def mongo_process_operations(expr_obj_list,
db,
job_id,
phenotype: PhenotypeModel,
phenotype_id,
phenotype_owner,
c: PhenotypeOperations,
final=False):
"""
Use MongoDB aggregation to evaluate NLPQL expressions.
"""
log('mongo_process_operations expr_object_list: ')
for expr_obj in expr_obj_list:
log(expr_obj)
context_var = phenotype.context.lower()
if 'document' == context_var:
# document IDs are in the report_id field
context_field = 'report_id'
else:
# patient IDs are in the subject field
context_field = 'subject'
# setup access to the Mongo collection
client = util.mongo_client()
mongo_db_obj = client[util.mongo_db]
mongo_collection_obj = mongo_db_obj['phenotype_results']
# ensure integer job_id; expression evaluator needs to lookup results
# by job_id, but a job id of 42 is different from a job_id of '42'
job_id = int(job_id)
try:
is_final_save = c['final']
for expr_obj in expr_obj_list:
# the 'is_final' flag only applies to the last subexpression
if expr_obj != expr_obj_list[-1]:
is_final = False
else:
is_final = is_final_save
# evaluate the (sub)expression in expr_obj
eval_result = expr_eval.evaluate_expression(expr_obj,
job_id,
context_field,
mongo_collection_obj)
# query MongoDB to get result docs
cursor = mongo_collection_obj.find({'_id': {'$in': eval_result.doc_ids}})
# initialize for MongoDB result document generation
phenotype_info = expr_result.PhenotypeInfo(
job_id=job_id,
phenotype_id=phenotype_id,
owner=phenotype_owner,
context_field=context_field,
is_final=is_final
)
# generate result documents
if expr_eval.EXPR_TYPE_MATH == eval_result.expr_type:
output_docs = expr_result.to_math_result_docs(eval_result,
phenotype_info,
cursor)
else:
assert expr_eval.EXPR_TYPE_LOGIC == eval_result.expr_type
# flatten the result set into a set of Mongo documents
doc_map, oid_list_of_lists = expr_eval.flatten_logical_result(eval_result,
mongo_collection_obj)
output_docs = expr_result.to_logic_result_docs(eval_result,
phenotype_info,
doc_map,
oid_list_of_lists)
if len(output_docs) > 0:
log('***** mongo_process_operations: writing {0} ' \
'output_docs *****'.format(len(output_docs)))
try:
mongo_collection_obj.insert_many(output_docs)
except pymongo.errors.BulkWriteError as e:
log('****** mongo_process_operations: ' \
'mongo insert_many failure ******')
log(e)
else:
log('mongo_process_operations ({0}): ' \
'no phenotype matches on "{1}".'.format(eval_result.expr_type,
eval_result.expr_text))
except Exception as exc:
log(exc, ERROR)
finally:
client.close()
def mongo_process_operations(expr_obj_list,
db,
job_id,
phenotype: PhenotypeModel,
phenotype_id,
phenotype_owner,
c: PhenotypeOperations,
final=False):
"""
Use MongoDB aggregation to evaluate NLPQL expressions.
"""
print('mongo_process_operations expr_object_list: ')
for expr_obj in expr_obj_list:
print(expr_obj)
context_var = phenotype.context.lower()
if 'document' == context_var:
# document IDs are in the report_id field
context_field = 'report_id'
else:
# patient IDs are in the subject field
context_field = 'subject'
# setup access to the Mongo collection
client = MongoClient(util.mongo_host, util.mongo_port)
mongo_db_obj = client[util.mongo_db]
mongo_collection_obj = mongo_db_obj['phenotype_results']
is_final_save = c['final']
for expr_obj in expr_obj_list:
# the 'is_final' flag only applies to the last subexpression
if expr_obj != expr_obj_list[-1]:
is_final = False
else:
is_final = is_final_save
# evaluate the (sub)expression in expr_obj
eval_result = expr_eval.evaluate_expression(expr_obj, job_id,
context_field,
mongo_collection_obj)
# query MongoDB to get result docs
cursor = mongo_collection_obj.find(
{'_id': {
'$in': eval_result.doc_ids
}})
# initialize for MongoDB result document generation
phenotype_info = expr_result.PhenotypeInfo(job_id=job_id,
phenotype_id=phenotype_id,
owner=phenotype_owner,
context_field=context_field,
is_final=is_final)
# generate result documents
if expr_eval.EXPR_TYPE_MATH == eval_result.expr_type:
output_docs = expr_result.to_math_result_docs(
eval_result, phenotype_info, cursor)
else:
assert expr_eval.EXPR_TYPE_LOGIC == eval_result.expr_type
# flatten the result set into a set of Mongo documents
doc_map, oid_list_of_lists = expr_eval.flatten_logical_result(
eval_result, mongo_collection_obj)
output_docs = expr_result.to_logic_result_docs(
eval_result, phenotype_info, doc_map, oid_list_of_lists)
if len(output_docs) > 0:
mongo_collection_obj.insert_many(output_docs)
else:
print('mongo_process_operations ({0}): ' \
'no phenotype matches on "{1}".'.format(eval_result.expr_type,
eval_result.expr_text))
client.close()
def mongo_process_operations(expr_obj_list,
db,
job_id,
phenotype: PhenotypeModel,
phenotype_id,
phenotype_owner,
c: PhenotypeOperations,
final=False):
"""
Use MongoDB aggregation to evaluate NLPQL expressions.
"""
print('mongo_process_operations expr_object_list: ')
for expr_obj in expr_obj_list:
print(expr_obj)
context_var = phenotype.context.lower()
if 'document' == context_var:
# document IDs are in the report_id field
context_field = 'report_id'
else:
# patient IDs are in the subject field
context_field = 'subject'
# setup access to the Mongo collection
client = MongoClient(util.mongo_host, util.mongo_port)
mongo_db_obj = client[util.mongo_db]
mongo_collection_obj = mongo_db_obj['phenotype_results']
is_final_save = c['final']
for expr_obj in expr_obj_list:
# the 'is_final' flag only applies to the last subexpression
if expr_obj != expr_obj_list[-1]:
is_final = False
else:
is_final = is_final_save
# evaluate the (sub)expression in expr_obj
eval_result = expr_eval.evaluate_expression(expr_obj,
job_id,
context_field,
mongo_collection_obj)
# query MongoDB to get result docs
cursor = mongo_collection_obj.find({'_id': {'$in': eval_result.doc_ids}})
# initialize for MongoDB result document generation
phenotype_info = expr_result.PhenotypeInfo(
job_id = job_id,
phenotype_id = phenotype_id,
owner = phenotype_owner,
context_field = context_field,
is_final = is_final
)
# generate result documents
if expr_eval.EXPR_TYPE_MATH == eval_result.expr_type:
output_docs = expr_result.to_math_result_docs(eval_result,
phenotype_info,
cursor)
else:
assert expr_eval.EXPR_TYPE_LOGIC == eval_result.expr_type
# flatten the result set into a set of Mongo documents
doc_map, oid_list_of_lists = expr_eval.flatten_logical_result(eval_result,
mongo_collection_obj)
output_docs = expr_result.to_logic_result_docs(eval_result,
phenotype_info,
doc_map,
oid_list_of_lists)
if len(output_docs) > 0:
mongo_collection_obj.insert_many(output_docs)
else:
print('mongo_process_operations ({0}): ' \
'no phenotype matches on "{1}".'.format(eval_result.expr_type,
eval_result.expr_text))
client.close()