def join_cubes(inputs, output, channels, resume=False, box=None):
"""Join cubes at specific channels
"""
if len(channels) != len(inputs):
raise ValueError("Length of channels(%i)!=inputs(%i)" %
(len(channels), len(inputs)))
# Concatenated image
imagename = os.path.expanduser(output)
# Join
if resume and os.path.isdir(imagename):
casalog.post('Skipping concatenated image: %s' % imagename)
else:
if os.path.isdir(imagename):
os.system('rm -rf %s' % imagename)
# Crop images
for i, (chans, inp) in enumerate(zip(channels, inputs)):
infile = os.path.expanduser(inp)
img_name = 'temp%i.image' % i
if os.path.isdir(img_name):
os.system('rm -rf temp*.image')
aux = crop_spectral_axis(infile, img_name, chans, box=box)
aux.close()
if i == 0:
filelist = img_name
else:
filelist += ' ' + img_name
# Concatenate
ia.imageconcat(outfile=imagename, infiles=filelist)
ia.close()
# Put rms
put_rms(imagename)
# Export fits
if resume and os.path.isfile(imagename + '.fits'):
casalog.post('Skipping FITS export')
else:
exportfits(imagename=imagename,
fitsimage=imagename + '.fits',
overwrite=True)
# Clean up
casalog.post('Cleaning up')
os.system('rm -rf temp*.image')
return True
if 'mask' not in impars:
impars['mask'] = maskname
imname = contimagename+"_robust{0}".format(robust)
if not os.path.exists(imname+".image.tt0"):
tclean(vis=continuum_ms,
field=field.encode(),
imagename=imname,
phasecenter=phasecenter,
outframe='LSRK',
veltype='radio',
usemask='user',
interactive=False,
cell=cellsize,
imsize=imsize,
antenna=antennae,
pbcor=True,
**impars
)
ia.open(imname+".image.tt0")
ia.sethistory(origin='almaimf_cont_imaging',
history=["{0}: {1}".format(key, val) for key, val in
impars.items()])
ia.close()
exportfits(imname+".image.tt0", imname+".image.tt0.fits")
exportfits(imname+".image.tt0.pbcor", imname+".image.tt0.pbcor.fits")
else:
logprint("Skipping completed file {0}".format(imname), origin='almaimf_cont_imaging')
pbcor=True,
**impars_thisiter)
ia.open(imname + ".image.tt0")
ia.sethistory(origin='almaimf_cont_imaging',
history=[
"{0}: {1}".format(key, val)
for key, val in impars.items()
])
ia.sethistory(origin='almaimf_cont_imaging',
history=[
"git_version: {0}".format(git_version),
"git_date: {0}".format(git_date)
])
ia.close()
exportfits(imname + ".image.tt0", imname + ".image.tt0.fits")
exportfits(imname + ".image.tt0.pbcor",
imname + ".image.tt0.pbcor.fits")
exportfits(imname + ".model.tt0", imname + ".model.tt0.fits")
exportfits(imname + ".residual.tt0", imname + ".residual.tt0.fits")
else:
logprint("Skipping completed file {0}".format(imname),
origin='almaimf_cont_imaging')
# reclean step (optional)
try:
maskname = make_custom_mask(field,
imname + ".image.tt0",
os.getenv('ALMAIMF_ROOTDIR'),
band,
rootdir=imaging_root,
def imaging(vis, trial_name, interactive, imsize, cellsize, glx_ctr, restfreq,
specmode, outframe, veltype, restoringbeam, weighting, robust,
scales, smallscalebias, dogrowprune,
growiterations, noisethreshold, minbeamfrac, sidelobethreshold,
gridder, pbmask, pblimit, threshold, niter_in, nsigma, cyclefactor,
minpsffraction, gain, w, nchan):
#
titles = ['.dirty', '', '.2.strong']
#
for i in range(2):
# niter
niter = 0 if i == 0 else niter_in
# masking method
usemask = 'auto-multithresh' if i == 2 else 'pb'
# deconvolver
deconvolver = 'multiscale'
#
tclean(vis=vis,
imagename=trial_name,
interactive=interactive,
intent='*TARGET*',
#
datacolumn='data',
nchan=nchan,
start=str(w * nchan / (-2)) + 'km/s',
width=str(w) + 'km/s',
# Image dimension
imsize=imsize,
cell=cellsize,
phasecenter=glx_ctr,
restfreq=restfreq,
specmode=specmode,
outframe=outframe,
veltype=veltype,
# Restore to common beam?
restoringbeam=restoringbeam,
# Weighting
weighting=weighting,
robust=robust,
# Methods
deconvolver=deconvolver,
scales=scales,
gain=gain,
smallscalebias=smallscalebias,
usemask=usemask,
dogrowprune=dogrowprune,
growiterations=growiterations,
noisethreshold=noisethreshold,
minbeamfrac=minbeamfrac,
sidelobethreshold=sidelobethreshold,
gridder=gridder,
pbmask=pbmask,
pblimit=pblimit,
pbcor=True,
# Stopping criteria
threshold=threshold,
niter=niter,
nsigma=nsigma,
cyclefactor=cyclefactor,
minpsffraction=minpsffraction)
#
nonpbcube = trial_name + '.image'
pbcube = trial_name + '.image.pbcor'
cubename = trial_name + '.image.pb'
tempcube = trial_name + titles[i] + '.cube.fits'
residualname = trial_name + '.residual'
tempresidual = trial_name + titles[i] + '.residual.fits'
m0name = cubename + '.integrated'
tempm0 = trial_name + titles[i] + '.m0.fits'
m1name = cubename + '.weighted_coord'
tempm1 = trial_name + titles[i] + '.m1.fits'
m2name = cubename + '.weighted_dispersion_coord'
tempm2 = trial_name + titles[i] + '.m2.fits'
for mxname in [m0name, m1name, m2name]:
if os.path.isdir(mxname):
rmtree(mxname)
#
print("...Calculating moment maps")
if i == 0:
rms = imstat(pbcube)['rms'][0]
excludepix = [-1000, (2 * rms)]
immoments(imagename=pbcube, moments=[0], axis='spectral',
excludepix=excludepix, outfile=m0name)
else:
# calculate mask here
# change masked_spec_width according to w
if w > 10:
spec_width = 1
else:
spec_width = 3
mask_dir, work_rms = \
make_mask_python(nonpbcube, masked_beam=2.0,
masked_spec_width=spec_width,
masked_snr=2.0, reject_area_in_beams=2)
# Use the PB corrected image!
immoments(imagename=pbcube, moments=[0, 1, 2], axis='spectral',
mask=mask_dir, excludepix=-1, outfile=cubename)
print("#\n")
print("...Exporting fits files of " + titles[i])
for (mxname, fitsname) in zip([pbcube, residualname, m0name, m1name,
m2name],
[tempcube, tempresidual, tempm0, tempm1,
tempm2]):
if (mxname in [residualname, m1name, m2name]) and i == 0:
continue
exportfits(imagename=mxname, fitsimage=fitsname)
# Blocking nan pixels
if mxname == pbcube:
data = fits.getdata(tempcube)
nanmask = np.any(np.isnan(data), axis=(0, 1))
del data
if mxname in [m0name, m1name, m2name]:
print("...Setting " + mxname + " NaNs to zeros")
data, hdr = fits.getdata(fitsname, header=True)
data[np.isnan(data)] = 0.0
data[0, 0][nanmask] = np.nan
fits.writeto(fitsname, data, hdr, overwrite=True)
with open(fitsname, 'rb') as f_in:
with gzip.open(fitsname + '.gz', 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(fitsname)
if mxname in [m0name, m1name, m2name]:
rmtree(mxname)
"""
print("...Plotting weighted m1 image")
plt.ioff()
m0 = fits.getdata(tempm0 + '.gz')[0, 0]
m1 = fits.getdata(tempm1 + '.gz')[0, 0]
#
cmap = cm.bwr_r
norm = mpl.colors.Normalize(vmin=np.nanmin(m1),
vmax=np.nanmax(m1))
m = cm.ScalarMappable(norm=norm, cmap=cmap)
with np.errstate(invalid='ignore'):
mm = m.to_rgba(m1)[:, :, :3]
temp = np.sum(mm, axis=2)
for i in range(3):
mm[:, :, i] /= temp
m0[m0 <= 0] = np.min(m0[m0 > 0])
m0 = np.log10(m0)
m0 -= m0.min()
m0 /= m0.max()
for i in range(3):
mm[:, :, i] *= m0
mm /= mm.max()
#
fig1, ax1 = plt.subplots(figsize=(12, 10))
fig2, ax2 = plt.subplots()
mpb = ax2.imshow(m1, cmap='bwr_r', origin='lower')
ax1.imshow(mm, origin='lower')
ax1.set_xticklabels([])
ax1.set_yticklabels([])
plt.colorbar(mpb, ax=ax1)
fig1.savefig(trial_name + titles[i] + '.m1.m0-weighted.png')
plt.close('all')
"""
if i == 1:
target = os.getcwd().split('/')[-1]
no_detection_check(image=pbcube,
pbimage=trial_name + '.pb',
target=target,
work_rms=work_rms,
chanwidth=w)
def everythings_imcontsub(image='', fitorder=1, masked_spec_width=3,
pbimage='', w=0):
assert False, 'Function under development!'
# 0) Definitions
imname_cont = image + '.imcont'
imname_contsub = image + '.imcontsub'
# 1) Load cube
vh = vishead(image)
numchan = vh # Need to write
# 1-1) Select channels according to channel number
chans = '0~10,' + str(numchan - 11) + '~' + str(numchan - 1)
# 2) imcontsub. save both cont and contsub
imcontsub(imagename=image,
linefile=imname_contsub,
contfile=imname_cont,
fitorder=fitorder,
chans=chans)
# 3) run masking for contsub image with pbcor image itself
mask_dir, work_rms = \
make_mask_python(imname_contsub, masked_beam=2.0,
masked_spec_width=masked_spec_width,
masked_snr=2.0, reject_area_in_beams=2)
# 4) run moment map building for contsub image
cubename = image + '.imcs'
tempcube = cubename + '.cube.fits'
m0name = cubename + '.integrated'
tempm0 = cubename + '.m0.fits'
m1name = cubename + '.weighted_coord'
tempm1 = cubename + '.m1.fits'
m2name = cubename + '.weighted_dispersion_coord'
tempm2 = cubename + '.m2.fits'
for mxname in [m0name, m1name, m2name]:
if os.path.isdir(mxname):
rmtree(mxname)
immoments(imagename=imname_contsub, moments=[0, 1, 2], axis='spectral',
mask=mask_dir, excludepix=-1, outfile=cubename)
for (mxname, fitsname) in zip([imname_contsub, m0name, m1name, m2name],
[tempcube, tempm0, tempm1, tempm2]):
exportfits(imagename=mxname, fitsimage=fitsname)
# Blocking nan pixels
if mxname == imname_contsub:
data = fits.getdata(tempcube)
nanmask = np.any(np.isnan(data), axis=(0, 1))
del data
if mxname in [m0name, m1name, m2name]:
print("...Setting " + mxname + " NaNs to zeros")
data, hdr = fits.getdata(fitsname, header=True)
data[np.isnan(data)] = 0.0
data[0, 0][nanmask] = np.nan
fits.writeto(fitsname, data, hdr, overwrite=True)
with open(fitsname, 'rb') as f_in:
with gzip.open(fitsname + '.gz', 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
os.remove(fitsname)
if mxname in [m0name, m1name, m2name]:
rmtree(mxname)
# 5) run no detection check
target = os.getcwd().split('/')[-1]
no_detection_check(image=imname_contsub,
pbimage=pbimage,
target=target,
work_rms=work_rms,
chanwidth=w)
# sidelobethreshold=0.2,
# minbeamfrac=0.5, #3
# negativethreshold=0.0,
# noisethreshold=3.0,lownoisethreshold=0.5,
# usemask='auto-multithresh',
usemask='pb',
interactive=False, #True,
cell=cellsize,
imsize=imsize,
weighting='briggs',
robust=robust,
pbcor=True,
antenna=antennae,
pblimit=0.1)
exportfits(imname + ".image.tt0", imname + ".image.tt0.fits")
exportfits(imname + ".image.tt0.pbcor",
imname + ".image.tt0.pbcor.fits")
logprint("Cleanest continuum images done.",
origin='almaimf_cont_3sigtemplate')
# ----------------------------------------------
# CLEAN FOR THE BEST SENSITIVITY CONTINUUM:
# Using here the splitted spw for line imaging:
# For B3: only spw 1,2,3 is used (spw 0 is for N2H+ J=1-0)
# For B6: only spw 6,7 is used
with open('to_image.json', 'r') as fh:
to_image = json.load(fh)
**impars)
for suffix in ('image', 'residual', 'model'):
ia.open(lineimagename + "." + suffix)
ia.sethistory(origin='almaimf_line_imaging',
history=[
"{0}: {1}".format(key, val)
for key, val in impars.items()
])
ia.sethistory(origin='almaimf_line_imaging',
history=[
"git_version: {0}".format(git_version),
"git_date: {0}".format(git_date)
])
ia.close()
impbcor(imagename=lineimagename + '.image',
pbimage=lineimagename + '.pb',
outfile=lineimagename + '.image.pbcor',
overwrite=True)
exportfits(lineimagename + ".image",
lineimagename + ".image.fits",
overwrite=True)
exportfits(lineimagename + ".image.pbcor",
lineimagename + ".image.pbcor.fits",
overwrite=True)
logprint("Completed {0}".format(vis), origin='almaimf_line_imaging')
logprint("Completed line_imaging.py run", origin='almaimf_line_imaging')
