code: def _crosscorr(x, y, **kwargs):
      
      sxy = _crosscov(x, y, **kwargs)
      # estimate sigma_x, sigma_y to normalize
      sx = np.std(x)
      sy = np.std(y)
      return sxy/(sx*sy)
docs: Returns the crosscorrelation sequence between two ndarrays.
      This is performed by calling fftconvolve on x, y[::-1]
  
      Parameters
  
  
      x: ndarray
      y: ndarray
      axis: time axis
      all_lags: {True/False}
      whether to return all nonzero lags, or to clip the length of r_xy
      to be the length of x and y. If False, then the zero lag correlation
      is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2
  
      Notes
  
  
      cross correlation is defined as
      rxy[k] := E{X[t]*Y[t+k]}/(E{X*X}E{Y*Y})**.5,
      where X,Y are zero mean random processes. It is the noramlized cross
      covariance.