Struct SplitGaussian 

pub struct SplitGaussian;

Split (asymmetric) Gaussian: a Gaussian with a different width on each side of the center. Covers both “asymmetric split-σ Gaussian” and “bi-Gaussian”.

f(x) = A · exp(−(x−c)² / (2σ²)) with σ = σ_L for x < c, else σ_R.

Parameters (in order): [amplitude, center, sigma_l, sigma_r]

• amplitude is the peak height at x == center (both branches equal A there, so the curve is continuous).
• center is the peak location.
• sigma_l / sigma_r are the left/right Gaussian widths.

The numpy oracle is identical — np.where(x < c, A·exp(−½((x−c)/σ_L)²), A·exp(−½((x−c)/σ_R)²)) — so parity is exact.

Trait Implementations

impl Model for SplitGaussian

fn jacobian(&self, x: &[f64], params: &[f64]) -> Vec<f64>

Central finite-difference Jacobian (the side-selecting branch makes the analytic derivative piecewise; numerical is simpler and matches log_normal).

fn eval(&self, x: &[f64], params: &[f64]) -> f64

Evaluate the model at coordinate x with the given params.

fn param_names(&self) -> Vec<Cow<'static, str>>

Ordered parameter names, matching the layout expected by eval and jacobian.

fn jacobian_into(&self, x: &[f64], params: &[f64], out: &mut [f64])

Fill a pre-allocated slice with Jacobian values (one entry per parameter).

fn eval_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch evaluation of a 1-D model: fill out[i] = eval([xs[i]], params).

fn jac_slice_into(&self, xs: &[f64], params: &[f64], out: &mut [f64])

Batch Jacobian for a 1-D model: fill out in row-major layout [i * params.len() + j] = d(model)/d(params[j]) at xs[i].

fn n_dims(&self) -> usize

Number of coordinate dimensions consumed from x. Defaults to 1.