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# -*- coding: utf-8 -*-
#
#
# $Date: 2005/11/04 14:06:36 $, by $Author: ivan $, $Revision: 1.1 $
#
"""
Graph pattern class used by the SPARQL implementation
"""
import sys, os, time, datetime
from rdflib.Literal import Literal
from rdflib.BNode import BNode
from rdflib.URIRef import URIRef
from rdflib import Variable
from types import *
from rdflib.syntax.NamespaceManager import NamespaceManager
from rdflib.Graph import Graph
from rdflib.sparql import _questChar, Debug, SPARQLError
def _createResource(v) :
"""Create an RDFLib Literal instance with the corresponding XML
Schema datatype set. If the variable is already an RDFLib
resource, it simply returns the resource; otherwise the
corresponding Literal. A SPARQLError Exception is raised if the
type is not implemented.
The Literal contains the string representation of the variable (as
Python does it by default) with the corresponding XML Schema URI
set.
@param v: Python variable
@return: either an RDFLib Literal (if 'v' is not an RDFLib Resource), or the same variable if it is already
an RDFLib resource (ie, Literal, BNode, or URIRef)
@raise SPARQLError: if the type of 'v' is not implemented
"""
if isinstance(v,Literal) or isinstance(v,BNode) or isinstance(v,URIRef) :
# just do nothing
return v
else :
return Literal(v) # Literal now does the datatype bits
def _isResQuest(r) :
"""
Is 'r' a request string (ie, of the form "?XXX")?
@rtype: Boolean
"""
if r and isinstance(r,basestring) and r[0] == _questChar :
return True
return False
class GraphPattern :
"""
Storage of one Graph Pattern, ie, the pattern tuples and the
possible (functional) constraints (filters)
"""
def __init__(self,patterns=[]) :
"""
@param patterns: an initial list of graph pattern tuples
"""
self.patterns = []
self.constraints = []
self.unbounds = []
self.bnodes = {}
if type(patterns) == list :
self.addPatterns(patterns)
elif type(patterns) == tuple :
self.addPattern(patterns)
else :
raise SPARQLError("illegal argument, pattern must be a tuple or a list of tuples")
def _generatePattern(self,tupl) :
"""
Append a tuple to the local patterns. Possible type literals
are converted to real literals on the fly. Each tuple should
be contain either 3 elements (for an RDF Triplet pattern) or
four, where the fourth element is a per-pattern constraint
(filter). (The general constraint of SPARQL can be optimized
by assigning a constraint to a specific pattern; because it
stops the graph expansion, its usage might be much more
optimal than the the 'global' constraint).
@param tupl: either a three or four element tuple
"""
if type(tupl) != tuple :
raise SPARQLError("illegal argument, pattern must be a tuple, got %s" % type(tupl))
if len(tupl) != 3 and len(tupl) != 4 :
raise SPARQLError("illegal argument, pattern must be a tuple of 3 or 4 element, got %s" % len(tupl))
if len(tupl) == 3 :
(s,p,o) = tupl
f = None
else :
(s,p,o,f) = tupl
final=[]
for c in (s,p,o) :
if _isResQuest(c) :
if not c in self.unbounds :
self.unbounds.append(c)
final.append(c)
elif isinstance(c, BNode):
#Do nothing - BNode name management is handled by SPARQL parser
# if not c in self.bnodes :
# self.bnodes[c] = BNode()
final.append(c)
else :
final.append(_createResource(c))
final.append(f)
return tuple(final)
def addPattern(self,tupl) :
"""
Append a tuple to the local patterns. Possible type literals
are converted to real literals on the fly. Each tuple should
be contain either 3 elements (for an RDF Triplet pattern) or
four, where the fourth element is a per-pattern constraint
(filter). (The general constraint of SPARQL can be optimized
by assigning a constraint to a specific pattern; because it
stops the graph expansion, its usage might be much more
optimal than the the 'global' constraint).
@param tupl: either a three or four element tuple
"""
self.patterns.append(self._generatePattern(tupl))
def insertPattern(self,tupl) :
"""
Insert a tuple to to the start of local patterns. Possible
type literals are converted to real literals on the fly. Each
tuple should be contain either 3 elements (for an RDF Triplet
pattern) or four, where the fourth element is a per-pattern
constraint (filter). (The general constraint of SPARQL can be
optimized by assigning a constraint to a specific pattern;
because it stops the graph expansion, its usage might be much
more optimal than the the 'global' constraint).
Semantically, the behaviour induced by a graphPattern does not
depend on the order of the patterns. However, due to the
behaviour of the expansion algorithm, users may control the
speed somewhat by adding patterns that would 'cut' the
expansion tree soon (ie, patterns that reduce the available
triplets significantly). API users may be able to do that,
hence this additional method.
@param tupl: either a three or four element tuple
"""
self.patterns.insert(0,self._generatePattern(tupl))
def addPatterns(self,lst) :
"""
Append a list of tuples to the local patterns. Possible type
literals are converted to real literals on the fly. Each
tuple should be contain either three elements (for an RDF
Triplet pattern) or four, where the fourth element is a
per-pattern constraint. (The general constraint of SPARQL can
be optimized by assigning a constraint to a specific pattern;
because it stops the graph expansion, its usage might be much
more optimal than the the 'global' constraint).
@param lst: list consisting of either a three or four element tuples
"""
for l in lst:
self.addPattern(l)
def insertPatterns(self,lst) :
"""
Insert a list of tuples to the start of the local
patterns. Possible type literals are converted to real
literals on the fly. Each tuple should be contain either
three elements (for an RDF Triplet pattern) or four, where the
fourth element is a per-pattern constraint. (The general
constraint of SPARQL can be optimized by assigning a
constraint to a specific pattern; because it stops the graph
expansion, its usage might be much more optimal than the the
'global' constraint).
Semantically, the behaviour induced by a graphPattern does not
depend on the order of the patterns. However, due to the
behaviour of the expansion algorithm, users may control the
speed somewhat by adding patterns that would 'cut' the
expansion tree soon (ie, patterns that reduce the available
triplets significantly). API users may be able to do that,
hence this additional method.
@param lst: list consisting of either a three or four element tuples
"""
for i in xrange(len(lst)-1,-1,-1) :
self.insertPattern(lst[i])
def addConstraint(self,func) :
"""
Add a global filter constraint to the graph pattern. 'func'
must be a method with a single input parameter (a dictionary)
returning a boolean. This method is I{added} to previously
added methods, ie, I{all} methods must return True to accept a
binding.
@param func: filter function
"""
if type(func) == FunctionType :
self.constraints.append(func)
else :
raise SPARQLError("illegal argument, constraint must be a function type, got %s" % type(func))
def addConstraints(self,lst) :
"""
Add a list of global filter constraints to the graph
pattern. Each function in the list must be a method with a
single input parameter (a dictionary) returning a
boolean. These methods are I{added} to previously added
methods, ie, I{all} methods must return True to accept a
binding.
@param lst: list of functions
"""
for l in lst:
self.addConstraint(l)
def construct(self,tripleStore,bindings) :
"""
Add triples to a tripleStore based on a variable bindings of
the patterns stored locally. The triples are patterned by the
current Graph Pattern. The method is used to construct a graph
after a successful querying.
@param tripleStore: an (rdflib) Triple Store
@param bindings: dictionary
"""
localBnodes = {}
for c in self.bnodes :
localBnodes[c] = BNode()
def bind(st) :
if _isResQuest(st) :
if st in bindings :
return bindings[st]
else :
if isinstance(self,GraphPattern2) :
return st
else :
return None
elif isinstance(st,BNode) :
for c in self.bnodes :
if self.bnodes[c] == st :
# this is a BNode that was created as part of building up the pattern
return localBnodes[c]
# if we got here, the BNode comes from somewhere else...
return st
else :
return st
for pattern in self.patterns :
(s,p,o,f) = pattern
triplet = []
valid = True
for res in (s,p,o) :
val = bind(res)
if val != None :
triplet.append(val)
else :
valid = False
break
if valid :
tripleStore.add(tuple(triplet))
def __add__(self,other) :
"""Adding means concatenating all the patterns and filters arrays"""
retval = GraphPattern()
retval += self
retval += other
return retval
def __iadd__(self,other) :
"""Adding means concatenating all the patterns and filters arrays"""
self.patterns += other.patterns
self.constraints += other.constraints
for c in other.unbounds :
if not c in self.unbounds :
self.unbounds.append(c)
for c in other.bnodes :
if not c in self.bnodes :
self.bnodes[c] = other.bnodes[c]
return self
def __str__(self) :
return self.__repr__()
def isEmpty(self) :
"""Is the pattern empty?
@rtype: Boolean
"""
return len(self.patterns) == 0
class BasicGraphPattern(GraphPattern) :
"""One, justified, problem with the current definition of L{GraphPattern<GraphPattern>} is that it
makes it difficult for users to use a literal of the type "?XXX", because any string beginning
with "?" will be considered to be an unbound variable. The only way of doing this is that the user
explicitly creates a Literal object and uses that as part of the pattern.
This class is a superclass of L{GraphPattern<GraphPattern>} which does I{not} do this, but requires the
usage of a separate variable class instance"""
def __init__(self,patterns=[],prolog=None) :
"""
@param patterns: an initial list of graph pattern tuples
"""
GraphPattern.__init__(self,patterns)
self.prolog = prolog
def canonicalTerm(self,term):
if isinstance(term,URIRef):
if self.prolog is not None:
namespace_manager = NamespaceManager(Graph())
for prefix,uri in self.prolog.prefixBindings.items():
namespace_manager.bind(prefix, uri, override=False)
try:
prefix,uri,localName=namespace_manager.compute_qname(term)
except:
return term
if prefix not in self.prolog.prefixBindings:
return term
else:
return u':'.join([prefix,localName])
else:
return term
elif isinstance(term,Literal):
return term.n3()
elif isinstance(term,BNode):
return term.n3()
else:
assert isinstance(term,Variable)
return term.n3()
def __repr__(self):
# from pprint import pformat
if self.constraints:
#return "Filter(.. a filter ..,BGP(%s))"%(','.join([pformat(p[:3]) for p in self.patterns]))
return "Filter(.. a filter ..,BGP(%s))"%(','.join([','.join([self.canonicalTerm(pat[0]),
self.canonicalTerm(pat[1]),
self.canonicalTerm(pat[2])])
for pat in self.patterns]))
else:
#return "BGP(%s)"%(','.join([repr(p[:3]) for p in self.patterns]))
return "BGP(%s)"%(','.join(['('+','.join([self.canonicalTerm(s),
self.canonicalTerm(p),
self.canonicalTerm(o)])+')'
for s,p,o,f in self.patterns]))
retval = " Patterns: %s\n" % self.patterns
retval += " Constraints: %s\n" % self.constraints
retval += " Unbounds: %s\n" % self.unbounds
return retval
def _generatePattern(self,tupl) :
"""
Append a tuple to the local patterns. Possible type literals
are converted to real literals on the fly. Each tuple should
be contain either 3 elements (for an RDF Triplet pattern) or
four, where the fourth element is a per-pattern constraint
(filter). (The general constraint of SPARQL can be optimized
by assigning a constraint to a specific pattern; because it
stops the graph expansion, its usage might be much more
optimal than the the 'global' constraint).
@param tupl: either a three or four element tuple
"""
if type(tupl) != tuple :
raise SPARQLError("illegal argument, pattern must be a tuple, got %s" % type(tupl))
if len(tupl) != 3 and len(tupl) != 4 :
raise SPARQLError("illegal argument, pattern must be a tuple of 3 or 4 element, got %s" % len(tupl))
if len(tupl) == 3 :
(s,p,o) = tupl
f = None
else :
(s,p,o,f) = tupl
final=[]
for c in (s,p,o) :
if isinstance(c,Variable) :
if not c in self.unbounds :
self.unbounds.append(c)
final.append(c)
elif isinstance(c, BNode):
#Do nothing - BNode name management is handled by SPARQL parser
final.append(c)
else :
final.append(_createResource(c))
final.append(f)
return tuple(final)
if __name__ == '__main__' :
v1 = Variable("a")
g = BasicGraphPattern([("a","?b",24),("?r","?c",12345),(v1,"?c",3333)])
print g
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