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Merged in python3 (pull request #5)
Port to python3, large code cleanup * Fixed command line for cosmotool * Fix path * Dump command line is log file * Fix important typo * Modify paths for example * Fix path again * Use an explicit constructor * Change file to open (python 2->3) * python3 fix for xrange in periodic_kdtree.py * Fixed index for Np, numPart, numZones, numZonesTot, partID, zoneID in catalogUtil.py
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392 changed files with 4092 additions and 260938 deletions
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import numpy as np
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import readsnap
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import readsubf
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import sys
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import time
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import random
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###############################################################################
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#this function returns an array containing the positions of the galaxies (kpc/h)
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#in the catalogue according to the fiducial density, M1 and alpha
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#CDM halos with masses within [min_mass,max_mass], are populated
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#with galaxies. The IDs and positions of the CDM particles belonging to the
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#different groups are read from the snapshots
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#If one needs to creates many catalogues, this function is not appropiate,
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#since it wastes a lot of time reading the snapshots and sorting the IDs
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#min_mass and max_mass are in units of Msun/h, not 1e10 Msun/h
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#mass_criteria: definition of the halo virial radius -- 't200' 'm200' 'c200'
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#fiducial_density: galaxy number density to be reproduced, in (h/Mpc)^3
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def hod(snapshot_fname,groups_fname,groups_number,min_mass,max_mass,
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fiducial_density,M1,alpha,mass_criteria,verbose=False):
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thres=1e-3 #controls the max relative error to accept a galaxy density
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#read the header and obtain the boxsize
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head=readsnap.snapshot_header(snapshot_fname)
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BoxSize=head.boxsize #BoxSize in kpc/h
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#read positions and IDs of DM particles: sort the IDs array
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DM_pos=readsnap.read_block(snapshot_fname,"POS ",parttype=-1) #kpc/h
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DM_ids=readsnap.read_block(snapshot_fname,"ID ",parttype=-1)-1
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sorted_ids=DM_ids.argsort(axis=0)
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#the particle whose ID is N is located in the position sorted_ids[N]
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#i.e. DM_ids[sorted_ids[N]]=N
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#the position of the particle whose ID is N would be:
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#DM_pos[sorted_ids[N]]
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#read the IDs of the particles belonging to the CDM halos
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halos_ID=readsubf.subf_ids(groups_fname,groups_number,0,0,
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long_ids=True,read_all=True)
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IDs=halos_ID.SubIDs-1
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del halos_ID
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#read CDM halos information
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halos=readsubf.subfind_catalog(groups_fname,groups_number,
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group_veldisp=True,masstab=True,
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long_ids=True,swap=False)
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if mass_criteria=='t200':
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halos_mass=halos.group_m_tophat200*1e10 #masses in Msun/h
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halos_radius=halos.group_r_tophat200 #radius in kpc/h
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elif mass_criteria=='m200':
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halos_mass=halos.group_m_mean200*1e10 #masses in Msun/h
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halos_radius=halos.group_r_mean200 #radius in kpc/h
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elif mass_criteria=='c200':
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halos_mass=halos.group_m_crit200*1e10 #masses in Msun/h
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halos_radius=halos.group_r_crit200 #radius in kpc/h
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else:
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print 'bad mass_criteria'
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sys.exit()
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halos_pos=halos.group_pos #positions in kpc/h
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halos_len=halos.group_len
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halos_offset=halos.group_offset
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halos_indexes=np.where((halos_mass>min_mass) & (halos_mass<max_mass))[0]
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del halos
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if verbose:
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print ' '
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print 'total halos found=',halos_pos.shape[0]
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print 'halos number density=',len(halos_pos)/(BoxSize*1e-3)**3
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#keep only the halos in the given mass range
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halo_mass=halos_mass[halos_indexes]
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halo_pos=halos_pos[halos_indexes]
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halo_radius=halos_radius[halos_indexes]
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halo_len=halos_len[halos_indexes]
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halo_offset=halos_offset[halos_indexes]
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del halos_indexes
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##### COMPUTE Mmin GIVEN M1 & alpha #####
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i=0; max_iterations=20 #maximum number of iterations
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Mmin1=min_mass; Mmin2=max_mass
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while (i<max_iterations):
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Mmin=0.5*(Mmin1+Mmin2) #estimation of the HOD parameter Mmin
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total_galaxies=0
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inside=np.where(halo_mass>Mmin)[0] #take all galaxies with M>Mmin
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mass=halo_mass[inside] #only halos with M>Mmin have central/satellites
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total_galaxies=mass.shape[0]+np.sum((mass/M1)**alpha)
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mean_density=total_galaxies*1.0/(BoxSize*1e-3)**3 #galaxies/(Mpc/h)^3
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if (np.absolute((mean_density-fiducial_density)/fiducial_density)<thres):
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i=max_iterations
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elif (mean_density>fiducial_density):
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Mmin1=Mmin
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else:
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Mmin2=Mmin
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i+=1
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if verbose:
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print ' '
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print 'Mmin=',Mmin
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print 'average number of galaxies=',total_galaxies
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print 'average galaxy density=',mean_density
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#########################################
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#just halos with M>Mmin; the rest do not host central/satellite galaxies
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inside=np.where(halo_mass>Mmin)[0]
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halo_mass=halo_mass[inside]
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halo_pos=halo_pos[inside]
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halo_radius=halo_radius[inside]
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halo_len=halo_len[inside]
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halo_offset=halo_offset[inside]
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del inside
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#compute number of satellites in each halo using the Poisson distribution
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N_mean_sat=(halo_mass/M1)**alpha #mean number of satellites
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N_sat=np.empty(len(N_mean_sat),dtype=np.int32)
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for i in range(len(N_sat)):
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N_sat[i]=np.random.poisson(N_mean_sat[i])
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N_tot=np.sum(N_sat)+len(halo_mass) #total number of galaxies in the catalogue
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if verbose:
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print ' '
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print np.min(halo_mass),'< M_halo <',np.max(halo_mass)
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print 'total number of galaxies=',N_tot
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print 'galaxy number density=',N_tot/(BoxSize*1e-3)**3
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#put satellites following the distribution of dark matter in groups
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if verbose:
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print ' '
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print 'Creating mock catalogue ...',
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pos_galaxies=np.empty((N_tot,3),dtype=np.float32)
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#index: variable that go through halos (may be several galaxies in a halo)
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#i: variable that go through all (central/satellites) galaxies
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#count: find number of galaxies that lie beyond its host halo virial radius
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index=0; count=0; i=0
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while (index<halo_mass.shape[0]):
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position=halo_pos[index] #position of the DM halo
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radius=halo_radius[index] #radius of the DM halo
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#save the position of the central galaxy
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pos_galaxies[i]=position; i+=1
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#if halo contains satellites, save their positions
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Nsat=N_sat[index]
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if Nsat>0:
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offset=halo_offset[index]
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length=halo_len[index]
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idss=sorted_ids[IDs[offset:offset+length]]
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#compute the distances to the halo center keeping those with R<Rvir
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pos=DM_pos[idss] #positions of the particles belonging to the halo
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posc=pos-position
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#this is to populate correctly halos closer to box boundaries
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if np.any((position+radius>BoxSize) + (position-radius<0.0)):
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inside=np.where(posc[:,0]>BoxSize/2.0)[0]
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posc[inside,0]-=BoxSize
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inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
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posc[inside,0]+=BoxSize
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inside=np.where(posc[:,1]>BoxSize/2.0)[0]
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posc[inside,1]-=BoxSize
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inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
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posc[inside,1]+=BoxSize
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inside=np.where(posc[:,2]>BoxSize/2.0)[0]
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posc[inside,2]-=BoxSize
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inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
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posc[inside,2]+=BoxSize
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radii=np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2)
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inside=np.where(radii<radius)[0]
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selected=random.sample(inside,Nsat)
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pos=pos[selected]
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#aditional, not esential check. Can be comment out
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posc=pos-position
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if np.any((posc>BoxSize/2.0) + (posc<-BoxSize/2.0)):
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inside=np.where(posc[:,0]>BoxSize/2.0)[0]
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posc[inside,0]-=BoxSize
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inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
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posc[inside,0]+=BoxSize
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inside=np.where(posc[:,1]>BoxSize/2.0)[0]
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posc[inside,1]-=BoxSize
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inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
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posc[inside,1]+=BoxSize
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inside=np.where(posc[:,2]>BoxSize/2.0)[0]
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posc[inside,2]-=BoxSize
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inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
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posc[inside,2]+=BoxSize
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r_max=np.max(np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2))
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if r_max>radius: #check no particles beyond Rv selected
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print position
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print radius
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print pos
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count+=1
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for j in range(Nsat):
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pos_galaxies[i]=pos[j]; i+=1
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index+=1
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if verbose:
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print 'done'
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#some final checks
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if i!=N_tot:
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print 'some galaxies missing:'
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print 'register',i,'galaxies out of',N_tot
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if count>0:
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print 'error:',count,'particles beyond the virial radius selected'
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return pos_galaxies
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###############################################################################
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#This function is equal to the above one, except that the snapshot read, halos
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#read and ID sorting it is not performing here. It is best suited when many
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#galaxy catalogues need to be created: for example, when iterating among M1 and
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#alpha trying to find the best combination that reproduces the measured wp(r)
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#VARIABLES:
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#DM_pos: array containing the positions of the CDM particles
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#sorted_ids: array containing the positions of the IDs in the snapshots.
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#sorted_ids[N] gives the position where the particle whose ID is N is located
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#IDs:IDs array as read from the subfind ID file
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#halo_mass: array containing the masses of the CDM halos in the mass interval
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#halo_pos: array containing the positions of the CDM halos in the mass interval
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#halo_radius: array containing the radii of the CDM halos in the mass interval
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#halo_len: array containing the len of the CDM halos in the mass interval
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#halo_offset: array containing the offset of the CDM halos in the mass interval
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#BoxSize: Size of the simulation Box. In Mpc/h
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#fiducial_density: galaxy number density to be reproduced, in (h/Mpc)^3
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def hod_fast(DM_pos,sorted_ids,IDs,halo_mass,halo_pos,halo_radius,halo_len,
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halo_offset,BoxSize,min_mass,max_mass,fiducial_density,
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M1,alpha,seed,verbose=False):
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problematic_cases=0 #number of problematic cases (e.g. halos with Rvir=0.0)
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thres=1e-3 #controls the max relative error to accept a galaxy density
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##### COMPUTE Mmin GIVEN M1 & alpha #####
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i=0; max_iterations=20 #maximum number of iterations
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Mmin1=min_mass; Mmin2=max_mass
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while (i<max_iterations):
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Mmin=0.5*(Mmin1+Mmin2) #estimation of the HOD parameter Mmin
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total_galaxies=0
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inside=np.where(halo_mass>Mmin)[0]
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mass=halo_mass[inside] #only halos with M>Mmin have central/satellites
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total_galaxies=mass.shape[0]+np.sum((mass/M1)**alpha)
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mean_density=total_galaxies*1.0/BoxSize**3
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if (np.absolute((mean_density-fiducial_density)/fiducial_density)<thres):
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i=max_iterations
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elif (mean_density>fiducial_density):
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Mmin1=Mmin
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else:
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Mmin2=Mmin
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i+=1
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if verbose:
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print ' '
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print 'Mmin=',Mmin
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print 'average number of galaxies=',total_galaxies
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print 'average galaxy density=',mean_density
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#########################################
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#just halos with M>Mmin; the rest do not host central/satellite galaxies
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inside=np.where(halo_mass>Mmin)[0]
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halo_mass=halo_mass[inside]
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halo_pos=halo_pos[inside]
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halo_radius=halo_radius[inside]
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halo_len=halo_len[inside]
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halo_offset=halo_offset[inside]
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del inside
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#compute number of satellites in each halo using the Poisson distribution
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np.random.seed(seed) #this is just to check convergence on w_p(r_p)
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N_mean_sat=(halo_mass/M1)**alpha #mean number of satellites
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N_sat=np.empty(len(N_mean_sat),dtype=np.int32)
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for i in range(len(N_sat)):
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N_sat[i]=np.random.poisson(N_mean_sat[i])
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N_tot=np.sum(N_sat)+len(halo_mass) #total number of galaxies in the catalogue
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if verbose:
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print ' '
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print np.min(halo_mass),'< M_halo <',np.max(halo_mass)
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print 'total number of galaxies=',N_tot
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print 'galaxy number density=',N_tot/BoxSize**3
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#put satellites following the distribution of dark matter in groups
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if verbose:
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print ' '
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print 'Creating mock catalogue ...',
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pos_galaxies=np.empty((N_tot,3),dtype=np.float32)
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#index: variable that go through halos (may be several galaxies in a halo)
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#i: variable that go through galaxies
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#count: find number of galaxies that lie beyond its host halo virial radius
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random.seed(seed) #this is just to check convergence on w_p(r_p)
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index=0; count=0; i=0
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while (index<halo_mass.size):
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position=halo_pos[index] #position of the DM halo
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radius=halo_radius[index] #radius of the DM halo
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#save the position of the central galaxy
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pos_galaxies[i]=position; i+=1
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#if halo contains satellites, save their positions
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Nsat=N_sat[index]
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if Nsat>0:
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offset=halo_offset[index]
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length=halo_len[index]
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idss=sorted_ids[IDs[offset:offset+length]]
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#compute the radius of those particles and keep those with R<Rvir
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pos=DM_pos[idss]
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posc=pos-position
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#this is to populate correctly halos closer to box boundaries
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if np.any((position+radius>BoxSize) + (position-radius<0.0)):
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inside=np.where(posc[:,0]>BoxSize/2.0)[0]
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posc[inside,0]-=BoxSize
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inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
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posc[inside,0]+=BoxSize
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inside=np.where(posc[:,1]>BoxSize/2.0)[0]
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posc[inside,1]-=BoxSize
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inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
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posc[inside,1]+=BoxSize
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inside=np.where(posc[:,2]>BoxSize/2.0)[0]
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posc[inside,2]-=BoxSize
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inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
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posc[inside,2]+=BoxSize
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radii=np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2)
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inside=np.where(radii<radius)[0]
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if len(inside)<Nsat:
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problematic_cases+=1
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print 'problematic case',len(inside),Nsat
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else:
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selected=random.sample(inside,Nsat)
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pos=pos[selected]
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#aditional, not esential check. Can be comment out
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#posc=pos-position
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#if np.any((posc>BoxSize/2.0) + (posc<-BoxSize/2.0)):
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# inside=np.where(posc[:,0]>BoxSize/2.0)[0]
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# posc[inside,0]-=BoxSize
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# inside=np.where(posc[:,0]<-BoxSize/2.0)[0]
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# posc[inside,0]+=BoxSize
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# inside=np.where(posc[:,1]>BoxSize/2.0)[0]
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# posc[inside,1]-=BoxSize
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# inside=np.where(posc[:,1]<-BoxSize/2.0)[0]
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# posc[inside,1]+=BoxSize
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# inside=np.where(posc[:,2]>BoxSize/2.0)[0]
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# posc[inside,2]-=BoxSize
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# inside=np.where(posc[:,2]<-BoxSize/2.0)[0]
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# posc[inside,2]+=BoxSize
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#r_max=np.max(np.sqrt(posc[:,0]**2+posc[:,1]**2+posc[:,2]**2))
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#if r_max>radius: #check no particles beyond Rv selected
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# print position
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# print radius
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# print pos
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# count+=1
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for j in range(Nsat):
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pos_galaxies[i]=pos[j]; i+=1
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index+=1
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if verbose:
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print 'done'
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#some final checks
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if i!=N_tot:
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print 'some galaxies missing:'
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print 'register',i,'galaxies out of',N_tot
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if count>0:
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print 'error:',count,'particles beyond the virial radius selected'
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return pos_galaxies
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###############################################################################
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##### example of use #####
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"""
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snapshot_fname='/data1/villa/b500p512nu0.6z99np1024tree/snapdir_017/snap_017'
|
||||
groups_fname='/home/villa/data1/b500p512nu0.6z99np1024tree'
|
||||
groups_number=17
|
||||
|
||||
### HALO CATALOGUE PARAMETERS ###
|
||||
mass_criteria='t200'
|
||||
min_mass=2e12 #Msun/h
|
||||
max_mass=2e15 #Msun/h
|
||||
|
||||
### HOD PARAMETERS ###
|
||||
fiducial_density=0.00111 #mean number density for galaxies with Mr<-21
|
||||
M1=8e13
|
||||
alpha=1.4
|
||||
|
||||
pos=hod(snapshot_fname,groups_fname,groups_number,min_mass,max_mass,fiducial_density,M1,alpha,mass_criteria,verbose=True)
|
||||
|
||||
print pos
|
||||
"""
|
||||
#+
|
||||
# VIDE -- Void IDentification and Examination -- ./python_tools/fit_hod/HOD_library.py
|
||||
# Copyright (C) 2010-2014 Guilhem Lavaux
|
||||
# Copyright (C) 2011-2014 P. M. Sutter
|
||||
#
|
||||
# This program is free software; you can redistribute it and/or modify
|
||||
# it under the terms of the GNU General Public License as published by
|
||||
# the Free Software Foundation; version 2 of the License.
|
||||
#
|
||||
#
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU General Public License for more details.
|
||||
#
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
#+
|
||||
import numpy as np
|
||||
import readsnap
|
||||
import readsubf
|
||||
import sys
|
||||
import time
|
||||
import random
|
||||
|
||||
###############################################################################
|
||||
#this function returns an array containing the positions of the galaxies (kpc/h)
|
||||
#in the catalogue according to the fiducial density, M1 and alpha
|
||||
#CDM halos with masses within [min_mass,max_mass], are populated
|
||||
#with galaxies. The IDs and positions of the CDM particles belonging to the
|
||||
#different groups are read from the snapshots
|
||||
#If one needs to creates many catalogues, this function is not appropiate,
|
||||
#since it wastes a lot of time reading the snapshots and sorting the IDs
|
||||
#min_mass and max_mass are in units of Msun/h, not 1e10 Msun/h
|
||||
#mass_criteria: definition of the halo virial radius -- 't200' 'm200' 'c200'
|
||||
#fiducial_density: galaxy number density to be reproduced, in (h/Mpc)^3
|
||||
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 ...', end=' ')
|
||||
|
||||
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
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#This function is equal to the above one, except that the snapshot read, halos
|
||||
#read and ID sorting it is not performing here. It is best suited when many
|
||||
#galaxy catalogues need to be created: for example, when iterating among M1 and
|
||||
#alpha trying to find the best combination that reproduces the measured wp(r)
|
||||
#VARIABLES:
|
||||
#DM_pos: array containing the positions of the CDM particles
|
||||
#sorted_ids: array containing the positions of the IDs in the snapshots.
|
||||
#sorted_ids[N] gives the position where the particle whose ID is N is located
|
||||
#IDs:IDs array as read from the subfind ID file
|
||||
#halo_mass: array containing the masses of the CDM halos in the mass interval
|
||||
#halo_pos: array containing the positions of the CDM halos in the mass interval
|
||||
#halo_radius: array containing the radii of the CDM halos in the mass interval
|
||||
#halo_len: array containing the len of the CDM halos in the mass interval
|
||||
#halo_offset: array containing the offset of the CDM halos in the mass interval
|
||||
#BoxSize: Size of the simulation Box. In Mpc/h
|
||||
#fiducial_density: galaxy number density to be reproduced, in (h/Mpc)^3
|
||||
def hod_fast(DM_pos,sorted_ids,IDs,halo_mass,halo_pos,halo_radius,halo_len,
|
||||
halo_offset,BoxSize,min_mass,max_mass,fiducial_density,
|
||||
M1,alpha,seed,verbose=False):
|
||||
|
||||
problematic_cases=0 #number of problematic cases (e.g. halos with Rvir=0.0)
|
||||
thres=1e-3 #controls the max relative error to accept a galaxy density
|
||||
|
||||
##### 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]
|
||||
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**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
|
||||
np.random.seed(seed) #this is just to check convergence on w_p(r_p)
|
||||
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**3)
|
||||
|
||||
#put satellites following the distribution of dark matter in groups
|
||||
if verbose:
|
||||
print(' ')
|
||||
print('Creating mock catalogue ...', end=' ')
|
||||
|
||||
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 galaxies
|
||||
#count: find number of galaxies that lie beyond its host halo virial radius
|
||||
random.seed(seed) #this is just to check convergence on w_p(r_p)
|
||||
index=0; count=0; i=0
|
||||
while (index<halo_mass.size):
|
||||
|
||||
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 radius of those particles and keep those with R<Rvir
|
||||
pos=DM_pos[idss]
|
||||
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]
|
||||
if len(inside)<Nsat:
|
||||
problematic_cases+=1
|
||||
print('problematic case',len(inside),Nsat)
|
||||
else:
|
||||
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
|
||||
###############################################################################
|
||||
|
||||
|
||||
|
||||
|
||||
##### example of use #####
|
||||
"""
|
||||
snapshot_fname='/data1/villa/b500p512nu0.6z99np1024tree/snapdir_017/snap_017'
|
||||
groups_fname='/home/villa/data1/b500p512nu0.6z99np1024tree'
|
||||
groups_number=17
|
||||
|
||||
### HALO CATALOGUE PARAMETERS ###
|
||||
mass_criteria='t200'
|
||||
min_mass=2e12 #Msun/h
|
||||
max_mass=2e15 #Msun/h
|
||||
|
||||
### HOD PARAMETERS ###
|
||||
fiducial_density=0.00111 #mean number density for galaxies with Mr<-21
|
||||
M1=8e13
|
||||
alpha=1.4
|
||||
|
||||
pos=hod(snapshot_fname,groups_fname,groups_number,min_mass,max_mass,fiducial_density,M1,alpha,mass_criteria,verbose=True)
|
||||
|
||||
print pos
|
||||
"""
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue