def _mini_mocha_galid(lib='bc03'):
''' pick 100 unique Lgal galids that roughly fall under the BGS target selection
for the mini mock challenge: r < 20.
'''
# gather all galids
galids = []
dir_inputs = os.path.join(UT.lgal_dir(), 'gal_inputs')
for finput in glob.glob(dir_inputs + '/*'):
galids.append(int(os.path.basename(finput).split('_')[2]))
galids = np.array(galids)
n_id = len(galids)
# get noiseless source spectra
_, spectra_s = _lgal_noiseless_spectra(galids, lib=lib)
# get DECAM photometry
photo, _ = FM.Photo_DESI(spectra_s['wave'], spectra_s['flux_dust'])
target_selection = (photo[:, 1] <= 20.)
print('%i Lgal galaxies within target_selection' %
np.sum(target_selection))
# now randomly choose 100 galids
mini_galids = np.random.choice(galids[target_selection],
size=100,
replace=False)
fids = os.path.join(UT.dat_dir(), 'mini_mocha', 'lgal.galids.%s.txt' % lib)
np.savetxt(fids,
mini_galids,
fmt='%i',
header='%i Lgal galids for mini mock challenge' %
len(mini_galids))
return None
def QA_fm_Lgal_mini_mocha(lib='bc03'):
''' quality assurance/sanity plots
'''
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['axes.xmargin'] = 1
mpl.rcParams['xtick.labelsize'] = 'x-large'
mpl.rcParams['xtick.major.size'] = 5
mpl.rcParams['xtick.major.width'] = 1.5
mpl.rcParams['ytick.labelsize'] = 'x-large'
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['ytick.major.width'] = 1.5
mpl.rcParams['legend.frameon'] = False
# read mini mocha data
fmm = h5py.File(
os.path.join(UT.dat_dir(), 'mini_mocha',
'lgal.mini_mocha.%s.v%s.hdf5' % (lib, version)), 'r')
ngal = fmm['spec_flux_source'][...].shape[0]
# plot BGS spectra and source spectra for sanity checks
fig = plt.figure(figsize=(15, 15))
for ii, i in enumerate(
np.random.choice(np.arange(ngal), size=3, replace=False)):
sub = fig.add_subplot(3, 1, ii + 1)
for band in ['b', 'r', 'z']:
sub.plot(fmm['spec_wave_%s_bgs' % band][...],
fmm['spec_flux_%s_bgs' % band][...][0, i, :],
c='C0')
sub.plot(fmm['spec_wave_source'][...],
fmm['spec_fiber_flux_source'][...][i, :],
c='k',
ls='--')
sub.set_xlim(3.6e3, 9.8e3)
sub.set_ylim(-2, 10)
sub.set_xlabel('wavelength', fontsize=25)
fig.savefig(os.path.join(UT.dat_dir(), 'mini_mocha',
'lgal.mini_mocha.%s.v%s.png' % (lib, version)),
bbox_inches='tight')
return None
def Fbestfit_specphoto(igal,
noise='bgs0_legacy',
sample='mini_mocha',
method='ifsps'):
''' file name of best-fit of photometry of spectral_challenge galaxy #igal
:param igal:
index of spectral_challenge galaxy
:param noise:
noise of the spectra. If noise == 'none', no noise. If noise =='legacy',
then legacy like noise. (default: 'none')
:param sample:
mini_mocha
:param method:
fitting method. (default: ifsps)
'''
model = 'vanilla'
f_bf = os.path.join(
UT.dat_dir(), sample, 'ifsps',
'lgal.specphoto.noise_%s.%s.%i.hdf5' % (noise, model, igal))
return f_bf
def compare_FSPS_Speculator():
'''
'''
tt_i = np.array([0.25, 0.25, 0.25, 0.25, 1e-4, 1e-4, 0., 1.5])
w0, lum0 = FSPS_sspLum(tt_i)
w1, lum1 = Speculator_sspLum(tt_i)
wlim0 = (w0 > 3e3)
wlim1 = (w1 > 3e3)
print(np.sum(lum0))
print(np.sum(lum1))
print(lum0[wlim0])
print(lum1[wlim1])
fig = plt.figure(figsize=(10, 5))
sub = fig.add_subplot(111)
sub.plot(w0, lum0, c='k', label='FSPS')
sub.plot(w1, lum1, c='C0', label='Speculator')
sub.set_xlim(3e3, 1e4)
ffig = os.path.join(UT.dat_dir(), '_fsps_speculator_comparison.png')
fig.savefig(ffig, bbox_inches='tight')
return None
def fm_TNG_minimocha():
''' generate spectroscopy and photometry for the mini Mock Challenge (MoCha)
* input: galaxy properties (SFH, ZH, etc), noiseless spectra
* "true" photometry directly from noiseless spectra
* assign photometric uncertainty and fiber flux using legacy imaging
* "measured" photometry and fiber flux + fiber source spectra (scaled down noiseless spectra),
* "BGS" spectra
notes
-----
* only 5000 galaxies for the mini_mocha
'''
from scipy.spatial import cKDTree as KDTree
# read TNG source spectra
ftng = h5py.File(os.path.join(UT.dat_dir(), 'tng', 'tng.hdf5'), 'r')
wave = ftng['wave'][...]
sed = ftng['sed_neb'][...][:5000, :] # SED with nebular emission lines
ngal = sed.shape[0]
print('%i illustris tng galaxies' % ngal)
_wave_s = wave * u.Angstrom
_spec_s = sed * u.Lsun / u.Hz
H0 = 70 * u.km / u.s / u.Mpc
# 0a. assign redshifts z ~ U(0.001, 0.4)
zred = np.random.uniform(0.001, 0.4, size=ngal)
# 0b. generate 'true' photometry from noiseless spectra
wave_s = np.tile(_wave_s.value, (ngal, 1)) # redshift wavelength
wave_s *= (1. + zred[:, None])
# convert fluxes
_spec = _spec_s / (
4. * np.pi *
(zred[:, None] * const.c / H0).to(u.cm)**2) / _wave_s**2 * const.c
spec_s = (_spec.to(u.erg / u.s / u.cm**2 / u.Angstrom)).value * 1e17
photo_true, mag_true = FM.Photo_DESI(wave_s, spec_s)
# r < 20 cut
target_selection = (mag_true[:, 1] <= 20.)
# compile meta data
meta = {}
meta['logM_total'] = ftng['logmstar'][...][:ngal][target_selection]
meta['sfr_100myr'] = 10.**ftng['logsfr.100'][...][:ngal][target_selection]
meta['redshift'] = zred[target_selection] # save to metadata
# 1. generate 'true' photometry from noiseless spectra
wave_s = wave_s[target_selection, :]
spec_s = spec_s[target_selection, :]
photo_true = photo_true[target_selection, :]
# 2. assign uncertainties to the photometry using BGS targets from the Legacy survey
bgs_targets = h5py.File(
os.path.join(UT.dat_dir(), 'bgs.1400deg2.rlim21.0.hdf5'), 'r')
n_targets = len(bgs_targets['ra'][...])
bands = ['g', 'r', 'z', 'w1', 'w2', 'w3', 'w4']
bgs_photo = np.zeros((n_targets, len(bands)))
bgs_photo_ivar = np.zeros((n_targets, len(bands)))
bgs_fiberflux = np.zeros(n_targets) # r-band fiber flux
for ib, band in enumerate(bands):
bgs_photo[:, ib] = bgs_targets['flux_%s' % band][...]
bgs_photo_ivar[:, ib] = bgs_targets['flux_ivar_%s' % band][...]
bgs_fiberflux = bgs_targets['fiberflux_r'][...]
# construct KD tree from BGS targets (currently downsampled)
bgs_features = np.array([
bgs_photo[:, 0], bgs_photo[:, 1], bgs_photo[:, 2],
bgs_photo[:, 0] - bgs_photo[:, 1], bgs_photo[:, 1] - bgs_photo[:, 2]
]).T
tree = KDTree(bgs_features)
# match ivars and fiberflux
match_features = np.array([
photo_true[:, 0], photo_true[:, 1], photo_true[:, 2],
photo_true[:, 0] - photo_true[:, 1],
photo_true[:, 1] - photo_true[:, 2]
]).T
dist, indx = tree.query(match_features)
photo_ivars = bgs_photo_ivar[indx, :]
photo_fiber_true = bgs_fiberflux[indx]
# 3.a. apply the uncertainty to the photometry to get "measured" photometry.
photo_meas = photo_true + photo_ivars**-0.5 * np.random.randn(
photo_true.shape[0], photo_true.shape[1])
# ***this needs to be checked; sometimes it gives greater than 1***
# ***this needs to be checked; sometimes it gives greater than 1***
# ***this needs to be checked; sometimes it gives greater than 1***
# ***this needs to be checked; sometimes it gives greater than 1***
f_fiber = np.clip(photo_fiber_true / photo_true[:, 1], None,
1.) # (r fiber flux) / (r total flux)
meta['logM_fiber'] = np.log10(f_fiber) + meta['logM_total']
# apply uncertainty to fiber flux as well
photo_fiber_meas = photo_fiber_true + f_fiber * photo_ivars[:,
1]**-0.5 * np.random.randn(
photo_true.
shape[0])
photo_ivar_fiber = f_fiber**-2 * photo_ivars[:, 1]
# 3.b. get fiber spectra by scaling down noiseless Lgal source spectra
spectra_fiber = spec_s * f_fiber[:, None] # 10e-17 erg/s/cm2/A
# 4. generate BGS like spectra
# read in sampled observing conditions, sky brightness, and exposure time
_fsky = os.path.join(UT.dat_dir(), 'mini_mocha',
'bgs.exposure.surveysim.150s.v0p4.sample.hdf5')
fsky = h5py.File(_fsky, 'r')
nexp = len(fsky['airmass'][...]) # number of exposures
wave_sky = fsky['wave'][...] # sky wavelength
sbright_sky = fsky['sky'][...]
# store to meta-data
for k in [
'airmass', 'moon_alt', 'moon_ill', 'moon_sep', 'seeing', 'sun_alt',
'sun_sep', 'texp_total', 'transp'
]:
meta[k] = fsky[k][...]
meta['wave_sky'] = wave_sky
meta['sbright_sky'] = sbright_sky
# generate BGS spectra for the exposures
spectra_bgs = {}
for iexp in range(nexp):
# sky brightness of exposure
Isky = [wave_sky, sbright_sky[iexp]]
fbgs = os.path.join(
UT.dat_dir(), 'mini_mocha',
'tng.mini_mocha.bgs_spec.%iof%i.fits' % (iexp + 1, nexp))
# interpolate source spectra onto linearly spaced wavelengths
wlin = np.linspace(1e3, 2e4, 19000)
spectra_fiber_lin = np.zeros((spectra_fiber.shape[0], len(wlin)))
for i in range(spectra_fiber.shape[0]):
spectra_fiber_lin[i] = np.interp(wlin, wave_s[i, :],
spectra_fiber[i, :])
bgs_spec = FM.Spec_BGS(
wlin, # wavelength
spectra_fiber_lin, # fiber spectra flux
fsky['texp_total'][...][iexp], # exp time
fsky['airmass'][...][iexp], # airmass
Isky,
filename=fbgs)
if iexp == 0:
spectra_bgs['wave_b'] = bgs_spec.wave['b']
spectra_bgs['wave_r'] = bgs_spec.wave['r']
spectra_bgs['wave_z'] = bgs_spec.wave['z']
spectra_bgs['flux_b'] = np.zeros(
(nexp, bgs_spec.flux['b'].shape[0],
bgs_spec.flux['b'].shape[1]))
spectra_bgs['flux_r'] = np.zeros(
(nexp, bgs_spec.flux['r'].shape[0],
bgs_spec.flux['r'].shape[1]))
spectra_bgs['flux_z'] = np.zeros(
(nexp, bgs_spec.flux['z'].shape[0],
bgs_spec.flux['z'].shape[1]))
spectra_bgs['ivar_b'] = np.zeros(
(nexp, bgs_spec.flux['b'].shape[0],
bgs_spec.flux['b'].shape[1]))
spectra_bgs['ivar_r'] = np.zeros(
(nexp, bgs_spec.flux['r'].shape[0],
bgs_spec.flux['r'].shape[1]))
spectra_bgs['ivar_z'] = np.zeros(
(nexp, bgs_spec.flux['z'].shape[0],
bgs_spec.flux['z'].shape[1]))
spectra_bgs['flux_b'][iexp] = bgs_spec.flux['b']
spectra_bgs['flux_r'][iexp] = bgs_spec.flux['r']
spectra_bgs['flux_z'][iexp] = bgs_spec.flux['z']
spectra_bgs['ivar_b'][iexp] = bgs_spec.ivar['b']
spectra_bgs['ivar_r'][iexp] = bgs_spec.ivar['r']
spectra_bgs['ivar_z'][iexp] = bgs_spec.ivar['z']
# write out everything
fmeta = os.path.join(UT.dat_dir(), 'mini_mocha', 'tng.mini_mocha.meta.p')
pickle.dump(meta, open(fmeta, 'wb')) # meta-data
fout = h5py.File(
os.path.join(UT.dat_dir(), 'mini_mocha', 'tng.mini_mocha.hdf5'), 'w')
fout.create_dataset('redshift', data=zred)
# photometry
for i, b in enumerate(bands):
# 'true'
fout.create_dataset('photo_flux_%s_true' % b, data=photo_true[:, i])
fout.create_dataset('photo_ivar_%s_true' % b, data=photo_ivars[:, i])
# 'measured'
fout.create_dataset('photo_flux_%s_meas' % b, data=photo_meas[:, i])
# fiber flux
fout.create_dataset('photo_fiberflux_r_true', data=photo_fiber_true)
fout.create_dataset('photo_fiberflux_r_meas', data=photo_fiber_meas)
fout.create_dataset('photo_fiberflux_r_ivar', data=photo_ivar_fiber)
fout.create_dataset('frac_fiber',
data=f_fiber) # fraction of flux in fiber
# spectroscopy
# noiseless source spectra
wlim = (_wave_s.value > 1e3) & (_wave_s.value < 2e4)
fout.create_dataset('spec_wave_source', data=wave_s[:, wlim])
fout.create_dataset('spec_flux_source', data=spec_s[:, wlim])
# noiseless source spectra in fiber
fout.create_dataset('spec_fiber_flux_source', data=spectra_fiber[:, wlim])
# BGS source spectra
for k in spectra_bgs.keys():
fout.create_dataset('spec_%s_bgs' % k, data=spectra_bgs[k])
fout.close()
return None
def cigale_dir(sample='mini_mocha'):
return os.path.join(UT.dat_dir(), sample, 'cigale')
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_pFF_spectra(igal, noise='none', iter_max=10, overwrite=False):
''' Fit Lgal spectra. `noise` specifies whether to fit spectra without noise or
with BGS-like noise.
: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)
'''
# read noiseless Lgal spectra of the spectral_challenge mocks
specs, meta = Data.Spectra(sim='lgal',
noise=noise,
lib='bc03',
sample='mini_mocha')
model = 'vanilla'
w_obs = specs['wave']
flux_obs = specs['flux'][igal]
if noise != 'none': ivar_obs = specs['ivar'][igal]
truths = [
meta['logM_fiber'][igal],
np.log10(meta['Z_MW'][igal]), meta['t_age_MW'][igal]
]
labels = ['$\log M_*$', '$\log Z$', r'$t_{\rm age}$']
if noise == 'none': # no noise
ivar_obs = np.ones(len(w_obs))
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', 'pff',
'lgal.spec.noise_%s.%s.%i.hdf5' % (noise, model, igal))
if os.path.isfile(f_bf):
if not overwrite:
print("** CAUTION: %s already exists **" % os.path.basename(f_bf))
# initiating fit
pff = Fitters.pseudoFirefly(model_name=model, prior=None)
bestfit = pff.Fit_spec(w_obs,
flux_obs,
ivar_obs,
meta['redshift'][igal],
mask='emline',
fit_cap=1000,
iter_max=10,
writeout=f_bf,
silent=False)
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('---------------')
fig = plt.figure(figsize=(12, 4))
sub = fig.add_subplot(111)
sub.plot(bestfit['wavelength_data'],
bestfit['flux_data'],
c='k',
zorder=1,
lw=0.5)
sub.plot(bestfit['wavelength_model'] * (1. + bestfit['redshift']),
bestfit['flux_model'],
c='C1')
sub.set_xlabel('Wavelength', fontsize=20)
sub.set_xlim(3500, 1e4)
sub.set_ylabel('Flux', fontsize=20)
sub.set_ylim(0., None)
fig.savefig(f_bf.replace('.hdf5', '.png'), 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
# --- plotting ---
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['axes.xmargin'] = 1
mpl.rcParams['xtick.labelsize'] = 'x-large'
mpl.rcParams['xtick.major.size'] = 5
mpl.rcParams['xtick.major.width'] = 1.5
mpl.rcParams['ytick.labelsize'] = 'x-large'
mpl.rcParams['ytick.major.size'] = 5
mpl.rcParams['ytick.major.width'] = 1.5
mpl.rcParams['legend.frameon'] = False
dir_fig = os.path.join(UT.dat_dir(), 'mini_mocha')
def mini_mocha_spec(noise='bgs0_legacy', sample='mini_mocha', method='ifsps'):
''' Compare properties inferred from forward modeled spectra 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',
def fm_Lgal_mini_mocha(lib='bc03'):
''' generate spectroscopy and photometry for the mini Mock Challenge (MoCha)
* input: galaxy properties (SFH, ZH, etc), noiseless spectra
* "true" photometry directly from noiseless spectra
* assign photometric uncertainty and fiber flux using legacy imaging
* "measured" photometry and fiber flux + fiber source spectra (scaled down noiseless spectra),
* "BGS" spectra
'''
from scipy.spatial import cKDTree as KDTree
# read in mini mocha galids
fids = os.path.join(UT.dat_dir(), 'mini_mocha', 'lgal.galids.%s.txt' % lib)
galids = np.loadtxt(fids, skiprows=1)
# get Lgal meta data
_meta = _lgal_metadata(galids)
# get noiseless source spectra
_meta_spec, spectra_s = _lgal_noiseless_spectra(galids, lib=lib)
# compile meta-data
meta = {}
for k in _meta.keys():
meta[k] = _meta[k]
for k in _meta_spec.keys():
meta[k] = _meta_spec[k]
print('%.2f < z < %.2f' % (meta['redshift'].min(), meta['redshift'].max()))
# 1. generate 'true' photometry from noiseless spectra
photo_true, _ = FM.Photo_DESI(spectra_s['wave'], spectra_s['flux_dust'])
# 2. assign uncertainties to the photometry using BGS targets from the Legacy survey
bgs_targets = h5py.File(
os.path.join(UT.dat_dir(), 'bgs.1400deg2.rlim21.0.hdf5'), 'r')
n_targets = len(bgs_targets['ra'][...])
bands = ['g', 'r', 'z', 'w1', 'w2', 'w3', 'w4']
bgs_photo = np.zeros((n_targets, len(bands)))
bgs_photo_ivar = np.zeros((n_targets, len(bands)))
bgs_fiberflux = np.zeros(n_targets) # r-band fiber flux
for ib, band in enumerate(bands):
bgs_photo[:, ib] = bgs_targets['flux_%s' % band][...]
bgs_photo_ivar[:, ib] = bgs_targets['flux_ivar_%s' % band][...]
bgs_fiberflux = bgs_targets['fiberflux_r'][...]
# construct KD tree from BGS targets (currently downsampled)
bgs_features = np.array([
bgs_photo[:, 0], bgs_photo[:, 1], bgs_photo[:, 2],
bgs_photo[:, 0] - bgs_photo[:, 1], bgs_photo[:, 1] - bgs_photo[:, 2]
]).T
tree = KDTree(bgs_features)
# match ivars and fiberflux
match_features = np.array([
photo_true[:, 0], photo_true[:, 1], photo_true[:, 2],
photo_true[:, 0] - photo_true[:, 1],
photo_true[:, 1] - photo_true[:, 2]
]).T
dist, indx = tree.query(match_features)
photo_ivars = bgs_photo_ivar[indx, :]
photo_fiber_true = bgs_fiberflux[indx]
# 3.a. apply the uncertainty to the photometry to get "measured" photometry.
photo_meas = photo_true + photo_ivars**-0.5 * np.random.randn(
photo_true.shape[0], photo_true.shape[1])
f_fiber = photo_fiber_true / photo_true[:,
1] # (r fiber flux) / (r total flux)
assert f_fiber.max() <= 1.
meta['logM_fiber'] = np.log10(f_fiber) + meta['logM_total']
# apply uncertainty to fiber flux as well
photo_fiber_meas = photo_fiber_true + f_fiber * photo_ivars[:,
1]**-0.5 * np.random.randn(
photo_true.
shape[0])
photo_ivar_fiber = f_fiber**-2 * photo_ivars[:, 1]
# 3.b. get fiber spectra by scaling down noiseless Lgal source spectra
spectra_fiber = spectra_s[
'flux_dust'] * f_fiber[:, None] # 10e-17 erg/s/cm2/A
# 4. generate BGS like spectra
# read in sampled observing conditions, sky brightness, and exposure time
_fsky = os.path.join(UT.dat_dir(), 'mini_mocha',
'bgs.exposure.surveysim.150s.v0p4.sample.hdf5')
fsky = h5py.File(_fsky, 'r')
nexp = len(fsky['airmass'][...]) # number of exposures
wave_sky = fsky['wave'][...] # sky wavelength
sbright_sky = fsky['sky'][...]
# store to meta-data
for k in [
'airmass', 'moon_alt', 'moon_ill', 'moon_sep', 'seeing', 'sun_alt',
'sun_sep', 'texp_total', 'transp'
]:
meta[k] = fsky[k][...]
meta['wave_sky'] = wave_sky
meta['sbright_sky'] = sbright_sky
# generate BGS spectra for the exposures
spectra_bgs = {}
for iexp in range(nexp):
# sky brightness of exposure
Isky = [wave_sky * u.Angstrom, sbright_sky[iexp]]
fbgs = os.path.join(
UT.dat_dir(), 'mini_mocha', 'lgal.bgs_spec.%s.v%s.%iof%i.fits' %
(lib, version, iexp + 1, nexp))
bgs_spec = FM.Spec_BGS(
spectra_s['wave'], # wavelength
spectra_fiber, # fiber spectra flux
fsky['texp_total'][...][iexp], # exp time
fsky['airmass'][...][iexp], # airmass
Isky,
filename=fbgs)
if iexp == 0:
spectra_bgs['wave_b'] = bgs_spec.wave['b']
spectra_bgs['wave_r'] = bgs_spec.wave['r']
spectra_bgs['wave_z'] = bgs_spec.wave['z']
spectra_bgs['flux_b'] = np.zeros(
(nexp, bgs_spec.flux['b'].shape[0],
bgs_spec.flux['b'].shape[1]))
spectra_bgs['flux_r'] = np.zeros(
(nexp, bgs_spec.flux['r'].shape[0],
bgs_spec.flux['r'].shape[1]))
spectra_bgs['flux_z'] = np.zeros(
(nexp, bgs_spec.flux['z'].shape[0],
bgs_spec.flux['z'].shape[1]))
spectra_bgs['ivar_b'] = np.zeros(
(nexp, bgs_spec.flux['b'].shape[0],
bgs_spec.flux['b'].shape[1]))
spectra_bgs['ivar_r'] = np.zeros(
(nexp, bgs_spec.flux['r'].shape[0],
bgs_spec.flux['r'].shape[1]))
spectra_bgs['ivar_z'] = np.zeros(
(nexp, bgs_spec.flux['z'].shape[0],
bgs_spec.flux['z'].shape[1]))
spectra_bgs['flux_b'][iexp] = bgs_spec.flux['b']
spectra_bgs['flux_r'][iexp] = bgs_spec.flux['r']
spectra_bgs['flux_z'][iexp] = bgs_spec.flux['z']
spectra_bgs['ivar_b'][iexp] = bgs_spec.ivar['b']
spectra_bgs['ivar_r'][iexp] = bgs_spec.ivar['r']
spectra_bgs['ivar_z'][iexp] = bgs_spec.ivar['z']
# write out everything
fmeta = os.path.join(UT.dat_dir(), 'mini_mocha',
'lgal.mini_mocha.%s.v%s.meta.p' % (lib, version))
fout = h5py.File(
os.path.join(UT.dat_dir(), 'mini_mocha',
'lgal.mini_mocha.%s.v%s.hdf5' % (lib, version)), 'w')
pickle.dump(meta, open(fmeta, 'wb')) # meta-data
# photometry
for i, b in enumerate(bands):
# 'true'
fout.create_dataset('photo_flux_%s_true' % b, data=photo_true[:, i])
fout.create_dataset('photo_ivar_%s_true' % b, data=photo_ivars[:, i])
# 'measured'
fout.create_dataset('photo_flux_%s_meas' % b, data=photo_meas[:, i])
# fiber flux
fout.create_dataset('photo_fiberflux_r_true', data=photo_fiber_true)
fout.create_dataset('photo_fiberflux_r_meas', data=photo_fiber_meas)
fout.create_dataset('photo_fiberflux_r_ivar', data=photo_ivar_fiber)
fout.create_dataset('frac_fiber',
data=f_fiber) # fraction of flux in fiber
# spectroscopy
# noiseless source spectra
wlim = (spectra_s['wave'] < 2e5) & (spectra_s['wave'] > 1e3
) # truncating the spectra
fout.create_dataset('spec_wave_source', data=spectra_s['wave'][wlim])
fout.create_dataset('spec_flux_source',
data=spectra_s['flux_dust'][:, wlim])
# noiseless source spectra in fiber
fout.create_dataset('spec_fiber_flux_source', data=spectra_fiber[:, wlim])
# BGS source spectra
for k in spectra_bgs.keys():
fout.create_dataset('spec_%s_bgs' % k, data=spectra_bgs[k])
fout.close()
return None
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
