9 repositorios
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 9 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.
Esta librería de visualización de datos en C++ es un framework de trazado científico utilizado para crear gráficos 2D y 3D, grafos de red y mapas geográficos. Opera como una librería de gráficos multi-backend, desacoplando la lógica de trazado de alto nivel de los motores de renderizado de bajo nivel para soportar varios backends de salida. El proyecto se distingue por una API de interfaz dual, que proporciona tanto una interfaz funcional global para prototipado rápido como una interfaz orientada a objetos para un control preciso. Cuenta con un motor de diseño basado en componentes para gestionar cuadrículas y subgráficos, junto con un estado de trazado en capas que permite superponer múltiples series de datos sin borrar los ejes. La librería cubre una amplia gama de capacidades de visualización, incluyendo trazado de funciones matemáticas, campos vectoriales y análisis de datos multidimensionales mediante mapas de calor y coordenadas paralelas. Incluye herramientas especializadas para la visualización de datos geográficos, como gráficos geobubble y geodensity, así como herramientas para renderizar redes de grafos dirigidos y no dirigidos. Las capacidades generales incluyen gestión de ejes, estilo estético con mapas de colores y exportación de gráficos de alta calidad. El proyecto utiliza CMake para la automatización de la compilación y la recuperación de dependencias para facilitar la instalación en diferentes sistemas operativos.
Provides a comprehensive system for adding text, arrows, rectangles, and ellipses to highlight specific data points.
Plotnine es una librería de visualización de datos para Python basada en la Gramática de Gráficos. Sirve como un framework de trazado estadístico declarativo y motor de trazado multipanel, permitiendo a los usuarios crear gráficos complejos mapeando variables de datos a propiedades visuales como posición, color y tamaño. El proyecto se distingue por su uso de un modelo de composición en capas y un motor de transformación estadística que realiza agregaciones y cálculos antes de renderizar visuales. Cuenta con un sistema integral para faceting multipanel, que permite dividir una sola visualización en una cuadrícula de sub-gráficos basados en variables categóricas. La librería cubre una amplia gama de capacidades, incluyendo diversas representaciones geométricas para gráficos de distribución, área y dispersión, así como visualización geoespacial para renderizar límites geográficos. Proporciona herramientas extensas para mapeo de escalas, proyecciones de coordenadas y estilo basado en temas para separar los elementos impulsados por datos de las propiedades estéticas no relacionadas con los datos. El framework utiliza un backend de Matplotlib para el renderizado e integra con dataframes tabulares mediante operaciones de tubería (piping).
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.
bqplot is an interactive data visualization library for IPython and Jupyter notebooks that utilizes a grammar of graphics. It functions as a tool for creating 2D charts and maps with real-time updates and bidirectional communication between the kernel and frontend. The library is distinguished by its ability to act as a geographic data visualization tool, rendering choropleth maps and spatial data via GeoJSON and custom projections. It also serves as a financial charting tool for producing OHLC and candle bar charts, and as an interactive dashboard framework for combining plotting widgets wit
Inserts text labels and reference lines into a figure to highlight specific data points.
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.