test fixes

Author: vyudu

src/inputoutput.jl

@@ -430,7 +430,7 @@ function add_input_disturbance(sys, dist::DisturbanceModel, inputs = nothing; kw
     augmented_sys = ODESystem(eqs, t, systems = [dsys], name = gensym(:outer))
     augmented_sys = extend(augmented_sys, sys)
 
-    (f_oop, f_ip), dvs, p, io_sys = generate_control_function(augmented_sys, all_inputs,
+    f, dvs, p, io_sys = generate_control_function(augmented_sys, all_inputs,
         [d]; kwargs...)
-    (f_oop, f_ip), augmented_sys, dvs, p, io_sys
+    f, augmented_sys, dvs, p, io_sys
 end

src/structural_transformation/utils.jl

@@ -96,6 +96,7 @@ function check_consistency(state::TransformationState, orig_inputs; nothrow = fa
     fullvars = get_fullvars(state)
     neqs = n_concrete_eqs(state)
     @unpack graph, var_to_diff = state.structure
+    @show equations(state.sys)
     highest_vars = computed_highest_diff_variables(complete!(state.structure))
     n_highest_vars = 0
     for (v, h) in enumerate(highest_vars)

src/systems/diffeqs/odesystem.jl

@@ -803,8 +803,7 @@ function validate_vars_and_find_ps!(auxvars, auxps, sysvars, iv)
 
     for var in auxvars
         if !iscall(var)
-            occursin(iv, var) && (var ∈ sts ||
-             throw(ArgumentError("Time-dependent variable $var is not an unknown of the system.")))
+            var ∈ sts || throw(ArgumentError("Time-independent variable $var is not an unknown of the system."))
         elseif length(arguments(var)) > 1
             throw(ArgumentError("Too many arguments for variable $var."))
         elseif length(arguments(var)) == 1

test/downstream/test_disturbance_model.jl

@@ -149,10 +149,10 @@ sol = solve(prob, Tsit5())
 ## Generate function for an augmented Unscented Kalman Filter =====================
 # temp = open_loop(model_with_disturbance, :d)
 outputs = [P.inertia1.phi, P.inertia2.phi, P.inertia1.w, P.inertia2.w]
-(f_oop1, f_ip), x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
+f, x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
     model_with_disturbance, [:u], [:d1, :d2, :dy], split = false)
 
-(f_oop2, f_ip2), x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
+f, x_sym, p_sym, io_sys = ModelingToolkit.generate_control_function(
     model_with_disturbance, [:u], [:d1, :d2, :dy],
     disturbance_argument = true, split = false)
 
@@ -168,22 +168,22 @@ x0, p = ModelingToolkit.get_u0_p(io_sys, op, op)
 x = zeros(5)
 u = zeros(1)
 d = zeros(3)
-@test f_oop2(x, u, p, t, d) == zeros(5)
+@test f(x, u, p, t, d) == zeros(5)
 @test measurement(x, u, p, 0.0) == [0, 0, 0, 0]
 @test measurement2(x, u, p, 0.0, d) == [0]
 
 # Add to the integrating disturbance input
 d = [1, 0, 0]
-@test sort(f_oop2(x, u, p, 0.0, d)) == [0, 0, 0, 1, 1] # Affects disturbance state and one velocity
+@test sort(f(x, u, p, 0.0, d)) == [0, 0, 0, 1, 1] # Affects disturbance state and one velocity
 @test measurement2(x, u, p, 0.0, d) == [0]
 
 d = [0, 1, 0]
-@test sort(f_oop2(x, u, p, 0.0, d)) == [0, 0, 0, 0, 1] # Affects one velocity
+@test sort(f(x, u, p, 0.0, d)) == [0, 0, 0, 0, 1] # Affects one velocity
 @test measurement(x, u, p, 0.0) == [0, 0, 0, 0]
 @test measurement2(x, u, p, 0.0, d) == [0]
 
 d = [0, 0, 1]
-@test sort(f_oop2(x, u, p, 0.0, d)) == [0, 0, 0, 0, 0] # Affects nothing
+@test sort(f(x, u, p, 0.0, d)) == [0, 0, 0, 0, 0] # Affects nothing
 @test measurement(x, u, p, 0.0) == [0, 0, 0, 0]
 @test measurement2(x, u, p, 0.0, d) == [1] # We have now disturbed the output
 

test/extensions/Project.toml

@@ -9,6 +9,7 @@ LabelledArrays = "2ee39098-c373-598a-b85f-a56591580800"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 Nemo = "2edaba10-b0f1-5616-af89-8c11ac63239a"
 NonlinearSolveHomotopyContinuation = "2ac3b008-d579-4536-8c91-a1a5998c2f8b"
+OrdinaryDiffEqNonlinearSolve = "127b3ac7-2247-4354-8eb6-78cf4e7c58e8"
 OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 SciMLStructures = "53ae85a6-f571-4167-b2af-e1d143709226"

test/input_output_handling.jl

@@ -173,7 +173,7 @@ end
         p = [rand()]
         x = [rand()]
         u = [rand()]
-        @test f[1](x, u, p, 1) ≈ -x + u
+        @test f(x, u, p, 1) ≈ -x + u
 
         # With disturbance inputs
         @variables x(t)=0 u(t)=0 [input = true] d(t)=0
@@ -191,7 +191,7 @@ end
         p = [rand()]
         x = [rand()]
         u = [rand()]
-        @test f[1](x, u, p, 1) ≈ -x + u
+        @test f(x, u, p, 1) ≈ -x + u
 
         ## With added d argument
         @variables x(t)=0 u(t)=0 [input = true] d(t)=0
@@ -210,7 +210,7 @@ end
         x = [rand()]
         u = [rand()]
         d = [rand()]
-        @test f[1](x, u, p, t, d) ≈ -x + u + [d[]^2]
+        @test f(x, u, p, t, d) ≈ -x + u + [d[]^2]
     end
 end
 
@@ -273,7 +273,7 @@ x = ModelingToolkit.varmap_to_vars(
     merge(ModelingToolkit.defaults(model),
         Dict(D.(unknowns(model)) .=> 0.0)), dvs)
 u = [rand()]
-out = f[1](x, u, p, 1)
+out = f(x, u, p, 1)
 i = findfirst(isequal(u[1]), out)
 @test i isa Int
 @test iszero(out[[1:(i - 1); (i + 1):end]])
@@ -333,7 +333,7 @@ model_outputs = [model.inertia1.w, model.inertia2.w, model.inertia1.phi, model.i
 @named dmodel = Blocks.StateSpace([0.0], [1.0], [1.0], [0.0]) # An integrating disturbance
 
 @named dist = ModelingToolkit.DisturbanceModel(model.torque.tau.u, dmodel)
-(f_oop, f_ip), outersys, dvs, p, io_sys = ModelingToolkit.add_input_disturbance(model, dist)
+f, outersys, dvs, p, io_sys = ModelingToolkit.add_input_disturbance(model, dist)
 
 @unpack u, d = outersys
 matrices, ssys = linearize(outersys, [u, d], model_outputs)
@@ -348,8 +348,8 @@ x0 = randn(5)
 x1 = copy(x0) + x_add # add disturbance state perturbation
 u = randn(1)
 pn = MTKParameters(io_sys, [])
-xp0 = f_oop(x0, u, pn, 0)
-xp1 = f_oop(x1, u, pn, 0)
+xp0 = f(x0, u, pn, 0)
+xp1 = f(x1, u, pn, 0)
 
 @test xp0 ≈ matrices.A * x0 + matrices.B * [u; 0]
 @test xp1 ≈ matrices.A * x1 + matrices.B * [u; 0]
@@ -402,7 +402,7 @@ outs = collect(complete(model).output.u)
 disturbed_input = ins[1]
 @named dist_integ = DisturbanceModel(disturbed_input, integrator)
 
-(f_oop, f_ip), augmented_sys, dvs, p = ModelingToolkit.add_input_disturbance(model,
+f, augmented_sys, dvs, p = ModelingToolkit.add_input_disturbance(model,
     dist_integ,
     ins)
 
@@ -447,7 +447,7 @@ end
     @named sys = ODESystem(eqs, t, [x], [])
 
     f, dvs, ps, io_sys = ModelingToolkit.generate_control_function(sys, simplify = true)
-    @test f[1]([0.5], nothing, MTKParameters(io_sys, []), 0.0) ≈ [1.0]
+    @test f([0.5], nothing, MTKParameters(io_sys, []), 0.0) ≈ [1.0]
 end
 
 @testset "With callable symbolic" begin
@@ -459,5 +459,5 @@ end
     p = MTKParameters(io_sys, [])
     u = [1.0]
     x = [1.0]
-    @test_nowarn f[1](x, u, p, 0.0)
+    @test_nowarn f(x, u, p, 0.0)
 end

test/odesystem.jl

@@ -472,14 +472,12 @@ sys = complete(sys)
 
 # check inputs
 let
-    @parameters f k d
-    @variables x(t) ẋ(t)
-    δ = D
+    @parameters k d
+    @variables x(t) ẋ(t) f(t) [input = true]
 
-    eqs = [δ(x) ~ ẋ, δ(ẋ) ~ f - k * x - d * ẋ]
-    @named sys = ODESystem(eqs, t, [x, ẋ], [f, d, k]; controls = [f])
-
-    calculate_control_jacobian(sys)
+    eqs = [D(x) ~ ẋ, D(ẋ) ~ f - k * x - d * ẋ]
+    @named sys = ODESystem(eqs, t, [x, ẋ], [d, k])
+    sys, _ = structural_simplify(sys, ([f], []))
 
     @test isequal(calculate_control_jacobian(sys),
         reshape(Num[0, 1], 2, 1))