def plot_Bayes_pval_map(priors, posterior):
"""
:param priors: list of xidplus.prior classes
:param posterior: xidplus.posterior class
:return: the default xidplus Bayesian P value map plot
"""
sns.set_style("white")
mod_map_array = postmaps.replicated_maps(priors, posterior, posterior.samples['lp__'].size)
Bayes_pvals = []
cmap = sns.diverging_palette(220, 20, as_cmap=True)
hdulists = list(map(lambda prior: postmaps.make_fits_image(prior, prior.sim), priors))
fig = plt.figure(figsize=(10 * len(priors), 10))
figs = []
for i in range(0, len(priors)):
figs.append(aplpy.FITSFigure(hdulists[i][1], figure=fig, subplot=(1, len(priors), i + 1)))
Bayes_pvals.append(postmaps.make_Bayesian_pval_maps(priors[i], mod_map_array[i]))
for i in range(0, len(priors)):
figs[i].show_markers(priors[i].sra, priors[i].sdec, edgecolor='black', facecolor='black',
marker='o', s=20, alpha=0.5)
figs[i].tick_labels.set_xformat('dd.dd')
figs[i].tick_labels.set_yformat('dd.dd')
figs[i]._data[
priors[i].sy_pix - np.min(priors[i].sy_pix) - 1, priors[i].sx_pix - np.min(priors[i].sx_pix) - 1] = \
Bayes_pvals[i]
figs[i].show_colorscale(vmin=-6, vmax=6, cmap=cmap)
figs[i].add_colorbar()
figs[i].colorbar.set_location('top')
return figs, fig
def plot_Bayes_pval_map(priors, posterior):
"""
:param priors: list of xidplus.prior classes
:param posterior: xidplus.posterior class
:return: the default xidplus Bayesian P value map plot
"""
sns.set_style("white")
mod_map_array = postmaps.replicated_maps(priors, posterior,
posterior.samples['lp__'].size)
Bayes_pvals = []
cmap = sns.diverging_palette(220, 20, as_cmap=True)
hdulists = list(
map(lambda prior: postmaps.make_fits_image(prior, prior.sim), priors))
fig = plt.figure(figsize=(10 * len(priors), 10))
figs = []
for i in range(0, len(priors)):
figs.append(
aplpy.FITSFigure(hdulists[i][1],
figure=fig,
subplot=(1, len(priors), i + 1)))
Bayes_pvals.append(
postmaps.make_Bayesian_pval_maps(priors[i], mod_map_array[i]))
for i in range(0, len(priors)):
figs[i].show_markers(priors[i].sra,
priors[i].sdec,
edgecolor='black',
facecolor='black',
marker='o',
s=20,
alpha=0.5)
figs[i].tick_labels.set_xformat('dd.dd')
figs[i].tick_labels.set_yformat('dd.dd')
figs[i]._data[
priors[i].sy_pix - np.min(priors[i].sy_pix) - 1, priors[i].sx_pix - np.min(priors[i].sx_pix) - 1] = \
Bayes_pvals[i]
figs[i].show_colorscale(vmin=-6, vmax=6, cmap=cmap)
figs[i].add_colorbar()
figs[i].colorbar.set_location('top')
return figs, fig
#priors[0].prior_flux_upper=(priors[0].prior_flux_upper-10.0+0.02)/np.max(priors[0].prf)
fit=MIPS.MIPS_24(priors[0],iter=1000)
posterior=xidplus.posterior_stan(fit,priors)
outfile=output_folder+'Tile_'+str(tiles[taskid-1])+'_'+str(order)
posterior=xidplus.posterior_stan(fit,priors)
xidplus.save(priors,posterior,outfile)
post_rep_map=postmaps.replicated_maps(priors,posterior,nrep=2000)
Bayes_P24=postmaps.Bayes_Pval_res(priors[0],post_rep_map[0])
cat=catalogue.create_MIPS_cat(posterior, priors[0], Bayes_P24)
kept_sources=moc_routines.sources_in_tile([tiles[taskid-1]],order,priors[0].sra,priors[0].sdec)
kept_sources=np.array(kept_sources)
cat[1].data=cat[1].data[kept_sources]
outfile=output_folder+'Tile_'+str(tiles[taskid-1])+'_'+str(order)
cat.writeto(outfile+'_MIPS24_cat.fits',overwrite=True)
Bayesian_Pval=postmaps.make_Bayesian_pval_maps(priors[0],post_rep_map[0])
wcs_temp=wcs.WCS(priors[0].imhdu)
ra,dec=wcs_temp.wcs_pix2world(priors[0].sx_pix,priors[0].sy_pix,0)
kept_pixels=np.array(moc_routines.sources_in_tile([tiles[taskid-1]],order,ra,dec))
Bayesian_Pval[np.invert(kept_pixels)]=np.nan
Bayes_24_map=postmaps.make_fits_image(priors[0],Bayesian_Pval)
Bayes_24_map.writeto(outfile+'_MIPS_24_Bayes_Pval.fits',overwrite=True)
