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AayushSabharwalAayushSabharwal
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test: test LinearProblem codegen
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‎test/linearproblem.jl

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using ModelingToolkit
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using LinearSolve
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using SciMLBase
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using StaticArrays
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using SparseArrays
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using Test
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using ModelingToolkit: t_nounits as t, D_nounits as D, SystemCompatibilityError
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@testset "Rejects non-affine systems" begin
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@variables x y
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@mtkbuild sys = System([0 ~ x^2 + y, 0 ~ x - y])
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@test_throws SystemCompatibilityError LinearProblem(sys, nothing)
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end
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@variables x[1:3] [irreducible = true]
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@parameters p[1:3, 1:3] q[1:3]
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@mtkbuild sys = System([p * x ~ q])
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# sanity check
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@test length(unknowns(sys)) == length(equations(sys)) == 3
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A = Float64[1 2 3; 4 3.5 1.7; 5.2 1.8 9.7]
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b = Float64[2, 5, 8]
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ps = [p => A, q => b]
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@testset "Basics" begin
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# Ensure it works without providing `u0`
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prob = LinearProblem(sys, ps)
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@test prob.u0 === nothing
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@test SciMLBase.isinplace(prob)
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@test prob.A ≈ A
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@test prob.b ≈ b
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@test eltype(prob.A) == Float64
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@test eltype(prob.b) == Float64
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@test prob.ps[p * q] ≈ A * b
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sol = solve(prob)
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test_broken SciMLBase.successful_retcode(sol)
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@test prob.A * sol.u - prob.b≈zeros(3) atol=1e-10
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A2 = rand(3, 3)
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b2 = rand(3)
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@testset "remake" begin
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prob2 = remake(prob; p = [p => A2, q => b2])
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@test prob2.ps[p] ≈ A2
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@test prob2.ps[q] ≈ b2
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@test prob2.A ≈ A2
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@test prob2.b ≈ b2
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end
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prob.ps[p] = A2
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@test prob.A ≈ A2
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prob.ps[q] = b2
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@test prob.b ≈ b2
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A2[1, 1] = prob.ps[p[1, 1]] = 1.5
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@test prob.A ≈ A2
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b2[1] = prob.ps[q[1]] = 2.5
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@test prob.b ≈ b2
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@testset "expression = Val{true}" begin
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prob3e = LinearProblem(sys, ps; expression = Val{true})
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@test prob3e isa Expr
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prob3 = eval(prob3e)
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@test prob3.u0 === nothing
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@test SciMLBase.isinplace(prob3)
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@test prob3.A ≈ A
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@test prob3.b ≈ b
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@test eltype(prob3.A) == Float64
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@test eltype(prob3.b) == Float64
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@test prob3.ps[p * q] ≈ A * b
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sol = solve(prob3)
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test_broken SciMLBase.successful_retcode(sol)
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@test prob3.A * sol.u - prob3.b≈zeros(3) atol=1e-10
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end
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end
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@testset "With `u0`" begin
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prob = LinearProblem(sys, [x => ones(3); ps])
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@test prob.u0 ≈ ones(3)
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@test SciMLBase.isinplace(prob)
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@test eltype(prob.u0) == Float64
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# Observed should work
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@test prob[x[1] + x[2]] ≈ 2.0
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@testset "expression = Val{true}" begin
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prob3e = LinearProblem(sys, [x => ones(3); ps]; expression = Val{true})
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@test prob3e isa Expr
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prob3 = eval(prob3e)
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@test prob3.u0 ≈ ones(3)
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@test eltype(prob3.u0) == Float64
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end
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end
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@testset "SArray OOP form" begin
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prob = LinearProblem(sys, SVector{2}(ps))
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@test prob.A isa SMatrix{3, 3, Float64}
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@test prob.b isa SVector{3, Float64}
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@test !SciMLBase.isinplace(prob)
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@test prob.ps[p * q] ≈ A * b
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sol = solve(prob)
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test_broken SciMLBase.successful_retcode(sol)
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@test prob.A * sol.u - prob.b≈zeros(3) atol=1e-10
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A2 = rand(3, 3)
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b2 = rand(3)
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@testset "remake" begin
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prob2 = remake(prob; p = [p => A2, q => b2])
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# Despite passing `Array` to `remake`
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@test prob2.A isa SMatrix{3, 3, Float64}
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@test prob2.b isa SVector{3, Float64}
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@test prob2.ps[p] ≈ A2
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@test prob2.ps[q] ≈ b2
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@test prob2.A ≈ A2
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@test prob2.b ≈ b2
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end
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@testset "expression = Val{true}" begin
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prob3e = LinearProblem(sys, SVector{2}(ps); expression = Val{true})
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@test prob3e isa Expr
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prob3 = eval(prob3e)
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@test prob3.A isa SMatrix{3, 3, Float64}
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@test prob3.b isa SVector{3, Float64}
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@test !SciMLBase.isinplace(prob3)
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@test prob3.ps[p * q] ≈ A * b
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sol = solve(prob3)
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test_broken SciMLBase.successful_retcode(sol)
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@test prob3.A * sol.u - prob3.b≈zeros(3) atol=1e-10
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end
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end
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@testset "u0_constructor" begin
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prob = LinearProblem{false}(sys, ps; u0_constructor = x -> SArray{Tuple{size(x)...}}(x))
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@test prob.A isa SMatrix{3, 3, Float64}
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@test prob.b isa SVector{3, Float64}
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@test prob.ps[p * q] ≈ A * b
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end
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@testset "sparse form" begin
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prob = LinearProblem(sys, ps; sparse = true)
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@test issparse(prob.A)
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@test !issparse(prob.b)
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sol = solve(prob)
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# This might end up failing because of
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test SciMLBase.successful_retcode(sol)
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A2 = rand(3, 3)
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prob.ps[p] = A2
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@test prob.A ≈ A2
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b2 = rand(3)
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prob.ps[q] = b2
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@test prob.b ≈ b2
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A2 = rand(3, 3)
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b2 = rand(3)
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@testset "remake" begin
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prob2 = remake(prob; p = [p => A2, q => b2])
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@test issparse(prob2.A)
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@test !issparse(prob2.b)
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@test prob2.ps[p] ≈ A2
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@test prob2.ps[q] ≈ b2
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@test prob2.A ≈ A2
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@test prob2.b ≈ b2
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end
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@testset "expression = Val{true}" begin
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prob3e = LinearProblem(sys, ps; sparse = true, expression = Val{true})
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@test prob3e isa Expr
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prob3 = eval(prob3e)
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@test issparse(prob3.A)
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@test !issparse(prob3.b)
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sol = solve(prob3)
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# This might end up failing because of
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# https://github.com/SciML/LinearSolve.jl/issues/532
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@test SciMLBase.successful_retcode(sol)
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end
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end

‎test/runtests.jl

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@safetestset "Namespacing test" include("namespacing.jl")
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@safetestset "Subsystem replacement" include("substitute_component.jl")
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@safetestset "Linearization Tests" include("linearize.jl")
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@safetestset "LinearProblem Tests" include("linearproblem.jl")
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end
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end
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