Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/networkx/classes/multidigraph.py @ 0:26e78fe6e8c4 draft

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"planemo upload commit c699937486c35866861690329de38ec1a5d9f783"
author shellac
date Sat, 02 May 2020 07:14:21 -0400
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1 # Copyright (C) 2004-2019 by
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2 # Aric Hagberg <hagberg@lanl.gov>
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3 # Dan Schult <dschult@colgate.edu>
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4 # Pieter Swart <swart@lanl.gov>
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5 # All rights reserved.
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6 # BSD license.
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7 #
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8 # Authors: Aric Hagberg <hagberg@lanl.gov>
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9 # Dan Schult <dschult@colgate.edu>
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10 # Pieter Swart <swart@lanl.gov>
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11 """Base class for MultiDiGraph."""
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12 from copy import deepcopy
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13
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14 import networkx as nx
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15 from networkx.classes.graph import Graph # for doctests
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16 from networkx.classes.digraph import DiGraph
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17 from networkx.classes.multigraph import MultiGraph
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18 from networkx.classes.coreviews import MultiAdjacencyView
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19 from networkx.classes.reportviews import OutMultiEdgeView, InMultiEdgeView, \
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20 DiMultiDegreeView, OutMultiDegreeView, InMultiDegreeView
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21 from networkx.exception import NetworkXError
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22
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23
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24 class MultiDiGraph(MultiGraph, DiGraph):
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25 """A directed graph class that can store multiedges.
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26
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27 Multiedges are multiple edges between two nodes. Each edge
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28 can hold optional data or attributes.
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29
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30 A MultiDiGraph holds directed edges. Self loops are allowed.
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31
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32 Nodes can be arbitrary (hashable) Python objects with optional
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33 key/value attributes. By convention `None` is not used as a node.
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34
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35 Edges are represented as links between nodes with optional
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36 key/value attributes.
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37
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38 Parameters
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39 ----------
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40 incoming_graph_data : input graph (optional, default: None)
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41 Data to initialize graph. If None (default) an empty
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42 graph is created. The data can be any format that is supported
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43 by the to_networkx_graph() function, currently including edge list,
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44 dict of dicts, dict of lists, NetworkX graph, NumPy matrix
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45 or 2d ndarray, SciPy sparse matrix, or PyGraphviz graph.
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46
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47 attr : keyword arguments, optional (default= no attributes)
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48 Attributes to add to graph as key=value pairs.
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49
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50 See Also
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51 --------
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52 Graph
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53 DiGraph
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54 MultiGraph
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55 OrderedMultiDiGraph
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56
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57 Examples
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58 --------
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59 Create an empty graph structure (a "null graph") with no nodes and
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60 no edges.
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61
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62 >>> G = nx.MultiDiGraph()
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63
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64 G can be grown in several ways.
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65
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66 **Nodes:**
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67
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68 Add one node at a time:
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69
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70 >>> G.add_node(1)
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71
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72 Add the nodes from any container (a list, dict, set or
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73 even the lines from a file or the nodes from another graph).
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74
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75 >>> G.add_nodes_from([2, 3])
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76 >>> G.add_nodes_from(range(100, 110))
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77 >>> H = nx.path_graph(10)
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78 >>> G.add_nodes_from(H)
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79
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80 In addition to strings and integers any hashable Python object
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81 (except None) can represent a node, e.g. a customized node object,
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82 or even another Graph.
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83
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84 >>> G.add_node(H)
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85
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86 **Edges:**
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87
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88 G can also be grown by adding edges.
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89
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90 Add one edge,
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91
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92 >>> key = G.add_edge(1, 2)
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93
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94 a list of edges,
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95
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96 >>> keys = G.add_edges_from([(1, 2), (1, 3)])
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97
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98 or a collection of edges,
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99
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100 >>> keys = G.add_edges_from(H.edges)
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101
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102 If some edges connect nodes not yet in the graph, the nodes
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103 are added automatically. If an edge already exists, an additional
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104 edge is created and stored using a key to identify the edge.
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105 By default the key is the lowest unused integer.
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106
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107 >>> keys = G.add_edges_from([(4,5,dict(route=282)), (4,5,dict(route=37))])
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108 >>> G[4]
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109 AdjacencyView({5: {0: {}, 1: {'route': 282}, 2: {'route': 37}}})
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110
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111 **Attributes:**
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112
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113 Each graph, node, and edge can hold key/value attribute pairs
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114 in an associated attribute dictionary (the keys must be hashable).
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115 By default these are empty, but can be added or changed using
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116 add_edge, add_node or direct manipulation of the attribute
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117 dictionaries named graph, node and edge respectively.
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118
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119 >>> G = nx.MultiDiGraph(day="Friday")
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120 >>> G.graph
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121 {'day': 'Friday'}
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122
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123 Add node attributes using add_node(), add_nodes_from() or G.nodes
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124
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125 >>> G.add_node(1, time='5pm')
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126 >>> G.add_nodes_from([3], time='2pm')
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127 >>> G.nodes[1]
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128 {'time': '5pm'}
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129 >>> G.nodes[1]['room'] = 714
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130 >>> del G.nodes[1]['room'] # remove attribute
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131 >>> list(G.nodes(data=True))
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132 [(1, {'time': '5pm'}), (3, {'time': '2pm'})]
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133
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134 Add edge attributes using add_edge(), add_edges_from(), subscript
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135 notation, or G.edges.
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136
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137 >>> key = G.add_edge(1, 2, weight=4.7 )
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138 >>> keys = G.add_edges_from([(3, 4), (4, 5)], color='red')
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139 >>> keys = G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
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140 >>> G[1][2][0]['weight'] = 4.7
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141 >>> G.edges[1, 2, 0]['weight'] = 4
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142
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143 Warning: we protect the graph data structure by making `G.edges[1, 2]` a
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144 read-only dict-like structure. However, you can assign to attributes
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145 in e.g. `G.edges[1, 2]`. Thus, use 2 sets of brackets to add/change
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146 data attributes: `G.edges[1, 2]['weight'] = 4`
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147 (For multigraphs: `MG.edges[u, v, key][name] = value`).
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148
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149 **Shortcuts:**
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150
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151 Many common graph features allow python syntax to speed reporting.
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152
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153 >>> 1 in G # check if node in graph
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154 True
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155 >>> [n for n in G if n<3] # iterate through nodes
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156 [1, 2]
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157 >>> len(G) # number of nodes in graph
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158 5
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159 >>> G[1] # adjacency dict-like view keyed by neighbor to edge attributes
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160 AdjacencyView({2: {0: {'weight': 4}, 1: {'color': 'blue'}}})
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161
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162 Often the best way to traverse all edges of a graph is via the neighbors.
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163 The neighbors are available as an adjacency-view `G.adj` object or via
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164 the method `G.adjacency()`.
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165
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166 >>> for n, nbrsdict in G.adjacency():
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167 ... for nbr, keydict in nbrsdict.items():
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168 ... for key, eattr in keydict.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, keys, weight in G.edges(data='weight', keys=True):
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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 methods and object-attributes.
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183 Reporting usually 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 MultiDiGraph class uses a dict-of-dict-of-dict-of-dict 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_key dicts keyed by neighbor. The edge_key dict holds each edge_attr
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199 dict keyed by edge key. The inner dict (edge_attr_dict) represents
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200 the edge data and holds edge attribute values keyed by attribute names.
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201
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202 Each of these four dicts in the dict-of-dict-of-dict-of-dict
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203 structure can be replaced by a user defined dict-like object.
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204 In general, the dict-like features should be maintained but
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205 extra features can be added. To replace one of the dicts create
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206 a new graph class by changing the class(!) variable holding the
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207 factory for that dict-like structure. The variable names are
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208 node_dict_factory, node_attr_dict_factory, adjlist_inner_dict_factory,
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209 adjlist_outer_dict_factory, edge_key_dict_factory, edge_attr_dict_factory
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210 and graph_attr_dict_factory.
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211
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212 node_dict_factory : function, (default: dict)
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213 Factory function to be used to create the dict containing node
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214 attributes, keyed by node id.
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215 It should require no arguments and return a dict-like object
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216
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217 node_attr_dict_factory: function, (default: dict)
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218 Factory function to be used to create the node attribute
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219 dict which holds attribute values keyed by attribute name.
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220 It should require no arguments and return a dict-like object
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221
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222 adjlist_outer_dict_factory : function, (default: dict)
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223 Factory function to be used to create the outer-most dict
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224 in the data structure that holds adjacency info keyed by node.
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225 It should require no arguments and return a dict-like object.
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226
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227 adjlist_inner_dict_factory : function, (default: dict)
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228 Factory function to be used to create the adjacency list
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229 dict which holds multiedge key dicts keyed by neighbor.
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230 It should require no arguments and return a dict-like object.
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231
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232 edge_key_dict_factory : function, (default: dict)
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233 Factory function to be used to create the edge key dict
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234 which holds edge data keyed by edge key.
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235 It should require no arguments and return a dict-like object.
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236
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237 edge_attr_dict_factory : function, (default: dict)
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238 Factory function to be used to create the edge attribute
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239 dict which holds attribute values keyed by attribute name.
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240 It should require no arguments and return a dict-like object.
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241
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242 graph_attr_dict_factory : function, (default: dict)
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243 Factory function to be used to create the graph attribute
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244 dict which holds attribute values keyed by attribute name.
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245 It should require no arguments and return a dict-like object.
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246
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247 Typically, if your extension doesn't impact the data structure all
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248 methods will inherited without issue except: `to_directed/to_undirected`.
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249 By default these methods create a DiGraph/Graph class and you probably
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250 want them to create your extension of a DiGraph/Graph. To facilitate
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251 this we define two class variables that you can set in your subclass.
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252
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253 to_directed_class : callable, (default: DiGraph or MultiDiGraph)
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254 Class to create a new graph structure in the `to_directed` method.
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255 If `None`, a NetworkX class (DiGraph or MultiDiGraph) is used.
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256
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257 to_undirected_class : callable, (default: Graph or MultiGraph)
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258 Class to create a new graph structure in the `to_undirected` method.
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259 If `None`, a NetworkX class (Graph or MultiGraph) is used.
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260
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261 Examples
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262 --------
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263
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264 Please see :mod:`~networkx.classes.ordered` for examples of
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265 creating graph subclasses by overwriting the base class `dict` with
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266 a dictionary-like object.
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267 """
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268 # node_dict_factory = dict # already assigned in Graph
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269 # adjlist_outer_dict_factory = dict
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270 # adjlist_inner_dict_factory = dict
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271 edge_key_dict_factory = dict
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272 # edge_attr_dict_factory = dict
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273
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274 def __init__(self, incoming_graph_data=None, **attr):
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275 """Initialize a graph with edges, name, or graph attributes.
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276
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277 Parameters
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278 ----------
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279 incoming_graph_data : input graph
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280 Data to initialize graph. If incoming_graph_data=None (default)
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281 an empty graph is created. The data can be an edge list, or any
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282 NetworkX graph object. If the corresponding optional Python
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283 packages are installed the data can also be a NumPy matrix
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284 or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
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285
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286 attr : keyword arguments, optional (default= no attributes)
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287 Attributes to add to graph as key=value pairs.
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288
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289 See Also
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290 --------
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291 convert
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292
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293 Examples
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294 --------
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295 >>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc
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296 >>> G = nx.Graph(name='my graph')
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297 >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges
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298 >>> G = nx.Graph(e)
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299
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300 Arbitrary graph attribute pairs (key=value) may be assigned
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301
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302 >>> G = nx.Graph(e, day="Friday")
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303 >>> G.graph
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304 {'day': 'Friday'}
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305
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306 """
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307 self.edge_key_dict_factory = self.edge_key_dict_factory
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308 DiGraph.__init__(self, incoming_graph_data, **attr)
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309
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310 @property
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311 def adj(self):
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312 """Graph adjacency object holding the neighbors of each node.
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313
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314 This object is a read-only dict-like structure with node keys
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315 and neighbor-dict values. The neighbor-dict is keyed by neighbor
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316 to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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317 the color of the edge `(3, 2, 0)` to `"blue"`.
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318
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319 Iterating over G.adj behaves like a dict. Useful idioms include
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320 `for nbr, datadict in G.adj[n].items():`.
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321
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322 The neighbor information is also provided by subscripting the graph.
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323 So `for nbr, foovalue in G[node].data('foo', default=1):` works.
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324
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325 For directed graphs, `G.adj` holds outgoing (successor) info.
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326 """
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327 return MultiAdjacencyView(self._succ)
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328
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329 @property
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330 def succ(self):
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331 """Graph adjacency object holding the successors of each node.
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332
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333 This object is a read-only dict-like structure with node keys
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334 and neighbor-dict values. The neighbor-dict is keyed by neighbor
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335 to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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336 the color of the edge `(3, 2, 0)` to `"blue"`.
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337
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338 Iterating over G.adj behaves like a dict. Useful idioms include
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339 `for nbr, datadict in G.adj[n].items():`.
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340
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341 The neighbor information is also provided by subscripting the graph.
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342 So `for nbr, foovalue in G[node].data('foo', default=1):` works.
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343
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344 For directed graphs, `G.succ` is identical to `G.adj`.
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345 """
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346 return MultiAdjacencyView(self._succ)
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347
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348 @property
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349 def pred(self):
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350 """Graph adjacency object holding the predecessors of each node.
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351
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352 This object is a read-only dict-like structure with node keys
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353 and neighbor-dict values. The neighbor-dict is keyed by neighbor
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354 to the edgekey-dict. So `G.adj[3][2][0]['color'] = 'blue'` sets
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355 the color of the edge `(3, 2, 0)` to `"blue"`.
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356
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357 Iterating over G.adj behaves like a dict. Useful idioms include
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358 `for nbr, datadict in G.adj[n].items():`.
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359 """
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360 return MultiAdjacencyView(self._pred)
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361
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362 def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
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363 """Add an edge between u and v.
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364
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365 The nodes u and v will be automatically added if they are
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366 not already in the graph.
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367
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368 Edge attributes can be specified with keywords or by directly
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369 accessing the edge's attribute dictionary. See examples below.
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370
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371 Parameters
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372 ----------
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373 u_for_edge, v_for_edge : nodes
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374 Nodes can be, for example, strings or numbers.
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375 Nodes must be hashable (and not None) Python objects.
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376 key : hashable identifier, optional (default=lowest unused integer)
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377 Used to distinguish multiedges between a pair of nodes.
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378 attr_dict : dictionary, optional (default= no attributes)
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379 Dictionary of edge attributes. Key/value pairs will
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380 update existing data associated with the edge.
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381 attr : keyword arguments, optional
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382 Edge data (or labels or objects) can be assigned using
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383 keyword arguments.
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384
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385 Returns
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386 -------
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387 The edge key assigned to the edge.
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388
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389 See Also
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390 --------
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391 add_edges_from : add a collection of edges
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392
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393 Notes
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394 -----
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395 To replace/update edge data, use the optional key argument
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396 to identify a unique edge. Otherwise a new edge will be created.
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397
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398 NetworkX algorithms designed for weighted graphs cannot use
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399 multigraphs directly because it is not clear how to handle
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400 multiedge weights. Convert to Graph using edge attribute
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401 'weight' to enable weighted graph algorithms.
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402
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403 Default keys are generated using the method `new_edge_key()`.
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404 This method can be overridden by subclassing the base class and
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405 providing a custom `new_edge_key()` method.
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406
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407 Examples
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408 --------
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409 The following all add the edge e=(1, 2) to graph G:
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410
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411 >>> G = nx.MultiDiGraph()
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412 >>> e = (1, 2)
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413 >>> key = G.add_edge(1, 2) # explicit two-node form
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414 >>> G.add_edge(*e) # single edge as tuple of two nodes
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415 1
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416 >>> G.add_edges_from( [(1, 2)] ) # add edges from iterable container
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417 [2]
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418
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419 Associate data to edges using keywords:
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420
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421 >>> key = G.add_edge(1, 2, weight=3)
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422 >>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0
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423 >>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)
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424
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425 For non-string attribute keys, use subscript notation.
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426
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427 >>> ekey = G.add_edge(1, 2)
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428 >>> G[1][2][0].update({0: 5})
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429 >>> G.edges[1, 2, 0].update({0: 5})
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430 """
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431 u, v = u_for_edge, v_for_edge
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432 # add nodes
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433 if u not in self._succ:
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434 self._succ[u] = self.adjlist_inner_dict_factory()
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435 self._pred[u] = self.adjlist_inner_dict_factory()
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436 self._node[u] = self.node_attr_dict_factory()
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437 if v not in self._succ:
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438 self._succ[v] = self.adjlist_inner_dict_factory()
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439 self._pred[v] = self.adjlist_inner_dict_factory()
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440 self._node[v] = self.node_attr_dict_factory()
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441 if key is None:
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442 key = self.new_edge_key(u, v)
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443 if v in self._succ[u]:
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444 keydict = self._adj[u][v]
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445 datadict = keydict.get(key, self.edge_key_dict_factory())
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446 datadict.update(attr)
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447 keydict[key] = datadict
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448 else:
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449 # selfloops work this way without special treatment
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450 datadict = self.edge_attr_dict_factory()
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451 datadict.update(attr)
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452 keydict = self.edge_key_dict_factory()
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453 keydict[key] = datadict
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454 self._succ[u][v] = keydict
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455 self._pred[v][u] = keydict
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456 return key
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457
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458 def remove_edge(self, u, v, key=None):
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459 """Remove an edge between u and v.
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460
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461 Parameters
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462 ----------
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463 u, v : nodes
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464 Remove an edge between nodes u and v.
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465 key : hashable identifier, optional (default=None)
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466 Used to distinguish multiple edges between a pair of nodes.
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467 If None remove a single (arbitrary) edge between u and v.
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468
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469 Raises
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470 ------
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471 NetworkXError
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472 If there is not an edge between u and v, or
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473 if there is no edge with the specified key.
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474
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475 See Also
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476 --------
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477 remove_edges_from : remove a collection of edges
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478
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479 Examples
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480 --------
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481 >>> G = nx.MultiDiGraph()
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482 >>> nx.add_path(G, [0, 1, 2, 3])
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483 >>> G.remove_edge(0, 1)
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484 >>> e = (1, 2)
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485 >>> G.remove_edge(*e) # unpacks e from an edge tuple
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486
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487 For multiple edges
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488
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489 >>> G = nx.MultiDiGraph()
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490 >>> G.add_edges_from([(1, 2), (1, 2), (1, 2)]) # key_list returned
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491 [0, 1, 2]
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492 >>> G.remove_edge(1, 2) # remove a single (arbitrary) edge
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493
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494 For edges with keys
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495
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496 >>> G = nx.MultiDiGraph()
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497 >>> G.add_edge(1, 2, key='first')
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498 'first'
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499 >>> G.add_edge(1, 2, key='second')
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500 'second'
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501 >>> G.remove_edge(1, 2, key='second')
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502
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503 """
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504 try:
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505 d = self._adj[u][v]
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506 except KeyError:
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507 raise NetworkXError(
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508 "The edge %s-%s is not in the graph." % (u, v))
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509 # remove the edge with specified data
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510 if key is None:
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511 d.popitem()
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512 else:
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513 try:
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514 del d[key]
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515 except KeyError:
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516 msg = "The edge %s-%s with key %s is not in the graph."
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517 raise NetworkXError(msg % (u, v, key))
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518 if len(d) == 0:
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diff changeset
519 # remove the key entries if last edge
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520 del self._succ[u][v]
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521 del self._pred[v][u]
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522
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523 @property
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524 def edges(self):
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525 """An OutMultiEdgeView of the Graph as G.edges or G.edges().
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526
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527 edges(self, nbunch=None, data=False, keys=False, default=None)
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528
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529 The OutMultiEdgeView provides set-like operations on the edge-tuples
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530 as well as edge attribute lookup. When called, it also provides
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531 an EdgeDataView object which allows control of access to edge
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532 attributes (but does not provide set-like operations).
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533 Hence, `G.edges[u, v]['color']` provides the value of the color
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534 attribute for edge `(u, v)` while
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535 `for (u, v, c) in G.edges(data='color', default='red'):`
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536 iterates through all the edges yielding the color attribute
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537 with default `'red'` if no color attribute exists.
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538
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diff changeset
539 Edges are returned as tuples with optional data and keys
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540 in the order (node, neighbor, key, data).
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diff changeset
541
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542 Parameters
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543 ----------
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544 nbunch : single node, container, or all nodes (default= all nodes)
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545 The view will only report edges incident to these nodes.
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546 data : string or bool, optional (default=False)
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547 The edge attribute returned in 3-tuple (u, v, ddict[data]).
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548 If True, return edge attribute dict in 3-tuple (u, v, ddict).
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549 If False, return 2-tuple (u, v).
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550 keys : bool, optional (default=False)
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551 If True, return edge keys with each edge.
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552 default : value, optional (default=None)
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553 Value used for edges that don't have the requested attribute.
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diff changeset
554 Only relevant if data is not True or False.
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555
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556 Returns
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557 -------
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558 edges : EdgeView
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559 A view of edge attributes, usually it iterates over (u, v)
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560 (u, v, k) or (u, v, k, d) tuples of edges, but can also be
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561 used for attribute lookup as `edges[u, v, k]['foo']`.
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562
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563 Notes
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564 -----
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565 Nodes in nbunch that are not in the graph will be (quietly) ignored.
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566 For directed graphs this returns the out-edges.
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567
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568 Examples
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569 --------
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570 >>> G = nx.MultiDiGraph()
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571 >>> nx.add_path(G, [0, 1, 2])
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572 >>> key = G.add_edge(2, 3, weight=5)
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573 >>> [e for e in G.edges()]
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diff changeset
574 [(0, 1), (1, 2), (2, 3)]
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575 >>> list(G.edges(data=True)) # default data is {} (empty dict)
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576 [(0, 1, {}), (1, 2, {}), (2, 3, {'weight': 5})]
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577 >>> list(G.edges(data='weight', default=1))
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578 [(0, 1, 1), (1, 2, 1), (2, 3, 5)]
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579 >>> list(G.edges(keys=True)) # default keys are integers
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580 [(0, 1, 0), (1, 2, 0), (2, 3, 0)]
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581 >>> list(G.edges(data=True, keys=True))
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diff changeset
582 [(0, 1, 0, {}), (1, 2, 0, {}), (2, 3, 0, {'weight': 5})]
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583 >>> list(G.edges(data='weight', default=1, keys=True))
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diff changeset
584 [(0, 1, 0, 1), (1, 2, 0, 1), (2, 3, 0, 5)]
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585 >>> list(G.edges([0, 2]))
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diff changeset
586 [(0, 1), (2, 3)]
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parents:
diff changeset
587 >>> list(G.edges(0))
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diff changeset
588 [(0, 1)]
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diff changeset
589
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diff changeset
590 See Also
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591 --------
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diff changeset
592 in_edges, out_edges
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593 """
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594 return OutMultiEdgeView(self)
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595
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596 # alias out_edges to edges
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diff changeset
597 out_edges = edges
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diff changeset
598
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599 @property
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diff changeset
600 def in_edges(self):
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diff changeset
601 """An InMultiEdgeView of the Graph as G.in_edges or G.in_edges().
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602
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603 in_edges(self, nbunch=None, data=False, keys=False, default=None)
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604
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605 Parameters
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606 ----------
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diff changeset
607 nbunch : single node, container, or all nodes (default= all nodes)
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608 The view will only report edges incident to these nodes.
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609 data : string or bool, optional (default=False)
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610 The edge attribute returned in 3-tuple (u, v, ddict[data]).
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611 If True, return edge attribute dict in 3-tuple (u, v, ddict).
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612 If False, return 2-tuple (u, v).
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613 keys : bool, optional (default=False)
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614 If True, return edge keys with each edge.
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615 default : value, optional (default=None)
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616 Value used for edges that don't have the requested attribute.
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617 Only relevant if data is not True or False.
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618
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619 Returns
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620 -------
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621 in_edges : InMultiEdgeView
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diff changeset
622 A view of edge attributes, usually it iterates over (u, v)
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623 or (u, v, k) or (u, v, k, d) tuples of edges, but can also be
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624 used for attribute lookup as `edges[u, v, k]['foo']`.
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625
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626 See Also
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627 --------
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628 edges
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629 """
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630 return InMultiEdgeView(self)
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631
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632 @property
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diff changeset
633 def degree(self):
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634 """A DegreeView for the Graph as G.degree or G.degree().
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635
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636 The node degree is the number of edges adjacent to the node.
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637 The weighted node degree is the sum of the edge weights for
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638 edges incident to that node.
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639
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640 This object provides an iterator for (node, degree) as well as
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641 lookup for the degree for a single node.
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642
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643 Parameters
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644 ----------
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645 nbunch : single node, container, or all nodes (default= all nodes)
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646 The view will only report edges incident to these nodes.
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647
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648 weight : string or None, optional (default=None)
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649 The name of an edge attribute that holds the numerical value used
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650 as a weight. If None, then each edge has weight 1.
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651 The degree is the sum of the edge weights adjacent to the node.
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652
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653 Returns
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654 -------
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655 If a single nodes is requested
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656 deg : int
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657 Degree of the node
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658
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659 OR if multiple nodes are requested
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660 nd_iter : iterator
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661 The iterator returns two-tuples of (node, degree).
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662
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663 See Also
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664 --------
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665 out_degree, in_degree
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666
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667 Examples
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668 --------
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669 >>> G = nx.MultiDiGraph()
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670 >>> nx.add_path(G, [0, 1, 2, 3])
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671 >>> G.degree(0) # node 0 with degree 1
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672 1
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673 >>> list(G.degree([0, 1, 2]))
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674 [(0, 1), (1, 2), (2, 2)]
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675
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676 """
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677 return DiMultiDegreeView(self)
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678
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679 @property
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680 def in_degree(self):
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681 """A DegreeView for (node, in_degree) or in_degree for single node.
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682
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683 The node in-degree is the number of edges pointing in to the node.
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684 The weighted node degree is the sum of the edge weights for
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685 edges incident to that node.
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686
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687 This object provides an iterator for (node, degree) as well as
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688 lookup for the degree for a single node.
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689
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690 Parameters
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691 ----------
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692 nbunch : single node, container, or all nodes (default= all nodes)
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693 The view will only report edges incident to these nodes.
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694
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695 weight : string or None, optional (default=None)
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696 The edge attribute that holds the numerical value used
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697 as a weight. If None, then each edge has weight 1.
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698 The degree is the sum of the edge weights adjacent to the node.
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699
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700 Returns
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701 -------
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702 If a single node is requested
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703 deg : int
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704 Degree of the node
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705
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706 OR if multiple nodes are requested
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707 nd_iter : iterator
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708 The iterator returns two-tuples of (node, in-degree).
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709
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710 See Also
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711 --------
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712 degree, out_degree
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713
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714 Examples
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715 --------
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716 >>> G = nx.MultiDiGraph()
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717 >>> nx.add_path(G, [0, 1, 2, 3])
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718 >>> G.in_degree(0) # node 0 with degree 0
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719 0
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720 >>> list(G.in_degree([0, 1, 2]))
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721 [(0, 0), (1, 1), (2, 1)]
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722
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723 """
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724 return InMultiDegreeView(self)
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725
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726 @property
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727 def out_degree(self):
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728 """Returns an iterator for (node, out-degree) or out-degree for single node.
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729
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730 out_degree(self, nbunch=None, weight=None)
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731
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732 The node out-degree is the number of edges pointing out of the node.
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733 This function returns the out-degree for a single node or an iterator
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734 for a bunch of nodes or if nothing is passed as argument.
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735
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736 Parameters
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737 ----------
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738 nbunch : single node, container, or all nodes (default= all nodes)
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739 The view will only report edges incident to these nodes.
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740
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741 weight : string or None, optional (default=None)
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742 The edge attribute that holds the numerical value used
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743 as a weight. If None, then each edge has weight 1.
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744 The degree is the sum of the edge weights.
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745
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746 Returns
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747 -------
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748 If a single node is requested
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749 deg : int
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750 Degree of the node
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751
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752 OR if multiple nodes are requested
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753 nd_iter : iterator
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754 The iterator returns two-tuples of (node, out-degree).
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755
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756 See Also
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757 --------
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758 degree, in_degree
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759
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760 Examples
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761 --------
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762 >>> G = nx.MultiDiGraph()
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763 >>> nx.add_path(G, [0, 1, 2, 3])
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764 >>> G.out_degree(0) # node 0 with degree 1
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765 1
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766 >>> list(G.out_degree([0, 1, 2]))
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767 [(0, 1), (1, 1), (2, 1)]
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768
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769 """
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770 return OutMultiDegreeView(self)
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771
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772 def is_multigraph(self):
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773 """Returns True if graph is a multigraph, False otherwise."""
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774 return True
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775
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776 def is_directed(self):
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777 """Returns True if graph is directed, False otherwise."""
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778 return True
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779
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780 def to_undirected(self, reciprocal=False, as_view=False):
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781 """Returns an undirected representation of the digraph.
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782
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783 Parameters
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784 ----------
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785 reciprocal : bool (optional)
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786 If True only keep edges that appear in both directions
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diff changeset
787 in the original digraph.
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788 as_view : bool (optional, default=False)
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diff changeset
789 If True return an undirected view of the original directed graph.
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790
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791 Returns
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792 -------
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793 G : MultiGraph
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794 An undirected graph with the same name and nodes and
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parents:
diff changeset
795 with edge (u, v, data) if either (u, v, data) or (v, u, data)
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796 is in the digraph. If both edges exist in digraph and
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parents:
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797 their edge data is different, only one edge is created
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parents:
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798 with an arbitrary choice of which edge data to use.
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799 You must check and correct for this manually if desired.
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800
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801 See Also
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802 --------
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803 MultiGraph, copy, add_edge, add_edges_from
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parents:
diff changeset
804
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805 Notes
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806 -----
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807 This returns a "deepcopy" of the edge, node, and
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parents:
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808 graph attributes which attempts to completely copy
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parents:
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809 all of the data and references.
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810
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811 This is in contrast to the similar D=MultiiGraph(G) which
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parents:
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812 returns a shallow copy of the data.
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813
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814 See the Python copy module for more information on shallow
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815 and deep copies, https://docs.python.org/2/library/copy.html.
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816
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817 Warning: If you have subclassed MultiDiGraph to use dict-like
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818 objects in the data structure, those changes do not transfer
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819 to the MultiGraph created by this method.
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parents:
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820
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821 Examples
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822 --------
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823 >>> G = nx.path_graph(2) # or MultiGraph, etc
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824 >>> H = G.to_directed()
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825 >>> list(H.edges)
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826 [(0, 1), (1, 0)]
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827 >>> G2 = H.to_undirected()
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828 >>> list(G2.edges)
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829 [(0, 1)]
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830 """
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831 graph_class = self.to_undirected_class()
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832 if as_view is True:
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diff changeset
833 return nx.graphviews.generic_graph_view(self, graph_class)
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834 # deepcopy when not a view
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diff changeset
835 G = graph_class()
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836 G.graph.update(deepcopy(self.graph))
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837 G.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
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838 if reciprocal is True:
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diff changeset
839 G.add_edges_from((u, v, key, deepcopy(data))
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parents:
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840 for u, nbrs in self._adj.items()
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841 for v, keydict in nbrs.items()
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parents:
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842 for key, data in keydict.items()
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parents:
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843 if v in self._pred[u] and key in self._pred[u][v])
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parents:
diff changeset
844 else:
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diff changeset
845 G.add_edges_from((u, v, key, deepcopy(data))
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846 for u, nbrs in self._adj.items()
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parents:
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847 for v, keydict in nbrs.items()
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parents:
diff changeset
848 for key, data in keydict.items())
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parents:
diff changeset
849 return G
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850
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diff changeset
851 def reverse(self, copy=True):
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diff changeset
852 """Returns the reverse of the graph.
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diff changeset
853
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854 The reverse is a graph with the same nodes and edges
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parents:
diff changeset
855 but with the directions of the edges reversed.
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856
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857 Parameters
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858 ----------
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859 copy : bool optional (default=True)
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860 If True, return a new DiGraph holding the reversed edges.
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diff changeset
861 If False, the reverse graph is created using a view of
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parents:
diff changeset
862 the original graph.
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863 """
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864 if copy:
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diff changeset
865 H = self.__class__()
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866 H.graph.update(deepcopy(self.graph))
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diff changeset
867 H.add_nodes_from((n, deepcopy(d)) for n, d in self._node.items())
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868 H.add_edges_from((v, u, k, deepcopy(d)) for u, v, k, d
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869 in self.edges(keys=True, data=True))
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diff changeset
870 return H
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diff changeset
871 return nx.graphviews.reverse_view(self)