Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/networkx/classes/multigraph.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 # Dan Schult <dschult@colgate.edu> |
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10 # Pieter Swart <swart@lanl.gov> |
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11 """Base class for MultiGraph.""" |
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12 from copy import deepcopy |
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13 |
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14 import networkx as nx |
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15 from networkx.classes.graph import Graph |
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16 from networkx.classes.coreviews import MultiAdjacencyView |
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17 from networkx.classes.reportviews import MultiEdgeView, MultiDegreeView |
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18 from networkx import NetworkXError |
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19 from networkx.utils import iterable |
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20 |
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21 |
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22 class MultiGraph(Graph): |
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23 """ |
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24 An undirected graph class that can store multiedges. |
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25 |
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26 Multiedges are multiple edges between two nodes. Each edge |
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27 can hold optional data or attributes. |
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28 |
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29 A MultiGraph holds undirected edges. Self loops are allowed. |
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30 |
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31 Nodes can be arbitrary (hashable) Python objects with optional |
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32 key/value attributes. By convention `None` is not used as a node. |
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33 |
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34 Edges are represented as links between nodes with optional |
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35 key/value attributes. |
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36 |
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37 Parameters |
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38 ---------- |
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39 incoming_graph_data : input graph (optional, default: None) |
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40 Data to initialize graph. If None (default) an empty |
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41 graph is created. The data can be any format that is supported |
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42 by the to_networkx_graph() function, currently including edge list, |
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43 dict of dicts, dict of lists, NetworkX graph, NumPy matrix |
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44 or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph. |
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45 |
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46 attr : keyword arguments, optional (default= no attributes) |
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47 Attributes to add to graph as key=value pairs. |
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48 |
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49 See Also |
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50 -------- |
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51 Graph |
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52 DiGraph |
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53 MultiDiGraph |
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54 OrderedMultiGraph |
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55 |
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56 Examples |
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57 -------- |
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58 Create an empty graph structure (a "null graph") with no nodes and |
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59 no edges. |
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60 |
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61 >>> G = nx.MultiGraph() |
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62 |
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63 G can be grown in several ways. |
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64 |
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65 **Nodes:** |
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66 |
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67 Add one node at a time: |
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68 |
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69 >>> G.add_node(1) |
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70 |
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71 Add the nodes from any container (a list, dict, set or |
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72 even the lines from a file or the nodes from another graph). |
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73 |
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74 >>> G.add_nodes_from([2, 3]) |
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75 >>> G.add_nodes_from(range(100, 110)) |
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76 >>> H = nx.path_graph(10) |
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77 >>> G.add_nodes_from(H) |
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78 |
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79 In addition to strings and integers any hashable Python object |
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80 (except None) can represent a node, e.g. a customized node object, |
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81 or even another Graph. |
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82 |
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83 >>> G.add_node(H) |
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84 |
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85 **Edges:** |
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86 |
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87 G can also be grown by adding edges. |
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88 |
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89 Add one edge, |
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90 |
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91 >>> key = G.add_edge(1, 2) |
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92 |
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93 a list of edges, |
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94 |
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95 >>> keys = G.add_edges_from([(1, 2), (1, 3)]) |
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96 |
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97 or a collection of edges, |
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98 |
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99 >>> keys = G.add_edges_from(H.edges) |
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100 |
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101 If some edges connect nodes not yet in the graph, the nodes |
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102 are added automatically. If an edge already exists, an additional |
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103 edge is created and stored using a key to identify the edge. |
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104 By default the key is the lowest unused integer. |
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105 |
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106 >>> keys = G.add_edges_from([(4,5,{'route':28}), (4,5,{'route':37})]) |
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107 >>> G[4] |
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108 AdjacencyView({3: {0: {}}, 5: {0: {}, 1: {'route': 28}, 2: {'route': 37}}}) |
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109 |
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110 **Attributes:** |
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111 |
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112 Each graph, node, and edge can hold key/value attribute pairs |
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113 in an associated attribute dictionary (the keys must be hashable). |
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114 By default these are empty, but can be added or changed using |
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115 add_edge, add_node or direct manipulation of the attribute |
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116 dictionaries named graph, node and edge respectively. |
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117 |
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118 >>> G = nx.MultiGraph(day="Friday") |
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119 >>> G.graph |
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120 {'day': 'Friday'} |
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121 |
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122 Add node attributes using add_node(), add_nodes_from() or G.nodes |
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123 |
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124 >>> G.add_node(1, time='5pm') |
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125 >>> G.add_nodes_from([3], time='2pm') |
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126 >>> G.nodes[1] |
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127 {'time': '5pm'} |
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128 >>> G.nodes[1]['room'] = 714 |
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129 >>> del G.nodes[1]['room'] # remove attribute |
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130 >>> list(G.nodes(data=True)) |
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131 [(1, {'time': '5pm'}), (3, {'time': '2pm'})] |
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132 |
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133 Add edge attributes using add_edge(), add_edges_from(), subscript |
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134 notation, or G.edges. |
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135 |
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136 >>> key = G.add_edge(1, 2, weight=4.7 ) |
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137 >>> keys = G.add_edges_from([(3, 4), (4, 5)], color='red') |
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138 >>> keys = G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})]) |
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139 >>> G[1][2][0]['weight'] = 4.7 |
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140 >>> G.edges[1, 2, 0]['weight'] = 4 |
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141 |
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142 Warning: we protect the graph data structure by making `G.edges[1, 2]` a |
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143 read-only dict-like structure. However, you can assign to attributes |
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144 in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change |
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145 data attributes: `G.edges[1, 2]['weight'] = 4` |
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146 (For multigraphs: `MG.edges[u, v, key][name] = value`). |
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147 |
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148 **Shortcuts:** |
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149 |
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150 Many common graph features allow python syntax to speed reporting. |
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151 |
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152 >>> 1 in G # check if node in graph |
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153 True |
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154 >>> [n for n in G if n<3] # iterate through nodes |
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155 [1, 2] |
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156 >>> len(G) # number of nodes in graph |
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157 5 |
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158 >>> G[1] # adjacency dict-like view keyed by neighbor to edge attributes |
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159 AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}}) |
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160 |
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161 Often the best way to traverse all edges of a graph is via the neighbors. |
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162 The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`. |
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163 |
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164 >>> for n, nbrsdict in G.adjacency(): |
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165 ... for nbr, keydict in nbrsdict.items(): |
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166 ... for key, eattr in keydict.items(): |
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167 ... if 'weight' in eattr: |
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168 ... # Do something useful with the edges |
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169 ... pass |
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170 |
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171 But the edges() method is often more convenient: |
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172 |
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173 >>> for u, v, keys, weight in G.edges(data='weight', keys=True): |
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174 ... if weight is not None: |
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175 ... # Do something useful with the edges |
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176 ... pass |
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177 |
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178 **Reporting:** |
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179 |
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180 Simple graph information is obtained using methods and object-attributes. |
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181 Reporting usually provides views instead of containers to reduce memory |
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182 usage. The views update as the graph is updated similarly to dict-views. |
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183 The objects `nodes, `edges` and `adj` provide access to data attributes |
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184 via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration |
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185 (e.g. `nodes.items()`, `nodes.data('color')`, |
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186 `nodes.data('color', default='blue')` and similarly for `edges`) |
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187 Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`. |
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188 |
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189 For details on these and other miscellaneous methods, see below. |
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190 |
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191 **Subclasses (Advanced):** |
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192 |
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193 The MultiGraph class uses a dict-of-dict-of-dict-of-dict data structure. |
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194 The outer dict (node_dict) holds adjacency information keyed by node. |
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195 The next dict (adjlist_dict) represents the adjacency information and holds |
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196 edge_key dicts keyed by neighbor. The edge_key dict holds each edge_attr |
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197 dict keyed by edge key. The inner dict (edge_attr_dict) represents |
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198 the edge data and holds edge attribute values keyed by attribute names. |
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199 |
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200 Each of these four dicts in the dict-of-dict-of-dict-of-dict |
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201 structure can be replaced by a user defined dict-like object. |
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202 In general, the dict-like features should be maintained but |
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203 extra features can be added. To replace one of the dicts create |
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204 a new graph class by changing the class(!) variable holding the |
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205 factory for that dict-like structure. The variable names are |
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206 node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory, |
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207 adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory |
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208 and graph_attr_dict_factory. |
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209 |
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210 node_dict_factory : function, (default: dict) |
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211 Factory function to be used to create the dict containing node |
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212 attributes, keyed by node id. |
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213 It should require no arguments and return a dict-like object |
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214 |
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215 node_attr_dict_factory: function, (default: dict) |
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216 Factory function to be used to create the node attribute |
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217 dict which holds attribute values keyed by attribute name. |
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218 It should require no arguments and return a dict-like object |
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219 |
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220 adjlist_outer_dict_factory : function, (default: dict) |
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221 Factory function to be used to create the outer-most dict |
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222 in the data structure that holds adjacency info keyed by node. |
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223 It should require no arguments and return a dict-like object. |
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224 |
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225 adjlist_inner_dict_factory : function, (default: dict) |
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226 Factory function to be used to create the adjacency list |
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227 dict which holds multiedge key dicts keyed by neighbor. |
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228 It should require no arguments and return a dict-like object. |
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229 |
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230 edge_key_dict_factory : function, (default: dict) |
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231 Factory function to be used to create the edge key dict |
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232 which holds edge data keyed by edge key. |
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233 It should require no arguments and return a dict-like object. |
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234 |
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235 edge_attr_dict_factory : function, (default: dict) |
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236 Factory function to be used to create the edge attribute |
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237 dict which holds attribute values keyed by attribute name. |
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238 It should require no arguments and return a dict-like object. |
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239 |
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240 graph_attr_dict_factory : function, (default: dict) |
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241 Factory function to be used to create the graph attribute |
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242 dict which holds attribute values keyed by attribute name. |
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243 It should require no arguments and return a dict-like object. |
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244 |
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245 Typically, if your extension doesn't impact the data structure all |
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246 methods will inherited without issue except: `to_directed/to_undirected`. |
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247 By default these methods create a DiGraph/Graph class and you probably |
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248 want them to create your extension of a DiGraph/Graph. To facilitate |
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249 this we define two class variables that you can set in your subclass. |
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250 |
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251 to_directed_class : callable, (default: DiGraph or MultiDiGraph) |
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252 Class to create a new graph structure in the `to_directed` method. |
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253 If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used. |
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254 |
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255 to_undirected_class : callable, (default: Graph or MultiGraph) |
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256 Class to create a new graph structure in the `to_undirected` method. |
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257 If `None`, a NetworkX class (Graph or MultiGraph) is used. |
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258 |
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259 Examples |
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260 -------- |
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261 |
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262 Please see :mod:`~networkx.classes.ordered` for examples of |
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263 creating graph subclasses by overwriting the base class `dict` with |
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264 a dictionary-like object. |
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265 """ |
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266 # node_dict_factory = dict # already assigned in Graph |
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267 # adjlist_outer_dict_factory = dict |
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268 # adjlist_inner_dict_factory = dict |
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269 edge_key_dict_factory = dict |
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270 # edge_attr_dict_factory = dict |
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271 |
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272 def to_directed_class(self): |
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273 """Returns the class to use for empty directed copies. |
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274 |
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275 If you subclass the base classes, use this to designate |
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276 what directed class to use for `to_directed()` copies. |
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277 """ |
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278 return nx.MultiDiGraph |
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279 |
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280 def to_undirected_class(self): |
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281 """Returns the class to use for empty undirected copies. |
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282 |
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283 If you subclass the base classes, use this to designate |
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284 what directed class to use for `to_directed()` copies. |
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285 """ |
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286 return MultiGraph |
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287 |
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288 def __init__(self, incoming_graph_data=None, **attr): |
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289 """Initialize a graph with edges, name, or graph attributes. |
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290 |
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291 Parameters |
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292 ---------- |
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293 incoming_graph_data : input graph |
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294 Data to initialize graph. If incoming_graph_data=None (default) |
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295 an empty graph is created. The data can be an edge list, or any |
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296 NetworkX graph object. If the corresponding optional Python |
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297 packages are installed the data can also be a NumPy matrix |
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298 or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph. |
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299 |
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300 attr : keyword arguments, optional (default= no attributes) |
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301 Attributes to add to graph as key=value pairs. |
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302 |
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303 See Also |
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304 -------- |
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305 convert |
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306 |
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307 Examples |
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308 -------- |
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309 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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310 >>> G = nx.Graph(name='my graph') |
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311 >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges |
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312 >>> G = nx.Graph(e) |
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313 |
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314 Arbitrary graph attribute pairs (key=value) may be assigned |
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315 |
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316 >>> G = nx.Graph(e, day="Friday") |
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317 >>> G.graph |
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318 {'day': 'Friday'} |
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319 |
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320 """ |
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321 self.edge_key_dict_factory = self.edge_key_dict_factory |
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322 Graph.__init__(self, incoming_graph_data, **attr) |
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323 |
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324 @property |
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325 def adj(self): |
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326 """Graph adjacency object holding the neighbors of each node. |
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327 |
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328 This object is a read-only dict-like structure with node keys |
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329 and neighbor-dict values. The neighbor-dict is keyed by neighbor |
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330 to the edgekey-data-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets |
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331 the color of the edge `(3, 2, 0)` to `"blue"`. |
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332 |
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333 Iterating over G.adj behaves like a dict. Useful idioms include |
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334 `for nbr, nbrdict in G.adj[n].items():`. |
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335 |
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336 The neighbor information is also provided by subscripting the graph. |
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337 So `for nbr, foovalue in G[node].data('foo', default=1):` works. |
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338 |
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339 For directed graphs, `G.adj` holds outgoing (successor) info. |
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340 """ |
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341 return MultiAdjacencyView(self._adj) |
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342 |
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343 def new_edge_key(self, u, v): |
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344 """Returns an unused key for edges between nodes `u` and `v`. |
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345 |
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346 The nodes `u` and `v` do not need to be already in the graph. |
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347 |
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348 Notes |
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349 ----- |
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350 In the standard MultiGraph class the new key is the number of existing |
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351 edges between `u` and `v` (increased if necessary to ensure unused). |
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352 The first edge will have key 0, then 1, etc. If an edge is removed |
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353 further new_edge_keys may not be in this order. |
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354 |
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355 Parameters |
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356 ---------- |
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357 u, v : nodes |
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358 |
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359 Returns |
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360 ------- |
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361 key : int |
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362 """ |
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363 try: |
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364 keydict = self._adj[u][v] |
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365 except KeyError: |
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366 return 0 |
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367 key = len(keydict) |
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368 while key in keydict: |
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369 key += 1 |
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370 return key |
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371 |
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372 def add_edge(self, u_for_edge, v_for_edge, key=None, **attr): |
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373 """Add an edge between u and v. |
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374 |
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375 The nodes u and v will be automatically added if they are |
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376 not already in the graph. |
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377 |
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378 Edge attributes can be specified with keywords or by directly |
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379 accessing the edge's attribute dictionary. See examples below. |
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380 |
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381 Parameters |
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382 ---------- |
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383 u_for_edge, v_for_edge : nodes |
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384 Nodes can be, for example, strings or numbers. |
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385 Nodes must be hashable (and not None) Python objects. |
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386 key : hashable identifier, optional (default=lowest unused integer) |
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387 Used to distinguish multiedges between a pair of nodes. |
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388 attr : keyword arguments, optional |
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389 Edge data (or labels or objects) can be assigned using |
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390 keyword arguments. |
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391 |
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392 Returns |
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393 ------- |
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394 The edge key assigned to the edge. |
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395 |
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396 See Also |
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397 -------- |
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398 add_edges_from : add a collection of edges |
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399 |
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400 Notes |
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401 ----- |
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402 To replace/update edge data, use the optional key argument |
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403 to identify a unique edge. Otherwise a new edge will be created. |
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404 |
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405 NetworkX algorithms designed for weighted graphs cannot use |
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406 multigraphs directly because it is not clear how to handle |
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407 multiedge weights. Convert to Graph using edge attribute |
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408 'weight' to enable weighted graph algorithms. |
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409 |
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410 Default keys are generated using the method `new_edge_key()`. |
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411 This method can be overridden by subclassing the base class and |
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412 providing a custom `new_edge_key()` method. |
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413 |
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414 Examples |
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415 -------- |
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416 The following all add the edge e=(1, 2) to graph G: |
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417 |
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418 >>> G = nx.MultiGraph() |
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419 >>> e = (1, 2) |
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420 >>> ekey = G.add_edge(1, 2) # explicit two-node form |
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421 >>> G.add_edge(*e) # single edge as tuple of two nodes |
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422 1 |
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423 >>> G.add_edges_from( [(1, 2)] ) # add edges from iterable container |
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424 [2] |
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425 |
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426 Associate data to edges using keywords: |
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427 |
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428 >>> ekey = G.add_edge(1, 2, weight=3) |
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429 >>> ekey = G.add_edge(1, 2, key=0, weight=4) # update data for key=0 |
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430 >>> ekey = G.add_edge(1, 3, weight=7, capacity=15, length=342.7) |
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431 |
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432 For non-string attribute keys, use subscript notation. |
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433 |
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434 >>> ekey = G.add_edge(1, 2) |
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435 >>> G[1][2][0].update({0: 5}) |
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436 >>> G.edges[1, 2, 0].update({0: 5}) |
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437 """ |
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438 u, v = u_for_edge, v_for_edge |
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439 # add nodes |
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440 if u not in self._adj: |
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441 self._adj[u] = self.adjlist_inner_dict_factory() |
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442 self._node[u] = self.node_attr_dict_factory() |
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443 if v not in self._adj: |
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444 self._adj[v] = self.adjlist_inner_dict_factory() |
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445 self._node[v] = self.node_attr_dict_factory() |
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446 if key is None: |
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447 key = self.new_edge_key(u, v) |
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448 if v in self._adj[u]: |
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449 keydict = self._adj[u][v] |
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450 datadict = keydict.get(key, self.edge_attr_dict_factory()) |
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451 datadict.update(attr) |
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452 keydict[key] = datadict |
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453 else: |
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454 # selfloops work this way without special treatment |
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455 datadict = self.edge_attr_dict_factory() |
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456 datadict.update(attr) |
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457 keydict = self.edge_key_dict_factory() |
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458 keydict[key] = datadict |
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459 self._adj[u][v] = keydict |
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460 self._adj[v][u] = keydict |
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461 return key |
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462 |
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463 def add_edges_from(self, ebunch_to_add, **attr): |
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464 """Add all the edges in ebunch_to_add. |
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465 |
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466 Parameters |
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467 ---------- |
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468 ebunch_to_add : container of edges |
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469 Each edge given in the container will be added to the |
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470 graph. The edges can be: |
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471 |
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472 - 2-tuples (u, v) or |
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473 - 3-tuples (u, v, d) for an edge data dict d, or |
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474 - 3-tuples (u, v, k) for not iterable key k, or |
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475 - 4-tuples (u, v, k, d) for an edge with data and key k |
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476 |
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477 attr : keyword arguments, optional |
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478 Edge data (or labels or objects) can be assigned using |
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479 keyword arguments. |
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480 |
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481 Returns |
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482 ------- |
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483 A list of edge keys assigned to the edges in `ebunch`. |
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484 |
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485 See Also |
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486 -------- |
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487 add_edge : add a single edge |
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488 add_weighted_edges_from : convenient way to add weighted edges |
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489 |
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490 Notes |
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491 ----- |
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492 Adding the same edge twice has no effect but any edge data |
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493 will be updated when each duplicate edge is added. |
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494 |
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495 Edge attributes specified in an ebunch take precedence over |
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496 attributes specified via keyword arguments. |
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497 |
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498 Default keys are generated using the method ``new_edge_key()``. |
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499 This method can be overridden by subclassing the base class and |
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500 providing a custom ``new_edge_key()`` method. |
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501 |
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502 Examples |
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503 -------- |
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504 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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505 >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples |
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506 >>> e = zip(range(0, 3), range(1, 4)) |
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507 >>> G.add_edges_from(e) # Add the path graph 0-1-2-3 |
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508 |
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509 Associate data to edges |
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510 |
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511 >>> G.add_edges_from([(1, 2), (2, 3)], weight=3) |
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512 >>> G.add_edges_from([(3, 4), (1, 4)], label='WN2898') |
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513 """ |
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514 keylist = [] |
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515 for e in ebunch_to_add: |
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516 ne = len(e) |
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517 if ne == 4: |
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518 u, v, key, dd = e |
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519 elif ne == 3: |
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520 u, v, dd = e |
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521 key = None |
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522 elif ne == 2: |
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523 u, v = e |
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524 dd = {} |
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525 key = None |
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526 else: |
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527 msg = "Edge tuple {} must be a 2-tuple, 3-tuple or 4-tuple." |
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528 raise NetworkXError(msg.format(e)) |
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529 ddd = {} |
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530 ddd.update(attr) |
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531 try: |
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532 ddd.update(dd) |
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533 except: |
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534 if ne != 3: |
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535 raise |
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536 key = dd |
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537 key = self.add_edge(u, v, key) |
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538 self[u][v][key].update(ddd) |
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539 keylist.append(key) |
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540 return keylist |
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541 |
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542 def remove_edge(self, u, v, key=None): |
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543 """Remove an edge between u and v. |
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544 |
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545 Parameters |
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546 ---------- |
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547 u, v : nodes |
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548 Remove an edge between nodes u and v. |
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549 key : hashable identifier, optional (default=None) |
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550 Used to distinguish multiple edges between a pair of nodes. |
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551 If None remove a single (arbitrary) edge between u and v. |
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552 |
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553 Raises |
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554 ------ |
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555 NetworkXError |
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556 If there is not an edge between u and v, or |
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557 if there is no edge with the specified key. |
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558 |
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559 See Also |
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560 -------- |
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561 remove_edges_from : remove a collection of edges |
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562 |
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563 Examples |
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564 -------- |
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565 >>> G = nx.MultiGraph() |
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566 >>> nx.add_path(G, [0, 1, 2, 3]) |
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567 >>> G.remove_edge(0, 1) |
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568 >>> e = (1, 2) |
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569 >>> G.remove_edge(*e) # unpacks e from an edge tuple |
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570 |
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571 For multiple edges |
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572 |
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573 >>> G = nx.MultiGraph() # or MultiDiGraph, etc |
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574 >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned |
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575 [0, 1, 2] |
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576 >>> G.remove_edge(1, 2) # remove a single (arbitrary) edge |
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577 |
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578 For edges with keys |
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579 |
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580 >>> G = nx.MultiGraph() # or MultiDiGraph, etc |
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581 >>> G.add_edge(1, 2, key='first') |
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582 'first' |
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583 >>> G.add_edge(1, 2, key='second') |
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584 'second' |
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585 >>> G.remove_edge(1, 2, key='second') |
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586 |
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587 """ |
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588 try: |
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589 d = self._adj[u][v] |
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590 except KeyError: |
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591 raise NetworkXError( |
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592 "The edge %s-%s is not in the graph." % (u, v)) |
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593 # remove the edge with specified data |
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594 if key is None: |
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595 d.popitem() |
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596 else: |
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597 try: |
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598 del d[key] |
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599 except KeyError: |
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600 msg = "The edge %s-%s with key %s is not in the graph." |
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601 raise NetworkXError(msg % (u, v, key)) |
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602 if len(d) == 0: |
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603 # remove the key entries if last edge |
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604 del self._adj[u][v] |
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605 if u != v: # check for selfloop |
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606 del self._adj[v][u] |
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607 |
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608 def remove_edges_from(self, ebunch): |
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609 """Remove all edges specified in ebunch. |
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610 |
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611 Parameters |
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612 ---------- |
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613 ebunch: list or container of edge tuples |
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614 Each edge given in the list or container will be removed |
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615 from the graph. The edges can be: |
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616 |
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617 - 2-tuples (u, v) All edges between u and v are removed. |
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618 - 3-tuples (u, v, key) The edge identified by key is removed. |
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619 - 4-tuples (u, v, key, data) where data is ignored. |
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620 |
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621 See Also |
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622 -------- |
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623 remove_edge : remove a single edge |
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624 |
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625 Notes |
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626 ----- |
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627 Will fail silently if an edge in ebunch is not in the graph. |
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628 |
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629 Examples |
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630 -------- |
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631 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc |
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632 >>> ebunch=[(1, 2), (2, 3)] |
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633 >>> G.remove_edges_from(ebunch) |
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634 |
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635 Removing multiple copies of edges |
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636 |
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637 >>> G = nx.MultiGraph() |
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638 >>> keys = G.add_edges_from([(1, 2), (1, 2), (1, 2)]) |
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639 >>> G.remove_edges_from([(1, 2), (1, 2)]) |
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640 >>> list(G.edges()) |
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641 [(1, 2)] |
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642 >>> G.remove_edges_from([(1, 2), (1, 2)]) # silently ignore extra copy |
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643 >>> list(G.edges) # now empty graph |
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644 [] |
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645 """ |
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646 for e in ebunch: |
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647 try: |
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648 self.remove_edge(*e[:3]) |
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649 except NetworkXError: |
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650 pass |
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651 |
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652 def has_edge(self, u, v, key=None): |
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653 """Returns True if the graph has an edge between nodes u and v. |
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654 |
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655 This is the same as `v in G[u] or key in G[u][v]` |
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656 without KeyError exceptions. |
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657 |
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658 Parameters |
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659 ---------- |
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660 u, v : nodes |
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661 Nodes can be, for example, strings or numbers. |
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662 |
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663 key : hashable identifier, optional (default=None) |
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664 If specified return True only if the edge with |
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665 key is found. |
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666 |
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667 Returns |
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668 ------- |
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669 edge_ind : bool |
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670 True if edge is in the graph, False otherwise. |
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671 |
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672 Examples |
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673 -------- |
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674 Can be called either using two nodes u, v, an edge tuple (u, v), |
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675 or an edge tuple (u, v, key). |
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676 |
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677 >>> G = nx.MultiGraph() # or MultiDiGraph |
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678 >>> nx.add_path(G, [0, 1, 2, 3]) |
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679 >>> G.has_edge(0, 1) # using two nodes |
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680 True |
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681 >>> e = (0, 1) |
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682 >>> G.has_edge(*e) # e is a 2-tuple (u, v) |
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683 True |
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684 >>> G.add_edge(0, 1, key='a') |
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685 'a' |
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686 >>> G.has_edge(0, 1, key='a') # specify key |
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687 True |
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688 >>> e=(0, 1, 'a') |
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689 >>> G.has_edge(*e) # e is a 3-tuple (u, v, 'a') |
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690 True |
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691 |
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692 The following syntax are equivalent: |
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693 |
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694 >>> G.has_edge(0, 1) |
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695 True |
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696 >>> 1 in G[0] # though this gives :exc:`KeyError` if 0 not in G |
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697 True |
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698 |
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699 """ |
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700 try: |
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701 if key is None: |
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702 return v in self._adj[u] |
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703 else: |
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704 return key in self._adj[u][v] |
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705 except KeyError: |
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706 return False |
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707 |
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708 @property |
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709 def edges(self): |
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710 """Returns an iterator over the edges. |
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711 |
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712 edges(self, nbunch=None, data=False, keys=False, default=None) |
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713 |
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714 The EdgeView provides set-like operations on the edge-tuples |
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715 as well as edge attribute lookup. When called, it also provides |
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716 an EdgeDataView object which allows control of access to edge |
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717 attributes (but does not provide set-like operations). |
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718 Hence, `G.edges[u, v]['color']` provides the value of the color |
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719 attribute for edge `(u, v)` while |
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720 `for (u, v, c) in G.edges(data='color', default='red'):` |
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721 iterates through all the edges yielding the color attribute. |
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722 |
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723 Edges are returned as tuples with optional data and keys |
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724 in the order (node, neighbor, key, data). |
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725 |
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726 Parameters |
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727 ---------- |
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728 nbunch : single node, container, or all nodes (default= all nodes) |
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729 The view will only report edges incident to these nodes. |
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730 data : string or bool, optional (default=False) |
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731 The edge attribute returned in 3-tuple (u, v, ddict[data]). |
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732 If True, return edge attribute dict in 3-tuple (u, v, ddict). |
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733 If False, return 2-tuple (u, v). |
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734 keys : bool, optional (default=False) |
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735 If True, return edge keys with each edge. |
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736 default : value, optional (default=None) |
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737 Value used for edges that don't have the requested attribute. |
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738 Only relevant if data is not True or False. |
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739 |
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740 Returns |
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741 ------- |
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742 edges : MultiEdgeView |
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743 A view of edge attributes, usually it iterates over (u, v) |
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744 (u, v, k) or (u, v, k, d) tuples of edges, but can also be |
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745 used for attribute lookup as `edges[u, v, k]['foo']`. |
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746 |
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747 Notes |
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748 ----- |
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749 Nodes in nbunch that are not in the graph will be (quietly) ignored. |
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750 For directed graphs this returns the out-edges. |
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751 |
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752 Examples |
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753 -------- |
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754 >>> G = nx.MultiGraph() # or MultiDiGraph |
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755 >>> nx.add_path(G, [0, 1, 2]) |
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756 >>> key = G.add_edge(2, 3, weight=5) |
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757 >>> [e for e in G.edges()] |
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758 [(0, 1), (1, 2), (2, 3)] |
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759 >>> G.edges.data() # default data is {} (empty dict) |
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760 MultiEdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]) |
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761 >>> G.edges.data('weight', default=1) |
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762 MultiEdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)]) |
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763 >>> G.edges(keys=True) # default keys are integers |
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764 MultiEdgeView([(0, 1, 0), (1, 2, 0), (2, 3, 0)]) |
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765 >>> G.edges.data(keys=True) |
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766 MultiEdgeDataView([(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]) |
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767 >>> G.edges.data('weight', default=1, keys=True) |
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768 MultiEdgeDataView([(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]) |
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769 >>> G.edges([0, 3]) |
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770 MultiEdgeDataView([(0, 1), (3, 2)]) |
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771 >>> G.edges(0) |
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772 MultiEdgeDataView([(0, 1)]) |
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773 """ |
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774 return MultiEdgeView(self) |
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775 |
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776 def get_edge_data(self, u, v, key=None, default=None): |
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777 """Returns the attribute dictionary associated with edge (u, v). |
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778 |
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779 This is identical to `G[u][v][key]` except the default is returned |
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780 instead of an exception is the edge doesn't exist. |
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781 |
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782 Parameters |
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783 ---------- |
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784 u, v : nodes |
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785 |
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786 default : any Python object (default=None) |
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787 Value to return if the edge (u, v) is not found. |
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788 |
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789 key : hashable identifier, optional (default=None) |
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790 Return data only for the edge with specified key. |
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791 |
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792 Returns |
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793 ------- |
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794 edge_dict : dictionary |
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795 The edge attribute dictionary. |
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796 |
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797 Examples |
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798 -------- |
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799 >>> G = nx.MultiGraph() # or MultiDiGraph |
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800 >>> key = G.add_edge(0, 1, key='a', weight=7) |
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801 >>> G[0][1]['a'] # key='a' |
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802 {'weight': 7} |
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803 >>> G.edges[0, 1, 'a'] # key='a' |
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804 {'weight': 7} |
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805 |
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806 Warning: we protect the graph data structure by making |
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807 `G.edges` and `G[1][2]` read-only dict-like structures. |
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808 However, you can assign values to attributes in e.g. |
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809 `G.edges[1, 2, 'a']` or `G[1][2]['a']` using an additional |
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810 bracket as shown next. You need to specify all edge info |
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811 to assign to the edge data associated with an edge. |
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812 |
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813 >>> G[0][1]['a']['weight'] = 10 |
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814 >>> G.edges[0, 1, 'a']['weight'] = 10 |
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815 >>> G[0][1]['a']['weight'] |
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816 10 |
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817 >>> G.edges[1, 0, 'a']['weight'] |
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818 10 |
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819 |
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820 >>> G = nx.MultiGraph() # or MultiDiGraph |
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821 >>> nx.add_path(G, [0, 1, 2, 3]) |
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822 >>> G.get_edge_data(0, 1) |
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823 {0: {}} |
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824 >>> e = (0, 1) |
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825 >>> G.get_edge_data(*e) # tuple form |
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826 {0: {}} |
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827 >>> G.get_edge_data('a', 'b', default=0) # edge not in graph, return 0 |
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828 0 |
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829 """ |
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830 try: |
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831 if key is None: |
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832 return self._adj[u][v] |
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833 else: |
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834 return self._adj[u][v][key] |
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835 except KeyError: |
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836 return default |
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837 |
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838 @property |
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839 def degree(self): |
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840 """A DegreeView for the Graph as G.degree or G.degree(). |
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841 |
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842 The node degree is the number of edges adjacent to the node. |
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843 The weighted node degree is the sum of the edge weights for |
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844 edges incident to that node. |
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845 |
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846 This object provides an iterator for (node, degree) as well as |
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847 lookup for the degree for a single node. |
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848 |
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849 Parameters |
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850 ---------- |
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851 nbunch : single node, container, or all nodes (default= all nodes) |
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852 The view will only report edges incident to these nodes. |
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853 |
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854 weight : string or None, optional (default=None) |
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855 The name of an edge attribute that holds the numerical value used |
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856 as a weight. If None, then each edge has weight 1. |
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857 The degree is the sum of the edge weights adjacent to the node. |
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858 |
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859 Returns |
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860 ------- |
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861 If a single node is requested |
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862 deg : int |
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863 Degree of the node, if a single node is passed as argument. |
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864 |
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865 OR if multiple nodes are requested |
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866 nd_iter : iterator |
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867 The iterator returns two-tuples of (node, degree). |
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868 |
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869 Examples |
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870 -------- |
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871 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc |
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872 >>> nx.add_path(G, [0, 1, 2, 3]) |
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873 >>> G.degree(0) # node 0 with degree 1 |
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874 1 |
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875 >>> list(G.degree([0, 1])) |
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876 [(0, 1), (1, 2)] |
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877 |
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878 """ |
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879 return MultiDegreeView(self) |
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880 |
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881 def is_multigraph(self): |
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882 """Returns True if graph is a multigraph, False otherwise.""" |
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883 return True |
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884 |
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885 def is_directed(self): |
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886 """Returns True if graph is directed, False otherwise.""" |
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887 return False |
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888 |
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889 def copy(self, as_view=False): |
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890 """Returns a copy of the graph. |
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891 |
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892 The copy method by default returns an independent shallow copy |
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893 of the graph and attributes. That is, if an attribute is a |
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894 container, that container is shared by the original an the copy. |
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895 Use Python's `copy.deepcopy` for new containers. |
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896 |
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897 If `as_view` is True then a view is returned instead of a copy. |
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898 |
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899 Notes |
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900 ----- |
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901 All copies reproduce the graph structure, but data attributes |
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902 may be handled in different ways. There are four types of copies |
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903 of a graph that people might want. |
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904 |
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905 Deepcopy -- A "deepcopy" copies the graph structure as well as |
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906 all data attributes and any objects they might contain. |
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907 The entire graph object is new so that changes in the copy |
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908 do not affect the original object. (see Python's copy.deepcopy) |
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909 |
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910 Data Reference (Shallow) -- For a shallow copy the graph structure |
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911 is copied but the edge, node and graph attribute dicts are |
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912 references to those in the original graph. This saves |
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913 time and memory but could cause confusion if you change an attribute |
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914 in one graph and it changes the attribute in the other. |
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915 NetworkX does not provide this level of shallow copy. |
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916 |
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917 Independent Shallow -- This copy creates new independent attribute |
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918 dicts and then does a shallow copy of the attributes. That is, any |
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919 attributes that are containers are shared between the new graph |
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920 and the original. This is exactly what `dict.copy()` provides. |
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921 You can obtain this style copy using: |
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922 |
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923 >>> G = nx.path_graph(5) |
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924 >>> H = G.copy() |
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925 >>> H = G.copy(as_view=False) |
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926 >>> H = nx.Graph(G) |
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927 >>> H = G.__class__(G) |
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928 |
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929 Fresh Data -- For fresh data, the graph structure is copied while |
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930 new empty data attribute dicts are created. The resulting graph |
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931 is independent of the original and it has no edge, node or graph |
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932 attributes. Fresh copies are not enabled. Instead use: |
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933 |
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934 >>> H = G.__class__() |
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935 >>> H.add_nodes_from(G) |
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936 >>> H.add_edges_from(G.edges) |
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937 |
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938 View -- Inspired by dict-views, graph-views act like read-only |
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939 versions of the original graph, providing a copy of the original |
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940 structure without requiring any memory for copying the information. |
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941 |
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942 See the Python copy module for more information on shallow |
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943 and deep copies, https://docs.python.org/2/library/copy.html. |
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944 |
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945 Parameters |
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946 ---------- |
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947 as_view : bool, optional (default=False) |
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948 If True, the returned graph-view provides a read-only view |
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949 of the original graph without actually copying any data. |
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950 |
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951 Returns |
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952 ------- |
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953 G : Graph |
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954 A copy of the graph. |
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955 |
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956 See Also |
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957 -------- |
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958 to_directed: return a directed copy of the graph. |
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959 |
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960 Examples |
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961 -------- |
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962 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc |
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963 >>> H = G.copy() |
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964 |
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965 """ |
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966 if as_view is True: |
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967 return nx.graphviews.generic_graph_view(self) |
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968 G = self.__class__() |
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969 G.graph.update(self.graph) |
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970 G.add_nodes_from((n, d.copy()) for n, d in self._node.items()) |
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971 G.add_edges_from((u, v, key, datadict.copy()) |
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972 for u, nbrs in self._adj.items() |
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973 for v, keydict in nbrs.items() |
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974 for key, datadict in keydict.items()) |
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975 return G |
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976 |
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977 def to_directed(self, as_view=False): |
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978 """Returns a directed representation of the graph. |
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979 |
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980 Returns |
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981 ------- |
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982 G : MultiDiGraph |
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983 A directed graph with the same name, same nodes, and with |
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984 each edge (u, v, data) replaced by two directed edges |
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985 (u, v, data) and (v, u, data). |
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986 |
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987 Notes |
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988 ----- |
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989 This returns a "deepcopy" of the edge, node, and |
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990 graph attributes which attempts to completely copy |
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991 all of the data and references. |
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992 |
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993 This is in contrast to the similar D=DiGraph(G) which returns a |
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994 shallow copy of the data. |
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995 |
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996 See the Python copy module for more information on shallow |
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997 and deep copies, https://docs.python.org/2/library/copy.html. |
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998 |
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999 Warning: If you have subclassed MultiGraph to use dict-like objects |
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1000 in the data structure, those changes do not transfer to the |
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1001 MultiDiGraph created by this method. |
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1002 |
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1003 Examples |
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1004 -------- |
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1005 >>> G = nx.Graph() # or MultiGraph, etc |
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1006 >>> G.add_edge(0, 1) |
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1007 >>> H = G.to_directed() |
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1008 >>> list(H.edges) |
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1009 [(0, 1), (1, 0)] |
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1010 |
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1011 If already directed, return a (deep) copy |
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1012 |
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1013 >>> G = nx.DiGraph() # or MultiDiGraph, etc |
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1014 >>> G.add_edge(0, 1) |
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1015 >>> H = G.to_directed() |
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1016 >>> list(H.edges) |
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1017 [(0, 1)] |
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1018 """ |
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1019 graph_class = self.to_directed_class() |
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1020 if as_view is True: |
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1021 return nx.graphviews.generic_graph_view(self, graph_class) |
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1022 # deepcopy when not a view |
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1023 G = graph_class() |
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1024 G.graph.update(deepcopy(self.graph)) |
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1025 G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items()) |
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1026 G.add_edges_from((u, v, key, deepcopy(datadict)) |
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1027 for u, nbrs in self.adj.items() |
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1028 for v, keydict in nbrs.items() |
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1029 for key, datadict in keydict.items()) |
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1030 return G |
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1031 |
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1032 def to_undirected(self, as_view=False): |
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1033 """Returns an undirected copy of the graph. |
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1034 |
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1035 Returns |
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1036 ------- |
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1037 G : Graph/MultiGraph |
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1038 A deepcopy of the graph. |
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1039 |
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1040 See Also |
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1041 -------- |
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1042 copy, add_edge, add_edges_from |
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1043 |
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1044 Notes |
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1045 ----- |
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1046 This returns a "deepcopy" of the edge, node, and |
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1047 graph attributes which attempts to completely copy |
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1048 all of the data and references. |
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1049 |
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1050 This is in contrast to the similar `G = nx.MultiGraph(D)` |
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1051 which returns a shallow copy of the data. |
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1052 |
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1053 See the Python copy module for more information on shallow |
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1054 and deep copies, https://docs.python.org/2/library/copy.html. |
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1055 |
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1056 Warning: If you have subclassed MultiiGraph to use dict-like |
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1057 objects in the data structure, those changes do not transfer |
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1058 to the MultiGraph created by this method. |
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1059 |
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1060 Examples |
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1061 -------- |
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1062 >>> G = nx.path_graph(2) # or MultiGraph, etc |
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1063 >>> H = G.to_directed() |
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1064 >>> list(H.edges) |
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1065 [(0, 1), (1, 0)] |
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1066 >>> G2 = H.to_undirected() |
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1067 >>> list(G2.edges) |
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1068 [(0, 1)] |
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1069 """ |
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1070 graph_class = self.to_undirected_class() |
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1071 if as_view is True: |
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1072 return nx.graphviews.generic_graph_view(self, graph_class) |
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1073 # deepcopy when not a view |
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1074 G = graph_class() |
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1075 G.graph.update(deepcopy(self.graph)) |
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1076 G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items()) |
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1077 G.add_edges_from((u, v, key, deepcopy(datadict)) |
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1078 for u, nbrs in self._adj.items() |
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1079 for v, keydict in nbrs.items() |
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1080 for key, datadict in keydict.items()) |
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1081 return G |
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1082 |
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1083 def number_of_edges(self, u=None, v=None): |
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1084 """Returns the number of edges between two nodes. |
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1085 |
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1086 Parameters |
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1087 ---------- |
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1088 u, v : nodes, optional (Gefault=all edges) |
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1089 If u and v are specified, return the number of edges between |
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1090 u and v. Otherwise return the total number of all edges. |
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1091 |
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1092 Returns |
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1093 ------- |
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1094 nedges : int |
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1095 The number of edges in the graph. If nodes `u` and `v` are |
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1096 specified return the number of edges between those nodes. If |
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1097 the graph is directed, this only returns the number of edges |
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1098 from `u` to `v`. |
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1099 |
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1100 See Also |
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1101 -------- |
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1102 size |
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1103 |
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1104 Examples |
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1105 -------- |
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1106 For undirected multigraphs, this method counts the total number |
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1107 of edges in the graph:: |
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1108 |
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1109 >>> G = nx.MultiGraph() |
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1110 >>> G.add_edges_from([(0, 1), (0, 1), (1, 2)]) |
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1111 [0, 1, 0] |
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1112 >>> G.number_of_edges() |
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1113 3 |
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1114 |
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1115 If you specify two nodes, this counts the total number of edges |
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1116 joining the two nodes:: |
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1117 |
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1118 >>> G.number_of_edges(0, 1) |
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1119 2 |
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1120 |
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1121 For directed multigraphs, this method can count the total number |
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1122 of directed edges from `u` to `v`:: |
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1123 |
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1124 >>> G = nx.MultiDiGraph() |
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1125 >>> G.add_edges_from([(0, 1), (0, 1), (1, 0)]) |
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1126 [0, 1, 0] |
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1127 >>> G.number_of_edges(0, 1) |
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1128 2 |
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1129 >>> G.number_of_edges(1, 0) |
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1130 1 |
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1131 |
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1132 """ |
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1133 if u is None: |
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1134 return self.size() |
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1135 try: |
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1136 edgedata = self._adj[u][v] |
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1137 except KeyError: |
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1138 return 0 # no such edge |
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1139 return len(edgedata) |