code: def chi2_vals(self, x):
          
  
          chi2_vals = np.ndarray((self._nx))
          for i in range(self._nx):
  
              mle = self._loglikes[i].mle()
              nll0 = self._loglikes[i].interp(mle)
              nll1 = self._loglikes[i].interp(x[i])
              chi2_vals[i] = 2.0 * np.abs(nll0 - nll1)
  
          return chi2_vals
docs: Compute the difference in the log-likelihood between the
          MLE in each energy bin and the normalization predicted by a
          global best-fit model.  This array can be summed to get a
          goodness-of-fit chi2 for the model.
  
          Parameters
          ----------
          x : `~numpy.ndarray`        
              An array of normalizations derived from a global fit to
              all energy bins.
  
          Returns
          -------
          chi2_vals : `~numpy.ndarray`
              An array of chi2 values for each energy bin.