annotate planemo/lib/python3.7/site-packages/rdflib/extras/external_graph_libs.py @ 1:56ad4e20f292 draft

"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author guerler
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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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'])