K3D-tools/K3D-jupyter

K3D-tools/K3D-jupyter has 1,043 GitHub stars and 125 forks. GitHot measured 0 stars in 24 hours and +1 in 7 days. Last checked 2026-11-12 08:22 UTC. Last push 2026-11-01.

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WebGL 3D plotting for Jupyter: meshes, isosurfaces, volumes, voxels and point clouds from NumPy arrays, with a path-traced cinematic renderer.

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README.md

Original

K3D Jupyter

K3D lets you create 3D plots backed by WebGL with high-level API (surfaces, isosurfaces, voxels, mesh, point clouds, vtk objects, volume renderer, colormaps, etc). The primary aim of K3D-jupyter is to be easy to use as a standalone package like matplotlib, but also to allow interoperation with existing libraries as VTK. K3D can be run as:

Jupyter Notebook / JupyterLab widget (anywidget) 🚀
Google Colab widget 🎉
VS Code notebooks 🧩
Standalone HTML/JS 📑

Since 3.0.0 a plot also chooses how it is lit, through plot.renderer: simple (the default rasteriser), advanced (image-based lighting with ambient occlusion) or cinematic (progressive path tracing). cinematic is experimental — it needs WebGL2 with renderable float textures, does not cover every object (volume_slice is not drawn, mip stays outside the light simulation, only the first volume is path traced), and its API and output may still change; simple and advanced are the stable choices. See Renderers.

Documentation: https://k3d-jupyter.org

Showcase:

Two frames from the renderers 3.0.0 added, both produced by the code in this repository — click either one for how it works.

advanced: image-based lighting and ambient occlusion. A million analytic sphere impostors, with the occlusion in the crevices between strands doing the sculpting.

cinematic: progressive path tracing. A Draco-compressed glTF read with k3d.glb: the stones refract with an index of 2.09 and throw light between their facets, the gold reflects the studio around it. 256 samples, denoised with Open Image Denoise. Model: "Sasha" by saber7711, CC-BY.

points_cloud

streamlines

volume_rendering

volume_slide_view

transfer_function_editor

YouTube:

Click to watch at YouTube:

Try it Now!

Watch: Interactive showcase gallery

Installation

PyPI

To install from PyPI use pip:

$ pip install k3d

Conda/Anaconda

To install from conda-forge use:

$ conda install -c conda-forge k3d

Google Colab

Since 3.0.0 (the anywidget migration) no extra steps are needed:

!pip install k3d

import k3d and plot - custom widget activation and the text protocol are no longer required.

Installing directly from GitHub

To install directly from this repository (requires git and node.js + npm to build):

$ pip install git+https://github.com/K3D-tools/K3D-jupyter

This also makes possible installing the most up-to-date development version (same requirements):

$ pip install git+https://github.com/K3D-tools/K3D-jupyter@devel

To install any historical version, replace devel above with any tag or commit hash.

Source

For a development installation (requires npm and node.js),

$ git clone https://github.com/K3D-tools/K3D-jupyter.git
$ cd K3D-jupyter
$ pip install -e .

No separate JupyterLab extension step is needed - the widget ships its own frontend module (anywidget).

Code of Conduct

K3D-jupyter follows the Python Software Foundation Code of Conduct in everything we do.

Kudos

Jupyter is my ❤️
OpenDreamKit is 🚀
Three.js is 🥇
The cinematic renderer traces its light with three-gpu-pathtracer and three-mesh-bvh by Garrett Johnson - thank you 🙏
Its denoiser is Intel's Open Image Denoise, run in the browser by oidn-web

Acknowledgments

Research Infrastructure project This package was created as part of the Horizon 2020 European OpenDreamKit (grant agreement #676541).