def gen_rand_cat_inMOC(n, RAmax, RAmin, DECmax, DECmin, catmoc):
'''
generate a random catalogue of positions within the given MOC
the positions are generated within the RA and DEC defined and
then removed if not within the given MOC. This is repeated
until n points lie within the MOC
'''
randfieldinmoc = 0
randfieldra = []
randfielddec = []
randfield = []
while n != 0:
#print (n)
randfieldtemp = generate_random_catalogue(n, RAmin, RAmax, DECmin,
DECmax)
randfieldtempra = randfieldtemp.ra.value
randfieldtempdec = randfieldtemp.dec.value
randfieldtempinmoc = (inMoc(randfieldtempra, randfieldtempdec, catmoc))
randfieldra = np.append(randfieldra,
randfieldtempra[randfieldtempinmoc])
randfielddec = np.append(randfielddec,
randfieldtempdec[randfieldtempinmoc])
n = n - sum(randfieldtempinmoc)
return (SkyCoord(randfieldra, randfielddec, unit='deg'))
def filter_catalogue(self, catalogue, colnames=("ra", "dec")):
"""
Filter a catalogue to the
"""
# TODO: Check if colnames in the catalogue
if self.moc == None:
print('filtering in a rectangular sky area')
return catalogue[
((catalogue[colnames[0]] >= self.ra_down) &
(catalogue[colnames[0]] <= self.ra_up) &
(catalogue[colnames[1]] >= self.dec_down) &
(catalogue[colnames[1]] <= self.dec_up))]
else:
print('filtering in a MOC')
return catalogue[ ( inMoc(catalogue[colnames[0]],catalogue[colnames[1]],self.moc) ) ]
n = (np.int(os.environ['SGE_TASK_ID']) - 1) * 10
ras = lofar_orig['RA'][n:n + 1]
decs = lofar_orig['DEC'][n:n + 1]
ID = lofar_orig['Source_Name'][n:n + 1]
from astropy.coordinates import SkyCoord
from astropy import units as u
c = SkyCoord(ra=ras * u.degree, dec=decs * u.degree)
import pymoc
moc_near = pymoc.util.catalog.catalog_to_moc(c, 60, 15)
moc_mask = inMoc(lofar_orig['RA'], lofar_orig['DEC'], moc_near)
moc = pymoc.util.catalog.catalog_to_moc(c, 120, 15)
prior = xidplus.prior(
radim_data, radim_err, radim_header, radim_header, moc=moc
) #Initialise with map, uncertianty map, wcs info and primary header
prior.prior_cat(lofar_orig['RA'][moc_mask],
lofar_orig['DEC'][moc_mask],
'prior_cat',
ID=lofar_orig['Source_Name'][moc_mask],
moc=moc) #Set input catalogue
prior.prior_bkg(
-5.0, 5) #Set prior on background (assumes Gaussian pdf with mu and sigma)
pind = np.arange(0, n_size) #*(1.0/radim_pixsize)
prior.set_prf(prf.array, pind, pind)
def gen_rand_cat_inMOC(n,RAmax,RAmin,DECmax,DECmin,catmoc):
'''
generate a random catalogue of positions within the given MOC
the positions are generated within the RA and DEC defined and
then removed if not within the given MOC. This is repeated
until n points lie within the MOC
'''
'''randfieldinmoc = 0
randfieldra = []
randfielddec = []
randfield = []
while n!=0:
#print (n)
randfieldtemp = generate_random_catalogue(n,RAmin,RAmax,DECmin,DECmax)
randfieldtempra = randfieldtemp.ra.value
randfieldtempdec = randfieldtemp.dec.value
randfieldtempinmoc = (inMoc(randfieldtempra,randfieldtempdec,catmoc))
randfieldra = np.append( randfieldra,randfieldtempra[randfieldtempinmoc])
randfielddec = np.append( randfielddec,randfieldtempdec[randfieldtempinmoc])
n = n - sum(randfieldtempinmoc)
return(SkyCoord(randfieldra,randfielddec,unit='deg'))'''
moc_area = catmoc.area_sq_deg
area = (abs(RAmax-RAmin)) * (abs(DECmax-DECmin))
ratio = area/moc_area
if ratio<1:
print('area ratio is less than 1 so something is wrong')
print('ratio of the random generation area compared to the moc area is: {}'.format(area/moc_area))
ra = []
dec = []
#randcoords = generate_random_catalogue(int(n*5*ratio),RAmin,RAmax,DECmin,DECmax)
#randra = randcoords.ra.value
#randdec = randcoords.dec.value
randra = np.random.uniform(RAmin, RAmax, int(2*n*ratio))
randdec = np.random.uniform(DECmin, DECmax, int(2*n*ratio))
mask = inMoc(randra,randdec,catmoc)
if np.sum(mask)>=n:
coords = SkyCoord(randra[mask][:n],randdec[mask][:n],unit='deg')
assert len(coords)==n,print('the number of coordinates generated is not equal to the number of radio sources in the moc. Something is wrong')
return(coords)
if np.sum(mask)<n:
while len(ra)<n:
ra = np.append(ra,randra[mask])
dec = np.append(dec,randdec[mask])
randcoords = generate_random_catalogue(n*5*ratio,RAmin,RAmax,DECmin,DECmax)
randra = randcoords.ra.value
randdec = randcoords.dec.value
mask = inMoc(randra,randdec,catmoc)
ra = np.append(ra,randra[mask])
dec = np.append(dec,randdec[mask])
coords = SkyCoord(ra[:n],dec[:n],unit='deg')
return(coords)
#Read in the Ldust predictions needed to increase the coerage of the prior list
ldust = Table.read('../../../../../HELP/dmu_products/dmu28/dmu28_Lockman-SWIRE/data/Lockman-SWIRE_Ldust_prediction_results.fits')
ldust.rename_column('id','help_id')
ldust['help_id'] = ldust['help_id'].astype(str)
#join Ldust predictions with the help table
ldust_id = [name[:-6] for name in list(ldust['help_id'])]
ldust['help_id'] = np.array(ldust_id)
ldust_cols = ['help_id','bayes.dust.luminosity','bayes.dust.luminosity_err','best.universe.redshift']
ldust = ldust[ldust_cols]
help_masterlist_ldust = join(help_masterlist,ldust,keys='help_id',join_type='outer')
#create a moc so that only radio sources in the prior area are selected
c = SkyCoord(ra=help_masterlist_ldust['ra'], dec=help_masterlist_ldust['dec'])
prior_moc=pymoc.util.catalog.catalog_to_moc(c,60,15)
radio_in_moc = inMoc(ra,dec,prior_moc)
#if radio_in_moc==False:
# sys.exit()
lofar_coords = SkyCoord(ra,dec,unit='deg')
help_coords = SkyCoord(help_masterlist['ra'],help_masterlist['dec'],unit='deg')
radius = 2
idx_help, idx_lofar, d2d, d3d = lofar_coords.search_around_sky(
help_coords, radius*u.arcsec)
#compute the predicted flux from the dust and usethis to construct the prior
from astropy.cosmology import Planck15 as cosmo
from astropy import units as u
f_pred=help_masterlist_ldust['bayes.dust.luminosity']/(4*np.pi*cosmo.luminosity_distance(help_masterlist_ldust['best.universe.redshift']).to(u.cm))
mask = np.isfinite(f_pred)
ldust_mask = (np.log10(f_pred)>8.5) & (np.isfinite(f_pred))
im500hdu=hdulist['image'].header
im500=hdulist['image'].data*1.0E3 #convert to mJy
nim500=hdulist['error'].data*1.0E3 #convert to mJy
w_500 = wcs.WCS(hdulist['image'].header)
pixsize500=3600.0*w_500.wcs.cd[1,1] #pixel size (in arcseconds)
hdulist.close()
from astropy.coordinates import SkyCoord
from astropy import units as u
c = SkyCoord(ra=ras*u.degree, dec=decs*u.degree)
import pymoc
moc=pymoc.util.catalog.catalog_to_moc(c,60,15)
#read in the prior cat and cut it down using the moc
prior_cat = Table.read('data/data_release/xidplus_prior_cat.fits')
mask = inMoc(prior_cat['ra'],prior_cat['dec'],moc)
prior_cat = prior_cat[mask]
#create a skycoord object with no radio source in it
#Then crossmatch with the radio source positions to find the nearest HELP source.
#if the nearest HELP source is within 2" remove it
mask = np.array(['ILTJ' not in name for name in prior_cat['help_id']])
prior_coords = SkyCoord(ra=prior_cat['ra'][mask],dec=prior_cat['dec'][mask])
idx,d2d,_ = c.match_to_catalog_sky(prior_coords)
mask = (d2d.value*3600)<2
help_id_near = prior_cat[idx]['help_id'][mask]
mask = np.array([name in help_id_near for name in prior_cat['help_id']])
prior_cat = prior_cat[~mask]
#---prior250--------