Module layout

source code

Layout (positioning) algorithms for graph drawing.

Date: $Date: 2005-06-15 08:53:26 -0600 (Wed, 15 Jun 2005) $

Author: Aric Hagberg (hagberg@lanl.gov) Dan Schult(dschult@colgate.edu)

Functions

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circular_layout(G, dim=2)
Circular layout.

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shell_layout(G, nlist=None, dim=2)
Shell layout.

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random_layout(G, dim=2)
Random layout.

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spring_layout(G, iterations=50, dim=2, node_pos=None)
Spring force model layout

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spectral_layout(G, dim=2, vpos=None, iterations=1000, eps=0.001)
Return the position vectors for drawing G using spectral layout.

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graph_low_ev_pi(uhat, G, eps=0.001, iterations=10000)
Power Iteration method to find smallest eigenvectors of Laplacian(G).

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_gershgorin_setup(G)
Return a list of matrix properties to be used to iteratively multiply B*v where v is a vector and B=g*I-L and g is the Gershgorin estimate of the largest eigenvalue of L=Laplacian(G).

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_graph_gershgorin_dot_v(gg_data, v)
Returns B*v where B=g*I-L and g is the Gershgorin estimate of the largest eigenvalue of L.

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_test_suite()

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Variables

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__credits__ = ''

__revision__ = '$Revision: 1033 $'

__warningregistry__ = {('Not importing directory \'/usr/lib/py...

Function Details

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circular_layout(G, dim=2)

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Circular layout.

Crude version that doesn't try to minimize edge crossings.

shell_layout(G, nlist=None, dim=2)

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Shell layout. Crude version that doesn't try to minimize edge crossings.

nlist is an optional list of lists of nodes to be drawn at each shell level. Only one shell with all nodes will be drawn if not specified.

graph_low_ev_pi(uhat, G, eps=0.001, iterations=10000)

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Power Iteration method to find smallest eigenvectors of Laplacian(G). Note: constant eigenvector has eigenvalue=0 but is not included in the count of smallest eigenvalues.

uhat -- list of p initial guesses (dicts) for the p eigenvectors. G -- The Graph from which Laplacian is calculated. eps -- tolerance for norm of change in eigenvalue estimate. iterations -- maximum number of iterations to use.

_gershgorin_setup(G)

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Return a list of matrix properties to be used to iteratively multiply B*v where v is a vector and B=g*I-L and g is the Gershgorin estimate of the largest eigenvalue of L=Laplacian(G).

Used as input to graph_gershgorin_dot_v()

_graph_gershgorin_dot_v(gg_data, v)

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Returns B*v where B=g*I-L and g is the Gershgorin estimate of the largest eigenvalue of L. (g=max( deg(n) + sum_u(|w_(n,u)|)

We use this to iterate and find the smallest eigenvectors of L.

Variables Details

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warningregistry

Value:

{('Not importing directory \'/usr/lib/python2.5/site-packages/Numeric\
': missing __init__.py',
  <type 'exceptions.ImportWarning'>,
  19): 1}