I need to evenly distribute clumped 3D data. 2D solutions would be terrific. Up to many millions of data points.
I am looking for the best method to evenly distribute [ie fully populate a correctly sized grid] clumped 3D or 2D data.
Sorting in numerous directions numerous times, with a shake to separate clumps a little now & again, is the method currently used. It is known that it is far from optimum. In general sorting is no good because it spreads/flattens clumps of points across a single surface.
Triangulation would seemingly be best [de-warp back to a regular grid] however I could never get the proper hull and had other problems.
Pressure equalization type methods seem over the top.
Can anybody point me in the direction of information on this?
Thanks for your time.
Currently used [inadequate] code 1 - allocates indexes for sorting in various directions [side to side, then on diagonals], 2 - performs the sorts independently; 3 - allocates 2D locations from the sorts; 4 - averages the locations obtained from the different sorts; 5 - shakes [attempted side to side & up/down movement of whole dataset leaving duplicates static] to declump; 6 - repeat as required up to 11 times.
I presume the "best" result would be the minimum total movement from original locations to final grided locations.
