<commit_before>"""
Laplacian, adjacency matrix, and spectrum of graphs.

Needs numpy array package: numpy.scipy.org.

"""
__author__ = """<NAME> (<EMAIL>)\<NAME> (<EMAIL>)\n<NAME>(<EMAIL>)"""
#    Copyright (C) 2004-2006 by 
#    <NAME> <<EMAIL>>
#    <NAME> <<EMAIL>>
#    <NAME> <<EMAIL>>
#    Distributed under the terms of the GNU Lesser General Public License
#    http://www.gnu.org/copyleft/lesser.html

try:
    import numpy as N
except:
    raise ImportError

import networkx

def adj_matrix(G,nodelist=None):
    """Return adjacency matrix of graph as a numpy matrix.

    This just calls networkx.convert.to_numpy_matrix.

    If you want a pure python adjacency matrix represntation try
    networkx.convert.to_dict_of_dicts with weighted=False,
    which will return a dictionary-of-dictionaries format that
    can be addressed as a sparse matrix.

    """
    return networkx.to_numpy_matrix(G,nodelist=nodelist,weighted=False)


def laplacian(G,nodelist=None):
    """Return standard combinatorial Laplacian of G as a numpy matrix.

    Return the matrix L = D - A, where

       D is the diagonal matrix in which the i'th entry is the degree of node i
       A is the adjacency matrix.

    """
    # this isn't the most efficient way to do this...
    n=G.order()
    I=N.identity(n)
    A=N.asarray(networkx.to_numpy_matrix(G,nodelist=nodelist))
    D=I*N.sum(A,axis=1)
    L=D-A
    return L


def normalized_laplacian(G,nodelist=None):
    """Return normalized Laplacian of G as a numpy matrix.

    See Spectral Graph Theory by <NAME>.
    CBMS Regional Conference Series in Mathematics, Number 92,<commit_msg>spurious "weighted" keyword in adj_matrix<commit_after>"""
Laplacian, adjacency matrix, and spectrum of graphs.

Needs numpy array package: numpy.scipy.org.

"""
__author__ = """<NAME> (<EMAIL>)\<NAME> (<EMAIL>)\n<NAME>(<EMAIL>)"""
#    Copyright (C) 2004-2006 by 
#    <NAME> <<EMAIL>>
#    <NAME> <<EMAIL>>
#    <NAME> <<EMAIL>>
#    Distributed under the terms of the GNU Lesser General Public License
#    http://www.gnu.org/copyleft/lesser.html

try:
    import numpy as N
except:
    raise ImportError

import networkx

def adj_matrix(G,nodelist=None):
    """Return adjacency matrix of graph as a numpy matrix.

    This just calls networkx.convert.to_numpy_matrix.

    If you want a pure python adjacency matrix represntation try
    networkx.convert.to_dict_of_dicts with weighted=False,
    which will return a dictionary-of-dictionaries format that
    can be addressed as a sparse matrix.

    """
    return networkx.to_numpy_matrix(G,nodelist=nodelist)


def laplacian(G,nodelist=None):
    """Return standard combinatorial Laplacian of G as a numpy matrix.

    Return the matrix L = D - A, where

       D is the diagonal matrix in which the i'th entry is the degree of node i
       A is the adjacency matrix.

    """
    # this isn't the most efficient way to do this...
    n=G.order()
    I=N.identity(n)
    A=N.asarray(networkx.to_numpy_matrix(G,nodelist=nodelist))
    D=I*N.sum(A,axis=1)
    L=D-A
    return L


def normalized_laplacian(G,nodelist=None):
    """Return normalized Laplacian of G as a numpy matrix.

    See Spectral Graph Theory by <NAME>.
    CBMS Regional Conference Series in Mathematics, Number 92,
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