test: fix inversemodel testset

Author: AayushSabharwal

test/downstream/inversemodel.jl

@@ -68,18 +68,10 @@ begin
     Ftf = tf(1, [(100), 1])^3
     Fss = ss(Ftf)
 
-    "Compute initial state that yields y0 as output"
-    function init_filter(y0)
-        (; A, B, C, D) = Fss
-        Fx0 = -A \ B * y0
-        @assert C * Fx0≈[y0] "C*Fx0*y0 ≈ y0 failed, got $(C*Fx0*y0) ≈ $(y0)]"
-        Fx0
-    end
-
     # Create an MTK-compatible constructor 
     function RefFilter(; name)
         sys = ODESystem(Fss; name)
-        delete!(defaults(sys), @nonamespace(sys.x)[1])
+        empty!(ModelingToolkit.get_defaults(sys))
         return sys
     end
 end
@@ -140,7 +132,7 @@ op = Dict(D(cm.inverse_tank.xT) => 1,
     cm.tank.xc => 0.65)
 tspan = (0.0, 1000.0)
 # https://github.com/SciML/ModelingToolkit.jl/issues/2786
-prob = ODEProblem(ssys, op, tspan; build_initializeprob = false)
+prob = ODEProblem(ssys, op, tspan)
 sol = solve(prob, Rodas5P())
 
 @test SciMLBase.successful_retcode(sol)
@@ -150,27 +142,29 @@ sol = solve(prob, Rodas5P())
 
 @test sol(tspan[2], idxs = cm.tank.xc)≈getp(prob, cm.ref.k)(prob) atol=1e-2 # Test that the inverse model led to the correct reference
 
-Sf, simplified_sys = Blocks.get_sensitivity_function(model, :y) # This should work without providing an operating opint containing a dummy derivative
-x, _ = ModelingToolkit.get_u0_p(simplified_sys, op)
-p = ModelingToolkit.MTKParameters(simplified_sys, op)
+# we need to provide `op` so the initialization system knows what to hold constant
+# the values don't matter
+Sf, simplified_sys = Blocks.get_sensitivity_function(model, :y; op); # This should work without providing an operating opint containing a dummy derivative
+x = state_values(Sf)
+p = parameter_values(Sf)
 # If this somehow passes, mention it on
 # https://github.com/SciML/ModelingToolkit.jl/issues/2786
 matrices1 = Sf(x, p, 0)
-matrices2, _ = Blocks.get_sensitivity(model, :y; op) # Test that we get the same result when calling the higher-level API
-@test_broken matrices1.f_x ≈ matrices2.A[1:7, 1:7]
+matrices2, _ = Blocks.get_sensitivity(model, :y; op); # Test that we get the same result when calling the higher-level API
+@test matrices1.f_x ≈ matrices2.A[1:7, 1:7]
 nsys = get_named_sensitivity(model, :y; op) # Test that we get the same result when calling an even higher-level API
 @test matrices2.A ≈ nsys.A
 
 # Test the same thing for comp sensitivities
 
-Sf, simplified_sys = Blocks.get_comp_sensitivity_function(model, :y) # This should work without providing an operating opint containing a dummy derivative
-x, _ = ModelingToolkit.get_u0_p(simplified_sys, op)
-p = ModelingToolkit.MTKParameters(simplified_sys, op)
+Sf, simplified_sys = Blocks.get_comp_sensitivity_function(model, :y; op); # This should work without providing an operating opint containing a dummy derivative
+x = state_values(Sf)
+p = parameter_values(Sf)
 # If this somehow passes, mention it on
 # https://github.com/SciML/ModelingToolkit.jl/issues/2786
 matrices1 = Sf(x, p, 0)
 matrices2, _ = Blocks.get_comp_sensitivity(model, :y; op) # Test that we get the same result when calling the higher-level API
-@test_broken matrices1.f_x ≈ matrices2.A[1:7, 1:7]
+@test matrices1.f_x ≈ matrices2.A[1:7, 1:7]
 nsys = get_named_comp_sensitivity(model, :y; op) # Test that we get the same result when calling an even higher-level API
 @test matrices2.A ≈ nsys.A
 
@@ -186,7 +180,9 @@ nsys = get_named_comp_sensitivity(model, :y; op) # Test that we get the same res
     )
 
     output = :y
-    lin_fun, ssys = Blocks.get_sensitivity_function(model, output)
+    # we need to provide `op` so the initialization system knows which
+    # values to hold constant
+    lin_fun, ssys = Blocks.get_sensitivity_function(model, output; op = op1)
     matrices1 = linearize(ssys, lin_fun, op = op1)
     matrices2 = linearize(ssys, lin_fun, op = op2)
     S1f = ss(matrices1...)