Introducing a new scientific computing library: syntropy.
Syntropy is a comprehensive package for information theory, aimed at both theoreticians and data analysts working on discrete, continuous, and mixed data.
1/N
github.com/thosvarley/s...
GitHub - thosvarley/syntropy: A python package for information-theoretic analysis of discrete and continuous data.
A python package for information-theoretic analysis of discrete and continuous data. - thosvarley/syntropy
This package grew out of my work in graduate school and my postdoc - inspired by the networkx package, I wanted a one-stop-shop for discrete and continuous multivariate information theory, all in Python for maximum accessibility. Years later, here it is. 2/N
I've included estimators for different data types: discrete estimators, covariance-based estimators for Gaussian data, and two different non-parametric estimators (normalizing flow-based and KNN-based). Also mixed, for data with discrete and continuous components. 3/N
I have classic measures (entropy, MI, CMI, etc), modern multivariate measures (O/S-information, TC, DTC, etc), and three different kinds of information decomposition (PID, PED, GID).
As far as I know, this is the most complete package on the market atm. 4/N
The goal is not to supplant other packages (e.g. JIDT/IDTxl are still my go-tos for information dynamics), but my goal was a pure-python package (i.e. no MATLAB, ever) to make everything as accessible as possible. 5/N
I am more than happy to continue expanding this package. If there's something you'd like to see, feel free to make a request, or roll it yourself. 6/N
I have no personal interest in Julia or MATLAB, but I do have Opus 4.5 and might try and see if it can automate the porting/translation process, just to see if it works.
