Python-Complementary-Languages
JET.jl
Python-Complementary-Languages | JET.jl | |
---|---|---|
8 | 13 | |
31 | 690 | |
- | - | |
2.1 | 9.0 | |
9 months ago | 9 days ago | |
Python | Julia | |
- | MIT License |
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Python-Complementary-Languages
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Julia is the better language for extending Python
Note that the OP is a Python user, not a Julia user. The Github profile is a bunch of Python packages and the Julia code wasn't even optimized (https://github.com/00sapo/cython_list_test/pull/5). If this test says anything, it at least would say that a inexperienced Python user could pick up Julia and do pretty well, even if the code they write isn't great.
Even if it doesn't say that, bashing people who use Julia for a repository made by a Python user is a new level of HN trolling.
- Julia is the best language to extend Python for scientific computing
JET.jl
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Prospects of utilising Rust in scientific computation?
An informative discussion on julia forum. Have you tried using https://github.com/aviatesk/JET.jl to minimize type instabilities?
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Julia v1.9.0 has been released
For instance, https://github.com/aviatesk/JET.jl is still in its relative infancy, but it's played a big role in detecting quite a few potential bugs that had never been reported to use by users or caught in our testing infrastructure. There's also been a lot developments like interfaces to RR the time travelling debugger https://rr-project.org/ which helps us better understand and catch some very hard to debug non-deterministic bugs.
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Julia Computing Raises $24M Series A
Have you seen Shuhei Tadowaki's work on JET.jl (?)
If you're curious: https://github.com/aviatesk/JET.jl
This may seem more about performance (than IDE development) but Shuhei is one of the driving contributors behind developing the capabilities to use compiler capabilities for IDE integration -- and indeed JET.jl contains the kernel of a number of these capabilities.
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I Hate Programming Language Advocacy (2000)
This is sort of being done right now, as dynamic languages have begun to adopt gradual typing... at least Python and Julia, that I know of.
If something like [JET.jl](https://github.com/aviatesk/JET.jl) become ubiquitous in Julia, one could add a function that pointed out all the places in the code where types are not fully inferred by the compiler.
It'll never be quite the same level of safety as a static language, however.
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From Julia to Rust
- Pattern matching (sometimes you don't want the overhead of a method lookup)
[1]: https://github.com/aviatesk/JET.jl
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Julia is the best language to extend Python for scientific computing
You can use the `@code_warntype` macro to check for type stability, which is very helpful for detecting such performance pitfalls on single function level. In the future, https://github.com/aviatesk/JET.jl may give a more powerful way to do it.
- Jet.jl: experimental type checker for Julia
- Jet.jl: A WIP compile time type checker for Julia
What are some alternatives?
py2many - Transpiler of Python to many other languages
julia - The Julia Programming Language
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
iminuit - Jupyter-friendly Python interface for C++ MINUIT2
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
shared_numpy - A simple library for creating shared memory numpy arrays
StaticArrays.jl - Statically sized arrays for Julia
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
HTTP.jl - HTTP for Julia
pypolyline - Fast Google Polyline encoding and decoding using a Rust binary
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia