I am trying to do something that may, or may not be possible. I want to use ForwardDiff, and create a Jacobian function. This function will be part of a `struct`

. A parameter container is also part of this `struct`

. Now, I want the Jacobian function to close over the entire `struct`

, not just the parameter container. A short description of that is present here: https://github.com/JuliaDiff/ForwardDiff.jl/issues/296

Unfortuantely, I do not know how to do it. At the moment here is my try:

```
# Here f must be of the form: f(x) -> SVector (ONE ARGUMENT!)
function generate_jacobian_oop(f::F, x::X) where {F, X}
# Test f structure:
L = length(x)
y = f(x)
@assert typeof(y) <: SVector
@assert length(y) == L
# Setup config
cfg = ForwardDiff.JacobianConfig(f, x)
FDjac(x, p) = ForwardDiff.jacobian(f, x, cfg)
return FDjac
end
mutable struct DiscreteDS{D, T<:Number, F, P, J}
state::SVector{D,T}
eom::F
p::P
jacob::J
function DiscreteDS{D, T, F, P}(state::SVector{D,T}, eom::F, p::P) where {D, T, F, P}
f = new(state, eom, p)
reducedeom = (x) -> eom(x, f.p)
f.jacob = generate_jacobian_oop(reducedeom, state)
return f
end
end
function DiscreteDS(state::SVector{D,T}, eom::F, p::P) where {D, T, F, P}
return DiscreteDS{D, T, F, P}(state, eom, p)
end
@inline henon_eom(x, p) = SVector{2}(1.0 - p[1]*x[1]^2 + x[2], p[2]*x[1])
p = [1.4, 0.3]
ds = DiscreteDS(SVector(0.0, 0.0), henon_eom, p)
```

This doesn’t run at the moment, because it tells me that there are too few parameters specified in `new`

.

But more importantly, I also do not know how to enforce the type parameter in the line `f.jacob = generate_jacobian_oop(reducedeom, state)`

…

Any help would be greatly appreciated!