bssunfold Documentation

bssunfold is a Python package for neutron spectrum unfolding using various algorithms.

Contents:

• Package Overview
  • Unfolding Methods
  • Built-in Response Functions
  • Dose Conversion Coefficients
  • Spectrum Comparison Metrics
  • Performance
• Detector Class
  • Detector
• Unfold Methods
  • unfold_cvxpy()
  • unfold_landweber()
  • unfold_mlem()
  • unfold_qpsolvers()
  • unfold_doroshenko()
  • unfold_kaczmarz()
  • unfold_lmfit()
  • unfold_mlem_odl()
  • unfold_mlem_stop()
  • unfold_combined()
  • unfold_gravel()
  • unfold_maxed()
  • unfold_tikhonov_legendre()
  • unfold_bayes()
  • unfold_bayes_spline_regularization()
  • unfold_statreg()
  • unfold_reconst()
  • unfold_scipy_direct_method()
  • unfold_tsvd()
  • unfold_parametric()
  • solve_parametric_cvxpy()
  • solve_parametric_qpsolvers()
  • solve_parametric_combined()
  • unfold_parametric2()
  • solve_parametric2()
  • solve_bon95_parametric()
  • directed_divergence_iteration()
  • solve_bon95_cvxpy()
  • solve_bon95_qpsolvers()
  • solve_bon95_combined()
  • unfold_fruit_like()
  • unfold_hybrid_parametric()
  • unfold_bayesian_parametric()
• Core Functions
  • solve_cvxpy()
  • solve_landweber()
  • solve_mlem()
  • solve_mlem_stop()
  • solve_qpsolvers()
  • solve_doroshenko()
  • solve_kaczmarz()
  • solve_lmfit()
  • solve_gravel()
  • solve_maxed()
  • solve_tikhonov_legendre()
  • solve_bayes()
  • solve_bayes_spline()
  • solve_statreg()
  • solve_reconst()
  • solve_scipy_direct()
  • solve_tsvd()
• Comparison Methods
  • compare_spectra()
  • compare_multiple()
• Comparison Metrics
  • Entropy-based
  • Distribution distances
  • Correlation
  • Error metrics
  • Kernel / similarity
  • Chi-squared family
  • Statistical tests
• Regularization Selection
  • select_regularization_parameter()
  • lcurve_selection()
  • gcv_selection()
  • discrepancy_principle_selection()
  • cosine_similarity_selection()
• Examples
  • Basic Usage
  • Parametric Unfolding
  • BON95 Parametric Unfolding
• Сравнение с оригинальным кодом RECONST
  • Построение матриц B и A_vec
  • Матрица Omega (сглаживание)
  • Решение системы D·f = A·β
  • Обеспечение неотрицательности
  • Автоматический подбор α и β
  • Производительность
  • Численная устойчивость
  • Ограничения Python-версии
  • Тестирование
  • Идентичность результатов

Indices and tables

• Index

• Module Index

• Search Page

Overview

BSSUnfold is a Python package for neutron spectrum unfolding from measurements obtained with Bonner Sphere Spectrometers (BSS). The package implements several mathematical algorithms for solving the inverse problem of unfolding neutron energy spectra from detector readings, with applications in radiation protection, nuclear physics research, and accelerator facilities. Iterative solvers are accelerated with Numba JIT compilation for 3–50x speedups.

Features

• Multiple Unfolding Algorithms (27 methods): - Tikhonov-type: CVXPY, qpsolvers (L1/L2/smoothness), Legendre basis, TSVD - Iterative: Landweber, MLEM (pure NumPy + ODL), MLEM-STOP, GRAVEL, Doroshenko, Kaczmarz - Bayesian: D’Agostini (Bayes), Bayes with spline regularisation - Maximum Entropy: MAXED (primal log-space dual minimisation) - Statistical Regularisation: Turchin’s method (StatReg), Fortran STREG1 port (Reconst) - Optimisation-based: lmfit (L1/L2/Elastic Net), Scipy direct (CG, GMRES, LSQR)

  • Pipeline: Combined approach for chaining multiple methods

  • Parametric: FRUIT-style thermal/epithermal/fast model (lmfit, cvxpy SQP, qpsolvers SQP, combined); BON95 4-component model with directed-divergence iterations

• Numba JIT-Accelerated Iterative Solvers: - @njit(cache=True) compiled inner loops for Doroshenko, Kaczmarz, MLEM, GRAVEL - 3–50x speedup on iterative solvers - Automatic disk caching; graceful fallback when numba is not installed

• Radiation Dose Calculations: - ICRP-116 conversion coefficients for effective dose

• Comprehensive Data Management: - Automatic response function processing - Uncertainty quantification via Monte Carlo methods

• Advanced Visualization: - Spectrum plotting with uncertainty bands - Detector reading comparisons