Hey! This sounds like a really interesting use case. If you run into any issues or need help with the visualization, please don't hesitate to post an issue on the repo. We can also think about adding an example demo of a manhattan plot to help too!
I think one nice thing that we have tried to do is limit super heavy dependencies and also separate optional dependencies to streamline things.
The quickest install would be `pip install fastplotlib`. This would be if you were interested in just having the barebones (no imgui or notebook) for desktop viz using something like glfw.
We can think about adding in our docs some kind of import time metrics.
We are definitely looking forward to adding more 3D graphics in the future, and this sounds really cool. Would you mind posting an issue on the repo? I think this is something we would want to have on the roadmap or at least an open issue to plan out how we could do this. Thanks!
Yes! The number of data points can range in the millions. Quite honestly, the quality of your GPU would be the limiting factor here. I will say, however, that for most use cases, an integrated GPU is sufficient. For reference, we have plotted upwards of 3 million points on a mid-range integrated GPU from 2017.
I will work on adding somewhere in our docs some metrics for this kind of thing (I think it could be helpful for many).
Fastplotlib is a next-gen plotting library that utilizes Vulkan, DX12, or Metal via WGPU, so it is very fast! We built this library for rapid prototyping and large-scale exploratory scientific visualization. This makes fastplotlib a great library for designing and developing machine learning models, especially in the realm of computer vision. Fastplotlib works in jupyterlab, Qt, and glfw, and also has optional imgui integration.
If you have any questions or would like to chat, feel free to reach out to us by posting a GitHub Issue or Discussion! We love engaging with our community!
