def _examine_distance(T, sim='simba', dem='slab_calzetti'):
''' compare ABC summary statistics to data
'''
# read pool
theta_T = np.loadtxt(os.path.join(abc_dir, 'theta.t%i.dat' % T))
rho_T = np.loadtxt(os.path.join(abc_dir, 'rho.t%i.dat' % T))
w_T = np.loadtxt(os.path.join(abc_dir, 'w.t%i.dat' % T))
theta_med = np.median(theta_T, axis=0)
# read simulations
_sim_sed = dustInfer._read_sed(sim)
wlim = (_sim_sed['wave'] > 1e3) & (_sim_sed['wave'] < 8e3)
#cens = _sim_sed['censat'].astype(bool) & (_sim_sed['logmstar'] > 8.5)
downsample = np.zeros(len(_sim_sed['logmstar'])).astype(bool)
downsample[::10] = True
f_downsample = 0.1
cens = _sim_sed['censat'].astype(bool) & (_sim_sed['logmstar'] > 9.4) & downsample
sim_sed = {}
sim_sed['sim'] = sim
sim_sed['logmstar'] = _sim_sed['logmstar'][cens].copy()
sim_sed['logsfr.100'] = _sim_sed['logsfr.100'][cens].copy()
sim_sed['wave'] = _sim_sed['wave'][wlim].copy()
sim_sed['sed_noneb'] = _sim_sed['sed_noneb'][cens,:][:,wlim].copy()
sim_sed['sed_onlyneb'] = _sim_sed['sed_onlyneb'][cens,:][:,wlim].copy()
# read SDSS observable
mag_edges, gr_edges, fuvnuv_edges, _x_obs, err_x = np.load(os.path.join(dat_dir, 'obs',
'tinker_SDSS_centrals_M9.7.Mr_complete.Mr_GR_FUVNUV.npy'),
allow_pickle=True)
x_obs = [np.sum(_x_obs), _x_obs]
print('----------------------------------------')
print('median posterior theta:', theta_med)
x_mod = dustInfer.sumstat_model(theta_med, sed=sim_sed, dem=dem,
f_downsample=f_downsample)
rho = dustInfer.distance_metric(x_obs, x_mod, method='L2', x_err=err_x)
print('L2', rho)
rho = dustInfer.distance_metric(x_obs, x_mod, method='L1', x_err=err_x)
print('L1', rho)
data_mod = dustInfer.sumstat_model(theta_med, sed=sim_sed, dem=dem,
f_downsample=f_downsample, return_datavector=True)
print('%f < R < %f' % (-1*data_mod[0].max(), -1*data_mod[0].min()))
print('%f < G-R < %f' % (data_mod[1].min(), data_mod[1].max()))
print('%f < FUV-NUV < %f' % (data_mod[2].min(), data_mod[2].max()))
print('theta:', np.array([2., 2.]))
x_mod = dustInfer.sumstat_model(np.array([2., 2.]), sed=sim_sed, dem=dem,
f_downsample=f_downsample)
rho = dustInfer.distance_metric(x_obs, x_mod, method='L2', x_err=err_x)
print('L2', rho)
rho = dustInfer.distance_metric(x_obs, x_mod, method='L1', x_err=err_x)
print('L1', rho)
data_mod = dustInfer.sumstat_model(np.array([2., 2.]), sed=sim_sed, dem=dem,
f_downsample=f_downsample, return_datavector=True)
print('%f < R < %f' % (-1*data_mod[0].max(), -1*data_mod[0].min()))
print('%f < G-R < %f' % (data_mod[1].min(), data_mod[1].max()))
print('%f < FUV-NUV < %f' % (data_mod[2].min(), data_mod[2].max()))
return None
def test_distance(name):
# read in observations
dat_dir = os.environ['GALPOPFM_DIR']
fsdss = os.path.join(dat_dir, 'obs', 'tinker_SDSS_centrals_M9.7.valueadd.hdf5')
sdss = h5py.File(fsdss, 'r')
F_mag_sdss = sdss['ABSMAG'][...][:,0]
N_mag_sdss = sdss['ABSMAG'][...][:,1]
R_mag_sdss = sdss['ABSMAG'][...][:,4]
Haflux_sdss = sdss['HAFLUX'][...]
Hbflux_sdss = sdss['HBFLUX'][...]
#Ha_sdss = Haflux_sdss * (4.*np.pi * (sdss['Z'][...] * 2.9979e10/2.2685e-18)**2) * 1e-17
#Hb_sdss = Hbflux_sdss * (4.*np.pi * (sdss['Z'][...] * 2.9979e10/2.2685e-18)**2) * 1e-17
x_obs = dustInfer.sumstat_obs(F_mag_sdss, N_mag_sdss, R_mag_sdss, Haflux_sdss, Hbflux_sdss, sdss['Z'][...])
# read SED for sims
sim_sed = dustInfer._read_sed(name)
# pass through the minimal amount of memory
wlim = (sim_sed['wave'] > 1e3) & (sim_sed['wave'] < 1e4)
sim_sed_wlim = {}
sim_sed_wlim['mstar'] = sim_sed['mstar']
sim_sed_wlim['wave'] = sim_sed['wave'][wlim]
for k in ['sed_noneb', 'sed_onlyneb']:
sim_sed_wlim[k] = sim_sed[k][:,wlim]
import time
for i in range(10):
t0 = time.time()
theta = np.array([0.1, 0.2+0.05*float(i), 1./0.44])
x_model = dustInfr.sumstat_model(theta, sim_sed_wlim, dem='slab_calzetti')
print('summary sstatitics takes %.f' % (time.time() - t0))
t0 = time.time()
print(dustInfer.distance_metric(x_model, x_obs))
print('distance takes %.f' % (time.time() - t0))
return None
elif statistic == '2d':
if distance_method == 'L2':
eps0 = [4.e-5, 0.002, 0.0005]
elif distance_method == 'L1':
eps0 = [0.01, 0.2, 0.1]
elif statistic == '3d':
if distance_method == 'L2':
eps0 = [4.e-5, 0.01]
elif distance_method == 'L1':
eps0 = [0.01, 0.2]
elif distance_method == 'L2_only':
eps0 = [rho_max_3d]
######################################################
# this will run on all processes =X
# read SED for sims
sim_sed = dustInfer._read_sed(sim)
# pass through the minimal amount of memory
wlim = (sim_sed['wave'] > 1e3) & (sim_sed['wave'] < 8e3)
# only keep centrals and impose mass limit as well.
# the lower limit log M* > 9.4 is padded by >0.25 dex to conservatively account
# for log M* and R magnitude scatter
downsample = np.zeros(len(sim_sed['logmstar'])).astype(bool)
downsample[::5] = True
f_downsample = 0.2
#downsample[:] = True
#f_downsample = 1.
mlim = (sim_sed['logmstar'] > 9.4)
cuts = mlim & downsample # mass limit
def abc_sumstat(T, sim='simba', dem='slab_calzetti', abc_dir=None):
''' compare ABC summary statistics to data
'''
####################################################################################
# read in SDSS measurements
####################################################################################
Rmag_edges, gr_edges, fuvnuv_edges, x_obs, x_obs_err = np.load(os.path.join(dat_dir, 'obs',
'tinker_SDSS_centrals_M9.7.Mr_complete.Mr_GR_FUVNUV.npy'), allow_pickle=True)
dRmag = Rmag_edges[1] - Rmag_edges[0]
dGR = gr_edges[1] - gr_edges[0]
dfuvnuv = fuvnuv_edges[1] - fuvnuv_edges[0]
#ranges = [(Rmag_edges[0], Rmag_edges[-1]), (gr_edges[0], gr_edges[-1]),
# (fuvnuv_edges[0], fuvnuv_edges[-1])]
ranges = [(Rmag_edges[0], Rmag_edges[-1]), (-1., 3.), (-1., 10.)]
nbar_obs = np.sum(x_obs)
x_obs = [nbar_obs, x_obs]
####################################################################################
# read pool
theta_T = np.loadtxt(os.path.join(abc_dir, 'theta.t%i.dat' % T))
rho_T = np.loadtxt(os.path.join(abc_dir, 'rho.t%i.dat' % T))
w_T = np.loadtxt(os.path.join(abc_dir, 'w.t%i.dat' % T))
theta_med = np.median(theta_T, axis=0)
print('median ABC theta', theta_med)
# read simulations
_sim_sed = dustInfer._read_sed(sim)
wlim = (_sim_sed['wave'] > 1e3) & (_sim_sed['wave'] < 8e3)
downsample = np.zeros(len(_sim_sed['logmstar'])).astype(bool)
downsample[::10] = True
f_downsample = 0.1
cens = _sim_sed['censat'].astype(bool) & (_sim_sed['logmstar'] > 9.4) & downsample
sim_sed = {}
sim_sed['sim'] = sim
sim_sed['logmstar'] = _sim_sed['logmstar'][cens].copy()
sim_sed['logsfr.100'] = _sim_sed['logsfr.100'][cens].copy()
sim_sed['wave'] = _sim_sed['wave'][wlim].copy()
sim_sed['sed_noneb'] = _sim_sed['sed_noneb'][cens,:][:,wlim].copy()
sim_sed['sed_onlyneb'] = _sim_sed['sed_onlyneb'][cens,:][:,wlim].copy()
x_mod = dustInfer.sumstat_model(theta_med, sed=sim_sed, dem=dem,
f_downsample=f_downsample, statistic='3d')
data_mod = dustInfer.sumstat_model(theta_med, sed=sim_sed, dem=dem,
f_downsample=f_downsample, return_datavector=True)
print('%f < R < %f' % (-1*data_mod[0].max(), -1*data_mod[0].min()))
print('%f < G-R < %f' % (data_mod[1].min(), data_mod[1].max()))
print('%f < FUV-NUV < %f' % (data_mod[2].min(), data_mod[2].max()))
########################################################################
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(221)
sub.pcolormesh(Rmag_edges, gr_edges, dfuvnuv * np.sum(x_obs[1], axis=2).T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Greys')
sub.text(0.95, 0.95, r'SDSS', ha='right', va='top', transform=sub.transAxes, fontsize=25)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([])
sub.set_ylabel(r'$G-R$', fontsize=20)
sub.set_ylim(ranges[1])
sub = fig.add_subplot(222)
sub.text(0.95, 0.95, r'SIMBA', ha='right', va='top', transform=sub.transAxes, fontsize=25)
sub.pcolormesh(Rmag_edges, gr_edges, dfuvnuv * np.sum(x_mod[1], axis=2).T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Oranges')
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([])
sub.set_ylim(ranges[1])
sub.set_yticklabels([])
sub = fig.add_subplot(223)
h = sub.pcolormesh(Rmag_edges, fuvnuv_edges, dGR * np.sum(x_obs[1], axis=1).T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Greys')
sub.set_xlabel(r'$M_r$', fontsize=20)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([-20, -21, -22, -23])
sub.set_ylabel(r'$FUV - NUV$', fontsize=20)
sub.set_ylim(ranges[2])
sub = fig.add_subplot(224)
sub.pcolormesh(Rmag_edges, fuvnuv_edges, dGR * np.sum(x_mod[1], axis=1).T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Oranges')
sub.set_xlabel(r'$M_r$', fontsize=20)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([-20, -21, -22, -23])
sub.set_ylim(ranges[2])
sub.set_yticklabels([])
fig.subplots_adjust(wspace=0.1, hspace=0.1, right=0.85)
cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7])
fig.colorbar(h, cax=cbar_ax)
fig.savefig(os.path.join(abc_dir, 'abc_sumstat.t%i.png' % T), bbox_inches='tight')
return None
def abc_attenuationt(T, sim='simba', dem='slab_calzetti', abc_dir=None):
'''
'''
# read pool
theta_T = np.loadtxt(os.path.join(abc_dir, 'theta.t%i.dat' % T))
rho_T = np.loadtxt(os.path.join(abc_dir, 'rho.t%i.dat' % T))
w_T = np.loadtxt(os.path.join(abc_dir, 'w.t%i.dat' % T))
theta_med = np.median(theta_T, axis=0)
wave = np.linspace(1e3, 1e4, 101)
flux = np.ones(len(wave))
i3000 = (np.abs(wave - 3000.)).argmin() # index at 3000A
# read simulations
_sim_sed = dustInfer._read_sed(sim)
cens = _sim_sed['censat'].astype(bool) & (_sim_sed['logmstar'] > 9.4)#
logms = _sim_sed['logmstar'][cens].copy()
logsfr = _sim_sed['logsfr.100'][cens].copy()
A_lambdas, highmass, sfing = [], [], []
for i in np.arange(np.sum(cens))[::100]:
if dem == 'slab_calzetti':
A_lambda = -2.5 * np.log10(dustFM.DEM_slabcalzetti(theta_med, wave,
flux, logms[i], logsfr[i], nebular=False))
elif dem == 'slab_noll_m':
A_lambda = -2.5 * np.log10(dustFM.DEM_slab_noll_m(theta_med, wave,
flux, logms[i], logsfr[i], nebular=False))
elif dem == 'slab_noll_msfr':
A_lambda = -2.5 * np.log10(dustFM.DEM_slab_noll_msfr(theta_med, wave,
flux, logms[i], logsfr[i], nebular=False))
A_lambdas.append(A_lambda)
if logms[i] > 10.5:
highmass.append(True)
else: highmass.append(False)
if logsfr[i] - logms[i] > -11.: sfing.append(True)
else: sfing.append(False)
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(311)
for hm, A_lambda in zip(highmass, A_lambdas):
if hm:
sub.plot(wave, A_lambda, c='C1', lw=0.1)
else:
sub.plot(wave, A_lambda, c='C0', lw=0.1)
sub.set_xlim(1.5e3, 1e4)
sub.set_xticklabels([])
sub.set_ylabel(r'$A_\lambda$', fontsize=25)
sub.set_yscale('log')
sub.set_ylim(1e-4, 20.)
sub = fig.add_subplot(312)
for sf, A_lambda in zip(sfing, A_lambdas):
if sf:
sub.plot(wave, A_lambda, c='C0', lw=0.1)
else:
sub.plot(wave, A_lambda, c='C1', lw=0.1)
sub.set_xlim(1.5e3, 1e4)
sub.set_xticklabels([])
sub.set_ylabel(r'$A_\lambda$', fontsize=25)
sub.set_yscale('log')
sub.set_ylim(0.1, 20.)
sub = fig.add_subplot(313)
for A_lambda in A_lambdas:
sub.plot(wave, A_lambda/A_lambda[i3000], c='k', lw=0.1)
sub.set_xlabel('Wavelength [$A$]', fontsize=25)
sub.set_xlim(1.5e3, 1e4)
sub.set_ylim(0., 10.)
sub.set_ylabel(r'$A_\lambda/A_{3000}$', fontsize=25)
fig.savefig(os.path.join(abc_dir, 'abc_attenuation.t%i.png' % T), bbox_inches='tight')
return None
def abc_sumstat(T, sim='simba', dem='slab_calzetti', sfr0_prescription='adhoc', abc_dir=None):
''' compare ABC summary statistics to data
'''
####################################################################################
# read in SDSS measurements
####################################################################################
r_edges, gr_edges, fn_edges, x_obs = dustInfer.sumstat_obs(statistic='2d', return_bins=True)
dr = r_edges[1] - r_edges[0]
dgr = gr_edges[1] - gr_edges[0]
dfn = fn_edges[1] - fn_edges[0]
ranges = [(r_edges[0], r_edges[-1]), (-1., 3.), (-1., 10.)]
nbar_obs, x_obs_gr, x_obs_fn = x_obs
####################################################################################
# read pool
####################################################################################
theta_T = np.loadtxt(os.path.join(abc_dir, 'theta.t%i.dat' % T))
rho_T = np.loadtxt(os.path.join(abc_dir, 'rho.t%i.dat' % T))
w_T = np.loadtxt(os.path.join(abc_dir, 'w.t%i.dat' % T))
theta_med = np.median(theta_T, axis=0)
####################################################################################
# read simulations
####################################################################################
_sim_sed = dustInfer._read_sed(sim)
wlim = (_sim_sed['wave'] > 1e3) & (_sim_sed['wave'] < 8e3)
cuts = (_sim_sed['logmstar'] > 9.4)
sim_sed = {}
sim_sed['sim'] = sim
sim_sed['logmstar'] = _sim_sed['logmstar'][cuts].copy()
sim_sed['logsfr.inst'] = _sim_sed['logsfr.inst'][cuts].copy()
sim_sed['wave'] = _sim_sed['wave'][wlim].copy()
sim_sed['sed_noneb'] = _sim_sed['sed_noneb'][cuts,:][:,wlim].copy()
sim_sed['sed_onlyneb'] = _sim_sed['sed_onlyneb'][cuts,:][:,wlim].copy()
nbar_mod, x_mod_gr, x_mod_fn = dustInfer.sumstat_model(theta_med, sed=sim_sed, dem=dem,
statistic='2d', sfr0_prescription=sfr0_prescription)
########################################################################
print('obs nbar = %.4e' % nbar_obs)
print('mod nbar = %.4e' % nbar_mod)
########################################################################
fig = plt.figure(figsize=(10,10))
sub = fig.add_subplot(221)
sub.pcolormesh(r_edges, gr_edges, x_obs_gr.T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Greys')
sub.text(0.95, 0.95, r'SDSS', ha='right', va='top', transform=sub.transAxes, fontsize=25)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([])
sub.set_ylabel(r'$G-R$', fontsize=20)
sub.set_ylim(ranges[1])
sub = fig.add_subplot(222)
sub.pcolormesh(r_edges, gr_edges, x_mod_gr.T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Oranges')
sub.text(0.95, 0.95, sim_sed['sim'].upper(), ha='right', va='top', transform=sub.transAxes, fontsize=25)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([])
sub.set_ylim(ranges[1])
sub.set_yticklabels([])
sub = fig.add_subplot(223)
h = sub.pcolormesh(r_edges, fn_edges, x_obs_fn.T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Greys')
sub.set_xlabel(r'$M_r$', fontsize=20)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([-20, -21, -22, -23])
sub.set_ylabel(r'$FUV - NUV$', fontsize=20)
sub.set_ylim(ranges[2])
sub = fig.add_subplot(224)
sub.pcolormesh(r_edges, fn_edges, x_mod_fn.T,
vmin=1e-5, vmax=1e-2, norm=mpl.colors.LogNorm(), cmap='Oranges')
sub.set_xlabel(r'$M_r$', fontsize=20)
sub.set_xlim(20., 23)
sub.set_xticks([20., 21., 22., 23])
sub.set_xticklabels([-20, -21, -22, -23])
sub.set_ylim(ranges[2])
sub.set_yticklabels([])
fig.subplots_adjust(wspace=0.1, hspace=0.1, right=0.85)
cbar_ax = fig.add_axes([0.875, 0.15, 0.02, 0.7])
fig.colorbar(h, cax=cbar_ax)
try:
fig.savefig(os.path.join(abc_dir, 'abc_sumstat.t%i.png' % T), bbox_inches='tight')
except RuntimeError:
fig.savefig(os.path.join(abc_dir, 'abc_sumstat.t%i.pdf' % T), bbox_inches='tight')
plt.close()
return None