"""
This example shows how a custom evaluation function can be added to
handle certain SPARQL Algebra elements.

A custom function is added that adds ``rdfs:subClassOf`` "inference" when
asking for ``rdf:type`` triples.

Here the custom eval function is added manually, normally you would use
setuptools and entry_points to do it:
i.e. in your setup.py::

    entry_points = {
        'rdf.plugins.sparqleval': [
            'myfunc =     mypackage:MyFunction',
            ],
    }
"""

# EvalBGP https://rdflib.readthedocs.io/en/stable/_modules/rdflib/plugins/sparql/evaluate.html
# Custom fct for rdf:type with auto infer super-classes: https://github.com/RDFLib/rdflib/blob/master/examples/custom_eval.py
# BGP = Basic Graph Pattern
# Docs rdflib custom fct: https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html
# StackOverflow: https://stackoverflow.com/questions/43976691/custom-sparql-functions-in-rdflib/66988421#66988421

# Another project: https://github.com/bas-stringer/scry/blob/master/query_handler.py
# https://www.w3.org/TR/sparql11-service-description/#example-turtle
# Federated query: https://www.w3.org/TR/2013/REC-sparql11-federated-query-20130321/#defn_service
# XML method: https://rdflib.readthedocs.io/en/stable/apidocs/rdflib.plugins.sparql.results.html#module-rdflib.plugins.sparql.results.xmlresults

import rdflib
from rdflib import Literal, URIRef
from rdflib.plugins.sparql import parser
from rdflib.plugins.sparql.algebra import pprintAlgebra, translateQuery
from rdflib.plugins.sparql.evaluate import evalBGP

# inferredSubClass = rdflib.RDFS.subClassOf * "*"  # any number of rdfs.subClassOf
biolink = URIRef("https://w3id.org/biolink/vocab/")


class Result:
    pass


def add_to_graph(ctx, drug, disease, score):
    bnode = rdflib.BNode()
    ctx.graph.add((bnode, rdflib.RDF.type, rdflib.RDF.Statement))
    ctx.graph.add((bnode, rdflib.RDF.subject, drug))
    ctx.graph.add((bnode, rdflib.RDF.predicate, biolink + "treats"))
    ctx.graph.add((bnode, rdflib.RDF.object, disease))
    ctx.graph.add((bnode, biolink + "category", biolink + "ChemicalToDiseaseOrPhenotypicFeatureAssociation"))
    ctx.graph.add((bnode, biolink + "has_confidence_level", score))


def get_triples(disease):
    drug = URIRef("http://bio2rdf.org/drugbank:DB00001")
    score = Literal("1.0")

    r = Result()
    r.drug = drug
    r.disease = disease
    r.score = score

    results = []
    results.append(r)
    return results


# def parseRelationalExpr(expr):


def custom_eval(ctx, part):
    """ """
    # print (part.name)

    if part.name == "Project":
        ctx.myvars = []

    # search extend for variable binding
    if part.name == "Extend" and hasattr(part, "expr") and not isinstance(part.expr, list):
        ctx.myvars.append(part.expr)

    # search for filter
    if part.name == "Filter" and hasattr(part, "expr"):
        if hasattr(part.expr, "expr"):
            if part.expr.expr["op"] == "=":
                part.expr.expr["expr"]
                d = part.expr.expr["other"]
                ctx.myvars.append(d)
        else:
            if part.expr["op"] == "=":
                part.expr["expr"]
                d = part.expr["other"]
                ctx.myvars.append(d)

    # search the BGP for the variable of interest
    if part.name == "BGP":
        triples = []
        for t in part.triples:
            if t[1] == rdflib.RDF.object:
                disease = t[2]
                # check first if the disease term is specified in the bgp triple
                if isinstance(disease, rdflib.term.URIRef):
                    ctx.myvars.append(disease)

                # fetch instances
                for d in ctx.myvars:
                    results = get_triples(d)
                    for r in results:
                        add_to_graph(ctx, r.drug, r.disease, r.score)

            triples.append(t)
        return evalBGP(ctx, triples)
    raise NotImplementedError()


if __name__ == "__main__":
    # add function directly, normally we would use setuptools and entry_points
    rdflib.plugins.sparql.CUSTOM_EVALS["exampleEval"] = custom_eval

    g = rdflib.Graph()

    q = """PREFIX openpredict: <https://w3id.org/um/openpredict/>
        PREFIX biolink: <https://w3id.org/biolink/vocab/>
        PREFIX omim: <http://bio2rdf.org/omim:>
        SELECT ?disease ?drug ?score
        {
            ?association a rdf:Statement ;
                rdf:subject ?drug ;
                rdf:predicate ?predicate ;
                #rdf:object omim:246300 ;
                rdf:object ?disease ;
                biolink:category biolink:ChemicalToDiseaseOrPhenotypicFeatureAssociation ;
                biolink:has_confidence_level ?score .
            #?disease dcat:identifier "OMIM:246300" .
            BIND(omim:1 AS ?disease)
            #FILTER(?disease = omim:2 || ?disease = omim:3)
            #VALUES ?disease { omim:5 omim:6 omim:7 }
        }"""

    pq = parser.parseQuery(q)
    tq = translateQuery(pq)
    pprintAlgebra(tq)

    # Find all FOAF Agents
    for x in g.query(q):
        print(x)