Sunspot Band6 Calibration for CASA 4.7: Difference between revisions

From CASA Guides
Jump to navigationJump to search
← Older edit
 
(13 intermediate revisions by the same user not shown)
Line 259: Line 259:
===Tsys+Tant calibration of the visibilities of the Sun===
===Tsys+Tant calibration of the visibilities of the Sun===


The standard method of Tsys calibration cannot be apply to the data of the Sun, because the antenna temperature of the Sun cannot be neglected for estimating the system equivalent flux density (SEFD). To estimate correct SEFD at the Sun, the solar observing sequence includes some special measurements, like the measurement of zero-signal level. The subroutines for creating and applying Tsys+Tant calibration tables are prepared by the ALMA solar development team. The subroutines are already imported at the section [#Confirm your version of CASA]. For the Tsys+Tant calibration of the solar data, you will execute only the following command.
The standard method of Tsys calibration cannot be apply to the data of the Sun, because the antenna temperature of the Sun cannot be neglected for estimating the system equivalent flux density (SEFD). To estimate correct SEFD at the Sun, the solar observing sequence includes some special measurements, like the measurement of zero-signal level. The subroutines for creating and applying Tsys+Tant calibration tables are prepared by the ALMA solar development team. The subroutines are already imported at the section [[#Confirm your version of CASA]]. For the Tsys+Tant calibration of the solar data, you will execute only the following command.


<source lang="python">
<source lang="python">
Line 271: Line 271:
#In Casa
#In Casa
sol_ampcal(mso, mso + '.tsys', exisTbl=True, outCSV=False)
sol_ampcal(mso, mso + '.tsys', exisTbl=True, outCSV=False)
</source>
===Flagging after Tsys and Tsys+Tant calibration===
Since we completed the Tsys and Tsys+Tant calibration, the data that are not used the image synthesis are flagged. At first, the data of auto-correlation and atmosphere calibration are flagged as follows:
<source lang="python">
#In Casa
flagdata(vis = mso, mode = 'manual', autocorr = True, flagbackup = False)
flagdata(vis = mso, mode = 'manual', intent = '*ATMOSPHERE*', flagbackup = False)
</source>
Next is the spectrum windows that are not used in later:
<source lang="python">
#In Casa
flagdata(vis = mso, mode = 'manual', spw = '0~4', flagbackup = False)
flagdata(vis = mso, mode = 'manual', spw = '13~36', flagbackup = False)
</source>
The data in the channels near the both edges of the spectrum window (~10 channels) are flagged:
<source lang="python">
#In Casa
flagdata(vis = mso, mode = 'manual', flagbackup = False, spw='5:0~9;116~127,7:0~9;116~127,9:0~9;116~127,11:0~9;116~127')
</source>
The some sub-scans at the start and end of the scientific scans are used to measure the sky with the setting of optimized for the Sun. Since the data are used only for estimating the antenna temperatures at the Sun, we flag the data:
<source lang="python">
#In Casa
msmd.open(mso)
sciScan = msmd.scansforintent('*OBSERVE_TARGET*')
msmd.done()
for i in range(0, len(sciScan)):
      subInf=aU.computeDurationOfScan(sciScan[i], vis=mso, returnSubscanTimes=True)
      subNum = subInf[1]
      flagdata(vis = mso, timerange = subInf[3][1], mode = 'manual', flagbackup = False)     
      flagdata(vis = mso, timerange = subInf[3][2],mode = 'manual', flagbackup = F)     
      flagdata(vis = mso, timerange = subInf[3][subNum-1], mode = 'manual', flagbackup = False)     
      flagdata(vis = mso, timerange = subInf[3][subNum], mode = 'manual', flagbackup = False)
</source>
As mentioned in the section [[#Tsys calibration of the visibilities of the calibrators]], the Tsys values of DA54 antennas are significantly large. Therefore, we flag the data of the antenna as follows:
<source lang="python">
#In Casa
flagdata(vis = mso, antenna = 'DA54', mode = 'manual', flagbackup = False)
</source>
Then, we store the current flagging state for each dataset using the {{flagmanager}}:
<source lang="python">
#In Casa
flagmanager(vis = mso, mode = 'save', versionname = 'priori2')
</source>
Now split out the CORRECTED data column, retaining spectral windows 5,7,9, and 11. This will get rid of the extraneous spectral windows.
<source lang="python">
#In Casa
split(vis = mso, outputvis = mss, datacolumn = 'corrected', scan =sel_scans, spw = '5,7,9,11', keepflags = True)
</source>
==Additional Data Inspection==
We will do some additional inspection.
The solar data frequently include zero values in some channels. To avoid their influence, we flag the data using the “clip” mode with “clipzeros=True” option.
<source lang="python">
#In Casa
flagdata(vis = mss, mode = 'clip', clipzeros = True, flagbackup = False)
</source>
The valleys appear in the plot of amplitude vs channel of the Spw #3. It shows the effect of the lines of the earth atmosphere. We need to flag the data.
<source lang="python">
#In Casa
flagdata(vis = mss, spw = '3:68~85', mode = 'manual', flagbackup = False)
flagdata(vis = mss, spw = '3:20~40', mode = 'manual', flagbackup = False)
</source>
Then, we store the current flagging state for each dataset using the {{flagmanager}}:
<source lang="python">
#In Casa
flagmanager(vis = mss, mode = 'save', versionname = 'Initial')
</source>
==Set up the Flux Calibration Model==
It is very rare that a useful planet for the flux calibration is located near the Sun. Therefore, we usually use a quasar near the Sun as a flux calibrator. In this observations, there is no quasar that flux is enough for the observations with the Mixer De-tuning mode, except nrao350. Therefore, the flux calibrator is the same as the phase calibrator in this observation.
To fill the model data column for nrao350 with a model for the flux density, we obtain the flux density and spectral index of the nro350 from the ALMA calibrator database.
<source lang="python">
#In Casa
intentSources=es.getIntentsAndSourceNames(mss)
ampCalId = intentSources['CALIBRATE_AMPLI']['id'] + intentSources['CALIBRATE_FLUX']['id']
calFieldNames = intentSources['CALIBRATE_AMPLI']['name'] + intentSources['CALIBRATE_FLUX']['name']
amp_cal_name=calFieldNames[1]
spwInfo=es.getSpwInfo(mss)
obs_freq="%fGHz"%(spwInfo[0]['refFreq']/1e9)
date=aU.getObservationStartDate(mss)
date_obs=date.split()[0]
spw1_flux=aU.getALMAFlux(sourcename=amp_cal_name, date=date_obs,frequency=obs_freq)
</source>
Then, fill the model data column for nrao350 with a model
<source lang="python">
#In Casa
setjy(vis = mss, field = '2', spw = '0,1,2,3', standard = 'manual', fluxdensity = [spw1_flux['fluxDensity'], 0, 0, 0], spix= spw1_flux['spectralIndex'], reffreq = obs_freq)
</source>
==Creating the Bandpass Calibration Table==
''The bandpass and gain calibrations are the same as the calibrations of non-solar data basically. Please refer to the other ALMA tutorials of the CASA guide too. The calibration method presented below is the same as that used in the QA2 process of the ALMA observatory.''
At first, we run {{gaincal}} on the bandpass calibrator to determine phase-only gain solutions. We will use solint='int' for the solution interval, which means that one gain solution will be determined for every integration time.
<source lang="python">
#In Casa
gaincal(vis = mss, caltable = mss + '.ap_pre_bandpass', field = '1', scan = '5', solint = 'int', refant = ref_ant, calmode = 'p')
</source>
The plots are created for the checking.
<source lang="python">
#In Casa
es.checkCalTable(mss+'.ap_pre_bandpass', msName=mss, interactive=False)
</source>
The plots look good. Next, create the bandpass calibration table:
<source lang="python">
#In Casa
bandpass(vis = mss, caltable = mss+'.bandpass', field = '1', scan = '5', solint = 'inf', refant = ref_ant, solnorm = True, bandtype = 'B', gaintable = mss+'.ap_pre_bandpass')
</source>
Check the bandpass calibration table using the plots created from the following command.
<source lang="python">
#In Casa
es.checkCalTable(mss+'.bandpass', msName=mss, interactive=False)
</source>
==Creating the Gain Calibration Table==
At first, we determine phase-only gain solutions of the calibrators, using the bandpass calibration table and solint='int' option, and create the plots for checking.
<source lang='python'>
#In Casa
gaincal(vis = mss, caltable = mss + '.phase_int', field = '1~2', solint = 'int', refant = ref_ant,gaintype = 'G',calmode = 'p',minsnr = 3.0, gaintable = mss + '.bandpass')
es.checkCalTable(mss + '.phase_int', msName=mss, interactive=False)
</source>
Using the bandpass and phase calibration tables, we obtain the amplitude-only gain solutions on the scan time, '''solint='inf''''.
<source lang='python'>
aincal(vis = mss, caltable = mss + '.ampli_inf', field = '1~2', solint = 'inf', refant = ref_ant, gaintype = 'T', calmode = 'a', minsnr = 3.0, gaintable = [mss + '.bandpass', mss + '.phase_int'])
es.checkCalTable(mss + '.ampli_inf', msName=mss, interactive=False)
</source>
Next we use the flux calibrator (whose flux density was set in {{setjy}} above) to derive the flux density of the other calibrators. Note that the flux table REPLACES the amp.gcal in terms of future application of the calibration to the data, i.e. the flux table contains both the amp.gcal and flux scaling. Unlike the gain calibration steps, this is not an incremental table.
<source lang='python'>
#In Casa
fluxscaleDict = fluxscale(vis = mss, caltable = mss + '.ampli_inf', fluxtable = mss + '.flux_inf', reference = '2')
es.fluxscale2(caltable = mss+'.ampli_inf', removeOutliers=True, msName=mss, writeToFile=True, preavg=10000)
</source>
Finally, we create the gain calibration table of phase-only gain solutions on the scan time, '''solint='inf''''.
<source lang='python'>
#In Casa
gaincal(vis = mss, caltable = mss+'.phase_inf', field = '1~2', solint = 'inf',\ refant = ref_ant, gaintype = 'G', calmode = 'p', minsnr = 3.0, gaintable = mss+'.bandpass')
es.checkCalTable(mss+'.phase_inf', msName=mss, interactive=False)
</source>
==Applying the Calibration Tables==
We apply the calibration solutions to each source individually, using the gainfield parameter to specify which calibrator's solutions should be applied from each of the gaintable calibration tables.
Applying the tables to the bandpass, flux and phase calibrators:
<source lang='python'>
#In Casa
for i in ['1', '2']:
      applycal(vis = mss,field = str(i), gaintable = [mss + '.bandpass', mss+'.phase_int', mss+'.flux_inf'], gainfield = ['', i, i], interp = 'linear,linear', calwt = True, flagbackup = False)
</source>
Next, applying them to the solar data:
<source lang='python'>
#In Casa
applycal(vis = mss, field = '0, 3~150', gaintable = [mss+'.bandpass', mss+'.phase_inf', mss+'.flux_inf'], gainfield = ['', '2', ''], interp = 'linear,linear', calwt = True, flagbackup = F)
</source>
Finally, split out the CORRECTED data column
<source lang='python'>
#In Casa
split(vis = mss, outputvis = msc, datacolumn = 'corrected', keepflags = True)
</source>
==Re-calculation of the direction==
<figure id="mos1">
[[File:sunspot_mos1.png|thumb|right|<xr id="mos1" nolink/> The pattern of mosaic BEFORE the re-calculation of the direction.]]
</figure>
<figure id="mos2">
[[File:sunspot_mos2.png|thumb|right|<xr id="mos2" nolink/> The pattern of mosaic AFTER the re-calculation of the direction.]]
</figure>
During most solar observations, the antennas are tracking a structure on the Sun according to the solar differential rotation. The image frame is fixed on the solar frame, but the frame is moving on the RA/Dec coordinate frame. If we do not do any measures, the pattern of pointing in the MOSAIC observation is shown in <xr id="mos1" /> , and the shape of the pattern is a rhombus though the correct shape is the square.
To correct the MOSAIC pattern, we re-calculate the coordinate of each field. At first, we modify the coordinate of the field “0” from the reference time using {{fixplanets}} task. The reference time has to be the time when the antennas are directed to the field “0”.
<source lang='python'>
#In Casa
reftime = '2015/12/18/19:49:00'
fixplanets(msc, field ='0', fixuvw = False, refant=ref_ant, reftime = reftime)
</source>
We define that the modified coordinate of field ’0’ is as the reference coordinate, and re-calculate the coordinate of each field, as follows.
<source lang='python'>
#In Casa
tb.open(msc+'/FIELD', nomodify=T)
phsCenOff = tb.getcol("PHASE_DIR")
tb.close()
refRaDec = aU.rad2radec(phsCenOff[0][0][0], phsCenOff[1][0][0],  prec=1, hmsdms=True, delimiter=' ') 
for i in range(3, 151):
    raOff = phsCenOff[0][0][i] * 180./pi * 60. *60.
    deOff = phsCenOff[1][0][i] * 180./pi * 60. *60.
    offRaDec = aU.radec2deg(aU.radecOffsetToRadec(refRaDec, raOff, deOff, prec=1))
    offRaDecF = 'J2000 ' + aU.deg2radec(offRaDec[0], offRaDec[1],  prec=1, hmsdms=True, delimiter=' ')       
    fixplanets(msc, field =str(i), fixuvw = False, direction = offRaDecF)
tb.open(msc+'/FIELD', nomodify=False)
tgt_refdir = tb.getcol("RefDir_Ref")
for id in range(3, len(tgt_refdir)):
    tb.putcell("RefDir_Ref", id, 21)
    tb.putcell("DelayDir_Ref", id, 21)
    tb.putcell("PhaseDir_Ref", id, 21)
tb.close()
</source>
Moreover, the direction in the pointing table has a bad influence to the coordinate system of the image synthesis. We erase the pointing table as follows:
<source lang='python'>
#In Casa
tb.open(msc+'/POINTING', nomodify = False)
a = tb.rownumbers()
tb.removerows(a)
</source>
As a result, the pattern of the pointing in the MOSAIC observation becomes the map, as shown in <xr id="mos2" />.
==Alternative way of the Calibration==
If you do not want to cut-&-paste all commands shown in above, you can use the script included in the file Sunspot_Band6_UncalibratedData.tgz. The script can be executed as follows, after downloading the file and installing the Analysis Utilities package.
The script creates the calibrated visibility data, automatically.
'''Caution: You need to wait for one night (or day) for completing the script. Of course, the duration depends on the computer resources of your system.'''
<source lang='python'>
# In a terminal outside CASA
tar -xvzf Sunspot_Band6_UncalibratedData.tgz
cd Sunspot_Band6_UncalibratedData
#Start CASA
casa
#In CASA
execfile(“SunRedUtil.py”)
execfile(“SunspotBand6Cal.py”)
</source>
</source>

Latest revision as of 12:50, 11 January 2018


Overview

The portion of the Sunspot_Band6 CASA Guide will cover the calibration of the raw visibility data. To follow this guide you must have downloaded the file Sunspot_Band6_UncalibratedData.tgz from Sunspot_Band6#Obtaining the Data.

Detail of the ALMA observations are provided at Sunspot_Band6

To skip to the image synthesis portion of the guide, see Sunspot_Band6_Imaging_for_CASA_4.7.

This guide is designed for CASA 4.7.

From next, we will show all commands for the calibration. If you do not want to cut-&-paste the commands, you can use the script as described in #Alternative way of the Calibration.

Before Starting the Calibration of Visibility Data

The “Analysis Utilities” package must be used for the calibration of solar raw visibility data. Therefore, before starting the tutorial, you need to install the package to your data-analysis environment. The documents and software of the package can be obtained from the Analysis Utilities page[1].

Unpack the Data

Once the file Sunspot_Band6_UncalibratedData.tgz had been download, unpack it as follows: 

 # In a terminal outside CASA
tar -xvzf Sunspot_Band6_UncalibratedData.tgz

cd Sunspot_Band6_UncalibratedData


#Start CASA
casa

Confirm your version of CASA

This guide has been written for CASA release 4.7. Please confirm your version before proceeding. 

# In Casa
version = casadef.casa_version 
print "You are using " + version 
if (version < '4.7.0'):
  print "YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE."
  print "PLEASE UPDATE IT BEFORE PROCEEDING." 
else:
  print "Your version of CASA is appropriate for this guide."

We need to import some scripts we will use during the calibration. 

import analysisUtils as aU
es = aU.stuffForScienceDataReduction() 
execfile(“SunRedUtil.py”)

“SunRedUtil.py” is included in the file Sunspot_Band6_UncalibratedData.tgz

Initial Inspection, A priori calibration

We start by defining the directory name of the ASDM and some directory names of the Measurement Sets (MS) for the calibration. 

 #In Casa
asdm ='uid___A002_Xae00c5_X2a8d' mso = asdm + '.ms'
mss = asdm + '_split.ms'
msc = mss + '.cal'

The raw data have been provided to you in the ASDM format. It is the native format of the data produced by the ALMA observatory.

Before we can proceed to the calibration, we will need to convert those data to the CASA MS format. This is done simply with the task importasdm. 

#In Casa
importasdm(asdm = asdm, vis = mso, asis='Antenna Station Receiver Source CalAtmosphere CalWVR CorrelatorMode SBSummary CalDevice ')

The usual first step is then to get some basic information about the data. We do this using the task listobs, which will output a detailed summary of each dataset supplied. 

#In Casa
listobs(mso, listfile = asdm + '_listobs.txt')

The output will be sent to the CASA Template:Logger, or saved in a text file. Here is a snippet extracted from the listobs output: 

================================================================================
  Date        Timerange (UTC)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
  18-Dec-2015/19:15:42.3 - 19:16:47.5     1      0 Sun                     236530  [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.016, 0.016, 0.016, 0.016, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_ATMOSPHERE#REFERENCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE,CALIBRATE_WVR#REFERENCE]
              19:17:31.8 - 19:19:26.4     2      1 J1924-2914             1081497  [4,25,26,27,28,29,30,31,32,33,34,35,36]  [1.15, 0.016, 0.016, 0.016, 0.016, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_POINTING#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:20:14.2 - 19:21:17.7     3      1 J1924-2914              895435  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_SIDEBAND_RATIO#IMAGE,CALIBRATE_SIDEBAND_RATIO#SIGNAL,CALIBRATE_WVR#IMAGE,CALIBRATE_WVR#SIGNAL]
              19:22:06.0 - 19:22:23.1     4      1 J1924-2914              177382  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE]
              19:23:11.6 - 19:28:27.9     5      1 J1924-2914             3244522  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_BANDPASS#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:29:14.5 - 19:31:09.2     6      2 nrao530                1081528  [4,25,26,27,28,29,30,31,32,33,34,35,36]  [1.15, 0.016, 0.016, 0.016, 0.016, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_POINTING#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:31:54.6 - 19:32:11.4     7      2 nrao530                 177413  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE]
              19:32:59.1 - 19:35:36.6     8      2 nrao530                1622261  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_FLUX#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:36:21.9 - 19:36:52.1     9      2 nrao530                 324446  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:37:41.4 - 19:37:57.8    10      0 Sun                     177413  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE]
              19:39:23.9 - 19:49:36.0    11      0 Sun                    3760610  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#OFF_SOURCE,CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#OFF_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              19:39:23.9 - 19:49:36.0    11      3 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              19:39:23.9 - 19:49:36.0    11      4 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
[...]
              19:39:23.9 - 19:49:36.0    11     79 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              19:50:21.7 - 19:50:52.0    12      2 nrao530                 324446  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              19:51:41.2 - 19:51:58.0    13      0 Sun                     177413  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE]
              19:53:26.0 - 20:03:37.0    14      0 Sun                    3742537  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#OFF_SOURCE,CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#OFF_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              19:53:26.0 - 20:03:37.0    14      3 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              19:53:26.0 - 20:03:37.0    14      4 Sun                      18042  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
            18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
[...]
19:53:26.0 - 20:03:37.0    14    150 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              20:04:22.7 - 20:04:53.7    15      2 nrao530                 324446  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
              20:05:43.3 - 20:06:00.2    16      0 Sun                     177413  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576, 0.576] [CALIBRATE_ATMOSPHERE#AMBIENT,CALIBRATE_ATMOSPHERE#HOT,CALIBRATE_ATMOSPHERE#OFF_SOURCE,CALIBRATE_WVR#AMBIENT,CALIBRATE_WVR#HOT,CALIBRATE_WVR#OFF_SOURCE]
              20:06:47.8 - 20:07:30.5    17      0 Sun                     227168  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#OFF_SOURCE,CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#OFF_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              20:06:47.8 - 20:07:30.5    17     10 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              20:06:47.8 - 20:07:30.5    17     11 Sun                      18042  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              20:06:47.8 - 20:07:30.5    17     12 Sun                      18011  [4,5,6,7,8,9,10,11,12]  [1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_WVR#ON_SOURCE,OBSERVE_TARGET#ON_SOURCE]
              20:08:16.5 - 20:08:47.1    18      2 nrao530                 324446  [0,1,2,3,4,5,6,7,8,9,10,11,12]  [0.016, 0.016, 0.016, 0.016, 1.15, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01, 2.02, 1.01] [CALIBRATE_PHASE#ON_SOURCE,CALIBRATE_WVR#ON_SOURCE]
           (nRows = Total number of rows per scan) 
Fields: 151
  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
  0    none Sun                 17:44:06.944771 -23.19.30.42665 ICRS    0        8499084
  1    none J1924-2914          19:24:51.055957 -29.14.30.12103 ICRS    1        5398836
  2    none nrao530             17:33:02.705760 -13.04.49.54800 ICRS    2        4178986
  3    none Sun                 17:44:06.379967 -23.20.38.51230 ICRS    0          36022
  4    none Sun                 17:44:07.341071 -23.20.38.51849 ICRS    0          36053
  5    none Sun                 17:44:08.302174 -23.20.38.52434 ICRS    0          36053
  6    none Sun                 17:44:09.263278 -23.20.38.52983 ICRS    0          36053
  7    none Sun                 17:44:10.224382 -23.20.38.53498 ICRS    0          36053
[...]
  149  none Sun                 17:44:18.760424 -23.18.24.86295 ICRS    0          18011
  150  none Sun                 17:44:19.721223 -23.18.24.86558 ICRS    0          18011
Spectral Windows:  (37 unique spectral windows and 2 unique polarization setups)
  SpwID  Name                                       #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz) BBC Num  Corrs  
  0      BB_1#SQLD                                       1   TOPO  230000.000   2000000.000   2000000.0 230000.0000        1  XX  YY
  1      BB_2#SQLD                                       1   TOPO  232000.000   2000000.000   2000000.0 232000.0000        2  XX  YY
  2      BB_3#SQLD                                       1   TOPO  246000.000   2000000.000   2000000.0 246000.0000        3  XX  YY
  3      BB_4#SQLD                                       1   TOPO  248000.000   2000000.000   2000000.0 248000.0000        4  XX  YY
  4      WVR#NOMINAL                                     4   TOPO  184550.000   1500000.000   7500000.0 187550.0000        0  XX
  5      X241538345#ALMA_RB_06#BB_1#SW-01#FULL_RES     128   TOPO  230992.188    -15625.000   2000000.0 230000.0000        1  XX  YY
  6      X241538345#ALMA_RB_06#BB_1#SW-01#CH_AVG         1   TOPO  229984.375   1796875.000   1796875.0 229984.3750        1  XX  YY
  7      X241538345#ALMA_RB_06#BB_2#SW-01#FULL_RES     128   TOPO  232992.188    -15625.000   2000000.0 232000.0000        2  XX  YY
  8      X241538345#ALMA_RB_06#BB_2#SW-01#CH_AVG         1   TOPO  231984.375   1796875.000   1796875.0 231984.3750        2  XX  YY
  9      X241538345#ALMA_RB_06#BB_3#SW-01#FULL_RES     128   TOPO  245007.813     15625.000   2000000.0 246000.0000        3  XX  YY
  10     X241538345#ALMA_RB_06#BB_3#SW-01#CH_AVG         1   TOPO  245984.375   1796875.000   1796875.0 245984.3750        3  XX  YY
  11     X241538345#ALMA_RB_06#BB_4#SW-01#FULL_RES     128   TOPO  247007.813     15625.000   2000000.0 248000.0000        4  XX  YY
  12     X241538345#ALMA_RB_06#BB_4#SW-01#CH_AVG         1   TOPO  247984.375   1796875.000   1796875.0 247984.3750        4  XX  YY
  13     BB_1#SQLD                                       1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        1  XX  YY
  14     BB_2#SQLD                                       1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        2  XX  YY
  15     BB_3#SQLD                                       1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        3  XX  YY
  16     BB_4#SQLD                                       1   TOPO  219559.000   2000000.000   2000000.0 219559.0000        4  XX  YY
  17     X241538345#ALMA_RB_06#BB_1#SW-01#FULL_RES     128   TOPO  220551.188    -15625.000   2000000.0 219559.0000        1  XX  YY
  18     X241538345#ALMA_RB_06#BB_1#SW-01#CH_AVG         1   TOPO  219543.375   1796875.000   1796875.0 219543.3750        1  XX  YY
  19     X241538345#ALMA_RB_06#BB_2#SW-01#FULL_RES     128   TOPO  220551.188    -15625.000   2000000.0 219559.0000        2  XX  YY
  20     X241538345#ALMA_RB_06#BB_2#SW-01#CH_AVG         1   TOPO  219543.375   1796875.000   1796875.0 219543.3750        2  XX  YY
  21     X241538345#ALMA_RB_06#BB_3#SW-01#FULL_RES     128   TOPO  220551.188    -15625.000   2000000.0 219559.0000        3  XX  YY
  22     X241538345#ALMA_RB_06#BB_3#SW-01#CH_AVG         1   TOPO  219543.375   1796875.000   1796875.0 219543.3750        3  XX  YY
  23     X241538345#ALMA_RB_06#BB_4#SW-01#FULL_RES     128   TOPO  220551.188    -15625.000   2000000.0 219559.0000        4  XX  YY
  24     X241538345#ALMA_RB_06#BB_4#SW-01#CH_AVG         1   TOPO  219543.375   1796875.000   1796875.0 219543.3750        4  XX  YY
  25     BB_1#SQLD                                       1   TOPO  221538.000   2000000.000   2000000.0 221538.0000        1  XX  YY
  26     BB_2#SQLD                                       1   TOPO  223538.000   2000000.000   2000000.0 223538.0000        2  XX  YY
  27     BB_3#SQLD                                       1   TOPO  237538.000   2000000.000   2000000.0 237538.0000        3  XX  YY
  28     BB_4#SQLD                                       1   TOPO  239538.000   2000000.000   2000000.0 239538.0000        4  XX  YY
  29     X0000000000#ALMA_RB_06#BB_1#SW-01#FULL_RES    128   TOPO  222530.188    -15625.000   2000000.0 221538.0000        1  XX  YY
  30     X0000000000#ALMA_RB_06#BB_1#SW-01#CH_AVG        1   TOPO  221514.562   1781250.000   1781250.0 221514.5625        1  XX  YY
  31     X0000000000#ALMA_RB_06#BB_2#SW-01#FULL_RES    128   TOPO  224530.188    -15625.000   2000000.0 223538.0000        2  XX  YY
  32     X0000000000#ALMA_RB_06#BB_2#SW-01#CH_AVG        1   TOPO  223514.562   1781250.000   1781250.0 223514.5625        2  XX  YY
  33     X0000000000#ALMA_RB_06#BB_3#SW-01#FULL_RES    128   TOPO  236545.813     15625.000   2000000.0 237538.0000        3  XX  YY
  34     X0000000000#ALMA_RB_06#BB_3#SW-01#CH_AVG        1   TOPO  237514.563   1781250.000   1781250.0 237514.5625        3  XX  YY
  35     X0000000000#ALMA_RB_06#BB_4#SW-01#FULL_RES    128   TOPO  238545.813     15625.000   2000000.0 239538.0000        4  XX  YY
  36     X0000000000#ALMA_RB_06#BB_4#SW-01#CH_AVG        1   TOPO  239514.563   1781250.000   1781250.0 239514.5625        4  XX  YY
Antennas: 31:
  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
                                                                     East         North     Elevation               x               y               z
  0    CM01  N602      7.0  m   -067.45.17.4  -22.53.22.3          8.8042     -527.8587       22.2034  2225080.354846 -5440132.955920 -2481524.789784
  1    CM02  J502      7.0  m   -067.45.17.7  -22.53.23.0          2.1073     -549.4461       22.1460  2225070.957857 -5440127.670516 -2481544.655003
  2    CM03  J503      7.0  m   -067.45.17.4  -22.53.23.2          9.2482     -555.0637       22.1304  2225076.734430 -5440122.931505 -2481549.824201
  3    CM06  N606      7.0  m   -067.45.17.1  -22.53.23.6         19.1995     -566.5684       22.1011  2225084.240492 -5440114.997537 -2481560.411621
  4    CM07  N601      7.0  m   -067.45.17.0  -22.53.22.5         21.0633     -532.5817       22.1865  2225090.999805 -5440126.600430 -2481529.134327
  5    CM08  J505      7.0  m   -067.45.18.0  -22.53.22.8         -7.2123     -541.3466       22.1685  2225063.532652 -5440134.133528 -2481537.202006
  6    CM09  N603      7.0  m   -067.45.17.7  -22.53.22.3         -0.0497     -527.8657       22.1913  2225072.154896 -5440136.294753 -2481524.791487
  7    CM10  J501      7.0  m   -067.45.17.4  -22.53.22.9         10.0863     -545.4959       22.1606  2225078.929507 -5440126.084513 -2481541.021572
  8    CM11  N604      7.0  m   -067.45.17.8  -22.53.23.7         -0.2657     -571.8966       22.0829  2225065.433709 -5440120.432522 -2481565.313207
  9    DA41  A004      12.0 m   -067.45.15.9  -22.53.28.0         52.6609     -704.4171       21.7726  2225094.796703 -5440052.421785 -2481687.277348
  10   DA49  A002      12.0 m   -067.45.16.3  -22.53.27.6         40.6333     -690.2503       21.8023  2225085.761255 -5440062.100754 -2481674.237730
  11   DA50  A038      12.0 m   -067.45.18.5  -22.53.29.4        -22.4285     -745.7518       22.0606  2225019.312629 -5440066.210860 -2481725.468896
  12   DA52  A018      12.0 m   -067.45.17.2  -22.53.28.1         16.8264     -706.6065       21.7531  2225061.301483 -5440065.182346 -2481689.286759
  13   DA54  A005      12.0 m   -067.45.14.8  -22.53.28.7         83.3315     -725.0764       21.7237  2225120.123751 -5440033.331382 -2481706.290680
  14   DA55  A047      12.0 m   -067.45.16.4  -22.53.30.3         38.4542     -775.2187       21.5966  2225071.160250 -5440032.158745 -2481752.434689
  15   DA57  A025      12.0 m   -067.45.18.7  -22.53.27.4        -26.4296     -685.5228       22.2053  2225024.528985 -5440089.533369 -2481670.039352
  16   DA59  A001      12.0 m   -067.45.16.9  -22.53.27.7         24.1880     -693.3966       21.7925  2225070.073933 -5440067.185105 -2481677.132442
  17   DA61  A006      12.0 m   -067.45.15.0  -22.53.28.0         79.0341     -702.0939       21.7778  2225119.549746 -5440043.278703 -2481685.139167
  18   DA62  A035      12.0 m   -067.45.16.6  -22.53.28.1         32.0366     -706.8051       21.7640  2225075.353584 -5440059.362225 -2481689.473934
  19   DA63  A039      12.0 m   -067.45.18.0  -22.53.29.6         -6.1070     -751.7850       22.0696  2225033.533416 -5440057.867935 -2481731.030436
  20   DA65  A030      12.0 m   -067.45.18.1  -22.53.27.2        -10.3876     -679.1070       21.8267  2225040.189208 -5440085.447750 -2481663.981462
  21   DV02  A007      12.0 m   -067.45.15.1  -22.53.27.3         74.0140     -681.2928       21.3255  2225117.808983 -5440052.282474 -2481665.800190
  22   DV07  A027      12.0 m   -067.45.19.0  -22.53.28.7        -35.0460     -726.6032       21.5989  2225010.293430 -5440077.487712 -2481707.648731
  23   DV11  A049      12.0 m   -067.45.14.6  -22.53.29.6         88.4465     -754.5446       20.1464  2225119.968124 -5440019.440510 -2481732.824666
  24   DV12  A017      12.0 m   -067.45.15.9  -22.53.26.8         51.3634     -665.5893       21.3604  2225099.169786 -5440066.540560 -2481651.346952
  25   DV13  A064      12.0 m   -067.45.14.7  -22.53.31.4         85.6567     -808.0278       21.0176  2225109.813656 -5440001.983403 -2481782.434609
  26   DV17  A031      12.0 m   -067.45.19.1  -22.53.27.1        -37.8149     -675.5186       21.7325  2225015.299768 -5440097.041914 -2481660.638998
  27   DV18  A009      12.0 m   -067.45.16.1  -22.53.26.1         48.2542     -644.4621       21.0152  2225099.282791 -5440075.029589 -2481631.749120
  28   DV20  A033      12.0 m   -067.45.19.4  -22.53.29.0        -47.3621     -735.6360       21.8836  2224997.663712 -5440079.140555 -2481716.080921
  29   DV22  A014      12.0 m   -067.45.15.1  -22.53.26.4         74.5090     -654.2102       20.9880  2225122.137554 -5440061.557701 -2481640.719077
  30   DV23  A003      12.0 m   -067.45.16.5  -22.53.27.0         35.5295     -672.6352       21.3390  2225083.469962 -5440069.979758 -2481657.829615


The first section of the output describes the detail of each scan, and the second section (from line 34) shows the information of the observing targets. This second section shows that three targets with 151 fields were observed: The Sun, J1924-2914, nrao350(J1733-1304). From the section, the J1924-2914 was observed for the calibrations of pointing [2]4, sideband ratio [3], atmosphere [4], and bandpass [5]. nrao350 was observed for the calibration of pointing [6], atmosphere [7], flux [8], and phase [9,12,15,18]. The Sun was observed for scientific observations [11,14,17], the calibration of atmosphere [10,16] and the measurement of zero-signal level [1]. After a prior calibration, we never use the data of the scans for the calibration of pointing, atmosphere, sideband ratio, and the measurement of zero-signal level. To reduce the size of the dataset for bandpass and gain calibrations, we will extract only the data for the calibrations from the raw dataset. For the extraction, we define the scanIDs for the bandpass/gain calibrations and scientific observations, now.

<figure id="antplots">

<xr id="antplots" nolink/>. “plotants” output for the dataset uid___A002_Xae00c5_X2a8d

</figure> 

#In Casa
sel_scans ='5,8,9,11,12,14,15,17,18'

The third section of the listobs output (from line 47) shows the information of the spectrum windows (Spw) in the dataset. From first and this sections, the scientific observations are done with the Spw 0~12, and the IDs of the Spw with 128 channels, which are used for image synthesis, are 5, 7, 9, and 11. Therefore, we will calibrate the data of SpwID 5, 7, 9, and 11 only. The data of spwID 0,1,2,3 are the data from the square-law detectors of the basebands. The data will be used for creating Tsys+Tant tables, and are archived as the auto-correlation data in the dataset. Thirty-one antennas were used for the dataset. Note that numbering in python always begins with "0", so the antennas have IDs 0-30. To see what the antenna configuration looked like at the time of this observation, we use the task plotants (<xr id="antplots" />) .

Flagging before creating Tsys and Tsys+Tant tables

Some scans in the data were used by the online system for pointing and sideband ratio calibration. These scans are no longer needed, and we can flag them easily with flagdata by selecting on 'intent'. 

#In Casa
flagdata(vis = mso, mode = 'manual', intent = '*POINTING*,*SIDEBAND_RATIO*', flagbackup = False)

The averaged data of each spectrum window is not used, so we flagged the averaged data as a follow. 

#In Casa
flagdata(vis = mso, mode = 'manual', spw = '6,8,10,12', flagbackup = False)

We will then store the current flagging state for each dataset using the flagmanager: 

#In Casa
flagmanager(vis = mso, mode = 'save', versionname = 'priori1')

Tsys calibration of the visibilities of the calibrators

<figure id="tsys">

<xr id="tsys" nolink/>. “plotants” output for the dataset uid___A002_Xae00c5_X2a8d

</figure> The Tsys calibration gives a first-order correction for the atmospheric opacity as a function of time and frequency, and associates weights with each visibility that persist through imaging. The MS dataset contains Tsys measurements; the task gencal is used to generate a calibration table. 

#In Casa
gencal(vis = mso, caltable = mso + '.tsys', caltype = 'tsys')
flagdata(vis = mso + '.tsys', mode = 'manual', spw = '5:0~9;116~127,7:0~9;116~127,9:0~9;116~127,11:0~9;116~127', flagbackup = False)
es.checkCalTable(mso+'.tsys', msName=mso, interactive=False)

Since the dataset obtained with TDM, the data in the channels near the both edges of the spectrum window (~10 channels) are flagged. Then, the plots for checking are created by the subroutine of Analysis Utilities package (<xr id="tsys" />). The Tsys of DA54 antennas are significant large, from the plot. In the later part, we will flag the data of the antenna.

We will apply the Tsys calibration table to the data of the calibrators with the task applycal, which reads the specified gain calibration tables, applies them to the (raw) data column, and writes the calibrated results into the corrected column. For non-solar observations, we also apply the WVR (Water Vapor Radiometer) calibration table to data. However, we must NOT apply the WVR table to the solar data, because the WVR receivers at the Sun occur the saturation. We apply the Tsys calibration table to the data of the bandpass calibrator: 

#In Casa
applycal(vis = mso, field = '1', spw = '5,7,9,11', gaintable = mso + '.tsys', gainfield = '1', interp = 'linear,linear', calwt = T, flagbackup = False)

In the observations, we do not the atmospheric calibration of the phase calibrator between the scientific scans, because a long suspension of scientific observations has a bad influence on science. Therefore, we apply the Tsys calibration table, which is created from the data of the atmosphere calibration at the flux calibrator or the Sun, to the phase calibrator, as follows. 

#In Casa
applycal(vis = mso, field = '2', spw = '5,7,9,11', gaintable = mso + '.tsys', gainfield = '2', interp = 'linear,linear', calwt = T, flagbackup = False)

You can use plotms to plot channel-averaged amplitudes as a function of time, comparing the DATA and CORRECTED columns after applying the Tsys correction. This way you can check that calibration has done what was expected, which is put the data onto the Kelvin temperature scale.

Tsys+Tant calibration of the visibilities of the Sun

The standard method of Tsys calibration cannot be apply to the data of the Sun, because the antenna temperature of the Sun cannot be neglected for estimating the system equivalent flux density (SEFD). To estimate correct SEFD at the Sun, the solar observing sequence includes some special measurements, like the measurement of zero-signal level. The subroutines for creating and applying Tsys+Tant calibration tables are prepared by the ALMA solar development team. The subroutines are already imported at the section #Confirm your version of CASA. For the Tsys+Tant calibration of the solar data, you will execute only the following command. 

#In Casa
sol_ampcal(mso, mso + '.tsys', exisTbl=False, outCSV=True)

The process takes long time, about one night or day. If you have already carried out the process before and there are the Tsys+Tant calibration tables (The directory name of the table includes “tsystant”), you can skip the generating of the tables using the following command. 

#In Casa
sol_ampcal(mso, mso + '.tsys', exisTbl=True, outCSV=False)

Flagging after Tsys and Tsys+Tant calibration

Since we completed the Tsys and Tsys+Tant calibration, the data that are not used the image synthesis are flagged. At first, the data of auto-correlation and atmosphere calibration are flagged as follows: 

#In Casa
flagdata(vis = mso, mode = 'manual', autocorr = True, flagbackup = False)
flagdata(vis = mso, mode = 'manual', intent = '*ATMOSPHERE*', flagbackup = False)

Next is the spectrum windows that are not used in later: 

#In Casa
flagdata(vis = mso, mode = 'manual', spw = '0~4', flagbackup = False)
flagdata(vis = mso, mode = 'manual', spw = '13~36', flagbackup = False)

The data in the channels near the both edges of the spectrum window (~10 channels) are flagged: 

#In Casa
flagdata(vis = mso, mode = 'manual', flagbackup = False, spw='5:0~9;116~127,7:0~9;116~127,9:0~9;116~127,11:0~9;116~127')

The some sub-scans at the start and end of the scientific scans are used to measure the sky with the setting of optimized for the Sun. Since the data are used only for estimating the antenna temperatures at the Sun, we flag the data: 

#In Casa
msmd.open(mso)
sciScan = msmd.scansforintent('*OBSERVE_TARGET*')
msmd.done()

for i in range(0, len(sciScan)):
      subInf=aU.computeDurationOfScan(sciScan[i], vis=mso, returnSubscanTimes=True)
      subNum = subInf[1]
      flagdata(vis = mso, timerange = subInf[3][1], mode = 'manual', flagbackup = False)       
      flagdata(vis = mso, timerange = subInf[3][2],mode = 'manual', flagbackup = F)       
      flagdata(vis = mso, timerange = subInf[3][subNum-1], mode = 'manual', flagbackup = False)       
      flagdata(vis = mso, timerange = subInf[3][subNum], mode = 'manual', flagbackup = False)

As mentioned in the section #Tsys calibration of the visibilities of the calibrators, the Tsys values of DA54 antennas are significantly large. Therefore, we flag the data of the antenna as follows: 

#In Casa
flagdata(vis = mso, antenna = 'DA54', mode = 'manual', flagbackup = False)

Then, we store the current flagging state for each dataset using the flagmanager: 

#In Casa
flagmanager(vis = mso, mode = 'save', versionname = 'priori2')

Now split out the CORRECTED data column, retaining spectral windows 5,7,9, and 11. This will get rid of the extraneous spectral windows. 

#In Casa
split(vis = mso, outputvis = mss, datacolumn = 'corrected', scan =sel_scans, spw = '5,7,9,11', keepflags = True)

Additional Data Inspection

We will do some additional inspection.

The solar data frequently include zero values in some channels. To avoid their influence, we flag the data using the “clip” mode with “clipzeros=True” option. 

#In Casa
flagdata(vis = mss, mode = 'clip', clipzeros = True, flagbackup = False)

The valleys appear in the plot of amplitude vs channel of the Spw #3. It shows the effect of the lines of the earth atmosphere. We need to flag the data. 

#In Casa
flagdata(vis = mss, spw = '3:68~85', mode = 'manual', flagbackup = False)
flagdata(vis = mss, spw = '3:20~40', mode = 'manual', flagbackup = False)

Then, we store the current flagging state for each dataset using the flagmanager: 

#In Casa
flagmanager(vis = mss, mode = 'save', versionname = 'Initial')

Set up the Flux Calibration Model

It is very rare that a useful planet for the flux calibration is located near the Sun. Therefore, we usually use a quasar near the Sun as a flux calibrator. In this observations, there is no quasar that flux is enough for the observations with the Mixer De-tuning mode, except nrao350. Therefore, the flux calibrator is the same as the phase calibrator in this observation. To fill the model data column for nrao350 with a model for the flux density, we obtain the flux density and spectral index of the nro350 from the ALMA calibrator database. 

#In Casa
intentSources=es.getIntentsAndSourceNames(mss)
ampCalId = intentSources['CALIBRATE_AMPLI']['id'] + intentSources['CALIBRATE_FLUX']['id']
calFieldNames = intentSources['CALIBRATE_AMPLI']['name'] + intentSources['CALIBRATE_FLUX']['name']

amp_cal_name=calFieldNames[1]
spwInfo=es.getSpwInfo(mss)
obs_freq="%fGHz"%(spwInfo[0]['refFreq']/1e9)

date=aU.getObservationStartDate(mss)
date_obs=date.split()[0]
spw1_flux=aU.getALMAFlux(sourcename=amp_cal_name, date=date_obs,frequency=obs_freq)

Then, fill the model data column for nrao350 with a model 

#In Casa
setjy(vis = mss, field = '2', spw = '0,1,2,3', standard = 'manual', fluxdensity = [spw1_flux['fluxDensity'], 0, 0, 0], spix= spw1_flux['spectralIndex'], reffreq = obs_freq)

Creating the Bandpass Calibration Table

The bandpass and gain calibrations are the same as the calibrations of non-solar data basically. Please refer to the other ALMA tutorials of the CASA guide too. The calibration method presented below is the same as that used in the QA2 process of the ALMA observatory.

At first, we run gaincal on the bandpass calibrator to determine phase-only gain solutions. We will use solint='int' for the solution interval, which means that one gain solution will be determined for every integration time. 

#In Casa
gaincal(vis = mss, caltable = mss + '.ap_pre_bandpass', field = '1', scan = '5', solint = 'int', refant = ref_ant, calmode = 'p')

The plots are created for the checking. 

#In Casa
es.checkCalTable(mss+'.ap_pre_bandpass', msName=mss, interactive=False)

The plots look good. Next, create the bandpass calibration table: 

#In Casa
bandpass(vis = mss, caltable = mss+'.bandpass', field = '1', scan = '5', solint = 'inf', refant = ref_ant, solnorm = True, bandtype = 'B', gaintable = mss+'.ap_pre_bandpass')

Check the bandpass calibration table using the plots created from the following command. 

#In Casa
es.checkCalTable(mss+'.bandpass', msName=mss, interactive=False)

Creating the Gain Calibration Table

At first, we determine phase-only gain solutions of the calibrators, using the bandpass calibration table and solint='int' option, and create the plots for checking. 

#In Casa
gaincal(vis = mss, caltable = mss + '.phase_int', field = '1~2', solint = 'int', refant = ref_ant,gaintype = 'G',calmode = 'p',minsnr = 3.0, gaintable = mss + '.bandpass')
es.checkCalTable(mss + '.phase_int', msName=mss, interactive=False)

Using the bandpass and phase calibration tables, we obtain the amplitude-only gain solutions on the scan time, solint='inf'. 

aincal(vis = mss, caltable = mss + '.ampli_inf', field = '1~2', solint = 'inf', refant = ref_ant, gaintype = 'T', calmode = 'a', minsnr = 3.0, gaintable = [mss + '.bandpass', mss + '.phase_int'])
es.checkCalTable(mss + '.ampli_inf', msName=mss, interactive=False)

Next we use the flux calibrator (whose flux density was set in setjy above) to derive the flux density of the other calibrators. Note that the flux table REPLACES the amp.gcal in terms of future application of the calibration to the data, i.e. the flux table contains both the amp.gcal and flux scaling. Unlike the gain calibration steps, this is not an incremental table. 

#In Casa
fluxscaleDict = fluxscale(vis = mss, caltable = mss + '.ampli_inf', fluxtable = mss + '.flux_inf', reference = '2') 
es.fluxscale2(caltable = mss+'.ampli_inf', removeOutliers=True, msName=mss, writeToFile=True, preavg=10000)

Finally, we create the gain calibration table of phase-only gain solutions on the scan time, solint='inf'. 

#In Casa
gaincal(vis = mss, caltable = mss+'.phase_inf', field = '1~2', solint = 'inf',\ refant = ref_ant, gaintype = 'G', calmode = 'p', minsnr = 3.0, gaintable = mss+'.bandpass')
 es.checkCalTable(mss+'.phase_inf', msName=mss, interactive=False)

Applying the Calibration Tables

We apply the calibration solutions to each source individually, using the gainfield parameter to specify which calibrator's solutions should be applied from each of the gaintable calibration tables. Applying the tables to the bandpass, flux and phase calibrators: 

#In Casa
for i in ['1', '2']: 
      applycal(vis = mss,field = str(i), gaintable = [mss + '.bandpass', mss+'.phase_int', mss+'.flux_inf'], gainfield = ['', i, i], interp = 'linear,linear', calwt = True, flagbackup = False)

Next, applying them to the solar data: 

#In Casa
applycal(vis = mss, field = '0, 3~150', gaintable = [mss+'.bandpass', mss+'.phase_inf', mss+'.flux_inf'], gainfield = ['', '2', ''], interp = 'linear,linear', calwt = True, flagbackup = F)

Finally, split out the CORRECTED data column 

#In Casa
split(vis = mss, outputvis = msc, datacolumn = 'corrected', keepflags = True)

Re-calculation of the direction

<figure id="mos1">

<xr id="mos1" nolink/> The pattern of mosaic BEFORE the re-calculation of the direction.

</figure> <figure id="mos2">

<xr id="mos2" nolink/> The pattern of mosaic AFTER the re-calculation of the direction.

</figure>

During most solar observations, the antennas are tracking a structure on the Sun according to the solar differential rotation. The image frame is fixed on the solar frame, but the frame is moving on the RA/Dec coordinate frame. If we do not do any measures, the pattern of pointing in the MOSAIC observation is shown in <xr id="mos1" /> , and the shape of the pattern is a rhombus though the correct shape is the square.

To correct the MOSAIC pattern, we re-calculate the coordinate of each field. At first, we modify the coordinate of the field “0” from the reference time using Template:Fixplanets task. The reference time has to be the time when the antennas are directed to the field “0”. 

#In Casa
reftime = '2015/12/18/19:49:00'
fixplanets(msc, field ='0', fixuvw = False, refant=ref_ant, reftime = reftime)

We define that the modified coordinate of field ’0’ is as the reference coordinate, and re-calculate the coordinate of each field, as follows. 

#In Casa
tb.open(msc+'/FIELD', nomodify=T)
phsCenOff = tb.getcol("PHASE_DIR")
tb.close()
refRaDec = aU.rad2radec(phsCenOff[0][0][0], phsCenOff[1][0][0],  prec=1, hmsdms=True, delimiter=' ')   
for i in range(3, 151):
    raOff = phsCenOff[0][0][i] * 180./pi * 60. *60.
    deOff = phsCenOff[1][0][i] * 180./pi * 60. *60.
    offRaDec = aU.radec2deg(aU.radecOffsetToRadec(refRaDec, raOff, deOff, prec=1))
    offRaDecF = 'J2000 ' + aU.deg2radec(offRaDec[0], offRaDec[1],  prec=1, hmsdms=True, delimiter=' ')         
    fixplanets(msc, field =str(i), fixuvw = False, direction = offRaDecF)

tb.open(msc+'/FIELD', nomodify=False)
tgt_refdir = tb.getcol("RefDir_Ref")
for id in range(3, len(tgt_refdir)):
    tb.putcell("RefDir_Ref", id, 21)
    tb.putcell("DelayDir_Ref", id, 21)
    tb.putcell("PhaseDir_Ref", id, 21)
tb.close()

Moreover, the direction in the pointing table has a bad influence to the coordinate system of the image synthesis. We erase the pointing table as follows: 

#In Casa
tb.open(msc+'/POINTING', nomodify = False)
a = tb.rownumbers()
tb.removerows(a)

As a result, the pattern of the pointing in the MOSAIC observation becomes the map, as shown in <xr id="mos2" />.

Alternative way of the Calibration

If you do not want to cut-&-paste all commands shown in above, you can use the script included in the file Sunspot_Band6_UncalibratedData.tgz. The script can be executed as follows, after downloading the file and installing the Analysis Utilities package. The script creates the calibrated visibility data, automatically. Caution: You need to wait for one night (or day) for completing the script. Of course, the duration depends on the computer resources of your system. 

# In a terminal outside CASA
tar -xvzf Sunspot_Band6_UncalibratedData.tgz

cd Sunspot_Band6_UncalibratedData

#Start CASA
casa

#In CASA
execfile(“SunRedUtil.py”)
execfile(“SunspotBand6Cal.py”)
Retrieved from "https://casaguides.nrao.edu/index.php?title=Sunspot_Band6_Calibration_for_CASA_4.7&oldid=23137"