Fix downstream test guess

ext/SciMLBaseMakieExt.jl

@@ -345,7 +345,7 @@ end
 function Makie.convert_arguments(::Type{<:Lines},
         sim::SciMLBase.EnsembleSummary;
         idxs = sim.u.u[1] isa AbstractArray ? eachindex(sim.u.u[1]) :
-                       1,
+               1,
         error_style = :ribbon, ci_type = :quantile,
         kwargs...)
     if ci_type == :SEM

src/ensemble/ensemble_solutions.jl

@@ -159,7 +159,7 @@ end
 
 @recipe function f(sim::EnsembleSummary;
         idxs = sim.u.u[1] isa AbstractArray ? eachindex(sim.u.u[1]) :
-                       1,
+               1,
         error_style = :ribbon, ci_type = :quantile)
     if ci_type == :SEM
         if sim.u.u[1] isa AbstractArray

src/problems/basic_problems.jl

@@ -232,7 +232,7 @@ For specifying Jacobians and mass matrices, see the
 * `kwargs`: The keyword arguments passed on to the solvers.
 """
 mutable struct NonlinearProblem{uType, isinplace, P, F, K, PT} <:
-       AbstractNonlinearProblem{uType, isinplace}
+               AbstractNonlinearProblem{uType, isinplace}
     f::F
     u0::uType
     p::P
@@ -322,8 +322,6 @@ function Base.setproperty!(prob::NonlinearProblem, s::Symbol, v, order::Symbol)
     Base.setfield!(prob, s, v, order)
 end
 
-
-
 @doc doc"""
 Defines a nonlinear least squares problem.
 

src/problems/bvp_problems.jl

@@ -360,15 +360,16 @@ struct SecondOrderBVProblem{uType, tType, isinplace, nlls, P, F, PT, K} <:
         _u0 = prepare_initial_state(u0)
         _tspan = promote_tspan(tspan)
         warn_paramtype(p)
-        prob_type = TP ? TwoPointSecondOrderBVProblem{iip}() : StandardSecondOrderBVProblem()
+        prob_type = TP ? TwoPointSecondOrderBVProblem{iip}() :
+                    StandardSecondOrderBVProblem()
 
         # Needed to ensure that `problem_type` doesn't get passed in kwargs
         if problem_type === nothing
             problem_type = prob_type
         else
             @assert prob_type===problem_type "This indicates incorrect problem type specification! Users should never pass in `problem_type` kwarg, this exists exclusively for internal use."
         end
-        
+
         return new{typeof(_u0), typeof(_tspan), iip, typeof(nlls), typeof(p), typeof(f),
             typeof(problem_type), typeof(kwargs)}(f, _u0, _tspan, p, problem_type, kwargs)
     end

src/remake.jl

@@ -63,7 +63,8 @@ function remake(prob::AbstractSciMLProblem; u0 = missing,
     _remake_internal(prob; kwargs..., u0, p)
 end
 
-function remake(prob::AbstractIntervalNonlinearProblem; p = missing, interpret_symbolicmap = true, use_defaults = false, kwargs...)
+function remake(prob::AbstractIntervalNonlinearProblem; p = missing,
+        interpret_symbolicmap = true, use_defaults = false, kwargs...)
     _, p = updated_u0_p(prob, [], p; interpret_symbolicmap, use_defaults)
     _remake_internal(prob; kwargs..., p)
 end
@@ -102,19 +103,25 @@ function remake(prob::ODEProblem; f = missing,
     iip = isinplace(prob)
 
     if f === missing
-        initializeprob, initializeprobmap = remake_initializeprob(prob.f.sys, prob.f, u0 === missing ? newu0 : u0, tspan[1], p === missing ? newp : p)
+        initializeprob, initializeprobmap = remake_initializeprob(
+            prob.f.sys, prob.f, u0 === missing ? newu0 : u0,
+            tspan[1], p === missing ? newp : p)
         if specialization(prob.f) === FunctionWrapperSpecialize
             ptspan = promote_tspan(tspan)
             if iip
-                _f = ODEFunction{iip, FunctionWrapperSpecialize}(wrapfun_iip(
-                    unwrapped_f(prob.f.f),
-                    (newu0, newu0, newp,
-                        ptspan[1])); initializeprob, initializeprobmap)
+                _f = ODEFunction{iip, FunctionWrapperSpecialize}(
+                    wrapfun_iip(
+                        unwrapped_f(prob.f.f),
+                        (newu0, newu0, newp,
+                            ptspan[1]));
+                    initializeprob, initializeprobmap)
             else
-                _f = ODEFunction{iip, FunctionWrapperSpecialize}(wrapfun_oop(
-                    unwrapped_f(prob.f.f),
-                    (newu0, newp,
-                        ptspan[1])); initializeprob, initializeprobmap)
+                _f = ODEFunction{iip, FunctionWrapperSpecialize}(
+                    wrapfun_oop(
+                        unwrapped_f(prob.f.f),
+                        (newu0, newp,
+                            ptspan[1]));
+                    initializeprob, initializeprobmap)
             end
         else
             _f = prob.f
@@ -138,7 +145,8 @@ function remake(prob::ODEProblem; f = missing,
     end
 
     if kwargs === missing
-        ODEProblem{isinplace(prob)}(_f, newu0, tspan, newp, prob.problem_type; prob.kwargs...,
+        ODEProblem{isinplace(prob)}(
+            _f, newu0, tspan, newp, prob.problem_type; prob.kwargs...,
             _kwargs...)
     else
         ODEProblem{isinplace(prob)}(_f, newu0, tspan, newp, prob.problem_type; kwargs...)

test/downstream/Project.toml

@@ -26,7 +26,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 BoundaryValueDiffEq = "5"
 ForwardDiff = "0.10"
 JumpProcesses = "9.10"
-ModelingToolkit = "8.37, 9"
+ModelingToolkit = "9"
 ModelingToolkitStandardLibrary = "2.7"
 NonlinearSolve = "2, 3"
 Optimization = "3"

test/downstream/modelingtoolkit_remake.jl

@@ -69,7 +69,7 @@ k = ShiftIndex(t)
 # Roundabout method to avoid having to specify values for previous timestep
 fn = DiscreteFunction(discsys)
 ps = ModelingToolkit.MTKParameters(discsys, p)
-discu0 = Dict([u0..., x(k-1) => 0.0, y(k-1) => 0.0, z(k-1) => 0.0])
+discu0 = Dict([u0..., x(k - 1) => 0.0, y(k - 1) => 0.0, z(k - 1) => 0.0])
 push!(syss, discsys)
 push!(probs, DiscreteProblem(fn, getindex.((discu0,), unknowns(discsys)), (0, 10), ps))
 
@@ -145,7 +145,8 @@ for (sys, prob) in zip(syss, probs)
     prob2 = @inferred baseType remake(prob; u0 = [x => 0.5σ + 1], p = [β => 0.5x + 1])
     @test ugetter(prob2) ≈ [15.0, 0.0, 0.0]
     @test pgetter(prob2) ≈ [28.0, 8.5, 10.0]
-    prob2 = @inferred baseType remake(prob; u0 = [sys.x => 0.5σ + 1], p = [sys.β => 0.5x + 1])
+    prob2 = @inferred baseType remake(
+        prob; u0 = [sys.x => 0.5σ + 1], p = [sys.β => 0.5x + 1])
     @test ugetter(prob2) ≈ [15.0, 0.0, 0.0]
     @test pgetter(prob2) ≈ [28.0, 8.5, 10.0]
     # Not testing `Symbol => expr` since nested substitution doesn't work with that

test/downstream/observables_autodiff.jl

@@ -69,7 +69,7 @@ function create_model(; C₁ = 3e-5, C₂ = 1e-6)
         systems = [
             resistor1, resistor2, capacitor1, capacitor2,
             source, input_signal, ground, ampermeter
-        ])
+        ], defaults = [resistor1.n.v => 0.0])
 end
 
 @testset "DAE Observable function AD" begin
@@ -96,7 +96,7 @@ end
 #         # mean(abs.(sol[sys.ampermeter.i] .- gt))
 #         sum(sol[sys.ampermeter.i])
 #     end
-# 
+#
 #     @test isnothing(gs_mtkp)
 #     @test length(gs_p_new) == length(p_new)
 # end