-ķ ē ¶" lines in HTML files). That may be because this is the only method of the 3 that has a *concept* of "junk" . 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 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. scs-||_t|_|_|i||ƒdS(s[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(). N(sisjunksselfsNonesasbsset_seqs(sselfsisjunksasb((s/usr/lib/python2.2/difflib.pys__init__‰s' cs|i|ƒ|i|ƒdS(s›Set the two sequences to be compared. >>> s = SequenceMatcher() >>> s.set_seqs("abcd", "bcde") >>> s.ratio() 0.75 N(sselfsset_seq1sasset_seq2sb(sselfsasb((s/usr/lib/python2.2/difflib.pysset_seqsĘs cs5||ijodSn||_t|_|_dS(sMSet 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_seq2(). N(sasselfsNonesmatching_blockssopcodes(sselfsa((s/usr/lib/python2.2/difflib.pysset_seq1Ņs  csH||ijodSn||_t|_|_t|_|iƒdS(sMSet the second sequence to be compared. The first sequence to be compared is not changed. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.set_seq2("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 .set_seq2(S) once and call .set_seq1(x) repeatedly for each of the other sequences. See also set_seqs() and set_seq1(). N(sbsselfsNonesmatching_blockssopcodess fullbcounts_SequenceMatcher__chain_b(sselfsb((s/usr/lib/python2.2/difflib.pysset_seq2ģs  cså|i}h|_}|i|_}xStt|ƒƒD]?}||}||ƒo||i |ƒn|g||= 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) iiN(sselfsasbsb2jsisbjunksalosblosbestisbestjsbestsizesj2lensnothingsxrangesahisisgetsj2lengetsnewj2lensjsbhisk(sselfsalosahisblosbhisbestisbsbestjsisnewj2lensbestsizesjsisbjunksasnothingsj2lengetsj2lensksb2j((s/usr/lib/python2.2/difflib.pysfind_longest_match:s4**    0 L)Tcsˆ|itj o |iSng|_t|iƒt|iƒf\}}|id|d||iƒ|ii ||dfƒ|iSdS(sČReturn 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)] iN( sselfsmatching_blockssNoneslensasbslaslbs_SequenceMatcher__helpersappend(sselfslbsla((s/usr/lib/python2.2/difflib.pysget_matching_blocks›s   $c sæ|i||||ƒ\}}} }| o||jo ||jo|i |||||ƒn|i |ƒ|| |jo|| |jo%|i || ||| ||ƒnndS(N( sselfsfind_longest_matchsalosahisblosbhisisjsksxs_SequenceMatcher__helpersanswersappend( sselfsalosahisblosbhisanswersjsisxsk((s/usr/lib/python2.2/difflib.pys__helperµs% "cs|itj o |iSnd}}g|_}xŻ|iƒD]Ļ\}}}d}||jo ||jo d}n/||jo d}n||jo d}n|o |i |||||fƒn||||f\}}|o |i d||||fƒnq?W|SdS(sZReturn 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) issreplacesdeletesinsertsequalN( sselfsopcodessNonesisjsanswersget_matching_blockssaisbjssizestagsappend(sselfsanswerstagsbjsisjsaissize((s/usr/lib/python2.2/difflib.pys get_opcodesĮs(          $csAtd„|iƒdƒ}d|t|iƒt|iƒSdS(sŅReturn a measure of the sequences' similarity (float in [0,1]). Where T is the total number of elements in both sequences, and M is the number of matches, this is 2,0*M / T. Note that this is 1 if the sequences are identical, and 0 if they have nothing in common. .ratio() is expensive to compute if you haven't already computed .get_matching_blocks() or .get_opcodes(), in which case you may want to try .quick_ratio() or .real_quick_ratio() first to get an upper bound. >>> s = SequenceMatcher(None, "abcd", "bcde") >>> s.ratio() 0.75 >>> s.quick_ratio() 0.75 >>> s.real_quick_ratio() 1.0 cs ||dS(Ni’’’’(ssumstriple(ssumstriple((s/usr/lib/python2.2/difflib.pyssif2.0N(sreducesselfsget_matching_blockssmatchesslensasb(sselfsmatches((s/usr/lib/python2.2/difflib.pysratioųs cs|itjo?h|_}x+|iD] }|i|dƒd|| 0. Optional arg cutoff (default 0.6) is a float in [0, 1]. Possibilities that don't score at least that similar to word are ignored. The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first. >>> get_close_matches("appel", ["ape", "apple", "peach", "puppy"]) ['apple', 'ape'] >>> import keyword as _keyword >>> get_close_matches("wheel", _keyword.kwlist) ['while'] >>> get_close_matches("apple", _keyword.kwlist) [] >>> get_close_matches("accept", _keyword.kwlist) ['except'] isn must be > 0: f0.0f1.0scutoff must be in [0.0, 1.0]: N(sns ValueErrorscutoffsresultsSequenceMatchersssset_seq2swords possibilitiessxsset_seq1sreal_quick_ratios quick_ratiosratiosappendssortsreverses_[1]sscore( swords possibilitiessnscutoffsssscores_[1]sresultsx((s/usr/lib/python2.2/difflib.pysget_close_matches;s$   9!   csPdt|ƒf\}}x-||jo|||jo|d7}qW|SdS(s} Return number of `ch` characters at the start of `line`. Example: >>> _count_leading(' abc', ' ') 3 iiN(slenslinesisnsch(slineschsisn((s/usr/lib/python2.2/difflib.pys_count_leadingos csStZdZeed„Zd„Zd„Zd„Zd„Zd„Z d„Z RS(se 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 = 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), 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. cs||_||_dS(s“ 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!). N(slinejunksselfscharjunk(sselfslinejunkscharjunk((s/usr/lib/python2.2/difflib.pys__init__Üs c #s t|i||ƒ} xš| iƒD]ā\}}}}}|djo"|i ||||||ƒ}nŠ|djo|id|||ƒ}na|djo|id|||ƒ}n8|djo|id|||ƒ}ntd| ‚x|D] } | VqöWq"Wd S( sŲ 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(1), ... 'ore\ntree\nemu\n'.splitlines(1))), - one ? ^ + ore ? ^ - two - three ? - + tree + emu sreplacesdeletes-sinserts+sequals s unknown tag N(sSequenceMatchersselfslinejunksasbscrunchers get_opcodesstagsalosahisblosbhis_fancy_replacesgs_dumps ValueErrorsline( sselfsasbsalosbhisgsahisblostagslinescruncher((s/usr/lib/python2.2/difflib.pyscomparešs   "   c#s0x)t||ƒD]}d|||fVqWdS(s4Generate comparison results for a same-tagged range.s%s %sN(sxrangesloshisistagsx(sselfstagsxsloshisi((s/usr/lib/python2.2/difflib.pys_dumpsc #s©||||jo4|id|||ƒ} |id|||ƒ}n1|id|||ƒ} |id|||ƒ}x)| |fD]}x|D] } | Vq“Wq†WdS(Ns+s-( sbhisblosahisalosselfs_dumpsbsfirstsassecondsgsline( sselfsasalosahisbsblosbhisgssecondslinesfirst((s/usr/lib/python2.2/difflib.pys_plain_replaces c#sXddf\}}t|iƒ}ttf\}}xńt ||ƒD]ą}||} |i| ƒxĄt ||ƒD]Æ}||}|| jo*|tjo||f\}}nqpn|i|ƒ|iƒ|jo#|iƒ|jo|iƒ|jo"|iƒ||f\}}}nqpWqCW||jo^|tjo5x*|i||||||ƒD] }|Vq`WdSn||df\}}}nt}x*|i||||||ƒD] }|Vq·W||||f\} }|tjo+d}} |i"| |ƒxć|i#ƒD]Õ\} }}}} ||| |f\}}| djo |d|7}| d|7} nz| djo|d |7}n[| d jo| d |7} n<| d jo |d |7}| d |7} nt+d| ‚qWx$|i,| ||| ƒD] }|VqWn d| Vx2|i||d|||d|ƒD] }|VqFWdS(s When replacing one block of lines with another, search the blocks for *similar* lines; the best-matching pair (if any) is used as a synch point, and intraline difference marking is done on the similar pair. Lots of work, but often worth it. Example: >>> d = Differ() >>> d._fancy_replace(['abcDefghiJkl\n'], 0, 1, ['abcdefGhijkl\n'], 0, 1) >>> print ''.join(d.results), - abcDefghiJkl ? ^ ^ ^ + abcdefGhijkl ? ^ ^ ^ f0.73999999999999999f0.75Nf1.0ssreplaces^sdeletes-sinserts+sequals s unknown tag s i(-s best_ratioscutoffsSequenceMatchersselfscharjunkscrunchersNoneseqiseqjsxrangesblosbhisjsbsbjsset_seq2salosahisisasaisset_seq1sreal_quick_ratios quick_ratiosratiosbest_isbest_js_plain_replaceslines _fancy_helpersaeltsbeltsatagssbtagssset_seqss get_opcodesstagsai1sai2sbj1sbj2slaslbs ValueErrors_qformat(sselfsasalosahisbsblosbhiscutoffsaisaeltsbjsbtagsstagsbj2sbj1satagsslinesbest_isbest_jslbslasisjsai1sai2seqiseqjs best_ratiosbeltscruncher((s/usr/lib/python2.2/difflib.pys_fancy_replace-sl      9*          'c #s g}||joK||jo"|i||||||ƒ}n|i d|||ƒ}n*||jo|i d|||ƒ}nx|D] }|VqŽWdS(Ns-s+( sgsalosahisblosbhisselfs_fancy_replacesasbs_dumpsline( sselfsasalosahisbsblosbhisgsline((s/usr/lib/python2.2/difflib.pys _fancy_helperŽs  " c#s«tt|dƒt|dƒƒ}t|t|| dƒƒ}||iƒ}||iƒ}d|V|odd||fVnd|V|odd||fVndS(s• Format "?" output and deal with leading tabs. Example: >>> d = Differ() >>> d._qformat('\tabcDefghiJkl\n', '\t\tabcdefGhijkl\n', ... ' ^ ^ ^ ', '+ ^ ^ ^ ') >>> for line in d.results: print repr(line) ... '- \tabcDefghiJkl\n' '? \t ^ ^ ^\n' '+ \t\tabcdefGhijkl\n' '? \t ^ ^ ^\n' s s s- s? %s%s s+ N(smins_count_leadingsalinesblinescommonsatagssrstripsbtags(sselfsalinesblinesatagssbtagsscommon((s/usr/lib/python2.2/difflib.pys_qformat›s( s__name__s __module__s__doc__sNones__init__scompares_dumps_plain_replaces_fancy_replaces _fancy_helpers_qformat(((s/usr/lib/python2.2/difflib.pysDiffer~s \ )   a Ns \s*#?\s*$cs||ƒtj SdS(sŅ 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 N(spatslinesNone(slinespat((s/usr/lib/python2.2/difflib.pys IS_LINE_JUNKĪs s cs||jSdS(sķ 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 N(schsws(schsws((s/usr/lib/python2.2/difflib.pysIS_CHARACTER_JUNKŽscst||ƒi||ƒSdS(sõ 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 N(sDifferslinejunkscharjunkscomparesasb(sasbslinejunkscharjunk((s/usr/lib/python2.2/difflib.pysndiffņs!c#s‡y&hdd<dd>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1), ... 'ore\ntree\nemu\n'.splitlines(1)) >>> diff = list(diff) >>> print ''.join(restore(diff, 1)), one two three >>> print ''.join(restore(diff, 2)), ore tree emu s- is+ is)unknown delta choice (must be 1 or 2): %rs N(sintswhichstagsKeyErrors ValueErrorsprefixessdeltasline(sdeltaswhichsprefixesstagsline((s/usr/lib/python2.2/difflib.pysrestores& cs#dk}dk}|i|ƒSdS(N(sdoctestsdifflibstestmod(sdoctestsdifflib((s/usr/lib/python2.2/difflib.pys_test6ss__main__(s __future__s generatorss__all__sSequenceMatchersget_close_matchess_count_leadingsDiffersrescompilesmatchs IS_LINE_JUNKsIS_CHARACTER_JUNKsndiffsrestores_tests__name__( sIS_CHARACTER_JUNKs_count_leadingsDiffers__all__srestores_testsndiffsres IS_LINE_JUNKs generatorssget_close_matchessSequenceMatcher((s/usr/lib/python2.2/difflib.pys?s$ ’’"4 ’O  $