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Looks very interesting for interactive visualization. I like the animation interface. Also love imgui, glad to see it here. I wish I had better plotting tools for publication quality images (though, honestly I'm pretty happy with matplotlib).
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Fastplotlib definitely works in Jupyterlab through jupyter-rfb https://github.com/vispy/jupyter_rfb I believe the performance is pretty decent, especially if you run the kernel locally Their docs also cover this as mentioned by @clewis7 below: https://www.fastplotlib.org/ver/dev/user_guide/faq.html#what...
Thanks Ivo! Just to add on, colab is weird and not performant, this PR outlines our attempts to get jupyter-rfb working on colab: https://github.com/vispy/jupyter_rfb/pull/77
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I've been looking into this issue with Datoviz [1] following a user request. It turns out there may be a way to achieve it using Vulkan [2] (which Datoviz is based on) and CuPy's UnownedMemory [3]. I wrote a simple proof of concept using only Vulkan and CuPy. I'm now working on a way for users to wrap a Datoviz GPU buffer as a CuPy array that directly references the Datoviz-managed GPU memory. This should, in princip…
Wow. So are you saying that you can have some array on the GPU that you setup with python via CuPy, then you call to the webbrowser and give it the pointer address for that GPU array, and the browser through WASM/WebGPU can access that same array? That sounds like a huge browser security hole.
I previously tried this on matplotlib and it took 20-30 minutes to make a single rendering because matplotlib only uses a single core on a cpu and doesn’t support gpu acceleration. I also tried Man im, but I couldn’t get an actual video file, and opengl seems to be a bit complicated to work with (I went and worked on other things though I should ask around about the video file output). Anyway, I’m excited about the prospect of a gpu accelerated dataviz tool that utilizes Vulkan, and I hope this library can cover my usecase.
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Writing and editting code is a lot more flexible, but it gets repetitive, and I have written the same stuff so many times. It's all adhoc, and it fixes the problem at the time, then it gets thrown away with the notebook only to be written again soon. As an example, I frequently want to run analytics on a dataframe. More complex summary stats. So you write a couple of functions, and have two for loops, iterating over…
I work with R and not python, so some things might not apply, but this: > [...] it fixes the problem at the time, then it gets thrown away with the notebook only to be written again soon. Is one of the reasons I stopped using notebooks. One solution to your problem might be to create a simple executable script that, when called on the file of your dataset in a shell, would produce the visualisation you need. If it's…
For larger datasets, real scripts are a better idea. I expect my stuff to work with datasets up to about 1Gb, caching is easy to layer on and would speed up work for larger datsets, but my code assumes the data fits in memory. It would be easier to add caching, the make sure I don't load an entire dataset into memory. (I don't serialize the entire dataframe to the browser though).
Haven't had the time to get very far yet, but will gladly contribute an example once I figure something out. Some of the ideas I want to eventually get to is to render shadertoys(interactively?) into a fpl subplot (haven't looked at the code at all, but might be doable), eventually run those interactively in the browser and do the network layout on the GPU with compute shaders (out of scope for fpl).
I’m not making neuroscience visualizations. I’m working with rather line graphs and would like to animate based on ~10000 points. I’m looking to convert these visuals to video for youtube, in hd and at 60fps using the HEVC/h.265 codec. I took a quick look at the documentation to see if this is possible and I didn’t see anything. Are or will this sort of rendering be supported? I previously tried this on matplotlib an…
I have watched recordings of your recent representation and decided to finally give it a try last week. My goal is to create some interactive network visualizations - like letting you click/box select nodes and edges to highlight subgraphs which sounds possible with the callbacks and selectors. Haven't had the time to get very far yet, but will gladly contribute an example once I figure something out. Some of the ide…
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Wow. So are you saying that you can have some array on the GPU that you setup with python via CuPy, then you call to the webbrowser and give it the pointer address for that GPU array, and the browser through WASM/WebGPU can access that same array? That sounds like a huge browser security hole.
Yea the security issue is why I'm pretty sure you can't do it on WGPU, but Vulkan and cupy can fully run locally so it doesn't have the same security concern.