code: def _exec_loop_moving_window(self, a_all, bd_all, mask, bd_idx):
          
          import scipy.linalg.lapack
  
          npt = bd_all.shape[0]
          n = bd_idx.shape[1]
          kvalues = np.zeros(npt)
          sigmasq = np.zeros(npt)
  
          for i in np.nonzero(~mask)[0]:
              b_selector = bd_idx[i]
              bd = bd_all[i]
  
              a_selector = np.concatenate((b_selector, np.array([a_all.shape[0] - 1])))
              a = a_all[a_selector[:, None], a_selector]
  
              if np.any(np.absolute(bd) <= self.eps):
                  zero_value = True
                  zero_index = np.where(np.absolute(bd) <= self.eps)
              else:
                  zero_value = False
                  zero_index = None
              b = np.zeros((n+1, 1))
              b[:n, 0] = - self.variogram_function(self.variogram_model_parameters, bd)
              if zero_value:
                  b[zero_index[0], 0] = 0.0
              b[n, 0] = 1.0
  
              x = scipy.linalg.solve(a, b)
  
              kvalues[i] = x[:n, 0].dot(self.VALUES[b_selector])
              sigmasq[i] = - x[:, 0].dot(b[:, 0])
  
          return kvalues, sigmasq
docs: Solves the kriging system by looping over all specified points.
          Uses only a certain number of closest points. Not very memory intensive,
          but the loop is done in pure Python.