def create_phenotype_terms():
print('Creating phenotype terms')
handle = os.path.join(FIXTURE_DIR, 'hpo.json')
with open(handle, 'r', encoding='utf-8') as f:
hpo_json = f.read()
g = obograph_util.convert_json_object(json.loads(hpo_json))
ont = Ontology(handle=handle, payload=g)
phenotype_terms = []
for class_node in get_classes(ont):
phenotype_terms.append(
PhenotypeTerm(term_id=class_node[0], label=class_node[1]))
PhenotypeTerm.objects.bulk_create(phenotype_terms)
def create_study_terms():
print('Creating study terms')
handle = os.path.join(FIXTURE_DIR, 'edda.json')
with open(handle, 'r', encoding='utf-8') as f:
edda_json = f.read()
g = obograph_util.convert_json_object(json.loads(edda_json))
ont = Ontology(handle=handle, payload=g)
study_terms = []
for class_node in get_classes(ont):
study_terms.append(
StudyTerm(term_id=class_node[0], label=class_node[1]))
StudyTerm.objects.bulk_create(study_terms)
def create_disease_terms():
print('Removing existing disease terms')
with connection.cursor() as cursor:
cursor.execute("truncate core_diseaseterm cascade")
print('Creating hdo disease terms')
handle = os.path.join(FIXTURE_DIR, 'hdo.json')
with open(handle, 'r', encoding='utf-8') as f:
hdo_json = f.read()
g = obograph_util.convert_json_object(json.loads(hdo_json))
ont = Ontology(handle=handle, payload=g)
disease_terms = []
for class_node in get_classes(ont):
disease_terms.append(DiseaseTerm(term_id=class_node[0], label=class_node[1]))
DiseaseTerm.objects.bulk_create(disease_terms)
def get(self, id):
"""
Returns evidence graph as a png
TODO - requires matplotlib which is hard to install
"""
args = parser.parse_args()
assoc = get_association(id, user_agent=USER_AGENT)
eg = {'graphs': [assoc.get('evidence_graph')]}
digraph = convert_json_object(eg)
#fp = tempfile.TemporaryFile()
nx.draw(digraph)
fn = '/tmp/' + id + '.png' # TODO
plt.savefig(fn)
return send_file(fn)
def test_obograph_to_assoc_results(self):
# Hits the mock solr manager in setup_class
results = self.golr_query.exec()
assoc = results['associations'][0] if len(
results['associations']) > 0 else {}
eg = {'graphs': [assoc.get('evidence_graph')]}
digraph = convert_json_object(eg, reverse_edges=False)['graph']
association_results = obograph_to_assoc_results(digraph)
results = json.dumps(
association_results,
default=lambda obj: getattr(obj, '__dict__', str(obj)))
expected_fh = os.path.join(os.path.dirname(__file__),
'resources/expected/test-evidence.json')
expected_results = json.dumps(json.load(open(expected_fh)))
assert results == expected_results
def create_gene_terms():
print('Creating gene terms')
handle = os.path.join(FIXTURE_DIR, 'hgnc.json')
with open(handle, 'r', encoding='utf-8') as f:
hgnc_json = f.read()
g = obograph_util.convert_json_object(json.loads(hgnc_json))
ont = Ontology(handle=handle, payload=g)
gene_terms = []
for n_id in ont.nodes():
n_dict = ont.node(n_id)
if 'type' in n_dict:
if ont.node_type(n_id) == 'CLASS':
for t in n_dict['meta']['basicPropertyValues']:
if t['pred'] == 'http://ncicb.nci.nih.gov/xml/owl/EVS/Hugo.owl#Approved_Symbol':
symbol = t['val']
if not symbol.endswith('~withdrawn'):
# print('{} {}'.format(n_id, symbol))
gene_terms.append(
GeneTerm(term_id=n_id, label=symbol))
break
GeneTerm.objects.bulk_create(gene_terms)
def create_ontology_from_obograph(og):
ont = None
g = obograph_util.convert_json_object(og)
ont = Ontology(handle=None, payload=g)
return ont
