code: def covariance_matrix(self,localizer=None):
          
  
  
  
  
          mean = np.array(self.mean(axis=0))
          delta = self.as_pyemu_matrix(typ=Cov)
          for i in range(self.shape[0]):
              delta.x[i, :] -= mean
          delta *= (1.0 / np.sqrt(float(self.shape[0] - 1.0)))
  
          if localizer is not None:
              delta = delta.T * delta
              return delta.hadamard_product(localizer)
  
          return delta.T * delta
docs: calculate the approximate covariance matrix implied by the ensemble using
          mean-differencing operation at the core of EnKF
  
          Parameters
          ----------
              localizer : pyemu.Matrix
                  covariance localizer to apply
  
          Returns
          -------
              cov : pyemu.Cov
                  covariance matrix