Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/networkx/classes/graphviews.py @ 2:6af9afd405e9 draft

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"planemo upload commit 0a63dd5f4d38a1f6944587f52a8cd79874177fc1"
author shellac
date Thu, 14 May 2020 14:56:58 -0400 (2020-05-14)
parents 26e78fe6e8c4
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1 # Copyright (C) 2004-2019 by
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2 # Aric Hagberg <hagberg@lanl.gov>
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3 # Dan Schult <dschult@colgate.edu>
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4 # Pieter Swart <swart@lanl.gov>
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5 # All rights reserved.
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6 # BSD license.
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7 #
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8 # Author: Aric Hagberg (hagberg@lanl.gov),
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9 # Pieter Swart (swart@lanl.gov),
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10 # Dan Schult(dschult@colgate.edu)
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11 """View of Graphs as SubGraph, Reverse, Directed, Undirected.
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12
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13 In some algorithms it is convenient to temporarily morph
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14 a graph to exclude some nodes or edges. It should be better
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15 to do that via a view than to remove and then re-add.
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16 In other algorithms it is convenient to temporarily morph
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17 a graph to reverse directed edges, or treat a directed graph
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18 as undirected, etc. This module provides those graph views.
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19
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20 The resulting views are essentially read-only graphs that
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21 report data from the orignal graph object. We provide an
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22 attribute G._graph which points to the underlying graph object.
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23
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24 Note: Since graphviews look like graphs, one can end up with
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25 view-of-view-of-view chains. Be careful with chains because
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26 they become very slow with about 15 nested views.
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27 For the common simple case of node induced subgraphs created
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28 from the graph class, we short-cut the chain by returning a
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29 subgraph of the original graph directly rather than a subgraph
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30 of a subgraph. We are careful not to disrupt any edge filter in
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31 the middle subgraph. In general, determining how to short-cut
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32 the chain is tricky and much harder with restricted_views than
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33 with induced subgraphs.
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34 Often it is easiest to use .copy() to avoid chains.
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35 """
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36 from networkx.classes.coreviews import UnionAdjacency, UnionMultiAdjacency, \
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37 FilterAtlas, FilterAdjacency, FilterMultiAdjacency
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38 from networkx.classes.filters import no_filter
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39 from networkx.exception import NetworkXError
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40 from networkx.utils import not_implemented_for
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41
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42 import networkx as nx
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43
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44 __all__ = ['generic_graph_view', 'subgraph_view', 'reverse_view']
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45
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46
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47 def generic_graph_view(G, create_using=None):
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48 if create_using is None:
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49 newG = G.__class__()
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50 else:
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51 newG = nx.empty_graph(0, create_using)
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52 if G.is_multigraph() != newG.is_multigraph():
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53 raise NetworkXError("Multigraph for G must agree with create_using")
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54 newG = nx.freeze(newG)
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55
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56 # create view by assigning attributes from G
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57 newG._graph = G
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58 newG.graph = G.graph
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59
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60 newG._node = G._node
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61 if newG.is_directed():
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62 if G.is_directed():
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63 newG._succ = G._succ
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64 newG._pred = G._pred
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65 newG._adj = G._succ
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66 else:
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67 newG._succ = G._adj
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68 newG._pred = G._adj
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69 newG._adj = G._adj
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70 elif G.is_directed():
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71 if G.is_multigraph():
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72 newG._adj = UnionMultiAdjacency(G._succ, G._pred)
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73 else:
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74 newG._adj = UnionAdjacency(G._succ, G._pred)
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75 else:
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76 newG._adj = G._adj
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77 return newG
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78
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79
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80 def subgraph_view(G, filter_node=no_filter, filter_edge=no_filter):
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81 """ View of `G` applying a filter on nodes and edges.
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82
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83 `subgraph_view` provides a read-only view of the input graph that excludes
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84 nodes and edges based on the outcome of two filter functions `filter_node`
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85 and `filter_edge`.
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86
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87 The `filter_node` function takes one argument --- the node --- and returns
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88 `True` if the node should be included in the subgraph, and `False` if it
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89 should not be included.
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90
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91 The `filter_edge` function takes two arguments --- the nodes describing an
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92 edge --- and returns `True` if the edge should be included in the subgraph,
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93 and `False` if it should not be included.
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94
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95 Both node and edge filter functions are called on graph elements as they
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96 are queried, meaning there is no up-front cost to creating the view.
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97
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98 Parameters
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99 ----------
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100 G : networkx.Graph
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101 A directed/undirected graph/multigraph
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102
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103 filter_node : callable, optional
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104 A function taking a node as input, which returns `True` if the node
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105 should appear in the view.
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106
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107 filter_edge : callable, optional
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108 A function taking as input the two nodes describing an edge, which
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109 returns `True` if the edge should appear in the view.
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110
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111 Returns
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112 -------
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113 graph : networkx.Graph
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114 A read-only graph view of the input graph.
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115
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116 Examples
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117 --------
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118 >>> import networkx as nx
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119 >>> G = nx.path_graph(6)
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120
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121 Filter functions operate on the node, and return `True` if the node should
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122 appear in the view:
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123
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124 >>> def filter_node(n1):
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125 ... return n1 != 5
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126 ...
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127 >>> view = nx.subgraph_view(
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128 ... G,
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129 ... filter_node=filter_node
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130 ... )
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131 >>> view.nodes()
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132 NodeView((0, 1, 2, 3, 4))
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133
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134 We can use a closure pattern to filter graph elements based on additional
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135 data --- for example, filtering on edge data attached to the graph:
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136
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137 >>> G[3][4]['cross_me'] = False
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138 >>> def filter_edge(n1, n2):
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139 ... return G[n1][n2].get('cross_me', True)
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140 ...
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141 >>> view = nx.subgraph_view(
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142 ... G,
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143 ... filter_edge=filter_edge
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144 ... )
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145 >>> view.edges()
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146 EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])
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147
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148 >>> view = nx.subgraph_view(
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149 ... G,
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150 ... filter_node=filter_node,
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151 ... filter_edge=filter_edge,
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152 ... )
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153 >>> view.nodes()
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154 NodeView((0, 1, 2, 3, 4))
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155 >>> view.edges()
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156 EdgeView([(0, 1), (1, 2), (2, 3)])
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157 """
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158 newG = nx.freeze(G.__class__())
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159 newG._NODE_OK = filter_node
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160 newG._EDGE_OK = filter_edge
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161
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162 # create view by assigning attributes from G
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163 newG._graph = G
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164 newG.graph = G.graph
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165
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166 newG._node = FilterAtlas(G._node, filter_node)
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167 if G.is_multigraph():
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168 Adj = FilterMultiAdjacency
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169
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170 def reverse_edge(u, v, k): return filter_edge(v, u, k)
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171 else:
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172 Adj = FilterAdjacency
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173
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174 def reverse_edge(u, v): return filter_edge(v, u)
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175 if G.is_directed():
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176 newG._succ = Adj(G._succ, filter_node, filter_edge)
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177 newG._pred = Adj(G._pred, filter_node, reverse_edge)
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178 newG._adj = newG._succ
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179 else:
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180 newG._adj = Adj(G._adj, filter_node, filter_edge)
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181 return newG
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182
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183
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184 @not_implemented_for('undirected')
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185 def reverse_view(G):
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186 """ View of `G` with edge directions reversed
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187
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188 `reverse_view` returns a read-only view of the input graph where
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189 edge directions are reversed.
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190
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191 Identical to digraph.reverse(copy=False)
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192
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193 Parameters
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194 ----------
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195 G : networkx.DiGraph
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196
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197 Returns
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198 -------
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199 graph : networkx.DiGraph
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200
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201 Examples
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202 --------
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203 >>> import networkx as nx
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204 >>> G = nx.DiGraph()
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205 >>> G.add_edge(1, 2)
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206 >>> G.add_edge(2, 3)
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207 >>> G.edges()
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208 OutEdgeView([(1, 2), (2, 3)])
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209
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210 >>> view = nx.reverse_view(G)
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211 >>> view.edges()
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212 OutEdgeView([(2, 1), (3, 2)])
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213 """
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214 newG = generic_graph_view(G)
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215 newG._succ, newG._pred = G._pred, G._succ
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216 newG._adj = newG._succ
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217 return newG