fix

Author: vyudu

src/linearization.jl

@@ -535,7 +535,6 @@ function linearize_symbolic(sys::AbstractSystem, inputs,
         if !iszero(Bs)
             if !allow_input_derivatives
                 der_inds = findall(vec(any(!iszero, Bs, dims = 1)))
-                @show typeof(der_inds)
                 error("Input derivatives appeared in expressions (-g_z\\g_u != 0), the following inputs appeared differentiated: $(ModelingToolkit.inputs(sys)[der_inds]). Call `linearize_symbolic` with keyword argument `allow_input_derivatives = true` to allow this and have the returned `B` matrix be of double width ($(2nu)), where the last $nu inputs are the derivatives of the first $nu inputs.")
             end
             B = [B [zeros(nx, nu); Bs]]

src/structural_transformation/symbolics_tearing.jl

@@ -378,7 +378,6 @@ function generate_derivative_variables!(
     # For variable x, make dummy derivative x_t if the
     # derivative is in the system
     for v in 1:length(var_to_diff)
-        @show graph
         dv = var_to_diff[v]
         dv isa Int || continue
         solved = var_eq_matching[dv] isa Int

src/systems/diffeqs/abstractodesystem.jl

@@ -146,10 +146,8 @@ function generate_W(sys::AbstractODESystem, γ = 1., dvs = unknowns(sys),
     sparse && (M = SparseArrays.sparse(M))
     J = calculate_jacobian(sys; simplify, sparse, dvs)
     W = ˍ₋gamma*M + J
-    @show W
 
     p = reorder_parameters(sys, ps)
-    @show length(p)
     return build_function_wrapper(sys, W, 
         dvs,
         p...,

src/systems/diffeqs/odesystem.jl

@@ -323,7 +323,6 @@ function ODESystem(deqs::AbstractVector{<:Equation}, iv, dvs, ps;
             cons = get_constraintsystem(sys)
             cons !== nothing && push!(conssystems, cons)
         end
-        @show conssystems
         @set! constraintsystem.systems = conssystems
     end
 

test/jacobiansparsity.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, ModelingToolkit, SparseArrays
+using ModelingToolkit, SparseArrays#, OrdinaryDiffEq
 
 N = 3
 xyd_brusselator = range(0, stop = 1, length = N)
@@ -51,7 +51,7 @@ JP = prob.f.jac_prototype
 
 # test sparse jacobian
 prob = ODEProblem(sys, u0, (0, 11.5), sparse = true, jac = true)
-@test_nowarn solve(prob, Rosenbrock23())
+#@test_nowarn solve(prob, Rosenbrock23())
 @test findnz(calculate_jacobian(sys, sparse = true))[1:2] ==
       findnz(prob.f.jac_prototype)[1:2]
 
@@ -74,7 +74,7 @@ f = DiffEqBase.ODEFunction(sys, u0 = nothing, sparse = true, jac = false)
 # test when u0 is not Float64
 u0 = similar(init_brusselator_2d(xyd_brusselator), Float32)
 prob_ode_brusselator_2d = ODEProblem(brusselator_2d_loop,
-    u0, (0.0, 11.5), p)
+                                     u0, (0.0, 11.5), p)
 sys = complete(modelingtoolkitize(prob_ode_brusselator_2d))
 
 prob = ODEProblem(sys, u0, (0, 11.5), sparse = true, jac = false)
@@ -85,6 +85,7 @@ prob = ODEProblem(sys, u0, (0, 11.5), sparse = true, jac = true)
 
 @testset "W matrix sparsity" begin
     t = ModelingToolkit.t_nounits
+    D = ModelingToolkit.D_nounits
     @parameters g
     @variables x(t) y(t) λ(t)
     eqs = [D(D(x)) ~ λ * x