def test_photometry(noise, sim='lgal', lib='bc03', sample='mini_mocha'):
photo, meta = Data.Photometry(sim=sim, noise=noise, lib=lib, sample=sample)
n_meta = len(meta['galid'])
assert n_meta == len(meta['redshift'])
n_photo = photo['flux'].shape[0]
assert n_meta == n_photo
def test_iSpeculator(data_type):
# initiate fitter
iSpec = Fitters.iSpeculator(model_name='emulator')
# default prior
prior = iSpec._default_prior()
specs, meta = Data.Spectra(sim='lgal',
noise='bgs0',
lib='bc03',
sample='mini_mocha')
photo, _ = Data.Photometry(sim='lgal',
noise='legacy',
lib='bc03',
sample='mini_mocha')
zred = meta['redshift'][0]
w_obs = specs['wave']
flux_obs = specs['flux'][0]
photo_obs = photo['flux'][0, :3]
if data_type == 'spec':
output = iSpec.MCMC_spec(w_obs,
flux_obs,
np.ones(len(w_obs)),
zred,
prior=prior,
nwalkers=20,
burnin=10,
niter=10,
silent=False)
assert 'theta_med' in output.keys()
elif data_type == 'photo':
output = iSpec.MCMC_photo(photo_obs,
np.ones(len(photo_obs)),
zred,
bands='desi',
prior=prior,
nwalkers=20,
burnin=10,
niter=10,
opt_maxiter=100,
silent=False)
assert 'theta_med' in output.keys()
def cigaledata(sim='lgal',
noise='none',
lib='bc03',
sample='mini_mocha',
f_out='lgal.mini_mocha.noise.none.dat'):
''' generate data file for CIGALE (mini mocha)'''
#reading data
photo, meta = Data.Photometry(sim='lgal',
noise=noise,
lib='bc03',
sample='mini_mocha')
#dir_sample = os.path.join(UT.dat_dir(), sample)
#reading ids and redshift
redshift = np.array(meta['redshift'][:])
galid = np.array(meta['galid'][:])
bands = ['g', 'r', 'z', 'w1', 'w2'] #, 'w3', 'w4']
for icol, band in enumerate(bands):
photo['flux'][:, icol] = photo[
'flux'][:, icol] * 3631 * 1e-6 # convert to mJansky [mJy]
if noise == 'none': # assume 0.1% flux error
sample = np.concatenate(
(
galid.reshape(-1, 1),
redshift.reshape(-1, 1) #id, redshift
,
photo['flux'][:, 0].reshape(-1, 1),
0.001 *
photo['flux'][:, 0].reshape(-1, 1) #flux_g,err_flux_g
,
photo['flux'][:, 1].reshape(-1, 1),
0.001 *
photo['flux'][:, 1].reshape(-1, 1) #flux_r,err_flux_r
,
photo['flux'][:, 2].reshape(-1, 1),
0.001 *
photo['flux'][:, 2].reshape(-1, 1) #flux_z,err_flux_z
,
photo['flux'][:, 3].reshape(-1, 1),
0.001 *
photo['flux'][:, 3].reshape(-1, 1) #flux_w1,err_flux_w1
,
photo['flux'][:, 4].reshape(-1, 1),
0.001 *
photo['flux'][:, 4].reshape(-1, 1) #flux_w2,err_flux_w2
#,photo['flux'][:,5].reshape(-1,1),0.01*photo['flux'][:,5].reshape(-1,1) #flux_w3,err_flux_w3
#,photo['flux'][:,6].reshape(-1,1),0.01*photo['flux'][:,6].reshape(-1,1) #flux_w4,err_flux_w4
),
axis=1)
if noise != 'none':
photo['ivar'][:, icol] = photo[
'ivar'][:,
icol]**-0.5 * 3631 * 1e-6 # inverse variance to error [mJy]
sample = np.concatenate(
(
galid.reshape(-1, 1),
redshift.reshape(-1, 1) #id, redshift
,
photo['flux'][:, 0].reshape(-1, 1),
photo['ivar'][:, 0].reshape(-1, 1) #flux_g,err_flux_g
,
photo['flux'][:, 1].reshape(-1, 1),
photo['ivar'][:, 1].reshape(-1, 1) #flux_r,err_flux_r
,
photo['flux'][:, 2].reshape(-1, 1),
photo['ivar'][:, 2].reshape(-1, 1) #flux_z,err_flux_z
,
photo['flux'][:, 3].reshape(-1, 1),
photo['ivar'][:, 3].reshape(-1, 1) #flux_w1,err_flux_w1
,
photo['flux'][:, 4].reshape(-1, 1),
photo['ivar'][:, 4].reshape(-1, 1) #flux_w2,err_flux_w2
#,photo['flux'][:,5].reshape(-1,1),photo['ivar'][:,5].reshape(-1,1) #flux_w3,err_flux_w3
#,photo['flux'][:,6].reshape(-1,1),photo['ivar'][:,6].reshape(-1,1) #flux_w4,err_flux_w4
),
axis=1)
np.savetxt(
os.path.join(cigale_dir(), f_out),
sample,
delimiter=' ',
header=
'id redshift DECam_g DECam_g_err DECam_r DECam_r_err DECam_z DECam_z_err WISE1 WISE1_err WISE2 WISE2_err'
) # WISE3 WISE3_err WISE4 WISE4_err')
def validate_sample(sim):
''' generate some plots to validate the mini Mock Challenge photometry
and spectroscopy
'''
# read photometry
photo, _ = Data.Photometry(sim=sim, noise='legacy', sample='mini_mocha')
photo_g = 22.5 - 2.5 * np.log10(photo['flux'][:, 0])
photo_r = 22.5 - 2.5 * np.log10(photo['flux'][:, 1])
photo_z = 22.5 - 2.5 * np.log10(photo['flux'][:, 2])
flux_g = photo['flux'][:, 0] * 1e-9 * 1e17 * UT.c_light() / 4750.**2 * (
3631. * UT.jansky_cgs())
flux_r = photo['flux'][:, 1] * 1e-9 * 1e17 * UT.c_light() / 6350.**2 * (
3631. * UT.jansky_cgs())
flux_z = photo['flux'][:, 2] * 1e-9 * 1e17 * UT.c_light() / 9250.**2 * (
3631. * UT.jansky_cgs()) # convert to 10^-17 ergs/s/cm^2/Ang
ivar_g = photo['ivar'][:, 0] * (1e-9 * 1e17 * UT.c_light() / 4750.**2 *
(3631. * UT.jansky_cgs()))**-2.
ivar_r = photo['ivar'][:, 1] * (1e-9 * 1e17 * UT.c_light() / 6350.**2 *
(3631. * UT.jansky_cgs()))**-2.
ivar_z = photo['ivar'][:, 2] * (
1e-9 * 1e17 * UT.c_light() / 9250.**2 *
(3631. * UT.jansky_cgs()))**-2. # convert to 10^-17 ergs/s/cm^2/Ang
# read BGS targets from imaging
bgs_targets = h5py.File(
os.path.join(UT.dat_dir(), 'bgs.1400deg2.rlim21.0.hdf5'), 'r')
n_targets = len(bgs_targets['ra'][...])
bgs_g = 22.5 - 2.5 * np.log10(bgs_targets['flux_g'][...])[::10]
bgs_r = 22.5 - 2.5 * np.log10(bgs_targets['flux_r'][...])[::10]
bgs_z = 22.5 - 2.5 * np.log10(bgs_targets['flux_z'][...])[::10]
# photometry validation
fig = plt.figure(figsize=(6, 6))
sub = fig.add_subplot(111)
DFM.hist2d(bgs_g - bgs_r,
bgs_r - bgs_z,
color='k',
levels=[0.68, 0.95],
range=[[-1., 3.], [-1., 3.]],
bins=40,
smooth=0.5,
plot_datapoints=True,
fill_contours=False,
plot_density=False,
linewidth=0.5,
ax=sub)
sub.scatter([-100.], [-100], c='k', s=1, label='BGS targets')
sub.scatter(photo_g - photo_r,
photo_r - photo_z,
c='C0',
s=1,
label='LGal photometry')
sub.legend(loc='upper left', handletextpad=0, markerscale=10, fontsize=20)
sub.set_xlabel('$g-r$', fontsize=25)
sub.set_xlim(-1., 3.)
sub.set_ylabel('$r-z$', fontsize=25)
sub.set_ylim(-1., 3.)
ffig = os.path.join(UT.dat_dir(), 'mini_mocha',
'mini_mocha.%s.photo.png' % sim)
fig.savefig(ffig, bbox_inches='tight')
fig.savefig(UT.fig_tex(ffig, pdf=True), bbox_inches='tight')
# read spectra
spec_s, _ = Data.Spectra(sim=sim, noise='none', sample='mini_mocha')
spec_bgs0, _ = Data.Spectra(sim=sim, noise='bgs0', sample='mini_mocha')
spec_bgs1, _ = Data.Spectra(sim=sim, noise='bgs1', sample='mini_mocha')
spec_bgs2, _ = Data.Spectra(sim=sim, noise='bgs2', sample='mini_mocha')
# read sky brightness
_fsky = os.path.join(UT.dat_dir(), 'mini_mocha',
'bgs.exposure.surveysim.150s.v0p4.sample.hdf5')
fsky = h5py.File(_fsky, 'r')
wave_sky = fsky['wave'][...] # sky wavelength
sbright_sky = fsky['sky'][...]
# spectra validationg
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
for i, spec_bgs in enumerate([spec_bgs2, spec_bgs0]):
wsort = np.argsort(spec_bgs['wave'])
if i == 0:
_plt, = sub.plot(spec_bgs['wave'][wsort],
spec_bgs['flux'][0, wsort],
c='C%i' % i,
lw=0.25)
else:
sub.plot(spec_bgs['wave'][wsort],
spec_bgs['flux'][0, wsort],
c='C%i' % i,
lw=0.25)
_plt_photo = sub.errorbar(
[4750, 6350, 9250], [flux_g[0], flux_r[0], flux_z[0]],
[ivar_g[0]**-0.5, ivar_r[0]**-0.5, ivar_z[0]**-0.5],
fmt='.r')
_plt_sim, = sub.plot(spec_s['wave'][0, :],
spec_s['flux'][0, :],
c='k',
ls='-',
lw=1)
_plt_sim0, = sub.plot(spec_s['wave'][0, :],
spec_s['flux_unscaled'][0, :],
c='k',
ls=':',
lw=0.25)
leg = sub.legend([_plt_sim0, _plt_photo, _plt_sim, _plt], [
'%s spectrum' % sim.upper(),
'%s photometry' % sim.upper(),
'%s fiber spectrum' % sim.upper(),
'%s BGS spectra' % sim.upper()
],
loc='upper right',
fontsize=17)
for legobj in leg.legendHandles:
legobj.set_linewidth(2.0)
sub.set_xlabel('Wavelength [$A$]', fontsize=20)
sub.set_xlim(3e3, 1e4)
sub.set_ylabel('flux [$10^{-17} erg/s/cm^2/A$', fontsize=20)
if sim == 'lgal': sub.set_ylim(-2., 8.)
elif sim == 'tng': sub.set_ylim(0., None)
ffig = os.path.join(UT.dat_dir(), 'mini_mocha',
'mini_mocha.%s.spectra.png' % sim)
fig.savefig(ffig, bbox_inches='tight')
fig.savefig(UT.fig_tex(ffig, pdf=True), bbox_inches='tight')
return None
def fit_spectrophotometry(igal,
sim='lgal',
noise='bgs0_legacy',
method='ifsps',
model='emulator',
nwalkers=100,
burnin=100,
niter='adaptive',
maxiter=200000,
opt_maxiter=100,
overwrite=False,
postprocess=False,
justplot=False):
''' Fit Lgal spectra. `noise` specifies whether to fit spectra without noise or
with BGS-like noise. Produces an MCMC chain and, if not on nersc, a corner plot of the posterior.
:param igal:
index of Lgal galaxy within the spectral_challenge
:param noise:
If 'bgs1'...'bgs8', fit BGS-like spectra. (default: 'none')
:param justplot:
If True, skip the fitting and plot the best-fit. This is mainly implemented
because I'm having issues plotting in NERSC. (default: False)
'''
noise_spec = noise.split('_')[0]
noise_photo = noise.split('_')[1]
# read noiseless Lgal spectra of the spectral_challenge mocks
specs, meta = Data.Spectra(sim=sim,
noise=noise_spec,
lib='bc03',
sample='mini_mocha')
# read Lgal photometry of the mini_mocha mocks
photo, _ = Data.Photometry(sim=sim,
noise=noise_photo,
lib='bc03',
sample='mini_mocha')
if meta['redshift'][igal] < 0.101:
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
return None
w_obs = specs['wave']
flux_obs = specs['flux'][igal]
ivar_obs = specs['ivar'][igal]
if method == 'ispeculator':
photo_obs = photo['flux'][igal, :3]
photo_ivar_obs = photo['ivar'][igal, :3]
else:
photo_obs = photo['flux'][igal, :5]
photo_ivar_obs = photo['ivar'][igal, :5]
# get fiber flux factor prior range based on measured fiber flux
f_fiber_true = (photo['fiberflux_r_true'][igal] /
photo['flux_r_true'][igal])
prior_width = np.max([
0.05,
5. * photo['fiberflux_r_ivar'][igal]**-0.5 / photo['flux'][igal, 1]
])
f_fiber_min = (
photo['fiberflux_r_meas'][igal]) / photo['flux'][igal, 1] - prior_width
f_fiber_max = (
photo['fiberflux_r_meas'][igal]) / photo['flux'][igal, 1] + prior_width
f_fiber_prior = [f_fiber_min, f_fiber_max]
print('--- input ---')
print('z = %f' % meta['redshift'][igal])
print('log M* total = %f' % meta['logM_total'][igal])
print('log M* fiber = %f' % meta['logM_fiber'][igal])
print('f_fiber = %f' % f_fiber_true)
print('log SFR 100myr = %f' % np.log10(meta['sfr_100myr'][igal]))
print('log Z_MW = %f' % np.log10(meta['Z_MW'][igal]))
f_mcmc = os.path.join(
UT.dat_dir(), 'mini_mocha', method,
'%s.specphoto.noise_%s.%s.%i.mcmc.hdf5' % (sim, noise, model, igal))
if method == 'ifsps':
ifitter = Fitters.iFSPS(model_name=model)
elif method == 'ispeculator':
ifitter = Fitters.iSpeculator(model_name=model)
print('--- mcmc ---')
if (justplot or not overwrite) and os.path.isfile(f_mcmc):
print(' reading... %s' % os.path.basename(f_mcmc))
# read in mcmc chain
fmcmc = h5py.File(f_mcmc, 'r')
mcmc = {}
for k in fmcmc.keys():
mcmc[k] = fmcmc[k][...]
fmcmc.close()
else:
if os.path.isfile(f_mcmc) and not overwrite:
print("** CAUTION: %s already exists **" %
os.path.basename(f_mcmc))
print(' writing... %s' % os.path.basename(f_mcmc))
# initiating fit
prior = ifitter._default_prior(f_fiber_prior=f_fiber_prior)
mcmc = ifitter.MCMC_spectrophoto(w_obs,
flux_obs,
ivar_obs,
photo_obs,
photo_ivar_obs,
meta['redshift'][igal],
mask='emline',
prior=prior,
nwalkers=nwalkers,
burnin=burnin,
niter=niter,
maxiter=maxiter,
opt_maxiter=opt_maxiter,
writeout=f_mcmc,
silent=False)
if postprocess:
print('--- postprocessing ---')
f_post = f_mcmc.replace('.mcmc.hdf5', '.postproc.hdf5')
mcmc = ifitter.postprocess(mcmc_output=mcmc, writeout=f_post)
i_fib = list(mcmc['theta_names'].astype(str)).index('f_fiber')
print('log M* total = %f' % mcmc['theta_med'][0])
print('log M* fiber = %f' %
(mcmc['theta_med'][0] + np.log10(mcmc['theta_med'][i_fib])))
print('---------------')
try:
# plotting on nersc never works.
if os.environ['NERSC_HOST'] == 'cori': return None
except KeyError:
labels = [lbl_dict[_t] for _t in mcmc['theta_names'].astype(str)]
truths = [None for _ in labels]
truths[0] = meta['logM_total'][igal]
truths[i_fib] = f_fiber_true
if postprocess:
i_sfr = list(
mcmc['theta_names'].astype(str)).index('logsfr.100myr')
truths[i_sfr] = np.log10(meta['sfr_100myr'][igal])
i_zw = list(mcmc['theta_names'].astype(str)).index('logz.mw')
truths[i_zw] = np.log10(meta['Z_MW'][igal])
print('log SFR 100myr = %f' %
np.median(mcmc['mcmc_chain'][:, i_sfr]))
print('log Z_MW = %f' % np.median(mcmc['mcmc_chain'][:, i_zw]))
# corner plot of the posteriors
fig = DFM.corner(mcmc['mcmc_chain'],
range=mcmc['prior_range'],
quantiles=[0.16, 0.5, 0.84],
levels=[0.68, 0.95],
nbin=40,
smooth=True,
truths=truths,
labels=labels,
label_kwargs={'fontsize': 20})
if postprocess:
fig.savefig(f_post.replace('.hdf5', '.png'), bbox_inches='tight')
else:
fig.savefig(f_mcmc.replace('.hdf5', '.png'), bbox_inches='tight')
plt.close()
fig = plt.figure(figsize=(18, 5))
gs = mpl.gridspec.GridSpec(1,
2,
figure=fig,
width_ratios=[1, 3],
wspace=0.2)
sub = plt.subplot(gs[0])
sub.errorbar(np.arange(len(photo_obs)),
photo_obs,
yerr=photo_ivar_obs**-0.5,
fmt='.k',
label='data')
sub.scatter(np.arange(len(photo_obs)),
mcmc['flux_photo_model'],
c='C1',
label='model')
sub.legend(loc='upper left',
markerscale=3,
handletextpad=0.2,
fontsize=15)
sub.set_xticks([0, 1, 2, 3, 4])
sub.set_xticklabels(['$g$', '$r$', '$z$', 'W1', 'W2'])
sub.set_xlim(-0.5, len(photo_obs) - 0.5)
sub = plt.subplot(gs[1])
sub.plot(w_obs, flux_obs, c='k', lw=1, label='data')
sub.plot(mcmc['wavelength_model'],
mcmc['flux_spec_model'],
c='C1',
ls='--',
lw=1,
label=method)
sub.legend(loc='upper right', fontsize=15)
sub.set_xlabel('wavelength [$A$]', fontsize=20)
sub.set_xlim(3600., 9800.)
sub.set_ylim(-1., 5.)
if postprocess:
fig.savefig(f_post.replace('.hdf5', '.bestfit.png'),
bbox_inches='tight')
else:
fig.savefig(f_mcmc.replace('.hdf5', '.bestfit.png'),
bbox_inches='tight')
plt.close()
return None
def fit_photometry(igal,
sim='lgal',
noise='legacy',
method='ifsps',
model='emulator',
nwalkers=100,
burnin=100,
niter='adaptive',
maxiter=200000,
opt_maxiter=100,
overwrite=False,
postprocess=False,
justplot=False):
''' Fit simulated photometry. `noise` specifies whether to fit spectra without noise or
with legacy-like noise. `dust` specifies whether to if spectra w/ dust or not.
Produces an MCMC chain and, if not on nersc, a corner plot of the posterior.
:param igal:
index of Lgal galaxy within the spectral_challenge
:param noise:
If 'none', fit noiseless photometry.
If 'legacy', fit Legacy-like photometry. (default: 'none')
:param justplot:
If True, skip the fitting and plot the best-fit. This is mainly implemented
because I'm having issues plotting in NERSC. (default: False)
'''
# read Lgal photometry of the mini_mocha mocks
photo, meta = Data.Photometry(sim=sim,
noise=noise,
lib='bc03',
sample='mini_mocha')
if meta['redshift'][igal] < 0.101:
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
# current Speculator wavelength doesn't extend far enough
return None
print('--- input ---')
print('z = %f' % meta['redshift'][igal])
print('log M* total = %f' % meta['logM_total'][igal])
print('log SFR 100myr = %f' % np.log10(meta['sfr_100myr'][igal]))
print('log Z MW = %f' % np.log10(meta['Z_MW'][igal]))
if method == 'ispeculator':
photo_obs = photo['flux'][igal, :3]
ivar_obs = photo['ivar'][igal, :3]
else:
photo_obs = photo['flux'][igal, :5]
ivar_obs = photo['ivar'][igal, :5]
f_mcmc = os.path.join(
UT.dat_dir(), 'mini_mocha', method,
'%s.photo.noise_%s.%s.%i.mcmc.hdf5' % (sim, noise, model, igal))
# initiating fit
if method == 'ifsps':
ifitter = Fitters.iFSPS(model_name=model)
elif method == 'ispeculator':
ifitter = Fitters.iSpeculator(model_name=model)
print('--- bestfit ---')
if (justplot or not overwrite) and os.path.isfile(f_mcmc):
# read in best-fit file with mcmc chain
print(' reading in %s' % f_mcmc)
fmcmc = h5py.File(f_mcmc, 'r')
mcmc = {}
for k in fmcmc.keys():
mcmc[k] = fmcmc[k][...]
fmcmc.close()
else:
if os.path.isfile(f_mcmc) and not overwrite:
print("** CAUTION: %s already exists **" % os.path.basename(f_bf))
print(' writing %s' % f_mcmc)
prior = ifitter._default_prior(f_fiber_prior=None)
mcmc = ifitter.MCMC_photo(photo_obs,
ivar_obs,
meta['redshift'][igal],
bands='desi',
prior=prior,
nwalkers=nwalkers,
burnin=burnin,
niter=niter,
maxiter=maxiter,
opt_maxiter=opt_maxiter,
writeout=f_mcmc,
silent=False)
if postprocess:
print('--- postprocessing ---')
f_post = f_mcmc.replace('.mcmc.hdf5', '.postproc.hdf5')
mcmc = ifitter.postprocess(mcmc_output=mcmc, writeout=f_post)
print('log M* total = %f' % mcmc['theta_med'][0])
print('---------------')
try:
# plotting on nersc never works.
if os.environ['NERSC_HOST'] == 'cori': return None
except KeyError:
labels = [lbl_dict[_t] for _t in mcmc['theta_names'].astype(str)]
truths = [None for _ in labels]
truths[0] = meta['logM_total'][igal]
if postprocess:
i_sfr = list(
mcmc['theta_names'].astype(str)).index('logsfr.100myr')
truths[i_sfr] = np.log10(meta['sfr_100myr'][igal])
i_zmw = list(mcmc['theta_names'].astype(str)).index('logz.mw')
truths[i_zmw] = np.log10(meta['Z_MW'][igal])
print('log SFR 100myr = %f' %
np.median(mcmc['mcmc_chain'][:, i_sfr]))
print('log Z MW = %f' % np.median(mcmc['mcmc_chain'][:, i_zmw]))
fig = DFM.corner(mcmc['mcmc_chain'],
range=mcmc['prior_range'],
quantiles=[0.16, 0.5, 0.84],
levels=[0.68, 0.95],
nbin=40,
smooth=True,
truths=truths,
labels=labels,
label_kwargs={'fontsize': 20})
if postprocess:
fig.savefig(f_post.replace('.hdf5', '.png'), bbox_inches='tight')
else:
fig.savefig(f_mcmc.replace('.hdf5', '.png'), bbox_inches='tight')
plt.close()
fig = plt.figure(figsize=(5, 3))
sub = fig.add_subplot(111)
sub.errorbar(np.arange(len(photo_obs)),
photo_obs,
yerr=ivar_obs**-0.5,
fmt='.k',
label='data')
sub.scatter(np.arange(len(photo_obs)),
mcmc['flux_photo_model'],
c='C1',
label='model')
sub.legend(loc='upper left',
markerscale=3,
handletextpad=0.2,
fontsize=15)
sub.set_xticks([0, 1, 2])
sub.set_xticklabels(['$g$', '$r$', '$z$'])
sub.set_xlim(-0.5, len(photo_obs) - 0.5)
if postprocess:
fig.savefig(f_post.replace('.hdf5', '.bestfit.png'),
bbox_inches='tight')
else:
fig.savefig(f_mcmc.replace('.hdf5', '.bestfit.png'),
bbox_inches='tight')
plt.close()
return None
def mini_mocha_specphoto(noise='bgs0_legacy',
sample='mini_mocha',
method='ifsps'):
''' Compare properties inferred from forward modeled photometry to input properties
'''
if noise != 'none':
noise_spec = noise.split('_')[0]
noise_photo = noise.split('_')[1]
else:
noise_spec = 'none'
noise_photo = 'none'
# read noiseless Lgal spectra of the spectral_challenge mocks
specs, meta = Data.Spectra(sim='lgal',
noise=noise_spec,
lib='bc03',
sample=sample)
# read Lgal photometry of the mini_mocha mocks
photo, _ = Data.Photometry(sim='lgal',
noise=noise_photo,
lib='bc03',
sample=sample)
Mstar_input = meta['logM_total'] # total mass
Z_MW_input = meta['Z_MW'] # mass-weighted metallicity
tage_input = meta['t_age_MW'] # mass-weighted age
theta_inf = []
for igal in range(100):
# read best-fit file and get inferred parameters
_fbf = Fbestfit_specphoto(igal, noise=noise, method=method)
fbf = h5py.File(_fbf, 'r')
theta_inf_i = np.array([
fbf['theta_2sig_minus'][...], fbf['theta_1sig_minus'][...],
fbf['theta_med'][...], fbf['theta_1sig_plus'][...],
fbf['theta_2sig_plus'][...]
])
theta_inf.append(theta_inf_i)
theta_inf = np.array(theta_inf)
# inferred properties
Mstar_inf = theta_inf[:, :, 0]
Z_MW_inf = 10**theta_inf[:, :, 1]
tage_inf = theta_inf[:, :, 2]
fig = plt.figure(figsize=(15, 4))
# compare total stellar mass
sub = fig.add_subplot(131)
sub.errorbar(Mstar_input,
Mstar_inf[:, 2],
yerr=[
Mstar_inf[:, 2] - Mstar_inf[:, 1],
Mstar_inf[:, 3] - Mstar_inf[:, 2]
],
fmt='.C0')
sub.plot([9., 12.], [9., 12.], c='k', ls='--')
sub.set_xlabel(r'input $\log~M_{\rm tot}$', fontsize=25)
sub.set_xlim(9., 12.)
sub.set_ylabel(r'inferred $\log~M_{\rm tot}$', fontsize=25)
sub.set_ylim(9., 12.)
# compare metallicity
sub = fig.add_subplot(132)
sub.errorbar(Z_MW_input,
Z_MW_inf[:, 2],
yerr=[
Z_MW_inf[:, 2] - Z_MW_inf[:, 1],
Z_MW_inf[:, 3] - Z_MW_inf[:, 2]
],
fmt='.C0')
sub.plot([1e-3, 1], [1e-3, 1.], c='k', ls='--')
sub.set_xlabel(r'input MW $Z$', fontsize=20)
sub.set_xscale('log')
sub.set_xlim(1e-3, 5e-2)
sub.set_ylabel(r'inferred MW $Z$', fontsize=20)
sub.set_yscale('log')
sub.set_ylim(1e-3, 5e-2)
# compare age
sub = fig.add_subplot(133)
sub.errorbar(tage_input,
tage_inf[:, 2],
yerr=[
tage_inf[:, 2] - tage_inf[:, 1],
tage_inf[:, 3] - tage_inf[:, 2]
],
fmt='.C0')
sub.plot([0, 13], [0, 13.], c='k', ls='--')
sub.set_xlabel(r'input MW $t_{\rm age}$', fontsize=20)
sub.set_xlim(0, 13)
sub.set_ylabel(r'inferred MW $t_{\rm age}$', fontsize=20)
sub.set_ylim(0, 13)
fig.subplots_adjust(wspace=0.4)
_ffig = os.path.join(
dir_fig,
'mini_mocha.%s.specphotofit.vanilla.noise_%s.png' % (method, noise))
fig.savefig(_ffig, bbox_inches='tight')
fig.savefig(UT.fig_tex(_ffig, pdf=True), bbox_inches='tight')
return None
def mock_challenge_photo(noise='none', dust=False, method='ifsps'):
''' Compare properties inferred from forward modeled photometry to input properties
'''
# read Lgal input input properties
_, meta = Data.Photometry(sim='lgal',
noise=noise,
lib='bc03',
sample='spectral_challenge')
Mstar_input = meta['logM_total'] # total mass
Z_MW_input = meta['Z_MW'] # mass-weighted metallicity
tage_input = meta['t_age_MW'] # mass-weighted age
theta_inf = []
for igal in range(97):
# read best-fit file and get inferred parameters
_fbf = Fbestfit_photo(igal, noise=noise, dust=dust, method=method)
fbf = h5py.File(_fbf, 'r')
theta_inf_i = np.array([
fbf['theta_2sig_minus'][...], fbf['theta_1sig_minus'][...],
fbf['theta_med'][...], fbf['theta_1sig_plus'][...],
fbf['theta_2sig_plus'][...]
])
theta_inf.append(theta_inf_i)
theta_inf = np.array(theta_inf)
# inferred properties
Mstar_inf = theta_inf[:, :, 0]
Z_MW_inf = 10**theta_inf[:, :, 1]
tage_inf = theta_inf[:, :, 2]
fig = plt.figure(figsize=(15, 4))
# compare total stellar mass
sub = fig.add_subplot(131)
sub.errorbar(Mstar_input,
Mstar_inf[:, 2],
yerr=[
Mstar_inf[:, 2] - Mstar_inf[:, 1],
Mstar_inf[:, 3] - Mstar_inf[:, 2]
],
fmt='.C0')
sub.plot([9., 12.], [9., 12.], c='k', ls='--')
sub.set_xlabel(r'input $\log~M_{\rm tot}$', fontsize=25)
sub.set_xlim(9., 12.)
sub.set_ylabel(r'inferred $\log~M_{\rm tot}$', fontsize=25)
sub.set_ylim(9., 12.)
# compare metallicity
sub = fig.add_subplot(132)
sub.errorbar(Z_MW_input,
Z_MW_inf[:, 2],
yerr=[
Z_MW_inf[:, 2] - Z_MW_inf[:, 1],
Z_MW_inf[:, 3] - Z_MW_inf[:, 2]
],
fmt='.C0')
sub.plot([1e-3, 1], [1e-3, 1.], c='k', ls='--')
sub.set_xlabel(r'input MW $Z$', fontsize=20)
sub.set_xscale('log')
sub.set_xlim(1e-3, 5e-2)
sub.set_ylabel(r'inferred MW $Z$', fontsize=20)
sub.set_yscale('log')
sub.set_ylim(1e-3, 5e-2)
# compare age
sub = fig.add_subplot(133)
sub.errorbar(tage_input,
tage_inf[:, 2],
yerr=[
tage_inf[:, 2] - tage_inf[:, 1],
tage_inf[:, 3] - tage_inf[:, 2]
],
fmt='.C0')
sub.plot([0, 13], [0, 13.], c='k', ls='--')
sub.set_xlabel(r'input MW $t_{\rm age}$', fontsize=20)
sub.set_xlim(0, 13)
sub.set_ylabel(r'inferred MW $t_{\rm age}$', fontsize=20)
sub.set_ylim(0, 13)
fig.subplots_adjust(wspace=0.4)
_ffig = os.path.join(
dir_fig, 'mock_challenge.photofit.%s.noise_%s.dust_%s.png' %
(method, noise, ['no', 'yes'][dust]))
fig.savefig(_ffig, bbox_inches='tight')
return None
spec_obs = specs['flux'][0]
spec_ivar_obs = specs['ivar'][0]
# read nonoise Lgal spectra
specs_non, _ = Data.Spectra(sim='lgal',
noise='none',
lib='bc03',
sample='mini_mocha')
w_obs_non = specs_non['wave']
spec_obs_non = specs_non['flux'][0]
spec_ivar_obs_non = np.ones(w_obs_non.shape[0])
t0 = time.time()
# read Lgal photometry of the mini_mocha mocks
photo, _ = Data.Photometry(sim='lgal',
noise='legacy',
lib='bc03',
sample='mini_mocha')
print('Data.Photometry takes %.3f sec' % (time.time() - t0)) # 0.019sec
photo_obs = photo['flux'][0, :5]
photo_ivar_obs = photo['ivar'][0, :5]
# initial fitter
ifsps = Fitters.iFSPS(model_name='vanilla', prior=None)
# middle of prior theta value
theta_test = np.average(np.array(ifsps.priors), axis=1)
# get mask
t0 = time.time()
_mask = ifsps._check_mask('emline', w_obs, spec_ivar_obs,
meta['redshift'][0])
def fit_spectrophotometry(igal, noise='none', nwalkers=100, burnin=100, niter=1000, overwrite=False, justplot=False):
''' Fit Lgal spectra. `noise` specifies whether to fit spectra without noise or
with BGS-like noise. Produces an MCMC chain and, if not on nersc, a corner plot of the posterior.
:param igal:
index of Lgal galaxy within the spectral_challenge
:param noise:
If 'none', fit noiseless spectra.
If 'bgs1'...'bgs8', fit BGS-like spectra. (default: 'none')
:param justplot:
If True, skip the fitting and plot the best-fit. This is mainly implemented
because I'm having issues plotting in NERSC. (default: False)
'''
if noise != 'none':
noise_spec = noise.split('_')[0]
noise_photo = noise.split('_')[1]
else:
noise_spec = 'none'
noise_photo = 'none'
# read noiseless Lgal spectra of the spectral_challenge mocks
specs, meta = Data.Spectra(sim='lgal', noise=noise_spec, lib='bc03', sample='mini_mocha')
# read Lgal photometry of the mini_mocha mocks
photo, _ = Data.Photometry(sim='lgal', noise=noise_photo, lib='bc03', sample='mini_mocha')
model = 'vanilla'
w_obs = specs['wave']
flux_obs = specs['flux'][igal]
if noise_spec != 'none':
ivar_obs = specs['ivar'][igal]
else:
ivar_obs = np.ones(len(w_obs))
photo_obs = photo['flux'][igal,:5]
if noise_photo != 'none':
photo_ivar_obs = photo['ivar'][igal,:5]
else:
photo_ivar_obs = np.ones(photo_obs.shape[0])
# get fiber flux factor prior range based on measured fiber flux
f_fiber_true = (photo['fiberflux_r_meas'][igal]/photo['flux_r_true'][igal])
f_fiber_min = (photo['fiberflux_r_meas'][igal] - 3.*photo['fiberflux_r_ivar'][igal]**-0.5)/photo['flux'][igal,1]
f_fiber_max = (photo['fiberflux_r_meas'][igal] + 3.*photo['fiberflux_r_ivar'][igal]**-0.5)/photo['flux'][igal,1]
f_fiber_prior = [f_fiber_min, f_fiber_max]
print(f_fiber_prior)
print(f_fiber_true)
print((photo['fiberflux_r_true'][igal]/photo['flux_r_true'][igal]))
truths = [meta['logM_total'][igal], np.log10(meta['Z_MW'][igal]), meta['t_age_MW'][igal], None, None, f_fiber_true]
labels = ['$\log M_*$', '$\log Z$', r'$t_{\rm age}$', 'dust2', r'$\tau$', r'$f_{\rm fiber}$']
print('--- input ---')
print('z = %f' % meta['redshift'][igal])
print('log M* total = %f' % meta['logM_total'][igal])
print('log M* fiber = %f' % meta['logM_fiber'][igal])
print('MW Z = %f' % meta['Z_MW'][igal])
print('MW tage = %f' % meta['t_age_MW'][igal])
f_bf = os.path.join(UT.dat_dir(), 'mini_mocha', 'ifsps', 'lgal.specphoto.noise_%s.%s.%i.hdf5' % (noise, model, igal))
if not justplot:
if os.path.isfile(f_bf):
if not overwrite:
print("** CAUTION: %s already exists **" % os.path.basename(f_bf))
# initiating fit
ifsps = Fitters.iFSPS(model_name=model, prior=None)
bestfit = ifsps.MCMC_spectrophoto(
w_obs,
flux_obs,
ivar_obs,
photo_obs,
photo_ivar_obs,
meta['redshift'][igal],
f_fiber_prior=f_fiber_prior,
mask='emline',
nwalkers=nwalkers,
burnin=burnin,
niter=niter,
writeout=f_bf,
silent=False)
else:
# read in best-fit file with mcmc chain
fbestfit = h5py.File(f_bf, 'r')
bestfit = {}
for k in fbestfit.keys():
bestfit[k] = fbestfit[k][...]
print('--- bestfit ---')
print('written to %s' % f_bf)
print('log M* total = %f' % bestfit['theta_med'][0])
print('log M* fiber = %f' % (bestfit['theta_med'][0] + np.log10(bestfit['theta_med'][-1])))
print('log Z = %f' % bestfit['theta_med'][1])
print('---------------')
try:
# plotting on nersc never works.
if os.environ['NERSC_HOST'] == 'cori': return None
except KeyError:
# corner plot of the posteriors
fig = DFM.corner(bestfit['mcmc_chain'], range=bestfit['priors'], quantiles=[0.16, 0.5, 0.84],
levels=[0.68, 0.95], nbin=40, smooth=True,
truths=truths, labels=labels, label_kwargs={'fontsize': 20})
fig.savefig(f_bf.replace('.hdf5', '.png'), bbox_inches='tight')
return None
def fit_photometry(igal, noise='none', nwalkers=100, burnin=100, niter=1000, overwrite=False, justplot=False):
''' Fit Lgal photometry. `noise` specifies whether to fit spectra without noise or
with legacy-like noise. `dust` specifies whether to if spectra w/ dust or not.
Produces an MCMC chain and, if not on nersc, a corner plot of the posterior.
:param igal:
index of Lgal galaxy within the spectral_challenge
:param noise:
If 'none', fit noiseless photometry.
If 'legacy', fit Legacy-like photometry. (default: 'none')
:param justplot:
If True, skip the fitting and plot the best-fit. This is mainly implemented
because I'm having issues plotting in NERSC. (default: False)
'''
# read Lgal photometry of the mini_mocha mocks
photo, meta = Data.Photometry(sim='lgal', noise=noise, lib='bc03', sample='mini_mocha')
model = 'vanilla'
photo_obs = photo['flux'][igal,:5]
if noise != 'none': ivar_obs = photo['ivar'][igal,:5]
truths = [meta['logM_total'][igal], np.log10(meta['Z_MW'][igal]), meta['t_age_MW'][igal], None, None]
labels = ['$\log M_*$', '$\log Z$', r'$t_{\rm age}$', 'dust2', r'$\tau$']
if noise == 'none': # no noise
ivar_obs = np.ones(photo_obs.shape[0])
print('--- input ---')
print('z = %f' % meta['redshift'][igal])
print('log M* total = %f' % meta['logM_total'][igal])
print('MW Z = %f' % meta['Z_MW'][igal])
print('MW tage = %f' % meta['t_age_MW'][igal])
f_bf = os.path.join(UT.dat_dir(), 'mini_mocha', 'ifsps', 'lgal.photo.noise_%s.%s.%i.hdf5' % (noise, model, igal))
if not justplot:
if os.path.isfile(f_bf):
if not overwrite:
print("** CAUTION: %s already exists **" % os.path.basename(f_bf))
return None
# initiate fitting
ifsps = Fitters.iFSPS(model_name=model, prior=None)
bestfit = ifsps.MCMC_photo(
photo_obs,
ivar_obs,
meta['redshift'][igal],
bands='desi',
nwalkers=nwalkers,
burnin=burnin,
niter=niter,
writeout=f_bf,
silent=False)
else:
# read in best-fit file with mcmc chain
fbestfit = h5py.File(f_bf, 'r')
bestfit = {}
for k in fbestfit.keys():
bestfit[k] = fbestfit[k][...]
print('--- bestfit ---')
print('written to %s ---' % f_bf)
print('log M* = %f' % bestfit['theta_med'][0])
print('log Z = %f' % bestfit['theta_med'][1])
print('---------------')
try:
# plotting on nersc never works.
if os.environ['NERSC_HOST'] == 'cori': return None
except KeyError:
fig = DFM.corner(bestfit['mcmc_chain'], range=bestfit['priors'], quantiles=[0.16, 0.5, 0.84],
levels=[0.68, 0.95], nbin=40, smooth=True,
truths=truths, labels=labels, label_kwargs={'fontsize': 20})
fig.savefig(f_bf.replace('.hdf5', '.png'), bbox_inches='tight')
return None
