iglu-python 0.1.5

Python implementation of the iglu package for continuous glucose monitoring data analysis

IGLU_PYTHON library

Concept

IGLU_PYTHON is a pure Python implementation of the widely-used IGLU (Interpreting GLUcose data) package. While the original IGLU implementation (referred to as iglu-r) is highly regarded in the research community, its R-based implementation has limited its adoption outside academic settings. The existing IGLU-PY solution provides a Python-to-R bridge but still requires a complete R installation and its dependencies.

IGLU_PYTHON reimplements all IGLU metric functions natively in Python, eliminating the need for R while maintaining full compatibility with the original package.

This project is proudly sponsored by Pheno.AI.

IGLU-R Compatibility

A significant focus of this project has been ensuring compatibility with the original R implementation of IGLU. To achieve this:

• The test suite includes validation against the original R implementation
• Test data is generated using tests/build_expected_values.py, which interfaces with the R implementation through an iglu-py adaptation layer
• Expected results are stored in tests/expected_results.json
• Each unit test in the package compares Python implementation results against the R-generated reference values

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
below_percent	✅
cogi	✅
conga	✅
cv_glu	✅
cv_measures	✅
ea1c	✅
episode_calculation	🟡 need fix in excl			no match in lv1_hypo_excl and lv1_hyper_excl
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
mean_glu	✅
median_glu	✅
modd	✅
pgs	✅
quantile_glu	✅
range_glu	✅
roc	✅
sd_glu	✅
sd_measures	✅
sd_roc	✅
process_data	✅
summary_glu	✅
CGMS2DayByDay	✅

Installation

Install IGLU_PYTHON using pip:

pip install iglu-python

For development installation:

git clone https://github.com/staskh/iglu_python.git
cd iglu_python
pip install -e .

Examples of Use

Basic Usage with DataFrame

import pandas as pd
import iglu_python as iglu

# Load your glucose data into a DataFrame
# Expected columns: 'id' (subject identifier) and 'gl' (glucose values)
# 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
})

# 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)

print(f"Mean glucose: {mean_glucose}")
print(f"CV: {cv}")
print(f"Time in range (70-180 mg/dL): {time_in_range}%")

Using with Time Series Data

import pandas as pd
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
})

# Calculate advanced metrics
mage = iglu.mage(data)
auc = iglu.auc(data)
gmi = iglu.gmi(data)

print(f"MAGE: {mage}")
print(f"AUC: {auc}")
print(f"GMI: {gmi}")

Multiple Input Formats

(Not yet fully implemented and tested)

import iglu_python as iglu
import numpy as np

# Using list (assumes 5-minute intervals)
glucose_list = [120, 135, 140, 125, 110, 95, 105, 115]
mean_from_list = iglu.mean_glu(glucose_list)

# Using NumPy array
glucose_array = np.array([120, 135, 140, 125, 110, 95, 105, 115])
cv_from_array = iglu.cv_glu(glucose_array)

# Using Pandas Series with DatetimeIndex
glucose_series = pd.Series(
    data=[120, 135, 140, 125, 110, 95, 105, 115],
    index=pd.date_range(start='2023-01-01', periods=8, freq='5min')
)
sd_from_series = iglu.sd_glu(glucose_series)

Notes on IGLU-R Compatibility

During our implementation and testing process, we identified several discrepancies between our Python implementation and the original R version of IGLU. While maintaining test compatibility remains a priority, we are actively working with the IGLU-R development team to investigate and resolve these issues.

Known Implementation Differences

Timezone Handling in check_data_columns

The function's timezone handling behavior requires clarification:

• When a specific timezone is provided, the function performs a timezone conversion (tz_convert) rather than timezone localization (tz_localize)
• This means timestamps are being transformed to the target timezone instead of being labeled with it
• The intended behavior needs to be confirmed with the original IGLU-R authors
• This difference in timezone handling may affect daily aggregation and analysis results

CGMS2DayByDay Function

The following issues have been identified in the R implementation:

1. Timezone Handling:

   • When using tz=UTC, data points are shifted one day earlier than expected
   • Status: Pending test case development to demonstrate the issue

2. Grid Alignment:

   • Results are shifted one grid index to the left from the expected values
   • Status: Pending test case development to demonstrate the issue

We are maintaining test compatibility while these issues are being investigated. Updates will be provided as we receive clarification from the IGLU-R development team.

Input Data Types

Most metric functions, in addition to a standard DataFrame, support multiple input formats for glucose readings:

• List[float]: Python list of glucose values
• np.array: NumPy array of glucose values
• pd.Series: Pandas Series of glucose values (with or without DatetimeIndex)

When using these sequence types (without timestamps), the functions assume a fixed 5-minute interval between measurements. For more precise analysis with variable time intervals, use the DataFrame input format with explicit timestamps or Series with DatetimeIndex .

ToDo

• implement Series/list/array as an input for all metrics (suing Series with DatetimeIndex)
• optimize code by NOT converting arrays/Series into DataFrames
• test and implement tz='UTC' timezone assignment
• clarify functionality correctness for CGMS2DayByDay