Struct CauchyDispersion 

pub struct CauchyDispersion;

Cauchy refractive-index dispersion: n(x) = a + b/x² + c/x⁴.

Parameters (in order): [a, b, c]

• a is the high-frequency (constant) refractive-index offset.
• b is the first dispersion coefficient (units of x²).
• c is the second dispersion coefficient (units of x⁴).

The independent variable x is a wavelength and must be > 0; the kernel is smooth and analytic there. At x == 0 the 1/x² / 1/x⁴ terms are undefined, so the kernel returns 0.0 (matching the numpy oracle np.where(x > 0, a + b/x² + c/x⁴, 0)), keeping numpy↔Rust parity exact. The analytical Jacobian is [1, 1/x², 1/x⁴].

Trait Implementations

impl Model for CauchyDispersion

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

Analytical Jacobian: ∂n/∂a = 1, ∂n/∂b = 1/x², ∂n/∂c = 1/x⁴ (for x > 0); all zero at x ≤ 0 where the kernel is clamped to 0.

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.