# fastplotlib/fastplotlib

Next-gen fast plotting library running on WGPU using the pygfx rendering engine

Repository: https://github.com/fastplotlib/fastplotlib
Canonical: https://ross.abutalabs.com/products/fastplotlib
Homepage: http://www.fastplotlib.org/ver/dev
Language: Python
License: Apache-2.0
License Family: permissive
Topics: gpu, scientific-visualization, visualization, vulkan, webgpu, wgpu, pygfx, interactive-visualizations, fast-visualization
Last push: 2026-08-24T21:15:12+00:00

## Health v2 (maintenance only)
Score: 80/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 45, longevity 100
- inputs: {"age_days": 1591, "days_push": 9, "days_rel": 291, "gap_med": 57, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1523, forks 65 (observed 2026-08-28T04:04:58.136733+00:00)

## What it is
fastplotlib is a GPU-accelerated scientific plotting library for Python built on the pygfx rendering engine, which uses WGPU (Vulkan, DX12, or Metal) for fast rendering. It provides an expressive, array-like API for interactive visualization of large-scale data across Jupyter, Qt, glfw, and wxPython.

## Use cases
- plot large datasets interactively in python
- gpu-accelerated scientific visualization
- real-time visualization of camera or instrument data
- interactive data exploration in jupyter lab
- rapid prototyping of algorithms with live plots
- visualize machine learning model data
- render plots offscreen with wgpu

## When to choose
- you need fast, interactive plotting of large-scale scientific data
- you want GPU-accelerated rendering via Vulkan, DX12, or Metal
- you need the same plotting code to run in Jupyter, Qt, glfw, or wxPython
- you are building real-time visualization for acquisition systems

## When to avoid
- you need publication-quality static figures with matplotlib-style APIs
- you need a fully stable, mature API (project is in late alpha)
- you have no GPU and need more than limited CPU software rendering
- you need simple quick charts where matplotlib or plotly suffice

## Facets
- artifact type: library
- maturity: experimental
- function: data-visualization, graphics, gpu-computing
- domain: data-visualization, data-science, machine-learning
- platform: python, cross-platform
- tags: wgpu, pygfx, gpu-accelerated, interactive-plotting, scientific-visualization, jupyter, vulkan, webgpu, gpu

## Member repositories
- fastplotlib/fastplotlib (main) score 80

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:58.136733+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:31:44.655274+00:00, confidence not recorded.
  - readme: https://github.com/fastplotlib/fastplotlib (fetched 2026-08-28T04:04:58.136733+00:00, sha 436b4cb87f31)
  - homepage: http://www.fastplotlib.org/ver/dev (fetched 2026-08-29T11:34:44.569807+00:00, sha 36e852a4552b)
  - site_page: https://www.fastplotlib.org/ver/dev/user_guide/faq.html (fetched 2026-08-29T11:34:44.578881+00:00, sha 4e2d7e80d235)
  - site_page: https://www.fastplotlib.org/ver/dev/api/graphic_features/index.html (fetched 2026-08-29T11:34:44.581185+00:00, sha f05bd12f35cf)
  - registry_pypi: https://pypi.org/pypi/fastplotlib/json (fetched 2026-08-29T11:34:44.582958+00:00, sha a060b300b54e)
- Data as of 2026-08-30T08:39:29.467469+00:00.