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 _sumstat_model_wrap(theta, dem=dem):
print(' [%s]' % ','.join('%.1f' % tt for tt in theta))
x_mod = dustInfer.sumstat_model(theta,
sed=shared_sim_sed,
dem=dem,
f_downsample=f_downsample,
statistic=statistic,
noise=True,
sfr0_prescription=sfr0)
if distance_method == 'L2_only':
# nbar not included in distance metric
return x_mod[1:]
return x_mod
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_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
def _sumstat_model_wrap(theta, dem=dem):
x_mod = dustInfer.sumstat_model(theta, sed=shared_sim_sed, dem=dem,
f_downsample=f_downsample, statistic=statistic)
return x_mod