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

README.md

@@ -19,50 +19,75 @@ A significant focus of this project has been ensuring compatibility with the ori
 This approach ensures that the Python implementation produces results consistent with the original R package.
 
 ## Unit Test Status
-Unless noted, iglu-r test is considered successful if it achieves precision of 0.001
-
-| Function | IGLU-R test compatibility | array/list/Series | TZ | Comments |
-|----------|---------------------------|-------------------|----|----------|
-| above_percent | ✅ | |||
-| active_percent | ✅ |
-| adrr | ✅ |
-| auc| 🟡 (0.01 precision) | || see [auc_evaluation.ipynb](https://github.com/staskh/iglu_python/blob/main/notebooks/auc_evaluation.ipynb)|
-| below_percent| ✅ |
-| cogi | ✅ |
-| conga | ✅ |
-| cv_glu | ✅ |
-| cv_measures | ✅ |
-| ea1c | ✅ |
-| episode_calculation |  ✅| || |
-| gmi | ✅ |
-| grade_eugly | ✅ |
-| grade_hyper | ✅ |
-| grade_hypo | ✅ |
-| grade | ✅ |
-| gri | ✅ |
-| gvp | ✅ |
-| hbgi | ✅ |
-| hyper_index | ✅ |
-| hypo_index | ✅ |
-| igc | ✅ |
-| j_index | ✅ |
-| lbgi | ✅ |
-| mad_glu | ✅ |
-| mag |  ✅ | || IMHO, Original R implementation has an error |
-| mage | ✅ | || See algorithm at [MAGE](https://irinagain.github.io/iglu/articles/MAGE.html) |
-| mean_glu | ✅ |
-| median_glu | ✅ |
-| modd | ✅ |
-| pgs |  ✅  | || |
-| quantile_glu |  ✅ |
-| range_glu | ✅ |
-| roc | ✅ |
-| sd_glu | ✅ |
-| sd_measures | ✅ |
-| sd_roc |  ✅ | |||
-| process_data | ✅ |
-| summary_glu | ✅ |
-| CGMS2DayByDay | ✅ |
+The current version of IGLU-PYTHON is test-compatible with IGLU-R v4.2.2
+
+Unless noted, IGLU-R test compatability is considered successful if it achieves precision of 0.001
+
+| Function | Description | IGLU-R test compatibility | list /ndarray /Series input | TZ | Comments |
+|----------|-------------|-------------|-------------------|----|----------|
+| above_percent | percentage of values above target thresholds| ✅ |✅ returns Dict[str,float] |||
+| active_percent | percentage of time CGM was active | ✅ | ✅ only Series(DatetimeIndex) returns Dict[str,float]|
+| adrr | average daily risk range | ✅ |✅ only Series(DatetimeIndex) returns float |
+| auc| Area Under Curve | 🟡 (0.01 precision) |✅ only Series(DatetimeIndex) returns float  || see [auc_evaluation.ipynb](https://github.com/staskh/iglu_python/blob/main/notebooks/auc_evaluation.ipynb)|
+| below_percent| percentage of values below target thresholds| ✅ | ✅ returns Dict[str,float]|
+| cogi |Coefficient of Glucose Irregularity | ✅ | ✅ returns float
+| conga | Continuous Overall Net Glycemic Action |✅ | ✅ only Series(DatetimeIndex) returns float
+| cv_glu | Coefficient of Variation | ✅|  ✅ returns float |
+| cv_measures |Coefficient of Variation subtypes (CVmean and CVsd) |✅  |✅ only Series(DatetimeIndex) returns Dict[str,float]| | 
+| ea1c |estimated A1C (eA1C) values| ✅ | ✅ returns float |
+| episode_calculation | Hypo/Hyperglycemic episodes with summary statistics|  ✅|  🟡 always returns DataFrame(s)|| |
+| gmi | Glucose Management Indicator | ✅ | ✅ returns float |
+| grade_eugly |percentage of GRADE score attributable to target range| ✅ | ✅ returns float 
+| grade_hyper |percentage of GRADE score attributable to hyperglycemia| ✅ |✅ returns float 
+| grade_hypo |percentage of GRADE score attributable to hypoglycemia| ✅ |✅ returns float 
+| grade |mean GRADE score| ✅ | ✅ returns float |
+| gri |Glycemia Risk Index | ✅ | ✅ returns float |
+| gvp |Glucose Variability Percentage| ✅ | ✅ only Series(DatetimeIndex) returns float
+| hbgi |High Blood Glucose Index| ✅ | ✅ returns float |
+| hyper_index |Hyperglycemia Index| ✅ |✅ returns float |
+| hypo_index |Hypoglycemia Index| ✅ |✅ returns float |
+| igc |Index of Glycemic Control| ✅ |✅ returns float |
+| in_range_percent |percentage of values within target ranges| ✅ | ✅ returns Dict[str,float]|
+| iqr_glu |glucose level interquartile range|✅ |✅ returns float |
+| j_index |J-Index score for glucose measurements| ✅ |✅ returns float |
+| lbgi | Low Blood Glucose Index| ✅ |✅ returns float |
+| m_value | M-value of Schlichtkrull et al | ✅ |✅ returns float |
+| mad_glu | Median Absolute Deviation | ✅ |✅ returns float |
+| mag | Mean Absolute Glucose| ✅ | ✅ only Series(DatetimeIndex) returns float ||| IMHO, Original R implementation has an error |
+| mage | Mean Amplitude of Glycemic Excursions|  ✅ |✅ only Series(DatetimeIndex) returns float || See algorithm at [MAGE](https://irinagain.github.io/iglu/articles/MAGE.html) |
+| mean_glu | Mean glucose value | ✅ | ✅ returns float|
+| median_glu |Median glucose value| ✅ |✅ returns float |
+| modd | Mean of Daily Differences| ✅ | ✅ only Series(DatetimeIndex) returns float|
+| pgs | Personal Glycemic State | ✅  |✅ only Series(DatetimeIndex) returns float| || 
+| quantile_glu |glucose level quantiles|  ✅ |✅ returns List[float] |
+| range_glu |glucose level range| ✅ |✅ returns float|
+| roc | Rate of Change| ✅ |🟡 always returns DataFrame|
+| sd_glu | standard deviation of glucose values| ✅ | ✅ returns float
+| sd_measures |various standard deviation subtypes| ✅ |✅ only Series(DatetimeIndex) returns Dict[str,float]|
+| sd_roc | standard deviation of the rate of change| ✅ |✅ only Series(DatetimeIndex) returns float ||
+| summary_glu | summary glucose level| ✅ |
+| process_data | Data Pre-Processor | ✅ |
+| CGMS2DayByDay |Interpolate glucose input| ✅ |
+
+### Input & Output 
+The implementation maintains compatibility with the R version while following Python best practices. The metrics can be used as:
+
+```Python
+import iglu_python ias iglu
+
+# With DataFrame input
+result_df = iglu.cv_glu(data)  # data should have 'id', 'time', and 'gl' columns
+# Return DataFrame with "id' and column(s) with value(s)
+
+# With Series input (some metrics require Series with DateTimeIndex)
+result_float = iglu.cv_glu(glucose_series)  # just glucose values
+# returns a single float value
+
+# Same with function that support list or ndarray
+result_float = iglu.cv_glu(glucose_list)  # list of glucose values
+# returns a single float value
+
+```
 
 # Installation
 
@@ -92,36 +117,32 @@ import iglu_python as iglu
 # Optional: datetime index or 'time' column
 data = pd.DataFrame({
     'id': ['Subject1'] * 100,
-    'time': pd.date_range(start='2023-01-01', periods=100, freq='5min')
-    'gl': [120, 135, 140, 125, 110, ...],  # glucose values in mg/dL
+    'time': pd.date_range(start='2023-01-01', periods=100, freq='5min'),
+    'gl': [120, 135, 140, 125, 110]*20  # glucose values in mg/dL
 })
 
 # Calculate glucose metrics
 mean_glucose = iglu.mean_glu(data)
 cv = iglu.cv_glu(data)
-time_in_range = iglu.active_percent(data, lltr=70, ultr=180)
+active = iglu.active_percent(data)
 
-print(f"Mean glucose: {mean_glucose}")
-print(f"CV: {cv}")
-print(f"Time in range (70-180 mg/dL): {time_in_range}%")
+print(f"Mean glucose: {mean_glucose['mean'][0]}")
+print(f"CV: {cv['CV'][0]}")
+print(f"CGM active percent: {active['active_percent'][0]}%")
 ```
 
 ### Using with Time Series Data
 
 ```python
 import pandas as pd
+import numpy as np
 import iglu_python as iglu
-from datetime import datetime, timedelta
 
 # Create time series data
 timestamps = pd.date_range(start='2023-01-01', periods=288, freq='5min')
 glucose_values = [120 + 20 * np.sin(i/48) + np.random.normal(0, 5) for i in range(288)]
 
-data = pd.DataFrame({
-    'id': ['Subject1'] * 288,
-    'time': timestamps,
-    'gl': glucose_values
-})
+data = pd.Series(glucose_values, index=timestamps)
 
 # Calculate advanced metrics
 mage = iglu.mage(data)

iglu_python/__init__.py

@@ -5,8 +5,11 @@
 from .below_percent import below_percent
 from .cogi import cogi
 from .conga import conga
+from .cv_glu import cv_glu
+from .cv_measures import cv_measures
 from .ea1c import ea1c
 from .episode_calculation import episode_calculation
+from .gmi import gmi
 from .grade import grade
 from .grade_eugly import grade_eugly
 from .grade_hyper import grade_hyper
@@ -37,7 +40,7 @@
 from .sd_measures import sd_measures
 from .sd_roc import sd_roc
 from .summary_glu import summary_glu
-from .utils import set_iglu_r_compatible, is_iglu_r_compatible, CGMS2DayByDay, check_data_columns, gd2d_to_df
+from .utils import CGMS2DayByDay, check_data_columns, gd2d_to_df, is_iglu_r_compatible, set_iglu_r_compatible
 
 __all__ = [
     "above_percent",
@@ -49,13 +52,16 @@
     "CGMS2DayByDay",
     "cogi",
     "conga",
+    "cv_glu",
+    "cv_measures",
     "ea1c",
     "episode_calculation",
     "gd2d_to_df",
     "grade",
     "grade_eugly",
     "grade_hyper",
     "grade_hypo",
+    "gmi",
     "gri",
     "gvp",
     "hbgi",

iglu_python/above_percent.py

@@ -1,14 +1,15 @@
 from typing import List, Union
 
+import numpy as np
 import pandas as pd
 
 from .utils import check_data_columns
 
 
 def above_percent(
-    data: Union[pd.DataFrame, pd.Series, list],
-    targets_above: List[int] = [140, 180, 250],
-) -> pd.DataFrame:
+    data: Union[pd.DataFrame, pd.Series, list,np.ndarray],
+    targets_above: List[int] = None,
+) -> pd.DataFrame|dict[str:float]:
     """
     Calculate percentage of values above target thresholds.
 
@@ -58,22 +59,13 @@ def above_percent(
     0       75.0       25.0
     """
     # Handle Series input
-    if isinstance(data, (pd.Series, list)):
-        # Convert targets to float
-        targets_above = [int(t) for t in targets_above]
+    if targets_above is None:
+        targets_above = [140, 180, 250]
+    if isinstance(data, (pd.Series, list,np.ndarray)):
+        if isinstance(data, (list, np.ndarray)):
+            data = pd.Series(data)
+        return above_percent_single(data, targets_above)
 
-        # Calculate total non-NA readings
-        total_readings = len(data.dropna())
-        if total_readings == 0:
-            return pd.DataFrame(columns=[f"above_{t}" for t in targets_above])
-
-        # Calculate percentages for each target
-        percentages = {}
-        for target in targets_above:
-            above_count = len(data[data > target])
-            percentages[f"above_{target}"] = (above_count / total_readings) * 100
-
-        return pd.DataFrame([percentages])
 
     # Handle DataFrame input
     data = check_data_columns(data)
@@ -85,19 +77,33 @@ def above_percent(
     # Process each subject
     for subject in data["id"].unique():
         subject_data = data[data["id"] == subject]
-        total_readings = len(subject_data.dropna(subset=["gl"]))
-
-        if total_readings == 0:
-            continue
-
-        # Calculate percentages for each target
-        percentages = {}
-        for target in targets_above:
-            above_count = len(subject_data[subject_data["gl"] > target])
-            percentages[f"above_{target}"] = (above_count / total_readings) * 100
-
+        percentages = above_percent_single(subject_data["gl"], targets_above)
         percentages["id"] = subject
         result.append(percentages)
 
     # Convert to DataFrame
-    return pd.DataFrame(result)
+    df = pd.DataFrame(result)
+    df = df[['id'] + [col for col in df.columns if col != 'id']]
+    return df
+
+def above_percent_single(data: pd.Series, targets_above: List[int] = None) -> dict[str:float]:
+    """
+    Calculate percentage of values above target thresholds for a single series/subject.
+    """
+    # Convert targets to float
+    if targets_above is None:
+        targets_above = [140, 180, 250]
+    targets_above = [int(t) for t in targets_above]
+
+    # Calculate total non-NA readings
+    total_readings = len(data.dropna())
+    if total_readings == 0:
+        return {f"above_{t}": 0 for t in targets_above}
+
+    # Calculate percentages for each target
+    percentages = {}
+    for target in targets_above:
+        above_count = len(data[data > target])
+        percentages[f"above_{target}"] = (above_count / total_readings) * 100
+
+    return percentages