feat: add specialized observed for solutions of DDEProblems

Project.toml

@@ -88,7 +88,7 @@ StableRNGs = "1.0"
 StaticArrays = "1.7"
 StaticArraysCore = "1.4"
 Statistics = "1.10"
-SymbolicIndexingInterface = "0.3.30"
+SymbolicIndexingInterface = "0.3.31"
 Tables = "1.11"
 Zygote = "0.6.67"
 julia = "1.10"

src/integrator_interface.jl

@@ -383,6 +383,15 @@ function set_ut!(integrator::DEIntegrator, u, t)
     set_t!(integrator, t)
 end
 
+"""
+    get_sol(integrator::DEIntegrator)
+
+Get the solution object contained in the integrator.
+"""
+function get_sol(integrator::DEIntegrator)
+    return integrator.sol
+end
+
 ### Addat isn't a real thing. Let's make it a real thing Gretchen
 
 function addat!(a::AbstractArray, idxs, val = nothing)
@@ -901,3 +910,7 @@ Checks if the integrator is adaptive
 function isadaptive(integrator::DEIntegrator)
     isdefined(integrator.opts, :adaptive) ? integrator.opts.adaptive : false
 end
+
+function SymbolicIndexingInterface.get_history_function(integ::AbstractDDEIntegrator)
+    DDESolutionHistoryWrapper(get_sol(integ))
+end

src/problems/dde_problems.jl

@@ -259,6 +259,8 @@ function DDEProblem(f::AbstractDDEFunction, args...; kwargs...)
     DDEProblem{isinplace(f)}(f, args...; kwargs...)
 end
 
+SymbolicIndexingInterface.get_history_function(prob::AbstractDDEProblem) = prob.h
+
 """
 $(TYPEDEF)
 """

src/solutions/ode_solutions.jl

@@ -354,6 +354,28 @@ function (sol::AbstractODESolution)(t::AbstractVector{<:Number}, ::Type{deriv},
     return DiffEqArray(u, t, p, sol; discretes)
 end
 
+struct DDESolutionHistoryWrapper{T}
+    sol::T
+end
+
+function (w::DDESolutionHistoryWrapper)(p, t; idxs = nothing)
+    w.sol(t; idxs)
+end
+function (w::DDESolutionHistoryWrapper)(out, p, t; idxs = nothing)
+    w.sol(out, t; idxs)
+end
+function (w::DDESolutionHistoryWrapper)(p, t, deriv::Type{Val{i}}; idxs = nothing) where {i}
+    w.sol(t, deriv; idxs)
+end
+function (w::DDESolutionHistoryWrapper)(
+        out, p, t, deriv::Type{Val{i}}; idxs = nothing) where {i}
+    w.sol(out, t, deriv; idxs)
+end
+
+function SymbolicIndexingInterface.get_history_function(sol::ODESolution)
+    DDESolutionHistoryWrapper(sol)
+end
+
 # public API, used by MTK
 """
     create_parameter_timeseries_collection(sys, ps, tspan)

test/downstream/Project.toml

@@ -1,5 +1,6 @@
 [deps]
 BoundaryValueDiffEq = "764a87c0-6b3e-53db-9096-fe964310641d"
+DelayDiffEq = "bcd4f6db-9728-5f36-b5f7-82caef46ccdb"
 DiffEqCallbacks = "459566f4-90b8-5000-8ac3-15dfb0a30def"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 JumpProcesses = "ccbc3e58-028d-4f4c-8cd5-9ae44345cda5"
@@ -26,6 +27,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 BoundaryValueDiffEq = "5"
+DelayDiffEq = "5"
 DiffEqCallbacks = "3, 4"
 ForwardDiff = "0.10"
 JumpProcesses = "9.10"

test/downstream/comprehensive_indexing.jl

@@ -1,6 +1,6 @@
 using ModelingToolkit, JumpProcesses, LinearAlgebra, NonlinearSolve, Optimization,
       OptimizationOptimJL, OrdinaryDiffEq, RecursiveArrayTools, SciMLBase,
-      SteadyStateDiffEq, StochasticDiffEq, SymbolicIndexingInterface,
+      SteadyStateDiffEq, StochasticDiffEq, DelayDiffEq, SymbolicIndexingInterface,
       DiffEqCallbacks, Test, Plots
 using ModelingToolkit: t_nounits as t, D_nounits as D
 
@@ -922,3 +922,52 @@ end
     @test_throws ErrorException sol(-0.1; idxs = sys.c)
     @test_throws ErrorException sol(-0.1; idxs = [sys.x, sys.x + sys.c])
 end
+
+@testset "DDEs" begin
+    function oscillator(; name, k = 1.0, τ = 0.01)
+        @parameters k=k τ=τ
+        @variables x(..)=0.1 y(t)=0.1 jcn(t)=0.0 delx(t)
+        eqs = [D(x(t)) ~ y,
+            D(y) ~ -k * x(t - τ) + jcn,
+            delx ~ x(t - τ)]
+        return System(eqs, t; name = name)
+    end
+    systems = @named begin
+        osc1 = oscillator(k = 1.0, τ = 0.01)
+        osc2 = oscillator(k = 2.0, τ = 0.04)
+    end
+    eqs = [osc1.jcn ~ osc2.delx,
+        osc2.jcn ~ osc1.delx]
+    @named coupledOsc = System(eqs, t)
+    @named coupledOsc = compose(coupledOsc, systems)
+    sys = structural_simplify(coupledOsc)
+    prob = DDEProblem(sys, [], (0.0, 10.0); constant_lags = [sys.osc1.τ, sys.osc2.τ])
+    # TODO: Remove this hack once MTK can generate appropriate observed functions
+    fn = prob.f
+    function fake_observed(_)
+        return function obsfn(u, h, p, t)
+            return u + h(p, t - 0.1)
+        end
+    end
+
+    struct NonMarkovianWrapper{S}
+        sys::S
+    end
+    SymbolicIndexingInterface.symbolic_container(x::NonMarkovianWrapper) = x.sys
+    SymbolicIndexingInterface.is_markovian(::NonMarkovianWrapper) = false
+    fn = DDEFunction(fn.f; observed = fake_observed, sys = NonMarkovianWrapper(fn.sys))
+    function fake_hist(p, t)
+        return ones(length(prob.u0)) .* t
+    end
+    prob = DDEProblem(
+        fn, prob.u0, fake_hist, prob.tspan, prob.p; constant_lags = prob.constant_lags)
+    sym = sys.osc1.delx
+    @test prob[sym] ≈ prob.u0 .+ (prob.tspan[1] - 0.1)
+    integ = init(prob, MethodOfSteps(Tsit5()))
+    step!(integ, 10.0, true)
+    # DelayDiffEq wraps `integ.f` and that doesn't contain `.observed`
+    # so the hack above doesn't work. `@reset` also doesn't work.
+    @test_broken integ[sym] ≈ integ.u + SciMLBase.get_sol(integ)(9.9)
+    sol = solve(prob, MethodOfSteps(Tsit5()))
+    @test sol[sym] ≈ sol.u .+ sol(sol.t .- 0.1).u
+end