本文整理汇总了Python中rdflib.graph.ConjunctiveGraph.subject_objects方法的典型用法代码示例。如果您正苦于以下问题：Python ConjunctiveGraph.subject_objects方法的具体用法？Python ConjunctiveGraph.subject_objects怎么用？Python ConjunctiveGraph.subject_objects使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类rdflib.graph.ConjunctiveGraph的用法示例。
在下文中一共展示了ConjunctiveGraph.subject_objects方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import subject_objects [as 别名]
def main():
parser = argparse.ArgumentParser(
description='OMIA integration test',
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument(
'--input', '-i', type=str, required=True,
help='Location of input ttl file')
args = parser.parse_args()
graph = ConjunctiveGraph()
graph.parse(args.input, format=rdflib_util.guess_format(args.input))
model_of = URIRef('http://purl.obolibrary.org/obo/RO_0003301')
models = graph.subject_objects(model_of)
model_len = len(list(models))
if model_len < EXPECTED_PAIRS:
logger.error("Not enough model_of predicates in graph:"
" {} expected {} check omia log for"
" warnings".format(model_len, EXPECTED_PAIRS))
exit(1)
omim_diseases = graph.objects(
subject=URIRef('https://monarchinitiative.org/model/OMIA-breed:18'),
predicate=model_of
)
if list(omim_diseases) != [URIRef('http://purl.obolibrary.org/obo/OMIM_275220')]:
logger.error("Missing breed to omim triple for {}".format('OMIA-breed:18'))
exit(1)
logger.info("PASSED")示例2: main
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import subject_objects [as 别名]
def main():
parser = argparse.ArgumentParser(
description='OMIA integration test',
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument(
'--input', '-i', type=str, required=True, help='Location of input ttl file')
args = parser.parse_args()
graph = ConjunctiveGraph()
graph.parse(args.input, format=rdflib_util.guess_format(args.input))
# "is model of": "RO:0003301"
# is_model_of = URIRef('OBO:RO_0003301')
is_model_of = URIRef('http://purl.obolibrary.org/obo/RO_0003301')
# if we curie_map & globaltt here we could ...
# (pfx lcl) = globaltt["is model of"].split(':')
# iri = curie_map[pfx] + '_'.join((pfx, lcl))
# is_model_of = URIRef(iri)
models = graph.subject_objects(is_model_of)
model_len = len(set(list(models)))
if model_len < EXPECTED_PAIRS:
LOG.error(
"Not enough <RO:is model of> predicates in graph: found {}, "
"expected {} check omia log for warnings".format(
model_len, EXPECTED_PAIRS))
exit(1)
# else:
# LOG.info(
# "Found {} model_of predicates in graph, expected at least: {}".format(
# model_len, EXPECTED_PAIRS))
breed = 'https://monarchinitiative.org/model/OMIA-breed:758'
disease = 'http://omim.org/entry/305100'
omim_diseases = graph.objects(
subject=URIRef(breed),
predicate=is_model_of
)
if list(omim_diseases) != [URIRef(disease)]:
LOG.error("Missing breed to omim triple for %s", breed)
LOG.error(list(omim_diseases))
exit(1)
LOG.info("PASSED")示例3: pprint
# 需要导入模块: from rdflib.graph import ConjunctiveGraph [as 别名]
# 或者: from rdflib.graph.ConjunctiveGraph import subject_objects [as 别名]
# just think .whatever((s, p, o))
# here we report on what we know
pprint(list(primer.subjects()))
pprint(list(primer.predicates()))
pprint(list(primer.objects()))
# and other things that make sense
# what do we know about pat?
pprint(list(primer.predicate_objects(myNS.pat)))
# who is what age?
pprint(list(primer.subject_objects(myNS.age)))
# Okay, so lets now work with a bigger
# dataset from the example, and start
# with a fresh new graph.
primer = ConjunctiveGraph()
# Lets start with a verbatim string straight from the primer text:
mySource = """