Infiltrator.jl
julia
Infiltrator.jl | julia | |
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5 | 350 | |
379 | 44,534 | |
2.9% | 0.4% | |
7.1 | 10.0 | |
17 days ago | about 14 hours ago | |
Julia | Julia | |
MIT License | MIT License |
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Infiltrator.jl
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I can never debug codes in Julia without issues. Help?
Also Infiltrator is very fast and useful but don't try to use it from the Vscode integrated terminal.
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Just downloaded Julia, what packages/other things do I need to download to have it all work properly?
The package Infiltrator.jl might be what you seek. It's not as good as inserting breakpoints like in Matlab but it's still better than printing everywhere haha
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Julia 1.7 has been released
Yes, it uses Debugger.jl, which relies on JuliaInterpreter.jl under the hood, so while you can tell the debugger to compile functions in certain modules, it will mostly interpret your code.
You might be interested in https://github.com/JuliaDebug/Infiltrator.jl, which uses an approach more similar to what you describe.
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Error handling and unwinding stacks in Julia
Another small thing is in the REPL when you trigger an error in Common lisp it drops you into the debugger where you can redefine code and retry directly from the stack without unwinding the entire stack. Does Julia have functionality similar to this? Currently when I trigger an error Julia just throw the error and goes right back to the top level prompt. To resolve this issue I've tried sprinkling my code with a combination of GitHub - JuliaDebug/Infiltrator.jl + Stack Traces · The Julia Language wrapped in try catch blocks so that if an error is singled it drops into a debugger of sorts. This is ok and it works but it isn't really as good. Is there a current package that can emulate what I am trying to do? I think that the REPL workflow is good in julia but the workflow stalls out when you run into errors that don't drop into debuggers and such.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
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?
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.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Debugger.jl - Julia debugger
NetworkX - Network Analysis in Python
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
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.
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
Diffractor.jl - Next-generation AD
Numba - NumPy aware dynamic Python compiler using LLVM
ResultTypes.jl - A Result type for Julia—it's like Nullables for Exceptions
F# - Please file issues or pull requests here: https://github.com/dotnet/fsharp