docs: def plot_data_filter(data, data_f, b, a, cutoff, fs):
      '''Plot frequency response and filter overlay for butter filtered data
  
      Args
      ----
      data: ndarray
          Signal array
      data_f: float
          Signal sampling rate
      b: array_like
          Numerator of a linear filter
      a: array_like
          Denominator of a linear filter
      cutoff: float
          Cutoff frequency for the filter
      fs: float
          Sampling rate of the signal
  
      Notes
      -----
      http://stackoverflow.com/a/25192640/943773
      '''
      import matplotlib.pyplot as plt
      import numpy
      import scipy.signal
  
      n = len(data)
      T = n/fs
      t = numpy.linspace(0, T, n, endpoint=False)
  
      # Calculate frequency response
      w, h = scipy.signal.freqz(b, a, worN=8000)
  
      # Plot the frequency response.
      fig, (ax1, ax2) = plt.subplots(2,1)
  
      ax1.title.set_text('Lowpass Filter Frequency Response')
      ax1.plot(0.5*fs * w/numpy.pi, numpy.abs(h), 'b')
      ax1.plot(cutoff, 0.5*numpy.sqrt(2), 'ko')
      ax1.axvline(cutoff, color='k')
      ax1.set_xlim(0, 0.5*fs)
      ax1.set_xlabel('Frequency [Hz]')
      ax2.legend()
  
      # Demonstrate the use of the filter.
      ax2.plot(t, data, linewidth=_linewidth, label='data')
      ax2.plot(t, data_f, linewidth=_linewidth, label='filtered data')
      ax2.set_xlabel('Time [sec]')
      ax2.legend()
  
      plt.show()
  
      return None