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)