Installation#

PyVista is supported on Python versions 3.7+. Previous versions of Python are no longer supported as outlined in this issue.

For the best experience, please considering using Anaconda as a virtual environment and package manager for Python and following the instructions to install PyVista with Anaconda.

Dependencies#

PyVista is built on top of the Visualization Toolkit (VTK) and NumPy - as such, the following projects are required dependencies of PyVista:

  • vtk - PyVista directly inherits types from the VTK library.

  • NumPy - NumPy arrays provide a core foundation for PyVista’s data array access.

  • imageio - This library is used for saving screenshots.

  • appdirs - Data management for our example datasets so users can download tutorials on the fly.

  • scooby - Reporting and debugging tools.

Optional Dependencies#

PyVista includes several optional dependencies for visualization and reading a variety of additional file formats, including:

  • cmocean - Colormaps for Oceanography.

  • colorcet - Perceptually accurate 256-color colormaps for use with Python.

  • ipyvtklink - Minimalist ipywidget to interface with any Python vtkRenderWindow.

  • matplotlib - Used for colormaps and 2D plotting with pyvista.ChartMPL.

  • meshio - Input/Output for many mesh formats.

  • pythreejs - Jupyter widgets based notebook extension that allows Jupyter to leverage the WebGL capabilities of modern browsers.

PyPI#

https://img.shields.io/pypi/v/pyvista.svg?logo=python&logoColor=white

PyVista can be installed from PyPI using pip:

pip install pyvista

To install all the additional packages that extend PyVista, install using pip with:

pip install pyvista[all]

Anaconda#

https://img.shields.io/conda/vn/conda-forge/pyvista.svg?logo=conda-forge&logoColor=white

To install this package with conda run:

conda install -c conda-forge pyvista

Installing the Current Development Branch from GitHub#

There may be features or bug-fixes that have been implemented in PyVista but have not made their way into a release. To install pyvista from the latest up-to-date development branch from github, use one of the following

pip install -U git+https://github.com/pyvista/pyvista.git@main

Alternatively, you can clone the repository with git and install it with pip.

git clone https://github.com/pyvista/pyvista.git
cd pyvista
pip install . -e

Note the development flag -e. This allows you to change pyvista in-place without having to reinstall it for each change.

Optional Dependencies#

The following are a list of optional dependencies and their purpose:

Package

Purpose

matplotlib

Using Colormaps

itkwidgets

Interactive notebook rendering

ipyvtklink

Interactive notebook rendering

sphinx_gallery

Capturing PyVista output for docs

colorcet

Perceptually uniform colormaps

cmocean

Oceanographic colormaps

imageio-ffmpeg

Saving movie files

tqdm

Status bars for monitoring filters

trimesh rtree pyembree

Vectorised ray tracing

Source / Developers#

Alternatively, you can install the latest version from GitHub by visiting PyVista, and downloading the source (cloning) by running:

git clone https://github.com/pyvista/pyvista.git
cd pyvista
python -m pip install -e .

The latest documentation for the main branch of PyVista can be found at dev.pyvista.org.

Test Installation#

You can test your installation by running an example:

>>> from pyvista import demos
>>> demos.plot_wave()

See other examples and demos:

>>> from pyvista import examples
>>> from pyvista import demos

List all available examples.

>>> print(dir(examples))

List all available demos.

Note

A more comprehensive testing suite is available after cloning the source repository. For details on how to clone and test the PyVista source, please see our Contributing Guide and specifically, the Testing section.

Running on CI Services#

Please head over to pyvista/setup-headless-display-action for details on setting up a headless display in GitHub Actions Workflows using PyVista.

In the past, we have also supported pyvista/gl-ci-helpers for setting up CI services like Travis and Azure Pipelines to run PyVista.

Running on MyBinder#

This section is for advanced users that would like to install and use PyVista with headless displays on notebook hosting services like MyBinder.

Please see this project for a convenient Cookiecutter to get started using PyVista on the notebook hosting service MyBinder.

To get started, the Docker container will need to have libgl1-mesa-dev and xvfb installed through apt-get. For MyBinder, include the following in a file called apt.txt:

libgl1-mesa-dev
xvfb

Then, you need to configure the headless display, for MyBinder, create a file called start and include the following set up script that will run every time your Docker container is launched:

#!/bin/bash
set -x
export DISPLAY=:99.0
export PYVISTA_OFF_SCREEN=true
export PYVISTA_USE_IPYVTK=true
which Xvfb
Xvfb :99 -screen 0 1024x768x24 > /dev/null 2>&1 &
sleep 3
set +x
exec "$@"

And that’s it! Include PyVista in your Python requirements and get to visualizing your data! If you need more help than this on setting up PyVista for these types of services, hop on Slack and chat with the developers or take a look at this repository that is currently using PyVista on MyBinder.

Running on Remote Servers#

Using PyVista on remote servers requires similar setup steps as in the above Docker case. As an example, here are the complete steps to use PyVista on AWS EC2 Ubuntu 18.04 LTS (ami-0a313d6098716f372 in us-east-1). Other servers would work similarly.

After logging into the remote server, install Miniconda and related packages:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p miniconda
echo '. $HOME/miniconda/etc/profile.d/conda.sh' >> ~/.bashrc && source ~/.bashrc
conda create --name vtk_env python=3.9
conda activate vtk_env
conda install nodejs  # required when importing pyvista in Jupyter
pip install jupyter pyvista ipyvtklink

# To avoid "ModuleNotFoundError: No module named 'vtkOpenGLKitPython' " when importing vtk
# https://stackoverflow.com/q/32389599
# https://askubuntu.com/q/629692
sudo apt update && sudo apt install python-qt4 libgl1-mesa-glx

Then, configure the headless display:

sudo apt-get install xvfb
export DISPLAY=:99.0
export PYVISTA_OFF_SCREEN=true
export PYVISTA_USE_IPYVTK=true
Xvfb :99 -screen 0 1024x768x24 > /dev/null 2>&1 &
sleep 3

Reconnect to the server with port-forwarding, and start Jupyter:

ssh -i "your-ssh-key" your-user-name@your-server-ip -L 8888:localhost:8888
conda activate vtk_env
jupyter lab --NotebookApp.token='' --no-browser --port=8888

Visit localhost:8888 in the web browser.

Running on WSL#

Similar to the example of the remote server above, the windows subsystem for Linux does not provide an x-server for visualization. Instead, the fastest way to get up and running on WSL is through JupyterLab.

First, make sure you have installed the correct environment through Miniconda and related packages:

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p miniconda
echo '. $HOME/miniconda/etc/profile.d/conda.sh' >> ~/.bashrc && source ~/.bashrc
conda create --name vtk_env python=3.9
conda activate vtk_env
conda install nodejs  # required when importing pyvista in Jupyter
pip install jupyter pyvista ipyvtklink

# To avoid "ModuleNotFoundError: No module named 'vtkOpenGLKitPython' " when importing vtk
# https://stackoverflow.com/q/32389599
# https://askubuntu.com/q/629692
sudo apt update && sudo apt install python-qt4 libgl1-mesa-glx

PyThreeJS Rendering in Jupyter#

The second option is to change the PyVista backend to use pythreejs.

To do this, first launch the Jupyter server:

jupyter lab --NotebookApp.token='' --no-browser --port=8888

Visit localhost:8888 in the web browser.

Finally change the PyVista backend to a web visualization library: pythreejs.

import pyvista
pyvista.global_theme.jupyter_backend='pythreejs'
pl = pyvista.Plotter(shape=(1, 2))
actor = pl.add_mesh(pyvista.Cube())
pl.subplot(0, 1)
actor = pl.add_mesh(pyvista.Sphere())
pl.set_background('orange', all_renderers=False)
pl.show()

Your visualizations should now be showing directly in the Jupyter frontend.