Installation¶
PyVista is supported on Python versions 3.5+, with temporary support for Python 2.7 as outlined in this issue.
Dependencies¶
PyVista is built on top of the Visualization Toolkit (VTK) and NumPy - as such, the following projects are required dependencies of PyVista:
Optional Dependencies¶
The following are a list of optional dependencies and their purpose:
Package |
Purpose |
|---|---|
|
Using Colormaps |
|
Background plotting |
|
IPython interactive tools |
|
Interactive notebook rendering |
|
Capturing PyVista output for docs |
Source / Developers¶
Alternatively, you can install the latest version from GitHub by visiting PyVista, downloading the source (or cloning), and running:
git clone https://github.com/pyvista/pyvista.git
cd pyvista
python -m pip install -e .
Test Installation¶
You can test your installation by running an example:
from pyvista import examples
examples.plot_wave()
See other examples:
from pyvista import examples
# list all examples
print(dir(examples))
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/gl-ci-hepers for details on 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_PANEL=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=3.7
conda activate vtk_env
conda install nodejs # required when importing pyvista in Jupyter
pip install jupyter pyvista panel
# 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_PANEL=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 notebook --NotebookApp.token='' --no-browser --port=8888
Visit localhost:8888 in the web browser.