import sys
import os
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import FLARE
from FLARE.SED import models
from FLARE.SED import SFZH
import FLARE.filters
SPS = models.SPS('BPASSv2.2.1.binary/ModSalpeter_300')
sfzh, sfr = SFZH.constant(SPS, {
'log10_duration': 8.,
'log10Z': -2.,
'log10M*': 8.
})
print('star formation rate: {0}'.format(sfr))
# plt.imshow(sfzh.T)
# plt.show()
SED = SPS.get_Lnu(sfzh)
plt.plot(np.log10(SED.stellar.lam), np.log10(SED.stellar.lnu))
plt.plot(np.log10(SED.total.lam), np.log10(SED.total.lnu))
plt.xlim([2.7, 4.])
def create_SED(self, p):
derived = {}
if 'beta' in p:
rest_lam = np.arange(0., 5000., 1.)
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=rest_lam *
(1. + p['z']))
sed = FLARE.SED.models.beta(rest_lam,
p['beta'],
10**p['log10L1500'],
normalisation_wavelength=1500.)
else:
# --- get SFH for a given choice of
sfzh, sfr = SFZH.constant(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10_duration': p['log10_duration'],
'log10Z': p['log10Z'],
'log10M*': p['log10M*']
})
# --- generate SED for a given choice of parameters
SED = self.SPS.get_Lnu(sfzh, {
'fesc': p['fesc'],
'log10tau_V': p['log10tau_V']
},
dust_model='very_simple')
sed = SED.total
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
restF = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
derived['log10L1500'] = np.log10(
sed.return_Lnu(
FLARE.filters.add_filters(['FAKE.TH.FUV'],
new_lam=sed.lam))['FAKE.TH.FUV'])
derived['log10SFR'] = np.log10(sfr)
if self.verbose:
print(rf'log10(L1500/erg/s/Hz)={derived["log10L1500"]:2f}')
print(rf'log10(SFR/M/yr)={derived["log10SFR"]:2f}')
sed.get_fnu(
self.cosmo, p['z']
) # --- generate observed frame spectrum (necessary to get broad band photometry)
sed.get_Fnu(self.F) # --- generate broadband photometry
Fnu = {f: [sed.Fnu[f]] for f in self.Filters}
# --- print out fluxes
if self.verbose:
print('-' * 5, 'SED')
for f in self.Filters:
print('{0}: {1:.2f}/nJy'.format(f, Fnu[f][0]))
return Fnu, derived
import os
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import FLARE
from FLARE.SED import models
from FLARE.SED import SFZH
SPS = models.SPS('BPASSv2.2.1.binary/ModSalpeter_300')
N = 100
for i, log10age in enumerate(np.linspace(6.0, 8.0, N)):
print(i)
sfzh = SFZH.simple(SPS, {'log10age': log10age, 'log10Z': -2.})
SED = SPS.get_Lnu(sfzh)
plt.plot(np.log10(SED.stellar.lam),
np.log10(SED.stellar.lnu),
c=cm.viridis(i / N),
lw=1,
alpha=0.5)
mx = np.max(np.log10(SED.stellar.lnu)) # only last one
plt.xlim([2.7, 4.])
plt.ylim([mx - 4., mx + 3.0])
plt.show()
def create_SED(self, p):
derived = {}
if self.SimType == 'beta':
rest_lam = np.arange(0., 5000., 1.)
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=rest_lam *
(1. + p['z']))
sed = FLARE.SED.models.beta(rest_lam,
p['beta'],
1.0,
normalisation_wavelength=1500.)
elif self.SimType == 'const':
# --- get SFH for a given choice of
# -- this was the old behaviour where we later rescaled by SNR
if 'SNR' in p:
sfzh, sfr = SFZH.constant(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10_duration': p['log10_duration'],
'log10Z': p['log10Z'],
'log10M*': 8.0
})
if 'log10M*' in p:
sfzh, sfr = SFZH.constant(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10_duration': p['log10_duration'],
'log10Z': p['log10Z'],
'log10M*': p['log10M*']
})
# --- generate SED for a given choice of parameters
SED = self.SPS.get_Lnu(sfzh, {
'fesc': p['fesc'],
'log10tau_V': p['log10tau_V']
},
dust=('simple', {
'slope': -1.0
}))
sed = SED.total
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
elif self.SimType == 'instantaneous': # instantaneous burst as close to age of the Universe as possible
# --- get SFH for a given choice of
sfzh, sfr = SFZH.instantaneous(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10age': p['log10age'],
'log10Z': p['log10Z'],
'log10M*': p['log10M*']
})
# --- generate SED for a given choice of parameters
SED = self.SPS.get_Lnu(sfzh, {
'fesc': 0.0,
'log10tau_V': p['log10tau_V']
},
dust=('simple', {
'slope': -1.0
}))
sed = SED.total
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
else:
print('WARNING! Incorrect simulation type set')
sed.get_fnu(
self.cosmo, p['z']
) # --- generate observed frame spectrum (necessary to get broad band photometry)
sed.get_Fnu(self.F) # --- generate broadband photometry
Fnu = {f: sed.Fnu[f] for f in self.Filters}
return Fnu
def create_SED(self, p):
derived = {}
if 'beta' in p:
rest_lam = np.arange(0., 5000., 1.)
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=rest_lam *
(1. + p['z']))
sed = FLARE.SED.models.beta(rest_lam,
p['beta'],
10**p['log10L1500'],
normalisation_wavelength=1500.)
else:
# --- get SFH for a given choice of
sfzh, sfr = SFZH.constant(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10_duration': p['log10_duration'],
'log10Z': p['log10Z'],
'log10M*': 8.0
})
# --- generate SED for a given choice of parameters
SED = self.SPS.get_Lnu(sfzh, {
'fesc': p['fesc'],
'log10tau_V': p['log10tau_V']
},
dust_model='very_simple')
sed = SED.total
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
restF = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
sed.get_fnu(
self.cosmo, p['z']
) # --- generate observed frame spectrum (necessary to get broad band photometry)
sed.get_Fnu(self.F) # --- generate broadband photometry
Fnu = {
f: sed.Fnu[f] / sed.Fnu[self.ReferenceFilter]
for f in self.Filters
}
# --- add in noise
ref_noise = Fnu[self.ReferenceFilter] / p['SNR']
ref_depth = FLARE.photom.m_to_flux(self.depths[self.ReferenceFilter])
noise_scaling = {
f: FLARE.photom.m_to_flux(self.depths[f]) / ref_depth
for f in self.Filters
}
obs = SimpleNamespace()
obs.error = {f: ref_noise * noise_scaling[f] for f in self.Filters}
obs.flux = {
f: Fnu[f] + obs.error[f] * np.random.randn()
for f in self.Filters
}
obs.flux = {
f: obs.flux[f] / obs.flux[self.ReferenceFilter]
for f in self.Filters
}
# --- print out fluxes
if self.verbose:
print('-' * 5, 'SED')
for f in self.Filters:
print(
f'{f}: {Fnu[f]:.2f} | {obs.flux[f]:.2f} {obs.error[f]:.2f} {obs.flux[f]/obs.error[f]:.2f}'
)
return Fnu, obs
def create_SED(self, p):
derived = {}
if 'beta' in p:
rest_lam = np.arange(0., 5000., 1.)
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=rest_lam *
(1. + p['z']))
sed = FLARE.SED.models.beta(rest_lam,
p['beta'],
10**p['log10L1500'],
normalisation_wavelength=1500.)
elif 'log10M*' in p:
print('WARNING: not yet implemented')
elif 'log10L1500' in p:
if 'duration' in p.keys():
p['log10_duration'] = np.log10(p['duration']) + 6.
sfzh, sfr = SFZH.constant(
self.SPS.grid['log10age'], self.SPS.grid['log10Z'], {
'log10_duration': p['log10_duration'],
'log10Z': p['log10Z'],
'log10M*': 0.0
})
# --- generate SED for a given choice of parameters
SED = self.SPS.get_Lnu(sfzh, {
'fesc': p['fesc'],
'log10tau_V': p['log10tau_V']
},
dust_model='very_simple')
sed = SED.total
log10L1500 = np.log10(
sed.return_Lnu(
FLARE.filters.add_filters(['FAKE.TH.FUV'],
new_lam=sed.lam))['FAKE.TH.FUV'])
derived['log10M*'] = p['log10L1500'] - log10L1500
derived['log10SFR'] = np.log10(sfr) + derived['log10M*']
sed.lnu *= 10**derived['log10M*']
self.F = FLARE.filters.add_filters(self.Filters,
new_lam=sed.lam * (1. + p['z']))
sed.get_fnu(
self.cosmo, p['z']
) # --- generate observed frame spectrum (necessary to get broad band photometry)
sed.get_Fnu(self.F) # --- generate broadband photometry
Fnu = {f: [sed.Fnu[f]] for f in self.Filters}
# --- print out fluxes
if self.verbose:
print('-' * 5, 'Derived Quantities')
for k, v in derived.items():
print(f'{k} {v:.2f}')
print('-' * 5, 'SED')
for f in self.Filters:
print('{0}: {1:.2f}/nJy'.format(f, Fnu[f][0]))
return Fnu, derived
sed.get_fnu(
cosmo, z
) # --- generate observed frame SED (necessary to get broad band photometry)
Fnu = sed.return_Fnu(F) # --- generate observed frame broadband photometry
FLARE.obs.misc.measure_beta(z, Fnu, F, verbose=True)
# --- using SPS model
SPS = FLARE.SED.models.SPS('BPASSv2.2.1.binary/Chabrier_300')
F = FLARE.filters.add_filters(filters, new_lam=SPS.lam * (1. + z))
sfzh, sfr = SFZH.constant(SPS.grid['log10age'], SPS.grid['log10Z'], {
'log10_duration': 8.,
'log10Z': -8.0,
'log10M*': 8.0
})
for log10tau_V in np.arange(-2., 2.0, 0.5):
SED = SPS.get_Lnu(sfzh, {
'fesc': 0.0,
'log10tau_V': log10tau_V
},
dust_model='very_simple')
sed = SED.total
sed.get_fnu(
cosmo, z