Wait, you lost me here.
I mean, once I have the flattened points in the dataset, to addcolumn out the specific bits of information I want. So rather than taking the first four, taking the first two, then wherever the next two happen to fall (I’ll have to do “indices math” to figure that out).
Are you saying that’s not possible and/or expensive?
Or are you saying something else?
Or more specifically, are you taking the first four columns in the ‘loudness’ dataset because you think those are four useful dimensions to take, or because they are the first four?
Part of this is to show potential use cases, and where they may be problematic given the interface/structure. From my testing a while back, using fluid.datasetquery~ on anything over a few points was already >10ms, and for the more “real-world” version test example I built, it was >30ms.
So I want to be able to build exactly I want to do, potentially with several steps of fluid.datasetquery~, along with fit-ing a fluid.kdtree~ at the bottom, to see what the actual cost is for real-time use.