-
-
Notifications
You must be signed in to change notification settings - Fork 71
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
4 changed files
with
636 additions
and
36 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,243 @@ | ||
#! format: off | ||
|
||
### Prepares Tests ### | ||
|
||
# Fetch packages | ||
using Catalyst, JumpProcesses, NonlinearSolve, OrdinaryDiffEq, SteadyStateDiffEq, StochasticDiffEq, Test | ||
|
||
# Sets rnd number. | ||
using StableRNGs | ||
rng = StableRNG(12345) | ||
seed = rand(rng, 1:100) | ||
|
||
|
||
### Basic Tests ### | ||
|
||
# Prepares a models and initial conditions/parameters (of different forms) to be used as problem inputs. | ||
begin | ||
model = @reaction_network begin | ||
@species Z(t) = Z0 | ||
@parameters k2=0.5 Z0 | ||
(kp,kd), 0 <--> X | ||
(k1,k2), X <--> Y | ||
(k1,k2), Y <--> Z | ||
end | ||
@unpack X, Y, Z, kp, kd, k1, k2, Z0 = model | ||
|
||
u0_alts = [ | ||
# Vectors not providing default values. | ||
[X => 4, Y => 5], | ||
[model.X => 4, model.Y => 5], | ||
[:X => 4, :Y => 5], | ||
# Vectors providing default values. | ||
[X => 4, Y => 5, Z => 10], | ||
[model.X => 4, model.Y => 5, model.Z => 10], | ||
[:X => 4, :Y => 5, :Z => 10], | ||
# Dicts not providing default values. | ||
Dict([X => 4, Y => 5]), | ||
Dict([model.X => 4, model.Y => 5]), | ||
Dict([:X => 4, :Y => 5]), | ||
# Dicts providing default values. | ||
Dict([X => 4, Y => 5, Z => 10]), | ||
Dict([model.X => 4, model.Y => 5, model.Z => 10]), | ||
Dict([:X => 4, :Y => 5, :Z => 10]), | ||
# Tuples not providing default values. | ||
(X => 4, Y => 5), | ||
(model.X => 4, model.Y => 5), | ||
(:X => 4, :Y => 5), | ||
# Tuples providing default values. | ||
(X => 4, Y => 5, Z => 10), | ||
(model.X => 4, model.Y => 5, model.Z => 10), | ||
(:X => 4, :Y => 5, :Z => 10) | ||
] | ||
tspan = (0.0, 10.0) | ||
p_alts = [ | ||
# Vectors not providing default values. | ||
[kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10], | ||
[model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10], | ||
[:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10], | ||
# Vectors providing default values. | ||
[kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10], | ||
[model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10], | ||
[:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10], | ||
# Dicts not providing default values. | ||
Dict([kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10]), | ||
Dict([model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10]), | ||
Dict([:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10]), | ||
# Dicts providing default values. | ||
Dict([kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10]), | ||
Dict([model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10]), | ||
Dict([:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10]), | ||
# Tuples not providing default values. | ||
(kp => 1.0, kd => 0.1, k1 => 0.25, Z0 => 10), | ||
(model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.Z0 => 10), | ||
(:kp => 1.0, :kd => 0.1, :k1 => 0.25, :Z0 => 10), | ||
# Tuples providing default values. | ||
(kp => 1.0, kd => 0.1, k1 => 0.25, k2 => 0.5, Z0 => 10), | ||
(model.kp => 1.0, model.kd => 0.1, model.k1 => 0.25, model.k2 => 0.5, model.Z0 => 10), | ||
(:kp => 1.0, :kd => 0.1, :k1 => 0.25, :k2 => 0.5, :Z0 => 10), | ||
] | ||
end | ||
|
||
# Perform ODE simulations (singular and ensemble). | ||
let | ||
# Creates normal and ensemble problems. | ||
base_oprob = ODEProblem(model, u0_alts[1], tspan, p_alts[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. | ||
for u0 in u0_alts, p in p_alts | ||
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, u0_alts[1], tspan, p_alts[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. | ||
for u0 in u0_alts, p in p_alts | ||
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, u0_alts[1], tspan, p_alts[1]) | ||
base_jprob = JumpProblem(model, 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. | ||
for u0 in u0_alts, p in p_alts | ||
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, u0_alts[1], p_alts[1]) | ||
base_sol = solve(base_nlprob, NewtonRaphson()) | ||
for u0 in u0_alts, p in p_alts | ||
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, u0_alts[1], p_alts[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. | ||
for u0 in u0_alts, p in p_alts | ||
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. | ||
let | ||
# Declares the model. | ||
rn = @reaction_network begin | ||
(k1,k2), X1 <--> X2 | ||
end | ||
@unpack k1, k2, X1, X2 = rn | ||
t = default_t() | ||
@species X3(t) | ||
@parameters k3 | ||
|
||
# Declares valid initial conditions and parameter values | ||
u0_valid = [X1 => 1, X2 => 2] | ||
ps_valid = [k1 => 0.5, k2 => 0.1] | ||
|
||
# Declares invalid initial conditions and parameters. This includes both cases where values are | ||
# missing, or additional ones are given. Includes vector/Tuple/Dict forms. | ||
u0s_invalid = [ | ||
# Missing a value. | ||
[X1 => 1], | ||
[rn.X1 => 1], | ||
[:X1 => 1], | ||
Dict([X1 => 1]), | ||
Dict([rn.X1 => 1]), | ||
Dict([:X1 => 1]), | ||
(X1 => 1), | ||
(rn.X1 => 1), | ||
(:X1 => 1), | ||
# Contain an additional value. | ||
[X1 => 1, X2 => 2, X3 => 3], | ||
[:X1 => 1, :X2 => 2, :X3 => 3], | ||
Dict([X1 => 1, X2 => 2, X3 => 3]), | ||
Dict([:X1 => 1, :X2 => 2, :X3 => 3]), | ||
(X1 => 1, X2 => 2, X3 => 3), | ||
(:X1 => 1, :X2 => 2, :X3 => 3) | ||
] | ||
ps_invalid = [ | ||
# Missing a value. | ||
[k1 => 1.0], | ||
[rn.k1 => 1.0], | ||
[:k1 => 1.0], | ||
Dict([k1 => 1.0]), | ||
Dict([rn.k1 => 1.0]), | ||
Dict([:k1 => 1.0]), | ||
(k1 => 1.0), | ||
(rn.k1 => 1.0), | ||
(:k1 => 1.0), | ||
# Contain an additional value. | ||
[k1 => 1.0, k2 => 2.0, k3 => 3.0], | ||
[:k1 => 1.0, :k2 => 2.0, :k3 => 3.0], | ||
Dict([k1 => 1.0, k2 => 2.0, k3 => 3.0]), | ||
Dict([:k1 => 1.0, :k2 => 2.0, :k3 => 3.0]), | ||
(k1 => 1.0, k2 => 2.0, k3 => 3.0), | ||
(:k1 => 1.0, :k2 => 2.0, :k3 => 3.0) | ||
] | ||
|
||
# Loops through all potential parameter sets, checking their inputs yield errors. | ||
for ps in [ps_valid; ps_invalid], u0 in [u0_valid; u0s_invalid] | ||
# Handles problems with/without tspan separately. Special check ensuring that valid inputs passes. | ||
for XProblem in [ODEProblem, SDEProblem, DiscreteProblem] | ||
if (ps == ps_valid) && (u0 == u0_valid) | ||
XProblem(rn, u0, (0.0, 1.0), ps); @test true; | ||
else | ||
# Several of these cases do not throw errors (https://github.com/SciML/ModelingToolkit.jl/issues/2624). | ||
@test_broken false | ||
continue | ||
@test_throws Exception XProblem(rn, u0, (0.0, 1.0), ps) | ||
end | ||
end | ||
for XProblem in [NonlinearProblem, SteadyStateProblem] | ||
if (ps == ps_valid) && (u0 == u0_valid) | ||
XProblem(rn, u0, ps); @test true; | ||
else | ||
@test_broken false | ||
continue | ||
@test_throws Exception XProblem(rn, u0, ps) | ||
end | ||
end | ||
end | ||
end |
Oops, something went wrong.