Click to generate new distribution with random r between 15 and 60 px (min distance between points)
Extract from the paper linked below
The algorithm takes as input the extent of the sample domain in Rn, the minimum distance r between samples, and a constant k
Step 0. Initialize an n-dimensional background grid for storing samples and accelerating spatial searches. We pick the cell size to be bounded by r/√n, so that each grid cell will contain at most one sample, and thus the grid can be implemented as a simple ndimensional array of integers: the default−1 indicates no sample, a non-negative integer gives the index of the sample located in a cell.
Step 1. Select the initial sample, x0, randomly chosen uniformly from the domain. Insert it into the background grid, and initialize the “active list” (an array of sample indices) with this index (zero).
Step 2. While the active list is not empty, choose a random index from it (say i). Generate up to k points chosen uniformly from the spherical annulus between radius r and 2r around xi.
For each point in turn, check if it is within distance r of existing samples (using the background grid to only test nearby samples). If a point is adequately far from existing samples, emit it as the next sample and add it to the active list. If after k attempts no such point is found, instead remove i from the active list.