Esempi in Python per sources_in_tile

Esempio n. 1

#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)

Esempio n. 2

File: XIDp_run_script_mips_tile.py Progetto: H-E-L-P/dmu_products

#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)