old_contents: import numpy as np
  import scipy as sp
  from scipy import linalg
  
  from pygsp import utils
  
  
  class operators(object):
      pass
  
  
  def grad(G, s):
      r"""
      Graph gradient
  
      """
      pass
  
  
  def grad_mat(G):
      r"""
      Gradient sparse matrix of the graph
  
      """
      pass
  
  
  @utils.graph_array_handler
  def compute_fourier_basis(G, exact=None, cheb_order=30, **kwargs):
  
      if hasattr(G, 'e') or hasattr(G, 'U'):
          print("This graph already has Laplacian eigenvectors or eigenvalues")
  
      if G.N > 3000:
          print("Performing full eigendecomposition of a large matrix\
                may take some time.")
  
      if False:
          # TODO
          pass
      else:
          if not hasattr(G, L):
              raise AttributeError("Graph Laplacian is missing")
          G.e, G.U = full_eigen(G.L)
  
      G.lmax = np.max(G.e)
  
      G.mu = np.max(np.abs(G.U))
  
  
  def full_eigen(L):
      eigenvectors, eigenvalues, _ = np.linalg.svd(L.todense())
  
      # Sort everything
      inds = np.argsort(eigenvalues)
      EVa = np.sort(eigenvalues)
  
      # TODO check if axis are good
      EVe = eigenvectors[:, inds]
  
      for val in EVe[0, :]:
          if val < 0:
              val = -val
  
      return EVa, EVe
  
new_contents: import numpy as np
  import scipy as sp
  from scipy import linalg
  
  from pygsp import utils
  
  
  class operators(object):
      pass
  
  
  def grad(G, s):
      r"""
      Graph gradient
  
      """
      pass
  
  
  def grad_mat(G):
      r"""
      Gradient sparse matrix of the graph
  
      """
      pass
  
  
  @utils.graph_array_handler
  def compute_fourier_basis(G, exact=None, cheb_order=30, **kwargs):
  
      if hasattr(G, 'e') or hasattr(G, 'U'):
          print("This graph already has Laplacian eigenvectors or eigenvalues")
  
      if G.N > 3000:
          print("Performing full eigendecomposition of a large matrix\
                may take some time.")
  
      if False:
          # TODO
          pass
      else:
          if not hasattr(G, 'L'):
              raise AttributeError("Graph Laplacian is missing")
          G.e, G.U = full_eigen(G.L)
  
      G.lmax = np.max(G.e)
  
      G.mu = np.max(np.abs(G.U))
  
  
  def compute_cheby_coeff(G, f, m=30, N=None, *args):
  
      if not N:
          N = m + 1
  
      if isinstance(f, list):
          Nf = len(f)
          c = np.zeros(m+1, Nf)
  
  
  def full_eigen(L):
      eigenvectors, eigenvalues, _ = np.linalg.svd(L.todense())
  
      # Sort everything
      inds = np.argsort(eigenvalues)
      EVa = np.sort(eigenvalues)
  
      # TODO check if axis are good
      EVe = eigenvectors[:, inds]
  
      for val in EVe[0, :]:
          if val < 0:
              val = -val
  
      return EVa, EVe
  
subject: fix and chby beginning