pySRFit

A modern, interactive GUI application for analysing muon spin rotation, relaxation and resonance (μSR) data. pySRFit is inspired by musrfit and uses a similar parameter and THEORY style.

Features

Core Capabilities

• Multi-beamline support: Load raw μSR data from PSI, ISIS, J-PARC and TRIUMF.
• Fitting: Perform single-run, global and sequential fits using MINUIT.
• Fourier transform: Display data and fits in the time domain or as Fourier transforms.
• Save dataset: Save complete analyses as .pysrfit projects.
• Generate a tabulated result table: Collect fitted projects into CSV result tables.
• Custom relaxation functions: Add custom Python relaxation functions.

Installation

pip install pysrfit

# Launch the GUI
pysrfit

Usage

Basic Workflow

1. Launch the application:

   pysrfit
   

2. Load data:

   • Configure the selected run in Runs / Data.

3. Model:

   • Define the parameters and THEORY model.

4. Fit and visualise:

   • Apply the parameters, fit the data, and save the .pysrfit project.

Supported Facilities

• PSI: MusrRoot .root files, tested with GPS, GPD, FLAME, HAL and LEM.
• ISIS: NeXus .nxs files.
• J-PARC: NeXus .nxs files, tested with MS1 and MD1.
• TRIUMF: binary MUD .msr and .mud files, tested with M20 and M15.

Theory

Consecutive THEORY lines multiply, while + starts an additive component:

asymmetry 1
simplExpo 2
TFieldCos 3 4
+
asymmetry 5

Built-in functions include exponential, stretched-exponential and Gaussian relaxation, cosine and Bessel oscillations, Gaussian and Lorentzian Kubo–Toyabe models, and their longitudinal-field and dynamic forms. Additional models can be registered with Python in Custom models.

Generalized two-state collision model

The two-state model from O. K. Forslund, Phys. Rev. B 112, 024309 (2025) accepts any registered static function:

twoState nu1 nu2 g | staticFunction1 ... | staticFunction2 ...

For example, symmetric switching between two Gaussian Kubo–Toyabe states is:

asymmetry 1
twoState 2 2 3 | statGssKt 4 | statGssKt 5

The first two references are the switching rates. The third is the probability of starting in state 1 and should be constrained between 0 and 1.

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License.

Citation

If you use pySRFit in your research, please cite: