3j1!dZddlmZddlmZddlZddlZddlmZddlZddl m Z ddl Z ddl m Z ddl mZdd l mZddlm Zdd lmZej(d ZGd d ej,ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,ZGddej,Z Gd d!ej,Z!Gd"d#ej,Z"Gd$d%ej,Z#Gd&d'ej,Z$Gd(d)ej,Z%Gd*d+ej,Z&Gd,d-ej,Z'Gd.d/ej,Z(Gd0d1ej,Z)Gd2d3ej,Z*Gd4d5ej,Z+Gd6d7ej,Z,Gd8d9ej,Z-Gd:d;ej,Z.Gd<d=ej,Z/Gd>d?ej,Z0Gd@dAej,Z1GdBdCej,Z2GdDdEej,Z3GdFdGej,Z4GdHdIej,Z5GdJdKej,Z6GdLdMej,Z7GdNdOej,Z8GdPdQej,Z9GdRdSej,Z:GdTdUej,Z;GdVdWej,Z<GdXdYej,Z=GdZd[ej,Z>Gd\d]ej,Z?Gd^d_ej,Z@Gd`daej,ZAGdbdcej,ZBGdddeej,ZCGdfdgej,ZDGdhdiej,ZEGdjdkej,ZFGdldmej,ZGGdndoej,ZHGdpdqej,ZIGdrdsej,ZJGdtduej,ZKGdvdwej,ZLGdxdyej,ZMGdzd{ej,ZNGd|d}ej,ZOGd~dej,ZPGddej,ZQGddej,ZRGddej,ZSGddej,ZTGddej,ZUGddej,ZVGddej,ZWGddej,ZXGddej,ZYGddej,ZZGddej,Z[Gddej,Z\Gddej,Z]Gddej,Z^Gddej,Z_Gddej,Z`Gddej,ZaGddej,ZbGddej,ZcGddej,ZdGddej,ZeGddej,ZfGddej,ZgGddej,ZhGddej,ZiGddej,ZjGddej,ZkGddej,ZlGddej,ZmGddej,ZnGddej,ZoGddej,ZpGddej,ZqGd„dej,ZrGdĄdej,ZsGdƄdej,ZtGdȄdej,ZuGdʄdej,ZvGd̄dej,ZwGd΄dej,ZxGdЄdej,ZyGd҄dej,ZzGdԄdej,Z{Gdքdej,Z|Gd؄dej,Z}Gdڄdej,Z~Gd܄de~ZGdބde~ZGdde~ZGdde~ZGdde~ZGdde~ZGdde~ZGdde~ZGdde~ZGdde~ZGddejZeddebecedeegfddeteuevewexeyeze{e|e}eeeeeeeeeegfddeeeeeeeeee e!e"e#e$e%de&e'e(dgfdde)e*e+e,e-e.e/e0e1e2e3e4e5e6e7e8e9e:e;ee?e@eAdgfdgdeCeDeEeFeGeHeIeJeKeLeMeNeOePeQdeReSeTeUeVeWeXeYeZdddde[e\e]e^e_e`eafddefegeheiejekelemeneodepeqderddddesgfdgdfgZdZgeeeeeeeeee e!e"e#e$e%e&e'e(eteveye{eeeeeeeeeeeQeReSeTeUeVeWeXeYeZe[e\e]e^e_e`eaefegehe)e*e+e,e-e.e/e0e1e2e3e4e5e6e7e8e;e<e=e>ZdZGdde jZedk(rddl Z e j$eyy)z The features implemented here are based on those found in jSymbolic and defined in Cory McKay's MA Thesis, "Automatic Genre Classification of MIDI Recordings" ) annotations) OrderedDictN)isclose)dedent)base) environment) exceptions21) Instrumentzfeatures.jSymbolicc.eZdZdZdZdfd ZdZxZS)MelodicIntervalHistogramFeatureaE A features array with bins corresponding to the values of the melodic interval histogram. 128 dimensions >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MelodicIntervalHistogramFeature(s) >>> f = fe.extract() >>> f.vector[0:5] [0.144..., 0.220..., 0.364..., 0.062..., 0.050...] M1c nt|dd|i|d|_d|_d|_d|_d|_y)N dataOrStreamzMelodic Interval HistogramzYA features array with bins corresponding to the values of the melodic interval histogram.Tsuper__init__name description isSequential dimensions normalizeselfrkeywords __class__s G/DATA/.local/lib/python3.12/site-packages/music21/features/jSymbolic.pyrz(MelodicIntervalHistogramFeature.__init__6s@ ?l?h?0 L cvt|jdD]\}}||jj|< y)E Do processing necessary, storing result in feature. midiIntervalHistogramN) enumeratedatafeaturevector)rivalues rprocessz'MelodicIntervalHistogramFeature.process@s6"$)),C"DEHAu%*DLL   "FrN__name__ __module__ __qualname____doc__idrr) __classcell__rs@rr r (s  B+rr c.eZdZdZdZdfd ZdZxZS)AverageMelodicIntervalFeaturez Average melodic interval (in semitones). >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.AverageMelodicIntervalFeature(s) >>> f = fe.extract() >>> f.vector [2.44...] M2c `t|dd|i|d|_d|_d|_d|_y)NrzAverage Melodic Intervalz(Average melodic interval (in semitones).Trrrrrrrrs rrz&AverageMelodicIntervalFeature.__init__Ts6 ?l?h?. E rcg}|jd}t|D]&\}}t|D]}|j|(|s t dt |t |z |jjd<yr!r"input lacks notesrN) r$r#rangeappendJSymbolicFeatureExceptionsumlenr%r&)rvalueshistor'r(js rr)z%AverageMelodicIntervalFeature.process\sw 12!%(HAu5\ a ")+,?@ @!$Vs6{!: Arr*r+r2s@rr4r4Hs B ;rr4c.eZdZdZdZdfd ZdZxZS) MostCommonMelodicIntervalFeaturez >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonMelodicIntervalFeature(s) >>> f = fe.extract() >>> f.vector [2] M3c `t|dd|i|d|_d|_d|_d|_y)NrzMost Common Melodic Intervalz,Melodic interval with the highest frequency.Tr7rr8rs rrz)MostCommonMelodicIntervalFeature.__init__vs6 ?l?h?2 I rc|jd}t|}|j|}||jjd<yr!r"rN)r$maxindexr%r&)rrBmaxValuemaxIndexs rr)z(MostCommonMelodicIntervalFeature.process~s>  12u:;;x(!) Arr*r+r2s@rrErEks B*rrEc.eZdZdZdZdfd ZdZxZS)0DistanceBetweenMostCommonMelodicIntervalsFeaturez >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.DistanceBetweenMostCommonMelodicIntervalsFeature(s) >>> f = fe.extract() >>> f.vector [1] M4c `t|dd|i|d|_d|_d|_d|_y)Nrz.Distance Between Most Common Melodic IntervalszvAbsolute value of the difference between the most common melodic interval and the second most common melodic interval.Tr7rr8rs rrz9DistanceBetweenMostCommonMelodicIntervalsFeature.__init__s: ?l?h?D 7!rc tj|jd}t|}|j |}d||<t|}|j |}t ||z |j jd<yrI)copydeepcopyr$rJrKabsr%r&)rrBrLrM secondValue secondIndexs rr)z8DistanceBetweenMostCommonMelodicIntervalsFeature.processsq  dii(?@Au:;;x(h%j kk+. !$X %;!< Arr*r+r2s@rrOrOs B =rrOc.eZdZdZdZdfd ZdZxZS)*MostCommonMelodicIntervalPrevalenceFeaturez Fraction of melodic intervals that belong to the most common interval. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonMelodicIntervalPrevalenceFeature(s) >>> f = fe.extract() >>> f.vector [0.364...] M5c `t|dd|i|d|_d|_d|_d|_y)Nrz'Most Common Melodic Interval PrevalencezFFraction of melodic intervals that belong to the most common interval.Tr7rr8rs rrz3MostCommonMelodicIntervalPrevalenceFeature.__init__s6 ?l?h?= c rctj|jd}t|}t |}|s t d||z |j jd<yr:)rSrTr$rJr?r>r%r&)rrBrLcounts rr)z2MostCommonMelodicIntervalPrevalenceFeature.processsV  dii(?@Au:E +,?@ @!)E!1 Arr*r+r2s@rrYrYs B 2rrYc.eZdZdZdZdfd ZdZxZS),RelativeStrengthOfMostCommonIntervalsFeaturez >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.RelativeStrengthOfMostCommonIntervalsFeature(s) >>> f = fe.extract() >>> f.vector [0.603...] M6c `t|dd|i|d|_d|_d|_d|_y)Nrz*Relative Strength of Most Common IntervalszFraction of melodic intervals that belong to the second most common interval divided by the fraction of melodic intervals belonging to the most common interval.Tr7rr8rs rrz5RelativeStrengthOfMostCommonIntervalsFeature.__init__s; ?l?h?@ c!rctj|jd}t|}t |}|j |}d||<t |}|s t d||z ||z z |jjd<y)r!r"rr;N) rSrTr$r?rJrKr>r%r&)rrBr]rLrMrVs rr)z4RelativeStrengthOfMostCommonIntervalsFeature.processs  dii(?@AE u:;;x(h%j +,?@ @"-"5(U:J!K Arr*r+r2s@rr_r_s BLrr_c.eZdZdZdZdfd ZdZxZS)%NumberOfCommonMelodicIntervalsFeaturez Number of melodic intervals that represent at least 9% of all melodic intervals. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.NumberOfCommonMelodicIntervalsFeature(s) >>> f = fe.extract() >>> f.vector [3] M7c `t|dd|i|d|_d|_d|_d|_y)Nrz"Number of Common Melodic IntervalszPNumber of melodic intervals that represent at least 9% of all melodic intervals.Tr7rr8rs rrz.NumberOfCommonMelodicIntervalsFeature.__init__s9 ?l?h?8 D rc|jd}t|}|s tdd}t|D]\}}||z dk\s|dz }||jj d<y)r!r"r;r ףp= ?r7N)r$r?r>r#r%r&)rrBtotalpostr'r]s rr)z-NumberOfCommonMelodicIntervalsFeature.process sn 12E +,?@ @!%(HAuu}$ )"& Arr*r+r2s@rrdrds B &rrdc.eZdZdZdZdfd ZdZxZS)AmountOfArpeggiationFeaturea Fraction of horizontal intervals that are repeated notes, minor thirds, major thirds, perfect fifths, minor sevenths, major sevenths, octaves, minor tenths or major tenths. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.AmountOfArpeggiationFeature(s) >>> f = fe.extract() >>> f.name 'Amount of Arpeggiation' >>> f.vector [0.333...] M8c `t|dd|i|d|_d|_d|_d|_y)NrzAmount of ArpeggiationzFraction of horizontal intervals that are repeated notes, minor thirds, major thirds, perfect fifths, minor sevenths, major sevenths, octaves, minor tenths or major tenths.Tr7rr8rs rrz$AmountOfArpeggiationFeature.__init__*s; ?l?h?, U!rc|jd}t|}|dk(rygd}t|}d}|D] }|||z } |s td||z |jjd<y)r!r"rN) r r;r$r?r>r%r&rrBritargetsr]ts rr)z#AmountOfArpeggiationFeature.process4sx 12E  A: 2E A U1X E+,?@ @!& Arr*r+r2s@rrlrls  B/rrlc.eZdZdZdZdfd ZdZxZS)RepeatedNotesFeaturez Fraction of notes that are repeated melodically >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.RepeatedNotesFeature(s) >>> f = fe.extract() >>> f.vector [0.144...] M9c `t|dd|i|d|_d|_d|_d|_y)NrzRepeated Notesz0Fraction of notes that are repeated melodically.Tr7rr8rs rrzRepeatedNotesFeature.__init__Ss6 ?l?h?$ M rc|jd}t|}|dk(rydg}t|}|s tdd}|D] }|||z } ||z |jjd<y)r!r"rNr;rxrys rr)zRepeatedNotesFeature.process[sz 12E  A: #E +,?@ @A U1X E!& Arr*r+r2s@rr}r}Gs B/rr}c.eZdZdZdZdfd ZdZxZS)ChromaticMotionFeaturez Fraction of melodic intervals corresponding to a semitone. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.ChromaticMotionFeature(s) >>> f = fe.extract() >>> f.vector [0.220...] m10c `t|dd|i|d|_d|_d|_d|_y)NrzChromatic Motionz;Fraction of melodic intervals corresponding to a semi-tone.Tr7rr8rs rrzChromaticMotionFeature.__init__zs6 ?l?h?& X rc|jd}t|}|s tddg}d}|D] }|||z } ||z |jjd<y)r!r"r;r7rNrxrys rr)zChromaticMotionFeature.processg 12E +,?@ @#A U1X E!& Arr*r+r2s@rrrn B /rrc.eZdZdZdZdfd ZdZxZS)StepwiseMotionFeaturez Fraction of melodic intervals that corresponded to a minor or major second >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.StepwiseMotionFeature(s) >>> f = fe.extract() >>> f.vector [0.584...] M11c `t|dd|i|d|_d|_d|_d|_y)NrzStepwise MotionzKFraction of melodic intervals that corresponded to a minor or major second.Tr7rr8rs rrzStepwiseMotionFeature.__init__s8 ?l?h?% : rc|jd}t|}|s tdddg}d}|D] }|||z } ||z |jjd<y)r!r"r;r7rNrxrys rr)zStepwiseMotionFeature.processi 12E +,?@ @a&A U1X E!& Arr*r+r2s@rrrs B /rrc.eZdZdZdZdfd ZdZxZS)MelodicThirdsFeaturez Fraction of melodic intervals that are major or minor thirds >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MelodicThirdsFeature(s) >>> f = fe.extract() >>> f.vector [0.113...] M12c `t|dd|i|d|_d|_d|_d|_y)NrzMelodic Thirdsz=Fraction of melodic intervals that are major or minor thirds.Tr7rr8rs rrzMelodicThirdsFeature.__init__s6 ?l?h?$ Z rc|jd}t|}|s tdddg}d}|D] }|||z } ||z |jjd<y)r!r"r;rprqrNrxrys rr)zMelodicThirdsFeature.processrrr*r+r2s@rrrrrrc.eZdZdZdZdfd ZdZxZS)MelodicFifthsFeaturez Fraction of melodic intervals that are perfect fifths >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MelodicFifthsFeature(s) >>> f = fe.extract() >>> f.vector [0.056...] M13c `t|dd|i|d|_d|_d|_d|_y)NrzMelodic Fifthsz6Fraction of melodic intervals that are perfect fifths.Tr7rr8rs rrzMelodicFifthsFeature.__init__s6 ?l?h?$ S rc|jd}t|}|s tddg}d}|D] }|||z } ||z |jjd<y)r!r"r;rrrNrxrys rr)zMelodicFifthsFeature.processrrr*r+r2s@rrrrrrc.eZdZdZdZdfd ZdZxZS)MelodicTritonesFeaturez Fraction of melodic intervals that are tritones >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MelodicTritonesFeature(s) >>> f = fe.extract() >>> f.vector [0.012...] M14c `t|dd|i|d|_d|_d|_d|_y)NrzMelodic Tritonesz0Fraction of melodic intervals that are tritones.Tr7rr8rs rrzMelodicTritonesFeature.__init__ s6 ?l?h?& M rc|jd}t|}|s tddg}d}|D] }|||z } ||z |jjd<y)r!r"r;rNrxrys rr)zMelodicTritonesFeature.processrrr*r+r2s@rrrrrrc.eZdZdZdZdfd ZdZxZS)MelodicOctavesFeaturez Fraction of melodic intervals that are octaves >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MelodicOctavesFeature(s) >>> f = fe.extract() >>> f.vector [0.018...] M15c `t|dd|i|d|_d|_d|_d|_y)NrzMelodic Octavesz/Fraction of melodic intervals that are octaves.Tr7rr8rs rrzMelodicOctavesFeature.__init__/s6 ?l?h?% L rc|jd}t|}|s tdgd}d}|D] }|||z } ||z |jjd<y)r!r"r;) ru0<HT`lxrNrxrys rr)zMelodicOctavesFeature.process7se 12E +,?@ @8A U1X E!& Arr*r+r2s@rrr#rrrc.eZdZdZdZdfd ZdZxZS)DirectionOfMotionFeaturea  Returns the fraction of melodic intervals that are rising rather than falling. Unisons are omitted. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.DirectionOfMotionFeature(s) >>> f = fe.extract() >>> f.vector [0.470...] m17c `t|dd|i|d|_d|_d|_d|_y)NrzDirection of MotionzBFraction of melodic intervals that are rising rather than falling.Tr7rr8rs rrz!DirectionOfMotionFeature.__init__Ts6 ?l?h?) _ rcd}d}g}|jjdkDrKt|jjD](}|jd|d}|j|*n |jd}|j||D]}|D]}|dkDr|dz }|dks|dz }!|s |s t d|||zz |j j d<y)r!rparts contourListr7r;N)r$ partsCountr<r=r>r%r&)rrisingfallingcBundler'cListcs rr)z DirectionOfMotionFeature.process\s 99  ! #499//0 '*1-m<u%1IIm,E NN5 !Eq5aKFUqLG  6+,?@ @!'7V+;!< Arr*r+r2s@rrrGs  B=rrc.eZdZdZdZdfd ZdZxZS)DurationOfMelodicArcsFeaturea Average number of notes that separate melodic peaks and troughs in any part. This is calculated as the total number of intervals (not counting unisons) divided by the number of times the melody changes direction. Example: C D E D C D E C C Intervals: [0] 2 2 -2 -2 2 2 -4 0 Changes direction (equivalent to +/- sign) three times. There are seven non-unison (nonzero) intervals. Thus, the duration of arcs is 7/3 ~= 2.333... >>> s = converter.parse("tinyNotation: c' d' e' d' c' d' e'2 c'2 c'2") >>> fe = features.jSymbolic.DurationOfMelodicArcsFeature(s) >>> fe.extract().vector [2.333...] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.DurationOfMelodicArcsFeature(s) >>> fe.extract().vector [1.74...] M18c `t|dd|i|d|_d|_d|_d|_y)NrzDuration of Melodic ArcszAverage number of notes that separate melodic peaks and troughs in any part. This is calculated as the total number of intervals (not counting unisons) divided by the number of times the melody changes direction.Tr7rr8rs rrz%DurationOfMelodicArcsFeature.__init__s; ?l?h?. S!rcg}|jjdkDrKt|jjD](}|jd|d}|j|*n |jd}|j|d}d}d}d}d}|D]K}|} |D]B} | dk7r|dz }| |k(r| dks|dz }|} | |k(r| dkDs+|dz }|} 3| dkDr|} ;| dksA|} DM|dk(rd} n||z } | |jj d<yr!rrrr7N)r$rr<r=r%r&) rrr'rdirection_changesnonUnison_intervals ASCENDING DESCENDING STATIONARYcurrent_directionintervalduration_of_melodic_arcss rr)z$DurationOfMelodicArcsFeature.processsG 99  ! #499//0 '*1-m<u%1IIm,E NN5 !   E * !q='1,'$ 1!|)Q.),6)&*4!|)Q.),5)!|,5)!A,6)"&  !'( $':=N'N $!9 Arr*r+r2s@rrrvs, B /:rrc.eZdZdZdZdfd ZdZxZS)SizeOfMelodicArcsFeatureaE Average span (in semitones) between melodic peaks and troughs in any part. Each time the melody changes direction begins a new arc. The average size of melodic arcs is defined as the total size of melodic intervals between changes of directions - or between the start of the melody and the first change of direction - divided by the number of direction changes. Example: C D E D C E D C C Intervals: [0] 2 2 -2 -2 2 2 -4 0 Changes direction (equivalent to +/- sign) three times. The total sum of interval distance up to the last change of direction is 12. We don't count the last interval, the descending major third, because it is not between changes of direction. Thus, the average size of melodic arcs is 12/3 = 4. >>> s = converter.parse("tinyNotation: c' d' e' d' c' d' e'2 c'2 c'2") >>> fe = features.jSymbolic.SizeOfMelodicArcsFeature(s) >>> fe.extract().vector [4.0] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.SizeOfMelodicArcsFeature(s) >>> fe.extract().vector [4.84...] M19c `t|dd|i|d|_d|_d|_d|_y)NrzSize of Melodic ArcsaeAverage span (in semitones) between melodic peaks and troughs in any part. Each time the melody changes direction begins a new arc. The average size ofmelodic arcs is defined as the total size of melodicintervals between changes of directions - or betweenthe start of the melody and the first change ofdirection - divided by the number of direction changes.Tr7rr8rs rrz!SizeOfMelodicArcsFeature.__init__s; ?l?h?* V!rcg}|jjdkDrKt|jjD](}|jd|d}|j|*n |jd}|j|d}d}d}d}d}|D]}|} d} |D]} | |k(r2| dkDr| t | z } | dks"|| z }|dz }|} t | } :| |k(r2| dkr| t | z } S| dkDsY|| z }|dz }|} t | } q| dkDr|} | t | z } | dks|} | t | z } |dk(rd} n||z } | |j j d<yr)r$rr<r=rUr%r&) rrr'rrsum_of_intervalsrrrrthis_arc_intervalrsize_of_melodic_arcss rr)z SizeOfMelodicArcsFeature.processs 99  ! #499//0 '*1-m<u%1IIm,E NN5 !   E *  ! !$ 1!|)S]:)!A(,==()Q.),6),/M)&*4!|)S]:)!A(,==()Q.),5),/M)!|,5))S]:)!A,6))S]:)7"B  !#$ #36G#G !5 Arr*r+r2s@rrrs6 B =6rrc.eZdZdZdZdfd ZdZxZS) MostCommonPitchPrevalenceFeaturez Fraction of Notes corresponding to the most common pitch. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonPitchPrevalenceFeature(s) >>> fe.extract().vector[0] 0.116... P1c nt|dd|i|d|_d|_d|_d|_d|_y)NrzMost Common Pitch PrevalencezrJrAr?r%r&)rrBpcMaxpcCounts rr)z(MostCommonPitchPrevalenceFeature.processPs\ 67+,?@ @ELLN#elln%!& Arr*r+r2s@rrr<s B 1rrc.eZdZdZdZdfd ZdZxZS)%MostCommonPitchClassPrevalenceFeaturez Fraction of Notes corresponding to the most common pitch class. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonPitchClassPrevalenceFeature(s) >>> fe.extract().vector [0.196...] P2c nt|dd|i|d|_d|_d|_d|_d|_y)Nrz"Most Common Pitch Class PrevalencezBFraction of Note Ons corresponding to the most common pitch class.Tr7Frrrs rrz.MostCommonPitchClassPrevalenceFeature.__init__js= ?l?h?8 _  rc|jd}|jt|}t|}|s t d|||z |j j d<yr!pitches.pitchClassHistogramr;rN)r$rKrJr?r>r%r&)rrBpcrs rr)z-MostCommonPitchClassPrevalenceFeature.processss\ 78[[U $e*+,?@ @!&rW!4 Arr*r+r2s@rrr_s B 5rrc.eZdZdZdZdfd ZdZxZS)#RelativeStrengthOfTopPitchesFeaturez The frequency of the 2nd most common pitch divided by the frequency of the most common pitch. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.RelativeStrengthOfTopPitchesFeature(s) >>> fe.extract().vector [0.947...] P3c nt|dd|i|d|_d|_d|_d|_d|_y)Nrz Relative Strength of Top Pitchesz]The frequency of the 2nd most common pitch divided by the frequency of the most common pitch.Tr7Frrrs rrz,RelativeStrengthOfTopPitchesFeature.__init__s@ ?l?h?6 Q  rc*|jd} |jddd\}}t|d|dz |jjd<y#t $r t dttf$rd|jjd<YywxYw)r!rrNr7rr;) r$ most_commonfloatr%r&ZeroDivisionErrorr> IndexError ValueErrorrrBpMaxpSeconds rr)z+RelativeStrengthOfTopPitchesFeature.processs 67 )!--a0!4MD'%*71:Q+?%@DLL   "  A+,?@ @J' )%(DLL   " )sAA;BBr*r+r2s@rrrs B )rrc.eZdZdZdZdfd ZdZxZS)(RelativeStrengthOfTopPitchClassesFeaturez >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.RelativeStrengthOfTopPitchClassesFeature(s) >>> fe.extract().vector [0.906...] P4c nt|dd|i|d|_d|_d|_d|_d|_y)Nrz&Relative Strength of Top Pitch ClassesziThe frequency of the 2nd most common pitch class divided by the frequency of the most common pitch class.Tr7Frrrs rrz1RelativeStrengthOfTopPitchClassesFeature.__init__s@ ?l?h?< W  rc tj|jd}|jt |}||}|s t dd||<|jt |}||}||z |j jd<yr)rSrTr$rKrJr>r%r&)rrB pIndexMax pCountMax pIndexSecond pCountSeconds rr)z0RelativeStrengthOfTopPitchClassesFeature.processs  dii(EFGKKE + )$ +,?@ @i{{3u:. \* !- !9 Arr*r+r2s@rrrs B:rrc.eZdZdZdZdfd ZdZxZS)&IntervalBetweenStrongestPitchesFeaturez Absolute value of the difference between the pitches of the two most common MIDI pitches. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.IntervalBetweenStrongestPitchesFeature(s) >>> fe.extract().vector [5] P5c `t|dd|i|d|_d|_d|_d|_y)Nrz"Interval Between Strongest PitcheszYAbsolute value of the difference between the pitches of the two most common MIDI pitches.Tr7rr8rs rrz/IntervalBetweenStrongestPitchesFeature.__init__s9 ?l?h?8 O rc|jd} |jddd\}}t|d|dz |jjd<y#t t f$rd|jjd<YywxYw)r!rrNrr)r$rrUr%r&rrrs rr)z.IntervalBetweenStrongestPitchesFeature.processs 67 )!--a0!4MD'%(d1g)=%>DLL   "J' )%(DLL   " )sAA(A?>A?r*r+r2s@rrrs B )rrc.eZdZdZdZdfd ZdZxZS)+IntervalBetweenStrongestPitchClassesFeaturez >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.IntervalBetweenStrongestPitchClassesFeature(s) >>> fe.extract().vector [5] P6c `t|dd|i|d|_d|_d|_d|_y)Nrz(Interval Between Strongest Pitch ClasseszeAbsolute value of the difference between the pitch classes of the two most common MIDI pitch classes.Tr7rr8rs rrz4IntervalBetweenStrongestPitchClassesFeature.__init__s9 ?l?h?> Q rctj|jd}|jt |}d||<|jt |}t ||z |j jd<yr!rrN)rSrTr$rKrJrUr%r&)rrBrrs rr)z3IntervalBetweenStrongestPitchClassesFeature.processsl dii(EFGKKE + i{{3u:. "%Y%=!> Arr*r+r2s@rrrs B ?rrc.eZdZdZdZdfd ZdZxZS)NumberOfCommonPitchesFeaturez Number of pitches that account individually for at least 9% of all notes. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.NumberOfCommonPitchesFeature(s) >>> fe.extract().vector [3] P7c `t|dd|i|d|_d|_d|_d|_y)NrzNumber of Common PitcheszINumber of pitches that account individually for at least 9% of all notes.Tr7rr8rs rrz%NumberOfCommonPitchesFeature.__init__!s8 ?l?h?. < rc|jd}t|j}d}|jD]}||z dk\s |dz }||jjd<y)r!rrrhr7N)r$r?rAr%r&)rrBrirjr]s rr)z$NumberOfCommonPitchesFeature.process*sc 67ELLN#\\^Eu}$ $"& Arr*r+r2s@rrrs B &rrc.eZdZdZdZdfd ZdZxZS)PitchVarietyFeaturez Number of pitches used at least once. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.PitchVarietyFeature(s) >>> fe.extract().vector [24] P8c `t|dd|i|d|_d|_d|_d|_y)Nrz Pitch Varietyz%Number of pitches used at least once.Tr7rr8rs rrzPitchVarietyFeature.__init__Bs6 ?l?h?# B rc|jd}d}t|D]\}}|dk\s |dz }||jjd<y)r!rrr7Nr$r#r%r&rrBrjr'r]s rr)zPitchVarietyFeature.processJsQ 67!%(HAuz )"& Arr*r+r2s@rr r 7 B &rr c.eZdZdZdZdfd ZdZxZS)PitchClassVarietyFeaturez Number of pitch classes used at least once. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.PitchClassVarietyFeature(s) >>> fe.extract().vector [10] P9c `t|dd|i|d|_d|_d|_d|_y)NrzPitch Class Varietyz+Number of pitch classes used at least once.Tr7rr8rs rrz!PitchClassVarietyFeature.__init__as6 ?l?h?) H rc|jd}d}t|D]\}}|dk\s |dz }||jjd<y)r!rrr7Nrrs rr)z PitchClassVarietyFeature.processisQ 78!%(HAuz )"& Arr*r+r2s@rrrVrrrc.eZdZdZdZdfd ZdZxZS) RangeFeaturez Difference between highest and lowest pitches. In semitones >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.RangeFeature(s) >>> fe.extract().vector [34] P10c `t|dd|i|d|_d|_d|_d|_y)NrRangez.Difference between highest and lowest pitches.Tr7rr8rs rrzRangeFeature.__init__s6 ?l?h? K rc|jd}|s tdt|j}t |j}||z |j j d<yr)r$r>minkeysrJr%r&)rrBminIndexrMs rr)zRangeFeature.processs[ 67+,?@ @uzz|$uzz|$!)H!4 Arr*r+r2s@rrrus B 5rrc.eZdZdZdZdfd ZdZxZS)MostCommonPitchFeaturez Bin label of the most common pitch. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonPitchFeature(s) >>> fe.extract().vector [61] P11c nt|dd|i|d|_d|_d|_d|_d|_y)NrzMost Common Pitchz#Bin label of the most common pitch.Tr7Frrrs rrzMostCommonPitchFeature.__init__s= ?l?h?' @  rc|jd} |jddd}||jjd<y#t$rd|jjd<YywxYw)r!rr7rrN)r$rr%r&r)rrB pNumberMaxs rr)zMostCommonPitchFeature.processsi 67 )**1-a03J%/DLL   " )%(DLL   " )s0A"A'&A'r*r+r2s@rr!r!s B )rr!c.eZdZdZdZdfd ZdZxZS)PrimaryRegisterFeaturez Average MIDI pitch. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.PrimaryRegisterFeature(s) >>> fe.extract().vector [61.12...] P12c nt|dd|i|d|_d|_d|_d|_d|_y)NrzPrimary RegisterzAverage MIDI pitch.Tr7Frrrs rrzPrimaryRegisterFeature.__init__s= ?l?h?& 0  rc|jd}|s tdtj|Dcgc]}|jc}|j j d<ycc}w)r!pitchesr;rN)r$r> statisticsmeanpsr%r&)rrBps rr)zPrimaryRegisterFeature.processsT )$+,?@ @!+1F1!$$1F!G A1FsA"r*r+r2s@rr'r's BHrr'c.eZdZdZdZdfd ZdZxZS)ImportanceOfBassRegisterFeaturez Fraction of Notes between MIDI pitches 0 and 54. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.ImportanceOfBassRegisterFeature(s) >>> fe.extract().vector [0.184...] P13c nt|dd|i|d|_d|_d|_d|_d|_y)NrzImportance of Bass Registerz3Fraction of Note Ons between MIDI pitches 0 and 54.Tr7Frrrs rrz(ImportanceOfBassRegisterFeature.__init__s= ?l?h?1 P  rc|jd}|s tdg}|jD]\}}|dks |j|t |}|t |j z |j jd<y)r!rr;6rNr$r>itemsr=r?rAr%r&rrBmatchesr'r] matchedSums rr)z'ImportanceOfBassRegisterFeature.process} 67+,?@ @ HAuBwu%&\ !+c%,,..A!A Arr*r+r2s@rr1r1 BBrr1c.eZdZdZdZdfd ZdZxZS)!ImportanceOfMiddleRegisterFeaturez Fraction of Notes between MIDI pitches 55 and 72 >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.ImportanceOfMiddleRegisterFeature(s) >>> fe.extract().vector [0.766...] P14c nt|dd|i|d|_d|_d|_d|_d|_y)NrzImportance of Middle Registerz4Fraction of Note Ons between MIDI pitches 55 and 72.Tr7Frrrs rrz*ImportanceOfMiddleRegisterFeature.__init__s= ?l?h?3 Q  rc,|jd}|s tdg}|jD]%\}}d|cxkrdksn|j|'t |}|t |j z |j jd<y)r!rr;7rrNr6r8s rr)z)ImportanceOfMiddleRegisterFeature.process s 67+,?@ @ HAuQ}"}u%&\ !+c%,,..A!A Arr*r+r2s@rr>r>r<rr>c.eZdZdZdZdfd ZdZxZS)ImportanceOfHighRegisterFeaturez Fraction of Notes between MIDI pitches 73 and 127. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.ImportanceOfHighRegisterFeature(s) >>> fe.extract().vector [0.049...] P15c nt|dd|i|d|_d|_d|_d|_d|_y)NrzImportance of High Registerz5Fraction of Note Ons between MIDI pitches 73 and 127.Tr7Frrrs rrz(ImportanceOfHighRegisterFeature.__init__(s= ?l?h?1 R  rc|jd}|s tdg}|jD]\}}|dk\s |j|t |}|t |j z |j jd<y)r!rr;IrNr6r8s rr)z'ImportanceOfHighRegisterFeature.process1r;rr*r+r2s@rrDrDr<rrDc.eZdZdZdZdfd ZdZxZS)MostCommonPitchClassFeaturez Bin label of the most common pitch class. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MostCommonPitchClassFeature(s) >>> fe.extract().vector [1] P16c `t|dd|i|d|_d|_d|_d|_y)NrzMost Common Pitch Classz)Bin label of the most common pitch class.Tr7rr8rs rrz$MostCommonPitchClassFeature.__init__Ms6 ?l?h?- F rc|jd}|jt|}||jjd<yrr$rKrJr%r&)rrBrs rr)z#MostCommonPitchClassFeature.processUs9 78KKE + !* Arr*r+r2s@rrJrJBs B+rrJc.eZdZdZdZdfd ZdZxZS)DominantSpreadFeaturez Not implemented Largest number of consecutive pitch classes separated by perfect 5ths that accounted for at least 9% each of the notes. P17c `t|dd|i|d|_d|_d|_d|_y)NrzDominant SpreadzwLargest number of consecutive pitch classes separated by perfect 5ths that accounted for at least 9% each of the notes.Tr7rr8rs rrzDominantSpreadFeature.__init__gs9 ?l?h?% ] rctdNznot yet implementedr>rs rr)zDominantSpreadFeature.processp'(=>>rr*r+r2s@rrPrP^ B?rrPc.eZdZdZdZdfd ZdZxZS)StrongTonalCentresFeaturez Not implemented Number of peaks in the fifths pitch histogram that each account for at least 9% of all Note Ons. P18c `t|dd|i|d|_d|_d|_d|_y)NrzStrong Tonal Centresz`Number of peaks in the fifths pitch histogram that each account for at least 9% of all Note Ons.Tr7rr8rs rrz"StrongTonalCentresFeature.__init__~s8 ?l?h?* ? rctdrTrUrVs rr)z!StrongTonalCentresFeature.processrWrr*r+r2s@rrZrZurXrrZc.eZdZdZdZdfd ZdZxZS)BasicPitchHistogramFeatureaB A feature extractor that finds a features array with bins corresponding to the values of the basic pitch histogram. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.BasicPitchHistogramFeature(s) >>> f = fe.extract() >>> f.vector [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006..., 0.0, 0.0, 0.006..., 0.006..., 0.030..., 0.0, 0.036..., 0.012..., 0.0, 0.006..., 0.018..., 0.061..., 0.0, 0.042..., 0.073..., 0.012..., 0.092..., 0.0, 0.116..., 0.061..., 0.006..., 0.085..., 0.018..., 0.110..., 0.0, 0.042..., 0.055..., 0.0, 0.049..., 0.0, 0.042..., 0.0, 0.0, 0.006..., 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] P19c nt|dd|i|d|_d|_d|_d|_d|_y)NrzBasic Pitch HistogramzTA features array with bins corresponding to the values of the basic pitch histogram.Trrrrs rrz#BasicPitchHistogramFeature.__init__s@ ?l?h?+ C rc|jdjD]\}}||jj|< y)r!rN)r$r7r%r&rr'r]s rr)z"BasicPitchHistogramFeature.processs: ">?EEGHAu%*DLL   "Hrr*r+r2s@rr_r_s* B+rr_c.eZdZdZdZdfd ZdZxZS)PitchClassDistributionFeaturea  A feature array with 12 entries where the first holds the frequency of the bin of the pitch class histogram with the highest frequency, and the following entries holding the successive bins of the histogram, wrapping around if necessary. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.PitchClassDistributionFeature(s) >>> f = fe.extract() >>> f.vector [0.196..., 0.073..., 0.006..., 0.098..., 0.036..., 0.177..., 0.0, 0.085..., 0.134..., 0.018..., 0.171..., 0.0] P20c |t|dd|i|d|_d|_d|_d|_d|_d|_y)NrzPitch Class DistributionzA feature array with 12 entries where the first holds the frequency of the bin of the pitch class histogram with the highest frequency, and the following entries holding the successive bins of the histogram, wrapping around if necessary.TruFr)rrrrrrrrrs rrz&PitchClassDistributionFeature.__init__sI ?l?h?. b! rcdg|jz}t|jdD] \}}|||< |jt |}t|D].\}}||j j ||z |jz<0y)r!rrN)rr#r$rKrJr%r&)rtempr'r]mvals rr)z%PitchClassDistributionFeature.processs sT__$!$)),I"JKHAuDGL JJs4y !oFAs=@DLL  Q$// 9 :&rr*r+r2s@rreres  B  Arrec.eZdZdZdZdfd ZdZxZS)FifthsPitchHistogramFeaturea A feature array with bins corresponding to the values of the 5ths pitch class histogram. Instead of the bins being arranged according to semitones -- [C, C#, D, etc.] -- they are arranged according to the circle of fifths: [C, G, D, A, E, B, F#, C#, G#, D#, A#, F]. Viewing such a histogram may draw attention to the prevalence of a tonal center, including the prevalence of dominant relationships in the piece. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.FifthsPitchHistogramFeature(s) >>> f = fe.extract() >>> f.vector [0.0, 0.0, 0.073..., 0.134..., 0.098..., 0.171..., 0.177..., 0.196..., 0.085..., 0.006..., 0.018..., 0.036...] P21c t|dd|i|d|_d|_d|_d|_d|_i|_tdD]}d|zdz|j|<y)NrzFifths Pitch HistogramzXA feature array with bins corresponding to the values of the 5ths pitch class histogram.Trurrr) rrrrrrr_mappingr<)rrrr'rs rrz$FifthsPitchHistogramFeature.__init__sl ?l?h?, :  rA !A|DMM! rct|jdD]+\}}||jj|j|<-y)r!rN)r#r$r%r&rprcs rr)z#FifthsPitchHistogramFeature.processs?"$)),I"JKHAu49DLL   a 0 1Lrr*r+r2s@rrmrms B ,:rrmc.eZdZdZdZdfd ZdZxZS)QualityFeaturea Set to 0 if the key signature indicates that a recording is major, set to 1 if it indicates that it is minor. In jSymbolic, this is set to 0 if key signature is unknown. See features.native.QualityFeature for a music21 improvement on this method Example: Handel, Rinaldo Aria (musicxml) is explicitly encoded as being in Major: >>> s = corpus.parse('handel/rinaldo/lascia_chio_pianga') >>> fe = features.jSymbolic.QualityFeature(s) >>> f = fe.extract() >>> f.vector [0] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.QualityFeature(s) >>> f = fe.extract() >>> f.vector [1] P22c `t|dd|i|d|_d|_d|_d|_y)NrQualityz Set to 0 if the key signature indicates that a recording is major, set to 1 if it indicates that it is minor and set to 0 if key signature is unknown. Tr7rr8rs rrzQualityFeature.__init__$s: ?l?h?  !rc|jd}d}|D](}|jdk(rd}n|jdk(s&d}n|d}||jjd<y)r!zflat.getElementsByClass(Key)Nmajorrminorr7)r$moder%r&)rallKeys keyFeaturexs rr)zQualityFeature.process0sk)):; Avv  7"    J!+ Arr*r+r2s@rrsrs s* B ,rrsc.eZdZdZdZdfd ZdZxZS)GlissandoPrevalenceFeaturez Not yet implemented in music21 Number of Note Ons that have at least one MIDI Pitch Bend associated with them divided by total number of pitched Note Ons. P23c `t|dd|i|d|_d|_d|_d|_y)NrzGlissando Prevalencez{Number of Note Ons that have at least one MIDI Pitch Bend associated with them divided by total number of pitched Note Ons.Tr7rr8rs rrz#GlissandoPrevalenceFeature.__init__Ls9 ?l?h?* ` rctdrTrUrVs rr)z"GlissandoPrevalenceFeature.processUrWrr*r+r2s@rrrCrXrrc.eZdZdZdZdfd ZdZxZS)AverageRangeOfGlissandosFeaturea( Not yet implemented in music21 Average range of MIDI Pitch Bends, where "range" is defined as the greatest value of the absolute difference between 64 and the second data byte of all MIDI Pitch Bend messages falling between the Note On and Note Off messages of any note P24c `t|dd|i|d|_d|_d|_d|_y)NrzAverage Range Of GlissandoszAverage range of MIDI Pitch Bends, where "range" is defined as the greatest value of the absolute difference between 64 and the second data byte of all MIDI Pitch Bend messages falling between the Note On and Note Off messages of any note.Tr7rr8rs rrz(AverageRangeOfGlissandosFeature.__init__es: ?l?h?1 + !rctdrTrUrVs rr)z'AverageRangeOfGlissandosFeature.processqrWrr*r+r2s@rrrZ B ?rrc.eZdZdZdZdfd ZdZxZS)VibratoPrevalenceFeaturez Not yet implemented in music21 Number of notes for which Pitch Bend messages change direction at least twice divided by total number of notes that have Pitch Bend messages associated with them. P25c `t|dd|i|d|_d|_d|_d|_y)NrzVibrato PrevalencezNumber of notes for which Pitch Bend messages change direction at least twice divided by total number of notes that have Pitch Bend messages associated with them.Tr7rr8rs rrz!VibratoPrevalenceFeature.__init__s; ?l?h?( R!rctdrTrUrVs rr)z VibratoPrevalenceFeature.processrWrr*r+r2s@rrrv B?rrc.eZdZdZdZdfd ZdZxZS)PrevalenceOfMicrotonesFeaturez not yet implemented Number of Note Ons that are preceded by isolated MIDI Pitch Bend messages as a fraction of the total number of Note Ons.' P26c `t|dd|i|d|_d|_d|_d|_y)NrzPrevalence Of MicrotoneszxNumber of Note Ons that are preceded by isolated MIDI Pitch Bend messages as a fraction of the total number of Note Ons.Tr7rr8rs rrz&PrevalenceOfMicrotonesFeature.__init__sB  %l %# %/ [ rctdrTrUrVs rr)z%PrevalenceOfMicrotonesFeature.processrWrr*r+r2s@rrrrrrc.eZdZdZdZdfd ZdZxZS)StrongestRhythmicPulseFeaturea Bin label of the beat bin of the peak with the highest frequency. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.StrongestRhythmicPulseFeature(sch) >>> f = fe.extract() >>> f.vector[0] 140 >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.StrongestRhythmicPulseFeature(s) >>> f = fe.extract() >>> f.vector [96] R1c `t|dd|i|d|_d|_d|_d|_y)NrzStrongest Rhythmic Pulsez5Bin label of the beat bin with the highest frequency.Tr7rr8rs rrz&StrongestRhythmicPulseFeature.__init__s6 ?l?h?. R rc|jd}|jt||jjd<yNzflat.secondsMap.beatHistogramrrNr beatHistos rr)z%StrongestRhythmicPulseFeature.processs2II=> !*Y!@ Arr*r+r2s@rrrs$ BArrc.eZdZdZdZdfd ZdZxZS)#SecondStrongestRhythmicPulseFeaturea Bin label of the beat bin of the peak with the second-highest frequency. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.SecondStrongestRhythmicPulseFeature(sch) >>> f = fe.extract() >>> f.vector[0] 70 >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.SecondStrongestRhythmicPulseFeature(s) >>> f = fe.extract() >>> f.vector [192] R2c `t|dd|i|d|_d|_d|_d|_y)NrzSecond Strongest Rhythmic PulsezHBin label of the beat bin of the peak with the second highest frequency.Tr7rr8rs rrz,SecondStrongestRhythmicPulseFeature.__init__s9 ?l?h?5 A rctj|jd}|jt|}d||<|jt||jj d<yr)rSr$rKrJr%r&)rr highestIndexs rr)z+SecondStrongestRhythmicPulseFeature.processsWIIdii(GHI  s9~6 "# ,!*Y!@ Arr*r+r2s@rrrs$ BArrc.eZdZdZdZdfd ZdZxZS).HarmonicityOfTwoStrongestRhythmicPulsesFeatureaf The bin label of the higher (in terms of bin label) of the two beat bins of the peaks with the highest frequency divided by the bin label of the lower. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.HarmonicityOfTwoStrongestRhythmicPulsesFeature(sch) >>> f = fe.extract() >>> f.vector[0] 2.0 >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.HarmonicityOfTwoStrongestRhythmicPulsesFeature(s) >>> f = fe.extract() >>> f.vector [0.5] R3c `t|dd|i|d|_d|_d|_d|_y)Nrz,Harmonicity of Two Strongest Rhythmic PulseszThe bin label of the higher (in terms of bin label) of the two beat bins of the peaks with the highest frequency divided by the bin label of the lower.Tr7rr8rs rrz7HarmonicityOfTwoStrongestRhythmicPulsesFeature.__init__ s; ?l?h?B E!rctj|jd}|jt|}d||<|jt|}t ||z |j j d<yr)rSr$rKrJrr%r&)rrr secondHighests rr)z6HarmonicityOfTwoStrongestRhythmicPulsesFeature.processsfIIdii(GHI  s9~6 "# ,!I7 !&|m'C!D Arr*r+r2s@rrrs& BErrc.eZdZdZdZdfd ZdZxZS)'StrengthOfStrongestRhythmicPulseFeatureaD Frequency of the beat bin with the highest frequency. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.StrengthOfStrongestRhythmicPulseFeature(sch) >>> fe.extract().vector[0] 0.853... R4c `t|dd|i|d|_d|_d|_d|_y)Nrz$Strength of Strongest Rhythmic Pulsez5Frequency of the beat bin with the highest frequency.Tr7rr8rs rrz0StrengthOfStrongestRhythmicPulseFeature.__init__*s6 ?l?h?: R rc~|jd}t|t|z |jjd<yr)r$rJr?r%r&rs rr)z/StrengthOfStrongestRhythmicPulseFeature.process2s2II=> !$Y#i.!@ Arr*r+r2s@rrrs  BArrc.eZdZdZdZdfd ZdZxZS)-StrengthOfSecondStrongestRhythmicPulseFeaturea] Frequency of the beat bin of the peak with the second-highest frequency. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.StrengthOfSecondStrongestRhythmicPulseFeature(sch) >>> fe.extract().vector[0] 0.121... R5c `t|dd|i|d|_d|_d|_d|_y)Nrz+Strength of Second Strongest Rhythmic PulsezHFrequency of the beat bin of the peak with the second highest frequency.Tr7rr8rs rrz6StrengthOfSecondStrongestRhythmicPulseFeature.__init__Es9 ?l?h?A A rctj|jd}t|}|jt |}d||<t |}||z |j j d<yr)rSr$r?rKrJr%r&)rrsumHistorrs rr)z5StrengthOfSecondStrongestRhythmicPulseFeature.processNsaIIdii(GHI y> s9~6 "# ,I !.!9 Arr*r+r2s@rrr7s  B:rrc.eZdZdZdZdfd ZdZxZS)0StrengthRatioOfTwoStrongestRhythmicPulsesFeaturea The frequency of the higher (in terms of frequency) of the two beat bins corresponding to the peaks with the highest frequency divided by the frequency of the lower. >>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.StrengthRatioOfTwoStrongestRhythmicPulsesFeature(sch) >>> fe.extract().vector[0] 7.0 R6c `t|dd|i|d|_d|_d|_d|_y)Nrz/Strength Ratio of Two Strongest Rhythmic PulseszThe frequency of the higher (in terms of frequency) of the two beat bins corresponding to the peaks with the highest frequency divided by the frequency of the lower.Tr7rr8rs rrz9StrengthRatioOfTwoStrongestRhythmicPulsesFeature.__init__is; ?l?h?E O!rctj|jd}t|}|j|}d||<t|}||z |jj d<yr)rSr$rJrKr%r&)rr theHighestrrs rr)z8StrengthRatioOfTwoStrongestRhythmicPulsesFeature.processss]IIdii(GHI ^  z2 "# ,I !+m!; Arr*r+r2s@rrrYs  B>> sch = corpus.parse('schoenberg/opus19', 2) >>> for p in sch.parts: ... p.insert(0, tempo.MetronomeMark('Langsam', 70)) >>> fe = features.jSymbolic.CombinedStrengthOfTwoStrongestRhythmicPulsesFeature(sch) >>> fe.extract().vector[0] 0.975... R7c `t|dd|i|d|_d|_d|_d|_y)Nrz2Combined Strength of Two Strongest Rhythmic PulseszZThe sum of the frequencies of the two beat bins of the peaks with the highest frequencies.Tr7rr8rs rrz^  z2 "# ,I ",}"<!H Arr*r+r2s@rrr~s  B Irrc.eZdZdZdZdfd ZdZxZS)NumberOfStrongPulsesFeaturez^ Not yet implemented Number of beat peaks with normalized frequencies over 0.1. R8c `t|dd|i|d|_d|_d|_d|_y)NrzNumber of Strong Pulsesz:Number of beat peaks with normalized frequencies over 0.1.Tr7rr8rs rrz$NumberOfStrongPulsesFeature.__init__s6 ?l?h?- W rctdrTrUrVs rr)z#NumberOfStrongPulsesFeature.processrWrr*r+r2s@rrrs B?rrc.eZdZdZdZdfd ZdZxZS)NumberOfModeratePulsesFeaturez^ Not yet implemented Number of beat peaks with normalized frequencies over 0.01. R9c `t|dd|i|d|_d|_d|_d|_y)NrzNumber of Moderate Pulsesz;Number of beat peaks with normalized frequencies over 0.01.Tr7rr8rs rrz&NumberOfModeratePulsesFeature.__init__s6 ?l?h?/ X rctdrTrUrVs rr)z%NumberOfModeratePulsesFeature.processrWrr*r+r2s@rrrs B?rrc(eZdZdZdZdfd ZxZS)%NumberOfRelativelyStrongPulsesFeaturez not yet implemented Number of beat peaks with frequencies at least 30% as high as the frequency of the bin with the highest frequency. R10c `t|dd|i|d|_d|_d|_d|_y)Nrz"Number of Relatively Strong PulseszrNumber of beat peaks with frequencies at least 30% as high as the frequency of the bin with the highest frequency.Tr7rr8rs rrz.NumberOfRelativelyStrongPulsesFeature.__init__s9 ?l?h?8 S rr*r,r-r.r/r0rr1r2s@rrrs Brrc.eZdZdZdZdfd ZdZxZS)RhythmicLoosenessFeaturea8 not yet implemented Average width of beat histogram peaks (in beats per minute). Width is measured for all peaks with frequencies at least 30% as high as the highest peak, and is defined by the distance between the points on the peak in question that are 30% of the height of the peak. R11c rt|dd|i|d|_td|_d|_d|_y)NrzRhythmic Loosenessa: Average width of beat histogram peaks (in beats per minute). Width is measured for all peaks with frequencies at least 30% as high as the highest peak, and is defined by the distance between the points on the peak in question that are 30% of the height of the peak.Tr7rrrrrrrrrs rrz!RhythmicLoosenessFeature.__init__sD ?l?h?( !#@A !rctdrTrUrVs rr)z RhythmicLoosenessFeature.processrWrr*r+r2s@rrrrrrc"eZdZdZdZddZdZy)PolyrhythmsFeaturea Not yet implemented Number of beat peaks with frequencies at least 30% of the highest frequency whose bin labels are not integer multiples or factors (using only multipliers of 1, 2, 3, 4, 6 and 8) (with an accepted error of +/- 3 bins) of the bin label of the peak with the highest frequency. This number is then divided by the total number of beat bins with frequencies over 30% of the highest frequency. R12Nc tjj|fd|i|d|_d|_d|_d|_y)Nr Polyrhythmsa Number of beat peaks with frequencies at least 30% of the highest frequency whose bin labels are not integer multiples or factors (using only multipliers of 1, 2, 3, 4, 6 and 8) (with an accepted error of +/- 3 bins) of the bin label of the peak with the highest frequency. This number is then divided by the total number of beat bins with frequencies over 30% of the highest frequency.Tr7)featuresModuleFeatureExtractorrrrrr)rrrs rrzPolyrhythmsFeature.__init__sL''00 =>J =3; =" .!rctdrTrUrVs rr)zPolyrhythmsFeature.processrWrr*)r,r-r.r/r0rr)rrrrrs  B ?rrc.eZdZdZdZdfd ZdZxZS)RhythmicVariabilityFeaturezi Not yet implemented Standard deviation of the bin values (except the first 40 empty ones). R13c `t|dd|i|d|_d|_d|_d|_y)NrzRhythmic VariabilityzFStandard deviation of the bin values (except the first 40 empty ones).Tr7rr8rs rrz#RhythmicVariabilityFeature.__init__%s6 ?l?h?* c rctdrTrUrVs rr)z"RhythmicVariabilityFeature.process-rWrr*r+r2s@rrrs B?rrc.eZdZdZdZdfd ZdZxZS)BeatHistogramFeaturez Not yet implemented A feature extractor that finds a feature array with entries corresponding to the frequency values of each of the bins of the beat histogram (except the first 40 empty ones). R14c nt|dd|i|d|_d|_d|_d|_d|_y)NrzBeat HistogramzA feature array with entries corresponding to the frequency values of each of the bins of the beat histogram (except the first 40 empty ones).TFrrrs rrzBeatHistogramFeature.__init__<sB ?l?h?$ @! rctdrTrUrVs rr)zBeatHistogramFeature.processGrWrr*r+r2s@rrr2s B ?rrc.eZdZdZdZdfd ZdZxZS)NoteDensityFeaturea Gives the average number of notes per second, taking into account the tempo at any moment in the piece. Unlike jSymbolic, music21 quantizes notes from MIDI somewhat before running this test; this function is meant to be run on encoded MIDI scores rather than recorded MIDI performances. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.NoteDensityFeature(s) >>> f = fe.extract() >>> f.vector [7.244...] R15c `t|dd|i|d|_d|_d|_d|_y)Nrz Note Densityz#Average number of notes per second.Tr7rr8rs rrzNoteDensityFeature.__init__\s6 ?l?h?" @ rc |jd}|Dcgc]}|d }}|j|sd|jjd<yt t ||dz |jjd<ycc}w)Nflat.secondsMapendTimeSecondsrrr)r$sortr%r&rr@r secondsMapbundle end_timess rr)zNoteDensityFeature.processds{YY01 =GGJ&V,-J G%(DLL   "%*3y>%:Yr]%JDLL   " Hs Br*r+r2s@rrrLs  B Krrc.eZdZdZdZdfd ZdZxZS)AverageNoteDurationFeaturea{ Average duration of notes in seconds. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.AverageNoteDurationFeature(s) >>> f = fe.extract() >>> f.vector [0.552...] >>> s.insert(0, tempo.MetronomeMark(number=240)) >>> fe = features.jSymbolic.AverageNoteDurationFeature(s) >>> f = fe.extract() >>> f.vector [0.220858...] R17c `t|dd|i|d|_d|_d|_d|_y)NrzAverage Note Durationz%Average duration of notes in seconds.Tr7rr8rs rrz#AverageNoteDurationFeature.__init__s6 ?l?h?+ B rc|jd}|s tdd}|D] }||dz } |t|z |jjd<yNrr;rdurationSecondsrr$r>r@r%r&)rrrirs rr)z"AverageNoteDurationFeature.processs\YY01 +,?@ @ F V-. .E!!&Z!8 Arr*r+r2s@rrrrs B9rrc.eZdZdZdZdfd ZdZxZS) VariabilityOfNoteDurationFeaturear Standard deviation of note durations in seconds. # In this piece, we have: # 9 half notes or tied pair of quarters # 98 untied quarters or tied pair of eighths # 56 untied eighths # BPM = 120 means a half note is a second. # Mean duration should thus be 0.44171779141104295 # and standard deviation should be 0.17854763448902145 >>> s = corpus.parse('bwv66.6') >>> for p in s.parts: ... p.insert(0, tempo.MetronomeMark(number=120)) >>> fe = features.jSymbolic.VariabilityOfNoteDurationFeature(s) >>> f = fe.extract() >>> f.vector[0] 0.178... R18c `t|dd|i|d|_d|_d|_d|_y)NrzVariability of Note Durationz0Standard deviation of note durations in seconds.Tr7rr8rs rrz)VariabilityOfNoteDurationFeature.__init__s6 ?l?h?2 M rc|jd}|s tdg}|D]}|j|dtj||j j d<y)Nrr;rr)r$r>r=r,pstdevr%r&)rrnote_durationsrs rr)z(VariabilityOfNoteDurationFeature.processsbYY01 +,?@ @ F  ! !&):"; <!!+!2!2>!B Arr*r+r2s@rrrs& BCrrc.eZdZdZdZdfd ZdZxZS)MaximumNoteDurationFeaturez Duration of the longest note (in seconds). >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MaximumNoteDurationFeature(s) >>> f = fe.extract() >>> f.vector [1.25] R19c `t|dd|i|d|_d|_d|_d|_y)NrzMaximum Note Durationz*Duration of the longest note (in seconds).Tr7rr8rs rrz#MaximumNoteDurationFeature.__init__s6 ?l?h?+ G rc|jd}|s tdd}|D]}|d|kDs |d}||jjd<yrr$r>r%r&)rr maxSecondsrs rr)z"MaximumNoteDurationFeature.processs^YY01 +,?@ @  F'(:5#$56 !", Arr*r+r2s@rrrs B,rrc.eZdZdZdZdfd ZdZxZS)MinimumNoteDurationFeaturez Duration of the shortest note (in seconds). >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MinimumNoteDurationFeature(s) >>> f = fe.extract() >>> f.vector [0.3125] R20c `t|dd|i|d|_d|_d|_d|_y)NrzMinimum Note Durationz+Duration of the shortest note (in seconds).Tr7rr8rs rrz#MinimumNoteDurationFeature.__init__s6 ?l?h?+ H rc|jd}|s td|dd}|D]}|d|ks |d}||jjd<y)Nrr;rrr)rr minSecondsrs rr)z"MinimumNoteDurationFeature.processshYY01 +,?@ @]#45  F'(:5#$56 !", Arr*r+r2s@rr r s B ,rr c.eZdZdZdZdfd ZdZxZS)StaccatoIncidenceFeaturea Number of notes with durations of less than a 10th of a second divided by the total number of notes in the recording. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.StaccatoIncidenceFeature(s) >>> f = fe.extract() >>> f.vector [0.0] R21c `t|dd|i|d|_d|_d|_d|_y)NrzStaccato IncidencezuNumber of notes with durations of less than a 10th of a second divided by the total number of notes in the recording.Tr7rr8rs rrz!StaccatoIncidenceFeature.__init__ s9 ?l?h?( a rc|jd}|s tdd}|D]}|ddks |dz }|t|z |jjd<y)Nrr;rrg?r7r)rrr]rs rr)z StaccatoIncidenceFeature.process seYY01 +,?@ @ F'(4/ !"'Z!8 Arr*r+r2s@rrrs  B9rrc.eZdZdZdZdfd ZdZxZS) AverageTimeBetweenAttacksFeatureaI Average time in seconds between Note On events (regardless of channel). >>> s = corpus.parse('bwv66.6') >>> for p in s.parts: ... p.insert(0, tempo.MetronomeMark(number=120)) >>> fe = features.jSymbolic.AverageTimeBetweenAttacksFeature(s) >>> f = fe.extract() >>> print(f.vector) [0.35] R22c `t|dd|i|d|_d|_d|_d|_y)NrzAverage Time Between AttackszGAverage time in seconds between Note On events (regardless of channel).Tr7rr8rs rrz)AverageTimeBetweenAttacksFeature.__init__0 s6 ?l?h?2 d rc|jd}|Dcgc]}|d }}|s td|jg}t|D]E\}}|t |dz k(rn/||dz}||z }t |ddr5|j |Gt|t |z |jjd<ycc}w Nr offsetSecondsr;r7rHz>abs_tolr) r$r>rr#r@rr=r?r%r& rrronsets differencesr'ooNextdifs rr)z(AverageTimeBetweenAttacksFeature.process8 sYY01 8BC f&) C+,?@ @  f%DAqCK!O#1q5ME!)C3T2""3'&"%[!1C 4D!D ADs Cr*r+r2s@rrr! s  BErrc.eZdZdZdZdfd ZdZxZS)&VariabilityOfTimeBetweenAttacksFeaturea Standard deviation of the times, in seconds, between Note On events (regardless of channel). >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.VariabilityOfTimeBetweenAttacksFeature(s) >>> f = fe.extract() >>> print(f.vector) [0.1875] R23c `t|dd|i|d|_d|_d|_d|_y)Nrz#Variability of Time Between Attacksz\Standard deviation of the times, in seconds, between Note On events (regardless of channel).Tr7rr8rs rrz/VariabilityOfTimeBetweenAttacksFeature.__init__Y s9 ?l?h?9 N rc|jd}|Dcgc]}|d }}|s td|jg}t|D]E\}}|t |dz k(rn/||dz}||z }t |ddr5|j |Gtj||jjd<ycc}wr) r$r>rr#r@rr=r,rr%r&rs rr)z.VariabilityOfTimeBetweenAttacksFeature.processb sYY01 8BC f&) C+,?@ @  f%DAqCK!O#1q5ME!)C3T2""3'&",!2!2;!? ADs Cr*r+r2s@rr$r$L s B@rr$c.eZdZdZdZdfd ZdZxZS),AverageTimeBetweenAttacksForEachVoiceFeaturea Average of average times in seconds between Note On events on individual channels that contain at least one note. >>> s = corpus.parse('bwv66.6') >>> for p in s.parts: ... p.insert(0, tempo.MetronomeMark(number=120)) >>> fe = features.jSymbolic.AverageTimeBetweenAttacksForEachVoiceFeature(s) >>> f = fe.extract() >>> print(f.vector[0]) 0.442... R24c `t|dd|i|d|_d|_d|_d|_y)Nrz+Average Time Between Attacks For Each VoicezqAverage of average times in seconds between Note On events on individual channels that contain at least one note.Tr7rr8rs rrz5AverageTimeBetweenAttacksForEachVoiceFeature.__init__ s9 ?l?h?A U rc g}g}|jjdkDr]t|jjD]:}|jd|d}|Dcgc]}|d }}|j|<n2|jd}|Dcgc]}|d }}|j||D]}|j g}t |D]E\}}|t |dz k(rn/||dz} | |z } t| ddr5|j| G|s td |jt|t |z t|t |z |jjd<ycc}wcc}w Nrrrrr7rrrzat least one part lacks notes) r$rr<r=rr#r@rr>r?r%r&) r onsetsByPart avgByPartr'rrrrr r!r"s rr)z4AverageTimeBetweenAttacksForEachVoiceFeature.process sz  99  ! #499//0!YYw/23DE @JK f&1 K##F+1 #45J>> s = corpus.parse('bwv66.6') >>> for p in s.parts: ... p.insert(0, tempo.MetronomeMark(number=120)) >>> fe = features.jSymbolic.AverageVariabilityOfTimeBetweenAttacksForEachVoiceFeature(s) >>> f = fe.extract() >>> f.vector [0.177...] R25c `t|dd|i|d|_d|_d|_d|_y)Nrz:Average Variability of Time Between Attacks For Each Voicez}Average standard deviation, in seconds, of time between Note On events on individual channels that contain at least one note.Tr7rr8rs rrzBAverageVariabilityOfTimeBetweenAttacksForEachVoiceFeature.__init__ s: ?l?h?P 1!rcg}g}|jjdkDr]t|jjD]:}|jd|d}|Dcgc]}|d }}|j|<n2|jd}|Dcgc]}|d }}|j||D]}|j g}t |D]E\}}|t |dz k(rn/||dz} | |z } t| ddr5|j| G|s td |jtj|t|t |z |jjd<ycc}wcc}wr-)r$rr<r=rr#r@rr>r,rr?r%r&) rr.stdDeviationByPartr'rrrrr r!r"s rr)zAAverageVariabilityOfTimeBetweenAttacksForEachVoiceFeature.process s~  99  ! #499//0!YYw/23DE @JK f&1 K##F+1 #45Jr*r+r2s@rr1r1 s  B=rr1c.eZdZdZdZdfd ZdZxZS)InitialTempoFeaturez Tempo in beats per minute at the start of the recording. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.InitialTempoFeature(s) >>> f = fe.extract() >>> f.vector # a default [96.0] R30c `t|dd|i|d|_d|_d|_d|_y)Nrz Initial Tempoz8Tempo in beats per minute at the start of the recording.Tr7rr8rs rrzInitialTempoFeature.__init__6 s6 ?l?h?# U rc|jd}|dd}|j|jjd<y)NmetronomeMarkBoundariesrr)r$ getQuarterBPMr%r&)rtriplesmms rr)zInitialTempoFeature.process> s;))56 QZ]!#!1!1!3 Arr*r+r2s@rr7r7) s B4rr7c.eZdZdZdZdfd ZdZxZS)InitialTimeSignatureFeaturea= A feature array with two elements. The first is the numerator of the first occurring time signature and the second is the denominator of the first occurring time signature. Both are set to 0 if no time signature is present. >>> s1 = stream.Stream() >>> s1.append(meter.TimeSignature('3/4')) >>> fe = features.jSymbolic.InitialTimeSignatureFeature(s1) >>> fe.extract().vector [3, 4] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.InitialTimeSignatureFeature(s) >>> f = fe.extract() >>> f.vector [4, 4] R31c `t|dd|i|d|_d|_d|_d|_y)NrzInitial Time SignaturezA feature array with two elements. The first is the numerator of the first occurring time signature and the second is the denominator of the first occurring time signature. Both are set to 0 if no time signature is present.Trrr8rs rrz$InitialTimeSignatureFeature.__init__[ s; ?l?h?, \!rc|jd}|sy|d}tjd|g|dj|jj d<|dj |jj d<y)N&flat.getElementsByClass(TimeSignature)rzfound tsr7)r$ environLocal printDebug numeratorr%r& denominator)relementstss rr)z#InitialTimeSignatureFeature.processf so99EF  a[R 01!)!!6!6 A!)!!8!8 Arr*r+r2s@rr@r@F s$ B 9rr@c.eZdZdZdZdfd ZdZxZS)CompoundOrSimpleMeterFeaturea Set to 1 if the initial meter is compound (numerator of time signature is greater than or equal to 6 and is evenly divisible by 3) and to 0 if it is simple (if the above condition is not fulfilled). >>> s1 = stream.Stream() >>> s1.append(meter.TimeSignature('3/4')) >>> fe = features.jSymbolic.CompoundOrSimpleMeterFeature(s1) >>> fe.extract().vector [0] >>> s2 = stream.Stream() >>> s2.append(meter.TimeSignature('9/8')) >>> fe.setData(s2) # change the data >>> fe.extract().vector [1] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.CompoundOrSimpleMeterFeature(s) >>> f = fe.extract() >>> f.vector [0] R32c `t|dd|i|d|_d|_d|_d|_y)NrzCompound Or Simple MeterzSet to 1 if the initial meter is compound (numerator of time signature is greater than or equal to 6 and is evenly divisible by 3) and to 0 if it is simple (if the above condition is not fulfilled).Tr7rr8rs rrz%CompoundOrSimpleMeterFeature.__init__ s; ?l?h?. I!rc|jd}|r0 |dj}|dk(rd|jj d<yyy#tj$rYywxYw)NrDrCompoundr7)r$beatDivisionCountNamer TimeSignatureExceptionr%r&)rrI countNames rr)z$CompoundOrSimpleMeterFeature.process sj99EF  $QK== J&)* ##A&'  66  sAAAr*r+r2s@rrLrLp s0 B  +rrLc.eZdZdZdZdfd ZdZxZS)TripleMeterFeaturea! Set to 1 if numerator of initial time signature is 3, set to 0 otherwise. >>> s1 = stream.Stream() >>> s1.append(meter.TimeSignature('5/4')) >>> fe = features.jSymbolic.TripleMeterFeature(s1) >>> fe.extract().vector [0] >>> s2 = stream.Stream() >>> s2.append(meter.TimeSignature('3/4')) >>> fe.setData(s2) # change the data >>> fe.extract().vector [1] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.TripleMeterFeature(s) >>> f = fe.extract() >>> f.vector [0] R33c `t|dd|i|d|_d|_d|_d|_y)Nrz Triple MeterzISet to 1 if numerator of initial time signature is 3, set to 0 otherwise.Tr7rr8rs rrzTripleMeterFeature.__init__ s8 ?l?h?" 2 rc|jd}|r-|djdk(rd|jjd<yyy)NrDrrpr7r$rGr%r&rrIs rr)zTripleMeterFeature.process s@99EF  --2%&DLL   "38rr*r+r2s@rrUrU s, B'rrUc.eZdZdZdZdfd ZdZxZS)QuintupleMeterFeaturea' Set to 1 if numerator of initial time signature is 5, set to 0 otherwise. >>> s1 = stream.Stream() >>> s1.append(meter.TimeSignature('5/4')) >>> fe = features.jSymbolic.QuintupleMeterFeature(s1) >>> fe.extract().vector [1] >>> s2 = stream.Stream() >>> s2.append(meter.TimeSignature('3/4')) >>> fe.setData(s2) # change the data >>> fe.extract().vector [0] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.QuintupleMeterFeature(s) >>> f = fe.extract() >>> f.vector [0] R34c `t|dd|i|d|_d|_d|_d|_y)NrzQuintuple MeterzISet to 1 if numerator of initial time signature is 5, set to 0 otherwise.Tr7rr8rs rrzQuintupleMeterFeature.__init__ s8 ?l?h?% 2 rc|jd}|r-|djdk(rd|jjd<yyy)NrDrr7rYrZs rr)zQuintupleMeterFeature.process s@99EF  --2%&DLL   "38rr*r+r2s@rr\r\ s, B'rr\c.eZdZdZdZdfd ZdZxZS)ChangesOfMeterFeatureab Returns 1 if the time signature is changed one or more times during the recording. >>> s1 = stream.Stream() >>> s1.append(meter.TimeSignature('3/4')) >>> fe = features.jSymbolic.ChangesOfMeterFeature(s1) >>> fe.extract().vector [0] >>> s2 = stream.Stream() >>> s2.append(meter.TimeSignature('3/4')) >>> s2.append(meter.TimeSignature('4/4')) >>> fe.setData(s2) # change the data >>> fe.extract().vector [1] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.ChangesOfMeterFeature(s) >>> f = fe.extract() >>> f.vector [0] R35c `t|dd|i|d|_d|_d|_d|_y)NrzChanges of MeterzPSet to 1 if the time signature is changed one or more times during the recordingTr7rr8rs rrzChangesOfMeterFeature.__init__ s8 ?l?h?& 9 rc|jd}t|dkry|d}|ddD].}|j|rd|jjd<yy)NrDr7r)r$r@ ratioEqualr%r&)rrIfirstes rr)zChangesOfMeterFeature.process s^99EF x=A   !"A##A&)* ##A&rr*r+r2s@rrbrb s. Brrbc.eZdZdZdZdfd ZdZxZS)DurationFeaturea2 A feature extractor that extracts the duration of the piece in seconds. >>> s = corpus.parse('bwv66.6') >>> for p in s.parts: ... p.insert(0, tempo.MetronomeMark(number=120)) >>> fe = features.jSymbolic.DurationFeature(s) >>> f = fe.extract() >>> f.vector[0] 18.0 R36c nt|dd|i|d|_d|_d|_d|_d|_y)NrDurationz+The total duration in seconds of the music.Fr7rrrs rrzDurationFeature.__init__/ s= ?l?h? H! rc|jd}|s td|Dcgc]}|d }}|j|d|jjd<ycc}w)Nrzinput lacks durationrrr)r$r>rr%r&rs rr)zDurationFeature.process8 seYY01 +,BC C=GGJ&V,-J G!*2 AHs Ar*r+r2s@rrjrj! s  B/rrjc(eZdZdZdZdfd ZxZS)OverallDynamicRangeFeaturezj Not implemented The maximum loudness minus the minimum loudness value. TODO: implement D1c `t|dd|i|d|_d|_d|_d|_y)NrzOverall Dynamic Rangez6The maximum loudness minus the minimum loudness value.Tr7rr8rs rrz#OverallDynamicRangeFeature.__init__Q s6 ?l?h?+ S rr*rr2s@rrprpG s Brrpc(eZdZdZdZdfd ZxZS)VariationOfDynamicsFeaturezh Not implemented Standard deviation of loudness levels of all notes. TODO: implement D2c `t|dd|i|d|_d|_d|_d|_y)NrzVariation of Dynamicsz3Standard deviation of loudness levels of all notes.Tr7rr8rs rrz#VariationOfDynamicsFeature.__init__e s6 ?l?h?+ P rr*rr2s@rrtrtZ s Brrtc(eZdZdZdZdfd ZxZS)%VariationOfDynamicsInEachVoiceFeaturez Not implemented The average of the standard deviations of loudness levels within each channel that contains at least one note. TODO: implement D3c `t|dd|i|d|_d|_d|_d|_y)Nrz#Variation of Dynamics In Each VoiceznThe average of the standard deviations of loudness levels within each channel that contains at least one note.Tr7rr8rs rrz.VariationOfDynamicsInEachVoiceFeature.__init__z s9 ?l?h?9 Z rr*rr2s@rrxrxn s Brrxc(eZdZdZdZdfd ZxZS)&AverageNoteToNoteDynamicsChangeFeaturez Not implemented Average change of loudness from one note to the next note in the same channel (in MIDI velocity units). TODO: implement D4c `t|dd|i|d|_d|_d|_d|_y)Nrz$Average Note To Note Dynamics ChangezgAverage change of loudness from one note to the next note in the same channel (in MIDI velocity units).Tr7rr8rs rrz/AverageNoteToNoteDynamicsChangeFeature.__init__ s9 ?l?h?: L rr*rr2s@rr|r| s Brr|c.eZdZdZdZdfd ZdZxZS)'MaximumNumberOfIndependentVoicesFeaturea Maximum number of different channels in which notes have sounded simultaneously. Here, Parts are treated as channels. >>> s = corpus.parse('handel/rinaldo/lascia_chio_pianga') >>> fe = features.jSymbolic.MaximumNumberOfIndependentVoicesFeature(s) >>> f = fe.extract() >>> f.vector [3] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.MaximumNumberOfIndependentVoicesFeature(s) >>> f = fe.extract() >>> f.vector [4] T1c `t|dd|i|d|_d|_d|_d|_y)Nrz$Maximum Number of Independent VoiceszuMaximum number of different channels in which notes have sounded simultaneously. Here, Parts are treated as channels.Tr7rr8rs rrz0MaximumNumberOfIndependentVoicesFeature.__init__ s9 ?l?h?: ` rcd}|jdD]^}tj}|jD]$}|jD]}|j |&t |t|}`||jjd<y)Nr'chordify.flat.getElementsByClass(Chord)) r$rGroupsr+groupsr=rJr@r%r&rfoundrgr/gSubs rr)z/MaximumNumberOfIndependentVoicesFeature.process sxDEA AYYHHDHHTN%s1v&EF"' Arr*r+r2s@rrr s" B 'rrc.eZdZdZdZdfd ZdZxZS)'AverageNumberOfIndependentVoicesFeaturea Average number of different channels in which notes have sounded simultaneously. Rests are not included in this calculation. Here, Parts are treated as voices >>> s = corpus.parse('handel/rinaldo/lascia_chio_pianga') >>> fe = features.jSymbolic.AverageNumberOfIndependentVoicesFeature(s) >>> f = fe.extract() >>> f.vector [1.528...] >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.AverageNumberOfIndependentVoicesFeature(s) >>> f = fe.extract() >>> f.vector [3.90...] T2c `t|dd|i|d|_d|_d|_d|_y)Nrz$Average Number of Independent VoiceszAverage number of different channels in which notes have sounded simultaneously. Rests are not included in this calculation. Here, Parts are treated as voicesTr7rr8rs rrz0AverageNumberOfIndependentVoicesFeature.__init__ s; ?l?h?: M!rchg}|jdD]c}tj}|jD]$}|jD]}|j |&|j t |e|s tdt|t |z |jjd<y)Nrr;r) r$rrr+rr=r@r>r?r%r&rs rr)z/AverageNumberOfIndependentVoicesFeature.process sDEA AYYHHDHHTN% LLQ F+,?@ @!$Uc%j!8 Arr*r+r2s@rrr s B 9rrc.eZdZdZdZdfd ZdZxZS)-VariabilityOfNumberOfIndependentVoicesFeatureaG Standard deviation of number of different channels in which notes have sounded simultaneously. Rests are not included in this calculation. >>> s = corpus.parse('bwv66.6') >>> fe = features.jSymbolic.VariabilityOfNumberOfIndependentVoicesFeature(s) >>> f = fe.extract() >>> f.vector [0.449...] T3c `t|dd|i|d|_d|_d|_d|_y)Nrz+Variability of Number of Independent VoiceszStandard deviation of number of different channels in which notes have sounded simultaneously. Rests are not included in this calculation.Tr7rr8rs rrz6VariabilityOfNumberOfIndependentVoicesFeature.__init__ s; ?l?h?A @!rcdg}|jdD]c}tj}|jD]$}|jD]}|j |&|j t |e|r-tj||jjd<ytd)Nrrr;) r$rrr+rr=r@r,rr%r&r>rs rr)z5VariabilityOfNumberOfIndependentVoicesFeature.process sDEA AYYHHDHHTN% LLQ F %/%6%6u%=DLL   "+,?@ @rr*r+r2s@rrr s  BArrc(eZdZdZdZdfd ZxZS)!VoiceEqualityNumberOfNotesFeaturez Not implemented TODO: implement Standard deviation of the total number of Note Ons in each channel that contains at least one note. T4c `t|dd|i|d|_d|_d|_d|_y)Nrz Voice Equality - Number of NoteszcStandard deviation of the total number of Note Ons in each channel that contains at least one note.Tr7rr8rs rrz*VoiceEqualityNumberOfNotesFeature.__init__+ s9 ?l?h?6 O rr*rr2s@rrr s Brrc(eZdZdZdZdfd ZxZS) VoiceEqualityNoteDurationFeature/ Not implemented TODO: implement T5c `t|dd|i|d|_d|_d|_d|_y)NrzVoice Equality - Note DurationzmStandard deviation of the total duration of notes in seconds in each channel that contains at least one note.Tr7rr8rs rrz)VoiceEqualityNoteDurationFeature.__init__> s9 ?l?h?4 O rr*rr2s@rrr5  Brrc(eZdZdZdZdfd ZxZS)VoiceEqualityDynamicsFeaturerT6c `t|dd|i|d|_d|_d|_d|_y)NrzVoice Equality - DynamicszbStandard deviation of the average volume of notes in each channel that contains at least one note.Tr7rr8rs rrz%VoiceEqualityDynamicsFeature.__init__Q s9 ?l?h?/ O rr*rr2s@rrrH rrrc(eZdZdZdZdfd ZxZS) VoiceEqualityMelodicLeapsFeaturerT7c rt|dd|i|d|_td|_d|_d|_y)NrzVoice Equality - Melodic Leapsz Standard deviation of the average melodic leap in MIDI pitches for each channel that contains at least one note.Tr7rrrs rrz)VoiceEqualityMelodicLeapsFeature.__init__d sD ?l?h?4 !#AB!rr*rr2s@rrr[ s B  rrc(eZdZdZdZdfd ZxZS)VoiceEqualityRangeFeaturez Not implemented Standard deviation of the differences between the highest and lowest pitches in each channel that contains at least one note. T8c rt|dd|i|d|_td|_d|_d|_y)NrzVoice Equality - Rangez Standard deviation of the differences between the highest and lowest pitches in each channel that contains at least one note.Tr7rrrs rrz"VoiceEqualityRangeFeature.__init__y sD ?l?h?, !#[\!rr*rr2s@rrrp s Brrc(eZdZdZdZdfd ZxZS)ImportanceOfLoudestVoiceFeature. Not implemented TODO: implement T9c rt|dd|i|d|_td|_d|_d|_y)NrzImportance of Loudest Voicez Difference between the average loudness of the loudest channel and the average loudness of the other channels that contain at least one note.Tr7rrrs rrz(ImportanceOfLoudestVoiceFeature.__init__ sB ?l?h?1 !#/0!rr*rr2s@rrr s B  rrc(eZdZdZdZdfd ZxZS)"RelativeRangeOfLoudestVoiceFeaturerT10c rt|dd|i|d|_td|_d|_d|_y)NrzRelative Range of Loudest Voicez Difference between the highest note and the lowest note played in the channel with the highest average loudness divided by the difference between the highest note and the lowest note overall in the piece.Tr7rrrs rrz+RelativeRangeOfLoudestVoiceFeature.__init__ sD ?l?h?5 !#RS!rr*rr2s@rrr  B  rrc(eZdZdZdZdfd ZxZS)RangeOfHighestLineFeaturerT12c rt|dd|i|d|_td|_d|_d|_y)NrzRange of Highest Linez Difference between the highest note and the lowest note played in the channel with the highest average pitch divided by the difference between the highest note and the lowest note in the piece.Tr7rrrs rrz"RangeOfHighestLineFeature.__init__ sD ?l?h?+ !#JK!rr*rr2s@rrr rrrc(eZdZdZdZdfd ZxZS)'RelativeNoteDensityOfHighestLineFeaturerT13c rt|dd|i|d|_td|_d|_d|_y)Nrz%Relative Note Density of Highest Linez Number of Note Ons in the channel with the highest average pitch divided by the average number of Note Ons in all channels that contain at least one note.Tr7rrrs rrz0RelativeNoteDensityOfHighestLineFeature.__init__ sB ?l?h?; !# !rr*rr2s@rrr  B  rrc(eZdZdZdZdfd ZxZS)#MelodicIntervalsInLowestLineFeaturerT15c rt|dd|i|d|_td|_d|_d|_y)Nrz Melodic Intervals in Lowest Linez Average melodic interval in semitones of the channel with the lowest average pitch divided by the average melodic interval of all channels that contain at least two notes.Tr7rrrs rrz,MelodicIntervalsInLowestLineFeature.__init__ sB ?l?h?6 !#9:!rr*rr2s@rrr rrrc(eZdZdZdZdfd ZxZS)VoiceSeparationFeaturez Not implemented Average separation in semitones between the average pitches of consecutive channels (after sorting based/non-average pitch) that contain at least one note. T20c rt|dd|i|d|_td|_d|_d|_y)NrzVoice Separationz Average separation in semi-tones between the average pitches of consecutive channels (after sorting based/non average pitch) that contain at least one note.Tr7rrrs rrzVoiceSeparationFeature.__init__ sB ?l?h?& !# !rr*rr2s@rrr rrrc.eZdZdZdZdfd ZdZxZS) PitchedInstrumentsPresentFeaturea Which pitched General MIDI Instruments are present. There is one entry for each instrument, which is set to 1.0 if there is at least one Note On in the recording corresponding to the instrument and to 0.0 if there is not. >>> s1 = stream.Stream() >>> s1.append(instrument.AcousticGuitar()) >>> s1.append(note.Note()) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.PitchedInstrumentsPresentFeature(s1) >>> fe.extract().vector [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] Default instruments will lack a `.midiProgram`, so they raise exceptions: >>> i = instrument.Instrument() >>> i.midiProgram is None True >>> s2 = stream.Stream() >>> s2.append(i) >>> s2.append(note.Note()) >>> fe2 = features.jSymbolic.PitchedInstrumentsPresentFeature(s2) >>> fe2.extract() Traceback (most recent call last): music21.features.jSymbolic.JSymbolicFeatureException: lacks a midiProgram I1c rt|dd|i|d|_td|_d|_d|_y)NrzPitched Instruments Presenta Which pitched General MIDI Instruments are present. There is one entry for each instrument, which is set to 1.0 if there is at least one Note On in the recording corresponding to the instrument and to 0.0 if there is not.Trrrrs rrz)PitchedInstrumentsPresentFeature.__init__& sB ?l?h?1 !#$% !rc|jd}|s td|jD]}|jtj }|j jsA|j&t|xs t|}t|dd|jj|j<y)r!partitionByInstrumentinput lacks instrumentsN lacks a midiProgramr7) r$r>rgetElementsByClassr rgrecursenotes midiProgramstrreprr%r&)rsr/r'iStrs rr)z(PitchedInstrumentsPresentFeature.process2 s II- .+,EF FA$$Z0668Ayy{  ==(q6,T!WD3tf>> s1 = stream.Stream() >>> s1.append(instrument.AcousticGuitar()) >>> s1.repeatAppend(note.Note(), 4) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.NotePrevalenceOfPitchedInstrumentsFeature(s1) >>> fe.extract().vector [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2..., 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] `.midiProgram` cannot be None: >>> s1.getInstruments().first().midiProgram = None >>> fe2 = features.jSymbolic.NotePrevalenceOfPitchedInstrumentsFeature(s1) >>> fe2.extract() Traceback (most recent call last): music21.features.jSymbolic.JSymbolicFeatureException: Acoustic Guitar lacks a midiProgram I3c `t|dd|i|d|_d|_d|_d|_y)Nrz&Note Prevalence of Pitched InstrumentszThe fraction of (pitched) notes played by each General MIDI Instrument. There is one entry for each instrument, which is set to the number of Note Ons played using the corresponding MIDI patch divided by the total number of Note Ons in the recording.Trrr8rs rrz2NotePrevalenceOfPitchedInstrumentsFeature.__init__ s; ?l?h?< X !rc|jd}t|jd}|s td|jD]}|j t j }|jj}|sC|j&t|xs t|}t|dt||z |jj|j<y)r!rrr;Nr)r$r?r>rrr rgrrrrrr@r%r&)rrrir/r'pNotesrs rr)z1NotePrevalenceOfPitchedInstrumentsFeature.process s II- .DII;<=+,?@ @A$$Z0668AYY[&&F==(q6,T!WD3tf !#=>!rr*rr2s@rrr s B  rrc(eZdZdZdZdfd ZxZS))TimePrevalenceOfPitchedInstrumentsFeaturea Not implemented The fraction of the total time of the recording in which a note was sounding for each (pitched) General MIDI Instrument. There is one entry for each instrument, which is set to the total time in seconds during which a given instrument was sounding one or more notes divided by the total length in seconds of the piece.' TODO: implement I5c `t|dd|i|d|_d|_d|_d|_y)Nrz&Time Prevalence of Pitched Instrumentsa>The fraction of the total time of the recording in which a note was sounding for each (pitched) General MIDI Instrument. There is one entry for each instrument, which is set to the total time in seconds during which a given instrument was sounding one or more notes divided by the total length in seconds of the piece.Trrr8rs rrz2TimePrevalenceOfPitchedInstrumentsFeature.__init__ s; ?l?h?< a !rr*rr2s@rrr s  B  rrc.eZdZdZdZdfd ZdZxZS)6VariabilityOfNotePrevalenceOfPitchedInstrumentsFeaturea Standard deviation of the fraction of Note Ons played by each (pitched) General MIDI instrument that is used to play at least one note. >>> s1 = stream.Stream() >>> s1.append(instrument.AcousticGuitar()) >>> s1.repeatAppend(note.Note(), 5) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.VariabilityOfNotePrevalenceOfPitchedInstrumentsFeature(s1) >>> fe.extract().vector [0.33333...] I6c `t|dd|i|d|_d|_d|_d|_y)Nrz5Variability of Note Prevalence of Pitched InstrumentszStandard deviation of the fraction of Note Ons played by each (pitched) General MIDI instrument that is used to play at least one note.Tr7rr8rs rrz?VariabilityOfNotePrevalenceOfPitchedInstrumentsFeature.__init__ s: ?l?h?K >!rcX|jd}t|jd}|s td|s tdg}|jD]_}|j t j }|jj}|sC|jt||z at|t|z }|Dcgc]}t||z d} }tjt| t| z |jjd<ycc}w)Nrrrr;rr)r$r?r>rrr rgrrr=r@powmathsqrtr%r&) rrricollr/r'rr-npartials rr)z>VariabilityOfNotePrevalenceOfPitchedInstrumentsFeature.process s II- .DII;<=+,EF F+,?@ @A$$Z0668AYY[&&F CK%/0 4y3t9$-12T3q4x#T2!%3w<#g,+F!G A3s D'r*r+r2s@rrr s BHrrc(eZdZdZdZdfd ZxZS)8VariabilityOfNotePrevalenceOfUnpitchedInstrumentsFeaturea[ Not implemented Standard deviation of the fraction of Note Ons played by each (unpitched) MIDI Percussion Key Map instrument that is used to play at least one note. It should be noted that only instruments 35 to 81 are included here, as they are the ones that are included in the official standard. TODO: implement I7c `t|dd|i|d|_d|_d|_d|_y)Nrz7Variability of Note Prevalence of Unpitched InstrumentsaStandard deviation of the fraction of Note Ons played by each (unpitched) MIDI Percussion Key Map instrument that is used to play at least one note. It should be noted that only instruments 35 to 81 are included here, as they are the ones that are included in the official standard.Tr7rr8rs rrzAVariabilityOfNotePrevalenceOfUnpitchedInstrumentsFeature.__init__"s> ?l?h?M  O  !rr*rr2s@rrrs  B  rrc.eZdZdZdZdfd ZdZxZS)!NumberOfPitchedInstrumentsFeaturear Total number of General MIDI patches that are used to play at least one note. >>> s1 = stream.Stream() >>> s1.append(instrument.AcousticGuitar()) >>> s1.append(note.Note()) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.NumberOfPitchedInstrumentsFeature(s1) >>> fe.extract().vector [2] I8c `t|dd|i|d|_d|_d|_d|_y)NrzNumber of Pitched InstrumentszMTotal number of General MIDI patches that are used to play at least one note.Tr7rr8rs rrz*NumberOfPitchedInstrumentsFeature.__init__?s8 ?l?h?3 6 rc|jd}d}|s td|jD]"}|jjs|dz }$||j j d<y)r!rrrr7N)r$r>rrrr%r&)rrr]r/s rr)z)NumberOfPitchedInstrumentsFeature.processHsc II- .+,EF FAyy{   "' Arr*r+r2s@rrr/s  B 'rrc(eZdZdZdZdfd ZxZS)#NumberOfUnpitchedInstrumentsFeaturea Not implemented Number of distinct MIDI Percussion Key Map patches that were used to play at least one note. It should be noted that only instruments 35 to 81 are included here, as they are the ones that are included in the official standard. TODO: implement I9c `t|dd|i|d|_d|_d|_d|_y)NrzNumber of Unpitched InstrumentszNumber of distinct MIDI Percussion Key Map patches that were used to play at least one note. It should be noted that only instruments 35 to 81 are included here, as they are the ones that are included in the official standard.Tr7rr8rs rrz,NumberOfUnpitchedInstrumentsFeature.__init__ds; ?l?h?5 J!rr*rr2s@rrrWs B  rrc(eZdZdZdZdfd ZxZS)PercussionPrevalenceFeaturez Not implemented Total number of Note Ons corresponding to unpitched percussion instruments divided by the total number of Note Ons in the recording. I10c `t|dd|i|d|_d|_d|_d|_y)NrzPercussion PrevalencezTotal number of Note Ons corresponding to unpitched percussion instruments divided by total number of Note Ons in the recording.Tr7rr8rs rrz$PercussionPrevalenceFeature.__init__zs9 ?l?h?+ ` rr*rr2s@rrrps Brrc*eZdZdZdfd ZdZxZS)InstrumentFractionFeaturez TODO: Add description of feature This subclass is in-turn subclassed by all FeatureExtractors that look at the proportional usage of an Instrument c 6t|dd|i|g|_y)Nrr)rr_targetProgramsrs rrz"InstrumentFractionFeature.__init__s" ?l?h? "rc|jd}t|jd}d}|s td|s td|jD]d}|j t j }|j|jvs?|t|jjz }f||z |jjd<y)r!rrrrr;N)r$r?r>rrr rgrr r@rrr%r&)rrrir]r/r's rr)z!InstrumentFractionFeature.processs II- .DII;<=+,EF F+,?@ @A$$Z0668A}} 4 44QYY[..//"' Arr*)r,r-r.r/rr)r1r2s@rrrs" /rrc(eZdZdZdZdfd ZxZS)StringKeyboardFractionFeaturea Fraction of all Note Ons belonging to string keyboard patches (General MIDI patches 1 to 8). >>> s1 = stream.Stream() >>> s1.append(instrument.Piano()) >>> s1.repeatAppend(note.Note(), 9) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.StringKeyboardFractionFeature(s1) >>> fe.extract().vector [0.9...] I11c t|dd|i|d|_d|_d|_d|_t d|_y)NrzString Keyboard Fractionz\Fraction of all Note Ons belonging to string keyboard patches (General MIDI patches 1 to 8).Tr7r)rrrrrrr<r rs rrz&StringKeyboardFractionFeature.__init__sD ?l?h?. = $Qxrr*rr2s@rr r   B ( (rr c(eZdZdZdZdfd ZxZS)AcousticGuitarFractionFeaturea A feature extractor that extracts the fraction of all Note Ons belonging to acoustic guitar patches (General MIDI patches 25 and 26). >>> s1 = stream.Stream() >>> s1.append(instrument.AcousticGuitar()) >>> s1.repeatAppend(note.Note(), 3) >>> s1.append(instrument.Tuba()) >>> s1.append(note.Note()) >>> fe = features.jSymbolic.AcousticGuitarFractionFeature(s1) >>> fe.extract().vector [0.75] I12c rt|dd|i|d|_d|_d|_d|_ddg|_y) NrzAcoustic Guitar Fractionz_Fraction of all Note Ons belonging to acoustic guitar patches (General MIDI patches 25 and 26).Tr7rrrrrrrrr rs rrz&AcousticGuitarFractionFeature.__init__sE ?l?h?. @  "Bxrr*rr2s@rrrrrrc(eZdZdZdZdfd ZxZS)ElectricGuitarFractionFeaturea. >>> s1 = stream.Stream() >>> s1.append(instrument.ElectricGuitar()) >>> s1.repeatAppend(note.Note(), 4) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 4) >>> fe = features.jSymbolic.ElectricGuitarFractionFeature(s1) >>> fe.extract().vector [0.5] I13c t|dd|i|d|_d|_d|_d|_t tdd|_y) NrzElectric Guitar Fractionz^Fraction of all Note Ons belonging to electric guitar patches (General MIDI patches 27 to 32).Tr7 r rrrrrrlistr<r rs rrz&ElectricGuitarFractionFeature.__init__sL ?l?h?. W #E"bM2rr*rr2s@rrrs  B 3 3rrc(eZdZdZdZdfd ZxZS)ViolinFractionFeaturea Fraction of all Note Ons belonging to violin patches (General MIDI patches 41 or 111). >>> s1 = stream.Stream() >>> s1.append(instrument.Violin()) >>> s1.repeatAppend(note.Note(), 2) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 8) >>> fe = features.jSymbolic.ViolinFractionFeature(s1) >>> fe.extract().vector [0.2...] I14c rt|dd|i|d|_d|_d|_d|_ddg|_y) NrzViolin FractionzVFraction of all Note Ons belonging to violin patches (General MIDI patches 41 or 111).Tr7(nrrrs rrzViolinFractionFeature.__init__ sE ?l?h?% @  "Cyrr*rr2s@rr r s  B ) )rr c(eZdZdZdZdfd ZxZS)SaxophoneFractionFeaturea Fraction of all Note Ons belonging to saxophone patches (General MIDI patches 65 through 68). # NOTE: incorrect >>> s1 = stream.Stream() >>> s1.append(instrument.SopranoSaxophone()) >>> s1.repeatAppend(note.Note(), 6) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 4) >>> fe = features.jSymbolic.SaxophoneFractionFeature(s1) >>> print(fe.extract().vector[0]) 0.6 I15c rt|dd|i|d|_d|_d|_d|_gd|_y)NrzSaxophone Fractionz]Fraction of all Note Ons belonging to saxophone patches (General MIDI patches 65 through 68).Tr7)@ABCrrrs rrz!SaxophoneFractionFeature.__init__&sA ?l?h?( D /rr*rr2s@rr&r&s  B 0 0rr&c(eZdZdZdZdfd ZxZS)BrassFractionFeaturea A feature extractor that extracts the fraction of all Note Ons belonging to brass patches (General MIDI patches 57 through 68). TODO: Conflict in source: only does 57-62? >>> s1 = stream.Stream() >>> s1.append(instrument.SopranoSaxophone()) >>> s1.repeatAppend(note.Note(), 6) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 4) >>> fe = features.jSymbolic.BrassFractionFeature(s1) >>> print(fe.extract().vector[0]) 0.4 I16c t|dd|i|d|_d|_d|_d|_t tdd|_y) NrzBrass FractionzYFraction of all Note Ons belonging to brass patches (General MIDI patches 57 through 68).Tr78>rrrs rrzBrassFractionFeature.__init__EsL ?l?h?$ D #E"bM2rr*rr2s@rr.r.2 B 3 3rr.c(eZdZdZdZdfd ZxZS)WoodwindsFractionFeaturea Fraction of all Note Ons belonging to woodwind patches (General MIDI patches 69 through 76). TODO: Conflict in source: does 69-79? >>> s1 = stream.Stream() >>> s1.append(instrument.Flute()) >>> s1.repeatAppend(note.Note(), 3) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 7) >>> fe = features.jSymbolic.WoodwindsFractionFeature(s1) >>> print(fe.extract().vector[0]) 0.3 I17c t|dd|i|d|_d|_d|_d|_t tdd|_y) NrzWoodwinds Fractionz\Fraction of all Note Ons belonging to woodwind patches (General MIDI patches 69 through 76).Tr7DPrrrs rrz!WoodwindsFractionFeature.__init__dsL ?l?h?( D #E"bM2rr*rr2s@rr5r5Qr3rr5c(eZdZdZdZdfd ZxZS) OrchestralStringsFractionFeaturea Fraction of all Note Ons belonging to orchestral strings patches (General MIDI patches 41 or 47). >>> s1 = stream.Stream() >>> s1.append(instrument.Violoncello()) >>> s1.repeatAppend(note.Note(), 4) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 6) >>> fe = features.jSymbolic.OrchestralStringsFractionFeature(s1) >>> print(fe.extract().vector[0]) 0.4 I18c t|dd|i|d|_d|_d|_d|_t tdd|_y) NrzOrchestral Strings FractionzaFraction of all Note Ons belonging to orchestral strings patches (General MIDI patches 41 or 47).Tr7).rrrs rrz)OrchestralStringsFractionFeature.__init__sK ?l?h?1 ? #E"bM2rr*rr2s@rr;r;ps  B 3 3rr;c(eZdZdZdZdfd ZxZS)StringEnsembleFractionFeaturez Not implemented Fraction of all Note Ons belonging to string ensemble patches (General MIDI patches 49 to 52). I19c rt|dd|i|d|_d|_d|_d|_gd|_y)NrzString Ensemble Fractionz^Fraction of all Note Ons belonging to string ensemble patches (General MIDI patches 49 to 52).Tr7)r123rrrs rrz&StringEnsembleFractionFeature.__init__s@ ?l?h?. ? /rr*rr2s@rrArAs B 0 0rrAc(eZdZdZdZdfd ZxZS)!ElectricInstrumentFractionFeaturea Fraction of all Note Ons belonging to electric instrument patches (General MIDI patches 5, 6, 17, 19, 27 through 32, 24 through 40). >>> s1 = stream.Stream() >>> s1.append(instrument.ElectricOrgan()) >>> s1.repeatAppend(note.Note(), 8) >>> s1.append(instrument.Tuba()) >>> s1.repeatAppend(note.Note(), 2) >>> fe = features.jSymbolic.ElectricInstrumentFractionFeature(s1) >>> print(fe.extract().vector[0]) 0.8 I20c rt|dd|i|d|_d|_d|_d|_gd|_y)NrzElectric Instrument Fractionz|Fraction of all Note Ons belonging to electric instrument patches (General MIDI patches 5, 6, 17, 19, 27 to 32 or 34 to 40).Tr7)rqr`rwr!"#$%&'rrrs rrz*ElectricInstrumentFractionFeature.__init__sD ?l?h?2 Y  Drr*rr2s@rrHrHs  B D DrrHc eZdZy)r>N)r,r-r.rrrr>r>srr>DIMPRTC)NNNNNNNNNNNNNNNNNNNNNNNNNNNNNc t||S#t$rtd|t$rtd|d|wxYw)aa Typical usage: >>> t5 = features.jSymbolic.getExtractorByTypeAndNumber('T', 5) >>> t5.__name__ 'VoiceEqualityNoteDurationFeature' >>> bachExample = corpus.parse('bach/bwv66.6') >>> fe = t5(bachExample) Features unimplemented in jSymbolic but documented in the dissertation return None >>> features.jSymbolic.getExtractorByTypeAndNumber('C', 20) is None True Totally unknown features return an exception: >>> features.jSymbolic.getExtractorByTypeAndNumber('L', 900) Traceback (most recent call last): music21.features.jSymbolic.JSymbolicFeatureException: Could not find any jSymbolic features of type L >>> features.jSymbolic.getExtractorByTypeAndNumber('C', 200) Traceback (most recent call last): music21.features.jSymbolic.JSymbolicFeatureException: jSymbolic features of type C do not have number 200 You could also find all the feature extractors this way: >>> fs = features.jSymbolic.extractorsById >>> for k in fs: ... for i in range(len(fs[k])): ... if fs[k][i] is not None: ... n = fs[k][i].__name__ ... if fs[k][i] not in features.jSymbolic.featureExtractors: ... n += ' (not implemented)' ... print(f'{k} {i} {n}') D 1 OverallDynamicRangeFeature (not implemented) D 2 VariationOfDynamicsFeature (not implemented) D 3 VariationOfDynamicsInEachVoiceFeature (not implemented) D 4 AverageNoteToNoteDynamicsChangeFeature (not implemented) I 1 PitchedInstrumentsPresentFeature I 2 UnpitchedInstrumentsPresentFeature (not implemented) I 3 NotePrevalenceOfPitchedInstrumentsFeature I 4 NotePrevalenceOfUnpitchedInstrumentsFeature (not implemented) I 5 TimePrevalenceOfPitchedInstrumentsFeature (not implemented) I 6 VariabilityOfNotePrevalenceOfPitchedInstrumentsFeature I 7 VariabilityOfNotePrevalenceOfUnpitchedInstrumentsFeature (not implemented) I 8 NumberOfPitchedInstrumentsFeature I 9 NumberOfUnpitchedInstrumentsFeature (not implemented) I 10 PercussionPrevalenceFeature (not implemented) I 11 StringKeyboardFractionFeature I 12 AcousticGuitarFractionFeature I 13 ElectricGuitarFractionFeature I 14 ViolinFractionFeature I 15 SaxophoneFractionFeature I 16 BrassFractionFeature I 17 WoodwindsFractionFeature I 18 OrchestralStringsFractionFeature I 19 StringEnsembleFractionFeature I 20 ElectricInstrumentFractionFeature M 1 MelodicIntervalHistogramFeature M 2 AverageMelodicIntervalFeature M 3 MostCommonMelodicIntervalFeature M 4 DistanceBetweenMostCommonMelodicIntervalsFeature M 5 MostCommonMelodicIntervalPrevalenceFeature M 6 RelativeStrengthOfMostCommonIntervalsFeature M 7 NumberOfCommonMelodicIntervalsFeature M 8 AmountOfArpeggiationFeature M 9 RepeatedNotesFeature M 10 ChromaticMotionFeature M 11 StepwiseMotionFeature M 12 MelodicThirdsFeature M 13 MelodicFifthsFeature M 14 MelodicTritonesFeature M 15 MelodicOctavesFeature M 17 DirectionOfMotionFeature M 18 DurationOfMelodicArcsFeature M 19 SizeOfMelodicArcsFeature P 1 MostCommonPitchPrevalenceFeature P 2 MostCommonPitchClassPrevalenceFeature P 3 RelativeStrengthOfTopPitchesFeature P 4 RelativeStrengthOfTopPitchClassesFeature P 5 IntervalBetweenStrongestPitchesFeature P 6 IntervalBetweenStrongestPitchClassesFeature P 7 NumberOfCommonPitchesFeature P 8 PitchVarietyFeature P 9 PitchClassVarietyFeature P 10 RangeFeature P 11 MostCommonPitchFeature P 12 PrimaryRegisterFeature P 13 ImportanceOfBassRegisterFeature P 14 ImportanceOfMiddleRegisterFeature P 15 ImportanceOfHighRegisterFeature P 16 MostCommonPitchClassFeature P 17 DominantSpreadFeature (not implemented) P 18 StrongTonalCentresFeature (not implemented) P 19 BasicPitchHistogramFeature P 20 PitchClassDistributionFeature P 21 FifthsPitchHistogramFeature P 22 QualityFeature P 23 GlissandoPrevalenceFeature (not implemented) P 24 AverageRangeOfGlissandosFeature (not implemented) P 25 VibratoPrevalenceFeature (not implemented) R 1 StrongestRhythmicPulseFeature (not implemented) R 2 SecondStrongestRhythmicPulseFeature (not implemented) R 3 HarmonicityOfTwoStrongestRhythmicPulsesFeature (not implemented) R 4 StrengthOfStrongestRhythmicPulseFeature (not implemented) R 5 StrengthOfSecondStrongestRhythmicPulseFeature (not implemented) R 6 StrengthRatioOfTwoStrongestRhythmicPulsesFeature (not implemented) R 7 CombinedStrengthOfTwoStrongestRhythmicPulsesFeature (not implemented) R 8 NumberOfStrongPulsesFeature (not implemented) R 9 NumberOfModeratePulsesFeature (not implemented) R 10 NumberOfRelativelyStrongPulsesFeature (not implemented) R 11 RhythmicLoosenessFeature (not implemented) R 12 PolyrhythmsFeature (not implemented) R 13 RhythmicVariabilityFeature (not implemented) R 14 BeatHistogramFeature (not implemented) R 15 NoteDensityFeature R 17 AverageNoteDurationFeature R 18 VariabilityOfNoteDurationFeature R 19 MaximumNoteDurationFeature R 20 MinimumNoteDurationFeature R 21 StaccatoIncidenceFeature R 22 AverageTimeBetweenAttacksFeature R 23 VariabilityOfTimeBetweenAttacksFeature R 24 AverageTimeBetweenAttacksForEachVoiceFeature R 25 AverageVariabilityOfTimeBetweenAttacksForEachVoiceFeature R 30 InitialTempoFeature R 31 InitialTimeSignatureFeature R 32 CompoundOrSimpleMeterFeature R 33 TripleMeterFeature R 34 QuintupleMeterFeature R 35 ChangesOfMeterFeature R 36 DurationFeature T 1 MaximumNumberOfIndependentVoicesFeature T 2 AverageNumberOfIndependentVoicesFeature T 3 VariabilityOfNumberOfIndependentVoicesFeature T 4 VoiceEqualityNumberOfNotesFeature (not implemented) T 5 VoiceEqualityNoteDurationFeature (not implemented) T 6 VoiceEqualityDynamicsFeature (not implemented) T 7 VoiceEqualityMelodicLeapsFeature (not implemented) T 8 VoiceEqualityRangeFeature (not implemented) T 9 ImportanceOfLoudestVoiceFeature (not implemented) T 10 RelativeRangeOfLoudestVoiceFeature (not implemented) T 12 RangeOfHighestLineFeature (not implemented) T 13 RelativeNoteDensityOfHighestLineFeature (not implemented) T 15 MelodicIntervalsInLowestLineFeature (not implemented) T 20 VoiceSeparationFeature (not implemented) z.Could not find any jSymbolic features of type zjSymbolic features of type z do not have number )extractorsByIdKeyErrorr>r) extractorTypenumbers rgetExtractorByTypeAndNumberrezsllWm,V44 N'<]O LN N W')-8LVH UW WWs 1?c d}d}tD]O}t|}tt|D]-}|| ||j}|dz }||tvs)|dz }/Qt d|d|d||z dy)z^ >>> features.jSymbolic.getCompletionStats() completion stats: 72/112 (0.6428...) rNr7zcompletion stats: /z ())rar<r@r,featureExtractorsprint) countTotal countCompletekgroupr'unused_ns rgetCompletionStatsrpts JM q!s5z"AQx# 8,,a 800!Q&M # }oQzl"]Z=W JJL C5)rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|)p1rrr5rFr}r)rrurwryr{rrr=rrSrTrrr}rrr&rs rtestRepeatedNotesFeaturezTest.testRepeatedNotesFeatures"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   4 4Q 7 JJL C5)rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|rrrr5rFr}r)rrurwryr{rrr=rrSrTrrrrrr&rs rtestChromaticMotionFeaturezTest.testChromaticMotionFeatures"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   6 6q 9 JJL C5)rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|rgUUUUUU?)rrurwryr{rrr=rrSrTrrrrrr&rs rtestStepwiseMotionFeaturezTest.testStepwiseMotionFeature$"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   5 5a 8 JJL E7+rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|rUUUUUU?)rrurwryr{rrr=rrSrTrrrrrr&rs rtestMelodicThirdsFeaturezTest.testMelodicThirdsFeature5"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   4 4Q 7 JJL E7+rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|)rrrr5r}r}gUUUUUU?)rrurwryr{rrr=rrSrTrrrrrr&rs rtestMelodicFifthsFeaturezTest.testMelodicFifthsFeatureFrrcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|)rrrr5r}d5r)rrurwryr{rrr=rrSrTrrrrrr&rs rtestMelodicTritonesFeaturezTest.testMelodicTritonesFeatureWs"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   6 6q 9 JJL E7+rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dgy) Nrrtrvrxrzr|rrrr5r}rr)rrurwryr{rrr=rrSrTrrrrrr&rs rtestMelodicOctavesFeaturezTest.testMelodicOctavesFeaturehrrcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dg|j }|j d}|j|jtj|d D]F}|j|}|j|jtj|H|jj|}|j} |j| j d g|j }|j d}|j|jtj|d D]F}|j|}|j|jtj|H|jj|}|j} |j| j d gy) Nrrtrvrxrzr|r?)rr-m2-M2-p5rr)rrrrr-p8r)rrurwryr{rrr=rrSrTrrrrrr&rs rtestDirectionOfMotionFeaturez!Test.testDirectionOfMotionFeatureys"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   8 8 ; JJL C5) MMO KK  4==+,-8A AA HHTYYt}}Q/0 19   8 8 ; JJL C5) MMO KK  4==+,-;A AA HHTYYt}}Q/0 1<   8 8 ; JJL C5)rcddlm}ddlm}ddlm}ddlm}|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j| j dg|j }|j d}|j|jtj|dD]F}|j|}|j|jtj|H|jj|}|j} |j#| j dd y) Nrrtrvrxrzr|rrrrr5r}rrrg?)rrurwryr{rrr=rrSrTrrrrrr&assertAlmostEqualrs r testDurationOfMelodicArcsFeaturez%Test.testDurationOfMelodicArcsFeatures]"! $ MMO KK  4==+,-5A AA HHTYYt}}Q/0 16   < rDrJrPrZr_rermrsrrrrrrrrrrrrrrrrrrrrrrr rrr$r)r1r7r@rLrUr\rbrjrprtrxr|rrrrrrrrrrrrrrrrrrrrrrrrrr rrr r&r.r5r;rArHFeatureExceptionr>rarerirpTestCaserrr,mainTestrrrrsy##  3)&{&&';< +n&E&E+@ ;N$C$C ;F*~'F'F*<"=''"=J2''2D$L''$LN!&N,K,K!&H)/."A"A)/X$/>::$/N!/^<<!/H"/N;;"/J!/>::!/H!/>::!/H!/^<<!/H!/N;;!/H,=~>>,=^S:>#B#BS:li6~>>i6` 1~'F'F 1F 5N,K,K 5F").*I*I")J%:~/N/N%:P )^-L-L )F!?''!?H&>#B#B&B&.99&>&~>>&>5>225@)^<<)@H^<<H<"Bn&E&E"BJ"B(G(G"BJ"Bn&E&E"BJ+."A"A+8?N;;?.? ? ??.'+!@!@'+T*AN$C$C*AZ&:."A"A&:R4,^444,n?!@!@?.?n&E&E?8?~>>?2?N$C$C?8AN$C$CAD#A.*I*I#AL'E'''ETAn.M.MA4:'':D"<''">?8?88?D?!@!@?*?>::?4#K88#KL"9!@!@"9J%C~'F'F%CP,!@!@,@,!@!@,@9~>>9D(E~'F'F(EV&@^-L-L&@R6A''6Ar9=''9=v4.994:'9."A"A'9T/+>#B#B/+d&'88&'R%'N;;%'P+N;;+\/n55/L!@!@&!@!@(N,K,K,^-L-L6('n.M.M('V*9n.M.M*9Z&A''&AR(G(G*~'F'F&>#B#B&~'F'F* ? ?(n&E&E()H)H( ? ?(n.M.M*.*I*I(^<<2@7~'F'F@7F?)H)H?D;I'';I|''6''>1H''1Hh''8%'(G(G%'P.*I*I2."A"A(/ ? ?/@($=(:($=(:3$=34)5):080:343>383>3'@3:0$=0.D(AD<  ? ?  ""-.    (*131>@)+#%%%  (%)+ .  '%(824-#  $  + .  (-+0.3$ ')'#!"%#"'  7 : &  & %&  ,&  7 &  0 &  6 &  9&  <&  $&  &&  .&  !&  &  #&  &  !& " #& $ #%& & )'& ( #)& * #+& , !-& . )/& 0 /1& 2 53& 4 B5& 6 7& 8 9& : ;& < =& > ?& @ $A& B %C& D E& F G& H I& J K& &N  //5)($(!'* !/ +    + .  cqqh]W@W#W"W% W 5 W / W1W*W WWWWWWW !W"#W$!%W&'W*%+W,.-W.;/W0&1W2"3W4"5W6"7W89W:;W<=W>?W@%AWB"CWD&EWNOWPQWR%SWTUWVWWXYWZ%[W\+]W^1_W`>aWdeWf gWh!iWjkWlmWnoWpqWt,uWv,wWx2yW|%}W~*W@(AWB-CWD+EWF0GWH!IWJKWLMWNOWPQWRSWT$UWV&WWX$YWZ [W\]W^"_W` aWbcWt\&J-8  J-Z  zGTr