refactor: update to ModelingToolkit@10

Author: AayushSabharwal

docs/src/libs/datadrivensparse/example_04.jl

@@ -29,9 +29,9 @@ using Test #src
     end
 end
 
-@mtkbuild sys = Autoregulation()
+@mtkcompile sys = Autoregulation()
 tspan = (0.0, 5.0)
-de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan, [])
+de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan)
 de_solution = solve(de_problem, Tsit5(), saveat = 0.005);
 #md plot(de_solution)
 

src/DataDrivenDiffEq.jl

@@ -13,7 +13,7 @@ using Parameters
 using Setfield
 
 @reexport using ModelingToolkit
-using ModelingToolkit: AbstractSystem, AbstractTimeDependentSystem
+using ModelingToolkit: AbstractSystem
 using SciMLStructures: SciMLStructures as SS
 using SymbolicUtils: operation, arguments, iscall, issym
 using Symbolics
@@ -67,7 +67,7 @@ const __EMPTY_VECTOR = Vector(undef, 0)
 
 # Basis with an indicator for implicit use
 abstract type AbstractDataDrivenFunction{Bool, Bool} end
-abstract type AbstractBasis <: AbstractTimeDependentSystem end
+abstract type AbstractBasis <: AbstractSystem end
 
 # Collect the DataInterpolations Methods into an Interpolation Type
 abstract type AbstractInterpolationMethod end

test/problem/problem.jl

@@ -183,9 +183,9 @@ end
         end
     end
 
-    @mtkbuild sys = Autoregulation()
+    @mtkcompile sys = Autoregulation()
     tspan = (0.0, 5.0)
-    de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan, [])
+    de_problem = ODEProblem{true, SciMLBase.NoSpecialize}(sys, [], tspan)
     de_solution = solve(de_problem, Tsit5(), saveat = 0.005)
     prob = DataDrivenProblem(de_solution)
     @test is_valid(prob)