old_contents: """
  Eigenvalue spectrum of graphs.
  """
  #    Copyright (C) 2004-2013 by
  #    Aric Hagberg <hagberg@lanl.gov>
  #    Dan Schult <dschult@colgate.edu>
  #    Pieter Swart <swart@lanl.gov>
  #    All rights reserved.
  #    BSD license.
  import networkx as nx
  __author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                          'Pieter Swart (swart@lanl.gov)',
                          'Dan Schult(dschult@colgate.edu)'])
  
  __all__ = ['laplacian_spectrum', 'adjacency_spectrum']
  
  
  def laplacian_spectrum(G, weight='weight'):
      """Return eigenvalues of the Laplacian of G
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      laplacian_matrix
      """
      from scipy.linalg import eigvals
      return eigvals(nx.laplacian_matrix(G,weight=weight).todense())
  
  def adjacency_spectrum(G, weight='weight'):
      """Return eigenvalues of the adjacency matrix of G.
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      adjacency_matrix
      """
      from scipy.linalg import eigvals
      return eigvals(nx.adjacency_matrix(G,weight=weight).todense())
  
  # fixture for nose tests
  def setup_module(module):
      from nose import SkipTest
      try:
          import scipy.linalg
      except:
          raise SkipTest("scipy.linalg not available")
  
new_contents: """
  Eigenvalue spectrum of graphs.
  """
  #    Copyright (C) 2004-2013 by
  #    Aric Hagberg <hagberg@lanl.gov>
  #    Dan Schult <dschult@colgate.edu>
  #    Pieter Swart <swart@lanl.gov>
  #    All rights reserved.
  #    BSD license.
  import networkx as nx
  __author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                          'Pieter Swart (swart@lanl.gov)',
                          'Dan Schult(dschult@colgate.edu)'])
  
  __all__ = ['laplacian_spectrum', 'adjacency_spectrum']
  
  
  def laplacian_spectrum(G, weight='weight'):
      """Return eigenvalues of the Laplacian of G
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      laplacian_matrix
      """
      from scipy.linalg import eigvalsh
      return eigvalsh(nx.laplacian_matrix(G,weight=weight).todense())
  
  def adjacency_spectrum(G, weight='weight'):
      """Return eigenvalues of the adjacency matrix of G.
  
      Parameters
      ----------
      G : graph
         A NetworkX graph
  
      weight : string or None, optional (default='weight')
         The edge data key used to compute each value in the matrix.
         If None, then each edge has weight 1.
  
      Returns
      -------
      evals : NumPy array
        Eigenvalues
  
      Notes
      -----
      For MultiGraph/MultiDiGraph, the edges weights are summed.
      See to_numpy_matrix for other options.
  
      See Also
      --------
      adjacency_matrix
      """
      from scipy.linalg import eigvals
      return eigvals(nx.adjacency_matrix(G,weight=weight).todense())
  
  # fixture for nose tests
  def setup_module(module):
      from nose import SkipTest
      try:
          import scipy.linalg
      except:
          raise SkipTest("scipy.linalg not available")
  
subject: Use symmetric eigen solver in laplacian_spectrum