test: fix tests

test/constants.jl

@@ -10,7 +10,7 @@ UMT = ModelingToolkit.UnitfulUnitCheck
 D = Differential(t)
 eqs = [D(x) ~ a]
 @named sys = ODESystem(eqs)
-prob = ODEProblem(sys, [0], [0.0, 1.0], [])
+prob = ODEProblem(complete(sys), [0], [0.0, 1.0], [])
 sol = solve(prob, Tsit5())
 
 newsys = MT.eliminate_constants(sys)

test/dae_jacobian.jl

@@ -49,7 +49,7 @@ du0 = [0.5, -2.0]
 p = [p1 => 1.5,
     p2 => 3.0]
 
-prob = DAEProblem(sys, du0, u0, tspan, p, jac = true, sparse = true)
+prob = DAEProblem(complete(sys), du0, u0, tspan, p, jac = true, sparse = true)
 sol = solve(prob, IDA(linear_solver = :KLU))
 
 @test maximum(sol - sol1) < 1e-12

test/discretesystem.jl

@@ -49,6 +49,7 @@ end
 u0 = [S => 990.0, I => 10.0, R => 0.0]
 p = [β => 0.05, c => 10.0, γ => 0.25, δt => 0.1, nsteps => 400]
 tspan = (0.0, ModelingToolkit.value(substitute(nsteps, p))) # value function (from Symbolics) is used to convert a Num to Float64
+sys = complete(sys)
 prob_map = DiscreteProblem(sys, u0, tspan, p)
 @test prob_map.f.sys === sys
 
@@ -70,6 +71,7 @@ eqs2 = [D(S) ~ S - infection2,
     D(R) ~ R + recovery2]
 
 @named sys = DiscreteSystem(eqs2; controls = [β, γ], tspan)
+sys = complete(sys)
 @test ModelingToolkit.defaults(sys) != Dict()
 
 prob_map2 = DiscreteProblem(sys)
@@ -193,6 +195,7 @@ RHS2 = RHS
     end
 
     @named sys = DiscreteSystem(eqs, t, us, ps; defaults = defs, preface = preface)
+    sys = complete(sys)
     prob = DiscreteProblem(sys, [], (0.0, 1.0))
     sol = solve(prob, FunctionMap(); dt = dt)
     @test c[1] + 1 == length(sol)

test/extensions/bifurcationkit.jl

@@ -9,7 +9,7 @@ let
     eqs = [0 ~ μ * x - x^3 + α * y,
         0 ~ -y]
     @named nsys = NonlinearSystem(eqs, [x, y], [μ, α])
-
+    nsys = complete(nsys)
     # Creates BifurcationProblem 
     bif_par = μ
     p_start = [μ => -1.0, α => 1.0]
@@ -62,7 +62,7 @@ let
     eqs = [D(x) ~ -x + a * y + x^2 * y,
         D(y) ~ b - a * y - x^2 * y]
     @named sys = ODESystem(eqs)
-
+    sys = complete(sys)
     # Creates BifurcationProblem
     bprob = BifurcationProblem(sys,
         [x => 1.5, y => 1.0],

test/funcaffect.jl

@@ -11,7 +11,7 @@ affect1!(integ, u, p, ctx) = integ.u[u.u] += 10
 
 @named sys = ODESystem(eqs, t, [u], [],
     discrete_events = [[4.0] => (affect1!, [u], [], nothing)])
-prob = ODEProblem(sys, [u => 10.0], (0, 10.0))
+prob = ODEProblem(complete(sys), [u => 10.0], (0, 10.0))
 sol = solve(prob, Tsit5())
 i4 = findfirst(==(4.0), sol[:t])
 @test sol.u[i4 + 1][1] > 10.0
@@ -62,7 +62,7 @@ end
 ctx1 = [10.0]
 @named sys = ODESystem(eqs, t, [u], [],
     discrete_events = [[4.0, 8.0] => (affect2!, [u], [], ctx1)])
-prob = ODEProblem(sys, [u => 10.0], (0, 10.0))
+prob = ODEProblem(complete(sys), [u => 10.0], (0, 10.0))
 sol = solve(prob, Tsit5())
 i4 = findfirst(==(4.0), sol[:t])
 @test sol.u[i4 + 1][1] > 10.0
@@ -79,7 +79,7 @@ end
 @parameters a = 10.0
 @named sys = ODESystem(eqs, t, [u], [a],
     discrete_events = [[4.0, 8.0] => (affect3!, [u], [a], nothing)])
-prob = ODEProblem(sys, [u => 10.0], (0, 10.0))
+prob = ODEProblem(complete(sys), [u => 10.0], (0, 10.0))
 
 sol = solve(prob, Tsit5())
 i4 = findfirst(==(4.0), sol[:t])
@@ -97,7 +97,7 @@ end
     discrete_events = [
         [4.0, 8.0] => (affect3!, [u], [a => :b], nothing),
     ])
-prob = ODEProblem(sys, [u => 10.0], (0, 10.0))
+prob = ODEProblem(complete(sys), [u => 10.0], (0, 10.0))
 
 sol = solve(prob, Tsit5())
 i4 = findfirst(==(4.0), sol[:t])
@@ -225,7 +225,7 @@ balls = compose(balls, [ball1, ball2])
 @test ModelingToolkit.has_discrete_events(balls)
 @test length(ModelingToolkit.affects(ModelingToolkit.discrete_events(balls))) == 2
 
-prob = ODEProblem(balls, [ball1.x => 10.0, ball1.v => 0, ball2.x => 10.0, ball2.v => 0],
+prob = ODEProblem(complete(balls), [ball1.x => 10.0, ball1.v => 0, ball2.x => 10.0, ball2.v => 0],
     (0, 3.0))
 sol = solve(prob, Tsit5())
 

test/jacobiansparsity.jl

@@ -40,7 +40,7 @@ end
 u0 = init_brusselator_2d(xyd_brusselator)
 prob_ode_brusselator_2d = ODEProblem(brusselator_2d_loop,
     u0, (0.0, 11.5), p)
-sys = modelingtoolkitize(prob_ode_brusselator_2d)
+sys = complete(modelingtoolkitize(prob_ode_brusselator_2d))
 
 # test sparse jacobian pattern only.
 prob = ODEProblem(sys, u0, (0, 11.5), sparse = true, jac = false)
@@ -74,7 +74,7 @@ f = DiffEqBase.ODEFunction(sys, u0 = nothing, sparse = true, jac = false)
 u0 = similar(init_brusselator_2d(xyd_brusselator), Float32)
 prob_ode_brusselator_2d = ODEProblem(brusselator_2d_loop,
     u0, (0.0, 11.5), p)
-sys = modelingtoolkitize(prob_ode_brusselator_2d)
+sys = complete(modelingtoolkitize(prob_ode_brusselator_2d))
 
 prob = ODEProblem(sys, u0, (0, 11.5), sparse = true, jac = false)
 @test eltype(prob.f.jac_prototype) == Float32

test/jumpsystem.jl

@@ -59,6 +59,7 @@ rate₃ = γ * I * h
 affect₃ = [I ~ I * h - 1, R ~ R + 1]
 j₃ = ConstantRateJump(rate₃, affect₃)
 @named js2 = JumpSystem([j₁, j₃], t, [S, I, R], [β, γ])
+js2 = complete(js2)
 u₀ = [999, 1, 0];
 p = (0.1 / 1000, 0.01);
 tspan = (0.0, 250.0);
@@ -80,6 +81,7 @@ m = getmean(jprob, Nsims)
 @variables S2(t)
 obs = [S2 ~ 2 * S]
 @named js2b = JumpSystem([j₁, j₃], t, [S, I, R], [β, γ], observed = obs)
+js2b = complete(js2b)
 dprob = DiscreteProblem(js2b, u₀map, tspan, parammap)
 jprob = JumpProblem(js2b, dprob, Direct(), save_positions = (false, false), rng = rng)
 sol = solve(jprob, SSAStepper(), saveat = tspan[2] / 10)
@@ -132,13 +134,15 @@ m2 = getmean(jprob, Nsims)
 maj1 = MassActionJump(2 * β / 2, [S => 1, I => 1], [S => -1, I => 1])
 maj2 = MassActionJump(γ, [I => 1], [I => -1, R => 1])
 @named js3 = JumpSystem([maj1, maj2], t, [S, I, R], [β, γ])
+js3 = complete(js3)
 dprob = DiscreteProblem(js3, u₀map, tspan, parammap)
 jprob = JumpProblem(js3, dprob, Direct(), rng = rng)
 m3 = getmean(jprob, Nsims)
 @test abs(m - m3) / m < 0.01
 
 # maj jump test with various dep graphs
 @named js3b = JumpSystem([maj1, maj2], t, [S, I, R], [β, γ])
+js3b = complete(js3b)
 jprobb = JumpProblem(js3b, dprob, NRM(), rng = rng)
 m4 = getmean(jprobb, Nsims)
 @test abs(m - m4) / m < 0.01
@@ -150,6 +154,7 @@ m4 = getmean(jprobc, Nsims)
 maj1 = MassActionJump(2.0, [0 => 1], [S => 1])
 maj2 = MassActionJump(γ, [S => 1], [S => -1])
 @named js4 = JumpSystem([maj1, maj2], t, [S], [β, γ])
+js4 = complete(js4)
 dprob = DiscreteProblem(js4, [S => 999], (0, 1000.0), [β => 100.0, γ => 0.01])
 jprob = JumpProblem(js4, dprob, Direct(), rng = rng)
 m4 = getmean(jprob, Nsims)
@@ -159,6 +164,7 @@ m4 = getmean(jprob, Nsims)
 maj1 = MassActionJump(2.0, [0 => 1], [S => 1])
 maj2 = MassActionJump(γ, [S => 2], [S => -1])
 @named js4 = JumpSystem([maj1, maj2], t, [S], [β, γ])
+js4 = complete(js4)
 dprob = DiscreteProblem(js4, [S => 999], (0, 1000.0), [β => 100.0, γ => 0.01])
 jprob = JumpProblem(js4, dprob, Direct(), rng = rng)
 sol = solve(jprob, SSAStepper());
@@ -177,6 +183,7 @@ end
 maj1 = MassActionJump(k1 * k3, [0 => 1], [A => -1, B => 1])
 maj2 = MassActionJump(k2, [B => 1], [A => 1, B => -1])
 @named js5 = JumpSystem([maj1, maj2], t, [A, B], [k1, k2, k3])
+js5 = complete(js5)
 p = [k1 => 2.0, k2 => 0.0, k3 => 0.5]
 u₀ = [A => 100, B => 0]
 tspan = (0.0, 2000.0)

test/lowering_solving.jl

@@ -30,6 +30,8 @@ p = [σ => 28.0,
     β => 8 / 3]
 
 tspan = (0.0, 100.0)
+
+sys = complete(sys)
 prob = ODEProblem(sys, u0, tspan, p, jac = true)
 probexpr = ODEProblemExpr(sys, u0, tspan, p, jac = true)
 sol = solve(prob, Tsit5())
@@ -52,7 +54,7 @@ lorenz2 = ODESystem(eqs, name = :lorenz2)
 @parameters γ
 connections = [0 ~ lorenz1.x + lorenz2.y + α * γ]
 @named connected = ODESystem(connections, t, [α], [γ], systems = [lorenz1, lorenz2])
-
+connected = complete(connected)
 u0 = [lorenz1.x => 1.0,
     lorenz1.y => 0.0,
     lorenz1.z => 0.0,

test/modelingtoolkitize.jl

@@ -52,7 +52,7 @@ x0 = zeros(2)
 p = [1.0, 100.0]
 
 prob = OptimizationProblem(rosenbrock, x0, p)
-sys = modelingtoolkitize(prob) # symbolicitize me captain!
+sys = complete(modelingtoolkitize(prob)) # symbolicitize me captain!
 
 prob = OptimizationProblem(sys, x0, p, grad = true, hess = true)
 sol = solve(prob, NelderMead())
@@ -141,7 +141,7 @@ problem = ODEProblem(SIRD_ac!, ℬ, 𝒯, 𝒫)
 @time solution = solve(problem, Tsit5(), saveat = 1:final_time);
 
 problem = ODEProblem(SIRD_ac!, ℬ, 𝒯, 𝒫)
-sys = modelingtoolkitize(problem)
+sys = complete(modelingtoolkitize(problem))
 fast_problem = ODEProblem(sys, ℬ, 𝒯, 𝒫)
 @time solution = solve(fast_problem, Tsit5(), saveat = 1:final_time)
 
@@ -179,7 +179,7 @@ u0 = [1.0, 0, 0, 0, 0]
 p = [9.8, 1]
 tspan = (0, 10.0)
 pendulum_prob = ODEProblem(pendulum_fun!, u0, tspan, p)
-pendulum_sys_org = modelingtoolkitize(pendulum_prob)
+pendulum_sys_org = complete(modelingtoolkitize(pendulum_prob))
 sts = states(pendulum_sys_org)
 pendulum_sys = dae_index_lowering(pendulum_sys_org)
 prob = ODEProblem(pendulum_sys, Pair[], tspan)
@@ -250,7 +250,7 @@ u0 = @LArray [9998.0, 1.0, 1.0, 1.0] (:S, :I, :R, :C)
 
 # Initiate ODE problem
 problem = ODEProblem(SIR!, u0, tspan, p)
-sys = modelingtoolkitize(problem)
+sys = complete(modelingtoolkitize(problem))
 
 @parameters t
 @test all(isequal.(parameters(sys), getproperty.(@variables(β, η, ω, φ, σ, μ), :val)))
@@ -304,6 +304,6 @@ noiseeqs = [0.1 * x,
     0.1 * z]
 
 @named sys = SDESystem(eqs, noiseeqs, t, [x, y, z], [sig, rho, beta]; tspan = (0, 1000.0))
-prob = SDEProblem(sys)
+prob = SDEProblem(complete(sys))
 sys = modelingtoolkitize(prob)
 @test ModelingToolkit.has_tspan(sys)

test/nonlinearsystem.jl

@@ -66,6 +66,7 @@ eqs = [0 ~ σ * a * h,
     0 ~ x * (ρ - z) - y,
     0 ~ x * y - β * z]
 @named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
+ns = complete(ns)
 nlsys_func = generate_function(ns, [x, y, z], [σ, ρ, β])
 nf = NonlinearFunction(ns)
 jac = calculate_jacobian(ns)
@@ -98,7 +99,7 @@ eqs1 = [
 
 lorenz = name -> NonlinearSystem(eqs1, [x, y, z, u, F], [σ, ρ, β], name = name)
 lorenz1 = lorenz(:lorenz1)
-@test_throws ArgumentError NonlinearProblem(lorenz1, zeros(5))
+@test_throws ArgumentError NonlinearProblem(complete(lorenz1), zeros(5))
 lorenz2 = lorenz(:lorenz2)
 @named connected = NonlinearSystem([s ~ a + lorenz1.x
         lorenz2.y ~ s * h
@@ -128,7 +129,7 @@ eqs = [0 ~ σ * (y - x),
     0 ~ x * (ρ - z) - y,
     0 ~ x * y - β * z * h]
 @named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
-np = NonlinearProblem(ns, [0, 0, 0], [1, 2, 3], jac = true, sparse = true)
+np = NonlinearProblem(complete(ns), [0, 0, 0], [1, 2, 3], jac = true, sparse = true)
 @test calculate_jacobian(ns, sparse = true) isa SparseMatrixCSC
 
 # issue #819
@@ -199,6 +200,7 @@ eq = [v1 ~ sin(2pi * t * h)
         u[4] ~ h]
 
     sys = NonlinearSystem(eqs, collect(u[1:4]), Num[], defaults = Dict([]), name = :test)
+    sys = complete(sys)
     prob = NonlinearProblem(sys, ones(length(states(sys))))
 
     sol = NonlinearSolve.solve(prob, NewtonRaphson())
@@ -223,6 +225,7 @@ testdict = Dict([:test => 1])
         0 ~ x * (b - z) - y,
         0 ~ x * y - c * z]
     @named sys = NonlinearSystem(eqs, [x, y, z], [a, b, c], defaults = Dict(x => 2.0))
+    sys = complete(sys)
     prob = NonlinearProblem(sys, ones(length(states(sys))))
 
     prob_ = remake(prob, u0 = [1.0, 2.0, 3.0], p = [1.1, 1.2, 1.3])

test/odesystem.jl

@@ -232,6 +232,7 @@ eqs = [D(y₁) ~ -k₁ * y₁ + k₃ * y₂ * y₃,
     0 ~ y₁ + y₂ + y₃ - 1,
     D(y₂) ~ k₁ * y₁ - k₂ * y₂^2 - k₃ * y₂ * y₃ * κ]
 @named sys = ODESystem(eqs, defaults = [k₁ => 100, k₂ => 3e7, y₁ => 1.0])
+sys = complete(sys)
 u0 = Pair[]
 push!(u0, y₂ => 0.0)
 push!(u0, y₃ => 0.0)
@@ -279,7 +280,7 @@ sol_dpmap = solve(prob_dpmap, Rodas5())
         sys1 = makesys(:sys1)
         sys2 = makesys(:sys2)
         @parameters t b=1.0
-        ODESystem(Equation[], t, [], [b]; systems = [sys1, sys2], name = :foo)
+        complete(ODESystem(Equation[], t, [], [b]; systems = [sys1, sys2], name = :foo))
     end
 
     sys = makecombinedsys()
@@ -435,6 +436,7 @@ default_u0 = [D(x) => 0.0, x => 10.0]
 default_p = [M => 1.0, b => 1.0, k => 1.0]
 @named sys = ODESystem(eqs, t, [x], ps; defaults = [default_u0; default_p], tspan)
 sys = ode_order_lowering(sys)
+sys = complete(sys)
 prob = ODEProblem(sys)
 sol = solve(prob, Tsit5())
 @test sol.t[end] == tspan[end]
@@ -448,6 +450,7 @@ prob = ODEProblem{false}(sys; u0_constructor = x -> SVector(x...))
 D = Differential(t)
 eqs = [D(x1) ~ -x1]
 @named sys = ODESystem(eqs, t, [x1, x2], [])
+sys = complete(sys)
 @test_throws ArgumentError ODEProblem(sys, [1.0, 1.0], (0.0, 1.0))
 @test_nowarn ODEProblem(sys, [1.0, 1.0], (0.0, 1.0), check_length = false)
 
@@ -512,6 +515,7 @@ eqs = [δ(x) ~ a * x - b * x * y
     Δ(x) ~ y
     U(y) ~ x + 1]
 @named de = ODESystem(eqs, t, [x, y], [a, b, c, d])
+de = complete(de)
 @test generate_difference_cb(de) isa ModelingToolkit.DiffEqCallbacks.DiscreteCallback
 
 # doesn't work with ODEFunction
@@ -589,6 +593,7 @@ eqs = [D(x) ~ foo(x, ms); D.(ms) .~ 1]
 @named sys = ODESystem(eqs, t, [x; ms], [])
 @named emptysys = ODESystem(Equation[], t)
 @named outersys = compose(emptysys, sys)
+outersys = complete(outersys)
 prob = ODEProblem(outersys, [sys.x => 1.0; collect(sys.ms) .=> 1:3], (0, 1.0))
 @test_nowarn solve(prob, Tsit5())
 
@@ -675,6 +680,7 @@ eqs[end] = D(D(z)) ~ α * x - β * y
     end
 
     @named sys = ODESystem(eqs, t, us, ps; defaults = defs, preface = preface)
+    sys = complete(sys)
     prob = ODEProblem(sys, [], (0.0, 1.0))
     sol = solve(prob, Euler(); dt = 0.1)
 
@@ -734,6 +740,7 @@ let
     D = Differential(t)
     eqs = [D(A) ~ -k1 * k2 * A]
     @named sys = ODESystem(eqs, t)
+    sys = complete(sys)
     u0map = [A => 1.0]
     pmap = (k1 => 1.0, k2 => 1)
     tspan = (0.0, 1.0)
@@ -887,6 +894,7 @@ let
     D = Differential(t)
     eqs = [D(A) ~ -k * A]
     @named osys = ODESystem(eqs, t)
+    osys = complete(osys)
     oprob = ODEProblem(osys, [A => 1.0], (0.0, 10.0), [k => 1.0]; check_length = false)
     @test_nowarn sol = solve(oprob, Tsit5())
 end
@@ -1002,7 +1010,7 @@ testdict = Dict([:name => "test"])
 eqs = [∂t(Q) ~ 1 / sin(P)
     ∂t(P) ~ log(-cos(Q))]
 @named sys = ODESystem(eqs, t, [P, Q], [])
-sys = debug_system(sys);
+sys = complete(debug_system(sys));
 prob = ODEProblem(sys, [], (0, 1.0));
 du = zero(prob.u0);
 if VERSION < v"1.8"