def fit_cat_F2_test(IDS, filt_list, fit_instructions, run):
fit_cat = pipes.fit_catalogue(IDS,
fit_instructions,
load_zfk2_cdfs_data,
spectrum_exists=False,
cat_filt_list=filt_list,
run=run)
fit_cat.fit(verbose=False)
fit_cat_nokbr = pipes.fit_catalogue(IDS,
fit_instructions,
load_zfk2_cdfs_data_nokbr,
spectrum_exists=False,
cat_filt_list=filt_list[:-2],
run=run + '_no_kbr')
fit_cat_nokbr.fit(verbose=False)
print('Done!')
return fit_cat, fit_cat_nokbr
def fitcatmodel(ID, run):
filt_list = np.loadtxt("filters/goodss_filt_list.txt", dtype="str")
exp = {}
exp["age"] = (0.1, 15.)
exp["tau"] = (0.3, 12.)
exp["massformed"] = (2., 15.)
exp["metallicity"] = (0., 2.5)
dust = {}
dust["type"] = "Cardelli"
dust["Av"] = (0., 2.)
fit_instructions = {}
fit_instructions["redshift"] = (0., 10.)
fit_instructions["exponential"] = exp
fit_instructions["dust"] = dust
fit_cat = pipes.fit_catalogue(ID, fit_instructions, load_goodss,\
spectrum_exists=False, cat_filt_list=filt_list, run=run)
fit_cat.fit(verbose=False)
fig = fit_cat.plot_spectrum_posterior(save=False, show=True)
fig = fit_cat.plot_sfh_posterior(save=False, show=True)
fig = fit_cat.plot_corner(save=False, show=True)
fit_instructions["t_bc"] = 0.01
fit_instructions["dblplaw"] = dblplaw
fit_instructions["dust"] = dust
fit_instructions["nebular"] = nebular
fit_instructions["calib"] = calib
return fit_instructions
# Load list of objects to be fitted from catalogue.
IDs = np.array(["UDS-HST020821SELECT"])
redshifts = np.array([1.0909])
# Fit the catalogue of objects.
cat_filt_list = get_cat_filt_list(IDs)
fit_instructions = get_fit_instructions()
fit_cat = pipes.fit_catalogue(IDs,
fit_instructions,
load_vandels,
run="no_noise",
cat_filt_list=cat_filt_list,
vary_filt_list=True,
redshifts=redshifts,
redshift_sigma=0.005,
make_plots=True,
time_calls=True,
full_catalogue=True,
n_posterior=1000)
fit_cat.fit(verbose=False)
fit_instructions["nebular"] = nebular
# load the catalog
cat_name = sys.argv[2]
if cat_name in ['cosmos', 'xmm', 'deep', 'elais']:
cat = Table.read('../catalogs/' + cat_name + '_to_fit_' + sys.argv[3] +
'.fits')
IDs = cat['ID']
redshifts = cat['Z_BEST']
redshift_sigmas = np.maximum(cat['Z_BEST'] - cat['Z_BEST68_LOW'],
(cat['Z_BEST68_HIGH'] - cat['Z_BEST68_LOW']) /
2).tolist()
fit_cat = pipes.fit_catalogue(IDs,
fit_instructions,
load_gal,
spectrum_exists=False,
cat_filt_list=filt_list_full,
run=sys.argv[1],
redshifts=redshifts,
redshift_sigma=redshift_sigmas)
else:
cat = Table.read('../catalogs/' + cat_name)
IDs = cat['ID']
redshifts = cat['z']
redshift_sigmas = cat['z_sigma'].tolist()
fit_cat = pipes.fit_catalogue(IDs,
fit_instructions,
load_gal_model_cat,
spectrum_exists=False,
cat_filt_list=filt_list_full,
run=sys.argv[1],
redshifts=redshifts,
ID = str(fit.galaxy.ID)
fit.posterior.get_advanced_quantities()
if "redshift" in fit.fitted_model.params:
redshift = np.median(fit.posterior.samples["redshift"])
else:
redshift = fit.fitted_model.model_components["redshift"]
# Plot the posterior photometry and full spectrum.
wavs = (fit.posterior.model_galaxy.wavelengths) * (1 + redshift) / 10
posterior = fit.posterior.samples["spectrum_full"]
spectrum = np.median(posterior, axis=0)
data = np.c_[wavs, spectrum]
distance = cosmo.luminosity_distance(redshift).value * (10**5)
uvj = fit.posterior.samples["uvj"]
umag = np.median(uvj[:, 0])
umag_obs = umag + 5 * np.log(distance)
fname = 'G13_spectra/' + ID + '_spectrum.dat'
np.savetxt(fname, data)
IDs = np.array([2408, 1424, 2782, 18180, 8785, 22211, 22085, 3973, 9209])
fit_cat = pipes.fit_catalogue(IDs, fit_instructions, load_goodss,\
spectrum_exists=False, cat_filt_list=filt_list, run="guo_2020atopten", make_plots = True, analysis_function=analysisfunc, full_catalogue=True)
fit_cat.fit(verbose=False, n_live=1000)
redshifts.append(redshift_percentile)
ssfrs.append(ssfr_percentile)
IDlist.append(ID)
i = np.where(IDs == ID)[0][0]
if (i % 20) == 0:
np.savetxt('redshift_percentiles', np.array(redshifts))
np.savetxt('ssfr_percentiles', np.array(ssfrs))
np.savetxt('subsample_IDs', np.array(IDlist))
if ID == IDs[-1]:
np.savetxt('redshift_percentiles_full', np.array(redshifts))
np.savetxt('ssfr_percentiles_full', np.array(ssfrs))
np.savetxt('subsample_IDs_full', np.array(IDlist))
fit_cat = pipes.fit_catalogue(IDs,
fit_instructions,
loaduds,
spectrum_exists=False,
cat_filt_list=filt_list,
run="uds_20to23kmag",
make_plots=False,
analysis_function=analysisfunc,
full_catalogue=True)
fit_cat.fit(verbose=False, n_live=1000, mpi_serial=False)
data['redshift_2.5'] = np.array(redshifts)
data['ssfr_97.5'] = np.array(ssfrs)
newtable = fits.BinTableHDU(data=data)
newtable.writeto('output_cats/subsample.fits')
dblplaw = {}
dblplaw["tau"] = (0., 15.)
dblplaw["alpha"] = (0.01, 1000.)
dblplaw["beta"] = (0.01, 1000.)
dblplaw["alpha_prior"] = "log_10"
dblplaw["beta_prior"] = "log_10"
dblplaw["massformed"] = (1., 13.)
dblplaw["metallicity"] = (0.01, 2.5)
dblplaw["metallicity_prior"] = "log_10"
dust = {}
dust["type"] = "Calzetti"
dust["Av"] = (0., 8.)
dust["eta"] = 2.
nebular = {}
nebular["logU"] = -3.
fit_instructions = {}
fit_instructions["redshift"] = (0., 10.)
#fit_instructions["exponential"] = exp
fit_instructions["dblplaw"] = dblplaw
fit_instructions["dust"] = dust
fit_instructions["t_bc"] = 0.01
fit_instructions["nebular"] = nebular
IDs = np.arange(1, 6001)
fit_cat = pipes.fit_catalogue(IDs, fit_instructions, load_goodss,\
spectrum_exists=False, cat_filt_list=filt_list, run="guo_v2")
fit_cat.fit(verbose=False)