docs: def corrcoef(time, crossf, integration_window=0.):
      """
      Calculate the correlation coefficient for given auto- and crosscorrelation
      functions. Standard settings yield the zero lag correlation coefficient.
      Setting integration_window > 0 yields the correlation coefficient of
      integrated auto- and crosscorrelation functions. The correlation coefficient
      between a zero signal with any other signal is defined as 0.
  
  
      Parameters
      ----------
      time : numpy.ndarray
          1 dim array of times corresponding to signal.
      crossf : numpy.ndarray 
          Crosscorrelation functions, 1st axis first unit, 2nd axis second unit,
          3rd axis times.
      integration_window: float
          Size of the integration window.
  
  
      Returns
      -------
      cc : numpy.ndarray
          2 dim array of correlation coefficient between two units.
  
      """
  
      N = len(crossf)
      cc = np.zeros(np.shape(crossf)[:-1])
      tbin = abs(time[1] - time[0])
      lim = int(integration_window / tbin)
      
      if len(time)%2 == 0:
          mid = len(time)/2-1
      else:
          mid = np.floor(len(time)/2.)
      
      for i in range(N):
          ai = np.sum(crossf[i, i][mid - lim:mid + lim + 1])
          offset_autoi = np.mean(crossf[i,i][:mid-1])
          for j in range(N):
              cij = np.sum(crossf[i, j][mid - lim:mid + lim + 1])
              offset_cross = np.mean(crossf[i,j][:mid-1])
              aj = np.sum(crossf[j, j][mid - lim:mid + lim + 1])
              offset_autoj = np.mean(crossf[j,j][:mid-1])
              if ai > 0. and aj > 0.:
                  cc[i, j] = (cij-offset_cross) / np.sqrt((ai-offset_autoi) * \
                      (aj-offset_autoj))
              else:
                  cc[i, j] = 0.
      
      return cc