Mercurial > repos > shellac > guppy_basecaller

annotate env/lib/python3.7/site-packages/rdflib/plugins/sparql/evaluate.py @ 3:758bc20232e8 draft

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"planemo upload commit 2a0fe2cc28b09e101d37293e53e82f61762262ec"
author shellac
date Thu, 14 May 2020 16:20:52 -0400
parents 26e78fe6e8c4
children
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1 """
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2 These method recursively evaluate the SPARQL Algebra
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3
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4 evalQuery is the entry-point, it will setup context and
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5 return the SPARQLResult object
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6
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7 evalPart is called on each level and will delegate to the right method
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8
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9 A rdflib.plugins.sparql.sparql.QueryContext is passed along, keeping
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10 information needed for evaluation
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11
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12 A list of dicts (solution mappings) is returned, apart from GroupBy which may
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13 also return a dict of list of dicts
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14
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15 """
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16
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17 import collections
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18
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19 from rdflib import Variable, Graph, BNode, URIRef, Literal
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20
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21 from rdflib.plugins.sparql import CUSTOM_EVALS
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22 from rdflib.plugins.sparql.parserutils import value
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23 from rdflib.plugins.sparql.sparql import (
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24 QueryContext, AlreadyBound, FrozenBindings, SPARQLError)
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25 from rdflib.plugins.sparql.evalutils import (
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26 _filter, _eval, _join, _diff, _minus, _fillTemplate, _ebv, _val)
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27
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28 from rdflib.plugins.sparql.aggregates import Aggregator
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29 from rdflib.plugins.sparql.algebra import Join, ToMultiSet, Values
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30
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31 def evalBGP(ctx, bgp):
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32
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33 """
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34 A basic graph pattern
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35 """
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36
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37 if not bgp:
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38 yield ctx.solution()
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39 return
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40
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41 s, p, o = bgp[0]
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42
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43 _s = ctx[s]
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44 _p = ctx[p]
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45 _o = ctx[o]
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46
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47 for ss, sp, so in ctx.graph.triples((_s, _p, _o)):
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48 if None in (_s, _p, _o):
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49 c = ctx.push()
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50 else:
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51 c = ctx
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52
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53 if _s is None:
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54 c[s] = ss
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55
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56 try:
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57 if _p is None:
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58 c[p] = sp
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59 except AlreadyBound:
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60 continue
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61
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62 try:
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63 if _o is None:
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64 c[o] = so
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65 except AlreadyBound:
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66 continue
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67
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68 for x in evalBGP(c, bgp[1:]):
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69 yield x
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70
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71
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72 def evalExtend(ctx, extend):
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73 # TODO: Deal with dict returned from evalPart from GROUP BY
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74
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75 for c in evalPart(ctx, extend.p):
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76 try:
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77 e = _eval(extend.expr, c.forget(ctx, _except=extend._vars))
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78 if isinstance(e, SPARQLError):
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79 raise e
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80
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81 yield c.merge({extend.var: e})
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82
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83 except SPARQLError:
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84 yield c
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85
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86
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87 def evalLazyJoin(ctx, join):
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88 """
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89 A lazy join will push the variables bound
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90 in the first part to the second part,
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91 essentially doing the join implicitly
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92 hopefully evaluating much fewer triples
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93 """
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94 for a in evalPart(ctx, join.p1):
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95 c = ctx.thaw(a)
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96 for b in evalPart(c, join.p2):
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97 yield b.merge(a) # merge, as some bindings may have been forgotten
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98
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99
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100 def evalJoin(ctx, join):
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101
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102 # TODO: Deal with dict returned from evalPart from GROUP BY
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103 # only ever for join.p1
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104
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105 if join.lazy:
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106 return evalLazyJoin(ctx, join)
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107 else:
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108 a = evalPart(ctx, join.p1)
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109 b = set(evalPart(ctx, join.p2))
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110 return _join(a, b)
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111
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112
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113 def evalUnion(ctx, union):
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114 res = set()
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115 for x in evalPart(ctx, union.p1):
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116 res.add(x)
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117 yield x
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118 for x in evalPart(ctx, union.p2):
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119 if x not in res:
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120 yield x
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121
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122
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123 def evalMinus(ctx, minus):
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124 a = evalPart(ctx, minus.p1)
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125 b = set(evalPart(ctx, minus.p2))
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126 return _minus(a, b)
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127
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128
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129 def evalLeftJoin(ctx, join):
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130 # import pdb; pdb.set_trace()
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131 for a in evalPart(ctx, join.p1):
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132 ok = False
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133 c = ctx.thaw(a)
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134 for b in evalPart(c, join.p2):
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135 if _ebv(join.expr, b.forget(ctx)):
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136 ok = True
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137 yield b
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138 if not ok:
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139 # we've cheated, the ctx above may contain
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140 # vars bound outside our scope
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141 # before we yield a solution without the OPTIONAL part
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142 # check that we would have had no OPTIONAL matches
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143 # even without prior bindings...
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144 p1_vars = join.p1._vars
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145 if p1_vars is None \
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146 or not any(_ebv(join.expr, b) for b in
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147 evalPart(ctx.thaw(a.remember(p1_vars)), join.p2)):
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148
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149 yield a
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150
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151
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152 def evalFilter(ctx, part):
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153 # TODO: Deal with dict returned from evalPart!
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154 for c in evalPart(ctx, part.p):
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155 if _ebv(part.expr, c.forget(ctx, _except=part._vars) if not part.no_isolated_scope else c):
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156 yield c
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157
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158
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159 def evalGraph(ctx, part):
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160
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161 if ctx.dataset is None:
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162 raise Exception(
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163 "Non-conjunctive-graph doesn't know about " +
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164 "graphs. Try a query without GRAPH.")
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165
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166 ctx = ctx.clone()
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167 graph = ctx[part.term]
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168 if graph is None:
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169
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170 for graph in ctx.dataset.contexts():
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171
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172 # in SPARQL the default graph is NOT a named graph
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173 if graph == ctx.dataset.default_context:
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174 continue
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175
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176 c = ctx.pushGraph(graph)
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177 c = c.push()
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178 graphSolution = [{part.term: graph.identifier}]
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179 for x in _join(evalPart(c, part.p), graphSolution):
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180 yield x
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181
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182 else:
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183 c = ctx.pushGraph(ctx.dataset.get_context(graph))
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184 for x in evalPart(c, part.p):
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185 yield x
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186
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187
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188 def evalValues(ctx, part):
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189 for r in part.p.res:
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190 c = ctx.push()
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191 try:
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192 for k, v in r.items():
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193 if v != 'UNDEF':
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194 c[k] = v
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195 except AlreadyBound:
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196 continue
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197
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198 yield c.solution()
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199
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200
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201 def evalMultiset(ctx, part):
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202
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203 if part.p.name == 'values':
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204 return evalValues(ctx, part)
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205
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206 return evalPart(ctx, part.p)
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207
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208
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209 def evalPart(ctx, part):
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210
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211 # try custom evaluation functions
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212 for name, c in list(CUSTOM_EVALS.items()):
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213 try:
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214 return c(ctx, part)
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215 except NotImplementedError:
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216 pass # the given custome-function did not handle this part
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217
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218 if part.name == 'BGP':
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219 # Reorder triples patterns by number of bound nodes in the current ctx
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220 # Do patterns with more bound nodes first
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221 triples = sorted(part.triples, key=lambda t: len([n for n in t if ctx[n] is None]))
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222
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223 return evalBGP(ctx, triples)
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224 elif part.name == 'Filter':
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225 return evalFilter(ctx, part)
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226 elif part.name == 'Join':
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227 return evalJoin(ctx, part)
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228 elif part.name == 'LeftJoin':
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229 return evalLeftJoin(ctx, part)
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230 elif part.name == 'Graph':
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231 return evalGraph(ctx, part)
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232 elif part.name == 'Union':
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233 return evalUnion(ctx, part)
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diff changeset
234 elif part.name == 'ToMultiSet':
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diff changeset
235 return evalMultiset(ctx, part)
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parents:
diff changeset
236 elif part.name == 'Extend':
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237 return evalExtend(ctx, part)
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parents:
diff changeset
238 elif part.name == 'Minus':
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parents:
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239 return evalMinus(ctx, part)
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240
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parents:
diff changeset
241 elif part.name == 'Project':
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242 return evalProject(ctx, part)
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parents:
diff changeset
243 elif part.name == 'Slice':
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244 return evalSlice(ctx, part)
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parents:
diff changeset
245 elif part.name == 'Distinct':
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246 return evalDistinct(ctx, part)
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parents:
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247 elif part.name == 'Reduced':
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248 return evalReduced(ctx, part)
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249
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parents:
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250 elif part.name == 'OrderBy':
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parents:
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251 return evalOrderBy(ctx, part)
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parents:
diff changeset
252 elif part.name == 'Group':
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253 return evalGroup(ctx, part)
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parents:
diff changeset
254 elif part.name == 'AggregateJoin':
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255 return evalAggregateJoin(ctx, part)
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256
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257 elif part.name == 'SelectQuery':
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diff changeset
258 return evalSelectQuery(ctx, part)
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parents:
diff changeset
259 elif part.name == 'AskQuery':
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260 return evalAskQuery(ctx, part)
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parents:
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261 elif part.name == 'ConstructQuery':
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262 return evalConstructQuery(ctx, part)
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263
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264 elif part.name == 'ServiceGraphPattern':
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265 raise Exception('ServiceGraphPattern not implemented')
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266
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267 elif part.name == 'DescribeQuery':
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268 raise Exception('DESCRIBE not implemented')
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269
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270 else:
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271 # import pdb ; pdb.set_trace()
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272 raise Exception('I dont know: %s' % part.name)
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273
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274
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275 def evalGroup(ctx, group):
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276
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277 """
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278 http://www.w3.org/TR/sparql11-query/#defn_algGroup
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279 """
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280 # grouping should be implemented by evalAggregateJoin
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281 return evalPart(ctx, group.p)
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282
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283
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284 def evalAggregateJoin(ctx, agg):
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285 # import pdb ; pdb.set_trace()
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286 p = evalPart(ctx, agg.p)
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287 # p is always a Group, we always get a dict back
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288
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289 group_expr = agg.p.expr
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290 res = collections.defaultdict(lambda: Aggregator(aggregations=agg.A))
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291
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292 if group_expr is None:
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293 # no grouping, just COUNT in SELECT clause
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294 # get 1 aggregator for counting
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295 aggregator = res[True]
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296 for row in p:
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297 aggregator.update(row)
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298 else:
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299 for row in p:
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300 # determine right group aggregator for row
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301 k = tuple(_eval(e, row, False) for e in group_expr)
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302 res[k].update(row)
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303
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304 # all rows are done; yield aggregated values
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305 for aggregator in res.values():
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306 yield FrozenBindings(ctx, aggregator.get_bindings())
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307
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308 # there were no matches
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309 if len(res) == 0:
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310 yield FrozenBindings(ctx)
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311
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312
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313 def evalOrderBy(ctx, part):
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314
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315 res = evalPart(ctx, part.p)
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316
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317 for e in reversed(part.expr):
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318
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319 reverse = bool(e.order and e.order == 'DESC')
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320 res = sorted(res, key=lambda x: _val(value(x, e.expr, variables=True)), reverse=reverse)
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321
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322 return res
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323
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324
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325 def evalSlice(ctx, slice):
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326 # import pdb; pdb.set_trace()
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327 res = evalPart(ctx, slice.p)
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328 i = 0
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329 while i < slice.start:
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330 next(res)
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331 i += 1
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332 i = 0
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333 for x in res:
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334 i += 1
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335 if slice.length is None:
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336 yield x
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337 else:
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338 if i <= slice.length:
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339 yield x
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340 else:
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341 break
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342
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343
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344 def evalReduced(ctx, part):
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345 """apply REDUCED to result
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346
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347 REDUCED is not as strict as DISTINCT, but if the incoming rows were sorted
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348 it should produce the same result with limited extra memory and time per
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349 incoming row.
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350 """
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351
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352 # This implementation uses a most recently used strategy and a limited
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353 # buffer size. It relates to a LRU caching algorithm:
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354 # https://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used_.28LRU.29
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355 MAX = 1
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356 # TODO: add configuration or determine "best" size for most use cases
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357 # 0: No reduction
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358 # 1: compare only with the last row, almost no reduction with
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359 # unordered incoming rows
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360 # N: The greater the buffer size the greater the reduction but more
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361 # memory and time are needed
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362
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363 # mixed data structure: set for lookup, deque for append/pop/remove
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364 mru_set = set()
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365 mru_queue = collections.deque()
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366
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367 for row in evalPart(ctx, part.p):
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368 if row in mru_set:
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369 # forget last position of row
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370 mru_queue.remove(row)
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371 else:
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372 #row seems to be new
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373 yield row
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374 mru_set.add(row)
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375 if len(mru_set) > MAX:
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376 # drop the least recently used row from buffer
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377 mru_set.remove(mru_queue.pop())
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378 # put row to the front
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379 mru_queue.appendleft(row)
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380
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381
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382 def evalDistinct(ctx, part):
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383 res = evalPart(ctx, part.p)
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384
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385 done = set()
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386 for x in res:
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387 if x not in done:
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388 yield x
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389 done.add(x)
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390
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391
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392 def evalProject(ctx, project):
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393 res = evalPart(ctx, project.p)
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394
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395 return (row.project(project.PV) for row in res)
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396
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397
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398 def evalSelectQuery(ctx, query):
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399
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400 res = {}
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401 res["type_"] = "SELECT"
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402 res["bindings"] = evalPart(ctx, query.p)
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403 res["vars_"] = query.PV
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404 return res
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405
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406
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407 def evalAskQuery(ctx, query):
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408 res = {}
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409 res["type_"] = "ASK"
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410 res["askAnswer"] = False
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411 for x in evalPart(ctx, query.p):
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412 res["askAnswer"] = True
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413 break
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414
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415 return res
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416
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417
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418 def evalConstructQuery(ctx, query):
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419 template = query.template
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420
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421 if not template:
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422 # a construct-where query
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423 template = query.p.p.triples # query->project->bgp ...
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424
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425 graph = Graph()
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426
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427 for c in evalPart(ctx, query.p):
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428 graph += _fillTemplate(template, c)
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429
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430 res = {}
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431 res["type_"] = "CONSTRUCT"
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432 res["graph"] = graph
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433
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434 return res
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435
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436
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437 def evalQuery(graph, query, initBindings, base=None):
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438
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439 initBindings = dict( ( Variable(k),v ) for k,v in initBindings.items() )
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440
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441 ctx = QueryContext(graph, initBindings=initBindings)
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442
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443 ctx.prologue = query.prologue
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444 main = query.algebra
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445
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446 if main.datasetClause:
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447 if ctx.dataset is None:
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448 raise Exception(
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449 "Non-conjunctive-graph doesn't know about " +
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450 "graphs! Try a query without FROM (NAMED).")
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451
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452 ctx = ctx.clone() # or push/pop?
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453
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454 firstDefault = False
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455 for d in main.datasetClause:
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456 if d.default:
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457
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458 if firstDefault:
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459 # replace current default graph
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460 dg = ctx.dataset.get_context(BNode())
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461 ctx = ctx.pushGraph(dg)
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462 firstDefault = True
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463
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464 ctx.load(d.default, default=True)
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465
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466 elif d.named:
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467 g = d.named
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468 ctx.load(g, default=False)
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469
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470 return evalPart(ctx, main)