# Low Dependency Usage

DifferentialEquations.jl is a large library containing the functionality of many different solver and addon packages. However in many cases you may want to cut down on the size of the dependency and only use the parts of the the library which are essential to your application. This is possible due to SciML's modular package structure.

## Common Example: Using only OrdinaryDiffEq.jl

One common example is using only the ODE solvers OrdinaryDiffEq.jl. The solvers all reexport SciMLBase.jl (which holds the problem and solution types) and so OrdinaryDiffEq.jl is all that's needed. Thus replacing

using DifferentialEquations

with

#Add the OrdinaryDiffEq Package first!
using OrdinaryDiffEq

will work if these are the only features you are using.

## Generalizing the Idea

In general, you will always need SciMLBase.jl, since it defines all of the fundamental types, but the solvers will automatically reexport it. For solvers, you typically only need that solver package. So SciMLBase+Sundials, SciMLBase+LSODA, etc. will get you the common interface with that specific solver setup. SciMLBase.jl is a very lightweight dependency, so there is no issue here! For PDEs, you normally need SciMLBase+DiffEqPDEBase in addition to the solver package.

For the addon packages, you will normally need SciMLBase, the solver package you choose, and the addon package. So for example, for parameter estimation you would likely want SciMLBase+OrdinaryDiffEq+DiffEqParamEstim. If you arne't sure which package a specific command is from, they using @which. For example, from the parameter estimation docs we have:

using DifferentialEquations
function f(du,u,p,t)
dx = p[1]*u[1] - u[1]*u[2]
dy = -3*u[2] + u[1]*u[2]
end

u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [1.5]
prob = ODEProblem(f,u0,tspan,p)
sol = solve(prob,Tsit5())
t = collect(range(0, stop=10, length=200))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
using RecursiveArrayTools
data = convert(Array,randomized)
cost_function = build_loss_objective(prob,t,data,Tsit5(),maxiters=10000)

If we wanted to know where build_loss_objective came from, we can do:

@which build_loss_objective(prob,t,data,Tsit5(),maxiters=10000)

(::DiffEqParamEstim.#kw##build_loss_objective)(::Array{Any,1}, ::DiffEqParamEstim.#build_loss_objective, prob::SciMLBase.DEProblem, t, data, alg)

This says it's in the DiffEqParamEstim.jl package. Thus in this case, we could have done

using OrdinaryDiffEq, DiffEqParamEstim

instead of the full using DifferentialEquations. Note that due to the way Julia dependencies work, any internal function in the package will work. The only dependencies you need to explicitly using are the functions you are specifically calling. Thus this method can be used to determine all of the DiffEq packages you are using.