All metrics completed, support for series, list and arrays as input

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)
@@ -13,7 +13,7 @@ def adrr(data: pd.DataFrame|pd.Series) -> pd.DataFrame|float:
 
     Parameters
     ----------
-    data : pd.DataFrame|pd.Series   
+    data : pd.DataFrame|pd.Series
         DataFrame object with column names "id", "time", and "gl".
         or a Timeseries of glucose values.
 
@@ -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.
 
@@ -20,7 +20,8 @@ def grade_eugly(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     lower : int, default=70
         Lower bound used for hypoglycemia cutoff, in mg/dL
     upper : int, default=140
@@ -63,7 +64,7 @@ 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 +92,4 @@ 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

@@ -17,7 +17,8 @@ def grade_hyper(data: Union[pd.DataFrame, pd.Series, np.ndarray, list], upper: i
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     upper : int, default=140
         Upper bound used for hyperglycemia cutoff, in mg/dL
 
@@ -75,12 +76,12 @@ 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

@@ -17,7 +17,8 @@ def grade_hypo(data: Union[pd.DataFrame, pd.Series, np.ndarray, list], lower: in
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     lower : int, default=80
         Lower bound used for hypoglycemia cutoff, in mg/dL
 
@@ -58,7 +59,7 @@ 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 +78,12 @@ 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

@@ -25,7 +25,7 @@ def hbgi(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.DataFrame
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
 
     Returns
@@ -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

@@ -20,7 +20,8 @@ def hyper_index(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     ULTR : int, default=140
         Upper Limit of Target Range, in mg/dL
     a : float, default=1.1
@@ -90,4 +91,4 @@ 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

@@ -20,7 +20,7 @@ def hypo_index(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
     LLTR : int, default=80
         Lower Limit of Target Range, in mg/dL
@@ -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

@@ -26,7 +26,8 @@ def igc(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     LLTR : int, default=80
         Lower Limit of Target Range, in mg/dL
     ULTR : int, default=140
@@ -86,12 +87,12 @@ 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 +103,4 @@ def igc_single(
     out_hypo = hypo_index(gl, LLTR=LLTR, b=b, d=d)
 
     out = out_hyper + out_hypo
-    return out
+    return out

iglu_python/iqr_glu.py

@@ -16,7 +16,8 @@ def iqr_glu(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.DataFr
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
 
     Returns
     -------
@@ -51,7 +52,7 @@ def iqr_glu(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.DataFr
             return np.nan
         # Calculate IQR for Series
         iqr_val = iqr_glu_single(data)
-        return iqr_val   
+        return iqr_val
 
     # Handle DataFrame input
     data = check_data_columns(data)
@@ -85,4 +86,4 @@ def iqr_glu_single(
         return np.nan
     # Calculate IQR for Series
     iqr_val = np.percentile(gl, 75) - np.percentile(gl, 25)
-    return iqr_val 
+    return iqr_val

iglu_python/j_index.py

@@ -17,7 +17,7 @@ def j_index(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.DataFr
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
 
     Returns
@@ -78,4 +78,4 @@ def j_index_single(gl:  pd.Series) -> float:
 
     # Calculate J-index
     j_index = 0.001 * (mean_gl + sd_gl) ** 2
-    return j_index
+    return j_index

iglu_python/lbgi.py

@@ -22,7 +22,8 @@ def lbgi(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.DataFrame
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns ['id', 'time', 'gl'] or Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns ['id', 'time', 'gl'] or Series of glucose values,
+        or a numpy array or list of glucose values
         in mg/dL
 
     Returns

iglu_python/m_value.py

@@ -23,7 +23,8 @@ def m_value(data: Union[pd.DataFrame, pd.Series, np.ndarray, list], r: float = 9
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, or a numpy array or list of glucose values
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
+        or a numpy array or list of glucose values
     r : float, default=90
         A reference value corresponding to basal glycemia in normal subjects
 

iglu_python/mad_glu.py

@@ -19,7 +19,7 @@ def mad_glu(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
     constant : float, default=1.4826
         Scaling factor to multiply the MAD value. The default value of 1.4826
@@ -31,7 +31,7 @@ def mad_glu(
     pd.DataFrame|float
         DataFrame with columns:
         - id: subject identifier (if DataFrame input)
-        - MAD: MAD value (median absolute deviation of glucose values). 
+        - MAD: MAD value (median absolute deviation of glucose values).
         If a Series of glucose values is passed, then a float is returned.
 
     Examples
@@ -74,4 +74,4 @@ def mad_glu_single(gl: pd.Series, constant: float = 1.4826) -> float:
     if len(gl) == 0:
         return np.nan
     mad_val = np.median(np.abs(gl - np.median(gl))) * constant
-    return mad_val
+    return mad_val

iglu_python/median_glu.py

@@ -1,7 +1,7 @@
 from typing import Union
 
-import pandas as pd
 import numpy as np
+import pandas as pd
 
 from .utils import check_data_columns
 
@@ -17,7 +17,7 @@ def median_glu(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.Dat
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
 
     Returns

iglu_python/modd.py

@@ -19,7 +19,7 @@ def modd(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
     lag : int, default=1
         Integer indicating which lag (# days) to use. Default is 1.
@@ -31,7 +31,7 @@ def modd(
     pd.DataFrame|float
         DataFrame with columns:
         - id: subject identifier (if DataFrame input)
-        - MODD: Mean of Daily Differences value. 
+        - MODD: Mean of Daily Differences value.
         If a Series of glucose values is passed, then a float is returned.
 
     References

iglu_python/pgs.py

@@ -23,7 +23,7 @@ def pgs(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
         Should only be data for 1 subject. In case multiple subject ids are detected,
         a warning is produced and only 1st subject is used.

iglu_python/quantile_glu.py

@@ -19,7 +19,7 @@ def quantile_glu(
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
     quantiles : List[float], default=[0, 25, 50, 75, 100]
         List of quantile values between 0 and 100

iglu_python/range_glu.py

@@ -16,7 +16,7 @@ def range_glu(data: Union[pd.DataFrame, pd.Series, np.ndarray, list]) -> pd.Data
     Parameters
     ----------
     data : Union[pd.DataFrame, pd.Series, np.ndarray, list]
-        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values, 
+        DataFrame with columns 'id', 'time', and 'gl', or a Series of glucose values,
         or a numpy array or list of glucose values
 
     Returns