Mercurial > repos > shellac > sam_consensus_v3
annotate env/lib/python3.9/site-packages/rdflib/compare.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 # -*- coding: utf-8 -*- |
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2 """ |
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3 A collection of utilities for canonicalizing and inspecting graphs. |
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4 |
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5 Among other things, they solve of the problem of deterministic bnode |
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6 comparisons. |
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7 |
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8 Warning: the time to canonicalize bnodes may increase exponentially on |
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9 degenerate larger graphs. Use with care! |
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10 |
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11 Example of comparing two graphs:: |
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12 |
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13 >>> g1 = Graph().parse(format='n3', data=''' |
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14 ... @prefix : <http://example.org/ns#> . |
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15 ... <http://example.org> :rel |
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16 ... <http://example.org/same>, |
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17 ... [ :label "Same" ], |
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18 ... <http://example.org/a>, |
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19 ... [ :label "A" ] . |
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20 ... ''') |
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21 >>> g2 = Graph().parse(format='n3', data=''' |
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22 ... @prefix : <http://example.org/ns#> . |
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23 ... <http://example.org> :rel |
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24 ... <http://example.org/same>, |
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25 ... [ :label "Same" ], |
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26 ... <http://example.org/b>, |
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27 ... [ :label "B" ] . |
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28 ... ''') |
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29 >>> |
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30 >>> iso1 = to_isomorphic(g1) |
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31 >>> iso2 = to_isomorphic(g2) |
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32 |
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33 These are not isomorphic:: |
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34 |
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35 >>> iso1 == iso2 |
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36 False |
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37 |
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38 Diff the two graphs:: |
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39 |
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40 >>> in_both, in_first, in_second = graph_diff(iso1, iso2) |
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41 |
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42 Present in both:: |
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43 |
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44 >>> def dump_nt_sorted(g): |
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45 ... for l in sorted(g.serialize(format='nt').splitlines()): |
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46 ... if l: print(l.decode('ascii')) |
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47 |
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48 >>> dump_nt_sorted(in_both) #doctest: +SKIP |
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49 <http://example.org> |
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50 <http://example.org/ns#rel> <http://example.org/same> . |
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51 <http://example.org> |
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52 <http://example.org/ns#rel> _:cbcaabaaba17fecbc304a64f8edee4335e . |
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53 _:cbcaabaaba17fecbc304a64f8edee4335e |
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54 <http://example.org/ns#label> "Same" . |
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55 |
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56 Only in first:: |
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57 |
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58 >>> dump_nt_sorted(in_first) #doctest: +SKIP |
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59 <http://example.org> |
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60 <http://example.org/ns#rel> <http://example.org/a> . |
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61 <http://example.org> |
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62 <http://example.org/ns#rel> _:cb124e4c6da0579f810c0ffe4eff485bd9 . |
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63 _:cb124e4c6da0579f810c0ffe4eff485bd9 |
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64 <http://example.org/ns#label> "A" . |
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65 |
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66 Only in second:: |
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67 |
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68 >>> dump_nt_sorted(in_second) #doctest: +SKIP |
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69 <http://example.org> |
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70 <http://example.org/ns#rel> <http://example.org/b> . |
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71 <http://example.org> |
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72 <http://example.org/ns#rel> _:cb558f30e21ddfc05ca53108348338ade8 . |
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73 _:cb558f30e21ddfc05ca53108348338ade8 |
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74 <http://example.org/ns#label> "B" . |
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75 """ |
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76 from __future__ import absolute_import |
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77 from __future__ import division |
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78 from __future__ import print_function |
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79 |
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80 |
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81 # TODO: |
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82 # - Doesn't handle quads. |
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83 # - Add warning and/or safety mechanism before working on large graphs? |
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84 # - use this in existing Graph.isomorphic? |
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85 |
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86 __all__ = ['IsomorphicGraph', 'to_isomorphic', 'isomorphic', |
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87 'to_canonical_graph', 'graph_diff', 'similar'] |
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88 |
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89 from rdflib.graph import Graph, ConjunctiveGraph, ReadOnlyGraphAggregate |
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90 from rdflib.term import BNode, Node |
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91 from hashlib import sha256 |
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92 |
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93 from datetime import datetime |
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94 from collections import defaultdict |
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95 |
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96 from six import text_type |
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97 |
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98 |
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99 def _total_seconds(td): |
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100 result = td.days * 24 * 60 * 60 |
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101 result += td.seconds |
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102 result += td.microseconds / 1000000.0 |
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103 return result |
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104 |
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105 |
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106 class _runtime(object): |
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107 def __init__(self, label): |
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108 self.label = label |
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109 |
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110 def __call__(self, f): |
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111 if self.label is None: |
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112 self.label = f.__name__ + "_runtime" |
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113 |
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114 def wrapped_f(*args, **kwargs): |
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115 start = datetime.now() |
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116 result = f(*args, **kwargs) |
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117 if 'stats' in kwargs and kwargs['stats'] is not None: |
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118 stats = kwargs['stats'] |
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119 stats[self.label] = _total_seconds(datetime.now() - start) |
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120 return result |
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121 return wrapped_f |
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122 |
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123 |
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124 class _call_count(object): |
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125 def __init__(self, label): |
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126 self.label = label |
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127 |
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128 def __call__(self, f): |
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129 if self.label is None: |
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130 self.label = f.__name__ + "_runtime" |
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131 |
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132 def wrapped_f(*args, **kwargs): |
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133 if 'stats' in kwargs and kwargs['stats'] is not None: |
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134 stats = kwargs['stats'] |
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135 if self.label not in stats: |
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136 stats[self.label] = 0 |
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137 stats[self.label] += 1 |
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138 return f(*args, **kwargs) |
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139 return wrapped_f |
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140 |
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141 |
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142 class IsomorphicGraph(ConjunctiveGraph): |
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143 """An implementation of the RGDA1 graph digest algorithm. |
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144 |
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145 An implementation of RGDA1 (publication below), |
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146 a combination of Sayers & Karp's graph digest algorithm using |
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147 sum and SHA-256 <http://www.hpl.hp.com/techreports/2003/HPL-2003-235R1.pdf> |
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148 and traces <http://pallini.di.uniroma1.it>, an average case |
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149 polynomial time algorithm for graph canonicalization. |
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150 |
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151 McCusker, J. P. (2015). WebSig: A Digital Signature Framework for the Web. |
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152 Rensselaer Polytechnic Institute, Troy, NY. |
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153 http://gradworks.umi.com/3727015.pdf |
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154 """ |
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155 |
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156 def __init__(self, **kwargs): |
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157 super(IsomorphicGraph, self).__init__(**kwargs) |
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158 |
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159 def __eq__(self, other): |
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160 """Graph isomorphism testing.""" |
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161 if not isinstance(other, IsomorphicGraph): |
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162 return False |
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163 elif len(self) != len(other): |
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164 return False |
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165 return self.internal_hash() == other.internal_hash() |
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166 |
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167 def __ne__(self, other): |
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168 """Negative graph isomorphism testing.""" |
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169 return not self.__eq__(other) |
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170 |
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171 def __hash__(self): |
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172 return super(IsomorphicGraph, self).__hash__() |
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173 |
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174 def graph_digest(self, stats=None): |
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175 """Synonym for IsomorphicGraph.internal_hash.""" |
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176 return self.internal_hash(stats=stats) |
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177 |
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178 def internal_hash(self, stats=None): |
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179 """ |
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180 This is defined instead of __hash__ to avoid a circular recursion |
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181 scenario with the Memory store for rdflib which requires a hash lookup |
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182 in order to return a generator of triples. |
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183 """ |
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184 return _TripleCanonicalizer(self).to_hash(stats=stats) |
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185 |
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186 |
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187 class Color: |
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188 def __init__(self, nodes, hashfunc, color=(), hash_cache=None): |
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189 if hash_cache is None: |
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190 hash_cache = {} |
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191 self._hash_cache = hash_cache |
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192 self.color = color |
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193 self.nodes = nodes |
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194 self.hashfunc = hashfunc |
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195 self._hash_color = None |
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196 |
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197 def __str__(self): |
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198 nodes, color = self.key() |
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199 return "Color %s (%s nodes)" % (color, nodes) |
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200 |
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201 def key(self): |
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202 return (len(self.nodes), self.hash_color()) |
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203 |
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204 def hash_color(self, color=None): |
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205 if color is None: |
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206 color = self.color |
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207 if color in self._hash_cache: |
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208 return self._hash_cache[color] |
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209 |
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210 def stringify(x): |
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211 if isinstance(x, Node): |
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212 return x.n3() |
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213 else: |
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214 return text_type(x) |
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215 if isinstance(color, Node): |
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216 return stringify(color) |
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217 value = 0 |
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218 for triple in color: |
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219 value += self.hashfunc(' '.join([stringify(x) for x in triple])) |
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220 val = u"%x" % value |
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221 self._hash_cache[color] = val |
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222 return val |
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223 |
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224 def distinguish(self, W, graph): |
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225 colors = {} |
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226 for n in self.nodes: |
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227 new_color = list(self.color) |
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228 for node in W.nodes: |
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229 new_color += [ |
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230 (1, p, W.hash_color()) |
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231 for s, p, o in graph.triples((n, None, node))] |
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232 new_color += [ |
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233 (W.hash_color(), p, 3) |
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234 for s, p, o in graph.triples((node, None, n))] |
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235 new_color = tuple(new_color) |
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236 new_hash_color = self.hash_color(new_color) |
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237 |
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238 if new_hash_color not in colors: |
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239 c = Color( |
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240 [], self.hashfunc, new_color, |
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241 hash_cache=self._hash_cache) |
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242 colors[new_hash_color] = c |
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243 colors[new_hash_color].nodes.append(n) |
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244 return colors.values() |
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245 |
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246 def discrete(self): |
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247 return len(self.nodes) == 1 |
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248 |
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249 def copy(self): |
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250 return Color( |
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251 self.nodes[:], self.hashfunc, self.color, |
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252 hash_cache=self._hash_cache) |
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253 |
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254 |
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255 class _TripleCanonicalizer(object): |
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256 |
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257 def __init__(self, graph, hashfunc=sha256): |
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258 self.graph = graph |
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259 |
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260 def _hashfunc(s): |
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261 h = hashfunc() |
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262 h.update(text_type(s).encode("utf8")) |
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263 return int(h.hexdigest(), 16) |
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264 self._hash_cache = {} |
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265 self.hashfunc = _hashfunc |
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266 |
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267 def _discrete(self, coloring): |
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268 return len([c for c in coloring if not c.discrete()]) == 0 |
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269 |
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270 def _initial_color(self): |
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271 """Finds an initial color for the graph. |
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272 |
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273 Finds an initial color fo the graph by finding all blank nodes and |
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274 non-blank nodes that are adjacent. Nodes that are not adjacent to blank |
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275 nodes are not included, as they are a) already colored (by URI or literal) |
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276 and b) do not factor into the color of any blank node. |
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277 """ |
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278 bnodes = set() |
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279 others = set() |
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280 self._neighbors = defaultdict(set) |
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281 for s, p, o in self.graph: |
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282 nodes = set([s, p, o]) |
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283 b = set([x for x in nodes if isinstance(x, BNode)]) |
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284 if len(b) > 0: |
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285 others |= nodes - b |
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286 bnodes |= b |
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287 if isinstance(s, BNode): |
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288 self._neighbors[s].add(o) |
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289 if isinstance(o, BNode): |
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290 self._neighbors[o].add(s) |
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291 if isinstance(p, BNode): |
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292 self._neighbors[p].add(s) |
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293 self._neighbors[p].add(p) |
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294 if len(bnodes) > 0: |
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295 return [ |
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296 Color(list(bnodes), self.hashfunc, hash_cache=self._hash_cache) |
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297 ] + [ |
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298 Color([x], self.hashfunc, x, hash_cache=self._hash_cache) |
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299 for x in others |
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300 ] |
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301 else: |
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302 return [] |
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303 |
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304 def _individuate(self, color, individual): |
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305 new_color = list(color.color) |
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306 new_color.append((len(color.nodes),)) |
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307 |
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308 color.nodes.remove(individual) |
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309 c = Color([individual], self.hashfunc, tuple(new_color), |
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310 hash_cache=self._hash_cache) |
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311 return c |
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312 |
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313 def _get_candidates(self, coloring): |
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314 candidates = [c for c in coloring if not c.discrete()] |
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315 for c in [c for c in coloring if not c.discrete()]: |
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316 for node in c.nodes: |
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317 yield node, c |
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318 |
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319 def _refine(self, coloring, sequence): |
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320 sequence = sorted(sequence, key=lambda x: x.key(), reverse=True) |
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321 coloring = coloring[:] |
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322 while len(sequence) > 0 and not self._discrete(coloring): |
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323 W = sequence.pop() |
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324 for c in coloring[:]: |
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325 if len(c.nodes) > 1 or isinstance(c.nodes[0], BNode): |
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326 colors = sorted(c.distinguish(W, self.graph), |
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327 key=lambda x: x.key(), |
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328 reverse=True) |
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329 coloring.remove(c) |
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330 coloring.extend(colors) |
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331 try: |
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332 si = sequence.index(c) |
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333 sequence = sequence[:si] + colors + sequence[si+1:] |
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334 except ValueError: |
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335 sequence = colors[1:] + sequence |
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336 combined_colors = [] |
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337 combined_color_map = dict() |
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338 for color in coloring: |
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339 color_hash = color.hash_color() |
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340 # This is a hash collision, and be combined into a single color for individuation. |
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341 if color_hash in combined_color_map: |
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342 combined_color_map[color_hash].nodes.extend(color.nodes) |
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343 else: |
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344 combined_colors.append(color) |
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345 combined_color_map[color_hash] = color |
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346 return combined_colors |
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347 |
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348 @_runtime("to_hash_runtime") |
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349 def to_hash(self, stats=None): |
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350 result = 0 |
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351 for triple in self.canonical_triples(stats=stats): |
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352 result += self.hashfunc(' '.join([x.n3() for x in triple])) |
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353 if stats is not None: |
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354 stats['graph_digest'] = "%x" % result |
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355 return result |
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356 |
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357 def _experimental_path(self, coloring): |
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358 coloring = [c.copy() for c in coloring] |
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359 while not self._discrete(coloring): |
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360 color = [x for x in coloring if not x.discrete()][0] |
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361 node = color.nodes[0] |
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362 new_color = self._individuate(color, node) |
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363 coloring.append(new_color) |
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364 coloring = self._refine(coloring, [new_color]) |
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365 return coloring |
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366 |
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367 def _create_generator(self, colorings, groupings=None): |
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368 if not groupings: |
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369 groupings = defaultdict(set) |
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370 for group in zip(*colorings): |
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371 g = set([c.nodes[0] for c in group]) |
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372 for n in group: |
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373 g |= groupings[n] |
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374 for n in g: |
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375 groupings[n] = g |
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376 return groupings |
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377 |
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378 @_call_count("individuations") |
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379 def _traces(self, coloring, stats=None, depth=[0]): |
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380 if stats is not None and 'prunings' not in stats: |
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381 stats['prunings'] = 0 |
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382 depth[0] += 1 |
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383 candidates = self._get_candidates(coloring) |
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384 best = [] |
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385 best_score = None |
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386 best_experimental = None |
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387 best_experimental_score = None |
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388 last_coloring = None |
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389 generator = defaultdict(set) |
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390 visited = set() |
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391 for candidate, color in candidates: |
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392 if candidate in generator: |
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393 v = generator[candidate] & visited |
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394 if len(v) > 0: |
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395 visited.add(candidate) |
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396 continue |
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397 visited.add(candidate) |
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398 coloring_copy = [] |
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399 color_copy = None |
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400 for c in coloring: |
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401 c_copy = c.copy() |
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402 coloring_copy.append(c_copy) |
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403 if c == color: |
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404 color_copy = c_copy |
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405 new_color = self._individuate(color_copy, candidate) |
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406 coloring_copy.append(new_color) |
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407 refined_coloring = self._refine(coloring_copy, [new_color]) |
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408 color_score = tuple([c.key() for c in refined_coloring]) |
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409 experimental = self._experimental_path(coloring_copy) |
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410 experimental_score = set([c.key() for c in experimental]) |
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411 if last_coloring: |
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412 generator = self._create_generator( |
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413 [last_coloring, experimental], |
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414 generator) |
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415 last_coloring = experimental |
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416 if best_score is None or best_score < color_score: |
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417 best = [refined_coloring] |
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418 best_score = color_score |
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419 best_experimental = experimental |
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420 best_experimental_score = experimental_score |
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421 elif best_score > color_score: |
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422 # prune this branch. |
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423 if stats is not None: |
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424 stats['prunings'] += 1 |
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425 elif experimental_score != best_experimental_score: |
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426 best.append(refined_coloring) |
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427 else: |
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428 # prune this branch. |
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429 if stats is not None: |
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430 stats['prunings'] += 1 |
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431 discrete = [x for x in best if self._discrete(x)] |
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432 if len(discrete) == 0: |
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433 best_score = None |
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434 best_depth = None |
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435 for coloring in best: |
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436 d = [depth[0]] |
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437 new_color = self._traces(coloring, stats=stats, depth=d) |
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438 color_score = tuple([c.key() for c in refined_coloring]) |
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439 if best_score is None or color_score > best_score: |
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440 discrete = [new_color] |
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441 best_score = color_score |
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442 best_depth = d[0] |
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443 depth[0] = best_depth |
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444 return discrete[0] |
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445 |
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446 def canonical_triples(self, stats=None): |
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447 if stats is not None: |
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448 start_canonicalization = datetime.now() |
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449 if stats is not None: |
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450 start_coloring = datetime.now() |
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451 coloring = self._initial_color() |
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452 if stats is not None: |
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453 stats['triple_count'] = len(self.graph) |
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454 stats['adjacent_nodes'] = max(0, len(coloring) - 1) |
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455 coloring = self._refine(coloring, coloring[:]) |
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456 if stats is not None: |
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457 stats['initial_coloring_runtime'] = _total_seconds(datetime.now() - start_coloring) |
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458 stats['initial_color_count'] = len(coloring) |
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459 |
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460 if not self._discrete(coloring): |
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461 depth = [0] |
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462 coloring = self._traces(coloring, stats=stats, depth=depth) |
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463 if stats is not None: |
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464 stats['tree_depth'] = depth[0] |
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465 elif stats is not None: |
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466 stats['individuations'] = 0 |
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467 stats['tree_depth'] = 0 |
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468 if stats is not None: |
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469 stats['color_count'] = len(coloring) |
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470 |
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471 bnode_labels = dict([(c.nodes[0], c.hash_color()) for c in coloring]) |
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472 if stats is not None: |
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473 stats["canonicalize_triples_runtime"] = _total_seconds(datetime.now() - start_coloring) |
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474 for triple in self.graph: |
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475 result = tuple(self._canonicalize_bnodes(triple, bnode_labels)) |
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476 yield result |
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477 |
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478 def _canonicalize_bnodes(self, triple, labels): |
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479 for term in triple: |
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480 if isinstance(term, BNode): |
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481 yield BNode(value="cb%s" % labels[term]) |
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482 else: |
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483 yield term |
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484 |
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485 |
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486 def to_isomorphic(graph): |
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487 if isinstance(graph, IsomorphicGraph): |
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488 return graph |
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489 result = IsomorphicGraph() |
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490 if hasattr(graph, "identifier"): |
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491 result = IsomorphicGraph(identifier=graph.identifier) |
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492 result += graph |
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493 return result |
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494 |
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495 |
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496 def isomorphic(graph1, graph2): |
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497 """Compare graph for equality. |
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498 |
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499 Uses an algorithm to compute unique hashes which takes bnodes into account. |
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500 |
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501 Examples:: |
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502 |
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503 >>> g1 = Graph().parse(format='n3', data=''' |
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504 ... @prefix : <http://example.org/ns#> . |
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505 ... <http://example.org> :rel <http://example.org/a> . |
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506 ... <http://example.org> :rel <http://example.org/b> . |
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507 ... <http://example.org> :rel [ :label "A bnode." ] . |
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508 ... ''') |
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509 >>> g2 = Graph().parse(format='n3', data=''' |
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510 ... @prefix ns: <http://example.org/ns#> . |
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511 ... <http://example.org> ns:rel [ ns:label "A bnode." ] . |
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512 ... <http://example.org> ns:rel <http://example.org/b>, |
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513 ... <http://example.org/a> . |
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514 ... ''') |
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515 >>> isomorphic(g1, g2) |
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516 True |
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517 |
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518 >>> g3 = Graph().parse(format='n3', data=''' |
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519 ... @prefix : <http://example.org/ns#> . |
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520 ... <http://example.org> :rel <http://example.org/a> . |
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521 ... <http://example.org> :rel <http://example.org/b> . |
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522 ... <http://example.org> :rel <http://example.org/c> . |
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523 ... ''') |
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524 >>> isomorphic(g1, g3) |
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525 False |
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526 """ |
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527 gd1 = _TripleCanonicalizer(graph1).to_hash() |
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528 gd2 = _TripleCanonicalizer(graph2).to_hash() |
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529 return gd1 == gd2 |
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530 |
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531 |
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532 def to_canonical_graph(g1, stats=None): |
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533 """Creates a canonical, read-only graph. |
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534 |
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535 Creates a canonical, read-only graph where all bnode id:s are based on |
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536 deterministical SHA-256 checksums, correlated with the graph contents. |
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537 """ |
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538 graph = Graph() |
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539 graph += _TripleCanonicalizer(g1).canonical_triples(stats=stats) |
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540 return ReadOnlyGraphAggregate([graph]) |
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541 |
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542 |
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543 def graph_diff(g1, g2): |
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544 """Returns three sets of triples: "in both", "in first" and "in second".""" |
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545 # bnodes have deterministic values in canonical graphs: |
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546 cg1 = to_canonical_graph(g1) |
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547 cg2 = to_canonical_graph(g2) |
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548 in_both = cg1 * cg2 |
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549 in_first = cg1 - cg2 |
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550 in_second = cg2 - cg1 |
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551 return (in_both, in_first, in_second) |
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552 |
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553 |
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554 _MOCK_BNODE = BNode() |
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555 |
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556 |
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557 def similar(g1, g2): |
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558 """Checks if the two graphs are "similar". |
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559 |
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560 Checks if the two graphs are "similar", by comparing sorted triples where |
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561 all bnodes have been replaced by a singular mock bnode (the |
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562 ``_MOCK_BNODE``). |
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563 |
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564 This is a much cheaper, but less reliable, alternative to the comparison |
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565 algorithm in ``isomorphic``. |
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566 """ |
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567 return all(t1 == t2 for (t1, t2) in _squashed_graphs_triples(g1, g2)) |
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568 |
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569 |
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570 def _squashed_graphs_triples(g1, g2): |
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571 for (t1, t2) in zip(sorted(_squash_graph(g1)), sorted(_squash_graph(g2))): |
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572 yield t1, t2 |
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573 |
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574 |
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575 def _squash_graph(graph): |
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576 return (_squash_bnodes(triple) for triple in graph) |
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577 |
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578 |
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579 def _squash_bnodes(triple): |
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580 return tuple((isinstance(t, BNode) and _MOCK_BNODE) or t for t in triple) |