jc5RUdZddlmZddlmZddlZddlmZddlZddl Z ddl Z ddl Z ddl m Z ddl mZddl mZdd l mZejd Zd d ddd dZdZddZdZddddZdZd dZdZ d!dZ d"dZgZded<edkrddl Z e j dSdS)#az tools for segmenting -- that is, dividing up a score into small, possibly overlapping sections -- for searching across pieces for similarity. Speed notes: this module is definitely a case where running PyPy rather than cPython will give you a 3-5x speedup. If you really want to do lots of comparisons, the `scoreSimilarity` method will use python-Levenshtein if it is installed, unless forceDifflib is set to True. python-Levenshtein can be installed via **pip install python-Levenshtein**. The ratios are very slightly different, but the speedup is between 10 and 100x! (But then PyPy probably won't work.) ) annotations) OrderedDictN)partial)common) converter)corpus) environmentzsearch.segment )segmentLengthsoverlap algorithmjittercddlm}||j}|j}||d\}}t |} ttj | z z } fdt| D} g} g} | D]}|tj d|z|z }td|}t|| dz }t|z| }|||}||||dz f}| || || | fS) a Translates a monophonic part with measures to a set of segments of length `segmentLengths` (measured in number of notes) with an overlap of `overlap` notes using a conversion algorithm of `algorithm` (default: search.translateStreamToStringNoRhythm). Returns two lists, a list of segments, and a list of tuples of measure start and end numbers that match the segments. If algorithm is None then a default algorithm of music21.search.translateStreamToStringNoRhythm is used >>> luca = corpus.parse('luca/gloria') >>> lucaCantus = luca.parts[0] >>> segments, measureLists = search.segment.translateMonophonicPartToSegments(lucaCantus) >>> segments[0:2] ['HJHEAAEHHCE@JHGECA@A>@A>@A>>> measureLists[0:2] [(1, 12), (7, 18)] >>> segments, measureLists = search.segment.translateMonophonicPartToSegments( ... lucaCantus, ... algorithm=search.translateDiatonicStreamToString) >>> segments[0:2] ['CRJOMTHCQNALRQPAGFEFDLFDCFEMOO', 'EFDLFDCFEMOOONPJDCBJSNTHLBOGFE'] >>> measureLists[0:2] [(1, 12), (7, 18)] r)searchNT)returnMeasuresc g|] }|z z Sr).0ir r s /Users/tedpeters/Documents/Projects/Claude Projects/Misc Tools/notename-sheets/.venv/lib/python3.11/site-packages/music21/search/segment.py z5translateMonophonicPartToSegments..]s#UUUQ.723UUU)music21rtranslateStreamToStringNoRhythmrecursenotesstreamlenintmathceilrangerandomrandintmaxminappend) inputStreamr r rrrnStream outputStrmeasures totalLengthnumberOfSegments segmentStarts segmentList measureList segmentStart segmentEndcurrentSegment measureTuples `` r!translateMonophonicPartToSegmentsr8-szN: !!##)0022G#)GDAAAIxi..K49[NW4L%MNNOOUUUUUUCS=T=TUUUMKK% ) ) rF{F;;; 1l++ <q99 6 DD "< #:; .a0HI >***<((((  %%rc r|j}g}|D]*}t|fi|\}}|||d+|S)au Creates segment and measure lists for each part of a score Returns list of dictionaries of segment and measure lists >>> bach = corpus.parse('bwv66.6') >>> scoreList = search.segment.indexScoreParts(bach) >>> scoreList[1]['segmentList'][0] '@B@@@@ED@DBDA=BB@?==B@@EBBDBBA' >>> scoreList[1]['measureList'][0:3] [(0, 7), (4, 9), (8, 9)] )r2r3)partsr8r*) scoreFilekeywordsscoreFileParts indexedListpartr2r3s rindexScorePartsr@rsx_NK  #D $$$$ [&&       rFc t|tjstj|}|j} t |fi|}n3#t $r&}|st d|d|d}n|Yd}~nd}~wwxYw|||fS)z5 Index one path in the context of multicore. zFailed on parse/index for, z: N) isinstancepathlibPathname indexOnePath Exceptionprint)filePathfailFastr<shortFp indexOutputes r_indexSingleMulticorerOs h - -*<))mG"888x88   ???A?? @ @ @KKG KKKK [( ++s A A5A00A5c Dtd|dd|d|ddS)NzIndexed r (/))rI)numRuntotalRun latestOutputs r_giveUpdatesMulticorerWs6 <\!_ < < < < < < <=====rT) giveUpdates runMulticorec |durt}nd}ttfi|}tt |D]?}t ||t jst j||||<@|rtj |||}ntj |||}i}|D] \} } } | | f|| <g} |D]} | || t| }|S)a Returns a dictionary of the lists from indexScoreParts for each score in scoreFilePaths >>> #_DOCS_SHOW searchResults = corpus.search('bwv190') >>> searchResults = corpus.corpora.CoreCorpus().search('bwv190') #_DOCS_HIDE >>> fpsNamesOnly = sorted([searchResult.sourcePath for searchResult in searchResults]) >>> len(fpsNamesOnly) 2 >>> scoreDict = search.segment.indexScoreFilePaths(fpsNamesOnly) >>> len(scoreDict['bwv190.7.mxl']) 4 >>> scoreDict['bwv190.7.mxl'][0]['measureList'] [(0, 9), (6, 15), (11, 20), (17, 25), (22, 31), (27, 32)] >>> scoreDict['bwv190.7.mxl'][0]['segmentList'][0] 'NNJLNOLLLJJIJLLLLNJJJIJLLJNNJL' TN)updateFunction) rWrrOr%r!rCrDrEr runParallelrunNonParallelr*r)scoreFilePathsrXrYr<r[ indexFuncrrpListUnOrderedrpDict outShortNameoutDataoriginalPathlibrpListp scoreDicts rindexScoreFilePathsrhsA4d.-::::I 3~&& ' '@@.+W\:: @ ' ^A-> ? ?N1  + ,  )+++ !/  )+++ F2A::. g#/"9 F !! fQi    F##I rc t|tjstj|}|st j|}nt j|}t|fi|}|S)z8 Index a single path. Returns a scoreDictEntry )rCrDrE is_absoluterparserr@)rJr<scoreObjscoreDictEntrys rrGrGsv h - -*<))    ! !-<))?8,,$X::::N rc$|td}n0t|ttfrt j|}|d5}tj ||dddn #1swxYwY|S)z Save the score dict from indexScoreFilePaths as a .json file for quickly reloading Returns the filepath (assumes you'll probably be using a temporary file) as a pathlib.Path() Nz.jsonwb) environLocal getTempFilerCstrbytesrDrEopenjsondump)rgrJfs r saveScoreDictrxs++G44 HsEl + +*<)) t    )Q                Os#BB  B ct|tjstj|}|d5}t j|}dddn #1swxYwY|S)z1 Load the scoreDictionary from filePath. rbN)rCrDrErtruload)rJrwrgs r loadScoreDictr|s h - -*<)) t  !IaLL !!!!!!!!!!!!!!! sA%%A),A)c|durtj|d|}nE ddlm}||d|}n&#t$rtj|d|}YnwxYw|S)z Returns either a difflib.SequenceMatcher or pyLevenshtein StringMatcher.StringMatcher object depending on what is installed. If forceDifflib is True then use difflib even if pyLevenshtein is installed: TrBr) StringMatcher)difflibSequenceMatcher Levenshteinr~ ImportError)seq2junk forceDifflibsmObject pyLevenshteins rgetDifflibOrPyLevrst*4T:: ? B B B B B B$224TBBHH ? ? ?.tR>>HHH ? Os; AAc  g d tt d d  f d}tD]} |} dz |durt dd dd tt|D]V t | d D]8\ }t|kr| d  ||9W S) a Find the level of similarity between each pair of segments in a scoreDict. This takes twice as long as it should because it does not cache the pairwise similarity. >>> filePaths = [] >>> for p in ('bwv197.5.mxl', 'bwv190.7.mxl', 'bwv197.10.mxl'): ... #_DOCS_SHOW source = corpus.search(p)[0].sourcePath ... source = corpus.corpora.CoreCorpus().search(p)[0].sourcePath #_DOCS_HIDE ... filePaths.append(source) >>> scoreDict = search.segment.indexScoreFilePaths(filePaths) >>> scoreSim = search.segment.scoreSimilarity(scoreDict, forceDifflib=True) #_DOCS_HIDE >>> #_DOCS_SHOW scoreSim = search.segment.scoreSimilarity(scoreDict) >>> len(scoreSim) 496 Returns a list of tuples of first score name, first score voice number, first score measure number, second score name, second score voice number, second score measure number, and similarity score (0 to 1). >>> for result in scoreSim[133:137]: ... result ('bwv197.5.mxl', 1, 1, (4, 10), 'bwv190.7.mxl', 3, 4, (22, 30), 0.13...) ('bwv197.5.mxl', 1, 1, (4, 10), 'bwv197.10.mxl', 0, 0, (0, 8), 0.2) ('bwv197.5.mxl', 1, 1, (4, 10), 'bwv197.10.mxl', 1, 0, (0, 7), 0.266...) ('bwv197.5.mxl', 1, 1, (4, 10), 'bwv197.10.mxl', 1, 1, (4, 9), 0.307...) rNc  t| }tD]}|}|}tt|D]}t||dD]\}}t|kr|||}||d|} |||| |f } |  sy|||| |f } | dS)N)rr2r3)rr%r! enumerateset_seq1ratior*) thisSegmentdlthatScoreNumber thatScoreKey thatScorepNum2thatSegmentNumber thatSegmentrthatMeasureNumbersimilarityTuplesimilarityTupleReversedrincludeReverse minimumLengthpNumrg scoreDictKeys scoreIndex segmentNumbersimilarityScoresthisMeasureNumber thisScoreKey totalScoress r doOneSegmentz%scoreSimilarity..doOneSegmentRsu { F F F$Z==$ E$ EO(9L!,/Is9~~..! E! E6?!%(77979 E E2%{;''-77 KK ,,,HHJJE(1%(8(GHY(Z%$%)$)) 'O%++O<<<)! $))$%) /+%++,CDDDDA E! E$ E$ ErrTz Comparing rQrRrSr2r3)r!listkeysr%rIr)rgrrXrrrthisScoreNumber thisScorethisSegmentOuterrrrrrrrrs`` `` @@@@@@@@rscoreSimilarityr'sHJi..K))**M DM'E'E'E'E'E'E'E'E'E'E'E'E'E'E'E'ER!-- / /$_5 l+ a $   J|JJzJJKJJJ K K K#i..)) / /D3rs #"""""######  &{&'788   A&A&A&A&A&J0,,,,(>>>%*%);;;;;|    &     2 ccccP  zNNNGr