'parallel_gridding': 2,
'auto_mask': 2.5
}
else:
kwargs = {
'baseline_averaging': '',
'parallel_gridding': 2,
'auto_mask': 2.0,
'fits_mask': maskname
}
#kwargs = {'temp_dir':'./', 'pol':'I', 'fits_mask':maskname, \
# 'use_idg':'', 'grid_with_beam':'', 'use_differential_lofar_beam':'', 'beam_aterm_update':600, 'auto_mask':2.0}
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=imgsizepix, scale='10arcsec', \
weight='briggs -0.3', niter=1000000, no_update_model_required='', minuv_l=30, maxuv_l=4500, mgain=0.85, \
parallel_deconvolution=512, local_rms='', auto_threshold=1.5, \
multiscale='', multiscale_scale_bias=0.6, \
join_channels='', fit_spectral_pol=3, channels_out=MSs.getChout(4.e6), deconvolution_channels=3, **kwargs)
os.system('cat logs/wsclean-c' + str(c) +
'.log | grep "background noise"')
w.done('imaging_c%02i' % c)
### DONE
if c == 0:
# make a mask for next cycle
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
im.makeMask(threshisl=5)
maskname = imagename + '-mask.fits'
imagenameM = 'img/wideM-' + str(c)
with w.if_todo('imaging_c%02i' % c):
logger.info('Cleaning (cycle: ' + str(c) + ')...')
if c == 0:
# make temp mask for cycle 0, in cycle 1 use the maske made from cycle 0 image
lib_util.run_wsclean(s,
'wsclean-c' + str(c) + '.log',
MSs.getStrWsclean(),
name=imagename,
size=imgsizepix,
scale='10arcsec',
weight='briggs -0.3',
niter=1000000,
no_update_model_required='',
minuv_l=30,
parallel_gridding=2,
baseline_averaging='',
maxuv_l=4500,
mgain=0.85,
parallel_deconvolution=512,
local_rms='',
auto_threshold=4,
join_channels='',
fit_spectral_pol=cc_fit_order,
channels_out=MSs.getChout(4.e6),
deconvolution_channels=cc_fit_order)
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
im.makeMask(threshpix=5)
kwargs = {
'do_predict': True,
def clean(p, MSs, size, res='normal', apply_beam=False):
"""
p = patch name
mss = list of mss to clean
size = in deg of the image
"""
# set pixscale and imsize
pixscale = MSs.resolution
if res == 'normal':
pixscale = float('%.1f' % (pixscale / 2.5))
elif res == 'high':
pixscale = float('%.1f' % (pixscale / 3.5))
elif res == 'low':
pass # no change
imsize = [0, 0]
imsize[0] = int(size[0] * 1.1 / (pixscale / 3600.)) # add 10%
imsize[1] = int(size[1] * 1.1 / (pixscale / 3600.)) # add 10%
imsize[0] += imsize[0] % 2
imsize[1] += imsize[1] % 2
if imsize[0] < 64: imsize[0] == 64
if imsize[1] < 64: imsize[1] == 64
logger.debug('Image size: ' + str(imsize) + ' - Pixel scale: ' +
str(pixscale))
if res == 'normal':
weight = 'briggs -0.1'
maxuv_l = None
elif res == 'high':
weight = 'briggs -0.6'
maxuv_l = None
elif res == 'low':
weight = 'briggs 0'
maxuv_l = 3500
# clean 1
logger.info('Cleaning (' + str(p) + ')...')
imagename = 'img/ddcal-' + str(p)
lib_util.run_wsclean(s, 'wscleanA-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, size=imsize, scale=str(pixscale)+'arcsec', \
weight=weight, niter=10000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.85, \
baseline_averaging='', parallel_deconvolution=512, auto_threshold=5, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
im.makeMask(threshpix=3)
# clean 2
logger.info('Cleaning w/ mask (' + str(p) + ')...')
imagename = 'img/ddcalM-' + str(p)
if apply_beam:
lib_util.run_wsclean(s, 'wscleanB-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=imsize, scale=str(pixscale)+'arcsec', \
weight=weight, niter=100000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.85, \
use_idg='', grid_with_beam='', use_differential_lofar_beam='', beam_aterm_update=400, \
multiscale='', multiscale_scales='0,10,20,40,80', \
parallel_deconvolution=512, local_rms='', auto_threshold=0.75, auto_mask=1.5, fits_mask=im.maskname, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
else:
lib_util.run_wsclean(s, 'wscleanB-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, size=imsize, save_source_list='', scale=str(pixscale)+'arcsec', \
weight=weight, niter=100000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.85, \
multiscale='', multiscale_scales='0,10,20,40,80',
baseline_averaging='', parallel_deconvolution=512, local_rms='', auto_threshold=0.75, auto_mask=1.5, fits_mask=im.maskname, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
os.system('cat logs/wscleanA-' + str(p) + '.log logs/wscleanB-' + str(p) +
'.log | grep "background noise"')
# correct phases - MS:DATA -> MS:CORRECTED_DATA
logger.info('Correcting Gp...')
MSs.run('DPPP '+parset_dir+'/DPPP-cor.parset msin=$pathMS cor.parmdb=self/solutions/cal-tec'+str(c)+'.h5 cor.correction=tec000', \
log='$nameMS_corGp-c'+str(c)+'.log', commandType='DPPP')
# set image size at 1.5 * FWHM
imgsizepix = int(1.5 * MSs.getListObj()[0].getFWHM() / (2. / 3600))
imgsizepix += imgsizepix % 2 # make even
if c >= 2:
imgsizepix *= 2 # last cycle make a very large image to catch source in the sidelobes
# clean mask clean
logger.info('Cleaning (cycle: ' + str(c) + ')...')
imagename = 'img/wide-' + str(c)
lib_util.run_wsclean(s, 'wscleanA-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, size=imgsizepix, scale='2arcsec', \
weight='briggs 0.', niter=10000, no_update_model_required='', mgain=0.9, \
baseline_averaging=5, parallel_deconvolution=256, auto_threshold=20, \
join_channels='', fit_spectral_pol=2, channels_out=8)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
im.makeMask(threshisl=4, atrous_do=False)
# baseline averaging possible as we cut longest baselines (also it is in time, where smearing is less problematic)
# TODO: add -parallel-deconvolution=256 when source lists can be saved (https://sourceforge.net/p/wsclean/tickets/141/)
logger.info('Cleaning w/ mask (cycle: ' + str(c) + ')...')
imagename = 'img/wideM-' + str(c)
lib_util.run_wsclean(s, 'wscleanB-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=imgsizepix, scale='2arcsec', \
weight='briggs 0.', niter=100000, no_update_model_required='', mgain=0.9, \
#multiscale='', multiscale_scales='0,5,10,20,40', \
baseline_averaging=5, auto_threshold=1, fits_mask=im.maskname, \
join_channels='', fit_spectral_pol=2, channels_out=8)
with w.if_todo('sub-field'):
# Low res image
logger.info('Cleaning wide 1...')
imagename = 'img/img-wide'
lib_util.run_wsclean(s,
'wsclean-wide.log',
MSs.getStrWsclean(),
name=imagename,
parallel_gridding=4,
baseline_averaging='',
size=2500,
scale='10arcsec',
weight='briggs -0.7',
taper_gaussian='30arcsec',
niter=1000000,
no_update_model_required='',
minuv_l=30,
mgain=0.75,
nmiter=0,
auto_threshold=5,
local_rms='',
local_rms_method='rms-with-min',
join_channels='',
fit_spectral_pol=2,
channels_out=2)
os.system('cat logs/wsclean-wide.log | grep "background noise"')
# makemask
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=region)
im.makeMask(threshpix=5, rmsbox=(50, 5))
# Plot solutions
lib_util.run_losoto(s, 'g-c'+str(c), [ms+'/cal-g-c'+str(c)+'.h5' for ms in MSs.getListStr()], \
[parset_dir+'/losoto-amp.parset', parset_dir+'/losoto-plot-amp.parset', parset_dir+'/losoto-plot-ph.parset'])
os.system('mv plots-g-c' + str(c) + ' ddcal/plots')
os.system('mv cal-g-c' + str(c) + '.h5 ddcal/solutions')
### DONE
###########################################################
# use idg and A-term to correct the data, single image
if aterm_imaging:
#wsclean -mem 90.0 -scale 0.0004166666666666667 -aterm-config /beegfs/rafferty/Data/LOFAR/Screens/Factor_sim/pipelines/image_1/sector_3/chunk9.ms.make_aterm_config -multiscale-scales 0 -size 1500 1500 -deconvolution-channels 4 -fits-mask /beegfs/rafferty/Data/LOFAR/Screens/Factor_sim/pipelines/image_1/sector_3/chunk9.ms.premask -j 6 -auto-mask 3.6 -idg-mode hybrid -channels-out 12 -local-rms-window 50 -mgain 0.5 -minuv-l 80.0 -fit-spectral-pol 3 -maxuv-l 1000000.0 -weighting-rank-filter 3 -aterm-kernel-size 32 -temp-dir /tmp -name /beegfs/rafferty/Data/LOFAR/Screens/Factor_sim/pipelines/image_1/sector_3/chunk9.ms.image -padding 1.2 -pol I -multiscale-shape gaussian -auto-threshold 1.0 -local-rms-method rms-with-min -weight briggs -0.5 -niter 13635 -no-update-model-required -multiscale -fit-beam -reorder -save-source-list -local-rms -join-channels -use-idg -apply-primary-beam -nmiter 4
lib_util.run_wsclean(s, 'wscleanDD-c%02i.log' %c, MSs.getStrWsclean(), name='img/wideDD-c%02i' %c, save_source_list='', size=imsize, scale=str(pixscale)+'arcsec', \
weight=weight, niter=100000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.85, \
use_idg='', grid_with_beam='', use_differential_lofar_beam='', beam_aterm_update=400, \
multiscale='', multiscale_scales='0,10,20,40,80', \
parallel_deconvolution=512, local_rms='', auto_threshold=0.75, auto_mask=1.5, fits_mask=im.maskname, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
# TODO: put proper names
os.system('cp img/wideDD-c%02i.MFS-image.fits ddcal/images/c%02i' %
(c, c))
mosaic_image = lib_img.Image('ddcal/images/c%02i/mos-MFS-image.fits' %
c,
userReg=userReg)
###########################################################
# facet imaging
else:
###########################################################
def clean(p, MSs, res='normal', size=[1, 1], empty=False, imagereg=None):
"""
p = patch name
mss = list of mss to clean
size = in deg of the image
"""
# set pixscale and imsize
pixscale = MSs.resolution
if res == 'normal':
pixscale = float('%.1f' % (pixscale / 2.5))
elif res == 'high':
pixscale = float('%.1f' % (pixscale / 3.5))
elif res == 'low':
pass # no change
imsize = [
int(size[0] * 1.5 / (pixscale / 3600.)),
int(size[1] * 1.5 / (pixscale / 3600.))
] # add 50%
imsize[0] += imsize[0] % 2
imsize[1] += imsize[1] % 2
if imsize[0] < 256: imsize[0] = 256
if imsize[1] < 256: imsize[1] = 256
logger.debug('Image size: ' + str(imsize) + ' - Pixel scale: ' +
str(pixscale))
if res == 'normal':
weight = 'briggs -0.3'
maxuv_l = None
elif res == 'high':
weight = 'briggs -0.6'
maxuv_l = None
elif res == 'low':
weight = 'briggs 0'
maxuv_l = 3500
else:
logger.error('Wrong "res": %s.' % str(res))
sys.exit()
if empty:
logger.info('Cleaning empty (' + str(p) + ')...')
imagename = 'img/empty-' + str(p)
lib_util.run_wsclean(s,
'wscleanE-' + str(p) + '.log',
MSs.getStrWsclean(),
name=imagename,
data_column='SUBTRACTED_DATA',
size=imsize,
scale=str(pixscale) + 'arcsec',
weight=weight,
niter=0,
no_update_model_required='',
minuv_l=30,
mgain=0,
baseline_averaging='')
else:
# clean 1
logger.info('Cleaning (' + str(p) + ')...')
imagename = 'img/extract-' + str(p)
lib_util.run_wsclean(s,
'wscleanA-' + str(p) + '.log',
MSs.getStrWsclean(),
name=imagename,
size=imsize,
scale=str(pixscale) + 'arcsec',
weight=weight,
niter=10000,
no_update_model_required='',
minuv_l=30,
maxuv_l=maxuv_l,
mgain=0.85,
baseline_averaging='',
parallel_deconvolution=512,
auto_threshold=5,
join_channels='',
fit_spectral_pol=3,
channels_out=ch_out,
deconvolution_channels=3)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
try:
im.makeMask(threshpix=10, rmsbox=(70, 5))
except:
logger.warning('Fail to create mask for %s.' % imagename +
'-MFS-image.fits')
return
if imagereg is not None:
lib_img.blank_image_reg(im.maskname,
imagereg,
inverse=True,
blankval=0.)
# clean 2
# TODO: add deconvolution_channels when bug fixed
logger.info('Cleaning w/ mask (' + str(p) + ')...')
imagenameM = 'img/extractM-' + str(p)
lib_util.run_wsclean(
s,
'wscleanB-' + str(p) + '.log',
MSs.getStrWsclean(),
name=imagenameM,
do_predict=True,
size=imsize,
scale=str(pixscale) + 'arcsec',
weight=weight,
niter=100000,
no_update_model_required='',
minuv_l=30,
maxuv_l=maxuv_l,
reuse_psf=imagename,
reuse_dirty=imagename,
mgain=0.85,
multiscale='',
baseline_averaging='',
parallel_deconvolution=512,
auto_threshold=0.7,
auto_mask=1.5,
fits_mask=im.maskname,
join_channels='',
fit_spectral_pol=3,
channels_out=ch_out) # , deconvolution_channels=3)
os.system('cat logs/wscleanB-' + str(p) +
'.log | grep "background noise"')
logger.info('Flag...')
MSs.run('DPPP ' + parset_dir + '/DPPP-flag2.parset msin=$pathMS',
log='$nameMS_flag2.log',
commandType='DPPP')
lib_util.check_rm('img')
os.makedirs('img')
imgsizepix = MSs.getListObj()[0].getFWHM() * 3600 / 5
logger.info('Cleaning low-res...')
imagename = 'img/calLR'
lib_util.run_wsclean(s, 'wscleanLR.log', MSs.getStrWsclean(), name=imagename, size=imgsizepix/5, scale='60arcsec', \
weight='briggs 0.', taper_gaussian='240arcsec', niter=10000, no_update_model_required='', minuv_l=30, maxuv_l=2000, mgain=0.85, \
use_idg='', grid_with_beam='', use_differential_lofar_beam='', beam_aterm_update=400, \
parallel_deconvolution=512, \
auto_mask=10, auto_threshold=1, pol='IQUV', join_polarizations='', join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
logger.info('Cleaning normal...')
imagename = 'img/cal'
lib_util.run_wsclean(s, 'wscleanA.log', MSs.getStrWsclean(), name=imagename, size=imgsizepix, scale='5arcsec', \
weight='briggs 0.', niter=10000, no_update_model_required='', minuv_l=30, mgain=0.85, \
baseline_averaging='', parallel_deconvolution=512, \
auto_threshold=20, join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits')
im.makeMask(threshisl=3)
logger.info('Cleaning w/ mask...')
'w') as file: # Use file to refer to the file object
file.write('aterms = [diagonal, beam]')
file.write('diagonal.images = [' + ' '.join(
sorted(glob.glob('ddcal/c%02i/aterm/TC*fits' % cmaj))) +
']')
file.write(
'diagonal.window = tukey\n diagonal.update_interval = 48.066724'
)
file.write(
'beam.differential = true\n beam.update_interval = 120\n beam.usechannelfreq = true'
)
# run the imager
lib_util.run_wsclean(s, 'wsclean-c'+str(cmaj)+'.log', MSs.getStrWsclean(), name=imagename, size='6000 6000', save_source_list='', scale='5arcsec', \
weight='briggs -0.3', niter=2000, no_update_model_required='', minuv_l=30, mgain=0.85, \
multiscale='', multiscale_scale_bias=0.65, multiscale_scales='0,10,20,40,80',
parallel_deconvolution=512, local_rms='', auto_threshold=0.5, auto_mask=1.5, \
join_channels='', fit_spectral_pol=3, channels_out=ch_out_idg, deconvolution_channels=3, \
temp_dir='./', pol='I', use_idg='', aterm_config=aterm_config_file, aterm_kernel_size=45, nmiter=4 )
sys.exit('Not implemente further on...')
else:
ddf_parms = {
'Data_MS': MSs.getStrDDF(),
'Data_ColName': 'CORRECTED_DATA',
'Data_Sort': 1,
'Output_Mode': 'Clean',
'Deconv_CycleFactor': 0,
'Deconv_MaxMinorIter': 1000000,
'Deconv_RMSFactor': 2.0,
logger.info('BP correction...')
if c == 0 and lofar_system == 'lba':
MSs.run('DPPP '+parset_dir+'/DPPP-cor.parset msin=$pathMS cor.updateweights=True cor.parmdb=cal-bp-c'+str(c)+'.h5 cor.correction=fulljones \
cor.soltab=\[amplitude000,phase000\]' , \
log='$nameMS_corBP3.log', commandType='DPPP')
else:
MSs.run('DPPP '+parset_dir+'/DPPP-cor.parset msin=$pathMS cor.updateweights=False cor.parmdb=cal-bp-c'+str(c)+'.h5 cor.correction=fulljones \
cor.soltab=\[amplitude000,phase000\]' , \
log='$nameMS_corBP3.log', commandType='DPPP')
logger.info('Cleaning (cycle %02i)...' % c)
imagename = 'img/img-c%02i' % c
if patch == 'CygA':
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1000, scale='1.5arcsec', \
weight='briggs -1.4', niter=50000, no_update_model_required='', nmiter=50, mgain=0.5, \
multiscale='', multiscale_scale_bias=0.5, multiscale_scales='0,5,10,20,40', \
fits_mask='/home/fdg/scripts/LiLF/parsets/LOFAR_ateam/masks/CygA.fits', \
baseline_averaging=5, auto_threshold=1, \
join_channels='', deconvolution_channels=8, fit_spectral_pol=4, channels_out=61)
elif patch == 'CasA':
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1300, scale='2arcsec', \
weight='briggs -1.2', niter=75000, no_update_model_required='', nmiter=50, mgain=0.5, \
multiscale='', multiscale_scale_bias=0.7, multiscale_scales='0,5,10,20,40,80', \
fits_mask='/home/fdg/scripts/LiLF/parsets/LOFAR_ateam/masks/CasA.fits', \
baseline_averaging=5, auto_threshold=1, \
join_channels='', deconvolution_channels=8, fit_spectral_pol=4, channels_out=61)
elif patch == 'TauA':
lib_util.run_wsclean(s, 'wscleanA-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1500, scale='1arcsec', \
weight='briggs -1.2', niter=30, update_model_required='', mgain=0.3, \
fits_mask='/home/fdg/scripts/LiLF/parsets/LOFAR_ateam/masks/pulsar.fits', \
#imagename = 'img/wide-'+str(c)
#lib_util.run_wsclean(s, 'wscleanA-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, size=imgsizepix, scale='10arcsec', \
# weight='briggs 0.', niter=10000, no_update_model_required='', minuv_l=30, maxuv_l=5000, mgain=0.85, \
# baseline_averaging=5, parallel_deconvolution=256, auto_threshold=3, \
# join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
## make mask
#im = lib_img.Image(imagename+'-MFS-image.fits', userReg=userReg, beamReg=beamReg)
#im.makeMask(threshisl = 4, only_beam=True)
# baseline averaging possible as we cut longest baselines (also it is in time, where smearing is less problematic)
logger.info('Cleaning (cycle: ' + str(c) + ')...')
imagename = 'img/wideM-' + str(c)
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), do_predict=c==0, name=imagename, save_source_list='', size=imgsizepix, scale='10arcsec', \
weight='briggs -0.3', niter=1000000, no_update_model_required='', minuv_l=30, maxuv_l=4500, mgain=0.85, \
parallel_deconvolution=256, baseline_averaging=5, local_rms='', auto_threshold=1.5, auto_mask=2.5, \
multiscale='', multiscale_scale_bias=0.75, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
os.system('cat logs/wsclean-c' + str(c) + '.log | grep "background noise"')
# add model and remove first sidelobe
if c == 0:
# TEST: reclean low-resolution
#logger.info('TEST: Cleaning low resolution...')
#imagename_lr = 'img/TESTpre-wide-lr'
#lib_util.run_wsclean(s, 'wscleanLR-pre.log', MSs.getStrWsclean(), name=imagename_lr, temp_dir='./', size=imgsizepix, scale='30arcsec', \
# weight='briggs 0.', niter=50000, no_update_model_required='', minuv_l=30, maxuvw_m=5000, mgain=0.8, \
# parallel_deconvolution=256, baseline_averaging=5, auto_mask=3, auto_threshold=0.5, \
# join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
# Correct BP CORRECTED_DATA -> CORRECTED_DATA
logger.info('BP correction...')
if c == 0 and lofar_system == 'lba':
MSs.run('DPPP '+parset_dir+'/DPPP-cor.parset msin=$pathMS cor.updateweights=True cor.parmdb=cal-iono-c'+str(c)+'.h5 cor.correction=amplitude000', \
log='$nameMS_corAMP3.log', commandType='DPPP')
else:
MSs.run('DPPP '+parset_dir+'/DPPP-cor.parset msin=$pathMS cor.updateweights=False cor.parmdb=cal-iono-c'+str(c)+'.h5 cor.correction=amplitude000', \
log='$nameMS_corAMP3.log', commandType='DPPP')
logger.info('Cleaning (cycle %i)...' % c)
imagename = 'img/img-c' + str(c)
if patch == 'CygA':
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1000, scale='1.5arcsec', \
weight='briggs -1.5', niter=50000, no_update_model_required='', mgain=0.5, \
use_weights_as_taper='',\
multiscale='', multiscale_scale_bias=0.7, \
fits_mask='/home/fdg/scripts/LiLF/parsets/LOFAR_ateam/masks/CygA.fits', \
baseline_averaging=5, auto_threshold=1, \
join_channels='', deconvolution_channels=10, fit_spectral_pol=7, channels_out=61)
elif patch == 'CasA':
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1300, scale='2arcsec', \
weight='briggs -1.2', niter=75000, no_update_model_required='', mgain=0.5, \
multiscale='', multiscale_scale_bias=0.7, \
fits_mask='/home/fdg/scripts/LiLF/parsets/LOFAR_ateam/masks/CasA.fits', \
baseline_averaging=5, auto_threshold=1, \
join_channels='', deconvolution_channels=10, fit_spectral_pol=7, channels_out=61)
elif patch == 'TauA':
lib_util.run_wsclean(s, 'wscleanA-c'+str(c)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=1200, scale='2arcsec', \
weight='briggs -1.2', niter=150, update_model_required='', mgain=0.5, \
lib_util.check_rm('img')
os.makedirs('img')
imgsizepix = int(MSs.getListObj()[0].getFWHM() * 3600 / 5)
logger.info('Cleaning normal...')
imagename = 'img/cal'
lib_util.run_wsclean(s,
'wscleanA.log',
MSs.getStrWsclean(),
name=imagename,
size=imgsizepix,
scale='5arcsec',
weight='briggs 0.',
niter=10000,
no_update_model_required='',
minuv_l=30,
mgain=0.85,
baseline_averaging='',
parallel_deconvolution=512,
auto_threshold=20,
join_channels='',
fit_spectral_pol=3,
channels_out=12,
deconvolution_channels=3)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits')
im.makeMask(threshpix=5)
logger.info('Cleaning w/ mask...')
lib_util.run_wsclean(s,
lib_util.run_losoto(s, 'Ga-c'+str(c), [ms+'/calGa.h5' for ms in MSs.getListStr()], \
[parset_dir+'/losoto-plot-ph.parset', parset_dir+'/losoto-plot-amp.parset', parset_dir+'/losoto-amp.parset'])
# Correct CORRECTED_DATA -> CORRECTED_DATA
logger.info('Correction AMP...')
MSs.run('DPPP ' + parset_dir + '/DPPP-cor.parset msin=$pathMS msin.datacolumn=CORRECTED_DATA cor.parmdb=cal-Ga-c'+str(c)+'.h5 cor.correction=amplitudeSmooth', \
log='$nameMS_corAMP-c'+str(c)+'.log', commandType='DPPP')
#################################################
# 2: Cleaning
logger.info('Cleaning (cycle: ' + str(c) + ')...')
imagename = 'img/imgM-%02i' % c
# if next is a "cont" then I need the do_predict
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), do_predict=True, name=imagename, size=2000, scale='1arcsec', \
weight='briggs -1', niter=1000000, no_update_model_required='', minuv_l=30, mgain=0.2, nmiter=0, \
auto_threshold=3, local_rms='', \
join_channels='', fit_spectral_pol=2, channels_out=2 )
os.system(
'cp -r img/imgM-%02i-MFS-image.fits img/imgMbkp-%02i-MFS-image.fits' %
(c, c))
os.system(
'cp -r img/imgM-%02i-MFS-model.fits img/imgMbkp-%02i-MFS-model.fits' %
(c, c))
os.system(
'cp -r img/imgM-%02i-MFS-residual.fits img/imgMbkp-%02i-MFS-residual.fits'
% (c, c))
lib_util.run_wsclean(s, 'wsclean-c'+str(c)+'.log', MSs.getStrWsclean(), do_predict=True, cont=True, name=imagename, size=2000, scale='1arcsec', \
weight='briggs -1', niter=1000000, no_update_model_required='', minuv_l=30, mgain=0.7, nmiter=0, \
auto_threshold=0.5, auto_mask=2.5, local_rms='', \
multiscale='', multiscale_scale_bias=0.7, \
join_channels='', fit_spectral_pol=2, channels_out=2 )
def clean(p, MSs, size, res='normal', apply_beam=False):
"""
p = patch name
mss = list of mss to clean
size = in deg of the image
"""
# set pixscale and imsize
pixscale = MSs.getListObj()[0].getResolution()
if res == 'normal':
pixscale = float('%.1f' % (pixscale / 2.))
elif res == 'high':
pixscale = float('%.1f' % (pixscale / 4.))
elif res == 'low':
pass # no change
imsize = [0, 0]
imsize[0] = int(size[0] * 1.05 / (pixscale / 3600.)) # add 5%
imsize[1] = int(size[1] * 1.05 / (pixscale / 3600.)) # add 5%
imsize[0] += imsize[0] % 2
imsize[1] += imsize[1] % 2
if imsize[0] < 64: imsize[0] == 64
if imsize[1] < 64: imsize[1] == 64
logger.debug('Image size: ' + str(imsize) + ' - Pixel scale: ' +
str(pixscale))
if res == 'normal':
weight = 'briggs 0'
maxuv_l = None
elif res == 'high':
weight = 'briggs -0.7'
maxuv_l = None
elif res == 'low':
weight = 'briggs 0'
maxuv_l = 3500
# clean 1
logger.info('Cleaning (' + str(p) + ')...')
imagename = 'img/ddcal-' + str(p)
lib_util.run_wsclean(s, 'wscleanA-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, size=imsize, scale=str(pixscale)+'arcsec', \
weight=weight, niter=10000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.8, \
baseline_averaging=5, parallel_deconvolution=256, auto_threshold=3, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
# make mask
im = lib_img.Image(imagename + '-MFS-image.fits', userReg=userReg)
im.makeMask(threshisl=4)
# clean 2
# TODO: add -parallel-deconvolution when source lists can be saved (https://sourceforge.net/p/wsclean/tickets/141/)
logger.info('Cleaning w/ mask (' + str(p) + ')...')
imagename = 'img/ddcalM-' + str(p)
if apply_beam:
lib_util.run_wsclean(s, 'wscleanB-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, save_source_list='', size=imsize, scale=str(pixscale)+'arcsec', \
weight=weight, niter=100000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.8, \
use_idg='', grid_with_beam='', use_differential_lofar_beam='', beam_aterm_update=400, \
multiscale='', \
auto_threshold=0.5, fits_mask=im.maskname, \
join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
logger.info('Cleaning V (' + str(p) + ')...')
imagename = 'img/ddcalV-' + str(p)
lib_util.run_wsclean(s, 'wscleanV-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, size=imgsize, scale=str(pixscale)+'srcsec', pol='V', \
weight='briggs 0.', niter=1000, no_update_model_required='', minuv_l=30, maxuv_l=5000, \
baseline_averaging=5)
else:
lib_util.run_wsclean(s, 'wscleanB-'+str(p)+'.log', MSs.getStrWsclean(), name=imagename, size=imsize, save_source_list='', scale=str(pixscale)+'arcsec', \
weight=weight, niter=50000, no_update_model_required='', minuv_l=30, maxuv_l=maxuv_l, mgain=0.85, \
multiscale='', \
auto_threshold=0.5, fits_mask=im.maskname, \
baseline_averaging=5, join_channels='', fit_spectral_pol=3, channels_out=9, deconvolution_channels=3)
os.system('cat logs/wscleanA-' + str(p) + '.log logs/wscleanB-' + str(p) +
'.log | grep "background noise"')
