4 repositorios
Widgets that provide mouse-driven zooming, panning, box selection, and data querying for real-time exploration of plotted data.
Distinct from Interactive Plotting Frameworks: Distinct from Interactive Plotting Frameworks: focuses on the interactive exploration widget itself rather than the broader framework for creating plots.
Explore 4 awesome GitHub repositories matching scientific & mathematical computing · Interactive Plot Exploration Widgets. Refine with filters or upvote what's useful.
Ships a widget providing mouse-driven zooming, panning, box selection, and data querying for real-time plot exploration.
Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The
Displays interactive plots and widgets that update in real-time as the data analysis workflow changes.
Shiny is a framework for building interactive web applications using R code, eliminating the need for HTML, CSS, or JavaScript. At its core, it provides a reactive programming model that automatically tracks data dependencies and re-executes only the parts of an application that depend on changed inputs. The framework handles server-side UI rendering and maintains persistent WebSocket connections between the browser and server for real-time updates without page reloads. The framework distinguishes itself through deep integration with the R ecosystem, including the ability to embed interactive
Generates plots that respond to mouse clicks, brush selections, and hover events.
PyQtGraph is a scientific plotting and graphics framework built for PyQt and PySide applications, providing fast, interactive 2D and 3D visualizations with GPU-accelerated rendering. It serves as both a real-time signal monitoring system for streaming time-series data and a toolkit for constructing interactive data dashboards with dockable panels, parameter trees, and custom widgets. The library also includes a node-based visual flowchart tool for building data processing pipelines and a scientific graphics export system that saves plots as PNG, SVG, or CSV and converts items to Matplotlib for
Provides ready-made GUI components for plotting, image viewing, parameter editing, and data exploration.