yt.lagos.HOPHaloFinder

class yt.lagos.HOPHaloFinder(pf, threshold=160, dm_only=True, padding=0.02)

HOP halo finder.

Halos are built by: 1. Calculating a density for each particle based on a smoothing kernel. 2. Recursively linking particles to other particles from lower density particles to higher. 3. Geometrically proximate chains are identified and 4. merged into final halos following merging rules.

Lower thresholds generally produce more halos, and the largest halos become larger. Also, halos become more filamentary and over-connected.

Eisenstein and Hut. “HOP: A New Group-Finding Algorithm for N-Body Simulations.” ApJ (1998) vol. 498 pp. 137-142

Parameters :

pf : EnzoStaticOutput object

threshold : float

The density threshold used when building halos. Default = 160.0.

dm_only : bool

If True, only dark matter particles are used when building halos. Default = False.

padding : float

When run in parallel, the finder needs to surround each subvolume with duplicated particles for halo finidng to work. This number must be no smaller than the radius of the largest halo in the box in code units. Default = 0.02.

Examples :

——- :

>>> pf = load(“RedshiftOutput0000”) :

>>> halos = HaloFinder(pf) :

Methods

nearest_neighbors_2D(haloID[, ...]) For a halo its nearest neighbors in 2D using the kd tree.
nearest_neighbors_3D(haloID[, ...]) For a halo its nearest neighbors in 3D using the kd tree.
write_out(filename) Write out standard halo information to a text file.
write_particle_lists(prefix) Write out the particle data for halos to HDF5 files.
write_particle_lists_txt(prefix) Write out the names of the HDF5 files containing halo particle data

Previous topic

yt.lagos.FOFHaloFinder.write_particle_lists_txt

Next topic

yt.lagos.HOPHaloFinder.nearest_neighbors_2D

This Page