test: update several tests to MTKv9, fix minor bug

src/scimlfunctions.jl

@@ -2293,7 +2293,7 @@ function ODEFunction{iip, specialize}(f;
 
     _f = prepare_function(f)
 
-    sys = something(sys, SymbolCache(syms, paramsyms, indepsym))
+    sys = sys_or_symbolcache(sys, syms, paramsyms, indepsym)
 
     @assert typeof(initializeprob) <:
             Union{Nothing, NonlinearProblem, NonlinearLeastSquaresProblem}
@@ -3897,7 +3897,7 @@ end
 function sys_or_symbolcache(sys, syms, paramsyms, indepsym = nothing)
     if sys === nothing &&
        (syms !== nothing || paramsyms !== nothing || indepsym !== nothing)
-        error("The use of keyword arguments `syms`, `paramsyms` and `indepsym` for `SciMLFunction`s is deprecated. Pass `sys = SymbolCache(syms, paramsyms, indepsym)` instead.")
+        Base.depwarn("The use of keyword arguments `syms`, `paramsyms` and `indepsym` for `SciMLFunction`s is deprecated. Pass `sys = SymbolCache(syms, paramsyms, indepsym)` instead.", :syms)
         sys = SymbolCache(syms, paramsyms, indepsym)
     end
     return sys

test/downstream/Project.toml

@@ -4,11 +4,13 @@ ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 NonlinearSolve = "8913a72c-1f9b-4ce2-8d82-65094dcecaec"
 Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationMOI = "fd9f6733-72f4-499f-8506-86b2bdd0dea1"
 OptimizationOptimJL = "36348300-93cb-4f02-beb5-3c3902f8871e"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
+SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"
 Sundials = "c3572dad-4567-51f8-b174-8c6c989267f4"
 SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
 Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -25,6 +27,7 @@ OrdinaryDiffEq = "6.33"
 RecursiveArrayTools = "3"
 SciMLBase = "2"
 SciMLSensitivity = "7.11"
+SciMLStructures = "1.1"
 Sundials = "4.11"
 SymbolicIndexingInterface = "0.3"
 Unitful = "1.12"

test/downstream/integrator_indexing.jl

@@ -1,6 +1,7 @@
 using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, StochasticDiffEq,
       SymbolicIndexingInterface, Test
 using ModelingToolkit: t_nounits as t, D_nounits as D
+using SciMLStructures: canonicalize, Tunable
 ### Tests on non-layered model (everything should work). ###
 
 @parameters a b c d
@@ -9,8 +10,8 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
     D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
 
-@named population_model = ODESystem(eqs, t)
-
+@named pop_model = ODESystem(eqs, t)
+population_model = complete(pop_model)
 # Tests on ODEProblem.
 u0 = [s1 => 2.0, s2 => 1.0]
 p = [a => 2.0, b => 1.0, c => 1.0, d => 1.0]
@@ -81,7 +82,8 @@ integrator[:s1] = 1.0
 # Tests on SDEProblem
 noiseeqs = [0.1 * s1,
     0.1 * s2]
-@named noisy_population_model = SDESystem(population_model, noiseeqs)
+@named noisy_population_model = SDESystem(pop_model, noiseeqs)
+noisy_population_model = complete(noisy_population_model)
 sprob = SDEProblem(noisy_population_model, u0, (0.0, 100.0), p)
 integrator = init(sprob, ImplicitEM())
 
@@ -120,7 +122,6 @@ integrator[:s1] = 1.0
 
 @parameters σ ρ β
 @variables x(t) y(t) z(t)
-D = Differential(t)
 
 eqs = [D(x) ~ σ * (y - x),
     D(y) ~ x * (ρ - z) - y,
@@ -133,9 +134,8 @@ eqs = [D(x) ~ σ * (y - x),
 @variables a(t) α(t)
 connections = [0 ~ lorenz1.x + lorenz2.y + a * γ,
     α ~ 2lorenz1.x + a * γ]
-@mtkbuild sys_simplified = ODESystem(
+@mtkbuild sys = ODESystem(
     connections, t, [a, α], [γ], systems = [lorenz1, lorenz2])
-sys_simplified = complete(structural_simplify(sys))
 
 u0 = [lorenz1.x => 1.0,
     lorenz1.y => 0.0,
@@ -154,7 +154,7 @@ p = [lorenz1.σ => 10.0,
     γ => 2.0]
 
 tspan = (0.0, 100.0)
-prob = ODEProblem(sys_simplified, u0, tspan, p)
+prob = ODEProblem(sys, u0, tspan, p)
 integrator = init(prob, Rodas4())
 step!(integrator, 100.0, true)
 
@@ -185,8 +185,8 @@ step!(integrator, 100.0, true)
 eqs = [D(q[1]) ~ 2q[1]
        D(q[2]) ~ 2.0]
 @named sys2 = ODESystem(eqs, t, [q...], [])
-sys2_simplified = complete(structural_simplify(sys2))
-prob2 = ODEProblem(sys2, [], (0.0, 5.0))
+sys2_simplified = structural_simplify(sys2)
+prob2 = ODEProblem(sys2_simplified, [], (0.0, 5.0))
 integrator2 = init(prob2, Tsit5())
 
 @test integrator2[q] isa Vector{Float64}
@@ -215,8 +215,9 @@ end
 
 # Tests various interface methods:
 @test_throws Any getp(sys, σ)(integrator)
-@test in(getp(sys, lorenz1.σ)(integrator), integrator.p)
-@test in(getp(sys, lorenz2.σ)(integrator), integrator.p)
+tunable, _, _ = canonicalize(Tunable(), integrator.p)
+@test in(getp(sys, lorenz1.σ)(integrator), tunable)
+@test in(getp(sys, lorenz2.σ)(integrator), tunable)
 @test_throws Any getp(sol, :σ)(sol)
 
 @test_throws Any integrator[x]
@@ -329,7 +330,6 @@ plot(sol,idxs=(t,α))
 using LinearAlgebra
 sts = @variables x(t)[1:3]=[1, 2, 3.0] y(t)=1.0
 ps = @parameters p[1:3] = [1, 2, 3]
-D = Differential(t)
 eqs = [collect(D.(x) .~ x)
        D(y) ~ norm(x) * y - x[1]]
 @mtkbuild sys = ODESystem(eqs, t, [sts...;], [ps...;])

test/downstream/problem_interface.jl

@@ -110,6 +110,7 @@ noiseeqs = [0.1 * x,
     0.1 * y,
     0.1 * z]
 @named noise_sys = SDESystem(sys, noiseeqs)
+noise_sys = complete(noise_sys)
 sprob = SDEProblem(noise_sys, u0, (0.0, 100.0), p)
 u0
 
@@ -181,7 +182,6 @@ set_tuple!(sprob, [10.0, 10.0])
 using LinearAlgebra
 sts = @variables x(t)[1:3]=[1, 2, 3.0] y(t)=1.0
 ps = @parameters p[1:3] = [1, 2, 3]
-D = Differential(t)
 eqs = [collect(D.(x) .~ x)
        D(y) ~ norm(x) * y - x[1]]
 @mtkbuild sys = ODESystem(eqs, t, [sts...;], [ps...;])

test/downstream/remake_autodiff.jl

@@ -2,7 +2,6 @@ using OrdinaryDiffEq, ModelingToolkit, Zygote, SciMLSensitivity
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @variables x(t) o(t)
-D = Differential(t)
 function lotka_volterra(; name = name)
     unknowns = @variables x(t)=1.0 y(t)=1.0 o(t)
     params = @parameters p1=1.5 p2=1.0 p3=3.0 p4=1.0

test/downstream/solution_interface.jl

@@ -29,7 +29,8 @@ sol = solve(oprob, Rodas4())
 noiseeqs = [0.1 * s1,
     0.1 * s2]
 @named noisy_population_model = SDESystem(population_model, noiseeqs)
-sprob = SDEProblem(complete(noisy_population_model), u0, (0.0, 100.0), p)
+noisy_population_model = complete(noisy_population_model)
+sprob = SDEProblem(noisy_population_model, u0, (0.0, 100.0), p)
 sol = solve(sprob, ImplicitEM())
 
 @test sol[s1] == sol[noisy_population_model.s1] == sol[:s1]
@@ -46,8 +47,8 @@ eqs = [D(x) ~ σ * (y - x),
     D(y) ~ x * (ρ - z) - y,
     D(z) ~ x * y - β * z]
 
-@named lorenz1 = ODESystem(eqs,t)
-@named lorenz2 = ODESystem(eqs,t)
+@named lorenz1 = ODESystem(eqs, t)
+@named lorenz2 = ODESystem(eqs, t)
 
 @parameters γ
 @variables a(t) α(t)

test/downstream/symbol_indexing.jl

@@ -4,14 +4,13 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @parameters σ ρ β
 @variables x(t) y(t) z(t)
-D = Differential(t)
 
 eqs = [D(x) ~ σ * (y - x),
     D(y) ~ x * (ρ - z) - y,
     D(z) ~ x * y - β * z]
 
-@named lorenz1 = ODESystem(eqs)
-@named lorenz2 = ODESystem(eqs)
+@named lorenz1 = ODESystem(eqs, t)
+@named lorenz2 = ODESystem(eqs, t)
 
 @parameters γ
 @variables a(t) α(t)
@@ -103,7 +102,7 @@ eqs = [D(q[1]) ~ 2q[1]
        D(q[2]) ~ 2.0]
 @named sys2 = ODESystem(eqs, t, [q...], [])
 sys2_simplified = structural_simplify(sys2)
-prob2 = ODEProblem(sys2, [], (0.0, 5.0))
+prob2 = ODEProblem(sys2_simplified, [], (0.0, 5.0))
 sol2 = solve(prob2, Tsit5())
 
 @test sol2[q] isa Vector{Vector{Float64}}
@@ -217,10 +216,10 @@ plot(sol,idxs=(t,α))
 using LinearAlgebra
 sts = @variables x(t)[1:3]=[1, 2, 3.0] y(t)=1.0
 ps = @parameters p[1:3] = [1, 2, 3]
-D = Differential(t)
 eqs = [collect(D.(x) .~ x)
        D(y) ~ norm(x) * y - x[1]]
 @named sys = ODESystem(eqs, t, [sts...;], [ps...;])
+sys = complete(sys)
 prob = ODEProblem(sys, [], (0, 1.0))
 sol = solve(prob, Tsit5())
 @test sol[x] isa Vector{<:Vector}
@@ -340,9 +339,9 @@ end
 # accessing parameters
 @variables x(t)
 @parameters tau
-D = Differential(t)
 
-@named fol = ODESystem([D(x) ~ (1 - x) / tau])
+@named fol = ODESystem([D(x) ~ (1 - x) / tau], t)
+fol = complete(fol)
 prob = ODEProblem(fol, [x => 0.0], (0.0, 10.0), [tau => 3.0])
 sol = solve(prob, Tsit5())
 @test getp(fol, tau)(sol) == 3
@@ -355,6 +354,7 @@ sol = solve(prob, Tsit5())
     @parameters a=1 b=1
     loss = (a - x)^2 + b * (y - x^2)^2
     @named sys = OptimizationSystem(loss, [x, y], [a, b])
+    sys = complete(sys)
     u0 = [x => 1.0
           y => 2.0]
     p = [a => 1.0

test/traits.jl

@@ -1,5 +1,6 @@
 using SciMLBase, Test
 using ModelingToolkit, OrdinaryDiffEq, DataFrames
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @test SciMLBase.Tables.isrowtable(ODESolution)
 @test SciMLBase.Tables.isrowtable(RODESolution)
@@ -9,8 +10,7 @@ using ModelingToolkit, OrdinaryDiffEq, DataFrames
 @test !SciMLBase.Tables.isrowtable(SciMLBase.QuadratureSolution)
 @test !SciMLBase.Tables.isrowtable(SciMLBase.OptimizationSolution)
 
-@variables t x(t)=1
-D = Differential(t)
+@variables x(t)=1
 eqs = [D(x) ~ -x]
 @named sys = ODESystem(eqs, t)
 sys = complete(sys)