This collection of Julia scripts automatically generates C header files from Julia functions marked with the @ccallable macro. This is useful when creating Julia libraries that need to be called from C/C++ code.
The main generator script that provides a simple and robust approach to generating C headers. It uses a manual definition approach where you explicitly define function signatures, avoiding complex AST parsing.
Features:
| ERROR: MethodError: Cannot `convert` an object of type Float32 to an object of type Vector{Float32} | |
| Closest candidates are: | |
| convert(::Type{Array{T, N}}, ::SizedArray{S, T, N, N, Array{T, N}}) where {S, T, N} at C:\Users\accou\.julia\packages\StaticArrays\0T5rI\src\SizedArray.jl:121 | |
| convert(::Type{Array{T, N}}, ::SizedArray{S, T, N, M, TData} where {M, TData<:AbstractArray{T, M}}) where {T, S, N} at C:\Users\accou\.julia\packages\StaticArrays\0T5rI\src\SizedArray.jl:115 | |
| convert(::Type{<:Array}, ::LabelledArrays.LArray) at C:\Users\accou\.julia\packages\LabelledArrays\lfn1b\src\larray.jl:133 | |
| ... | |
| Stacktrace: | |
| [1] setproperty!(x::OrdinaryDiffEq.ODEIntegrator{Tsit5, false, Vector{Float32}, Float32}, f::Symbol, v::Float32) | |
| @ Base .\Base.jl:43 | |
| [2] initialize!(integrator::OrdinaryDiffEq.ODEIntegrator{Tsit5, false, Vector{Float32}, Float32}) |
| function (ˍ₋out, ˍ₋arg1, ˍ₋arg2, t) | |
| #= C:\Users\accou\.julia\packages\SymbolicUtils\v2ZkM\src\code.jl:349 =# | |
| #= C:\Users\accou\.julia\packages\SymbolicUtils\v2ZkM\src\code.jl:350 =# | |
| #= C:\Users\accou\.julia\packages\SymbolicUtils\v2ZkM\src\code.jl:351 =# | |
| begin | |
| begin | |
| #= C:\Users\accou\.julia\packages\Symbolics\vQXbU\src\build_function.jl:452 =# | |
| #= C:\Users\accou\.julia\packages\SymbolicUtils\v2ZkM\src\code.jl:398 =# @inbounds begin | |
| #= C:\Users\accou\.julia\packages\SymbolicUtils\v2ZkM\src\code.jl:394 =# | |
| ˍ₋out[1] = (/)((*)((*)((*)((*)((*)((*)(2, ˍ₋arg2[4]), ˍ₋arg2[2]), ˍ₋arg1[1]), (+)(0.5, (*)(0.5, (tanh)((/)((+)(ˍ₋arg1[14], (/)((*)((*)(ˍ₋arg2[4], ˍ₋arg2[1]), (sqrt)((+)((+)((^)((+)((*)((*)(ˍ₋arg1[9], (sin)(ˍ₋arg1[6])), (sqrt)(ˍ₋arg1[1])), (*)((*)((*)(-1, ˍ₋arg1[10]), (cos)(ˍ₋arg1[6])), (sqrt)(ˍ₋arg1[1]))), 2), (^)((+)((+)((/)((*)((*)(ˍ₋arg1[9], (+)(ˍ₋arg1[2], (*)((+)((+)(2, (*)(ˍ₋arg1[2], (cos)(ˍ₋arg1[6]))), (*)(ˍ₋arg1[3], ( |
| <!DOCTYPE html> | |
| <HTML lang = "en"> | |
| <HEAD> | |
| <meta charset="UTF-8"/> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> | |
| <title>Forward and Reverse Automatic Differentiation In A Nutshell</title> | |
| <script type="text/x-mathjax-config"> | |
| MathJax.Hub.Config({ |
Tested is DifferentialEquations.jl vs Python's SciPy ODE solvers. Notes:
| using Cassette, DiffRules | |
| using Core: CodeInfo, SlotNumber, SSAValue, ReturnNode, GotoIfNot | |
| const printbranch = true | |
| Cassette.@context HasBranchingCtx | |
| function Cassette.overdub(ctx::HasBranchingCtx, f, args...) | |
| if Cassette.canrecurse(ctx, f, args...) | |
| return Cassette.recurse(ctx, f, args...) |
The Jax developers optimized a differential equation benchmark in this issue which used DiffEqFlux.jl as a performance baseline. The Julia code from there was updated to include some standard performance tricks and is the benchmark code here. Thus both codes have been optimized by the library developers.
| retcode: Success | |
| Interpolation: 3rd order Hermite | |
| t: [0.0, 0.5, 1.0] | |
| u: Vector{Num}[[x0, y0], [x0 + 0.08333333333333333((1.5x0) + (1.5(x0 + (0.5((1.5(x0 + (0.25((1.5(x0 + (0.25((1.5x0) - (x0*y0))))) - ((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))))))) - ((x0 + (0.25((1.5(x0 + (0.25((1.5x0) - (x0*y0))))) - ((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))))))*(y0 + (0.25(((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))) - (3(y0 + (0.25((x0*y0) - (3y0))))))))))))) + (2((1.5(x0 + (0.25((1.5x0) - (x0*y0))))) + (1.5(x0 + (0.25((1.5(x0 + (0.25((1.5x0) - (x0*y0))))) - ((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))))))) - ((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))) - ((x0 + (0.25((1.5(x0 + (0.25((1.5x0) - (x0*y0))))) - ((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))))))*(y0 + (0.25(((x0 + (0.25((1.5x0) - (x0*y0))))*(y0 + (0.25((x0*y0) - (3y0))))) - (3(y0 + (0.25((x0*y0) - (3y0))))))))))) - (x0*y0) - ((x0 + (0.5( |
| using OrdinaryDiffEq | |
| using Plots | |
| using Flux, DiffEqFlux, Optim | |
| function lotka_volterra(du,u,p,t) | |
| x, y = u | |
| α, β, δ, γ = p | |
| du[1] = dx = α*x - β*x*y | |
| du[2] = dy = -δ*y + γ*x*y | |
| end |
| # -------------------------------------------------------------------------------- | |
| # Ewing model | |
| # translation by: [email protected] (July 2020) | |
| # -------------------------------------------------------------------------------- | |
| using DifferentialEquations | |
| using Plots | |
| using LabelledArrays | |
| using StaticArrays |