def do_test_json(cat, num, inputpath, expectedpath, context, options):
base = TC_BASE + inputpath
input_obj = _load_json(inputpath)
input_graph = ConjunctiveGraph()
to_rdf(
input_obj,
input_graph,
base=base,
context_data=context,
produce_generalized_rdf=True,
)
expected_json = _load_json(expectedpath)
use_native_types = True # CONTEXT in input_obj
result_json = from_rdf(
input_graph,
context,
base=TC_BASE + inputpath,
use_native_types=options.get("useNativeTypes", use_native_types),
use_rdf_type=options.get("useRdfType", False),
)
def _prune_json(data):
if CONTEXT in data:
data.pop(CONTEXT)
if GRAPH in data:
data = data[GRAPH]
# def _remove_empty_sets(obj):
return data
expected_json = _prune_json(expected_json)
result_json = _prune_json(result_json)
_compare_json(expected_json, result_json)
def _construct(compiler, sources, query=None):
dataset = ConjunctiveGraph()
if not isinstance(sources, list):
sources = [sources]
for sourcedfn in sources:
source = sourcedfn['source']
graph = dataset.get_context(URIRef(sourcedfn.get('dataset') or source))
if isinstance(source, (dict, list)):
context_data = sourcedfn['context']
if not isinstance(context_data, list):
context_data = compiler.load_json(context_data )['@context']
context_data = [compiler.load_json(ctx)['@context']
if isinstance(ctx, unicode) else ctx
for ctx in context_data]
to_rdf(source, graph, context_data=context_data)
elif isinstance(source, Graph):
graph += source
else:
graph += compiler.cached_rdf(source)
if not query:
return graph
with compiler.path(query).open() as fp:
result = dataset.query(fp.read())
g = Graph()
for spo in result:
g.add(spo)
return g
def _test_parser(cat, num, inputpath, expectedpath, context, options):
input_obj = _load_json(inputpath)
expected_graph = _load_nquads(expectedpath)
base = TC_BASE + inputpath
result_graph = ConjunctiveGraph()
to_rdf(input_obj, result_graph, base=base, context_data=context,
produce_generalized_rdf = options.get('produceGeneralizedRdf', False))
assert isomorphic(
result_graph, expected_graph), "Expected:\n%s\nGot:\n%s" % (
expected_graph.serialize(format='turtle'),
result_graph.serialize(format='turtle'))
def _test_parser(cat, num, inputpath, expectedpath, context, options):
input_obj = _load_json(inputpath)
expected_graph = _load_nquads(expectedpath)
base = TC_BASE + inputpath
result_graph = ConjunctiveGraph()
to_rdf(input_obj, result_graph, base=base, context_data=context,
produce_generalized_rdf = options.get('produceGeneralizedRdf', False))
assert isomorphic(
result_graph, expected_graph), "Expected:\n%s\nGot:\n%s" % (
expected_graph.serialize(format='turtle'),
result_graph.serialize(format='turtle'))
def _test_json(cat, num, inputpath, expectedpath, context, options):
base = TC_BASE + inputpath
input_obj = _load_json(inputpath)
input_graph = ConjunctiveGraph()
to_rdf(input_obj, input_graph, base=base, context_data=context)
expected_json = _load_json(expectedpath)
result_json = from_rdf(input_graph, context, base=TC_BASE + inputpath,
use_native_types=options.get('useNativeTypes', False),
use_rdf_type=options.get('useRdfType', False))
def _prune_json(data):
if CONTEXT in data:
data.pop(CONTEXT)
if GRAPH in data:
data = data[GRAPH]
return data
expected_json = _prune_json(expected_json)
result_json = _prune_json(result_json)
_compare_json(expected_json, result_json)
def _test_json(cat, num, inputpath, expectedpath, context, options):
base = TC_BASE + inputpath
input_obj = _load_json(inputpath)
input_graph = ConjunctiveGraph()
to_rdf(input_obj, input_graph, base=base, context_data=context,
produce_generalized_rdf=True)
expected_json = _load_json(expectedpath)
use_native_types = True # CONTEXT in input_obj
result_json = from_rdf(input_graph, context, base=TC_BASE + inputpath,
use_native_types=options.get('useNativeTypes', use_native_types),
use_rdf_type=options.get('useRdfType', False))
def _prune_json(data):
if CONTEXT in data:
data.pop(CONTEXT)
if GRAPH in data:
data = data[GRAPH]
#def _remove_empty_sets(obj):
return data
expected_json = _prune_json(expected_json)
result_json = _prune_json(result_json)
_compare_json(expected_json, result_json)
"label": "rdfs:label",
}
}
organizations = []
with open(sys.argv[1]) as infile:
for count, row in enumerate(csv.DictReader(infile)):
name = row['org_name']
oid = row['org_ID']
org_uri = 'org{}'.format(oid)
org_type = row['org_vivo_uri']
org = {}
org['uri'] = org_uri
org['a'] = org_type
org['label'] = name
org.update(org_ctx)
organizations.append(org)
g = Graph()
g.namespace_manager = ns_mgr
out = to_rdf(organizations, g)
print out.serialize(format='turtle')
def graph_from_ld(data):
g = ConjunctiveGraph()
out = to_rdf(data, g)
return g
e.update(faculty_ctx)
vc['hasEmail'] = vcard_email_uri
fac.append(e)
#Phone
phone = row.get('phone')
if phone != '':
p = {}
p['uri'] = vcard_phone_uri
p['a'] = ["vcard:Telephone", "vcard:Work", "vcard:Voice"]
p['telephone'] = phone
p.update(faculty_ctx)
fac.append(p)
#Fax - really, 2014.
fax = row.get('fax')
if fax != '':
f = {}
f['uri'] = vcard_fax_uri
f['a'] = ["vcard:Telephone", "vcard:Work", "vcard:Fax"]
f['telephone'] = fax
f.update(faculty_ctx)
fac.append(f)
vc['hasPhone'] = [vcard_phone_uri, vcard_fax_uri]
g = Graph()
out = to_rdf(fac, g)
out.namespace_manager = ns_mgr
print out.serialize(format='turtle')
#Phone
phone = row.get('phone')
if phone != '':
p = {}
p['uri'] = vcard_phone_uri
p['a'] = ["vcard:Telephone", "vcard:Work", "vcard:Voice"]
p['telephone'] = phone
p.update(faculty_ctx)
fac.append(p)
#Fax - really, 2014.
fax = row.get('fax')
if fax != '':
f = {}
f['uri'] = vcard_fax_uri
f['a'] = ["vcard:Telephone", "vcard:Work", "vcard:Fax"]
f['telephone'] = fax
f.update(faculty_ctx)
fac.append(f)
vc['hasPhone'] = [vcard_phone_uri, vcard_fax_uri]
g = Graph()
out = to_rdf(fac, g)
out.namespace_manager = ns_mgr
print out.serialize(format='turtle')
def to_graph(self, prepped):
g = to_rdf(prepped, Graph())
return g
import json
import yaml
from rdflib import Graph
from rdflib_jsonld import parser
import sys
ctxpath = sys.argv[1]
fpath = sys.argv[2]
with open(fpath) as fp:
data = yaml.load(fp)
with open(ctxpath) as fp:
ctx = json.load(fp)
graph = Graph()
parser.to_rdf(data, graph, context_data=ctx)
print graph.serialize(format='turtle')
"a": "@type",
"uri": "@id",
"vivo": "http://vivoweb.org/ontology/core#",
"rdfs": "http://www.w3.org/2000/01/rdf-schema#",
"label": "rdfs:label",
}
}
organizations = []
with open(sys.argv[1]) as infile:
for count, row in enumerate(csv.DictReader(infile)):
name = row['org_name']
oid = row['org_ID']
org_uri = 'org{}'.format(oid)
org_type = row['org_vivo_uri']
org = {}
org['uri'] = org_uri
org['a'] = org_type
org['label'] = name
org.update(org_ctx)
organizations.append(org)
g = Graph()
g.namespace_manager = ns_mgr
out = to_rdf(organizations, g)
print out.serialize(format='turtle')
def to_graph(self, prepped):
g = to_rdf(prepped, Graph())
return g
for row in csv.DictReader(infile):
print row
#tax_id = row.get('EIN')
#print "tax_id: " + str(tax_id)
d = {}
d['uri'] = uuid_uri()
d['label'] = row.get('NAME')
print "NAME: " + str(d['label'])
d['a'] = 'foaf:Organization'
d.update(org_ctx)
vco = uuid_uri()
vcard_address = {}
vcard_address['uri'] = uuid_uri()
#vco['vcard:hasAddress'] = vcard_address
d['vco'] = vco
orgs.append(d)
print orgs
g = Graph()
out = to_rdf(orgs, g)
#out.namespace_manager = ns_mgr
print out.serialize(format='turtle')
