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mozilla/metrics-graphics

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metricsgraphicsjs.org↗

Metrics Graphics

metrics-graphics is a data visualization library and declarative graphics framework designed to create principled data graphics and layouts. It functions as a statistical graphics engine that maps raw data to geometric shapes and structured objects to render complex, data-driven layouts.

The toolkit specializes in rendering time-series data through line charts and scatterplots using a consistent layout system. It also provides capabilities for statistical distribution mapping, including the creation of rug plots to represent one-dimensional data density.

The system covers a broad surface of data visualization engineering, including custom chart layout design and declarative data visualization. It utilizes a compositional model to translate structured specifications into final pixels.

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Features

  • Time-Series Visualizers - Provides a comprehensive toolkit for rendering time-series data as line charts, scatterplots, and histograms.
  • Statistical Plotting Libraries - Maps raw data to geometric shapes and statistical distributions like rug plots and histograms.
  • Visualization Engines - Builds principled charts and layouts to represent complex datasets in a visual format.
  • Data Visualization Libraries - Provides a library for creating principled data graphics and layouts optimized for concise representations of complex datasets.
  • Geometric Data Mappings - Translates input arrays into geometric shapes like lines or rectangles based on defined mapping rules.
  • Data-Driven Shape Generators - Builds complex visual layouts by mapping raw data arrays to geometric shapes and screen coordinates.
  • Declarative Rendering - Separates visual properties from rendering implementation by translating structured object definitions into final pixels.
  • Declarative Visualization Grammars - Defines visual properties through structured specifications that a rendering engine translates into pixels.
  • Scale-Based Coordinate Mappings - Maps raw data values to screen coordinates using linear and logarithmic scales to ensure geometric accuracy.
  • Chart Layout Components - Creates precise and consistent data graphics using a compositional system of nested elements and axis-relative positioning.
  • Component Composition Patterns - Utilizes a compositional model that builds complex graphics by nesting small primitive elements into a hierarchical tree structure.
  • Declarative Visualization Frameworks - Implements a compositional model that defines visual properties via structured objects to render complex data-driven layouts.
  • Data-Driven Layouts - Develops precise layouts for data-driven graphics that follow a consistent design system.
  • Rug Plots - Provides the capability to represent data point density along a specific axis using rug plots.
  • Axis-Relative Positioning - Implements a system for calculating marker and label placements relative to data boundaries to maintain alignment across chart types.
  • Hierarchical Layouts - Organizes nested graphical elements into a parent-child structure to coordinate scaling and positioning across the canvas.
  • Statistical Distribution Visualizers - Visualizes data density and frequency through rug plots and histograms to understand dataset spread.
  • Data Visualization - Library for concise, optimized data charts.
  • Data Visualization - Concise and efficient D3-based charting library.
  • Game Engines - Library for concise data graphics and layouts.
7,403 stars·463 forks·TypeScript·28 views

Star history

Star history chart for mozilla/metrics-graphicsStar history chart for mozilla/metrics-graphics

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does mozilla/metrics-graphics do?

metrics-graphics is a data visualization library and declarative graphics framework designed to create principled data graphics and layouts. It functions as a statistical graphics engine that maps raw data to geometric shapes and structured objects to render complex, data-driven layouts.

What are the main features of mozilla/metrics-graphics?

The main features of mozilla/metrics-graphics are: Time-Series Visualizers, Statistical Plotting Libraries, Visualization Engines, Data Visualization Libraries, Geometric Data Mappings, Data-Driven Shape Generators, Declarative Rendering, Declarative Visualization Grammars.

Which projects share features with mozilla/metrics-graphics?

Projects with overlapping indexed features include: antvis/g2 — G2 is a declarative data visualization engine that constructs complex charts and graphical representations by mapping… has2k1/plotnine — Plotnine is a data visualization library for Python based on the Grammar of Graphics. It serves as a declarative… d3/d3 — D3 is a modular library providing low-level primitives for creating data-driven visualizations. It functions as a… apache/echarts — Apache ECharts is a JavaScript data visualization library used for rendering interactive charts and complex data… epochjs/epoch — Epoch is a CSS-stylable charting engine and visualization library designed for real-time and statistical data. It… recharts/recharts — This project is a declarative data visualization library that provides a composable suite of user interface components…

Projects sharing features with Metrics Graphics

These projects share indexed features with Metrics Graphics. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • antvis/g2antvis avatar

    antvis/G2

    12,524View on GitHub↗

    G2 is a declarative data visualization engine that constructs complex charts and graphical representations by mapping raw data to visual elements through a systematic grammar of graphics. It functions as a modular framework for building custom analytical visualizations, allowing users to define visual encodings and coordinate systems independently of the underlying data. The library distinguishes itself through a multi-backend rendering pipeline that supports Canvas, SVG, and WebGL, ensuring consistent graphical performance across different environments. Its architecture relies on a plugin-ba

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  • has2k1/plotninehas2k1 avatar

    has2k1/plotnine

    4,598View on GitHub↗

    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

    Pythondata-analysisgrammargraphics
    View on GitHub↗4,598
  • d3/d3d3 avatar

    d3/d3

    113,118View on GitHub↗

    D3 is a modular library providing low-level primitives for creating data-driven visualizations. It functions as a flexible framework that allows for direct control over visual presentation by mapping abstract data dimensions to graphical properties, such as position, color, and size, without imposing predefined chart abstractions. The library distinguishes itself by offering specialized tools for complex data representation, including algorithmic layouts for hierarchical structures and geographic projection utilities for mapping spherical coordinates. It also includes a comprehensive suite fo

    Shellchartchartsd3
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  • apache/echartsapache avatar

    apache/echarts

    66,629View on GitHub↗

    Apache ECharts is a JavaScript data visualization library used for rendering interactive charts and complex data visualizations in web browsers. It functions as a canvas-based charting engine and a statistical data visualization suite that transforms datasets into visual representations. The framework provides specialized capabilities for three-dimensional data visualization, including the generation of 3D plots and globe visualizations. It also serves as a web-based geographic mapping tool for overlaying heatmaps, routes, and data distributions onto interactive maps. The library covers a br

    TypeScriptapachecanvascharting-library
    View on GitHub↗66,629
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