pybaseline_loop

pybaseline_loop is a graphical Python application for batch processing and baseline correction of SWV (Square Wave Voltammetry) data files. It provides a user-friendly interface to select input folders, configure data import options, and export processed results and plots.

Features

• Batch processing of .txt SWV data files.
• Automatic smoothing of signals using the Savitzky-Golay filter.
• Baseline estimation and correction using the asPLS algorithm from pybaselines.
• Export of processed data to CSV or Excel.
• Export of annotated signal plots as PNG images.
• Results summary in a multi-index Excel file.
• Progress bar and log window for monitoring processing.
• Cross-platform GUI (Windows, macOS, Linux).

Requirements

• Python 3.8+
• numpy
• pandas
• matplotlib
• pybaselines
• scipy
• openpyxl
• tkinter (usually included with Python)

Install dependencies with:

pip install pybaselines numpy pandas matplotlib scipy openpyxl

Usage

1. Launch the application:

   python pybaseline_loop.py

2. Select the input folder containing your .txt SWV files.

3. Configure import options:

   • Column separator (Tab, Comma, Semicolon, Space)
   • Decimal separator (Point or Comma)
   • Export options for processed data (None, CSV, Excel)
   • Export options for plots (None, PNG)

4. Click Lancer l'analyse to start processing.

5. Monitor progress in the log window and progress bar.

6. Open the results folder using the provided button after processing.

Input File Format

• The application expects .txt files with two columns: Potential and Current.
• The first row is skipped (assumed to be a header).
• File names must match the pattern:
  *_NN_SWV_CNN_loopN.txt
  where:
  • NN = variant/frequency (2 digits)
  • CNN = channel (e.g., C01)
  • loopN = iteration number

Output

• Processed data files (.csv or .xlsx) for each input file (optional).
• Annotated plots (.png) for each input file (optional).
• A summary Excel file with all results, organized by loop, channel, and variant.

GUI Overview

• Dossier d'entrée: Select the folder containing .txt files.
• Paramètres de lecture: Set column and decimal separators.
• Export des fichiers traités: Choose export format for processed data.
• Export des graphiques: Choose whether to export plots.
• Progression du traitement: Shows progress bar.
• Journal de traitement: Displays processing log and errors.
• Lancer l'analyse: Start processing.
• Ouvrir le dossier de résultats: Open the output folder.

Example

Suppose your folder contains files like:

sample_01_SWV_C01_loop1.txt
sample_01_SWV_C01_loop2.txt
sample_02_SWV_C02_loop1.txt
...

After processing, you will find:

• sample_01_SWV_C01_loop1.png (if plot export enabled)
• sample_01_SWV_C01_loop1.csv or .xlsx (if data export enabled)
• YourFolderName.xlsx (summary file with all results)

License

MIT License. See LICENCE for details.

Note:
This application uses the asPLS algorithm from pybaselines for baseline correction.