Struct CountsModel 

pub struct CountsModel<'a> {
    pub transmission_model: &'a dyn FitModel,
    pub flux: &'a [f64],
    pub background: &'a [f64],
    pub n_params: usize,
}

Convert transmission model + counts to a counts-based forward model.

Given: Y_model = flux × T_model(θ) + background

This wraps a transmission model to predict observed counts.

The flux and background slices must have the same length as the transmission vector returned by the inner model. In debug builds, evaluate() asserts this invariant.

Fields

transmission_model: &'a dyn FitModel

Underlying transmission model.

flux: &'a [f64]

Incident flux (counts per bin in open beam, after normalization).

background: &'a [f64]

Background counts per bin.

n_params: usize

Total parameter count in the wrapped model.

Trait Implementations

impl<'a> FitModel for CountsModel<'a>

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

Analytical Jacobian: ∂Y/∂θ = flux · ∂T_inner/∂θ.

Background is constant w.r.t. θ and drops out.

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

Evaluate the model for the given parameters.

impl<'a> ForwardModel for CountsModel<'a>

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

Predict model output for the given parameter vector.

fn n_data(&self) -> usize

Number of data points in the model output.

fn n_params(&self) -> usize

Number of parameters (total, including fixed).

fn jacobian( &self, _params: &[f64], _free_param_indices: &[usize], _y_current: &[f64], ) -> Option<Vec<Vec<f64>>>

Analytical Jacobian (column-major layout).