" lines in HTML files). That may be because this is the only method of the 3 that has a *concept* of "junk" . 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(), 2)) 0.87 >>> 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. """ def __init__(self, isjunk=None, a='', b='', autojunk=True): """Construct a SequenceMatcher. Optional arg isjunk is None (the default), or a one-argument function that takes a sequence element and returns true iff the element is junk. None is equivalent to passing "lambda x: 0", i.e. no elements are considered to be junk. 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. Optional arg a is the first of two sequences to be compared. By default, an empty string. The elements of a must be hashable. See also .set_seqs() and .set_seq1(). Optional arg b is the second of two sequences to be compared. By default, an empty string. The elements of b must be hashable. See also .set_seqs() and .set_seq2(). Optional arg autojunk should be set to False to disable the "automatic junk heuristic" that treats popular elements as junk (see module documentation for more information). """ # Members: # a # first sequence # b # second sequence; differences are computed as "what do # we need to do to 'a' to change it into 'b'?" # b2j # for x in b, b2j[x] is a list of the indices (into b) # at which x appears; junk and popular elements do not appear # fullbcount # for x in b, fullbcount[x] == the number of times x # appears in b; only materialized if really needed (used # only for computing quick_ratio()) # matching_blocks # a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k]; # ascending & non-overlapping in i and in j; terminated by # a dummy (len(a), len(b), 0) sentinel # opcodes # a list of (tag, i1, i2, j1, j2) tuples, where tag is # one of # 'replace' a[i1:i2] should be replaced by b[j1:j2] # 'delete' a[i1:i2] should be deleted # 'insert' b[j1:j2] should be inserted # 'equal' a[i1:i2] == b[j1:j2] # isjunk # a user-supplied function taking a sequence element and # returning true iff the element is "junk" -- this has # subtle but helpful effects on the algorithm, which I'll # get around to writing up someday . # DON'T USE! Only __chain_b uses this. Use "in self.bjunk". # bjunk # the items in b for which isjunk is True. # bpopular # nonjunk items in b treated as junk by the heuristic (if used). self.isjunk = isjunk self.a = self.b = None self.autojunk = autojunk self.set_seqs(a, b) def set_seqs(self, a, b): """Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 """ self.set_seq1(a) self.set_seq2(b) def set_seq1(self, 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(). """ if a is self.a: return self.a = a self.matching_blocks = self.opcodes = None def set_seq2(self, 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(). """ if b is self.b: return self.b = b self.matching_blocks = self.opcodes = None self.fullbcount = None self.__chain_b() # For each element x in b, set b2j[x] to a list of the indices in # b where x appears; the indices are in increasing order; note that # the number of times x appears in b is len(b2j[x]) ... # when self.isjunk is defined, junk elements don't show up in this # map at all, which stops the central find_longest_match method # from starting any matching block at a junk element ... # b2j also does not contain entries for "popular" elements, meaning # elements that account for more than 1 + 1% of the total elements, and # when the sequence is reasonably large (>= 200 elements); this can # be viewed as an adaptive notion of semi-junk, and yields an enormous # speedup when, e.g., comparing program files with hundreds of # instances of "return NULL;" ... # note that this is only called when b changes; so for cross-product # kinds of matches, it's best to call set_seq2 once, then set_seq1 # repeatedly def __chain_b(self): # Because isjunk is a user-defined (not C) function, and we test # for junk a LOT, it's important to minimize the number of calls. # Before the tricks described here, __chain_b was by far the most # time-consuming routine in the whole module! If anyone sees # Jim Roskind, thank him again for profile.py -- I never would # have guessed that. # The first trick is to build b2j ignoring the possibility # of junk. I.e., we don't call isjunk at all yet. Throwing # out the junk later is much cheaper than building b2j "right" # from the start. b = self.b self.b2j = b2j = {} for i, elt in enumerate(b): indices = b2j.setdefault(elt, []) indices.append(i) # Purge junk elements self.bjunk = junk = set() isjunk = self.isjunk if isjunk: for elt in b2j.keys(): if isjunk(elt): junk.add(elt) for elt in junk: # separate loop avoids separate list of keys del b2j[elt] # Purge popular elements that are not junk self.bpopular = popular = set() n = len(b) if self.autojunk and n >= 200: ntest = n // 100 + 1 for elt, idxs in b2j.items(): if len(idxs) > ntest: popular.add(elt) for elt in popular: # ditto; as fast for 1% deletion del b2j[elt] def find_longest_match(self, alo=0, ahi=None, blo=0, bhi=None): """Find longest matching block in a[alo:ahi] and b[blo:bhi]. By default it will find the longest match in the entirety of a and b. If isjunk is not defined: Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where alo >> 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, return (alo, blo, 0). >>> s = SequenceMatcher(None, "ab", "c") >>> s.find_longest_match(0, 2, 0, 1) Match(a=0, b=0, size=0) """ # CAUTION: stripping common prefix or suffix would be incorrect. # E.g., # ab # acab # Longest matching block is "ab", but if common prefix is # stripped, it's "a" (tied with "b"). UNIX(tm) diff does so # strip, so ends up claiming that ab is changed to acab by # inserting "ca" in the middle. That's minimal but unintuitive: # "it's obvious" that someone inserted "ac" at the front. # Windiff ends up at the same place as diff, but by pairing up # the unique 'b's and then matching the first two 'a's. a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__ if ahi is None: ahi = len(a) if bhi is None: bhi = len(b) besti, bestj, bestsize = alo, blo, 0 # find longest junk-free match # during an iteration of the loop, j2len[j] = length of longest # junk-free match ending with a[i-1] and b[j] j2len = {} nothing = [] for i in range(alo, ahi): # look at all instances of a[i] in b; note that because # b2j has no junk keys, the loop is skipped if a[i] is junk j2lenget = j2len.get newj2len = {} for j in b2j.get(a[i], nothing): # a[i] matches b[j] if j < blo: continue if j >= bhi: break k = newj2len[j] = j2lenget(j-1, 0) + 1 if k > bestsize: besti, bestj, bestsize = i-k+1, j-k+1, k j2len = newj2len # Extend the best by non-junk elements on each end. In particular, # "popular" non-junk elements aren't in b2j, which greatly speeds # the inner loop above, but also means "the best" match so far # doesn't contain any junk *or* popular non-junk elements. while besti > alo and bestj > blo and \ not isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ not isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize += 1 # Now that we have a wholly interesting match (albeit possibly # empty!), we may as well suck up the matching junk on each # side of it too. Can't think of a good reason not to, and it # saves post-processing the (possibly considerable) expense of # figuring out what to do with it. In the case of an empty # interesting match, this is clearly the right thing to do, # because no other kind of match is possible in the regions. while besti > alo and bestj > blo and \ isbjunk(b[bestj-1]) and \ a[besti-1] == b[bestj-1]: besti, bestj, bestsize = besti-1, bestj-1, bestsize+1 while besti+bestsize < ahi and bestj+bestsize < bhi and \ isbjunk(b[bestj+bestsize]) and \ a[besti+bestsize] == b[bestj+bestsize]: bestsize = bestsize + 1 return Match(besti, bestj, bestsize) def get_matching_blocks(self): """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 in j. New in Python 2.5, it's also guaranteed that 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'. IOW, adjacent triples never describe adjacent equal blocks. The last triple is a dummy, (len(a), len(b), 0), and is the only triple with n==0. >>> s = SequenceMatcher(None, "abxcd", "abcd") >>> list(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)] """ if self.matching_blocks is not None: return self.matching_blocks la, lb = len(self.a), len(self.b) # This is most naturally expressed as a recursive algorithm, but # at least one user bumped into extreme use cases that exceeded # the recursion limit on their box. So, now we maintain a list # ('queue`) of blocks we still need to look at, and append partial # results to `matching_blocks` in a loop; the matches are sorted # at the end. queue = [(0, la, 0, lb)] matching_blocks = [] while queue: alo, ahi, blo, bhi = queue.pop() i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi) # a[alo:i] vs b[blo:j] unknown # a[i:i+k] same as b[j:j+k] # a[i+k:ahi] vs b[j+k:bhi] unknown if k: # if k is 0, there was no matching block matching_blocks.append(x) if alo < i and blo < j: queue.append((alo, i, blo, j)) if i+k < ahi and j+k < bhi: queue.append((i+k, ahi, j+k, bhi)) matching_blocks.sort() # It's possible that we have adjacent equal blocks in the # matching_blocks list now. Starting with 2.5, this code was added # to collapse them. i1 = j1 = k1 = 0 non_adjacent = [] for i2, j2, k2 in matching_blocks: # Is this block adjacent to i1, j1, k1? if i1 + k1 == i2 and j1 + k1 == j2: # Yes, so collapse them -- this just increases the length of # the first block by the length of the second, and the first # block so lengthened remains the block to compare against. k1 += k2 else: # Not adjacent. Remember the first block (k1==0 means it's # the dummy we started with), and make the second block the # new block to compare against. if k1: non_adjacent.append((i1, j1, k1)) i1, j1, k1 = i2, j2, k2 if k1: non_adjacent.append((i1, j1, k1)) non_adjacent.append( (la, lb, 0) ) self.matching_blocks = list(map(Match._make, non_adjacent)) return self.matching_blocks def get_opcodes(self): """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 == the i2 from the tuple preceding it, and likewise for j1 == the previous j2. The tags are strings, with these meanings: '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] >>> 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) """ if self.opcodes is not None: return self.opcodes i = j = 0 self.opcodes = answer = [] for ai, bj, size in self.get_matching_blocks(): # invariant: we've pumped out correct diffs to change # a[:i] into b[:j], and the next matching block is # a[ai:ai+size] == b[bj:bj+size]. So we need to pump # out a diff to change a[i:ai] into b[j:bj], pump out # the matching block, and move (i,j) beyond the match tag = '' if i < ai and j < bj: tag = 'replace' elif i < ai: tag = 'delete' elif j < bj: tag = 'insert' if tag: answer.append( (tag, i, ai, j, bj) ) i, j = ai+size, bj+size # the list of matching blocks is terminated by a # sentinel with size 0 if size: answer.append( ('equal', ai, i, bj, j) ) return answer def get_grouped_opcodes(self, 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)]] """ codes = self.get_opcodes() if not codes: codes = [("equal", 0, 1, 0, 1)] # Fixup leading and trailing groups if they show no changes. if codes[0][0] == 'equal': tag, i1, i2, j1, j2 = codes[0] codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2 if codes[-1][0] == 'equal': tag, i1, i2, j1, j2 = codes[-1] codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n) nn = n + n group = [] for tag, i1, i2, j1, j2 in codes: # End the current group and start a new one whenever # there is a large range with no changes. if tag == 'equal' and i2-i1 > nn: group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n))) yield group group = [] i1, j1 = max(i1, i2-n), max(j1, j2-n) group.append((tag, i1, i2, j1 ,j2)) if group and not (len(group)==1 and group[0][0] == 'equal'): yield group def ratio(self): """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 """ matches = sum(triple[-1] for triple in self.get_matching_blocks()) return _calculate_ratio(matches, len(self.a) + len(self.b)) def quick_ratio(self): """Return an upper bound on ratio() relatively quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute. """ # viewing a and b as multisets, set matches to the cardinality # of their intersection; this counts the number of matches # without regard to order, so is clearly an upper bound if self.fullbcount is None: self.fullbcount = fullbcount = {} for elt in self.b: fullbcount[elt] = fullbcount.get(elt, 0) + 1 fullbcount = self.fullbcount # avail[x] is the number of times x appears in 'b' less the # number of times we've seen it in 'a' so far ... kinda avail = {} matches = 0 for elt in self.a: if elt in avail: numb = avail[elt] else: numb = fullbcount.get(elt, 0) avail[elt] = numb - 1 if numb > 0: matches += 1 return _calculate_ratio(matches, len(self.a) + len(self.b)) def real_quick_ratio(self): """Return an upper bound on ratio() very quickly. This isn't defined beyond that it is an upper bound on .ratio(), and is faster to compute than either .ratio() or .quick_ratio(). """ la, lb = len(self.a), len(self.b) # can't have more matches than the number of elements in the # shorter sequence return _calculate_ratio(min(la, lb), la + lb) __class_getitem__ = classmethod(GenericAlias) def 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'] """ if not n > 0: raise ValueError("n must be > 0: %r" % (n,)) if not 0.0 >> 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 """ return Differ(linejunk, charjunk).compare(a, b) def _mdiff(fromlines, tolines, context=None, linejunk=None, charjunk=IS_CHARACTER_JUNK): r"""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. """ import re # regular expression for finding intraline change indices change_re = re.compile(r'(\++|\-+|\^+)') # create the difference iterator to generate the differences diff_lines_iterator = ndiff(fromlines,tolines,linejunk,charjunk) def _make_line(lines, format_key, side, num_lines=[0,0]): """Returns line of text with user's change markup and line formatting. lines -- list of lines from the ndiff generator to produce a line of text from. When producing the line of text to return, the lines used are removed from this list. format_key -- '+' return first line in list with "add" markup around the entire line. '-' return first line in list with "delete" markup around the entire line. '?' return first line in list with add/delete/change intraline markup (indices obtained from second line) None return first line in list with no markup side -- indice into the num_lines list (0=from,1=to) num_lines -- from/to current line number. This is NOT intended to be a passed parameter. It is present as a keyword argument to maintain memory of the current line numbers between calls of this function. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ num_lines[side] += 1 # Handle case where no user markup is to be added, just return line of # text with user's line format to allow for usage of the line number. if format_key is None: return (num_lines[side],lines.pop(0)[2:]) # Handle case of intraline changes if format_key == '?': text, markers = lines.pop(0), lines.pop(0) # find intraline changes (store change type and indices in tuples) sub_info = [] def record_sub_info(match_object,sub_info=sub_info): sub_info.append([match_object.group(1)[0],match_object.span()]) return match_object.group(1) change_re.sub(record_sub_info,markers) # process each tuple inserting our special marks that won't be # noticed by an xml/html escaper. for key,(begin,end) in reversed(sub_info): text = text[0:begin]+'\0'+key+text[begin:end]+'\1'+text[end:] text = text[2:] # Handle case of add/delete entire line else: text = lines.pop(0)[2:] # if line of text is just a newline, insert a space so there is # something for the user to highlight and see. if not text: text = ' ' # insert marks that won't be noticed by an xml/html escaper. text = '\0' + format_key + text + '\1' # Return line of text, first allow user's line formatter to do its # thing (such as adding the line number) then replace the special # marks with what the user's change markup. return (num_lines[side],text) def _line_iterator(): """Yields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from a differencing iterator, processes them and yields them. When it can it yields both a "from" and a "to" line, otherwise it will yield one or the other. In addition to yielding the lines of from/to text, a boolean flag is yielded to indicate if the text line(s) have differences in them. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ lines = [] num_blanks_pending, num_blanks_to_yield = 0, 0 while True: # Load up next 4 lines so we can look ahead, create strings which # are a concatenation of the first character of each of the 4 lines # so we can do some very readable comparisons. while len(lines) < 4: lines.append(next(diff_lines_iterator, 'X')) s = ''.join([line[0] for line in lines]) if s.startswith('X'): # When no more lines, pump out any remaining blank lines so the # corresponding add/delete lines get a matching blank line so # all line pairs get yielded at the next level. num_blanks_to_yield = num_blanks_pending elif s.startswith('-?+?'): # simple intraline change yield _make_line(lines,'?',0), _make_line(lines,'?',1), True continue elif s.startswith('--++'): # in delete block, add block coming: we do NOT want to get # caught up on blank lines yet, just process the delete line num_blanks_pending -= 1 yield _make_line(lines,'-',0), None, True continue elif s.startswith(('--?+', '--+', '- ')): # in delete block and see an intraline change or unchanged line # coming: yield the delete line and then blanks from_line,to_line = _make_line(lines,'-',0), None num_blanks_to_yield,num_blanks_pending = num_blanks_pending-1,0 elif s.startswith('-+?'): # intraline change yield _make_line(lines,None,0), _make_line(lines,'?',1), True continue elif s.startswith('-?+'): # intraline change yield _make_line(lines,'?',0), _make_line(lines,None,1), True continue elif s.startswith('-'): # delete FROM line num_blanks_pending -= 1 yield _make_line(lines,'-',0), None, True continue elif s.startswith('+--'): # in add block, delete block coming: we do NOT want to get # caught up on blank lines yet, just process the add line num_blanks_pending += 1 yield None, _make_line(lines,'+',1), True continue elif s.startswith(('+ ', '+-')): # will be leaving an add block: yield blanks then add line from_line, to_line = None, _make_line(lines,'+',1) num_blanks_to_yield,num_blanks_pending = num_blanks_pending+1,0 elif s.startswith('+'): # inside an add block, yield the add line num_blanks_pending += 1 yield None, _make_line(lines,'+',1), True continue elif s.startswith(' '): # unchanged text, yield it to both sides yield _make_line(lines[:],None,0),_make_line(lines,None,1),False continue # Catch up on the blank lines so when we yield the next from/to # pair, they are lined up. while(num_blanks_to_yield < 0): num_blanks_to_yield += 1 yield None,('','\n'),True while(num_blanks_to_yield > 0): num_blanks_to_yield -= 1 yield ('','\n'),None,True if s.startswith('X'): return else: yield from_line,to_line,True def _line_pair_iterator(): """Yields from/to lines of text with a change indication. This function is an iterator. It itself pulls lines from the line iterator. Its difference from that iterator is that this function always yields a pair of from/to text lines (with the change indication). If necessary it will collect single from/to lines until it has a matching pair from/to pair to yield. Note, this function is purposefully not defined at the module scope so that data it needs from its parent function (within whose context it is defined) does not need to be of module scope. """ line_iterator = _line_iterator() fromlines,tolines=[],[] while True: # Collecting lines of text until we have a from/to pair while (len(fromlines)==0 or len(tolines)==0): try: from_line, to_line, found_diff = next(line_iterator) except StopIteration: return if from_line is not None: fromlines.append((from_line,found_diff)) if to_line is not None: tolines.append((to_line,found_diff)) # Once we have a pair, remove them from the collection and yield it from_line, fromDiff = fromlines.pop(0) to_line, to_diff = tolines.pop(0) yield (from_line,to_line,fromDiff or to_diff) # Handle case where user does not want context differencing, just yield # them up without doing anything else with them. line_pair_iterator = _line_pair_iterator() if context is None: yield from line_pair_iterator # Handle case where user wants context differencing. We must do some # storage of lines until we know for sure that they are to be yielded. else: context += 1 lines_to_write = 0 while True: # Store lines up until we find a difference, note use of a # circular queue because we only need to keep around what # we need for context. index, contextLines = 0, [None]*(context) found_diff = False while(found_diff is False): try: from_line, to_line, found_diff = next(line_pair_iterator) except StopIteration: return i = index % context contextLines[i] = (from_line, to_line, found_diff) index += 1 # Yield lines that we have collected so far, but first yield # the user's separator. if index > context: yield None, None, None lines_to_write = context else: lines_to_write = index index = 0 while(lines_to_write): i = index % context index += 1 yield contextLines[i] lines_to_write -= 1 # Now yield the context lines after the change lines_to_write = context-1 try: while(lines_to_write): from_line, to_line, found_diff = next(line_pair_iterator) # If another change within the context, extend the context if found_diff: lines_to_write = context-1 else: lines_to_write -= 1 yield from_line, to_line, found_diff except StopIteration: # Catch exception from next() and return normally return _file_template = """ Diff comparison %(table)s%(legend)s """ _styles = """ :root {color-scheme: light dark} table.diff { font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace; border: medium; } .diff_header { background-color: #e0e0e0; font-weight: bold; } td.diff_header { text-align: right; padding: 0 8px; } .diff_next { background-color: #c0c0c0; padding: 4px 0; } .diff_add {background-color:palegreen} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa} table.diff[summary="Legends"] { margin-top: 20px; border: 1px solid #ccc; } table.diff[summary="Legends"] th { background-color: #e0e0e0; padding: 4px 8px; } table.diff[summary="Legends"] td { padding: 4px 8px; } @media (prefers-color-scheme: dark) { .diff_header {background-color:#666} .diff_next {background-color:#393939} .diff_add {background-color:darkgreen} .diff_chg {background-color:#847415} .diff_sub {background-color:darkred} table.diff[summary="Legends"] {border-color:#555} table.diff[summary="Legends"] th{background-color:#666} }""" _table_template = """ %(header_row)s %(data_rows)s """ _legend = """

Legends
Colors  Added  Changed Deleted	Links (f)irst change (n)ext change (t)op

""" class HtmlDiff(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. """ _file_template = _file_template _styles = _styles _table_template = _table_template _legend = _legend _default_prefix = 0 def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None, charjunk=IS_CHARACTER_JUNK): """HtmlDiff instance initializer Arguments: tabsize -- tab stop spacing, defaults to 8. wrapcolumn -- column number where lines are broken and wrapped, defaults to None where lines are not wrapped. linejunk,charjunk -- 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. """ self._tabsize = tabsize self._wrapcolumn = wrapcolumn self._linejunk = linejunk self._charjunk = charjunk def make_file(self, 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 """ return (self._file_template % dict( styles=self._styles, legend=self._legend, table=self.make_table(fromlines, tolines, fromdesc, todesc, context=context, numlines=numlines), charset=charset )).encode(charset, 'xmlcharrefreplace').decode(charset) def _tab_newline_replace(self,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. """ def expand_tabs(line): # hide real spaces line = line.replace(' ','\0') # expand tabs into spaces line = line.expandtabs(self._tabsize) # replace spaces from expanded tabs back into tab characters # (we'll replace them with markup after we do differencing) line = line.replace(' ','\t') return line.replace('\0',' ').rstrip('\n') fromlines = [expand_tabs(line) for line in fromlines] tolines = [expand_tabs(line) for line in tolines] return fromlines,tolines def _split_line(self,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. """ # if blank line or context separator, just add it to the output list if not line_num: data_list.append((line_num,text)) return # if line text doesn't need wrapping, just add it to the output list size = len(text) max = self._wrapcolumn if (size