text: import numpy as np
  
  vector = [
      0, 6, 25, 20, 15, 8, 15, 6, 0, 6, 0, -5, -15, -3, 4, 10, 8, 13, 8, 10, 3,
      1, 20, 7, 3, 0
  ]
  
  # This is an extract from Laurent Garnier Cryspy Bacon track.
  # https://www.youtube.com/watch?v=6SFD7fz8fWc
  # Converted from MP3 to WAV, then imported as a Numpy array.
  # No Copyright infringement intended. For educational purpose only.
  cb = np.array([-0.010406, -0.0080872, -0.0066528, -0.0074158, -0.0072937, -0.0063782, -0.005249, -0.0038147, -0.0021973, -0.0011902, -0.0014954, -0.00036621, 0.00030518, -0.00027466, -0.00073242, 0.0014648, 0.0025635, 0.0019531, 0.0033875, 0.0037842, 0.0047913, 0.0059509, 0.0049133, 0.0059814, 0.007019, 0.0062256, 0.0069885, 0.0074463, 0.007782, 0.0086975, 0.0089722, 0.0097656, 0.0083313, 0.0087585, 0.01001, 0.010345, 0.011139, 0.010345, 0.01123, 0.012482, 0.012329, 0.012573, 0.011505, 0.010773, 0.011017, 0.011658, 0.011353, 0.010498, 0.010803, 0.010773, 0.011932, 0.013184, 0.013428, 0.012665, 0.012451, 0.011993, 0.010559, 0.01004, 0.0097351, 0.0098267, 0.0099792, 0.0098267, 0.0092468, 0.0089111, 0.0088501, 0.0090027, 0.0088196, 0.0079346, 0.0068054, 0.006897, 0.0077209, 0.0073853, 0.0069275, 0.0046692, 0.0021362, 0.0013733, 0.00021362, -0.0024414, -0.0045776, -0.0067444, -0.0083313, -0.0088806, -0.011688, -0.012207, -0.012756, -0.01532, -0.014984, -0.017212, -0.020844, -0.022247, -0.022614, -0.023773, -0.02356, -0.024323, -0.025177, -0.024353, -0.02594, -0.026794, -0.02829, -0.029236, -0.029388, -0.030304, -0.030914, -0.032593, -0.032867, -0.03363, -0.032867, -0.03009, -0.032501, -0.035034, -0.035339, -0.035492, -0.035614, -0.037018, -0.037476, -0.039001, -0.039886, -0.038818, -0.039215, -0.039917, -0.041107, -0.041931, -0.042206, -0.042419, -0.043762, -0.043427, -0.041992, -0.043915, -0.043854, -0.045929, -0.047607, -0.046265, -0.045807, -0.045135, -0.045715, -0.045898, -0.04538, -0.043579, -0.043518, -0.04425, -0.043823, -0.044617, -0.045227, -0.044159, -0.042877, -0.044708, -0.043732, -0.043152, -0.043182, -0.041473, -0.041718, -0.04303, -0.043121, -0.042236, -0.043335, -0.043121, -0.041321, -0.042328, -0.043274, -0.040955, -0.040375, -0.041687, -0.039063, -0.038177, -0.039215, -0.037354, -0.037079, -0.036835, -0.035797, -0.035492, -0.034241, -0.032776, -0.033295, -0.033112, -0.03186, -0.031891, -0.032074, -0.030182, -0.030365, -0.03125, -0.030334, 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  def plot_peaks(x, indexes, algorithm=None, mph=None, mpd=None):
      """Plot results of the peak dectection."""
      try:
          import matplotlib.pyplot as plt
      except ImportError:
          print('matplotlib is not available.')
          return
      _, ax = plt.subplots(1, 1, figsize=(8, 4))
      ax.plot(x, 'b', lw=1)
      if indexes.size:
          label = 'peak'
          label = label + 's' if indexes.size > 1 else label
          ax.plot(indexes, x[indexes], '+', mfc=None, mec='r', mew=2, ms=8,
                  label='%d %s' % (indexes.size, label))
          ax.legend(loc='best', framealpha=.5, numpoints=1)
      ax.set_xlim(-.02*x.size, x.size*1.02-1)
      ymin, ymax = x[np.isfinite(x)].min(), x[np.isfinite(x)].max()
      yrange = ymax - ymin if ymax > ymin else 1
      ax.set_ylim(ymin - 0.1*yrange, ymax + 0.1*yrange)
      ax.set_xlabel('Data #', fontsize=14)
      ax.set_ylabel('Amplitude', fontsize=14)
      ax.set_title('%s (mph=%s, mpd=%s)' % (algorithm, mph, mpd))
      plt.show()
  