d)ddlmZddlmZmZmZddlmZddlm Z m Z ddl m Z ddl mZdddd"dZ d#d$dZdddd"dZdddd%dZ d#d&dZdddd%dZ d#d&dZddd'dZddd(d!ZdS))) annotations)CallableHashableSequence)is_none)EditopsOpcodes)_block_similarity) similarityN) processor score_cutoffs1Sequence[Hashable]s2r (Callable[..., Sequence[Hashable]] | Noner int | Nonereturnintc|||}||}t|t|z}t||}|d|zz }|||kr|n|dzS)a Calculates the minimum number of insertions and deletions required to change one sequence into the other. This is equivalent to the Levenshtein distance with a substitution weight of 2. Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : int, optional Maximum distance between s1 and s2, that is considered as a result. If the distance is bigger than score_cutoff, score_cutoff + 1 is returned instead. Default is None, which deactivates this behaviour. Returns ------- distance : int distance between s1 and s2 Examples -------- Find the Indel distance between two strings: >>> from rapidfuzz.distance import Indel >>> Indel.distance("lewenstein", "levenshtein") 3 Setting a maximum distance allows the implementation to select a more efficient implementation: >>> Indel.distance("lewenstein", "levenshtein", score_cutoff=1) 2 N)lenlcs_seq_similarity)rrr r maximumlcs_simdists _/home/feoh/.local/pipx/venvs/poetry/lib/python3.11/site-packages/rapidfuzz/distance/Indel_py.pydistancersw^ Yr]] Yr]]"ggBG R((G Q[ D (DL,@,@44|VWGWWblockdict[Hashable, int]ct|t|z}t|||}|d|zz }|||kr|n|dzS)Nrr)rlcs_seq_block_similarity)r rrr rrrs r_block_distancer$GsX "ggBG&ub"55G Q[ D (DL,@,@44|VWGWWrc|||}||}t|t|z}t||}||z }|||kr|ndS)a Calculates the Indel similarity in the range [max, 0]. This is calculated as ``(len1 + len2) - 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 : 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 Nr)rr)rrr r rrsims rr r Ssl@ Yr]] Yr]]"ggBG B  D D.C'3,+>+>33QFr float | Nonefloatct|st|rdS|||}||}t|t|z}t||}|r||z nd}|||kr|ndS)a8 Calculates a normalized levenshtein similarity in the range [1, 0]. This is calculated as ``distance / (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 ?Nrr)rrr)rrr r rr norm_dists rnormalized_distancer,}s>r{{gbkks Yr]] Yr]]"ggBG B  D")0wqI%-l1J1J99QRRrct|t|z}t|||}|r||z nd}|||kr|ndS)Nrr)rr$)r rrr rrr+s r_block_normalized_distancer.sW "ggBG 5"b ) )D")0wqI%-l1J1J99QRRrct|st|rdS|||}||}t||}d|z }|||kr|ndS)a Calculates a normalized indel similarity in the range [0, 1]. This is calculated as ``1 - normalized_distance`` Parameters ---------- s1 : Sequence[Hashable] First string to compare. s2 : Sequence[Hashable] Second string to compare. processor: callable, optional Optional callable that is used to preprocess the strings before comparing them. Default is None, which deactivates this behaviour. score_cutoff : float, optional Optional argument for a score threshold as a float between 0 and 1.0. For norm_sim < score_cutoff 0 is returned instead. Default is 0, which deactivates this behaviour. Returns ------- norm_sim : float normalized similarity between s1 and s2 as a float between 0 and 1.0 Examples -------- Find the normalized Indel similarity between two strings: >>> from rapidfuzz.distance import Indel >>> Indel.normalized_similarity("lewenstein", "levenshtein") 0.85714285714285 Setting a score_cutoff allows the implementation to select a more efficient implementation: >>> Indel.normalized_similarity("lewenstein", "levenshtein", score_cutoff=0.9) 0.0 When a different processor is used s1 and s2 do not have to be strings >>> Indel.normalized_similarity(["lewenstein"], ["levenshtein"], processor=lambda s: s[0]) 0.8571428571428572 gNr*r)rr,)rrr r r+norm_sims rnormalized_similarityr1svdr{{gbkks Yr]] Yr]]#B++IYH$,L0H0H88qPrcFt|||}d|z }|||kr|ndS)Nr*r)r.)r rrr r+r0s r_block_normalized_similarityr3s7 +5"b99IYH$,L0H0H88qPrr rc@|||}||}t)ua 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 [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 Indel >>> for tag, src_pos, dest_pos in Indel.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] )NotImplementedErrorrrr s reditopsr8s+X Yr]] Yr]] rr cJt|||S)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 [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 Indel >>> a = "qabxcd" >>> b = "abycdf" >>> for tag, i1, i2, j1, j2 in Indel.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) r4)r8 as_opcodesr7s ropcodesr;1s&d 2rY / / / : : < <rBs#"""""//////////$$$$$$;;;;;;;;VVVVVVIIIIII;?# 6X6X6X6X6X6Xz $ X X X X X ;?# 'G'G'G'G'G'G\;?!% )S)S)S)S)S)S`"& S S S S S ;?!% ;Q;Q;Q;Q;Q;QD"& QQQQQ;? 000000n;? 2=2=2=2=2=2=2=2=r