def reset_global_particle_IDs(obj):
''' Maps particle lists from currently loaded ID's to the ID's corresponding to the full snapshot '''
# determine full particle numbers in snapshot
from caesar.property_manager import has_ptype, get_property
offset = np.zeros(len(obj.data_manager.ptypes) + 1, dtype=np.int64)
for ip, p in enumerate(obj.data_manager.ptypes):
if not has_ptype(obj, p):
continue
count = len(get_property(obj, 'mass', p))
if p == 'gas':
offset[ip + 1] = offset[ip] + obj.simulation.ngas
obj.simulation.ngas = count
elif p == 'star':
offset[ip + 1] = offset[ip] + obj.simulation.nstar
obj.simulation.nstar = count
elif p == 'bh':
offset[ip + 1] = offset[ip] + obj.simulation.nbh
obj.simulation.nbh = count
elif p == 'dust':
offset[ip + 1] = offset[ip] + obj.simulation.ndust
obj.simulation.ndust = count
elif p == 'dm':
offset[ip + 1] = offset[ip] + obj.simulation.ndm
obj.simulation.ndm = count
elif p == 'dm2':
offset[ip + 1] = offset[ip] + obj.simulation.ndm2
obj.simulation.ndm2 = count
elif p == 'dm3':
offset[ip + 1] = offset[ip] + obj.simulation.ndm3
obj.simulation.ndm3 = count
# reset lists
for group_type in obj.group_types:
group_list = 'obj.%s_list' % group_type
for ip, p in enumerate(obj.data_manager.ptypes):
if not has_ptype(obj, p):
continue
for group in eval(group_list):
part_list = 'group.%s' % plist_dict[p]
mylist = eval(part_list)
mylist = obj.data_manager.indexes[mylist + offset[ip]]
if p == 'gas': group.glist = mylist
if p == 'star': group.slist = mylist
if p == 'bh': group.bhlist = mylist
if p == 'dust': group.dlist = mylist
if p == 'dm': group.dmlist = mylist
if p == 'dm2': group.dm2list = mylist
if p == 'dm3': group.dm3list = mylist
return
def load_global_lists(obj):
# initialize global particle lists
from caesar.property_manager import has_ptype
for group_type in obj.group_types:
glist = np.full(obj.simulation.ngas, -1, dtype=np.int32)
slist = np.full(obj.simulation.nstar, -1, dtype=np.int32)
bhlist = np.full(obj.simulation.nbh, -1, dtype=np.int32)
dlist = np.full(obj.simulation.ndust, -1, dtype=np.int32)
dmlist = np.full(obj.simulation.ndm, -1, dtype=np.int32)
dm2list = np.full(obj.simulation.ndm2, -1, dtype=np.int32)
group_list = 'obj.%s_list' % group_type
for group in eval(group_list):
for ip, p in enumerate(obj.data_manager.ptypes):
if not has_ptype(obj, p):
continue
part_list = 'group.%s' % plist_dict[p]
if p == 'gas': glist[eval(part_list)] = group.GroupID
if p == 'star': slist[eval(part_list)] = group.GroupID
if p == 'bh': bhlist[eval(part_list)] = group.GroupID
if p == 'dust': dlist[eval(part_list)] = group.GroupID
if p == 'dm': dmlist[eval(part_list)] = group.GroupID
if p == 'dm2': dm2list[eval(part_list)] = group.GroupID
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_bhlist' % group_type, bhlist)
setattr(obj.global_particle_lists, '%s_dlist' % group_type, dlist)
setattr(obj.global_particle_lists, '%s_dmlist' % group_type, dmlist)
setattr(obj.global_particle_lists, '%s_dm2list' % group_type, dm2list)
return
def create_attributes(self, obj):
"""After loading in a yt dataset, store local attributes here."""
ds = obj.yt_dataset
self.cosmological_simulation = ds.cosmological_simulation
self.XH = 0.76
self.redshift = ds.current_redshift
self.scale_factor = 1.0 / (1.0 + self.redshift)
self.time = ds.current_time
self.omega_matter = ds.omega_matter
self.omega_lambda = ds.omega_lambda
self.fullpath = ds.fullpath
self.basename = ds.basename
self.hubble_constant = ds.hubble_constant
self.parameters = ds.parameters
if not self.cosmological_simulation:
# correct for NON comoving coordinates in non-cosmo sims
if obj.units['length'].endswith(
'cm') and obj.units['length'] != 'cm':
obj.units['length'] = obj.units['length'][:-2]
self.boxsize = ds.domain_width[0].to(obj.units['length'])
self.boxsize_units = str(self.boxsize.units)
sru = 'kpc'
if self.cosmological_simulation: sru += 'cm'
self.search_radius = ds.quan(500.0, sru).to(obj.units['length'])
H0 = self.hubble_constant * 100.0
if self.cosmological_simulation:
Om_0 = ds.cosmology.omega_matter
Ol_0 = ds.cosmology.omega_lambda
Ok_0 = ds.cosmology.omega_curvature
self.E_z = np.sqrt(Ol_0 + Ok_0 * (1.0 + self.redshift)**2 + Om_0 *
(1.0 + self.redshift)**3)
self.Om_z = Om_0 * (1.0 + self.redshift)**3 / self.E_z**2
H_z = H0 * self.E_z
else:
H_z = H0
if hasattr(ds, 'cosmology') and hasattr(ds.cosmology,
'omega_matter'):
self.Om_z = ds.cosmology.omega_matter
else:
self.Om_z = 0.3
self.H_z = ds.quan(H_z * 3.24077929e-20, '1/s')
self.G = ds.quan(4.51691362044e-39,
'kpc**3/(Msun * s**2)') ## kpc^3 / (Msun s^2)
self.critical_density = ds.quan(
(3.0 * self.H_z.d**2) / (8.0 * np.pi * self.G.d), 'Msun / kpc**3')
#Kitayama & Suto 1996 v.469, p.480
fomega = self.Om_z * (1. + self.redshift)**3 / (
self.Om_z * (1. + self.redshift)**3 + (1. - self.Om_z - Ol_0) *
(1. + self.redshift)**2 + Ol_0)
virial_density = (177.65287921960845 * (1. + 0.4093 *
(1. / fomega - 1.)**0.9052) -
1.) * self.Om_z #18*pi*pi
self.Densities = np.array([
virial_density * self.critical_density.to('Msun/kpc**3').d,
200. * self.critical_density.to('Msun/kpc**3').d,
500. * self.critical_density.to('Msun/kpc**3').d,
2500. * self.critical_density.to('Msun/kpc**3').d
])
self.Densities = ds.arr(self.Densities, 'Msun/kpc**3')
from caesar.property_manager import has_ptype
self.baryons_present = False
if has_ptype(obj, 'gas') or has_ptype(obj, 'star'):
self.baryons_present = True
def get_mean_interparticle_separation(obj):
"""Calculate the mean interparticle separation and Omega Baryon.
Parameters
----------
obj : :class:`main.CAESAR`
Main caesar object.
Returns
-------
mips : float
Mean inter-particle separation used for calculating FOF's b
parameter.
"""
if hasattr(obj.simulation, 'mean_interparticle_separation'):
return obj.simulation.mean_interparticle_separation
if obj.yt_dataset.cosmological_simulation == 0:
mylog.info(
'Non-cosmological data set detected -- setting units as specified in fubar.py'
)
UT = obj.yt_dataset.current_time.to('s/h')
UL = obj.yt_dataset.length_unit.to('cm/h')
GRAV = obj.yt_dataset.quan(6.672e-8, 'cm**3/(g*s**2)')
else:
UT = obj.yt_dataset.time_unit.to('s/h') / obj.yt_dataset.scale_factor
UL = obj.yt_dataset.length_unit.to('cmcm/h')
GRAV = obj.yt_dataset.quan(6.672e-8, 'cmcm**3/(g*s**2)')
UM = obj.yt_dataset.mass_unit.to('g/h')
G = GRAV / UL**3 * UM * UT**2 ## to system units
Hubble = obj.yt_dataset.quan(3.2407789e-18, 'h/s') * UT
dmmass = get_property(obj, 'mass', 'dm').to('code_mass')
ndm = len(dmmass)
dmmass = np.sum(dmmass)
# Only need this when the second family (not low res one!) of dark matter particles is presented.
# if 'dm2' in obj.data_manager.ptypes:
# dmmass2 = get_property(obj, 'mass', 'dm2').to('code_mass')
# ndm += len(dmmass2)
# dmmass += np.sum(dmmass2)
gmass = obj.yt_dataset.arr(np.array([0.0]), 'code_mass')
smass = obj.yt_dataset.arr(np.array([0.0]), 'code_mass')
bhmass = obj.yt_dataset.arr(np.array([0.0]), 'code_mass')
dustmass = obj.yt_dataset.arr(np.array([0.0]), 'code_mass')
from caesar.property_manager import has_ptype
if has_ptype(obj, 'gas'):
gmass = get_property(obj, 'mass', 'gas').to('code_mass')
if has_ptype(obj, 'star'):
smass = get_property(obj, 'mass', 'star').to('code_mass')
if obj.data_manager.blackholes and has_ptype(obj, 'bh'):
bhmass = get_property(obj, 'mass', 'bh').to('code_mass')
if obj.data_manager.dust and has_ptype(obj, 'dust'):
dustmass = get_property(obj, 'mass', 'dust').to('code_mass')
bmass = np.sum(gmass) + np.sum(smass) + np.sum(bhmass) + np.sum(dustmass)
"""
DM = obj.data_manager
dmmass = DM.mass[DM.dmlist]
gmass = DM.mass[DM.glist]
smass = DM.mass[DM.slist]
bhmass = DM.mass[DM.bhlist]
ndm = len(dmmass)
dmmass = obj.yt_dataset.quan(np.sum(dmmass), obj.units['mass']).to('code_mass')
bmass = obj.yt_dataset.quan(np.sum(gmass) + np.sum(smass) + np.sum(bhmass), obj.units['mass']).to('code_mass')
"""
#if its an idealized simulation, then there's no cosmology and we just take z=0 Planck15 values
if obj.yt_dataset.cosmological_simulation == 0:
from astropy.cosmology import Planck15
Om = Planck15.Om0
Ob = Planck15.Ob0
else:
Om = obj.yt_dataset.cosmology.omega_matter
Ob = (bmass / (bmass + dmmass) * Om).d
rhodm = ((Om - Ob) * 3.0 * Hubble**2 / (8.0 * np.pi * G)).d
rhodm = obj.yt_dataset.quan(rhodm, 'code_mass/code_length**3')
mips = ((dmmass / ndm / rhodm)**(1. / 3.)).to(obj.units['length'])
efres = int(obj.simulation.boxsize.d / mips.d)
obj.simulation.omega_baryon = float(Ob)
obj.simulation.effective_resolution = efres
obj.simulation.mean_interparticle_separation = mips
mylog.info('Calculated Omega_Baryon=%g and %d^3 effective resolution' %
(Ob, efres))
return obj.simulation.mean_interparticle_separation
def create_attributes(self, obj):
"""After loading in a yt dataset, store local attributes here."""
ds = obj.yt_dataset
self.cosmological_simulation = ds.cosmological_simulation
self.XH = 0.76
self.redshift = ds.current_redshift
self.scale_factor = 1.0 / (1.0 + self.redshift)
self.time = ds.current_time
self.omega_matter = ds.omega_matter
self.omega_lambda = ds.omega_lambda
self.fullpath = ds.fullpath
self.basename = ds.basename
self.hubble_constant = ds.hubble_constant
self.parameters = ds.parameters
if not self.cosmological_simulation:
# correct for NON comoving coordinates in non-cosmo sims
if obj.units['length'].endswith('cm') and obj.units['length'] != 'cm':
obj.units['length'] = obj.units['length'][:-2]
self.boxsize = ds.domain_width[0].to(obj.units['length'])
self.boxsize_units = str(self.boxsize.units)
sru = 'kpc'
if self.cosmological_simulation: sru += 'cm'
self.search_radius = ds.arr([300,1000,3000], sru).to(obj.units['length']) # default values
H0 = ds.quan(self.hubble_constant * 100.0 * 3.24077929e-20, '1/s')
if self.cosmological_simulation:
Om_0 = ds.cosmology.omega_matter
Ol_0 = ds.cosmology.omega_lambda
Ok_0 = ds.cosmology.omega_curvature
self.E_z = np.sqrt(
Ol_0 +
Ok_0 * (1.0 + self.redshift)**2 +
Om_0 * (1.0 + self.redshift)**3
)
self.Om_z = Om_0 * (1.0 + self.redshift)**3 / self.E_z**2
H_z = H0 * self.E_z
#Kitayama & Suto 1996 v.469, p.480
fomega = Om_0*(1.+self.redshift)**3 / self.E_z**2
if fomega >=1.0: mylog.warning('fomega out of bounds! fomega=%g %g %g %g'%(fomega,self.Om_z,self.redshift,Ol_0))
else:
H_z = H0
if hasattr(ds, 'cosmology') and hasattr(ds.cosmology, 'omega_matter'):
self.Om_z = ds.cosmology.omega_matter
else:
self.Om_z = 0.3
fomega = self.Om_z
self.H_z = H_z
self.G = ds.quan(4.51691362044e-39, 'kpc**3/(Msun * s**2)') ## kpc^3 / (Msun s^2)
self.critical_density = ds.quan(
(3.0 * H_z**2) / (8.0 * np.pi * self.G.d),
'Msun / kpc**3'
)
virial_density = (177.65287921960845*(1. + 0.4093*(1./fomega - 1.)**0.9052) - 1.)*self.Om_z #Kitayama & Suto 1996 v.469, p.480
#print(self.critical_density, self.critical_density.to('g/cm**3'),self.H_z.to('1/s'),(3.0 * H0.d**2) / (8.0 * np.pi * self.G.d))
# Romeel: Removed virial density, because the FOF with b=0.2*MIS
# only finds contour enclosing ~200, so halos do not have all the particles they
# need to compute spherical ~100xrhocrit.
#self.Densities = np.array([virial_density*self.critical_density.to('Msun/kpc**3').d,
self.Densities = np.array([ 200.*self.critical_density.to('Msun/kpc**3').d,
500.*self.critical_density.to('Msun/kpc**3').d,
2500.*self.critical_density.to('Msun/kpc**3').d])
self.Densities = ds.arr(self.Densities, 'Msun/kpc**3')
from caesar.property_manager import has_ptype
self.baryons_present = False
if has_ptype(obj, 'gas') or has_ptype(obj, 'star'):
self.baryons_present = True
