to=time.time()
log_posterior = posterior_obj.log_posterior(posterior_obj.theta, posterior_obj.phi, log_posterior_elements, n_pol_eff, model, normalize=True)
print "\t\t", time.time()-to
# lbc_log_posteriors[inj_id,:] = lbc_log_posterior
print "\tposterior by hand"
to=time.time()
posterior = np.exp(log_posterior) ### find posterior
print "\t\t", time.time()-to
#=================================================
# PLOT POSTERIOR
#=================================================
if not opts.skip_plots:
estang = stats.estang(posterior, nside=nside)
print "\tplotting posterior"
figname = "%s/inj=%d%s.png"%(opts.output_dir, inj_id, opts.tag)
logfigname = "%s/inj=%d-log%s.png"%(opts.output_dir, inj_id, opts.tag)
to=time.time()
posterior_obj.plot(figname, posterior=posterior/pixarea, title="posterior\nNo bins=%d\nlogBayes=%.3f\n$\\rho_{net}$=%.3f"%(np.sum(model),lb,snr_net), unit="prob/deg$^2$", inj=injang, est=estang)
posterior_obj.plot(logfigname, posterior=log_posterior-np.log(pixarea), title="log(posterior)\nNo bins=%d\nlogBayes=%.3f\n$\\rho_{net}$=%.3f"%(np.sum(model),lb,snr_net), unit="log(prob/deg$^2$)", inj=injang, est=estang)
print "\t\t", time.time()-to
#=================================================
# SAVE POSTERIOR
#=================================================
if not opts.skip_fits:
print "\twriting posterior to file"
fits = "%s/inj-%d%s.fits"%(opts.output_dir, inj_id, opts.tag)
to=time.time()
print "reading map from", fits
post, header = hp.read_map(fits, h=True, verbose=False)
npix = len(post)
if (dict(header)['ORDERING'] == 'NEST'): ### convert to RING ordering
post = hp.nest2ring(nside, post)
nside = hp.npix2nside(npix)
if opts.verbose:
print " nside=%d" % nside
if opts.rankmap: ### convert maps to rankmpas, not posteriors
post = stats.rankmap(post, npix=npix, normed=True)
d['post'] = post
d['npix'] = npix
d['nside'] = nside
d['estang'] = stats.estang(post, nside=nside)
#=================================================
### iterate through pairs and compute statistics
for ind, label1 in enumerate(labels):
d1 = maps[label1]
Post1 = d1['post']
nside1 = d1['nside']
for label2 in labels[ind + 1:]:
d2 = maps[label2]
Post2 = d2['post']
nside2 = d2['nside']
### normalize posterior
posterior /= sum(posterior)
if opts.verbose and opts.time: print "\t", time.time()-to, "sec"
if opts.scatter: ### recored the average prior for the scatter plot
prior_map += _prior
#======================
# save, plot, and summarize posterior
#======================
### find the posterior's mode and pull out injected location
if opts.plot_posteriors or opts.stats or opts.scatter:
### estimated pixel
est_theta, est_phi = stats.estang(posterior, nside=nside)
### injected pixel
inj_theta = toa_event['theta_inj']
inj_phi = toa_event['phi_inj']
if opts.deg:
inj_theta *= utils.deg2rad
inj_phi *= utils.deg2rad
### record positions for scatter
if opts.scatter:
estangs[toa_ind] = np.array([est_theta, est_phi])
injangs[toa_ind] = np.array([inj_theta, inj_phi])
### write posteriors
if opts.write_posteriors:
