3 个仓库
Processes for creating non-interactive, fixed-format image files of data visualizations.
Distinct from Interactive Visualization Rendering: Distinct from Interactive Visualization Rendering: focuses on producing flat image files for static environments rather than reactive, user-driven displays.
Explore 3 awesome GitHub repositories matching data & databases · Static Visualization Generation. Refine with filters or upvote what's useful.
Nivo is a responsive charting framework and a React data visualization library that uses D3 for its underlying math logic. It serves as both a collection of interactive chart components for web applications and a server-side visualization engine for generating static data chart images. The project distinguishes itself by providing a containerized chart rendering API, allowing the visualization engine to be deployed via Docker to serve rendered graphics as images or files through a programmatic interface. It also features a motion engine for animated data transitions, ensuring smooth visual sh
Production of non-interactive chart images for environments where a live runtime is unavailable.
This project is an educational collection of interactive Jupyter notebooks designed to illustrate fundamental machine learning algorithms and mathematical principles. It serves as a resource for bridging the gap between abstract equations and practical implementation through a combination of narrative text and executable code. The collection utilizes a modular architecture where individual algorithm implementations are isolated to facilitate independent study. It incorporates both interactive code examples and static graphical assets to represent complex statistical concepts and model behavio
Generates static graphical assets to provide clear visual representations of mathematical concepts.
mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for
Creates static and interactive charts and saves them as images or HTML files.