add package extension for Meshes.jl

.github/workflows/CI.yml

@@ -63,7 +63,7 @@ jobs:
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
         with:
-          directories: src,examples
+          directories: src,examples,ext
       - uses: codecov/codecov-action@v4
         with:
           files: lcov.info

Project.toml

@@ -20,20 +20,30 @@ TrixiBase = "9a0f1c46-06d5-4909-a5a3-ce25d3fa3284"
 TypedPolynomials = "afbbf031-7a57-5f58-a1b9-b774a0fad08d"
 WriteVTK = "64499a7a-5c06-52f2-abe2-ccb03c286192"
 
+[weakdeps]
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
+
+[extensions]
+KernelInterpolationMeshesExt = "Meshes"
+
 [compat]
 DiffEqCallbacks = "3"
 ForwardDiff = "0.10.36"
 LinearAlgebra = "1"
+Meshes = "0.46"
 Printf = "1"
 ReadVTK = "0.2"
 RecipesBase = "1.3.4"
 Reexport = "1.2"
 SciMLBase = "2.26"
 SimpleUnPack = "1.1"
 SpecialFunctions = "2"
-StaticArrays = "1"
+StaticArrays = "1.9"
 TimerOutputs = "0.5.23"
 TrixiBase = "0.1.3"
 TypedPolynomials = "0.4.1"
 WriteVTK = "1.18"
 julia = "1.10"
+
+[extras]
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"

README.md

@@ -36,10 +36,10 @@ julia> using Pkg
 julia> Pkg.add("Plots")
 ```
 
-To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) and
-for solving time-dependent partial differential equations [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a
-similar way as above for Plots.jl. See the documentation for more examples on how to use these packages in combination with
-KernelInterpolation.jl.
+To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) or
+[Meshes.jl](https://github.com/JuliaGeometry/Meshes.jl) and for solving time-dependent partial differential equations
+[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. See the documentation for more
+examples on how to use these packages in combination with KernelInterpolation.jl.
 
 ## Usage
 
@@ -129,6 +129,7 @@ for more details.
 ## Referencing
 
 You can directly refer to KernelInterpolation.jl as
+
 ```bibtex
 @misc{lampert2024kernel,
   title={{K}ernel{I}nterpolation.jl: {M}ultivariate (generalized) scattered data interpolation

docs/Project.toml

@@ -1,9 +1,11 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
 
 [compat]
 Documenter = "1"
+Meshes = "0.46"
 Plots = "1.9"
 QuasiMonteCarlo = "0.3"

docs/src/development.md

@@ -19,7 +19,7 @@ cd KernelInterpolation.jl
 mkdir run
 cd run
 julia --project=. -e 'using Pkg; Pkg.develop(PackageSpec(path=".."))' # Install local KernelInterpolation.jl clone
-julia --project=. -e 'using Pkg; Pkg.add(["Plots", "QuasiMonteCarlo", "OrdinaryDiffEq"])' # Install additional packages
+julia --project=. -e 'using Pkg; Pkg.add(["Plots", "QuasiMonteCarlo", "Meshes", "OrdinaryDiffEq"])' # Install additional packages
 ```
 
 If you use other packages for executing KernelInterpolation.jl, you can add them to the project in the `run`

docs/src/index.md

@@ -36,10 +36,10 @@ julia> using Pkg
 julia> Pkg.add("Plots")
 ```
 
-To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) and
-for solving time-dependent partial differential equations [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a
-similar way as above for Plots.jl. See the documentation for more examples on how to use these packages in combination with
-KernelInterpolation.jl.
+To create special node sets, you might also want to install [QuasiMonteCarlo.jl](https://github.com/SciML/QuasiMonteCarlo.jl) or
+[Meshes.jl](https://github.com/JuliaGeometry/Meshes.jl) and for solving time-dependent partial differential equations
+[OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl) in a similar way as above for Plots.jl. See the documentation for more
+examples on how to use these packages in combination with KernelInterpolation.jl.
 
 ## Usage
 
@@ -129,6 +129,7 @@ for more details.
 ## Referencing
 
 You can directly refer to KernelInterpolation.jl as
+
 ```bibtex
 @misc{lampert2024kernel,
   title={{K}ernel{I}nterpolation.jl: {M}ultivariate (generalized) scattered data interpolation

docs/src/nodesets.md

@@ -84,6 +84,22 @@ nodes_halton = NodeSet(nodes_matrix')
 
 For the available sampling algorithms in QuasiMonteCarlo.jl, see the [overview in the documentation](https://docs.sciml.ai/QuasiMonteCarlo/stable/samplers/).
 
+Another possibility to create more advanced [`NodeSet`](@ref)s is by using the package [Meshes.jl](https://github.com/JuliaGeometry/Meshes.jl) and the sampling
+algorithms defined therein. For example, we can create a regularly sampled set of nodes on the surface of a sphere by running:
+
+```@example nodesets
+using Meshes: Meshes, Sphere, Point, RegularSampling
+sphere = Sphere(Point(0.0, 0.0, 0.0), 1.0)
+sampler = RegularSampling(20, 30)
+points = Meshes.sample(sphere, sampler)
+nodes = NodeSet(collect(points))
+```
+
+For more information on the available sampling algorithms in Meshes.jl, see the [documentation](https://juliageometry.github.io/MeshesDocs/stable/algorithms/sampling/).
+In the documentation of Meshes.jl, you can also find information on how to create more complex geometries like ellipsoids, tori, and many more.
+In general, a `PointSet` from Meshes.jl or a `Vector` of `Point`s can be directly passed to the constructor of a [`NodeSet`](@ref) and vice versa can a
+[`NodeSet`](@ref) be passed to the constructor of a `PointSet`.
+
 More complicated [`NodeSet`](@ref)s consisting of different shapes can be created, e.g., by `merge`ing different [`NodeSet`](@ref)s.
 
 ## Visualizing [`NodeSet`](@ref)s

docs/src/pdes.md

@@ -211,7 +211,7 @@ c_0 = (u_0)_X.
 
 For the solution of the DAE system, KernelInterpolation.jl uses the library [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl), which already provides a
 wide range of time integration methods. Note that this is a differential-algebraic equation (DAE) system, which is more difficult to solve than a simple ODE system. Thus,
-we are restricted to specialized time integration methods, which can handle DAEs. We recommend using the `Rodas5` method, which is a Rosenbrock method for stiff DAEs. See
+we are restricted to specialized time integration methods, which can handle DAEs. We recommend using the `Rodas5P` method, which is a Rosenbrock method for stiff DAEs. See
 also the [documentation of OrdinaryDiffEq.jl](https://docs.sciml.ai/DiffEqDocs/latest/tutorials/dae_example/) for more information.
 
 In KernelInterpolation.jl, you can use the constructor of a [`Semidiscretization`](@ref) in a very similar way as [`SpatialDiscretization`](@ref), but with the additional

ext/KernelInterpolationMeshesExt.jl

@@ -0,0 +1,23 @@
+module KernelInterpolationMeshesExt
+
+using Meshes: Meshes, Point, PointSet, to
+using KernelInterpolation: KernelInterpolation
+
+# Meshes.jl uses Unitful.jl for units, which is not available in KernelInterpolation.jl
+# Thus, we need to remove the units from the Point struct
+val(u) = u.val
+val(p::Point) = val.(to(p))
+
+function KernelInterpolation.NodeSet(points::Vector{P}) where P <: Point
+    return KernelInterpolation.NodeSet(val.(points))
+end
+
+function KernelInterpolation.NodeSet(points::PointSet)
+    return KernelInterpolation.NodeSet(points.geoms)
+end
+
+function Meshes.PointSet(nodes::KernelInterpolation.NodeSet)
+    return Meshes.PointSet(Tuple.(nodes.nodes))
+end
+
+end

src/KernelInterpolation.jl

@@ -9,7 +9,7 @@ using RecipesBase: RecipesBase, @recipe, @series
 using SciMLBase: ODEFunction, ODEProblem, ODESolution, DiscreteCallback, u_modified!
 using SimpleUnPack: @unpack
 using SpecialFunctions: besselk, loggamma
-using StaticArrays: StaticArrays, MVector
+using StaticArrays: StaticArrays, MVector, SVector
 using Reexport: @reexport
 using TimerOutputs: TimerOutputs, print_timer, reset_timer!
 @reexport using TrixiBase: trixi_include

src/nodes.jl

@@ -38,6 +38,11 @@ function NodeSet(nodes::Vector{MVector{Dim, RealT}}) where {Dim, RealT}
     q = separation_distance(nodes)
     NodeSet{Dim, RealT}(nodes, q)
 end
+function NodeSet(nodes::Vector{SVector{Dim, RealT}}) where {Dim, RealT}
+    nodes = MVector.(nodes)
+    q = separation_distance(nodes)
+    NodeSet{Dim, RealT}(nodes, q)
+end
 function NodeSet(nodes::AbstractVector{Vector{RealT}}) where {RealT}
     n = length(nodes)
     @assert n > 0

test/Project.toml

@@ -2,6 +2,7 @@
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Meshes = "eacbb407-ea5a-433e-ab97-5258b1ca43fa"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
@@ -14,8 +15,9 @@ WriteVTK = "64499a7a-5c06-52f2-abe2-ccb03c286192"
 Aqua = "0.8.3"
 ExplicitImports = "1.0.1"
 LinearAlgebra = "1"
+Meshes = "0.46"
 OrdinaryDiffEq = "6.68"
 Plots = "1.25.11"
 Random = "1"
-StaticArrays = "1"
+StaticArrays = "1.9"
 Test = "1"

test/test_examples_pde.jl

@@ -47,10 +47,13 @@ EXAMPLES_DIR = joinpath(examples_dir(), "PDEs")
                               pde_test=true, atol=1e-6) # stability issues
     end
 
-    @ki_testset "advection_3d_basic.jl" begin
-        @test_include_example(joinpath(EXAMPLES_DIR, "advection_3d_basic.jl"),
-                              l2=0.055338785034078526, linf=0.004385483831323006,
-                              pde_test=true, tspan=(0.0, 0.1))
+    # Windows CI suddenly takes much smaller time steps for some reason
+    if !Sys.iswindows()
+        @ki_testset "advection_3d_basic.jl" begin
+            @test_include_example(joinpath(EXAMPLES_DIR, "advection_3d_basic.jl"),
+                                  l2=0.055338785034078526, linf=0.004385483831323006,
+                                  pde_test=true, tspan=(0.0, 0.1))
+        end
     end
 
     @ki_testset "advection_diffusion_2d_basic.jl" begin

test/test_unit.jl

@@ -5,6 +5,7 @@ using KernelInterpolation
 using LinearAlgebra: norm, Symmetric, I
 using OrdinaryDiffEq: solve, Rodas5P
 using StaticArrays: MVector
+using Meshes: Sphere, Point, PointSet, RegularSampling, sample
 using Plots
 
 include("test_util.jl")
@@ -553,6 +554,16 @@ include("test_util.jl")
         for node in nodeset14_1
             @test isapprox(norm(node), 1.0)
         end
+
+        # Meshes.jl extension
+        sphere = Sphere(Point(0.0, 0.0, 0.0), 1.0)
+        sampler = RegularSampling(5, 6)
+        points = sample(sphere, sampler)
+        nodeset_15 = @test_nowarn NodeSet(collect(points))
+        @test nodeset_15 isa NodeSet{3, Float64}
+        @test length(nodeset_15) == 32
+        ps = PointSet(Point(1.0, 2.0, 3.0), Point(4.0, 5.0, 6.0))
+        @test PointSet(NodeSet(ps)) == ps
     end
 
     @testset "Interpolation" begin