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"""

Module difflib -- helpers for computing deltas between objects.

Function get_close_matches(word, possibilities, n=3, cutoff=0.6):

Use SequenceMatcher to return list of the best "good enough" matches.

Function context_diff(a, b):

For two lists of strings, return a delta in context diff format.

Function ndiff(a, b):

Return a delta: the difference between `a` and `b` (lists of strings).

Function restore(delta, which):

Return one of the two sequences that generated an ndiff delta.

Function unified_diff(a, b):

For two lists of strings, return a delta in unified diff format.

Class SequenceMatcher:

A flexible class for comparing pairs of sequences of any type.

Class Differ:

For producing human-readable deltas from sequences of lines of text.

Class HtmlDiff:

For producing HTML side by side comparison with change highlights.

"""

__all__ = ['get_close_matches', 'ndiff', 'restore', 'SequenceMatcher',

'Differ','IS_CHARACTER_JUNK', 'IS_LINE_JUNK', 'context_diff',

'unified_diff', 'diff_bytes', 'HtmlDiff', 'Match']

from heapq import nlargest as _nlargest

from collections import namedtuple as _namedtuple

Match = _namedtuple('Match', 'a b size')

def _calculate_ratio(matches, length):

if length:

return 2.0 * matches / length

return 1.0

class SequenceMatcher:

"""

SequenceMatcher is a flexible class for comparing pairs of sequences of

any type, so long as the sequence elements are hashable. The basic

algorithm predates, and is a little fancier than, an algorithm

published in the late 1980's by Ratcliff and Obershelp under the

hyperbolic name "gestalt pattern matching". The basic idea is to find

the longest contiguous matching subsequence that contains no "junk"

elements (R-O doesn't address junk). The same idea is then applied

recursively to the pieces of the sequences to the left and to the right

of the matching subsequence. This does not yield minimal edit

sequences, but does tend to yield matches that "look right" to people.

SequenceMatcher tries to compute a "human-friendly diff" between two

sequences. Unlike e.g. UNIX(tm) diff, the fundamental notion is the

longest *contiguous* & junk-free matching subsequence. That's what

catches peoples' eyes. The Windows(tm) windiff has another interesting

notion, pairing up elements that appear uniquely in each sequence.

That, and the method here, appear to yield more intuitive difference

reports than does diff. This method appears to be the least vulnerable

to synching up on blocks of "junk lines", though (like blank lines in

ordinary text files, or maybe "<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:

>>> print(round(s.ratio(), 3))

0.866

>>>

If you're only interested in where the sequences match,

.get_matching_blocks() is handy:

>>> for block in s.get_matching_blocks():

... print("a[%d] and b[%d] match for %d elements" % block)

a[0] and b[0] match for 8 elements

a[8] and b[17] match for 21 elements

a[29] and b[38] match for 0 elements

Note that the last tuple returned by .get_matching_blocks() is always a

dummy, (len(a), len(b), 0), and this is the only case in which the last

tuple element (number of elements matched) is 0.

If you want to know how to change the first sequence into the second,

use .get_opcodes():

>>> for opcode in s.get_opcodes():

... print("%6s a[%d:%d] b[%d:%d]" % opcode)

equal a[0:8] b[0:8]

insert a[8:8] b[8:17]

equal a[8:29] b[17:38]

See the Differ class for a fancy human-friendly file differencer, which

uses SequenceMatcher both to compare sequences of lines, and to compare

sequences of characters within similar (near-matching) lines.

See also function get_close_matches() in this module, which shows how

simple code building on SequenceMatcher can be used to do useful work.

Timing: Basic R-O is cubic time worst case and quadratic time expected

case. SequenceMatcher is quadratic time for the worst case and has

expected-case behavior dependent in a complicated way on how many

elements the sequences have in common; best case time is linear.

Methods:

__init__(isjunk=None, a='', b='')

Construct a SequenceMatcher.

set_seqs(a, b)

Set the two sequences to be compared.

set_seq1(a)

Set the first sequence to be compared.

set_seq2(b)

Set the second sequence to be compared.

find_longest_match(alo, ahi, blo, bhi)

Find longest matching block in a[alo:ahi] and b[blo:bhi].

get_matching_blocks()

Return list of triples describing matching subsequences.

get_opcodes()

Return list of 5-tuples describing how to turn a into b.

ratio()

Return a measure of the sequences' similarity (float in [0,1]).

quick_ratio()

Return an upper bound on .ratio() relatively quickly.

real_quick_ratio()

Return an upper bound on ratio() very quickly.

"""

def __init__(self, isjunk=None, a='', b='', autojunk=True):

"""Construct a SequenceMatcher.

Optional arg isjunk is None (the default), or a one-argument

function that takes a sequence element and returns true iff the

element is junk. None is equivalent to passing "lambda x: 0", i.e.

no elements are considered to be junk. For example, pass

lambda x: x in " \\t"

if you're comparing lines as sequences of characters, and don't

want to synch up on blanks or hard tabs.

Optional arg a is the first of two sequences to be compared. By

default, an empty string. The elements of a must be hashable. See

also .set_seqs() and .set_seq1().

Optional arg b is the second of two sequences to be compared. By

default, an empty string. The elements of b must be hashable. See

also .set_seqs() and .set_seq2().

Optional arg autojunk should be set to False to disable the

"automatic junk heuristic" that treats popular elements as junk

(see module documentation for more information).

"""

# Members:

# a

# first sequence

# b

# second sequence; differences are computed as "what do

# we need to do to 'a' to change it into 'b'?"

# b2j

# for x in b, b2j[x] is a list of the indices (into b)

# at which x appears; junk and popular elements do not appear

# fullbcount

# for x in b, fullbcount[x] == the number of times x

# appears in b; only materialized if really needed (used

# only for computing quick_ratio())

# matching_blocks

# a list of (i, j, k) triples, where a[i:i+k] == b[j:j+k];

# ascending & non-overlapping in i and in j; terminated by

# a dummy (len(a), len(b), 0) sentinel

# opcodes

# a list of (tag, i1, i2, j1, j2) tuples, where tag is

# one of

# 'replace' a[i1:i2] should be replaced by b[j1:j2]

# 'delete' a[i1:i2] should be deleted

# 'insert' b[j1:j2] should be inserted

# 'equal' a[i1:i2] == b[j1:j2]

# isjunk

# a user-supplied function taking a sequence element and

# returning true iff the element is "junk" -- this has

# subtle but helpful effects on the algorithm, which I'll

# get around to writing up someday <0.9 wink>.

# DON'T USE! Only __chain_b uses this. Use "in self.bjunk".

# bjunk

# the items in b for which isjunk is True.

# bpopular

# nonjunk items in b treated as junk by the heuristic (if used).

self.isjunk = isjunk

self.a = self.b = None

self.autojunk = autojunk

self.set_seqs(a, b)

def set_seqs(self, a, b):

"""Set the two sequences to be compared.

>>> s = SequenceMatcher()

>>> s.set_seqs("abcd", "bcde")

>>> s.ratio()

0.75

"""

self.set_seq1(a)

self.set_seq2(b)

def set_seq1(self, 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

>>>

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().

"""

if b is self.b:

return

self.b = b

self.matching_blocks = self.opcodes = None

self.fullbcount = None

self.__chain_b()

# For each element x in b, set b2j[x] to a list of the indices in

# b where x appears; the indices are in increasing order; note that

# the number of times x appears in b is len(b2j[x]) ...

# when self.isjunk is defined, junk elements don't show up in this

# map at all, which stops the central find_longest_match method

# from starting any matching block at a junk element ...

# b2j also does not contain entries for "popular" elements, meaning

# elements that account for more than 1 + 1% of the total elements, and

# when the sequence is reasonably large (>= 200 elements); this can

# be viewed as an adaptive notion of semi-junk, and yields an enormous

# speedup when, e.g., comparing program files with hundreds of

# instances of "return NULL;" ...

# note that this is only called when b changes; so for cross-product

# kinds of matches, it's best to call set_seq2 once, then set_seq1

# repeatedly

def __chain_b(self):

# Because isjunk is a user-defined (not C) function, and we test

# for junk a LOT, it's important to minimize the number of calls.

# Before the tricks described here, __chain_b was by far the most

# time-consuming routine in the whole module! If anyone sees

# Jim Roskind, thank him again for profile.py -- I never would

# have guessed that.

# The first trick is to build b2j ignoring the possibility

# of junk. I.e., we don't call isjunk at all yet. Throwing

# out the junk later is much cheaper than building b2j "right"

# from the start.

b = self.b

self.b2j = b2j = {}

for i, elt in enumerate(b):

indices = b2j.setdefault(elt, [])

indices.append(i)

# Purge junk elements

self.bjunk = junk = set()

isjunk = self.isjunk

if isjunk:

for elt in b2j.keys():

if isjunk(elt):

junk.add(elt)

for elt in junk: # separate loop avoids separate list of keys

del b2j[elt]

# Purge popular elements that are not junk

self.bpopular = popular = set()

n = len(b)

if self.autojunk and n >= 200:

ntest = n // 100 + 1

for elt, idxs in b2j.items():

if len(idxs) > ntest:

popular.add(elt)

for elt in popular: # ditto; as fast for 1% deletion

del b2j[elt]

def find_longest_match(self, 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)

Match(a=0, b=4, size=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)

Match(a=1, b=0, size=4)

If no blocks match, return (alo, blo, 0).

>>> s = SequenceMatcher(None, "ab", "c")

>>> s.find_longest_match(0, 2, 0, 1)

Match(a=0, b=0, size=0)

"""

# CAUTION: stripping common prefix or suffix would be incorrect.

# E.g.,

# ab

# acab

# Longest matching block is "ab", but if common prefix is

# stripped, it's "a" (tied with "b"). UNIX(tm) diff does so

# strip, so ends up claiming that ab is changed to acab by

# inserting "ca" in the middle. That's minimal but unintuitive:

# "it's obvious" that someone inserted "ac" at the front.

# Windiff ends up at the same place as diff, but by pairing up

# the unique 'b's and then matching the first two 'a's.

a, b, b2j, isbjunk = self.a, self.b, self.b2j, self.bjunk.__contains__

besti, bestj, bestsize = alo, blo, 0

# find longest junk-free match

# during an iteration of the loop, j2len[j] = length of longest

# junk-free match ending with a[i-1] and b[j]

j2len = {}

nothing = []

for i in range(alo, ahi):

# look at all instances of a[i] in b; note that because

# b2j has no junk keys, the loop is skipped if a[i] is junk

j2lenget = j2len.get

newj2len = {}

for j in b2j.get(a[i], nothing):

# a[i] matches b[j]

if j < blo:

continue

if j >= bhi:

break

k = newj2len[j] = j2lenget(j-1, 0) + 1

if k > bestsize:

besti, bestj, bestsize = i-k+1, j-k+1, k

j2len = newj2len

# Extend the best by non-junk elements on each end. In particular,

# "popular" non-junk elements aren't in b2j, which greatly speeds

# the inner loop above, but also means "the best" match so far

# doesn't contain any junk *or* popular non-junk elements.

while besti > alo and bestj > blo and \

not isbjunk(b[bestj-1]) and \

a[besti-1] == b[bestj-1]:

besti, bestj, bestsize = besti-1, bestj-1, bestsize+1

while besti+bestsize < ahi and bestj+bestsize < bhi and \

not isbjunk(b[bestj+bestsize]) and \

a[besti+bestsize] == b[bestj+bestsize]:

bestsize += 1

# Now that we have a wholly interesting match (albeit possibly

# empty!), we may as well suck up the matching junk on each

# side of it too. Can't think of a good reason not to, and it

# saves post-processing the (possibly considerable) expense of

# figuring out what to do with it. In the case of an empty

# interesting match, this is clearly the right thing to do,

# because no other kind of match is possible in the regions.

while besti > alo and bestj > blo and \

isbjunk(b[bestj-1]) and \

a[besti-1] == b[bestj-1]:

besti, bestj, bestsize = besti-1, bestj-1, bestsize+1

while besti+bestsize < ahi and bestj+bestsize < bhi and \

isbjunk(b[bestj+bestsize]) and \

a[besti+bestsize] == b[bestj+bestsize]:

bestsize = bestsize + 1

return Match(besti, bestj, bestsize)

def get_matching_blocks(self):

"""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. New in Python 2.5, it's also guaranteed that if

(i, j, n) and (i', j', n') are adjacent triples in the list, and

the second is not the last triple in the list, then i+n != i' or

j+n != j'. IOW, adjacent triples never describe adjacent equal

blocks.

The last triple is a dummy, (len(a), len(b), 0), and is the only

triple with n==0.

>>> s = SequenceMatcher(None, "abxcd", "abcd")

>>> list(s.get_matching_blocks())

[Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]

"""

if self.matching_blocks is not None:

return self.matching_blocks

la, lb = len(self.a), len(self.b)

# This is most naturally expressed as a recursive algorithm, but

# at least one user bumped into extreme use cases that exceeded

# the recursion limit on their box. So, now we maintain a list

# ('queue`) of blocks we still need to look at, and append partial

# results to `matching_blocks` in a loop; the matches are sorted

# at the end.

queue = [(0, la, 0, lb)]

matching_blocks = []

while queue:

alo, ahi, blo, bhi = queue.pop()

i, j, k = x = self.find_longest_match(alo, ahi, blo, bhi)

# a[alo:i] vs b[blo:j] unknown

# a[i:i+k] same as b[j:j+k]

# a[i+k:ahi] vs b[j+k:bhi] unknown

if k: # if k is 0, there was no matching block

matching_blocks.append(x)

if alo < i and blo < j:

queue.append((alo, i, blo, j))

if i+k < ahi and j+k < bhi:

queue.append((i+k, ahi, j+k, bhi))

matching_blocks.sort()

# It's possible that we have adjacent equal blocks in the

# matching_blocks list now. Starting with 2.5, this code was added

# to collapse them.

i1 = j1 = k1 = 0

non_adjacent = []

for i2, j2, k2 in matching_blocks:

# Is this block adjacent to i1, j1, k1?

if i1 + k1 == i2 and j1 + k1 == j2:

# Yes, so collapse them -- this just increases the length of

# the first block by the length of the second, and the first

# block so lengthened remains the block to compare against.

k1 += k2

else:

# Not adjacent. Remember the first block (k1==0 means it's

# the dummy we started with), and make the second block the

# new block to compare against.

if k1:

non_adjacent.append((i1, j1, k1))

i1, j1, k1 = i2, j2, k2

if k1:

non_adjacent.append((i1, j1, k1))

non_adjacent.append( (la, lb, 0) )

self.matching_blocks = list(map(Match._make, non_adjacent))

return self.matching_blocks

def get_opcodes(self):

"""Return list of 5-tuples describing how to turn a into b.

Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple

has i1 == j1 == 0, and remaining tuples have i1 == the i2 from the

tuple preceding it, and likewise for j1 == the previous j2.

The tags are strings, with these meanings:

'replace': a[i1:i2] should be replaced by b[j1:j2]

'delete': a[i1:i2] should be deleted.

Note that j1==j2 in this case.

'insert': b[j1:j2] should be inserted at a[i1:i1].

Note that i1==i2 in this case.

'equal': a[i1:i2] == b[j1:j2]

>>> a = "qabxcd"

>>> b = "abycdf"

>>> s = SequenceMatcher(None, a, b)

>>> for tag, i1, i2, j1, j2 in s.get_opcodes():

... print(("%7s a[%d:%d] (%s) b[%d:%d] (%s)" %

... (tag, i1, i2, a[i1:i2], j1, j2, b[j1:j2])))

delete a[0:1] (q) b[0:0] ()

equal a[1:3] (ab) b[0:2] (ab)

replace a[3:4] (x) b[2:3] (y)

equal a[4:6] (cd) b[3:5] (cd)

insert a[6:6] () b[5:6] (f)

"""

if self.opcodes is not None:

return self.opcodes

i = j = 0

self.opcodes = answer = []

for ai, bj, size in self.get_matching_blocks():

# invariant: we've pumped out correct diffs to change

# a[:i] into b[:j], and the next matching block is

# a[ai:ai+size] == b[bj:bj+size]. So we need to pump

# out a diff to change a[i:ai] into b[j:bj], pump out

# the matching block, and move (i,j) beyond the match

tag = ''

if i < ai and j < bj:

tag = 'replace'

elif i < ai:

tag = 'delete'

elif j < bj:

tag = 'insert'

if tag:

answer.append( (tag, i, ai, j, bj) )

i, j = ai+size, bj+size

# the list of matching blocks is terminated by a

# sentinel with size 0

if size:

answer.append( ('equal', ai, i, bj, j) )

return answer

def get_grouped_opcodes(self, n=3):

""" Isolate change clusters by eliminating ranges with no changes.

Return a generator of groups with up to n lines of context.

Each group is in the same format as returned by get_opcodes().

>>> from pprint import pprint

>>> a = list(map(str, range(1,40)))

>>> b = a[:]

>>> b[8:8] = ['i'] # Make an insertion

>>> b[20] += 'x' # Make a replacement

>>> b[23:28] = [] # Make a deletion

>>> b[30] += 'y' # Make another replacement

>>> pprint(list(SequenceMatcher(None,a,b).get_grouped_opcodes()))

[[('equal', 5, 8, 5, 8), ('insert', 8, 8, 8, 9), ('equal', 8, 11, 9, 12)],

[('equal', 16, 19, 17, 20),

('replace', 19, 20, 20, 21),

('equal', 20, 22, 21, 23),

('delete', 22, 27, 23, 23),

('equal', 27, 30, 23, 26)],

[('equal', 31, 34, 27, 30),

('replace', 34, 35, 30, 31),

('equal', 35, 38, 31, 34)]]

"""

codes = self.get_opcodes()

if not codes:

codes = [("equal", 0, 1, 0, 1)]

# Fixup leading and trailing groups if they show no changes.

if codes[0][0] == 'equal':

tag, i1, i2, j1, j2 = codes[0]

codes[0] = tag, max(i1, i2-n), i2, max(j1, j2-n), j2

if codes[-1][0] == 'equal':

tag, i1, i2, j1, j2 = codes[-1]

codes[-1] = tag, i1, min(i2, i1+n), j1, min(j2, j1+n)

nn = n + n

group = []

for tag, i1, i2, j1, j2 in codes:

# End the current group and start a new one whenever

# there is a large range with no changes.

if tag == 'equal' and i2-i1 > nn:

group.append((tag, i1, min(i2, i1+n), j1, min(j2, j1+n)))

yield group

group = []

i1, j1 = max(i1, i2-n), max(j1, j2-n)

group.append((tag, i1, i2, j1 ,j2))

if group and not (len(group)==1 and group[0][0] == 'equal'):

yield group

def ratio(self):

"""Return a measure of the sequences' similarity (float in [0,1]).

Where T is the total number of elements in both sequences, and

M is the number of matches, this is 2.0*M / T.

Note that this is 1 if the sequences are identical, and 0 if

they have nothing in common.

.ratio() is expensive to compute if you haven't already computed

.get_matching_blocks() or .get_opcodes(), in which case you may

want to try .quick_ratio() or .real_quick_ratio() first to get an

upper bound.

>>> s = SequenceMatcher(None, "abcd", "bcde")

>>> s.ratio()

0.75

>>> s.quick_ratio()

0.75

>>> s.real_quick_ratio()

1.0

"""

matches = sum(triple[-1] for triple in self.get_matching_blocks())

return _calculate_ratio(matches, len(self.a) + len(self.b))

def quick_ratio(self):

"""Return an upper bound on ratio() relatively quickly.

This isn't defined beyond that it is an upper bound on .ratio(), and

is faster to compute.

"""

# viewing a and b as multisets, set matches to the cardinality

# of their intersection; this counts the number of matches

# without regard to order, so is clearly an upper bound

if self.fullbcount is None:

self.fullbcount = fullbcount = {}

for elt in self.b:

fullbcount[elt] = fullbcount.get(elt, 0) + 1

fullbcount = self.fullbcount

# avail[x] is the number of times x appears in 'b' less the

# number of times we've seen it in 'a' so far ... kinda

avail = {}

availhas, matches = avail.__contains__, 0

for elt in self.a:

if availhas(elt):

numb = avail[elt]

else:

numb = fullbcount.get(elt, 0)

avail[elt] = numb - 1

if numb > 0:

matches = matches + 1

return _calculate_ratio(matches, len(self.a) + len(self.b))

def real_quick_ratio(self):

"""Return an upper bound on ratio() very quickly.

This isn't defined beyond that it is an upper bound on .ratio(), and

is faster to compute than either .ratio() or .quick_ratio().

"""

la, lb = len(self.a), len(self.b)

# can't have more matches than the number of elements in the

# shorter sequence

return _calculate_ratio(min(la, lb), la + lb)

def get_close_matches(word, possibilities, n=3, cutoff=0.6):

"""Use SequenceMatcher to return list of the best "good enough" matches.

word is a sequence for which close matches are desired (typically a

string).

possibilities is a list of sequences against which to match word

(typically a list of strings).

Optional arg n (default 3) is the maximum number of close matches to

return. n must be > 0.

Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities

that don't score at least that similar to word are ignored.

The best (no more than n) matches among the possibilities are returned

in a list, sorted by similarity score, most similar first.

>>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"])

['apple', 'ape']

>>> import keyword as _keyword

>>> get_close_matches("wheel", _keyword.kwlist)

['while']

>>> get_close_matches("Apple", _keyword.kwlist)

[]

>>> get_close_matches("accept", _keyword.kwlist)

['except']

"""

if not n > 0:

raise ValueError("n must be > 0: %r" % (n,))

if not 0.0 <= cutoff <= 1.0:

raise ValueError("cutoff must be in [0.0, 1.0]: %r" % (cutoff,))

result = []

s = SequenceMatcher()

s.set_seq2(word)

for x in possibilities:

s.set_seq1(x)

if s.real_quick_ratio() >= cutoff and \

s.quick_ratio() >= cutoff and \

s.ratio() >= cutoff:

result.append((s.ratio(), x))

# Move the best scorers to head of list

result = _nlargest(n, result)

# Strip scores for the best n matches

return [x for score, x in result]

def _keep_original_ws(s, tag_s):

"""Replace whitespace with the original whitespace characters in `s`"""

return ''.join(

c if tag_c == " " and c.isspace() else tag_c

for c, tag_c in zip(s, tag_s)

)

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(keepends=True)

>>> len(text1)

>>> 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(keepends=True)

Next we instantiate a Differ object:

>>> d = Differ()

Note that when instantiating a Differ object we may pass functions to

filter out line and character 'junk'. See Differ.__init__ for details.

Finally, we compare the two:

>>> result = list(d.compare(text1, text2))

'result' is a list of strings, so let's pretty-print it:

>>> from pprint import pprint as _pprint

>>> _pprint(result)

[' 1. Beautiful is better than ugly.\n',

'- 2. Explicit is better than implicit.\n',

'- 3. Simple is better than complex.\n',

'+ 3. Simple is better than complex.\n',

'? ++\n',

'- 4. Complex is better than complicated.\n',

'? ^ ---- ^\n',

'+ 4. Complicated is better than complex.\n',

'? ++++ ^ ^\n',

'+ 5. Flat is better than nested.\n']

As a single multi-line string it looks like this:

>>> print(''.join(result), end="")

1. Beautiful is better than ugly.

- 2. Explicit is better than implicit.

- 3. Simple is better than complex.

+ 3. Simple is better than complex.

? ++

- 4. Complex is better than complicated.

? ^ ---- ^

+ 4. Complicated is better than complex.

? ++++ ^ ^

+ 5. Flat is better than nested.

Methods:

__init__(linejunk=None, charjunk=None)

Construct a text differencer, with optional filters.

compare(a, b)

Compare two sequences of lines; generate the resulting delta.

"""

def __init__(self, linejunk=None, charjunk=None):

"""

Construct a text differencer, with optional filters.

The two optional keyword parameters are for filter functions:

- `linejunk`: A function that should accept a single string argument,

and return true iff the string is junk. The module-level function

`IS_LINE_JUNK` may be used to filter out lines without visible

characters, except for at most one splat ('#'). It is recommended

to leave linejunk None; the underlying SequenceMatcher class has

an adaptive notion of "noise" lines that's better than any static

definition the author has ever been able to craft.

- `charjunk`: A function that should accept a string of length 1. The

module-level function `IS_CHARACTER_JUNK` may be used to filter out

whitespace characters (a blank or tab; **note**: bad idea to include

newline in this!). Use of IS_CHARACTER_JUNK is recommended.

"""

self.linejunk = linejunk

self.charjunk = charjunk

def compare(self, a, b):

r"""

Compare two sequences of lines; generate the resulting delta.

Each sequence must contain individual single-line strings ending with

newlines. Such sequences can be obtained from the `readlines()` method

of file-like objects. The delta generated also consists of newline-

terminated strings, ready to be printed as-is via the writeline()

method of a file-like object.

Example:

>>> print(''.join(Differ().compare('one\ntwo\nthree\n'.splitlines(True),

... 'ore\ntree\nemu\n'.splitlines(True))),

... end="")

- one

? ^

+ ore

? ^

- two

- three

? -

+ tree

+ emu

"""

cruncher = SequenceMatcher(self.linejunk, a, b)

for tag, alo, ahi, blo, bhi in cruncher.get_opcodes():

if tag == 'replace':

g = self._fancy_replace(a, alo, ahi, b, blo, bhi)

elif tag == 'delete':

g = self._dump('-', a, alo, ahi)

elif tag == 'insert':

g = self._dump('+', b, blo, bhi)

elif tag == 'equal':

g = self._dump(' ', a, alo, ahi)

else:

raise ValueError('unknown tag %r' % (tag,))

yield from g

def _dump(self, tag, x, lo, hi):

"""Generate comparison results for a same-tagged range."""

for i in range(lo, hi):

yield '%s %s' % (tag, x[i])

def _plain_replace(self, a, alo, ahi, b, blo, bhi):

assert alo < ahi and blo < bhi

# dump the shorter block first -- reduces the burden on short-term

# memory if the blocks are of very different sizes

if bhi - blo < ahi - alo:

first = self._dump('+', b, blo, bhi)

second = self._dump('-', a, alo, ahi)

else:

first = self._dump('-', a, alo, ahi)

second = self._dump('+', b, blo, bhi)

for g in first, second:

yield from g

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()

>>> results = d._fancy_replace(['abcDefghiJkl\n'], 0, 1,

... ['abcdefGhijkl\n'], 0, 1)

>>> print(''.join(results), end="")

- abcDefghiJkl

? ^ ^ ^

+ abcdefGhijkl

? ^ ^ ^

"""

# don't synch up unless the lines have a similarity score of at

# least cutoff; best_ratio tracks the best score seen so far

best_ratio, cutoff = 0.74, 0.75

cruncher = SequenceMatcher(self.charjunk)

eqi, eqj = None, None # 1st indices of equal lines (if any)

# search for the pair that matches best without being identical

# (identical lines must be junk lines, & we don't want to synch up

# on junk -- unless we have to)

for j in range(blo, bhi):

bj = b[j]

cruncher.set_seq2(bj)

for i in range(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

yield from 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

# pump out diffs from before the synch point

yield from 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':

View remainder of file in raw view

cpython/Lib/difflib.py at 3.8 · python-emscripten/cpython · GitHub

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