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Copy pathIRAS32_spectral_line_SB.py
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IRAS32_spectral_line_SB.py
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"""
eDisk data reduction script
This script was written for CASA 6.1.1/6.2
Originally derived from DSHARP reduction scripts
Datasets calibrated (in order of date observed):
SB1:
LB1:
reducer: Zhe-Yu Daniel Lin
"""
""" Starting matter """
#sys.path.append('/home/casa/contrib/AIV/science/analysis_scripts/') #CHANGE THIS TO YOUR PATH TO THE SCRIPTS!
import analysisUtils as au
import analysisUtils as aU
import string
import os
import glob
import numpy as np
import sys
import pickle
#execfile('../reduction_utils3.py', globals())
execfile('/lustre/cv/projects/edisk/reduction_utils3.py', globals())
###############################################################
################ SETUP/METADATA SPECIFICATION #################
################ USERS NEED TO SET STUFF HERE #################
###############################################################
### Use MPI CASA for faster imaging (start casa with mpicasa -n XX CASA; where XX is the number of processes >= 2)
parallel=True
### if True, can run script non-interactively if later parameters properly set
skip_plots = False
### Add field names (corresponding to the field in the MS) here and prefix for
### filenameing (can be different but try to keep same)
### Only make different if, for example, the field name has a space
field = 'IRAS32'
prefix = 'IRAS32'
### always include trailing slashes!!
WD_path = '/lustre/cv/projects/edisk/IRAS32_test/'
SB_path = WD_path+'SB/'
LB_path = WD_path+'LB/'
### scales for multi-scale clean
SB_scales = [0, 5] #[0, 5, 10, 20]
LB_scales = [0, 5, 30] #[0, 5, 30, 100, 200]
### automasking parameters for very extended emission
#sidelobethreshold=2.0
#noisethreshold=3.0
#lownoisethreshold=1.0
#smoothfactor=2.0
### automasking parameters for compact emission (uncomment to use)
sidelobethreshold=2.0
noisethreshold=4.0
lownoisethreshold=1.5
smoothfactor=1.0
#read in final data_params from continuum to ensure we get the phase centers for each MS
with open(prefix+'.pickle', 'rb') as handle:
data_params = pickle.load(handle)
###############################################################
#################### SHIFT PHASE CENTERS ######################
###############################################################
selectedVis='vis'
#selectedVis='vis_shift'
if selectedVis == 'vis_shift':
for i in data_params.keys():
data_params[i]['vis_shift']=prefix+'_'+i+'_shift.ms'
os.system('rm -rf '+data_params[i]['vis_shift']+'*')
fixvis(vis=data_params[i]['vis'], outputvis=data_params[i]['vis_shift'],
field=data_params[i]['field'],
phasecenter='J2000 '+data_params[i]['phasecenter'])
fixplanets(vis=data_params[i]['vis_shift'], field=data_params[i]['field'],
direction=data_params[i]['common_dir'])
###############################################################
############### SCALE DATA RELATIVE TO ONE EB #################
###############################################################
### Uses scaling from continuum data
for i in data_params.keys():
rescale_flux(data_params[i][selectedVis].replace(WD_path,''), [data_params[i]['gencal_scale']])
data_params[i]['vis_rescaled']=data_params[i][selectedVis].replace('.ms','_rescaled.ms')
with open(prefix+'.pickle', 'wb') as handle:
pickle.dump(data_params, handle, protocol=pickle.HIGHEST_PROTOCOL)
###############################################################
##### APPLY SELF-CALIBRATION SOLUTIONS TO LINE DATA ###########
###############################################################
### Gain tables and spw mapping saved to data dictionaries during selfcal and used as arguments here
for i in data_params.keys():
n_tables=len(data_params[i]['selfcal_tables'])
interp_list=['linearPD']*n_tables
applycal(vis=data_params[i]['vis_rescaled'], spw='',
gaintable=data_params[i]['selfcal_tables'],spwmap=data_params[i]['selfcal_spwmap'], interp=interp_list,
calwt=True, applymode='calonly')
split(vis=data_params[i]['vis_rescaled'],outputvis=data_params[i]['vis_rescaled'].replace('.ms','.ms.selfcal'),datacolumn='corrected')
data_params[i]['vis_selfcal']=data_params[i]['vis_rescaled'].replace('.ms','.ms.selfcal')
### cleanup
os.system('rm -rf '+data_params[i]['vis_rescaled'])
if selectedVis=='vis_shift':
os.system('rm -rf '+data_params[i]['vis_shift'])
with open(prefix+'.pickle', 'wb') as handle:
pickle.dump(data_params, handle, protocol=pickle.HIGHEST_PROTOCOL)
###############################################################
################## DO CONTINUUM SUBTRACTION ###################
###############################################################
### Get channels to exclude for continuum fitting (same as the ones
### we flagged for doing making continuum MS)
for i in data_params.keys():
flagchannels_string = get_flagchannels(data_params[i], prefix)
print(flagchannels_string)
### Get spws for argument list to uvcontsub
spws_string = get_contsub_spws_indivdual_ms(data_params[i], prefix,only_cont_spws=True)
print(spws_string)
### Run uvcontsub on combined, self-cal applied dataset; THIS WILL TAKE MANY HOURS PER EB
contsub(data_params[i]['vis_selfcal'], prefix, spw=spws_string,flagchannels=flagchannels_string,excludechans=True)
os.system('rm -rf '+prefix+'_'+i+'_spectral_line.ms') ### remove existing spectral line MS if present
os.system('mv '+data_params[i]['vis_selfcal'].replace('.selfcal','.selfcal.contsub')+' '+prefix+'_'+i+'_spectral_line.ms')
os.system('rm -rf '+data_params[i]['vis_selfcal'])
data_params[i]['vis_contsub']=prefix+'_'+i+'_spectral_line.ms'
with open(prefix+'.pickle', 'wb') as handle:
pickle.dump(data_params, handle, protocol=pickle.HIGHEST_PROTOCOL)
###############################################################
################ TAR UP FINAL CONTSUBBED DATA #################
###############################################################
for i in data_params.keys():
if 'SB' in i:
os.system('rm -rf '+data_params[i]['vis_contsub']+'.tgz')
os.system('tar czf '+data_params[i]['vis_contsub']+'.tgz '+data_params[i]['vis_contsub'])
###############################################################
############ RUN A FINAL SPECTRAL LINE IMAGE SET ##############
###############################################################
### generate list of MS files to image
vislist=[]
for i in data_params.keys():
if 'SB' in i:
vislist.append(data_params[i]['vis_contsub'])
### Dictionary defining the spectral line imaging parameters.
image_list = {
### C18O images
"C18O":dict(chanstart='-5.5km/s', chanwidth='0.167km/s',
nchan=120, linefreq='219.56035410GHz', linespw='3',
robust=[0.5,-0.5,0.]),
### 13CO images
"13CO":dict(chanstart='-5.5km/s', chanwidth='0.167km/s',
nchan=120, linefreq='220.39868420GHz', linespw='1',
robust=[0.5,-0.5]),
### 12CO images
"12CO":dict(chanstart='-100.0km/s', chanwidth='0.635km/s',
nchan=315, linefreq='230.538GHz', linespw='6',
robust=[0.5]),
### SO Images
"SO":dict(chanstart='-5.5km/s', chanwidth='0.167km/s',
nchan=120, linefreq='219.94944200GHz', linespw='2',
robust=[0.5]),
### H2CO 3(2,1)-2(2,0) Images
"H2CO_3_21-2_20_218.76GHz":dict(chanstart='-5.5km/s',
chanwidth='0.167km/s', nchan=120, linefreq='218.76006600GHz',
linespw='0', robust=[0.5]),
### H2CO 3(0,3)-2(0,2) Images
"H2CO_3_03-2_02_218.22GHz":dict(chanstart='-10km/s',
chanwidth='1.34km/s', nchan=23, linefreq='218.22219200GHz',
linespw='4', robust=[0.5]),
### H2CO 3(2,2)-2(2,1) Images
"H2CO_3_22-2_21_218.47GHz":dict(chanstart='-10km/s',
chanwidth='1.34km/s', nchan=23, linefreq='218.47563200GHz',
linespw='4', robust=[0.5]),
### c-C3H2 217.82 GHz Images
"c-C3H2_217.82":dict(chanstart='-10km/s', chanwidth='1.34km/s',
nchan=23, linefreq='217.82215GHz', linespw='4', robust=[0.5]),
### c-C3H2 217.94 GHz Images
"cC3H2_217.94":dict(chanstart='-10km/s', chanwidth='1.34km/s',
nchan=23, linefreq='217.94005GHz', linespw='4', robust=[0.5]),
### c-C3H2 218.16 GHz Images
"cC3H2_218.16":dict(chanstart='-10km/s', chanwidth='1.34km/s',
nchan=23, linefreq='218.16044GHz', linespw='4', robust=[0.5]),
### DCN Images
"DCN":dict(chanstart='-10km/s', chanwidth='1.34km/s', nchan=23,
linefreq='217.2386GHz', linespw='4', robust=[0.5]),
### CH3OH Images
"CH3OH":dict(chanstart='-10km/s', chanwidth='1.34km/s', nchan=23,
linefreq='218.44006300GHz', linespw='4', robust=[0.5]),
### SiO Images
"SiO":dict(chanstart='-100km/s', chanwidth='1.34km/s', nchan=150,
linefreq='217.10498000GHz', linespw='4', robust=[0.5])
}
### Loop through the spectral line images and make images.
cellsize='0.025arcsec'
imsize=1600
for line in image_list:
for robust in image_list[line]["robust"]:
imagename = prefix+f'_SB_'+line+'_robust_'+str(robust)
sigma = get_sensitivity(data_params, specmode='cube', \
spw=[image_list[line]["linespw"]], chan=450,robust=robust,imsize=imsize,cellsize=cellsize)
tclean_spectral_line_wrapper(vislist, imagename,
image_list[line]["chanstart"], image_list[line]["chanwidth"],
image_list[line]["nchan"], image_list[line]["linefreq"],
image_list[line]["linespw"], SB_scales, threshold=3.0*sigma,
imsize=imsize, cellsize=cellsize,robust=robust,
sidelobethreshold=sidelobethreshold, noisethreshold=noisethreshold,
lownoisethreshold=lownoisethreshold,smoothfactor=smoothfactor,parallel=parallel,
phasecenter=data_params['SB1']['common_dir'].replace('J2000','ICRS'))
###############################################################
################ CLEANUP AND FITS CONVERSION ##################
###############################################################
import glob
### Remove extra image products
os.system('rm -rf *.residual* *.psf* *.model* *dirty* *.sumwt* *.gridwt* *.workdirectory')
### Remove fits files and pbcor files from previous iterations.
os.system("rm -rf *.pbcor* *.fits")
imagelist=glob.glob('*.image') + glob.glob('*.image.tt0')
for image in imagelist:
impbcor(imagename=image,pbimage=image.replace('image','pb'),outfile=image.replace('image','pbcor'))
exportfits(imagename=image.replace('image','pbcor'),fitsimage=image.replace('image','pbcor')+'.fits',overwrite=True,dropdeg=True)
exportfits(imagename=image,fitsimage=image+'.fits',overwrite=True,dropdeg=True)
imagelist=glob.glob('*.mask')
for image in imagelist:
exportfits(imagename=image,fitsimage=image+'.fits',overwrite=True,dropdeg=True)
os.system('gzip '+image+'.fits')
os.system('rm -rf *initcont*.pb')
imagelist=glob.glob('*.pb') + glob.glob('*.pb.tt0')
for image in imagelist:
exportfits(imagename=image,fitsimage=image+'.fits',overwrite=True,dropdeg=True)
os.system('gzip '+image+'.fits')
###############################################################
################# Make Plots of Everything ####################
###############################################################
import sys
sys.argv = ['../edisk/plot_final_images.py',prefix]
execfile('../edisk/plot_final_images.py')
### Remove rescaled selfcal MSfiles
os.system('rm -rf *rescaled.ms.*')
os.system('rm -rf scale*')
### Make a directory to put the final products
os.system('rm -rf export')
os.system('mkdir export')
os.system('mv *.fits export/')
os.system('mv *.tgz export/')
os.system('mv *.pdf export/')