Bump compat for OrdinaryDiffEq v7 / StochasticDiffEq v7 ecosystem

Project.toml

@@ -1,7 +1,7 @@
 name = "JumpProcesses"
 uuid = "ccbc3e58-028d-4f4c-8cd5-9ae44345cda5"
 authors = ["Chris Rackauckas <accounts@chrisrackauckas.com>"]
-version = "9.26.0"
+version = "9.27.0"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
@@ -45,8 +45,9 @@ Graphs = "1.11"
 KernelAbstractions = "0.9"
 LinearAlgebra = "1"
 LinearSolve = "3"
-OrdinaryDiffEq = "6"
-OrdinaryDiffEqCore = "3, 4"
+OrdinaryDiffEq = "6, 7"
+OrdinaryDiffEqCore = "3.17, 4"
+OrdinaryDiffEqFunctionMap = "1, 2"
 Pkg = "1"
 PoissonRandom = "0.4"
 Random = "1"
@@ -57,7 +58,7 @@ SciMLBase = "2.115, 3.1"
 StableRNGs = "1"
 StaticArrays = "1.9.8"
 Statistics = "1"
-StochasticDiffEq = "6.82"
+StochasticDiffEq = "6.82, 7"
 SymbolicIndexingInterface = "0.3.36"
 Test = "1"
 julia = "1.10"
@@ -70,6 +71,7 @@ FastBroadcast = "7034ab61-46d4-4ed7-9d0f-46aef9175898"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
+OrdinaryDiffEqFunctionMap = "d3585ca7-f5d3-4ba6-8057-292ed1abd90f"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
@@ -78,4 +80,4 @@ StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ADTypes", "Aqua", "ExplicitImports", "FastBroadcast", "LinearAlgebra", "LinearSolve", "OrdinaryDiffEq", "Pkg", "SafeTestsets", "StableRNGs", "Statistics", "StochasticDiffEq", "Test"]
+test = ["ADTypes", "Aqua", "ExplicitImports", "FastBroadcast", "LinearAlgebra", "LinearSolve", "OrdinaryDiffEq", "OrdinaryDiffEqFunctionMap", "Pkg", "SafeTestsets", "StableRNGs", "Statistics", "StochasticDiffEq", "Test"]

docs/Project.toml

@@ -20,8 +20,8 @@ Distributions = "0.25"
 Documenter = "1.1"
 Graphs = "1.9"
 JumpProcesses = "9.13.2"
-OrdinaryDiffEq = "6.59"
+OrdinaryDiffEq = "6.59, 7"
 Plots = "1.39"
 PointProcesses = "0.5"
 StableRNGs = "1.0"
-StochasticDiffEq = "6.63"
+StochasticDiffEq = "6.63, 7"

docs/src/assets/Project.toml

@@ -20,8 +20,8 @@ Distributions = "0.25"
 Documenter = "1.1"
 Graphs = "1.9"
 JumpProcesses = "9.13.2"
-OrdinaryDiffEq = "6.59"
+OrdinaryDiffEq = "6.59, 7"
 Plots = "1.39"
 PointProcesses = "0.5"
 StableRNGs = "1.0"
-StochasticDiffEq = "6.63"
+StochasticDiffEq = "6.63, 7"

src/SSA_stepper.jl

@@ -113,6 +113,28 @@ end
 (integrator::SSAIntegrator)(t) = copy(integrator.u)
 (integrator::SSAIntegrator)(out, t) = (out .= integrator.u)
 
+# SciMLBase v3 / DiffEqBase v7 renamed the integrator's `u_modified` field to
+# `derivative_discontinuity` and internal callback code now reads/writes the
+# field directly (e.g. `integrator.derivative_discontinuity`, not via a
+# method). Aliasing here so both names access the same underlying storage —
+# v6-era callbacks that look for `:u_modified` and v7-era callbacks that look
+# for `:derivative_discontinuity` both keep working without renaming the
+# struct field (which would be a breaking ABI change).
+@inline function Base.getproperty(integrator::SSAIntegrator, sym::Symbol)
+    sym === :derivative_discontinuity && return getfield(integrator, :u_modified)
+    return getfield(integrator, sym)
+end
+
+@inline function Base.setproperty!(integrator::SSAIntegrator, sym::Symbol, val)
+    sym === :derivative_discontinuity &&
+        return setfield!(integrator, :u_modified, convert(Bool, val))
+    return setfield!(integrator, sym, convert(fieldtype(typeof(integrator), sym), val))
+end
+
+function Base.propertynames(integrator::SSAIntegrator, private::Bool = false)
+    return (fieldnames(SSAIntegrator)..., :derivative_discontinuity)
+end
+
 function DiffEqBase.u_modified!(integrator::SSAIntegrator, bool::Bool)
     integrator.u_modified = bool
 end

test/constant_rate.jl

@@ -1,4 +1,4 @@
-using JumpProcesses, DiffEqBase, OrdinaryDiffEq, Statistics
+using JumpProcesses, DiffEqBase, OrdinaryDiffEq, OrdinaryDiffEqFunctionMap, Statistics
 using Test
 using StableRNGs
 rng = StableRNG(12345)

test/ensemble_problems.jl

@@ -108,7 +108,7 @@ end
         jprob = make_ssa_jump_prob()
         sol = solve(EnsembleProblem(jprob), SSAStepper(), EnsembleThreads();
             trajectories = 4)
-        @test length(sol) == 4
+        @test length(sol.u) == 4
     end
 
     @testset "ODE + VR ($agg)" for agg in (VR_FRM(), VR_Direct(), VR_DirectFW())
@@ -117,7 +117,7 @@ end
         # randexp!(_jump_prob.rng, ...) on the shared original problem.
         sol = solve(EnsembleProblem(jprob), Tsit5(), EnsembleThreads();
             trajectories = 4, save_everystep = false)
-        @test length(sol) == 4
+        @test length(sol.u) == 4
     end
 
     @testset "SDE + VR (VR_FRM): unique trajectories" begin
@@ -126,7 +126,7 @@ end
         # resetted_jump_problem, so trajectories should be distinct.
         sol = solve(EnsembleProblem(jprob), EM(), EnsembleThreads();
             trajectories = 4, dt = 0.01, save_everystep = false)
-        @test length(sol) == 4
+        @test length(sol.u) == 4
         finals = [sol.u[i].u[end][1] for i in 1:4]
         @test length(unique(finals)) > 1
     end

test/ensemble_uniqueness.jl

@@ -1,4 +1,4 @@
-using OrdinaryDiffEq, JumpProcesses, Test
+using OrdinaryDiffEq, OrdinaryDiffEqFunctionMap, JumpProcesses, Test
 using StableRNGs, Random
 
 j1 = ConstantRateJump((u, p, t) -> 10, (integrator) -> integrator.u[1] += 1)
@@ -11,7 +11,7 @@ dprob = DiscreteProblem(u0, (0.0, 100.0))
 # This ensures different trajectories while maintaining reproducibility.
 # Generate seeds from a seeded RNG for reproducibility of ensemble results.
 function make_seeded_prob_func(dprob, aggregator, jumps, base_rng)
-    return function prob_func(prob, i, repeat)
+    return function prob_func(prob, ctx)
         seed = rand(base_rng, UInt64)
         JumpProblem(dprob, aggregator, jumps...; rng = StableRNG(seed))
     end

test/saveat_regression.jl

@@ -17,7 +17,7 @@ Nsims = 10_000
 sol = JumpProcesses.solve(EnsembleProblem(jprob), SSAStepper(), saveat = ts,
     trajectories = Nsims)
 
-for i in 1:length(sol)
+for i in 1:length(sol.u)
     NA .+= sol.u[i][1, :]
 end
 

test/sir_model.jl

@@ -1,4 +1,4 @@
-using JumpProcesses, DiffEqBase, OrdinaryDiffEq
+using JumpProcesses, DiffEqBase, OrdinaryDiffEq, OrdinaryDiffEqFunctionMap
 using Test
 using StableRNGs
 rng = StableRNG(12345)

test/splitcoupled.jl

@@ -1,4 +1,4 @@
-using JumpProcesses, DiffEqBase, OrdinaryDiffEq, StochasticDiffEq, Statistics
+using JumpProcesses, DiffEqBase, OrdinaryDiffEq, OrdinaryDiffEqFunctionMap, StochasticDiffEq, Statistics
 using Test
 using StableRNGs
 rng = StableRNG(12345)

test/thread_safety.jl

@@ -26,11 +26,11 @@ let
 
     for agg in (VR_FRM(), VR_Direct(), VR_DirectFW())
         jump_prob = JumpProblem(ode_prob, Direct(), vrj; vr_aggregator = agg)
-        prob_func(prob, i, repeat) = deepcopy(prob)
+        prob_func(prob, ctx) = deepcopy(prob)
         prob = EnsembleProblem(jump_prob, prob_func = prob_func)
         sol = solve(prob, Tsit5(), EnsembleThreads(), trajectories = 400,
             save_everystep = false)
-        firstrx_time = [sol.u[i].t[findfirst(>(sol.u[i].t[1]), sol.u[i].t)] for i in 1:length(sol)]
+        firstrx_time = [sol.u[i].t[findfirst(>(sol.u[i].t[1]), sol.u[i].t)] for i in 1:length(sol.u)]
         @test allunique(firstrx_time)
     end
 end

test/variable_rate.jl

@@ -41,8 +41,8 @@ integrator = init(jump_prob_gill, Tsit5())
 sol_gill = solve(jump_prob_gill, Tsit5())
 sol_gill = solve(jump_prob, Rosenbrock23(autodiff = AutoFiniteDiff()))
 sol_gill = solve(jump_prob, Rosenbrock23())
-@test maximum([sol.u[i][2] for i in 1:length(sol)]) <= 1e-12
-@test maximum([sol.u[i][3] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][2] for i in 1:length(sol.u)]) <= 1e-12
+@test maximum([sol.u[i][3] for i in 1:length(sol.u)]) <= 1e-12
 
 g = function (du, u, p, t)
     du[1] = u[1]
@@ -52,8 +52,8 @@ jump_prob = JumpProblem(prob, jump, jump2; vr_aggregator = VR_FRM(), rng = rng)
 sol = solve(jump_prob, SRIW1())
 jump_prob_gill = JumpProblem(prob, jump, jump2; vr_aggregator = VR_Direct(), rng = rng)
 sol_gill = solve(jump_prob_gill, SRIW1())
-@test maximum([sol.u[i][2] for i in 1:length(sol)]) <= 1e-12
-@test maximum([sol.u[i][3] for i in 1:length(sol)]) <= 1e-12
+@test maximum([sol.u[i][2] for i in 1:length(sol.u)]) <= 1e-12
+@test maximum([sol.u[i][3] for i in 1:length(sol.u)]) <= 1e-12
 
 function ff(du, u, p, t)
     if p == 0