def _li_ml_dustmass(field, data):
li_ml_dgr = dgr_ert(data["gasmetals"], data["PartType0",
"StarFormationRate"],
data["PartType0", "Masses"])
li_ml_dustmass = ((10.**li_ml_dgr) *
data["PartType0", "Masses"]).in_units('code_mass')
#ds.parameters['li_ml_dustmass'] = li_ml_dustmass
##return (data.ds.arr(data.ds.parameters['li_ml_dustmass'].value,'code_mass'))
return li_ml_dustmass
def li_ml(pf, refined):
wTrue = np.where(np.array(refined) == True)[0]
wFalse = np.where(np.array(refined) == False)[0]
ad = pf.all_data()
density_smoothed = ad["gassmootheddensity"]
metallicity_smoothed = ad["gassmoothedmetals"]
masses_smoothed = ad["gassmoothedmasses"]
star_masses_smoothed = ad["starsmoothedmasses"]
dgr = dgr_ert(metallicity_smoothed, star_masses_smoothed, masses_smoothed)
dust_to_gas_ratio = 10.**(dgr)
dust_smoothed = np.zeros(len(refined))
dust_smoothed[wFalse] = dust_to_gas_ratio * density_smoothed
return dust_smoothed
def gadget_field_add(fname,
bounding_box=None,
ds=None,
add_smoothed_quantities=True):
def _starmetals_00(field, data):
el_dict = {
'He': '01',
'C': '02',
'N': '03',
'O': '04',
'Ne': '05',
'Mg': '06',
'Si': '07',
'S': '08',
'Ca': '09',
'Fe': '10'
}
el_str = field.name[1]
if '_' in el_str:
el_name = field.name[1][field.name[1].find('_') + 1:]
el_num = el_dict[el_name]
else:
el_num = '00'
return data[('PartType4', 'Metallicity_' + el_num)]
def _starmetals(field, data):
return data[('PartType4', 'Metallicity')]
def _starcoordinates(field, data):
return data[('PartType4', 'Coordinates')]
def _starformationtime(field, data):
return data[('PartType4', 'StellarFormationTime')]
def _starmasses(field, data):
return data[("PartType4", "Masses")]
def _diskstarcoordinates(field, data):
return data[('PartType2', 'Coordinates')]
def _diskstarmasses(field, data):
return data[("PartType2", "Masses")]
def _bulgestarcoordinates(field, data):
return data[('PartType3', 'Coordinates')]
def _bulgestarmasses(field, data):
return data[("PartType3", "Masses")]
def _gasdensity(field, data):
return data[('PartType0', 'Density')]
def _gasmetals_00(field, data):
return data[('PartType0', 'Metallicity_00')]
def _gasmetals(field, data):
return data[('PartType0', 'Metallicity')]
def _gascoordinates(field, data):
return data[('PartType0', 'Coordinates')]
def _gasmasses(field, data):
return data[('PartType0', 'Masses')]
def _gasfh2(field, data):
try:
return data[('PartType0', 'FractionH2')]
except:
return data[('PartType0', 'metallicity')] * 0.
def _gassfr(field, data):
return data[('PartType0', 'StarFormationRate')]
def _gassmootheddensity(field, data):
if yt.__version__ == '4.0.dev0':
return data.ds.parameters['octree'][('PartType0', 'density')]
else:
return data[("deposit", "PartType0_smoothed_density")]
def _gassmoothedmetals(field, data):
if yt.__version__ == '4.0.dev0':
return data.ds.parameters['octree'][('PartType0', 'metallicity')]
else:
return data[("deposit", "PartType0_smoothed_metallicity")]
def _gassmoothedmasses(field, data):
if yt.__version__ == '4.0.dev0':
return data.ds.parameters['octree'][('PartType0', 'Masses')]
else:
return data[('deposit', 'PartType0_mass')]
def _metaldens_00(field, data):
return (data["PartType0", "Density"] *
data["PartType0", "Metallicity_00"])
def _metaldens(field, data):
return (data["PartType0", "Density"] *
data["PartType0", "Metallicity"])
def _metalmass_00(field, data):
return (data["PartType0", "Masses"] *
(data["PartType0", "Metallicity_00"].value))
def _metalmass(field, data):
return (data["PartType0", "Masses"] *
(data["PartType0", "Metallicity"].value))
def _metalsmoothedmasses(field, data):
if yt.__version__ == '4.0.dev0':
return (data.ds.parameters['octree'][('PartType0', 'Masses')] *
data.ds.parameters['octree'][('PartType0', 'metallicity')])
else:
return (data[('deposit', 'PartType0_smoothed_metalmass')].value)
def _dustmass_manual(field, data):
return (data.ds.arr(data[("PartType0", "Dust_Masses")].value,
'code_mass'))
def _dustmass_dtm(field, data):
return (data["PartType0", "metalmass"] * cfg.par.dusttometals_ratio)
def _li_ml_dustmass(field, data):
return (data.ds.arr(data.ds.parameters['li_ml_dustmass'].value,
'code_mass'))
def _dustsmoothedmasses(field, data):
if yt.__version__ == '4.0.dev0':
return (data.ds.parameters['octree'][('PartType0', 'Dust_Masses')])
else:
return (data.ds.arr(
data[("deposit", "PartType0_sum_Dust_Masses")].value,
'code_mass'))
def _li_ml_dustsmoothedmasses(field, data):
if yt.__version__ == '4.0.dev0':
return (data.ds.parameters['octree'][('PartType0',
'li_ml_dustmass')])
else:
return (data.ds.arr(
data[("deposit", "PartType0_sum_li_ml_dustmass")].value,
'code_mass'))
def _stellarages(field, data):
ad = data.ds.all_data()
if data.ds.cosmological_simulation == False:
simtime = data.ds.current_time.in_units('Gyr')
simtime = simtime.value
age = simtime - data.ds.arr(
ad[('PartType4', 'StellarFormationTime')], 'Gyr').value
# make the minimum age 1 million years
age[np.where(age < 1.e-3)[0]] = 1.e-3
print('\n--------------')
print(
'[gadget2pd: ] Idealized Galaxy Simulation Assumed: Simulation time is (Gyr): ',
simtime)
print('--------------\n')
else:
yt_cosmo = yt.utilities.cosmology.Cosmology(
hubble_constant=data.ds.hubble_constant,
omega_matter=data.ds.omega_matter,
omega_lambda=data.ds.omega_lambda)
simtime = yt_cosmo.t_from_z(ds.current_redshift).in_units(
'Gyr').value # Current age of the universe
scalefactor = data[('PartType4', 'StellarFormationTime')].value
formation_z = (1. / scalefactor) - 1.
formation_time = yt_cosmo.t_from_z(formation_z).in_units(
'Gyr').value
age = simtime - formation_time
# Minimum age is set to 1 Myr (FSPS doesn't work properly for ages below 1 Myr)
age[np.where(age < 1.e-3)[0]] = 1.e-3
print('\n--------------')
print(
'[gadget2pd: ] Cosmological Galaxy Simulation Assumed: Current age of Universe is (Gyr): ',
simtime)
print('--------------\n')
age = data.ds.arr(age, 'Gyr')
return age
def _starsmoothedmasses(field, data):
return data[('deposit', 'PartType4_mass')]
def _bhluminosity(field, data):
ad = data.ds.all_data()
mdot = ad[("PartType5", "BH_Mdot")]
# give it a unit since usually these are dimensionless in yt
mdot = data.ds.arr(mdot, "code_mass/code_time")
c = yt.utilities.physical_constants.speed_of_light_cgs
bhluminosity = (cfg.par.BH_eta * mdot * c**2.).in_units("erg/s")
print("[front_ends/gadget2pd:] Generating the black hole luminosity")
if cfg.par.BH_var:
return bhluminosity * cfg.par.bhlfrac
else:
return bhluminosity
def _bhcoordinates(field, data):
return data["PartType5", "Coordinates"]
def _bhsed_nu(field, data):
bhluminosity = data["bhluminosity"]
log_lum_lsun = np.log10(bhluminosity[0].in_units("Lsun"))
nu, bhlum = agn_spectrum(log_lum_lsun)
# the last 4 numbers aren't part of the SED
nu = nu[0:-4]
nu = 10.**nu
nu = yt.YTArray(nu, "Hz")
return nu
def _bhsed_sed(field, data):
bhluminosity = data["bhluminosity"]
nholes = len(bhluminosity)
# get len of nu just for the 0th hole so we know how long the vector is
log_lum_lsun = np.log10(bhluminosity[0].in_units("Lsun"))
nu, l_band_vec = agn_spectrum(log_lum_lsun)
nu = nu[0:-4]
n_nu = len(nu)
bh_sed = np.zeros([nholes, n_nu])
for i in range(nholes):
log_lum_lsun = np.log10(bhluminosity[i].in_units("Lsun"))
nu, l_band_vec = agn_spectrum(log_lum_lsun)
l_band_vec = 10.**l_band_vec
l_band_vec = l_band_vec[0:-4]
for l in range(len(l_band_vec)):
l_band_vec[l] = data.ds.quan(l_band_vec[l], "erg/s")
bh_sed[i, :] = l_band_vec
bh_sed = yt.YTArray(bh_sed, "erg/s")
return bh_sed
# load the ds (but only if this is our first passthrough and we pass in fname)
if fname != None:
if yt.__version__ == '4.0.dev0':
ds = yt.load(fname)
ds.index
ad = ds.all_data()
else:
ds = yt.load(fname,
bounding_box=bounding_box,
over_refine_factor=cfg.par.oref,
n_ref=cfg.par.n_ref)
ds.index
ad = ds.all_data()
#if we're in the 4.x branch of yt, load up the octree for smoothing
if yt.__version__ == '4.0.dev0':
left = np.array([pos[0] for pos in bounding_box])
right = np.array([pos[1] for pos in bounding_box])
#octree = ds.octree(left, right, over_refine_factor=cfg.par.oref, n_ref=cfg.par.n_ref, force_build=True)
octree = ds.octree(left, right, n_ref=cfg.par.n_ref)
ds.parameters['octree'] = octree
# for the metal fields have a few options since gadget can have different nomenclatures
ad = ds.all_data()
if ('PartType4', 'Metallicity_00') in ds.derived_field_list:
try:
ds.add_field(('starmetals'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_He'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_C'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_N'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_O'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_Ne'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_Mg'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_Si'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_S'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_Ca'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('starmetals_Fe'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
except:
ds.add_field(('starmetals'),
function=_starmetals_00,
units="code_metallicity",
particle_type=True)
else:
ds.add_field(('starmetals'),
function=_starmetals,
units="code_metallicity",
particle_type=True)
if ('PartType0', 'Metallicity_00') in ds.derived_field_list:
ds.add_field(('gasmetals'),
function=_gasmetals_00,
units="code_metallicity",
particle_type=True)
ds.add_field(('metaldens'),
function=_metaldens_00,
units="g/cm**3",
particle_type=True)
# we add this as part type 0 (a non-general name) as it gets
# smoothed immediately and that's all we end up using downstream
ds.add_field(('PartType0', 'metalmass'),
function=_metalmass_00,
units="g",
particle_type=True)
else:
ds.add_field(('gasmetals'),
function=_gasmetals,
units="code_metallicity",
particle_type=True)
ds.add_field(('metaldens'),
function=_metaldens,
units="g/cm**3",
particle_type=True)
ds.add_field(('PartType0', 'metalmass'),
function=_metalmass,
units="g",
particle_type=True)
metalmass_fn = add_volume_weighted_smoothed_field("PartType0",
"Coordinates", "Masses",
"SmoothingLength",
"Density", "metalmass",
ds.field_info)
if add_smoothed_quantities == True:
ds.add_field(('metalsmoothedmasses'),
function=_metalsmoothedmasses,
units='code_metallicity',
particle_type=True)
# get the dust mass
if cfg.par.dust_grid_type == 'dtm':
ds.add_field(('dustmass'),
function=_dustmass_dtm,
units='code_mass',
particle_type=True)
if cfg.par.dust_grid_type == 'manual':
#if ('PartType0', 'Dust_Masses') in ds.derived_field_list:
ds.add_field(('dustmass'),
function=_dustmass_manual,
units='code_mass',
particle_type=True)
ds.add_deposited_particle_field(("PartType0", "Dust_Masses"), "sum")
if add_smoothed_quantities == True:
ds.add_field(('dustsmoothedmasses'),
function=_dustsmoothedmasses,
units='code_mass',
particle_type=True)
#if we have the Li, Narayanan & Dave 2019 Extreme Randomized Trees
#dust model in place, create a field for these so that
#dust_grid_gen can use these dust masses
if cfg.par.dust_grid_type == 'li_ml':
#get the dust to gas ratio
ad = ds.all_data()
li_ml_dgr = dgr_ert(ad["PartType0", "Metallicity_00"],
ad["PartType0",
"StarFormationRate"], ad["PartType0", "Masses"])
li_ml_dustmass = ((10.**li_ml_dgr) *
ad["PartType0", "Masses"]).in_units('code_mass')
#this is an icky way to pass this to the function for ds.add_field in the next line. but such is life.
ds.parameters['li_ml_dustmass'] = li_ml_dustmass
ds.add_field(('PartType0', 'li_ml_dustmass'),
function=_li_ml_dustmass,
units='code_mass',
particle_type=True)
ds.add_deposited_particle_field(("PartType0", "li_ml_dustmass"), "sum")
if add_smoothed_quantities == True:
ds.add_field(("li_ml_dustsmoothedmasses"),
function=_li_ml_dustsmoothedmasses,
units='code_mass',
particle_type=True)
ds.add_field(('starmasses'),
function=_starmasses,
units='g',
particle_type=True)
ds.add_field(('starcoordinates'),
function=_starcoordinates,
units='cm',
particle_type=True)
ds.add_field(('starformationtime'),
function=_starformationtime,
units='dimensionless',
particle_type=True)
ds.add_field(('stellarages'),
function=_stellarages,
units='Gyr',
particle_type=True)
if ('PartType2', 'Masses') in ds.derived_field_list:
ds.add_field(('diskstarmasses'),
function=_diskstarmasses,
units='g',
particle_type=True)
ds.add_field(('diskstarcoordinates'),
function=_diskstarcoordinates,
units='cm',
particle_type=True)
if ('PartType3', 'Masses') in ds.derived_field_list:
ds.add_field(('bulgestarmasses'),
function=_bulgestarmasses,
units='g',
particle_type=True)
ds.add_field(('bulgestarcoordinates'),
function=_bulgestarcoordinates,
units='cm',
particle_type=True)
if add_smoothed_quantities == True:
ds.add_field(('starsmoothedmasses'),
function=_starsmoothedmasses,
units='g',
particle_type=True)
ds.add_field(('gasdensity'),
function=_gasdensity,
units='g/cm**3',
particle_type=True)
# Gas Coordinates need to be in Comoving/h as they'll get converted later.
ds.add_field(('gascoordinates'),
function=_gascoordinates,
units='cm',
particle_type=True)
if add_smoothed_quantities == True:
ds.add_field(('gassmootheddensity'),
function=_gassmootheddensity,
units='g/cm**3',
particle_type=True)
ds.add_field(('gassmoothedmetals'),
function=_gassmoothedmetals,
units='code_metallicity',
particle_type=True)
ds.add_field(('gassmoothedmasses'),
function=_gassmoothedmasses,
units='g',
particle_type=True)
ds.add_field(('gasmasses'),
function=_gasmasses,
units='g',
particle_type=True)
ds.add_field(('gasfh2'),
function=_gasfh2,
units='dimensionless',
particle_type=True)
ds.add_field(('gassfr'), function=_gassfr, units='g/s', particle_type=True)
if cfg.par.BH_SED == True:
if ('PartType5', 'BH_Mass') in ds.derived_field_list:
nholes = len(ds.all_data()[('PartType5', 'BH_Mass')])
print("The number of black holes is:", nholes)
if nholes > 0:
if cfg.par.BH_model == 'Nenkova':
from powderday.agn_models.nenkova import Nenkova2008
agn_spectrum = Nenkova2008(
*cfg.par.nenkova_params).agn_spectrum
else:
from powderday.agn_models.hopkins import agn_spectrum
if cfg.par.BH_var:
from powderday.agn_models.hickox import vary_bhluminosity
cfg.par.bhlfrac = vary_bhluminosity(nholes)
ds.add_field(("bhluminosity"),
function=_bhluminosity,
units='erg/s',
particle_type=True)
ds.add_field(("bhcoordinates"),
function=_bhcoordinates,
units="cm",
particle_type=True)
ds.add_field(("bhnu"),
function=_bhsed_nu,
units='Hz',
particle_type=True)
ds.add_field(("bhsed"),
function=_bhsed_sed,
units="erg/s",
particle_type=True)
else:
print('No black holes found (length of BH_Mass field is 0)')
return ds
def gadget_field_add(fname,
bounding_box=None,
ds=None,
add_smoothed_quantities=True):
def _gassmoothinglength(field, data):
return data[('PartType0', 'SmoothingLength')].in_units('pc')
def _starmetals_00(field, data):
el_dict = {
'He': '01',
'C': '02',
'N': '03',
'O': '04',
'Ne': '05',
'Mg': '06',
'Si': '07',
'S': '08',
'Ca': '09',
'Fe': '10'
}
el_str = field.name[1]
if '_' in el_str:
el_name = field.name[1][field.name[1].find('_') + 1:]
el_num = el_dict[el_name]
else:
el_num = '00'
return data[('PartType4', 'Metallicity_' + el_num)]
def _starmetals(field, data):
return data[('PartType4', 'Metallicity')]
def _starcoordinates(field, data):
return data[('PartType4', 'Coordinates')]
def _starformationtime(field, data):
return data[('PartType4', 'StellarFormationTime')]
def _starmasses(field, data):
return data[("PartType4", "Masses")]
def _diskstarcoordinates(field, data):
return data[('PartType2', 'Coordinates')]
def _diskstarmasses(field, data):
return data[("PartType2", "Masses")]
def _bulgestarcoordinates(field, data):
return data[('PartType3', 'Coordinates')]
def _bulgestarmasses(field, data):
return data[("PartType3", "Masses")]
def _gasdensity(field, data):
return data[('PartType0', 'Density')]
def _gasmetals_00(field, data):
el_dict = {
'He': '01',
'C': '02',
'N': '03',
'O': '04',
'Ne': '05',
'Mg': '06',
'Si': '07',
'S': '08',
'Ca': '09',
'Fe': '10'
}
el_str = field.name[1]
if '_' in el_str:
el_name = field.name[1][field.name[1].find('_') + 1:]
el_num = el_dict[el_name]
else:
el_num = '00'
return data[('PartType0', 'Metallicity_' + el_num)]
def _gasmetals(field, data):
return data[('PartType0', 'Metallicity')]
def _gascoordinates(field, data):
return data[('PartType0', 'Coordinates')]
def _gasmasses(field, data):
return data[('PartType0', 'Masses')]
def _gasfh2(field, data):
try:
return data[('PartType0', 'FractionH2')]
except:
return data[('PartType0', 'metallicity')] * 0.
def _gassfr(field, data):
return data[('PartType0', 'StarFormationRate')]
def _gassmootheddensity(field, data):
if float(yt.__version__[0:3]) >= 4:
return data.ds.parameters['octree'][('PartType0', 'density')]
else:
return data[("deposit", "PartType0_smoothed_density")]
def _gassmoothedmetals(field, data):
if float(yt.__version__[0:3]) >= 4:
try:
el_str = field.name[1]
if '_' in el_str:
el_name = field.name[1][field.name[1].find('_') + 1:] + "_"
else:
el_name = ""
return data.ds.parameters['octree'][('PartType0',
el_name + 'metallicity')]
except:
return data.ds.parameters['octree'][('PartType0',
'metallicity')]
else:
try:
el_str = field.name[1]
if '_' in el_str:
el_name = field.name[1][field.name[1].find('_') + 1:] + "_"
else:
el_name = ""
return data[("deposit",
"PartType0_smoothed_" + el_name + "metallicity")]
except:
return data[("deposit", "PartType0_smoothed_metallicity")]
def _gassmoothedmasses(field, data):
if float(yt.__version__[0:3]) >= 4:
return data.ds.parameters['octree'][('PartType0', 'Masses')]
else:
return data[('deposit', 'PartType0_mass')]
def _metaldens_00(field, data):
return (data["PartType0", "Density"] *
data["PartType0", "Metallicity_00"])
def _metaldens(field, data):
return (data["PartType0", "Density"] *
data["PartType0", "Metallicity"])
def _metalmass_00(field, data):
return (data["PartType0", "Masses"] *
(data["PartType0", "Metallicity_00"].value))
def _metalmass(field, data):
return (data["PartType0", "Masses"] *
(data["PartType0", "Metallicity"].value))
def _metalsmoothedmasses(field, data):
if float(yt.__version__[0:3]) >= 4:
return (data.ds.parameters['octree'][('PartType0', 'Masses')] *
data.ds.parameters['octree'][('PartType0', 'metallicity')])
else:
return (data[('deposit', 'PartType0_smoothed_metalmass')].value)
def _dustmass_manual(field, data):
if cfg.par.otf_extinction == True:
return (data.ds.arr(data[("PartType3", "Masses")].value,
'code_mass'))
else:
return (data.ds.arr(data[("PartType0", "Dust_Masses")].value,
'code_mass'))
def _dustmass_dtm(field, data):
return (data["PartType0", "metalmass"] * cfg.par.dusttometals_ratio)
def _li_ml_dustmass(field, data):
return (data.ds.arr(data.ds.parameters['li_ml_dustmass'].value,
'code_mass'))
def _dustmass_rr(field, data):
#hard coded values from remy-ruyer table 1
a = 2.21
alpha = 2.02
x_sun = 8.69
x = 12. + np.log10(data["PartType0", "Metallicity_00"] /
cfg.par.solar * 10.**(x_sun - 12.))
y = a + alpha * (x_sun - np.asarray(x))
gas_to_dust_ratio = 10.**(y)
dust_to_gas_ratio = 1. / gas_to_dust_ratio
return dust_to_gas_ratio * data["PartType0", "Masses"]
def _dustmass_li_bestfit(field, data):
log_dust_to_gas_ratio = (2.445 * np.log10(
data["PartType0", "Metallicity_00"] / cfg.par.solar)) - (2.029)
dust_to_gas_ratio = 10.**(log_dust_to_gas_ratio)
return dust_to_gas_ratio * data["PartType0", "Masses"]
def _dustsmoothedmasses(field, data):
if float(yt.__version__[0:3]) >= 4:
if cfg.par.otf_extinction == True:
dsm = ds.arr(
data.ds.parameters['octree'][('PartType3', 'Masses')],
'code_mass')
else:
dsm = ds.arr(
data.ds.parameters['octree'][('PartType0', 'Dust_Masses')],
'code_mass')
#return (data.ds.parameters['octree'][('PartType0','Dust_Masses')])
else:
if cfg.par.otf_extinction == True:
dsm = (data.ds.arr(
data[("deposit", "PartType3_sum_Masses")].value,
'code_mass'))
else:
dsm = (data.ds.arr(
data[("deposit", "PartType0_sum_Dust_Masses")].value,
'code_mass'))
return dsm
def _li_ml_dustsmoothedmasses(field, data):
if float(yt.__version__[0:3]) >= 4:
return (data.ds.parameters['octree'][('PartType0',
'li_ml_dustmass')])
else:
return (data.ds.arr(
data[("deposit", "PartType0_sum_li_ml_dustmass")].value,
'code_mass'))
def _stellarages(field, data):
ad = data.ds.all_data()
if data.ds.cosmological_simulation == False:
simtime = data.ds.current_time.in_units('Gyr')
simtime = simtime.value
age = simtime - data.ds.arr(
ad[('PartType4', 'StellarFormationTime')], 'Gyr').value
# make the minimum age 1 million years
age[np.where(age < 1.e-3)[0]] = 1.e-3
print('\n--------------')
print(
'[gadget2pd: ] Idealized Galaxy Simulation Assumed: Simulation time is (Gyr): ',
simtime)
print('--------------\n')
else:
yt_cosmo = yt.utilities.cosmology.Cosmology(
hubble_constant=data.ds.hubble_constant,
omega_matter=data.ds.omega_matter,
omega_lambda=data.ds.omega_lambda)
simtime = yt_cosmo.t_from_z(ds.current_redshift).in_units(
'Gyr').value # Current age of the universe
scalefactor = data[('PartType4', 'StellarFormationTime')].value
formation_z = (1. / scalefactor) - 1.
formation_time = yt_cosmo.t_from_z(formation_z).in_units(
'Gyr').value
age = simtime - formation_time
# Minimum age is set to 1 Myr (FSPS doesn't work properly for ages below 1 Myr)
age[np.where(age < 1.e-3)[0]] = 1.e-3
print('\n--------------')
print(
'[gadget2pd: ] Cosmological Galaxy Simulation Assumed: Current age of Universe is (Gyr): ',
simtime)
print('--------------\n')
age = data.ds.arr(age, 'Gyr')
return age
def _starsmoothedmasses(field, data):
return data[('deposit', 'PartType4_mass')]
def _bhluminosity(field, data):
ad = data.ds.all_data()
mdot = ad[("PartType5", "BH_Mdot")]
# give it a unit since usually these are dimensionless in yt
mdot = data.ds.arr(mdot, "code_mass/code_time")
c = yt.utilities.physical_constants.speed_of_light_cgs
bhluminosity = (cfg.par.BH_eta * mdot * c**2.).in_units("erg/s")
print("[front_ends/gadget2pd:] Generating the black hole luminosity")
if cfg.par.BH_var:
return bhluminosity * cfg.par.bhlfrac
else:
return bhluminosity
def _bhcoordinates(field, data):
return data["PartType5", "Coordinates"]
def _bhsed_nu(field, data):
bhluminosity = data["bhluminosity"]
log_lum_lsun = np.log10(bhluminosity[0].in_units("Lsun"))
nu, bhlum = agn_spectrum(log_lum_lsun)
# the last 4 numbers aren't part of the SED
nu = nu[0:-4]
nu = 10.**nu
nu = yt.YTArray(nu, "Hz")
return nu
def _bhsed_sed(field, data):
bhluminosity = data["bhluminosity"]
nholes = len(bhluminosity)
# get len of nu just for the 0th hole so we know how long the vector is
log_lum_lsun = np.log10(bhluminosity[0].in_units("Lsun"))
nu, l_band_vec = agn_spectrum(log_lum_lsun)
nu = nu[0:-4]
n_nu = len(nu)
bh_sed = np.zeros([nholes, n_nu])
for i in range(nholes):
log_lum_lsun = np.log10(bhluminosity[i].in_units("Lsun"))
nu, l_band_vec = agn_spectrum(log_lum_lsun)
l_band_vec = 10.**l_band_vec
l_band_vec = l_band_vec[0:-4]
for l in range(len(l_band_vec)):
l_band_vec[l] = data.ds.quan(l_band_vec[l], "erg/s")
bh_sed[i, :] = l_band_vec
bh_sed = yt.YTArray(bh_sed, "erg/s")
return bh_sed
#functions used for OTF_Extinction
def _dust_density(field, data):
return data.ds.arr(data[('PartType3', 'Dust_Density')],
'code_mass/code_length**3')
def _size_with_units(field, data):
return data.ds.parameters['size']
# load the ds (but only if this is our first passthrough and we pass in fname)
if fname != None:
if float(yt.__version__[0:3]) >= 4:
ds = yt.load(fname)
#ds.sph_smoothing_style = "gather"
ds.index
ad = ds.all_data()
else:
ds = yt.load(fname,
bounding_box=bounding_box,
over_refine_factor=cfg.par.oref,
n_ref=cfg.par.n_ref)
ds.index
ad = ds.all_data()
#We're assuming that the particle type that the active dust is
#in in PartType3 and adding it to the sph types so that it can
#be deposited onto the octree
if cfg.par.otf_extinction: ds._sph_ptypes = ('PartType0', 'PartType3')
#if we're in the 4.x branch of yt, load up the octree for smoothing
if float(yt.__version__[0:3]) >= 4:
left = np.array([pos[0] for pos in bounding_box])
right = np.array([pos[1] for pos in bounding_box])
#octree = ds.octree(left, right, over_refine_factor=cfg.par.oref, n_ref=cfg.par.n_ref, force_build=True)
octree = ds.octree(left, right, n_ref=cfg.par.n_ref)
ds.parameters['octree'] = octree
print('BOUNDING BOX:', bounding_box, 'LEFT: ', left, 'RIGHT: ', right)
# for the metal fields have a few options since gadget can have different nomenclatures
ad = ds.all_data()
if ('PartType4', 'Metallicity_00') in ds.derived_field_list:
try:
ds.add_field(('star', 'metals'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_He'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_C'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_N'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_O'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_Ne'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_Mg'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_Si'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_S'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_Ca'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('star', 'metals_Fe'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
except:
ds.add_field(('star', 'metals'),
function=_starmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
else:
ds.add_field(('star', 'metals'),
function=_starmetals,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
if ('PartType0', 'Metallicity_00') in ds.derived_field_list:
try:
ds.add_field(('gas', 'metals'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_He'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_C'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_N'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_O'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_Ne'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_Mg'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_Si'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_S'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_Ca'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('gas', 'metals_Fe'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
except:
ds.add_field(('gas', 'metals'),
function=_gasmetals_00,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('metal', 'dens'),
function=_metaldens_00,
sampling_type='particle',
units="g/cm**3",
particle_type=True)
# we add this as part type 0 (a non-general name) as it gets
# smoothed immediately and that's all we end up using downstream
ds.add_field(('PartType0', 'metalmass'),
function=_metalmass_00,
sampling_type='particle',
units="g",
particle_type=True)
else:
ds.add_field(('gas', 'metals'),
function=_gasmetals,
sampling_type='particle',
units="code_metallicity",
particle_type=True)
ds.add_field(('metal', 'dens'),
function=_metaldens,
sampling_type='particle',
units="g/cm**3",
particle_type=True)
ds.add_field(('PartType0', 'metalmass'),
function=_metalmass,
sampling_type='particle',
units="g",
particle_type=True)
#this line is deprecated and no longer used (and will throw an error in sufficiently new yt hashes)
#metalmass_fn = add_volume_weighted_smoothed_field("PartType0", "Coordinates", "Masses",
# "SmoothingLength", "Density", "metalmass",
# ds.field_info)
if add_smoothed_quantities == True:
ds.add_field(('metal', 'smoothedmasses'),
function=_metalsmoothedmasses,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'masses'),
function=_gasmasses,
sampling_type='particle',
units='g',
particle_type=True)
ds.add_field(('gas', 'fh2'),
function=_gasfh2,
sampling_type='particle',
units='dimensionless',
particle_type=True)
ds.add_field(('gas', 'sfr'),
function=_gassfr,
sampling_type='particle',
units='g/s',
particle_type=True)
ds.add_field(('gas', 'smoothinglength'),
function=_gassmoothinglength,
sampling_type='particle',
units='pc',
particle_type=True)
# get the dust mass
if cfg.par.dust_grid_type == 'dtm':
ds.add_field(('dust', 'mass'),
function=_dustmass_dtm,
sampling_type='particle',
units='code_mass',
particle_type=True)
if cfg.par.dust_grid_type == 'manual':
#if ('PartType0', 'Dust_Masses') in ds.derived_field_list:
ds.add_field(('dust', 'mass'),
function=_dustmass_manual,
sampling_type='particle',
units='code_mass',
particle_type=True)
if cfg.par.otf_extinction:
#we need to add this density field so that the masses can be projected onto the octree in _dustsmoothedmasses
ds.add_field(('PartType3', 'density'),
function=_dust_density,
units='code_mass/code_length**3',
sampling_type='particle',
particle_type=True)
ds.add_deposited_particle_field(("PartType3", "Masses"), "sum")
else:
ds.add_deposited_particle_field(("PartType0", "Dust_Masses"),
"sum")
if add_smoothed_quantities == True:
ds.add_field(('dust', 'smoothedmasses'),
function=_dustsmoothedmasses,
sampling_type='particle',
units='code_mass',
particle_type=True)
if cfg.par.dust_grid_type == 'rr':
#ds.add_field(("dust','mass"),function=_dustmass_rr,sampling_type='particle',units='code_mass',particle_type=True)
ds.add_field(('dust', 'mass'),
function=_dustmass_rr,
sampling_type='particle',
units='code_mass',
particle_type=True)
if cfg.par.dust_grid_type == 'li_bestfit':
ds.add_field(('dust', 'mass'),
function=_dustmass_li_bestfit,
sampling_type='particle',
units='code_mass',
particle_type=True)
#if we have the Li, Narayanan & Dave 2019 Extreme Randomized Trees
#dust model in place, create a field for these so that
#dust_grid_gen can use these dust masses
if cfg.par.dust_grid_type == 'li_ml':
#get the dust to gas ratio
ad = ds.all_data()
li_ml_dgr = dgr_ert(ad["PartType0", "Metallicity_00"],
ad["PartType0",
"StarFormationRate"], ad["PartType0", "Masses"])
li_ml_dustmass = ((10.**li_ml_dgr) *
ad["PartType0", "Masses"]).in_units('code_mass')
#this is an icky way to pass this to the function for ds.add_field in the next line. but such is life.
ds.parameters['li_ml_dustmass'] = li_ml_dustmass
ds.add_field(('PartType0', 'li_ml_dustmass'),
function=_li_ml_dustmass,
sampling_type='particle',
units='code_mass',
particle_type=True)
#just adding a new field that is called 'dustmass' so that we
#can save it later in analytics.
ds.add_field(("dust','mass"),
function=_li_ml_dustmass,
sampling_type='particle',
units='code_mass',
particle_type=True)
ds.add_deposited_particle_field(("PartType0", "li_ml_dustmass"), "sum")
if add_smoothed_quantities == True:
ds.add_field(("li_ml_dustsmoothedmasses"),
function=_li_ml_dustsmoothedmasses,
sampling_type='particle',
units='code_mass',
particle_type=True)
ds.add_field(('star', 'masses'),
function=_starmasses,
sampling_type='particle',
units='g',
particle_type=True)
ds.add_field(('star', 'coordinates'),
function=_starcoordinates,
sampling_type='particle',
units='cm',
particle_type=True)
ds.add_field(('star', 'formationtime'),
function=_starformationtime,
sampling_type='particle',
units='dimensionless',
particle_type=True)
ds.add_field(('stellar', 'ages'),
function=_stellarages,
sampling_type='particle',
units='Gyr',
particle_type=True)
if ('PartType2', 'Masses') in ds.derived_field_list:
ds.add_field(('diskstar', 'masses'),
function=_diskstarmasses,
sampling_type='particle',
units='g',
particle_type=True)
ds.add_field(('diskstar', 'coordinates'),
function=_diskstarcoordinates,
sampling_type='particle',
units='cm',
particle_type=True)
if ('PartType3', 'Masses') in ds.derived_field_list:
ds.add_field(('bulgestar', 'masses'),
function=_bulgestarmasses,
sampling_type='particle',
units='g',
particle_type=True)
ds.add_field(('bulgestar', 'coordinates'),
function=_bulgestarcoordinates,
sampling_type='particle',
units='cm',
particle_type=True)
if add_smoothed_quantities == True:
ds.add_field(('star', 'smoothedmasses'),
function=_starsmoothedmasses,
sampling_type='particle',
units='g',
particle_type=True)
ds.add_field(('gas', 'density'),
function=_gasdensity,
sampling_type='particle',
units='g/cm**3',
particle_type=True)
# Gas Coordinates need to be in Comoving/h as they'll get converted later.
ds.add_field(('gas', 'coordinates'),
function=_gascoordinates,
sampling_type='particle',
units='cm',
particle_type=True)
if add_smoothed_quantities == True:
ds.add_field(('gas', 'smootheddensity'),
function=_gassmootheddensity,
sampling_type='particle',
units='g/cm**3',
particle_type=True)
ds.add_field(('gas', 'smoothedmasses'),
function=_gassmoothedmasses,
sampling_type='particle',
units='g',
particle_type=True)
#try:
ds.add_field(('gas', 'smoothedmetals'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_He'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_C'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_N'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_O'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_Ne'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_Mg'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_Si'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_S'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_Ca'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
ds.add_field(('gas', 'smoothedmetals_Fe'),
function=_gassmoothedmetals,
sampling_type='particle',
units='code_metallicity',
particle_type=True)
#except:
# ds.add_field(('gassmoothedmetals'), function=_gassmoothedmetals, sampling_type='particle',units='code_metallicity', particle_type=True)
if cfg.par.BH_SED == True:
if ('PartType5', 'BH_Mass') in ds.derived_field_list:
nholes = len(ds.all_data()[('PartType5', 'BH_Mass')])
print("The number of black holes is:", nholes)
if nholes > 0:
if cfg.par.BH_model == 'Nenkova':
from powderday.agn_models.nenkova import Nenkova2008
agn_spectrum = Nenkova2008(
*cfg.par.nenkova_params).agn_spectrum
else:
from powderday.agn_models.hopkins import agn_spectrum
if cfg.par.BH_var:
from powderday.agn_models.hickox import vary_bhluminosity
cfg.par.bhlfrac = vary_bhluminosity(nholes)
ds.add_field(("bh','luminosity"),
function=_bhluminosity,
sampling_type='particle',
units='erg/s',
particle_type=True)
ds.add_field(("bh','coordinates"),
function=_bhcoordinates,
sampling_type='particle',
units="cm",
particle_type=True)
ds.add_field(("bh','nu"),
function=_bhsed_nu,
sampling_type='particle',
units='Hz',
particle_type=True)
ds.add_field(("bh','sed"),
function=_bhsed_sed,
sampling_type='particle',
units="erg/s",
particle_type=True)
else:
print('No black holes found (length of BH_Mass field is 0)')
#OTF EXTINCTION
if cfg.par.otf_extinction:
if add_smoothed_quantities == True:
print("==============================================\n")
print("[front_ends/gadget2pd:] Entering OTF Field Addition\n")
print("==============================================\n")
#add the dust density field
ds.add_field(('PartType3', 'density'),
function=_dust_density,
units='code_mass/code_length**3',
sampling_type='particle',
particle_type=True)
ad = ds.all_data()
nsizes = ad['PartType3',
'Dust_Size'].shape[1] #number of dust size bins
#now loop through th esize bins and project them each onto
#their own octree. we'll then, after projecting them into
#individual grids, collate those grids back together into a
#master size grid
for isize in range(nsizes):
ds.parameters['size'] = 0 #just to clear it out
#we have to slice it before we add the field. if you try to slice
#in the field defintion (i.e., in _size_with_units), yt freaks
ds.parameters['size'] = ad['PartType3', 'Dust_Size'][:, isize]
#actually add the sliced field now. we do this so that we can
#prepare for depositing onto the octree. we call this a dummy size
#since this is just there for a place holder to deposit into a dummy octree
print('adding and depositing fields for dust size bin ' +
str(isize))
ds.add_field(('PartType3', 'dummy_size_bin' + str(isize)),
function=_size_with_units,
sampling_type='particle',
units='dimensionless',
particle_type=True,
force_override=True)
#deposit onto the octree.
if float(yt.__version__[0:3]) < 4:
ds.add_deposited_particle_field(
('PartType3', 'dummy_size_bin' + str(isize)), "sum")
print(
np.sum(ad["PartType3", "dummy_size_bin" + str(isize)]))
else:
#just call a dummy octree so that its deposited
dum_size_octree = octree[('PartType3',
'dummy_size_bin' + str(isize))]
print(np.sum(dum_size_octree))
#save in mater array that we lazily stuff into parameters.
#Note this has to be done here; attempting to do this
#downstream can cause the entire octree_of_sizes array to
#take on the value of the -1 size bin. this is likely due
#to the order of operations of ds and octree construction
#in zoom.py
if isize == 0:
if float(yt.__version__[0:3]) < 4:
octree_of_sizes = np.zeros(
(ad[('deposit', 'PartType3_sum_dummy_size_bin' +
str(isize))].shape[0], nsizes))
else:
octree_of_sizes = np.zeros(
(len(octree[('PartType3',
'dummy_size_bin' + str(isize))]),
nsizes))
if float(yt.__version__[0:3]) < 4:
octree_of_sizes[:, isize] = ad[(
'deposit',
'PartType3_sum_dummy_size_bin' + str(isize))]
else:
octree_of_sizes[:, isize] = octree[('PartType3',
'dummy_size_bin' +
str(isize))]
#------------------------
#DEBUG BLOCK
#if cfg.par.OTF_EXTINCTION_MRN_FORCE == True:
# loga = np.linspace(-4,0,octree_of_sizes.shape[1])
# a = 10.**loga
# mrn_dn_da = a**(-3.5)
# da = [a[i+1]-a[i] for i in range(len(a)-1)]
# da.append(da[-1])
# mrn_dn = mrn_dn_da*da
# for i in range(octree_of_sizes.shape[0]):
# octree_of_sizes[i,:] = mrn_dn
#------------------------
octree_of_sizes[np.isnan(
octree_of_sizes
)] = 0 #just because the density can be zero in some extreme cases which screws up the particle deposition
ds.parameters['octree_of_sizes'] = octree_of_sizes
return ds
def tipsy_field_add(fname,bounding_box = None ,ds=None,add_smoothed_quantities=True):
def _gassmoothinglength(field,data):
return data[("Gas", "smoothing_length")].in_units('pc')
def _starmetals(field,data):
return data[("newstars", "Metals")]
def _starcoordinates(field,data):
return data[("newstars", "Coordinates")]
def _starformationtime(field,data):
return data[("newstars", "FormationTime")]
def _stellarages(field,data):
ad = data.ds.all_data()
simtime = data.ds.current_time.in_units('Gyr')
simtime = simtime.value
age = simtime - data[("newstars","FormationTime")].in_units('Gyr').value
age[np.where(age < 1.e-3)[0]] = 1.e-3
age = data.ds.arr(age,'Gyr')
return age
def _starmasses(field,data):
return data[("newstars", "Mass")]
def _diskstarcoordinates(field,data):
return data[("diskstars", "Coordinates")]
def _diskstarmasses(field,data):
return data[("diskstars", "Mass")]
def _gasdensity(field,data):
return data[("Gas", "Density")]
def _gasmetals(field,data):
return data[("Gas", "Metals")]
def _gascoordinates(field,data):
return data[("Gas", "Coordinates")]
def _gassmootheddensity(field,data):
if float(yt.__version__[0:3]) >= 4:
return data.ds.parameters['octree'][("Gas", "Density")]
else:
return data[("deposit", "Gas_density")]
def _gassmoothedmetals(field,data):
if float(yt.__version__[0:3]) >= 4:
return data.ds.parameters['octree'][("Gas", "Metals")]
else:
return data[("deposit", "Gas_smoothed_metallicity")]
# Does this field exist?
def _gassmoothedmasses(field,data):
if float(yt.__version__[0:3]) >= 4:
return data.ds.parameters['octree'][("Gas", "Mass")]
else:
return data[("deposit", "Gas_mass")]
def _gasmasses(field,data):
return data[("Gas","Mass")]
def _dustmass_dtm(field,data):
return (data[("Gas","Metals")]*data[("Gas","Mass")]*cfg.par.dusttometals_ratio)
def _li_ml_dustmass(field,data):
return (data.ds.arr(data.ds.parameters['li_ml_dustmass'].value,'code_mass'))
def _dustmass_rr(field,data):
#hard coded values from remy-ruyer table 1
a = 2.21
alpha = 2.02
x_sun = 8.69
x = 12.+np.log10(data["Gas","Metals"]/cfg.par.solar * 10.**(x_sun-12.) )
y = a + alpha*(x_sun-np.asarray(x))
gas_to_dust_ratio = 10.**(y)
dust_to_gas_ratio = 1./gas_to_dust_ratio
return dust_to_gas_ratio * data["Gas","Mass"]
def _dustmass_li_bestfit(field,data):
log_dust_to_gas_ratio = (2.445*np.log10(data["Gas","Metals"]/cfg.par.solar))-(2.029)
dust_to_gas_ratio = 10.**(log_dust_to_gas_ratio)
return dust_to_gas_ratio * data["Gas","Mass"]
def _dustsmoothedmasses(field, data):
if float(yt.__version__[0:3]) >= 4:
return (data.ds.parameters['octree'][("dust","mass")])
else:
return (data.ds.arr(data[("Gas", "dust_mass")].value, 'code_mass'))
def _li_ml_dustsmoothedmasses(field,data):
return (data.ds.parameters['octree'][("Gas", "li_ml_dustmass")])
def _metaldens(field,data):
return (data["Gas","Density"]*data["Gas","Metals"])
def _gasfh2(field,data):
try: return data[("Gas","H2")]
except: return (data.ds.arr(data[("Gas", "Mass")].value*0, 'dimensionless'))
def _gassfr(field,data):
instsfr = data.ds.arr(data[("Gas", "Mass")].value*0, 'Msun/yr')
# temperature below which SF can happen (K)
try: tempcut = ds.quan(ds.parameters["dTempMax"],'K')
except: tempcut = ds.quan(10**3,'K') # default for g14 sims
denscut = ds.quan(100*1.67*10**-24,'g/cm**3') # density above which SF can happen - default for g14 sims
# star formation timescale (s)
try: deltat = ds.quan(ds.parameters["dDeltaStarForm"]*3.15569*10**7,'s')
except: deltat = ds.quan(10**6*3.15569*10**7,'s') # 1 Myr
# Star formation efficiency
try: cstarfac = ds.parameters["dCStar"]
except: cstarfac = 0.15
# Mass at which stars form (Msol)
try: massform = ds.quan(ds.parameters["dInitStarMass"]*ds.parameters["dMsolUnit"],'Msun')
except: massform = max(data[("newstars", "Mass")])
SFposs = np.where((data[("Gas","Temperature")]<tempcut) & (data[("Gas","Density")]>denscut))
try: cstar = cstarfac*data[("Gas","H2")][SFposs] # SF efficiency with molecular hydrogen
except: cstar = cstarfac*data[("Gas","Mass")][SFposs]/data[("Gas","Mass")][SFposs]
dynt = 1.0/np.sqrt(data[("Gas","Density")][SFposs]*4.0*np.pi*G)
SFeff = 1.0 - np.e**(-1.0*cstar*deltat/dynt)
instsfr[SFposs] = (data[("Gas","Mass")][SFposs]/massform)*SFeff
return instsfr
if fname != None:
ds = yt.load(fname)
ds.index
#if we're in the 4.x branch of yt, load up the octree for smoothing
if float(yt.__version__[0:3]) >= 4:
left = np.array([pos[0] for pos in bounding_box])
right = np.array([pos[1] for pos in bounding_box])
# Add option for scatter vs gather to parameters_master file?
octree = ds.octree(left,right,n_ref=cfg.par.n_ref)
ds.parameters['octree'] = octree
#set up particle filters to figure out which stars might have been
#initalized with the simulation. we'll call stars that are formed
#in the simulation 'new_stars' (no matter how old they are), and
#stars that are initalized with the simulation as 'diskstars'.
#Assume that no cosmology someone uses will give us a Hubble time
#> 15 Gyr.
def newstars(pfilter, data):
age = data.ds.current_time - data[pfilter.filtered_type, "creation_time"]
filter = np.logical_and(age.in_units('Gyr') <= 15, age.in_units('Gyr') >= 0)
return filter
def diskstars(pfilter, data):
age = data.ds.current_time - data[pfilter.filtered_type, "creation_time"]
filter = np.logical_or(age.in_units('Gyr') >= 15, age.in_units('Gyr') <= 0)
return filter
add_particle_filter("newstars", function=newstars, filtered_type='Stars', requires=["creation_time"])
add_particle_filter("diskstars", function=diskstars, filtered_type='Stars', requires=["creation_time"])
ds.add_particle_filter("newstars")
ds.add_particle_filter("diskstars")
ad = ds.all_data()
#add the regular powderday fields
ds.add_field(("star","metals"),function=_starmetals,units="code_metallicity",sampling_type='particle')
ds.add_field(("star","coordinates"),function=_starcoordinates,units='cm',sampling_type='particle')
ds.add_field(("star","formationtime"),function=_starformationtime,units='Gyr',sampling_type='particle')
ds.add_field(("stellar","ages"),function=_stellarages,units='Gyr',sampling_type='particle')
ds.add_field(("star","masses"),function=_starmasses,units='g',sampling_type='particle')
ds.add_field(("gas","density"),function=_gasdensity,units='g/cm**3',sampling_type='particle')
ds.add_field(("gas","metals"),function=_gasmetals,units="code_metallicity",sampling_type='particle')
ds.add_field(("gas","coordinates"),function=_gascoordinates,units='cm',sampling_type='particle')
if add_smoothed_quantities == True:
ds.add_field(("gas","smootheddensity"),function=_gassmootheddensity,units='g/cm**3',sampling_type='particle')
ds.add_field(("gas","smoothedmetals"),function=_gassmoothedmetals,units='code_metallicity',sampling_type='particle')
ds.add_field(("gas","smoothedmasses"),function=_gassmoothedmasses,units='g',sampling_type='particle')
ds.add_field(("gas","masses"),function=_gasmasses,units='g',sampling_type='particle')
ds.add_field(("gas","fh2"),function=_gasfh2,units='dimensionless',sampling_type='particle')
ds.add_field(("gas","sfr"),function=_gassfr,units='g/s',sampling_type='particle')
ds.add_field(("gas","smoothinglength"),function=_gassmoothinglength,units='pc',sampling_type='particle')
ds.add_field(("metal","dens"),function=_metaldens,units="g/cm**3", sampling_type='particle')
#only add the disk star fields if there are any disk stars
if len(ad["diskstars","Mass"]) > 0 :
ds.add_field(("diskstar","masses"),function=_diskstarmasses,units='g',sampling_type='particle')
ds.add_field(("diskstar","coordinates"),function=_diskstarcoordinates,units='cm',sampling_type='particle')
#add the dust mass, but only if we're using the DTM dust mass
if cfg.par.dust_grid_type == 'dtm':
ds.add_field(("dust","mass"), function=_dustmass_dtm,units='code_mass',sampling_type='particle')
if cfg.par.dust_grid_type == 'rr':
ds.add_field(("dust","mass"),function=_dustmass_rr, units='code_mass',sampling_type='particle')
if cfg.par.dust_grid_type == 'li_ml':
if float(yt.__version__[0:3]) < 4:
raise KeyError("li_ml is not implemented for tipsy front ends with yt<4. Please set another dust grid type in the parameters or upgrade to yt4")
#the issue is that we can't smooth the dust onto a grid that
#is necessary in dust_grid_gen/li_ml_oct because yt3.x
#requires a field: ('PartType0', 'particle_position').
else:
#get the dust to gas ratio
ad = ds.all_data()
li_ml_dgr = dgr_ert(ad["Gas","Metals"],ad["gas","sfr"],ad["Gas","Mass"])
li_ml_dustmass = ((10.**li_ml_dgr)*ad["Gas","Mass"]).in_units('code_mass')
#this is an icky way to pass this to the function for ds.add_field in the next line. but such is life.
ds.parameters['li_ml_dustmass'] = li_ml_dustmass
ds.add_field(("Gas","li_ml_dustmass"),function=_li_ml_dustmass,units='code_mass',sampling_type='particle')
#just adding a new field that is called 'dustmass' so that we
#can save it later in analytics.
ds.add_field(("dust","mass"),function=_li_ml_dustmass,units='code_mass',sampling_type='particle')
ds.add_deposited_particle_field(("Gas","li_ml_dustmass"),"sum")
if add_smoothed_quantities == True:
ds.add_field(("Gas","li_ml_dustsmoothedmasses"), function=_li_ml_dustsmoothedmasses,units='code_mass',sampling_type='particle')
if cfg.par.dust_grid_type == 'li_bestfit':
ds.add_field(("dust","mass"),function=_dustmass_li_bestfit, units='code_mass',sampling_type='particle')
ad = ds.all_data()
return ds
