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comparison src/SbpOperators/boundaryops/normal_derivative.jl @ 1329:e94ddef5e72f refactor/grids

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Clean up documentation for changed types in SbpOperatorClean up documentation for changed types in SbpOperatorss
author Jonatan Werpers <jonatan@werpers.com>
date Tue, 02 May 2023 22:09:33 +0200
parents 54c3ed752730
children 08f06bfacd5c
comparison
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1328:f00a205ae347 1329:e94ddef5e72f
1 """ 1 """
2 normal_derivative(g, closure_stencil::Stencil, boundary) 2 normal_derivative(g, stencil_set::StencilSet, boundary)
3 normal_derivative(g::TensorGrid, stencil_set::StencilSet, boundary::TensorGridBoundary)
4 normal_derivative(g::EquidistantGrid, stencil_set::StencilSet, boundary)
3 5
4 Creates the normal derivative boundary operator `d` as a `LazyTensor` 6 Creates the normal derivative boundary operator `d` as a `LazyTensor`
5 7
6 `d` computes the normal derivative of a grid function on `boundary` a `Stencil` `closure_stencil`. 8 `d` computes the normal derivative of a grid function on `boundary` using the
7 `d'` is the prolongation of the normal derivative of a grid function to the whole grid using the same `closure_stencil`. 9 'd1' stencil in the given stencil_set. `d'` is the prolongation of the normal
8 On a one-dimensional `grid`, `d` is a `BoundaryOperator`. On a multi-dimensional `grid`, `d` is the inflation of 10 derivative of a grid function to the whole grid using the same stencil. On a
9 a `BoundaryOperator`. 11 one-dimensional `grid`, `d` is a `BoundaryOperator`. On a multi-dimensional
12 `grid`, `d` is the inflation of a `BoundaryOperator`.
10 13
11 See also: [`BoundaryOperator`](@ref), [`LazyTensors.inflate`](@ref). 14 See also: [`BoundaryOperator`](@ref), [`LazyTensors.inflate`](@ref).
12 """ 15 """
13 #TODO: Check docstring 16 function normal_derivative end
17
18
14 function normal_derivative(g::TensorGrid, stencil_set::StencilSet, boundary::TensorGridBoundary) 19 function normal_derivative(g::TensorGrid, stencil_set::StencilSet, boundary::TensorGridBoundary)
15 op = normal_derivative(g.grids[grid_id(boundary)], stencil_set, boundary_id(boundary)) 20 op = normal_derivative(g.grids[grid_id(boundary)], stencil_set, boundary_id(boundary))
16 return LazyTensors.inflate(op, size(g), grid_id(boundary)) 21 return LazyTensors.inflate(op, size(g), grid_id(boundary))
17 end 22 end
18 23