8 مستودعات
Capabilities for rendering plots as inline images within interactive notebook environments.
Distinguishing note: No candidate covers notebook-specific plot rendering; candidates are all unrelated service offerings.
Explore 8 awesome GitHub repositories matching development tools & productivity · Notebook Plot Rendering. Refine with filters or upvote what's useful.
This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
Arranges multiple plots in grid layouts to enable side-by-side comparison of datasets.
Folium is a Python library that builds interactive Leaflet.js maps directly from Python data structures, enabling geographic data visualization in Jupyter notebooks or as standalone HTML pages. It creates maps centered on given coordinates with configurable zoom, tiles, and dimensions, and supports embedding those maps inside web routes for serving in browsers. The library provides a comprehensive set of tools for data-driven map creation, including choropleth maps that bind tabular data to geographic geometries, colormap application to markers and polygons, and GeoJSON data overlay and visua
Arranges multiple independent maps in a grid for comparative geographic viewing.
ScottPlot is a cross-platform, high-performance charting library for .NET that renders interactive plots across desktop and web GUI frameworks including Windows Forms, WPF, MAUI, Avalonia, Blazor, and WinUI. It provides an optimized rendering engine capable of displaying millions of data points with interactive pan, zoom, and live data streaming, while also supporting image export to formats like PNG and SVG for file output, cloud applications, and notebooks. The library distinguishes itself through a comprehensive set of chart types including scatter, line, bar, pie, heatmap, financial, rada
Ships a custom formatter so plots render as inline PNGs in .NET interactive notebooks.
Provides a plot rendering engine that outputs to virtual DOM and other formats.
This C++ data visualization library is a scientific plotting framework used to create 2D and 3D charts, network graphs, and geographic maps. It operates as a multi-backend graphics library, decoupling high-level plotting logic from low-level rendering engines to support various output backends. The project distinguishes itself with a dual-interface API, providing both a global functional interface for rapid prototyping and an object-oriented interface for precise control. It features a component-based layout engine for managing tiled grids and subplots, alongside a layered plot state that all
Generates a grid of axes combining histograms and scatter plots to visualize data correlations.
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
Renders interactive plots directly within Jupyter notebook cells for exploratory data analysis.
Makie.jl is a high-performance Julia data visualization library and hardware-accelerated plotting engine used to create interactive 2D and 3D visualizations. It functions as a reactive visualization framework where plots update automatically via observables and compute graphs, and as a vector graphics generator for high-resolution academic output. The system is distinguished by its backend-agnostic rendering pipeline, which supports OpenGL, WebGL, and ray-traced scenes. It employs a grammar-of-graphics approach to map variables to aesthetic attributes and utilizes a hierarchical scene graph t
Organizes multiple visual elements into rows and columns with automatically calculated dimensions.
Patchwork is a layout manager for combining multiple ggplot2 graphics into a single complex arrangement. It functions as a multi-plot composition tool and data visualization orchestrator, allowing independent graphics to be arranged into grids and nested layouts using additive and functional syntax. The system differentiates itself through a broadcast-based style application that propagates themes and scales across all subplots to maintain visual consistency. It also features guide-merging reconciliation to identify and collapse redundant legends into a single shared global guide. The framew
Provides additive and functional syntax to arrange multiple plots side-by-side or stacked vertically in grids.