code: def get_waveform_plot(
      filename,
      samplerate=0,
      block_size=4096,
      ax=None,
      downsample=2**4,
  ):
      import matplotlib.pyplot as plt
  
      if not ax:
          fig = plt.figure()
          ax = fig.add_subplot(111)
      hop_s = block_size
  
      allsamples_max = zeros(
          0,
      )
      downsample = downsample  # to plot n samples / hop_s
  
      a = source(filename, samplerate, hop_s)  # source file
      if samplerate == 0:
          samplerate = a.samplerate
  
      total_frames = 0
      while True:
          samples, read = a()
          # keep some data to plot it later
          new_maxes = (
              abs(samples.reshape(hop_s // downsample, downsample))
          ).max(axis=0)
          allsamples_max = hstack([allsamples_max, new_maxes])
          total_frames += read
          if read < hop_s:
              break
      allsamples_max = (allsamples_max > 0) * allsamples_max
      allsamples_max_times = [
          (float(t) / downsample) * hop_s
          for t in range(len(allsamples_max))
      ]
  
      ax.plot(allsamples_max_times, allsamples_max, "-b")
      ax.plot(allsamples_max_times, -allsamples_max, "-b")
      ax.axis(
          xmin=allsamples_max_times[0], xmax=allsamples_max_times[-1]
      )
  
      set_xlabels_sample2time(ax, allsamples_max_times[-1], samplerate)
      return ax
  
imports: import sys
  from aubio import source
  from numpy import zeros, hstack
label_desc: Use image node_id 1 for calling a global function with example usage: get_waveform_plot(filename, samplerate, block_size, ax, downsample) and returns: ax