#! /usr/bin/env python import sysfrom aubio import source, pitch if len(sys.argv) < 2:    print("Usage: %s <filename> [samplerate]" % sys.argv[0])    sys.exit(1) filename = sys.argv[1] downsample = 1samplerate = 44100 // downsampleif len( sys.argv ) > 2: samplerate = int(sys.argv[2]) win_s = 4096 // downsample # fft sizehop_s = 512  // downsample # hop size s = source(filename, samplerate, hop_s)samplerate = s.samplerate tolerance = 0.8 pitch_o = pitch("yin", win_s, hop_s, samplerate)pitch_o.set_unit("midi")pitch_o.set_tolerance(tolerance) pitches = []confidences = [] # total number of frames readtotal_frames = 0while True:    samples, read = s()    pitch = pitch_o(samples)[0]    #pitch = int(round(pitch))    confidence = pitch_o.get_confidence()    #if confidence < 0.8: pitch = 0.    print("%f %f %f" % (total_frames / float(samplerate), pitch, confidence))    pitches += [pitch]    confidences += [confidence]    total_frames += read    if read < hop_s: break if 0: sys.exit(0) #print pitchesimport os.pathfrom numpy import array, maimport matplotlib.pyplot as pltfrom demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time skip = 1 pitches = array(pitches[skip:])confidences = array(confidences[skip:])times = [t * hop_s for t in range(len(pitches))] fig = plt.figure() ax1 = fig.add_subplot(311)ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1)plt.setp(ax1.get_xticklabels(), visible = False)ax1.set_xlabel('') def array_from_text_file(filename, dtype = 'float'):    filename = os.path.join(os.path.dirname(__file__), filename)    return array([line.split() for line in open(filename).readlines()],        dtype = dtype) ax2 = fig.add_subplot(312, sharex = ax1)ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'if os.path.isfile(ground_truth):    ground_truth = array_from_text_file(ground_truth)    true_freqs = ground_truth[:,2]    true_freqs = ma.masked_where(true_freqs < 2, true_freqs)    true_times = float(samplerate) * ground_truth[:,0]    ax2.plot(true_times, true_freqs, 'r')    ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )# plot raw pitchesax2.plot(times, pitches, '.g')# plot cleaned up pitchescleaned_pitches = pitches#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)ax2.plot(times, cleaned_pitches, '.-')#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )#ax2.axis( ymin = 55, ymax = 70 )plt.setp(ax2.get_xticklabels(), visible = False)ax2.set_ylabel('f0 (midi)') # plot confidenceax3 = fig.add_subplot(313, sharex = ax1)# plot the confidenceax3.plot(times, confidences)# draw a line at toleranceax3.plot(times, [tolerance]*len(confidences))ax3.axis( xmin = times[0], xmax = times[-1])ax3.set_ylabel('confidence')set_xlabels_sample2time(ax3, times[-1], samplerate)plt.show()#plt.savefig(os.path.basename(filename) + '.svg')