def plot_map(priors):
"""Plot of the fitted maps, with fitted objects overplotted
:param priors: list of xidplus.prior classes
:return: the default xidplus map plot
"""
sns.set_style("white")
cmap = sns.cubehelix_palette(8, start=.5, rot=-.75, 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)))
for i in range(0, len(priors)):
vmin = np.min(priors[i].sim)
vmax = np.max(priors[i].sim)
figs[i].show_colorscale(vmin=vmin, vmax=vmax, cmap=cmap)
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].add_colorbar()
figs[i].colorbar.set_location('top')
return figs, fig
def plot_map(priors):
"""Plot of the fitted maps, with fitted objects overplotted
:param priors: list of xidplus.prior classes
:return: the default xidplus map plot
"""
sns.set_style("white")
cmap=sns.cubehelix_palette(8, start=.5, rot=-.75,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)))
for i in range(0,len(priors)):
vmin=np.min(priors[i].sim)
vmax=np.max(priors[i].sim)
figs[i].show_colorscale(vmin=vmin,vmax=vmax,cmap=cmap)
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].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
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)
#tiles=Master['tiles']
#order=Master['order']
outfile=output_folder+'Master_prior.pkl'
with open(outfile, 'rb') as f:
obj=pickle.load(f)
priors=obj['priors']
bands=['PACS_100', 'PACS_160']
#hdulists=[]
#for b in bands:
# hdulists.append(fits.open(output_folder+'dmu26_XID+PACS_'+b+'_EGS_Bayes_Pval.fits'))
hdulists=list(map(lambda prior: postmaps.make_fits_image(prior,np.full_like(prior.sim,np.nan)),priors))
failed_tiles=[]
for i in range(0,len(tiles)):
print('On tile '+str(i)+' out of '+str(len(tiles)))
try:
for b in range(0,len(priors)):
Bayes_tile=fits.open(output_folder+'Tile_'+str(tiles[i])+'_'+str(order)+'_'+bands[b]+'_Bayes_Pval.fits')
x_ind,y_ind=np.meshgrid(np.arange(0,Bayes_tile[1].header['NAXIS1'],dtype=np.int16)-Bayes_tile[1].header['CRPIX1']+hdulists[b][1].header['CRPIX1'],np.arange(0,Bayes_tile[1].header['NAXIS2'],dtype=np.int16)-Bayes_tile[1].header['CRPIX2']+hdulists[b][1].header['CRPIX2'])
good=Bayes_tile[1].data>-6
hdulists[b][1].data[y_ind[good].astype(np.int16),x_ind[good].astype(np.int16)]=Bayes_tile[1].data[good]
#First run: Tiles.pkl
#####################
with open(output_folder+'Tiles.pkl',"rb") as f:
Master = pickle.load(f)
tiles=Master['tiles']
order=Master['order']
outfile=output_folder+'Master_prior.pkl'
with open(outfile, 'rb') as f:
obj=pickle.load(f)
priors=obj['priors']
hdulist24=postmaps.make_fits_image(priors[0],np.full_like(priors[0].sim,np.nan))
failed_tiles=[]
for i in range(0,len(tiles)):
print('On tile '+str(i)+' out of '+str(len(tiles)))
try:
Bayes_24_tile=fits.open(output_folder+'Tile_'+str(tiles[i])+'_'+str(order)+'_MIPS_24_Bayes_Pval.fits')
x_ind,y_ind=np.meshgrid(np.arange(0,Bayes_24_tile[1].header['NAXIS1'],dtype=np.int16)-Bayes_24_tile[1].header['CRPIX1']+hdulist24[1].header['CRPIX1'],np.arange(0,Bayes_24_tile[1].header['NAXIS2'],dtype=np.int16)-Bayes_24_tile[1].header['CRPIX2']+hdulist24[1].header['CRPIX2'])
good=Bayes_24_tile[1].data>-6
hdulist24[1].data[y_ind[good].astype(np.int16),x_ind[good].astype(np.int16)]=Bayes_24_tile[1].data[good]
