636 lines
24 KiB
Python
Executable File
636 lines
24 KiB
Python
Executable File
#! /usr/bin/env python
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# Module ndiff version 1.6.0
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# Released to the public domain 08-Dec-2000,
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# by Tim Peters (tim.one@home.com).
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# Provided as-is; use at your own risk; no warranty; no promises; enjoy!
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"""ndiff [-q] file1 file2
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or
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ndiff (-r1 | -r2) < ndiff_output > file1_or_file2
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Print a human-friendly file difference report to stdout. Both inter-
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and intra-line differences are noted. In the second form, recreate file1
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(-r1) or file2 (-r2) on stdout, from an ndiff report on stdin.
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In the first form, if -q ("quiet") is not specified, the first two lines
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of output are
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-: file1
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+: file2
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Each remaining line begins with a two-letter code:
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"- " line unique to file1
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"+ " line unique to file2
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" " line common to both files
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"? " line not present in either input file
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Lines beginning with "? " attempt to guide the eye to intraline
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differences, and were not present in either input file. These lines can be
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confusing if the source files contain tab characters.
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The first file can be recovered by retaining only lines that begin with
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" " or "- ", and deleting those 2-character prefixes; use ndiff with -r1.
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The second file can be recovered similarly, but by retaining only " " and
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"+ " lines; use ndiff with -r2; or, on Unix, the second file can be
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recovered by piping the output through
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sed -n '/^[+ ] /s/^..//p'
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See module comments for details and programmatic interface.
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"""
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__version__ = 1, 5, 0
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# SequenceMatcher tries to compute a "human-friendly diff" between
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# two sequences (chiefly picturing a file as a sequence of lines,
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# and a line as a sequence of characters, here). Unlike e.g. UNIX(tm)
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# diff, the fundamental notion is the longest *contiguous* & junk-free
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# matching subsequence. That's what catches peoples' eyes. The
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# Windows(tm) windiff has another interesting notion, pairing up elements
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# that appear uniquely in each sequence. That, and the method here,
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# appear to yield more intuitive difference reports than does diff. This
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# method appears to be the least vulnerable to synching up on blocks
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# of "junk lines", though (like blank lines in ordinary text files,
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# or maybe "<P>" lines in HTML files). That may be because this is
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# the only method of the 3 that has a *concept* of "junk" <wink>.
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#
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# Note that ndiff makes no claim to produce a *minimal* diff. To the
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# contrary, minimal diffs are often counter-intuitive, because they
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# synch up anywhere possible, sometimes accidental matches 100 pages
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# apart. Restricting synch points to contiguous matches preserves some
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# notion of locality, at the occasional cost of producing a longer diff.
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#
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# With respect to junk, an earlier version of ndiff simply refused to
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# *start* a match with a junk element. The result was cases like this:
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# before: private Thread currentThread;
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# after: private volatile Thread currentThread;
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# If you consider whitespace to be junk, the longest contiguous match
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# not starting with junk is "e Thread currentThread". So ndiff reported
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# that "e volatil" was inserted between the 't' and the 'e' in "private".
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# While an accurate view, to people that's absurd. The current version
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# looks for matching blocks that are entirely junk-free, then extends the
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# longest one of those as far as possible but only with matching junk.
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# So now "currentThread" is matched, then extended to suck up the
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# preceding blank; then "private" is matched, and extended to suck up the
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# following blank; then "Thread" is matched; and finally ndiff reports
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# that "volatile " was inserted before "Thread". The only quibble
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# remaining is that perhaps it was really the case that " volatile"
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# was inserted after "private". I can live with that <wink>.
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#
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# NOTE on junk: the module-level names
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# IS_LINE_JUNK
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# IS_CHARACTER_JUNK
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# can be set to any functions you like. The first one should accept
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# a single string argument, and return true iff the string is junk.
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# The default is whether the regexp r"\s*#?\s*$" matches (i.e., a
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# line without visible characters, except for at most one splat).
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# The second should accept a string of length 1 etc. The default is
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# whether the character is a blank or tab (note: bad idea to include
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# newline in this!).
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#
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# After setting those, you can call fcompare(f1name, f2name) with the
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# names of the files you want to compare. The difference report
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# is sent to stdout. Or you can call main(args), passing what would
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# have been in sys.argv[1:] had the cmd-line form been used.
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import string
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TRACE = 0
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# define what "junk" means
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import re
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def IS_LINE_JUNK(line, pat=re.compile(r"\s*#?\s*$").match):
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return pat(line) is not None
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def IS_CHARACTER_JUNK(ch, ws=" \t"):
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return ch in ws
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del re
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class SequenceMatcher:
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def __init__(self, isjunk=None, a='', b=''):
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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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# b2jhas
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# b2j.has_key
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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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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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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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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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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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# 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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self.b2j = b2j = {}
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self.b2jhas = b2jhas = b2j.has_key
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for i in xrange(len(b)):
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elt = b[i]
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if b2jhas(elt):
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b2j[elt].append(i)
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else:
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b2j[elt] = [i]
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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, junkdict = self.isjunk, {}
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if isjunk:
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for elt in b2j.keys():
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if isjunk(elt):
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junkdict[elt] = 1 # value irrelevant; it's a set
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del b2j[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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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
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that starts earliest in a, and of all those maximal matching
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blocks that start earliest in a, return the one that starts
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earliest in b.
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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
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no junk element appears in the block. Then that block is
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extended as far as possible by matching (only) junk elements on
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both sides. So the resulting block never matches on junk except
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as identical junk happens to be adjacent to an "interesting"
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match.
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If no blocks match, return (alo, blo, 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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# 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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if TRACE:
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print "get_matching_blocks", alo, ahi, blo, bhi
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print " returns", besti, bestj, bestsize
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return besti, bestj, bestsize
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def get_matching_blocks(self):
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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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if TRACE:
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print '*** matching blocks', self.matching_blocks
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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):
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i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi)
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# a[alo:i] vs b[blo:j] unknown
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# a[i:i+k] same as b[j:j+k]
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# a[i+k:ahi] vs b[j+k:bhi] unknown
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if k:
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if alo < i and blo < j:
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self.__helper(alo, i, blo, j, answer)
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answer.append(x)
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if i+k < ahi and j+k < bhi:
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self.__helper(i+k, ahi, j+k, bhi, answer)
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def ratio(self):
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"""Return a measure of the sequences' similarity (float in [0,1]).
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Where T is the total number of elements in both sequences, and
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M is the number of matches, this is 2*M / T.
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Note that this is 1 if the sequences are identical, and 0 if
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they have nothing in common.
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"""
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matches = reduce(lambda sum, triple: sum + triple[-1],
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self.get_matching_blocks(), 0)
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return 2.0 * matches / (len(self.a) + len(self.b))
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def quick_ratio(self):
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"""Return an upper bound on ratio() relatively quickly."""
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# viewing a and b as multisets, set matches to the cardinality
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# of their intersection; this counts the number of matches
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# without regard to order, so is clearly an upper bound
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if self.fullbcount is None:
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self.fullbcount = fullbcount = {}
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for elt in self.b:
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fullbcount[elt] = fullbcount.get(elt, 0) + 1
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fullbcount = self.fullbcount
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# avail[x] is the number of times x appears in 'b' less the
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# number of times we've seen it in 'a' so far ... kinda
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avail = {}
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availhas, matches = avail.has_key, 0
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for elt in self.a:
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if availhas(elt):
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numb = avail[elt]
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else:
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numb = fullbcount.get(elt, 0)
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avail[elt] = numb - 1
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if numb > 0:
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matches = matches + 1
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return 2.0 * matches / (len(self.a) + len(self.b))
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def real_quick_ratio(self):
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"""Return an upper bound on ratio() very quickly"""
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la, lb = len(self.a), len(self.b)
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# can't have more matches than the number of elements in the
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# shorter sequence
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return 2.0 * min(la, lb) / (la + lb)
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def get_opcodes(self):
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if self.opcodes is not None:
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return self.opcodes
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i = j = 0
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self.opcodes = answer = []
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for ai, bj, size in self.get_matching_blocks():
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# invariant: we've pumped out correct diffs to change
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# a[:i] into b[:j], and the next matching block is
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# a[ai:ai+size] == b[bj:bj+size]. So we need to pump
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# out a diff to change a[i:ai] into b[j:bj], pump out
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# the matching block, and move (i,j) beyond the match
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tag = ''
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if i < ai and j < bj:
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tag = 'replace'
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elif i < ai:
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tag = 'delete'
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elif j < bj:
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tag = 'insert'
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if tag:
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answer.append( (tag, i, ai, j, bj) )
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i, j = ai+size, bj+size
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# the list of matching blocks is terminated by a
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# sentinel with size 0
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if size:
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answer.append( ('equal', ai, i, bj, j) )
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return answer
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# meant for dumping lines
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def dump(tag, x, lo, hi):
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for i in xrange(lo, hi):
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print tag, x[i],
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def plain_replace(a, alo, ahi, b, blo, bhi):
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assert alo < ahi and blo < bhi
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# dump the shorter block first -- reduces the burden on short-term
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# memory if the blocks are of very different sizes
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if bhi - blo < ahi - alo:
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dump('+', b, blo, bhi)
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dump('-', a, alo, ahi)
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else:
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dump('-', a, alo, ahi)
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dump('+', b, blo, bhi)
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# When replacing one block of lines with another, this guy searches
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# the blocks for *similar* lines; the best-matching pair (if any) is
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# used as a synch point, and intraline difference marking is done on
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# the similar pair. Lots of work, but often worth it.
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def fancy_replace(a, alo, ahi, b, blo, bhi):
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if TRACE:
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print '*** fancy_replace', alo, ahi, blo, bhi
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dump('>', a, alo, ahi)
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dump('<', b, blo, bhi)
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# don't synch up unless the lines have a similarity score of at
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# least cutoff; best_ratio tracks the best score seen so far
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best_ratio, cutoff = 0.74, 0.75
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cruncher = SequenceMatcher(IS_CHARACTER_JUNK)
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eqi, eqj = None, None # 1st indices of equal lines (if any)
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# search for the pair that matches best without being identical
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# (identical lines must be junk lines, & we don't want to synch up
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# on junk -- unless we have to)
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for j in xrange(blo, bhi):
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bj = b[j]
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cruncher.set_seq2(bj)
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for i in xrange(alo, ahi):
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ai = a[i]
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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
|
|
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:
|
|
print '*** best_ratio', best_ratio, best_i, best_j
|
|
dump('>', a, best_i, best_i+1)
|
|
dump('<', b, best_j, best_j+1)
|
|
|
|
# pump out diffs from before the synch point
|
|
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`
|
|
printq(aelt, belt, atags, btags)
|
|
else:
|
|
# the synch pair is identical
|
|
print ' ', aelt,
|
|
|
|
# pump out diffs from after the synch point
|
|
fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi)
|
|
|
|
def fancy_helper(a, alo, ahi, b, blo, bhi):
|
|
if alo < ahi:
|
|
if blo < bhi:
|
|
fancy_replace(a, alo, ahi, b, blo, bhi)
|
|
else:
|
|
dump('-', a, alo, ahi)
|
|
elif blo < bhi:
|
|
dump('+', b, blo, bhi)
|
|
|
|
# Crap to deal with leading tabs in "?" output. Can hurt, but will
|
|
# probably help most of the time.
|
|
|
|
def printq(aline, bline, atags, btags):
|
|
common = min(count_leading(aline, "\t"),
|
|
count_leading(bline, "\t"))
|
|
common = min(common, count_leading(atags[:common], " "))
|
|
print "-", aline,
|
|
if count_leading(atags, " ") < len(atags):
|
|
print "?", "\t" * common + atags[common:]
|
|
print "+", bline,
|
|
if count_leading(btags, " ") < len(btags):
|
|
print "?", "\t" * common + btags[common:]
|
|
|
|
def count_leading(line, ch):
|
|
i, n = 0, len(line)
|
|
while i < n and line[i] == ch:
|
|
i += 1
|
|
return i
|
|
|
|
def fail(msg):
|
|
import sys
|
|
out = sys.stderr.write
|
|
out(msg + "\n\n")
|
|
out(__doc__)
|
|
return 0
|
|
|
|
# open a file & return the file object; gripe and return 0 if it
|
|
# couldn't be opened
|
|
def fopen(fname):
|
|
try:
|
|
return open(fname, 'r')
|
|
except IOError, detail:
|
|
return fail("couldn't open " + fname + ": " + str(detail))
|
|
|
|
# open two files & spray the diff to stdout; return false iff a problem
|
|
def fcompare(f1name, f2name):
|
|
f1 = fopen(f1name)
|
|
f2 = fopen(f2name)
|
|
if not f1 or not f2:
|
|
return 0
|
|
|
|
a = f1.readlines(); f1.close()
|
|
b = f2.readlines(); f2.close()
|
|
|
|
cruncher = SequenceMatcher(IS_LINE_JUNK, a, b)
|
|
for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():
|
|
if tag == 'replace':
|
|
fancy_replace(a, alo, ahi, b, blo, bhi)
|
|
elif tag == 'delete':
|
|
dump('-', a, alo, ahi)
|
|
elif tag == 'insert':
|
|
dump('+', b, blo, bhi)
|
|
elif tag == 'equal':
|
|
dump(' ', a, alo, ahi)
|
|
else:
|
|
raise ValueError, 'unknown tag ' + `tag`
|
|
|
|
return 1
|
|
|
|
# crack args (sys.argv[1:] is normal) & compare;
|
|
# return false iff a problem
|
|
|
|
def main(args):
|
|
import getopt
|
|
try:
|
|
opts, args = getopt.getopt(args, "qr:")
|
|
except getopt.error, detail:
|
|
return fail(str(detail))
|
|
noisy = 1
|
|
qseen = rseen = 0
|
|
for opt, val in opts:
|
|
if opt == "-q":
|
|
qseen = 1
|
|
noisy = 0
|
|
elif opt == "-r":
|
|
rseen = 1
|
|
whichfile = val
|
|
if qseen and rseen:
|
|
return fail("can't specify both -q and -r")
|
|
if rseen:
|
|
if args:
|
|
return fail("no args allowed with -r option")
|
|
if whichfile in "12":
|
|
restore(whichfile)
|
|
return 1
|
|
return fail("-r value must be 1 or 2")
|
|
if len(args) != 2:
|
|
return fail("need 2 filename args")
|
|
f1name, f2name = args
|
|
if noisy:
|
|
print '-:', f1name
|
|
print '+:', f2name
|
|
return fcompare(f1name, f2name)
|
|
|
|
def restore(which):
|
|
import sys
|
|
tag = {"1": "- ", "2": "+ "}[which]
|
|
prefixes = (" ", tag)
|
|
for line in sys.stdin.readlines():
|
|
if line[:2] in prefixes:
|
|
print line[2:],
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
args = sys.argv[1:]
|
|
if "-profile" in args:
|
|
import profile, pstats
|
|
args.remove("-profile")
|
|
statf = "ndiff.pro"
|
|
profile.run("main(args)", statf)
|
|
stats = pstats.Stats(statf)
|
|
stats.strip_dirs().sort_stats('time').print_stats()
|
|
else:
|
|
main(args)
|