NGC 5921: red-shifted HI emission 5.7.2: Difference between revisions

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




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</source>
</source>


<!--
Now, in CASA, set up paths and global variables to facilitate the data reduction. These operations can be performed on-the-fly if you are reducing data interactively, but it's better to have them prepared from the start in the scripting environment.
Now, in CASA, set up paths and global variables to facilitate the data reduction. These operations can be performed on-the-fly if you are reducing data interactively, but it's better to have them prepared from the start in the scripting environment.


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imname = prefix + '.cleanimg'
imname = prefix + '.cleanimg'
</source>
</source>
-->


We'll use a python ''os'' command to get the appropriate CASA path for your installation. The use of '''os.environ.get''' is explained in [[#os.environ.get | the Appendix]].
We'll use a python ''os'' command to get the appropriate CASA path for your installation in order to import the data. The use of '''os.environ.get''' is explained in [[#os.environ.get | the Appendix]].


<source lang="python">
<source lang="python">
# In CASA
%cpaste
# Press Enter or Return, then copy/paste the following:
import os
pathname=os.environ.get('CASAPATH').split()[0]
pathname=os.environ.get('CASAPATH').split()[0]
fitsdata=pathname+'/data/demo/NGC5921.fits'
fitsdata=pathname+'/data/demo/NGC5921.fits'
--
</source>
</source>


Scripts are of course modified and repeated to the satisfaction of observer. To help clean up the bookkeeping and further avoid issues of write privileges, remove prior versions of the measurement set and calibration tables.
Scripts are of course modified and repeated to the satisfaction of observer. To help clean up the bookkeeping and further avoid issues of write privileges, remove prior versions of the measurement set and calibration tables.


This can be done with the rmtables('table_name') command.
<!--
<div style="background-color: #dddddd;">
<div style="background-color: #dddddd;">
'''Tip:''' The first command in the following code block removes the measurement set. Depending on the size of the source data, refilling a measurement set can be time-consuming, and so you may want to consider editing a separate script that preserves the measurement set but skips the '''importuvfits''' command [[#Import the Data | given below]]. This NGC5921 dataset, however, is not large and refilling goes quickly.
'''Tip:''' The first command in the following code block removes the measurement set. Depending on the size of the source data, refilling a measurement set can be time-consuming, and so you may want to consider editing a separate script that preserves the measurement set but skips the '''importuvfits''' command [[#Import the Data | given below]]. This NGC5921 dataset, however, is not large and refilling goes quickly.
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os.system('rm -rf '+prefix+'*')
os.system('rm -rf '+prefix+'*')
</source>
</source>
-->


== Import the Data ==
== Import the Data ==


The next step is to import the multisource UVFITS data to a CASA measurement set via the {{importuvfits}} filler.  
The next step is to import the multisource UVFITS data to a CASA measurement set via the {{importuvfits}} filler. Note that you can set each parameter for any particular task one-by-one, or you could supply the task and input parameters with one command. Here we will set each parameter value first, save them, and run the import task. Throughout the remaining tutorial, we will call upon tasks with a single command.
 
<!--
<source lang="python">
importuvfits(fitsfile=fitsdata, vis='ngc5921.demo.ms')
saveinputs('importuvfits', 'ngc5921.demo.importuvfits.saved')
</source>
-->


<source lang="python">
<source lang="python">
# Safest to start from task defaults
# Safest to start from task defaults
default('importuvfits')
default('importuvfits')
# Set up the MS filename and save as new global variable
msfile = prefix + '.ms'
# Use task importuvfits
# Use task importuvfits
fitsfile = fitsdata
fitsfile = fitsdata
vis = msfile
vis='ngc5921.demo.ms'
saveinputs('importuvfits',prefix+'.importuvfits.saved')
saveinputs('importuvfits', 'ngc5921.demo.importuvfits.saved')
importuvfits()
importuvfits()
</source>
</source>
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Calibration of spectral line data broadly follows the approach for [[Calibrating a VLA 5 GHz continuum survey | continuum data]], except that the amplitude and phase corrections are a function of frequency and so must be corrected by '''bandpass calibration.''' The basic calibration steps follow.
Calibration of spectral line data broadly follows the approach for [[Calibrating a VLA 5 GHz continuum survey | continuum data]], except that the amplitude and phase corrections are a function of frequency and so must be corrected by '''bandpass calibration.''' The basic calibration steps follow.
* [[#Setting the Flux Scale | Set the flux scale]] of the primary calibrator, here, 1331+305 = 3C 286.
* [[#Setting the Flux Scale | Set the flux scale]] of the primary calibrator, here, 1331+305 = 3C 286.
* [[#Bandpass Calibration | Determine bandpass corrections]] based on the primary calibrator. In the script that follows, the bandpass calibration is stored in ''ngc5921.demo.bcal'', which is itself referenced by the python variable ''btable.''
* [[#Bandpass Calibration | Determine bandpass corrections]] based on the primary calibrator. In the script that follows, the bandpass calibration is stored in ''ngc5921.demo.bcal''.
* [[#Inspect the Bandpass Response Curve | Inspect the bandpass correction]] to determine viable channels for averaging and imaging. We want to toss out end channels where the response is poor.
* [[#Inspect the Bandpass Response Curve | Inspect the bandpass correction]] to determine viable channels for averaging and imaging. We want to toss out end channels where the response is poor.
* [[#Gain Calibration | Determine the gain calibrations]] on the bandpass-corrected and channel-averaged data. In this step, we effectively turn the spectral line data into a single-channel continuum data set and calibrate accordingly. The calibration is stored in ''ngc5921.demo.gcal'', which is itself referenced by the python variable ''gtable.''
* [[#Gain Calibration | Determine the gain calibrations]] on the bandpass-corrected and channel-averaged data. In this step, we effectively turn the spectral line data into a single-channel continuum data set and calibrate accordingly. The calibration is stored in ''ngc5921.demo.gcal''.
* [[#Inspect the Calibration Solutions | Inspect the gain calibration solutions]] to look for any aberrant solutions that hint at bad calibrator data.
* [[#Inspect the Calibration Solutions | Inspect the gain calibration solutions]] to look for any aberrant solutions that hint at bad calibrator data.
* [[#Apply the Solutions | Apply the calibration solutions]] to the source (N5921_2). This action literally adds a new column of data to the measurement set. This new column contains the data with the gain calibration and bandpass calibration applied, but it does not overwrite the raw data in case the calibration needs revision.
* [[#Apply the Solutions | Apply the calibration solutions]] to the source (N5921_2). This action literally adds a new column of data to the measurement set. This new column contains the data with the gain calibration and bandpass calibration applied, but it does not overwrite the raw data in case the calibration needs revision.
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{{setjy | Setjy}} also looks up the radio SED for common flux calibrators and automatically assigns the total flux density.
{{setjy | Setjy}} also looks up the radio SED for common flux calibrators and automatically assigns the total flux density.


<source lang="python">
# 1331+305 = 3C286 is our primary calibrator. Use the wildcard on the end of the source name
# This is 1.4GHz D-config and 1331+305 is sufficiently unresolved that we dont need a model image. 
# For higher frequencies (particularly in A and B config) you would want to use one.
setjy(vis='ngc5921.demo.ms', field='1331+305*', modimage='')
</source>
<!--
<source lang="python">
<source lang="python">
default('setjy')
default('setjy')
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setjy()
setjy()
</source>
</source>
-->


A summary of the operation is sent to the logger window. Here's a listing of the output.
A summary of the operation is sent to the logger window. Here's a listing of the output.
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<source lang="python">
<source lang="python">
# We can first do the bandpass on the single 5min scan on 1331+305. At 1.4GHz phase stablility should be sufficient to do this without
# a first (rough) gain calibration. This will give us the relative antenna gain as a function of frequency.
bandpass(vis='ngc5921.demo.ms', caltable='ngc5921.demo.bcal', field='0', selectdata=False, bandtype='B', solint='inf', combine='scan', refant='15')
</source>
* field='0' : Use the flux calibrator 1331+305 = 3C286 (FIELD_ID 0) as bandpass calibrator.
* bandtype='B' : Choose bandpass solution type. Pick standard time-binned B (rather than BPOLY).
* solint='inf' and combine='scan' : Set solution interval arbitrarily long (get single bandpass).
* refant = '15' : Reference antenna Name 15 (15=VLA:N2) (Id 14)
<!--
default('bandpass')
default('bandpass')
# We can first do the bandpass on the single 5min scan on 1331+305
# We can first do the bandpass on the single 5min scan on 1331+305
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saveinputs('bandpass',prefix+'.bandpass.saved')
saveinputs('bandpass',prefix+'.bandpass.saved')
bandpass()
bandpass()
</source>
-->


=== Inspect the Bandpass Response Curve ===
=== Inspect the Bandpass Response Curve ===
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[[File:ngc5921-bandpass.png | thumb | Bandpass response curves generated by {{plotcal}}. The solutions for different antennas are indicated by differently colored plotting symbols. Plots for individual antennas can be generated by setting '''iteration = 'antenna'''' for {{plotcal}}.]]
[[File:ngc5921-bandpass.png | thumb | Bandpass response curves generated by {{plotcal}}. The solutions for different antennas are indicated by differently colored plotting symbols. Plots for individual antennas can be generated by setting '''iteration = 'antenna'''' for {{plotcal}}.]]


In the [[#Gain Calibration | gain calibration]] to follow, we will effectively convert the spectral line data into a continuum data set. Before proceeding, we need to inspect the bandpass calibration to make sure that it contains no bad values and also to inspect which channels to average to produce the continuum data. {{plotcal | Plotcal}} is the standard tool for plotting calibration solutions. The following script produces the figure at right.  
In the [[#Gain Calibration | gain calibration]] to follow, we will effectively convert the spectral line data into a continuum data set. Before proceeding, we need to inspect the bandpass calibration to make sure that it contains no bad values and also to inspect which channels to average to produce the continuum data. {{plotcal | Plotcal}} is the standard tool for plotting calibration solutions. The following commands produce the figure at right.  


By inspection, the amplitude response curve is flat over channels 6~56; that channel range will be used to generate the continuum data for gain calibration.
<source lang="python">
# Set up 2x1 panels - upper panel amp vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=211, yaxis='amp', showgui=True)
</source>
 
<source lang="python">
# Set up 2x1 panels - lower panel phase vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=212, yaxis='phase', showgui=True)
</source>
 
By inspection, the amplitude response curve is flat over channels 6~56; that channel range will be used to generate the continuum data for gain calibration. If you want to further inspect the plots interactively and iterate over antenna, set iteration = 'antenna'


Notice that {{plotcal}} is run twice: once to display gain amplitudes as a function of channel (frequency), and again to plot gain phases as a function of channel.
Notice that {{plotcal}} is run twice: once to display gain amplitudes as a function of channel (frequency), and again to plot gain phases as a function of channel.


<!--
If the '''scriptmode''' is set to '''False''', the plot is saved to ngc5921.demo.plotcal.png.
If the '''scriptmode''' is set to '''False''', the plot is saved to ngc5921.demo.plotcal.png.
-->


<source lang="python">
<!--
default('plotcal')
default('plotcal')
caltable = btable
caltable = btable
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# iteration = 'antenna'
# iteration = 'antenna'
plotcal()
plotcal()
</source>
-->


=== Gain Calibration ===
=== Gain Calibration ===
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* Note that fluxscale() determines the flux density of the phase calibrator and accordingly adjusts its model and calibration solutions. A report of the results are sent to the logger window.
* Note that fluxscale() determines the flux density of the phase calibrator and accordingly adjusts its model and calibration solutions. A report of the results are sent to the logger window.


<source lang="python">
# Armed with the bandpass, we now solve for the time-dependent antenna gains using our previously determined bandpass.
# Note this will automatically be applied to all sources not just the one used to determine the bandpass
gaincal(vis='ngc5921.demo.ms', caltable='ngc5921.demo.gcal', gaintable='ngc5921.demo.bcal', interp='nearest', field='0,1',
        spw='0:6~56', gaintype='G', solint='inf', calmode='ap', refant='15', minsnr=1.0)
</source>
<source lang="python">
# Now we will transfer the flux scale to the phase calibrator.
# We will be using 1331+305 (the source we did setjy on) as our flux standard reference.
# Note its extended name as in the FIELD table summary above (it has a VLA seq number appended)
fluxscale(vis='ngc5921.demo.ms', fluxtable='ngc5921.demo.fluxscale', caltable='ngc5921.demo.gcal', reference='1331*', transfer='1445*')
</source>
<!--
<source lang="python">
<source lang="python">
default('gaincal')
default('gaincal')
# Armed with the bandpass, we now solve for the
 
# time-dependent antenna gains
vis = msfile
vis = msfile
# set the name for the output gain caltable
# set the name for the output gain caltable
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fluxscale()
fluxscale()
</source>
</source>
-->


The output from {{fluxscale}} follows. A relatively large uncertainty for the phase calibrator is a sign that something went wrong, perhaps bad solutions in {{gaincal}}. Here, the phase calibrator scaled to 2.486 &plusmn; 0.001 Jy, which looks reasonable.
The output from {{fluxscale}} follows. A relatively large uncertainty for the phase calibrator is a sign that something went wrong, perhaps bad solutions in {{gaincal}}. Here, the phase calibrator scaled to 2.486 &plusmn; 0.001 Jy, which looks reasonable.
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<source lang="python">
<source lang="python">
# Set up 2x1 panels - upper panel amp vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=211, yaxis='amp', showgui=True)
</source>
<source lang="python">
# Set up 2x1 panels - lower panel phase vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=212, yaxis='phase', showgui=True)
</source>
The amp and phase coherence looks good. If you want to do this interactively and iterate over antenna, set iteration = 'antenna'.
<!--
default('plotcal')
default('plotcal')
caltable = ftable
caltable = ftable
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#iteration = 'antenna'
#iteration = 'antenna'
plotcal()
plotcal()
</source>
-->


=== Apply the Solutions ===
=== Apply the Solutions ===
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</source>
</source>


We want to correct the calibrators using themselves and transfer from 1445+099 to itself and the target N5921. Start with the fluxscale/gain and bandpass tables. We will pick the 1445+099 out of the gain table for transfer and use all of the bandpass table.
<source lang="python">
applycal(vis='ngc5921.demo.ms', field='1,2', gaintable=['ngc5921.demo.gcal','ngc5921.demo.bcal'], gainfield=['1','*'],
        interp=['linear','nearest'], spwmap=[], selectdata=False)
</source>
Now for completeness apply 1331+305 to itself.
<source lang="python">
applycal(vis='ngc5921.demo.ms', field='0', gaintable=['ngc5921.demo.gcal','ngc5921.demo.bcal'], gainfield=['0','*'],
        interp=['linear','nearest'], spwmap=[], selectdata=False)
</source>
<!---
In the script snippet below, the python global variables ''ftable'' and ''btable'' replace the full table names.
In the script snippet below, the python global variables ''ftable'' and ''btable'' replace the full table names.


<source lang="python">
default('applycal')
default('applycal')
vis = msfile
vis = msfile
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saveinputs('applycal',prefix+'.applycal.saved')
saveinputs('applycal',prefix+'.applycal.saved')
applycal()
applycal()
</source>
 
-->


== Plot the Spectrum ==
== Plot the Spectrum ==
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We can do this in {{plotms}}.
We can do this in {{plotms}}.


<source lang="bash">
<source lang="python">
plotms(vis=msfile, selectdata=True, field='N5921*', spw='0:6~56', \
plotms(vis='ngc5921.demo.ms', selectdata=True, field='N5921*', spw='0:6~56', averagedata=True, avgtime='3600', avgscan=True,  
      averagedata=True, avgtime='3600', avgscan=True, avgbaseline=True, \
      avgbaseline=True, xaxis='channel', yaxis='amp', ydatacolumn='corrected')
      xaxis='channel', yaxis='amp', ydatacolumn='corrected')
</source>
</source>


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<source lang="python">
<source lang="python">
split(vis='ngc5921.demo.ms', outputvis='ngc5921.demo.src.split.ms', field='N5921*', spw='', datacolumn='corrected')
</source>
<!--
default('split')
default('split')
vis = msfile
vis = msfile
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split()
split()
print "Created "+splitms
print "Created "+splitms
</source>
-->


This action generated a new measurement set called ''ngc5921.demo.src.split.ms'' and copied the ''calibrated'' source data (datacolumn = 'corrected') into it.
This action generated a new measurement set called ''ngc5921.demo.src.split.ms'' and copied the ''calibrated'' source data (datacolumn = 'corrected') into it.


{{uvcontsub|Uvcontsub}} subtracts the continuum from the data in the visibility (''u'', ''v'') plane.  
{{uvcontsub|Uvcontsub}} subtracts the continuum from the data in the visibility (''u'', ''v'') plane. We will be using channels 4-6 and 50-59 for continuum.


<source lang="python">
<source lang="python">
uvcontsub(vis='ngc5921.demo.src.split.ms', field='N5921*', fitspw='0:4~6;50~59', spw='0', solint=0.0, fitorder=0, want_cont=True)
</source>
<!--
default('uvcontsub')
default('uvcontsub')
vis = splitms
vis = splitms
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uvcontsub()
uvcontsub()
</source>
</source>
-->


Notice that {{uvcontsub}} splits two new measurement sets, 'ngc5921.demo.ms.cont', which contains an average of the continuum channels, and 'ngc5921.demo.ms.contsub', which contains the continuum-subtracted spectral line data.
Notice that {{uvcontsub}} splits two new measurement sets, 'ngc5921.demo.ms.cont', which contains an average of the continuum channels, and 'ngc5921.demo.ms.contsub', which contains the continuum-subtracted spectral line data.
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<source lang="python">
<source lang="python">
# identify the continuum subtracted measurement set
# Image the continuum subtracted measurement set
clean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.cleanimg', field='0', mode='channel', nchan=46, start=5, width=1,
      spw='', gain=0.1, imsize=[256,256], psfmode='clark', imagermode='', cell=['15.0arcsec','15.0arcsec'], niter=6000,
      threshold='8.0mJy', weighting='briggs', robust=0.5, mask = [108,108,148,148], interactive=False)
</source>
 
<!--
srcsplitms = splitms + '.contsub'
srcsplitms = splitms + '.contsub'
#=====================================================================
#=====================================================================
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saveinputs('clean',prefix+'.clean.saved')
saveinputs('clean',prefix+'.clean.saved')
clean()
clean()
</source>
-->


Use {{imhead}} to look at the cube header:
Use {{imhead}} to look at the cube header:


<source lang="python">
<source lang="python">
imhead(imagename='ngc5921.demo.cleanimg.image', mode='summary')
</source>
<!--
clnimage = imname+'.image' # store the clean image in a python global for future use
clnimage = imname+'.image' # store the clean image in a python global for future use
default('imhead')
default('imhead')
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mode = 'summary'
mode = 'summary'
imhead()
imhead()
</source>
-->


The output, as follows, appears in the logger window.
The output, as follows, appears in the logger window.
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== Additional Science Products ==
== Additional Science Products ==


If things went well, you should now have a spectral line cube (''ngc5921.demo.cleanimg.img'') as a primary science product. The demo script illustrates further how to generate cube statistics (using {{imstat}}), an integrated spectrum, and moment maps.  
If things went well, you should now have a spectral line cube (''ngc5921.demo.cleanimg.image'') as a primary science product. The demo script illustrates further how to generate cube statistics (using {{imstat}}), an integrated spectrum, and moment maps.  


=== Cube Statistics ===
=== Cube Statistics ===
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<source lang="python">
<source lang="python">
cubestat=imstat(imagename='ngc5921.demo.cleanimg.image', box='')
</source>
<!--
default('imstat')
default('imstat')
imagename = clnimage # or imagename = "ngc5921.demo.cleanimg.img"
imagename = clnimage # or imagename = "ngc5921.demo.cleanimg.img"
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#box = '108,108,148,148'
#box = '108,108,148,148'
cubestats = imstat()
cubestats = imstat()
</source>
-->


The output goes to the logger window.
The output goes to the logger window.
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<source lang="python">
<source lang="python">
viewer(infile='ngc5921.demo.cleanimg.image')
</source>
<!--
# Store the name of the clean image into a python global
# Store the name of the clean image into a python global
clnimage = imname+'.image'
clnimage = imname+'.image'
viewer(clnimage)
viewer(clnimage)
</source>
-->


To generate the integrated spectrum, perform the following tasks.
To generate the integrated spectrum, perform the following tasks.
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Cube moments are maps of weighted sums along the velocity axis. In CASA, they are generated by the task {{immoments}}. The zeroth moment (moments = 0) is a sum of intensities along the velocity axis (the integrated intensity map); the first moment (moment = 1) is the sum of velocities weighted by intensity (the ''velocity field''); the second moment (moment = 2) is a map of the velocity dispersion; see the {{immoments|helpfile}} for additional options.
Cube moments are maps of weighted sums along the velocity axis. In CASA, they are generated by the task {{immoments}}. The zeroth moment (moments = 0) is a sum of intensities along the velocity axis (the integrated intensity map); the first moment (moment = 1) is the sum of velocities weighted by intensity (the ''velocity field''); the second moment (moment = 2) is a map of the velocity dispersion; see the {{immoments|helpfile}} for additional options.


The following example produces maps of the zeroth and first moments, or the integrated intensity and velocity field. The respective measurement sets are ''ngc5921.demo.moments.integrated'' and ''ngc5921.demo.moments.weighted_coord'', stored in the python globals ''momzeroimage'' and ''momoneimage''.
The following example produces maps of the zeroth and first moments, or the integrated intensity and velocity field. The respective measurement sets are the moment zero image ''ngc5921.demo.moments.integrated'' and moment one image''ngc5921.demo.moments.weighted_coord''.


We will do the zeroth and first moments and mask out noisy pixels using hard global limits. We will also collapse along the spectral (channel) axis and include all planes.


<source lang="python">
<source lang="python">
immoments(imagename='ngc5921.demo.cleanimg.image', moments=[0,1], excludepix=[-100, 0.009], axis='spectral', chans='', outfile='ngc5921.demo.moments')
</source>
* moments = [0,1] : Do zeroth and first moments
* excludepix = [-100,0.009] : Mask out noisy pixels using hard global limits
* axis  = 'spectral' : Collapse along the spectral (channel) axis
* chans = '' :Include all planes
<!--
default('immoments')
default('immoments')
imagename = clnimage
imagename = clnimage
Line 806: Line 936:
momzeroimage = momfile + '.integrated'
momzeroimage = momfile + '.integrated'
momoneimage = momfile + '.weighted_coord'
momoneimage = momfile + '.weighted_coord'
</source>
-->


To examine the moment images, use {{viewer}}; the resulting moment zero image is displayed at right.
To examine the moment images, use {{viewer}}; the resulting moment zero image is displayed at right.
Note that you may have to play with the color map (Data Display Options button in viewer) in order to replicate the image in this tutorial.


<source lang="python">
<source lang="python">
viewer(momzeroimage)
viewer(infile='ngc5921.demo.moments.integrated')
</source>
</source>


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To export the (''u'', ''v'') data and image cube as FITS files, use {{exportuvfits}} and {{exportfits}}, respectively.
To export the (''u'', ''v'') data and image cube as FITS files, use {{exportuvfits}} and {{exportfits}}, respectively.


Here's how to export the continuum-subtracted (''u'', ''v'') data. Note that this snippet has {{exportuvfits}} spawned to the background (async = True).
Here's how to export the continuum-subtracted (''u'', ''v'') data.


<source lang="python">
<source lang="python">
exportuvfits(vis='ngc5921.demo.src.split.ms.contsub', fitsfile='ngc5921.demo.contsub.uvfits', datacolumn='corrected', multisource=True)
</source>
<!--
default('exportuvfits')
default('exportuvfits')
srcuvfits = prefix + '.contsub.uvfits' # recall: prefix = 'ngc5921.demo'
srcuvfits = prefix + '.contsub.uvfits' # recall: prefix = 'ngc5921.demo'
Line 829: Line 965:
async = True
async = True
myhandle = exportuvfits()
myhandle = exportuvfits()
</source>
-->


And now, the FITS cube.
And now, the FITS cube.
<source lang="python">
<source lang="python">
exportfits(imagename='ngc5921.demo.cleanimg.image', fitsimage='ngc5921.demo.cleanimg.fits')
</source>
<!--
default('exportfits')
default('exportfits')
clnfits = prefix + '.cleanimg.fits'
clnfits = prefix + '.cleanimg.fits'
Line 840: Line 980:
saveinputs('exportfits',prefix+'.exportfits.saved')
saveinputs('exportfits',prefix+'.exportfits.saved')
myhandle2 = exportfits()
myhandle2 = exportfits()
</source>
-->


The moment maps (or any CASA images) can be similarly exported using {{exportfits}}.
The moment maps (or any CASA images) can be similarly exported using {{exportfits}}.
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[[Pre-upgrade VLA Tutorials | &#8629; '''Pre-upgrade VLA Tutorials''']]
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Revision as of 21:08, 10 May 2018

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

Overview

The technique used to calibrate and image continuum datasets generally applies to spectral line observations, except that an additional calibration step is required. Bandpass calibration flattens the spectral response of the observations, ensuring that spectral channel images are properly calibrated in amplitude and phase.

The following tutorial derives from an annotated script provided in the CASA Cookbook. The script is largely reproduced and additionally annotated with figures and illustrations. It is assumed that this tutorial will be used interactively, and so interactive pauses in the original script have been removed.

The data are included with the CASA installation.

Setting up the CASA Environment

Start up CASA in the directory you want to use. 

# in bash
mkdir NGC5921
cd NGC5921
casa


We'll use a python os command to get the appropriate CASA path for your installation in order to import the data. The use of os.environ.get is explained in the Appendix. 

# In CASA
%cpaste

# Press Enter or Return, then copy/paste the following:
import os
pathname=os.environ.get('CASAPATH').split()[0]
fitsdata=pathname+'/data/demo/NGC5921.fits'
--

Scripts are of course modified and repeated to the satisfaction of observer. To help clean up the bookkeeping and further avoid issues of write privileges, remove prior versions of the measurement set and calibration tables.

This can be done with the rmtables('table_name') command.


Import the Data

The next step is to import the multisource UVFITS data to a CASA measurement set via the importuvfits filler. Note that you can set each parameter for any particular task one-by-one, or you could supply the task and input parameters with one command. Here we will set each parameter value first, save them, and run the import task. Throughout the remaining tutorial, we will call upon tasks with a single command. 


# Safest to start from task defaults
default('importuvfits')
# Use task importuvfits
fitsfile = fitsdata
vis='ngc5921.demo.ms'
saveinputs('importuvfits', 'ngc5921.demo.importuvfits.saved')
importuvfits()

Saveinputs saves the parameters of a given command to specified text file, handy to debug a script and see what actually was run. The parameters of importuvfits are saved to the file "ngc5921.demo.importuvfits.saved". A listing of this file follows. Notice that it is executable with execfile in CASA (remove the # commenting symbol before importuvfits to have the execfile run the command). 

CASA <71>: os.system('cat ngc5921.demo.importuvfits.saved')
taskname           = "importuvfits"
fitsfile           =  "/usr/lib64/casapy/30.0.9709test-001/data/demo/NGC5921.fits"
vis                =  "ngc5921.demo.ms"
antnamescheme      =  "old"
#importuvfits(fitsfile="/usr/lib64/casapy/30.0.9709test-001/data/demo/NGC5921.fits",vis="ngc5921.demo.ms",antnamescheme="old")

A Summary of the Data

We'll need to have a look at the observing tables to learn the calibrator and source names. The relevant command is listobs.

Logger output of listobs.
listobs(vis='ngc5921.demo.ms', verbose=True)

The output goes to the logger window; see the screenshot at right.

Tip: You can control the text size of the logger window using <ctrl>-A (smaller font) and <ctrl>-L (larger font) in Linux (<Command>-A and <Command>-L on MacOS X).

A more complete listing of the listobs output follows. 

2011-04-25 19:52:17 INFO listobs	##########################################
2011-04-25 19:52:17 INFO listobs	##### Begin Task: listobs            #####
2011-04-25 19:52:17 INFO  	listobs::::casa
2011-04-25 19:52:17 INFO listobs	================================================================================
2011-04-25 19:52:17 INFO listobs	           MeasurementSet Name:  /Science/VLA_tutorial_check/NGC_5921_red-shifted_HI/ngc5921.demo.ms      MS Version 2
2011-04-25 19:52:17 INFO listobs	================================================================================
2011-04-25 19:52:17 INFO listobs	   Observer: TEST     Project:   
2011-04-25 19:52:17 INFO listobs	Observation: VLA
2011-04-25 19:52:17 INFO listobs	Data records: 22653       Total integration time = 5280 seconds
2011-04-25 19:52:17 INFO listobs	   Observed from   13-Apr-1995/09:19:00.0   to   13-Apr-1995/10:47:00.0 (TAI)
2011-04-25 19:52:17 INFO  	listobs::ms::summary
2011-04-25 19:52:17 INFO listobs	   ObservationID = 0         ArrayID = 0
2011-04-25 19:52:17 INFO listobs	  Date        Timerange (TAI)          Scan  FldId FieldName nVis   Int(s)   SpwIds      ScanIntent
2011-04-25 19:52:17 INFO listobs	  13-Apr-1995/09:19:00.0 - 09:24:30.0     1      0 1331+305000* 4509   30       [0]                         
2011-04-25 19:52:17 INFO listobs	              09:27:30.0 - 09:29:30.0     2      1 1445+099000* 1890   30       [0]                         
2011-04-25 19:52:17 INFO listobs	              09:33:00.0 - 09:48:00.0     3      2 N5921_2      6048   30       [0]                         
2011-04-25 19:52:17 INFO listobs	              09:50:30.0 - 09:51:00.0     4      1 1445+099000* 756    30       [0]                         
2011-04-25 19:52:17 INFO listobs	              10:22:00.0 - 10:23:00.0     5      1 1445+099000* 1134   30       [0]                         
2011-04-25 19:52:17 INFO listobs	              10:26:00.0 - 10:43:00.0     6      2 N5921_2      6804   30       [0]                         
2011-04-25 19:52:17 INFO listobs	              10:45:30.0 - 10:47:00.0     7      1 1445+099000* 1512   30       [0]                         
2011-04-25 19:52:17 INFO listobs	           (nVis = Total number of time/baseline visibilities per scan) 
2011-04-25 19:52:17 INFO listobs	Fields: 3
2011-04-25 19:52:17 INFO listobs	  ID   Code Name         RA            Decl           Epoch   SrcId nVis   
2011-04-25 19:52:17 INFO listobs	  0    C    1331+305000* 13:31:08.2873 +30.30.32.9590 J2000   0     4509   
2011-04-25 19:52:17 INFO listobs	  1    A    1445+099000* 14:45:16.4656 +09.58.36.0730 J2000   1     5292   
2011-04-25 19:52:17 INFO listobs	  2         N5921_2      15:22:00.0000 +05.04.00.0000 J2000   2     12852  
2011-04-25 19:52:17 INFO listobs	   (nVis = Total number of time/baseline visibilities per field) 
2011-04-25 19:52:17 INFO listobs	Spectral Windows:  (1 unique spectral windows and 1 unique polarization setups)
2011-04-25 19:52:17 INFO listobs	  SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)TotBW(kHz)  Ref(MHz)    Corrs   
2011-04-25 19:52:17 INFO listobs	  0          63 LSRK  1412.66507  24.4140625  1550.19688  1413.42801  RR  LL  
2011-04-25 19:52:17 INFO listobs	Sources: 3
2011-04-25 19:52:17 INFO listobs	  ID   Name         SpwId RestFreq(MHz)  SysVel(km/s) 
2011-04-25 19:52:17 INFO listobs	  0    1331+305000* 0     1420.405752    0            
2011-04-25 19:52:17 INFO listobs	  1    1445+099000* 0     1420.405752    0            
2011-04-25 19:52:17 INFO listobs	  2    N5921_2      0     1420.405752    0            
2011-04-25 19:52:17 INFO listobs	Antennas: 27:
2011-04-25 19:52:17 INFO listobs	  ID   Name  Station   Diam.    Long.         Lat.         
2011-04-25 19:52:17 INFO listobs	  0    1     VLA:N7    25.0 m   -107.37.07.2  +33.54.12.9  
2011-04-25 19:52:17 INFO listobs	  1    2     VLA:W1    25.0 m   -107.37.05.9  +33.54.00.5  
2011-04-25 19:52:17 INFO listobs	  2    3     VLA:W2    25.0 m   -107.37.07.4  +33.54.00.9  
2011-04-25 19:52:17 INFO listobs	  3    4     VLA:E1    25.0 m   -107.37.05.7  +33.53.59.2  
2011-04-25 19:52:17 INFO listobs	  4    5     VLA:E3    25.0 m   -107.37.02.8  +33.54.00.5  
2011-04-25 19:52:17 INFO listobs	  5    6     VLA:E9    25.0 m   -107.36.45.1  +33.53.53.6  
2011-04-25 19:52:17 INFO listobs	  6    7     VLA:E6    25.0 m   -107.36.55.6  +33.53.57.7  
2011-04-25 19:52:17 INFO listobs	  7    8     VLA:W8    25.0 m   -107.37.21.6  +33.53.53.0  
2011-04-25 19:52:17 INFO listobs	  8    9     VLA:N5    25.0 m   -107.37.06.7  +33.54.08.0  
2011-04-25 19:52:17 INFO listobs	  9    10    VLA:W3    25.0 m   -107.37.08.9  +33.54.00.1  
2011-04-25 19:52:17 INFO listobs	  10   11    VLA:N4    25.0 m   -107.37.06.5  +33.54.06.1  
2011-04-25 19:52:17 INFO listobs	  11   12    VLA:W5    25.0 m   -107.37.13.0  +33.53.57.8  
2011-04-25 19:52:17 INFO listobs	  12   13    VLA:N3    25.0 m   -107.37.06.3  +33.54.04.8  
2011-04-25 19:52:17 INFO listobs	  13   14    VLA:N1    25.0 m   -107.37.06.0  +33.54.01.8  
2011-04-25 19:52:17 INFO listobs	  14   15    VLA:N2    25.0 m   -107.37.06.2  +33.54.03.5  
2011-04-25 19:52:17 INFO listobs	  15   16    VLA:E7    25.0 m   -107.36.52.4  +33.53.56.5  
2011-04-25 19:52:17 INFO listobs	  16   17    VLA:E8    25.0 m   -107.36.48.9  +33.53.55.1  
2011-04-25 19:52:17 INFO listobs	  17   18    VLA:W4    25.0 m   -107.37.10.8  +33.53.59.1  
2011-04-25 19:52:17 INFO listobs	  18   19    VLA:E5    25.0 m   -107.36.58.4  +33.53.58.8  
2011-04-25 19:52:17 INFO listobs	  19   20    VLA:W9    25.0 m   -107.37.25.1  +33.53.51.0  
2011-04-25 19:52:17 INFO listobs	  20   21    VLA:W6    25.0 m   -107.37.15.6  +33.53.56.4  
2011-04-25 19:52:17 INFO listobs	  21   22    VLA:E4    25.0 m   -107.37.00.8  +33.53.59.7  
2011-04-25 19:52:17 INFO listobs	  23   24    VLA:E2    25.0 m   -107.37.04.4  +33.54.01.1  
2011-04-25 19:52:17 INFO listobs	  24   25    VLA:N6    25.0 m   -107.37.06.9  +33.54.10.3  
2011-04-25 19:52:17 INFO listobs	  25   26    VLA:N9    25.0 m   -107.37.07.8  +33.54.19.0  
2011-04-25 19:52:17 INFO listobs	  26   27    VLA:N8    25.0 m   -107.37.07.5  +33.54.15.8  
2011-04-25 19:52:17 INFO listobs	  27   28    VLA:W7    25.0 m   -107.37.18.4  +33.53.54.8  
2011-04-25 19:52:17 INFO  	listobs::::casa
2011-04-25 19:52:17 INFO listobs	##### End Task: listobs              #####
2011-04-25 19:52:17 INFO listobs	##########################################

Key Information from listobs

Certainly the output of listobs is dense with information, but there are some particularly vital data that we'll need for the calibration.

  • The calibrators are 1331+305* (3C286, the flux and bandpass calibrator) and 1445+099* (the phase calibrator). We can use wild-cards 1331* and 1445* since they uniquely identify the sources.
  • The calibrator field indices are field='0' (1331+305) and field='1' (1445+099).
  • The name of the source in the observations list is N5921_2, or field = '2'.
  • The data were taken in a single IF (a single spectral window, SpwID = 0), divided into 63 channels.
  • Only RR and LL correlations are present; cross-pols are absent.

Flagging

Flag the autocorrelations

We don't need the autocorrelation data, and we can use flagdata to get rid of them. You shouldn't have to specify the measurement set, because the variable vis is already set, but it never hurts to be cautious. 

flagdata(vis="ngc5921.demo.ms", autocorr=True)

Interactive Flagging

Plotms settings for flagging spectral line data. Click to enlarge.


plotms is a good tool for flagging spectral line data. Check out the tutorial that describes editing VLA continuum data. Spectral line data of course require some consideration of channels and channel averaging. 

plotms()


The figure at right highlights the settings needed for effective editing of a spectral line data set. The key settings are as follows.

  • Specify the measurement set in File Location; the Browse button allows you to hunt down the measurement set.
  • It's better to edit one source at a time. In the illustrated example, the flux / bandpass calibrator 1331+305* is displayed.
  • Average the channels. First, specify the central channels to remove band edge effects. Channels 6~56 in the first spectral window (IF) are appropriate (see #Inspect the Bandpass Response Curve, below). In the Channel Averaging box, enter 51 channels to average over all channels in the given range.
  • Ideally you want the channels to have the same (u, v) coverage (projected baseline spacings as viewed from the source); otherwise, the beam (point spread function) will be different for each channel. Therefore, if you flag data from a given channel it's usually a good idea to flag those data from all channels. Under the Flagging tab, specify Extend flags to Channel.


With these settings, interactive flagging proceeds as for continuum data. When you're satisfied with the edits, File → Quit to return to the CASA prompt.

Calibration

Calibration of spectral line data broadly follows the approach for continuum data, except that the amplitude and phase corrections are a function of frequency and so must be corrected by bandpass calibration. The basic calibration steps follow.

  • Set the flux scale of the primary calibrator, here, 1331+305 = 3C 286.
  • Determine bandpass corrections based on the primary calibrator. In the script that follows, the bandpass calibration is stored in ngc5921.demo.bcal.
  • Inspect the bandpass correction to determine viable channels for averaging and imaging. We want to toss out end channels where the response is poor.
  • Determine the gain calibrations on the bandpass-corrected and channel-averaged data. In this step, we effectively turn the spectral line data into a single-channel continuum data set and calibrate accordingly. The calibration is stored in ngc5921.demo.gcal.
  • Inspect the gain calibration solutions to look for any aberrant solutions that hint at bad calibrator data.
  • Apply the calibration solutions to the source (N5921_2). This action literally adds a new column of data to the measurement set. This new column contains the data with the gain calibration and bandpass calibration applied, but it does not overwrite the raw data in case the calibration needs revision.


Setting the Flux Scale

setjy generates a point source model for the primary calibrator, 1331+305 = 3C286. These data are of low angular resolution, and so the point source model is adequate for our purposes. For observations with higher angular resolution (longer baseline configurations), you can specify a model of the calibrator using the modimage parameter (see the tutorial Calibrating a VLA 5 GHz continuum survey#Set the Flux Scale for an example of how to use modimage).

setjy also looks up the radio SED for common flux calibrators and automatically assigns the total flux density. 

# 1331+305 = 3C286 is our primary calibrator. Use the wildcard on the end of the source name
# This is 1.4GHz D-config and 1331+305 is sufficiently unresolved that we dont need a model image.  
# For higher frequencies (particularly in A and B config) you would want to use one.
setjy(vis='ngc5921.demo.ms', field='1331+305*', modimage='')


A summary of the operation is sent to the logger window. Here's a listing of the output. 

2011-04-25 19:50:53 INFO setjy	##########################################
2011-04-25 19:50:53 INFO setjy	##### Begin Task: setjy              #####
2011-04-25 19:50:53 INFO  	setjy::::casa
2011-04-25 19:50:53 INFO setjy	Adding MODEL_DATA and CORRECTED_DATA columns
2011-04-25 19:50:53 INFO setjy	Initializing MODEL_DATA (to unity) and CORRECTED_DATA (to DATA)
2011-04-25 19:50:54 INFO setjy	Initialized 22653 rows.
2011-04-25 19:50:54 INFO setjy	1331+30500002_0  spwid=  0  [I=14.79, Q=0, U=0, V=0] Jy, (Perley-Butler 2010)
2011-04-25 19:50:54 INFO setjy	Selecting data
2011-04-25 19:50:54 INFO setjy	Selected 4509 out of 22653 visibilities.
2011-04-25 19:50:54 INFO setjy	Selecting 63 channels, starting at 0, stepped by 1, for spw 0
2011-04-25 19:50:54 INFO setjy	Fourier transforming: replacing MODEL_DATA column
2011-04-25 19:50:54 INFO setjy	Processing after subtracting componentlist .setjy_1331+30500002_0_1.41342GHz49820d.cl
2011-04-25 19:50:54 INFO setjy	Performing interferometric gridding...
2011-04-25 19:50:55 INFO  	setjy::::casa
2011-04-25 19:50:55 INFO setjy	##### End Task: setjy                #####
2011-04-25 19:50:55 INFO setjy	##########################################

Bandpass Calibration

The flux calibrator 1331+305 = 3C 286 now has a point-source model assigned to it. Since the point-source model doesn't change over this narrow range of frequencies, we can use the model to determine amplitude and phase (gain) corrections for each channel independently. The result is the bandpass calibration.

As for any antenna-based calibration scheme, we have to pick an antenna to act as the reference point for the calibration. Any antenna will do, but it's better to pick one near the center of the array. For the remainder of the calibration, we will use refant = '15'. 

# We can first do the bandpass on the single 5min scan on 1331+305. At 1.4GHz phase stablility should be sufficient to do this without
# a first (rough) gain calibration. This will give us the relative antenna gain as a function of frequency.
bandpass(vis='ngc5921.demo.ms', caltable='ngc5921.demo.bcal', field='0', selectdata=False, bandtype='B', solint='inf', combine='scan', refant='15')
  • field='0' : Use the flux calibrator 1331+305 = 3C286 (FIELD_ID 0) as bandpass calibrator.
  • bandtype='B' : Choose bandpass solution type. Pick standard time-binned B (rather than BPOLY).
  • solint='inf' and combine='scan' : Set solution interval arbitrarily long (get single bandpass).
  • refant = '15' : Reference antenna Name 15 (15=VLA:N2) (Id 14)


Inspect the Bandpass Response Curve

Bandpass response curves generated by plotcal. The solutions for different antennas are indicated by differently colored plotting symbols. Plots for individual antennas can be generated by setting iteration = 'antenna' for plotcal.

In the gain calibration to follow, we will effectively convert the spectral line data into a continuum data set. Before proceeding, we need to inspect the bandpass calibration to make sure that it contains no bad values and also to inspect which channels to average to produce the continuum data. plotcal is the standard tool for plotting calibration solutions. The following commands produce the figure at right. 

# Set up 2x1 panels - upper panel amp vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=211, yaxis='amp', showgui=True)

# Set up 2x1 panels - lower panel phase vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=212, yaxis='phase', showgui=True)

By inspection, the amplitude response curve is flat over channels 6~56; that channel range will be used to generate the continuum data for gain calibration. If you want to further inspect the plots interactively and iterate over antenna, set iteration = 'antenna'

Notice that plotcal is run twice: once to display gain amplitudes as a function of channel (frequency), and again to plot gain phases as a function of channel.


Gain Calibration

From inspection of the bandpass response curve, we can average channels 6~56 to produce continuum data for the calibrators. For VLA data, this averaging is specified through the spw (spectral window) parameter, which takes the form IF:Channel-range, as follows. 

spw = '0:6~56'

That is, there is only one spectral window (IF), spw = 0, and we want to average channels 6~56 within that spectral window.

Gain calibrations are otherwise determined as for continuum data.

  • gaincal() is run only on the calibrators, 1331+305 (flux calibrator) and 1445+099 (phase calibrator).
  • The default model for gain calibrations is a 1 Jy point-source. The flux scale is overridden by setjy, which has been performed for the flux calibrator. We need to transfer that flux scale to the phase calibrator using fluxscale().
  • Note that fluxscale() determines the flux density of the phase calibrator and accordingly adjusts its model and calibration solutions. A report of the results are sent to the logger window.
# Armed with the bandpass, we now solve for the time-dependent antenna gains using our previously determined bandpass.
# Note this will automatically be applied to all sources not just the one used to determine the bandpass

gaincal(vis='ngc5921.demo.ms', caltable='ngc5921.demo.gcal', gaintable='ngc5921.demo.bcal', interp='nearest', field='0,1', 
        spw='0:6~56', gaintype='G', solint='inf', calmode='ap', refant='15', minsnr=1.0)

# Now we will transfer the flux scale to the phase calibrator. 
# We will be using 1331+305 (the source we did setjy on) as our flux standard reference.
# Note its extended name as in the FIELD table summary above (it has a VLA seq number appended)

fluxscale(vis='ngc5921.demo.ms', fluxtable='ngc5921.demo.fluxscale', caltable='ngc5921.demo.gcal', reference='1331*', transfer='1445*')


The output from fluxscale follows. A relatively large uncertainty for the phase calibrator is a sign that something went wrong, perhaps bad solutions in gaincal. Here, the phase calibrator scaled to 2.486 ± 0.001 Jy, which looks reasonable. 

2011-04-25 19:54:25 INFO fluxscale	##########################################
2011-04-25 19:54:25 INFO fluxscale	##### Begin Task: fluxscale          #####
2011-04-25 19:54:25 INFO  	fluxscale::::casa
2011-04-25 19:54:25 INFO fluxscale	Opening MS: ngc5921.demo.ms for calibration.
2011-04-25 19:54:25 INFO fluxscale	Initializing nominal selection to the whole MS.
2011-04-25 19:54:26 INFO fluxscale	Beginning fluxscale--(MSSelection version)-------
2011-04-25 19:54:26 INFO fluxscale	 Found reference field(s): 1331+30500002_0
2011-04-25 19:54:26 INFO fluxscale	 Found transfer field(s):  1445+09900002_0
2011-04-25 19:54:26 INFO fluxscale	 Flux density for 1445+09900002_0 in SpW=0 (freq=1.41342e+09 Hz) is: 2.53882 +/- 0.00218946 (SNR = 1159.57, N = 54)
2011-04-25 19:54:26 INFO fluxscale	Storing result in ngc5921.demo.fluxscale
2011-04-25 19:54:26 INFO fluxscale	Writing solutions to table: ngc5921.demo.fluxscale
2011-04-25 19:54:26 INFO  	fluxscale::::casa
2011-04-25 19:54:26 INFO fluxscale	##### End Task: fluxscale            #####
2011-04-25 19:54:26 INFO fluxscale	##########################################

Inspect the Calibration Solutions

Gain calibration solutions from gaincal and fluxscale.

Now inspect the results of gaincal. The setup is identical to that used to plot the bandpass response curve. The only change is that we are plotting the gaintable ngc5921.demo.gcal, and we're looking at solutions for both of the calibrator sources. The results are shown at right. 

# Set up 2x1 panels - upper panel amp vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=211, yaxis='amp', showgui=True)

# Set up 2x1 panels - lower panel phase vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=212, yaxis='phase', showgui=True)

The amp and phase coherence looks good. If you want to do this interactively and iterate over antenna, set iteration = 'antenna'.


Apply the Solutions

Next, apply the calibration solutions to the calibrators themselves, and finally transfer the calibration solutions by interpolation (or nearest-neighbor sampling) to the source. The relevant task is applycal, which fills out a new column (CORRECTED_DATA) of calibrated data in the measurement set without wiping out the raw data column. The application is identical to that used for continuum data, except that the bandpass table is also included in the calibration. To apply multiple calibrations at once, provide the gaintable parameter with a list of calibration tables, as follows. 

gaintable = ['ngc5921.demo.gcal', 'ngc5921.demo.bcal']

We want to correct the calibrators using themselves and transfer from 1445+099 to itself and the target N5921. Start with the fluxscale/gain and bandpass tables. We will pick the 1445+099 out of the gain table for transfer and use all of the bandpass table. 

applycal(vis='ngc5921.demo.ms', field='1,2', gaintable=['ngc5921.demo.gcal','ngc5921.demo.bcal'], gainfield=['1','*'], 
         interp=['linear','nearest'], spwmap=[], selectdata=False)

Now for completeness apply 1331+305 to itself. 

applycal(vis='ngc5921.demo.ms', field='0', gaintable=['ngc5921.demo.gcal','ngc5921.demo.bcal'], gainfield=['0','*'], 
         interp=['linear','nearest'], spwmap=[], selectdata=False)


Plot the Spectrum

plotms settings to produce the integrated spectrum from the calibrated visibilities data.

Before we attempt to image the 21 cm cube of the source, we need to subtract off the underlying continuum, which means we need to plot the integrated spectrum of the source to determine the continuum channels.

We can do this in plotms. 

plotms(vis='ngc5921.demo.ms', selectdata=True, field='N5921*', spw='0:6~56', averagedata=True, avgtime='3600', avgscan=True, 
       avgbaseline=True, xaxis='channel', yaxis='amp', ydatacolumn='corrected')

Note that we have entered all the relevent parameters via the task interface, as an alternative to entering each option into the GUI. If the symbols appear too small, the size may be increased via the Display tab: change the Unflagged Points Symbol to 'Custom' and increase the number of pixels for the plotting symbol. The resulting plot is illustrated in the figure at right. Briefly, we want to average both in time and over baselines to get the signal-to-noise necessary to reveal the 21 cm profile (see Averaging data in plotms for more details on averaging options). If you wish to enter the values directly into the GUI, you can follow the (Tab)Command convention of the flagging tutorial with the following settings :

  • (Data)field = N5921*
  • (Data)spw = 0:6~56
  • (Data)Averaging → Time = 3600 (average over some long time window)
  • (Data)Averaging → Scan = True (checkmark; average in time across scan boundaries)
  • (Data)Averaging → All Baselines = True (checkmark)
  • (Axes)X Axis = Channel
  • (Axes)Y Axis = Amp

From inspection of this plot, it looks like channels 4~6 and 50~59 contain line-free channels, suitable to use for continuum subtraction.

Continuum Subtraction

The next step is to split off the NGC 5921 data from the multisource measurement set and subtract the continuum. Splitting uses the split command, as follows. 

split(vis='ngc5921.demo.ms', outputvis='ngc5921.demo.src.split.ms', field='N5921*', spw='', datacolumn='corrected')


This action generated a new measurement set called ngc5921.demo.src.split.ms and copied the calibrated source data (datacolumn = 'corrected') into it.

uvcontsub subtracts the continuum from the data in the visibility (u, v) plane. We will be using channels 4-6 and 50-59 for continuum. 

uvcontsub(vis='ngc5921.demo.src.split.ms', field='N5921*', fitspw='0:4~6;50~59', spw='0', solint=0.0, fitorder=0, want_cont=True)


Notice that uvcontsub splits two new measurement sets, 'ngc5921.demo.ms.cont', which contains an average of the continuum channels, and 'ngc5921.demo.ms.contsub', which contains the continuum-subtracted spectral line data.

Imaging

Plot of amplitude vs. projected baseline length (in units of the observing wavelength) produced by casaplotms. The maximum baseline is just below 5 kilo-lambda.


Now we can generate the primary science product, a clean data cube (ra, dec, velocity) from the continuum-subtracted (u, v, channel) measurement set, ngc5921.demo.ms.contsub. Things to consider in using clean:

  • To ensure channels aren't averaged prior to imaging, choose mode='channel'.
  • Specify the channels to image using start = 5, width = 1 (no averaging over channels), nchan = 46; only channels 5~51 will be imaged.
  • The maximum baseline is just under 5 kilolambda (see the figure at right), and so the expected synthetic beam is roughly 1.22 × 206265 / 5000 = 50 arcseconds (subject to the details of u, v weighting). Pixels should sample the beam better than 3 times, so 15 arcseconds is a good choice of pixel size (cell = ['15.0arcsec','15.0arcsec']).
  • We only want to clean down to the noise, which is easily determined by trial-and-error imaging of a single channel (choosing nchan=1 and start appropriately). Here, clean stops when the maximum residual on the channel is below threshold='8.0mJy'.
# Image the continuum subtracted measurement set
clean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.cleanimg', field='0', mode='channel', nchan=46, start=5, width=1,
      spw='', gain=0.1, imsize=[256,256], psfmode='clark', imagermode='', cell=['15.0arcsec','15.0arcsec'], niter=6000,
      threshold='8.0mJy', weighting='briggs', robust=0.5, mask = [108,108,148,148], interactive=False)


Use imhead to look at the cube header: 

imhead(imagename='ngc5921.demo.cleanimg.image', mode='summary')


The output, as follows, appears in the logger window. 

2011-04-25 20:10:29 INFO imhead	##########################################
2011-04-25 20:10:29 INFO imhead	##### Begin Task: imhead             #####
2011-04-25 20:10:29 INFO  	imhead::::casa
2011-04-25 20:10:29 INFO  	ImageAnalysis::summary
2011-04-25 20:10:29 INFO ImageAnalysis	Image name       : ngc5921.demo.cleanimg.image
2011-04-25 20:10:29 INFO ImageAnalysis	Object name      : N5921_2
2011-04-25 20:10:29 INFO ImageAnalysis	Image type       : PagedImage
2011-04-25 20:10:29 INFO ImageAnalysis	Image quantity   : Intensity
2011-04-25 20:10:29 INFO ImageAnalysis	Pixel mask(s)    : None
2011-04-25 20:10:29 INFO ImageAnalysis	Region(s)        : None
2011-04-25 20:10:29 INFO ImageAnalysis	Image units      : Jy/beam
2011-04-25 20:10:29 INFO ImageAnalysis	Restoring Beam   : 51.7693 arcsec, 47.2192 arcsec, -170.658 deg
2011-04-25 20:10:29 INFO  	ImageAnalysis::summary
2011-04-25 20:10:29 INFO ImageAnalysis	Direction reference : J2000
2011-04-25 20:10:29 INFO ImageAnalysis	Spectral  reference : LSRK
2011-04-25 20:10:29 INFO ImageAnalysis	Velocity  type      : RADIO
2011-04-25 20:10:29 INFO ImageAnalysis	Rest frequency      : 1.42041e+09 Hz
2011-04-25 20:10:29 INFO ImageAnalysis	Pointing center     :  15:22:00.000000  +05.04.00.000000
2011-04-25 20:10:29 INFO ImageAnalysis	Telescope           : VLA
2011-04-25 20:10:29 INFO ImageAnalysis	Observer            : TEST
2011-04-25 20:10:29 INFO ImageAnalysis	Date observation    : 1995/04/13/09:33:00
2011-04-25 20:10:29 INFO ImageAnalysis	Telescope position: [-1.60119e+06m, -5.04198e+06m, 3.55488e+06m] (ITRF)
2011-04-25 20:10:29 INFO  	ImageAnalysis::summary+
2011-04-25 20:10:29 INFO ImageAnalysis	Axis Coord Type      Name             Proj Shape Tile   Coord value at pixel    Coord incr Units
2011-04-25 20:10:29 INFO ImageAnalysis	------------------------------------------------------------------------------------------------ 
2011-04-25 20:10:29 INFO ImageAnalysis	0    0     Direction Right Ascension   SIN   256   64  15:22:00.000   128.00 -1.500000e+01 arcsec
2011-04-25 20:10:29 INFO ImageAnalysis	1    0     Direction Declination       SIN   256   64 +05.04.00.000   128.00  1.500000e+01 arcsec
2011-04-25 20:10:29 INFO ImageAnalysis	2    1     Stokes    Stokes                    1    1             I
2011-04-25 20:10:29 INFO ImageAnalysis	3    2     Spectral  Frequency                46    8   1.41279e+09     0.00 2.4414062e+04 Hz
2011-04-25 20:10:29 INFO ImageAnalysis	                     Velocity                               1607.99     0.00 -5.152860e+00 km/s
2011-04-25 20:10:29 INFO  	imhead::::casa
2011-04-25 20:10:29 INFO imhead	##### End Task: imhead               #####
2011-04-25 20:10:29 INFO imhead	##########################################

Additional Science Products

If things went well, you should now have a spectral line cube (ngc5921.demo.cleanimg.image) as a primary science product. The demo script illustrates further how to generate cube statistics (using imstat), an integrated spectrum, and moment maps.

Cube Statistics

imstat is the tool for displaying statistics of images and cubes. The following example displays the statistics for the whole cube. 

cubestat=imstat(imagename='ngc5921.demo.cleanimg.image', box='')


The output goes to the logger window. 

2011-04-25 20:12:13 INFO imstat	##########################################
2011-04-25 20:12:13 INFO imstat	##### Begin Task: imstat             #####
2011-04-25 20:12:13 INFO  	imstat::::casa
2011-04-25 20:12:13 INFO imstat	No region specified. Using full positional plane.
2011-04-25 20:12:13 INFO imstat	Using all spectral channels.
2011-04-25 20:12:13 INFO imstat	Using polarizations I
2011-04-25 20:12:13 INFO imstat	Determining stats for image ngc5921.demo.cleanimg.image
2011-04-25 20:12:13 INFO imstat	Selected bounding box : 
2011-04-25 20:12:13 INFO imstat	    [0, 0, 0, 0] to [255, 255, 0, 45]  (15:24:08.404, +04.31.59.181, I, 1.41279e+09Hz to 15:19:52.390, +05.35.44.246, I, 1.41389e+09Hz)
2011-04-25 20:12:13 INFO imstat	Regions --- 
2011-04-25 20:12:13 INFO imstat	         -- bottom-left corner (pixel) [blc]:  [0, 0, 0, 0]
2011-04-25 20:12:13 INFO imstat	         -- top-right corner (pixel) [trc]:    [255, 255, 0, 45]
2011-04-25 20:12:13 INFO imstat	         -- bottom-left corner (world) [blcf]: 15:24:08.404, +04.31.59.181, I, 1.41279e+09Hz
2011-04-25 20:12:13 INFO imstat	         -- top-right corner (world) [trcf]:   15:19:52.390, +05.35.44.246, I, 1.41389e+09Hz
2011-04-25 20:12:13 INFO imstat	Values --- 
2011-04-25 20:12:13 INFO imstat	         -- flux density [flux]:     4.14845 Jy
2011-04-25 20:12:13 INFO imstat	         -- number of points [npts]:                3.01466e+06
2011-04-25 20:12:13 INFO imstat	         -- maximum value [max]:                    0.0555024 Jy/beam
2011-04-25 20:12:13 INFO imstat	         -- minimum value [min]:                    -0.0107881 Jy/beam
2011-04-25 20:12:13 INFO imstat	         -- position of max value (pixel) [maxpos]: [134, 134, 0, 38]
2011-04-25 20:12:13 INFO imstat	         -- position of min value (pixel) [minpos]: [230, 0, 0, 15]
2011-04-25 20:12:13 INFO imstat	         -- position of max value (world) [maxposf]: 15:21:53.976, +05.05.29.998, I, 1.41371e+09Hz
2011-04-25 20:12:13 INFO imstat	         -- position of min value (world) [maxposf]: 15:20:17.679, +04.31.59.470, I, 1.41315e+09Hz
2011-04-25 20:12:13 INFO imstat	         -- Sum of pixel values [sum]:               51.0692 Jy/beam
2011-04-25 20:12:13 INFO imstat	         -- Sum of squared pixel values [sumsq]:     12.2781 Jy/beam.Jy/beam
2011-04-25 20:12:13 INFO  	imstat::::
2011-04-25 20:12:13 INFO imstat	Statistics --- 
2011-04-25 20:12:13 INFO imstat	        -- Mean of the pixel values [mean]:         1.69403e-05 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Variance of the pixel values :           4.07252e-06 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Standard deviation of the Mean [sigma]:  0.00201805 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Root mean square [rms]:                  0.00201812 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Median of the pixel values [median]:     -1.18009e-05 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Median of the deviations [medabsdevmed]: 0.00126199 Jy/beam
2011-04-25 20:12:13 INFO imstat	        -- Quartile [quartile]:                     0.00252393 Jy/beam
2011-04-25 20:12:13 INFO  	imstat::::casa
2011-04-25 20:12:13 INFO imstat	##### End Task: imstat               #####
2011-04-25 20:12:13 INFO imstat	##########################################

The Integrated Spectrum

Example of the viewer rectangle selection tool on one channel of the NGC 5921 21 cm data cube. The spectral profile window is shown at right.


We saw earlier how to generate an integrated spectrum from the (u, v) measurement set. Here's how to produce the integrated spectrum from the spectral line cube. First, load the cube into viewer. 

viewer(infile='ngc5921.demo.cleanimg.image')


To generate the integrated spectrum, perform the following tasks.

  • Use the player controls to inspect the cube one channel at a time.
  • From the viewer Tools menu, select Spectral Profile. A new graphics window should appear.
  • By default, the rectangle selection tool is assigned to the right mouse button, and you can just right-click and drag a box over the region where you want to (spatially) integrate the spectrum. See the figure at upper right.
  • Alternatively, you can assign one of the other selection tools by right-clicking on the appropriate button.
  • The spectrum now appears in the graphics window; see the figure at right.

You can save the integrated spectrum to a text file by clicking the button on the graphics window. There are also buttons to print the figure or save the figure to disk.

Cube Moments

The moment 0 (integrated intensity) 21 cm image of NGC 5921, produced using immoments

Cube moments are maps of weighted sums along the velocity axis. In CASA, they are generated by the task immoments. The zeroth moment (moments = 0) is a sum of intensities along the velocity axis (the integrated intensity map); the first moment (moment = 1) is the sum of velocities weighted by intensity (the velocity field); the second moment (moment = 2) is a map of the velocity dispersion; see the immoments for additional options.

The following example produces maps of the zeroth and first moments, or the integrated intensity and velocity field. The respective measurement sets are the moment zero image ngc5921.demo.moments.integrated and moment one imagengc5921.demo.moments.weighted_coord.

We will do the zeroth and first moments and mask out noisy pixels using hard global limits. We will also collapse along the spectral (channel) axis and include all planes. 

immoments(imagename='ngc5921.demo.cleanimg.image', moments=[0,1], excludepix=[-100, 0.009], axis='spectral', chans='', outfile='ngc5921.demo.moments')
  • moments = [0,1] : Do zeroth and first moments
  • excludepix = [-100,0.009] : Mask out noisy pixels using hard global limits
  • axis = 'spectral' : Collapse along the spectral (channel) axis
  • chans = :Include all planes


To examine the moment images, use viewer; the resulting moment zero image is displayed at right.

Note that you may have to play with the color map (Data Display Options button in viewer) in order to replicate the image in this tutorial. 

viewer(infile='ngc5921.demo.moments.integrated')

Export the Data

To export the (u, v) data and image cube as FITS files, use exportuvfits and exportfits, respectively.

Here's how to export the continuum-subtracted (u, v) data. 

exportuvfits(vis='ngc5921.demo.src.split.ms.contsub', fitsfile='ngc5921.demo.contsub.uvfits', datacolumn='corrected', multisource=True)


And now, the FITS cube. 

exportfits(imagename='ngc5921.demo.cleanimg.image', fitsimage='ngc5921.demo.cleanimg.fits')


The moment maps (or any CASA images) can be similarly exported using exportfits.

Appendix: Python Notes

os.system

os.system allows you to run shell commands from within python / CASA. For example: 

import os
os.system('ls -sF')

will give an OS-level listing of the current directory's contents.

os.environ.get

It's worth having a look at the output of the os.environ.get command to understand the python syntax (alternative: os.getenv). You can think of os.environ as a list of operating system environment variables, and get is a method that extracts information about the requested environment variable, here, CASAPATH. Get returns a string of whitespace separated information, and .split() turns that string into a list. The array index [0] extracts the first element of that list, which contains the path.

To illustrate, here is some example python I/O in CASA. 

CASA <12>: print os.environ.get('CASAPATH')
/usr/lib64/casapy/30.0.9709test-001 linux local el5bld64b

CASA <13>: print os.environ.get('CASAPATH').split()
['/usr/lib64/casapy/30.0.9709test-001', 'linux', 'local', 'el5bld64b']

CASA <14>: print os.environ.get('CASAPATH').split()[0]
/usr/lib64/casapy/30.0.9709test-001

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Last checked on CASA Version 5.1.1.

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