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  • Committer: Package Import Robot
  • Author(s): Sandro Tosi
  • Date: 2012-08-11 12:41:30 UTC
  • mfrom: (1.4.1) (5.1.13 sid)
  • Revision ID: package-import@ubuntu.com-20120811124130-whr6uso7fncyg8bi
Tags: 1.7-1
* New upstream release
* debian/patches/changeset_*
  - removed, included upstream

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networkx/generators/stochastic.py
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"""
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Stocastic graph.
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"""
 
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"""Stocastic graph."""
 
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import networkx as nx
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#    Copyright (C) 2010 by 
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#    Aric Hagberg <hagberg@lanl.gov>
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#    Dan Schult <dschult@colgate.edu>
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#    All rights reserved.
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#    BSD license.
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__author__ = "Aric Hagberg <hagberg@lanl.gov>"
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__all__ = ['stochastic_graph']
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import networkx as nx
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def stochastic_graph(G,copy=True):
 
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def stochastic_graph(G, copy=True, weight='weight'):
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    """Return a right-stochastic representation of G.
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    A right-stochastic graph is a weighted graph in which all of
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    copy : boolean, optional
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      If True make a copy of the graph, otherwise modify original graph
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    weight : key (optional)
 
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      Edge data key used for weight.  If None all weights are set to 1.
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    """        
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    if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:
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        raise Exception("stochastic_graph not implemented for Multi(Di)Graphs")
 
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        raise Exception("stochastic_graph not implemented for multigraphs")
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    if not G.is_directed():
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        raise Exception("stochastic_graph not implemented for undirected graphs")
 
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        raise Exception("stochastic_graph not defined for undirected graphs")
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    if copy:
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        W=nx.DiGraph(G)
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    else:
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        W=G # reference original graph, no copy
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    try:        
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        degree=W.out_degree(weighted=True)
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    except:
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        degree=W.out_degree()
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#    for n in W:
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#        for p in W[n]:
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#            weight=G[n][p].get('weight',1.0)
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#            W[n][p]['weight']=weight/degree[n]        
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    degree=W.out_degree(weight=weight)
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    for (u,v,d) in W.edges(data=True):
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        d['weight']=d.get('weight',1.0)/degree[u]
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        d[weight]=d.get(weight,1.0)/degree[u]
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    return W