added optional read-only flag with default True to API

biasanalyzer/api.py

@@ -40,7 +40,7 @@ def set_config(self, config_file_path: str):
             except ValidationError as ex:
                 notify_users(f"configuration yaml file is not valid with validation error: {ex}", level="error")
 
-    def set_root_omop(self):
+    def set_root_omop(self, read_only=True):
         if not self.config:
             notify_users(
                 "no valid configuration to set root OMOP CDM data. "
@@ -62,7 +62,7 @@ def set_root_omop(self):
             self.bias_db = BiasDatabase(":memory:", omop_db_url=db_url)
         elif db_type == "duckdb":
             db_path = self.config["root_omop_cdm_database"].get("database", ":memory:")
-            self.omop_cdm_db = OMOPCDMDatabase(db_path)
+            self.omop_cdm_db = OMOPCDMDatabase(db_path, read_only=read_only)
             self.bias_db = BiasDatabase(":memory:", omop_db_url=db_path)
         else:
             notify_users(f"Unsupported database type: {db_type}")

biasanalyzer/cohort.py

@@ -10,7 +10,7 @@
 from biasanalyzer.concept import ConceptHierarchy
 from biasanalyzer.config import load_cohort_creation_config
 from biasanalyzer.database import BiasDatabase, OMOPCDMDatabase
-from biasanalyzer.models import CohortDefinition, DOMAIN_MAPPING
+from biasanalyzer.models import DOMAIN_MAPPING, CohortDefinition
 from biasanalyzer.utils import clean_string, hellinger_distance, notify_users
 
 
@@ -60,7 +60,7 @@ def get_concept_stats(
         Get cohort concept statistics such as concept prevalence
         """
         if concept_type not in DOMAIN_MAPPING:
-            raise ValueError(f'input concept_type {concept_type} is not a valid concept type to get concept stats')
+            raise ValueError(f"input concept_type {concept_type} is not a valid concept type to get concept stats")
 
         cohort_stats = self.bias_db.get_cohort_concept_stats(
             self.cohort_id,

biasanalyzer/database.py

@@ -1,6 +1,5 @@
 # ruff: noqa: S608
 import gc
-import platform
 from datetime import datetime
 from typing import Optional
 
@@ -322,13 +321,13 @@ class OMOPCDMDatabase:
     _instance = None  # indicating a singleton with only one instance of the class ever created
     _database_type = None
 
-    def __new__(cls, *args, **kwargs):
+    def __new__(cls, db_url, read_only=True):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
-            cls._instance._initialize(*args, **kwargs)  # Initialize only once
+            cls._instance._initialize(db_url, read_only=read_only)  # Initialize only once
         return cls._instance
 
-    def _initialize(self, db_url):
+    def _initialize(self, db_url, read_only=True):
         if db_url.endswith(".duckdb"):
             # close any potential global connections if any
             for obj in gc.get_objects():  # pragma: no cover
@@ -340,11 +339,8 @@ def _initialize(self, db_url):
 
             # Handle DuckDB connection
             try:
-                if platform.system().lower() == "windows":  # pragma: no cover
-                    # it is critical to set duckdb connection to be read-only on windows platform
-                    self.engine = duckdb.connect(db_url, read_only=True)
-                else:
-                    self.engine = duckdb.connect(db_url)
+                # it is critical to set duckdb connection to be read-only on windows and Mac platforms
+                self.engine = duckdb.connect(db_url, read_only=read_only)
                 notify_users(f"Connected to the DuckDB database: {db_url}.")
             except duckdb.Error as e:  # pragma: no cover
                 notify_users(f"Failed to connect to DuckDB: {e}", level="error")
@@ -573,4 +569,4 @@ def close(self):
         else:
             self.engine.dispose()  # pragma: no cover
         OMOPCDMDatabase._instance = None
-        notify_users("Connection to the OMOP CDM database closed.")
+        notify_users("Connection to the OMOP CDM database closed.")

pyproject.toml

@@ -10,9 +10,9 @@ include = [
     {path = "biasanalyzer/sql_templates/*.sql", format=["sdist", "wheel"]}
 ]
 [tool.poetry.dependencies]
-python = ">=3.8.10,<3.13"
+python = ">=3.9,<3.13"
 duckdb = "^1.1.1"
-pandas = "2.0.3"
+pandas = "^2.1.4"
 
 scipy = [
     {version = ">=1.10.1,<1.11", markers = "python_version<'3.12'"},

scripts/ingest_csvs_to_omop_duckdb.py

@@ -11,45 +11,30 @@
 """
 
 import argparse
-import sys
 import csv
+import sys
 import time
 from pathlib import Path
 
 import duckdb
 
-
 FILENAME_STEM_TO_TABLE_NAME_MAPPING = {
     # 'demographics': 'person'
     # 'conditions': 'condition_occurrence'
     # 'drugs': 'drug_exposure'
     # 'procedures': 'procedure_occurrence'
     # 'visits': 'visit_occurrence'
-    'observations': 'observation'
+    "observations": "observation"
 }
 
 COLUMN_MAPPINGS = {
-    "person": {
-        "deid_pat_id": "person_id"
-    },
-    "condition_occurrence": {
-        "deid_pat_id": "person_id"
-    },
-    "drug_exposure": {
-        "deid_pat_id": "person_id"
-    },
-    "procedure_occurrence": {
-        "deid_pat_id": "person_id"
-    },
-    "visit_occurrence": {
-        "deid_pat_id": "person_id"
-    },
-    "observation": {
-        "deid_pat_id": "person_id"
-    },
-    "measurement": {
-        "deid_pat_id": "person_id"
-    },
+    "person": {"deid_pat_id": "person_id"},
+    "condition_occurrence": {"deid_pat_id": "person_id"},
+    "drug_exposure": {"deid_pat_id": "person_id"},
+    "procedure_occurrence": {"deid_pat_id": "person_id"},
+    "visit_occurrence": {"deid_pat_id": "person_id"},
+    "observation": {"deid_pat_id": "person_id"},
+    "measurement": {"deid_pat_id": "person_id"},
 }
 
 OMOP_TABLE_SCHEMAS = {
@@ -71,7 +56,7 @@
         "race_source_value",
         "race_source_concept_id",
         "ethnicity_source_value",
-        "ethnicity_source_concept_id"
+        "ethnicity_source_concept_id",
     ],
     "condition_occurrence": [
         "condition_occurrence_id",
@@ -89,9 +74,9 @@
         "visit_detail_id",
         "condition_source_value",
         "condition_source_concept_id",
-        "condition_status_source_value"
+        "condition_status_source_value",
     ],
-    'drug_exposure': [
+    "drug_exposure": [
         "drug_exposure_id",
         "person_id",
         "drug_concept_id",
@@ -114,9 +99,9 @@
         "drug_source_value",
         "drug_source_concept_id",
         "route_source_value",
-        "dose_unit_source_value"
+        "dose_unit_source_value",
     ],
-    'procedure_occurrence': [
+    "procedure_occurrence": [
         "procedure_occurrence_id",
         "person_id",
         "procedure_concept_id",
@@ -132,9 +117,9 @@
         "visit_detail_id",
         "procedure_source_value",
         "procedure_source_concept_id",
-        "modifier_source_value"
+        "modifier_source_value",
     ],
-    'visit_occurrence': [
+    "visit_occurrence": [
         "visit_occurrence_id",
         "person_id",
         "visit_concept_id",
@@ -151,9 +136,9 @@
         "admitted_from_source_value",
         "discharged_to_concept_id",
         "discharged_to_source_value",
-        "preceding_visit_occurrence_id"
+        "preceding_visit_occurrence_id",
     ],
-    'observation': [
+    "observation": [
         "observation_id",
         "person_id",
         "observation_concept_id",
@@ -174,28 +159,29 @@
         "qualifier_source_value",
         "value_source_value",
         "observation_event_id",
-        "obs_event_field_concept_id"
-    ]
+        "obs_event_field_concept_id",
+    ],
 }
 
+
 def load_csv_to_duckdb(con, csv_path: Path, table_name: str):
     """Load a single CSV file into DuckDB."""
     t0 = time.time()
     print(f"loading {table_name} from {csv_path}")
 
     # read and normalize header
-    with open(csv_path, "r", newline="") as f:
+    with open(csv_path, newline="") as f:
         reader = csv.reader(f)
         raw_header = next(reader)
 
     # normalize: lower case + strip quotes/spaces
-    raw_header = [h.strip().replace('"', '') for h in raw_header]
+    raw_header = [h.strip().replace('"', "") for h in raw_header]
     header = [h.lower() for h in raw_header]
-    print(f'normalized header: {header}')
+    print(f"normalized header: {header}")
 
     mapping = COLUMN_MAPPINGS.get(table_name, {})
     final_cols = [mapping.get(col, col) for col in header]
-    print(f'mapped header: {final_cols}')
+    print(f"mapped header: {final_cols}")
 
     expected = OMOP_TABLE_SCHEMAS.get(table_name, [])
     final_set = set(final_cols)
@@ -209,15 +195,15 @@ def load_csv_to_duckdb(con, csv_path: Path, table_name: str):
     extra = final_set - set(expected)
     if extra:
         print(f"WARNING: Extra columns in CSV for {table_name}: {sorted(extra)}")
-        print(f"Extra columns will NOT be ingested.")
+        print("Extra columns will NOT be ingested.")
 
     select_clauses = []
     for orig, new in zip(raw_header, final_cols):
         if new not in expected:
             # skip extra columns entirely
             continue
         if orig != new:
-            select_clauses.append(f'{orig} AS {new}')
+            select_clauses.append(f"{orig} AS {new}")
         else:
             select_clauses.append(orig)
 
@@ -268,9 +254,13 @@ def main():
         required=False,
         help="Directory containing OMOP vocabulary CSVs (concept, concept_relationship, etc.)",
     )
-    parser.add_argument("--output", type=Path,
-                        default=Path("Y:/OMOP_duckdb/omop.duckdb"),
-                        required=False, help="Output DuckDB file path")
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("Y:/OMOP_duckdb/omop.duckdb"),
+        required=False,
+        help="Output DuckDB file path",
+    )
 
     args = parser.parse_args()
 

tests/conftest.py

@@ -273,7 +273,7 @@ def test_db():
 
     # mock configuration file
     bias = BIAS(config_file_path=config_file)
-    bias.set_root_omop()
+    bias.set_root_omop(read_only=False)
 
     yield bias  # Provide the connection to the test
 

tests/query_based/test_cohort_creation.py

@@ -86,10 +86,10 @@ def test_cohort_creation_baseline(caplog, test_db):
 
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 5)
-    assert_equal(patient_ids, {'106', '108', '110', '111', '112'})
+    assert_equal(patient_ids, {"106", "108", "110", "111", "112"})
     # select two patients to check for cohort_start_date and cohort_end_date automatically computed
-    patient_106 = next(item for item in cohort.data if item["subject_id"] == '106')
-    patient_108 = next(item for item in cohort.data if item["subject_id"] == '108')
+    patient_106 = next(item for item in cohort.data if item["subject_id"] == "106")
+    patient_108 = next(item for item in cohort.data if item["subject_id"] == "108")
 
     # Replace dates with actual values from your test data
     assert_equal(
@@ -127,7 +127,7 @@ def test_cohort_creation_study(test_db):
     assert cohort.data is not None, "Cohort creation wrongly returned None data"
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 4)
-    assert_equal(patient_ids, {'108', '110', '111', '112'})
+    assert_equal(patient_ids, {"108", "110", "111", "112"})
 
 
 def test_cohort_creation_study2(caplog, test_db):
@@ -155,7 +155,7 @@ def test_cohort_creation_study2(caplog, test_db):
     assert cohort.data is not None, "Cohort creation wrongly returned None data"
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 1)
-    assert_equal(patient_ids, {'106'})
+    assert_equal(patient_ids, {"106"})
 
 
 def test_cohort_creation_all(caplog, test_db):
@@ -191,7 +191,7 @@ def test_cohort_creation_all(caplog, test_db):
     patient_ids = set([item["subject_id"] for item in cohort.data])
     print(f"patient_ids: {patient_ids}", flush=True)
     assert_equal(len(patient_ids), 2)
-    assert_equal(patient_ids, {'108', '110'})
+    assert_equal(patient_ids, {"108", "110"})
 
 
 def test_cohort_creation_multiple_temporary_groups_with_no_operator(test_db):
@@ -214,7 +214,7 @@ def test_cohort_creation_multiple_temporary_groups_with_no_operator(test_db):
     patient_ids = set([item["subject_id"] for item in cohort.data])
     print(f"patient_ids: {patient_ids}", flush=True)
     assert_equal(len(patient_ids), 2)
-    assert_equal(patient_ids, {'108', '110'})
+    assert_equal(patient_ids, {"108", "110"})
 
 
 def test_cohort_creation_mixed_domains(test_db):
@@ -242,7 +242,7 @@ def test_cohort_creation_mixed_domains(test_db):
     patient_ids = set([item["subject_id"] for item in cohort.data])
     print(f"patient_ids: {patient_ids}", flush=True)
     assert_equal(len(patient_ids), 3)
-    assert_equal(patient_ids, {'1', '2', '6'})
+    assert_equal(patient_ids, {"1", "2", "6"})
     start_dates = [item["cohort_start_date"] for item in cohort.data]
     assert_equal(len(start_dates), 3)
     assert_equal(start_dates, [datetime.date(2020, 6, 1), datetime.date(2020, 6, 1), datetime.date(2018, 1, 1)])
@@ -356,10 +356,10 @@ def test_cohort_creation_negative_instance(test_db):
 
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 6)  # Female patients 1, 2, 3, 5
-    assert_equal(patient_ids, {'1', '2', '3', '5', '6', '7'})
+    assert_equal(patient_ids, {"1", "2", "3", "5", "6", "7"})
 
     # Verify dates for a specific patient (e.g., patient 1 with last diabetes diagnosis)
-    patient_1 = next(item for item in cohort.data if item["subject_id"] == '1')
+    patient_1 = next(item for item in cohort.data if item["subject_id"] == "1")
     assert_equal(
         patient_1["cohort_start_date"],
         datetime.date(2020, 6, 1),
@@ -392,10 +392,10 @@ def test_cohort_creation_offset(test_db):
 
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 6)  # Female patients 1, 2, 3, 5
-    assert_equal(patient_ids, {'1', '2', '3', '5', '6', '7'})
+    assert_equal(patient_ids, {"1", "2", "3", "5", "6", "7"})
 
     # Verify dates for a specific patient (e.g., patient 1 with offset)
-    patient_1 = next(item for item in cohort.data if item["subject_id"] == '1')
+    patient_1 = next(item for item in cohort.data if item["subject_id"] == "1")
     # Diabetes on 2020-06-01: -730 days = 2018-06-02, +180 days = 2020-11-28
     assert_equal(
         patient_1["cohort_start_date"],
@@ -435,10 +435,10 @@ def test_cohort_creation_negative_instance_offset(test_db):
 
     patient_ids = set([item["subject_id"] for item in cohort.data])
     assert_equal(len(patient_ids), 6)
-    assert_equal(patient_ids, {'1', '2', '3', '5', '6', '7'})
+    assert_equal(patient_ids, {"1", "2", "3", "5", "6", "7"})
 
     # Verify dates for a specific patient (e.g., patient 1 with last diabetes and offset)
-    patient_1 = next(item for item in cohort.data if item["subject_id"] == '1')
+    patient_1 = next(item for item in cohort.data if item["subject_id"] == "1")
     # Last diabetes on 2020-06-01: +180 days = 2020-11-28
     assert_equal(
         patient_1["cohort_start_date"],