IMLCV.tools._rbfinterp_pythran

IMLCV.tools._rbfinterp_pythran#

Attributes#

NAME_TO_FUNC

Functions#

linear(r)

thin_plate_spline(r)

cubic(r)

quintic(r)

multiquadric(r)

inverse_multiquadric(r)

inverse_quadratic(r)

gaussian(r)

get_d(x, metric, epsilon)

cv_norm(x, y, metric, eps)

cv_vals(x, power, metric)

eval_kernel_matrix(x, y, metric, eps, kernel_func)

Evaluate RBFs, with centers at x, at x.

eval_polynomial_matrix(x, metric, powers)

Evaluate monomials, with exponents from powers, at x.

evaluate_system(coeffs, x, y, metric, kernel, epsilon, ...)

Construct the coefficients needed to evaluate

Module Contents#

IMLCV.tools._rbfinterp_pythran.linear(r)#
IMLCV.tools._rbfinterp_pythran.thin_plate_spline(r)#
IMLCV.tools._rbfinterp_pythran.cubic(r)#
IMLCV.tools._rbfinterp_pythran.quintic(r)#
IMLCV.tools._rbfinterp_pythran.multiquadric(r)#
IMLCV.tools._rbfinterp_pythran.inverse_multiquadric(r)#
IMLCV.tools._rbfinterp_pythran.inverse_quadratic(r)#
IMLCV.tools._rbfinterp_pythran.gaussian(r)#
IMLCV.tools._rbfinterp_pythran.NAME_TO_FUNC: dict[str, Callable[[jax.Array], jax.Array]]#
IMLCV.tools._rbfinterp_pythran.get_d(x: jax.Array, metric: IMLCV.base.CV.CvMetric, epsilon: jax.Array | float)#
IMLCV.tools._rbfinterp_pythran.cv_norm(x: IMLCV.base.CV.CV, y: IMLCV.base.CV.CV, metric: IMLCV.base.CV.CvMetric, eps: jax.Array | float)#
IMLCV.tools._rbfinterp_pythran.cv_vals(x: IMLCV.base.CV.CV, power: jax.Array, metric: IMLCV.base.CV.CvMetric)#
IMLCV.tools._rbfinterp_pythran.eval_kernel_matrix(x: IMLCV.base.CV.CV, y: IMLCV.base.CV.CV, metric: IMLCV.base.CV.CvMetric, eps: jax.Array | float, kernel_func: Callable[[jax.Array], jax.Array])#

Evaluate RBFs, with centers at x, at x.

IMLCV.tools._rbfinterp_pythran.eval_polynomial_matrix(x: IMLCV.base.CV.CV, metric: IMLCV.base.CV.CvMetric, powers: jax.Array)#

Evaluate monomials, with exponents from powers, at x.

IMLCV.tools._rbfinterp_pythran.evaluate_system(coeffs: jax.Array, x: IMLCV.base.CV.CV, y: IMLCV.base.CV.CV, metric: IMLCV.base.CV.CvMetric, kernel: str, epsilon: float | jax.Array, powers: jax.Array)#

Construct the coefficients needed to evaluate the RBF.

Parameters:

  • x ((Q, N) float ndarray) – Evaluation point coordinates.

  • y ((P, N) float ndarray) – Data point coordinates.

  • kernel (str) – Name of the RBF.

  • epsilon (float) – Shape parameter.

  • powers ((R, N) int ndarray) – The exponents for each monomial in the polynomial.

  • shift ((N,) float ndarray) – Shifts the polynomial domain for numerical stability.

  • scale ((N,) float ndarray) – Scales the polynomial domain for numerical stability.

Return type:

(Q, P + R) float ndarray

Contents