Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/networkx/classes/function.py @ 0:26e78fe6e8c4 draft
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author | shellac |
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date | Sat, 02 May 2020 07:14:21 -0400 |
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1 # Copyright (C) 2004-2019 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 # |
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8 # Authors: Aric Hagberg <hagberg@lanl.gov> |
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9 # Pieter Swart <swart@lanl.gov> |
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10 # Dan Schult <dschult@colgate.edu> |
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11 """Functional interface to graph methods and assorted utilities. |
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12 """ |
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13 |
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14 from collections import Counter |
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15 from itertools import chain |
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16 try: |
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17 from itertools import zip_longest |
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18 except ImportError: |
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19 from itertools import izip_longest as zip_longest |
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20 |
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21 import networkx as nx |
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22 from networkx.utils import pairwise, not_implemented_for |
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23 |
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24 from networkx.classes.graphviews import subgraph_view, reverse_view |
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25 |
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26 __all__ = ['nodes', 'edges', 'degree', 'degree_histogram', 'neighbors', |
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27 'number_of_nodes', 'number_of_edges', 'density', |
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28 'is_directed', 'info', 'freeze', 'is_frozen', |
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29 'subgraph', 'subgraph_view', 'induced_subgraph', 'reverse_view', |
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30 'edge_subgraph', 'restricted_view', |
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31 'to_directed', 'to_undirected', |
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32 'add_star', 'add_path', 'add_cycle', |
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33 'create_empty_copy', 'set_node_attributes', |
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34 'get_node_attributes', 'set_edge_attributes', |
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35 'get_edge_attributes', 'all_neighbors', 'non_neighbors', |
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36 'non_edges', 'common_neighbors', 'is_weighted', |
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37 'is_negatively_weighted', 'is_empty', |
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38 'selfloop_edges', 'nodes_with_selfloops', 'number_of_selfloops', |
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39 ] |
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40 |
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41 |
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42 def nodes(G): |
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43 """Returns an iterator over the graph nodes.""" |
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44 return G.nodes() |
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45 |
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46 |
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47 def edges(G, nbunch=None): |
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48 """Returns an edge view of edges incident to nodes in nbunch. |
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49 |
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50 Return all edges if nbunch is unspecified or nbunch=None. |
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51 |
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52 For digraphs, edges=out_edges |
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53 """ |
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54 return G.edges(nbunch) |
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55 |
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56 |
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57 def degree(G, nbunch=None, weight=None): |
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58 """Returns a degree view of single node or of nbunch of nodes. |
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59 If nbunch is omitted, then return degrees of *all* nodes. |
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60 """ |
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61 return G.degree(nbunch, weight) |
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62 |
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63 |
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64 def neighbors(G, n): |
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65 """Returns a list of nodes connected to node n. """ |
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66 return G.neighbors(n) |
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67 |
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68 |
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69 def number_of_nodes(G): |
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70 """Returns the number of nodes in the graph.""" |
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71 return G.number_of_nodes() |
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72 |
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73 |
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74 def number_of_edges(G): |
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75 """Returns the number of edges in the graph. """ |
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76 return G.number_of_edges() |
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77 |
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78 |
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79 def density(G): |
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80 r"""Returns the density of a graph. |
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81 |
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82 The density for undirected graphs is |
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83 |
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84 .. math:: |
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85 |
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86 d = \frac{2m}{n(n-1)}, |
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87 |
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88 and for directed graphs is |
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89 |
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90 .. math:: |
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91 |
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92 d = \frac{m}{n(n-1)}, |
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93 |
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94 where `n` is the number of nodes and `m` is the number of edges in `G`. |
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95 |
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96 Notes |
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97 ----- |
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98 The density is 0 for a graph without edges and 1 for a complete graph. |
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99 The density of multigraphs can be higher than 1. |
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100 |
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101 Self loops are counted in the total number of edges so graphs with self |
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102 loops can have density higher than 1. |
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103 """ |
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104 n = number_of_nodes(G) |
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105 m = number_of_edges(G) |
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106 if m == 0 or n <= 1: |
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107 return 0 |
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108 d = m / (n * (n - 1)) |
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109 if not G.is_directed(): |
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110 d *= 2 |
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111 return d |
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112 |
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113 |
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114 def degree_histogram(G): |
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115 """Returns a list of the frequency of each degree value. |
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116 |
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117 Parameters |
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118 ---------- |
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119 G : Networkx graph |
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120 A graph |
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121 |
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122 Returns |
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123 ------- |
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124 hist : list |
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125 A list of frequencies of degrees. |
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126 The degree values are the index in the list. |
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127 |
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128 Notes |
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129 ----- |
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130 Note: the bins are width one, hence len(list) can be large |
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131 (Order(number_of_edges)) |
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132 """ |
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133 counts = Counter(d for n, d in G.degree()) |
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134 return [counts.get(i, 0) for i in range(max(counts) + 1)] |
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135 |
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136 |
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137 def is_directed(G): |
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138 """ Return True if graph is directed.""" |
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139 return G.is_directed() |
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140 |
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141 |
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142 def frozen(*args, **kwargs): |
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143 """Dummy method for raising errors when trying to modify frozen graphs""" |
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144 raise nx.NetworkXError("Frozen graph can't be modified") |
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145 |
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146 |
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147 def freeze(G): |
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148 """Modify graph to prevent further change by adding or removing |
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149 nodes or edges. |
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150 |
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151 Node and edge data can still be modified. |
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152 |
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153 Parameters |
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154 ---------- |
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155 G : graph |
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156 A NetworkX graph |
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157 |
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158 Examples |
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159 -------- |
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160 >>> G = nx.path_graph(4) |
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161 >>> G = nx.freeze(G) |
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162 >>> try: |
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163 ... G.add_edge(4, 5) |
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164 ... except nx.NetworkXError as e: |
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165 ... print(str(e)) |
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166 Frozen graph can't be modified |
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167 |
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168 Notes |
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169 ----- |
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170 To "unfreeze" a graph you must make a copy by creating a new graph object: |
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171 |
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172 >>> graph = nx.path_graph(4) |
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173 >>> frozen_graph = nx.freeze(graph) |
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174 >>> unfrozen_graph = nx.Graph(frozen_graph) |
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175 >>> nx.is_frozen(unfrozen_graph) |
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176 False |
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177 |
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178 See Also |
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179 -------- |
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180 is_frozen |
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181 """ |
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182 G.add_node = frozen |
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183 G.add_nodes_from = frozen |
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184 G.remove_node = frozen |
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185 G.remove_nodes_from = frozen |
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186 G.add_edge = frozen |
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187 G.add_edges_from = frozen |
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188 G.add_weighted_edges_from = frozen |
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189 G.remove_edge = frozen |
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190 G.remove_edges_from = frozen |
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191 G.clear = frozen |
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192 G.frozen = True |
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193 return G |
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194 |
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195 |
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196 def is_frozen(G): |
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197 """Returns True if graph is frozen. |
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198 |
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199 Parameters |
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200 ---------- |
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201 G : graph |
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202 A NetworkX graph |
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203 |
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204 See Also |
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205 -------- |
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206 freeze |
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207 """ |
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208 try: |
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209 return G.frozen |
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210 except AttributeError: |
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211 return False |
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212 |
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213 |
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214 def add_star(G_to_add_to, nodes_for_star, **attr): |
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215 """Add a star to Graph G_to_add_to. |
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216 |
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217 The first node in `nodes_for_star` is the middle of the star. |
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218 It is connected to all other nodes. |
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219 |
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220 Parameters |
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221 ---------- |
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222 G_to_add_to : graph |
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223 A NetworkX graph |
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224 nodes_for_star : iterable container |
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225 A container of nodes. |
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226 attr : keyword arguments, optional (default= no attributes) |
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227 Attributes to add to every edge in star. |
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228 |
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229 See Also |
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230 -------- |
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231 add_path, add_cycle |
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232 |
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233 Examples |
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234 -------- |
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235 >>> G = nx.Graph() |
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236 >>> nx.add_star(G, [0, 1, 2, 3]) |
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237 >>> nx.add_star(G, [10, 11, 12], weight=2) |
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238 """ |
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239 nlist = iter(nodes_for_star) |
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240 try: |
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241 v = next(nlist) |
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242 except StopIteration: |
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243 return |
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244 G_to_add_to.add_node(v) |
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245 edges = ((v, n) for n in nlist) |
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246 G_to_add_to.add_edges_from(edges, **attr) |
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247 |
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248 |
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249 def add_path(G_to_add_to, nodes_for_path, **attr): |
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250 """Add a path to the Graph G_to_add_to. |
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251 |
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252 Parameters |
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253 ---------- |
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254 G_to_add_to : graph |
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255 A NetworkX graph |
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256 nodes_for_path : iterable container |
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257 A container of nodes. A path will be constructed from |
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258 the nodes (in order) and added to the graph. |
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259 attr : keyword arguments, optional (default= no attributes) |
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260 Attributes to add to every edge in path. |
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261 |
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262 See Also |
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263 -------- |
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264 add_star, add_cycle |
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265 |
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266 Examples |
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267 -------- |
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268 >>> G = nx.Graph() |
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269 >>> nx.add_path(G, [0, 1, 2, 3]) |
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270 >>> nx.add_path(G, [10, 11, 12], weight=7) |
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271 """ |
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272 nlist = iter(nodes_for_path) |
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273 try: |
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274 first_node = next(nlist) |
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275 except StopIteration: |
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276 return |
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277 G_to_add_to.add_node(first_node) |
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278 G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist)), **attr) |
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279 |
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280 |
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281 def add_cycle(G_to_add_to, nodes_for_cycle, **attr): |
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282 """Add a cycle to the Graph G_to_add_to. |
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283 |
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284 Parameters |
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285 ---------- |
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286 G_to_add_to : graph |
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287 A NetworkX graph |
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288 nodes_for_cycle: iterable container |
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289 A container of nodes. A cycle will be constructed from |
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290 the nodes (in order) and added to the graph. |
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291 attr : keyword arguments, optional (default= no attributes) |
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292 Attributes to add to every edge in cycle. |
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293 |
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294 See Also |
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295 -------- |
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296 add_path, add_star |
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297 |
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298 Examples |
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299 -------- |
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300 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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301 >>> nx.add_cycle(G, [0, 1, 2, 3]) |
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302 >>> nx.add_cycle(G, [10, 11, 12], weight=7) |
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303 """ |
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304 nlist = iter(nodes_for_cycle) |
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305 try: |
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306 first_node = next(nlist) |
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307 except StopIteration: |
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308 return |
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309 G_to_add_to.add_node(first_node) |
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310 G_to_add_to.add_edges_from(pairwise(chain((first_node,), nlist), cyclic=True), **attr) |
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311 |
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312 |
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313 def subgraph(G, nbunch): |
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314 """Returns the subgraph induced on nodes in nbunch. |
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315 |
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316 Parameters |
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317 ---------- |
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318 G : graph |
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319 A NetworkX graph |
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320 |
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321 nbunch : list, iterable |
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322 A container of nodes that will be iterated through once (thus |
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323 it should be an iterator or be iterable). Each element of the |
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324 container should be a valid node type: any hashable type except |
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325 None. If nbunch is None, return all edges data in the graph. |
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326 Nodes in nbunch that are not in the graph will be (quietly) |
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327 ignored. |
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328 |
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329 Notes |
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330 ----- |
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331 subgraph(G) calls G.subgraph() |
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332 """ |
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333 return G.subgraph(nbunch) |
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334 |
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335 |
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336 def induced_subgraph(G, nbunch): |
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337 """Returns a SubGraph view of `G` showing only nodes in nbunch. |
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338 |
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339 The induced subgraph of a graph on a set of nodes N is the |
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340 graph with nodes N and edges from G which have both ends in N. |
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341 |
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342 Parameters |
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343 ---------- |
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344 G : NetworkX Graph |
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345 nbunch : node, container of nodes or None (for all nodes) |
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346 |
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347 Returns |
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348 ------- |
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349 subgraph : SubGraph View |
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350 A read-only view of the subgraph in `G` induced by the nodes. |
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351 Changes to the graph `G` will be reflected in the view. |
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352 |
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353 Notes |
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354 ----- |
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355 To create a mutable subgraph with its own copies of nodes |
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356 edges and attributes use `subgraph.copy()` or `Graph(subgraph)` |
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357 |
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358 For an inplace reduction of a graph to a subgraph you can remove nodes: |
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359 `G.remove_nodes_from(n in G if n not in set(nbunch))` |
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360 |
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361 If you are going to compute subgraphs of your subgraphs you could |
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362 end up with a chain of views that can be very slow once the chain |
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363 has about 15 views in it. If they are all induced subgraphs, you |
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364 can short-cut the chain by making them all subgraphs of the original |
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365 graph. The graph class method `G.subgraph` does this when `G` is |
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366 a subgraph. In contrast, this function allows you to choose to build |
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367 chains or not, as you wish. The returned subgraph is a view on `G`. |
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368 |
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369 Examples |
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370 -------- |
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371 >>> import networkx as nx |
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372 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc |
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373 >>> H = G.subgraph([0, 1, 2]) |
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374 >>> list(H.edges) |
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375 [(0, 1), (1, 2)] |
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376 """ |
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377 induced_nodes = nx.filters.show_nodes(G.nbunch_iter(nbunch)) |
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378 return nx.graphviews.subgraph_view(G, induced_nodes) |
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379 |
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380 |
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381 def edge_subgraph(G, edges): |
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382 """Returns a view of the subgraph induced by the specified edges. |
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383 |
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384 The induced subgraph contains each edge in `edges` and each |
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385 node incident to any of those edges. |
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386 |
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387 Parameters |
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388 ---------- |
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389 G : NetworkX Graph |
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390 edges : iterable |
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391 An iterable of edges. Edges not present in `G` are ignored. |
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392 |
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393 Returns |
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394 ------- |
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395 subgraph : SubGraph View |
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396 A read-only edge-induced subgraph of `G`. |
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397 Changes to `G` are reflected in the view. |
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398 |
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399 Notes |
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400 ----- |
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401 To create a mutable subgraph with its own copies of nodes |
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402 edges and attributes use `subgraph.copy()` or `Graph(subgraph)` |
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403 |
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404 If you create a subgraph of a subgraph recursively you can end up |
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405 with a chain of subgraphs that becomes very slow with about 15 |
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406 nested subgraph views. Luckily the edge_subgraph filter nests |
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407 nicely so you can use the original graph as G in this function |
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408 to avoid chains. We do not rule out chains programmatically so |
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409 that odd cases like an `edge_subgraph` of a `restricted_view` |
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410 can be created. |
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411 |
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412 Examples |
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413 -------- |
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414 >>> import networkx as nx |
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415 >>> G = nx.path_graph(5) |
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416 >>> H = G.edge_subgraph([(0, 1), (3, 4)]) |
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417 >>> list(H.nodes) |
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418 [0, 1, 3, 4] |
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419 >>> list(H.edges) |
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420 [(0, 1), (3, 4)] |
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421 """ |
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422 nxf = nx.filters |
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423 edges = set(edges) |
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424 nodes = set() |
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425 for e in edges: |
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426 nodes.update(e[:2]) |
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427 induced_nodes = nxf.show_nodes(nodes) |
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428 if G.is_multigraph(): |
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429 if G.is_directed(): |
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430 induced_edges = nxf.show_multidiedges(edges) |
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431 else: |
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432 induced_edges = nxf.show_multiedges(edges) |
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433 else: |
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434 if G.is_directed(): |
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435 induced_edges = nxf.show_diedges(edges) |
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436 else: |
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437 induced_edges = nxf.show_edges(edges) |
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438 return nx.graphviews.subgraph_view(G, induced_nodes, induced_edges) |
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439 |
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440 |
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441 def restricted_view(G, nodes, edges): |
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442 """Returns a view of `G` with hidden nodes and edges. |
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443 |
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444 The resulting subgraph filters out node `nodes` and edges `edges`. |
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445 Filtered out nodes also filter out any of their edges. |
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446 |
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447 Parameters |
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448 ---------- |
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449 G : NetworkX Graph |
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450 nodes : iterable |
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451 An iterable of nodes. Nodes not present in `G` are ignored. |
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452 edges : iterable |
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453 An iterable of edges. Edges not present in `G` are ignored. |
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454 |
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455 Returns |
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456 ------- |
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457 subgraph : SubGraph View |
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458 A read-only restricted view of `G` filtering out nodes and edges. |
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459 Changes to `G` are reflected in the view. |
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460 |
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461 Notes |
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462 ----- |
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463 To create a mutable subgraph with its own copies of nodes |
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464 edges and attributes use `subgraph.copy()` or `Graph(subgraph)` |
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465 |
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466 If you create a subgraph of a subgraph recursively you may end up |
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467 with a chain of subgraph views. Such chains can get quite slow |
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468 for lengths near 15. To avoid long chains, try to make your subgraph |
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469 based on the original graph. We do not rule out chains programmatically |
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470 so that odd cases like an `edge_subgraph` of a `restricted_view` |
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471 can be created. |
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472 |
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473 Examples |
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474 -------- |
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475 >>> import networkx as nx |
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476 >>> G = nx.path_graph(5) |
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477 >>> H = nx.restricted_view(G, [0], [(1, 2), (3, 4)]) |
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478 >>> list(H.nodes) |
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479 [1, 2, 3, 4] |
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480 >>> list(H.edges) |
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481 [(2, 3)] |
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482 """ |
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483 nxf = nx.filters |
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484 hide_nodes = nxf.hide_nodes(nodes) |
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485 if G.is_multigraph(): |
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486 if G.is_directed(): |
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487 hide_edges = nxf.hide_multidiedges(edges) |
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488 else: |
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489 hide_edges = nxf.hide_multiedges(edges) |
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490 else: |
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491 if G.is_directed(): |
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492 hide_edges = nxf.hide_diedges(edges) |
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493 else: |
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494 hide_edges = nxf.hide_edges(edges) |
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495 return nx.graphviews.subgraph_view(G, hide_nodes, hide_edges) |
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496 |
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497 |
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498 def to_directed(graph): |
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499 """Returns a directed view of the graph `graph`. |
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500 |
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501 Identical to graph.to_directed(as_view=True) |
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502 Note that graph.to_directed defaults to `as_view=False` |
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503 while this function always provides a view. |
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504 """ |
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505 return graph.to_directed(as_view=True) |
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506 |
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507 |
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508 def to_undirected(graph): |
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509 """Returns an undirected view of the graph `graph`. |
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510 |
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511 Identical to graph.to_undirected(as_view=True) |
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512 Note that graph.to_undirected defaults to `as_view=False` |
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513 while this function always provides a view. |
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514 """ |
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515 return graph.to_undirected(as_view=True) |
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516 |
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517 |
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518 def create_empty_copy(G, with_data=True): |
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519 """Returns a copy of the graph G with all of the edges removed. |
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520 |
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521 Parameters |
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522 ---------- |
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523 G : graph |
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524 A NetworkX graph |
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525 |
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526 with_data : bool (default=True) |
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527 Propagate Graph and Nodes data to the new graph. |
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528 |
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529 See Also |
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530 ----- |
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531 empty_graph |
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532 |
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533 """ |
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534 H = G.__class__() |
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535 H.add_nodes_from(G.nodes(data=with_data)) |
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536 if with_data: |
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537 H.graph.update(G.graph) |
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538 return H |
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539 |
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540 |
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541 def info(G, n=None): |
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542 """Print short summary of information for the graph G or the node n. |
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543 |
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544 Parameters |
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545 ---------- |
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546 G : Networkx graph |
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547 A graph |
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548 n : node (any hashable) |
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549 A node in the graph G |
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550 """ |
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551 info = '' # append this all to a string |
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552 if n is None: |
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553 info += "Name: %s\n" % G.name |
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554 type_name = [type(G).__name__] |
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555 info += "Type: %s\n" % ",".join(type_name) |
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556 info += "Number of nodes: %d\n" % G.number_of_nodes() |
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557 info += "Number of edges: %d\n" % G.number_of_edges() |
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558 nnodes = G.number_of_nodes() |
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559 if len(G) > 0: |
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560 if G.is_directed(): |
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561 deg = sum(d for n, d in G.in_degree()) / float(nnodes) |
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562 info += "Average in degree: %8.4f\n" % deg |
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563 deg = sum(d for n, d in G.out_degree()) / float(nnodes) |
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564 info += "Average out degree: %8.4f" % deg |
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565 else: |
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566 s = sum(dict(G.degree()).values()) |
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567 info += "Average degree: %8.4f" % (float(s) / float(nnodes)) |
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568 |
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569 else: |
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570 if n not in G: |
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571 raise nx.NetworkXError("node %s not in graph" % (n,)) |
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572 info += "Node % s has the following properties:\n" % n |
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573 info += "Degree: %d\n" % G.degree(n) |
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574 info += "Neighbors: " |
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575 info += ' '.join(str(nbr) for nbr in G.neighbors(n)) |
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576 return info |
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577 |
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578 |
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579 def set_node_attributes(G, values, name=None): |
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580 """Sets node attributes from a given value or dictionary of values. |
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581 |
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582 .. Warning:: The call order of arguments `values` and `name` |
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583 switched between v1.x & v2.x. |
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584 |
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585 Parameters |
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586 ---------- |
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587 G : NetworkX Graph |
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588 |
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589 values : scalar value, dict-like |
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590 What the node attribute should be set to. If `values` is |
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591 not a dictionary, then it is treated as a single attribute value |
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592 that is then applied to every node in `G`. This means that if |
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593 you provide a mutable object, like a list, updates to that object |
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594 will be reflected in the node attribute for every node. |
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595 The attribute name will be `name`. |
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596 |
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597 If `values` is a dict or a dict of dict, it should be keyed |
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598 by node to either an attribute value or a dict of attribute key/value |
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599 pairs used to update the node's attributes. |
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600 |
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601 name : string (optional, default=None) |
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602 Name of the node attribute to set if values is a scalar. |
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603 |
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604 Examples |
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605 -------- |
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606 After computing some property of the nodes of a graph, you may want |
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607 to assign a node attribute to store the value of that property for |
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608 each node:: |
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609 |
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610 >>> G = nx.path_graph(3) |
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611 >>> bb = nx.betweenness_centrality(G) |
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612 >>> isinstance(bb, dict) |
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613 True |
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614 >>> nx.set_node_attributes(G, bb, 'betweenness') |
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615 >>> G.nodes[1]['betweenness'] |
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616 1.0 |
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617 |
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618 If you provide a list as the second argument, updates to the list |
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619 will be reflected in the node attribute for each node:: |
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620 |
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621 >>> G = nx.path_graph(3) |
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622 >>> labels = [] |
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623 >>> nx.set_node_attributes(G, labels, 'labels') |
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624 >>> labels.append('foo') |
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625 >>> G.nodes[0]['labels'] |
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626 ['foo'] |
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627 >>> G.nodes[1]['labels'] |
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628 ['foo'] |
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629 >>> G.nodes[2]['labels'] |
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630 ['foo'] |
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631 |
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632 If you provide a dictionary of dictionaries as the second argument, |
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633 the outer dictionary is assumed to be keyed by node to an inner |
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634 dictionary of node attributes for that node:: |
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635 |
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636 >>> G = nx.path_graph(3) |
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637 >>> attrs = {0: {'attr1': 20, 'attr2': 'nothing'}, 1: {'attr2': 3}} |
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638 >>> nx.set_node_attributes(G, attrs) |
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639 >>> G.nodes[0]['attr1'] |
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640 20 |
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641 >>> G.nodes[0]['attr2'] |
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642 'nothing' |
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643 >>> G.nodes[1]['attr2'] |
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644 3 |
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645 >>> G.nodes[2] |
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646 {} |
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647 |
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648 """ |
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649 # Set node attributes based on type of `values` |
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650 if name is not None: # `values` must not be a dict of dict |
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651 try: # `values` is a dict |
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652 for n, v in values.items(): |
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653 try: |
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654 G.nodes[n][name] = values[n] |
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655 except KeyError: |
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656 pass |
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657 except AttributeError: # `values` is a constant |
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658 for n in G: |
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659 G.nodes[n][name] = values |
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660 else: # `values` must be dict of dict |
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661 for n, d in values.items(): |
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662 try: |
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663 G.nodes[n].update(d) |
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664 except KeyError: |
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665 pass |
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666 |
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667 |
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668 def get_node_attributes(G, name): |
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669 """Get node attributes from graph |
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670 |
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671 Parameters |
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672 ---------- |
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673 G : NetworkX Graph |
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674 |
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675 name : string |
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676 Attribute name |
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677 |
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678 Returns |
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679 ------- |
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680 Dictionary of attributes keyed by node. |
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681 |
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682 Examples |
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683 -------- |
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684 >>> G = nx.Graph() |
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685 >>> G.add_nodes_from([1, 2, 3], color='red') |
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686 >>> color = nx.get_node_attributes(G, 'color') |
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687 >>> color[1] |
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688 'red' |
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689 """ |
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690 return {n: d[name] for n, d in G.nodes.items() if name in d} |
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691 |
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692 |
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693 def set_edge_attributes(G, values, name=None): |
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694 """Sets edge attributes from a given value or dictionary of values. |
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695 |
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parents:
diff
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|
696 .. Warning:: The call order of arguments `values` and `name` |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
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|
697 switched between v1.x & v2.x. |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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|
698 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
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|
699 Parameters |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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|
700 ---------- |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
changeset
|
701 G : NetworkX Graph |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
changeset
|
702 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
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|
703 values : scalar value, dict-like |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
704 What the edge attribute should be set to. If `values` is |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
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|
705 not a dictionary, then it is treated as a single attribute value |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
changeset
|
706 that is then applied to every edge in `G`. This means that if |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
changeset
|
707 you provide a mutable object, like a list, updates to that object |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
708 will be reflected in the edge attribute for each edge. The attribute |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
709 name will be `name`. |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
changeset
|
710 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
711 If `values` is a dict or a dict of dict, it should be keyed |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
712 by edge tuple to either an attribute value or a dict of attribute |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
713 key/value pairs used to update the edge's attributes. |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
714 For multigraphs, the edge tuples must be of the form ``(u, v, key)``, |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
715 where `u` and `v` are nodes and `key` is the edge key. |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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|
716 For non-multigraphs, the keys must be tuples of the form ``(u, v)``. |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
diff
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|
717 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
718 name : string (optional, default=None) |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
719 Name of the edge attribute to set if values is a scalar. |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
720 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
721 Examples |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
722 -------- |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
723 After computing some property of the edges of a graph, you may want |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
724 to assign a edge attribute to store the value of that property for |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
725 each edge:: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
726 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
727 >>> G = nx.path_graph(3) |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
728 >>> bb = nx.edge_betweenness_centrality(G, normalized=False) |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
729 >>> nx.set_edge_attributes(G, bb, 'betweenness') |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
730 >>> G.edges[1, 2]['betweenness'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
731 2.0 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
732 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
733 If you provide a list as the second argument, updates to the list |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
734 will be reflected in the edge attribute for each edge:: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
735 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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|
736 >>> labels = [] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
737 >>> nx.set_edge_attributes(G, labels, 'labels') |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
738 >>> labels.append('foo') |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
739 >>> G.edges[0, 1]['labels'] |
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parents:
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|
740 ['foo'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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|
741 >>> G.edges[1, 2]['labels'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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|
742 ['foo'] |
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parents:
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changeset
|
743 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
744 If you provide a dictionary of dictionaries as the second argument, |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
745 the entire dictionary will be used to update edge attributes:: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
746 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
747 >>> G = nx.path_graph(3) |
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parents:
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changeset
|
748 >>> attrs = {(0, 1): {'attr1': 20, 'attr2': 'nothing'}, |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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|
749 ... (1, 2): {'attr2': 3}} |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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|
750 >>> nx.set_edge_attributes(G, attrs) |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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|
751 >>> G[0][1]['attr1'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
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|
752 20 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
753 >>> G[0][1]['attr2'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
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parents:
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changeset
|
754 'nothing' |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
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changeset
|
755 >>> G[1][2]['attr2'] |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
756 3 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
757 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
758 """ |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
759 if name is not None: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
760 # `values` does not contain attribute names |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
761 try: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
762 # if `values` is a dict using `.items()` => {edge: value} |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
763 if G.is_multigraph(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
764 for (u, v, key), value in values.items(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
765 try: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
766 G[u][v][key][name] = value |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
767 except KeyError: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
768 pass |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
769 else: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
770 for (u, v), value in values.items(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
771 try: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
772 G[u][v][name] = value |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
773 except KeyError: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
774 pass |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
775 except AttributeError: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
776 # treat `values` as a constant |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
777 for u, v, data in G.edges(data=True): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
778 data[name] = values |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
779 else: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
780 # `values` consists of doct-of-dict {edge: {attr: value}} shape |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
781 if G.is_multigraph(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
782 for (u, v, key), d in values.items(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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|
783 try: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
784 G[u][v][key].update(d) |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
785 except KeyError: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
786 pass |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
787 else: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
788 for (u, v), d in values.items(): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
789 try: |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
790 G[u][v].update(d) |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
791 except KeyError: |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
792 pass |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
793 |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
794 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
795 def get_edge_attributes(G, name): |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
796 """Get edge attributes from graph |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
797 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
798 Parameters |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
799 ---------- |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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changeset
|
800 G : NetworkX Graph |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
801 |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
802 name : string |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
803 Attribute name |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
804 |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
805 Returns |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
806 ------- |
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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
807 Dictionary of attributes keyed by edge. For (di)graphs, the keys are |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
808 2-tuples of the form: (u, v). For multi(di)graphs, the keys are 3-tuples of |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
809 the form: (u, v, key). |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
diff
changeset
|
810 |
26e78fe6e8c4
"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
shellac
parents:
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811 Examples |
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812 -------- |
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813 >>> G = nx.Graph() |
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814 >>> nx.add_path(G, [1, 2, 3], color='red') |
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815 >>> color = nx.get_edge_attributes(G, 'color') |
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816 >>> color[(1, 2)] |
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817 'red' |
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818 """ |
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819 if G.is_multigraph(): |
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820 edges = G.edges(keys=True, data=True) |
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821 else: |
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822 edges = G.edges(data=True) |
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823 return {x[:-1]: x[-1][name] for x in edges if name in x[-1]} |
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824 |
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825 |
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826 def all_neighbors(graph, node): |
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827 """Returns all of the neighbors of a node in the graph. |
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828 |
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829 If the graph is directed returns predecessors as well as successors. |
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830 |
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831 Parameters |
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832 ---------- |
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833 graph : NetworkX graph |
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834 Graph to find neighbors. |
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835 |
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836 node : node |
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837 The node whose neighbors will be returned. |
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838 |
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839 Returns |
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840 ------- |
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841 neighbors : iterator |
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842 Iterator of neighbors |
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843 """ |
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844 if graph.is_directed(): |
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845 values = chain(graph.predecessors(node), graph.successors(node)) |
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846 else: |
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847 values = graph.neighbors(node) |
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848 return values |
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849 |
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850 |
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851 def non_neighbors(graph, node): |
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852 """Returns the non-neighbors of the node in the graph. |
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853 |
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854 Parameters |
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855 ---------- |
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856 graph : NetworkX graph |
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857 Graph to find neighbors. |
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858 |
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859 node : node |
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860 The node whose neighbors will be returned. |
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861 |
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862 Returns |
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863 ------- |
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864 non_neighbors : iterator |
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865 Iterator of nodes in the graph that are not neighbors of the node. |
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866 """ |
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867 nbors = set(neighbors(graph, node)) | {node} |
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868 return (nnode for nnode in graph if nnode not in nbors) |
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869 |
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870 |
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871 def non_edges(graph): |
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872 """Returns the non-existent edges in the graph. |
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873 |
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874 Parameters |
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875 ---------- |
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876 graph : NetworkX graph. |
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877 Graph to find non-existent edges. |
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878 |
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879 Returns |
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880 ------- |
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881 non_edges : iterator |
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882 Iterator of edges that are not in the graph. |
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883 """ |
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884 if graph.is_directed(): |
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885 for u in graph: |
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886 for v in non_neighbors(graph, u): |
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887 yield (u, v) |
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888 else: |
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889 nodes = set(graph) |
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890 while nodes: |
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891 u = nodes.pop() |
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892 for v in nodes - set(graph[u]): |
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893 yield (u, v) |
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894 |
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895 |
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896 @not_implemented_for('directed') |
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897 def common_neighbors(G, u, v): |
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898 """Returns the common neighbors of two nodes in a graph. |
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899 |
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900 Parameters |
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901 ---------- |
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902 G : graph |
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903 A NetworkX undirected graph. |
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904 |
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905 u, v : nodes |
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906 Nodes in the graph. |
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907 |
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908 Returns |
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909 ------- |
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910 cnbors : iterator |
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911 Iterator of common neighbors of u and v in the graph. |
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912 |
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913 Raises |
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914 ------ |
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915 NetworkXError |
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916 If u or v is not a node in the graph. |
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917 |
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918 Examples |
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919 -------- |
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920 >>> G = nx.complete_graph(5) |
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921 >>> sorted(nx.common_neighbors(G, 0, 1)) |
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922 [2, 3, 4] |
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923 """ |
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924 if u not in G: |
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925 raise nx.NetworkXError('u is not in the graph.') |
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926 if v not in G: |
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927 raise nx.NetworkXError('v is not in the graph.') |
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928 |
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929 # Return a generator explicitly instead of yielding so that the above |
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930 # checks are executed eagerly. |
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931 return (w for w in G[u] if w in G[v] and w not in (u, v)) |
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932 |
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933 |
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934 def is_weighted(G, edge=None, weight='weight'): |
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935 """Returns True if `G` has weighted edges. |
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936 |
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937 Parameters |
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938 ---------- |
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939 G : graph |
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940 A NetworkX graph. |
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941 |
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942 edge : tuple, optional |
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943 A 2-tuple specifying the only edge in `G` that will be tested. If |
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944 None, then every edge in `G` is tested. |
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945 |
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946 weight: string, optional |
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947 The attribute name used to query for edge weights. |
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948 |
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949 Returns |
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950 ------- |
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951 bool |
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952 A boolean signifying if `G`, or the specified edge, is weighted. |
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953 |
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954 Raises |
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955 ------ |
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956 NetworkXError |
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957 If the specified edge does not exist. |
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958 |
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959 Examples |
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960 -------- |
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961 >>> G = nx.path_graph(4) |
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962 >>> nx.is_weighted(G) |
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963 False |
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964 >>> nx.is_weighted(G, (2, 3)) |
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965 False |
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966 |
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967 >>> G = nx.DiGraph() |
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968 >>> G.add_edge(1, 2, weight=1) |
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969 >>> nx.is_weighted(G) |
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970 True |
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971 |
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972 """ |
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973 if edge is not None: |
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974 data = G.get_edge_data(*edge) |
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975 if data is None: |
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976 msg = 'Edge {!r} does not exist.'.format(edge) |
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977 raise nx.NetworkXError(msg) |
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978 return weight in data |
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979 |
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980 if is_empty(G): |
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981 # Special handling required since: all([]) == True |
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982 return False |
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983 |
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984 return all(weight in data for u, v, data in G.edges(data=True)) |
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985 |
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986 |
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987 def is_negatively_weighted(G, edge=None, weight='weight'): |
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988 """Returns True if `G` has negatively weighted edges. |
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989 |
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990 Parameters |
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991 ---------- |
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992 G : graph |
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993 A NetworkX graph. |
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994 |
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995 edge : tuple, optional |
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996 A 2-tuple specifying the only edge in `G` that will be tested. If |
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997 None, then every edge in `G` is tested. |
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998 |
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999 weight: string, optional |
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1000 The attribute name used to query for edge weights. |
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1001 |
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1002 Returns |
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1003 ------- |
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1004 bool |
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1005 A boolean signifying if `G`, or the specified edge, is negatively |
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1006 weighted. |
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1007 |
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1008 Raises |
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1009 ------ |
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1010 NetworkXError |
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1011 If the specified edge does not exist. |
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1012 |
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1013 Examples |
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1014 -------- |
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1015 >>> G = nx.Graph() |
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1016 >>> G.add_edges_from([(1, 3), (2, 4), (2, 6)]) |
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1017 >>> G.add_edge(1, 2, weight=4) |
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1018 >>> nx.is_negatively_weighted(G, (1, 2)) |
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1019 False |
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1020 >>> G[2][4]['weight'] = -2 |
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1021 >>> nx.is_negatively_weighted(G) |
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1022 True |
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1023 >>> G = nx.DiGraph() |
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1024 >>> edges = [('0', '3', 3), ('0', '1', -5), ('1', '0', -2)] |
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1025 >>> G.add_weighted_edges_from(edges) |
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1026 >>> nx.is_negatively_weighted(G) |
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1027 True |
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1028 |
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1029 """ |
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1030 if edge is not None: |
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1031 data = G.get_edge_data(*edge) |
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1032 if data is None: |
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1033 msg = 'Edge {!r} does not exist.'.format(edge) |
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1034 raise nx.NetworkXError(msg) |
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1035 return weight in data and data[weight] < 0 |
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1036 |
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1037 return any(weight in data and data[weight] < 0 |
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1038 for u, v, data in G.edges(data=True)) |
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1039 |
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1040 |
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1041 def is_empty(G): |
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1042 """Returns True if `G` has no edges. |
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1043 |
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1044 Parameters |
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1045 ---------- |
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1046 G : graph |
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1047 A NetworkX graph. |
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1048 |
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1049 Returns |
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1050 ------- |
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1051 bool |
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1052 True if `G` has no edges, and False otherwise. |
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1053 |
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1054 Notes |
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1055 ----- |
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1056 An empty graph can have nodes but not edges. The empty graph with zero |
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1057 nodes is known as the null graph. This is an $O(n)$ operation where n |
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1058 is the number of nodes in the graph. |
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1059 |
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1060 """ |
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1061 return not any(G.adj.values()) |
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1062 |
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1063 |
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1064 def nodes_with_selfloops(G): |
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1065 """Returns an iterator over nodes with self loops. |
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1066 |
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1067 A node with a self loop has an edge with both ends adjacent |
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1068 to that node. |
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1069 |
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1070 Returns |
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1071 ------- |
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1072 nodelist : iterator |
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1073 A iterator over nodes with self loops. |
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1074 |
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1075 See Also |
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1076 -------- |
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1077 selfloop_edges, number_of_selfloops |
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1078 |
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1079 Examples |
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1080 -------- |
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1081 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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1082 >>> G.add_edge(1, 1) |
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1083 >>> G.add_edge(1, 2) |
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1084 >>> list(nx.nodes_with_selfloops(G)) |
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1085 [1] |
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1086 |
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1087 """ |
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1088 return (n for n, nbrs in G.adj.items() if n in nbrs) |
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1089 |
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1090 |
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1091 def selfloop_edges(G, data=False, keys=False, default=None): |
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1092 """Returns an iterator over selfloop edges. |
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1093 |
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1094 A selfloop edge has the same node at both ends. |
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1095 |
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1096 Parameters |
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1097 ---------- |
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1098 data : string or bool, optional (default=False) |
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1099 Return selfloop edges as two tuples (u, v) (data=False) |
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1100 or three-tuples (u, v, datadict) (data=True) |
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1101 or three-tuples (u, v, datavalue) (data='attrname') |
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1102 keys : bool, optional (default=False) |
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1103 If True, return edge keys with each edge. |
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1104 default : value, optional (default=None) |
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1105 Value used for edges that don't have the requested attribute. |
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1106 Only relevant if data is not True or False. |
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1107 |
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1108 Returns |
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1109 ------- |
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1110 edgeiter : iterator over edge tuples |
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1111 An iterator over all selfloop edges. |
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1112 |
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1113 See Also |
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1114 -------- |
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1115 nodes_with_selfloops, number_of_selfloops |
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1116 |
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1117 Examples |
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1118 -------- |
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1119 >>> G = nx.MultiGraph() # or Graph, DiGraph, MultiDiGraph, etc |
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1120 >>> ekey = G.add_edge(1, 1) |
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1121 >>> ekey = G.add_edge(1, 2) |
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1122 >>> list(nx.selfloop_edges(G)) |
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1123 [(1, 1)] |
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1124 >>> list(nx.selfloop_edges(G, data=True)) |
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1125 [(1, 1, {})] |
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1126 >>> list(nx.selfloop_edges(G, keys=True)) |
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1127 [(1, 1, 0)] |
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1128 >>> list(nx.selfloop_edges(G, keys=True, data=True)) |
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1129 [(1, 1, 0, {})] |
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1130 """ |
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1131 if data is True: |
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1132 if G.is_multigraph(): |
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1133 if keys is True: |
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1134 return ((n, n, k, d) |
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1135 for n, nbrs in G.adj.items() |
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1136 if n in nbrs for k, d in nbrs[n].items()) |
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1137 else: |
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1138 return ((n, n, d) |
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1139 for n, nbrs in G.adj.items() |
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1140 if n in nbrs for d in nbrs[n].values()) |
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1141 else: |
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1142 return ((n, n, nbrs[n]) for n, nbrs in G.adj.items() if n in nbrs) |
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1143 elif data is not False: |
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1144 if G.is_multigraph(): |
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1145 if keys is True: |
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1146 return ((n, n, k, d.get(data, default)) |
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1147 for n, nbrs in G.adj.items() |
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1148 if n in nbrs for k, d in nbrs[n].items()) |
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1149 else: |
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1150 return ((n, n, d.get(data, default)) |
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1151 for n, nbrs in G.adj.items() |
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1152 if n in nbrs for d in nbrs[n].values()) |
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1153 else: |
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1154 return ((n, n, nbrs[n].get(data, default)) |
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1155 for n, nbrs in G.adj.items() if n in nbrs) |
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1156 else: |
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1157 if G.is_multigraph(): |
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1158 if keys is True: |
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1159 return ((n, n, k) |
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1160 for n, nbrs in G.adj.items() |
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1161 if n in nbrs for k in nbrs[n]) |
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1162 else: |
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1163 return ((n, n) |
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1164 for n, nbrs in G.adj.items() |
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1165 if n in nbrs for d in nbrs[n].values()) |
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1166 else: |
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1167 return ((n, n) for n, nbrs in G.adj.items() if n in nbrs) |
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1168 |
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1169 |
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1170 def number_of_selfloops(G): |
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1171 """Returns the number of selfloop edges. |
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1172 |
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1173 A selfloop edge has the same node at both ends. |
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1174 |
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1175 Returns |
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1176 ------- |
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1177 nloops : int |
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1178 The number of selfloops. |
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1179 |
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1180 See Also |
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1181 -------- |
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1182 nodes_with_selfloops, selfloop_edges |
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1183 |
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1184 Examples |
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1185 -------- |
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1186 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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1187 >>> G.add_edge(1, 1) |
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1188 >>> G.add_edge(1, 2) |
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1189 >>> nx.number_of_selfloops(G) |
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1190 1 |
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1191 """ |
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1192 return sum(1 for _ in nx.selfloop_edges(G)) |