def __init__(self,catdir,snapnum,nbins,rmin=1e-2,rmax=1.,useSubhaloID=False,\
useFOF=False, useStellarhalfmassRad=None, useReduced=False, radinkpc=False,\
NR=False, binwidth=0.1, debug=False, useSubhaloes=False, testconvergence=False,):
assert type(nbins) is int and nbins >= 0, 'parameter nbins must be int'
# Set main parameters
if catdir.find('/output') < 0:
self.snapdir = catdir + '/output/'
else:
self.snapdir = catdir
self.snapnum = snapnum
self.useSubhaloID = useSubhaloID
self.useStellarhalfmassRad = False
self.radinkpc = radinkpc
print '\n\tAxialRatio: ', self.snapdir
# Read snapshot header for boxsize
snapstr = str(snapnum).zfill(3)
self.header = readsnapHDF5.snapshot_header(self.snapdir + '/snapdir_' +
snapstr + '/snap_' +
snapstr)
self.boxsize = self.header.boxsize
print '\tAxialRatio: Boxsize =', self.boxsize
self.parttypes = [1]
if self.header.cooling == 1:
self.parttypes.append(0)
if self.header.sfr == 1:
self.parttypes.append(4)
print '\tAvailable parttypes =', self.parttypes
# Set other parameters
self.setparams(nbins,rmin,rmax,useFOF,useStellarhalfmassRad,useReduced,NR,\
binwidth, debug, testconvergence)
# Read SUBFIND catalogue
keysel = [
"Group_R_Crit200", "GroupFirstSub", "Group_M_Crit200", "GroupPos"
]
if useSubhaloes is True:
print 'adding keysel for subhaloes'
keysel += ["GroupNsubs", "SubhaloPos", "SubhaloMass"]
if useSubhaloID is True:
print 'adding keysel for using SubhaloIDs'
assert useStellarhalfmassRad is True
assert useFOF is False
if useStellarhalfmassRad is True:
assert self.header.sfr == 1, 'Simulation does not have stars'
print 'adding keysel for StellarhalfmassRad'
keysel += [
"SubhaloPos", "SubhaloMassInRadType", "SubhaloHalfmassRadType"
]
self.cat = readsubfHDF5.subfind_catalog(self.snapdir,
snapnum,
keysel=keysel)
def __init__(self,
base,
num,
long_ids=False,
snapbase='snap',
double_output=False,
verbose=False,
run=None):
self.base = base
self.snapbase = snapbase
self.num = num
self.num_pad = str(num).zfill(3)
self.verbose = verbose
self.part_types = [0, 1, 4, 5]
keysel = [
"ngroups", "nsubs", "GroupLenType", "GroupNsubs", "GroupFirstSub",
"SubhaloLenType"
]
self.cat = readsubfHDF5.subfind_catalog(base,
num,
long_ids=long_ids,
double_output=double_output,
keysel=keysel)
if not hasattr(self.cat, "GroupLenType"):
raise RuntimeError("Subfind catalog has no group or subhalo "
"information.")
self.filenames = naming.get_snap_filenames(self.base, self.snapbase,
self.num)
offsets = readsubfHDF5.get_offsets(self.cat, self.part_types, self.num,
run)
self.group_offset, self.halo_offset = offsets
head = readsnapHDF5.snapshot_header(self.filenames[0])
self.file_num = head.filenum
assert (self.file_num == len(self.filenames))
ntypes = 6
self.file_type_numbers = np.zeros([self.file_num, ntypes],
dtype="int64")
cumcount = np.zeros(ntypes, dtype="int64")
# Store in file_type_numbers[i, :] the cumulative number of particles
# in all previous files. Note we never need to open the last file.
for i in range(0, self.file_num - 1):
if self.verbose:
print("READHALO: initial read of file: %s" % self.filenames[i])
head = readsnapHDF5.snapshot_header(self.filenames[i])
cumcount[:] += head.npart[:]
self.file_type_numbers[i + 1, :] = cumcount[:]
def __init__(self,catdir,snapnum=135,savepartids=False,\
GetRotMat=True,RotateParticles=False,GetCoef=True,GetInit=True,GetInitStar=False):
self.catdir=catdir
self.snapnum=snapnum
self.cat=readsubfHDF5.subfind_catalog(catdir,snapnum,keysel=keysel)
#self.rvir=self.cat.Group_R_Crit200[groupid]/h*kpc
self.savepartids=savepartids
self.RotateParticles=RotateParticles
self.GetRotMat=GetRotMat
self.GetCoef=GetCoef
self.GetInit=GetInit
self.GetInitStar=GetInitStar
print 'You have chosen the following:'
if self.RotateParticles: print 'Rotate particles using ',self.RotateParticles
if self.GetRotMat: print 'Save rotmat'
if self.GetCoef: print 'Save Knlm coefs'
if self.GetInit: print 'Save DM init file'
if self.GetInitStar: print 'Save Stellar init file'
self.fields={1:["Coordinates","Velocities"],4:["Coordinates","Velocities","Masses"],\
0:["Coordinates","InternalEnergy","Masses","ElectronAbundance"]}
if catdir.find('1820DM') >0:
self.whichdm='1820DM'
self.dmpartmass=0.00052946428432085776
elif catdir.find('1820FP') >0:
self.whichdm='1820FP'
self.dmpartmass=0.00044089652436109581
elif catdir.find('910DM') >0:
self.whichdm='910DM'
self.dmpartmass=0.0042357142745668621
elif catdir.find('910FP') >0:
self.whichdm='910FP'
self.dmpartmass=0.0035271721948887664
else:
raise ValueError('Cant find DM or FP!')
print self.catdir,self.snapnum
print self.whichdm,self.dmpartmass
if self.whichdm.find('DM')>0:
if self.GetInitStar:
self.GetInitStar=False
print 'Warning: no stellar init for DMO runs!'
#self.catdir=cat.filebase.split(self.whichdm)[0]+self.whichdm+'/output/'
N=self.cat.filebase.find('/groups_')
if int(self.cat.filebase[N+8:N+11]) != self.snapnum:
raise ValueError('Mismatching specification of SnapNum with Subfind catalogue')
def find_vt_tracer_ids(snap_num,sub_id,base,scale_factor,gal_radfac): print "Finding velocity tracer IDs..." cat = readsubfHDF5.subfind_catalog(base, snap_num) # I assume that the subhalo is the primary subhalo in its group sub_list = cat.GroupFirstSub grp_id = np.argmin( np.abs(sub_id-sub_list) ) sub_pos = cat.SubhaloPos[sub_id] sub_mass = cat.SubhaloMass[sub_id] sub_Rvir = cat.Group_R_Crit200[grp_id] gal_rad = sub_Rvir * gal_radfac #* 0.1 #snapname = base + "snapdir_"+str(snap_num).zfill(3)+"/snap_"+str(snap_num).zfill(3) snapname = base + "snap_"+str(snap_num).zfill(3) #Find all velocity tracers within 0.1 Rvir of the subhalo vt_pos = readsnapHDF5.read_block(snapname,"POS ",parttype=2) vt_vel = readsnapHDF5.read_block(snapname,"VEL ",parttype=2) vt_ids = readsnapHDF5.read_block(snapname,"ID ",parttype=2) #readsnapHDF5.list_blocks(snapname+".hdf5") vt_x = np.logical_and(vt_pos[:,0] > sub_pos[0]-gal_rad, vt_pos[:,0] < sub_pos[0]+gal_rad) vt_y = np.logical_and(vt_pos[:,1] > sub_pos[1]-gal_rad, vt_pos[:,1] < sub_pos[1]+gal_rad) vt_z = np.logical_and(vt_pos[:,2] > sub_pos[2]-gal_rad, vt_pos[:,2] < sub_pos[2]+gal_rad) vt_ind = np.logical_and(np.logical_and(vt_x,vt_y),vt_z) gal_vt_pos = vt_pos[vt_ind] - sub_pos #gal_vt_vel = vt_vel[vt_ind] gal_vt_rad = np.sqrt(gal_vt_pos[:,0]**2 + gal_vt_pos[:,1]**2 + gal_vt_pos[:,2]**2) vt_ind2 = gal_vt_rad < gal_rad gal_vt_pos = gal_vt_pos[vt_ind2] #gal_vt_vel = gal_vt_vel[vt_ind2] gal_vt_ids = vt_ids[vt_ind][vt_ind2] print "Done finding velocity tracer IDs!" return gal_vt_ids
def get_subhalo_ids(base,snap_num,sub_id): cat = readsubfHDF5.subfind_catalog(base, snap_num) sub_list = cat.GroupFirstSub grp_id = np.argmin( np.abs(sub_id-sub_list) ) #snapname = base + "snapdir_"+str(snap_num).zfill(3)+"/snap_"+str(snap_num).zfill(3) snapname = base + "/snap_"+str(snap_num).zfill(3) redshift = readsnapHDF5.snapshot_header(snapname).redshift scale_factor = 1./(1.+redshift) # Get full bound-particle ID list: all_bound_ids = get_full_bound_id_list(base,snap_num) # First need to construct the offset table: groupOffset = np.zeros(cat.ngroups, dtype="int64") haloOffset = np.zeros(cat.nsubs, dtype="int64") for i in range(1,cat.ngroups): groupOffset[i] = groupOffset[i-1] + cat.GroupLen[i-1] for GrNr in range(0,cat.ngroups): #GrNr means Group number nsubs = cat.GroupNsubs[GrNr] if nsubs > 0: SubNr = cat.GroupFirstSub[GrNr] haloOffset[SubNr] = groupOffset[GrNr] if nsubs > 1: subOffsets = np.cumsum(cat.SubhaloLen[SubNr:SubNr+nsubs-2]) haloOffset[SubNr+1:SubNr+nsubs-1] = groupOffset[GrNr] + subOffsets sub_offset = haloOffset[sub_id] sub_len = cat.SubhaloLen[sub_id] sub_ids = all_bound_ids[haloOffset[sub_id]:haloOffset[sub_id]+cat.SubhaloLen[sub_id]] return sub_ids
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)
import snapshot
import readsubfHDF5
import numpy as np
import tables
which='DM'
dir='/n/hernquistfs1/Illustris/Runs/L75n1820'+which+'/output/'
snapnum=135
massrange=[11,15]
nbins=20
cat=readsubfHDF5.subfind_catalog(dir,135,\
keysel=["Group_M_Crit200","Group_R_Crit200","GroupVel","GroupPos","GroupFirstSub"])
massg=cat.Group_M_Crit200/0.704*1e10
N=len(massg)
gallist=np.nonzero((massg>10**massrange[0]) & (massg < 10**massrange[1]))[0]
edges=10**np.linspace(np.log10(1e-2),np.log10(1),nbins+1)
y=(edges[1:]+edges[:-1])/2
with tables.open_file('1820'+which+'VelDisp.hdf5','w') as f:
s2=f.create_carray('/','Veldisp2',tables.Float32Col(),(N,nbins))
rs2=f.create_carray('/','RadialVeldisp2',tables.Float32Col(),(N,nbins))
ts2=f.create_carray('/','TanVeldisp2',tables.Float32Col(),(N,nbins))
B=f.create_carray('/','VelAniso',tables.Float32Col(),(N,nbins))
r=f.create_carray('/','Radius',tables.Float32Col(),(nbins,))
for gal in gallist:
part=snapshot.loadSubhalo(dir,snapnum,cat.GroupFirstSub[gal],1,["Coordinates","Velocities"])
box = header.boxsize
print 'Directory: ', fdir
print 'Snapshot: ', snap
print 'Redshift: ', header.redshift
print 'Hubble: ', header.hubble
print 'Massarr ', header.massarr
print 'Halo mass range for calculation: 10^[{},{}] M_sun'.format(
args.minmass, args.maxmass)
#1: Create and write useful information
cat = readsubfHDF5.subfind_catalog(fdir,
snap,
keysel=[
"Group_R_Crit200", "GroupFirstSub",
"Group_M_Crit200",
"SubhaloMassInRadType",
"SubhaloHalfmassRadType",
"SubhaloMassInHalfRadType", "SubhaloPos"
])
first = cat.GroupFirstSub[:]
#mgal=cat.SubhaloMassInRadType[cat.GroupFirstSub[:]]
ngroups = cat.ngroups
groupmass = cat.Group_M_Crit200 * 1e10 / hubble
groups = np.nonzero((groupmass > 10**args.minmass)
& (groupmass < 10**args.maxmass))[0]
print 'To calculate {} groups'.format(len(groups))
with tables.open_file(fout, 'w') as f:
f.create_carray("/", "Group_M_Crit200", tables.Float32Col(), (ngroups, ))
with open('../match' + res + '_' + vel + '_' + str(snapnum) + '.dat',
'rb') as f:
matched = pickle.load(f)
with open('../SIGOidx' + res + '_' + vel + '_' + str(snapnum) + '.dat',
'rb') as f:
SIGOidx = pickle.load(f)
with open(
'../luminosity/GP_luminosity' + res + '_' + vel + '_' + str(snapnum) +
'.dat', 'rb') as f:
gp = pickle.load(f)
prefix = '../../../'
run = "14Mpc_118kms_Cooling_OldArepo"
snap = 5
name = 'clump'
cat_118kms = readsubfHDF5.subfind_catalog(
"../../../14Mpc_118kms_Cooling_OldArepo/output/GasOnly_FOF", snap)
jobdir = 'paperplots/' # 'Plots_projections/'
def h100toSIGOidx(idx):
try:
return np.where(SIGOidx == idx)[0][0]
except:
print "No SIGO found"
raise KeyError
def plotProjection(kind, index, boxsize=1.):
#index is which SIGO it is, ihalo refers to the numbering in halo100_indices
ihalo = SIGOidx[index]
def readhalo(
base, snapbase, num, block_name, parttype, fof_num, sub_num, long_ids=False, double_output=False, verbose=False
):
global FlagRead, cat, GroupOffset, HaloOffset, multiple, filename, Parttype, FileTypeNumbers, FileNum
if (FlagRead == False) | ((parttype in Parttype) == False):
if verbose:
print "READHALO: INITIAL READ"
# add parttype to list
Parttype.append(parttype)
if verbose:
print "READHALO: Parttype = ", Parttype
# read in catalog
cat = readsubfHDF5.subfind_catalog(
base,
num,
long_ids=long_ids,
double_output=double_output,
keysel=["GroupLenType", "GroupNsubs", "GroupFirstSub", "SubhaloLenType", "SubhaloMassType"],
)
if cat.ngroups == 0:
if verbose:
print "READHALO: no groups in catalog... returning"
return
if FlagRead == False:
GroupOffset = np.zeros([cat.ngroups, 6], dtype="int64")
HaloOffset = np.zeros([cat.nsubs, 6], dtype="int64")
filename = base + "/" + snapbase + "_" + str(num).zfill(3)
multiple = False
if os.path.exists(filename + ".hdf5") == False:
filename = (
base + "/snapdir_" + str(num).zfill(3) + "/" + snapbase + "_" + str(num).zfill(3) + "." + str(0)
)
multiple = True
if os.path.exists(filename + ".hdf5") == False:
print "READHALO: [error] file not found : ", filename
sys.exit()
FlagRead = True
# construct offset tables
k = 0
for i in range(0, cat.ngroups):
if i > 0:
GroupOffset[i, parttype] = GroupOffset[i - 1, parttype] + cat.GroupLenType[i - 1, parttype]
if cat.GroupNsubs[i] > 0:
HaloOffset[k, parttype] = GroupOffset[i, parttype]
k += 1
for j in range(1, cat.GroupNsubs[i]):
HaloOffset[k, parttype] = HaloOffset[k - 1, parttype] + cat.SubhaloLenType[k - 1, parttype]
k += 1
if k != cat.nsubs:
print "READHALO: problem with offset table", k, cat.nsubs
sys.exit()
# construct file tables
if multiple:
filename = base + "/snapdir_" + str(num).zfill(3) + "/" + snapbase + "_" + str(num).zfill(3) + "." + str(0)
else:
filename = base + "/" + snapbase + "_" + str(num).zfill(3)
head = snapHDF5.snapshot_header(filename)
FileNum = head.filenum
FileTypeNumbers = np.zeros([FileNum, 6], dtype="int64")
cumcount = np.zeros(6, dtype="int64")
for fnr in range(0, FileNum - 1):
if multiple:
filename = (
base + "/snapdir_" + str(num).zfill(3) + "/" + snapbase + "_" + str(num).zfill(3) + "." + str(fnr)
)
else:
filename = base + "/" + snapbase + "_" + str(num).zfill(3)
if verbose:
print "READHALO: initial reading file :", filename
head = snapHDF5.snapshot_header(filename)
cumcount[:] += head.npart[:]
FileTypeNumbers[fnr + 1, :] = cumcount[:]
if (sub_num >= 0) & (fof_num < 0):
off = HaloOffset[sub_num, parttype]
left = cat.SubhaloLenType[sub_num, parttype]
if verbose:
print "READHALO: nr / particle # / mass :", sub_num, cat.SubhaloLenType[
sub_num, parttype
], cat.SubhaloMassType[sub_num, parttype].astype("float64")
if (fof_num >= 0) & (sub_num < 0):
off = GroupOffset[fof_num, parttype]
left = cat.GroupLenType[fof_num, parttype]
if verbose:
print "READHALO: nr / particle # / mass :", fof_num, cat.GroupLenType[fof_num, parttype], cat.GroupMassType[
fof_num, parttype
].astype("float64")
if (sub_num >= 0) & (fof_num >= 0):
real_sub_num = sub_num + cat.GroupFirstSub[fof_num]
off = HaloOffset[real_sub_num, parttype]
left = cat.SubhaloLenType[real_sub_num, parttype]
if verbose:
print "READHALO: nr / particle # / mass :", real_sub_num, cat.SubhaloLenType[
real_sub_num, parttype
], cat.SubhaloMassType[real_sub_num, parttype].astype("float64")
if left == 0:
if verbose:
print "READHALO: no particles of type... returning"
return
# get first file that contains particles of required halo/fof/etc
findex = np.argmax(FileTypeNumbers[:, parttype] > off) - 1
# in case we reached the end argmax returns 0
if findex == -1:
findex = FileNum - 1
if verbose:
print "READHALO: first file that contains particles =", findex
for fnr in range(0, findex):
off -= FileTypeNumbers[fnr + 1, parttype] - FileTypeNumbers[fnr, parttype]
# read data from file
first = True
for fnr in range(findex, FileNum):
if multiple:
filename = (
base + "/snapdir_" + str(num).zfill(3) + "/" + snapbase + "_" + str(num).zfill(3) + "." + str(fnr)
)
else:
filename = base + "/" + snapbase + "_" + str(num).zfill(3)
if verbose:
print "READHALO: reading file :", filename
head = snapHDF5.snapshot_header(filename)
nloc = head.npart[parttype]
if nloc > off:
if verbose:
print "READHALO: data"
start = off
if nloc - off > left:
count = left
else:
count = nloc - off
if first == True:
data = snapHDF5.read_block(filename, block_name, parttype, slab_start=start, slab_len=count)
first = False
else:
data = np.append(
data, snapHDF5.read_block(filename, block_name, parttype, slab_start=start, slab_len=count), axis=0
)
left -= count
off += count
if left == 0:
break
off -= nloc
return data
else:
min_mass = littleh # 1e10 Msun in 1/1e10 Msun / h
max_mass = 100 * littleh # 1e12 Msun
search_query = "?mass_stars__gt=" + str(min_mass)
cut1 = get(url_sbhalos + search_query)
cut1['count']
cut1 = get(url_sbhalos + search_query, {
'limit': cut1['count'],
'order_by': 'id'
})
if args.local:
subs = np.array([sub['id'] for sub in cut1['results']], dtype='i')
cat = readsubfHDF5.subfind_catalog(
args.local, snapnum, keysel=['SubhaloPos', 'SubhaloMass'])
sub_dat = np.hstack(
(subs.reshape(subs.size,
1), cat.SubhaloMass[subs].reshape(subs.size, 1),
cat.SubhaloPos[subs]))
del cat
else:
keys = ('id', 'mass', 'pos_x', 'pos_y', 'pos_z')
sub_dat = np.array([ itemgetter(*keys)(get(sub['url'])) \
for sub in cut1['results'] ])
np.savetxt(
folder + 'subhalo_mass_positions.csv',
sub_dat,
delimiter=',',
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 #=============================================================================================== redshift = rs.snapshot_header(snapname).redshift scale_factor = 1./(1.+redshift) print "z: ",redshift print "a: ",scale_factor #cat = readsubf.subfind_catalog(base,snapnum,masstab=True) cat = readsubfHDF5.subfind_catalog(base,snapnum) 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")
def __init__(self, res, vel, snapnum):
#self.res = res
#self.vel = vel
#self.snapnum = snapnum
if res == "1.12Mpc":
s_res = '112Mpc'
elif res == "1.4Mpc":
s_res = '14Mpc'
if vel == "Sig0":
s_vel = "Sig0"
elif vel == "11.8kms":
s_vel = '118kms'
snapnum = int(snapnum)
filename = "/n/hernquistfs3/mvogelsberger/GlobularClusters/InterfaceWArepo_All_" + res + '_' + vel + "/output/"
filename2 = filename + "GasOnly_FOF" #Used for readsubfHDF5
########## CHANGED FILENAME3 TO GROUPORDERED IN GAS ONLY
filename3 = filename2 + "/snap-groupordered_" + str(snapnum).zfill(
3) #Used for hdf5lib, snapHDF5
#### Not sure if this works with change but don't care about 2.8
if res == '2.8Mpc':
filename3 = filename + "snapdir_" + str(snapnum).zfill(
3) + "/snap_" + str(snapnum).zfill(3)
#Units
GRAVITY_cgs = 6.672e-8
UnitLength_in_cm = 3.085678e21 # code length unit in cm/h
UnitMass_in_g = 1.989e43 # code length unit in g/h
UnitVelocity_in_cm_per_s = 1.0e5
UnitTime_in_s = UnitLength_in_cm / UnitVelocity_in_cm_per_s
UnitDensity_in_cgs = UnitMass_in_g / np.power(UnitLength_in_cm, 3)
UnitPressure_in_cgs = UnitMass_in_g / UnitLength_in_cm / np.power(
UnitTime_in_s, 2)
UnitEnergy_in_cgs = UnitMass_in_g * np.power(
UnitLength_in_cm, 2) / np.power(UnitTime_in_s, 2)
GCONST = GRAVITY_cgs / np.power(
UnitLength_in_cm, 3) * UnitMass_in_g * np.power(UnitTime_in_s, 2)
critical_density = 3.0 * .1 * .1 / 8.0 / np.pi / GCONST #.1 is for 1/Mpc to 1/kpc, also in units of h^2
header = snap.snapshot_header(filename3)
if res == "2.8Mpc":
fs = hdf5lib.OpenFile(filename3 + ".0.hdf5")
else:
fs = hdf5lib.OpenFile(filename3 + ".hdf5")
red = hdf5lib.GetAttr(fs, "Header", "Redshift")
atime = hdf5lib.GetAttr(fs, "Header", "Time")
boxSize = hdf5lib.GetAttr(fs, "Header", "BoxSize")
boxSize *= atime #convert from ckpc/h to kpc/h
Omega0 = hdf5lib.GetAttr(fs, "Header", "Omega0")
OmegaLambda = hdf5lib.GetAttr(fs, "Header", "OmegaLambda")
fs.close()
cat = readsubfHDF5.subfind_catalog(filename2, snapnum)
Omega_a = Omega0 / (Omega0 + OmegaLambda * atime * atime * atime)
critical_density *= (Omega0 / Omega_a)
r200 = cat.Group_R_Crit200
r200 *= atime #convert from ckpc/h to kpc/h
m200 = cat.Group_M_Crit200
haloCMvel = cat.GroupVel
haloCMvel *= 1. / atime #convert from km/s/a to km/s
haloPos = cat.GroupPos
haloPos *= atime #convert from ckpc/h to kpc/h
#Read in particles
#read in all simulation masses to calculate cosmic baryon fraction
massgassim = snap.read_block(filename + "snap_" +
str(snapnum).zfill(3),
"MASS",
parttype=0)
massdmsim = snap.read_block(filename + "snap_" + str(snapnum).zfill(3),
"MASS",
parttype=1)
massgas = snap.read_block(filename3, "MASS", parttype=0)
massdm = snap.read_block(filename3, "MASS", parttype=1)
posgas = snap.read_block(filename3, "POS ", parttype=0)
posdm = snap.read_block(filename3, "POS ", parttype=1)
velgas = snap.read_block(filename3, "VEL ", parttype=0)
veldm = snap.read_block(filename3, "VEL ", parttype=1)
#redefine position units from ckpc/h to kpc/h
posgas *= atime
posdm *= atime
#redefine velocity units from kmsqrt(a)/s to km/s
velgas *= np.sqrt(atime)
veldm *= np.sqrt(atime)
fb = massgassim.sum(dtype="float64") / (
massgassim.sum(dtype="float64") + massdmsim.sum(dtype="float64"))
gaslimit = .4 # Set the limit for gas fraction in plots
#boxSize hubble flow correction for halo CM velocity subtraction
boxSizeVel = boxSize * .1 * UnitLength_in_cm / UnitVelocity_in_cm_per_s * np.sqrt(
Omega0 / atime / atime / atime + OmegaLambda)
#load particle indices
pGas = snap.read_block(filename3, "POS ", parttype=0)
mGas = snap.read_block(filename3, "MASS", parttype=0)
pDM = snap.read_block(filename3, "POS ", parttype=1)
halo100_indices = np.where(cat.GroupLenType[:, 0] > 100)[0]
startAllGas = []
endAllGas = []
for i in halo100_indices:
startAllGas += [np.sum(cat.GroupLenType[:i, 0])]
endAllGas += [startAllGas[-1] + cat.GroupLenType[i, 0]]
#Initialize arrays
spinparam = np.zeros(np.size(halo100_indices))
jsptotspinparam = np.zeros(np.size(halo100_indices))
jspgasspinparam = np.zeros(np.size(halo100_indices))
jspdmspinparam = np.zeros(np.size(halo100_indices))
gasfrac = np.zeros(np.size(halo100_indices))
costheta = np.zeros(np.size(halo100_indices)) #misalignment angle
v200 = np.zeros(np.size(halo100_indices))
velgasall = np.zeros(np.size(halo100_indices))
veldmall = np.zeros(np.size(halo100_indices))
virialratio = np.zeros(np.size(halo100_indices))
numGas = np.zeros(np.size(halo100_indices))
numDM = np.zeros(np.size(halo100_indices))
j200gas = np.zeros(np.size(halo100_indices))
j200dm = np.zeros(np.size(halo100_indices))
j200 = np.zeros(np.size(halo100_indices))
totmass = np.zeros(np.size(halo100_indices))
gasmass = np.zeros(np.size(halo100_indices))
DMmass = np.zeros(np.size(halo100_indices))
rmax = np.zeros(np.size(halo100_indices))
rmin = np.zeros(np.size(halo100_indices))
j200gasNoNorm = np.zeros(np.size(halo100_indices))
closestm200 = np.zeros(np.size(halo100_indices))
#some radii are errors and negative, will have a value of 1 to be excluded
negradii = np.zeros(np.size(halo100_indices))
#Indexing for global variable works because halos are ordered from largest to smallest so <100 particles are at the end and not counted.
for i in halo100_indices:
exec("cm = cm_%s_%s_%d[0][i]" % (s_res, s_vel, snapnum))
exec("rotation = rotation_%s_%s_%d[0][i]" %
(s_res, s_vel, snapnum))
exec("radii = radii_%s_%s_%d[0][i]" % (s_res, s_vel, snapnum))
#some radii are errors and negative, will have a value of 1 to be excluded
if radii[0] < 0.:
negradii[i] = 1.
else:
maxrad = radii[2]
maxrad *= atime #convert from ckpc to kpc
exec("mDM=mDM_%s_%s_%d[0][i]" % (s_res, s_vel, snapnum))
exec("DMinEll=DMindices_%s_%s_%d[0][i]" %
(s_res, s_vel, snapnum))
exec("m200dm = M200dm_%s_%s[snapnum-10][i]" % (s_res, s_vel))
#Check if CM is buggy
if np.sum(cm == np.array([0., 0., 0.])) == 3:
# it's probbaly an error; recompute com
totalGas = np.sum(mGas[startAllGas[i]:endAllGas[i]])
cm = np.array([
np.sum(pGas[startAllGas[i]:endAllGas[i], j] *
mGas[startAllGas[i]:endAllGas[i]]) / totalGas
for j in range(3)
])
# Get positions of gas particles
P = pGas[startAllGas[i]:endAllGas[i]]
# Shift coordinate system to center on the center of the ellipsoid
Precentered = dx_wrap(P - cm, boxSize / atime)
# Rotate coordinated to the the axes point along x,y,z directions:
Precentered = np.array(
[np.dot(pp, rotation.T) for pp in Precentered])
# Figure out which particles are inside the ellipsoid
inEll = (Precentered[:, 0]**2. / radii[0]**2. +
Precentered[:, 1]**2. / radii[1]**2 +
Precentered[:, 2]**2. / radii[2]**2) <= 1.
#remove halo CM velocity
tempvelgas = dx_wrap(
velgas[startAllGas[i]:endAllGas[i]][inEll] - haloCMvel[i],
boxSizeVel)
tempveldm = dx_wrap(veldm[DMinEll] - haloCMvel[i], boxSizeVel)
#redefine positions wrt COM
tempposgas = dx_wrap(
posgas[startAllGas[i]:endAllGas[i]][inEll] - haloPos[i],
boxSize)
tempposdm = dx_wrap(posdm[DMinEll] - haloPos[i], boxSize)
numDM[i] = np.size(tempposdm)
numGas[i] = np.size(tempposgas)
#Calculating j200
#j200 of all particles
j200vecgas = np.sum(
np.cross(tempposgas, tempvelgas) *
massgas[startAllGas[i]:endAllGas[i]][inEll][:, np.newaxis],
axis=0)
j200vecdm = np.sum(np.cross(tempposdm, tempveldm) *
massdm[DMinEll][:, np.newaxis],
axis=0)
#if np.size(tempveldm)!=0: #can be no dm particles!
# costheta[i] = np.dot(j200vecgas,j200vecdm)/np.linalg.norm(j200vecgas)/np.linalg.norm(j200vecdm)
j200vec = j200vecgas + j200vecdm
j200[i] = np.linalg.norm(j200vec)
j200dm[i] = np.linalg.norm(j200vecdm)
j200gas[i] = np.linalg.norm(j200vecgas)
j200gasNoNorm[i] = np.linalg.norm(j200vecgas)
gasmass[i] = np.sum(
massgas[startAllGas[i]:endAllGas[i]][inEll])
totmass[i] = gasmass[i] + mDM
DMmass[i] = mDM
rmax[i] = radii[2]
rmin[i] = radii[0]
closestm200[i] = m200dm
#using fudicial m200~6mgas
#get r200 from analytic formula in Barkana,Loeb 01 review
if gasmass[i] != 0.: #Some ellpsoids fit nothing
m200fid = 6. * gasmass[i]
omgz = .27 * atime**(-3.) / (.27 * atime**(-3.) + .73)
dfact = omgz - 1.
delc = 18. * np.pi**2. + 82 * dfact - 39. * dfact**2.
r200fid = .784 * (m200fid * 100.)**(1. / 3.) * (
.27 / omgz * delc / 18. / np.pi**2)**(-1. /
3.) * 10 * atime
v200fid = np.sqrt(GCONST * (m200fid) / r200fid)
j200gas[i] *= 1. / np.sqrt(2) / (
gasmass[i]) / v200fid / r200fid
j200[i] *= 1. / np.sqrt(2) / (
totmass[i]) / v200fid / r200fid
if mDM != 0.:
j200dm[i] *= 1. / np.sqrt(2) / mDM / v200fid / r200fid
gasfrac[i] = gasmass[i] / totmass[i]
#Reindex to account for shrunken ellipsoids with gas particles >100
goodidx, = np.where(np.logical_and(numGas > 100, negradii == 0.))
self.j200gas = j200gas[goodidx]
self.j200dm = j200dm[goodidx]
self.j200 = j200[goodidx]
self.j200gasNoNorm = j200gasNoNorm[goodidx]
self.gasfrac = gasfrac[goodidx]
self.totmass = totmass[goodidx]
self.totmass *= 10**10
#costheta = costheta[goodidx]
self.rmax = rmax[goodidx]
self.rmin = rmin[goodidx]
#thetadeg = np.arccos(costheta)*180./np.pi
self.gasmass = gasmass[goodidx]
self.closestm200 = closestm200[goodidx]
#Reindex the Rmin, R200_DM params
exec("self.rclosest = Rmin_%s_%s[snapnum-10][goodidx]" %
(s_res, s_vel))
exec("self.R200dm = R200dm_%s_%s[snapnum-10][goodidx]" %
(s_res, s_vel))
fig_gas_z = plt.figure(figsize=(5, 5))
fig_star_z = plt.figure(figsize=(5, 5))
fig_cgmf = plt.figure(figsize=(5, 5))
ax_sfr = fig_sfr.add_subplot(1, 1, 1)
ax_gas_z = fig_gas_z.add_subplot(1, 1, 1)
ax_star_z = fig_star_z.add_subplot(1, 1, 1)
ax_cgmf = fig_cgmf.add_subplot(1, 1, 1)
for redshift in target_redshifts:
diff = np.abs(redshift - redshifts)
snap = snapshots[diff == diff.min()]
cat = readsubfHDF5.subfind_catalog(dir,
snap[0],
keysel=[
'SubhaloMassType', 'SubhaloSFR',
'SubhaloGasMetallicity',
'SubhaloGasMetallicitySfr'
])
stellar_masses = cat.SubhaloMassType[:,
4] * 1e10 / little_h # units of M_solar
sfr = cat.SubhaloSFR # units of M_solar / yr
gas_z = cat.SubhaloGasMetallicity # unitless metallicity
gas_sfr_z = cat.SubhaloGasMetallicitySfr # unitless metallicity
star_z = readsubfHDF5.subhalo_stellar_metallicities(snap=snap[0])
# read this guy in manually...
file = '/n/home01/ptorrey/ReviewPlots/cold_gas_masses_z' + str(
redshift) + '.hdf5'
f = hdf5lib.OpenFile(file, mode='r')
def __init__(self,overwrite=False):
# self.run = "c0_128"
# self.fnummax = 8
# self.base="/n/hernquistfs1/Illustris/SmallBox/GFM/Production/Cosmo/Cosmo0_V6/L25n128/output/"
# self.snapnum_arr = np.array([120])
self.very_large_sim = True
self.run = "ill1"
self.fnummax=512 # future: find this automatically instead of having it as an input
self.base="/n/ghernquist/Illustris/Runs/Illustris-1/output/"
self.snapnum_arr = np.array([120])
group_min_mass = 10.**11
group_max_mass = 10.**18.
self.dat_str_list = ["Masses","Coordinates","GFM_Metals","GFM_Metallicity","Velocities","Density","Volume","InternalEnergy","ElectronAbundance","NeutralHydrogenAbundance","SmoothingLength"] #future: add "Radius"
self.savebase = '/n/home04/jsuresh/scratch1/Test/'
# self.savebase = '/n/home04/jsuresh/data1/Projects/Feedback_and_CGM/CGM_new/data/CGM_snaps/'
# Here's where the magic happens
comm = MPI.COMM_WORLD
self.rank = comm.Get_rank()
print "my rank = {}".format(self.rank)
self.size = comm.Get_size()
# print "my size = {}".format(size)
if self.rank == 0: print "Done with MPI comm/rank/size initialization!"
if self.fnummax % self.size != 0:
raise Exception("# of processes does not divide into # of subfiles!")
for snapnum in self.snapnum_arr:
if self.rank == 0:
# Create necessary folder if it does not exist:
CGM_snapdir = self.savebase+"{}/s{}/".format(self.run,snapnum)
if not os.path.isdir(CGM_snapdir):
print "Trying to create {}".format(CGM_snapdir)
os.mkdir(CGM_snapdir)
# Get header information before proceeding:
fname = self.base+"snapdir_"+str(snapnum).zfill(3)+"/snap_"+str(snapnum).zfill(3)+".0.hdf5"
print "fname ",fname
f = h5py.File(fname,'r')
self.redshift=f["Header"].attrs["Redshift"]
self.hubble=f["Header"].attrs["HubbleParam"]
self.box=f["Header"].attrs["BoxSize"]
self.omegam=f["Header"].attrs["Omega0"]
self.omegal=f["Header"].attrs["OmegaLambda"]
f.close()
cat=readsubfHDF5.subfind_catalog(self.base,snapnum,subcat=False,keysel=["Group_M_Crit200","GroupPos","Group_R_Crit200"]) #,"GroupBHMass","GroupBHMdot"
# Select by minimum group mass threshold:
self.grp_mass = np.array(cat.Group_M_Crit200)
m = AU.PhysicalMass(self.grp_mass)
mass_select = np.logical_and(m > group_min_mass, m < group_max_mass)
self.grp_mass = self.grp_mass[mass_select]
self.grp_ids = np.arange(np.float64(cat.ngroups))
self.grp_ids = self.grp_ids[mass_select]
self.grp_pos = np.array(cat.GroupPos)[mass_select]
self.grp_Rvir = np.array(cat.Group_R_Crit200)[mass_select]
# self.grp_BHmass = np.array(cat.GroupBHMass)[mass_select]
# self.grp_BHMdot = np.array(cat.GroupBHMdot)[mass_select]
self.n_selected_groups = np.float32(np.size(self.grp_mass))
if not overwrite:
# First remove all groups which already have data files output:
keep = np.ones_like(self.grp_ids,dtype=bool)
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
filepath = self.savebase+ "{}/s{}/{}.hdf5".format(self.run,snapnum,str(int(grp_id)).zfill(5))
if os.path.isfile(filepath):
if self.rank == 0:
print "File {} already exists! Skipping...".format(filepath)
keep[i] = False
else:
pass
self.grp_ids = self.grp_ids[keep]
self.grp_pos = self.grp_pos[keep]
self.grp_Rvir = self.grp_Rvir[keep]
self.grp_mass = self.grp_mass[keep]
self.n_selected_groups = np.float32(np.size(self.grp_mass))
print "CGM snapshots will be written for the following group ids: {}".format(self.grp_ids)
else:
self.redshift = None
self.hubble = None
self.box = None
self.omegam = None
self.omegal = None
self.grp_mass = None
self.grp_ids = None
self.grp_pos = None
self.grp_Rvir = None
self.n_selected_groups = None
if self.size > 1:
# Broadcast necessary data from root process to other processes
self.redshift = comm.bcast(self.redshift,root=0)
self.hubble = comm.bcast(self.hubble,root=0)
self.box = comm.bcast(self.box,root=0)
self.omegam = comm.bcast(self.omegam,root=0)
self.omegal = comm.bcast(self.omegal,root=0)
self.grp_mass = comm.bcast(self.grp_mass,root=0)
self.grp_ids = comm.bcast(self.grp_ids,root=0)
self.grp_pos = comm.bcast(self.grp_pos,root=0)
self.grp_Rvir = comm.bcast(self.grp_Rvir,root=0)
self.n_selected_groups = comm.bcast(self.n_selected_groups,root=0)
if self.very_large_sim:
# Read in positions from entire snapshot:
pos = self.subprocess_load_data(snapnum,"Coordinates")
if self.rank == 0: print "Done loading full position array in snapshot"
# Build KDtree of gas positions:
gas_kdtree = cKDTree(pos)
print "Rank {} is done building its gas KD tree".format(self.rank)
# Loop over all group positions, and find indices of gas close to this group position
index_dict = {}
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
gpos = self.grp_pos[i]
# r200 = self.grp_Rvir[i]
dist_thresh = AU.CodePosition(500.*np.sqrt(3),self.redshift)
ind = self.get_gas_ind(gpos,dist_thresh,gas_kdtree)
index_dict[grp_id] = np.copy(ind)
# Now we have full index dictionary for all groups. We no longer need the KD-tree.
# try: print "Size of index dictionary in bytes: ",sys.getsizeof(index_dict)
# except: print "try1 failed"
# try: print "Size of KD-tree in bytes: ",sys.getsizeof(gas_kdtree)
# except: print "try2 failed"
del gas_kdtree
# Create data structure which will house data for all groups simultaneously:
group_dict = {}
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
group_dict[grp_id] = {}
# Now get position information for every group, since we already have the position array in memory.
# Then delete the pos array, since it is probably very large.
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
ind = index_dict[grp_id]
group_dict[grp_id]["Coordinates"] = pos[ind]
del pos
print "Rank {} is done with the Coordinates field for all groups!".format(self.rank)
# Now do the same for all other desired fields, deleting each one after we are done with it.
for dat_str in self.dat_str_list:
if dat_str == "Coordinates":
pass
else:
dat = self.subprocess_load_data(snapnum,dat_str)
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
ind = index_dict[grp_id]
group_dict[grp_id][dat_str] = dat[ind]
del dat
print "Rank {} is done with the {} field for all groups!".format(self.rank,dat_str)
# We have now loaded all of the data for all of the groups! First delete the large index_dict:
del index_dict
if self.size == 1:
# If there is only one process, then just trivially loop through all groups and save their data
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
save_dict = group_dict[grp_id]
self.save_group_data(snapnum,i,save_dict)
elif self.size > 1:
# If there are subprocesses, then send the data to the rank-0 process, where it will be saved.
comm.Barrier()
print "Rank {} hit the comm barrier!".format(self.rank)
# We now send data from the subprocesses to the rank-0 process, group by group.
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
if self.rank == 0:
save_dict = {}
# save_dict = group_dict[grp_id]
for dat_str in self.dat_str_list:
if group_dict.has_key(grp_id) and group_dict[grp_id].has_key(dat_str):
foo = np.copy(group_dict[grp_id][dat_str])
else:
foo = None
# Now gather from all of the subprocesses to rank 0:
foo = comm.gather(foo,root=0)
if self.rank == 0:
savedat = self._cat_gather_list(foo)
save_dict[dat_str] = savedat
# Rank 0 now has all of the data for this group saved in save_dict. Save to file:
if self.rank == 0:
self.save_group_data(snapnum,i,save_dict)
# del save_dict # HERE
# Now all processes remove this grp_id entry from their memory
# if group_dict.has_key(grp_id):
# del group_dict[grp_id]
# else:
# pass
elif not self.very_large_sim:
# Read in all data from snapshot a single time:
local_dict = {}
for dat_str in self.dat_str_list:
local_dict[dat_str] = subprocess_load_data(dat_str)
if rank == 0: print "Done loading full snapshot"
# Build KDtree of gas positions:
pos = local_dict['Coordinates']
gas_kdtree = cKDTree(pos)
# Loop over all group positions, and find indices of gas close to this group position
for i in np.arange(n_selected_groups):
grp_id = self.grp_ids[i]
gpos = self.grp_pos[i]
# r200 = self.grp_Rvir[i]
# Check whether CGM data file already exists:
filepath = self.savebase+ "{}/s{}/{}.hdf5".format(run,snapnum,str(int(grp_id)).zfill(5))
if os.path.isfile(filepath):
if rank == 0:
print "File {} already exists! Skipping...".format(filepath)
else:
pass
else: # If CGM data file does not exist yet, then continue:
dist_thresh = AU.CodePosition(500.*np.sqrt(3),redshift)
ind = get_gas_ind(gpos,dist_thresh,gas_kdtree,box)
send_dict = {}
if np.size(ind) > 0:
for dat_str in self.dat_str_list:
send_dict[dat_str] = local_dict[dat_str][ind]
# Future: hide this in another function
# Now that data has been compiled for this process, send it to rank-0 process
if rank == 0:
save_dict = send_dict
if size > 1:
comm.Barrier()
print "comm barrier!"
for dat_str in self.dat_str_list:
for temp_rank in range(1,size):
if rank == temp_rank:
if send_dict.has_key(dat_str):
comm.send(send_dict[dat_str],dest=0,tag=i)
else:
comm.send('nothing',dest=0,tag=i)
elif rank == 0:
hold = comm.recv(source=temp_rank,tag=i)
#print "this is what was received from rank {}: {}".format(temp_rank,hold)
if hold != 'nothing':
if save_dict.has_key(dat_str):
full_dict[dat_str] = np.append(save_dict[dat_str],hold,axis=0)
else:
full_dict[dat_str] = hold
# Now data for this halo has been sent to rank-0 process. Time to save it.
if rank==0:
print "Saving group file now to: {}".format(filepath)
f=h5py.File(filepath,'a')
grp = f.create_group("Header")
grp.attrs["hubble"]=hubble
grp.attrs["omegam"]=omegam
grp.attrs["omegal"]=omegal
grp.attrs["redshift"]=redshift
grp.attrs["box"]=box
grp.attrs["grp_id"] = grp_id
grp.attrs["grp_mass"] = self.grp_mass[i]
grp.attrs["grp_pos"] = self.grp_pos[i]
grp.attrs["grp_Rvir"] = self.grp_Rvir[i]
grp.attrs["grp_BHmass"] = self.grp_BHmass[i]
grp.attrs["grp_BHMdot"] = self.grp_BHMdot[i]
p_grp = f.create_group('PartType0')
for key in save_dict:
#print save_dict[key]
p_grp.create_dataset(key,data=save_dict[key])
f.close()
smhm = np.zeros(n_bins)
# ============================================== #
scalefactors = illustris.load_scalefactors()
redshifts = 1.0 / scalefactors - 1.0
snapshots = np.arange(redshifts.shape[0])
# ============================================== #
fig = plt.figure(figsize=(5, 5))
ax = fig.add_subplot(1, 1, 1)
for redshift in target_redshifts:
diff = np.abs(redshift - redshifts)
snap = snapshots[diff == diff.min()]
cat = readsubfHDF5.subfind_catalog(dir,
snap[0],
keysel=[
'SubhaloMassType', 'SubhaloGrNr',
'GroupFirstSub', 'Group_M_Crit200'
])
stellar_masses = cat.SubhaloMassType[:, 4] * 1e10 / little_h
halo_masses = cat.SubhaloMassType[:, 1] * 1e10 / little_h
m200c_halo_masses = cat.Group_M_Crit200 * 1e10 / little_h
gr_first_sub = cat.GroupFirstSub # the first subhalo ID for each group
sub_grnr = cat.SubhaloGrNr # the group ID for each subhalo
sub_nr = np.arange(stellar_masses.shape[0]) # the subhalo ID
sub_first_in_group = gr_first_sub[
sub_grnr] # the first subhalo for the group to which this subhalo belongs
is_central = sub_first_in_group == sub_nr # compare the "" "" against this subhalo ID. If matches, is a central
#+ "&mass_stars__lt=" + str(max_mass) \
#+ "&halfmassrad_stars__gt=" + str(2 / a0 * littleh) # 2 kpc
cut1 = get(url_sbhalos + search_query)
cut1['count']
cut1 = get(url_sbhalos + search_query, {
'limit': cut1['count'],
'order_by': 'id'
})
sub_list = cut1['results']
sub_ids = np.array([sub['id'] for sub in cut1['results']], dtype='i')
if args.local:
cat = readsubfHDF5.subfind_catalog(
args.local,
snapnum, #grpcat=False, subcat=False,
keysel=['GroupFirstSub', 'SubhaloGrNr'])
sat = np.zeros(cat.SubhaloGrNr.size, dtype=bool)
sat[sub_ids] = (sub_ids != cat.GroupFirstSub[cat.SubhaloGrNr[sub_ids]])
del cat
gc.collect()
else:
sub_ids = None
if args.local:
sat = None
my_subs = scatter_work(sub_ids, rank, size)
sub_ids = comm.bcast(sub_ids, root=0)
if args.local:
sat = comm.bcast(sat, root=0)
def __init__(self,snap_dir,snapnum,savefile,group_min_mass=0,group_max_mass=10e16,rmin_pkpc=300,Rvir_min=1,load_savefile=False,BH_dat=True,fuzz_implemented=False):
""" Initiate class
Parameters:
snap_dir - directory of snapshots
snapnum - snapshot number
savefile - where to save calculation output """
# Initiate variables
self.snap_dir = snap_dir
self.snapnum = snapnum
self.savefile = savefile
self.group_min_mass = group_min_mass
self.group_max_mass = group_max_mass
self.rmin_pkpc = rmin_pkpc
self.Rvir_min = Rvir_min
self.BH_dat = BH_dat
#self.load_savefile = load_savefile
#self.fuzz_implemented = fuzz_implemented
if load_savefile:
pass
#self.load_savefile()
else:
ts = time.time()
# Get basic parameters of snapshot
f=hdfsim.get_file(snapnum,snap_dir,0)
self.redshift=f["Header"].attrs["Redshift"]
self.hubble=f["Header"].attrs["HubbleParam"]
self.box=AU.PhysicalPosition(f["Header"].attrs["BoxSize"],self.redshift,hubble=self.hubble)
self.omegam=f["Header"].attrs["Omega0"]
self.omegal=f["Header"].attrs["OmegaLambda"]
print "redshift: ",self.redshift
f.close()
# Other useful things:
self.rho_barycrit = 0.0456*AU.GetRhoCrit0(hubble=self.hubble)
self.tab = cc.CloudyTable(self.redshift)
self.Hz = AU.CalcH_kms_over_Mpc(self.redshift, OmegaM=self.omegam, OmegaL=self.omegal, hubble=self.hubble)
# Get group mass data so we can make a mass cut
self.cat = readsubfHDF5.subfind_catalog(self.snap_dir,self.snapnum,long_ids=True)
self.n_all_groups = np.float64(self.cat.ngroups)
self.n_all_subs = np.float64(self.cat.nsubs)
self.grp_ids = np.arange(self.n_all_groups)
self.grp_mass = AU.PhysicalMass(np.array(self.cat.Group_M_Crit200),hubble=self.hubble)
print "Total # of groups in snapshot: ",self.n_all_groups
# Impose minimum halo mass cut and get additional group data:
mass_select = np.logical_and(self.grp_mass > self.group_min_mass,self.grp_mass < self.group_max_mass)
self.grp_ids = self.grp_ids[mass_select]
self.grp_mass = self.grp_mass[mass_select]
self.grp_pos = AU.PhysicalPosition(np.array(self.cat.GroupPos[mass_select]),self.redshift,hubble=self.hubble)
self.grp_Rvir = AU.PhysicalPosition(np.array(self.cat.Group_R_Crit200[mass_select]),self.redshift,hubble=self.hubble)
self.grp_vel = AU.PeculiarVelocity(np.array(self.cat.GroupVel[mass_select]),self.redshift)
if self.BH_dat:
self.grp_BHmass = AU.PhysicalMass(np.array(self.cat.GroupBHMass[mass_select]),hubble=self.hubble)
self.grp_BHMdot = np.array(self.cat.GroupBHMdot[mass_select])
self.n_selected_groups = np.float64(np.size(self.grp_mass))
print "# of selected groups: ",self.n_selected_groups
# Construct group/halo tables (assumes subfind-ordered snapshot)
self.GroupOffset, self.HaloOffset = subfind_tables.constructtables(self.cat)
self.GroupLenType = self.cat.GroupLenType
self.SubhaloLenType = self.cat.SubhaloLenType
print "Time for preamble: {}".format(time.time()-ts)
# Get gas positions, masses, and metallicities for this subfile
ts = time.time()
self.get_gas_data()
print "# of gas cells: ",self.ngas
print "Time to get gas data: {}".format(time.time()-ts)
# Generate kdtree for particle positions:
self.gas_kdtree = cKDTree(self.full_gas_pos,leafsize=10)
# Generate particle flag
ts = time.time()
self.full_gas_flag = subfind_tables.generate_particle_haloflags(self.snap_dir,self.snapnum,self.cat)
print "Time to generate flags: {}".format(time.time()-ts)
# Calculate background mass-weighted metallicity
ts = time.time()
self.metal_bg = self.calc_background_metallicity()
print "background metallicity: ",self.metal_bg
print "Time to calculate metal bg: {}".format(time.time()-ts)
# Calculate enrichment radius for all groups
self.extract_CGM_allhalos()
self.save_data()
snap_nums = np.arange(snap_earliest,snap_latest) snap_nums = snap_nums[::-1] #reverse cat = readsubfHDF5.subfind_catalog(base, snap_num) # I assume that the subhalo is the primary subhalo in its group #sub_list = cat.GroupFirstSub #grp_id = np.argmin( np.abs(sub_id-sub_list) ) grp_id = cat.SubhaloParent[sub_id] sub_partIDs = get_subhalo_ids(base,snap_num,sub_id) sub_pos = cat.SubhaloPos[sub_id] sub_mass = cat.SubhaloMass[sub_id] sub_vel = cat.SubhaloVel[sub_id] sub_Rvir = cat.Group_R_Crit200[grp_id] new_match_flag = 1
def __init__(self, overwrite=False):
# self.run = "c0_128"
# self.fnummax = 8
# self.base="/n/hernquistfs1/Illustris/SmallBox/GFM/Production/Cosmo/Cosmo0_V6/L25n128/output/"
# self.snapnum_arr = np.array([120])
self.very_large_sim = True
self.run = "ill1"
self.fnummax = 512 # future: find this automatically instead of having it as an input
self.base = "/n/ghernquist/Illustris/Runs/Illustris-1/output/"
self.snapnum_arr = np.array([120])
group_min_mass = 10.**11
group_max_mass = 10.**18.
self.dat_str_list = [
"Masses", "Coordinates", "GFM_Metals", "GFM_Metallicity",
"Velocities", "Density", "Volume", "InternalEnergy",
"ElectronAbundance", "NeutralHydrogenAbundance", "SmoothingLength"
] #future: add "Radius"
self.savebase = '/n/home04/jsuresh/scratch1/Test/'
# self.savebase = '/n/home04/jsuresh/data1/Projects/Feedback_and_CGM/CGM_new/data/CGM_snaps/'
# Here's where the magic happens
comm = MPI.COMM_WORLD
self.rank = comm.Get_rank()
print "my rank = {}".format(self.rank)
self.size = comm.Get_size()
# print "my size = {}".format(size)
if self.rank == 0: print "Done with MPI comm/rank/size initialization!"
if self.fnummax % self.size != 0:
raise Exception(
"# of processes does not divide into # of subfiles!")
for snapnum in self.snapnum_arr:
if self.rank == 0:
# Create necessary folder if it does not exist:
CGM_snapdir = self.savebase + "{}/s{}/".format(
self.run, snapnum)
if not os.path.isdir(CGM_snapdir):
print "Trying to create {}".format(CGM_snapdir)
os.mkdir(CGM_snapdir)
# Get header information before proceeding:
fname = self.base + "snapdir_" + str(snapnum).zfill(
3) + "/snap_" + str(snapnum).zfill(3) + ".0.hdf5"
print "fname ", fname
f = h5py.File(fname, 'r')
self.redshift = f["Header"].attrs["Redshift"]
self.hubble = f["Header"].attrs["HubbleParam"]
self.box = f["Header"].attrs["BoxSize"]
self.omegam = f["Header"].attrs["Omega0"]
self.omegal = f["Header"].attrs["OmegaLambda"]
f.close()
cat = readsubfHDF5.subfind_catalog(
self.base,
snapnum,
subcat=False,
keysel=["Group_M_Crit200", "GroupPos",
"Group_R_Crit200"]) #,"GroupBHMass","GroupBHMdot"
# Select by minimum group mass threshold:
self.grp_mass = np.array(cat.Group_M_Crit200)
m = AU.PhysicalMass(self.grp_mass)
mass_select = np.logical_and(m > group_min_mass,
m < group_max_mass)
self.grp_mass = self.grp_mass[mass_select]
self.grp_ids = np.arange(np.float64(cat.ngroups))
self.grp_ids = self.grp_ids[mass_select]
self.grp_pos = np.array(cat.GroupPos)[mass_select]
self.grp_Rvir = np.array(cat.Group_R_Crit200)[mass_select]
# self.grp_BHmass = np.array(cat.GroupBHMass)[mass_select]
# self.grp_BHMdot = np.array(cat.GroupBHMdot)[mass_select]
self.n_selected_groups = np.float32(np.size(self.grp_mass))
if not overwrite:
# First remove all groups which already have data files output:
keep = np.ones_like(self.grp_ids, dtype=bool)
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
filepath = self.savebase + "{}/s{}/{}.hdf5".format(
self.run, snapnum,
str(int(grp_id)).zfill(5))
if os.path.isfile(filepath):
if self.rank == 0:
print "File {} already exists! Skipping...".format(
filepath)
keep[i] = False
else:
pass
self.grp_ids = self.grp_ids[keep]
self.grp_pos = self.grp_pos[keep]
self.grp_Rvir = self.grp_Rvir[keep]
self.grp_mass = self.grp_mass[keep]
self.n_selected_groups = np.float32(np.size(self.grp_mass))
print "CGM snapshots will be written for the following group ids: {}".format(
self.grp_ids)
else:
self.redshift = None
self.hubble = None
self.box = None
self.omegam = None
self.omegal = None
self.grp_mass = None
self.grp_ids = None
self.grp_pos = None
self.grp_Rvir = None
self.n_selected_groups = None
if self.size > 1:
# Broadcast necessary data from root process to other processes
self.redshift = comm.bcast(self.redshift, root=0)
self.hubble = comm.bcast(self.hubble, root=0)
self.box = comm.bcast(self.box, root=0)
self.omegam = comm.bcast(self.omegam, root=0)
self.omegal = comm.bcast(self.omegal, root=0)
self.grp_mass = comm.bcast(self.grp_mass, root=0)
self.grp_ids = comm.bcast(self.grp_ids, root=0)
self.grp_pos = comm.bcast(self.grp_pos, root=0)
self.grp_Rvir = comm.bcast(self.grp_Rvir, root=0)
self.n_selected_groups = comm.bcast(self.n_selected_groups,
root=0)
if self.very_large_sim:
# Read in positions from entire snapshot:
pos = self.subprocess_load_data(snapnum, "Coordinates")
if self.rank == 0:
print "Done loading full position array in snapshot"
# Build KDtree of gas positions:
gas_kdtree = cKDTree(pos)
print "Rank {} is done building its gas KD tree".format(
self.rank)
# Loop over all group positions, and find indices of gas close to this group position
index_dict = {}
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
gpos = self.grp_pos[i]
# r200 = self.grp_Rvir[i]
dist_thresh = AU.CodePosition(500. * np.sqrt(3),
self.redshift)
ind = self.get_gas_ind(gpos, dist_thresh, gas_kdtree)
index_dict[grp_id] = np.copy(ind)
# Now we have full index dictionary for all groups. We no longer need the KD-tree.
# try: print "Size of index dictionary in bytes: ",sys.getsizeof(index_dict)
# except: print "try1 failed"
# try: print "Size of KD-tree in bytes: ",sys.getsizeof(gas_kdtree)
# except: print "try2 failed"
del gas_kdtree
# Create data structure which will house data for all groups simultaneously:
group_dict = {}
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
group_dict[grp_id] = {}
# Now get position information for every group, since we already have the position array in memory.
# Then delete the pos array, since it is probably very large.
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
ind = index_dict[grp_id]
group_dict[grp_id]["Coordinates"] = pos[ind]
del pos
print "Rank {} is done with the Coordinates field for all groups!".format(
self.rank)
# Now do the same for all other desired fields, deleting each one after we are done with it.
for dat_str in self.dat_str_list:
if dat_str == "Coordinates":
pass
else:
dat = self.subprocess_load_data(snapnum, dat_str)
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
ind = index_dict[grp_id]
group_dict[grp_id][dat_str] = dat[ind]
del dat
print "Rank {} is done with the {} field for all groups!".format(
self.rank, dat_str)
# We have now loaded all of the data for all of the groups! First delete the large index_dict:
del index_dict
if self.size == 1:
# If there is only one process, then just trivially loop through all groups and save their data
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
save_dict = group_dict[grp_id]
self.save_group_data(snapnum, i, save_dict)
elif self.size > 1:
# If there are subprocesses, then send the data to the rank-0 process, where it will be saved.
comm.Barrier()
print "Rank {} hit the comm barrier!".format(self.rank)
# We now send data from the subprocesses to the rank-0 process, group by group.
for i in np.arange(self.n_selected_groups):
grp_id = self.grp_ids[i]
if self.rank == 0:
save_dict = {}
# save_dict = group_dict[grp_id]
for dat_str in self.dat_str_list:
if group_dict.has_key(grp_id) and group_dict[
grp_id].has_key(dat_str):
foo = np.copy(group_dict[grp_id][dat_str])
else:
foo = None
# Now gather from all of the subprocesses to rank 0:
foo = comm.gather(foo, root=0)
if self.rank == 0:
savedat = self._cat_gather_list(foo)
save_dict[dat_str] = savedat
# Rank 0 now has all of the data for this group saved in save_dict. Save to file:
if self.rank == 0:
self.save_group_data(snapnum, i, save_dict)
# del save_dict # HERE
# Now all processes remove this grp_id entry from their memory
# if group_dict.has_key(grp_id):
# del group_dict[grp_id]
# else:
# pass
elif not self.very_large_sim:
# Read in all data from snapshot a single time:
local_dict = {}
for dat_str in self.dat_str_list:
local_dict[dat_str] = subprocess_load_data(dat_str)
if rank == 0: print "Done loading full snapshot"
# Build KDtree of gas positions:
pos = local_dict['Coordinates']
gas_kdtree = cKDTree(pos)
# Loop over all group positions, and find indices of gas close to this group position
for i in np.arange(n_selected_groups):
grp_id = self.grp_ids[i]
gpos = self.grp_pos[i]
# r200 = self.grp_Rvir[i]
# Check whether CGM data file already exists:
filepath = self.savebase + "{}/s{}/{}.hdf5".format(
run, snapnum,
str(int(grp_id)).zfill(5))
if os.path.isfile(filepath):
if rank == 0:
print "File {} already exists! Skipping...".format(
filepath)
else:
pass
else: # If CGM data file does not exist yet, then continue:
dist_thresh = AU.CodePosition(500. * np.sqrt(3),
redshift)
ind = get_gas_ind(gpos, dist_thresh, gas_kdtree, box)
send_dict = {}
if np.size(ind) > 0:
for dat_str in self.dat_str_list:
send_dict[dat_str] = local_dict[dat_str][ind]
# Future: hide this in another function
# Now that data has been compiled for this process, send it to rank-0 process
if rank == 0:
save_dict = send_dict
if size > 1:
comm.Barrier()
print "comm barrier!"
for dat_str in self.dat_str_list:
for temp_rank in range(1, size):
if rank == temp_rank:
if send_dict.has_key(dat_str):
comm.send(send_dict[dat_str],
dest=0,
tag=i)
else:
comm.send('nothing', dest=0, tag=i)
elif rank == 0:
hold = comm.recv(source=temp_rank,
tag=i)
#print "this is what was received from rank {}: {}".format(temp_rank,hold)
if hold != 'nothing':
if save_dict.has_key(dat_str):
full_dict[dat_str] = np.append(
save_dict[dat_str],
hold,
axis=0)
else:
full_dict[dat_str] = hold
# Now data for this halo has been sent to rank-0 process. Time to save it.
if rank == 0:
print "Saving group file now to: {}".format(
filepath)
f = h5py.File(filepath, 'a')
grp = f.create_group("Header")
grp.attrs["hubble"] = hubble
grp.attrs["omegam"] = omegam
grp.attrs["omegal"] = omegal
grp.attrs["redshift"] = redshift
grp.attrs["box"] = box
grp.attrs["grp_id"] = grp_id
grp.attrs["grp_mass"] = self.grp_mass[i]
grp.attrs["grp_pos"] = self.grp_pos[i]
grp.attrs["grp_Rvir"] = self.grp_Rvir[i]
grp.attrs["grp_BHmass"] = self.grp_BHmass[i]
grp.attrs["grp_BHMdot"] = self.grp_BHMdot[i]
p_grp = f.create_group('PartType0')
for key in save_dict:
#print save_dict[key]
p_grp.create_dataset(key, data=save_dict[key])
f.close()
Omega0 = header.omega0 OmegaLambda = header.omegaL massDMParticle = header.massarr[1] #all DM particles have same mass #redshift evolution of critical_density critical_density *= Omega0 + atime**3 * OmegaLambda critical_density_gas = critical_density * baryonfraction #load particle indices and catalogs pGas = snapHDF5.read_block(filename3,"POS ", parttype=0) mGas = snapHDF5.read_block(filename3,"MASS", parttype=0) vGas = snapHDF5.read_block(filename3,"VEL ", parttype=0) rGas = snapHDF5.read_block(filename3,"RHO ",parttype=0) uGas = snapHDF5.read_block(filename3,"U ",parttype=0) pDM = snapHDF5.read_block(filename3,"POS ",parttype=1) catGas = readsubfHDF5.subfind_catalog(filename2, snapnum) halo100_indices= np.where(catGas.GroupLenType[:,0] >100)[0] startAllGas = [] endAllGas = [] for i in halo100_indices: startAllGas += [np.sum(catGas.GroupLenType[:i,0])] endAllGas += [startAllGas[-1] + catGas.GroupLenType[i,0]] cms = catGas.GroupPos / hubbleparam / atime #convert to physical cvel = catGas.GroupVel / atime #Initialize arrays #some radii are errors and negative, will have a value of 1 to be excluded negradii = np.zeros(np.size(halo100_indices))
def run_series(base_id="base400", snap_num=314):
import numpy as np
# import readsubf
import readsubfHDF5
import os
from base_lookup import directory
from run_find_disks import run_find_disks
# from run_J_profile import run_J_profile
from subsnap_write import subsnap_write
import time
import glob
#############
# Control Parameters
base = directory(base_id)[0]
snap_num = 311
# snap_num = 314 #directory(base_id)[1]
# snap_num = 189 #for z = 2
# snap_num = 250
base_dir = base_id + "/"
save_dir = base_dir + "snap" + str(snap_num).zfill(3) + "/"
resnap_folder = "resnaps/"
subsnap_folder = save_dir + "subsnaps/"
dat_folder = "data/"
# Overwrite past analysis?
overw = 1
# (Analysis restricted to only primary subhalos in given group)
# Analyze subhalos within a given mass range:
M_botlim = 100.0
M_toplim = 1000.0
# Analyze set # of subhalos?
analyze_N = -1
# JOB CONTROL:
# Concurrent jobs?
job_limit = 10
# Time between job submission?
wait_time = 10 # seconds
############################################################################################
if not os.path.exists(base_dir):
os.system("mkdir " + base_dir)
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)
cat = readsubfHDF5.subfind_catalog(base, snap_num)
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 = [1136, 1302, 1536]
# sub_ids = [8037]
# sub_ids = [4987]
# sub_ids = [0]
"""
sub_ids = [4024,\
4987,\
5517,\
5704,\
5791,\
6265,\
6911,\
7542,\
7721,\
7746,\
8005,\
8037]
"""
if analyze_N > 0.0:
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, dat_dir):
if overw == 0:
subsnap_name = dat_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_find_disks(base_id, base, snap_num, sub_id, grp_name)
# run_J_profile(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_find_disks(base_id, base, snap_num, sub_id, grp_name)
# run_J_profile(base_id, base, snap_num, sub_id, grp_name)
print "Waiting ", str(wait_time), "seconds to submit next job..."
time.sleep(wait_time)
################################################
dat_dir = save_dir + dat_folder
dat_list = glob.glob(dat_dir + "*.dat")
n_matches = len(sub_ids)
for i in np.arange(n_matches):
print str(i + 1) + " 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, dat_dir=dat_dir)
print "Job submission done!"
critical_density *= Omega0 + atime**3 * OmegaLambda
critical_density_gas = critical_density * baryonfraction
print(filename3)
#load particle indices and catalogs
pGas = snapHDF5.read_block(filename4, "POS ", parttype=0) #correct 4
iGas2 = snapHDF5.read_block(filename4, "ID ",
parttype=0) #correct 4 to filename3_032!
mGas = snapHDF5.read_block(filename3, "MASS", parttype=0)
eGas = snapHDF5.read_block(filename3, "U ", parttype=0)
dGas = snapHDF5.read_block(filename3, "RHO ", parttype=0)
xHI = snapHDF5.read_block(filename3, "HI ", parttype=0)
if str(species) == 'H2':
xH2I = snapHDF5.read_block(filename3, "H2I ", parttype=0)
pDM = snapHDF5.read_block(filename3, "POS ", parttype=1)
cat = readsubfHDF5.subfind_catalog(filename2, snapnum)
r200 = cat.Group_R_Crit200
print('loding catalog done')
def find_adress(j):
found_ad = np.where(iGas2 == j)
if j not in iGas2:
print('ID ' + str(j) + 'is not in the snapnumber ' +
str(snapnum))
#fp_result.write('ID ' + str(j) + 'is not in the snapnumber ' + str(snapnum) + '\n')
return found_ad
for j in ID:
if j in iGas2:
adress = np.where(iGas == j)
print('ID ' + str(j) + ' adress is ' + str(adress) + '\n')
def __init__(self, res, vel, snapnum):
self.vel = vel
self.res = res
self.snapnum = int(snapnum)
self.s_vel = vel.replace(".","")
self.s_res = res.replace(".","")
#File paths
filename = "/n/hernquistfs3/mvogelsberger/GlobularClusters/InterfaceWArepo_All_" + self.res + '_' + self.vel + "/output/"
filename2 = filename + "DM_FOF" #Used for readsubfHDF5
filename3 = filename + "snap_" + str(self.snapnum).zfill(3) #Used for hdf5lib, snapHDF5
#Read header information
header = snapHDF5.snapshot_header(filename3)
with hdf5lib.OpenFile(filename3 + ".hdf5") as fs:
red = hdf5lib.GetAttr(fs, "Header", "Redshift")
atime = hdf5lib.GetAttr(fs, "Header", "Time")
boxSize = hdf5lib.GetAttr(fs, "Header", "BoxSize")
boxSize *= atime / hubbleparam #convert from ckpc/h to kpc
Omega0 = hdf5lib.GetAttr(fs, "Header", "Omega0")
OmegaLambda = hdf5lib.GetAttr(fs, "Header", "OmegaLambda")
#Read halo catalog
cat = readsubfHDF5.subfind_catalog(filename2, self.snapnum)
#critical_density *= 1. / (Omega0 + OmegaLambda * atime * atime * atime) #redshift correction
r200 = cat.Group_R_Crit200
r200 *= atime / hubbleparam #convert from ckpc/h to kpc
m200 = cat.Group_M_Crit200
m200 *= 1. / hubbleparam #convert to 10^10 M_sun
haloCMvel = cat.GroupVel
haloCMvel *= 1. / atime #convert from km/s/a to km/s
haloPos = cat.GroupPos
haloPos *= atime / hubbleparam #convert from ckpc/h to kpc
#Initialize arrays
spinparamTotal = np.zeros(np.size(r200))
spinparamGas = np.zeros(np.size(r200))
spinparamDM = np.zeros(np.size(r200))
gasfrac = np.zeros(np.size(r200))
costheta = np.zeros(np.size(r200)) #misalignment angle
v200 = np.zeros(np.size(r200))
numGas = np.zeros(np.size(r200))
numDM = np.zeros(np.size(r200))
#Read in particles
massgas = snapHDF5.read_block(filename3, "MASS", parttype=0)
massdm = snapHDF5.read_block(filename3, "MASS", parttype=1)
posgas = snapHDF5.read_block(filename3, "POS ", parttype=0)
posdm = snapHDF5.read_block(filename3, "POS ", parttype=1)
velgas = snapHDF5.read_block(filename3, "VEL ", parttype=0)
veldm = snapHDF5.read_block(filename3, "VEL ", parttype=1)
#redefine position units from ckpc/h to kpc
posgas *= atime / hubbleparam
posdm *= atime / hubbleparam
#redefine velocity units from kmsqrt(a)/s to km/s
velgas *= np.sqrt(atime)
veldm *= np.sqrt(atime)
#boxSize hubble flow correction for halo CM velocity subtraction
boxSizeVel = boxSize * hubbleparam * .1 * np.sqrt(Omega0/atime/atime/atime + OmegaLambda)
#load particle indices
over300idx, indgas, inddm = np.load('particleindex_' + self.res + '_' + self.vel + '_' + str(self.snapnum) + '.npy')
over300idx = over300idx.astype(int)
over1 = []
for i,j in enumerate(over300idx):
#remove halo CM velocity
tempvelgas = dx_wrap(velgas[indgas[i]] - haloCMvel[j],boxSizeVel)
tempveldm = dx_wrap(veldm[inddm[i]] - haloCMvel[j],boxSizeVel)
#redefine positions wrt COM
tempposgas = dx_wrap(posgas[indgas[i]] - haloPos[j],boxSize)
tempposdm = dx_wrap(posdm[inddm[i]] - haloPos[j],boxSize)
numDM[j] = np.size(tempposdm)
numGas[j] = np.size(tempposgas)
#Calculating j200
#j200 of all particles
j200vecgas = np.sum(np.cross(tempposgas,tempvelgas)*massgas[indgas[i]][:, np.newaxis],axis=0)
j200vecdm = np.sum(np.cross(tempposdm,tempveldm)*massdm[inddm[i]][:, np.newaxis],axis=0)
if np.size(tempvelgas)!=0: #can be no gas particles!
costheta[j] = np.dot(j200vecgas,j200vecdm)/np.linalg.norm(j200vecgas)/np.linalg.norm(j200vecdm)
j200vec = j200vecgas + j200vecdm
j200 = np.linalg.norm(j200vec)
j200gas = np.linalg.norm(j200vecgas)
j200dm = np.linalg.norm(j200vecdm)
v200[j] = np.sqrt(GCONST*m200[j]/r200[j])
#Bullock spin parameter
totalmass = massgas[indgas[i]].sum(dtype='float64') + massdm[inddm[i]].sum(dtype='float64')
spinparamTotal[j] = j200/np.sqrt(2)/v200[j]/r200[j]/totalmass
if np.size(tempveldm)!=0: #tempveldm can be empty no dm particles!
spinparamDM[j] = j200dm/np.sqrt(2)/v200[j]/r200[j]/massdm[inddm[i]].sum(dtype='float64')
if np.size(tempvelgas)!=0: #tempvelgas can be empty no gas particles!
spinparamGas[j] = j200gas/np.sqrt(2)/v200[j]/r200[j]/massgas[indgas[i]].sum(dtype='float64')
gasfrac[j] = massgas[indgas[i]].sum(dtype='float64') / (massgas[indgas[i]].sum(dtype='float64') + massdm[inddm[i]].sum(dtype='float64'))
#Reindex over300idx to account for SO halos with DM particles >300
over300idx2 = over300idx[numDM[over300idx] > 300]
#Plotting
#Redfine in terms of over300idx2
self.spinparamTotal = spinparamTotal[over300idx2]
self.spinparamGas = spinparamGas[over300idx2]
self.spinparamDM = spinparamDM[over300idx2]
self.gasfrac = gasfrac[over300idx2]
self.m200 = m200[over300idx2]
self.m200 *= 10**10 #Convert to solar mass.
self.costheta = costheta[over300idx2]
self.gasfracCosTheta = self.gasfrac[self.costheta!=0.]
self.m2002 = self.m200[self.costheta!=0.]
self.costheta = self.costheta[self.costheta!=0.] #take out the 0 gas components
self.thetadeg = np.arccos(self.costheta)*180./np.pi
def find_MC_tracer_ids(snap_num,sub_id,base,scale_factor,gal_radfac,gas_pos,gas_ids,star_pos,star_ids,tracer_ids,parent_ids): print "Finding MC tracer IDs..." cat = readsubfHDF5.subfind_catalog(base, snap_num) # I assume that the subhalo is the primary subhalo in its group sub_list = cat.GroupFirstSub grp_id = np.argmin( np.abs(sub_id-sub_list) ) sub_pos = cat.SubhaloPos[sub_id] sub_mass = cat.SubhaloMass[sub_id] sub_Rvir = cat.Group_R_Crit200[grp_id] gal_rad = sub_Rvir * gal_radfac # * 0.1 #snapname = base + "snapdir_"+str(snap_num).zfill(3)+"/snap_"+str(snap_num).zfill(3) snapname = base + "snap_"+str(snap_num).zfill(3) #Find all gas and star particles within 0.1 Rvir of the subhalo # GAS # #gas_pos = readsnapHDF5.read_block(snapname,"POS ",parttype=0) #gas_ids = readsnapHDF5.read_block(snapname,"ID ",parttype=0) #readsnapHDF5.list_blocks(snapname+".hdf5") gas_x = np.logical_and(gas_pos[:,0] > sub_pos[0]-gal_rad, gas_pos[:,0] < sub_pos[0]+gal_rad) gas_y = np.logical_and(gas_pos[:,1] > sub_pos[1]-gal_rad, gas_pos[:,1] < sub_pos[1]+gal_rad) gas_z = np.logical_and(gas_pos[:,2] > sub_pos[2]-gal_rad, gas_pos[:,2] < sub_pos[2]+gal_rad) gas_xyz_ind = np.logical_and(np.logical_and(gas_x,gas_y),gas_z) gas_pos = gas_pos[gas_xyz_ind] gas_pos = gas_pos - sub_pos gas_r_ind = np.sqrt(gas_pos[:,0]**2 + gas_pos[:,1]**2 + gas_pos[:,2]**2) < gal_rad gal_gas_ids = gas_ids[gas_xyz_ind][gas_r_ind] print "len(gal_gas_ids )",len(gal_gas_ids) # Clear gas data: gas_pos = 0. gas_ids = 0. gas_x = 0. gas_y = 0. gas_z = 0. gas_xyz_ind = 0. gas_r_ind = 0. # STARS # #star_pos = readsnapHDF5.read_block(snapname,"POS ",parttype=4) #star_ids = readsnapHDF5.read_block(snapname,"ID ",parttype=4) #readsnapHDF5.list_blocks(snapname+".hdf5") star_x = np.logical_and(star_pos[:,0] > sub_pos[0]-gal_rad, star_pos[:,0] < sub_pos[0]+gal_rad) star_y = np.logical_and(star_pos[:,1] > sub_pos[1]-gal_rad, star_pos[:,1] < sub_pos[1]+gal_rad) star_z = np.logical_and(star_pos[:,2] > sub_pos[2]-gal_rad, star_pos[:,2] < sub_pos[2]+gal_rad) star_xyz_ind = np.logical_and(np.logical_and(star_x,star_y),star_z) star_pos = star_pos[star_xyz_ind] star_pos = star_pos - sub_pos star_r_ind = np.sqrt(star_pos[:,0]**2 + star_pos[:,1]**2 + star_pos[:,2]**2) < gal_rad gal_star_ids = star_ids[star_xyz_ind][star_r_ind] print "len(gal_star_ids )",len(gal_star_ids) # Clear star data: star_pos = 0. star_ids = 0. star_x = 0. star_y = 0. star_z = 0. star_xyz_ind = 0. star_r_ind = 0. # Now find all MC tracers associated with the gas and star particles we have selected: pre_in1d = time.time() temp1 = tracer_ids[np.in1d(parent_ids,gal_gas_ids)] temp2 = tracer_ids[np.in1d(parent_ids,gal_star_ids)] post_in1d = time.time() print "in1d time: ",post_in1d-pre_in1d # update arrays after each iteration? gal_MC_ids = np.append(temp1,temp2) print "Done finding MC tracer IDs..." return gal_MC_ids
#Read header information header = snapHDF5.snapshot_header(filename3) red = header.redshift atime = header.time boxSize = header.boxsize Omega0 = header.omega0 OmegaLambda = header.omegaL massDMParticle = header.massarr[1] #all DM particles have same mass #redshift evolution of critical_density critical_density *= Omega0 + atime**3 * OmegaLambda critical_density_gas = critical_density * baryonfraction #Read halo catalog catGas = readsubfHDF5.subfind_catalog(filename + "GasOnly_FOF", snapnum) catDM = readsubfHDF5.subfind_catalog(filename + "DM_FOF", snapnum) #Get CM & R200 of all halos w/ >300 DM particles, >100 gas particles GroupPos_Gas = catGas.GroupPos[catGas.GroupLenType[:, 0] > 100] GroupPos_DM = catDM.GroupPos[catDM.GroupLenType[:, 1] > 300] R200_DM = catDM.Group_R_Crit200[catDM.GroupLenType[:, 1] > 300] M200_DM = catDM.Group_M_Crit200[catDM.GroupLenType[:, 1] > 300] #Filter for nonzero R200 GroupPos_DM = GroupPos_DM[R200_DM != 0.] M200_DM = M200_DM[R200_DM != 0.] R200_DM = R200_DM[R200_DM != 0.] #Allocate arrays matchingHalos = []
redshift = readsnapHDF5.snapshot_header(snapname).redshift scale_factor = 1./(1.+redshift) gal_radfac = scale_factor * 2.5 gal_vt_ids = find_vt_tracer_ids(snap_num,sub_id,base,scale_factor,gal_radfac) gal_MC_ids = find_MC_tracer_ids(snap_num,sub_id,base,scale_factor,gal_radfac) n_vt = np.uint32(gal_vt_ids.size) print "n_vt ",n_vt n_MC = np.uint32(gal_MC_ids.size) print "n_MC ",n_MC MC_vt_ratio = float(n_MC)/float(n_vt) print "n_MC/n_vt ", MC_vt_ratio cat = readsubfHDF5.subfind_catalog(base, snap_num) # I assume that the subhalo is the primary subhalo in its group #sub_list = cat.GroupFirstSub #grp_id = np.argmin( np.abs(sub_id-sub_list) ) grp_id = cat.SubhaloParent[sub_id] sub_partIDs = get_subhalo_ids(base,snap_num,sub_id) sub_pos = cat.SubhaloPos[sub_id] sub_mass = cat.SubhaloMass[sub_id] sub_vel = cat.SubhaloVel[sub_id] sub_Rvir = cat.Group_R_Crit200[grp_id] elif resume == 1:
import readsubfHDF5
import numpy as np
Base1 = '/n/hernquistfs1/Illustris/Runs/Illustris-3/output/'
Base2 = '/n/hernquistfs1/Illustris/Runs/Illustris-Dark-3/output/'
MatchBase = "./output/"
snapnum = 100
fname = MatchBase + "/matchdata/sub_match_" + str(snapnum).zfill(3)
cat1 = readsubfHDF5.subfind_catalog(Base1,
snapnum,
keysel=["SubhaloPos", "SubhaloLenType"])
cat2 = readsubfHDF5.subfind_catalog(Base2,
snapnum,
keysel=["SubhaloPos", "SubhaloLenType"])
ch = 200
print cat1.SubhaloPos[ch, :]
print cat2.SubhaloPos[match_halonr[ch], :]
scalefactors = illustris.load_scalefactors()
redshifts = 1.0 / scalefactors - 1.0
snapshots = np.arange(redshifts.shape[0])
# ============================================== #
start_time = time.time()
end_time = time.time()
count = 1
min_sf_density = 1000.0
for redshift in target_redshifts:
diff = np.abs(redshift - redshifts)
snap = snapshots[diff == diff.min()]
if this_task == 0:
cat = readsubfHDF5.subfind_catalog(
dir, snap[0], keysel=['SubhaloMassType', 'SubhaloSFR'])
else:
cat = None
cat = comm.bcast(cat, root=0)
stellar_mass = cat.SubhaloMassType[:, 4] * 1e10 / little_h
total_gas_mass = cat.SubhaloMassType[:, 0] * 1e10 / little_h
sfr = cat.SubhaloSFR
n_subs = cat.nsubs
cold_gas_mass = np.zeros(n_subs) # local array
cold_gas_mass_fraction = np.zeros(n_subs) # local array
global_cold_gas_mass = np.zeros(n_subs) # global array
global_cold_gas_mass_fraction = np.zeros(n_subs) # global array
readhaloHDF5.reset()
#Last updated: 1/18/15.
#importing libraries. Got 1/5/15 version of readsnapHDF5 from Volgelsburger repo.
import readsubfHDF5
import numpy as np
import cosmo_const as cc
#switch variable. If = 0, then runs
this_task = 0
#loading in catalog of halos
if this_task==0:
cat = readsubfHDF5.subfind_catalog('/n/ghernquist/Illustris/Runs/Illustris-1/output/', 135, keysel=['Group_M_Crit200', 'Group_M_Mean200', 'Group_M_TopHat200', 'SubhaloMassType'] )
else:
cat = None
massInsideData = np.transpose(np.load('./kdTree_complete_10-300kpc_dm_bigdata_illustris-1.npy'))
haloNum = np.array(massInsideData[0])
groupNum = np.array(massInsideData[1])
m_crit200 = np.empty(len(groupNum))
m_mean200 = np.empty(len(groupNum))
m_topHat200 = np.empty(len(groupNum))
m_star = np.empty(len(groupNum))
m_blackhole = np.empty(len(groupNum))
i = 0
while (i<len(groupNum)):
m_crit200[i] = cat.Group_M_Crit200[groupNum[i]]*1e10*(cc.h_little**-1.)
m_mean200[i] = cat.Group_M_Mean200[groupNum[i]]*1e10*(cc.h_little**-1.)
m_topHat200[i] = cat.Group_M_TopHat200[groupNum[i]]*1e10*(cc.h_little**-1.)
m_star[i] = (cat.SubhaloMassType[:,4])[haloNum[i]]*1e10*(cc.h_little**-1.)
m_blackhole[i] = (cat.SubhaloMassType[:,5])[haloNum[i]]*1e10*(cc.h_little**-1.)
import readsubfHDF5
import numpy as np
import hdfsim
base = "/n/hernquistfs1/mvogelsberger/projects/GFM/Production/Cosmo/Cosmo0_V6/L25n512/output/"
snapnum = 68
halonum = 1
cat = readsubfHDF5.subfind_catalog(base,snapnum,long_ids=True)
GasFuzzOffset = cat.GroupFuzzOffsetType[:,0]
start = GasFuzzOffset[halonum]
end = GasFuzzOffset[halonum+1]-1
totngas = 0
for fnum in xrange(0,500):
try:
f=hdfsim.get_file(snapnum,base,fnum)
except IOError:
break
NumPart_ThisFile = f["Header"].attrs["NumPart_ThisFile"][0]
if totngas < start and totngas+NumPart_ThisFile > start:
newstart = start - totngas
newend = end - totngas
bar=f["PartType0"]
snap_spacing = 1 # Mass cutoff for halos selected at latest_snap mass_thresh = 10. # Total number of halos to run for (mainly for testing purposes) N_tot = -1 # Minimum tracer ID: tracer_MIN = np.uint64(1000000010) ############################ pre_setup = time.time() # Read in full subfind catalog cat = readsubfHDF5.subfind_catalog(base, latest_snap) # Look only at primary halos sub_list = cat.GroupFirstSub #grp_ids = np.argmin( np.abs(sub_id-sub_list) ) #grp_id = cat.SubhaloParent[sub_list] # Apply mass threshold: sub_mass = cat.SubhaloMass[sub_list] mass_cutoff_ind = sub_mass > mass_thresh sub_list = sub_list[mass_cutoff_ind] # If N_tot is set, limit sub_list to that size if N_tot > 0: sub_list = sub_list[: N_tot]
def readhalo(base, snapbase, num, block_name, parttype, fof_num, sub_num, long_ids=False, double_output=False, verbose=False):
global FlagRead, cat, GroupOffset, HaloOffset, multiple, filename, Parttype, FileTypeNumbers, FileNum
if (FlagRead==False) | ((parttype in Parttype) == False):
if (verbose):
print("READHALO: INITIAL READ")
#add parttype to list
Parttype.append(parttype)
if (verbose):
print("READHALO: Parttype = ", Parttype)
#read in catalog
cat = readsubfHDF5.subfind_catalog(base, num, long_ids=long_ids, double_output=double_output, keysel=["GroupLenType","GroupNsubs","GroupFirstSub","SubhaloLenType","SubhaloMassType"])
if (cat.ngroups==0):
if (verbose):
print("READHALO: no groups in catalog... returning")
return
if (FlagRead==False):
GroupOffset = np.zeros([cat.ngroups, 6], dtype="int64")
HaloOffset = np.zeros([cat.nsubs, 6], dtype="int64")
filename = base+"/"+snapbase+"_"+str(num).zfill(3)
multiple=False
if (os.path.exists(filename+".hdf5")==False):
filename = base+"/snapdir_"+str(num).zfill(3)+"/"+snapbase+"_"+str(num).zfill(3)+"."+str(0)
multiple=True
if (os.path.exists(filename+".hdf5")==False):
print("READHALO: [error] file not found : ", filename)
sys.exit()
FlagRead=True
#construct offset tables
k=0
for i in range(0, cat.ngroups):
if (i>0):
GroupOffset[i, parttype] = GroupOffset[i-1, parttype] + cat.GroupLenType[i-1, parttype]
if (cat.GroupNsubs[i]>0):
HaloOffset[k, parttype] = GroupOffset[i, parttype]
k+=1
for j in range(1, cat.GroupNsubs[i]):
HaloOffset[k, parttype] = HaloOffset[k-1, parttype] + cat.SubhaloLenType[k-1, parttype]
k+=1
if (k!=cat.nsubs):
print("READHALO: problem with offset table", k, cat.nsubs)
sys.exit()
#construct file tables
if (multiple):
filename = base+"/snapdir_"+str(num).zfill(3)+"/"+snapbase+"_"+str(num).zfill(3)+"."+str(0)
else:
filename = base+"/"+snapbase+"_"+str(num).zfill(3)
head = snapHDF5.snapshot_header(filename)
FileNum = head.filenum+1
FileTypeNumbers = np.zeros([FileNum, 6], dtype="int64")
cumcount = np.zeros(6, dtype="int64")
for fnr in range(0, FileNum-1):
if (multiple):
filename = base+"/snapdir_"+str(num).zfill(3)+"/"+snapbase+"_"+str(num).zfill(3)+"."+str(fnr)
else:
filename = base+"/"+snapbase+"_"+str(num).zfill(3)
if (verbose):
print("READHALO: initial reading file :", filename)
head = snapHDF5.snapshot_header(filename)
cumcount[:] += head.npart[:]
FileTypeNumbers[fnr+1, :] = cumcount[:]
if (sub_num>=0) & (fof_num < 0):
off = HaloOffset[sub_num, parttype]
left = cat.SubhaloLenType[sub_num, parttype]
if (verbose):
print("READHALO: nr / particle # / mass :", sub_num, cat.SubhaloLenType[sub_num, parttype], cat.SubhaloMassType[sub_num, parttype].astype("float64"))
if (fof_num>=0) & (sub_num < 0):
off = GroupOffset[fof_num, parttype]
left = cat.GroupLenType[fof_num, parttype]
if (verbose):
print("READHALO: nr / particle # / mass :", fof_num, cat.GroupLenType[fof_num, parttype], cat.GroupMassType[fof_num, parttype].astype("float64"))
if (sub_num>=0) & (fof_num>=0):
real_sub_num = sub_num + cat.GroupFirstSub[fof_num]
off = HaloOffset[real_sub_num, parttype]
left = cat.SubhaloLenType[real_sub_num, parttype]
if (verbose):
print("READHALO: nr / particle # / mass :", real_sub_num, cat.SubhaloLenType[real_sub_num, parttype], cat.SubhaloMassType[real_sub_num, parttype].astype("float64"))
if (left==0):
if (verbose):
print("READHALO: no particles of type... returning")
return
#get first file that contains particles of required halo/fof/etc
findex = np.argmax(FileTypeNumbers[:, parttype] > off) - 1
#in case we reached the end argmax returns 0
if (findex == -1):
findex = FileNum - 1
if (verbose):
print("READHALO: first file that contains particles =", findex)
for fnr in range(0, findex):
off -= FileTypeNumbers[fnr+1, parttype] - FileTypeNumbers[fnr, parttype]
#read data from file
first=True
for fnr in range(findex, FileNum):
if (multiple):
filename = base+"/snapdir_"+str(num).zfill(3)+"/"+snapbase+"_"+str(num).zfill(3)+"."+str(fnr)
else:
filename = base+"/"+snapbase+"_"+str(num).zfill(3)
if (verbose):
print("READHALO: reading file :", filename)
head = snapHDF5.snapshot_header(filename)
nloc = head.npart[parttype]
if (nloc > off):
if (verbose):
print("READHALO: data")
start = off
if (nloc - off > left):
count = left
else:
count = nloc - off
if (first==True):
data = snapHDF5.read_block(filename, block_name, parttype, slab_start=start, slab_len=count)
first=False
else:
data = np.append(data, snapHDF5.read_block(filename, block_name, parttype, slab_start=start, slab_len=count), axis=0)
left -= count
off += count
if (left==0):
break
off -= nloc
return data
#prints arguments from initial command line
print sys.argv
print snap
ext = '000'+str(snap)
ext = ext[-3:]
#loading in txt file with all particle files listed
partFileArray = np.genfromtxt(partFileTxt, dtype='str')
print partFileArray
#loading in catalog of halos
if this_task==0:
cat = readsubfHDF5.subfind_catalog('/n/ghernquist/Illustris/Runs/'+run+'/output/', snap, keysel=['SubhaloPos', 'SubhaloMass', 'GroupFirstSub', 'SubhaloGrNr', 'Group_M_Crit200'] )
else:
cat = None
print "Loaded In Halo Catalog"
groupFirstSub = cat.GroupFirstSub[np.where(cat.GroupFirstSub != -1)]
all_subhalo_nrs = np.arange(cat.SubhaloMass.shape[0]) #making an array of values 0 to the number of halos - 1 (index number array)
subhalo_masses = cat.SubhaloMass * 1e10 * (cc.h_little**-1.) #correcting array of halo masses to be in units of Msun
groupNum = cat.SubhaloGrNr
groupMcrit200 = cat.Group_M_Crit200[np.where(cat.GroupFirstSub != -1)]*(cc.h_little**-1.)*(10.**10.)
#finding primary halos within mass range (e11.5-e12.5)
#groupFirstSub = groupFirstSub[(subhalo_masses[groupFirstSub] > (10.**11.5)) & (subhalo_masses[groupFirstSub] < (10.**12.5))]
groupFirstSub = groupFirstSub[(groupMcrit200 > (10.**11.)) & (groupMcrit200 < (10.**13.))]
print groupFirstSub
def calculate_halo(sub_id): #submit job to queue
# I assume that the subhalo is the primary subhalo in its group
sub_list = cat.GroupFirstSub
grp_id = np.argmin( np.abs(sub_id-sub_list) )
#grp_id = cat.SubhaloParent[sub_id]
sub_partIDs = get_subhalo_ids(base,snap_num,sub_id)
sub_pos = cat.SubhaloPos[sub_id]
sub_mass = cat.SubhaloMass[sub_id]
sub_vel = cat.SubhaloVel[sub_id]
sub_Rvir = cat.Group_R_Crit200[grp_id]
frame_num = 0
for current_snap in snap_nums:
# Find the halo in this snapshot that corresponds to the halo in our original snapshot:
print "on snapshot number ",current_snap
old_match_flag = new_match_flag
old_subid = new_subid
old_partIDs = new_partIDs
old_subpos = new_subpos
old_submass = new_submass
old_subvel = new_subvel
#new_snapname = base + "snapdir_"+str(current_snap).zfill(3)+"/snap_"+str(current_snap).zfill(3)
new_snapname = base + "/snap_"+str(current_snap).zfill(3)
new_cat = readsubfHDF5.subfind_catalog(base, current_snap)
redshift = readsnapHDF5.snapshot_header(new_snapname).redshift
scale_factor = 1./(1.+redshift)
#readsnapHDF5.list_blocks(new_snapname+".hdf5")
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# MC tracer information:
MC_ids = readsnapHDF5.read_block(new_snapname,"TFID",parttype=3) # May want to fix this for very large # of tracers (read in 1 snapshot at a time)
MC_ind = np.in1d(MC_ids,gal_MC_ids,assume_unique=True) #This step takes a long time!
MC_ids = 0.
print "MC_ind.sum() ",MC_ind.sum()
# now match the tracers with their parent particles:
parent_ids = readsnapHDF5.read_block(new_snapname,"TRPI",parttype=3)[MC_ind]
#parent_ids = np.unique(parent_ids)
parent_ids.sort()
(unique_parent_ids,unique_arg) = np.unique(parent_ids,return_index=True)
foo = parent_ids.size-unique_arg[-1] # the number of reps for the very last unique entry in parent_ids
rep_count = np.append(np.diff(unique_arg),foo)
gas_ids = readsnapHDF5.read_block(new_snapname,"ID ",parttype=0)
gas_ind = np.in1d(gas_ids,unique_parent_ids,assume_unique=True)
gas_ids = 0.
gas_pos = readsnapHDF5.read_block(new_snapname,"POS ",parttype=0)
pos1 = gas_pos[gas_ind]
gas_pos = 0.
gas_vel = readsnapHDF5.read_block(new_snapname,"VEL ",parttype=0)
vel1 = gas_vel[gas_ind]
gas_vel = 0.
# now find which ones are stars (and get their properties):
star_ids = readsnapHDF5.read_block(new_snapname,"ID ",parttype=4)
star_ind = np.in1d(star_ids,unique_parent_ids,assume_unique=True)
star_ids = 0.
star_pos = readsnapHDF5.read_block(new_snapname,"POS ",parttype=4)
pos2 = star_pos[star_ind]
star_pos = 0.
star_vel = readsnapHDF5.read_block(new_snapname,"VEL ",parttype=4)
vel2 = star_vel[star_ind]
star_vel = 0.
pos = np.concatenate((pos1,pos2))
pos1 = 0.
pos2 = 0.
vel = np.concatenate((vel1,vel2))
vel1 = 0.
vel2 = 0.
gal_MC_pos = np.repeat(pos,rep_count,axis=0)
gal_MC_vel = np.repeat(vel,rep_count,axis=0)
print "len(gal_MC_pos) ",len(gal_MC_pos)
print "len(gal_MC_vel) ",len(gal_MC_vel)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Generate image:
#generate_tracer_image()
print "not generating image "
# Generate savefile: # save: snap_num, # of vts, gal_vt_ids at z=0, sub_id at that snap,
# subhalo_pos at that snap, Rvir at that time,
filename = save_dir + str(current_snap).zfill(3)+".dat"
f = open(filename,'wb')
primary_halo_flag = np.uint32(primary_halo_flag)
gal_MC_posx = gal_MC_pos[:,0]
gal_MC_posy = gal_MC_pos[:,1]
gal_MC_posz = gal_MC_pos[:,2]
gal_MC_velx = gal_MC_vel[:,0]
gal_MC_vely = gal_MC_vel[:,1]
gal_MC_velz = gal_MC_vel[:,2]
print "len(gal_MC_posx) ",len(gal_MC_posx)
print "len(gal_MC_velz) ",len(gal_MC_velz)
# Saving stuff to file:
# general stuff
current_snap.astype("uint32").tofile(f)
redshift.astype("float64").tofile(f)
# MC tracer data
n_MC.astype("uint32").tofile(f)
gal_MC_ids.astype("uint64").tofile(f)
gal_MC_posx.astype("float64").tofile(f)
gal_MC_posy.astype("float64").tofile(f)
gal_MC_posz.astype("float64").tofile(f)
gal_MC_velx.astype("float64").tofile(f)
gal_MC_vely.astype("float64").tofile(f)
gal_MC_velz.astype("float64").tofile(f)
# halo data
primary_halo_flag.astype("uint32").tofile(f)
new_subid.astype("uint32").tofile(f)
new_subpos.astype("float64").tofile(f)
new_subvel.astype("float64").tofile(f)
new_subRvir.astype("float64").tofile(f)
if False:
if primary_halo_flag == 1: pass
elif primary_halo_flag == 0:
primary_halo_pos.astype("float64").tofile(f)
primary_halo_vel.astype("float64").tofile(f)
f.close()
frame_num = frame_num + 1
time4 = time.time()
print "done!"
return
