def make_mask(imagename,
thresh,
verbose=False,
options=None,
external_mask=None,
catcher=None):
if catcher: catcher.check()
# mask_use specifies a mask file to use
if options is None:
options = o # attempt to get global
fname = imagename + '.mask.fits'
runcommand = "MakeMask.py --RestoredIm=%s --Th=%s --Box=50,2" % (imagename,
thresh)
if options['restart'] and os.path.isfile(fname):
warn('File ' + fname + ' already exists, skipping MakeMask step')
if verbose:
print 'Would have run', runcommand
else:
run(runcommand,
dryrun=options['dryrun'],
log=logfilename('MM-' + imagename + '.log', options=options),
quiet=options['quiet'])
if external_mask is not None:
merge_mask(fname, external_mask, fname)
def make_list(workdir='.',force=False):
g=sorted(glob.glob(workdir+'/*.ms'))
full_mslist=[]
start_times=[]
for ms in g:
ff=check_flagged(ms)
t0,t1=get_timerange(ms)
print(ms,ff)
if ff<0.8:
full_mslist.append(os.path.basename(ms))
start_times.append(t0)
full_mslist = np.array(full_mslist)
# check for multiple observations
Ustart_times = np.unique(start_times)
if len(full_mslist)<18:
warn('Too few MS found for normal running: only %i' % len(full_mslist))
if not force: return False
if len(full_mslist)<24:
warn('Warning -- only %i ms found' % len(full_mslist))
# ensure lists contain all ms from all observations and same subset for each observation
write_full_mslist = np.array(())
write_mslist = np.array(())
for start_time in Ustart_times:
write_full_mslist = np.hstack((write_full_mslist,full_mslist[start_times==start_time]))
write_mslist = np.hstack((write_mslist,full_mslist[start_times==start_time][2::4]))
open(workdir+'/big-mslist.txt','w').writelines(ms+'\n' for ms in write_full_mslist)
open(workdir+'/mslist.txt','w').writelines(ms+'\n' for ms in write_mslist)
return 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_list(workdir='.'):
g=sorted(glob.glob(workdir+'/*.ms'))
full_mslist=[]
start_times=[]
for ms in g:
ff=check_flagged(ms)
t0,t1=get_timerange(ms)
print ms,ff
if ff<0.8:
full_mslist.append(os.path.basename(ms))
start_times.append(t0)
full_mslist = np.array(full_mslist)
# check for multiple observations
Ustart_times = np.unique(start_times)
if len(full_mslist)<18:
warn('Too few MS found for normal running: only %i' % len(full_mslist))
return False
if len(full_mslist)<24:
warn('Warning -- only %i ms found' % len(full_mslist))
# ensure lists contain all ms from all observations and same subset for each observation
write_full_mslist = np.array(())
write_mslist = np.array(())
for start_time in Ustart_times:
write_full_mslist = np.hstack((write_full_mslist,full_mslist[start_times==start_time]))
write_mslist = np.hstack((write_mslist,full_mslist[start_times==start_time][2::4]))
open(workdir+'/big-mslist.txt','w').writelines(ms+'\n' for ms in write_full_mslist)
open(workdir+'/mslist.txt','w').writelines(ms+'\n' for ms in write_mslist)
return True
def make_external_mask(fname,
templatename,
use_tgss=True,
options=None,
extended_use=None,
clobber=False):
if options is None:
options = o # attempt to get global
if options['restart'] and os.path.isfile(fname) and not clobber:
warn('External mask already exists, not creating it')
else:
report('Make blank external mask')
hdus = fits.open(templatename)
hdus[0].data = np.zeros_like(hdus[0].data, dtype=np.int32)
hdus.writeto(fname, clobber=True)
hdus.close()
if use_tgss and options['tgss'] is not None:
report('Merging the mask with TGSS catalogue')
# TGSS path is provided, this means we want to add the positions of bright TGSS sources to the mask
modify_mask(fname,
fname,
options['tgss'],
options['tgss_radius'],
options['tgss_flux'],
do_extended=options['tgss_extended'],
cellsize=options['cellsize'],
pointsize=options['tgss_pointlike'])
if options['region'] is not None:
report('Merging with mask with user-specified region')
add_manual_mask(fname, options['region'], fname)
if options['extended_size'] is not None and extended_use is not None:
report('Merging with automatic extended mask')
merge_mask(fname, extended_use, fname)
def killms_data(imagename,
mslist,
outsols,
clusterfile=None,
colname='CORRECTED_DATA',
niterkf=6,
dicomodel=None,
uvrange=None,
wtuv=None,
robust=None,
catcher=None,
options=None):
if options is None:
options = o # attempt to get global if it exists
cache_dir = find_cache_dir(options)
# run killms individually on each MS -- allows restart if it failed in the middle
filenames = [l.strip() for l in open(mslist, 'r').readlines()]
for f in filenames:
if catcher: catcher.check()
checkname = f + '/killMS.' + outsols + '.sols.npz'
if options['restart'] and os.path.isfile(checkname):
warn('Solutions file ' + checkname +
' already exists, not running killMS step')
else:
runcommand = "killMS.py --MSName %s --SolverType KAFCA --PolMode Scalar --BaseImageName %s --dt %f --BeamMode LOFAR --LOFARBeamMode=A --NIterKF %i --CovQ 0.1 --LambdaKF=%f --NCPU %i --OutSolsName %s --NChanSols %i --PowerSmooth=%f --InCol %s --DDFCacheDir=%s" % (
f, imagename, options['dt'], niterkf, options['LambdaKF'],
options['NCPU_killms'], outsols, options['NChanSols'],
options['PowerSmooth'], colname, cache_dir)
if robust is None:
runcommand += ' --Weighting Natural'
else:
runcommand += ' --Weighting Briggs --Robust=%f' % robust
if uvrange is not None:
if wtuv is not None:
runcommand += ' --WTUV=%f --WeightUVMinMax=%f,%f' % (
wtuv, uvrange[0], uvrange[1])
else:
runcommand += ' --UVMinMax=%f,%f' % (uvrange[0],
uvrange[1])
if clusterfile is not None:
runcommand += ' --NodesFile ' + clusterfile
if dicomodel is not None:
runcommand += ' --DicoModel ' + dicomodel
if options['nobar']:
runcommand += ' --DoBar=0'
rootfilename = outsols.split('/')[-1]
f = f.replace("/", "_")
run(runcommand,
dryrun=options['dryrun'],
log=logfilename('KillMS-' + f + '_' + rootfilename + '.log',
options=options),
quiet=options['quiet'])
def update_frequencies(rootname, freq):
g = glob.glob(rootname + '*.fits')
for f in g:
with fits.open(f) as hdu:
if 'CRVAL4' in hdu[0].header:
warn('Updating FITS header for %s to freq of %f' % (f, freq))
hdu[0].header['CRVAL4'] = freq
hdu[0].header['RESTFRQ'] = freq
hdu.writeto(f, overwrite=True)
del hdu[0].data
def do_plot_facet_offsets(t, regfile, savefig=None):
''' convenience function to plot offsets '''
if savefig is not None and os.path.isfile(savefig):
warn('Figure file %s exists, not re-making it' % savefig)
else:
cra, cdec = get_centpos()
r = RegPoly(regfile, cra, cdec)
if isinstance(t, str):
t = Table.read(t)
if 'Facet' not in t.columns:
r.add_facet_labels(t)
plot_offsets(t, r.clist, 'red')
if savefig is not None:
plt.savefig(savefig)
def do_plot_facet_offsets(t,regfile,savefig=None):
''' convenience function to plot offsets '''
if savefig is not None and os.path.isfile(savefig):
warn('Figure file %s exists, not re-making it' % savefig)
else:
cra,cdec=get_centpos()
r=RegPoly(regfile,cra,cdec)
if isinstance(t,str):
t=Table.read(t)
if 'Facet' not in t.columns:
r.add_facet_labels(t)
plot_offsets(t,r.clist,'red')
if savefig is not None:
plt.savefig(savefig)
def crossmatch_image(lofarcat, auxcatname, options=None):
if options is None:
options = o
auxcat = options[auxcatname]
if options['restart'] and os.path.isfile(lofarcat + '_' + auxcatname +
'_match.fits'):
warn('File ' + lofarcat + '_' + auxcatname +
'_match.fits already exists, skipping source matching step')
else:
t = Table.read(lofarcat)
tab = Table.read(auxcat)
match_catalogues(t, tab, o[auxcatname + '_matchrad'], auxcatname)
t = t[~np.isnan(t[auxcatname + '_separation'])]
t.write(lofarcat + '_' + auxcatname + '_match.fits')
def mask_dicomodel(indico, maskname, outdico, catcher=None):
if catcher: catcher.check()
if o['restart'] and os.path.isfile(outdico):
warn('File ' + outdico +
' already exists, skipping MaskDicoModel step')
return False
else:
runcommand = "MaskDicoModel.py --MaskName=%s --InDicoModel=%s --OutDicoModel=%s" % (
maskname, indico, outdico)
run(runcommand,
dryrun=o['dryrun'],
log=logfilename('MaskDicoModel-' + maskname + '.log'),
quiet=o['quiet'])
return True
def make_model(maskname, imagename, catcher=None):
# returns True if the step was run, False if skipped
if catcher: catcher.check()
fname = imagename + '.npy'
if o['restart'] and os.path.isfile(fname):
warn('File ' + fname + ' already exists, skipping MakeModel step')
return False
else:
runcommand = "MakeModel.py --MaskName=%s --BaseImageName=%s --NCluster=%i --DoPlot=0" % (
maskname, imagename, o['ndir'])
run(runcommand,
dryrun=o['dryrun'],
log=logfilename('MakeModel-' + maskname + '.log'),
quiet=o['quiet'])
return True
def smooth_solutions(mslist, ddsols, interval, catcher=None):
outsols = ddsols + '.Smooth'
filenames = [l.strip() for l in open(mslist, 'r').readlines()]
for f in filenames:
if catcher: catcher.check()
checkname = f + '/killMS.' + outsols + '.sols.npz'
if o['restart'] and os.path.isfile(checkname):
warn('Solutions file ' + checkname +
' already exists, not running smoothing step')
else:
smoothsols.main(options=dotdict({
'MSName': f,
'Order': 2,
'Plot': False,
'SolsFile': ddsols,
'WSize': interval
}))
return outsols
def crossmatch_image(lofarcat,auxcatname,options=None,catdir='.'):
if options is None:
options = o
auxcat = options[auxcatname]
crossmatchname=lofarcat + '_' + auxcatname + '_match.fits'
if options['restart'] and os.path.isfile(crossmatchname):
warn('File ' + crossmatchname+ ' already exists, skipping source matching step')
t=Table.read(crossmatchname)
matches=len(t)
del(t)
else:
t=Table.read(lofarcat)
tab=Table.read(catdir+'/'+auxcat)
matches=match_catalogues(t,tab,o[auxcatname+'_matchrad'],auxcatname)
t=t[~np.isnan(t[auxcatname+'_separation'])]
t.write(lofarcat+'_'+auxcatname+'_match.fits')
return matches
def crossmatch_image(lofarcat,auxcatname,options=None,catdir='.'):
if options is None:
options = o
auxcat = options[auxcatname]
crossmatchname=lofarcat + '_' + auxcatname + '_match.fits'
if options['restart'] and os.path.isfile(crossmatchname):
warn('File ' + crossmatchname+ ' already exists, skipping source matching step')
t=Table.read(crossmatchname)
matches=len(t)
del(t)
else:
t=Table.read(lofarcat)
tab=Table.read(catdir+'/'+auxcat)
matches=match_catalogues(t,tab,o[auxcatname+'_matchrad'],auxcatname)
t=t[~np.isnan(t[auxcatname+'_separation'])]
t.write(lofarcat+'_'+auxcatname+'_match.fits')
return matches
def check_imaging_weight(mslist_name):
# returns a boolean that says whether it did something
result = False
report('Checking for IMAGING_WEIGHT in input MSS')
mslist = [s.strip() for s in open(mslist_name).readlines()]
for ms in mslist:
t = pt.table(ms)
try:
dummy = t.getcoldesc('IMAGING_WEIGHT')
except RuntimeError:
dummy = None
t.close()
if dummy is not None:
warn('Table ' + ms + ' already has imaging weights')
else:
pt.addImagingColumns(ms)
result = True
return result
def ddf_shift(imagename, shiftfile, catcher=None, options=None, verbose=False):
if catcher: catcher.check()
if options is None:
options = o # attempt to get global if it exists
cache_dir = find_cache_dir(options)
runcommand = 'DDF.py ' + imagename + '.parset --Output-Name=' + imagename + '_shift --Image-Mode=RestoreAndShift --Output-ShiftFacetsFile=' + shiftfile + ' --Predict-InitDicoModel ' + imagename + '.DicoModel --Cache-SmoothBeam=force --Cache-Dir=' + cache_dir
fname = imagename + '_shift.app.facetRestored.fits'
if options['restart'] and os.path.isfile(fname):
warn('File ' + fname + ' already exists, skipping DDF-shift step')
if verbose:
print 'would have run', runcommand
else:
run(runcommand,
dryrun=options['dryrun'],
log=logfilename('DDF-' + imagename + '_shift.log',
options=options),
quiet=options['quiet'])
def sfind_image(catprefix,pbimage,nonpbimage,sfind_pixel_fraction,options=None):
if options is None:
options = o
f = fits.open(nonpbimage)
imsizex = f[0].header['NAXIS1']
imsizey = f[0].header['NAXIS2']
f.close()
kwargs={}
if o['sfind_pixel_fraction']<1.0:
lowerx,upperx = int(((1.0-sfind_pixel_fraction)/2.0)*imsizex),int(((1.0-sfind_pixel_fraction)/2.0)*imsizex + sfind_pixel_fraction*imsizex)
lowery,uppery = int(((1.0-sfind_pixel_fraction)/2.0)*imsizey),int(((1.0-sfind_pixel_fraction)/2.0)*imsizey + sfind_pixel_fraction*imsizey)
kwargs['trim_box']=(lowerx,upperx,lowery,uppery)
if options['restart'] and os.path.isfile(catprefix +'.cat.fits'):
warn('File ' + catprefix +'.cat.fits already exists, skipping source finding step')
else:
img = bdsm.process_image(pbimage, detection_image=nonpbimage, thresh_isl=4.0, thresh_pix=5.0, rms_box=(160,50), 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=True,atrous_jmax=4, flagging_opts=True, flag_maxsize_fwhm=0.5,advanced_opts=True, blank_limit=None,**kwargs)
img.write_catalog(outfile=catprefix +'.cat.fits',catalog_type='srl',format='fits',correct_proj='True')
img.export_image(outfile=catprefix +'.rms.fits',img_type='rms',img_format='fits',clobber=True)
img.export_image(outfile=catprefix +'.resid.fits',img_type='gaus_resid',img_format='fits',clobber=True)
img.export_image(outfile=catprefix +'.pybdsmmask.fits',img_type='island_mask',img_format='fits',clobber=True)
img.write_catalog(outfile=catprefix +'.cat.reg',catalog_type='srl',format='ds9',correct_proj='True')
def sfind_image(catprefix,pbimage,nonpbimage,sfind_pixel_fraction,options=None):
if options is None:
options = o
f = fits.open(nonpbimage)
imsizex = f[0].header['NAXIS1']
imsizey = f[0].header['NAXIS2']
f.close()
kwargs={}
if options['sfind_pixel_fraction']<1.0:
lowerx,upperx = int(((1.0-sfind_pixel_fraction)/2.0)*imsizex),int(((1.0-sfind_pixel_fraction)/2.0)*imsizex + sfind_pixel_fraction*imsizex)
lowery,uppery = int(((1.0-sfind_pixel_fraction)/2.0)*imsizey),int(((1.0-sfind_pixel_fraction)/2.0)*imsizey + sfind_pixel_fraction*imsizey)
kwargs['trim_box']=(lowerx,upperx,lowery,uppery)
if options['restart'] and os.path.isfile(catprefix +'.cat.fits'):
warn('File ' + catprefix +'.cat.fits already exists, skipping source finding step')
else:
img = bdsm.process_image(pbimage, detection_image=nonpbimage, thresh_isl=4.0, thresh_pix=5.0, rms_box=(160,50), 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=True,atrous_jmax=4, flagging_opts=True, flag_maxsize_fwhm=0.5,advanced_opts=True, ncores=options['NCPU'], blank_limit=None,**kwargs)
img.write_catalog(outfile=catprefix +'.cat.fits',catalog_type='srl',format='fits',correct_proj='True')
img.export_image(outfile=catprefix +'.rms.fits',img_type='rms',img_format='fits',clobber=True)
img.export_image(outfile=catprefix +'.resid.fits',img_type='gaus_resid',img_format='fits',clobber=True)
img.export_image(outfile=catprefix +'.pybdsmmask.fits',img_type='island_mask',img_format='fits',clobber=True)
img.write_catalog(outfile=catprefix +'.cat.reg',catalog_type='srl',format='ds9',correct_proj='True')
def run_reprocess(wd=None):
# by default assume we're in the working directory at this point
if wd is not None:
os.chdir(wd)
update_status(None,'Running')
solsfile = glob.glob('DDS3_full*smoothed.npz')
if len(solsfile) < 1:
die('Cannot find the correct solution file -- exiting')
solsfile = str(solsfile[0])
o = options('reprocess-vlow.cfg',option_list)
cubefiles=['image_full_vlow_QU.cube.dirty.fits','image_full_vlow_QU.cube.dirty.corr.fits']
cthreads=[]
flist=[]
ddf_kw = {}
do_polcubes('DATA','[DDS3_full_smoothed,DDS3_full_slow]',[o['image_uvmin'],1.600000],'image_full_vlow',ddf_kw,beamsize=o['vlow_psf_arcsec'],imsize=o['vlow_imsize'],cellsize=o['vlow_cell'],robust=o['vlow_robust'],options=o,catcher=None)
if o['compress_polcubes']:
for cubefile in cubefiles:
if o['restart'] and os.path.isfile(cubefile+'.fz'):
warn('Compressed cube file '+cubefile+'.fz already exists, not starting compression thread')
else:
report('Starting compression thread for '+cubefile)
thread = threading.Thread(target=compress_fits, args=(cubefile,o['fpack_q']))
thread.start()
cthreads.append(thread)
flist.append(cubefile)
if o['compress_polcubes']:
# cthreads and flist exist
for thread in cthreads:
if thread.isAlive():
warn('Waiting for a compression thread to finish')
thread.join()
if o['delete_compressed']:
for f in flist:
warn('Deleting compressed file %s' % f)
os.remove(f)
update_status(None,'Complete')
def do_run_pipeline(name, basedir):
if name[0] != 'P' and name[0] != 'L':
die('This code should be used only with field or observation names',
database=False)
do_field = (name[0] == 'P')
try:
qsubfile = sys.argv[2]
except:
qsubfile = '/home/mjh/pipeline-master/ddf-pipeline/torque/pipeline.qsub'
workdir = basedir + '/' + name
try:
os.mkdir(workdir)
except OSError:
warn('Working directory already exists')
report('Downloading data')
if do_field:
success = download_field(name, basedir=basedir)
else:
success = download_dataset('https://lofar-webdav.grid.sara.nl',
'/SKSP/' + name + '/',
basedir=basedir)
if not success:
die('Download failed, see earlier errors', database=False)
report('Unpacking data')
try:
unpack(workdir=workdir)
except RuntimeError:
if do_field:
update_status(name, 'List failed', workdir=workdir)
raise
if do_field:
update_status(name, 'Unpacked', workdir=workdir)
report('Deleting tar files')
os.system('rm ' + workdir + '/*.tar.gz')
os.system('rm ' + workdir + '/*.tar')
averaged = False
report('Checking structure')
g = glob.glob(workdir + '/*.ms')
msl = MSList(None, mss=g)
uobsids = set(msl.obsids)
for thisobs in uobsids:
# check one MS with each ID
for m, ch, o in zip(msl.mss, msl.channels, msl.obsids):
if o == thisobs:
channels = len(ch)
print 'MS', m, 'has', channels, 'channels'
if channels > 20:
update_status(name, 'Averaging', workdir=workdir)
print 'Averaging needed for', thisobs, '!'
averaged = True
average(wildcard=workdir + '/*' + thisobs + '*')
os.system('rm -r ' + workdir + '/*' + thisobs +
'*pre-cal.ms')
break
report('Making ms lists')
success = make_list(workdir=workdir)
if do_field:
list_db_update(success, workdir=workdir)
if not success:
die('make_list could not construct the MS list', database=False)
report('Creating custom config file from template')
make_custom_config(name, workdir, do_field, averaged)
# now run the job
do_run_job(name, basedir=basedir, qsubfile=None, do_field=do_field)
cra, cdec = get_centpos()
r = RegPoly(regfile, cra, cdec)
if isinstance(t, str):
t = Table.read(t)
if 'Facet' not in t.columns:
r.add_facet_labels(t)
plot_offsets(t, r.clist, 'red')
if savefig is not None:
plt.savefig(savefig)
if __name__ == '__main__':
# Main loop
if len(sys.argv) < 2:
warn(
'quality_pipeline.py must be called with at least one parameter file\nor a command-line option list.\nE.g "pipeline.py example.cfg second_example.cfg --solutions-robust=0.1"\nSee below for a complete list of possible options with their default values.'
)
print_options(option_list)
sys.exit(1)
o = options(sys.argv[1:], option_list)
if o['pbimage'] is None:
die('pbimage must be specified')
if o['nonpbimage'] is None:
die('nonpbimage must be specified')
if o['list'] is not None:
# fix up the new list-type options
for i, cat in enumerate(o['list']):
try:
o[cat] = o['filenames'][i]
except:
def plot_position_offset(catalog,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if os.path.isfile(outname):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
x = scat[auxcatname+'_dRA']
y = scat[auxcatname+'_dDEC']
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
plt.xlabel('$RA_{\\rm LOFAR} - RA_{\\rm %s}$ (arcsec)' % auxcatname)
plt.ylabel('$DEC_{\\rm LOFAR} - DEC_{\\rm %s}$ (arcsec)' % auxcatname)
plt.xticks(rotation=270)
axScatter.plot(np.zeros(40),np.arange(-20,20),'k--')
axScatter.plot(np.arange(-20,20),np.zeros(40),'k--')
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
plt.xticks(rotation=270)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# the scatter plot:
axScatter.scatter(x, y,marker='+',s=2,alpha=0.3)
binwidth = 0.25
xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])
lim = (int(xymax/binwidth) + 1) * binwidth
axScatter.set_xlim((-lim, lim))
axScatter.set_ylim((-lim, lim))
bins = np.arange(-lim, lim + binwidth, binwidth)
axHistx.hist(x, bins=bins)
axHisty.hist(y, bins=bins, orientation='horizontal')
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axScatter.add_artist(Ellipse((np.median(x),np.median(y)),np.std(x),np.std(y),angle=0,linewidth=2,fill=False,color='k',zorder=10000))
plt.savefig(outname)
plt.close('all')
plt.cla()
plt.clf()
def plot_flux_errors(catalog,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if os.path.isfile(outname.replace('.png','_integrated.png')):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
radialseps = sepn(scat['RA']*deg2rad,scat['DEC']*deg2rad,fieldra*deg2rad,fielddec*deg2rad)*rad2deg
plt.plot(np.sort(scat['Total_flux']),np.sort(scat['Total_flux'])*np.median(np.array(scat[auxcatname+'_Total_flux']/options[auxcatname+'_fluxfactor'])/np.array(scat['Total_flux'])),'r--')
plt.plot(scat['Total_flux'],scat[auxcatname+'_Total_flux']/options[auxcatname+'_fluxfactor'],'ko')
plt.xlabel('Integrated LOFAR flux (Jy)')
plt.ylabel('Integrated '+auxcatname+' flux (Jy)')
plt.xlim(xmin=0.01,xmax=0.5)
plt.ylim(ymin=0.01,ymax=0.5)
plt.semilogx()
plt.semilogy()
equality = np.arange(0,10,0.01)
plt.plot(equality,equality,'k-')
plt.savefig(outname.replace('.png','_integrated.png'))
plt.close('all')
plt.cla()
plt.clf()
plt.plot(np.sort(scat['Peak_flux']),np.sort(scat['Peak_flux'])*np.median(np.array(scat[auxcatname+'_Peak_flux']/options['%s_fluxfactor'%auxcatname])/np.array(scat['Peak_flux'])),'r--')
plt.plot(scat['Peak_flux'],scat[auxcatname+'_Peak_flux']/options[auxcatname+'_fluxfactor'],'ko')
plt.xlabel('Peak LOFAR flux (Jy)')
plt.ylabel('Peak '+auxcatname+' flux (Jy)')
plt.xlim(xmin=0.01,xmax=0.5)
plt.ylim(ymin=0.01,ymax=0.5)
plt.semilogx()
plt.semilogy()
equality = np.arange(0,10,0.01)
plt.plot(equality,equality,'k-')
plt.savefig(outname.replace('.png','_peak.png'))
plt.close('all')
plt.cla()
plt.clf()
plt.plot(radialseps,scat['Total_flux']/(scat[auxcatname+'_Total_flux']/options[auxcatname+'_fluxfactor']),'bo',alpha=0.4,markersize=3)
equality = np.arange(-0.01,10,0.01)
fractionrange = np.arange(0.99,1.01,0.002)
for i in fractionrange:
plt.plot(equality,1.0*i+0*equality,'k-')
plt.plot(equality,1.0+0*equality,'k-')
plt.xlabel('Distance from pointing centre (deg)')
plt.ylabel('Integrated LOFAR flux / Integrated '+auxcatname+' flux')
plt.xlim(xmin=0.0,xmax=2)
plt.ylim(ymin=0.5,ymax=2.0)
distancerange = np.arange(0,np.max(radialseps)+0.15,0.15)
for i in range(0,len(distancerange)-1):
distancemin = distancerange[i]
distancemax = distancerange[i+1]
binvals = np.array([])
for j in range(0,len(radialseps)):
if distancemin < radialseps[j] < distancemax:
binvals = np.append(binvals,scat['Total_flux'][j]/(scat[auxcatname+'_Total_flux'][j]/options[auxcatname+'_fluxfactor']))
midpoint = (distancemin+distancemax)/2.0
if len(binvals) > 0:
booterrl,booterrh = bootstrap(binvals, 100000, np.mean, 0.05)
booterrl,booterrh = bootstrap(binvals, 100000, np.median, 0.05)
# print booterrl,booterrh, binvals
plt.errorbar(midpoint,np.median(binvals),xerr=(distancemin-distancemax)/2.0,yerr=[[np.median(binvals)-booterrl],[booterrh-np.median(binvals)]],fmt='--o',ecolor='b',color='b',zorder=999999)
plt.savefig(outname.replace('.png','_total_radial.png'))
plt.close('all')
plt.cla()
plt.clf()
return outsols
def full_clearcache(o):
clearcache(o['mslist'], o)
clearcache('temp_mslist.txt', o)
if o['full_mslist'] is not None:
clearcache(o['full_mslist'], o)
if __name__ == '__main__':
# Main loop
report('Welcome to ddf-pipeline, version ' + __version__)
if len(sys.argv) < 2:
warn(
'pipeline.py must be called with at least one parameter file or a command-line\noption list.\nE.g "pipeline.py example.cfg second_example.cfg --solutions-robust=0.1"\nSee below for a complete list of possible options with their default values.'
)
print_options(option_list)
sys.exit(1)
o = options(sys.argv[1:], option_list)
if o['catch_signal']:
catcher = Catcher()
else:
catcher = None
uvrange = [o['image_uvmin'], o['uvmax']]
killms_uvrange = [0, 1000]
if o['solutions_uvmin'] is not None:
killms_uvrange[0] = o['solutions_uvmin']
def plot_flux_ratios(catalog,fitsimage,outname,options=None):
if options is None:
options = o
if os.path.isfile(outname):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fluxratios = scat['Total_flux']/scat['Peak_flux']
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
radialseps = sepn(scat['RA']*deg2rad,scat['DEC']*deg2rad,fieldra*deg2rad,fielddec*deg2rad)*rad2deg
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.8
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
plt.xlabel('Distance from pointing centre (deg)')
plt.ylabel('Integrated flux / Peak flux')
plt.xticks(rotation=270)
axScatter.plot([0,3.0],[1.0,1.0],'k-')
smids = []
svals = []
distancerange = np.arange(0,max(radialseps),0.3)
for i in range(0,len(distancerange)-1):
distancemin = distancerange[i]
distancemax = distancerange[i+1]
binvals = np.array([])
for j in range(0,len(radialseps)):
if distancemin < radialseps[j] < distancemax:
binvals = np.append(binvals,fluxratios[j])
midpoint = (distancemin+distancemax)/2.0
print i,binvals
#bsmear = bandwidth_smearing2(6*arcsec2deg,150E6,midpoint,4*48.8E3)
#tsmear = time_smearing2(16.0,midpoint,6*arcsec2deg)
#smids.append(midpoint)
#svals.append(1.0/(bsmear*tsmear))
#booterrl,booterrh = bootstrap(binvals, 100000, np.mean, 0.05)
if len(binvals)>0:
booterrl,booterrh = bootstrap(binvals, 100000, np.median, 0.05)
axScatter.errorbar(midpoint,np.median(binvals),xerr=(distancemin-distancemax)/2.0,yerr=[[np.median(binvals)-booterrl],[booterrh-np.median(binvals)]],fmt='--o',ecolor='b',color='b',zorder=999999)
#axScatter.plot(smids,svals,'k--')
axHisty = plt.axes(rect_histy)
plt.xticks(rotation=270)
# no labels
axHisty.yaxis.set_major_formatter(nullfmt)
# the scatter plot:
axScatter.scatter(radialseps,fluxratios,color='blue',marker='+',s=10,alpha=0.5)
binwidth = 0.05
axScatter.set_xlim((0, 3.0))
axScatter.set_ylim((0.8, 2.5))
bins = np.arange(0.8, 3, binwidth)
axHisty.hist(fluxratios, bins=bins, orientation='horizontal',color='blue',alpha=0.5)
axHisty.set_ylim(axScatter.get_ylim())
plt.savefig(outname)
plt.close('all')
plt.cla()
plt.clf()
def plot_flux_ratios(catalog,fitsimage,outname,options=None):
if options is None:
options = o
if os.path.isfile(outname):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fluxratios = old_div(scat['Total_flux'],scat['Peak_flux'])
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
radialseps = sepn(scat['RA']*deg2rad,scat['DEC']*deg2rad,fieldra*deg2rad,fielddec*deg2rad)*rad2deg
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.8
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
plt.xlabel('Distance from pointing centre (deg)')
plt.ylabel('Integrated flux / Peak flux')
plt.xticks(rotation=270)
axScatter.plot([0,3.0],[1.0,1.0],'k-')
smids = []
svals = []
distancerange = np.arange(0,max(radialseps),0.3)
for i in range(0,len(distancerange)-1):
distancemin = distancerange[i]
distancemax = distancerange[i+1]
binvals = np.array([])
for j in range(0,len(radialseps)):
if distancemin < radialseps[j] < distancemax:
binvals = np.append(binvals,fluxratios[j])
midpoint = (distancemin+distancemax)/2.0
#print i,binvals
#bsmear = bandwidth_smearing2(6*arcsec2deg,150E6,midpoint,4*48.8E3)
#tsmear = time_smearing2(16.0,midpoint,6*arcsec2deg)
#smids.append(midpoint)
#svals.append(1.0/(bsmear*tsmear))
#booterrl,booterrh = bootstrap(binvals, 100000, np.mean, 0.05)
if len(binvals)>0:
booterrl,booterrh = bootstrap(binvals, 100000, np.median, 0.05)
axScatter.errorbar(midpoint,np.median(binvals),xerr=(distancemin-distancemax)/2.0,yerr=[[np.median(binvals)-booterrl],[booterrh-np.median(binvals)]],fmt='--o',ecolor='b',color='b',zorder=999999)
#axScatter.plot(smids,svals,'k--')
axHisty = plt.axes(rect_histy)
plt.xticks(rotation=270)
# no labels
axHisty.yaxis.set_major_formatter(nullfmt)
# the scatter plot:
axScatter.scatter(radialseps,fluxratios,color='blue',marker='+',s=10,alpha=0.5)
binwidth = 0.05
axScatter.set_xlim((0, 3.0))
axScatter.set_ylim((0.8, 2.5))
bins = np.arange(0.8, 3, binwidth)
axHisty.hist(fluxratios, bins=bins, orientation='horizontal',color='blue',alpha=0.5)
axHisty.set_ylim(axScatter.get_ylim())
plt.savefig(outname)
plt.close('all')
plt.cla()
plt.clf()
def do_run_subtract(name,basedir,inarchivedir,outarchivedir,force=False):
startdir = os.getcwd()
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
sdb.close()
fields = extractdict['fields'].split(',')
extract_status = extractdict['extract_status'].split(',')
print 'Working on ',name, 'in fields', fields,'which have status',extract_status
for i in range(0,len(fields)):
os.chdir(startdir)
if not(extract_status[i] == 'EREADY' or (force and extract_status[i] == 'STARTED')):
continue
field = fields[i]
workdir=basedir+'/'+name
try:
os.mkdir(workdir)
except OSError:
warn('Working directory already exists')
print 'In directory', os.getcwd()
os.chdir(workdir)
# Update status to running here
extract_status[i] = 'STARTED'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to STARTED for',field,name
time.sleep(2.0)
report('Copying data from %s'%inarchivedir)
# WANT TO MAKE THIS INTO A RSYNC SO THAT IT CAN BE DONE OUTSIDE LEIDEN
#os.system('cp -r %s/%s %s'%(inarchivedir,field,workdir))
do_rsync_download(field,inarchivedir,workdir)
# Update status to copied here
extract_status[i] = 'COPIED'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to COPIED for',field,name
# Create boxfile
create_ds9_region('%s.ds9.reg'%name,extractdict['ra'],extractdict['decl'],extractdict['size'])
# Run subtract code
print os.getcwd(), 'working here'
os.chdir(field)
print ('sub-sources-outside-region.py -b %s/%s.ds9.reg -p %s'%(workdir,name,name))
result=os.system('sub-sources-outside-region.py -b %s/%s.ds9.reg -p %s'%(workdir,name,name))
if result!=0:
raise RuntimeError('sub-sources-outside-region.py failed with error code %i' % result)
# Archive the results need an rsync code this is just the *archive file that needs to be archived.
#os.system('mkdir %s/%s'%(outarchivedir,name))
#os.system('mkdir %s/%s/%s'%(outarchivedir,name,field))
os.chdir(workdir)
f = glob.glob('%s/*.archive*'%(field))
do_rsync_upload(name,field,f)
#print ('cp -r %s_%s.dysco.sub.shift.avg.weights.ms.archive %s/%s/%s'%(field,name,outarchivedir,name,field))
#os.system('cp -r %s_%s.dysco.sub.shift.avg.weights.ms.archive %s/%s/%s'%(field,name,outarchivedir,name,field))
# update the database to give success
extract_status[i] = 'EDONE'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to EDONE for',field,name
# update the database to give selfcal status as SREADY
selfcal_status = 'SREADY'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['selfcal_status'] = selfcal_status
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to SREADY for',name
def run_bootstrap(o):
colname='DATA_DI_CORRECTED'
if o['mslist'] is None:
die('MS list must be specified')
if o['logging'] is not None and not os.path.isdir(o['logging']):
os.mkdir(o['logging'])
# check the data supplied
if o['frequencies'] is None or o['catalogues'] is None:
die('Frequencies and catalogues options must be specified')
if "DDF_PIPELINE_CATALOGS" not in os.environ.keys():
warn("You need to define the environment variable DDF_PIPELINE_CATALOGS where your catalogs are located")
sys.exit(2)
o["tgss"]=o["tgss"].replace("$$",os.environ["DDF_PIPELINE_CATALOGS"])
o["catalogues"]=[l.replace("$$",os.environ["DDF_PIPELINE_CATALOGS"]) for l in o["catalogues"]]
lCat=o["catalogues"]+[o["tgss"]]
for fCat in lCat:
if not os.path.isfile(fCat):
warn("Catalog %s does not exist"%fCat)
sys.exit(2)
cl=len(o['catalogues'])
if o['names'] is None:
o['names']=[os.path.basename(x).replace('.fits','') for x in o['catalogues']]
if o['radii'] is None:
o['radii']=[10]*cl
if o['groups'] is None:
o['groups']=range(cl)
if (len(o['frequencies'])!=cl or len(o['radii'])!=cl or
len(o['names'])!=cl or len(o['groups'])!=cl):
die('Names, groups, radii and frequencies entries must be the same length as the catalogue list')
low_uvrange=[o['image_uvmin'],2.5*206.0/o['low_psf_arcsec']]
if o['low_imsize'] is not None:
low_imsize=o['low_imsize'] # allow over-ride
else:
low_imsize=o['imsize']*o['cellsize']/o['low_cell']
low_robust=o['low_robust']
# Clear the shared memory
run('CleanSHM.py',dryrun=o['dryrun'])
# We use the individual ms in mslist.
m=MSList(o['mslist'])
Uobsid = set(m.obsids)
for obsid in Uobsid:
warn('Running bootstrap for obsid %s' % obsid)
freqs=[]
omslist=[]
for ms,ob,f in zip(m.mss,m.obsids,m.freqs):
if ob==obsid:
omslist.append(ms)
freqs.append(f)
if len(freqs)<4:
die('Not enough frequencies to bootstrap. Check your mslist or MS naming scheme')
# sort to work in frequency order
freqs,omslist = (list(x) for x in zip(*sorted(zip(freqs, omslist), key=lambda pair: pair[0])))
for f,ms in zip(freqs,omslist):
print ms,f
# generate the sorted input mslist
with open('temp_mslist.txt','w') as f:
for line in omslist:
f.write(line+'\n')
# Clean in cube mode
# As for the main pipeline, first make a dirty map
ddf_image('image_bootstrap_'+obsid+'_init','temp_mslist.txt',
cleanmask=None,cleanmode='SSD',ddsols='DDS0',
applysols='P',majorcycles=0,robust=low_robust,
uvrange=low_uvrange,beamsize=o['low_psf_arcsec'],
imsize=low_imsize,cellsize=o['low_cell'],
options=o,colname=colname,automask=True,
automask_threshold=15,smooth=True,cubemode=True,
conditional_clearcache=True)
external_mask='bootstrap_external_mask.fits'
make_external_mask(external_mask,'image_bootstrap_'+obsid+'_init.dirty.fits',use_tgss=True,clobber=False,cellsize='low_cell',options=o)
# Deep SSD clean with this external mask and automasking
ddf_image('image_bootstrap_'+obsid,'temp_mslist.txt',
cleanmask=external_mask,reuse_psf=True,reuse_dirty=True,
cleanmode='SSD',ddsols='DDS0',applysols='P',
majorcycles=5,robust=low_robust,uvrange=low_uvrange,
beamsize=o['low_psf_arcsec'],imsize=low_imsize,
cellsize=o['low_cell'],options=o,
colname=colname,automask=True,
automask_threshold=15,smooth=True,cubemode=True,
conditional_clearcache=False)
if os.path.isfile('image_bootstrap_'+obsid+'.cube.int.restored.pybdsm.srl'):
warn('Source list exists, skipping source extraction')
else:
warn('Running PyBDSM, please wait...')
img=bdsm.process_image('image_bootstrap_'+obsid+'.cube.int.restored.fits',thresh_pix=5,rms_map=True,atrous_do=True,atrous_jmax=2,group_by_isl=True,rms_box=(80,20), adaptive_rms_box=True, adaptive_thresh=80, rms_box_bright=(35,7),mean_map='zero',spectralindex_do=True,specind_maxchan=1,debug=True,kappa_clip=3,flagchan_rms=False,flagchan_snr=False,incl_chan=True,spline_rank=1)
# Write out in ASCII to work round bug in pybdsm
img.write_catalog(catalog_type='srl',format='ascii',incl_chan='true')
img.export_image(img_type='rms',img_format='fits')
from make_fitting_product import make_catalogue
import fitting_factors
import find_outliers
# generate the fitting product
if os.path.isfile(obsid+'crossmatch-1.fits'):
warn('Crossmatch table exists, skipping crossmatch')
else:
t = pt.table(omslist[0]+ '/FIELD', readonly=True, ack=False)
direction = t[0]['PHASE_DIR']
ra, dec = direction[0]
if (ra<0):
ra+=2*np.pi
ra*=180.0/np.pi
dec*=180.0/np.pi
cats=zip(o['catalogues'],o['names'],o['groups'],o['radii'])
make_catalogue('image_bootstrap_'+obsid+'.cube.int.restored.pybdsm.srl',ra,dec,2.5,cats,outnameprefix=obsid)
freqlist=open(obsid+'frequencies.txt','w')
for n,f in zip(o['names'],o['frequencies']):
freqlist.write('%f %s_Total_flux %s_E_Total_flux False\n' % (f,n,n))
for i,f in enumerate(freqs):
freqlist.write('%f Total_flux_ch%i E_Total_flux_ch%i True\n' % (f,i+1,i+1))
freqlist.close()
# Now call the fitting code
if os.path.isfile(obsid+'crossmatch-results-1.npy'):
warn('Results 1 exists, skipping first fit')
else:
fitting_factors.run_all(1, name=obsid)
nreject=-1 # avoid error if we fail somewhere
if os.path.isfile(obsid+'crossmatch-2.fits'):
warn('Second crossmatch exists, skipping outlier rejection')
else:
nreject=find_outliers.run_all(1, name=obsid)
if os.path.isfile(obsid+'crossmatch-results-2.npy'):
warn('Results 2 exists, skipping second fit')
else:
if nreject==0:
shutil.copyfile(obsid+'crossmatch-results-1.npy',obsid+'crossmatch-results-2.npy')
if os.path.isfile(obsid+'crossmatch-results-2.npy'):
warn('Results 2 exists, skipping first fit')
else:
fitting_factors.run_all(2, name=obsid)
# Now apply corrections
if o['full_mslist'] is None:
die('Need big mslist to apply corrections')
if not(o['dryrun']):
warn('Applying corrections to MS list')
scale=np.load(obsid+'crossmatch-results-2.npy')[:,0]
# InterpolatedUS gives us linear interpolation between points
# and extrapolation outside it
spl = InterpolatedUnivariateSpline(freqs, scale, k=1)
bigmslist=[s.strip() for s in open(o['full_mslist']).readlines()]
obigmslist = [ms for ms in bigmslist if obsid in ms]
for ms in obigmslist:
t = pt.table(ms)
try:
dummy=t.getcoldesc('SCALED_DATA')
except RuntimeError:
dummy=None
t.close()
if dummy is not None:
warn('Table '+ms+' has already been corrected, skipping')
else:
# in this version we need to scale both the original data and the data in colname
t = pt.table(ms+'/SPECTRAL_WINDOW', readonly=True, ack=False)
frq=t[0]['REF_FREQUENCY']
factor=spl(frq)
print frq,factor
t=pt.table(ms,readonly=False)
desc=t.getcoldesc(o['colname'])
desc['name']='SCALED_DATA'
t.addcols(desc)
d=t.getcol(o['colname'])
d*=factor
t.putcol('SCALED_DATA',d)
try:
dummy=t.getcoldesc(colname)
except RuntimeError:
dummy=None
if dummy is not None:
desc=t.getcoldesc(colname)
newname=colname+'_SCALED'
desc['name']=newname
t.addcols(desc)
d=t.getcol(colname)
d*=factor
t.putcol(newname,d)
t.close()
if os.path.isfile('image_bootstrap.app.mean.fits'):
warn('Mean bootstrap image exists, not creating it')
else:
warn('Creating mean bootstrap image')
hdus=[]
for obsid in Uobsid:
hdus.append(fits.open('image_bootstrap_'+obsid+'.app.restored.fits'))
for i in range(1,len(Uobsid)):
hdus[0][0].data+=hdus[i][0].data
hdus[0][0].data/=len(Uobsid)
hdus[0].writeto('image_bootstrap.app.mean.fits')
warn('Figure file %s exists, not re-making it' % savefig)
else:
cra,cdec=get_centpos()
r=RegPoly(regfile,cra,cdec)
if isinstance(t,str):
t=Table.read(t)
if 'Facet' not in t.columns:
r.add_facet_labels(t)
plot_offsets(t,r.clist,'red')
if savefig is not None:
plt.savefig(savefig)
if __name__=='__main__':
# Main loop
if len(sys.argv)<2:
warn('quality_pipeline.py must be called with at least one parameter file\nor a command-line option list.\nE.g "pipeline.py example.cfg second_example.cfg --solutions-robust=0.1"\nSee below for a complete list of possible options with their default values.')
print_options(option_list)
sys.exit(1)
o=options(sys.argv[1:],option_list)
if o['pbimage'] is None:
die('pbimage must be specified')
if o['nonpbimage'] is None:
die('nonpbimage must be specified')
if o['list'] is not None:
# fix up the new list-type options
for i,cat in enumerate(o['list']):
try:
o[cat]=o['filenames'][i]
except:
pass
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'])
image_root='image_full_ampphase_di_m.NS'
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:
if method=='pslocal':
data=Table.read(method+'/'+method+'.txt',format='ascii')
data['RA'].name='ra'
data['DEC'].name='dec'
data.write(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'
gaulfile=catfile.replace('cat','gaul')
if os.path.isfile(catfile):
warn('Catalogue already exists, skipping pybdsf run')
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')
img.write_catalog(outfile=gaulfile,catalog_type='gaul',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'
cra,cdec=get_centpos()
report('Set up structure')
NDir=np.load("image_dirin_SSD_m.npy.ClusterCat.npy").shape[0]
oo=Offsets(method,n=NDir,imroot=image_root,cellsize=o['cellsize'],fitmethod=o['fit'])
report('Label table')
lofar_l=oo.r.add_facet_labels(lofar)
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')
def do_run_selfcal(name,basedir,inarchivedir,outarchivedir):
startdir = os.getcwd()
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
sdb.close()
fields = extractdict['fields'].split(',')
selfcal_status = extractdict['selfcal_status']
extract_status = extractdict['extract_status'].split(',')
print 'Working on ',name, 'in fields', fields,'current selfcal status',selfcal_status
workdir=basedir+'/'+name
try:
os.mkdir(workdir)
except OSError:
warn('Working directory already exists')
print 'In directory', os.getcwd()
os.chdir(workdir)
# Update status to running here
selfcal_status = 'STARTED'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['selfcal_status'] = selfcal_status
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to STARTED for',name
time.sleep(2.0)
print 'Starting rsync'
fieldstring = ''
for fieldid, field in enumerate(fields):
print field, fields
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
sdb.close()
extract_status = extractdict['extract_status'].split(',')
if extract_status[fieldid] == 'EDONE':
cmd = '%s/%s/%s/%s_%s*archive*'%(inarchivedir,name,field,field,name)
observations = check_output('ssh [email protected] ls -d ' + cmd, shell=True)
print 'ssh [email protected] ls -d ' + cmd
observations = observations.split('\n')[:-1] # remove last empty item in this list
else:
observations = []
print 'DATA LOCATIONS', observations
print 'FIELDS', fields
print 'EXTRACT STATUS', extract_status
for observation in observations:
print observation
report('Copying data from %s'%observation)
#'+ inarchivedir +'/' + name + '/' + field +'/' + field +'_'+name +'.dysco.sub.shift.avg.weights.ms.archive')
do_rsync_download(observation.split('/')[-1],inarchivedir +'/'+name + '/'+field +'/',workdir)
fieldstring += observation.split('/')[-1] + ' '
#'%s_%s.dysco.sub.shift.avg.weights.ms.archive '%(field,name)
fieldstring = fieldstring[:-1]
# Update status to copied here
report('Updating %s status to copied'%name)
selfcal_status = 'COPIED'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['selfcal_status'] = selfcal_status
uvminstr = str(extractdict['uvmin'])
uvmin = extractdict['uvmin']
sdb.db_set('reprocessing',extractdict)
sdb.close()
# Create boxfile
report('Create ds9 region file for extraction')
create_ds9_region('%s.ds9.reg'%name,extractdict['ra'],extractdict['decl'],extractdict['size'])
# Run subtract code
print os.getcwd(), 'working here'
if uvmin > 0.0:
print ('runwsclean.py --uvmin=%s -b %s.ds9.reg -i %s %s'%(uvminstr,name,name+"_image",fieldstring))
os.system('runwsclean.py --uvmin=%s -b %s.ds9.reg -i %s %s'%(uvminstr,name,name+"_image",fieldstring))
else:
print ('runwsclean.py -b %s.ds9.reg -i %s %s'%(name,name+"_image",fieldstring))
os.system('runwsclean.py -b %s.ds9.reg -i %s %s'%(name,name+"_image",fieldstring))
report('Archiving the results to %s'%outarchivedir)
os.chdir(workdir)
f = glob.glob('%s.ds9.tar.gz'%(name))
do_rsync_upload(name,outarchivedir,f)
# update the database to give success
selfcal_status = 'SDONE'
sdb=SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['selfcal_status'] = selfcal_status
sdb.db_set('reprocessing',extractdict)
sdb.close()
print 'Updated status to SDONE for',name
def run_bootstrap(o):
colname = 'DATA_DI_CORRECTED'
if o['mslist'] is None:
die('MS list must be specified')
if o['logging'] is not None and not os.path.isdir(o['logging']):
os.mkdir(o['logging'])
# check the data supplied
if o['frequencies'] is None or o['catalogues'] is None:
die('Frequencies and catalogues options must be specified')
if "DDF_PIPELINE_CATALOGS" not in os.environ.keys():
warn(
"You need to define the environment variable DDF_PIPELINE_CATALOGS where your catalogs are located"
)
sys.exit(2)
o["tgss"] = o["tgss"].replace("$$", os.environ["DDF_PIPELINE_CATALOGS"])
o["catalogues"] = [
l.replace("$$", os.environ["DDF_PIPELINE_CATALOGS"])
for l in o["catalogues"]
]
lCat = o["catalogues"] + [o["tgss"]]
for fCat in lCat:
if not os.path.isfile(fCat):
warn("Catalog %s does not exist" % fCat)
sys.exit(2)
cl = len(o['catalogues'])
if o['names'] is None:
o['names'] = [
os.path.basename(x).replace('.fits', '') for x in o['catalogues']
]
if o['radii'] is None:
o['radii'] = [10] * cl
if o['groups'] is None:
o['groups'] = range(cl)
if (len(o['frequencies']) != cl or len(o['radii']) != cl
or len(o['names']) != cl or len(o['groups']) != cl):
die('Names, groups, radii and frequencies entries must be the same length as the catalogue list'
)
low_uvrange = [o['image_uvmin'], 2.5 * 206.0 / o['low_psf_arcsec']]
if o['low_imsize'] is not None:
low_imsize = o['low_imsize'] # allow over-ride
else:
low_imsize = o['imsize'] * o['cellsize'] / o['low_cell']
low_robust = o['low_robust']
# Clear the shared memory
run('CleanSHM.py', dryrun=o['dryrun'])
# We use the individual ms in mslist.
m = MSList(o['mslist'])
Uobsid = set(m.obsids)
for obsid in Uobsid:
warn('Running bootstrap for obsid %s' % obsid)
freqs = []
omslist = []
for ms, ob, f in zip(m.mss, m.obsids, m.freqs):
if ob == obsid:
omslist.append(ms)
freqs.append(f)
if len(freqs) < 4:
die('Not enough frequencies to bootstrap. Check your mslist or MS naming scheme'
)
# sort to work in frequency order
freqs, omslist = (list(x) for x in zip(
*sorted(zip(freqs, omslist), key=lambda pair: pair[0])))
for f, ms in zip(freqs, omslist):
print ms, f
# generate the sorted input mslist
with open('temp_mslist.txt', 'w') as f:
for line in omslist:
f.write(line + '\n')
# Clean in cube mode
# As for the main pipeline, first make a dirty map
ddf_image('image_bootstrap_' + obsid + '_init',
'temp_mslist.txt',
cleanmask=None,
cleanmode='SSD',
ddsols='DDS0',
applysols='P',
majorcycles=0,
robust=low_robust,
uvrange=low_uvrange,
beamsize=o['low_psf_arcsec'],
imsize=low_imsize,
cellsize=o['low_cell'],
options=o,
colname=colname,
automask=True,
automask_threshold=15,
smooth=True,
cubemode=True,
conditional_clearcache=True)
external_mask = 'bootstrap_external_mask.fits'
make_external_mask(external_mask,
'image_bootstrap_' + obsid + '_init.dirty.fits',
use_tgss=True,
clobber=False,
cellsize='low_cell',
options=o)
# Deep SSD clean with this external mask and automasking
ddf_image('image_bootstrap_' + obsid,
'temp_mslist.txt',
cleanmask=external_mask,
reuse_psf=True,
reuse_dirty=True,
cleanmode='SSD',
ddsols='DDS0',
applysols='P',
majorcycles=5,
robust=low_robust,
uvrange=low_uvrange,
beamsize=o['low_psf_arcsec'],
imsize=low_imsize,
cellsize=o['low_cell'],
options=o,
colname=colname,
automask=True,
automask_threshold=15,
smooth=True,
cubemode=True,
conditional_clearcache=False)
if os.path.isfile('image_bootstrap_' + obsid +
'.cube.int.restored.pybdsm.srl'):
warn('Source list exists, skipping source extraction')
else:
warn('Running PyBDSM, please wait...')
img = bdsm.process_image('image_bootstrap_' + obsid +
'.cube.int.restored.fits',
thresh_pix=5,
rms_map=True,
atrous_do=True,
atrous_jmax=2,
group_by_isl=True,
rms_box=(80, 20),
adaptive_rms_box=True,
adaptive_thresh=80,
rms_box_bright=(35, 7),
mean_map='zero',
spectralindex_do=True,
specind_maxchan=1,
debug=True,
kappa_clip=3,
flagchan_rms=False,
flagchan_snr=False,
incl_chan=True,
spline_rank=1)
# Write out in ASCII to work round bug in pybdsm
img.write_catalog(catalog_type='srl',
format='ascii',
incl_chan='true')
img.export_image(img_type='rms', img_format='fits')
from make_fitting_product import make_catalogue
import fitting_factors
import find_outliers
# generate the fitting product
if os.path.isfile(obsid + 'crossmatch-1.fits'):
warn('Crossmatch table exists, skipping crossmatch')
else:
t = pt.table(omslist[0] + '/FIELD', readonly=True, ack=False)
direction = t[0]['PHASE_DIR']
ra, dec = direction[0]
if (ra < 0):
ra += 2 * np.pi
ra *= 180.0 / np.pi
dec *= 180.0 / np.pi
cats = zip(o['catalogues'], o['names'], o['groups'], o['radii'])
make_catalogue('image_bootstrap_' + obsid +
'.cube.int.restored.pybdsm.srl',
ra,
dec,
2.5,
cats,
outnameprefix=obsid)
freqlist = open(obsid + 'frequencies.txt', 'w')
for n, f in zip(o['names'], o['frequencies']):
freqlist.write('%f %s_Total_flux %s_E_Total_flux False\n' %
(f, n, n))
for i, f in enumerate(freqs):
freqlist.write('%f Total_flux_ch%i E_Total_flux_ch%i True\n' %
(f, i + 1, i + 1))
freqlist.close()
# Now call the fitting code
if os.path.isfile(obsid + 'crossmatch-results-1.npy'):
warn('Results 1 exists, skipping first fit')
else:
fitting_factors.run_all(1, name=obsid)
nreject = -1 # avoid error if we fail somewhere
if os.path.isfile(obsid + 'crossmatch-2.fits'):
warn('Second crossmatch exists, skipping outlier rejection')
else:
nreject = find_outliers.run_all(1, name=obsid)
if os.path.isfile(obsid + 'crossmatch-results-2.npy'):
warn('Results 2 exists, skipping second fit')
else:
if nreject == 0:
shutil.copyfile(obsid + 'crossmatch-results-1.npy',
obsid + 'crossmatch-results-2.npy')
if os.path.isfile(obsid + 'crossmatch-results-2.npy'):
warn('Results 2 exists, skipping first fit')
else:
fitting_factors.run_all(2, name=obsid)
# Now apply corrections
if o['full_mslist'] is None:
die('Need big mslist to apply corrections')
if not (o['dryrun']):
warn('Applying corrections to MS list')
scale = np.load(obsid + 'crossmatch-results-2.npy')[:, 0]
# InterpolatedUS gives us linear interpolation between points
# and extrapolation outside it
spl = InterpolatedUnivariateSpline(freqs, scale, k=1)
bigmslist = [s.strip() for s in open(o['full_mslist']).readlines()]
obigmslist = [ms for ms in bigmslist if obsid in ms]
for ms in obigmslist:
t = pt.table(ms)
try:
dummy = t.getcoldesc('SCALED_DATA')
except RuntimeError:
dummy = None
t.close()
if dummy is not None:
warn('Table ' + ms +
' has already been corrected, skipping')
else:
# in this version we need to scale both the original data and the data in colname
t = pt.table(ms + '/SPECTRAL_WINDOW',
readonly=True,
ack=False)
frq = t[0]['REF_FREQUENCY']
factor = spl(frq)
print frq, factor
t = pt.table(ms, readonly=False)
desc = t.getcoldesc(o['colname'])
desc['name'] = 'SCALED_DATA'
t.addcols(desc)
d = t.getcol(o['colname'])
d *= factor
t.putcol('SCALED_DATA', d)
try:
dummy = t.getcoldesc(colname)
except RuntimeError:
dummy = None
if dummy is not None:
desc = t.getcoldesc(colname)
newname = colname + '_SCALED'
desc['name'] = newname
t.addcols(desc)
d = t.getcol(colname)
d *= factor
t.putcol(newname, d)
t.close()
if os.path.isfile('image_bootstrap.app.mean.fits'):
warn('Mean bootstrap image exists, not creating it')
else:
warn('Creating mean bootstrap image')
hdus = []
for obsid in Uobsid:
hdus.append(
fits.open('image_bootstrap_' + obsid + '.app.restored.fits'))
for i in range(1, len(Uobsid)):
hdus[0][0].data += hdus[i][0].data
hdus[0][0].data /= len(Uobsid)
hdus[0].writeto('image_bootstrap.app.mean.fits')
def plot_flux_errors(catalog,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if os.path.isfile(outname.replace('.png','_integrated.png')):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
radialseps = sepn(scat['RA']*deg2rad,scat['DEC']*deg2rad,fieldra*deg2rad,fielddec*deg2rad)*rad2deg
plt.plot(np.sort(scat['Total_flux']),np.sort(scat['Total_flux'])*np.median(old_div(np.array(old_div(scat[auxcatname+'_Total_flux'],options[auxcatname+'_fluxfactor'])),np.array(scat['Total_flux']))),'r--')
plt.plot(scat['Total_flux'],old_div(scat[auxcatname+'_Total_flux'],options[auxcatname+'_fluxfactor']),'ko')
plt.xlabel('Integrated LOFAR flux (Jy)')
plt.ylabel('Integrated '+auxcatname+' flux (Jy)')
plt.xlim(xmin=0.01,xmax=0.5)
plt.ylim(ymin=0.01,ymax=0.5)
plt.semilogx()
plt.semilogy()
equality = np.arange(0,10,0.01)
plt.plot(equality,equality,'k-')
plt.savefig(outname.replace('.png','_integrated.png'))
plt.close('all')
plt.cla()
plt.clf()
plt.plot(np.sort(scat['Peak_flux']),np.sort(scat['Peak_flux'])*np.median(old_div(np.array(old_div(scat[auxcatname+'_Peak_flux'],options['%s_fluxfactor'%auxcatname])),np.array(scat['Peak_flux']))),'r--')
plt.plot(scat['Peak_flux'],old_div(scat[auxcatname+'_Peak_flux'],options[auxcatname+'_fluxfactor']),'ko')
plt.xlabel('Peak LOFAR flux (Jy)')
plt.ylabel('Peak '+auxcatname+' flux (Jy)')
plt.xlim(xmin=0.01,xmax=0.5)
plt.ylim(ymin=0.01,ymax=0.5)
plt.semilogx()
plt.semilogy()
equality = np.arange(0,10,0.01)
plt.plot(equality,equality,'k-')
plt.savefig(outname.replace('.png','_peak.png'))
plt.close('all')
plt.cla()
plt.clf()
plt.plot(radialseps,old_div(scat['Total_flux'],(old_div(scat[auxcatname+'_Total_flux'],options[auxcatname+'_fluxfactor']))),'bo',alpha=0.4,markersize=3)
equality = np.arange(-0.01,10,0.01)
fractionrange = np.arange(0.99,1.01,0.002)
for i in fractionrange:
plt.plot(equality,1.0*i+0*equality,'k-')
plt.plot(equality,1.0+0*equality,'k-')
plt.xlabel('Distance from pointing centre (deg)')
plt.ylabel('Integrated LOFAR flux / Integrated '+auxcatname+' flux')
plt.xlim(xmin=0.0,xmax=2)
plt.ylim(ymin=0.5,ymax=2.0)
distancerange = np.arange(0,np.max(radialseps)+0.15,0.15)
for i in range(0,len(distancerange)-1):
distancemin = distancerange[i]
distancemax = distancerange[i+1]
binvals = np.array([])
for j in range(0,len(radialseps)):
if distancemin < radialseps[j] < distancemax:
binvals = np.append(binvals,old_div(scat['Total_flux'][j],(old_div(scat[auxcatname+'_Total_flux'][j],options[auxcatname+'_fluxfactor']))))
midpoint = (distancemin+distancemax)/2.0
if len(binvals) > 0:
booterrl,booterrh = bootstrap(binvals, 100000, np.mean, 0.05)
booterrl,booterrh = bootstrap(binvals, 100000, np.median, 0.05)
# print booterrl,booterrh, binvals
plt.errorbar(midpoint,np.median(binvals),xerr=(distancemin-distancemax)/2.0,yerr=[[np.median(binvals)-booterrl],[booterrh-np.median(binvals)]],fmt='--o',ecolor='b',color='b',zorder=999999)
plt.savefig(outname.replace('.png','_total_radial.png'))
plt.close('all')
plt.cla()
plt.clf()
def do_run_subtract(name, basedir, inarchivedir, outarchivedir, force=False):
startdir = os.getcwd()
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
sdb.close()
fields = extractdict['fields'].split(',')
extract_status = extractdict['extract_status'].split(',')
try:
bad_pointings = extractdict['bad_pointings'].split(',')
except AttributeError:
bad_pointings = ['']
print('Working on ', name, 'in fields', fields, 'which have status',
extract_status)
for i in range(0, len(fields)):
os.chdir(startdir)
if not (extract_status[i] == 'EREADY' or
(force and extract_status[i] == 'STARTED')):
continue
field = fields[i]
if field in bad_pointings:
print('Field', field,
'in bad pointings -- skipping and setting to BADP')
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
extract_status[i] = 'BADP'
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing', extractdict)
sdb.close()
continue
workdir = basedir + '/' + name
try:
os.mkdir(workdir)
except OSError:
warn('Working directory already exists')
print('In directory', os.getcwd())
os.chdir(workdir)
# Update status to running here
extract_status[i] = 'STARTED'
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing', extractdict)
sdb.close()
print('Updated status to STARTED for', field, name)
time.sleep(2.0)
report('Copying data from %s' % inarchivedir)
# WANT TO MAKE THIS INTO A RSYNC SO THAT IT CAN BE DONE OUTSIDE LEIDEN
#os.system('cp -r %s/%s %s'%(inarchivedir,field,workdir))
do_rsync_download(field, inarchivedir, workdir)
# Update status to copied here
extract_status[i] = 'COPIED'
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing', extractdict)
sdb.close()
print('Updated status to COPIED for', field, name)
# Create boxfile
create_ds9_region('%s.ds9.reg' % name, extractdict['ra'],
extractdict['decl'], extractdict['size'])
# Run subtract code
print(os.getcwd(), 'working here')
os.chdir(field)
print('sub-sources-outside-region.py -b %s/%s.ds9.reg -p %s' %
(workdir, name, name))
result = os.system(
'sub-sources-outside-region.py -b %s/%s.ds9.reg -p %s' %
(workdir, name, name))
if result != 0:
raise RuntimeError(
'sub-sources-outside-region.py failed with error code %i' %
result)
# Archive the results need an rsync code this is just the *archive file that needs to be archived.
#os.system('mkdir %s/%s'%(outarchivedir,name))
#os.system('mkdir %s/%s/%s'%(outarchivedir,name,field))
os.chdir(workdir)
f = glob.glob('%s/*.archive*' % (field))
do_rsync_upload(name, field, f)
#print ('cp -r %s_%s.dysco.sub.shift.avg.weights.ms.archive %s/%s/%s'%(field,name,outarchivedir,name,field))
#os.system('cp -r %s_%s.dysco.sub.shift.avg.weights.ms.archive %s/%s/%s'%(field,name,outarchivedir,name,field))
# update the database to give success
extract_status[i] = 'EDONE'
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['extract_status'] = ','.join(extract_status)
sdb.db_set('reprocessing', extractdict)
sdb.close()
print('Updated status to EDONE for', field, name)
# update the database to give selfcal status as SREADY
selfcal_status = 'SREADY'
sdb = SurveysDB()
extractdict = sdb.get_reprocessing(name)
extractdict['selfcal_status'] = selfcal_status
sdb.db_set('reprocessing', extractdict)
sdb.close()
print('Updated status to SREADY for', name)
import sys
import os
rootdir = '/data/lofar/mjh'
os.chdir(rootdir)
name = sys.argv[1]
try:
qsubfile = sys.argv[2]
except:
qsubfile = '/home/mjh/git/ddf-pipeline/pipeline.qsub'
try:
os.mkdir(name)
except OSError:
warn('Working directory already exists')
pass
os.chdir(name)
report('Downloading data')
if not download_dataset('https://lofar-webdav.grid.sara.nl',
'/SKSP/' + name + '/'):
die('Download failed to get the right number of files')
report('Unpacking data')
unpack()
report('Deleting tar files')
os.system('rm *.tar.gz')
report('Making ms lists')
if make_list():
with SurveysDB() as sdb:
sdb.cur.execute('select * from fields where status="Archived" or status="Complete" order by ra')
result=sdb.cur.fetchall()
print 'There are',len(result),'complete datasets'
if not skip_construct:
separator('Preparing release directory')
releasefiles=['image_full_low_stokesV.dirty.fits','image_full_vlow_QU.cube.dirty.corr.fits.fz','image_full_low_QU.cube.dirty.corr.fits.fz','image_full_vlow_QU.cube.dirty.fits.fz','image_full_low_QU.cube.dirty.fits.fz','image_full_low_m.int.restored.fits','image_full_low_m.app.restored.fits','image_full_ampphase_di_m.NS.tessel.reg','image_full_ampphase_di_m.NS_shift.int.facetRestored.fits','image_full_ampphase_di_m.NS_shift.app.facetRestored.fits','image_full_ampphase_di_m.NS_Band0_shift.int.facetRestored.fits','image_full_ampphase_di_m.NS_Band1_shift.int.facetRestored.fits','image_full_ampphase_di_m.NS_Band2_shift.int.facetRestored.fits','astromap.fits','DynSpec*.tgz']
os.chdir(workdir+'/fields')
for r in result:
id=r['id']
if not os.path.isdir(id):
warn('Directory %s does not exist, making it' % id)
os.mkdir(id)
tdir=workdir+'/fields/'+id
if r['clustername']=='Herts':
location=r['location']
resolved_release=[]
for f in releasefiles:
if '*' in f:
resolved_release+=[os.path.basename(g) for g in glob.glob(location+'/'+f)]
else:
resolved_release.append(f)
if location:
for f in resolved_release:
source=location+'/'+f
if not os.path.isfile(tdir+'/'+f) or (os.path.isfile(source) and os.path.getmtime(source)>os.path.getmtime(tdir+'/'+f)):
def plot_position_offset(catalog,fitsimage,outname,auxcatname,options=None):
if options is None:
options = o
if os.path.isfile(outname):
warn('Plot file %s exists, not making it' % outname)
else:
scat = Table.read(catalog)
fitsimage = fits.open(fitsimage)
fieldra = fitsimage[0].header['CRVAL1']
fielddec = fitsimage[0].header['CRVAL2']
fitsimage.close()
x = scat[auxcatname+'_dRA']
y = scat[auxcatname+'_dDEC']
nullfmt = NullFormatter() # no labels
# definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# start with a rectangular Figure
plt.figure(1, figsize=(8, 8))
axScatter = plt.axes(rect_scatter)
plt.xlabel('$RA_{\\rm LOFAR} - RA_{\\rm %s}$ (arcsec)' % auxcatname)
plt.ylabel('$DEC_{\\rm LOFAR} - DEC_{\\rm %s}$ (arcsec)' % auxcatname)
plt.xticks(rotation=270)
axScatter.plot(np.zeros(40),np.arange(-20,20),'k--')
axScatter.plot(np.arange(-20,20),np.zeros(40),'k--')
axHistx = plt.axes(rect_histx)
axHisty = plt.axes(rect_histy)
plt.xticks(rotation=270)
# no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHisty.yaxis.set_major_formatter(nullfmt)
# the scatter plot:
axScatter.scatter(x, y,marker='+',s=2,alpha=0.3)
binwidth = 0.25
xymax = np.max([np.max(np.fabs(x)), np.max(np.fabs(y))])
lim = (int(old_div(xymax,binwidth)) + 1) * binwidth
axScatter.set_xlim((-lim, lim))
axScatter.set_ylim((-lim, lim))
bins = np.arange(-lim, lim + binwidth, binwidth)
axHistx.hist(x, bins=bins)
axHisty.hist(y, bins=bins, orientation='horizontal')
axHistx.set_xlim(axScatter.get_xlim())
axHisty.set_ylim(axScatter.get_ylim())
axScatter.add_artist(Ellipse((np.median(x),np.median(y)),np.std(x),np.std(y),angle=0,linewidth=2,fill=False,color='k',zorder=10000))
plt.savefig(outname)
plt.close('all')
plt.cla()
plt.clf()
hdus = []
for i in range(3):
h = fits.open('band%i-mosaic.fits' % i)
hdus.append(h)
y, x = h[0].data.shape
newdata = np.zeros((3, y, x), dtype=np.float32)
for i, h in enumerate(hdus):
newdata[i, :, :] = h[0].data
ohdu = hdus[0]
outfile = 'band-cube.fits'
if os.path.isfile(outfile):
warn('Output file exists, skipping cube generation')
else:
ohdu[0].data = newdata
ohdu[0].header['NAXIS3'] = 3
ohdu[0].header['CTYPE3'] = 'FREQ'
ohdu[0].header['CUNIT3'] = 'Hz'
ohdu[0].header['CRPIX3'] = 1
ohdu[0].header['CRVAL3'] = 128e6
ohdu[0].header['CDELT3'] = 16.0e6
ohdu[0].header['WCSAXES'] = 3
ohdu.writeto(outfile, overwrite=True)
os.system('chmod u+w .')
os.system('chmod u+w mosaic-blanked_pybdsm')
catprefix = 'bandcat'
img = bdsf.process_image(outfile,
import os
rootdir='/disks/paradata/shimwell/LoTSS-DR2/ongoing-leiden-runs'
os.chdir(rootdir)
name=sys.argv[1]
if name[0]!='P' and name[0]!='L':
die('This code should be used only with field or observation names',database=False)
do_field=(name[0]=='P')
try:
os.mkdir(name)
except OSError:
warn('Working directory already exists')
pass
os.chdir(name)
report('Downloading data')
if do_field:
success=download_field(name)
else:
success=download_dataset('https://lofar-webdav.grid.sara.nl','/SKSP/'+name+'/')
if not success:
die('Download failed, see earlier errors',database=False)
report('Unpacking data')
unpack()
if do_field:
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'])
image_root = 'image_full_ampphase_di_m.NS'
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:
if method == 'pslocal':
data = Table.read(method + '/' + method + '.txt', format='ascii')
data['RA'].name = 'ra'
data['DEC'].name = 'dec'
data.write(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'
gaulfile = catfile.replace('cat', 'gaul')
if os.path.isfile(catfile):
warn('Catalogue already exists, skipping pybdsf run')
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')
img.write_catalog(outfile=gaulfile,
catalog_type='gaul',
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'
cra, cdec = get_centpos()
report('Set up structure')
NDir = np.load("image_dirin_SSD_m.npy.ClusterCat.npy").shape[0]
oo = Offsets(method,
n=NDir,
imroot=image_root,
cellsize=o['cellsize'],
fitmethod=o['fit'])
report('Label table')
lofar_l = oo.r.add_facet_labels(lofar)
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')
'image_full_ampphase_di_m.NS_shift.int.facetRestored.fits',
'image_full_ampphase_di_m.NS_shift.app.facetRestored.fits',
'image_full_ampphase_di_m.NS_Band0_shift.int.facetRestored.fits',
'image_full_ampphase_di_m.NS_Band1_shift.int.facetRestored.fits',
'image_full_ampphase_di_m.NS_Band0_shift.app.facetRestored.fits',
'image_full_ampphase_di_m.NS_Band1_shift.app.facetRestored.fits',
'image_full_ampphase_di_m.NS_Band2_shift.app.facetRestored.fits',
'astromap.fits', 'DynSpec*.tgz'
]
os.chdir(workdir + '/fields')
for r in result:
id = r['id']
print('Doing', id)
if not os.path.isdir(id):
warn('Directory %s does not exist, making it' % id)
os.mkdir(id)
tdir = workdir + '/fields/' + id
if r['clustername'] == 'Herts' and r['location'] != "":
location = r['location']
resolved_release = []
for f in releasefiles:
if '*' in f:
resolved_release += [
os.path.basename(g)
for g in glob.glob(location + '/' + f)
]
else:
resolved_release.append(f)
if location:
def ddf_image(imagename,
mslist,
cleanmask=None,
cleanmode='HMP',
ddsols=None,
applysols=None,
threshold=None,
majorcycles=3,
use_dicomodel=False,
robust=0,
beamsize=None,
beamsize_minor=None,
beamsize_pa=None,
reuse_psf=False,
reuse_dirty=False,
verbose=False,
saveimages=None,
imsize=None,
cellsize=None,
uvrange=None,
colname='CORRECTED_DATA',
peakfactor=0.1,
dicomodel_base=None,
options=None,
do_decorr=None,
normalization=None,
dirty_from_resid=False,
clusterfile=None,
HMPsize=None,
automask=True,
automask_threshold=10.0,
smooth=False,
noweights=False,
cubemode=False,
apply_weights=True,
catcher=None,
rms_factor=3.0):
if catcher: catcher.check()
# saveimages lists _additional_ images to save
if saveimages is None:
saveimages = ''
saveimages += 'onNeds'
if options is None:
options = o # attempt to get global if it exists
if HMPsize is None:
HMPsize = options['HMPsize']
if do_decorr is None:
do_decorr = options['do_decorr']
if beamsize is None:
beamsize = options['psf_arcsec']
if imsize is None:
imsize = options['imsize']
if cellsize is None:
cellsize = options['cellsize']
cache_dir = find_cache_dir(options)
if majorcycles > 0:
fname = imagename + '.app.restored.fits'
else:
fname = imagename + '.dirty.fits'
runcommand = "DDF.py --Output-Name=%s --Data-MS=%s --Deconv-PeakFactor %f --Data-ColName %s --Parallel-NCPU=%i --Image-Mode=Clean --Beam-CenterNorm=1 --Deconv-CycleFactor=0 --Deconv-MaxMinorIter=1000000 --Deconv-MaxMajorIter=%s --Deconv-Mode %s --Beam-Model=LOFAR --Beam-LOFARBeamMode=A --Weight-Robust %f --Image-NPix=%i --CF-wmax 50000 --CF-Nw 100 --Output-Also %s --Image-Cell %f --Facets-NFacets=11 --SSDClean-NEnlargeData 0 --Freq-NDegridBand 1 --Beam-NBand 1 --Facets-DiamMax 1.5 --Facets-DiamMin 0.1 --Deconv-RMSFactor=%f --Data-Sort 1 --Cache-Dir=%s" % (
imagename, mslist, peakfactor, colname, options['NCPU_DDF'],
majorcycles, cleanmode, robust, imsize, saveimages, float(cellsize),
rms_factor, cache_dir)
if beamsize_minor is not None:
runcommand += ' --Output-RestoringBeam %f,%f,%f' % (
beamsize, beamsize_minor, beamsize_pa)
elif beamsize is not None:
runcommand += ' --Output-RestoringBeam %f' % (beamsize)
if apply_weights:
runcommand += ' --Weight-ColName="IMAGING_WEIGHT"'
else:
runcommand += ' --Weight-ColName="None"'
if cubemode:
channels = len(open(mslist).readlines())
runcommand += ' --Output-Cubes I --Freq-NBand=%i' % channels
else:
runcommand += ' --Freq-NBand=2'
if do_decorr:
runcommand += ' --RIME-DecorrMode=FT'
if cleanmode == 'SSD':
runcommand += ' --SSDClean-SSDSolvePars [S,Alpha] --SSDClean-BICFactor 0'
if clusterfile is not None:
runcommand += ' --Facets-CatNodes=%s' % clusterfile
if automask:
runcommand += ' --Mask-Auto=1 --Mask-SigTh=%.2f' % automask_threshold
if cleanmask is not None:
runcommand += ' --Mask-External=%s' % cleanmask
if applysols is not None:
if normalization is not None:
if normalization[:3] == 'Abs':
normalization = 'Mean' + normalization # backward compat. hack
runcommand += ' --DDESolutions-GlobalNorm=' + normalization
runcommand += ' --DDESolutions-DDModeGrid=%s --DDESolutions-DDModeDeGrid=%s --DDESolutions-DDSols=%s' % (
applysols, applysols, ddsols)
if use_dicomodel:
if dicomodel_base is not None:
runcommand += ' --Predict-InitDicoModel=%s.DicoModel' % dicomodel_base
else:
raise RuntimeError(
'use_dicomodel is set but no dicomodel supplied')
if threshold is not None:
runcommand += ' --Deconv-FluxThreshold=%f' % threshold
if uvrange is not None:
runcommand += ' --Selection-UVRangeKm=[%f,%f]' % (uvrange[0],
uvrange[1])
if dirty_from_resid and reuse_dirty:
raise RuntimeError('Cannot combine reuse_dirty and dirty_from_resid')
if dirty_from_resid:
# possible that crashes could destroy the cache, so need to check
if os.path.exists(cache_dir + '/' + mslist + '.ddfcache/LastResidual'):
runcommand += ' --Cache-Dirty forceresidual'
if reuse_dirty:
if os.path.exists(cache_dir + '/' + mslist + '.ddfcache/Dirty'):
runcommand += ' --Cache-Dirty forcedirty'
if reuse_psf:
if os.path.exists(cache_dir + '/' + mslist + '.ddfcache/PSF'):
runcommand += ' --Cache-PSF force'
if HMPsize is not None:
runcommand += ' --SSDClean-MinSizeInitHMP=%i' % HMPsize
if options['nobar']:
runcommand += ' --Log-Boring=1'
if smooth:
runcommand += ' --Beam-Smooth=1'
if options['restart'] and os.path.isfile(fname):
warn('File ' + fname + ' already exists, skipping DDF step')
if verbose:
print 'would have run', runcommand
else:
run(runcommand,
dryrun=options['dryrun'],
log=logfilename('DDF-' + imagename + '.log', options=options),
quiet=options['quiet'])
