# Interfacing problems, integrators, and solutions

When simulating a model, one begins with creating a problem. Next, a simulation is performed on a problem, during which the state of the simulation is recorded through an integrator. Finally, the simulation output is returned as a solution. This tutorial describes how to access, or modify the state, or parameter, values of problems, integrators, and solutions structures.

Generally, when we have a structure simulation_struct and want to interface with the state (or parameter) G, we use simulation_struct[:G] to access the value, and simulation_struct[:G] = 5.0 to set it to a new value. However, see the following examples for full details.

## Interfacing problem objects

We begin by demonstrating how we can interface with problem objects. We will demonstrate using a ODEProblem, however, it works similarily for other problem types.

using Catalyst
rn = @reaction_network begin
(k1,k2), X1 <--> X2
end

u0 = [:X1 => 1.0, :X2 => 5.0]
p = [:k1 => 5.0, :k2 => 2.0]
oprob = ODEProblem(rn, u0, (0.0,10.0), p)

We can find the value of a state simply by interfacing with the corresponding symbol:

oprob[:X1]
1.0

with the notation being identical for parameters:

oprob[:k1]
5.0

If we want to change a state's initial condition value, we use the following notation

oprob[:X1] = 10.0
10.0

with parameters using the same notation.

#### Remaking problems using the remake function

Typically, when modifying problems, it is recommended to use the remake function. Unlike when we do oprob[:X1] = 10.0 (which modifies the problem in question), remake creates a new problem object. The remake function takes a problem as input, and any fields you wish to modify (and their new values) as optional inputs. Thus, we can do:

using DifferentialEquations
@unpack X1, X2, k1, k2 = rn
oprob1 = ODEProblem(rn, u0, (0.0,10.0), p)
oprob2 = remake(oprob1; u0=[X1 => 10.0, X2 => 50.0], tspan=(0.0,100.0), p=[k1 => 50.0,k2 => 20.0])

and we can now check the fields of oprob2

oprob2.u0
2-element Vector{Float64}:
10.0
50.0
oprob2.tspan
(0.0, 100.0)
oprob2.p
2-element Vector{Float64}:
50.0
20.0

Please note that, currently, remake does not work while giving Symbols as input (e.g [:X1 => 10.0, :X2 => 50.0]), but we need to unpack the symbolic variables and use them instead (please see the end of this tutorial for more information on using symbolic variables rather than Symbols).

When using remake, we only have to provide the fields that we actually wish to change, e.g.

oprob3 = remake(oprob1; u0=[X1 => 10.0, X2 => 50.0])

will only update the initial conditions.

## Interfacing integrator objects

During a simulation, the solution is stored in an integrator object, we will here describe how to interface with these. The primary circumstance under which a user may wish to do so is when using callbacks. We can create an integrator by calling init on our problem (while circumstances where the user might want to use init function exist, since integrators are automatically created during simulations, these are rare):

integrator = init(oprob)

Using a similar syntax to problems, we can get the current values of a state within the integrator:

integrator[:X1]

or a parameter:

integrator[:k1]

Similarly, we can update their values using:

integrator[:X1] = 10.0

Please read this with regards to updating integrators of JumpProblems.

## Interfacing solution objects

Finally, we consider solution objects. First, we simulate our problem:

sol = solve(oprob)

For solutions, when we access a state, we get its whole simulation vector:

sol[:X1]

while when we access a parameter we only get a single value:

sol[:k1]

Finally, we note that we cannot change the values of solution states or parameters (i.e. both sol[:X1] = 0.0 and sol[:k1] = 0.0 generate errors).

## Interfacing using symbolic representation

Catalyst is built on an intermediary representation implemented by (ModelingToolkit.jl)[https://github.com/SciML/ModelingToolkit.jl]. ModelingToolkit is a modelling framework where one first declares a set of symbolic variables and parameters using e.g.

using ModelingToolkit
@parameters σ ρ β
@variables t x(t) y(t) z(t)

and then uses these to build systems of equations. Here, these symbolic variables (x, y, and z) and parameters (σ, ρ, and β) can be used to interface a problem, integrator, and solution object (like we did previously, but using Symbols, e.g. :X). Since Catalyst models are built on ModelingToolkit, these models also contain similar symbolic variables and parameters.

using Catalyst
rn = @reaction_network begin
(k1,k2), X1 <--> X2
end

@unpack k1,k2,X1,X2 = rn
\begin{align*} \mathrm{X1} &\xrightleftharpoons[k2]{k1} \mathrm{X2} \end{align*}

Here, we first list the parameters and variables (for reaction systems the latter are typically species) we wish to import (in this case we select all, but we could select only a subset), next we denote from which model (here rn) from which we wish to import from. Next, these values can be used directly to interface with e.g. an ODEProblem:

u0 = [X1 => 1.0, X2 => 5.0]
p = [:k1 => 5.0, :k2 => 2.0]
oprob = ODEProblem(rn, u0, (0.0,10.0), p)

oprob[k1]
5.0

To interface with integrators and solutions we use a similar syntax.

Finally, instead of using @unpack to access a symbolic variable or parameter, we can access it directly using rn.X1, and thus access a state of our ODEProblem using

oprob[rn.X1]
1.0