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from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
from sqlalchemy.future import select
import datetime
import asyncio
async def main():
cdm = CdmEngineFactory() # Creates SQLite database by default
# Postgres example (db='mysql' also supported)
# cdm = CdmEngineFactory(db='pgsql', host='', port=5432,
# user='', pw='',
# name='', schema='cdm6')
engine = cdm.engine
# Create Tables if required
await cdm.init_models(metadata)
# Create vocabulary if required
vocab = CdmVocabulary(cdm)
# vocab.create_vocab('/path/to/csv/files') # Uncomment to load vocabulary csv files
# Add a cohort
async with cdm.session() as session:
async with session.begin():
session.add(Cohort(cohort_definition_id=2, subject_id=100,
cohort_end_date=datetime.datetime.now(),
cohort_start_date=datetime.datetime.now()))
await session.commit()
# Query the cohort
stmt = select(Cohort).where(Cohort.subject_id == 100)
result = await session.execute(stmt)
for row in result.scalars():
print(row)
assert row.subject_id == 100
# Query the cohort pattern 2
cohort = await session.get(Cohort, 1)
print(cohort)
assert cohort.subject_id == 100
# Convert result to a pandas dataframe
vec = CdmVector()
vec.result = result
print(vec.df.dtypes)
result = await vec.sql_df(cdm, 'TEST') # TEST is defined in sqldict.py
for row in result:
print(row)
result = await vec.sql_df(cdm, query='SELECT * from cohort')
for row in result:
print(row)
# Close session
await session.close()
await engine.dispose()
# Run the main function
asyncio.run(main())
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