module(difflib) """ Module difflib -- helpers for computing deltas between objects. Function get_close_matches(word, possibilities, n=3, cutoff=0.6): Use SequenceMatcher to return list of the best "good enough" matches. Function context_diff(a, b): For two lists of strings, return a delta in context diff format. Function ndiff(a, b): Return a delta: the difference between `a` and `b` (lists of strings). Function restore(delta, which): Return one of the two sequences that generated an ndiff delta. Function unified_diff(a, b): For two lists of strings, return a delta in unified diff format. Class SequenceMatcher: A flexible class for comparing pairs of sequences of any type. Class Differ: For producing human-readable deltas from sequences of lines of text. Class HtmlDiff: For producing HTML side by side comparison with change highlights. """ __all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher', 'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff', 'unified_diff', 'diff_bytes', 'HtmlDiff', 'Match'] from heapq import nlargest as _nlargest from collections import namedtuple as _namedtuple Match = _namedtuple('Match', 'a b size') def _calculate_ratio(matches, length): if length: return 2.0 * matches / length return 1.0 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 "

" 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(), 3)) 0.866 >>> If you're only interested in where the sequences match, .get_matching_blocks() is handy: >>> for block in s.get_matching_blocks(): ... print("a[%d] and b[%d] match for %d elements" % block) a[0] and b[0] match for 8 elements a[8] and b[17] match for 21 elements a[29] and b[38] match for 0 elements Note that the last tuple returned by .get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0. If you want to know how to change the first sequence into the second, use .get_opcodes(): >>> for opcode in s.get_opcodes(): ... print("%6s a[%d:%d] b[%d:%d]" % opcode) equal a[0:8] b[0:8] insert a[8:8] b[8:17] equal a[8:29] b[17:38] See the Differ class for a fancy human-friendly file differencer, which uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines. See also function get_close_matches() in this module, which shows how simple code building on SequenceMatcher can be used to do useful work. Timing: Basic R-O is cubic time worst case and quadratic time expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear. 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='', 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 <0.9 wink>. # 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).