+ ʇi,^RIHt^RIHtHt^RIHtHt^RIH t H t RRRR/Rlt RR lt RRRR/R lt RRRR/R ltRRRR/R ltR tRR/RltRR/RltR#)) annotations) common_affixconv_sequences)is_none setupPandas)EditopEditops processorN score_cutoffcVeV!V4pV!V4pV'g^#\W4wr^\V4,^, p/pVPp^pVFpV!V^4V,WX&V^,pK VF)p V!V ^4p WJ,p WK,WK, ,pK+ \V4\V4)RP R4p VeW8dV #^#)a Calculates the length of the longest common subsequence Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the similarity is smaller than score_cutoff, 0 is returned instead. Default is None, which deactivates this behaviour. Returns ------- similarity : int similarity between s1 and s2 N0)rlengetbincount) s1s2r r Sblock block_getxch1ch2Matchesuress &&$$ q/Users/max/.openclaw/workspace/postharvest/merge_env/lib/python3.14/site-packages/rapidfuzz/distance/LCSseq_py.py similarityr s< r] r]  B #FB c"gA E I AsA&*  aC# K Uqu  a&#b'  " "3 'C'3+>3FQFc(V'g^#^\V4,^, pVPpVF)pV!V^4pWG,pWH,WH, ,pK+ \V4\V4)RPR4p VeW8dV #^#rNr )rrrr) rrrr rrrrrrs &&&& r_block_similarityr"Bs  c"gA IC# K Uqu  a&#b'  " "3 'C'3+>3FQFrcVeV!V4pV!V4p\W4wr\\V4\V44p\W4pWE, pVeWc8:dV#V^,#)a Calculates the LCS distance in the range [0, max]. This is calculated as ``max(len1, len2) - similarity``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the distance is bigger than score_cutoff, score_cutoff + 1 is returned instead. Default is None, which deactivates this behaviour. Returns ------- distance : int distance between s1 and s2 Examples -------- Find the LCS distance between two strings: >>> from rapidfuzz.distance import LCSseq >>> LCSseq.distance("lewenstein", "levenshtein") 2 Setting a maximum distance allows the implementation to select a more efficient implementation: >>> LCSseq.distance("lewenstein", "levenshtein", score_cutoff=1) 2 )rmaxrr)rrr r maximumsimdists&&$$ rdistancer(Xsi^ r] r] B #FB#b'3r7#G R C =D (D,@4W|VWGWWrc@\4\V4'g\V4'dR#VeV!V4pV!V4pV'd V'g^#\W4wr\\ V4\ V44p\ W4V, pVeWS8:dV#^#)a Calculates a normalized LCS similarity in the range [1, 0]. This is calculated as ``distance / max(len1, len2)``. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is 1.0, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 0 and 1.0 ?)rrrr$rr()rrr r r%norm_sims&&$$ rnormalized_distancer,s>Mr{{gbkk r] r] R B #FB#b'3r7#G')H$,0H8PqPrc\4\V4'g\V4'dR#VeV!V4pV!V4pR\W4, pVeWC8dV#^#)a  Calculates a normalized LCS similarity in the range [0, 1]. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 Examples -------- Find the normalized LCS similarity between two strings: >>> from rapidfuzz.distance import LCSseq >>> LCSseq.normalized_similarity("lewenstein", "levenshtein") 0.8181818181818181 Setting a score_cutoff allows the implementation to select a more efficient implementation: >>> LCSseq.normalized_similarity("lewenstein", "levenshtein", score_cutoff=0.9) 0.0 When a different processor is used s1 and s2 do not have to be strings >>> LCSseq.normalized_similarity(["lewenstein"], ["levenshtein"], processor=lambda s: s[0]) 0.81818181818181 gr*)rrr,)rrr r r+s&&$$ rnormalized_similarityr.s[dMr{{gbkk r] r](00H$,0H8PqPrcV'g^.3#^\V4,^, p/pVPp^pVFpV!V^4V,W6&V^,pK .pVF:pV!V^4p W),p W*,W*, ,pVPV4K< \V4\V4)RP R4p W3#r!)rrappendrr) rrrrrrrmatrixrrrr&s && r_matrixr2s 2w c"gA E I AsA&*  aFC# K Uqu  a  a&#b'  " "3 'C =rcVeV!V4pV!V4p\W4wr\W4wr4W\V4V, pW\V4V, p\W4wrV\ .^^4p\V4V,V,Vn\V4V,V,Vn\V4\V4,^V,, pV^8XdV#R.V,p \V4p \V4p V ^8wdV ^8wdWk^, ,^V ^, ,,'d0V^,pV ^,p \RW,W,4W&KgV ^,p V 'dQWk^, ,^V ^, ,,'g'V^,p\RW,W,4W&KV ^,p KV ^8wd0V^,pV ^,p \RW,W,4W&K6V ^8wd0V^,pV ^,p \RW,W,4W&K6WnV#)u Return Editops describing how to turn s1 into s2. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. Returns ------- editops : Editops edit operations required to turn s1 into s2 Notes ----- The alignment is calculated using an algorithm of Heikki Hyyrö, which is described in [6]_. It has a time complexity and memory usage of ``O([N/64] * M)``. References ---------- .. [6] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation." Stringology (2004). Examples -------- >>> from rapidfuzz.distance import LCSseq >>> for tag, src_pos, dest_pos in LCSseq.editops("qabxcd", "abycdf"): ... print(("%7s s1[%d] s2[%d]" % (tag, src_pos, dest_pos))) delete s1[0] s2[0] delete s1[3] s2[2] insert s1[4] s2[2] insert s1[6] s2[5] Ndeleteinsert) rrrr2r _src_len _dest_lenr_editops) rrr prefix_len suffix_lenr&r1editopsr' editop_listcolrows &&$ rr;r;sX r] r] B #FB)"1J R:- .B R:- .B"/KCb!QG2w+j8GB*,z9G r7SW q3w &D qy&4-K b'C b'C (sax '?aC!Gn - - AID 1HC &x1A3CS TK  1HCF7OqS1W~>> $*8S5EsGW$X !q (   q"8S-=s?OP  (   q"8S-=s?OP " Nrc8\WVR7P4#)u Return Opcodes describing how to turn s1 into s2. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. Returns ------- opcodes : Opcodes edit operations required to turn s1 into s2 Notes ----- The alignment is calculated using an algorithm of Heikki Hyyrö, which is described in [7]_. It has a time complexity and memory usage of ``O([N/64] * M)``. References ---------- .. [7] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation." Stringology (2004). Examples -------- >>> from rapidfuzz.distance import LCSseq >>> a = "qabxcd" >>> b = "abycdf" >>> for tag, i1, i2, j1, j2 in LCSseq.opcodes(a, b): ... 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) delete a[3:4] (x) b[2:2] () insert a[4:4] () b[2:3] (y) equal a[4:6] (cd) b[3:5] (cd) insert a[6:6] () b[5:6] (f) )r )r; as_opcodes)rrr s&&$ropcodesrAxsd 2Y / : : <rGs#=1=5G 5G  5GpG,7X 7X  7Xt-Q -Q  -Q`;Q ;Q  ;Q|0] ]@2= 2=r