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I actually use TypeScript/JavaScript a lot for this reason, especially for biological algorithms that I want to run in the browser. The developer tooling is also as good as you can hope for, especially when using VS Code. I actually wrote a circular RNA sequence deduplication algorithm in it just recently [1].
With respect to the identifier resolution in Nim, it strikes me as more of a matter of preference. Especially given the universal function call syntax in Nim, at least it's consistent. For example, Nim treats "ATGCA".lowerCase() the same as lowercase("ATGCA"). I do appreciate the fact that you can use a chaining syntax instead of a nesting one when doing multiple function calls but this is also a matter of style more than substance.
[1] https://github.com/Benjamin-Lee/viroiddb/blob/main/scripts/c...
You make a fair point that using optimized numerical libraries instead of string methods will be ridiculously fast because they're compiled anyway. For example, scikit-bio does just this for their reverse complement operation [1]. However, they use an 8 bit representation since they need to be able to represent the extended IUPAC notation for ambiguous bases, which includes things like the character N for "aNy" nucleotide [2]. One could get creative with a 4 bit encoding and still end up saving space (assuming you don't care about the distinction between upper versus lowercase characters in your sequence [2]). Or, if you know in advance your sequence is unambiguous (unlikely in DNA sequencing-derived data) you could use the 2 bit encoding. When dealing with short nucleotide sequences, another approach is to encode the sequence as an integer. I would love to see a library—Python, Nim, or otherwise—that made using the most efficient encoding for a sequence transparent to the developer.
[1] https://github.com/biocore/scikit-bio/blob/b470a55a8dfd054ae...
[2] https://en.wikipedia.org/wiki/Nucleic_acid_notation
[3]
If anyone is interested to see how Nim fares against some other programming languages, here are some benchmarks: https://github.com/kostya/benchmarks
Another nim & python thread that has not been mentioned yet here
https://news.ycombinator.com/item?id=28506531 - project allows creating pythonic bindings for your nim libraries pretty easily, which can be useful if you still want to write most of your toplevel code in python, but leverage nim's speed when it matters.
If you want to make your nim code even more "pythonic" there is a https://github.com/Yardanico/nimpylib, and for calling some python code from nim there is a https://github.com/yglukhov/nimpy
Another nim & python thread that has not been mentioned yet here
https://news.ycombinator.com/item?id=28506531 - project allows creating pythonic bindings for your nim libraries pretty easily, which can be useful if you still want to write most of your toplevel code in python, but leverage nim's speed when it matters.
If you want to make your nim code even more "pythonic" there is a https://github.com/Yardanico/nimpylib, and for calling some python code from nim there is a https://github.com/yglukhov/nimpy
The thing with Python is it's usually pretty easy to optimise quite impressively.
E.g. random example:
Sprinkle some cdef's in your python and suddenly you're faster than c++
https://github.com/luizsol/PrimesResult
https://github.com/PlummersSoftwareLLC/Primes/blob/drag-race...
The thing with Python is it's usually pretty easy to optimise quite impressively.
E.g. random example:
Sprinkle some cdef's in your python and suddenly you're faster than c++
https://github.com/luizsol/PrimesResult
https://github.com/PlummersSoftwareLLC/Primes/blob/drag-race...
Apparently there's also a data processing library for Nim called Arraymancer[0] that's inspired by Numpy and PyTorch. It claims to be faster than both.
[0] https://mratsim.github.io/Arraymancer/
There’s been a tremendous amount of work optimizing blas _and_ ensuring it’s numerically stable. Julia made a good choice to use blas first. Though it’s good to see new native implementations.
For Nim, there’s also NimTorch which is interesting in that it builds on Nim’s C++ target to generate native PyTorch code. Even Python is technically a second class citizen for the C++ code. Most ML libraries are C++ all the way down.
https://github.com/sinkingsugar/nimtorch
Not necessarily true with Julia. Many libraries like DifferentialEquations.jl are Julia all of the way down because the pure Julia BLAS tools outperform OpenBLAS and MKL in certain areas. For example see:
https://github.com/YingboMa/RecursiveFactorization.jl/pull/2...
So a stiff ODE solve is pure Julia, LU-factorizations and all.