Add test

Author: YingboMa

test/discretesystem.jl

@@ -32,6 +32,7 @@ u0 = [S => 990.0, I => 10.0, R => 0.0]
 p = [β => 0.05, c => 10.0, γ => 0.25, δt => 0.1, nsteps => 400]
 tspan = (0.0, ModelingToolkit.value(substitute(nsteps, p))) # value function (from Symbolics) is used to convert a Num to Float64
 prob_map = DiscreteProblem(sys, u0, tspan, p)
+@test prob_map.f.sys === sys
 
 # Solution
 using OrdinaryDiffEq

test/nonlinearsystem.jl

@@ -79,6 +79,7 @@ sH = calculate_hessian(ns)
       getfield.(sparse.(sH), :rowval)
 
 prob = NonlinearProblem(ns, ones(3), ones(3))
+@test prob.f.sys === ns
 sol = solve(prob, NewtonRaphson())
 @test sol.u[1] ≈ sol.u[2]
 

test/odesystem.jl

@@ -213,6 +213,7 @@ p2 = (k₁ => 0.04,
       k₃ => 1e4)
 tspan = (0.0, 100000.0)
 prob1 = ODEProblem(sys, u0, tspan, p)
+@test prob1.f.sys === sys
 prob12 = ODEProblem(sys, u0, tspan, [0.04, 3e7, 1e4])
 prob13 = ODEProblem(sys, u0, tspan, (0.04, 3e7, 1e4))
 prob14 = ODEProblem(sys, u0, tspan, p2)

test/optimizationsystem.jl

@@ -41,6 +41,7 @@ p = [sys1.a => 6.0
      β => 10.0]
 
 prob = OptimizationProblem(combinedsys, u0, p, grad = true)
+@test prob.f.sys === combinedsys
 sol = solve(prob, NelderMead())
 @test sol.minimum < -1e5
 
@@ -54,6 +55,7 @@ sol = solve(prob2, BFGS(initial_stepnorm = 0.0001), allow_f_increases = true)
 prob = OptimizationProblem(sys2, [x => 0.0, y => 0.0], [a => 1.0, b => 100.0],
                            lcons = [-1.0, -1.0], ucons = [500.0, 500.0], grad = true,
                            hess = true)
+@test prob.f.sys === sys2
 sol = solve(prob, IPNewton(), allow_f_increases = true)
 @test sol.minimum < 1.0
 sol = solve(prob, Ipopt.Optimizer())

test/sdesystem.jl

@@ -71,6 +71,7 @@ parammap = [
 ]
 
 prob = SDEProblem(de, u0map, (0.0, 100.0), parammap)
+@test prob.f.sys === de
 @test size(prob.noise_rate_prototype) == (3, 3)
 @test prob.noise_rate_prototype isa Matrix
 sol = solve(prob, EM(), dt = 0.001)