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

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K3D Jupyter

Downloads Anaconda-Server Badge Build Status Total Alerts Language Grade: JavaScript Language Grade: Python

Jupyter notebook extension for 3D visualization.

points_cloud

streamlines

volume_rendering

YouTube:

Volume renderer

Volume renderer

Volume renderer

Volume renderer

Try it Now!

Interactive showcase gallery: Documentation Status

Documentation is generated at readthedocs: Documentation Status

Jupyter version: Binder

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

Installing directly from GitHub

To install directy 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 .

Then, if required, JupyterLab installation:

$ jupyter labextension install ./js

JupyterLab

Then, if required, JupyterLab installation:

$ jupyter labextension install k3d

Please notice that support for jupyterLab is still experimental.

Developer's How To

Please make sure to take a look at the HOW-TO.md document.

About

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

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  • Python 87.4%
  • JavaScript 11.9%
  • GLSL 0.5%
  • Jupyter Notebook 0.1%
  • HTML 0.1%
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