I want to solve a PDE-Problem that is a kind of an advection problem with dynamic boundary conditions. The PDE itself is solved with ModelingToolkit and Method of lines. the entire calculation should be called finally from one function in python with juliacall.

For the dynamic BCs i did an interpolation on the measured data and afterwards the function were registered symbolic with e.g. @register_symbolic func(t)

Now my problem is that since finally everything should be called from python inside one function also the several @ register_symbolics need to be put in a function, but that caused errors.

This question is related to Registering a time dependent function inside a function in ModelingToolkit.jl, where @ChrisRackauckas said to do this a callable struct is needed, but I don´t know how I can do this. Any help is greatly appreciated.

Here is a minimal example of my code.

```
using PythonCall
using DomainSets
using Interpolations
using MethodOfLines
using ModelingToolkit
using OrdinaryDiffEq
function itp(interval::PyArray, function_vals::PyArray, t)
""" returns interpolation"""
interpolation = LinearInterpolation(interval["time"], interval[function_vals])
itpFunction(t) = interpolation(t)
return itpFunction(t)
end
function PDE_solve(interval_time::Float64,
T_fInit::Num,
Te_amb::Num,
eq_params)
Dt = Differential(t); Dx = Differential(x);
eq1 = Dt(T_s(t, x)) ~ - a1_m * (T_s(t, x)- Te_amb) - a2_m * (T_s(t, x)- Te_amb)^2 - (alpha) * (T_s(t, x) - T_f(t, x)))
...second equation definition....
ic_bc = [T_f(0.0, x) ~ T_fInit]
domain = [x ∈ Interval(0.0, 1.0),
t ∈ Interval(0.0, interval_time)]
@named sys = PDESystem(eqs=[eq1, eq2], bcs=ic_bc, domain=domain, ivs= [t, x], dvs=[T_f(t, x), T_s(t, x)])
# Determine the number of spatial grid points (Nx)
Nx = 100
@time discretization = MOLFiniteDifference([x => Nx], t, advection_scheme = UpwindScheme(),
approx_order = 2)
# Convert the PDE problem into an ODE problem
@time prob = discretize(sys, discretization)
@time sol = solve(prob, alg, abstol=1e-6, reltol=1e-4, saveat=4.0, dense=false
return sol, discrete_, grid
end
fun(t) = itp(time_index, func_data)
@register_symbolic T_f_in(t)
...more registering of itp functions...
@parameters t, x;
@variables T_f(..), T_s(..)
sol = PDE_solve(data["index"], T_f_in(t), Te_amb(t), parameters)
```