| #! /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. |
| |
| 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 |
| >>> get_close_matches("wheel", keyword.kwlist) |
| ['while'] |
| >>> get_close_matches("apple", keyword.kwlist) |
| [] |
| >>> get_close_matches("accept", keyword.kwlist) |
| ['except'] |
| |
| 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. |
| |
| 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 Tools/scripts/ndiff.py for a fancy human-friendly file differencer, |
| which uses SequenceMatcher both to view files as sequences of lines, and |
| lines as sequences of characters. |
| |
| 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. |
| |
| SequenceMatcher methods: |
| |
| __init__(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. |
| |
| Optional arg b is the second of two sequences to be compared. By |
| default, an empty string. The elements of b must be hashable. |
| |
| set_seqs(a, b) |
| Set the two sequences to be compared. |
| |
| >>> s = SequenceMatcher() |
| >>> s.set_seqs("abcd", "bcde") |
| >>> s.ratio() |
| 0.75 |
| |
| 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(). |
| |
| find_longest_match(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) |
| |
| get_matching_blocks() |
| Return list of triples describing matching subsequences. |
| |
| Each triple is of the form (i, j, n), and means that |
| a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i |
| and 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)] |
| |
| get_opcodes() |
| Return list of 5-tuples describing how to turn a into b. |
| |
| Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has |
| i1 == j1 == 0, and remaining tuples have i1 == 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) |
| |
| 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 |
| |
| quick_ratio() |
| 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. |
| |
| real_quick_ratio(): |
| 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(). |
| """ |
| |
| TRACE = 0 |
| |
| class SequenceMatcher: |
| 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 |
| >>> 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 _test(): |
| import doctest, difflib |
| return doctest.testmod(difflib) |
| |
| if __name__ == "__main__": |
| _test() |