docs: def compound_powerspec(data, tbin, Df=None, pointProcess=False):
      """
      Calculate the power spectrum of the compound/sum signal.
      data is first summed across units, then the power spectrum is calculated.
  
      If pointProcess=True, power spectra are normalized by the length T of
      the time series.
  
      
      Parameters
      ----------
      data : numpy.ndarray, 
          1st axis unit, 2nd axis time
      tbin : float, 
          binsize in ms
      Df : float/None, 
          window width of sliding rectangular filter (smoothing),
          None -> no smoothing
      pointProcess : bool, 
          if set to True, powerspectrum is normalized to signal length T
                   
  
  
      Returns
      -------
      freq : tuple
          numpy.ndarray of frequencies
      POW : tuple
          1 dim numpy.ndarray, frequency series
  
  
      Examples
      --------
      >>> compound_powerspec(np.array([analog_sig1, analog_sig2]), tbin, Df=Df)
      Out[1]: (freq,POW)
      >>> POW.shape
      Out[2]: (len(analog_sig1),)
  
      """
  
      return powerspec([np.sum(data, axis=0)], tbin, Df=Df, units=True,
          pointProcess=pointProcess)