X-Ray Spectrum Fitter

A modern, standalone GUI application built with CustomTkinter for loading, visualizing, and fitting Gaussian peaks to spectrum data. This tool is specifically designed to work with data files from applications like the ESRF PyMca, supporting its common data structures.

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🚀 Features

• Modern Interface: Clean and theme-able UI built with customtkinter.
• Data Loading: Loads .txt and .csv files, automatically detecting common delimiters (comma, tab, whitespace).
• PyMca-Aware: Optimized for ESRF PyMca data. It correctly parses files with Channel, Counts, and Energy columns.
• Axis Selection: Toggle the plot's x-axis between Channel and Energy (if energy data is present).
• Interactive Plotting: Powered by Matplotlib, including navigation, zoom, and pan controls.
• Automatic Fitting:
  • Uses scipy.signal.find_peaks for automatic peak detection.
  • Adjust peak-finding sensitivity with a "Auto Peak Height" slider.
  • Performs a composite Gaussian fit for up to 10 peaks using scipy.optimize.curve_fit.
• Manual Fitting:
  • A "Manual" mode opens a new window with sliders for all fit parameters (center, width, height, baseline).
  • Visually see your manual adjustments update on the main plot in real-time.
  • "Reset to Auto-Fit" button to discard manual changes.
• Export Results:
  • "Show Results" button displays all fit parameters in a clean pop-up window.
  • "Save Results" button exports the complete fit parameters and function definitions to a .txt file.

💾 Data Format

This tool is designed to be flexible but is optimized for data from PyMca.

• File Types: .txt or .csv.
• Delimiters: Automatically detects comma (,), semicolon (;), tab, or whitespace.
• Headers: Automatically attempts to skip a single header line if it contains non-numeric text.
• Column Structure: The script expects data in the following order:
  1. Column 1: Channel
  2. Column 2: Counts
  3. Last Column: Energy

PS: While PyMca may label last column as Energy (eV), this tool can process data where this column is actually Wavelength (nm).

🛠️ Installation & Requirements

This script requires Python 3 and the libraries listed below.

1. Clone the repository:

   git clone [https://github.com/ocakiroglu/XRay-Spectrum-Data-Fitting.git](https://github.com/ocakiroglu/XRay-Spectrum-Data-Fitting.git)
   cd XRay-Spectrum-Data-Fitting

2. Create a requirements.txt file in the same directory with the following content:

   customtkinter
   numpy
   scipy
   matplotlib
   

3. Set up your Python environment:

   You can use either a standard venv or conda.

   ---

   Option 1: Using venv (standard Python)

   # Create a virtual environment
   python -m venv venv
   
   # Activate it
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate
   
   # Install libraries
   pip install -r requirements.txt

   ---

   Option 2: Using conda

   # Create a new conda environment (e.g., named 'spectrum_fitter')
   # We recommend Python 3.13.5 or newer
   conda create --name spectrum_fitter python=3.13.5
   
   # Activate the environment
   conda activate spectrum_fitter
   
   # Install libraries using pip (easiest way)
   pip install -r requirements.txt

   (Note: Using pip install -r requirements.txt within the active conda environment is the simplest way, as customtkinter is not on the default conda channels.)

🏃 How to Use

Activate the environment and then follow this steps:

1. Run the application:

   python gaussian_fit_on_spectrum_GUI_modern_v3.py

2. Basic Workflow:

   • Click "Open File" to load your .txt or .csv spectrum file.
   • Select your desired x-axis ("Energy" or "Channel") using the radio buttons.
   • Define the area you want to fit by typing in the "Fit Range" boxes and pressing Enter or clicking "Apply Range".
   • Adjust the "Auto Peak Height" slider until the "Number of Peaks" label shows the correct number of peaks for your selected range. The plot will update with an automatic fit.
   • (Optional) Click the "Manual" button to open the slider window. Adjust the parameters as needed. Your changes will appear on the main plot.
   • Click "Show Results" to view the final parameters or "Save Results" to export them to a text file.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

🙏 Acknowledgments

This code was developed with coding assistance from Google's Gemini and Github Copilot.