scinim
The core types and functions of the SciNim ecosystem (by SciNim)
OpticsPolynomials.jl
Polynomials used in optics. Zernike, Legendre, etc (by JuliaOptics)
| scinim | OpticsPolynomials.jl | |
|---|---|---|
| 1 | 1 | |
| 35 | 6 | |
| - | - | |
| 4.2 | 10.0 | |
| 5 months ago | over 3 years ago | |
| Nim | Julia | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
scinim
Posts with mentions or reviews of scinim.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-10.
-
Comparing a Rust extension to other methods of speeding up python
I have never tried nim, but according to nimpy's README, it looks like you can work with Numpy arrays via the buffer protocol or scinim. I'd be curious to see how it performs.
OpticsPolynomials.jl
Posts with mentions or reviews of OpticsPolynomials.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-10.
-
Comparing a Rust extension to other methods of speeding up python
I gave Julia a rather serious try, converting the world's fastest numerical optics library to the language. It ended up being mildly faster (2-3x) for some things, but overall programs did not run meaningfully faster (and significantly slower when comparing jl to python on GPU -- I would have to write specializations of all the functions for GPU, which is a horrific prospect).
What are some alternatives?
When comparing scinim and OpticsPolynomials.jl you can also consider the following projects:
nimpy - Nim - Python bridge
ndarray_comparison - Benchmark of toy calculation on an n-dimensional array using python, numba, cython, pythran and rust
nimporter - Compile Nim Extensions for Python On Import!