Computational Python Packages

blosc2

A high-performance library for compressed ND arrays and columnar tables, with compute and indexing engines

tripy

Simple polygon triangulation algorithms in pure python

bhv

Boolean Hypervectors with various operators for experiments in hyperdimensional computing (HDC).

probnode

Probability expression representation and calculation library

hbayesdm

Python interface for hBayesDM, hierarchical Bayesian modeling of RL-DM tasks

fairtool

A CLI tool for FAIR processing of computational materials science data.

latentcor

Fast Computation of Latent Correlations for Mixed Data

fastgrouper

Fast groupby-apply operations in python.

thoad

**thoad** (Torch High Order Automatic Differentiation) is a lightweight reverse-mode autodifferentiation engine written entirely in Python that works over PyTorch’s computational graph to compute **high order partial derivatives**. Unlike PyTorch’s native autograd - which is limited to first-order native partial derivatives - **thoad** is able to performantly propagate arbitray-order derivatives throughout the graph, enabling more advanced gradient-based computations.