julia-vim
julia
julia-vim | julia | |
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21 | 350 | |
744 | 44,534 | |
0.8% | 0.5% | |
4.3 | 10.0 | |
13 days ago | 5 days ago | |
Vim Script | Julia | |
GNU General Public License v3.0 or later | MIT License |
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julia-vim
- IDE with graphs to the side for Julia?
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just started learning swift and this blew my mind
There's a handy Vim plug-in for Julia that will convert latex commands to symbols so it's a one character difference for a pretty notable improvement to readability when you start to get into longer equations.
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Doing Latex preview in vim inside python comments?
Nowadays during my master thesis does lots of equations appear in my python code, but I would love to use tex rendering in some way, like latex preview in emacs. However, I know that there is some great latex rendering such as tex-conceal.vim and latex_to_unicode in julia-vim, but I am not able to make it work for python comments. Any idea on how to solve this?
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The Must-Have Neovim Plugins for Julia
There is a plugin I tried which is called julia-vim. However, this plugin is too broken for me. It conflicts with other completion plugins which makes it so hard to either fix or manage my configuration and keymaps. Fortunately, I found cmp-latex-symbols, a completion plugin that as described in the README
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How do you collate and organize research notes?
I use Git and plain text files. The Julia addon for Vim allows one to write UTF-8 math symbols with LaTeX commands.
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What input method would you prefer for Unicode characters in a neovim plugin?
I use julia.vim for unicode support. I find it a bit more responsive than agda-vim, and it has more symbols (the list is autogenerated). but I have two gripes with it:
- How to search and replace my variables with unicode?
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Any Julia users here to help a n00b?
Ideally, yes, I want to use LSP in order for it to work as close as possible from my nvim with julia-vim, coc.nvim and vim-julia-cell. At least until I'm more familiarized with Emacs. I really do want to learn, but I cannot just stop my daily work, so the best world possible would be to be able to keep working while learning.
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Help with IDE's for Julia
I use vim+vim_slime along with the julia plugin configured with tab latex to unicode conversion. This means I can have a REPL open in a split vim buffer and send chunks from my script directly to the REPL. It's really lightweight and fast. I'm working to make some functional snippets too.
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Julia workflow for vim users
Since I was still missing some more advanced Vim features, I did some search and now have a pretty decent terminal based environment. The base is Tmux + Neovim- I open 2 panes, where one is used for coding and the other is Julia REPL. I use julia-vim plugin for base syntax and code highlights and vim-slime with vim-julia-cell for live sending of the code from the Neovim to the REPL.
julia
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Top Paying Programming Technologies 2024
34. Julia - $74,963
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Optimize sgemm on RISC-V platform
I don't believe there is any official documentation on this, but https://github.com/JuliaLang/julia/pull/49430 for example added prefetching to the marking phase of a GC which saw speedups on x86, but not on M1.
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Dart 3.3
3. dispatch on all the arguments
the first solution is clean, but people really like dispatch.
the second makes calling functions in the function call syntax weird, because the first argument is privileged semantically but not syntactically.
the third makes calling functions in the method call syntax weird because the first argument is privileged syntactically but not semantically.
the closest things to this i can think of off the top of my head in remotely popular programming languages are: nim, lisp dialects, and julia.
nim navigates the dispatch conundrum by providing different ways to define free functions for different dispatch-ness. the tutorial gives a good overview: https://nim-lang.org/docs/tut2.html
lisps of course lack UFCS.
see here for a discussion on the lack of UFCS in julia: https://github.com/JuliaLang/julia/issues/31779
so to sum up the answer to the original question: because it's only obvious how to make it nice and tidy like you're wanting if you sacrifice function dispatch, which is ubiquitous for good reason!
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Julia 1.10 Highlights
https://github.com/JuliaLang/julia/blob/release-1.10/NEWS.md
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Best Programming languages for Data Analysis📊
Visit official site: https://julialang.org/
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Potential of the Julia programming language for high energy physics computing
No. It runs natively on ARM.
julia> versioninfo() Julia Version 1.9.3 Commit bed2cd540a1 (2023-08-24 14:43 UTC) Build Info: Official https://julialang.org/ release
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Rust std:fs slower than Python
https://github.com/JuliaLang/julia/issues/51086#issuecomment...
So while this "fixes" the issue, it'll introduce a confusing time delay between you freeing the memory and you observing that in `htop`.
But according to https://jemalloc.net/jemalloc.3.html you can set `opt.muzzy_decay_ms = 0` to remove the delay.
Still, the musl author has some reservations against making `jemalloc` the default:
https://www.openwall.com/lists/musl/2018/04/23/2
> It's got serious bloat problems, problems with undermining ASLR, and is optimized pretty much only for being as fast as possible without caring how much memory you use.
With the above-mentioned tunables, this should be mitigated to some extent, but the general "theme" (focusing on e.g. performance vs memory usage) will likely still mean "it's a tradeoff" or "it's no tradeoff, but only if you set tunables to what you need".
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Eleven strategies for making reproducible research the norm
I have asked about Julia's reproducibility story on the Guix mailing list in the past, and at the time Simon Tournier didn't think it was promising. I seem to recall Julia itself didnt have a reproducible build. All I know now is that github issue is still not closed.
https://github.com/JuliaLang/julia/issues/34753
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Julia as a unifying end-to-end workflow language on the Frontier exascale system
I don't really know what kind of rebuttal you're looking for, but I will link my HN comments from when this was first posted for some thoughts: https://news.ycombinator.com/item?id=31396861#31398796. As I said, in the linked post, I'm quite skeptical of the business of trying to assess relative buginess of programming in different systems, because that has strong dependencies on what you consider core vs packages and what exactly you're trying to do.
However, bugs in general suck and we've been thinking a fair bit about what additional tooling the language could provide to help people avoid the classes of bugs that Yuri encountered in the post.
The biggest class of problems in the blog post, is that it's pretty clear that `@inbounds` (and I will extend this to `@assume_effects`, even though that wasn't around when Yuri wrote his post) is problematic, because it's too hard to write. My proposal for what to do instead is at https://github.com/JuliaLang/julia/pull/50641.
Another common theme is that while Julia is great at composition, it's not clear what's expected to work and what isn't, because the interfaces are informal and not checked. This is a hard design problem, because it's quite close to the reasons why Julia works well. My current thoughts on that are here: https://github.com/Keno/InterfaceSpecs.jl but there's other proposals also.
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Getaddrinfo() on glibc calls getenv(), oh boy
Doesn't musl have the same issue? https://github.com/JuliaLang/julia/issues/34726#issuecomment...
I also wonder about OSX's libc. Newer versions seem to have some sort of locking https://github.com/apple-open-source-mirror/Libc/blob/master...
but older versions (from 10.9) don't have any lockign: https://github.com/apple-oss-distributions/Libc/blob/Libc-99...
What are some alternatives?
vim-slime - A vim plugin to give you some slime. (Emacs)
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
jupyter - An interface to communicate with Jupyter kernels.
NetworkX - Network Analysis in Python
LoopVectorization.jl - Macro(s) for vectorizing loops.
Lua - Lua is a powerful, efficient, lightweight, embeddable scripting language. It supports procedural programming, object-oriented programming, functional programming, data-driven programming, and data description.
vim-julia-cell - Run Julia cells in Vim
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
lspsaga.nvim-cmp
Numba - NumPy aware dynamic Python compiler using LLVM
coc.nvim - Nodejs extension host for vim & neovim, load extensions like VSCode and host language servers.
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp