Exported Functions

RadialBasisFunctions.directionalMethod
function directional(data, eval_points, v, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the directional derivative, Directional.

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RadialBasisFunctions.directionalMethod
function directional(data, v, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the directional derivative, Directional.

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RadialBasisFunctions.gradientMethod
function gradient(data, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the gradient, Gradient.

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RadialBasisFunctions.gradientMethod
function gradient(data, eval_points, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the gradient, Gradient. The resulting operator will only evaluate at eval_points.

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RadialBasisFunctions.partialMethod
function partial(data, eval_points, order, dim, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the partial derivative, Partial. The resulting operator will only evaluate at eval_points.

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RadialBasisFunctions.partialMethod
function partial(data, order, dim, basis; k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is the partial derivative, Partial, of order with respect to dim.

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RadialBasisFunctions.regridMethod
function regrid(data, eval_points, basis=PHS(3; poly_deg=2); k=autoselect_k(data, basis))

Builds a RadialBasisOperator where the operator is an regrid from one set of points to another, data -> eval_points.

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RadialBasisFunctions.∂virtualMethod
function ∂virtual(data, eval_points, dim, Δ, basis; k=autoselect_k(data, basis))

Builds a virtual RadialBasisOperator whichi will be evaluated at eval_points where the operator is the partial derivative with respect to dim. Virtual operators interpolate the data to structured points at a distance Δ for which standard finite difference formulas can be applied.

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RadialBasisFunctions.∂virtualMethod
function ∂virtual(data, dim, Δ, basis; k=autoselect_k(data, basis))

Builds a virtual RadialBasisOperator whichi will be evaluated at the input points (data) where the operator is the partial derivative with respect to dim. Virtual operators interpolate the data to structured points at a distance Δ for which standard finite difference formulas can be applied.

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RadialBasisFunctions.DirectionalType
Directional{Dim,T} <: ScalarValuedOperator

Operator for the directional derivative, or the inner product of the gradient and a direction vector.

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Private

RadialBasisFunctions._calculate_thread_offsetsMethod
_calculate_thread_offsets(adjl, boundary_flag, nchunks)

Calculate the starting offsets for each thread when filling LHS and RHS matrices.

  • lhs_offsets: Starting indices for internal-to-internal connections
  • rhs_offsets: Starting indices for internal-to-boundary connections

Returns a tuple of (lhsoffsets, rhsoffsets).

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RadialBasisFunctions._return_global_matricesMethod
_return_global_matrices(I_lhs, J_lhs, V_lhs, I_rhs, J_rhs, V_rhs, boundary_flag)

Constructs sparse matrix representation of the global linear system.

Arguments

  • I_lhs, J_lhs, V_lhs: COO format components for LHS matrix
  • I_rhs, J_rhs, V_rhs: COO format components for RHS matrix
  • boundary_flag: Boolean array indicating boundary nodes

Returns

  • For single operators: tuple of (lhsmatrix, rhsmatrix)
  • For multiple operators: tuple of (lhsmatrices, rhsmatrices)
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