age = np.array([],dtype="float64")
pot = np.array([],dtype="float64")
for parttype in parttype_list:
ids = rs.read_block(snapname,"ID ",parttype=parttype)
all_ids = np.append(all_ids, ids)
types = np.append(types, np.zeros(len(ids))+parttype)
ngas = (types == 0).sum()
ndm = (types == 1).sum()
nstar = (types == 4).sum()
maxid = np.max(all_ids)
all_bound_ids = readsubf.subf_ids(base,snapnum,0, 0, long_ids=True,read_all=True).SubIDs
rev = np.zeros(maxid+1) - 1
rev[all_bound_ids] = np.arange(len(all_bound_ids),dtype="uint32")
r = rev[all_ids]
index1 = r > -1
index2 = np.argsort(r[index1])
#=================================================================
# List which blocks are in the snapshot:
rs.list_blocks(snapname+".0.hdf5")
print "\n Are these the required blocks?"
raw_input("Press Enter to continue...")
if myrank==0:
#read positions and IDs of DM particles: sort the IDs array
DM_pos=readsnap.read_block(snapshot_fname,"POS ",parttype=-1)
DM_ids=readsnap.read_block(snapshot_fname,"ID ",parttype=-1)
print len(DM_ids),np.min(DM_ids),np.max(DM_ids)
sorted_ids=DM_ids.argsort(axis=0)
del DM_ids
#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
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
DM_pos = readsnap.read_block(snapshot_fname,"POS ",parttype=-1) #kpc/h
DM_vel = readsnap.read_block(snapshot_fname,"VEL ",parttype=-1) #km/s
#IDs should go from 0 to N-1, instead from 1 to N
DM_ids = readsnap.read_block(snapshot_fname,"ID ",parttype=-1)-1
if np.min(DM_ids)!=0 or np.max(DM_ids)!=(len(DM_pos)-1):
print 'Error!!!!'; print 'IDs should go from 0 to N-1'
print len(DM_ids),np.min(DM_ids),np.max(DM_ids)
sorted_ids = DM_ids.argsort(axis=0); del DM_ids
#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
#again the IDs should go from 0 to N-1
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
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
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]]
