#! /usr/bin/env python import sysimport numpy as npfrom aubio import source, pvoc, specdesc win_s = 512                 # fft sizehop_s = win_s // 4          # hop size if len(sys.argv) < 2:    print("Usage: %s <filename> [samplerate]" % sys.argv[0])    sys.exit(1) filename = sys.argv[1] samplerate = 0if len( sys.argv ) > 2: samplerate = int(sys.argv[2]) s = source(filename, samplerate, hop_s)samplerate = s.samplerate pv = pvoc(win_s, hop_s) methods = ['default', 'energy', 'hfc', 'complex', 'phase', 'specdiff', 'kl',        'mkl', 'specflux', 'centroid', 'slope', 'rolloff', 'spread', 'skewness',        'kurtosis', 'decrease',] all_descs = {}o = {} for method in methods:    cands = []    all_descs[method] = np.array([])    o[method] = specdesc(method, win_s) total_frames = 0downsample = 2 while True:    samples, read = s()    fftgrain = pv(samples)    #outstr = "%f" % ( total_frames / float(samplerate) )    for method in methods:        specdesc_val = o[method](fftgrain)[0]        all_descs[method] = np.append(all_descs[method], specdesc_val)        #outstr += " %f" % specdesc_val    #print(outstr)    total_frames += read    if read < hop_s: break if 1:    print("done computing, now plotting")    import matplotlib.pyplot as plt    from demo_waveform_plot import get_waveform_plot    from demo_waveform_plot import set_xlabels_sample2time    fig = plt.figure()    plt.rc('lines',linewidth='.8')    wave = plt.axes([0.1, 0.75, 0.8, 0.19])    get_waveform_plot(filename, samplerate, block_size = hop_s, ax = wave )    wave.yaxis.set_visible(False)    wave.xaxis.set_visible(False)     all_desc_times = [ x * hop_s  for x in range(len(all_descs["default"])) ]    n_methods = len(methods)    for i, method in enumerate(methods):        #ax = fig.add_subplot (n_methods, 1, i)        #plt2 = plt.axes([0.1, 0.1, 0.8, 0.65], sharex = plt1)        ax = plt.axes ( [0.1, 0.75 - ((i+1) * 0.65 / n_methods),  0.8, 0.65 / n_methods], sharex = wave )        ax.plot(all_desc_times, all_descs[method], '-', label = method)        #ax.set_ylabel(method, rotation = 0)        ax.xaxis.set_visible(False)        ax.yaxis.set_visible(False)        ax.axis(xmax = all_desc_times[-1], xmin = all_desc_times[0])        ax.annotate(method, xy=(-10, 0),  xycoords='axes points',                horizontalalignment='right', verticalalignment='bottom',                )    set_xlabels_sample2time(ax, all_desc_times[-1], samplerate)    #plt.ylabel('spectral descriptor value')    ax.xaxis.set_visible(True)    plt.show()