hfof

Friends of Friends group finding via spatial hashing

hfof is an open-source friends-of-friends (FoF) group finder in 3d and 2d (periodic and non-periodic), based on the following paper: arxiv:1805.04911.

Once you have downloaded hfof you will probably want to do the following:

Install

Install (also builds the C-functions)

python setup.py install [--prefix=/myhome/my-site-packages]

and run the tests,

python setup.py test

Examples

These probably need at least python 2.7 and a (non-ancient) numpy and scipy.

Labal an example set of particles with by their connectivity

In a python environment try

from hfof import fof, example_data
import numpy as np

pos = example_data.get_pos() # (32768,3) positions in [0,1]
pos[:,2] *= 0.0 # project (squash) z-dimension
r_cut = 0.004 # linking length

fof_labels = fof(pos, r_cut, boxsize=1.0) # integer labels from 0...

# color by number of particles in group
clrs = np.bincount(fof_labels)[fof_labels]
import pylab as pl
pl.scatter(pos[:,0],pos[:,1], c=np.power(clrs,0.3), s=1.0, edgecolors='none')
pl.show()

All of these are equivalent to running

python examples/example1.py

If you only want to do clustering on 2-d data I have now added the function

# pos an (N,2) array of positions
fof_labels = fof2d(pos, r_cut, boxsize=None) # integer labels from 0...

Notes on the 2d implementation in version 0.2

The 2d FoF (function fof2d) is not described in the paper mentioned above, although it broadly follows the same methodology as the 3d. The 2d domain is split in blocks which are further decomposed into 8x8 (=64) cells. The blocks are entered into a hash-table, and as each point is added to a cell, we test for connection against the neighbouring cells. In 2d this involves checking 10 neighbouring cells (excluding itself, and all those with larger indices), which can be found in (up to) 4 blocks (including itself).

The code to pre-compute the neighbour masks is given in hfof\ngb_vals.py and the actual computation is done in src\fof64_2d.c.