def build_model(fixed_metallicity=None, luminosity_distance=None, **extras):
"""Construct a model. This method defines a number of parameter
specification dictionaries and uses them to initialize a
`models.sedmodel.SedModel` object.
:param object_redshift:
If given, given the model redshift to this value.
:param add_dust: (optional, default: False)
Switch to add (fixed) parameters relevant for dust emission.
:param add_neb: (optional, default: False)
Switch to add (fixed) parameters relevant for nebular emission, and
turn nebular emission on.
:param luminosity_distance: (optional)
If present, add a `"lumdist"` parameter to the model, and set it's
value (in Mpc) to this. This allows one to decouple redshift from
distance, and fit, e.g., absolute magnitudes (by setting
luminosity_distance to 1e-5 (10pc))
"""
from prospect.models.templates import TemplateLibrary, adjust_continuity_agebins
from prospect.models import priors, sedmodel, transforms
model_params = TemplateLibrary['continuity_sfh']
### BASIC PARAMETERS ###
model_params["imf_type"] = {
'N': 1,
'isfree': False,
'init': 1, #1=Chabrier
'prior': None
}
model_params['zred'] = {
'N': 1,
'isfree': True,
'init': obj_red,
"prior": priors.TopHat(mini=obj_red - 0.05, maxi=obj_red + 0.05)
}
model_params['add_igm_absorption'] = {
'N': 1,
'isfree': False,
'init': 1,
'units': None,
'prior': None
}
model_params['add_agb_dust_model'] = {
'N': 1,
'isfree': False,
'init': 1,
'units': None,
'prior': None
}
# model_params['pmetals'] = {'N': 1,
# 'isfree': False,
# 'init': -99,
# 'units': '',
# 'prior': priors.TopHat(mini=-3, maxi=-1)}
### SFH ###
tuniv = WMAP9.age(obj_red).value
model_params = adjust_continuity_agebins(model_params,
tuniv=tuniv,
nbins=7)
### DUST ABSORPTION ###
model_params['dust_type'] = {
'N': 1,
'isfree': False,
'init': 4, #4=Kriek & Conroy
'units': 'index',
'prior': None
}
model_params['dust1'] = {
'N': 1,
'isfree': False,
'depends_on': transforms.dustratio_to_dust1,
'init': 0.,
'units': 'optical depth towards young stars'
}
model_params['dust_ratio'] = {
'N': 1,
'isfree': True,
'init': 1.0,
'init_disp': 0.8,
'disp_floor': 0.8,
'units': 'ratio of birth-cloud to diffuse dust',
'prior': priors.ClippedNormal(mini=0.0, maxi=2.0, mean=1.0, sigma=0.3)
}
model_params['dust2'] = {
'N': 1,
'isfree': True,
'init': 1.0,
'init_disp': 0.25,
'disp_floor': 0.15,
'units': 'optical depth at 5500AA',
'prior': priors.ClippedNormal(mini=0.0, maxi=4.0, mean=0.3, sigma=1)
}
model_params['dust_index'] = {
'N': 1,
'isfree': True,
'init': 0.0,
'init_disp': 0.25,
'disp_floor': 0.15,
'units': 'power-law multiplication of Calzetti',
'prior': priors.TopHat(mini=-2.0, maxi=0.5)
}
### DUST EMISSION ###
model_params.update(TemplateLibrary["dust_emission"])
### NEBULAR EMISSION ###
model_params['add_neb_emission'] = {
'N': 1,
'isfree': False,
'init': True,
'prior': None
}
model_params['add_neb_continuum'] = {
'N': 1,
'isfree': False,
'init': True,
'prior': None
}
model_params['gas_logz'] = {
'N': 1,
'isfree': True,
'init': 0.0,
'units': r'log Z/Z_\odot',
'prior': priors.TopHat(mini=-2.0, maxi=0.5)
}
model_params['gas_logu'] = {
'N': 1,
'isfree': True,
'init': -1.0,
'units': '',
'prior': priors.TopHat(mini=-4.0, maxi=-1.0)
}
### CALIBRATION ###
model_params['polyorder'] = {'N': 1, 'init': 10, 'isfree': False}
model_params['spec_norm'] = {
'N': 1,
'init': 1.0,
'isfree': True,
'prior': priors.Normal(sigma=0.2, mean=1.0),
'units': 'f_true/f_obs'
}
model_params['spec_jitter'] = {
"N": 1,
"isfree": True,
"init": 1.0,
"prior": priors.TopHat(mini=0., maxi=4.0)
}
model_params['f_outlier_spec'] = {
"N": 1,
"isfree": True,
"init": 0.01,
"prior": priors.TopHat(mini=1e-5, maxi=0.5)
}
model_params['nsigma_outlier_spec'] = {
"N": 1,
"isfree": False,
"init": 5.0
}
### SMOOTHING ###
model_params.update(TemplateLibrary["spectral_smoothing"])
model_params["sigma_smooth"]["prior"] = priors.TopHat(mini=150, maxi=250)
# Now instantiate the model using this new dictionary of parameter specifications
model = sedmodel.SedModel(model_params)
return model
def build_model_un(object_redshift=None,
fixed_metallicity=None,
add_duste=False,
**extras):
from prospect.models.sedmodel import SedModel
from prospect.models.templates import TemplateLibrary
from prospect.models import priors
from prospect.models import transforms
from prospect.models.templates import adjust_continuity_agebins
from astropy.cosmology import WMAP9 as cosmos
import prospect
# Get (a copy of) one of the prepackaged model set dictionaries.
# Get the 2018 prospector-alpha model manually
model_params = (TemplateLibrary["continuity_sfh"])
model_params.update(TemplateLibrary["dust_emission"])
model_params.update(TemplateLibrary["nebular"])
model_params.update(TemplateLibrary["agn"])
# Set the dust and agn emission free
model_params["fagn"]["isfree"] = True
model_params["agn_tau"]["isfree"] = True
# Complexify the dust attenuation
model_params["dust_type"] = {
"N": 1,
"isfree": False,
"init": 4,
"units": "FSPS index"
}
model_params["dust2"]["prior"] = priors.TopHat(mini=0.0, maxi=4.0)
model_params["dust1"] = {
"N": 1,
"isfree": False,
'depends_on': transforms.dustratio_to_dust1,
"init": 0.0,
"units": "optical depth towards young stars"
}
model_params["dust_ratio"] = {
"N": 1,
"isfree": True,
"init": 1.0,
"units": "ratio of birth-cloud to diffuse dust",
"prior": priors.ClippedNormal(mini=0.0, maxi=2.0, mean=1.0, sigma=0.3)
}
model_params["dust_index"] = {
"N": 1,
"isfree": True,
"init": 0.0,
"units": "power-law multiplication of Calzetti",
"prior": priors.TopHat(mini=-2.0, maxi=0.5)
}
# in Gyr
tuniv = cosmos.age(object_redshift).value
model_params = adjust_continuity_agebins(model_params, tuniv)
model_params["duste_qpah"]["isfree"] = False
model_params["duste_umin"]["isfree"] = False
model_params["duste_gamma"]["isfree"] = False
model_params["duste_qpah"]["init"] = 2.0
model_params["duste_umin"]["init"] = 1.0
model_params["duste_gamma"]["init"] = 0.01
model_params["duste_qpah"]["prior"] = priors.TopHat(mini=0.0, maxi=7.0)
model_params["duste_umin"]["prior"] = priors.TopHat(mini=0.1, maxi=25.0)
model_params["duste_gamma"]["prior"] = priors.TopHat(mini=0.0, maxi=1.0)
model_params["duste_qpah"]["disp_floor"] = 3.0
model_params["duste_umin"]["disp_floor"] = 4.5
model_params["duste_gamma"]["disp_floor"] = 0.15
model_params["duste_qpah"]["init_disp"] = 3.0
model_params["duste_umin"]["init_disp"] = 5.0
model_params["duste_gamma"]["init_disp"] = 0.2
model_params['gas_logz']["isfree"] = True
model_params['gas_logz']["init"] = 0.0
model_params['gas_logz']["prior"] = priors.TopHat(mini=-2.0, maxi=0.5)
model_params['gas_logu']["isfree"] = False
model_params['gas_logu']["init"] = -1.0
model_params['gas_logu']["prior"] = priors.TopHat(mini=-4.0, maxi=-1.0)
# Now add the lumdist parameter by hand as another entry in the dictionary.
# This will control the distance since we are setting the redshift to zero.
# In `build_obs` above we used a distance of 10pc to convert from absolute to apparent magnitudes,
# so we use that here too, since the `maggies` are appropriate for that distance.
# Let's make some changes to values appropriate for our objects and data
model_params["logzsol"]["prior"] = priors.TopHat(mini=-2.0, maxi=0.2)
model_params["dust_index"]["prior"] = priors.TopHat(mini=-2.2, maxi=0.4)
model_params["agn_tau"]["prior"] = priors.LogUniform(mini=5, maxi=1.5e2)
model_params["dust2"]["prior"] = priors.TopHat(mini=1e-6, maxi=3.0)
model_params['dust_type']['init'] = 0
model_params['fagn']['init'] = 0.5
model_params['dust2']['init'] = 0.1
# If we are going to be using emcee, it is useful to provide a
# minimum scale for the cloud of walkers (the default is 0.1)
model_params["agn_tau"]["disp_floor"] = 1
model_params["dust2"]["disp_floor"] = 1e-2
model_params["logzsol"]["disp_floor"] = 1e-3
model_params['fagn']['disp_floor'] = 1e-3
model_params['dust_index']['disp_floor'] = 1e-3
# Change the model parameter specifications based on some keyword arguments
if object_redshift is not None:
# make sure zred is fixed
model_params["zred"]['isfree'] = False
# And set the value to the object_redshift keyword
model_params["zred"]['init'] = object_redshift
# Change fit orders
tparams = {}
parnames = [m for m in model_params]
fit_order = [
'logmass', 'dust_index', 'dust2', 'logzsol', 'fagn', 'dust_ratio'
]
for param in fit_order:
tparams[param] = model_params[param]
for param in model_params:
if param not in fit_order:
tparams[param] = model_params[param]
model_params = tparams
########
model_params['add_neb_emission']['init'] = False
########
model_params['sfh']['init'] = 0
# Now instantiate the model object using this dictionary of parameter specifications
model = SedModel(model_params)
#print(model_params['agebins'])
return model
def build_model(objid=1,
non_param_sfh=False,
dirichlet_sfh=False,
add_duste=False,
add_neb=False,
add_agn=False,
switch_off_mix=False,
marginalize_neb=True,
n_bins_sfh=8,
use_eline_prior=False,
add_jitter=False,
fit_continuum=False,
switch_off_phot=False,
switch_off_spec=False,
fixed_dust=False,
**extras):
"""Construct a model. This method defines a number of parameter
specification dictionaries and uses them to initialize a
`models.sedmodel.SedModel` object.
:param object_redshift:
If given, given the model redshift to this value.
:param add_dust: (optional, default: False)
Switch to add (fixed) parameters relevant for dust emission.
:param add_neb: (optional, default: False)
Switch to add (fixed) parameters relevant for nebular emission, and
turn nebular emission on.
"""
# read in data table
obs = build_obs(objid=objid)
# get SFH template
if non_param_sfh and not dirichlet_sfh:
model_params = TemplateLibrary["continuity_sfh"]
elif dirichlet_sfh:
model_params = TemplateLibrary["dirichlet_sfh"]
else:
model_params = TemplateLibrary["parametric_sfh"]
# fit for redshift
# use catalog value as center of the prior
model_params["zred"]['isfree'] = True
model_params["zred"]["init"] = obs['redshift']
model_params["zred"]["prior"] = priors.TopHat(mini=obs['redshift'] - 0.005,
maxi=obs['redshift'] + 0.005)
# get SFH template
if non_param_sfh:
t_univ = cosmo.age(obs['redshift']).value
tbinmax = 0.95 * t_univ * 1e9
lim1, lim2, lim3, lim4 = 7.4772, 8.0, 8.5, 9.0
agelims = [0, lim1, lim2, lim3] + np.log10(
np.linspace(10**lim4, tbinmax,
n_bins_sfh - 4)).tolist() + [np.log10(t_univ * 1e9)]
if dirichlet_sfh:
model_params = adjust_dirichlet_agebins(model_params,
agelims=agelims)
model_params["total_mass"]["prior"] = priors.LogUniform(mini=3e9,
maxi=1e12)
else:
model_params = adjust_continuity_agebins(model_params,
tuniv=t_univ,
nbins=n_bins_sfh)
agebins = np.array([agelims[:-1], agelims[1:]])
model_params['agebins']['init'] = agebins.T
model_params["logmass"]["prior"] = priors.TopHat(mini=9.5,
maxi=12.0)
else:
model_params["tau"]["prior"] = priors.LogUniform(mini=1e-1, maxi=10)
model_params["tage"]["prior"] = priors.TopHat(
mini=1e-3, maxi=cosmo.age(obs['redshift']).value)
model_params["mass"]["prior"] = priors.LogUniform(mini=3e9, maxi=1e12)
# metallicity (no mass-metallicity prior yet!)
if fixed_dust:
model_params["logzsol"]["prior"] = priors.ClippedNormal(mini=-1.0,
maxi=0.19,
mean=0.0,
sigma=0.15)
else:
model_params["logzsol"]["prior"] = priors.TopHat(mini=-1.0, maxi=0.19)
# complexify the dust
if fixed_dust:
model_params['dust_type']['init'] = 2
model_params["dust2"]["prior"] = priors.ClippedNormal(mini=0.0,
maxi=4.0,
mean=0.3,
sigma=1)
model_params['dust1'] = {
"N": 1,
"isfree": False,
"init": 0.0,
"units": "optical depth towards young stars",
"prior": None
}
else:
model_params['dust_type']['init'] = 4
model_params["dust2"]["prior"] = priors.ClippedNormal(mini=0.0,
maxi=4.0,
mean=0.3,
sigma=1)
model_params["dust_index"] = {
"N": 1,
"isfree": True,
"init": 0.0,
"units": "power-law multiplication of Calzetti",
"prior": priors.TopHat(mini=-1.0, maxi=0.4)
}
def to_dust1(dust1_fraction=None, dust1=None, dust2=None, **extras):
return (dust1_fraction * dust2)
model_params['dust1'] = {
"N": 1,
"isfree": False,
'depends_on': to_dust1,
"init": 0.0,
"units": "optical depth towards young stars",
"prior": None
}
model_params['dust1_fraction'] = {
'N': 1,
'isfree': True,
'init': 1.0,
'prior': priors.ClippedNormal(mini=0.0,
maxi=2.0,
mean=1.0,
sigma=0.3)
}
# velocity dispersion
model_params.update(TemplateLibrary['spectral_smoothing'])
model_params["sigma_smooth"]["prior"] = priors.TopHat(mini=40.0,
maxi=400.0)
# Change the model parameter specifications based on some keyword arguments
if add_duste:
# Add dust emission (with fixed dust SED parameters)
model_params.update(TemplateLibrary["dust_emission"])
model_params['duste_gamma']['isfree'] = True
model_params['duste_gamma']['init'] = 0.01
model_params['duste_gamma']['prior'] = priors.LogUniform(mini=1e-4,
maxi=0.1)
model_params['duste_qpah']['isfree'] = True
model_params['duste_qpah']['prior'] = priors.TopHat(mini=0.5, maxi=7.0)
model_params['duste_umin']['isfree'] = True
model_params['duste_umin']['init'] = 1.0
model_params['duste_umin']['prior'] = priors.ClippedNormal(mini=0.1,
maxi=15.0,
mean=2.0,
sigma=1.0)
if add_agn:
# Allow for the presence of an AGN in the mid-infrared
model_params.update(TemplateLibrary["agn"])
model_params['fagn']['isfree'] = True
model_params['fagn']['prior'] = priors.LogUniform(mini=1e-5, maxi=3.0)
model_params['agn_tau']['isfree'] = True
model_params['agn_tau']['prior'] = priors.LogUniform(mini=5.0,
maxi=150.)
if add_neb:
# Add nebular emission
model_params.update(TemplateLibrary["nebular"])
model_params['gas_logu']['isfree'] = True
model_params['gas_logu']['init'] = -2.0
model_params['gas_logz']['isfree'] = True
_ = model_params["gas_logz"].pop("depends_on")
model_params['nebemlineinspec'] = {
'N': 1,
'isfree': False,
'init': False
}
if marginalize_neb:
model_params.update(TemplateLibrary['nebular_marginalization'])
#model_params.update(TemplateLibrary['fit_eline_redshift'])
model_params['eline_prior_width']['init'] = 3.0
model_params['use_eline_prior']['init'] = use_eline_prior
# only marginalize over a few (strong) emission lines
if True:
#SPS_HOME = os.getenv('SPS_HOME')
#emline_info = np.genfromtxt(SPS_HOME + '/data/emlines_info.dat', dtype=[('wave', 'f8'), ('name', 'S20')], delimiter=',')
to_fit = [
'[OII]3726', '[OII]3729', 'H 3798', 'H 3835', 'H 3889',
'H 3970', '[NeIII]3870', 'H delta 4102', 'H gamma 4340',
'[OIII]4364', 'H beta 4861', '[OIII]4960', '[OIII]5007',
'[NII]6549', 'H alpha 6563', '[NII]6585'
]
#idx = np.array([1 if name in to_fit else 0 for name in emline_info['name']], dtype=bool)
model_params['lines_to_fit']['init'] = to_fit
# model_params['use_eline_prior']['init'] = False
else:
model_params['nebemlineinspec']['init'] = True
# This removes the continuum from the spectroscopy. Highly recommend
# using when modeling both photometry & spectroscopy
if fit_continuum:
# order of polynomial that's fit to spectrum
polyorder_estimate = int(
np.clip(
np.round((np.min([7500 * (obs['redshift'] + 1), 9150.0]) -
np.max([3525.0 * (obs['redshift'] + 1), 6000.0])) /
(obs['redshift'] + 1) * 100), 10, 30))
model_params['polyorder'] = {
'N': 1,
'init': polyorder_estimate,
'isfree': False
}
# fit for normalization of spectrum
# model_params['spec_norm'] = {'N': 1,
# 'init': 0.8,
# 'isfree': True,
# 'prior': priors.Normal(sigma=0.2, mean=0.8),
# 'units': 'f_true/f_obs'}
# This is a pixel outlier model. It helps to marginalize over
# poorly modeled noise, such as residual sky lines or
# even missing absorption lines
if not switch_off_mix:
model_params['f_outlier_spec'] = {
"N": 1,
"isfree": True,
"init": 0.01,
"prior": priors.TopHat(mini=1e-5, maxi=0.5)
}
model_params['nsigma_outlier_spec'] = {
"N": 1,
"isfree": False,
"init": 50.0
}
# This is a multiplicative noise inflation term. It inflates the noise in
# all spectroscopic pixels as necessary to get a good fit.
if add_jitter:
model_params['spec_jitter'] = {
"N": 1,
"isfree": True,
"init": 1.0,
"prior": priors.TopHat(mini=1.0, maxi=5.0)
}
# Now instantiate the model using this new dictionary of parameter specifications
model = PolySpecModel(model_params)
return model