annotate env/lib/python3.9/site-packages/networkx/readwrite/json_graph/adjacency.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author shellac
date Mon, 22 Mar 2021 18:12:50 +0000
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1 from itertools import chain
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2 import networkx as nx
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3
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4 __all__ = ["adjacency_data", "adjacency_graph"]
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5
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6 _attrs = dict(id="id", key="key")
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7
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8
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9 def adjacency_data(G, attrs=_attrs):
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10 """Returns data in adjacency format that is suitable for JSON serialization
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11 and use in Javascript documents.
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12
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13 Parameters
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14 ----------
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15 G : NetworkX graph
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16
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17 attrs : dict
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18 A dictionary that contains two keys 'id' and 'key'. The corresponding
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19 values provide the attribute names for storing NetworkX-internal graph
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20 data. The values should be unique. Default value:
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21 :samp:`dict(id='id', key='key')`.
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22
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23 If some user-defined graph data use these attribute names as data keys,
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24 they may be silently dropped.
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25
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26 Returns
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27 -------
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28 data : dict
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29 A dictionary with adjacency formatted data.
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30
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31 Raises
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32 ------
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33 NetworkXError
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34 If values in attrs are not unique.
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35
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36 Examples
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37 --------
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38 >>> from networkx.readwrite import json_graph
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39 >>> G = nx.Graph([(1, 2)])
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40 >>> data = json_graph.adjacency_data(G)
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41
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42 To serialize with json
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43
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44 >>> import json
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45 >>> s = json.dumps(data)
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46
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47 Notes
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48 -----
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49 Graph, node, and link attributes will be written when using this format
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50 but attribute keys must be strings if you want to serialize the resulting
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51 data with JSON.
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52
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53 The default value of attrs will be changed in a future release of NetworkX.
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54
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55 See Also
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56 --------
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57 adjacency_graph, node_link_data, tree_data
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58 """
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59 multigraph = G.is_multigraph()
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60 id_ = attrs["id"]
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61 # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
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62 key = None if not multigraph else attrs["key"]
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63 if id_ == key:
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64 raise nx.NetworkXError("Attribute names are not unique.")
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65 data = {}
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66 data["directed"] = G.is_directed()
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67 data["multigraph"] = multigraph
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68 data["graph"] = list(G.graph.items())
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69 data["nodes"] = []
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70 data["adjacency"] = []
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71 for n, nbrdict in G.adjacency():
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72 data["nodes"].append(dict(chain(G.nodes[n].items(), [(id_, n)])))
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73 adj = []
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74 if multigraph:
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75 for nbr, keys in nbrdict.items():
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76 for k, d in keys.items():
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77 adj.append(dict(chain(d.items(), [(id_, nbr), (key, k)])))
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78 else:
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79 for nbr, d in nbrdict.items():
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80 adj.append(dict(chain(d.items(), [(id_, nbr)])))
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81 data["adjacency"].append(adj)
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82 return data
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83
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84
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85 def adjacency_graph(data, directed=False, multigraph=True, attrs=_attrs):
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86 """Returns graph from adjacency data format.
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87
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88 Parameters
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89 ----------
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90 data : dict
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91 Adjacency list formatted graph data
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92
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93 Returns
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94 -------
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95 G : NetworkX graph
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96 A NetworkX graph object
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97
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98 directed : bool
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99 If True, and direction not specified in data, return a directed graph.
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100
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101 multigraph : bool
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102 If True, and multigraph not specified in data, return a multigraph.
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103
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104 attrs : dict
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105 A dictionary that contains two keys 'id' and 'key'. The corresponding
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106 values provide the attribute names for storing NetworkX-internal graph
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107 data. The values should be unique. Default value:
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108 :samp:`dict(id='id', key='key')`.
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109
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110 Examples
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111 --------
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112 >>> from networkx.readwrite import json_graph
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113 >>> G = nx.Graph([(1, 2)])
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114 >>> data = json_graph.adjacency_data(G)
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115 >>> H = json_graph.adjacency_graph(data)
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116
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117 Notes
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118 -----
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119 The default value of attrs will be changed in a future release of NetworkX.
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120
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121 See Also
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122 --------
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123 adjacency_graph, node_link_data, tree_data
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124 """
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125 multigraph = data.get("multigraph", multigraph)
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126 directed = data.get("directed", directed)
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127 if multigraph:
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128 graph = nx.MultiGraph()
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129 else:
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130 graph = nx.Graph()
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131 if directed:
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132 graph = graph.to_directed()
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133 id_ = attrs["id"]
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134 # Allow 'key' to be omitted from attrs if the graph is not a multigraph.
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135 key = None if not multigraph else attrs["key"]
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136 graph.graph = dict(data.get("graph", []))
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137 mapping = []
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138 for d in data["nodes"]:
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139 node_data = d.copy()
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140 node = node_data.pop(id_)
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141 mapping.append(node)
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142 graph.add_node(node)
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143 graph.nodes[node].update(node_data)
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144 for i, d in enumerate(data["adjacency"]):
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145 source = mapping[i]
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146 for tdata in d:
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147 target_data = tdata.copy()
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148 target = target_data.pop(id_)
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149 if not multigraph:
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150 graph.add_edge(source, target)
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151 graph[source][target].update(tdata)
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152 else:
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153 ky = target_data.pop(key, None)
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154 graph.add_edge(source, target, key=ky)
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155 graph[source][target][ky].update(tdata)
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156 return graph