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params_prosp_parrot.py
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params_prosp_parrot.py
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import sys, os
import numpy as np
from astropy.cosmology import WMAP9 as cosmo
from prospect.models import priors, transforms
from prospect.models import priors_beta as PZ
def params_parrot_phisfh(obs=None, **extra):
'''mass + metallicity + sfh priors from p-beta
'''
fit_order = ['nzsfh',
'dust2',
'dust_index',
'dust1_fraction',
'log_fagn',
'log_agn_tau',
'gas_logz',
'duste_qpah',
'duste_umin',
'log_duste_gamma']
# -------------
# MODEL_PARAMS
model_params = {}
# --- BASIC PARAMETERS ---
model_params['nzsfh'] = {'N': 9, 'isfree': True, 'init': np.array([0.5,8,0.0,0,0,0,0,0,0]),
'prior': PZ.PhiSFH(zred_mini=1e-3, zred_maxi=20.0,
mass_mini=6.0, mass_maxi=12.5,
z_mini=-1.98, z_maxi=0.19,
logsfr_ratio_mini=-5.0, logsfr_ratio_maxi=5.0,
logsfr_ratio_tscale=0.3, nbins_sfh=7,
const_phi=True)}
model_params['zred'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_zred,
'init': 0.5,
'prior': priors.FastUniform(a=1e-3, b=20.0)}
model_params['logmass'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_logmass,
'init': 8.0,
'units': 'Msun',
'prior': priors.FastUniform(a=6.0, b=12.5)}
model_params['logzsol'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_logzsol,
'init': -0.5,
'units': r'$\log (Z/Z_\odot)$',
'prior': priors.FastUniform(a=-1.98, b=0.19)}
# --- SFH ---
model_params['logsfr_ratios'] = {'N': 6, 'isfree': False,
'depends_on': transforms.nzsfh_to_logsfr_ratios,
'init': 0.0,
'prior': priors.FastTruncatedEvenStudentTFreeDeg2(hw=np.ones(6)*5.0, sig=np.ones(6)*0.3)}
# --- Dust Absorption ---
model_params['dust1_fraction'] = {'N': 1, 'isfree': True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=2.0, mu=1.0, sig=0.3)}
model_params['dust2'] = {'N': 1, 'isfree': True, 'init': 0.0,
'units': '',
'prior': priors.FastTruncatedNormal(a=0.0, b=4.0, mu=0.3, sig=1.0)}
model_params['dust_index'] = {'N': 1, 'isfree': True, 'init': 0.7,
'units': '',
'prior': priors.FastUniform(a=-1.0, b=0.4)}
# --- Nebular Emission ---
model_params['gas_logz'] = {'N': 1, 'isfree': True, 'init': -0.5,
'units': r'log Z/Z_\odot',
'prior': priors.FastUniform(a=-2.0, b=0.5)}
# --- AGN dust ---
model_params['log_fagn'] = {'N': 1, 'isfree': True, 'init': -7.0e-5,
'prior': priors.FastUniform(a=-5.0, b=np.log10(3.0))}
model_params['log_agn_tau'] = {'N': 1, 'isfree': True, 'init': np.log10(20.0),
'prior': priors.FastUniform(a=np.log10(5.0), b=np.log10(150.0))}
# --- Dust Emission ---
model_params['duste_qpah'] = {'N':1, 'isfree':True, 'init': 2.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=7.0, mu=2.0, sig=2.0)}
model_params['duste_umin'] = {'N':1, 'isfree':True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.1, b=25.0, mu=1.0, sig=10.0)}
model_params['log_duste_gamma'] = {'N':1, 'isfree':True, 'init': -2.0,
'prior': priors.FastTruncatedNormal(a=-4.0, b=0.0, mu=-2.0, sig=1.0)}
#---- Units ----
model_params['peraa'] = {'N': 1, 'isfree': False, 'init': False}
model_params['mass_units'] = {'N': 1, 'isfree': False, 'init': 'mformed'}
extra = [k for k in model_params.keys() if k not in fit_order]
fit_order = fit_order + list(extra)
tparams = {}
for i in fit_order:
tparams[i] = model_params[i]
for i in list(model_params.keys()):
if i not in fit_order:
tparams[i] = model_params[i]
model_params = tparams
return (model_params, fit_order)
def params_parrot_phisfhfixzred(obs=None, **extra):
'''mass + metallicity + sfh priors from p-beta
redshift fixed to obs['zspec']
'''
fit_order = ['nzsfh',
'dust2',
'dust_index',
'dust1_fraction',
'log_fagn',
'log_agn_tau',
'gas_logz',
'duste_qpah',
'duste_umin',
'log_duste_gamma']
# -------------
# MODEL_PARAMS
model_params = {}
# --- BASIC PARAMETERS ---
model_params['nzsfh'] = {'N': 9, 'isfree': True, 'init': np.array([0.5,8,0.0,0,0,0,0,0,0]),
'prior': PZ.PhiSFHfixZred(zred=obs['zspec'],
mass_mini=6.0, mass_maxi=12.5,
z_mini=-1.98, z_maxi=0.19,
logsfr_ratio_mini=-5.0, logsfr_ratio_maxi=5.0,
logsfr_ratio_tscale=0.3, nbins_sfh=7,
const_phi=True)}
model_params['zred'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_zred,
'init': 0.5,
'prior': priors.FastUniform(a=1e-3, b=20.0)}
model_params['logmass'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_logmass,
'init': 8.0,
'units': 'Msun',
'prior': priors.FastUniform(a=6.0, b=12.5)}
model_params['logzsol'] = {'N': 1, 'isfree': False,
'depends_on': transforms.nzsfh_to_logzsol,
'init': -0.5,
'units': r'$\log (Z/Z_\odot)$',
'prior': priors.FastUniform(a=-1.98, b=0.19)}
# --- SFH ---
model_params['logsfr_ratios'] = {'N': 6, 'isfree': False,
'depends_on': transforms.nzsfh_to_logsfr_ratios,
'init': 0.0,
'prior': priors.FastTruncatedEvenStudentTFreeDeg2(hw=np.ones(6)*5.0, sig=np.ones(6)*0.3)}
# --- Dust Absorption ---
model_params['dust1_fraction'] = {'N': 1, 'isfree': True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=2.0, mu=1.0, sig=0.3)}
model_params['dust2'] = {'N': 1, 'isfree': True, 'init': 0.0,
'units': '',
'prior': priors.FastTruncatedNormal(a=0.0, b=4.0, mu=0.3, sig=1.0)}
model_params['dust_index'] = {'N': 1, 'isfree': True, 'init': 0.7,
'units': '',
'prior': priors.FastUniform(a=-1.0, b=0.4)}
# --- Nebular Emission ---
model_params['gas_logz'] = {'N': 1, 'isfree': True, 'init': -0.5,
'units': r'log Z/Z_\odot',
'prior': priors.FastUniform(a=-2.0, b=0.5)}
# --- AGN dust ---
model_params['log_fagn'] = {'N': 1, 'isfree': True, 'init': -7.0e-5,
'prior': priors.FastUniform(a=-5.0, b=np.log10(3.0))}
model_params['log_agn_tau'] = {'N': 1, 'isfree': True, 'init': np.log10(20.0),
'prior': priors.FastUniform(a=np.log10(5.0), b=np.log10(150.0))}
# --- Dust Emission ---
model_params['duste_qpah'] = {'N':1, 'isfree':True, 'init': 2.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=7.0, mu=2.0, sig=2.0)}
model_params['duste_umin'] = {'N':1, 'isfree':True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.1, b=25.0, mu=1.0, sig=10.0)}
model_params['log_duste_gamma'] = {'N':1, 'isfree':True, 'init': -2.0,
'prior': priors.FastTruncatedNormal(a=-4.0, b=0.0, mu=-2.0, sig=1.0)}
#---- Units ----
model_params['peraa'] = {'N': 1, 'isfree': False, 'init': False}
model_params['mass_units'] = {'N': 1, 'isfree': False, 'init': 'mformed'}
extra = [k for k in model_params.keys() if k not in fit_order]
fit_order = fit_order + list(extra)
tparams = {}
for i in fit_order:
tparams[i] = model_params[i]
for i in list(model_params.keys()):
if i not in fit_order:
tparams[i] = model_params[i]
model_params = tparams
return (model_params, fit_order)
def params_parrot_alpha(obs=None, **extra):
'''p-alpha priors
'''
fit_order = ['zred', 'logmass', 'logzsol',
'logsfr_ratios',
'dust2',
'dust_index',
'dust1_fraction',
'log_fagn',
'log_agn_tau',
'gas_logz',
'duste_qpah',
'duste_umin',
'log_duste_gamma']
# -------------
# MODEL_PARAMS
model_params = {}
# --- BASIC PARAMETERS ---
model_params['zred'] = {'N': 1, 'isfree': True,
'init': 0.5,
'prior': priors.FastUniform(a=1e-3, b=20.0)}
model_params['logmass'] = {'N': 1, 'isfree': True,
'init': 8.0,
'units': 'Msun',
'prior': priors.FastUniform(a=6.0, b=12.5)}
model_params['logzsol'] = {'N': 1, 'isfree': True,
'init': -0.5,
'units': r'$\log (Z/Z_\odot)$',
'prior': priors.FastUniform(a=-1.98, b=0.19)}
# --- SFH ---
model_params['logsfr_ratios'] = {'N': 6, 'isfree': True, 'init': np.zeros(6),
'prior': priors.FastTruncatedEvenStudentTFreeDeg2(hw=np.ones(6)*5.0, sig=np.ones(6)*0.3)}
# --- Dust Absorption ---
model_params['dust1_fraction'] = {'N': 1, 'isfree': True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=2.0, mu=1.0, sig=0.3)}
model_params['dust2'] = {'N': 1, 'isfree': True, 'init': 0.0,
'units': '',
'prior': priors.FastTruncatedNormal(a=0.0, b=4.0, mu=0.3, sig=1.0)}
model_params['dust_index'] = {'N': 1, 'isfree': True, 'init': 0.7,
'units': '',
'prior': priors.FastUniform(a=-1.0, b=0.4)}
# --- Nebular Emission ---
model_params['gas_logz'] = {'N': 1, 'isfree': True, 'init': -0.5,
'units': r'log Z/Z_\odot',
'prior': priors.FastUniform(a=-2.0, b=0.5)}
# --- AGN dust ---
model_params['log_fagn'] = {'N': 1, 'isfree': True, 'init': -7.0e-5,
'prior': priors.FastUniform(a=-5.0, b=np.log10(3.0))}
model_params['log_agn_tau'] = {'N': 1, 'isfree': True, 'init': np.log10(20.0),
'prior': priors.FastUniform(a=np.log10(5.0), b=np.log10(150.0))}
# --- Dust Emission ---
model_params['duste_qpah'] = {'N':1, 'isfree':True, 'init': 2.0,
'prior': priors.FastTruncatedNormal(a=0.0, b=7.0, mu=2.0, sig=2.0)}
model_params['duste_umin'] = {'N':1, 'isfree':True, 'init': 1.0,
'prior': priors.FastTruncatedNormal(a=0.1, b=25.0, mu=1.0, sig=10.0)}
model_params['log_duste_gamma'] = {'N':1, 'isfree':True, 'init': -2.0,
'prior': priors.FastTruncatedNormal(a=-4.0, b=0.0, mu=-2.0, sig=1.0)}
#---- Units ----
model_params['peraa'] = {'N': 1, 'isfree': False, 'init': False}
model_params['mass_units'] = {'N': 1, 'isfree': False, 'init': 'mformed'}
extra = [k for k in model_params.keys() if k not in fit_order]
fit_order = fit_order + list(extra)
tparams = {}
for i in fit_order:
tparams[i] = model_params[i]
for i in list(model_params.keys()):
if i not in fit_order:
tparams[i] = model_params[i]
model_params = tparams
return (model_params, fit_order)