blob: fbaae526d7621083cf5864e931c1e482cf02d167 [file] [log] [blame]
###
Module difflib -- helpers for computing deltas between objects.
Function getCloseMatches(word, possibilities, n=3, cutoff=0.6):
Use SequenceMatcher to return list of the best "good enough" matches.
Function contextDiff(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 unifiedDiff(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.
###
# Requires
{floor, max, min} = Math
{Heap} = require('heap')
assert = require('assert')
# Helper functions
_calculateRatio = (matches, length) ->
if length then (2.0 * matches / length) else 1.0
_arrayCmp = (a, b) ->
[la, lb] = [a.length, b.length]
for i in [0...min(la, lb)]
return -1 if a[i] < b[i]
return 1 if a[i] > b[i]
la - lb
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":
>>> isjunk = (c) -> c is ' '
>>> s = new SequenceMatcher(isjunk,
'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:
>>> s.ratio().toPrecision(3)
'0.866'
If you're only interested in where the sequences match,
.getMatchingBlocks() is handy:
>>> for [a, b, size] in s.getMatchingBlocks()
... console.log("a[#{a}] and b[#{b}] match for #{size} elements");
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 [op, a1, a2, b1, b2] in s.getOpcodes()
... console.log "#{op} a[#{a1}:#{a2}] b[#{b1}:#{b2}]"
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 getCloseMatches() 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:
constructor(isjunk=null, a='', b='')
Construct a SequenceMatcher.
setSeqs(a, b)
Set the two sequences to be compared.
setSeq1(a)
Set the first sequence to be compared.
setSeq2(b)
Set the second sequence to be compared.
findLongestMatch(alo, ahi, blo, bhi)
Find longest matching block in a[alo:ahi] and b[blo:bhi].
getMatchingBlocks()
Return list of triples describing matching subsequences.
getOpcodes()
Return list of 5-tuples describing how to turn a into b.
ratio()
Return a measure of the sequences' similarity (float in [0,1]).
quickRatio()
Return an upper bound on .ratio() relatively quickly.
realQuickRatio()
Return an upper bound on ratio() very quickly.
###
constructor: (@isjunk, a='', b='', @autojunk=true) ->
###
Construct a SequenceMatcher.
Optional arg isjunk is null (the default), or a one-argument
function that takes a sequence element and returns true iff the
element is junk. Null is equivalent to passing "(x) -> 0", i.e.
no elements are considered to be junk. For example, pass
(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 .setSeqs() and .setSeq1().
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 .setSeqs() and .setSeq2().
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 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 quickRatio())
# matchingBlocks
# 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 __chainB uses this. Use isbjunk.
# isbjunk
# for x in b, isbjunk(x) == isjunk(x) but much faster;
# DOES NOT WORK for x in a!
# isbpopular
# for x in b, isbpopular(x) is true iff b is reasonably long
# (at least 200 elements) and x accounts for more than 1 + 1% of
# its elements (when autojunk is enabled).
# DOES NOT WORK for x in a!
@a = @b = null
@setSeqs(a, b)
setSeqs: (a, b) ->
###
Set the two sequences to be compared.
>>> s = new SequenceMatcher()
>>> s.setSeqs('abcd', 'bcde')
>>> s.ratio()
0.75
###
@setSeq1(a)
@setSeq2(b)
setSeq1: (a) ->
###
Set the first sequence to be compared.
The second sequence to be compared is not changed.
>>> s = new SequenceMatcher(null, 'abcd', 'bcde')
>>> s.ratio()
0.75
>>> s.setSeq1('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 .setSeq2(S) once and call .setSeq1(x)
repeatedly for each of the other sequences.
See also setSeqs() and setSeq2().
###
return if a is @a
@a = a
@matchingBlocks = @opcodes = null
setSeq2: (b) ->
###
Set the second sequence to be compared.
The first sequence to be compared is not changed.
>>> s = new SequenceMatcher(null, 'abcd', 'bcde')
>>> s.ratio()
0.75
>>> s.setSeq2('abcd')
>>> 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 .setSeq2(S) once and call .setSeq1(x)
repeatedly for each of the other sequences.
See also setSeqs() and setSeq1().
###
return if b is @b
@b = b
@matchingBlocks = @opcodes = null
@fullbcount = null
@_chainB()
# 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 b2j[x].length ...
# when @isjunk is defined, junk elements don't show up in this
# map at all, which stops the central findLongestMatch method
# from starting any matching block at a junk element ...
# also creates the fast isbjunk function ...
# 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 setSeq2 once, then setSeq1
# repeatedly
_chainB: ->
# Because isjunk is a user-defined function, and we test
# for junk a LOT, it's important to minimize the number of calls.
# Before the tricks described here, __chainB 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 = @b
@b2j = b2j = {}
for elt, i in b
indices = if b2j.hasOwnProperty(elt) then b2j[elt] else b2j[elt] = []
indices.push(i)
# Purge junk elements
junk = {}
isjunk = @isjunk
if isjunk
for elt in Object.keys(b2j)
if isjunk(elt)
junk[elt] = true
delete b2j[elt]
# Purge popular elements that are not junk
popular = {}
n = b.length
if @autojunk and n >= 200
ntest = floor(n / 100) + 1
for elt, idxs of b2j
if idxs.length > ntest
popular[elt] = true
delete b2j[elt]
# Now for x in b, isjunk(x) == x in junk, but the latter is much faster.
# Sicne the number of *unique* junk elements is probably small, the
# memory burden of keeping this set alive is likely trivial compared to
# the size of b2j.
@isbjunk = (b) -> junk.hasOwnProperty(b)
@isbpopular = (b) -> popular.hasOwnProperty(b)
findLongestMatch: (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.
>>> isjunk = (x) -> x is ' '
>>> s = new SequenceMatcher(isjunk, ' abcd', 'abcd abcd')
>>> s.findLongestMatch(0, 5, 0, 9)
[1, 0, 4]
>>> s = new SequenceMatcher(null, 'ab', 'c')
>>> s.findLongestMatch(0, 2, 0, 1)
[0, 0, 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] = [@a, @b, @b2j, @isbjunk]
[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 = {}
for i in [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
newj2len = {}
for j in (if a[i] of b2j then b2j[a[i]] else [])
# a[i] matches b[j]
continue if j < blo
break if j >= bhi
k = newj2len[j] = (j2len[j-1] or 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] is 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] is b[bestj+bestsize]
bestsize++
# 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] is 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] is b[bestj+bestsize]
bestsize++
[besti, bestj, bestsize]
getMatchingBlocks: ->
###
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. 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, [a.length, b.length, 0], and is the only
triple with n==0.
>>> s = new SequenceMatcher(null, 'abxcd', 'abcd')
>>> s.getMatchingBlocks()
[[0, 0, 2], [3, 2, 2], [5, 4, 0]]
###
return @matchingBlocks if @matchingBlocks
[la, lb] = [@a.length, @b.length]
# 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]]
matchingBlocks = []
while queue.length
[alo, ahi, blo, bhi] = queue.pop()
[i, j, k] = x = @findLongestMatch(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
matchingBlocks.push(x)
if alo < i and blo < j
queue.push([alo, i, blo, j])
if i+k < ahi and j+k < bhi
queue.push([i+k, ahi, j+k, bhi])
matchingBlocks.sort(_arrayCmp)
# It's possible that we have adjacent equal blocks in the
# matching_blocks list now.
i1 = j1 = k1 = 0
nonAdjacent = []
for [i2, j2, k2] in matchingBlocks
# Is this block adjacent to i1, j1, k1?
if i1 + k1 is i2 and j1 + k1 is 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
nonAdjacent.push([i1, j1, k1])
[i1, j1, k1] = [i2, j2, k2]
if k1
nonAdjacent.push([i1, j1, k1])
nonAdjacent.push([la, lb, 0])
@matchingBlocks = nonAdjacent
getOpcodes: ->
###
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]
>>> s = new SequenceMatcher(null, 'qabxcd', 'abycdf')
>>> s.getOpcodes()
[ [ 'delete' , 0 , 1 , 0 , 0 ] ,
[ 'equal' , 1 , 3 , 0 , 2 ] ,
[ 'replace' , 3 , 4 , 2 , 3 ] ,
[ 'equal' , 4 , 6 , 3 , 5 ] ,
[ 'insert' , 6 , 6 , 5 , 6 ] ]
###
return @opcodes if @opcodes
i = j = 0
@opcodes = answer = []
for [ai, bj, size] in @getMatchingBlocks()
# invariant: we've pumped out correct diffs to change
# a[0...i] into b[0...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'
else if i < ai
tag = 'delete'
else if j < bj
tag = 'insert'
if tag
answer.push([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.push(['equal', ai, i, bj, j])
answer
getGroupedOpcodes: (n=3) ->
###
Isolate change clusters by eliminating ranges with no changes.
Return a list groups with upto n lines of context.
Each group is in the same format as returned by get_opcodes().
>>> a = [1...40].map(String)
>>> b = a.slice()
>>> b[8...8] = 'i'
>>> b[20] += 'x'
>>> b[23...28] = []
>>> b[30] += 'y'
>>> s = new SequenceMatcher(null, a, b)
>>> s.getGroupedOpcodes()
[ [ [ '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 = @getOpcodes()
unless codes.length
codes = [['equal', 0, 1, 0, 1]]
# Fixup leading and trailing groups if they show no changes.
if codes[0][0] is 'equal'
[tag, i1, i2, j1, j2] = codes[0]
codes[0] = [tag, max(i1, i2-n), i2, max(j1, j2-n), j2]
if codes[codes.length-1][0] is 'equal'
[tag, i1, i2, j1, j2] = codes[codes.length-1]
codes[codes.length-1] = [tag, i1, min(i2, i1+n), j1, min(j2, j1+n)]
nn = n + n
groups = []
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 is 'equal' and i2-i1 > nn
group.push([tag, i1, min(i2, i1+n), j1, min(j2, j1+n)])
groups.push(group)
group = []
[i1, j1] = [max(i1, i2-n), max(j1, j2-n)]
group.push([tag, i1, i2, j1, j2])
if group.length and not (group.length is 1 and group[0][0] is 'equal')
groups.push(group)
groups
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
.getMatchingBlocks() or .getOpcodes(), in which case you may
want to try .quickRatio() or .realQuickRatio() first to get an
upper bound.
>>> s = new SequenceMatcher(null, 'abcd', 'bcde')
>>> s.ratio()
0.75
>>> s.quickRatio()
0.75
>>> s.realQuickRatio()
1.0
###
matches = 0
for match in @getMatchingBlocks()
matches += match[2]
_calculateRatio(matches, @a.length + @b.length)
quickRatio: ->
###
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
unless @fullbcount
@fullbcount = fullbcount = {}
for elt in @b
fullbcount[elt] = (fullbcount[elt] or 0) + 1
fullbcount = @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 = {}
matches = 0
for elt in @a
if avail.hasOwnProperty(elt)
numb = avail[elt]
else
numb = fullbcount[elt] or 0
avail[elt] = numb - 1
if numb > 0
matches++
_calculateRatio(matches, @a.length + @b.length)
realQuickRatio: ->
###
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 .quickRatio().
###
[la, lb] = [@a.length, @b.length]
# can't have more matches than the number of elements in the
# shorter sequence
_calculateRatio(min(la, lb), la + lb)
getCloseMatches = (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.
>>> getCloseMatches('appel', ['ape', 'apple', 'peach', 'puppy'])
['apple', 'ape']
>>> KEYWORDS = require('coffee-script').RESERVED
>>> getCloseMatches('wheel', KEYWORDS)
['when', 'while']
>>> getCloseMatches('accost', KEYWORDS)
['const']
###
unless n > 0
throw new Error("n must be > 0: (#{n})")
unless 0.0 <= cutoff <= 1.0
throw new Error("cutoff must be in [0.0, 1.0]: (#{cutoff})")
result = []
s = new SequenceMatcher()
s.setSeq2(word)
for x in possibilities
s.setSeq1(x)
if s.realQuickRatio() >= cutoff and
s.quickRatio() >= cutoff and
s.ratio() >= cutoff
result.push([s.ratio(), x])
# Move the best scorers to head of list
result = Heap.nlargest(n, result, _arrayCmp)
# Strip scores for the best n matches
(x for [score, x] in result)
_countLeading = (line, ch) ->
###
Return number of `ch` characters at the start of `line`.
>>> _countLeading(' abc', ' ')
3
###
[i, n] = [0, line.length]
while i < n and line[i] is ch
i++
i
class Differ
###
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.
>>> text1 = ['1. Beautiful is better than ugly.\n',
... '2. Explicit is better than implicit.\n',
... '3. Simple is better than complex.\n',
... '4. Complex is better than complicated.\n']
>>> text1.length
4
>>> text2 = ['1. Beautiful is better than ugly.\n',
... '3. Simple is better than complex.\n',
... '4. Complicated is better than complex.\n',
... '5. Flat is better than nested.\n']
Next we instantiate a Differ object:
>>> d = new Differ()
Note that when instantiating a Differ object we may pass functions to
filter out line and character 'junk'.
Finally, we compare the two:
>>> result = d.compare(text1, text2)
[ ' 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' ]
Methods:
constructor(linejunk=null, charjunk=null)
Construct a text differencer, with optional filters.
compare(a, b)
Compare two sequences of lines; generate the resulting delta.
###
constructor: (@linejunk, @charjunk) ->
###
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 null.
- `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.
###
compare: (a, b) ->
###
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:
>>> d = new Differ
>>> d.compare(['one\n', 'two\n', 'three\n'],
... ['ore\n', 'tree\n', 'emu\n'])
[ '- one\n',
'? ^\n',
'+ ore\n',
'? ^\n',
'- two\n',
'- three\n',
'? -\n',
'+ tree\n',
'+ emu\n' ]
###
cruncher = new SequenceMatcher(@linejunk, a, b)
lines = []
for [tag, alo, ahi, blo, bhi] in cruncher.getOpcodes()
switch tag
when 'replace'
g = @_fancyReplace(a, alo, ahi, b, blo, bhi)
when 'delete'
g = @_dump('-', a, alo, ahi)
when 'insert'
g = @_dump('+', b, blo, bhi)
when 'equal'
g = @_dump(' ', a, alo, ahi)
else
throw new Error("unknow tag (#{tag})")
lines.push(line) for line in g
lines
_dump: (tag, x, lo, hi) ->
###
Generate comparison results for a same-tagged range.
###
("#{tag} #{x[i]}" for i in [lo...hi])
_plainReplace: (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 = @_dump('+', b, blo, bhi)
second = @_dump('-', a, alo, ahi)
else
first = @_dump('-', a, alo, ahi)
second = @_dump('+', b, blo, bhi)
lines = []
lines.push(line) for line in g for g in [first, second]
lines
_fancyReplace: (a, alo, ahi, b, blo, bhi) ->
###
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 = new Differ
>>> d._fancyReplace(['abcDefghiJkl\n'], 0, 1,
... ['abcdefGhijkl\n'], 0, 1)
[ '- abcDefghiJkl\n',
'? ^ ^ ^\n',
'+ abcdefGhijkl\n',
'? ^ ^ ^\n' ]
###
# don't synch up unless the lines have a similarity score of at
# least cutoff; best_ratio tracks the best score seen so far
[bestRatio, cutoff] = [0.74, 0.75]
cruncher = new SequenceMatcher(@charjunk)
[eqi, eqj] = [null, null] # 1st indices of equal lines (if any)
lines = []
# 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 [blo...bhi]
bj = b[j]
cruncher.setSeq2(bj)
for i in [alo...ahi]
ai = a[i]
if ai is bj
if eqi is null
[eqi, eqj] = [i, j]
continue
cruncher.setSeq1(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.realQuickRatio() > bestRatio and
cruncher.quickRatio() > bestRatio and
cruncher.ratio() > bestRatio
[bestRatio, besti, bestj] = [cruncher.ratio(), i, j]
if bestRatio < cutoff
# no non-identical "pretty close" pair
if eqi is null
# no identical pair either -- treat it as a straight replace
for line in @_plainReplace(a, alo, ahi, b, blo, bhi)
lines.push(line)
return lines
# no close pair, but an identical pair -- synch up on that
[besti, bestj, bestRatio] = [eqi, eqj, 1.0]
else
# there's a close pair, so forget the identical pair (if any)
eqi = null
# a[besti] very similar to b[bestj]; eqi is null iff they're not
# identical
# pump out diffs from before the synch point
for line in @_fancyHelper(a, alo, besti, b, blo, bestj)
lines.push(line)
# do intraline marking on the synch pair
[aelt, belt] = [a[besti], b[bestj]]
if eqi is null
# pump out a '-', '?', '+', '?' quad for the synched lines
atags = btags = ''
cruncher.setSeqs(aelt, belt)
for [tag, ai1, ai2, bj1, bj2] in cruncher.getOpcodes()
[la, lb] = [ai2 - ai1, bj2 - bj1]
switch tag
when 'replace'
atags += Array(la+1).join('^')
btags += Array(lb+1).join('^')
when 'delete'
atags += Array(la+1).join('-')
when 'insert'
btags += Array(lb+1).join('+')
when 'equal'
atags += Array(la+1).join(' ')
btags += Array(lb+1).join(' ')
else
throw new Error("unknow tag (#{tag})")
for line in @_qformat(aelt, belt, atags, btags)
lines.push(line)
else
# the synch pair is identical
lines.push(' ' + aelt)
# pump out diffs from after the synch point
for line in @_fancyHelper(a, besti+1, ahi, b, bestj+1, bhi)
lines.push(line)
lines
_fancyHelper: (a, alo, ahi, b, blo, bhi) ->
g = []
if alo < ahi
if blo < bhi
g = @_fancyReplace(a, alo, ahi, b, blo, bhi)
else
g = @_dump('-', a, alo, ahi)
else if blo < bhi
g = @_dump('+', b, blo, bhi)
g
_qformat: (aline, bline, atags, btags) ->
###
Format "?" output and deal with leading tabs.
Example:
>>> d = new Differ
>>> d._qformat('\tabcDefghiJkl\n', '\tabcdefGhijkl\n',
[ '- \tabcDefghiJkl\n',
'? \t ^ ^ ^\n',
'+ \tabcdefGhijkl\n',
'? \t ^ ^ ^\n' ]
###
lines = []
# Can hurt, but will probably help most of the time.
common = min(_countLeading(aline, '\t'),
_countLeading(bline, '\t'))
common = min(common, _countLeading(atags[0...common], ' '))
common = min(common, _countLeading(btags[0...common], ' '))
atags = atags[common..].replace(/\s+$/, '')
btags = btags[common..].replace(/\s+$/, '')
lines.push('- ' + aline)
if atags.length
lines.push("? #{Array(common+1).join('\t')}#{atags}\n")
lines.push('+ ' + bline)
if btags.length
lines.push("? #{Array(common+1).join('\t')}#{btags}\n")
lines
# 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>.
IS_LINE_JUNK = (line, pat=/^\s*#?\s*$/) ->
###
Return 1 for ignorable line: iff `line` is blank or contains a single '#'.
Examples:
>>> IS_LINE_JUNK('\n')
true
>>> IS_LINE_JUNK(' # \n')
true
>>> IS_LINE_JUNK('hello\n')
false
###
pat.test(line)
IS_CHARACTER_JUNK = (ch, ws=' \t') ->
###
Return 1 for ignorable character: iff `ch` is a space or tab.
Examples:
>>> IS_CHARACTER_JUNK(' ').should.be.true
true
>>> IS_CHARACTER_JUNK('\t').should.be.true
true
>>> IS_CHARACTER_JUNK('\n').should.be.false
false
>>> IS_CHARACTER_JUNK('x').should.be.false
false
###
ch in ws
_formatRangeUnified = (start, stop) ->
###
Convert range to the "ed" format'
###
# Per the diff spec at http://www.unix.org/single_unix_specification/
beginning = start + 1 # lines start numbering with one
length = stop - start
return "#{beginning}" if length is 1
beginning-- unless length # empty ranges begin at line just before the range
"#{beginning},#{length}"
unifiedDiff = (a, b, {fromfile, tofile, fromfiledate, tofiledate, n, lineterm}={}) ->
###
Compare two sequences of lines; generate the delta as a unified diff.
Unified diffs are a compact way of showing line changes and a few
lines of context. The number of context lines is set by 'n' which
defaults to three.
By default, the diff control lines (those with ---, +++, or @@) are
created with a trailing newline.
For inputs that do not have trailing newlines, set the lineterm
argument to "" so that the output will be uniformly newline free.
The unidiff format normally has a header for filenames and modification
times. Any or all of these may be specified using strings for
'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
Example:
>>> unifiedDiff('one two three four'.split(' '),
... 'zero one tree four'.split(' '), {
... fromfile: 'Original'
... tofile: 'Current',
... fromfiledate: '2005-01-26 23:30:50',
... tofiledate: '2010-04-02 10:20:52',
... lineterm: ''
... })
[ '--- Original\t2005-01-26 23:30:50',
'+++ Current\t2010-04-02 10:20:52',
'@@ -1,4 +1,4 @@',
'+zero',
' one',
'-two',
'-three',
'+tree',
' four' ]
###
fromfile ?= ''
tofile ?= ''
fromfiledate ?= ''
tofiledate ?= ''
n ?= 3
lineterm ?= '\n'
lines = []
started = false
for group in (new SequenceMatcher(null, a, b)).getGroupedOpcodes()
unless started
started = true
fromdate = if fromfiledate then "\t#{fromfiledate}" else ''
todate = if tofiledate then "\t#{tofiledate}" else ''
lines.push("--- #{fromfile}#{fromdate}#{lineterm}")
lines.push("+++ #{tofile}#{todate}#{lineterm}")
[first, last] = [group[0], group[group.length-1]]
file1Range = _formatRangeUnified(first[1], last[2])
file2Range = _formatRangeUnified(first[3], last[4])
lines.push("@@ -#{file1Range} +#{file2Range} @@#{lineterm}")
for [tag, i1, i2, j1, j2] in group
if tag is 'equal'
lines.push(' ' + line) for line in a[i1...i2]
continue
if tag in ['replace', 'delete']
lines.push('-' + line) for line in a[i1...i2]
if tag in ['replace', 'insert']
lines.push('+' + line) for line in b[j1...j2]
lines
_formatRangeContext = (start, stop) ->
###
Convert range to the "ed" format'
###
# Per the diff spec at http://www.unix.org/single_unix_specification/
beginning = start + 1 # lines start numbering with one
length = stop - start
beginning-- unless length # empty ranges begin at line just before the range
return "#{beginning}" if length <= 1
"#{beginning},#{beginning + length - 1}"
# See http://www.unix.org/single_unix_specification/
contextDiff = (a, b, {fromfile, tofile, fromfiledate, tofiledate, n, lineterm}={}) ->
###
Compare two sequences of lines; generate the delta as a context diff.
Context diffs are a compact way of showing line changes and a few
lines of context. The number of context lines is set by 'n' which
defaults to three.
By default, the diff control lines (those with *** or ---) are
created with a trailing newline. This is helpful so that inputs
created from file.readlines() result in diffs that are suitable for
file.writelines() since both the inputs and outputs have trailing
newlines.
For inputs that do not have trailing newlines, set the lineterm
argument to "" so that the output will be uniformly newline free.
The context diff format normally has a header for filenames and
modification times. Any or all of these may be specified using
strings for 'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
If not specified, the strings default to blanks.
Example:
>>> a = ['one\n', 'two\n', 'three\n', 'four\n']
>>> b = ['zero\n', 'one\n', 'tree\n', 'four\n']
>>> contextDiff(a, b, {fromfile: 'Original', tofile: 'Current'})
[ '*** Original\n',
'--- Current\n',
'***************\n',
'*** 1,4 ****\n',
' one\n',
'! two\n',
'! three\n',
' four\n',
'--- 1,4 ----\n',
'+ zero\n',
' one\n',
'! tree\n',
' four\n' ]
###
fromfile ?= ''
tofile ?= ''
fromfiledate ?= ''
tofiledate ?= ''
n ?= 3
lineterm ?= '\n'
prefix =
insert : '+ '
delete : '- '
replace : '! '
equal : ' '
started = false
lines = []
for group in (new SequenceMatcher(null, a, b)).getGroupedOpcodes()
unless started
started = true
fromdate = if fromfiledate then "\t#{fromfiledate}" else ''
todate = if tofiledate then "\t#{tofiledate}" else ''
lines.push("*** #{fromfile}#{fromdate}#{lineterm}")
lines.push("--- #{tofile}#{todate}#{lineterm}")
[first, last] = [group[0], group[group.length-1]]
lines.push('***************' + lineterm)
file1Range = _formatRangeContext(first[1], last[2])
lines.push("*** #{file1Range} ****#{lineterm}")
if ((tag in ['replace', 'delete']) for [tag, _, _, _, _] in group).some((x) -> x)
for [tag, i1, i2, _, _] in group
if tag isnt 'insert'
for line in a[i1...i2]
lines.push(prefix[tag] + line)
file2Range = _formatRangeContext(first[3], last[4])
lines.push("--- #{file2Range} ----#{lineterm}")
if ((tag in ['replace', 'insert']) for [tag, _, _, _, _] in group).some((x) -> x)
for [tag, _, _, j1, j2] in group
if tag isnt 'delete'
for line in b[j1...j2]
lines.push(prefix[tag] + line)
lines
ndiff = (a, b, linejunk, charjunk=IS_CHARACTER_JUNK) ->
###
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 null, and is
recommended;
- 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!).
Example:
>>> a = ['one\n', 'two\n', 'three\n']
>>> b = ['ore\n', 'tree\n', 'emu\n']
>>> ndiff(a, b)
[ '- one\n',
'? ^\n',
'+ ore\n',
'? ^\n',
'- two\n',
'- three\n',
'? -\n',
'+ tree\n',
'+ emu\n' ]
###
(new Differ(linejunk, charjunk)).compare(a, b)
restore = (delta, which) ->
###
Generate one of the two sequences that generated a delta.
Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
lines originating from file 1 or 2 (parameter `which`), stripping off line
prefixes.
Examples:
>>> a = ['one\n', 'two\n', 'three\n']
>>> b = ['ore\n', 'tree\n', 'emu\n']
>>> diff = ndiff(a, b)
>>> restore(diff, 1)
[ 'one\n',
'two\n',
'three\n' ]
>>> restore(diff, 2)
[ 'ore\n',
'tree\n',
'emu\n' ]
###
tag = {1: '- ', 2: '+ '}[which]
throw new Error("unknow delta choice (must be 1 or 2): #{which}") unless tag
prefixes = [' ', tag]
lines = []
for line in delta
if line[0...2] in prefixes
lines.push(line[2..])
lines
# exports to global
exports._arrayCmp = _arrayCmp
exports.SequenceMatcher = SequenceMatcher
exports.getCloseMatches = getCloseMatches
exports._countLeading = _countLeading
exports.Differ = Differ
exports.IS_LINE_JUNK = IS_LINE_JUNK
exports.IS_CHARACTER_JUNK = IS_CHARACTER_JUNK
exports._formatRangeUnified = _formatRangeUnified
exports.unifiedDiff = unifiedDiff
exports._formatRangeContext = _formatRangeContext
exports.contextDiff = contextDiff
exports.ndiff = ndiff
exports.restore = restore