8 مستودعات
Adding text, shapes, or images to specific coordinates in a visualization to highlight data points.
Distinct from Visual Annotations: Distinct from Visual Annotations [f0_mt1] which is specific to audio data, and Automated Visual Data Annotation [f0_mt3] which is for ML training.
Explore 8 awesome GitHub repositories matching graphics & multimedia · Plot Annotations. Refine with filters or upvote what's useful.
ggplot2 is a data visualization library for R based on a formal grammar of graphics. It provides a declarative plotting framework that allows users to create complex graphics by combining geometric objects, statistical summaries, and coordinate systems. The system is distinguished by a layered approach to composition, where visualizations are built incrementally by stacking independent geometric, statistical, and coordinate layers. It utilizes a hierarchical styling engine to manage non-data elements such as backgrounds, fonts, and margins, and includes a multi-panel faceting tool for splitti
Provides tools to add text and geometric shapes to specific plot coordinates for highlighting key information.
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
Places always-visible text annotations over the data area in pixel coordinates.
Adds text labels, markers, legends, and custom colors or line styles to highlight observations and match a theme.
This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi
Provides guidance on adding descriptive text and markers to plots to highlight key data points.
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
Provides a comprehensive system for adding text, arrows, rectangles, and ellipses to highlight specific data points.
Plotnine is a data visualization library for Python based on the Grammar of Graphics. It serves as a declarative statistical plotting framework and multi-panel plotting engine, allowing users to create complex charts by mapping data variables to visual properties such as position, color, and size. The project is distinguished by its use of a layered composition model and a statistical transformation engine that performs aggregations and computations before rendering visuals. It features a comprehensive system for multi-panel faceting, which enables the splitting of a single visualization into
Adds text, shapes, or images to specific coordinates in a visualization to highlight data points.
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
Adds text labels, arrows, legends, and region-of-interest selectors directly onto plotted data.
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 the ability to add overarching titles, subtitles, and captions to a group of combined graphics.