Struct NormalizedTransmissionModel 

pub struct NormalizedTransmissionModel<M: FitModel> { /* private fields */ }

Wraps a transmission model with SAMMY-style normalization and background.

T_out(E) = Anorm × T_inner(E) + BackA + BackB / √E + BackC × √E + BackD × exp(−BackF / √E)

The normalization and background parameters are additional entries in the parameter vector, appended after the density (and optional temperature) parameters of the inner model.

The exponential tail (BackD, BackF) is optional. When back_d_index and back_f_index are None, the model reduces to the 4-parameter form.

SAMMY Reference

SAMMY manual Sec III.E.2 — NORMAlization and BACKGround cards. SAMMY fits up to 6 background terms; we implement all 6: Anorm, constant BackA, 1/√E term BackB, √E term BackC, exponential amplitude BackD, exponential decay BackF.

Implementations

impl<M: FitModel> NormalizedTransmissionModel<M>

pub fn new( inner: M, energies: &[f64], anorm_index: usize, back_a_index: usize, back_b_index: usize, back_c_index: usize, ) -> Self

Create a new normalized transmission model (4-parameter, no exponential tail).

Arguments

• inner — The inner transmission model (Beer-Lambert).
• energies — Energy grid in eV (must be positive).
• anorm_index — Index of Anorm in the parameter vector.
• back_a_index — Index of BackA in the parameter vector.
• back_b_index — Index of BackB in the parameter vector.
• back_c_index — Index of BackC in the parameter vector.

pub fn new_with_exponential( inner: M, energies: &[f64], anorm_index: usize, back_a_index: usize, back_b_index: usize, back_c_index: usize, back_d_index: usize, back_f_index: usize, ) -> Self

Create a normalized transmission model with the SAMMY exponential tail.

Adds BackD × exp(−BackF / √E) to the 4-parameter background model.

Arguments

• back_d_index — Index of BackD (exponential amplitude) in the parameter vector.
• back_f_index — Index of BackF (exponential decay constant) in the parameter vector.

Trait Implementations

impl<M: FitModel> FitModel for NormalizedTransmissionModel<M>

fn analytical_jacobian( &self, params: &[f64], free_param_indices: &[usize], y_current: &[f64], ) -> Option<FlatMatrix>

Analytical Jacobian for the normalized transmission model.

For each free parameter:

• If it belongs to the inner model (density or temperature): ∂T_out/∂p = Anorm × ∂T_inner/∂p (inner Jacobian scaled by Anorm)
• ∂T_out/∂Anorm = T_inner(E)
• ∂T_out/∂BackA = 1
• ∂T_out/∂BackB = 1/√E
• ∂T_out/∂BackC = √E
• ∂T_out/∂BackD = exp(−BackF / √E)
• ∂T_out/∂BackF = −BackD × exp(−BackF / √E) / √E

fn evaluate(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>

Evaluate the model for the given parameters.

impl<M: FitModel> ForwardModel for NormalizedTransmissionModel<M>

fn predict(&self, params: &[f64]) -> Result<Vec<f64>, FittingError>

Predict model output for the given parameter vector.

fn jacobian( &self, params: &[f64], free_param_indices: &[usize], y_current: &[f64], ) -> Option<Vec<Vec<f64>>>

Analytical Jacobian (column-major layout).

fn n_data(&self) -> usize

Number of data points in the model output.

fn n_params(&self) -> usize

Number of parameters (total, including fixed).