View source for CARMA spectral line mosaic M99 3.1

== Overview ==

This tutorial describes the data reduction for CO (1-0) data observed toward the galaxy M99 (NGC 4254) using CARMA. 
These data were kindly provided by the CARMA STING team and should not be used for scientific purposes. More information about STING and these data in particular can be found in Rahman et al. (2010) and [http://www.astro.umd.edu/~bolatto/STING/ CARMA STING webpage].
 
In this tutorial we follow common CARMA practice to utilize a wide band (500 MHz) spectral window to calibrate narrow band (6 MHz) spectral windows in order to achieve better signal-to-noise.

The tutorial below assumes that you have followed the initial miriad data reduction and export to fits steps described at [http://casaguides.nrao.edu/index.php?title=Importing_Data_from_MIRIAD Importing_Data_from_MIRIAD].

A tar file of the resulting fits files can be downloaded from http://casa.nrao.edu/Data/CARMA/M99/M99_CARMA.fits.tar

After downloading, unpack the data in the directory you will be working in with

tar -xvf M99_CARMA.fits.tar

this will create a directory called fits.

== Import fits files ==

Below we show an example of creating a python dictionary and using "for" loops in python to run importuvfits multiple times, and then use the output as the input to the concat task. 

<source lang="python">
myfiles = []
for i in range(4,7):
        msfile = "c0104I"+str(i)+".ms"
        importuvfits(fitsfile="fits/c0104I."+str(i)+".fits",
                     vis=msfile)
        myfiles.append(msfile)
</source>

<source lang="python">
concat(vis=myfiles,concatvis='c0104I',timesort=True)
</source>

<source lang="python">
listobs(vis='c0104I')
</source>

Look at the logger output.  Notice that the CARMA data comes into
CASA with antenna names that are numbers and that CASA also creates
an antenna id number for each antenna (it also creates id numbers
for fields and spw). Both the antenna name and id can be used to
identify an antenna, which is very confusing if both are numbers --
but not the same number. The python commands below will append the
antenna names with CA to more easily distinguish them from their
ids. ALMA will already have antenna names that are strings, as does
the EVLA. This step is only needed for data that comes into CASA via
importuvfits.

<source lang="python">
tb.open("c0104I/ANTENNA",nomodify=False)
namelist=tb.getcol("NAME").tolist()
for i in range(len(namelist)):
	name = 'CA'+namelist[i]
	print ' Changing '+namelist[i]+' to '+name
	namelist[i]=name

tb.putcol("NAME",namelist)
tb.close()
</source>

<source lang="python">
listobs(vis='c0104I')
</source>

<pre style="background-color: #fffacd;">
 Fields: 22
   ID   Code Name         RA            Decl           Epoch   nVis   
   0         MARS         09:15:50.0585 +17.21.03.8918 J2000   9450   
   1         3C273        12:29:06.7000 +02.03.08.5980 J2000   60480  
   2         NGC4254      12:18:47.8090 +14.24.13.9342 J2000   24570  
   3         NGC4254      12:18:49.6000 +14.24.13.9342 J2000   24570  
   4         NGC4254      12:18:51.3910 +14.24.13.9342 J2000   24570  
   5         NGC4254      12:18:52.2865 +14.24.36.4672 J2000   24570  
   6         NGC4254      12:18:50.4955 +14.24.36.4672 J2000   24570  
   7         NGC4254      12:18:48.7045 +14.24.36.4672 J2000   24570  
   8         NGC4254      12:18:46.9135 +14.24.36.4672 J2000   24570  
   9         NGC4254      12:18:46.0179 +14.24.59.0000 J2000   24570  
   10        NGC4254      12:18:47.8090 +14.24.59.0000 J2000   24570  
   11        NGC4254      12:18:49.6000 +14.24.59.0000 J2000   24570  
   12        NGC4254      12:18:51.3910 +14.24.59.0000 J2000   24570  
   13        NGC4254      12:18:53.1821 +14.24.59.0000 J2000   24570  
   14        NGC4254      12:18:52.2865 +14.25.21.5328 J2000   24570  
   15        NGC4254      12:18:50.4955 +14.25.21.5328 J2000   24570  
   16        NGC4254      12:18:48.7045 +14.25.21.5328 J2000   24570  
   17        NGC4254      12:18:46.9135 +14.25.21.5328 J2000   22680  
   18        NGC4254      12:18:47.8090 +14.25.44.0658 J2000   22680  
   19        NGC4254      12:18:49.6000 +14.25.44.0658 J2000   22680  
   20        NGC4254      12:18:51.3910 +14.25.44.0658 J2000   22680  
   21        3C274        12:30:49.4230 +12.23.28.0440 J2000   45360  
    (nVis = Total number of time/baseline visibilities per field) 
 Spectral Windows:  (3 unique spectral windows and 1 unique polarization setups)
   SpwID  Chans Frame Ch1(MHz)    ChanWid(kHz)TotBW(kHz)  Ref(MHz)    Corrs
   0          15 LSRK  113978.067  31250       468750      113978.067  RR  
   1          63 LSRK  114289.671  976.5625    61523.4375  114289.671  RR  
   2          63 LSRK  114341.036  976.5625    61523.4375  114341.036  RR  
 The SOURCE table is empty: see the FIELD table
 Antennas: 15:
   ID   Name  Station   Diam.    Long.         Lat.         
   0    CA1     ANT1      10.4 m   -116.45.01.0  +37.13.43.4  
   1    CA2     ANT2      10.4 m   -116.59.48.3  +37.52.00.3  
   2    CA3     ANT3      10.4 m   -120.06.43.6  +37.35.02.8  
   3    CA4     ANT4      10.4 m   -119.38.33.8  +38.26.22.2  
   4    CA5     ANT5      10.4 m   -119.13.25.0  +35.03.23.4  
   5    CA6     ANT6      10.4 m   -119.06.39.2  +38.54.24.4  
   6    CA7     ANT7      6.1  m   -119.30.34.9  +36.58.55.6  
   7    CA8     ANT8      6.1  m   -118.08.34.0  +37.16.45.8  
   8    CA9     ANT9      6.1  m   -118.46.59.9  +36.48.41.4  
   9    CA10    ANT10     6.1  m   -118.29.04.6  +36.03.45.6  
   10   CA11    ANT11     6.1  m   -118.48.40.9  +37.15.57.8  
   11   CA12    ANT12     6.1  m   -119.33.27.5  +36.06.46.1  
   12   CA13    ANT13     6.1  m   -118.31.43.5  +37.05.19.4  
   13   CA14    ANT14     6.1  m   -118.13.18.7  +36.50.34.6  
   14   CA15    ANT15     6.1  m   -117.23.50.2  +36.25.48.2  
</pre>

<pre style="background-color: #E0FFFF;">
Observing Strategy:

Bandpass, Gain, and Flux calibrator: 3c273 field id=1
Secondary Gain Calibrator 3C274 field id=21
Extra flux calibrator Mars field id=0

The target mosaic is in field ids 2~20

There is one wideband channel (spw=0) and two narrow band channels
(spw=1,2)
</pre>

== Initial Inspection and Flagging ==

First, flag edge channels: 

<source lang="python">
flagdata(vis='c0104I',mode='manualflag',selectdata=True,spw='0:0~1;13~14')
</source>

<source lang="python">
flagdata(vis='c0104I',mode='manualflag',selectdata=True,spw='1~2:0~1;61~62')
</source>

Let's look at the wide band channel on all sources:

[[Image:M99plotms1.png|thumb| Amplitude as a function of time for all sources, with each field shown in a different color.]]

<source lang="python">
plotms(vis='c0104I',xaxis='time',yaxis='amp',field='1~21', spw='0',
      avgchannel='15')
</source>

Click "Display" tab in the plotms GUI and select "Colorize by: Field",
then click the plot button.


'''There are a few things to notice:'''

First there is some very noticeably bad target data near the end of
the observation. Use the zoom button to zoom in on the bad timerange and
then click the Mark Region icon and put a box over a modest number of the
offending high points, and then press the Locate button. 

Information about the located points will display to the terminal ('''not''' the Log Messages window).  This will tell
you that the offending telescope is antenna name=CA13 (id=12) and
that the field-ids are 2~11. Looking at the plot the bad time range
is 05:43:00~05:54:00.

[[Image:M99plotms2.png|thumb|Zoom on bad target data, with small marked region.]]
[[Image:M99plotms3.png|thumb|Zoom on last few scans of 3c273 and 3c274.]]

Click the "Clear Regions" button, and then the "house" button to
unzoom.


Next note that the first two scans on 3c273 and the last scan on
3c273 show some low points. Also the last scan on 3c274 shows low
points.  Again use zoom, mark, and locate to see if you can see a
pattern.

The first two scans on 3c273 don't show a particular pattern -- no
particular antenna or individual time range appears to be
responsible. This is usually a good indication that it cannot be
calibrated out. Also note that it is unlikely that the target data
in between is good, though this is not obvious because the target is
weak. However, 3c274 does look ok, before the second 3c273 scan.
Unfortunately, there is no way to calibrate the first target scans
even if they are good, with no calibrator scan to precede them.  For
now we will flag 3c273 scans and the target in that timerange.

As for the last scans, there is a pattern: problems with the last scan on 3c273 appears to be caused by antenna CA7 (in scan 272)
and problems with the last scan on 3c274 appears to be related to antenna CA8 (scan 255).

Looking at the lowest points on all the 3c273 scans, baseline
CA2-CA8 appears low, we will also wait to see if this calibrates out.

We will flag the narrow band channels wherever we see problems in
wideband. You can either make separate plotting commands, or combine
as below. To demonstrate different possibilities we use timerange to
identify some data, and scan number others.

<source lang="python">
flagdata(vis='c0104I',spw='0~2',
	 antenna=['CA13','','CA7','CA8'],
	 field=['2~11','1~20','1','21'],
	 scan=['','','272','255'],
	 timerange=['05:43:00~05:54:00','00:25:00~00:55:00','',''])
</source>

Note that each of these flagging commands could have been written out separately, but combining them into one command will run much faster than the combined time of multiple calls as the data only have to be searched once.

== Set the Absolute Flux Scale ==

Often for millimeter observations, the absolute flux scale is determined by observing a planet and using a model of its flux as a function of baseline length (most planets are not point sources at mm frequencies using typical arrays). This is because the flux of planets change in a mostly predictable way (depending primarily on distance at any given time) than quasars. CASA does not yet have this functionality (we expect it for the next release). The flux of 3c273 is inserted from CARMA flux monitoring (which used the Mars observation to derive the 3c273 flux).

<source lang="python">
setjy(vis='c0104I', field='3C273',fluxdensity=[17.8,0.,0.,0.], spw='0~2')
</source>

== Calibrate the Wideband Bandpass ==



Lets start by looking at the bandpass calibrator's phase as a
function of channel for the wideband data.

[[Image:M99plotmswide1.png|thumb|Bandpass calibrator's phase as a function of channel.]]
<source lang="python">
plotms(vis='c0104I',xaxis='channel',yaxis='phase',
       field='1',spw='0',antenna='CA1',
       avgtime='1e8',avgscan=T)
</source>

Select colorize by antenna2, and select "custom" for unflagged
points, and then raise the style to 3 and you will see that the
phase does not change a great deal on individual baselines over
the wide band channel (spw=0). This means that averaging over some 
channels will not introduce significant closure errors. Check
other antennas if you like.

Now look at the phase as a function of time. With avgtime a large
number, but no avgscan=T, this command will show the average phase
per scan. Since the cache changes, the colorize option changes
too. You will have to select these again as above.

[[Image:M99plotmswide2.png|thumb|Bandpass calibrator's phase as a function of time.]]
<source lang="python">
plotms(vis='c0104I',xaxis='time',yaxis='phase',
       field='1',spw='0',
       antenna='CA1',
       avgchannel='15',avgtime='1e8')
</source>

Here you see significant variation with time. This is not
surprising as the bandpass calibrator was observed over a fairly
long period of time (~6 hours). In this case, it is important to calibrate the
phase before solving for the bandpass. We choose a fairly narrow
channel range since the bandpass phase as a function of frequency
has not been taken out yet (though for these data it is quite well
behaved).

<source lang="python">
gaincal(vis='c0104I',caltable='c0104I.bpphase_widecal',
        field='1',spw='0:5~9',
        refant='CA9',calmode='p',solint='inf',minsnr=2.0)
</source>

The use of solint='inf' here without setting combine will get one
solution per scan.

Look at the solutions

[[Image:M99plotcalwide1.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.bpphase_widecal',
        xaxis='time',yaxis='phase',
        iteration='antenna',subplot=321)
</source>

Note the phase drift is > 100 degrees on some antennas -- this is
why it would not have been appropriate to vector average these
data into one bp solution before taking out the phase. Use the
"Next" button to page through all antennas.

Having done a preliminary phase solution we can now combine all the
3C273 scans into one bandpass solution. Note the default combine
parameter is different for [[bandpass]] : combine='scan'.

<source lang="python">
bandpass(vis='c0104I',caltable='c0104I.bp_widecal',
         interp='',field='1',spw='0',
         bandtype='B',solint='inf',
         refant='CA9',solnorm=F,
         gaintable=['c0104I.bpphase_widecal'],
         spwmap=[[]])
</source>

[[Image:M99plotcalwide2.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.bp_widecal',xaxis='chan',yaxis='amp',
        iteration='antenna',subplot=321)
</source>

[[Image:M99plotcalwide3.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.bp_widecal',xaxis='chan',yaxis='phase',
        iteration='antenna',subplot=321)
</source>

== Calibrate the Wideband Gains ==

Now resolve for phase and also amplitude while applying the
bandpass solutions, for both calibrators 3c273 and 3c274. We will
not use the first gain solution again.

First, do a phase solution on the timescale of an individual integration. (Consulting the listobs will reveal that each scan is about 5 minutes long, built up of 10-second integrations.)

<source lang="python">
gaincal(vis='c0104I',caltable='c0104I.phase_widecal',
        field='1,21',spw='0',
        refant='CA9',calmode='p',solint='int',minsnr=2.0,
        gaintable=['c0104I.bp_widecal'],
        spwmap=[[]])
</source>

[[Image:M99plotcalwide4.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.phase_widecal',xaxis='time',yaxis='phase',
        iteration='antenna',subplot=321,plotrange=[0,0,-180,180])
</source>

Note the significant phase variation within a single scan. If we
used an average phase over each whole scan to derive the amplitude
solutions, it would decorrelate the subsequent amplitude
corrections.

So use the phase calibration just derived to get an amplitude solution over each scan:

<source lang="python">
gaincal(vis='c0104I',caltable='c0104I.amp_inf_widecal',
        field='1,21',spw='0',
        refant='CA9',calmode='ap',solint='inf',minsnr=2.0,
        gaintable=['c0104I.bp_widecal','c0104I.phase_widecal'],
        spwmap=[[]])
</source>

[[Image:M99plotcalwide5a.png|thumb|Gains for 3C274 (top points) and 3C273 (bottom points).]]
<source lang="python">
plotcal(caltable='c0104I.amp_inf_widecal',xaxis='time',yaxis='amp',
        iteration='antenna',subplot=321)
</source>

Note the different gains for 3c273 scans and 3c274 scans, this
just reflects that the two calibrators have different strengths.

Next plot the phase from the amplitude solutions -- this will be
the residual phase after taking out the integration-time based
solutions.  This gives a good idea of the residual phase
noise. If this is more than a few degrees, the cause should be
investigated. Note that the scatter for 3c274 is larger than 3c273
-- this reflects that 3c274 is significantly weaker and thus has
lower S/N. 

[[Image:M99plotcalwide6a.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.amp_inf_widecal',xaxis='time',yaxis='phase',
        iteration='antenna',subplot=321)
</source>

Here we re-run the phase only calibration but now getting one
solution per scan to apply later to the target. An alternative
would be to smooth the integration time phase calibration table,
but this is a bit simpler.

<source lang="python">
gaincal(vis='c0104I',caltable='c0104I.phase_inf_widecal',
        field='1,21',spw='0',
        refant='CA9',calmode='p',solint='inf',minsnr=2.0,
        gaintable=['c0104I.bp_widecal'],
        spwmap=[[]])
</source>

[[Image:M99plotcalwide7a.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.phase_inf_widecal',xaxis='time',yaxis='phase',
        iteration='antenna',subplot=321,plotrange=[0,0,-180,180])
</source>

This is a very important plot to look at carefully as these are
the phase solutions that will be applied to the target. Note that
the phases for 3c273 and 3c274 are very similar, but not exactly
the same. Unfortunately we can not color by field here to make it
easier to see.

== Calibrate the Narrow Band Bandpasses ==

Solve for narrow band bandpasses, applying wideband phase and
amplitude corrections. Because there are 3 spw in the data, you must
set a placeholder for all three in spwmap, even though we are only
solving for 2 of them.

<source lang="python">
bandpass(vis='c0104I',caltable='c0104I.bp_narrowcal',
         interp='',field='1',spw='1,2',
         bandtype='B',solint='inf',
         refant='CA9',solnorm=F,
         gaintable=['c0104I.phase_widecal','c0104I.amp_inf_widecal'],
         spwmap=[[0,0,0],[0,0,0]])
</source>

[[Image:M99plotcalnarrow1.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.bp_narrowcal',xaxis='chan',yaxis='amp',
        iteration='antenna',subplot=331)
</source>

[[Image:M99plotcalnarrow2.png|thumb|]]
<source lang="python">
plotcal(caltable='c0104I.bp_narrowcal',xaxis='chan',yaxis='phase',
        iteration='antenna',subplot=331)
</source>

== Calibrate Absolute Flux ==

<source lang="python">
fluxscale(vis='c0104I',caltable='c0104I.amp_inf_widecal',
          fluxtable='c0104I.fluxcal',reference='1')
</source>
[[Image:M99plotcalflux2.png|thumb|Plotcal for the fluxscale solution.]]
<pre style="background-color: #fffacd;">
Flux density for 3C274 in SpW=0 is: 3.11626 +/- 0.0146614 (SNR = 212.549, nAnt= 15)
</pre>

Check that the flux table looks as expected: the amplitude table scaled by correct flux.

<source lang="python">
plotcal(caltable='c0104I.fluxcal',xaxis='time',yaxis='amp',
        iteration='antenna',subplot=331)
</source>

== Applycal and Inspect ==

We run applycal separately for the wide and narrow band data so we can apply the appropriate bandpass tables.

Applying solutions to 3C273:
<source lang="python">
applycal(vis='c0104I',field='1',spw='0',
        gaintable=['c0104I.bp_widecal','c0104I.phase_widecal','c0104I.fluxcal'],
        spwmap=[[],[],[]],gainfield=['1','1','1'])
</source>

<source lang="python">
applycal(vis='c0104I',field='1',spw='1,2',
        gaintable=['c0104I.bp_narrowcal','c0104I.phase_widecal','c0104I.fluxcal'],
        spwmap=[[],[0,0,0],[0,0,0]],gainfield=['1','1','1'])
</source>

Applying solutions to 3C274:
<source lang="python">
applycal(vis='c0104I',field='21',spw='0',
        gaintable=['c0104I.bp_widecal','c0104I.phase_widecal','c0104I.fluxcal'],
        spwmap=[[],[],[]],gainfield=['1','21','21'])
</source>

<source lang="python">
applycal(vis='c0104I',field='21',spw='1,2',
        gaintable=['c0104I.bp_narrowcal','c0104I.phase_widecal','c0104I.fluxcal'],
        spwmap=[[],[0,0,0],[0,0,0]],gainfield=['1','21','21'])
</source>

For the target we will use both gain calibrators, so whichever is closest in time will be applied to the target.

<source lang="python">
applycal(vis='c0104I',field='2~20',spw='0',
        gaintable=['c0104I.bp_widecal','c0104I.phase_inf_widecal','c0104I.fluxcal'],
        spwmap=[[],[],[]],gainfield=['1','1,21','1,21'])
</source>

<source lang="python">
applycal(vis='c0104I',field='2~20',spw='1,2',
        gaintable=['c0104I.bp_narrowcal','c0104I.phase_inf_widecal','c0104I.fluxcal'],
        spwmap=[[],[0,0,0],[0,0,0]],gainfield=['1','1,21','1,21'])
</source>


You should now see, for example, that the phases are flat across all spw for 3C273, and amplitudes match the monitored calibrator fluxes for this date (with the source data at the bottom).
[[Image:M99plotmsfinp.png|thumb|]]
<source lang="python">
plotms(vis='c0104I',xaxis='frequency',ydatacolumn='corrected',
       field='1',avgtime='1e8')
</source>

[[Image:M99plotmsfina.png|thumb|]]
<source lang="python">
plotms(vis='c0104I',xaxis='time',yaxis='amp',ydatacolumn='corrected',
       field='1~21',spw='0',
       avgchannel='15',avgtime='1e8')
</source>

== Deconvolution and Imaging ==

[[Image:.png|thumb|]]
<source lang="python">
plotms(vis='c0104I',xaxis='velocity',yaxis='amp',ydatacolumn='corrected',
       field='2',spw='1~2',avgtime='1e8')
</source>

in plotms in the "trans" tab set the CO rest frequency (115271.2
MHz) in order to see the velocity range of the narrow band
channels. Unfortunately the individual pointings are a bit too
weak to see the UV vector averaged CO signal but you can at least
see the observed velocity range.

Go to display tab and chose colorize by spw. Notice that the edge
channels are a bit noisy -- especially a concern in the overlap
region. We will exclude these below.

[[Image:M99_interactive.png|thumb|The interactive viewer with the .flux contours in magenta and the clean mask in white contours.]]
The clean call below, uses the interactive clean mode. This is important for complex extended emission as is seen in the CO(1-0) for M99. Below we provide a brief description of one easy way you might make a clean mask for this image. Of course making a clean mask for each channel individually would be better if you have the time and patience.

<source lang="python">
clean(vis='c0104I',imagename='M99_cube_nearest',spw='1~2:3~59',field='2~20',
      phasecenter='11',
      cell='0.9arcsec',imsize=450,
      mode='velocity',start='2268km/s',width='10.0km/s',
      interpolation='nearest',
      imagermode='mosaic',cyclefactor=2,
      restfreq='115.2712GHz',interactive=T,
      mask='../M99_cube.mask',
      minpb=0.1,pbcor=F,
      niter=5000,threshold='40mJy')
</source>

When the interactive viewer pops up, click the "folder" icon in top left. From file gui select "M99_cube_nearest.flux" and then select "display as" contour. This will show the convolution of the primary beam coverage. Next click the "wrench" next to the file folder icon. This will open the "Data Display Options" gui. At the top, tab over until you see the "M99_cube_nearest.flux-contour" tab.  Now change the line color to magenta (from foreground). Now click the zoom button in the Display panel and zoom in on the mosaic region. Chose "all channels" toggle and then chose the polygon tool from the top, and make a clean mask around the outermost contour (this happens to be the 0.2 contour level). Double click inside the polygon area, and you should see it turn white. If you now use the tape deck to move back and forth through the cube you can check that all the emission falls within the masked region. Once you are satisfied with the mask, '''go to the data drop down menu and close the "M99_cube_nearest.flux-contour" file. If you don't do this you will get a table lock and the final image will not be constructed.''' Then hit the blue arrow to continue cleaning with this mask until the threshold is reached. More clean masking tips and techniques are described in the [[EVLA_spectral_line_IRC10216]] casaguide.

== Image Analysis ==

First lets see what the rms noise level in a single channel is using the viewer.

<source lang="python">
viewer(infile='M99_cube_nearest.image')
</source>

Then use the tape deck to go to a line free channel, select the box region tool and make a box. When you double click in the box, the image statistics for the whole cube will print to the terminal and for that window to a pop up window. Move the box around a bit to see what the variation is. You should get something like 22 - 25 mJy. If you want the box tool to go away, hit the escape key.

Next make integrated intensity maps (moment 0) and integrated velocity maps (moment 1). to do this, we'll want to know what channels the line emission starts on. The first channel with significant emission is channel 1, while the last channel with significant emission is channel 23.

[[Image:moment_maps_2.png|thumb|Example of moment 0 and moment 1 images side by side.]]
For moment zero, its best to limit the calculation to image channels with significant signal in them, but not to apply a flux cutoff, as this will bias the derived integrated intensities upward. 

<source lang="python">
immoments(imagename='M99_cube_nearest.image',moments=[0],
          axis='spectral',
          chans='1~23',outfile='M99_cube.moment0')
</source>

For moment 1, it is essential to apply a conservative flux cutoff to limit the calculation to 
high signal-to-noise areas.  Here we use about 5sigma.

<source lang="python">
immoments(imagename='M99_cube_nearest.image',moments=[1],
          axis='spectral',
          chans='1~23',excludepix=[-100,0.125],
          outfile='M99_cube.moment1')
</source>

[[Image:moment_maps.png|thumb|Example of moment 1 raster superposed with white moment 0 contours.]]
Next you can open the viwer by typing 
<source lang="python">
viewer
</source>

You can open both moment maps as rasters and blink between them. You might also try opening the "p wrench tool" (Viewer Canvas Manager), go to "Number of panels" and up the number in x to 2. Then select blink in the Viewer display to see both images side by side. It will help to make your viewer window fairly wide.

Also try opening the moment1 as a raster and moment0 as a contour map as in the example (I've played with the default contour levels in the "Data display options".

== ==
{{Checked 3.0.2}}

--[[User:CBrogan|Crystal Brogan]]