Scientific Computing on the Erlang VM by Duncan McGreggor.
From the post:
This tutorial brings in the New Year by introducing the Erlang/LFE scientific computing library lsci – a ports wrapper of NumPy and SciPy (among others) for the Erlang ecosystem. The topic of the tutorial is polynomial curve-fitting for a given data set. Additionally, this post further demonstrates py usage, the previously discussed Erlang/LFE library for running Python code from the Erlang VM.
Background
The content of this post was taken from a similar tutorial done by the same author for the Python Lisp Hy in an IPython notebook. It, in turn, was completely inspired by the Clojure Incantor tutorial on the same subject, by David Edgar Liebke.
This content is also available in the lsci examples directory.
Introduction
The lsci library (pronounced “Elsie”) provides access to the fast numerical processing libraries that have become so popular in the scientific computing community. lsci is written in LFE but can be used just as easily from Erlang.
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Just in case Erlang was among your New Year’s Resolutions. 😉
Well, that’s not the only reason. You are going to encounter data processing that was performed in systems or languages that are strange to you. Assuming access to the data and a sufficient explanation of what was done, you need to be able to verify analysis in a language comfortable to you.
There isn’t now nor is there likely to be a shortage of languages and applications for data processing. Apologies to the various evangelists who dream of world domination for their favorite. Unless and until that happy day for someone arrives, the rest of us need to survive in a multilingual and multi-application space.
Which means having the necessary tools for data analysis/verification in your favorite tool suite counts for a lot. It is the difference in taking someone’s word for analysis and verifying the analysis for yourself. There is a world of difference between those two positions.