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")
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)
else:
logger.info("PASSED")
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")
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")
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")
pprint(list(primer)) # 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 = """ @prefix : <http://www.w3.org/2000/10/swap/Primer#>. @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
# 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 = """
