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

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[[Category: VLA]] [[Category: Calibration]] [[Category: Imaging]] [[Category: Spectral Line]]
[[Category: VLA]] [[Category: Calibration]] [[Category: Imaging]] [[Category: Spectral Line]]
{{Checked 5.7.2}}


== Overview ==
== Overview ==
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</source>
</source>


CASA issues a warning when running this command, but this can be disregarded.


=== Interactive Flagging ===
=== Interactive Flagging ===
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* Specify the measurement set in '''File'''; the '''Browse''' button allows you to hunt down the measurement set.
* Specify the measurement set in '''File'''; 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.
* 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 plotms() specify: spw=0:06~56. In the '''Channel Averaging''' box, enter 51 channels to average over all channels in the given range.
* 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 plotms() specify: spw=0:6~56. 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.'''
* 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 [[Data flagging with plotms | continuum data]]. When you're satisfied with the edits, '''File &rarr; Quit''' to return to the CASA prompt.
With these settings, interactive flagging proceeds as for [[Data flagging with plotms | continuum data]]. When you're satisfied with the edits, '''File &rarr; Quit''' to return to the CASA prompt.


== Calibration ==
== Calibration ==
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=== Setting the Flux Scale ===
=== Setting the Flux Scale ===


{{setjy | Setjy}} generates a source model for the primary calibrator, 1331+305 = 3C286. From CASA 5.3+ the default standard is Perley-Butler 2017, and includes resolved structure of the calibrators. This is 1.4GHz D-config and 1331+305 is sufficiently unresolved that, in principle, we don't need a model image; however, here we proceed with applying the detailed model, as a good practise.
{{setjy | Setjy}} generates a source model for the primary calibrator, 1331+305 = 3C286. From CASA 5.3+ the default standard is Perley-Butler 2017, and includes resolved structure of the calibrators. This is 1.4GHz D-config and 1331+305 is sufficiently unresolved that, in principle, we don't need a model image; however, here we proceed with applying the detailed model, as a good practice.


{{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.
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=== Inspect the Bandpass Response Curve ===
=== Inspect the Bandpass Response Curve ===


[[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_casa5.5.png | thumb | Bandpass response curves generated by {{plotms}}. The solutions for different antennas are indicated by differently colored plotting symbols. Plots for individual antennas can be generated by setting '''iteraxis = 'antenna'''' for {{plotms}}.]]


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.  
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. Plotms is the standard tool for plotting calibration solutions. The following commands produce the figure at right.  


<source lang="python">
<source lang="python">
# Set up 2x1 panels - upper panel amp vs. channel
# Set up 2x1 panels - upper panel amp vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=211, yaxis='amp', showgui=True)
plotms(vis='ngc5921.demo.bcal', field='0', gridrows=2, gridcols=1, plotindex=0, rowindex=0, xaxis='channel', yaxis='amp', showgui=True, clearplots=False, coloraxis='antenna1')
</source>
</source>


<source lang="python">
<source lang="python">
# Set up 2x1 panels - lower panel phase vs. channel
# Set up 2x1 panels - lower panel phase vs. channel
plotcal(caltable='ngc5921.demo.bcal', field='0', subplot=212, yaxis='phase', showgui=True)
plotms(vis='ngc5921.demo.bcal', field='0', gridrows=2, gridcols=1, plotindex=1, rowindex=1, xaxis='channel', yaxis='phase', showgui=True, clearplots=False, coloraxis='antenna1')
</source>
</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'
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 iteraxis = '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 plotms 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.


<!--
<!--
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<source lang="python">
<source lang="python">
# Set up 2x1 panels - upper panel amp vs. time
# Set up 2x1 panels - upper panel amp vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=211, yaxis='amp', showgui=True)
plotms(vis='ngc5921.demo.fluxscale', field='0,1', gridrows=2, gridcols=1, plotindex=0, rowindex=0, yaxis='amp', showgui=True, clearplots=False, coloraxis='antenna1')
</source>
</source>


<source lang="python">
<source lang="python">
# Set up 2x1 panels - lower panel phase vs. time
# Set up 2x1 panels - lower panel phase vs. time
plotcal(caltable='ngc5921.demo.fluxscale', field='0,1', subplot=212, yaxis='phase', showgui=True)
plotms(vis='ngc5921.demo.fluxscale', field='0,1', gridrows=2, gridcols=1, plotindex=1, rowindex=1, yaxis='phase', showgui=True, clearplots=False, coloraxis='antenna1')
</source>
</source>


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


<!--
<!--
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* (Axes)'''Y Axis''' = Amp
* (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.
Now remove 0:6~56 from the spw field to see all channels. 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 ==
== Continuum Subtraction ==
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[[File:spectrum-uvplot.png|thumb|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.]]
[[File:spectrum-uvplot.png|thumb|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}}:
Now we can generate the primary science product, a {{tclean}} data cube (ra, dec, velocity) from the continuum-subtracted (''u'', ''v'', channel) measurement set, ''ngc5921.demo.ms.contsub''. Things to consider in using {{tclean}}:
* To ensure channels aren't averaged prior to imaging, choose '''mode='channel''''.
* 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.
* 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 &times; 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']''').
* The maximum baseline is just under 5 kilolambda (see the figure at right), and so the expected synthetic beam is roughly 1.22 &times; 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''''.  
* We only want to {{tclean}} down to the noise, which is easily determined by trial-and-error imaging of a single channel (choosing '''nchan=1''' and '''start''' appropriately). Here, {{tclean}} stops when the maximum residual on the channel is below '''threshold='8.0mJy''''.  


<!--
<source lang="python">
<source lang="python">
# Image the continuum subtracted measurement set
# Image the continuum subtracted measurement set
clean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.clean', 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)
clean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.clean', 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>
</source>
-->


 
<!-- As of CASA 5.3, the preferred and recommended deconvolution task is {{tclean}}(). The {{tclean}}() execution equivalent to the above {{clean}}() one is following: -->
As of CASA 5.3, the preferred and recommended deconvolution task is {{tclean}}(). The {{tclean}}() execution equivalent to the above {{clean}}() one is following:


<source lang="python">
<source lang="python">
tclean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.tclean', field='0', datacolumn='data', specmode='cube', nchan=46, start=5, width=1, spw='', deconvolver='hogbom', gridder='standard' niter=6000, gain=0.1, threshold='8.0mJy', imsize=[256,256], cell=['15.0arcsec','15.0arcsec'], weighting='briggs', robust=0.5,  mask = 'box[[108pix,108pix],[148pix,148pix]]', interactive=False)
# Image the continuum subtracted measurement set
tclean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.tclean', field='0', datacolumn='data', specmode='cube', nchan=46, start=5, width=1, spw='', deconvolver='hogbom', gridder='standard', niter=6000, gain=0.1, threshold='8.0mJy', imsize=[256,256], cell=['15.0arcsec','15.0arcsec'], weighting='briggs', robust=0.5,  mask = 'box[[108pix,108pix],[148pix,148pix]]', interactive=False, pblimit=-0.2)
</source>
</source>


<!--
Execute only one, either {{tclean}}() or {{clean}}().
Execute only one, either {{tclean}}() or {{clean}}().
-->


<!--
<!--
Line 914: Line 918:


<source lang="python">
<source lang="python">
imhead(imagename='ngc5921.demo.clean.image', mode='summary')
imhead(imagename='ngc5921.demo.tclean.image', mode='summary')
</source>
</source>


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The output, as follows, appears in the logger window.
The output, as follows, appears in the logger window.
<!--


<pre>
<pre>
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2018-09-13 00:26:03 INFO imhead ##########################################
2018-09-13 00:26:03 INFO imhead ##########################################
</pre>
</pre>
-->
<pre>
2019-09-03 22:25:25 INFO imhead ##### Begin Task: imhead            #####
2019-09-03 22:25:25 INFO imhead imhead(imagename="ngc5921.demo.tclean.image",mode="summary",hdkey="",hdvalue="",verbose=False)
2019-09-03 22:25:25 INFO ImageMetaData  
2019-09-03 22:25:25 INFO ImageMetaData Image name      : ngc5921.demo.tclean.image
2019-09-03 22:25:25 INFO ImageMetaData Object name      : N5921_2
2019-09-03 22:25:25 INFO ImageMetaData Image type      : PagedImage
2019-09-03 22:25:25 INFO ImageMetaData Image quantity  : Intensity
2019-09-03 22:25:25 INFO ImageMetaData Pixel mask(s)    : None
2019-09-03 22:25:25 INFO ImageMetaData Region(s)        : None
2019-09-03 22:25:25 INFO ImageMetaData Image units      : Jy/beam
2019-09-03 22:25:25 INFO ImageMetaData Restoring Beams
2019-09-03 22:25:25 INFO ImageMetaData Pol  Type Chan        Freq    Vel
2019-09-03 22:25:25 INFO ImageMetaData   I    Max    7 1.41296e+09 1571.92  51.6639 arcsec x  47.3594 arcsec pa=  8.3205 deg
2019-09-03 22:25:25 INFO ImageMetaData   I    Min  43 1.41384e+09 1386.42  51.5897 arcsec x  47.3127 arcsec pa=  8.3869 deg
2019-09-03 22:25:25 INFO ImageMetaData   I Median  13 1.41310e+09 1541.00  51.6611 arcsec x  47.3318 arcsec pa=  8.1425 deg
2019-09-03 22:25:25 INFO ImageMetaData  
2019-09-03 22:25:25 INFO ImageMetaData Direction reference : J2000
2019-09-03 22:25:25 INFO ImageMetaData Spectral  reference : LSRK
2019-09-03 22:25:25 INFO ImageMetaData Velocity  type      : RADIO
2019-09-03 22:25:25 INFO ImageMetaData Rest frequency      : 1.42041e+09 Hz
2019-09-03 22:25:25 INFO ImageMetaData Pointing center    :  15:22:00.000000  +05.04.00.000000
2019-09-03 22:25:25 INFO ImageMetaData Telescope          : VLA
2019-09-03 22:25:25 INFO ImageMetaData Observer            : TEST
2019-09-03 22:25:25 INFO ImageMetaData Date observation    : 1995/04/13/09:33:00
2019-09-03 22:25:25 INFO ImageMetaData Telescope position: [-1.60119e+06m, -5.04198e+06m, 3.55488e+06m] (ITRF)
2019-09-03 22:25:25 INFO ImageMetaData  
2019-09-03 22:25:25 INFO ImageMetaData Axis Coord Type      Name            Proj Shape Tile  Coord value at pixel    Coord incr Units
2019-09-03 22:25:25 INFO ImageMetaData ------------------------------------------------------------------------------------------------
2019-09-03 22:25:25 INFO ImageMetaData 0    0    Direction Right Ascension  SIN  256  64  15:22:00.000  128.00 -1.500000e+01 arcsec
2019-09-03 22:25:25 INFO ImageMetaData 1    0    Direction Declination      SIN  256  64 +05.04.00.000  128.00  1.500000e+01 arcsec
2019-09-03 22:25:25 INFO ImageMetaData 2    1    Stokes    Stokes                    1    1            I
2019-09-03 22:25:25 INFO ImageMetaData 3    2    Spectral  Frequency                46    8  1.41279e+09    0.00 2.4414062e+04 Hz
2019-09-03 22:25:25 INFO ImageMetaData                     Velocity                              1607.99    0.00 -5.152860e+00 km/s
2019-09-03 22:25:25 INFO imhead ##### End Task: imhead              #####
2019-09-03 22:25:25 INFO imhead ##########################################
</pre>


<!--
<!--
Line 1,001: Line 1,048:
== Additional Science Products ==
== Additional Science Products ==


If things went well, you should now have a spectral line cube (''ngc5921.demo.clean.image'') 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.tclean.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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{{imstat|Imstat}} is the tool for displaying statistics of images and cubes. The following example displays the statistics for an empty region of the whole cube.
{{imstat|Imstat}} is the tool for displaying statistics of images and cubes. The following example displays the statistics for an empty region of the whole cube.


<!--
<source lang="python">
<source lang="python">
cubestat=imstat(imagename='ngc5921.demo.clean.image', box='10,10,100,100')
cubestat=imstat(imagename='ngc5921.demo.clean.image', box='10,10,100,100')
</source>
</source>


<!--
 
default('imstat')
default('imstat')
imagename = clnimage # or imagename = "ngc5921.demo.cleanimg.img"
imagename = clnimage # or imagename = "ngc5921.demo.cleanimg.img"
Line 1,019: Line 1,067:
#box = '108,108,148,148'
#box = '108,108,148,148'
cubestats = imstat()
cubestats = imstat()
-->
 


The output goes to the logger window.
The output goes to the logger window.
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If you executed {{tclean}}() instead of {{clean}}() in previous steps, then your image statistics may look like this:
If you executed {{tclean}}() instead of {{clean}}() in previous steps, then your image statistics may look like this:
-->


<source lang="python">
<source lang="python">
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</source>
</source>


<!--
<pre>
<pre>
2018-09-13 23:57:15 INFO imstat ##########################################
2018-09-13 23:57:15 INFO imstat ##########################################
Line 1,129: Line 1,180:
2018-09-13 23:57:15 INFO imstat ##########################################
2018-09-13 23:57:15 INFO imstat ##########################################
</pre>
</pre>
-->


 
<pre>
There are small variations, but in principle the two methods give similar results.
2019-09-03 22:40:15 INFO imstat ##########################################
 
2019-09-03 22:40:15 INFO imstat ##### Begin Task: imstat            #####
2019-09-03 22:40:15 INFO imstat imstat(imagename="ngc5921.demo.tclean.image",axes=-1,region="",box="10,10,100,100",chans="",
2019-09-03 22:40:15 INFO imstat         stokes="",listit=True,verbose=True,mask="",stretch=False,
2019-09-03 22:40:15 INFO imstat         logfile="",append=True,algorithm="classic",fence=-1,center="mean",
2019-09-03 22:40:15 INFO imstat         lside=True,zscore=-1,maxiter=-1,clmethod="auto",niter=3)
2019-09-03 22:40:15 INFO imstat Using specified box(es) 10,10,100,100
2019-09-03 22:40:15 INFO imstat Determining stats for image ngc5921.demo.tclean.image
2019-09-03 22:40:15 INFO imstat Selected bounding box :
2019-09-03 22:40:15 INFO imstat     [10, 10, 0, 0] to [100, 100, 0, 45]  (15:23:58.379, +04.34.29.305, I, 1.41279e+09Hz to 15:22:28.105, +04.56.59.962, I, 1.41389e+09Hz)
2019-09-03 22:40:15 INFO imstat Statistics calculated using Classic algorithm
2019-09-03 22:40:15 INFO imstat Regions ---
2019-09-03 22:40:15 INFO imstat         -- bottom-left corner (pixel) [blc]:  [10, 10, 0, 0]
2019-09-03 22:40:15 INFO imstat         -- top-right corner (pixel) [trc]:    [100, 100, 0, 45]
2019-09-03 22:40:15 INFO imstat         -- bottom-left corner (world) [blcf]: 15:23:58.379, +04.34.29.305, I, 1.41279e+09Hz
2019-09-03 22:40:15 INFO imstat         -- top-right corner (world) [trcf]:  15:22:28.105, +04.56.59.962, I, 1.41389e+09Hz
2019-09-03 22:40:15 INFO imstat Values ---
2019-09-03 22:40:15 INFO imstat         -- flux [flux]:                            1.99425 Jy.km/s
2019-09-03 22:40:15 INFO imstat         -- number of points [npts]:                380926
2019-09-03 22:40:15 INFO imstat         -- maximum value [max]:                    0.00953276 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- minimum value [min]:                    -0.0100478 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- position of max value (pixel) [maxpos]: [85, 63, 0, 8]
2019-09-03 22:40:15 INFO imstat         -- position of min value (pixel) [minpos]: [30, 18, 0, 7]
2019-09-03 22:40:15 INFO imstat         -- position of max value (world) [maxposf]: 15:22:43.151, +04.47.44.907, I, 1.41298e+09Hz
2019-09-03 22:40:15 INFO imstat         -- position of min value (world) [minposf]: 15:23:38.319, +04.36.29.518, I, 1.41296e+09Hz
2019-09-03 22:40:15 INFO imstat         -- Sum of pixel values [sum]:              4.76561 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Sum of squared pixel values [sumsq]:    1.38125 Jy/beam.Jy/beam
2019-09-03 22:40:15 INFO imstat Statistics ---
2019-09-03 22:40:15 INFO imstat         -- Mean of the pixel values [mean]:        1.25106e-05 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Variance of the pixel values :          3.62589e-06 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Standard deviation of the Mean [sigma]:  0.00190418 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Root mean square [rms]:                  0.00190421 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Median of the pixel values [median]:    6.37541e-06 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Median of the deviations [medabsdevmed]: 0.00127676 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- IQR [quartile]:                          0.00255348 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- First quartile [q1]:                    -0.00126604 Jy/beam
2019-09-03 22:40:15 INFO imstat         -- Third quartile [q3]:                    0.00128744 Jy/beam
2019-09-03 22:40:15 INFO imstat Sum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat Mean column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat Std_dev column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat Minimum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat Maximum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat Npts          Sum          Mean          Rms          Std_dev      Minimum      Maximum   
2019-09-03 22:40:15 INFO imstat 3.809260e+05  4.765615e+00  1.251060e-05  1.904215e-03  1.904176e-03 -1.004781e-02  9.532760e-03
2019-09-03 22:40:15 INFO imstat ##### End Task: imstat              #####
2019-09-03 22:40:15 INFO imstat ##########################################
</pre>


<!--
<!--
Line 1,182: Line 1,279:


<source lang="python">
<source lang="python">
viewer(infile='ngc5921.demo.clean.image')
viewer(infile='ngc5921.demo.tclean.image')
</source>
</source>


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* The spectrum now appears in the graphics window; see the figure at right.
* 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 [[File:save-as-text-file.png]] button on the graphics window. There are also buttons to print the figure or save the figure to disk.
You can save the integrated spectrum to a text file by clicking the [[File:save_to_text.png]] button on the graphics window. There are also buttons to print the figure or save the figure to disk.




Line 1,212: Line 1,309:


<source lang="python">
<source lang="python">
immoments(imagename='ngc5921.demo.clean.image', moments=[0,1], excludepix=[-100, 0.009], axis='spectral', chans='', outfile='ngc5921.demo.moments')
immoments(imagename='ngc5921.demo.tclean.image', moments=[0,1], excludepix=[-100, 0.009], axis='spectral', chans='', outfile='ngc5921.demo.moments')
</source>
</source>


Line 1,270: Line 1,367:
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')
exportfits(imagename='ngc5921.demo.tclean.image', fitsimage='ngc5921.demo.tclean.fits')
</source>
</source>


Line 1,318: Line 1,415:
[[Pre-upgrade VLA Tutorials | &#8629; '''Pre-upgrade VLA Tutorials''']]
[[Pre-upgrade VLA Tutorials | &#8629; '''Pre-upgrade VLA Tutorials''']]


{{Checked 5.3.0}}
{{Checked 5.7.2}}

Latest revision as of 17:08, 13 February 2023

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

Last checked on CASA Version 5.7.2.

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.

DATA: 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. 

2018-09-12 20:19:48 INFO listobs	##########################################
2018-09-12 20:19:48 INFO listobs	##### Begin Task: listobs            #####
2018-09-12 20:19:48 INFO listobs	listobs(vis="ngc5921.demo.ms",selectdata=True,spw="",field="",antenna="",
2018-09-12 20:19:48 INFO listobs	        uvrange="",timerange="",correlation="",scan="",intent="",
2018-09-12 20:19:48 INFO listobs	        feed="",array="",observation="",verbose=True,listfile="",
2018-09-12 20:19:48 INFO listobs	        listunfl=False,cachesize=50,overwrite=False)
2018-09-12 20:19:49 INFO listobs	================================================================================
2018-09-12 20:19:49 INFO listobs	           MeasurementSet Name:  /lustre/aoc/sciops/akapinsk/oldVLA/ngc5921/ngc5921.demo.ms      MS Version 2
2018-09-12 20:19:49 INFO listobs	================================================================================
2018-09-12 20:19:49 INFO listobs	   Observer: TEST     Project:   
2018-09-12 20:19:49 INFO listobs	Observation: VLA
2018-09-12 20:19:49 INFO listobs	Computing scan and subscan properties...
2018-09-12 20:19:49 INFO listobs	Data records: 22653       Total elapsed time = 5310 seconds
2018-09-12 20:19:49 INFO listobs	   Observed from   13-Apr-1995/09:18:45.0   to   13-Apr-1995/10:47:15.0 (TAI)
2018-09-12 20:19:49 INFO listobs	   
2018-09-12 20:19:49 INFO listobs	   ObservationID = 0         ArrayID = 0
2018-09-12 20:19:49 INFO listobs	  Date        Timerange (TAI)          Scan  FldId FieldName             nRows     SpwIds   Average Interval(s)    ScanIntent
2018-09-12 20:19:49 INFO listobs	  13-Apr-1995/09:18:45.0 - 09:24:45.0     1      0 1331+30500002_0           4509  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              09:27:15.0 - 09:29:45.0     2      1 1445+09900002_0           1890  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              09:32:45.0 - 09:48:15.0     3      2 N5921_2                   6048  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              09:50:15.0 - 09:51:15.0     4      1 1445+09900002_0            756  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              10:21:45.0 - 10:23:15.0     5      1 1445+09900002_0           1134  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              10:25:45.0 - 10:43:15.0     6      2 N5921_2                   6804  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	              10:45:15.0 - 10:47:15.0     7      1 1445+09900002_0           1512  [0]  [30] 
2018-09-12 20:19:49 INFO listobs	           (nRows = Total number of rows per scan) 
2018-09-12 20:19:49 INFO listobs	Fields: 3
2018-09-12 20:19:49 INFO listobs	  ID   Code Name                RA               Decl           Epoch   SrcId      nRows
2018-09-12 20:19:49 INFO listobs	  0         1331+30500002_0     13:31:08.287300 +30.30.32.95900 J2000   0           4509
2018-09-12 20:19:49 INFO listobs	  1         1445+09900002_0     14:45:16.465600 +09.58.36.07300 J2000   1           5292
2018-09-12 20:19:49 INFO listobs	  2         N5921_2             15:22:00.000000 +05.04.00.00000 J2000   2          12852
2018-09-12 20:19:49 INFO listobs	Spectral Windows:  (1 unique spectral windows and 1 unique polarization setups)
2018-09-12 20:19:49 INFO listobs	  SpwID  Name   #Chans   Frame   Ch0(MHz)  ChanWid(kHz)  TotBW(kHz) CtrFreq(MHz)  Corrs  
2018-09-12 20:19:49 INFO listobs	  0      none      63   LSRK    1412.665        24.414      1550.2   1413.4219   RR  LL
2018-09-12 20:19:49 INFO listobs	Sources: 3
2018-09-12 20:19:49 INFO listobs	  ID   Name                SpwId RestFreq(MHz)  SysVel(km/s) 
2018-09-12 20:19:49 INFO listobs	  0    1331+30500002_0     0     1420.405752    0            
2018-09-12 20:19:49 INFO listobs	  1    1445+09900002_0     0     1420.405752    0            
2018-09-12 20:19:49 INFO listobs	  2    N5921_2             0     1420.405752    0            
2018-09-12 20:19:49 INFO listobs	Antennas: 27:
2018-09-12 20:19:49 INFO listobs	  ID   Name  Station   Diam.    Long.         Lat.                Offset from array center (m)                ITRF Geocentric coordinates (m)        
2018-09-12 20:19:49 INFO listobs	                                                                     East         North     Elevation               x               y               z
2018-09-12 20:19:49 INFO listobs	  0    1     VLA:N7    25.0 m   -107.37.07.2  +33.54.12.9        -30.2623      345.7477       -0.8872 -1601155.613187 -5041783.882304  3555162.343090
2018-09-12 20:19:49 INFO listobs	  1    2     VLA:W1    25.0 m   -107.37.05.9  +33.54.00.5          3.5004      -39.7725        0.9883 -1601188.991307 -5042000.530918  3554843.409670
2018-09-12 20:19:49 INFO listobs	  2    3     VLA:W2    25.0 m   -107.37.07.4  +33.54.00.9        -37.1358      -25.0262        1.0383 -1601225.244615 -5041980.431775  3554855.677111
2018-09-12 20:19:49 INFO listobs	  3    4     VLA:E1    25.0 m   -107.37.05.7  +33.53.59.2          6.9833      -79.6414        1.1565 -1601192.444530 -5042022.911771  3554810.411780
2018-09-12 20:19:49 INFO listobs	  4    5     VLA:E3    25.0 m   -107.37.02.8  +33.54.00.5         81.5188      -37.9632        1.0246 -1601114.335629 -5042023.211477  3554844.931655
2018-09-12 20:19:49 INFO listobs	  5    6     VLA:E9    25.0 m   -107.36.45.1  +33.53.53.6        536.8977     -250.3175        0.1183 -1600715.915813 -5042273.186780  3554668.167811
2018-09-12 20:19:49 INFO listobs	  6    7     VLA:E6    25.0 m   -107.36.55.6  +33.53.57.7        267.7566     -124.8145        1.2815 -1600951.554888 -5042125.947753  3554772.987072
2018-09-12 20:19:49 INFO listobs	  7    8     VLA:W8    25.0 m   -107.37.21.6  +33.53.53.0       -401.2640     -270.6305        2.2293 -1601614.059494 -5042001.699973  3554652.484758
2018-09-12 20:19:49 INFO listobs	  8    9     VLA:N5    25.0 m   -107.37.06.7  +33.54.08.0        -16.9948      194.1215       -0.1368 -1601168.756077 -5041869.099542  3555036.914367
2018-09-12 20:19:49 INFO listobs	  9    10    VLA:W3    25.0 m   -107.37.08.9  +33.54.00.1        -74.4964      -50.1921        1.1608 -1601265.132224 -5041982.597979  3554834.857504
2018-09-12 20:19:49 INFO listobs	  10   11    VLA:N4    25.0 m   -107.37.06.5  +33.54.06.1        -11.7487      134.3686        0.1774 -1601173.922897 -5041902.701204  3554987.495105
2018-09-12 20:19:49 INFO listobs	  11   12    VLA:W5    25.0 m   -107.37.13.0  +33.53.57.8       -179.2554     -120.8635        1.4872 -1601376.990711 -5041988.712764  3554776.381187
2018-09-12 20:19:49 INFO listobs	  12   13    VLA:N3    25.0 m   -107.37.06.3  +33.54.04.8         -8.2438       94.5297        0.3947 -1601177.362708 -5041925.112425  3554954.550128
2018-09-12 20:19:49 INFO listobs	  13   14    VLA:N1    25.0 m   -107.37.06.0  +33.54.01.8         -0.0030        0.0445        0.8773 -1601185.580779 -5041978.216463  3554876.396287
2018-09-12 20:19:49 INFO listobs	  14   15    VLA:N2    25.0 m   -107.37.06.2  +33.54.03.5         -4.7904       54.7090        0.5774 -1601180.839839 -5041947.470902  3554921.600805
2018-09-12 20:19:49 INFO listobs	  15   16    VLA:E7    25.0 m   -107.36.52.4  +33.53.56.5        348.8969     -162.6653        1.0336 -1600880.544215 -5042170.427468  3554741.431900
2018-09-12 20:19:49 INFO listobs	  16   17    VLA:E8    25.0 m   -107.36.48.9  +33.53.55.1        438.6654     -204.5038        0.5027 -1600801.910482 -5042219.412805  3554706.408864
2018-09-12 20:19:49 INFO listobs	  17   18    VLA:W4    25.0 m   -107.37.10.8  +33.53.59.1       -122.0163      -82.2819        1.2624 -1601315.866196 -5041985.352573  3554808.279150
2018-09-12 20:19:49 INFO listobs	  18   19    VLA:E5    25.0 m   -107.36.58.4  +33.53.58.8        195.8349      -91.2758        1.2155 -1601014.427180 -5042086.300814  3554800.787928
2018-09-12 20:19:49 INFO listobs	  19   20    VLA:W9    25.0 m   -107.37.25.1  +33.53.51.0       -491.1000     -331.2429        2.5539 -1601709.998072 -5042006.975455  3554602.355417
2018-09-12 20:19:49 INFO listobs	  20   21    VLA:W6    25.0 m   -107.37.15.6  +33.53.56.4       -244.9704     -165.2178        1.6861 -1601447.161927 -5041992.554228  3554739.677219
2018-09-12 20:19:49 INFO listobs	  21   22    VLA:E4    25.0 m   -107.37.00.8  +33.53.59.7        133.6478      -62.2829        1.0919 -1601068.773396 -5042051.970054  3554824.783566
2018-09-12 20:19:49 INFO listobs	  23   24    VLA:E2    25.0 m   -107.37.04.4  +33.54.01.1         40.6649      -18.9151        0.9550 -1601150.040469 -5042000.665669  3554860.702914
2018-09-12 20:19:49 INFO listobs	  24   25    VLA:N6    25.0 m   -107.37.06.9  +33.54.10.3        -23.2197      265.3902       -0.4819 -1601162.569974 -5041829.054708  3555095.873969
2018-09-12 20:19:49 INFO listobs	  25   26    VLA:N9    25.0 m   -107.37.07.8  +33.54.19.0        -46.5533      532.1581       -1.8550 -1601139.422904 -5041679.082136  3555316.518142
2018-09-12 20:19:49 INFO listobs	  26   27    VLA:N8    25.0 m   -107.37.07.5  +33.54.15.8        -38.0437      434.7201       -1.3387 -1601147.894127 -5041733.868915  3555235.935926
2018-09-12 20:19:49 INFO listobs	  27   28    VLA:W7    25.0 m   -107.37.18.4  +33.53.54.8       -319.1171     -215.2368        1.9407 -1601526.340031 -5041996.897001  3554698.302182
2018-09-12 20:19:49 INFO listobs	##### End Task: listobs              #####
2018-09-12 20:19:49 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)

CASA issues a warning when running this command, but this can be disregarded.

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; 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 plotms() specify: spw=0:6~56. 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 will be 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 first stored in ngc5921.demo.gcal. In the second part of this step we correct the fluxscale of the .gcal table, and store the final calibration solutions with correct fluxes in ngc5921.demo.fluxscale (this is the table that needs to be applied later to the data, not the .gcal version).
  • 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 source model for the primary calibrator, 1331+305 = 3C286. From CASA 5.3+ the default standard is Perley-Butler 2017, and includes resolved structure of the calibrators. This is 1.4GHz D-config and 1331+305 is sufficiently unresolved that, in principle, we don't need a model image; however, here we proceed with applying the detailed model, as a good practice.

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
setjy(vis='ngc5921.demo.ms', field='1331+305*', model='3C286_L.im')


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

2018-09-12 20:43:38 INFO setjy	##########################################
2018-09-12 20:43:38 INFO setjy	##### Begin Task: setjy              #####
2018-09-12 20:43:38 INFO setjy	setjy(vis="ngc5921.demo.ms",field="1331+305*",spw="",selectdata=False,timerange="",
2018-09-12 20:43:38 INFO setjy	        scan="",intent="",observation="",scalebychan=True,standard="Perley-Butler 2017",
2018-09-12 20:43:38 INFO setjy	        model="3C286_L.im",modimage="",listmodels=False,fluxdensity=-1,spix=0.0,
2018-09-12 20:43:38 INFO setjy	        reffreq="1GHz",polindex=[],polangle=[],rotmeas=0.0,fluxdict={},
2018-09-12 20:43:38 INFO setjy	        useephemdir=False,interpolation="nearest",usescratch=False,ismms=False)
2018-09-12 20:43:38 INFO setjy	{'field': '1331+305*'}
2018-09-12 20:43:38 INFO Imager	Opening MeasurementSet [...]
2018-09-12 20:43:38 INFO setjy	Using /home/casa/data/distro/nrao/VLA/CalModels/3C286_L.im for modimage.
2018-09-12 20:43:38 INFO setjy	CASA Version 5.3.0-143  
2018-09-12 20:43:38 INFO setjy	   
2018-09-12 20:43:39 INFO imager	Using channel dependent flux densities
2018-09-12 20:43:39 INFO imager	Selected 4509 out of 22653 rows.
2018-09-12 20:43:39 INFO imager	1331+30500002_0 (fld ind 0) spw 0  [I=15.016, Q=0, U=0, V=0] Jy @ 1.4127e+09Hz, (Perley-Butler 2017)
2018-09-12 20:43:40 INFO imager	Using model image /home/casa/data/distro/nrao/VLA/CalModels/3C286_L.im
2018-09-12 20:43:40 INFO imager	Scaling spw(s) [0]'s model image by channel to  I = 15.0159, 15.0118, 15.0077 Jy @(1.41265e+09, 1.41343e+09, 1.41419e+09)Hz (LSRK) for visibility prediction (a few representative values are shown).
2018-09-12 20:43:40 INFO imager	The model image's reference pixel is 0.00904522 arcsec from 1331+30500002_0's phase center.
2018-09-12 20:43:40 INFO imager	Will clear any existing model with matching field=1331+30500002_0 and spw=*
2018-09-12 20:43:40 INFO  	Clearing model records in MS header for selected fields.
2018-09-12 20:43:40 INFO  	 1331+30500002_0 (id = 0) deleted.
2018-09-12 20:43:40 INFO imager	Selected 4509 out of 22653 rows.
2018-09-12 20:43:40 INFO setjy	##### End Task: setjy                #####
2018-09-12 20:43:40 INFO setjy	##########################################


Bandpass Calibration

The flux calibrator 1331+305 = 3C 286 now has a model assigned to it. Since the bandwidth of our observations is only 1.55 MHz, the model doesn't change over this narrow range of frequencies, so we can use it 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 plotms. The solutions for different antennas are indicated by differently colored plotting symbols. Plots for individual antennas can be generated by setting iteraxis = 'antenna' for plotms.

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. Plotms 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
plotms(vis='ngc5921.demo.bcal', field='0', gridrows=2, gridcols=1, plotindex=0, rowindex=0, xaxis='channel', yaxis='amp', showgui=True, clearplots=False, coloraxis='antenna1')

# Set up 2x1 panels - lower panel phase vs. channel
plotms(vis='ngc5921.demo.bcal', field='0', gridrows=2, gridcols=1, plotindex=1, rowindex=1, xaxis='channel', yaxis='phase', showgui=True, clearplots=False, coloraxis='antenna1')

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 iteraxis = 'antenna'

Notice that plotms 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.
  • Unless you use parameter incremental=True while executing fluxscale() (the default is False), the resulting .fluxscale table will replace the .gcal table at this point. This particularly important in the applycal() stage.
# 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')



# 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. 

2018-09-12 22:38:05 INFO fluxscale	##########################################
2018-09-12 22:38:05 INFO fluxscale	##### Begin Task: fluxscale          #####
2018-09-12 22:38:05 INFO fluxscale	fluxscale(vis="ngc5921.demo.ms",caltable="ngc5921.demo.gcal",fluxtable="ngc5921.demo.fluxscale",reference="1331*",transfer="1445*",
2018-09-12 22:38:05 INFO fluxscale	        listfile="",append=False,refspwmap=[-1],gainthreshold=-1.0,antenna="",
2018-09-12 22:38:05 INFO fluxscale	        timerange="",scan="",incremental=False,fitorder=1,display=False)
2018-09-12 22:38:05 INFO fluxscale	****Using NEW VI2-driven calibrater tool****
2018-09-12 22:38:05 INFO fluxscale	Opening MS: ngc5921.demo.ms for calibration.
2018-09-12 22:38:05 INFO fluxscale	Initializing nominal selection to the whole MS.
2018-09-12 22:38:05 INFO fluxscale	Beginning fluxscale--(MSSelection version)-------
2018-09-12 22:38:05 INFO fluxscale	 Found reference field(s): 1331+30500002_0
2018-09-12 22:38:05 INFO fluxscale	 Found transfer field(s):  1445+09900002_0
2018-09-12 22:38:05 INFO fluxscale	 Flux density for 1445+09900002_0 in SpW=0 (freq=1.41342e+09 Hz) is: 2.52609 +/- 0.00218206 (SNR = 1157.67, N = 54)
2018-09-12 22:38:05 INFO fluxscale	Storing result in ngc5921.demo.fluxscale
2018-09-12 22:38:05 INFO fluxscale	Writing solutions to table: ngc5921.demo.fluxscale
2018-09-12 22:38:06 INFO fluxscale	CASA Version 5.3.0-143  
2018-09-12 22:38:06 INFO fluxscale	   
2018-09-12 22:38:07 INFO fluxscale	##### End Task: fluxscale            #####
2018-09-12 22:38:07 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
plotms(vis='ngc5921.demo.fluxscale', field='0,1', gridrows=2, gridcols=1, plotindex=0, rowindex=0, yaxis='amp', showgui=True, clearplots=False, coloraxis='antenna1')

# Set up 2x1 panels - lower panel phase vs. time
plotms(vis='ngc5921.demo.fluxscale', field='0,1', gridrows=2, gridcols=1, plotindex=1, rowindex=1, yaxis='phase', showgui=True, clearplots=False, coloraxis='antenna1')

The amp and phase coherence looks good. If you want to do this interactively and iterate over antenna, set iteraxis = '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.fluxscale', '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. Also, note that the table .fluxscale has the .gcal solutions with the correct flux scale applied, and so there is no need to invoke the .gcal again in the applycal() command below. 

applycal(vis='ngc5921.demo.ms', field='1,2', gaintable=['ngc5921.demo.fluxscale','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.fluxscale','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

Now remove 0:6~56 from the spw field to see all channels. 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 the data into 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 tclean data cube (ra, dec, velocity) from the continuum-subtracted (u, v, channel) measurement set, ngc5921.demo.ms.contsub. Things to consider in using tclean:

  • 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 tclean down to the noise, which is easily determined by trial-and-error imaging of a single channel (choosing nchan=1 and start appropriately). Here, tclean stops when the maximum residual on the channel is below threshold='8.0mJy'.


# Image the continuum subtracted measurement set
tclean(vis='ngc5921.demo.src.split.ms.contsub', imagename='ngc5921.demo.tclean', field='0', datacolumn='data', specmode='cube', nchan=46, start=5, width=1, spw='', deconvolver='hogbom', gridder='standard', niter=6000, gain=0.1, threshold='8.0mJy', imsize=[256,256], cell=['15.0arcsec','15.0arcsec'], weighting='briggs', robust=0.5,  mask = 'box[[108pix,108pix],[148pix,148pix]]', interactive=False, pblimit=-0.2)


Use imhead to look at the cube header: 

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


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


2019-09-03 22:25:25 INFO imhead	##### Begin Task: imhead             #####
2019-09-03 22:25:25 INFO imhead	imhead(imagename="ngc5921.demo.tclean.image",mode="summary",hdkey="",hdvalue="",verbose=False)
2019-09-03 22:25:25 INFO ImageMetaData	   
2019-09-03 22:25:25 INFO ImageMetaData	Image name       : ngc5921.demo.tclean.image
2019-09-03 22:25:25 INFO ImageMetaData	Object name      : N5921_2
2019-09-03 22:25:25 INFO ImageMetaData	Image type       : PagedImage
2019-09-03 22:25:25 INFO ImageMetaData	Image quantity   : Intensity
2019-09-03 22:25:25 INFO ImageMetaData	Pixel mask(s)    : None
2019-09-03 22:25:25 INFO ImageMetaData	Region(s)        : None
2019-09-03 22:25:25 INFO ImageMetaData	Image units      : Jy/beam
2019-09-03 22:25:25 INFO ImageMetaData	Restoring Beams 
2019-09-03 22:25:25 INFO ImageMetaData	Pol   Type Chan        Freq     Vel
2019-09-03 22:25:25 INFO ImageMetaData	  I    Max    7 1.41296e+09 1571.92   51.6639 arcsec x   47.3594 arcsec pa=  8.3205 deg
2019-09-03 22:25:25 INFO ImageMetaData	  I    Min   43 1.41384e+09 1386.42   51.5897 arcsec x   47.3127 arcsec pa=  8.3869 deg
2019-09-03 22:25:25 INFO ImageMetaData	  I Median   13 1.41310e+09 1541.00   51.6611 arcsec x   47.3318 arcsec pa=  8.1425 deg
2019-09-03 22:25:25 INFO ImageMetaData	   
2019-09-03 22:25:25 INFO ImageMetaData	Direction reference : J2000
2019-09-03 22:25:25 INFO ImageMetaData	Spectral  reference : LSRK
2019-09-03 22:25:25 INFO ImageMetaData	Velocity  type      : RADIO
2019-09-03 22:25:25 INFO ImageMetaData	Rest frequency      : 1.42041e+09 Hz
2019-09-03 22:25:25 INFO ImageMetaData	Pointing center     :  15:22:00.000000  +05.04.00.000000
2019-09-03 22:25:25 INFO ImageMetaData	Telescope           : VLA
2019-09-03 22:25:25 INFO ImageMetaData	Observer            : TEST
2019-09-03 22:25:25 INFO ImageMetaData	Date observation    : 1995/04/13/09:33:00
2019-09-03 22:25:25 INFO ImageMetaData	Telescope position: [-1.60119e+06m, -5.04198e+06m, 3.55488e+06m] (ITRF)
2019-09-03 22:25:25 INFO ImageMetaData	   
2019-09-03 22:25:25 INFO ImageMetaData	Axis Coord Type      Name             Proj Shape Tile   Coord value at pixel    Coord incr Units
2019-09-03 22:25:25 INFO ImageMetaData	------------------------------------------------------------------------------------------------ 
2019-09-03 22:25:25 INFO ImageMetaData	0    0     Direction Right Ascension   SIN   256   64  15:22:00.000   128.00 -1.500000e+01 arcsec
2019-09-03 22:25:25 INFO ImageMetaData	1    0     Direction Declination       SIN   256   64 +05.04.00.000   128.00  1.500000e+01 arcsec
2019-09-03 22:25:25 INFO ImageMetaData	2    1     Stokes    Stokes                    1    1             I
2019-09-03 22:25:25 INFO ImageMetaData	3    2     Spectral  Frequency                46    8   1.41279e+09     0.00 2.4414062e+04 Hz
2019-09-03 22:25:25 INFO ImageMetaData	                     Velocity                               1607.99     0.00 -5.152860e+00 km/s
2019-09-03 22:25:25 INFO imhead	##### End Task: imhead               #####
2019-09-03 22:25:25 INFO imhead	##########################################



Additional Science Products

If things went well, you should now have a spectral line cube (ngc5921.demo.tclean.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 an empty region of the whole cube. 


cubestat=imstat(imagename='ngc5921.demo.tclean.image', box='10,10,100,100')



2019-09-03 22:40:15 INFO imstat	##########################################
2019-09-03 22:40:15 INFO imstat	##### Begin Task: imstat             #####
2019-09-03 22:40:15 INFO imstat	imstat(imagename="ngc5921.demo.tclean.image",axes=-1,region="",box="10,10,100,100",chans="",
2019-09-03 22:40:15 INFO imstat	        stokes="",listit=True,verbose=True,mask="",stretch=False,
2019-09-03 22:40:15 INFO imstat	        logfile="",append=True,algorithm="classic",fence=-1,center="mean",
2019-09-03 22:40:15 INFO imstat	        lside=True,zscore=-1,maxiter=-1,clmethod="auto",niter=3)
2019-09-03 22:40:15 INFO imstat	Using specified box(es) 10,10,100,100
2019-09-03 22:40:15 INFO imstat	Determining stats for image ngc5921.demo.tclean.image
2019-09-03 22:40:15 INFO imstat	Selected bounding box : 
2019-09-03 22:40:15 INFO imstat	    [10, 10, 0, 0] to [100, 100, 0, 45]  (15:23:58.379, +04.34.29.305, I, 1.41279e+09Hz to 15:22:28.105, +04.56.59.962, I, 1.41389e+09Hz)
2019-09-03 22:40:15 INFO imstat	Statistics calculated using Classic algorithm
2019-09-03 22:40:15 INFO imstat	Regions --- 
2019-09-03 22:40:15 INFO imstat	         -- bottom-left corner (pixel) [blc]:  [10, 10, 0, 0]
2019-09-03 22:40:15 INFO imstat	         -- top-right corner (pixel) [trc]:    [100, 100, 0, 45]
2019-09-03 22:40:15 INFO imstat	         -- bottom-left corner (world) [blcf]: 15:23:58.379, +04.34.29.305, I, 1.41279e+09Hz
2019-09-03 22:40:15 INFO imstat	         -- top-right corner (world) [trcf]:   15:22:28.105, +04.56.59.962, I, 1.41389e+09Hz
2019-09-03 22:40:15 INFO imstat	Values --- 
2019-09-03 22:40:15 INFO imstat	         -- flux [flux]:                            1.99425 Jy.km/s
2019-09-03 22:40:15 INFO imstat	         -- number of points [npts]:                380926
2019-09-03 22:40:15 INFO imstat	         -- maximum value [max]:                    0.00953276 Jy/beam
2019-09-03 22:40:15 INFO imstat	         -- minimum value [min]:                    -0.0100478 Jy/beam
2019-09-03 22:40:15 INFO imstat	         -- position of max value (pixel) [maxpos]: [85, 63, 0, 8]
2019-09-03 22:40:15 INFO imstat	         -- position of min value (pixel) [minpos]: [30, 18, 0, 7]
2019-09-03 22:40:15 INFO imstat	         -- position of max value (world) [maxposf]: 15:22:43.151, +04.47.44.907, I, 1.41298e+09Hz
2019-09-03 22:40:15 INFO imstat	         -- position of min value (world) [minposf]: 15:23:38.319, +04.36.29.518, I, 1.41296e+09Hz
2019-09-03 22:40:15 INFO imstat	         -- Sum of pixel values [sum]:               4.76561 Jy/beam
2019-09-03 22:40:15 INFO imstat	         -- Sum of squared pixel values [sumsq]:     1.38125 Jy/beam.Jy/beam
2019-09-03 22:40:15 INFO imstat	Statistics --- 
2019-09-03 22:40:15 INFO imstat	        -- Mean of the pixel values [mean]:         1.25106e-05 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Variance of the pixel values :           3.62589e-06 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Standard deviation of the Mean [sigma]:  0.00190418 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Root mean square [rms]:                  0.00190421 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Median of the pixel values [median]:     6.37541e-06 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Median of the deviations [medabsdevmed]: 0.00127676 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- IQR [quartile]:                          0.00255348 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- First quartile [q1]:                     -0.00126604 Jy/beam
2019-09-03 22:40:15 INFO imstat	        -- Third quartile [q3]:                     0.00128744 Jy/beam
2019-09-03 22:40:15 INFO imstat	Sum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat	Mean column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat	Std_dev column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat	Minimum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat	Maximum column unit = Jy/beam
2019-09-03 22:40:15 INFO imstat	Npts          Sum           Mean          Rms           Std_dev       Minimum       Maximum     
2019-09-03 22:40:15 INFO imstat	 3.809260e+05  4.765615e+00  1.251060e-05  1.904215e-03  1.904176e-03 -1.004781e-02  9.532760e-03
2019-09-03 22:40:15 INFO imstat	##### End Task: imstat               #####
2019-09-03 22:40:15 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.tclean.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.tclean.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.tclean.image', fitsimage='ngc5921.demo.tclean.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

↵ Pre-upgrade VLA Tutorials

Last checked on CASA Version 5.7.2.

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