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annotate planemo/lib/python3.7/site-packages/networkx/generators/stochastic.py @ 1:56ad4e20f292 draft

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"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author guerler
date Fri, 31 Jul 2020 00:32:28 -0400
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1 # Copyright (C) 2010-2013 by
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2 # Aric Hagberg <hagberg@lanl.gov>
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3 # Dan Schult <dschult@colgate.edu>
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4 # Pieter Swart <swart@lanl.gov>
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5 # All rights reserved.
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6 # BSD license.
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7 """Functions for generating stochastic graphs from a given weighted directed
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8 graph.
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9
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10 """
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11
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12 from networkx.classes import DiGraph
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13 from networkx.classes import MultiDiGraph
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14 from networkx.utils import not_implemented_for
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15
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16 __author__ = "Aric Hagberg <aric.hagberg@gmail.com>"
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17 __all__ = ['stochastic_graph']
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18
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19
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20 @not_implemented_for('undirected')
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21 def stochastic_graph(G, copy=True, weight='weight'):
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22 """Returns a right-stochastic representation of directed graph `G`.
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23
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24 A right-stochastic graph is a weighted digraph in which for each
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25 node, the sum of the weights of all the out-edges of that node is
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26 1. If the graph is already weighted (for example, via a 'weight'
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27 edge attribute), the reweighting takes that into account.
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28
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29 Parameters
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30 ----------
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31 G : directed graph
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32 A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
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33
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34 copy : boolean, optional
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35 If this is True, then this function returns a new graph with
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36 the stochastic reweighting. Otherwise, the original graph is
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37 modified in-place (and also returned, for convenience).
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38
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39 weight : edge attribute key (optional, default='weight')
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40 Edge attribute key used for reading the existing weight and
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41 setting the new weight. If no attribute with this key is found
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42 for an edge, then the edge weight is assumed to be 1. If an edge
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43 has a weight, it must be a a positive number.
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44
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45 """
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46 if copy:
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47 G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
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48 # There is a tradeoff here: the dictionary of node degrees may
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49 # require a lot of memory, whereas making a call to `G.out_degree`
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50 # inside the loop may be costly in computation time.
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51 degree = dict(G.out_degree(weight=weight))
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52 for u, v, d in G.edges(data=True):
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53 if degree[u] == 0:
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54 d[weight] = 0
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55 else:
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56 d[weight] = d.get(weight, 1) / degree[u]
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57 return G