Spectrum 
========

>>> try:
...     import numpy.linalg
...	eigenvalues=numpy.linalg.eigvals
... except ImportError:
...     raise ImportError,"numpy not found"


>>> from networkx.generators.degree_seq import *
>>> from networkx.spectrum import *
>>> import sys
>>> deg=[3,2,2,1,0]
>>> G=havel_hakimi_graph(deg)

Adjacency Matrix
----------------

>>> NA=N.array([[0, 1, 1, 1, 0], [1, 0, 1, 0, 0], [1, 1, 0, 0, 0], [1, 0, 0, 0, 0], [0, 0, 0, 0, 0]])
>>> s=N.sum(N.sum((adj_matrix(G)-NA)))
>>> print float(s) 
0.0

Laplacian
---------
>>> NL=N.array([[3, -1, -1, -1, 0], [-1, 2, -1, 0, 0], [-1, -1, 2, 0, 0], [-1, 0, 0, 1, 0], [0, 0, 0, 0, 0]])
>>> L= laplacian(G)
>>> s=N.sum(N.sum((adj_matrix(G)-NA)))
>>> print float(s) 
0.0

>>> NGL=N.array([[3, -1, -1, -1, 0], [-1, 2, -1, 0, 0], [-1, -1, 2, 0, 0], [-1, 0, 0, 1, 0], [0, 0, 0, 0, 0]])

>>> GL=generalized_laplacian(G)
>>> for row in GL:
...    for entry in row:
...        sys.stdout.write("%5.2f "%entry)
...    print 
 1.00 -0.41 -0.41 -0.58  0.00 
-0.41  1.00 -0.50  0.00  0.00 
-0.41 -0.50  1.00  0.00  0.00 
-0.58  0.00  0.00  1.00  0.00 
 0.00  0.00  0.00  0.00  0.00 

Eigenvalues
-----------

>>> e=eigenvalues(L)
>>> print map(int,map(round,sorted(e)))
[0, 0, 1, 3, 4]

>>> e=eigenvalues(GL)
>>> for ev in sorted(e):
...     print "%5.2f"%abs(ev)
 0.00
 0.00
 0.77
 1.50
 1.73