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Next-gen plotting library built using the [`pygfx`](https://github.com/pygfx/pygfx) rendering engine that can utilize[Vulkan](https://en.wikipedia.org/wiki/Vulkan), [DX12](https://en.wikipedia.org/wiki/DirectX#DirectX_12), or [Metal](https://developer.apple.com/metal/) via WGPU, so it is very fast! `fastplotlib` is an expressive plotting library that enables rapid prototyping for large scale exploratory scientific visualization.
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Next-gen plotting library built using the [`pygfx`](https://github.com/pygfx/pygfx) rendering engine that utilizes[Vulkan](https://en.wikipedia.org/wiki/Vulkan), [DX12](https://en.wikipedia.org/wiki/DirectX#DirectX_12), or [Metal](https://developer.apple.com/metal/) via WGPU, so it is very fast! `fastplotlib` is an expressive plotting library that enables rapid prototyping for large scale exploratory scientific visualization.
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> **Note**
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>
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> `fastplotlib` is currently in the **late alpha stage**, but you're welcome to use it or contribute! See our [Roadmap](https://github.com/kushalkolar/fastplotlib/issues/55). See this for a discussion on API stability: https://github.com/fastplotlib/fastplotlib/issues/121
> `fastplotlib` is currently in the **late alpha stage**, but you're welcome to use it or contribute! See our [Roadmap](https://github.com/kushalkolar/fastplotlib/issues/55). Also, see this for a discussion on API stability: https://github.com/fastplotlib/fastplotlib/issues/121
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# What are *some* things I can do with `fastplotlib`?
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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.)
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# Supported frameworks
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@@ -30,11 +47,7 @@ Next-gen plotting library built using the [`pygfx`](https://github.com/pygfx/pyg
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:heavy_check_mark:`glfw`\
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:heavy_check_mark:`wxPython`
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**Notes:**\
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:heavy_check_mark: Non-blocking interactive Qt/PySide output is supported in ipython and notebooks, see the [interactive shells section in the user guide](http://fastplotlib.org/ver/dev/user_guide/guide.html#using-fastplotlib-in-an-interactive-shell)\
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:grey_exclamation: We do not officially support `jupyter notebook` through `jupyter_rfb`, this may change with notebook v7\
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:grey_exclamation: We only support jupyterlab for use in notebooks. This means that we do not support VSCode notebooks or any other python notebook platform. Jupyterlab is the most reliable way to use widget-based libraries in notebooks.\
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:disappointed:[`jupyter_rfb`](https://github.com/vispy/jupyter_rfb) does not work in collab, see https://github.com/vispy/jupyter_rfb/pull/77
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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) :smile: See the [FAQ](https://www.fastplotlib.org/ver/dev/user_guide/faq.html) for more details on where and how you can use `fastplotlib`.
We strongly recommend installing ``simplejpeg`` for use in notebooks, you must first install [libjpeg-turbo](https://libjpeg-turbo.org/)
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- If you use ``conda``, you can get ``libjpeg-turbo`` through conda.
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- If you are on linux you can get it through your distro's package manager.
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- If you are on linux, you can get it through your distro's package manager.
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- For Windows and Mac compiled binaries are available on their release page: https://github.com/libjpeg-turbo/libjpeg-turbo/releases
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Once you have ``libjpeg-turbo``:
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pip install simplejpeg
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```
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> **Note**
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> **Note:**
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> `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 here: https://github.com/fastplotlib/fastplotlib/releases
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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
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##GPU drivers and requirements
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# GPU drivers and requirements
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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.
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More detailed information on GPUs and drivers is here: http://fastplotlib.org/ver/dev/user_guide/gpu.html
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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
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# :heart: Contributing
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# Contributing :heart:
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We welcome contributions! See the contributing guide: https://github.com/fastplotlib/fastplotlib/blob/main/CONTRIBUTING.md
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You can also take a look at our [**Roadmap for 2025**](https://github.com/fastplotlib/fastplotlib/issues/55) and [**Issues**](https://github.com/fastplotlib/fastplotlib/issues) for ideas on how to contribute!
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- `glfw`
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- `wxPython`
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Note: Use in Google Colab is not highly functional. We recommend using an inexpensive alternative cloud provider
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such as CodeOcean or Lambda Cloud. We have tested these and `fastplotlib` works very well.
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Notes:
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- `jupyter_rfb <https://github.com/vispy/jupyter_rfb>`_ does not work in Google Collab, see https://github.com/vispy/jupyter_rfb/pull/77
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- Non-blocking interactive Qt/PySide output is supported in ipython and notebooks, see the `interactive shells section in the user guide <http://fastplotlib.org/ver/dev/user_guide/guide.html#using-fastplotlib-in-an-interactive-shell>`_
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- We do not officially support `jupyter notebook` through `jupyter_rfb` and strongly recommend using `jupyter lab`. Jupyter Notebook v7 might work, but this has not been extensively tested.
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- We only support `jupyterlab` for use in notebooks. This means that we do not support VSCode notebooks or any other python notebook platform. Jupyterlab is the most reliable way to use widget-based libraries in notebooks.
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