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ChrisRackauckasChrisRackauckas
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Merge pull request SciML#61 from ChrisRackauckas-Claude/interface-check-20251230-000251
Add type validation and interface compatibility tests
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‎README.md‎

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@@ -26,6 +26,17 @@ MATLABDiffEq.jl is simply a solver on the DiffEq common interface, so for detail
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However, the only options implemented are those for error calculations
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(`timeseries_errors`), `saveat`, and tolerances.
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### Type Requirements
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Since this package sends data to MATLAB for computation, it only supports types
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that MATLAB can handle:
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- **Supported types:** `Float64`, integers (`Int64`, etc.), and `Complex{Float64}`
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- **Not supported:** `BigFloat`, `Float32`, GPU arrays (`CuArray`, `JLArray`), or other custom array types
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If you need arbitrary precision or GPU computing, use the native Julia solvers
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from [DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/) instead.
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Note that the algorithms are defined to have the same name as the MATLAB algorithms,
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but are not exported. Thus to use `ode45`, you would specify the algorithm as
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`MATLABDiffEq.ode45()`.

‎src/MATLABDiffEq.jl‎

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@@ -5,6 +5,39 @@ using Reexport
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using MATLAB, ModelingToolkit
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using PrecompileTools
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# MATLAB only supports Float64 arrays. Check if a type is MATLAB-compatible.
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_is_matlab_compatible_eltype(::Type{Float64}) = true
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_is_matlab_compatible_eltype(::Type{<:Integer}) = true # MATLAB can convert integers
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_is_matlab_compatible_eltype(::Type{<:Complex{Float64}}) = true
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_is_matlab_compatible_eltype(::Type) = false
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function _check_matlab_compatible(u0, tspan)
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T = eltype(u0)
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if !_is_matlab_compatible_eltype(T)
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throw(ArgumentError(
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"MATLABDiffEq.jl requires Float64-compatible element types. " *
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"Got eltype(u0) = $T. MATLAB does not support arbitrary precision " *
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"(BigFloat) or GPU arrays (JLArrays, CuArrays). Please convert your " *
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"initial conditions to Float64: u0 = Float64.(u0)"
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))
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end
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tT = eltype(tspan)
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if !_is_matlab_compatible_eltype(tT)
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throw(ArgumentError(
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"MATLABDiffEq.jl requires Float64-compatible time span types. " *
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"Got eltype(tspan) = $tT. MATLAB does not support arbitrary precision " *
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"(BigFloat). Please use Float64 for tspan: tspan = Float64.(tspan)"
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))
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end
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# Check that the array type itself is a standard Julia array
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if !(u0 isa Array || u0 isa Number)
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@warn "MATLABDiffEq.jl works best with standard Julia Arrays. " *
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"Got $(typeof(u0)). The array will be converted to a standard Array " *
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"before being sent to MATLAB."
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end
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return nothing
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end
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# Handle ModelingToolkit API changes: states -> unknowns
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if isdefined(ModelingToolkit, :unknowns)
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const mtk_states = ModelingToolkit.unknowns
@@ -35,6 +68,9 @@ function DiffEqBase.__solve(
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callback = nothing,
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kwargs...
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) where {uType, tupType, isinplace, AlgType <: MATLABAlgorithm}
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# Validate that input types are MATLAB-compatible
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_check_matlab_compatible(prob.u0, prob.tspan)
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tType = eltype(tupType)
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if prob.tspan[end] - prob.tspan[1] < tType(0)
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# Also precompile with missing keys (common case)
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_ = buildDEStats(Dict{String, Any}())
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# Precompile type compatibility checks
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_ = _is_matlab_compatible_eltype(Float64)
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_ = _is_matlab_compatible_eltype(Int64)
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_ = _is_matlab_compatible_eltype(Complex{Float64})
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_ = _is_matlab_compatible_eltype(BigFloat)
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_ = _check_matlab_compatible([1.0, 2.0], (0.0, 1.0))
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_ = _check_matlab_compatible(1.0, (0.0, 1.0))
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end
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end
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‎test/interface_tests.jl‎

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# Interface compatibility tests for MATLABDiffEq.jl
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# These tests verify type checking and interface compliance
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using Test
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@testset "Interface Compatibility" begin
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@testset "MATLAB type compatibility checks" begin
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# Test _is_matlab_compatible_eltype function
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@test MATLABDiffEq._is_matlab_compatible_eltype(Float64) == true
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@test MATLABDiffEq._is_matlab_compatible_eltype(Float32) == false
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@test MATLABDiffEq._is_matlab_compatible_eltype(Int64) == true
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@test MATLABDiffEq._is_matlab_compatible_eltype(Int32) == true
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@test MATLABDiffEq._is_matlab_compatible_eltype(Complex{Float64}) == true
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@test MATLABDiffEq._is_matlab_compatible_eltype(Complex{Float32}) == false
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@test MATLABDiffEq._is_matlab_compatible_eltype(BigFloat) == false
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@test MATLABDiffEq._is_matlab_compatible_eltype(BigInt) == false
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@test MATLABDiffEq._is_matlab_compatible_eltype(Rational{Int}) == false
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end
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@testset "_check_matlab_compatible validation" begin
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# Valid Float64 inputs should pass
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@test MATLABDiffEq._check_matlab_compatible([1.0, 2.0], (0.0, 1.0)) === nothing
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@test MATLABDiffEq._check_matlab_compatible(1.0, (0.0, 1.0)) === nothing
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@test MATLABDiffEq._check_matlab_compatible([1, 2, 3], (0, 10)) === nothing # Integers OK
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# Complex Float64 should pass
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@test MATLABDiffEq._check_matlab_compatible([1.0 + 2.0im], (0.0, 1.0)) === nothing
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# BigFloat u0 should throw ArgumentError
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@test_throws ArgumentError MATLABDiffEq._check_matlab_compatible(
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BigFloat[1.0, 2.0], (0.0, 1.0)
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)
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# BigFloat tspan should throw ArgumentError
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@test_throws ArgumentError MATLABDiffEq._check_matlab_compatible(
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[1.0, 2.0], (BigFloat(0.0), BigFloat(1.0))
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)
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# Float32 should throw ArgumentError (MATLAB expects Float64)
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@test_throws ArgumentError MATLABDiffEq._check_matlab_compatible(
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Float32[1.0, 2.0], (0.0, 1.0)
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)
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end
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@testset "Error messages are helpful" begin
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# Test that error messages contain useful information
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try
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MATLABDiffEq._check_matlab_compatible(BigFloat[1.0], (0.0, 1.0))
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@test false # Should not reach here
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catch e
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@test e isa ArgumentError
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@test occursin("BigFloat", e.msg)
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@test occursin("Float64", e.msg)
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@test occursin("MATLABDiffEq", e.msg)
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end
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try
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MATLABDiffEq._check_matlab_compatible([1.0], (BigFloat(0.0), BigFloat(1.0)))
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@test false # Should not reach here
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catch e
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@test e isa ArgumentError
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@test occursin("tspan", lowercase(e.msg))
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end
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end
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@testset "buildDEStats is type-generic" begin
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# Test that buildDEStats works with different Dict types
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stats1 = Dict{String, Any}("nfevals" => 100, "nsteps" => 50)
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result1 = MATLABDiffEq.buildDEStats(stats1)
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@test result1.nf == 100
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@test result1.naccept == 50
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# Test with empty dict
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stats2 = Dict{String, Any}()
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result2 = MATLABDiffEq.buildDEStats(stats2)
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@test result2.nf == 0
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@test result2.naccept == 0
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# Test with all fields
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stats3 = Dict{String, Any}(
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"nfevals" => 200,
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"nfailed" => 10,
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"nsteps" => 190,
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"nsolves" => 100,
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"npds" => 20,
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"ndecomps" => 15
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)
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result3 = MATLABDiffEq.buildDEStats(stats3)
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@test result3.nf == 200
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@test result3.nreject == 10
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@test result3.naccept == 190
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@test result3.nsolve == 100
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@test result3.njacs == 20
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@test result3.nw == 15
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end
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@testset "Algorithm structs instantiation" begin
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# Test that all algorithm structs can be instantiated
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@test MATLABDiffEq.ode23() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode45() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode113() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode23s() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode23t() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode23tb() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode15s() isa MATLABDiffEq.MATLABAlgorithm
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@test MATLABDiffEq.ode15i() isa MATLABDiffEq.MATLABAlgorithm
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end
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end

‎test/runtests.jl‎

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using DiffEqBase, MATLABDiffEq, ParameterizedFunctions, Test
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# Interface tests - these test type validation without needing MATLAB runtime
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include("interface_tests.jl")
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# The following tests require MATLAB runtime to be available
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# They test the actual ODE solving functionality
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f = @ode_def_bare LotkaVolterra begin
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dx = a * x - b * x * y
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dy = -c * y + d * x * y

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