def __init__(self, title, maxval):
self._pbar = tqdm(leave=True, total=maxval, desc=title)
self.i = 0
def fubar(obj, group_type, **kwargs):
"""Group finding procedure.
FUBAR stands for Friends-of-friends Unbinding after Rockstar; the
name is no longer valid, but it stuck. Here we perform an FOF
operation for each grouping and create the master caesar lists.
For halos we consider dark matter + gas + stars. For galaxies
however, we only consider high density gas and stars (dust and
blackholes if included).
For clouds we consider all gas particles.
Parameters
----------
obj : :class:`main.CAESAR`
Main caesar object.
group_type : str
Can be either 'halo', 'galaxy' or 'cloud'; determines what objects
we find with FOF.
"""
#pdb.set_trace()
pos = obj.data_manager.pos
unbind = False
unbind_str = 'unbind_%s' % group_types[group_type]
if unbind_str in obj._kwargs and \
isinstance(obj._kwargs[unbind_str], bool):
unbind = obj._kwargs[unbind_str]
setattr(obj.simulation, unbind_str, unbind)
if group_type == 'galaxy':
if not obj.simulation.baryons_present:
return
if ('fof6d' in obj._kwargs and obj._kwargs['fof6d'] == True):
#set default parameters
mingrp = 16
LL_factor = 0.02
vel_LL = 1.0
nproc = 1
LL = get_mean_interparticle_separation(obj) * get_b(
obj, group_type) # get MIS and omega_baryon
if ('fof6d_mingrp' in obj._kwargs
and obj._kwargs['fof6d_mingrp'] is not None):
mingrp = obj._kwargs['fof6d_mingrp']
if ('fof6d_LL_factor' in obj._kwargs
and obj._kwargs['fof6d_LL_factor'] is not None):
LL_factor = obj._kwargs['fof6d_LL_factor']
if ('fof6d_vel_LL' in obj._kwargs
and obj._kwargs['fof6d_vel_LL'] is not None):
vel_LL = obj._kwargs['fof6d_vel_LL']
if ('nproc' in obj._kwargs and obj._kwargs['nproc'] is not None):
nproc = obj._kwargs['nproc']
snapname = ('%s/%s' %
(obj.simulation.fullpath, obj.simulation.basename))
mylog.info("Running FOF6D")
nparts, gas_index, star_index, bh_index = run_fof_6d(
snapname, mingrp, LL_factor, vel_LL, nproc)
fof_tags = np.concatenate((gas_index, star_index, bh_index))
high_rho_indexes = get_high_density_gas_indexes(obj)
if ('fof6d_outfile' in obj._kwargs):
fof6d_file = obj._kwargs['fof6d_outfile']
mylog.info('Writing fof6d particle group info to %s' %
fof6d_file)
with h5py.File(
fof6d_file,
'w') as hf: # overwrites existing fof6d group file
hf.create_dataset('nparts', data=nparts, compression=1)
hf.create_dataset('gas_index',
data=gas_index,
compression=1)
hf.create_dataset('star_index',
data=star_index,
compression=1)
hf.create_dataset('bh_index', data=bh_index, compression=1)
hf.close()
#assert(obj.simulation.ngas == len(gas_index)) & (obj.simulation.nstar == len(star_index)) & (obj.simulation.nbh == len(bh_index)),'[fubar/fubar]: Assertion failed: Wrong number of particles in fof6d calculation'
elif ('fof6d_file' in obj._kwargs
and obj._kwargs['fof6d_file'] is not None):
# use galaxy info from fof6d hdf5 file
fof6d_file = obj._kwargs['fof6d_file']
LL = get_mean_interparticle_separation(obj) * get_b(
obj, group_type) # get MIS and omega_baryon
import os
if os.path.isfile(fof6d_file):
mylog.info('Galaxy IDs from fof6d file %s' % fof6d_file)
else:
mylog.info('fof6d file %s not found!' % fof6d_file)
hf = h5py.File(fof6d_file, 'r')
npfof6d = hf['nparts']
assert (obj.simulation.ngas == npfof6d[0]) & (
obj.simulation.nstar == npfof6d[1]
) & (
obj.simulation.nbh == npfof6d[2]
), 'Assertion failed: Wrong number of particles in fof6d file: %s' % npfof6d
gas_indexes = hf['gas_index']
star_indexes = hf['star_index']
bh_indexes = hf['bh_index']
fof_tags = np.concatenate((gas_indexes, star_indexes, bh_indexes))
else:
# here we want to perform 3D FOF on high density gas + stars
mylog.info('Groups based on YT 3DFOF')
high_rho_indexes = get_high_density_gas_indexes(obj)
pos0 = pos
pos = np.concatenate(
(pos0[obj.data_manager.glist][high_rho_indexes],
pos0[obj.data_manager.slist]))
if obj.data_manager.blackholes:
pos = np.concatenate((pos, pos0[obj.data_manager.bhlist]))
if obj.data_manager.dust:
pos = np.concatenate((pos, pos0[obj.data_manager.dlist]))
LL = get_mean_interparticle_separation(obj) * get_b(
obj, group_type)
fof_tags = fof(obj, pos, LL, group_type=group_type)
gtags = np.full(obj.simulation.ngas, -1, dtype=np.int64)
gtags[high_rho_indexes] = fof_tags[0:len(high_rho_indexes)]
fof_tags = np.concatenate(
(gtags, fof_tags[len(high_rho_indexes)::]))
elif group_type == 'cloud':
#don't run if there's no baryons
if not obj.simulation.baryons_present:
return
#also don't run if fofclouds isn't set
if ('fofclouds' not in obj._kwargs) or (obj._kwargs['fofclouds']
== False):
mylog.warning(
'No clouds: fofclouds either not set, or is set to false: not performing 3D group search for GMCs'
)
return
# here we want to perform FOF on all gas
pos = pos[obj.data_manager.glist]
LL = get_mean_interparticle_separation(obj) * get_b(obj, group_type)
if ('ll_cloud' in obj._kwargs) and isinstance(obj._kwargs['ll_cloud'],
(int, float)):
LL = obj._ds.quan(float(obj._kwargs['ll_cloud']), 'kpccm')
fof_tags = fof(obj, pos, LL, group_type=group_type)
elif group_type == 'halo':
if ('fof_from_snap' in obj._kwargs
and obj._kwargs['fof_from_snap'] == 1):
mylog.info('Using Halo fof ID from snapshots')
fof_tags = obj.data_manager.haloid - 1
else:
LL = get_mean_interparticle_separation(obj) * get_b(
obj, group_type)
fof_tags = fof(obj, pos, LL, group_type=group_type, **kwargs)
#print 'fof_tags',len(fof_tags[fof_tags>=0]),max(fof_tags),np.shape(fof_tags),fof_tags[fof_tags>=0]
else:
mylog.warning('group-type %s not recognized' % group_type)
tag_sort = np.argsort(fof_tags)
unique_groupIDs = np.unique(fof_tags)
groupings = {}
for GroupID in unique_groupIDs:
if GroupID < 0: continue
groupings[GroupID] = create_new_group(obj, group_type)
if len(groupings) == 0:
mylog.warning('No %s found!' % group_types[group_type])
return
tags = fof_tags
nparts = len(tags)
for i in range(0, nparts):
index = tag_sort[i]
tag = tags[index]
if tag < 0: continue
groupings[tag]._append_global_index(index)
if unbind: mylog.info('Unbinding %s' % group_types[group_type])
for v in tqdm(groupings.values(),
total=len(groupings),
desc='Processing %s' % group_types[group_type]):
v._process_group()
n_invalid = 0
group_list = []
for v in six.itervalues(groupings):
if not v._valid:
n_invalid += 1
continue
group_list.append(v)
mylog.info('Disregarding %d invalid %s (%d left)' %
(n_invalid, group_types[group_type], len(group_list)))
# sort by mass
group_list.sort(key=lambda x: x.masses['total'], reverse=True)
for i in range(0, len(group_list)):
group_list[i].GroupID = i
# initialize global lists
glist = np.full(obj.simulation.ngas, -1, dtype=np.int32)
slist = np.full(obj.simulation.nstar, -1, dtype=np.int32)
dmlist = np.full(obj.simulation.ndm, -1, dtype=np.int32)
if 'dm2' in obj.data_manager.ptypes:
dm2list = np.full(obj.simulation.ndm2, -1, dtype=np.int32)
if 'dm3' in obj.data_manager.ptypes:
dm3list = np.full(obj.simulation.ndm3, -1, dtype=np.int32)
bhlist = np.full(obj.simulation.nbh, -1, dtype=np.int32)
dlist = np.full(obj.simulation.ndust, -1, dtype=np.int32)
for group in group_list:
glist[group.glist] = group.GroupID
slist[group.slist] = group.GroupID
dmlist[group.dmlist] = group.GroupID
if 'dm2' in obj.data_manager.ptypes:
dm2list[group.dm2list] = group.GroupID
if 'dm3' in obj.data_manager.ptypes:
dm3list[group.dm3list] = group.GroupID
bhlist[group.bhlist] = group.GroupID
dlist[group.dlist] = group.GroupID
if not hasattr(group, 'unbound_indexes'):
continue
glist[group.unbound_indexes[ptype_ints['gas']]] = -2
slist[group.unbound_indexes[ptype_ints['star']]] = -2
dmlist[group.unbound_indexes[ptype_ints['dm']]] = -2
if 'dm2' in obj.data_manager.ptypes:
dm2list[group.unbound_indexes[ptype_ints['dm2']]] = -2
if 'dm3' in obj.data_manager.ptypes:
dm3list[group.unbound_indexes[ptype_ints['dm3']]] = -2
bhlist[group.unbound_indexes[ptype_ints['bh']]] = -2
dlist[group.unbound_indexes[ptype_ints['dust']]] = -2
setattr(obj.global_particle_lists, '%s_glist' % group_type, glist)
setattr(obj.global_particle_lists, '%s_slist' % group_type, slist)
setattr(obj.global_particle_lists, '%s_dmlist' % group_type, dmlist)
if 'dm2' in obj.data_manager.ptypes:
setattr(obj.global_particle_lists, '%s_dm2list' % group_type, dm2list)
if 'dm3' in obj.data_manager.ptypes:
setattr(obj.global_particle_lists, '%s_dm3list' % group_type, dm3list)
setattr(obj.global_particle_lists, '%s_bhlist' % group_type, bhlist)
setattr(obj.global_particle_lists, '%s_dlist' % group_type, dlist)
calculate_local_densities(obj, group_list)
if group_type == 'halo':
obj.halos = group_list
obj.nhalos = len(obj.halos)
#for ig in range(obj.nhalos):
# if ig < 5: print('%d: dm %g gas %g star %g bh %g [%g %g %g] r200 %g vc %g sig %g %g %g'%(ig,np.log10(obj.halos[ig].masses['dm']),np.log10(obj.halos[ig].masses['gas']),np.log10(obj.halos[ig].masses['stellar']),np.log10(obj.halos[ig].masses['bh']),obj.halos[ig].pos[0],obj.halos[ig].pos[1],obj.halos[ig].pos[2],obj.halos[ig].radii['r500c'],obj.halos[ig].virial_quantities['circular_velocity'],obj.halos[ig].velocity_dispersions['gas'],obj.halos[ig].velocity_dispersions['stellar'],obj.halos[ig].velocity_dispersions['dm']))
elif group_type == 'galaxy':
obj.galaxies = group_list
obj.ngalaxies = len(obj.galaxies)
if group_type == 'cloud':
obj.clouds = group_list
obj.nclouds = len(obj.clouds)
