Installation | GPU Drivers | Documentation | Examples | Contributing
Next-gen plotting library built using the pygfx rendering engine that utilizes Vulkan, DX12, or Metal via WGPU, so it is very fast! fastplotlib is an expressive plotting library that enables rapid prototyping for large scale exploratory scientific visualization.
Note:
fastplotlibis currently in the late alpha stage, but you're welcome to use it or contribute! See our Roadmap. Also, see this for a discussion on API stability: fastplotlib#121
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GPU-accelerated visualization
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interactive visualization via an intuitive and expressive API
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rapid prototyping and algorithm design
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easy exploration and fast rendering of large-scale data
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design, develop, evaluate, and ship machine learning models
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create visualizations for real-time acquisition systems for scientific instruments (cameras, etc.)
fastplotlib can run on anything that pygfx can also run, this includes:
✔️ Jupyter lab, using jupyter_rfb
✔️ PyQt and PySide
✔️ glfw
✔️ wxPython
Write your code once and run it anywhere. Whether you are using Qt, glfw, jupyter lab, or doing offscreen rendering, fastplotlib works across all major platforms (Linux, Windows, Mac OS X) 😄 See the FAQ for more details on where and how you can use fastplotlib.
http://www.fastplotlib.org/ver/dev
Questions, issues, ideas? You are welcome to post an issue or post on the discussion forum! 😃
To install use pip:
# with imgui and jupyterlab
pip install -U "fastplotlib[notebook,imgui]"
# minimal install, install glfw, pyqt6 or pyside6 separately
pip install -U fastplotlib
# with imgui
pip install -U "fastplotlib[imgui]"
# to use in jupyterlab without imgui
pip install -U "fastplotlib[notebook]"We strongly recommend installing simplejpeg for use in notebooks, you must first install libjpeg-turbo
- If you use
conda, you can getlibjpeg-turbothrough conda. - If you are on linux, you can get it through your distro's package manager.
- For Windows and Mac compiled binaries are available on their release page: https://github.com/libjpeg-turbo/libjpeg-turbo/releases
Once you have 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,imgui]"
# install latest pygfx
pip install git+https://github.com/pygfx/pygfx.git@mainSee Contributing for more details on development
Examples gallery: http://fastplotlib.org/ver/dev/_gallery/index.html
User guide: http://fastplotlib.org/ver/dev/user_guide/guide.html
fastplotlib code is identical across notebook (jupyterlab), and desktop use with Qt/PySide or glfw.
Notebooks
The quickstart.ipynb tutorial notebook is a great way to get familiar with the API: https://github.com/fastplotlib/fastplotlib/tree/main/examples/notebooks/quickstart.ipynb
Generally if your GPU is from 2017 or later it should be fine. Modern integrated graphics are usually fine for many use cases. The exact requirements will depend on how complex your visualization is and how many objects you need to render.
More detailed information on GPUs and drivers is here: http://fastplotlib.org/ver/dev/user_guide/gpu.html
For more detailed information, such as use on cloud computing infrastructure, see the WGPU docs: https://wgpu-py.readthedocs.io/en/stable/start.html#cloud-compute
We welcome contributions! See the contributing guide: https://github.com/fastplotlib/fastplotlib/blob/main/CONTRIBUTING.md
You can also take a look at our Roadmap for 2025 and Issues for ideas on how to contribute!
A special thanks to all of the pygfx developers and the amazing work they have done.