def make_catalogue(name,c_ra,c_dec,radius,cats,outnameprefix=''):
# cats needs to be a list of catalogues with a filename, short
# name, group ID and matching radius in arcsec.
# group IDs are a set of objects -- can be anything -- such that we require at least one flux from each group.
# Each catalogue needs RA, DEC, Total_flux and E_Total_flux.
t=Table.read(name,format='ascii.commented_header',header_start=-1)
print 'Total table length is',len(t)
if len(t)==0:
raise RuntimeError('No sources in table from pybdsm')
t=filter_catalogue(t,c_ra,c_dec,radius)
print 'Filtered within',radius,'degrees:',len(t)
if len(t)==0:
raise RuntimeError('No sources in central part of image')
t=t[t['Total_flux']>0.15]
print 'Bright sources:',len(t)
if len(t)==0:
raise RuntimeError('No bright sources for crossmatching')
# Filter for isolated sources
t=select_isolated_sources(t,100)
print 'Remove close neighbours:',len(t)
if len(t)==0:
raise RuntimeError('No sources in table before crossmatching')
ctab=[]
groups=[]
for n,sh,group,cmrad in cats:
tab=Table.read(n)
ctab.append(filter_catalogue(tab,c_ra,c_dec,radius))
groups.append(group)
print 'Table',sh,'has',len(ctab[-1]),'entries'
groups=set(groups)
# now do cross-matching
for g in groups:
t['g_count_'+str(g)]=0
for i,(n,sh,group,cmrad) in enumerate(cats):
tab=ctab[i]
match_catalogues(t,tab,cmrad,sh,group=group)
#
# t[sh+'_flux']=np.nan
# t[sh+'_e_flux']=np.nan
# for r in t:
# dist=np.sqrt((np.cos(c_dec*np.pi/180.0)*(tab['RA']-r['RA']))**2.0+(tab['DEC']-r['DEC'])**2.0)*3600.0
# stab=tab[dist<cmrad]
# if len(stab)==1:
# # got a unique match
# r[sh+'_flux']=stab[0]['Total_flux']
# r[sh+'_e_flux']=stab[0]['E_Total_flux']
# r['g_count_'+str(group)]+=1
# Now reject sources that have no match in a given group
for g in groups:
t=t[t['g_count_'+str(g)]>0]
if len(t)==0:
raise RuntimeError('No crossmatches exist after group matching')
t.write(outnameprefix+'crossmatch-1.fits',overwrite=True)
def filter_catalog(singlecat,matchedcat,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if options['restart'] and os.path.isfile(outname):
warn('File ' + outname +' already exists, skipping source filtering step')
else:
matchedcat = Table.read(matchedcat)
singlecat = Table.read(singlecat)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
print 'Originally',len(matchedcat),'sources'
matchedcat=filter_catalogue(matchedcat,fieldra,fielddec,3.0)
print '%i sources after filtering for 3.0 deg from centre' % len(matchedcat)
matchedcat=matchedcat[matchedcat['DC_Maj']<10.0] # ERROR!
print '%i sources after filtering for sources over 10arcsec in LOFAR' % len(matchedcat)
# not implemented yet!
#tooextendedsources_aux = np.array(np.where(matchedcat[1].data[options['%s_match_majkey2'%auxcatname]] > options['%s_filtersize'%auxcatname])).flatten()
#print '%s out of %s sources filtered out as over %sarcsec in %s'%(np.size(tooextendedsources_aux),len(allsources),options['%s_filtersize'%auxcatname],auxcatname)
matchedcat=select_isolated_sources(matchedcat,30.0)
print '%i sources after filtering for isolated sources in LOFAR' % len(matchedcat)
matchedcat.write(outname)
def filter_catalog(singlecat,matchedcat,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if options['restart'] and os.path.isfile(outname):
warn('File ' + outname +' already exists, skipping source filtering step')
else:
matchedcat = Table.read(matchedcat)
singlecat = Table.read(singlecat)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
print('Originally',len(matchedcat),'sources')
matchedcat=filter_catalogue(matchedcat,fieldra,fielddec,3.0)
print('%i sources after filtering for 3.0 deg from centre' % len(matchedcat))
matchedcat=matchedcat[matchedcat['DC_Maj']<10.0] # ERROR!
print('%i sources after filtering for sources over 10arcsec in LOFAR' % len(matchedcat))
# not implemented yet!
#tooextendedsources_aux = np.array(np.where(matchedcat[1].data[options['%s_match_majkey2'%auxcatname]] > options['%s_filtersize'%auxcatname])).flatten()
#print '%s out of %s sources filtered out as over %sarcsec in %s'%(np.size(tooextendedsources_aux),len(allsources),options['%s_filtersize'%auxcatname],auxcatname)
matchedcat=select_isolated_sources(matchedcat,30.0)
print('%i sources after filtering for isolated sources in LOFAR' % len(matchedcat))
matchedcat.write(outname)
def make_catalogue(name, c_ra, c_dec, radius, cats, outnameprefix=''):
# cats needs to be a list of catalogues with a filename, short
# name, group ID and matching radius in arcsec.
# group IDs are a set of objects -- can be anything -- such that we require at least one flux from each group.
# Each catalogue needs RA, DEC, Total_flux and E_Total_flux.
t = Table.read(name, format='ascii.commented_header', header_start=-1)
print('Total table length is', len(t))
if len(t) == 0:
raise RuntimeError('No sources in table from pybdsm')
t = filter_catalogue(t, c_ra, c_dec, radius)
print('Filtered within', radius, 'degrees:', len(t))
if len(t) == 0:
raise RuntimeError('No sources in central part of image')
t = t[t['Total_flux'] > 0.15]
print('Bright sources:', len(t))
if len(t) == 0:
raise RuntimeError('No bright sources for crossmatching')
# Filter for isolated sources
t = select_isolated_sources(t, 100)
print('Remove close neighbours:', len(t))
if len(t) == 0:
raise RuntimeError('No sources in table before crossmatching')
ctab = []
groups = []
for n, sh, group, cmrad in cats:
tab = Table.read(n)
ctab.append(filter_catalogue(tab, c_ra, c_dec, radius))
groups.append(group)
print('Table', sh, 'has', len(ctab[-1]), 'entries')
groups = set(groups)
# now do cross-matching
for g in groups:
t['g_count_' + str(g)] = 0
for i, (n, sh, group, cmrad) in enumerate(cats):
tab = ctab[i]
match_catalogues(t, tab, cmrad, sh, group=group)
#
# t[sh+'_flux']=np.nan
# t[sh+'_e_flux']=np.nan
# for r in t:
# dist=np.sqrt((np.cos(c_dec*np.pi/180.0)*(tab['RA']-r['RA']))**2.0+(tab['DEC']-r['DEC'])**2.0)*3600.0
# stab=tab[dist<cmrad]
# if len(stab)==1:
# # got a unique match
# r[sh+'_flux']=stab[0]['Total_flux']
# r[sh+'_e_flux']=stab[0]['E_Total_flux']
# r['g_count_'+str(group)]+=1
# Now reject sources that have no match in a given group
for g in groups:
t = t[t['g_count_' + str(g)] > 0]
if len(t) == 0:
raise RuntimeError('No crossmatches exist after group matching')
t.write(outnameprefix + 'crossmatch-1.fits', overwrite=True)