Tohban EOVSA Imaging Tutorial A-Z: Difference between revisions
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====Step 5: Quick-look imaging ==== | ====Step 5: Quick-look imaging ==== <!-- ====Step 6: Making the full set of images==== --> | ||
For further analysis of the event, follow the link given below. | For further analysis of the event, follow the link given below. | ||
https://colab.research.google.com/drive/1lSLLxgOG6b8kgu9Sk6kSKvrViyubnXG6?usp=sharing#scrollTo=xbXyyLmCFCGL | https://colab.research.google.com/drive/1lSLLxgOG6b8kgu9Sk6kSKvrViyubnXG6?usp=sharing#scrollTo=xbXyyLmCFCGL | ||
Revision as of 20:24, 30 November 2021
Connection details to pipeline server
Step 1: Downloading raw data (IDB) on Pipeline machine
On CASA of the Pipeline machine, which has the complete EOVSA SQL database.
If you are not using mobaxterm, directly SSH to Pipeline through your Linux or Mac computer and start tcsh to run CASA.
from astropy.time import Time import os trange = Time(['2017-08-21 20:15:00', '2017-08-21 20:25:00']) #Change accordingly #### (Optional) change output path, default current directory "./" ##### outpath = './msdata/' if not os.path.exists(outpath): os.makedirs(outpath) ###################################################### msfiles = importeovsa(idbfiles=trange, ncpu=1, visprefix=outpath)
OR (for a time range longer than 10 minutes)
from suncasa.tasks import task_calibeovsa as calibeovsa from suncasa.tasks import task_importeovsa as timporteovsa from split_cli import split_cli as split import dump_tsys as dt from util import Time import numpy as np import os from glob import glob from eovsapy import util trange = Time(['2017-08-21 20:15:00', '2017-08-21 20:35:00']) #Change accordingly idbdir = util.get_idbdir(trange[0]) info = dt.rd_fdb(trange[0]) for k, v in info.iteritems(): info[k] = info[k][~(info[k] == '')] sidx = np.where( np.logical_and(info['SOURCEID'] == 'Sun', info['PROJECTID'] == 'NormalObserving') & np.logical_and( info['ST_TS'].astype(np.float) >= trange[0].lv, info['ST_TS'].astype(np.float) <= trange[ 1].lv)) filelist = info['FILE'][sidx] outpath = './msdata/' if not os.path.exists(outpath): os.makedirs(outpath) inpath = idbdir + '{}/'.format(trange[0].datetime.strftime("%Y%m%d")) ncpu = 1 msfiles = timporteovsa.importeovsa(idbfiles=[inpath + ll for ll in filelist], ncpu=ncpu, timebin="0s", width=1, visprefix=outpath, nocreatms=False, doconcat=False, modelms="", doscaling=False, keep_nsclms=False, udb_corr=True)
If you come across errors with calibeovsa, add following lines to your ~/.casa/init.py file.
import sys sys.path.append('/common/python') sys.path.append('/common/python/packages/pipeline_casa') execfile('/common/python/suncasa/tasks/mytasks.py')
Step 2: Concatenate all the 10 mins data, if there are any
Follow this step if there are more than one .ms files, if not run step 3 directly.
# This is to set the path/name for the concatenated files concatvis = os.path.basename(msfiles[0])[:11] + '_concat.ms' vis = calibeovsa(msfiles, doconcat=True, concatvis=concatvis, msoutdir=outpath)
Step 3: Calibration
This will calibrate the input visibility, write out calibration tables under /data1/eovsa/caltable/, and apply the calibration. If doimage=True, a quicklook image will be produced (by integrating over the entire time).
calibeovsa(vis='IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True) #Change the vis filename accordingly
One needs to transfer the created ms data files from pipeline to inti server, which has all the casatasks installed, in order to run the rest of the imaging steps.
Connection details to Inti server
For Windows, on Mobaxterm,
- With your NJIT VPN connected, connect to one of the afsconnect servers (for example, afsaccess3.njit.edu) using your UCID and password.
- ssh -X UCID@inti.hpcnet.campus.njit.edu
To avoid typing the full inti address each time you attempt for ssh, you may wish to add the following lines with your username to C:\Program Files\Git\etc\ssh\ssh_config on Windows and /.ssh/config on Mac and Linux machines.
Host inti Hostname inti.hpcnet.campus.njit.edu User USERNAME
To have sufficient disk space with EOVSA data analysis over Inti, use your dedicated directory YOURDIRECTORY at the location given below. If you do not have a directory, Please take help from Sijie in creating one.
cd /inti/data/users/YOURDIRECTORY
For Linux or Mac machine, ssh -X UCID@inti.hpcnet.campus.njit.edu
Transferring data files between servers
A direct transfer between the servers is possible, but with some issues (this method will be updated soon).
Alternatively, one can follow the below procedure to do the transfer.
For Windows, on mobaxterm, drag and drop the ms file to your local machine or use scp command as given below.
scp -r -C userid@pipeline:/your/folder/msfile /localmachine/destination/folder/
On mobaxterm and on your local terminal, use the following command to finally copy the ms file to Inti.
scp -r -C /localmachine/destination/folder/msfile UCID@inti.hpcnet.campus.njit.edu:/inti/data/users/YOURDIRECTORY
For Mac and Linux, SCP can be used in the same way. Add the following lines to your SSH config file, to bypass ovsa.njit.edu from copying. vi ~/.ssh/config
Host ovsa HostName ovsa.njit.edu User guest Host pipeline Hostname pipeline.solar.pvt User userid ProxyCommand ssh -W %h:%p guest@ovsa.njit.edu
Software details on the servers
On Inti, when logging in for the first time, please add the following lines to your accounts ~/.bashrc file.
>>vi ~/.bashrc Insert the text given below and save it.
#### setting start #### if [ $HOSTNAME == "baozi.hpcnet.campus.njit.edu" ]; then source /srg/.setenv_baozi fi if [ $HOSTNAME == "inti.hpcnet.campus.njit.edu" ]; then source /inti/.setenv_inti fi if [ $HOSTNAME == "guko.resource.campus.njit.edu" ]; then source /data/data/.setenv_guko fi #### setting end ####
Both CASA 5 and 6 are available on Inti.
Enter the bash environment on inti, and load the desired casa environment. To load CASA 5, enter bash environment by giving >>bash
>> loadcasa5 >> suncasa #This should load the software making you ready for the analysis
Or to load CASA 6, enter bash environment by giving >>bash
>> loadcasa6 >> ipython #This should load the software making you ready for the analysis
Here, for example, to use clean, first start ipython as given above, then type in >>from casatasks import tclean
Step 4: Self-calibration on Inti server
https://github.com/binchensun/casa-eovsa/blob/master/slfcal_example.py
from suncasa.utils import helioimage2fits as hf import os import numpy as np import pickle from matplotlib import gridspec as gridspec from sunpy import map as smap from matplotlib import pyplot as plt from split_cli import split_cli as split import time # =========== task handlers ============= dofullsun=0 # initial full-sun imaging ##Change accordingly domasks=1 # get masks ##Change accordingly doslfcal=1 # main cycle of doing selfcalibration ##Change accordingly doapply=1 # apply the results ##Change accordingly doclean_slfcaled=1 # perform clean for self-calibrated data ##Change accordingly # ============ declaring the working directories ============ workdir = os.getcwd()+'/' #main working directory. Using current directory in this example slfcaldir = workdir+'slfcal/' #place to put all selfcalibration products imagedir = slfcaldir+'images/' #place to put all selfcalibration images maskdir = slfcaldir+'masks/' #place to put clean masks imagedir_slfcaled = slfcaldir+'images_slfcaled/' #place to put final self-calibrated images caltbdir = slfcaldir+'caltbs/' # place to put calibration tables # make these directories if they do not already exist dirs = [workdir, slfcaldir, imagedir, maskdir, imagedir_slfcaled, caltbdir] for d in dirs: if not os.path.exists(d): os.makedirs(d) # ============ Split a short time for self-calibration =========== # input visibility ms_in = workdir + 'IDB20170821_concat_original.ms' ##Change accordingly # output, selfcaled, visibility ms_slfcaled = workdir + os.path.basename(ms_in).replace('original','slfcaled') # intermediate small visibility for selfcalbration # selected time range for generating self-calibration solutions trange='2017/08/21/20:21:10~2017/08/21/20:21:30' ##Change accordingly slfcalms = slfcaldir+'slfcalms.XX.slfcal' slfcaledms = slfcaldir+'slfcalms.XX.slfcaled' if not os.path.exists(slfcalms): split(vis=ms_in,outputvis=slfcalms,datacolumn='data',timerange=trange,correlation='XX') # ============ Prior definitions for spectral windows, antennas, pixel numbers ========= spws=[str(s+1) for s in range(30)] antennas='0~12' npix=512 nround=3 #number of slfcal cycles # parameters specific to the event (found from step 1) phasecenter='J2000 10h02m59 11d58m14' ##Change accordingly xran=[280,480] ##Change accordingly yran=[-50,150] ##Change accordingly # =========== Step 1, doing a full-Sun image to find out phasecenter and appropriate field of view ========= if dofullsun: #initial mfs clean to find out the image phase center im_init='fullsun_init' os.system('rm -rf '+im_init+'*') tclean(vis=slfcalms, antenna='0~12', imagename=im_init, spw='1~15', specmode='mfs', timerange=trange, imsize=[npix], cell=['5arcsec'], niter=1000, gain=0.05, stokes='I', restoringbeam=['30arcsec'], interactive=False, pbcor=True) hf.imreg(vis=slfcalms,imagefile=im_init+'.image.pbcor',fitsfile=im_init+'.fits', timerange=trange,usephacenter=False,verbose=True) clnjunks = ['.flux', '.mask', '.model', '.psf', '.residual','.sumwt','.pb','.image'] for clnjunk in clnjunks: if os.path.exists(im_init + clnjunk): os.system('rm -rf '+im_init + clnjunk) from sunpy import map as smap from matplotlib import pyplot as plt fig = plt.figure(figsize=(6,6)) ax = fig.add_subplot(111) eomap=smap.Map(im_init+'.fits') #eomap.data=eomap.data.reshape((npix,npix)) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot(axes = ax) eomap.draw_limb() plt.show() viewer(im_init+'.image.pbcor') # =========== Step 2 (optional), generate masks ========= # if skipped, will not use any masks if domasks: clearcal(slfcalms) delmod(slfcalms) antennas='0~12' pol='XX' imgprefix=maskdir+'slf_t0' # step 1: set up the clean masks img_init=imgprefix+'_init_ar_' os.system('rm -rf '+img_init+'*') #spwrans_mask=['1~5','6~12','13~20','21~30'] spwrans_mask=['1~12'] #convert to a list of spws spwrans_mask_list = [[str(i) for i in (np.arange(int(m.split('~')[0]),int(m.split('~')[1])))] for m in spwrans_mask] masks=[] imnames=[] for spwran in spwrans_mask: imname=img_init+spwran.replace('~','-') try: tclean(vis=slfcalms, antenna='0~12', imagename=imname, spw=spwran, specmode='mfs', timerange=trange, imsize=[npix], cell=['2arcsec'], niter=1000, gain=0.05, stokes='XX', restoringbeam=['20arcsec'], phasecenter=phasecenter, weighting='briggs', robust=1.0, interactive=True, datacolumn='data', pbcor=True, savemodel='modelcolumn') imnames.append(imname+'.image') masks.append(imname+'.mask') clnjunks = ['.flux', '.model', '.psf', '.residual'] for clnjunk in clnjunks: if os.path.exists(imname + clnjunk): os.system('rm -rf '+ imname + clnjunk) except: print('error in cleaning spw: '+spwran) pickle.dump(masks,open(slfcaldir+'masks.p','wb')) # =========== Step 3, main step of selfcalibration ========= if doslfcal: if os.path.exists(slfcaldir+'masks.p'): masks=pickle.load(open(slfcaldir+'masks.p','rb')) if not os.path.exists(slfcaldir+'masks.p'): print 'masks do not exist. Use default mask' masks=[] os.system('rm -rf '+imagedir+'*') os.system('rm -rf '+caltbdir+'*') #first step: make a mock caltable for the entire database print('Processing ' + trange) slftbs=[] calprefix=caltbdir+'slf' imgprefix=imagedir+'slf' tb.open(slfcalms+'/SPECTRAL_WINDOW') reffreqs=tb.getcol('REF_FREQUENCY') bdwds=tb.getcol('TOTAL_BANDWIDTH') cfreqs=reffreqs+bdwds/2. tb.close() # starting beam size at 3.4 GHz in arcsec sbeam=40. strtmp=[m.replace(':','') for m in trange.split('~')] timestr='t'+strtmp[0]+'-'+strtmp[1] refantenna='0' # number of iterations for each round niters=[100, 300, 500] # roburst value for weighting the baselines robusts=[1.0, 0.5, 0.0] # apply calibration tables? Set to true for most cases doapplycal=[1,1,1] # modes for calibration, 'p' for phase-only, 'a' for amplitude only, 'ap' for both calmodes=['p','p','a'] # setting uvranges for model image (optional, not used here) uvranges=['','',''] for n in range(nround): slfcal_tb_g= calprefix+'.G'+str(n) fig = plt.figure(figsize=(8.4,7.)) gs = gridspec.GridSpec(5, 6) for s,sp in enumerate(spws): print 'processing spw: '+sp cfreq=cfreqs[int(sp)] # setting restoring beam size (not very useful for selfcal anyway, but just to see the results) bm=max(sbeam*cfreqs[1]/cfreq, 6.) slfcal_img = imgprefix+'.spw'+sp.zfill(2)+'.slfcal'+str(n) # only the first round uses nearby spws for getting initial model if n == 0: spbg=max(int(sp)-2,1) sped=min(int(sp)+2,30) spwran=str(spbg)+'~'+str(sped) print('using spw {0:s} as model'.format(spwran)) if 'spwrans_mask' in vars(): for m, spwran_mask in enumerate(spwrans_mask): if sp in spwran_mask: mask = masks[m] print('using mask {0:s}'.format(mask)) findmask = True if not findmask: print('mask not found. Do use any masks') else: spwran = sp if 'spwrans_mask' in vars(): for m, spwran_mask in enumerate(spwrans_mask): if sp in spwran_mask: mask = masks[m] print 'using mask {0:s}'.format(mask) findmask = True if not findmask: print('mask not found. Do use any masks') try: tclean(vis=slfcalms, antenna=antennas, imagename=slfcal_img, uvrange=uvranges[n], spw=spwran, specmode='mfs', timerange=trange, imsize=[npix], cell=['2arcsec'], niter=niters[n], gain=0.05, stokes='XX', #use pol XX image as the model weighting='briggs', robust=robusts[n], phasecenter=phasecenter, mask=mask, restoringbeam=[str(bm)+'arcsec'], pbcor=False, interactive=False, savemodel='modelcolumn') if os.path.exists(slfcal_img+'.image'): fitsfile=slfcal_img+'.fits' hf.imreg(vis=slfcalms,imagefile=slfcal_img+'.image',fitsfile=fitsfile, timerange=trange,usephacenter=False,toTb=True,verbose=False,overwrite=True) clnjunks = ['.mask','.flux', '.model', '.psf', '.residual', '.image','.pb','.image.pbcor','.sumwt'] for clnjunk in clnjunks: if os.path.exists(slfcal_img + clnjunk): os.system('rm -rf '+ slfcal_img + clnjunk) ax = fig.add_subplot(gs[s]) eomap=smap.Map(fitsfile) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot(axes = ax) eomap.draw_limb() #eomap.draw_grid() ax.set_title(' ') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) ax.set_xlim(xran) ax.set_ylim(yran) os.system('rm -f '+ fitsfile) except: print 'error in cleaning spw: '+sp print 'using nearby spws for initial model' sp_e=int(sp)+2 sp_i=int(sp)-2 if sp_i < 1: sp_i = 1 if sp_e > 30: sp_e = 30 sp_=str(sp_i)+'~'+str(sp_e) try: tget(tclean) spw=sp_ print('using spw {0:s} as model'.format(sp_)) tclean() except: print 'still not successful. abort...' break gaincal(vis=slfcalms, refant=refantenna,antenna=antennas,caltable=slfcal_tb_g,spw=sp, uvrange='',\ gaintable=[],selectdata=True,timerange=trange,solint='inf',gaintype='G',calmode=calmodes[n],\ combine='',minblperant=4,minsnr=2,append=True) if not os.path.exists(slfcal_tb_g): print 'No solution found in spw: '+sp figname=imagedir+'slf_t0_n{:d}.png'.format(n) plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) plt.savefig(figname) time.sleep(10) plt.close() if os.path.exists(slfcal_tb_g): slftbs.append(slfcal_tb_g) slftb=[slfcal_tb_g] os.chdir(slfcaldir) if calmodes[n] == 'p': plotcal(caltable=slfcal_tb_g,antenna='1~12',xaxis='freq',yaxis='phase',\ subplot=431,plotrange=[-1,-1,-180,180],iteration='antenna',figfile=slfcal_tb_g+'.png',showgui=False) if calmodes[n] == 'a': plotcal(caltable=slfcal_tb_g,antenna='1~12',xaxis='freq',yaxis='amp',\ subplot=431,plotrange=[-1,-1,0,2.],iteration='antenna',figfile=slfcal_tb_g+'.png',showgui=False) os.chdir(workdir) if doapplycal[n]: clearcal(slfcalms) delmod(slfcalms) applycal(vis=slfcalms,gaintable=slftb,spw=','.join(spws),selectdata=True,\ antenna=antennas,interp='nearest',flagbackup=False,applymode='calonly',calwt=False) if n < nround-1: prompt=raw_input('Continuing to selfcal?') #prompt='y' if prompt.lower() == 'n': if os.path.exists(slfcaledms): os.system('rm -rf '+slfcaledms) split(slfcalms,slfcaledms,datacolumn='corrected') print 'Final calibrated ms is {0:s}'.format(slfcaledms) break if prompt.lower() == 'y': slfcalms_=slfcalms+str(n) if os.path.exists(slfcalms_): os.system('rm -rf '+slfcalms_) split(slfcalms,slfcalms_,datacolumn='corrected') slfcalms=slfcalms_ else: if os.path.exists(slfcaledms): os.system('rm -rf '+slfcaledms) split(slfcalms,slfcaledms,datacolumn='corrected') print 'Final calibrated ms is {0:s}'.format(slfcaledms) # =========== Step 4: Apply self-calibration tables ========= if doapply: import glob os.chdir(workdir) clearcal(ms_in) clearcal(slfcalms) applycal(vis=slfcalms,gaintable=slftbs,spw=','.join(spws),selectdata=True,\ antenna=antennas,interp='linear',flagbackup=False,applymode='calonly',calwt=False) applycal(vis=ms_in,gaintable=slftbs,spw=','.join(spws),selectdata=True,\ antenna=antennas,interp='linear',flagbackup=False,applymode='calonly',calwt=False) if os.path.exists(ms_slfcaled): os.system('rm -rf '+ms_slfcaled) split(ms_in, ms_slfcaled,datacolumn='corrected') # =========== Step 5: Generate final self-calibrated images (optional) ========= if doclean_slfcaled: import glob pol='XX' print('Processing ' + trange) img_final=imagedir_slfcaled+'/slf_final_{0}_t0'.format(pol) vis = ms_slfcaled spws=[str(s+1) for s in range(30)] tb.open(vis+'/SPECTRAL_WINDOW') reffreqs=tb.getcol('REF_FREQUENCY') bdwds=tb.getcol('TOTAL_BANDWIDTH') cfreqs=reffreqs+bdwds/2. tb.close() sbeam=30. from matplotlib import gridspec as gridspec from sunpy import map as smap from matplotlib import pyplot as plt fitsfiles=[] for s,sp in enumerate(spws): cfreq=cfreqs[int(sp)] bm=max(sbeam*cfreqs[1]/cfreq,4.) imname=img_final+'_s'+sp.zfill(2) fitsfile=imname+'.fits' if not os.path.exists(fitsfile): print 'cleaning spw {0:s} with beam size {1:.1f}"'.format(sp,bm) try: tclean(vis=vis, antenna=antennas, imagename=imname, spw=sp, specmode='mfs', timerange=trange, imsize=[npix], cell=['1arcsec'], niter=1000, gain=0.05, stokes=pol, weighting='briggs', robust=2.0, restoringbeam=[str(bm)+'arcsec'], phasecenter=phasecenter, mask='', pbcor=True, interactive=False) except: print 'cleaning spw '+sp+' unsuccessful. Proceed to next spw' continue if os.path.exists(imname+'.image.pbcor'): imn = imname+'.image.pbcor' hf.imreg(vis=vis,imagefile=imn,fitsfile=fitsfile, timerange=trange,usephacenter=False,toTb=True,verbose=False) fitsfiles.append(fitsfile) junks=['.flux','.model','.psf','.residual','.mask','.image','.pb','.image.pbcor','.sumwt'] for junk in junks: if os.path.exists(imname+junk): os.system('rm -rf '+imname+junk) else: print('fits file '+fitsfile+' already exists, skip clean...') fitsfiles.append(fitsfile) fig = plt.figure(figsize=(8.4,7.)) gs = gridspec.GridSpec(5, 6) for s,sp in enumerate(spws): cfreq=cfreqs[int(sp)] ax = fig.add_subplot(gs[s]) eomap=smap.Map(fitsfiles[s]) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot(axes = ax) eomap.draw_limb() ax.set_title(' ') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) ax.set_xlim(xran) ax.set_ylim(yran) plt.text(0.98,0.85,'{0:.1f} GHz'.format(cfreq/1e9),transform=ax.transAxes,ha='right',color='w',fontweight='bold') plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) plt.show()
Step 5: Quick-look imaging
For further analysis of the event, follow the link given below. https://colab.research.google.com/drive/1lSLLxgOG6b8kgu9Sk6kSKvrViyubnXG6?usp=sharing#scrollTo=xbXyyLmCFCGL