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Commit 007730b1 authored by Tim Peters's avatar Tim Peters
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SF patch #445412 extract ndiff functionality to difflib, from 

David Goodger.
parent ab1c4af3
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Lib/difflib.py
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......@@ -4,291 +4,142 @@
Module difflib -- helpers for computing deltas between objects.
Function get_close_matches(word, possibilities, n=3, cutoff=0.6):
Use SequenceMatcher to return list of the best "good enough" matches.
word is a sequence for which close matches are desired (typically a
string).
Function ndiff(a, b):
Return a delta: the difference between `a` and `b` (lists of strings).
possibilities is a list of sequences against which to match word
(typically a list of strings).
Function restore(delta, which):
Return one of the two sequences that generated an ndiff delta.
Optional arg n (default 3) is the maximum number of close matches to
return. n must be > 0.
Class SequenceMatcher:
A flexible class for comparing pairs of sequences of any type.
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.
Class Differ:
For producing human-readable deltas from sequences of lines of text.
"""
The best (no more than n) matches among the possibilities are returned
in a list, sorted by similarity score, most similar first.
__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',
'Differ']
>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword
>>> get_close_matches("wheel", keyword.kwlist)
['while']
>>> get_close_matches("apple", keyword.kwlist)
[]
>>> get_close_matches("accept", keyword.kwlist)
['except']
TRACE = 0
class SequenceMatcher:
"""
SequenceMatcher is a flexible class for comparing pairs of sequences of
any type, so long as the sequence elements are hashable. The basic
algorithm predates, and is a little fancier than, an algorithm
published in the late 1980's by Ratcliff and Obershelp under the
hyperbolic name "gestalt pattern matching". The basic idea is to find
the longest contiguous matching subsequence that contains no "junk"
elements (R-O doesn't address junk). The same idea is then applied
recursively to the pieces of the sequences to the left and to the right
of the matching subsequence. This does not yield minimal edit
sequences, but does tend to yield matches that "look right" to people.
SequenceMatcher tries to compute a "human-friendly diff" between two
sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the
longest *contiguous* & junk-free matching subsequence. That's what
catches peoples' eyes. The Windows(tm) windiff has another interesting
notion, pairing up elements that appear uniquely in each sequence.
That, and the method here, appear to yield more intuitive difference
reports than does diff. This method appears to be the least vulnerable
to synching up on blocks of "junk lines", though (like blank lines in
ordinary text files, or maybe "<P>" lines in HTML files). That may be
because this is the only method of the 3 that has a *concept* of
"junk" <wink>.
Example, comparing two strings, and considering blanks to be "junk":
>>> s = SequenceMatcher(lambda x: x == " ",
... "private Thread currentThread;",
... "private volatile Thread currentThread;")
>>>
.ratio() returns a float in [0, 1], measuring the "similarity" of the
sequences. As a rule of thumb, a .ratio() value over 0.6 means the
sequences are close matches:
Class SequenceMatcher
SequenceMatcher is a flexible class for comparing pairs of sequences of any
type, so long as the sequence elements are hashable. The basic algorithm
predates, and is a little fancier than, an algorithm published in the late
1980's by Ratcliff and Obershelp under the hyperbolic name "gestalt pattern
matching". The basic idea is to find the longest contiguous matching
subsequence that contains no "junk" elements (R-O doesn't address junk).
The same idea is then applied recursively to the pieces of the sequences to
the left and to the right of the matching subsequence. This does not yield
minimal edit sequences, but does tend to yield matches that "look right"
to people.
Example, comparing two strings, and considering blanks to be "junk":
>>> s = SequenceMatcher(lambda x: x == " ",
... "private Thread currentThread;",
... "private volatile Thread currentThread;")
>>>
.ratio() returns a float in [0, 1], measuring the "similarity" of the
sequences. As a rule of thumb, a .ratio() value over 0.6 means the
sequences are close matches:
>>> print round(s.ratio(), 3)
0.866
>>>
If you're only interested in where the sequences match,
.get_matching_blocks() is handy:
>>> for block in s.get_matching_blocks():
... print "a[%d] and b[%d] match for %d elements" % block
a[0] and b[0] match for 8 elements
a[8] and b[17] match for 6 elements
a[14] and b[23] match for 15 elements
a[29] and b[38] match for 0 elements
Note that the last tuple returned by .get_matching_blocks() is always a
dummy, (len(a), len(b), 0), and this is the only case in which the last
tuple element (number of elements matched) is 0.
If you want to know how to change the first sequence into the second, use
.get_opcodes():
>>> for opcode in s.get_opcodes():
... print "%6s a[%d:%d] b[%d:%d]" % opcode
equal a[0:8] b[0:8]
insert a[8:8] b[8:17]
equal a[8:14] b[17:23]
equal a[14:29] b[23:38]
See Tools/scripts/ndiff.py for a fancy human-friendly file differencer,
which uses SequenceMatcher both to view files as sequences of lines, and
lines as sequences of characters.
See also function get_close_matches() in this module, which shows how
simple code building on SequenceMatcher can be used to do useful work.
Timing: Basic R-O is cubic time worst case and quadratic time expected
case. SequenceMatcher is quadratic time for the worst case and has
expected-case behavior dependent in a complicated way on how many
elements the sequences have in common; best case time is linear.
SequenceMatcher methods:
__init__(isjunk=None, a='', b='')
Construct a SequenceMatcher.
Optional arg isjunk is None (the default), or a one-argument function
that takes a sequence element and returns true iff the element is junk.
None is equivalent to passing "lambda x: 0", i.e. no elements are
considered to be junk. For example, pass
lambda x: x in " \\t"
if you're comparing lines as sequences of characters, and don't want to
synch up on blanks or hard tabs.
Optional arg a is the first of two sequences to be compared. By
default, an empty string. The elements of a must be hashable.
Optional arg b is the second of two sequences to be compared. By
default, an empty string. The elements of b must be hashable.
set_seqs(a, b)
Set the two sequences to be compared.
>>> s = SequenceMatcher()
>>> s.set_seqs("abcd", "bcde")
>>> s.ratio()
0.75
set_seq1(a)
Set the first sequence to be compared.
The second sequence to be compared is not changed.
>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.set_seq1("bcde")
>>> s.ratio()
1.0
>>> print round(s.ratio(), 3)
0.866
>>>
SequenceMatcher computes and caches detailed information about the
second sequence, so if you want to compare one sequence S against many
sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for
each of the other sequences.
If you're only interested in where the sequences match,
.get_matching_blocks() is handy:
See also set_seqs() and set_seq2().
>>> for block in s.get_matching_blocks():
... print "a[%d] and b[%d] match for %d elements" % block
a[0] and b[0] match for 8 elements
a[8] and b[17] match for 6 elements
a[14] and b[23] match for 15 elements
a[29] and b[38] match for 0 elements
set_seq2(b)
Set the second sequence to be compared.
Note that the last tuple returned by .get_matching_blocks() is always a
dummy, (len(a), len(b), 0), and this is the only case in which the last
tuple element (number of elements matched) is 0.
The first sequence to be compared is not changed.
If you want to know how to change the first sequence into the second,
use .get_opcodes():
>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.set_seq2("abcd")
>>> s.ratio()
1.0
>>>
>>> for opcode in s.get_opcodes():
... print "%6s a[%d:%d] b[%d:%d]" % opcode
equal a[0:8] b[0:8]
insert a[8:8] b[8:17]
equal a[8:14] b[17:23]
equal a[14:29] b[23:38]
SequenceMatcher computes and caches detailed information about the
second sequence, so if you want to compare one sequence S against many
sequences, use .set_seq2(S) once and call .set_seq1(x) repeatedly for
each of the other sequences.
See also set_seqs() and set_seq1().
find_longest_match(alo, ahi, blo, bhi)
Find longest matching block in a[alo:ahi] and b[blo:bhi].
If isjunk is not defined:
Return (i,j,k) such that a[i:i+k] is equal to b[j:j+k], where
alo <= i <= i+k <= ahi
blo <= j <= j+k <= bhi
and for all (i',j',k') meeting those conditions,
k >= k'
i <= i'
and if i == i', j <= j'
In other words, of all maximal matching blocks, return one that starts
earliest in a, and of all those maximal matching blocks that start
earliest in a, return the one that starts earliest in b.
>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
(0, 4, 5)
If isjunk is defined, first the longest matching block is determined as
above, but with the additional restriction that no junk element appears
in the block. Then that block is extended as far as possible by
matching (only) junk elements on both sides. So the resulting block
never matches on junk except as identical junk happens to be adjacent
to an "interesting" match.
Here's the same example as before, but considering blanks to be junk.
That prevents " abcd" from matching the " abcd" at the tail end of the
second sequence directly. Instead only the "abcd" can match, and
matches the leftmost "abcd" in the second sequence:
>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
>>> s.find_longest_match(0, 5, 0, 9)
(1, 0, 4)
If no blocks match, return (alo, blo, 0).
>>> s = SequenceMatcher(None, "ab", "c")
>>> s.find_longest_match(0, 2, 0, 1)
(0, 0, 0)
get_matching_blocks()
Return list of triples describing matching subsequences.
Each triple is of the form (i, j, n), and means that
a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i
and in j.
The last triple is a dummy, (len(a), len(b), 0), and is the only triple
with n==0.
>>> s = SequenceMatcher(None, "abxcd", "abcd")
>>> s.get_matching_blocks()
[(0, 0, 2), (3, 2, 2), (5, 4, 0)]
get_opcodes()
Return list of 5-tuples describing how to turn a into b.
Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has
i1 == j1 == 0, and remaining tuples have i1 == the i2 from the tuple
preceding it, and likewise for j1 == the previous j2.
The tags are strings, with these meanings:
'replace': a[i1:i2] should be replaced by b[j1:j2]
'delete': a[i1:i2] should be deleted.
Note that j1==j2 in this case.
'insert': b[j1:j2] should be inserted at a[i1:i1].
Note that i1==i2 in this case.
'equal': a[i1:i2] == b[j1:j2]
>>> a = "qabxcd"
>>> b = "abycdf"
>>> s = SequenceMatcher(None, a, b)
>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
... print ("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %
... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2]))
delete a[0:1] (q) b[0:0] ()
equal a[1:3] (ab) b[0:2] (ab)
replace a[3:4] (x) b[2:3] (y)
equal a[4:6] (cd) b[3:5] (cd)
insert a[6:6] () b[5:6] (f)
ratio()
Return a measure of the sequences' similarity (float in [0,1]).
Where T is the total number of elements in both sequences, and M is the
number of matches, this is 2,0*M / T. Note that this is 1 if the
sequences are identical, and 0 if they have nothing in common.
.ratio() is expensive to compute if you haven't already computed
.get_matching_blocks() or .get_opcodes(), in which case you may want to
try .quick_ratio() or .real_quick_ratio() first to get an upper bound.
>>> s = SequenceMatcher(None, "abcd", "bcde")
>>> s.ratio()
0.75
>>> s.quick_ratio()
0.75
>>> s.real_quick_ratio()
1.0
quick_ratio()
Return an upper bound on .ratio() relatively quickly.
This isn't defined beyond that it is an upper bound on .ratio(), and
is faster to compute.
real_quick_ratio():
Return an upper bound on ratio() very quickly.
This isn't defined beyond that it is an upper bound on .ratio(), and
is faster to compute than either .ratio() or .quick_ratio().
"""
See the Differ class for a fancy human-friendly file differencer, which
uses SequenceMatcher both to compare sequences of lines, and to compare
sequences of characters within similar (near-matching) lines.
TRACE = 0
See also function get_close_matches() in this module, which shows how
simple code building on SequenceMatcher can be used to do useful work.
Timing: Basic R-O is cubic time worst case and quadratic time expected
case. SequenceMatcher is quadratic time for the worst case and has
expected-case behavior dependent in a complicated way on how many
elements the sequences have in common; best case time is linear.
Methods:
__init__(isjunk=None, a='', b='')
Construct a SequenceMatcher.
set_seqs(a, b)
Set the two sequences to be compared.
set_seq1(a)
Set the first sequence to be compared.
set_seq2(b)
Set the second sequence to be compared.
find_longest_match(alo, ahi, blo, bhi)
Find longest matching block in a[alo:ahi] and b[blo:bhi].
get_matching_blocks()
Return list of triples describing matching subsequences.
get_opcodes()
Return list of 5-tuples describing how to turn a into b.
ratio()
Return a measure of the sequences' similarity (float in [0,1]).
quick_ratio()
Return an upper bound on .ratio() relatively quickly.
real_quick_ratio()
Return an upper bound on ratio() very quickly.
"""
class SequenceMatcher:
def __init__(self, isjunk=None, a='', b=''):
"""Construct a SequenceMatcher.
Optional arg isjunk is None (the default), or a one-argument
function that takes a sequence element and returns true iff the
element is junk. None is equivalent to passing "lambda x: 0", i.e.
element is junk. None is equivalent to passing "lambda x: 0", i.e.
no elements are considered to be junk. For example, pass
lambda x: x in " \\t"
if you're comparing lines as sequences of characters, and don't
......@@ -742,12 +593,12 @@ def get_close_matches(word, possibilities, n=3, cutoff=0.6):
>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])
['apple', 'ape']
>>> import keyword
>>> get_close_matches("wheel", keyword.kwlist)
>>> import keyword as _keyword
>>> get_close_matches("wheel", _keyword.kwlist)
['while']
>>> get_close_matches("apple", keyword.kwlist)
>>> get_close_matches("apple", _keyword.kwlist)
[]
>>> get_close_matches("accept", keyword.kwlist)
>>> get_close_matches("accept", _keyword.kwlist)
['except']
"""
......@@ -773,6 +624,463 @@ def get_close_matches(word, possibilities, n=3, cutoff=0.6):
# Strip scores.
return [x for score, x in result]
def _count_leading(line, ch):
"""
Return number of `ch` characters at the start of `line`.
Example:
>>> _count_leading(' abc', ' ')
3
"""
i, n = 0, len(line)
while i < n and line[i] == ch:
i += 1
return i
class Differ:
r"""
Differ is a class for comparing sequences of lines of text, and
producing human-readable differences or deltas. Differ uses
SequenceMatcher both to compare sequences of lines, and to compare
sequences of characters within similar (near-matching) lines.
Each line of a Differ delta begins with a two-letter code:
'- ' line unique to sequence 1
'+ ' line unique to sequence 2
' ' line common to both sequences
'? ' line not present in either input sequence
Lines beginning with '? ' attempt to guide the eye to intraline
differences, and were not present in either input sequence. These lines
can be confusing if the sequences contain tab characters.
Note that Differ makes no claim to produce a *minimal* diff. To the
contrary, minimal diffs are often counter-intuitive, because they synch
up anywhere possible, sometimes accidental matches 100 pages apart.
Restricting synch points to contiguous matches preserves some notion of
locality, at the occasional cost of producing a longer diff.
Example: Comparing two texts.
First we set up the texts, sequences of individual single-line strings
ending with newlines (such sequences can also be obtained from the
`readlines()` method of file-like objects):
>>> text1 = ''' 1. Beautiful is better than ugly.
... 2. Explicit is better than implicit.
... 3. Simple is better than complex.
... 4. Complex is better than complicated.
... '''.splitlines(1)
>>> len(text1)
4
>>> text1[0][-1]
'\n'
>>> text2 = ''' 1. Beautiful is better than ugly.
... 3. Simple is better than complex.
... 4. Complicated is better than complex.
... 5. Flat is better than nested.
... '''.splitlines(1)
Next we instantiate a Differ object:
>>> d = Differ()
Note that when instantiating a Differ object we may pass functions to
filter out line and character 'junk'. See Differ.__init__ for details.
Finally, we compare the two:
>>> result = d.compare(text1, text2)
'result' is a list of strings, so let's pretty-print it:
>>> from pprint import pprint as _pprint
>>> _pprint(result)
[' 1. Beautiful is better than ugly.\n',
'- 2. Explicit is better than implicit.\n',
'- 3. Simple is better than complex.\n',
'+ 3. Simple is better than complex.\n',
'? ++\n',
'- 4. Complex is better than complicated.\n',
'? ^ ---- ^\n',
'+ 4. Complicated is better than complex.\n',
'? ++++ ^ ^\n',
'+ 5. Flat is better than nested.\n']
As a single multi-line string it looks like this:
>>> print ''.join(result),
1. Beautiful is better than ugly.
- 2. Explicit is better than implicit.
- 3. Simple is better than complex.
+ 3. Simple is better than complex.
? ++
- 4. Complex is better than complicated.
? ^ ---- ^
+ 4. Complicated is better than complex.
? ++++ ^ ^
+ 5. Flat is better than nested.
Methods:
__init__(linejunk=None, charjunk=None)
Construct a text differencer, with optional filters.
compare(a, b)
Compare two sequences of lines; return the resulting delta (list).
"""
def __init__(self, linejunk=None, charjunk=None):
"""
Construct a text differencer, with optional filters.
The two optional keyword parameters are for filter functions:
- `linejunk`: A function that should accept a single string argument,
and return true iff the string is junk. The module-level function
`IS_LINE_JUNK` may be used to filter out lines without visible
characters, except for at most one splat ('#').
- `charjunk`: A function that should accept a string of length 1. The
module-level function `IS_CHARACTER_JUNK` may be used to filter out
whitespace characters (a blank or tab; **note**: bad idea to include
newline in this!).
"""
self.linejunk = linejunk
self.charjunk = charjunk
self.results = []
def compare(self, a, b):
r"""
Compare two sequences of lines; return the resulting delta (list).
Each sequence must contain individual single-line strings ending with
newlines. Such sequences can be obtained from the `readlines()` method
of file-like objects. The list returned is also made up of
newline-terminated strings, ready to be used with the `writelines()`
method of a file-like object.
Example:
>>> print ''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(1),
... 'ore\ntree\nemu\n'.splitlines(1))),
- one
? ^
+ ore
? ^
- two
- three
? -
+ tree
+ emu
"""
cruncher = SequenceMatcher(self.linejunk, a, b)
for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():
if tag == 'replace':
self._fancy_replace(a, alo, ahi, b, blo, bhi)
elif tag == 'delete':
self._dump('-', a, alo, ahi)
elif tag == 'insert':
self._dump('+', b, blo, bhi)
elif tag == 'equal':
self._dump(' ', a, alo, ahi)
else:
raise ValueError, 'unknown tag ' + `tag`
results = self.results
self.results = []
return results
def _dump(self, tag, x, lo, hi):
"""Store comparison results for a same-tagged range."""
for i in xrange(lo, hi):
self.results.append('%s %s' % (tag, x[i]))
def _plain_replace(self, a, alo, ahi, b, blo, bhi):
assert alo < ahi and blo < bhi
# dump the shorter block first -- reduces the burden on short-term
# memory if the blocks are of very different sizes
if bhi - blo < ahi - alo:
self._dump('+', b, blo, bhi)
self._dump('-', a, alo, ahi)
else:
self._dump('-', a, alo, ahi)
self._dump('+', b, blo, bhi)
def _fancy_replace(self, a, alo, ahi, b, blo, bhi):
r"""
When replacing one block of lines with another, search the blocks
for *similar* lines; the best-matching pair (if any) is used as a
synch point, and intraline difference marking is done on the
similar pair. Lots of work, but often worth it.
Example:
>>> d = Differ()
>>> d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ['abcdefGhijkl\n'], 0, 1)
>>> print ''.join(d.results),
- abcDefghiJkl
? ^ ^ ^
+ abcdefGhijkl
? ^ ^ ^
"""
if TRACE:
self.results.append('*** _fancy_replace %s %s %s %s\n'
% (alo, ahi, blo, bhi))
self._dump('>', a, alo, ahi)
self._dump('<', b, blo, bhi)
# don't synch up unless the lines have a similarity score of at
# least cutoff; best_ratio tracks the best score seen so far
best_ratio, cutoff = 0.74, 0.75
cruncher = SequenceMatcher(self.charjunk)
eqi, eqj = None, None # 1st indices of equal lines (if any)
# search for the pair that matches best without being identical
# (identical lines must be junk lines, & we don't want to synch up
# on junk -- unless we have to)
for j in xrange(blo, bhi):
bj = b[j]
cruncher.set_seq2(bj)
for i in xrange(alo, ahi):
ai = a[i]
if ai == bj:
if eqi is None:
eqi, eqj = i, j
continue
cruncher.set_seq1(ai)
# computing similarity is expensive, so use the quick
# upper bounds first -- have seen this speed up messy
# compares by a factor of 3.
# note that ratio() is only expensive to compute the first
# time it's called on a sequence pair; the expensive part
# of the computation is cached by cruncher
if cruncher.real_quick_ratio() > best_ratio and \
cruncher.quick_ratio() > best_ratio and \
cruncher.ratio() > best_ratio:
best_ratio, best_i, best_j = cruncher.ratio(), i, j
if best_ratio < cutoff:
# no non-identical "pretty close" pair
if eqi is None:
# no identical pair either -- treat it as a straight replace
self._plain_replace(a, alo, ahi, b, blo, bhi)
return
# no close pair, but an identical pair -- synch up on that
best_i, best_j, best_ratio = eqi, eqj, 1.0
else:
# there's a close pair, so forget the identical pair (if any)
eqi = None
# a[best_i] very similar to b[best_j]; eqi is None iff they're not
# identical
if TRACE:
self.results.append('*** best_ratio %s %s %s %s\n'
% (best_ratio, best_i, best_j))
self._dump('>', a, best_i, best_i+1)
self._dump('<', b, best_j, best_j+1)
# pump out diffs from before the synch point
self._fancy_helper(a, alo, best_i, b, blo, best_j)
# do intraline marking on the synch pair
aelt, belt = a[best_i], b[best_j]
if eqi is None:
# pump out a '-', '?', '+', '?' quad for the synched lines
atags = btags = ""
cruncher.set_seqs(aelt, belt)
for tag, ai1, ai2, bj1, bj2 in cruncher.get_opcodes():
la, lb = ai2 - ai1, bj2 - bj1
if tag == 'replace':
atags += '^' * la
btags += '^' * lb
elif tag == 'delete':
atags += '-' * la
elif tag == 'insert':
btags += '+' * lb
elif tag == 'equal':
atags += ' ' * la
btags += ' ' * lb
else:
raise ValueError, 'unknown tag ' + `tag`
self._qformat(aelt, belt, atags, btags)
else:
# the synch pair is identical
self.results.append(' ' + aelt)
# pump out diffs from after the synch point
self._fancy_helper(a, best_i+1, ahi, b, best_j+1, bhi)
def _fancy_helper(self, a, alo, ahi, b, blo, bhi):
if alo < ahi:
if blo < bhi:
self._fancy_replace(a, alo, ahi, b, blo, bhi)
else:
self._dump('-', a, alo, ahi)
elif blo < bhi:
self._dump('+', b, blo, bhi)
def _qformat(self, aline, bline, atags, btags):
r"""
Format "?" output and deal with leading tabs.
Example:
>>> d = Differ()
>>> d._qformat('\tabcDefghiJkl\n', '\t\tabcdefGhijkl\n',
... ' ^ ^ ^ ', '+ ^ ^ ^ ')
>>> for line in d.results: print repr(line)
...
'- \tabcDefghiJkl\n'
'? \t ^ ^ ^\n'
'+ \t\tabcdefGhijkl\n'
'? \t ^ ^ ^\n'
"""
# Can hurt, but will probably help most of the time.
common = min(_count_leading(aline, "\t"),
_count_leading(bline, "\t"))
common = min(common, _count_leading(atags[:common], " "))
atags = atags[common:].rstrip()
btags = btags[common:].rstrip()
self.results.append("- " + aline)
if atags:
self.results.append("? %s%s\n" % ("\t" * common, atags))
self.results.append("+ " + bline)
if btags:
self.results.append("? %s%s\n" % ("\t" * common, btags))
# With respect to junk, an earlier version of ndiff simply refused to
# *start* a match with a junk element. The result was cases like this:
# before: private Thread currentThread;
# after: private volatile Thread currentThread;
# If you consider whitespace to be junk, the longest contiguous match
# not starting with junk is "e Thread currentThread". So ndiff reported
# that "e volatil" was inserted between the 't' and the 'e' in "private".
# While an accurate view, to people that's absurd. The current version
# looks for matching blocks that are entirely junk-free, then extends the
# longest one of those as far as possible but only with matching junk.
# So now "currentThread" is matched, then extended to suck up the
# preceding blank; then "private" is matched, and extended to suck up the
# following blank; then "Thread" is matched; and finally ndiff reports
# that "volatile " was inserted before "Thread". The only quibble
# remaining is that perhaps it was really the case that " volatile"
# was inserted after "private". I can live with that <wink>.
import re
def IS_LINE_JUNK(line, pat=re.compile(r"\s*#?\s*$").match):
r"""
Return 1 for ignorable line: iff `line` is blank or contains a single '#'.
Examples:
>>> IS_LINE_JUNK('\n')
1
>>> IS_LINE_JUNK(' # \n')
1
>>> IS_LINE_JUNK('hello\n')
0
"""
return pat(line) is not None
def IS_CHARACTER_JUNK(ch, ws=" \t"):
r"""
Return 1 for ignorable character: iff `ch` is a space or tab.
Examples:
>>> IS_CHARACTER_JUNK(' ')
1
>>> IS_CHARACTER_JUNK('\t')
1
>>> IS_CHARACTER_JUNK('\n')
0
>>> IS_CHARACTER_JUNK('x')
0
"""
return ch in ws
del re
def ndiff(a, b, linejunk=IS_LINE_JUNK, charjunk=IS_CHARACTER_JUNK):
r"""
Compare `a` and `b` (lists of strings); return a `Differ`-style delta.
Optional keyword parameters `linejunk` and `charjunk` are for filter
functions (or None):
- linejunk: A function that should accept a single string argument, and
return true iff the string is junk. The default is module-level function
IS_LINE_JUNK, which filters out lines without visible characters, except
for at most one splat ('#').
- charjunk: A function that should accept a string of length 1. The
default is module-level function IS_CHARACTER_JUNK, which filters out
whitespace characters (a blank or tab; note: bad idea to include newline
in this!).
Tools/scripts/ndiff.py is a command-line front-end to this function.
Example:
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
... 'ore\ntree\nemu\n'.splitlines(1))
>>> print ''.join(diff),
- one
? ^
+ ore
? ^
- two
- three
? -
+ tree
+ emu
"""
return Differ(linejunk, charjunk).compare(a, b)
def restore(delta, which):
r"""
Return one of the two sequences that generated a delta.
Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
lines originating from file 1 or 2 (parameter `which`), stripping off line
prefixes.
Examples:
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
... 'ore\ntree\nemu\n'.splitlines(1))
>>> print ''.join(restore(diff, 1)),
one
two
three
>>> print ''.join(restore(diff, 2)),
ore
tree
emu
"""
try:
tag = {1: "- ", 2: "+ "}[int(which)]
except KeyError:
raise ValueError, ('unknown delta choice (must be 1 or 2): %r'
% which)
prefixes = (" ", tag)
results = []
for line in delta:
if line[:2] in prefixes:
results.append(line[2:])
return results
def _test():
import doctest, difflib
return doctest.testmod(difflib)
......
Misc/ACKS
View file @ 007730b1
......@@ -144,6 +144,7 @@ Harry Henry Gebel
Thomas Gellekum
Ben Gertzfield
Jonathan Giddy
David Goodger
Hans de Graaff
Duncan Grisby
Dag Gruneau
......
Misc/NEWS
View file @ 007730b1
......@@ -3,6 +3,9 @@ What's New in Python 2.2a2?
Tests
- regrtest.py now knows which tests are expected to be skipped on some
platforms, allowing to give clearer test result output.
- Several new tests in the standard test suite, with special thanks to
Nick Mathewson.
......@@ -14,6 +17,10 @@ Core
Library
- New class Differ and new functions ndiff() and restore() in difflib.py.
These package the algorithms used by the popular Tools/scripts/ndiff.py,
for progammatic reuse.
- New function xml.sax.saxutils.quoteattr(): Quote an XML attribute
value using the minimal quoting required for the value; more
reliable than using xml.sax.saxutils.escape() for attribute values.
......
Tools/scripts/ndiff.py
View file @ 007730b1
#! /usr/bin/env python
# Module ndiff version 1.6.0
# Module ndiff version 1.7.0
# Released to the public domain 08-Dec-2000,
# by Tim Peters (tim.one@home.com).
# Provided as-is; use at your own risk; no warranty; no promises; enjoy!
# ndiff.py is now simply a front-end to the difflib.ndiff() function.
# Originally, it contained the difflib.SequenceMatcher class as well.
# This completes the raiding of reusable code from this formerly
# self-contained script.
"""ndiff [-q] file1 file2
or
ndiff (-r1 | -r2) < ndiff_output > file1_or_file2
......@@ -39,217 +44,13 @@ The second file can be recovered similarly, but by retaining only " " and
recovered by piping the output through
sed -n '/^[+ ] /s/^..//p'
See module comments for details and programmatic interface.
"""
__version__ = 1, 5, 0
# SequenceMatcher tries to compute a "human-friendly diff" between
# two sequences (chiefly picturing a file as a sequence of lines,
# and a line as a sequence of characters, here). Unlike e.g. UNIX(tm)
# diff, the fundamental notion is the longest *contiguous* & junk-free
# matching subsequence. That's what catches peoples' eyes. The
# Windows(tm) windiff has another interesting notion, pairing up elements
# that appear uniquely in each sequence. That, and the method here,
# appear to yield more intuitive difference reports than does diff. This
# method appears to be the least vulnerable to synching up on blocks
# of "junk lines", though (like blank lines in ordinary text files,
# or maybe "<P>" lines in HTML files). That may be because this is
# the only method of the 3 that has a *concept* of "junk" <wink>.
#
# Note that ndiff 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.
#
# 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>.
#
# NOTE on junk: the module-level names
# IS_LINE_JUNK
# IS_CHARACTER_JUNK
# can be set to any functions you like. The first one should accept
# a single string argument, and return true iff the string is junk.
# The default is whether the regexp r"\s*#?\s*$" matches (i.e., a
# line without visible characters, except for at most one splat).
# The second should accept a string of length 1 etc. The default is
# whether the character is a blank or tab (note: bad idea to include
# newline in this!).
#
# After setting those, you can call fcompare(f1name, f2name) with the
# names of the files you want to compare. The difference report
# is sent to stdout. Or you can call main(args), passing what would
# have been in sys.argv[1:] had the cmd-line form been used.
from difflib import SequenceMatcher
import string
TRACE = 0
# define what "junk" means
import re
def IS_LINE_JUNK(line, pat=re.compile(r"\s*#?\s*$").match):
return pat(line) is not None
def IS_CHARACTER_JUNK(ch, ws=" \t"):
return ch in ws
del re
# meant for dumping lines
def dump(tag, x, lo, hi):
for i in xrange(lo, hi):
print tag, x[i],
def plain_replace(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:
dump('+', b, blo, bhi)
dump('-', a, alo, ahi)
else:
dump('-', a, alo, ahi)
dump('+', b, blo, bhi)
# When replacing one block of lines with another, this guy searches
# 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.
__version__ = 1, 7, 0
def fancy_replace(a, alo, ahi, b, blo, bhi):
if TRACE:
print '*** fancy_replace', alo, ahi, blo, bhi
dump('>', a, alo, ahi)
dump('<', b, blo, bhi)
# don't synch up unless the lines have a similarity score of at
# least cutoff; best_ratio tracks the best score seen so far
best_ratio, cutoff = 0.74, 0.75
cruncher = SequenceMatcher(IS_CHARACTER_JUNK)
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
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
import difflib, sys
def fail(msg):
import sys
out = sys.stderr.write
out(msg + "\n\n")
out(__doc__)
......@@ -273,18 +74,8 @@ def fcompare(f1name, f2name):
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`
diff = difflib.ndiff(a, b)
sys.stdout.writelines(diff)
return 1
......@@ -323,16 +114,14 @@ def main(args):
print '+:', f2name
return fcompare(f1name, f2name)
# read ndiff output from stdin, and print file1 (which=='1') or
# file2 (which=='2') to stdout
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:],
restored = difflib.restore(sys.stdin.readlines(), which)
sys.stdout.writelines(restored)
if __name__ == '__main__':
import sys
args = sys.argv[1:]
if "-profile" in args:
import profile, pstats
......
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