mirror of https://github.com/python/cpython
1119 lines
41 KiB
Python
1119 lines
41 KiB
Python
#! /usr/bin/env python
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"""
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Module difflib -- helpers for computing deltas between objects.
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Function get_close_matches(word, possibilities, n=3, cutoff=0.6):
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Use SequenceMatcher to return list of the best "good enough" matches.
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Function ndiff(a, b):
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Return a delta: the difference between `a` and `b` (lists of strings).
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Function restore(delta, which):
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Return one of the two sequences that generated an ndiff delta.
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Class SequenceMatcher:
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A flexible class for comparing pairs of sequences of any type.
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Class Differ:
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For producing human-readable deltas from sequences of lines of text.
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"""
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__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',
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'Differ']
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class SequenceMatcher:
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"""
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SequenceMatcher is a flexible class for comparing pairs of sequences of
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any type, so long as the sequence elements are hashable. The basic
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algorithm predates, and is a little fancier than, an algorithm
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published in the late 1980's by Ratcliff and Obershelp under the
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hyperbolic name "gestalt pattern matching". The basic idea is to find
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the longest contiguous matching subsequence that contains no "junk"
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elements (R-O doesn't address junk). The same idea is then applied
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recursively to the pieces of the sequences to the left and to the right
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of the matching subsequence. This does not yield minimal edit
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sequences, but does tend to yield matches that "look right" to people.
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SequenceMatcher tries to compute a "human-friendly diff" between two
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sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the
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longest *contiguous* & junk-free matching subsequence. That's what
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catches peoples' eyes. The Windows(tm) windiff has another interesting
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notion, pairing up elements that appear uniquely in each sequence.
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That, and the method here, appear to yield more intuitive difference
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reports than does diff. This method appears to be the least vulnerable
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to synching up on blocks of "junk lines", though (like blank lines in
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ordinary text files, or maybe "<P>" lines in HTML files). That may be
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because this is the only method of the 3 that has a *concept* of
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"junk" <wink>.
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Example, comparing two strings, and considering blanks to be "junk":
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>>> s = SequenceMatcher(lambda x: x == " ",
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... "private Thread currentThread;",
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... "private volatile Thread currentThread;")
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>>>
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.ratio() returns a float in [0, 1], measuring the "similarity" of the
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sequences. As a rule of thumb, a .ratio() value over 0.6 means the
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sequences are close matches:
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>>> print round(s.ratio(), 3)
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0.866
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>>>
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If you're only interested in where the sequences match,
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.get_matching_blocks() is handy:
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>>> for block in s.get_matching_blocks():
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... print "a[%d] and b[%d] match for %d elements" % block
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a[0] and b[0] match for 8 elements
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a[8] and b[17] match for 6 elements
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a[14] and b[23] match for 15 elements
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a[29] and b[38] match for 0 elements
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Note that the last tuple returned by .get_matching_blocks() is always a
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dummy, (len(a), len(b), 0), and this is the only case in which the last
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tuple element (number of elements matched) is 0.
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If you want to know how to change the first sequence into the second,
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use .get_opcodes():
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>>> for opcode in s.get_opcodes():
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... print "%6s a[%d:%d] b[%d:%d]" % opcode
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equal a[0:8] b[0:8]
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insert a[8:8] b[8:17]
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equal a[8:14] b[17:23]
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equal a[14:29] b[23:38]
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See the Differ class for a fancy human-friendly file differencer, which
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uses SequenceMatcher both to compare sequences of lines, and to compare
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sequences of characters within similar (near-matching) lines.
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See also function get_close_matches() in this module, which shows how
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simple code building on SequenceMatcher can be used to do useful work.
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Timing: Basic R-O is cubic time worst case and quadratic time expected
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case. SequenceMatcher is quadratic time for the worst case and has
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expected-case behavior dependent in a complicated way on how many
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elements the sequences have in common; best case time is linear.
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Methods:
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__init__(isjunk=None, a='', b='')
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Construct a SequenceMatcher.
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set_seqs(a, b)
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Set the two sequences to be compared.
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set_seq1(a)
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Set the first sequence to be compared.
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set_seq2(b)
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Set the second sequence to be compared.
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find_longest_match(alo, ahi, blo, bhi)
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Find longest matching block in a[alo:ahi] and b[blo:bhi].
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get_matching_blocks()
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Return list of triples describing matching subsequences.
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get_opcodes()
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Return list of 5-tuples describing how to turn a into b.
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ratio()
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Return a measure of the sequences' similarity (float in [0,1]).
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quick_ratio()
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Return an upper bound on .ratio() relatively quickly.
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real_quick_ratio()
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Return an upper bound on ratio() very quickly.
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"""
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def __init__(self, isjunk=None, a='', b=''):
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"""Construct a SequenceMatcher.
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Optional arg isjunk is None (the default), or a one-argument
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function that takes a sequence element and returns true iff the
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element is junk. None is equivalent to passing "lambda x: 0", i.e.
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no elements are considered to be junk. For example, pass
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lambda x: x in " \\t"
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if you're comparing lines as sequences of characters, and don't
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want to synch up on blanks or hard tabs.
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Optional arg a is the first of two sequences to be compared. By
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default, an empty string. The elements of a must be hashable. See
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also .set_seqs() and .set_seq1().
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Optional arg b is the second of two sequences to be compared. By
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default, an empty string. The elements of b must be hashable. See
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also .set_seqs() and .set_seq2().
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"""
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# Members:
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# a
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# first sequence
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# b
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# second sequence; differences are computed as "what do
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# we need to do to 'a' to change it into 'b'?"
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# b2j
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# for x in b, b2j[x] is a list of the indices (into b)
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# at which x appears; junk elements do not appear
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# fullbcount
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# for x in b, fullbcount[x] == the number of times x
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# appears in b; only materialized if really needed (used
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# only for computing quick_ratio())
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# matching_blocks
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# a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];
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# ascending & non-overlapping in i and in j; terminated by
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# a dummy (len(a), len(b), 0) sentinel
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# opcodes
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# a list of (tag, i1, i2, j1, j2) tuples, where tag is
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# one of
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# 'replace' a[i1:i2] should be replaced by b[j1:j2]
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# 'delete' a[i1:i2] should be deleted
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# 'insert' b[j1:j2] should be inserted
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# 'equal' a[i1:i2] == b[j1:j2]
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# isjunk
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# a user-supplied function taking a sequence element and
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# returning true iff the element is "junk" -- this has
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# subtle but helpful effects on the algorithm, which I'll
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# get around to writing up someday <0.9 wink>.
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# DON'T USE! Only __chain_b uses this. Use isbjunk.
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# isbjunk
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# for x in b, isbjunk(x) == isjunk(x) but much faster;
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# it's really the has_key method of a hidden dict.
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# DOES NOT WORK for x in a!
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# isbpopular
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# for x in b, isbpopular(x) is true iff b is reasonably long
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# (at least 200 elements) and x accounts for more than 1% of
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# its elements. DOES NOT WORK for x in a!
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self.isjunk = isjunk
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self.a = self.b = None
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self.set_seqs(a, b)
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def set_seqs(self, a, b):
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"""Set the two sequences to be compared.
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>>> s = SequenceMatcher()
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>>> s.set_seqs("abcd", "bcde")
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>>> s.ratio()
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0.75
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"""
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self.set_seq1(a)
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self.set_seq2(b)
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def set_seq1(self, a):
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"""Set the first sequence to be compared.
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The second sequence to be compared is not changed.
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>>> s = SequenceMatcher(None, "abcd", "bcde")
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>>> s.ratio()
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0.75
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>>> s.set_seq1("bcde")
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>>> s.ratio()
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1.0
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>>>
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SequenceMatcher computes and caches detailed information about the
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second sequence, so if you want to compare one sequence S against
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many sequences, use .set_seq2(S) once and call .set_seq1(x)
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repeatedly for each of the other sequences.
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See also set_seqs() and set_seq2().
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"""
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if a is self.a:
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return
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self.a = a
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self.matching_blocks = self.opcodes = None
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def set_seq2(self, b):
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"""Set the second sequence to be compared.
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The first sequence to be compared is not changed.
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>>> s = SequenceMatcher(None, "abcd", "bcde")
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>>> s.ratio()
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0.75
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>>> s.set_seq2("abcd")
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>>> s.ratio()
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1.0
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>>>
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SequenceMatcher computes and caches detailed information about the
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second sequence, so if you want to compare one sequence S against
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many sequences, use .set_seq2(S) once and call .set_seq1(x)
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repeatedly for each of the other sequences.
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See also set_seqs() and set_seq1().
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"""
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if b is self.b:
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return
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self.b = b
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self.matching_blocks = self.opcodes = None
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self.fullbcount = None
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self.__chain_b()
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# For each element x in b, set b2j[x] to a list of the indices in
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# b where x appears; the indices are in increasing order; note that
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# the number of times x appears in b is len(b2j[x]) ...
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# when self.isjunk is defined, junk elements don't show up in this
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# map at all, which stops the central find_longest_match method
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# from starting any matching block at a junk element ...
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# also creates the fast isbjunk function ...
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# b2j also does not contain entries for "popular" elements, meaning
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# elements that account for more than 1% of the total elements, and
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# when the sequence is reasonably large (>= 200 elements); this can
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# be viewed as an adaptive notion of semi-junk, and yields an enormous
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# speedup when, e.g., comparing program files with hundreds of
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# instances of "return NULL;" ...
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# note that this is only called when b changes; so for cross-product
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# kinds of matches, it's best to call set_seq2 once, then set_seq1
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# repeatedly
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def __chain_b(self):
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# Because isjunk is a user-defined (not C) function, and we test
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# for junk a LOT, it's important to minimize the number of calls.
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# Before the tricks described here, __chain_b was by far the most
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# time-consuming routine in the whole module! If anyone sees
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# Jim Roskind, thank him again for profile.py -- I never would
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# have guessed that.
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# The first trick is to build b2j ignoring the possibility
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# of junk. I.e., we don't call isjunk at all yet. Throwing
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# out the junk later is much cheaper than building b2j "right"
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# from the start.
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b = self.b
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n = len(b)
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self.b2j = b2j = {}
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populardict = {}
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for i, elt in enumerate(b):
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if elt in b2j:
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indices = b2j[elt]
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if n >= 200 and len(indices) * 100 > n:
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populardict[elt] = 1
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del indices[:]
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else:
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indices.append(i)
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else:
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b2j[elt] = [i]
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# Purge leftover indices for popular elements.
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for elt in populardict:
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del b2j[elt]
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# Now b2j.keys() contains elements uniquely, and especially when
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# the sequence is a string, that's usually a good deal smaller
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# than len(string). The difference is the number of isjunk calls
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# saved.
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isjunk = self.isjunk
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junkdict = {}
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if isjunk:
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for d in populardict, b2j:
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for elt in d.keys():
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if isjunk(elt):
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junkdict[elt] = 1
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del d[elt]
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# Now for x in b, isjunk(x) == junkdict.has_key(x), but the
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# latter is much faster. Note too that while there may be a
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# lot of junk in the sequence, the number of *unique* junk
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# elements is probably small. So the memory burden of keeping
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# this dict alive is likely trivial compared to the size of b2j.
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self.isbjunk = junkdict.has_key
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self.isbpopular = populardict.has_key
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def find_longest_match(self, alo, ahi, blo, bhi):
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"""Find longest matching block in a[alo:ahi] and b[blo:bhi].
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If isjunk is not defined:
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Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
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alo <= i <= i+k <= ahi
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blo <= j <= j+k <= bhi
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and for all (i',j',k') meeting those conditions,
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k >= k'
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i <= i'
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and if i == i', j <= j'
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In other words, of all maximal matching blocks, return one that
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starts earliest in a, and of all those maximal matching blocks that
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start earliest in a, return the one that starts earliest in b.
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>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
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>>> s.find_longest_match(0, 5, 0, 9)
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(0, 4, 5)
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If isjunk is defined, first the longest matching block is
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determined as above, but with the additional restriction that no
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junk element appears in the block. Then that block is extended as
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far as possible by matching (only) junk elements on both sides. So
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the resulting block never matches on junk except as identical junk
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happens to be adjacent to an "interesting" match.
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Here's the same example as before, but considering blanks to be
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junk. That prevents " abcd" from matching the " abcd" at the tail
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end of the second sequence directly. Instead only the "abcd" can
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match, and matches the leftmost "abcd" in the second sequence:
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>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
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>>> s.find_longest_match(0, 5, 0, 9)
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(1, 0, 4)
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If no blocks match, return (alo, blo, 0).
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>>> s = SequenceMatcher(None, "ab", "c")
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>>> s.find_longest_match(0, 2, 0, 1)
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(0, 0, 0)
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"""
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# CAUTION: stripping common prefix or suffix would be incorrect.
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# E.g.,
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# ab
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# acab
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# Longest matching block is "ab", but if common prefix is
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# stripped, it's "a" (tied with "b"). UNIX(tm) diff does so
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# strip, so ends up claiming that ab is changed to acab by
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# inserting "ca" in the middle. That's minimal but unintuitive:
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# "it's obvious" that someone inserted "ac" at the front.
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# Windiff ends up at the same place as diff, but by pairing up
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# the unique 'b's and then matching the first two 'a's.
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a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.isbjunk
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besti, bestj, bestsize = alo, blo, 0
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# find longest junk-free match
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# during an iteration of the loop, j2len[j] = length of longest
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# junk-free match ending with a[i-1] and b[j]
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j2len = {}
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nothing = []
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for i in xrange(alo, ahi):
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# look at all instances of a[i] in b; note that because
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# b2j has no junk keys, the loop is skipped if a[i] is junk
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j2lenget = j2len.get
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newj2len = {}
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for j in b2j.get(a[i], nothing):
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# a[i] matches b[j]
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if j < blo:
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continue
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if j >= bhi:
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break
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k = newj2len[j] = j2lenget(j-1, 0) + 1
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if k > bestsize:
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besti, bestj, bestsize = i-k+1, j-k+1, k
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j2len = newj2len
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# Extend the best by non-junk elements on each end. In particular,
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# "popular" non-junk elements aren't in b2j, which greatly speeds
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# the inner loop above, but also means "the best" match so far
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# doesn't contain any junk *or* popular non-junk elements.
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while besti > alo and bestj > blo and \
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not isbjunk(b[bestj-1]) and \
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a[besti-1] == b[bestj-1]:
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besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
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while besti+bestsize < ahi and bestj+bestsize < bhi and \
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not isbjunk(b[bestj+bestsize]) and \
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a[besti+bestsize] == b[bestj+bestsize]:
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bestsize += 1
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# Now that we have a wholly interesting match (albeit possibly
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# empty!), we may as well suck up the matching junk on each
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# side of it too. Can't think of a good reason not to, and it
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# saves post-processing the (possibly considerable) expense of
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# figuring out what to do with it. In the case of an empty
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# interesting match, this is clearly the right thing to do,
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# because no other kind of match is possible in the regions.
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while besti > alo and bestj > blo and \
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isbjunk(b[bestj-1]) and \
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a[besti-1] == b[bestj-1]:
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besti, bestj, bestsize = besti-1, bestj-1, bestsize+1
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while besti+bestsize < ahi and bestj+bestsize < bhi and \
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isbjunk(b[bestj+bestsize]) and \
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a[besti+bestsize] == b[bestj+bestsize]:
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bestsize = bestsize + 1
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return besti, bestj, bestsize
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def get_matching_blocks(self):
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"""Return list of triples describing matching subsequences.
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Each triple is of the form (i, j, n), and means that
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a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in
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i and in j.
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The last triple is a dummy, (len(a), len(b), 0), and is the only
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triple with n==0.
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>>> s = SequenceMatcher(None, "abxcd", "abcd")
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>>> s.get_matching_blocks()
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[(0, 0, 2), (3, 2, 2), (5, 4, 0)]
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"""
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if self.matching_blocks is not None:
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return self.matching_blocks
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self.matching_blocks = []
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la, lb = len(self.a), len(self.b)
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self.__helper(0, la, 0, lb, self.matching_blocks)
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self.matching_blocks.append( (la, lb, 0) )
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return self.matching_blocks
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# builds list of matching blocks covering a[alo:ahi] and
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# b[blo:bhi], appending them in increasing order to answer
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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 = list(d.compare(text1, text2))
|
|
|
|
'result' is a list of strings, so let's pretty-print it:
|
|
|
|
>>> from pprint import pprint as _pprint
|
|
>>> _pprint(result)
|
|
[' 1. Beautiful is better than ugly.\n',
|
|
'- 2. Explicit is better than implicit.\n',
|
|
'- 3. Simple is better than complex.\n',
|
|
'+ 3. Simple is better than complex.\n',
|
|
'? ++\n',
|
|
'- 4. Complex is better than complicated.\n',
|
|
'? ^ ---- ^\n',
|
|
'+ 4. Complicated is better than complex.\n',
|
|
'? ++++ ^ ^\n',
|
|
'+ 5. Flat is better than nested.\n']
|
|
|
|
As a single multi-line string it looks like this:
|
|
|
|
>>> print ''.join(result),
|
|
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; generate the resulting delta.
|
|
"""
|
|
|
|
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 ('#'). It is recommended
|
|
to leave linejunk None; as of Python 2.3, the underlying
|
|
SequenceMatcher class has grown an adaptive notion of "noise" lines
|
|
that's better than any static definition the author has ever been
|
|
able to craft.
|
|
|
|
- `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!). Use of IS_CHARACTER_JUNK is recommended.
|
|
"""
|
|
|
|
self.linejunk = linejunk
|
|
self.charjunk = charjunk
|
|
|
|
def compare(self, a, b):
|
|
r"""
|
|
Compare two sequences of lines; generate the resulting delta.
|
|
|
|
Each sequence must contain individual single-line strings ending with
|
|
newlines. Such sequences can be obtained from the `readlines()` method
|
|
of file-like objects. The delta generated also consists of newline-
|
|
terminated strings, ready to be printed as-is via the writeline()
|
|
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':
|
|
g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
|
|
elif tag == 'delete':
|
|
g = self._dump('-', a, alo, ahi)
|
|
elif tag == 'insert':
|
|
g = self._dump('+', b, blo, bhi)
|
|
elif tag == 'equal':
|
|
g = self._dump(' ', a, alo, ahi)
|
|
else:
|
|
raise ValueError, 'unknown tag ' + `tag`
|
|
|
|
for line in g:
|
|
yield line
|
|
|
|
def _dump(self, tag, x, lo, hi):
|
|
"""Generate comparison results for a same-tagged range."""
|
|
for i in xrange(lo, hi):
|
|
yield '%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:
|
|
first = self._dump('+', b, blo, bhi)
|
|
second = self._dump('-', a, alo, ahi)
|
|
else:
|
|
first = self._dump('-', a, alo, ahi)
|
|
second = self._dump('+', b, blo, bhi)
|
|
|
|
for g in first, second:
|
|
for line in g:
|
|
yield line
|
|
|
|
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
|
|
? ^ ^ ^
|
|
"""
|
|
|
|
# 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
|
|
for line in self._plain_replace(a, alo, ahi, b, blo, bhi):
|
|
yield line
|
|
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
|
|
|
|
# pump out diffs from before the synch point
|
|
for line in self._fancy_helper(a, alo, best_i, b, blo, best_j):
|
|
yield line
|
|
|
|
# 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`
|
|
for line in self._qformat(aelt, belt, atags, btags):
|
|
yield line
|
|
else:
|
|
# the synch pair is identical
|
|
yield ' ' + aelt
|
|
|
|
# pump out diffs from after the synch point
|
|
for line in self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi):
|
|
yield line
|
|
|
|
def _fancy_helper(self, a, alo, ahi, b, blo, bhi):
|
|
g = []
|
|
if alo < ahi:
|
|
if blo < bhi:
|
|
g = self._fancy_replace(a, alo, ahi, b, blo, bhi)
|
|
else:
|
|
g = self._dump('-', a, alo, ahi)
|
|
elif blo < bhi:
|
|
g = self._dump('+', b, blo, bhi)
|
|
|
|
for line in g:
|
|
yield line
|
|
|
|
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()
|
|
|
|
yield "- " + aline
|
|
if atags:
|
|
yield "? %s%s\n" % ("\t" * common, atags)
|
|
|
|
yield "+ " + bline
|
|
if btags:
|
|
yield "? %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')
|
|
True
|
|
>>> IS_LINE_JUNK(' # \n')
|
|
True
|
|
>>> IS_LINE_JUNK('hello\n')
|
|
False
|
|
"""
|
|
|
|
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(' ')
|
|
True
|
|
>>> IS_CHARACTER_JUNK('\t')
|
|
True
|
|
>>> IS_CHARACTER_JUNK('\n')
|
|
False
|
|
>>> IS_CHARACTER_JUNK('x')
|
|
False
|
|
"""
|
|
|
|
return ch in ws
|
|
|
|
del re
|
|
|
|
def ndiff(a, b, linejunk=None, 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 None, and is
|
|
recommended; as of Python 2.3, an adaptive notion of "noise" lines is
|
|
used that does a good job on its own.
|
|
|
|
- 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"""
|
|
Generate one of the two sequences that generated a delta.
|
|
|
|
Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
|
|
lines originating from file 1 or 2 (parameter `which`), stripping off line
|
|
prefixes.
|
|
|
|
Examples:
|
|
|
|
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
|
|
... 'ore\ntree\nemu\n'.splitlines(1))
|
|
>>> diff = list(diff)
|
|
>>> 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)
|
|
for line in delta:
|
|
if line[:2] in prefixes:
|
|
yield line[2:]
|
|
|
|
def _test():
|
|
import doctest, difflib
|
|
return doctest.testmod(difflib)
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|