Installation | GPU Drivers | Documentation | Examples | Contributing

A fast plotting library built using the pygfx rendering engine that can utilize Vulkan, DX12, or Metal via WGPU, so it is very fast! We also aim to be an expressive plotting library that enables rapid prototyping for large scale explorative scientific visualization.

SciPy 2023 Talk

Notebooks from talk: https://github.com/fastplotlib/fastplotlib-scipy2023

Supported frameworks

fastplotlib can run on anything that pygfx can also run, this includes:

:heavy_check_mark: Jupyter lab, using jupyter_rfb
:heavy_check_mark: PyQt and PySide
:heavy_check_mark: glfw
:heavy_check_mark: wxPython

Notes:
:heavy_check_mark: Non-blocking Qt output is supported in ipython and notebooks by using %gui qt before creating plots. This hook only supports pyqt6 at the moment.
:grey_exclamation: We do not officially support jupyter notebook through jupyter_rfb, this may change with notebook v7
:disappointed: jupyter_rfb does not work in collab yet, see https://github.com/vispy/jupyter_rfb/pull/77

Note

fastplotlib is currently in the alpha stage with breaking changes every ~month, but you're welcome to try it out or contribute! See our Roadmap. See this for a discussion on API stability: https://github.com/fastplotlib/fastplotlib/issues/121

Documentation

http://fastplotlib.readthedocs.io/

The Quickstart guide is not interactive. We recommend cloning/downloading the repo and trying out the desktop or notebook examples: https://github.com/kushalkolar/fastplotlib/tree/main/examples

If someone wants to integrate pyodide with pygfx we would be able to have live interactive examples! :smiley:

Questions, issues, ideas? Post an issue or post on the discussion forum!

Installation

Install using pip.

Minimal, use with your own Qt or glfw applications

pip install fastplotlib

This does not give you Qt or glfw, you will have to install one of them yourself depending on your preference.

Notebook

pip install "fastplotlib[notebook]"

Optional: install simplejpeg for much faster notebook visualization, you will need C compilers and libjpeg-turbo to install it:

pip install simplejpeg

Note

fastplotlib and pygfx are fast evolving projects, the version available through pip might be outdated, you will need to follow the "For developers" instructions below if you want the latest features. You can find the release history on pypi here: https://pypi.org/project/fastplotlib/#history

For developers

git clone https://github.com/kushalkolar/fastplotlib.git
cd fastplotlib

# install all extras in place
pip install -e ".[notebook,docs,tests]"

Examples

Note

fastplotlib and pygfx are fast evolving, you may require the latest pygfx and fastplotlib from github to use the examples in the main branch.

Note that fastplotlib code is basically identical between desktop and notebook usage. The differences are:

• Running in Qt or glfw require a fastplotlib.run() call (which is really just a wgpu run() call)
• Notebooks plots have ipywidget-based toolbars and widgets 😄

Desktop examples using glfw or Qt

GLFW examples are here. GLFW is a "minimal" desktop framework.

https://github.com/fastplotlib/fastplotlib/tree/main/examples/desktop

Qt examples are here:

https://github.com/fastplotlib/fastplotlib/tree/main/examples/qt

Some of the examples require imageio:

pip install imageio

Notebook examples

Notebook examples are here:

https://github.com/fastplotlib/fastplotlib/tree/main/examples/notebooks

Start with simple.ipynb.

Some of the examples require imageio:

pip install imageio

Video

You can watch our SciPy 2023 talk if you prefer watching demos: https://github.com/fastplotlib/fastplotlib#scipy-talk

Graphics drivers

You will need a relatively modern GPU (newer integrated GPUs in CPUs are usually fine). Generally if your GPU is from 2017 or later it should be fine.

For more information see: https://wgpu-py.readthedocs.io/en/stable/start.html#platform-requirements

Windows:

Vulkan drivers should be installed by default on Windows 11, but you will need to install your GPU manufacturer's driver package (Nvidia or AMD). If you have an integrated GPU within your CPU, you might still need to install a driver package too, check your CPU manufacturer's info.

We also recommend installing C compilers so that you can install simplejpeg which improves remote frame buffer performance in notebooks.

Linux:

Debian based distros:

sudo apt install mesa-vulkan-drivers
# for better performance with the remote frame buffer install libjpeg-turbo
sudo apt install libjpeg-turbo

For other distros install the appropriate vulkan driver package, and optionally the corresponding libjpeg-turbo package for better remote-frame-buffer performance in jupyter notebooks.

CPU/software rendering (Lavapipe)

If you do not have a GPU you can perform limited software rendering using lavapipe. This should get you everything you need for that on Debian or Ubuntu based distros:

sudo apt install llvm-dev libturbojpeg* libgl1-mesa-dev libgl1-mesa-glx libglapi-mesa libglx-mesa0 mesa-common-dev mesa-vulkan-drivers

Mac OSX:

WGPU uses Metal instead of Vulkan on Mac. You will need at least Mac OSX 10.13. The OS should come with Metal pre-installed so you should be good to go!

:heart: Contributing

We welcome contributions! See the contributing guide: https://github.com/kushalkolar/fastplotlib/blob/main/CONTRIBUTING.md

You can also take a look at our Roadmap for 2024 and Issues for ideas on how to contribute!