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,bH‰W Hˆf†[H‰W L‰à]A\ÃèTHÿÿHƒìHƒÄÃ%s undecomposable Sequence???%s cannot allocate memory%s item #%i is not a String%s item #%i is not a Unicodehammingdistance%s internal errorjarosetratio%s weight #%i is not a Number%s weight #%i is negativesetmedianquickmedianmedian_improve%s argument types don't matchseqratiojaro_winkler%s negative prefix weightsubtract_editinversematching_blocksopcodeseditopsapply_edit_levenshteinequalreplaceinsertdelete%s expected list of Strings or Unicodes%s expected two strings of the same length%s expected two unicodes of the same length%s expected two Strings or two Unicodes%s first argument must be a Sequence%s second argument must be a Sequence%s both sequences must consist of items of the same type%s got %i strings but %i weights%s first argument must be a String or Unicode%s third argument must be a Floatsubtract_edit subsequence is not a subsequence or is invalidsubtract_edit expected two lists of edit operationsinverse expected a list of edit operationsmatching_blocks second and third argument must specify sizesapply_edit edit operations are invalid or inapplicableopcodes first argument must be a List of edit operationsopcodes second and third argument must specify sizesopcodes edit operation list is invalidopcodes expected two Strings or two Unicodeseditops first argument must be a List of edit operationseditops second and third argument must specify sizeseditops edit operation list is invalideditops expected two Strings or two Unicodesapply_edit first argument must be a List of edit operationsapply_edit first argument must be a list of edit operationsapply_edit expected two Strings or two UnicodesA C extension module for fast computation of:
- Levenshtein (edit) distance and edit sequence manipulation
- string similarity
- approximate median strings, and generally string averaging
- string sequence and set similarity

Levenshtein has a some overlap with difflib (SequenceMatcher).  It
supports only strings, not arbitrary sequence types, but on the
other hand it's much faster.

It supports both normal and Unicode strings, but can't mix them, all
arguments to a function (method) have to be of the same type (or its
subclasses).
Compute absolute Levenshtein distance of two strings.

distance(string1, string2)

Examples (it's hard to spell Levenshtein correctly):

>>> distance('Levenshtein', 'Lenvinsten')
4
>>> distance('Levenshtein', 'Levensthein')
2
>>> distance('Levenshtein', 'Levenshten')
1
>>> distance('Levenshtein', 'Levenshtein')
0

Yeah, we've managed it at last.
Compute similarity of two strings.

ratio(string1, string2)

The similarity is a number between 0 and 1, it's usually equal or
somewhat higher than difflib.SequenceMatcher.ratio(), because it's
based on real minimal edit distance.

Examples:

>>> ratio('Hello world!', 'Holly grail!')  # doctest: +ELLIPSIS
0.583333...
>>> ratio('Brian', 'Jesus')
0.0

Really?  I thought there was some similarity.
Compute Hamming distance of two strings.

hamming(string1, string2)

The Hamming distance is simply the number of differing characters.
That means the length of the strings must be the same.

Examples:
>>> hamming('Hello world!', 'Holly grail!')
7
>>> hamming('Brian', 'Jesus')
5
Compute Jaro string similarity metric of two strings.

jaro(string1, string2)

The Jaro string similarity metric is intended for short strings like
personal last names.  It is 0 for completely different strings and
1 for identical strings.

Examples:
>>> jaro('Brian', 'Jesus')
0.0
>>> jaro('Thorkel', 'Thorgier')  # doctest: +ELLIPSIS
0.779761...
>>> jaro('Dinsdale', 'D')  # doctest: +ELLIPSIS
0.708333...
Compute Jaro string similarity metric of two strings.

jaro_winkler(string1, string2[, prefix_weight])

The Jaro-Winkler string similarity metric is a modification of Jaro
metric giving more weight to common prefix, as spelling mistakes are
more likely to occur near ends of words.

The prefix weight is inverse value of common prefix length sufficient
to consider the strings *identical*.  If no prefix weight is
specified, 1/10 is used.

Examples:

>>> jaro_winkler('Brian', 'Jesus')
0.0
>>> jaro_winkler('Thorkel', 'Thorgier')  # doctest: +ELLIPSIS
0.867857...
>>> jaro_winkler('Dinsdale', 'D')  # doctest: +ELLIPSIS
0.7375...
>>> jaro_winkler('Thorkel', 'Thorgier', 0.25)
1.0
Find an approximate generalized median string using greedy algorithm.

median(string_sequence[, weight_sequence])

You can optionally pass a weight for each string as the second
argument.  The weights are interpreted as item multiplicities,
although any non-negative real numbers are accepted.  Use them to
improve computation speed when strings often appear multiple times
in the sequence.

Examples:

>>> median(['SpSm', 'mpamm', 'Spam', 'Spa', 'Sua', 'hSam'])
'Spam'
>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
...          'Leveshtei', 'Lenshtein', 'Lvenstein',
...          'Levenhtin', 'evenshtei']
>>> median(fixme)
'Levenshtein'

Hm.  Even a computer program can spell Levenshtein better than me.
Improve an approximate generalized median string by perturbations.

median_improve(string, string_sequence[, weight_sequence])

The first argument is the estimated generalized median string you
want to improve, the others are the same as in median().  It returns
a string with total distance less or equal to that of the given string.

Note this is much slower than median().  Also note it performs only
one improvement step, calling median_improve() again on the result
may improve it further, though this is unlikely to happen unless the
given string was not very similar to the actual generalized median.

Examples:

>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
...          'Leveshtei', 'Lenshtein', 'Lvenstein',
...          'Levenhtin', 'evenshtei']
>>> median_improve('spam', fixme)
'enhtein'
>>> median_improve(median_improve('spam', fixme), fixme)
'Levenshtein'

It takes some work to change spam to Levenshtein.
Find a very approximate generalized median string, but fast.

quickmedian(string[, weight_sequence])

See median() for argument description.

This method is somewhere between setmedian() and picking a random
string from the set; both speedwise and quality-wise.

Examples:

>>> fixme = ['Levnhtein', 'Leveshein', 'Leenshten',
...          'Leveshtei', 'Lenshtein', 'Lvenstein',
...          'Levenhtin', 'evenshtei']
>>> quickmedian(fixme)
'Levnshein'
Find set median of a string set (passed as a sequence).

setmedian(string[, weight_sequence])

See median() for argument description.

The returned string is always one of the strings in the sequence.

Examples:

>>> setmedian(['ehee', 'cceaes', 'chees', 'chreesc',
...            'chees', 'cheesee', 'cseese', 'chetese'])
'chees'

You haven't asked me about Limburger, sir.
Compute similarity ratio of two sequences of strings.

seqratio(string_sequence1, string_sequence2)

This is like ratio(), but for string sequences.  A kind of ratio()
is used to to measure the cost of item change operation for the
strings.

Examples:

>>> seqratio(['newspaper', 'litter bin', 'tinny', 'antelope'],
...          ['caribou', 'sausage', 'gorn', 'woody'])
0.21517857142857144
Compute similarity ratio of two strings sets (passed as sequences).

setratio(string_sequence1, string_sequence2)

The best match between any strings in the first set and the second
set (passed as sequences) is attempted.  I.e., the order doesn't
matter here.

Examples:

>>> setratio(['newspaper', 'litter bin', 'tinny', 'antelope'],
...          ['caribou', 'sausage', 'gorn', 'woody'])  # doctest: +ELLIPSIS
0.281845...

No, even reordering doesn't help the tinny words to match the
woody ones.
Find sequence of edit operations transforming one string to another.

editops(source_string, destination_string)
editops(edit_operations, source_length, destination_length)

The result is a list of triples (operation, spos, dpos), where
operation is one of 'equal', 'replace', 'insert', or 'delete';  spos
and dpos are position of characters in the first (source) and the
second (destination) strings.  These are operations on signle
characters.  In fact the returned list doesn't contain the 'equal',
but all the related functions accept both lists with and without
'equal's.

Examples:

>>> editops('spam', 'park')
[('delete', 0, 0), ('insert', 3, 2), ('replace', 3, 3)]

The alternate form editops(opcodes, source_string, destination_string)
can be used for conversion from opcodes (5-tuples) to editops (you can
pass strings or their lengths, it doesn't matter).
Find sequence of edit operations transforming one string to another.

opcodes(source_string, destination_string)
opcodes(edit_operations, source_length, destination_length)

The result is a list of 5-tuples with the same meaning as in
SequenceMatcher's get_opcodes() output.  But since the algorithms
differ, the actual sequences from Levenshtein and SequenceMatcher
may differ too.

Examples:

>>> for x in opcodes('spam', 'park'):
...     print(x)
...
('delete', 0, 1, 0, 0)
('equal', 1, 3, 0, 2)
('insert', 3, 3, 2, 3)
('replace', 3, 4, 3, 4)

The alternate form opcodes(editops, source_string, destination_string)
can be used for conversion from editops (triples) to opcodes (you can
pass strings or their lengths, it doesn't matter).
Invert the sense of an edit operation sequence.

inverse(edit_operations)

In other words, it returns a list of edit operations transforming the
second (destination) string to the first (source).  It can be used
with both editops and opcodes.

Examples:

>>> inverse(editops('spam', 'park'))
[('insert', 0, 0), ('delete', 2, 3), ('replace', 3, 3)]
>>> editops('park', 'spam')
[('insert', 0, 0), ('delete', 2, 3), ('replace', 3, 3)]
Apply a sequence of edit operations to a string.

apply_edit(edit_operations, source_string, destination_string)

In the case of editops, the sequence can be arbitrary ordered subset
of the edit sequence transforming source_string to destination_string.

Examples:

>>> e = editops('man', 'scotsman')
>>> apply_edit(e, 'man', 'scotsman')
'scotsman'
>>> apply_edit(e[:3], 'man', 'scotsman')
'scoman'

The other form of edit operations, opcodes, is not very suitable for
such a tricks, because it has to always span over complete strings,
subsets can be created by carefully replacing blocks with 'equal'
blocks, or by enlarging 'equal' block at the expense of other blocks
and adjusting the other blocks accordingly.

Examples:
>>> a, b = 'spam and eggs', 'foo and bar'
>>> e = opcodes(a, b)
>>> apply_edit(inverse(e), b, a)
'spam and eggs'
>>> e[4] = ('equal', 10, 13, 8, 11)
>>> a, b, e
>>> apply_edit(e, a, b)
'foo and ggs'
Find identical blocks in two strings.

matching_blocks(edit_operations, source_length, destination_length)

The result is a list of triples with the same meaning as in
SequenceMatcher's get_matching_blocks() output.  It can be used with
both editops and opcodes.  The second and third arguments don't
have to be actually strings, their lengths are enough.

Examples:

>>> a, b = 'spam', 'park'
>>> matching_blocks(editops(a, b), a, b)
[(1, 0, 2), (4, 4, 0)]
>>> matching_blocks(editops(a, b), len(a), len(b))
[(1, 0, 2), (4, 4, 0)]

The last zero-length block is not an error, but it's there for
compatibility with difflib which always emits it.

One can join the matching blocks to get two identical strings:
>>> a, b = 'dog kennels', 'mattresses'
>>> mb = matching_blocks(editops(a,b), a, b)
>>> ''.join([a[x[0]:x[0]+x[2]] for x in mb])
'ees'
>>> ''.join([b[x[1]:x[1]+x[2]] for x in mb])
'ees'
Subtract an edit subsequence from a sequence.

subtract_edit(edit_operations, subsequence)

The result is equivalent to
editops(apply_edit(subsequence, s1, s2), s2), except that is
constructed directly from the edit operations.  That is, if you apply
it to the result of subsequence application, you get the same final
string as from application complete edit_operations.  It may be not
identical, though (in amibuous cases, like insertion of a character
next to the same character).

The subtracted subsequence must be an ordered subset of
edit_operations.

Note this function does not accept difflib-style opcodes as no one in
his right mind wants to create subsequences from them.

Examples:

>>> e = editops('man', 'scotsman')
>>> e1 = e[:3]
>>> bastard = apply_edit(e1, 'man', 'scotsman')
>>> bastard
'scoman'
>>> apply_edit(subtract_edit(e, e1), bastard, 'scotsman')
'scotsman'

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