Image Continuum CASA 6.1.1

This guide should be used after completing Prepare the data for Imaging. You should have created calibrated_final.ms prior to proceeding.

Check CASA version

This template is for use in CASA versions 4.4 and greater. The following code checks the version of CASA and exits if it is less than 4.4.0. You can download the appropriate version of CASA from Obtaining CASA . See Prepare for Imaging section on the main page of this guide for more information.

# in CASA
import re

if casadef.casa_version < '4.4.0' :
    sys.exit("Please use CASA version greater than or equal to 4.4.0 with this script")

Create an Averaged Continuum MS

<figure id="Calibrated_final_Field0_Spw0.png">

spw 0

</figure> <figure id="Calibrated_final_Field0_Spw3.png">

Plots showing each spectral window for TW Hydra with channel vs amplitude. Spw 0 has a strong spectral line and spw 3 is a continuum only window.

</figure>

Appropriate averaging of a measurement set can reduce overall data volume, making imaging faster with clean. Below, we outline a procedure to create a spectrally averaged measurement set for continuum imaging.

The first step is to identify which spectral windows you would like to include in the continuum. In general, you should include all spectral windows with bandwidths greater than 250MHz to maximize the available continuum bandwidth. Once you have determined which spws you would like to use to form the continuum ms, set the contspw variable:

# in CASA
# Set spws to be used to form continuum
contspws = '0,1,2,3'

The next step is to identify the spectral lines in all spectral windows (spws) you will use to make the continuum image. If you find line emission, you will need to flag the relevant channels in each spectral window before averaging to exclude this emission from the channel averaged data set. The averaged data set can be used to produce a continuum image.

Two methods commonly used to identical spectral line emission are: 1) a channel vs. amplitude plot of the visibilities and 2) a dirty image of the cube.

To use the first method, create a channel vs. amplitude plot using plotms. An example command is given below. You may wish to change the averaging and the uvrange to identify extended emission:

# in CASA
plotms(vis=finalvis, xaxis='channel', yaxis='amplitude',
   	ydatacolumn='data',
   	avgtime='1e8', avgscan=True, avgchannel='1',
   	iteraxis='spw' )

The second, and more accurate, method is to use clean to make a quick dirty image cube of channels with niter set to zero and mode=’channel’. Inspecting this channel cube for line emission gives a better defined channel range to flag. The command below gives the basic command to create dirty image cubes. This should be repeated for each spw and science field.

#In CASA 


testimagename=’testImage’
field=[‘0’] #list all target fields
spw=[‘0,1,2,3’] #list all target spw’s

for i in field:
      for j in spw:  
            clean(vis=finalvis,
            	  imagename=testimagename+’Field_’+str(i)+’_spw_’+str(j), 
            	  field=str(i),
      	          spw=str(j),
	          #      phasecenter=phasecenter, # uncomment if mosaic.      
      	          mode='channel',
      	          outframe=outframe, 
      	          niter=0,
      	          interactive=True,
      	          cell=cell,
      	          imsize=imsize, 
      	          weighting=weighting, 
      	          robust=robust,
      	          imagermode=imagermode)

<figure id="Wt_vs_Freq_spw0123.png">

The weight spectrum vs Frequency for the calibrated_final.ms for each science spectral window. Notice how the weights in each individual spectral window do not vary wildly and have no outliers but the overall weight measure is different for each spectral window due to the different observing frequency.

</figure> The first step to flagging the spectral line channels in your data is to use the flagmanager task to save the state of the data before any flagging is applied. You will need to revert back to the non spectral line flagged dataset when line imaging is done later on. In addition, if you accidentally flag any data you can easily correct this using the *.before_cont_flags file.

# in CASA
flagmanager(vis=finalvis,mode='save',
versionname='before_cont_flags')

Initialize the weights in the ms using initweights.

# in CASA
initweights(vis=finalvis,wtmode='weight',dowtsp=True)

The exact spectral window and channel ranges to flag in the flagchannels variable needs to be specified. For example, if spws 2&3 have a line between channels 1201 and 2199 and spectral windows 0 and 1 are line-free the command would be:

# in CASA
flagchannels='2:1201~2199,3:1201~2199' # modify the channel range for your dataset

<figure id="Calibrated_final_field0_spw0_LineChannelFlagged.png">

Amp vs channel for spectral window 0 for TW Hydra with the spectral line flagged out.

</figure> Next, use the task flagdata to apply these flags. After this is done it is good to check that the flags were applied correctly by using plotms to inspect the flagged ms.

# in CASA
flagdata(vis=finalvis,mode='manual',
      	spw=flagchannels,flagbackup=False)

# check that flags are as expected, NOTE must check reload on plotms
# gui if its still open.
plotms(vis=finalvis,yaxis='amp',xaxis='channel',
   	avgchannel='1',avgtime='1e8',avgscan=True,iteraxis='spw')

Before imaging the continuum there are several things that can be done to speed up cleaning of the final continuum image. One of these is to spectrally average several channels together within a single continuum window thus reducing the total number of channels while maintaining the same bandwidth.

Refer to How to Calculate Bandwidth Smearing for the derivation of the information about bandwidth smearing. Conservative values for various ALMA bands are suggested below.

This table lists the maximum bandwidth allowed for a reduction in peak response to a point source over the field of view of 1% for a a square and Gaussian bandpass. This tables lists this for various observing frequencies and baselines for the two bandpass cases. For example, if your observation was at 385 GHz and your maximum baseline was 10,000 meters then for a Gaussian bandpass the maximum bandwidth that can be averaged together is 109.62 MHz bandwidth.

125MHz/ChanWid(MHz) = number of channels to average for bands 3,4,5,6 250MHz/ChanWid(MHz) = number of channels to average for band 7

For example, if you have 128 channels and a channel width of 15.625 MHz in band 6, the way to find the number of channels to average together is

125 MHz / 15.625 MHz = 8 channels

It is always a good check to make sure the number of channels you are averaging together is an integer multiple of the total number of channels original. This will tell you how many channels will remain in the ms. Using the example above averaging every 8 channels of 128 channels leaves 16 final channels each with a channel width of 125MHz.

split averages the number of channels you specify within the width command and will save a new ms file called calibrated_final_cont.ms.

# in CASA
contvis='calibrated_final_cont.ms'
rmtables(contvis)
os.system('rm -rf ' + contvis + '.flagversions')
split2(vis=finalvis,
 spw=contspws,
 outputvis=contvis,
 width=[8,8,8,8], # number of channels to average together. The final channel width should be less than 125MHz in Bands 3, 4, and 6 and 250MHz in Band 7.
 datacolumn='data')

Now you should check the weights of the new continuum measurement set. The ratio of the weights value between Time Domain Mode (TDM) and Frequency Domain Mode (FDM) windows should be approximately equal to the ratio of the channel widths.

# in CASA
# update the antenna and field parameters for your dataset
plotms(vis=contvis, yaxis='wtsp',xaxis='freq',spw='',antenna='DA42',field='0')

Use flagmanager to restore the ms file to its original unflagged state. <figure id="Amp_vs_uvdist.png">

Amplitude vs UVDistance plot of the continuum, colored by spw.

</figure>

# in CASA
# If you flagged any line channels, restore the previous flags
flagmanager(vis=finalvis,mode='restore',
        	versionname='before_cont_flags')

It is a good practice to inspect the continuum after this in order to confirm it is alright for future cleaning.The uv-coverage should be consistent and you should not see large outliers.

# in CASA
plotms(vis=contvis,xaxis='uvdist',yaxis='amp',coloraxis='spw')

Image Parameters

Before imaging, you should set a few key parameters that you will use throughout the rest of the script.

Set the field id for the science target you are interested in imaging. You can use listobs file generated during the imaging prep step to find this information. If you are imaging a mosaic then you will have to select all the fields. For example, field = ‘3~25’. You can also set the field variable to the name of the source, as long as it matches the name in the listobs file. Do not leave the field parameter blank. If you leave the field parameter blank, clean will attempt to image all sources in the calibrated.ms.

# in CASA
# update for your ms
field='0'

Uncomment the imagermode that is relevant to your dataset. Set the phase center by field id or coordinates if you are imaging a mosaic. Check the spatial setup in the weblog (for pipeline calibrations) or qa2 report (for manual calibrations) to find the phase center. You should choose the central field for the phase center in order to get the best results.

If you are unsure which field to use for the phase center after looking at the weblog then you may use the following Analysis Utilities command:

au.pickCellSize(‘calibrated_final.ms',imsize=True).

This will return a four element array with that contains the calculated cell size, the X axis number of pixels, the Y axis number of pixels, and the field number that is most centered in the mosaic. You may use this as the phase center field id in your script. If you haven't installed Analysis Utilities, see the [Guide_NA_ImagingTemplate#Obtaining Analysis Utilities | Obtaining Analysis Utilities] section for instructions.

Another method of finding the central field number uses the {plotmosaic} Analysis Utilities au.plotmosaic(‘calibrated_final.ms’). This method will produce a plot of all fields with their corresponding field numbers plotted on the sky. You can also set the phase center with the coordinates at the center of this plot.

# in CASA
# imagermode='csclean' # uncomment if single field
# imagermode='mosaic' # uncomment if mosaic or if combining one 7m and one 12m pointing.
# phasecenter=3 # uncomment and set to field number for phase
            	# center. Note lack of ''.  Use the weblog to
            	# determine which pointing to use. Remember that the
            	# field ids for each pointing will be re-numbered
            	# after your initial split. You can also specify the
            	# phase center using coordinates, e.g.,
            	# phasecenter='J2000 19h30m00 -40d00m00'

<figure id="Calibrated_final_AmpVsUVWave.png">

Amplitude versus UV wave for the calibrated_final.ms showing the maximum uv wave point for determining cell size.

</figure>

Next you will determine the cell (or pixel) size. To do this, you need to determine the approximate resolution of your observations. Then you divide the resolution in arcsec by 5 to 8 to adequately sample the PSF. The resolution of a particular interferometer observations can be estimated from the length of the longest baseline:

resolution (radian) ~ (observed wavelength) / (length of longest baseline)

If the baseline is measured in wavelengths, this becomes

resolution (radian) ~ 1.0 / (length of longest baseline in wavelengths)

To convert from radians to arcsec, we multiple by 206265.0 to obtain:

resolution(arcsec) ~ 206265.0/(longest baseline in wavelengths) 

To determine the longest baseline in wavelengths use plotms with xaxis=’uvwave’ and yaxis=’amp’ on your ms file. Figure 1 shows an example of this plot. It is generally better to oversample your beam than to undersample, particularly for observations with poor uv-coverage.

cellsize(arc seconds) = resolution(arcsec)/5 

The next step is to determine the image size in pixels. There are two methods of doing this depending on if you are imaging one field or a mosaic.

If this is a single field, the image size can usually be approximated to be the same size as the primary beam of the telescope. The ALMA 12m primary beam in arcsec scales as 6300 / nu[GHz] and the ALMA 7m primary beam in arcsec scales as 10608 / nu[GHz], where nu[GHz] is the sky frequency. However, if there is significant point source and/or extended emission beyond the edges of your initial images, you should increase the imsize to incorporate more emission.

For mosaics, you can get the imsize from the spatial tab of the OT. The parameters "p length" and "q length" specify the dimensions of the mosaic. If you're imaging a mosaic, pad the imsize substantially to avoid artifacts. To know if you have padded enough you should, in your images, be able to see the edges of the outside fields being cut off. <figure id="Antennae_Antennae_North.Cont.Dirty.image.png">

</figure> <figure id="Antennae_North.Cont.Dirty.smallIMSize.image.png">

A mosaic of Antennae showing under padding and correctly padded images. For the correctly padded image you can see the edges of the primary beam field whereas the under padded does not cut off.

</figure> Note that the imsize parameter is in PIXELS, not arcsec, so you will need to divide the image size in arcsec by the pixel size to determine a value for imsize.

If you would like to check any of these calculations you may use the following command, au.pickCellSize('calibrated_final.ms', imsize=True), in CASA. This will return a four element array of the calculated cell size, the x axis imsize, the y axis imsize, and the central field id number.

# in CASA
cell='1arcsec' # cell size for imaging.
imsize = [128,128] # size of image in pixels.

When imaging you will need to set two specific velocity parameters called outframe and veltype. Outframe is the coordinate system used for the observation and can be found in the OT under field setup. A list of acceptable outframes that can be used in CASA can be found at https://help.almascience.org/index.php?/Knowledgebase/Article/View/86/0/what-are-the-frequency-reference-frames-in-casa. Note: heliocentric(hel) is deprecated in CASA. Use barycentric(bary) in this case.

You will also have to set the veltype for the clean command. This variable has only two options available, radio and optical. It is standard to leave this set to ‘radio’ in all projects regardless of the velocity frame used in the project.

# in CASA
outframe='bary' # velocity reference frame. See science goals.
veltype='radio' # velocity type.

The last four parameters that must be set are associated with how clean will weight and clean the data. Weighting determines how much influence data points in the UV plane have on the final image. There are several weighting schemes that can be used such as briggs, natural and uniform weighting.

• Natural: Natural weighting uses the same weights as that of the data weights when collected. This weighting scheme is the default in clean and gives low rms noise to the image but a poor angular resolution.

• Uniform: Uniform weighting will give all points in the UV plane the same influence regardless of their weight. The sidelobes will be reduced because the uv-plane is filled more uniformly. This gives more weight to longer baselines so angular resolution is enhanced while sensitivity is degraded.

• Briggs and the Robust Parameter: This is the most common weighting used and gives a balance between the low rms of natural weighting while also giving the excellent angular resolution of uniform weighting. Briggs weighting will vary the weighting given to UV cells with the robust parameter. Setting this to 2 gives Natural weighting, -2 Uniform weighting, and a number in between a variance of the two. Refer to Brigg's Thesis for more information.

Currently, robust is set to 0.5 which gives a good compromise between natural and uniform weighting. You may find, after imaging, that you have to decrease the rms noise or angular resolution based on the desired performance requested in the proposal. Playing with the robust parameter can affect your final rms noise in the image and also the angular resolution.

The niter and threshold parameters affect when clean will stop the cleaning process. niter is the maximum number of iterations allowed and once this number has reached zero interactive cleaning will finish. Once a certain flux level, set by threshold, has been reached, clean will stop. This is by default set at 0.0mJy but may be increased to prevent cleaning too deeply into the thermal noise.

There are several methods to determine what to set threshold to if you desire to change it. One method is to make a clean mask by setting niter equal to zero, then inspecting the image produced using viewer. Select a box around an area of no emission to determine the rms noise of the image, which should be the approximate value of the threshold parameter. Follow the instructions located in the “Image the Spectral Line Data” section of EVLA Spectral Line Imaging Analysis IRC+10216. If you plan on cleaning non-interactively then you must set threshold to a value other than 0.

# in CASA
weighting = 'briggs'
robust=0.5
niter=1000
threshold = '0.0mJy'

For more information on the various options available in clean, refer to David Wilner’s Imaging and Deconvolution presentation from the 2016 Synthesis Imaging Workshop. Use this table to find specific slides for different parameters you will set below.

Imaging Parameters

Pixel and Image Size	Slides 40-41
Weighting	Slides 42-48, 60-61
Deconvolution Algorithms	Slides 50-53, 62

Imaging the Continuum

<figure id="Cont_before_clean.png">

During interactive clean, the GUI will show an initial dirty image. From this GUI, create the clean mask.

</figure> <figure id="Cont_before_clean_mask.png">

After a mask is created, the green arrow will be illuminated. Press this to begin the first 100 iterations. Once cleaning is complete, press the red "X" to finish.

</figure> <figure id="Final_Cont_residual.png">

The final residual image.

</figure> Now that you have set all of the imaging parameters you will need in clean you can proceed to imaging the continuum. The First Look at Imaging CASAGuide gives an introduction to cleaning and imaging.

# in CASA
contvis = 'calibrated_final_cont.ms'         
contimagename = 'calibrated_final_cont'

If you have already run clean on the ms and would like to start over, be sure to use the following commands before cleaning the continuum.

# in CASA
# Clearcal clears the calibrated column of the ms.
# clearcal(contvis)
# Delmod removes any model column put in by clean.
# delmod(contvis)

clean creates a variety of files after it completes. If these files exist in the directory, clean will begin working on the files that already exist. The following command will delete all of these files. You may also change the cont_image_name parameter if you would like to keep the files you created previously.

# in CASA
for ext in ['.flux','.image','.mask','.model','.pbcor','.psf','.residual','.flux.pbcoverage','.pb','.wtsum']:
	rmtables(contimagename+ext)

If you are imaging a mosaic, the phasecenter parameter should be set. Refer to the imaging parameters section of this guide to see how to find this for your project.

Type “help clean()” in CASA if you would like to explore the possible parameters of clean. Mode=’mfs’ sets the spectral gridding type to multi-frequency synthesis and creates a continuum image. Leave the psfmode to use during minor cycles at the default ‘clark’ .

clean interactively as the threshold is set at 0 mJy. The mask parameter may also be added if you have an existing file.

# in CASA
clean(vis=contvis,
      imagename=contimagename,
      field=field,
#      phasecenter=phasecenter, # uncomment if mosaic.      
      mode='mfs',
      psfmode='clark',
      imsize = imsize, 
      cell= cell, 
      weighting = weighting, 
      robust = robust,
      niter = niter, 
      threshold = threshold, 
      interactive = True,
      imagermode = imagermode)

Figure 11 shows the clean GUI that will appear. If no source is apparent, no cleaning should be done. Press the red “X” to complete the task. If a source is apparent, create a mask around it. Once a mask is created, the green arrow will be illuminated and you can begin the first round of cleaning.

The logger window gives you vital information on the progression of clean. Once the cycle is complete, the residuals will appear in the GUI. You should continue to iterate until the region inside the mask matches the noise outside the mask. You may need multiple cycles depending on the complexity of the source.

The red "X" will stop clean where you are, the blue arrow will stop the interactive part of clean, but continue to clean non-interactively until reaching the number of iterations requested (niter) or the flux density threshold (whichever comes first), and the green circle arrow will clean until it reaches the "iterations" parameter on the left side of the green area. These are the only safe exit buttons to use during clean. Otherwise your data may be corrupted.

The final continuum image.

After creating the continuum image, check the RMS and beam size to make sure the results match the expected values. Once you are happy with your continuum image(s), you can continue to Self-Calibration Template or Spectral Line Imaging Template. If you do not wish to self-calibrate or create line cubes, continue with this guide to create primary beam corrected images and fits files.

Apply a primary beam correction

<figure id="TW_Hya_Calibrated_final.pbcor.png">

TW Hydra image after a primary beam correction is applied to correct the lower rms found at the edges of the images from the primary beam.

</figure> Once all imaging is finished, diagnostic images will be made. The first of these is a primary beam correction image of the data. Each telescope's primary beam is, for the most part, approximately a Gaussian with small side lobes that can be approximated to zero. Because of this, the center of the beam has much more sensitivity than the edges. To correct for this, flux is added to the edges of the image in order for all pixels across the beam to have the same relative brightness based on the beam pattern.

impbcor is used to produce the primary beam corrected images . You will need the *.flux and *.pbcor files.

# in CASA
import glob

myimages = glob.glob("*.image")

rmtables('*.pbcor')
for image in myimages:
	pbimage = image.rsplit('.',1)[0]+'.flux'
	outfile = image.rsplit('.',1)[0]+'.pbcor'
	impbcor(imagename=image, pbimage=pbimage, outfile = outfile)

Export the images

Use exportfits to create fits files of the *.flux and *.pbcor files.

# in CASA
import glob

myimages = glob.glob("*.pbcor")
for image in myimages:
    exportfits(imagename=image, fitsimage=image+'.fits',overwrite=True)

myimages = glob.glob("*.flux")
for image in myimages:
    exportfits(imagename=image, fitsimage=image+'.fits',overwrite=True)

Create Diagnostic PNGs

The First Look at Image Analysis guide gives an introduction to a variety of options to begin image analysis, including using immoments to create moment maps. The commands create png files of the continuum image and moment 8 maps.

<source lang="python">

1. in CASA

os.system("rm -rf *.png") mycontimages = glob.glob("calibrated*.image") for cimage in mycontimages:

   max=imstat(cimage)['max'][0]
   min=-0.1*max
   outimage = cimage+'.png'
   os.system('rm -rf '+outimage)
   imview(raster={'file':cimage,'range':[min,max]},out=outimage)