Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/networkx/algorithms/components/connected.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 """Connected components.""" |
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2 import networkx as nx |
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3 from networkx.utils.decorators import not_implemented_for |
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4 from ...utils import arbitrary_element |
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5 |
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6 __all__ = [ |
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7 "number_connected_components", |
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8 "connected_components", |
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9 "is_connected", |
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10 "node_connected_component", |
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11 ] |
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12 |
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13 |
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14 @not_implemented_for("directed") |
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15 def connected_components(G): |
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16 """Generate connected components. |
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17 |
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18 Parameters |
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19 ---------- |
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20 G : NetworkX graph |
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21 An undirected graph |
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22 |
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23 Returns |
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24 ------- |
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25 comp : generator of sets |
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26 A generator of sets of nodes, one for each component of G. |
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27 |
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28 Raises |
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29 ------ |
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30 NetworkXNotImplemented |
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31 If G is directed. |
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32 |
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33 Examples |
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34 -------- |
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35 Generate a sorted list of connected components, largest first. |
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36 |
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37 >>> G = nx.path_graph(4) |
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38 >>> nx.add_path(G, [10, 11, 12]) |
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39 >>> [len(c) for c in sorted(nx.connected_components(G), key=len, reverse=True)] |
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40 [4, 3] |
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41 |
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42 If you only want the largest connected component, it's more |
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43 efficient to use max instead of sort. |
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44 |
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45 >>> largest_cc = max(nx.connected_components(G), key=len) |
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46 |
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47 To create the induced subgraph of each component use: |
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48 |
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49 >>> S = [G.subgraph(c).copy() for c in nx.connected_components(G)] |
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50 |
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51 See Also |
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52 -------- |
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53 strongly_connected_components |
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54 weakly_connected_components |
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55 |
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56 Notes |
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57 ----- |
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58 For undirected graphs only. |
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59 |
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60 """ |
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61 seen = set() |
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62 for v in G: |
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63 if v not in seen: |
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64 c = _plain_bfs(G, v) |
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65 seen.update(c) |
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66 yield c |
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67 |
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68 |
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69 def number_connected_components(G): |
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70 """Returns the number of connected components. |
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71 |
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72 Parameters |
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73 ---------- |
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74 G : NetworkX graph |
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75 An undirected graph. |
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76 |
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77 Returns |
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78 ------- |
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79 n : integer |
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80 Number of connected components |
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81 |
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82 See Also |
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83 -------- |
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84 connected_components |
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85 number_weakly_connected_components |
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86 number_strongly_connected_components |
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87 |
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88 Notes |
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89 ----- |
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90 For undirected graphs only. |
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91 |
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92 """ |
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93 return sum(1 for cc in connected_components(G)) |
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94 |
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95 |
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96 @not_implemented_for("directed") |
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97 def is_connected(G): |
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98 """Returns True if the graph is connected, False otherwise. |
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99 |
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100 Parameters |
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101 ---------- |
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102 G : NetworkX Graph |
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103 An undirected graph. |
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104 |
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105 Returns |
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106 ------- |
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107 connected : bool |
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108 True if the graph is connected, false otherwise. |
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109 |
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110 Raises |
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111 ------ |
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112 NetworkXNotImplemented |
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113 If G is directed. |
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114 |
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115 Examples |
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116 -------- |
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117 >>> G = nx.path_graph(4) |
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118 >>> print(nx.is_connected(G)) |
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119 True |
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120 |
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121 See Also |
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122 -------- |
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123 is_strongly_connected |
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124 is_weakly_connected |
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125 is_semiconnected |
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126 is_biconnected |
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127 connected_components |
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128 |
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129 Notes |
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130 ----- |
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131 For undirected graphs only. |
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132 |
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133 """ |
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134 if len(G) == 0: |
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135 raise nx.NetworkXPointlessConcept( |
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136 "Connectivity is undefined ", "for the null graph." |
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137 ) |
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138 return sum(1 for node in _plain_bfs(G, arbitrary_element(G))) == len(G) |
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139 |
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140 |
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141 @not_implemented_for("directed") |
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142 def node_connected_component(G, n): |
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143 """Returns the set of nodes in the component of graph containing node n. |
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144 |
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145 Parameters |
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146 ---------- |
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147 G : NetworkX Graph |
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148 An undirected graph. |
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149 |
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150 n : node label |
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151 A node in G |
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152 |
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153 Returns |
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154 ------- |
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155 comp : set |
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156 A set of nodes in the component of G containing node n. |
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157 |
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158 Raises |
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159 ------ |
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160 NetworkXNotImplemented |
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161 If G is directed. |
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162 |
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163 See Also |
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164 -------- |
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165 connected_components |
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166 |
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167 Notes |
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168 ----- |
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169 For undirected graphs only. |
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170 |
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171 """ |
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172 return _plain_bfs(G, n) |
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173 |
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174 |
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175 def _plain_bfs(G, source): |
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176 """A fast BFS node generator""" |
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177 G_adj = G.adj |
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178 seen = set() |
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179 nextlevel = {source} |
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180 while nextlevel: |
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181 thislevel = nextlevel |
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182 nextlevel = set() |
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183 for v in thislevel: |
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184 if v not in seen: |
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185 seen.add(v) |
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186 nextlevel.update(G_adj[v]) |
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187 return seen |