def load_model(agelims=[], nbins_sfh=7, sigma=0.3, df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate t_universe at z=1
tuniv = WMAP9.age(1.0).value*1e9
# now construct the nonparametric SFH
# current scheme: last bin is 15% age of the Universe, first two are 0-30, 30-100
# remaining N-3 bins spaced equally in logarithmic space
tbinmax = (tuniv*0.85)
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),nbins_sfh-2).tolist() + [np.log10(tuniv)]
agebins = np.array([agelims[:-1], agelims[1:]])
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
# insert redshift into model dictionary
model_params[n.index('zred')]['init'] = 0.0
return sedmodel.SedModel(model_params)
def load_model(nbins_sfh=6,sigma=0.3,datfile=None,objname=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# create SFH bins
with open(datfile,'r') as f:
data = json.load(f)
zred = float(data[objname]['redshift'])
model_params[n.index('zred')]['init'] = zred
tuniv = WMAP9.age(zred).value*1e9
# now construct the nonparametric SFH
# set number of components
# set logsfr_ratio prior
# propagate to agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = SFR_Ratio(mean=np.full(nbins_sfh-1,0.0),sigma=np.full(nbins_sfh-1,sigma))
model_params.append({'name': 'tuniv', 'N': 1,
'isfree': False,
'init': tuniv})
# set mass-metallicity prior
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
return sedmodel.SedModel(model_params)
def load_model(objname, field, agelims=[], **extras):
# REDSHIFT
# open file, load data
photname, zname, filtername, filts = get_names(field)
with open(photname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(photname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float) for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == objname # creates array of True/False: True when dat[id] = objname
zred = zout['z_spec'][idx][0] # use z_spec
if zred == -99: # if z_spec doesn't exist
zred = zout['z_peak'][idx][0] # use z_phot
print(zred, 'zred')
# CALCULATE AGE OF THE UNIVERSE (TUNIV) AT REDSHIFT ZRED
tuniv = WMAP9.age(zred).value
print(tuniv, 'tuniv')
n = [p['name'] for p in model_params]
# model_params[n.index('tage')]['prior_args']['maxi'] = tuniv
# NONPARAMETRIC SFH # NEW
agelims = [0.0, 7.7, 8.0, 9.0, (9.0 + (np.log10(tuniv*1e9) - 9.0)/3), (9.0 + 2 * (np.log10(tuniv*1e9) - 9.0)/3),
np.log10(tuniv*1e9)]
ncomp = len(agelims) - 1
agebins = np.array([agelims[:-1], agelims[1:]]) # why agelims[1:] instead of agelims[0:]?
# INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY
zind = n.index('zred')
model_params[zind]['init'] = zred
# SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# FRACTIONAL MASS INITIALIZATION # NEW
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('sfr_fraction')]['N'] = ncomp-1
model_params[n.index('sfr_fraction')]['prior_args'] = {
'maxi': np.full(ncomp-1, 1.0),
'mini': np.full(ncomp-1, 0.0),
# NOTE: ncomp instead of ncomp-1 makes the prior take into
# account the implicit Nth variable too
}
model_params[n.index('sfr_fraction')]['init'] = np.zeros(ncomp-1)+1./ncomp
model_params[n.index('sfr_fraction')]['init_disp'] = 0.02
# CREATE MODEL
model = BurstyModel(model_params)
return model
def load_model(objname=None, datdir=None, nbins_sfh=7, sigma=0.3, df=2, agelims=[], zred=None, runname=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
datname = datdir + objname.split('_')[0] + '_' + runname + '.dat'
dat = ascii.read(datname)
idx = dat['phot_id'] == int(objname.split('_')[-1])
zred = float(dat['z_best'][idx])
tuniv = WMAP9.age(zred).value*1e9
model_params[n.index('zred')]['init'] = zred
# now construct the nonparametric SFH
# current scheme: last bin is 15% age of the Universe, first two are 0-30, 30-100
# remaining N-3 bins spaced equally in logarithmic space
tbinmax = (tuniv*0.85)
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),nbins_sfh-2).tolist() + [np.log10(tuniv)]
agebins = np.array([agelims[:-1], agelims[1:]])
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
return sedmodel.SedModel(model_params)
def load_model(nbins_sfh=7,sigma=0.3,df=2.,agelims=None,objname=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# replace nbins_sfh
nbins_sfh = 4 + (int(objname)-1) / 9
# create SFH bins
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),nbins_sfh-2).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
return sedmodel.SedModel(model_params)
def load_model(datname='', objname='', **extras):
###### REDSHIFT ######
hdulist = fits.open(datname)
idx = hdulist[1].data['Name'] == objname
zred = hdulist[1].data['cz'][idx][0] / 3e5
hdulist.close()
#### TUNIV #####
tuniv = WMAP9.age(zred).value
#### TAGE #####
tage_init = 1.1
tage_mini = 0.11 # FSPS standard
tage_maxi = tuniv
#### INSERT MAXIMUM AGE AND REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
pnames = [m['name'] for m in model_params]
zind = pnames.index('zred')
model_params[zind]['init'] = zred
tind = pnames.index('tage')
model_params[tind]['prior_args']['maxi'] = tuniv
model = BurstyModel(model_params)
return model
def cosmoAge(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
Myr=False):
"""
Get the Age of the Universe at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
age = cosmo.age(redshift)
if not Myr:
return age.value
else:
return age.to(u.Myr).value
def draw_sfrs(objs,
flds,
new_logmass=None,
ndraw=1e4,
alpha_sfh=1.0,
pfile=None,
show=True):
# pfile=e_params or n_params
# let's do it
ndraw = int(ndraw)
zred = np.array([3.5])
logmass = np.array([10.])
smass_factor = 0.8 # add in a not-unreasonable stellar mass <--> total mass conversion
minssfr, maxssfr = 1e-14, 1e-5
# new redshift, new model
if new_logmass is None:
new_logmass = 10.17
new_SFRs = []
for i in range(len(objs)):
new_model = pfile.load_model(objname=objs[i], field=flds[i])
new_zred = new_model.params['zred']
tuniv = WMAP9.age(new_zred).value
new_SFRs.append(new_logmass / tuniv)
return new_SFRs
def load_model(alpha_sfh=0.2,agelims=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# create SFH bins
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('mass')]['init'] = np.full(ncomp,1e6)
model_params[n.index('mass')]['prior'] = priors.TopHat(mini=np.full(ncomp,1e5), maxi=np.full(ncomp,1e12))
return sedmodel.SedModel(model_params)
def load_model(nbins_sfh=5,sigma=0.3,df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# create SFH bins
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value*1e9
# now construct the nonparametric SFH
# set number of components
# set logsfr_ratio prior
# propagate to agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
model_params[n.index('logsfr_ratio30')]['prior'] = priors.StudentT(mean=0.0,
scale=sigma,
df=df)
model_params[n.index('logsfr_ratiomax')]['prior'] = priors.StudentT(mean=0.0,
scale=sigma,
df=df)
model_params.append({'name': 'tuniv', 'N': 1,
'isfree': False,
'init': tuniv})
return sedmodel.SedModel(model_params)
def cosmoAge(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
Myr=False):
"""
Get the Age of the Universe at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
age = cosmo.age(redshift)
if not Myr:
return age.value
else:
return age.to(u.Myr).value
def load_model(**extras):
# set tage_max, fix redshift
n = [p['name'] for p in model_params]
zred = 0.0001
tuniv = WMAP9.age(zred).value
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
return sedmodel.SedModel(model_params)
def load_model(**extras):
# set tage_max, fix redshift
n = [p['name'] for p in model_params]
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
return sedmodel.SedModel(model_params)
def load_model(objname=None, datdir=None, runname=None, agelims=[], zred=None, alpha_sfh=0.3, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
hdu = fits.open(APPS+'/prospector_alpha/data/3dhst/shivaei_sample.fits')
fields = np.array([f.replace('-','') for f in hdu[1].data['FIELD']])
ids = hdu[1].data['V4ID'].astype(str)
idx_obj = (fields == objname.split('_')[0]) & (ids == objname.split('_')[1])
zred = float(hdu[1].data['Z_MOSFIRE'][idx_obj][0])
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# now we do the computational z-fraction setup
# number of zfrac variables = (number of SFH bins - 1)
# set initial with a constant SFH
# if alpha_SFH is a vector, use this as the alpha array
# else assume all alphas are the same
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('z_fraction')]['N'] = ncomp-1
if type(alpha_sfh) != type(np.array([])):
alpha = np.repeat(alpha_sfh,ncomp-1)
else:
alpha = alpha_sfh
tilde_alpha = np.array([alpha[i-1:].sum() for i in xrange(1,ncomp)])
model_params[n.index('z_fraction')]['prior'] = priors.Beta(alpha=tilde_alpha, beta=alpha, mini=0.0, maxi=1.0)
model_params[n.index('z_fraction')]['init'] = np.array([(i-1)/float(i) for i in range(ncomp,1,-1)])
model_params[n.index('z_fraction')]['init_disp'] = 0.02
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
# set gas-phase metallicity prior
# log(Z/Zsun) = -3.07 for model
mean = hdu[1].data['m_12LOGOH'][idx_obj][0]
if (mean > -100):
gas_logz_mean = np.clip((mean - 12) + 3.06, -2, 0.5)
sigma = (hdu[1].data['U68_12LOGOH'] - hdu[1].data['L68_12LOGOH'])[idx_obj][0] / 2.
model_params[n.index('gas_logz')]['prior'] = priors.ClippedNormal(mean=gas_logz_mean,sigma=sigma,mini=-2,maxi=0.5)
return sedmodel.SedModel(model_params)
def load_model(objname='', datname='', fastname='', agelims=[], **extras):
###### REDSHIFT ######
### open file, load data
'''
with open(datname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1],'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(datname, comments = '#', delimiter=' ',
dtype = dtype)
'''
with open(fastname, 'r') as f:
hdr = f.readline().split()
# dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
fast = np.loadtxt(fastname, comments='#', delimiter=' ') # , dtype=dtype)
'''
with open(fastname, 'r') as f:
# drop the comment hash
header = f.readline().split()
fast = np.genfromtxt(fastname, comments='#', dtype=np.dtype([(n, np.float) for n in header]))
f.close()
'''
idx = 0 # fast['id'] == objname
print(fast)
zred = fast[1] # 3.0815
print(zred)
#### INITIAL VALUES
tau = 10 ** fast[2] / 1e9 # 10 ** 7.80 / 1e9 #
tage = 10 ** fast[4] / 1e9 # 10 ** 8.40 / 1e9 #
logmass = fast[6] # 11.23 #
dust2 = fast[5] / 1.086 # 1.70 / 1.086 #
# logzsol = 0 # np.log10(fast['metal'])
n = [p['name'] for p in model_params]
model_params[n.index('tau')]['init'] = tau
model_params[n.index('tage')]['init'] = tage
model_params[n.index('logmass')]['init'] = logmass
model_params[n.index('dust2')]['init'] = dust2
# model_params[n.index('logzsol')]['init'] = logzsol
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
model_params[n.index('tage')]['prior_args']['maxi'] = tuniv
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE MODEL
model = BurstyModel(model_params)
return model
def load_model(**extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# set tmax = tuniv
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
return sedmodel.SedModel(model_params)
def CSFH(SPS, p, redshift=0.0, log10Z=-2.):
log10ages = SPS.grid['log10age']
iZ = (np.abs(SPS.grid['log10Z'] - log10Z)).argmin()
SFZH = np.zeros((len(SPS.grid['log10age']), len(SPS.grid['log10Z'])))
from astropy.cosmology import WMAP9 as cosmo
dz = 0.01
z = np.arange(10., redshift, -dz)
ages = -(cosmo.age(z).to('yr').value - cosmo.age(redshift).to('yr').value)
csfh = lambda z, p1, p2, p3, p4: p1 * (1 + z)**p2 / (1 +
((1 + z) / p3)**p4)
sfrd = csfh(z, *p)
f = lambda x: np.interp(x, ages, sfrd[::-1])
start = 0.
for ia, log10age in enumerate(log10ages[:-1]):
end = 10**log10age
# determine the amount of star formation in this bin
sf = integrate.quad(f, start, end)[0]
SFZH[ia, iZ] = sf
# print('{0:.1f}-{1:.1f}: SF={2:.2f}'.format(start/1E6, end/1E6, sf))
start = end
SFZH /= np.sum(SFZH)
SFR = csfh(redshift, *p)
return SFZH, SFR
def tuniv_ssfr_prior(objs, flds, pfile=None):
# NEW TUNIV PRIOR
ssfrs = []
for i in range(len(objs)):
model = pfile.load_model(
objname=objs[i], field=flds[i]) # load_model prints zred, tuniv
tuniv = WMAP9.age(model.params['zred']).value
ssfrs.append(1 / tuniv)
print(i)
perc = np.percentile(ssfrs, [16., 50., 84.])
print(perc)
return perc
def make_galaxy():
## integrate Zg(mstar)
#### take mstar as an array
## Zstar = int(Zg, mstar) ?
## can be done numerically? or analytically?
## let's start numerically because I'm lazy
sfr = 15. * u.Msun / u.yr # msun per year
nsteps = 2000
time_start = 2 * u.Gyr
init_Mstar = 1.e6 * u.Msun
dt = (cosmo.age(z=0) - time_start) / nsteps
galaxy = initialize_galaxy(nsteps)
weight = np.zeros(nsteps) * u.Msun
zstar = np.zeros(len(galaxy))
Mzstar = np.zeros(len(galaxy)) * u.Msun
for i in range(nsteps):
galaxy['age'][i] = i * dt
galaxy['dt'][i] = dt
galaxy['lookback'][i] = (nsteps - i - 1) * dt
galaxy['sfr'][i] = sfr
if i == 0:
galaxy['Ms'][i] = init_Mstar
else:
galaxy['Ms'][i] = galaxy['Ms'][
i - 1] + sfr * dt - sfr * (1 - fml(galaxy['age'][i])) * dt
weight[i] = sfr * (1 - fml(galaxy['age'][i])) * dt
galaxy['tlogoh'][i] = get_tlogoh(np.log10(galaxy['Ms'][i].value),
"am13")
Zism = (O_AMU / HELIUM_CORR) * np.power(10.0, galaxy['tlogoh'] - 12)
galaxy['Zism'] = Zism
zstar[i], sum_weight = np.average(galaxy['Zism'],
weights=weight,
returned=True)
Mzstar[i] = np.sum(galaxy['Zism'] * weight) ## / np.cumsum(weight)
## how many metals made?
dMoxyiidt = Y_O_II * galaxy['sfr']
Moxymade = np.cumsum(dMoxyiidt * galaxy['dt'])
## Need to account for metals made by stars existing in 0th timestep; assume from eq(2) of peeples14
Moxyinit = np.power(10.0, P.oxyii(np.log10(
galaxy['Ms'][0].value))) * u.Msun
galaxy['Moxymade'] = Moxymade + Moxyinit
galaxy['dMoxymadedt'] = dMoxyiidt
galaxy['Zstar'] = zstar
galaxy['Mzstar'] = Mzstar
return galaxy
def load_model(objname=None, datdir=None, runname=None, agelims=[], **extras):
###### REDSHIFT ######
### open file, load data
# this is zgris
datname = datdir + objname.split('_')[0] + '_' + runname + '.dat'
dat = ascii.read(datname)
zred = dat['z_max_grism'][np.array(dat['phot_id']) == int(objname.split('_')[-1])][0]
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH #####
# six bins, four spaced equally in logarithmic space AFTER t=100 Myr + BEFORE tuniv-1 Gyr
if tuniv > 5:
tbinmax = (tuniv-2)*1e9
else:
tbinmax = (tuniv-1)*1e9
agelims = [agelims[0]] + np.linspace(agelims[1],np.log10(tbinmax),5).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
### computational z-fraction setup
# N-1 bins
# set initial by drawing randomly from the prior
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('z_fraction')]['N'] = ncomp-1
tilde_alpha = np.array([ncomp-i for i in xrange(1,ncomp)])
model_params[n.index('z_fraction')]['prior'] = priors.Beta(alpha=tilde_alpha, beta=np.ones_like(tilde_alpha),mini=0.0,maxi=1.0)
model_params[n.index('z_fraction')]['init'] = np.array([(i-1)/float(i) for i in range(ncomp,1,-1)])
model_params[n.index('z_fraction')]['init_disp'] = 0.02
### apply SDSS mass-metallicity prior
model_params[n.index('logzsol')]['prior'] = MassMet(mini=-1.98, maxi=0.19)
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE MODEL
model = SedMet(model_params)
return model
def grid(Nage=80, NZ=20, nebular=True, dust=False):
"""
Generate grid of spectra with FSPS
Returns:
spec (array, float) spectra, dimensions NZ*Nage
metallicities (array, float) metallicity array, units Z / Zsol
scale_factors (array, flota) age array in units of the scale factor
wl (array, float) wavelength array in Angstroms
"""
if dust:
sp = fsps.StellarPopulation(zcontinuous=1,
sfh=0,
logzsol=0.0,
add_neb_emission=nebular,
dust_type=2,
dust2=0.2,
cloudy_dust=True,
dust1=0.0)
else:
sp = fsps.StellarPopulation(zcontinuous=1,
sfh=0,
cloudy_dust=True,
logzsol=0.0,
add_neb_emission=nebular)
wl = np.array(sp.get_spectrum(tage=13, peraa=True)).T[:, 0]
ages = np.logspace(-3.5,
np.log10(cosmo.age(0).value - 0.4),
num=Nage,
base=10)
scale_factors = cosmo.scale_factor(
[z_at_value(cosmo.lookback_time, age * u.Gyr) for age in ages])
metallicities = np.linspace(-3, 1, num=NZ) # log(Z / Zsol)
spec = np.zeros((len(metallicities), len(ages), len(wl)))
for i, Z in enumerate(metallicities):
for j, a in enumerate(ages):
sp.params['logzsol'] = Z
if nebular: sp.params['gas_logz'] = Z
spec[i, j] = sp.get_spectrum(tage=a, peraa=True)[1] # Lsol / AA
return spec, scale_factors, metallicities, wl
def load_model(objname=None, datdir=None, runname=None, agelims=[], zred=None, alpha_sfh=0.2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
datname = datdir + objname.split('_')[0] + '_' + runname + '.dat'
dat = ascii.read(datname)
idx = dat['phot_id'] == int(objname.split('_')[-1])
zred = float(dat['z_best'][idx])
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# now we do the computational z-fraction setup
# number of zfrac variables = (number of SFH bins - 1)
# set initial with a constant SFH
# if alpha_SFH is a vector, use this as the alpha array
# else assume all alphas are the same
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('z_fraction')]['N'] = ncomp-1
if type(alpha_sfh) != type(np.array([])):
alpha = np.repeat(alpha_sfh,ncomp-1)
else:
alpha = alpha_sfh
tilde_alpha = np.array([alpha[i-1:].sum() for i in xrange(1,ncomp)])
model_params[n.index('z_fraction')]['prior'] = priors.Beta(alpha=tilde_alpha, beta=alpha, mini=0.0, maxi=1.0)
model_params[n.index('z_fraction')]['init'] = np.array([(i-1)/float(i) for i in range(ncomp,1,-1)])
model_params[n.index('z_fraction')]['init_disp'] = 0.02
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def make_scattered_galaxy(filename, **kwargs):
print "attempting to read in ", filename
input_galaxy = ascii.read(filename)
galaxy = initialize_galaxy(len(input_galaxy))
dt = 1.e7
print "assuming timestep is 1.e7 years!!!!!"
galaxy['dt'] += 1.e7*u.yr
galaxy['sfr'] = np.power(10.0,input_galaxy['SFR']) * u.Msun/u.yr
galaxy['dMrdt'] = np.power(10.0,input_galaxy['Mrec'])* u.Msun / galaxy['dt']
# galaxy['dMrdt'].unit = u.Msun / u.yr
## Need to get gas masses from Gergo's code
fake_radius = 3000. + np.zeros(len(galaxy['sfr']))
gas = ig.gas_masses(galaxy['sfr'], input_galaxy['Ms'], fake_radius)
Mgas, Mhi, Mh2 = gas[0], gas[1], gas[2]
galaxy['Mg'] = Mgas * HELIUM_CORR * u.Msun
galaxy['Mhi'] = Mhi * u.Msun
galaxy['Mh2'] = Mh2 * u.Msun
galaxy['Ms'] = np.power(10.0,input_galaxy['Ms']) * u.Msun
col_dummy = Column(name='z',data=input_galaxy['z'])
galaxy.add_column(col_dummy)
print "ASSUMING PROBABLY A WRONG COSMOLOGY FYI"
age = cosmo.age(galaxy['z']) ### THIS COSMOLOGY IS WRONG
galaxy['age'] = age
tlb = cosmo.lookback_time(galaxy['z'])
galaxy['lookback'] = tlb
galaxy.remove_column('Mh')
galaxy.remove_column('Mcgm')
galaxy.remove_column('tlogoh')
## check to make sure sorted so earlier is earlier
galaxy.sort('age')
print galaxy
return galaxy
def read_in_galaxy(filename, index):
print "attempting to read in",filename
if ".pkl" in filename:
print "assuming that",filename,"is a pickle file with only a dict of galaxies in it"
galaxies = pickle.load(open(filename,"r"))
print "for now we're only dealing with one galaxy at a time; taking galaxy #",index
assert(index in galaxies.keys()), "index %i not in file :-(" % index
galaxy = galaxies[index]
col_z = Column(name='z',data=galaxies['z'])
galaxy.add_column(col_z,0)
elif "peter" in filename:
print "assuming that",filename,"is an ascii file from peter behroozi and gergo popping with Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2"
print "also assuming that",filename,"only has one iteration"
z,Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2 = np.loadtxt(filename,usecols=(0,7*index+1,7*index+2,7*index+3,7*index+4,7*index+5,7*index+6,7*index+7),unpack=True)
galaxy = QTable([z,Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2],names=('z','Mh','Ms','dMhdt','sfr','Mg','Mhi','Mh2'))
## need to add units
galaxy['Mh'].unit = u.Msun
galaxy['Ms'].unit = u.Msun
galaxy['Mg'].unit = u.Msun
galaxy['Mhi'].unit = u.Msun
galaxy['Mh2'].unit = u.Msun
galaxy['dMhdt'].unit = u.Msun / u.yr
if 'dMrdt' in galaxy.colnames:
galaxy['dMrdt'].unit = u.Msun / u.yr
galaxy['sfr'].unit = u.Msun / u.yr
### Helium is not included in the gas masses!!
galaxy['Mg'] = galaxy['Mg'] * HELIUM_CORR
### Let's make a CGM! ###
galaxy['Mcgm'] = galaxy['Mh']*cosmo.Ob0/cosmo.Om0 - galaxy['Ms'] - galaxy['Mg']
age = cosmo.age(galaxy['z'])
col_age = Column(name='age', data=age)
galaxy.add_column(col_age,1)
tlb = cosmo.lookback_time(galaxy['z'])
col_tlb = Column(name='lookback', data=tlb)
galaxy.add_column(col_tlb,2)
## check to make sure sorted so earlier is earlier
galaxy.sort('age')
return galaxy
def load_model(objname='',datname='', agelims=[], **extras):
###### REDSHIFT ######
hdulist = fits.open(datname)
idx = hdulist[1].data['Name'] == objname
zred = hdulist[1].data['cz'][idx][0] / 3e5
lumdist = hdulist[1].data['Dist'][idx][0]
hdulist.close()
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH ######
agelims[-1] = np.log10(tuniv*1e9)
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### FRACTIONAL MASS INITIALIZATION
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('z_fraction')]['N'] = ncomp-1
tilde_alpha = np.array([ncomp-i for i in xrange(1,ncomp)])
model_params[n.index('z_fraction')]['prior'] = priors.Beta(alpha=tilde_alpha, beta=np.ones_like(tilde_alpha),mini=0.0,maxi=1.0)
model_params[n.index('z_fraction')]['init'] = model_params[n.index('z_fraction')]['prior'].sample()
model_params[n.index('z_fraction')]['init_disp'] = 0.02
model_params[n.index('sfr_fraction')]['N'] = ncomp-1
model_params[n.index('sfr_fraction')]['prior'] = priors.TopHat(maxi=np.full(ncomp-1,1.0), mini=np.full(ncomp-1,0.0))
model_params[n.index('sfr_fraction')]['init'] = np.zeros(ncomp-1)+1./ncomp
model_params[n.index('sfr_fraction')]['init_disp'] = 0.02
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
model_params[n.index('zred')]['init'] = zred
model_params[n.index('lumdist')]['init'] = lumdist
#### CREATE MODEL
model = BurstyModel(model_params)
return model
def load_model(objname='', datname='', agelims=[], **extras):
###### REDSHIFT ######
hdulist = fits.open(datname)
idx = hdulist[1].data['Name'] == objname
zred = hdulist[1].data['cz'][idx][0] / 3e5
hdulist.close()
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH ######
agelims[-1] = np.log10(tuniv*1e9)
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
mass_init = expsfh(agelims, **extras)*1e5
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### FRACTIONAL MASS
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('sfr_fraction')]['N'] = ncomp-1
model_params[n.index('sfr_fraction')]['init'] = mass_init[:-1] / np.sum(mass_init)
model_params[n.index('sfr_fraction')]['prior_args'] = {
'maxi':np.full(ncomp-1,1.0),
'mini':np.full(ncomp-1,0.0),
'alpha':1.0,
'alpha_sum':ncomp
# NOTE: ncomp instead of ncomp-1 makes the prior take into account the implicit Nth variable too
}
model_params[n.index('sfr_fraction')]['init_disp'] = 0.15
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE MODEL
model = BurstyModel(model_params)
return model
def load_model(objname=None, datloc=None, agelims=[], zred=None, nbins_sfh=7, sigma=0.3,df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
### open file, load data
with open(datloc, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1],'S20')] + [(nam, np.float) for nam in hdr[2:]])
dat = np.loadtxt(datloc, comments = '#', dtype = dtype)
obj_idx = (dat['ID'] == objname)
zred = dat['zz'][obj_idx]
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def test_magnitudes():
bt = StarlightBase(
'/Users/william/BasesDir/Base.bc03.Padova1994.chab.All.hdf5',
'Base.bc03.Padova1994.chab.All')
i_met = 0
i_z = 0
z = np.array([0.5, 1.0, 1.5])
# Generate random parameters to test:
z_age_limit = cosmo.age(0.01).to('yr').value
t0_young = np.log10(6e6) + np.random.rand() * (np.log10(5e9) -
np.log10(6e6))
tau_young = np.log10(.001) + np.random.rand() * (np.log10(.001) -
np.log10(1000))
t0_old = np.log10(1e9) + np.random.rand() * (np.log10(z_age_limit) -
np.log10(1e9))
tau_old = np.log10(.001) + np.random.rand() * (np.log10(.001) -
np.log10(1000))
frac_young = np.random.rand()
a_v = 2 * np.random.rand()
print 'Parameters: ', z_age_limit, t0_young, tau_young, t0_old, tau_old, frac_young, a_v
# Create the CSP with it
csp_model = n_component(bt.ageBase)
csp_model.add_exp(t0_young, tau_young, frac_young)
csp_model.add_exp(t0_old, tau_old, 1 - frac_young)
# Calculate the analytic integral
spec = np.sum(bt.f_ssp[i_met] * csp_model.get_sfh()[:, np.newaxis],
axis=0) # Base spectra [??units??]
spec *= 10**(-0.4 * (Cardelli_RedLaw(bt.l_ssp) * a_v))
mags_analytic = mag_in_z(bt.l_ssp, spec, z[i_z], filters)
# Calculate the Taylor Approximation
for n_taylor in range(1, 10):
t0 = time.time()
m, a, f = taylor_matrix_ssp(bt, n_taylor, z, filters)
mags_taylor = mag_in_z_taylor(csp_model.get_sfh(), a_v, i_z, i_met, m,
a)
print 'n_taylor, t, max |delta_m| = ', n_taylor, time.time(
) - t0, np.max(np.abs(mags_taylor - mags_analytic))
def load_model(objname=None, datloc=None, agelims=[0.0,7.4772,8.0,8.5,9.0,9.5,9.8,10.0], zred=None, nbins_sfh=7, sigma=0.3,df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
dat = fits.open(datloc)[1].data
obj_idx = (dat['CATAID'] == int(objname))
zred = dat['Z'][obj_idx]
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [float(np.log10(tuniv*1e9))]
if type(agelims[-3]) == type([]): # workaround for odyssey numpy
agelims = [np.atleast_1d(x)[0] for x in agelims]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def load_model(objname=None, datdir=None, runname=None, zred=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
datname = datdir + objname.split('_')[0] + '_' + runname + '.dat'
dat = ascii.read(datname)
idx = dat['phot_id'] == int(objname.split('_')[-1])
zred = float(dat['z_best'][idx])
tuniv = WMAP9.age(zred).value
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def load_model(objname=None, datdir=None, agelims=[], runname=None, zred=None, **extras):
###### REDSHIFT ######
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
datname = datdir + objname.split('_')[0] + '_' + runname + '.dat'
dat = ascii.read(datname)
idx = dat['phot_id'] == int(objname.split('_')[-1])
zred = float(dat['z_best'][idx])
tuniv = WMAP9.age(zred).value
# Update parameters
n = [p['name'] for p in model_params]
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
model_params[n.index('zred')]['init'] = zred
#### CREATE MODEL
model = sedmodel.SedModel(model_params)
return model
def load_model(objname=None, datloc=None, agelims=[], zred=None, nbins_sfh=7, sigma=0.3,df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
dat = fits.open(datloc)[1].data
obj_idx = (dat['CATAID'] == int(objname))
zred = dat['Z'][obj_idx]
tuniv = WMAP9.age(zred).value
# update prior for tage
model_params[n.index('tage')]['prior'].update(maxi=tuniv)
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def load_model(objname='',datname='', agelims=[], **extras):
###### REDSHIFT ######
hdulist = fits.open(datname)
idx = hdulist[1].data['Name'] == objname
zred = hdulist[1].data['cz'][idx][0] / 3e5
hdulist.close()
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH ######
agelims[-1] = np.log10(tuniv*1e9)
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### FRACTIONAL MASS INITIALIZATION
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('mass')]['prior_args'] = {'maxi':np.full(ncomp,1e5),
'mini':np.full(ncomp,1e14),}
model_params[n.index('mass')]['init'] = np.full(ncomp,1e10)
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE MODEL
model = BurstyModel(model_params)
return model
def load_model(alpha_sfh=0.2,agelims=None, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# create SFH bins
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# last bin is 15% age of the Universe, first two are 0-30, 30-100
tbinmax = (tuniv*0.85)*1e9
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),len(agelims)-3).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# now we do the computational z-fraction setup
# number of zfrac variables = (number of SFH bins - 1)
# set initial with a constant SFH
# if alpha_SFH is a vector, use this as the alpha array
# else assume all alphas are the same
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('z_fraction')]['N'] = ncomp-1
if type(alpha_sfh) != type(np.array([])):
alpha = np.repeat(alpha_sfh,ncomp-1)
else:
alpha = alpha_sfh
tilde_alpha = np.array([alpha[i-1:].sum() for i in xrange(1,ncomp)])
model_params[n.index('z_fraction')]['prior'] = priors.Beta(alpha=tilde_alpha, beta=alpha, mini=0.0, maxi=1.0)
model_params[n.index('z_fraction')]['init'] = np.array([(i-1)/float(i) for i in range(ncomp,1,-1)])
model_params[n.index('z_fraction')]['init_disp'] = 0.02
return sedmodel.SedModel(model_params)
def load_model(objname='', agelims=[], **extras):
###### LOAD REDSHIFT ######
zred = 0.0
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH ######
agelims[-1] = np.log10(tuniv*1e9)
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
mass_init = expsfh(agelims, **extras)*1e5
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
model_params[n.index('logmass')]['N'] = ncomp
model_params[n.index('logmass')]['init'] = np.log10(mass_init)
model_params[n.index('logmass')]['prior_args'] = {'maxi':np.full(ncomp,14.0), 'mini':np.full(ncomp,1.0)}
model_params[n.index('logmass')]['init_disp'] = 0.3
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('mass')]['init'] = mass_init
model_params[n.index('mass')]['prior_args'] = {'maxi':np.full(ncomp,10**14.0), 'mini':np.full(ncomp,10**1.0)}
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE AND RETURN MODEL
model = BurstyModel(model_params)
return model
def load_model(objname='',datname='', agelims=[], nbins_sfh=7, sigma=0.3, df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# set redshift, tuniv
hdulist = fits.open(datname)
idx = hdulist[1].data['Name'] == objname
zred = hdulist[1].data['cz'][idx][0] / 3e5
tuniv = WMAP9.age(zred).value*1e9
lumdist = hdulist[1].data['Dist'][idx][0]
hdulist.close()
# now construct the nonparametric SFH
# current scheme: last bin is 15% age of the Universe, first two are 0-30, 30-100
# remaining N-3 bins spaced equally in logarithmic space
tbinmax = (tuniv*0.85)
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),nbins_sfh-2).tolist() + [np.log10(tuniv)]
agebins = np.array([agelims[:-1], agelims[1:]])
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
model_params[n.index('lumdist')]['init'] = lumdist
return sedmodel.SedModel(model_params)
def __init__(self,
template_magnitudes,
filters,
z,
base_file,
base_path,
taylor_file=None,
magerr=1e-14,
is_single=False):
# Template magnitudes
self.template_magnitudes = template_magnitudes
# self.chi2_constant = np.average(template_magnitudes) # This will be summed to chi2, avoiding to get them ~ 0.
self.filters = filters
self.z = z
self.z_age_limit = cosmo.age(z).to('yr').value
# Base
self.bt = StarlightBase(base_file, base_path)
## Metallicity
aux = np.log10(self.bt.metBase)
aux2 = (aux[1:] - aux[0:-1]) / 2
self.met_min = aux - np.append(aux2[0], aux2)
self.met_max = aux + np.append(aux2, aux2[-1])
self.met_low = min(self.met_min)
self.met_upp = max(self.met_max)
# Extinction Law
self.q = Cardelli_RedLaw(self.bt.l_ssp)
# If Taylor expansion:
if taylor_file:
self.m, self.a, self.tz, self.int_f = load_taylor(config, filters)
# Magnitude error
self.magerr = magerr
def load_model(nbins_sfh=7, sigma=0.3, df=2, agelims=[], objname=None, datdir=None, runname=None, zred=None,**extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
if zred is None:
datname = datdir + 'joel_'+objname.split('_')[0].lower() + '.csv'
dat = read_csv(datname)
idx = dat['ID'] == int(objname.split('_')[-1])
zred = float(dat['REDSHIFT'][idx])
tuniv = WMAP9.age(zred).value*1e9
# now construct the nonparametric SFH
# current scheme: last bin is 15% age of the Universe, first two are 0-30, 30-100
# remaining N-3 bins spaced equally in logarithmic space
tbinmax = (tuniv*0.85)
agelims = agelims[:2] + np.linspace(agelims[2],np.log10(tbinmax),nbins_sfh-2).tolist() + [np.log10(tuniv)]
agebins = np.array([agelims[:-1], agelims[1:]])
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = nbins_sfh
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = nbins_sfh
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
# set mass-metallicity prior
# insert redshift into model dictionary
model_params[n.index('massmet')]['prior'] = MassMet(z_mini=-1.98, z_maxi=0.19, mass_mini=7, mass_maxi=12.5)
model_params[n.index('zred')]['init'] = zred
return sedmodel.SedModel(model_params)
def load_model(agelims=[], nbins_sfh=7, sigma=0.3, df=2, **extras):
# we'll need this to access specific model parameters
n = [p['name'] for p in model_params]
# first calculate redshift and corresponding t_universe
# if no redshift is specified, read from file
zred = model_params[n.index('zred')]['init']
tuniv = WMAP9.age(zred).value
# now construct the nonparametric SFH
# current scheme: six bins, four spaced equally in logarithmic
# space AFTER t=100 Myr + BEFORE tuniv-1 Gyr
tbinmax = (tuniv-4)*1e9
if tbinmax < 0:
tbinmax = (tuniv-0.25)*1e9
agelims = agelims[:1] + np.linspace(agelims[1],np.log10(tbinmax),len(agelims)-2).tolist() + [np.log10(tuniv*1e9)]
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
# load into `agebins` in the model_params dictionary
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# load nvariables and agebins
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
model_params[n.index('mass')]['N'] = ncomp
model_params[n.index('logsfr_ratios')]['N'] = nbins_sfh-1
model_params[n.index('logsfr_ratios')]['init'] = np.full(nbins_sfh-1,0.0) # constant SFH
model_params[n.index('logsfr_ratios')]['prior'] = priors.StudentT(mean=np.full(nbins_sfh-1,0.0),
scale=np.full(nbins_sfh-1,sigma),
df=np.full(nbins_sfh-1,df))
return sedmodel.SedModel(model_params)
def load_model(objname='', agelims=[], **extras):
###### LOAD REDSHIFT ######
zred = 0.0
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
#### NONPARAMETRIC SFH ######
agelims[-1] = np.log10(tuniv*1e9)
agebins = np.array([agelims[:-1], agelims[1:]])
ncomp = len(agelims) - 1
mass_init = expsfh(agelims, **extras)*1e5
#### ADJUST MODEL PARAMETERS #####
n = [p['name'] for p in model_params]
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### FRACTIONAL MASS
# N-1 bins, last is set by x = 1 - np.sum(fracmass)
model_params[n.index('fracmass')]['N'] = ncomp-1
model_params[n.index('fracmass')]['init'] = mass_init[:-1] / np.sum(mass_init)
model_params[n.index('fracmass')]['prior_args'] = {'maxi':np.full(ncomp-1,1.0), 'mini':np.full(ncomp-1,0.0)}
model_params[n.index('fracmass')]['init_disp'] = 0.15
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### CREATE MODEL
model = BurstyModel(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
Create a custom cosmology object >>> from astropy.cosmology import FlatLambdaCDM >>> cosmo = FlatLambdaCDM(H0=70, Om0=0.3) >>> cosmo FlatLambdaCDM(H0=70, Om0=0.3, Ode0=0.7) Compute the comoving volume to z=6.5 in cubic Mpc using this cosmology >>> cosmo.comoving_volume(6.5) 2521696198211.6924 Compute the age of the universe in Gyr using the pre-defined WMAP 5-year and WMAP 9-year cosmologies >>> from astropy.cosmology import WMAP5, WMAP9 >>> WMAP5.age(0) 13.723782349795023 >>> WMAP9.age(0) 13.768899510689097 Create a cosmology with a varying `w' >>> from astropy.cosmology import Flatw0waCDM >>> cosmo = Flatw0waCDM(H0=70, Om0=0.3, w0=-1, wa=0.2) Find the separation in proper kpc at z=4 corresponding to 10 arcsec in this cosmology compared to a WMAP9 cosmology >>> cosmo.kpc_proper_per_arcmin(4) * 10 / 60. 68.87214405278925 >>> WMAP9.kpc_proper_per_arcmin(4) * 10 / 60. 71.21374615575363
def sfr_flux_relationship():
# initialize
thetas = copy.copy(mod.initial_theta)
thetas[mod.theta_index['fagn']] = 0.0
thetas[mod.theta_index['dust2']] = prosp_dutils.av_to_dust2(20)
# grids and constants
ngrid_sfr, ngrid_zred = 31, 30
sfr_grid = np.linspace(1,1000,ngrid_sfr)
zred_grid = np.linspace(0.1,3,ngrid_zred)
mags = np.zeros(shape=(ngrid_zred,ngrid_sfr,len(obs['filters'])))
wlam = np.array([f.wave_effective for f in obs['filters']])
flux_conversion = 3631*1e-23
tuniv = WMAP9.age(mod.params['zred']).value * 1e9
# loop
for j in range(ngrid_zred):
# set zred
mod.params['zred'] = zred_grid[j]
for i in range(ngrid_sfr):
# set SFR value
thetas[0] = np.log10(sfr_grid[i]*tuniv)
_, mags[j,i,:], _ = mod.mean_model(thetas, obs, sps=sps)
# convert to cgs
mags *= flux_conversion
# plot values
colors = ['#e31a1c', '#ff7f00','#33a02c','#1f78b4','#6a3d9a','ochre']
labels = ['10$\mu$m','12$\mu$m','15$\mu$m','18$\mu$m','21$\mu$m','24$\mu$m']
fig, ax = plt.subplots(1,2, figsize=(7, 3.5))
zred_idx = np.abs(zred_grid-2).argmin()
sfr_idx = np.abs(sfr_grid-100).argmin()
for j in range(5):
ax[0].plot(np.log10(sfr_grid),np.log10(mags[zred_idx,:,j]),'o-',color=colors[j],label=labels[j],lw=1.4,ms=2.4)
ax[1].plot(zred_grid,np.log10(mags[:,sfr_idx,j]),'o-',color=colors[j],label=labels[j],lw=1.4,ms=2.4)
ax[0].legend()
ax[1].legend()
ax[0].set_title('z=2')
ax[1].set_title('SFR=100 M$_{\odot}$/yr')
for a in ax: a.set_ylabel(r'log(f$_{\nu}$) [cgs]')
ax[0].set_xlabel(r'log(SFR) [M$_{\odot}$/yr]')
ax[1].set_xlabel(r'redshift')
plt.tight_layout()
plt.savefig('flux_sfr_redshift.png',dpi=200)
plt.close()
ascii_write(mags,zred_grid,sfr_grid,'redshift','SFR','flux_sfr_redshift.dat')
# loop over fagn
thetas[mod.theta_index['agn_tau']] = 10
fagn_grid = np.array([0.0,0.05,0.5])
mags = np.zeros(shape=(ngrid_zred,len(fagn_grid),len(obs['filters'])))
# loop
for j in range(len(fagn_grid)):
# set fagn
thetas[mod.theta_index['fagn']] = fagn_grid[j]
for i in range(ngrid_zred):
# set zred
mod.params['zred'] = zred_grid[i]
_, mags[i,j,:], _ = mod.mean_model(thetas, obs, sps=sps)
# convert to cgs
mags *= flux_conversion
# plot values
fig, ax = plt.subplots(1,1, figsize=(4, 4))
for j in range(5): ax.plot(zred_grid,np.log10(mags[:,0,j]/mags[:,-1,j]),'o-',color=colors[j],label=labels[j],lw=1.4,ms=2.4)
ax.set_ylim(-1.5,0)
ax.legend()
ax.set_ylabel(r'log(f$_{\nu}$/f$_{\nu,AGN}$)')
ax.set_xlabel(r'redshift')
plt.tight_layout()
plt.savefig('agn.png',dpi=200)
plt.close()
ascii_write(mags,zred_grid,fagn_grid,'redshift','fagn','flux_redshift_agn.dat')
# loop over PAH
thetas[mod.theta_index['fagn']] = 0.0
qpah_grid = np.array([0.0,2,7])
mags = np.zeros(shape=(ngrid_zred,len(qpah_grid),len(obs['filters'])))
# loop
for j in range(len(qpah_grid)):
# set qph
mod.params['duste_qpah'] = qpah_grid[j]
for i in range(ngrid_zred):
# set zred
mod.params['zred'] = zred_grid[i]
_, mags[i,j,:], _ = mod.mean_model(thetas, obs, sps=sps)
# convert to cgs
mags *= flux_conversion
# plot values
fig, ax = plt.subplots(1,1, figsize=(4, 4))
for j in range(5): ax.plot(zred_grid,np.log10(mags[:,0,j]/mags[:,-1,j]),'o-',color=colors[j],label=labels[j],lw=1.4,ms=2.4)
ax.set_ylim(-1.5,0.5)
ax.legend()
ax.set_ylabel(r'log(f$_{\nu,lowPAH}$/f$_{\nu,highPAH}$)')
ax.set_xlabel(r'redshift')
ax.axhline(0, linestyle='--', color='k',lw=1,zorder=-1)
plt.tight_layout()
plt.savefig('qpah.png',dpi=200)
plt.close()
ascii_write(mags,zred_grid,qpah_grid,'redshift','qpah','flux_redshift_qpah.dat')
print 'Loading gas cell mass...'
gas_mass = dd['gas', 'cell_mass']
print 'Loading gas cell position...'
gas_x = dd['index', 'x'].in_units('kpc')
gas_y = dd['index', 'y'].in_units('kpc')
gas_z = dd['index', 'z'].in_units('kpc')
print 'Loading star positions...'
star_x = dd['stars', 'particle_position_x'].in_units('kpc')
star_y = dd['stars', 'particle_position_y'].in_units('kpc')
star_z = dd['stars', 'particle_position_z'].in_units('kpc')
star_mass = dd['stars', 'particle_mass'].in_units('Msun')
star_creation_time = dd['stars', 'particle_creation_time'].in_units('yr')
star_age_all = ds.arr(cosmo.age(ds.current_redshift).value, 'Gyr').in_units('yr') - star_creation_time
print 'Loading star velocities...'
star_vx = dd['stars', 'particle_velocity_x'].in_units('km/s')
star_vy = dd['stars', 'particle_velocity_y'].in_units('km/s')
star_vz = dd['stars', 'particle_velocity_z'].in_units('km/s')
#Recenter the gas cells to galaxy center
cen_x = star_x[id_cen_star-1] - ds.arr(cen_star_offset[0], 'kpc')
cen_y = star_y[id_cen_star-1] - ds.arr(cen_star_offset[1], 'kpc')
cen_z = star_z[id_cen_star-1] - ds.arr(cen_star_offset[2], 'kpc')
cen_vx = star_vx[id_cen_star-1] - ds.arr(cen_star_voffset[0], 'km/s')
cen_vy = star_vy[id_cen_star-1] - ds.arr(cen_star_voffset[1], 'km/s')
cen_vz = star_vz[id_cen_star-1] - ds.arr(cen_star_voffset[2], 'km/s')
from astropy.cosmology import WMAP9 as cosmo
import h5py
from magal.io.readfilterset import FilterSet
from magal.photometry.syntphot import spec2filterset
from magal.util.cosmo import zcor
import pystarlight.io
# #### Defs #####
bases_dir = '%s/BasesDir/' % os.environ.get('HOME')
# base_file = '%s/Base.bc03.Padova1994.chab.All' % bases_dir
base_file = '%s/Base.BC03.N' % bases_dir
bt = atpy.Table(base_file, '/Users/william/BasesDir/', type='starlightv4_base', read_basedir=True)
base_ages = bt['age_base']
base_maxage = np.max(base_ages)
min_z_age = cosmo.age(0.001).to('yr').value
if base_maxage > min_z_age:
base_maxage = min_z_age
from parametric_library import lib_guess
Z_model = 0.02 # Z_\odot
flag_Z = (bt['Z_base'] == bt['Z_base'][np.argmin((Z_model - bt['Z_base']) ** 2)]) # Get a mask to the desired metallicity
n_model = 10000
_i_norm = int(np.argwhere(bt.l_ssp[0] == 4020.))
at_flux = []
def univ_age():
z = ((df_sel.sort_values(['redshift'], ascending=False).reset_index(drop=True))['redshift'][0])
u_age = (cosmo.age(z).value)*1000
u_age = np.round(u_age, decimals=4)
return u_age
red = df6['redshift']
flu = df6['fluence']
fig = plt.figure(figsize=(14,8))
ax = fig.add_subplot(111)
ax.scatter(red, flu, color='DarkBlue')
ax.grid(True)
plt.title("Modeling GRB's observed by Swift", fontsize=36)
plt.xlabel("Redshift", fontsize=24)
plt.ylabel("Fluence(erg/cm^2)", fontsize=24)
plt.ylim([0,.00005])
plt.xlim([0,12])
plt.show()
continue
elif plot_choice.lower() == 'ra':
z = df6['redshift']
u_age = (cosmo.age(z).value)*1000
fig = plt.figure(figsize=(14,8))
ax = fig.add_subplot(111)
ax.scatter(u_age, z, color='#CC0000')
ax.grid(True)
ax.annotate('Big Bang', fontsize=20, xy=(0, 0), xytext=(20, 2),
arrowprops=dict(facecolor='black'),
)
plt.title("GRB Redshifts and Age of the Universe", fontsize=36)
plt.xlabel("Age of Universe(in millions of years)", fontsize=24)
plt.ylabel("Redshift", fontsize=24)
plt.ylim([0,12])
plt.xlim([0,2000])
plt.show()
continue
elif plot_choice == 'q':
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 time(self):
return cosmo.age(self.z)
def load_model(objname, field, agelims=[], **extras):
# REDSHIFT
# open file, load data
photname, zname, filtername, filts = get_names(field)
with open(photname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(photname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float) for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == objname # creates array of True/False: True when dat[id] = objname
# zred = zout['z_spec'][idx][0] # use z_spec
# if zred == -99: # if z_spec doesn't exist
# zred = zout['z_peak'][idx][0] # use z_phot
zred = 3.5
print(zred, 'zred')
# CALCULATE AGE OF THE UNIVERSE (TUNIV) AT REDSHIFT ZRED
tuniv = WMAP9.age(zred).value
print(tuniv, 'tuniv')
n = [p['name'] for p in model_params]
# model_params[n.index('tage')]['prior_args']['maxi'] = tuniv
# NONPARAMETRIC SFH # NEW
'''
agelims = [0.0, 8.0, 8.7, 9.0, (9.0 + (np.log10(tuniv*1e9) - 9.0)/3), (9.0 + 2 * (np.log10(tuniv*1e9) - 9.0)/3),
np.log10(tuniv*1e9)]
'''
agelims = [0.0, 7.7, 8.0, 9.0, (9.0 + (np.log10(tuniv*1e9) - 9.0)/3), (9.0 + 2 * (np.log10(tuniv*1e9) - 9.0)/3),
np.log10(tuniv*1e9)]
ncomp = len(agelims) - 1
agebins = np.array([agelims[:-1], agelims[1:]]) # why agelims[1:] instead of agelims[0:]?
# print(agebins, 'grab these agebins')
# INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY
zind = n.index('zred')
model_params[zind]['init'] = zred
# model_params[n.index('tage')]['init'] = tuniv # set tage to the age of the Universe
# set dust and metallicity to be the same dust and metallicity you find in the actual fits:
get_out = '/home/jonathan/.conda/envs/snowflakes/lib/python2.7/site-packages/prospector/git/out/out_efico/'
for plop in os.listdir(get_out):
if plop.startswith(str(objname)) and plop.endswith(".h5"):
# print(str(objname))
get = gmd.printer(get_out + plop) # [mass, dust, metal, gasmet]
model_params[n.index('logmass')]['init'] = 9.9 # 9. # get[0]
model_params[n.index('dust2')]['init'] = 0.3 # get[1]
model_params[n.index('logzsol')]['init'] = -0.06 # -1.7 # get[2]
model_params[n.index('gas_logz')]['init'] = get[3]
# SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# FRACTIONAL MASS INITIALIZATION # NEW
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('sfr_fraction')]['N'] = ncomp-1
model_params[n.index('sfr_fraction')]['prior_args'] = {
'maxi': np.full(ncomp-1, 1.0),
'mini': np.full(ncomp-1, 0.0),
# NOTE: ncomp instead of ncomp-1 makes the prior take into
# account the implicit Nth variable too
}
eelg_frac1 = 0.3 # mfrac in most recent bin
eelg_frac2 = 0.05 # mfrac in second most recent bin
sf_inits = np.zeros(ncomp-1)+(1.-(eelg_frac1 + eelg_frac2))/(ncomp-2.)
sf_inits[0] = eelg_frac1
sf_inits[1] = eelg_frac2
# print(sf_inits, 'look at me!!!')
model_params[n.index('sfr_fraction')]['init'] = sf_inits # np.zeros(ncomp-1)+1./ncomp
model_params[n.index('sfr_fraction')]['init_disp'] = 0.02
# CREATE MODEL
model = BurstyModel(model_params)
print('model')
# print(model_params)
# print(model_params[n.index('sfr_fraction')])
return model
import numpy as np
import matplotlib.pyplot as plt
from astropy.cosmology import WMAP9 as cosmo
import astropy.units as u
from astropy.cosmology import Planck13, z_at_value
import pandas as pd
dataframe = pd.read_csv('glenna.csv')
r_z = np.array(dataframe['redshift'])
for n in range(len(r_z)):
##find age at z= whatever...
age = cosmo.age(r_z[n])
# age13 = cosmo.age(13)
#add 1 billion years
newage = age + (1 * u.Gyr)
## convert newage (the age plus a billion years) back to redshift:
newredshift = z_at_value(Planck13.age, newage)
print(age)
print(newredshift)
redshiftdata = np.column_stack((r_z, age, newredshift))
np.savetxt('/Users/Olivia/Desktop/redshiftdata.csv',
redshiftdata,
fmt='%.11',
delimiter=',',
header='r_z, age, redshift')
def load_model(objname, field, agelims=[], **extras):
# REDSHIFT
# open file, load data
photname, zname, fname, filtername, filts = get_names(field)
with open(photname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(photname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float)
for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == objname # creates T/F array: True when dat[id] = objname
zred = zout['z_spec'][idx][0] # use z_spec
if zred == -99: # if z_spec doesn't exist
zred = zout['z_peak'][idx][0] # use z_phot
print(zred, 'zred')
# NEW FOUT (MASS-METALLICITY)
with open(fname, 'r') as ff:
hdr_f = ff.readline().split()
dtype_f = np.dtype([(hdr_f[1], 'S20')] + [(n, np.float)
for n in hdr_f[2:]])
fout = np.loadtxt(fname, comments='#', delimiter=' ', dtype=dtype_f)
idx_f = fout[
'id'] == objname # creates T/F array: True when fout[id] = objname
lmass = fout['lmass'][idx_f][0]
print(lmass, 'lmass')
# met = [-1.5, 0.5, -0.3]
if lmass <= 9.7:
met = [-1.0, 0.0, -0.6]
elif 9.7 < lmass <= 10.0:
met = [-0.9, 0.1, -0.4]
elif 10.0 < lmass <= 10.3:
met = [-0.8, 0.15, -0.2]
elif lmass > 10.3:
met = [-0.2, 0.3, 0.0]
# CALCULATE AGE OF THE UNIVERSE (TUNIV) AT REDSHIFT ZRED
tuniv = WMAP9.age(zred).value
print(tuniv, 'tuniv')
n = [p['name'] for p in model_params]
# model_params[n.index('tage')]['prior_args']['maxi'] = tuniv
# NEW FOUT MASS-METALLICITY
model_params[n.index('logzsol')]['prior_args'] = {
'mini': met[0],
'maxi': met[1]
} # {'mini': -0.8, 'maxi': 0.15}
model_params[n.index('logzsol')]['init'] = met[2] # -0.25
# 4942_cosmos_noelgmet was run with met[2] = -0.4
# 4942_cosmos_noelg25 was run with met[2] = -0.25
# NONPARAMETRIC SFH
agelims = [
0.0, 8.48, 8.78, 8.95, (8.95 + (np.log10(tuniv * 1e9) - 8.95) / 3),
(8.95 + 2 * (np.log10(tuniv * 1e9) - 8.95) / 3),
np.log10(tuniv * 1e9)
]
# 0, 300, 600, 900, three evenly spaced to tuniv
ncomp = len(agelims) - 1
agebins = np.array([agelims[:-1], agelims[1:]
]) # why agelims[1:] instead of agelims[0:]?
# INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY
zind = n.index('zred')
model_params[zind]['init'] = zred
# SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
# FRACTIONAL MASS INITIALIZATION # NEW
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('sfr_fraction')]['N'] = ncomp - 1
model_params[n.index('sfr_fraction')]['prior_args'] = {
'maxi': np.full(ncomp - 1, 1.0),
'mini': np.full(ncomp - 1, 0.0),
# NOTE: ncomp instead of ncomp-1 makes the prior take into
# account the implicit Nth variable too
}
model_params[n.index('sfr_fraction')]['init'] = np.zeros(ncomp -
1) + 1. / ncomp
model_params[n.index('sfr_fraction')]['init_disp'] = 0.02
# CREATE MODEL
model = BurstyModel(model_params)
return model
# lib_param[key] = guess[key]
# print lib_param
#
# spec = lib.get_model_spectrum(0)[0]
# # plt.clf()
# cut = np.bitwise_and(spec['wl'] > 3000, spec['wl'] < 9000)
# plt.plot(spec['wl'][cut], spec['flux'][cut])
# # raw_input('next...')
f = FilterSet('/Users/william/doutorado/photo_filters/Alhambra_24.hdf5')
f.load('Alhambra_24', 1)
n_filters = len(f.filter_wls)
aux_base = StarlightBase(base_file, base_path)
base_maxage = np.max(aux_base.ageBase)
min_z_age = cosmo.age(0.001).to('yr').value
if base_maxage > min_z_age:
base_maxage = min_z_age
allow_overwrite_bpz = True
allow_overwrite_lib = True
bpz_lib_file = '/Users/william/tmp_out/pzT_parametric_noelines.hdf5'
parametric_lib = '/Users/william/tmp_out/zT_weights_noelines.hdf5'
if allow_overwrite_bpz:
try:
os.unlink(bpz_lib_file)
except OSError:
pass
if allow_overwrite_lib:
try:
def load_model(objname='', datname='', zname='', agelims=[], **extras):
###### REDSHIFT ######
### open file, load data
with open(datname, 'r') as f:
hdr = f.readline().split()
dtype = np.dtype([(hdr[1], 'S20')] + [(n, np.float) for n in hdr[2:]])
dat = np.loadtxt(datname, comments='#', delimiter=' ', dtype=dtype)
with open(zname, 'r') as fz:
hdr_z = fz.readline().split()
dtype_z = np.dtype([(hdr_z[1], 'S20')] + [(n, np.float)
for n in hdr_z[2:]])
zout = np.loadtxt(zname, comments='#', delimiter=' ', dtype=dtype_z)
idx = dat['id'] == objname
zred = zout['z_spec'][idx][0] # use z_spec
if zred == -99: # if z_spec doesn't exist
zred = zout['z_peak'][idx][0] # use z_phot
print(zred, 'zred')
#### CALCULATE TUNIV #####
tuniv = WMAP9.age(zred).value
print(tuniv, 'tuniv')
n = [p['name'] for p in model_params]
model_params[n.index('tage')]['prior_args']['maxi'] = tuniv
#### NONPARAMETRIC SFH ###### # NEW
# agelims[-1] = np.log10(tuniv*1e9)
ncomp = len(agelims) - 1
agelims = [
0.0, 7.0, 8.0, (8.0 + (np.log10(tuniv * 1e9) - 8.0) / 4),
(8.0 + 2 * (np.log10(tuniv * 1e9) - 8.0) / 4),
(8.0 + 3 * (np.log10(tuniv * 1e9) - 8.0) / 4),
np.log10(tuniv * 1e9)
]
agebins = np.array([agelims[:-1],
agelims[1:]]) # agelims[1:] or agelims[0:]?
# calculate the somethings: [0, a, b, b + (f-b)/4, b + 2*(f-b)/4, b + 3*(f-b)/4, b + 4*(f-b)/4 = f]
#### INSERT REDSHIFT INTO MODEL PARAMETER DICTIONARY ####
zind = n.index('zred')
model_params[zind]['init'] = zred
#### SET UP AGEBINS
model_params[n.index('agebins')]['N'] = ncomp
model_params[n.index('agebins')]['init'] = agebins.T
#### FRACTIONAL MASS INITIALIZATION # NEW # BUCKET2: don't want all 5 bins equal for quickstart (also try 4 bins)
# N-1 bins, last is set by x = 1 - np.sum(sfr_fraction)
model_params[n.index('sfr_fraction')]['N'] = ncomp - 1
model_params[n.index('sfr_fraction')]['prior_args'] = {
'maxi': np.full(ncomp - 1, 1.0),
'mini': np.full(ncomp - 1, 0.0),
# NOTE: ncomp instead of ncomp-1 makes the prior take into
# account the implicit Nth variable too
}
# use 5 bins (6th is implicit, so sum over the 5 bins = 1 - 6th bin)
model_params[n.index('sfr_fraction')]['init'] = np.array(
[0.7, 0.2, 0.05, 0.03, 0.01]) # np.zeros(ncomp-1)+1./ncomp
model_params[n.index('sfr_fraction')]['init_disp'] = 0.02
#### CREATE MODEL
model = BurstyModel(model_params)
return model
def fit_one_SED(gal_ID,base_dust_curve,filters,bpass_folder,photometry,EBVres=0.0025,Rv=2.74,maxage=5e8):
# Determine which ages to use based on age of the universe at z=redshift
redshift = photometry.data[str(gal_ID)]['redshift']
ages = 10.**(np.array([6. + 0.1*_i for _i in range(51)]))
cage = cosmo.age(redshift).to_value(units.year)
tage = np.where(cage-maxage > ages)[0]
nages = len(tage)
# BPASS SPECTRA FILE NAMES/LOCATIONS
bpass_spectra = glob.glob(f'{bpass_folder}/spectra*.gz')
# CREATE EBV AND AGE ARRAYS
EBVs = np.arange(0.,0.2,EBVres)
ages = np.arange(0,nages,1).astype(int)+1
# SETTING BOUNDS ON RETURNED MASSES FOR OUTPUT MODEL,
# PREVENTS NEGATIVE MASS MODELS BEING INCLUDED
bnd = [0.,1.e13]
bounds = []
for i in range(33):
bounds.append(tuple(bnd))
bounds = tuple(bounds)
# DETERMINE WHICH FILTERS ARE IN USE AND SET UP PHOTOMETRY FOR FITTING
fit_filters = photometry.get_fit_filters(gal_ID,filters)
wav = filter_wavs(filters,fit_filters)
flux,error = fit_photometry(photometry.data[str(gal_ID)]['phot'],fit_filters)
grid,tft = bpt.make_grid(bpass_spectra[0],filters,fit_filters,redshift)
alosses = np.zeros(len(bpass_spectra))
aopts = np.zeros((len(bpass_spectra),grid.shape[0]))
aEBVs = np.zeros(len(bpass_spectra))
for j,bps in enumerate(bpass_spectra):
grid,tft = bpt.make_grid(bps,filters,fit_filters,redshift)
tFit = list(set(tft))
theta = np.zeros(grid.shape[0])*1.e8
losses = np.copy(EBVs)*0.
topts = np.empty((len(EBVs),len(theta)))
for i,EBV in enumerate(EBVs):
dust_grid = dm.add_grid(wav,grid,base_dust_curve.wav,base_dust_curve.base_curve,EBV,redshift,Rv=Rv)
Res = opt.minimize(fun = lrm.cost,
x0 = theta,
args = (dust_grid[:,tFit],flux[tFit],error[tFit]),
method = 'TNC',
jac = lrm.grad,
bounds = bounds
)
theta_opt = Res.x
SED_opt = lrm.h(theta_opt,dust_grid)
topts[i,:] = theta_opt
losses[i] = lrm.SED_loss(SED_opt,flux[tFit],error[tFit])
tbest = np.where(losses == np.min(losses))[0]
tEBV = EBVs[tbest]
tOPT = topts[tbest][0,:]
alosses[j] = losses[tbest]
aopts[j] = tOPT
aEBVs[j] = tEBV
abest = np.where(alosses == np.min(alosses))[0][0]
EBV_out = aEBVs[abest]
OPT_out = aopts[abest]
swav,out_spec = bpt.out_spec(OPT_out,
bpass_spectra[abest],
redshift,
base_dust_curve.wav,
base_dust_curve.base_curve,
EBV_out
)
bpf,tf2 = bpt.BPASS_phot(swav,out_spec,fit_filters,filters,redshift)
return {'wav':swav,
'spec':out_spec,
'BPASS_mod':bpass_spectra[abest],
'theta_opt':OPT_out,
'EBV':EBV_out,
'attenuation_curve':np.exp((-1.)*(np.interp(swav,base_dust_curve.wav,base_dust_curve.base_curve)*EBV_out*Rv/1.086)),
'z':redshift,
'obs_phot':{
'wav':wav/(1.+redshift),
'flx':flux,
'err':error,
'fit':tFit
}
}
