code: def getIntervals(self, alpha):
          
          limit_vals_lo = np.ndarray((self._nx))
          limit_vals_hi = np.ndarray((self._nx))
  
          for i in range(self._nx):
              lo_lim, hi_lim = self._loglikes[i].getInterval(alpha)
              limit_vals_lo[i] = lo_lim
              limit_vals_hi[i] = hi_lim
  
          return limit_vals_lo, limit_vals_hi
docs: Evaluate the two-sided intervals corresponding to a C.L. of
          (1-alpha)%.
  
          Parameters
          ----------
          alpha :  float
             limit confidence level.
  
          Returns
          -------
          limit_vals_hi : `~numpy.ndarray`
              An array of lower limit values.
  
          limit_vals_lo : `~numpy.ndarray`
              An array of upper limit values.