test: test updates

Author: vyudu

src/systems/systems.jl

@@ -17,13 +17,11 @@ $(SIGNATURES)
 Structurally simplify algebraic equations in a system and compute the
 topological sort of the observed equations in `sys`.
 
-### Optional Arguments:
-+ optional argument `io` may take a tuple `(inputs, outputs)`. This will convert all `inputs` to parameters and allow them to be unconnected, i.e., simplification will allow models where `n_unknowns = n_equations - n_inputs`.
-
 ### Optional Keyword Arguments:
 + When `simplify=true`, the `simplify` function will be applied during the tearing process.
 + `allow_symbolic=false`, `allow_parameter=true`, and `conservative=false` limit the coefficient types during tearing. In particular, `conservative=true` limits tearing to only solve for trivial linear systems where the coefficient has the absolute value of ``1``.
 + `fully_determined=true` controls whether or not an error will be thrown if the number of equations don't match the number of inputs, outputs, and equations.
++ `inputs`, `outputs` and `disturbance_inputs` are passed as keyword arguments.` All inputs` get converted to parameters and are allowed to be unconnected, allowing models where `n_unknowns = n_equations - n_inputs`.
 + `sort_eqs=true` controls whether equations are sorted lexicographically before simplification or not.
 """
 function structural_simplify(

test/clock.jl

@@ -65,10 +65,11 @@ By inference:
 ci, varmap = infer_clocks(sys)
 eqmap = ci.eq_domain
 tss, inputs, continuous_id = ModelingToolkit.split_system(deepcopy(ci))
-sss, = ModelingToolkit._structural_simplify!(
-    deepcopy(tss[continuous_id]), (inputs[continuous_id], ()))
+sss = ModelingToolkit._structural_simplify!(
+    deepcopy(tss[continuous_id]), inputs = inputs[continuous_id], outputs = [])
 @test equations(sss) == [D(x) ~ u - x]
-sss, = ModelingToolkit._structural_simplify!(deepcopy(tss[1]), (inputs[1], ()))
+sss = ModelingToolkit._structural_simplify!(
+    deepcopy(tss[1]), inputs = inputs[1], outputs = [])
 @test isempty(equations(sss))
 d = Clock(dt)
 k = ShiftIndex(d)

test/code_generation.jl

@@ -70,7 +70,7 @@ end
         @parameters p[1:2] (f::Function)(..)
         @named sys = ODESystem(
             [D(x[0]) ~ p[1] * x[0] + x[2], D(x[1]) ~ p[2] * f(x) + x[2]], t)
-        sys, = structural_simplify(sys, ([x[2]], []))
+        sys, = structural_simplify(sys, inputs = [x[2]], outputs = [])
         @test is_parameter(sys, x[2])
         prob = ODEProblem(sys, [x[0] => 1.0, x[1] => 1.0], (0.0, 1.0),
             [p => ones(2), f => sum, x[2] => 2.0])

test/input_output_handling.jl

@@ -50,7 +50,7 @@ end
 @test !is_bound(sys31, sys1.v[2])
 
 # simplification turns input variables into parameters
-ssys, _ = structural_simplify(sys, ([u], []))
+ssys = structural_simplify(sys, inputs = [u], outputs = [])
 @test ModelingToolkit.isparameter(unbound_inputs(ssys)[])
 @test !is_bound(ssys, u)
 @test u ∈ Set(unbound_inputs(ssys))
@@ -281,7 +281,7 @@ i = findfirst(isequal(u[1]), out)
 @variables x(t) u(t) [input = true]
 eqs = [D(x) ~ u]
 @named sys = ODESystem(eqs, t)
-@test_nowarn structural_simplify(sys, ([u], []))
+@test_nowarn structural_simplify(sys, inputs = [u], outputs = [])
 
 #=
 ## Disturbance input handling
@@ -366,9 +366,9 @@ eqs = [D(y₁) ~ -k₁ * y₁ + k₃ * y₂ * y₃ + u1
 @named sys = ODESystem(eqs, t)
 m_inputs = [u[1], u[2]]
 m_outputs = [y₂]
-sys_simp, input_idxs = structural_simplify(sys, (; inputs = m_inputs, outputs = m_outputs))
+sys_simp = structural_simplify(sys, inputs = m_inputs, outputs = m_outputs)
 @test isequal(unknowns(sys_simp), collect(x[1:2]))
-@test length(input_idxs) == 2
+@test length(inputs(sys_simp)) == 2
 
 # https://github.com/SciML/ModelingToolkit.jl/issues/1577
 @named c = Constant(; k = 2)

test/odesystem.jl

@@ -1283,11 +1283,11 @@ end
     @named sys = ODESystem(
         [D(u) ~ (sum(u) + sum(x) + sum(p) + sum(o)) * x, o ~ prod(u) * x],
         t, [u..., x..., o...], [p...])
-    sys1, = structural_simplify(sys, ([x...], []))
+    sys1 = structural_simplify(sys, inputs = [x...], outputs = [])
     fn1, = ModelingToolkit.generate_function(sys1; expression = Val{false})
     ps = MTKParameters(sys1, [x => 2ones(2), p => 3ones(2, 2)])
     @test_nowarn fn1(ones(4), ps, 4.0)
-    sys2, = structural_simplify(sys, ([x...], []); split = false)
+    sys2 = structural_simplify(sys, inputs = [x...], outputs = [], split = false)
     fn2, = ModelingToolkit.generate_function(sys2; expression = Val{false})
     ps = zeros(8)
     setp(sys2, x)(ps, 2ones(2))
@@ -1396,7 +1396,7 @@ end
            o[2] ~ sum(p) * sum(x)]
 
     @named sys = ODESystem(eqs, t, [u..., x..., o], [p...])
-    sys1, = structural_simplify(sys, ([x...], [o...]), split = false)
+    sys1 = structural_simplify(sys, inputs = [x...], outputs = [o...], split = false)
 
     @test_nowarn ModelingToolkit.build_explicit_observed_function(sys1, u; inputs = [x...])
 

test/reduction.jl

@@ -233,7 +233,7 @@ eqs = [D(x) ~ σ * (y - x)
        u ~ z + a]
 
 lorenz1 = ODESystem(eqs, t, name = :lorenz1)
-lorenz1_reduced, _ = structural_simplify(lorenz1, ([z], []))
+lorenz1_reduced, _ = structural_simplify(lorenz1, inputs = [z], outputs = [])
 @test z in Set(parameters(lorenz1_reduced))
 
 # #2064