Skip to content

Commit

Permalink
Merge pull request #957 from SciML/tests___new___vector_valued
Browse files Browse the repository at this point in the history 
[v14 - Awaiting MTK fix] Tests for vector valued species/parameters
  • Loading branch information
@TorkelE
TorkelE committed Jun 16, 2024
2 parents 50312ad + f4fed5a commit 134041d
Showing 1 changed file with 178 additions and 1 deletion.
179 changes: 178 additions & 1 deletion test/upstream/mtk_problem_inputs.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -158,6 +158,183 @@ let
end
end

### Vector Species/Parameters Tests ###

begin
# Declares the model (with vector species/parameters, with/without default values, and observables).
t = default_t()
@species X(t)[1:2] Y(t)[1:2] = [10.0, 20.0] XY(t)[1:2]
@parameters p[1:2] d[1:2] = [0.2, 0.5]
rxs = [
Reaction(p[1], [], [X[1]]),
Reaction(p[2], [], [X[2]]),
Reaction(d[1], [X[1]], []),
Reaction(d[2], [X[2]], []),
Reaction(p[1], [], [Y[1]]),
Reaction(p[2], [], [Y[2]]),
Reaction(d[1], [Y[1]], []),
Reaction(d[2], [Y[2]], [])
]
observed = [XY[1] ~ X[1] + Y[1], XY[2] ~ X[2] + Y[2]]
@named model_vec = ReactionSystem(rxs, t; observed)
model_vec = complete(model_vec)

# Declares various u0 versions (scalarised and vector forms).
u0_alts_vec = [
# Vectors not providing default values.
[X => [1.0, 2.0]],
[X[1] => 1.0, X[2] => 2.0],
[model_vec.X => [1.0, 2.0]],
[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0],
[:X => [1.0, 2.0]],
# Vectors providing default values.
[X => [1.0, 2.0], Y => [10.0, 20.0]],
[X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0],
[model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]],
[model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0],
[:X => [1.0, 2.0], :Y => [10.0, 20.0]],
# Dicts not providing default values.
Dict([X => [1.0, 2.0]]),
Dict([X[1] => 1.0, X[2] => 2.0]),
Dict([model_vec.X => [1.0, 2.0]]),
Dict([model_vec.X[1] => 1.0, model_vec.X[2] => 2.0]),
Dict([:X => [1.0, 2.0]]),
# Dicts providing default values.
Dict([X => [1.0, 2.0], Y => [10.0, 20.0]]),
Dict([X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0]),
Dict([model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]]),
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]),
Dict([:X => [1.0, 2.0], :Y => [10.0, 20.0]]),
# Tuples not providing default values.
(X => [1.0, 2.0]),
(X[1] => 1.0, X[2] => 2.0),
(model_vec.X => [1.0, 2.0]),
(model_vec.X[1] => 1.0, model_vec.X[2] => 2.0),
(:X => [1.0, 2.0]),
# Tuples providing default values.
(X => [1.0, 2.0], Y => [10.0, 20.0]),
(X[1] => 1.0, X[2] => 2.0, Y[1] => 10.0, Y[2] => 20.0),
(model_vec.X => [1.0, 2.0], model_vec.Y => [10.0, 20.0]),
(model_vec.X[1] => 1.0, model_vec.X[2] => 2.0, model_vec.Y[1] => 10.0, model_vec.Y[2] => 20.0),
(:X => [1.0, 2.0], :Y => [10.0, 20.0]),
]

# Declares various ps versions (vector forms only).
p_alts_vec = [
# Vectors not providing default values.
[p => [1.0, 2.0]],
[model_vec.p => [1.0, 2.0]],
[:p => [1.0, 2.0]],
# Vectors providing default values.
[p => [4.0, 5.0], d => [0.2, 0.5]],
[model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]],
[:p => [4.0, 5.0], :d => [0.2, 0.5]],
# Dicts not providing default values.
Dict([p => [1.0, 2.0]]),
Dict([model_vec.p => [1.0, 2.0]]),
Dict([:p => [1.0, 2.0]]),
# Dicts providing default values.
Dict([p => [4.0, 5.0], d => [0.2, 0.5]]),
Dict([model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]]),
Dict([:p => [4.0, 5.0], :d => [0.2, 0.5]]),
# Tuples not providing default values.
(p => [1.0, 2.0]),
(model_vec.p => [1.0, 2.0]),
(:p => [1.0, 2.0]),
# Tuples providing default values.
(p => [4.0, 5.0], d => [0.2, 0.5]),
(model_vec.p => [4.0, 5.0], model_vec.d => [0.2, 0.5]),
(:p => [4.0, 5.0], :d => [0.2, 0.5]),
]

# Declares a timespan.
tspan = (0.0, 10.0)
end

# Perform ODE simulations (singular and ensemble).
let
# Creates normal and ensemble problems.
base_oprob = ODEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
base_sol = solve(base_oprob, Tsit5(); saveat = 1.0)
base_eprob = EnsembleProblem(base_oprob)
base_esol = solve(base_eprob, Tsit5(); trajectories = 2, saveat = 1.0)

# Simulates problems for all input types, checking that identical solutions are found.
@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
# for u0 in u0_alts_vec, p in p_alts_vec
# oprob = remake(base_oprob; u0, p)
# @test base_sol == solve(oprob, Tsit5(); saveat = 1.0)
# eprob = remake(base_eprob; u0, p)
# @test base_esol == solve(eprob, Tsit5(); trajectories = 2, saveat = 1.0)
# end
end

# Perform SDE simulations (singular and ensemble).
let
# Creates normal and ensemble problems.
base_sprob = SDEProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
base_sol = solve(base_sprob, ImplicitEM(); seed, saveat = 1.0)
base_eprob = EnsembleProblem(base_sprob)
base_esol = solve(base_eprob, ImplicitEM(); seed, trajectories = 2, saveat = 1.0)

# Simulates problems for all input types, checking that identical solutions are found.
@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
# for u0 in u0_alts_vec, p in p_alts_vec
# sprob = remake(base_sprob; u0, p)
# @test base_sol == solve(sprob, ImplicitEM(); seed, saveat = 1.0)
# eprob = remake(base_eprob; u0, p)
# @test base_esol == solve(eprob, ImplicitEM(); seed, trajectories = 2, saveat = 1.0)
# end
end

# Perform jump simulations (singular and ensemble).
let
# Creates normal and ensemble problems.
base_dprob = DiscreteProblem(model_vec, u0_alts_vec[1], tspan, p_alts_vec[1])
base_jprob = JumpProblem(model_vec, base_dprob, Direct(); rng)
base_sol = solve(base_jprob, SSAStepper(); seed, saveat = 1.0)
base_eprob = EnsembleProblem(base_jprob)
base_esol = solve(base_eprob, SSAStepper(); seed, trajectories = 2, saveat = 1.0)

# Simulates problems for all input types, checking that identical solutions are found.
@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
# for u0 in u0_alts_vec, p in p_alts_vec
# jprob = remake(base_jprob; u0, p)
# @test base_sol == solve(base_jprob, SSAStepper(); seed, saveat = 1.0)
# eprob = remake(base_eprob; u0, p)
# @test base_esol == solve(eprob, SSAStepper(); seed, trajectories = 2, saveat = 1.0)
# end
end

# Solves a nonlinear problem (EnsembleProblems are not possible for these).
let
base_nlprob = NonlinearProblem(model_vec, u0_alts_vec[1], p_alts_vec[1])
base_sol = solve(base_nlprob, NewtonRaphson())
@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
# for u0 in u0_alts_vec, p in p_alts_vec
# nlprob = remake(base_nlprob; u0, p)
# @test base_sol == solve(nlprob, NewtonRaphson())
# end
end

# Perform steady state simulations (singular and ensemble).
let
# Creates normal and ensemble problems.
base_ssprob = SteadyStateProblem(model_vec, u0_alts_vec[1], p_alts_vec[1])
base_sol = solve(base_ssprob, DynamicSS(Tsit5()))
base_eprob = EnsembleProblem(base_ssprob)
base_esol = solve(base_eprob, DynamicSS(Tsit5()); trajectories = 2)

# Simulates problems for all input types, checking that identical solutions are found.
@test_broken false # Cannot remake problem (https://github.com/SciML/ModelingToolkit.jl/issues/2804).
# for u0 in u0_alts_vec, p in p_alts_vec
# ssprob = remake(base_ssprob; u0, p)
# @test base_sol == solve(ssprob, DynamicSS(Tsit5()))
# eprob = remake(base_eprob; u0, p)
# @test base_esol == solve(eprob, DynamicSS(Tsit5()); trajectories = 2)
# end
end

### Checks Errors On Faulty Inputs ###

# Checks various erroneous problem inputs, ensuring that these throw errors.
Expand Down Expand Up @@ -244,4 +421,4 @@ let
end
end
end
end
end

0 comments on commit 134041d

Please sign in to comment.