View source for VLA 5 GHz continuum survey of Seyfert galaxies 5.7.2

[[Category:VLA]] [[Category:Calibration]] [[Category:Imaging]] [[Category:Self-Calibration]] [[Category:Continuum]]

{{Checked 5.7.2}}


Disclaimer: Due to continuous CASA software updates, GUI images may look different on more recent versions than those shown here. 


This particular tutorial demonstrates how to calibrate a VLA continuum survey, consisting of a flux denisty scale calibrator, target sources, and their respective complex gain calibrators. What distinguishes this example is that it includes multiple target sources and multiple complex gain calibrators.


== Importing VLA Archive Data ==

Download your data from the [http://archive.nrao.edu: VLA Archive]; in this example we'll use the publicly available survey [https://archive.nrao.edu/archive/ArchiveQuery?QUERY_ID=9999&QUERY_MODE=Prepare+Download&PROTOCOL=HTML&SORT_PARM=Starttime&SORT_ORDER=Asc&LOCKMODE=PROJECT&SITE_CODE=AOC&DBHOST=CHEWBACCA&PROJECT_CODE=AG733&OBSERVER=&TELESCOPE=ALL&TIMERANGE1=&TIMERANGE2=&PASSWD=&QUERYTYPE=ARCHIVE&SUBMIT=Submit+Query: AG733], which consists of VLA C-configuration, C-band (5 GHz) continuum observations of a handful of Seyfert galaxies. 

With some sensible fiddling of the web interface, you should be able to download two archive formatted files, one from 06-Dec-09 and one from 06-Dec-13, with names like '''AG733_A061209.xp1''' and '''AG733_B061213.xp1''' or '''2006-12/vla2006-12-09.dat''' and '''2006-12/vla2006-12-13.dat'''.

[[Image:PreEVLA_Seyfert_Example_ListObs.png|thumb|Example snippet of listobs output for program AG733.]]

It's helpful to make a subdirectory to store the VLA archive data and the CASA measurement sets before starting CASA. We call this subdirectory AG733 and place the downloaded files in it. Linux-level commands are given assuming you are using bash as your default shell.

<source lang="bash">
# In bash
cd AG733
casa
</source>

To load the data in CASA, we employ the '''{{importvla}}''' command.[[Image:PreEVLA_Seyfert_PlotAnts.png|thumb|Graphics window produced by {{plotants}}. Shown are the antennas of the VLA for the program AG733.]]

<source lang="python">
# In CASA
# Before importing, get rid of any original version of the measurement set
if os.path.exists('ag733.ms'): rmtables('ag733.ms')
# let glob find the archive files, store in a python global "myFiles"
from glob import glob as filesearch 
myFiles = filesearch("AG733*.xp?")
importvla(archivefiles=myFiles,vis='ag733.ms')
listobs('ag733.ms')
vishead('ag733.ms')
</source>

The output measurement set, i.e., the data to be worked on, is ''ag733.ms.'' Note that the output of {{listobs}} and {{vishead}} go to the CASA message log window.

{{listobs}} provides a list of scans, sources and their respective indices, and antennas. You can also get a plot of antennas using {{plotants}}. The antennas plot aids the selection of a reference antenna; the antenna ''VA05'' looks like a reasonable choice.

<source lang="python">
# In CASA
vis = 'ag733.ms'
plotants()
</source>

You may want to '''close''' {{plotants}} before moving on in the script to avoid locked tables.

''' <pre style="color: red"> Note: Some older VLA data may give errors in more recent CASA versions when attempting to generate a plot of antennas. If this occurs, use the listobs task and choose an antenna that was located on a pad close to the center of the array (Pads numbers increase with distance from the center of the array, ie. "VLA:_N1" and "EVLA:W2" are pads close to the center of the array, where as, "VLA:_N18" is not).
</pre>'''


== Editing the Measurement Set ==

This technique is detailed in the tutorial [[Data flagging with plotms]]. Here's a shorter version. 

<source lang="python">
# In CASA
plotms() 
</source>

[[plotms|Plotms]] brings up a graphical display that allows you to plot various aspects of your data.

[[Image:PreEVLA_Seyfert_PlotMS.png|300px|The plotms GUI with proper field and timerange options selected.]]

In this example, the measurement set was selected by navigating to and highlighting the measurement set ag733.ms (which will look like a directory to your file browser, because measurement sets are actually directory structures). A specific subset of data (the flux density scale calibrator 3C48 = 0137+331 on the first observing day) was selected by specifying in the appropriate "Data -> Selection" boxes:

* field = 0137+331, and
* timerange = 2006/12/09/0:0:0~23:0:0.

For this specific field, we see no obvious bad data. Note that the low points are actually cross-polarization data; you can see this by entering:

* corr = RR, LL

For the sake of this example, we'll clip out the early part of the observations, say, all the points before 04:00:45:

Firstly, box the region to be flagged by using the "Mark Regions" button [[Image:MarkRegionsButton.png]]. Next, go to the "Flag" tab at top, and select "Extend flags" and "Correlation", so that we will delete the RL and LR data as well (despite the fact that we're not plotting it). Then, press the "Go" or "Flag" button [[Image:FlagThoseData.png‎]] to zap those data. 

The {{viewer}} tool provides utilities to flag data in a manner similar to the AIPS task TVFLG; see the tutorial [[Data flagging with viewer]]. 

If we leave the field and timerange filter boxes blank and plot again, we see a number of visibilities with extremely large amplitudes. If you have trouble seeing the points, adjust their size by going to "Display", setting "Unflagged Points Symbol" to "Custom", and selecting a larger pixel size for the "circle", "square", or "diamond" options and replotting. Highlight these few extremely high amplitude points and flag them as before. Now we'll reload the plot by selecting the "Reload" checkbox and clicking "Plot". When the plot reloads with its new adjusted scale, we notice that there are even more bad points. By highlighting and locating these new bad points (described in detail in the [[data flagging with plotms]] tutorial), we find most of the bad data can be attributed to one antenna, 'VA15', at specific times, and one time range where all antennas contribute. Close inspection will show there is also some bad data on VA05 and VA22 as well. You may need to use the Zoom Tool [[File:casaplotms-zoom.png]] to see all of the bad points clearly. In the text below we flag these points using {{flagdata}}; you can also flag data interactively in the {{plotms}} display (though this is generally not encouraged since it does not save backup versions of the flags, and it can be easy to flag data by mistake or miss data that's not plotted). 

<source lang="python">
# In CASA
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/04:45:10.0~04:45:20.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/06:12:30.0~06:14:10.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/13/04:32:40.0~04:35:30.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/06:12:30.0~06:12:40.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/06:13:00.0~06:13:40.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/06:13:50.0~06:14:00.0')
flagdata(vis='ag733.ms', antenna='VA15', timerange='2006/12/09/06:14:00.0~06:14:10.0')
flagdata(vis='ag733.ms', antenna='VA05', timerange='2006/12/09/04:30:00.0~04:30:10.0')
flagdata(vis='ag733.ms', antenna='VA22', timerange='2006/12/09/06:28:10.0~06:28:20.0')
flagdata(vis='ag733.ms', timerange='2006/12/09/06:24:50.0~06:25:00.0')
</source>

Plot the data again to make sure the amplitudes look reasonable (if {{plotms}} is already open, just hit Shift+Plot to reload with the current flags): 

<source lang="python">
# In CASA
plotms() 
</source>

=== Careful with Cross-Pols ===

By default, {{plotms}} plots all of the data, including cross-polarization. All those low-amplitude points in your data are probably not junk. They are real cross-polarization data. Flagging those data will cause grief later on. Take a look at the tutorial [[Data flagging with plotms#Cross-Pol Data of Bright Sources | data flagging with plotms]].

Tread lightly!  And, whenever possible, flag with {{flagdata}} and keep careful track of what you flag in a separate file.

==A Python Interlude==

CASA uses python as an interactive wrapper and scripting language. Python offers the ability to define global variables on the fly, and it's worth using such variables to take care of bookkeeping. First we'll define some ''list'' variables that store calibrator and source information. 

<source lang="python">
# In CASA
FDS_callist = ['0137+331']
CG_callist = ['2250+143', '0119+321', '0237+288', '0239-025',
                '0323+055', '0339-017', '0423-013'] 
sourcelist = ['NGC7469', 'MRK0993', 'MRK1040', 'NGC1056', 'NGC1068',
              'NGC1194', 'NGC1241', 'NGC1320', 'F04385-0828',
              'NGC1667'] 
allcals = FDS_callist + CG_callist
print allcals
</source>

It will also be helpful later to have a way of referencing which complex gain calibrator should be assigned to which source. We can define the matching explicitly by using a ''dictionary'' variable.

<source lang="python">
# In CASA
calDict = {'NGC7469':'2250+143',
           'MRK0993':'0119+321', 
           'MRK1040':'0237+288', 
           'NGC1056':'0237+288',
           'NGC1068':'0239-025',
           'NGC1194':'0323+055',
           'NGC1241':'0323+055',
           'NGC1320':'0339-017',
           'F04385-0828':'0423-013',
           'NGC1667':'0423-013'}
</source>

Those variables being defined, we can proceed with the calibration (and not have to type source and calibrator names over and over).

== Calibration ==

=== Set the Flux Denisty Scale ===
As in AIPS, CASA sets the flux density scale using an observation of a flux density scale calibrator (here, 0137+331) and the task {{setjy}}.  For this example, we'll determine the calibration in reference to a model image of the calibrator source. Look at the inputs of {{setjy}} and get it ready to go!

<source lang="python"> 
# In CASA
# restore setjy back to its defaults
default setjy
# shows several of the current and possible input parameters for the current task
inp
vis = 'ag733.ms'
field = '0137+331'
# For the model image, need to specify the correct directory, something like the following
#
# load the CASA path from environment variable
import os
casapath = os.environ.get('CASAPATH').split()[0]
# generate full path to the image
model = casapath + '/data/nrao/VLA/CalModels/3C48_C.im'
go
</source>

That example was set up to demonstrate some AIPS heritage. You could equally well run {{setjy}} the following way:
<source lang="python"> 
# In CASA
import os
casapath = os.environ.get('CASAPATH').split()[0]
setjy(vis = 'ag733.ms', field = '0137+331', model = casapath + '/data/nrao/VLA/CalModels/3C48_C.im')
</source>

=== Determine calibration solutions ===

At this stage the data have an overall flux density scaling determined, but full gain solutions aren't there yet. The relevant task is {{gaincal}}, which will produce the table ''cal.G.''

<source lang="python">
# In CASA
rmtables('cal.G') # get rid of any old, unmeritorious versions of the calibration
rmtables('gc.cal')# will give a warning if the tables don't already exist, which can be safely ignored

# First we'll generate an antenna zenith-angle dependent gain curve calibration table
gencal(vis='ag733.ms', caltype='gc', caltable='gc.cal') 

#
# Some caution on the following step. The solution interval is set to 'inf,' meaning to average over entire
# scans of each calibrator in turn. If a given scan is however longer than the coherence time (esp. on long baselines), 
# some decorrelation will occur and the flux density scale calibration will be thrown off. Suggest, e.g., solint='1min' or solint='2min' 
# as reasonable alternatives to solint='inf'. Trial and error may be your friend here.
#
gaincal(vis='ag733.ms',caltable='cal.G', field=','.join(allcals), solint='inf',
        refant='VA05', gaintable=['gc.cal'], append=False)
plotms(vis='cal.G', coloraxis='Antenna1', yaxis='amp', xaxis='time', timerange='2006/12/09/04:0:0~06:30:0')
</source>

There's some python trickery buried in this example. If you are unfamiliar with the string method ''join,'' try the following example. Recall that we had defined the variable allcals above in [[#A_Python_Interlude|A Python Interlude]].
<source lang="python">
# In CASA
print allcals 
print ','.join(allcals)
</source>

It's worth playing with the timerange, etc. in {{plotms}} to explore the solutions. You may notice that antenna 'EA26' does not appear in the solutions on the second day. For completeness, we'll flag it manually from the measurement set and then repeat the calibration.

<source lang="python">
# In CASA
flagdata(vis='ag733.ms',antenna='EA26', timerange='2006/12/13/0:0:0~24:0:0')
rmtables('cal.G') 
gaincal(vis='ag733.ms',caltable='cal.G', field=','.join(allcals), solint='inf',
        refant='VA05', gaintable=['gc.cal'], append=False )
</source>

Go back and re-plot the calibration; this example shows even more AIPS heritage.

<source lang="python">
# In CASA
tget plotms
plotms()
</source>

Using the task {{fluxscale}}, bootstrap the flux density scale onto the complex gain calibrators.

<source lang="python">
# In CASA
rmtables('cal.Gflx') # remove any old versions of the calibration table, if necessary
myFluxes = fluxscale(vis='ag733.ms', caltable='cal.G', reference=','.join(FDS_callist), 
                     transfer=','.join(CG_callist), fluxtable='cal.Gflx', append=False)
</source>

Here, the calibration table ''cal.G'' is modified and stored as 'cal.Gflx.' The Python dictionary "myFluxes" will also contain information about the flux density scaling and other useful tidbits (type "myFluxes" to see its contents).  So far, solutions have been generated only for the calibrators, and they have not yet been transfered to the sources.

We could equally well have used reference='0137+337', but use of the ''join'' method allows for the more general possibility of multiple flux density scale calibrators.

=== Apply the Calibrations ===

The task in CASA is {{applycal}}.  We loop over sources and calibrators to properly match them:

<source lang="python">
# In CASA
for source, calibrator in calDict.iteritems():
    applycal(vis='ag733.ms', field=source,
             gaintable='cal.Gflx', gainfield=calibrator)
</source>

=== Baseline-based corrections ===

[[File:PreEVLA_Seyfert_IntClean_Noblcal.png|thumb|Initial tclean image of NGC1068 ''without'' blcal.]] 
[[File:PreELA_Seyfert_IntClean_blcal.png|thumb|Initial tclean image of NGC1068 ''with'' blcal applied.]]

Although it makes only modest improvements for the present example, observations including a mix of EVLA and VLA telescopes may benefit from performing baseline-based corrections on a strong calibrator. Now that we have initial antenna-based solutions, we can use {{blcal}}.

<source lang="python">
# In CASA
default('blcal')
vis = 'ag733.ms'
# output baseline-based calibration solutions
caltable = 'cal.BL' 
# use the strong flux density scale calibrator to determine baseline-based solutions
field = '0137+331' 
# generate a solution for each calibrator scan
solint = 'inf' 
gaintable = 'cal.Gflx'
# calibration table with the best antenna-based solutions, so far
gainfield = '0137+331' 
# calibrator for the BL calibrator; in this example, gain cal and bl cal are the same
interp = 'nearest'
# do the first day's data
timerange = '2006/12/09/0:0:0~24:0:0'
blcal()
# now the second day
timerange = '2006/12/13/0:0:0~24:0:0'
blcal()
</source>

This operation generates the calibration table ''cal.BL''. Solutions can be inspected with {{plotcal}} (this functionality may transfer to {{plotms}} in the near future). Now we have to use {{applycal}} again to apply both the antenna-based and baseline-based calibration solutions to the data.

<source lang="python">
# In CASA
for source, calibrator in calDict.iteritems():
    applycal(vis='ag733.ms', field=source,
             gaintable=['cal.Gflx','cal.BL'], gainfield=calibrator)
</source>

Did the {{blcal}} operation make a difference here? Getting ahead of ourselves a little, the plots at right show the initial {{tclean}} images of NGC1068, one with {{blcal}} applied, and one without, and both images are displayed with the same stretch. There does appear a slight reduction of artifacts with {{blcal}} applied, but of course your mileage will vary.

=== Splitting the calibrated source data from the multisource measurement set ===

To split the calibrated source data out of the measurement set, use the CASA task {{split}}. 

<source lang="python">
# In CASA
splitfile = 'NGC1667.split.ms'
rmtables(splitfile) # get rid of any old versions before splitting
split(vis='ag733.ms',outputvis=splitfile, datacolumn='corrected', field='NGC1667')
</source>

Instead of having to manually repeat the split task for each source, you can take advantage of scripting to loop over the sources:

<source lang="python">
# In CASA
for source in sourcelist:
    splitfile = source + '.split.ms'
    rmtables(splitfile)
    split(vis='ag733.ms',outputvis=splitfile, 
          datacolumn='corrected', field=source)
</source>

== Imaging ==

[[Image:PreEVLA_Seyfert_ExampleViewer.png|thumb|An example desktop display of ''{{viewer}},'' showing the image display panel, the data display options window, and the data loader.]]

Use {{tclean}} for [http://www.aoc.nrao.edu/events/synthesis/2008/lectures/wilner_synthesis08.pdf imaging] and [http://adsabs.harvard.edu/abs/1974A%26AS...15..417H clean deconvolution]. Like IMAGR in AIPS, it's worth some time mulling over all of the options. A list of some of the various keywords is given in the example, below. Most of the options should be relatively familiar to the AIPS user.  <!--, but one keyword bears mentioning in this example: '''calready = True'''. By selecting this option, the CLEAN model is transformed back to (''u'',''v'') space and stored as as a new column in the visibility data. The transformed CLEAN model will be necessary for [[#Self-calibration|self calibration]], described below. -->

For this snapshot survey, the example chooses a fairly conservative, slow clean to accommodate the less-than-ideal beam. You could probably get away with more efficient cleaning, but it's better to play it safe in scripted self-calibration.

<source lang="python">
# In CASA
default(tclean)
savemodel           =      'modelcolumn'
specmode            =      'mfs'        
niter               =      2000      
gain                =      0.1       
threshold           =      '4.5e-5Jy'      
deconvolver         =      'clark_exp'            
cyclefactor         =      3              
interactive         =      False      
mask                =      []       
imsize              =      [1024, 1024]      
cell                =      ['0.75arcsec', '0.75arcsec']
stokes              =      'I'        
weighting           =      'natural'        
uvtaper             =      []      
pbcor               =      False      
pblimit             =      0.1 
selectdata          =      False                  
vis                 =      'NGC1068.split.ms'
imagename           =      'NGC1068_img'
rmtables(imagename + '*')
tclean()
</source>

Take a look at the image using {{viewer}}. From the GUI, select the .image file you want to view, select ''Raster Image'', and then dismiss and close your way through to the display.

<source lang="python">
# In CASA
viewer()
</source>

In the interest of time for this tutorial, we've chosen to only image NGC1068; however, should one be interested, all of the sources can be imaged by looping over a similar {{tclean}} command.

<source lang="python">
# In CASA
default(tclean)
savemodel           =      'modelcolumn'
specmode            =      'mfs'        
niter               =      2000      
gain                =      0.1       
threshold           =      '4.5e-5Jy'      
deconvolver         =      'clark_exp'            
cyclefactor         =      3              
interactive         =      False      
mask                =      []       
imsize              =      [1024, 1024]      
cell                =      ['0.75arcsec', '0.75arcsec']
stokes              =      'I'        
weighting           =      'natural'        
uvtaper             =      []      
pbcor               =      False      
pblimit             =      0.1 
selectdata          =      False                  
for source in sourcelist:
    splitfile = source + '.split.ms'
    vis = splitfile
    imagename = source + '_img'
    rmtables(imagename + '*') # get ride of earlier versions of the image
    tclean()
</source>

==Self-calibration==
There's not much more that can be done for the fainter sources, but the brighter sources will almost certainly show some residual calibration artifacts that can be cleaned up with self-calibration. The strategy is to repeat using the primary calibration application {{gaincal}}, only this time we use the source itself as a calibrator.

NGC 1068 is a good example of a difficult source. Firstly, it's very bright, so residual complex gain calibration errors should be obvious. Secondly, like the larger fraction of the sky, it's located painfully close to the celestial equator, meaning that the (''u'',''v'') coverage is particularly poor.

To view the calibration artifacts, use {{viewer}} to display the image of NGC 1068, but adjust the display levels so that the displayed image saturates at 30 mJy. To do this, select the leftmost button bearing a wrench icon. The "Data Display Options" GUI opens, and you can enter the desired display stretch in the '''Data Range''' text box. To cutoff the stretch at 30 mJy,

* Data Range: [-0.005,0.03]

You can see some residual systematics in the background and sidelobe artifacts that suggest the complex gain calibration isn't as good as it might be.

The process of self-calibration will involve iterating between (''i'') improved {{tclean}} models of the source <!--(which are updated in the "split" visibility file so long as calready = True)--> and (''i''+1) improved calibrations generated by {{gaincal}}.  For simplicity, in this example we will repeatedly generate new calibration tables so that we can avoid using {{accum}}.

=== Step ''i'' ===

In principle, we could start with the initial {{tclean}} model that we generated above. For this example, we'll take a more cautious approach and re-run {{tclean}} interactively.

<source lang="python">
# In CASA
source = 'NGC1068' # set up a global variable to store the source we're working on
tget tclean
vis = source + '.split.ms'
imagename = source + '_img0'
interactive = True
cycleniter = 10
go
</source>

The trick to interactive cleaning is to draw ''clean boxes'' around the sources as they appear in the residual image. By default, clean boxes are set by ''right-clicking'' on the image and dragging. The box can be edited until you ''double right-click'' inside the box, which then sets that box for cleaning. Press the green icon to continue cleaning the image; on the next major cycle, a new residual map will be displayed, affording the option to add clean boxes as new sources appear where they were once swamped by sidelobes.

Of course, for self-calibration, it pays to stop the cleaning early when artifacts start to appear in the residuals!

=== Step ''i'' + 1 ===

To start, we should already have a clean model for NGC 1068 stored in the visibility database. Now we can set up {{gaincal}}.

<source lang="python">
# In CASA
default('gaincal') # reset inputs for gaincal
vis = source + '.split.ms'
caltable = source + '.gcal0'
gaintype = 'G'
calmode = 'p' # phase-only self-calibration at this stage
solint = '10 min' # set up a fairly coarse correction interval in this early stage
minsnr = 3.0
gaintable = ''
field = source
gainfield = source # are these necessary?
gaincal() # or go
</source>

=== Pause for Reflection and Troubleshooting ===

Self-calibration is a method to fine-tune your original calibration, and, as such, should produce relatively small complex gain corrections assuming the primary calibration was applied correctly. Now is a good time to run {{plotms}} on your new calibration table (''NGC1068.gcal0'') to make sure the phase corrections range from a few to perhaps 10 or 20 degrees. If you see phase corrections greatly exceeding 20 degrees, then there's a chance the primary calibration wasn't applied to the split database correctly (you could check this by searching your CASA Log file for errors during the applycal tasks). To check the phase corrections, run the following and step through each antenna:  

<!--* the primary calibration wasn't applied to the split database correctly, and should be checked, or -->
<!--* the model wasn't updated in the last iteration of {{clean}}, meaning you should verify that ''calready = True.''-->
<source lang="python">
# In CASA
plotms(caltable, iteraxis='antenna', xaxis='time', yaxis='phase', plotrange=[-1,-1,-180,180])
</source>

=== Step ''i'' + 2 ===

Assuming {{plotms}} gave reasonable results, next apply the calibration.

<source lang="python">
# In CASA
default('applycal')
vis = source + '.split.ms'
gaintable = source + '.gcal0'
gainfield = source
go
</source>

And now we can generate a new {{tclean}} model.

<source lang="python">
# In CASA
tget tclean
imagename=source + '_img1' # choose an image root name that matches the future cal table to facilitate bookkeeping
go
</source>

=== Further Steps ===

Assuming things are going well, we can now continue to repeat the process until we are satisfied with the image. 

[[Image:PreEVLA_Seyfert_Final.png|thumb|Final, self-calibrated image of NGC1068.]]

<source lang="python">
# In CASA
tget gaincal
caltable = source +'.gcal1'
solint = "5 min" # halve the solution interval. 
go

# good idea to plotms() again here!

tget applycal
gaintable = source + '.gcal1'
go

tget tclean
imagename = source + '_img2'
go
</source>

And so on, decreasing the solution interval as much as the S/N and sampling allow.

Note that {{gaincal}} always uses the 'data' column (not the recently self-calibrated 'corrected' column) to find gain solutions. Therefore, these iterations simply serve to improve the data model, and gain solutions at each iteration are independent (e.g., the solution from the second self calibration iteration does ''not'' accumulate on top of the solution from the first interval. Use the CASA task {{accum}} if you wish to accumulate gain solutions).  

=== One Last Iteration: Amplitude & Phase Self Calibration ===

After you have improved the phase calibration as much as possible, one last iteration where both phase ''and'' amplitude are calibrated can be beneficial. A&P self calibration should be performed with long solution intervals---typically the length of a scan. 

<source lang="python">
# In CASA
default('gaincal') # reset inputs for gaincal
vis = source + '.split.ms'
caltable = source + '.gcalap'
gaintype = 'G'
calmode = 'ap' # it's finally time to calibrate amplitudes, too
solint = 'inf' # one solution interval per scan (in conjunction with combine='') 
combine = ''
minsnr = 3.0
gaintable = source + '.gcal1' # apply your best phase calibration on the fly.
gaincal() # or go
</source>

Here, we are applying the best phase calibration solution on the fly by setting the ''gaintable'' parameter, so that this 'ap' self calibration iteration will only make small tweaks on top of the phase solutions we just worked so hard to find. Check your solutions in {{plotms}}---amplitudes should not vary much and should be close to unity. Complex gain tweaks should be minimal and close to zero. If the solutions look reasonable, go ahead and apply the calibration:

<source lang="python">
# In CASA
default('applycal')
vis = source + '.split.ms'
gaintable = [source + '.gcal1', source + '.gcalap']
gainfield = source
go
</source>

The critical difference here from phase-only self calibration iterations is that we are applying ''two'' gain tables---the best phase solutions (source + '.gcal1') and the A&P solutions (source + '.gcalap'). Now you should be ready for one final imaging run with {{tclean}}!  For consistency, we will run it with the same command we used the first time:

<source lang="python">
# In CASA
default(tclean)
savemodel           =      'modelcolumn'
specmode            =      'mfs'        
niter               =      2000      
gain                =      0.1       
threshold           =      '4.5e-5Jy'      
deconvolver         =      'clark_exp'           
cyclefactor         =      3                    
interactive         =      False      
mask                =      []       
imsize              =      [1024, 1024]      
cell                =      ['0.75arcsec', '0.75arcsec']
stokes              =      'I'        
weighting           =      'natural'        
uvtaper             =      []      
pbcor               =      False      
pblimit             =      0.1      
selectdata          =      False    
vis                 =      'NGC1068.split.ms'
imagename           =      'NGC1068_img4'
rmtables(imagename + '*') # get rid of earlier versions of the image
tclean()
</source>

You can use the {{viewer}} to inspect your final image:

<source lang="python">
# In CASA
viewer('NGC1068_img4.image')
</source>

{{Checked 5.7.2}}

[[Pre-upgrade VLA Tutorials | &#8629; '''Pre-upgrade VLA Tutorials''']]