if "DDF_PIPELINE_CATALOGS" in os.environ.keys():
o['catdir']=os.environ["DDF_PIPELINE_CATALOGS"]
if o['logging'] is not None and not os.path.isdir(o['logging']):
os.mkdir(o['logging'])
# pybdsm source finding
sfind_image(o['catprefix'],o['pbimage'],o['nonpbimage'],o['sfind_pixel_fraction'])
# facet labels -- do this now for generality
cra,cdec=get_centpos()
t=Table.read(o['catprefix'] + '.cat.fits')
tesselfile=o['catprefix']+'.tessel.reg'
if 'Facet' not in t.columns:
t=label_table(t,tesselfile,cra,cdec)
t.write(o['catprefix'] + '.cat.fits',overwrite=True)
catsources=len(t)
# matching with catalogs
removelist=[]
for cat in o['list']:
print 'Doing catalogue',cat
if crossmatch_image(o['catprefix'] + '.cat.fits',cat,catdir=o['catdir'])>10:
filter_catalog(o['catprefix'] + '.cat.fits',o['catprefix']+'.cat.fits_'+cat+'_match.fits',o['pbimage'],o['catprefix']+'.cat.fits_'+cat+'_match_filtered.fits',cat,options=o)
else:
print 'Insufficient matches, abandoning catalogue'
removelist.append(cat)
for cat in removelist:
o['list'].remove(cat)
try:
o[cat + '_fluxfactor'] = o['fluxfactor'][i]
except:
pass
if o['logging'] is not None and not os.path.isdir(o['logging']):
os.mkdir(o['logging'])
# pybdsm source finding
sfind_image(o['catprefix'], o['pbimage'], o['nonpbimage'],
o['sfind_pixel_fraction'])
# facet labels -- do this now for generality
t = Table.read(o['catprefix'] + '.cat.fits')
if 'Facet' not in t.columns:
t = label_table(t, 'image_full_ampphase1m.tessel.reg')
t.write(o['catprefix'] + '.cat.fits', overwrite=True)
# matching with catalogs
for cat in o['list']:
print 'Doing catalogue', cat
crossmatch_image(o['catprefix'] + '.cat.fits', cat)
filter_catalog(o['catprefix'] + '.cat.fits',
o['catprefix'] + '.cat.fits_' + cat + '_match.fits',
o['pbimage'],
o['catprefix'] + '.cat.fits_' + cat +
'_match_filtered.fits',
cat,
options=o)
# Filter catalogs (only keep isolated compact sources within 3deg of pointing centre)
if "DDF_PIPELINE_CATALOGS" in os.environ.keys():
o['catdir'] = os.environ["DDF_PIPELINE_CATALOGS"]
if o['logging'] is not None and not os.path.isdir(o['logging']):
os.mkdir(o['logging'])
# pybdsm source finding
sfind_image(o['catprefix'], o['pbimage'], o['nonpbimage'],
o['sfind_pixel_fraction'])
# facet labels -- do this now for generality
cra, cdec = get_centpos()
t = Table.read(o['catprefix'] + '.cat.fits')
tesselfile = o['catprefix'] + '.tessel.reg'
if 'Facet' not in t.columns:
t = label_table(t, tesselfile, cra, cdec)
t.write(o['catprefix'] + '.cat.fits', overwrite=True)
catsources = len(t)
# matching with catalogs
removelist = []
for cat in o['list']:
print 'Doing catalogue', cat
if crossmatch_image(o['catprefix'] + '.cat.fits',
cat,
catdir=o['catdir']):
filter_catalog(o['catprefix'] + '.cat.fits',
o['catprefix'] + '.cat.fits_' + cat + '_match.fits',
o['pbimage'],
o['catprefix'] + '.cat.fits_' + cat +
def do_offsets(o):
# o is the options file
if o['mode'] != 'normal' and o['mode'] != 'test':
raise NotImplementedError('Offsets called with mode ' + o['mode'])
if o['second_selfcal']:
image_root = 'image_full_ampphase2'
else:
image_root = 'image_full_ampphase1m'
method = o['method']
report('Determining astrometric offsets with method ' + method +
' in mode ' + o['mode'])
report('Merging downloaded catalogues')
if os.path.isfile(method + '.fits'):
warn('Merged file exists, reading from disk instead')
data = Table.read(method + '.fits')
else:
kwargs = {}
if 'panstarrs' in method:
kwargs['rastr'] = 'ramean'
kwargs['decstr'] = 'decmean'
data = merge_cat(method, **kwargs)
if o['mode'] == 'test':
image_root += '_shift'
method += '-test'
report('Running PyBDSM on LOFAR image, please wait...')
catfile = image_root + '.offset_cat.fits'
if os.path.isfile(catfile):
warn('Catalogue already exists')
else:
if o['mode'] == 'test':
suffix = 'facetRestored'
else:
suffix = 'restored'
pbimage = image_root + '.int.' + suffix + '.fits'
nonpbimage = image_root + '.app.' + suffix + '.fits'
img = bdsm.process_image(pbimage,
detection_image=nonpbimage,
thresh_isl=4.0,
thresh_pix=5.0,
rms_box=(150, 15),
rms_map=True,
mean_map='zero',
ini_method='intensity',
adaptive_rms_box=True,
adaptive_thresh=150,
rms_box_bright=(60, 15),
group_by_isl=False,
group_tol=10.0,
output_opts=True,
output_all=True,
atrous_do=False,
flagging_opts=True,
flag_maxsize_fwhm=0.5,
advanced_opts=True,
blank_limit=None)
img.write_catalog(outfile=catfile,
catalog_type='srl',
format='fits',
correct_proj='True')
lofar = Table.read(catfile)
print len(lofar), 'LOFAR sources before filtering'
filter = (lofar['E_RA'] * 3600.0) < 2.0
filter &= (lofar['E_DEC'] * 3600.0) < 2.0
filter &= (lofar['Maj'] * 3600.0) < 10
lofar = lofar[filter]
print len(lofar), 'LOFAR sources after filtering'
regfile = image_root + '.tessel.reg'
lofar_l = label_table(lofar, regfile)
oo = Offsets(method,
n=o['ndir'],
imroot=image_root,
cellsize=o['cellsize'],
fitmethod=o['fit'])
report('Finding offsets')
oo.find_offsets(lofar_l, data)
report('Fitting offsets')
oo.fit_offsets()
report('Making plots and saving output')
#oo.plot_fits(method+'-fits.pdf')
oo.save_fits()
oo.plot_offsets()
if 'test' not in o['mode']:
oo.save(method + '-fit_state.pickle')
report('Making astrometry error map, please wait')
oo.make_astrometry_map('astromap.fits', 20)
oo.offsets_to_facetshift('facet-offset.txt')