code: def localize(self, i, **kwargs):
          r
          s = np.zeros(self.G.N)
          s[i] = 1
          return np.sqrt(self.G.N) * self.filter(s, **kwargs)
docs: r"""Localize the kernels at a node (to visualize them).
  
          That is particularly useful to visualize a filter in the vertex domain.
  
          A kernel is localized on vertex :math:`v_i` by filtering a Kronecker
          delta :math:`\delta_i` as
  
          .. math:: (g(L) \delta_i)(j) = g(L)(i,j),
                    \text{ where } \delta_i(j) =
                    \begin{cases} 0 \text{ if } i \neq j, \\
                                  1 \text{ if } i = j.    \end{cases}
  
          Parameters
          ----------
          i : int
              Index of the node where to localize the kernel.
          kwargs: dict
              Parameters to be passed to the :meth:`analyze` method.
  
          Returns
          -------
          s : ndarray
              Kernel localized at vertex i.
  
          Examples
          --------
          Visualize heat diffusion on a grid by localizing the heat kernel.
  
          >>> import matplotlib
          >>> N = 20
          >>> DELTA = N//2 * (N+1)
          >>> G = graphs.Grid2d(N)
          >>> G.estimate_lmax()
          >>> g = filters.Heat(G, 100)
          >>> s = g.localize(DELTA)
          >>> _ = G.plot(s, highlight=DELTA)