test: update downstream tests

Author: AayushSabharwal

test/downstream/inversemodel.jl

@@ -17,7 +17,7 @@ using ModelingToolkit: t_nounits as t, D_nounits as D
 connect = ModelingToolkit.connect;
 rc = 0.25 # Reference concentration
 
-@component function MixingTank(; name, c0 = 0.8, T0 = 308.5, a1 = 0.2674, a21 = 1.815, a22 = 0.4682, b = 1.5476, k0 = 1.05e14, ϵ = 34.2894)
+@component function MixingTank(; name, c0 = 0.8, T0 = 308.5, a1 = 0.2674, a21 = 1.815, a22 = 0.4682, b = 1.5476, k0 = 1.05e14, ϵ = 34.2894, xc = nothing, xT = nothing)
     pars = @parameters begin
         c0 = c0, [description = "Nominal concentration"]
         T0 = T0, [description = "Nominal temperature"]
@@ -34,11 +34,13 @@ rc = 0.25 # Reference concentration
         c = RealOutput()
         T = RealOutput()
     end
-
+    
+    xc = @something(xc, c0)
+    xT = @something(xT, T0)
     vars = @variables begin
         gamma(t), [description = "Reaction speed"]
-        xc(t) = c0, [description = "Concentration"]
-        xT(t) = T0, [description = "Temperature"]
+        xc(t) = xc, [description = "Concentration"]
+        xT(t) = xT, [description = "Temperature"]
         xT_c(t), [description = "Cooling temperature"]
     end
 
@@ -73,7 +75,7 @@ begin
     function RefFilter(; name)
         sys = System(Fss; name)
         "Compute initial state that yields y0 as output"
-        empty!(ModelingToolkit.get_defaults(sys))
+        empty!(ModelingToolkit.get_initial_conditions(sys))
         return sys
     end
 end
@@ -132,7 +134,9 @@ cm = complete(model)
 
 op = Dict(
     cm.filter.y.u => 0.8 * (1 - 0.42),
-    D(cm.filter.y.u) => 0
+    D(cm.filter.y.u) => 0,
+    cm.noise_filter.x => nothing,
+    cm.controller.int.x => nothing,
 )
 tspan = (0.0, 1000.0)
 # https://github.com/SciML/ModelingToolkit.jl/issues/2786
@@ -178,18 +182,23 @@ nsys = get_named_comp_sensitivity(model, :y; op)
 @testset "Issue #3319" begin
     op1 = Dict(
         cm.filter.y.u => 0.8 * (1 - 0.42),
-        cm.tank.xc => 0.65
+        cm.tank.xc => 0.65,
+        cm.noise_filter.x => nothing,
     )
 
     op2 = Dict(
         cm.filter.y.u => 0.8 * (1 - 0.42),
-        cm.tank.xc => 0.45
+        cm.tank.xc => 0.45,
+        cm.noise_filter.x => nothing,
     )
 
     output = :y
     # we need to provide `op` so the initialization system knows which
     # values to hold constant
-    lin_fun, ssys = get_sensitivity_function(model, output; op = op1)
+    #
+    # Printing `lin_fun` in a tuple (so it doesn't hit the pretty-printing method)
+    # will likely segfault Julia. Somehow.
+    lin_fun, ssys = get_sensitivity_function(model, output; op = op1);
     matrices1, extras1 = linearize(ssys, lin_fun, op = op1)
     matrices2, extras2 = linearize(ssys, lin_fun, op = op2)
     @test extras1.x != extras2.x

test/downstream/linearization_dd.jl

@@ -7,16 +7,23 @@ using ModelingToolkitStandardLibrary.Blocks
 using ModelingToolkitStandardLibrary.Mechanical.MultiBody2D
 using ModelingToolkitStandardLibrary.Mechanical.TranslationalPosition
 using Test
+import Symbolics
 import NonlinearSolve
+using Setfield: @set
 
 using ControlSystemsMTK
 using ControlSystemsMTK.ControlSystemsBase: sminreal, minreal, poles
 connect = ModelingToolkit.connect
 
+function rm_bindings(sys)
+    @set sys.bindings = empty(bindings(sys))
+end
+
 @independent_variables t
 D = Differential(t)
 
 @named link1 = Link(; m = 0.2, l = 10, I = 1, g = -9.807)
+link1 = rm_bindings(link1)
 @named cart = TranslationalPosition.Mass(; m = 1, s = 0)
 @named fixed = Fixed()
 @named force = Force(use_support = false)
@@ -64,13 +71,13 @@ lsyss,
 
 dummyder = setdiff(unknowns(sysss), unknowns(model))
 # op2 = merge(ModelingToolkit.guesses(model), op, Dict(x => 0.0 for x in dummyder))
-op2 = merge(ModelingToolkit.defaults(syss), op)
+op2 = merge(ModelingToolkit.initial_conditions(syss), op)
 op2[link1.fy1] = -op2[link1.g] * op2[link1.m]
 op2[cart.f] = 0
 
-@test substitute(lsyss.A, op2) ≈ lsys.A
+@test Symbolics.value.(substitute(lsyss.A, op2; fold = Val(true))) ≈ lsys.A
 # We cannot pivot symbolically, so the part where a linear solve is required
 # is not reliable.
-@test substitute(lsyss.B, op2)[1:6, 1] ≈ lsys.B[1:6, 1]
-@test substitute(lsyss.C, op2) ≈ lsys.C
-@test substitute(lsyss.D, op2) ≈ lsys.D
+@test Symbolics.value.(substitute(lsyss.B, op2; fold = Val(true)))[1:6, 1] ≈ lsys.B[1:6, 1]
+@test Symbolics.value.(substitute(lsyss.C, op2; fold = Val(true))) ≈ lsys.C
+@test Symbolics.value.(substitute(lsyss.D, op2; fold = Val(true))) ≈ lsys.D