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diff --git a/test/docutils_difflib.py b/test/docutils_difflib.py new file mode 100644 index 000000000..a41d4d5ba --- /dev/null +++ b/test/docutils_difflib.py @@ -0,0 +1,1089 @@ +#! /usr/bin/env python + +""" +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 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. + +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. +""" + +__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher', + 'Differ'] + +TRACE = 0 + +class SequenceMatcher: + + """ + 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 synching 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 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 .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[8:14] b[17:23] + equal a[14:29] b[23:38] + + 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. + + Methods: + + __init__(isjunk=None, a='', b='') + Construct a SequenceMatcher. + + set_seqs(a, b) + Set the two sequences to be compared. + + set_seq1(a) + Set the first sequence to be compared. + + set_seq2(b) + Set the second sequence to be compared. + + find_longest_match(alo, ahi, blo, bhi) + Find longest matching block in a[alo:ahi] and b[blo:bhi]. + + get_matching_blocks() + Return list of triples describing matching subsequences. + + get_opcodes() + Return list of 5-tuples describing how to turn a into b. + + ratio() + Return a measure of the sequences' similarity (float in [0,1]). + + quick_ratio() + Return an upper bound on .ratio() relatively quickly. + + real_quick_ratio() + Return an upper bound on ratio() very quickly. + """ + + def __init__(self, isjunk=None, a='', b=''): + """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(). + """ + + # 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 elements do not appear + # b2jhas + # b2j.has_key + # 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 <0.9 wink>. + # DON'T USE! Only __chain_b uses this. Use isbjunk. + # isbjunk + # for x in b, isbjunk(x) == isjunk(x) but much faster; + # it's really the has_key method of a hidden dict. + # DOES NOT WORK for x in a! + + self.isjunk = isjunk + self.a = self.b = None + 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 ... + # also creates the fast isbjunk function ... + # 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 = {} + self.b2jhas = b2jhas = b2j.has_key + for i in xrange(len(b)): + elt = b[i] + if b2jhas(elt): + b2j[elt].append(i) + else: + b2j[elt] = [i] + + # Now b2j.keys() contains elements uniquely, and especially when + # the sequence is a string, that's usually a good deal smaller + # than len(string). The difference is the number of isjunk calls + # saved. + isjunk, junkdict = self.isjunk, {} + if isjunk: + for elt in b2j.keys(): + if isjunk(elt): + junkdict[elt] = 1 # value irrelevant; it's a set + del b2j[elt] + + # Now for x in b, isjunk(x) == junkdict.has_key(x), but the + # latter is much faster. Note too that while there may be a + # lot of junk in the sequence, the number of *unique* junk + # elements is probably small. So the memory burden of keeping + # this dict alive is likely trivial compared to the size of b2j. + self.isbjunk = junkdict.has_key + + def find_longest_match(self, alo, ahi, blo, bhi): + """Find longest matching block in a[alo:ahi] and b[blo:bhi]. + + If isjunk is not defined: + + Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where + alo <= i <= i+k <= ahi + blo <= j <= j+k <= bhi + and for all (i',j',k') meeting those conditions, + k >= k' + i <= i' + and if i == i', j <= j' + + 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 is defined, 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, return (alo, blo, 0). + + >>> s = SequenceMatcher(None, "ab", "c") + >>> s.find_longest_match(0, 2, 0, 1) + (0, 0, 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.isbjunk + 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 xrange(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 + + # 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 + + if TRACE: + print "get_matching_blocks", alo, ahi, blo, bhi + print " returns", besti, bestj, bestsize + return 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. + + The last triple is a dummy, (len(a), len(b), 0), and is the only + triple with n==0. + + >>> s = SequenceMatcher(None, "abxcd", "abcd") + >>> s.get_matching_blocks() + [(0, 0, 2), (3, 2, 2), (5, 4, 0)] + """ + + if self.matching_blocks is not None: + return self.matching_blocks + self.matching_blocks = [] + la, lb = len(self.a), len(self.b) + self.__helper(0, la, 0, lb, self.matching_blocks) + self.matching_blocks.append( (la, lb, 0) ) + if TRACE: + print '*** matching blocks', self.matching_blocks + return self.matching_blocks + + # builds list of matching blocks covering a[alo:ahi] and + # b[blo:bhi], appending them in increasing order to answer + + def __helper(self, alo, ahi, blo, bhi, answer): + 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 alo < i and blo < j: + self.__helper(alo, i, blo, j, answer) + answer.append(x) + if i+k < ahi and j+k < bhi: + self.__helper(i+k, ahi, j+k, bhi, answer) + + 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 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 = reduce(lambda sum, triple: sum + triple[-1], + self.get_matching_blocks(), 0) + return 2.0 * 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 = {} + availhas, matches = avail.has_key, 0 + for elt in self.a: + if availhas(elt): + numb = avail[elt] + else: + numb = fullbcount.get(elt, 0) + avail[elt] = numb - 1 + if numb > 0: + matches = matches + 1 + return 2.0 * 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 2.0 * min(la, lb) / (la + lb) + +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: " + `n`) + if not 0.0 <= cutoff <= 1.0: + raise ValueError("cutoff must be in [0.0, 1.0]: " + `cutoff`) + result = [] + s = SequenceMatcher() + s.set_seq2(word) + for x in possibilities: + s.set_seq1(x) + if s.real_quick_ratio() >= cutoff and \ + s.quick_ratio() >= cutoff and \ + s.ratio() >= cutoff: + result.append((s.ratio(), x)) + # Sort by score. + result.sort() + # Retain only the best n. + result = result[-n:] + # Move best-scorer to head of list. + result.reverse() + # Strip scores. + return [x for score, x in result] + + +def _count_leading(line, ch): + """ + Return number of `ch` characters at the start of `line`. + + Example: + + >>> _count_leading(' abc', ' ') + 3 + """ + + i, n = 0, len(line) + while i < n and line[i] == ch: + i += 1 + return i + +class Differ: + r""" + 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(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 Differ.__init__ for details. + + Finally, we compare the two: + + >>> result = 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), + 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. + + Methods: + + __init__(linejunk=None, charjunk=None) + Construct a text differencer, with optional filters. + + compare(a, b) + Compare two sequences of lines; return the resulting delta (list). + """ + + def __init__(self, linejunk=None, charjunk=None): + """ + Construct a text differencer, with optional filters. + + The two optional keyword parameters are for filter functions: + + - `linejunk`: A function that should accept a single string argument, + and return true iff the string is junk. The module-level function + `IS_LINE_JUNK` may be used to filter out lines without visible + characters, except for at most one splat ('#'). + + - `charjunk`: A function that should accept a string of length 1. The + module-level function `IS_CHARACTER_JUNK` may be used to filter out + whitespace characters (a blank or tab; **note**: bad idea to include + newline in this!). + """ + + self.linejunk = linejunk + self.charjunk = charjunk + self.results = [] + + def compare(self, a, b): + r""" + Compare two sequences of lines; return the resulting delta (list). + + 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 list returned is also made up of + newline-terminated strings, ready to be used with the `writelines()` + method of a file-like object. + + Example: + + >>> print ''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(1), + ... 'ore\ntree\nemu\n'.splitlines(1))), + - one + ? ^ + + ore + ? ^ + - two + - three + ? - + + tree + + emu + """ + + cruncher = SequenceMatcher(self.linejunk, a, b) + for tag, alo, ahi, blo, bhi in cruncher.get_opcodes(): + if tag == 'replace': + self._fancy_replace(a, alo, ahi, b, blo, bhi) + elif tag == 'delete': + self._dump('-', a, alo, ahi) + elif tag == 'insert': + self._dump('+', b, blo, bhi) + elif tag == 'equal': + self._dump(' ', a, alo, ahi) + else: + raise ValueError, 'unknown tag ' + `tag` + results = self.results + self.results = [] + return results + + def _dump(self, tag, x, lo, hi): + """Store comparison results for a same-tagged range.""" + for i in xrange(lo, hi): + self.results.append('%s %s' % (tag, x[i])) + + def _plain_replace(self, a, alo, ahi, b, blo, bhi): + assert alo < ahi and blo < bhi + # dump the shorter block first -- reduces the burden on short-term + # memory if the blocks are of very different sizes + if bhi - blo < ahi - alo: + self._dump('+', b, blo, bhi) + self._dump('-', a, alo, ahi) + else: + self._dump('-', a, alo, ahi) + self._dump('+', b, blo, bhi) + + def _fancy_replace(self, a, alo, ahi, b, blo, bhi): + r""" + 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() + >>> d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ['abcdefGhijkl\n'], 0, 1) + >>> print ''.join(d.results), + - abcDefghiJkl + ? ^ ^ ^ + + abcdefGhijkl + ? ^ ^ ^ + """ + + if TRACE: + self.results.append('*** _fancy_replace %s %s %s %s\n' + % (alo, ahi, blo, bhi)) + self._dump('>', a, alo, ahi) + self._dump('<', b, blo, bhi) + + # don't synch up unless the lines have a similarity score of at + # least cutoff; best_ratio tracks the best score seen so far + best_ratio, cutoff = 0.74, 0.75 + cruncher = SequenceMatcher(self.charjunk) + eqi, eqj = None, None # 1st indices of equal lines (if any) + + # search for the pair that matches best without being identical + # (identical lines must be junk lines, & we don't want to synch up + # on junk -- unless we have to) + for j in xrange(blo, bhi): + bj = b[j] + cruncher.set_seq2(bj) + for i in xrange(alo, ahi): + ai = a[i] + if ai == bj: + if eqi is None: + eqi, eqj = i, j + continue + cruncher.set_seq1(ai) + # computing similarity is expensive, so use the quick + # upper bounds first -- have seen this speed up messy + # compares by a factor of 3. + # note that ratio() is only expensive to compute the first + # time it's called on a sequence pair; the expensive part + # of the computation is cached by cruncher + if cruncher.real_quick_ratio() > best_ratio and \ + cruncher.quick_ratio() > best_ratio and \ + cruncher.ratio() > best_ratio: + best_ratio, best_i, best_j = cruncher.ratio(), i, j + if best_ratio < cutoff: + # no non-identical "pretty close" pair + if eqi is None: + # no identical pair either -- treat it as a straight replace + self._plain_replace(a, alo, ahi, b, blo, bhi) + return + # no close pair, but an identical pair -- synch up on that + best_i, best_j, best_ratio = eqi, eqj, 1.0 + else: + # there's a close pair, so forget the identical pair (if any) + eqi = None + + # a[best_i] very similar to b[best_j]; eqi is None iff they're not + # identical + if TRACE: + self.results.append('*** best_ratio %s %s %s %s\n' + % (best_ratio, best_i, best_j)) + self._dump('>', a, best_i, best_i+1) + self._dump('<', b, best_j, best_j+1) + + # pump out diffs from before the synch point + self._fancy_helper(a, alo, best_i, b, blo, best_j) + + # do intraline marking on the synch pair + aelt, belt = a[best_i], b[best_j] + if eqi is None: + # pump out a '-', '?', '+', '?' quad for the synched lines + atags = btags = "" + cruncher.set_seqs(aelt, belt) + for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes(): + la, lb = ai2 - ai1, bj2 - bj1 + if tag == 'replace': + atags += '^' * la + btags += '^' * lb + elif tag == 'delete': + atags += '-' * la + elif tag == 'insert': + btags += '+' * lb + elif tag == 'equal': + atags += ' ' * la + btags += ' ' * lb + else: + raise ValueError, 'unknown tag ' + `tag` + self._qformat(aelt, belt, atags, btags) + else: + # the synch pair is identical + self.results.append(' ' + aelt) + + # pump out diffs from after the synch point + self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi) + + def _fancy_helper(self, a, alo, ahi, b, blo, bhi): + if alo < ahi: + if blo < bhi: + self._fancy_replace(a, alo, ahi, b, blo, bhi) + else: + self._dump('-', a, alo, ahi) + elif blo < bhi: + self._dump('+', b, blo, bhi) + + def _qformat(self, aline, bline, atags, btags): + r""" + Format "?" output and deal with leading tabs. + + Example: + + >>> d = Differ() + >>> d._qformat('\tabcDefghiJkl\n', '\t\tabcdefGhijkl\n', + ... ' ^ ^ ^ ', '+ ^ ^ ^ ') + >>> for line in d.results: print repr(line) + ... + '- \tabcDefghiJkl\n' + '? \t ^ ^ ^\n' + '+ \t\tabcdefGhijkl\n' + '? \t ^ ^ ^\n' + """ + + # Can hurt, but will probably help most of the time. + common = min(_count_leading(aline, "\t"), + _count_leading(bline, "\t")) + common = min(common, _count_leading(atags[:common], " ")) + atags = atags[common:].rstrip() + btags = btags[common:].rstrip() + + self.results.append("- " + aline) + if atags: + self.results.append("? %s%s\n" % ("\t" * common, atags)) + + self.results.append("+ " + bline) + if btags: + self.results.append("? %s%s\n" % ("\t" * common, btags)) + +# With respect to junk, an earlier version of ndiff simply refused to +# *start* a match with a junk element. The result was cases like this: +# before: private Thread currentThread; +# after: private volatile Thread currentThread; +# If you consider whitespace to be junk, the longest contiguous match +# not starting with junk is "e Thread currentThread". So ndiff reported +# that "e volatil" was inserted between the 't' and the 'e' in "private". +# While an accurate view, to people that's absurd. The current version +# looks for matching blocks that are entirely junk-free, then extends the +# longest one of those as far as possible but only with matching junk. +# So now "currentThread" is matched, then extended to suck up the +# preceding blank; then "private" is matched, and extended to suck up the +# following blank; then "Thread" is matched; and finally ndiff reports +# that "volatile " was inserted before "Thread". The only quibble +# remaining is that perhaps it was really the case that " volatile" +# was inserted after "private". I can live with that <wink>. + +import re + +def IS_LINE_JUNK(line, pat=re.compile(r"\s*#?\s*$").match): + r""" + Return 1 for ignorable line: iff `line` is blank or contains a single '#'. + + Examples: + + >>> IS_LINE_JUNK('\n') + 1 + >>> IS_LINE_JUNK(' # \n') + 1 + >>> IS_LINE_JUNK('hello\n') + 0 + """ + + return pat(line) is not None + +def IS_CHARACTER_JUNK(ch, ws=" \t"): + r""" + Return 1 for ignorable character: iff `ch` is a space or tab. + + Examples: + + >>> IS_CHARACTER_JUNK(' ') + 1 + >>> IS_CHARACTER_JUNK('\t') + 1 + >>> IS_CHARACTER_JUNK('\n') + 0 + >>> IS_CHARACTER_JUNK('x') + 0 + """ + + return ch in ws + +del re + +def ndiff(a, b, linejunk=IS_LINE_JUNK, charjunk=IS_CHARACTER_JUNK): + r""" + Compare `a` and `b` (lists of strings); return a `Differ`-style delta. + + Optional keyword parameters `linejunk` and `charjunk` are for filter + functions (or None): + + - linejunk: A function that should accept a single string argument, and + return true iff the string is junk. The default is module-level function + IS_LINE_JUNK, which filters out lines without visible characters, except + for at most one splat ('#'). + + - charjunk: A function that should accept a string of length 1. 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. + + Example: + + >>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), + ... 'ore\ntree\nemu\n'.splitlines(1)) + >>> print ''.join(diff), + - one + ? ^ + + ore + ? ^ + - two + - three + ? - + + tree + + emu + """ + return Differ(linejunk, charjunk).compare(a, b) + +def restore(delta, which): + r""" + Return 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(1), + ... 'ore\ntree\nemu\n'.splitlines(1)) + >>> print ''.join(restore(diff, 1)), + one + two + three + >>> print ''.join(restore(diff, 2)), + ore + tree + emu + """ + try: + tag = {1: "- ", 2: "+ "}[int(which)] + except KeyError: + raise ValueError, ('unknown delta choice (must be 1 or 2): %r' + % which) + prefixes = (" ", tag) + results = [] + for line in delta: + if line[:2] in prefixes: + results.append(line[2:]) + return results + +def _test(): + import doctest, difflib + return doctest.testmod(difflib) + +if __name__ == "__main__": + _test() |