Mercurial > repos > guerler > springsuite
annotate planemo/lib/python3.7/site-packages/rdflib/extras/external_graph_libs.py @ 1:56ad4e20f292 draft
"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author | guerler |
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date | Fri, 31 Jul 2020 00:32:28 -0400 |
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1 #!/usr/bin/env python2.7 |
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2 # encoding: utf-8 |
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3 |
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4 |
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5 |
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6 |
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7 |
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8 """Convert (to and) from rdflib graphs to other well known graph libraries. |
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9 |
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10 Currently the following libraries are supported: |
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11 - networkx: MultiDiGraph, DiGraph, Graph |
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12 - graph_tool: Graph |
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13 |
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14 Doctests in this file are all skipped, as we can't run them conditionally if |
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15 networkx or graph_tool are available and they would err otherwise. |
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16 see ../../test/test_extras_external_graph_libs.py for conditional tests |
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17 """ |
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18 |
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19 import logging |
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20 logger = logging.getLogger(__name__) |
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21 |
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22 _identity = lambda x: x |
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23 |
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24 def _rdflib_to_networkx_graph( |
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25 graph, |
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26 nxgraph, |
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27 calc_weights, |
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28 edge_attrs, |
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29 transform_s=_identity, transform_o=_identity): |
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30 """Helper method for multidigraph, digraph and graph. |
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31 |
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32 Modifies nxgraph in-place! |
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33 |
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34 Arguments: |
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35 graph: an rdflib.Graph. |
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36 nxgraph: a networkx.Graph/DiGraph/MultiDigraph. |
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37 calc_weights: If True adds a 'weight' attribute to each edge according |
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38 to the count of s,p,o triples between s and o, which is meaningful |
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39 for Graph/DiGraph. |
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40 edge_attrs: Callable to construct edge data from s, p, o. |
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41 'triples' attribute is handled specially to be merged. |
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42 'weight' should not be generated if calc_weights==True. |
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43 (see invokers below!) |
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44 transform_s: Callable to transform node generated from s. |
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45 transform_o: Callable to transform node generated from o. |
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46 """ |
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47 assert callable(edge_attrs) |
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48 assert callable(transform_s) |
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49 assert callable(transform_o) |
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50 import networkx as nx |
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51 for s, p, o in graph: |
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52 ts, to = transform_s(s), transform_o(o) # apply possible transformations |
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53 data = nxgraph.get_edge_data(ts, to) |
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54 if data is None or isinstance(nxgraph, nx.MultiDiGraph): |
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55 # no edge yet, set defaults |
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56 data = edge_attrs(s, p, o) |
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57 if calc_weights: |
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58 data['weight'] = 1 |
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59 nxgraph.add_edge(ts, to, **data) |
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60 else: |
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61 # already have an edge, just update attributes |
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62 if calc_weights: |
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63 data['weight'] += 1 |
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64 if 'triples' in data: |
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65 d = edge_attrs(s, p, o) |
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66 data['triples'].extend(d['triples']) |
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67 |
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68 def rdflib_to_networkx_multidigraph( |
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69 graph, |
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70 edge_attrs=lambda s, p, o: {'key': p}, |
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71 **kwds): |
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72 """Converts the given graph into a networkx.MultiDiGraph. |
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73 |
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74 The subjects and objects are the later nodes of the MultiDiGraph. |
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75 The predicates are used as edge keys (to identify multi-edges). |
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76 |
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77 Arguments: |
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78 graph: a rdflib.Graph. |
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79 edge_attrs: Callable to construct later edge_attributes. It receives |
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80 3 variables (s, p, o) and should construct a dictionary that is |
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81 passed to networkx's add_edge(s, o, **attrs) function. |
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82 |
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83 By default this will include setting the MultiDiGraph key=p here. |
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84 If you don't want to be able to re-identify the edge later on, you |
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85 can set this to `lambda s, p, o: {}`. In this case MultiDiGraph's |
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86 default (increasing ints) will be used. |
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87 |
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88 Returns: |
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89 networkx.MultiDiGraph |
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90 |
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91 >>> from rdflib import Graph, URIRef, Literal |
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92 >>> g = Graph() |
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93 >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') |
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94 >>> p, q = URIRef('p'), URIRef('q') |
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95 >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] |
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96 >>> for t in edges: |
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97 ... g.add(t) |
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98 ... |
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99 >>> mdg = rdflib_to_networkx_multidigraph(g) |
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100 >>> len(mdg.edges()) |
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101 4 |
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102 >>> mdg.has_edge(a, b) |
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103 True |
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104 >>> mdg.has_edge(a, b, key=p) |
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105 True |
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106 >>> mdg.has_edge(a, b, key=q) |
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107 True |
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108 |
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109 >>> mdg = rdflib_to_networkx_multidigraph(g, edge_attrs=lambda s,p,o: {}) |
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110 >>> mdg.has_edge(a, b, key=0) |
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111 True |
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112 >>> mdg.has_edge(a, b, key=1) |
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113 True |
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114 """ |
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115 import networkx as nx |
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116 mdg = nx.MultiDiGraph() |
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117 _rdflib_to_networkx_graph(graph, mdg, False, edge_attrs, **kwds) |
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118 return mdg |
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119 |
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120 def rdflib_to_networkx_digraph( |
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121 graph, |
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122 calc_weights=True, |
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123 edge_attrs=lambda s, p, o: {'triples': [(s, p, o)]}, |
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124 **kwds): |
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125 """Converts the given graph into a networkx.DiGraph. |
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126 |
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127 As an rdflib.Graph() can contain multiple edges between nodes, by default |
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128 adds the a 'triples' attribute to the single DiGraph edge with a list of |
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129 all triples between s and o. |
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130 Also by default calculates the edge weight as the length of triples. |
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131 |
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132 Args: |
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133 graph: a rdflib.Graph. |
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134 calc_weights: If true calculate multi-graph edge-count as edge 'weight' |
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135 edge_attrs: Callable to construct later edge_attributes. It receives |
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136 3 variables (s, p, o) and should construct a dictionary that is |
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137 passed to networkx's add_edge(s, o, **attrs) function. |
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138 |
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139 By default this will include setting the 'triples' attribute here, |
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140 which is treated specially by us to be merged. Other attributes of |
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141 multi-edges will only contain the attributes of the first edge. |
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142 If you don't want the 'triples' attribute for tracking, set this to |
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143 `lambda s, p, o: {}`. |
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144 |
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145 Returns: |
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146 networkx.DiGraph |
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147 |
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148 >>> from rdflib import Graph, URIRef, Literal |
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149 >>> g = Graph() |
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150 >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') |
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151 >>> p, q = URIRef('p'), URIRef('q') |
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152 >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] |
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153 >>> for t in edges: |
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154 ... g.add(t) |
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155 ... |
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156 >>> dg = rdflib_to_networkx_digraph(g) |
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157 >>> dg[a][b]['weight'] |
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158 2 |
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159 >>> sorted(dg[a][b]['triples']) == [(a, p, b), (a, q, b)] |
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160 True |
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161 >>> len(dg.edges()) |
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162 3 |
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163 >>> dg.size() |
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164 3 |
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165 >>> dg.size(weight='weight') |
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166 4.0 |
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167 |
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168 >>> dg = rdflib_to_networkx_graph(g, False, edge_attrs=lambda s,p,o:{}) |
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169 >>> 'weight' in dg[a][b] |
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170 False |
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171 >>> 'triples' in dg[a][b] |
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172 False |
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173 """ |
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174 import networkx as nx |
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175 dg = nx.DiGraph() |
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176 _rdflib_to_networkx_graph(graph, dg, calc_weights, edge_attrs, **kwds) |
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177 return dg |
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178 |
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179 |
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180 def rdflib_to_networkx_graph( |
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181 graph, |
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182 calc_weights=True, |
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183 edge_attrs=lambda s, p, o: {'triples': [(s, p, o)]}, |
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184 **kwds): |
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185 """Converts the given graph into a networkx.Graph. |
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186 |
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187 As an rdflib.Graph() can contain multiple directed edges between nodes, by |
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188 default adds the a 'triples' attribute to the single DiGraph edge with a |
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189 list of triples between s and o in graph. |
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190 Also by default calculates the edge weight as the len(triples). |
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191 |
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192 Args: |
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193 graph: a rdflib.Graph. |
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194 calc_weights: If true calculate multi-graph edge-count as edge 'weight' |
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195 edge_attrs: Callable to construct later edge_attributes. It receives |
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196 3 variables (s, p, o) and should construct a dictionary that is |
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197 passed to networkx's add_edge(s, o, **attrs) function. |
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198 |
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199 By default this will include setting the 'triples' attribute here, |
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200 which is treated specially by us to be merged. Other attributes of |
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201 multi-edges will only contain the attributes of the first edge. |
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202 If you don't want the 'triples' attribute for tracking, set this to |
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203 `lambda s, p, o: {}`. |
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204 |
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205 Returns: |
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206 networkx.Graph |
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207 |
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208 >>> from rdflib import Graph, URIRef, Literal |
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209 >>> g = Graph() |
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210 >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') |
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211 >>> p, q = URIRef('p'), URIRef('q') |
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212 >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] |
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213 >>> for t in edges: |
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214 ... g.add(t) |
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215 ... |
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216 >>> ug = rdflib_to_networkx_graph(g) |
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217 >>> ug[a][b]['weight'] |
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218 3 |
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219 >>> sorted(ug[a][b]['triples']) == [(a, p, b), (a, q, b), (b, p, a)] |
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220 True |
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221 >>> len(ug.edges()) |
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222 2 |
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223 >>> ug.size() |
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224 2 |
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225 >>> ug.size(weight='weight') |
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226 4.0 |
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227 |
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228 >>> ug = rdflib_to_networkx_graph(g, False, edge_attrs=lambda s,p,o:{}) |
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229 >>> 'weight' in ug[a][b] |
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230 False |
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231 >>> 'triples' in ug[a][b] |
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232 False |
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233 """ |
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234 import networkx as nx |
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235 g = nx.Graph() |
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236 _rdflib_to_networkx_graph(graph, g, calc_weights, edge_attrs, **kwds) |
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237 return g |
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238 |
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239 |
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240 def rdflib_to_graphtool( |
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241 graph, |
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242 v_prop_names=[str('term')], |
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243 e_prop_names=[str('term')], |
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244 transform_s=lambda s, p, o: {str('term'): s}, |
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245 transform_p=lambda s, p, o: {str('term'): p}, |
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246 transform_o=lambda s, p, o: {str('term'): o}, |
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247 ): |
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248 """Converts the given graph into a graph_tool.Graph(). |
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249 |
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250 The subjects and objects are the later vertices of the Graph. |
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251 The predicates become edges. |
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252 |
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253 Arguments: |
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254 graph: a rdflib.Graph. |
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255 v_prop_names: a list of names for the vertex properties. The default is |
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256 set to ['term'] (see transform_s, transform_o below). |
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257 e_prop_names: a list of names for the edge properties. |
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258 transform_s: callable with s, p, o input. Should return a dictionary |
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259 containing a value for each name in v_prop_names. By default is set |
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260 to {'term': s} which in combination with v_prop_names = ['term'] |
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261 adds s as 'term' property to the generated vertex for s. |
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262 transform_p: similar to transform_s, but wrt. e_prop_names. By default |
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263 returns {'term': p} which adds p as a property to the generated |
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264 edge between the vertex for s and the vertex for o. |
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265 transform_o: similar to transform_s. |
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266 |
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267 Returns: |
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268 graph_tool.Graph() |
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269 |
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270 >>> from rdflib import Graph, URIRef, Literal |
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271 >>> g = Graph() |
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272 >>> a, b, l = URIRef('a'), URIRef('b'), Literal('l') |
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273 >>> p, q = URIRef('p'), URIRef('q') |
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274 >>> edges = [(a, p, b), (a, q, b), (b, p, a), (b, p, l)] |
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275 >>> for t in edges: |
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276 ... g.add(t) |
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277 ... |
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278 >>> mdg = rdflib_to_graphtool(g) |
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279 >>> len(list(mdg.edges())) |
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280 4 |
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281 >>> from graph_tool import util as gt_util |
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282 >>> vpterm = mdg.vertex_properties['term'] |
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283 >>> va = gt_util.find_vertex(mdg, vpterm, a)[0] |
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284 >>> vb = gt_util.find_vertex(mdg, vpterm, b)[0] |
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285 >>> vl = gt_util.find_vertex(mdg, vpterm, l)[0] |
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286 >>> (va, vb) in [(e.source(), e.target()) for e in list(mdg.edges())] |
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287 True |
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288 >>> epterm = mdg.edge_properties['term'] |
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289 >>> len(list(gt_util.find_edge(mdg, epterm, p))) == 3 |
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290 True |
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291 >>> len(list(gt_util.find_edge(mdg, epterm, q))) == 1 |
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292 True |
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293 |
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294 >>> mdg = rdflib_to_graphtool( |
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295 ... g, |
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296 ... e_prop_names=[str('name')], |
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297 ... transform_p=lambda s, p, o: {str('name'): unicode(p)}) |
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298 >>> epterm = mdg.edge_properties['name'] |
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299 >>> len(list(gt_util.find_edge(mdg, epterm, unicode(p)))) == 3 |
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300 True |
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301 >>> len(list(gt_util.find_edge(mdg, epterm, unicode(q)))) == 1 |
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302 True |
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303 """ |
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304 import graph_tool as gt |
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305 g = gt.Graph() |
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306 |
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307 vprops = [(vpn, g.new_vertex_property('object')) for vpn in v_prop_names] |
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308 for vpn, vprop in vprops: |
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309 g.vertex_properties[vpn] = vprop |
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310 eprops = [(epn, g.new_edge_property('object')) for epn in e_prop_names] |
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311 for epn, eprop in eprops: |
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312 g.edge_properties[epn] = eprop |
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313 node_to_vertex = {} |
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314 for s, p, o in graph: |
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315 sv = node_to_vertex.get(s) |
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316 if sv is None: |
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317 v = g.add_vertex() |
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318 node_to_vertex[s] = v |
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319 tmp_props = transform_s(s, p, o) |
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320 for vpn, vprop in vprops: |
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321 vprop[v] = tmp_props[vpn] |
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322 sv = v |
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323 |
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324 ov = node_to_vertex.get(o) |
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325 if ov is None: |
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326 v = g.add_vertex() |
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327 node_to_vertex[o] = v |
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328 tmp_props = transform_o(s, p, o) |
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329 for vpn, vprop in vprops: |
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330 vprop[v] = tmp_props[vpn] |
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331 ov = v |
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332 |
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333 e = g.add_edge(sv, ov) |
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334 tmp_props = transform_p(s, p, o) |
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335 for epn, eprop in eprops: |
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336 eprop[e] = tmp_props[epn] |
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337 return g |
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338 |
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339 |
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340 if __name__ == '__main__': |
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341 import sys |
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342 import logging.config |
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343 logging.basicConfig(level=logging.DEBUG) |
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344 |
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345 import nose |
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346 nose.run(argv=[sys.argv[0], sys.argv[0], '-v', '--with-doctest']) |