Struct CountsBackgroundScaleModel 

pub struct CountsBackgroundScaleModel<'a> {
    pub transmission_model: &'a dyn FitModel,
    pub flux: &'a [f64],
    pub background: &'a [f64],
    pub alpha1_index: usize,
    pub alpha2_index: usize,
    pub n_params: usize,
}

Counts model with optional nuisance scaling of signal and detector background.

Given a transmission model T(θ), predicts:

Y(E) = α₁ · [Φ(E) · T(θ)] + α₂ · B(E)

where α₁ and α₂ are parameter-vector entries.

Fields

transmission_model: &'a dyn FitModel

Underlying transmission model.

flux: &'a [f64]

Incident flux spectrum.

background: &'a [f64]

Detector background spectrum.

alpha1_index: usize

Index of α₁ in the parameter vector.

alpha2_index: usize

Index of α₂ in the parameter vector.

n_params: usize

Total parameter count in the wrapped model.

Trait Implementations

impl<'a> FitModel for CountsBackgroundScaleModel<'a>

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

Evaluate the model for the given parameters.

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

Optionally provide an analytical Jacobian.

impl<'a> ForwardModel for CountsBackgroundScaleModel<'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).