Difference between revisions of "EVLA high frequency spectral line tutorial - IRC+10216 - calibration"

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(The Observing Log, Antenna Position Corrections, Opacities, Gaincurves, and other Calibration "Priors")
(Analyze the Image Cubes)
 
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'''This page is under development'''
 
'''This page is under development'''
 +
 +
This CASA Guide provides a simplified approach to the editing, calibration, and imaging of a galactic EVLA observation for use in NRAO Community Day Events.  For a complete description of the data reduction process see [[EVLA high frequency Spectral Line tutorial - IRC+10216 part1]] and [[EVLA high frequency Spectral Line tutorial - IRC+10216 part2]].
  
 
[[Category:EVLA]][[Category:Calibration]][[Category:Spectral Line]]
 
[[Category:EVLA]][[Category:Calibration]][[Category:Spectral Line]]
Line 6: Line 8:
 
[[Image:irc10216_dust.jpg|thumb|VLT V-band image of IRC+10216 showing dust rings out to a radius of 90" by Leão et al. (2006, A&A, 455, 187).]]
 
[[Image:irc10216_dust.jpg|thumb|VLT V-band image of IRC+10216 showing dust rings out to a radius of 90" by Leão et al. (2006, A&A, 455, 187).]]
  
This tutorial describes the data reduction for two spectral lines observed toward the AGB star IRC+10216.
+
This tutorial describes the data reduction for two spectral lines, HC3N and SiS, observed toward the AGB star IRC+10216 by the EVLA in D-configuration.
In this EVLA OSRO1 mode observation one subband was observed in each of two basebands, the subbands were centered on the HC3N and SiS lines near 36 GHz. The raw data were loaded into CASA with {{importevla}}, where zero and shadowed data were flagged. Then the data were "{{split}}", so we could average from the native 1 second integrations to 10 seconds, select only antennas with Ka-band receivers, and select only spectral windows (called spw in CASA) with Ka-band data. This produces a significantly smaller dataset for processing.
 
 
 
IRC+10216 (CW Leo) is the brightest star in the sky at 5 microns but only 16th magnitude visually.  It was discovered during the first survey
 
of the infrared sky, carried out by Bob Leighton and Gerry Neugebauer in 1965.  An Asymptotic Giant Branch star, it is a Mira-type variable going through prodigious episodic mass loss.  The dust condensed from the atmosphere during the mass loss is responsible for the millimeter emission; the continuum emission seen at radio wavelengths probes the actual stellar photosphere.  Molecules form along with the dust, and a steady state chemistry occurs in the dense inner regions (Tsuji 1973 A&A 23, 411).  As the density of material drops, the chemistry freezes.  But the molecules continue their long coast outward into the Galaxy, and as the shell thins ultraviolet light from the ambient galactic radiation field penetrates and initiates a new chemistry in the gas.
 
 
 
SiS, a simple molecule created in the dense inner envelope chemistry is photodissociated as it coasts out into the shell.  The result is the
 
centrally condensed emission we see in the present observations. Interestingly, in the next lower transition at 18 GHz, the line shape is
 
much different from what we see here.  At the extreme velocities in the profile, very bright narrow emission is seen which has been interpreted
 
as maser emission.  The interested student can find EVLA observations of this line in the archive.
 
 
 
HC3N, a much more complex species, is created by the photochemistry which becomes active as atoms and pieces of molecules destroyed by
 
ultraviolet radiation undergo the next phase of chemistry in the shell. HC3N has many vibrational modes which may be excited in addition to
 
its rotational modes.  Owing to this, it can re-radiate energy absorbed from ultraviolet radiation more effectively than some molecules with a
 
single bond.  Eventually it too is destroyed however, but during its brief existence its rise to abundance in the envelope results in a ring of emission, which is what is observed in this image made with the EVLA. A recent model by Cordiner & Millar (2009, ApJ, 697, 68) describes a
 
new chemical model for the shell, which also takes into account the variation of mass loss by the star.  They show that in addition to
 
purely chemical effects, local gas and dust density peaks play a role in shaping the observed emission.
 
  
 
==Getting the data==
 
==Getting the data==
  
The data for this tutorial can be obtained by anonymous FTP from <tt>ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/</tt>.  Download all 4 files TAR in this directory.
+
The data for this tutorial can be obtained by anonymous FTP from <tt>ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/</tt>.  Download all 4 TAR files.
  
 
For example,
 
For example,
Line 34: Line 20:
 
wget 'ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/*'
 
wget 'ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/*'
 
</source>
 
</source>
 
== How to Use This casaguide==
 
 
[[Image:clean.png|thumb|Inputs from one of the clean commands from this tutorial]]
 
There are a number of possible ways to run CASA. Many aspects are described in [[Getting_Started_in_CASA]]. You should review this page if you are new to CASA. In brief you can run CASA interactively by looking at the inputs to tasks with '''inp taskname''' (example: inp clean), setting the parameters one by one (example: selectdata=T) as you desire and then run '''go'''. After setting parameters one by one in a task and then looking at the inputs again, you will notice that the parameters that have been set to something other than their defaults are blue. If you have mistyped any parameters, they will be red and must be fixed for the task to run correctly. You can get more detailed help on any task by typing '''help taskname''' (example: help clean). Once a task is run you can get the same parameters back by running '''tget taskname''' (example: tget clean); subsequent runs will overwrite the previous tget file.
 
 
The second way to run CASA is to provide task function calls. This tutorial is made up of such calls, which were developed by looking at the inputs for each task and deciding what needed to be changed from default values. For task function calls, '''only parameters that you want to be different from their defaults need to be set'''. A series of task function calls can be combined together into a script, and run with '''execfile('scriptname.py')'''. It is possible to extract a script containing all the CASA task function calls in this and other casaguides using the method described at the [[Extracting_scripts_from_these_tutorials]] page.
 
 
If you are a relative novice or just new to CASA it is strongly recommended to work through this tutorial by cutting and pasting the task function calls provided below after you have read all the associated explanations.  Work at your own pace, look at the inputs to the tasks to see what other options exist, and read the help files. Later, when you are more comfortable, you might try to extract the script, modify it for your purposes, and begin to reduce other data.
 
  
 
==Initial Inspection and Flagging==
 
==Initial Inspection and Flagging==
  
{{listobs}} provides almost all relevant observational parameters such as correlator setup (frequencies, bandwidths, channel number and widths, polarization products), sources, scans, scan intents, and antenna locations:
+
GEt a summary listing of the data set using {{listobs}}.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
listobs(vis='day2_TDEM0003_10s_norx')
+
vis = 'day2_TDEM0003_20s_full'
 +
listobs(vis=vis, verbose=True)
 
</source>
 
</source>
  
Line 57: Line 35:
 
<pre style="background-color: #fffacd;">
 
<pre style="background-color: #fffacd;">
 
Fields: 4
 
Fields: 4
   ID  Code Name         RA           Decl           Epoch  SrcId nVis  
+
   ID  Code Name               RA             Decl         Epoch  SrcId nVis
   2    D    J0954+1743   09:54:56.8236 +17.43.31.2224 J2000  2    65326 
+
   2    D    J0954+1743         09:54:56.82363 +17.43.31.2224 J2000  2    32726
   3    NONE IRC+10216   09:47:57.3820 +13.16.40.6600 J2000  3    208242
+
   3    NONE IRC+10216           09:47:57.38200 +13.16.40.6600 J2000  3    99540
   5    F    J1229+0203   12:29:06.6997 +02.03.08.5982 J2000  5    10836 
+
   5    F    J1229+0203         12:29:06.69973 +02.03.08.5982 J2000  5    5436
   7    E    J1331+3030   13:31:08.2880 +30.30.32.9589 J2000  7    5814 
+
   7    E    J1331+3030         13:31:08.28798 +30.30.32.9589 J2000  7    2736
   (nVis = Total number of time/baseline visibilities per field)  
+
   (nVis = Total number of time/baseline visibilities per field)
 
Spectral Windows:  (2 unique spectral windows and 1 unique polarization setups)
 
Spectral Windows:  (2 unique spectral windows and 1 unique polarization setups)
   SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)TotBW(kHz)  Ref(MHz)    Corrs          
+
   SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz) TotBW(kHz)  Corrs
   0          64 TOPO  36387.2295  125         8000        36387.2295  RR  RL  LR  LL
+
   0          64 TOPO  36387.2295  125           8000        RR  RL  LR  LL
   1          64 TOPO  36304.542  125         8000        36304.542  RR  RL  LR  LL
+
   1          64 TOPO  36304.542  125           8000        RR  RL  LR  LL
 
Sources: 10
 
Sources: 10
   ID  Name         SpwId RestFreq(MHz)  SysVel(km/s)  
+
   ID  Name               SpwId RestFreq(MHz)  SysVel(km/s)
   0    J1008+0730   0    0.03639232    -0.026      
+
   0    J1008+0730         0    0.03639232    -0.026
   0    J1008+0730   1    0.03639232    -0.026      
+
   0    J1008+0730         1    0.03639232    -0.026
   2    J0954+1743   0    0.03639232    -0.026      
+
   2    J0954+1743         0    0.03639232    -0.026
   2    J0954+1743   1    0.03639232    -0.026      
+
   2    J0954+1743         1    0.03639232    -0.026
   3    IRC+10216   0    0.03639232    -0.026      
+
   3    IRC+10216           0    0.03639232    -0.026
   3    IRC+10216   1    0.03639232    -0.026      
+
   3    IRC+10216           1    0.03639232    -0.026
   5    J1229+0203   0    0.03639232    -0.026      
+
   5    J1229+0203         0    0.03639232    -0.026
   5    J1229+0203   1    0.03639232    -0.026      
+
   5    J1229+0203         1    0.03639232    -0.026
   7    J1331+3030   0    0.03639232    -0.026      
+
   7    J1331+3030         0    0.03639232    -0.026
   7    J1331+3030   1    0.03639232    -0.026      
+
   7    J1331+3030         1    0.03639232    -0.026
 
Antennas: 19:
 
Antennas: 19:
   ID  Name  Station  Diam.    Long.        Lat.        
+
   ID  Name  Station  Diam.    Long.        Lat.
   0    ea01  W09      25.0 m  -107.37.25.2  +33.53.51.0
+
   0    ea01  W09      25.0 m  -107.37.25.2  +33.53.51.0
   1    ea02  E02      25.0 m  -107.37.04.4  +33.54.01.1
+
   1    ea02  E02      25.0 m  -107.37.04.4  +33.54.01.1
   2    ea03  E09      25.0 m  -107.36.45.1  +33.53.53.6
+
   2    ea03  E09      25.0 m  -107.36.45.1  +33.53.53.6
   3    ea04  W01      25.0 m  -107.37.05.9  +33.54.00.5
+
   3    ea04  W01      25.0 m  -107.37.05.9  +33.54.00.5
   4    ea05  W08      25.0 m  -107.37.21.6  +33.53.53.0
+
   4    ea05  W08      25.0 m  -107.37.21.6  +33.53.53.0
   5    ea07  N06      25.0 m  -107.37.06.9  +33.54.10.3
+
   5    ea07  N06      25.0 m  -107.37.06.9  +33.54.10.3
   6    ea08  N01      25.0 m  -107.37.06.0  +33.54.01.8
+
   6    ea08  N01      25.0 m  -107.37.06.0  +33.54.01.8
   7    ea09  E06      25.0 m  -107.36.55.6  +33.53.57.7
+
   7    ea09  E06      25.0 m  -107.36.55.6  +33.53.57.7
   8    ea12  E08      25.0 m  -107.36.48.9  +33.53.55.1
+
   8    ea12  E08      25.0 m  -107.36.48.9  +33.53.55.1
   9    ea15  W06      25.0 m  -107.37.15.6  +33.53.56.4
+
   9    ea15  W06      25.0 m  -107.37.15.6  +33.53.56.4
   10  ea19  W04      25.0 m  -107.37.10.8  +33.53.59.1
+
   10  ea19  W04      25.0 m  -107.37.10.8  +33.53.59.1
   11  ea20  N05      25.0 m  -107.37.06.7  +33.54.08.0
+
   11  ea20  N05      25.0 m  -107.37.06.7  +33.54.08.0
   12  ea21  E01      25.0 m  -107.37.05.7  +33.53.59.2
+
   12  ea21  E01      25.0 m  -107.37.05.7  +33.53.59.2
   13  ea22  N04      25.0 m  -107.37.06.5  +33.54.06.1
+
   13  ea22  N04      25.0 m  -107.37.06.5  +33.54.06.1
   14  ea23  E07      25.0 m  -107.36.52.4  +33.53.56.5
+
   14  ea23  E07      25.0 m  -107.36.52.4  +33.53.56.5
   15  ea24  W05      25.0 m  -107.37.13.0  +33.53.57.8
+
   15  ea24  W05      25.0 m  -107.37.13.0  +33.53.57.8
   16  ea25  N02      25.0 m  -107.37.06.2  +33.54.03.5
+
   16  ea25  N02      25.0 m  -107.37.06.2  +33.54.03.5
   17  ea27  E03      25.0 m  -107.37.02.8  +33.54.00.5
+
   17  ea27  E03      25.0 m  -107.37.02.8  +33.54.00.5
   18  ea28  N08      25.0 m  -107.37.07.5  +33.54.15.8
+
   18  ea28  N08      25.0 m  -107.37.07.5  +33.54.15.8
 
</pre>
 
</pre>
  
In addition to source names, antenna names, etc. CASA indexes each of these parameters staring from 0. When, say a field is specified, one can use the index or the name, both is accepted.
+
We summarize the observing strategy in this table.
 
 
 
 
Note that the Rest Frequency and Systemic Velocity are wrong in the listobs log by a factor 10^6 and 1000, respectively, given the quoted units (MHz) and (km/s). This was due to a temporary error in the EVLA Observing Tool that has subsequently been fixed. Because the sky frequencies are correct, and we set the rest frequency explicitly later in the deconvolution stage, this does not present a problem for the data reductions.  
 
  
<pre style="background-color: #E0FFFF;">
+
{| border="1" align="center" cellpadding="10" cellspacing="0"
Summary of Observing Strategy
+
! Gain calibrator
Gain Calibrator: J0954+1743 field id=2
+
| J0954+1743
Bandpass Calibrator: J1229+0203   field id=5
+
| field id = 2
Flux Calibrator: J1331+3030 (3C286) field id=7
+
|-
Target: IRC+10216  field id=3
+
! Bandpass calibrator
Ka-band spws = 0,1
+
| J1229+0203
</pre>
+
| field id = 5
 +
|-
 +
! Flux calibrator
 +
| J1331+3030 (3C286)
 +
| field id = 7
 +
|-
 +
! Science target
 +
| IRC+10216
 +
  | field id = 3
 +
|}
  
 
[[Image:Ant_locations.png|thumb|Antenna locations from running plotants ]]
 
[[Image:Ant_locations.png|thumb|Antenna locations from running plotants ]]
Look at a graphical plot of the antenna locations and save hardcopy
 
in case you want it later. This will be useful for picking a reference antenna --
 
typically a good choice is an antenna close to the center of the array. Unless it
 
shows problems after inspection of the data, we provisionally chose ea02.
 
 
[[Image:elevationvstime.png|thumb|Elevation as a function of time (after selecting colorize by field).]]
 
<source lang="python">
 
# In CASA
 
plotants(vis='day2_TDEM0003_10s_norx',figfile='ant_locations.png')
 
</source>
 
  
Next, let's look at the elevation as a function of time for all sources. It's not the case for these data, but if the elevation is very low (usually at start or end of track) you may want to flag. Also, how near in elevation your flux calibrator is to your target will impact your ultimate absolute flux calibration accuracy. Unfortunately, the target and flux calibrator are not particularly well-matched for this observation, as you can show by plotting the elevation for each source (each sources has a different colors). We will be using data in spectral window 0, channels 4 to 60 for this plot as given by the '''spw='0:4~60'''' parameter, the general CASA selection syntax is described in the [http://www.aoc.nrao.edu/~sbhatnag/misc/msselection/msselection.html Measurement Selection Syntax Document]:
+
Create a plot of antenna positions using {{plotants}}.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',
+
plotants(vis=vis)
      xaxis='time',yaxis='elevation',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60', coloraxis='field')
 
 
</source>
 
</source>
 
Thus we are strongly dependent on the opacity and gaincurve corrections to get the flux scale right for these data. (This is something to keep in mind when planning observations!)
 
  
 
[[Image:plotallfields.png|thumb|Result of plotms]]
 
[[Image:plotallfields.png|thumb|Result of plotms]]
 
[[Image:Zoom1_mark.png|thumb|Zooming in and marking region (hatched box)]]
 
[[Image:Zoom1_mark.png|thumb|Zooming in and marking region (hatched box)]]
  
Next, let's look at all the source amplitudes as a function of time.
+
Next, let's look at all the source amplitudes as a function of time using {{plotms}}.
 +
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',
+
plotms(vis=vis, xaxis='time', yaxis='amp', correlation='RR,LL',
      xaxis='time',yaxis='amp',correlation='RR,LL',
+
       avgchannel='64', spw='0:4~60', coloraxis='field')
       avgchannel='64',spw='0:4~60', coloraxis='field')
 
 
</source>
 
</source>
 
  
 
Now zoom in on the region very near zero amplitude for sources J0954+1743 and IRC+10216. To zoom, select the  
 
Now zoom in on the region very near zero amplitude for sources J0954+1743 and IRC+10216. To zoom, select the  
 
Zoom tool in lower left corner of  the {{plotms}} GUI, then you can left click to draw a box.  
 
Zoom tool in lower left corner of  the {{plotms}} GUI, then you can left click to draw a box.  
Look for the low values (you may want to zoom a few times to really see the suspect points clearly). Now use the Mark Region and Locate buttons (located along the bottom of the GUI) to see which antenna is causing problems. The output is be shown in the logger. Since all the "located" baselines include ea12, this is the responsible antenna.
+
Look for the low values (you may want to zoom a few times to really see the suspect points clearly). Now use the Mark Region and Locate buttons (located along the bottom of the GUI) to see which antenna is causing problems. The output will be shown in the logger. Since all the "located" baselines include ea12, this is the responsible antenna.
 
 
<pre style="background-color: #98FB98;">
 
IMPORTANT NOTES ON PLOTMS:
 
 
 
* When using the locate button it is important to have only selected a modest number
 
of points with the mark region tool (see example of marked region in the thumnail),
 
otherwise the response will be very slow and possibly hang the tool
 
(all of the information will be output to your terminal window, not the logger).
 
 
 
* Throughout the tutorial, when you are done marking/locate use the Clear Regions
 
tool to get rid of the marked box before plotting other things.
 
 
 
* After flagdata command flagging, you have to force a complete reload of the cache
 
to look at the same plot again with the new flags applied. To do this change anything
 
in the plotms GUI (the colorize parameter, antenna, spw, anything) and hit the
 
plot button.
 
 
 
* If the plotms tool does get hung during a plot try clicking the cancel button on the
 
load progress GUI, and/or if you see a "table locked" message try typing
 
clearstat on the CASA command line.
 
 
 
* Occasionally plotms will get into a strange state that you cannot clear from inside.
 
We are working on these issues, but for now, when all else fails, exit from casapy and
 
restart. 
 
 
 
</pre>
 
  
 
Now click the clear region button, and then go back to the zoom button to zoom in further to note exactly what the time range is: 03:41:00~04:10:00.
 
Now click the clear region button, and then go back to the zoom button to zoom in further to note exactly what the time range is: 03:41:00~04:10:00.
Line 190: Line 133:
 
If you set antenna to 'ea12' and zoom in on this intial timerange, you can also see that ea12 is bad during the same time range as for spw 0.  You can also see this by entering '!ea07' for antenna, which removes ea07 from the plot (in CASA selection, ! deselects).  
 
If you set antenna to 'ea12' and zoom in on this intial timerange, you can also see that ea12 is bad during the same time range as for spw 0.  You can also see this by entering '!ea07' for antenna, which removes ea07 from the plot (in CASA selection, ! deselects).  
  
We can set up a flagging command to get both bad antennas for the
+
We can flag the bad data using {{flagdata}}.
appropriate time and spw:
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
flagdata(vis='day2_TDEM0003_10s_norx',
+
flagdata(vis=vis,
 
         field=['2,3','2,3'],
 
         field=['2,3','2,3'],
 
         spw=['','1'],
 
         spw=['','1'],
Line 207: Line 149:
 
needed; however, flagdata will run fastest if you put as many constraints as possible.
 
needed; however, flagdata will run fastest if you put as many constraints as possible.
  
Now remove the !ea07 from antenna and replot both spw, zooming in to
+
==Set Up the Model for the Flux Calibrator==
be sure that all obviously low points are gone. Also zoom in and
 
check 3C286 (J1229+0203 is already obvious because it is so bright!).
 
 
 
[[Image:IRC10216_uvdist1.png|thumb|Amplitude vs. uv-distance for IRC+10216, both spw]]
 
  
Lets look more closely at IRC+10216:
+
We set the model for the flux calibrator using {{setjy}}.  First, check the availability of calibration models.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',field='3',
+
setjy(vis=vis,listmodimages=T)
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0~1:4~60', coloraxis='spw')
 
 
</source>
 
</source>
  
You can see a
+
There is no Ka-band model of 3C286.  We will use the K-band model instead.
that there are some noisy high points. But now try
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',field='3',
+
setjy(vis=vis,field='7',spw='0~1',
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
+
      modimage='3C286_K.im')
      avgchannel='64',spw='0~1:4~60', coloraxis='spw')
 
 
</source>
 
</source>
  
Most of the high points on IRC+10216 are due to large scale emission on short baselines, but there is still some noisy stuff -- for a target like this with extended emission it's best to wait until later to decide what to do about it. We will not be able to get adequate calibration for antennas that are truly bad (even if they don't stand out here) so these will be obvious later.
+
{{setjy}} scales the total flux in the model image to that appropriate for your individual spectral window frequencies according to the calibrator's flux and reports this number to the logger.
 
 
==Set Up the Model for the Flux Calibrator==
 
 
 
Next, we set the model for the flux calibrator. Depending on your observing frequency and angular resolution you can do this several ways. In the past, one typically used a point source (constant flux) model for
 
the flux calibrator, possibly with a uvrange cutoff if necessary. More recently for the VLA/EVLA,  model images for the most common flux calibrators have been made available for use in cases where the sources are somewhat resolved. This is most likely to be true at higher frequencies and at higher resolutions (more extended arrays).
 
 
 
Currently, CASA contains models for the most common calibrators at the most common frequencies but not yet the new EVLA frequency bands S, Ku, and Ka. These will be added as son as they become available. One may check the availability of calibration models in {{setjy}}:
 
 
 
 
 
<source lang="python">
 
# In CASA
 
setjy(vis='day2_TDEM0003_10s_norx',listmodimages=T)
 
</source>
 
 
 
The terminal will now show the available models, e.g. 3C286_C.im, 3C48_K.im etc. ({{setjy}} will search in the working directory for images that may contain models, as well as in a CASA directory where known calibrator models are stored)
 
 
 
As mentioned above, the Ka band does not yet have a model incorporated at this time as the full configuration cycle has not ended yet (the A-configuration data has not yet obtained as of Aug 2011). However, models of Ka band that are good for D, C, and B configurations are available from the [https://science.nrao.edu/facilities/evla/data-processing/flux-calibrator-models-for-new-evla-bands  EVLA calibrator model webpage for the new frequency bands].
 
 
 
From this page, please download the file 36.68 GHz CASA image of 3C286, this is the [http://www.aoc.nrao.edu/evlacal/3C286_36.68G.model.tgz direct link.] To do so, you may run
 
 
 
 
 
<source lang='bash'>
 
# in a terminal, outside of CASA:
 
wget http://www.aoc.nrao.edu/evlacal/3C286_36.68G.model.tgz
 
tar xzvf 3C286_36.68G.model.tgz
 
</source>
 
 
 
to create the model '''3C286_36.68G.model''' on disk.
 
 
 
{{setjy}} scales the total flux in the model image to that appropriate for your individual spectral window frequencies according to the calibrator's flux and reports this number to the logger -- it is a good idea to save this information for your records.
 
 
 
 
 
<source lang="python">
 
# In CASA
 
setjy(vis='day2_TDEM0003_10s_norx',field='7',spw='0~1',
 
      modimage='3C286_36.68G.model')
 
</source>
 
  
 
<pre style="background-color: #fffacd;">
 
<pre style="background-color: #fffacd;">
 
The logger output for each spw is:
 
The logger output for each spw is:
J1331+3030 rawspwid=  0  [I=1.776, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
+
J1331+3030 (fld ind 7) spw 0  [I=1.7762, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
J1331+3030 rawspwid=  1  [I=1.779, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
+
J1331+3030 (fld ind 7) spw 1  [I=1.7794, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
 
</pre>
 
</pre>
  
 
The absolute fluxes for the frequencies have now been determined and one can proceed to the bandpass and complex gain calibrations.
 
The absolute fluxes for the frequencies have now been determined and one can proceed to the bandpass and complex gain calibrations.
  
==Bandpass==
+
==Bandpass Calibration==
 
 
Before determining the bandpass solution, we need to inspect phase and amplitude
 
variations with time and frequency on the bandpass calibrator to
 
decide how best to proceed. We limit the number of antennas to make
 
the plot easier to see. We chose ea02 as it seems like a good
 
candidate for the reference antenna.
 
 
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='channel',yaxis='phase',correlation='RR',
 
      avgtime='1e8',spw='0:4~60',antenna='ea02&ea23')
 
</source>
 
[[Image:Nobandpass_phase.png|thumb|Phase as a function of channel for ea02 (after Custom and upping "Style" to 3.)]]
 
The phase variation is modest ~10 degrees.  Now expand to all baselines that include ea02, then hit the "Plot" button.
 
  
<source lang="python">
+
Correct the phase variations with time before solving for the bandpass to
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='channel',yaxis='phase',correlation='RR',
 
      avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
 
</source>
 
[[Image:Nobandpass_phasetime.png|thumb|Phase as a function of time for all baselines with antenna ea02 (after Custom and upping "Style" to 3.)]]
 
From this
 
you can see that the phase variation across the bandpass is
 
modest. Next check LL, and spw=1, both correlations. Also check
 
other antennas if you like.
 
 
 
Now look at the phase as a function of time.
 
 
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='time',yaxis='phase',correlation='RR',
 
      avgchannel='64',spw='0:4~60',antenna='ea02&ea23')
 
</source>
 
 
 
 
 
Expand to all antennas with ea02
 
 
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='time',yaxis='phase',correlation='RR',
 
      avgchannel='64',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
 
</source>
 
 
 
You may want to select "Custom" under "unflagged points symbol" and then change Style from "2" to "3" under the "Display" tab, "Unflagged Points Symbol" .
 
 
 
You can see that the phase variations are smooth, but do vary
 
significantly over the 5 minutes of observation -- in most cases by
 
a few 10s of degrees. Zoom in to see this better if you want.
 
 
 
The conclusion from this investigation is that you need to correct
 
the phase variations with time before solving for the bandpass to
 
 
prevent decorrelation of the vector averaged bandpass
 
prevent decorrelation of the vector averaged bandpass
solution. Since the phase variation as a function of channel is
+
solution.  
modest, you can average over several channels to increase the signal
 
to noise of the phase vs. time solution. If the phase variation as a
 
function of channel is larger you may need to use only a few
 
channels to prevent introducing delay-based closure errors as can happen from averaging over
 
non-bandpass corrected channels with large phase variations.
 
  
 +
[[Image:Prebp_phasecal2.png|thumb|Phase only calibration before bandpass. The 4 lines are both polarizations in both spw, unfortunately two of them get the same color green at the moment.]]
  
Since the bandpass calibrator is quite strong we do the phase-only
 
solution on the integration time of 10 seconds (solint='int').
 
 
Remember that previously we determined the '''myTau''' variable as a list of opacities. It's repeated here but may still be in your CASA session.
 
 
[[Image:Prebp_phasecal2.png|thumb|Phase only calibration before bandpass. The 4 lines are both polarizations in both spw, unfortunately two of them get the same color green at the moment.]]
 
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
myTau=[0.041019209411983566, 0.040779609355637569]
+
gaincal(vis=vis, caltable='bpphase.gcal', field='5', spw='0~1:20~40',
gaincal(vis='day2_TDEM0003_10s_norx',caltable='bpphase.gcal',
+
         refant='ea02', gaintype='G', calmode='p',solint='int',
        field='5',spw='0~1:20~40',
+
         gaincurve=T)
         refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaintable=['antpos.cal'],
 
         opacity=myTau,gaincurve=T)
 
 
</source>
 
</source>
  
Plot the solutions
+
Plot the solutions using {{plotcal}}.
  
 
<source lang="python">
 
<source lang="python">
Line 367: Line 198:
 
         iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
 
         iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
 
</source>
 
</source>
 
These solutions will appear in the CASA plotter gui. If you closed it after plotting the antennas above, it should reopen. If it is still open from before, the new plots should just appear. After you are done looking at the first set of plots, push the "Next" button on the GUI to see the next set of antennas.
 
  
 
Next we can apply this phase solution on the fly while determining
 
Next we can apply this phase solution on the fly while determining
 
the bandpass solutions on the timescale of the bandpass calibrator scan (solint='inf').  
 
the bandpass solutions on the timescale of the bandpass calibrator scan (solint='inf').  
 
We also use the opacity list now instead of myTau - but both options will work.
 
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
bandpass(vis='day2_TDEM0003_10s_norx',caltable='bandpass.bcal',field='5',
+
bandpass(vis=vis, caltable='bandpass.bcal', field='5', refant='ea02',
        refant='ea02',solint='inf',solnorm=T,
+
        solint='inf', solnorm=T, gaintable='bpphase.gcal',
        gaintable=['antpos.cal','bpphase.gcal'],
+
        gaincurve=T)
        opacity=[0.041, 0.0408],gaincurve=T)
 
 
</source>
 
</source>
 
'''A few words about solint and combine:'''
 
 
The use of  solint='inf' in {{bandpass}} will derive one bandpass
 
solution for the whole J1229+0203 scan. Note that if there had been two observations of the bandpass calibrator (for example), this command would have combined the data from both scans to form one bandpass solution, because the default of the combine parameter '''for {{bandpass}}''' is combine='scan'. To solve for one bandpass for each bandpass calibrator scan you would also need to include combine='''' '''' in the bandpass call. In all calibration tasks, regardless of solint, scan boundaries are only crossed when combine='scan'. Likewise, field (spw) boundaries are only crossed if combine='field' (combine='spw'), the latter two are not generally good ideas for bandpass solutions. 
 
  
 
Plot the solutions, amplitude and phase:
 
Plot the solutions, amplitude and phase:
Line 406: Line 226:
 
</source>
 
</source>
  
 +
==Remaining Calibration==
  
 +
In this Community Day Event guide, we will skip over the remaining calibration steps.  However, you can refer to [[EVLA high frequency Spectral Line tutorial - IRC+10216 part1]] for full details.  To summarize the missing steps, you bootstrap the flux densities of the secondary calibrators by
  
 +
# doing a phase only calibration on all calibrators using {{gaincal}},
 +
# doing amplitude only calibration on all calibrators while applying the phase-only solutions, and
 +
# deriving the flux density of the secondary calibrators while applying the previously obtained solutions.
  
Note that phases on ea12 look noiser than on other antennas. This
+
Then, you can calibrate the target source using the phase and amplitude solutions you have obtained. Apply the target source calibration solutions using {{applycal}}. Use {{plotms}} to examine the calibrated data. If more flagging is required, redo all calibration steps. When the data look good, {{split}} the target source into a separate measurement set.  Subtract the continuum flux using {{uvcontsub}}. Make Doppler corrections using {{cvel}}, or let {{clean}} do the Doppler corrections on the fly.
jitter could indicate bad pointing; note that ea12 had just come back in
 
the array.
 
 
 
This step isn't necessary from a calibration perspective, but if you
 
want to go ahead and check the bandpass calibration on the bandpass
 
calibrator you can run {{applycal}} here. In future we hope to plot
 
corrected data on-the-fly without this {{applycal}} step. Later applycals
 
will overwrite this one, so no need to worry.
 
 
 
[[Image:Applybandpass_phase.png|thumb|Phase as a function of channel, plotting the corrected data (after Custom and upping "Style" to 3.)]]
 
 
 
<source lang="python">
 
applycal(vis='day2_TDEM0003_10s_norx',field='5',
 
        gaintable=['antpos.cal','bandpass.bcal'],
 
        gainfield=['','5'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
 
 
Similar to {{flagdata}}, {{applycal}} works like a matrix. The first entries in the lists are to be used together, so are the second entries etc. (except for the opacity list, which is referring to spws). All will be applied to the 'field' selection. In the above example, 'antpos.cal' from any field is applied to source '5', and the 'bandpass.cal' that was obtained for field '5' (the bandpass observation) is also applied to field '2'. Again, lists within the lists are fine. 
 
 
 
<source lang="python">
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='channel',yaxis='phase',ydatacolumn='corrected',
 
      correlation='RR',
 
      avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
 
</source>
 
 
 
<source lang="python">
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',
 
      xaxis='channel',yaxis='amp',ydatacolumn='corrected',
 
      correlation='RR',
 
      avgtime='1e8',spw='0:4~60',antenna='ea02', coloraxis='antenna2')
 
</source>
 
 
 
Note that the phase and amplitude as a function of channel are very flat now.
 
 
 
==Gain Calibration==
 
 
 
Now that we have a bandpass solution to apply we can solve for the antenna-based phase and amplitude gain calibration. Since the phase changes on a much shorter timescale than the amplitude, we will solve for them separately. In particular, if the phase changes significantly over a scan time, the amplitude would be decorrelated, if the un-corrected phase were averaged over this timescale. Note that we re-solve for the gain solutions of the bandpass calibrator, so we can derive new solutions that are corrected for the bandpass shape. Since the bandpass calibrator will not be used again, this is not strictly necessary, but is useful to check its calibrated flux density for example. We use a minimum signal-to-noise of 2 here as it seems to be a good compromise for using good data without rejecting too many solutions (minsnr=2).  
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='intphase.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass.bcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
[[Image:allcal_phaseint2.png|thumb|Plot of phase solutions on an integration time.]]
 
 
 
Here solint='int' coupled with calmode='p' will derive a single phase solution for each 10 second integration. Note that the bandpass table is applied on-the-fly before solving for the phase solutions, however the bandpass is NOT applied to the data permanently until applycal is run later on.
 
 
 
Note that quite a few solutions are rejected due to SNR<2 (printed to terminal). For the most part it
 
is only one or two solutions out of >30 so this isn't too worrying. Take note if you see large numbers of rejected solutions per integration. This is likely an indication that solint is too short for the S/N of the data.
 
 
 
Now look at the phase solution, and note the obvious scatter within a scan time.
 
 
 
<source lang="python">
 
# In CASA
 
plotcal(caltable='intphase.gcal',xaxis='time',yaxis='phase',
 
        iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
 
</source>
 
 
 
Although solint='int' (i.e. the integration time of 10 seconds) is the best choice to apply before for solving for the amplitude solutions, it is not a good idea to use this to apply to the target. This is because the phase-scatter within a scan can dominate the interpolation between calibrator scans. Instead, we also solve for the phase on the scan time, solint='inf' (but combine='''' '''', since we want one solution per scan) for application to the target later on. '''Unlike the bandpass task,''' for gaincal, the default of the combine parameter is combine='''' ''''.
 
[[Image:allcal_phaseinf2.png|thumb|Plot of phase solutions on a scan time.]]
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='scanphase.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='inf',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass.bcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
plotcal(caltable='scanphase.gcal',xaxis='time',yaxis='phase',
 
        iteration='antenna',subplot=331,plotrange=[0,0,-180,180])
 
</source>
 
 
 
Note that there are no failed solutions here because of the added S/N afforded by the longer solint.
 
Alternatively, instead of making a separate phase solution for application to the target, one can also run {{smoothcal}} to smooth the solutions derived on the integration time.
 
 
 
Next we apply the bandpass and solint='int' phase-only calibration solutions on-the-fly to derive amplitude solutions.
 
Here the use of solint='inf', but combine='''' '''' will result in one solution per scan interval.
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='amp.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='ap',solint='inf',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass.bcal','intphase.gcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
[[Image:allcal_ampphase.png|thumb|Plot of residual phase solutions on a scan time]]
 
 
 
Now let's look at the resulting phase solutions.  Since we have taken out the phase as best we can by applying the solint='int' phase-only solution, this plot will give a good idea of the residual phase error. If you see scatter of more than a few degrees here, you should consider going back and looking for more data to flag, particularly bad timeranges etc.
 
 
 
<source lang="python">
 
# In CASA     
 
plotcal(caltable='amp.gcal',xaxis='time',yaxis='phase',
 
        iteration='antenna',subplot=331)
 
</source>
 
 
 
Indeed, both antenna ea12 (all times) and ea23 (first 1/3 of observation) show particularly large residual phase noise.
 
[[Image:allcal_amp.png|thumb|Plot of amplitude solutions on a scan time]]
 
<source lang="python">
 
# In CASA
 
plotcal(caltable='amp.gcal',xaxis='time',yaxis='amp',
 
        iteration='antenna',subplot=331)
 
</source>
 
 
 
Note that the amplitude solutions for ea12 are very low; this is another indication that this antenna is dubious.
 
 
 
Next we use the flux calibrator (whose flux density was set in {{setjy}} above) to derive the flux 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
 
fluxscale(vis='day2_TDEM0003_10s_norx',caltable='amp.gcal',
 
          fluxtable='flux.cal',reference='7')
 
</source>
 
 
 
[[Image:allcal_flux.png|thumb|Plot of flux corrected amplitude solutions.]]
 
It is a good idea to note down for your records the derived flux densities:
 
  
<pre style="background-color: #fffacd;">
+
This tutorial picks up where [[ EVLA high frequency spectral line tutorial - IRC+10216 - calibration]] leaves off.  
Flux density for J0954+1743 in SpW=0 is: 0.237135 +/- 0.000858511 (SNR = 276.216, nAnt= 19)
 
Flux density for J0954+1743 in SpW=1 is: 0.247597 +/- 0.00063498 (SNR = 389.928, nAnt= 19)
 
Flux density for J1229+0203 in SpW=0 is: 26.508 +/- 0 (SNR = inf, nAnt= 19)
 
Flux density for J1229+0203 in SpW=1 is: 26.2517 +/- 0 (SNR = inf, nAnt= 19)
 
  
</pre>
+
==Imaging==
  
Obviously, the signal-to-noise for J1229+0203 can't be infinity! This is just an indication that there is only one scan for this source, and we derived a scan based amplitude solution, so there is no variation to calculate.  
+
[[Image:irc10216_uvspec.png|thumb|UV-plot of the spectral line signal in both spw for IRC+10216.]]
  
Next, check that the flux.cal table looks as expected.
+
The continuum-subtracted spectral line data is contained in <tt>IRC10216_spls.ms</tt>.  Use {{plotms}} to plot the lines.
  
 
<source lang="python">
 
<source lang="python">
# In CASA
+
plotms(vis='IRC10216_spls.ms', xaxis='channel', yaxis='amp',
plotcal(caltable='flux.cal',xaxis='time',yaxis='amp',
+
      avgtime='1e8', avgscan=T, coloraxis='spw')
        iteration='antenna',subplot=331)
 
 
</source>
 
</source>
  
==Applycal and Inspect==
+
Now it is time to image the visibility data using {{clean}}. For illustration, we will clean channel 22 of the SiS line.
 
 
Now we apply the calibration to each source, according to which tables are appropriate, and which source should be used to do that particular calibration. For the calibrators, all bandpass solutions come from the bandpass calibrator (id=5), and the phase and amplitude calibration comes from their own solutions.
 
 
 
'''Note:''' In applycal we set calwt=F. It is very important to turn off this parameter which determines if the weights are calibrated along with the data. Data from antennas with better receiver performance and/or longer integration times should have higher weights, and it can be advantageous to factor this information into the calibration. During the VLA era, meaningful weights were available for each visibility. However, EVLA is not yet recording the information necessary to calculate meaningful weights. Since these data weights are used at the imaging stage you can get strange results from having calwt=T when the input weights are themselves not meaningful, especially for self-calibration on resolved sources (your flux calibrator and target for example). In a few months EVLA data will again have meaningful weights and the default calwt=T will likely again be the best option.
 
 
 
<source lang="python">
 
# In CASA
 
# for the gain/phase calibrator (field '2'):
 
applycal(vis='day2_TDEM0003_10s_norx',field='2',
 
        gaintable=['antpos.cal','bandpass.bcal','intphase.gcal','flux.cal'],
 
        gainfield=['','5','2','2'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
# for the bandpass calibrator (field '5'):
+
os.system('rm -rf ch22.*') # remove previously generated image, if it exists
applycal(vis='day2_TDEM0003_10s_norx',field='5',
+
clean(vis='IRC10216_spls.ms', imagename='ch22', spw='1:22~22',
        gaintable=['antpos.cal','bandpass.bcal','intphase.gcal','flux.cal'],
+
      mode='channel', nchan=1, start='', width=1, niter=100000,  
        gainfield=['','5','5','5'],
+
      gain=0.1, threshold='3.0mJy', psfmode='clark', imagermode='csclean',
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
+
      interactive=T, npercycle=100, imsize=300, cell=['0.4arcsec', '0.4arcsec'],
 +
      stokes='I', weighting='briggs', robust=0.5)  
 
</source>
 
</source>
  
<source lang="python">
+
After running the above command, make a region in the CASA viewer. Double-click inside the region and clean by clicking the green circular arrow. After each cycle, click the green circular arrow again if the flux inside the region is brighter than the flux peaks outside the region. When you are finished cleaning, click the red 'STOP' button.  
# In CASA
 
# for the flux calibrator (field '7'):
 
applycal(vis='day2_TDEM0003_10s_norx',field='7',
 
        gaintable=['antpos.cal','bandpass.bcal','intphase.gcal','flux.cal'],
 
        gainfield=['','5','7','7'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
  
For the target we apply the bandpass from id=5, and the calibration from the gain calibrator (id=2):
+
Open the resulting image using the {{viewer}}.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
# for the target source IRC10216 (field '3'):
+
viewer("ch22.image")
applycal(vis='day2_TDEM0003_10s_norx',field='3',
 
        gaintable=['antpos.cal','bandpass.bcal','scanphase.gcal','flux.cal'],
 
        gainfield=['','5','2','2'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
 
</source>
 
</source>
  
Now inspect the corrected data:
+
In the viewer, make a region and click inside it to get statistics about the portion of the image in the region.
[[Image:applycal_inspect.png|thumb|Plot of calibrated amplitudes over time.]]
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='5',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna1')
 
</source>
 
  
This plot shows some data deviating from the average amplitudes. Use methods described above to
+
==Analyze the Image Cubes==
mark a region for a small number of deviant data points, and click "Locate". You will find that ea12 is responsible.
 
  
<source lang="python">
+
Open the HC3N image cube in the {{viewer}}.
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna2')
 
</source>
 
 
 
Here we see some problems, with high points. Mark some regions
 
and locate in {{plotms}} to find out which antennas and in which spws. Pay special
 
attention to antennas that have been called out already as showing some dubious behavior.
 
 
 
What you find is that ea07 which we flagged spw=1 above, is also bad for the same timerange in spw=0. This was not obvious in the raw data, because spw=0 was adjusted in the on-line system by a gain attenuator, while spw=1 wasn't. So a lack of power on this antenna can look like very low (and obvious) amplitudes in spw=1 but not for spw=0. Looking carefully you'll see that ea07 is actually pretty noisy throughout.
 
[[Image:ea12.png|thumb|Plot of antenna ea12 by itself]]
 
[[Image:ea23.png|thumb|Plot of antenna ea23 by itself]]
 
 
 
From the locate we also find that ea12 and ea23 show some high points; to see this, replot baselines using each of them alone:
 
 
 
<source lang="python">
 
plotms(vis='day2_TDEM0003_10s_norx',field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='ea12', coloraxis='antenna2')
 
</source>
 
 
 
<source lang="python">
 
plotms(vis='day2_TDEM0003_10s_norx',field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='ea23', coloraxis='antenna2')
 
</source>
 
 
 
it may be a a good idea to flag ea12 completely - it's just a bit noisy all around and ea23 is pretty noisy during the first scans between initial and second pointing. Recall that these are antennas we became suspicious of while inspecting the calibration solutions.
 
 
 
[[Image:target_uvdist.png|thumb|IRC+10216 as a function of uv-distance.]]
 
Now lets see how the target looks. Because the target has resolved structure, its best to look at it as
 
a function of uvdistance. We'll go ahead and exclude the three antennas we already know have problems.
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',field='3',ydatacolumn='corrected',
+
viewer("IRC10216_HC3N.image")
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='!ea07;!ea12;!ea23', coloraxis='antenna2')
 
 
</source>
 
</source>
  
you can see that the spikes
+
Click the 'Play' button to view all channels in the cube.  Select '''Tool --> Spectral profile...''' from the pull down menu.  Create a region on the image to view the spectrum within the region.
are caused by a single antenna. Use, zoom, mark, and locate to see which one.
 
Also look at spw=1.
 
  
Turns out to be ea28; to confirm, replot with antenna=!ea28, and
+
Open the SiS image cube in the viewer.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',field='3',ydatacolumn='corrected',
+
viewer("IRC10216_SiS.image")
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='!ea07;!ea12;!ea23;!ea28', coloraxis='antenna2')
 
 
</source>
 
</source>
  
To see if it's restricted to a certain time
+
Determine what channels in the cube have emission.  Then make moment maps using {{immoments}}.
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
plotms(vis='day2_TDEM0003_10s_norx',field='3',ydatacolumn='corrected',
+
os.system('rm -rf IRC10216_Sis.mom0') # remove previously generated map, if it exists
      xaxis='time',yaxis='amp',correlation='RR,LL',
+
immoments(imagename="IRC10216_SiS.image", moments=[0], axis="spectral",
      avgchannel='64',spw='0:4~60',antenna='ea28', coloraxis='antenna1')
+
          chans="12~40", outfile="IRC10216_Sis.mom0")  
 
</source>
 
</source>
  
 
+
Open the moment map in the viewer.
Baselines with ea28 clearly show issues until about two-thirds of the way through the observation.
 
Plot another distant antenna to compare. We will go ahead and flag it all, since its hanging far out on the north
 
arm by itself.
 
 
 
The additional data we've identified as bad need to be flagged, and then all the calibration steps will need to be run
 
again.
 
  
 
<source lang="python">
 
<source lang="python">
 
# In CASA
 
# In CASA
flagdata(vis='day2_TDEM0003_10s_norx',
+
viewer("IRC10216_Sis.mom0")
        field=['',''],
 
        spw=['',''],
 
        antenna=['ea07,ea12,ea28','ea07,ea23'],
 
        timerange=['','03:21:40~04:10:00'])
 
 
</source>
 
</source>
  
==Redo Calibration after more Flagging==
+
Overlay contours by selecting '''Data --> Open''' from the pull down menu, selecting the moment-0 map, and clicking 'contour map'.
 
 
After flagging, you'll need to repeat the calibration steps above.  Here, we append  _redo to the table names to distinguish them from the first round, in case we want to compare with previous versions.
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='bpphase_redo.gcal',
 
        field='5',spw='0~1:20~40',
 
        refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaintable=['antpos.cal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
bandpass(vis='day2_TDEM0003_10s_norx',caltable='bandpass_redo.bcal',
 
        field='5',
 
        refant='ea02',solint='inf',solnorm=T,
 
        gaintable=['antpos.cal','bpphase_redo.gcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='intphase_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='int',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass_redo.bcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='scanphase_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='p',solint='inf',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass_redo.bcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
gaincal(vis='day2_TDEM0003_10s_norx',caltable='amp_redo.gcal',
 
        field='2,5,7',spw='0~1:4~60',
 
        refant='ea02',calmode='ap',solint='inf',minsnr=2.0,
 
        gaintable=['antpos.cal','bandpass_redo.bcal','intphase_redo.gcal'],
 
        opacity=[0.0410, 0.0408],gaincurve=T)
 
</source>
 
 
<source lang="python">
 
# In CASA     
 
fluxscale(vis='day2_TDEM0003_10s_norx',caltable='amp_redo.gcal',
 
          fluxtable='flux_redo.cal',reference='7')
 
</source>
 
 
 
<pre style="background-color: #fffacd;">
 
Flux density for J0954+1743 in SpW=0 is: 0.235345 +/- 0.000879422
 
    (SNR = 267.613, nAnt= 16)
 
Flux density for J0954+1743 in SpW=1 is: 0.241996 +/- 0.000930228
 
    (SNR = 260.147, nAnt= 16)
 
Flux density for J1229+0203 in SpW=0 is: 25.2479 +/- 0
 
    (SNR = inf, nAnt= 16)
 
Flux density for J1229+0203 in SpW=1 is: 24.9907 +/- 0
 
    (SNR = inf, nAnt= 16)
 
 
 
Flux density for J0954+1743 in SpW=0 is: 0.247052 +/- 0.000946345 (SNR = 261.059, nAnt= 16)
 
Flux density for J0954+1743 in SpW=1 is: 0.254038 +/- 0.00097531 (SNR = 260.47, nAnt= 16)
 
Flux density for J1229+0203 in SpW=0 is: 26.5079 +/- 0 (SNR = inf, nAnt= 16)
 
Flux density for J1229+0203 in SpW=1 is: 26.2335 +/- 0 (SNR = inf, nAnt= 16)
 
</pre>
 
 
 
Feel free to pause here and remake the calibration solution plots from above, just be sure to put in the revised table names.
 
 
 
==Redo Applycal and Inspect==
 
 
 
Now, apply all the new calibrations, which will overwrite the old ones.  These commands are identical to those above, with the exception of the _redo part of each calibration filename.
 
 
 
<source lang="python">
 
# In CASA
 
applycal(vis='day2_TDEM0003_10s_norx',field='2',
 
        gaintable=['antpos.cal','bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','2','2'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
applycal(vis='day2_TDEM0003_10s_norx',field='5',
 
        gaintable=['antpos.cal','bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','5','5'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
applycal(vis='day2_TDEM0003_10s_norx',field='7',
 
        gaintable=['antpos.cal','bandpass_redo.bcal','intphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','7','7'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
 
 
[[Image:gaincal_corrflag.png|thumb|Gain calibrator after further flagging and recalibration]]
 
[[Image:target_corrflag.png|thumb|IRC+10216 after further flagging and recalibration (after selecting colorize by spw).]]
 
 
 
<source lang="python">
 
# In CASA
 
applycal(vis='day2_TDEM0003_10s_norx',field='3',
 
        gaintable=['antpos.cal','bandpass_redo.bcal','scanphase_redo.gcal','flux_redo.cal'],
 
        gainfield=['','5','2','2'],
 
        opacity=[0.0410, 0.0408],gaincurve=T,calwt=F)
 
</source>
 
 
 
Now you can inspect the calibrated data again. Except for random scatter things look pretty good.
 
 
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='2',ydatacolumn='corrected',
 
      xaxis='time',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0:4~60',antenna='', coloraxis='antenna2')
 
</source>
 
 
 
You can use the Mark and Locate buttons to assess that the remaining scatter seems random, i.e. no particular antenna or time range appears to be responsible.
 
 
 
<source lang="python">
 
# In CASA
 
plotms(vis='day2_TDEM0003_10s_norx',field='3',ydatacolumn='corrected',
 
      xaxis='uvdist',yaxis='amp',correlation='RR,LL',
 
      avgchannel='64',spw='0~1:4~60',antenna='', coloraxis='spw')
 
</source>
 
 
 
==Split==
 
 
 
Now we split the data into individual files. This is not strictly necessary, as you can select the appropriate fields in later clean stages, but it is safer in case for example you get confused with later processing and want to fall back to this point (this is especially a good idea if you plan to do continuum subtraction or self calibration later on). It also makes smaller individual files in case you want to copy to another machine or colleague.
 
 
 
Here, we split off the data for the phase calibrator and the target:
 
 
 
<source lang="python">
 
# In CASA
 
split(vis='day2_TDEM0003_10s_norx',outputvis='J0954',
 
      field='2')
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
split(vis='day2_TDEM0003_10s_norx',outputvis='IRC10216',
 
      field='3')
 
</source>
 
 
 
To reinitialize the scratch columns for use by later tasks, we need to run clearcal for both new datasets
 
 
 
<source lang="python">
 
# In CASA
 
clearcal(vis='J0954')
 
</source>
 
 
 
<source lang="python">
 
# In CASA
 
clearcal(vis='IRC10216')
 
</source>
 
 
 
This concludes the calibration phase of the data reductions. The tutorial continues with continuum subtraction, imaging, and image analysis in
 
[[EVLA high frequency Spectral Line tutorial - IRC+10216 part2]].
 
  
 
[[Main Page | &#8629; '''CASAguides''']]
 
[[Main Page | &#8629; '''CASAguides''']]
 
--[[User:Cbrogan|Crystal Brogan]]   
 
--additions:[[User:Jott| Juergen Ott]]
 
  
 
{{Checked 3.3.0}}
 
{{Checked 3.3.0}}

Latest revision as of 16:57, 6 January 2012

This page is under development

This CASA Guide provides a simplified approach to the editing, calibration, and imaging of a galactic EVLA observation for use in NRAO Community Day Events. For a complete description of the data reduction process see EVLA high frequency Spectral Line tutorial - IRC+10216 part1 and EVLA high frequency Spectral Line tutorial - IRC+10216 part2.

Overview

VLT V-band image of IRC+10216 showing dust rings out to a radius of 90" by Leão et al. (2006, A&A, 455, 187).

This tutorial describes the data reduction for two spectral lines, HC3N and SiS, observed toward the AGB star IRC+10216 by the EVLA in D-configuration.

Getting the data

The data for this tutorial can be obtained by anonymous FTP from ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/. Download all 4 TAR files.

For example,

# In UNIX
wget 'ftp://ftp.aoc.nrao.edu/staff/gvanmoor/community_day/*'

Initial Inspection and Flagging

GEt a summary listing of the data set using listobs.

# In CASA
vis = 'day2_TDEM0003_20s_full'
listobs(vis=vis, verbose=True)

Below we have cut and pasted the most relevant output from the logger.

Fields: 4
  ID   Code Name                RA              Decl          Epoch   SrcId nVis
  2    D    J0954+1743          09:54:56.82363 +17.43.31.2224 J2000   2     32726
  3    NONE IRC+10216           09:47:57.38200 +13.16.40.6600 J2000   3     99540
  5    F    J1229+0203          12:29:06.69973 +02.03.08.5982 J2000   5     5436
  7    E    J1331+3030          13:31:08.28798 +30.30.32.9589 J2000   7     2736
   (nVis = Total number of time/baseline visibilities per field)
Spectral Windows:  (2 unique spectral windows and 1 unique polarization setups)
  SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)  TotBW(kHz)  Corrs
  0          64 TOPO  36387.2295  125           8000        RR  RL  LR  LL
  1          64 TOPO  36304.542   125           8000        RR  RL  LR  LL
Sources: 10
  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s)
  0    J1008+0730          0     0.03639232     -0.026
  0    J1008+0730          1     0.03639232     -0.026
  2    J0954+1743          0     0.03639232     -0.026
  2    J0954+1743          1     0.03639232     -0.026
  3    IRC+10216           0     0.03639232     -0.026
  3    IRC+10216           1     0.03639232     -0.026
  5    J1229+0203          0     0.03639232     -0.026
  5    J1229+0203          1     0.03639232     -0.026
  7    J1331+3030          0     0.03639232     -0.026
  7    J1331+3030          1     0.03639232     -0.026
Antennas: 19:
  ID   Name  Station   Diam.    Long.         Lat.
  0    ea01  W09       25.0 m   -107.37.25.2  +33.53.51.0
  1    ea02  E02       25.0 m   -107.37.04.4  +33.54.01.1
  2    ea03  E09       25.0 m   -107.36.45.1  +33.53.53.6
  3    ea04  W01       25.0 m   -107.37.05.9  +33.54.00.5
  4    ea05  W08       25.0 m   -107.37.21.6  +33.53.53.0
  5    ea07  N06       25.0 m   -107.37.06.9  +33.54.10.3
  6    ea08  N01       25.0 m   -107.37.06.0  +33.54.01.8
  7    ea09  E06       25.0 m   -107.36.55.6  +33.53.57.7
  8    ea12  E08       25.0 m   -107.36.48.9  +33.53.55.1
  9    ea15  W06       25.0 m   -107.37.15.6  +33.53.56.4
  10   ea19  W04       25.0 m   -107.37.10.8  +33.53.59.1
  11   ea20  N05       25.0 m   -107.37.06.7  +33.54.08.0
  12   ea21  E01       25.0 m   -107.37.05.7  +33.53.59.2
  13   ea22  N04       25.0 m   -107.37.06.5  +33.54.06.1
  14   ea23  E07       25.0 m   -107.36.52.4  +33.53.56.5
  15   ea24  W05       25.0 m   -107.37.13.0  +33.53.57.8
  16   ea25  N02       25.0 m   -107.37.06.2  +33.54.03.5
  17   ea27  E03       25.0 m   -107.37.02.8  +33.54.00.5
  18   ea28  N08       25.0 m   -107.37.07.5  +33.54.15.8

We summarize the observing strategy in this table.

Gain calibrator J0954+1743 field id = 2
Bandpass calibrator J1229+0203 field id = 5
Flux calibrator J1331+3030 (3C286) field id = 7
Science target IRC+10216 field id = 3
Antenna locations from running plotants

Create a plot of antenna positions using plotants.

# In CASA
plotants(vis=vis)
Result of plotms
Zooming in and marking region (hatched box)

Next, let's look at all the source amplitudes as a function of time using plotms.

# In CASA
plotms(vis=vis, xaxis='time', yaxis='amp', correlation='RR,LL',
       avgchannel='64', spw='0:4~60', coloraxis='field')

Now zoom in on the region very near zero amplitude for sources J0954+1743 and IRC+10216. To zoom, select the Zoom tool in lower left corner of the plotms GUI, then you can left click to draw a box. Look for the low values (you may want to zoom a few times to really see the suspect points clearly). Now use the Mark Region and Locate buttons (located along the bottom of the GUI) to see which antenna is causing problems. The output will be shown in the logger. Since all the "located" baselines include ea12, this is the responsible antenna.

Now click the clear region button, and then go back to the zoom button to zoom in further to note exactly what the time range is: 03:41:00~04:10:00.

Check the other sideband by changing spw to 1:4~60. You will have to rezoom. If you have trouble, click on the Mark icon and then back to zoom. In spw=1, ea07 is bad from the beginning until after next pointing run: 03:21:40~04:10:00. To see this, compare the amplitudes when antenna is set to 'ea07' and when it is set to one of the other antennas, such as 'ea08'.

If you set antenna to 'ea12' and zoom in on this intial timerange, you can also see that ea12 is bad during the same time range as for spw 0. You can also see this by entering '!ea07' for antenna, which removes ea07 from the plot (in CASA selection, ! deselects).

We can flag the bad data using flagdata.

# In CASA
flagdata(vis=vis,
         field=['2,3','2,3'],
         spw=['','1'],
         antenna=['ea12','ea07'],
         timerange=['03:41:00~04:10:00','03:21:40~04:10:00'])

flagdata works by spanning up a matrix. The first entries in each list must be taken as one flagging command, as well as the second entries etc. Lists within lists are fine. In the above example, the first flagging command is issued for fields 2 and 3 for all spws and within the 03:41:00~04:10:00 timerange. A second command is again for the fields 2 and 3 but for spw 1 only and for the second timerange in the list '03:21:40~04:10:00'.

Note that because the chosen timerange is limited to fields 2 and 3, the field parameter is not really needed; however, flagdata will run fastest if you put as many constraints as possible.

Set Up the Model for the Flux Calibrator

We set the model for the flux calibrator using setjy. First, check the availability of calibration models.

# In CASA
setjy(vis=vis,listmodimages=T)

There is no Ka-band model of 3C286. We will use the K-band model instead.

# In CASA
setjy(vis=vis,field='7',spw='0~1',
      modimage='3C286_K.im')

setjy scales the total flux in the model image to that appropriate for your individual spectral window frequencies according to the calibrator's flux and reports this number to the logger.

The logger output for each spw is:
J1331+3030 (fld ind 7) spw 0  [I=1.7762, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
J1331+3030 (fld ind 7) spw 1  [I=1.7794, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)

The absolute fluxes for the frequencies have now been determined and one can proceed to the bandpass and complex gain calibrations.

Bandpass Calibration

Correct the phase variations with time before solving for the bandpass to prevent decorrelation of the vector averaged bandpass solution.

Phase only calibration before bandpass. The 4 lines are both polarizations in both spw, unfortunately two of them get the same color green at the moment.
# In CASA
gaincal(vis=vis, caltable='bpphase.gcal', field='5', spw='0~1:20~40',
        refant='ea02', gaintype='G', calmode='p',solint='int',
        gaincurve=T)

Plot the solutions using plotcal.

# In CASA
plotcal(caltable='bpphase.gcal',xaxis='time',yaxis='phase',
        iteration='antenna',subplot=331,plotrange=[0,0,-180,180])

Next we can apply this phase solution on the fly while determining the bandpass solutions on the timescale of the bandpass calibrator scan (solint='inf').

# In CASA
bandpass(vis=vis, caltable='bandpass.bcal', field='5', refant='ea02',
         solint='inf', solnorm=T, gaintable='bpphase.gcal',
         gaincurve=T)

Plot the solutions, amplitude and phase:

Amplitude Bandpass solutions
Phase Bandpass solutions
# In CASA
plotcal(caltable='bandpass.bcal',xaxis='chan',yaxis='amp',
        iteration='antenna',subplot=331)


# In CASA
plotcal(caltable='bandpass.bcal',xaxis='chan',yaxis='phase',
        iteration='antenna',subplot=331)

Remaining Calibration

In this Community Day Event guide, we will skip over the remaining calibration steps. However, you can refer to EVLA high frequency Spectral Line tutorial - IRC+10216 part1 for full details. To summarize the missing steps, you bootstrap the flux densities of the secondary calibrators by

  1. doing a phase only calibration on all calibrators using gaincal,
  2. doing amplitude only calibration on all calibrators while applying the phase-only solutions, and
  3. deriving the flux density of the secondary calibrators while applying the previously obtained solutions.

Then, you can calibrate the target source using the phase and amplitude solutions you have obtained. Apply the target source calibration solutions using applycal. Use plotms to examine the calibrated data. If more flagging is required, redo all calibration steps. When the data look good, split the target source into a separate measurement set. Subtract the continuum flux using uvcontsub. Make Doppler corrections using cvel, or let clean do the Doppler corrections on the fly.

This tutorial picks up where EVLA high frequency spectral line tutorial - IRC+10216 - calibration leaves off.

Imaging

UV-plot of the spectral line signal in both spw for IRC+10216.

The continuum-subtracted spectral line data is contained in IRC10216_spls.ms. Use plotms to plot the lines.

plotms(vis='IRC10216_spls.ms', xaxis='channel', yaxis='amp',
       avgtime='1e8', avgscan=T, coloraxis='spw')

Now it is time to image the visibility data using clean. For illustration, we will clean channel 22 of the SiS line.

# In CASA
os.system('rm -rf ch22.*') # remove previously generated image, if it exists
clean(vis='IRC10216_spls.ms', imagename='ch22', spw='1:22~22',
      mode='channel', nchan=1, start='', width=1, niter=100000, 
      gain=0.1, threshold='3.0mJy', psfmode='clark', imagermode='csclean',
      interactive=T, npercycle=100, imsize=300, cell=['0.4arcsec', '0.4arcsec'],
      stokes='I', weighting='briggs', robust=0.5)

After running the above command, make a region in the CASA viewer. Double-click inside the region and clean by clicking the green circular arrow. After each cycle, click the green circular arrow again if the flux inside the region is brighter than the flux peaks outside the region. When you are finished cleaning, click the red 'STOP' button.

Open the resulting image using the viewer.

# In CASA
viewer("ch22.image")

In the viewer, make a region and click inside it to get statistics about the portion of the image in the region.

Analyze the Image Cubes

Open the HC3N image cube in the viewer.

# In CASA
viewer("IRC10216_HC3N.image")

Click the 'Play' button to view all channels in the cube. Select Tool --> Spectral profile... from the pull down menu. Create a region on the image to view the spectrum within the region.

Open the SiS image cube in the viewer.

# In CASA
viewer("IRC10216_SiS.image")

Determine what channels in the cube have emission. Then make moment maps using immoments.

# In CASA
os.system('rm -rf IRC10216_Sis.mom0') # remove previously generated map, if it exists
immoments(imagename="IRC10216_SiS.image", moments=[0], axis="spectral",
          chans="12~40", outfile="IRC10216_Sis.mom0")

Open the moment map in the viewer.

# In CASA
viewer("IRC10216_Sis.mom0")

Overlay contours by selecting Data --> Open from the pull down menu, selecting the moment-0 map, and clicking 'contour map'.

CASAguides

Last checked on CASA Version 3.3.0.