Installation | GPU Drivers | Documentation | Examples | Contributing
Next-gen plotting library built using the pygfx rendering engine that can utilize Vulkan, DX12, or Metal via WGPU, so it is very fast! fastplotlib is an expressive plotting library that enables rapid prototyping for large scale explorative scientific visualization.
Note that the API is currently evolving quickly. We recommend using the latest notebooks from the repo but the general concepts are similar to those from the API shown in the video.
fastplotlib can run on anything that pygfx can also run, this includes:
✔️ Jupyter lab, using jupyter_rfb
✔️ PyQt and PySide
✔️ glfw
✔️ wxPython
Notes:
✔️ Non-blocking Qt/PySide output is supported in ipython and notebooks by using %gui qt. This must be called before importing fastplotlib!
:grey_exclamation: We do not officially support jupyter notebook through jupyter_rfb, this may change with notebook v7
😞 jupyter_rfb does not work in collab, see vispy/jupyter_rfb#77
Note
fastplotlibis currently in the late alpha stage, but you're welcome to try it out or contribute! See our Roadmap. See this for a discussion on API stability: #121
http://fastplotlib.readthedocs.io/
The examples are interactive if you run them locally on your computer. If someone wants to integrate pyodide with pygfx we would be able to have live interactive examples on the website!
Questions, issues, ideas? You are welcome to post an issue or post on the discussion forum! 😃
pip install fastplotlibThis does not give you PyQt/PySide or glfw, you will have to install your preferred GUI framework separately.
pip install "fastplotlib[notebook]"Strongly recommended: install simplejpeg for much faster notebook visualization, this requires you to first install libjpeg-turbo
pip install simplejpegNote
fastplotlibandpygfxare fast evolving projects, the version available through pip might be outdated, you will need to follow the "For developers" instructions below if you want the latest features.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 here: https://github.com/fastplotlib/fastplotlib/releases
Make sure you have git-lfs installed.
git clone https://github.com/fastplotlib/fastplotlib.git
cd fastplotlib
# install all extras in place
pip install -e ".[notebook,docs,tests]"
# install latest pygfx
pip install git+https://github.com/pygfx/pygfx.git@mainSe Contributing for more details on development
Examples gallery: https://fastplotlib.readthedocs.io/en/latest/_gallery/index.html
Note:
fastplotlibandpygfxare fast evolving, you will probably require the latestpygfxandfastplotlibfrom github to use the examples in the main branch.
fastplotlib code is identical across notebook (jupyter), and desktop use with Qt/PySide or glfw.
Even if you do not intend to use notebooks with fastplotlib, the quickstart.ipynb tutorial notebook is the best way to get familiar with the API: https://github.com/fastplotlib/fastplotlib/tree/main/examples/notebooks/quickstart.ipynb
The specifics for running fastplotlib in different GUI frameworks are:
- Running in
glfwrequires afastplotlib.run()call (which is really just awgpurun()call) - With
Qtyou can encapsulate it within aQApplication, seeexamples/qt - Notebooks plots have ipywidget-based toolbars and widgets. There are plans to move toward an identical in-canvas toolbar with UI elements across all supported frameworks 😄
See these for examples on embedding within a Qt app. Note that you can also use fastplotlib with qt interactively using %gui qt in jupyter or ipython.
https://github.com/fastplotlib/fastplotlib/tree/main/examples/qt
Notebook examples are here, these include examples on selector tools.
https://github.com/fastplotlib/fastplotlib/tree/main/examples/notebooks
Our SciPy 2023 talk walks through numerous demos: https://github.com/fastplotlib/fastplotlib#scipy-talk
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 detailed information, such as use on cloud computing infrastructure, see: https://wgpu-py.readthedocs.io/en/stable/start.html#platform-requirements
Some more information on GPUs is here: https://fastplotlib.readthedocs.io/en/latest/user_guide/gpu.html
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.
You will generally need a linux distro that is from ~2020 or newer (ex. Ubuntu 18.04 won't work), this is due to the glibc requirements of the wgpu-native binary.
Debian based distros:
sudo apt install mesa-vulkan-drivers
# for better performance with the remote frame buffer install libjpeg-turbo
sudo apt install libjpeg-turboFor other distros install the appropriate vulkan driver package, and optionally the corresponding libjpeg-turbo package for better remote-frame-buffer performance in jupyter notebooks.
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-driversWGPU 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!
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 2025 and Issues for ideas on how to contribute!