+ ʇiB^RIHt^RIHtHt^RIHtHt^RIH t ^RI H t H t RtRtRtR RR R R R R R /RltR RR R R R R R /RltR RR R R R R R /RltR RR R R R R R /RltRtR R R R /RltR R R R /RltR #)) annotations) common_affixconv_sequences)is_none setupPandas)Indel_py)EditopEditopsc\V4p\V4pVwrVpW6,WE,,pW48d)\WV,W4, V,,4pV#\WV,WC, V,,4pV#)N)lenmin) s1s2weightslen1len2insertdeletereplacemax_dists &&& v/Users/max/.openclaw/workspace/postharvest/merge_env/lib/python3.14/site-packages/rapidfuzz/distance/Levenshtein_py.py_levenshtein_maximumr ss r7D r7D%FG}t},H |x4;&2H!HI Ox4;&2H!HI Oc\V4pVwrEp\\^V^,V,V44pVFpV^,p V^;;,V, uu&\V4F`p T p W ,V8wd5\Wz,V,Wz^,,V,W,4p Wz^,,p WV ^,&Kb K VR,#)r)r listranger ) rrrrrrrcachech2tempixs &&& r_uniform_genericr#s r7D%FG q4!8v-v6 7EQx aFtAAu|6)5Q<&+@$.QQ>> from rapidfuzz.distance import Levenshtein >>> Levenshtein.distance("lewenstein", "levenshtein") 2 Setting a maximum distance allows the implementation to select a more efficient implementation: >>> Levenshtein.distance("lewenstein", "levenshtein", score_cutoff=1) 2 It is possible to select different weights by passing a `weight` tuple. >>> Levenshtein.distance("lewenstein", "levenshtein", weights=(1,1,2)) 3 r&r&r&)r&r&)rr4Indeldistancer#)rrrr5r6r7_dists&&$$$$ rr<r<PsD A r] r] B #FB'Y. ( I ~~b%0 (D,@4W|VWGWWrcTpVeV!V4pV!V4p\W4wrT;'gRp\WV4p\WVR7pWx, p VeW8dV #^#)aO Calculates the levenshtein similarity in the range [max, 0] using custom costs for insertion, deletion and substitution. This is calculated as ``max - distance``, where max is the maximal possible Levenshtein distance given the lengths of the sequences s1/s2 and the weights. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. weights : tuple[int, int, int] or None, optional The weights for the three operations in the form (insertion, deletion, substitution). Default is (1, 1, 1), which gives all three operations a weight of 1. processor : callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the similarity is smaller than score_cutoff, 0 is returned instead. Default is None, which deactivates this behaviour. score_hint : int, optional Expected similarity between s1 and s2. This is used to select a faster implementation. Default is None, which deactivates this behaviour. Returns ------- similarity : int similarity between s1 and s2 Raises ------ ValueError If unsupported weights are provided a ValueError is thrown rr9)rrr<) rrrr5r6r7r=maximumr>sims &&$$$$ r similarityrCso` A r] r] B #FB""G"273G BG ,D .C'3+>3FQFrc.Tp\4\V4'g\V4'dR#VeV!V4pV!V4p\W4wrT;'gRp\WV4p\ WVR7pV'd W, M^p VeW8:dV #^#)aw Calculates a normalized levenshtein distance in the range [1, 0] using custom costs for insertion, deletion and substitution. This is calculated as ``distance / max``, where max is the maximal possible Levenshtein distance given the lengths of the sequences s1/s2 and the weights. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. weights : tuple[int, int, int] or None, optional The weights for the three operations in the form (insertion, deletion, substitution). Default is (1, 1, 1), which gives all three operations a weight of 1. processor : callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_dist > score_cutoff 1.0 is returned instead. Default is None, which deactivates this behaviour. score_hint : float, optional Expected normalized distance between s1 and s2. This is used to select a faster implementation. Default is None, which deactivates this behaviour. Returns ------- norm_dist : float normalized distance between s1 and s2 as a float between 1.0 and 0.0 Raises ------ ValueError If unsupported weights are provided a ValueError is thrown ?r@r9)rrrrr<) rrrr5r6r7r=rAr> norm_dists &&$$$$ rnormalized_distancerGs^ AMr{{gbkk r] r] B #FB""G"273G BG ,D")qI%-1J9RQRRrcTp\4\V4'g\V4'dR#VeV!V4pV!V4p\W4wrT;'gRp\WVR7pRV, pVeW8dV#^#)a Calculates a normalized levenshtein similarity in the range [0, 1] using custom costs for insertion, deletion and substitution. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. weights : tuple[int, int, int] or None, optional The weights for the three operations in the form (insertion, deletion, substitution). Default is (1, 1, 1), which gives all three operations a weight of 1. 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 None, which deactivates this behaviour. score_hint : int, optional Expected normalized similarity between s1 and s2. This is used to select a faster implementation. Default is None, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 Raises ------ ValueError If unsupported weights are provided a ValueError is thrown Examples -------- Find the normalized Levenshtein similarity between two strings: >>> from rapidfuzz.distance import Levenshtein >>> Levenshtein.normalized_similarity("lewenstein", "levenshtein") 0.81818181818181 Setting a score_cutoff allows the implementation to select a more efficient implementation: >>> Levenshtein.normalized_similarity("lewenstein", "levenshtein", score_cutoff=0.85) 0.0 It is possible to select different weights by passing a `weight` tuple. >>> Levenshtein.normalized_similarity("lewenstein", "levenshtein", weights=(1,1,2)) 0.85714285714285 When a different processor is used s1 and s2 do not have to be strings >>> Levenshtein.normalized_similarity(["lewenstein"], ["levenshtein"], processor=lambda s: s[0]) 0.81818181818181 gr@rEr9)rrrrG) rrrr5r6r7r=rFnorm_sims &&$$$$ rnormalized_similarityrJs~N AMr{{gbkk r] r] B #FB""G#BG>> from rapidfuzz.distance import Levenshtein >>> for tag, src_pos, dest_pos in Levenshtein.editops("qabxcd", "abycdf"): ... print(("%7s s1[%d] s2[%d]" % (tag, src_pos, dest_pos))) delete s1[1] s2[0] replace s1[3] s2[2] insert s1[6] s2[5] Nrrr) rrr rOr _src_len _dest_lenr _editops)rrr5r7r= prefix_len suffix_lenr>r(r)editops editop_listcolrows&&$$ rrVrVs^ A r] r] B #FB)"1J R:- .B R:- .B2?LDbb!QG2w+j8GB*,z9G qy&4-K b'C b'C (sax Ag;!a. ) ) AID 1HC &x1A3CS TK  1HC7 qS1W~66 $*8S5EsGW$X !q7bg%AID(.y#:JCL\(]K% (   q"8S-=s?OP  (   q"8S-=s?OP " Nrc8\WW#R7P4#)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. score_hint : int, optional Expected distance between s1 and s2. This is used to select a faster implementation. 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 [9]_. It has a time complexity and memory usage of ``O([N/64] * M)``. References ---------- .. [9] Hyyrö, Heikki. "A Note on Bit-Parallel Alignment Computation." Stringology (2004). Examples -------- >>> from rapidfuzz.distance import Levenshtein >>> a = "qabxcd" >>> b = "abycdf" >>> for tag, i1, i2, j1, j2 in Levenshtein.opcodes("qabxcd", "abycdf"): ... 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) )r5r7)rV as_opcodes)rrr5r7s&&$$ropcodesr\sj 2Y F Q Q SSrr9) __future__rrapidfuzz._common_pyrrrapidfuzz._utilsrrrapidfuzz.distancerr;!rapidfuzz.distance._initialize_pyr r rr#r4r<rCrGrJrOrVr\rrrcs;#=10= $!HOX  OX  OX  OXOXd:G  :G  :G  :G:Gz=S  =S  =S  =S=S@TQ  TQ  TQ  TQTQn&,Rd d  dN5T 5T  5Tr