ruff formaiting

Author: staskh

iglu_python/utils.py

@@ -232,6 +232,10 @@ def CGMS2DayByDay(
     interp_data = interp_data.reshape(n_days, n_points_per_day)
 
     # Get actual dates
+    if IGLU_R_COMPATIBLE:
+        # convert start_time into naive datetime
+        start_time = start_time.tz_localize(None)
+        
     actual_dates = [start_time + pd.Timedelta(days=i) for i in range(n_days)]
 
     return interp_data, actual_dates, dt0

notebooks/episode_calculation_evaluation.ipynb

@@ -88,7 +88,7 @@
    "source": [
     "@iglu_py.bridge.df_conversion\n",
     "def my_episode_calculation(data: pd.DataFrame, **kwargs):\n",
-    "    results = bridge.iglu_r.episode_calculation(data, **kwargs)\n",
+    "    results = iglu_py.bridge.iglu_r.episode_calculation(data, **kwargs)\n",
     "\n",
     "    if isinstance(results, pd.DataFrame):\n",
     "        return results\n",

tests/test_CGMS2DayByDay.py

@@ -47,7 +47,7 @@ def test_CGMS2DayByDay_iglu_r_compatible(scenario):
     expected_results = scenario["results"]
     expected_interp_data = np.array(expected_results["gd2d"], dtype=np.float64)
     expected_interp_data = np.where(
-        expected_interp_data == None, np.nan, expected_interp_data
+        expected_interp_data is None, np.nan, expected_interp_data
     )
     expected_interp_data = expected_interp_data.astype(np.float64)
     expected_actual_dates = [pd.Timestamp(d) for d in expected_results["actual_dates"]]

tests/test_conga.py

@@ -109,7 +109,7 @@ def test_conga_empty():
     """Test CONGA with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.conga(empty_data)
+        iglu.conga(empty_data)
 
 
 def test_conga_constant_glucose():

tests/test_ea1c.py

@@ -118,7 +118,7 @@ def test_ea1c_empty():
     """Test eA1C with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.ea1c(empty_data)
+        iglu.ea1c(empty_data)
 
 
 def test_ea1c_constant_glucose():

tests/test_episode_calculation.py

@@ -33,7 +33,7 @@ def test_episode_calculation_iglu_r_compatible(scenario):
     expected_results = scenario["results"]
     if "data" in expected_results:
         # this is extended expected result, with two separate dataframes
-        assert kwargs["return_data"] == True
+        assert kwargs["return_data"]
         expected_episodes_df = pd.DataFrame(expected_results['episodes']).reset_index(drop=True)
         expected_data_df = pd.DataFrame(expected_results['data']).reset_index(drop=True)
     else :
@@ -46,15 +46,15 @@ def test_episode_calculation_iglu_r_compatible(scenario):
     if "time" in df.columns:
         df["time"] = pd.to_datetime(df["time"])
 
-    if "return_data" in kwargs and kwargs["return_data"] == True :
+    if "return_data" in kwargs and kwargs["return_data"]:
         result_episodes_df, result_data_df = iglu.episode_calculation(df, **kwargs)
     else:
         result_episodes_df = iglu.episode_calculation(df, **kwargs)
 
     assert result_episodes_df is not None
 
     # Compare DataFrames with precision to 0.001 for numeric columns
-    if "return_data" in kwargs and kwargs["return_data"] == True :
+    if "return_data" in kwargs and kwargs["return_data"] :
         flag_columns = ['lv1_hypo', 'lv2_hypo', 'lv1_hyper', 'lv2_hyper', 'ext_hypo', 'lv1_hypo_excl', 'lv1_hyper_excl']
         for col in flag_columns:
             result_data_df[col] = result_data_df[col].astype(bool)
@@ -207,7 +207,7 @@ def test_episode_calculation_empty():
     """Test episode calculation with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.episode_calculation(empty_data)
+        iglu.episode_calculation(empty_data)
 
 
 def test_episode_calculation_constant_glucose():

tests/test_grade_hypo.py

@@ -113,7 +113,7 @@ def test_grade_hypo_empty():
     """Test GRADE hypoglycemia with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.grade_hypo(empty_data)
+        iglu.grade_hypo(empty_data)
 
 
 def test_grade_hypo_constant_glucose():

tests/test_gri.py

@@ -113,7 +113,7 @@ def test_gri_empty():
     """Test GRI with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.gri(empty_data)
+        iglu.gri(empty_data)
 
 
 def test_gri_constant_glucose():

tests/test_j_index.py

@@ -120,7 +120,7 @@ def test_j_index_empty_data():
     # Empty DataFrame
     data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.j_index(data)
+        iglu.j_index(data)
 
 
 def test_j_index_missing_values():

tests/test_median_glu.py

@@ -110,7 +110,7 @@ def test_median_glu_empty():
     """Test median_glu with empty data"""
     empty_data = pd.DataFrame(columns=["id", "time", "gl"])
     with pytest.raises(ValueError):
-        result = iglu.median_glu(empty_data)
+        iglu.median_glu(empty_data)
 
 
 def test_median_glu_constant_glucose():