def make_image (msname="$MS",column="${im.COLUMN}",imager='$IMAGER',
dirty=True,restore=False,restore_lsm=True,psf=False,
dirty_image="${im.DIRTY_IMAGE}",
model_image="${im.MODEL_IMAGE}",
restored_image="${im.RESTORED_IMAGE}",
residual_image="${im.RESIDUAL_IMAGE}",
psf_image="${im.PSF_IMAGE}",
algorithm="${im.CLEAN_ALGORITHM}",
fullrest_image="${im.FULLREST_IMAGE}",
restoring_options="${im.RESTORING_OPTIONS}",
channelize=None,lsm="$LSM",**kw0):
""" run casa imager """
im.IMAGER = II(imager)
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL,bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
do_moresane=False
if algorithm.lower() in ['moresane','pymoresane']:
do_moresane = True
from im import moresane
imager,msname,column,lsm,dirty_image,psf_image,restored_image,residual_image,\
model_image,algorithm,fullrest_image,restoring_options = \
interpolate_locals("imager msname column lsm dirty_image psf_image restored_image "
"residual_image model_image algorithm fullrest_image restoring_options")
makedir('$DESTDIR')
if restore and column != "CORRECTED_DATA":
abort("Due to imager limitations, restored images can only be made from the CORRECTED_DATA column.")
# setup imager options
kw0.update(dict(chanstart=ms.CHANSTART,chanstep=ms.CHANSTEP,nchan=ms.NUMCHANS));
if 'nchan' not in kw0 or 'start' not in kw0:
if channelize is None:
channelize = im.IMAGE_CHANNELIZE;
if channelize == 0:
kw0.update(nchan=1,width=ms.NUMCHANS);
elif channelize > 0:
kw0.update(nchan=ms.NUMCHANS//channelize,start=ms.CHANSTART,width=channelize);
kw0.update(vis=msname,imagename=im.BASENAME_IMAGE)
def make_dirty():
info("im.casa.make_image: making dirty image $dirty_image")
kw = kw0.copy()
kw['niter'] = 0
_run(dirty=dirty_image,makepsf=psf,psf=psf_image,**kw)
if dirty:
make_dirty()
if do_moresane and restore:
if np.logical_or(not dirty,not psf):
psf = True
make_dirty()
opts = restore if isinstance(restore,dict) else {}
info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
moresane.deconv(dirty_image,psf_image,model_image=model_image,
residual_image=residual_image,restored_image=restored_image,**opts)
elif restore:
info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
kw = kw0.copy()
kw['psfmode'] = algorithm if algorithm in 'clark clarkstokes hogbom' else 'clark'
if isinstance(restore,dict):
kw.update(restore)
_run(model=model_image,residual=residual_image,restored=restored_image,makepsf=psf,psf=psf_image,clean=True,**kw)
if restore:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image")
opts = restore_lsm if isinstance(restore_lsm,dict) else {}
tigger_restore(restoring_options,"-f",restored_image,lsm,fullrest_image,kwopt_to_command_line(**opts))
def make_image(msname="$MS",
column="${im.COLUMN}",
imager='$IMAGER',
dirty=True,
restore=False,
restore_lsm=True,
psf=False,
dirty_image="${im.DIRTY_IMAGE}",
model_image="${im.MODEL_IMAGE}",
restored_image="${im.RESTORED_IMAGE}",
residual_image="${im.RESIDUAL_IMAGE}",
psf_image="${im.PSF_IMAGE}",
algorithm="${im.CLEAN_ALGORITHM}",
fullrest_image="${im.FULLREST_IMAGE}",
restoring_options="${im.RESTORING_OPTIONS}",
channelize=None,
lsm="$LSM",
**kw0):
""" run casa imager """
_imager = im.IMAGER
im.IMAGER = II(imager)
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL, bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
do_moresane = False
if algorithm.lower() in ['moresane', 'pymoresane']:
do_moresane = True
from im import moresane
imager,msname,column,lsm,dirty_image,psf_image,restored_image,residual_image,\
model_image,algorithm,fullrest_image,restoring_options = \
interpolate_locals("imager msname column lsm dirty_image psf_image restored_image "
"residual_image model_image algorithm fullrest_image restoring_options")
makedir('$DESTDIR')
if restore and column != "CORRECTED_DATA":
abort(
"Due to imager limitations, restored images can only be made from the CORRECTED_DATA column."
)
# setup imager options
kw0.update(
dict(chanstart=ms.CHANSTART, chanstep=ms.CHANSTEP, nchan=ms.NUMCHANS))
if 'nchan' not in kw0 or 'start' not in kw0:
if channelize is None:
channelize = im.IMAGE_CHANNELIZE
if channelize == 0:
kw0.update(nchan=1, width=ms.NUMCHANS)
elif channelize > 0:
kw0.update(nchan=ms.NUMCHANS // channelize,
start=ms.CHANSTART,
width=channelize)
kw0.update(vis=msname, imagename=im.BASENAME_IMAGE)
def make_dirty():
info("im.casa.make_image: making dirty image $dirty_image")
kw = kw0.copy()
kw['niter'] = 0
_run(dirty=dirty_image, makepsf=psf, psf=psf_image, **kw)
if dirty:
make_dirty()
if do_moresane and restore:
if np.logical_or(not dirty, not psf):
psf = True
make_dirty()
opts = restore if isinstance(restore, dict) else {}
info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
moresane.deconv(dirty_image,
psf_image,
model_image=model_image,
residual_image=residual_image,
restored_image=restored_image,
**opts)
elif restore:
info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
kw = kw0.copy()
kw['psfmode'] = algorithm if algorithm in 'clark clarkstokes hogbom' else 'clark'
if isinstance(restore, dict):
kw.update(restore)
_run(model=model_image,
residual=residual_image,
restored=restored_image,
makepsf=psf,
psf=psf_image,
clean=True,
**kw)
if restore:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image")
opts = restore_lsm if isinstance(restore_lsm, dict) else {}
tigger_restore(restoring_options, "-f", restored_image, lsm,
fullrest_image, kwopt_to_command_line(**opts))
im.IMAGER = _imager
def make_image(msname='$MS',image_prefix='${im.BASENAME_IMAGE}',column='${im.COLUMN}',
mslist=None, # if given, overrieds msname
path='${WSCLEAN_PATH}',
imager='$IMAGER',
restore=False,
dirty=True,
psf=False,
restore_lsm=False,
lsm='$LSM',
algorithm='${im.CLEAN_ALGORITHM}',
channelize=None,
psf_image='${im.PSF_IMAGE}',
dirty_image='${im.DIRTY_IMAGE}',
model_image='${im.MODEL_IMAGE}',
residual_image='${im.RESIDUAL_IMAGE}',
restored_image='${im.RESTORED_IMAGE}',
fullrest_image='${im.FULLREST_IMAGE}',
restoring_options='${im.RESTORING_OPTIONS}',
keep_component_images=False,
**kw):
""" run WSCLEAN """
makedir('$DESTDIR')
_imager = im.IMAGER
im.IMAGER = II(imager)
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL,bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
path,msname,image_prefix,column,dirty_image,model_image,residual_image,restored_image,psf_image,channelize,\
fullrest_image,restoring_options = \
interpolate_locals('path msname image_prefix column dirty_image model_image residual_image '
'restored_image psf_image channelize fullrest_image restoring_options')
# Check if WSCLEAN is where it is said to be
path = argo.findImager(path,imager_name='WSCLEAN')
# wsclean requires a WEIGHT_SPECTRUM column in the MS
if wsclean_version()[0]<1.6:
argo.addcol(msname,colname='WEIGHT_SPECTRUM',valuetype='float',init_with=1)
if 'datacolumn' not in kw.keys():
kw['datacolumn'] = column
# Cater for moresane
do_moresane = False
if restore and algorithm.lower() in ['moresane','pymoresane']:
kw['niter'] = 0
kw['makepsf'] = True
psf = True
dirty = True
if isinstance(restore,dict):
kw0 = restore.copy()
else:
kw0 = {}
restore = False
do_moresane = True
from im import moresane
else:
if isinstance(restore,dict):
kw.update(restore)
restore = True
elif not isinstance(restore,bool):
restore = False
kw['name'] = image_prefix
# Check channel selection options in kw
if 'channelrange' in kw.keys():
if isinstance(kw['channelrange'],str):
start,end = map(int,kw['channelrange'].split())
else:
start,end = kw['channelrange']
else:
start,end = ms.CHANSTART,ms.CHANSTART+ms.NUMCHANS;
# if multiple MSs are specified, adjust channel range
if mslist:
end = ms.TOTAL_CHANNELS*(len(mslist)-1) + end
kw['channelrange'] = "%d %d"%(start,end);
nr = 1
if not channelize:
channelize = im.IMAGE_CHANNELIZE
if channelize:
nr = (end-start)//channelize
kw['channelsout'] = nr
if nr ==1:
channelize=False
if dirty: info("im.wsclean.make_image: making dirty image $dirty_image")
if restore: info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
if psf and not restore:
kw['makepsf'] = True
if 'pol' in kw.keys():
pol = kw['pol']
elif 'stokes' in kw.keys():
pol = kw['pol'] = kw.pop('stokes')
else:
pol = stokes
kw['pol'] = pol
if ',' in pol:
pol = pol.split(',')
kw['clean'] = restore
# also accepts list of MSs
_run(mslist or msname,**kw)
# delete gridding image unless user wants it
if not KEEP_GRIDDING_IMAGE:
rm_fr('$image_prefix-gridding.fits')
# delete first-residual images
first_residual_images = glob.glob(II("$image_prefix-*-first-residual.fits"))
if first_residual_images:
rm_fr(" ".join(first_residual_images))
#TODO(sphe): always keep wsclean MFS images?
# Combine images if needed
# mfs = mode if 'mode' not in kw.keys() else kw['mode']
# mfs = mode=='mfs'
# abort(mfs,mode)
def eval_list(vals):
l = []
for val in vals:
l.append(II(val))
return l
def combine_pol(pol,image_prefix=None,mfs=False):
dirtys = eval_list(['$image_prefix-%s-dirty.fits'%d for d in pol])
if dirty:
argo.combine_fits(dirtys,outname=dirty_image,ctype='STOKES',keep_old=keep_component_images)
else:
for item in dirtys:
rm_fr(item)
if restore:
model = eval_list(['$image_prefix-%s-model.fits'%d for d in pol])
argo.combine_fits(model,outname=model_image,ctype='STOKES',keep_old=keep_component_images)
residual = eval_list(['$image_prefix-%s-residual.fits'%d for d in pol])
argo.combine_fits(residual,outname=residual_image,ctype='STOKES',keep_old=keep_component_images)
restored = eval_list(['$image_prefix-%s-image.fits'%d for d in pol])
argo.combine_fits(restored,outname=restored_image,ctype='STOKES',keep_old=keep_component_images)
if mfs:
model_mfs = eval_list(['$image_prefix-MFS-%s-model.fits'%d for d in pol])
argo.combine_fits(model_mfs,
outname=model_image.replace('.model.fits','-MFS.model.fits'),
ctype='STOKES',keep_old=keep_component_images)
residual_mfs = eval_list(['$image_prefix-MFS-%s-residual.fits'%d for d in pol])
argo.combine_fits(residual_mfs,
outname=residual_image.replace('.residual.fits','-MFS.residual.fits'),
ctype='STOKES',keep_old=keep_component_images)
restored_mfs = eval_list(['$image_prefix-MFS-%s-image.fits'%d for d in pol])
argo.combine_fits(restored_mfs,
outname=restored_image.replace('.restored.fits','-MFS.restored.fits'),
ctype='STOKES',keep_old=False)
else:
for fits in ['$image_prefix-%s-image.fits'%d for d in pol]:
rm_fr(fits)
if not channelize:
if psf:
x.mv('${image_prefix}-psf.fits $psf_image')
elif restore:
rm_fr('${image_prefix}-psf.fits')
if len(pol)>1:
combine_pol(pol,image_prefix)
else:
if dirty:
x.mv('${image_prefix}-dirty.fits $dirty_image')
else:
rm_fr('${image_prefix}-dirty.fits')
if restore:
x.mv('${image_prefix}-model.fits $model_image')
x.mv('${image_prefix}-residual.fits $residual_image')
x.mv('${image_prefix}-image.fits $restored_image')
# x.mv('${image_prefix}-psf.fits $psf_image')
else:
rm_fr('${image_prefix}-image.fits')
else:
# Combine component images from wsclean
labels = ['%04d'%d for d in range(nr)]
psfs = eval_list(['$image_prefix-%s-psf.fits'%d for d in labels])
if psf:
argo.combine_fits(psfs,outname=II('$image_prefix.psf.fits'),ctype='FREQ',keep_old=keep_component_images)
elif restore:
for fits in psfs:
rm_fr(fits)
for i in pol:
if len(pol) == 1:
i = ''
if i :
i = '-%s'%i
dirtys = eval_list(['$image_prefix-%s$i-dirty.fits'%d for d in labels])
if dirty:
argo.combine_fits(dirtys,
outname=II('$image_prefix$i-dirty.fits') if i else dirty_image,
ctype='FREQ',keep_old=keep_component_images)
if not restore:
xo.sh('rm -fr ${image_prefix}*image*.fits')
else:
for fits in dirtys:
rm_fr(fits)
if restore:
model = eval_list(['$image_prefix-%s$i-model.fits'%d for d in labels])
argo.combine_fits(model,outname=II('$image_prefix$i-model.fits') if i else model_image,
ctype='FREQ',keep_old=keep_component_images)
residual = eval_list(['$image_prefix-%s$i-residual.fits'%d for d in labels])
argo.combine_fits(residual,outname=II('$image_prefix$i-residual.fits') if i else residual_image,
ctype='FREQ',keep_old=keep_component_images)
restored = eval_list(['$image_prefix-%s$i-image.fits'%d for d in labels])
argo.combine_fits(restored,outname=II('$image_prefix$i-image.fits') if i else restored_image ,
ctype='FREQ',keep_old=keep_component_images)
if len(pol)==1:
for old,new in zip([image_prefix+'-MFS-%s.fits'%img for img in 'model residual image'.split()],
[model_image.replace('.model.fits','-MFS.model.fits'),
residual_image.replace('.residual.fits','-MFS.residual.fits'),
restored_image.replace('.restored.fits','-MFS.restored.fits')]):
#TODO(sphe) Should we always keep wsclean MFS images?
#if mfs:
x.mv('$old $new')
#else:
# rm_fr(old)
if len(pol)>1:
combine_pol(pol,image_prefix,mfs=True)
if do_moresane:
info(" im.moresane.deconv: making estored image $restored_image \
model is $model_image, residual is $residual_image)")
moresane.deconv(dirty_image,psf_image,model_image=model_image,
residual_image=residual_image,
restored_image=restored_image,**kw0)
if restore or do_moresane:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image");
opts = restore_lsm if isinstance(restore_lsm,dict) else {};
tigger_restore(restoring_options,"-f",
restored_image,lsm,
fullrest_image,
kwopt_to_command_line(**opts));
im.IMAGER = _imager
def make_image(
msname='$MS',
image_prefix='${im.BASENAME_IMAGE}',
column='${im.COLUMN}',
mslist=None, # if given, overrieds msname
path='${WSCLEAN_PATH}',
imager='$IMAGER',
restore=False,
dirty=True,
psf=False,
restore_lsm=False,
lsm='$LSM',
algorithm='${im.CLEAN_ALGORITHM}',
channelize=None,
psf_image='${im.PSF_IMAGE}',
dirty_image='${im.DIRTY_IMAGE}',
model_image='${im.MODEL_IMAGE}',
residual_image='${im.RESIDUAL_IMAGE}',
restored_image='${im.RESTORED_IMAGE}',
fullrest_image='${im.FULLREST_IMAGE}',
restoring_options='${im.RESTORING_OPTIONS}',
keep_component_images=False,
**kw):
""" run WSCLEAN """
makedir('$DESTDIR')
_imager = im.IMAGER
im.IMAGER = II(imager)
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL, bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
path,msname,image_prefix,column,dirty_image,model_image,residual_image,restored_image,psf_image,channelize,\
fullrest_image,restoring_options = \
interpolate_locals('path msname image_prefix column dirty_image model_image residual_image '
'restored_image psf_image channelize fullrest_image restoring_options')
# Check if WSCLEAN is where it is said to be
path = argo.findImager(path, imager_name='WSCLEAN')
# wsclean requires a WEIGHT_SPECTRUM column in the MS
if wsclean_version()[0] < 1.6:
argo.addcol(msname,
colname='WEIGHT_SPECTRUM',
valuetype='float',
init_with=1)
if 'datacolumn' not in list(kw.keys()):
kw['datacolumn'] = column
# Cater for moresane
do_moresane = False
if restore and algorithm.lower() in ['moresane', 'pymoresane']:
kw['niter'] = 0
kw['makepsf'] = True
psf = True
dirty = True
if isinstance(restore, dict):
kw0 = restore.copy()
else:
kw0 = {}
restore = False
do_moresane = True
from im import moresane
else:
if isinstance(restore, dict):
kw.update(restore)
restore = True
elif not isinstance(restore, bool):
restore = False
kw['name'] = image_prefix
# Check channel selection options in kw
if 'channelrange' in list(kw.keys()):
if isinstance(kw['channelrange'], str):
start, end = list(map(int, kw['channelrange'].split()))
else:
start, end = kw['channelrange']
else:
start, end = ms.CHANSTART, ms.CHANSTART + ms.NUMCHANS
# if multiple MSs are specified, adjust channel range
if mslist:
end = ms.TOTAL_CHANNELS * (len(mslist) - 1) + end
kw['channelrange'] = "%d %d" % (start, end)
nr = 1
if not channelize:
channelize = im.IMAGE_CHANNELIZE
if channelize:
nr = (end - start) // channelize
kw['channelsout'] = nr
if nr == 1:
channelize = False
if dirty: info("im.wsclean.make_image: making dirty image $dirty_image")
if restore:
info(" making restored image $restored_image\
(model is $model_image, residual is $residual_image)")
if psf and not restore:
kw['makepsf'] = True
if 'pol' in list(kw.keys()):
pol = kw['pol']
elif 'stokes' in list(kw.keys()):
pol = kw['pol'] = kw.pop('stokes')
else:
pol = stokes
kw['pol'] = pol
if ',' in pol:
pol = pol.split(',')
kw['clean'] = restore
# also accepts list of MSs
_run(mslist or msname, **kw)
# delete gridding image unless user wants it
if not KEEP_GRIDDING_IMAGE:
rm_fr('$image_prefix-gridding.fits')
# delete first-residual images
first_residual_images = glob.glob(
II("$image_prefix-*-first-residual.fits"))
if first_residual_images:
rm_fr(" ".join(first_residual_images))
#TODO(sphe): always keep wsclean MFS images?
# Combine images if needed
# mfs = mode if 'mode' not in kw.keys() else kw['mode']
# mfs = mode=='mfs'
# abort(mfs,mode)
def eval_list(vals):
l = []
for val in vals:
l.append(II(val))
return l
def combine_pol(pol, image_prefix=None, mfs=False):
dirtys = eval_list(['$image_prefix-%s-dirty.fits' % d for d in pol])
if dirty:
argo.combine_fits(dirtys,
outname=dirty_image,
ctype='STOKES',
keep_old=keep_component_images)
else:
for item in dirtys:
rm_fr(item)
if restore:
model = eval_list(['$image_prefix-%s-model.fits' % d for d in pol])
argo.combine_fits(model,
outname=model_image,
ctype='STOKES',
keep_old=keep_component_images)
residual = eval_list(
['$image_prefix-%s-residual.fits' % d for d in pol])
argo.combine_fits(residual,
outname=residual_image,
ctype='STOKES',
keep_old=keep_component_images)
restored = eval_list(
['$image_prefix-%s-image.fits' % d for d in pol])
argo.combine_fits(restored,
outname=restored_image,
ctype='STOKES',
keep_old=keep_component_images)
if mfs:
model_mfs = eval_list(
['$image_prefix-MFS-%s-model.fits' % d for d in pol])
argo.combine_fits(model_mfs,
outname=model_image.replace(
'.model.fits', '-MFS.model.fits'),
ctype='STOKES',
keep_old=keep_component_images)
residual_mfs = eval_list(
['$image_prefix-MFS-%s-residual.fits' % d for d in pol])
argo.combine_fits(residual_mfs,
outname=residual_image.replace(
'.residual.fits', '-MFS.residual.fits'),
ctype='STOKES',
keep_old=keep_component_images)
restored_mfs = eval_list(
['$image_prefix-MFS-%s-image.fits' % d for d in pol])
argo.combine_fits(restored_mfs,
outname=restored_image.replace(
'.restored.fits', '-MFS.restored.fits'),
ctype='STOKES',
keep_old=False)
else:
for fits in ['$image_prefix-%s-image.fits' % d for d in pol]:
rm_fr(fits)
if not channelize:
if psf:
x.mv('${image_prefix}-psf.fits $psf_image')
elif restore:
rm_fr('${image_prefix}-psf.fits')
if len(pol) > 1:
combine_pol(pol, image_prefix)
else:
if dirty:
x.mv('${image_prefix}-dirty.fits $dirty_image')
else:
rm_fr('${image_prefix}-dirty.fits')
if restore:
x.mv('${image_prefix}-model.fits $model_image')
x.mv('${image_prefix}-residual.fits $residual_image')
x.mv('${image_prefix}-image.fits $restored_image')
# x.mv('${image_prefix}-psf.fits $psf_image')
else:
rm_fr('${image_prefix}-image.fits')
else:
# Combine component images from wsclean
labels = ['%04d' % d for d in range(nr)]
psfs = eval_list(['$image_prefix-%s-psf.fits' % d for d in labels])
if psf:
argo.combine_fits(psfs,
outname=II('$image_prefix.psf.fits'),
ctype='FREQ',
keep_old=keep_component_images)
elif restore:
for fits in psfs:
rm_fr(fits)
for i in pol:
if len(pol) == 1:
i = ''
if i:
i = '-%s' % i
dirtys = eval_list(
['$image_prefix-%s$i-dirty.fits' % d for d in labels])
if dirty:
argo.combine_fits(dirtys,
outname=II('$image_prefix$i-dirty.fits')
if i else dirty_image,
ctype='FREQ',
keep_old=keep_component_images)
if not restore:
xo.sh('rm -fr ${image_prefix}*image*.fits')
else:
for fits in dirtys:
rm_fr(fits)
if restore:
model = eval_list(
['$image_prefix-%s$i-model.fits' % d for d in labels])
argo.combine_fits(model,
outname=II('$image_prefix$i-model.fits')
if i else model_image,
ctype='FREQ',
keep_old=keep_component_images)
residual = eval_list(
['$image_prefix-%s$i-residual.fits' % d for d in labels])
argo.combine_fits(residual,
outname=II('$image_prefix$i-residual.fits')
if i else residual_image,
ctype='FREQ',
keep_old=keep_component_images)
restored = eval_list(
['$image_prefix-%s$i-image.fits' % d for d in labels])
argo.combine_fits(restored,
outname=II('$image_prefix$i-image.fits')
if i else restored_image,
ctype='FREQ',
keep_old=keep_component_images)
if len(pol) == 1:
for old, new in zip([
image_prefix + '-MFS-%s.fits' % img
for img in 'model residual image'.split()
], [
model_image.replace('.model.fits',
'-MFS.model.fits'),
residual_image.replace('.residual.fits',
'-MFS.residual.fits'),
restored_image.replace('.restored.fits',
'-MFS.restored.fits')
]):
#TODO(sphe) Should we always keep wsclean MFS images?
#if mfs:
x.mv('$old $new')
#else:
# rm_fr(old)
if len(pol) > 1:
combine_pol(pol, image_prefix, mfs=True)
if do_moresane:
info(" im.moresane.deconv: making estored image $restored_image \
model is $model_image, residual is $residual_image)")
moresane.deconv(dirty_image,
psf_image,
model_image=model_image,
residual_image=residual_image,
restored_image=restored_image,
**kw0)
if restore or do_moresane:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image")
opts = restore_lsm if isinstance(restore_lsm, dict) else {}
tigger_restore(restoring_options, "-f", restored_image, lsm,
fullrest_image, kwopt_to_command_line(**opts))
im.IMAGER = _imager
def make_image (msname="$MS",column="${im.COLUMN}",imager='$IMAGER',
dirty=True,restore=False,restore_lsm=True,psf=False,
dirty_image="${im.DIRTY_IMAGE}",
restored_image="${im.RESTORED_IMAGE}",
residual_image="${im.RESIDUAL_IMAGE}",
psf_image="${im.PSF_IMAGE}",
model_image="${im.MODEL_IMAGE}",
fullrest_image="${im.FULLREST_IMAGE}",
restoring_options="${im.RESTORING_OPTIONS}",
algorithm="${im.CLEAN_ALGORITHM}",
channelize=None,lsm="$LSM",
double_psf=None,**kw0):
"""Makes image(s) from MS. Set dirty and restore to True or False to make the appropriate images. You can also
set either to a dict of options to be passed to the imager. If restore=True and restore_lsm is True and 'lsm' is set,
it will also make a full-restored image (i.e. will restore the LSM into the image) with tigger-restore. Use this when
deconvolving residual images. Note that RESTORING_OPTIONS are passed to tigger-restore.
'channelize', if set, overrides the IMAGE_CHANNELIZE setting. If both are None, the options in the 'imager' module take effect.
'algorithm' is the deconvolution algorithm to use (hogbom, clark, csclean, multiscale, entropy)
'dirty_image', etc. sets the image names, with defaults determined by the globals DIRTY_IMAGE, etc.
""";
_imager = im.IMAGER
im.IMAGER = II(imager)
# retain lwimager label for dirty maps and psf_maps
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL,bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
do_moresane = False
if algorithm.lower() in ['moresane','pymoresane']:
from im import moresane
do_moresane = True
imager,msname,column,lsm,dirty_image,psf_image,restored_image,residual_image,model_image,algorithm,\
fullrest_image,restoring_options,double_psf = \
interpolate_locals("imager msname column lsm dirty_image psf_image restored_image "
"residual_image model_image algorithm fullrest_image restoring_options double_psf");
makedir('$DESTDIR');
if restore and column != "CORRECTED_DATA":
abort("Due to imager limitations, restored images can only be made from the CORRECTED_DATA column.");
# setup imager options
kw0.update(dict(chanstart=ms.CHANSTART,chanstep=ms.CHANSTEP,nchan=ms.NUMCHANS));
if 'img_nchan' not in kw0 or 'img_chanstart' not in kw0:
if channelize is None:
channelize = im.IMAGE_CHANNELIZE;
if channelize == 0:
kw0.update(img_nchan=1,img_chanstart=ms.CHANSTART,img_chanstep=ms.NUMCHANS);
elif channelize > 0:
kw0.update(img_nchan=ms.NUMCHANS//channelize,img_chanstart=ms.CHANSTART,img_chanstep=channelize);
kw0.update(ms=msname,data=column);
def make_dirty(**kw1):
info("im.lwimager.make_image: making dirty image $dirty_image");
kw = kw0.copy();
if type(dirty) is dict:
kw.update(dirty);
kw.update(kw1)
kw['operation'] = 'image';
_run(image=dirty_image,**kw);
if dirty: make_dirty()
def make_psf(**kw1):
info("im.lwimager.make_image: making PSF image $psf_image");
kw = kw0.copy();
if type(psf) is dict:
kw.update(psf);
kw['operation'] = 'image';
kw['data'] = 'psf';
kw['stokes'] = "I";
kw.update(kw1)
_run(image=psf_image,**kw);
if psf: make_psf()
if do_moresane and restore:
# Moresane does better with a double sized PSF
double_psf = double_psf or im.DOUBLE_PSF
if double_psf:
_npix = int(npix)*2 if 'npix' not in kw0.keys() else (kw0['npix'])*2
make_psf(npix=_npix)
elif not psf:
make_psf()
if not dirty: make_dirty()
opts = restore if isinstance(restore,dict) else {}
moresane.deconv(dirty_image,psf_image,model_image=model_image,
residual_image=residual_image,restored_image=restored_image,**opts)
elif restore:
info("im.lwimager.make_image: making restored image $restored_image");
info(" (model is $model_image, residual is $residual_image)");
kw = kw0.copy();
if type(restore) is dict:
kw.update(restore);
kw.setdefault("operation",algorithm or "clark");
temp_images = [];
## if fixed model was specified as a fits image, convert to CASA image
if kw.pop('fixed',None):
kw['fixed'] = 1;
if not os.path.exists(model_image):
warn("fixed=1 (use prior model) specified, but $model_image does not exist, ignoring");
elif not os.path.isdir(model_image):
info("converting prior model $model_image into CASA image");
modimg = model_image+".img";
temp_images.append(modimg);
argo.fits2casa(model_image,modimg);
model_image = modimg;
## if mask was specified as a fits image, convert to CASA image
mask = kw.get("mask");
if mask and not isinstance(mask,str):
kw['mask'] = mask = im.MASK_IMAGE;
imgmask = None;
if mask and os.path.exists(mask) and not os.path.isdir(mask):
info("converting clean mask $mask into CASA image");
kw['mask'] = imgmask = mask+".img";
argo.fits2casa(mask,imgmask);
temp_images.append(imgmask);
## run the imager
_run(restored=restored_image,model=model_image,residual=residual_image,**kw)
## delete CASA temp images if created above
for img in temp_images:
rm_fr(img);
if restore:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image");
opts = restore_lsm if isinstance(restore_lsm,dict) else {};
tigger_restore(restoring_options,"-f",restored_image,lsm,fullrest_image,kwopt_to_command_line(**opts));
im.IMAGER = _imager
def make_image (msname="$MS",column="${im.COLUMN}",imager='$IMAGER',
dirty=True,restore=False,restore_lsm=True,psf=False,
dirty_image="${im.DIRTY_IMAGE}",
restored_image="${im.RESTORED_IMAGE}",
residual_image="${im.RESIDUAL_IMAGE}",
psf_image="${im.PSF_IMAGE}",
model_image="${im.MODEL_IMAGE}",
fullrest_image="${im.FULLREST_IMAGE}",
restoring_options="${im.RESTORING_OPTIONS}",
algorithm="${im.CLEAN_ALGORITHM}",
channelize=None,lsm="$LSM",
double_psf=None,**kw0):
"""Makes image(s) from MS. Set dirty and restore to True or False to make the appropriate images. You can also
set either to a dict of options to be passed to the imager. If restore=True and restore_lsm is True and 'lsm' is set,
it will also make a full-restored image (i.e. will restore the LSM into the image) with tigger-restore. Use this when
deconvolving residual images. Note that RESTORING_OPTIONS are passed to tigger-restore.
'channelize', if set, overrides the IMAGE_CHANNELIZE setting. If both are None, the options in the 'imager' module take effect.
'algorithm' is the deconvolution algorithm to use (hogbom, clark, csclean, multiscale, entropy)
'dirty_image', etc. sets the image names, with defaults determined by the globals DIRTY_IMAGE, etc.
""";
_imager = im.IMAGER
im.IMAGER = II(imager)
# retain lwimager label for dirty maps and psf_maps
#Add algorithm label if required
if im.DECONV_LABEL and restore:
if isinstance(im.DECONV_LABEL,bool):
if im.DECONV_LABEL:
im.DECONV_LABEL = algorithm
elif im.DECONV_LABEL is False:
im.DECONV_LABEL = None
do_moresane = False
if algorithm.lower() in ['moresane','pymoresane']:
from im import moresane
do_moresane = True
imager,msname,column,lsm,dirty_image,psf_image,restored_image,residual_image,model_image,algorithm,\
fullrest_image,restoring_options,double_psf = \
interpolate_locals("imager msname column lsm dirty_image psf_image restored_image "
"residual_image model_image algorithm fullrest_image restoring_options double_psf");
makedir('$DESTDIR');
if restore and column != "CORRECTED_DATA":
abort("Due to imager limitations, restored images can only be made from the CORRECTED_DATA column.");
# setup imager options
kw0.update(dict(chanstart=ms.CHANSTART,chanstep=ms.CHANSTEP,nchan=ms.NUMCHANS));
if 'img_nchan' not in kw0 or 'img_chanstart' not in kw0:
if channelize is None:
channelize = im.IMAGE_CHANNELIZE;
if channelize == 0:
kw0.update(img_nchan=1,img_chanstart=ms.CHANSTART,img_chanstep=ms.NUMCHANS);
elif channelize > 0:
kw0.update(img_nchan=ms.NUMCHANS//channelize,img_chanstart=ms.CHANSTART,img_chanstep=channelize);
kw0.update(ms=msname,data=column);
def make_dirty(**kw1):
info("im.lwimager.make_image: making dirty image $dirty_image");
kw = kw0.copy();
if type(dirty) is dict:
kw.update(dirty);
kw.update(kw1)
kw['operation'] = 'image';
_run(image=dirty_image,**kw);
if dirty: make_dirty()
def make_psf(**kw1):
info("im.lwimager.make_image: making PSF image $psf_image");
kw = kw0.copy();
if type(psf) is dict:
kw.update(psf);
kw['operation'] = 'image';
kw['data'] = 'psf';
kw['stokes'] = "I";
kw.update(kw1)
_run(image=psf_image,**kw);
if psf: make_psf()
if do_moresane and restore:
# Moresane does better with a double sized PSF
double_psf = double_psf or im.DOUBLE_PSF
if double_psf:
_npix = int(npix)*2 if 'npix' not in list(kw0.keys()) else (kw0['npix'])*2
make_psf(npix=_npix)
elif not psf:
make_psf()
if not dirty: make_dirty()
opts = restore if isinstance(restore,dict) else {}
moresane.deconv(dirty_image,psf_image,model_image=model_image,
residual_image=residual_image,restored_image=restored_image,**opts)
elif restore:
info("im.lwimager.make_image: making restored image $restored_image");
info(" (model is $model_image, residual is $residual_image)");
kw = kw0.copy();
if type(restore) is dict:
kw.update(restore);
kw.setdefault("operation",algorithm or "clark");
temp_images = [];
## if fixed model was specified as a fits image, convert to CASA image
if kw.pop('fixed',None):
kw['fixed'] = 1;
if not os.path.exists(model_image):
warn("fixed=1 (use prior model) specified, but $model_image does not exist, ignoring");
elif not os.path.isdir(model_image):
info("converting prior model $model_image into CASA image");
modimg = model_image+".img";
temp_images.append(modimg);
argo.fits2casa(model_image,modimg);
model_image = modimg;
## if mask was specified as a fits image, convert to CASA image
mask = kw.get("mask");
if mask and not isinstance(mask,str):
kw['mask'] = mask = im.MASK_IMAGE;
imgmask = None;
if mask and os.path.exists(mask) and not os.path.isdir(mask):
info("converting clean mask $mask into CASA image");
kw['mask'] = imgmask = mask+".img";
argo.fits2casa(mask,imgmask);
temp_images.append(imgmask);
## run the imager
_run(restored=restored_image,model=model_image,residual=residual_image,**kw)
## delete CASA temp images if created above
for img in temp_images:
rm_fr(img);
if restore:
if lsm and restore_lsm:
info("Restoring LSM into FULLREST_IMAGE=$fullrest_image");
opts = restore_lsm if isinstance(restore_lsm,dict) else {};
tigger_restore(restoring_options,"-f",restored_image,lsm,fullrest_image,kwopt_to_command_line(**opts));
im.IMAGER = _imager
