Glucose Data Processor

Overview

This Python script is designed to process glucose monitoring data from a CSV file downloaded from LibreView. It cleans and transforms the data by removing unnecessary columns, combining relevant glucose measurement columns, filtering data based on specific date-time ranges, and finally sorting the results in ascending order by date-time.

Features

• Data Cleaning: Combines "Glukosewert-Verlauf mg/dL" and "Glukose-Scan mg/dL" into a single column ("BG") while removing all other columns.
• Error Handling: Safely converts glucose data to numeric format, excluding invalid or non-numeric values.
• Date-Time Formatting: Adjusts the format of the "Gerätezeitstempel" column to "DD-MM-YYYY HH:MM".
• Data Filtering: Filters rows based on specified date-time ranges for selected dates.
• Sorting: Orders the filtered results in ascending order based on the date-time column.
• Output: Generates a cleaned and filtered CSV file with only the necessary columns, renamed for readability.

Requirements

• Python 3.x
• Libraries:
  • pandas

You can install the dependencies using the following command:

pip install -r requirements.tx

How to Use

1. Set Up Your Environment:

• Create and activate a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

• Install dependencies:

pip install -r requirements.txt

2. Prepare Your CSV File:

• Ensure you have a CSV file exported from LibreView. The file should include columns such as “Gerätezeitstempel”, “Glukosewert-Verlauf mg/dL”, and “Glukose-Scan mg/dL”.
• Place the file in the same directory as the script or have its location ready.

3. Run the Script:

• Execute the script by running:

python glucose_data_processor.py

• Use the file selection dialog to choose your CSV file.

4. Review the Output:

• The processed data will be saved in a file named filtered_glucose.csv in the same directory as the script.

Customization

• Date-Time Filtering:
• The script uses hardcoded date-time ranges to filter data. You can modify the time_ranges list in the script to customize the filtering criteria:

time_ranges = [
    ("20-01-2025 07:19", "20-01-2025 16:05"),
    ("21-01-2025 03:58", "21-01-2025 10:25"),
]

• Column Names:
• If your dataset uses different column names, update the script to match those column names in the pd.read_csv and data processing sections.

Known Limitations

• Input File Format: The script assumes the input CSV file follows the format exported by LibreView. Any deviations may require script modifications.
• Hardcoded Date-Time Ranges: The filtering is static and based on pre-defined ranges. For more dynamic filtering, user input or external configuration would be required.
• Locale-Specific Parsing: The script assumes a dayfirst=True format for dates. Adjust if the date format differs.

Contributing

Contributions to improve or extend the script are welcome. Feel free to:

• Fork the repository
• Make changes or enhancements
• Submit a pull request with a detailed description of the changes

License

This project is open-source and available under the MIT License.

Acknowledgments

This script was created to simplify and streamline glucose data analysis, particularly for LibreView datasets.