This repository is an interactive, cyberpunk-styled periodic table of elements web application. It displays all 118 elements with detailed data, supports multiple visualization modes (standard, heatmap for properties like radius, electronegativity, ionization energy, melting point, boiling point, density), category filtering, real-time search, and a 3D atomic model viewer. The application is fully bilingual (Chinese/English) and responsive across devices. The architecture is a single-page application built with TypeScript and Vite. The codebase is modular, with clear separation into modules for table rendering, heatmap, 3D atom, modal, media loading, search, and legend. State is managed centrally, and internationalization is handled via typed translation dictionaries. The project includes unit tests using Vitest and a CI/CD pipeline for GitHub Pages deployment. Key technical highlights include a pure-function electron configuration algorithm (Aufbau principle with exceptions), CSS 3D transforms for the atom model, and a lightweight i18n system. The code is type-safe with strict TypeScript and follows modern frontend best practices.
MuscleMap is a SwiftUI SDK for rendering interactive human body muscle maps with highlighting, heatmaps, and gesture support. It provides male and female body models in front and back views, supports tap/long-press/drag interactions, pinch-to-zoom, multi-select with undo/redo, pulse animations, and extensive localization (11 languages). Built entirely in SwiftUI with zero external dependencies, it uses SVG path parsing for precise muscle rendering and offers both SwiftUI and UIKit interfaces.
This repository contains a Jupyter notebook that processes Strava data exports to generate interactive running heatmaps. The core functionality involves parsing GPS and sensor data from .fit.gz files, filtering activities by type and date, detecting a home location, rasterizing GPS tracks into multiple value grids (frequency, pace, heart rate, gradient, elevation change), applying Gaussian blurring and normalization, and rendering the results as overlay layers on a Leaflet basemap via Folium. The output is a single HTML file with six toggleable layers showing different aspects of running patterns. The code demonstrates solid data processing techniques including efficient caching, coordinate transformations, and custom visualization logic, though it remains tightly coupled as a monolithic notebook.
This repository is a Python application/library for generating 2D heatmaps from coordinate data. The core module src/heatmap77/heatmap.py implements a complete pipeline: file readers parse plain text, CSV, GPX, and ESRI Shapefile inputs; coordinate systems and projections (equirectangular, mercator) map lat/lon to pixel space; linear and gaussian kernels spread point mass; sparse matrix classes accumulate contributions with optional decay; and an ImageMaker renders the result to PNG/PPM, optionally compositing over OSM tiles or custom backgrounds. The package exposes both a CLI entry point (heatmap77) and a programmatic API used by the examples.
This is an R/Bioconductor package for making enriched heatmaps, primarily used in epigenomics to visualize enrichment of genomic signals (e.g., histone modifications, DNA methylation) around target regions (e.g., transcription start sites). The package provides a workflow: normalize signals to a matrix via `normalizeToMatrix()`, then draw a heatmap with `EnrichedHeatmap()`, which is a subclass of ComplexHeatmap. It includes specialized annotation (`anno_enriched()`), row ordering by enrichment scores, smoothing, and support for categorical signals. The architecture leverages S3 classes (normalizedMatrix) and integrates with GenomicRanges for genomic interval operations. It is well-documented with vignettes and a pkgdown site, and includes Rcpp for performance-critical distance calculations. While the package is actively maintained, there are some signs of technical drift such as deprecated arguments and an old CI configuration.