def query_scigraph_for_curies(self, label: str, prefixes:List[str] = ['UBERON', 'ILX']) -> list:
curies = []
# return []
# for prefix in prefixes:
# BUG: prefixes cant be used because it gives random errors if the prefix isn't exact
for prefix in prefixes:
neighbors = [v.OntTerm for v in OntTerm.query(label=label.strip(), prefix=prefix)]
if not neighbors:
continue
for neighbor in neighbors:
oid = OntId(neighbor)
curies.append(oid.curie)
return curies
def get_curies_from_scigraph_label_query(label: str,
prefixes: List[str] = [
'UBERON', 'ILX', 'PAXRAT'
]) -> list:
curies = set()
for prefix in prefixes:
# TODO: if not stipped the label will return nothing. Seems to be trailing spaces
neighbors = [
v.OntTerm
for v in OntTerm.query(label=label.strip(), prefix=prefix)
]
if not neighbors:
continue
for neighbor in neighbors:
curies.add(OntId(neighbor).curie)
return list(curies)
def main():
sparc_terms = convert_view_text_to_dict()
print('Linearizing graph')
sparc_terms_unpaired = list(linearize_graph(sparc_terms))
print('Adding ids to terms list')
sparc_terms_paired = pair_terms(sparc_terms_unpaired)
# embed()
avoid_in_bl = [
'Gross anatomy',
'Internal anatomy',
'Segmental Anatomy',
'Atlas Nomenclature',
'Allen Mouse Brainstem',
'Paxinos Rar Brainstem'
'Berman Cat Brainstem',
'Nerves (also includes Cranial)',
'UBERON',
]
accepted_prefixes_for_bl = [
'UBERON',
'PAXRAT',
]
print('Building sparc terms list txt')
sparc_terms_text, sparc_terms_bl = '', []
for term in sparc_terms_paired:
sparc_terms_text += ' ' * 4 * term.tier_level + '\t'.join(
[term.label] + term.curies) + '\n'
if not term.curies and term.label.strip() not in avoid_in_bl:
sparc_terms_bl.append(term.label)
# Labels with no ID
# Slow as heck
print(
f'Building list for terms with no IDs with len:{len(sparc_terms_bl)}')
for i, bl_term in enumerate(sparc_terms_bl):
neighbors = OntTerm.search(bl_term.split(' ')[0])
for neighbor in neighbors:
if set(neighbor.split(' ')) == set(bl_term.split(' ')):
# if neighbor.curie.split(':')[0] in accepted_prefixes_for_bl:
view_text_bl[i] += '\t' + neighbor.curie
# Original IDs with wrong label
print('Building list for terms with wrong IDs')
sparc_terms_with_bad_ids = []
for term in sparc_terms_unpaired:
for curie in term.curies:
onts = OntTerm.query(curie=curie)
if not onts:
continue
for ont in onts:
if term.label.lower().strip() != ont.label.lower().strip():
sparc_terms_with_bad_ids.append({
**vars(term), 'searched_label':
ont.label
})
# embed()
with open(resources / 'sparc_terms_populated3.txt', 'w') as outfile:
outfile.write(sparc_terms_text)
with open(resources / 'sparc_terms_unpopulated_terms3.txt',
'w') as outfile:
outfile.write('\n'.join(sorted(sparc_terms_bl)))
with open(resources / 'sparc_terms_with_bad_ids3.json', 'w') as outfile:
json.dump(sparc_terms_with_bad_ids, outfile, indent=4)