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:
Input file:
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 [1]: %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?)