ruff reformating

Author: staskh

iglu_python/adrr.py

@@ -1,10 +1,10 @@
-import warnings
 
 import numpy as np
 import pandas as pd
 
 from .utils import check_data_columns
 
+
 def adrr(data: pd.DataFrame|pd.Series) -> pd.DataFrame|float:
     """
     Calculate average daily risk range (ADRR)
@@ -83,7 +83,7 @@ def adrr_single(data: pd.DataFrame|pd.Series) -> float:
     data_filtered = data.dropna()
     if len(data_filtered) == 0:
         return np.nan
-    
+
     # Group by date and calculate daily risk for each day
     daily_risks = data_filtered.groupby(data_filtered.index.date).apply(
         lambda x: _calculate_daily_risk(x)

iglu_python/ea1c.py

@@ -72,9 +72,9 @@ def ea1c_single(data: pd.Series) -> float:
     """Calculate eA1C for a single subject"""
     if not isinstance(data, pd.Series):
         raise ValueError("Data must be a pandas Series")
-    
+
     data = data.dropna()
     if len(data) == 0:
         return np.nan
 
-    return (46.7 + data.mean()) / 28.7
+    return (46.7 + data.mean()) / 28.7

iglu_python/grade_eugly.py

@@ -9,7 +9,7 @@
 
 def grade_eugly(
     data: Union[pd.DataFrame, pd.Series, np.ndarray, list], lower: int = 70, upper: int = 140
-) -> pd.DataFrame|float:  
+) -> pd.DataFrame|float:
     """
     Calculate percentage of GRADE score attributable to target range.
 
@@ -63,7 +63,7 @@ def grade_eugly(
         if isinstance(data, (np.ndarray, list)):
             data = pd.Series(data)
         return grade_eugly_single(data, lower, upper)
-    
+
     # Handle DataFrame input
     data = check_data_columns(data)
 
@@ -91,4 +91,4 @@ def grade_eugly_single(data: pd.Series, lower: int = 70, upper: int = 140) -> fl
         return np.nan
 
     eugly_percent = (np.sum(grade_scores[in_range]) / total_grade) * 100
-    return eugly_percent
+    return eugly_percent

iglu_python/grade_hyper.py

@@ -75,12 +75,12 @@ def grade_hyper_single(data: pd.Series, upper: int = 140) -> float:
 
     # Calculate GRADE scores
     grade_scores = _grade_formula(data)
-    
+
     # Calculate percentage above upper bound
     above_upper = data > upper
     total_grade = np.sum(grade_scores)
     if total_grade == 0:
         return np.nan
 
     hyper_percent = (np.sum(grade_scores[above_upper]) / total_grade) * 100
-    return hyper_percent
+    return hyper_percent

iglu_python/grade_hypo.py

@@ -58,7 +58,7 @@ def grade_hypo(data: Union[pd.DataFrame, pd.Series, np.ndarray, list], lower: in
         if isinstance(data, (np.ndarray, list)):
             data = pd.Series(data)
         return grade_hypo_single(data, lower)
-    
+
     # Handle DataFrame input
     data = check_data_columns(data)
 
@@ -77,12 +77,12 @@ def grade_hypo_single(data: pd.Series, lower: int = 80) -> float:
 
     # Calculate GRADE scores
     grade_scores = _grade_formula(data)
-    
+
     # Calculate percentage below lower bound
     below_lower = data < lower
     total_grade = np.sum(grade_scores)
     if total_grade == 0:
         return np.nan
 
     hypo_percent = (np.sum(grade_scores[below_lower]) / total_grade) * 100
-    return hypo_percent
+    return hypo_percent

iglu_python/gvp.py

@@ -63,7 +63,7 @@ def gvp(data: Union[pd.DataFrame, pd.Series]) -> pd.DataFrame|float:
         return gvp_single(data)
 
     # Handle DataFrame input
-    data = check_data_columns(data) 
+    data = check_data_columns(data)
     data.set_index("time", inplace=True, drop=True)
 
     out = data.groupby('id').agg(
@@ -138,4 +138,4 @@ def gvp_single(subj_data):
     if base_length == 0:
         return np.nan
 
-    return (np.sum(added_length) / base_length - 1) * 100
+    return (np.sum(added_length) / base_length - 1) * 100

iglu_python/hbgi.py

@@ -88,4 +88,4 @@ def calculate_hbgi_single(glucose_values: pd.Series) -> float:
     n = len(glucose_values)
     hbgi_value = 10 * np.sum(fbg[glucose_values >= 112.5] ** 2) / n
 
-    return hbgi_value
+    return hbgi_value

iglu_python/hyper_index.py

@@ -90,4 +90,4 @@ def hyper_index_single(
     hyper_values = gl[gl > ULTR] - ULTR
     hyper_index = np.sum(hyper_values**a) / (len(gl) * c)
 
-    return hyper_index   
+    return hyper_index

iglu_python/hypo_index.py

@@ -67,7 +67,7 @@ def hypo_index(
         if isinstance(data, (np.ndarray, list)):
             data = pd.Series(data)
         return hypo_index_single(data, LLTR, b, d)
-    
+
     data = check_data_columns(data)
     out = data.groupby('id').agg(
         hypo_index = ("gl", lambda x: hypo_index_single(x, LLTR, b, d))

iglu_python/igc.py

@@ -86,12 +86,12 @@ def igc(
     return out
 
 def igc_single(
-    gl: pd.Series, 
-    LLTR: int = 80, 
-    ULTR: int = 140, 
-    a: float = 1.1, 
-    b: float = 2, 
-    c: int = 30, 
+    gl: pd.Series,
+    LLTR: int = 80,
+    ULTR: int = 140,
+    a: float = 1.1,
+    b: float = 2,
+    c: int = 30,
     d: int = 30
 ) -> float:
     """
@@ -102,4 +102,4 @@ def igc_single(
     out_hypo = hypo_index(gl, LLTR=LLTR, b=b, d=d)
 
     out = out_hyper + out_hypo
-    return out
+    return out