DaemonMode.jl
RCall.jl
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DaemonMode.jl | RCall.jl | |
---|---|---|
22 | 8 | |
268 | 310 | |
- | 1.0% | |
4.7 | 5.5 | |
4 months ago | 15 days 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.
RCall.jl
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Makie, a modern and fast plotting library for Julia
I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
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Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
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Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
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I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl
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translate R code to Julia code
I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl
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Julia 1.6: what has changed since Julia 1.0?
You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.
I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.
I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.
[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl
[RCall.jl]: https://github.com/JuliaInterop/RCall.jl
[Julia Interop]: https://github.com/JuliaInterop
[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html
What are some alternatives?
julia - The Julia Programming Language
Makie.jl - Interactive data visualizations and plotting in Julia
org-mode - This is a MIRROR only, do not send PR.
HTTP.jl - HTTP for Julia
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
julia-numpy-fortran-test - Comparing Julia vs Numpy vs Fortran for performance and code simplicity
PyCall.jl - Package to call Python functions from the Julia language
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
Revise.jl - Automatically update function definitions in a running Julia session
DataFramesMeta.jl - Metaprogramming tools for DataFrames
cmssw - CMS Offline Software