SDE solution symbolic indexing not working

[isaacsas]

Describe the bug 🐞

SDE solution symbolic indexing gives an error.

Expected behavior

SDE solution symbolic indexing works.

Minimal Reproducible Example 👇

using Catalyst, DifferentialEquations, Plots

rn = @reaction_network begin
    (k1,k2), X1 <--> X2
end
rn = complete(rn)
u0 = [:X1 => 10.0, :X2 => 10.0]
tspan = (0.0, 10.0)
p_1 = [:k1 => 1.0, :k2 => 1.0]

sprob = SDEProblem(rn, u0, tspan, p_1)
ssol = solve(sprob)

# fails while analogous ODE/jump indexing works
t = [1.0, 2.0]
ssol(t; idxs = rn.X1)

# fails while analogous ODE/jump indexing works
plot(ssol; idxs = rn.X1, ylimit = (0.0, 20.0))

Error & Stacktrace ⚠️

ERROR: ArgumentError: invalid index: X1(t) of type SymbolicUtils.BasicSymbolic{Real}
Stacktrace:
  [1] to_index(i::SymbolicUtils.BasicSymbolic{Real})
    @ Base ./indices.jl:300
  [2] to_index(x::Num)
    @ Symbolics ~/.julia/packages/Symbolics/eV38N/src/num.jl:162
  [3] to_index
    @ ./indices.jl:277 [inlined]
  [4] _to_indices1
    @ ./indices.jl:359 [inlined]
  [5] to_indices
    @ ./indices.jl:354 [inlined]
  [6] to_indices
    @ ./indices.jl:345 [inlined]
  [7] getindex
    @ ./abstractarray.jl:1288 [inlined]
  [8] maybeview
    @ ./views.jl:149 [inlined]
  [9] dotview
    @ ./broadcast.jl:1244 [inlined]
 [10] sde_interpolant(Θ::Float64, dt::Float64, u0::Vector{Float64}, u1::Vector{Float64}, idxs::Num, deriv::Type{Val{0}})
    @ StochasticDiffEq ~/.julia/packages/StochasticDiffEq/adUnd/src/dense.jl:32
 [11] (::StochasticDiffEq.var"#90#91"{Vector{…}, Num, DataType, Symbol, Float64, Vector{…}, Vector{…}})(j::Int64)
    @ StochasticDiffEq ~/.julia/packages/StochasticDiffEq/adUnd/src/dense.jl:133
 [12] iterate(g::Base.Generator, s::Vararg{Any})
    @ Base ./generator.jl:47 [inlined]
 [13] _collect(c::Vector{…}, itr::Base.Generator{…}, ::Base.EltypeUnknown, isz::Base.HasShape{…})
    @ Base ./array.jl:854
 [14] collect_similar(cont::Vector{Int64}, itr::Base.Generator{Vector{…}, StochasticDiffEq.var"#90#91"{…}})
    @ Base ./array.jl:763
 [15] map(f::Function, A::Vector{Int64})
    @ Base ./abstractarray.jl:3282
 [16] sde_interpolation(tvals::Vector{…}, id::StochasticDiffEq.LinearInterpolationData{…}, idxs::Num, deriv::Type, p::Vector{…}, continuity::Symbol)
    @ StochasticDiffEq ~/.julia/packages/StochasticDiffEq/adUnd/src/dense.jl:114
 [17] LinearInterpolationData
    @ StochasticDiffEq ~/.julia/packages/StochasticDiffEq/adUnd/src/interp_func.jl:7 [inlined]
 [18] #_#461
    @ SciMLBase ~/.julia/packages/SciMLBase/dU5UI/src/solutions/rode_solutions.jl:66 [inlined]
 [19] RODESolution
    @ SciMLBase ~/.julia/packages/SciMLBase/dU5UI/src/solutions/rode_solutions.jl:64 [inlined]
 [20] top-level scope
    @ REPL[13]:1
Some type information was truncated. Use `show(err)` to see complete types.

Environment (please complete the following information):

• Output of using Pkg; Pkg.status()

Status `/private/var/folders/8s/kss4zcyx10v_l22h64zdzs6m0000gn/T/jl_7q9J8Y/Project.toml`
  [479239e8] Catalyst v13.5.1
  [0c46a032] DifferentialEquations v7.12.0
  [91a5bcdd] Plots v1.39.0

• Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)

Status `/private/var/folders/8s/kss4zcyx10v_l22h64zdzs6m0000gn/T/jl_7q9J8Y/Manifest.toml`
  [47edcb42] ADTypes v0.2.6
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⌃ [764a87c0] BoundaryValueDiffEq v5.6.0
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  [05823500] OpenLibm_jll v0.8.1+2
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.8.0+1
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`

• Output of versioninfo()

Julia Version 1.10.0
Commit 3120989f39b (2023-12-25 18:01 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (arm64-apple-darwin22.4.0)
  CPU: 16 × Apple M3 Max
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, apple-m1)
  Threads: 1 on 12 virtual cores