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Merge pull request #805 from SciML/new_indexing_and_sim_tests
New indexing and simulation input tests
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‎test/meta/mtk_problem_inputs.jl

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#! format: off
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### Prepares Tests ###
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# Fetch packages
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using Catalyst, JumpProcesses, NonlinearSolve, OrdinaryDiffEq, SteadyStateDiffEq, StochasticDiffEq, Test
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# Sets rnd number.
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using StableRNGs
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rng = StableRNG(12345)
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seed = rand(rng, 1:100)
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### Basic Tests ###
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# Prepares a models and initial conditions/parameters (of different forms) to be used as problem inputs.
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begin
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model = @reaction_network begin
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@species Z(t) = Z0
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@parameters k2=0.5 Z0
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(kp,kd), 0 <--> X
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(k1,k2), X <--> Y
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(k1,k2), Y <--> Z
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end
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@unpack X, Y, Z, kp, kd, k1, k2, Z0 = model
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u0_alts = [
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# Vectors not providing default values.
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[X => 4, Y => 5],
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[model.X => 4, model.Y => 5],
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[:X => 4, :Y => 5],
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# Vectors providing default values.
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[X => 4, Y => 5, Z => 10],
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[model.X => 4, model.Y => 5, model.Z => 10],
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[:X => 4, :Y => 5, :Z => 10],
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# Dicts not providing default values.
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Dict([X => 4, Y => 5]),
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Dict([model.X => 4, model.Y => 5]),
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Dict([:X => 4, :Y => 5]),
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# Dicts providing default values.
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Dict([X => 4, Y => 5, Z => 10]),
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Dict([model.X => 4, model.Y => 5, model.Z => 10]),
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Dict([:X => 4, :Y => 5, :Z => 10]),
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# Tuples not providing default values.
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(X => 4, Y => 5),
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(model.X => 4, model.Y => 5),
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(:X => 4, :Y => 5),
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# Tuples providing default values.
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(X => 4, Y => 5, Z => 10),
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(model.X => 4, model.Y => 5, model.Z => 10),
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(:X => 4, :Y => 5, :Z => 10)
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]
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tspan = (0.0, 10.0)
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p_alts = [
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# Vectors not providing default values.
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[kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10],
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[model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10],
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[:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10],
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# Vectors providing default values.
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[kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10],
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[model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10],
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[:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10],
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# Dicts not providing default values.
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Dict([kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10]),
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Dict([model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10]),
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Dict([:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10]),
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# Dicts providing default values.
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Dict([kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10]),
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Dict([model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10]),
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Dict([:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10]),
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# Tuples not providing default values.
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(kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10),
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(model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10),
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(:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10),
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# Tuples providing default values.
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(kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10),
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(model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10),
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(:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10),
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]
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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, u0_alts[1], tspan, p_alts[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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for u0 in u0_alts, p in p_alts
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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, u0_alts[1], tspan, p_alts[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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for u0 in u0_alts, p in p_alts
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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, u0_alts[1], tspan, p_alts[1])
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base_jprob = JumpProblem(model, 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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for u0 in u0_alts, p in p_alts
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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, u0_alts[1], p_alts[1])
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base_sol = solve(base_nlprob, NewtonRaphson())
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for u0 in u0_alts, p in p_alts
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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, u0_alts[1], p_alts[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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for u0 in u0_alts, p in p_alts
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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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# Checks various erroneous problem inputs, ensuring that these throw errors.
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let
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# Declares the model.
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rn = @reaction_network begin
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(k1,k2), X1 <--> X2
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end
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@unpack k1, k2, X1, X2 = rn
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t = default_t()
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@species X3(t)
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@parameters k3
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# Declares valid initial conditions and parameter values
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u0_valid = [X1 => 1, X2 => 2]
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ps_valid = [k1 => 0.5, k2 => 0.1]
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# Declares invalid initial conditions and parameters. This includes both cases where values are
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# missing, or additional ones are given. Includes vector/Tuple/Dict forms.
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u0s_invalid = [
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# Missing a value.
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[X1 => 1],
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[rn.X1 => 1],
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[:X1 => 1],
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Dict([X1 => 1]),
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Dict([rn.X1 => 1]),
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Dict([:X1 => 1]),
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(X1 => 1),
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(rn.X1 => 1),
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(:X1 => 1),
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# Contain an additional value.
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[X1 => 1, X2 => 2, X3 => 3],
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[:X1 => 1, :X2 => 2, :X3 => 3],
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Dict([X1 => 1, X2 => 2, X3 => 3]),
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Dict([:X1 => 1, :X2 => 2, :X3 => 3]),
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(X1 => 1, X2 => 2, X3 => 3),
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(:X1 => 1, :X2 => 2, :X3 => 3)
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]
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ps_invalid = [
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# Missing a value.
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[k1 => 1.0],
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[rn.k1 => 1.0],
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[:k1 => 1.0],
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Dict([k1 => 1.0]),
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Dict([rn.k1 => 1.0]),
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Dict([:k1 => 1.0]),
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(k1 => 1.0),
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(rn.k1 => 1.0),
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(:k1 => 1.0),
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# Contain an additional value.
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[k1 => 1.0, k2 => 2.0, k3 => 3.0],
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[:k1 => 1.0, :k2 => 2.0, :k3 => 3.0],
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Dict([k1 => 1.0, k2 => 2.0, k3 => 3.0]),
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Dict([:k1 => 1.0, :k2 => 2.0, :k3 => 3.0]),
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(k1 => 1.0, k2 => 2.0, k3 => 3.0),
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(:k1 => 1.0, :k2 => 2.0, :k3 => 3.0)
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]
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# Loops through all potential parameter sets, checking their inputs yield errors.
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for ps in [ps_valid; ps_invalid], u0 in [u0_valid; u0s_invalid]
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# Handles problems with/without tspan separately. Special check ensuring that valid inputs passes.
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for XProblem in [ODEProblem, SDEProblem, DiscreteProblem]
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if (ps == ps_valid) && (u0 == u0_valid)
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XProblem(rn, u0, (0.0, 1.0), ps); @test true;
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else
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# Several of these cases do not throw errors (https://github.com/SciML/ModelingToolkit.jl/issues/2624).
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@test_broken false
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continue
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@test_throws Exception XProblem(rn, u0, (0.0, 1.0), ps)
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end
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end
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for XProblem in [NonlinearProblem, SteadyStateProblem]
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if (ps == ps_valid) && (u0 == u0_valid)
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XProblem(rn, u0, ps); @test true;
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else
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@test_broken false
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continue
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@test_throws Exception XProblem(rn, u0, ps)
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end
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end
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end
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end

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