add print tests

src/basis/basis.jl

@@ -23,11 +23,3 @@ unicode_order(::Val{6}) = "⁶"
 unicode_order(::Val{7}) = "⁷"
 unicode_order(::Val{8}) = "⁸"
 unicode_order(::Val{9}) = "⁹"
-
-function Base.show(io::IO, basis::B) where {B<:AbstractRadialBasis}
-    if basis.poly_deg < 0
-        print(io, "\n  No Monomial augmentation")
-    else
-        print(io, "\n  Monomial: degree $(basis.poly_deg)")
-    end
-end

src/basis/gaussian.jl

@@ -10,8 +10,6 @@ struct Gaussian{T,D<:Int} <: AbstractRadialBasis
         if all(ε .< 0)
             throw(ArgumentError("Shape parameter should be > 0. (ε=$ε)"))
         end
-        poly_deg < -1 &&
-            throw(ArgumentError("Augmented Monomial degree must be at least 0 (constant)."))
         return new{T,D}(ε, poly_deg)
     end
 end
@@ -47,11 +45,8 @@ end
 function Base.show(io::IO, rbf::Gaussian)
     print(io, "Gaussian, exp(-(ε*r)²)")
     print(io, "\n├─Shape factor: ε = $(rbf.ε)")
-    if rbf.poly_deg < 0
-        print(io, "\n└─No Monomial augmentation")
-    else
-        print(io, "\n└─Polynomial augmentation: degree $(rbf.poly_deg)")
-    end
+    print(io, "\n└─Polynomial augmentation: degree $(rbf.poly_deg)")
+    return nothing
 end
 
 print_basis(rbf::Gaussian) = "Gaussian (ε = $(rbf.ε))"

src/basis/inverse_multiquadric.jl

@@ -7,8 +7,6 @@ struct IMQ{T,D<:Int} <: AbstractRadialBasis
         if all(ε .< 0)
             throw(ArgumentError("Shape parameter should be > 0. (ε=$ε)"))
         end
-        poly_deg < -1 &&
-            throw(ArgumentError("Augmented Monomial degree must be at least 0 (constant)."))
         return new{T,D}(ε, poly_deg)
     end
 end

src/interpolation.jl

@@ -55,11 +55,10 @@ function Base.show(io::IO, op::Interpolator)
     println(io, "├─Input type: ", typeof(first(op.x)))
     println(io, "├─Output type: ", typeof(first(op.y)))
     println(io, "├─Number of points: ", length(op.x))
-    return println(
+    return print(
         io,
         "└─Basis: ",
         print_basis(op.rbf_basis),
-        " with degree $(_get_deg(op.monomial_basis)) Monomial",
+        " with degree $(degree(op.monomial_basis)) Monomial",
     )
 end
-_get_deg(::MonomialBasis{Dim,Deg}) where {Dim,Deg} = Deg

src/operators/directional.jl

@@ -101,4 +101,4 @@ function update_weights!(op::RadialBasisOperator{<:Directional})
 end
 
 # pretty printing
-print_op(op::Directional) = "Directional Gradient (∇f⋅v)"
+print_op(op::Directional) = "Directional Derivative (∇f⋅v)"

src/operators/laplacian.jl

@@ -30,4 +30,4 @@ function laplacian(
 end
 
 # pretty printing
-print_op(op::Laplacian) = "Laplacian (∇²f or Δf)"
+print_op(op::Laplacian) = "Laplacian (∇²f)"

test/Project.toml

@@ -3,6 +3,6 @@ HaltonSequences = "13907d55-377f-55d6-a9d6-25ac19e11b95"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
-StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+StaticArraysCore = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/basis/gaussian.jl

@@ -1,8 +1,8 @@
 using RadialBasisFunctions
 import RadialBasisFunctions as RBF
-using StaticArrays
+using StaticArraysCore
 
-@testset "Constructors" begin
+@testset "Constructors and Printing" begin
     g = Gaussian()
     @test g isa Gaussian
     @test g.ε == 1
@@ -13,7 +13,11 @@ using StaticArrays
     @test g.poly_deg == 0
 
     @test_throws ArgumentError Gaussian(-1)
-    @test_throws ArgumentError Gaussian(; poly_deg=-2)
+
+    @test repr(g) == """
+    Gaussian, exp(-(ε*r)²)
+    ├─Shape factor: ε = 5.0
+    └─Polynomial augmentation: degree 0"""
 end
 
 x₁ = SVector(1.0, 2)

test/basis/inverse_multiquadric.jl

@@ -1,8 +1,8 @@
 using RadialBasisFunctions
 import RadialBasisFunctions as RBF
-using StaticArrays
+using StaticArraysCore
 
-@testset "Constructors" begin
+@testset "Constructors and Printing" begin
     imq = IMQ()
     @test imq isa IMQ
     @test imq.ε == 1
@@ -13,7 +13,11 @@ using StaticArrays
     @test imq.poly_deg == 0
 
     @test_throws ArgumentError IMQ(-1)
-    @test_throws ArgumentError IMQ(; poly_deg=-2)
+
+    @test repr(imq) == """
+    Inverse Multiquadrics, 1/sqrt((r*ε)²+1)
+    ├─Shape factor: ε = 5.0
+    └─Polynomial augmentation: degree 0"""
 end
 
 x₁ = SVector(1.0, 2)

test/basis/monomial.jl

@@ -1,6 +1,12 @@
 using RadialBasisFunctions
 import RadialBasisFunctions as RBF
-using StaticArrays
+using StaticArraysCore
+
+@testset "Construction and Printing" begin
+    @test_throws ArgumentError MonomialBasis(1, -1)
+    m = MonomialBasis(1, 0)
+    @test repr(m) == "MonomialBasis of degree 0 in 1 dimensions"
+end
 
 @testset "dim=1, deg=0" begin
     inputs = (SVector(2.0), 2.0)

test/basis/polyharmonic_spline.jl

@@ -1,8 +1,8 @@
 using RadialBasisFunctions
 import RadialBasisFunctions as RBF
-using StaticArrays
+using StaticArraysCore
 
-@testset "Constructors" begin
+@testset "Constructors and Printing" begin
     phs = PHS()
     @test phs isa PHS3
     @test phs.poly_deg == 2
@@ -12,6 +12,10 @@ using StaticArrays
 
     @test_throws ArgumentError PHS(2; poly_deg=-1)
     @test_throws ArgumentError PHS(3; poly_deg=-2)
+
+    @test repr(phs) == """
+    Polyharmonic spline (r⁵)
+    └─Polynomial augmentation: degree 0"""
 end
 
 @testset "PHS, n=1" begin

test/operators/directional.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 using LinearAlgebra
@@ -45,3 +45,8 @@ end
     exact = map((x, vv) -> SVector(df_dx(x), df_dy(x)) ⋅ vv, x2, v)
     @test mean_percent_error(∇v(y), exact) < 10
 end
+
+@testset "Printing" begin
+    ∇v = Directional((1, 2), (3, 4))
+    @test RadialBasisFunctions.print_op(∇v) == "Directional Derivative (∇f⋅v)"
+end

test/operators/gradient.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 
@@ -30,3 +30,8 @@ end
     @test mean_percent_error(∇y[1], df_dx.(x2)) < 10
     @test mean_percent_error(∇y[2], df_dy.(x2)) < 10
 end
+
+@testset "Printing" begin
+    ∇ = Gradient((1, 2))
+    @test RadialBasisFunctions.print_op(∇) == "Gradient (∇f)"
+end

test/operators/interpolation.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using HaltonSequences
 
 """
@@ -24,3 +24,10 @@ interp = Interpolator(x, y, PHS(3; poly_deg=2))
 
 xnew = SVector(0.5, 0.5)
 @test abs(interp(xnew) - franke(xnew)) < 1e-5
+
+@test repr(interp) == """
+Interpolator
+├─Input type: StaticArraysCore.SVector{2, Float64}
+├─Output type: Float64
+├─Number of points: 10000
+└─Basis: Polyharmonic spline (r³) with degree 2 Monomial"""

test/operators/laplacian.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 
@@ -31,3 +31,8 @@ end
     ∇² = laplacian(x, x2, PHS(3; poly_deg=4))
     @test mean_percent_error(∇²(y), ∇²f.(x2)) < 10
 end
+
+@testset "Printing" begin
+    ∇ = Laplacian(identity)
+    @test RadialBasisFunctions.print_op(∇) == "Laplacian (∇²f)"
+end

test/operators/monomial.jl

@@ -0,0 +1,5 @@
+using RadialBasisFunctions
+
+@test_throws ArgumentError RadialBasisFunctions.ℒMonomialBasis(1, -1, identity)
+m = RadialBasisFunctions.ℒMonomialBasis(1, 0, identity)
+@test repr(m) == "ℒMonomialBasis of degree 0 in 1 dimensions"

test/operators/operator_algebra.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using LinearAlgebra
 using Statistics
 using HaltonSequences

test/operators/partial.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 
@@ -69,3 +69,8 @@ end
     @test mean_percent_error(∂x(y), df_dx.(x2)) < 10
     @test mean_percent_error(∂y(y), df_dy.(x2)) < 10
 end
+
+@testset "Printing" begin
+    ∂ = Partial(identity, 1, 2)
+    @test RadialBasisFunctions.print_op(∂) == "∂ⁿf/∂xᵢ (n = 1, i = 2)"
+end

test/operators/regrid.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 
@@ -15,3 +15,5 @@ y = f.(x)
 x2 = map(x -> SVector{2}(rand(2)), 1:100)
 r = regrid(x, x2, PHS(3; poly_deg=2))
 @test mean_percent_error(r(y), f.(x2)) < 0.1
+
+@test RadialBasisFunctions.print_op(r.ℒ) == "regrid"

test/operators/virtual.jl

@@ -1,5 +1,5 @@
 using RadialBasisFunctions
-using StaticArrays
+using StaticArraysCore
 using Statistics
 using HaltonSequences
 

test/solve.jl

@@ -1,6 +1,6 @@
 using RadialBasisFunctions
 import RadialBasisFunctions as RBF
-using StaticArrays
+using StaticArraysCore
 using LinearAlgebra
 
 x = [SVector(1.0, 2.0), SVector(2.0, 1.0), SVector(1.5, 0.0)]