Merge branch 'release/4.1.0'

CHANGELOG.md

@@ -1,8 +1,8 @@
 # Changelog
 
-## [Unreleased](https://github.com/dermatologist/pyomop/tree/HEAD)
+## [4.0.0](https://github.com/dermatologist/pyomop/tree/4.0.0) (2023-11-19)
 
-[Full Changelog](https://github.com/dermatologist/pyomop/compare/3.2.0...HEAD)
+[Full Changelog](https://github.com/dermatologist/pyomop/compare/3.2.0...4.0.0)
 
 **Closed issues:**
 

README.md

@@ -27,64 +27,67 @@ pip install pyomop
 pip install -e .
 ```
 
-## Usage >= 4.0.0 (Async)
+## Usage >= 4.0.0 (Async) Example
 ```
 from pyomop import CdmEngineFactory, CdmVocabulary, CdmVector, Cohort, Vocabulary, metadata
 from sqlalchemy.future import select
 import datetime
 import asyncio
 
-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
-asyncio.run(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
-
-# Close session
-await session.close()
-await engine.dispose()
-
-# Convert result to a pandas dataframe
-vec = CdmVector()
-vec.result = result
-print(vec.df.dtypes)
-
-# Execute SQL statetements
-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)
-
+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())
 ```
 
 ## Usage <=3.2.0

src/pyomop/main.py

@@ -1,4 +1,5 @@
 import click
+import asyncio
 from . import CdmEngineFactory
 from . import CdmVocabulary
 from . import metadata
@@ -29,12 +30,11 @@ def cli(verbose, create, dbtype, host, port, user, pw, name, schema, vocab):
         print("verbose")
     if create:
         cdm = CdmEngineFactory(dbtype, host, port, user, pw, name, schema)
-        engine = cdm.engine
-        metadata.create_all(engine)
+        asyncio.run(cdm.init_models(metadata))
     if vocab is not '':
         cdm = CdmEngineFactory(dbtype, host, port, user, pw, name, schema)
-        vocab = CdmVocabulary(cdm)
-        vocab.create_vocab(vocab)
+        _vocab = CdmVocabulary(cdm)
+        _vocab.create_vocab(vocab)
         print("Done")
 
 def main_routine():

t_install.py

@@ -37,10 +37,6 @@ async def main():
     print(cohort)
     assert cohort.subject_id == 100
 
-    # Close session
-    await session.close()
-    await engine.dispose()
-
     # Convert result to a pandas dataframe
     vec = CdmVector()
     vec.result = result
@@ -55,5 +51,9 @@ async def main():
         print(row)
 
 
+    # Close session
+    await session.close()
+    await engine.dispose()
+
 # Run the main function
 asyncio.run(main())