def export_terms(dest_star, job):
v = self.variable_table(dest_star)
keys = job.concepts['keys']
names = job.concepts['names']
paths = I2B2MetaData.keys_to_paths(keys)
v.create(bind=dest_db)
dest_db.execute(v.insert(),
[dict(id=id,
item_key=key,
concept_path=path,
name_char=name,
name=strip_counts(name))
for (id, (path, key, name)) in
enumerate(zip(paths, keys, names))])
[(q_cd, result_cd), (q_md, result_md)] = job.term_info()
cd = dest_star.tables['concept_dimension']
md = dest_star.tables['modifier_dimension']
values = lambda _: dict(concept_path=bindparam('concept_path'),
concept_cd=bindparam('concept_cd'))
tc.copy_in_chunks(dest_db, result_cd, cd,
'concept_dimension', [],
values=values)
values = lambda _: dict(concept_path=bindparam('modifier_path'),
concept_cd=bindparam('modifer_cd'))
tc.copy_in_chunks(dest_db, result_cd, md,
'modifier_dimension', [])
def export_patients(dest_star, job):
pd = dest_star.tables['patient_dimension']
vd = dest_star.tables['visit_dimension']
[(pat_q, pat_data), (enc_q, enc_data)] = job.demographics()
tc.copy_in_chunks(dest_db, pat_data, pd,
'demographics (patient_dimension)', [])
tc.copy_in_chunks(dest_db, enc_data, vd,
'demographics (visit_dimension)', [])
return dest_db.execute(
'select count(*) from patient_dimension').scalar()
def export_data(dest_star, job):
q, data = job.patient_data()
obs = dest_star.tables['observation_fact']
dest_db.execute(obs.delete())
tc.copy_in_chunks(dest_db, data, obs,
'patient data', [])
