def runSpec(catalog_entry, mask, objno):
print catalog_entry['OBJNO'], catalog_entry['MASK'], catalog_entry[
'ZBEST'], catalog_entry['RA'], catalog_entry['DEC']
t0 = time.time()
path_to_spectrum = glob.glob(
join(os.environ['DEEP2_DIR'], 'spectra', mask, '*',
'*' + objno + '*_fc_tc.dat'))
print path_to_spectrum
if len(path_to_spectrum) >= 1:
spec = gs.GalaxySpectrumFIREFLY("-", milky_way_reddening=True)
spec.openObservedDEEP2pectrum(catalog_entry)
ok = (spec.bad_flags == 1) & (spec.flux > 0)
return np.median(spec.flux[ok] / spec.error[ok])
else:
return -1.
def generic(self, wave, flux, redshift=None, fractional_error=0.1):
''' Read in **observed** frame wavelength [Ang] and flux [1e-17 erg/s/A/cm^2]
to class compatible with gs.GalaxySpectrumFIREFLY object
'''
self.ra = 0.
self.dec = 0.
if redshift is None:
self.redshift = 0.01
else:
self.redshift = redshift
self.wavelength = wave
self.restframe_wavelength = self.wavelength / (1. + self.redshift)
#self.wavelength = self.restframe_wavelength * (1. + self.redshift)
self.flux = flux
self.error = self.flux * fractional_error
self.bad_flags = np.ones(len(self.restframe_wavelength))
self.vdisp = 70.
self.trust_flag = 1
self.objid = 0
lines_mask = self.emissionlineMask(self.restframe_wavelength)
self.wavelength = self.wavelength[~lines_mask]
self.restframe_wavelength = self.restframe_wavelength[~lines_mask]
influx = self.flux[~lines_mask]
inerror = self.error[~lines_mask]
inbad_flags = self.bad_flags[~lines_mask]
self.flux, self.error, self.bad_flags = gs.remove_bad_data(
influx, inerror, inbad_flags)
self.r_instrument = np.zeros(len(self.wavelength))
for wi, w in enumerate(self.wavelength):
if w < 6000:
self.r_instrument[wi] = (2270.0 - 1560.0) / (
6000.0 - 3700.0) * w + 420.0
else:
self.r_instrument[wi] = (2650.0 - 1850.0) / (
9000.0 - 6000.0) * w + 250.0
self.ebv_mw = 0.0
return None
def DESIlike(self, desi_spec, redshift=None):
''' Read in desispec.io spectra object and return class compatible with
gs.GalaxySpectrumFIREFLY object
'''
if redshift is None:
self.redshift = 0.
else:
self.redshift = redshift
self.wavelength = np.concatenate(
[desi_spec.wave[b] for b in ['b', 'r', 'z']])
self.restframe_wavelength = self.wavelength / (1. + self.redshift)
self.flux = np.concatenate(
[desi_spec.flux[b][0] for b in ['b', 'r', 'z']])
self.error = np.concatenate(
[desi_spec.ivar[b][0]**(-0.5) for b in ['b', 'r', 'z']])
self.bad_flags = np.ones(len(self.restframe_wavelength))
self.vdisp = 70.
self.trust_flag = 1
self.objid = 0
lines_mask = self.emissionlineMask(self.restframe_wavelength)
self.wavelength = self.wavelength[~lines_mask]
self.restframe_wavelength = self.restframe_wavelength[~lines_mask]
influx = self.flux[~lines_mask]
inerror = self.error[~lines_mask]
inbad_flags = self.bad_flags[~lines_mask]
self.flux, self.error, self.bad_flags = gs.remove_bad_data(
influx, inerror, inbad_flags)
# some generic instrument resolution. This is only used when downgrade_models=True,
# which at this time I don't know what that's useful for...
self.r_instrument = np.zeros(len(self.wavelength))
#for wi, w in enumerate(self.wavelength):
# if w < 6000:
# self.r_instrument[wi] = (2270.0-1560.0)/(6000.0-3700.0)*w + 420.0
# else:
# self.r_instrument[wi] = (2650.0-1850.0)/(9000.0-6000.0)*w + 250.0
self.ebv_mw = 0.0
return None
def convert_spec_2_fits(catalog_entry, output_file, mask, objno):
path_to_spectrum = glob.glob(
join(deep2_dir, 'spectra', mask, '*', '*' + objno + '*_fc_tc.dat'))
print path_to_spectrum
if len(path_to_spectrum) >= 1:
spec = gs.GalaxySpectrumFIREFLY("-", milky_way_reddening=True)
spec.openObservedDEEP2pectrum(catalog_entry)
prihdr = fits.Header()
prihdr['FILE'] = os.path.basename(output_file)
prihdr['MASK'] = catalog_entry['MASK']
prihdr['OBJNO'] = catalog_entry['OBJNO']
prihdr['RA'] = catalog_entry['RA']
prihdr['DEC'] = catalog_entry['DEC']
prihdr['redshift'] = catalog_entry['ZBEST']
prihdu = fits.PrimaryHDU(header=prihdr)
waveCol = fits.Column(name="wavelength",
format="D",
unit="Angstrom",
array=spec.wavelength)
dataCol = fits.Column(name="flux",
format="D",
unit="1e-17erg/s/cm2/Angstrom",
array=spec.flux)
errorCol = fits.Column(name="flux_error",
format="D",
unit="1e-17erg/s/cm2/Angstrom",
array=spec.error)
cols = fits.ColDefs([waveCol, dataCol, errorCol])
tbhdu = fits.BinTableHDU.from_columns(cols)
complete_hdus = fits.HDUList([prihdu, tbhdu])
if os.path.isfile(output_file):
os.remove(output_file)
complete_hdus.writeto(output_file)
def runSpec_DR16(specLiteFile):
t0 = time.time()
baseN = os.path.basename(specLiteFile).split('-')
plate = baseN[1]
mjd = baseN[2]
fibre, dum = os.path.basename(baseN[3]).split('.')
# This needs to be changed to your own directory
outputFolder = join(os.environ['FF_DIR'], 'output')
output_file = join(outputFolder, 'spFly-' +
os.path.basename(specLiteFile)[5:-5]) + ".fits"
if os.path.isfile(output_file):
print('already done', time.time() - t0)
return 0.
if os.path.isdir(outputFolder) == False:
os.mkdir(outputFolder)
# Firefly modules and parameters
import GalaxySpectrumFIREFLY as gs
import StellarPopulationModel as spm
import astropy.cosmology as co
cosmo = co.Planck15
models_key = 'm11'
testing = False #true: smaller wavelength range, older age
spec = gs.GalaxySpectrumFIREFLY(specLiteFile, milky_way_reddening=True)
spec.openObservedSDSSSpectrum(survey='sdssMain', testing=True)
ageMin = 0.
ageMax = 20.
ZMin = 0.001
ZMax = 10.
print(spec.hdulist[2].data['CLASS'][0], spec.hdulist[2].data['Z'][0],
spec.hdulist[2].data['Z_ERR'][0],
spec.hdulist[2].data['ZWARNING'][0])
if spec.hdulist[2].data['CLASS'][0] == "GALAXY" and spec.hdulist[2].data[
'Z'][0] > spec.hdulist[2].data['Z_ERR'][0] and spec.hdulist[
2].data['Z_ERR'][0] > 0 and (
(spec.hdulist[2].data['ZWARNING_NOQSO'][0] == 0) |
(spec.hdulist[2].data['ZWARNING_NOQSO'][0] == 4)):
print('Output file:')
print(output_file)
print("--------------------------")
prihdr = spm.pyfits.Header()
prihdr['FILE'] = os.path.basename(output_file)
prihdr['PLATE'] = plate
prihdr['MJD'] = mjd
prihdr['FIBERID'] = fibre
prihdr['MODELS'] = models_key
prihdr['FITTER'] = "FIREFLY"
prihdr['AGEMIN'] = str(ageMin)
prihdr['AGEMAX'] = str(ageMax)
prihdr['ZMIN'] = str(ZMin)
prihdr['ZMAX'] = str(ZMax)
prihdr['redshift'] = spec.hdulist[2].data['Z'][0]
prihdr['HIERARCH age_universe'] = np.round(
cosmo.age(spec.hdulist[2].data['Z'][0]).value, 3)
prihdu = spm.pyfits.PrimaryHDU(header=prihdr)
tables = [prihdu]
did_not_converged = 0.
try:
model_1 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['MILES'],
imfs=['cha'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=False,
write_results=True)
model_1.fit_models_to_data()
tables.append(model_1.tbhdu)
print("m1", time.time() - t0)
except (ValueError):
tables.append(model_1.create_dummy_hdu())
did_not_converged += 1
print('did not converge')
try:
model_1 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['ELODIE'],
imfs=['cha'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=False,
write_results=True)
model_1.fit_models_to_data()
tables.append(model_1.tbhdu)
print("m2", time.time() - t0)
except (ValueError):
tables.append(model_1.create_dummy_hdu())
did_not_converged += 1
print('did not converge')
#try :
#model_1 = spm.StellarPopulationModel(spec, output_file, cosmo, models = models_key, model_libs = ['STELIB'], imfs = ['cha'], age_limits = [ageMin,ageMax], downgrade_models = False, data_wave_medium = 'vacuum', Z_limits = [ZMin,ZMax], use_downgraded_models = False, write_results = False)
#model_1.fit_models_to_data()
#tables.append( model_1.tbhdu )
#print( "m3", time.time()-t0)
#except (ValueError):
#tables.append( model_1.create_dummy_hdu() )
#did_not_converged +=1
#print('did not converge')
if did_not_converged < 2:
complete_hdus = spm.pyfits.HDUList(tables)
if os.path.isfile(output_file):
os.remove(output_file)
complete_hdus.writeto(output_file)
print("time used =", time.time() - t0, "seconds")
return 1. #spec, model_1
def create_figure_add_chi2(filename, model_hdus):
sp = gs.GalaxySpectrumFIREFLY(filename, milky_way_reddening=False)
sp.openGAMAsimulatedSpectrum()
spec = interp1d(sp.restframe_wavelength, sp.flux)
err = interp1d(sp.restframe_wavelength, sp.error)
wl_data_min = n.min(sp.restframe_wavelength)
wl_data_max = n.max(sp.restframe_wavelength)
# now creates the figure per model
fig = p.figure(0, figsize=(7, 10), frameon=False) #, tight_layout=True)
rect = 0.2, 0.15, 0.85, 0.95
#ax = fig.add_axes(rect, frameon=False)
# panel with the spectrum
fig.add_subplot(3, 1, 1)
p.plot(sp.restframe_wavelength[::2],
sp.flux[::2],
'k',
rasterized=True,
alpha=0.5,
label='data')
p.plot(model_hdus[0].data['wavelength'],
model_hdus[0].data['model_flux'],
label='model')
p.yscale('log')
mean_data = n.median(sp.flux)
p.ylim((mean_data / 8., mean_data * 8.))
p.xlabel('Wavelength [Angstrom]')
p.ylabel(r'Flux [$f_\lambda$ $10^{-17}$ erg/cm2/s/A]')
p.title(os.path.basename(filename))
# second panel distribution of residuals
fig.add_subplot(3, 1, 2)
for hdu in model_hdus:
#print hdu
ok_model = (hdu.data['wavelength'] >
wl_data_min) & (hdu.data['wavelength'] < wl_data_max)
wl_model = hdu.data['wavelength'][ok_model]
#print spec(wl_model),hdu.data['model_flux'][ok_model],err(wl_model)
chi2s = (spec(wl_model) -
hdu.data['model_flux'][ok_model]) / err(wl_model)
p.hist(chi2s,
bins=n.arange(-2, 2, 0.1),
normed=True,
histtype='step',
label=hdu.header['IMF'] + hdu.header['library'] + ", EBV=" +
str(n.round(hdu.header['EBV'], 3)) + r", $\chi^2=$" +
str(n.round(n.sum(chi2s**2.) / (len(chi2s) - 2.), 4)))
p.ylim((-0.02, 1.02))
#p.yscale('log')
p.xlabel('(data-model)/error')
p.ylabel('Normed distribution')
hdu.header['chi2'] = n.sum(chi2s**2.)
hdu.header['ndof'] = len(chi2s) - 2.
p.plot(n.arange(-2, 2, 0.005),
gaussD.pdf(n.arange(-2, 2, 0.005)),
'k--',
label=r'N(0,1)',
lw=0.5)
p.grid()
p.legend(frameon=False, loc=0, fontsize=8)
fig.add_subplot(3, 1, 3)
tpl = n.transpose(
n.array([[
hdu.header['age_lightW'], hdu.header['stellar_mass'],
hdu.header['age_lightW_up'] - hdu.header['age_lightW'],
hdu.header['age_lightW'] - hdu.header['age_lightW_low'],
hdu.header['stellar_mass_up'] - hdu.header['stellar_mass'],
hdu.header['stellar_mass'] - hdu.header['stellar_mass_low']
] for hdu in model_hdus]))
p.errorbar(tpl[0],
tpl[1],
xerr=[tpl[2], tpl[3]],
yerr=[tpl[4], tpl[5]],
barsabove=True,
fmt='o')
#p.axvline(prihdr['age_universe'], color='r', ls='dashed')
idsUP = n.argsort(tpl[1])
iterList = model_hdus[idsUP]
for jj, hdu in enumerate(iterList):
p.annotate(hdu.header['IMF'] + hdu.header['library'] +
r", $\log(Z/Z_\odot)=$" +
str(n.round(hdu.header['metallicity_lightW'], 4)),
xy=(hdu.header['age_lightW'], hdu.header['stellar_mass']),
xycoords='data',
xytext=(0.85, (jj + 0.5) / len(iterList)),
textcoords='axes fraction',
arrowprops=dict(facecolor='black',
shrink=0.05,
width=0.2,
headwidth=3),
horizontalalignment='right',
verticalalignment='top',
fontsize=9)
p.ylabel(r'$\log_{10}(M/[M_\odot])$')
p.xlabel(r'$\log_{10}(age/[yr])$')
#p.ylim((9,12.5))
p.grid()
p.savefig(path_2_im(filename))
p.clf()
return model_hdus
def runSpec(specLiteFile):
baseN = os.path.basename(specLiteFile).split('-')
plate = baseN[1]
mjd = baseN[2]
fibre, dum = os.path.basename(baseN[3]).split('.')
t0 = time.time()
spec = gs.GalaxySpectrumFIREFLY(specLiteFile, milky_way_reddening=True)
spec.openObservedSDSSSpectrum(survey='sdssMain')
ageMin = 0.
ageMax = np.log10(cosmo.age(spec.redshift).value * 1e9)
ZMin = 0.001
ZMax = 4.
if spec.hdulist[2].data['CLASS'][0] == "GALAXY" and spec.hdulist[2].data[
'Z'][0] > spec.hdulist[2].data['Z_ERR'][0] and spec.hdulist[2].data[
'Z_ERR'][0] > 0 and spec.hdulist[2].data['ZWARNING'][0] == 0:
# This needs to be changed to your own directory
outputFolder = join(os.environ['FF_DIR'], 'output', 'plate')
output_file = join(outputFolder, 'spFly-' +
os.path.basename(specLiteFile)[5:-5]) + ".fits"
print("Start time=", t0)
print(output_file)
print("--------------------------")
if os.path.isdir(outputFolder) == False:
os.mkdir(outputFolder)
prihdr = spm.pyfits.Header()
prihdr['FILE'] = os.path.basename(output_file)
prihdr['PLATE'] = plate
prihdr['MJD'] = mjd
prihdr['FIBERID'] = fibre
prihdr['MODELS'] = models_key
prihdr['FITTER'] = "FIREFLY"
prihdr['AGEMIN'] = str(ageMin)
prihdr['AGEMAX'] = str(ageMax)
prihdr['ZMIN'] = str(ZMin)
prihdr['ZMAX'] = str(ZMax)
prihdr['redshift'] = spec.hdulist[2].data['Z'][0]
prihdr['HIERARCH age_universe'] = np.round(
cosmo.age(spec.hdulist[2].data['Z'][0]).value, 3)
prihdu = spm.pyfits.PrimaryHDU(header=prihdr)
tables = [prihdu]
did_not_converged = 0.
try:
model_1 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['MILES'],
imfs=['cha'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_1.fit_models_to_data()
tables.append(model_1.tbhdu)
# print "m1", time.time()-t0
except (ValueError):
tables.append(model_1.create_dummy_hdu())
did_not_converged += 1
try:
model_2 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['MILES'],
imfs=['ss'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_2.fit_models_to_data()
tables.append(model_2.tbhdu)
# print "m2", time.time()-t0
except (ValueError):
tables.append(model_2.create_dummy_hdu())
did_not_converged += 1
try:
model_3 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['MILES'],
imfs=['kr'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_3.fit_models_to_data()
tables.append(model_3.tbhdu)
# print "m3", time.time()-t0
except (ValueError):
tables.append(model_3.create_dummy_hdu())
did_not_converged += 1
try:
model_4 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['ELODIE'],
imfs=['cha'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_4.fit_models_to_data()
# print "m4", time.time()-t0
tables.append(model_4.tbhdu)
did_not_converged += 1
except (ValueError):
tables.append(model_4.create_dummy_hdu())
try:
model_5 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['ELODIE'],
imfs=['ss'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_5.fit_models_to_data()
tables.append(model_5.tbhdu)
# print "m5", time.time()-t0
except (ValueError):
tables.append(model_5.create_dummy_hdu())
did_not_converged += 1
try:
model_6 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['ELODIE'],
imfs=['kr'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_6.fit_models_to_data()
tables.append(model_6.tbhdu)
# print "m6", time.time()-t0
except (ValueError):
tables.append(model_6.create_dummy_hdu())
did_not_converged += 1
try:
model_7 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['STELIB'],
imfs=['cha'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_7.fit_models_to_data()
# print "m4", time.time()-t0
tables.append(model_7.tbhdu)
except (ValueError):
tables.append(model_7.create_dummy_hdu())
did_not_converged += 1
try:
model_8 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['STELIB'],
imfs=['ss'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_8.fit_models_to_data()
tables.append(model_8.tbhdu)
# print "m5", time.time()-t0
except (ValueError):
tables.append(model_8.create_dummy_hdu())
did_not_converged += 1
try:
model_9 = spm.StellarPopulationModel(spec,
output_file,
cosmo,
models=models_key,
model_libs=['STELIB'],
imfs=['kr'],
age_limits=[ageMin, ageMax],
downgrade_models=False,
data_wave_medium='vacuum',
Z_limits=[ZMin, ZMax],
use_downgraded_models=True,
write_results=False)
model_9.fit_models_to_data()
tables.append(model_9.tbhdu)
# print "m6", time.time()-t0
except (ValueError):
tables.append(model_9.create_dummy_hdu())
did_not_converged += 1
if did_not_converged < 9:
complete_hdus = spm.pyfits.HDUList(tables)
if os.path.isfile(output_file):
os.remove(output_file)
complete_hdus.writeto(output_file)
print("time used =", time.time() - t0, "seconds")
return spec
import GalaxySpectrumFIREFLY as gs
## Index band blue continuum red continuum Units name
#01 4142.125 4177.125 4080.125 4117.625 4244.125 4284.125 1 CN_1
iii, b0, b1, bc0, bc1, rc0, rc1, unit, name = n.loadtxt('../../data/lick_indices/indices_table.txt', unpack=True, dtype='str')
li0 = b0.astype('float')
li1 = b1.astype('float')
dir_elodie = os.path.join( os.environ['STELLARPOPMODELS_DIR'], 'data', 'SSP_M11_ELODIE')
dir_miles = os.path.join( os.environ['STELLARPOPMODELS_DIR'], 'data', 'SSP_M11_MILES')
dir_stelib = os.path.join( os.environ['STELLARPOPMODELS_DIR'], 'data', 'SSP_M11_STELIB')
IMF = {'cha': 'Ch', 'kr': 'Kr', 'ss': 'Sa'}
spec_sdss = gs.GalaxySpectrumFIREFLY( '/home/comparat/SDSS/26/spectra/0266/spec-0266-51602-0004.fits' )
spec_sdss.openObservedSDSSSpectrum('sdssMain')
spec_boss = gs.GalaxySpectrumFIREFLY( '/home/comparat/SDSS/v5_10_0/spectra/3586/spec-3586-55181-0024.fits' )
spec_boss.openObservedSDSSSpectrum('sdss4')
path_2_deep2 = '/home/comparat/data2/firefly/v1_1_0/DEEP2/spectra/2/deep2-2249-22052688.fits'
spec_deep2 = gs.GalaxySpectrumFIREFLY( path_2_deep2 )
spec_deep2.openObservedDEEP2pectrum(path_2_deep2, survey='deep2_fits')
file_list_elodie = n.array([
# chabrier
#os.path.join(dir_elodie, 'ssp_M11_ELODIE.chaz001'),
os.path.join(dir_elodie, 'ssp_M11_ELODIE.chaz002'),
#os.path.join(dir_elodie, 'ssp_M11_ELODIE.chaz004'),
# kroupa
# for one galaxy spectrum
import GalaxySpectrumFIREFLY as gs
import StellarPopulationModel as spm
#spec_deep2_file = "D:\data\spectraStacks\content\data\O2_3728\O2_3728-DEEP2R24.2-z0.927_stack_N_100_R_8686_L_1.84133041845e+41.fits"
spec_vvds_file = "D:\data\spectraStacks\content\data\O2_3728\O2_3728-VVDSDEEPI24-z0.612_stack_N_400_R_217_L_2.1326925665e+41.fits"
"""
spec_deep2=gs.GalaxySpectrumFIREFLY(spec_deep2_file, milky_way_reddening=False)
spec_deep2.openObservedStack()
file =os.path.basename(spec_deep2_file)
outFile = join(os.environ['SPECTRASTACKS_DIR'], "fits", file[:7], file)
model_deep2 = spm.StellarPopulationModel(spec_deep2, outFile, cosmo, models = 'm11', model_libs = ['MILES'], imfs = ['ss'], age_limits = [6,10], downgrade_models = True, data_wave_medium = 'air', Z_limits = [-3.,1.],suffix="-SPM-MILES.fits", use_downgraded_models = False)
model_deep2.fit_models_to_data()
"""
spec_vvds = gs.GalaxySpectrumFIREFLY(spec_vvds_file, milky_way_reddening=False)
spec_vvds.openObservedStack()
file = os.path.basename(spec_vvds_file)
outFile = join(os.environ['SPECTRASTACKS_DIR'], "fits", file[:7], file)
model_vvds = spm.StellarPopulationModel(spec_vvds,
outFile,
cosmo,
models='m11',
model_libs=['MILES'],
imfs=['ss'],
age_limits=[6, 10],
downgrade_models=True,
data_wave_medium='air',
Z_limits=[-3., 1.],
suffix="-SPM-MILES.fits",
use_downgraded_models=False)