move another labelledarray

Author: ChrisRackauckas

test/labelledarrays.jl

@@ -41,3 +41,51 @@ d = LVector(x = 1.0, y = 2.0, z = 3.0)
 @test ff.jac(d, p, ForwardDiff.Dual(0.0, 1.0)) isa Array
 @inferred ff.jac(d, p, ForwardDiff.Dual(0.0, 1.0))
 @test eltype(ff.jac(d, p, ForwardDiff.Dual(0.0, 1.0))) <: ForwardDiff.Dual
+
+## https://github.com/SciML/ModelingToolkit.jl/issues/1054
+using LabelledArrays
+using ModelingToolkit
+
+# ODE model: simple SIR model with seasonally forced contact rate
+function SIR!(du, u, p, t)
+
+    # Unknowns
+    (S, I, R) = u[1:3]
+    N = S + I + R
+
+    # params
+    β = p.β
+    η = p.η
+    φ = p.φ
+    ω = 1.0 / p.ω
+    μ = p.μ
+    σ = p.σ
+
+    # FOI
+    βeff = β * (1.0 + η * cos(2.0 * π * (t - φ) / 365.0))
+    λ = βeff * I / N
+
+    # change in unknowns
+    du[1] = (μ * N - λ * S - μ * S + ω * R)
+    du[2] = (λ * S - σ * I - μ * I)
+    du[3] = (σ * I - μ * R - ω * R)
+    du[4] = (σ * I) # cumulative incidence
+end
+
+# Solver settings
+tmin = 0.0
+tmax = 10.0 * 365.0
+tspan = (tmin, tmax)
+
+# Initiate ODE problem
+theta_fix = [1.0 / (80 * 365)]
+theta_est = [0.28, 0.07, 1.0 / 365.0, 1.0, 1.0 / 5.0]
+p = @LArray [theta_est; theta_fix] (:β, :η, :ω, :φ, :σ, :μ)
+u0 = @LArray [9998.0, 1.0, 1.0, 1.0] (:S, :I, :R, :C)
+
+# Initiate ODE problem
+problem = ODEProblem(SIR!, u0, tspan, p)
+sys = complete(modelingtoolkitize(problem))
+
+@test all(isequal.(parameters(sys), getproperty.(@variables(β, η, ω, φ, σ, μ), :val)))
+@test all(isequal.(Symbol.(unknowns(sys)), Symbol.(@variables(S(t), I(t), R(t), C(t)))))

test/modelingtoolkitize.jl

@@ -1,6 +1,6 @@
 using OrdinaryDiffEq, ModelingToolkit, DataStructures, Test
 using Optimization, RecursiveArrayTools, OptimizationOptimJL
-using LabelledArrays, SymbolicIndexingInterface
+using SymbolicIndexingInterface
 using ModelingToolkit: t_nounits as t, D_nounits as D
 using SciMLBase: parameterless_type
 
@@ -210,54 +210,6 @@ tspan = (0.0, 1.0)
 prob = ODEProblem(k, x0, tspan)
 sys = modelingtoolkitize(prob)
 
-## https://github.com/SciML/ModelingToolkit.jl/issues/1054
-using LabelledArrays
-using ModelingToolkit
-
-# ODE model: simple SIR model with seasonally forced contact rate
-function SIR!(du, u, p, t)
-
-    # Unknowns
-    (S, I, R) = u[1:3]
-    N = S + I + R
-
-    # params
-    β = p.β
-    η = p.η
-    φ = p.φ
-    ω = 1.0 / p.ω
-    μ = p.μ
-    σ = p.σ
-
-    # FOI
-    βeff = β * (1.0 + η * cos(2.0 * π * (t - φ) / 365.0))
-    λ = βeff * I / N
-
-    # change in unknowns
-    du[1] = (μ * N - λ * S - μ * S + ω * R)
-    du[2] = (λ * S - σ * I - μ * I)
-    du[3] = (σ * I - μ * R - ω * R)
-    du[4] = (σ * I) # cumulative incidence
-end
-
-# Solver settings
-tmin = 0.0
-tmax = 10.0 * 365.0
-tspan = (tmin, tmax)
-
-# Initiate ODE problem
-theta_fix = [1.0 / (80 * 365)]
-theta_est = [0.28, 0.07, 1.0 / 365.0, 1.0, 1.0 / 5.0]
-p = @LArray [theta_est; theta_fix] (:β, :η, :ω, :φ, :σ, :μ)
-u0 = @LArray [9998.0, 1.0, 1.0, 1.0] (:S, :I, :R, :C)
-
-# Initiate ODE problem
-problem = ODEProblem(SIR!, u0, tspan, p)
-sys = complete(modelingtoolkitize(problem))
-
-@test all(isequal.(parameters(sys), getproperty.(@variables(β, η, ω, φ, σ, μ), :val)))
-@test all(isequal.(Symbol.(unknowns(sys)), Symbol.(@variables(S(t), I(t), R(t), C(t)))))
-
 # https://github.com/SciML/ModelingToolkit.jl/issues/1158
 
 function ode_prob(du, u, p::NamedTuple, t)