SANS Model Fitter

A flexible, model-agnostic Python template for fitting Small-Angle Neutron Scattering (SANS) data using the SasModels library.

Features

• Model-Agnostic Design: Works with any model from the SasModels library (cylinder, sphere, core_shell, etc.)
• Multiple Fitting Engines: Supports both BUMPS (default) and LMFit optimization engines
• Flexible Data Loading: Reads CSV, XML, and HDF5 formats via sasdata
• Q-Range Restriction: Fit only a chosen [qmin, qmax] window (e.g. trim beam-stop or background-dominated points)
• User-Friendly Parameter Management: Easy-to-use interface for setting parameter values, bounds, and fitting flags
• Interactive Visualization: Automatic plotting of data, fitted model, and residuals with Plotly
• Result Export: Save fitted parameters and curves to CSV files

Installation

Option 1: Using pip (recommended for users)

# Clone the repository
git clone https://github.com/ai4se1dk/SANS-fitter.git
cd SANS-fitter

# Install the package
pip install -e .

# Or install with development dependencies
pip install -e ".[dev]"

Option 2: Using Pixi (recommended for development)

# Clone the repository
git clone https://github.com/ai4se1dk/SANS-fitter.git
cd SANS-fitter

# Install dependencies with Pixi
pixi install

# Run tests
pixi run test

# Run demo notebook
pixi run run-demo

Quick Start

from sans_fitter import SANSFitter

# Create fitter instance
fitter = SANSFitter()

# Load your data
fitter.load_data('my_sans_data.csv')

# Set the model (any model from SasModels!)
fitter.set_model('cylinder')

# Optionally restrict the Q range used for fitting
fitter.set_q_range(qmin=0.01, qmax=0.3)

# View initial parameter values
fitter.get_params()

# Configure parameters for fitting
fitter.set_param('radius', value=20, min=1, max=100, vary=True)
fitter.set_param('length', value=400, min=10, max=1000, vary=True)
fitter.set_param('scale', value=0.1, min=0, max=1, vary=True)
fitter.set_param('background', value=0.01, min=0, max=1, vary=True)

# View current parameters
fitter.get_params()

# Perform the fit (using BUMPS by default)
result = fitter.fit(engine='bumps', method='amoeba')

# Visualize results
fitter.plot_results(show_residuals=True)

# Save results
fitter.save_results('fit_results.csv')

Switching Models

The fitter is completely model-agnostic. Simply load a different model:

# Try with a sphere model instead
fitter.set_model('sphere')
fitter.get_params()  # See different parameters!

fitter.set_param('radius', value=25, min=5, max=100, vary=True)
result = fitter.fit()

Switching Fitting Engines

# Use BUMPS (default)
result = fitter.fit(engine='bumps', method='amoeba')

# Or use LMFit
result = fitter.fit(engine='lmfit', method='leastsq')

Working with Structure Factors

Combine any SasModels form factor with an interaction model to capture correlated systems.

fitter.set_model('sphere')

# Apply a structure factor (creates sphere@hardsphere product model)
fitter.set_structure_factor('hardsphere', radius_effective_mode='link_radius')

# Inspect linked parameters and run the fit as usual
fitter.get_params()
result = fitter.fit()

# Remove the structure factor to go back to the pure form factor
fitter.remove_structure_factor()

• Supported structure factors: hardsphere, hayter_msa, squarewell, stickyhardsphere.
• Radius handling: use radius_effective_mode='link_radius' to keep radius_effective equal to the form-factor radius, or leave the default unconstrained to fit it independently.
• State helpers: get_structure_factor() returns the active structure factor so notebooks/scripts can branch as needed.

Available Methods

BUMPS methods:

• 'amoeba' - Nelder-Mead simplex (default, robust)
• 'lm' - Levenberg-Marquardt
• 'newton' - Newton's method
• 'de' - Differential evolution

LMFit methods:

• 'leastsq' - Levenberg-Marquardt (default)
• 'least_squares' - Trust Region Reflective
• 'differential_evolution' - Global optimizer
• 'powell', 'nelder', etc.

Demo Notebook

See sans_fitter_demo.ipynb for a comprehensive demonstration with examples.

Design Philosophy

This implementation follows a template pattern where:

1. The core fitting logic is abstracted into a reusable class
2. Models are loaded dynamically from SasModels - no hardcoded model assumptions
3. Parameters are discovered automatically from the model definition
4. Multiple optimization engines are supported through a unified interface
5. The user maintains full control over parameter initialization and bounds

Implementation Details

Engine Adapters

The fitter implements adapter patterns for both BUMPS and LMFit:

• BUMPS: Uses native sasmodels.bumps_model integration
• LMFit: Uses sasmodels.direct_model.DirectModel with a custom residual function

Parameter Management

Parameters are stored internally with:

• value: Current/initial value
• min, max: Bounds
• vary: Fitting flag
• description: From model metadata

This allows the fitter to work with any model without prior knowledge of its parameters.

License

BSD 3-Clause License. See LICENSE for the full text.

References

• SasModels: https://github.com/SasView/sasmodels
• BUMPS: https://github.com/bumps/bumps
• LMFit: https://lmfit.github.io/lmfit-py/
• Plotly: https://plotly.com/python/