View source for IRAS16293 Band9 - Imaging for CASA 4.0

*'''This portion of the guide covers imaging of the fully calibrated data see: [[IRAS16293_Band9_-_Calibration_for_CASA_4.0]]'''.

<pre style="background-color: #ffa07a;">
WARNING: On June 15, 2012 the calibration guide and the final data products (calibrated science 
data: IRAS16293_Band9_CalibratedMS_FIXED.tgz and reference images: IRAS16293_Band9_ReferenceImages_FIXED.tgz)) 
were changed to correct for a 1.2" position error in the phase calibrator (1625-254). Without 
correction, the science images will suffer from a similar offset. 
</pre>

== Overview ==
This section of the casa guide continues from [http://casaguides.nrao.edu/index.php?title=IRAS16293_Band9_-_Calibration_for_CASA_4.0 IRAS16293_Band9_-_Calibration_for_CASA_4.0] . If you completed the calibration section, then you already have the final calibrated dataset. If you just started to work on this IRAS16293 B9 data, you can download the fully calibrated dataset '''IRAS16293_Band9_CalibratedMS''' from

[https://almascience.nrao.edu/almadata/sciver/IRAS16293B9 North America]

[http://almascience.eso.org/almadata/sciver/IRAS16293B9 Europe]

[http://almascience.nao.ac.jp/almadata/sciver/IRAS16293B9 East Asia]

This casa guide covers imaging of both continuum and spectral lines of IRAS16293 Band 9. '''This guide requires CASA 4.0'''. In this casa guide you will also need the [http://casaguides.nrao.edu/index.php?title=Analysis_Utilities Analysis Utils] package, so make sure you have it imported into CASA.

==Confirm your version of CASA==

This guide has been written for CASA release 3.4.0.  Please confirm your version before proceeding.
<source lang="python">
# In CASA
version = casadef.casa_version
print "You are using " + version
if (version < '4.0.0'):
    print "YOUR VERSION OF CASA IS TOO OLD FOR THIS GUIDE."
    print "PLEASE UPDATE IT BEFORE PROCEEDING."
else:
    print "Your version of CASA is appropriate for this guide."
</source>

==Install Analysis Utilities==

Analysis Utilities (or analysisUtils for short) is a small set of Python scripts that provide a number of analysis and plotting utilities for ALMA data reduction. This guide uses a few of these utilities. They are very easy to install (just download and untar). See 

http://casaguides.nrao.edu/index.php?title=Analysis_Utilities

for a full description and download instructions. If you do not wish to do this, see the CASA 3.3 version of the guide linked at the top of this page for alternative (but slow) plotting options. Analysis Utilities are updated frequently so if its been a while since you installed it, its probably worth doing it again. If you are at an ALMA site or ARC, the analysis utilities are probably already installed and up to date.

== List the contents of the Data==

Before we start with the imaging itself, do a {{listobs}} to the calibrated dataset, so you can check that everything is ok.

<source lang="python">
# listobs to check data
listobs(vis='IRAS16293_Band9.fixed.rebin.ms',
        listfile='IRAS16293_Band9.fixed.rebin.ms.listobs',verbose=F)
</source>

You should see the next information in the .listobs file.

<pre style="background-color: #fffacd;">
Fields: 7
  ID   Code Name                RA              Decl          Epoch   SrcId nRows  
  0    none IRAS16293-2422-a    16:32:22.99200 -24.28.36.0000 J2000   0     7811   
  1    none IRAS16293-2422-a    16:32:22.47925 -24.28.36.0000 J2000   0     7829   
  2    none IRAS16293-2422-a    16:32:22.73563 -24.28.36.0000 J2000   0     7829   
  3    none IRAS16293-2422-a    16:32:22.73563 -24.28.32.5000 J2000   0     7829   
  4    none IRAS16293-2422-a    16:32:22.47925 -24.28.29.0000 J2000   0     7429   
  5    none IRAS16293-2422-a    16:32:22.73563 -24.28.29.0000 J2000   0     7151   
  6    none IRAS16293-2422-a    16:32:22.99200 -24.28.29.0000 J2000   0     7151   
Spectral Windows:  (4 unique spectral windows and 1 unique polarization setups)
  SpwID  #Chans Frame Ch1(MHz)    ChanWid(kHz)  TotBW(kHz)  Corrs  
  0        3840 TOPO  703312.744  488.28125     1875000     XX  YY  
  1        3840 TOPO  692237.256  -488.28125    1875000     XX  YY  
  2        3840 TOPO  690437.256  -488.28125    1875000     XX  YY  
  3        3840 TOPO  688437.256  -488.28125    1875000     XX  YY  
Antennas: 13 'name'='station' 
   ID=   0-3: 'DA41'='A003', 'DA43'='A075', 'DA47'='A026', 'DV02'='A077', 
   ID=   4-8: 'DV03'='A137', 'DV07'='A076', 'DV09'='A046', 'DV10'='A071', 
   ID=  9-13: 'DV12'='A011', 'DV13'='A072', 'DV14'='A025', 'DV17'='A138', 
</pre>

'''If you are using the calibrated data from the science portal, it is essential to do the following.'''

<source lang="python">
# In CASA
tb.open('IRAS16293_Band9.fixed.rebin.ms/POINTING', nomodify = False)
a = tb.rownumbers()
tb.removerows(a)
tb.close()
</source>

== Continuum Emission Imaging ==
As shown in Figure 2 of [[IRAS16293Band9]], these observations have a 7 pointing mosaic, which you can visualize with the next command (see also Figure 1).

[[File:mosaic_pattern.png|200px|thumb|right|'''Fig. 1.''' Pointings showing the mosaic used for the observations. The circles mark the FWHM of the primary beam response.]]

<source lang="python">
# Make a plot of the mosaic pattern with field ids identified
au.plotMosaic(vis='IRAS16293_Band9.fixed.rebin.ms',sourceid='0',
              doplot=True,figfile='mosaic_pattern.png')
</source>

You might wonder which of these pointings or fields have stronger line emission. You can determine this by running the next command. We set spw='1' which contains CO (6-5), and you can check all the plots for all the fields by just hitting the arrow for "next". Also see Figure 2 for an example of the output.

[[File:IRAS16293_Band9.AVG.ms_uvamp_f0_spw1.png|200px|thumb|right|'''Fig. 2.''' UV-amp distribution for the field 0 (spw 1) in the mosaic.]]

<source lang="python">
# See which mosaic fields have the brightest line emission
plotms(vis='IRAS16293_Band9.fixed.rebin.ms', 
       field='',xaxis='uvdist', yaxis='amp',
       spw='1', avgchannel='3840',coloraxis='field',
       iteraxis='field')
</source>

Another way to visualize the uv-amp distribution is with the next {{plotms}}. This will load all the fields or pointing into the plot, and you can change interactively between the spws using the appropriate button in plotms. See Figure 3 for a plot of spw 0.

[[File:IRAS16293_Band9.AVG.ms_uvamp_allf_spw0.png|200px|thumb|right|'''Fig. 3.''' UV-amp distribution for spw 0 using all the fields or pointings in the mosaic.]]

<source lang="python">
# Iterate over spws all fields
plotms(vis='IRAS16293_Band9.fixed.rebin.ms', 
       field='',xaxis='uvdist', yaxis='amp',
       spw='', avgchannel='3840',coloraxis='field',
       iteraxis='spw')
</source>

We need to note the channels that do not present line emission. To identify them we can plot the amplitude vs frequency for all the spectral windows. Figure 4 shows the output from the next command for spw 0 (field 3).

[[File:amp_vs_channel.spw1.png|200px|thumb|right|'''Fig. 4.''' Frequency-amplitude plot for spw=1 of field=3.  ]]

<source lang="python">
# Select Channels for uvcontsub. Average every 8 channels for
# increased S/N.

plotms(vis='IRAS16293_Band9.fixed.rebin.ms', 
       xaxis='channel', yaxis='amp',field='3',
       spw='', avgtime='1e8',avgscan=T,coloraxis='corr',
       avgchannel='',iteraxis='spw',ydatacolumn='corrected',
       xselfscale=True,yselfscale=True)
</source>

Using the plots from the previous command you can select a block of channels that are free of strong line emission. To do this in the most rigorous way, you would first need to make dirty image cubes and then examine the spectra on and near the continuum sources, because many more weaker lines will become apparent in the cubes. Here we provide you with the channel selection we used for continuum emission.

The next {{flagmanager}} command will save the flags state in case you might need to re-do the steps from this point.

<source lang="python">
flagmanager(vis='IRAS16293_Band9.fixed.rebin.ms',mode='save',versionname='Original')
</source>

Now we will flag the channels identified to have line emission, so we will be ready to proceed with the continuum imaging.

<source lang="python">
# flagging the line channels
flagdata(vis='IRAS16293_Band9.fixed.rebin.ms',
         spw='0:1700~2500,1:400~500;1100~1900;3000~3450,2:1700~2200;3100~3839,3:0~800;1600~2900;3300~3839',action='apply', flagbackup=F)
</source>

To help {{clean}} to be faster, you can average 200 channels, as you do not need to have a high spectral resolution for continuum imaging.

<source lang="python">
split(vis='IRAS16293_Band9.fixed.rebin.ms',
      outputvis='IRAS16293_Band9.fixed.CONT.msAVG',field='',
      datacolumn='data',width=200,spw='0~3:20~3819')
</source>

=== Clean and self-calibrate the continuum ===

Now we proceed with the cleaning itself. For the {{clean}} task you will need to choose the right cell size, map size, etc. Below are the parameters that were used in this casaguide. Note that the command will be interactive and you will have to set the cleaning boxes.

<source lang="python">
# cleaning 1
os.system('rm -rf *CONTIN*')
clean(vis='IRAS16293_Band9.fixed.CONT.msAVG',
     imagename='IRAS16293_Band9.fixed.CONTIN',
     spw='0~3',field='',phasecenter='3',
     mode='mfs',imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=T,
     weighting='briggs',robust=0.5,
     niter=10000, threshold='0.3mJy',usescratch=F)
</source>

It is important to check if all fields have some emission in the model data column, if not, or if it is very weak you would want to exclude from the gaincal below. Because the bright Source B is within the primary beam of all the observed fields, all fields have strong emission present in the image model. In Figure 5 you can see an example for the output from next command.
[[File:IRAS16293_Band9.AVG.ms_tamp_f2_allspw.png|200px|thumb|right|'''Fig. 5.''' Time-amplitude plot for field 2. All spw are plotted.]]

<source lang="python">
# checking the model data for all the fields
plotms(vis='IRAS16293_Band9.fixed.CONT.msAVG', 
      xaxis='time', yaxis='amp',field='',iteraxis='field',
      spw='', avgchannel='4000',coloraxis='field',
      ydatacolumn='model',xselfscale=True,yselfscale=True)
</source>

Now we proceed with {{gaincal}} to perform self calibration (for phases) on the data. Even with minsnr=3.0 there will be some failed solutions.

<source lang="python">
# get one scan per field
os.system('rm -rf cont_pcal1')
gaincal(vis='IRAS16293_Band9.fixed.CONT.msAVG',caltable='cont_pcal1',gaintype='T',
        refant='DV14',calmode='p',combine='',spw='',field='',
        solint='inf',minsnr=3.0,minblperant=4)
</source>

The solutions for five antennas are shown in Figure 6, and are produced with the next {{plotcal}}. Be sure to check all the antennas.

[[File:cont_pcal1.png|200px|thumb|right|'''Fig. 6.''' Phase solutions for the first self-cal run.]]

<source lang="python">
plotcal(caltable='cont_pcal1',xaxis='time',yaxis='phase',
        spw='',iteration='antenna',subplot=511,plotrange=[0,0,-80,80],
        antenna='',timerange='')

# The phases are remarkably stable for Band 9! 
</source>

We then apply the table to the data to proceed right after with the proper cleaning, to check the improvement of the data.

<source lang="python">
# application of the table
applycal(vis='IRAS16293_Band9.fixed.CONT.msAVG',spwmap=[],spw='',
       gaintable=['cont_pcal1'],calwt=F,flagbackup=F)
</source>

Now you are ready for the second cleaning. For this one we are using the mask resulting from the previous cleaning. In the next blocks of commands we will repeat this process (clean, viewer, gaincal, plotcal, applycal, clean, etc.) to improve the overall quality of the image.

<source lang="python">
# cleaning 2
os.system('rm -rf *CONTIN_P1*')
clean(vis='IRAS16293_Band9.fixed.CONT.msAVG',
     imagename='IRAS16293_Band9.fixed.CONTIN_P1',
     spw='0~3',field='',phasecenter='3',
     mode='mfs',imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=T,
     weighting='briggs',robust=0.5,
     mask='IRAS16293_Band9.fixed.CONTIN.mask',
     niter=10000, threshold='0.3mJy', usescratch=F)

# visualize the image
imview (raster=[{'file':'IRAS16293_Band9.fixed.CONTIN.image',
                 'range': [-0.01,0.5]},
                {'file': 'IRAS16293_Band9.fixed.CONTIN_P1.image',
                 'range': [-0.01,0.5]}])
</source>

Second {{gaincal}} step for self calibration.

<source lang="python">
# second self-cal
os.system('rm -rf cont_pcal2')
gaincal(vis='IRAS16293_Band9.fixed.CONT.msAVG',caltable='cont_pcal2',gaintype='T',
        refant='DV14',calmode='p',combine='',spw='',field='',
        solint='inf',minsnr=3.0,minblperant=4)

#Checking the table
plotcal(caltable='cont_pcal2',xaxis='time',yaxis='phase',
        spw='',iteration='antenna',subplot=511,plotrange=[0,0,-80,80],
        antenna='',timerange='')

# Applying results to the data
applycal(vis='IRAS16293_Band9.fixed.CONT.msAVG',spwmap=[],spw='',
       gaintable=['cont_pcal2'],calwt=F,flagbackup=F)
</source>

We continue with the next cleaning.

<source lang="python">
# Cleaning 3
os.system('rm -rf *CONTIN_P2*')
clean(vis='IRAS16293_Band9.fixed.CONT.msAVG',
     imagename='IRAS16293_Band9.fixed.CONTIN_P2',
     spw='0~3',field='',phasecenter='3',
     mode='mfs',imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=T,
     weighting='briggs',robust=0.5,
     mask='IRAS16293_Band9.fixed.CONTIN.mask',
     niter=10000, threshold='0.3mJy')

# to view the resulting image
imview (raster=[{'file':'IRAS16293_Band9.fixed.CONTIN_P1.image',
                 'range': [-0.01,0.5]},
                {'file': 'IRAS16293_Band9.fixed.CONTIN_P2.image',
                 'range': [-0.01,0.5]}])

# There is very little change, the bold could try another iteration using a shorter
# solint phase self-cal. For now, moving on to amplitude.
</source>

[[File:cont_apcal.png|200px|thumb|right|'''Fig. 6.''' Amplitude self-calibration solutions.]]

Next iteration with gaincal, now using amplitude and phase for "calmode".

<source lang="python">
# For amplitude self-cal we only want to get one solution per spw, per dataset
os.system('rm -rf cont_apcal')
gaincal(vis='IRAS16293_Band9.fixed.CONT.msAVG',caltable='cont_apcal',gaintype='T',
        refant='DV14',calmode='ap',combine='scan,field',spw='',field='',
        solint='8000s',minsnr=3.0,minblperant=4,gaintable='cont_pcal2')

# ploting the table
plotcal(caltable='cont_apcal',xaxis='time',yaxis='amp',
        spw='',iteration='antenna',subplot=511,plotrange=[0,0,0.5,2],
        antenna='',timerange='')

# and applying to the data
applycal(vis='IRAS16293_Band9.fixed.CONT.msAVG',spwmap=[],spw='',
       gaintable=['cont_pcal2','cont_apcal'],calwt=F,flagbackup=F)

# A large correction is present for the 2nd dataset. This amplitude
# error was causing many of the artifacts noticeable in the previous
# images.
</source>

Continuing the final cleaning step.

<source lang="python">
# Cleaning 4
os.system('rm -rf *CONTIN_AP*')
clean(vis='IRAS16293_Band9.fixed.CONT.msAVG',
     imagename='IRAS16293_Band9.fixed.CONTIN_AP',
     spw='0~3',field='',phasecenter='3',
     mode='mfs',imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=T,
     weighting='briggs',robust=0.5,
     mask='IRAS16293_Band9.fixed.CONTIN_P2.mask',
     niter=10000, threshold='0.3mJy')

# and to view the image
imview (raster=[{'file':'IRAS16293_Band9.fixed.CONTIN_P2.image',
                 'range': [-0.01,0.5]},
                {'file': 'IRAS16293_Band9.fixed.CONTIN_AP.image',
                 'range': [-0.01,0.5]}])
</source>

We use now {{impbcor}} to produce an image corrected by the primary beam response.  We are
outputting a sub-region of the cube because the area covered is large compared to 
the area of interest, and we want to keep the file size of the result as small
as possible.

<source lang="python">
# correcting for primary beam
impbcor(imagename='IRAS16293_Band9.fixed.CONTIN_AP.image',
        pbimage='IRAS16293_Band9.fixed.CONTIN_AP.flux',
        outfile='IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim',
        box='150,104,345,299')

# and checking the image
imview (raster=[{'file': 'IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim',
                 'range': [-0.01,1.5],'colorwedge':True}],
        out='IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim.png')

</source>

In Figure 7 we show the final image of the self-calibration process. Source A is the weaker source to the SE and Source B is the stronger source to the NW. The rms noise is about 18 mJy/beam, and the restoring beam is 0.29 x 0.17 arcsec, PA=-71 deg with robust=0.5 weighting. The peak intensities for the two components are about 3.19 (B) and 1.07 (A) Jy/beam, while the flux densities are about 11.9 (B) and 9 (A) Jy.

[[File:IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim_c.png|200px|thumb|right|'''Fig. 7.''' Continuum image resulted from the complete process of self calibration for the mosaic of IRAS16293 Band 9.]]


Now you can export your final image in FITS format, so you can analyze it in other software packages if desired.
<source lang="python">
exportfits(imagename='IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim',
           fitsimage='IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim.fits')
</source>

== Spectral Line Imaging ==

<source lang="python">
# Restore the flags made for the continuum imaging
flagmanager(vis='IRAS16293_Band9.fixed.rebin.ms',mode='restore',versionname='Original')
</source>

Then we need to subtract the continuum, using the line free channels

<source lang="python">
# Subtract the continuum from the line data
contspw='0:20~1700;2500~3820,1:20~400;500~1100;1900~3000;3450~3800,2:20~1700;2200~3100,3:800~1600;2900~3300'
uvcontsub(vis='IRAS16293_Band9.fixed.rebin.ms',fitspw=contspw,fitorder=1,combine='spw')
</source>

Before we commence with the cleaning we also need to apply the tables we produced in the self calibration steps.

<source lang="python">
# Apply final self-calibration to the line data
applycal(vis='IRAS16293_Band9.fixed.rebin.ms.contsub',spwmap=[],spw='',
       gaintable=['cont_pcal2','cont_apcal'],calwt=F,flagbackup=F)
</source>

To make your work more effective we recommend you to clean cubes 1/2 spw at a time to make the image cubes a manageable size. Also, average every 2 channels since this is the native velocity resolution.

Some notes for this sections: the threshold was chosen after doing a couple of smaller test images. This is about 3 sigma in channels with weak line emission. The mask was also chosen from a few test images. With this set, there is no need to do interactive cleaning. Note however, that this clean mask does not do a good job on the extended emission of CO(6-5).

=== Clean each spw ===

First clean the first half of each spw.

<source lang="python">
# In CASA
os.system('rm -rf *spw*a*')
spw=[0,1,2,3]
for i in spw:
  clean(vis='IRAS16293_Band9.fixed.rebin.ms.contsub',
     imagename='IRAS16293_Band9.fixed.ms.spw'+str(i)+'a',
     spw=str(i),field='',phasecenter='3',
     mode='channel',outframe='LSRK',
     nchan=950,start=20,width=2,
     imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=F,
     mask=['circle[[16h32m22.61s,-24d28m32.49s ], 0.75arcsec]','circle[[16h32m22.86s,-24d28m36.57s ], 1.0arcsec]'],
     weighting='briggs',robust=0.5,
     niter=200, threshold='220.0mJy',usescratch=F)
</source>

Next clean the 2nd half of each spw.

<source lang="python">
# In CASA
os.system('rm -rf *spw*b*')
spw=[0,1,2,3]
for i in spw:
  clean(vis='IRAS16293_Band9.fixed.rebin.ms.contsub',
     imagename='IRAS16293_Band9.fixed.ms.spw'+str(i)+'b',
     spw=str(i),field='',phasecenter='3',
     mode='channel',outframe='LSRK',
     nchan=950,start=1918,width=2,
     imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=F,
     mask=['circle[[16h32m22.61s,-24d28m32.49s ], 0.75arcsec]','circle[[16h32m22.86s,-24d28m36.57s ], 1.0arcsec]'],
     weighting='briggs',robust=0.5,
     niter=200, threshold='220.0mJy',usescratch=F)
</source>

=== Create a velocity cube for H13CN (J=8-7) ===

In this section we provide an example for how to create a Doppler corrected velocity cube for one of the interesting lines in the data: H13CN (J=8-7).  

A good reference for finding rest frequencies is http://www.splatalogue.net/. Another good reference for Band 9 specifically is the 450 micron CSO survey of Orion: http://iopscience.iop.org/0067-0049/132/2/281/

First, determine the velocity range for H13CN 690.55207 GHz
with the help of the next {{plotms}}

<source lang="python">
plotms(vis='IRAS16293_Band9.fixed.rebin.ms.contsub',
       xaxis='velocity', yaxis='amp',field='',
       spw='1', avgtime='1e8',avgscan=T,coloraxis='field',
       transform=True,freqframe='LSRK',restfreq='690.55207GHz',
       avgchannel='8',
       ydatacolumn='corrected',plotfile='Vel_H13CN.png')
</source>

Now do an interactive clean, making clean boxes for the emission.  Stop
cleaning when noise like residuals remain. Number of iterations
between showing interactive window can be controlled within the
interactive viewer. We used about 400 iterations.

<source lang="python">
os.system('rm -rf IRAS16293_Band9.fixed.H13CN_8_7*') 
clean(vis='IRAS16293_Band9.fixed.rebin.ms.contsub',
     imagename='IRAS16293_Band9.fixed.H13CN_8_7',
     spw='1',field='',phasecenter='3',
     mode='velocity',outframe='LSRK',restfreq='690.55207GHz',
     nchan=60,start='-10km/s',width='0.5km/s',
     imagermode='mosaic',
     imsize=500,cell='0.04arcsec',minpb=0.25,
     interactive=T,
     weighting='briggs',robust=0.5,
     niter=500, threshold='220.0mJy')
</source>

You need to correct for the primary beam attenuation and subimage the cubes to a 
smaller more manageable size since most of the mosaic is empty.

<source lang="python">
spw=[0,1,2,3]
for i in spw:
  impbcor(imagename='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'a.image',
        pbimage='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'a.flux',
        outfile='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'a.image.pbcor.subim',
        box='150,104,345,299')

spw=[0,1,2,3]
for i in spw:
  impbcor(imagename='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'b.image',
        pbimage='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'b.flux',
        outfile='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'b.image.pbcor.subim',
        box='150,104,345,299')

impbcor(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image',
        pbimage='IRAS16293_Band9.fixed.H13CN_8_7.flux',
        outfile='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',
        box='150,104,345,299')
</source>


To enable analysis in other software packages, you can export the data from CASA to FITS format using {{exportfits}}

<source lang="python">
spw=[0,1,2,3]
for i in spw:
  exportfits(imagename='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'a.image.pbcor.subim',
             fitsimage='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'a.image.pbcor.subim.fits')

spw=[0,1,2,3]
for i in spw:
  exportfits(imagename='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'b.image.pbcor.subim',
             fitsimage='IRAS16293_Band9.fixed.rebin.ms.spw'+str(i)+'b.image.pbcor.subim.fits')


exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',
             fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.fits')
</source>

=== Moment Maps for H13CN (J=8-7) ===
To proceed with the moment maps generation, you will need to determine the channel range and pixel range for inclusion for higher order moments.

<source lang="python">
viewer('IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim')
</source>

Generation of moment 0

<source lang="python">
os.system('rm -rf IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.mom0')
immoments(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',moments=[0],
          outfile='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.mom0',
          chans='10~44')
</source>

which you can see in Figure 8. To produce that figure run the following command

[[File:IRAS16293_Band9.fixed.H13CN_8_7.mom0.png|200px|thumb|right|'''Fig. 8.''' Moment 0 map for the spectral line H13CN J=8-7.]]

[[File:IRAS16293_Band9.fixed.H13CN_8_7.Positive_mom1.png|200px|thumb|right|'''Fig. 9.''' Positive part of the moment 1 map.]]

[[File:IRAS16293_Band9.fixed.H13CN_8_7.Negative_mom1.png|200px|thumb|right|'''Fig. 10.''' Negative part of the moment 1.]]

<source lang="python">
imview( raster=[ {'file':'IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.mom0',
                 'range': [-1.,5.5]} ], 
        contour={'file':'IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim', 
                 'base':0, 'unit':0.025, 'levels':[3]},
        out='IRAS16293_Band9.fixed.H13CN_8_7.mom0.png')
</source>

One cannot make higher order moments that simultaneously include
both positive and negative emission. So we separate them
below. Also, we need to restrict the higher order moments to regions
of real signal. Below we demonstrate using the clean mask for this
purpose. We must first subimage it to match the primary beam corrected image. We
also set the included pixels to about 5sigma.

<source lang="python">
immath(imagename='IRAS16293_Band9.fixed.H13CN_8_7.mask',
       mode='evalexpr',expr='IM0',box='150,104,345,299',
       outfile='IRAS16293_Band9.fixed.H13CN_8_7.mask.subim')
</source>

Producing moment 1 and 2

<source lang="python">
os.system('rm -rf IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive*')
immoments(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',moments=[1,2],
          outfile='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive',
          mask='IRAS16293_Band9.fixed.H13CN_8_7.mask.subim',
          chans='10~44',includepix=[0.4,100])

os.system('rm -rf IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative*')
immoments(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',moments=[1,2],
          outfile='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative',
          mask='IRAS16293_Band9.fixed.H13CN_8_7.mask.subim',
          chans='10~44',includepix=[-100,-0.4])
</source>

Visualizing the maps

<source lang="python">
imview( raster=[ {'file':'IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative.weighted_coord',
                 'colorwedge':True}], 
        contour={'file':'IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim', 
                 'base':0, 'unit':0.025, 'levels':[3]},
        out='IRAS16293_Band9.fixed.H13CN_8_7.Negative_mom1.png')
</source>

<source lang="python">
imview( raster=[ {'file':'IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive.weighted_coord',
                 'colorwedge':True}], 
        contour={'file':'IRAS16293_Band9.fixed.CONTIN_AP.image.pbcor.subim', 
                 'base':0, 'unit':0.025, 'levels':[3]},
        out='IRAS16293_Band9.fixed.H13CN_8_7.Positive_mom1.png')
</source>

And finally export the result to FITS format

<source lang="python">
exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.fits')

exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.mom0',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.mom0.fits')
exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative.weighted_coord',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative.weighted_coord.fits')
exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive.weighted_coord',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive.weighted_coord.fits')
exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative.weighted_dispersion_coord',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Negative.weighted_dispersion_coord.fits')
exportfits(imagename='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive.weighted_dispersion_coord',
           fitsimage='IRAS16293_Band9.fixed.H13CN_8_7.image.pbcor.subim.Positive.weighted_dispersion_coord.fits')
</source>

{{Checked 3.4.0}}