fix: use public api in diffusion_function

CHANGELOG.md

@@ -1,3 +1,8 @@
+# main
+
+- Support StochasticDiffEq v6.96+, where the diffusion function is no longer reachable via `integrator.g`. The extension now reads it from the `SDEProblem` (`prob.g`), which is a stable SciMLBase accessor that works across both old and new StochasticDiffEq versions.
+- Run the multiplicative and non-diagonal noise examples on the `CoupledSDEs` docs page so they actually integrate a trajectory.
+
 # v3.15
 
 - New function `named_variables(ds)` for getting the variable names of an MTK-generated dynamical system.

Project.toml

@@ -1,7 +1,7 @@
 name = "DynamicalSystemsBase"
 uuid = "6e36e845-645a-534a-86f2-f5d4aa5a06b4"
 repo = "https://github.com/JuliaDynamics/DynamicalSystemsBase.jl.git"
-version = "3.15.6"
+version = "3.16.0"
 
 [deps]
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"

docs/src/CoupledSDEs.md

@@ -84,6 +84,8 @@ function g!(du, u, p, t)
     return nothing
 end
 sde = CoupledSDEs(f!, rand(2)./10; g=g!)
+tr = trajectory(sde, 1.0)
+plot_trajectory(tr...)
 ```
 
 #### Non-diagonal noise
@@ -105,6 +107,8 @@ function g!(du, u, p, t)
 end
 diffeq = (alg = RKMilCommute(), reltol = 1e-3, abstol = 1e-3, dt=0.1)
 sde = CoupledSDEs(f!, rand(2)./10; g=g!, noise_prototype = zeros(2, 2), diffeq = diffeq)
+tr = trajectory(sde, 1.0)
+plot_trajectory(tr...)
 ```
 
 !!! warning

ext/src/CoupledSDEs.jl

@@ -85,6 +85,10 @@ function DynamicalSystemsBase.CoupledSDEs(
     end
 
     solver, remaining = _decompose_into_sde_solver_and_remaining(diffeq)
+    # The default `dtmin` from SciML scales with `tspan`. With our open-ended
+    # `tspan = (0, 1e11)` it becomes ~1e-5, which is too coarse for the SDE
+    # adaptive controller and causes spurious `DtLessThanMin` aborts.
+    remaining = haskey(remaining, :dtmin) ? remaining : merge((dtmin = 0.0,), remaining)
     integ = __init(
         prob,
         solver;

ext/src/classification.jl

@@ -50,7 +50,8 @@ function diffusion_function(g, IIP, noise_prototype)
 end
 
 function diffusion_function(ds::CoupledSDEs{IIP}) where {IIP}
-    return diffusion_function(ds.integ.g, IIP, referrenced_sciml_prob(ds).noise_rate_prototype)
+    prob = referrenced_sciml_prob(ds)
+    return diffusion_function(prob.g, IIP, prob.noise_rate_prototype)
 end
 
 """

src/core_systems/continuous_time_ode.jl

@@ -52,7 +52,7 @@ to access the solvers. The default `diffeq` is:
 $(DynamicalSystemsBase.DEFAULT_DIFFEQ)
 
 `diffeq` keywords can also include `callback` for [event handling
-](http://docs.juliadiffeq.org/latest/features/callback_functions.html).
+](https://docs.sciml.ai/DiffEqDocs/stable/features/callback_functions/).
 
 The convenience constructors `CoupledODEs(prob::ODEProblem [, diffeq])` and
 `CoupledODEs(ds::CoupledODEs [, diffeq])` are also available.

test/continuous.jl

@@ -41,6 +41,7 @@ end
     prob = lorenz_oop.integ.sol.prob
     ds = CoupledODEs(prob, (alg = Vern9(), abstol = 0.0, reltol = 1.0e-6, verbose = false))
     @test ds.integ.alg isa Vern9
-    @test ds.integ.opts.verbose == false
+    # SciML moved from Bool verbose to a `DEVerbosity` struct of per-toggle verbosities.
+    @test nameof(typeof(ds.integ.opts.verbose.linear_verbosity)) == :None
 
 end

test/jacobian.jl

@@ -48,23 +48,23 @@ end
 
     jac = jacobian(ds)
     @test jac isa GeneratedFunctionWrapper
-    @test jac([1.0, 1.0], [], 0.0) == [-3 0;0 3]
+    @test jac([1.0, 1.0], prob.p, 0.0) == [-3 0;0 3]
 
     @testset "CoupledSDEs" begin
         # just to check if MTK @brownian does not give any problems
         using StochasticDiffEq
-        @brownian β
+        @brownians β
         eqs = [
             D(u[1]) ~ 3.0 * u[1] + β,
             D(u[2]) ~ -3.0 * u[2] + β,
         ]
-        @mtkbuild sys = System(eqs, t)
+        @mtkcompile sys = System(eqs, t)
 
         prob = SDEProblem(sys, [1.0, 1.0], (0.0, 1.0), jac = true)
         sde = CoupledSDEs(prob)
 
         jac = jacobian(sde)
         @test jac isa GeneratedFunctionWrapper
-        @test jac([1.0, 1.0], [], 0.0) == [-3 0; 0 3]
+        @test jac([1.0, 1.0], prob.p, 0.0) == [-3 0; 0 3]
     end
 end

test/stochastic.jl

@@ -73,15 +73,16 @@ end
         diffeq = (alg = SRA(), abstol = 1.0e-3, reltol = 1.0e-3, verbose = false)
     )
     @test lorenz_SRA.integ.alg isa SRA
-    @test lorenz_SRA.integ.opts.verbose == false
+    # SciML moved from Bool verbose to a `DEVerbosity` struct of per-toggle verbosities.
+    @test nameof(typeof(lorenz_SRA.integ.opts.verbose.linear_verbosity)) == :None
 
     # also test SDEproblem creation
     prob = lorenz_SRA.integ.sol.prob
 
     ds = CoupledSDEs(prob, (alg = SRA(), abstol = 0.0, reltol = 1.0e-3, verbose = false))
 
     @test ds.integ.alg isa SRA
-    @test ds.integ.opts.verbose == false
+    @test nameof(typeof(ds.integ.opts.verbose.linear_verbosity)) == :None
 
     @test_throws ArgumentError CoupledSDEs(prob; diffeq = (alg = SRA(),))
 
@@ -104,7 +105,10 @@ end
         corr = CoupledSDEs(f, zeros(2); covariance = [1 0.3; 0.3 1])
         corr_alt = CoupledSDEs(f, zeros(2); g = g, noise_prototype = zeros(2, 2))
         @test corr.noise_type == corr_alt.noise_type
-        @test all(corr.integ.g(zeros(2), (), 0.0) .== corr_alt.integ.g(zeros(2), (), 0.0))
+        @test all(
+            DynamicalSystemsBase.referrenced_sciml_prob(corr).g(zeros(2), (), 0.0) .==
+                DynamicalSystemsBase.referrenced_sciml_prob(corr_alt).g(zeros(2), (), 0.0)
+        )
     end
 
     @testset "ArgumentError" begin