def subhalo_positions(groups_fname,
groups_number,
mass_interval,
min_mass,
max_mass,
velocities=False):
#read SUBFIND CDM subhalos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
Pos = halos.sub_pos / 1e3 #Mpc/h
Vel = halos.sub_vel #km/s ---> they are physical velocities!
if mass_interval:
a = halos.sub_mass > min_mass
b = halos.sub_mass < max_mass
c = a * b
halos_indexes = np.where(c == True)[0]
del a, b, c, halos
Pos = Pos[halos_indexes]
Vel = Vel[halos_indexes]
if velocities:
return [Pos, Vel]
else:
return Pos
def center_halo(halo): import numpy as np import readsubf # Initialize variables: base_id = halo.base_id base = halo.base snap_num = halo.snap_num sub_id = halo.sub_id save_dir = halo.save_dir interact = halo.interact type_thresh = halo.type_thresh scale_factor = halo.scale_factor all_pos = halo.all_pos all_vel = halo.all_vel all_mass = halo.all_mass all_pot = halo.all_pot all_T_gas = halo.all_T_gas all_rho_gas = halo.all_rho_gas #all_age = halo.all_age # Move to the frame where the most tightly bound particle is at rest, and at the origin: print "Re-centering..." cat = readsubf.subfind_catalog(base,snap_num,masstab=True) halo_cent_pos = cat.sub_pos[sub_id] halo_cent_vel = cat.sub_vel[sub_id] #rescale to physical coordinates: halo_cent_pos = halo_cent_pos * scale_factor halo_cent_vel = halo_cent_vel * np.sqrt(scale_factor) for j in np.arange(3): all_pos[j] = all_pos[j] - halo_cent_pos all_vel[j] = all_vel[j] - halo_cent_vel # Calculate radii for all particles: all_radii = [0]*3 for j in np.arange(3): all_radii[j] = np.zeros(len(all_pos[j])) all_radii[j] = np.float64(all_radii[j]) for k in np.arange(len(all_pos[j])): all_radii[j][k] = np.sqrt(np.dot(all_pos[j][k],all_pos[j][k])) print "np.sort(all_radii[0]): ", np.sort(all_radii[0]) # Exporting variables halo.all_pos = all_pos halo.all_vel = all_vel halo.all_radii = all_radii
def group_catalog(
self,
hdf5_names=["GroupPos", "Group_M_Crit200", "Group_R_Crit200"],
masstab=True,
group_veldisp=True,
file_prefix="",
files=-1,
path="",
dirname="groups_",
filename="fof_subhalo_tab_",
):
"""Read data from the group catalog corresponding to the snapshot.
Usage:
my_snapshot.group_catalog(<hdf5_names>, <masstab>, <group_veldisp>, <file_prefix>, <files>, <path>, <dirname>, <filename>)
Arguments:
hdf5_names List of hdf5 names of the data fields to be loaded (see 'my_snapshot.show_group_catalog_contents()'), optional, default '['GroupPos', 'Group_M_Crit200', 'Group_R_Crit200']'
masstab Only needed for Gadget format, optional
group_veldisp Only needed for Gadget format, optional
file_prefix Prefix for the group directory, optional, default ''
files List of files to be loaded from the group catalog, optional, default '-1' (all files)
path path where the group catalog is stored, optional, default: same path as snapshot data
dirname directory name for the group catalog subdirectories, optional, default 'groups_'
filename filename for the individual catalog files, optional, default '/fof_subhalo_tab_'
Example:
my_snapshot.group_catalog(['GroupPos', 'SubhaloPos'])
This will load the positions of all groups and subhalos.
"""
if not self.hdf5:
self.cat = readsubf.subfind_catalog(
self.directory + file_prefix,
self.snapnum,
masstab=masstab,
group_veldisp=group_veldisp,
)
else:
self.fast_group_catalog(
hdf5_names=hdf5_names,
files=files,
path=path,
dirname=dirname,
filename=filename,
file_prefix=file_prefix,
)
def center_halo(halo): import numpy as np import readsubf # Initialize variables: base_id = halo.base_id base = halo.base snap_num = halo.snap_num sub_id = halo.sub_id save_dir = halo.save_dir interact = halo.interact type_thresh = halo.type_thresh gal_pos = halo.gal_pos gal_vel = halo.gal_vel gal_mass = halo.gal_mass gal_pot = halo.gal_pot gal_T_gas = halo.gal_T_gas gal_rho_gas = halo.gal_rho_gas #gal_age = halo.gal_age # Move to the frame where the center of the galaxy is at rest, and at the origin: print "Re-centering..." cat = readsubf.subfind_catalog(base,snap_num,masstab=True) gal_cent_pos = cat.sub_pos[sub_id] gal_cent_vel = cat.sub_vel[sub_id] for j in np.arange(3): gal_pos[j] = gal_pos[j] - gal_cent_pos gal_vel[j] = gal_vel[j] - gal_cent_vel # Exporting variables halo.gal_pos = gal_pos halo.gal_vel = gal_vel
def halo_positions(groups_fname, groups_number, mass_interval, min_mass,
max_mass):
#read SUBFIND CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
Pos = halos.group_pos / 1e3 #Mpc/h
if mass_interval:
a = halos.group_m_mean200 > min_mass
b = halos.group_m_mean200 < max_mass
c = a * b
halos_indexes = np.where(c == True)[0]
del a, b, c, halos
Pos = Pos[halos_indexes]
return Pos
def find_all_halos(num, base, min_mass):
"""Get a halo catalogue and return its members, filtering out those with masses below min_mass.
Select halos via their M_200 mass, defined in terms of the critical density.
Arguments:
num - snapnumber
base - simulation directory
min_mass - minimum mass of halos to use
Returns:
ind - list of halo indices used
sub_mass - halo masses in M_sun /h
sub_cofm - halo positions
sub_radii - R_Crit200 for halo radii"""
try:
subs=readsubf.subfind_catalog(base,num,masstab=True,long_ids=True)
#Get list of halos resolved, using a mass cut; cuts off at about 2e9 for 512**3 particles.
ind=np.where(subs.group_m_crit200 > min_mass)
#Store the indices of the halos we are using
#Get particle center of mass, use group catalogue.
sub_cofm=np.array(subs.group_pos[ind])
#halo masses in M_sun/h: use M_200
sub_mass=np.array(subs.group_m_crit200[ind])*UnitMass_in_g/SolarMass_in_g
#r200 in kpc.
sub_radii = np.array(subs.group_r_crit200[ind])
del subs
except IOError:
# We might have the halo catalog stored in the new format, which is HDF5.
subs=readsubfHDF5.subfind_catalog(base, num,long_ids=True)
#Get list of halos resolved, using a mass cut; cuts off at about 2e9 for 512**3 particles.
ind=np.where(subs.Group_M_Crit200 > min_mass)
#Store the indices of the halos we are using
#Get particle center of mass, use group catalogue.
sub_cofm=np.array(subs.GroupPos[ind])
#halo masses in M_sun/h: use M_200
sub_mass=np.array(subs.Group_M_Crit200[ind])*UnitMass_in_g/SolarMass_in_g
#r200 in kpc/h (comoving).
sub_radii = np.array(subs.Group_R_Crit200[ind])
del subs
return (ind, sub_mass,sub_cofm,sub_radii)
def mass_function(groups_fname,
groups_number,
obj,
BoxSize,
bins,
f_out,
min_mass=None,
max_mass=None,
long_ids_flag=True,
SFR_flag=False):
#bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
#mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
#dM=bins_mass[1:]-bins_mass[:-1]
if obj == 'FoF':
#read FoF halos information
fof = readfof.FoF_catalog(groups_fname,
groups_number,
long_ids=long_ids_flag,
swap=False,
SFR=SFR_flag)
F_pos = fof.GroupPos / 1e3 #positions in Mpc/h
F_mass = fof.GroupMass * 1e10 #masses in Msun/h
F_part = fof.GroupLen #number particles belonging to the group
F_Mpart = F_mass[0] / F_part[0] #mass of a single particle in Msun/h
del fof
#some verbose
print '\nNumber of FoF halos=', len(F_pos)
print '%f < X_fof < %f' % (np.min(F_pos[:, 0]), np.max(F_pos[:, 0]))
print '%f < Y_fof < %f' % (np.min(F_pos[:, 1]), np.max(F_pos[:, 1]))
print '%f < Z_fof < %f' % (np.min(F_pos[:, 2]), np.max(F_pos[:, 2]))
print '%e < M_fof < %e\n' % (np.min(F_mass), np.max(F_mass))
#Correct the masses of the FoF halos
F_mass = F_Mpart * (F_part * (1.0 - F_part**(-0.6)))
#compute the minimum and maximum mass
if min_mass == None:
min_mass = np.min(F_mass)
if max_mass == None:
max_mass = np.max(F_mass)
print 'M_min = %e' % (min_mass)
print 'M_max = %e\n' % (max_mass)
#find the masses and mass intervals
bins_mass = np.logspace(np.log10(min_mass), np.log10(max_mass),
bins + 1)
mass_mean = 10**(0.5 *
(np.log10(bins_mass[1:]) + np.log10(bins_mass[:-1])))
dM = bins_mass[1:] - bins_mass[:-1]
#compute the number of halos within each mass interval
number = np.histogram(F_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
elif obj == 'halos_m200':
#read CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
H_pos = halos.group_pos / 1e3 #positions in Mpc/h
H_mass = halos.group_m_mean200 * 1e10 #masses in Msun/h
H_radius_m = halos.group_r_mean200 / 1e3 #radius in Mpc/h
del halos
#some verbose
print '\nNumber of halos=', len(H_pos)
print '%f < X < %f' % (np.min(H_pos[:, 0]), np.max(H_pos[:, 0]))
print '%f < Y < %f' % (np.min(H_pos[:, 1]), np.max(H_pos[:, 1]))
print '%f < Z < %f' % (np.min(H_pos[:, 2]), np.max(H_pos[:, 2]))
print '%e < M < %e\n' % (np.min(H_mass), np.max(H_mass))
#compute the minimum and maximum mass
if min_mass == None:
min_mass = np.min(H_mass)
if max_mass == None:
max_mass = np.max(H_mass)
#compute the number of halos within each mass interval
bins_mass = np.logspace(np.log10(min_mass), np.log10(max_mass),
bins + 1)
mass_mean = 10**(0.5 *
(np.log10(bins_mass[1:]) + np.log10(bins_mass[:-1])))
dM = bins_mass[1:] - bins_mass[:-1]
number = np.histogram(H_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
elif obj == 'halos_c200':
#read CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
H_pos = halos.group_pos / 1e3 #positions in Mpc/h
H_mass = halos.group_m_crit200 * 1e10 #masses in Msun/h
H_radius_m = halos.group_r_crit200 / 1e3 #radius in Mpc/h
del halos
#some verbose
print '\nNumber of halos=', len(H_pos)
print '%f < X < %f' % (np.min(H_pos[:, 0]), np.max(H_pos[:, 0]))
print '%f < Y < %f' % (np.min(H_pos[:, 1]), np.max(H_pos[:, 1]))
print '%f < Z < %f' % (np.min(H_pos[:, 2]), np.max(H_pos[:, 2]))
print '%e < M < %e\n' % (np.min(H_mass), np.max(H_mass))
#compute the minimum and maximum mass
if min_mass == None:
min_mass = np.min(H_mass)
if max_mass == None:
max_mass = np.max(H_mass)
#compute the number of halos within each mass interval
bins_mass = np.logspace(np.log10(min_mass), np.log10(max_mass),
bins + 1)
mass_mean = 10**(0.5 *
(np.log10(bins_mass[1:]) + np.log10(bins_mass[:-1])))
dM = bins_mass[1:] - bins_mass[:-1]
number = np.histogram(H_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
elif obj == 'subhalos':
#read CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
S_pos = halos.sub_pos / 1e3 #positions in Mpc/h
S_mass = halos.sub_mass * 1e10 #masses in Msun/h
del halos
#some verbose
print '\nNumber of subhalos=', len(S_pos)
print '%f < X < %f' % (np.min(S_pos[:, 0]), np.max(S_pos[:, 0]))
print '%f < Y < %f' % (np.min(S_pos[:, 1]), np.max(S_pos[:, 1]))
print '%f < Z < %f' % (np.min(S_pos[:, 2]), np.max(S_pos[:, 2]))
print '%e < M < %e\n' % (np.min(S_mass), np.max(S_mass))
#compute the minimum and maximum mass
if min_mass == None:
min_mass = np.min(S_mass)
if max_mass == None:
max_mass = np.max(S_mass)
#compute the number of halos within each mass interval
bins_mass = np.logspace(np.log10(min_mass), np.log10(max_mass),
bins + 1)
mass_mean = 10**(0.5 *
(np.log10(bins_mass[1:]) + np.log10(bins_mass[:-1])))
dM = bins_mass[1:] - bins_mass[:-1]
number = np.histogram(S_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
else:
print 'bad object type selected'
sys.exit()
MF = number / (dM * BoxSize**3)
delta_MF = np.sqrt(number) / (dM * BoxSize**3)
f = open(f_out, 'w')
for i in range(bins):
f.write(
str(mass_mean[i]) + ' ' + str(MF[i]) + ' ' + str(delta_MF[i]) +
'\n')
f.close()
def pairwise_match(base1,base2,snapnum1,snapnum2,ids1=-1,ids2=-1): import readsubf import numpy as np # Important parameters: minM=10. maxM=10.**5 massDiff_thresh = 0.3 #ratio centerDiff_thresh = 30. #kpc box_size = 20000. #kpc # Load subfind catalogues cat = [readsubf.subfind_catalog(base1,snapnum1,masstab=True),\ readsubf.subfind_catalog(base2,snapnum2,masstab=True)] nruns = 2 #len(cat_list) sub_ids = [0]*nruns M = [0]*nruns pos = [0]*nruns match_ids1 = np.array([]) match_ids2 = np.array([]) for i in np.arange(nruns): # Load only primary subhalos within groups, or load preselected list of IDs sub_ids[i] = cat[i].group_firstsub #if (i == 0 and ids1 == -1) or (i == 1 and ids2 == -1): sub_ids[i] = cat[i].group_firstsub #elif i == 0: sub_ids[0] = ids1 #elif i == 1: sub_ids[1] = ids2 M[i] = cat[i].sub_mass[sub_ids[i][:-1]] pos[i] = cat[i].sub_pos[sub_ids[i][:-1]] # Apply mass cuts: M_cut1 = M[i] > minM sub_ids[i] = sub_ids[i][M_cut1] M[i] = M[i][M_cut1] M_cut2 = M[i] < maxM sub_ids[i] = sub_ids[i][M_cut2] M[i] = M[i][M_cut2] for j in np.arange(len(sub_ids[0])): locM = M[0][j] locPos = pos[0][j] massDiff = abs(M[1]-locM)/locM M_matches = abs(massDiff) < massDiff_thresh N_match1 = M_matches.sum() #print "N_match1 ",N_match1 if N_match1 > 0: match1_ids = sub_ids[1][M_matches] match1_pos = pos[1][M_matches] posDiff = locPos - match1_pos centerDiff = np.arange(N_match1) for k in np.arange(N_match1): centerDiff[k] = np.sqrt( np.dot(posDiff[k],posDiff[k])) pos_matches = centerDiff < centerDiff_thresh #pos_matches = np.logical_or(centerDiff < centerDiff_thresh,centerDiff > box_size-centerDiff_thresh) N_match2 = pos_matches.sum() #print "N_match2 ",N_match2 if N_match2 == 1: match_ids1 = np.append(match_ids1,sub_ids[0][j]) match_ids2 = np.append(match_ids2,sub_ids[1][M_matches][pos_matches]) elif N_match2 > 1: print "More than 1 possible match for a halo found!" closest = np.argmin(centerDiff[pos_matches]) match_ids1 = np.append(match_ids1,sub_ids[0][j]) match_ids2 = np.append(match_ids2,sub_ids[1][M_matches][pos_matches][closest]) N_matches = len(match_ids1) print "\n\n"+"Matched "+str(N_matches)+" Halos" #print "Matching catalog: " #for i in np.arange(N_matches): # print "1: ",match_ids1[i], " -- 2: ",match_ids2[i] return [match_ids1,match_ids2]
#import CalcHsml #=============================================================================================== resnap_name = "base102" base = directory(resnap_name)[0] snapnum = 313 #directory(resnap_name)[1] print resnap_name snapname = base + "/snapdir_"+str(snapnum).zfill(3)+ "/snap_"+str(snapnum).zfill(3) #snapname = base + "/snapdir_"+str(snapnum).zfill(3)+"_SAVE"+ "/snap_"+str(snapnum).zfill(3) print "snapname ", snapname #=============================================================================================== cat = readsubf.subfind_catalog(base,snapnum,masstab=True) nsubs = cat.nsubs print str(nsubs) + " subhalos!\n" parttype_list = [0,1,4] all_ids = np.array([],dtype="uint32") types = np.array([],dtype="uint32") mass = np.array([],dtype="float64") pos = np.array([],dtype="float64") vel = np.array([],dtype="float64") u = np.array([],dtype="float64") T = np.array([],dtype="float64") rho = np.array([],dtype="float64") sfr = np.array([],dtype="float64")
#the particle whose ID is N is located in the position sorted_ids[N]
#i.e. DM_ids[sorted_ids[N]]=N
#the position of the particle whose ID is N would be:
#DM_pos[sorted_ids[N]]
#read the IDs of the particles belonging to the CDM halos
halos_ID=readsubf.subf_ids(groups_fname,groups_number,0,0,
long_ids=True,read_all=True)
IDs=halos_ID.SubIDs
del halos_ID
print 'subhalos IDs=',np.min(IDs),np.max(IDs)
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
if mass_criteria=='t200':
halos_mass=halos.group_m_tophat200*1e10 #masses in Msun/h
halos_radius=halos.group_r_tophat200 #radius in kpc/h
elif mass_criteria=='m200':
halos_mass=halos.group_m_mean200*1e10 #masses in Msun/h
halos_radius=halos.group_r_mean200 #radius in kpc/h
elif mass_criteria=='c200':
halos_mass=halos.group_m_crit200*1e10 #masses in Msun/h
halos_radius=halos.group_r_crit200 #radius in kpc/h
else:
print 'bad mass_criteria'
sys.exit()
halos_pos=halos.group_pos
halos_len=halos.group_len
def bary_mass(code_type="Arepo_ENERGY",res="512_20Mpc"):
import numpy as np
import readsubf
import os
from run_bd import run_bd
import bd
#############
#Control Parameters
base_folder = "/n/hernquistfs1/mvogelsberger/ComparisonProject/"
#code_type = "Gadget"
#code_type = "Arepo_ENERGY"
out_folder = "/output"
snap_num = 314
# Interactive mode?
interact = 0
# Restrict analysis to only primary subhalos in given group?
first_subs_only = 0
# Analyze all subhalos within a given mass range:
M_botlim = 25.
M_toplim = 100.
############################################################################################
resnap = code_type+"_"+res+"_resnap"+str(snap_num).zfill(3)+'.dat'
snap_type = 1
snap_name = resnap
out_folder = "/output"
base = base_folder + res + "/" + code_type + out_folder
save_dir = code_type[0]+res+"_test"+"/"
if not os.path.exists(save_dir):
os.system('mkdir '+save_dir)
#os.path.mkdir(save_dir)
cat = readsubf.subfind_catalog(base,snap_num,masstab=True)
if first_subs_only == 1: sub_ids = cat.group_firstsub
else: sub_ids = np.arange(cat.nsubs)
M_subs = cat.sub_mass[sub_ids]
ind = np.logical_and(M_subs >= M_botlim, M_subs < M_toplim)
M_subs = M_subs[ind]
sub_ids = sub_ids[ind]
N_subs = np.uint32(len(sub_ids))
print "writing ",N_subs," subhalos"
err_flag = np.zeros(N_subs)
bary_mass = np.zeros(N_subs)
star_mass = np.zeros(N_subs)
sub_posx = np.zeros(N_subs)
sub_posy = np.zeros(N_subs)
sub_posz = np.zeros(N_subs)
sub_velx = np.zeros(N_subs)
sub_vely = np.zeros(N_subs)
sub_velz = np.zeros(N_subs)
for i in np.arange(N_subs):
sub_id = sub_ids[i]
print "sub_id ",sub_id
halo = bd.Halo()
halo.interact = interact
halo.code_type = code_type
halo.res = res
halo.snap_num = snap_num
halo.sub_id = sub_id
halo.save_dir = save_dir
halo.snap_name = snap_name
halo.base = base
halo.snap_type = snap_type
halo.err_flag = 0
bd.read_halo(halo)
if halo.err_flag != 1: bd.recenter_halo(halo)
print "sub_pos ",halo.sub_pos
sub_posx[i] = halo.sub_pos[0]
sub_posy[i] = halo.sub_pos[1]
sub_posz[i] = halo.sub_pos[2]
sub_velx[i] = halo.sub_vel[0]
sub_vely[i] = halo.sub_vel[1]
sub_velz[i] = halo.sub_vel[2]
err_flag[i] = halo.err_flag
bary_mass[i] = halo.bary_mass
star_mass[i] = halo.star_mass
print "outputting close pairs list :)"
g = open(save_dir+"bary.dat",'wb')
sub_ids = np.uint32(sub_ids)
err_flag = np.uint32(err_flag)
M_subs = np.float64(M_subs)
N_subs.astype("uint32").tofile(g)
sub_ids.astype("uint32").tofile(g)
err_flag.astype("uint32").tofile(g)
sub_posx.astype("float64").tofile(g)
sub_posy.astype("float64").tofile(g)
sub_posz.astype("float64").tofile(g)
sub_velx.astype("float64").tofile(g)
sub_vely.astype("float64").tofile(g)
sub_velz.astype("float64").tofile(g)
M_subs.astype("float64").tofile(g)
bary_mass.astype("float64").tofile(g)
star_mass.astype("float64").tofile(g)
g.close()
print "sub_pos x",sub_posx
print "sub_pos y",sub_posy
print "sub_pos z",sub_posz
def hod(snapshot_fname,
groups_fname,
groups_number,
min_mass,
max_mass,
fiducial_density,
M1,
alpha,
mass_criteria,
verbose=False):
thres = 1e-3 #controls the max relative error to accept a galaxy density
#read the header and obtain the boxsize
head = readsnap.snapshot_header(snapshot_fname)
BoxSize = head.boxsize #BoxSize in kpc/h
#read positions and IDs of DM particles: sort the IDs array
DM_pos = readsnap.read_block(snapshot_fname, "POS ", parttype=-1) #kpc/h
DM_ids = readsnap.read_block(snapshot_fname, "ID ", parttype=-1) - 1
sorted_ids = DM_ids.argsort(axis=0)
#the particle whose ID is N is located in the position sorted_ids[N]
#i.e. DM_ids[sorted_ids[N]]=N
#the position of the particle whose ID is N would be:
#DM_pos[sorted_ids[N]]
#read the IDs of the particles belonging to the CDM halos
halos_ID = readsubf.subf_ids(groups_fname,
groups_number,
0,
0,
long_ids=True,
read_all=True)
IDs = halos_ID.SubIDs - 1
del halos_ID
#read CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
if mass_criteria == 't200':
halos_mass = halos.group_m_tophat200 * 1e10 #masses in Msun/h
halos_radius = halos.group_r_tophat200 #radius in kpc/h
elif mass_criteria == 'm200':
halos_mass = halos.group_m_mean200 * 1e10 #masses in Msun/h
halos_radius = halos.group_r_mean200 #radius in kpc/h
elif mass_criteria == 'c200':
halos_mass = halos.group_m_crit200 * 1e10 #masses in Msun/h
halos_radius = halos.group_r_crit200 #radius in kpc/h
else:
print('bad mass_criteria')
sys.exit()
halos_pos = halos.group_pos #positions in kpc/h
halos_len = halos.group_len
halos_offset = halos.group_offset
halos_indexes = np.where((halos_mass > min_mass)
& (halos_mass < max_mass))[0]
del halos
if verbose:
print(' ')
print('total halos found=', halos_pos.shape[0])
print('halos number density=', len(halos_pos) / (BoxSize * 1e-3)**3)
#keep only the halos in the given mass range
halo_mass = halos_mass[halos_indexes]
halo_pos = halos_pos[halos_indexes]
halo_radius = halos_radius[halos_indexes]
halo_len = halos_len[halos_indexes]
halo_offset = halos_offset[halos_indexes]
del halos_indexes
##### COMPUTE Mmin GIVEN M1 & alpha #####
i = 0
max_iterations = 20 #maximum number of iterations
Mmin1 = min_mass
Mmin2 = max_mass
while (i < max_iterations):
Mmin = 0.5 * (Mmin1 + Mmin2) #estimation of the HOD parameter Mmin
total_galaxies = 0
inside = np.where(halo_mass > Mmin)[0] #take all galaxies with M>Mmin
mass = halo_mass[
inside] #only halos with M>Mmin have central/satellites
total_galaxies = mass.shape[0] + np.sum((mass / M1)**alpha)
mean_density = total_galaxies * 1.0 / (BoxSize *
1e-3)**3 #galaxies/(Mpc/h)^3
if (np.absolute(
(mean_density - fiducial_density) / fiducial_density) < thres):
i = max_iterations
elif (mean_density > fiducial_density):
Mmin1 = Mmin
else:
Mmin2 = Mmin
i += 1
if verbose:
print(' ')
print('Mmin=', Mmin)
print('average number of galaxies=', total_galaxies)
print('average galaxy density=', mean_density)
#########################################
#just halos with M>Mmin; the rest do not host central/satellite galaxies
inside = np.where(halo_mass > Mmin)[0]
halo_mass = halo_mass[inside]
halo_pos = halo_pos[inside]
halo_radius = halo_radius[inside]
halo_len = halo_len[inside]
halo_offset = halo_offset[inside]
del inside
#compute number of satellites in each halo using the Poisson distribution
N_mean_sat = (halo_mass / M1)**alpha #mean number of satellites
N_sat = np.empty(len(N_mean_sat), dtype=np.int32)
for i in range(len(N_sat)):
N_sat[i] = np.random.poisson(N_mean_sat[i])
N_tot = np.sum(N_sat) + len(
halo_mass) #total number of galaxies in the catalogue
if verbose:
print(' ')
print(np.min(halo_mass), '< M_halo <', np.max(halo_mass))
print('total number of galaxies=', N_tot)
print('galaxy number density=', N_tot / (BoxSize * 1e-3)**3)
#put satellites following the distribution of dark matter in groups
if verbose:
print(' ')
print('Creating mock catalogue ...', )
pos_galaxies = np.empty((N_tot, 3), dtype=np.float32)
#index: variable that go through halos (may be several galaxies in a halo)
#i: variable that go through all (central/satellites) galaxies
#count: find number of galaxies that lie beyond its host halo virial radius
index = 0
count = 0
i = 0
while (index < halo_mass.shape[0]):
position = halo_pos[index] #position of the DM halo
radius = halo_radius[index] #radius of the DM halo
#save the position of the central galaxy
pos_galaxies[i] = position
i += 1
#if halo contains satellites, save their positions
Nsat = N_sat[index]
if Nsat > 0:
offset = halo_offset[index]
length = halo_len[index]
idss = sorted_ids[IDs[offset:offset + length]]
#compute the distances to the halo center keeping those with R<Rvir
pos = DM_pos[
idss] #positions of the particles belonging to the halo
posc = pos - position
#this is to populate correctly halos closer to box boundaries
if np.any((position + radius > BoxSize) +
(position - radius < 0.0)):
inside = np.where(posc[:, 0] > BoxSize / 2.0)[0]
posc[inside, 0] -= BoxSize
inside = np.where(posc[:, 0] < -BoxSize / 2.0)[0]
posc[inside, 0] += BoxSize
inside = np.where(posc[:, 1] > BoxSize / 2.0)[0]
posc[inside, 1] -= BoxSize
inside = np.where(posc[:, 1] < -BoxSize / 2.0)[0]
posc[inside, 1] += BoxSize
inside = np.where(posc[:, 2] > BoxSize / 2.0)[0]
posc[inside, 2] -= BoxSize
inside = np.where(posc[:, 2] < -BoxSize / 2.0)[0]
posc[inside, 2] += BoxSize
radii = np.sqrt(posc[:, 0]**2 + posc[:, 1]**2 + posc[:, 2]**2)
inside = np.where(radii < radius)[0]
selected = random.sample(inside, Nsat)
pos = pos[selected]
#aditional, not esential check. Can be comment out
posc = pos - position
if np.any((posc > BoxSize / 2.0) + (posc < -BoxSize / 2.0)):
inside = np.where(posc[:, 0] > BoxSize / 2.0)[0]
posc[inside, 0] -= BoxSize
inside = np.where(posc[:, 0] < -BoxSize / 2.0)[0]
posc[inside, 0] += BoxSize
inside = np.where(posc[:, 1] > BoxSize / 2.0)[0]
posc[inside, 1] -= BoxSize
inside = np.where(posc[:, 1] < -BoxSize / 2.0)[0]
posc[inside, 1] += BoxSize
inside = np.where(posc[:, 2] > BoxSize / 2.0)[0]
posc[inside, 2] -= BoxSize
inside = np.where(posc[:, 2] < -BoxSize / 2.0)[0]
posc[inside, 2] += BoxSize
r_max = np.max(
np.sqrt(posc[:, 0]**2 + posc[:, 1]**2 + posc[:, 2]**2))
if r_max > radius: #check no particles beyond Rv selected
print(position)
print(radius)
print(pos)
count += 1
for j in range(Nsat):
pos_galaxies[i] = pos[j]
i += 1
index += 1
if verbose:
print('done')
#some final checks
if i != N_tot:
print('some galaxies missing:')
print('register', i, 'galaxies out of', N_tot)
if count > 0:
print('error:', count, 'particles beyond the virial radius selected')
return pos_galaxies
def load_subhalos(base_list,snapnum): import numpy as np import readsubf global cat, N_subs, subhalo_Offset, subhalo_Len, subhalo_Pos, subhalo_VelDisp, \ subhalo_Vel, subhalo_Vmax, subhalo_GrNr, subhalo_Parent, subhalo_CM, subhalo_VmaxRad, \ subhalo_IDMostBound, subhalo_Mass, subhalo_Spin, subhalo_HmassRad, subhalo_IDs cat = [0]*len(base_list) N_subs = [0]*len(base_list) subhalo_Offset = [0]*len(base_list) subhalo_Len = [0]*len(base_list) subhalo_Pos = [0]*len(base_list) subhalo_VelDisp = [0]*len(base_list) subhalo_Vel = [0]*len(base_list) subhalo_Vmax = [0]*len(base_list) subhalo_GrNr = [0]*len(base_list) subhalo_Parent = [0]*len(base_list) subhalo_CM = [0]*len(base_list) subhalo_VmaxRad = [0]*len(base_list) subhalo_IDMostBound = [0]*len(base_list) subhalo_Mass = [0]*len(base_list) subhalo_Spin = [0]*len(base_list) subhalo_HmassRad = [0]*len(base_list) subhalo_IDs = [0]*len(base_list) global TEST TEST = [0]*len(base_list) for i in np.arange(len(base_list)): cat[i] = readsubf.subfind_catalog(base_list[i],snapnum,masstab=True) N_subs[i] = cat[i].nsubs TEST[i] = cat[i].group_firstsub print str(N_subs[i])+ " subhalos!" subhalo_Offset[i] = np.zeros(N_subs[i]) subhalo_Len[i] = np.zeros(N_subs[i]) subhalo_Pos[i] = np.zeros([N_subs[i],3]) subhalo_VelDisp[i] = np.zeros(N_subs[i]) subhalo_Vel[i] = np.zeros([N_subs[i],3]) subhalo_Vmax[i] = np.zeros(N_subs[i]) subhalo_GrNr[i] = np.zeros(N_subs[i]) subhalo_Parent[i] = np.zeros(N_subs[i]) subhalo_CM[i] = np.zeros([N_subs[i],3]) subhalo_VmaxRad[i] = np.zeros(N_subs[i]) subhalo_IDMostBound[i] = np.zeros(N_subs[i]) subhalo_Mass[i] = np.zeros(N_subs[i]) subhalo_Spin[i] = np.zeros([N_subs[i],3]) subhalo_HmassRad[i] = np.zeros(N_subs[i]) subhalo_IDs[i] = [0]*N_subs[i] all_ids = readsubf.subf_ids(base_list[i],snapnum,0, 0, long_ids=True,read_all=True).SubIDs for j in np.arange(N_subs[i]): subhalo_Offset[i][j] = cat[i].sub_offset[j] subhalo_Len[i][j] = cat[i].sub_len[j] subhalo_Pos[i][j] = cat[i].sub_pos[j] subhalo_VelDisp[i][j] = cat[i].sub_veldisp[j] subhalo_Vel[i][j] = cat[i].sub_vel[j] subhalo_Vmax[i][j] = cat[i].sub_vmax[j] subhalo_GrNr[i][j] = cat[i].sub_grnr[j] subhalo_Parent[i][j] = cat[i].sub_parent[j] subhalo_CM[i][j] = cat[i].sub_cm[j] subhalo_VmaxRad[i][j] = cat[i].sub_vmaxrad[j] subhalo_IDMostBound[i][j] = cat[i].sub_id_mostbound[j] subhalo_Mass[i][j] = cat[i].sub_mass[j] subhalo_Spin[i][j] = cat[i].sub_spin[j] subhalo_HmassRad[i][j] = cat[i].sub_halfmassrad[j] subhalo_IDs[i][j] = all_ids[subhalo_Offset[i][j]:subhalo_Offset[i][j]]
def read_halo(halo): import numpy as np import readsubf # Initialize variables: base = halo.base snap_type = halo.snap_type snap_name = halo.snap_name snap_num = halo.snap_num sub_id = halo.sub_id code_type = halo.code_type res = halo.res save_dir = halo.save_dir sub_id = halo.sub_id type_thresh = 25 #snap_type == 0: regular HDF5 snapshot #snap_type == 1: a "resorted" snapshot according to the subfind catalog #snap_type == 2: a *.snap file created by subsnap_write, which only contains the information for the specific halo in question print "Reading snapshot..." if snap_type == 0: gal_ids = [0]*3 gal_pos = [0]*3 gal_vel = [0]*3 gal_mass = [0]*3 gal_pot = [0]*3 gal_T_gas = [0]*3 print "opening "+snap_name # 0th element: gas. 1st element: dm. 2nd element: stars gal_ids[0] = rs.read_block(snap_name,"ID ",parttype=0) gal_ids[1] = rs.read_block(snap_name,"ID ",parttype=1) gal_ids[2] = np.append(rs.read_block(snap_name,"ID ",parttype=2),\ rs.read_block(snap_name,"ID ",parttype=3)) gal_pos[0] = rs.read_block(snap_name,"POS ",parttype=0) gal_pos[1] = rs.read_block(snap_name,"POS ",parttype=1) gal_pos[2] = np.append(rs.read_block(snap_name,"POS ",parttype=2),\ rs.read_block(snap_name,"POS ",parttype=3),axis=0) gal_vel[0] = rs.read_block(snap_name,"VEL ",parttype=0) gal_vel[1] = rs.read_block(snap_name,"VEL ",parttype=1) gal_vel[2] = np.append(rs.read_block(snap_name,"VEL ",parttype=2),\ rs.read_block(snap_name,"VEL ",parttype=3),axis=0) gal_mass[0] = rs.read_block(snap_name,"MASS",parttype=0) gal_mass[1] = rs.read_block(snap_name,"MASS",parttype=1) gal_mass[2] = np.append(rs.read_block(snap_name,"MASS",parttype=2),\ rs.read_block(snap_name,"MASS",parttype=3)) disk_mass = rs.read_block(snap_name,"MASS",parttype=2) bulge_mass = rs.read_block(snap_name,"MASS",parttype=3) gal_pot[0] = rs.read_block(snap_name,"POT ",parttype=0) gal_pot[1] = rs.read_block(snap_name,"POT ",parttype=1) gal_pot[2] = np.append(rs.read_block(snap_name,"POT ",parttype=2),\ rs.read_block(snap_name,"POT ",parttype=3)) total_D = disk_mass.sum() total_B = bulge_mass.sum() accepted_D_T = total_D/(total_D+total_B) print "Accepted D/T: ", accepted_D_T gal_T_gas = conv_T(rs.read_block(snap_name,"U ",parttype=0)) #cosmological runs: #type 0: gas, type 1: dm, type 4: stars #arrays: # spot 0: gas, spot 1: dm, spot 2: stars #ptorrey isolated runs: #type 0: gas, type 1: dm, type 2: disk stars, type 3: bulge stars, type 4: formed stars (0 initially) elif snap_type == 1: ######################################## # READ IN DATA FROM RE-SORTED SNAPSHOT # ######################################## gal_ids = [0]*3 gal_pos = [0]*3 gal_vel = [0]*3 gal_mass = [0]*3 gal_pot = [0]*3 #gal_T_gas = [0]*3 #gal_age = [0]*3 print "opening "+snap_name f = open(snap_name, 'rb') f.seek(0) nsub = np.fromfile(f,dtype="uint32",count=1)[0] npart = np.fromfile(f,dtype="uint32",count=1)[0] cat = readsubf.subfind_catalog(base,snap_num,masstab=True) sub_pos = cat.sub_pos[sub_id] sub_cm = cat.sub_cm[sub_id] sub_vel = cat.sub_vel[sub_id] halo.sub_pos = sub_pos #print "sub_pos ",sub_pos halo.sub_cm = sub_cm halo.sub_vel = sub_vel temp = sub_pos-sub_cm cent_offset = np.sqrt(np.dot(temp,temp)) halo.cent_offset = cent_offset #if np.sqrt(np.dot(pos_offset,pos_offset)) > 10.: sub_offset = cat.sub_offset[sub_id] sub_len = cat.sub_len[sub_id] sub_mass = cat.sub_mass[sub_id] halo.sub_mass = sub_mass types = np.fromfile(f,dtype="uint32",count=npart)[sub_offset:sub_offset+sub_len] mass = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] posx = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] posy = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] posz = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] velx = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] vely = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] velz = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] T = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] rho = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] sfr = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] age = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] pot = np.fromfile(f,dtype="float64",count=npart)[sub_offset:sub_offset+sub_len] ### GAS ### index_gas = types == 0 ngas = index_gas.sum() print "Ngas "+str(ngas) if ngas < type_thresh: print "Insufficient gas in this galaxy!" halo.err_flag = 1 return gal_mass[0] = mass[index_gas] gal_pot[0] = pot[index_gas] gas_len = len(gal_mass[0]) gal_pos[0] = np.zeros([gas_len,3]) gal_pos[0][:,0] = posx[index_gas] gal_pos[0][:,1] = posy[index_gas] gal_pos[0][:,2] = posz[index_gas] gal_vel[0] = np.zeros([gas_len,3]) gal_vel[0][:,0] = velx[index_gas] gal_vel[0][:,1] = vely[index_gas] gal_vel[0][:,2] = velz[index_gas] gal_T_gas = T[index_gas] gal_rho_gas = rho[index_gas] ### DM ### index_dm = types == 1 ndm = index_dm.sum() print "Ndm "+str(ndm) if ndm < type_thresh: print "Insufficient dm in this galaxy!" halo.err_flag = 1 return gal_mass[1] = mass[index_dm] gal_pot[1] = pot[index_dm] dm_len = len(gal_mass[1]) gal_pos[1] = np.zeros([dm_len,3]) gal_pos[1][:,0] = posx[index_dm] gal_pos[1][:,1] = posy[index_dm] gal_pos[1][:,2] = posz[index_dm] gal_vel[1] = np.zeros([dm_len,3]) gal_vel[1][:,0] = velx[index_dm] gal_vel[1][:,1] = vely[index_dm] gal_vel[1][:,2] = velz[index_dm] ### STARS ### index_star = types == 4 nstar = index_star.sum() print "Nstar "+str(nstar) if nstar < type_thresh: print "Insufficient stars in this galaxy!" halo.err_flag = 1 return gal_mass[2] = mass[index_star] gal_pot[2] = pot[index_star] star_len = len(gal_mass[2]) gal_pos[2] = np.zeros([star_len,3]) gal_pos[2][:,0] = posx[index_star] gal_pos[2][:,1] = posy[index_star] gal_pos[2][:,2] = posz[index_star] gal_vel[2] = np.zeros([star_len,3]) gal_vel[2][:,0] = velx[index_star] gal_vel[2][:,1] = vely[index_star] gal_vel[2][:,2] = velz[index_star] gal_age = age[index_star] print "done loading from snapshot" f.close() elif snap_type == 2: ###################################################### # READ IN DATA FROM *.snap FILE ASSOCIATED WITH HALO # ###################################################### subsnap_name = save_dir+"S"+str(snap_num).zfill(3)+"_H"+str(sub_id).zfill(5)+".snap" g = open(subsnap_name,"rb") #gal_ids = [0]*3 gal_pos = [0]*3 gal_vel = [0]*3 gal_mass = [0]*3 gal_pot = [0]*3 ### GAS ### ngas = np.fromfile(g,dtype="uint32",count=1) if ngas < type_thresh: print "Insufficient gas in this galaxy!" halo.err_flag = 1 return else: # Mass gal_mass[0] = np.fromfile(g,dtype="float64",count=ngas) # Potential gal_pot[0] = np.fromfile(g,dtype="float64",count=ngas) # Positions gal_pos[0] = np.zeros([ngas,3]) gal_pos[0][:,0] = np.fromfile(g,dtype="float64",count=ngas) gal_pos[0][:,1] = np.fromfile(g,dtype="float64",count=ngas) gal_pos[0][:,2] = np.fromfile(g,dtype="float64",count=ngas) # Velocities gal_vel[0] = np.zeros([ngas,3]) gal_vel[0][:,0] = np.fromfile(g,dtype="float64",count=ngas) gal_vel[0][:,1] = np.fromfile(g,dtype="float64",count=ngas) gal_vel[0][:,2] = np.fromfile(g,dtype="float64",count=ngas) # Temperature gal_T_gas = np.fromfile(g,dtype="float64",count=ngas) # Density gal_rho_gas = np.fromfile(g,dtype="float64",count=ngas) ### DM ### ndm = np.fromfile(g,dtype="uint32",count=1) if ndm < type_thresh: print "Insufficient dm in this galaxy!" halo.err_flag = 1 return else: # Mass gal_mass[1] = np.fromfile(g,dtype="float64",count=ndm) # Potential gal_pot[1] = np.fromfile(g,dtype="float64",count=ndm) # Positions gal_pos[1] = np.zeros([ndm,3]) gal_pos[1][:,0] = np.fromfile(g,dtype="float64",count=ndm) gal_pos[1][:,1] = np.fromfile(g,dtype="float64",count=ndm) gal_pos[1][:,2] = np.fromfile(g,dtype="float64",count=ndm) # Velocities gal_vel[1] = np.zeros([ndm,3]) gal_vel[1][:,0] = np.fromfile(g,dtype="float64",count=ndm) gal_vel[1][:,1] = np.fromfile(g,dtype="float64",count=ndm) gal_vel[1][:,2] = np.fromfile(g,dtype="float64",count=ndm) ### STARS ### nstar = np.fromfile(g,dtype="uint32",count=1) if nstar < type_thresh: print "Insufficient stars in this galaxy!" halo.err_flag = 1 return else: # Mass gal_mass[2] = np.fromfile(g,dtype="float64",count=nstar) # Potential gal_pot[2] = np.fromfile(g,dtype="float64",count=nstar) # Positions gal_pos[2] = np.zeros([nstar,3]) gal_pos[2][:,0] = np.fromfile(g,dtype="float64",count=nstar) gal_pos[2][:,1] = np.fromfile(g,dtype="float64",count=nstar) gal_pos[2][:,2] = np.fromfile(g,dtype="float64",count=nstar) # Velocities gal_vel[2] = np.zeros([nstar,3]) gal_vel[2][:,0] = np.fromfile(g,dtype="float64",count=nstar) gal_vel[2][:,1] = np.fromfile(g,dtype="float64",count=nstar) gal_vel[2][:,2] = np.fromfile(g,dtype="float64",count=nstar) # Age gal_age = np.fromfile(g,dtype="float64",count=nstar) print "done loading from *.snap file" g.close() # Exporting variables halo.gal_pos = gal_pos halo.gal_vel = gal_vel halo.gal_mass= gal_mass halo.gal_pot = gal_pot halo.gal_T_gas = gal_T_gas halo.gal_rho_gas = gal_rho_gas halo.gal_age = gal_age halo.type_thresh = type_thresh
def sub_list(snapnum=314,M_botlim=10.,M_toplim=10.**5,code_type="Arepo_ENERGY",res="512_20Mpc",first_subs_only=1,matching=1):
import readsubf
import numpy as np
import readsnapHDF5 as rs
base = "/n/hernquistfs1/mvogelsberger/ComparisonProject/"
out_folder = "/output"
if matching == 0:
base_out = base + res + "/" + code_type + out_folder
cat = readsubf.subfind_catalog(base_out,snapnum,masstab=True)
if first_subs_only == 1: sub_ids = cat.group_firstsub
else: print "Have not yet added functionality for analyzing not-first-in-group subhalos"
M_subs = cat.sub_mass[sub_ids]
ind = np.logical_and(M_subs > M_botlim, M_subs < M_toplim)
M_subs = M_subs[ind]
sub_ids = sub_ids[ind]
return sub_ids
if matching == 1:
save_catalog = 0
massDiffThresh = 1.0 #unitless
posDiffThresh = 60 #kpc
code_type1 = "Arepo_ENERGY"
code_type2 = "Gadget"
code_type_list = [code_type1,code_type2]
base1 = base + res + "/" + code_type1 + out_folder
base2 = base + res + "/" + code_type2 + out_folder
base_out = [base1,base2]
cat = [readsubf.subfind_catalog(base_out[0],snapnum,masstab=True),readsubf.subfind_catalog(base_out[1],snapnum,masstab=True)]
if first_subs_only == 1: sub_ids = [cat[0].group_firstsub, cat[1].group_firstsub]
else: print "Have not yet added functionality for analyzing not-first-in-group subhalos"
N_subs = [len(sub_ids[0]),len(sub_ids[1])]
M = [cat[0].sub_mass[sub_ids[0]],cat[1].sub_mass[sub_ids[1]]]
pos = [cat[0].sub_pos[sub_ids[0]],cat[1].sub_pos[sub_ids[1]]]
matching_catalog = []
print "N_subs[0]",N_subs[0]
for i in np.arange(N_subs[0]):
locMass = M[0][i]
locPos = pos[0][i]
if locMass > M_botlim and locMass < M_toplim: # Only match halos in given mass range
displace = locPos - pos[1]
posDiff = np.arange(len(displace))
for j in np.arange(len(displace)):
posDiff[j] = np.sqrt( np.dot(displace[j],displace[j]))
if np.min(posDiff) < posDiffThresh: #require centers to overlap within given distance
ind1 = posDiff < posDiffThresh
N_match1 = ind1.sum()
M1 = M[1][ind1]
pos1 = pos[1][ind1]
massDiff = abs(M1-locMass)/locMass
if np.min(massDiff) < massDiffThresh: #require mass to match within given range
ind2 = massDiff < massDiffThresh
N_match = ind2.sum()
if N_match > 1:
print "More than 1 possible match for a halo found!"
ind2 = np.argmin(massDiff)
matching_catalog.append([sub_ids[0][i],sub_ids[1][ind1][ind2]])
N_matches = len(matching_catalog)
print "\n\n"+"Matched "+str(N_matches)+" Halos"
matching_catalog = np.array(matching_catalog)
print "Matching catalog: "
print matching_catalog
if save_catalog == 1:
# Save the matching catalog into a file:
filename = res+'_'+'matching_groups_'+str(snapnum).zfill(3)+'.dat'
#data = {'base_list': base_list, 'matching_catalog': matching_catalog}
output = open(filename, 'wb')
N_matches = np.uint32(N_matches)
N_matches.astype("uint32").tofile(output)
matching_catalog1 = matching_catalog[:,0]
matching_catalog2 = matching_catalog[:,1]
matching_catalog1.astype("uint32").tofile(output)
matching_catalog2.astype("uint32").tofile(output)
output.close()
return matching_catalog
def run_series(base_id="base102", snap_num=313):
import numpy as np
import readsubf
import os
from base_lookup import directory
from run_J_align import run_J_align
from subsnap_write import subsnap_write
import time
import glob
#############
#Control Parameters
base = directory(base_id)[0]
snap_num = 313 #directory(base_id)[1]
save_dir = base_id + "/"
resnap_folder = 'resnaps/'
subsnap_folder = save_dir+'subsnaps/'
# Overwrite past analysis?
overw = 0
# (Analysis restricted to only primary subhalos in given group)
# Analyze subhalos within a given mass range:
M_botlim = 10.
M_toplim = 1000.
# Analyze set # of subhalos?
analyze_N = 3#-1.
# JOB CONTROL:
#Concurrent jobs?
job_limit = 10
#Time between job submission?
wait_time = 1 #seconds
############################################################################################
if not os.path.exists(save_dir):
os.system('mkdir '+save_dir)
if not os.path.exists(subsnap_folder):
os.system('mkdir '+subsnap_folder)
cat = readsubf.subfind_catalog(base,snap_num,masstab=True)
sub_ids = cat.group_firstsub
M_subs = cat.sub_mass[sub_ids]
ind = np.logical_and(M_subs >= M_botlim, M_subs < M_toplim)
M_subs = M_subs[ind]
sub_ids = sub_ids[ind]
#N_subs = np.uint32(M_subs.size)
# Can insert specific subhalos instead:
sub_ids = [469]
if analyze_N > 0.:
sub_ids = sub_ids[0:analyze_N]
print "Will submit "+str(len(sub_ids))+" jobs.\n"
raw_input("Press Enter to continue...")
#################################################################################################
resnap = resnap_folder+base_id+"_"+"S"+str(snap_num).zfill(3)+'.resnap'
#Write intermediate .snap files:
subsnap_write(resnap_name=resnap,base=base,snap_num=snap_num,sub_ids=sub_ids,save_dir=save_dir)
# Job group control:
grp_name = "/bd_decomp"
add_grp = "bgadd -L "+str(job_limit)+" "+grp_name
mod_grp = "bgmod -L "+str(job_limit)+" "+grp_name
os.system(add_grp)
os.system(mod_grp)
################################################
def submit_job(base_id, base, snap_num, sub_id, dat_list, save_dir):
if overw == 0:
subsnap_name = save_dir+str(sub_id).zfill(5)+".dat"
if subsnap_name in dat_list:
print subsnap_name
print "Done with this one!"
else:
print "Could not find ",subsnap_name,"... submitting job."
#run_bd(base_id, base, snap_num, sub_id, grp_name)
run_J_align(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ",str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
else:
#run_bd(base_id, base, snap_num, sub_id, grp_name)
run_J_align(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ",str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
################################################
dat_list = glob.glob(save_dir+'*.dat')
n_matches = len(sub_ids)
for i in np.arange(n_matches):
print str(i) +" of "+str(n_matches)
submit_job(base_id=base_id,base=base,snap_num=snap_num,sub_id=sub_ids[i],dat_list=dat_list,save_dir=save_dir)
print "Job submission done!"
def mass_function(groups_fname,groups_number,obj,BoxSize,bins,f_out,
min_mass=None,max_mass=None,
long_ids_flag=True,SFR_flag=False):
#bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
#mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
#dM=bins_mass[1:]-bins_mass[:-1]
if obj=='FoF':
#read FoF halos information
fof=readfof.FoF_catalog(groups_fname,groups_number,
long_ids=long_ids_flag,swap=False,SFR=SFR_flag)
F_pos=fof.GroupPos/1e3 #positions in Mpc/h
F_mass=fof.GroupMass*1e10 #masses in Msun/h
F_part=fof.GroupLen #number particles belonging to the group
F_Mpart=F_mass[0]/F_part[0] #mass of a single particle in Msun/h
del fof
#some verbose
print '\nNumber of FoF halos=',len(F_pos)
print '%f < X_fof < %f'%(np.min(F_pos[:,0]),np.max(F_pos[:,0]))
print '%f < Y_fof < %f'%(np.min(F_pos[:,1]),np.max(F_pos[:,1]))
print '%f < Z_fof < %f'%(np.min(F_pos[:,2]),np.max(F_pos[:,2]))
print '%e < M_fof < %e\n'%(np.min(F_mass),np.max(F_mass))
#Correct the masses of the FoF halos
F_mass=F_Mpart*(F_part*(1.0-F_part**(-0.6)))
#compute the minimum and maximum mass
if min_mass==None:
min_mass=np.min(F_mass)
if max_mass==None:
max_mass=np.max(F_mass)
print 'M_min = %e'%(min_mass)
print 'M_max = %e\n'%(max_mass)
#find the masses and mass intervals
bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
dM=bins_mass[1:]-bins_mass[:-1]
#compute the number of halos within each mass interval
number=np.histogram(F_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
elif obj=='halos_m200':
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
H_pos=halos.group_pos/1e3 #positions in Mpc/h
H_mass=halos.group_m_mean200*1e10 #masses in Msun/h
H_radius_m=halos.group_r_mean200/1e3 #radius in Mpc/h
del halos
#some verbose
print '\nNumber of halos=',len(H_pos)
print '%f < X < %f'%(np.min(H_pos[:,0]),np.max(H_pos[:,0]))
print '%f < Y < %f'%(np.min(H_pos[:,1]),np.max(H_pos[:,1]))
print '%f < Z < %f'%(np.min(H_pos[:,2]),np.max(H_pos[:,2]))
print '%e < M < %e\n'%(np.min(H_mass),np.max(H_mass))
#compute the minimum and maximum mass
if min_mass==None:
min_mass=np.min(H_mass)
if max_mass==None:
max_mass=np.max(H_mass)
#compute the number of halos within each mass interval
bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
dM=bins_mass[1:]-bins_mass[:-1]
number=np.histogram(H_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
elif obj=='halos_c200':
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
H_pos=halos.group_pos/1e3 #positions in Mpc/h
H_mass=halos.group_m_crit200*1e10 #masses in Msun/h
H_radius_m=halos.group_r_crit200/1e3 #radius in Mpc/h
del halos
#some verbose
print '\nNumber of halos=',len(H_pos)
print '%f < X < %f'%(np.min(H_pos[:,0]),np.max(H_pos[:,0]))
print '%f < Y < %f'%(np.min(H_pos[:,1]),np.max(H_pos[:,1]))
print '%f < Z < %f'%(np.min(H_pos[:,2]),np.max(H_pos[:,2]))
print '%e < M < %e\n'%(np.min(H_mass),np.max(H_mass))
#compute the minimum and maximum mass
if min_mass==None:
min_mass=np.min(H_mass)
if max_mass==None:
max_mass=np.max(H_mass)
#compute the number of halos within each mass interval
bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
dM=bins_mass[1:]-bins_mass[:-1]
number=np.histogram(H_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
elif obj=='subhalos':
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
S_pos=halos.sub_pos/1e3 #positions in Mpc/h
S_mass=halos.sub_mass*1e10 #masses in Msun/h
del halos
#some verbose
print '\nNumber of subhalos=',len(S_pos)
print '%f < X < %f'%(np.min(S_pos[:,0]),np.max(S_pos[:,0]))
print '%f < Y < %f'%(np.min(S_pos[:,1]),np.max(S_pos[:,1]))
print '%f < Z < %f'%(np.min(S_pos[:,2]),np.max(S_pos[:,2]))
print '%e < M < %e\n'%(np.min(S_mass),np.max(S_mass))
#compute the minimum and maximum mass
if min_mass==None:
min_mass=np.min(S_mass)
if max_mass==None:
max_mass=np.max(S_mass)
#compute the number of halos within each mass interval
bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
dM=bins_mass[1:]-bins_mass[:-1]
number=np.histogram(S_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
else:
print 'bad object type selected'
sys.exit()
MF=number/(dM*BoxSize**3)
delta_MF=np.sqrt(number)/(dM*BoxSize**3)
f=open(f_out,'w')
for i in range(bins):
f.write(str(mass_mean[i])+' '+str(MF[i])+' '+str(delta_MF[i])+'\n')
f.close()
def hod(snapshot_fname,groups_fname,groups_number,min_mass,max_mass,
fiducial_density,M1,alpha,mass_criteria,verbose=False):
thres=1e-3 #controls the max relative error to accept a galaxy density
#read the header and obtain the boxsize
head=readsnap.snapshot_header(snapshot_fname)
BoxSize=head.boxsize #BoxSize in kpc/h
#read positions and IDs of DM particles: sort the IDs array
DM_pos=readsnap.read_block(snapshot_fname,"POS ",parttype=-1) #kpc/h
DM_ids=readsnap.read_block(snapshot_fname,"ID ",parttype=-1)-1
sorted_ids=DM_ids.argsort(axis=0)
#the particle whose ID is N is located in the position sorted_ids[N]
#i.e. DM_ids[sorted_ids[N]]=N
#the position of the particle whose ID is N would be:
#DM_pos[sorted_ids[N]]
#read the IDs of the particles belonging to the CDM halos
halos_ID=readsubf.subf_ids(groups_fname,groups_number,0,0,
long_ids=True,read_all=True)
IDs=halos_ID.SubIDs-1
del halos_ID
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
if mass_criteria=='t200':
halos_mass=halos.group_m_tophat200*1e10 #masses in Msun/h
halos_radius=halos.group_r_tophat200 #radius in kpc/h
elif mass_criteria=='m200':
halos_mass=halos.group_m_mean200*1e10 #masses in Msun/h
halos_radius=halos.group_r_mean200 #radius in kpc/h
elif mass_criteria=='c200':
halos_mass=halos.group_m_crit200*1e10 #masses in Msun/h
halos_radius=halos.group_r_crit200 #radius in kpc/h
else:
print 'bad mass_criteria'
sys.exit()
halos_pos=halos.group_pos #positions in kpc/h
halos_len=halos.group_len
halos_offset=halos.group_offset
halos_indexes=np.where((halos_mass>min_mass) & (halos_mass<max_mass))[0]
del halos
if verbose:
print ' '
print 'total halos found=',halos_pos.shape[0]
print 'halos number density=',len(halos_pos)/(BoxSize*1e-3)**3
#keep only the halos in the given mass range
halo_mass=halos_mass[halos_indexes]
halo_pos=halos_pos[halos_indexes]
halo_radius=halos_radius[halos_indexes]
halo_len=halos_len[halos_indexes]
halo_offset=halos_offset[halos_indexes]
del halos_indexes
##### COMPUTE Mmin GIVEN M1 & alpha #####
i=0; max_iterations=20 #maximum number of iterations
Mmin1=min_mass; Mmin2=max_mass
while (i<max_iterations):
Mmin=0.5*(Mmin1+Mmin2) #estimation of the HOD parameter Mmin
total_galaxies=0
inside=np.where(halo_mass>Mmin)[0] #take all galaxies with M>Mmin
mass=halo_mass[inside] #only halos with M>Mmin have central/satellites
total_galaxies=mass.shape[0]+np.sum((mass/M1)**alpha)
mean_density=total_galaxies*1.0/(BoxSize*1e-3)**3 #galaxies/(Mpc/h)^3
if (np.absolute((mean_density-fiducial_density)/fiducial_density)<thres):
i=max_iterations
elif (mean_density>fiducial_density):
Mmin1=Mmin
else:
Mmin2=Mmin
i+=1
if verbose:
print ' '
print 'Mmin=',Mmin
print 'average number of galaxies=',total_galaxies
print 'average galaxy density=',mean_density
#########################################
#just halos with M>Mmin; the rest do not host central/satellite galaxies
inside=np.where(halo_mass>Mmin)[0]
halo_mass=halo_mass[inside]
halo_pos=halo_pos[inside]
halo_radius=halo_radius[inside]
halo_len=halo_len[inside]
halo_offset=halo_offset[inside]
del inside
#compute number of satellites in each halo using the Poisson distribution
N_mean_sat=(halo_mass/M1)**alpha #mean number of satellites
N_sat=np.empty(len(N_mean_sat),dtype=np.int32)
for i in range(len(N_sat)):
N_sat[i]=np.random.poisson(N_mean_sat[i])
N_tot=np.sum(N_sat)+len(halo_mass) #total number of galaxies in the catalogue
if verbose:
print ' '
print np.min(halo_mass),'< M_halo <',np.max(halo_mass)
print 'total number of galaxies=',N_tot
print 'galaxy number density=',N_tot/(BoxSize*1e-3)**3
#put satellites following the distribution of dark matter in groups
if verbose:
print ' '
print 'Creating mock catalogue ...',
pos_galaxies=np.empty((N_tot,3),dtype=np.float32)
#index: variable that go through halos (may be several galaxies in a halo)
#i: variable that go through all (central/satellites) galaxies
#count: find number of galaxies that lie beyond its host halo virial radius
index=0; count=0; i=0
while (index<halo_mass.shape[0]):
position=halo_pos[index] #position of the DM halo
radius=halo_radius[index] #radius of the DM halo
#save the position of the central galaxy
pos_galaxies[i]=position; i+=1
#if halo contains satellites, save their positions
Nsat=N_sat[index]
if Nsat>0:
offset=halo_offset[index]
length=halo_len[index]
idss=sorted_ids[IDs[offset:offset+length]]
#compute the distances to the halo center keeping those with R<Rvir
pos=DM_pos[idss] #positions of the particles belonging to the halo
posc=pos-position
#this is to populate correctly halos closer to box boundaries
if np.any((position+radius>BoxSize) + (position-radius<0.0)):
inside=np.where(posc[:,0]>BoxSize/2.0)[0]
posc[inside,0]-=BoxSize
inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
posc[inside,0]+=BoxSize
inside=np.where(posc[:,1]>BoxSize/2.0)[0]
posc[inside,1]-=BoxSize
inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
posc[inside,1]+=BoxSize
inside=np.where(posc[:,2]>BoxSize/2.0)[0]
posc[inside,2]-=BoxSize
inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
posc[inside,2]+=BoxSize
radii=np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2)
inside=np.where(radii<radius)[0]
selected=random.sample(inside,Nsat)
pos=pos[selected]
#aditional, not esential check. Can be comment out
posc=pos-position
if np.any((posc>BoxSize/2.0) + (posc<-BoxSize/2.0)):
inside=np.where(posc[:,0]>BoxSize/2.0)[0]
posc[inside,0]-=BoxSize
inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
posc[inside,0]+=BoxSize
inside=np.where(posc[:,1]>BoxSize/2.0)[0]
posc[inside,1]-=BoxSize
inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
posc[inside,1]+=BoxSize
inside=np.where(posc[:,2]>BoxSize/2.0)[0]
posc[inside,2]-=BoxSize
inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
posc[inside,2]+=BoxSize
r_max=np.max(np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2))
if r_max>radius: #check no particles beyond Rv selected
print position
print radius
print pos
count+=1
for j in range(Nsat):
pos_galaxies[i]=pos[j]; i+=1
index+=1
if verbose:
print 'done'
#some final checks
if i!=N_tot:
print 'some galaxies missing:'
print 'register',i,'galaxies out of',N_tot
if count>0:
print 'error:',count,'particles beyond the virial radius selected'
return pos_galaxies
def mass_function_fsigma(groups_fname, groups_number, f_out, min_mass,
max_mass, bins, BoxSize, obj, Omega_M, k, Pk):
rhoM = rho_crit * Omega_M
bins_mass = np.logspace(np.log10(min_mass), np.log10(max_mass), bins + 1)
mass_mean = 10**(0.5 *
(np.log10(bins_mass[1:]) + np.log10(bins_mass[:-1])))
if obj == 'FoF':
#read FoF halos information
fof = readfof.FoF_catalog(groups_fname,
groups_number,
long_ids=True,
swap=False)
F_pos = fof.GroupPos / 1e3 #positions in Mpc/h
F_mass = fof.GroupMass * 1e10 #masses in Msun/h
F_part = fof.GroupLen #number particles belonging to the group
F_Mpart = F_mass[0] / F_part[0] #mass of a single particle in Msun/h
del fof
#Correct the masses of the FoF halos
F_mass = F_Mpart * (F_part * (1.0 - F_part**(-0.6)))
#some verbose
print 'Number of FoF halos=', len(F_pos)
print np.min(F_pos[:, 0]), '< X_fof <', np.max(F_pos[:, 0])
print np.min(F_pos[:, 1]), '< Y_fof <', np.max(F_pos[:, 1])
print np.min(F_pos[:, 2]), '< Z_fof <', np.max(F_pos[:, 2])
print np.min(F_mass), '< M_fof <', np.max(F_mass)
number = np.histogram(F_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
elif obj == 'halos_m200':
#read CDM halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=True,
swap=False)
H_pos = halos.group_pos / 1e3 #positions in Mpc/h
H_mass = halos.group_m_mean200 * 1e10 #masses in Msun/h
H_radius_m = halos.group_r_mean200 / 1e3 #radius in Mpc/h
del halos
#some verbose
print 'Number of halos=', len(H_pos)
print np.min(H_pos[:, 0]), '< X_fof <', np.max(H_pos[:, 0])
print np.min(H_pos[:, 1]), '< Y_fof <', np.max(H_pos[:, 1])
print np.min(H_pos[:, 2]), '< Z_fof <', np.max(H_pos[:, 2])
print np.min(H_mass), '< M_fof <', np.max(H_mass)
number = np.histogram(H_mass, bins=bins_mass)[0]
print number
print np.sum(number, dtype=np.float64)
else:
print 'bad object type selected'
sys.exit()
sigma_mean = np.empty(bins)
f_sigma = np.empty(bins)
delta_f_sigma = np.empty(bins)
for i in range(bins):
R1 = (3.0 * bins_mass[i] / (4.0 * pi * rhoM))**(1.0 / 3.0)
sigma1 = sigma(k, Pk, R1)
R2 = (3.0 * bins_mass[i + 1] / (4.0 * pi * rhoM))**(1.0 / 3.0)
sigma2 = sigma(k, Pk, R2)
sigma_mean[i] = 0.5 * (sigma2 + sigma1)
f_sigma[i] = (number[i] / np.log(sigma2 / sigma1)) / BoxSize**3
f_sigma[i] = -(mass_mean[i] / rhoM) * f_sigma[i]
delta_f_sigma[i] = (np.sqrt(number[i]) /
np.log(sigma2 / sigma1)) / BoxSize**3
delta_f_sigma[i] = -(mass_mean[i] / rhoM) * delta_f_sigma[i]
f = open(f_out, 'w')
for i in range(bins):
f.write(
str(sigma_mean[i]) + ' ' + str(f_sigma[i]) + ' ' +
str(delta_f_sigma[i]) + ' ' + str(mass_mean[i]) + '\n')
f.close()
sys.exit()
#just keep with the particles having HI masses
M_HI = M_HI[IDs]
pos = pos[IDs]
R = R[IDs]
del IDs
#compute the value of Omega_HI
print 'Omega_HI = %e' % (np.sum(M_HI, dtype=np.float64) / BoxSize**3 /
rho_crit)
#read FoF/Subfind halos information
halos = readsubf.subfind_catalog(groups_fname,
groups_number,
group_veldisp=True,
masstab=True,
long_ids=long_ids_flag,
swap=False)
pos_SO = halos.group_pos / 1e3 #Mpc/h
M_SO = halos.group_m_mean200 * 1e10 #Msun/h. SO mass
R_SO = halos.group_r_mean200 / 1e3 #Mpc/h
M_FoF = halos.group_mass * 1e10 #Msun/h. FoF mass
del halos
"""#write X-Y positions and R of the halos
f=open('borrar.dat','w')
for i in range(len(R_SO)):
print i
if M_FoF[i]>8.75e8:
f.write(str(pos_SO[i,0])+' '+str(pos_SO[i,1])+' '+str(pos_SO[i,2])+\
' '+str(R_SO[i])+' '+str(M_FoF[i])+'\n')
f.close()"""
def recenter_halo(halo): import numpy as np import readsubf # Initialize variables: base = halo.base snap_type = halo.snap_type snap_name = halo.snap_name snap_num = halo.snap_num sub_id = halo.sub_id code_type = halo.code_type res = halo.res save_dir = halo.save_dir sub_id = halo.sub_id gal_pos = halo.gal_pos gal_vel = halo.gal_vel gal_mass = halo.gal_mass gal_pot = halo.gal_pot gal_T_gas = halo.gal_T_gas gal_rho_gas = halo.gal_rho_gas gal_age = halo.gal_age type_thresh = halo.type_thresh # Move to the frame where the center of the galaxy is at rest, and at the origin: print "Re-centering..." if sub_id != -1: cat = readsubf.subfind_catalog(base,snap_num,masstab=True) gal_cent_pos = cat.sub_pos[sub_id] gal_cent_vel = cat.sub_vel[sub_id] for j in np.arange(3): gal_pos[j] = gal_pos[j] - gal_cent_pos gal_vel[j] = gal_vel[j] - gal_cent_vel # Cut off stars at a certain distance from the center: set by kpc_cutoff kpc_cutoff = 30. halo.kpc_cutoff = kpc_cutoff # First a crude cut (to save memory): for j in np.arange(3): #particle types for k in np.arange(3): #dimensions (x,y,z) bool_cut = abs(gal_pos[j][:,k]) < kpc_cutoff if bool_cut.sum() < type_thresh: print "Not enough particles within kpc_cutoff" halo.err_flag = 1 return else: gal_pos[j] = gal_pos[j][bool_cut] gal_vel[j] = gal_vel[j][bool_cut] gal_mass[j] = gal_mass[j][bool_cut] gal_pot[j] = gal_pot[j][bool_cut] if j == 0: gal_T_gas = gal_T_gas[bool_cut] gal_rho_gas = gal_rho_gas[bool_cut] elif j == 2: gal_age = gal_age[bool_cut] # Better cut (radial): #Calculate radii: radii = [0]*3 for j in np.arange(3): radii[j] = np.zeros(len(gal_pos[j])) for k in np.arange(len(gal_pos[j])): radii[j][k] = np.sqrt(np.dot(gal_pos[j][k],gal_pos[j][k])) for j in np.arange(3): #particle types bool_cut2 = abs(radii[j]) < kpc_cutoff if bool_cut2.sum() < type_thresh: print "Not enough particles within kpc_cutoff" halo.err_flag = 1 return else: gal_pos[j] = gal_pos[j][bool_cut2] gal_vel[j] = gal_vel[j][bool_cut2] gal_mass[j] = gal_mass[j][bool_cut2] gal_pot[j] = gal_pot[j][bool_cut2] radii[j] = radii[j][bool_cut2] if j == 0: gal_T_gas = gal_T_gas[bool_cut2] gal_rho_gas = gal_rho_gas[bool_cut2] elif j == 2: gal_age = gal_age[bool_cut2] # Calculate baryonic mass, stellar mass, gas fraction... gas_mass = gal_mass[0].sum() star_mass = gal_mass[2].sum() bary_mass = gas_mass + star_mass gas_frac = gas_mass/(gas_mass+star_mass) # Exporting variables halo.gal_pos = gal_pos halo.gal_vel = gal_vel halo.gal_mass= gal_mass halo.gal_pot = gal_pot halo.gal_T_gas = gal_T_gas halo.gal_rho_gas = gal_rho_gas halo.gal_age = gal_age halo.radii = radii halo.gas_mass = gas_mass halo.star_mass = star_mass halo.bary_mass = bary_mass halo.gas_frac = gas_frac
def mass_function_fsigma(groups_fname,groups_number,f_out,min_mass,max_mass,
bins,BoxSize,obj,Omega_M,k,Pk):
rhoM=rho_crit*Omega_M
bins_mass=np.logspace(np.log10(min_mass),np.log10(max_mass),bins+1)
mass_mean=10**(0.5*(np.log10(bins_mass[1:])+np.log10(bins_mass[:-1])))
if obj=='FoF':
#read FoF halos information
fof=readfof.FoF_catalog(groups_fname,groups_number,
long_ids=True,swap=False)
F_pos=fof.GroupPos/1e3 #positions in Mpc/h
F_mass=fof.GroupMass*1e10 #masses in Msun/h
F_part=fof.GroupLen #number particles belonging to the group
F_Mpart=F_mass[0]/F_part[0] #mass of a single particle in Msun/h
del fof
#Correct the masses of the FoF halos
F_mass=F_Mpart*(F_part*(1.0-F_part**(-0.6)))
#some verbose
print 'Number of FoF halos=',len(F_pos)
print np.min(F_pos[:,0]),'< X_fof <',np.max(F_pos[:,0])
print np.min(F_pos[:,1]),'< Y_fof <',np.max(F_pos[:,1])
print np.min(F_pos[:,2]),'< Z_fof <',np.max(F_pos[:,2])
print np.min(F_mass),'< M_fof <',np.max(F_mass)
number=np.histogram(F_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
elif obj=='halos_m200':
#read CDM halos information
halos=readsubf.subfind_catalog(groups_fname,groups_number,
group_veldisp=True,masstab=True,
long_ids=True,swap=False)
H_pos=halos.group_pos/1e3 #positions in Mpc/h
H_mass=halos.group_m_mean200*1e10 #masses in Msun/h
H_radius_m=halos.group_r_mean200/1e3 #radius in Mpc/h
del halos
#some verbose
print 'Number of halos=',len(H_pos)
print np.min(H_pos[:,0]),'< X_fof <',np.max(H_pos[:,0])
print np.min(H_pos[:,1]),'< Y_fof <',np.max(H_pos[:,1])
print np.min(H_pos[:,2]),'< Z_fof <',np.max(H_pos[:,2])
print np.min(H_mass),'< M_fof <',np.max(H_mass)
number=np.histogram(H_mass,bins=bins_mass)[0]
print number; print np.sum(number,dtype=np.float64)
else:
print 'bad object type selected'
sys.exit()
sigma_mean=np.empty(bins); f_sigma=np.empty(bins)
delta_f_sigma=np.empty(bins)
for i in range(bins):
R1=(3.0*bins_mass[i]/(4.0*pi*rhoM))**(1.0/3.0)
sigma1=sigma(k,Pk,R1)
R2=(3.0*bins_mass[i+1]/(4.0*pi*rhoM))**(1.0/3.0)
sigma2=sigma(k,Pk,R2)
sigma_mean[i]=0.5*(sigma2+sigma1)
f_sigma[i]=(number[i]/np.log(sigma2/sigma1))/BoxSize**3
f_sigma[i]=-(mass_mean[i]/rhoM)*f_sigma[i]
delta_f_sigma[i]=(np.sqrt(number[i])/np.log(sigma2/sigma1))/BoxSize**3
delta_f_sigma[i]=-(mass_mean[i]/rhoM)*delta_f_sigma[i]
f=open(f_out,'w')
for i in range(bins):
f.write(str(sigma_mean[i])+' '+str(f_sigma[i])+' '+str(delta_f_sigma[i])+' '+str(mass_mean[i])+'\n')
f.close()
