Add tests for PiecewiseLegendrePolyVector

SpM-lab · Nov 14, 2024 · e619fb8 · e619fb8
1 parent e7b487e
commit e619fb8
Showing 1 changed file with 200 additions and 0 deletions.
diff --git a/test/poly.jl b/test/poly.jl
@@ -70,6 +70,206 @@ isdefined(Main, :sve_logistic) || include("_conftest.jl")
         end
     end
 
+    @testset "PiecewiseLegendrePolyVector" begin
+#=
+julia> # The following data and knots are generated by
+julia> using SparseIR
+julia> sve_result = SparseIR.SVEResult(LogisticKernel(1.0))
+julia> data1 = sve_result.u[1].data
+julia> data2 = sve_result.u[2].data
+julia> data3 = sve_result.u[3].data
+julia> knots1 = sve_result.u[1].knots
+julia> knots2 = sve_result.u[2].knots
+julia> knots3 = sve_result.u[3].knots
+julia> l1 = sve_result.u[1].l
+julia> l2 = sve_result.u[2].l
+julia> l3 = sve_result.u[3].l
+=#
+        begin
+            data1 = reshape(
+                [
+                    0.49996553669802485
+                    -0.009838135710548356
+                    0.003315915376286483
+                    -2.4035906967802686e-5
+                    3.4824832610792906e-6
+                    -1.6818592059096e-8
+                    1.5530850593697272e-9
+                    -5.67191158452736e-12
+                    3.8438802553084145e-13
+                    -1.12861464373688e-15
+                    -1.4028528586225198e-16
+                    5.199431653846204e-18
+                    -3.490774002228127e-16
+                    4.339342349553959e-18
+                    -8.247505551908268e-17
+                    7.379549188001237e-19
+                    0.49996553669802485
+                    0.009838135710548356
+                    0.003315915376286483
+                    2.4035906967802686e-5
+                    3.4824832610792906e-6
+                    1.6818592059096e-8
+                    1.5530850593697272e-9
+                    5.67191158452736e-12
+                    3.8438802553084145e-13
+                    1.12861464373688e-15
+                    -1.4028528586225198e-16
+                    -5.199431653846204e-18
+                    -3.490774002228127e-16
+                    -4.339342349553959e-18
+                    -8.247505551908268e-17
+                    -7.379549188001237e-19
+                ],
+                16,
+                2,
+            )
+
+            knots1 = [-1.0, 0.0, 1.0]
+            l1 = 0
+        end
+
+        begin
+            data2 = reshape(
+                [
+                    -0.43195475509329695
+                    0.436151579050162
+                    -0.005257007544885257
+                    0.0010660519696441624
+                    -6.611545612452212e-6
+                    7.461310619506964e-7
+                    -3.2179499894475862e-9
+                    2.5166526274315926e-10
+                    -8.387341925898803e-13
+                    5.008268649326024e-14
+                    3.7750894390998034e-17
+                    -2.304983535459561e-16
+                    3.0252856483620636e-16
+                    -1.923751082183687e-16
+                    7.201014354168769e-17
+                    -3.2715804561902326e-17
+                    0.43195475509329695
+                    0.436151579050162
+                    0.005257007544885257
+                    0.0010660519696441624
+                    6.611545612452212e-6
+                    7.461310619506964e-7
+                    3.2179499894475862e-9
+                    2.5166526274315926e-10
+                    8.387341925898803e-13
+                    5.008268649326024e-14
+                    -3.7750894390998034e-17
+                    -2.304983535459561e-16
+                    -3.0252856483620636e-16
+                    -1.923751082183687e-16
+                    -7.201014354168769e-17
+                    -3.2715804561902326e-17
+                ],
+                16,
+                2,
+            )
+
+            knots2 = [-1.0, 0.0, 1.0]
+            l2 = 1
+        end
+
+        begin
+            data3 = reshape(
+                [
+                    -0.005870438661638806
+                    -0.8376202388555938
+                    0.28368166184926036
+                    -0.0029450618222246236
+                    0.0004277118923277169
+                    -2.4101642603229184e-6
+                    2.2287962786878678e-7
+                    -8.875091544426018e-10
+                    6.021488924175155e-11
+                    -1.8705305570705647e-13
+                    9.924398482443944e-15
+                    4.299521053905097e-16
+                    -1.0697019178666955e-16
+                    3.6972269778329906e-16
+                    -8.848885164903329e-17
+                    6.327687614609368e-17
+                    -0.005870438661638806
+                    0.8376202388555938
+                    0.28368166184926036
+                    0.0029450618222246236
+                    0.0004277118923277169
+                    2.4101642603229184e-6
+                    2.2287962786878678e-7
+                    8.875091544426018e-10
+                    6.021488924175155e-11
+                    1.8705305570705647e-13
+                    9.924398482443944e-15
+                    -4.299521053905097e-16
+                    -1.0697019178666955e-16
+                    -3.6972269778329906e-16
+                    -8.848885164903329e-17
+                    -6.327687614609368e-17
+                ],
+                16,
+                2,
+            )
+
+            knots3 = [-1.0, 0.0, 1.0]
+            l3 = 2
+        end
+
+        pwlp1 = SparseIR.PiecewiseLegendrePoly(data1, knots1, l1)
+        pwlp2 = SparseIR.PiecewiseLegendrePoly(data2, knots2, l2)
+        pwlp3 = SparseIR.PiecewiseLegendrePoly(data3, knots3, l3)
+
+        polys = SparseIR.PiecewiseLegendrePolyVector([pwlp1, pwlp2, pwlp3])
+
+        @test length(polys) == 3
+
+        polys = SparseIR.PiecewiseLegendrePolyVector(
+            [
+                SparseIR.PiecewiseLegendrePoly(data1, knots1, l1)
+                SparseIR.PiecewiseLegendrePoly(data2, knots2, l2)
+                SparseIR.PiecewiseLegendrePoly(data3, knots3, l3)
+            ],
+        )
+
+        @test SparseIR.xmin(polys) == SparseIR.xmin(pwlp1)
+        @test SparseIR.xmax(polys) == SparseIR.xmax(pwlp1)
+        @test SparseIR.knots(polys) == SparseIR.knots(pwlp1)
+        @test SparseIR.Δx(polys) == SparseIR.Δx(pwlp1)
+        @test SparseIR.polyorder(polys) == SparseIR.polyorder(pwlp1)
+        @test SparseIR.norms(polys) == SparseIR.norms(pwlp1)
+        @test SparseIR.symm(polys) == SparseIR.symm.([pwlp1, pwlp2, pwlp3])
+
+        @testset "polys(x::Float64)" begin
+            x = rand(StableRNG(42))
+            @test polys(x) == [pwlp1(x), pwlp2(x), pwlp3(x)]
+            @test SparseIR.data(polys) ==
+                cat(SparseIR.data.([pwlp1, pwlp2, pwlp3])..., dims = 3)
+        end
+
+        @testset "polys(x::Array)" begin
+            x = rand(StableRNG(42), 2, 1, 4)
+            tar = polys(x)
+            @test size(tar) == (3, 2, 1, 4)
+            ref = reshape(vcat([[pwlp1(e), pwlp2(e), pwlp3(e)] for e in x]...), 3, 2, 1, 4)
+            @test tar == ref
+        end
+
+        @testset "PiecewiseLegendrePolyVector(polys::PiecewiseLegendrePolyVector, knots::AbstractVector)" begin
+            new_knots = [-1.0, 0.0, 1.0]
+
+            new_polys = SparseIR.PiecewiseLegendrePolyVector(
+                polys, new_knots,
+                symm=zeros(Int, size(SparseIR.data(polys), 3)),
+            )
+
+            @test SparseIR.data(new_polys) == SparseIR.data(polys)
+            @test SparseIR.knots(new_polys) == SparseIR.knots(new_polys)
+            @test SparseIR.Δx(new_polys) == diff(new_knots)
+        end
+    end
+
     @testset "deriv" begin
         # independent from sve.jl
         # https://github.com/SpM-lab/SparseIR.jl/issues/51