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- Use this package: https://github.com/BeastyBlacksmith/ProtoStructs.jl . It gives you a simple @proto macro that you can prepend to the struct definition and makes all changes immediate.
> And if that’s what you want to stick to, I guess you could program in that style by making the “object” the first method argument, and wrapping the functions along with the struct definition in a module.
You might not even need to do that. This package was released recently, implementing OOP: https://github.com/Suzhou-Tongyuan/ObjectOriented.jl
> > This gives the package authors a tool to basically "profile" the loading time of their package, which will help them optimize the loading time. So there _will_ be downstream improvement to package loading for us users too.
It lead to https://github.com/SciML/RecursiveArrayTools.jl/pull/217 . 6228.5 ms to 292.7 ms isn't too shabby.
some of the error messages are about to get much better (see https://github.com/JuliaLang/julia/pull/46372).
I find Julia to be pretty good for interactive work, but there definitely are some rough edges that still need smoothing.
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
For some examples of people porting existing C++ Fortran libraries to julia, you should check out https://github.com/JuliaLinearAlgebra/Octavian.jl, https://github.com/dgleich/GenericArpack.jl, https://github.com/apache/arrow-julia (just off the top of my head). These are all ports of C++ or Fortran libraries that match (or exceed) performance of the original, and in the case of Arrow.jl is faster, more general, and 10x less code.
> One thing that supports this view is that there are several Julia packages that are wrappers around existing C/Fortran/C++ libraries, and basically no examples (that I know) of people porting existing libraries to Julia.
As with the others, I'll strongly disagree and chime in with a few examples off the top of my head:
* ITensors.jl : They started moving from a C++ to Julia a couple years ago and now their webpage doesn't even mention their original C++ implementation on its homepage anymore https://itensor.org/
* DifferentialEquations.jl : This has many state of the art differentiatial equation solving facilities in it, many of which are improvements over old Fortran libraries.
* SpecialFunctions.jl, Julia's own libm, Bessels.jl, SLEEFPirates.jl : Many core math functions have ancient Fortran or C implementations from OpenLibm or whatever, and they're being progressively replaced with better, faster versions written in pure julia that outperform the old versions.