imagename=imagename,
niter=0,
threshold='1000mJy',
robust=0.5,
gridder='mosaic',
scales=[0,3,9,27],
deconvolver='mtmfs',
specmode='mfs',
nterms=2,
weighting='briggs',
pblimit=0.2,
interactive=False,
outframe='LSRK',
savemodel='none',
)
makefits(imagename)
# create a mask based on region selection (no thresholding here)
dirtyimagename = imagename+".image.tt0.pbcor"
exportfits(dirtyimagename, dirtyimagename+".fits", overwrite=True)
reg = pyregion.open('cleanbox_regions_SgrB2.reg')
imghdu = fits.open(dirtyimagename+".fits")[0]
#mask = reg.get_mask(imghdu)[None, None, :, :]
mask = reg.get_mask(header=wcs.WCS(imghdu.header).celestial.to_header(),
shape=imghdu.data.shape[2:])
imghdu.data = mask.astype('int16')
imghdu.header['BITPIX'] = 16
imghdu.writeto('cleanbox_mask_SgrB2.fits', clobber=True)
cleanbox_mask_image = 'cleanbox_mask_SgrB2.image'
importfits(fitsimage='cleanbox_mask_SgrB2.fits',
# re-clean a wider area now
full_imagename = (imagename +
"_{field}_r{robust}_allcont_clean1e4_{threshold}".format(
field=selfcal_fields[0].replace(" ", "_"),
robust=0.5,
threshold='0.5mJy',
))
os.system('rm -r {0}.mask'.format(full_imagename))
tclean(
vis=cont_vis,
field=selfcal_fields[0],
spw='',
imsize=2000,
phasecenter='J2000 17h47m20.166 -28d23m04.968',
cell='0.01arcsec',
imagename=full_imagename,
robust=0.5,
nterms=2,
deconvolver='mtmfs',
specmode='mfs',
gridder=gridder,
niter=10000,
threshold='3.5mJy',
scales=[0, 3, 9, 27],
savemodel='none',
mask='',
)
makefits(full_imagename, cleanup=False)
robust=0.0,
phasecenter=phasecenter[field],
imsize=imsize[field],
cell=['0.01 arcsec'],
threshold='1 Jy',
niter=0,
gridder='standard',
specmode='mfs',
deconvolver='mtmfs',
outframe='LSRK',
savemodel='none',
scales=[0, 3, 9, 27],
nterms=2,
selectdata=True,
)
makefits(imagename)
if not os.path.exists('cleanbox_mask_{0}.fits'.format(field_nospace)):
dirtyimage = imagename + '.image.tt0.pbcor'
if not os.path.exists(dirtyimage):
if os.path.exists(dirtyimage + ".fits"):
importfits(fitsimage=dirtyimage + ".fits",
imagename=dirtyimage)
else:
raise IOError("Missing dirty image file & dirty FITS image")
ia.open(dirtyimage)
ia.calcmask(mask=dirtyimage +
" > {0}".format(mask_threshold[field] / 1e3),
name='dirty_mask_{0}'.format(field_nospace))
imagename=imagename,
niter=10000,
threshold='1mJy',
robust=0.5,
gridder='mosaic',
scales=[0, 3, 9, 27],
deconvolver='mtmfs',
specmode='mfs',
nterms=2,
weighting='briggs',
pblimit=0.2,
interactive=False,
outframe='LSRK',
savemodel='none',
)
makefits('18A-229_mosaic_for_selfcal', cleanup=False)
#myclean(['../'+x for x in good_Q_mses],
# name='18A-229_combined_for_selfcal',
# threshold='2mJy', # no signal at 5...
# spws=[Qmses[x] for x in good_Q_mses],
# )
#
# myclean(['../'+x for x in Kamses],
# name='18A-229_combined_for_selfcal',
# threshold='2mJy',
# cell='0.015arcsec',
# fields=['Sgr B2 MN Ka', 'Sgr B2 MS Ka', 'Sgr B2 S Ka', 'Sgr B2 DS1 Ka', 'Sgr B2 DS2 Ka'],
# spws=[x for x in Kamses.values()],
# )
#
imagename=imagename,
niter=0,
threshold='1000mJy',
robust=0.5,
gridder='mosaic',
scales=[0,3,9],
deconvolver='mtmfs',
specmode='mfs',
nterms=2,
weighting='briggs',
pblimit=0.2,
interactive=False,
outframe='LSRK',
savemodel='none',
)
makefits(imagename)
cleanbox_mask = 'cleanbox_mask.mask'
cleanbox_mask_image = 'cleanbox_mask_SgrB2.image'
if not os.path.exists(cleanbox_mask) or not os.path.exists(cleanbox_mask_image):
# create a mask based on region selection (no thresholding here)
dirtyimagename = imagename+".image.tt0.pbcor"
exportfits(dirtyimagename, dirtyimagename+".fits", overwrite=True)
reg = pyregion.open('cleanbox_regions_SgrB2.reg')
imghdu = fits.open(dirtyimagename+".fits")[0]
#mask = reg.get_mask(imghdu)[None, None, :, :]
mask = reg.get_mask(header=wcs.WCS(imghdu.header).celestial.to_header(),
shape=imghdu.data.shape[2:])
imghdu.data = mask.astype('int16')
imghdu.header['BITPIX'] = 16