StarWarsArrays.jl VS Cython

I raise you https://github.com/giordano/StarWarsArrays.jl

Why not Star Wars Indices (4,5,6,1,2,3,7,8,9...)? https://github.com/giordano/StarWarsArrays.jl

> The documentation disagrees about string indices not starting with 1 As priorly said, I'm speaking about strings, not `String` in particular. So, to write code which work for all AbstractString (which have basic string functions), you must not assume that the first indexing is 1, you can have degenerate cases such as : https://github.com/giordano/StarWarsArrays.jl (this is for vectors, but creating a similar type, for AbstractString isn't impossible) or just strings with an offset indexing.

There are other choices like https://github.com/simonster/TwoBasedIndexing.jl and https://github.com/giordano/StarWarsArrays.jl if you do not like 1-based indexing.

This is a total non issue as indexing is an operation that is subject to multiple dispatch. For a humorous example see https://github.com/giordano/StarWarsArrays.jl

The cool thing with Julia is that array indices aren't inherent properties, and may be changed locally by using appropriate wrappers. This means that the same underlying array may start at 0 in one part of the code, at 1 in another, and perhaps use the star-wars indexing in yet another section if that's necessary.

Adding https://github.com/giordano/StarWarsArrays.jl to the list for some extra spice

You should also check out https://github.com/giordano/StarWarsArrays.jl and https://github.com/giordano/RandomBasedArrays.jl

The approach that I favour is to use Cython. The nice thing with this approach is that your code is still written as (almost) Python, but so long as you define all required types correctly it will automatically create the C extension for you. Early versions of Cython required using Cython specific typing (Python didn't have type hints when Cython was created), but it can now use Python's type hints.

and again, everything that was released after using an older version of cython.

Just for reference,

* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

Profile and optimize the hotspots with cython (or whatever the cool kids are using these days... It's been a while.)

JIT essentially means generating machine code for the language on the fly, either during loading of the interpreter (method JIT), or by profiling and optimizing hotspots (tracing JIT). The language itself can be statically or dynamically typed. You could also compile a dynamic language ahead of time, for example, cython.

Cython - - embed demo