2.11 Get transform of objects with optransform.2.10 Solver and wrangle for branching structures.2.9 Solver sop and wrangles for simulation.2.7 Access group names procedurally in vex.2.6 CamelCase to snake_case with regex and re_replace.2.1.1 That's hard to read, summarise please.

1.16 Blurring attributes with vex and point clouds.1.15 Get values from other points, other geo.1.13 Wrangles vs hscript vs vops vs everything else.1.12.6 Rotate prims around an edge, alternate version.1.12.2 Rotate geometry with sin and cos.1.12.1 Rotate a single point with sin and cos.1.9 Example: Random delete points by threshold.1.3 Implicit vs explicit attribute type.So if we have point_many, prim_many functions in addition to min(vector array), max(vector array), avg(vector array), etc it would be extremely useful. From what SESI told me the point() vex function is written expecting to be able to amortize setup time across many procs, and a function like point_many that let you fetch a batch of points at once, returning the result in a VEX array would make it a lot faster. This is actually a case even Python have comparable performance to VEX. VEX functions like min(string attr)/max(string attr)/avg(string attr) would be helpful. I think avg() on a big array of x, y, z's might be a way faster, but costs creating such an array doesn't make any sense. Looks like llvm/vex doesn't do its best here. Pure speculation here, but this is probably due to point()/import() functions which force atomic access to the attribute's array and per element compute (bye bye simd), unlike HDK which has these days very cache/simd friendly batch access to attributes. SYmek I would say this code has a good chance to have comparable performance with C++ node, but apparently not (it's ~x3.5 slower from AttributePromote in average mode, which is not bad, as it's still bellow a second for 5mil points, but still…). I have made my own l1 median it is important to remember that ICE = SOPS and VOPs, not just VOPS Hopes this helps! Centroid( Check out the setAttribute VOP For weighted averages or thongs of the sort, point cloud VOPs are awesome and fast For other things, attribPromote and python operators (ie python snippets) are very easy I guess the reason there is not a single node is that you would do each statistic operation in one way or another for speed and versatility but remember you can always make your own statistics digital asset for yourself (or share it in orbolt store).

You can also extend quite a bit with python nodes which even allow you to use snippets of inline cpp code (see inlinecpp module) numpy us also available with hou For average of all points, you can also add a vector detail attribute and set each axis to this expression: Centroid(“./youroperator”, D_X) Centroid(“./youroperator”, D_Y) Centroid(“./youroperator”, D_Z) For things like sum, attribVOP is the way to go (since you can simd that). Its a very important advantage as it allows for processing of massive data (ie scan data) in a fraction of time Fir doing operations on all points against each other (ie statistics), I feel in most cases regular operators (sops, pops, etc ) are better. Yes, but it is that way foe an important reason, the vex/vop contexts run in a highly multithreaded single instruction multiple data (simd) archittexture, which is why is so fast.

Houdini vex to find multiples of 4