Learned about a new Python Library: Numba

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/Physics

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  • NumPy

    The fundamental package for scientific computing with Python.

    Lately I had to deal with a lot of numeric and of course as lazy as I am I used Python. The downside of python is being relativly slow.I talked to some friends studying CS and they told me about Numba apparently it's a just-in-time compiler compiling your Python code into Machine-Language .I already knew about numpy but when it came to computations which needed nested loops I just couldn't find any efficient and readable way to program it. Numba solves this problem relatively easy. You can just do the programm in python. Of course you'll have to adopt your code a little and there will definitely be at least a few problems to make it more efficient and get it running. I recommend having a solid Python knowledge, including working with libs, etc. I used this tutorial and the doc to get into it.

  • Numba

    NumPy aware dynamic Python compiler using LLVM

    Lately I had to deal with a lot of numeric and of course as lazy as I am I used Python. The downside of python is being relativly slow.I talked to some friends studying CS and they told me about Numba apparently it's a just-in-time compiler compiling your Python code into Machine-Language .I already knew about numpy but when it came to computations which needed nested loops I just couldn't find any efficient and readable way to program it. Numba solves this problem relatively easy. You can just do the programm in python. Of course you'll have to adopt your code a little and there will definitely be at least a few problems to make it more efficient and get it running. I recommend having a solid Python knowledge, including working with libs, etc. I used this tutorial and the doc to get into it.

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  • julia

    The Julia Programming Language

    Numba can do pretty amazing things, but I feel that the disadvantages that come with bolt-on JIT are severe enough to put me off. Numba isn't terribly flexible, and the syntax is unique to Numba. If one is going to the pain of managing an environment and learning syntax, then you can do worse than trying out Julia. With Julia you get flexible loop composition, SIMD, and parallelism out of the box. Throw in free C calls, broadcast fusion, Numpy / Matlab syntax, and calls to all the fundamental vectorised ops... and you've got a stew going.

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