test: add tests for changing data in ParametrizedInterpolation

Author: SebastianM-C

Project.toml

@@ -39,9 +39,12 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 ControlSystemsBase = "aaaaaaaa-a6ca-5380-bf3e-84a91bcd477e"
 DataInterpolations = "82cc6244-b520-54b8-b5a6-8a565e85f1d0"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
+SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
+SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Aqua", "LinearAlgebra", "OrdinaryDiffEq", "SafeTestsets", "Test", "ControlSystemsBase", "DataInterpolations"]
+test = ["Aqua", "LinearAlgebra", "OrdinaryDiffEq", "Optimization", "SafeTestsets", "Test", "ControlSystemsBase", "DataInterpolations", "SciMLStructures", "SymbolicIndexingInterface"]

test/Blocks/sources.jl

@@ -6,6 +6,9 @@ using ModelingToolkitStandardLibrary.Blocks: smooth_sin, smooth_cos, smooth_damp
                                              smooth_triangular, triangular, square
 using OrdinaryDiffEq: ReturnCode.Success
 using DataInterpolations
+using SymbolicIndexingInterface
+using SciMLStructures: SciMLStructures, Tunable
+using Optimization
 
 @testset "Constant" begin
     @named src = Constant(k = 2)
@@ -479,8 +482,8 @@ end
 
 @testset "ParametrizedInterpolation" begin
     @variables y(t) = 0
-    @parameters u[1:15] = rand(15)
-    @parameters x[1:15] = 0:14.0
+    u = rand(15)
+    x = 0:14.0
 
     @testset "LinearInterpolation" begin
         @named i = ParametrizedInterpolation(LinearInterpolation, u, x)
@@ -493,6 +496,41 @@ end
         sol = solve(prob)
 
         @test SciMLBase.successful_retcode(sol)
+
+        prob2 = remake(prob, p=[i.data => ones(15)])
+        sol2 = solve(prob2)
+
+        @test SciMLBase.successful_retcode(sol2)
+        @test all(only.(sol2.u) .≈ sol2.t) # the solution for y' = 1 is y(t) = t
+
+        set_data! = setp(prob2, i.data)
+        set_data!(prob2, zeros(15))
+        sol3 = solve(prob2)
+        @test SciMLBase.successful_retcode(sol3)
+        @test iszero(sol3)
+
+        function loss(x, p)
+            prob0, set_data! = p
+            ps = parameter_values(prob0)
+            arr, repack, alias = SciMLStructures.canonicalize(Tunable(), ps)
+            T = promote_type(eltype(x), eltype(arr))
+            promoted_ps = SciMLStructures.replace(Tunable(), ps, T.(arr))
+            prob = remake(prob0; p = promoted_ps)
+
+            set_data!(prob, x)
+            sol = solve(prob)
+            sum(abs2.(only.(sol.u) .- sol.t))
+        end
+
+        set_data! = setp(prob, i.data)
+        of = OptimizationFunction(loss, AutoForwardDiff())
+        op = OptimizationProblem(of, u, (prob, set_data!), lb = zeros(15), ub = fill(2.0, 15))
+
+        # check that type changing works
+        @test length(ForwardDiff.gradient(x -> of(x, (prob, set_data!)), u)) == 15
+
+        r = solve(op, Optimization.LBFGS(), maxiters = 1000)
+        @test of(r.u, (prob, set_data!)) < of(u, (prob, set_data!))
     end
 
     @testset "BSplineInterpolation" begin