Tohban EOVSA Imaging Tutorial A-Z V2: Difference between revisions

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This tutorial describes the step-by-step procedure to download EOVSA IDB data, obtain and calibrate the measurement sets (.ms), and, transfer them to the inti server for self-calibration and further analysis.


=== Pre-requisites ===
Accounts on Pipeline and Inti or Baozi servers. Sijie and Bin can help with the account set-up for pipeline, inti and boazi.
==== How to connect to pipeline ====
'''Windows:''' On a windows machine Mobaxterm can be used to connect to Pipeline. For details, see ...
'''Linux/Unix:''' On a linux/Mac the ssh commands can be used:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
ssh -L 5902:helios.solar.pvt:20000 guest@ovsa.njit.edu                                    ### when prompted, enter password to guest@ovsa ###
ssh -XY your_username@pipeline                                                            ### add your username and enter password to your pipeline account, when prompted ###
</pre>
Alternatively, to avoid typing the longer commands, proxy commands can be added to the ssh config file:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
vim ~/.ssh/config                                                                          ### press 'i' to insert content and esc +':wq' to write and quit ###
Host *
        ForwardAgent yes
Host *
        ForwardX11 yes
Host ovsa
        HostName ovsa.njit.edu
        User guest
Host pipeline
        Hostname pipeline.solar.pvt
        User your_username                                                                ### Insert your pipeline username here ###
        ProxyCommand ssh -W %h:%p guest@ovsa.njit.edu
</pre>
Upon saving the .ssh/config file and restarting the terminal, you can connect to pipeline directly using:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
ssh -XY pipeline
</pre>
==== How to connect to the inti server ====
NOTE: To connect to inti you must have a NJIT UCID. The arcs have granted access to inti for all SRG members. If you encounter any issue with the login. Please let arcs folks (arcs@njit.edu) know.
'''Windows:''' As mentioned above, on a windows machine Mobaxterm can be used to connect to inti. For details, see ...
'''Linux/Unix:''' On a linux/Mac the ssh command can be used:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
ssh -XY UCID@inti.hpcnet.campus.njit.edu                                                  ### insert UCID here and enter your NJIT UCID password, when prompted ###
</pre>
When logging in for the first time, please add the following lines (demarcated by #) in your ~/.bashrc file in order to use the casa on inti.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
#### 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 ####
</pre>
Proxy commands for inti can also be added to the .ssh/config file for convenience:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
vim ~/.ssh/config                                                                          ### press 'i' to insert content and esc +':wq' to write and quit ###
Host inti
        Hostname inti.hpcnet.campus.njit.edu
        User UCID                                                                          ### insert NJIT UCID here ###
</pre>
after the proxy is set up, connect to inti by simply typing:
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
ssh -XY inti                                                                              ### enter your NJIT UCID password when prompted ###
</pre>
=== Downloading data and Preliminary Calibration  ===
==== Step 1: Downloading raw data (IDB) on Pipeline machine====
The initial downloading of data and applying refcal and phasecal to the .ms files is performed on the pipeline since the complete EOVSA SQL database is stored here for now.
If you are not using mobaxterm, directly SSH to Pipeline through your Linux or Mac computer and start tcsh to run CASA.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
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/'                                                                      ###Change accordingly###
if not os.path.exists(outpath):
    os.makedirs(outpath)
######################################################
msfiles = importeovsa(idbfiles=trange, ncpu=1, visprefix=outpath)
</pre>
NOTE: The above step currently gives an error with importeovsa. This is because the data location was changed and the code doesn't know where to look for the IDB files. Sijie is looking into the problem. We will update the page when it is fixed. For now, please use the method below for all time ranges.
OR (for a time range longer than 10 minutes)
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
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/'                                                                      ###Change accordingly###
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)
</pre>
If you come across errors with calibeovsa, add following lines to your ~/.casa/init.py file.
<pre style="background-color: #FCEBD9">
import sys
sys.path.append('/common/python')
sys.path.append('/common/python/packages/pipeline_casa')
execfile('/common/python/suncasa/tasks/mytasks.py')
</pre>
==== 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. If doimage=True, a quicklook image will be produced (by integrating over the entire time) as shown below.
<pre style="background-color: #FCEBD9">
# This is to set the path/name for the concatenated files
concatvis = os.path.basename(msfiles[0])[:11] + '_concat_cal.ms'
vis = calibeovsa.calibeovsa(msfiles, doconcat=True, concatvis=concatvis, caltype=['refpha','phacal'], doimage=True)
</pre>
[[file:Figure1_imagingtutorial.png|frame|center|800px|'''Figure 1:''' Quick-look full-Sun image after the initial calibration.]]
==== Step 3: Calibration ====
This will calibrate the input visibility, write out calibration tables under /data1/eovsa/caltable/, and apply the calibration.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
vis = calibeovsa.calibeovsa('IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True)            ###Change the vis filename accordingly###
# Append '_cal' to the ms filename and split the corrected column to the new caled ms
vis_str = str(' '.join(vis))
caled_vis=vis_str.replace('.ms','_cal.ms')
split(vis=' '.join(vis),outputvis=caled_vis,datacolumn='corrected',timerange='',correlation='XX')
</pre>
One needs to transfer the created caled ms data files (xxx_concat_cal.ms or xxx_cal.ms) 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.
<pre style="background-color: #FCEBD9">
Host inti
Hostname inti.hpcnet.campus.njit.edu
User USERNAME                                                                                              ###Insert UCID/inti username here###
</pre>
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.
<pre style="background-color: #FCEBD9">
cd /inti/data/users/YOURDIRECTORY   
</pre>
For Linux or Mac machine,
ssh -X UCID@inti.hpcnet.campus.njit.edu
=== Transferring data files between servers ===
For directly transferring your calibrated .ms data between the Pipeline and Inti servers, follow the below given steps.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
1. Log into Inti using your username.
ssh -X USERNAME@inti.hpcnet.campus.njit.edu
Then create a tunnel into Pipeline from Inti.
ssh -L 8888:pipeline.solar.pvt:22 guest@ovsa.njit.edu
2. Log into Inti again from a new terminal.
Change to your working directory and give this command to copy your data on Pipeline.
scp -v -C -r -P 8888 USERNAMEofPipeline@localhost:PATHofMSDATA/MSfilename ./
Eg: scp -v -C -r -P 8888 shaheda@localhost:/data1/shaheda/IDB20220118173922_cal.ms ./
where, MSfilename, PATHofMSDATA are your .ms data and its path on Pipeline machine.
</pre>
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.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
scp -r -C userid@pipeline:/your/folder/msfile /localmachine/destination/folder/
</pre>
On mobaxterm and on your local terminal, use the following command to finally copy the ms file to Inti.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
scp -r -C /localmachine/destination/folder/msfile UCID@inti.hpcnet.campus.njit.edu:/inti/data/users/YOURDIRECTORY
</pre>
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
<pre style="background-color: #FCEBD9">
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
</pre>
=== 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.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
#### 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 ####
</pre>
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
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
>> loadcasa5
>> suncasa              #This should load the software making you ready for the analysis
</pre>
Or to load CASA 6, enter bash environment by giving >>bash
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
>> loadcasa6
>> ipython              #This should load the software making you ready for the analysis
</pre>
Here, for example, to use clean, first start ipython as given above, then type in >>from casatasks import tclean
====Step 4: Self-calibration====
[[file:Figure3_imagingtutorial.png|thumb|center|500px|Figure 2: Cotton-Schwab clean major and minor cycles. [Source: http://www.aoc.nrao.edu/~rurvashi/ImagingAlgorithmsInCasa/node2.html].]]
Follow the below given steps to run the self-calibration of the imaging data and produce the calibrated images in .fits format. https://github.com/binchensun/casa-eovsa/blob/master/slfcal_example.py
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
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 = 1 # 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 + 'IDB20170821202020_cal.ms'                                    ###Change the initial calibrated (through calibeovsa) vis accordingly###
# output, selfcaled, visibility
ms_slfcaled = workdir + os.path.basename(ms_in).replace('cal','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
# =========== 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']    #Do not run the next 4 lines if needed to view and assess the subset of clean process images
    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')
</pre>
[[file:Figure2_imagingtutorial.png|thumb|center|500px|Figure 3: Full-Sun image after initial clean to find the flare location.]]
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
# parameters specific to the event (found from step 1)
phasecenter='J2000 10h02m59 11d58m07'                                              ###Change accordingly###
xran=[280,480]                                                                      ###Change accordingly###
yran=[-50,150]                                                                      ###Change accordingly###
# =========== 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']  #Do not run the next 4 lines if needed to view and assess the subset of clean process images
            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'))
</pre>
[[file:Figure4_imagingtutorial.PNG|thumb|center|800px|Figure 4: Interactive clean window to create masks over the source. The white outline surrounding the source is the mask selected by the polygon drawing option.]]
The outlines drawn for masks can be created by any of the icons with the letter 'R' in the Viewer window. The instructions for doing this can be found by hovering over those icons.
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
# =========== 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        #Change accordingly
    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']  #Do not run the next 4 lines if needed to view and assess the subset of clean process images
                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)
</pre>
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
# =========== 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')
</pre>
[[file:Slf.G0.png|thumb|center|500px|Figure 1: Phase before self-calibration]]
[[file:Slf.G1.png|thumb|center|500px|Figure 2: Phase after self-calibration]]
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
# =========== 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']  #Do not run the next 4 lines if needed to view and assess the subset of clean process images
            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()
</pre>
The .fits files of the self calibrated images at each frequency for the given time are saved at /slfcal/images_slfcaled in your working directory.
[[file:Slf_t0_n0.png|thumb|center|500px|Figure 3: Multi-frequency images before self-calibration]]
[[file:Slf_t0_n1.png|thumb|center|500px|Figure 4: Multi-frequency images after self-calibration]]
====Step 5: Quick-look imaging ==== <!-- ====Step 6: Making the full set of images====  -->
For spectral imaging analysis of the event, follow [http://www.ovsa.njit.edu/wiki/index.php/EOVSA_Data_Analysis_Tutorial#Spectral_Imaging_with_SunCASA this tutorial] using the self-calibrated data obtained from the previous step or use [https://colab.research.google.com/drive/1lSLLxgOG6b8kgu9Sk6kSKvrViyubnXG6?usp=sharing#scrollTo=xbXyyLmCFCGL this link].
<pre style="background-color: #FCEBD9;overflow: auto;width: auto;">
from suncasa.utils import qlookplot as ql    ## (Optional) Supply the npz file of the dynamic spectrum from previous step.
            ## If not provided, the program will generate a new one from the visibility.
                                            ## set the time interval
from suncasa import dspec as ds
import time
visibility_data = 'IDB20170821202020_slfcaled.ms'
specfile = visibility_data + '.dspec.npz'
d = ds.Dspec(visibility_data, bl='4&9', specfile=specfile)
timerange = '19:02:00~19:02:10'            ## select frequency range from 2.5 GHz to 3.5 GHz
spw = '2~5'                                ## select stokes XX
stokes = 'XX'                              ## turn off AIA image plotting, default is True
plotaia = False
xycen = [375, 45]  ## image center for clean in solar X-Y in arcsec
cell=['2.0arcsec'] ## pixel size
imsize=[128]  ## x and y image size in pixels.
fov = [100,100]  ## field of view of the zoomed-in panels in unit of arcsec
spw = ['{}'.format(s) for s in range(1,31)]
clevels = [0.5, 1.0]  ## contour levels to fill in between.
calpha=0.35  ## now tune down the alpha
restoringbeam=['6arcsec']
ql.qlookplot(vis=msfile, specfile=specfile, timerange=timerange, spw=spw, stokes=stokes, \
            restoringbeam=restoringbeam,imsize=imsize,cell=cell, \
            xycen=xycen,fov=fov,clevels=clevels,calpha=calpha)
</pre>
Page Last Updated: 03/29/2022

Latest revision as of 23:16, 7 July 2022

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