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‎test/upstream/mtk_problem_inputs.jl

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@@ -175,6 +175,202 @@ let
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end
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end
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### Vector Species/Parameters Tests ###
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begin
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# Declares the model (with vector species/parameters, with/without default values, and observables).
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t = default_t()
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@species X(t)[1:2] Y(t)[1:2] = [10.0, 20.0] XY(t)[1:2]
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@parameters p[1:2] d[1:2] = [0.2, 0.5]
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rxs = [
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Reaction(p[1], [], [X[1]]),
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Reaction(p[2], [], [X[2]]),
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Reaction(d[1], [X[1]], []),
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Reaction(d[2], [X[2]], []),
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Reaction(p[1], [], [Y[1]]),
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Reaction(p[2], [], [Y[2]]),
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Reaction(d[1], [Y[1]], []),
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Reaction(d[2], [Y[2]], [])
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]
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observed = [XY[1] ~ X[1] + Y[1], XY[2] ~ X[2] + Y[2]]
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@named model_vec = ReactionSystem(rxs, t; observed)
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model_vec = complete(model_vec)
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# Declares various u0 versions (scalarised and vector forms).
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u0_alts_vec = [
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# Vectors not providing default values.
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[X => [1.0, 2.0]],
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[X[1] => 1.0, X[2] => 2.0],
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[model_vec.X => [1.0, 2.0]],
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[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0],
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[:X => [1.0, 2.0]],
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# Vectors providing default values.
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[X => [1.0, 2.0], Y => [10.0, 20.0]],
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[X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0],
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[model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]],
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[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0],
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[:X => [1.0, 2.0], :Y => [10.0, 20.0]],
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# Static vectors not providing default values.
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SA[X => [1.0, 2.0]],
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SA[X[1] => 1.0, X[2] => 2.0],
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SA[model_vec.X => [1.0, 2.0]],
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SA[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0],
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SA[:X => [1.0, 2.0]],
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# Static vectors providing default values.
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SA[X => [1.0, 2.0], Y => [10.0, 20.0]],
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SA[X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0],
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SA[model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]],
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SA[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0],
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SA[:X => [1.0, 2.0], :Y => [10.0, 20.0]],
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# Dicts not providing default values.
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Dict([X => [1.0, 2.0]]),
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Dict([X[1] => 1.0, X[2] => 2.0]),
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Dict([model_vec.X => [1.0, 2.0]]),
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Dict([model_vec.X[1] => 1.0, model_vec.X[2] => 2.0]),
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Dict([:X => [1.0, 2.0]]),
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# Dicts providing default values.
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Dict([X => [1.0, 2.0], Y => [10.0, 20.0]]),
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Dict([X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0]),
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Dict([model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]]),
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Dict([model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0]),
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Dict([:X => [1.0, 2.0], :Y => [10.0, 20.0]]),
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# Tuples not providing default values.
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(X => [1.0, 2.0]),
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(X[1] => 1.0, X[2] => 2.0),
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(model_vec.X => [1.0, 2.0]),
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(model_vec.X[1] => 1.0, model_vec.X[2] => 2.0),
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(:X => [1.0, 2.0]),
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# Tuples providing default values.
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(X => [1.0, 2.0], Y => [10.0, 20.0]),
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(X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0),
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(model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]),
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(model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0),
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(:X => [1.0, 2.0], :Y => [10.0, 20.0]),
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]
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# Declares various ps versions (vector forms only).
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p_alts_vec = [
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# Vectors not providing default values.
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[p => [1.0, 2.0]],
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[model_vec.p => [1.0, 2.0]],
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[:p => [1.0, 2.0]],
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# Vectors providing default values.
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[p => [4.0, 5.0], d => [0.2, 0.5]],
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[model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]],
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[:p => [4.0, 5.0], :d => [0.2, 0.5]],
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# Static vectors not providing default values.
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SA[p => [1.0, 2.0]],
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SA[model_vec.p => [1.0, 2.0]],
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SA[:p => [1.0, 2.0]],
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# Static vectors providing default values.
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SA[p => [4.0, 5.0], d => [0.2, 0.5]],
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SA[model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]],
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SA[:p => [4.0, 5.0], :d => [0.2, 0.5]],
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# Dicts not providing default values.
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Dict([p => [1.0, 2.0]]),
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Dict([model_vec.p => [1.0, 2.0]]),
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Dict([:p => [1.0, 2.0]]),
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# Dicts providing default values.
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Dict([p => [4.0, 5.0], d => [0.2, 0.5]]),
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Dict([model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]]),
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Dict([:p => [4.0, 5.0], :d => [0.2, 0.5]]),
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# Tuples not providing default values.
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(p => [1.0, 2.0]),
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(model_vec.p => [1.0, 2.0]),
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(:p => [1.0, 2.0]),
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# Tuples providing default values.
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(p => [4.0, 5.0], d => [0.2, 0.5]),
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(model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]),
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(:p => [4.0, 5.0], :d => [0.2, 0.5]),
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]
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# Declares a timespan.
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tspan = (0.0, 10.0)
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end
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# Perform ODE simulations (singular and ensemble).
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let
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# Creates normal and ensemble problems.
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base_oprob = ODEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
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base_sol = solve(base_oprob, Tsit5(); saveat = 1.0)
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base_eprob = EnsembleProblem(base_oprob)
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base_esol = solve(base_eprob, Tsit5(); trajectories = 2, saveat = 1.0)
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# Simulates problems for all input types, checking that identical solutions are found.
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@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
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# for u0 in u0_alts_vec, p in p_alts_vec
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# oprob = remake(base_oprob; u0, p)
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# @test base_sol == solve(oprob, Tsit5(); saveat = 1.0)
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# eprob = remake(base_eprob; u0, p)
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# @test base_esol == solve(eprob, Tsit5(); trajectories = 2, saveat = 1.0)
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# end
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end
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# Perform SDE simulations (singular and ensemble).
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let
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# Creates normal and ensemble problems.
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base_sprob = SDEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
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base_sol = solve(base_sprob, ImplicitEM(); seed, saveat = 1.0)
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base_eprob = EnsembleProblem(base_sprob)
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base_esol = solve(base_eprob, ImplicitEM(); seed, trajectories = 2, saveat = 1.0)
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# Simulates problems for all input types, checking that identical solutions are found.
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@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
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# for u0 in u0_alts_vec, p in p_alts_vec
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# sprob = remake(base_sprob; u0, p)
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# @test base_sol == solve(sprob, ImplicitEM(); seed, saveat = 1.0)
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# eprob = remake(base_eprob; u0, p)
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# @test base_esol == solve(eprob, ImplicitEM(); seed, trajectories = 2, saveat = 1.0)
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# end
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end
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# Perform jump simulations (singular and ensemble).
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let
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# Creates normal and ensemble problems.
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base_dprob = DiscreteProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
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base_jprob = JumpProblem(model_vec, base_dprob, Direct(); rng)
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base_sol = solve(base_jprob, SSAStepper(); seed, saveat = 1.0)
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base_eprob = EnsembleProblem(base_jprob)
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base_esol = solve(base_eprob, SSAStepper(); seed, trajectories = 2, saveat = 1.0)
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# Simulates problems for all input types, checking that identical solutions are found.
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@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
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# for u0 in u0_alts_vec, p in p_alts_vec
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# jprob = remake(base_jprob; u0, p)
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# @test base_sol == solve(base_jprob, SSAStepper(); seed, saveat = 1.0)
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# eprob = remake(base_eprob; u0, p)
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# @test base_esol == solve(eprob, SSAStepper(); seed, trajectories = 2, saveat = 1.0)
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# end
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end
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# Solves a nonlinear problem (EnsembleProblems are not possible for these).
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let
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base_nlprob = NonlinearProblem(model_vec, u0_alts_vec[1], p_alts_vec[1])
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base_sol = solve(base_nlprob, NewtonRaphson())
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@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
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# for u0 in u0_alts_vec, p in p_alts_vec
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# nlprob = remake(base_nlprob; u0, p)
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# @test base_sol == solve(nlprob, NewtonRaphson())
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# end
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end
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# Perform steady state simulations (singular and ensemble).
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let
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# Creates normal and ensemble problems.
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base_ssprob = SteadyStateProblem(model_vec, u0_alts_vec[1], p_alts_vec[1])
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base_sol = solve(base_ssprob, DynamicSS(Tsit5()))
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base_eprob = EnsembleProblem(base_ssprob)
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base_esol = solve(base_eprob, DynamicSS(Tsit5()); trajectories = 2)
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# Simulates problems for all input types, checking that identical solutions are found.
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@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
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# for u0 in u0_alts_vec, p in p_alts_vec
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# ssprob = remake(base_ssprob; u0, p)
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# @test base_sol == solve(ssprob, DynamicSS(Tsit5()))
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# eprob = remake(base_eprob; u0, p)
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# @test base_esol == solve(eprob, DynamicSS(Tsit5()); trajectories = 2)
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# end
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end
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### Checks Errors On Faulty Inputs ###
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