DaemonMode.jl
TwoBasedIndexing.jl
| DaemonMode.jl | TwoBasedIndexing.jl | |
|---|---|---|
| 22 | 11 | |
| 269 | 57 | |
| - | - | |
| 4.7 | 0.0 | |
| 5 months ago | almost 7 years ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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DaemonMode.jl
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Potential of the Julia programming language for high energy physics computing
Thats for an entry point, you can search `Base.@main` to see a little summary of it. Later it will be able to be callable with `juliax` and `juliac` i.e. `~juliax test.jl` in shell.
DynamicalSystems looks like a heavy project. I don't think you can do much more on your own. There have been recent features in 1.10 that lets you just use the portion you need (just a weak dependency), and there is precompiletools.jl but these are on your side.
You can also look into https://github.com/dmolina/DaemonMode.jl for running a Julia process in the background and do your stuff in the shell without startup time until the standalone binaries are there.
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Julia 1.9.0 lives up to its promise
> If I were to use e.g. Rust with polars, load time would be virtually none.
Because you're compiling...
And if you need to do the same in Julia, you should also pre-compile or some other method like https://github.com/dmolina/DaemonMode.jl (their demo shows loading a database, with subsequent loads after the first one taking roughly ~0.2% of the first)
- Administrative Scripting with Julia
- GNU Octave 8.1
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Ask HN: Why is Julia so underrated?
Well, not nicely certainly, but:
https://github.com/dmolina/DaemonMode.jl
> portable
Neither is python - it just relies on universal availability. Over time…
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Is Julia suitable today as a scripting language?
You can get around a lot of these problems with DaemonMode.jl though.
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Julia performance, startup.jl, and sysimages
You might want DaemonMode.jl
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Can I execute code in Julia REPL if I'm connected to a remote server?
https://github.com/dmolina/DaemonMode.jl can possibly help in the future. Leaving it here so that people know this is planned.
- Ask HN: Why hasn't the Deep Learning community embraced Julia yet?
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Compile for faster execution?
If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
TwoBasedIndexing.jl
- PyCharm is the worst IDE I have used. /s
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Obviously European elevators are designed by C programmers!
We need to evolve 2 based indexing is the superior choice
- I promise it won’t hurt you
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The counter-intuitive rise of Python in scientific computing
There are other choices like https://github.com/simonster/TwoBasedIndexing.jl and https://github.com/giordano/StarWarsArrays.jl if you do not like 1-based indexing.
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what a wonderful world
Not a problem, there are packages that let you have two-based indexing.
- Kill it bevor it lays eggs
- How to start a war
- Why does Julia adopt 1-based index?
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some may hate it, some may love it
The one and only indexing is https://github.com/simonster/TwoBasedIndexing.jl
- Why not Julia?
What are some alternatives?
julia - The Julia Programming Language
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Makie.jl - Interactive data visualizations and plotting in Julia
OffsetArrays.jl - Fortran-like arrays with arbitrary, zero or negative starting indices.
HTTP.jl - HTTP for Julia
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
StarWarsArrays.jl - Arrays indexed as the order of Star Wars movies