j4dZddlmZddlmZddlmZdZGddZ d d Z d Z e d krddlZej dSdS) z This module takes two XML files and displays the number of measures that differ between the two before and after running the combined correction models ) annotations) correctors) converterFceZdZdZddZedZejdZdZdZ dZ d Z d Z d Z d Zd ZdZdS)OmrGroundTruthPairz Object for making comparisons between an OMR score and the GroundTruth Takes in a path to the OMR and a path to the groundTruth (or a pair of music21.stream.Score objects). See below for examples. NcNd|_d|_t|dr|j|_||_n||_d|_||_t|dr|j|_||_ n||_d|_ | |_ dS)NfilePath) _overriddenDebugnumberOfDifferenceshasattrr omrPath omrM21Score getOmrScoreomrScore groundPathgroundM21ScoregetGroundScore groundScore)selfomrgrounds /Users/tedpeters/Documents/Projects/Claude Projects/Misc Tools/notename-sheets/.venv/lib/python3.11/site-packages/music21/omr/evaluators.py__init__zOmrGroundTruthPair.__init__"s $#' 3 # # $>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> ssOMR = omrGTP.getOmrScore() >>> ssOMR zparsing OMR score)rprintrrparser rScoreCorrectorrs rrzOmrGroundTruthPair.getOmrScoreTsP : ' % & & & =(t|<>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> ssGT = omrGTP.getGroundScore() >>> ssGT zparsing Ground Truth score)rr'rrr(rrr)rs rrz!OmrGroundTruthPair.getGroundScoregsQ : 0 . / / /" C"+/$/"B"BD ()<===rc||krdSdS)z\ define the substitution cost for x and y (2 if x and y are unequal else 0) r)rxys r substCostzOmrGroundTruthPair.substCosts 6611rcdS)z; define the insertion cost for x and y (1) r-rr.s r insertCostzOmrGroundTruthPair.insertCost qrcdS)z: define the deletion cost for x and y (1) r2r-r3s r deleteCostzOmrGroundTruthPair.deleteCostr5rc t|}t|fdt|dzD}td|dzD];}||dz d|||dz z||d<z=OmrGroundTruthPair.minEditDist...s---1Q---rr2)range)r;jms rr=z2OmrGroundTruthPair.minEditDist..s2DDD!--a!e ---DDDrr2r)lenr>r4r7minr0)rtargetsourcendistancer<r?r@s @r minEditDistzOmrGroundTruthPair.minEditDists KK KKDDDDuQU||DDDq!a% Q QA%a!e_Q/$//&Q-2P2PPHQKNNq!a% Q QA%a[Q/$//&Q-2P2PPHQKNNq!a% X XA1a!e__ X X!$Xa!e_Q%7!%;%-a[Q%7!%;%-a!e_QU%;*...AqSTu *V*V&W"X"X A X {1~rc,d|_|j}|j}t t |D]8}||}||}|||}|xj|z c_9|jS)a Returns the total edit distance as an Int between the two scores This function is based on James H. Martin's minimum edit distance. >>> omrPath = omr.correctors.K525omrShortPath >>> ground = omr.correctors.K525groundTruthShortPath >>> omrGTP = omr.evaluators.OmrGroundTruthPair(omr=omrPath, ground=ground) >>> differences = omrGTP.getDifferences() >>> differences 32 r)r rr$rr>rArG)romrListgtListpartNumomrPartgtPart measureErrorss rgetDifferencesz!OmrGroundTruthPair.getDifferencess$% -,,..!..00S\\** 6 6Gg&GG_F ,,Wf==M  $ $ 5 $ $ $''r)NN)__name__ __module__ __qualname____doc__rpropertyrsetterr"r%rrr0r4r7rGrOr-rrrrs1111*))X) \))\)111(((;;;&>>>J  .(((((rrNc|t}d}t||}|rtd||}n|}|rtd||j}|} |rtdg} d} d} |durh| j|} | }|r0td |td | j|j| ntt| jD]}| j|} | }| t|z } | }| || t| j|jz } |rLtd td td | tdtd| }|rLtd td td|tdtd| | |}|rtdt||}|rItd|td| td| | j||| | d}|S)a Get a dictionary showing the efficacy of the omr.correctors.ScoreCorrector on an OMR Score by comparing it to the GroundTruth. Set debug to True to see a lot of intermediary steps. >>> omrFilePath = omr.correctors.K525omrShortPath >>> groundTruthFilePath = omr.correctors.K525groundTruthShortPath >>> returnDict = omr.evaluators.evaluateCorrectingModel(omrFilePath, groundTruthFilePath) >>> for name in sorted(list(returnDict.keys())): ... (name, returnDict[name]) ('newEditDistance', 20) ('numberOfFlaggedMeasures', 13) ('originalEditDistance', 32) ('totalNumberOfMeasures', 84) Nr2)rrzgetting differenceszOriginal edit distancez2Running Horizontal Model (Prior-based-on-distance)rTzIncorrect measure indices:z Hashed notes:z+for each entry in the array below, we have zt[flagged measure part, flagged measure index, source measure part, source measure index, source measure probability]zHORIZONTAL CORRECTING ARRAYz"**********************************z-Running Vertical Model (Prior-based-on-Parts)zVERTICAL CORRECTING MEASURESz]Finding best from Horizontal and Vertical and replacing flagged measures with source measuresz4done replacing flagged measures with source measuresznew edit distancez%number of flagged measures originallyztotal number of measures)originalEditDistancenewEditDistancenumberOfFlaggedMeasurestotalNumberOfMeasures)rrr'rOr singlePartsgetIncorrectMeasureIndices hashedNotesrunHorizontalCorrectionModelr>rAappendrunVerticalCorrectionModelgenerateCorrectedScorescoreshow)r groundTruthPathroriginalDifferences runOnePartpnomrGTPr myOmrScorescorrectingArrayHorAllPartnumberOfIncorrectMeasuresnumberOfTotalMeasures scorePartincorrectMeasureIndicestempPNcorrectingArrayHorOnePartcorrectingArrayVertAllPart priorScorenewNumberOfDifferences returnDicts revaluateCorrectingModelrvsY& } B GO D D DF % #$$$"$33551 = &(;<<<JA D BCCC " !TM"% "+"F"F"H"H  B .0G H H H /1=#4#@ A A A..0000C ..// L LF f-I&/&J&J&L&L # %-D)E)E E %(1(N(N(P(P % % , ,-F G G G !Sv)>)J%K%K K ! ! ? ;<<< D E E E +-FGGG 2333 =>>>!"!=!=!?!? 1 ;<<< E F F F ,.HIII 2333 0 1 1 1))*CE_``J  DEEE j#2244  !#9::: 57PQQQ (*?@@@  *=%;-F+@BBJ rctj|}|}d}d}|j}t |D]\}}||}t |D]\} } | |vr |dz }d} t |D]\} } | | kr | | krd} n| dur>t t|D]!}||kr ||| }|| krd} n"| dur|dz }||fS)a Essentially it's the ratio of amount of rhythmic similarity within a piece, which gives an upper bound on what the omr.corrector.prior measure should be able to achieve for the flagged measures. If a piece has low rhythmic similarity in general, then there's no way for a correct match to be found within the unflagged measures in the piece. Returns a tuple of the total number of NON-flagged measures and the total number of those measures that have a rhythmic match. Takes in a stream.Score. >>> c = converter.parse(omr.correctors.K525omrShortPath) # first 21 measures >>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c) >>> (totalUnflagged, totalUnflaggedWithMatches) (71, 64) >>> print( float(totalUnflaggedWithMatches) / totalUnflagged ) 0.901... Schoenberg has low autoCorrelation. >>> c = corpus.parse('schoenberg/opus19/movement6') >>> totalUnflagged, totalUnflaggedWithMatches = omr.evaluators.autoCorrelationBestMeasure(c) >>> (totalUnflagged, totalUnflaggedWithMatches) (18, 6) >>> print( float(totalUnflaggedWithMatches) / totalUnflagged ) 0.333... rr2FT)rr)r$r[ enumerater\r>rA) inputScoress allHashes totalMeasures totalMatchesr[pNum pHashArrayincorrectIndicesr<mHashmatchr?nHash otherPNum otherHashs rautoCorrelationBestMeasurer7sj:  ": . .B!!IML.K%i00""j&t,GGII!*-- " "HAu$$$ Q ME&j11  566E>> EE" ~~!&s;'7'7!8!8I D(( )) 4Q 7I E)) $*}}! 5 "6 < ((r__main__)NNF) rS __future__r music21.omrrmusic21rrrrvrrPmainTestr-rrrs#""""""""""" w(w(w(w(w(w(w(w(t=AAFccccLB)B)B)J zNNNGr