Shameless plug: I'm actively working on a similar project, Datoviz [1], a C/C++ library with thin Python bindings (ctypes). It supports both 2D and 3D but is currently less mature and feature-complete than fastplotlib. It is also lower level (high-level capabilities will soon be provided by VisPy 2.0 which will be built on top of Datoviz, among other possible backends).
My focus is primarily on raw performance, visual quality, and scalability for large datasets—millions, tens of millions of points, or even more.
"Fast" is a bold claim, given the complete lack of benchmarks and the fact that it's written entirely in Python...
I'm certain the host heavy lifting is done by numpy which is a python wrapper around Fortran and C. The visualization heavy lifting is done by pygfx/wgpu-py. wgpu-py has C. I think wgpu-py compiles to WASM to run in the browser. More and more packages are taking this route.
Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
I have to agree with your point about EDA. The library is neat, but even the example of covariance matrix animation is a bit contrived. Every pixel has a covariance with every other pixel, so sliding though the rows of the covariance matrix generates as many faces on the right as there are pixels in a photograph of a face. However the pixels that strongly co-vary will produce very similar right side "face" pictures.…
Aren't you missing the entire point of exploratory data analysis? Eigenfaces are an example of what you can come up with as the end product of your data exploration, after you've tried many ways of looking at the data and determined that eigenfaces are useful.
Your whole third paragraph seems to be criticizing the core purpose of exploratory data analysis as though one should always be able to skip directly to the next phase of having a standardized representation. When entering a new problem domain, somebody needs to actually look at the data in a somewhat raw form. Using the strengths of the human vision system to get a rough idea of what the typical data looks like and the frequency and character of outliers isn't dumping the job of exploratory data analysis onto the reader, it's how the job actually gets done in the first place.
Shameless plug: I'm actively working on a similar project, Datoviz [1], a C/C++ library with thin Python bindings (ctypes). It supports both 2D and 3D but is currently less mature and feature-complete than fastplotlib. It is also lower level (high-level capabilities will soon be provided by VisPy 2.0 which will be built on top of Datoviz, among other possible backends). My focus is primarily on raw performance, visua…
Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
My hot take is that 3D plotting feels bad because 3D plots are bad. You can usually find some alternative way of representing the data
Very cool effort. That said, and it's probably because of the kind of work that I do, but I have almost never found the four challenges to be any kind of a problem for me. Although I do think there is some kind of contradiction there. Plotting (exploratory data analyis ("EDA"), really) is all about distilling key insights and finding features hidden in data. But you have to some kind of intuition about where the need…
I agree on the refinement of matplotlib, we all need it to be better at resource handling, lower memory use, it often get boggy quickly.
Shameless plug: I'm actively working on a similar project, Datoviz [1], a C/C++ library with thin Python bindings (ctypes). It supports both 2D and 3D but is currently less mature and feature-complete than fastplotlib. It is also lower level (high-level capabilities will soon be provided by VisPy 2.0 which will be built on top of Datoviz, among other possible backends). My focus is primarily on raw performance, visua…
I have always admired your datoviz library from afar and check the vispy2/vispy2-sandbox libraries on GitHub every few months to check up on it. When do you think 'soon' is?? Really looking forward to it!
I have to agree with your point about EDA. The library is neat, but even the example of covariance matrix animation is a bit contrived. Every pixel has a covariance with every other pixel, so sliding though the rows of the covariance matrix generates as many faces on the right as there are pixels in a photograph of a face. However the pixels that strongly co-vary will produce very similar right side "face" pictures.…
Aren't you missing the entire point of exploratory data analysis? Eigenfaces are an example of what you can come up with as the end product of your data exploration, after you've tried many ways of looking at the data and determined that eigenfaces are useful. Your whole third paragraph seems to be criticizing the core purpose of exploratory data analysis as though one should always be able to skip directly to the ne…
Eigendecomposition of the covariance matrix, essentially PCA, is probably the first non-trivial step in the analysis of any dataset. The idea in the comment above seems to be that it's more useful to combine some basic knowledge of statistics with simpler visualisation techniques, rather than to quickly generate thousands of shallower plots. Being able to generate thousands of plot is useful, of course, but I would agree that promoting good data-analysis culture is more beneficial.