erfa
LibTIFF.jl
| erfa | LibTIFF.jl | |
|---|---|---|
| 2 | 1 | |
| 125 | 0 | |
| 0.0% | - | |
| 4.0 | 6.5 | |
| 9 days ago | 6 months ago | |
| C | Julia | |
| GNU General Public License v3.0 or later | MIT License |
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erfa
- Julia 1.10 Released
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License Adherence Help
I'm working on a pure Rust approximation of astropy. Up til now, I was able to recreate the intent by looking at an external API, but I'm moving on to functionality that I don't understand enough to implement without basically copying the code. Astropy uses the BSD-3 license, and it wraps the ERFA library which uses a custom license. My project currently uses the MIT license. My PR is here - my question is have I attributed everything correctly, or is there anything I need to change for everything to be above-board?
LibTIFF.jl
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Julia 1.10 Released
Are there solid C interfaces that can be used?
A large part of why I started using Julia is because calling into other languages through the C FFI is pretty easy and efficient. Most of the wrappers are a single line. If there is not existing driver support, I would pass the C headers through Clang.jl, which automatically wraps the C API in a C header.
https://github.com/JuliaInterop/Clang.jl
I most recently did this with libtiff. Here is the Clang.jl code to generate the bindings. It's less than 30 lines of sterotypical code.
https://github.com/mkitti/LibTIFF.jl/tree/main/gen
The generated bindings with a few tweaks is here:
https://github.com/mkitti/LibTIFF.jl/blob/main/src/LibTIFF.j...
What are some alternatives?
astro-rs - Astronomy utils written in Rust
Torch.jl - Sensible extensions for exposing torch in Julia.
astropy - Astronomy and astrophysics core library
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.
assist - ASSIST is a software package for ephemeris-quality integrations of test particles.
Clang.jl - C binding generator and Julia interface to libclang
PyCallChainRules.jl - Differentiate python calls from Julia
Lux.jl - Explicitly Parameterized Neural Networks in Julia