def generate_tracks(mt, aexp_list, z0_clusters):
"""tracks most massive progenitors for reach halo"""
mt.add_column("is_main_line", "BOOLEAN", table="mergertree", default=0)
properties = [
"x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"
]
z0_halos = mt.get_halo_properties(z0_clusters, properties, aexp_list[0])
for cluster in z0_clusters:
print "Finding progenitor line for z0 id: " + str(cluster)
tree = mt.get_full_tree(cluster)
parent = z0_halos[z0_halos["id"] == cluster].squeeze()
progenitor_line = []
for i, aexp in enumerate(aexp_list[1:]):
parent_id = parent["id"]
progenitors = tree[(tree['aexp'] == aexp)
& (tree['parent_id'] == parent_id)]
#this can be pandas-ized
t1 = WMAP5.age(1. / aexp_list[i - 1] - 1.)
t2 = WMAP5.age(1. / aexp_list[i] - 1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#apply smaller searchbox to exclude far off merger candidates
searchbox = ut.define_searchbox(parent,
dt,
mt.boxsize,
search_distance=1.5)
#identify most massive progenitor
if not progenitors.empty:
main_progenitor = identify_most_massive_progenitor(
progenitors, searchbox=searchbox)
if main_progenitor is None:
break
mt.mark_main_line(main_progenitor['aexp'],
main_progenitor['id'],
parent_id=main_progenitor['parent_id'])
parent = main_progenitor
progenitor_line.append(parent["id"])
else:
break
print "end of tree for cluster " + str(cluster)
print progenitor_line
mt.commit()
def generate_tracks(mt, aexp_list, z0_clusters) :
"""tracks most massive progenitors for reach halo"""
mt.add_column("is_main_line", "BOOLEAN", table="mergertree", default=0)
properties = ["x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"]
z0_halos = mt.get_halo_properties(z0_clusters, properties, aexp_list[0])
for cluster in z0_clusters:
print "Finding progenitor line for z0 id: "+str(cluster)
tree = mt.get_full_tree(cluster)
parent = z0_halos[z0_halos["id"] == cluster].squeeze()
progenitor_line = []
for i,aexp in enumerate(aexp_list[1:]):
parent_id = parent["id"]
progenitors = tree[(tree['aexp'] == aexp) &
(tree['parent_id'] == parent_id)]
#this can be pandas-ized
t1 = WMAP5.age(1./aexp_list[i-1]-1.)
t2 = WMAP5.age(1./aexp_list[i]-1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#apply smaller searchbox to exclude far off merger candidates
searchbox = ut.define_searchbox(parent, dt, mt.boxsize,
search_distance=1.5)
#identify most massive progenitor
if not progenitors.empty :
main_progenitor = identify_most_massive_progenitor(progenitors,
searchbox=searchbox)
if main_progenitor is None:
break
mt.mark_main_line(main_progenitor['aexp'], main_progenitor['id'],
parent_id = main_progenitor['parent_id'])
parent = main_progenitor
progenitor_line.append(parent["id"])
else:
break
print "end of tree for cluster "+str(cluster)
print progenitor_line
mt.commit()
def load_tmerger_from_file(sim, dir, aexp=1.0):
last_merger = sim.load_last_merger_epochs(dir,aexp)
tmerger = {}
for id in last_merger.keys():
merger_z = (1./float(last_merger[id])-1)
merger_time = WMAP5.age(merger_z)
cosmic_age = WMAP5.age(1./float(aexp)-1)
# merger_time = cosmocalc(merger_z)["zage_Gyr"]
# cosmic_age = cosmocalc(1./float(aexp)-1)["zage_Gyr"]
tmerger[id] = cosmic_age - merger_time
return tmerger
def load_tmerger_from_file(sim, dir, aexp=1.0):
last_merger = sim.load_last_merger_epochs(dir,aexp)
tmerger = {}
for id in last_merger.keys():
merger_z = (1./float(last_merger[id])-1)
merger_time = WMAP5.age(merger_z)
cosmic_age = WMAP5.age(1./float(aexp)-1)
# merger_time = cosmocalc(merger_z)["zage_Gyr"]
# cosmic_age = cosmocalc(1./float(aexp)-1)["zage_Gyr"]
tmerger[id] = cosmic_age - merger_time
return tmerger
def calc_age_from_aexp(aexp): return WMAP5.age(1./float(aexp)-1).value
if len(expected_matches) > 0:
matches = mt.get_halo_properties(expected_matches, properties, child_aexp)
for match in matches:
print match
print "x distance: ", halo["x"] - match["x"]
print "y distance: ", halo["y"] - match["y"]
print "z distance: ", halo["z"] - match["z"]
shared_particles = list( set(map(itemgetter(0),particles_parent[phalo]))
& set(map(itemgetter(0),particles_child[match["id"]])) )
print "shared particles: ", len(shared_particles )
print "mass ratio: ", float(match["M_hc"])/float(halos["M_hc"])
# print "Checking cluster,", parent_aexp, phalo
#determine time difference between the two epochs in seconds
t1 = WMAP5.age(1./parent_aexp-1.).value
t2 = WMAP5.age(1./child_aexp-1.).value
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#search cube determined by parent halo velocity + parent halo radius
pos = np.array([halo["x"], halo["y"], halo["z"]])
vel = np.array([halo["vx"], halo["vy"], halo["vz"]])
searchbox = mt.define_searchbox(pos, vel, dt, halo["r_hc"], search_distance = search_distance_multiplier)
print searchbox
chalos = mt.get_halos_within_distance(child_aexp,searchbox, [],
masscut = 0.01*halo["M_hc"])
print sorted(chalos)
#loop through satellites within search box
prog_found = False
def generate_links(mt, aexp_list, z0_clusters, particles_dir) :
"""Generates links from halo particle data"""
if restart:
phalos, last_aexp = mt.restart_mergertree()
print "restarting merger tree code from %0.4f" % last_aexp
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, last_aexp)
else:
phalos = z0_clusters
z0id_0 = dict((phalo, phalo) for phalo in phalos)
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, aexp_list[0])
particles_parent = cart_io.read_halo_particles(particles_file, clusters=phalos)
for i0 in range(len(aexp_list)-1):
i1 = i0 + 1
parent_aexp = aexp_list[i0]
child_aexp = aexp_list[i1]
if restart and child_aexp >= last_aexp:
continue
t1 = WMAP5.age(1./parent_aexp-1.)
t2 = WMAP5.age(1./child_aexp-1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
print "Finding matches for %0.4f, %0.4f" % (parent_aexp, child_aexp)
print str(len(phalos))+" halos identified for matching"
#get halo properties from database
properties = ["x","y","z", "vx","vy","vz", "M_hc", "r_hc", "num_particles"]
halos = mt.get_halo_properties(phalos, properties, parent_aexp)
#load child_particles
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir, child_aexp)
particles_child = cart_io.read_halo_particles(particles_file, min_np = 0)
links_data = []
z0id_1 = {}
#loop through parent halos
for phalo_id in phalos:
phalo = halos[halos["id"] == phalo_id].squeeze()
logging.debug( "Checking cluster,", parent_aexp, phalo )
searchbox = ut.define_searchbox(phalo, dt, mt.boxsize,
search_distance=search_distance_multiplier)
chalos = mt.get_halos_within_distance(child_aexp, searchbox, ["x","y","z"],
mass_cut = 0.01*phalo["M_hc"])
prog_found = False
if not chalos.empty:
z0id = z0id_0[phalo_id]
this_parent_particles_set = set(map(itemgetter(0),particles_parent[phalo_id]))
min_joint_particles = min_joint_particles_factor * len(this_parent_particles_set)
#loop through satellites within search box
for i, chalo in chalos.iterrows():
chalo_id = chalo["id"]
#consider redoing with np.intersect1d(parent, child)
shared_particles = this_parent_particles_set.intersection(map(itemgetter(0),particles_child[chalo_id]))
if len(shared_particles) > min_joint_particles:
ratio = float(len(particles_child[chalo_id]))/float(len(particles_parent[phalo_id]))
logging.debug( "Possible progenitor found: ", chalo_id, len(shared_particles), ratio)
distance = ut.distance_between_halos(chalo, phalo, mt.boxsize)
links_data.append((parent_aexp, child_aexp, phalo_id, chalo_id, len(shared_particles),
ratio, distance, z0id, 0 ))
z0id_1[chalo_id] = z0id
prog_found = True
if not prog_found:
logging.debug("No matches found for halo "+str(phalo_id)+" at aexp = "+str(child_aexp))
mt.mark_leaf(parent_aexp, phalo_id)
mt.insert("mergertree", links_data)
mt.commit()
z0id_0 = z0id_1
phalos = z0id_0.keys()
particles_parent = particles_child
def generate_links(mt, aexp_list, z0_clusters, particles_dir):
"""Generates links from halo particle data"""
if restart:
phalos, last_aexp = mt.restart_mergertree()
print "restarting merger tree code from %0.4f" % last_aexp
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
last_aexp)
else:
phalos = z0_clusters
z0id_0 = dict((phalo, phalo) for phalo in phalos)
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
aexp_list[0])
particles_parent = cart_io.read_halo_particles(particles_file,
clusters=phalos)
for i0 in range(len(aexp_list) - 1):
i1 = i0 + 1
parent_aexp = aexp_list[i0]
child_aexp = aexp_list[i1]
if restart and child_aexp >= last_aexp:
continue
t1 = WMAP5.age(1. / parent_aexp - 1.)
t2 = WMAP5.age(1. / child_aexp - 1.)
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
print "Finding matches for %0.4f, %0.4f" % (parent_aexp, child_aexp)
print str(len(phalos)) + " halos identified for matching"
#get halo properties from database
properties = [
"x", "y", "z", "vx", "vy", "vz", "M_hc", "r_hc", "num_particles"
]
halos = mt.get_halo_properties(phalos, properties, parent_aexp)
#load child_particles
particles_file = "%s/halo_particles_a%0.4f.dat" % (particles_dir,
child_aexp)
particles_child = cart_io.read_halo_particles(particles_file, min_np=0)
links_data = []
z0id_1 = {}
#loop through parent halos
for phalo_id in phalos:
phalo = halos[halos["id"] == phalo_id].squeeze()
logging.debug("Checking cluster,", parent_aexp, phalo)
searchbox = ut.define_searchbox(
phalo,
dt,
mt.boxsize,
search_distance=search_distance_multiplier)
chalos = mt.get_halos_within_distance(child_aexp,
searchbox, ["x", "y", "z"],
mass_cut=0.01 *
phalo["M_hc"])
prog_found = False
if not chalos.empty:
z0id = z0id_0[phalo_id]
this_parent_particles_set = set(
map(itemgetter(0), particles_parent[phalo_id]))
min_joint_particles = min_joint_particles_factor * len(
this_parent_particles_set)
#loop through satellites within search box
for i, chalo in chalos.iterrows():
chalo_id = chalo["id"]
#consider redoing with np.intersect1d(parent, child)
shared_particles = this_parent_particles_set.intersection(
map(itemgetter(0), particles_child[chalo_id]))
if len(shared_particles) > min_joint_particles:
ratio = float(len(particles_child[chalo_id])) / float(
len(particles_parent[phalo_id]))
logging.debug("Possible progenitor found: ", chalo_id,
len(shared_particles), ratio)
distance = ut.distance_between_halos(
chalo, phalo, mt.boxsize)
links_data.append(
(parent_aexp, child_aexp, phalo_id, chalo_id,
len(shared_particles), ratio, distance, z0id, 0))
z0id_1[chalo_id] = z0id
prog_found = True
if not prog_found:
logging.debug("No matches found for halo " + str(phalo_id) +
" at aexp = " + str(child_aexp))
mt.mark_leaf(parent_aexp, phalo_id)
mt.insert("mergertree", links_data)
mt.commit()
z0id_0 = z0id_1
phalos = z0id_0.keys()
particles_parent = particles_child
Create a custom cosmology object >>> from astropy.cosmology import FlatLambdaCDM >>> cosmo = FlatLambdaCDM(H0=70, Om0=0.3) >>> cosmo FlatLambdaCDM(H0=70, Om0=0.3, Ode0=0.7) Compute the comoving volume to z=6.5 in cubic Mpc using this cosmology >>> cosmo.comoving_volume(6.5) 2521696198211.6924 Compute the age of the universe in Gyr using the pre-defined WMAP 5-year and WMAP 9-year cosmologies >>> from astropy.cosmology import WMAP5, WMAP9 >>> WMAP5.age(0) 13.723782349795023 >>> WMAP9.age(0) 13.768899510689097 Create a cosmology with a varying `w' >>> from astropy.cosmology import Flatw0waCDM >>> cosmo = Flatw0waCDM(H0=70, Om0=0.3, w0=-1, wa=0.2) Find the separation in proper kpc at z=4 corresponding to 10 arcsec in this cosmology compared to a WMAP9 cosmology >>> cosmo.kpc_proper_per_arcmin(4) * 10 / 60. 68.87214405278925 >>> WMAP9.kpc_proper_per_arcmin(4) * 10 / 60. 71.21374615575363
print match
print "x distance: ", halo["x"] - match["x"]
print "y distance: ", halo["y"] - match["y"]
print "z distance: ", halo["z"] - match["z"]
shared_particles = list(
set(map(itemgetter(0), particles_parent[phalo]))
& set(map(itemgetter(0), particles_child[match["id"]]))
)
print "shared particles: ", len(shared_particles)
print "mass ratio: ", float(match["M_hc"]) / float(
halos["M_hc"])
# print "Checking cluster,", parent_aexp, phalo
#determine time difference between the two epochs in seconds
t1 = WMAP5.age(1. / parent_aexp - 1.).value
t2 = WMAP5.age(1. / child_aexp - 1.).value
dt = math.fabs(t1 - t2) * 1.0e9 * 3.15569e7 #convert to seconds
#search cube determined by parent halo velocity + parent halo radius
pos = np.array([halo["x"], halo["y"], halo["z"]])
vel = np.array([halo["vx"], halo["vy"], halo["vz"]])
searchbox = mt.define_searchbox(
pos,
vel,
dt,
halo["r_hc"],
search_distance=search_distance_multiplier)
print searchbox
chalos = mt.get_halos_within_distance(child_aexp,
def calc_age_from_aexp(aexp): return WMAP5.age(1./float(aexp)-1).value
