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How to use analyze_frames method in ATX

Best Python code snippet using ATX

analyzeFrames.py

Source:analyzeFrames.py

1"""2Analyzes an array of frames using one of these methods: 3intensity, centroid, gauss, or PCA4VARIABLE SET BY SCRIPT:5data     RESULTS OF ANALYSIS, SHAPE = (NUMFRAMES,NUMDIMENSIONS)6         WHERE NUMDIMENSIONS IS THE NUMBER OF DIMENSIONS OF DATA7         PRODUCED BY THE ANALYSIS METHOD CHOSE8         9v. 2021 01 2710"""11# here define the array of frames to analyze12analyze_frames = frames13# here define the method of tracking14analyze_method = 'pca'    # either 'intensity, 'centroid', 'gauss' or 'pca'15# if pca method is chosen, must define pca principle components here16# if other method, can leave these commented out17analyze_components =  [ V[0], V[1], V[2] ]18analyze_meanframe = meanframe19# choose to plot tracking results versus frame number also20scatter_plot = True21full_plot = True22## use below to overide parameter values from common.py23######################################################################24analyze_background = False      # set to background to use default25analyze_normalize = normalize       # set to normalize to use default26############################################################27############################################################28num_frames = len(analyze_frames)29print( num_frames, 'frames loaded for analysis.')30# subtract background if requested31if analyze_background == True:32    print("Background correction will be applied.")33# now perform tracking depending on method chosen34if analyze_method == 'intensity':35    temp = pa.statframes(analyze_frames, back = analyze_background)36    data = temp[:,0:3]37    ylabel = ['max','min','sum']38elif analyze_method == 'centroid':39    temp = pa.statframes(analyze_frames, back = analyze_background)40    data = temp[:,3:8]41    ylabel = ['<x>','<y>','<xx>','<xy>','<yy>']42elif analyze_method == 'gauss':43    data = pa.fitframes(analyze_frames, back = analyze_background)44    ylabel = ['x','y','sigma','amp','err']45elif analyze_method == 'pca':46#    meanframe = track_frames.mean(axis=0)47    data = pa.pcaframes(analyze_frames, analyze_meanframe, analyze_components)48    datadim = data.shape[1]49    ylabel = []50    for i in range(datadim):51        ylabel.append('comp {}'.format(i) )52    53else:54    print("Choose intensity, centroid, gauss or pca method.")55numpoints = len(data)56if numpoints > 0:57    print( analyze_method, 'analysis complete.', numpoints, 'frames processed.')58    print("Means of results:")59    print(data.mean(axis=0))60# create plots61if full_plot == True:62    xlabel = 'frame number'63    plot_title = analyze_method + ' analysis plots'64    pa.makeplot(data, xlabel=xlabel, ylabel=ylabel,title=plot_title)65if scatter_plot == True:66    x, y = data[:,0], data[:,1] 67    xmax, ymax = max(x), max(y)68    xmin, ymin = min(x), min(y)69    xcenter = (xmax+xmin)/2.070    ycenter = (ymax+ymin)/2.071    span = max( xmax-xmin, ymax-ymin )72    left = xcenter - 0.55*span73    right = xcenter + 0.55*span74    bottom = ycenter - 0.55*span75    top = ycenter + 0.55*span76    clrs = np.arange(numpoints)*255.0/numpoints77    figc, axc = plt.subplots(2,2,sharex='col',sharey='row')78    figc.suptitle(analyze_method + ' analysis scatter plot ' )79    axc[1,0].set_aspect(1.0)80    axc[1,0].set_xlim(left,right)81    axc[1,0].set_ylim(top,bottom)82    axc[1,0].scatter(x,y,c=clrs,cmap='prism',marker='.',)83    axc[1,0].grid(True)84    axc[0,0].plot(x,range(numpoints),'b.-')85    axc[1,1].plot(range(numpoints),y,'b.-')86    axc[1,0].set_xlabel('x')87    axc[1,0].set_ylabel('y')88    axc[0,0].set_ylabel('frame number')...

librosa_analysis.py

Source:librosa_analysis.py

...10        scipy.spatial.distance.pdist(X.T, metric="cosine"))11    return D[:-1, :-1]12def analyze_file(infile, debug=False):13    y, sr = librosa.load(infile, sr=44100)14    return analyze_frames(y, sr, debug)15def analyze_frames(y, sr, debug=False):16    A = {}17    hop_length = 12818    # First, get the track duration19    A['duration'] = float(len(y)) / sr20    # Then, get the beats21    if debug: print "> beat tracking"22    tempo, beats = librosa.beat.beat_track(y, sr, hop_length=hop_length)23    # Push the last frame as a phantom beat24    A['tempo'] = tempo25    A['beats'] = librosa.frames_to_time(26        beats, sr, hop_length=hop_length).tolist()27    if debug: print "beats count: ", len(A['beats'])28    if debug: print "> spectrogram"29    S = librosa.feature.melspectrogram(y, sr,30                                       n_fft=2048,31                                       hop_length=hop_length,32                                       n_mels=80,33                                       fmax=8000)34    S = S / S.max()35    # A['spectrogram'] = librosa.logamplitude(librosa.feature.sync(S, beats)**2).T.tolist()36    # Let's make some beat-synchronous mfccs37    if debug: print "> mfcc"38    S = librosa.feature.mfcc(S=librosa.logamplitude(S), n_mfcc=40)39    A['timbres'] = librosa.feature.sync(S, beats).T.tolist()40    if debug: print "timbres count: ", len(A['timbres'])41    # And some chroma42    if debug: print "> chroma"43    S = np.abs(librosa.stft(y, hop_length=hop_length))44    # Grab the harmonic component45    H = librosa.decompose.hpss(S)[0]46    # H = librosa.hpss.hpss_median(S, win_P=31, win_H=31, p=1.0)[0]47    A['chroma'] = librosa.feature.sync(librosa.feature.chromagram(S=H, sr=sr),48                                        beats,49                                        aggregate=np.median).T.tolist()50    # Relative loudness51    S = S / S.max()52    S = S**253    if debug: print "> dists"54    dists = structure(np.vstack([np.array(A['timbres']).T, np.array(A['chroma']).T]))55    A['dense_dist'] = dists56    edge_lens = [A["beats"][i] - A["beats"][i - 1]57                 for i in xrange(1, len(A["beats"]))]58    A["avg_beat_duration"] = np.mean(edge_lens)59    A["med_beat_duration"] = np.median(edge_lens)60    return A61if __name__ == '__main__':62    import sys63    from radiotool.composer import Song64    song = Song(sys.argv[1], cache_dir=None)65    frames = song.all_as_mono()...

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