Welcome to nres’s documentation!

nres (pronounced N-res) is a simple yet powerful package for fitting neutron resonances. It allows quick and easy quantitative fitting of total cross-section for neutron absorption resonances transmission spectrum in the epithermal and fast energy regions.

Features

  • Flexible and simple way to generate and combine cross-sections from different isotopic materials.

  • Built-in database of many useful materials and elements.

  • Cross-sections taken from ENDF8.0.

  • Built on lmfit for intuitive and powerful fit exploration.

  • Python API leveraging popular libraries like numpy and pandas.

  • Methods to define response functions and background functions.

  • Plotting utilities for concise result visualization.

  • Fast cross-section integration and convolution with response function using C++ core code.

  • NEW in v0.4: Grouped data support for imaging detectors and multi-sample measurements.

  • NEW in v0.4: Advanced rebinning capabilities for time-of-flight data.

  • NEW in v0.4: Parallel fitting with progress tracking and memory management.

  • NEW in v0.4: Save/load functionality for models and results.

  • NEW in v0.4: Enhanced visualization with transmission maps and multi-parameter plots.

Installation

To install from source, you can clone the repository and install it using pip:

git clone https://github.com/TsvikiHirsh/nres
cd nres
pip install .

Basic Usage

Here’s a quick example of how to use nres:

# Import nres
import nres

# Define material
Si = nres.CrossSection("Silicon")

# Load data
data = nres.Data.from_transmission("silicon.dat")

# Define model
model = nres.TransmissionModel(Si, vary_background=True)

# Fit using lmfit
result = model.fit(data, emin=0.4e6, emax=1.7e6)

# Plot fit results
result.plot()
Fit results

For more detailed examples and advanced usage, please refer to our Jupyter notebook demo.

Contributing

See CONTRIBUTING.md for instructions on how to contribute.

License

Distributed under the terms of the MIT license.

Contact

For questions, issues, or contributions, please visit the GitHub repository.

Contents:

Indices and tables