I am trying to compare a neighbour list search to other implementations, and
scipy has one. I am benchmarking it but it seem to be so slow that I don’t really trust what I’m doing. I am just doing this:
import numpy as np from scipy.spatial import KDTree def pp(points) : kd_tree = KDTree(points) pairs = kd_tree.query_pairs(r=0.05) return pairs points = np.random.random((10000,3)) #%timeit pp(points)
then I run:
% ipython3 -i nn.py In : %timeit pp(points) 2.78 s ± 134 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
It that correct?
Any tips on how to get the neighbour list faster (by tunning parameters of this package or using something else?)