try to make rngs deterministic

src/SSA_stepper.jl

@@ -184,10 +184,11 @@ function DiffEqBase.__init(jump_prob::JumpProblem,
         end
     else
         cb = deepcopy(jump_prob.jump_callback.discrete_callbacks[end])
+        rng = cb.condition.rng
         if seed === nothing
-            Random.seed!(cb.condition.rng, rand(UInt64))
+            Random.seed!(rng, rand(UInt64))
         else
-            Random.seed!(cb.condition.rng, seed)
+            Random.seed!(rng, seed)
         end
     end
     opts = (callback = CallbackSet(callback),)

src/problem.jl

@@ -44,8 +44,8 @@ then be passed within a single [`JumpSet`](@ref) or as subsequent sequential arg
 $(FIELDS)
 
 ## Keyword Arguments
-- `rng`, the random number generator to use. On 1.7 and up defaults to Julia's built-in
-  generator, below 1.7 uses RandomNumbers.jl's `Xorshifts.Xoroshiro128Star(rand(UInt64))`.
+- `rng`, the random number generator to use. Defaults to Julia's built-in
+  generator.
 - `save_positions=(true,true)`, specifies whether to save the system's state (before, after)
   the jump occurs.
 - `spatial_system`, for spatial problems the underlying spatial structure.

src/solve.jl

@@ -47,12 +47,12 @@ end
 
 function resetted_jump_problem(_jump_prob, seed)
     jump_prob = deepcopy(_jump_prob)
+    rng = jump_prob.jump_callback.discrete_callbacks[1].condition.rng
     if !isempty(jump_prob.jump_callback.discrete_callbacks)
         if seed === nothing
-            Random.seed!(jump_prob.jump_callback.discrete_callbacks[1].condition.rng,
-                rand(UInt64))
+            Random.seed!(rng, rand(UInt64))
         else
-            Random.seed!(jump_prob.jump_callback.discrete_callbacks[1].condition.rng, seed)
+            Random.seed!(rng, seed)
         end
     end
 

test/variable_rate.jl

@@ -275,18 +275,20 @@ end
 # https://github.com/SciML/JumpProcesses.jl/issues/320
 # note that even with the seeded StableRNG this test is not 
 # deterministic for some reason.
-function getmean(Nsims, prob, alg, dt, tsave)
+function getmean(Nsims, prob, alg, dt, tsave, seed)
     umean = zeros(length(tsave))
     for i in 1:Nsims
-        sol = solve(prob, alg; saveat = dt)
+        sol = solve(prob, alg; saveat = dt, seed)
         umean .+= Array(sol(tsave; idxs = 1))
+        seed += 1
     end
     umean ./= Nsims
     return umean
 end
 
 let
-    rng = StableRNG(12345)
+    seed = 12345
+    rng = StableRNG(seed)
     b = 2.0
     d = 1.0
     n0 = 1
@@ -320,7 +322,8 @@ let
     dt = 0.1
     tsave = range(tspan[1], tspan[2]; step = dt)
     for alg in (Tsit5(), Rodas5P(linsolve = QRFactorization()))
-        umean = getmean(Nsims, sjm_prob, alg, dt, tsave)
+        umean = getmean(Nsims, sjm_prob, alg, dt, tsave, seed)
         @test all(abs.(umean .- n.(tsave)) .< 0.05 * n.(tsave))
+        seed += Nsims 
     end
 end