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