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ChrisRackauckasChrisRackauckas
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Update solution_interface.jl
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‎test/downstream/solution_interface.jl‎

Lines changed: 121 additions & 117 deletions
Original file line numberDiff line numberDiff line change
@@ -6,27 +6,29 @@ using Plots: Plots, plot
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### Tests on non-layered model (everything should work). ###
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@parameters a b c d
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@variables s1(t) s2(t)
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eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
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D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
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@mtkcompile population_model = System(eqs, t)
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# Tests on ODEProblem.
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u0 = [s1 => 2.0, s2 => 1.0]
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p = [a => 2.0, b => 1.0, c => 1.0, d => 1.0]
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tspan = (0.0, 1000000.0)
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oprob = ODEProblem(population_model, [u0; p], tspan)
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sol = solve(oprob, Rodas4())
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@test sol[s1] == sol[population_model.s1] == sol[:s1]
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@test sol[s2] == sol[population_model.s2] == sol[:s2]
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@test sol[s1][end] ≈ 1.0
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@test_throws Exception sol[a]
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@test_throws Exception sol[population_model.a]
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@test_throws Exception sol[:a]
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@testset "Basic indexing" begin
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@parameters a b c d
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@variables s1(t) s2(t)
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eqs = [D(s1) ~ a * s1 / (1 + s1 + s2) - b * s1,
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D(s2) ~ +c * s2 / (1 + s1 + s2) - d * s2]
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@mtkcompile population_model = System(eqs, t)
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# Tests on ODEProblem.
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u0 = [s1 => 2.0, s2 => 1.0]
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p = [a => 2.0, b => 1.0, c => 1.0, d => 1.0]
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tspan = (0.0, 1000000.0)
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oprob = ODEProblem(population_model, [u0; p], tspan)
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sol = solve(oprob, Rodas4())
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@test sol[s1] == sol[population_model.s1] == sol[:s1]
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@test sol[s2] == sol[population_model.s2] == sol[:s2]
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@test sol[s1][end] ≈ 1.0
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@test_throws Exception sol[a]
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@test_throws Exception sol[population_model.a]
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@test_throws Exception sol[:a]
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end
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@testset "plot ODE solution" begin
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Plots.unicodeplots()
@@ -51,102 +53,104 @@ sol = solve(oprob, Rodas4())
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@test_nowarn plot(sol; plot_analytic = true)
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end
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# Tests on SDEProblem
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noiseeqs = [0.1 * s1,
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0.1 * s2]
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@named noisy_population_model = SDESystem(population_model, noiseeqs)
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noisy_population_model = complete(noisy_population_model)
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sprob = SDEProblem(noisy_population_model, [u0; p], (0.0, 100.0))
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sol = solve(sprob, ImplicitEM())
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@test sol[s1] == sol[noisy_population_model.s1] == sol[:s1]
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@test sol[s2] == sol[noisy_population_model.s2] == sol[:s2]
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@test_throws Exception sol[a]
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@test_throws Exception sol[noisy_population_model.a]
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@test_throws Exception sol[:a]
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@test_nowarn sol(0.5, idxs = noisy_population_model.s1)
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### Tests on layered model (some things should not work). ###
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@parameters σ ρ β
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@variables x(t) y(t) z(t)
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eqs = [D(x) ~ σ * (y - x),
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D(y) ~ x * (ρ - z) - y,
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D(z) ~ x * y - β * z]
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@named lorenz1 = System(eqs, t)
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@named lorenz2 = System(eqs, t)
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@parameters γ
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@variables a(t) α(t)
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connections = [0 ~ lorenz1.x + lorenz2.y + a * γ,
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α ~ 2lorenz1.x + a * γ]
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@mtkcompile sys = System(connections, t, [a, α], [γ], systems = [lorenz1, lorenz2])
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u0 = [lorenz1.x => 1.0,
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lorenz1.y => 0.0,
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lorenz1.z => 0.0,
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lorenz2.x => 0.0,
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lorenz2.y => 1.0,
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lorenz2.z => 0.0]
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p = [lorenz1.σ => 10.0,
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lorenz1.ρ => 28.0,
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lorenz1.β => 8 / 3,
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lorenz2.σ => 10.0,
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lorenz2.ρ => 28.0,
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lorenz2.β => 8 / 3,
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γ => 2.0]
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tspan = (0.0, 100.0)
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prob = ODEProblem(sys, [u0; p], tspan)
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sol = solve(prob, Rodas4())
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@test_throws ArgumentError sol[x]
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@test in(sol[lorenz1.x], [getindex.(sol.u, i) for i in 1:length(unknowns(sol.prob.f.sys))])
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@test_throws KeyError sol[:x]
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### Non-symbolic indexing tests
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@test sol[:, 1] isa AbstractVector
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@test sol[:, 1:2] isa AbstractDiffEqArray
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@test sol[:, [1, 2]] isa AbstractDiffEqArray
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sol1 = sol(0.0:1.0:10.0)
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@test sol1.u isa Vector
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@test first(sol1.u) isa Vector
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@test length(sol1.u) == 11
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@test length(sol1.t) == 11
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sol2 = sol(0.1)
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@test sol2 isa Vector
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@test length(sol2) == length(unknowns(sys))
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@test first(sol2) isa Real
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sol3 = sol(0.0:1.0:10.0, idxs = [lorenz1.x, lorenz2.x])
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sol7 = sol(0.0:1.0:10.0, idxs = [2, 1])
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@test sol7.u isa Vector
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@test first(sol7.u) isa Vector
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@test length(sol7.u) == 11
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@test length(sol7.t) == 11
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@test collect(sol7[t]) ≈ sol3.t
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@test collect(sol7[t, 1:5]) ≈ sol3.t[1:5]
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sol8 = sol(0.1, idxs = [2, 1])
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@test sol8 isa Vector
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@test length(sol8) == 2
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@test first(sol8) isa Real
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sol9 = sol(0.0:1.0:10.0, idxs = 2)
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@test sol9.u isa Vector
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@test first(sol9.u) isa Real
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@test length(sol9.u) == 11
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@test length(sol9.t) == 11
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@test collect(sol9[t]) ≈ sol3.t
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@test collect(sol9[t, 1:5]) ≈ sol3.t[1:5]
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sol10 = sol(0.1, idxs = 2)
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@test sol10 isa Real
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@testset "Symbolic Indexing" begin
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# Tests on SDEProblem
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noiseeqs = [0.1 * s1,
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0.1 * s2]
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@named noisy_population_model = SDESystem(population_model, noiseeqs)
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noisy_population_model = complete(noisy_population_model)
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sprob = SDEProblem(noisy_population_model, [u0; p], (0.0, 100.0))
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sol = solve(sprob, ImplicitEM())
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@test sol[s1] == sol[noisy_population_model.s1] == sol[:s1]
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@test sol[s2] == sol[noisy_population_model.s2] == sol[:s2]
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@test_throws Exception sol[a]
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@test_throws Exception sol[noisy_population_model.a]
69+
@test_throws Exception sol[:a]
70+
@test_nowarn sol(0.5, idxs = noisy_population_model.s1)
71+
### Tests on layered model (some things should not work). ###
72+
73+
@parameters σ ρ β
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@variables x(t) y(t) z(t)
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eqs = [D(x) ~ σ * (y - x),
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D(y) ~ x * (ρ - z) - y,
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D(z) ~ x * y - β * z]
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@named lorenz1 = System(eqs, t)
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@named lorenz2 = System(eqs, t)
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@parameters γ
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@variables a(t) α(t)
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connections = [0 ~ lorenz1.x + lorenz2.y + a * γ,
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α ~ 2lorenz1.x + a * γ]
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@mtkcompile sys = System(connections, t, [a, α], [γ], systems = [lorenz1, lorenz2])
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u0 = [lorenz1.x => 1.0,
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lorenz1.y => 0.0,
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lorenz1.z => 0.0,
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lorenz2.x => 0.0,
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lorenz2.y => 1.0,
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lorenz2.z => 0.0]
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p = [lorenz1.σ => 10.0,
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lorenz1.ρ => 28.0,
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lorenz1.β => 8 / 3,
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lorenz2.σ => 10.0,
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lorenz2.ρ => 28.0,
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lorenz2.β => 8 / 3,
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γ => 2.0]
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tspan = (0.0, 100.0)
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prob = ODEProblem(sys, [u0; p], tspan)
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sol = solve(prob, Rodas4())
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@test_throws ArgumentError sol[x]
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@test in(sol[lorenz1.x], [getindex.(sol.u, i) for i in 1:length(unknowns(sol.prob.f.sys))])
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@test_throws KeyError sol[:x]
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### Non-symbolic indexing tests
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@test sol[:, 1] isa AbstractVector
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@test sol[:, 1:2] isa AbstractDiffEqArray
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@test sol[:, [1, 2]] isa AbstractDiffEqArray
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sol1 = sol(0.0:1.0:10.0)
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@test sol1.u isa Vector
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@test first(sol1.u) isa Vector
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@test length(sol1.u) == 11
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@test length(sol1.t) == 11
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sol2 = sol(0.1)
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@test sol2 isa Vector
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@test length(sol2) == length(unknowns(sys))
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@test first(sol2) isa Real
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sol3 = sol(0.0:1.0:10.0, idxs = [lorenz1.x, lorenz2.x])
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sol7 = sol(0.0:1.0:10.0, idxs = [2, 1])
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@test sol7.u isa Vector
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@test first(sol7.u) isa Vector
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@test length(sol7.u) == 11
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@test length(sol7.t) == 11
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@test collect(sol7[t]) ≈ sol3.t
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@test collect(sol7[t, 1:5]) ≈ sol3.t[1:5]
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sol8 = sol(0.1, idxs = [2, 1])
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@test sol8 isa Vector
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@test length(sol8) == 2
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@test first(sol8) isa Real
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sol9 = sol(0.0:1.0:10.0, idxs = 2)
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@test sol9.u isa Vector
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@test first(sol9.u) isa Real
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@test length(sol9.u) == 11
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@test length(sol9.t) == 11
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@test collect(sol9[t]) ≈ sol3.t
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@test collect(sol9[t, 1:5]) ≈ sol3.t[1:5]
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sol10 = sol(0.1, idxs = 2)
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@test sol10 isa Real
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end
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@testset "Plot idxs" begin
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@variables x(t) y(t)

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