Tessellate your favorite 3D surfaces (technically, 2D manifolds) with triangles, hexagons, or a number of other curated tiling types!

A space shooter game you can play in your terminal.

Asciimatics is a package to help people create full-screen text UIs (from interactive forms to ASCII animations) on any platform.

A simple, configurable, cross-platform youtube-dl wrapper for downloading and managing youtube audio (with added features).

A* algorithm visualizer written in pygame.

A gravity simulation using the runge kutter integration method.

Tetris game made using pygame and python.

Shellscript for synchronization of files and directories (a simple alternative to RSYNC)

Parsr, is a minimal-footprint document (image, pdf) cleaning, parsing and extraction toolchain which generates readily available, organized and usable data for data scientists and developers.

It provides users with clean structured and label-enriched information set for ready-to-use applications ranging from data entry and document analysis automation, archival, and many others.

Currently, Parsr can perform:

• Document Hierarchy Regeneration - Words, Lines and Paragraphs
• Headings Detection
• Table Detection and Reconstruction
• Lists Detection
• Text Order Detection
• Named Entity Recognition (Dates, Percentages, etc)
• Key-Value Pair Detection (for the extraction of specific form-based entries)
• Page Number Detection
• Header-Footer Detection
• Link Detection
• Whitespace Removal

Provides an implementation of today's most used tokenizers, with a focus on performance and versatility.

Main features:

• Train new vocabularies and tokenize, using today's most used tokenizers.
• Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU.
• Easy to use, but also extremely versatile.
• Designed for research and production.
• Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token.
• Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

CleverCSV provides a drop-in replacement for the Python csv package with improved dialect detection for messy CSV files. It also provides a handy command line tool that can standardize a messy file or generate Python code to import it.

Karate Club is an unsupervised machine learning extension library for NetworkX.

Karate Club consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping commmunity detection methods. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences, workshops, and pieces from prominent journals.

Any C structure can be stored in a hash table using uthash. Just add a UT_hash_handle to the structure and choose one or more fields in your structure to act as the key. Then use these macros to store, retrieve or delete items from the hash table.

A fast, efficient universal vector embedding utility package.

Dead simple games made with word vectors.

exa is a replacement for the venerable ls command. exa sports more features and arguably better defaults. Free and open source software.

Formerly known as what, it is a CLI utility for displaying current network utilization by process, connection and remote IP/hostname.

Collections: double linked list, deque, RBtree, channels.

The idea of RosettaGit is to present solutions to the same task in as many different programming languages as possible. It demonstrates how languages are similar and different and can help you learn new approaches to solving problems.

Particle filter is a Monte Carlo algorithm used to solve statistical inference problems. In this project, the turtle location and heading direction in maze was infered using particle filter. The green turtle is the actual location while the orange turtule is the estimated location. The arrows are particles. Blue arrows stand for low probability particles while red arrows stand for high probability particles. There are four sensors installed on the front, back, left and right of the turtle. The sensors measure its perpendicular distances to the closest walls in four directions, possibly bounded by some sensor limit.