Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/classes/graphviews.py @ 0:4f3585e2f14b draft default tip
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author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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1 """View of Graphs as SubGraph, Reverse, Directed, Undirected. |
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2 |
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3 In some algorithms it is convenient to temporarily morph |
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4 a graph to exclude some nodes or edges. It should be better |
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5 to do that via a view than to remove and then re-add. |
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6 In other algorithms it is convenient to temporarily morph |
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7 a graph to reverse directed edges, or treat a directed graph |
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8 as undirected, etc. This module provides those graph views. |
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9 |
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10 The resulting views are essentially read-only graphs that |
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11 report data from the orignal graph object. We provide an |
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12 attribute G._graph which points to the underlying graph object. |
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13 |
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14 Note: Since graphviews look like graphs, one can end up with |
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15 view-of-view-of-view chains. Be careful with chains because |
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16 they become very slow with about 15 nested views. |
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17 For the common simple case of node induced subgraphs created |
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18 from the graph class, we short-cut the chain by returning a |
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19 subgraph of the original graph directly rather than a subgraph |
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20 of a subgraph. We are careful not to disrupt any edge filter in |
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21 the middle subgraph. In general, determining how to short-cut |
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22 the chain is tricky and much harder with restricted_views than |
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23 with induced subgraphs. |
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24 Often it is easiest to use .copy() to avoid chains. |
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25 """ |
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26 from networkx.classes.coreviews import ( |
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27 UnionAdjacency, |
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28 UnionMultiAdjacency, |
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29 FilterAtlas, |
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30 FilterAdjacency, |
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31 FilterMultiAdjacency, |
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32 ) |
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33 from networkx.classes.filters import no_filter |
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34 from networkx.exception import NetworkXError |
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35 from networkx.utils import not_implemented_for |
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36 |
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37 import networkx as nx |
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38 |
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39 __all__ = ["generic_graph_view", "subgraph_view", "reverse_view"] |
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40 |
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41 |
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42 def generic_graph_view(G, create_using=None): |
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43 if create_using is None: |
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44 newG = G.__class__() |
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45 else: |
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46 newG = nx.empty_graph(0, create_using) |
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47 if G.is_multigraph() != newG.is_multigraph(): |
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48 raise NetworkXError("Multigraph for G must agree with create_using") |
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49 newG = nx.freeze(newG) |
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50 |
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51 # create view by assigning attributes from G |
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52 newG._graph = G |
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53 newG.graph = G.graph |
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54 |
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55 newG._node = G._node |
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56 if newG.is_directed(): |
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57 if G.is_directed(): |
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58 newG._succ = G._succ |
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59 newG._pred = G._pred |
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60 newG._adj = G._succ |
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61 else: |
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62 newG._succ = G._adj |
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63 newG._pred = G._adj |
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64 newG._adj = G._adj |
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65 elif G.is_directed(): |
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66 if G.is_multigraph(): |
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67 newG._adj = UnionMultiAdjacency(G._succ, G._pred) |
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68 else: |
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69 newG._adj = UnionAdjacency(G._succ, G._pred) |
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70 else: |
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71 newG._adj = G._adj |
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72 return newG |
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73 |
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74 |
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75 def subgraph_view(G, filter_node=no_filter, filter_edge=no_filter): |
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76 """ View of `G` applying a filter on nodes and edges. |
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77 |
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78 `subgraph_view` provides a read-only view of the input graph that excludes |
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79 nodes and edges based on the outcome of two filter functions `filter_node` |
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80 and `filter_edge`. |
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81 |
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82 The `filter_node` function takes one argument --- the node --- and returns |
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83 `True` if the node should be included in the subgraph, and `False` if it |
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84 should not be included. |
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85 |
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86 The `filter_edge` function takes two (or three arguments if `G` is a |
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87 multi-graph) --- the nodes describing an edge, plus the edge-key if |
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88 parallel edges are possible --- and returns `True` if the edge should be |
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89 included in the subgraph, and `False` if it should not be included. |
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90 |
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91 Both node and edge filter functions are called on graph elements as they |
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92 are queried, meaning there is no up-front cost to creating the view. |
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93 |
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94 Parameters |
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95 ---------- |
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96 G : networkx.Graph |
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97 A directed/undirected graph/multigraph |
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98 |
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99 filter_node : callable, optional |
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100 A function taking a node as input, which returns `True` if the node |
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101 should appear in the view. |
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102 |
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103 filter_edge : callable, optional |
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104 A function taking as input the two nodes describing an edge (plus the |
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105 edge-key if `G` is a multi-graph), which returns `True` if the edge |
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106 should appear in the view. |
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107 |
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108 Returns |
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109 ------- |
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110 graph : networkx.Graph |
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111 A read-only graph view of the input graph. |
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112 |
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113 Examples |
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114 -------- |
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115 >>> G = nx.path_graph(6) |
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116 |
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117 Filter functions operate on the node, and return `True` if the node should |
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118 appear in the view: |
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119 |
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120 >>> def filter_node(n1): |
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121 ... return n1 != 5 |
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122 ... |
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123 >>> view = nx.subgraph_view(G, filter_node=filter_node) |
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124 >>> view.nodes() |
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125 NodeView((0, 1, 2, 3, 4)) |
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126 |
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127 We can use a closure pattern to filter graph elements based on additional |
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128 data --- for example, filtering on edge data attached to the graph: |
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129 |
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130 >>> G[3][4]["cross_me"] = False |
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131 >>> def filter_edge(n1, n2): |
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132 ... return G[n1][n2].get("cross_me", True) |
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133 ... |
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134 >>> view = nx.subgraph_view(G, filter_edge=filter_edge) |
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135 >>> view.edges() |
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136 EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)]) |
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137 |
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138 >>> view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge,) |
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139 >>> view.nodes() |
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140 NodeView((0, 1, 2, 3, 4)) |
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141 >>> view.edges() |
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142 EdgeView([(0, 1), (1, 2), (2, 3)]) |
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143 """ |
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144 newG = nx.freeze(G.__class__()) |
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145 newG._NODE_OK = filter_node |
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146 newG._EDGE_OK = filter_edge |
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147 |
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148 # create view by assigning attributes from G |
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149 newG._graph = G |
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150 newG.graph = G.graph |
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151 |
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152 newG._node = FilterAtlas(G._node, filter_node) |
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153 if G.is_multigraph(): |
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154 Adj = FilterMultiAdjacency |
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155 |
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156 def reverse_edge(u, v, k): |
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157 return filter_edge(v, u, k) |
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158 |
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159 else: |
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160 Adj = FilterAdjacency |
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161 |
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162 def reverse_edge(u, v): |
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163 return filter_edge(v, u) |
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164 |
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165 if G.is_directed(): |
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166 newG._succ = Adj(G._succ, filter_node, filter_edge) |
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167 newG._pred = Adj(G._pred, filter_node, reverse_edge) |
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168 newG._adj = newG._succ |
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169 else: |
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170 newG._adj = Adj(G._adj, filter_node, filter_edge) |
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171 return newG |
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172 |
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173 |
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174 @not_implemented_for("undirected") |
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175 def reverse_view(G): |
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176 """ View of `G` with edge directions reversed |
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177 |
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178 `reverse_view` returns a read-only view of the input graph where |
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179 edge directions are reversed. |
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180 |
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181 Identical to digraph.reverse(copy=False) |
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182 |
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183 Parameters |
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184 ---------- |
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185 G : networkx.DiGraph |
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186 |
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187 Returns |
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188 ------- |
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189 graph : networkx.DiGraph |
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190 |
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191 Examples |
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192 -------- |
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193 >>> G = nx.DiGraph() |
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194 >>> G.add_edge(1, 2) |
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195 >>> G.add_edge(2, 3) |
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196 >>> G.edges() |
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197 OutEdgeView([(1, 2), (2, 3)]) |
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198 |
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199 >>> view = nx.reverse_view(G) |
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200 >>> view.edges() |
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201 OutEdgeView([(2, 1), (3, 2)]) |
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202 """ |
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203 newG = generic_graph_view(G) |
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204 newG._succ, newG._pred = G._pred, G._succ |
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205 newG._adj = newG._succ |
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206 return newG |