Merge pull request #537 from AayushSabharwal/as/mtkv10

refactor: update to ModelingToolkitv10

Author: ChrisRackauckas

Project.toml

@@ -30,13 +30,13 @@ DataInterpolations = "4, 5, 6, 7, 8"
 DiffEqBase = "6"
 DocStringExtensions = "0.7, 0.8, 0.9"
 MLUtils = "0.3, 0.4"
-ModelingToolkit = "9"
+ModelingToolkit = "10"
+OrdinaryDiffEqTsit5 = "1"
 Parameters = "0.12"
 ProgressMeter = "1.6"
 QuadGK = "2.4"
 RecipesBase = "1"
 Reexport = "1.0"
-OrdinaryDiffEqTsit5 = "1"
 SciMLStructures = "1"
 Setfield = "1"
 Statistics = "1"

docs/Project.toml

@@ -13,7 +13,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 DataDrivenDiffEq = "1"
 Documenter = "1"
 Literate = "2.14"
-ModelingToolkit = "9"
+ModelingToolkit = "10"
 OrdinaryDiffEq = "6"
 Plots = "1"
 StableRNGs = "1"

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

@@ -13,13 +13,13 @@ 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
 using Symbolics: scalarize, variable, value
 @reexport using ModelingToolkit: unknowns, parameters, independent_variable, observed,
-                                 controls, get_iv, get_observed
+                                 get_iv, get_observed
 
 using Random
 using QuadGK
@@ -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
@@ -98,7 +98,7 @@ include("./basis/utils.jl")
 include("./basis/type.jl")
 export Basis
 export jacobian, dynamics
-export implicit_variables, states
+export implicit_variables, states, controls
 export get_parameter_values, get_parameter_map
 
 include("./utils/basis_generators.jl")

src/basis/type.jl

@@ -263,6 +263,18 @@ function states(b::AbstractBasis)
     return getfield(b, :unknowns)
 end
 
+function controls(b::AbstractBasis)
+    ctrls = getfield(b, :ctrls)
+    systems = getfield(b, :systems)
+    isempty(systems) && return ctrls
+
+    ctrls = copy(ctrls)
+    for sys in systems
+        append!(ctrls, unknowns(sys, controls(sys)))
+    end
+    return ctrls
+end
+
 # For internal use
 is_implicit(b::Basis{X, <:Any}) where {X} = X
 is_controlled(b::Basis{<:Any, X}) where {X} = X

src/problem/type.jl

@@ -326,7 +326,7 @@ function ModelingToolkit.observed(p::AbstractDataDrivenProblem, i = :, j = :)
     isempty(x) ? x : getindex(x, i, j)
 end
 
-function ModelingToolkit.controls(p::AbstractDataDrivenProblem, i = :, j = :)
+function controls(p::AbstractDataDrivenProblem, i = :, j = :)
     x = getfield(p, :U)
     isempty(x) ? x : getindex(x, i, j)
 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)