test: update several tests to MTKv9, fix minor bug

Author: AayushSabharwal

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}

test/downstream/Project.toml

@@ -4,6 +4,7 @@ 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"

test/downstream/ensemble_multi_prob.jl

@@ -1,18 +1,18 @@
 using ModelingToolkit, OrdinaryDiffEq, Test
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
-@variables t, x(t), y(t)
-D = Differential(t)
+@variables x(t), y(t)
 
 @named sys1 = ODESystem([D(x) ~ x,
-    D(y) ~ -y])
+    D(y) ~ -y], t)
 @named sys2 = ODESystem([D(x) ~ 2x,
-    D(y) ~ -2y])
+    D(y) ~ -2y], t)
 @named sys3 = ODESystem([D(x) ~ 3x,
-    D(y) ~ -3y])
+    D(y) ~ -3y], t)
 
-prob1 = ODEProblem(sys1, [1.0, 1.0], (0.0, 1.0))
-prob2 = ODEProblem(sys2, [2.0, 2.0], (0.0, 1.0))
-prob3 = ODEProblem(sys3, [3.0, 3.0], (0.0, 1.0))
+prob1 = ODEProblem(complete(sys1), [1.0, 1.0], (0.0, 1.0))
+prob2 = ODEProblem(complete(sys2), [2.0, 2.0], (0.0, 1.0))
+prob3 = ODEProblem(complete(sys3), [3.0, 3.0], (0.0, 1.0))
 
 # test that when passing a vector of problems, trajectories and the prob_func are chosen appropriately
 ensemble_prob = EnsembleProblem([prob1, prob2, prob3])

test/downstream/integrator_indexing.jl

@@ -1,11 +1,11 @@
 using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, StochasticDiffEq,
       SymbolicIndexingInterface, Test
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 ### Tests on non-layered model (everything should work). ###
 
-@parameters t a b c d
+@parameters a b c d
 @variables s1(t) s2(t)
-D = Differential(t)
 
 eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
     D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
@@ -119,9 +119,8 @@ integrator[noisy_population_model.s2] = 10.0
 integrator[:s1] = 1.0
 @test integrator[s1] == integrator[noisy_population_model.s1] == integrator[:s1] == 1.0
 
-@parameters t σ ρ β
+@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,
@@ -327,10 +326,8 @@ plot(sol,idxs=(t,α))
 =#
 
 using LinearAlgebra
-@variables t
 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...;])

test/downstream/problem_interface.jl

@@ -1,15 +1,15 @@
 using ModelingToolkit, OrdinaryDiffEq, Test
 using SymbolicIndexingInterface
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @parameters σ ρ β
-@variables t x(t) y(t) z(t)
-D = Differential(t)
+@variables x(t) y(t) z(t)
 
 eqs = [D(D(x)) ~ σ * (y - x),
     D(y) ~ x * (ρ - z) - y,
     D(z) ~ x * y - β * z]
 
-@named sys = ODESystem(eqs)
+@named sys = ODESystem(eqs, t)
 
 sys = structural_simplify(sys)
 
@@ -181,10 +181,8 @@ set_tuple!(sprob, [10.0, 10.0])
 @test get_tuple(sprob) == (10.0, 10.0)
 
 using LinearAlgebra
-@variables t
 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...;])

test/downstream/remake_autodiff.jl

@@ -1,7 +1,7 @@
 using OrdinaryDiffEq, ModelingToolkit, Zygote, SciMLSensitivity
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
-@variables t x(t) o(t)
-D = Differential(t)
+@variables x(t) o(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

@@ -1,18 +1,15 @@
 using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, StochasticDiffEq, Test
-# compat for MTKv8 and v9
-unknowns = isdefined(ModelingToolkit, :states) ? ModelingToolkit.states :
-           ModelingToolkit.unknowns
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 ### Tests on non-layered model (everything should work). ###
 
-@parameters t a b c d
+@parameters a b c d
 @variables s1(t) s2(t)
-D = Differential(t)
 
 eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
     D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
 
-@named population_model = ODESystem(eqs)
+@mtkbuild population_model = ODESystem(eqs, t)
 
 # Tests on ODEProblem.
 u0 = [s1 => 2.0, s2 => 1.0]
@@ -32,6 +29,7 @@ sol = solve(oprob, Rodas4())
 noiseeqs = [0.1 * s1,
     0.1 * s2]
 @named noisy_population_model = SDESystem(population_model, noiseeqs)
+noisy_population_model = complete(noisy_population_model)
 sprob = SDEProblem(noisy_population_model, u0, (0.0, 100.0), p)
 sol = solve(sprob, ImplicitEM())
 
@@ -42,16 +40,15 @@ sol = solve(sprob, ImplicitEM())
 @test_throws Exception sol[:a]
 ### Tests on layered model (some things should not work). ###
 
-@parameters t σ ρ β
+@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)

test/downstream/symbol_indexing.jl

@@ -1,19 +1,16 @@
 using ModelingToolkit, OrdinaryDiffEq, RecursiveArrayTools, SymbolicIndexingInterface, Test
 using Optimization, OptimizationOptimJL
-# compat for MTKv8 and v9
-unknowns = isdefined(ModelingToolkit, :states) ? ModelingToolkit.states :
-           ModelingToolkit.unknowns
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
-@parameters t σ ρ β
+@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)
@@ -106,7 +103,7 @@ 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}}
@@ -218,13 +215,12 @@ plot(sol,idxs=(t,α))
 =#
 
 using LinearAlgebra
-@variables t
 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}
@@ -342,11 +338,11 @@ for (sym, oldval, newval, check_inference) in [
 end
 
 # accessing parameters
-@variables t x(t)
+@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
@@ -359,6 +355,7 @@ 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)