3jBJdZddlmZddlZddlZddlmZddlmZddlmZddlm Z ddlm Z dd l m Z ejd ZGd d ejZGd dZGddZGddej&ZegZedk(rddlZej.eyy)an This module describes classes for performing windowed and overlapping windowed analysis. The :class:`music21.analysis.windowed.WindowedAnalysis` provides a reusable framework for systematic overlapping window analysis at the starting at the level of the quarter note and moving to the size of an entire :class:`music21.stream.Stream`. Modular analysis procedures inherit from :class:`music21.analysis.discrete.DiscreteAnalysis`. The :class:`music21.analysis.discrete.KrumhanslSchmuckler` (for algorithmic key detection) and :class:`music21.analysis.discrete.Ambitus` (for pitch range analysis) classes provide examples. ) annotationsN) exceptions21)common) environment)meter)stream)DiscreteAnalysisExceptionzanalysis.windowedc eZdZy)WindowedAnalysisExceptionN)__name__ __module__ __qualname__F/DATA/.local/lib/python3.12/site-packages/music21/analysis/windowed.pyr r 'srr cDeZdZdZdZddZddZ d d dZy) WindowedAnalysisa Create a WindowedAnalysis object. The provided `analysisProcessor` must provide a `process()` method that, when given a windowed Stream (a Measure) returns two element tuple containing (a) a data value (implementation dependent) and (b) a color code. c||_t|tjs t d|j r|j }||_|j|_ y)Nznon-stream provided as argument) processor isinstancerStreamr hasPartLikeStreamsflatten _srcStreamgetMinimumWindowStream_windowedStream)self streamObjanalysisProcessors r__init__zWindowedAnalysis.__init__6sU*)V]]3+,MN N  ' ' )!))+I##::<rctj}|jdtj||j j |}|jd|s td|jd|S)a- Take the loaded stream and restructure it into measures of 1 quarter note duration. >>> s = corpus.parse('bach/bwv324') >>> p = analysis.discrete.Ambitus() Placing one part into analysis: >>> wa = analysis.windowed.WindowedAnalysis(s.parts[0], p) >>> post = wa.getMinimumWindowStream() >>> len(post.getElementsByClass(stream.Measure)) 42 >>> post.getElementsByClass(stream.Measure).first() Time signature set to 1/4 time signature >>> post.getElementsByClass(stream.Measure).first().timeSignature leaves one note in this measure >>> len(post.getElementsByClass(stream.Measure)[1].notes) 1 Placing a score with parts into analysis will automatically flatten the stream. So these two calls are equivalent: >>> wa1 = analysis.windowed.WindowedAnalysis(s, p) >>> wa2 = analysis.windowed.WindowedAnalysis(s.flatten(), p) r) meterStreamMeasurezMaking measures failedT)inPlace) rrinsertr TimeSignaturer makeMeasuresremoveByNotOfClassr makeTies)r timeSignaturer"measureds rrz'WindowedAnalysis.getMinimumWindowStreamAsyHmmo 1e11-@A ??//K/H ##I.+,DE E$'rc t|j}|dk(r ||z dz}nU|dk(r0||z dz}t|}||k7r8tjddn |dk(r|}nt j d|d g|z}d g|z}t|}|dk(rv|D]m} tj} t| | |zD] } | j|j| " |jj| \|| <|| <o||fS|dk(rd } | |z} t|D]} t| t|j} tj} t| | D] } | j|j| " |jj| \|| <|| <| } | |z} ||fS|dk(rg}t||z dzD]n} tj} g}t| | |zD]1} | j|j| |j| 3|j| |gpt|D]b} tj} |D]"\}}| |vs |D]}| j|$ |jj| \|| <|| <d||fS#t$rd \|| <|| <Y6wxYw#t$rd \|| <|| <Y>> s = corpus.parse('bach/bwv66.6') >>> p = analysis.discrete.Ambitus() >>> wa = analysis.windowed.WindowedAnalysis(s.flatten(), p) >>> len(wa._windowedStream) 36 >>> a, b = wa.analyze(1) >>> len(a), len(b) (36, 36) >>> a, b = wa.analyze(4) >>> len(a), len(b) (33, 33) >>> a, b = wa.analyze(1, windowType='noOverlap') >>> len(a), len(b) (37, 37) >>> a, b = wa.analyze(4, windowType='noOverlap') >>> len(a), len(b) (10, 10) >>> a, b = wa.analyze(1, windowType='adjacentAverage') >>> len(a), len(b) (36, 36) overlap noOverlapzJmaxWindowCount is not divisible by windowSize, possibly undefined behavior) stackleveladjacentAveragezUnknown windowType: r))NNrz#ffffff)lenrintwarningswarnrMusic21Exceptionrangerrappendrprocessr min)r windowSize windowTypemaxWindowCount windowCountwindowCountFloatdatacolorwindowCountIndicesicurrentjstartend overlapped participants dataStreamms ranalyzezWindowedAnalysis.analyzets^PT112  "(:59K ; &- :Q> ./K;. ` , ,(K//2Fzl0ST Ts[ k!";/  "' --/q!j.1ANN4#7#7#:;2C(,(>(>w(G%DGU1X(rU{[; &E*$C;'#s4#7#789 --/uc*ANN4#7#7#:;+C(,(>(>w(G%DGU1X j((TU{5, ,J>J6:; --/! q!j.1ANN4#7#7#:; ''*2!!7L"9:<>* --/0:,J L(!+A#NN1-",1;C(,(>(>w(G%DGU1X+U{c1C(B%DGU1XC 1C(B%DGU1XC:1C(B%DGU1XCs6!$J6$K $K,6K KK)(K),LLc|t|j}n|}|t|j}n|}|d}n>|jdvrd}n)|jdvrd}n|jdvrd}g}g} g} t|trt t ||dz|} nOtj|\} } g} |} | j||tt | z}||dzkDrn2|r*t|j}|| vr| j|| D]O}|j|| \}}|j|| j|d |i}| j|Q|| | fS) a  Main method for windowed analysis across one or more window sizes. Calls :meth:`~music21.analysis.WindowedAnalysis.analyze` for the number of different window sizes to be analyzed. The `minWindow` and `maxWindow` set the range of window sizes in quarter lengths. The `windowStepSize` parameter determines the increment between these window sizes, in quarter lengths. If `minWindow` or `maxWindow` is None, the largest window size available will be set. If `includeTotalWindow` is True, the largest window size will always be added. >>> s = corpus.parse('bach/bwv324') >>> ksAnalyzer = analysis.discrete.KrumhanslSchmuckler() placing one part into analysis >>> sopr = s.parts[0] >>> wa = analysis.windowed.WindowedAnalysis(sopr, ksAnalyzer) >>> solutions, colors, meta = wa.process(1, 1, includeTotalWindow=False) >>> len(solutions) # we only have one series of windows 1 >>> solutions, colors, meta = wa.process(1, 2, includeTotalWindow=False) >>> len(solutions) # we have two series of windows 2 >>> solutions[1] [(, 'major', 0.6844...), (, 'minor', 0.8308...), (, 'major', 0.6844...), (, 'minor', 0.8308...),...] >>> colors[1] ['#ffb5ff', '#9b519b', '#ffd752', '#9b519b', ...] >>> meta [{'windowSize': 1}, {'windowSize': 2}] r-r-) nooverlapnonoverlappingr/)adjacentaverager2r.g?)r=r<) r3rlowerrr4listr8r getNumFromStrr9roundrM)r minWindow maxWindowwindowStepSizer=includeTotalWindow maxLength minLengthsolutionMatrix colorMatrix metaMatrix windowSizesnumjunkx totalWindowrDsolution colorNamemetas rr:zWindowedAnalysis.processs`  D001I!I  D001I!I  "J    ; ."J    #B B$J    #6 6*J  nc *uY A ~NOK,,^rs # ?&{&&':;  = = h7h7Z <<]8  ]F   zGTr