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Add JET.jl static analysis tests and improve type stability
- Add function barrier pattern to DataDrivenProblem constructor for type stability - Add JET.jl static analysis tests for key functions - Configure JET tests to run in CI environments - Add JET as a test dependency with broad version compat The new `_construct_datadrivenproblem` internal function ensures that the type parameters (cType, probType) are known at compile time through Val wrappers, improving type stability of problem construction. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Project.toml

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name = "DataDrivenDiffEq"
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uuid = "2445eb08-9709-466a-b3fc-47e12bd697a2"
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authors = ["Julius Martensen <julius.martensen@gmail.com>"]
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version = "1.12.0"
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authors = ["Julius Martensen <julius.martensen@gmail.com>"]
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[deps]
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CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
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DataInterpolations = "4, 5, 6, 7, 8"
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DiffEqBase = "6"
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DocStringExtensions = "0.7, 0.8, 0.9"
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JET = "0.9, 0.10, 0.11"
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MLUtils = "0.3, 0.4"
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ModelingToolkit = "11"
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OrdinaryDiffEqTsit5 = "1"
@@ -48,11 +49,12 @@ Symbolics = "7"
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julia = "1.10"
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[extras]
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JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
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OrdinaryDiffEqTsit5 = "b1df2697-797e-41e3-8120-5422d3b24e4a"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["Test", "Random", "SafeTestsets", "Pkg", "OrdinaryDiffEqTsit5"]
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test = ["Test", "Random", "SafeTestsets", "Pkg", "OrdinaryDiffEqTsit5", "JET"]

src/problem/type.jl

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@@ -114,6 +114,15 @@ end
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Base.eltype(::AbstractDataDrivenProblem{T}) where {T} = T
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# Internal constructor that is type-stable once cType and probType are known
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# This function barrier ensures the inner construction is fully specialized
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function _construct_datadrivenproblem(
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::Val{cType}, ::Val{probType}, dType::Type{T}, X, t, DX, Y, U, p, name
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) where {cType, probType, T}
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promoted = _promote(X, t, DX, Y, U, p)
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return DataDrivenProblem{T, cType, probType}(promoted..., name)
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end
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function DataDrivenProblem(
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probType, X, t, DX, Y, U, p; name = gensym(:DDProblem),
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kwargs...
@@ -131,7 +140,10 @@ function DataDrivenProblem(
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end
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end
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return DataDrivenProblem{dType, cType, probType}(_promote(X, t, DX, Y, U, p)..., name)
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# Use function barrier with Val types for type stability
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return _construct_datadrivenproblem(
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Val(cType), Val(probType), dType, X, t, DX, Y, U, p, name
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)
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end
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function remake_problem(

test/jet_tests.jl

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using DataDrivenDiffEq
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using JET
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using Test
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# Note: JET analysis on symbolic packages can be slow and may report many
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# false positives from the underlying symbolic infrastructure (Symbolics.jl,
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# ModelingToolkit.jl). This test file focuses on core DataDrivenDiffEq
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# functionality with concrete types to catch actual type stability issues.
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#
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# We use @test_opt with target_modules to only check DataDrivenDiffEq code,
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# and we use broken=true for tests that detect expected polymorphic behavior
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# (like problem type dispatch) rather than actual bugs.
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@testset "JET Static Analysis" begin
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@testset "Basis generator type stability" begin
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# Test basis generators with concrete types
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# These should be fully type-stable as they don't involve symbolic computation
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.polynomial_basis(
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2, 3
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)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.monomial_basis(2, 3)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.chebyshev_basis(2, 3)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.sin_basis(2, 3)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.cos_basis(2, 3)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.fourier_basis(2, 3)
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end
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@testset "Problem accessor type stability" begin
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X = rand(2, 10)
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t = collect(1.0:10.0)
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DX = rand(2, 10)
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prob = ContinuousDataDrivenProblem(X, t, DX)
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# Test simple accessor functions that should be type-stable
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# These accessors only check type parameters, not field values
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_autonomous(prob)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_discrete(prob)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_continuous(prob)
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq.is_direct(prob)
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# Note: has_timepoints checks isempty on AbstractVector field, which has
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# expected runtime dispatch due to the abstract field type design choice
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end
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@testset "Internal constructor type stability" begin
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# Test the internal type-stable constructor directly
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X = rand(2, 10)
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t = collect(1.0:10.0)
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DX = rand(2, 10)
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Y = Matrix{Float64}(undef, 0, 0)
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U = Matrix{Float64}(undef, 0, 0)
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p = Float64[]
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# The internal constructor should be fully type-stable
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@test_opt target_modules = (DataDrivenDiffEq,) DataDrivenDiffEq._construct_datadrivenproblem(
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Val(false),
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Val(DataDrivenDiffEq.DDProbType(3)),
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Float64,
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X,
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t,
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DX,
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Y,
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U,
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p,
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:test
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)
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end
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end

test/runtests.jl

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include("./commonsolve/commonsolve.jl")
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end
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end
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# Run JET analysis tests only in CI or when GROUP explicitly includes JET
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# Note: JET tests may take longer due to static analysis overhead
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if get(ENV, "CI", "false") == "true" || get(ENV, "RUN_JET_TESTS", "false") == "true"
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@safetestset "JET Static Analysis" begin
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include("./jet_tests.jl")
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end
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end
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else
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dev_subpkg(GROUP)
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subpkg_path = joinpath(dirname(@__DIR__), "lib", GROUP)

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