# Best practices for defining stochastic affect! functions for Jump Problems (RNG)
**URL:** https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202
**Category:** Modelling & Simulations
**Tags:** diffeq, random
**Created:** [September 13, 2022, 10:39am UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202 "2022-09-13T10:39:18Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@tom-plaa](https://discourse.julialang.org/u/tom-plaa)
#### Post date: [September 13, 2022, 10:39am UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/1 "2022-09-13T10:39:18Z")
I have created a small module based on **DifferentialEquations** to help me define a certain kind of stochastic differential equations where the stochastic processes are Lévy processes. As the jumps are stochastic, the kind of “affect functions” that act on the integrator object at jump times end up being similar to:
```julia
function affect!(integrator)
integrator.u[1] += some_function(rand())
return nothing
end
```
I currently make use of the **rand** function but I don’t even include **Random** in my module’s dependencies, nor do I declare the RNG anywhere. Should I do so? If so, what’s the cleanest way of doing this? I am assuming that it’s not a good idea to declare the RNG as a global variable akin to
```julia
module MyModule
import Random: Xoshiro
rng = Xoshiro()
function affect!(integrator)
integrator.u[1] += some_function(rand(rng))
return nothing
end
end
```
---
### Author:  [@isaacsas](https://discourse.julialang.org/u/isaacsas)
#### Post date: [September 13, 2022, 1:02pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/2 "2022-09-13T13:02:25Z")
You could make it an optional parameter with a default value in whatever function serves as the starting point for using your package / setting up problems with it.
Note also that `JumpProblem`s can take `rng` as a keyword argument, which sets the internal random number generator used for simulating the jump process. So you could configure your package to ensure they use the same rng. (Not sure if `SDEProblem`s allow it to be set too.)
---
### Author:  [@tom-plaa](https://discourse.julialang.org/u/tom-plaa)
#### Post date: [September 13, 2022, 2:30pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/3 "2022-09-13T14:30:16Z")
Don’t I need to a define a particular instance of the generator?
I thought I needed to do something akin to
```julia
rng = Xoshiro()
JumpProblem(..., rng=rng)
```
and that the excerpt below wouldn’t work:
```julia
JumpProblem(..., rng = Xoshiro())
```
Or do you mean instantiating it on startup? I use instantiating functions for a few minor configurations, but those are simple deterministic things whose purpose is to make finding files and data easier. I feel uneasy about having an init function export it as a global variable, and if I just define a function that gets read by every simulation function, it will always create a new instance of the rng everytime it’s called.
> So you could configure your package to ensure they use the same rng.
By this do you mean passing the same rng on both calls? In other words:
`rand(rng)` and `JumpProblem(..., rng=rng)`.
---
### Author:  [@isaacsas](https://discourse.julialang.org/u/isaacsas)
#### Post date: [September 13, 2022, 4:33pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/4 "2022-09-13T16:33:40Z")
We just define a default generator instance in JumpProcesses, see [https://github.com/SciML/JumpProcesses.jl/blob/23aa6ac0f78b05cff1fa573a2b8d3f06ff34d585/src/JumpProcesses.jl#L33](https://github.com/SciML/JumpProcesses.jl/blob/23aa6ac0f78b05cff1fa573a2b8d3f06ff34d585/src/JumpProcesses.jl#L33), and set the default value of the `rng` keyword to it, see [https://github.com/SciML/JumpProcesses.jl/blob/23aa6ac0f78b05cff1fa573a2b8d3f06ff34d585/src/problem.jl#L164](https://github.com/SciML/JumpProcesses.jl/blob/23aa6ac0f78b05cff1fa573a2b8d3f06ff34d585/src/problem.jl#L164), when calling `JumpProblem`.
So there is a module-wide default that is created and set to the default Julia generator, but it can be overridden if the user sets the keyword argument when calling `JumpProblem`.
> By this do you mean passing the same rng on both calls? In other words:
> `rand(rng)` and `JumpProblem(..., rng=rng)`.
Yes.
---
### Author:  [@tom-plaa](https://discourse.julialang.org/u/tom-plaa)
#### Post date: [September 14, 2022, 10:29am UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/5 "2022-09-14T10:29:19Z")
If I understand correctly, there is a global variable defined within the module, then. Does this guarantee that the rng used for the jump part ends up being the same as the one used in the diffusion component (for example)?
To follow your suggestion of using the same rng for the affect function, would I need to import the cons from the package?
Something like
```julia
import DifferentialEquations.JumpProcesses: DEFAULT_RNG
function affect!(integrator)
integrator.u[1] += some_function(rand(DEFAULT_RNG))
return nothing
end
```
---
### Author:  [@isaacsas](https://discourse.julialang.org/u/isaacsas)
#### Post date: [September 14, 2022, 1:18pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/6 "2022-09-14T13:18:59Z")
I wouldn’t suggest using `JumpProcesses.DEFAULT_RNG` as that is internal to the package. I was suggesting you could define an analogous default within your module, and an analogous way for users of your module to pass in an alternative rng. You could then pass whatever `rng` has been selected in your module down into JumpProcesses and other sub-libraries.
Unfortunately, I don’t think StochasticDiffEq currently let’s you change the `rng` in the default usage via `SDEProblem`, but @ChrisRackauckas would know better about that.
---
### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [September 14, 2022, 4:14pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/7 "2022-09-14T16:14:47Z")
> [@isaacsas](#):
>
> I don’t think StochasticDiffEq currently let’s you change the `rng` in the default usage via `SDEProblem`, but @ChrisRackauckas would know better about that.
It does, you just have to define a noise process and then pass it as noise.
---
### Author:  [@tom-plaa](https://discourse.julialang.org/u/tom-plaa)
#### Post date: [September 15, 2022, 12:43pm UTC](https://discourse.julialang.org/t/best-practices-for-defining-stochastic-affect-functions-for-jump-problems-rng/87202/8 "2022-09-15T12:43:42Z")
Alright, thank you both. I’ll try to see if it’s possible to refactor what I have using a custom noise process, and go from there.