Analyzing Data

Analysis Modules

Two helpful libraries are our modules and analysis tools.

git clone # Python 2.7+
git clone # Python 2.7+

Add these to your PYTHONPATH environment variable, e.g. for .cshrc add:

setenv PYTHONPATH /path/to/modules:$PYTHONPATH
setenv PYTHONPATH /path/to/analysis:$PYTHONPATH

Lastly you will need to install asciitable, h5py and pandas.

These tools aren't critical, but they may make your life a lot easier.

Halo Catalogs

Once you have the modules ready, uou can load a ROCKSTAR catalogue for a given snapshot simply as:

rscat = htils.load_rscat(hpath,319,verbose=True) # snapshot = 319 (z = 0)

Once you do this however, you will have access to the following methods:

def __init__(self, dir, snap_num, version=2, sort_by='mvir', base='halos_', digits=2, AllParticles=False):
def get_particles_from_halo(self, haloID):
# @param haloID: id number of halo. Not its row position in matrix
# @return: a list of particle IDs in the Halo
def get_subhalos_from_halo(self,haloID):
#Retrieve subhalos only one level deep.
#Does not get sub-sub halos, etc.
def get_subhalos_from_halos(self,haloIDs):
#Returns an array of pandas data frames of subhalos. one data frame
#for each host halo. returns only first level of subhalos.
def get_subhalos_from_halos_flat(self,haloIDs):
#Returns a flattened pandas data frame of all subhalos within
#the hosts given by haloIDs. Returns only first level of subhalos.
def get_hosts(self):
# Get host halo frame only
def get_subs(self):
# Get subhalo frame only
def get_all_subs_recurse(self,haloID):
# Retrieve all subhalos: sub and sub-sub, etc.
# just need mask of all subhalos, then return data frame subset
def get_all_subhalos_from_halo(self,haloID):
# Retrieve all subhalos: sub and sub-sub, etc.
# return pandas data frame of subhalos
def get_all_sub_particles_from_halo(self,haloID):
#returns int array of particle IDs belonging to all substructure
#within host of haloID
def get_all_particles_from_halo(self,haloID):
#returns int array of all particles belonging to haloID
def get_all_num_particles_from_halo(self,haloID):
# Get the actual number of particles 'total_npart' from halo as opposed to 'npart'.
# mainly for versions less than 7
def get_block_from_halo(self, snapshot_dir, haloID, blockname, allparticles=True):
# quick load a block (hdf5 block) of particles belong to halo.
# e.g. you want particle positions for haloid = 10 (use blockname="pos")
# this works fastest on snapshots ordered by id and requires import readsnapHDF5_greg
def H(self):
#returns hubble parameter for rockstar run
def get_most_gravbound_particles_from_halo(self,snapshot_dir, haloID):
# Gets most bound particles just based on potential energy for specific halo ID
def get_most_bound_particles_from_halo(self, snapshot_dir, haloID):
# Gets most bound particles for halo based on pot. energy and kin. energy
# if potential block does not exist, it is calculate assuming a spherical halo
def getversion(self):
# returns the version of rockstar the run was done within
# this will include versions made by Alex Ji, Greg Dooley & Brendan Griffen

One workflow might look as follows:

import haloutils as htils
# load the first 24 halos
# (just change 14 to 11 for the lower resolution halos)
hpaths = htils.get_paper_paths_lx(14)
hpath = hpaths[0] # select first Caterpillar halo
# strip down the path to just the halo id
parentid = htils.get_parent_hid(hpath)
# get the central host id of the zoom-in halo
# parentid is named from the parent simulation
# the zooms have different ids
zoomid = htils.load_zoomid(hpath)
# Return the pandas data frame with all the halos,
# return mvir for example
lastsnap = htils.get_lastsnap(hpath)
halos = htils.load_rscat(hpath,lastsnap,verbose=True)
mvir_host = halos.ix[zoomid]['mvir'] # units of Msol/h
# get one specific parameter of the host at
# z = 0 (quick version of above)
mvir_host = htils.get_quant_zoom(hpath,'mvir') # units of Msol/h
# return all halo virial masses
all_mvir = halos['mvir'] # units of Msol/h
# get the merger tree of the host and all its subs
cat = htils.load_mtc(hpath,haloids=[zoomid])
# read in every halo's merger tree
all_trees = htils.load_mtc(hpath,indexbyrsid=True)
tree = all_trees[zoomid]
# if you feed more than one id to the above
# it would be cat[0],cat[1] etc.
# you can now access the main branch > try tree. then tab complete to see other functions
mainbranch = tree.getMainBranch()
# we also have a short hand version:
# mainbranch = htils.get_mainbranch(hpath)
# which is very fast and skips the reading of the entire progenitor tree if you don't need dit
# see further down this page for more options
# output the main branch virial mass
print mainbranch['mvir']

Here is an example of some of these in action:

# load required modules
import haloutils as htils
import numpy as np
# select Cat-1 halo
hpaths = htils.get_paper_paths_lx(14)[0]
# select the last snapshot (z = 0)
snapshot = htils.get_lastsnap(hpath)
# load rockstar id of the host halo
zoomid = htils.load_zoomid(hpath)
#Load Halo Catalogue
halos = htils.load_rscat(hpath,snapshot)
#Select host halos
hosts = halos.get_hosts()
#Select subhalos
subs = halos.get_subs()
# Get positions of subs and hosts
print hosts[['posX','posY','posZ']]
print subs[['posX','posY','posZ']]
#Get particle ids from halo of interest (here it is the host)
print halos.get_particles_from_halo(zoomid)
#Get virial radius of a specific halo id (in this case the host)
print halos.ix[zoomid]['rvir'] # units of kpc/h

Merger Trees

Similarly the merger tree catalogues (once loaded) have a number of its own functions.

def getMainBranch(self, row=0):
@param row: row of the halo you want the main branch for. Defaults to row 0
Uses getSubTree, then finds the smallest dfid that has no progenitors
@return: all halos that are in the main branch of the halo specified by row (in a np structured array)
def getMMP(self, row):
@param row: row number (int) of halo considered
@return: row number of the most massive parent, or None if no parent
def getNonMMPprogenitors(self,row):
return row index of all progenitors that are not the most massive
These are all the subhalos that were destroyed in the interval

These can be used in the following example:

# load required modules
import haloutils as htils
import numpy as np
# select the caterpillar halo of interest
# based on halo id and resolution level
hid = 1387186
lx = 14
hpath = htils.hid_hpath_lx(hid,lx)
# select the last snapshot (z = 0)
snapshot = htils.get_lastsnap(hpath)
# load rockstar id of the host halo
zoomid = htils.load_zoomid(hpath)
# Load every tree (VERY slow)
trees = htils.load_mtc(hpath)
# Just look at the first tree
# tree = cat[0]
# You can access all trees via: cat[0], cat[1] etc.
# If you want to load the tree for a particular ID from the rockstar catalogue
trees = htils.load_mtc(haloids=[zoomid]) # in this case the host (quite slow)
tree = cat[0] # tree will contain all progenitors, including subhalos
# Just say you want to index the merger tree by the z = 0 root rockstar id
# (i.e. the base of the tree). This is quite powerful because you might select
# halos of interest in the rockstar catalogue then want to know, just for those
# what their merger tree is (e.g. say you just want dwarf systems of a particular size)
trees = htils.load_mtc(haloids=[zoomid],indexbyrsid=True)
tree = trees[zoomid]
# You can just loop through rockstar ids (if you gave it more than one id above)
# and get out the accretion histories for a small sample of trees quite quickly
# to get the main branch
main_branch = tree.getMainBranch()
# print mass evolution
for mass,scale in zip(main_branch['mvir'],main_branch['scale']):
print "%3.2f: %3.2e" % (scale,mass)
# output
1.00: 2.86e+14
0.99: 2.89e+14
0.98: 2.90e+14
0.97: 2.90e+14
0.95: 2.88e+14
0.94: 2.86e+14
0.93: 2.83e+14
0.92: 2.80e+14
0.91: 2.76e+14
0.90: 2.73e+14
0.89: 2.64e+14
0.88: 2.47e+14 ...

A function to find the descendant branch of any halo in merger tree catalogue. You should use it as follows:

# get a tree of interest
mtc = haloutils.load_zoom_mtc(hpath)
host = mtc.Trees[0]
# make a dictionary that maps ids to rows
desc_map = host.get_desc_map()
# get the descendent branch.
desc_branch = host.getDescBranch(row, desc_map)
# similarly for main branches, you can use a dictionary that speeds up the
# getMainBranch call substantially.
mmp_map = host.get_mmp_map()
main_branch = host.getMainBranch(row, mmp_map)
# you can get the branches without making the map as below, but they will be much slower.
# only faster if you use ~ < 10 calls to the function.
desc_branch = host.getDescBranch(row)
main_branch = host.getMainBranch(row)

Particle Data

If you want the Gadget header:

# get the first halo in the catalogue
hpaths = htils.get_paper_paths_lx(14)[0]
# read its Gadget header
header = htils.get_halo_header(hpath)
# header contains the typical Gadget info
header.boxsize header.massarr header.omegaL
header.cooling header.metals header.redshift
header.double header.nall header.nall_highword header.stellar_age
header.filenum header.npart header.time
header.hubble header.omega0

Be sure to divide the relevant quantities (pos, rvir etc.) by header.hubble. See the Gadget section in the sidebar for more information on the header and block types available.

If you wanted to get the postions of all the particles for a specific halo (or any block).

pos = htils.load_partblock(hpath,zoomid,"POS ") # units Mpc/h
# "VEL ", "ID ", "MASS" etc. also work (notice the space)
# check readsnapshots/ for the other block names
# you can call in the caterpillar modules
print pos*1000. # kpc/h
# output
[ 32085.45117188 57312.79296875 44314.35546875]
[ 27002.18554688 10062.73242188 9899.70019531]
[ 26711.08789062 9560.22460938 10165.18847656]
[ 49757.3515625 21470.00195312 6461.90917969]

If you want to read in the entire block, use the following:

import haloutils as htils
hid = 1387186
lx = 14
hpath = htils.hid_hpath_lx(hid,lx)
pos = htils.load_partblock(hpath,319,"POS ")
mass = htils.load_partblock(hpath,319,"MASS")

Note that the mass block will have different values depending on how many layers of refinement there are for that zoom in simulation. If you use this code on a parent simulation it will be an array of length N all of the same value because there is only one particle type.

If you wanted just the ids for a selection of particle ids:

pos = htils.load_partblock(hpath,zoomid,"POS ",partids=[listofids]) # units Mpc/h