def firstlook(cloud, line='NH3_11'):
print('Now %s' % line)
a_rms, b_rms = basebox(cloud, line)
cmin, cmax = chanmin(cloud, line), chanmax(cloud, line)
# assuming the peak of emission is somewhere
# in the middle of the bandwidth:
cmid = (cmin+cmax)/2
# we can find the corresponding gap in basebox:
bcen, acen = [[i,i+1] for i in range(len(a_rms)-1)
if b_rms[i]<cmid<a_rms[i+1]][0]
index_peak=first_look.create_index([b_rms[bcen]],[a_rms[acen]])
index_rms=first_look.create_index(a_rms, b_rms)
file_in = '../%s/%s_%s.fits' % (keys[cloud]['region'],
keys[cloud]['region'], line)
s = SpectralCube.read(file_in)
s = s.with_spectral_unit(u.km/u.s,velocity_convention='radio')
file_out=file_in.replace('.fits','_base1.fits')
file_new=first_look.baseline(file_in, file_out,
index_clean=index_rms, polyorder=1)
first_look.peak_rms(file_new, index_rms=index_rms,
index_peak=index_peak)
def map_cloud(cloud, do_sdfits=False, do_calibration=False, do_imaging=True, keys=keys):
source = keys[cloud]['source' ]
region = keys[cloud]['region' ]
windows = keys[cloud]['windows']
nblocks = keys[cloud]['nblocks']
scans = keys[cloud]['scans' ]
gains = keys[cloud]['gains' ]
beam = keys[cloud]['beam' ]
# TODO: make --windows (or better yet, --lines) an cmd argument
# pulls all unique values from a dictionary of {'ifnum':'lineName', ...} form:
lines = []
_ = [(WindowDict[ifn],lines.append(WindowDict[ifn]))
for ifn in WindowDict if WindowDict[ifn] not in lines]
# NOTE: "lines" list controls the imaging loop,
# while "windows" list controls calibration!
# TODO: resolve lines/windows ambiguity!
# Convert VEGAS raw data to sdfits
if do_sdfits:
import subprocess
assert type(scans) is list # better safe than sorry
unique_sessions = set([s['session'] for s in scans])
for session in unique_sessions:
# all scan blocks within one session are
# parsed in a "s1:s2,s3:s4,..." format and
# then sent to sdfits-test for data-crunching
scan_blocks = ','.join(['%i:%i'%(s['start'],s['end'])
for s in scans
if s['session'] is session])
sdfits_dir = ' AGBT15B_313_%.2i' % session
sdfits_args = ' -scans=\"%s\"' % scan_blocks
# TODO: oops I also need to properly set the output dir!
subprocess.Popen('sdfits-test -backends=vegasi'+
sdfits_args+sdfits_dir)
# it's being quite slow on the import, moved inside the script
import GAS_gridregion
data_dir='/lustre/pipeline/scratch/vsokolov/'
# Run the GAS pipeline wrapper
if do_calibration:
for window in windows:
# TODO: this is way too slow; gbtpipeline can accept
# arguments like -m "50:60,80:90", rewrite the
# GAS wrapper to accept faster arguments
for block in range(nblocks):
GAS_gridregion.doPipeline(
SessionNumber = scans[block]['session'],
StartScan = scans[block]['start'],
EndScan = scans[block]['end' ],
Source = source,
Gains = gains,
Region = region,
Window = str(window),
OutputRoot = data_dir+region+'/',
overwrite = True )
# Image the calibrated data
if do_imaging:
# TODO: trim and implement proper vlsr corrections
# cloud 'I' had a somewhat mismatched vlsr
startChannel, endChannel = (2800, 4600) \
if cloud is 'I' else (3200, 5000)
for line in lines:
GAS_gridregion.griddata(rootdir=data_dir,
region=region,
indir=region+'_'+line,
outfile=region+'_'+line,
startChannel = startChannel,
endChannel = endChannel,
doBaseline = True,
baselineRegion =
basebox(cloud, line)+
startChannel,
useBeam = beam,
file_extension='')
