CodeGraph is a Python command-line tool that performs static analysis on Python source code to generate dependency graphs. It parses Python files to extract functions, classes, and import statements, then builds a graph showing how these entities depend on each other. The tool outputs an interactive HTML visualization (using D3.js by default) or can export the data to CSV or print it to the console. The architecture is modular, consisting of a parser (using Python's tokenize module), a core module that builds the dependency graph, utility functions for file discovery, and a visualizer that supports both D3.js (interactive) and matplotlib (legacy) backends. The tool does not execute the code being analyzed, making it safe and fast for large codebases. Key strengths include a robust parser that handles various import styles and class inheritance, and a flexible visualization system that provides features like search, collapsing/expanding nodes, and detailed tooltips. The project is actively maintained with comprehensive tests and supports multiple Python versions.
This repository provides a self-contained JavaScript library (`radar.js`) that renders an interactive 'Tech Radar' visualization using D3.js v7. The radar chart displays technology entries organized into four quadrants and four concentric rings (e.g., ADOPT, TRIAL, ASSESS, HOLD), with automatic collision-avoidance positioning via D3's force simulation. The library is used by Zalando to visualize their internal technology choices and is published as a static page. The codebase consists of a single global function (`radar_visualization`) that accepts a configuration object, making it easy to embed in any HTML page. It includes features like hover tooltips, clickable links, a legend with text wrapping, optional ring description table, and a print layout mode. The repository also contains historical release versions of the library. It is not a data pipeline or research project; it is a purely client-side visualization tool.
SequenceServer is a Ruby/Sinatra web application that provides a graphical interface for running NCBI BLAST+ and DIAMOND sequence similarity searches against custom databases. It manages BLAST database formatting, job submission with background execution, result parsing, and interactive visualization of alignments using D3.js and Circos-style plots. The backend uses a thread pool for concurrent job processing, YAML-based job persistence, and a plugin system for extensibility. The frontend is built with React and includes components for database selection, query input, and result exploration with export to SVG/PNG. The application is designed for bioinformatics researchers who need a self-hosted BLAST server with a modern web UI, supporting multiple BLAST programs (blastn, blastp, blastx, tblastn, tblastx) and DIAMOND. It includes comprehensive test suites for multiple BLAST versions and CI/CD pipelines for Docker and AppImage deployment.
Make.md is an Obsidian plugin (manifest id `make-md`) that turns a plain vault into a structured knowledge workspace. In production it registers custom leaf views (Spaces, Navigator, Blink), patches Obsidian's workspace and CodeMirror editor, and maintains a metadata layer called `Superstate` indexed by web workers. It ships a React-based frame layout engine where notes/spaces are rendered as configurable frame trees with style/action expressions evaluated at runtime, an MDB layer that reads and writes SQLite `.mdb` files via sql.js, and a predicate/filter/aggregate system powering table, calendar, list, and chart views. The plugin also includes a custom mathjs-based formula language, an HTML export pipeline, and a D3-driven visualization pipeline.
bertviz is a Python library for interactive visualization of attention in Transformer-based NLP models. It provides three complementary views: Head View (per-head attention patterns), Model View (attention flow between tokens across layers), and Neuron View (query/key interactions within individual attention heads). The library is designed for Jupyter notebooks, rendering D3.js-based interactive HTML directly in cell output. It accepts attention tensors from most HuggingFace models via output_attentions=True, and includes a vendored, modified copy of older HuggingFace transformers code to expose query/key vectors for the Neuron View on BERT, GPT-2, RoBERTa, XLNet, and similar architectures.