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

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"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author guerler
date Fri, 31 Jul 2020 00:32:28 -0400
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1 # Copyright (C) 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 # Author: Aric Hagberg (hagberg@lanl.gov),
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9 # Pieter Swart (swart@lanl.gov),
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10 # Dan Schult(dschult@colgate.edu)
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11 """Base class for undirected graphs.
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12
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13 The Graph class allows any hashable object as a node
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14 and can associate key/value attribute pairs with each undirected edge.
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15
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16 Self-loops are allowed but multiple edges are not (see MultiGraph).
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17
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18 For directed graphs see DiGraph and MultiDiGraph.
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19 """
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20 import warnings
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21 from copy import deepcopy
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22 from collections.abc import Mapping
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23
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24 import networkx as nx
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25 from networkx.classes.coreviews import AtlasView, AdjacencyView
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26 from networkx.classes.reportviews import NodeView, EdgeView, DegreeView
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27 from networkx.exception import NetworkXError
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28 import networkx.convert as convert
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29 from networkx.utils import pairwise
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30
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31
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32 class Graph(object):
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33 """
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34 Base class for undirected graphs.
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35
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36 A Graph stores nodes and edges with optional data, or attributes.
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37
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38 Graphs hold undirected edges. Self loops are allowed but multiple
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39 (parallel) edges are not.
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40
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41 Nodes can be arbitrary (hashable) Python objects with optional
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42 key/value attributes. By convention `None` is not used as a node.
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43
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44 Edges are represented as links between nodes with optional
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45 key/value attributes.
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46
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47 Parameters
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48 ----------
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49 incoming_graph_data : input graph (optional, default: None)
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50 Data to initialize graph. If None (default) an empty
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51 graph is created. The data can be any format that is supported
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52 by the to_networkx_graph() function, currently including edge list,
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53 dict of dicts, dict of lists, NetworkX graph, NumPy matrix
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54 or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
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55
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56 attr : keyword arguments, optional (default= no attributes)
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57 Attributes to add to graph as key=value pairs.
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58
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59 See Also
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60 --------
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61 DiGraph
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62 MultiGraph
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63 MultiDiGraph
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64 OrderedGraph
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65
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66 Examples
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67 --------
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68 Create an empty graph structure (a "null graph") with no nodes and
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69 no edges.
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70
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71 >>> G = nx.Graph()
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72
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73 G can be grown in several ways.
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74
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75 **Nodes:**
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76
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77 Add one node at a time:
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78
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79 >>> G.add_node(1)
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80
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81 Add the nodes from any container (a list, dict, set or
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82 even the lines from a file or the nodes from another graph).
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83
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84 >>> G.add_nodes_from([2, 3])
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85 >>> G.add_nodes_from(range(100, 110))
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86 >>> H = nx.path_graph(10)
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87 >>> G.add_nodes_from(H)
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88
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89 In addition to strings and integers any hashable Python object
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90 (except None) can represent a node, e.g. a customized node object,
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91 or even another Graph.
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92
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93 >>> G.add_node(H)
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94
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95 **Edges:**
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96
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97 G can also be grown by adding edges.
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98
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99 Add one edge,
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100
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101 >>> G.add_edge(1, 2)
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102
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103 a list of edges,
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104
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105 >>> G.add_edges_from([(1, 2), (1, 3)])
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106
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107 or a collection of edges,
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108
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109 >>> G.add_edges_from(H.edges)
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110
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111 If some edges connect nodes not yet in the graph, the nodes
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112 are added automatically. There are no errors when adding
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113 nodes or edges that already exist.
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114
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115 **Attributes:**
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116
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117 Each graph, node, and edge can hold key/value attribute pairs
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118 in an associated attribute dictionary (the keys must be hashable).
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119 By default these are empty, but can be added or changed using
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120 add_edge, add_node or direct manipulation of the attribute
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121 dictionaries named graph, node and edge respectively.
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122
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123 >>> G = nx.Graph(day="Friday")
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124 >>> G.graph
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125 {'day': 'Friday'}
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126
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127 Add node attributes using add_node(), add_nodes_from() or G.nodes
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128
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129 >>> G.add_node(1, time='5pm')
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130 >>> G.add_nodes_from([3], time='2pm')
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131 >>> G.nodes[1]
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132 {'time': '5pm'}
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133 >>> G.nodes[1]['room'] = 714 # node must exist already to use G.nodes
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134 >>> del G.nodes[1]['room'] # remove attribute
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135 >>> list(G.nodes(data=True))
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136 [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
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137
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138 Add edge attributes using add_edge(), add_edges_from(), subscript
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139 notation, or G.edges.
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140
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141 >>> G.add_edge(1, 2, weight=4.7 )
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142 >>> G.add_edges_from([(3, 4), (4, 5)], color='red')
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143 >>> G.add_edges_from([(1, 2, {'color': 'blue'}), (2, 3, {'weight': 8})])
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144 >>> G[1][2]['weight'] = 4.7
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145 >>> G.edges[1, 2]['weight'] = 4
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146
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147 Warning: we protect the graph data structure by making `G.edges` a
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148 read-only dict-like structure. However, you can assign to attributes
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149 in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
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150 data attributes: `G.edges[1, 2]['weight'] = 4`
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151 (For multigraphs: `MG.edges[u, v, key][name] = value`).
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152
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153 **Shortcuts:**
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154
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155 Many common graph features allow python syntax to speed reporting.
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156
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157 >>> 1 in G # check if node in graph
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158 True
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159 >>> [n for n in G if n < 3] # iterate through nodes
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160 [1, 2]
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161 >>> len(G) # number of nodes in graph
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162 5
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163
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164 Often the best way to traverse all edges of a graph is via the neighbors.
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165 The neighbors are reported as an adjacency-dict `G.adj` or `G.adjacency()`
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166
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167 >>> for n, nbrsdict in G.adjacency():
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168 ... for nbr, eattr in nbrsdict.items():
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169 ... if 'weight' in eattr:
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170 ... # Do something useful with the edges
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171 ... pass
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172
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173 But the edges() method is often more convenient:
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174
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175 >>> for u, v, weight in G.edges.data('weight'):
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176 ... if weight is not None:
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177 ... # Do something useful with the edges
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178 ... pass
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179
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180 **Reporting:**
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181
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182 Simple graph information is obtained using object-attributes and methods.
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183 Reporting typically provides views instead of containers to reduce memory
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184 usage. The views update as the graph is updated similarly to dict-views.
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185 The objects `nodes, `edges` and `adj` provide access to data attributes
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186 via lookup (e.g. `nodes[n], `edges[u, v]`, `adj[u][v]`) and iteration
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187 (e.g. `nodes.items()`, `nodes.data('color')`,
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188 `nodes.data('color', default='blue')` and similarly for `edges`)
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189 Views exist for `nodes`, `edges`, `neighbors()`/`adj` and `degree`.
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190
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191 For details on these and other miscellaneous methods, see below.
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192
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193 **Subclasses (Advanced):**
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194
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195 The Graph class uses a dict-of-dict-of-dict data structure.
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196 The outer dict (node_dict) holds adjacency information keyed by node.
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197 The next dict (adjlist_dict) represents the adjacency information and holds
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198 edge data keyed by neighbor. The inner dict (edge_attr_dict) represents
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199 the edge data and holds edge attribute values keyed by attribute names.
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200
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201 Each of these three dicts can be replaced in a subclass by a user defined
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202 dict-like object. In general, the dict-like features should be
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203 maintained but extra features can be added. To replace one of the
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204 dicts create a new graph class by changing the class(!) variable
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205 holding the 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_attr_dict_factory and graph_attr_dict_factory.
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208
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209 node_dict_factory : function, (default: dict)
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210 Factory function to be used to create the dict containing node
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211 attributes, keyed by node id.
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212 It should require no arguments and return a dict-like object
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213
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214 node_attr_dict_factory: function, (default: dict)
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215 Factory function to be used to create the node attribute
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216 dict which holds attribute values keyed by attribute name.
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217 It should require no arguments and return a dict-like object
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218
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219 adjlist_outer_dict_factory : function, (default: dict)
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220 Factory function to be used to create the outer-most dict
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221 in the data structure that holds adjacency info keyed by node.
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222 It should require no arguments and return a dict-like object.
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223
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224 adjlist_inner_dict_factory : function, (default: dict)
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225 Factory function to be used to create the adjacency list
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226 dict which holds edge data keyed by neighbor.
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227 It should require no arguments and return a dict-like object
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228
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229 edge_attr_dict_factory : function, (default: dict)
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230 Factory function to be used to create the edge attribute
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231 dict which holds attribute values keyed by attribute name.
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232 It should require no arguments and return a dict-like object.
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233
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234 graph_attr_dict_factory : function, (default: dict)
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235 Factory function to be used to create the graph attribute
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236 dict which holds attribute values keyed by attribute name.
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237 It should require no arguments and return a dict-like object.
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238
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239 Typically, if your extension doesn't impact the data structure all
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240 methods will inherit without issue except: `to_directed/to_undirected`.
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241 By default these methods create a DiGraph/Graph class and you probably
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242 want them to create your extension of a DiGraph/Graph. To facilitate
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243 this we define two class variables that you can set in your subclass.
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244
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245 to_directed_class : callable, (default: DiGraph or MultiDiGraph)
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246 Class to create a new graph structure in the `to_directed` method.
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247 If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
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248
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249 to_undirected_class : callable, (default: Graph or MultiGraph)
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250 Class to create a new graph structure in the `to_undirected` method.
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251 If `None`, a NetworkX class (Graph or MultiGraph) is used.
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252
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253 Examples
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254 --------
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255
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256 Create a low memory graph class that effectively disallows edge
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257 attributes by using a single attribute dict for all edges.
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258 This reduces the memory used, but you lose edge attributes.
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259
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260 >>> class ThinGraph(nx.Graph):
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261 ... all_edge_dict = {'weight': 1}
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262 ... def single_edge_dict(self):
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263 ... return self.all_edge_dict
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264 ... edge_attr_dict_factory = single_edge_dict
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265 >>> G = ThinGraph()
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266 >>> G.add_edge(2, 1)
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267 >>> G[2][1]
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268 {'weight': 1}
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269 >>> G.add_edge(2, 2)
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270 >>> G[2][1] is G[2][2]
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271 True
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272
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273 Please see :mod:`~networkx.classes.ordered` for more examples of
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274 creating graph subclasses by overwriting the base class `dict` with
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275 a dictionary-like object.
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276 """
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277 node_dict_factory = dict
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278 node_attr_dict_factory = dict
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279 adjlist_outer_dict_factory = dict
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280 adjlist_inner_dict_factory = dict
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281 edge_attr_dict_factory = dict
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282 graph_attr_dict_factory = dict
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283
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284 def to_directed_class(self):
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285 """Returns the class to use for empty directed copies.
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286
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287 If you subclass the base classes, use this to designate
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288 what directed class to use for `to_directed()` copies.
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289 """
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290 return nx.DiGraph
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291
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292 def to_undirected_class(self):
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293 """Returns the class to use for empty undirected copies.
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294
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295 If you subclass the base classes, use this to designate
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296 what directed class to use for `to_directed()` copies.
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297 """
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298 return Graph
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299
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300 def __init__(self, incoming_graph_data=None, **attr):
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301 """Initialize a graph with edges, name, or graph attributes.
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302
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303 Parameters
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304 ----------
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305 incoming_graph_data : input graph (optional, default: None)
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306 Data to initialize graph. If None (default) an empty
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307 graph is created. The data can be an edge list, or any
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308 NetworkX graph object. If the corresponding optional Python
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309 packages are installed the data can also be a NumPy matrix
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310 or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
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311
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312 attr : keyword arguments, optional (default= no attributes)
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313 Attributes to add to graph as key=value pairs.
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314
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315 See Also
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316 --------
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317 convert
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318
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319 Examples
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320 --------
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321 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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322 >>> G = nx.Graph(name='my graph')
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323 >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
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324 >>> G = nx.Graph(e)
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325
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326 Arbitrary graph attribute pairs (key=value) may be assigned
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327
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328 >>> G = nx.Graph(e, day="Friday")
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329 >>> G.graph
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330 {'day': 'Friday'}
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331
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332 """
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333 self.graph_attr_dict_factory = self.graph_attr_dict_factory
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334 self.node_dict_factory = self.node_dict_factory
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335 self.node_attr_dict_factory = self.node_attr_dict_factory
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336 self.adjlist_outer_dict_factory = self.adjlist_outer_dict_factory
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337 self.adjlist_inner_dict_factory = self.adjlist_inner_dict_factory
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338 self.edge_attr_dict_factory = self.edge_attr_dict_factory
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339
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340 self.graph = self.graph_attr_dict_factory() # dictionary for graph attributes
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341 self._node = self.node_dict_factory() # empty node attribute dict
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342 self._adj = self.adjlist_outer_dict_factory() # empty adjacency dict
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343 # attempt to load graph with data
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344 if incoming_graph_data is not None:
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345 convert.to_networkx_graph(incoming_graph_data, create_using=self)
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346 # load graph attributes (must be after convert)
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347 self.graph.update(attr)
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348
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349 @property
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350 def adj(self):
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351 """Graph adjacency object holding the neighbors of each node.
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352
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353 This object is a read-only dict-like structure with node keys
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354 and neighbor-dict values. The neighbor-dict is keyed by neighbor
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355 to the edge-data-dict. So `G.adj[3][2]['color'] = 'blue'` sets
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356 the color of the edge `(3, 2)` to `"blue"`.
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357
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358 Iterating over G.adj behaves like a dict. Useful idioms include
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359 `for nbr, datadict in G.adj[n].items():`.
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360
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361 The neighbor information is also provided by subscripting the graph.
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362 So `for nbr, foovalue in G[node].data('foo', default=1):` works.
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363
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364 For directed graphs, `G.adj` holds outgoing (successor) info.
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365 """
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366 return AdjacencyView(self._adj)
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367
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368 @property
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369 def name(self):
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370 """String identifier of the graph.
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371
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372 This graph attribute appears in the attribute dict G.graph
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373 keyed by the string `"name"`. as well as an attribute (technically
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374 a property) `G.name`. This is entirely user controlled.
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375 """
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376 return self.graph.get('name', '')
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377
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378 @name.setter
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379 def name(self, s):
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380 self.graph['name'] = s
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381
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382 def __str__(self):
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383 """Returns the graph name.
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384
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385 Returns
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386 -------
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387 name : string
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388 The name of the graph.
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389
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390 Examples
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391 --------
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392 >>> G = nx.Graph(name='foo')
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393 >>> str(G)
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394 'foo'
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395 """
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396 return self.name
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397
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398 def __iter__(self):
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399 """Iterate over the nodes. Use: 'for n in G'.
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400
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401 Returns
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402 -------
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403 niter : iterator
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404 An iterator over all nodes in the graph.
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405
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406 Examples
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407 --------
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408 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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409 >>> [n for n in G]
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410 [0, 1, 2, 3]
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411 >>> list(G)
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412 [0, 1, 2, 3]
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413 """
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414 return iter(self._node)
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415
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416 def __contains__(self, n):
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417 """Returns True if n is a node, False otherwise. Use: 'n in G'.
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418
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419 Examples
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420 --------
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421 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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422 >>> 1 in G
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423 True
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424 """
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425 try:
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426 return n in self._node
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427 except TypeError:
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428 return False
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429
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430 def __len__(self):
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431 """Returns the number of nodes in the graph. Use: 'len(G)'.
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432
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433 Returns
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434 -------
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435 nnodes : int
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436 The number of nodes in the graph.
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437
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438 See Also
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439 --------
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440 number_of_nodes, order which are identical
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441
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442 Examples
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443 --------
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444 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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445 >>> len(G)
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446 4
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447
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448 """
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449 return len(self._node)
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450
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451 def __getitem__(self, n):
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452 """Returns a dict of neighbors of node n. Use: 'G[n]'.
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453
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454 Parameters
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455 ----------
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456 n : node
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457 A node in the graph.
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458
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459 Returns
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460 -------
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461 adj_dict : dictionary
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462 The adjacency dictionary for nodes connected to n.
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463
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464 Notes
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465 -----
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466 G[n] is the same as G.adj[n] and similar to G.neighbors(n)
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467 (which is an iterator over G.adj[n])
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468
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469 Examples
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470 --------
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471 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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472 >>> G[0]
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473 AtlasView({1: {}})
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474 """
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475 return self.adj[n]
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476
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477 def add_node(self, node_for_adding, **attr):
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478 """Add a single node `node_for_adding` and update node attributes.
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479
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480 Parameters
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481 ----------
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482 node_for_adding : node
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483 A node can be any hashable Python object except None.
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484 attr : keyword arguments, optional
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485 Set or change node attributes using key=value.
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486
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487 See Also
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488 --------
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489 add_nodes_from
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490
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491 Examples
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492 --------
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493 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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494 >>> G.add_node(1)
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495 >>> G.add_node('Hello')
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496 >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
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497 >>> G.add_node(K3)
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498 >>> G.number_of_nodes()
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499 3
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500
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501 Use keywords set/change node attributes:
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502
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503 >>> G.add_node(1, size=10)
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504 >>> G.add_node(3, weight=0.4, UTM=('13S', 382871, 3972649))
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505
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506 Notes
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507 -----
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508 A hashable object is one that can be used as a key in a Python
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509 dictionary. This includes strings, numbers, tuples of strings
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510 and numbers, etc.
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511
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512 On many platforms hashable items also include mutables such as
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513 NetworkX Graphs, though one should be careful that the hash
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514 doesn't change on mutables.
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515 """
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516 if node_for_adding not in self._node:
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517 self._adj[node_for_adding] = self.adjlist_inner_dict_factory()
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518 attr_dict = self._node[node_for_adding] = self.node_attr_dict_factory()
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519 attr_dict.update(attr)
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520 else: # update attr even if node already exists
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521 self._node[node_for_adding].update(attr)
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522
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523 def add_nodes_from(self, nodes_for_adding, **attr):
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524 """Add multiple nodes.
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525
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526 Parameters
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527 ----------
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528 nodes_for_adding : iterable container
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529 A container of nodes (list, dict, set, etc.).
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530 OR
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531 A container of (node, attribute dict) tuples.
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532 Node attributes are updated using the attribute dict.
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533 attr : keyword arguments, optional (default= no attributes)
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534 Update attributes for all nodes in nodes.
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535 Node attributes specified in nodes as a tuple take
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536 precedence over attributes specified via keyword arguments.
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537
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538 See Also
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539 --------
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540 add_node
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541
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542 Examples
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543 --------
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544 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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545 >>> G.add_nodes_from('Hello')
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546 >>> K3 = nx.Graph([(0, 1), (1, 2), (2, 0)])
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547 >>> G.add_nodes_from(K3)
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548 >>> sorted(G.nodes(), key=str)
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549 [0, 1, 2, 'H', 'e', 'l', 'o']
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550
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551 Use keywords to update specific node attributes for every node.
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552
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553 >>> G.add_nodes_from([1, 2], size=10)
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554 >>> G.add_nodes_from([3, 4], weight=0.4)
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555
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556 Use (node, attrdict) tuples to update attributes for specific nodes.
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557
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558 >>> G.add_nodes_from([(1, dict(size=11)), (2, {'color':'blue'})])
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559 >>> G.nodes[1]['size']
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560 11
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561 >>> H = nx.Graph()
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562 >>> H.add_nodes_from(G.nodes(data=True))
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563 >>> H.nodes[1]['size']
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564 11
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565
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566 """
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567 for n in nodes_for_adding:
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568 # keep all this inside try/except because
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569 # CPython throws TypeError on n not in self._node,
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570 # while pre-2.7.5 ironpython throws on self._adj[n]
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571 try:
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572 if n not in self._node:
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573 self._adj[n] = self.adjlist_inner_dict_factory()
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574 attr_dict = self._node[n] = self.node_attr_dict_factory()
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575 attr_dict.update(attr)
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576 else:
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577 self._node[n].update(attr)
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578 except TypeError:
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579 nn, ndict = n
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580 if nn not in self._node:
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581 self._adj[nn] = self.adjlist_inner_dict_factory()
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582 newdict = attr.copy()
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583 newdict.update(ndict)
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584 attr_dict = self._node[nn] = self.node_attr_dict_factory()
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585 attr_dict.update(newdict)
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586 else:
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587 olddict = self._node[nn]
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588 olddict.update(attr)
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589 olddict.update(ndict)
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590
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591 def remove_node(self, n):
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592 """Remove node n.
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593
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594 Removes the node n and all adjacent edges.
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595 Attempting to remove a non-existent node will raise an exception.
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596
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597 Parameters
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598 ----------
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599 n : node
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600 A node in the graph
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601
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602 Raises
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603 -------
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604 NetworkXError
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605 If n is not in the graph.
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606
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607 See Also
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608 --------
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609 remove_nodes_from
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610
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611 Examples
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612 --------
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613 >>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
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614 >>> list(G.edges)
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615 [(0, 1), (1, 2)]
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616 >>> G.remove_node(1)
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617 >>> list(G.edges)
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618 []
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619
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620 """
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621 adj = self._adj
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622 try:
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623 nbrs = list(adj[n]) # list handles self-loops (allows mutation)
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624 del self._node[n]
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625 except KeyError: # NetworkXError if n not in self
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626 raise NetworkXError("The node %s is not in the graph." % (n,))
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627 for u in nbrs:
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628 del adj[u][n] # remove all edges n-u in graph
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629 del adj[n] # now remove node
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630
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631 def remove_nodes_from(self, nodes):
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632 """Remove multiple nodes.
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633
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634 Parameters
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635 ----------
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636 nodes : iterable container
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637 A container of nodes (list, dict, set, etc.). If a node
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638 in the container is not in the graph it is silently
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639 ignored.
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640
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641 See Also
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642 --------
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643 remove_node
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644
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645 Examples
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646 --------
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647 >>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
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648 >>> e = list(G.nodes)
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649 >>> e
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650 [0, 1, 2]
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651 >>> G.remove_nodes_from(e)
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diff changeset
652 >>> list(G.nodes)
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653 []
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654
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655 """
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656 adj = self._adj
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657 for n in nodes:
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658 try:
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659 del self._node[n]
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660 for u in list(adj[n]): # list handles self-loops
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diff changeset
661 del adj[u][n] # (allows mutation of dict in loop)
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662 del adj[n]
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663 except KeyError:
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664 pass
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665
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666 @property
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667 def nodes(self):
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668 """A NodeView of the Graph as G.nodes or G.nodes().
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669
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670 Can be used as `G.nodes` for data lookup and for set-like operations.
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671 Can also be used as `G.nodes(data='color', default=None)` to return a
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diff changeset
672 NodeDataView which reports specific node data but no set operations.
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diff changeset
673 It presents a dict-like interface as well with `G.nodes.items()`
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674 iterating over `(node, nodedata)` 2-tuples and `G.nodes[3]['foo']`
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675 providing the value of the `foo` attribute for node `3`. In addition,
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676 a view `G.nodes.data('foo')` provides a dict-like interface to the
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diff changeset
677 `foo` attribute of each node. `G.nodes.data('foo', default=1)`
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diff changeset
678 provides a default for nodes that do not have attribute `foo`.
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679
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680 Parameters
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681 ----------
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682 data : string or bool, optional (default=False)
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683 The node attribute returned in 2-tuple (n, ddict[data]).
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684 If True, return entire node attribute dict as (n, ddict).
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685 If False, return just the nodes n.
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686
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687 default : value, optional (default=None)
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parents:
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688 Value used for nodes that don't have the requested attribute.
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parents:
diff changeset
689 Only relevant if data is not True or False.
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690
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691 Returns
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diff changeset
692 -------
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693 NodeView
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diff changeset
694 Allows set-like operations over the nodes as well as node
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diff changeset
695 attribute dict lookup and calling to get a NodeDataView.
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diff changeset
696 A NodeDataView iterates over `(n, data)` and has no set operations.
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parents:
diff changeset
697 A NodeView iterates over `n` and includes set operations.
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698
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699 When called, if data is False, an iterator over nodes.
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700 Otherwise an iterator of 2-tuples (node, attribute value)
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701 where the attribute is specified in `data`.
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702 If data is True then the attribute becomes the
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703 entire data dictionary.
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704
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705 Notes
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706 -----
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diff changeset
707 If your node data is not needed, it is simpler and equivalent
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parents:
diff changeset
708 to use the expression ``for n in G``, or ``list(G)``.
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diff changeset
709
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710 Examples
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711 --------
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712 There are two simple ways of getting a list of all nodes in the graph:
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diff changeset
713
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diff changeset
714 >>> G = nx.path_graph(3)
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parents:
diff changeset
715 >>> list(G.nodes)
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diff changeset
716 [0, 1, 2]
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parents:
diff changeset
717 >>> list(G)
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parents:
diff changeset
718 [0, 1, 2]
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diff changeset
719
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diff changeset
720 To get the node data along with the nodes:
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diff changeset
721
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diff changeset
722 >>> G.add_node(1, time='5pm')
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parents:
diff changeset
723 >>> G.nodes[0]['foo'] = 'bar'
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guerler
parents:
diff changeset
724 >>> list(G.nodes(data=True))
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guerler
parents:
diff changeset
725 [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
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guerler
parents:
diff changeset
726 >>> list(G.nodes.data())
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parents:
diff changeset
727 [(0, {'foo': 'bar'}), (1, {'time': '5pm'}), (2, {})]
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
728
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
729 >>> list(G.nodes(data='foo'))
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guerler
parents:
diff changeset
730 [(0, 'bar'), (1, None), (2, None)]
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parents:
diff changeset
731 >>> list(G.nodes.data('foo'))
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parents:
diff changeset
732 [(0, 'bar'), (1, None), (2, None)]
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diff changeset
733
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diff changeset
734 >>> list(G.nodes(data='time'))
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guerler
parents:
diff changeset
735 [(0, None), (1, '5pm'), (2, None)]
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guerler
parents:
diff changeset
736 >>> list(G.nodes.data('time'))
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
737 [(0, None), (1, '5pm'), (2, None)]
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diff changeset
738
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parents:
diff changeset
739 >>> list(G.nodes(data='time', default='Not Available'))
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
740 [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
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guerler
parents:
diff changeset
741 >>> list(G.nodes.data('time', default='Not Available'))
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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parents:
diff changeset
742 [(0, 'Not Available'), (1, '5pm'), (2, 'Not Available')]
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guerler
parents:
diff changeset
743
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
744 If some of your nodes have an attribute and the rest are assumed
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
745 to have a default attribute value you can create a dictionary
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
746 from node/attribute pairs using the `default` keyword argument
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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parents:
diff changeset
747 to guarantee the value is never None::
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diff changeset
748
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parents:
diff changeset
749 >>> G = nx.Graph()
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parents:
diff changeset
750 >>> G.add_node(0)
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parents:
diff changeset
751 >>> G.add_node(1, weight=2)
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guerler
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diff changeset
752 >>> G.add_node(2, weight=3)
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guerler
parents:
diff changeset
753 >>> dict(G.nodes(data='weight', default=1))
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guerler
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diff changeset
754 {0: 1, 1: 2, 2: 3}
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diff changeset
755
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756 """
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diff changeset
757 nodes = NodeView(self)
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diff changeset
758 # Lazy View creation: overload the (class) property on the instance
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diff changeset
759 # Then future G.nodes use the existing View
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diff changeset
760 # setattr doesn't work because attribute already exists
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diff changeset
761 self.__dict__['nodes'] = nodes
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diff changeset
762 return nodes
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diff changeset
763
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diff changeset
764 def number_of_nodes(self):
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diff changeset
765 """Returns the number of nodes in the graph.
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diff changeset
766
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diff changeset
767 Returns
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diff changeset
768 -------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
769 nnodes : int
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parents:
diff changeset
770 The number of nodes in the graph.
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parents:
diff changeset
771
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diff changeset
772 See Also
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diff changeset
773 --------
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diff changeset
774 order, __len__ which are identical
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775
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776 Examples
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777 --------
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778 >>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
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779 >>> G.number_of_nodes()
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780 3
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781 """
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782 return len(self._node)
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783
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784 def order(self):
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785 """Returns the number of nodes in the graph.
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786
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787 Returns
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788 -------
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789 nnodes : int
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790 The number of nodes in the graph.
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791
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792 See Also
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793 --------
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794 number_of_nodes, __len__ which are identical
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795
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796 Examples
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797 --------
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798 >>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
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799 >>> G.order()
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800 3
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801 """
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802 return len(self._node)
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803
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804 def has_node(self, n):
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805 """Returns True if the graph contains the node n.
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806
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807 Identical to `n in G`
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808
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809 Parameters
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810 ----------
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811 n : node
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812
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813 Examples
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814 --------
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815 >>> G = nx.path_graph(3) # or DiGraph, MultiGraph, MultiDiGraph, etc
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816 >>> G.has_node(0)
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817 True
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818
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819 It is more readable and simpler to use
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820
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821 >>> 0 in G
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822 True
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823
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824 """
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825 try:
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826 return n in self._node
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827 except TypeError:
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828 return False
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829
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830 def add_edge(self, u_of_edge, v_of_edge, **attr):
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831 """Add an edge between u and v.
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832
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833 The nodes u and v will be automatically added if they are
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834 not already in the graph.
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835
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836 Edge attributes can be specified with keywords or by directly
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837 accessing the edge's attribute dictionary. See examples below.
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838
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839 Parameters
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840 ----------
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841 u, v : nodes
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842 Nodes can be, for example, strings or numbers.
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843 Nodes must be hashable (and not None) Python objects.
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844 attr : keyword arguments, optional
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845 Edge data (or labels or objects) can be assigned using
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846 keyword arguments.
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847
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848 See Also
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849 --------
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850 add_edges_from : add a collection of edges
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851
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852 Notes
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853 -----
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854 Adding an edge that already exists updates the edge data.
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855
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856 Many NetworkX algorithms designed for weighted graphs use
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857 an edge attribute (by default `weight`) to hold a numerical value.
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858
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859 Examples
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860 --------
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861 The following all add the edge e=(1, 2) to graph G:
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862
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863 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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864 >>> e = (1, 2)
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865 >>> G.add_edge(1, 2) # explicit two-node form
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866 >>> G.add_edge(*e) # single edge as tuple of two nodes
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867 >>> G.add_edges_from([(1, 2)]) # add edges from iterable container
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868
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869 Associate data to edges using keywords:
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870
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871 >>> G.add_edge(1, 2, weight=3)
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872 >>> G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
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873
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874 For non-string attribute keys, use subscript notation.
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875
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876 >>> G.add_edge(1, 2)
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877 >>> G[1][2].update({0: 5})
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878 >>> G.edges[1, 2].update({0: 5})
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879 """
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880 u, v = u_of_edge, v_of_edge
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881 # add nodes
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882 if u not in self._node:
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883 self._adj[u] = self.adjlist_inner_dict_factory()
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884 self._node[u] = self.node_attr_dict_factory()
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885 if v not in self._node:
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886 self._adj[v] = self.adjlist_inner_dict_factory()
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887 self._node[v] = self.node_attr_dict_factory()
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888 # add the edge
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889 datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
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890 datadict.update(attr)
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891 self._adj[u][v] = datadict
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892 self._adj[v][u] = datadict
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893
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894 def add_edges_from(self, ebunch_to_add, **attr):
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895 """Add all the edges in ebunch_to_add.
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896
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897 Parameters
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898 ----------
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899 ebunch_to_add : container of edges
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900 Each edge given in the container will be added to the
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901 graph. The edges must be given as as 2-tuples (u, v) or
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902 3-tuples (u, v, d) where d is a dictionary containing edge data.
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903 attr : keyword arguments, optional
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904 Edge data (or labels or objects) can be assigned using
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905 keyword arguments.
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906
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907 See Also
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diff changeset
908 --------
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parents:
diff changeset
909 add_edge : add a single edge
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parents:
diff changeset
910 add_weighted_edges_from : convenient way to add weighted edges
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parents:
diff changeset
911
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parents:
diff changeset
912 Notes
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parents:
diff changeset
913 -----
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parents:
diff changeset
914 Adding the same edge twice has no effect but any edge data
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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parents:
diff changeset
915 will be updated when each duplicate edge is added.
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parents:
diff changeset
916
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diff changeset
917 Edge attributes specified in an ebunch take precedence over
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parents:
diff changeset
918 attributes specified via keyword arguments.
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diff changeset
919
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diff changeset
920 Examples
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diff changeset
921 --------
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parents:
diff changeset
922 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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diff changeset
923 >>> G.add_edges_from([(0, 1), (1, 2)]) # using a list of edge tuples
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parents:
diff changeset
924 >>> e = zip(range(0, 3), range(1, 4))
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parents:
diff changeset
925 >>> G.add_edges_from(e) # Add the path graph 0-1-2-3
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diff changeset
926
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diff changeset
927 Associate data to edges
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928
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diff changeset
929 >>> G.add_edges_from([(1, 2), (2, 3)], weight=3)
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diff changeset
930 >>> G.add_edges_from([(3, 4), (1, 4)], label='WN2898')
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931 """
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diff changeset
932 for e in ebunch_to_add:
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diff changeset
933 ne = len(e)
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diff changeset
934 if ne == 3:
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diff changeset
935 u, v, dd = e
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936 elif ne == 2:
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diff changeset
937 u, v = e
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diff changeset
938 dd = {} # doesn't need edge_attr_dict_factory
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939 else:
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diff changeset
940 raise NetworkXError(
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diff changeset
941 "Edge tuple %s must be a 2-tuple or 3-tuple." % (e,))
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diff changeset
942 if u not in self._node:
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diff changeset
943 self._adj[u] = self.adjlist_inner_dict_factory()
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parents:
diff changeset
944 self._node[u] = self.node_attr_dict_factory()
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diff changeset
945 if v not in self._node:
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diff changeset
946 self._adj[v] = self.adjlist_inner_dict_factory()
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parents:
diff changeset
947 self._node[v] = self.node_attr_dict_factory()
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diff changeset
948 datadict = self._adj[u].get(v, self.edge_attr_dict_factory())
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diff changeset
949 datadict.update(attr)
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diff changeset
950 datadict.update(dd)
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parents:
diff changeset
951 self._adj[u][v] = datadict
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diff changeset
952 self._adj[v][u] = datadict
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diff changeset
953
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diff changeset
954 def add_weighted_edges_from(self, ebunch_to_add, weight='weight', **attr):
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diff changeset
955 """Add weighted edges in `ebunch_to_add` with specified weight attr
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diff changeset
956
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957 Parameters
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diff changeset
958 ----------
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959 ebunch_to_add : container of edges
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diff changeset
960 Each edge given in the list or container will be added
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diff changeset
961 to the graph. The edges must be given as 3-tuples (u, v, w)
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962 where w is a number.
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963 weight : string, optional (default= 'weight')
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diff changeset
964 The attribute name for the edge weights to be added.
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965 attr : keyword arguments, optional (default= no attributes)
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diff changeset
966 Edge attributes to add/update for all edges.
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diff changeset
967
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968 See Also
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969 --------
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970 add_edge : add a single edge
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diff changeset
971 add_edges_from : add multiple edges
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diff changeset
972
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973 Notes
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974 -----
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diff changeset
975 Adding the same edge twice for Graph/DiGraph simply updates
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diff changeset
976 the edge data. For MultiGraph/MultiDiGraph, duplicate edges
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diff changeset
977 are stored.
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diff changeset
978
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diff changeset
979 Examples
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980 --------
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diff changeset
981 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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diff changeset
982 >>> G.add_weighted_edges_from([(0, 1, 3.0), (1, 2, 7.5)])
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983 """
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984 self.add_edges_from(((u, v, {weight: d}) for u, v, d in ebunch_to_add),
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985 **attr)
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diff changeset
986
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diff changeset
987 def remove_edge(self, u, v):
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diff changeset
988 """Remove the edge between u and v.
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989
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990 Parameters
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991 ----------
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992 u, v : nodes
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diff changeset
993 Remove the edge between nodes u and v.
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diff changeset
994
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995 Raises
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996 ------
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diff changeset
997 NetworkXError
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diff changeset
998 If there is not an edge between u and v.
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diff changeset
999
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1000 See Also
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1001 --------
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1002 remove_edges_from : remove a collection of edges
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diff changeset
1003
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diff changeset
1004 Examples
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diff changeset
1005 --------
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diff changeset
1006 >>> G = nx.path_graph(4) # or DiGraph, etc
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diff changeset
1007 >>> G.remove_edge(0, 1)
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diff changeset
1008 >>> e = (1, 2)
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diff changeset
1009 >>> G.remove_edge(*e) # unpacks e from an edge tuple
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diff changeset
1010 >>> e = (2, 3, {'weight':7}) # an edge with attribute data
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diff changeset
1011 >>> G.remove_edge(*e[:2]) # select first part of edge tuple
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diff changeset
1012 """
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1013 try:
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diff changeset
1014 del self._adj[u][v]
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diff changeset
1015 if u != v: # self-loop needs only one entry removed
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1016 del self._adj[v][u]
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diff changeset
1017 except KeyError:
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diff changeset
1018 raise NetworkXError("The edge %s-%s is not in the graph" % (u, v))
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diff changeset
1019
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1020 def remove_edges_from(self, ebunch):
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diff changeset
1021 """Remove all edges specified in ebunch.
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diff changeset
1022
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1023 Parameters
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1024 ----------
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1025 ebunch: list or container of edge tuples
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diff changeset
1026 Each edge given in the list or container will be removed
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diff changeset
1027 from the graph. The edges can be:
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diff changeset
1028
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1029 - 2-tuples (u, v) edge between u and v.
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parents:
diff changeset
1030 - 3-tuples (u, v, k) where k is ignored.
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diff changeset
1031
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1032 See Also
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1033 --------
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1034 remove_edge : remove a single edge
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1035
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1036 Notes
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1037 -----
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1038 Will fail silently if an edge in ebunch is not in the graph.
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1039
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1040 Examples
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1041 --------
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1042 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1043 >>> ebunch=[(1, 2), (2, 3)]
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1044 >>> G.remove_edges_from(ebunch)
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1045 """
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1046 adj = self._adj
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1047 for e in ebunch:
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1048 u, v = e[:2] # ignore edge data if present
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1049 if u in adj and v in adj[u]:
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1050 del adj[u][v]
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1051 if u != v: # self loop needs only one entry removed
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1052 del adj[v][u]
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1053
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1054 def update(self, edges=None, nodes=None):
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1055 """Update the graph using nodes/edges/graphs as input.
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1056
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1057 Like dict.update, this method takes a graph as input, adding the
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1058 graph's nodes and edges to this graph. It can also take two inputs:
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1059 edges and nodes. Finally it can take either edges or nodes.
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1060 To specify only nodes the keyword `nodes` must be used.
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1061
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1062 The collections of edges and nodes are treated similarly to
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1063 the add_edges_from/add_nodes_from methods. When iterated, they
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1064 should yield 2-tuples (u, v) or 3-tuples (u, v, datadict).
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1065
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1066 Parameters
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1067 ----------
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1068 edges : Graph object, collection of edges, or None
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1069 The first parameter can be a graph or some edges. If it has
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1070 attributes `nodes` and `edges`, then it is taken to be a
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1071 Graph-like object and those attributes are used as collections
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1072 of nodes and edges to be added to the graph.
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1073 If the first parameter does not have those attributes, it is
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1074 treated as a collection of edges and added to the graph.
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1075 If the first argument is None, no edges are added.
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1076 nodes : collection of nodes, or None
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1077 The second parameter is treated as a collection of nodes
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1078 to be added to the graph unless it is None.
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1079 If `edges is None` and `nodes is None` an exception is raised.
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1080 If the first parameter is a Graph, then `nodes` is ignored.
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1081
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1082 Examples
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1083 --------
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1084 >>> G = nx.path_graph(5)
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1085 >>> G.update(nx.complete_graph(range(4,10)))
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1086 >>> from itertools import combinations
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1087 >>> edges = ((u, v, {'power': u * v})
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1088 ... for u, v in combinations(range(10, 20), 2)
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1089 ... if u * v < 225)
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1090 >>> nodes = [1000] # for singleton, use a container
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1091 >>> G.update(edges, nodes)
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1092
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1093 Notes
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1094 -----
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1095 It you want to update the graph using an adjacency structure
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1096 it is straightforward to obtain the edges/nodes from adjacency.
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1097 The following examples provide common cases, your adjacency may
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1098 be slightly different and require tweaks of these examples.
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1099
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1100 >>> # dict-of-set/list/tuple
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1101 >>> adj = {1: {2, 3}, 2: {1, 3}, 3: {1, 2}}
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1102 >>> e = [(u, v) for u, nbrs in adj.items() for v in nbrs]
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1103 >>> G.update(edges=e, nodes=adj)
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1104
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1105 >>> DG = nx.DiGraph()
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1106 >>> # dict-of-dict-of-attribute
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1107 >>> adj = {1: {2: 1.3, 3: 0.7}, 2: {1: 1.4}, 3: {1: 0.7}}
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1108 >>> e = [(u, v, {'weight': d}) for u, nbrs in adj.items()
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1109 ... for v, d in nbrs.items()]
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1110 >>> DG.update(edges=e, nodes=adj)
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1111
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1112 >>> # dict-of-dict-of-dict
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1113 >>> adj = {1: {2: {'weight': 1.3}, 3: {'color': 0.7, 'weight':1.2}}}
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1114 >>> e = [(u, v, {'weight': d}) for u, nbrs in adj.items()
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1115 ... for v, d in nbrs.items()]
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1116 >>> DG.update(edges=e, nodes=adj)
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1117
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1118 >>> # predecessor adjacency (dict-of-set)
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1119 >>> pred = {1: {2, 3}, 2: {3}, 3: {3}}
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1120 >>> e = [(v, u) for u, nbrs in pred.items() for v in nbrs]
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1121
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1122 >>> # MultiGraph dict-of-dict-of-dict-of-attribute
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1123 >>> MDG = nx.MultiDiGraph()
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1124 >>> adj = {1: {2: {0: {'weight': 1.3}, 1: {'weight': 1.2}}},
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1125 ... 3: {2: {0: {'weight': 0.7}}}}
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1126 >>> e = [(u, v, ekey, d) for u, nbrs in adj.items()
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1127 ... for v, keydict in nbrs.items()
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1128 ... for ekey, d in keydict.items()]
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1129 >>> MDG.update(edges=e)
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1130
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1131 See Also
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1132 --------
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1133 add_edges_from: add multiple edges to a graph
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1134 add_nodes_from: add multiple nodes to a graph
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1135 """
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1136 if edges is not None:
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1137 if nodes is not None:
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1138 self.add_nodes_from(nodes)
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1139 self.add_edges_from(edges)
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1140 else:
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1141 # check if edges is a Graph object
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1142 try:
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1143 graph_nodes = edges.nodes
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1144 graph_edges = edges.edges
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1145 except AttributeError:
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1146 # edge not Graph-like
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1147 self.add_edges_from(edges)
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1148 else: # edges is Graph-like
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1149 self.add_nodes_from(graph_nodes.data())
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1150 self.add_edges_from(graph_edges.data())
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1151 self.graph.update(edges.graph)
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1152 elif nodes is not None:
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1153 self.add_nodes_from(nodes)
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1154 else:
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1155 raise NetworkXError("update needs nodes or edges input")
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1156
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1157 def has_edge(self, u, v):
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1158 """Returns True if the edge (u, v) is in the graph.
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parents:
diff changeset
1159
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parents:
diff changeset
1160 This is the same as `v in G[u]` without KeyError exceptions.
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parents:
diff changeset
1161
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parents:
diff changeset
1162 Parameters
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parents:
diff changeset
1163 ----------
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parents:
diff changeset
1164 u, v : nodes
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parents:
diff changeset
1165 Nodes can be, for example, strings or numbers.
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parents:
diff changeset
1166 Nodes must be hashable (and not None) Python objects.
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parents:
diff changeset
1167
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parents:
diff changeset
1168 Returns
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parents:
diff changeset
1169 -------
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parents:
diff changeset
1170 edge_ind : bool
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parents:
diff changeset
1171 True if edge is in the graph, False otherwise.
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parents:
diff changeset
1172
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parents:
diff changeset
1173 Examples
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parents:
diff changeset
1174 --------
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parents:
diff changeset
1175 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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parents:
diff changeset
1176 >>> G.has_edge(0, 1) # using two nodes
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parents:
diff changeset
1177 True
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parents:
diff changeset
1178 >>> e = (0, 1)
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parents:
diff changeset
1179 >>> G.has_edge(*e) # e is a 2-tuple (u, v)
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parents:
diff changeset
1180 True
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parents:
diff changeset
1181 >>> e = (0, 1, {'weight':7})
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parents:
diff changeset
1182 >>> G.has_edge(*e[:2]) # e is a 3-tuple (u, v, data_dictionary)
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parents:
diff changeset
1183 True
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parents:
diff changeset
1184
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parents:
diff changeset
1185 The following syntax are equivalent:
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diff changeset
1186
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parents:
diff changeset
1187 >>> G.has_edge(0, 1)
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parents:
diff changeset
1188 True
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parents:
diff changeset
1189 >>> 1 in G[0] # though this gives KeyError if 0 not in G
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parents:
diff changeset
1190 True
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parents:
diff changeset
1191
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diff changeset
1192 """
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parents:
diff changeset
1193 try:
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diff changeset
1194 return v in self._adj[u]
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parents:
diff changeset
1195 except KeyError:
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parents:
diff changeset
1196 return False
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parents:
diff changeset
1197
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diff changeset
1198 def neighbors(self, n):
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parents:
diff changeset
1199 """Returns an iterator over all neighbors of node n.
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diff changeset
1200
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parents:
diff changeset
1201 This is identical to `iter(G[n])`
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parents:
diff changeset
1202
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parents:
diff changeset
1203 Parameters
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parents:
diff changeset
1204 ----------
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parents:
diff changeset
1205 n : node
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parents:
diff changeset
1206 A node in the graph
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parents:
diff changeset
1207
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parents:
diff changeset
1208 Returns
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parents:
diff changeset
1209 -------
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parents:
diff changeset
1210 neighbors : iterator
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parents:
diff changeset
1211 An iterator over all neighbors of node n
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diff changeset
1212
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parents:
diff changeset
1213 Raises
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diff changeset
1214 ------
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parents:
diff changeset
1215 NetworkXError
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diff changeset
1216 If the node n is not in the graph.
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parents:
diff changeset
1217
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diff changeset
1218 Examples
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diff changeset
1219 --------
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parents:
diff changeset
1220 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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parents:
diff changeset
1221 >>> [n for n in G.neighbors(0)]
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parents:
diff changeset
1222 [1]
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parents:
diff changeset
1223
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diff changeset
1224 Notes
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parents:
diff changeset
1225 -----
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parents:
diff changeset
1226 It is usually more convenient (and faster) to access the
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parents:
diff changeset
1227 adjacency dictionary as ``G[n]``:
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parents:
diff changeset
1228
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parents:
diff changeset
1229 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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parents:
diff changeset
1230 >>> G.add_edge('a', 'b', weight=7)
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parents:
diff changeset
1231 >>> G['a']
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parents:
diff changeset
1232 AtlasView({'b': {'weight': 7}})
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parents:
diff changeset
1233 >>> G = nx.path_graph(4)
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parents:
diff changeset
1234 >>> [n for n in G[0]]
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parents:
diff changeset
1235 [1]
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parents:
diff changeset
1236 """
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parents:
diff changeset
1237 try:
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diff changeset
1238 return iter(self._adj[n])
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parents:
diff changeset
1239 except KeyError:
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diff changeset
1240 raise NetworkXError("The node %s is not in the graph." % (n,))
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parents:
diff changeset
1241
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parents:
diff changeset
1242 @property
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parents:
diff changeset
1243 def edges(self):
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parents:
diff changeset
1244 """An EdgeView of the Graph as G.edges or G.edges().
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parents:
diff changeset
1245
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parents:
diff changeset
1246 edges(self, nbunch=None, data=False, default=None)
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diff changeset
1247
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parents:
diff changeset
1248 The EdgeView provides set-like operations on the edge-tuples
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guerler
parents:
diff changeset
1249 as well as edge attribute lookup. When called, it also provides
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parents:
diff changeset
1250 an EdgeDataView object which allows control of access to edge
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guerler
parents:
diff changeset
1251 attributes (but does not provide set-like operations).
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guerler
parents:
diff changeset
1252 Hence, `G.edges[u, v]['color']` provides the value of the color
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guerler
parents:
diff changeset
1253 attribute for edge `(u, v)` while
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guerler
parents:
diff changeset
1254 `for (u, v, c) in G.edges.data('color', default='red'):`
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1255 iterates through all the edges yielding the color attribute
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guerler
parents:
diff changeset
1256 with default `'red'` if no color attribute exists.
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parents:
diff changeset
1257
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parents:
diff changeset
1258 Parameters
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parents:
diff changeset
1259 ----------
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parents:
diff changeset
1260 nbunch : single node, container, or all nodes (default= all nodes)
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parents:
diff changeset
1261 The view will only report edges incident to these nodes.
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parents:
diff changeset
1262 data : string or bool, optional (default=False)
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parents:
diff changeset
1263 The edge attribute returned in 3-tuple (u, v, ddict[data]).
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parents:
diff changeset
1264 If True, return edge attribute dict in 3-tuple (u, v, ddict).
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parents:
diff changeset
1265 If False, return 2-tuple (u, v).
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guerler
parents:
diff changeset
1266 default : value, optional (default=None)
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parents:
diff changeset
1267 Value used for edges that don't have the requested attribute.
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parents:
diff changeset
1268 Only relevant if data is not True or False.
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parents:
diff changeset
1269
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parents:
diff changeset
1270 Returns
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parents:
diff changeset
1271 -------
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parents:
diff changeset
1272 edges : EdgeView
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parents:
diff changeset
1273 A view of edge attributes, usually it iterates over (u, v)
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guerler
parents:
diff changeset
1274 or (u, v, d) tuples of edges, but can also be used for
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1275 attribute lookup as `edges[u, v]['foo']`.
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guerler
parents:
diff changeset
1276
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parents:
diff changeset
1277 Notes
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parents:
diff changeset
1278 -----
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parents:
diff changeset
1279 Nodes in nbunch that are not in the graph will be (quietly) ignored.
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guerler
parents:
diff changeset
1280 For directed graphs this returns the out-edges.
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guerler
parents:
diff changeset
1281
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parents:
diff changeset
1282 Examples
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parents:
diff changeset
1283 --------
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parents:
diff changeset
1284 >>> G = nx.path_graph(3) # or MultiGraph, etc
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guerler
parents:
diff changeset
1285 >>> G.add_edge(2, 3, weight=5)
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guerler
parents:
diff changeset
1286 >>> [e for e in G.edges]
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guerler
parents:
diff changeset
1287 [(0, 1), (1, 2), (2, 3)]
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1288 >>> G.edges.data() # default data is {} (empty dict)
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1289 EdgeDataView([(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})])
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1290 >>> G.edges.data('weight', default=1)
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diff changeset
1291 EdgeDataView([(0, 1, 1), (1, 2, 1), (2, 3, 5)])
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1292 >>> G.edges([0, 3]) # only edges incident to these nodes
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1293 EdgeDataView([(0, 1), (3, 2)])
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diff changeset
1294 >>> G.edges(0) # only edges incident to a single node (use G.adj[0]?)
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1295 EdgeDataView([(0, 1)])
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1296 """
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1297 return EdgeView(self)
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1298
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1299 def get_edge_data(self, u, v, default=None):
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1300 """Returns the attribute dictionary associated with edge (u, v).
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1301
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1302 This is identical to `G[u][v]` except the default is returned
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1303 instead of an exception if the edge doesn't exist.
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1304
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1305 Parameters
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1306 ----------
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1307 u, v : nodes
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1308 default: any Python object (default=None)
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1309 Value to return if the edge (u, v) is not found.
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1310
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1311 Returns
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1312 -------
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1313 edge_dict : dictionary
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1314 The edge attribute dictionary.
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1315
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1316 Examples
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1317 --------
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1318 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1319 >>> G[0][1]
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1320 {}
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1321
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1322 Warning: Assigning to `G[u][v]` is not permitted.
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1323 But it is safe to assign attributes `G[u][v]['foo']`
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1324
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1325 >>> G[0][1]['weight'] = 7
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1326 >>> G[0][1]['weight']
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1327 7
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1328 >>> G[1][0]['weight']
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1329 7
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1330
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1331 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1332 >>> G.get_edge_data(0, 1) # default edge data is {}
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1333 {}
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1334 >>> e = (0, 1)
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1335 >>> G.get_edge_data(*e) # tuple form
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1336 {}
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1337 >>> G.get_edge_data('a', 'b', default=0) # edge not in graph, return 0
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1338 0
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1339 """
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1340 try:
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1341 return self._adj[u][v]
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1342 except KeyError:
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1343 return default
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1344
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1345 def adjacency(self):
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1346 """Returns an iterator over (node, adjacency dict) tuples for all nodes.
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1347
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1348 For directed graphs, only outgoing neighbors/adjacencies are included.
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1349
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1350 Returns
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1351 -------
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1352 adj_iter : iterator
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1353 An iterator over (node, adjacency dictionary) for all nodes in
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1354 the graph.
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1355
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1356 Examples
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1357 --------
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1358 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1359 >>> [(n, nbrdict) for n, nbrdict in G.adjacency()]
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1360 [(0, {1: {}}), (1, {0: {}, 2: {}}), (2, {1: {}, 3: {}}), (3, {2: {}})]
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1361
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1362 """
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1363 return iter(self._adj.items())
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1364
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1365 @property
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1366 def degree(self):
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1367 """A DegreeView for the Graph as G.degree or G.degree().
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1368
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1369 The node degree is the number of edges adjacent to the node.
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1370 The weighted node degree is the sum of the edge weights for
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1371 edges incident to that node.
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1372
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1373 This object provides an iterator for (node, degree) as well as
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1374 lookup for the degree for a single node.
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1375
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1376 Parameters
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1377 ----------
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1378 nbunch : single node, container, or all nodes (default= all nodes)
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1379 The view will only report edges incident to these nodes.
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1380
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1381 weight : string or None, optional (default=None)
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1382 The name of an edge attribute that holds the numerical value used
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1383 as a weight. If None, then each edge has weight 1.
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1384 The degree is the sum of the edge weights adjacent to the node.
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1385
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1386 Returns
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1387 -------
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1388 If a single node is requested
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1389 deg : int
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1390 Degree of the node
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1391
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1392 OR if multiple nodes are requested
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1393 nd_view : A DegreeView object capable of iterating (node, degree) pairs
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1394
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1395 Examples
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1396 --------
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1397 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1398 >>> G.degree[0] # node 0 has degree 1
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1399 1
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1400 >>> list(G.degree([0, 1, 2]))
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1401 [(0, 1), (1, 2), (2, 2)]
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1402 """
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1403 return DegreeView(self)
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1404
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1405 def clear(self):
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1406 """Remove all nodes and edges from the graph.
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1407
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1408 This also removes the name, and all graph, node, and edge attributes.
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1409
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1410 Examples
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1411 --------
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1412 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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1413 >>> G.clear()
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1414 >>> list(G.nodes)
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diff changeset
1415 []
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diff changeset
1416 >>> list(G.edges)
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diff changeset
1417 []
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1418
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1419 """
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diff changeset
1420 self._adj.clear()
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diff changeset
1421 self._node.clear()
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diff changeset
1422 self.graph.clear()
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1423
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diff changeset
1424 def is_multigraph(self):
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diff changeset
1425 """Returns True if graph is a multigraph, False otherwise."""
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diff changeset
1426 return False
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diff changeset
1427
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diff changeset
1428 def is_directed(self):
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diff changeset
1429 """Returns True if graph is directed, False otherwise."""
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diff changeset
1430 return False
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1431
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1432 def copy(self, as_view=False):
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diff changeset
1433 """Returns a copy of the graph.
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diff changeset
1434
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1435 The copy method by default returns an independent shallow copy
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1436 of the graph and attributes. That is, if an attribute is a
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diff changeset
1437 container, that container is shared by the original an the copy.
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diff changeset
1438 Use Python's `copy.deepcopy` for new containers.
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1439
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1440 If `as_view` is True then a view is returned instead of a copy.
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1441
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1442 Notes
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diff changeset
1443 -----
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1444 All copies reproduce the graph structure, but data attributes
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diff changeset
1445 may be handled in different ways. There are four types of copies
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diff changeset
1446 of a graph that people might want.
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1447
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diff changeset
1448 Deepcopy -- A "deepcopy" copies the graph structure as well as
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diff changeset
1449 all data attributes and any objects they might contain.
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diff changeset
1450 The entire graph object is new so that changes in the copy
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parents:
diff changeset
1451 do not affect the original object. (see Python's copy.deepcopy)
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diff changeset
1452
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diff changeset
1453 Data Reference (Shallow) -- For a shallow copy the graph structure
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1454 is copied but the edge, node and graph attribute dicts are
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diff changeset
1455 references to those in the original graph. This saves
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
1456 time and memory but could cause confusion if you change an attribute
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diff changeset
1457 in one graph and it changes the attribute in the other.
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diff changeset
1458 NetworkX does not provide this level of shallow copy.
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diff changeset
1459
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diff changeset
1460 Independent Shallow -- This copy creates new independent attribute
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1461 dicts and then does a shallow copy of the attributes. That is, any
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1462 attributes that are containers are shared between the new graph
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diff changeset
1463 and the original. This is exactly what `dict.copy()` provides.
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diff changeset
1464 You can obtain this style copy using:
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diff changeset
1465
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diff changeset
1466 >>> G = nx.path_graph(5)
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parents:
diff changeset
1467 >>> H = G.copy()
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diff changeset
1468 >>> H = G.copy(as_view=False)
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parents:
diff changeset
1469 >>> H = nx.Graph(G)
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parents:
diff changeset
1470 >>> H = G.__class__(G)
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diff changeset
1471
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1472 Fresh Data -- For fresh data, the graph structure is copied while
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diff changeset
1473 new empty data attribute dicts are created. The resulting graph
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1474 is independent of the original and it has no edge, node or graph
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diff changeset
1475 attributes. Fresh copies are not enabled. Instead use:
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diff changeset
1476
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diff changeset
1477 >>> H = G.__class__()
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diff changeset
1478 >>> H.add_nodes_from(G)
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parents:
diff changeset
1479 >>> H.add_edges_from(G.edges)
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diff changeset
1480
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diff changeset
1481 View -- Inspired by dict-views, graph-views act like read-only
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diff changeset
1482 versions of the original graph, providing a copy of the original
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1483 structure without requiring any memory for copying the information.
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diff changeset
1484
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diff changeset
1485 See the Python copy module for more information on shallow
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diff changeset
1486 and deep copies, https://docs.python.org/2/library/copy.html.
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diff changeset
1487
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1488 Parameters
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1489 ----------
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1490 as_view : bool, optional (default=False)
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diff changeset
1491 If True, the returned graph-view provides a read-only view
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diff changeset
1492 of the original graph without actually copying any data.
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diff changeset
1493
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1494 Returns
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diff changeset
1495 -------
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1496 G : Graph
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diff changeset
1497 A copy of the graph.
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diff changeset
1498
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1499 See Also
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diff changeset
1500 --------
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diff changeset
1501 to_directed: return a directed copy of the graph.
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diff changeset
1502
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1503 Examples
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diff changeset
1504 --------
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diff changeset
1505 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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diff changeset
1506 >>> H = G.copy()
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diff changeset
1507
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1508 """
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1509 if as_view is True:
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diff changeset
1510 return nx.graphviews.generic_graph_view(self)
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diff changeset
1511 G = self.__class__()
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diff changeset
1512 G.graph.update(self.graph)
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diff changeset
1513 G.add_nodes_from((n, d.copy()) for n, d in self._node.items())
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diff changeset
1514 G.add_edges_from((u, v, datadict.copy())
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diff changeset
1515 for u, nbrs in self._adj.items()
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diff changeset
1516 for v, datadict in nbrs.items())
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diff changeset
1517 return G
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diff changeset
1518
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diff changeset
1519 def to_directed(self, as_view=False):
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diff changeset
1520 """Returns a directed representation of the graph.
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diff changeset
1521
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1522 Returns
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diff changeset
1523 -------
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1524 G : DiGraph
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diff changeset
1525 A directed graph with the same name, same nodes, and with
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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diff changeset
1526 each edge (u, v, data) replaced by two directed edges
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diff changeset
1527 (u, v, data) and (v, u, data).
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1528
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1529 Notes
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diff changeset
1530 -----
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1531 This returns a "deepcopy" of the edge, node, and
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diff changeset
1532 graph attributes which attempts to completely copy
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diff changeset
1533 all of the data and references.
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1534
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diff changeset
1535 This is in contrast to the similar D=DiGraph(G) which returns a
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diff changeset
1536 shallow copy of the data.
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diff changeset
1537
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1538 See the Python copy module for more information on shallow
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diff changeset
1539 and deep copies, https://docs.python.org/2/library/copy.html.
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1540
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diff changeset
1541 Warning: If you have subclassed Graph to use dict-like objects
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diff changeset
1542 in the data structure, those changes do not transfer to the
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diff changeset
1543 DiGraph created by this method.
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diff changeset
1544
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diff changeset
1545 Examples
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diff changeset
1546 --------
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diff changeset
1547 >>> G = nx.Graph() # or MultiGraph, etc
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parents:
diff changeset
1548 >>> G.add_edge(0, 1)
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diff changeset
1549 >>> H = G.to_directed()
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parents:
diff changeset
1550 >>> list(H.edges)
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diff changeset
1551 [(0, 1), (1, 0)]
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diff changeset
1552
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diff changeset
1553 If already directed, return a (deep) copy
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diff changeset
1554
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diff changeset
1555 >>> G = nx.DiGraph() # or MultiDiGraph, etc
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parents:
diff changeset
1556 >>> G.add_edge(0, 1)
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diff changeset
1557 >>> H = G.to_directed()
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diff changeset
1558 >>> list(H.edges)
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diff changeset
1559 [(0, 1)]
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diff changeset
1560 """
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diff changeset
1561 graph_class = self.to_directed_class()
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1562 if as_view is True:
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diff changeset
1563 return nx.graphviews.generic_graph_view(self, graph_class)
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diff changeset
1564 # deepcopy when not a view
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diff changeset
1565 G = graph_class()
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diff changeset
1566 G.graph.update(deepcopy(self.graph))
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diff changeset
1567 G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
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1568 G.add_edges_from((u, v, deepcopy(data))
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1569 for u, nbrs in self._adj.items()
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diff changeset
1570 for v, data in nbrs.items())
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diff changeset
1571 return G
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1572
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1573 def to_undirected(self, as_view=False):
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1574 """Returns an undirected copy of the graph.
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diff changeset
1575
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1576 Parameters
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diff changeset
1577 ----------
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1578 as_view : bool (optional, default=False)
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diff changeset
1579 If True return a view of the original undirected graph.
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1580
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1581 Returns
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1582 -------
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1583 G : Graph/MultiGraph
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1584 A deepcopy of the graph.
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1585
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1586 See Also
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1587 --------
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1588 Graph, copy, add_edge, add_edges_from
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1589
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1590 Notes
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1591 -----
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1592 This returns a "deepcopy" of the edge, node, and
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1593 graph attributes which attempts to completely copy
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1594 all of the data and references.
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1595
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1596 This is in contrast to the similar `G = nx.DiGraph(D)` which returns a
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diff changeset
1597 shallow copy of the data.
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1598
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1599 See the Python copy module for more information on shallow
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diff changeset
1600 and deep copies, https://docs.python.org/2/library/copy.html.
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1601
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1602 Warning: If you have subclassed DiGraph to use dict-like objects
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1603 in the data structure, those changes do not transfer to the
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1604 Graph created by this method.
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1605
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1606 Examples
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1607 --------
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1608 >>> G = nx.path_graph(2) # or MultiGraph, etc
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1609 >>> H = G.to_directed()
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1610 >>> list(H.edges)
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diff changeset
1611 [(0, 1), (1, 0)]
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1612 >>> G2 = H.to_undirected()
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1613 >>> list(G2.edges)
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1614 [(0, 1)]
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1615 """
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1616 graph_class = self.to_undirected_class()
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1617 if as_view is True:
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1618 return nx.graphviews.generic_graph_view(self, graph_class)
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diff changeset
1619 # deepcopy when not a view
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diff changeset
1620 G = graph_class()
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diff changeset
1621 G.graph.update(deepcopy(self.graph))
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diff changeset
1622 G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
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diff changeset
1623 G.add_edges_from((u, v, deepcopy(d))
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diff changeset
1624 for u, nbrs in self._adj.items()
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diff changeset
1625 for v, d in nbrs.items())
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diff changeset
1626 return G
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diff changeset
1627
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1628 def subgraph(self, nodes):
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diff changeset
1629 """Returns a SubGraph view of the subgraph induced on `nodes`.
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diff changeset
1630
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diff changeset
1631 The induced subgraph of the graph contains the nodes in `nodes`
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diff changeset
1632 and the edges between those nodes.
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diff changeset
1633
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diff changeset
1634 Parameters
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diff changeset
1635 ----------
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1636 nodes : list, iterable
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diff changeset
1637 A container of nodes which will be iterated through once.
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diff changeset
1638
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1639 Returns
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1640 -------
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diff changeset
1641 G : SubGraph View
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diff changeset
1642 A subgraph view of the graph. The graph structure cannot be
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diff changeset
1643 changed but node/edge attributes can and are shared with the
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diff changeset
1644 original graph.
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1645
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1646 Notes
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diff changeset
1647 -----
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diff changeset
1648 The graph, edge and node attributes are shared with the original graph.
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diff changeset
1649 Changes to the graph structure is ruled out by the view, but changes
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diff changeset
1650 to attributes are reflected in the original graph.
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diff changeset
1651
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1652 To create a subgraph with its own copy of the edge/node attributes use:
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diff changeset
1653 G.subgraph(nodes).copy()
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diff changeset
1654
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diff changeset
1655 For an inplace reduction of a graph to a subgraph you can remove nodes:
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diff changeset
1656 G.remove_nodes_from([n for n in G if n not in set(nodes)])
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diff changeset
1657
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diff changeset
1658 Subgraph views are sometimes NOT what you want. In most cases where
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diff changeset
1659 you want to do more than simply look at the induced edges, it makes
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parents:
diff changeset
1660 more sense to just create the subgraph as its own graph with code like:
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diff changeset
1661
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1662 ::
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diff changeset
1663
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diff changeset
1664 # Create a subgraph SG based on a (possibly multigraph) G
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diff changeset
1665 SG = G.__class__()
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diff changeset
1666 SG.add_nodes_from((n, G.nodes[n]) for n in largest_wcc)
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1667 if SG.is_multigraph:
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diff changeset
1668 SG.add_edges_from((n, nbr, key, d)
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parents:
diff changeset
1669 for n, nbrs in G.adj.items() if n in largest_wcc
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parents:
diff changeset
1670 for nbr, keydict in nbrs.items() if nbr in largest_wcc
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diff changeset
1671 for key, d in keydict.items())
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parents:
diff changeset
1672 else:
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parents:
diff changeset
1673 SG.add_edges_from((n, nbr, d)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1674 for n, nbrs in G.adj.items() if n in largest_wcc
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1675 for nbr, d in nbrs.items() if nbr in largest_wcc)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1676 SG.graph.update(G.graph)
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guerler
parents:
diff changeset
1677
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parents:
diff changeset
1678 Examples
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parents:
diff changeset
1679 --------
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guerler
parents:
diff changeset
1680 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1681 >>> H = G.subgraph([0, 1, 2])
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parents:
diff changeset
1682 >>> list(H.edges)
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parents:
diff changeset
1683 [(0, 1), (1, 2)]
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parents:
diff changeset
1684 """
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parents:
diff changeset
1685 induced_nodes = nx.filters.show_nodes(self.nbunch_iter(nodes))
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1686 # if already a subgraph, don't make a chain
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1687 subgraph = nx.graphviews.subgraph_view
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guerler
parents:
diff changeset
1688 if hasattr(self, '_NODE_OK'):
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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parents:
diff changeset
1689 return subgraph(self._graph, induced_nodes, self._EDGE_OK)
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guerler
parents:
diff changeset
1690 return subgraph(self, induced_nodes)
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parents:
diff changeset
1691
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parents:
diff changeset
1692 def edge_subgraph(self, edges):
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1693 """Returns the subgraph induced by the specified edges.
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guerler
parents:
diff changeset
1694
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parents:
diff changeset
1695 The induced subgraph contains each edge in `edges` and each
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1696 node incident to any one of those edges.
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parents:
diff changeset
1697
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parents:
diff changeset
1698 Parameters
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parents:
diff changeset
1699 ----------
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parents:
diff changeset
1700 edges : iterable
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parents:
diff changeset
1701 An iterable of edges in this graph.
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parents:
diff changeset
1702
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parents:
diff changeset
1703 Returns
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parents:
diff changeset
1704 -------
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parents:
diff changeset
1705 G : Graph
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parents:
diff changeset
1706 An edge-induced subgraph of this graph with the same edge
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guerler
parents:
diff changeset
1707 attributes.
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parents:
diff changeset
1708
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parents:
diff changeset
1709 Notes
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parents:
diff changeset
1710 -----
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guerler
parents:
diff changeset
1711 The graph, edge, and node attributes in the returned subgraph
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1712 view are references to the corresponding attributes in the original
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guerler
parents:
diff changeset
1713 graph. The view is read-only.
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parents:
diff changeset
1714
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parents:
diff changeset
1715 To create a full graph version of the subgraph with its own copy
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1716 of the edge or node attributes, use::
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parents:
diff changeset
1717
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guerler
parents:
diff changeset
1718 >>> G.edge_subgraph(edges).copy() # doctest: +SKIP
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guerler
parents:
diff changeset
1719
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parents:
diff changeset
1720 Examples
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parents:
diff changeset
1721 --------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1722 >>> G = nx.path_graph(5)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1723 >>> H = G.edge_subgraph([(0, 1), (3, 4)])
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guerler
parents:
diff changeset
1724 >>> list(H.nodes)
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guerler
parents:
diff changeset
1725 [0, 1, 3, 4]
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guerler
parents:
diff changeset
1726 >>> list(H.edges)
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guerler
parents:
diff changeset
1727 [(0, 1), (3, 4)]
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guerler
parents:
diff changeset
1728
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parents:
diff changeset
1729 """
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parents:
diff changeset
1730 return nx.edge_subgraph(self, edges)
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guerler
parents:
diff changeset
1731
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parents:
diff changeset
1732 def size(self, weight=None):
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1733 """Returns the number of edges or total of all edge weights.
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guerler
parents:
diff changeset
1734
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parents:
diff changeset
1735 Parameters
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parents:
diff changeset
1736 ----------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
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parents:
diff changeset
1737 weight : string or None, optional (default=None)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1738 The edge attribute that holds the numerical value used
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1739 as a weight. If None, then each edge has weight 1.
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guerler
parents:
diff changeset
1740
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parents:
diff changeset
1741 Returns
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parents:
diff changeset
1742 -------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1743 size : numeric
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guerler
parents:
diff changeset
1744 The number of edges or
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1745 (if weight keyword is provided) the total weight sum.
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guerler
parents:
diff changeset
1746
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1747 If weight is None, returns an int. Otherwise a float
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1748 (or more general numeric if the weights are more general).
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guerler
parents:
diff changeset
1749
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parents:
diff changeset
1750 See Also
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guerler
parents:
diff changeset
1751 --------
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guerler
parents:
diff changeset
1752 number_of_edges
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guerler
parents:
diff changeset
1753
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parents:
diff changeset
1754 Examples
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guerler
parents:
diff changeset
1755 --------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1756 >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc
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guerler
parents:
diff changeset
1757 >>> G.size()
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1758 3
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guerler
parents:
diff changeset
1759
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1760 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1761 >>> G.add_edge('a', 'b', weight=2)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1762 >>> G.add_edge('b', 'c', weight=4)
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guerler
parents:
diff changeset
1763 >>> G.size()
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1764 2
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guerler
parents:
diff changeset
1765 >>> G.size(weight='weight')
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guerler
parents:
diff changeset
1766 6.0
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parents:
diff changeset
1767 """
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guerler
parents:
diff changeset
1768 s = sum(d for v, d in self.degree(weight=weight))
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guerler
parents:
diff changeset
1769 # If `weight` is None, the sum of the degrees is guaranteed to be
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1770 # even, so we can perform integer division and hence return an
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1771 # integer. Otherwise, the sum of the weighted degrees is not
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1772 # guaranteed to be an integer, so we perform "real" division.
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1773 return s // 2 if weight is None else s / 2
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guerler
parents:
diff changeset
1774
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guerler
parents:
diff changeset
1775 def number_of_edges(self, u=None, v=None):
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1776 """Returns the number of edges between two nodes.
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guerler
parents:
diff changeset
1777
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parents:
diff changeset
1778 Parameters
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guerler
parents:
diff changeset
1779 ----------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1780 u, v : nodes, optional (default=all edges)
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1781 If u and v are specified, return the number of edges between
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1782 u and v. Otherwise return the total number of all edges.
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1783
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1784 Returns
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guerler
parents:
diff changeset
1785 -------
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1786 nedges : int
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1787 The number of edges in the graph. If nodes `u` and `v` are
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1788 specified return the number of edges between those nodes. If
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1789 the graph is directed, this only returns the number of edges
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1790 from `u` to `v`.
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1791
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1792 See Also
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guerler
parents:
diff changeset
1793 --------
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guerler
parents:
diff changeset
1794 size
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guerler
parents:
diff changeset
1795
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guerler
parents:
diff changeset
1796 Examples
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guerler
parents:
diff changeset
1797 --------
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guerler
parents:
diff changeset
1798 For undirected graphs, this method counts the total number of
56ad4e20f292 "planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
guerler
parents:
diff changeset
1799 edges in the graph:
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guerler
parents:
diff changeset
1800
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1801 >>> G = nx.path_graph(4)
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1802 >>> G.number_of_edges()
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1803 3
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1804
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1805 If you specify two nodes, this counts the total number of edges
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1806 joining the two nodes:
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1807
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1808 >>> G.number_of_edges(0, 1)
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1809 1
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1810
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1811 For directed graphs, this method can count the total number of
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1812 directed edges from `u` to `v`:
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1813
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1814 >>> G = nx.DiGraph()
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1815 >>> G.add_edge(0, 1)
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1816 >>> G.add_edge(1, 0)
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1817 >>> G.number_of_edges(0, 1)
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1818 1
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1819
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1820 """
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1821 if u is None:
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1822 return int(self.size())
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1823 if v in self._adj[u]:
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1824 return 1
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1825 return 0
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1826
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1827 def nbunch_iter(self, nbunch=None):
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1828 """Returns an iterator over nodes contained in nbunch that are
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1829 also in the graph.
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1830
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1831 The nodes in nbunch are checked for membership in the graph
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1832 and if not are silently ignored.
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1833
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1834 Parameters
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1835 ----------
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1836 nbunch : single node, container, or all nodes (default= all nodes)
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1837 The view will only report edges incident to these nodes.
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1838
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1839 Returns
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1840 -------
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1841 niter : iterator
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1842 An iterator over nodes in nbunch that are also in the graph.
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1843 If nbunch is None, iterate over all nodes in the graph.
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1844
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1845 Raises
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1846 ------
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1847 NetworkXError
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1848 If nbunch is not a node or or sequence of nodes.
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1849 If a node in nbunch is not hashable.
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1850
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1851 See Also
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1852 --------
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1853 Graph.__iter__
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1854
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1855 Notes
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1856 -----
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1857 When nbunch is an iterator, the returned iterator yields values
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1858 directly from nbunch, becoming exhausted when nbunch is exhausted.
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1859
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1860 To test whether nbunch is a single node, one can use
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1861 "if nbunch in self:", even after processing with this routine.
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1862
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1863 If nbunch is not a node or a (possibly empty) sequence/iterator
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1864 or None, a :exc:`NetworkXError` is raised. Also, if any object in
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1865 nbunch is not hashable, a :exc:`NetworkXError` is raised.
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1866 """
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1867 if nbunch is None: # include all nodes via iterator
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1868 bunch = iter(self._adj)
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1869 elif nbunch in self: # if nbunch is a single node
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1870 bunch = iter([nbunch])
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1871 else: # if nbunch is a sequence of nodes
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1872 def bunch_iter(nlist, adj):
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1873 try:
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1874 for n in nlist:
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1875 if n in adj:
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1876 yield n
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1877 except TypeError as e:
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1878 message = e.args[0]
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1879 # capture error for non-sequence/iterator nbunch.
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1880 if 'iter' in message:
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1881 msg = "nbunch is not a node or a sequence of nodes."
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1882 raise NetworkXError(msg)
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1883 # capture error for unhashable node.
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1884 elif 'hashable' in message:
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1885 msg = "Node {} in sequence nbunch is not a valid node."
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1886 raise NetworkXError(msg.format(n))
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1887 else:
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1888 raise
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1889 bunch = bunch_iter(nbunch, self._adj)
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1890 return bunch