keep_original_ws(s, tag_s)

  Replace whitespace with the original whitespace characters in s

lxml.html._difflib._mdiff(fromlines, tolines, context=None, linejunk=None, charjunk=<cyfunction IS_CHARACTER_JUNK>)

  Returns generator yielding marked up from/to side by side differences.

  Arguments: fromlines – list of text lines to compared to tolines tolines – list of text lines to be compared to fromlines context – number of context lines to display on each side of difference,

  > if None, all from/to text lines will be generated.

  linejunk – passed on to ndiff (see ndiff documentation) charjunk – passed on to ndiff (see ndiff documentation)

  This function returns an iterator which returns a tuple: (from line tuple, to line tuple, boolean flag)

  from/to line tuple – (line num, line text)

    line num – integer or None (to indicate a context separation) line text – original line text with following markers inserted:

    > ‘0+’ – marks start of added text ‘0-’ – marks start of deleted text ‘0^’ – marks start of changed text ‘1’ – marks end of added/deleted/changed text

  boolean flag – None indicates context separation, True indicates

    either “from” or “to” line contains a change, otherwise False.

  This function/iterator was originally developed to generate side by side file difference for making HTML pages (see HtmlDiff class for example usage).

  Note, this function utilizes the ndiff function to generate the side by side difference markup. Optional ndiff arguments may be passed to this function and they in turn will be passed to ndiff.

lxml.html._difflib.context_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')

  Compare two sequences of lines; generate the delta as a context diff.

  Context diffs are a compact way of showing line changes and a few lines of context. 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 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.

  Example:

      >>> print(''.join(context_diff('one\ntwo\nthree\nfour\n'.splitlines(True),
      ...       'zero\none\ntree\nfour\n'.splitlines(True), 'Original', 'Current')),
      ...       end="")
      *** Original
      --- Current
      ***************
      *** 1,4 ****
        one
      ! two
      ! three
        four
      --- 1,4 ----
      + zero
        one
      ! tree
        four

lxml.html._difflib.diff_bytes(dfunc, a, b, fromfile=b'', tofile=b'', fromfiledate=b'', tofiledate=b'', n=3, lineterm=b'\n')

  Compare a and b, two sequences of lines represented as bytes rather than str. This is a wrapper for dfunc, which is typically either unified_diff() or context_diff(). Inputs are losslessly converted to strings so that dfunc only has to worry about strings, and encoded back to bytes on return. This is necessary to compare files with unknown or inconsistent encoding. All other inputs (except n) must be bytes rather than str.

lxml.html._difflib.get_close_matches(word, possibilities, n=3, cutoff=0.6)

  Use SequenceMatcher to return list of the best “good enough” matches.

  word is a sequence for which close matches are desired (typically a string).

  possibilities is a list of sequences against which to match word (typically a list of strings).

  Optional arg n (default 3) is the maximum number of close matches to return. n must be > 0.

  Optional arg cutoff (default 0.6) is a float in [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 as _keyword
      >>> get_close_matches("wheel", _keyword.kwlist)
      ['while']
      >>> get_close_matches("Apple", _keyword.kwlist)
      []
      >>> get_close_matches("accept", _keyword.kwlist)
      ['except']

lxml.html._difflib.ndiff(a, b, linejunk=None, charjunk=<cyfunction IS_CHARACTER_JUNK>)

  Compare a and b (lists of strings); return a Differ-style delta.

  Optional keyword parameters linejunk and charjunk are for filter functions, or can be None:

  - linejunk: A function that should accept a single string argument and return true iff the string is junk. The default is None, and is recommended; the underlying SequenceMatcher class has an adaptive notion of “noise” lines.

  - charjunk: A function that accepts a character (string of length 1), and returns true iff the character is junk. The default is the module-level function IS_CHARACTER_JUNK, which filters out whitespace characters (a blank or tab; note: it’s a bad idea to include newline in this!).

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

  Example:

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

lxml.html._difflib.restore(delta, which)

  Generate one of the two sequences that generated a delta.

  Given a delta produced by Differ.compare() or ndiff(), extract lines originating from file 1 or 2 (parameter which), stripping off line prefixes.

  Examples:

      >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
      ...              'ore\ntree\nemu\n'.splitlines(keepends=True))
      >>> diff = list(diff)
      >>> print(''.join(restore(diff, 1)), end="")
      one
      two
      three
      >>> print(''.join(restore(diff, 2)), end="")
      ore
      tree
      emu

lxml.html._difflib.unified_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')

  Compare two sequences of lines; generate the delta as a unified diff.

  Unified diffs are a compact way of showing line changes and a few lines of context. 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 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 unidiff 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.

  Example:

      >>> for line in unified_diff('one two three four'.split(),
      ...             'zero one tree four'.split(), 'Original', 'Current',
      ...             '2005-01-26 23:30:50', '2010-04-02 10:20:52',
      ...             lineterm=''):
      ...     print(line)
      --- Original        2005-01-26 23:30:50
      +++ Current         2010-04-02 10:20:52
      @@ -1,4 +1,4 @@
      +zero
       one
      -two
      -three
      +tree
       four